A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data.

The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.

OSCAA: A two-dimensional Gaussian mixture model for copy number variation association analysis.

Copy number variants (CNVs) are prevalent in the human genome and are found to have a profound effect on genomic organization and human diseases. Discovering disease-associated CNVs is critical for understanding the pathogenesis of diseases and aiding their diagnosis and treatment. However, traditional methods for assessing the association between CNVs and disease risks adopt a two-stage strategy conducting quantitative CNV measurements first and then testing for association, which may lead to biased association estimation and low statistical power, serving as a major barrier in routine genome-wide assessment of such variation. In this article, we developed One-Stage CNV-disease Association Analysis (OSCAA), a flexible algorithm to discover disease-associated CNVs for both quantitative and qualitative traits. OSCAA employs a two-dimensional Gaussian mixture model that is built upon the PCs from copy number intensities, accounting for technical biases in CNV detection while simultaneously testing for their effect on outcome traits. In OSCAA, CNVs are identified and their associations with disease risk are evaluated simultaneously in a single step, taking into account the uncertainty of CNV identification in the statistical model. Our simulations demonstrated that OSCAA outperformed the existing one-stage method and traditional two-stage methods by yielding a more accurate estimate of the CNV-disease association, especially for short CNVs or CNVs with weak signals. In conclusion, OSCAA is a powerful and flexible approach for CNV association testing with high sensitivity and specificity, which can be easily applied to different traits and clinical risk predictions.

SOCS3 regulates pathological retinal angiogenesis through modulating SPP1 expression in microglia and macrophages.

Pathological ocular angiogenesis has long been associated with myeloid cell activation. However, the precise cellular and molecular mechanisms governing the intricate crosstalk between the immune system and vascular changes during ocular neovascularization formation remain elusive. In this study, we demonstrated that the absence of the suppressor of cytokine signaling 3 (SOCS3) in myeloid cells led to a substantial accumulation of microglia and macrophage subsets during the neovascularization process. Our single-cell RNA sequencing data analysis revealed a remarkable increase in the expression of the secreted phosphoprotein 1 (Spp1) gene within these microglia and macrophages, identifying subsets of Spp1-expressing microglia and macrophages during neovascularization formation in angiogenesis mouse models. Notably, the number of Spp1-expressing microglia and macrophages exhibited further elevation during neovascularization in mice lacking myeloid SOCS3. Moreover, our investigation unveiled the Spp1 gene as a direct transcriptional target gene of signal transducer and activator of transcription 3. Importantly, pharmaceutical activation of SOCS3 or blocking of SPP1 resulted in a significant reduction in pathological neovascularization. In conclusion, our study highlights the pivotal role of the SOCS3/STAT3/SPP1 axis in the regulation of pathological retinal angiogenesis.

CDK8 and CDK19: positive regulators of signal-induced transcription and negative regulators of Mediator complex proteins.

We have conducted a detailed transcriptomic, proteomic and phosphoproteomic analysis of CDK8 and its paralog CDK19, alternative enzymatic components of the kinase module associated with transcriptional Mediator complex and implicated in development and diseases. This analysis was performed using genetic modifications of CDK8 and CDK19, selective CDK8/19 small molecule kinase inhibitors and a potent CDK8/19 PROTAC degrader. CDK8/19 inhibition in cells exposed to serum or to agonists of NFκB or protein kinase C (PKC) reduced the induction of signal-responsive genes, indicating a pleiotropic role of Mediator kinases in signal-induced transcriptional reprogramming. CDK8/19 inhibition under basal conditions initially downregulated a small group of genes, most of which were inducible by serum or PKC stimulation. Prolonged CDK8/19 inhibition or mutagenesis upregulated a larger gene set, along with a post-transcriptional increase in the proteins comprising the core Mediator complex and its kinase module. Regulation of both RNA and protein expression required CDK8/19 kinase activities but both enzymes protected their binding partner cyclin C from proteolytic degradation in a kinase-independent manner. Analysis of isogenic cell populations expressing CDK8, CDK19 or their kinase-inactive mutants revealed that CDK8 and CDK19 have the same qualitative effects on protein phosphorylation and gene expression at the RNA and protein levels, whereas differential effects of CDK8 versus CDK19 knockouts were attributable to quantitative differences in their expression and activity rather than different functions.

