CN117480262A - Biomarkers and methods for classifying subjects after viral exposure - Google Patents

Biomarkers and methods for classifying subjects after viral exposure Download PDF

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CN117480262A
CN117480262A CN202280039341.2A CN202280039341A CN117480262A CN 117480262 A CN117480262 A CN 117480262A CN 202280039341 A CN202280039341 A CN 202280039341A CN 117480262 A CN117480262 A CN 117480262A
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A·J·曼
G·盖尼高勒特
A·巴萨尔
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Abstract

A method of predicting whether a subject will develop an acute symptom of a disease after exposure or potential exposure to a respiratory virus, the method comprising analyzing a biomarker in a biological sample obtained from the subject and comparing the biomarker to a reference for the biomarker, wherein the biomarker comprises or is derived from an expression level of one or more genes selected from the group consisting of: PHF20, ABCA1, APBA2, MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K. Related prediction methods, methods of conducting clinical trials or field studies, computer programs, classification algorithms, computer readable media and computer implemented methods are also disclosed.

Description

Biomarkers and methods for classifying subjects after viral exposure
Technical Field
The present invention relates to biomarkers for predicting whether a subject will develop an acute symptom or sign of a disease after exposure or possible exposure to a respiratory virus, such as influenza virus. The present invention provides methods for predicting whether a subject will develop a severe or complex form of a disease. As disclosed herein, the invention includes methods of conducting clinical trials or field studies that include analyzing biomarkers, but more generally, the biomarkers of the invention can be used in any healthcare or non-healthcare environment; for example, patients infected with respiratory viruses are classified to identify those patients who are prone to develop acute signs or symptoms and therefore may require medical intervention. The subject may have been administered a pharmaceutical product to treat or prevent a respiratory disease, and thus the biomarkers of the invention may be used as a companion analysis product to predict the likely efficacy of the pharmaceutical product. The invention further provides computer programs, computer readable media, computer implemented methods and classification algorithms for generating or utilizing the biomarkers of the invention.
Background
Acute upper and lower respiratory tract infections are a major public health problem and are also a major cause of morbidity and mortality worldwide. Viruses are the leading cause of respiratory disease and include RNA viruses such as Respiratory Syncytial Virus (RSV), influenza virus, parainfluenza virus, metapneumovirus, rhinovirus (HRV) and coronavirus (Hodinka, "Respiratory RNA Viruses", microbiol spectra., month 8, 2016; 4 (4)).
CDC estimated that 2500 ten thousand people had influenza in the united states, 1100 ten thousand people had hospitalized for influenza, 31 ten thousand people had hospitalized for influenza, and 12,000 people died from pneumonia and influenza (rolifes et al, 2016) during 2015-2016. In 2003, the annual economic burden of influenza was estimated to be around $870 billion in the united states alone (Molinari et al, 2007). The cost of influenza is obviously enormous and any method of treating or diagnosing influenza would be of great value.
Influenza infects people of all ages and leads to a range of consequences, from asymptomatic infections and mild respiratory disease to severe respiratory disease and even death. Thus, different subjects exposed to the same influenza virus may be asymptomatic, mildly symptomatic, subclinical, presenting with acute symptoms, or in need of medical care, or even emergency hospitalization (Cox et al, 1999). In addition, the proportion of asymptomatic or subclinical infections, the degree of infectivity, and the proportion of detoxification that occurs before symptoms appear can all affect the potential impact of control measures and decisions regarding treatment and drug management (Lau et al, 2010).
The design of experiments in the current human challenge model for assessing influenza, RSV or HRV, studying therapeutic drugs and agents, depends on:
"general administration" -general treatment of all virus-vaccinated subjects on a day after vaccination (e.g., 24 hours or 28 hours after vaccination), whether or not the subjects are infected;
"triggered dosing" -treatment of subjects with only one or two of the following:
i. their first (or confirmed) PCR positive airway samples (i.e., only those samples expected to be infected after inoculation);
initial respiratory symptoms indicative of onset of viral infection;
"triggered dosing + general dosing" (DeVincenzo et al, NEJM 2014; deVincenzo et al, NEJM 2015) -this uses the principle of triggered dosing as the primary endpoint, however, any subject that still has no positive virus sample (or symptom) on some day after inoculation, e.g., day 5, will subsequently receive medication. In this case, a subject to whom the drug is commonly administered may be included, which is analyzed in two sub-analysis methods:
i. themselves as subsets;
binding to the triggered subset.
In a research model such as the human challenge model, knowing in advance who will develop obvious symptoms will allow the administration of the research drug to be triggered only in subjects who would otherwise continue to develop acute symptoms of influenza-like disease. A method that can predict who will develop acute symptoms of influenza-like disease would allow identification of subjects suitable for administration of the drug. Benefits of this volunteer selection dosing method include:
a. By evaluating the effects of drugs that may continue to present with acute symptoms of influenza-like disease only, the ability to detect a reduction in clinically relevant diseases is improved. This is in sharp contrast to experimental designs where the triggering of treatment may be based on the presence of viral shedding/symptoms or drug administration to all vaccinators. The prior selection of appropriate test subjects can avoid the problems associated with assessing the efficacy of a drug in a population, as the ability to detect differences in a population is more difficult (i.e., uninfected, asymptomatic, or only mildly infected and very small viral load people).
b. Fewer people may unnecessarily contact the drug, thereby:
i. reduce drug requirements, thereby reducing manufacturing and cost effectiveness;
providing a treatment regimen with improved benefits: by selecting a risk profile that provides therapy only to patients presenting with acute symptoms;
providing improved benefits: the risk profile of the drug and study is reduced by requiring fewer people to contact the study drug.
Thus, there remains a need for methods of predicting whether a subject will develop acute symptoms of an influenza-like disease, to make informed therapeutic decisions, to administer proper levels of care, and/or to improve the experimental design of research drugs for treating influenza-like disease.
Woods et al, "A Host Transcriptional Signature for Presymptomatic Detection of Infection in Humans Exposed to Influenza H1N1 or H3N2", PLOS ONE, month 1 of 2013; 8 (1) e52198 describes the generation of viral gene signatures (or factors) for symptomatic influenza, enabling detection of 94% of infection cases. Gene signatures were detected as early as 29 hours after exposure, with the highest accuracy reported to be achieved on average 43 hours (p=0.003, H1N 1) and 38 hours (p value equal to 0.005, H3N 2) before the peak of clinical symptoms.
While Woods et al disclose methods of identifying subjects infected with respiratory viruses before symptoms appear, such methods do not predict whether an individual will develop acute symptoms of influenza-like disease.
Disclosure of Invention
The present invention provides a biomarker for predicting whether a subject will develop an acute symptom of a disease upon or likely to be exposed to a respiratory virus, wherein the biomarker comprises or is derived from the expression level of one or more genes selected from the group consisting of PHF20, ABCA1, APBA2, MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K measured in a biological sample obtained from the subject upon or likely to be exposed to a respiratory virus.
Biomarkers in the context of the present invention are measurable indicators of a biological state or condition, in particular an output for predicting whether a subject will develop an acute symptom of a disease. The output may be a digital output. In certain embodiments, a biomarker of the present invention may be a composite biomarker comprising the expression level of two or more genes of a gene set or the expression level of at least one gene of a gene set in combination with at least one other factor as described herein.
The subject may be a human or non-human mammal.
Acute symptoms of the disease may consist of symptoms of influenza-like or other respiratory diseases, as disclosed herein.
Acute symptoms of influenza-like or other respiratory diseases refer to a subject experiencing more than four symptoms, and these symptoms alone or in combination interfere with daily activities. The symptoms include runny nose, nasal obstruction, sore throat, sneezing, ear pain, cough, shortness of breath, asthma, chest distress, headache, restlessness, myalgia, muscle and/or joint pain, elevated body temperature, chill, and fever. Elevated body temperature may be a temperature above 38 ℃, optionally accompanied by a cough, optionally onset within the last 8-12 (e.g. 10) days. In a common human challenge study involving vaccinating with respiratory viruses, where symptoms of influenza-like or other respiratory diseases are assessed, a portion of the subjects will develop acute symptoms, and the symptoms of these subjects will be scored at the 85 th percentile, e.g., the total VAS or CAT score of the subject will be at the 85 th percentile. For example, a total VAS score of greater than or equal to 25 units, or a CAT score of greater than or equal to 10 units, corresponds to acute symptoms.
Thus, in certain embodiments, a subject may be identified as susceptible to progression to a complex form of influenza-like disease or other respiratory disease. Complex respiratory diseases (e.g., influenza) are defined as diseases requiring hospitalization and/or having symptoms and signs of lower respiratory tract infections (hypoxia, dyspnea, pulmonary infiltration), central nervous system involvement and/or significant deterioration of underlying medical conditions.
If a subject is predicted to develop an acute symptom of an influenza-like or other respiratory disease, such as a complex influenza or other respiratory disease, then the subject is predicted to continue to exhibit the acute symptom as described above in accordance with the present invention. Acute symptoms of influenza-like or other respiratory diseases may be predicted to occur in a subject, but the subject may resolve himself or may take action to prevent the subject from developing acute symptoms; for example, a drug may be administered. Thus, a subject predicted to develop acute symptoms of influenza-like or other respiratory diseases will not inevitably develop acute symptoms of influenza-like or other respiratory diseases.
Respiratory viruses include all respiratory viral infections including Respiratory Syncytial Virus (RSV), parainfluenza virus (HPIV), metapneumovirus (HMPV), rhinovirus (HRV), coronavirus, adenovirus (HAdV), enterovirus (EV), bocavirus (HBoV), paraenteric virus (HPeV), influenza (including influenza a and influenza b).
A gene set in the context of the present invention is a group of genes whose expression levels can be analyzed and used to predict the progression and/or outcome of an influenza-like disease or other respiratory disease in a subject. A gene subset is a group of genes selected from a gene set that can be used to predict a certain stage of disease progression, such as early, mid or late progression of acute symptoms of influenza-like or other respiratory diseases, or at a certain point in time after inoculation, or exposure to respiratory viruses, for example up to 25 hours (e.g., 13-25 hours), or 37-49 hours, or 49-61 hours, which in certain embodiments may be referred to as early, mid or late, respectively. In some cases, it may not be possible to determine when a subject is exposed to respiratory viruses, and furthermore, some subjects develop symptoms faster than others, so disease progression and stage of disease progression may be estimated based on the assessed symptoms.
In particular, the gene set of the present invention may include one or more genes (including two genes, three genes, four genes, five genes, six genes, etc.) selected from the following genes: PHF20, ABCA1, APBA2, MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K.
In certain embodiments, the gene set may consist of up to 16 genes (typically up to 10 genes, and more typically up to 6 genes), including one or more genes (including two genes, three genes, four genes, five genes, six genes, etc.) selected from the group consisting of: PHF20, ABCA1, APBA2, MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K.
In certain embodiments, the gene set may comprise PHF20. In certain embodiments, the gene set may comprise NOL9. In certain embodiments, the gene set may include PHF20 and NOL9.
The gene set of the present invention may consist of one, two, three, four, five or six genes selected from the following PHF20, ABCA1, APBA2, MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K. Thus, for example, a gene set of the invention may consist of one, two, three, four, five or six genes including PHF20 and optionally NOL9.
Unless otherwise indicated, the present invention does not exclude the possibility of including within the gene set one or more other genes not specifically disclosed herein, which may be found to further improve the accuracy, sensitivity or specificity of the methods of the invention.
The first gene subset may comprise PHF20, ABCA1, APBA2, MORC2, SNU13 and DCUN1D2.
Thus, the first gene subset may comprise the expression level of PHF20. In addition to the expression level of PHF20, the first gene subset may comprise the expression level of one or both of APBA2 and ABCA 1. When the first gene subset comprises the expression level of PHF20 and the expression level of one or both of APBA2 and ABCA1, the first gene subset may additionally comprise the expression level of one, two or three of MORC2, SNU13 and DCUN1D2.
The first gene subset may consist of one, two, three, four, five or six of PHF20, ABCA1, APBA2, MORC2, SNU13 and DCUN1D2.
The first gene subset may consist of one gene, which is PHF20. The first gene subset may consist of two genes, one of which is PHF20. The first gene subset may consist of three genes, one of which is PHF20. The first gene subset may consist of four genes, one of which is PHF20. The first gene subset may consist of five genes, one of which is PHF20. The first gene subset may consist of six genes, one of which is PHF20.
The first gene subset may consist of two genes, one of which is PHF20 and the other of which is APBA2 or ABCA1. The first gene subset may consist of three genes, including PHF20, accompanied by one or both of APBA2 and ABCA1. The first gene subset may consist of four genes, including PHF20, accompanied by one or both of APBA2 and ABCA1. The first gene subset may consist of five genes, including PHF20, accompanied by one or both of APBA2 and ABCA1. The first gene subset may consist of six genes, including PHF20, accompanied by one or both of APBA2 and ABCA1.
The second gene subset may comprise MAX, NOL9, MPRIP, HP, BST1 and TM9SF2.
Thus, the second gene subset may comprise the expression level of one or more of NOL9, HP and MAX (in particular NOL 9). In addition to the expression levels of one or more of HP, MAX and NOL9 (in particular NOL 9), the second gene subset may comprise the expression levels of one or both of BST1 and MPRIP. When the second gene subset comprises the expression level of one or more of HP, MAX and NOL9 (in particular NOL 9), and the expression level of one or both of BST1 and MPRIP, the second gene subset may additionally comprise the expression level of TM9SF2.
The second gene subset may consist of one, two, three, four, five or six of MAX, NOL9, MPRIP, HP, BST1 and TM9SF 2.
The second gene subset may consist of a gene which is NOL9, HP or MAX (in particular NOL 9). The second gene subset may consist of two genes, one or both of which is selected from NOL9, HP and MAX (in particular NOL 9). The second gene subset may consist of three genes, one, two or all of which are selected from NOL9, HP and MAX (in particular NOL 9). The second gene subset may consist of four genes, one, two or three selected from NOL9, HP and MAX (in particular NOL 9). The second gene subset may consist of five genes, one, two or three of which are selected from NOL9, HP and MAX (in particular NOL 9). The second gene subset may consist of six genes, one, two or three of which are selected from NOL9, HP and MAX (in particular NOL 9).
The second gene subset may consist of two genes, one of which is NOL9, HP or MAX (in particular NOL 9) and the other of which is BST1 or MPRIP. The second gene subset may consist of three genes, including one or more of NOL9, HP, or MAX (in particular NOL 9), accompanied by one or both of BST1 and MPRIP. The second gene subset may consist of four genes including one or more of NOL9, HP or MAX (in particular NOL 9) with one or both of BST1 and MPRIP. The second gene subset may consist of five genes, including one or more of NOL9, HP, or MAX (in particular NOL 9), accompanied by one or both of BST1 and MPRIP. The second gene subset may consist of six genes, including one or more of NOL9, HP, or MAX (in particular NOL 9), accompanied by one or both of BST1 and MPRIP.
The third gene subset may comprise HOMER3, NSUN6, HP, EPHA4 and BMP2K.
Thus, the third gene subset may comprise the expression level of one or both of HP and home 3. In addition to the expression level of one or both of HP and home 3, the third gene subset may comprise the expression level of one or both of EPHA4 and BMP2K. When the third gene subset comprises the expression level of one or both of HP and home 3, the expression level of one or both of EPHA4 and BMP2K, the third gene subset may additionally comprise the expression level of NSUN 6.
The third gene subset may consist of one, two, three, four or five of HOMER3, NSUN6, HP, EPHA4 and BMP2K.
The third gene subset may consist of one gene, which is HP or HOMER3. The third gene subset may consist of two genes, one or both of which is selected from HP and home 3. The third gene subset may consist of three genes, one or two of which are selected from HP and home 3. The third gene subset may consist of four genes, one or two of which are selected from HP and home 3. The third gene subset may consist of five genes, one or both of which are selected from HP and home 3.
The third gene subset may consist of two genes, one of which is HP or home 3; the other is EPHA4 or BMP2K. The third gene subset may consist of three genes, including one or both of HP and HOMER3, accompanied by one or both of EPHA4 and BMP2K. The third gene subset may consist of four genes, including one or both of HP and HOMER3, accompanied by one or both of EPHA4 and BMP2K. The third gene subset may consist of five genes, including one or both of HP and HOMER3, accompanied by one or both of EPHA4 and BMP2K.
In certain embodiments, any of the above-described gene sets may further comprise 1 to 2 genes in addition to the genes listed above without departing from the essential features of the sets of the present disclosure.
As shown in the examples below, genes that have been identified according to the invention as predicting the appearance of an acute symptom of influenza-like or other respiratory disease in a subject, as described above, exhibit altered expression levels following inoculation with a virus in a subject that subsequently continues to exhibit the acute symptom relative to a subject that does not exhibit the acute symptom. This suggests that these genes may identify subjects more likely to develop acute symptoms of influenza-like or other respiratory diseases. Since symptoms of a viral infection occur faster in certain subjects than in other subjects, altered expression of one or more genes according to the invention can predict acute symptoms before the subject exhibits any symptoms of the infection, or an early diagnostic indicator of acute symptoms at about the same time that the subject begins to exhibit the first symptoms of the infection.
The expression level of one or more genes in a biological sample may be measured using any suitable method known in the art for quantifying the expression level of a gene, particularly a mammalian gene. In certain embodiments, the expression level of one or more genes according to the invention may be measured by quantifying mRNA transcripts of the one or more genes in a biological sample.
Preferably, a PCR-based method, such as RT-qPCR, may be used. Examples of RT-qPCR based methods are disclosed in U.S. Pat. No. 7,101,663, the contents of which are incorporated herein by reference. One advantage of real-time PCR is that it is relatively simple and convenient to use.
Alternatively, a gene expression microarray of the type disclosed in, for example, U.S. Pat. No. 6,040,138, the contents of which are incorporated herein by reference, may be used, wherein a pool of labeled target cRNA molecules obtained by transcribing double-stranded cDNA derived from mRNA transcripts isolated in a biological sample and fragmenting the resulting cRNA transcripts are hybridized with oligonucleotide probes having specific sequences immobilized at specific positions on a solid support. After incubation of cRNA targets with surface-bound probes, the array is washed and the labels on the targets can be used to quantify the amount of targets bound to any given feature on the array. The amount of target cRNA bound to a given surface is proportional to the level of expression of the corresponding gene.
Alternatively, RNA-seq can be used to quantify, discover and analyze RNA. This uses a new generation of sequencing for cDNA converted from RNA (Wang et al 2009).
Suitably, the biological sample may be a blood or breath sample. In particular, the sample may be a sample containing immune cells.
In certain embodiments, the expression level of each of the one or more genes may be compared to a respective reference level. The reference level may be a threshold expression level indicative of influenza-like or other respiratory disease acute symptoms or a prediction of acute symptom development. Alternatively, the reference level may be a baseline level of expression that indicates that the subject is unlikely to develop acute symptoms of influenza-like or other respiratory diseases. Significantly altered expression (increased or decreased expression) of one or more genes relative to their respective baseline levels, e.g., at least 1.1x, preferably at least 1.5x or 2x, or 3x, or 4x, or 5x, etc., up to 100x, may be indicative of, or predictive of, a subject's acute symptoms of an influenza-like or other respiratory disease.
