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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

ViDGER: An R package for integrative interpretation of differential gene expression results of RNA-seq data

Differential gene expression (DGE) is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes (DEGs) across two or more conditions. Interpretation of the DGE results can be non-intuitive and time consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. Here we present an R package, ViDGER (Visualization of Differential Gene Expression Results using R), which contains nine functions that generate information-rich visualizations for the interpretation of DGE results from three widely-used tools, Cuffdiff, DESeq2, and edgeR.

bioinformatics↗
bioRxiv2023

Transformer with Convolution and Graph-Node co-embedding: An accurate and interpretable vision backbone for predicting gene expressions from local histopathological image

Inferring gene expressions from histopathological images has always been a fascinating but challenging task due to the huge differences between the two modal data. Previous works have used modified DenseNet121 to encode the local images and make gene expression predictions. And later works improved the prediction accuracy of gene expression by incorporating the coordinate information from images and using all spots in the tissue region as input. While these methods were limited in use due to model complexity, large demand on GPU memory, and insufficient encoding of local images, thus the results had low interpretability, relatively low accuracy, and over-smooth prediction of gene expression among neighbor spots. In this paper, we propose TCGN, (Transformer with Convolution and Graph-Node co-embedding method) for gene expression prediction from H&E stained pathological slide images. TCGN consists of convolutional layers, transformer encoders, and graph neural networks, and is the first to integrate these blocks in a general and interpretable computer vision backbone for histopathological image analysis. We trained TCGN and compared its performance with three existing methods on a publicly available spatial transcriptomic dataset. Even in the absence of the coordinates information and neighbor spots, TCGN still outperformed the existing methods by 5% and achieved 10 times higher prediction accuracy than the counterpart model. Besides its higher accuracy, our model is also small enough to be run on a personal computer and does not need complex building graph preprocessing compared to the existing methods. Moreover, TCGN is interpretable in recognizing special cell morphology and cell-cell interactions compared to models using all spots as input that are not interpretable. A more accurate omics information prediction from pathological images not only links genotypes to phenotypes so that we can predict more biomarkers that are expensive to test from histopathological images that are low-cost to obtain, but also provides a theoretical basis for future modeling of multi-modal data. Our results support that TCGN is a useful tool for inferring gene expressions from histopathological images and other potential histopathological image analysis studies. HighlightsO_LIFirst deep learning model to integrate CNN, GNN, and transformer for image analysis C_LIO_LIAn interpretable model that uses cell morphology and organizations to predict genes C_LIO_LIHigher gene expression prediction accuracy without global information C_LIO_LIAccurately predicted genes are related to immune escape and abnormal metabolism C_LIO_LIPredict important biomarkers for breast cancer accurately from cheaper images C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/542669v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1c6a4dforg.highwire.dtl.DTLVardef@7266d4org.highwire.dtl.DTLVardef@bcfd0aorg.highwire.dtl.DTLVardef@188d452_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗
bioRxiv2023

Space and epigenetic inheritance determine inter-individual differences in siderophore gene expression in bacterial colonies

Heterogeneity in gene expression among cells in clonal groups is common in bacteria. Albeit ubiquitous, it often remains unclear what the sources of variation are and whether variation has functional significance. Here, we tracked the expression of genes involved in the synthesis of iron-chelating siderophores (pyoverdine and pyochelin) in individual cells of the bacterium Pseudomonas aeruginosa during colony growth on surfaces using time-lapse fluorescence microscopy, to explore potential sources and functions of cellular heterogeneity. Regarding sources, we found that the physical position of cells within the colony and epigenetic gene expression inheritance from mother to daughter cells significantly contributed to cellular heterogeneity. In contrast, cell pole age and cellular lifespan had no effect. Regarding functions, our results indicate that cells optimize their siderophore investment strategies (pyoverdine vs. pyochelin) along a gradient from the centre to the edge of the colony. Moreover, we found evidence that cell lineages with above-average siderophore investment increase the fitness of cell lineages with below-average investment through cooperative sharing of secreted siderophores. Altogether, our study highlights that single-cell experiments combined with automated image and cell-tracking analyses can identify sources of heterogeneity and yield adaptive explanations for gene expression variation among clonal bacterial cells.