BISC: accurate inference of transcriptional bursting kinetics from single-cell transcriptomic data.

Gene expression in mammalian cells is inherently stochastic and mRNAs are synthesized in discrete bursts. Single-cell transcriptomics provides an unprecedented opportunity to explore the transcriptome-wide kinetics of transcriptional bursting. However, current analysis methods provide limited accuracy in bursting inference due to substantial noise inherent to single-cell transcriptomic data. In this study, we developed BISC, a Bayesian method for inferring bursting parameters from single cell transcriptomic data. Based on a beta-gamma-Poisson model, BISC modeled the mean-variance dependency to achieve accurate estimation of bursting parameters from noisy data. Evaluation based on both simulation and real intron sequential RNA fluorescence in situ hybridization data showed improved accuracy and reliability of BISC over existing methods, especially for genes with low expression values. Further application of BISC found bursting frequency but not bursting size was strongly associated with gene expression regulation. Moreover, our analysis provided new mechanistic insights into the functional role of enhancer and superenhancer by modulating both bursting frequency and size. BISC also formulated a downstream framework to identify differential bursting (in frequency and size separately) genes in samples under different conditions. Applying to multiple datasets (a mouse embryonic cell and fibroblast dataset, a human immune cell dataset and a human pancreatic cell dataset), BISC identified known cell-type signature genes that were missed by differential expression analysis, providing additional insights in understanding the cell-specific stochastic gene transcription. Applying to datasets of human lung and colon cancers, BISC successfully detected tumor signature genes based on alterations in bursting kinetics, which illustrates its value in understanding disease development regarding transcriptional bursting. Collectively, BISC provides a new tool for accurately inferring bursting kinetics and detecting differential bursting genes. This study also produced new insights in the role of transcriptional bursting in regulating gene expression, cell identity and tumor progression.

Shall genomic correlation structure be considered in copy number variants detection?

Copy number variation has been identified as a major source of genomic variation associated with disease susceptibility. With the advent of whole-exome sequencing (WES) technology, massive WES data have been generated, allowing for the identification of copy number variants (CNVs) in the protein-coding regions with direct functional interpretation. We have previously shown evidence of the genomic correlation structure in array data and developed a novel chromosomal breakpoint detection algorithm, LDcnv, which showed significantly improved detection power through integrating the correlation structure in a systematic modeling manner. However, it remains unexplored whether the genomic correlation exists in WES data and how such correlation structure integration can improve the CNV detection accuracy. In this study, we first explored the correlation structure of the WES data using the 1000 Genomes Project data. Both real raw read depth and median-normalized data showed strong evidence of the correlation structure. Motivated by this fact, we proposed a correlation-based method, CORRseq, as a novel release of the LDcnv algorithm in profiling WES data. The performance of CORRseq was evaluated in extensive simulation studies and real data analysis from the 1000 Genomes Project. CORRseq outperformed the existing methods in detecting medium and large CNVs. In conclusion, it would be more advantageous to model genomic correlation structure in detecting relatively long CNVs. This study provides great insights for methodology development of CNV detection with NGS data.

SCRIP: an accurate simulator for single-cell RNA sequencing data.

