ComBat-seq: batch effect adjustment for RNA-seq count data

A pan-cancer analysis of MARCH8: molecular characteristics, clinical relevance, and immuno-oncology features

Membrane-associated RING-CH8 (MARCH8) is a member of the recently discovered MARCH family of ubiquitin ligases. MARCH8 has been shown to participate in immune responses. However, the role of MARCH8 in prognosis and immunology in human cancers remains largely unknown. The expression of MARCH8 protein was detected via immunohistochemistry in non-small cell lung cancer (NSCLC) and non-cancerous lung tissues. The study investigated the role of MARCH8 in tumor immunity through pan-cancer analysis of multiple databases. MARCH8 genetic alternations and expression were explored with the cBioPortal, GTEx, and TCGA databases. We investigated the role of MARCH8 expression in clinical relevance, prognosis, tumor immune microenvironment, immune checkpoint (ICP) with a series of bioinformatics tools and methods, such as TISIDB database, ESTIMATE, and CIBERSORT method. MARCH8 expression showed cancer-specific dysregulation and was associated with the prognosis of patients in various cancers. MARCH8 was related to the tumor microenvironment and participated in tumor immune regulation. Furthermore, low expression of MARCH8 was associated with poor prognosis and might serve as an independent prognostic biomarker for NSCLC patients. The comprehensive pan-cancer analysis revealed the potential of MARCH8 in tumor-targeted therapy, and suggested MARCH8 as a promising prognostic and immunological pan-cancer biomarker.

Identification of Periodontal Disease Diagnostic Markers Via Data Cross-Validation

INTRODUCTION AND AIMS

Periodontitis is a globally prevalent disease that is clinically diagnosed when the periodontal tissues are pathologically affected. Therefore, it is vital to identify novel periodontitis-associated biomarkers that will aid in diagnosing or treating potential patients with periodontitis.

METHODS

The GSE16134 and GSE10334 datasets were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes between periodontitis and healthy samples. Single-sample gene set enrichment analysis was performed to identify significantly involved signalling pathways. Weighted gene correlation network analysis was used to identify key molecular modules. Hub genes were screened using key genes to construct a diagnosis and prediction model of periodontitis. Microenvironment cell population-counter was used to analyse immune cell infiltration patterns in periodontal diseases.

RESULTS

Single-sample gene set enrichment analysis revealed that periodontitis involves the PI3K/AKT/mTOR signalling pathway and associated module genes (667 genes). Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the module genes revealed that periodontitis involves the type I interferon, rhythmic process, and response to type I interferon signalling pathways. GSEA identified 21 core genes associated with periodontitis and classified them into two clusters, A and B. Genomics of Drug Sensitivity in Cancer analysis revealed that AKT.inhibitor.VIII had high drug sensitivity in the cluster A subtype. Monocytes and myeloid dendritic cell infiltration were enriched in the cluster A subtype, whereas natural killer T cell infiltration was enriched in the cluster B subtype.

CONCLUSION

The pathway and gene modules identified in this study may help comprehensively diagnose periodontitis and provide a novel method for evaluating new treatments.

CLINICAL RELEVANCE

Our results are beneficial for classifying periodontitis subtypes and treatment using targeted medicine.

Molecular endotypes in sepsis: integration of multicohort transcriptomics based on RNA sequencing

BACKGROUND

The heterogeneity of host responses in sepsis has hindered efforts to develop targeted therapies for this large patient population. Although growing evidence has identified sepsis endotypes based on the microarray data, studies using RNA-seq data-which offers higher sensitivity and a broader dynamic range-remain limited. We hypothesized that integrating RNA-seq data from patients with sepsis would reveal molecular endotypes with distinct biological and clinical signatures.

METHODS

In this meta-analysis, we systematically searched for publicly available RNA-seq datasets of sepsis. Using identified datasets, we applied a consensus clustering algorithm to identify distinct endotypes. To characterize the biological differences between these endotypes, we performed gene-set enrichment analysis and immune cell deconvolution. Next, we investigated the association between these endotypes and mortality risks. We finally developed gene classifiers for endotype stratification and validated our endotype classification by applying these classifiers to an external cohort.

RESULTS

A total of 280 adults with sepsis from four datasets were included in this analysis. Using an unsupervised approach, we identified three distinct endotypes: coagulopathic (n = 83, 30%), inflammatory (n = 118, 42%), and adaptive endotype (n = 79, 28%). The coagulopathic endotype exhibited upregulated coagulation signaling, along with an increased monocyte and neutrophil composition, although the adaptive endotype demonstrated enhanced adaptive immune cell responses, marked by elevated T and B cell compositions. The inflammatory endotype was characterized by upregulated TNF-α/NF-κB signaling and IL-6/JAK/STAT3 pathways with an increased neutrophil composition. Patients with the coagulopathic endotype had a significantly higher risk of mortality than those with the adaptive endotype (30% vs. 16%, odds ratio 2.19, 95% confidence interval 1.04-4.78, p = 0.04). To enable the practical application of these findings, we developed endotype classification models and identified 14 gene classifiers. In a validation cohort of 123 patients, we consistently identified these three endotypes. Furthermore, the mortality risk pattern was reproduced, with the coagulopathic endotype showing greater mortality risk than the adaptive endotype (34% vs 18%, p = 0.10).

CONCLUSIONS

This multicohort RNA-seq meta-analysis identified three biologically and clinically distinct sepsis endotypes characterized by coagulopathic, adaptive, and inflammatory responses. This endotype-based approach to patient stratification may facilitate the development of more precise therapeutic strategies for sepsis.

Rapid reprogramming and stabilization of homoeolog expression bias in hexaploid wheat biparental populations

BACKGROUND

Differences in the relative level of expression of homoeologs, known as homoeolog expression bias, are widely observed in allopolyploids. While the evolution of homoeolog expression bias through hybridization has been characterized, on shorter timescales such as those found in crop breeding programs, the extent to which homoeolog expression bias is preserved or altered between generations remains elusive.

RESULTS

Here we use biparental mapping populations of hexaploid wheat (Triticum aestivum) with a common "Paragon" parent to explore the inheritance of homoeolog expression bias in the F5 generation. We found that homoeolog expression bias is inherited for 26-27% of triads in both populations. Most triads conserved a similar homoeolog expression bias pattern as one or both parents. Inherited patterns were largely driven by changes in the expression of one homoeolog, allowing homoeolog expression bias in subsequent generations to match parental expression. Novel patterns of homoeolog expression bias occurred more frequently in the biparental population from a landrace × elite cross, than in the population with two elite parents.

CONCLUSIONS

These results demonstrate that there is significant reprogramming and stabilization of homoeolog expression bias within a small number of generations that differs significantly based on the parental lines used in the crossing.

Quality Control Standards for Batch Effect Evaluation and Correction in Mass Spectrometry Imaging

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) allows spatial molecular profiling. Despite many successful applications, an appropriate control of technical variations is still lacking for result reproducibility assessment and for maximizing the MSI data quality. To address this, we introduce a novel quality control standard (QCS) design and data analysis pipeline accounting for variability due to sample preparation and instrument performance. Firstly, we created a tissue mimicking QCS consisting of propranolol in a gelatin matrix. We showed that this QCS mimics ion suppression of propranolol in the tissue. Next, a three-day batch experiment demonstrated the QCS's performance to longitudinal technical variations, establishing it as an effective indicator of batch effects. Then three computational approaches for batch effect correction were applied for the first time to MALDI-MSI data, leading to a significant reduction of QCS variation and to improved sample clustering by using multivariate principal component analysis. Altogether, we offer the designed QCS in combination with a data correction pipeline for MALDI-MSI users for batch effect evaluation and correction.

Bridging the gap between R and Python in bulk transcriptomic data analysis with InMoose

We introduce InMoose, an open-source Python environment aimed at omic data analysis. We illustrate its capabilities for bulk transcriptomic data analysis. Due to its wide adoption, Python has grown as a de facto standard in fields increasingly important for bioinformatic pipelines, such as data science, machine learning, or artificial intelligence (AI). As a general-purpose language, Python is also recognized for its versatility and scalability. InMoose aims at bringing state-of-the-art tools, historically written in R, to the Python ecosystem. InMoose focuses on providing drop-in replacements for R tools, to ensure consistency and reproducibility between R-based and Python-based pipelines. The first development phase has focused on bulk transcriptomic data, with current capabilities encompassing data simulation, batch effect correction, and differential analysis and meta-analysis.

A standardized framework for robust fragmentomic feature extraction from cell-free DNA sequencing data

Fragmentomics features of cell-free DNA represent promising non-invasive biomarkers for cancer diagnosis. A lack of systematic evaluation of biases in feature quantification hinders the adoption of such applications. We compare features derived from whole-genome sequencing of ten healthy donors using nine library kits and ten data-processing routes and validated in 1182 plasma samples from published studies. Our results clarify the variations from library preparation and feature quantification methods. We design the Trim Align Pipeline and cfDNAPro R package as unified interfaces for data pre-processing, feature extraction, and visualization to standardize multi-modal feature engineering and integration for machine learning.

