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bioRxiv2025

Differential gene expression drives cell-cycle-dependent transition from monopolar to bipolar growth in fission yeast.

Cell polarity is important for maintaining cell structure and function. In S. pombe, after division, cells are monopolar and grow from the old end that existed from the previous generation. Cells transition to bipolar growth once the cell reaches a certain size, when the new end generated after division initiates growth. Bipolarity occurs as a consequence of cell growth, where the two cell ends have sufficient growth-promoting factors. However, the transition to bipolarity does not occur in G1-arrested cells even with enhanced cell growth, suggesting a role for the cell-cycle phase in this process. To identify how the cell cycle impacts monopolar to bipolar transition, we performed high-throughput mRNA sequencing to detect differentially expressed genes in G1/S-arrested and G2-phase cells of the cell cycle mutant cdc10-129. Gene expression analysis using the DESeq2 R package identified 65 unique genes upregulated in G1/S phase and 35 in G2 phase cells. Enrichment analysis shows that G1/S phase cells upregulated the MAPK pheromone-response pathway, protein folding, rRNA processing, and heat-shock protein binding. G2 phase cells showed upregulation of plasma membrane maintenance and cell wall organization. Protein-protein interaction networks identified the cdc15-hob3-rho1-bgs1 hub in G2 phase cells. These genes are known to promote bipolar growth and regulate cell wall biogenesis. In G1/S phase cells spk1-byr2-ste11 hub was identified, which is required for pheromone-response and nutritional stress-dependent G1-arrest. We find that Spk1 also prevents precocious bipolar growth. Thus, we hypothesize that the pheromone response pathway, in addition to promoting G1-G0 transition, also prevents bipolar growth. In nutrient-rich conditions, this pathway is downregulated, G1-arrest is alleviated, the cell cycle progresses, and bipolarity occurs. Our findings suggest that in nutrition-rich conditions, stress response pathways are downregulated enabling transition from monopolar to bipolar growth, along with cell cycle progression.

cell biology↗

bioRxiv2026

Integrated transcriptomic analysis identifies Paternally Expressed Gene 10 as a novel candidate biomarker associated with aggressive features in liposarcoma

Abstract Background Liposarcoma is a rare cancer, comprising four major histological subtypes: well-differentiated, dedifferentiated, myxoid and pleomorphic liposarcoma. Therapeutic options remain limited for advanced disease, and additional biomarkers that may support biological characterization, diagnosis, or risk stratification are needed. Methods Bulk RNA sequencing (RNA-seq) was performed on paired tumour and adjacent non-tumour tissues from an analysable subset of 16 patients (22 tumour specimens) within a clinicopathological cohort of 21 patients. Differential gene expression analysis, principal component analysis, gene set enrichment (GSEA) analysis and single-sample GSEA (ssGSEA) were used to define subtype-specific transcriptional programs and imprinted-gene dysregulation. PEG10 expression was validated by RT-qPCR and RNA fluorescence in situ hybridization (FISH), and its cellular localization was explored by combined FISH and immunofluorescence for P53, vimentin, Ki67, and CD34. PEG10 expression in liposarcoma was further investigated in an external single-cell RNA-sequencing dataset of dedifferentiated liposarcoma. An exploratory survival analysis was performed in the pan-sarcoma TCGA-SARC cohort. Results Transcriptomic profiling identified distinct subtype-specific gene expression patterns and broad dysregulation of imprinted genes in liposarcoma compared with adjacent non-tumour tissue. Among all the imprinted genes, PEG10 showed a marked tumour-to-normal expression difference and good discriminatory performance between tumour and normal tissues in the discovery cohort (AUC=0.867). In the TCGA sarcoma cohort high PEG10 expression was associated with shorter overall survival. Immuno-FISH and single-cell analyses revealed that PEG10 was predominantly detected in tumour and proliferating cell populations. Importantly, PEG10 expression was higher in high-grade tumours. In bulk RNA-seq data, PEG10 expression correlated with hypoxia, extracellular matrix remodelling and invasion ssGSEA signatures. In the external single-cell RNA-seq dataset PEG10high tumours showed a reduced immune cell infiltration. Conclusions This study provides an integrated transcriptomic characterisation of liposarcoma and identifies PEG10 as a candidate tumour-associated biomarker linked to aggressive disease features. The diagnostic and prognostic value of PEG10 requires validation in larger, independent, subtype-balanced liposarcoma cohorts, and its potential therapeutic relevance requires direct functional investigation.