microbiology↗
bioRxiv2020

Analysis of Gene Expression from Systemic Lupus Erythematosus Synovium Reveals a Profile of Activated Immune Cells and Inflammatory Pathways

Arthritis is a common manifestation of systemic lupus erythematosus (SLE) yet understanding of the underlying pathogenic mechanisms remains incomplete. We, therefore, interrogated gene expression profiles of SLE synovium to gain insight into the nature of lupus arthritis (LA), using osteoarthritis (OA) and rheumatoid arthritis (RA) as comparators. Knee synovia from SLE, OA, and RA patients were analyzed for differentially expressed genes (DEGs) and also by Weighted Gene Co-expression Network Analysis (WGCNA) to identify modules of highly co-expressed genes. Genes upregulated and/or co-expressed in LA revealed numerous immune/inflammatory cells dominated by a myeloid phenotype, whereas OA was characteristic of fibroblasts and RA of T- and B-cells. Upstream regulator analysis identified CD40L and inflammatory cytokines as drivers of the LA gene expression profile. Genes governing trafficking of immune cells into the synovium by chemokines were identified, but not in situ generation of germinal centers. GSVA confirmed activation of specific myeloid and lymphoid cell types in LA. Numerous therapies were predicted to target LA, including TNF, NF{kappa}B, MAPK, and CDK inhibitors. Detailed gene expression analysis identified a unique pattern of cellular components and physiologic pathways operative in LA, as well as drugs potentially able to target this common manifestation of SLE.

immunology↗
bioRxiv2019

Meta-Analysis of Transcriptomic Variation in T cell Populations Reveals Novel Signatures of Gene Expression and Splicing

Distinct T cell subtypes are typically defined by the expression of distinct gene repertoires. However, there is variability between studies regarding the markers used to define each T cell subtype. Moreover, previous analysis of gene expression in T cell subsets has largely focused on gene expression rather than alternative splicing. Here we take a meta-analysis approach, comparing eleven independent RNA-Seq studies of human Th1, Th2, Th17 and/or Treg cells to identify transcriptomic features that correlate consistently with subtype. We find that known master-regulators are consistently enriched in the appropriate subtype, however, cytokines and other genes often used as markers are more variable. Importantly, we also identify previously unknown transcriptomic markers that consistently differentiate between subsets, including a few Treg-specific splicing patterns. Together this work highlights the heterogeneity in gene expression between isolates of the same subtype, but also suggests additional markers that can be used to define functional groupings.

immunology↗
bioRxiv2018

Genetic architecture of gene expression traits across diverse populations

For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n=233), Hispanic (HIS, n=352), and European (CAU, n=578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene, TACSTD2, was the same across populations with R2 > 0.86 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at .\n\nAuthor summaryMost genome-wide association studies (GWAS) have been conducted in populations of European ancestry leading to a disparity in understanding the genetics of complex traits between populations. For many complex traits, gene regulation is critical, given the consistent enrichment of regulatory variants among trait-associated variants. However, it is still unknown how the effects of these key variants differ across populations. We used data from MESA to study the underlying genetic architecture of gene expression by optimizing gene expression prediction within and across diverse populations. The populations with genotype and gene expression data available are from individuals with African American (AFA, n=233), Hispanic (HIS, n=352), and European (CAU, n=578) ancestry. After calculating the prediction performance, we found that there are many genes that were well predicted in one population are poorly predicted in another. We further show that a training set with ancestry similar to the test set resulted in better gene expression predictions, demonstrating the need to incorporate diverse populations in genomic studies. Our gene expression prediction models and performance statistics are publicly available to facilitate future transcriptome mapping studies in diverse populations.

genomics↗
bioRxiv2020

Epigenome Prediction of Gene Expression using a Dynamical System Approach

Gene regulation is an important fundamental biological process. The regulation of gene expression is managed through a variety of methods including epigentic processes (e.g., DNA methylation). Understanding the role of epigenetic changes in gene expression is a fundamental question of molecular biology. Predictions of gene expression values from epigenetic data have tremendous research and clinical potential. Despite active research, studies to date have focused on using statistical models to predict gene expression from methylation data. In contrast, dynamical systems can be used to generate a model to predict gene expression using epigenetic data and a gene regulatory network (GRN) which can also serve as a mechanistic hypothesis. Here we present a novel stochastic dynamical systems model that predicts gene expression levels from methylation data of genes in a given GRN. Software for dataset preparation, model parameter fitting and prediction generation, and reporting are available at https://github.com/kordk/stoch_epi_lib.

