CN112423648A

CN112423648A - Method for screening desynchronization indexes

Info

Publication number: CN112423648A
Application number: CN201880095725.XA
Authority: CN
Inventors: 徐璎; 杨凌; 张陶
Original assignee: Suzhou University
Current assignee: Suzhou University
Priority date: 2018-07-18
Filing date: 2018-07-18
Publication date: 2021-02-26
Anticipated expiration: 2038-07-18
Also published as: WO2020014884A1; CN112423648B

Abstract

The invention provides an automatic judgment method for screening desynchronization indexes after large-scale rhythmic data are obtained. The method provides possibility for screening large-scale rhythmicity data, and has essential improvement compared with the existing non-automatic judgment technology. By defining two indexes to successfully screen the desynchronization phenotype of the mouse, the method can effectively screen the desynchronization of the human body biological rhythm data.

Description

Method for screening desynchronization indexes

Technical Field

The invention relates to a biomarker and a screening method for screening circadian desynchronization.

Background

Biological clocks play a critical role between synchronized physiological behavior and the environment. It allows the organism to predict daily periodic events to adapt to ambient light, changes in dark cycles, and thus make the physical state better. The discovery of the genetic basis of circadian rhythms has expanded the understanding of how biological clocks produce circadian rhythms (Takahashi, J.S. transcriptional architecture of the mammalian clock. Nat Rev Genet 18, 164-. The biological clock in mammals is considered a hierarchical system that includes the 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. supervisory nucleus: cell autonomy and network properties. annu Rev. Physiol 72, 551-77 (2010); Takahashi, j.s.transphysiological architecture of the mammalian circular clock. nat Rev gene 18, 164. quadrature 179 (2017)). The architecture of this system raises the following issues: how does this dynamic network participate in ambient light/dark cycle synchronization? Importantly, modern lifestyle factors and different time types are closely related to diurnal traction (circumferential entry) in the areas of (Kondravova, A.A. & Kondravov, R.V. the circumferential lock and pathway of the managing brake. Nat Rev Neurosci 13, 325-35 (2012); Morris, C.J.et al. endogenesis circumferential lock and System and circumferential lock and analysis of environmental impact metals in humans. Proe Natl Act. Sci. S A112, E2015-34 (2015), Garault, M.Ordovas, Sch32. Madrid. J.A. Theioblossob, environmental impact, D.E.D. J.S. J.O.J. The. environmental impact and D.S.7. J.S. M.S. D. M. J.M. Maidrid.J.D.P.D. J.D. J. Cheng. J. and D.E.S.7. environmental impact and D.S. No. 7. E.S. 7. D.S. E.S. J. 7. environmental impact, D.S. 7. D. E.S. 7. environmental impact, D.S.S.S. 7. D.S. 7. D.S.S.D.D.D.D.A.D.D.D.A.D.D.D.A.A.A.A.A.A.A.A. 7. No. 7. E.D.D.A. 7. E.A. E.A.A. C. E.A. E.A.A.A. C. E.A. 7. environmental impact metals and No. 7. 282-93(2016).). The circadian system is genetically encoded, with significant cross-interactions between different signaling pathways. Thus, large-scale screening in organisms can provide new molecular components and understand the mechanisms of circadian traction. However, unlike the assessment of circadian rhythm by cycle length and rhythmicity, biomarkers for circadian traction are not, in fact, clear, and the present invention is intended to address this issue.

In the human body, dysfunction or misalignment of circadian rhythms with environmental signals alters the time of the sleep-wake cycle. Carrying hPER2^S662G/CSNK1D ^T44A/hCRy2 ^A260TMutant mice mimic the human behavioral rhythm and show a jump point that does not pull activity during the light/dark cycle (Hirano, A.et al.A crypto chrome 2 mutation induced slow phase in human. Elife 5 (2016); Xu, Y.et al.functional sequences of a CKelta mutation using a similar advanced slow phase synthesis. Nature 434, 640-4 (2005); Xu, Y.et al.Moduling of a human circulation mutation induced stresses inter-phase regulation by PER 2.128, 59-70 (2007)). The S662 site in PER2 is conserved in human PER1 (S714). Mice carrying the S714G mutation in PER1 were able to show abnormalities in feeding behavior and metabolic balance (Liu, z.et al. per1 phosphorylation specificities feeding rhythm in mice. cell Rep 7, 1509-20 (2014)).