In certain embodiments, the methods may involve separate reference levels for each gene. According to the invention, altered expression of at least one gene, preferably two or more genes, relative to their respective reference levels may be indicative of, or predictive of the development of, acute symptoms of influenza-like or other respiratory diseases.
In certain embodiments, the reference level of the or each gene may be a previously measured expression level of that gene in the same subject. In particular, the reference level of the or each gene may comprise a baseline expression level of the gene of the subject, which is measured when the subject is known not to be infected with a respiratory virus such as influenza. When a subject can obtain previous expression levels of one or more genes measured at more than one previous occasion, the reference level for each gene can comprise an average of the multiple previous levels.
Thus, in some cases, a subject may be tested once to obtain baseline levels of one or more genes that form a reference level, which may then be used in the event of suspected viral infection or routinely checked for comparison to contemporaneous expression levels to predict whether the subject is likely to develop acute symptoms of influenza-like or other respiratory illness.
Exposure to respiratory viruses, or possibly to respiratory viruses, including any contact or possible contact with respiratory viruses, including exposure to community acquired respiratory virus infection, exposure to respiratory viruses in a home, nursing home, hospital, or military setting. Exposure also includes vaccinating the subject during the human challenge model and/or the clinical trial.
As mentioned above, it is not always possible to determine when a subject is exposed to respiratory viruses. In addition, different subjects exhibited symptoms at different time points, with some subjects exhibiting symptoms earlier than others. Thus, the progression of influenza-like or other respiratory diseases may be measured on a relative scale and may be referred to as early, mid or late progression towards possibly occurring acute symptoms of influenza-like or other respiratory diseases. In other cases, the precise time of exposure to respiratory viruses is known, e.g., after inoculation in a model of human attack, and the time in hours from exposure can be measured, e.g., up to 25 hours (e.g., 13-25 hours) after exposure.
As explained in the figures and examples, a single gene set comprising one or more genes may be analyzed using a first algorithm. Alternatively, a combination of gene sets and gene subsets may be analyzed. Different gene sets and gene subsets may be analyzed simultaneously, for example, using the same biological sample or samples obtained over similar or identical time frames. The different gene sets and gene subsets may be analyzed sequentially with a first gene set or gene subset analyzed in a sample collected at a first time point using a first algorithm, a second gene set or gene subset analyzed in a sample collected at a second time point using a second algorithm, and a third gene set or gene subset analyzed in a sample collected at a third time point using a third algorithm, etc.
As noted above, the biomarkers of the invention may be based on a number of input variables or factors, including the level of gene expression, e.g., the age of the subject, or other potential conditions (e.g., asthma) that the subject may have, may be included in the variables used to calculate the biomarkers. Thus, the biomarker may be a composite biomarker. The output of the biomarker may be a numerical value. The value may be determined using a threshold, reference level, or baseline level, for example, a value above a certain reference level predicts that the subject will develop acute symptoms of influenza-like or other respiratory disease.
The biomarker may be computer-generated and comprise an output variable of a classification algorithm that uses the expression level of one or more genes in the gene set as an input variable; or one or more genes of the first subset of genes; or one or more genes in the second subset of genes; or one or more genes in the third gene subset.
The classification algorithm may be configured to prioritize the accuracies such that the algorithm produces the maximum number of correct predictions. Thus, the classification algorithm may be configured to prioritize Negative Predictive Value (NPV), i.e. the proportion of negative test results that are truly negative, in order to minimize the number of subjects who are not expected to develop acute symptoms of influenza-like disease but who actually develop acute symptoms of influenza-like or other respiratory diseases.
The classification algorithm may be derived from a training dataset using as input variables expression levels of one or more genes from the gene set measured in biological samples obtained from a group of subjects at a predetermined time after exposure to the respiratory virus, wherein the subject group is classified into two classes according to whether or not acute symptoms of influenza-like or other respiratory disease occur after exposure to the respiratory virus, and wherein the classification algorithm operates on the expression levels to produce a class-differentiated output variable.
A variety of classification algorithms may be used by those skilled in the art to classify subjects into two or more categories based on their symptom scores. Similarly, many machine learning techniques can be used to derive classification algorithms that can classify new subjects according to the expression levels of one or more genes using training data sets that include more than two categories and the expression levels of their respective one or more genes. The performance of a classification algorithm constructed using a machine learning process may be verified using one or more known verification methods, such as cross-validation, and statistical parameters (e.g., accuracy, sensitivity, specificity) are calculated so that one skilled in the art may obtain a classification algorithm that is best suited for classifying a subject based on the expression levels of one or more genes of the subject.
Acute symptoms of influenza-like or other respiratory diseases in a group of subjects in the training dataset may be assessed by assessing one or more symptoms of influenza-like or other respiratory diseases at a series of preset times after exposure to the respiratory virus. The subject evaluates one or more symptoms using a diary card, optionally visually simulates panic symptoms diary cards (VAS), or optionally records classification symptoms (CAT) using a modified normalized symptom score (e.g., a modified jackson score). The symptoms evaluated may include runny nose, nasal obstruction, sore throat, sneezing, ear pain, cough, shortness of breath, headache, restlessness, myalgia, muscle and/or joint pain, coldness and fever.
The first class of subjects may record a total VAS of greater than or equal to 25 units and/or a total CAT score of greater than 10 units, or may display one or more of the following: maximum variance of total VAS or CAT up to symptom peaks; maximum variance of total VAS or CAT during isolation; or total VAS or CAT up to the steepest gradient of the symptom peak (regression line slope).
In general, the machine learning process and the resulting classification algorithm may be performed using a computer.
The gene set and gene subset may be selected by: i) Analyzing expression levels in biological samples obtained from a subject group in a training set of data over a series of preset times following exposure to a virus; ii) identifying genes nominally associated with acute symptoms of influenza-like or other respiratory disease, and iii) selecting an identified gene set that exhibits a maximum predictive value for the development of acute symptoms of influenza-like or other respiratory disease at an expression level at a predetermined time after exposure to the virus using a variable selection procedure.
The variable selection process may comprise subjecting the expression level of the identified gene to a repeated gradient enhancement process for a predetermined time after exposure to the respiratory virus, selecting a set of 1, 2, 3, 4, 5 or 6 genes most commonly selected by the gradient enhancement process. The variable selection process is performed by a gradient enhancer (GBM; friedman 2001;Friedman 2002) as shown in FIG. 6 of the drawings. In the context of the present invention, differential expression analysis of genes between subjects presenting with acute symptoms of influenza-like or other respiratory diseases and subjects not presenting with acute symptoms of influenza-like or other respiratory diseases is performed by applying a cubic p-spline model. The nominal correlation resulting from the cubic spline analysis is input into a variable selection process that includes a gradient enhancer and an iterative search is performed using fifty starts (seeds) to determine the best gene predictors for the appearance of acute symptoms of influenza-like or other respiratory illness.
Biomarkers can be used to assign subjects to groups in clinical trials or to make therapeutic decisions. The subjects assigned to one subset were administered the drug, while the subjects assigned to the other subset were not administered the drug or did not receive the drug until the end of the trial or study. Biomarkers can also be used to monitor the efficacy of a drug by assessing whether a subject administered the drug is likely to develop acute symptoms of the disease after exposure or possible exposure to respiratory viruses. The medicament may comprise a therapeutic or prophylactic agent, such as a vaccine.
Accordingly, the present invention provides a method of predicting whether a subject will develop acute or complex symptoms of a disease after exposure to or possible exposure to a respiratory virus, the method comprising analysing a biomarker according to the present invention and comparing the biomarker to a reference for the biomarker.
The invention also provides a method of conducting a clinical trial or on-site study in which a group of subjects are exposed to a respiratory virus, the method comprising analyzing a biomarker according to the invention for each subject and comparing the biomarker to a reference for the biomarker to predict whether the subject is likely to develop an acute symptom of the disease, and including subjects predicted to develop an acute symptom of the disease in a first subset of the clinical trial or on-site study, and including subjects predicted not to develop an acute symptom of the disease in a second subset.
As described above, the methods of the invention may include comparing the biomarker to a reference for the biomarker or to a baseline for the biomarker. The baseline for the biomarker may be determined when the subject is known to not be infected with respiratory viruses. The disease may be an influenza-like disease or other respiratory disease.
Medicaments include all substances used in medical treatment and include vaccines, medicaments, placebo and research medicaments, for example research medicaments which are the subject of clinical trials. Thus, drugs include licensed, unlicensed and research drugs. The medicament also includes products that have been given marketing authorization but are being tested for different purposes, or are being tested for efficacy when assembled in different ways, or are being tested for more information about authorized uses.
The invention also provides a computer program for predicting whether a subject will develop acute symptoms of a disease, such as an influenza-like disease, after exposure to or possible exposure to a respiratory virus, comprising instructions which, when executed by a computer, cause the computer to generate a biomarker according to the invention.
The present invention also provides a classification algorithm for predicting whether a subject will develop an acute symptom of a disease (e.g., an influenza-like disease or other respiratory disease) after exposure or possible exposure to a respiratory virus, wherein the classification algorithm is derived by analyzing the expression level of one or more genes in a subject who has developed an acute symptom of the disease and comparing the expression level with the expression level in a subject who has not developed an acute symptom of the disease, wherein the one or more genes are PHF20, ABCA1, APBA2, MORC2, SNU13 DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4, and BMP2K.
The invention also provides a computer readable medium and/or a computer program comprising instructions which, when executed by a computer, cause the computer to perform a classification algorithm according to the invention.
The present invention also provides a computer-implemented method for predicting whether a subject will develop an acute symptom of a disease, such as an influenza-like disease, by analyzing the expression level of one or more genes in a subject that has developed an acute symptom of the disease after vaccination with a respiratory virus and comparing the expression level with the expression level of a subject that does not develop an acute symptom of the disease after vaccination with a respiratory virus, wherein the one or more genes are PHF20, ABCA1, APBA2, MORC2, SNU13 DCUN1D2, MAX, nop 9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4, and BMP2K.
The present invention also provides a computer-implemented method, wherein the method comprises displaying a graphical user interface of the biomarker to a user. It is also contemplated that computer-implemented aspects of the invention may be performed by more than one computer, e.g., two or more computers operating in different locations. More than two computers may communicate via a data channel (e.g., the internet).
The present invention also provides a method of predicting whether a subject will develop an acute symptom of a disease after exposure or potential exposure to a respiratory virus, comprising estimating the time elapsed after exposure or potential exposure to a respiratory virus by analyzing the expression level of one or more genes selected from the group consisting of: PHF20, ABCA1, APBA2, MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4; selecting a biomarker described herein that exhibits a maximum predictive value for the development of an acute symptom of a disease at the time; and comparing the biomarker to a reference for the biomarker.
For example, in certain embodiments, the elapsed time may be estimated to be about one day (i.e., about 23-26 hours, e.g., 25 hours) after exposure or possible exposure to the respiratory virus. If so, the selected biomarker may comprise the expression level of one or more genes from the first gene subset described herein.
The time elapsed after exposure or possible exposure to respiratory viruses can be estimated to be about 1.5-2 days (i.e., about 37-49 hours). If so, the selected biomarker may comprise the expression level of one or more genes from the second gene subset described herein.
The time elapsed after exposure or possible exposure to respiratory viruses can be estimated to be about 2-2.5 days (i.e., about 49-61 hours). If so, the selected biomarker may comprise the expression level of one or more genes from the third gene subset described herein.
The invention also provides a kit for use in the method according to the invention. The kit may comprise one or more reagents that allow detection, optionally quantification, of one or more nucleotides, or one or more peptides, corresponding to one or more genes from the gene set or first, second or third gene subsets described herein. The kit may be used to detect one or more analytes in or extracted from a biological sample, such as, but not limited to, blood, serum, plasma, urine, saliva, tissue biopsies, stool, sputum, skin, nose or throat samples. The kit may include means for performing an assay (e.g., a lateral flow assay). The device is used by the patient himself (without the help of a doctor), for example in the home (as opposed to a hospital or other medical institution). The device may comprise a strip of porous material capable of supporting capillary flow, with a region for receiving a sample; a region comprising a reagent for detecting an analyte; a detection zone; and a control region. In use, a reaction between the reagent and the analyte may be detected in the detection zone, for example by a colour change of the material in the detection zone. In use, the control zone may be used as a reference against which to benchmark the reaction detected in the detection zone. The device may include more than one test strip, for example the device may include two, three, four, five or six test strips in communication with the same or separate receiving areas.
The literature references and sequence listing of the above genes are included at the end of the present specification. It is to be understood that references and sequence listing necessarily disclose specific alleles and are included by way of example only. The invention is not limited to the use of such specific alleles, but may also be practiced using expression products of different variants of one or more genes.
Drawings
The following description refers purely by way of example to the accompanying drawings of an embodiment of the invention.
In the drawings:
fig. 1 is a graph showing how subjects are treated by a typical clinical study as described in example 1 below.
Fig. 2 is a graph showing the total peak VAS levels for infected (left) and uninfected (right) subjects.
Figure 3 is a graph showing the maximum variance change in VAS score, indicating that four individuals with acute symptom peaks of influenza-like disease all experienced a total VAS variance change of greater than 30 units.
FIG. 4 is a graph showing peak levels of peak classification scores for infected (left) and uninfected (right) subjects in three studies (hVIVO-1, duke-2).
Fig. 5 is a principal component analysis to show greater homogeneity after adjustments to the study.
Fig. 6 is a flow chart demonstrating a variable selection process performed by the gradient booster.
Fig. 7 is a flow chart demonstrating a scenario in which 1 to 3 algorithms are run in parallel to assign subjects to groups for action (e.g., dosing, additional clinical assessment). These scenarios include: in a human virus challenge model, in a field study, or in a community, wherein a subject is exposed to respiratory viruses.
Fig. 8 is a flow chart showing a scenario in which each genetic algorithm is used in turn to assign subjects into groups to take action (e.g., administration of drugs, additional clinical evaluations) in a human virus challenge model.
Examples
Example 1
As described below, subjects from three independent studies were determined to include a subset of subjects exhibiting acute symptoms of influenza-like disease based on peak symptom scores during isolation.
Method
Using Affymetrix TM HG-U133 Plus 2.0 microarrays were subjected to transcriptomic analysis throughout post-inoculation isolation. Differential expression analysis between subjects who developed acute symptoms of influenza-like disease and subjects who did not developed acute symptoms of influenza-like disease was performed by applying a cubic p-spline model. The nominal correlation generated by cubic spline analysis was entered into the variable selection process to determine the best 6 gene predictors for acute symptoms of influenza-like disease on day 1, day 2, and night 2 post inoculation. These genes can be used to distinguish between subjects who develop acute symptoms of influenza-like disease at different times after exposure to the virus within the model and subjects who do not develop acute symptoms of influenza-like disease.
This analysis combines the data of three independent studies, the largest of which was performed by hVIVO (hVIVO-1), and the two published studies were also performed in hVIVO challenge models of Duke university medical center (Duke-1 (Zaas et al 2009) and Duke-2 (Woods et al 2013), with the index GSE52428 in GEO and published):
table 1-each study demonstrates the following; the virus used, the number of subjects, the microarray platform used, the time point at which the PaxGene blood samples were taken, the method in the diary cards for measuring symptoms (vas=visual analog scale, cat=modified jackson score/classification), the number of diary cards recorded a day and the method for confirming influenza infection.
For hVIVO-1, 60 healthy volunteers were inoculated intranasally with H3N2 Perth/16/2009 influenza A, of which 27 volunteers were used for this analysis. For Duke-1, 17 healthy volunteers were inoculated intranasally with H3N2A/Wisconsin/67/2005 influenza A. For Duke-2, 24 healthy volunteers were inoculated intranasally with H1N1A/Wisconsin/59/2007 influenza A. All volunteers provided informed consent and received extensive pre-group health screening (fig. 1), and any volunteers with significant baseline antibodies to the influenza strain used were excluded. After about 48 hours of isolation (about noon on study day 0), a predetermined dose of influenza a virus was instilled into the bilateral nostrils of the subject using standard pipetting methods. Volunteers underwent clinical measurements and samples were collected until the isolation was released, then at each visit.
In hVIVO-1, 33 out of 60 subjects were infected after inoculation (as evidenced by confirmed viral shedding), 25 were determined to be uninfected, and 2 were indeterminate. Metaphase analysis was performed after the first 27 vaccinations and all samples from each subject were sent for gene microarray analysis. One of the 27 subjects did not complete the isolation and was therefore excluded from the analysis. Of the 26 subjects with viable microarray data, 13 were identified as diagnosed with infection, 11 uninfected, and 2 indeterminate.
In Duke-1, 9 out of 17 subjects were infected after inoculation (as evidenced by confirmed viral shedding), 8 were determined to be uninfected. Four dilutions (6.41TCID50/mL, 5.25TCID50/mL, 4.41TCID50/mL and 3.08TCID50/mL) were used, with 4 to 5 subjects receiving each dose.
In Duke-2, 9 out of 24 subjects were infected after inoculation (as evidenced by confirmed viral shedding), and 15 were determined to be uninfected. Four dilutions (2.35TCID50/mL, 1.8TCID50/mL, 1.25TCID50/mL and 1.4TCID50/mL) were used, with 4 to 6 subjects receiving each dose. One subject was excluded from secondary infection.
Based on qualitative virus culture and quantitative influenza RT-PCR data for epithelial lining fluid in the hvvo study, subjects had been infected with influenza. Epithelial lining fluid was collected from nasopharyngeal FLOQ swabs twice daily (the first sample was collected about 20 hours after inoculation, starting on day 1, morning). Nasal collection was continued throughout the isolation period. For Duke's university study, the infection status of the subjects was obtained from Woods et al, 2013.
During the period of isolation, subjects self-assessed their symptoms three times daily using a categorical and continuous (visual analog scale, VAS) symptom diary card. Classification symptoms were recorded using a modified normalized symptom score. The modified jackson score required the subject to rank 10 symptoms including: upper respiratory symptoms (runny nose, nasal obstruction, sore throat, sneezing and ear pain), lower respiratory symptoms (cough and shortness of breath) and systemic symptoms (headache, myalgia, and muscle and/or joint pain) were scored on a scale of 0-3 on the scale of "asymptomatic", "only apparent", "troublesome but still active" and "troublesome and not active daily". hVIVO-1 includes wheezing and cold/fever in addition to 10 symptoms. Furthermore, shortness of breath at rest and wheezing at rest (grade 4 = symptoms at rest) were also recorded using only additional grades of these symptoms. VAS symptoms were measured along a 10cm line in mm.
To determine which subjects are considered to have significant symptoms, subjects are first identified in hvvo-1 using the VAS data recorded along with the classification score. The total VAS score was calculated for all 58 evaluable participants at each time point during the quarantine period in hVIVO-1. The peak VAS score for each participant was determined and a range of 0-20 units was observed for uninfected individuals. However, in the infected group, four individuals experienced a total VAS >25 units (fig. 2). The four individuals differ in other respects from the other participants; maximum variance of total VAS up to symptom peak, maximum variance of total VAS during isolation, steepest gradient of total VAS up to symptom peak (regression line slope)
To solve the problem of whether these four individuals can be identified early in the quarantine, a piecewise estimate of variance and variance variation during quarantine is calculated.
