bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.01.24.701467

Transcriptomic Analysis Reveals Inflammatory and Metabolic Dysregulation in Unexplained Female Infertility

Abstract

Infertility is a complex condition affecting both the male and female population. Influenced by multiple factors, it remains a constant challenge due to limited understanding of endometrial abnormalities. With this study we aim to investigate the molecular basis of infertility using transcriptomic analysis of endometrial tissue from the NCBI GEO dataset GSE92324. We performed exploratory data analysis using Principal Component Analysis (PCA) to find samples variance followed by differential gene expression (DGE) analysis using DESeq2 package where we identified 168 significant genes with adjusted p-value < 0.05 and |log2FC| > 2. Upregulated genes included GPX3, CXCL14, and PPARGC1A and downregulated genes included WNK4, GJB2, and TRPM6. Functional enrichment using KEGG and GO showed that differentially expressed genes (DEGs) are involved in immune-inflammatory pathways, lipid metabolism and steroid biosynthesis pathways. Through Ingenuity Pathway Analysis (IPA) we identified affected canonical pathways such as increased innate immune responses, altered lipid metabolism and inhibition of mitochondrial dysfunction. Upstream regulator analysis highlighted PTEN, PRKAA1, HDAC4, IL10RA, and RAD51, which were impacting metabolic pathways and anti-inflammatory signalling. Further, through Weighted Gene Co-expression Network Analysis (WGCNA) we found a Turquoise module that had very strong and highly significant negative correlation (cor = - 0.84, respectively and P < 0.0001) with traits of interest. This led to the discovery of C7orf50 as a novel insight involved in cholesterol metabolism linked to infertility. This integrative approach reveals crucial genes, co-expression modules, and underlying pathways involved in female infertility. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/701467v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@4418a6org.highwire.dtl.DTLVardef@ae7900org.highwire.dtl.DTLVardef@89f581org.highwire.dtl.DTLVardef@154f1a9_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIFrom the dataset GSE92324 total of 168 significant DEGs associated with unexplained infertility were identified using adjusted p-value < 0.05 and |log2FC| > and < 2. C_LIO_LIIn comparison with the CTD list we identified five genes C1orf106, C15orf59, LINC00461, C15orf48, and C10orf99 previously unknown as having direct evidence of involvement in infertility. C_LIO_LIWGCNA analysis highlighted the turquoise module as highly associated and gave the novel gene C7orf50 associated with cholesterol metabolism. C_LIO_LIIPA revealed PTEN, PRKAA1, IL10RA, and RAD51 as potential upstream regulators and inflammatory pathways, mitochondrial dysfunction as canonical pathways. C_LIO_LIThe study highlights a novel link between GI inflammation and endometrial receptivity. C_LI

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

PATIAL, R., Ray, S., Singh, K., Sobti, R. C.. 2026-01-26. Transcriptomic Analysis Reveals Inflammatory and Metabolic Dysregulation in Unexplained Female Infertility. https://doi.org/10.64898/2026.01.24.701467

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

A meta-interaction basis for cell-cell communication in tissues

Tissue function depends on signals exchanged between cells and the responses they elicit. Yet whether diverse cell-cell interactions in situ form recurrent sender-receiver programs remains unclear. We present SpiderNet, an interpretable representation-learning framework that discovers such directed programs as a compact basis of cell-cell meta-interactions (MIs) from spatial transcriptomics. SpiderNet jointly learns which sender regulators, ligand-receptor pairs, and receiver targets define each MI and where each program is active across neighboring cell pairs. The resulting representation traces multicellular relays and links communication to cell states, perturbation responses, and phenotypes. SpiderNet recovers ground-truth MIs and their molecular components in simulations and, in real tissues, shows stronger direction-specific agreement with independently curated regulatory programs in senders and receivers than alternative methods. Across more than 5.8 million spatially profiled cells, SpiderNet resolves an SPP1-THBS relay linking monocytes, fibroblasts, and tumor cells within an immune-suppressive ovarian cancer niche, predicts T-cell responses to held-out melanoma-cell perturbations, and identifies a T-cell-associated brain-aging program and age-predictive signals that transfer across regions and platforms. It reveals a recurrent pan-cancer COLLAGEN-linked fibroblast-tumor program whose projected abundance in independent cohorts is associated with poorer survival and non-response to immunotherapy. SpiderNet thus establishes MIs as a reusable organizational layer between molecular interactions and tissue phenotypes, providing a framework to resolve, compare, trace, and perturb multicellular regulation in situ.