The International mouse phenotype Association (IMPC) systematic genotyping of knockout mice resulted in a gene function catalog (de Angelis, M.H. et al, analysis of the genomic gene function through-based genetic sciences a consortium of human genes 371, pg 969, 2015. Nature Genetics 47(2015), Dickinson, M.E. et al, high-through gene discovery of novel genetic languages Na tube 537, 508-514 (2016); Beckers, J.Wurst, W. & de Angelis, M.H. 15 mouse models: enhanced genes, genetic analysis and 10. Nature 2009). Parameters of indirect calorimetry were included in the IMPC analysis results and a standardized protocol was developed by the IMPC phenotype working group (https:// www.mou sephenotype. org/impress/protocol/86). Activity parameters were monitored using a metabolic chamber equipped with infrared beam interruption rather than a cage with wheels to avoid artificially increasing or decreasing activity. Food intake monitoring systems are also integrated for investigating circadian patterns and behaviour. Thus, systematic gene screening using IMPC data sets for diurnal tractions is feasible and bypasses the generation of mutants, which saves time, expense and labor.

On the other hand, the experimental animals are animals which are artificially fed, control the microorganisms carried by the experimental animals, have definite genetic background or clear sources and are used for scientific research, teaching, production, verification and scientific experiments. At present, experimental animals mainly include mice, rabbits, and the like.

At present, particularly for experiments of mice, activities, diet and body temperature need to be recorded through a metabolism cage, and the influence of different factors on rhythmicity indexes needs to be researched. During the data recording process, many accidental factors can interfere with the obtained data, such as: sudden food drops, sudden human events, sudden changes in environmental conditions, etc. Therefore, there is a need for reliable biomarkers that can more accurately determine desynchronization in biological clocks, since there are occasional interfering data in the data in addition to data in the normal range.

Disclosure of Invention

Although the parameters of takeoff point Onset, peak phase, morning peak, amplitude, etc. are deviated to some extent in some individuals deviated from the normal rhythm, the inventors found that only Onset, peak phase can be an index for indicating the desynchronization state of circadian rhythm and further screening rhythm-related genes in large-scale data. While other parameters are not used to indicate desynchronization. The Onset and peak phase parameters are the indexes which can be used for judging desynchronization and are proposed by the inventor for the first time.

In view of this, the present invention provides a method for determining the circadian desynchronization state of a subject by detecting at least one of the following indicators:

1) the jump point of the activity and/or food intake rhythm;

2) peak phase of activity and/or food intake rhythm.

In one embodiment, the circadian rhythm desynchronization state of the subject is judged by the method, and genetic genes related to abnormal circadian traction are screened, wherein the genetic genes preferably comprise at least one of Slc7A11, Rhbdl1, span and Oxtr genes.

A method for screening circadian rhythm desynchronization associated genes of a subject, comprising the steps of:

1. wherein the subject's circadian desynchronization is judged by detecting at least one of the following indicators:

1) the jump point of the activity and/or food intake rhythm;

2) peak phase of activity and/or food intake rhythm.

2. By judging the desynchronization state of the circadian rhythm of the testee, the genetic genes related to abnormal circadian traction are screened.

Wherein the genetic genes preferably comprise at least one of Slc7A11, Rhbdl1, Spop and Oxtr genes.

In one embodiment, the subject comprises a mammal, preferably at least one of a human, mouse, rat, monkey.

In another aspect, there is provided a method of screening for jump-off and peak phase of activity and/or food intake rhythm from large scale data, the programming preferably using Matlab and implemented, comprising the following implementation steps:

1) carrying out data preprocessing on the rhythmic data of the physiology and behavior of the 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 the rhythmic data;

3) defining a jump-off point, wherein the jump-off point is a time point of beginning of each type of rhythmicity data 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 are outside of normal physiological ranges during the resting phase;

1-2) removing interference points generated under the sudden change of data caused by the time of the alternate change of the external environment; if the alternative point is a local maximum or minimum and is a sudden jump point caused by the alternative change of the external environment, the point is removed in the fitting stage;

1-3) removing a point (11) with a particularly large difference with two adjacent points in front and back.

In one embodiment, as data preprocessing, data outside of the normal physiological range during the resting stage may be removed, for example; removing interference points generated under the sudden change of data caused by the time when the external environment changes alternately; if the alternative point is a local maximum or minimum and is a sudden jump point caused by the alternative change of the external environment, the point is removed in the fitting stage; removing a point (11) with a particularly large difference with two adjacent points in front and back.

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) the activity data of the mice exceeds 4000 times/hour in daytime;

b) data for more than 0.65 grams per hour of day in dietary data;

c) human heart rate data is greater than 200 beats/minute data during the night.

In a specific embodiment, in the above step 1-3), the absolute value of the difference between the value of the point (11) and the values of the two points on the left and right of the point is greater than a threshold, the threshold may be set to be four fifths of the maximum value in a period or other ratios, and the ratio may be set differently for different data.

In the onset definition process, the judgment of the take-off threshold is more important, and the influence on the accuracy of the method is larger; the gaussian function fits the original data, and the initial value of the fit has a large influence on the accuracy in the phase obtaining process.

The prior processing of the metabolism cage data is mainly fit by a cosine function, only one peak exists, and the method uses a plurality of Gaussian functions for fitting, so that the method is more accurate to the situation of multiple peaks.