systems biology↗
bioRxiv2025

Defining and detecting global transcriptional amplitude in circadian gene expression

Many genes exhibit circadian rhythms in expression. The amplitude of oscillation, both in core clock and circadian output genes, may differ from person to person. Mutations in core clock genes are known to alter global rhythmic properties, and researchers often informally discuss "circadian amplitude." Yet, it remains unclear whether, in the general population, differences in transcriptional amplitude are largely gene-specific, or if they reflect a global, transcriptome-wide pattern -- whether some individuals have globally higher or lower amplitude across the set of all rhythmic genes. We used Cosinor regression to reanalyze four human skin time-series transcriptomic datasets (paired epidermis/dermis samples, N=11, N=19) and found that, using either absolute or relative amplitude measures, distributions of gene amplitudes tended to cluster by subject. Using a non-parametric, permutation-based statistical test, we found that in many subjects this global amplitude trend was statistically significant (p [≤] 0.01). Furthermore, we found that when rhythmic genes were divided into two sets based on peak time (genes peaking before-noon and after-noon), the subjects global amplitude in one set predicted global amplitude in the other set (p [≤] 0.05). We also found that in the paired epidermis/dermis datasets, subjects global amplitude in epidermis predicted their global amplitude in the dermis (p [≤] 0.05). After identifying these trends in the skin datasets, we then found that evidence for subject-specific transcriptional rhythm strength replicated across 6 additional human time-course datasets from adipose, muscle, and blood. Perhaps surprisingly, across datasets, we found that neither established metrics of core clock transcriptional organization nor the amplitude of core clock transcription were strongly correlated with subject-specific global amplitude.

cell biology↗
bioRxiv2021

Infection of immune competent macrophages expressing functional Slc11a1 alters global gene expression, regulation of metal ions, and infection outcomes

Nutritional immunity involves cellular and physiological responses to invading pathogens, such as limiting iron availability, increasing exposure to bactericidal copper, and manipulating zinc to restrict the growth of pathogens. Manipulation of zinc at the host-pathogen interface depends on both the pathogens identity and the nature of the host cell. Here we examine infection of bone marrow-derived macrophages from 129S6/SvEvTac mice by Salmonella Typhimurium. Unlike Balb/c and C57BL/6 mice, 129S6/SvEvTac mice possess a functional Slc11a1 (Nramp-1), a phagosomal transporter of divalent cations. We carried out global RNA sequencing upon treatment with live or heat-killed Salmonella at 2 Hrs and 18 Hrs post-infection and observed widespread changes in metal transport, metal-dependent, and metal homeostasis genes, suggesting significant remodeling of iron, copper, and zinc availability by host cells. Changes in host cell gene expression suggest infection increases cytosolic zinc while simultaneously limiting zinc within the phagosome. Using a genetically encoded sensor, we demonstrate that cytosolic labile zinc increases 36-fold 12 hrs post-infection. Further, manipulation of zinc in the media alters bacterial clearance and replication, with zinc depletion inhibiting both processes. Comparing our results to published data on infection of C57BL/6 macrophages revealed notable differences in metal regulation and the global immune response, with 129S6 macrophages transitioning from M1 to M2 polarization over the course of infection and showing signs of recovery. Our results reveal that functional Slc11a1 profoundly affects the transcriptional landscape upon infection. Further, our results indicate that manipulation of zinc at the host-pathogen interface is more nuanced than that of iron or copper. 129S6 macrophage leverage intricate means of manipulating zinc availability and distribution to limit the pathogens access to zinc while simultaneously ensuring sufficient zinc to support the immune response. Author summaryMetal ions play an important role in influencing how immune cells such as macrophages respond to infection by pathogens. Because metal ions are both essential to survival, as well toxic when present is excessive amounts, the host and the pathogen have evolved diverse strategies to regulate metal acquisition and availability. Here, we show that the metal transporter slc11a1 plays a critical role in defining the host response to Salmonella infection. Infection causes widespread changes in expression of metal regulatory genes to limit the pathogens access to iron, increase its exposure to copper, and remodel zinc to ensure increased zinc in the cytosol and limited zinc for the pathogen. Macrophages expressing functional slc11a1 have a different profile of metal regulation and vastly different outcomes compared to immune compromised macrophage, demonstrating significantly different nutritional immune responses in immune competent versus immune compromised macrophages.

cell biology↗
bioRxiv2022

The Botrytis cinerea Gene Expression Browser.

