CN112423648B - Method for screening desynchronization indexes - Google Patents
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Abstract
The invention provides an automatic judging method for screening desynchronization indexes after large-scale rhythmic data acquisition. The invention provides possibility for screening large-scale rhythmicity data, and compared with the existing non-automatic judging technology, the method has substantial progress. By defining two indexes to successfully screen the desynchronization phenotype of the mice, the invention can effectively screen the desynchronization of the biological rhythm data of the human body.
Description
Technical Field
The present invention relates to a biomarker for screening circadian desynchronization and a screening method.
Background
Biological clocks play a critical role in synchronizing physiological behavior with the environment. It allows organisms to predict daily periodic events to accommodate changes in ambient light, dark periods, and thus better physical state. The discovery of the genetic basis of circadian rhythms has expanded the awareness of how biological clocks produce circadian rhythms (Takahashi, J.S. trans-script architecture of the mammalian circadian clock Nat Rev Genet 18, 164-179 (2017); partch, C.L., green, C.B. & Takahashi, J.S. molecular architecture of the mammalian circadian clock trends Cell Biol 24, 90-9 (2014)). Biological clocks in mammals are considered a hierarchical system that includes perception of environmental inputs, integration of time-related information into the biological clock, transmission of adjusted time information to physiological behaviors, and subsequent feedback of tissue information to the biological clock (Welsh, d.k., takahashi, J.S & Kay, s.a. superior nucleic acids: cell autonomy and network properties, an nu Rev Physiol 72, 551-77 (2010); takahashi, j.s. trans-national architecture of the mammalian circadian clock nature Rev Genet 18, 164-179 (2017)). The architecture of the system causes the following problems: how does this dynamic network participate in ambient light/dark cycle synchronization? Importantly, modern lifestyle factors and different time types are closely related to circadian traction (circadian entrainment) ((kondriov, A.A.) kondriov, r.v. the circadian clock and pathology of the ageing brin. Nat Rev Neurosci 13, 325-35 (2012); morris, c.j. et al Endogenous circadian system and circadian misalignment impact glucose tolerance via separate mechanisms in humans. Proc Natl Acad Sci USA112, E2225-34 (2015); garauet, m.), ordovas, J.M.) Madrid, J. A. The chronobiosystem, etiology and pathophysiology of obsity. Int J Obes (Lond) 34, 1667-83 (2010); stochard, e.r. Et al Circadian Entrainment to the Natural Light-Dark Cycle across Seasons and the enod. Curr Biol 27, 508-513 (2017)), an, J. Circadian System and Glucose Metabolism: implications for Physiology and circadian dispersion 27, window 282, a. The molecular pathway of which is not understood by the molecular pathway between the two circadian rhythms is the great-length of the circadian system, and the circadian pathway is distinct and is therefore not understood to be the great-scale of the circadian pathway of the invention.
In humans, dysfunction or misalignment of the circadian clock with the ambient signal can alter the time of the sleep-awake cycle. Carrying hPER2 S662G /CSNK1D T44A /hCRY2 A260T Mutant mice mimic human behavior rhythms and can display a jump point that does not pull activity during the light/dark cycle (Hirano, A.et al. A. Cryptochrome 2 mutation yields advanced sleep phase in humans.Elife 5 (2016); xu, Y.et al. Functional consequences of a CKIdelta mutation causing familial advanced sleep phase syndrome. Nature 434, 640-4 (2005); xu, Y.et al modeling of a human circadian mutation yields insights into clock regulation by PER2.Cell 128, 59-70 (2007)). The S662 site in PER2 is conserved in human PER1 (S714). Mice carrying the S714G mutation in PER1 are able to exhibit abnormalities in feeding behavior and metabolic balance (Liu, z.et al, PER1 phosphorylation specifies feeding rhythm in mice.cell Rep 7, 1509-20 (2014)).
Systematic surface profiling of knockout mice by the International Mouse Phenotyping Consortium (IMPC) resulted in a catalog of gene functions (de Angelis, M.H.et al analysis of mammalian g ene function through broad-based phenotypic screens across a consortium of mouse clinics (vol 47, pg 969, 2015); nature Genetics 47 (2015); dickinson, M.E. et al high-throughput discovery of novel developmental phenotypes.Na tube 537, 508-514 (2016); beckers, J., wurst, W. & de Angelis, M.H.Towar ds better mouse models: enhanced genotypes, systemic phenotyping and envir otype modelling. Nat Rev Genet 10, 371-80 (2009)). Parameters of the indirect calorimetric method are included in the IMPC analysis results, and a standardized protocol (https:// www.mou settype. Org/impress/protocol/86) was developed by the IMPC phenotype working group. The activity parameters are monitored using a metabolic chamber equipped with infrared beam interruption instead of a cage with wheels to avoid artificially enhancing or attenuating the activity. Food intake monitoring systems are also integrated for investigation of circadian patterns and behaviour. Thus, systematic gene screening using the IMPC dataset for circadian traction is feasible and bypasses the generation of mutants, thus saving time, expense and labor.