It was observed that the total VAS variance varied by more than 30 units for all four individuals with the most severe symptoms during peak hours (FIG. 3). The maximum variance variation for the remaining individuals is less than 25 units. In addition, this change was consistently observed at day 2, time point 2, and in 3 of 4 cases, the change occurred before the peak of symptoms (table 2).
TABLE 2
Individual body Variance change on day 2; TPT 2 Peak time point
1 35.64 Day 3; TPT 1
2 88.14 Day 2; TPT 2
3 48.83 Day 2; TPT 3
4 70.57 Day 2; TPT 3
After Duke-1 and Duke-2 are included in the analysis, the severity score must be adjusted for use with the classification symptom score because there is no VAS available in Duke-1 and Duke-2. For these three studies, a similar analysis as for VAS (above) was now performed for the classification of symptoms. It was observed that in these three studies, there were 11 individuals with a peak total classification score of greater than or equal to 10 units (10 subjects with microarray data). Including the four members of hvvo-1 already discussed. This threshold was also passed by 4 individuals in the H1N1 study and 3 individuals in the H3N2 study (fig. 4). As with VAS, abrupt changes in classification score variance are associated with acute symptoms of influenza-like disease.
Blood was collected into RNA PAXGene at predetermined time intervals TM In the collection tube. This occurs once on day-1, on the morning of the inoculation (about 5 hours before inoculation), and then once every 12 hours for hVIVO-1 and Duke-2, and once every 8 hours for the remainder of the isolation.
All three studies were performedAs a microarray, the human genome U133A 2.0 array (Affymetrix, santa Clara, calif.). Microarray data from both studies at Duke university were obtained from both studies covered by Liu et al (2016). The public data contained 22,277 probe sets, a subset of 54,675 probe sets available for hVIVO-1.
Principal Component Analysis (PCA) showed systematic differences in the three transcriptome datasets (fig. 5). The conclusion is that direct merging can lead to spurious results. Therefore, study adjustments were made to transcriptomic measurements and PCA replicates (fig. 5).
To exclude informationless probe sets, two sets were considered, namely, the stringent and non-severe set. For each molecule in each group, a filtration rate was calculated, reflecting the variation over time as well as the variation between individuals. At the recommended threshold, 13,806 probe sets provided information in at least one set and were further explored.
Differential expression analysis (Straube et al) between subjects who developed acute symptoms of influenza-like disease (n=10) and subjects who did not developed acute symptoms of influenza-like disease (n=56) was performed by applying a cubic p-spline model. Group x time interactions were tested and q <0.05 for a total of 1052 transcripts after adjustment of False Discovery Rate (FDR) (Benjamini et al 1995).
For the development of molecular features, the nominal correlation generated by cubic p-spline analysis was input into the variable selection process to determine the best predictors of the occurrence of acute symptoms of influenza-like disease at three time points after inoculation; day 1, day 2 morning and day 2 evening (approximately 13 to 25, 37 to 49 and 49 to 61 hours after inoculation, respectively).
Variable selection was performed by a gradient enhancer (GBM; friedman 2001;Friedman 2002), the number of molecules to be selected being limited to six for best results. Fig. 6 shows the procedure followed.
Logical regression is applied to generate a predictive model (feature or gene set or gene subset) of the selected variable. The characteristic properties (sensitivity, specificity, positive and negative predictive values) are determined at the time point on which the model is based and at all other time points considered. Sensitivity-the proportion of cases where the test result is positive. Specificity-the proportion of cases for which the test results were negative. Positive Predictive Value (PPV) -the proportion of positive detection results that are true positives. Negative Predictive Value (NPV) -the proportion of negative detection results that are true negative. AUC (area under subject operating characteristics (ROC) curve) was determined.
Day 1 (morning) features
Table 3 shows the variables selected by gradient enhancement, table 4 shows the characteristics of logistic regression generation, and finally table 5 shows the test performance characteristics at all time points considered. It can be seen that the features or gene subsets performed well on day 1 data (AUC > 0.80).
This feature or gene subset includes the genes PHF20, ABCA1, APBA2, MORC2, SNU13 and DCUN1D2.
Table 3: variables selected on day 1 (morning)
Table 4: day 1 (morning) features
Estimation Standard error Z value Pr(＞|z|)
(intercept) -2.03 0.65 -3.14 0.001679
X203504_s_at -5.02 2.17 -2.32 0.020531
X209422_at -7.49 6.43 -1.16 0.244544
X216863_s_at -1.48 6.33 -0.23 0.815441
X201076_at -5.42 9.01 -0.60 0.547252
X219116_s_at -6.21 4.78 -1.30 0.193459
X209870_s_at -6.88 4.98 -1.38 0.167406
Table 5: test performance of day 1 (morning) features
Training Testing Case of cases N AUC Optimization Cutting off Propn positive Sensitivity of Specificity (specificity) Accuracy of PPC NPV
Day 1 morning Base line 10 63 0.71 Accuracy of NA NA 0.00 1.00 0.84 NA 0.84
NPV 0.02 0.68 0.90 0.36 0.44 0.21 0.95
Day 1 morning Day 1 morning 9 62 0.92 Accuracy of 0.24 0.21 0.89 0.91 0.90 0.62 0.98
NPV 0.24 0.21 0.89 0.91 0.90 0.62 0.98
1 stMorning and evening Afternoon of day 1 10 62 0.63 Accuracy of 0.64 0.08 0.30 0.96 0.85 0.60 0.88
NPV 0.03 0.77 0.90 0.25 0.35 0.19 0.93
Day 1 morning Day 2 morning 10 65 0.81 Accuracy of 1.00 0.02 0.10 1.00 0.86 1.00 0.86
NPV 0.04 0.69 0.90 0.35 0.43 0.20 0.95
Day 1 morning Afternoon of day 2 10 66 0.89 Accuracy of 0.81 0.21 0.80 0.89 0.88 0.57 0.96
NPV 0.81 0.21 0.80 0.89 0.88 0.57 0.96
Day 2 (morning) features
Table 6 shows the variables selected by gradient enhancement, table 7 shows the characteristics of logistic regression generation, and finally table 8 shows the test performance characteristics at all time points considered. The feature performed well on day 2 (morning).
This feature or gene subset includes genes MAX, NOL9, MPRIP, HP, BST1, TM9SF2.
Table 6: variables selected on day 2 (morning)
Table 7: day 2 (morning) features
Estimation Standard error Z value Pr(＞|z|)
(intercept) -3.28 0.90 -3.63 0.000280
X212197_x_at 1.07 7.98 0.13 0.893670
X214108_at 6.88 3.87 1.78 0.075326
X218754_at -0.01 7.01 0.00 0.999142
X205715_at -2.08 4.32 -0.48 0.629615
X208470_s_at 4.75 2.81 1.69 0.090620
X201078_at 7.39 10.83 0.68 0.494829
Table 8: test performance of day 2 (morning) features
Day 2 (afternoon) features
Table 9 shows the variables selected by gradient enhancement, table 10 shows the characteristics of logistic regression generation, and finally table 11 shows the test performance characteristics at all time points considered.
This feature or gene subset includes the genes HOMER3, NSUN6, HP, EPHA4, BMP2K.
Table 9: variables selected on day 2 (afternoon)
Table 10: day 2 (afternoon) features
Estimation Standard error Z value Pr(＞|z|)
(intercept) -19.21 18.67 -1.03 0.303403
X206114_at -13.07 19.53 -0.67 0.50355
X208470_s_at 16.03 16.49 0.97 0.33089
X215489_x_at 57.62 66.78 0.86 0.388274
X222128_at -17.21 15.54 -1.11 0.268238
X59644_at 55.37 62.33 0.89 0.374308
Table 11: test performance of day 2 (afternoon) features
Once the gene subset is determined, the minimum number of genes required to provide a good prediction of whether a subject will continue to develop acute symptoms of influenza-like disease can be determined. AUC values above 0.8 provide a good prediction that a subject will develop acute symptoms of influenza-like disease. The parameters with the lowest relative influence are excluded and updated features are derived. Model performance (sensitivity, specificity, PPV and NPV) is derived for the updated model. ROC curves were plotted and AUCs were tabulated. The other model parameters decrease in order of increasing relative influence. In this way, model properties based on the characteristics of 5, 4, 3, 2 and 1 genes at different time points were determined (tables 12 to 14). It can be seen that any one of the 1, 2, 3, 4, 5 or 6 genes can predict the development of acute symptoms of influenza-like disease.
Table 12-day 1 morning algorithm performance AUC values
Table 13-day 2 morning algorithm performance AUC values
Table 14-pm algorithm performance AUC values on day 2
Example 2
A group of individuals were recruited into a model of human viral attack and inoculated with respiratory viruses. It is beneficial to identify subjects in advance who will develop acute symptoms of influenza-like disease, allowing these subjects to be selectively administered study or licensed drugs (drugs/vaccine/placebo) as early as possible. This will increase the ability to detect clinically relevant reductions in disease responsive to drugs simply by assessing the effect of the drug in individuals who will/may have developed acute symptoms of influenza-like disease. This will also reduce the subject from unnecessarily contacting the study drug. This will also reduce the amount of drug required.
Volunteers were screened to determine whether they were eligible to evaluate efficacy of study drugs in human challenge studies with respiratory viruses (particularly influenza viruses).
Qualified volunteers arrived at the clinic, and baseline samples were collected and clinical measures were taken prior to exposure to virus (e.g., vaccination). Baseline values were obtained at various time points using one or more blood samples prior to inoculation.
Blood samples (e.g., paxgene RNA samples twice, three times or more per day) are periodically taken before and after virus exposure, while clinical measurements of the disease are made.
Expression levels of specific gene sets and subsets were measured in real time from blood paxgenes. Blood gene expression was assessed as in example 1 using an Affymetrix HG-U133 Plus 2.0 microarray chip for measuring transcript expression. Microarray data was preprocessed using RMA background correction and quantile normalization. One can use the absolute value of each gene at a given time point, or where baseline gene levels are obtained and available, gene levels after exposure to the virus can be baseline normalized for each subject (i.e., compared to the baseline expression levels of that gene or gene set or gene subset for the subject).
Three independent gene subsets (i.e., 3 algorithms) can be used to identify which individuals are suffering from acute symptoms of influenza-like disease
The a.3 gene subsets were:
i. subset a: PHF20, ABCA1, APBA2, MORC2, SNU13 and DCUN1D2
Subset B: MAX, NOL9, MPRIP, HP, BST1 and TM9SF2
Subset C: HOMER3, NSUN6, HP, EPHA4 and BMP2K
In one case, different genetic subgraphs were used at different time points (fig. 8, subset a, followed by subset B, followed by subset C). Alternatively, 1, 2 or 3 gene subsets are used simultaneously, rather than sequentially (FIG. 7), and may be repeated at several points after inoculation.
a. For both methods, a positive result of any test immediately triggered the subject to receive study drug (drug/vaccine/placebo).
b. Alternatively, two positive results or three positive results are required to trigger dosing.
For each gene subset, the stringency can be changed by modifying the threshold at which positive results are obtained. For example, the threshold for gene subset 1 may be set more stringent to avoid false positives. Gene subset 2 is then set to have a lower stringency and gene subset 3 is set to even lower stringency, thereby increasing the chance of identifying and administering as early as possible all subjects who will develop acute symptoms of influenza-like disease.
In addition to using gene subsets, the results can be combined with diagnostic tests to confirm that the subject has a respiratory viral infection associated with the test (e.g., a viral test).
In addition to using gene subsets, the results can also be combined with measurements of symptom variance/gradient changes.
Other actions that may be triggered with drug administration include increasing observations/samples/measurements of persons predicted to develop acute symptoms of influenza-like disease, or decreasing observations/samples/measurements of persons predicted not to develop acute symptoms of influenza-like disease.
By using the present invention as part of a design of trial decision, only those subjects most likely to develop acute symptoms of influenza-like disease are included in a statistical analysis of the efficacy of the study drug, which has the benefits as previously described.
In some cases, when the algorithm does not report a positive test result, the subject may be dosed at a predetermined time point (e.g., day 4) after exposure or inoculation. These subjects formed further analytical subsets.
Example 3
A group of individuals are enrolled in efficacy site studies and are infected with respiratory viruses, particularly influenza viruses, in the community. After exposure, it is beneficial to identify subjects in advance who will develop acute symptoms of influenza-like disease, allowing these subjects to be selectively administered study or licensed drugs (drugs/vaccine/placebo) as early as possible. By evaluating only the effects of drugs in individuals who may continue to develop acute symptoms of influenza-like disease, the ability to detect a reduction in clinically relevant diseases may be improved. This will reduce unnecessary exposure of the subject to the study drug and reduce the amount of drug required.
In this example, volunteers have been screened to determine whether they are eligible to evaluate efficacy of study drugs in clinical field trials against respiratory viruses (particularly influenza).
Qualified volunteers arrived at the clinic and baseline samples and clinical measurements were taken as they participated in the study. Baseline values will be obtained using one or more blood samples at various time points after enrollment and prior to community infection with respiratory viruses.
Blood samples (e.g., paxgene RNA samples) are taken after exposure of a home contactor with respiratory tract infection to the virus, or after exhibiting preliminary symptoms of respiratory tract disease (the test subject may or may not use a study questionnaire/symptom diary card that records these symptoms).
Specific gene subsets in blood paxgenes were measured in real time using the methods described in examples 1 and 2. One can use the absolute value of each gene at a given time point, or where baseline gene levels are obtained and available, gene levels after exposure to the virus can be baseline normalized (i.e., compared to baseline) for each subject.
Three independent gene subsets (i.e., 3 algorithms) can be used to identify which individuals will progress to acute symptoms of influenza-like disease
The a.3 gene subsets were:
i. subset a: PHF20, ABCA1, APBA2, MORC2, SNU13 and DCUN1D2
Subset B: MAX, NOL9, MPRIP, HP, BST1 and TM9SF2
Subset C: HOMER3, NSUN6, HP, EPHA4 and BMP2K
In one case, 1, 2 or 3 gene subsets are used simultaneously (fig. 7), and can be repeated at several points over time.
a. A positive result of any test immediately triggered the subject to receive study drug (drug/vaccine/placebo).
b. Alternatively, two positive results or three positive results are required to trigger dosing.
For each gene subset, the stringency can be changed by modifying the threshold at which positive results are obtained. For example, the threshold for gene subset 1 may be set more stringent to avoid false positives. Gene subset 2 is then set to have a lower stringency and gene subset 3 is set to even lower stringency, thereby increasing the chance of identifying and administering as early as possible all subjects who will develop acute symptoms of influenza-like disease.
In addition to using gene subsets, the results can be combined with diagnostic tests to confirm that the subject has a respiratory viral infection associated with the test (e.g., a viral test).
In addition to using gene subsets, in the case of using symptom diary cards, the results can be combined with measurements of variance/gradient changes of symptoms.
Other actions that may be triggered with the administration of a drug include increasing observations/samples/measurements of persons predicted to develop acute symptoms of influenza-like disease or decreasing observations/samples/measurements of persons predicted not to develop acute symptoms of influenza-like disease.
By using the present invention as part of a design of trial decision, only those subjects most likely to develop acute symptoms of influenza-like disease are included in a statistical analysis of the efficacy of the study drug, which has the benefits as previously described.
In some cases, when the algorithm does not report positive, the subject may be dosed at a predetermined time point (e.g., day 4, day 5) after exposure, and these subjects develop a secondary subset analysis.
Example 4
The subject may be infected with respiratory viruses in the community or may be exposed to an infected person in the community for a prolonged period of time. The community environment may include in-home (family members, family contacts), at work sites, on the way of transportation (e.g., in trains, coaches, planes, ships), in nursing homes (colleagues, family guests, caregivers), as hospital inpatients (inpatients, medical staff, guests), in military environments (colleagues). After exposure, it is beneficial to identify in advance those who will develop acute symptoms of influenza-like disease, so that intervention can be performed as early as possible. Interventions include assisting in diversion to healthcare professionals, treatment with antiviral drugs (e.g., duffy) earlier than otherwise, administration of immunomodulatory drugs or combinations of antiviral and immunomodulatory agents, separation (quarantine) from others, inclusion in studies (IMP) assays, transmission studies), initiation of disease/biomarker monitoring sampling.
The subject may be infected with respiratory viruses in the community or may be exposed to an infected person in the community for a prolonged period of time.
After one or more triggers, a blood sample after virus exposure can be collected and the gene level quantified:
a. positive diagnostic tests (e.g., based on viral replication, diagnostic biomarkers)
b. Early symptoms of respiratory viral disease
c. Prolonged contact with infected contact person
A specific set or subset of genes in a blood sample is measured in real time (e.g., using point-of-care testing). One can use the absolute value of each gene at a given time point, or where baseline gene levels are obtained or obtainable, gene levels after exposure to the virus can be baseline normalized (i.e., compared to baseline) for each subject.
In one case, three independent gene sub-sets (i.e., three algorithms) are used to identify which individuals are likely to develop acute symptoms of influenza-like disease
The a.3 gene subsets were:
i. subset a: PHF20, ABCA1, APBA2, MORC2, SNU13 and DCUN1D2
Subset B: MAX, NOL9, MPRIP, HP, BST1 and TM9SF2
Subset C: HOMER3, NSUN6, HP, EPHA4 and BMP2K
In another case, one, two or three gene subsets are used simultaneously (fig. 7), and can be repeated at several time points.
a. A positive result of any test may trigger one or more of the following:
i. to assist in transferring to the medical care professional,
treatment with antiviral drugs (e.g. duffy) can be performed earlier than otherwise,
administering an immunomodulator drug or a combination of an antiviral drug and an immunomodulator,
isolation from others (isolation, use of a transmission barrier such as a mask),
inclusion studies (e.g. IMP test, disease study, transmission study),
initiating disease/biomarker monitoring sampling
b. Alternatively, two positive results or three positive results are required to trigger the procedure, as described above.
For each gene subset, the stringency can be changed by modifying the threshold at which positive results are obtained. For example, the threshold for gene subset 1 may be set more stringent to avoid false positives. The gene subset 2 is then set to have a lower stringency and the gene subset 3 is set to an even lower stringency, thereby increasing the chance of early identification and interference.
In addition to using gene subsets, the results may also be combined with diagnostic tests that confirm that the subject has a respiratory viral infection (if not already performed).
Gene sequence
Each of the identified genes listed below includesThe human genome U133A 2.0 array (Affymetrix, santa Clara, calif.) has a unique probe name, gene ID and complete sequence of the entire gene. It should be understood that the present invention is not limited to the use of +.>Human genome U133a 2.0 array; the expression level of one or more genes disclosed herein can be measured using any suitable method known to those of skill in the art, using any suitable probe capable of uniquely binding to a corresponding nucleic acid sequence representing the one or more genes.