In one embodiment, the step 2) includes:

2-1) searching the rhythmic data for three local peaks, wherein the circadian data has at least one peak in one cycle;

2-2) fitting the data subjected to data preprocessing by using a Gaussian fitting method, taking the local maximum value and the corresponding time point thereof found in the step 1-2) as initial values of phases in Gaussian fitting, and fitting rhythmic data to obtain a fitting curve;

2-3) selecting the time point corresponding to the first peak of the fitting results as the phase of the measured index.

In one embodiment, the step 3) includes:

3-1) determining a first candidate point: dividing the rhythmic data into a high-state area and a low-state area, setting a time point corresponding to the first time point of reaching the high state as a first candidate point of a jump point, wherein the time point starts 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;

3-2) determining a second candidate point: in the process of changing from the low state area to the high state area, starting at least 3 hours before the nearest external environment transition point, when the difference between the point at the position and the mean value of the first three points is greater than a threshold value, the point at the position is regarded as a jump-off point, and the point corresponding time at the position is a second candidate point of the jump-off point;

3-3) the earlier one of the first candidate point and the second candidate point is a time point of take-off.

In one embodiment, the method for dividing the rhythmic data into high-state regions and low-state regions in step 3-1) is k-means clustering.

In the invention, the peak phase and the 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 desynchronization; the biological definition of the peak value phase is the time corresponding to the highest peak of various rhythmicity data, and the peak value phase indicates that the physiological index of the rhythmicity reaches the highest value at the moment; the jump point is defined as the time point when the variation trend of various rhythmicity indexes changes from low to high in one cycle.

In one embodiment, the aforementioned method further comprises:

4) the effect quantity is used to compare the peak phase of the screening object and the deviation degree of the take-off point from the corresponding baseline.

In a specific embodiment, the step 4) includes:

4-1) the effect quantity is a statistic used for measuring the size of the influence caused by a certain factor, and is independent of the size of a statistical sample;

4-2) the specific calculation method of the degree of deviation is to screen the difference between the mean of the peak phase or the jump point of the object and the mean of the baseline of the peak phase or the jump point, and divide the difference by the combined standard deviation.

Wherein, the judgment standard deviating from the normal condition is as follows: the peak phase (trip point) to baseline difference for a certain mutant was outside the 2-fold standard deviation of the distribution of peak phase (trip point) to baseline differences for all screened subjects.

The inventors therefore designed a method and identified 4 new genes involved in circadian traction (Slc7a11, Rhbdl1, Spop or Oxtr).

The invention provides application of substances for preparing or detecting Slc7A11, Rhbdl1, Spop or Oxtr genes in preparing products for detecting abnormal day and night traction diseases.

Advantageous effects

The invention provides an automatic judgment method for screening desynchronization indexes after large-scale rhythmic data are obtained. The method comprises the following basic characteristics: 1) removing unreal and interfered data in a programmed mode; 2) defining the peak phase and the jump point of the rhythmic physiological data as biological indexes of the screening; 3) the effect quantity is used to compare the peak phase and the deviation degree of the take-off point of the screening object from the corresponding base line, thereby determining the desynchronization degree. The method provides possibility for screening large-scale rhythmicity data, and has essential improvement compared with the existing non-automatic judgment technology. By defining two indexes to successfully screen the desynchronization phenotype of the mouse, the method can become an effective method for screening desynchronization of the human body biological rhythm data, for example, the data of portable intelligent monitoring equipment is used for screening the synchronization 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 refer to like parts throughout the drawings. In the drawings:

figure 1 shows a screening strategy for the determination of biomarkers using IMPC resources and the identification of aberrant circadian bias phenotypes.

Figure 2 shows the trend of the takeoff point, peak phase and amplitude recorded by indirect calorimetry of four C57BL6N mice with over 1200 centers. 1 (red) represents the strongest; 0 (blue) represents the weakest.

Fig. 3 shows the trip point, peak phase distribution at four centers by visual evaluation. (a-h) histograms of the trip point and peak value data distributions obtained at four centers (ICS, WTSI, RBRC and TCP) under 12-hour light and 12-hour dark cycles. For the activity (a-d), N-1200C 57BL/6N mice, food intake (e-h) was measured by indirect calorimetry over time in N-1206C 57BL/6N mice. Pink column: jump point, red column: the peak phase.

Fig. 4 shows the four-center takeoff point, peak phase distribution being evaluated by auto-discovery analysis. (a-h) histograms of the trip point and peak value data distributions obtained at four centers (ICS, WTSI, RBRC and TCP) under 12-hour light and 12-hour dark cycles. For the activity (a-d), N-1200C 57BL/6N mice, food intake (e-h) was measured by indirect calorimetry over time in N-1206C 57BL/6N mice. Pink column: jump point, red column: the peak phase.