On the other hand, the experimental animals are animals which are artificially fed, carry out control on microorganisms, have definite genetic background or clear sources and are used for scientific research, teaching, production, verification and scientific experiments. Currently, laboratory animals mainly include mice, rabbits, and the like.
At present, particularly for experiments of mice, activities, diets and body temperatures need to be recorded through a metabolism cage, and influences of different factors on rhythmicity indexes are studied. Many accidental factors during the data recording process can interfere with the obtained data, such as: sudden drop of food, sudden artificial events, sudden changes in environmental conditions, etc. Therefore, there is also interference data generated by accidental factors in addition to the data in the normal range, so that a reliable biomarker capable of more accurately judging desynchronization in biological clocks is needed.
Disclosure of Invention
Although some deviations from normal rhythms occur to some extent in individuals, the parameter jump point Onset, peak phase, moving peak, amplitude, etc., the inventors have found that only Onset, peak phase may be an indicator for indicating circadian rhythm desynchronization status and further screening for rhythm-related genes in large-scale data. While other parameters cannot be used to indicate desynchronization. These Oset and peak phase parameters are the first indicators that the inventors propose to be used for determining desynchronization.
In view of this, the present invention provides a method of determining a subject circadian rhythm desynchronization status, wherein the subject circadian rhythm desynchronization is determined by detecting at least one of the following indicators:
1) A take-off point of activity and/or food intake rhythms;
2) Peak phase of activity and/or food intake rhythms.
In one embodiment, the subject circadian rhythm desynchronization status is determined by the above method, and genetic genes associated with abnormal circadian traction, preferably including at least one of the Slc7All, rhbdll, spop and Oxtr genes, are selected.
A method of screening a subject for circadian rhythm desynchronization related genes comprising the steps of:
1. wherein the subject circadian rhythm desynchronization is determined by detecting at least one of the following indicators:
1) A take-off point of activity and/or food intake rhythms;
2) Peak phase of activity and/or food intake rhythms.
2. Screening genetic genes related to abnormal circadian traction by judging the desynchronization state of the circadian rhythm of the subject.
Wherein the genetic gene preferably comprises at least one of the Slc7All, rhbdll, spop and Oxtr genes.
In one embodiment, the subject comprises a mammal, preferably at least one of a human, a mouse, a rat, a monkey.
In another aspect, there is also provided a method of screening activity and/or food intake rhythms for take-off points and peak phases from large-scale data, programmed preferably using Matlab and implemented, comprising the steps of:
1) Carrying out data preprocessing on rhythmic data of physiology and behavior of an organism, wherein the rhythmic data is obtained by sampling and collecting in a set time interval;
2) Defining a peak phase, wherein the peak phase is a time point corresponding to the highest peak of rhythmic data;
3) A starting point is defined, wherein the starting point is a time point when various types of rhythmic data start to change from low to high in one period.
In one embodiment of the present invention, the step 1) includes:
1-1) removing data that exceeds the normal physiological range during the rest phase;
1-2) removing interference points generated under abrupt change of data caused by the moment that external environment is alternately changed; if the alternating points are local maximum values or minimum values and are kick points caused by alternating external environment, removing the points in the fitting stage;
1-3) removing points (11) which are particularly large in distance from the front and rear adjacent points.
In one embodiment, as data preprocessing, data outside of normal physiological ranges during rest periods may be removed, for example; removing interference points generated under abrupt change of data caused by the moment of alternating change of external environment; if the alternating points are local maximum values or minimum values and are kick points caused by alternating external environment, removing the points in the fitting stage; the point (11) which is extremely large in the distance between the two adjacent points is removed.
In a specific embodiment, in the step 1-1), the data beyond the normal physiological range is determined according to different physiological index types, wherein the different physiological index types include one or more of the following data:
a) Data of day activity exceeding 4000 times/hour in the mouse activity data;
b) Data exceeding 0.65 g/hr during daytime in diet data;
c) The heart rate data is greater than 200 times/minute data at night.
In a specific embodiment, in the above steps 1-3), the absolute value of the difference between the value of the point (11) and the value of the two points on the left and right is greater than a threshold, the threshold may be set to be four fifths of the maximum value in one period or other ratio, and the ratio may be set differently for different data.
In the process of onset definition, it is important to judge the threshold value of the jump, and the influence on the accuracy of the method is larger; the original data is fitted by the Gaussian function, and in the process of obtaining the phase, the accuracy is greatly influenced by the initial value of the fitting.
The prior processing of the metabolism cage data is mainly cosine function fitting, only one peak can exist, and the invention uses a plurality of Gaussian functions for fitting, so that the method is more accurate for the multimodal situation.
In one embodiment, the step 2) includes:
2-1) searching three local peaks for rhythmic data, wherein the circadian rhythmic data has at least one peak in one period;
2-2) fitting the data subjected to data preprocessing by using a Gaussian fitting method, and using the local maximum value found in the step 1-2) and the corresponding time point thereof as an initial value of a phase in Gaussian fitting to fit rhythmic data to obtain a fitting curve;
2-3) selecting the time point corresponding to the first peak of the fitting result as the phase of the measured index.
