JP2006510382A - Using gene expression profiles to identify, monitor, and treat infections, and characterize inflammatory conditions associated with infections - Google Patents

Abstract

The present invention provides a method for determining a profile data set for a subject with an infectious disease or a subject with an infection-related infectious disease based on a sample of the subject that provides the RNA source. , Provided in various embodiments. The method includes using amplification to measure the amount of RNA corresponding to at least two components of Table 1. The profile data set consists of each component's measure and amplification is performed under measurement conditions that are substantially reproducible.

Description

(Technical field of the invention)
The present invention relates to the use of gene expression data, in particular gene expression for identifying, monitoring, and treating infections, and for characterizing and evaluating infection symptoms in subjects that induce or are associated with infections. It relates to using data.

(Conventional technology)
Prior art uses gene expression data to determine the presence or absence of specific markers that are useful in the diagnosis of specific conditions, and in some situations, to achieve increased diagnostic accuracy or sensitivity The cumulative addition to marker overexpression has been described. Information about any condition of a particular patient and the patient's response to the type and dosage of a therapeutic or nutritional supplement improves patient outcomes and benefits, not only in terms of medical efficiency for the medical industry It became an important issue for today's clinical medicine.

(Summary of the Invention)
In the first embodiment, a profile data set of a subject with an infectious disease or a subject having an infection symptom associated with an infectious disease is based on a sample obtained from the subject (a sample that provides an RNA source). A method is provided for determining. The method includes measuring the amount of RNA corresponding to at least two components of Table 1 and using amplification to reach the criteria for each component, wherein the profile data set comprises: Amplification is performed under substantially reproducible measurement conditions.

  The subject also has uncertain signs of systemic infection, including at least one of increased white blood cell count, increased body temperature, increased heart rate, and increased or decreased blood pressure compared to medical normative values. At least one of blunt trauma or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or dental or gynecological examination or treatment. It may be an infectious symptom arising from one.

  In another embodiment, the substantially reproducible measurement conditions may be in a range of reproducibility greater than 5 percent or in a range of reproducibility greater than 3 percent. Moreover, the amplification efficiencies of all the components are substantially the same, and the amplification efficiencies of all the components are within 2 percent or less than 1 percent. In such embodiments, the sample can be selected from the group consisting of blood obtained from a subject, blood fraction, body fluid, cell group, and tissue.

  In another embodiment, a method is provided for characterizing an infection or an infection associated with an infection in a subject based on a sample obtained from a subject providing an RNA source. The method includes evaluating a profile data set of a plurality of members, each member having a plurality of selected component measurements to allow characterization of systemic infection uncertainties. Quantitative criteria for the amount of one different RNA component within a panel of components, such criteria for each component being obtained under substantially reproducible measurement conditions.

  The subject also has uncertain signs of systemic infection, including at least one of increased white blood cell count, increased body temperature, increased heart rate, and increased or decreased blood pressure as compared to medical normative values. Or the subject has arisen from at least one of blunt or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or dental or gynecological examination or procedure It may have an uncertain sign of systemic infection related to infection symptoms. In such embodiments, the evaluation may further comprise comparing the profile data set to a baseline profile data set of the panel, wherein the baseline profile data set is , Or an infection characterized or associated with the infection.

  In another embodiment, the amplification efficiencies of all components are substantially similar, and the infection or infection associated with the infection is a microbial infection, more particularly a bacterial infection, or a parasitic infection, or a virus By infection, or fungal infection, otherwise systemic inflammatory response syndrome (SIRS). More specifically, the infection or infection associated with the infection is due to bacteremia, viremia, or fungiemia, or due to sepsis caused by any type of microorganism. Further, the infectious disease or an infectious disease related to the infectious disease relates to a partial tissue of the subject, and the sample is derived from a tissue of a body fluid different from the partial tissue, Also good.

  In another embodiment, the profile data set is stored in a digital storage medium, and the profile data set is stored in a database in a record corresponding to one profile data set. Including being stored as

  Yet another embodiment provides a method for assessing an infection or infection-related infection symptom in a subject providing an RNA source based on a first sample obtained from said subject. . The method is the step of deriving from the sample a first profile data set comprising a plurality of members from the sample, each member having an infection or infection-related infection. Quantitative criteria for the amount of different RNA components in a single panel composed of multiple components selected to allow symptom assessment, such criteria for each component being substantive Includes steps obtained under reproducible measurement conditions. The method also includes generating calibrated profile data for the panel, wherein each member of the calibrated profile data is associated with a corresponding member of the first profile data set and the baseline of the panel. A function with a corresponding member of the profile data set and the baseline profile data set is associated with the infection or an infection symptom associated with the infection to be evaluated, An assessment of the infection or infection-related infection symptoms of the subject by the calibrated profile data being a comparison of the first profile data set and the baseline profile data set Is obtained.

  In a related embodiment, the subject has a systemic infection comprising at least one of an increase in white blood cell count, an increase in body temperature, an increase in heart rate, and an increase or decrease in blood pressure compared to a medical normative value. Infectious symptoms associated with the infectious disease or blunt or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or dental or gynecological examination or treatment Infectious symptoms that arise from one.

  Further, the baseline profile data set may be derived from one or more other samples obtained from the same subject under an environment different from the first sample, and the environment is (I) the time at which the first sample was taken, (ii) the site from which the first sample was taken, and (iii) the biological of the subject when the first sample was taken It may be selected from the group consisting of states.

  The interval between the first sample and the one or more other samples exceeds at least one month, and the interval between the first sample and the one or more other samples is at least 12. It is possible to take the one or more other samples after a month or before or after the intervention.

  In such an embodiment, the first sample may be derived from the blood of the subject, and the baseline profile data set may be derived from a tissue or body fluid of the subject other than blood. Alternatively, the first sample may be derived from blood of the subject, and the baseline profile data set may be derived from tissue or body fluid of the subject other than blood.

  In another embodiment, the baseline protofield data set is the first prototypical data set for at least one of age, nutritional history, symptoms, clinical indicators, medications, physical activity, weight, and environmental exposure. It may be derived from one or more other samples taken when the subject is in a biological state different from the biological state of the subject when the sample is taken The baseline profile data set may be derived from one or more other samples obtained from one or more different subjects.

  In addition, the one or more other samples are similar to the subject in age group, gender, ethnicity, geographical location, nutritional history, symptoms, clinical indicators, medication, physical activity, body weight, And at least one of environmental exposures. In another embodiment, the clinical index may be used to evaluate at least one or more other samples of infectious diseases or infection-related infectious symptoms, and at least one or more other types of infectious diseases. Interpreting the calibrated profile data in the context of a clinical index of the method may further include the at least one other clinical index being blood chemistry, urinalysis, x-ray or Selected from the group consisting of other radiological or metabolic imaging techniques, other chemical assays, and physical findings.

  In such embodiments, the infection or infection associated with the infection may be due to a microbial infection, a bacterial infection, a eukaryotic parasite infection, a viral infection, a fungal infection, or the infection or Infectious symptoms associated with the infection may be due to systemic inflammatory response syndrome (SIRS) or sepsis caused by bacteremia, viremia or fungiemia, or any type of microorganism Also good.

  In yet another embodiment, the function is a mathematical function that is different from a simple difference, such as the first profile data set for a corresponding member of the baseline profile data set. The second function or the logarithmic function of the ratio of the corresponding members. In a related embodiment, each member of the calibrated profile data is D = F (1.1) −F (0.9), where F is an amount greater than the amount D when the second function. If it has a changing value, it has biological significance. In such an embodiment, acquisition of the first sample and quantification of the first profile data set was performed at a first location and stored on a digital storage medium at a second location. Using the network to access the database, the calibrated profile data is generated and the database is updated to reflect the first profile data set quantified from the sample. May be. Also, the use of the network may include access to a global computer network.

  In a related embodiment, the quantitative criteria is determined by amplification and the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent, or less than about 1 percent.

  Yet another embodiment is a method of providing an index indicating an infectious disease associated with an infectious disease or an infectious disease associated with an infectious disease based on a first sample from the subject providing an RNA source. The method is a step of deriving a profile data set from the first sample, the profile data set comprising a plurality of members, each member being capable of characterizing an uncertain sign of systemic infection A quantitative criterion for the amount of one different RNA component within a panel of components selected to be at least two of the components of the gene expression panel of Table 1 Including, and deriving said profile data set, achieving such criteria for each component under substantially reproducible measurement conditions, At least one basis of the profile data set for an exponential function giving a mapping of at least one criterion And by applying to at least one criterion not 確徴 systemic infection, and generating an index relating to infection symptoms associated the with the infection or infection in a subject.

  The subject also has uncertain signs of systemic infection, including at least one of an increase in white blood cell count, an increase in body temperature, an increase in heart rate, and an increase or decrease in blood pressure compared to a medical normative value. Infectious symptoms associated with the infection may be blunt or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or dental or gynecological examination or treatment. It may be an infectious symptom resulting from at least one.

In a related embodiment, the exponential function has the formula: I = ΣC _i M _i P ^{(i) where} I is the index, M _i is the value of member i of the profile data set, C _i is a constant, And P (i) is constructed as a linear sum of a plurality of terms with an index Mi is raised, and the sum is formed for integer values i up to the number of members in the data set. Further, the values C _i and P (i) are determined using a statistical method such as latent class modeling, and the latent class related to the uncertain signs of the systemic infection derived clinically and experimentally. Correlate with data including modeling. In an alternative embodiment, a normative value of the exponential function determined for an associated set of subjects is provided, allowing the indicator to be interpreted relative to the normative value. Providing the normative value includes configuring the exponential function such that the normative value is approximately 1, or providing the normative value is that the normative value is approximately zero; The exponential function may be configured such that a deviation of the exponential function from 0 is expressed in standard deviation units. In yet another embodiment, the related sets of the plurality of subjects commonly have an age group, gender, ethnicity, geographic location, nutritional history, symptoms, clinical indicators, medications, physical activity , Body weight, and / or environmental exposure, or in common, age group, gender, ethnicity, geographical location, nutritional history, symptoms, clinical indicators, medication, physical activity, There is a characteristic that is at least one of body weight and environmental exposure.

  In another embodiment, using clinical indicators, interpreting the calibrated profile data in the context of at least one other clinical indicator, the associated set of infections or infections of the plurality of subjects. The at least one other clinical indicator may be blood chemistry, urinalysis, X-ray or other radiological or metabolic imaging techniques, other chemicals Selected from the group consisting of tests, as well as physical findings. The quantitative criteria may also be determined by amplification and the measurement conditions are substantially reproducible with a difference in amplification efficiency of less than about 2 percent or less than about 1 percent for all components. The measurement conditions are within a range of reproducibility exceeding 5 percent or within a range of reproducibility exceeding 3 percent.

  In such embodiments, the infection or infection associated with the infection being assessed relates to a partial tissue of the subject, and the first sample is of the partial tissue. Derived from a different type of tissue or body fluid, where the infection or infection associated with the infection is a microbial infection, more specifically a bacterial infection, more particularly a eukaryotic parasite infection, viral infection, fungal infection, Or the infection is due to systemic inflammatory response syndrome (SIRS).

87. The amount of one different RNA component in one panel from a plurality of components, each selected from at least one of the other samples, each of which is selected to allow characterization of systemic infection uncertainty Deriving at least one other profile data set comprising a plurality of members that are quantitative criteria, wherein said at least one other sample has at least one time point, nutritional history, symptoms, clinical Derived from the same subject taken in an environment different from that of the first sample with respect to indicators, medications, physical activity, weight, and environmental exposure;
At least one criterion from the at least one other profile data set is related to the infection or an infection-related infection symptom of the subject under a different environment than that of the first sample The mapping of the at least one other profile data set from the at least one criterion under different circumstances to produce at least one other indicator is one of the infectious symptoms associated with the infection or infection. Applying an exponential function to provide for one criterion;
62. A method for providing an indication according to claim 61, further comprising:

A related embodiment is that the exponential function provides an indicator with 2, 3, 4, or 5 components including disease state, severity, or disease course. In addition, the exponential function is represented by the formula: I = ΣC _i M _i P ⁽ⁱ⁾ (where I is an index, M _i is the value of member i of the profile data set, C _i is a constant, and P (i ) is configured as a linear sum of a plurality of terms with index) which is pulled up M _i, for integer values i to the number of members the data set, there is the above sum are formed The values C _i and P (i) are determined using statistical methods such as latent class modeling, and are related to uncertainties of the systemic infection derived clinically and experimentally. Correlate with data including class modeling.

  Alternatively, by providing a normative value of the exponential function determined in relation to an associated set of subjects, the at least one other indicator can be interpreted in relation to the normative value. Here, giving the normative value means that the normative value is about 1 or that the normative value is about 0, and that the deviation of the exponential function from 0 is a standard deviation unit. It is possible to construct the exponential function as: Such embodiments include at least one other selected from the group consisting of blood chemistry, urinalysis, X-ray or other radiological or metabolic imaging techniques, other chemical assays, and physical findings. Using a clinical index to assess an associated set of infections or infection-related symptoms of the plurality of subjects by interpreting the calibrated profile data of the context of the clinical index of Can also be mentioned.

  As in other embodiments, the quantitative criteria may be determined by amplification and the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent or the difference is less than about 1 percent The substantially reproducible measurement conditions are within a reproducibility range of greater than 5 percent or a reproducibility range of greater than 3 percent.

  Further, the infectious disease or an infectious disease related to the infectious disease relates to a partial tissue of the subject, and the first sample is a tissue or body fluid of a type different from that of the partial tissue. Derived from.

  Yet another embodiment includes a method for providing an indicator, wherein the infection or infection associated with the infection is a microbial infection, more particularly a bacterial infection, more particularly a eukaryotic parasitic infection, a viral infection. Or the fungal infection is due to a systemic inflammatory response syndrome (SIRS) and the panel of components comprises at least two components of Table 1.

  Other embodiments provide a method of assessing a subject's infection or infection-related infection symptoms based on a first sample that provides the subject's RNA source. The method is the step of deriving a first profile data set from the sample, the first profile data set comprising a plurality of members, each member having an infectious disease or component measurement A quantitative measure of the amount of different RNA components in a single panel composed of multiple components selected to allow assessment of infection symptoms associated with the infection, and for each component Such a criterion is obtained under substantially reproducible measurement conditions. The invention also includes the step of generating calibrated profile data for the panel, wherein each member of the calibrated profile data corresponds to a corresponding member of the first profile data set and a baseline of the panel A function with corresponding members of the profile data set, and each member of the baseline profile data set is determined with respect to an associated set of multiple quantities of one component of the component in the panel A baseline profile data set, the baseline profile data set being associated with the infection or an infection symptom associated with the infection to be evaluated, the calibrated profile data Are the first profile data set and the baseline profile data set. Because it is compared with the evaluation of infection symptoms associated with the infection or infection of the subject is provided.

  In such embodiments, the subject has a systemic infection comprising at least one of an increase in white blood cell count, an increase in body temperature, an increase in heart rate, and an increase or decrease in blood pressure compared to a medical normative value. Or the symptoms associated with the infection may be blunt trauma or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or dental or gynecological examination or It may be an infectious symptom resulting from at least one of the treatments.

  Further, the related sets of the plurality of subjects commonly have age group, gender, ethnicity, geographical location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environment. A set of healthy subjects having at least one characteristic of exposure. As in other embodiments, the quantitative criteria may be determined by amplification, wherein the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent or the difference is less than about 1 percent; The measurement conditions that are substantially reproducible are within a reproducibility range of greater than 5 percent or a reproducibility range of greater than 3 percent.

  In such embodiments, the infection or infection associated with the infection being assessed relates to a partial tissue of the subject, and the first sample is of the partial tissue. It is possible to store the calibrated profile data in a digital storage medium that originates from a different type of tissue or body fluid than the one, such as storing the data as a record in a database. Further, the baseline profile data set is derived from one or more types of other samples obtained from the same subject under an environment different from the first sample, and the first sample and the first sample The interval between taking one or more other types of samples exceeds at least one month, and the interval between taking the first sample and one or more other types of samples exceeds at least 12 months, or before treatment intervention Alternatively, after the intervention, take one or more other samples. Further, the first sample is derived from the blood of the subject, and the baseline profile data set is derived from a tissue or body fluid of the subject other than blood, or the first sample is the subject The baseline profile data set is derived from blood.

  Yet another embodiment is a method for evaluating an infectious disease of a subject or an infectious symptom associated with an infectious disease based on a first sample of the subject and a second sample derived from a predetermined indicator cell group. These samples provide a source of RNA. The method includes applying the first sample or a portion thereof to the predetermined population of indicator cells. The method is also a step of deriving a first profile data set from the sample, the first profile data set including a plurality of members, each member having an infectious component measurement Quantitative criteria for the amount of different RNA components in a single panel composed of multiple components selected to allow assessment of infection symptoms associated with the disease or infection, each component Are obtained under substantially reproducible measurement conditions, and generating calibrated profile data for the panel, each member of the calibrated profile data Is the corresponding member of the first profile data set and the baseline profile data set of the panel. And each member of the baseline profile data set is a normative criterion determined with respect to an associated set of multiple quantities of one component of the component in the panel. And a baseline profile data set is associated with the infection to be evaluated or an infection symptom associated with the infection, wherein the calibrated profile data is the first profile data The comparison of the set with the baseline profile data set provides an assessment of the infection or infection associated with the subject in the subject.

  In a related embodiment, the subject has a systemic infection comprising at least one of an increase in white blood cell count, an increase in body temperature, an increase in heart rate, and an increase or decrease in blood pressure compared to a medical normative value. May be uncertain or the symptoms associated with the infection may be blunt trauma or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or dental or gynecological It may be an infectious symptom resulting from at least one of examination or treatment.

  In addition, the related sets of the plurality of subjects commonly include age group, gender, ethnicity, geographical location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environment. Has at least one characteristic of exposure. Alternatively, using clinical indicators, interpret the context of the calibrated profile data in at least one other clinical indicator to infect an infection or infection-related infection of the multiple subjects The set may be evaluated and the at least one other clinical indicator may include blood chemistry, urinalysis, x-ray or other radiological or metabolic imaging techniques, other chemical tests, and physical findings Selected from the group consisting of

  As in the other examples, the quantitative criteria is determined by amplification, and the measurement conditions are substantially less than about 2 percent or less than about 1 percent difference in amplification efficiency for all components, The reproducible measurement conditions are within a range of reproducibility exceeding 5 percent or within a range of reproducibility exceeding 3 percent. The infectious disease being evaluated is related to a partial tissue of the subject, and the first sample is derived from a tissue or body fluid of a type different from that of the partial tissue, The infection symptom associated with or associated with an infection is a microbial infection.

