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Results for “gene expression”

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bioRxiv2026

The obesity associated BDNF intron-3 cis-regulatory element, BE5.1, modulates the effects of early-life stress on weight gain, anxiety and gene expression in a sex dependent manner.

Early-life stress (ELS) has been associated with increases in obesity and anxiety and affects males and females in different ways. Animal and human studies indicate that ELS has maladaptive effects on brain development due, in part, to disruptions in the expression of genes that include brain-derived neurotrophic factor (BDNF). Our previous studies identified a cis-regulatory element (CRE, BE5.1) within BDNF intron-3 that harbours a SNP associated with obesity and anxiety. We used maternal separation (MS) to examine the role of BE5.1 in controlling resilience to the maladaptive effects of ELS. At 9-11 weeks, BE5.1KO mice experience a delay in weight gain compared to WT mice, a phenotype partly reversed by MS in BE5.1KO females. The effects of either MS or BE5.1KO decreased anxiety-like behaviour in males whereas MS had little effect in females who displayed increased anxiety-like behaviour in animals lacking BE5.1. We also found that social behaviours are influenced by BE5.1 deletion in females but are more influenced by MS in males. Furthermore, deletion of BE5.1 and MS together significantly decreased marble burying behaviour in male mice, an effect negated by diazepam. RNA-seq analyses suggest that, in hypothalamus, BE5.1 is required to stabilize or down-regulate the transcription of 26 key genes, with diverse neuroregulatory roles, in response to MS as these 26 genes are strongly upregulated in the absence of BE5.1. We also provide evidence that three of these genes; Prkcg, Foxg1 and Mef2c, modulate BE5.1 activity in an allele-specific manner. Taken together, these results suggest that the BE5.1CRE acts as a buffer element that protects against the maladaptive effects of MS on gene expression and later life behaviours in a sex-dependent manner.

molecular biology↗

bioRxiv2026

Chromatin states coordinate diurnal gene expression in the Drosophila retina

Daily biological rhythms enable organisms to anticipate and respond to predictable diurnal environmental changes. While the circadian clock maintains rhythms under constant conditions, it remains unclear what gates the time-of-day expression of genes that are rhythmic under light:dark cycles. In the retina, restricting biological processes to specific times of the day is crucial for homeostasis because disrupting these rhythms causes retinal degeneration. We show that in light-sensitive photoreceptor neurons of Drosophila, the circadian transcription factors only account for the timing of a minority of rhythmic genes. Instead, integrative analysis of the daily chromatin landscape and transcription factor footprints reveals that promoter, enhancer, or repressive signatures strongly correspond with phase. Further, genetic modulation of the repressive and promoter chromatin states altered gene expression in a phase-specific manner. Together, our data support a model where the genome is organized into discrete chromatin signatures that collaborate with transcription factors to restrict diurnal transcription to distinct phases of the day.

genomics↗

bioRxiv2024

Haplotype phased genome of 'Fairchild' mandarin highlights influence of local chromatin state on gene expression

BackgroundCis-regulatory sequences control gene expression through the coordinated action of transcription factors and their associated partners. Both genetic and epigenetic perturbation of cis-regulatory sequences can lead to novel patterns of gene expression. Phased genome assemblies now enable the local dissection of linkages between cis-regulatory sequences, including their epigenetic state, and gene expression to further characterize gene regulation in heterozygous genomes. ResultsWe assembled a locally phased genome for a mandarin hybrid named Fairchild to explore the molecular signatures of allele-specific gene expression. With genome phasing, genes with allele-specific expression were paired with haplotype-specific chromatin states, including levels of chromatin accessibility, histone modifications, and DNA methylation. We found that 30% of variation in allele-specific expression could be attributed to haplotype associated factors, with allelic levels of chromatin accessibility and three histone modifications in gene bodies having the most influence. Structural variants in promoter regions were also associated with allele-specific expression, including specific enrichments of hAT and MULE-MuDR DNA transposon sequences. Mining of cis-regulatory sequences underlying regions with allelic variation in chromatin accessibility revealed a paternally-associated sequence motif bound by ERF48, a target of the Polycomb repressive complex 2 (PRC2), and sequence similarity of this motif corresponded to local levels of H3K27me3, a signature of PRC2 activity. ConclusionsUsing a locally phased assembly of a heterozygous citrus cultivar, we dissected the interplay between genetic variants and molecular phenotypes with the goal of revealing functional cis-regulatory sequences and exploring the evolution of gene regulation.