In one embodiment, the step 3) includes:
3-1) determining a first candidate point: dividing rhythmic data into a high-state area and a low-state area, and setting a time point when a first reaches a high state as a first candidate point of a starting point at least 3 hours before a nearest external environment transition point in the process of changing from the low-state area to the high-state area;
3-2) determining a second candidate point: starting at least 3 hours before the nearest external environment transition point in the process of changing from the low-state area to the high-state area, when the difference between the average value of the point at the position and the first three points is larger than a threshold value, the point at the position is regarded as a take-off point, and the time corresponding to the point at the position is the second candidate point of the take-off point:
3-3) the earlier one of the first candidate point and the second candidate point is the time point of the take-off.
In one embodiment, in the step 3-1), the method of classifying rhythmic data into two types of high-state regions and low-state regions is a k-means clustering method.
In the invention, peak phase and take-off point are used as indexes for large-scale screening desynchronization.
Further, two biological indexes (Biomarker) of Peak phase (Peak phase) and jump point (Onset) are used as indexes for large-scale screening and desynchronization; the peak phase, whose biological definition is the time corresponding to the highest peak of various rhythmic data, indicates that the rhythmic physiological index reaches the highest value at that time; the jump point, the biology of which is defined as the point in time when the trend of various rhythmicity indexes changes from low to high in one cycle.
In one embodiment, the foregoing method further comprises:
4) The peak phase of the screening object is compared with the degree of deviation of the jump point from the corresponding baseline using the effect quantity.
In a specific embodiment, the step 4) includes:
4-1) the effect amount is a statistic for measuring the magnitude of the effect caused by a certain factor, independent of the size of the statistical sample;
4-2) the deviation degree is specifically calculated by dividing the difference between the mean value of the peak phase or jump point of the screening object and the mean value of the base line of the peak phase or jump point by the joint standard deviation.
Wherein, the judgment standard of the deviation from the normal condition is: the peak phase (jump point) to baseline difference for a mutant is outside 2 standard deviations of the distribution of peak phase (jump point) to baseline differences for all screening subjects.
The inventors have thus devised a method and determined 4 new genes involved in circadian traction (Slc 7All, rhbdll, spop or Oxtr).
The invention provides application of a substance for preparing or detecting Slc7All, rhbdll, spop or Oxtr genes in preparing a product for detecting abnormal circadian traction diseases.
Advantageous effects
The invention provides an automatic judging method for screening desynchronization indexes after large-scale rhythmic data acquisition. The method comprises the following basic characteristics: 1) Programmatically implementing the removal of unrealistic and disturbed data: 2) The peak phase and the jump point of rhythmic physiological data are defined as biological indexes of the screening: 3) The degree of desynchronization is determined by comparing the peak phase of the screened object with the degree of departure of the jump point from the corresponding baseline using the effect amounts. The invention provides possibility for screening large-scale rhythmicity data, and compared with the existing non-automatic judging technology, the method has substantial progress. By defining two indexes to successfully screen the desynchronization phenotype of the mice, the invention can become an effective method for screening the desynchronization of the biological rhythm data of the human body, for example, the data of portable intelligent monitoring equipment is used for screening the synchronous conditions such as sleep disorder, metabolic disorder and the like.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 shows a screening strategy for determining biomarkers using IMPC resources and identification of abnormal circadian deviation phenotypes.
Figure 2 shows the trend of the jump point, peak phase and amplitude recorded by indirect thermography with four C57BL6N mice centered over 1200. 1 (red) represents the strongest: 0 (blue) represents the weakest.
Fig. 3 shows the jump point, peak phase distribution by visual assessment of the four centers. (a-h) histograms of the distribution of the jump and peak phase data obtained at four centers (ICS, WTSI, RBRC and TCP) under 12 hours light and 12 hours dark cycles. For activity (a-d), n=1200C 57BL/6N mice, food intake (e-h) of n=1206C 57BL/6N mice measured by indirect calorimetry over time. Pink column: trip point, red column: peak phase.
Fig. 4 shows the evaluation of the jump point, peak phase distribution of four centers by auto discovery analysis. (a-h) histograms of the distribution of the jump and peak phase data obtained at four centers (ICS, WTSI, RBRC and TCP) under 12 hours light and 12 hours dark cycles. For activity (a-d), n=1200C 57BL/6N mice, food intake (e-h) of n=1206C 57BL/6N mice measured by indirect calorimetry over time. Pink column: trip point, red column: peak phase.
Fig. 5 shows that the Bland-Altman method calculates the average difference between the two methods, i.e. the visual and automatic detection of the activity split measurement. A 95% consistency limit (1.96 s.d.) was calculated to determine if the auto-discovery assay could replace the visual assessment. (a-d) shows the activity take-off point data and (e-h) shows the peak phase activity data obtained by the four centers (ICS, WTSI, RBRC and TCP).
Fig. 6 shows that the Bland-Altman method calculates the average difference between the two methods, i.e. visual and automatic assay analytical measurement of food intake. (a-d) shows the take-off point data and (e-h) shows the peak phase data obtained by the four centers (ICS, WTSI, RBRC and TCP).
