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PubMed2026

Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.

We present Tahoe-100M, a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions. This parallel profiling of thousands of perturbations at single-cell resolution with minimal batch effects is enabled by the Mosaic platform, which multiplexes genetically distinct cell models into balanced "cell villages." Beyond cataloging transcriptomic shifts, Tahoe-100M systematically quantifies cellular phenotypes, including proliferation, cytotoxicity, lineage-specific vulnerabilities, and cell-cycle changes. It captures population-level transcriptomic heterogeneity, characterizing whether drug responses drive cells toward divergent fates or convergent states. Pathway-based signatures define drug-induced expression programs, classify mechanisms of action, reveal off-target activities, and expose adaptive stress responses associated with resistance. By unifying cellular and molecular readouts, this broadly applicable perturbation atlas advances our ability to model gene regulation, drug response, and network dynamics. Its public release enables the training of AI frameworks to advance predictive models of cell behavior.

Humans↗
PubMed2026

Comprehensive Viral Detection and Profiling of Plasma Cell-Free RNA in Patients With Suspected Hemophagocytic Lymphohistiocytosis.

Hemophagocytic lymphohistiocytosis (HLH) is a severe, rapidly progressive disease. While viral infection is considered a common etiology of pediatric HLH, specific causative viruses other than the Epstein-Barr virus (EBV) have been rarely identified. This study utilized metagenomic next-generation sequencing (NGS) to identify potential causative pathogens in plasma samples from 17 pediatric patients with suspected HLH. Additionally, one case each of confirmed EBV- and cytomegalovirus (CMV)-associated HLH was analyzed for methodological validation. Plasma cell-free RNA (cfRNA) profiling was performed using NGS data to assess the host transcriptome response. Significant viral reads of human herpesvirus-6B, human herpesvirus-7, and Hubei reo-like virus (HRLV) 14 were detected using metagenomic NGS in one patient each. Plasma cfRNA profiles from five patients with viral infection (including EBV and CMV) were compared to those of 14 patients without viral infection. By comparing the two patient groups, 1053 differentially expressed genes were identified. The gene ontology (GO) term of "adaptive immune response" (GO: 0002250) was significantly enriched among upregulated genes in the virus-positive group. Furthermore, an isolated cluster consisting specifically of mitochondrial RNAs, was identified in the upregulated genes of the virus-positive group. Using metagenomic NGS, several candidate viral pathogens were identified in patients with suspected infection-related HLH. The viral genome of HRLV 14, previously undetected in human clinical samples, was identified in one patient. The results from plasma cfRNA profiling suggest that mitochondrial RNAs may reflect the underlying pathogenesis of virus-associated HLH and have potential utility as disease biomarkers.

Humans↗
PubMed2026

Advancing insect research through cell line transcriptomics.

This review emphasizes the significance of insect cell lines in transcriptomic research, highlighting their role as vital tools for uncovering cellular and molecular mechanisms of insect physiology, immune responses, and adaptation to environmental stressors. Cell lines derived from tissues such as the midgut, fat body, nervous system, and reproductive organs enable researchers to examine gene expression changes in a controlled setting, making discoveries that are difficult to achieve through whole-organism studies. High-throughput sequencing and single-cell RNA sequencing (scRNA-seq) have identified genes linked to detoxification, stress response, development, and immune defense, offering valuable insights for future applications in agriculture, pest control, and biotechnology. To organize this information clearly, we have summarized key findings in a table, providing an accessible overview of each cell line's important roles in transcriptomic research. This method not only highlights the adaptability of insect cell lines in functional genomics but also underscores their usefulness as model systems in pest management, virology, and bioengineering. Through utilizing transcriptomics, insect cell lines continue to advance our understanding of insect biology and foster the development of innovative strategies for sustainable crop protection and biotechnological use.

Animals↗
PubMed2006

OMICS-driven biomarker discovery in nutrition and health.