APOBEC affects tumor evolution and age at onset of lung cancer in smokers

Most solid tumors harbor somatic mutations attributed to off-target activities of APOBEC3A (A3A) and/or APOBEC3B (A3B). However, how APOBEC3A/B enzymes affect tumor evolution in the presence of exogenous mutagenic processes is largely unknown. Here, multi-omics profiling of 309 lung cancers from smokers identifies two subtypes defined by low (LAS) and high (HAS) APOBEC mutagenesis. LAS are enriched for A3B-like mutagenesis and KRAS mutations; HAS for A3A-like mutagenesis and TP53 mutations. Compared to LAS, HAS have older age at onset and high proportions of newly generated progenitor-like cells likely due to the combined tobacco smoking- and APOBEC3A-associated DNA damage and apoptosis. Consistently, HAS exhibit high expression of pulmonary healing signaling pathway, stemness markers, distal cell-of-origin, more neoantigens, slower clonal expansion, but no smoking-associated genomic/epigenomic changes. With validation in 184 lung tumor samples, these findings show how heterogeneity in mutational burden across co-occurring mutational processes and cell types contributes to tumor development.

RNAcare: integrating clinical data with transcriptomic evidence using rheumatoid arthritis as a case study

BACKGROUND

Gene expression analysis is a crucial tool for uncovering the biological mechanisms that underlie differences between patient subgroups, offering insights that can inform clinical decisions. However, despite its potential, gene expression analysis remains challenging for clinicians due to the specialised skills required to access, integrate, and analyse large datasets. Existing tools primarily focus on RNA-Seq data analysis, providing user-friendly interfaces but often falling short in several critical areas: they typically do not integrate clinical data, lack support for patient-specific analyses, and offer limited flexibility in exploring relationships between gene expression and clinical outcomes in disease cohorts. Users, including clinicians with a general knowledge of transcriptomics, however, who may have limited programming experience, are increasingly seeking tools that go beyond traditional analysis. To overcome these issues, computational tools must incorporate advanced techniques, such as machine learning, to better understand how gene expression correlates with patient symptoms of interest.

RESULTS

Our RNAcare platform, addresses these limitations by offering an interactive and reproducible solution specifically designed for analysing transcriptomic data from patient samples in a clinical context. This enables researchers to directly integrate gene expression data with clinical features, perform exploratory data analysis, and identify patterns among patients with similar diseases. By enabling users to integrate transcriptomic and clinical data, and customise the target label, the platform facilitates the analysis of the relationships between gene expression and clinical symptoms like pain and fatigue. This allows users to generate hypotheses and illustrative visualisations/reports to support their research. As proof of concept, we use RNAcare to link inflammation-related genes to pain and fatigue in rheumatoid arthritis (RA) and detect signatures in the drug response group, confirming previous findings.

CONCLUSION

We present a novel computational platform allowing the interpretation of clinical and transcriptomics data in real-time. The platform can be used for data generated by the user, such as the patient data presented here or using published datasets. The platform is available at https://rna-care.mvls.gla.ac.uk/ , and its source code is https://github.com/sii-scRNA-Seq/RNAcare/ .

Activation of the mechanosensitive ion channels TRPV4 and PIEZO1 downregulates key regulatory systems in the chondrocyte mechanome

BACKGROUND

Chondrocytes, the only native cell type in cartilage, use mechanosensitive ion channels such as Transient Receptor Potential Vanilloid 4 (TRPV4) and PIEZO1 to transduce mechanical forces into transcriptomic changes that regulate cell behavior under both physiologic and pathologic conditions. Recent work has identified and characterized the differentially expressed genes (DEGs) that are upregulated following TRPV4 or PIEZO1 activation, but the transcriptomic systems downregulated by these ion channels also represent an important aspect of the chondrocyte regulatory process that remains poorly studied.

METHODS

Here, we utilized previously established bulk RNAsequencing libraries to analyze the transcriptomes downregulated by activation of TRPV4 and PIEZO1 through differential gene expression analysis (using DESeq2), Gene Ontology, RT-qPCR, and Weighted Gene Correlation Network Analysis (WGCNA).

RESULTS

TRPV4 and PIEZO1 activations downregulated largely unique sets of DEGs, though the set of DEGs downregulated by TRPV4 exhibited a notable overlap with genes downregulated by treatment with inflammatory mediator Interleukin-1 (IL-1). The DEG set downregulated by PIEZO1 activation included genes associated with the G2/M cell cycle checkpoint, a system that checks cells for DNA damage prior to entry into mitosis, and this result was confirmed with RT-qPCR. WGCNA revealed modules of gene regulation negatively correlated with TRPV4, PIEZO1, and IL-1, outlining how these downregulated DEGs may interact to form gene regulatory networks (GRNs).

CONCLUSION

This study complements previous work in describing the full mechanosensitive transcriptome (or "mechanome") of differential gene expression in response to activation of mechanosensitive ion channels TRPV4 and PIEZO1 Q2 and suggests potential avenues for future therapeutic treatment design.

Genome-scale metabolic modelling identifies reactions mediated by SNP-SNP interactions associated with yeast sporulation

Genome-scale metabolic models (GEMs) are powerful tools used to understand the functional effects of genetic variants. However, the impact of single nucleotide polymorphisms (SNPs) in transcription factors and their interactions on metabolic fluxes remains largely unexplored. Using gene expression data from a yeast allele replacement panel grown during sporulation, we constructed co-expression networks and SNP-specific GEMs. Analysis of co-expression networks revealed that during sporulation, SNP-SNP interactions impact the connectivity of metabolic regulators involved in glycolysis, steroid and histidine biosynthesis, and amino acid metabolism. Further, genome-scale differential flux analysis identified reactions within six major metabolic pathways associated with sporulation efficiency variation. Notably, autophagy was predicted to act as a pentose pathway-dependent compensatory mechanism supplying critical precursors like nucleotides and amino acids, enhancing sporulation. Our study highlights how transcription factor polymorphisms interact to shape metabolic pathways in yeast, offering insights into genetic variants associated with metabolic traits in genome-wide association studies.

Multimodal spatial proteomic profiling in acute myeloid leukemia

Acute myeloid leukemia (AML) resides in an immune-rich microenvironment, yet, immune-based therapies have faltered in eliciting durable responses. Bridging this paradox requires a comprehensive understanding of leukemic interactions within the bone marrow microenvironment. We optimized a high-throughput tissue-microarray-based pipeline for high-plex spatial immunofluorescence and mass cytometry imaging on a single slide, capturing immune, tumor, and structural components. Using unbiased clustering on the spatial K function, we unveiled the presence of tertiary lymphoid-like aggregates in bone marrow, which we validated using spatial transcriptomics and an independent proteomics approach. We then found validated TLS signatures predictive of outcomes in AML using an integrated public 480-patient transcriptomic dataset. By harnessing high-plex spatial proteomics, we open the possibility of discovering novel structures and interactions that underpin leukemic immune response. Further, our study's methodologies and resources can be adapted for other bone marrow diseases where decalcification and autofluorescence present challenges.

The inconsistent pathogenesis of endometriosis and adenomyosis: insights from endometrial metabolome and microbiome

Endometriosis (EM) and adenomyosis (AM) are interrelated gynecological disorders characterized by the aberrant presence of endometrial tissue and are frequently linked with chronic pelvic pain and infertility, yet their pathogenetic mechanisms remain largely unclear. In this cross-sectional study, we analyzed endometrial samples from 244 participants, split into 91 EM patients, 56 AM patients, and 97 healthy controls (HC). We conducted untargeted liquid chromatography-mass spectrometry (LC-MS) and 5R 16S rRNA sequencing to examine endometrial metabolome and microbiome profiles. Additionally, we integrated transcriptomic analysis using nine transcriptomic data sets to investigate the biological basis of these conditions. Metabolomic profiling and 16S rRNA sequencing revealed distinct metabolic and microbial signatures. Specific pathways, including linoleic acid and glycerophospholipid metabolism, show significant alterations in both conditions. Notably, four metabolites, including phosphatidylcholine 40:8 [PC(40:8)], exhibited marked changes in both EM and AM, suggesting shared pathological features. Furthermore, taxonomic analysis identified unique bacterial species associated with each condition, particularly those belonging to the phylum Proteobacteria, which correlated with altered metabolic signatures. Machine learning models demonstrated high predictive accuracy for differentiating between AM, EM, and HC based on metabolic and microbial signatures. Integrative analysis with transcriptomic data highlighted distinct pathways related to immune response and signaling transduction for each condition. Our study provides fresh insights into the pathogenesis of AM and EM through a multi-omic approach, suggesting potential inconsistencies in the underlying pathogenetic mechanisms.

IMPORTANCE

Existing research highlighted a connection between endometriosis (EM) and adenomyosis (AM), underscoring their overlapping symptoms and potential shared pathophysiological mechanisms. Although the role of microbiota in inflammatory conditions has been acknowledged, comprehensive investigations into the endometrial microbiota in cases of EM and AM have been limited. Previous studies identified distinct microbial communities associated with these conditions; however, they were constrained by small sample sizes and a lack of integrated analyses of microbiota and metabolomics. Furthermore, the ongoing debate over whether EM and AM should be classified as separate diseases or related phenotypes emphasizes the necessity for further exploration of their molecular interactions. Our study uncovers distinct microbial and metabolic signatures associated with each condition, revealing both shared and unique pathways that may contribute to their pathogenesis. Furthermore, the integration of transcriptomic data offers valuable insights into the complex interactions underlying these disorders.