Figure 7 shows the jump point and peak phase phenotyping of each individual mouse of the mutant strain. (a-h) profile of oscillating activity and food intake over time (left), data expressed as average (n=7-8). Phase loss reproduces the starting point (purple point) and peak phase (red point) blue point and line of each mutant mouse representing the wild-type jump point (inner circle) and peak phase (second circle) of mean ± s.d. The red column indicates anomalies. The time of day is indicated in hours and the dark phase is indicated in shadows.
FIG. 8 is a schematic diagram of interfering data points in data preprocessing of a method of screening for desynchronization indicators after large-scale rhythmic data acquisition;
FIG. 9 shows a schematic diagram of raw data and a bimodal fitted curve of a method of screening desynchronization indicators after large-scale rhythmic data acquisition;
FIG. 10 shows a schematic diagram of Oset positions of three mouse activities of a method of screening for desynchronization indicators after large-scale rhythmic data acquisition;
fig. 11 shows a schematic diagram of the activity position of four mouse activities of the method of screening for desynchronization indicators after large-scale rhythmic data acquisition.
Detailed Description
The present invention will be further described with reference to the following specific embodiments and experimental data. Although specific terms are used hereinafter for the sake of clarity, these terms are not intended to define or limit the scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The terms "Peak phase" and "Peak phase" used in the present invention are interchangeable, and the biological meaning thereof means the time corresponding to the highest Peak of various types of rhythmic data, indicating that the rhythmic physiological index reaches the highest value at that time.
The terms "jump point" and "Onset" as used herein, are used in the biological sense to refer to the time points at which the trend of various rhythmic indicators changes from low to high in one cycle.
In the present invention, the term "kick point" refers to a point of change of data due to a change of external environment.
The term "synchronization" as used herein refers to whether or not the body is synchronized with the circadian rhythm of the environment, and if not synchronized, i.e. "desynchronized", it is clinically occurring that the Chronotypes are not synchronized with the circadian rhythm of the environment, i.e. early-onset, late-onset, or various metabolism in the body, and the physiological index is not optimal.
In the present invention, the term "data preprocessing" refers to the elimination of some of the data, such as the elimination of data that is outside of normal physiological ranges during rest phases; removing interference points generated under abrupt change of data caused by the moment of alternating change of external environment; if the alternating point is a local maximum or a coom minimum and is a kick point caused by the alternating change of the external environment, removing the point in the fitting stage; and removing points (11) with absolute values of numerical differences between the two points which are adjacent to each other in front and behind being larger than a threshold value.
In the present invention, the term "threshold" means a value of two thirds or more of the maximum value in one cycle, preferably four fifths or more of the maximum value in one cycle.
In the present invention, the term "set time interval" means that collected data is collected through a certain time interval, which may be the same or different from each other, for example, data is collected every hour, data is collected every minute, and a specific interval time may be adjusted according to the routine of those skilled in the art.
In the present invention, the term "abrupt change" refers to a change in data caused by the time when external environments are alternately changed, and such a change due to the change in environments is an abrupt change. Since there is a significant difference between the change in the environment and the data before the change, there is preferably a difference of 10% or more, 20% or more, 30% or more, 50% or more, 80% or more.
In the present invention, the term "large-scale screening" refers to a process of calculating biological indicators from large-scale data of a biological body's rhythms, such as activities and/or food intake rhythms.
In the present invention, the term "large-scale data" refers to data concerning the rhythms of several organisms such as activities and/or food intake rhythms, wherein the number of organisms is two or more, preferably 10 or more, 100 or more, 1000 or more, 10000 or more.
In the invention, the term "high-state region" refers to high-value rhythm data obtained by clustering data points with high and low values according to a K-means cluster classification method, and the corresponding term "low-state region" refers to low-value rhythm data obtained by clustering data points with high and low values according to a K-means cluster classification method.
The experimental methods in the following examples are conventional methods unless otherwise specified.
Example 1 screening Using IMPC resources
The inventors performed systematic and unbiased analysis of activity and food intake measured by indirect calorimetry (fig. 1) by designing a methodology framework to quantify the synchronicity of activity and food intake behavior, gene modification induced light/dark cycles. We downloaded and used heat maps from IMPC, institute of physical and chemical biological resource center (RBRC), phenogenomics Toronto Center (TCP), institute Clinique de la Souris (ICS), and Wellcome Trust Sanger Institute (WTSI) to visualize trends and determine reliable biomarkers. The inventors then used an empirical visual assessment and used an algorithm to obtain the value of the biomarker. Control biomarker values are used to evaluate phenotypic bias within and between different centers.
Based on the distribution of biomarkers in the mutant line, a critical percentile value for the first primary screen was determined. Secondary screening was then performed based on primary screening results using appropriate assays, including percentage of similar phenotypes, normalized effect size, and statistical analysis. Finally, the screening results were validated using a correlation analysis (fig. 1) between the circadian system and the phenotypic association analysis.