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Reference to the literature
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Cox NJ,Subbarao K.Influenza.Lancet 1999；354:1277-82
DeVincenzo et al 2014, oral GS-5806activity in a respiratory syncytial virus challenge study.N Engl J Med.2014, 8/21; 371 (8):711-22.
DeVincenzo et al 2015,Activity of Oral ALS-008176 in a Respiratory Syncytial Virus Challenge Study.N Engl J Med.2015, 11, 19; 373 (21):2048-58.
Friedman JH(2001).Greedy function approximation:a gradient boosting machine.Ann Statist 29(5):1189-1232.
Friedman JH(2002).Stochastic gradient boosting.Comput Stat Data Anal 38(4):367-378.
Hodika, "Respiratory RNA Viruses", microbiol spectrum, month 8 of 2016; 4 (4)
Lau,L.L.H.,Cowling,B.J.,Fang,V.J.,Chan,K.-H.,Lau,E.H.Y.,Lipsitch,M.,…Leung,G.M.(2010).Viral shedding and clinical illness in naturally acquired influenza virus infections.The Journal of Infectious Diseases,201(10),1509–1516
Liu, T.Y. et al An individualized predictor of health and disease using paired reference and target samples BMC Bioinformatics 2016.17, page 47
Molinari, n.m. (2007), the annual impact of seasonal influenza in the US: measuring disease burden and costs, Month 28 of 2007, pages 5086-5096 Rolfes MA, foppa IM, garg S, flannery B, brammer L, singleton JA et al Estimated Influenza Illnesses, medical vitamins, hospitalizations and Deaths Averted by Vaccination in the United states 2016, 12, 9
Straube J, gorse A-D, PROOF Centre of Excellence Team et al (2015). A linear mixed model spline framework for analysing time course' omics data.PLoS ONE 10 (8): e0134540.
Wang,Z.,Gerstein,M.,&Snyder,M.(2009).RNA-Seq:a revolutionary tool for transcriptomics.Nature Reviews.Genetics,10(1),57–63.
Woods CW, mcClain MT, chen M, zaas AK et al (2013). A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H N1 or H3N2.PLoS ONE 8 (1): e52198.
Zaas, A.K., et al Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans.cell Host Microbe,2009.6 (3): pages 207-17.
Sequence listing
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Leu Phe Ile Phe Leu Ile Leu Ile Ser Val Arg Leu Ser Tyr Pro Pro
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Ser Lys Leu Lys Ser Lys Thr Ser Gln Leu Ser Gly Gly Met Gln
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Arg Lys Leu Ser Val Ala Leu Ala Phe Val Gly Gly Ser Lys Val
1040 1045 1050
Val Ile Leu Asp Glu Pro Thr Ala Gly Val Asp Pro Tyr Ser Arg
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Arg Gly Ile Trp Glu Leu Leu Leu Lys Tyr Arg Gln Gly Arg Thr
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Ile Ile Leu Ser Thr His His Met Asp Glu Ala Asp Val Leu Gly
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Asp Arg Ile Ala Ile Ile Ser His Gly Lys Leu Cys Cys Val Gly
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Ser Ser Leu Phe Leu Lys Asn Gln Leu Gly Thr Gly Tyr Tyr Leu
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Ser Gln Ser Ser Ser Asp Ala Gly Leu Gly Ser Asp His Glu Ser
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Asp Thr Leu Thr Ile Asp Val Ser Ala Ile Ser Asn Leu Ile Arg
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Lys His Val Ser Glu Ala Arg Leu Val Glu Asp Ile Gly His Glu
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Leu Thr Tyr Val Leu Pro Tyr Glu Ala Ala Lys Glu Gly Ala Phe
1205 1210 1215
Val Glu Leu Phe His Glu Ile Asp Asp Arg Leu Ser Asp Leu Gly
1220 1225 1230
Ile Ser Ser Tyr Gly Ile Ser Glu Thr Thr Leu Glu Glu Ile Phe
1235 1240 1245
Leu Lys Val Ala Glu Glu Ser Gly Val Asp Ala Glu Thr Ser Asp
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Gly Thr Leu Pro Ala Arg Arg Asn Arg Arg Ala Phe Gly Asp Lys
1265 1270 1275
Gln Ser Cys Leu Arg Pro Phe Thr Glu Asp Asp Ala Ala Asp Pro
1280 1285 1290
Asn Asp Ser Asp Ile Asp Pro Glu Ser Arg Glu Thr Asp Leu Leu
1295 1300 1305
Ser Gly Met Asp Gly Lys Gly Ser Tyr Gln Val Lys Gly Trp Lys
1310 1315 1320
Leu Thr Gln Gln Gln Phe Val Ala Leu Leu Trp Lys Arg Leu Leu
1325 1330 1335
Ile Ala Arg Arg Ser Arg Lys Gly Phe Phe Ala Gln Ile Val Leu
1340 1345 1350
Pro Ala Val Phe Val Cys Ile Ala Leu Val Phe Ser Leu Ile Val
1355 1360 1365
Pro Pro Phe Gly Lys Tyr Pro Ser Leu Glu Leu Gln Pro Trp Met
1370 1375 1380
Tyr Asn Glu Gln Tyr Thr Phe Val Ser Asn Asp Ala Pro Glu Asp
1385 1390 1395
Thr Gly Thr Leu Glu Leu Leu Asn Ala Leu Thr Lys Asp Pro Gly
1400 1405 1410
Phe Gly Thr Arg Cys Met Glu Gly Asn Pro Ile Pro Asp Thr Pro
1415 1420 1425
Cys Gln Ala Gly Glu Glu Glu Trp Thr Thr Ala Pro Val Pro Gln
1430 1435 1440
Thr Ile Met Asp Leu Phe Gln Asn Gly Asn Trp Thr Met Gln Asn
1445 1450 1455
Pro Ser Pro Ala Cys Gln Cys Ser Ser Asp Lys Ile Lys Lys Met
1460 1465 1470
Leu Pro Val Cys Pro Pro Gly Ala Gly Gly Leu Pro Pro Pro Gln
1475 1480 1485
Arg Lys Gln Asn Thr Ala Asp Ile Leu Gln Asp Leu Thr Gly Arg
1490 1495 1500
Asn Ile Ser Asp Tyr Leu Val Lys Thr Tyr Val Gln Ile Ile Ala
1505 1510 1515
Lys Ser Leu Lys Asn Lys Ile Trp Val Asn Glu Phe Arg Tyr Gly
1520 1525 1530
Gly Phe Ser Leu Gly Val Ser Asn Thr Gln Ala Leu Pro Pro Ser
1535 1540 1545
Gln Glu Val Asn Asp Ala Ile Lys Gln Met Lys Lys His Leu Lys
1550 1555 1560
Leu Ala Lys Asp Ser Ser Ala Asp Arg Phe Leu Asn Ser Leu Gly
1565 1570 1575
Arg Phe Met Thr Gly Leu Asp Thr Lys Asn Asn Val Lys Val Trp
1580 1585 1590
Phe Asn Asn Lys Gly Trp His Ala Ile Ser Ser Phe Leu Asn Val
1595 1600 1605
Ile Asn Asn Ala Ile Leu Arg Ala Asn Leu Gln Lys Gly Glu Asn
1610 1615 1620
Pro Ser His Tyr Gly Ile Thr Ala Phe Asn His Pro Leu Asn Leu
1625 1630 1635
Thr Lys Gln Gln Leu Ser Glu Val Ala Leu Met Thr Thr Ser Val
1640 1645 1650
Asp Val Leu Val Ser Ile Cys Val Ile Phe Ala Met Ser Phe Val
1655 1660 1665
Pro Ala Ser Phe Val Val Phe Leu Ile Gln Glu Arg Val Ser Lys
1670 1675 1680
Ala Lys His Leu Gln Phe Ile Ser Gly Val Lys Pro Val Ile Tyr
1685 1690 1695
Trp Leu Ser Asn Phe Val Trp Asp Met Cys Asn Tyr Val Val Pro
1700 1705 1710
Ala Thr Leu Val Ile Ile Ile Phe Ile Cys Phe Gln Gln Lys Ser
1715 1720 1725
Tyr Val Ser Ser Thr Asn Leu Pro Val Leu Ala Leu Leu Leu Leu
1730 1735 1740
Leu Tyr Gly Trp Ser Ile Thr Pro Leu Met Tyr Pro Ala Ser Phe
1745 1750 1755
Val Phe Lys Ile Pro Ser Thr Ala Tyr Val Val Leu Thr Ser Val
1760 1765 1770
Asn Leu Phe Ile Gly Ile Asn Gly Ser Val Ala Thr Phe Val Leu
1775 1780 1785
Glu Leu Phe Thr Asp Asn Lys Leu Asn Asn Ile Asn Asp Ile Leu
1790 1795 1800
Lys Ser Val Phe Leu Ile Phe Pro His Phe Cys Leu Gly Arg Gly
1805 1810 1815
Leu Ile Asp Met Val Lys Asn Gln Ala Met Ala Asp Ala Leu Glu
1820 1825 1830
Arg Phe Gly Glu Asn Arg Phe Val Ser Pro Leu Ser Trp Asp Leu
1835 1840 1845
Val Gly Arg Asn Leu Phe Ala Met Ala Val Glu Gly Val Val Phe
1850 1855 1860
Phe Leu Ile Thr Val Leu Ile Gln Tyr Arg Phe Phe Ile Arg Pro
1865 1870 1875
Arg Pro Val Asn Ala Lys Leu Ser Pro Leu Asn Asp Glu Asp Glu
1880 1885 1890
Asp Val Arg Arg Glu Arg Gln Arg Ile Leu Asp Gly Gly Gly Gln
1895 1900 1905
Asn Asp Ile Leu Glu Ile Lys Glu Leu Thr Lys Ile Tyr Arg Arg
1910 1915 1920
Lys Arg Lys Pro Ala Val Asp Arg Ile Cys Val Gly Ile Pro Pro
1925 1930 1935
Gly Glu Cys Phe Gly Leu Leu Gly Val Asn Gly Ala Gly Lys Ser
1940 1945 1950
Ser Thr Phe Lys Met Leu Thr Gly Asp Thr Thr Val Thr Arg Gly
1955 1960 1965
Asp Ala Phe Leu Asn Lys Asn Ser Ile Leu Ser Asn Ile His Glu
1970 1975 1980
Val His Gln Asn Met Gly Tyr Cys Pro Gln Phe Asp Ala Ile Thr
1985 1990 1995
Glu Leu Leu Thr Gly Arg Glu His Val Glu Phe Phe Ala Leu Leu
2000 2005 2010
Arg Gly Val Pro Glu Lys Glu Val Gly Lys Val Gly Glu Trp Ala
2015 2020 2025
Ile Arg Lys Leu Gly Leu Val Lys Tyr Gly Glu Lys Tyr Ala Gly
2030 2035 2040
Asn Tyr Ser Gly Gly Asn Lys Arg Lys Leu Ser Thr Ala Met Ala
2045 2050 2055
Leu Ile Gly Gly Pro Pro Val Val Phe Leu Asp Glu Pro Thr Thr
2060 2065 2070
Gly Met Asp Pro Lys Ala Arg Arg Phe Leu Trp Asn Cys Ala Leu
2075 2080 2085
Ser Val Val Lys Glu Gly Arg Ser Val Val Leu Thr Ser His Ser
2090 2095 2100
Met Glu Glu Cys Glu Ala Leu Cys Thr Arg Met Ala Ile Met Val
2105 2110 2115
Asn Gly Arg Phe Arg Cys Leu Gly Ser Val Gln His Leu Lys Asn
2120 2125 2130
Arg Phe Gly Asp Gly Tyr Thr Ile Val Val Arg Ile Ala Gly Ser
2135 2140 2145
Asn Pro Asp Leu Lys Pro Val Gln Asp Phe Phe Gly Leu Ala Phe
2150 2155 2160
Pro Gly Ser Val Leu Lys Glu Lys His Arg Asn Met Leu Gln Tyr
2165 2170 2175
Gln Leu Pro Ser Ser Leu Ser Ser Leu Ala Arg Ile Phe Ser Ile
2180 2185 2190
Leu Ser Gln Ser Lys Lys Arg Leu His Ile Glu Asp Tyr Ser Val
2195 2200 2205
Ser Gln Thr Thr Leu Asp Gln Val Phe Val Asn Phe Ala Lys Asp
2210 2215 2220
Gln Ser Asp Asp Asp His Leu Lys Asp Leu Ser Leu His Lys Asn
2225 2230 2235
Gln Thr Val Val Asp Val Ala Val Leu Thr Ser Phe Leu Gln Asp
2240 2245 2250
Glu Lys Val Lys Glu Ser Tyr Val
2255 2260
<210> 3
<211> 749
<212> PRT
<213> person
<400> 3
Met Ala His Arg Lys Leu Glu Ser Val Gly Ser Gly Met Leu Asp His
1 5 10 15
Arg Val Arg Pro Gly Pro Val Pro His Ser Gln Glu Pro Glu Ser Glu
20 25 30
Asp Met Glu Leu Pro Leu Glu Gly Tyr Val Pro Glu Gly Leu Glu Leu
35 40 45
Ala Ala Leu Arg Pro Glu Ser Pro Ala Pro Glu Glu Gln Glu Cys His
50 55 60
Asn His Ser Pro Asp Gly Asp Ser Ser Ser Asp Tyr Val Asn Asn Thr
65 70 75 80
Ser Glu Glu Glu Asp Tyr Asp Glu Gly Leu Pro Glu Glu Glu Glu Gly
85 90 95
Ile Thr Tyr Tyr Ile Arg Tyr Cys Pro Glu Asp Asp Ser Tyr Leu Glu
100 105 110
Gly Met Asp Cys Asn Gly Glu Glu Tyr Leu Ala His Ser Ala His Pro
115 120 125
Val Asp Thr Asp Glu Cys Gln Glu Ala Val Glu Glu Trp Thr Asp Ser
130 135 140
Ala Gly Pro His Pro His Gly His Glu Ala Glu Gly Ser Gln Asp Tyr
145 150 155 160
Pro Asp Gly Gln Leu Pro Ile Pro Glu Asp Glu Pro Ser Val Leu Glu
165 170 175
Ala His Asp Gln Glu Glu Asp Gly His Tyr Cys Ala Ser Lys Glu Gly
180 185 190
Tyr Gln Asp Tyr Tyr Pro Glu Glu Ala Asn Gly Asn Thr Gly Ala Ser
195 200 205
Pro Tyr Arg Leu Arg Arg Gly Asp Gly Asp Leu Glu Asp Gln Glu Glu
210 215 220
Asp Ile Asp Gln Ile Val Ala Glu Ile Lys Met Ser Leu Ser Met Thr
225 230 235 240
Ser Ile Thr Ser Ala Ser Glu Ala Ser Pro Glu His Gly Pro Glu Pro
245 250 255
Gly Pro Glu Asp Ser Val Glu Ala Cys Pro Pro Ile Lys Ala Ser Cys
260 265 270
Ser Pro Ser Arg His Glu Ala Arg Pro Lys Ser Leu Asn Leu Leu Pro
275 280 285
Glu Ala Lys His Pro Gly Asp Pro Gln Arg Gly Phe Lys Pro Lys Thr
290 295 300
Arg Thr Pro Glu Glu Arg Leu Lys Trp Pro His Glu Gln Val Cys Asn
305 310 315 320
Gly Leu Glu Gln Pro Arg Lys Gln Gln Arg Ser Asp Leu Asn Gly Pro
325 330 335
Val Asp Asn Asn Asn Ile Pro Glu Thr Lys Lys Val Ala Ser Phe Pro
340 345 350
Ser Phe Val Ala Val Pro Gly Pro Cys Glu Pro Glu Asp Leu Ile Asp
355 360 365
Gly Ile Ile Phe Ala Ala Asn Tyr Leu Gly Ser Thr Gln Leu Leu Ser
370 375 380
Glu Arg Asn Pro Ser Lys Asn Ile Arg Met Met Gln Ala Gln Glu Ala
385 390 395 400
Val Ser Arg Val Lys Arg Met Gln Lys Ala Ala Lys Ile Lys Lys Lys
405 410 415
Ala Asn Ser Glu Gly Asp Ala Gln Thr Leu Thr Glu Val Asp Leu Phe
420 425 430
Ile Ser Thr Gln Arg Ile Lys Val Leu Asn Ala Asp Thr Gln Glu Thr
435 440 445
Met Met Asp His Ala Leu Arg Thr Ile Ser Tyr Ile Ala Asp Ile Gly
450 455 460
Asn Ile Val Val Leu Met Ala Arg Arg Arg Met Pro Arg Ser Ala Ser
465 470 475 480
Gln Asp Cys Ile Glu Thr Thr Pro Gly Ala Gln Glu Gly Lys Lys Gln
485 490 495
Tyr Lys Met Ile Cys His Val Phe Glu Ser Glu Asp Ala Gln Leu Ile
500 505 510
Ala Gln Ser Ile Gly Gln Ala Phe Ser Val Ala Tyr Gln Glu Phe Leu
515 520 525
Arg Ala Asn Gly Ile Asn Pro Glu Asp Leu Ser Gln Lys Glu Tyr Ser
530 535 540
Asp Ile Ile Asn Thr Gln Glu Met Tyr Asn Asp Asp Leu Ile His Phe
545 550 555 560
Ser Asn Ser Glu Asn Cys Lys Glu Leu Gln Leu Glu Lys His Lys Gly
565 570 575
Glu Ile Leu Gly Val Val Val Val Glu Ser Gly Trp Gly Ser Ile Leu
580 585 590
Pro Thr Val Ile Leu Ala Asn Met Met Asn Gly Gly Pro Ala Ala Arg
595 600 605
Ser Gly Lys Leu Ser Ile Gly Asp Gln Ile Met Ser Ile Asn Gly Thr
610 615 620
Ser Leu Val Gly Leu Pro Leu Ala Thr Cys Gln Gly Ile Ile Lys Gly
625 630 635 640
Leu Lys Asn Gln Thr Gln Val Lys Leu Asn Ile Val Ser Cys Pro Pro
645 650 655
Val Thr Thr Val Leu Ile Lys Arg Pro Asp Leu Lys Tyr Gln Leu Gly
660 665 670
Phe Ser Val Gln Asn Gly Ile Ile Cys Ser Leu Met Arg Gly Gly Ile
675 680 685
Ala Glu Arg Gly Gly Val Arg Val Gly His Arg Ile Ile Glu Ile Asn
690 695 700
Gly Gln Ser Val Val Ala Thr Ala His Glu Lys Ile Val Gln Ala Leu
705 710 715 720
Ser Asn Ser Val Gly Glu Ile His Met Lys Thr Met Pro Ala Ala Met
725 730 735
Phe Arg Leu Leu Thr Gly Gln Glu Thr Pro Leu Tyr Ile
740 745
<210> 4
<211> 1032
<212> PRT
<213> person
<400> 4
Met Ala Phe Thr Asn Tyr Ser Ser Leu Asn Arg Ala Gln Leu Thr Phe
1 5 10 15
Glu Tyr Leu His Thr Asn Ser Thr Thr His Glu Phe Leu Phe Gly Ala
20 25 30
Leu Ala Glu Leu Val Asp Asn Ala Arg Asp Ala Asp Ala Thr Arg Ile
35 40 45
Asp Ile Tyr Ala Glu Arg Arg Glu Asp Leu Arg Gly Gly Phe Met Leu
50 55 60
Cys Phe Leu Asp Asp Gly Ala Gly Met Asp Pro Ser Asp Ala Ala Ser
65 70 75 80
Val Ile Gln Phe Gly Lys Ser Ala Lys Arg Thr Pro Glu Ser Thr Gln
85 90 95
Ile Gly Gln Tyr Gly Asn Gly Leu Lys Ser Gly Ser Met Arg Ile Gly
100 105 110
Lys Asp Phe Ile Leu Phe Thr Lys Lys Glu Asp Thr Met Thr Cys Leu
115 120 125
Phe Leu Ser Arg Thr Phe His Glu Glu Glu Gly Ile Asp Glu Val Ile
130 135 140
Val Pro Leu Pro Thr Trp Asn Ala Arg Thr Arg Glu Pro Val Thr Asp
145 150 155 160