Exploratory Analysis of Molecular Subtypes in Early-Stage Osteosarcoma: Identifying Resistance and Optimizing Therapy

Background: Osteosarcoma (OS) is a highly aggressive bone malignancy with limited treatment options and poor prognosis. This exploratory study aimed to identify molecular subtypes of early-stage, treatment-naive OS to guide precise therapeutic strategies. Methods: We analyzed RNA-seq data obtained from tumor tissues from 102 OS patients using a non-negative matrix factorization algorithm (NMF) to classify the tumors into three subtypes: S1, S2, and S3. Differential gene expression was evaluated using DESeq2, followed by functional enrichment analysis with clusterProfiler and CancerHallmarks. The tumor microenvironment was assessed through ESTIMATE and CIBERSORT, and drug sensitivity was predicted using OncoPredict. SAOS-2 and MG63 cells, representing the S1 subtype, were used in the viability essays to determine the effect of hesperidin, a natural phenolic compound noted for its anti-cancer potential, alone and in combination with doxorubicin and 5-fluorouracil. Results: This study revealed three OS subtypes: S1 was enriched in cell cycle regulation, vesicular transport, and RNA metabolism while S2 and S3 were enriched in pathways related to extracellular matrix organization and protein translation, respectively. S1 displayed high tumor purity, significant chemoresistance, and overexpression of KIF20 A, correlating with poor prognosis. AURKB, a hesperidin target, was implicated in S1 pathogenesis. In vitro, hesperidin significantly reduced the viability of SAOS-2 and MG63 cells and enhanced doxorubicin efficacy. Conclusions: Our findings support the molecular subclassification of OS, emphasizing subtype-specific mechanisms of tumor progression and chemoresistance, with hesperidin offering potential as a therapeutic adjunct for high-risk OS patients.

CellHit: a web server to predict and analyze cancer patients' drug responsiveness

We present the CellHit web server (https://cellhit.bioinfolab.sns.it/), a web-based platform designed to predict and analyze cancer patients' responsiveness to drugs using transcriptomic data. By leveraging extensive pharmacogenomics datasets from the Genomics of Drug Sensitivity in Cancer v1 and v2 (GDSC) and Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) and transcriptomic data from the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas Program (TCGA). CellHit integrates a computational pipeline for preprocessing, gene imputation, and robust alignment between patient and cell line transcriptomic data with pre-trained SOTA models for drug sensitivity prediction. The pipeline employs batch correction, enhanced Celligner methodology, and Parametric UMAP for stable and actionable alignment. The intuitive interface requires no programming expertise, offering interactive visualizations, including low-dimensional embeddings and drug sensitivity heatmaps for the input transcriptomic samples. Results feature contextual metadata, SHAP-based feature importance, and transcriptomic neighbors from reference datasets, simplifying interpretation and hypothesis generation. CellHit provides precomputed predictions across TCGA samples and offers the ability to run custom analyses online on input samples, democratizing precision oncology by enabling rapid, interpretable predictions accessible the research community.

Machine learning identifies genes linked to neurological disorders induced by equine encephalitis viruses, traumatic brain injuries, and organophosphorus nerve agents

Venezuelan, eastern, and western equine encephalitis viruses (collectively referred to as equine encephalitis viruses---EEV) cause serious neurological diseases and pose a significant threat to the civilian population and the warfighter. Likewise, organophosphorus nerve agents (OPNA) are highly toxic chemicals that pose serious health threats of neurological deficits to both military and civilian personnel around the world. Consequently, only a select few approved research groups are permitted to study these dangerous chemical and biological warfare agents. This has created a significant gap in our scientific understanding of the mechanisms underlying neurological diseases. Valuable insights may be gleaned by drawing parallels to other extensively researched neuropathologies, such as traumatic brain injuries (TBI). By examining combined gene expression profiles, common and unique molecular characteristics may be discovered, providing new insights into medical countermeasures (MCMs) for TBI, EEV infection and OPNA neuropathologies and sequelae. In this study, we collected transcriptomic datasets for neurological disorders caused by TBI, EEV, and OPNA injury, and implemented a framework to normalize and integrate gene expression datasets derived from various platforms. Effective machine learning approaches were developed to identify critical genes that are either shared by or distinctive among the three neuropathologies. With the aid of deep neural networks, we were able to extract important association signals for accurate prediction of different neurological disorders by using integrated gene expression datasets of VEEV, OPNA, and TBI samples. Gene ontology and pathway analyses further identified neuropathologic features with specific gene product attributes and functions, shedding light on the fundamental biology of these neurological disorders. Collectively, we highlight a workflow to analyze published transcriptomic data using machine learning, which can be used for both identification of gene biomarkers that are unique to specific neurological conditions, as well as genes shared across multiple neuropathologies. These shared genes could serve as potential neuroprotective drug targets for conditions like EEV, TBI, and OPNA.

Giant transposons promote strain heterogeneity in a major fungal pathogen

Fungal infections are difficult to prevent and treat in large part due to strain heterogeneity, which confounds diagnostic predictability. Yet, the genetic mechanisms driving strain-to-strain variation remain poorly understood. Here, we determined the extent to which Starships-giant transposons capable of mobilizing numerous fungal genes-generate genetic and phenotypic variability in the opportunistic human pathogen Aspergillus fumigatus. We analyzed 519 diverse strains, including 11 newly sequenced with long-read technology and multiple isolates of the same reference strain, to reveal 20 distinct Starships that are generating genomic heterogeneity over timescales relevant for experimental reproducibility. Starship-mobilized genes encode diverse functions, including known biofilm-related virulence factors and biosynthetic gene clusters, and many are differentially expressed during infection and antifungal exposure in a strain-specific manner. These findings support a new model of fungal evolution wherein Starships help generate variation in genome structure, gene content, and expression among fungal strains. Together, our results demonstrate that Starships are a previously hidden mechanism generating genotypic and, in turn, phenotypic heterogeneity in a major human fungal pathogen.IMPORTANCENo "one size fits all" option exists for treating fungal infections in large part due to genetic and phenotypic variability among strains. Accounting for strain heterogeneity is thus fundamental for developing efficacious treatments and strategies for safeguarding human health. Here, we report significant progress toward achieving this goal by uncovering a previously hidden mechanism generating heterogeneity in the human fungal pathogen Aspergillus fumigatus: giant transposons, called Starships, that span dozens of kilobases and mobilize fungal genes as cargo. By conducting a systematic investigation of these unusual transposons in a single fungal species, we demonstrate their contributions to population-level variation at the genome, pangenome, and transcriptome levels. The Starship compendium we develop will not only help predict variation introduced by these elements in laboratory experiments but will serve as a foundational resource for determining how Starships impact clinically relevant phenotypes, such as antifungal resistance and pathogenicity.

Spatial transcriptional landscape of human heart failure

BACKGROUND AND AIMS

Heart failure (HF) remains a significant clinical challenge due to its diverse aetiologies and complex pathophysiology. The molecular alterations specific to distinct cell types and histological patterns during HF progression are still poorly characterized. This study aimed to explore cell-type- and histology-specific gene expression profiles in cardiomyopathies.

METHODS

Ninety tissue cores from 44 participants, encompassing various forms of cardiomyopathy and control samples with diverse histological features, were analysed using the GeoMx Whole Human Transcriptome Atlas. Data on cell types, clinical information, and histological features were integrated to examine gene expression profiles in cardiomyopathy.

RESULTS

The study characterized the cellular composition of ventricular myocardium and validated the GeoMx platform's efficiency in compartmentalizing specific cell types, demonstrating high accuracy for cardiomyocytes but limitations for endothelial cells and fibroblasts. Differentially expressed genes, including UCHL1 from cardiomyocytes, were associated with degeneration, while CCL14, ACKR1, and PLVAP from endothelial cells were linked to fibrosis. Multiplex immunohistochemistry and integrative analysis of prior sc/snRNA-seq data identified a PLVAP, ACKR1, and CCL14-positive pro-inflammatory endothelial cell subtype linked to fibrosis in HF. Downregulation of ribosomal proteins in cardiomyocytes was associated with myocyte disarray in hypertrophic cardiomyopathy. Additionally, pronounced inflammatory responses were observed in end-stage HF. Combined histological and clinical analysis identified CRIP3, PFKFB2, and TAX1BP3 as novel contributors to HF pathogenesis.

CONCLUSIONS

These findings highlight the critical role of cell-enriched and histology-specific transcriptome mapping in understanding the complex pathophysiological landscape of failing hearts, offering molecular insights and potential therapeutic targets for future interventions.