Example 2 screening and determination of reliable and quantifiable biomarkers
Baseline data from C57BL/6N is critical to reliability in and/or within the center in determining effective biomarkers that can be used for screening. The inventors first obtained over 1200 wild-type C57BL/6N mouse parameters that were related to the activity and/or food intake cycle of the indirect calorimetric method, and then used the heat map as raw data. As expected, the results show that the Onset jump point and amplitude of activity and food intake were observed for each mouse (fig. 2). In fig. 2, the left and right panels of the graph are heat maps, respectively, using raw data of activity and diet of 4 central wild-type mice, each row representing the activity or diet of one mouse. The grey part in the horizontal bar above the figure indicates night, the white part indicates day, and the numbers below the horizontal bar indicate the corresponding time. The data for each mouse activity or diet was normalized using its maximum, i.e., 1 for each row.
Both activity rhythms and food intake show two peaks: the evening (E) peak is a "strong" oscillator and the early morning (M) peak is a "weak" oscillator. Furthermore, the heat map presents a corresponding pattern and sharp transitions between the two peaks, reflecting a fixed phase relationship (fig. 2). Observations indicate that these parameters may be potential biomarkers for screening.
These biomarkers were evaluated by optimizing the conditions for drawing the scatter plots, and at 1 hour intervals within 21 hours to produce a total of 1200 scatter plots for activity/rest and 1206 scatter plots for food intake (ZT 0: on, ZT12: off); these pictures are stored in the Cam-Su GRC database. The time to take off and peak phase of activity and/or food intake are assessed by two experts in the field of cross validation. The time to jump that activity and/or food intake is reliably detected can yield the lowest variance (fig. 3 a-d).
The E-peak phase of activity and/or food intake showed a broader distribution than the onset of activity and this phenomenon was found in all mice (fig. 3E-h). The set variance of RBRC is higher than in other centers (FIG. 3 b), which may be due to different illumination times in the house (WTS 1:7:30-19:30; TCP:7:00-19:00; ICS:7:00-19:00; RBRC 11:00-23:00). Based on these results, this data analysis is therefore limited to intra-center comparisons rather than inter-center comparisons.
TABLE 1
Table 1 is the statistical results of the activity and diet data. EP phases ave=average of the grading peak. Ep=scoring peak represents the first peak in the night. EP fields sd=standard deviation of the imaging peak. MP phases sd=standard deviation of moving peak, mp=moving peak represents a peak around the on-time. EP amp min = the minimum of the center sizing peak amplitude. EP amp max = the maximum value of the center sizing peak amplitude. EP amp ave = average of the center sizing peak amplitudes. EP amp SD = standard deviation of the center sizing peak amplitude.
From the above data, the standard deviation of MP phases is greater than that of EP phases, and it can also be seen from FIG. 2 that EP phases are substantially concentrated within 3 hours, whereas MP phases are more diffuse.
For EP amp itself, we use the coefficient of variation CV to examine the degree of dispersion of the period itself, cv=ep amp SD/EP amp ave. If the coefficient of variation is generally greater than 15%, the data should be rejected in consideration of possible abnormality. Therefore, MP phases themselves are much more versatile to use as biomarkers. I.e. including the peak values (M) from the amplitudes and the morning fail to produce meaningful estimated value.
To achieve objective evaluation of large-scale screening, we developed an algorithm to automatically find biomarkers to study the utility of IMPC resources. The M and E peak phases and amplitudes are first fitted using a multiple gaussian function. This technique is based on the assumption that the activity in the morning and evening includes at least two oscillators, each with a normal height profile overlapping each other to adjust the beginning and end of the activity. Night data was split into two parts using k-means: activity and physical form. The first active point after Zeitgeber Time (ZT) 10 is searched and the corresponding ZT value is used as the candidate trip point. Then, the difference between the first data point after searching ZT10 and its average of the first three points reaches a certain threshold. The corresponding ZT value is used as another candidate takeoff point. Of these two candidate points, the previous point is considered as the Oset take-off point. The model was then applied to a dataset of 1200 more C57BL/6N mice. The results of Oset trip point and activity period and food intake are summarized in each center, respectively (FIGS. 4 a-h).
The two methods were then compared using the Bland-Altman method to determine if the autodiscovery assay could replace the visual assessment. The Bland-Altman plot shows that the difference between the two methods is acceptable over a 95% range of identity, with more accurate results obtained at the take-off point than during the peak (FIGS. 5a-h and 6 a-h). The inferred jump point is easily visually assessed as a significantly steeper rise from the inactive state, while the peak phase sometimes shows a plateau, which may lead to deviations in peak phase identification.
Finally, we established positive mutant lines and by using hPER2 S662G Mice are used to evaluate the advance of activity take-off points and Fbxl is used 3-/- Delay of mice for active take-off point using hPERL S714G Mice were used to evaluate food intake advance and Nestin-Cre for feeding: zbtb20 -/- The delay of peak phase was evaluated to evaluate markers and auto-discovery analysis (fig. 7 a-f). Automatic seek determination of trip point and peak phase by indirect thermography recordingTo estimate. The normalized effect magnitude (d) was used to estimate the phenotypic deviation, where the absolute difference between mutant and wild-type controls was scaled by the phenotypic standard deviation, i.e., d= (mutation jump/peak phase-control jump/peak phase)/σ. These positive controls show high detectable d values at the time of the take-off and peak phase, indicating that auto-discovery assays and biomarkers would be a reliable tool for searching for diurnal carried genetic components.