genomics↗

bioRxiv2024

Genetic variants associated with cell-type-specific intra-individual gene expression variability reveal new mechanisms of genome regulation

Gene expression levels can vary substantially across cells, even in a seemingly homogeneous cell population. Identifying the relationships between genetic variation and gene expression is critical for understanding the mechanisms of genome regulation. However, the genetic control of gene expression variability among the cells within individuals has yet to be extensively examined. This is primarily due to the statistical challenges, such as the need for sufficiently powered cohorts and adjusting mean-variance dependence. Here, we introduce MEOTIVE (Mapping genetic Effects On inTra-Individual Variability of gene Expression), a novel statistical framework to identify genetic effects on the gene expression variability (sc-veQTL) accounting for the mean-variance dependence. Using single-cell RNA-seq data of 1.2 million peripheral blood mononuclear cells from 980 human donors, we identified 14 - 3,488 genes with significant sc-veQTLs (study-wide q-value < 0.05) across different blood cell types, 2,103 of which were shared across more than one cell type. We further detected 55 SNP-gene pairs (in 34 unique genes) by directly linking genetic variations with gene expression dispersion (sc-deQTL) regardless of mean-variance dependence, and these genes were enriched in biological processes relevant to immune response and viral infection. An example is rs1131017 (p<9.08x10-52), a sc-veQTL in the 5UTR of RPS26, which shows a ubiquitous dispersion effect across cell types, with higher dispersion levels associated with lower auto-immune disease risk, including rheumatoid arthritis and type 1 diabetes. Another example is LYZ, which is associated with antibacterial activity against bacterial species and was only detected with a monocyte-specific deQTL (rs1384) located at the 3 UTR region (p=1.48x10-11) and replicated in an independent cohort. Our results demonstrate an efficient and robust statistical method to identify genetic effects on gene expression variability and how these associations and their involved pathways confer auto-immune disease risk. This analytical framework provides a new approach to unravelling the genetic regulation of gene expression at the single-cell resolution, advancing our understanding of complex biological processes.

genomics↗

bioRxiv2022

Predicting gene expression from cell morphology in human induced pluripotent stem cells

Purification is essential before differentiating human induced pluripotent stem cells (hiPSCs) into cells that fully express particular differentiation marker genes. High-quality iPSC clones are typically purified through gene expression profiling or visual inspection of the cell morphology; however, the relationship between the two methods remains unclear. We investigated the relationship between gene expression levels and morphology by analyzing live-cell phase-contrast images and mRNA profiles collected during the purification process. We employed this data and an unsupervised image feature extraction method to build a model that predicts gene expression levels from morphology. As a benchmark, we confirmed that the method can predict the gene expression levels from tissue images for cancer genes, performing as well as state-of-the-art methods. We then applied the method to iPSCs and identified two genes that are well-predicted from cell morphology. Although strong batch effects resulting from the reprogramming process preclude the ability to use the same model to predict across batches, prediction within a reprogramming batch is sufficiently robust to provide a practical approach for estimating expression levels of a few genes and monitoring the purification process.

bioengineering↗

bioRxiv2020

Tissue heterogeneity is prevalent in gene expression studies

BackgroundLack of reproducibility in gene expression studies has recently attracted much attention in and beyond the biomedical research community. Previous efforts have identified many underlying factors, such as batch effects and incorrect sample annotations. Recently, tissue heterogeneity, a consequence of unintended profiling of cells of other origins than the tissue of interest, was proposed as a source of variance that exacerbates irreproducibility and is commonly ignored. ResultsHere, we systematically analyzed 2,692 publicly available gene expression datasets including 78,332 samples for tissue heterogeneity. We found a prevalence of tissue heterogeneity in gene expression data that affects on average 5-15% of the samples, depending on the tissue type. We distinguish cases of severe heterogeneity, which may be caused by mistakes in annotation or sample handling, from cases of moderate heterogeneity, which are more likely caused by tissue infiltration or sample contamination. ConclusionsTissue heterogeneity is a widespread issue in publicly available gene expression datasets and thus an important source of variance that should not be ignored. We advocate the application of quality control methods such as BioQC to detect tissue heterogeneity prior to mining or analysing gene expression data.