genomics↗

bioRxiv2021

Controlling gene expression timing through gene regulatory architecture

Gene networks typically involve the regulatory control of multiple genes with related function. This connectivity enables correlated control of the levels and timing of gene expression. Here we study how gene expression timing in the single-input module motif can be encoded in the regulatory DNA of a gene. Using stochastic simulations, we examine the role of binding affinity, TF regulatory function and network size in controlling the mean first-passage time to reach a fixed fraction of steady-state expression for both an auto-regulated TF gene and a target gene. We also examine how the variability in first-passage time depends on these factors. We find that both network size and binding affinity can dramatically speed up or slow down the response time of network genes, in some cases predicting more than a 100-fold change compared to that for a constitutive gene. Furthermore, these factors can also significantly impact the fidelity of this response. Importantly, these effects do not occur at "extremes" of network size or binding affinity, but rather in an intermediate window of either quantity. Author summaryRegulated genes are able to respond to stimuli in order to ramp up or down production of specific proteins. Although there is considerable focus on the magnitude (or fold-change) of the response and how that depends on the architectural details of the regulatory DNA, the dynamics, which dictates the response time of the gene, is another key feature of a gene that is encoded within the DNA. Unraveling the rules that dictate both the response time of a gene and the precision of that response encoded in the DNA poses a fundamental problem. In this manuscript, we systematically investigate how the response time of genes in auto-regulatory networks is controlled by the molecular details of the network. In particular, we find that network size and TF-binding affinity are key parameters that can slow, in the case of auto-activation, or speed up, in the case of auto-repression, the response time of not only the auto-regulated gene but also the genes that are controlled by the auto-regulated TF. In addition, we find that the precision of the response depends crucially on these characteristics.

systems biology↗

bioRxiv2026

PFOA exposure amplifies normal developmental gene expression programs in the African Killifish, Nothobranchius furzeri

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants associated with developmental abnormalities and adverse health outcomes, yet it remains unclear whether PFAS exposure imposes novel transcriptional programs during development or perturbs endogenous developmental processes. Here, we continuously exposed African killifish (Nothobranchius furzeri) to an environmentally relevant concentration of perfluorooctanoic acid (PFOA) from egg laying through juvenile development to mimic prenatal-to-adolescent exposure and performed whole-transcriptome sequencing at two developmental stages. Despite four weeks of embryonic exposure, newly hatched juveniles, approximately equivalent to human infants, exhibited remarkably limited transcriptional responses, with only a few differentially expressed genes identified. In contrast, older juveniles, equivalent to human adolescents, exposed for eight weeks displayed a dramatic expansion of transcriptional perturbation, with approximately 30-fold more differentially expressed genes spanning pathways involved in cell-cycle regulation, endocrine signaling, immune function, oxidative stress, and lipid metabolism. Unexpectedly, more than half of the PFOA-induced genes were the same genes that normally increase during juvenile maturation, representing a highly significant enrichment of the endogenous developmental program. These findings indicate that the embryonic transcriptome is largely buffered against chronic PFOA exposure, whereas post-hatch stages exhibit heightened vulnerability. Rather than inducing a distinct toxicological state, PFOA predominantly acted by amplifying existing developmental gene regulatory programs. Our results identify the juvenile stage following hatching, equivalent to human neonatal and adolescent developmental stages, as a critical window of PFAS susceptibility and suggest that environmental contaminants may exert their effects by exaggerating normal developmental trajectories, with potential consequences for growth, maturation, and long-term health.