MOTIVATION: Recent advancements in single-cell RNA sequencing (scRNA-seq) have enabled time-efficient transcriptome profiling in individual cells. To optimize sequencing protocols and develop reliable analysis methods for various application scenarios, solid simulation methods for scRNA-seq data are required. However, due to the noisy nature of scRNA-seq data, currently available simulation methods cannot sufficiently capture and simulate important properties of real data, especially the biological variation. In this study, we developed scRNA-seq information producer (SCRIP), a novel simulator for scRNA-seq that is accurate and enables simulation of bursting kinetics. RESULTS: Compared to existing simulators, SCRIP showed a significantly higher accuracy of stimulating key data features, including mean-variance dependency in all experiments. SCRIP also outperformed other methods in recovering cell-cell distances. The application of SCRIP in evaluating differential expression analysis methods showed that edgeR outperformed other examined methods in differential expression analyses, and ZINB-WaVE improved the AUC at high dropout rates. Collectively, this study provides the research community with a rigorous tool for scRNA-seq data simulation. AVAILABILITY AND IMPLEMENTATION: https://CRAN.R-project.org/package=SCRIP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrating genomic correlation structure improves copy number variations detection.

MOTIVATION: Copy number variation plays important roles in human complex diseases. The detection of copy number variants (CNVs) is identifying mean shift in genetic intensities to locate chromosomal breakpoints, the step of which is referred to as chromosomal segmentation. Many segmentation algorithms have been developed with a strong assumption of independent observations in the genetic loci, and they assume each locus has an equal chance to be a breakpoint (i.e. boundary of CNVs). However, this assumption is violated in the genetics perspective due to the existence of correlation among genomic positions, such as linkage disequilibrium (LD). Our study showed that the LD structure is related to the location distribution of CNVs, which indeed presents a non-random pattern on the genome. To generate more accurate CNVs, we proposed a novel algorithm, LDcnv, that models the CNV data with its biological characteristics relating to genetic dependence structure (i.e. LD). RESULTS: We theoretically demonstrated the correlation structure of CNV data in SNP array, which further supports the necessity of integrating biological structure in statistical methods for CNV detection. Therefore, we developed the LDcnv that integrated the genomic correlation structure with a local search strategy into statistical modeling of the CNV intensities. To evaluate the performance of LDcnv, we conducted extensive simulations and analyzed large-scale HapMap datasets. We showed that LDcnv presented high accuracy, stability and robustness in CNV detection and higher precision in detecting short CNVs compared to existing methods. This new segmentation algorithm has a wide scope of potential application with data from various high-throughput technology platforms. AVAILABILITY AND IMPLEMENTATION: https://github.com/FeifeiXiaoUSC/LDcnv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A pilot study that provides evidence of epigenetic changes among mother-child pairs living proximal to mining in the US.

Environmental exposures to chemicals can disrupt gene expression, and the effects could be mediated by methylation. This investigation focused on methylation of genes associated with exposure to metals. Mother-child pairs from three locations in Montana were recruited, and buccal cells were collected for genome-wide methylation assay. Four pairs were from Butte, where there is mining and a Superfund site, four pairs were from Anaconda with a Superfund site, and four pairs were from Missoula with neither a mine nor a Superfund site. Principal component analysis, linear mixed models, hierarchical clustering and heatmap, and gene set enrichment analysis were used to visualize the profiles, identify the top associated methylation loci, and investigate the involved pathways. Distinctly higher or lower methylation in samples from Butte were found at the top differentially methylated loci. The 200 genes harboring the most hypermethylated loci were significantly enriched in genes involved in actin cytoskeleton regulation, ABC transporters, leukocyte transendothelial migration, focal adhesion, and adherens junction, which plays a role in pathogenesis of disease, including autism spectrum disorders. This study lays a foundation for inquiry about genetic changes associated with environmental exposure to metals for people living in proximity to Superfund and open pit mining.

Mature Vascular Smooth Muscle Cells, but Not Endothelial Cells, Serve as the Major Cellular Source of Intimal Hyperplasia in Vein Grafts.