FIG. 5 illustrates the Bland-Altman method calculating the average difference between the two methods, i.e., the visual and automatic detection analysis measures of activity. A 95% consistency limit (1.96s.d.) was calculated to determine whether an auto-discovery assay could replace the visual assessment. (a-d) shows the active takeoff point data, and (e-h) shows the peak phase activity data obtained by the four centers (ICS, WTSI, RBRC, and TCP).

FIG. 6 shows the Bland-Altman method calculating the mean difference between the two methods, i.e., visual and automated assay analytical measurements of food intake. (a-d) shows the takeoff point data, and (e-h) shows the peak phase data obtained at the four centers (ICS, WTSI, RBRC, and TCP).

FIG. 7 shows the takeoff point and peak phase phenotype analysis of each individual mouse of the mutant. (a-h) profile of the shaking activity and food intake over time (left), data are presented as mean values (n-7-8). The phase vectors reproduce the starting point (purple dots) and peak phase (red dots) for each mutant mouse. The blue dots and lines represent the mean ± s.d. wild-type jump-off (inner circle) and peak (second circle). The red column indicates an anomaly. The time of day is indicated in hours and the dark phase is shaded.

FIG. 8 is a schematic diagram of interference data points in data pre-processing for a method of screening desynchronization indicators after large-scale rhythmic data acquisition;

FIG. 9 shows a schematic 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 Onset position diagram of three mouse activities for a method of screening desynchronization indicators after large-scale rhythmic data acquisition;

figure 11 shows an activity location diagram of four mouse activities for a method of screening desynchronization indicators after large-scale rhythmic data acquisition.

Detailed Description

The present invention will be further described below by way of specific embodiments and experimental data. Although specific terms are used below for the sake of clarity, these terms are not meant 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 refers to the time corresponding to the highest Peak of various rhythmic data, which indicates that the physiological index of rhythmic reaches the highest value at that time.

The terms "jump point" and "Onset" used in the present invention mean the time point at which the variation trend of various rhythmical indicators changes from low to high in a cycle.

In the present invention, the term "jumping point" refers to a data change point due to a change in external environment.

The term "synchronization" used in the present invention means whether the circadian rhythms of the body and the environment are synchronized, if they are not synchronized, i.e., "desynchronization", chronotypees will appear clinically, and the circadian rhythms of the environment are not synchronized, i.e., early-morning sleep, late-evening sleep, or various metabolism in vivo, and the physiological indexes cannot reach the best state.

In the present invention, the term "data preprocessing" refers to the elimination of some data from the data, for example, the elimination of data that is outside the normal physiological range during the resting stage; removing interference points generated under the sudden change of data caused by the time when the external environment changes alternately; if the alternative point is a local maximum or minimum and is a sudden jump point caused by the alternative change of the external environment, the point is removed in the fitting stage; and removing points (11) with absolute values of the difference between the values of the two points adjacent to the front and the back being larger than a threshold value.

In the present invention, the term "threshold value" means a value of two thirds or more of the maximum value in one cycle, and preferably a value of four fifths or more of the maximum value in one cycle.

In the present invention, the term "set time interval" means that the collected data are collected at certain time intervals, the time intervals may be the same or different from each other, the time intervals may be, for example, hourly data collection and every minute data collection, and the specific time intervals 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 the external environment alternately changes, and such a change due to a change in the environment is an abrupt change. Since there is a significant difference between the change in the environment and the data before the change, it is preferable that there is a difference of 10% or more, 20% or more, 30% or more, 50% or more, or 80% or more.

In the present invention, the term "large-scale screening" refers to a process of calculating a biological index from large-scale data of rhythms of organisms such as activity and/or food intake rhythms.

In the present invention, the term "large-scale data" refers to data on rhythms such as activity and/or food intake rhythms of several organisms, in which the number of organisms is two or more, preferably 10 or more, 100 or more, 1000 or more, 10000 or more.

In the present invention, the term "high-state region" refers to high-value rhythm data obtained by high-low value clustering of data points by rhythm data according to the K-means cluster classification method, and correspondingly, the term "low-state region" refers to low-value rhythm data obtained by high-low value clustering of data points by rhythm data according to the K-means cluster classification method.

The experimental procedures in the following examples are conventional unless otherwise specified.

Example 1 screening Using IMPC resources

The inventors have conducted systematic and unbiased analysis of activity and food intake as measured by indirect calorimetry (fig. 1) by designing a methodology to quantify activity and food intake behavior, the synchronicity of gene-modifications induced light/dark cycles. We downloaded and used heat maps from IMPC, Institute of physiochemistry, biological resources 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 empirical visual assessment and used algorithms to obtain values for biomarkers. Control biomarker values were used to assess phenotypic bias within and between different centers.