Developmental Biology↗

bioRxiv2024

Gene expression and eQTL analysis reflect the heterogeneity in the inflammatory status of the duodenal epithelial lining in coeliac disease

In coeliac disease (CeD), the epithelial lining (EL) of the small intestine is severely damaged through a complex auto-inflammatory response that results in intraepithelial lymphocytes (IELs) attacking epithelial cells (ECs). To better understand the changes occurring in the EL in CeD, including after initiation of a gluten-free diet, and the regulatory mechanisms that affect mucosal homeostasis, we investigated ECs and IELs in the CeD duodenal EL using RNA-seq and eQTL analysis on predicted cell types. The study included 82 duodenal biopsies from volunteers, grouped into controls (CTRL), gluten-free diet treated CeD (TCD) and untreated CeD (UCD). We identified 2,868 differential expressed genes, which clustered into four sets with specific functions related to CeD. Two sets, one upregulated for cell cycle function and one downregulated for digestion, transmembrane transport, and laminin pathways, defined three groups of samples based on inflammation status: non-inflamed, mild inflammation or severe inflammation. These inflammation states correlated with but were not specific to disease state. The remaining two sets of genes were enriched for immune, extracellular matrix, and barrier functions. These latter two sets allowed the classification of samples into their disease conditions: CTRL, TCD, and UCD. Finally, deconvoluting eQTL effects from ECs and immune cells in the EL identified 7 and 9 cell-type-mediated eQTL genes, respectively. In sum, we identified genes expressed in the duodenal EL whose expression might be used as biomarkers to assess CeD condition and its mucosal and immune status. HighlightsO_LIGene expression in epithelial lining in CeD patients reflects inflammation status C_LIO_LIThe transcriptome can classify epithelium as no, mild, or severe inflammation C_LIO_LICell cycle, absorption, and basal lamina genes are indicators of mucosal damage C_LIO_LIImmune and extracellular matrix genes distinguish untreated from treated cases C_LIO_LIPredicted-cell-type eQTLs pinpoint effects in immune and epithelial cells C_LI

immunology↗

bioRxiv2024

Split minireporters facilitate monitoring of gene expression and peptide production in linear cell-free transcription-translation systems

Cell-free transcription-translation (TXTL) systems expressing genes from linear dsDNA enable rapid prototyping of genetic devices while avoiding cloning steps. However, repetitive inclusion of a reporter gene is an incompressible cost and sometimes accounts for most of the synthesized DNA length. Here we present minireporters based on split-GFP systems that reassemble into functional fluorescent proteins and can be used to monitor gene expression in E. coli TXTL. The 135 bp GFP10-11 fragment produces a fluorescent signal comparable to its full-length GFP counterpart when reassembling with its complementary protein synthesized from the 535 bp fragment expressed in TXTL. We show that minireporters can be used to characterize promoter libraries, with data qualitatively comparable to full-length GFP, and matching with in vivo expression measurements. We also use minireporters as small fusion tags to measure TXTL protein and peptide production yield. Finally, we generalize our concept by providing a luminescent minireporter based on split-nanoluciferase. The [~]80% gene sequence length reduction afforded by minireporters lowers synthesis costs and liberates space for testing larger devices while producing a reliable output. In the peptide production context, the small size of minireporters compared to full-length GFP is less likely to bias peptide solubility assays. We anticipate that minireporters will facilitate rapid and cost-efficient genetic device prototyping, protein production, and interaction assays.

synthetic biology↗

bioRxiv2026

Topological dependent modifications of gene expression in the preeclamptic placenta under aspirin treatment.