Overall, we defined two powerful biomarkers by analyzing the significant trend of 1200 more C57BL/6N standardized mice. Our auto-discovery method can avoid subjective impact on experience, providing fast lookup using large-scale resources.
Example 3 discovery of genetic determinants of circadian phenotype
Effective phenotypic screening was performed on 312 knockout mice and 203 heterozygous mice, representing 498 genes, with further screening by three criteria: (1) 50% of the mice exhibited very similar phenotypes; (2) The effect size (d) > 1.2 represents the difference (representing the difference is very large); and (3) the statistical significance (p-value) between the mutant and the phenotypic baseline is less than 0.001. We found 4 novel mutant genes (Slc 7A11 -/- 、Rhbdll +/- 、Spop +/- 、Oxtr -/- ) Associated with circadian rhythms (fig. 7).
Example 4 data pretreatment:
the data obtained by experimental recording is usually accompanied by certain noise. In particular, the time interval for recording the metabolic cage data of the mice is relatively large and not continuous, usually in the interval of twenty minutes to one hour. At longer intervals, some fluctuations have a greater impact on the analysis of the data for various rhythmicity indicators in the mice. In addition, some systematic effects can also lead to large fluctuations in the data of mouse activity and diet rhythms, such as: some central data have sudden improvement in activity data at ZT12 (off time), and a small peak is formed, which has a great influence on the real phase judgment of the first peak. Fluctuations in the data can make further data analysis difficult or even lead to erroneous analysis results. Still other data are data that are significantly far from normal. Therefore, data preprocessing is performed on the data before entry analysis of the data. Occasional and systematic fluctuations are removed.
The implementation purpose is as follows: bad data points are accurately judged and removed. Embodiments are described below: the original data was read out using Matlab and judged in three steps. In the first step, data which is significantly far from the normal range is found, such as: data with mice active more than 4000 times/hour or food intake more than 0.65 g during the day will be rejected. And secondly, judging systematic errors, uniformly and independently judging the data in a certain center when the ZT12 is started, judging whether the data is suddenly started or not, and then removing the data points suddenly started when the ZT12 is started. And thirdly, removing a point with a particularly large difference from the front and rear adjacent points, firstly judging whether the point is a point with a local maximum value, if so, judging whether the difference between the data point and the adjacent data exceeds a certain threshold value, if so, defining the point as not conforming to the overall trend, and removing the point.
Implementation results: as shown in fig. 8, three types of dead spots are removed so that the data is smoother. In fig. 8, three types of data are represented as dead pixels, left: the data are obviously beyond the conventional range; in (a): particularly high points of kick do not correspond to overall trends; right: ZT12 increases suddenly, resulting in a small peak.
Example 5. Fitting the data of mouse rhythmicity, the phase of each type of physiological index rhythms of the mouse is obtained:
Various rhythmic physiological indicators of mice can be recorded by metabolic cages. The data recorded by the metabolic cages are all discrete and are typically recorded at intervals of thirty minutes or one hour. Although the raw data can see a clear and distinct circadian rhythm. Several peaks are observed in the original data of various rhythmic physiological indexes of mice, however, the observed peaks can only be said to be peaks of the detected data points due to higher dispersion of the data, and cannot represent actual phases of activities and diets, even the first activity peak is difficult to judge by the data of some mice. Therefore, in judging the phases of various rhythmic data of mice, some fitting means are needed to fit the trend of the original data, so that a smooth curve is obtained and then accurate phase values can be obtained. Previous studies have focused on the circadian rhythm at the RNA level, which is usually a peak, so that the means of fitting is predominantly a sine (cosine) function. However, many physiological indicators of mice, such as: the rhythms of activity, diet, oxygen consumption, and exhaled carbon dioxide amount present a bimodal appearance, and may even appear as trimodal. Thus, a simple sine (cosine) function has not been able to meet the requirements of the fitting, because the function resulting from the linear superposition of the same periodic sine (cosine) functions is still unimodal. Therefore, we choose the gaussian function as the smallest unit of the fit, and the multiple peaks are superimposed by multiple gaussian functions, and the superimposed function is a multimodal function. The parameters obtained after fitting have corresponding biological significance, wherein the parameters corresponding to the mean value of each Gaussian function represent the phase of each peak.
The implementation purpose is as follows: accurately obtain the phase of various physiological index rhythms of the mice. Embodiments are described below: we fit the rhythmic data of the mice using gaussian functions, specifically as follows. First we find the position of the peak in the raw data, we define the position of the peak as the local maximum point higher than the data points of both the two adjacent front and rear points. When searching peak value, the number of default peak value is at most three. In the case of a trimodal fit, the fit function is a superposition of three gaussian functions, as in equation (1). Then, the time and peak value corresponding to the searched peak value are used as initial values of the phase and amplitude at the time of fitting. And fitting the initial value obtained by the above with a Gaussian function after superposition. Finally, the phase of the mouse's activity and diet is obtained from the parameters obtained after fitting.
Implementation results: as shown in fig. 9, the fitted curve matches the original data well, and the phase of the peak is apparent. In fig. 9, left: original metabolism cage data, shaded portions represent the night; right: raw data and a fitted curve, wherein a red curve is obtained by fitting according to the raw data.