According to the distribution of the biomarkers in the mutant line, the critical percentile value of the first primary screening is determined. Secondary screening is then performed based on primary screening results using appropriate analyses, including percentage of similar phenotypes, normalized effect size, and statistical analysis. Finally, the screening results were validated using a correlation analysis between the circadian system and the phenotypic correlation analysis (fig. 1).

Example 2 screening and determination of reliable and quantifiable biomarkers

The baseline data from C57BL/6N is crucial for reliability in the center and/or 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 correlate with activity and/or food intake cycles of indirect calorimetry, and then used the heatmap as raw data. As expected, the results showed 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 using raw data of activity and diet, respectively, of 4 central wild-type mice, each row representing activity or diet of one mouse. The grey part of the horizontal bar above the figure represents night, the white part represents day and the numbers below the horizontal bar represent the corresponding time. Data for each mouse activity or diet was normalized using its maximum value, i.e., 1 for each row.

Both the active rhythm and food intake show two peaks: the peak value in the evening (E) is a 'strong' oscillator, and the peak value in the early morning (M) is a 'weak' oscillator. In addition, the heatmap exhibits 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 under which the scatter plots were drawn, and at 1 hour intervals over 21 hours to produce a total of 1200 scatter plots for activity/rest and 1206 scatter plots for food intake (ZT 0: on light, ZT 12: off light); 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 evaluated by experts in the two-bit cross validation field. Reliably detecting the time-to-jump of activity and/or food intake yields the lowest variance (fig. 3 a-d).

The phase of the E peak of activity and/or food intake showed a broader distribution than the onset of activity and this was found in all mice (fig. 3E-h). The onset variance of RBRC is higher than other centers (FIG. 3b), which may be due to different lighting times inside the house (WTSI: 7: 30-19: 30; TCP: 7: 00-19: 00; ICS: 7: 00-19: 00; RBRC 11: 00-23: 00). Based on these results, the data analysis of this time is therefore limited to comparisons within the center rather than comparisons between centers.

TABLE 1

Table 1 is a statistical result of activity and diet data. EP phases ave is the mean value of evolving peak. EP-evolving peak indicates the first peak in the night. EP phases SD ═ evolution peak standard deviation. MP phases SD is the standard deviation of the burning peak, and MP is the burning peak representing the peak around the time of lamp-on. EP amp min is the minimum of the central evolution peak amplitude. EP amp max is the maximum value of the central evolution peak amplitude. EP amp ave is the average of the amplitude of this central evolution peak. EP amp SD — standard deviation of the central evolution peak amplitude.

From the above data, the standard deviation of MP phases is larger than that of EP phases, and from FIG. 2, it can be seen that EP phases are basically concentrated within 3 hours, while MP phases are relatively dispersed.

For EP amps per se, we examined the degree of dispersion per se using the coefficient of variation, CV, EP amp SD/EP amp ave. It is generally considered that if the coefficient of variation is greater than 15%, the data may be abnormal and should be discarded. Thus, the variation of MP phases per se is largely unavailable as a biomarker. I.e., failure to produce meaningful estimates including values from amplitude and morning peaks (M).

To achieve objective assessment 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 multiple gaussian functions. This technique is based on the assumption that morning and evening activities include at least two oscillators, each having a normal height profile that overlaps each other to adjust the start and end of the activity. The nighttime data was divided into two parts using k-means: activities and rest. The first activity point after Zeitgeber Time (ZT)10 is searched and the corresponding ZT value is used as a candidate takeoff point. Then, the first data point after ZT10 was searched, and the difference between the first three data points and the average value of the first three data points reached a certain threshold. The corresponding ZT value is used as another candidate takeoff point. Of these two candidate points, the previous point is considered to be the Onset trip point. This model was then applied to a data set of 1200 more C57BL/6N mice. Results for Onset takeoff and active period and food intake were summarized separately at each center (fig. 4 a-h).

The two methods are then compared using the Bland-Altman method to determine if the auto-discovery assay can replace the visual assessment. The Bland-Altman graph shows that the difference between the two methods is acceptable within a 95% consistency range, with more accurate results obtained at the trip point than during the peak (fig. 5a-h and fig. 6 a-h). Inferring the trip point is easily viewed visually as a sharp rise from the inactive state, and the peak phase sometimes shows a plateau, which may lead to a deviation in peak phase identification.

Finally, we established positive mutant lines and by using hPER2^S662GMice used to assess the advancement of the active takeoff point, using Fbxl^3-/-Mice were used for delay of active takeoff using hPER1^S714GMice were used to assess food intake advancement of feeding and Nestin-Cre: zbtb20^-/-Delay in peak phase was evaluated to evaluate markers and auto-discovery analysis (fig. 7 a-f). The trip point and peak phase are estimated using an automatic seek determination using indirect calorimetry recordings. The phenotypic deviation was estimated using the normalized effect size (d), where the absolute difference between mutant and wild-type controls was scaled in units of phenotypic standard deviation, i.e., d ═ (mutant takeoff/peak phase-control takeoff/peak phase)/σ. These positive controls showed high detectable d-values at the time of the takeoff and peak position, indicating that the auto-discovery assay and biomarkers would be reliable tools for searching for genetic components carried around day and night.