bioinformatics↗

bioRxiv2023

Gene expression shifts in Emperor penguin adaptation to the extreme Antarctic environment

Gene expression can accelerate ecological divergence by rapidly tweaking the response of an organism to novel environments, with more divergent environments exerting stronger selection and supposedly, requiring faster adaptive responses. Organisms adapted to extreme environments provide ideal systems to test this hypothesis, particularly when compared to related species with milder ecological niches. The Emperor penguin (Aptenodytes forsteri) is the only warm-blooded vertebrate breeding in the harsh Antarctic winter, in stark contrast with the less cold-adapted sister species, the King penguin (A. patagonicus). Assembling the first de novo transcriptomes and analysing multi-tissue (brain, kidney, liver, muscle, skin) RNAseq data from natural populations of both species, we quantified the shifts in tissue-enhanced genes, co-expression gene networks, and differentially expressed genes characterising Emperor penguin adaptation to extreme Antarctic ecology. Our analyses revealed the crucial role played by muscle and liver in temperature homeostasis, fasting and whole-body energy metabolism (glucose/insulin regulation, lipid metabolism, fatty acid beta-oxidation, and blood coagulation). Repatterning at the regulatory level appears as more important in the brain of the Emperor penguin, showing the lowest signature of differential gene expression but the largest co-expression gene network shift. Nevertheless, over-expressed genes related to mTOR signalling in the brain and the liver support their central role in cold and fasting responses. Besides contributing to understanding the genetics underlying complex traits, like body energy reservoir management, our results provide a first insight into the role of gene expression in adaptation to one of the most extreme environmental conditions endured by an endotherm.

evolutionary biology↗

bioRxiv2020

Leaf trichome distribution pattern in Arabidopsis reveals gene expression variation associated with environmental adaptation

Gene expression varies stochastically even in both heterogenous and homogeneous cell populations. This variation is not simply useless noise; rather, it is important for many biological processes. Unicellular organisms or cultured cell lines are useful for analyzing the variation in gene expression between cells; however, owing to technical challenges, the biological relevance of this variation in multicellular organisms such as higher plants remains unclear. Here, we addressed the biological relevance of this variation between cells by examining the genetic basis of trichome distribution patterns in Arabidopsis thaliana. The distribution pattern of a trichome on a leaf is stochastic and can be mathematically represented using Turings reaction-diffusion (RD) model. We analyzed simulations based on the RD model and found that the variability in the trichome distribution pattern increased with the increase in stochastic variation in a particular gene expression. Moreover, differences in heat-dependent variability of the trichome distribution pattern between the accessions showed a strong correlation with environmental factors to which each accession was adapted. Taken together, we successfully visualized variations in gene expression by quantifying the variability in the Arabidopsis trichome distribution pattern. Thus, our data provide evidence for the biological importance of variations in gene expression for environmental adaptation.

plant biology↗

bioRxiv2015

CIDER: a pipeline for detecting waves of coordinated transcriptional regulation in gene expression time-course data

Cell adaptability to environmental changes is conferred by complex transcriptional regulatory networks, which respond to external stimuli by modulating the expression dynamics of each gene. Hence, deciphering the network of transcriptional regulation is remarkably important, but proves to be extremely challenging, mainly due to the unfavorable ratio between the number of available observations and the number of parameters to estimate. Most of the existing computational methods for the inference of transcriptional networks consider steady-state gene expression datasets, and produce models of transcriptional regulation best explaining the observed static gene expression.\n\nGene expression time-courses are an emergent typology of gene expression data, paving the way to the characterization of the time-dependent dynamics of transcriptional regulation.\n\nIn this work we introduce the Complexity Invariant Dynamic Time Warping motif EnRichment (CIDER) analysis, a novel computational pipeline to identify the prominent waves of coordinated gene transcription induced in cells by external stimuli, and determine which TFs are involved in the coordination of gene transcription. The CIDER pipeline combines unsupervised time series clustering and motif enrichment analysis to first detect transcriptional expression patterns, and then identify the TFs over-represented in the promoter regions of gene sets with similar expression dynamics.\n\nThe ability of CIDER to correctly identify regulatory interactions is assessed on a realistic synthetic dataset of gene expression time-courses, generated by simulating the effects of knock-out perturbations on the E. coli regulatory network.\n\nThe CIDER source code and the validation datasets are available on request from the corresponding author.

