Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine

Multiomics2Targets identifies targets from cancer cohorts profiled with transcriptomics, proteomics, and phosphoproteomics

The availability of data from profiling of cancer patients with multiomics is rapidly increasing. However, integrative analysis of such data for personalized target identification is not trivial. Multiomics2Targets is a platform that enables users to upload transcriptomics, proteomics, and phosphoproteomics data matrices collected from the same cohort of cancer patients. After uploading the data, Multiomics2Targets produces a report that resembles a research publication. The uploaded matrices are processed, analyzed, and visualized using the tools Enrichr, KEA3, ChEA3, Expression2Kinases, and TargetRanger to identify and prioritize proteins, genes, and transcripts as potential targets. Figures and tables, as well as descriptions of the methods and results, are automatically generated. Reports include an abstract, introduction, methods, results, discussion, conclusions, and references and are exportable as citable PDFs and Jupyter Notebooks. Multiomics2Targets is applied to analyze version 3 of the Clinical Proteomic Tumor Analysis Consortium (CPTAC3) pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype. Multiomics2Targets is available from https://multiomics2targets.maayanlab.cloud/.

Biodegradation characteristics of p-Chloroaniline and the mechanism of co-metabolism with aniline by Pseudomonas sp. CA-1

Co-metabolism is a promising method to optimize the biodegradation of p-Chloroaniline (PCA). In this study, Pseudomonas sp. CA-1 could reduce 76.57 % of PCA (pH = 8, 70 mg/L), and 20 mg/L aniline as the co-substrate improved the degradation efficiency by 12.50 %. Further, the response and co-metabolism mechanism of CA-1 to PCA were elucidated. The results revealed that PCA caused deformation and damage on the surface of CA-1, and the -OH belonging to polysaccharides and proteins offered adsorption sites for the contact between CA-1 and PCA. Subsequently, PCA entered the cell through transporters and was degraded by various oxidoreductases accompanied by deamination, hydroxylation, and ring-cleavage reactions. Thus, the key metabolite 4-chlorocatechol was identified and two PCA degradation pathways were proposed. Besides, aniline further enhanced the antioxidant capacity of CA-1, stimulated the expression of catechol 2,3-dioxygenase and promoted meta-cleavage efficiency of PCA. The findings provide new insights into the treatment of PCA-aniline co-pollution.

(Re-)Viewing Role of Intracellular Glucose Beyond Extracellular Regulation of Glucose-Stimulated Insulin Secretion by Pancreatic Cells

For glucose-stimulated insulin secretion (GSIS) by pancreatic β-cells in animals, it is believed that ATP generated from glucose metabolism is primarily responsible. However, this ignores two well-established aspects in literature: (a) intracellular ATP generation from other sources resulting in an overall pool of ATP, regardless of the original source, and (b) that intracellular glucose transport is 10- to 100-fold higher than intracellular glucose phosphorylation in β-cells. The latter especially provides an earlier unaddressed, but highly appealing, observation pertaining to (at least transient) the presence of intracellular glucose molecules. Could these intracellular glucose molecules be responsible for the specificity of GSIS to glucose (instead of the widely believed ATP production from its metabolism)? In this work, we provide a comprehensive compilation of literature on glucose and GSIS using various cellular systems - all studies focus only on the extracellular role of glucose in GSIS. Further, we carried out a comprehensive analysis of differential gene expression in Mouse Insulinoma 6 (MIN6) cells, exposed to low and high extracellular glucose concentrations (EGC), from the existing whole transcriptome data. The expression of other genes involved in glycolysis, Krebs cycle, and electron transport chain was found to be unaffected by EGC, except Gapdh, Atp6v0a4, and Cox20. Remarkably, 3 upregulated genes (Atp6v0a4, Cacnb4, Kif11) in high EGC were identified to have an association with cellular secretion. Using glucose as a possible ligand for the 3 proteins, computational investigations were carried out (that will require future 'wet validation', both in vitro and in vivo, e.g., using primary islets and animal models). The glucose-affinity/binding scores (in kcal/mol) obtained were also compared with glucose binding scores for positive controls (GCK and GLUT2), along with negative controls (RPA1, KU70-80, POLA1, ACAA1A, POLR1A). The binding affinity scores of glucose molecules for the 3 proteins were found to be closer to positive controls. Therefore, we report the glucose binding ability of 3 secretion-related proteins and a possible direct role of intracellular glucose molecules in GSIS.