Overall, we defined two strong biomarkers by analyzing 1200 significant trends of C57BL/6N normalized mice. The automatic discovery method can avoid the influence of subjective aspects on experience, and provides quick search by using large-scale resources.

Example 3 discovery of genetic determinants of circadian phenotypes

Efficient phenotypic screening of 312 knockout mice and 203 heterozygote mice, representing 498 genes, was performed with three criteria for further screening: (1) 50% of the mice showed very similar phenotypes; (2) effect size (d) > 1.2 represents difference (representing very large difference); and (3) statistical significance (p-value) between mutant and phenotypic baseline was less than 0.001. We found 4 novel mutant genes (Slc7A 11)^- ^/-、Rhbd11 ^+/-、Spop ^+/-、Oxtr ^-/-) Related to the circadian rhythm (fig. 7).

Example 4. data preprocessing:

the data obtained by experimental recording are usually accompanied by certain noise. In particular, the recording time interval of mouse metabolic cage data is relatively large and not continuous, typically twenty minutes to one hour. When the time interval is longer, some fluctuation has a great influence on the analysis of data of various rhythmicity indexes of the mouse. In addition, some systemic effects also lead to large fluctuations in the data on mouse activity and eating rhythm, such as: the activity data of some central data at ZT12 (at the moment of turning off the lamp) is suddenly improved to form a small peak, and the phase judgment of the real first peak is greatly influenced. The fluctuation of the data brings great difficulty to further data analysis and even leads to wrong analysis conclusion. Still other data are data that are significantly far from normal ranges. Therefore, we pre-process the data before analyzing the data entries. Occasional and systematic fluctuations are removed.

The implementation purpose is as follows: and accurately judging and removing bad data points. The implementation scheme is as follows: and reading the original data by using Matlab, and judging in three steps. In the first step, data significantly far from the normal range are found, such as: data of more than 4000 times of activity/hour or more than 0.65 g of food intake of the mice in the daytime are rejected. And secondly, judging systematic errors, uniformly and independently judging whether the data of a certain center jump at ZT12, and then removing data points jumping suddenly at ZT 12. And thirdly, removing points with particularly large difference with front and back adjacent points, firstly judging whether the points are the points with local maximum values, if so, then judging whether the difference value between the data point and the adjacent data exceeds a certain threshold value, if so, defining the points as not conforming to the integral trend, and removing the points.

The implementation results are as follows: as shown in fig. 8, three types of dead spots are removed, so that the data is relatively smooth. In fig. 8, three types of data dead pixel are represented, left: data significantly out of the conventional range; the method comprises the following steps: the particularly high points of the kick, which do not correspond to the overall trend; and (3) right: ZT12 suddenly increased, resulting in a small peak.

Example 5 fitting the rhythmicity data of the mice, the phases of various types of physiological index rhythms of the mice are obtained:

various rhythmic physiological indexes of the mice can be recorded through a metabolism cage. The data recorded for the metabolic cages are discrete, typically recorded at thirty minute or one hour intervals. Although the raw data can be seen as a clear and distinct circadian rhythm. The original data of various rhythmic physiological indexes of the mice can observe a plurality of obvious peaks, however, the peaks observed due to the high dispersion of the data can only be regarded as the peaks of the detected data points and cannot represent the real phases of activities and diets, and even the data of some mice can hardly judge the first activity peak. Therefore, some fitting means are needed to fit the trend of the original data when judging the phase of various types of rhythmic data of the mouse, a smooth curve is obtained, and then an accurate phase value can be obtained. Previous studies have been directed primarily to the circadian rhythm at the RNA level, which is usually a peak, and thus the means of fitting is dominated by sine (cosine) functions. However, mice have many physiological indicators, such as: the rhythm of activity, diet, oxygen consumption, amount of exhaled carbon dioxide presents a double peak, and may even appear triple. Therefore, a simple sine (cosine) function cannot meet the requirement of fitting, because the linear superposition of the sine (cosine) functions with the same period still has a single peak. Therefore, a Gaussian function is selected as a minimum unit of fitting, a plurality of peaks are superposed by the Gaussian function, and a function obtained after superposition is a multi-peak function. And each parameter obtained after fitting has the corresponding biological significance, wherein the parameter corresponding to each Gaussian function mean value represents the phase position of each peak.