Specifically, to get the phase of the mice' activities and diet we fit using least squares. We used a linear fit of three gaussian functions as our fit function, using the lsqcurvefit function in Matlab. The form of the fitting function is as follows:
a 3 ，a 6 And a 9 Respectively representing the phases of the three peaks,and->Respectively represent the peak values of three peaks, a 2 ，a 5 And a 8 Respectively represent the width of three peaks, a 10 Is the minimum of the fitted curve. The fitting initial values and fitting parameter ranges for the phases of the appropriate three peaks are important for least squares fitting. We therefore first find the appropriate initial values and parameter ranges for gaussian fitting from the raw data. Time corresponding to three peaks (ZT peak1 ，ZT peak2 And ZT peak3 ) As a 3 ，a 6 And a 9 Is set to an initial value of (1); a, a 2 ，a 5 And a 8 Is artificially set to 2; a, a 1 ，a 4 And a 7 The initial value is +.>Is->Wherein m is 1 ，m 2 And m 3 Is the peak value measured by three peaks. If there are only two peaks, then m 3 Set to 0 if there is only one peak m 2 And m 3 Are set to 0. Fitting diet data a 10 Initial value of 0.05, a when fitting active data 10 The initial value of (c) is 750,fitting other data is set according to specific conditions. Then we set the upper and lower bounds of the respective parameters. a, a 3 ，a 6 And a 9 The parameter range of (1) is [ ZT ] peak1 -3，ZT pea1 +3]，[ZT peak2 -3，ZT peak2 +3]And [ ZT ] peak3 -3，ZT peak3 +3]。a 3 ，a 4 And a 7 The parameter range of (2) is +.>And->a 2 ，a 5 And a 8 The parameter ranges of (2) are all set to 0,4]. Fitting diet data a 10 The parameter range of (5) is [0,0.15 ]]Fitting activity data a 10 The parameter range of (1) is [0, 1500]And setting other data according to specific conditions when fitting the other data.
Example 6 Oset defining mouse activity and diet:
the various rhythmic physiological indexes of the mice have obvious activities and rest under the condition of external illumination traction, the various rhythmic physiological indexes of the mice can be converted from a lower state to an active state before and after the light turning-off time under the normal condition, then the various rhythmic physiological indexes can gradually rise to reach a first peak value, the time point of starting conversion is an obvious demarcation point of the various indexes of the mice in the high state and the low state, and is a conversion point from the rest state in daytime to the first living crossing state at night. The point can represent the condition that the rhythms of various rhythmic physiological indexes of the mice are synchronized by external illumination environment as the phase of the rhythmic physiological indexes. In particular, the Onset variation can also be used to characterize the response of various rhythmic physiological indicators of mice to light. Onset, as well as phase, can be used as a biomarker for screening to find genetic factors that affect this process.
The implementation purpose is as follows: accurately defines the starting position (Oset) of the rise of various rhythmic physiological indexes of the mice. Embodiments are described below: we judge one by one from ZT9 if each data point meets our definition of Onset. Oset has two types of characteristics, firstly, the Oset point reaches an active state in value, and secondly, dynamically, oset is usually a point of sudden jump. Therefore, we define the Onset point based on these two characteristics. First class definition: the first class of classification is determined from the size of the data. We used the K-means classification method to classify the night data points into two categories, high and low. From ZT9, we consider this point as the first Onset candidate point, the first point in time corresponding to the point at which the night activity level is reached. Second class definition: the second class of definitions is determined by determining the kick condition of the data point. The absolute value of the difference between the average of the first three points and the first peak of each data point is multiplied by a coefficient α as a threshold value from ZT9, where α is artificially set. If the difference between a point and the average of its first three points is greater than this threshold, we treat this point as the second Oset candidate point. One of the two Onset ZT values that is smaller is determined as the Onset time point of the time series.
Implementation results: as shown in fig. 10, the start position (Onset) of the mouse's activity and diet is accurately defined. In fig. 10, blue dots are raw data, and shaded portions represent black nights; red dots in the figure are defined Onset dots. The horizontal line of the daytime part represents the average of the three points before Onset, and the horizontal line of the night represents the boundary between the two parts of activity and rest.
As further shown in FIG. 11, 4 known asynchronized mutant mice, zbtb20 -/- The first peak of mouse activity became smaller and the second peak increased: PER2 S662G The movement of the mice is advanced; fbxl3 -/- The movement of the mice is lagging; PER1 S714G Mice had a lag in diet. Table 2 shows the effect values corresponding to the phase and take-off point of the diet and activity of each mutant mouse known to be out of sync with the external environment.
TABLE 2
In summary, by adopting the technical scheme, the automatic and rapid screening method for the various rhythmic physiological index data of the large-scale experimental animal can automatically, rapidly and accurately find the activities of the mutant mice and the values of the dietary rhythmic phase and Oset during large-scale screening, so that the screening efficiency of the activity data of the experimental animal is effectively improved.
Incorporated by reference
The entire disclosure of each patent document and scientific document cited herein is incorporated by reference for all purposes.