While traditional nutrition research has dealt with providing nutrients to nourish populations, it nowadays focuses on improving health of individuals through diet. Modern nutritional research is aiming at health promotion and disease prevention and on performance improvement. As a consequence of these ambitious objectives, the disciplines "nutrigenetics" and "nutrigenomics" have evolved. Nutrigenetics asks the question how individual genetic disposition, manifesting as single nucleotide polymorphisms, copy-number polymorphisms and epigenetic phenomena, affects susceptibility to diet. Nutrigenomics addresses the inverse relationship, that is how diet influences gene transcription, protein expression and metabolism. A major methodological challenge and first pre-requisite of nutrigenomics is integrating genomics (gene analysis), transcriptomics (gene expression analysis), proteomics (protein expression analysis) and metabonomics (metabolite profiling) to define a "healthy" phenotype. The long-term deliverable of nutrigenomics is personalised nutrition for maintenance of individual health and prevention of disease. Transcriptomics serves to put proteomic and metabolomic markers into a larger biological perspective and is suitable for a first "round of discovery" in regulatory networks. Metabonomics is a diagnostic tool for metabolic classification of individuals. The great asset of this platform is the quantitative, non-invasive analysis of easily accessible human body fluids like urine, blood and saliva. This feature also holds true to some extent for proteomics, with the constraint that proteomics is more complex in terms of absolute number, chemical properties and dynamic range of compounds present. Apart from addressing the most complex "-ome", proteomics represents the only platform that delivers not only markers for disposition and efficacy but also targets of intervention. The Omics disciplines applied in the context of nutrition and health have the potential to deliver biomarkers for health and comfort, reveal early indicators for disease disposition, assist in differentiating dietary responders from non-responders, and, last but not least, discover bioactive, beneficial food components. This paper reviews the state-of-the-art of the three Omics platforms, discusses their implication in nutrigenomics and elaborates on applications in nutrition and health such as digestive health, allergy, diabetes and obesity, nutritional intervention and nutrient bioavailability. Proteomic developments, applications and potential in the field of nutrition have been specifically addressed in another review issued by our group.

Animals↗
PubMed2024

Acute MeCP2 loss in adult mice reveals transcriptional and chromatin changes that precede neurological dysfunction and inform pathogenesis.

Mutations in the X-linked methyl-CpG-binding protein 2 (MECP2) gene cause Rett syndrome, a severe childhood neurological disorder. MeCP2 is a well-established transcriptional repressor, yet upon its loss, hundreds of genes are dysregulated in both directions. To understand what drives such dysregulation, we deleted Mecp2 in adult mice, circumventing developmental contributions and secondary pathogenesis. We performed time series transcriptional, chromatin, and phenotypic analyses of the hippocampus to determine the immediate consequences of MeCP2 loss and the cascade of pathogenesis. We find that loss of MeCP2 causes immediate and bidirectional progressive dysregulation of the transcriptome. To understand what drives gene downregulation, we profiled genome-wide histone modifications and found that a decrease in histone H3 acetylation (ac) at downregulated genes is among the earliest molecular changes occurring well before any measurable deficiencies in electrophysiology and neurological function. These data reveal a molecular cascade that drives disease independent of any developmental contributions or secondary pathogenesis.

Animals↗
PubMed2006

Application of transcriptional and biological network analyses in mouse germ-cell transcriptomes.

Serial analysis of gene expression (SAGE) provides a global analysis platform for profiling mRNA populations present in cells of interest without the constraint of gene selection and the ambiguous nature of data obtained. However, most of the reports on SAGE and germ cell development are limited to descriptive analyses. Here, we report a series of bioinformatic analyses using recently published SAGE data on the transcriptome of mouse type A spermatogonia (Spga), pachytene spermatocytes (Spcy), and round spermatids (Sptd). Tags with a total count of > or =20 in three SAGE libraries were examined. Our aim was to identify and discover potential transcriptional regulators and pathways involved at different stages of spermatogenesis. Unsupervised hierarchical clustering based on tag expression and Gene Ontology analysis were applied to identify genes and biological processes overrepresented at a particular stage of development. The 5' cis-regulatory elements were examined for common regulators in different functional clusters. Potential biological networks were also constructed to reveal the link between the gene candidates. Biological pathways related to the three germ cell stages were constructed. A number of known transcription regulators in spermatogenesis, including NF-kappaB, SP1, AP-1, and EGR, were identified. Novel promoter elements such as the E box in Spga-specific genes, GATA in Spcy-specific genes, and GKLF in Sptd-specific genes were also observed. Taken together, our approach is reliable and provides a foundation for the generation of novel biological hypotheses for studying spermatogenesis.