Transcriptomic Features of Recurrence Rates in Idiopathic Subglottic Stenosis

OBJECTIVE

Idiopathic subglottic stenosis (iSGS) is a rare disease characterized by narrowing of the upper airway and affects near-exclusively females. Patients often experience recurrent disease and require repeated surgical dilations. The pathophysiology underlying the broad spectrum of disease severity within iSGS remains unknown. In the current study, we sought to identify transcriptomic differences between iSGS patients with markedly different recurrence rates.

METHODS

Prospectively collected clinical and bulk RNA sequencing data from subglottic tissues of 56 female iSGS patients with 1-4 years of follow-up were analyzed. DESeq2 was used to perform differential expression analysis, comparing samples from the highest (1.19-1.87 dilations/year) versus the lowest (0.30-0.65 dilations/year) quartile of surgical dilation rate (i.e., high vs. low recurrence groups).

RESULTS

In total, 220 genes were significantly differentially expressed between the high and low recurrence groups (adjusted p < 0.1 and log2 fold change > |1|). Pathway enrichment analyses showed that the high recurrence group had significantly increased expression of genes involved in adaptive immune responses (e.g., immunoglobulin subunit genes) and extracellular matrix organization (e.g., COMP, NID2) (adjusted p < 0.1). In contrast, the low recurrence group had significantly increased expression of genes involved in cilia structure and function (e.g., CFAP43, DNAI2) (adjusted p < 0.1), suggesting a relatively increased abundance of respiratory cilia.

CONCLUSION

Transcriptomic profiling suggests that lower recurrence rates in iSGS are associated with retention of respiratory cilia, while adaptive immune responses and increased extracellular matrix deposition are present in those with higher recurrence rates. These results hold promise for the development of prognostic markers and identification of therapeutic targets for iSGS.

Exploring ribosome biogenesis in lung adenocarcinoma to advance prognostic methods and immunotherapy strategies

BACKGROUND

Lung adenocarcinoma (LUAD) presents a considerable danger to human health and has evolved into a major public health concern. Ribosome biogenesis (RiboSis) is a critical process for synthesizing ribosomes, closely associated with cancer initiation, progression, and treatment resistance, potentially serving as a target for future cancer therapies.

METHODS

Utilizing single-cell RNA sequencing (scRNA-seq) technology, a single-cell atlas of LUAD was delineated, focusing on the analysis of T cell subpopulations. Cells were scored based on the expression patterns of 331 genes associated with RiboSis across different cell types, and monocle2 was employed to analyze the developmental trajectory of CD4+ T cells. Employing various machine learning algorithms, a ribosome biogenesis-related signature (RBS) was constructed and compared to 140 published LUAD prognostic models. The relationship between RBS risk scores and various factors in LUAD patients, including prognosis, the tumor immune microenvironment (TIME), responsiveness to immunotherapy, and sensitivity to pharmacological treatments was specifically analyzed. Immunohistochemistry was utilized to validate the expression levels of immune markers in the high- and low- RBS groups, and in vitro experiments were performed to validate the functional role of the pivotal gene KIF23 in the progression of LUAD.

RESULTS

Using single-cell analysis, two distinct T cell subtypes were identified: CD8+ interferon (IFN) response T cells and CD4+ stress response T cells. It was observed that CD4+ naive-like T cells exhibit high expression of RiboSis-related genes, with a gradual decrease in RiboSis activity as CD4+ T cells develop. Compared to other prognostic models, RBS demonstrated superior performance in prognosis prediction. The low-RBS group exhibited a tumor microenvironment (TME) more favorable for efficient immune monitoring and reaction, higher responsiveness to immunotherapy, and a better prognosis. Immunohistochemistry confirmed higher expression levels of immune markers in the low-RBS group, while in vitro experiments validated the promoting role of KIF23 in LUAD cell proliferation, migration and invasion.

CONCLUSION

This study delves into the relationship between RiboSis and LUAD cell subpopulations, identifying a potent prognostic biomarker for LUAD. This biomarker aids in assessing immunotherapy efficacy in LUAD patients, ultimately enhancing their prognosis and guiding clinical decision-making.

Transcriptome signatures of the medial prefrontal cortex underlying GABAergic control of resilience to chronic stress exposure

Analyses of postmortem human brains and preclinical studies of rodents have identified somatostatin (SST)-positive, dendrite-targeting GABAergic interneurons as key elements that regulate the vulnerability to stress-related psychiatric disorders. Conversely, genetically induced disinhibition of SST neurons (induced by Cre-mediated deletion of the γ2 GABAA receptor subunit gene selectively from SST neurons, SSTCre:γ2f/f mice) results in stress resilience. Similarly, chronic chemogenetic activation of SST neurons in the medial prefrontal cortex (mPFC) results in stress resilience but only in male and not in female mice. Here, we used RNA sequencing of the mPFC of SSTCre:γ2f/f mice to characterize the transcriptome changes underlying GABAergic control of stress resilience. We found that stress resilience of male but not female SSTCre:γ2f/f mice is characterized by resilience to chronic stress-induced transcriptome changes in the mPFC. Interestingly, the transcriptome of non-stressed SSTCre:γ2f/f (stress-resilient) male mice resembled that of chronic stress-exposed SSTCre (stress-vulnerable) mice. However, the behavior and the serum corticosterone levels of non-stressed SSTCre:γ2f/f mice showed no signs of physiological stress. Most strikingly, chronic stress exposure of SSTCre:γ2f/f mice was associated with an almost complete reversal of their chronic stress-like transcriptome signature, along with pathway changes suggesting stress-induced enhancement of mRNA translation. Behaviorally, the SSTCre:γ2f/f mice were not only resilient to chronic stress-induced anhedonia - they also showed an inversed, anxiolytic-like behavioral response to chronic stress exposure that mirrored the chronic stress-induced reversal of the chronic stress-like transcriptome signature. We conclude that GABAergic dendritic inhibition by SST neurons exerts bidirectional control over behavioral vulnerability and resilience to chronic stress exposure that is mirrored in bidirectional changes in the expression of putative stress resilience genes, through a sex-specific brain substrate.

Multi-omics analysis of Helicobacter pylori-associated gastric cancer identifies hub genes as a novel therapeutic biomarker

Helicobacter pylori infection is one of the most common gastric pathogens; however, the molecular mechanisms driving its progression to gastric cancer remain poorly understood. This study aimed to identify the key transcriptomic drivers and therapeutic targets of H. pylori-associated gastric cancer through an integrative transcriptomic analysis. This analysis integrates microarray and RNA-seq datasets to identify significant differentially expressed genes (DEGs) involved in the progression of H. pylori-associated gastric cancer. In addition to independent analyses, data were integrated using ComBat to detect consistent expression patterns of hub genes. This approach revealed distinct clustering patterns and stage-specific transcriptional changes in common DEGs across disease progression, including H. pylori infection, gastritis, atrophy, and gastric cancer. Genes such as TPX2, MKI67, EXO1, and CTHRC1 exhibited progressive upregulation from infection to cancer, highlighting involvement in cell cycle regulation, DNA repair, and extracellular matrix remodeling. These findings provide insights into molecular shifts linking inflammation-driven infection to malignancy. Furthermore, network analysis identified hub genes, including CXCL1, CCL20, IL12B, and STAT4, which are enriched in immune pathways such as chemotaxis, leukocyte migration, and cytokine signaling. This emphasizes their role in immune dysregulation and tumor development. Expression profiling demonstrated the upregulation of hub genes in gastric cancer and stage-specific changes correlating with disease progression. Finally, drug-gene interaction analysis identified therapeutic opportunities, with hub genes interacting with approved drugs like abatacept and zoledronic acid, as well as developmental drugs such as adjuvant and relapladib. These findings highlight the key role of these hub genes as biomarkers and therapeutic targets, providing a foundation for advancing precision medicine in H. pylori-associated gastric cancer. Overall, this study paves the way for advancing precision medicine in H. pylori-associated gastric cancer by providing insights into the development of early detection biomarkers, risk stratification, and targeted therapies. This supports the clinical translation of precision medicine strategies in H. pylori-associated gastric cancer.

Unraveling the dynamics of osteoblast differentiation in MC3T3-E1 cells: Transcriptomic insights into matrix mineralization and cell proliferation

Unraveling the intricacies of osteoblast differentiation is crucial for advancing our comprehension of bone biology. This study investigated the complicated molecular events orchestrating osteoblast differentiation in MC3T3-E1 cells, a well-established in vitro culture model. Employing longitudinal RNA-sequencing analysis, we explored transcriptomic changes at the pivotal time points of 0, 1, 4, 7, 10, 14, and 21 days and categorized osteogenic differentiation into proliferation, matrix maturation, and mineralization stages. Notably, we observed a simultaneous increase in matrix mineralization and cell proliferation during the mineralization stage, accompanied by a positive correlation between proliferation-associated genes and those enriched in ossification. Additionally, we identified the presence of proliferating cells over the mineralizing matrix layers. These results could serve as a model for understanding the principles by which bone lining cells are formed on the calcified bone matrix and the mechanism by which new osteoblasts are recruited during the bone remodeling process.