Preeclampsia is a hypertensive disorder of pregnancy characterized by systemic endothelial dysfunction and placental inflammatory activation. Low dose aspirin reduces preeclampsia incidence when initiated early, yet its molecular effects on the diseased human placenta remain poorly defined. We performed a topologically resolved transcriptomic analysis of placentas from two independent cohorts of preeclamptic women treated or not with aspirin. Placental samples were obtained either from the villous surface (syncytiotrophoblast rich) or from deep parenchymal regions (stromal and endothelial rich). In villous samples, microarray profiling identified 1,836 differentially expressed genes, predominantly down regulated. Gene set enrichment demonstrated marked suppression of pathways central to hypertensive placental pathology, including TNF/NF {kappa}B signaling, hypoxia responsive programs, glycolysis, and estrogen response cascades. RNA seq analysis of parenchymal samples identified 645 differentially expressed genes, again dominated by down regulation of cell cycle progression, mTORC1 signaling, inflammatory pathways, and metabolic stress responses. Across cohorts, 116 genes were consistently down regulated by aspirin and enriched for hypoxia, NF {kappa}B, estrogen response, and inflammatory signatures. Promoter analysis implicated transcription factors central to vascular inflammation and stress signaling, including NF {kappa}B1, RELA, HIF1A, PPARG, and PARP1. Functional validation in BeWo trophoblasts confirmed aspirin dependent modulation of key placental transcripts and altered secretion of MMP9, ANGPT2, and soluble endoglin (sENG) molecules directly implicated in endothelial dysfunction and hypertensive disease. These findings demonstrate that aspirin attenuates core inflammatory, hypoxic, and metabolic pathways across multiple placental lineages in preeclampsia, providing a mechanistic framework linking early aspirin administration to improved vascular and placental adaptation in hypertensive pregnancy. The action of aspirin upon differentiation of trophoblasts contribute to explain why aspirin protects only when administered early in pregnancy, before the end of the first trimester.

molecular biology↗

bioRxiv2021

Type 2 Diabetes Modifies Skeletal Muscle Gene Expression Response to Gastric Bypass Surgery

Roux-en-Y gastric bypass (RYGB) is an effective treatment for type 2 diabetes mellitus (T2DM) which can result in remission of clinical symptoms, yet mechanisms for improved skeletal muscle health are poorly understood. We sought to define the impact of existing T2DM on RYGB-induced muscle transcriptome changes. MethodsVastus lateralis biopsy transcriptomes were generated pre- and 1-yr post-RYGB in black adult females with (T2D; n = 5, age=51{+/-}6 yr, BMI=53.0{+/-}5.8 kg/m2) and without (CON; n = 7,43{+/-}6 yr,51.0{+/-}9.2 kg/m2) T2DM. Insulin, glucose, and HOMA-IR were measured in blood at the same time points. ANCOVA detected differentially expressed genes (p< 0.01, Fold change<|1.2|), which were used to identify enriched biological pathways. ResultsPre-RYGB, 95 probes were downregulated with T2D including subunits of mitochondrial complex I. Post-RYGB, the T2D group had normalized gene expression when compared to their non-diabetic counterparts with only 3 probes remaining significantly different. In the T2D, we identified 52 probes upregulated from pre- to post-RYGB, including NDFUB7 and NDFUA1. ConclusionBlack females with T2DM show extensive down regulation of genes across aerobic metabolism pathways prior to RYGB, which resolves 1 year post-RYGB and is related to improvements in clinical markers. These data support efficacy of RYGB for improving skeletal muscle health, especially in patients with T2DM.

molecular biology↗

bioRxiv2026

Combining RNA-seq and proteomics boosts sensitivity to detect differentially expressed genes by rpMeta

Differential gene expression analysis is a powerful technique to characterize biological systems. Differentially expressed genes (DEGs) are commonly identified by analysis of RNA sequencing datasets or mass spectrometry proteomics datasets. Increasingly, multi-omic studies include both RNA sequencing and proteomics from the same biospecimens. However, the common multi-omic DEG analysis practice involves separate DEG analysis by RNA and by protein with limited comparison by fold change correlations or Venn Diagrams. This disjoint practice may not utilize all of the available signal from both datatypes as transcripts and proteins have correlated expression levels. To integrate RNA and proteomics, we developed a new method, RNA protein meta-analysis called rpMeta. rpMeta takes RNA and protein expression data and calculates DEGs by RNA, by protein, and by meta-analysis models, following user-specified options. Spike-in, downsampling and cross-cohort benchmarks revealed that the meta-analysis model had greater sensitivity than RNA and protein models. In a real-world use case, the meta-analysis model discovered new genes and biological processes in lung cancer subtypes that were not detectable by the RNA or protein models. To streamline interpretation, rpMeta produces publication-ready data visualizations of the integrated DEGs. rpMeta is an open-source R package, available at https://github.com/zhangx8/rpMeta.

