Machine learning analysis of bleeding status in venous thromboembolism patients.

Background: Anticoagulation therapy is the mainstay of therapy for patients with venous thromboembolism (VTE). However, continuing or stopping anticoagulants after the first 3 to 6 months is a difficult decision that requires ascertainment of the risk of bleeding and recurrent VTE. Despite the development of several statistical models to predict bleeding, the benefit of machine learning (ML) models has not been investigated in depth. Objectives: To assess the benefits of ML algorithms in bleeding risk evaluation in VTE patients and gain insight into their baseline information. Methods: The baseline clinical, demographic, and genotype information was collected for 2542 patients with VTE who were on extended anticoagulation therapy. Six unsupervised dimensionality reduction and clustering ML algorithms were used to visualize and cluster the data for patients with major bleeding (118 patients) and nonbleeders. Eight supervised ML algorithms were trained and compared with the previously derived clinical models using a 5-fold nested cross-validation scheme. Results: The baseline dataset for bleeders and nonbleeders showed a high degree of similarity. Two novel clusters were discovered within the dataset for bleeders based on the presence of isolated pulmonary embolism or isolated deep vein thrombosis, though the difference in bleeding risks was not statistically significant (P = .32). The supervised analysis showed that the ML and clinical models have similar discrimination (c-statistics, ∼62%) and calibration performance (Brier score, ∼0.045). Conclusion: The clinical variables recorded at baseline are not distinctive enough to improve bleeding prediction beyond the performance of the existing models, and other strategies or data modalities should be considered.

A deep-learning approach to predict bleeding risk over time in patients on extended anticoagulation therapy.

BACKGROUND: Thus far, all the clinical models developed to predict major bleeding in patients on extended anticoagulation therapy use the baseline predictors to stratify patients into different risk groups. Therefore, these models do not account for the clinical changes and events that occur after the baseline visit, which can modify risk of bleeding. However, it is difficult to develop predictive models from the routine follow-up clinical interviews, which are irregular sequences of multivariate time series data. OBJECTIVES: To demonstrate that deep learning can incorporate patient time series follow-up data to improve prediction of major bleeding. METHODS: We used the baseline and follow-up data that were collected over 8 years in a longitudinal cohort study of 2542 patients, of whom 118 had major bleeding. Four supervised neural network-based machine-learning models were trained on the baseline, follow-up, or both datasets using 70% of the data. The performance of these models was evaluated, along with modified versions of 6 previously developed clinical models, on the remaining 30% of the data. RESULTS: An ensemble of feedforward and recurrent neural networks that used the baseline and follow-up data was the best-performing model, achieving a sensitivity and a specificity of 61% and 82%, respectively, in identifying major bleeding, and it outperformed the previously developed clinical models in terms of area under the receiver operating characteristic curve (82%) and area under the precision-recall curve (14%). CONCLUSION: Time series follow-up data can improve major bleeding prediction in patients on extended anticoagulation therapy.

Empirical Bayes single nucleotide variant-calling for next-generation sequencing data.

One of the fundamental computational problems in cancer genomics is the identification of single nucleotide variants (SNVs) from DNA sequencing data. Many statistical models and software implementations for SNV calling have been developed in the literature, yet, they still disagree widely on real datasets. Based on an empirical Bayesian approach, we introduce a local false discovery rate (LFDR) estimator for germline SNV calling. Our approach learns model parameters without prior information, and simultaneously accounts for information across all sites in the genomic regions of interest. We also propose another LFDR-based algorithm that reliably prioritizes a given list of mutations called by any other variant-calling algorithm. We use a suite of gold-standard cell line data to compare our LFDR approach against a collection of widely used, state of the art programs. We find that our LFDR approach approximately matches or exceeds the performance of all of these programs, despite some very large differences among them. Furthermore, when prioritizing other algorithms' calls by our LFDR score, we find that by manipulating the type I-type II tradeoff we can select subsets of variant calls with minimal loss of sensitivity but dramatic increases in precision.

A Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks.