Redistribution of Ancestral Functions Underlies the Evolution of Venom Production in Marine Predatory Snails

Venom-secreting glands are highly specialized organs evolved throughout the animal kingdom to synthetize and secrete toxins for predation and defense. Venom is extensively studied for its toxin components and application potential; yet, how animals become venomous remains poorly understood. Venom systems therefore offer a unique opportunity to understand the molecular mechanisms underlying functional innovation. Here, we conducted a multispecies multi-tissue comparative transcriptomics analysis of 12 marine predatory gastropod species, including species with venom glands and species with homologous non-venom-producing glands, to examine how specialized functions evolve through gene expression changes. We found that while the venom gland specialized for the mass production of toxins, its homologous glands retained the ancestral digestive functions. The functional divergence and specialization of the venom gland were achieved through a redistribution of its ancestral digestive functions to other organs, specifically the esophagus. This entailed concerted expression changes and accelerated transcriptome evolution across the entire digestive system. The increase in venom gland secretory capacity was achieved through the modulation of an ancient secretory machinery, particularly genes involved in endoplasmic reticulum stress and unfolded protein response. This study shifts the focus from the well-explored evolution of toxins to the lesser-known evolution of the organ and mechanisms responsible for venom production. As such, it contributes to elucidating the molecular mechanisms underlying organ evolution at a fine evolutionary scale, highlighting the specific events that lead to functional divergence.

Profiling the cell-specific small non-coding RNA transcriptome of the human placenta

The human placenta is the composite of multiple cell types, each which contributes uniquely to placental function. Small non-coding RNAs (sncRNAs) are regulators of gene expression and can be cell-specific. The sncRNA transcriptome of individual placental cell types has not yet been investigated due to difficulties in their procurement and isolation. Using a custom sequencing method, we explored the expression of seven sncRNA species (miRNA, piRNA, rRNA, scaRNA, snRNA, snoRNA, tRNA) from whole chorionic villi and four major sample-matched FACS-sorted cell type (cytotrophoblast, stromal, endothelial, Hofbauer) samples from 9 first trimester and 17 term placentas. After normalization for technical variables, samples clustered primarily by cell type lineage. No sncRNAs were uniquely expressed by cell type, however, mean expression differed by cell type for 115 sncRNAs. Known placentally-expressed sncRNAs showed differing expression by cell type and trimester. Expression of few sncRNAs varied by sex. Lastly, sample-matched sncRNA expression and DNA methylation correlation was not significant, although high correlation (> R2 ± 0.6) was observed for some sncRNA-CpG pairs. This study represents the first exploration of the sncRNA transcriptome of bulk placental villi and placental cell types, informing about the expression and regulatory patterns underlying human placental development.

T-bet+CD8+ T cells govern anti-PD-1 responses in microsatellite-stable gastric cancers

More than 90% of advanced gastric cancers (GC) are microsatellite-stable (MSS). Compared to the high response rate of immune checkpoint inhibitors (ICI) in microsatellite-instability-high (MSI-H) GCs, only 10% of unstratified MSS GCs respond to ICIs. In this study, we apply semi-supervised learning to stratify potential ICI responders in MSS GCs, achieving high accuracy, quantified by an area under the curve of 0.924. Spatial analysis of the tumor microenvironment of ICI-sensitive GCs reveals a high level of T-bet+ CD8 + T cell infiltration in their tumor compartments. T-bet+ CD8 + T cells exhibit superior anti-tumor activity due to their increased ability to infiltrate tumors and secrete cytotoxic molecules. Adoptive transfer of T-bet+ CD8 + T cells boosts anti-tumor immunity and confers susceptibility to ICIs in immune-ignorant MSS GCs in a humanized mouse model. Spatial RNA sequencing suggests a positive-feedback loop between T-bet+ T cells and PD-L1+ tumor cells, which eventually drives T cell exhaustion and can therefore be leveraged for ICI therapy. In summary, our research provides insights into the underlying mechanism of anti-tumor immunity and deepens our understanding of varied ICI responses in MSS GCs.

Reliable RNA-seq analysis from FFPE specimens as a means to accelerate cancer-related health disparities research

Whole transcriptome sequencing (WTS/ RNA-Seq) is a ubiquitous tool for investigating cancer biology. RNA isolated from frozen sources limits possible studies for analysis of associations with phenotypes or clinical variables requiring long-term follow-up. Although good correlations are reported in RNA-Seq data from paired frozen and formalin fixed paraffin embedded (FFPE) samples, uncertainties regarding RNA quality, methods of extraction, and data reliability are hurdles to utilization of archival samples. We compared three different platforms for performing RNA-seq using archival FFPE oropharyngeal squamous carcinoma (OPSCC) specimens stored up to 20 years, as part of an investigation of transcriptional profiles related to health disparities. We developed guidelines to purify DNA and RNA from FFPE tissue and perform downstream RNA-seq and DNA SNP arrays. RNA was extracted from 150 specimens, with an average yield of 401.8 ng/cm2 of tissue. Most samples yielded sufficient RNA reads >13,000 protein coding genes which could be used to differentiate HPV-associated from HPV-independent OPSCCs. Co-isolated DNA was used to identify reliably define patient ancestry which correlated well with patient-reported race. Utilizing the methods described in this study provides a robust, reliable, and standardized means of DNA & RNA extraction from FFPE as well as a means by which to assure the quality of the data generated. Optimized RNA extraction techniques, combined with robust bioinformatic approaches designed to optimize data homogenization, analysis and biological validation can revolutionize our ability to transcriptomically profile large solid tumor sets derived from ancestrally varied patient populations.

Transcriptomic Biomarkers in Blood Indicative of the Administration of Recombinant Human Erythropoietin to Thoroughbred Horses

Erythropoiesis-stimulating agents (ESAs) continue to be a significant threat to the integrity of human and equine sports. Besides conventional direct testing, monitoring the biomarkers associated with the effects of ESAs may provide a complementary approach via indirect detection to enhance doping control. In this study, we applied RNA-sequencing (RNA-seq) to discover blood RNA biomarkers in Thoroughbred horses after administration with a long-acting form of recombinant human erythropoietin (rhEPO), methoxy polyethylene glycol epoetin beta, Mircera®. A single subcutaneous administration of Mircera® at ~ 4.2 μg/kg was effective in elevating haematocrit, haemoglobin and erythrocyte levels to varying extents in as early as 4 days post-administration in all three horses, which persisted for 40 days post-administration (the last sample collected). RNA-seq was applied to analyse blood transcriptomic changes. Differential gene expression analysis has allowed the identification of 46 genes that showed dramatic and temporary upregulation at 4-11 days after Mircera® administration. STRING analysis has identified the functional enrichment of 15 genes important for erythropoiesis and erythrocyte function, supporting the idea of an increased release into the peripheral circulation of residual RNA-containing reticulocytes after rhEPO exposure, which would otherwise mature normally inside the bone marrow in horses. Machine learning of blood transcriptomes has enabled the discrimination of samples with or without Mircera administration. Therefore, our study has provided new insights into the biological mechanism of erythropoiesis caused by rhEPO administration in horses and has provided evidence supporting the control of misuse of ESAs by monitoring the equine blood transcriptome.

AFED, a comprehensive resource for Aspergillus flavus gene expression profiling

The Aspergillus flavus expression database (AFED) is a comprehensive resource dedicated to exploring gene expression in A. flavus, a significant fungal pathogen that threatens food security by contaminating crops with aflatoxin. Given the complex regulation of aflatoxin biosynthesis and the lack of centralized expression data resources for this important pathogen, a database integrating diverse experimental conditions is essential for understanding its biology and developing control strategies. Public RNA sequencing data were used to quantify gene expression abundance for 604 A. flavus samples from 52 experiments. Using abundance data, we created an AFED accessible through a web-based interface that allows for the expression profiles of genes to be conveniently examined across different growth conditions and life cycle stages. Expression profiles can be visualized through either an interactive bar plot for single gene queries or a heatmap for multiple gene queries. A gene co-expression network based on samples containing at least 10 million mapped reads is also available, which allows users to identify genes that are co-expressed with an individual gene or set of genes and displays the functional enrichment among the co-expressed genes. Database URL: https://a-flavus-expression-db-jyqnpeuvta-uc.a.run.app.

A single-cell genomic atlas for the effects of chronic ethanol exposure in the mouse dorsal striatum

Alcohol use disorder (AUD) is characterized by compulsive drinking, which is thought to be mediated by effects of chronic intermittent ethanol exposure on the dorsal striatum, the input nucleus of the basal ganglia. Despite significant efforts to understand the impact of ethanol on the dorsal striatum, the rich diversity of striatal cell types and multitude of ethanol targets expressed by them necessitates an unbiased, discovery-based approach. In this study, we used single-nuclei RNA-sequencing (snRNA-seq; n = 86,715 cells) to examine the impact of chronic intermittent ethanol exposure on the dorsal striatum in C57BL/6 male and female mice. We detected 462 differentially expressed genes at FDR < 0.05, the majority of which were mapped to spiny projection neurons (SPNs), the most prominent cell type in the striatum. Gene co-expression network analysis and functional annotation of differentially expressed genes revealed down-regulation of postsynaptic intracellular signaling cascades in SPNs. Inflammation-related genes were down-regulated across many neuronal and non-neuronal cell types. Gene set enrichment analyses also pointed to altered states of rare cell types, including the induction of angiogenesis-related genes in vascular cells. A gene module down-regulated specifically in canonical SPNs was enriched for calcium-signaling genes and components of glutamatergic synapses, as well as for genes associated with genetic risk for AUD. Genetic perturbations of six of this module's hub genes - Foxp1, Bcl11b, Pde10a, Rarb, Rgs9, and Itgr1 - had causal effects on its expression in the mouse striatum and/or on the broader set of differentially expressed genes in alcohol-exposed mice. These data provide important clues as to the impact of ethanol on striatal biology and provide a key resource for future investigation.