To analyze and visualize comprehensive gene expression patterns in the phytopathogenic fungus Botrytis cinerea, we developed BEB -- a web-based B. cinerea gene expression browser. This tool and associated databases (DB) contain manually-curated RNA-Seq experiments conducted in B. cinerea. BEB allows easy gene expression analyses of genes of interest under different culture conditions by providing publication-ready heatmaps depicting transcripts levels. BEB is a computationally-inexpensive web-based application and gene expression DB that allows effortless visualization of the transcript levels of genes of interest without needing advanced computational skills. BEB also provides details of each experiment under analysis and user-defined gene expression clustering and visualization options. If needed, tables of gene expression values can be downloaded for further exploration, employing more sophisticated bioinformatics tools. The BEB implementation is based on open-source computational technologies that can be easily deployed for other organisms of interest with little additional effort. To demonstrate BEBs usability and potential, we selected genes of interest in B. cinerea to determine their expression patterns across different conditions. We thus focused our analysis on secondary metabolite gene clusters, chromosome-wide gene expression, previously described virulence factors, and reference genes, leading to a comprehensive expression overview of these groups of genes in this relevant fungal phytopathogen.

bioinformatics↗
bioRxiv2023

Integrating gene mutation spectra from tumors and the general population with gene expression topological networks to identify novel cancer driver genes

BackgroundUnderstanding the genetics underlying cancer development and progression is the most important goal of biomedical research to improve patient survival rates. Recently, researchers have proposed computationally combining the mutational burden with biological networks as a novel means to identify cancer driver genes. However, these approaches treated all mutations as having the same functional impact on genes and incorporated gene-gene interaction networks without considering tissue specificity, which may have hampered our ability to identify novel cancer drivers. MethodsWe have developed a framework, DGAT-cancer that integrates the predicted pathogenicity of somatic mutation in cancers and germline variants in the healthy population, with topological networks of gene expression in tumor tissues, and the gene expression levels in tumor and paracancerous tissues in predicting cancer drivers. These features were filtered by an unsupervised approach, Laplacian selection, and those selected were combined by Hotelling and Box-Cox transformations to score genes. Finally, the scored genes were subjected to Gibbs sampling to determine the probability that a given gene is a cancer driver. ResultsThis method was applied to nine types of cancer, and achieved the best area under the precision-recall curve compared to three commonly used methods, leading to the identification of 571 novel cancer drivers. One of the top genes, EEF1A1 was experimentally confirmed as a cancer driver of glioma. Knockdown of EEF1A1 led to a ~ 41-50% decrease in glioma size and improved the temozolomide sensitivity of glioma cells. ConclusionBy combining the pathogenic status of mutational spectra in tumors alongside the spectrum of variation in the healthy population, with gene expression in both tumors and paracancerous tissues, DGAT-cancer has significantly improved our ability to detect novel cancer driver genes.

bioinformatics↗
bioRxiv2023

Model integration of circadian and sleep-wake driven contributions to rhythmic gene expression reveals novel regulatory principles

Transcriptome studies aim at gaining insight into the molecular pathways underlying biological processes. Analyses of gene-expression dynamics in research on circadian rhythms and sleep homeostasis describe these two processes independently, using separate models such as sinusoidal oscillations and exponential saturating functions. Rhythmically expressed genes are, however, influenced by both processes. We therefore implemented a driven, damped harmonic oscillator model which can accommodate both types of dynamics by varying the degree of damping. This makes it possible to estimate the contribution of circadian and sleep-wake driven influences on the expression of a gene within the framework of a single model. We applied the model to cortex, liver, and blood data obtained in mice and humans. The model reliably captured a wide range of rhythmic dynamics under various experimental conditions, including the long-term amplitude reduction of cortical clock-gene rhythms observed after sleep deprivation. Cortical gene expression was generally influenced more by sleep-wake driven than circadian factors, while the opposite was observed in liver and blood. Importantly, the model suggested that sleep-wake state can alter gene expression with a delayed, long-lasting response not previously considered. Our model further predicted that, perhaps paradoxically, the gain in sleep time after sleep deprivation, delayed re-establishing baseline expression rhythms of intrinsically oscillatory transcripts indicating that similar to insufficient sleep, also excess sleep can impact rhythmic gene expression. Because of the tissue- and gene-specific responses, sleep deprivation led to a profound intra- and inter-tissue desynchronization which in the cortex lasted well beyond phenotypic sleep-wake recovery. The results demonstrate that analyzing rhythmic gene expression must take the complex interactions between circadian and sleep-wake influences into account. The model is a versatile tool with a low number of free parameters to fit and predict gene expression under a variety of conditions relevant to society.