Elementary flux modes (EFMs) are minimal, steady state pathways characterizing a flux network. Fundamentally, all steady state fluxes in a network are decomposable into a linear combination of EFMs. While there is typically no unique set of EFM weights that reconstructs these fluxes, several optimization-based methods have been proposed to constrain the solution space by enforcing some notion of parsimony. However, it has long been recognized that optimization-based approaches may fail to uniquely identify EFM weights and return different feasible solutions across objective functions and solvers. Here we show that, for flux networks only involving single molecule transformations, these problems can be avoided by imposing a Markovian constraint on EFM weights. Our Markovian constraint guarantees a unique solution to the flux decomposition problem, and that solution is arguably more biophysically plausible than other solutions. We describe an algorithm for computing Markovian EFM weights via steady state analysis of a certain discrete-time Markov chain, based on the flux network, which we call the cycle-history Markov chain. We demonstrate our method with a differential analysis of EFM activity in a lipid metabolic network comparing healthy and Alzheimer's disease patients. Our method is the first to uniquely decompose steady state fluxes into EFM weights for any unimolecular metabolic network.

Transcriptional reprogramming of skeletal muscle stem cells by the niche environment.

Adult stem cells are indispensable for tissue regeneration, but their function declines with age. The niche environment in which the stem cells reside plays a critical role in their function. However, quantification of the niche effect on stem cell function is lacking. Using muscle stem cells (MuSC) as a model, we show that aging leads to a significant transcriptomic shift in their subpopulations accompanied by locus-specific gain and loss of chromatin accessibility and DNA methylation. By combining in vivo MuSC transplantation and computational methods, we show that the expression of approximately half of all age-altered genes in MuSCs from aged male mice can be restored by exposure to a young niche environment. While there is a correlation between gene reversibility and epigenetic alterations, restoration of gene expression occurs primarily at the level of transcription. The stem cell niche environment therefore represents an important therapeutic target to enhance tissue regeneration in aging.

Quantification of Muscle Satellite Stem Cell Divisions by High-Content Analysis.

High-content screening is commonly performed on 2D cultured cells, which is high throughput but has low biological relevance. In contrast, single myofiber culture assay preserves the satellite cell niche between the basal lamina and sarcolemma and consequently has high biological relevance but is low throughput. We describe here a high-content screening method that utilizes single myofiber culture that addresses the caveats of both techniques. Our method utilizes the transgenic reporter allele Myf5-Cre:R26R-eYFP to differentiate stem and committed cells within a dividing couplet that can be quantified by high-content throughput immunodetection and bioinformatic analysis.

Costs of Next-Generation Sequencing Assays in Non-Small Cell Lung Cancer: A Micro-Costing Study.

Background: Next-generation sequencing (NGS) of tumor genomes has changed and improved cancer treatment over the past few decades. It can inform clinicians on the optimal therapeutic approach in many of the solid and hematologic cancers, including non-small lung cancer (NSCLC). Our study aimed to determine the costs of NGS assays for NSCLC diagnostics. Methods: We performed a micro-costing study of four NGS assays (Trusight Tumor 170 Kit (Illumina), Oncomine Focus (Thermo Fisher), QIAseq Targeted DNA Custom Panel and QIASeq Targeted RNAscan Custom Panel (Qiagen), and KAPA HyperPlus/SeqCap EZ (Roche)) at the StemCore Laboratories, the Ottawa Hospital, Canada. We used a time-and-motion approach to measure personnel time and a pre-defined questionnaire to collect resource utilization. The unit costs were based on market prices. The cost data were reported in 2019 Canadian dollars. Results: Based on a case throughput of 500 cases per year, the per-sample cost for TruSight Tumor 170 Kit, QIASeq Targeted DNA Custom Panel and QIASeq Targeted RNAscan Custom Panel, Oncomine Focus, and HyperPlus/SeqCap EZ were CAD 1778, CAD 599, CAD 1100 and CAD 1270, respectively. The key cost drivers were library preparation (34-60%) and sequencing (31-51%), followed by data analysis (6-13%) and administrative support (2-7%). Conclusions: Trusight Tumor 170 Kit was the most expensive NGS assay for NSCLC diagnostics; however, an economic evaluation is required to identify the most cost-effective NGS assay. Our study results could help inform decisions to select a robust platform for NSCLC diagnostics from fine needle aspirates, and future economic evaluations of the NGS platforms to guide treatment selections for NSCLC patients.