OBJECTIVE: Neointima formation is a primary cause of intermediate to late vein graft (VG) failure. However, the precise source of neointima cells in VGs remains unclear. Approach and Results: Herein we clarify the relative contributions of mature vascular smooth muscle cells (SMCs) and endothelial cells (ECs) to neointima formation in a mouse model of VG remodeling via the genetic-inducible fate mapping approaches. Regardless of the magnitude of neointima formation, the recipient arterial and the donor venous SMCs contributed ≈55% of the neointima cells at the anastomotic regions, whereas only donor venous SMCs donated ≈68% of the neointima cells at the middle bodies. A small portion of the SMC-derived cells became non-SMC cells, most likely vascular stem cells, and constituted 2% to 11% of the cells in each major layer of VGs. In addition, the recipient arterial ECs were the major cellular source of re-endothelialization but did not contribute to neointima formation. The donor venous ECs donated ≈17% neointima cells in the VGs with mild neointima formation and conditional media from ECs after endothelial-to-mesenchymal transition suppressed vascular SMC dedifferentiation. CONCLUSIONS: The recipient arterial and donor venous mature SMCs dominate but contribute distinctly to intimal hyperplasia at the anastomosis and the middle body regions of VGs. The recipient arterial ECs are the major cellular source of re-endothelialization but do not donate neointima formation in VGs. Only the donor venous ECs undergo endothelial-to-mesenchymal transition. Endothelial-to-mesenchymal transition is marginal for generating neointima cells but is likely required for controlling the quality of VG remodeling.

Post-traumatic Headache and Mild Traumatic Brain Injury: Brain Networks and Connectivity.

PURPOSE OF REVIEW: Post-traumatic headache (PTH) consequent to mild traumatic brain injury (mTBI) is a complex, multidimensional, chronic neurological disorder. The purpose of this review is to evaluate the current neuroimaging studies on mTBI and PTH with a specific focus on brain networks and connectivity patterns. RECENT FINDINGS: We present findings on PTH incidence and prevalence, as well as the latest neuroimaging research findings on mTBI and PTH. Additionally, we propose a new strategy in studying PTH following mTBI. The diversity and heterogeneity of pathophysiological mechanisms underlying mild traumatic brain injury pose unique challenges on how we interpret neuroimaging findings in PTH. Evaluating alterations in the intrinsic brain network connectivity patterns using novel imaging and analytical techniques may provide additional insights into PTH disease state and therefore inform effective treatment strategies.

A Cluster-Based Approach for Identifying Prognostic microRNA Signatures in Digestive System Cancers.

Cancer remains the second leading cause of death all over the world. Aberrant expression of miRNA has shown diagnostic and prognostic value in many kinds of cancer. This study aims to provide a novel strategy to identify reliable miRNA signatures and develop improved cancer prognostic models from reported cancer-associated miRNAs. We proposed a new cluster-based approach to identify distinct cluster(s) of cancers and corresponding miRNAs. Further, with samples from TCGA and other independent studies, we identified prognostic markers and validated their prognostic value in prediction models. We also performed KEGG pathway analysis to investigate the functions of miRNAs associated with the cancer cluster of interest. A distinct cluster with 28 cancers and 146 associated miRNAs was identified. This cluster was enriched by digestive system cancers. Further, we screened out 8 prognostic miRNA signatures for STAD, 5 for READ, 18 for PAAD, 24 for LIHC, 12 for ESCA and 18 for COAD. These identified miRNA signatures demonstrated strong abilities in discriminating the overall survival time between high-risk group and low-risk group (p-value < 0.05) in both TCGA training and test datasets, as well as four independent Gene Expression Omnibus (GEO) validation datasets. We also demonstrated that these cluster-based miRNA signatures are superior to signatures identified in single cancers for prognosis. Our study identified significant miRNA signatures with improved prognosis accuracy in digestive system cancers. It also provides a novel method/strategy for cancer prognostic marker selection and offers valuable methodological directions to similar research topics.

SARS-CoV-2 Impairs Dendritic Cells and Regulates DC-SIGN Gene Expression in Tissues.