'Single-subject studies'-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases

MOTIVATION

Identifying altered transcripts between very small human cohorts is particularly challenging and is compounded by the low accrual rate of human subjects in rare diseases or sub-stratified common disorders. Yet, single-subject studies (S3) can compare paired transcriptome samples drawn from the same patient under two conditions (e.g. treated versus pre-treatment) and suggest patient-specific responsive biomechanisms based on the overrepresentation of functionally defined gene sets. These improve statistical power by: (i) reducing the total features tested and (ii) relaxing the requirement of within-cohort uniformity at the transcript level. We propose Inter-N-of-1, a novel method, to identify meaningful differences between very small cohorts by using the effect size of 'single-subject-study'-derived responsive biological mechanisms.

RESULTS

In each subject, Inter-N-of-1 requires applying previously published S3-type N-of-1-pathways MixEnrich to two paired samples (e.g. diseased versus unaffected tissues) for determining patient-specific enriched genes sets: Odds Ratios (S3-OR) and S3-variance using Gene Ontology Biological Processes. To evaluate small cohorts, we calculated the precision and recall of Inter-N-of-1 and that of a control method (GLM+EGS) when comparing two cohorts of decreasing sizes (from 20 versus 20 to 2 versus 2) in a comprehensive six-parameter simulation and in a proof-of-concept clinical dataset. In simulations, the Inter-N-of-1 median precision and recall are > 90% and >75% in cohorts of 3 versus 3 distinct subjects (regardless of the parameter values), whereas conventional methods outperform Inter-N-of-1 at sample sizes 9 versus 9 and larger. Similar results were obtained in the clinical proof-of-concept dataset.

AVAILABILITY AND IMPLEMENTATION

R software is available at Lussierlab.net/BSSD.

The overexpression of DNA repair genes in invasive ductal and lobular breast carcinomas: Insights on individual variations and precision medicine

In the era of precision medicine, analyzing the transcriptomic profile of patients is essential to tailor the appropriate therapy. In this study, we explored transcriptional differences between two invasive breast cancer subtypes; infiltrating ductal carcinoma (IDC) and lobular carcinoma (LC) using RNA-Seq data deposited in the TCGA-BRCA project. We revealed 3854 differentially expressed genes between normal ductal tissues and IDC. In addition, IDC to LC comparison resulted in 663 differentially expressed genes. We then focused on DNA repair genes because of their known effects on patients' response to therapy and resistance. We here report that 36 DNA repair genes are overexpressed in a significant number of both IDC and LC patients' samples. Despite the upregulation in a significant number of samples, we observed a noticeable variation in the expression levels of the repair genes across patients of the same cancer subtype. The same trend is valid for the expression of miRNAs, where remarkable variations between patients' samples of the same cancer subtype are also observed. These individual variations could lie behind the differential response of patients to treatment. The future of cancer diagnostics and therapy will inevitably depend on high-throughput genomic and transcriptomic data analysis. However, we propose that performing analysis on individual patients rather than a big set of patients' samples will be necessary to ensure that the best treatment is determined, and therapy resistance is reduced.

Personalized beyond Precision: Designing Unbiased Gold Standards to Improve Single-Subject Studies of Personal Genome Dynamics from Gene Products