BATL: Bayesian annotations for targeted lipidomics.

MOTIVATION: Bioinformatic tools capable of annotating, rapidly and reproducibly, large, targeted lipidomic datasets are limited. Specifically, few programs enable high-throughput peak assessment of liquid chromatography-electrospray ionization tandem mass spectrometry data acquired in either selected or multiple reaction monitoring modes. RESULTS: We present here Bayesian Annotations for Targeted Lipidomics, a Gaussian naïve Bayes classifier for targeted lipidomics that annotates peak identities according to eight features related to retention time, intensity, and peak shape. Lipid identification is achieved by modeling distributions of these eight input features across biological conditions and maximizing the joint posterior probabilities of all peak identities at a given transition. When applied to sphingolipid and glycerophosphocholine selected reaction monitoring datasets, we demonstrate over 95% of all peaks are rapidly and correctly identified. AVAILABILITY AND IMPLEMENTATION: BATL software is freely accessible online at https://complimet.ca/batl/ and is compatible with Safari, Firefox, Chrome and Edge. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

WACS: improving ChIP-seq peak calling by optimally weighting controls.

BACKGROUND: Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq), initially introduced more than a decade ago, is widely used by the scientific community to detect protein/DNA binding and histone modifications across the genome. Every experiment is prone to noise and bias, and ChIP-seq experiments are no exception. To alleviate bias, the incorporation of control datasets in ChIP-seq analysis is an essential step. The controls are used to account for the background signal, while the remainder of the ChIP-seq signal captures true binding or histone modification. However, a recurrent issue is different types of bias in different ChIP-seq experiments. Depending on which controls are used, different aspects of ChIP-seq bias are better or worse accounted for, and peak calling can produce different results for the same ChIP-seq experiment. Consequently, generating "smart" controls, which model the non-signal effect for a specific ChIP-seq experiment, could enhance contrast and increase the reliability and reproducibility of the results. RESULT: We propose a peak calling algorithm, Weighted Analysis of ChIP-seq (WACS), which is an extension of the well-known peak caller MACS2. There are two main steps in WACS: First, weights are estimated for each control using non-negative least squares regression. The goal is to customize controls to model the noise distribution for each ChIP-seq experiment. This is then followed by peak calling. We demonstrate that WACS significantly outperforms MACS2 and AIControl, another recent algorithm for generating smart controls, in the detection of enriched regions along the genome, in terms of motif enrichment and reproducibility analyses. CONCLUSIONS: This ultimately improves our understanding of ChIP-seq controls and their biases, and shows that WACS results in a better approximation of the noise distribution in controls.

Absolute quantification of transcription factors in human erythropoiesis using selected reaction monitoring mass spectrometry.

Quantitative changes in transcription factor (TF) abundance regulate dynamic cellular processes, including cell fate decisions. Protein copy number provides information about the relative stoichiometry of TFs that can be used to determine how quantitative changes in TF abundance influence gene regulatory networks. In this protocol, we describe a targeted selected reaction monitoring (SRM)-based mass-spectrometry method to systematically measure the absolute protein concentration of nuclear TFs as human hematopoietic stem and progenitor cells differentiate along the erythropoietic lineage. For complete details on the use and execution of this protocol, please refer to Gillespie et al. (2020).

Myf6/MRF4 is a myogenic niche regulator required for the maintenance of the muscle stem cell pool.

The function and maintenance of muscle stem cells (MuSCs) are tightly regulated by signals originating from their niche environment. Skeletal myofibers are a principle component of the MuSC niche and are in direct contact with the muscle stem cells. Here, we show that Myf6 establishes a ligand/receptor interaction between muscle stem cells and their associated muscle fibers. Our data show that Myf6 transcriptionally regulates a broad spectrum of myokines and muscle-secreted proteins in skeletal myofibers, including EGF. EGFR signaling blocks p38 MAP kinase-induced differentiation of muscle stem cells. Homozygous deletion of Myf6 causes a significant reduction in the ability of muscle to produce EGF, leading to a deregulation in EGFR signaling. Consequently, although Myf6-knockout mice are born with a normal muscle stem cell compartment, they undergo a progressive reduction in their stem cell pool during postnatal life due to spontaneous exit from quiescence. Taken together, our data uncover a novel role for Myf6 in promoting the expression of key myokines, such as EGF, in the muscle fiber which prevents muscle stem cell exhaustion by blocking their premature differentiation.