systems biology↗
bioRxiv2024

UPF1 is required for gene expression in mitochondria and for the elimination of paternal mtDNA

UPF1 is a conserved RNA helicase in eukaryotes typically defined by its role in nonsense-mediated mRNA decay (NMD). This study presents ChIP-seq evidence indicating an RNA-dependent interaction of UPF1 with mtDNA, consistent with an RNA-mediated association. Mitochondrial localisation was observed as discrete foci throughout the mitochondrial network, frequently in proximity to nucleoids, as confirmed microscopically by immunostaining and GFP tagging of UPF1 in different fly tissues and S2 cells. Depletion of UPF1, but not depletion of the other core NMD factors UPF2 or UPF3, in salivary glands results in smaller mitochondria and, unexpectedly, increased respiratory activity. This increase is observed specifically in salivary glands and correlates with elevated expression of nuclear genes involved in respiratory chain assembly and mitochondrial translation. Changes in mtDNA-encoded gene expression are also observed upon the depletion of UPF1. These findings indicate that UPF1 operates in mitochondria and, independently of canonical NMD, is linked to mitochondrial gene expression and may coordinate nuclear and mitochondrial gene expression in a tissue-specific manner.

cell biology↗
bioRxiv2019

On the cross-population portability of gene expression prediction models

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction. Author summaryAdvances in RNA sequencing technology have reduced the cost of measuring gene expression at a genome-wide level. However, sequencing enough human RNA samples for adequately-powered disease association studies remains prohibitively costly. To this end, modern transcriptome-wide association analysis tools leverage existing paired genotype-expression datasets by creating models to predict gene expression using genotypes. These predictive models enable researchers to perform cost-effective association tests with gene expression in independently genotyped samples. However, most of these models use European reference data, and the extent to which gene expression prediction models work across populations is not fully resolved. We observe that these models predict gene expression worse than expected in a dataset of African-Americans when derived from European-descent individuals. Using simulations, we show that gene expression predictive model performance depends on both the amount of shared genotype predictors as well as the genetic relatedness between populations. Our findings suggest a need to carefully select reference populations for prediction and point to a pressing need for more genetically diverse genotype-expression datasets.

genomics↗
bioRxiv2024

Cross-expression analysis reveals patterns of coordinated gene expression in spatial transcriptomics

Spatial transcriptomics promises to transform our understanding of tissue biology by molecularly profiling individual cells in situ. A fundamental question they allow us to ask is how nearby cells orchestrate their gene expression. Rather than focus on how these cells (samples) communicate with each other, we reframe the problem to investigate how genes (features) coordinate their expression between neighboring cells. To study these phenomena - called cross-expression - we compare all genes to find pairs that coordinate their expression between adjacent cells, thereby avoiding curating gene lists or annotating cell types. Our end-to-end method recovers ligand-receptor pairs as cross-expressing genes and finds gene combinations that mark anatomical regions, complementing marker gene-based region annotation. Leveraging the overlapping genes across different panels, we use multiple atlas-scale adult mouse brain datasets (~25 million cells, 695 samples, 8 technologies) to create an integrated, meta-analytic cross-expression network, whose communities are enriched in spatial processes such as synaptic signaling and G protein coupled receptor activity. Highlighting cross-expressions biological utility, our network shows that genes Drd1 and Gpr6, which are individually implicated in Parkinsons disease (PD) and are being pursued as therapeutic targets, are cross-expressed within the striatum, hinting at their joint role in PD pathophysiology. We provide an efficient R package (https://github.com/gillislab/CrossExpression/) to computationally analyze and visually explore cross-expression patterns, which allow us to better understand how genes coordinate their expression in space to perform tissue-level functions.