  In a related embodiment, the baseline profile data set is derived from one or more other samples obtained from the same subject in a different environment than the first sample, and the first profile After the interval between the sample and the one or more other samples exceeds at least one month, and the interval between the first sample and the one or more other samples exceeds at least 12 months, Alternatively, the one or more other samples are taken before or after the intervention. In such embodiments, the first sample is derived from the subject's blood, and the baseline profile data set is derived from the subject's tissue or body fluid other than blood, or the first sample The sample is derived from the subject's tissue or body fluid, and the baseline profile data set is derived from blood.

  In another embodiment of the present invention, an infection of a target cell group affected by the first factor based on a sample derived from the target cell group to which the first factor has been administered and providing an RNA source, or A method for assessing infection symptoms associated with an infection is presented. The method is the step of deriving a first profile data set from the sample, the first profile data set comprising a plurality of members, each member having an infectious disease or component measurement A quantitative measure of the amount of different RNA components in a single panel composed of multiple components selected to allow assessment of infection symptoms associated with the infection, and for each component Such criteria are obtained under substantially reproducible measurement conditions and generating calibrated profile data for the panel, wherein each member of the calibrated profile data is Corresponding member of the first profile data set and corresponding member of the baseline profile data set of the panel And each member of the baseline profile data set is a normative criterion determined with respect to an associated set of a plurality of subjects in one quantity of the component in the panel; A baseline profile data set associated with the infection to be assessed or an infection symptom associated with the infection, wherein the calibrated profile data is the first profile data set. And the baseline profile data set provides an assessment of the infection or infection associated with the subject in the subject. The target cell population exhibits uncertain signs of systemic infection, including at least one of increased white blood cell count, increased body temperature, increased heart rate, and increased or decreased blood pressure compared to medical normative values It may be a thing. The infectious symptom associated with the infectious disease is an infectious symptom resulting from at least one of blunt trauma or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or dental or gynecological examination or treatment. May be. The related set of the plurality of subjects may be a set of a plurality of healthy subjects. Alternatively, the related sets of the plurality of subjects commonly have age group, gender, ethnicity, geographical location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environment It may have at least one characteristic of exposure. In such cases, a clinical indicator may be used to evaluate an associated set of infections or infectious symptoms associated with the plurality of subjects, the method comprising at least one other clinical indicator. Further interpreting the calibrated profile data in the context of: wherein the at least one other clinical indicator is blood chemistry, urinalysis, x-ray or other radiological or metabolic It may be selected from the group consisting of imaging techniques, other chemical assays, and physical findings. The quantitative criteria can be determined by amplification, and the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent, or less than about 1 percent. The substantially reproducible measurement conditions may be in a range of reproducibility greater than 5 percent or greater than 3 percent. Further, the infectious disease or the infectious symptom related to the infectious disease to be evaluated may be related to a partial tissue of the subject, and the first sample is of the partial tissue. Are from different types of tissues or body fluids. In addition, the infectious diseases or infectious symptoms related to infectious diseases are microbial infection, bacterial infection, eukaryotic parasite infection, viral infection, fungal infection, systemic inflammatory response syndrome (SIRS), bacteremia, viremia, fungi It may be due to blood or sepsis from any type of microorganism. A related embodiment of the method may further include storing the profile data set on a digital storage medium, and storing the profile data set in a database It may include storing the data set as one record. This embodiment has the limitation that the first sample is derived from the blood of the subject and the baseline profile data set is derived from the tissue or body fluid of the subject other than blood. Also good. Alternatively, the first sample may be derived from the tissue or body fluid of the subject, and the baseline profile data set may be derived from blood. Similarly, the baseline profile data set may be derived from one or more other samples obtained from the same subject obtained in a different environment than the first sample. One or more such other samples can be taken before or after a therapeutic intervention or over a time interval that is at least one month between the initial sample and the sample. The interval between the first sample and the one or more other samples exceeds at least one month, and the interval between the first sample and the one or more other samples is at least 12 months. Take one or more other samples after or before or after the intervention.

  Another embodiment of the invention relates to an infection of a target cell group affected by a second factor or an infection symptom associated with an infection of a target cell group affected by a first factor. A first sample that is derived from the target cell population to which the first factor has been administered and has an infection symptom associated with the infection and that provides an RNA source; and the second factor has been administered. And a second sample derived from the target cell group and providing an RNA source. Such a method is the steps of deriving a first profile data set from the first sample and deriving a second profile data set from the second sample, wherein the first profile data set The data set and the second profile data set include a plurality of members, each member selected such that the measurement of the component allows an assessment of the infection or infection-related infection symptoms A quantitative measure of the amount of different RNA components in a panel composed of a plurality of components affected by the first factor in relation to the second factor, each component Such a criterion for the element is obtained under substantially reproducible measurement conditions and a first calibration profile data set for said panel Generating a second calibration profile data set; (i) each member of the first calibration profile data set includes a corresponding member of the first profile data set and the panel (Ii) each member of the second calibration profile data set is a function of a corresponding member of the second profile data set for the panel A baseline reference of a quantity of one of the plurality of components of the panel that is a function of the baseline profile data set and is determined with respect to an associated set of subjects. The data set is related to the infection or infection-related The first and second calibration profile data sets are compared between the first profile data set and the baseline profile set and the second profile data set. And the baseline profile data set, the first cell in relation to the infection of the target cell group affected by the second factor or an infection symptom associated with the infection. An assessment of the infection of the target cell group affected by these factors or an infection symptom associated with the infection is given. The target cell population exhibits uncertain signs of systemic infection, including at least one of increased white blood cell count, increased body temperature, increased heart rate, and increased or decreased blood pressure compared to medical normative values There may be things. Similarly, the target cell population was associated with blunt or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or an infection symptom resulting from at least one dental or gynecological examination or treatment. It may present an uncertain sign of systemic infection. The first factor may be a drug and the second factor may be a complex mixture or a dietary supplement. The quantitative criteria may be determined by amplification, and the measurement conditions may be that the difference in amplification efficiency for all components is less than about 2 percent, or less than about 1 percent. The substantially reproducible measurement conditions may be in a range of reproducibility greater than 5 percent or greater than 3 percent. Further, the infectious disease or the infectious symptom related to the infectious disease to be evaluated may be related to a partial tissue of the subject, and the first sample is of the partial tissue. Are from different types of tissues or body fluids. In addition, the infectious diseases or infectious symptoms related to infectious diseases are microbial infection, bacterial infection, eukaryotic parasite infection, viral infection, fungal infection, systemic inflammatory response syndrome (SIRS), bacteremia, viremia, fungi It may be due to blood or sepsis from any type of microorganism. The method may further include storing the first and second profile data sets on a digital storage medium. Storing the first and second profile data sets further includes storing each data set as a record in the data base. The baseline profile data set is derived from one or more other samples obtained from the same subject in a different environment from the first sample, or different from those of the second sample It may be. The first sample may be derived from blood of the subject, and the baseline profile data set may be derived from tissue or body fluid of the subject other than blood. The first sample may be derived from the tissue or body fluid of the subject, and the baseline profile data set may be derived from blood.

In yet another embodiment of the present invention, an index indicating an inflammatory state of the subject having an uncertain sign of systemic infection is provided based on the first sample derived from the subject and being an RNA source. A method is shown. The method is the step of deriving a profile data set from the first sample, wherein the profile data set includes a plurality of members, each member such that a measurement of the component indicates the inflammatory condition. A quantitative criterion for the amount of different RNA components in a panel composed of a plurality of selected components, the panel being at least two of the components of the gene expression panel of Table 1 And deriving said profile data set to achieve such criteria for each component under substantially reproducible measurement conditions; and
Applying a mapping of at least one criterion obtained from the profile data set to an exponential function that provides at least one criterion for the inflammatory condition to generate an indicator related to the inflammatory condition of the sample. The exponential function uses data from the baseline profile data set of the panel, and each member of the baseline data set is associated with a plurality of subjects of one quantity of the panel components Normative criteria determined for the set, the baseline data set being associated with the inflammatory condition to be evaluated. The subject exhibits uncertain signs of systemic infection, including at least one of increased white blood cell count, increased body temperature, increased heart rate, and increased or decreased blood pressure compared to medical normative values. It may be. Alternatively, the uncertain signs of systemic infection may be blunt or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or infection resulting from at least one of dental or gynecological examinations or procedures Associated with symptoms. There may be two, three, four, or five at least one criterion of the profile data set applied to an exponential function.

Yet another embodiment provides a method of using an indicator to guide therapeutic intervention to a subject with an infection or a subject with an infection symptom associated with the infection. The method is a step of providing an indicator according to any of the embodiments described above,
Comparing the index with a normative value of the index determined for a related set of subjects to determine a difference, and using the difference between the index and the normative value of the index to treat The therapeutic intervention is a microbial specific therapy, a bacteria specific therapy, a fungal specific therapy, or a virus specific therapy, or is eukaryotic parasite specific.

  Another embodiment provides a pathogen type within the class of pathogens of interest to a patient with an infectious disease or a patient having an infectious disease associated with the infection, from the subject and providing an RNA source A method of distinguishing based on at least one sample is provided. The method includes determining at least one profile data set for the subject and determining in relation to at least one related set of samples in the class of the pathogen of interest. Comparing the generated profile data set to at least one baseline profile data set to determine a difference, and using the difference, in the at least one profile data set for the subject Distinguishing the pathogen type from the pathogen class of the at least one baseline profile data set, wherein the pathogen class is a microorganism. Alternatively, the class of parasites are bacteria and the difference is used to distinguish gram positive bacterial pathogens from gram negative pathogen bacteria. More particularly, the class of parasites are viruses, and the difference is used to distinguish DNA viral pathogens from RNA viral pathogens. More particularly, said class of parasites are eukaryotic parasites and the difference is used to distinguish plasmodium parasite pathogens from trypanosoma pathogens.

  Yet another embodiment provides a pathogen type within a class of pathogens targeted for a patient with an infection or a patient with an infection-related infection based on at least one sample of the subject, A method of using an index to distinguish is provided. The method includes providing at least one indicator based on any of the previously disclosed embodiments for the subject and associating the at least one indicator with at least one related set of subjects. Comparing the at least one normative value of the indicator determined in the step, and using the at least one difference between the at least one indicator and the at least one normative value for the indicator, Distinguishing the type of the pathogen from the class.

(Detailed description of specific embodiment)
Definitions The following terms shall have the indicated meanings unless the context requires otherwise.

  An “algorithm” is a set of rules for describing a biological state. This set of rules is defined exclusively algebraically but may also include alternative or complex decision points that require domain specific knowledge, expert interpretation, or other clinical indicators. Is possible.

  An “agent” is a “composition” or “stimulus” as defined herein, or a combination of a composition and a stimulus.

  “Amplification” in the context of a quantitative RT-PCR assay is a function of the number of DNA replications tracked to quantitatively measure the concentration of DNA.

  As used herein, “amplification” refers to the sensitivity and specificity of a quantitative analysis technique. Thus, amplification defines a measurement of the concentration of a component that is substantially similar in efficiency to amplification and thus the degree of sensitivity and reproducibility for measuring all components. It is evaluated under the conditions.

A “baseline profile data set” is associated with a component of a gene expression panel that results from the assessment of a biological sample (or sample group or set of samples) under a desired biological condition A set of values that are used for mathematically normative purposes.
A desired biological state is, for example, the state of a subject (or subject group or set of subjects) prior to exposure to a drug or in the presence or absence of an untreated disease. is there. Alternatively, or in addition, the desired biological state may be the health condition of the subject or group of subjects or set of subjects. Alternatively or in addition, the desired biological state may include age group, gender, ethnicity, geographic location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environmental exposure It may be related to a group of subjects or a set of subjects selected on the basis of at least one of them.

  A “set” or “population” consisting of a plurality of samples or a plurality of subjects is a predetermined or selected group consisting of a plurality of samples or a plurality of subjects. There is a potential commonality or correlation between members of a sample or set of subjects or groups.

  “Population of cells” refers to any group of cells, and there is a potential commonality or interrelationship among members contained in the cell group, Examples include a group consisting of a plurality of cells obtained from one organ or a group consisting of a plurality of cells obtained from cell culture or biopsy.

  A “biological condition” of a subject is the state of the subject in the relevant area under observation. Such areas can include some aspect of the subject that can be monitored for changes in conditions such as trauma; aging; infection; tissue degeneration; developmental stage;

  As described above, a condition in this context is considered chronic or acute, or simply transient. Furthermore, the biological state of interest may be manifested throughout the organism or population of cells throughout the organism or group of cells, or a particular organ (eg, skin, heart, eyes, or blood). In any case, monitoring the condition directly with a sample from the affected cell population or indirectly with a sample from another location of the subject. Is possible. The term “biological condition” encompasses “physical condition”.

  A “body fluid” of a subject includes blood, urine, spinal fluid, lymph, mucosal secretions, prostate fluid, semen, hemolymph, or any other body fluid known in the art.

  A “calibrated profile data set” is a function of the members of the first profile data set and the corresponding members of the baseline profile data set for a given component of the panel .

  A “clinical indicator” is any physiological data used alone or in conjunction with other data in assessing the physiological state of cell collection or biological collection.

  As a “composition”, any physiological condition or combination of physiological conditions, chemicals, dietary supplements, pharmaceuticals, homeopathic preparations, anti-symptomatic therapeutic preparations, natural therapeutic preparations, combinations of compounds, Examples include complex mixtures of food supplements, minerals, and substances, including toxins, foods.

  “Deriving” a profile data set from a sample is to determine a set of values associated with a component of a gene expression panel, (i) directly measuring such component in a biological sample Or (ii) determining by measuring such components in the original sample or a second biological sample exposed to the material derived from the original sample.

  A “distinct RNA or protein constituent” in a panel composed of a plurality of components is an independent expression product of a gene, regardless of RNA or protein. A gene “expression product” includes a gene product regardless of the protein produced by translation of RNA or messenger RNA.

  The “Gene Expression Panel” is a set of experimentally identified components, each component being a distinct expression product of a gene, whether RNA or protein, The set of components is selected such that the measurement of the set of components defines the measurement of the biological state of interest.

  A “Gene Expression Profile” is a set of values related to a component of a gene expression panel that results from an assessment of a biological sample (or sample group or set of samples).

  “Gene Expression Profile Inflammation Index” is the value of an exponential function that provides a mapping from an instance of a gene expression profile to a monovalent criterion for an inflammatory condition.

  A subject's “health” includes the subject's psychological, emotional, physical, mental, paratherapeutic, paratherapeutic, and homeopathic conditions.

  An “index” is an arithmetically or mathematically derived numerical property that arises as an aid in simplifying or disclosing or informing more complex quantitative information. A disease or group index can be determined by application of a particular algorithm to multiple subjects or samples having a common biological state.

  “Inflammation” is used herein in its general sense and is induced by any number of chemical, physical, or biological factors or combinations of factors. Sustained acute or chronic, simple or purulent, local or generalized, cellular or tissue response.

  “Inflammatory state” is used to indicate the relative biological state of a subject resulting from inflammation or to characterize the degree of inflammation.

  A “large number” data set based on a common panel of genes is sufficient to allow statistically significant conclusions to be made for instances of a data set based on the same panel A large number of data sets.

  The “normal” state of the subject to whom the composition is administered means the state prior to administration, even if the subject in the previous period happens to suffer from a disease.

  A “panel” of genes is a set of genes that includes at least two components.

  A “sample” from a subject includes a single cell or a plurality of cells or cell fragments or part of a body fluid, such as venipuncture, drainage, ejaculation, massage, biopsy, needle aspirate, wash fluid Samples, scrapes, surgical incisions or interventions, or other means well known to those skilled in the art are taken from the subject.

  A “Signature Profile” is an experimentally validated subset of gene expression profiles that are chosen to identify biological states, agents, or physiological mechanisms of action.

  The “Signature Panel” is a subset of the gene expression panel, the components of which are chosen so that they can distinguish between biological states, agents, or physiological mechanisms of action.

  A “subject” is a cell, tissue, or living body (human or non-human) and, under observation, in vivo, ex vivo, or in vitro. ). When referring to assessing the biological status of a subject based on a sample obtained from a subject, use blood or other tissue samples from the human subject to assess the status of the human subject. However, using the blood sample itself as the above-mentioned subject, for example, evaluating the effect of a treatment or an action factor on the sample is also included.

  “Stimulus” means (i) physical interaction with a monitored subject, eg treatment with ultraviolet A or B or light for seasonal affective disorder, treatment of psoriasis with psoralen, or implanted radioactive seed, Treatment of melanoma with other radiation, and (ii) any physical, psychological, or mental activity or inactivity of the monitored subject.

  “Therapeutic” is whether it is a biological, chemical, physical, metaphysical, or combination thereof intended to maintain or alter the biological state of the monitored subject. Including all interventions.

  PCT Patent Application Publication No. WO01 filed for invention by the inventor and entitled “System and Method for Characterizing Biological Conditions or Agents Using Calibrated Gene Expression Profiles” / 25473 (issued April 12, 2001) (incorporated herein) includes (i) biological conditions (included with respect to health and disease) and (ii) biological conditions (health, toxicity, treatment) And the use of a gene expression panel to assess the effects of one or more agents on treatment and inclusion (with respect to drug interactions).

  In particular, a gene expression panel can be used to predict a set of biological states of interest; prediction of toxicological or dose effects of a composition or mixed composition on an individual or population or a set of individuals or groups of cells; Determining how two or more agents administered in the treatment interact to detect either synergistic, additive, neutral, or toxic activity; Conducting preclinical and clinical trials by providing new criteria for useful profile data sets to reveal; and those patients prior to conducting Phase 1 or Phase 2 trials Measuring the therapeutic efficacy of natural or synthetic compositions or stimuli that can be formulated alone or in combination or as a mixture Or it may be used for. These gene expression panels can be used on samples derived from subjects to assess their biological status.

  The selection of the gene expression panel is made such that quantitative measurements of RNA or protein components constitute measurements of the biological state of the subject. In one type of arrangement, a calibrated profile data set is used. Each member of this calibrated profile data is a function of (i) independent component criteria of the gene expression panel and (ii) baseline.