bioinformatics↗

bioRxiv2022

Comprehensive analysis of Arabidopsis thaliana DNA polymerase epsilon catalytic subunit A and B mutants - an insight into differentially expressed genes and protein-protein interactions

One of the main replicative enzymes in most eukaryotes, DNA polymerase {varepsilon} (POLE), is composed of four subunits, namely a single catalytic and three regulatory subunits. In Arabidopsis, the catalytic subunit of POLE is encoded by two genes: Arabidopsis thaliana DNA POLYMERASE EPSILON CATALYTIC SUBUNIT A (AtPOL2A) and B (AtPOL2B). Although studies have shown AtPOL2A to be involved in various biological processes, the role of AtPOL2B is unclear. Here, we investigated the transcriptomes of both atpol2a and atpol2b mutants, and the promoter sequences to provide a better insight into the targets of AtPOL2s at the molecular level. In the present study, leaf cDNA libraries of four AtPOL2 mutants (atpol2a-1 and atpol2b-1, -2 and - 3) were sequenced using the Illumina platform. Analysis of gene expression profiles identified a total of 198, 76, 141 and 67 differentially expressed genes in atpol2a-1, atpol2b-1, atpol2b-2 and atpol2b-3, respectively; the majority of pericentromeric transposable elements were transcriptionally active in atpol2a-1 as compared to atpol2b mutants and wild type. Protein-protein interaction network analysis and molecular docking identified three (CER1, RPA1E and AT5G60250) and two (PR1 and AT5G48490) proteins as potential interactors (cluster size > 60 and balanced score < -900) of AtPOL2A and AtPOL2B, respectively; Interestingly, these five proteins also showed a significant interaction between POLE catalytic subunit of Saccharomyces cerevisiae. Our in silico promoter analysis showed that the AtPOL2A promoter sequence is overrepresented with cis-acting regulatory elements (CREs) associate with cell cycle regulation, meristematic/reproductive tissue-specific pattern of expression and MYB protein recognition, whereas the AtPOL2B promoter sequence was mainly enriched with stress-responsive elements. The information provided here has led to the identification of targets of AtPOL2s at the molecular level and CREs putatively associated with the regulation of AtPOL2s. To our knowledge, this study provides the first comparative transcriptome profiling of single-gene mutants of AtPOL2s.

molecular biology↗

bioRxiv2026

On the predictability of progression-free survival in ovarian cancer from NanoString gene expression data

In the treatment of high grade serous ovarian cancer (HGSC), patients initially diagnosed with unresectable tumors are first treated with neoadjuvant chemotherapy (NACT) to reduce tumor burden prior to surgery. Analysis of matched pre- and post-NACT samples from the same patients enables the investigation of chemotherapy impacts and the biomarkers of progression. Although the tumor immune microenvironment (TIME) has increasingly been recognized as critical in shaping the development and progression of HGSC, we lack a comprehensive understanding of how chemotherapy remodels the TIME. Previous studies have found evidence for a general inflammatory response post-NACT, despite inconsistencies regarding which differentially expressed genes and pathways are implicated. We combine matched NanoString gene expression data from multiple sources to create a large dataset of matched pre- and post- NACT samples (N=83, with 29 novel to this study) and investigate reproducibility. Further, we use machine learning methods to investigate whether patient progression-free survival (PFS) can be predicted from the observed impact of chemotherapy on the TIME as represented by the comprehensive set of NanoString features. We find overall low predictability of PFS from all NanoString features, suggesting that previous results may have been limited by small sample size effects and that larger datasets are needed to identify more generalizable and translatable findings. We identify a set of differential expression features that are the most important for predicting patient outcomes that can be validated in future computational and biological studies. Author summaryA subset of patients with high grade serous ovarian cancer are treated with chemotherapy before surgery to reduce tumor burden. We investigate a large dataset of samples taken before and after chemotherapy. These matched samples enable an investigation of how the environment around tumors, for example immune cell infiltration, reacts to chemotherapy, providing insights into biomarkers for treatment response and treatments that could complement chemotherapy. This larger dataset only partially replicates results from previous studies, while also providing new insights. Machine learning models designed to predict the time to patient recurrence from available biomarkers indicate that they are not strongly predictive of patient outcomes, in contrast to past studies. These results suggest that larger datasets are needed. We identify a set of genes that change with chemotherapy and are indicative of and potentially useful for predicting time to disease recurrence and can be further investigated.