AZD8701, an Antisense Oligonucleotide Targeting FOXP3 mRNA, as Monotherapy and in Combination with Durvalumab: A Phase I Trial in Patients with Advanced Solid Tumors

PURPOSE

AZD8701 uses next-generation antisense oligonucleotide (ASO) technology to selectively reduce human forkhead box P3 (FOXP3) expression in regulatory T cells, reversing their immunosuppressive function. FOXP3 ASO alone or with PD-(L)1 inhibition attenuated tumor growth in mice. We report a phase I study of AZD8701 alone or combined with durvalumab in patients with advanced solid tumors.

PATIENTS AND METHODS

Eligible patients had solid tumors and received prior standard-of-care treatment, including anti-PD-(L)1 therapy. Patient cohorts were treated with AZD8701 intravenously weekly at escalating doses, either alone (60-960 mg) or combined (240-720 mg) with durvalumab 1,500 mg intravenous every 4 weeks. The primary objective was safety and tolerability, with the aim of determining the MTD.

RESULTS

Forty-five patients received AZD8701 monotherapy, and 18 received AZD8701 with durvalumab. One dose-limiting toxicity (increased alanine aminotransferase) occurred with AZD8701 960 mg. The most common adverse events related to AZD8701 monotherapy were fatigue (22.2%), asthenia, pyrexia, and increased alanine aminotransferase (20% each); the safety profile was similar when combined with durvalumab. With AZD8701 monotherapy, 24.4% and 15.6% of the patients had stable disease for ≥16 and ≥24 weeks, respectively; one patient treated with AZD8701 720 mg and durvalumab had a partial response. FOXP3 mRNA changes were heterogeneous (8/13 patients showed a reduction), with no clear dose relationship. ASO accumulated in the tumor epithelium and stroma.

CONCLUSIONS

This study demonstrates the clinical feasibility of ASO therapy, with generally manageable adverse events, FOXP3 knockdown, and ASO delivery to the tumor.

Gulf War Illness Induced Sex-Specific Transcriptional Differences Under Stressful Conditions

Gulf War Illness (GWI) is a multi-symptom disorder affecting 1990-1991 Persian Gulf War veterans and is characterized by post-exertional malaise, neurological symptoms, immune deregulation, and exhaustion. Causation is not understood, and effective diagnostics and therapies have not yet been developed. In this work, we analyzed stress-related, sex-specific transcriptomic shifts in GWI subjects and healthy controls through RNA sequencing of peripheral blood mononuclear cells (PBMCs). Blood samples at baseline (T0), at maximal exertion (T1), and four hours post-exertion (T2) were analyzed. In female subjects with GWI, pathways associated with pro-inflammatory processes were found to be deregulated, and in male GWI subjects, pathways related to IL-12 signaling and lymphocytic activation were deregulated at T1 compared to T0. During recovery from stress, pathways corresponding to immune responses and microglial cell activation were altered in female GWI subjects, and apoptotic signaling changed in males with GWI. Documented sex-specific immune deregulation leads to finding better biomarkers. Targeting sex-specific transcriptomic markers of the disease could lead to new therapies for GWI.

Identification of the key genes in Sanhe cattle for health and milk composition traits based on the WGCNA

Sanhe cattle are domestically bred dual-purpose (i.e., milk and meat) cattle in northeast China that exhibit exceptional adaptability, resilience, and milk composition traits. Nevertheless, few studies that have analyzed the transcriptome of Sanhe cattle and elucidated the key pathways and genes accountable for its immune and production traits. Weighted gene co-expression network analysis is effective for cattle genetic studies. In this study, with Holstein cattle (n = 82) serving as the dairy breed, we aimed to investigate the manifestations and regulatory pathways of the health and milk composition traits for the Sanhe cattle population (n = 61). We employed principal composition analysis and pathway scoring to compare the distinct characteristics between the 2 cattle breeds using transcriptome, routine blood examination, milk composition, and dairy herd improvement data. We then identified 20 hub genes and potentially crucial pathways that were specifically and significantly correlated with the health and milk composition traits of Sanhe cattle by weighted co-expression network analysis and enrichment analysis. We discovered 56 hub genes that might give rise to commonalities and differences in health and milk composition between Sanhe cattle and Holstein cattle. Finally, we examined the differences in hub gene expression in health and milk composition traits between Sanhe cattle and Holstein cattle using correlation analysis. Overall, this study establishes a foundation for further exploration of the molecular markers related to the health and milk composition traits of Sanhe cattle.

Identification of a deubiquitinating gene-related signature in ovarian cancer using integrated transcriptomic analysis and machine learning framework

BACKGROUND

Ovarian carcinoma represents an aggressive malignancy with poor prognosis and limited therapeutic efficacy. While deubiquitinating (DUB) genes are known to regulate crucial cellular processes and cancer progression, their specific roles in ovarian carcinoma remain poorly understood.

METHODS

We conducted an integrated analysis of single-cell RNA sequencing and bulk transcriptome data from public databases. DUB genes were identified through Genecard database. Using the Seurat package, we performed cell clustering and differential expression analysis. Cell-cell communications were analyzed using CellChat. A DUB-related risk signature (DRS) was developed using machine learning approaches through integration of GEO and TCGA datasets. The prognostic value and immune characteristics of the signature were systematically evaluated.

RESULTS

Our analysis revealed eight distinct cell subtypes in the tumor microenvironment, including epithelial, fibroblast, myeloid, and Treg cells. DUB-high cells were predominantly found in Treg and myeloid populations, exhibiting elevated expression of tumor-related pathways and enhanced cell-cell communication networks, particularly between fibroblasts and myeloid cells. Conversely, DUB-low cells were enriched in epithelial populations with reduced immune activity. The DRS model demonstrated robust prognostic value across multiple independent cohorts. High-risk patients, as classified by the DRS, showed significantly poorer survival outcomes and distinct immune infiltration patterns compared to low-risk patients.

CONCLUSION

This study provides comprehensive insights into DUB gene expression patterns across different cell populations in ovarian carcinoma. The established DRS model offers a promising tool for risk stratification and may guide personalized therapeutic strategies. Our findings highlight the potential role of DUB genes in modulating the tumor immune microenvironment and patient outcomes in ovarian carcinoma.

Estrogen influences class-switched memory B cell frequency only in humans with two X chromosomes

Sex differences in immunity are well-documented, though mechanisms underpinning these differences remain ill-defined. Here, in a human-only ex vivo study, we demonstrate that postpubertal cisgender females have higher levels of CD19+CD27+IgD- class-switched memory B cells compared with age-matched cisgender males. This increase is only observed after puberty and before menopause, suggesting a strong influence for sex hormones. Accordingly, B cells express high levels of estrogen receptor 2 (ESR2), and class-switch-regulating genes are enriched for ESR2-binding sites. In a gender-diverse cohort, blockade of natal estrogen in transgender males (XX karyotype) reduced class-switched memory B cell frequency, while gender-affirming estradiol treatment in transgender females (XY karyotype) did not increase these levels. In postmenopausal cis-females, class-switched memory B cells were increased in those taking hormone replacement therapy (HRT) compared with those who were not. These data demonstrate that sex hormones and chromosomes work in tandem to impact immune responses, with estrogen only influencing the frequency of class-switched memory B cells in individuals with an XX chromosomal background.

Integrative Bioinformatic Analysis of Cellular Senescence Genes in Ovarian Cancer: Molecular Subtyping, Prognostic Risk Stratification, and Chemoresistance Prediction

Background: Ovarian cancer (OC) is a heterogeneous malignancy associated with a poor prognosis, necessitating robust biomarkers for risk stratification and therapy optimization. Cellular senescence-related genes (CSGs) are emerging as pivotal regulators of tumorigenesis and immune modulation, yet their prognostic and therapeutic implications in OC remain underexplored. Methods: We integrated RNA-sequencing data from TCGA-OV (n = 376), GTEx (n = 88), and GSE26712 (n = 185) to identify differentially expressed CSGs (DE-CSGs). Consensus clustering, Cox regression, LASSO-penalized modeling, and immune infiltration analyses were employed to define molecular subtypes, construct a prognostic risk score, and characterize tumor microenvironment (TME) dynamics. Drug sensitivity was evaluated using the Genomics of Drug Sensitivity in Cancer (GDSC)-derived chemotherapeutic response profiles. Results: Among 265 DE-CSGs, 31 were prognostic in OC, with frequent copy number variations (CNVs) in genes such as STAT1, FOXO1, and CCND1. Consensus clustering revealed two subtypes (C1/C2): C2 exhibited immune-rich TME, elevated checkpoint expression (PD-L1, CTLA4), and poorer survival. A 19-gene risk model stratified patients into high-/low-risk groups, validated in GSE26712 (AUC: 0.586-0.713). High-risk patients showed lower tumor mutation burden (TMB), immune dysfunction, and resistance to Docetaxel/Olaparib. Six hub genes (HMGB3, MITF, CKAP2, ME1, CTSD, STAT1) were independently predictive of survival. Conclusions: This study establishes CSGs as critical determinants of OC prognosis and immune evasion. The molecular subtypes and risk model provide actionable insights for personalized therapy, while identified therapeutic vulnerabilities highlight opportunities to overcome chemoresistance through senescence-targeted strategies.