  For the purposes of this description and the following claims, we consider a degree of measurement reproducibility greater than 20 percent as a measurement condition provided “substantially repeatable”. In particular, it is required that substantially the same measurement should result in substantially the same expression level each time when the measurement is obtained corresponding to the expression level of a component contained in a particular sample. In this way, it is possible to compare the expression levels of the constituent elements of the gene expression panel from sample to sample. The criteria for reproducibility are when all measurements for this component are distorted, even if the expression level measurement for that particular component is inaccurate (eg, well below 30%). Is also systematically distorted, meaning that the measurement of the expression level of its constituents can be meaningfully compared. In this way, it is possible to obtain useful information and make a meaningful comparison with respect to the expression of the components under different circumstances.

  In addition to reproducibility criteria, quantitative measurement of components under conditions where all components have substantially the same amplification efficiency (within 1-2 percent and generally within 1 percent). It is desirable that the second criterion for performing is also satisfied. If both of these criteria are met, the measurement of the expression level of one component is meaningfully compared with the expression level of another component of a given sample from sample to sample.

  This embodiment uses an indicator or algorithm derived from quantitative measurement of components, and optionally uses an indicator or algorithm obtained from either expert analysis or computerized biology. The use of which controls or normalizes the effects of (a) analysis of complex data sets, (b) no informational value or otherwise minor variance in gene expression levels between samples or between subjects Simplifying the characterization of a composite data set for comparison with other composite data sets, databases, or indicators or algorithms derived from the composite data set, (d) subject biology (E) a series of biological conditions of interest, either alone or in combination To determine the therapeutic efficacy of a natural or synthetic composition or stimulus that can be formulated as a mixture, (f) the toxicological effects of the composition or mixture on an individual or a population or a set of individuals or cells Or to predict dose effect, (g) how two or more agents administered in a single treatment detect either synergistic, additive, neutral, or toxic activity To determine how to interact, (h) conduct preclinical and clinical trials by providing new criteria for useful profile data sets to reveal disease status As well as prior administration studies for those patients prior to conducting Phase 1 or Phase 2 studies.

  Gene expression profiling and the use of index characterization for specific conditions and / or agents can be used to reduce the cost of phase 3 trials and are beyond the scope of phase 3 trials The selection of an appropriate medication in a class of medications for a particular patient directed to a unique physiology; diagnosis or determination of a medical condition or infection progression preceding the onset of symptoms, or the therapeutic It can also be used for diagnosis of side effects related to administration; healthcare management of patients; and quality control for different batches of one agent or a mixture of agents.

Subject
Since the methods disclosed herein are well known to those skilled in the art, it is well known to those skilled in the art that all cells transcribe RNA and how RNA is extracted from all types of cells. Can be applied to cells of humans, mammals, or other living organisms without the need for

Selection of Gene Expression Panel Components A general approach for selecting gene expression panel components is disclosed in PCT Application Publication No. WO01 / 25473. We have designed extensive gene expression panels and have experimentally confirmed that each panel provides a quantitative measure of biological status derived from a sample of blood or other tissue. For each panel, experiments confirmed that gene expression profiles using panel components provide biological status information (we are helpful in obtaining biological status information elsewhere) This shows that, inter alia, gene expression profiles can be used to measure the effects of treatment, as well as providing a target for therapeutic intervention). Examples of gene expression panels are shown in the table attached hereto as follows, in addition to a brief description of each panel component.

Table 1. Inflammatory gene expression panel Table 2. Diabetes gene expression panel Table 3. Prostate gene expression panel Table 4. Skin reaction gene expression panel Table 5. Liver metabolism and disease gene expression panel Table 6. Endothelial gene expression panel Table 7. Cell Health and Apoptosis Gene Expression Panel Table 8. Cytokine gene expression panel
Table 9. TNF / IL1 inhibitory gene expression panel
Table 10. Chemokine gene expression panel
Table 11. Breast cancer gene expression panel
Table 12. Infectious disease gene expression panel
One skilled in the art can construct and experimentally verify other panels according to the principles clearly expressed in this application.

Assay design We generally performed quadruplicates to run one sample on one panel. That is, the sample is divided into a plurality of aliquots, and the concentration of each component in one gene expression panel is measured for each aliquot. With a total of 900 component assays in quadruplicate for each assay, we have an average coefficient of variation (standard deviation / mean) * 100) of less than 2 percent, generally less than 1 percent, between results for each assay The knowledge that it is. This number is a measure of what we call “intra-assay variability”. We also performed assays under different conditions using the same sample material. For the 72 assays, from the concentration measurements of the components in a 24 member panel and the results from such concentration measurements determined at three different conditions over time, we found an average coefficient of variation of 5 percent. Less than, generally less than 2 percent. This number is the criterion we call “inter-assay variability”.

  We have found it useful in using quadruple test results to identify and eliminate data points that are statistically “outliners”. That is, such data points differ, for example, by a rate that is greater than 3% of the average of all four values, and do not result from any systematic distortion, for example, greater than 1%. Such data points are different by, for example, a larger percentage, such as 4 values and any systematic distortion, eg, greater than 1%, that is 3% of all the averages where it does not occur. In addition, if a set of four data points are excluded by this procedure, all data for the associated component is discarded.

Measurement of gene expression relative to components in the panel To determine the amount of specific RNA in a sample, we transcribed RNA from the sample related to the components of the gene expression panel using methods well known to those skilled in the art. Were extracted and quantified (for details see the protocol below, see also PCT application publication number WO 98/24935, incorporated herein as an RNA analysis protocol).

  Briefly, RNA is extracted from a sample of a subject (eg, tissue, body fluid, or culture medium in which a group of cells can grow). For example, cells can be lysed and RNA can be eluted into an appropriate solution (a solution in which a DNase reaction is performed). First strand synthesis can be performed using reverse transcriptase. Gene amplification, more specifically a quantitative PCR assay, can then be performed to calibrate the gene of interest to a marker such as 18s rRNA ((Hirayama et al., Blood 92, 1998: 46-52) Sample measurements are made in multiple repeats (eg, 4 repeats) The relative quantification of mRNA is determined by the difference in threshold cycle between the internal standard and the gene of interest. In an embodiment of the present invention, quantitative PCR is performed using amplification, described as reagents and equipment marketed by Applied Biosystems (Forster City, Calif.) Predefined amplification of target transcripts For efficiency reasons, the signal from the amplified target is detectable. (Eg, cycle number) is considered to be directly related to the amount of a particular message transcript in the sample measured, as well as other quantifications such as fluorescence, enzyme activity, decays per minute, absorbance, etc. The quantification signal correlates to a known concentration of a target template (eg, a reference standard curve) or quantifies the number of target templates in a known sample when normalized to a standard with limited variation Can be used for

  Standards with limited variability can be used to quantify the number of target templates in unknown samples, correlated or normalized with a known concentration of the target template (eg a reference calibration curve). Although amplification methods are not limited, quantitative gene expression techniques can utilize target transcript amplification. Alternatively, or in combination with amplification of the target transcript, reporter signal amplification can also be used. Amplification of the target template can be achieved by isothermal gene amplification strategies or by gene amplification by thermal cycling such as PCR.

  It is desirable to obtain a definable and reproducible correlation between the amplified target or reporter and the concentration of the starting template. What we have found is that, for example, this objective is to carefully observe consistent primer-template ratios and to strictly adhere to narrow tolerance levels of experimental amplification efficiency (eg, 99.0 to 100 % Relative efficiency, typically 99.8 to 100% relative efficiency). For example, in determining gene expression levels in relation to a single gene expression profile, all components of this panel have a similar and limited range of primer-template ratios (eg, within a 10-fold range) and It is necessary to maintain amplification efficiency (eg, in the range of less than 1%) to allow accurate and accurate relative measurements for each component. For the purposes of this description and the following claims, we consider amplification efficiency to be “substantially similar” if it differs by no more than about 10%. Preferably, the difference is less than about 2%, more preferably less than about 1%. These constraints must be observed over the full range of measured concentration levels associated with the associated biological state. Thus, the various embodiments herein meet the criteria that measurements are achieved under substantially reproducible measurement conditions and that the amplification efficiencies of all components are substantially similar. On the other hand, nevertheless, by adjusting for assay results that do not meet these criteria directly, by compensating for errors in this way, such that the criteria are met after appropriately adjusting the assay results, Achieving such measurement conditions is within the scope of the present invention as claimed herein.

  In practice, we conduct tests to ensure that these conditions are met. For example, we typically design and manufacture a large number of primer-probe sets and experimentally determine which set gives the best performance. Primer-probe design and manufacture can be enhanced using computer techniques known in the art, and in the course of experimental validation we have selected a set of primer-probe combinations Related to features.

  The reverse primer must be complementary to the coding DNA strand. In one embodiment, the primer must be located in the intron-exon junction and is no more than 3 bases at the 3 ′ prime end of the reverse primer complementary to the proximal exon (greater than 3 bases). Is complementary, it will tend to amplify genomic DNA competitively).

  In an embodiment of the present invention, the primer probe must amplify a cDNA of less than 110 bases in length, from a related but biologically unrelated locus, genomic DNA or transcript or Do not amplify cDNA.

  A suitable target for the selected primer / probe is the first strand cDNA that would be prepared in one embodiment and is described as follows.

(A) Use of whole blood for ex vivo assessment of the biological state affected by the agent Human blood is obtained by venipuncture and the sample is prepared in an amount sufficient for at least three time points. Prepare for assay separately for baseline, unstimulated, and stimulated. Typical stimulants include lipopolysaccharide (LPS), phytohemagglutinin (PHA), and heat-killed staphylococci (HKS) or carrageen, used individually (typically) or in combination May be. Aliquots of heparinized whole blood were mixed without irritants and kept at 37 ° C. for 30 minutes in an atmosphere of 5% CO ₂ . Additional test compound may be added at this point and held at different times according to the expected pharmacokinetics of the test compound. At predetermined times, cells were harvested by centrifugation, plasma was removed by various standard means, and RNA was extracted.

Nucleic acid, RNA, and / or DNA is purified from cells, tissues or body fluids of a test group of cells or indicator cell lines. RNA is preferentially obtained from nucleic acid mixtures using various standard procedures (or RNA Isolation Strategies, pp. 55-104, in RNA Methods for isolation and charactorization, char- acterization, R & D, 19 E. Farrell, Jr., Ed., Academic Press, in this case, a filter-based RNA isolation system (Ambion (RNAqueous ^™ , Phenol-free Total RNA Isolation Kit, Catalog # 1912, 8th, 8th, 8th, 9th, 8th, 9th, 8th, 9th, 8th, 9th, 8th, 9th, 8th, 9th, 8th, 9th, 8th, 9th, 8th Obtained from).

  According to one procedure, a whole blood assay for gene expression profiles was performed as follows. That is, human whole blood was collected into a 10 ml vacutainer tube containing heparin / sodium. The blood sample was gently mixed by inverting the tube 4-5 times. Blood was used within 10-15 minutes of blood collection. In the experiment, the blood was diluted 2-fold per sample, ie for each time point (0.6 ml whole blood + 0.6 ml stimulant). An assay medium was prepared and stimuli added appropriately.

Next, an aliquot of whole blood (0.6 ml) was added to each 12 × 75 mm polypropylene tube. Add 0.6 ml of 2XLPS ( E. coli serotype 0127: B8, Sigma # L3880 or serotype 055, Sigma # L4005, 10 ng / ml, subject to change in lot) to LPS tubes did. Next, 0.6 ml of assay medium was added to the “control” tube, and the tube was duplicated for each condition. Closed the cap tightly. The sample was mixed by inverting the tube 2-3 times. The cap was loosened to the first stop and the tube was incubated at 37 ° C., 5% CO ₂ for 6 hours. At 6 hours, the sample was gently mixed with the suspended blood cells and 1 ml was removed from each tube (micropipette with a barrier tip) and transferred to a 2 ml “Dolphin” microcentrifuge tube (Costar # 3213).

  The samples were then centrifuged for 5 minutes at 500 × g and ambient temperature (IEC centrifuge or equivalent, with a microcentrifuge adapter in a swinging bucket) to remove as much serum as possible from each tube. The cell pellet was placed on ice and RNA was extracted as quickly as possible using the Ambion RNAqueous kit.

(B) Amplification strategy Specific RNA is amplified using message specific primers or random primers. Specific primers are obtained from public databases (eg, Unigene, National Center for Biotechnology Information, National Library of Medicine, Bethesda, Med.) Containing information from genomic and cDNA libraries obtained from humans or other animals. Was synthesized from In order to preferentially amplify from a specific RNA obtained from a test sample or an indicator sample, a primer is selected. For example, RT PCR, Chapter 15 in RNA Methods, A laboratory guide for isolation and charac- terization , 2nd edition, 1998, Robert E. et al . Farrell, Jr. Ed. , Academic Press; or Chapter 22 pp. 143-151, RNA isolation and char- acterization protocols , Methods in molecular biology, Volume 86, 1998, R .; Rapley and D.C. L. Manning Eds. , Human Press, or 14 in Statistical refinement of primer design parameters, Chapter 5, pp. 55-72, PCR applications: protocols for functional genomics, M.M. A. Innis, D.D. H. Gelfand and J.M. J. et al. Sninsky, Eds. , 1999, Academic Press). Amplification is either performed under isothermal conditions, or carried out using a thermal cycler (e.g., Applied Biosystems, Foster City, Calif obtained from ABI9600 or 9700 or 7700;.. Nucleic acid detection methods , pp 1-24, in Molecular methods for virus detection , D. L. Wiedbrauk and D. H., Farkas, Eds., 1995, Academic Press). Amplified nucleic acid is detected using fluorescently labeled detection primers identified and synthesized from known databases as described for amplification primers (eg, Taqman ^™ PCR Reagent Kit, Protocol, part number 402823 revision A , 1996, Applied Biosystems, Foster City Calif.).

  In this case, the amplified DNA is detected and quantified using an ABIPrism 7700 Sequence Detection System obtained from Applied Biosystems (Foster City, Calif.). The amount of specific RNA contained in a test sample or obtained from an indicator cell line can be related to the amount of fluorescence observed (see, eg, Advances in quantitative PCR technology: 5'nuclease assays, YS Lie and C. J. Petropolus, Current Opinion in Biotechnology, 1998, 9: 43-48, or Rapid thermal cycling and PCR kinetics in fol. Innis, DH Gelfa d and J. J. Sninsky, Eds., See, 1999, Academic Press).

  As a specific implementation of the approach described here, we describe in detail the procedure for the synthesis of first strand cDNA for use in PCR. This procedure can be used for both RNA extracted from cultured cells (ie, THP-1 cells) and whole blood RNA.

Material 1. Applied Biosystems TAQMAN Reverse Transcription Reagents Kit (P / N 808-0234). Kit components: 10 × TaqMan RT buffer, 25 ml magnesium chloride, deoxyNTP mixture, random hexamer, RNase inhibitor, MultiScribe reverse transcriptase (50 U / ml) (2) RNAase / DNase free water (DEPC treatment) Water, obtained from Ambion (P / N 9915G) or equivalent).

Method 1. RNase inhibitor and MultiScribe reverse transcriptase are immediately placed on ice. All other reagents can be thawed at room temperature and placed on ice.

  2. Remove the RNA sample from the −80 ° C. freezer, thaw at room temperature and place immediately on ice.

  3. Prepare the following reverse transcriptase reagent cocktail for each 100 ml RT reaction (prepare extra cocktail to allow pipetting errors for multiple samples).

1 Reactant (ml) 11X, eg 10 samples (ml)
10X RT buffer 10.0 110.0
25 mM MgCl ₂ 22.0 242.0
dNTP 20.0 220.0
Random hexamer 5.0 55.0
RNase inhibitor 2.5 27.5
Water 18.5 203.5
Total: 80.0 880.0 (80ml per sample)
4). Bring each RNA sample to a total volume of 20 ml in a 1.5 ml microcentrifuge tube (eg, take 10 ml RNA for THP-1 RNA and dilute to 20 ml with RNase / DNAase free water. 20 ml for whole blood RNA Use total RNA), add steps 5, 2, 3 to 80 ml RT reaction mixture. Mix by pipetting up and down.

  5). Incubate the sample at room temperature for 10 minutes.

  6). Samples are incubated at 37 ° C. for 1 hour.

  7). Incubate the sample at 90 ° C. for 10 minutes.

  8). Quickly centrifuge the sample in a microcentrifuge.

  9. If PCR is to be performed immediately, place the sample on ice, otherwise store the sample at −20 ° C. for future use.

  10. PCR QC is performed on all RT samples using 18S and b-actin (see SOP 200-020).

  In order to enable measurement of the components of the gene expression panel, the use of the primer / probe based on the first strand cDNA as described above is as follows.

Set up a 24-gene human gene expression panel for inflammation.
Material 1. 20X primer / probe mixture for each gene of interest.

  2. 20S endogenous control 20X primer / probe mixture.

  3.2X TACMAN universal PCR master mix.

  4). CDNA transcribed from RNA extracted from cells.

  5). Plate (Applied Biosystems 96-Well Optical Reaction Plate).

  6). Cap (Applied Biosystems Optical Cap) or optical clear film.

  7). Detection device (Applied Biosystem Prism 7700 Sequence Detector).

Method 1, Stock of each primer-probe mixture containing primer-probe for the gene of interest, 20S endogenous control 20X primer-probe, and 2X PCR master mix as follows. Make a sufficient excess (eg, about 10% excess) to allow for pipetting errors. The following example illustrates a typical setup for one gene with quadruple samples testing two conditions (two plates).

1X (1 well) 9X (comparable to 2 plates)
2X Master Mix 12.50 112.50
20X18S primer / probe mixture 1.25 11.25
20X target gene primer / probe mixture 1.25 11.25
Total 15.00 135.00

2. Dilute 95 μl of cDNA95 in 2000 μl of water to make a cDNA target stock. The amount of cDNA is adjusted to obtain a Ct value of 10-18, typically 12-13.

  3. Pipette 15 [mu] l of primer-probe mixture into the appropriate wells of a plate (Applied Biosystems 96-Well Optical Reaction Plate).

  4). Pipette 10 μl of cDNA stock solution into each well of a plate (Applied Biosystems 96-Well Optical Reaction Plate).

  5). The plate is sealed with a cap (Applied Biosystems Optical Cap) or optical clear film.

  6). Plates are analyzed with a detector (AB Prism 7700 Sequence Detector).

  The methods described here can be applied using proteins and sensitive quantitative techniques such as enzyme-linked immunosorbent assay (ELISA) or mass spectrometry can be used to measure the amount of protein components. Are well known to those skilled in the art (see WO 98/24935, incorporated herein).