cancer biology↗

bioRxiv2021

Robust transcriptional profiling and identification of differentially expressed genes with low input RNA sequencing of adult hippocampal neural stem and progenitor populations

Multipotent neural stem cells (NSCs) are found in several isolated niches of the adult mammalian brain where they have unique potential to assist in tissue repair. Modern transcriptomics offer high-throughput methods for identifying disease or injury associated gene expression signatures in endogenous adult NSCs, but they require adaptation to accommodate the rarity of NSCs. Bulk RNA sequencing (RNAseq) of NSCs requires pooling several mice, which impedes application to labor-intensive injury models. Alternatively, single cell RNAseq can profile hundreds to thousands of cells from a single mouse and is increasingly used to study NSCs. The consequences of the low RNA input from a single NSC on downstream identification of differentially expressed genes (DEGs) remains largely unexplored. Here, to clarify the role that low RNA input plays in NSC DEG identification, we directly compared DEGs in an oxidative stress model of cultured NSCs by bulk and single cell sequencing. While both methods yielded DEGs that were replicable, single cell sequencing DEGs derived from genes with higher relative transcript counts compared to all detected genes and exhibited smaller fold changes than DEGs identified by bulk RNAseq. The loss of high fold-change DEGs in the single cell platform presents an important limitation for identifying disease-relevant genes. To facilitate identification of such genes, we determined an RNA-input threshold that enables transcriptional profiling of NSCs comparable to standard bulk sequencing and used it to establish a workflow for in vivo profiling of endogenous NSCs. We then applied this workflow to identify DEGs after lateral fluid percussion injury, a labor-intensive animal model of traumatic brain injury. Our work suggests that single cell RNA sequencing may underestimate the diversity of pathologic DEGs but population level transcriptomic analysis can be adapted to capture more of these DEGs with similar efficacy and diversity as standard bulk sequencing. Together, our data and workflow will be useful for investigators interested in understanding and manipulating adult hippocampal NSC responses to various stimuli.

neuroscience↗

bioRxiv2021

Bioinformatics analysis of differentially expressed genes in non alcoholic fatty liver disease using next generation sequencing data

Non alcoholic fatty liver disease (NAFLD) is the most common metabolic disease in humans, affecting the majority of individuals. In the current investigation, we aim to identify potential key genes linked with NAFLD through bioinformatics analyses of next generation sequencing (NGS) dataset. NGS dataset of GSE135251 from the Gene Expression Omnibus (GEO) database were retrieved. Differentially expressed genes (DEGs) were obtained by DESeq2 package. g:Profiler database was further used to identify the potential gene ontology (GO) and REACTOME pathways. Protein-protein interaction (PPI) network was constructed using the Hippie interactome database. miRNet and NetworkAnalyst databases were used to establish a miRNA-hub gene regulatory network and TF-hub gene regulatory network for the hub genes. Hub genes were verified based on receiver operating characteristic curve (ROC) analysis. Totally, 951 DEGs were identified including 476 up regulated genes and 475 down regulated genes screened in NAFLD and normal control. GO showed that DEGs were significantly enhanced for signaling and regulation of biological quality. REACTOME pathway analysis revealed that DEGs were enriched in signaling by interleukins and extracellular matrix organization. ESR2, JUN, PTN, PTGER3, CEBPB, IKBKG, HSPA8, SFN, CDKN1A and E2F1 were indicated as hub genes from PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Furthermore, ROC analysis revealed that ESR2, JUN, PTN, PTGER3, CEBPB, IKBKG, HSPA8, SFN, CDKN1A and E2F1 might serve as diagnostic biomarkers in NAFLD. The current investigation provided insights into the molecular mechanism of NAFLD that might be useful in further investigations.