Background: Developing patient-centric baseline standards that enable the detection of clinically significant outlier gene products on a genome-scale remains an unaddressed challenge required for advancing personalized medicine beyond the small pools of subjects implied by "precision medicine". This manuscript proposes a novel approach for reference standard development to evaluate the accuracy of single-subject analyses of transcriptomes and offers extensions into proteomes and metabolomes. In evaluation frameworks for which the distributional assumptions of statistical testing imperfectly model genome dynamics of gene products, artefacts and biases are confounded with authentic signals. Model confirmation biases escalate when studies use the same analytical methods in the discovery sets and reference standards. In such studies, replicated biases are confounded with measures of accuracy. We hypothesized that developing method-agnostic reference standards would reduce such replication biases. We propose to evaluate discovery methods with a reference standard derived from a consensus of analytical methods distinct from the discovery one to minimize statistical artefact biases. Our methods involve thresholding effect-size and expression-level filtering of results to improve consensus between analytical methods. We developed and released an R package "referenceNof1" to facilitate the construction of robust reference standards. Results: Since RNA-Seq data analysis methods often rely on binomial and negative binomial assumptions to non-parametric analyses, the differences create statistical noise and make the reference standards method dependent. In our experimental design, the accuracy of 30 distinct combinations of fold changes (FC) and expression counts (hereinafter "expression") were determined for five types of RNA analyses in two different datasets. This design was applied to two distinct datasets: Breast cancer cell lines and a yeast study with isogenic biological replicates in two experimental conditions. Furthermore, the reference standard (RS) comprised all RNA analytical methods with the exception of the method testing accuracy. To mitigate biases towards a specific analytical method, the pairwise Jaccard Concordance Index between observed results of distinct analytical methods were calculated for optimization. Optimization through thresholding effect-size and expression-level reduced the greatest discordances between distinct methods' analytical results and resulted in a 65% increase in concordance. Conclusions: We have demonstrated that comparing accuracies of different single-subject analysis methods for clinical optimization in transcriptomics requires a new evaluation framework. Reliable and robust reference standards, independent of the evaluated method, can be obtained under a limited number of parameter combinations: Fold change (FC) ranges thresholds, expression level cutoffs, and exclusion of the tested method from the RS development process. When applying anticonservative reference standard frameworks (e.g., using the same method for RS development and prediction), most of the concordant signal between prediction and Gold Standard (GS) cannot be confirmed by other methods, which we conclude as biased results. Statistical tests to determine DEGs from a single-subject study generate many biased results requiring subsequent filtering to increase reliability. Conventional single-subject studies pertain to one or a few patient's measures over time and require a substantial conceptual framework extension to address the numerous measures in genome-wide analyses of gene products. The proposed referenceNof1 framework addresses some of the inherent challenges for improving transcriptome scale single-subject analyses by providing a robust approach to constructing reference standards.

The intergenerational toxic effects on offspring of medaka fish Oryzias melastigma from parental benzo[a]pyrene exposure via interference of the circadian rhythm

Benzo[a]pyrene (BaP), a widely existed polycyclic aromatic hydrocarbon pollutant in aquatic environment, has toxic effects on marine animals and their generations, but the intergenerational immunotoxic mechanism underlying has not been clearly understood. In the study, the offspring of marine medaka (oryzias melastigma) which were exposed to 0.5 μg L^-1 BaP suffered from circadian rhythm oscillation disorders and severe DNA damage. Many clock-associated genes like per1 were significantly modulated in offspring, both per1 and p53 were significantly inhibited that altered the progression of cell cycle and inhibited DNA repair, which possibly resulted in the increased mortality of offspring. The hypermethylation of the per1 promotor and abnormal levels of N⁶-methyladenosine (m⁶A) suggested that the underlying mechanism was probably related to the epigenetic modification. Moreover, the offspring from paternal BaP exposure had more severe DNA damage and a higher degree of hypermethylation than those from maternal exposure. F1 larvae from BaP-exposed parents were more sensitive to BaP exposure, showing that the expression of immune and metabolism-related genes were significantly up-regulated. Taken together, the parental toxicity induced by BaP could be passed to F1 generation and the mechanism underlying was probably associated with a characteristic circadian rhythm disorder.

Interpretation of 'Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes

Calculating Differentially Expressed Genes (DEGs) from RNA-sequencing requires replicates to estimate gene-wise variability, a requirement that is at times financially or physiologically infeasible in clinics. By imposing restrictive transcriptome-wide assumptions limiting inferential opportunities of conventional methods (edgeR, NOISeq-sim, DESeq, DEGseq), comparing two conditions without replicates (TCWR) has been proposed, but not evaluated. Under TCWR conditions (e.g., unaffected tissue vs. tumor), differences of transformed expression of the proposed individualized DEG (iDEG) method follow a distribution calculated across a local partition of related transcripts at baseline expression; thereafter the probability of each DEG is estimated by empirical Bayes with local false discovery rate control using a two-group mixture model. In extensive simulation studies of TCWR methods, iDEG and NOISeq are more accurate at 5%<DEGs<20% (precision>90%, recall>75%, false_positive_rate<1%) and 30%<DEGs<40% (precision=recall~90%), respectively. The proposed iDEG method borrows localized distribution information from the same individual, a strategy that improves accuracy to compare transcriptomes in absence of replicates at low DEGsconditions. http://www.lussiergroup.org/publications/iDEG.