Baseline profile data set Analysis of samples from a single individual and a large population yields a library associated with a particular panel or series of panels. These profile data sets can be stored as records in the library for use as baseline profile data sets. The term “baseline” suggests that the stored baseline profile data set provides a calibrated profile data set with information about the biological state or agent. It functions as. Baseline profile data sets can be stored in a library and classified as a number of cross-reference means. One form of taxonomy may depend on the characteristics of the panel from which the data set is derived. Another form of classification may be by a specific biological state. The concept of biological state encompasses any state in which a cell or group of cells is found at any time. This state may reflect sample placement, subject sex, or other discriminators. This condition can represent the geography of the sample (subject or any other discriminator nature). A part of the discriminator may be overlapped. The library can be accessed for records associated with a single subject or a particular clinical trial. Baseline profile data set classifications can be further annotated with medical information about specific subjects, symptoms, specific agents, and the like.

  The selection of the baseline profile data set to create the calibrated profile data to be set is not only related to the biological condition being monitored or predicted and similarly evaluated, The intended use of the calibrated panel is, for example, for drug development for monitoring, quality control, or other applications. Change the exposure to stimuli, drugs, or complex compounds at different times, from the same subject from which the first profile data set is obtained, or from another subject to the baseline profile data set It may be desirable to access or may be from the same or a different group or set of subjects.

  The profile data set may be from the same subject from which the first data set is obtained, and sample collection may be at a different or similar location at a different or similar time. Or in a different biological state or similar biological state. For example, FIG. 5 shows a procedure in which a sample is taken before or after stimulation. A profile data set obtained from an unstimulated sample can serve as a basic profile data set for samples collected after stimulation. The baseline data set may be derived from a library containing a profile data set of a group of subjects or a set of subjects having a number of predetermined characteristics or biological states. The baseline profile data set may correspond to a number of ex vivo or in vivo characteristics associated with in vitro cell culture. The resulting calibrated profile data set is then stored as a record in a database or library (Figure 6), with or without the baseline profile database, and as needed. Can be stored with the first profile data set (although the first profile data set is usually incorporated into the baseline profile data set under appropriate taxonomy criteria) . The remarkable consistency of gene expression profiles with a given biological state is useful for storing profile data and can be used among others for normative reference purposes. A normative reference indicates the extent to which a subject meets a given biological state (healthy or diseased state), and may instead or in addition help subject the target to clinical intervention it can.

  The selected baseline profile data set can also be used as a standard for judging production lots in terms of efficacy, toxicity, etc. When the effect of a therapeutic agent is measured, the baseline data set matches the gene expression profile taken prior to administering the therapeutic agent. If quality control has been determined for a newly manufactured product, the baseline data set will match the criteria for that product. However, any suitable normalization technique can be used. For example, an average baseline profile data set can be obtained from a reliable source of naturally grown herbal dietary supplements and released in lots of compounds that are compared over time between different lots. It is possible to show consistency or lack of consistency.

Calibrated data If we have the reproducibility achieved in the measurement of gene expression described above in relation to the “gene expression panel” and “gene amplification”, we can If so, conclude that the difference is due to a difference in biological state. Thus, we have found that the calibrated profile data set is very reproducible with samples taken from the same individual under the same conditions. We have found that similarly calibrated profile data sets are reproducible with samples tested repeatedly. We have derived a “calibrated profile data set” that is obtained when a sample from a subject is exposed to a compound ex vivo from a sample that has been exposed to the sample in vivo. We also found repeated instances corresponding to calibrated profile data. Importantly, we have a number of calibrated profile data sets that are equivalent to those obtained from in vivo or ex vivo cell populations of indicator cell lines treated with the agent. If you have found that you can offer. In addition, we administer a sample from the subject on the indicator cell, including the health status, symptoms, therapeutic intervention, aging, or exposure of the subject to environmental stimuli or toxins. It has been discovered that a useful calibrated profile data set can be provided.

Calculation and assistance with calibrated profile data sets The calibrated profile data can be represented in a spreadsheet or visually, for example in a bar chart or tabular format or in a three-dimensional display . The function relating the baseline and the profile data may be a ratio expressed as a logarithm. It is possible to show the components by item on the X-axis and place a logarithmic scale on the Y-axis. Express the members of the calibrated data set as a positive value that symbolizes relative enhancement of gene expression relative to the baseline or as a negative value that represents a relative decrease in gene expression Is possible.

  Each member of the calibrated profile data must be reproducible within the range associated with similar samples taken from the subject under similar conditions. For example, the calibrated profile data may have an order of magnitude reproducibility for similar samples taken from the subject under similar conditions. More particularly, the member may have a reproducibility within 50%, in particular within 20%, generally within 10%. More particularly, members can be reproducible within 50% (especially reproducible within 20%) and generally within 10%. According to an embodiment of the present invention, a pattern with no increase, decrease, or change in relative gene expression from each of the plurality of loci tested in the gene expression panel prepares a calibrated profile set The set can be used for information related to biological status, biological efficiency of agent treatment conditions, or a group of subjects or samples or a set of subjects or samples ( Information for comparison with a set) or information for comparing cell groups. This pattern of properties can be used to identify potential drug trial candidates and is used alone or in combination with clinical indicators that are diagnosed or predictive of a biological condition Or it can be used to guide the development of pharmaceuticals or dietary supplements through manufacturing, testing, and marketing.

  Quantity data obtained from quantitative gene expression and quantity data obtained from gene expression calibrated in relation to the baseline profile data set are stored in a database or digital storage medium to manage patient health, clinical It is possible to search for conducting tests or characterization of drugs. The data can be physically retrieved via the World Wide Web, e-mail, Internet access site, or by hard copy so that it can be collected and pooled from geographically distant locations. Or can be transferred to a wireless network (FIG. 8).

  In one embodiment of the present invention, the descriptive records are stored in one database or multiple databases, and the stored data is stored in the raw gene expression data (secondary) prior to conversion by use of the baseline profile data set. A single profile data set), for example, whether the baseline profile data set comes from a particular signature panel and other annotations that facilitate the interpretation and use of the data. Including a record of the baseline profile data set used to generate the calibrated profile data.

  Since the data is in a general-purpose format, data processing can be easily performed by a computer. Organize the data so that an output that arbitrarily corresponds to the graphical representation of the calibrated data set is obtained.

For example, a separate sample from the subject with at least one RNA or protein may be denoted as P _I. The first profile data set derived from the sample P _I and M _j. Here, M _j is the quantitative measure of the independent RNA or protein vigorous elements P _I. Record R _i is the ratio of M to P and relates to, for example, age, diet, ethnicity, gender, geographic location, medical illness, mental disorder, medication, physical activity, weight, and environmental exposure It may be annotated with additional data of the subject. Further, the data processing can further include access data from a second state database that can include additional medical data that is not currently maintained in the calibrated profile data. In this context, data access may be via a computer network.

  The data storage device on the computer can provide information in a form accessible by the user. Thus, the user can load information into the second access site, including downloading information. However, access can be restricted to users with passwords or other security measures so that the medical records contained therein are protected. A feature of this embodiment of the invention is the user's ability to add new or annotated records to the data set so that the records become part of the biological information. A graphical representation of a calibrated profile data set associated with a product such as a drug provides an opportunity to standardize the product with a calibrated profile (especially a signature profile). The profile is used as a feature that indicates relative efficacy, differences in mechanism of action, etc. compared to other drugs approved or similar in different or different applications.

  Various embodiments of the present invention may also be implemented as computer program products for use by a computer system. The product can include program code to derive a first profile data set to produce a calibrated profile. Such an implementation may be implemented in a computer system via a tangible representation medium (eg, a computer readable medium (eg, diskette, CD-ROM, ROM or fixed disk)) or a modem or other interface device (eg, a communication adapter coupled to a network). Any of the media that can be sent to can include a series of machine language instructions. Network coupling may be, for example, via optical or wired communication lines, or via wireless technology (eg, microwave, infrared, or other electrical transmission technology) or a combination thereof. The series of machine language instructions preferably implements all or part of the functionality already described herein with respect to the system and all or part of the functionality previously described herein with respect to the system. To do. Those skilled in the art should appreciate that such machine language instructions can be written in a number of programming languages for many computer architectures or operating systems. Furthermore, such instructions can be stored in any memory element (eg, semiconductor, magnetic element, optical element, or other memory element) and any communication technology (eg, optical technology, infrared technology, It is also possible to transmit using microwave technology or other transmission technology. Such computer program products are distributed as removable media (eg, packaged software) with printed or electronic documents attached and preloaded into a computer system (eg, system ROM or fixed) Distribution on a disk) or from a bulletin board on a server or network (eg, the Internet or the World Wide Web). It is also expected that the computer system may be provided with a guidance module or the like for deriving for deriving the first data set and the calibration profile data set.

  Calibration profile data sets in the form of graphs or tables, related databases, and calculated indicators or derived algorithms together with information derived from panels, databases, data sets, or indicators or algorithms Or separately, it is a necessity sold for various purposes, as described in WO 01/25473.

Indicator structure (i) Significant consistency of gene expression profiles for the biological state across a group or set of analytes or samples or cells, and (ii) Specificity of amplification for all components of the panel Combined with the use of a procedure that provides a substantially reproducible measurement of the components of the gene expression panel that yields the gene expression profile under measurement conditions that are substantially similar in efficiency , Thereby enabling the use of an indicator by which a biological state is measured.

  An indicator can be constructed using an exponential function that maps a value in a gene expression profile to a single value associated with the biological state at hand. The value within the gene expression profile is the amount of each component of the gene expression panel that corresponds to the gene expression profile. The quantities of these components form a profile data set, and the above index with a single value exponential function is generated from the members of the profile data set.

An exponential function is conveniently constructed as a linear sum of terms, and each term is what we call a “contribution function” of the members of the profile data set. For example, the contribution function may be a constant multiplied by the index of the member of the profile data set. Therefore, the exponential function is
I = ΣCiMiP (i)
Where I is the index, Mi is the value of member i of the profile data set, Ci is a constant, and P (i) is the index by which Mi is raised. For integer values i up to the number of members in the data set, the sum is formed, so we have a linear polynomial.

Since the values Ci and P (i) can be determined in several ways, the indicator I gives information on the relevant biological state. One method applies statistical techniques (eg, latent class modeling) to the profile data set or other data related to biological conditions that correlate clinical data or experimentally derived data. That is. In this regard, for example, Statistical Innovations, Belmont, Mass. , CALLED LATENTED GOLD (registered trademark) software. Web page www. statistical innovations. com / lg / . This web page is incorporated herein.

  Alternatively, other simpler modeling techniques can be used in a manner known in the art. The exponential function of inflammation, for example, is constructed in such a way that a higher degree of inflammation (as determined in the profile data set of the inflammatory gene expression profile) correlates with a large value of the exponential function. Thus, in a simple embodiment, each P (i) is +1 or -1, depending on whether the component increases or decreases with increasing inflammation. As explained in more detail below, we have constructed a meaningful inflammatory index that is proportional to the following equation:

1/4 {IL1A} +1/4 {IL1B} +1/4 {TNF} +1/4 {INFG} -1 / {IL10},
Where curly brackets around the component indicate such component measurements, and the component is a subset of the inflammatory gene expression panel of Table 1.

  Since the baseline profile data set described above is used to provide an appropriate normative reference, it can also be used to generate a profile data set calibrated as described above. Based on the genetic reference, the index characterizing the gene expression profile can give a normative value of the exponential function used to create the index. Since this normative value is determined in relation to an associated group or set of subjects or samples, or in relation to an associated cell group, the indicator can be interpreted with respect to the normative value. The related group or set of subjects or samples, or related cell groups, include age range, gender, ethnicity, geographical location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environmental exposure It is possible to have at least one characteristic in common.

  For example, the indicator may be related to a normative gene expression profile for a group or set of healthy subjects such that an indication of about 1 characterizes the normative gene expression profile of healthy subjects. Can be built. Further assume that the biological condition that is the subject of the above indication is inflammation. That is, a reading of 1 in this example corresponds to a gene expression profile that is comparable to the standard for healthy subjects. A substantially higher reading can identify a subject experiencing an inflammatory condition. However, using 1 as the normative value identification is the only possible choice, and another logical choice is to use 0 as the normative value identification. With this selection, the deviation of the index from zero can be expressed in standard deviation units, so that the value present between -1 and +1 includes 90% of the normative distribution reference group or set of subjects. . Since we have found that gene expression profile values (and indicators based on them) usually tend to be distributed, considerable information can be obtained from the zero-centric indicators thus constructed. It is done. Therefore, it is easy to use the indicator in the purpose setting for disease diagnosis and treatment. The selection of 0 for the normative value and the use of standard deviation units are illustrated, for example, in FIG.

Example
Example 1: Acute Inflammation Index Assisted by Analysis of a Large Complex Data Set In one embodiment of the invention, using the index value or algorithm described above, a complex data set is used to provide information regarding the inflammatory status of a subject. Having a single index value can be reduced. This is illustrated in FIGS. 1A and 1B.

  FIG. 1A is titled that subject's source precision inflammation profile tracking results in a large composite data set. FIG. 1 shows the results of assaying 24 genes from an inflammatory gene expression panel (shown in Table 1) on separate 8 days during the course of optic neuritis in one male subject.

FIG. 1B shows the use of an acute inflammation index. The data shown in FIG. 1A is expressed by the following formula: (1/4 {IL1A} +1/4 {IE1B} +1/4 {TNF} +1/4 {INFG})
It is shown in this figure after calculation using an exponential function proportional to.

Example 2: Use of an acute inflammatory index or algorithm to monitor the biological status of a sample or subject The inflammatory status of a subject can be past history, future history, response to treatment, etc. Clarify information about. Acute inflammatory indicators are used to reveal such information regarding the biological state of the subject. This is illustrated in FIG.

  The results of the assay for daily inflammatory gene expression (shown for 24 genes in each column of FIG. 1A) are displayed in individual histograms after calculation. The index reveals a clear trend in inflammatory conditions that appear to correlate with therapeutic intervention (FIG. 2).

  FIG. 2 is a graphical representation of an acute inflammatory index calculated at 9 different and important clinical milestones from blood obtained from a single patient undergoing medical treatment for optic neuritis . Changes in the index value of the acute inflammatory index are strongly correlated with the expected effects of therapeutic intervention. Four clinical control points are identified in the figure at the top of the acute inflammation index, (1) before treatment with steroids, (2) treatment with 1 g IV solmedrol per day, (3) per day Includes post-treatment with 60 mg oral prednisone (tapering to 10 mg per day), and (4) post-treatment. The data set is the same as in FIG. The index is proportional to 1/4 {ElIA} +1/4 {ILlB} +1/4 {TNF} +1/4 {INFG} -1 / {IL10}. As expected, the acute inflammatory index falls rapidly with treatment with IV steroids and rises during treatment with less effective oral prednisone, after steroids are stopped and completely metabolized, before treatment. Return to level.

Example 3: Use of acute inflammatory indicators to set dose , concentration and timing, as shown in FIG. 3, for compounds under development or to be tested in human and non-human subjects. Acute inflammation indicators may be used as a common reference value for therapeutic compounds or therapeutic interventions without a common mechanism of action. As indicated by the above indicators, a compound that elicits a genetic response to a compound but fails to improve a known biological condition is compared with a different compound with a changing effect in treating the biological condition be able to.

  As characterized by the acute inflammation index, FIG. 3 shows the effect of a single dose treatment of ibuprofen 800 mg on a single donor. 800 mg of over-the-counter ibuprofen was taken at time = 0 and time = 48. Gene expression values for the five inflammation-related loci shown were determined at time = 2, 4, 6, 48, 50, 56, and 96 hours as follows. As expected, the acute inflammation index falls immediately after taking the nonsteroidal anti-inflammatory ibuprofen and returns to baseline after 48 hours. The second dose at T = 48 follows the same kinetics as the first dose and returns to baseline at the end of the experiment at T = 96.

Experimental Example 4: Use of an acute inflammatory index to characterize the efficacy, safety, and physiological mode of action of an agent, which is complex during development and / or in nature. This is illustrated in FIG.

  FIG. 4 shows the calculated acute inflammation index visually shown for five different conditions: (A) untreated whole blood, (B) DMSO (inactive carrier compound in vitro) ) Treated whole blood, (C) treated in vitro with dexamethasone (0.08 μg / ml), otherwise unstimulated whole blood, (D) lipogenic in vitro. Whole blood stimulated with polysaccharides (known pro-inflammatory compounds) (LPS, 1 ng / ml), and (E) LPS (1 ng / ml) and dexamethasone (0.08 μg / ml) in glassware (in vitro) Whole blood treated with is included. Dexamethasone is used as a prescription compound that is commonly used medically as an anti-inflammatory steroid compound. The acute inflammation index is calculated from an experimentally determined gene expression level of an inflammation-related gene expressed in human whole blood obtained from a single patient. The result of mRNA expression is expressed in this example as that of Ct, but for the genes IL1A, IL1B, TNF, IFNG, and IL10, for example, relative fluorescence units, copy number or any other quantifiable, It can also be expressed as an accurate and calibrated form. From the gene expression value, the acute inflammation value is based on the equation: 1/4 {IL1A} +1/4 {IL1B} +1/4 {TNF} +1/4 {INFG} -1 / {IL10} Was determined.

Example 5: Expression and use of group normative values for gene expression profiles FIGS. 6 and 7 show gene expression profiles obtained from whole blood from two different patient groups (patient sets) (inflammatory gene expression in Table 1). The arithmetic average value of 48 loci in the panel) is shown. These patient sets are normal or undiagnosed. The first set of patients, identified as Bonfils (diamond plot), consists of 17 patients identified as donors at the Bonfils Blood Center, Denver, Colorado. The second set of patients consisted of 9 donors and gene expression profiles were obtained from assays performed 4 times over 4 weeks. The subjects of this second set of patients (plots shown as squares) were recruited from employees of Source Precision Medicine, Inc., the assignee of the present specification. The results for loci 1-24 (hereinafter often referred to as inflammation 48A loci) are shown in FIG. 6, and the results for loci 25-48 (hereinafter often referred to as inflammation 48B loci) are shown in FIG.

  The consistency between the gene expression levels of the two different patient sets is dramatic. Both patient sets show gene expression for each of the 48 loci that may not be significantly different from each other. This observation suggests that there is a gene expression profile that is a “normal” expression template for human inflammatory genes, and using the inflammatory gene expression panel (or a subset thereof) in Table 1, its expression pattern The group normal expression pattern can be used, for example, to guide medical intervention for any biological condition that results in a change from the normal expression pattern.