Equivalent means
The present invention may be embodied in other specific forms without departing from its essential characteristics. The foregoing embodiments are, therefore, to be considered as illustrative and not restrictive of the invention described herein. The scope of the invention is indicated by the appended claims rather than by the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are intended to be embraced therein.
Claims (7)
1. A method of determining a subject circadian rhythm desynchronization status, wherein the subject circadian rhythm desynchronization is determined by detecting at least one of the following indicators:
1) A take-off point of activity and/or food intake rhythms;
2) Peak phase of activity and/or food intake rhythms;
the method also comprises the following steps:
screening for genetic genes associated with abnormal circadian traction by determining a circadian rhythm desynchronization status of the subject, wherein the genetic genes include at least one of the Slc7a11, rhbdl1, spop and Oxtr genes; the subject comprises at least one of a human, a mouse, a rat, and a monkey in a mammal; wherein, the jump point of the activity and/or food intake rhythm, the peak value phase of the activity and/or food intake rhythm are selected from big data;
The method of screening big data for activity and/or food intake rhythms for take-off points, peak phases of activity and/or food intake rhythms comprises the following implementation steps:
1) Carrying out data preprocessing on rhythmic data of physiology and behavior of an organism, wherein the rhythmic data is obtained by sampling and collecting in a set time interval;
2) Defining a peak phase, wherein the peak phase is a time point corresponding to the highest peak of rhythmicity data, and wherein:
2-1) searching three local peaks for rhythmic data, wherein the circadian rhythmic data has at least one peak in one period;
2-2) fitting the data subjected to data preprocessing by using a Gaussian fitting method, and using the local peak and the corresponding time point searched in the step 2-1) as an initial value of a phase in Gaussian fitting to fit rhythmic data to obtain a fitting curve;
2-3) selecting a time point corresponding to the first peak of the fitting result as the phase of the measured index;
3) Defining a jump point, wherein the jump point is a starting time point of various rhythmicity data in a period from low to high, and wherein:
3-1) determining a first candidate point: dividing rhythmic data into a high-state area and a low-state area, and setting a time point when a first reaches a high state as a first candidate point of a starting point at least 3 hours before a nearest external environment transition point in the process of changing from the low-state area to the high-state area;
3-2) determining a second candidate point: starting at least 3 hours before the nearest external environment transition point in the process of changing from the low-state region to the high-state region, wherein the first time point corresponding to the point of which the difference between the average values of the first three points is larger than the threshold value is the second candidate point of the jump point;
3-3) the earlier one of the first candidate point and the second candidate point is the time point of the take-off;
wherein step 1) in the method of screening the big data for the onset of an activity and/or a food intake rhythm, the peak phase of an activity and/or a food intake rhythm comprises:
1-1) removing data that exceeds the normal physiological range during the rest phase;
1-2) removing interference points generated under abrupt change of data caused by the moment that external environment is alternately changed; if the time point corresponding to the moment of occurrence of the alternating change is a local maximum value or a local minimum value and is a jump point caused by the alternating change of the external environment, removing the point in the fitting stage;
1-3) removing points (11) with absolute values of which the numerical differences from the front and rear adjacent points are larger than a threshold value, wherein the threshold value is set to be more than two thirds of the maximum value in one period.
2. The method according to claim 1, wherein in step 1-1) of the method of screening big data for activity and/or food intake rhythms for take-off points, peak phases of activity and/or food intake rhythms, the data out of normal physiological range is based on different physiological index types, wherein the different physiological index types comprise one or more of the following data:
a) Data of day activity exceeding 4000 times/hour in the mouse activity data;
b) Data exceeding 0.65 g/hr during daytime in diet data;
c) The heart rate data is greater than 200 times/minute data at night.
3. A method according to claim 1, characterized in that in step 1-3) of the method of screening the big data for the jump point of the activity and/or food intake rhythm, the peak phase of the activity and/or food intake rhythm, the threshold value is set to at least four fifths or more of the maximum value in one cycle.
4. The method according to claim 1, wherein in the step 3-1) of the method of screening out the onset of activity and/or food intake rhythms, the peak phase of activity and/or food intake rhythms from the big data, the method of classifying rhythmic data into two types of high-state areas and low-state areas is a k-means clustering method.
5. The method of any one of claims 1 to 4, wherein peak phase and take-off point are indicators of large-scale screening desynchronization.
6. The method according to any one of claims 1 to 4, wherein the method of screening the big data for the onset of activity and/or food intake rhythms, the peak phase of activity and/or food intake rhythms further comprises the steps of:
4) The peak phase of the screening object is compared with the degree of deviation of the jump point from the corresponding baseline using the effect quantity.
7. The method according to claim 6, wherein step 4) of the method of screening activity and/or food intake rhythms for take-off points, movements and/or peak phases of food intake rhythms from big data comprises:
4-1) the effect amount is a statistic for measuring the magnitude of the effect caused by a certain factor, independent of the size of the statistical sample;
4-2) the specific calculation method of the deviation degree is that the difference between the mean value of the peak phase or the jump point of the screening object and the mean value of the base line of the peak phase or the jump point is divided by the joint standard deviation.
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