Bioinformatics↗

bioRxiv2020

Harnessing the central dogma for stringent multi-level control of gene expression

Strictly controlled inducible gene expression is crucial when engineering biological systems where even tiny amounts of a protein have a large impact on function or host cell viability. In these cases, leaky protein production must be avoided at all costs, but ideally without affecting the achievable range of expression. Here, we demonstrate how the central dogma offers a simple way to effectively address this challenge. By simultaneously regulating both transcription and translation, we show how relative basal expression of an inducible system can be greatly reduced, with minimal impact on the maximum induced expression rate. Using this approach, we create several stringent expression systems displaying >1000-fold change in their output after induction in vivo and up to a 350-fold change when used in a cell-free expression system. Furthermore, we find that multi-level regulation is able to suppress transcriptional noise and creates a digital-like switch when transitioning between on and off states. This work provides foundational knowledge and a genetic toolkit of parts to create multi-level gene expression controllers for those working with toxic genes or requiring precise regulation and propagation of cellular signals. It also demonstrates the value of exploring more complex and diverse regulatory designs for synthetic biology.

synthetic biology↗

bioRxiv2020

Analysis of gene expression and connectivity on hippocampus of Alzheimer's disease by a new comprehensive approach

Gene expression and gene connectivity describe two different functional aspects of a gene. These two different measures reveal different information about the involvement of genes in disorders. Previous case-control gene expression studies have often focused on expression level of individual genes. Correlated expression relationships among genes, measured as gene connectivity, have obtained limited attention. We developed a comprehensive method, TRIple Differentiation (TRID), to assess these two measures, both separately and jointly. We applied TRID to gene expression data in hippocampus tissue samples from three Alzheimers disease (AD) microarray datasets. Following TRID, comparisons among the three datasets showed poor consistency for disease-associated individual genes but reproducible changes of disease-associated biological pathways annotated for functional protein-protein interaction (PPI) modules identified from network analysis. Our results suggest that changes of gene expression in hippocampus of AD patients are highly heterogeneous at the individual gene level, while biological pathways annotated for PPI modules identified based on TRID weights demonstrate consistency among the three datasets. The R package TRID can be accessed from GitHub (https://github.com/shannjiang/TRID).

systems biology↗

bioRxiv2018

Satellite DNA-containing gigantic introns in a unique gene expression program during Drosophila spermatogenesis

Intron gigantism, where genes contain megabase-sized introns, is observed across species, yet little is known about its purpose or regulation. Here we identify a unique gene expression program utilized for the proper expression of genes with intron gigantism. We find that two Drosophila genes with intron gigantism, kl-3 and kl-5, are transcribed in a spatiotemporal manner over the course of spermatocyte differentiation, which spans ~90 hours. The introns of these genes contain megabases of simple satellite DNA repeats that comprise over 99% of the gene loci, and these satellite-DNA containing introns are transcribed. We identify two RNA-binding proteins that specifically localize to kl-3 and kl-5 transcripts and are needed for the successful transcription or processing of these genes. We propose that genes with intron gigantism require a unique gene expression program, which may serve as a platform to regulate gene expression during cellular differentiation.

developmental biology↗

bioRxiv2019

Meta-analysis of Gene Expression Microarray Datasets in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls. We used pre-existing, publicly available microarray expression datasets to conduct a meta-analysis. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we ran an analysis of variance with a linear model with disease state, age, sex, smoking status and study as effects that also included binary interactions. We found 1,513 statistically significant (Benjamini-Hochberg-adjusted p-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from a Tukey test post-hoc analysis (Benjamini-Hochberg-adjusted p-value <0.05 and 10% two-tailed quantiles of mean differences between COPD and control), to identify 304 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the 304 genes as features, which enabled prediction of disease status with 84% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures.