Asn Val Glu Lys Phe Ala Ile Glu Thr Glu Leu Ile Tyr Lys Tyr Ser
165 170 175
Pro Phe Arg Thr Glu Glu Glu Val Met Thr Gln Phe Met Lys Ile Pro
180 185 190
Gly Asp Ser Gly Thr Leu Val Ile Ile Phe Asn Leu Lys Leu Met Asp
195 200 205
Asn Gly Glu Pro Glu Leu Asp Ile Ile Ser Asn Pro Arg Asp Ile Gln
210 215 220
Met Ala Glu Thr Ser Pro Glu Gly Thr Lys Pro Glu Arg Arg Ser Phe
225 230 235 240
Arg Ala Tyr Ala Ala Val Leu Tyr Ile Asp Pro Arg Met Arg Ile Phe
245 250 255
Ile His Gly His Lys Val Gln Thr Lys Arg Leu Ser Cys Cys Leu Tyr
260 265 270
Lys Pro Arg Met Tyr Lys Tyr Thr Ser Ser Arg Phe Lys Thr Arg Ala
275 280 285
Glu Gln Glu Val Lys Lys Ala Glu His Val Ala Arg Ile Ala Glu Glu
290 295 300
Lys Ala Arg Glu Ala Glu Ser Lys Ala Arg Thr Leu Glu Val Arg Leu
305 310 315 320
Gly Gly Asp Leu Thr Arg Asp Ser Arg Val Met Leu Arg Gln Val Gln
325 330 335
Asn Arg Ala Ile Thr Leu Arg Arg Glu Ala Asp Val Lys Lys Arg Ile
340 345 350
Lys Glu Ala Lys Gln Arg Ala Leu Lys Glu Pro Lys Glu Leu Asn Phe
355 360 365
Val Phe Gly Val Asn Ile Glu His Arg Asp Leu Asp Gly Met Phe Ile
370 375 380
Tyr Asn Cys Ser Arg Leu Ile Lys Met Tyr Glu Lys Val Gly Pro Gln
385 390 395 400
Leu Glu Gly Gly Met Ala Cys Gly Gly Val Val Gly Val Val Asp Val
405 410 415
Pro Tyr Leu Val Leu Glu Pro Thr His Asn Lys Gln Asp Phe Ala Asp
420 425 430
Ala Lys Glu Tyr Arg His Leu Leu Arg Ala Met Gly Glu His Leu Ala
435 440 445
Gln Tyr Trp Lys Asp Ile Ala Ile Ala Gln Arg Gly Ile Ile Lys Phe
450 455 460
Trp Asp Glu Phe Gly Tyr Leu Ser Ala Asn Trp Asn Gln Pro Pro Ser
465 470 475 480
Ser Glu Leu Arg Tyr Lys Arg Arg Arg Ala Met Glu Ile Pro Thr Thr
485 490 495
Ile Gln Cys Asp Leu Cys Leu Lys Trp Arg Thr Leu Pro Phe Gln Leu
500 505 510
Ser Ser Val Glu Lys Asp Tyr Pro Asp Thr Trp Val Cys Ser Met Asn
515 520 525
Pro Asp Pro Glu Gln Asp Arg Cys Glu Ala Ser Glu Gln Lys Gln Lys
530 535 540
Val Pro Leu Gly Thr Phe Arg Lys Asp Met Lys Thr Gln Glu Glu Lys
545 550 555 560
Gln Lys Gln Leu Thr Glu Lys Ile Arg Gln Gln Gln Glu Lys Leu Glu
565 570 575
Ala Leu Gln Lys Thr Thr Pro Ile Arg Ser Gln Ala Asp Leu Lys Lys
580 585 590
Leu Pro Leu Glu Val Thr Thr Arg Pro Ser Thr Glu Glu Pro Val Arg
595 600 605
Arg Pro Gln Arg Pro Arg Ser Pro Pro Leu Pro Ala Val Ile Arg Asn
610 615 620
Ala Pro Ser Arg Pro Pro Ser Leu Pro Thr Pro Arg Pro Ala Ser Gln
625 630 635 640
Pro Arg Lys Ala Pro Val Ile Ser Ser Thr Pro Lys Leu Pro Ala Leu
645 650 655
Ala Ala Arg Glu Glu Ala Ser Thr Ser Arg Leu Leu Gln Pro Pro Glu
660 665 670
Ala Pro Arg Lys Pro Ala Asn Thr Leu Val Lys Thr Ala Ser Arg Pro
675 680 685
Ala Pro Leu Val Gln Gln Leu Ser Pro Ser Leu Leu Pro Asn Ser Lys
690 695 700
Ser Pro Arg Glu Val Pro Ser Pro Lys Val Ile Lys Thr Pro Val Val
705 710 715 720
Lys Lys Thr Glu Ser Pro Ile Lys Leu Ser Pro Ala Thr Pro Ser Arg
725 730 735
Lys Arg Ser Val Ala Val Ser Asp Glu Glu Glu Val Glu Glu Glu Ala
740 745 750
Glu Arg Arg Lys Glu Arg Cys Lys Arg Gly Arg Phe Val Val Lys Glu
755 760 765
Glu Lys Lys Asp Ser Asn Glu Leu Ser Asp Ser Ala Gly Glu Glu Asp
770 775 780
Ser Ala Asp Leu Lys Arg Ala Gln Lys Asp Lys Gly Leu His Val Glu
785 790 795 800
Val Arg Val Asn Arg Glu Trp Tyr Thr Gly Arg Val Thr Ala Val Glu
805 810 815
Val Gly Lys His Val Val Arg Trp Lys Val Lys Phe Asp Tyr Val Pro
820 825 830
Thr Asp Thr Thr Pro Arg Asp Arg Trp Val Glu Lys Gly Ser Glu Asp
835 840 845
Val Arg Leu Met Lys Pro Pro Ser Pro Glu His Gln Ser Leu Asp Thr
850 855 860
Gln Gln Glu Gly Gly Glu Glu Glu Val Gly Pro Val Ala Gln Gln Ala
865 870 875 880
Ile Ala Val Ala Glu Pro Ser Thr Ser Glu Cys Leu Arg Ile Glu Pro
885 890 895
Asp Thr Thr Ala Leu Ser Thr Asn His Glu Thr Ile Asp Leu Leu Val
900 905 910
Gln Ile Leu Arg Asn Cys Leu Arg Tyr Phe Leu Pro Pro Ser Phe Pro
915 920 925
Ile Ser Lys Lys Gln Leu Ser Ala Met Asn Ser Asp Glu Leu Ile Ser
930 935 940
Phe Pro Leu Lys Glu Tyr Phe Lys Gln Tyr Glu Val Gly Leu Gln Asn
945 950 955 960
Leu Cys Asn Ser Tyr Gln Ser Arg Ala Asp Ser Arg Ala Lys Ala Ser
965 970 975
Glu Glu Ser Leu Arg Thr Ser Glu Arg Lys Leu Arg Glu Thr Glu Glu
980 985 990
Lys Leu Gln Lys Leu Arg Thr Asn Ile Val Ala Leu Leu Gln Lys Val
995 1000 1005
Gln Glu Asp Ile Asp Ile Asn Thr Asp Asp Glu Leu Asp Ala Tyr
1010 1015 1020
Ile Glu Asp Leu Ile Thr Lys Gly Asp
1025 1030
<210> 5
<211> 128
<212> PRT
<213> person
<400> 5
Met Thr Glu Ala Asp Val Asn Pro Lys Ala Tyr Pro Leu Ala Asp Ala
1 5 10 15
His Leu Thr Lys Lys Leu Leu Asp Leu Val Gln Gln Ser Cys Asn Tyr
20 25 30
Lys Gln Leu Arg Lys Gly Ala Asn Glu Ala Thr Lys Thr Leu Asn Arg
35 40 45
Gly Ile Ser Glu Phe Ile Val Met Ala Ala Asp Ala Glu Pro Leu Glu
50 55 60
Ile Ile Leu His Leu Pro Leu Leu Cys Glu Asp Lys Asn Val Pro Tyr
65 70 75 80
Val Phe Val Arg Ser Lys Gln Ala Leu Gly Arg Ala Cys Gly Val Ser
85 90 95
Arg Pro Val Ile Ala Cys Ser Val Thr Ile Lys Glu Gly Ser Gln Leu
100 105 110
Lys Gln Gln Ile Gln Ser Ile Gln Gln Ser Ile Glu Arg Leu Leu Val
115 120 125
<210> 6
<211> 259
<212> PRT
<213> person
<400> 6
Met His Lys Leu Lys Ser Ser Gln Lys Asp Lys Val Arg Gln Phe Met
1 5 10 15
Ala Cys Thr Gln Ala Gly Glu Arg Thr Ala Ile Tyr Cys Leu Thr Gln
20 25 30
Asn Glu Trp Arg Leu Asp Glu Ala Thr Asp Ser Phe Phe Gln Asn Pro
35 40 45
Asp Ser Leu His Arg Glu Ser Met Arg Asn Ala Val Asp Lys Lys Lys
50 55 60
Leu Glu Arg Leu Tyr Gly Arg Tyr Lys Asp Pro Gln Asp Glu Asn Lys
65 70 75 80
Ile Gly Val Asp Gly Ile Gln Gln Phe Cys Asp Asp Leu Ser Leu Asp
85 90 95
Pro Ala Ser Ile Ser Val Leu Val Ile Ala Trp Lys Phe Arg Ala Ala
100 105 110
Thr Gln Cys Glu Phe Ser Arg Lys Glu Phe Leu Asp Gly Met Thr Glu
115 120 125
Leu Gly Cys Asp Ser Met Glu Lys Leu Lys Ala Leu Leu Pro Arg Leu
130 135 140
Glu Gln Glu Leu Lys Asp Thr Ala Lys Phe Lys Asp Phe Tyr Gln Phe
145 150 155 160
Thr Phe Thr Phe Ala Lys Asn Pro Gly Gln Lys Gly Leu Asp Leu Glu
165 170 175
Met Ala Val Ala Tyr Trp Lys Leu Val Leu Ser Gly Arg Phe Lys Phe
180 185 190
Leu Asp Leu Trp Asn Thr Phe Leu Met Glu His His Lys Arg Ser Ile
195 200 205
Pro Arg Asp Thr Trp Asn Leu Leu Leu Asp Phe Gly Asn Met Ile Ala
210 215 220
Asp Asp Met Ser Asn Tyr Asp Glu Glu Gly Ala Trp Pro Val Leu Ile
225 230 235 240
Asp Asp Phe Val Glu Tyr Ala Arg Pro Val Val Thr Gly Gly Lys Arg
245 250 255
Ser Leu Phe
<210> 7
<211> 160
<212> PRT
<213> person
<400> 7
Met Ser Asp Asn Asp Asp Ile Glu Val Glu Ser Asp Glu Glu Gln Pro
1 5 10 15
Arg Phe Gln Ser Ala Ala Asp Lys Arg Ala His His Asn Ala Leu Glu
20 25 30
Arg Lys Arg Arg Asp His Ile Lys Asp Ser Phe His Ser Leu Arg Asp
35 40 45
Ser Val Pro Ser Leu Gln Gly Glu Lys Ala Ser Arg Ala Gln Ile Leu
50 55 60
Asp Lys Ala Thr Glu Tyr Ile Gln Tyr Met Arg Arg Lys Asn His Thr
65 70 75 80
His Gln Gln Asp Ile Asp Asp Leu Lys Arg Gln Asn Ala Leu Leu Glu
85 90 95
Gln Gln Val Arg Ala Leu Glu Lys Ala Arg Ser Ser Ala Gln Leu Gln
100 105 110
Thr Asn Tyr Pro Ser Ser Asp Asn Ser Leu Tyr Thr Asn Ala Lys Gly
115 120 125
Ser Thr Ile Ser Ala Phe Asp Gly Gly Ser Asp Ser Ser Ser Glu Ser
130 135 140
Glu Pro Glu Glu Pro Gln Ser Arg Lys Lys Leu Arg Met Glu Ala Ser
145 150 155 160
<210> 8
<211> 702
<212> PRT
<213> person
<400> 8
Met Ala Asp Ser Gly Leu Leu Leu Lys Arg Gly Ser Cys Arg Ser Thr
1 5 10 15
Trp Leu Arg Val Arg Lys Ala Arg Pro Gln Leu Ile Leu Ser Arg Arg
20 25 30
Pro Arg Arg Arg Leu Gly Ser Leu Arg Trp Cys Gly Arg Arg Arg Leu
35 40 45
Arg Trp Arg Leu Leu Gln Ala Gln Ala Ser Gly Val Asp Trp Arg Glu
50 55 60
Gly Ala Arg Gln Val Ser Arg Ala Ala Ala Ala Arg Arg Pro Asn Thr
65 70 75 80
Ala Thr Pro Ser Pro Ile Pro Ser Pro Thr Pro Ala Ser Glu Pro Glu
85 90 95
Ser Glu Pro Glu Leu Glu Ser Ala Ser Ser Cys His Arg Pro Leu Leu
100 105 110
Ile Pro Pro Val Arg Pro Val Gly Pro Gly Arg Ala Leu Leu Leu Leu
115 120 125
Pro Val Glu Gln Gly Phe Thr Phe Ser Gly Ile Cys Arg Val Thr Cys
130 135 140
Leu Tyr Gly Gln Val Gln Val Phe Gly Phe Thr Ile Ser Gln Gly Gln
145 150 155 160
Pro Ala Gln Asp Ile Phe Ser Val Tyr Thr His Ser Cys Leu Ser Ile
165 170 175
His Ala Leu His Tyr Ser Gln Pro Glu Lys Ser Lys Lys Glu Leu Lys
180 185 190
Arg Glu Ala Arg Asn Leu Leu Lys Ser His Leu Asn Leu Asp Asp Arg
195 200 205
Arg Trp Ser Met Gln Asn Phe Ser Pro Gln Cys Ser Ile Val Leu Leu
210 215 220
Glu His Leu Lys Thr Ala Thr Val Asn Phe Ile Thr Ser Tyr Pro Gly
225 230 235 240
Ser Ser Tyr Ile Phe Val Gln Glu Ser Pro Thr Pro Gln Ile Lys Pro
245 250 255
Glu Tyr Leu Ala Leu Arg Ser Val Gly Ile Arg Arg Glu Lys Lys Arg
260 265 270
Lys Gly Leu Gln Leu Thr Glu Ser Thr Leu Ser Ala Leu Glu Glu Leu
275 280 285
Val Asn Val Ser Cys Glu Glu Val Asp Gly Cys Pro Val Ile Leu Val
290 295 300
Cys Gly Ser Gln Asp Val Gly Lys Ser Thr Phe Asn Arg Tyr Leu Ile
305 310 315 320
Asn His Leu Leu Asn Ser Leu Pro Cys Val Asp Tyr Leu Glu Cys Asp
325 330 335
Leu Gly Gln Thr Glu Phe Thr Pro Pro Gly Cys Ile Ser Leu Leu Asn
340 345 350
Ile Thr Glu Pro Val Leu Gly Pro Pro Phe Thr His Leu Arg Thr Pro
355 360 365
Gln Lys Met Val Tyr Tyr Gly Lys Pro Ser Cys Lys Asn Asn Tyr Glu
370 375 380
Asn Tyr Ile Asp Ile Val Lys Tyr Val Phe Ser Ala Tyr Lys Arg Glu
385 390 395 400
Ser Pro Leu Ile Val Asn Thr Met Gly Trp Val Ser Asp Gln Gly Leu
405 410 415
Leu Leu Leu Ile Asp Leu Ile Arg Leu Leu Ser Pro Ser His Val Val
420 425 430
Gln Phe Arg Ser Asp His Ser Lys Tyr Met Pro Asp Leu Thr Pro Gln
435 440 445
Tyr Val Asp Asp Met Asp Gly Leu Tyr Thr Lys Ser Lys Thr Lys Met
450 455 460
Arg Asn Arg Arg Phe Arg Leu Ala Ala Phe Ala Asp Ala Leu Glu Phe
465 470 475 480
Ala Asp Glu Glu Lys Glu Ser Pro Val Glu Phe Thr Gly His Lys Leu
485 490 495
Ile Gly Val Tyr Thr Asp Phe Ala Phe Arg Ile Thr Pro Arg Asn Arg
500 505 510
Glu Ser His Asn Lys Ile Leu Arg Asp Leu Ser Ile Leu Ser Tyr Leu
515 520 525
Ser Gln Leu Gln Pro Pro Met Pro Lys Pro Leu Ser Pro Leu His Ser
530 535 540
Leu Thr Pro Tyr Gln Val Pro Phe Asn Ala Val Ala Leu Arg Ile Thr
545 550 555 560
His Ser Asp Val Ala Pro Thr His Ile Leu Tyr Ala Val Asn Ala Ser
565 570 575
Trp Val Gly Leu Cys Lys Ile Gln Asp Asp Val Arg Gly Tyr Thr Asn
580 585 590
Gly Pro Ile Leu Leu Ala Gln Thr Pro Ile Cys Asp Cys Leu Gly Phe
595 600 605
Gly Ile Cys Arg Gly Ile Asp Met Glu Lys Arg Leu Tyr His Ile Leu
610 615 620
Thr Pro Val Pro Pro Glu Glu Leu Arg Thr Val Asn Cys Leu Leu Val
625 630 635 640
Gly Ala Ile Ala Ile Pro His Cys Val Leu Lys Cys Gln Arg Gly Ile
645 650 655
Glu Gly Thr Val Pro Tyr Val Thr Thr Asp Tyr Asn Phe Lys Leu Pro
660 665 670
Gly Ala Ser Glu Lys Ile Gly Ala Arg Glu Pro Glu Glu Ala His Lys
675 680 685
Glu Lys Pro Tyr Arg Arg Pro Lys Phe Cys Arg Lys Met Lys
690 695 700
<210> 9
<211> 1025
<212> PRT
<213> person
<400> 9
Met Ser Ala Ala Lys Glu Asn Pro Cys Arg Lys Phe Gln Ala Asn Ile
1 5 10 15
Phe Asn Lys Ser Lys Cys Gln Asn Cys Phe Lys Pro Arg Glu Ser His
20 25 30
Leu Leu Asn Asp Glu Asp Leu Thr Gln Ala Lys Pro Ile Tyr Gly Gly
35 40 45
Trp Leu Leu Leu Ala Pro Asp Gly Thr Asp Phe Asp Asn Pro Val His
50 55 60
Arg Ser Arg Lys Trp Gln Arg Arg Phe Phe Ile Leu Tyr Glu His Gly
65 70 75 80
Leu Leu Arg Tyr Ala Leu Asp Glu Met Pro Thr Thr Leu Pro Gln Gly
85 90 95
Thr Ile Asn Met Asn Gln Cys Thr Asp Val Val Asp Gly Glu Gly Arg
100 105 110
Thr Gly Gln Lys Phe Ser Leu Cys Ile Leu Thr Pro Glu Lys Glu His
115 120 125
Phe Ile Arg Ala Glu Thr Lys Glu Ile Val Ser Gly Trp Leu Glu Met
130 135 140
Leu Met Val Tyr Pro Arg Thr Asn Lys Gln Asn Gln Lys Lys Lys Arg
145 150 155 160
Lys Val Glu Pro Pro Thr Pro Gln Glu Pro Gly Pro Ala Lys Val Ala
165 170 175
Val Thr Ser Ser Ser Ser Ser Ser Ser Ser Ser Ser Ser Ile Pro Ser
180 185 190
Ala Glu Lys Val Pro Thr Thr Lys Ser Thr Leu Trp Gln Glu Glu Met
195 200 205
Arg Thr Lys Asp Gln Pro Asp Gly Ser Ser Leu Ser Pro Ala Gln Ser
210 215 220
Pro Ser Gln Ser Gln Pro Pro Ala Ala Ser Ser Leu Arg Glu Pro Gly
225 230 235 240
Leu Glu Ser Lys Glu Glu Glu Ser Ala Met Ser Ser Asp Arg Met Asp
245 250 255
Cys Gly Arg Lys Val Arg Val Glu Ser Gly Tyr Phe Ser Leu Glu Lys
260 265 270
Thr Lys Gln Asp Leu Lys Ala Glu Glu Gln Gln Leu Pro Pro Pro Leu
275 280 285
Ser Pro Pro Ser Pro Ser Thr Pro Asn His Arg Arg Ser Gln Val Ile
290 295 300
Glu Lys Phe Glu Ala Leu Asp Ile Glu Lys Ala Glu His Met Glu Thr
305 310 315 320
Asn Ala Val Gly Pro Ser Pro Ser Ser Asp Thr Arg Gln Gly Arg Ser
325 330 335
Glu Lys Arg Ala Phe Pro Arg Lys Arg Asp Phe Thr Asn Glu Ala Pro
340 345 350
Pro Ala Pro Leu Pro Asp Ala Ser Ala Ser Pro Leu Ser Pro His Arg
355 360 365
Arg Ala Lys Ser Leu Asp Arg Arg Ser Thr Glu Pro Ser Val Thr Pro
370 375 380
Asp Leu Leu Asn Phe Lys Lys Gly Trp Leu Thr Lys Gln Tyr Glu Asp
385 390 395 400
Gly Gln Trp Lys Lys His Trp Phe Val Leu Ala Asp Gln Ser Leu Arg
405 410 415
Tyr Tyr Arg Asp Ser Val Ala Glu Glu Ala Ala Asp Leu Asp Gly Glu
420 425 430
Ile Asp Leu Ser Ala Cys Tyr Asp Val Thr Glu Tyr Pro Val Gln Arg
435 440 445
Asn Tyr Gly Phe Gln Ile His Thr Lys Glu Gly Glu Phe Thr Leu Ser
450 455 460
Ala Met Thr Ser Gly Ile Arg Arg Asn Trp Ile Gln Thr Ile Met Lys
465 470 475 480
His Val His Pro Thr Thr Ala Pro Asp Val Thr Ser Ser Leu Pro Glu
485 490 495
Glu Lys Asn Lys Ser Ser Cys Ser Phe Glu Thr Cys Pro Arg Pro Thr
500 505 510
Glu Lys Gln Glu Ala Glu Leu Gly Glu Pro Asp Pro Glu Gln Lys Arg
515 520 525
Ser Arg Ala Arg Glu Arg Arg Arg Glu Gly Arg Ser Lys Thr Phe Asp
530 535 540
Trp Ala Glu Phe Arg Pro Ile Gln Gln Ala Leu Ala Gln Glu Arg Val
545 550 555 560
Gly Gly Val Gly Pro Ala Asp Thr His Glu Pro Leu Arg Pro Glu Ala