Unravelling Cancer Immunity: Coagulation.Sig and BIRC2 as Predictive Immunotherapeutic Architects

Immune checkpoint inhibitors (ICIs) represent a groundbreaking advancement in cancer therapy, substantially improving patient survival rates. Our comprehensive research reveals a significant positive correlation between coagulation scores and immune-related gene expression across 30 diverse cancer types. Notably, tumours exhibiting high coagulation scores demonstrated enhanced infiltration of cytotoxic immune cells, including CD8+ T cells, natural killer (NK) cells, and macrophages. Leveraging the TCGA pan-cancer database, we developed the Coagulation.Sig model, a sophisticated predictive framework utilising a coagulation-related genes (CRGs) to forecast immunotherapy outcomes. Through rigorous analysis of ten ICI-treated cohorts, we identified and validated seven critical CRGs: BIRC2, HMGB1, STAT2, IFNAR1, BID, SPATA2, IL33 and IFNG, which form the foundation of our predictive model. Functional analyses revealed that low-risk tumours characterised by higher immune cell populations, particularly CD8+ T cells, demonstrated superior ICI responses. These tumours also exhibited increased mutation rates, elevated neoantigen loads, and greater TCR/BCR diversity. Conversely, high-risk tumours displayed pronounced intratumor heterogeneity (ITH) and elevated NRF2 pathway activity, mechanisms strongly associated with immune evasion. Experimental validation highlighted BIRC2 as a promising therapeutic target. Targeted BIRC2 knockdown, when combined with anti-PD-1 therapy, significantly suppressed tumour growth, enhanced CD8+ T cell infiltration, and amplified IFN-γ and TNF-α secretion in tumour models. Our findings position the Coagulation.Sig model as a novel, comprehensive approach to personalised cancer treatment, with BIRC2 emerging as both a predictive biomarker and a potential therapeutic intervention point.

Deciphering the Oncogenic Landscape of Hepatocytes Through Integrated Single-Nucleus and Bulk RNA-Seq of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality, while the hepatocyte mechanisms driving oncogenesis remains poorly understood. In this study, single-nucleus RNA sequencing of samples from 22 HCC patients revealed 10 distinct hepatocyte subtypes, including beneficial Hep0, predominantly malignant Hep2, and immunosuppressive Hep9. These subtypes were strongly associated with patient prognosis, confirmed in TCGA-LIHC and Fudan HCC cohorts through hepatocyte composition deconvolution. A quantile-based scoring method is developed to integrate data from 29 public HCC datasets, creating a Quantile Distribution Model (QDM) with excellent diagnostic accuracy (Area Under the Curve, AUC = 0.968-0.982). QDM was employed to screen potential biomarkers, revealing that PDE7B functions as a key gene whose suppression promotes HCC progression. Guided by the genes specific to Hep0/2/9 subtypes, HCC is categorized into metabolic, inflammatory, and matrix classes, which are distinguishable in gene mutation frequencies, survival times, enriched pathways, and immune infiltration. Meanwhile, the sensitive drugs of the three HCC classes are identified, namely ouabain, teniposide, and TG-101348. This study presents the largest single-cell hepatocyte dataset to date, offering transformative insights into hepatocarcinogenesis and a comprehensive framework for advancing HCC diagnostics, prognostics, and personalized treatment strategies.

Landscape and regulation of mRNA translation in the early C. elegans embryo

Animal embryos rely on regulated translation of maternally deposited mRNAs to drive early development. Using low-input ribosome profiling combined with RNA sequencing on precisely staged embryos, we measured mRNA translation during the first four cell cycles of C. elegans development. We uncovered stage-specific patterns of developmentally coordinated translational regulation. We confirmed that mRNA localization correlates with translational eLiciency, though initial translational repression in germline precursors occurs before P-granule association. Our analysis suggests that the RNA-binding protein OMA-1 represses the translation of its target mRNAs in a stage-specific manner, while indirectly promoting the translational eLiciency of other transcripts. These findings illuminate how post-transcriptional mechanisms shape the embryonic proteome to direct cell diLerentiation, with implications for understanding similar regulation across species where maternal factors guide early development.

A hollow fiber membrane-based liver organoid-on-a-chip model for examining drug metabolism and transport

Liver-on-a-chip models predictive for both metabolism, and blood and canalicular transport of drug candidates in humans are lacking. Here, we established a bioengineered and 3Rs-complied (animal component-free) hepatocyte-like millifluidic system based on 3D hollow fiber membranes (HFMs), recombinant human laminin 332 coating and adult human stem cell-derived organoids. Organoid fragments formed polarized and tight monolayers on HFMs with improved hepatocyte-like maturation, as compared to standard 3D organoid cultures in Matrigel from matched donors. Gene expression profiling and immunofluorescence revealed that hepatocyte-like monolayers expressed a broad panel of phase I (e.g. CYP3A4, CYP2D6, CYP2C9) and II (e.g. UGTs, SULTs) drug-metabolizing enzymes and drug transporters (e.g. MDR1, MRP3, OATP1B3). Moreover, statically cultured monolayers displayed phase I and II metabolism of a cocktail of six relevant compounds, including midazolam and 7-hydroxycoumarin. We also demonstrated the disposition of midazolam in the basal/blood-like circulation and apical/canalicular-like compartment of the millifluidic chip. Finally, we studied the bioavailability of midazolam and coumarin on-a-chip in combination with a small intestine-like system. In conclusion, we generated a proof-of-concept liver organoid-on-a-chip model for examining metabolism and transport of drugs, which can be further developed to predict pharmacokinetics' (PK)/absorption, distribution, metabolism and excretion (ADME) profiles in humans.

Reference-informed evaluation of batch correction for single-cell omics data with overcorrection awareness

Batch effect correction (BEC) is fundamental to integrate multiple single-cell RNA sequencing datasets, and its success is critical to empower in-depth interrogation for biological insights. However, no simple metric is available to evaluate BEC performance with sensitivity to data overcorrection, which erases true biological variations and leads to false biological discoveries. Here, we propose RBET, a reference-informed statistical framework for evaluating the success of BEC. Using extensive simulations and six real data examples including scRNA-seq and scATAC-seq datasets with different numbers of batches, batch effect sizes and numbers of cell types, we demonstrate that RBET evaluates the performance of BEC methods more fairly with biologically meaningful insights from data, while other methods may lead to false results. Moreover, RBET is computationally efficient, sensitive to overcorrection and robust to large batch effect sizes. Thus, RBET provides a robust guideline on selecting case-specific BEC method, and the concept of RBET is extendable to other modalities.

A complex phylogeny of lineage plasticity in metastatic castration resistant prostate cancer

Aggressive variant and androgen receptor (AR)-independent castration resistant prostate cancers (CRPC) represent the most significant diagnostic and therapeutic challenges in prostate cancer. This study examined a case of simultaneous progression of both adenocarcinoma and squamous tumors from the same common origin. Using whole-genome and transcriptome sequencing from 17 samples collected over >6 years, we established the clonal relationship of all samples, defined shared complex structural variants, and demonstrated both divergent and convergent evolution at AR. Squamous CRPC-associated circulating tumor DNA was identified at clinical progression prior to biopsy detection of any squamous differentiation. Dynamic changes in the detection rate of histology-specific clones in circulation reflected histology-specific sensitivity to treatment. This dataset serves as an illustration of non-neuroendocrine transdifferentiation and highlights the importance of serial sampling at progression in CRPC for the detection of emergent non-adenocarcinoma histologies with implications for the treatment of lineage plasticity and transdifferentiation in metastatic CRPC.

Simulation study of factors affecting the accuracy of transcriptome models under complex environments

Characterization of molecular responses in real and complex field environments is essential for understanding the environmental response of plants. Field transcriptomics prediction consists of modeling of transcriptomes in outdoor fields with various environmental variables: Meteorological parameters, atmospheric gases, soil conditions, herbivores, management, etc. It is the most comprehensive method of studying gene expression dynamics in complex environments. However, it is not clear what factors influence the accuracy of field transcriptome models. In this study, a novel simulation system was developed. Using the system, we performed a large-scale simulation to reveal the factors affecting the accuracy of the models. We found that the factors that had the greatest impact on the accuracy are, in order of importance, the expression pattern of the gene, the number of samples in the training data, the diurnal coverage of the training data, and the temperature coverage of the training data. Validation using actually measured transcriptome data showed similar results to the simulations. Our simulation system and the analysis results will be helpful for developing efficient sampling strategies for training data and for generating simulated data for benchmarking new modelling methods. It will also be valuable to dissect the relative importance of various factors behind transcriptome dynamics in the real environment.