Accuracy and reproducibility of somatic point mutation calling in clinical-type targeted sequencing data.

BACKGROUND: Treating cancer depends in part on identifying the mutations driving each patient's disease. Many clinical laboratories are adopting high-throughput sequencing for assaying patients' tumours, applying targeted panels to formalin-fixed paraffin-embedded tumour tissues to detect clinically-relevant mutations. While there have been some benchmarking and best practices studies of this scenario, much variant calling work focuses on whole-genome or whole-exome studies, with fresh or fresh-frozen tissue. Thus, definitive guidance on best choices for sequencing platforms, sequencing strategies, and variant calling for clinical variant detection is still being developed. METHODS: Because ground truth for clinical specimens is rarely known, we used the well-characterized Coriell cell lines GM12878 and GM12877 to generate data. We prepared samples to mimic as closely as possible clinical biopsies, including formalin fixation and paraffin embedding. We evaluated two well-known targeted sequencing panels, Illumina's TruSight 170 hybrid-capture panel and the amplification-based Oncomine Focus panel. Sequencing was performed on an Illumina NextSeq500 and an Ion Torrent PGM respectively. We performed multiple replicates of each assay, to test reproducibility. Finally, we applied four different freely-available somatic single-nucleotide variant (SNV) callers to the data, along with the vendor-recommended callers for each sequencing platform. RESULTS: We did not observe major differences in variant calling success within the regions that each panel covers, but there were substantial differences between callers. All had high sensitivity for true SNVs, but numerous and non-overlapping false positives. Overriding certain default parameters to make them consistent between callers substantially reduced discrepancies, but still resulted in high false positive rates. Intersecting results from multiple replicates or from different variant callers eliminated most false positives, while maintaining sensitivity. CONCLUSIONS: Reproducibility and accuracy of targeted clinical sequencing results depend less on sequencing platform and panel than on variability between replicates and downstream bioinformatics. Differences in variant callers' default parameters are a greater influence on algorithm disagreement than other differences between the algorithms. Contrary to typical clinical practice, we recommend employing multiple variant calling pipelines and/or analyzing replicate samples, as this greatly decreases false positive calls.

Establishment of macaque trophoblast stem cell lines derived from cynomolgus monkey blastocysts.

The placenta forms a maternal-fetal junction that supports many physiological functions such as the supply of nutrition and exchange of gases and wastes. Establishing an in vitro culture model of human and non-human primate trophoblast stem/progenitor cells is important for investigating the process of early placental development and trophoblast differentiation. In this study, we have established five trophoblast stem cell (TSC) lines from cynomolgus monkey blastocysts, named macTSC #1-5. Fibroblast growth factor 4 (FGF4) enhanced proliferation of macTSCs, while other exogenous factors were not required to maintain their undifferentiated state. macTSCs showed a trophoblastic gene expression profile and trophoblast-like DNA methylation status and also exhibited differentiation capacity towards invasive trophoblast cells and multinucleated syncytia. In a xenogeneic chimera assay, these stem cells contributed to trophectoderm (TE) development in the chimeric blastocysts. macTSC are the first primate trophoblast cell lines whose proliferation is promoted by FGF4. These cell lines provide a valuable in vitro culture model to analyze the similarities and differences in placental development between human and non-human primates.

Absolute Quantification of Transcription Factors Reveals Principles of Gene Regulation in Erythropoiesis.

Dynamic cellular processes such as differentiation are driven by changes in the abundances of transcription factors (TFs). However, despite years of studies, our knowledge about the protein copy number of TFs in the nucleus is limited. Here, by determining the absolute abundances of 103 TFs and co-factors during the course of human erythropoiesis, we provide a dynamic and quantitative scale for TFs in the nucleus. Furthermore, we establish the first gene regulatory network of cell fate commitment that integrates temporal protein stoichiometry data with mRNA measurements. The model revealed quantitative imbalances in TFs' cross-antagonistic relationships that underlie lineage determination. Finally, we made the surprising discovery that, in the nucleus, co-repressors are dramatically more abundant than co-activators at the protein level, but not at the RNA level, with profound implications for understanding transcriptional regulation. These analyses provide a unique quantitative framework to understand transcriptional regulation of cell differentiation in a dynamic context.