Animals↗
PubMed2005

Transcriptome analysis of the secretion stress response of Bacillus subtilis.

Transcription profiling of all protein-encoding genes of Bacillus subtilis was carried out under several secretion stress conditions in the exponential growth phase. Cells that secreted AmyQ alpha-amylase at a high level were stressed only moderately: seven genes were induced, most significantly htrA and htrB, encoding quality control proteases, and yqxL, encoding a putative CorA-type Mg(2+) transporter. These three genes were induced more strongly by severe secretion stress (prsA3 mutant secreting AmyQ), suggesting that their expression responds to protein misfolding. In addition, 17 other genes were induced, including the liaIHGFSR (yvqIHGFEC) operon, csaA and ffh, encoding chaperones involved in the pretranslocational phase of secretion, and genes involved in cell wall synthesis/modification. Severe secretion stress caused downregulation of 23 genes, including the prsA paralogue yacD. Analysis of a cssS knockout mutant indicated that the absence of the CssRS two-component system, and consequently the absence of the HtrA and HtrB proteases, caused secretion stress. The results also suggest that the htrA and htrB genes comprise the CssRS regulon. B. subtilis cells respond to secretion/folding stress by various changes in gene expression, which can be seen as an attempt to combat the stress condition.

Bacillus subtilis↗
PubMed2005

Pathway and ontology analysis: emerging approaches connecting transcriptome data and clinical endpoints.

The increasing use of gene expression profiling offers great promise in clinical research into disease biology and its treatment. Along with the ability to measure changing expression levels in thousands of genes at once, comes the challenge of analyzing and interpreting the vast sets of data generated. Analysis tools are evolving rapidly to meet such challenges. The next step is to interpret observed changes in terms of the biological properties or relationships underlying them. One powerful approach is to make associations between the genes that are under investigation and well-known biochemical or signaling pathways, and further to assess the significance of such associations. Similarly, genes can be mapped to standardized biological categories via an ontology resource. We discuss these approaches and several web-based resources and tools designed to facilitate such analyses. This information can be used to facilitate understanding and to help design more focused experiments for validating the relevance and importance of these biological pathways and processes in human disease and therapeutics.

Animals↗
PubMed1999

The Genexpress IMAGE knowledge base of the human brain transcriptome: a prototype integrated resource for functional and computational genomics.

Expression profiles of 5058 human gene transcripts represented by an array of 7451 clones from the first IMAGE Consortium cDNA library from infant brain have been collected by semiquantitative hybridization of the array with complex probes derived by reverse transcription of mRNA from brain and five other human tissues. Twenty-one percent of the clones corresponded to transcripts that could be classified in general categories of low, moderate, or high abundance. These expression profiles were integrated with cDNA clone and sequence clustering and gene mapping information from an upgraded version of the Genexpress Index. For seven gene transcripts found to be transcribed preferentially or specifically in brain, the expression profiles were confirmed by Northern blot analyses of mRNA from eight adult and four fetal tissues, and 15 distinct regions of brain. In four instances, further documentation of the sites of expression was obtained by in situ hybridization of rat-brain tissue sections. A systematic effort was undertaken to further integrate available cytogenetic, genetic, physical, and genic map informations through radiation-hybrid mapping to provide a unique validated map location for each of these genes in relation to the disease map. The resulting Genexpress IMAGE Knowledge Base is illustrated by five examples presented in the printed article with additional data available on a dedicated Web site at the address http://idefix.upr420.vjf.cnrs.fr/EXPR++ +/ welcome.html.

Brain Chemistry↗
PubMed2004

Gene expression profiling of human cancers.