The implementation purpose is as follows: accurately obtain the phases of various physiological index rhythms of the mice. The implementation scheme is as follows: we fit the rhythmic data of the mice using a gaussian function, as follows. First, we find the position of the peak in the original data, and we define the position of the peak as a local maximum point higher than the data points of two adjacent points. When searching for a peak, the number of the default peaks is at most three. When fitting three peaks, the fitting function is the superposition of three Gaussian functions, which is shown in formula (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 values obtained by the steps by using the superposed Gaussian function. Finally, the phases of mouse activity and diet are obtained from the parameters obtained after fitting.

The implementation results are as follows: as shown in fig. 9, the fitted curve matches the raw data well and the phase of the peak is significant. In fig. 9, left: raw metabolism cage data, shaded to indicate the night; and (3) right: the red curve is obtained by fitting according to the original data instead.

Specifically, to obtain the phases of mouse activity and diet, we used a least squares fit. We used linear fits of three gaussian functions as our fitting function, fitting was done using the lsqcurvefit function in Matlab. The form of the fitting function is as follows:

a ₃，a ₆and a₉Respectively representing the phases of the three peaks,

and

respectively represent the peak values of three peaks, a₂，a ₅And a₈Each representing the width of three peaks, a₁₀Is the minimum of the fitted curve. The appropriate initial values of the fit and ranges of the fit parameters for the phases of the three peaks are important for the least squares fit. Therefore, we first find the fit for Gaussian fitting from the raw dataInitial values and parameter ranges. Time of correspondence of three peaks (ZT)_peak1，ZT _peak2And ZT_peak3) As a is₃，a ₆And a₉An initial value of (1); a is₂，a ₅And a₈Is artificially set to 2; a is₁，a ₄And a₇Is initially of

And

wherein m is₁，m ₂And m₃Is the peak measured by three peaks. If there are only two peaks, then m₃Set to 0 if there is only one peak m₂And m₃Are set to 0. Fitting diet data a₁₀Is 0.05, a when fitting the activity data₁₀Is 750, and is set as the case may be when fitting other data. Then, we set the upper and lower bounds of the respective parameters. a is₃，a ₆And a₉Has a parameter range of [ ZT_peak1-3，ZT _peak1+3]，[ZT _peak2-3，ZT _peak2+3]And [ ZT ]_peak3-3，ZT _peak3+3]。a ₁，a ₄And a₇In a parameter range of

And

a ₂，a ₅and a₈Are all set to [0, 4 ]]. Fitting diet data a ₁₀Is [0, 0.15 ]]Fitting Activity data time a₁₀Has a parameter range of [0, 1500 ]]And the other data are set according to specific conditions when being fitted.

Example 6 Onset to define mouse Activity and diet:

the rhythmic physiological indexes of the mouse have two parts of obvious activity and rest under the condition of external illumination traction, generally, the rhythmic physiological indexes of the mouse can be changed from a low state to an active state before and after the light-off time, then the rhythmic physiological indexes can gradually rise to reach a first peak value, and the time point for starting the change is an obvious boundary point of the high state and the low state of the indexes of the mouse and a transition point of the rest state in the daytime to the first living state at night. The point can represent the condition that the rhythms of various rhythmical physiological indexes of the mouse are synchronized by the external illumination environment like the phases of various rhythmical physiological indexes. In particular, the change of Onset can also be used for characterizing the response of various rhythmic physiological indexes of the mice to illumination. Onset, like the phase, can be used as a biological marker for screening to find genetic factors affecting this process.

The implementation purpose is as follows: accurately defining the initial position (Onset) of the rising of various rhythmic physiological indexes of the mouse. The implementation scheme is as follows: we start with ZT9 and judge one by one whether each data point satisfies our definition of Onset. Onstet has two types of features, firstly, numerically Onstet point reaches active state, and secondly, dynamically, Onstet is usually a point of sudden jump. Therefore, we define an Onset point according to these two features. The first category defines: the first category is judged from the size of the data. We use the K-means classification method to classify the data points at night into two types, high-state region and low-state region. The point in time corresponding to the point at which the first night activity level was reached, starting with ZT9, we considered this point as the first Onset candidate point. The second class defines: the second class of definitions is determined by determining the snap-through condition of the data point. The absolute value of the difference between the average value of the first three points of each data point and the first peak is multiplied by a coefficient α as a threshold value from ZT9, where α is artificially set. If the difference between a certain point and the average of the first three points is greater than the threshold, we will use this point as the second Onset candidate. The one with the smaller ZT values of the two Onset is determined as the Onset time point of the time series.

The implementation results are as follows: as shown in fig. 10, the starting position (Onset) of the mouse activity and diet was accurately defined. In fig. 10, blue dots are raw data, and shaded portions indicate nighttime; the red dots in the figure are defined Onset points. The horizontal line in the day part represents the mean of the three points before Onstet, and the horizontal line in the night represents the dividing line between the active and resting parts.