Bayesian correlation is a robust gene similarity measure for single-cell RNA-seq data.

Assessing similarity is highly important for bioinformatics algorithms to determine correlations between biological information. A common problem is that similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single-cell RNA-seq (scRNA-seq) data because read counts are much lower compared to bulk RNA-seq. Recently, a Bayesian correlation scheme that assigns low similarity to genes that have low confidence expression estimates has been proposed to assess similarity for bulk RNA-seq. Our goal is to extend the properties of the Bayesian correlation in scRNA-seq data by considering three ways to compute similarity. First, we compute the similarity of pairs of genes over all cells. Second, we identify specific cell populations and compute the correlation in those populations. Third, we compute the similarity of pairs of genes over all clusters, by considering the total mRNA expression. We demonstrate that Bayesian correlations are more reproducible than Pearson correlations. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We show that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population. We conclude that Bayesian correlation is a robust similarity measure in scRNA-seq data.

Human gene expression variability and its dependence on methylation and aging.

BACKGROUND: Phenotypic variability of human populations is partly the result of gene polymorphism and differential gene expression. As such, understanding the molecular basis for diversity requires identifying genes with both high and low population expression variance and identifying the mechanisms underlying their expression control. Key issues remain unanswered with respect to expression variability in human populations. The role of gene methylation as well as the contribution that age, sex and tissue-specific factors have on expression variability are not well understood. RESULTS: Here we used a novel method that accounts for sampling error to classify human genes based on their expression variability in normal human breast and brain tissues. We find that high expression variability is almost exclusively unimodal, indicating that variance is not the result of segregation into distinct expression states. Genes with high expression variability differ markedly between tissues and we find that genes with high population expression variability are likely to have age-, but not sex-dependent expression. Lastly, we find that methylation likely has a key role in controlling expression variability insofar as genes with low expression variability are likely to be non-methylated. CONCLUSIONS: We conclude that gene expression variability in the human population is likely to be important in tissue development and identity, methylation, and in natural biological aging. The expression variability of a gene is an important functional characteristic of the gene itself and the classification of a gene as one with Hyper-Variability or Hypo-Variability in a human population or in a specific tissue should be useful in the identification of important genes that functionally regulate development or disease.

Transcriptomically Guided Mesendoderm Induction of Human Pluripotent Stem Cells Using a Systematically Defined Culture Scheme.

Human pluripotent stem cells (hPSCs) are an essential cell source in tissue engineering, studies of development, and disease modeling. Efficient, broadly amenable protocols for rapid lineage induction of hPSCs are of great interest in the stem cell biology field. We describe a simple, robust method for differentiation of hPSCs into mesendoderm in defined conditions utilizing single-cell seeding (SCS) and BMP4 and Activin A (BA) treatment. BA treatment was readily incorporated into existing protocols for chondrogenic and endothelial progenitor cell differentiation, while fine-tuning of BA conditions facilitated definitive endoderm commitment. After prolonged differentiation in vitro or in vivo, BA pretreatment resulted in higher mesoderm and endoderm levels at the expense of ectoderm formation. These data demonstrate that SCS with BA treatment is a powerful method for induction of mesendoderm that can be adapted for use in mesoderm and endoderm differentiation.

High-resolution genome-wide expression analysis of single myofibers using SMART-Seq.