bioinformatics↗

Heterogeneous Graph Contrastive Learning for Drug-Gene-Disease Motif Prediction

Drug repurposing and target discovery offer critical strategies for advancing therapeutic development by uncovering the potential biological pathways and novel associations among drugs, genes, and diseases. However, experimental discovery remains expensive and time-consuming, which limits the scalability of large-scale studies. In addition, existing computational approaches often struggle to effectively integrate heterogeneous biomedical data, capture the complex higher-order topological signatures of biological interactomes, and generalize to unseen entities. Here, we present HANAMI (Heterogeneous grAph coNtrastive leArning for drug-gene-disease Motif predIction), a multi-view deep graph learning framework designed to model complex interactions among drugs, genes, and diseases. HANAMI integrates diverse heterogeneous biomedical knowledge, including chemical structures, genomic sequences, and clinical phenotypes, and leverages relation-aware topology encoding, structure-aware aggregation, and contrastive learning to enable accurate motif prediction with biological context from the network. Systematic evaluation on benchmark datasets shows that HANAMI achieves up to 6% improvements over existing state-of-the-art methods in predicting drug-gene-disease motifs. The framework further demonstrates strong inductive generalization, maintaining an [~]18% performance advantage in zero-shot settings involving previously unseen entities. Beyond predictive performance, HANAMI effectively prioritizes drug-disease relationships investigated in Phase II or III trials while identifying candidate genes that suggest plausible mechanistic links. Together, HANAMI provides a computational framework for interpreting complex biomedical interactions, offering a scalable foundation to accelerate drug repurposing and therapeutic innovation.

bioinformatics↗

PTMExplorer: A Multi-Dimensional Integrative Visualization Platform for Protein Post-Translational Modification Function and Structure

Deciphering the functions of post-translational modifications (PTMs) is a critical bridge connecting large-scale modification proteomics data to mechanistic studies. However, most existing tools for visualizing PTM omics data are limited to site catalogs or single-dimensional feature displays. They lack the capability to simultaneously map user-derived differential modification sites onto multi-dimensional contexts, including protein three-dimensional (3D) structure, evolutionary conservation, functional sites, and disease associations. This limitation makes it difficult for researchers to rapidly assess the biological importance of candidate sites from among a vast number of differentially modified sites. Here, we present PTMExplorer, an interactive platform for the multi-dimensional visualization of protein PTMs. PTMExplorer comprises three core modules: PTM Inspector, built upon ProtVista, provides a multi-track, sequence-feature integrated view incorporating intrinsically disordered region (IDR) prediction (via flDPnn), surface accessibility calculation (via FreeSASA), and UniProt functional annotations; PTM 3D Locator, leveraging the Nightingale/Mol* engine, anchors modification sites onto AlphaFold/Protein Data Bank (PDB) 3D structures through residue mapping via PDBe-SIFTS; and PTM Overview, utilizing the R circlize package, presents a panoramic polar circos plot illustrating modification distribution and inter-group differential regulation. Additionally, three major disease-associated modification databases (PTMD, qPTM, and PhosCancer) are integrated as PTM-Disease Nexus, enabling co-localization comparison between user-defined differential sites and reported disease-related sites. PTMExplorer currently supports eight model organisms, accepts user-uploaded differential analysis results, and provides multi-dimensional annotations and various visualization options (https://www.bioladder.cn/PTMExplorer/). Using a multi-omics dataset from hepatocellular carcinoma (18 patients, 9 modification types) as a case study, we demonstrate the practical utility of PTMExplorer in screening potential biomarkers, revealing multi-modification coordination mechanisms, and distinguishing between absolute and relative quantification patterns.

bioinformatics↗