As shown in FIG. 11, 4 desynchronized mutant mice, Zbtb20, were known^-/-The first peak of mouse activity decreased and the second peak increased; PER2^S662GThe mice move ahead; fbxl3^-/-The mice have lagged activity; PER1^S714GThe mice had a lag in diet. Table 2 shows the effect values corresponding to the phase and the take-off point of each of the mutant mouse activities and diets known to be desynchronized with the external environment.

TABLE 2

In summary, by adopting the above technical scheme, the method for automatically and rapidly screening various rhythmic physiological index data of large-scale experimental animals can automatically, rapidly and accurately find the values of the activity, the diet rhythm phase and the Onset of the mutant mice during large-scale screening, thereby effectively improving the screening efficiency of the activity data of the experimental animals.

Is incorporated by reference

The entire disclosure of each patent and scientific literature cited herein is incorporated by reference for all purposes.

Equivalence of

The present invention may be embodied in other specific forms without departing from its essential characteristics. Accordingly, the foregoing examples are to be considered as illustrative and not limiting 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

A method of judging the circadian desynchronization state of a subject, wherein the circadian desynchronization of the subject is judged by detecting at least one of the following indicators:

1) the jump point of the activity and/or food intake rhythm;

2) peak phase of activity and/or food intake rhythm.
The method of claim 1, further comprising the steps of:

screening genetic genes related to abnormal circadian traction by judging the circadian rhythm desynchronization state of a subject, wherein the genetic genes preferably comprise at least one of Slc7A11, Rhbdl1, Spop and Oxtr genes.
The method of claim 1 or 2, wherein the subject comprises a mammal, preferably at least one of a human, a mouse, a rat, a monkey.
A method of screening for the jump-off and peak phase of an activity and/or food intake rhythm from large-scale data, comprising the steps of:

1) carrying out data preprocessing on the rhythmic data of the physiology and behavior of the 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 a highest peak of the rhythmic data, and the peak phase comprises:

2-1) searching the rhythmic data for three local peaks, wherein the circadian data has at least one peak in one cycle;

2-2) fitting the data subjected to data preprocessing by using a Gaussian fitting method, taking the local maximum value and the corresponding time point thereof found in the step 1-2) as initial values of phases in Gaussian fitting, and fitting rhythmic data to obtain a fitting curve;

2-3) selecting the time point corresponding to the first peak of the fitting results as the phase of the measured index.

3) Defining a jump-off point, wherein the jump-off point is a time point of beginning of each type of rhythmic data to change from low to high in one cycle, wherein:

3-1) determining a first candidate point: dividing the rhythmic data into a high-state area and a low-state area, setting a time point corresponding to the first time point of reaching the high state as a first candidate point of a jump point, wherein the time point starts 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;

3-2) determining a second candidate point: in the process of changing from the low state area to the high state area, starting at least 3 hours before the nearest external environment transition point, when the difference between the point at the position and the mean value of the first three points is greater than a threshold value, the point at the position is regarded as a jump-off point, and the point corresponding time at the position is a second candidate point of the jump-off point;

3-3) the earlier one of the first candidate point and the second candidate point is a time point of take-off.
The method of claim 4, wherein step 1) comprises:

1-1) removing data that are outside of normal physiological ranges during the resting phase;

1-2) removing interference points generated under the sudden change of data caused by the time of the alternate change of the external environment; if the alternative point is a local maximum or minimum and is a sudden jump point caused by the alternative change of the external environment, the point is removed in the fitting stage;

1-3) removing points (11) with absolute values of difference between the front and rear adjacent points larger than a threshold value, wherein the threshold value is set to be more than two thirds of the maximum value in one period.
The method according to claim 5, wherein in step 1-1), the data beyond the normal physiological range is determined according to different physiological index types, wherein the different physiological index types comprise more than one of the following data:

a) the activity data of the mice exceeds 4000 times/hour in daytime;

b) data for more than 0.65 grams per hour of day in dietary data;

c) human heart rate data is greater than 200 beats/minute data during the night.
The method of claim 5, wherein in steps 1-3), the threshold is set to at least four fifths or more of a maximum value within a period.
The method as set forth in claim 4, wherein in the step 3-1), the method of classifying the rhythmic data into the high-state region and the low-state region is k-means clustering.
The method of any one of claims 4 to 8, wherein the peak phase and the trip point are indicators of large-scale screening desynchronization.
The method of any of claims 4 to 8, further comprising:

4) the effect quantity is used to compare the peak phase of the screening object and the deviation degree of the take-off point from the corresponding baseline.
The method of claim 10, wherein step 4) comprises:

4-1) the effect quantity is a statistic used for measuring the size of the influence caused by a certain factor, and is independent of the size of a statistical sample;

4-2) the specific calculation method 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 baseline of the peak phase or the jump point is divided by the combined standard deviation.
Application of substances for preparing or detecting Slc7A11, Rhbdl1, Spop or Oxtr genes in preparation of products for detecting abnormal diurnal traction diseases.