565 570 575
Glu Pro Gly Glu Leu Glu Arg Glu Arg Ala Arg Arg Arg Glu Glu Arg
580 585 590
Arg Lys Arg Phe Gly Met Leu Asp Ala Thr Asp Gly Pro Gly Thr Glu
595 600 605
Asp Ala Ala Leu Arg Met Glu Val Asp Arg Ser Pro Gly Leu Pro Met
610 615 620
Ser Asp Leu Lys Thr His Asn Val His Val Glu Ile Glu Gln Arg Trp
625 630 635 640
His Gln Val Glu Thr Thr Pro Leu Arg Glu Glu Lys Gln Val Pro Ile
645 650 655
Ala Pro Val His Leu Ser Ser Glu Asp Gly Gly Asp Arg Leu Ser Thr
660 665 670
His Glu Leu Thr Ser Leu Leu Glu Lys Glu Leu Glu Gln Ser Gln Lys
675 680 685
Glu Ala Ser Asp Leu Leu Glu Gln Asn Arg Leu Leu Gln Asp Gln Leu
690 695 700
Arg Val Ala Leu Gly Arg Glu Gln Ser Ala Arg Glu Gly Tyr Val Leu
705 710 715 720
Gln Ala Thr Cys Glu Arg Gly Phe Ala Ala Met Glu Glu Thr His Gln
725 730 735
Lys Lys Ile Glu Asp Leu Gln Arg Gln His Gln Arg Glu Leu Glu Lys
740 745 750
Leu Arg Glu Glu Lys Asp Arg Leu Leu Ala Glu Glu Thr Ala Ala Thr
755 760 765
Ile Ser Ala Ile Glu Ala Met Lys Asn Ala His Arg Glu Glu Met Glu
770 775 780
Arg Glu Leu Glu Lys Ser Gln Arg Ser Gln Ile Ser Ser Val Asn Ser
785 790 795 800
Asp Val Glu Ala Leu Arg Arg Gln Tyr Leu Glu Glu Leu Gln Ser Val
805 810 815
Gln Arg Glu Leu Glu Val Leu Ser Glu Gln Tyr Ser Gln Lys Cys Leu
820 825 830
Glu Asn Ala His Leu Ala Gln Ala Leu Glu Ala Glu Arg Gln Ala Leu
835 840 845
Arg Gln Cys Gln Arg Glu Asn Gln Glu Leu Asn Ala His Asn Gln Glu
850 855 860
Leu Asn Asn Arg Leu Ala Ala Glu Ile Thr Arg Leu Arg Thr Leu Leu
865 870 875 880
Thr Gly Asp Gly Gly Gly Glu Ala Thr Gly Ser Pro Leu Ala Gln Gly
885 890 895
Lys Asp Ala Tyr Glu Leu Glu Val Leu Leu Arg Val Lys Glu Ser Glu
900 905 910
Ile Gln Tyr Leu Lys Gln Glu Ile Ser Ser Leu Lys Asp Glu Leu Gln
915 920 925
Thr Ala Leu Arg Asp Lys Lys Tyr Ala Ser Asp Lys Tyr Lys Asp Ile
930 935 940
Tyr Thr Glu Leu Ser Ile Ala Lys Ala Lys Ala Asp Cys Asp Ile Ser
945 950 955 960
Arg Leu Lys Glu Gln Leu Lys Ala Ala Thr Glu Ala Leu Gly Glu Lys
965 970 975
Ser Pro Asp Ser Ala Thr Val Ser Gly Tyr Asp Ile Met Lys Ser Lys
980 985 990
Ser Asn Pro Asp Phe Leu Lys Lys Asp Arg Ser Cys Val Thr Arg Gln
995 1000 1005
Leu Arg Asn Ile Arg Ser Lys Ser Val Ile Glu Gln Val Ser Trp
1010 1015 1020
Asp Thr
1025
<210> 10
<211> 406
<212> PRT
<213> person
<400> 10
Met Ser Ala Leu Gly Ala Val Ile Ala Leu Leu Leu Trp Gly Gln Leu
1 5 10 15
Phe Ala Val Asp Ser Gly Asn Asp Val Thr Asp Ile Ala Asp Asp Gly
20 25 30
Cys Pro Lys Pro Pro Glu Ile Ala His Gly Tyr Val Glu His Ser Val
35 40 45
Arg Tyr Gln Cys Lys Asn Tyr Tyr Lys Leu Arg Thr Glu Gly Asp Gly
50 55 60
Val Tyr Thr Leu Asn Asp Lys Lys Gln Trp Ile Asn Lys Ala Val Gly
65 70 75 80
Asp Lys Leu Pro Glu Cys Glu Ala Asp Asp Gly Cys Pro Lys Pro Pro
85 90 95
Glu Ile Ala His Gly Tyr Val Glu His Ser Val Arg Tyr Gln Cys Lys
100 105 110
Asn Tyr Tyr Lys Leu Arg Thr Glu Gly Asp Gly Val Tyr Thr Leu Asn
115 120 125
Asn Glu Lys Gln Trp Ile Asn Lys Ala Val Gly Asp Lys Leu Pro Glu
130 135 140
Cys Glu Ala Val Cys Gly Lys Pro Lys Asn Pro Ala Asn Pro Val Gln
145 150 155 160
Arg Ile Leu Gly Gly His Leu Asp Ala Lys Gly Ser Phe Pro Trp Gln
165 170 175
Ala Lys Met Val Ser His His Asn Leu Thr Thr Gly Ala Thr Leu Ile
180 185 190
Asn Glu Gln Trp Leu Leu Thr Thr Ala Lys Asn Leu Phe Leu Asn His
195 200 205
Ser Glu Asn Ala Thr Ala Lys Asp Ile Ala Pro Thr Leu Thr Leu Tyr
210 215 220
Val Gly Lys Lys Gln Leu Val Glu Ile Glu Lys Val Val Leu His Pro
225 230 235 240
Asn Tyr Ser Gln Val Asp Ile Gly Leu Ile Lys Leu Lys Gln Lys Val
245 250 255
Ser Val Asn Glu Arg Val Met Pro Ile Cys Leu Pro Ser Lys Asp Tyr
260 265 270
Ala Glu Val Gly Arg Val Gly Tyr Val Ser Gly Trp Gly Arg Asn Ala
275 280 285
Asn Phe Lys Phe Thr Asp His Leu Lys Tyr Val Met Leu Pro Val Ala
290 295 300
Asp Gln Asp Gln Cys Ile Arg His Tyr Glu Gly Ser Thr Val Pro Glu
305 310 315 320
Lys Lys Thr Pro Lys Ser Pro Val Gly Val Gln Pro Ile Leu Asn Glu
325 330 335
His Thr Phe Cys Ala Gly Met Ser Lys Tyr Gln Glu Asp Thr Cys Tyr
340 345 350
Gly Asp Ala Gly Ser Ala Phe Ala Val His Asp Leu Glu Glu Asp Thr
355 360 365
Trp Tyr Ala Thr Gly Ile Leu Ser Phe Asp Lys Ser Cys Ala Val Ala
370 375 380
Glu Tyr Gly Val Tyr Val Lys Val Thr Ser Ile Gln Asp Trp Val Gln
385 390 395 400
Lys Thr Ile Ala Glu Asn
405
<210> 11
<211> 318
<212> PRT
<213> person
<400> 11
Met Ala Ala Gln Gly Cys Ala Ala Ser Arg Leu Leu Gln Leu Leu Leu
1 5 10 15
Gln Leu Leu Leu Leu Leu Leu Leu Leu Ala Ala Gly Gly Ala Arg Ala
20 25 30
Arg Trp Arg Gly Glu Gly Thr Ser Ala His Leu Arg Asp Ile Phe Leu
35 40 45
Gly Arg Cys Ala Glu Tyr Arg Ala Leu Leu Ser Pro Glu Gln Arg Asn
50 55 60
Lys Asn Cys Thr Ala Ile Trp Glu Ala Phe Lys Val Ala Leu Asp Lys
65 70 75 80
Asp Pro Cys Ser Val Leu Pro Ser Asp Tyr Asp Leu Phe Ile Asn Leu
85 90 95
Ser Arg His Ser Ile Pro Arg Asp Lys Ser Leu Phe Trp Glu Asn Ser
100 105 110
His Leu Leu Val Asn Ser Phe Ala Asp Asn Thr Arg Arg Phe Met Pro
115 120 125
Leu Ser Asp Val Leu Tyr Gly Arg Val Ala Asp Phe Leu Ser Trp Cys
130 135 140
Arg Gln Lys Asn Asp Ser Gly Leu Asp Tyr Gln Ser Cys Pro Thr Ser
145 150 155 160
Glu Asp Cys Glu Asn Asn Pro Val Asp Ser Phe Trp Lys Arg Ala Ser
165 170 175
Ile Gln Tyr Ser Lys Asp Ser Ser Gly Val Ile His Val Met Leu Asn
180 185 190
Gly Ser Glu Pro Thr Gly Ala Tyr Pro Ile Lys Gly Phe Phe Ala Asp
195 200 205
Tyr Glu Ile Pro Asn Leu Gln Lys Glu Lys Ile Thr Arg Ile Glu Ile
210 215 220
Trp Val Met His Glu Ile Gly Gly Pro Asn Val Glu Ser Cys Gly Glu
225 230 235 240
Gly Ser Met Lys Val Leu Glu Lys Arg Leu Lys Asp Met Gly Phe Gln
245 250 255
Tyr Ser Cys Ile Asn Asp Tyr Arg Pro Val Lys Leu Leu Gln Cys Val
260 265 270
Asp His Ser Thr His Pro Asp Cys Ala Leu Lys Ser Ala Ala Ala Ala
275 280 285
Thr Gln Arg Lys Ala Pro Ser Leu Tyr Thr Glu Gln Arg Ala Gly Leu
290 295 300
Ile Ile Pro Leu Phe Leu Val Leu Ala Ser Arg Thr Gln Leu
305 310 315
<210> 12
<211> 663
<212> PRT
<213> person
<400> 12
Met Ser Ala Arg Leu Pro Val Leu Ser Pro Pro Arg Trp Pro Arg Leu
1 5 10 15
Leu Leu Leu Ser Leu Leu Leu Leu Gly Ala Val Pro Gly Pro Arg Arg
20 25 30
Ser Gly Ala Phe Tyr Leu Pro Gly Leu Ala Pro Val Asn Phe Cys Asp
35 40 45
Glu Glu Lys Lys Ser Asp Glu Cys Lys Ala Glu Ile Glu Leu Phe Val
50 55 60
Asn Arg Leu Asp Ser Val Glu Ser Val Leu Pro Tyr Glu Tyr Thr Ala
65 70 75 80
Phe Asp Phe Cys Gln Ala Ser Glu Gly Lys Arg Pro Ser Glu Asn Leu
85 90 95
Gly Gln Val Leu Phe Gly Glu Arg Ile Glu Pro Ser Pro Tyr Lys Phe
100 105 110
Thr Phe Asn Lys Lys Glu Thr Cys Lys Leu Val Cys Thr Lys Thr Tyr
115 120 125
His Thr Glu Lys Ala Glu Asp Lys Gln Lys Leu Glu Phe Leu Lys Lys
130 135 140
Ser Met Leu Leu Asn Tyr Gln His His Trp Ile Val Asp Asn Met Pro
145 150 155 160
Val Thr Trp Cys Tyr Asp Val Glu Asp Gly Gln Arg Phe Cys Asn Pro
165 170 175
Gly Phe Pro Ile Gly Cys Tyr Ile Thr Asp Lys Gly His Ala Lys Asp
180 185 190
Ala Cys Val Ile Ser Ser Asp Phe His Glu Arg Asp Thr Phe Tyr Ile
195 200 205
Phe Asn His Val Asp Ile Lys Ile Tyr Tyr His Val Val Glu Thr Gly
210 215 220
Ser Met Gly Ala Arg Leu Val Ala Ala Lys Leu Glu Pro Lys Ser Phe
225 230 235 240
Lys His Thr His Ile Asp Lys Pro Asp Cys Ser Gly Pro Pro Met Asp
245 250 255
Ile Ser Asn Lys Ala Ser Gly Glu Ile Lys Ile Ala Tyr Thr Tyr Ser
260 265 270
Val Ser Phe Glu Glu Asp Asp Lys Ile Arg Trp Ala Ser Arg Trp Asp
275 280 285
Tyr Ile Leu Glu Ser Met Pro His Thr His Ile Gln Trp Phe Ser Ile
290 295 300
Met Asn Ser Leu Val Ile Val Leu Phe Leu Ser Gly Met Val Ala Met
305 310 315 320
Ile Met Leu Arg Thr Leu His Lys Asp Ile Ala Arg Tyr Asn Gln Met
325 330 335
Asp Ser Thr Glu Asp Ala Gln Glu Glu Phe Gly Trp Lys Leu Val His
340 345 350
Gly Asp Ile Phe Arg Pro Pro Arg Lys Gly Met Leu Leu Ser Val Phe
355 360 365
Leu Gly Ser Gly Thr Gln Ile Leu Ile Met Thr Phe Val Thr Leu Phe
370 375 380
Phe Ala Cys Leu Gly Phe Leu Ser Pro Ala Asn Arg Gly Ala Leu Met
385 390 395 400
Thr Cys Ala Val Val Leu Trp Val Leu Leu Gly Thr Pro Ala Gly Tyr
405 410 415
Val Ala Ala Arg Phe Tyr Lys Ser Phe Gly Gly Glu Lys Trp Lys Thr
420 425 430
Asn Val Leu Leu Thr Ser Phe Leu Cys Pro Gly Ile Val Phe Ala Asp
435 440 445
Phe Phe Ile Met Asn Leu Ile Leu Trp Gly Glu Gly Ser Ser Ala Ala
450 455 460
Ile Pro Phe Gly Thr Leu Val Ala Ile Leu Ala Leu Trp Phe Cys Ile
465 470 475 480
Ser Val Pro Leu Thr Phe Ile Gly Ala Tyr Phe Gly Phe Lys Lys Asn
485 490 495
Ala Ile Glu His Pro Val Arg Thr Asn Gln Ile Pro Arg Gln Ile Pro
500 505 510
Glu Gln Ser Phe Tyr Thr Lys Pro Leu Pro Gly Ile Ile Met Gly Gly
515 520 525
Ile Leu Pro Phe Gly Cys Ile Phe Ile Gln Leu Phe Phe Ile Leu Asn
530 535 540
Ser Ile Trp Ser His Gln Met Tyr Tyr Met Phe Gly Phe Leu Phe Leu
545 550 555 560
Val Phe Ile Ile Leu Val Ile Thr Cys Ser Glu Ala Thr Ile Leu Leu
565 570 575
Cys Tyr Phe His Leu Cys Ala Glu Asp Tyr His Trp Gln Trp Arg Ser
580 585 590
Phe Leu Thr Ser Gly Phe Thr Ala Val Tyr Phe Leu Ile Tyr Ala Val
595 600 605
His Tyr Phe Phe Ser Lys Leu Gln Ile Thr Gly Thr Ala Ser Thr Ile
610 615 620
Leu Tyr Phe Gly Tyr Thr Met Ile Met Val Leu Ile Phe Phe Leu Phe
625 630 635 640
Thr Gly Thr Ile Gly Phe Phe Ala Cys Phe Trp Phe Val Thr Lys Ile
645 650 655
Tyr Ser Val Val Lys Val Asp
660
<210> 13
<211> 361
<212> PRT
<213> person
<400> 13
Met Ser Thr Ala Arg Glu Gln Pro Ile Phe Ser Thr Arg Ala His Val
1 5 10 15
Phe Gln Ile Asp Pro Ala Thr Lys Arg Asn Trp Ile Pro Ala Gly Lys
20 25 30
His Ala Leu Thr Val Ser Tyr Phe Tyr Asp Ala Thr Arg Asn Val Tyr
35 40 45
Arg Ile Ile Ser Ile Gly Gly Ala Lys Ala Ile Ile Asn Ser Thr Val
50 55 60
Thr Pro Asn Met Thr Phe Thr Lys Thr Ser Gln Lys Phe Gly Gln Trp
65 70 75 80
Ala Asp Ser Arg Ala Asn Thr Val Tyr Gly Leu Gly Phe Ala Ser Glu
85 90 95
Gln His Leu Thr Gln Phe Ala Glu Lys Phe Gln Glu Val Lys Glu Ala
100 105 110
Ala Arg Leu Ala Arg Glu Lys Ser Gln Asp Gly Gly Glu Leu Thr Ser
115 120 125
Pro Ala Leu Gly Leu Ala Ser His Gln Val Pro Pro Ser Pro Leu Val
130 135 140
Ser Ala Asn Gly Pro Gly Glu Glu Lys Leu Phe Arg Ser Gln Ser Ala
145 150 155 160
Asp Ala Pro Gly Pro Thr Glu Arg Glu Arg Leu Lys Lys Met Leu Ser
165 170 175
Glu Gly Ser Val Gly Glu Val Gln Trp Glu Ala Glu Phe Phe Ala Leu
180 185 190
Gln Asp Ser Asn Asn Lys Leu Ala Gly Ala Leu Arg Glu Ala Asn Ala
195 200 205
Ala Ala Ala Gln Trp Arg Gln Gln Leu Glu Ala Gln Arg Ala Glu Ala
210 215 220
Glu Arg Leu Arg Gln Arg Val Ala Glu Leu Glu Ala Gln Ala Ala Ser
225 230 235 240
Glu Val Thr Pro Thr Gly Glu Lys Glu Gly Leu Gly Gln Gly Gln Ser
245 250 255
Leu Glu Gln Leu Glu Ala Leu Val Gln Thr Lys Asp Gln Glu Ile Gln
260 265 270
Thr Leu Lys Ser Gln Thr Gly Gly Pro Arg Glu Ala Leu Glu Ala Ala
275 280 285
Glu Arg Glu Glu Thr Gln Gln Lys Val Gln Asp Leu Glu Thr Arg Asn
290 295 300
Ala Glu Leu Glu His Gln Leu Arg Ala Met Glu Arg Ser Leu Glu Glu
305 310 315 320
Ala Arg Ala Glu Arg Glu Arg Ala Arg Ala Glu Val Gly Arg Ala Ala
325 330 335
Gln Leu Leu Asp Val Ser Leu Phe Glu Leu Ser Glu Leu Arg Glu Gly
340 345 350
Leu Ala Arg Leu Ala Glu Ala Ala Pro
355 360
<210> 15
<211> 469
<212> PRT
<213> person
<400> 15
Met Ser Ile Phe Pro Lys Ile Ser Leu Arg Pro Glu Val Glu Asn Tyr
1 5 10 15
Leu Lys Glu Gly Phe Met Asn Lys Glu Ile Val Thr Ala Leu Gly Lys
20 25 30
Gln Glu Ala Glu Arg Lys Phe Glu Thr Leu Leu Lys His Leu Ser His
35 40 45
Pro Pro Ser Phe Thr Thr Val Arg Val Asn Thr His Leu Ala Ser Val
50 55 60
Gln His Val Lys Asn Leu Leu Leu Asp Glu Leu Gln Lys Gln Phe Asn
65 70 75 80
Gly Leu Ser Val Pro Ile Leu Gln His Pro Asp Leu Gln Asp Val Leu
85 90 95
Leu Ile Pro Val Ile Gly Pro Arg Lys Asn Ile Lys Lys Gln Gln Cys
100 105 110
Glu Ala Ile Val Gly Ala Gln Cys Gly Asn Ala Val Leu Arg Gly Ala
115 120 125
His Val Tyr Ala Pro Gly Ile Val Ser Ala Ser Gln Phe Met Lys Ala
130 135 140
Gly Asp Val Ile Ser Val Tyr Ser Asp Ile Lys Gly Lys Cys Lys Lys
145 150 155 160
Gly Ala Lys Glu Phe Asp Gly Thr Lys Val Phe Leu Gly Asn Gly Ile
165 170 175
Ser Glu Leu Ser Arg Lys Glu Ile Phe Ser Gly Leu Pro Glu Leu Lys
180 185 190
Gly Met Gly Ile Arg Met Thr Glu Pro Val Tyr Leu Ser Pro Ser Phe
195 200 205
Asp Ser Val Leu Pro Arg Tyr Leu Phe Leu Gln Asn Leu Pro Ser Ala
210 215 220
Leu Val Ser His Val Leu Asn Pro Gln Pro Gly Glu Lys Ile Leu Asp
225 230 235 240
Leu Cys Ala Ala Pro Gly Gly Lys Thr Thr His Ile Ala Ala Leu Met
245 250 255
His Asp Gln Gly Glu Val Ile Ala Leu Asp Lys Ile Phe Asn Lys Val
260 265 270
Glu Lys Ile Lys Gln Asn Ala Leu Leu Leu Gly Leu Asn Ser Ile Arg
275 280 285
Ala Phe Cys Phe Asp Gly Thr Lys Ala Val Lys Leu Asp Met Val Glu
290 295 300
Asp Thr Glu Gly Glu Pro Pro Phe Leu Pro Glu Ser Phe Asp Arg Ile
305 310 315 320
Leu Leu Asp Ala Pro Cys Ser Gly Met Gly Gln Arg Pro Asn Met Ala
325 330 335
Cys Thr Trp Ser Val Lys Glu Val Ala Ser Tyr Gln Pro Leu Gln Arg
340 345 350
Lys Leu Phe Thr Ala Ala Val Gln Leu Leu Lys Pro Glu Gly Val Leu
355 360 365
Val Tyr Ser Thr Cys Thr Ile Thr Leu Ala Glu Asn Glu Glu Gln Val
370 375 380
Ala Trp Ala Leu Thr Lys Phe Pro Cys Leu Gln Leu Gln Pro Gln Glu
385 390 395 400
Pro Gln Ile Gly Gly Glu Gly Met Arg Gly Ala Gly Leu Ser Cys Glu
405 410 415
Gln Leu Lys Gln Leu Gln Arg Phe Asp Pro Ser Ala Val Pro Leu Pro
420 425 430
Asp Thr Asp Met Asp Ser Leu Arg Glu Ala Arg Arg Glu Asp Met Leu
435 440 445
Arg Leu Ala Asn Lys Asp Ser Ile Gly Phe Phe Ile Ala Lys Phe Val
450 455 460
Lys Cys Lys Ser Thr
465
<210> 16
<211> 986
<212> PRT
<213> person
<400> 16