DNA microarrays allow us to visualize simultaneously the expression of potentially all genes within a cell population or tissue sample-revealing the "transcriptome." The analysis of this type of data is commonly called "gene expression profiling" (GEP) because it provides a comprehensive picture of the pattern of gene expression in a particular biological sample. For this reason microarrays are revolutionizing life sciences research and are leading to the development of novel and powerful methods for investigating cancer biology, classifying cancers, and predicting clinical outcome of cancers. Several recent high-profile reports have revealed how clustering of GEP data can clearly identify clinically (and prognostically) important subtypes of cancer among patients considered by established clinicopathological criteria to have similar tumors. Accurate "prognostic signatures" can be obtained from GEP data, which represent relatively small numbers of genes. These signatures can be valuable in directing appropriate treatment and in predicting clinical outcome, and they generally outperform other systems based on clinical and histological criteria. In this paper the basic principles of DNA microarray technology and the different types of microarray platforms available will be introduced, and the power of the technique will be illustrated by reviewing some recent GEP studies on selected cancers, including a preliminary analysis of hepatocellular carcinoma from our Palermo laboratory. GEP is likely to be adopted in the future as a key decision-making tool in the clinical arena. However, several issues relating to data analysis, reproducibility, cross-comparability, validation, and cost need to be resolved before the technology can be adopted broadly in this context.

Carcinoma, Hepatocellular↗
PubMed2026

Human brown fat metabolism associates with systemic branched-chain amino acids homeostasis.

Circulating branched-chain amino acids (BCAAs) are linked with insulin resistance, but the human tissues contributing to systemic BCAA homeostasis remain incompletely defined. Brown adipose tissue (BAT) is a metabolically active adipose depot associated with favourable insulin sensitivity, yet its role in BCAA metabolism in humans remains unclear. We tested whether human BAT metabolism is associated with circulating BCAA levels, BAT-resident BCAA-catabolic signatures, and longitudinal changes in systemic BCAA homeostasis. We studied 83 adults who underwent metabolic phenotyping, PET-CT assessment of cold-stimulated BAT metabolism, and serum metabolomic profiling at room temperature and during acute mild cold exposure. Supraclavicular BAT biopsies from 25 participants were analysed by transcriptomics and metabolomics, and 40 participants were re-examined for circulating BCAA profiles after approximately five years. Participants with high BAT metabolism had lower circulating BCAA levels than those with low BAT metabolism. Within BAT, metabolically active individuals exhibited lower relative BCAA abundance together with higher expression of genes involved in BCAA catabolism. These BAT BCAA-catabolic signatures aligned with thermogenic capacity and indices of systemic insulin sensitivity. In contrast, individuals with low BAT metabolism showed increases in circulating BCAAs over five years. Integrative analyses further linked circulating lipopolysaccharide, a marker of metabolic endotoxemia, with higher BAT BCAA and aminomalonate abundance, together with transcriptional patterns involving inflammatory and mitochondrial pathways. Together, these findings identify human BAT metabolism as a tissue phenotype linked to systemic BCAA homeostasis and extend the role of human BAT beyond thermogenesis, suggesting that BAT-associated BCAA handling may contribute to systemic metabolic health.

Humans↗
PubMed2026

Gene expression profiling identifies potential biomarkers for vaso-occlusive episodes in sickle cell disease.

Vaso-occlusive episodes (VOEs) or acute pain events, involving complex interactions between sickle erythrocytes and other blood cells, are a hallmark of sickle cell disease (SCD). In this study, we analyzed changes in peripheral blood transcriptomes between steady state and VOEs in individuals with SCD. We followed a cohort of 174 individuals with SCD with or without chronic pain and collected peripheral blood at clinic visits (steady state) and during hospitalizations (VOEs). We performed RNA-Seq profiling of CD45+ leukocytes and CD71+ erythroid cells. Pathways linked to complement activation, coagulation, and IL-6/JAK/STAT3 signaling were enriched during VOEs in the CD45+ cells. Contrastingly, the CD71+ cells showed an enrichment of pathways related to the cell cycle, such as mTORC1 signaling and the G2M checkpoint during VOEs. We then analyzed the expression changes of genes in patients with longitudinal data to determine potential biomarkers for VOEs. Expression of 4 genes - FAM20A, IL1B, MS4A4A, and SERPINB2 - was elevated during VOEs compared with steady state in the majority of patients. Furthermore, our results indicate that patients experiencing chronic pain exhibited 44% increased enrichment of significant pathways during VOEs when compared with patients without chronic pain.

Humans↗
PubMed2025

MOADE: a multimodal autoencoder for dissociating bulk multi-omics data.