A mouse model of cardiac immunoglobulin light chain amyloidosis reveals insights into tissue accumulation and toxicity of amyloid fibrils

Immunoglobulin light chain (LC) amyloidosis (AL) is one of the most common types of systemic amyloidosis but there is no reliable in vivo model for better understanding this disease. Here, we develop a transgenic mouse model producing a human AL LC. We show that the soluble full length LC is not toxic but a single injection of pre-formed amyloid fibrils or an unstable fragment of the LC leads to systemic amyloid deposits associated with early cardiac dysfunction. AL fibrils in mice are highly similar to that of human, arguing for a conserved mechanism of amyloid fibrils formation. Overall, this transgenic mice closely reproduces human cardiac AL amyloidosis and shows that a partial degradation of the LC is likely to initiate the formation of amyloid fibrils in vivo, which in turn leads to cardiac dysfunction. This is a valuable model for research on AL amyloidosis and preclinical evaluation of new therapies.

Impact of BRCA mutations, age, surgical indication, and hormone status on the molecular phenotype of the human Fallopian tube

The human Fallopian tube (FT) is an important organ in the female reproductive system and has been implicated as a site of origin for pelvic serous cancers, including high-grade serous tubo-ovarian carcinoma (HGSC). We have generated comprehensive whole-genome bisulfite sequencing, RNA-seq, and proteomic data of over 100 human FTs, with detailed clinical covariate annotations. Our results challenge existing paradigms that extensive epigenetic, transcriptomic and proteomic alterations exist in the FTs from women carrying heterozygous germline BRCA1/2 pathogenic variants. We find minimal differences between BRCA1/2 carriers and non-carriers prior to loss of heterozygosity. Covariates such as age and surgical indication can confound BRCA1/2-related differences reported in the literature, mainly through their impact on cell composition. We systematically document and highlight the degree of variations across normal human FT, defining five groups capturing major cellular and molecular changes across various reproductive stages, pregnancy, and aging. We are able to associate gene, protein, and epigenetic changes with these and other clinical covariates, but not heterozygous BRCA1/2 mutation status. This sheds new light into prevention and early detection of tumorigenesis in populations at high-risk for ovarian cancer.

CREB3 gain of function variants protect against ALS

Amyotrophic lateral sclerosis (ALS) is a fatal and rapidly evolving neurodegenerative disease arising from the loss of glutamatergic corticospinal neurons (CSN) and cholinergic motoneurons (MN). Here, we performed comparative cross-species transcriptomics of CSN using published snRNA-seq data from the motor cortex of ALS and control postmortem tissues, and performed longitudinal RNA-seq on CSN purified from male Sod1G86R mice. We report that CSN undergo ER stress and altered mRNA translation, and identify the transcription factor CREB3 and its regulatory network as a resilience marker of ALS, not only amongst vulnerable neuronal populations, but across all neuronal populations as well as other cell types. Using genetic and epidemiologic analyses we further identify the rare variant CREB3R119G (rs11538707) as a positive disease modifier in ALS. Through gain of function, CREB3R119G decreases the risk of developing ALS and the motor progression rate of ALS patients.

Depletion of tumor-derived CXCL5 improves T cell infiltration and anti-PD-1 therapy response in an obese model of pancreatic cancer

BACKGROUND

CXCR1/2 inhibitors are being implemented with immunotherapies in PDAC clinical trials. CXC-ligands are a family of cytokines responsible for stimulating these receptors; while typically secreted by activated immune cells, fibroblasts, and even adipocytes, they are also secreted by immune-evasive cancer cells. CXC-ligand release is known to occur in response to inflammatory stimuli. Adipose tissue is an endocrine organ and a source of inflammatory signaling peptides. Importantly, adipose-derived cytokines and chemokines are implicated as potential drivers of tumor cell immune evasion; cumulatively, these findings suggest that targeting CXC-ligands may be beneficial in the context of obesity.

METHODS

RNA-sequencing of human PDAC cell lines was used to assess influences of adipose conditioned media on the cancer cell transcriptome. The adipose-induced secretome of PDAC cells was validated with ELISA for induction of CXCL5 secretion. Human tissue data from CPTAC was used to correlate IL-1β and TNF expression with both CXCL5 mRNA and protein levels. CRISPR-Cas9 was used to knockout CXCL5 from a murine PDAC KPC cell line to assess orthotopic tumor studies in syngeneic, diet-induced obese mice. Flow cytometry and immunohistochemistry were used to compare the immune profiles between tumors with or without CXCL5. Mice-bearing CXCL5 competent or deficient tumors were monitored for differential tumor size in response to anti-PD-1 immune checkpoint blockade therapy.

RESULTS

Human adipose tissue conditioned media stimulates CXCL5 secretion from PDAC cells via either IL-1β or TNF; neutralization of both is required to significantly block the release of CXCL5 from tumor cells. Ablation of CXCL5 from tumors promoted an enriched immune phenotype with an unanticipatedly increased number of exhausted CD8 T cells. Application of anti-PD-1 treatment to control tumors failed to alter tumor growth, yet treatment of CXCL5-deficient tumors showed response by significantly diminished tumor mass.

CONCLUSIONS

In summary, our findings show that both TNF and IL-1β can stimulate CXCL5 release from PDAC cells in vitro, which correlates with expression in patient data. CXCL5 depletion in vivo alone is sufficient to promote T cell infiltration into tumors, increasing efficacy and requiring checkpoint blockade inhibition to alleviate tumor burden.

Wnt signaling restores evolutionary loss of regenerative potential in Hydra

The regenerative potential of animals varies widely, even among closely-related species. In a comparative study of regeneration across the Hydra genus, we found that while most species exhibit robust whole-body regeneration, Hydra oligactis and other members of the Oligactis clade consistently fail to regenerate their feet. To investigate the mechanisms underlying this deficiency, we analyzed transcriptional responses during head and foot regeneration in H. oligactis. Our analysis revealed that the general injury response in H. oligactis lacks activation of Wnt signaling, a pathway essential for Hydra vulgaris foot regeneration. Notably, transient treatment with a Wnt agonist in H. oligactis triggered a foot-specific transcriptional program, successfully rescuing foot regeneration. Our transcriptional profiling also revealed dlx2 as a likely high-level regulator of foot regeneration, dependent on Wnt signaling activation. Our study establishes a comparative framework for understanding the molecular basis of regeneration and its evolutionary loss in closely-related species.

Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial

Anti-angiogenic (AA) therapy is a cornerstone of metastatic clear cell renal cell carcinoma (ccRCC) treatment, but not everyone responds, and predictive biomarkers are lacking. CD31, a marker of vasculature, is insufficient, and the Angioscore, an RNA-based angiogenesis quantification method, is costly, associated with delays, difficult to standardize, and does not account for tumor heterogeneity. Here, we developed an interpretable deep learning (DL) model that predicts the Angioscore directly from ubiquitous histopathology slides yielding a visual vascular network (H&E DL Angio). H&E DL Angio achieves a strong correlation with the Angioscore across multiple cohorts (spearman correlations of 0.77 and 0.73). Using this approach, we found that angiogenesis inversely correlates with grade and stage and is associated with driver mutation status. Importantly, DL Angio expediently predicts AA response in both a real-world and IMmotion150 trial cohorts, out-performing CD31, and closely approximating the Angioscore (c-index 0.66 vs 0.67) at a fraction of the cost.

A deep learning tissue classifier based on differential co-expression genes predicts the pregnancy outcomes of cattle†

Economic losses in cattle farms are frequently associated with failed pregnancies. Some studies found that the transcriptomic profiles of blood and endometrial tissues in cattle with varying pregnancy outcomes display discrepancies even before artificial insemination (AI) or embryo transfer (ET). In the study, 330 samples from seven distinct sources and two tissue types were integrated and divided into two groups based on the ability to establish and maintain pregnancy after AI or ET: P (pregnant) and NP (nonpregnant). By analyzing gene co-variation and employing machine learning algorithms, the objective was to identify genes that could predict pregnancy outcomes in cattle. Initially, within each tissue type, the top 100 differentially co-expressed genes (DCEGs) were identified based on the analysis of changes in correlation coefficients and network topological structure. Subsequently, these genes were used in models trained by seven different machine learning algorithms. Overall, models trained on DCEGs exhibited superior predictive accuracy compared to those trained on an equivalent number of differential expression genes. Among them, the deep learning models based on differential co-expression genes in blood and endometrial tissue achieved prediction accuracies of 91.7% and 82.6%, respectively. Finally, the importance of DCEGs was ranked using SHapley Additive exPlanations (SHAP) and enrichment analysis, identifying key signaling pathways that influence pregnancy. In summary, this study identified a set of genes potentially affecting pregnancy by analyzing the overall co-variation of gene connections between multiple sources. These key genes facilitated the development of interpretable machine learning models that accurately predict pregnancy outcomes in cattle.