bioinformatics↗

bioRxiv2023

Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes

High-dimensional, spatially resolved analysis of intact tissue samples promises to transform biomedical research and diagnostics, but existing spatial omics technologies are costly and labor-intensive. We present FISHnCHIPs for highly sensitive in situ profiling of cell types and gene expression programs. FISHnCHIPs achieves this by simultaneously imaging [~]2-35 co-expressed genes that are spatially co-localized in tissues, resulting in similar spatial information as single-gene FISH, but at [~]2-20-fold higher sensitivity. Using FISHnCHIPs, we imaged up to 53 gene modules from the mouse kidney and mouse brain, and demonstrated high-speed, large field-of-view profiling of a whole tissue section. FISHnCHIPS also revealed spatially restricted localizations of cancer-associated fibroblasts in a human colorectal cancer biopsy. Overall, FISHnCHIPs enables robust and scalable spatial transcriptomics analysis of tissues with normal physiology or undergoing pathogenesis.

genomics↗

bioRxiv2023

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed.. Using the g:Profiler, the function and pathway enrichment analysis of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst database was used to construct the targeted microRNAs (miRNAs) and transcription factors (TFs) of the hub genes. Finally receiver operating characteristic (ROC) curve analysis was used to validates the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8 and EFHC2 were selected. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

bioinformatics↗

bioRxiv2023

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes

BackgroundPrevious pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters. MethodsWe first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships. ResultsWe identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that [~]85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community. ConclusionsOverall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.

genomics↗

bioRxiv2023

Non-additive genetic components contribute significantly to population-wide gene expression variation

Gene expression variation, an essential step between genomic variation and phenotypic landscape, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence the heritability of expression traits remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originated from genetically divergent yeast isolates. We estimated the broad- and narrow-sense heritability across 5,087 transcript abundance traits and showed that non-additive components account for 36% of the phenotypic variance on average. By comparing allelic expression ratios in the hybrid and the corresponding parental pair, we identified regulatory changes in 25% of all cases, with a majority acting in trans. We further showed that trans-regulation could underlie coordinated expression variation across highly connected genes, resulting in significantly higher non-additive variance and most likely in some of the missing heritability of gene expression traits. HighlightsO_LIDiallel panel for dissecting genetic components underlying gene expression variation C_LIO_LINon-additive components account for 36% of gene expression trait heritability C_LIO_LIMost cis- regulatory variation is buffered by additional trans-acting variants C_LIO_LIGenes under trans- regulation show high non-additive variance and functional coherence C_LI

genetics↗

bioRxiv2017

Gene expression profiling reveals U1 snRNA regulates cancer gene expression

U1 small nuclear RNA (U1 snRNA), as one of the most abundant noncoding RNA in eukaryotic cells plays an important role in splicing of pre-mRNAs. Compared to other studies which have focused on the primary function of U1 snRNA and the neurodegenerative diseases caused by the abnormalities of U1 snRNA, this study is to investigate how the U1 snRNA over-expression affects the expression of genes on a genome-wide scale. In this study, we built a model of U1 snRNA over-expression in a rat cell line. By comparing the gene expression profiles of U1 snRNA over-expressed cells with those of their controls using the microarray experiments, 916 genes or loci were identified significantly differentially expressed. These 595 up-regulated genes and 321 down-regulated genes were further analyzed using the annotations from the GO terms and the KEGG database. As a result, three of 12 enriched pathways are well-known cancer pathways, while nine of them were associated to cancers in previous studies. The further analysis of 73 genes involved in 12 pathways suggests that U1 snRNA regulates cancer gene expression. The microarray data with ID GSE84304 is available in the NCBI GEO database.

cancer biology↗