In single cell biology, the complexity of tissues may hinder lineage cell mapping or tumor microenvironment decomposition, requiring digital dissociation of bulk tissues. Many deconvolution methods focus on transcriptomic assay, not easily applicable to other omics due to ambiguous cell markers and reference-to-target difference. Here, we present MOADE, a multimodal autoencoder pipeline linking multi-dimensional features to jointly predict personalized multi-omic profiles and cellular compositions, using pseudo-bulk data constructed by internal non-transcriptomic reference and external scRNA-seq data. MOADE is evaluated through rigorous simulation experiments and real multi-omic data from multiple tissue types, outperforming nine deconvolution pipelines with superior generalizability and fidelity.

Humans↗
PubMed2026

Integrative genomic and transcriptomic analyses identify key regulators of skin pigmentation in Larimichthys crocea.

The yellow body coloration of large yellow croaker (Larimichthys crocea) constitutes a crucial economic trait, yet its underlying genetic regulatory mechanisms remain poorly understood. This study systematically elucidated the molecular basis of body color variation by integrating genome resequencing and skin transcriptome analyses, combined with the contextual analysis of key pigmentation-related genes and phenotypic histological validation. 200 phenotyped individuals (including yellow-selected lines, F1 progeny, and normal control groups, all derived from a well-characterized aquaculture stock) identified 39 significantly associated SNPs (-log₁₀(P) ≥ 6), mapping to multiple candidate genes. These genes were significantly enriched in pathways related to pigment deposition (GO:0033059), melanosome organization (GO:0032438), melanogenesis, and tyrosine metabolism. Cross-developmental stage transcriptome analysis revealed 2395 differentially expressed genes (DEGs). Multi-omics integration identified eight overlapping candidate genes, including tyrp1, slc45a2, oca2, and dgat2, among which tyrp1 was prioritized for in-depth validation based on its core regulatory role in eumelanin synthesis, significant SNP association signal, and consistent downregulation in transcriptomic data. Experimental validation demonstrated that the g.895C > T mutation in exon 2 of tyrp1b was strongly significantly associated with the yellow phenotype: the frequency of mutant genotypes (TT/CT) reached 92.86%in the yellow-selected group, whereas the control group exclusively exhibited the wild-type genotype (CC). qPCR confirmed significantly downregulated tyrp1b expression in the skin of yellow individuals, consistent with the transcriptome trend. Histological and stereomicroscopic observations of skin tissues further validated the physiological basis of the yellow phenotype, revealing a significant reduction in melanophore number and abnormal melanosome morphology in yellow-phenotype individuals, accompanied by increased xanthophore density. These results suggest that tyrp1b mutation is strongly associated with the yellow phenotype. However, the presence of a wild-type CC individual in the yellow group indicates that this mutation is not strictly required for yellow coloration, suggesting that other genetic or environmental factors may also contribute to the phenotype, Additionally, downregulation of the carotenoid metabolism gene bco2 coupled with upregulation of xdh, together with the functional changes of slc45a2 and oca2, may synergistically promote xanthophore pigment deposition, contributing to the yellow phenotype. As melanin synthesis in large yellow croaker relies on the conserved tyrosinase pathway and transporter proteins, mutations in associated genes (tyrp1b, slc45a2, oca2) represent a primary underlying cause for the loss of melanin-based coloration and transition to a yellow phenotype in L. crocea. These findings provide key molecular targets and a theoretical foundation for molecular breeding of body color in this species, and also enrich the understanding of xanthism regulatory mechanisms in teleosts.

Animals↗
PubMed2005

Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling.

Metabolome analysis technologies are still in early development because, unlike genome, transcriptome and proteome analyses, metabolome analysis has to deal with a highly diverse range of biomolecules. Combinations of different analytical platforms are therefore required for comprehensive metabolomic studies. Each of these platforms covers only part of the metabolome. To establish multiparallel technologies, thorough standardization of each measured metabolite is required. Standardization is best achieved by addition of a specific stable isotope-labeled compound, a mass isotopomer, for each metabolite. This suggestion, at first glance, seems unrealistic because of cost and time constraints. A possible solution to this problem is discussed in this article. Saturation in vivo labeling with stable isotopes enables the biosynthesis of differentially mass-labeled metabolite mixtures, which can be exploited for highly standardized metabolite profiling by mass isotopomer ratios.