  In a similar context, FIG. 8 also shows a gene expression profile obtained from whole blood of two different patient groups (patient set) (again using 48 loci of the inflammatory gene expression panel of Table 1). Indicates the arithmetic mean value. A set of patients (the expression values of which are indicated by triangular data points) are 24 normative and undiagnosed subjects (and therefore no known inflammatory disease). The other set of patients (whose expression values are indicated by diamond-type dots) were four patients with rheumatoid arthritis and treatment failed (thus having unstable rheumatoid arthritis).

  As remarkable as the consistency of the data from the two different normal patient sets shown in FIGS. 6 and 7 is the systematic diversification of the data from the normal and diseased patient sets shown in FIG. systematic diversity). At 45 of the 48 inflammatory loci shown, subjects with unstable rheumatoid arthritis on average showed increased inflammatory gene expression (cycle threshold Ct) compared to subjects without disease. Reduction). The data thus further depends on what is being used for gene expression if the accuracy and calibration of the underlying assay is carefully designed and controlled based on what is taught herein. Demonstrate what is possible to identify groups in different biological states.

  FIG. 9 shows, in a manner similar to FIG. 8, using the 24 loci of the inflammatory gene expression panel of Table 1 obtained from whole blood of two separate patient sets as well, the expression average of the gene expression profile (shows) Indicates the value. A set of patients (whose expression values are indicated by diamond-type data points) is 17 normal and undiagnosed subjects who are blood donors (and thus have no known inflammatory disease). The other set of patients (expressed as square data points) is 16 subjects, normal and undiagnosed, observed over 6 months, and the average of their expression values is square Represented by data points. Thus, in this way, the average cross-sectional gene expression value of the group in the first healthy state is quite consistent with the average longitudinal gene expression value of the group in the second healthy state, in the form of gene versus gene. The variation in the measured expression value is about 7% or less.

  FIG. 10 shows the displayed gene expression values (from the inflammatory gene expression panel of Table 1) obtained from whole blood of 44 normal and undiagnosed blood donors (data for the 10 subjects shown). 14 loci are used). Also, the gene expression values for each member of the group (set) are fairly consistent with those for all sets. It is also visually represented by a consistent peak height for each genetic locus. Of the above set of subjects, subjects different from those represented here, and other gene loci show results that are consistent with those shown here.

  As a result of these principles, and in various embodiments of the present invention, group normative values for gene expression profiles can be obtained by comparing individual subjects with respect to biological status, including health and / or disease objectives. Can be used. In one embodiment, the normative value for the gene expression profile is a “calibrated profile data set for subjects that reveal deviations in gene expression of such subjects from the group normative value. "(As defined at the beginning of this section) is used as the baseline. Group normative values of gene expression profiles can also be used as baseline values in constructing exponential functions according to embodiments of the present invention. As a result, an exponential function can be constructed, for example, to reveal in relation to the normative value as well as to the range of individual inflammation manifestations.

Example 6: Consistency of expression values of components of gene expression panels over time as a reliable indicator of biological status . FIG. 11 shows the expression levels for each of the four genes (from the inflammatory gene expression panel of Table 1) in a single subject and was assayed monthly for 8 months. It can be seen that the expression level shows remarkable consistency with time.

  Figures 12 and 13 show, in each case, 48 genes (inflammatory gene expression panel of Table 1) of a separate single subject (selected in each case based on good physical condition and not taking the drug). In the case of FIG. 12, the assay was performed every week for 4 weeks, and in the case of FIG. 13, the assay was performed every month for 6 months. In each case, the expression levels also show significant consistency over time and are similar across individuals.

  FIG. 14 is also assayed using the inflammatory gene expression panel of Table 1 and shows the effect of anti-inflammatory steroid administration over time on return gene expression in a single human subject. In this case, 24 out of 48 loci are shown. The subject had a baseline blood sample in a PAXRNA isolation tube and took a single dose of 60 mg prednisone (anti-inflammatory prescription steroid). Additional blood samples were drawn at 2 and 24 hours after a single oral dose. Results regarding gene expression are shown for all three time points. Here, the baseline sample value is shown as one unit on the x-axis. As expected, oral treatment with prednisone resulted in decreased expression of most inflammation-related loci, as shown by the bar graph 2 hours after administration. However, in the bar graph 24 hours after administration, most gene loci showed a decrease in gene expression level at 2 hours, while an increase in gene expression level was observed at 24 hours.

  Despite the baseline in FIG. 14 being based on the pre-drug gene expression values associated with the one tested, we found from the previous examples that healthy individuals were treated with the inflammatory gene expression panel in Table 1 (or Know that it is useful for group normative values in the expression profile. In an attempt to return the inflammatory gene expression level from FIG. 14 to that shown in FIG. 6 and FIG. 7 (normal or set level), interference with normal expression is likely that prednisone is significantly metabolized to the inactive form. Taka, subject. As a result, it induced a compensatory gene expression response that was over-compensated for drug-induced reactions.

  In a manner similar to FIG. 14, FIG. 15 shows the effect over time on five genes (from the inflammatory gene expression panel of Table 1) through a whole blood sample obtained from a human subject who received a single prednisone administration. ). Samples were taken at the time of prednisone administration (t = 0) and then at 2 and 24 hours after such administration. Each whole blood sample was challenged by adding 0.1 ng / ml lipopolysaccharide (gram positive endotoxin), and after the challenge, the gene expression profile of the sample was determined. In the 2-hour sample, it was shown that the expression of 5 loci of the inflammatory gene expression panel was remarkably reduced with respect to the expression level at the time of administration (t = 0). At 24 hours after administration, the inhibitory effect by prednisone is no longer clear, and at 3 out of 5 loci, gene expression is actually higher than t = 0, and at the molecular level it has a well-known rebound effect quantitatively. Indicated.

  FIG. 16 also shows the effect of administration of a TNF inhibitor compound over time in the expression of inflammatory genes in one human subject suffering from rheumatoid arthritis, where the expression is normal (ie, diagnostically uncertain) And (healthy) patient set is shown in comparison with the already determined homologous mean (relative to FIGS. 6 and 7). As part of a larger international study involving patients with rheumatoid arthritis, subject follow-up was performed over a 12-week period. The subjects were included in this study because they did not respond to conservative medications for rheumatoid arthritis and because they changed their treatment and started rapid treatment with TNF-inhibiting compounds. Blood was collected from the subject before the start of a new treatment (visit 1). Blood was collected after the start of the new treatment, 4 weeks after the change of treatment (visit 2), 8 weeks after the start of the new treatment (visit 3), and 12 weeks (visit 4). Blood was collected in PAXRNA isolation tubes and frozen at room temperature for 2 hours followed by −30 ° C.

  The frozen sample was sent to the central laboratory of Source Precision Medicine (Boulder, Colo, the assignee of the present application) to measure the expression level of the genes contained in 48 genes of the inflammatory gene expression panel in Table 1. . The blood sample was thawed and RNA was extracted according to the procedure recommended by the manufacturer. RNA was converted to cDNA and the expression level of 48 inflammatory genes was determined. The expression results are shown in FIG. 16 for 11 out of 48 loci. When the expression results for 11 loci were compared from Visit 1 to the group average of normal blood donors in the United States, the subject showed a significant difference. Similarly, at each subsequent physician visit for each locus, the gene expression level is compared to the same normal mean value. Data obtained from visits 2, 3, and 4 demonstrate the effect of treatment changes. At each visit following treatment change, the level of inflammatory gene expression at 10 out of 11 loci was determined by the homologous mean value previously determined for a normal (ie, diagnostically uncertain, healthy) patient set. close.

  FIG. 17A further illustrates the consistency of inflammatory gene expression, in which a set of 44 normal and undiagnosed blood donors (from the inflammatory gene expression panel of Table 1) Illustrated for the locus. For each individual, the loci show a range of values within the standard deviation ± 2 of the mean expression value, representing 95% of the normally dispersed group. Despite the large range of confidence intervals (95%), the measured gene expression value (ΔCT) is surprisingly within 10% of the average value, regardless of the expression level involved. As will be described in more detail later, because of known facts, the biological status indicator is configured to define a measurement of the status. This is possible as a result of the combination of the two situations. (I) the presence of significant consistency of gene expression profiles with respect to biological status across the group, and (ii) substantially reproducibility of the components of the gene expression panel that yields the gene expression profile under the measurement conditions A procedure that provides a simple measurement is used, and the specificity and efficiency of amplification for all components of the panel is substantially similar, which provides a measurement of biological status. Therefore, here, the function of the expression value of the typical component locus in FIG. 17A is the inflammation index value (indication of 1 corresponds to the component expression value of the healthy person as shown in the right part of FIG. 17A). Used to generate normalization).

  In FIG. 17B, inflammation index values are determined for each member of a set of 42 normal and undiagnosed donors, and the resulting results despite a relatively small subject set size It can be seen that the distribution of index values (shown) closely approximates the normal distribution. By the deviation from the median calibrated in standard deviation units, the index value is indicated in relation to the median based on 0. In this way, 90% of the subject is set within the range of 0 values of +1 and -1. We have various configured indicators that show similar behavior.

  FIG. 17C illustrates the use of the same index as FIG. 17B, with a median inflammation set to zero for a normal group of multiple subjects, and normal and diseased subjects with a standard deviation with respect to their median value. Plot unit. Inflammatory index values were determined for each member of the standard (70 (black bars) undiagnosed group). It can be seen that the resulting index value distribution (shown in FIG. 17C) is very close to the normal distribution. Similarly, index values were calculated for individuals in groups with two diseases. These groups include (1) patients with rheumatoid arthritis treated with methotrexate (methotrexate) trying to turn treatment into more effective drugs (eg TNF inhibitors) (hatched bars), and (2) other than methotrexate Rheumatoid arthritis patients treated with a disease modifying anti-rheumatic drug (DMARDS), and the patient is trying to change the treatment to a more effective drug (eg, methotrexate). Both groups of subjects show index values that are distorted upward (indicating increased inflammation) compared to the normal distribution. Thus, this figure illustrates the usefulness of indicators against those derived from gene expression profile data to assess disease state and provide a target and quantifiable treatment subject. When these two groups of subjects were properly treated, the index values from both groups returned to a more normal distribution (data not shown here).

  FIG. 18 plots gene expression profiles for the same 7 loci (two 6 subject groups consisting of rheumatoid arthritis patients) as in FIG. 17A in a manner similar to FIG. 17A. One group (referred to as “stable” in the figure) consists of patients who responded well to the treatment, and the other group (referred to as “unstable” in the figure) is sufficient for the treatment. It consists of patients who did not respond, and changes in their treatment are planned. It can be seen that the expression values for the stable patient group are within the 95% confidence interval, while the expression values for the unstable patient group for five of the seven loci are outside this range. The right part of the figure shows that the average inflammation index of the unstable group is 9.3 and the average inflammation index of the stable group is 1 and 8, whereas it is 1 in patients in the normal undiagnosed group Show. In this case (rheumatoid arthritis), the indicator thus provides a measure of the range of underlying inflammatory conditions. Therefore, in addition to providing a measure of biological status, the indicator can be used on the basis of the effect of treatment as well to provide a target for therapeutic intervention.

  FIG. 19 illustrates the use of an inflammation index to evaluate a single subject suffering from rheumatoid arthritis that did not respond sufficiently to conventional treatment with methotrexate. Inflammatory indicators for this subject are shown on the far right side at the start of a new treatment (TNF inhibitor) and then continue to the right, proceeding sequentially for 2 weeks, 6 weeks, and 12 weeks . The indicator could be seen progressing towards the standard, consistent with the physician's observation of the patient as it responded to the new treatment.

  FIG. 20 similarly evaluates three subjects with rheumatoid arthritis who are not fully responsive to conventional treatment with methotrexate, at the start of a new treatment (also with a TNF inhibitor), and 2 Illustrating the use of an inflammation index to do after weeks and 6 weeks. In each case, it was generally seen that the index moved back to normal, consistent with the physician's observation of the patient as it responded to the new treatment.

  Each of FIG. 21 to FIG. 23 shows an inflammation index for an international group consisting of a plurality of patients suffering from rheumatoid arthritis, and each patient is characterized as being stable by the subject treating physician ( That is, there are no plans to change treatment). FIG. 21 shows the indices for each of the 10 patients in the group, who have been treated with methotrexate, which is known to reduce symptoms without addressing the underlying disease. It has been. Figure 22 shows the indicators for each of the 10 patients in the group being treated with Enbrel (TNF inhibitor), and Figure 23 is being treated with Remicade (another TNF inhibitor). An index is shown for each of the 10 patients. While the inflammation index of each of the patients in FIG. 21 is increased compared to the standard, in FIG. 22, patients treated with embrel as a class have an inflammation index very close to the standard (80% of the normal range). You can see that In FIG. 23, all but one of the patients undergoing treatment with Remicade are normal or less, two of the patients have an abnormally low inflammation index, and the immunosuppressive response to this drug is (In fact, the study shows that remicade is associated with severe infections in some subjects, where the immunosuppressive effect is quantified). Also, in FIG. 23, one subject has an inflammation index that is significantly above the normal range. The subject was also on a prescription plan for an anti-inflammatory steroid (prednisone) that was actually tapering. That is, within about one week after sampling the inflammation index, the subject experienced a significant increase in clinical symptoms.

  Surprisingly, these examples are derived from an assay of blood taken from a subject and show measurements related to the arthritic state of the subject. Since the measurement is related to the degree of inflammation, it can be expected that other inflammation, including cardiovascular disease, can be monitored, for example, at the same time.

  FIG. 24 illustrates the use of an inflammatory index to evaluate a single subject suffering from inflammatory bowel disease, and treatment with remicade was initiated on the patient in three doses. The graph shows the inflammation index immediately before the first treatment and 24 hours after the first treatment. The index rose just before the second dose, but was in the normal range before the third dose. Again, the indicator (in addition to providing a measure of biological status) is used here to measure the effect of treatment (remicade) and provides a target for therapeutic intervention in terms of both dose and schedule.

  FIG. 25 shows the genes for 24 loci of whole blood (from the inflammatory gene expression panel in Table 1) that are treated with ibuprofen in vitro for other non-steroidal anti-inflammatory drugs (NSAIDs). The expression profile is shown. The ibuprofen profile is in front. It can be seen that all NSAIDs including ibuprofen share a substantially similar profile in that the pattern of gene expression within the locus is similar. Despite these similarities, each drug has its own signature signature.

  FIG. 26 illustrates how the effects of two antagonizing anti-inflammatory compounds can be compared objectively, quantitatively, accurately, and reproducibly. In this example, the expression of each of the two genes (from the inflammatory gene expression panel of Table 1) is expressed in whole blood in vitro in a variable dose of each drug (0.08-250 μg / ml). Market leader drugs show a complex interaction between dose and inflammatory gene response. Paradoxically, as the dose increases, in the case of market leaders, gene expression at both loci first decreases and then increases. Because more consistent responses occur for other compounds, gene expression at both loci decreases more consistently as dose is increased.

  FIGS. 27-41 illustrate the use of gene expression panels in the initial identification and monitoring of infectious diseases. These figures are plots of the response in the expression product of the gene shown in whole blood to administration of various infectious agents or products associated with the infectious agent. In each figure, gene expression levels are “calibrated” with respect to baseline expression levels determined for whole blood prior to administration of the associated infectious agent, as defined herein. In this respect, the figure is virtually similar to the various drawings (eg, FIG. 15) of the patent application WO 01/25473 referenced below. Concentration changes are shown proportionately, and baseline level 1 of a particular locus is monitored at an appropriate time after the addition of an infectious agent or other stimulus, and the expression level is the same as before the stimulus addition. It corresponds to the expression level of a certain locus. The proportional (ratiometric) change in concentration is plotted on a logarithmic scale. The bar below the unified line represents a decrease in concentration, and the bar above the unified line represents an increase in concentration. The size of each bar indicates the magnitude of the rate of change. In WO 01/25473, and in other experiments, gene expression profiles derived in vitro by exposing whole blood to stimuli under appropriate conditions are due to exposure to the corresponding stimuli. It has been shown that gene expression profiles derived in vivo can be represented.

  FIG. 27 uses a novel bacterial gene expression panel of 24 genes developed to distinguish different bacterial states in the host biological system. Two different stimuli are used, namely lipoteichoic acid (LTA), a gram positive cell wall component, and lipopolysaccharide (LPS), a gram negative cell wall component. The final concentration immediately after stimulant administration was 100 ng / mL, and the ratiometric change in expression was monitored over 2 and 6 hours of each stimulation after administration with respect to the level prior to administration. It can be seen that differential gene expression can be observed as early as 2 hours after administration, for example, the IFNA2 locus can distinguish between gram-positive and gram-negative bacteria as well as others. Like that.

  FIG. 28 shows a single locus WFNG for LTA from three different sources: S. pyogenes, B. subtilis, and S. aureus. It is a figure which shows differential gene expression. Each stimulus was administered to achieve a concentration of 100 ng / mL and the response was monitored 1, 2, 4, 6, and 24 hours after administration. The results suggest that gene expression profiles can be used to distinguish between different infectious agents (here different species of gram positive bacteria).

29 and 13, irritants Staphylococcus aureus (S. aureus), and stimulation of E. coli (E. coli) of (concentrations displayed respectively, immediately after administration of 10 ⁷ and ¹⁰ 6 CFU / mL) Each of the responses of the inflammation 48A and 48B loci in whole blood to administration (described above in connection with FIGS. 6 and 7) is shown, which was monitored for 2 days post-dose with respect to baseline before administration. The figure shows that many of the loci respond to the presence of bacterial infection within 2 hours after infection.

  FIGS. 31 and 32 correspond to FIGS. 29 and 30, respectively, and are similar to them (except that monitoring takes place 6 hours after administration). More loci respond to the presence of infection. Various loci (eg, IL2) exhibit expression levels that distinguish the two infectious agents.

FIG. 33 shows the administration of stimulants of E. coli (again at a concentration immediately after administration of 10 ⁶ CFU / ml) as well as E. coli by-products (but lacking E. coli). Figure 5 shows the response of the inflammation 48A locus to administration of stimulants of E. coli filtrate containing. Responses were monitored at 2, 6, and 24 hours after dosing. For example, it was found that the responses over time of the loci IL1B, IL18, and CSF3 to E. coli and E. coli filtrates were different.

  FIG. 34 is similar to FIG. 33, where the compared reaction is for stimuli from E. coli filtrate and is known to bind to polymyxin B (lipopolysaccharide (LPS)). To the stimulus derived from the E. coli filtrate to which the added antibody) is added. Tests of IL1B reaction have shown that, for example, LPS was not able to affect the reaction of the above locus to E. coli filtrate, so that IL1B against E. coli filtrate. It was shown not to be an agent in the reaction.