The current spreading coronavirus SARS-CoV-2 is highly infectious and pathogenic. In this study, we screened the gene expression of three host receptors (ACE2, DC-SIGN and L-SIGN) of SARS coronaviruses and dendritic cells (DCs) status in bulk and single cell transcriptomic datasets of upper airway, lung or blood of COVID-19 patients and healthy controls. In COVID-19 patients, DC-SIGN gene expression was interestingly decreased in lung DCs but increased in blood DCs. Within DCs, conventional DCs (cDCs) were depleted while plasmacytoid DCs (pDCs) were augmented in the lungs of mild COVID-19. In severe cases, we identified augmented types of immature DCs (CD22+ or ANXA1+ DCs) with MHCII downregulation. In this study, our observation indicates that DCs in severe cases stimulate innate immune responses but fail to specifically present SARS-CoV-2. It provides insights into the profound modulation of DC function in severe COVID-19.

A gene expression-based immune signature for lung adenocarcinoma prognosis.

BACKGROUND: Lung adenocarcinoma (LUAD) has become the most frequent histologic type of lung cancer in the past several decades. Recent successes with immune checkpoint blockade therapy have demonstrated that the manipulation of the immune system is a very potent treatment for LUAD. This study aims to explore the role of immune-related genes in the development of LUAD and establish a signature that can predict overall survival for LUAD patients. METHODS: To identify the differential expression genes (DEGs) between normal and tumor tissues, we developed an analysis strategy to combine an independent-sample design and a paired-sample design using RNA-seq transcriptomic profiling data of The Cancer Genome Atlas LUAD samples. Further, we selected prognostic markers from DEGs and evaluated their prognostic value in a prediction model. RESULTS: We identified and validated PD1, PDL1 and CTLA4 genes as prognostic markers, which are well-known immune checkpoints, and revealed two new potential prognostic immune checkpoints for LUAD, HHLA2 (logFC = 2.55, FDR = 1.89 × 10-6) and VTCN1 (logFC = -2.86, FDR = 1.72 × 10-11). Furthermore, we identified an 18-gene LUAD prognostic biomarker panel and observed that the classified high-risk group presented a significantly shorter overall survival time (HR = 3.57, p value = 4.07 × 10-10). The prediction model was validated in five independent high-throughput gene expression datasets. CONCLUSIONS: The identified DEG features may serve as potential biomarkers for prognosis prediction of LUAD patients and immunotherapy. Based on that assumption, we identified a gene expression-based immune signature for lung adenocarcinoma prognosis.

An accurate and powerful method for copy number variation detection.

MOTIVATION: Integration of multiple genetic sources for copy number variation detection (CNV) is a powerful approach to improve the identification of variants associated with complex traits. Although it has been shown that the widely used change point based methods can increase statistical power to identify variants, it remains challenging to effectively detect CNVs with weak signals due to the noisy nature of genotyping intensity data. We previously developed modSaRa, a normal mean-based model on a screening and ranking algorithm for copy number variation identification which presented desirable sensitivity with high computational efficiency. To boost statistical power for the identification of variants, here we present a novel improvement that integrates the relative allelic intensity with external information from empirical statistics with modeling, which we called modSaRa2. RESULTS: Simulation studies illustrated that modSaRa2 markedly improved both sensitivity and specificity over existing methods for analyzing array-based data. The improvement in weak CNV signal detection is the most substantial, while it also simultaneously improves stability when CNV size varies. The application of the new method to a whole genome melanoma dataset identified novel candidate melanoma risk associated deletions on chromosome bands 1p22.2 and duplications on 6p22, 6q25 and 19p13 regions, which may facilitate the understanding of the possible roles of germline copy number variants in the etiology of melanoma. AVAILABILITY AND IMPLEMENTATION: http://c2s2.yale.edu/software/modSaRa2 or https://github.com/FeifeiXiaoUSC/modSaRa2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Δ9-Tetrahydrocannabinol Prevents Mortality from Acute Respiratory Distress Syndrome through the Induction of Apoptosis in Immune Cells, Leading to Cytokine Storm Suppression.