molecular biology↗

bioRxiv2020

Control of Vein-Forming, Striped Gene-Expression by Auxin Signaling

Activation of gene expression in striped domains is a key building block of biological patterning, from the recursive formation of veins in plant leaves to that of ribs and vertebrae in our bodies. In animals, gene expression is activated in striped domains by the differential affinity of broadly expressed transcription factors for their target genes and the combinatorial interaction between such target genes. In plants, how gene expression is activated in striped domains is instead unknown. We address this question for the broadly expressed MONOPTEROS (MP) transcription factor and its target gene ARABIDOPSIS THALIANA HOMEOBOX FACTOR8 (ATHB8). We find that ATHB8 promotes vein formation and that such vein-forming function depends on both levels of ATHB8 expression and width of ATHB8 expression domains. We further find that ATHB8 expression is activated in striped domains by a combination of (1) activation of ATHB8 expression through binding of peak levels of MP to a low-affinity MP-binding site in the ATHB8 promoter and (2) repression of ATHB8 expression by MP target genes of the INDOLE-3-ACETIC-ACID-INDUCIBLE family such as BODENLOS. Our findings suggest that a common regulatory logic controls activation of gene expression in striped domains in both plants and animals despite the independent evolution of their multicellularity.

plant biology↗

bioRxiv2023

Evolution of gene expression across brain regions in behaviorally divergent deer mice

The evolution of innate behaviors is ultimately due to genetic variation likely acting in the nervous system. Gene regulation may be particularly important because it can evolve in a modular brain-region specific fashion through the concerted action of cis- and trans-regulatory changes. Here, to investigate transcriptional variation and its regulatory basis across the brain, we perform RNA sequencing (RNA-Seq) on ten brain subregions in two sister species of deer mice (Peromyscus maniculatus and P. polionotus) - which differ in a range of innate behaviors, including their social system - and their F1 hybrids. We find that most of the variation in gene expression distinguishes subregions, followed by species. Interspecific differential expression (DE) is pervasive (52-59% of expressed genes), whereas the number of DE genes between sexes is modest overall ([~]3%). Interestingly, the identity of DE genes varies considerably across brain regions. Much of this modularity is due to cis-regulatory divergence, and while 43% of genes were consistently assigned to the same gene regulatory class across subregions (e.g., conserved, cis-, or trans-regulatory divergence), a similar number were assigned to two or more different gene regulatory classes. Together, these results highlight the modularity of gene expression differences and divergence in the brain, which may be key to explain how the evolution of brain gene expression can contribute to the astonishing diversity of animal behaviors.

evolutionary biology↗

bioRxiv2023

Tools for Genetic Engineering and Gene Expression Control in Novosphingobium aromaticivorans and Rhodobacter sphaeroides