FIG. 35 shows whole blood inflammation 48A against stimuli of S. aureus (at a concentration immediately after administration of 10 ⁷ CFU / ml) monitored at 2, 6 and 24 hours after administration. Illustrates the response of a locus. It can be seen that the response can relate the direction and magnitude of change over time to expression (see, eg, IL5 and IL15).

Figures 36 and 37 show stimulation and S. aureus (S. aureus) from 10 ⁷ and 10 immediately after administration from E. coli monitored at 6 hours (at concentrations of 10 ⁶ and 10 ² CFU / mL immediately after administration). ² shows the respective responses of the inflammation 48A and 48B loci to stimuli from (at a concentration of ² CFU / mL). In particular, it can be seen that at various loci (eg B7 (FIG. 36), TACI, PLA2G7, and CIQA (FIG. 37)), E. coli produces a significantly more pronounced response than S. aureus. The data strongly suggest that the gene expression profile can be used to sensitively identify the presence of Gram negative bacteria and distinguish them from Gram positive bacteria.

FIGS. 38 and 39 show inflammation in response to stimulation by high concentrations of S. aureus and E. coli (concentrations immediately after administration are 10 ⁷ and 10 ⁶ CFU / mL, respectively). Responses at 48B and 48A loci are monitored and shown at 2, 6 and 24 hours after administration, respectively. Responses over time at many loci are accompanied by changes in strength and direction. FIG. 40 is similar to FIG. 39 but shows the response of the inflammation 48B locus.

FIG. 41 similarly shows the response to stimulation by high concentrations of S. aureus and E. coli at 24 hours after administration (respectively the concentrations immediately after administration are ¹⁰⁷ and At a concentration of 10 ⁶ CFU / mL). As in FIGS. 20 and 21, it reacts at several loci (eg, GRO1 and GRO2) that are distinguished by the type of infection.

  FIG. 42 illustrates the application of a statistical T-test to identify potential members of a signature gene expression panel that can distinguish between normal subjects and subjects with unstable rheumatoid arthritis. . The gray box is very effective in distinguishing the two sets of subjects individually (t-test P-value seen in the right box in each example), thus signature gene expression for rheumatoid arthritis Genes that can represent potential members of the panel are indicated.

  FIG. 43 illustrates the expression levels of 8 patients estimated to have bacteremia for a panel of 17 genes. The data suggests that patients with bacteremia have the potential to have a characteristic pattern of gene expression.

  FIG. 44 illustrates the application of a statistical T-test to the identification of potential members of a signature gene expression panel that can distinguish between normal subjects and subjects suffering from bacteremia. Since the gray box is very effective in distinguishing between two sets of subjects individually (t-test P value found in the right box in each example), the signature gene expression panel for bacteremia Indicates potential members of.

  FIG. 45 illustrates the application of an algorithm (shown in the figure) that provides an index for rheumatoid arthritis (RA) as applied to normal subjects, rheumatoid arthritis (RA) patients, and bacteremia patients, respectively. The index easily distinguishes RA subjects from normal and bacteremia subjects.

  FIG. 46 illustrates application of an algorithm (shown in the figure) that provides an indicator for bacteremia as applied to normal subjects, rheumatoid arthritis (RA) patients, and bacteremia patients, respectively. The indicator readily distinguishes bacteremia subjects from both normal subjects and rheumatoid arthritis subjects.

These data show that a gene expression profile with sufficient accuracy and calibration as described herein (1) can determine a subset of individuals in a known biological state, (2) Used to monitor a patient's response to treatment, (3) used to assess the efficacy and safety of treatment, and (4) one or more related gene expression profiles, a normative value. We support our conclusion that it can be used to guide the patient's medical management by adjusting the treatment to approximate a target set of multiple values, or other desired or achievable values. We have shown that gene expression profiles provide meaningful information even when derived from ex vivo treatments of blood or other tissues. We have also shown that gene expression profiles derived from peripheral whole blood provide information on a wide range of conditions that are not directly or schematically related to blood.
Furthermore, in embodiments of the present invention, gene expression profiles can be used for characterization and early identification (including prodromal symptoms) of infectious diseases (eg sepsis). This characterization includes bacterial and viral infections, specific subtypes of pathogens, stages of natural history of infection (eg, early or late), including distinguishing between infected and non-infectious individuals, and Prognosis is mentioned. In order to achieve such identification and distinguish in such a manner, the use of statistical approaches by the above algorithm is within the scope of the various embodiments herein.

The foregoing features of the invention will be readily understood by reference to the following detailed description, taken in conjunction with the accompanying drawings, in which:
FIG. 1A performed a gene assay 24 from the Source Inflammation Gene Panel (shown in Table 1) for a total of 8 days on separate days during the course of optic neuritis in one male subject. Results are shown. FIG. 1B illustrates the use of an inflammatory indicator associated with the data of FIG. 1A. FIG. 2 is a graphical representation of the same inflammatory index calculated at nine different important clinical milestones. FIG. 3 shows the effect of a single dose treatment of ibuprofen 800 mg on a single donor characterized by the index. FIG. 4 shows the calculated acute inflammation index visually shown for five different conditions. FIG. 5 shows a viral response index (Viral Response Index) for monitoring the course of upper respiratory tract inflammation (URI). It is a Virtual Response Index. FIGS. 6 and 7 compare two different groups using gene expression profiles (Gene Expression Profiles) (for 48 loci of inflammatory genes in Table 1). FIGS. 6 and 7 compare two different groups using gene expression profiles (Gene Expression Profiles) (for 48 loci of inflammatory genes in Table 1). FIG. 8 compares the normal group with the rheumatoid arthritis group from a time course study. FIG. 9 compares two normal groups (one is a longitudinal section and the other is a transverse section). FIG. 10 shows gene expression levels that appear for various individuals in the normal group. FIG. 11 shows the expression levels for each of the four genes (from the inflammatory gene expression panel in Table 1) of one subject assayed monthly for 8 months. FIGS. 12 and 13 show that each of the different subjects (in each case in good condition and not taking the drug) assayed once a week for 4 weeks in FIG. 12 and once a month for 6 months in FIG. The expression levels for each of 48 genes (selected based on the above) (in the inflammatory gene expression panel of Table 1) are shown in the same manner in each case. FIGS. 12 and 13 show that each of the different subjects (in each case in good condition and not taking the drug) assayed once a week for 4 weeks in FIG. 12 and once a month for 6 months in FIG. The expression levels for each of 48 genes (selected based on the above) (in the inflammatory gene expression panel of Table 1) are shown in the same manner in each case. FIG. 14 shows the effect of anti-inflammatory steroid administration over time on the inflammatory gene expression of a single human subject as assayed using the inflammatory gene expression panel of Table 1. FIG. 15 is similar to FIG. 14 for the effects over time on five genes (from the inflammatory gene expression panel of Table 1) through a whole blood sample obtained from a human subject that received a single dose of prednisone. Show by method. FIG. 16 also shows the time course effect of TNF inhibitor compound administration on inflammatory gene expression in one human subject suffering from rheumatoid arthritis. Here, however, its expression is shown relative to a pre-determined allogeneic mean value for the normal (ie undiagnosed and healthy) group. FIG. 17A further illustrates the consistency of inflammatory gene expression in one group. FIG. 17B shows a normal distribution of index values obtained from an undiagnosed group. FIG. 17C illustrates the use of the same index as FIG. 17B, with the median inflammation in the normal group set to zero and normal and diseased subjects plotted against that median. FIG. 18 shows seven groups of the same loci as in FIG. 17A in two groups (responders vs. non-responders) of groups of 6 subjects with rheumatoid arthritis in a manner similar to that of FIG. 17A. Plot the gene expression profile for. FIG. 19 is a diagram for explaining the use of an inflammation index to evaluate a single subject suffering from rheumatoid arthritis that has not sufficiently responded to conventional treatment with methotrexate. It is. FIG. 20 similarly illustrates the use of an inflammation index to evaluate three subjects with rheumatoid arthritis who did not respond well to conventional treatment with methotrexate. Each of FIG. 21 to FIG. 23 shows an inflammation index for one international group consisting of a plurality of subjects suffering from rheumatoid arthritis, and three treatment plans are implemented. Each of FIG. 21 to FIG. 23 shows an inflammation index for one international group consisting of a plurality of subjects suffering from rheumatoid arthritis, and three treatment plans are implemented. Each of FIG. 21 to FIG. 23 shows an inflammation index for one international group consisting of a plurality of subjects suffering from rheumatoid arthritis, and three treatment plans are implemented. FIG. 24 illustrates the use of an inflammatory index to evaluate a single subject suffering from inflammatory bowel disease. FIG. 25 shows the genes for 24 loci (of the inflammatory gene expression panel of Table 1) for whole blood treated with ibuprofen in vitro in relation to other non-steroidal anti-inflammatory drugs. The expression profile (Gene Expression Profiles) is shown. FIG. 26 illustrates how the effects of two antagonistic anti-inflammatory compounds can be compared objectively, quantitatively, accurately, and reproducibly. Figures 27-41 illustrate the use of gene expression panels in the initial identification and monitoring of infections FIG. 28 shows three different sources: pyogenes, B.I. Figure 5 shows differential gene expression of a single locus IFNG against LTA from subtilis and S. aureus. Figures 29 and 30 show the response after 2 hours of inflammation locus 48A and 48B in whole blood to administration of Gram positive and Gram negative microorganisms, respectively (described in connection with Figures 6 and 7, respectively). Show. Figures 29 and 30 show the response after 2 hours of inflammation locus 48A and 48B in whole blood to administration of Gram positive and Gram negative microorganisms, respectively (described in connection with Figures 6 and 7, respectively). Show. FIGS. 31 and 32 are similar to them corresponding to FIGS. 29 and 30, respectively, except that here monitoring occurs 6 hours after administration. FIGS. 31 and 32 are similar to them corresponding to FIGS. 29 and 30, respectively, except that here monitoring occurs 6 hours after administration. FIG. 33 compares the gene expression reaction induced by E. coli and the gene expression reaction induced by E. coli filtrate without microorganisms. FIG. 34 is similar to FIG. 33, but the reaction compared here is for stimulation with E. coli filtrate alone and stimulation with E. coli filtrate with polymyxin B added. . FIG. 35 illustrates the gene expression response induced by Staphylococcus aureus (S. aureus) at 2, 6, and 24 hours after administration. Figures 36-41 compare gene expression induced by E. coli and S. aureus at various concentrations and times. Figures 36-41 compare gene expression induced by E. coli and S. aureus at various concentrations and times. Figures 36-41 compare gene expression induced by E. coli and S. aureus at various concentrations and times. Figures 36-41 compare gene expression induced by E. coli and S. aureus at various concentrations and times. Figures 36-41 compare gene expression induced by E. coli and S. aureus at various concentrations and times. Figures 36-41 compare gene expression induced by E. coli and S. aureus at various concentrations and times. FIG. 42 illustrates the application of a statistical T-test to the identification of potential members of a signature gene expression panel that can distinguish between normal subjects and subjects suffering from unstable rheumatoid arthritis. FIG. 43 illustrates the expression levels of 8 patients estimated to have bacteremia for one panel of 17 genes. FIG. 44 illustrates the application of a statistical T-test to the identification of potential members of a signature gene expression panel that can distinguish between normal subjects and subjects suffering from bacteremia. FIG. 45 illustrates the application of an algorithm (shown in the figure) that provides an index for rheumatoid arthritis (RA) as applied to normal subjects, rheumatoid arthritis (RA) patients, and bacteremia patients, respectively. FIG. 46 illustrates application of an algorithm (shown in the figure) that provides an indicator for bacteremia as applied to normal subjects, rheumatoid arthritis (RA) patients, and bacteremia patients, respectively.

Claims (262)

