Search

JP-7855179-B2 - Methods for monitoring and diagnosing health and disease conditions

JP7855179B2JP 7855179 B2JP7855179 B2JP 7855179B2JP-7855179-B2

Inventors

  • ロウリー ジェシー ワース
  • ロンディーナ マシュー
  • ヴォーラ ディーパク

Assignees

  • ユニヴァーシティー オブ ユタ リサーチ ファウンデーション
  • デューク ユニバーシティ

Dates

Publication Date
20260508
Application Date
20201218
Priority Date
20191220

Claims (9)

  1. A method for constructing a longitudinal transcriptome-wide expression profile, a) Measuring the expression levels of multiple genes present in multiple biological samples collected from two or more healthy subjects, wherein the multiple biological samples consist of isolated platelets, the multiple biological samples are collected at a first time point and a second time point, the first and second time points are different, and the measurement includes RNA sequencing. b) For multiple biological samples collected from two or more healthy subjects, identify the genes with the lowest fluctuations in expression levels across the first and second time points as the most stable genes, and identify the genes with the highest fluctuations in expression levels as the most unstable genes. c) Includes calculating repetition indices for the stable genes identified in b) and thereby constructing the longitudinal transcriptome-wide expression profile in healthy subjects, The aforementioned method.
  2. The method according to claim 1, wherein the method is repeated at least once for a plurality of biological samples, the plurality of biological samples consist of isolated platelets and are obtained from a second healthy subject, the plurality of biological samples are obtained from the second healthy subject at a first time point and a second time point, and the first and second time points are the same time points as for the first healthy subject.
  3. The method according to claim 1, wherein the above method is repeated for a plurality of biological samples, the plurality of biological samples consist of isolated platelets, obtained from two or more healthy subjects, and collected at different time points.
  4. The method according to claim 1, wherein the expression level is measured using a device selected from the group consisting of a microarray, a bead array, and a liquid array.
  5. The method according to claim 1, further comprising repeating steps a) to c) using multiple biological samples obtained from one or more different healthy subjects, and obtaining additional biological samples from one or more different healthy subjects, wherein the additional biological samples from each of the one or more healthy subjects are obtained at different time points.
  6. Furthermore, the method according to claim 5, comprising comparing longitudinal transcriptome-wide expression profiles from different healthy subjects.
  7. The method according to claim 1, wherein the longitudinal transcriptome-wide expression profile functions as a criterion for disease diagnosis.
  8. The method according to claim 1, wherein the repeatability index is 0.60 to 0.90.
  9. The method according to claim 1, wherein the repeatability index is 0.0 to 1.0.

Description

Cross-reference of related applications This application claims the benefit as of the filing date of U.S. Provisional Application No. 62/951,004, filed on December 20, 2019. The contents of this prior application are incorporated in their entirety by reference. Statement Regarding Federally Funded Research: This invention was made with government support under grant numbers AG048022 and HL144957, awarded by the National Institutes of Health. The government has certain rights in this invention. Incorporation of Sequence Listing This application includes a sequence listing submitted concurrently with the filing of this application via EFS-Web, which includes the filename "21101_0410P1_SL.txt", which is 4,096 bytes in size, was created on December 6, 2020, and is incorporated herein by reference in its entirety. This disclosure relates to compositions and methods relating to healthy gene expression signature criteria for platelets that can be used for monitoring and diagnosing health and disease states in subjects. These platelet healthy gene expression signature criteria can be used to screen for disease states, diagnose them, monitor their onset, monitor their progression, identify and diagnose them, or as prognostic indicators. Treatment plans can also be established and evaluated using these platelet healthy gene expression signature criteria. Current gene expression diagnostics rely on the use of large cross-sectional cohorts, including healthy controls, to account for noise from inter-individual and intra-individual variability. There are no available criteria for expected variability in platelets from healthy individuals over time. Cross-sectional studies require a large number of healthy controls to indirectly account for intra-individual variability. These cross-sectional studies do not directly correct for intra-individual variability or genetic variants that may significantly affect gene expression. Diagnostic tests need more accurate "healthy" criteria. A method for preparing a transcriptome-wide expression profile of a biological sample is disclosed herein, comprising: a) measuring the expression levels of one or more genes present in a first biological sample, wherein the measurement includes RNA sequencing; b) determining the gene expression levels from the measured expression levels obtained in step a); c) combining the results of steps a) and b) to generate a transcriptome-wide expression profile; and d) providing the transcriptome-wide expression profile as a dataset, wherein the first biological sample consists of isolated platelets. This shows the intra-individual and inter-individual stability of platelet RNA expression over a period of 4 months (Cohort 1) to 4 years (Cohort 2). Figure 1A shows unsupervised clustering and heatmaps of total RNA expression in platelets derived from Cohort 1 samples. The histogram on the left of each heatmap shows the distribution of distances between sample pairs, and the intensity of the blue color indicates the similarity between sample pairs. Samples clustered as neighbors in the heatmap dendrogram reflect the transcriptome with the highest similarity. The nearest neighbor self-pairs are highlighted in yellow and gray, and the nearest neighbor non-self-pairs are highlighted in orange.This shows the intra-individual and inter-individual stability of platelet RNA expression over 4 months (Cohort 1) to 4 years (Cohort 2). Figure 1B shows an example of an individual correlation plot of transcripts from Cohort 1. Each data point represents the normalized logarithmic expression level (RLD) of a single transcript from a specified donor at time 0 (x-axis) versus time 0, 2 weeks, 4 months, or 4 years (y-axis), either within the same individual (upper panel) or between different individuals (lower panel). Points are heat-colored according to density. P-values are derived from Pearson correlations.Figures 1C and 1F show the intra-individual and inter-individual stability of platelet RNA expression over a period of 4 months (Cohort 1) to 4 years (Cohort 2). Figures 1C and 1F show box plots summarizing the intra-individual and inter-individual RNA expression Pearson correlations for all tested time points (left) or individually specified time points (right). Note that for the specified time points in Figure 1F, the mean inter-individual correlation did not significantly decrease when comparing further analyzed samples. For example, there was no significant difference when comparing the mean intra-individual correlation at time point 0 versus 2 weeks to the mean intra-individual correlation at time point 0 versus 4 years. Box plots for Cohort 1 (Figure 1C) before and after adjustment for age, sex, and race are shown; these are not adjusted for Cohort 2 (Figure 1F) due to the smaller sample size. P-values are from the Wilcoxon test and are adjusted.This shows the intra-individual and inter-individual stability of platelet RNA expression over a period of 4 months (Cohort