Alphaproteobacteria have a variety of cellular and metabolic features that provide important insights into biological systems and enable biotechnologies. For example, some species are capable of converting plant biomass into valuable biofuels and bioproducts have the potential to form the backbone of the sustainable bioeconomy. Among the Alphaproteobacteria, Novosphingobium aromaticivorans, Rhodobacter sphaeroides, and Zymomonas mobilis, show particular promise as organisms that can be engineered to convert extracted plant lignin or sugars into bioproducts and biofuels. Genetic manipulation of these bacteria is needed to introduce engineered pathways and modulate expression of native genes with the goal of enhancing bioproduct output. Although recent work has expanded the genetic toolkit for Z. mobilis, N. aromaticivorans and R. sphaeroides still need facile, reliable approaches to deliver genetic payloads to the genome and to control gene expression. Here, we expand the platform of genetic tools for N. aromaticivorans and R. sphaeroides to address these issues. We demonstrate that Tn7 transposition is an effective approach for introducing engineered DNA into the chromosome of N. aromaticivorans and R. sphaeroides. We screen a synthetic promoter library to identify inducible promoters with strong, regulated activity in both organisms. Combining Tn7 integration with promoters from our library, we establish CRISPR interference systems for N. aromaticivorans and R. sphaeroides that can target essential genes and modulate engineered pathways. We anticipate that these systems will greatly facilitate both genetic engineering and gene function discovery efforts in these industrially important species and other Alphaproteobacteria. IMPORTANCEIt is important to increase our understanding of the microbial world to improve health, agriculture, the environment and biotechnology. For example, building a sustainable bioeconomy depends on the efficient conversion of plant material to valuable biofuels and bioproducts by microbes. One limitation in this conversion process is that microbes with otherwise excellent properties for conversion are challenging to genetically engineer. Here, we report systems to overcome that barrier in the Alphaproteobacteria, Novosphingobium aromaticivorans and Rhodobacter sphaeroides, by producing genetic tools that allow easy insertion of engineered pathways into their genomes and to precisely control gene expression by inducing genes with synthetic promoters or repressing genes using CRISPR interference. These tools can be used in future work to gain additional insight into these and other Alphaproteobacteria and to optimize yield of biofuels and bioproducts.

microbiology↗

bioRxiv2024

Engineered Transcription Factor Binding Arrays for DNA-based Gene Expression Control in Mammalian Cells

Manipulating gene expression in mammalian cells is critical for cell engineering applications. Here we explore the potential of transcription factor (TF) recognition element arrays as DNA tools for modifying free TF levels in cells and thereby controlling gene expression. We first demonstrate proof-of-concept, showing that Tet TF-binding recognition element (RE) arrays of different lengths can tune gene expression and alter gene circuit performance in a predictable manner. We then open-up the approach to interface with any TF with a known binding site by developing a new method called Cloning Troublesome Repeats in Loops (CTRL) that can assemble plasmids with up to 256 repeats of any RE sequence. Transfection of RE array plasmids assembled by CTRL into mammalian cells show potential to modify host cell gene regulation at longer array sizes by sequestration of the TF of interest. RE array plasmids built using CTRL were demonstrated to target both synthetic and native mammalian TFs, illustrating the ability to use these tools to modulate genetic circuits and instruct cell fate. Together this work advances our ability to assemble repetitive DNA arrays and showcases the use of TF-binding RE arrays as a method for manipulating mammalian gene expression, thus expanding the possibilities for mammalian cell engineering.

synthetic biology↗

bioRxiv2024

Aberrant expression of histone H2B variants reshape chromatin and alter oncogenic gene expression programs

Chromatin architecture governs DNA accessibility and gene expression. Thus, any perturbations to chromatin can significantly alter gene expression programs and promote disease. Prior studies demonstrate that every amino acid in a histone is functionally significant, and that even a single amino acid substitution can drive specific cancers. We previously observed that naturally occurring H2B variants are dysregulated during the epithelial to mesenchymal transition (EMT) in bronchial epithelial cells. Naturally occurring H2B variants differ from canonical H2B by only a few amino acids, yet single amino acid changes in other histone variants (e.g., H3.3) can drive cancer. We therefore hypothesized that H2B variants might function like oncohistones, and investigated how they modify chromatin architecture, dynamics, and function. We find that H2B variants are frequently dysregulated in many cancers, and correlate with patient prognosis. Despite high sequence similarity, mutations in each H2B variant tend to occur at specific "hotspots" in cancer. Some H2B variants cause tighter DNA wrapping around nucleosomes, leading to more compact chromatin structures and reduced transcription factor accessibility to nucleosomal DNA. They also altered genome-wide accessibility to oncogenic regulatory elements and genes, with concomitant changes in oncogenic gene expression programs. Although we did not observe changes in cell proliferation or migration in vitro, our Gene Ontology (GO) analyses of ATAC-seq peaks and RNA-seq data indicated significant changes in oncogenic pathways. These findings suggest that H2B variants may influence early-stage, cancer-associated regulatory mechanisms, potentially setting the stage for oncogenesis later on. Thus, H2B variant expression could serve as an early cancer biomarker, and H2B variants might be novel therapeutic targets.

cancer biology↗