A method for determining a profile data set of a subject having an infection or having an infection related to an infection based on a sample obtained from a subject providing an RNA source, comprising:
Measuring the amount of RNA corresponding to at least two components of Table 1 and using amplification to reach the criteria for each component;
A method for determining a profile data set, wherein the profile data set is composed of criteria for each component and is amplified under substantially reproducible measurement conditions. The subject has uncertain signs of systemic infection, including at least one of increased white blood cell count, increased body temperature, increased heart rate, and increased or decreased blood pressure compared to medical normative values; The method of claim 1. Infectious symptoms associated with said infection are blunt trauma or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or at least one of dental or gynecological examination or treatment The method of claim 1. The method for determining a profile data set according to claim 1, wherein the substantially reproducible measurement conditions are within a range of reproducibility greater than 5 percent. The method for determining a profile data set according to claim 1, wherein the substantially reproducible measurement conditions are within a reproducibility range of greater than 3 percent. The method for determining a profile data set according to claim 1, wherein the amplification efficiencies of all components are substantially similar. 7. The method for determining a profile data set according to claim 6, wherein the amplification efficiency of all the components is within 2 percent. The method for determining a profile data set according to claim 6, wherein the amplification efficiency of all the components is less than 1 percent. The method according to any one of claims 1 to 8, wherein the sample is selected from the group consisting of blood, blood fraction, body fluid, cell group, and tissue obtained from the subject. A method for characterizing an infection or an infection associated with an infection in a subject based on a sample obtained from a subject providing an RNA source comprising:
Evaluating a multi-member profile data set, each member comprising a plurality of components selected such that the component measurement allows for characterization of uncertain signs of systemic infection A method that is a quantitative measure of the amount of one different RNA component within a panel, wherein such criteria for each component are obtained under substantially reproducible measurement conditions. The subject has uncertain signs of systemic infection, including at least one of increased white blood cell count, increased body temperature, increased heart rate, and increased or decreased blood pressure compared to medical normative values; The method of claim 10. Systemic, the subject is associated with blunt or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or infection symptoms resulting from at least one of dental or gynecological examinations or procedures 11. The method of claim 10, wherein the method has an uncertain sign of infection. The evaluation further comprises comparing the profile data set to a baseline profile data set of the panel, wherein the baseline profile data set is characterized by or associated with the infection 11. The method for characterizing an infection or infection-related infection symptom in a subject according to claim 10, wherein the symptom is associated with an infection symptom. 11. The method for characterizing an infection or infection-related infection in a subject according to claim 10, wherein the amplification efficiencies of all components are substantially similar. 11. The method of claim 10, wherein the infection or an infection symptom associated with the infection is due to a microbial infection. 11. The method of claim 10, wherein the infection or an infection symptom associated with the infection is due to a bacterial infection. 11. The method of claim 10, wherein the infection or infection associated with the infection is due to a eukaryotic parasite infection. 11. The method of claim 10, wherein the infection or an infection symptom associated with the infection is due to a viral infection. 11. The method of claim 10, wherein the infection or an infection symptom associated with an infection is due to a fungal infection. 11. The method of claim 10, wherein the infection or infection associated with the infection is due to a systemic inflammatory response syndrome (SIRS). 11. The method of claim 10, wherein the infection or infection associated with the infection is due to bacteremia, viremia, or fungiemia. 11. The method of claim 10, wherein the infection or infection associated with the infection is due to sepsis caused by any type of microorganism. 11. The infection or an infection symptom associated with an infection is related to a partial tissue of a subject, and the sample is derived from a tissue of a type of body fluid different from the partial tissue. the method of. The method according to claim 1, further comprising storing the profile data set on a digital storage medium. 27. The method of claim 26, wherein storing the profile data set includes storing the profile data set as a record in a database. A method for assessing an infection or infection-related infection in a subject providing an RNA source based on a first sample obtained from said subject, comprising:
Deriving, from the sample, a first profile data set comprising a plurality of members from the sample, each member having a component measurement to assess an infection or an infection-related infection symptom. A quantitative measure of the amount of different RNA components in a panel composed of multiple components selected to allow such criteria to be substantially reproducible Steps obtained under certain measurement conditions;
Generating calibrated profile data for the panel, each member of the calibrated profile data being a corresponding member of the first profile data set and a baseline profile data set of the panel A function with a corresponding member, and wherein the baseline profile data set is associated with the infection or an infection associated with the infection being assessed;
Have
Evaluation of the infection or infection-related infection symptoms of the subject by the calibrated profile data being a comparison of the first profile data set and the baseline profile data set Is obtained. The subject exhibits uncertain signs of systemic infection, including at least one of an increase in white blood cell count, an increase in body temperature, an increase in heart rate, and an increase or decrease in blood pressure compared to a medical normative value; 30. The method of claim 28. Infectious symptoms associated with said infection are blunt trauma or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or at least one of dental or gynecological examination or treatment 30. The method of claim 28. 30. The method of claim 28, wherein the baseline profile data set is derived from one or more other samples obtained from the same subject in a different environment than the first sample. The environment is (i) the time when the first sample was taken, (ii) the part where the first sample was taken, and (iii) the subject when the first sample was taken 32. The method of claim 31, wherein the method is selected from the group consisting of: 32. The method of claim 31, wherein the interval between the first sample and the one or more other samples exceeds at least one month before the one or more other samples are taken. 32. The method of claim 31, wherein the interval between the first sample and the one or more other samples exceeds at least 12 months before the one or more other samples are taken. 32. The method of claim 31, wherein the one or more other samples are taken prior to therapeutic intervention. 32. The method of claim 31, wherein the one or more other samples are taken after a therapeutic intervention. 29. The method of claim 28, wherein the first sample is from the subject's blood and the baseline profile data set is from the subject's tissue or body fluid other than blood. 30. The method of claim 28, wherein the first sample is derived from the subject's tissue or body fluid and the baseline profile data set is derived from blood. The baseline protofield data set was taken from the first sample for at least one of age, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environmental exposure. 32. The method of claim 31, wherein the method is derived from one or more other samples taken at a time when the subject is in a biological state that is different from the biological state of the subject at the time. 30. The method of claim 28, wherein the baseline profile data set is derived from one or more other samples obtained from one or more different subjects. The one or more other samples, like the subject, are age group, gender, ethnicity, geographic location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environment 41. The method of claim 40, having at least one of the exposures. The clinical index is used to assess an infection or infection-related infection symptom of at least one other sample, and in the context of at least one other clinical indicator, the calibrated profile 42. The method of claim 41, further comprising interpreting the data. The at least one other clinical indicator is selected from the group consisting of blood chemistry, urinalysis, X-ray or other radiological or metabolic imaging techniques, other chemical assays, and physical findings; 43. The method of claim 42. 30. The method of claim 28, wherein the infection or an infection symptom associated with an infection is due to a microbial infection. 30. The method of claim 28, wherein the infection or an infection symptom associated with an infection is due to a bacterial infection. 30. The method of claim 28, wherein the infection or infection associated with the infection is due to a eukaryotic parasite infection. 29. The method of claim 28, wherein the infection or an infection symptom associated with an infection is due to a viral infection. 30. The method of claim 28, wherein the infection symptom associated with the infection is due to a fungal infection. 30. The method of claim 28, wherein the infection or an infection symptom associated with the infection is due to a systemic inflammatory response syndrome (SIRS). 29. The method of claim 28, wherein the infection or infection associated with the infection is due to bacteremia, viremia, or fungiemia. 29. The method of claim 28, wherein the infection or infection associated with the infection is due to sepsis resulting from any type of microorganism. 30. The method of claim 28, wherein the function is a mathematical function and is different from a simple difference. 53. The method of claim 52, wherein the function is a second function of a ratio of corresponding members of the first profile data set to corresponding members of the baseline profile data set. 54. The method of claim 53, wherein the function is a logarithmic function. If each member of the calibrated profile data has a value that varies by an amount greater than the amount D when D = F (1.1) −F (0.9) and F is the second function 54. The method of claim 53, wherein the method has biological significance. Acquisition of the first sample and quantification of the first profile data set is performed at a first location, and generation of the calibrated profile data is performed on a digital storage medium at a second location. 30. The method of claim 28, comprising using a network to access a stored database. 57. The method of claim 56, further comprising updating the database to reflect a first profile data set quantified from the sample. 57. The method of claim 56, wherein using the network includes access to a global computer network. 30. The method of claim 28, wherein the quantitative criteria is determined by amplification, and the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent. 29. The method of claim 28, wherein the quantitative criteria is determined by amplification, and the measurement condition is that the difference in amplification efficiency for all components is less than about 1 percent. A method of providing an index indicating an infectious disease associated with an infectious disease or an infectious disease of the subject based on a first sample from the subject providing an RNA source, comprising:
Deriving a profile data set from the first sample, the profile data set comprising a plurality of members, each member selected to allow characterization of systemic infection uncertainty A quantitative measure of the amount of one different RNA component within a panel of a plurality of components, wherein the panel comprises at least two of the components of the gene expression panel of Table 1, and
Deriving the profile data set, achieving such criteria for each component under substantially reproducible measurement conditions;
Applying at least one criterion of the profile data set to an exponential function that provides a mapping of at least one criterion of the profile data set to at least one criterion of systemic infection uncertainty Generating an indicator related to the infection or an infection related to the infection of the subject;
Including methods. The subject exhibits uncertain signs of systemic infection, including at least one of increased white blood cell count, increased body temperature, increased heart rate, and increased or decreased blood pressure as compared to medical normative values; 62. The method of claim 61. Infectious symptoms associated with said infection are blunt trauma or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or at least one of dental or gynecological examination or treatment 62. The method of claim 61. 62. The method of providing an indicator according to claim 61, wherein the exponential function has two components including disease state, severity, or disease course. 64. The method of claim 61, wherein the exponential function has three components, the exponential function comprising disease state, severity, or disease course. 64. The method of claim 61, wherein the exponential function has four components that include the disease state, severity, or disease course. 62. The method of claim 61, wherein the exponential function has five components, the exponential function comprising disease state, severity, or disease course. The exponential function is the formula:
I = ΣC _i M _i P ⁽ⁱ⁾
Where I is the index, M _i is the value of member i of the profile data set, C _i is a constant, and P (i) is the index by which M _i is raised)
Is constructed as a linear sum of terms with
The sum is formed for integer values i up to the number of members in the data set;
68. A method of providing an index according to any one of claims 61 to 67. The value C _i and P (i) are determined using a statistical method such as latent class modeling, and the latent class modeling related to the uncertain signs of the systemic infection derived clinically and experimentally 69. The method of providing an index of claim 68, wherein the index is correlated with data comprising: 69. The method of claim 68, further comprising providing an indicator for a normative value of the exponential function determined for an associated set of subjects to allow the indicator to be interpreted relative to the normative value. the method of. 71. The method of claim 70, wherein providing the normative value comprises configuring the exponential function such that the normative value is approximately one. Providing the normative value comprises configuring the exponential function such that the normative value is approximately zero and a deviation of the exponential function from zero is expressed in standard deviation units. 71. The method according to 71. The related sets of subjects are commonly associated with age group, gender, ethnicity, geographical location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environmental exposure. 72. The method of claim 71, having at least one of the characteristics. The related sets of subjects are commonly associated with age group, gender, ethnicity, geographical location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environmental exposure. 73. The method of claim 72, having at least one of the characteristics. A clinical indicator is used to evaluate the infection or an infection-related infection symptom of an associated set of the plurality of subjects and the calibrated in the context of at least one other clinical indicator 69. The method of claim 68, further comprising interpreting profile data. The at least one other clinical indicator is selected from the group consisting of blood chemistry, urinalysis, X-ray or other radiological or metabolic imaging techniques, other chemical assays, and physical findings; 75. The method of claim 74. 62. The method of claim 61, wherein the quantitative criteria is determined by amplification and the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent. 62. The method of providing an indicator according to claim 61, wherein the quantitative criteria is determined by amplification and the measurement condition is that the difference in amplification efficiency for all components is less than about 1 percent. 62. A method for providing an index according to claim 61, wherein the substantially reproducible measurement conditions are within a reproducibility range of greater than 5 percent. 62. A method for providing an index according to claim 61, wherein the substantially reproducible measurement conditions are within a range of reproducibility greater than 3 percent. The infection or infection associated with the infection being assessed relates to a partial tissue of the subject, and the first sample is a different type of tissue or different from that of the partial tissue 62. A method of providing an index according to claim 61, derived from bodily fluids. 62. The method of claim 61, wherein the infection or an infection symptom associated with an infection is due to a microbial infection. 62. The method of claim 61, wherein the infection or an infection symptom associated with an infection is due to a bacterial infection. 62. The method of claim 61, wherein the infection or infection associated with the infection is due to a eukaryotic parasite infection. 62. The method of claim 61, wherein the infection or an infection symptom associated with an infection is due to a viral infection. 62. The method of claim 61, wherein the infection or an infection symptom associated with an infection is due to a fungal infection. 62. The method of claim 61, wherein the infection or infection associated with the infection is due to a systemic inflammatory response syndrome (SIRS). The amount of one different RNA component in one panel from a plurality of components, each selected from at least one of the other samples, each of which is selected to allow characterization of systemic infection uncertainty Deriving at least one other profile data set comprising a plurality of members that are quantitative criteria, wherein said at least one other sample has at least one time point, nutritional history, symptoms, clinical Derived from the same subject taken in an environment different from that of the first sample with respect to indicators, medications, physical activity, weight, and environmental exposure;
At least one criterion from the at least one other profile data set is related to the infection or an infection-related infection symptom of the subject under a different environment than that of the first sample The mapping of the at least one other profile data set from the at least one criterion under different circumstances to produce at least one other indicator is one of the infectious symptoms associated with the infection or infection. Applying an exponential function to provide for one criterion;
62. The method of providing an indicator according to claim 61, further comprising: The related sets of subjects are commonly associated with age group, gender, ethnicity, geographical location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environmental exposure. 90. The method of claim 87, having at least one of the characteristics. The related sets of subjects are commonly associated with age group, gender, ethnicity, geographical location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environmental exposure. 90. The method of claim 88, having at least one of the characteristics. 88. The method of providing an index according to claim 87, wherein the exponential function has two components including disease state, severity, or disease course. 90. The method of claim 87, wherein the exponential function has three components, the exponential function comprising disease state, severity, or disease course. 88. The method of claim 87, wherein the exponential function has four components, the exponential function comprising disease state, severity, or disease course. 90. The method of claim 87, wherein the exponential function has five components, the exponential function comprising disease state, severity, or disease course. The exponential function is the formula:
I = ΣC _i M _i P ⁽ⁱ⁾
Where I is the index, M _i is the value of member i of the profile data set, C _i is a constant, and P (i) is the index by which M _i is raised)
Is constructed as a linear sum of terms with
The sum is formed for integer values i up to the number of members in the data set;
94. A method for providing an index according to any one of claims 87-93. The value C _i and P (i) are determined using a statistical method such as latent class modeling, and the latent class modeling related to the uncertain signs of the systemic infection derived clinically and experimentally 95. A method of providing an indication according to claim 94, wherein the indicator is correlated with data comprising: 90. The method of claim 87, further comprising: providing an indicator for a normative value of an exponential function determined for a related set of subjects to allow the indicator to be interpreted relative to the normative value. the method of. 99. The method of claim 96, wherein providing the normative value comprises configuring the exponential function such that the normative value is approximately one. Providing the normative value comprises configuring the exponential function such that the normative value is approximately zero and a deviation of the exponential function from zero is expressed in standard deviation units. 97. The method according to 97. The calibrated profile wherein a clinical index is used to assess an associated set of infections or infection-related symptoms of the plurality of subjects and is in the context of at least one other clinical indicator 95. The method of claim 94, further comprising interpreting the data. The at least one other clinical indicator is selected from the group consisting of blood chemistry, urinalysis, X-ray or other radiological or metabolic imaging techniques, other chemical assays, and physical findings; 140. The method of claim 139. 88. The method of claim 87, wherein the quantitative criteria is determined by amplification, and the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent. 88. The method of providing an indicator according to claim 87, wherein the quantitative criteria is determined by amplification, and the measurement condition is that the difference in amplification efficiency for all components is less than about 1 percent. 88. A method for providing an index according to claim 87, wherein the substantially reproducible measurement conditions are within a range of reproducibility greater than 5 percent. 88. The method for determining a profile data set of claim 87, wherein the substantially reproducible measurement conditions are within a reproducibility range of greater than 3 percent. The infectious disease being evaluated or an infectious disease related to the infectious disease is related to a partial tissue of the subject, and the first sample is a type of tissue different from that of the partial tissue. 88. The method of claim 87, wherein the method is derived from a body fluid. 88. The method of claim 87, wherein the infection or an infection symptom associated with an infection is due to a microbial infection and the panel of components comprises at least two components of Table 1. 88. The method of claim 87, wherein the infection or an infection symptom associated with an infection is due to a microbial infection and the panel of components comprises at least two components of Table 1. 88. The method of claim 87, wherein the infection or an infection symptom associated with an infection is a eukaryotic parasite infection, and the panel of components comprises at least two components of Table 1. 88. The method of claim 87, wherein the infection or an infection symptom associated with an infection is a viral infection, and the panel of components comprises at least two components of Table 1. 88. The method of claim 87, wherein the infection or infection associated with the infection is a fungal infection, and the panel of components comprises at least two components of Table X. 88. The infection or infection-related infection symptom is systemic inflammatory response syndrome (SIRS), and the panel of components comprises at least two components of Table X. Method. Uncertain signs of systemic infection. A method of assessing an infectious disease of a subject or an infectious symptom associated with an infection based on a first sample that provides the RNA source of the subject,
Deriving a first profile data set from the sample, wherein the first profile data set includes a plurality of members, each member having a component measurement associated with an infection or infection A quantitative measure of the amount of different RNA components in a single panel composed of a plurality of components selected to allow assessment of the affected infection symptoms, such criteria for each component Steps obtained under substantially reproducible measurement conditions,
Generating calibrated profile data for the panel, each member of the calibrated profile data being a corresponding member of the first profile data set and a baseline profile data set of the panel A normative criterion that is a function of corresponding members, and that each member of the baseline profile data set is determined with respect to an associated set of one quantity of the component of the component in the panel And the baseline profile data set is associated with the infection or infection-related infection symptom being evaluated, and
Have
Since the calibrated profile data is a comparison of the first profile data set and the baseline profile data set, an assessment of the infection or infection-related infection symptoms of the subject A method is provided. The subject exhibits uncertain signs of systemic infection, including at least one of increased white blood cell count, increased body temperature, increased heart rate, and increased or decreased blood pressure as compared to medical normative values; 113. The method of claim 112. Infectious symptoms associated with said infection are blunt trauma or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or at least one of dental or gynecological examination or treatment 113. The method of claim 112. 113. The method of claim 112, wherein the related set of multiple subjects is a set of multiple healthy subjects. The related sets of subjects are commonly associated with age group, gender, ethnicity, geographical location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environmental exposure. 113. The method of claim 112, wherein the method has at least one of the characteristics. For the relevant set of subjects, a clinical indicator is used to assess an infection or an inflammatory condition associated with an infection, and the calibrated profile data in the context of at least one other clinical indicator is 115. The method of claim 114, further comprising interpreting. The at least one other clinical indicator is selected from the group consisting of blood chemistry, urinalysis, X-ray or other radiological or metabolic imaging techniques, other chemical assays, and physical findings; 116. The method of claim 115. 113. The method of claim 112, wherein the quantitative criteria is determined by amplification and the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent. 113. The method of claim 112, wherein the measurement condition is that the difference in amplification efficiency for all components is less than about 1 percent. 113. The method of claim 112, wherein the substantially reproducible measurement conditions are within a range of reproducibility greater than 5 percent. 113. The method of claim 112, wherein the measurement conditions that are substantially reproducible are within a range of reproducibility greater than 3 percent. The infectious disease being evaluated or an infectious disease related to the infectious disease is related to a partial tissue of the subject, and the first sample is a type of tissue different from that of the partial tissue. 113. The method of claim 112, wherein the method is derived from a body fluid. 113. The method of claim 112, wherein the infection or an infection symptom associated with an infection is due to a microbial infection. 113. The method of claim 112, wherein the infection or infection associated with an infection is due to a bacterial infection. 113. The method of claim 112, wherein the infection or an infection symptom associated with an infection is due to a eukaryotic parasitic infection. 113. The method of claim 112, wherein the infection or infection associated with an infection is due to a viral infection. 113. The method of claim 112, wherein the infection or infection associated with the infection is due to a fungal infection. 113. The method of claim 112, wherein the infection or infection associated with the infection is due to a systemic inflammatory response syndrome (SIRS). 113. The method of claim 112, wherein the infection or infection associated with the infection is due to bacteremia, viremia, or fungiemia. 113. The method of claim 112, wherein the infection or infection associated with the infection is sepsis from any type of microorganism. 113. The method of claim 112, further comprising storing the profile data set on a digital storage medium. 131. The method of claim 130, wherein storing the profile data set comprises storing the profile data set as a record in a database. 113. The method of claim 112, wherein the baseline profile data set is derived from one or more other samples obtained from the same subject in a different environment than the first sample. 135. The method of claim 132, wherein the one or more other samples undergo a pre-treatment intervention. 135. The method of claim 132, wherein the one or more other samples undergo a post-treatment intervention. 135. The method of claim 132, wherein the interval between taking the first sample and the one or more other samples exceeds at least one month before the one or more other samples are taken. 143. The method of claim 132, wherein the interval between the first sample and the one or more other samples exceeds at least 12 months before the one or more other samples are taken. 113. The method of claim 112, wherein the first sample is from the subject's blood and the baseline profile data set is from the subject's tissue or body fluid other than blood. 113. The method of claim 112, wherein the first sample is derived from the subject's tissue or body fluid and the baseline profile data set is derived from blood. An infectious disease of a subject or an infectious symptom associated with an infectious disease is evaluated based on a first sample of the subject and a second sample derived from a predetermined indicator cell group, and the sample serves as an RNA source. A method of providing,