Biochemistry↗
PubMed2025

LungGENIE: the lung gene-expression and network imputation engine.

BACKGROUND: Few cohorts have study populations large enough to conduct molecular analysis of ex vivo lung tissue for genomic analyses. Transcriptome imputation is a non-invasive alternative with many potential applications. We present a novel transcriptome-imputation method called the Lung Gene Expression and Network Imputation Engine (LungGENIE) that uses principal components from blood gene-expression levels in a linear regression model to predict lung tissue-specific gene-expression. METHODS: We use paired blood and lung RNA sequencing data from the Genotype-Tissue Expression (GTEx) project to train LungGENIE models. We replicate model performance in a unique dataset, where we generated RNA sequencing data from paired lung and blood samples available through the SUNY Upstate Biorepository (SUBR). We further demonstrate proof-of-concept application of LungGENIE models in an independent blood RNA sequencing data from the Genetic Epidemiology of COPD (COPDGene) study. RESULTS: We show that LungGENIE prediction accuracies have higher correlation to measured lung tissue expression compared to existing cis-expression quantitative trait loci-based methods (median Pearson's r = 0.25, IQR 0.19-0.32), with close to half of the reliably predicted transcripts being replicated in the testing dataset. Finally, we demonstrate significant correlation of differential expression results in chronic obstructive pulmonary disease (COPD) from imputed lung tissue gene-expression and differential expression results experimentally determined from lung tissue. CONCLUSION: Our results demonstrate that LungGENIE provides complementary results to existing expression quantitative trait loci-based methods and outperforms direct blood to lung results across internal cross-validation, external replication, and proof-of-concept in an independent dataset. Taken together, we establish LungGENIE as a tool with many potential applications in the study of lung diseases.

Humans↗
PubMed2026

Network methods for diagonal integration of unpaired single-cell multiomics data: a review.

MOTIVATION: Advances in single-cell sequencing have enabled multiomics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays. RESULTS: We survey the complete computational pipeline for constructing mechanistic proteogenomic networks from unpaired single-cell data, covering: (i) unimodal network inference such as knowledge-based approaches, probabilistic graphical models, temporal directionality inference, and generative and foundation model strategies that establish the transcriptomic scaffold; (ii) cross-modal integration architectures such as network propagation, graph neural networks (scMRDR, scmFormer, scCotag), and consensus frameworks designed explicitly for the unpaired proteomics setting; and (iii) benchmarking paradigms spanning network reconstruction (BEELINE, GRETA, CausalBench) and multi-task integration evaluation (scMultiBench, SCMMIB), with guidance on metric selection under network sparsity and class imbalance. We identify three principal axes of future development: generative proteomic translation from transcriptomic precursors, inductive prior embedding in next-generation architectures, and perturbation-based causal benchmarking. AVAILABILITY AND IMPLEMENTATION: This is a review article; no novel software is distributed. A curated benchmark resource table, methods starter guide, and per-method bottleneck annotations are provided in the Supplementary Material.

Multiomics↗
PubMed2025

HERV Modulation in Colorectal Carcinoma Patients: A Snapshot of Endogenous Retroviral Transcriptome.

Human endogenous retroviruses (HERVs) are proviral relics of infections that affected primates' germ line. Many HERV elements retain a residual capacity to encode transcripts and proteins that have been occasionally domesticated for the host physiology. In addition, HERV transcriptional modulation is of great interest to clarify the etiology of complex disorders such as cancer, even if a few studies assessed the specific HERV loci modulated in tumor tissues. In the present work, we used a transcriptomic approach to investigate the specific expression of ~3300 HERV loci in paired tumor and normal tissues of 7 colorectal cancer (CRC) patients. A total of 102 HERVs were significantly modulated in CRC, with a general tendency towards downregulation. Of note, among the 42 upregulated HERVs 23 belonged to the HERV-H group, that is the most investigated in CRC. De novo transcriptome reconstruction and qPCR validation allowed to identify a transcript from a HERV-H locus on chromosome Xp22.3 with high specific expression in CRC samples, potentially encoding for a partial Pol protein. These results provide a detailed description of HERV transcriptional variations in CRC and its interindividual variability, identifying a HERV-H transcript that deserves further investigation for its possible impact on tumor progression.

Endogenous Retroviruses↗