bioinformatics↗
bioRxiv2023

Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets

BackgroundRobust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches. ResultsWe established a novel gene expression similarity-based workflow for the discovery of immune cell type signatures that leverages multiple datasets, here four single cell expression datasets from three different cancer types. We used our immune cell signatures to train random forest classifiers for immune cell type assignment of single-cell RNA-seq datasets. We obtained similar or better prediction results compared to commonly used methods for cell type assignment in two independent benchmarking datasets. Our gene signature set yields higher prediction scores than other published immune cell type gene sets in our random forest approach. Discussion and conclusionWe demonstrated the quality of our immune cell signatures and their strong performance in a random forest-based cell typing approach. We argue that classifying cells based on our comparably slim sets of genes accompanied by a random forest-based approach not only matches or outperforms widely used published approaches. It also facilitates unbiased downstream statistical analyses of differential gene expression between cell types for 90% of all genes whose expression profiles have not been used for cell type classification.

genomics↗
bioRxiv2023

Molecular subtypes of high grade serous ovarian cancer across racial groups and gene expression platforms

IntroductionHigh-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by race may contribute to poorer HGSC survival among Black versus non-Hispanic White individuals. MethodsWe included newly generated RNA-Seq data from Black and White individuals, and array-based genotyping data from four existing studies of White and Japanese individuals. We assigned subtypes using K-means clustering. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. Following mapping to The Cancer Genome Atlas (TCGA)-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset. ResultsCluster-specific gene expression was similar across gene expression platforms. Comparing the Black study population to the White and Japanese study populations, the immunoreactive subtype was more common (39% versus 23%-28%) and the differentiated subtype less common (7% versus 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA-Seq data; compared to mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases (Black population HR=0.79 [0.55, 1.13], White population HR=0.86 [0.62, 1.19]). ConclusionsA single, platform-agnostic pipeline can be used to assign HGSC gene expression subtypes. While the observed prevalence of HGSC subtypes varied by race, subtype-specific survival was similar. Statement of SignificanceA single pipeline was used to subtype ovarian high-grade serous carcinoma (HGSC) with array-based or RNA-Seq gene expression data. Subtype distributions differed by race, but subtype-specific survival was similar across racial groups.

cancer biology↗
bioRxiv2021

Comparative analysis of transcriptomic profiles among ascidians, zebrafish, and mice: insights from tissue-specific gene expression

Tissue/organ-specific genes (TSGs) are important not only for understanding organ development and function, but also for investigating the evolutionary lineages of organs in animals. Here, we investigate the TSGs of 9 adult tissues of an ascidian, Ciona intestinalis Type A (Ciona robusta), which lies in the important position of being the sister group of vertebrates. RNA-seq and qRT-PCR identified the Ciona TSGs in each tissue, and BLAST searches identified their homologs in zebrafish and mice. Tissue distributions of the vertebrate homologs were analyzed and clustered using public RNA-seq data for 12 zebrafish and 30 mouse tissues. Among the vertebrate homologs of the Ciona TSGs in the neural complex, 48% and 63% showed high expression in the zebrafish and mouse brain, respectively, suggesting that the central nervous system is evolutionarily conserved in chordates. In contrast, vertebrate homologs of Ciona TSGs in the ovary, pharynx, and intestine were not consistently highly expressed in the corresponding tissues of vertebrates, suggesting that these organs have evolved in Ciona-specific lineages. Intriguingly, more TSG homologs of the Ciona stomach were highly expressed in the vertebrate liver (17-29%) and intestine (22-33%) than in the mouse stomach (5%). Expression profiles for these gene suggest that the biological roles of the Ciona stomach are distinct from those of their vertebrate counterparts. Collectively, Ciona tissues were categorized into 3 groups: i) high similarity to the corresponding vertebrate tissues (neural complex and heart), ii) low similarity to the corresponding vertebrate tissues (ovary, pharynx, and intestine), and iii) low similarity to the corresponding vertebrate tissues, but high similarity to other vertebrate tissues (stomach, endostyle, and siphons). The present study provides transcriptomic catalogs of adult ascidian tissues and significant insights into the evolutionary lineages of the brain, heart, and digestive tract of chordates.

evolutionary biology↗