Applying the first sample or a portion thereof to the predetermined population of indicator cells;
Deriving a first profile data set from the sample, wherein the first profile data set includes a plurality of members, each member having a component measurement associated with an infection or infection A quantitative measure of the amount of different RNA components in a single panel composed of a plurality of components selected to allow assessment of the affected infection symptoms, such criteria for each component Steps obtained under substantially reproducible measurement conditions,
Generating calibrated profile data for the panel, each member of the calibrated profile data being a corresponding member of the first profile data set and a baseline profile data set of the panel And each member of the baseline profile data set is determined with respect to an associated set of a plurality of subjects of one quantity of the component in the panel A baseline and baseline profile data set is associated with the infection or infection associated with the infection being assessed, and
Have
Since the calibrated profile data is a comparison of the first profile data set and the baseline profile data set, an assessment of the infection or infection-related infection symptoms of the subject A method is provided. The subject exhibits uncertain signs of systemic infection, including at least one of increased white blood cell count, increased body temperature, increased heart rate, and increased or decreased blood pressure as compared to medical normative values; 140. The method of claim 139. Infectious symptoms associated with said infection are blunt trauma or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or at least one of dental or gynecological examination or treatment 140. The method of claim 139. 140. The method of claim 139, wherein the related set of multiple subjects is a set of multiple healthy subjects. The related set of subjects is commonly associated with age group, gender, ethnicity, geographic location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environmental exposure. 140. The method of claim 139, having at least one of the characteristics. The calibrated profile wherein a clinical index is used to assess an associated set of infections or infection-related symptoms of the plurality of subjects and is in the context of at least one other clinical indicator 145. The method of claim 143, further comprising interpreting the data. The at least one other clinical indicator is selected from the group consisting of blood chemistry, urinalysis, X-ray or other radiological or metabolic imaging techniques, other chemical assays, and physical findings; 145. The method of claim 144. 140. The method of claim 139, wherein the quantitative criteria is determined by amplification and the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent. 140. The method of claim 139, wherein the quantitative criteria is determined by amplification and the measurement condition is that the difference in amplification efficiency for all components is less than about 1 percent. 140. The method of claim 139, wherein the measurement conditions that are substantially reproducible are within a range of reproducibility greater than 5 percent. 140. The method of claim 139, wherein the measurement conditions that are substantially reproducible are within a range of reproducibility greater than 3 percent. The infectious disease being evaluated or an infectious disease related to the infectious disease is related to a partial tissue of the subject, and the first sample is a type of tissue different from that of the partial tissue. 140. The method of claim 139, wherein the method is derived from a body fluid. 140. The method of claim 139, wherein the infection or infection associated with the infection is due to a microbial infection. 140. The method of claim 139, wherein the infection or infection associated with the infection is due to a bacterial infection. 140. The method of claim 139, wherein the infection or an infection symptom associated with an infection is due to a eukaryotic parasitic infection. 140. The method of claim 139, wherein the infection or infection associated with the infection is due to a viral infection. 140. The method of claim 139, wherein the infection or infection associated with the infection is due to a fungal infection. 140. The method of claim 139, wherein the infection or infection associated with the infection is due to a systemic inflammatory response syndrome (SIRS). 140. The method of claim 139, wherein the infection or infection associated with the infection is due to bacteremia, viremia, or fungiemia. 140. The method of claim 139, wherein the infection or infection associated with the infection is sepsis from any type of microorganism. 140. The method of claim 139, wherein the infection or infection associated with the infection is sepsis from any type of microorganism. 140. The method of claim 139, further comprising storing the profile data set on a digital storage medium. 140. The method of claim 139, wherein the baseline profile data set is derived from one or more other samples obtained from the same subject in a different environment than the first sample. 164. The method of claim 161, wherein the one or more other samples undergo a pre-treatment intervention. 164. The method of claim 161, wherein the one or more other samples undergo a post-treatment intervention. 163. The method of claim 161, wherein the interval between the first sample and the one or more other samples exceeds at least one month before the one or more other samples are taken. 163. The method of claim 161, wherein the interval between the first sample and the one or more other samples exceeds at least 12 months before the one or more other samples are taken. 140. The method of claim 139, wherein the first sample is from the subject's blood and the baseline profile data set is from the subject's tissue or body fluid other than blood. 140. The method of claim 139, wherein the first sample is derived from the tissue or fluid of the subject and the baseline profile data set is derived from blood. Infection or infection associated with the target cell group affected by the first factor, based on a sample that is derived from the target cell group to which the first factor has been administered and that provides an RNA source A method of assessing symptoms,
Deriving a first profile data set from the sample, wherein the first profile data set includes a plurality of members, each member having a component measurement effected by the first factor; Quantitative criteria for the amount of different RNA components in a single panel composed of multiple components selected to allow assessment of the infection or the associated symptoms associated with the infection Such criteria for each component are obtained under substantially reproducible measurement conditions; and
Generating calibrated profile data for the panel, each member of the calibrated profile data being a corresponding member of the first profile data set and a baseline profile data set of the panel And each member of the baseline data set is a normative criterion determined with respect to an associated set of target cells of one quantity of the component in the panel. And a baseline profile data set is associated with the infection or infection-related infection symptom being assessed, and
Have
Since the calibrated profile data is a comparison of the first profile data set and the baseline profile data set, the infection of the target cell group affected by the first factor Or a method, wherein an assessment of an infection symptom associated with an infection is provided. The subject exhibits uncertain signs of systemic infection, including at least one of an increase in white blood cell count, an increase in body temperature, an increase in heart rate, and an increase or decrease in blood pressure compared to a medical normative value; 168. The method of claim 168. Infectious symptoms associated with said infection are blunt trauma or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or at least one of dental or gynecological examination or treatment 168. The method of claim 168. 169. The method of claim 168, wherein the related set of multiple target cell groups is a set of multiple healthy subjects. The related set of target cells is commonly shared by age group, gender, ethnicity, geographical location, nutritional history, symptoms, clinical indicators, medication, physical activity, weight, and environment. 169. The method of claim 168, wherein the method has at least one characteristic of exposure. A clinical index is used to assess an associated set of infections comprising the plurality of target cell populations or an infection symptom associated with the infection, and the calibrated in the context of at least one other clinical indicator. 173. The method of claim 172, further comprising interpreting the profile data. The at least one other clinical indicator is selected from the group consisting of blood chemistry, urinalysis, X-ray or other radiological or metabolic imaging techniques, other chemical assays, and physical findings; 178. The method of claim 173. 169. The method of claim 168, wherein the quantitative criteria is determined by amplification and the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent. 169. The method of claim 168, wherein the substantially reproducible measurement conditions are within a range of reproducibility greater than 5 percent. 169. The method of claim 168, wherein the substantially reproducible measurement conditions are within a range of reproducibility greater than 3 percent. The infectious disease being evaluated or an infectious disease related to the infectious disease is related to a partial tissue of the subject, and the first sample is a type of tissue different from that of the partial tissue. 169. The method of claim 168, wherein the method is derived from a body fluid. 169. The method of claim 168, wherein the infection or an infection symptom associated with an infection is due to a microbial infection. 169. The method of claim 168, wherein the infection or infection associated with the infection is due to bacteria. 169. The method of claim 168, wherein the infection or infection associated with the infection is due to a eukaryotic parasite infection. 169. The method of claim 168, wherein the infection or infection associated with the infection is due to a viral infection. 169. The method of claim 168, wherein the infection or infection associated with the infection is due to a fungal infection. 168. The method of claim 168, wherein the infection or an infection symptom associated with an infection is systemic inflammatory response syndrome (SIRS). 169. The method of claim 168, wherein the infection or infection associated with the infection is due to bacteremia, viremia, or fungiemia. 169. The method of claim 168, wherein the infection or an infection symptom associated with an infection is sepsis from any type of microorganism. 169. The method of claim 168, further comprising storing the profile data set on a digital storage medium. 189. The method of claim 188, wherein storing the profile data set includes storing the profile data set as a record in a database. 169. The method of claim 168, wherein the first sample is derived from the subject's blood and the baseline profile data set is derived from a tissue or body fluid of the subject other than blood. 169. The method of claim 168, wherein the first sample is derived from the subject's tissue or body fluid and the baseline profile data set is derived from blood. 169. The method of claim 168, wherein the baseline profile data set is derived from one or more other samples obtained from the same subject in a different environment than the first sample. 193. The method of claim 192, wherein the one or more other samples undergo a pre-treatment intervention. 193. The method of claim 192, wherein the one or more other samples undergo a post-treatment intervention. 193. The method of claim 192, wherein the interval between taking the first sample and the one or more other samples exceeds at least one month before taking the one or more other samples. Infections or infections related to target cells affected by the first factor in relation to infections or infections related to infections of the target cells affected by the second factor A symptom is derived from the target cell group to which the first factor has been administered, and from a first sample that provides an RNA source, the target cell group to which the second factor has been administered, and A second sample that provides an RNA source, and an evaluation method based on:
Deriving a first profile data set from the first sample and deriving a second profile data set from the second sample, wherein the first profile data set and the first profile data set The two profile data sets include a plurality of members, each member being selected such that the component measurement allows for the assessment of an infection or an infection symptom associated with the infection, and A quantitative measure of the amount of different RNA components in a panel composed of a plurality of components affected by the first factor in relation to two factors, such as for each component A step in which a reference is obtained under substantially reproducible measurement conditions;
Generating a first calibration profile data set and a second calibration profile data set for the panel; (i) each member of the first calibration profile data set is A function of the corresponding member of the first profile data set and the corresponding member of the baseline profile data set of the panel; (ii) each member of the second calibration profile data set is A function of the corresponding member of the second profile data set for the panel and the baseline profile data set of the plurality of components of the panel determined with respect to an associated set of subjects A quantity of normative criteria, and the baseline profile data set Associated with infection symptoms associated with the infection or infection by the steps,
Have
The first and second calibration profile data sets are a comparison of the first profile data set and the baseline profile set, and the second profile data set and the baseline profile data set. Because of the comparison with the set, in relation to the infection of the target cell group affected by the second factor or an infection symptom associated with the infection, the affected by the first factor A method wherein an assessment of infection of a target cell population or infection associated with an infection is given. The subject exhibits uncertain signs of systemic infection, including at least one of increased white blood cell count, increased body temperature, increased heart rate, and increased or decreased blood pressure as compared to medical normative values; 196. The method of claim 196. Infectious symptoms associated with said infection are associated with blunt or penetrating trauma, surgery, endocarditis, urinary tract infection, pneumonia, or at least one of dental or gynecological examination or treatment 196. The method of claim 196, wherein the method presents uncertain signs of systemic infection. 196. The method of claim 196, wherein the first factor is a first drug and the second factor is a second drug. 196. The method of claim 196, wherein the first factor is a drug and the second factor is a complex mixture. 196. The method of claim 196, wherein the first factor is a drug and the second factor is a dietary supplement. 196. The method of claim 196, wherein the quantitative criteria is determined by amplification and the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent. 196. The method of claim 196, wherein the quantitative criteria is determined by amplification, and the measurement condition is that the difference in amplification efficiency for all components is less than about 1 percent. 196. The method of claim 196, wherein the substantially reproducible measurement conditions are within a range of reproducibility greater than 5 percent. 196. The method of claim 196, wherein the substantially reproducible measurement conditions are within a range of reproducibility greater than 3 percent. The infectious disease being evaluated or an infectious disease related to the infectious disease is related to a partial tissue of the subject, and the first sample is a type of tissue different from that of the partial tissue. 196. The method of claim 196, wherein the method is derived from a body fluid. 196. The method of claim 196, wherein the infection or an infection symptom associated with an infection is due to a microbial infection. 196. The method of claim 196, wherein the infection or infection associated with the infection is due to a bacterial infection. 196. The method of claim 196, wherein the infection or infection associated with the infection is due to a eukaryotic parasite infection. 196. The method of claim 196, wherein the infection or infection associated with the infection is due to a viral infection. 197. The method of claim 196, wherein the infection or infection associated with the infection is due to a fungal infection. 196. The method of claim 196, wherein the infection or an infection symptom associated with an infection is systemic inflammatory response syndrome (SIRS). 196. The method of claim 196, wherein the infection or infection associated with the infection is due to bacteremia, viremia, or fungiemia. 196. The method of claim 196, wherein the infection or infection associated with the infection is sepsis from any type of microorganism. 196. The method of claim 196, further comprising storing the first and second profile data sets on a digital storage medium. 197. The method of claim 196, wherein storing the first and second profile data sets further comprises storing each data set as a record in a data base. 196. The method of claim 196, wherein the baseline profile data set is derived from one or more other samples obtained from the same subject under a different environment than the first sample. 196. The method of claim 196, wherein the baseline profile data set is derived from one or more other samples obtained from the same subject in a different environment than the second sample. 196. The method of claim 196, wherein the first sample is from the subject's blood and the baseline profile data set is from the subject's tissue or body fluid other than blood. 196. The method of claim 196, wherein the first sample is derived from the subject's tissue or body fluid and the baseline profile data set is derived from blood. A method for providing an index indicating an inflammatory state of the subject having an uncertain sign of systemic infection based on the first sample derived from the subject and being an RNA source,
Deriving a profile data set from the first sample, the profile data set comprising a plurality of members, each member selected such that the measurement of the component indicates the inflammatory condition A quantitative measure of the amount of different RNA components in a panel composed of a plurality of identified components, the panel comprising at least two of the components of the gene expression panel of Table 1; When,
Deriving the profile data set, achieving such criteria for each component under substantially reproducible measurement conditions;
Applying a mapping of at least one criterion obtained from the profile data set to an exponential function that provides at least one criterion for the inflammatory condition to generate an indicator related to the inflammatory condition of the sample;
Have
The exponential function uses data from the baseline profile data set of the panel, each member of the baseline data set being an associated set of multiple subjects, one quantity of the components of the panel The normative criteria determined for the method, wherein the baseline data set is associated with the inflammatory condition to be evaluated. The subject exhibits uncertain signs of systemic infection, including at least one of an increase in white blood cell count, an increase in body temperature, an increase in heart rate, and an increase or decrease in blood pressure compared to a medical normative value; 223. The method of claim 221. The subject exhibits uncertain signs of systemic infection, including at least one of increased white blood cell count, increased body temperature, increased heart rate, and increased or decreased blood pressure compared to medical normative values; 223. The method of claim 221. 223. The method for providing an index of claim 221, wherein the exponential function has two components including disease state, severity, or disease course. 223. The method of providing an index according to claim 221, wherein the exponential function has three components including disease state, severity, or disease course. 223. The method for providing an indicator of claim 221, wherein the exponential function has four components including disease state, severity, or disease course. 223. The method of providing an indicator of claim 221, wherein the exponential function has five components including disease state, severity, or disease course. The exponential function is the formula: I = ΣC _i M _i P ^{(i) where} I is the index, M _i is the value of member i of the profile data set, C _i is a constant, and P (i) is constructed as a linear sum of multiple terms with M _i raised), and the sum is formed for integer values i up to the number of members in the data set; 227. A method for providing an index according to claim 221-227. The values C _i and P (i) are determined using statistical methods such as latent class modeling, and the latent class modeling related to the uncertainties of the systemic infection derived clinically and experimentally 229. The method of providing an indicator of claim 228, wherein the indicator is correlated with data comprising: 223. The method of claim 221, wherein the indicator is interpreted relative to the normative value by providing the indicator for a normative value determined for an associated set of subjects. 223. The method of claim 221, wherein the quantitative criteria is determined by amplification and the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent. 223. The method of providing an indicator according to claim 221, wherein the quantitative criteria is determined by amplification and the measurement condition is that the difference in amplification efficiency for all components is less than about 1 percent. 223. The method of providing an indicator according to claim 221, wherein the quantitative criteria is determined by amplification and the measurement condition is that the difference in amplification efficiency for all components is less than about 2 percent. 223. The method for providing an index of claim 221, wherein the substantially reproducible measurement conditions are within a range of reproducibility greater than 5 percent. 223. The method for determining a profile data set of claim 221, wherein the substantially reproducible measurement conditions are within a range of reproducibility greater than 3 percent. 224. The inflammatory condition is assessed in relation to a partial tissue of the subject, and the first sample is derived from a different type of tissue or body fluid than that of the partial tissue. Method. 223. The method of claim 221, wherein the inflammatory condition is a microbial infection. 223. The method of claim 221, wherein the inflammatory condition is a bacterial infection. 223. The method of claim 221, wherein the inflammatory condition is a eukaryotic parasite infection. 223. The method of claim 221, wherein the inflammatory condition is a viral infection. 223. The method of claim 221, wherein the inflammatory condition is a fungal infection. 223. The method of claim 221, wherein the inflammatory condition is systemic inflammatory response syndrome (SIRS). Deriving at least one other profile data set from at least one of the other samples, wherein the at least one other profile data set comprises a plurality of members, each member comprising the component Is a quantitative measure of the amount of different RNA components in a panel composed of a plurality of selected components such that the at least one other sample is , At least one time point, with respect to nutritional history, symptoms, clinical indicators, medications, physical activity, body weight, and environmental exposure, the same subject taken in a different environment than that of the first sample. Derived from the specimen, steps,
What is that of the first sample relative to an exponential function that provides a mapping derived from at least one criterion of the at least one other profile data to at least one criterion of the infectious symptom under different circumstances? At least one from the at least one other profile data set to produce at least one other indicator related to the infection or infection-related infection symptoms of the subject under different circumstances Applying the criteria, steps,
223. The method of providing an indication according to claim 221, further comprising: A method of using an indicator to guide therapeutic intervention to a subject who has an infection or who has an infection related to the infection,
245. Providing an indicator according to any of claims 61, 87, 221 or 243, and comparing the indicator with a normative value of the indicator determined for a related set of subjects. Using the difference between the indicator and a normative value of the indicator to guide therapeutic intervention;
Using indicators to guide therapeutic interventions, including: 224. The method of using an indicator to guide a therapeutic intervention according to claim 224, wherein the therapeutic intervention is a microbe specific therapy. 227. The method of using an indicator to guide a therapeutic intervention according to claim 224, wherein the therapeutic intervention is a bacteria specific therapy. 224. The method of using an indicator to guide a therapeutic intervention according to claim 224, wherein the therapeutic intervention is a fungal specific therapy. 224. The method of using an indicator to guide a therapeutic intervention according to claim 224, wherein the therapeutic intervention is a virus specific therapy. 224. The method of using an indicator to guide a therapeutic intervention according to claim 224, wherein the therapeutic intervention is a eukaryotic parasite-specific treatment. 224. The method of using an indicator to guide therapeutic intervention according to claim 224, wherein the therapeutic intervention is directed to the host immune system. Based on at least one sample that is from the subject and that provides an RNA source, the type of pathogen within the class of pathogens of interest to patients with infections or patients with infection-related infections Is a way of distinguishing,
Determining at least one profile data set according to claim 1 for said subject and associated with at least one related set of samples in said class of said pathogens of interest; Comparing said determined profile data set with at least one baseline profile data set to determine a difference;
The at least one profile data set of claim 1 for the subject determined in relation to at least one related set of samples within the class of the pathogen of interest. Determining at least one baseline profile data set as the profile data set determined in relation to at least one related set of samples in the class of the pathogen of interest; Comparing and determining a difference, and using the difference, the pathogen type in the at least one profile data set for the subject is converted to the pathogen in the at least one baseline profile data set. Distinguishing from the class of steps, and using the difference, Distinguishing the pathogen type in the at least one profile data set from the pathogen class in the at least one baseline profile data set; and
A method of distinguishing pathogen types within a class of pathogens, including: 251. The method of distinguishing pathogen types within a pathogen class according to claim 251, wherein the pathogen class is a microorganism. 252. The method of distinguishing pathogen types within a pathogen class according to claim 252, wherein the pathogen class is a bacterium. 254. The method of distinguishing pathogen types within a class of bacterial pathogens according to claim 253, wherein the difference is used to distinguish gram positive bacterial pathogens from gram negative pathogen bacteria. 254. The method of distinguishing pathogen types within a pathogen class according to claim 252, wherein the pathogen class is a fungus. 259. The method of distinguishing pathogen types within the class of fungal pathogens of claim 255, wherein the difference is used to distinguish acute Candida pathogens from chronic Candida pathogens. 254. The method of distinguishing pathogen types within a pathogen class according to claim 252, wherein the pathogen class is a virus. 258. The method of distinguishing pathogen types within a class of viral pathogens of claim 257, wherein the difference is used to distinguish DNA viral pathogens from RNA viral pathogens. 258. The method of distinguishing pathogen types within the class of viral pathogens of claim 257, wherein the difference is used to distinguish rhinovirus pathogens from influenza pathogens. 254. The method of distinguishing pathogen types within a pathogen class according to claim 252, wherein the pathogen class is a eukaryotic parasite. 262. The method of distinguishing pathogen types within a class of eukaryotic parasite pathogens according to claim 260, wherein the difference is used to distinguish plasmodium parasite pathogens from trypanosoma pathogens. An indicator is used to distinguish between pathogen types within a class of pathogens targeted by a patient who has an infection or who has an infection related to an infection based on at least one sample of the subject A method,
Providing at least one indicator according to any of claims 61, 87, 221 or 243 to the subject;
Comparing the at least one indicator to at least one normative value of the indicator determined in relation to at least one related set of a plurality of subjects;
Obtaining at least one difference;
Using the at least one difference between the at least one indicator and the at least one normative value for the indicator to distinguish the pathogen type from the pathogen class;
A method of using an indicator to distinguish between pathogen types within a class of pathogens, including:

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