Skeletal muscle is a heterogeneous tissue. Individual myofibers that make up muscle tissue exhibit variation in their metabolic and contractile properties. Although biochemical and histological assays are available to study myofiber heterogeneity, efficient methods to analyze the whole transcriptome of individual myofibers are lacking. Here, we report on a single-myofiber RNA-sequencing (smfRNA-Seq) approach to analyze the whole transcriptome of individual myofibers by combining single-fiber isolation with Switching Mechanism at 5' end of RNA Template (SMART) technology. Using smfRNA-Seq, we first determined the genes that are expressed in the whole muscle, including in nonmyogenic cells. We also analyzed the differences in the transcriptome of myofibers from young and old mice to validate the effectiveness of this new method. Our results suggest that aging leads to significant changes in the expression of metabolic genes, such as Nos1, and structural genes, such as Myl1, in myofibers. We conclude that smfRNA-Seq is a powerful tool to study developmental, disease-related, and age-related changes in the gene expression profile of skeletal muscle.

Preclinical rationale for entinostat in embryonal rhabdomyosarcoma.

BACKGROUND: Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in the pediatric cancer population. Survival among metastatic RMS patients has remained dismal yet unimproved for years. We previously identified the class I-specific histone deacetylase inhibitor, entinostat (ENT), as a pharmacological agent that transcriptionally suppresses the PAX3:FOXO1 tumor-initiating fusion gene found in alveolar rhabdomyosarcoma (aRMS), and we further investigated the mechanism by which ENT suppresses PAX3:FOXO1 oncogene and demonstrated the preclinical efficacy of ENT in RMS orthotopic allograft and patient-derived xenograft (PDX) models. In this study, we investigated whether ENT also has antitumor activity in fusion-negative eRMS orthotopic allografts and PDX models either as a single agent or in combination with vincristine (VCR). METHODS: We tested the efficacy of ENT and VCR as single agents and in combination in orthotopic allograft and PDX mouse models of eRMS. We then performed CRISPR screening to identify which HDAC among the class I HDACs is responsible for tumor growth inhibition in eRMS. To analyze whether ENT treatment as a single agent or in combination with VCR induces myogenic differentiation, we performed hematoxylin and eosin (H&E) staining in tumors. RESULTS: ENT in combination with the chemotherapy VCR has synergistic antitumor activity in a subset of fusion-negative eRMS in orthotopic "allografts," although PDX mouse models were too hypersensitive to the VCR dose used to detect synergy. Mechanistic studies involving CRISPR suggest that HDAC3 inhibition is the primary mechanism of cell-autonomous cytoreduction in eRMS. Following cytoreduction in vivo, residual tumor cells in the allograft models treated with chemotherapy undergo a dramatic, entinostat-induced (70-100%) conversion to non-proliferative rhabdomyoblasts. CONCLUSION: Our results suggest that the targeting class I HDACs may provide a therapeutic benefit for selected patients with eRMS. ENT's preclinical in vivo efficacy makes ENT a rational drug candidate in a phase II clinical trial for eRMS.

RECAP reveals the true statistical significance of ChIP-seq peak calls.

MOTIVATION: Chromatin Immunopreciptation (ChIP)-seq is used extensively to identify sites of transcription factor binding or regions of epigenetic modifications to the genome. A key step in ChIP-seq analysis is peak calling, where genomic regions enriched for ChIP versus control reads are identified. Many programs have been designed to solve this task, but nearly all fall into the statistical trap of using the data twice-once to determine candidate enriched regions, and again to assess enrichment by classical statistical hypothesis testing. This double use of the data invalidates the statistical significance assigned to enriched regions, thus the true significance or reliability of peak calls remains unknown. RESULTS: Using simulated and real ChIP-seq data, we show that three well-known peak callers, MACS, SICER and diffReps, output biased P-values and false discovery rate estimates that can be many orders of magnitude too optimistic. We propose a wrapper algorithm, RECAP, that uses resampling of ChIP-seq and control data to estimate a monotone transform correcting for biases built into peak calling algorithms. When applied to null hypothesis data, where there is no enrichment between ChIP-seq and control, P-values recalibrated by RECAP are approximately uniformly distributed. On data where there is genuine enrichment, RECAP P-values give a better estimate of the true statistical significance of candidate peaks and better false discovery rate estimates, which correlate better with empirical reproducibility. RECAP is a powerful new tool for assessing the true statistical significance of ChIP-seq peak calls. AVAILABILITY AND IMPLEMENTATION: The RECAP software is available through www.perkinslab.ca or on github at https://github.com/theodorejperkins/RECAP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.