Met Ala Gly Ile Phe Tyr Phe Ala Leu Phe Ser Cys Leu Phe Gly Ile
1 5 10 15
Cys Asp Ala Val Thr Gly Ser Arg Val Tyr Pro Ala Asn Glu Val Thr
20 25 30
Leu Leu Asp Ser Arg Ser Val Gln Gly Glu Leu Gly Trp Ile Ala Ser
35 40 45
Pro Leu Glu Gly Gly Trp Glu Glu Val Ser Ile Met Asp Glu Lys Asn
50 55 60
Thr Pro Ile Arg Thr Tyr Gln Val Cys Asn Val Met Glu Pro Ser Gln
65 70 75 80
Asn Asn Trp Leu Arg Thr Asp Trp Ile Thr Arg Glu Gly Ala Gln Arg
85 90 95
Val Tyr Ile Glu Ile Lys Phe Thr Leu Arg Asp Cys Asn Ser Leu Pro
100 105 110
Gly Val Met Gly Thr Cys Lys Glu Thr Phe Asn Leu Tyr Tyr Tyr Glu
115 120 125
Ser Asp Asn Asp Lys Glu Arg Phe Ile Arg Glu Asn Gln Phe Val Lys
130 135 140
Ile Asp Thr Ile Ala Ala Asp Glu Ser Phe Thr Gln Val Asp Ile Gly
145 150 155 160
Asp Arg Ile Met Lys Leu Asn Thr Glu Ile Arg Asp Val Gly Pro Leu
165 170 175
Ser Lys Lys Gly Phe Tyr Leu Ala Phe Gln Asp Val Gly Ala Cys Ile
180 185 190
Ala Leu Val Ser Val Arg Val Phe Tyr Lys Lys Cys Pro Leu Thr Val
195 200 205
Arg Asn Leu Ala Gln Phe Pro Asp Thr Ile Thr Gly Ala Asp Thr Ser
210 215 220
Ser Leu Val Glu Val Arg Gly Ser Cys Val Asn Asn Ser Glu Glu Lys
225 230 235 240
Asp Val Pro Lys Met Tyr Cys Gly Ala Asp Gly Glu Trp Leu Val Pro
245 250 255
Ile Gly Asn Cys Leu Cys Asn Ala Gly His Glu Glu Arg Ser Gly Glu
260 265 270
Cys Gln Ala Cys Lys Ile Gly Tyr Tyr Lys Ala Leu Ser Thr Asp Ala
275 280 285
Thr Cys Ala Lys Cys Pro Pro His Ser Tyr Ser Val Trp Glu Gly Ala
290 295 300
Thr Ser Cys Thr Cys Asp Arg Gly Phe Phe Arg Ala Asp Asn Asp Ala
305 310 315 320
Ala Ser Met Pro Cys Thr Arg Pro Pro Ser Ala Pro Leu Asn Leu Ile
325 330 335
Ser Asn Val Asn Glu Thr Ser Val Asn Leu Glu Trp Ser Ser Pro Gln
340 345 350
Asn Thr Gly Gly Arg Gln Asp Ile Ser Tyr Asn Val Val Cys Lys Lys
355 360 365
Cys Gly Ala Gly Asp Pro Ser Lys Cys Arg Pro Cys Gly Ser Gly Val
370 375 380
His Tyr Thr Pro Gln Gln Asn Gly Leu Lys Thr Thr Lys Val Ser Ile
385 390 395 400
Thr Asp Leu Leu Ala His Thr Asn Tyr Thr Phe Glu Ile Trp Ala Val
405 410 415
Asn Gly Val Ser Lys Tyr Asn Pro Asn Pro Asp Gln Ser Val Ser Val
420 425 430
Thr Val Thr Thr Asn Gln Ala Ala Pro Ser Ser Ile Ala Leu Val Gln
435 440 445
Ala Lys Glu Val Thr Arg Tyr Ser Val Ala Leu Ala Trp Leu Glu Pro
450 455 460
Asp Arg Pro Asn Gly Val Ile Leu Glu Tyr Glu Val Lys Tyr Tyr Glu
465 470 475 480
Lys Asp Gln Asn Glu Arg Ser Tyr Arg Ile Val Arg Thr Ala Ala Arg
485 490 495
Asn Thr Asp Ile Lys Gly Leu Asn Pro Leu Thr Ser Tyr Val Phe His
500 505 510
Val Arg Ala Arg Thr Ala Ala Gly Tyr Gly Asp Phe Ser Glu Pro Leu
515 520 525
Glu Val Thr Thr Asn Thr Val Pro Ser Arg Ile Ile Gly Asp Gly Ala
530 535 540
Asn Ser Thr Val Leu Leu Val Ser Val Ser Gly Ser Val Val Leu Val
545 550 555 560
Val Ile Leu Ile Ala Ala Phe Val Ile Ser Arg Arg Arg Ser Lys Tyr
565 570 575
Ser Lys Ala Lys Gln Glu Ala Asp Glu Glu Lys His Leu Asn Gln Gly
580 585 590
Val Arg Thr Tyr Val Asp Pro Phe Thr Tyr Glu Asp Pro Asn Gln Ala
595 600 605
Val Arg Glu Phe Ala Lys Glu Ile Asp Ala Ser Cys Ile Lys Ile Glu
610 615 620
Lys Val Ile Gly Val Gly Glu Phe Gly Glu Val Cys Ser Gly Arg Leu
625 630 635 640
Lys Val Pro Gly Lys Arg Glu Ile Cys Val Ala Ile Lys Thr Leu Lys
645 650 655
Ala Gly Tyr Thr Asp Lys Gln Arg Arg Asp Phe Leu Ser Glu Ala Ser
660 665 670
Ile Met Gly Gln Phe Asp His Pro Asn Ile Ile His Leu Glu Gly Val
675 680 685
Val Thr Lys Cys Lys Pro Val Met Ile Ile Thr Glu Tyr Met Glu Asn
690 695 700
Gly Ser Leu Asp Ala Phe Leu Arg Lys Asn Asp Gly Arg Phe Thr Val
705 710 715 720
Ile Gln Leu Val Gly Met Leu Arg Gly Ile Gly Ser Gly Met Lys Tyr
725 730 735
Leu Ser Asp Met Ser Tyr Val His Arg Asp Leu Ala Ala Arg Asn Ile
740 745 750
Leu Val Asn Ser Asn Leu Val Cys Lys Val Ser Asp Phe Gly Met Ser
755 760 765
Arg Val Leu Glu Asp Asp Pro Glu Ala Ala Tyr Thr Thr Arg Gly Gly
770 775 780
Lys Ile Pro Ile Arg Trp Thr Ala Pro Glu Ala Ile Ala Tyr Arg Lys
785 790 795 800
Phe Thr Ser Ala Ser Asp Val Trp Ser Tyr Gly Ile Val Met Trp Glu
805 810 815
Val Met Ser Tyr Gly Glu Arg Pro Tyr Trp Asp Met Ser Asn Gln Asp
820 825 830
Val Ile Lys Ala Ile Glu Glu Gly Tyr Arg Leu Pro Pro Pro Met Asp
835 840 845
Cys Pro Ile Ala Leu His Gln Leu Met Leu Asp Cys Trp Gln Lys Glu
850 855 860
Arg Ser Asp Arg Pro Lys Phe Gly Gln Ile Val Asn Met Leu Asp Lys
865 870 875 880
Leu Ile Arg Asn Pro Asn Ser Leu Lys Arg Thr Gly Thr Glu Ser Ser
885 890 895
Arg Pro Asn Thr Ala Leu Leu Asp Pro Ser Ser Pro Glu Phe Ser Ala
900 905 910
Val Val Ser Val Gly Asp Trp Leu Gln Ala Ile Lys Met Asp Arg Tyr
915 920 925
Lys Asp Asn Phe Thr Ala Ala Gly Tyr Thr Thr Leu Glu Ala Val Val
930 935 940
His Val Asn Gln Glu Asp Leu Ala Arg Ile Gly Ile Thr Ala Ile Thr
945 950 955 960
His Gln Asn Lys Ile Leu Ser Ser Val Gln Ala Met Arg Thr Gln Met
965 970 975
Gln Gln Met His Gly Arg Met Val Pro Val
980 985
<210> 17
<211> 1161
<212> PRT
<213> person
<400> 17
Met Lys Lys Phe Ser Arg Met Pro Lys Ser Glu Gly Gly Ser Gly Gly
1 5 10 15
Gly Ala Ala Gly Gly Gly Ala Gly Gly Ala Gly Ala Gly Ala Gly Cys
20 25 30
Gly Ser Gly Gly Ser Ser Val Gly Val Arg Val Phe Ala Val Gly Arg
35 40 45
His Gln Val Thr Leu Glu Glu Ser Leu Ala Glu Gly Gly Phe Ser Thr
50 55 60
Val Phe Leu Val Arg Thr His Gly Gly Ile Arg Cys Ala Leu Lys Arg
65 70 75 80
Met Tyr Val Asn Asn Met Pro Asp Leu Asn Val Cys Lys Arg Glu Ile
85 90 95
Thr Ile Met Lys Glu Leu Ser Gly His Lys Asn Ile Val Gly Tyr Leu
100 105 110
Asp Cys Ala Val Asn Ser Ile Ser Asp Asn Val Trp Glu Val Leu Ile
115 120 125
Leu Met Glu Tyr Cys Arg Ala Gly Gln Val Val Asn Gln Met Asn Lys
130 135 140
Lys Leu Gln Thr Gly Phe Thr Glu Pro Glu Val Leu Gln Ile Phe Cys
145 150 155 160
Asp Thr Cys Glu Ala Val Ala Arg Leu His Gln Cys Lys Thr Pro Ile
165 170 175
Ile His Arg Asp Leu Lys Val Glu Asn Ile Leu Leu Asn Asp Gly Gly
180 185 190
Asn Tyr Val Leu Cys Asp Phe Gly Ser Ala Thr Asn Lys Phe Leu Asn
195 200 205
Pro Gln Lys Asp Gly Val Asn Val Val Glu Glu Glu Ile Lys Lys Tyr
210 215 220
Thr Thr Leu Ser Tyr Arg Ala Pro Glu Met Ile Asn Leu Tyr Gly Gly
225 230 235 240
Lys Pro Ile Thr Thr Lys Ala Asp Ile Trp Ala Leu Gly Cys Leu Leu
245 250 255
Tyr Lys Leu Cys Phe Phe Thr Leu Pro Phe Gly Glu Ser Gln Val Ala
260 265 270
Ile Cys Asp Gly Asn Phe Thr Ile Pro Asp Asn Ser Arg Tyr Ser Arg
275 280 285
Asn Ile His Cys Leu Ile Arg Phe Met Leu Glu Pro Asp Pro Glu His
290 295 300
Arg Pro Asp Ile Phe Gln Val Ser Tyr Phe Ala Phe Lys Phe Ala Lys
305 310 315 320
Lys Asp Cys Pro Val Ser Asn Ile Asn Asn Ser Ser Ile Pro Ser Ala
325 330 335
Leu Pro Glu Pro Met Thr Ala Ser Glu Ala Ala Ala Arg Lys Ser Gln
340 345 350
Ile Lys Ala Arg Ile Thr Asp Thr Ile Gly Pro Thr Glu Thr Ser Ile
355 360 365
Ala Pro Arg Gln Arg Pro Lys Ala Asn Ser Ala Thr Thr Ala Thr Pro
370 375 380
Ser Val Leu Thr Ile Gln Ser Ser Ala Thr Pro Val Lys Val Leu Ala
385 390 395 400
Pro Gly Glu Phe Gly Asn His Arg Pro Lys Gly Ala Leu Arg Pro Gly
405 410 415
Asn Gly Pro Glu Ile Leu Leu Gly Gln Gly Pro Pro Gln Gln Pro Pro
420 425 430
Gln Gln His Arg Val Leu Gln Gln Leu Gln Gln Gly Asp Trp Arg Leu
435 440 445
Gln Gln Leu His Leu Gln His Arg His Pro His Gln Gln Gln Gln Gln
450 455 460
Gln Gln Gln Gln Gln Gln Gln Gln Gln Gln Gln Gln Gln Gln Gln Gln
465 470 475 480
Gln Gln Gln Gln Gln Gln His His His His His His His His Leu Leu
485 490 495
Gln Asp Ala Tyr Met Gln Gln Tyr Gln His Ala Thr Gln Gln Gln Gln
500 505 510
Met Leu Gln Gln Gln Phe Leu Met His Ser Val Tyr Gln Pro Gln Pro
515 520 525
Ser Ala Ser Gln Tyr Pro Thr Met Met Pro Gln Tyr Gln Gln Ala Phe
530 535 540
Phe Gln Gln Gln Met Leu Ala Gln His Gln Pro Ser Gln Gln Gln Ala
545 550 555 560
Ser Pro Glu Tyr Leu Thr Ser Pro Gln Glu Phe Ser Pro Ala Leu Val
565 570 575
Ser Tyr Thr Ser Ser Leu Pro Ala Gln Val Gly Thr Ile Met Asp Ser
580 585 590
Ser Tyr Ser Ala Asn Arg Ser Val Ala Asp Lys Glu Ala Ile Ala Asn
595 600 605
Phe Thr Asn Gln Lys Asn Ile Ser Asn Pro Pro Asp Met Ser Gly Trp
610 615 620
Asn Pro Phe Gly Glu Asp Asn Phe Ser Lys Leu Thr Glu Glu Glu Leu
625 630 635 640
Leu Asp Arg Glu Phe Asp Leu Leu Arg Ser Asn Arg Leu Glu Glu Arg
645 650 655
Ala Ser Ser Asp Lys Asn Val Asp Ser Leu Ser Ala Pro His Asn His
660 665 670
Pro Pro Glu Asp Pro Phe Gly Ser Val Pro Phe Ile Ser His Ser Gly
675 680 685
Ser Pro Glu Lys Lys Ala Glu His Ser Ser Ile Asn Gln Glu Asn Gly
690 695 700
Thr Ala Asn Pro Ile Lys Asn Gly Lys Thr Ser Pro Ala Ser Lys Asp
705 710 715 720
Gln Arg Thr Gly Lys Lys Thr Ser Val Gln Gly Gln Val Gln Lys Gly
725 730 735
Asn Asp Glu Ser Glu Ser Asp Phe Glu Ser Asp Pro Pro Ser Pro Lys
740 745 750
Ser Ser Glu Glu Glu Glu Gln Asp Asp Glu Glu Val Leu Gln Gly Glu
755 760 765
Gln Gly Asp Phe Asn Asp Asp Asp Thr Glu Pro Glu Asn Leu Gly His
770 775 780
Arg Pro Leu Leu Met Asp Ser Glu Asp Glu Glu Glu Glu Glu Lys His
785 790 795 800
Ser Ser Asp Ser Asp Tyr Glu Gln Ala Lys Ala Lys Tyr Ser Asp Met
805 810 815
Ser Ser Val Tyr Arg Asp Arg Ser Gly Ser Gly Pro Thr Gln Asp Leu
820 825 830
Asn Thr Ile Leu Leu Thr Ser Ala Gln Leu Ser Ser Asp Val Ala Val
835 840 845
Glu Thr Pro Lys Gln Glu Phe Asp Val Phe Gly Ala Val Pro Phe Phe
850 855 860
Ala Val Arg Ala Gln Gln Pro Gln Gln Glu Lys Asn Glu Lys Asn Leu
865 870 875 880
Pro Gln His Arg Phe Pro Ala Ala Gly Leu Glu Gln Glu Glu Phe Asp
885 890 895
Val Phe Thr Lys Ala Pro Phe Ser Lys Lys Val Asn Val Gln Glu Cys
900 905 910
His Ala Val Gly Pro Glu Ala His Thr Ile Pro Gly Tyr Pro Lys Ser
915 920 925
Val Asp Val Phe Gly Ser Thr Pro Phe Gln Pro Phe Leu Thr Ser Thr
930 935 940
Ser Lys Ser Glu Ser Asn Glu Asp Leu Phe Gly Leu Val Pro Phe Asp
945 950 955 960
Glu Ile Thr Gly Ser Gln Gln Gln Lys Val Lys Gln Arg Ser Leu Gln
965 970 975
Lys Leu Ser Ser Arg Gln Arg Arg Thr Lys Gln Asp Met Ser Lys Ser
980 985 990
Asn Gly Lys Arg His His Gly Thr Pro Thr Ser Thr Lys Lys Thr Leu
995 1000 1005
Lys Pro Thr Tyr Arg Thr Pro Glu Arg Ala Arg Arg His Lys Lys
1010 1015 1020
Val Gly Arg Arg Asp Ser Gln Ser Ser Asn Glu Phe Leu Thr Ile
1025 1030 1035
Ser Asp Ser Lys Glu Asn Ile Ser Val Ala Leu Thr Asp Gly Lys
1040 1045 1050
Asp Arg Gly Asn Val Leu Gln Pro Glu Glu Ser Leu Leu Asp Pro
1055 1060 1065
Phe Gly Ala Lys Pro Phe His Ser Pro Asp Leu Ser Trp His Pro
1070 1075 1080
Pro His Gln Gly Leu Ser Asp Ile Arg Ala Asp His Asn Thr Val
1085 1090 1095
Leu Pro Gly Arg Pro Arg Gln Asn Ser Leu His Gly Ser Phe His
1100 1105 1110
Ser Ala Asp Val Leu Lys Met Asp Asp Phe Gly Ala Val Pro Phe
1115 1120 1125
Thr Glu Leu Val Val Gln Ser Ile Thr Pro His Gln Ser Gln Gln
1130 1135 1140
Ser Gln Pro Val Glu Leu Asp Pro Phe Gly Ala Ala Pro Phe Pro
1145 1150 1155
Ser Lys Gln
1160

Claims (63)

1. A method of predicting whether a subject will develop an acute symptom of a disease after exposure or potential exposure to a respiratory virus, comprising analyzing a biomarker of a biological sample obtained from the subject and comparing the biomarker to a reference for the biomarker, wherein the biomarker comprises or is derived from an expression level of one or more genes selected from the group consisting of: PHF20, ABCA1, APBA2, MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K.
2. The method of claim 1, wherein the gene set consists of one, two, three, four, five or six genes selected from the group consisting of: PHF20, ABCA1, APBA2, MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4 and BMP2K.
3. The method of claim 1 or claim 2, wherein the biomarker comprises the expression level of one or more genes selected from a first subset of genes comprising PHF20, ABCA1, APBA2, MORC2, SNU13, and DCUN1D 2.
4. The method of claim 3, wherein the first gene subset comprises the expression level of PHF 20.
5. The method of claim 4, wherein the first gene subset further comprises the expression level of one or both of APBA2 and ABCA 1.
6. The method of claim 5, wherein the first gene subset further comprises expression levels of one, two, or three of MORC2, SNU13, and DCUN1D 2.
7. The method of any one of claims 3 to 6, wherein the first gene subset consists of one, two, three, four, five or six of PHF20, ABCA1, APBA2, MORC2, SNU13 and DCUN1D 2.
8. The method of claim 1 or claim 2, wherein the biomarker comprises the expression level of one or more genes selected from a second subset of genes comprising MAX, NOL9, MPRIP, HP, BST1 and TM9SF 2.
9. The method of claim 8, wherein the second subset of genes comprises expression levels of one or more of noi 9, HP, and MAX.
10. The method of claim 9, wherein the second subset of genes further comprises expression levels of one or both of BST1 and MPRIP.
11. The method of claim 10, wherein the second gene subset further comprises expression levels of TM9SF 2.
12. The method of any one of claims 8 to 11, wherein the second gene subset consists of one, two, three, four, five or six of MAX, NOL9, MPRIP, HP, BST1 and TM9SF 2.
13. The method of claim 1 or claim 2, wherein the biomarker comprises the expression level of one or more genes selected from a third gene subset comprising home 3, NSUN6, HP, EPHA4, and BMP 2K.
14. The method of claim 13, wherein the third gene subset comprises the expression level of one or both of HP and home 3.
15. The method of claim 14, wherein the third gene subset further comprises the expression level of one or both of EPHA4 and BMP 2K.