Acute Respiratory Distress Syndrome (ARDS) causes up to 40% mortality in humans and is difficult to treat. ARDS is also one of the major triggers of mortality associated with coronavirus-induced disease (COVID-19). We used a mouse model of ARDS induced by Staphylococcal enterotoxin B (SEB), which triggers 100% mortality, to investigate the mechanisms through which Δ9-tetrahydrocannabinol (THC) attenuates ARDS. SEB was used to trigger ARDS in C3H mice. These mice were treated with THC and analyzed for survival, ARDS, cytokine storm, and metabolome. Additionally, cells isolated from the lungs were used to perform single-cell RNA sequencing and transcriptome analysis. A database analysis of human COVID-19 patients was also performed to compare the signaling pathways with SEB-mediated ARDS. The treatment of SEB-mediated ARDS mice with THC led to a 100% survival, decreased lung inflammation, and the suppression of cytokine storm. This was associated with immune cell apoptosis involving the mitochondrial pathway, as suggested by single-cell RNA sequencing. A transcriptomic analysis of immune cells from the lungs revealed an increase in mitochondrial respiratory chain enzymes following THC treatment. In addition, metabolomic analysis revealed elevated serum concentrations of amino acids, lysine, n-acetyl methionine, carnitine, and propionyl L-carnitine in THC-treated mice. THC caused the downregulation of miR-185, which correlated with an increase in the pro-apoptotic gene targets. Interestingly, the gene expression datasets from the bronchoalveolar lavage fluid (BALF) of human COVID-19 patients showed some similarities between cytokine and apoptotic genes with SEB-induced ARDS. Collectively, this study suggests that the activation of cannabinoid receptors may serve as a therapeutic modality to treat ARDS associated with COVID-19.

Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data.

Motivation: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different gene expression profiling platforms present a unique problem when analyzing data generated from different studies. Currently, there is a lack of effective methods designed to eliminate platform-based bias. We present a method to normalize and classify RNA-seq data using machine learning classifiers trained on DNA microarray data and molecular subtypes in two datasets: breast invasive carcinoma (BRCA) and colorectal cancer (CRC). Results: Multiple analyses show that feature specific quantile normalization (FSQN) successfully removes platform-based bias from RNA-seq data, regardless of feature scaling or machine learning algorithm. We achieve up to 98% accuracy for BRCA data and 97% accuracy for CRC data in assigning molecular subtypes to RNA-seq data normalized using FSQN and a support vector machine trained exclusively on DNA microarray data. We find that maximum accuracy was achieved when normalizing RNA-seq datasets that contain at least 25 samples. FSQN allows comparison of RNA-seq data to existing DNA microarray datasets. Using these techniques, we can successfully leverage information from existing gene expression data in new analyses despite different platforms used for gene expression profiling. Availability and implementation: FSQN has been submitted as an R package to CRAN. All code used for this study is available on Github (https://github.com/jenniferfranks/FSQN). Contact: michael.l.whitfield@dartmouth.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Local sequence and sequencing depth dependent accuracy of RNA-seq reads.

BACKGROUND: Many biases and spurious effects are inherent in RNA-seq technology, resulting in a non-uniform distribution of sequencing read counts for each base position in a gene. Therefore, a base-level strategy is required to model the non-uniformity. Also, the properties of sequencing read counts can be leveraged to achieve a more precise estimation of the mean and variance of measurement. RESULTS: In this study, we aimed to unveil the effects on RNA-seq accuracy from multiple factors and develop accurate modeling of RNA-seq reads in comparison. We found that the overdispersion rate decreased when sequencing depth increased on the base level. Moreover, the influence of local sequence(s) on the overdispersion rate was notable but no longer significant after adjusting the effect from sequencing depth. Based on these findings, we propose a desirable beta-binomial model with a dynamic overdispersion rate on the base-level proportion of sequencing read counts from two samples. CONCLUSIONS: The current study provides thorough insights into the impact of overdispersion at the position level and especially into its relationship with sequencing depth, local sequence, and preparation protocol. These properties of RNA-seq will aid in improvement of the quality control procedure and development of statistical methods for RNA-seq downstream analyses.