16. The method of claim 15, wherein the third gene subset further comprises an expression level of NSUN 6.
17. The method of any one of claims 13 to 16, wherein the third gene subset consists of one, two, three, four or five of home 3, NSUN6, HP, EPHA4 and BMP 2K.
18. The method of any one of claims 3 to 17, wherein the biomarker is associated with a relative time course progression leading to progression to an acute symptom of the disease such that the first gene subset is associated with an early stage during progression leading to an acute symptom of the disease, the second gene subset is associated with a mid-stage during progression leading to an acute symptom of the disease, and the third gene subset is associated with a later stage during progression leading to an acute symptom of the disease.
19. The method of any of the preceding claims, wherein the biological sample is obtained from the subject up to about 25 hours after exposure or possible exposure to respiratory viruses.
20. The method of claim 19, wherein the biomarker comprises the expression level of one, two, three, four, five, six or more genes selected from the first gene subset defined in any one of claims 3 to 7.
21. The method of any one of the preceding claims 1 to 18, wherein the biological sample is obtained from the subject about 37-49 hours after exposure or possible exposure to respiratory viruses.
22. The method of claim 21, wherein the biomarker comprises the expression level of one, two, three, four, five, six or more genes selected from the second gene subset defined in any one of claims 8 to 12.
23. The method of any one of claims 1 to 18, wherein the biological sample is obtained from the subject about 49-61 hours after exposure or possible exposure to respiratory viruses.
24. The method of claim 23, wherein the biomarker comprises the expression level of one, two, three, four, five or more genes selected from a third subset of genes as defined in any one of claims 13 to 17.
25. The method of any one of the preceding claims, wherein the biomarker is computer-generated and comprises an output variable of a classification algorithm using one or more genes of the gene set; or one or more genes of the first subset of genes; or one or more genes in the second subset of genes; or the expression level of one or more genes in the third gene subset as an input variable.
26. The method of claim 25, wherein the output variable comprises a numerical value.
27. The method of claim 25 or claim 26, wherein the classification algorithm is derived by machine learning from a training dataset using as input variables expression levels of one or more genes from the gene set, the expression levels being measured from biological samples obtained from a group of subjects at a predetermined time after exposure to the respiratory virus, wherein the group of subjects are classified into two classes according to whether acute symptoms of the disease occur after their exposure to the respiratory virus, and wherein the classification algorithm operates on the expression levels to produce output variables that distinguish the classes.
28. The method of claim 27, wherein the classification algorithm comprises a generalized regression-based algorithm or a decision tree.
29. The method of claim 28, wherein the classification algorithm is configured to prioritize accuracy.
30. The method of claim 28, wherein the classification algorithm is configured to prioritize Negative Predictive Value (NPV).
31. The method of any one of claims 27 to 30, wherein the acute symptoms of disease in the group of subjects in the training dataset are assessed by assessing one or more symptoms of disease at a series of preset times following exposure to the respiratory virus.
32. The method of claim 31, wherein the subject evaluates one or more symptoms using a diary card, optionally using a visual analog scoring symptom diary card (VAS), or optionally using a modified standardized symptom score, such as a modified jackson score, recording classification symptoms (CAT).
33. The method of claim 31 or claim 32, wherein two classes of subjects in the training dataset are distinguished by one or more parameters based on an assessment of one or more symptoms including runny nose, nasal congestion, sore throat, sneezing, ear pain, coughing, shortness of breath, headache, discomfort, myalgia, muscle and/or joint pain, coldness and fever.
34. The method of claim 32 or claim 33, wherein the first class comprises subjects who record a total VAS of greater than or equal to 25 units and/or a total CAT score of greater than 10 units.
35. The method of any one of claims 32 to 34, wherein the first class comprises a subject exhibiting one or more of: maximum variance of total VAS or CAT up to symptom peaks; maximum variance of total VAS or CAT during isolation; or the steepest gradient of total VAS or CAT up to the symptom peak (regression line slope).
36. The method of any one of claims 27 to 35, wherein the gene set and gene subset are selected by: i) Analyzing expression levels in biological samples obtained from a subject group in a training set of data over a series of preset times following exposure to a virus; ii) identifying genes that are indicative of nominal correlation with acute symptoms of the disease, and iii) selecting a set of identified genes whose expression levels at a predetermined time after exposure to the virus exhibit the greatest predictive value for the development of acute symptoms of the disease using a variable selection procedure.
37. The method of claim 36, wherein the variable selection process comprises subjecting the expression level of the identified gene to a repeated gradient enhancement process a predetermined time after exposure to the respiratory virus, and selecting a group of 1, 2, 3, 4, 5, or 6 genes that are most frequently selected by the gradient enhancement process.
38. The method of any one of the preceding claims, wherein the biomarker is compared to a baseline for the biomarker, wherein the baseline for the biomarker is determined prior to exposure or possible exposure of the subject to the respiratory virus.
39. The method of any one of the preceding claims, wherein the subject has taken a pharmaceutical product before or after exposure or possible exposure to respiratory viruses.
40. The method of any of the preceding claims, wherein the subject has undergone a positive diagnostic test for a respiratory viral disease, develops symptoms of a respiratory viral disease, and/or has been chronically exposed to at least one other person infected with a respiratory virus.
41. The method of any one of the preceding claims, wherein the subject is subjected to more than two tests for the same or different biomarkers as defined in any one of claims 1 to 31 and if the result of at least one or more of the tests is positive, it is indicative that the subject is predicted to develop an acute symptom.
42. The method of claim 41, wherein the threshold for obtaining positive results is different for the two or more tests, the threshold for at least one test is configured to minimize false positives, and the threshold for at least another test is configured to have fewer false positives than the one test.
43. The method of any one of the preceding claims, further comprising administering a therapeutic or prophylactic treatment to the subject if the subject is predicted to develop an acute symptom.
44. The method of claim 43, wherein the treatment comprises administration of an antiviral or immunomodulatory agent.
45. A method of predicting whether a subject will develop an acute symptom of a disease after exposure or possible exposure to a respiratory virus, comprising estimating the time elapsed after exposure or possible exposure to a respiratory virus by analyzing the expression level of one or more genes selected from the group consisting of: PHF20, ABCA1, APBA2, MORC2, SNU13, DCUN1D2, MAX, NOL9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4; selecting a biomarker as defined in any of claims 1 to 37 that exhibits a maximum predictive value for the development of acute symptoms of a disease at said time; and comparing the biomarker to a reference for the biomarker.
46. A method of conducting a clinical trial or field study in which a group of subjects are exposed to a respiratory virus, the method comprising analyzing the biomarker as defined in any one of claims 1 to 237 for each subject and comparing the biomarker to a reference to the biomarker to predict whether a subject is likely to develop an acute symptom of a disease, and incorporating subjects predicted to develop an acute symptom of a disease into a first subset of the clinical trial or field study.
47. The method of claim 46, wherein the biomarker is compared to a baseline for the biomarker, wherein the baseline for the biomarker is determined prior to exposure or possible exposure of the subject to the respiratory virus.
48. The method of claim 46 or claim 47, wherein the subjects in the first subset are administered a drug after being predicted to develop an acute symptom of the disease.
49. The method of any one of claims 46 to 48, further comprising incorporating into the second subset a subject predicted to not develop acute symptoms of influenza-like disease.
50. The method of claim 49, wherein the subjects in the second subset are not administered a drug during the trial or study, or are administered a drug at a predetermined time after the start of the trial or study.
51. The method of any one of the preceding claims, wherein the respiratory virus is Respiratory Syncytial Virus (RSV), parainfluenza virus (HPIV), metapneumovirus (HMPV), rhinovirus (HRV), coronavirus, adenovirus (HAdV), enterovirus (EV), bocavirus (HBoV), paraenteric virus (HPeV), or influenza virus.
52. The method of any one of the preceding claims, wherein the biological sample is a blood or respiratory tract sample.
53. The method of any one of the preceding claims, wherein the expression level of one or more genes is measured by quantifying mRNA transcripts of the one or more genes in the biological sample.
54. The method of any one of the preceding claims, wherein mRNA transcripts in a biological sample are quantified by one or more of: PCR-based methods, such as RT-qPCR; or a gene expression microarray; or RNA sequencing.
55. A computer program for predicting whether a subject will develop acute symptoms of a disease after exposure or potential exposure to a respiratory virus, comprising instructions which, when executed by a computer, cause the computer to generate a biomarker according to the invention.
56. The computer program of claim 55, wherein the computer program compares the biomarker to a reference for the biomarker.
57. The computer program of claim 55 or claim 56, wherein the computer program compares the biomarker to a baseline for the biomarker.
58. A classification algorithm for predicting whether a subject will develop an acute symptom of a disease after exposure to or potential exposure to a respiratory virus, wherein the classification algorithm is derived by analyzing the expression level of one or more genes in a subject that has developed an acute symptom of a disease and comparing the expression level in a subject that does not develop an acute symptom of a disease, wherein the one or more genes are PHF20, ABCA1, APBA2, MORC2, SNU13 DCUN1D2, MAX, nop 9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4, and BMP2K.
59. The classification algorithm of claim 58, wherein the acute symptom of the disease is assessed in the subject by assessing one or more symptoms of the disease.
60. The classification algorithm of claim 58 or 59, wherein the classification algorithm is computer-executed and comprises receiving in a computer a data set comprising expression levels of one or more genes from one or more subjects and executing software on the computer to predict whether the one or more subjects will develop acute symptoms of the disease.
61. A computer readable medium and/or computer program comprising instructions which, when executed by a computer, cause the computer to perform the classification algorithm of any one of claims 58 to 60.
62. A computer-implemented method for predicting whether a subject will develop an acute symptom of a disease, wherein a biomarker is generated by analyzing the expression level of one or more genes in a subject that has developed an acute symptom of a disease after vaccination with a respiratory virus and comparing the expression level with the expression level in a subject that does not develop an acute symptom of a disease after vaccination with a respiratory virus, wherein the one or more genes are PHF20, ABCA1, APBA2, MORC2, SNU13 DCUN1D2, MAX, nop 9, MPRIP, HP, BST1, TM9SF2, HOMER3, NSUN6, EPHA4, and BMP2K.
63. The computer-implemented method of claim 62, wherein the method comprises displaying a graphical user interface of the biomarker to a user.
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