CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses

Comprehensive Analysis of circRNA Expression and circRNA-miRNA-mRNA Networks in the Ventral Hippocampus of the Rat: Impact of Chronic Stress and Biological Sex

This study provides new insights into how sex and chronic stress influence circRNA expression in the rat ventral hippocampus, a region critical for emotional processing. We identified 206 sex-biased circRNAs and 194 stress-responsive circRNAs, highlighting distinct expression profiles. Parental genes of male circRNAs were primarily enriched in synaptic transmission pathways, while those of female circRNAs were associated with axon guidance, emphasizing sex-specific molecular differences. Chronic stress also triggered miRNA changes unique to each sex, revealing divergent regulatory mechanisms. The identified circRNA-miRNA-mRNA axes, modulated under stress, appear to regulate the translation of numerous potential mRNA targets. In males, stress positively regulated neuroprotective pathways, suggesting a compensatory response to mitigate stress-induced damage. In contrast, females exhibited a broader translational network that favored mRNA expression without distinct pathway-specific actions. However, the smaller repressed network in females─characterized by a higher circRNA-to-miRNA and mRNA ratio─may indicate a more selective and targeted regulatory mechanism, with many interactions linked to anti-inflammatory processes. Coexpression analysis revealed two male-specific modules with altered activity under stress. These were associated with processes such as reticulum stress and actin dynamics, the latter linked to dendritic spine loss and depressive-like behaviors, extensively documented in chronically stressed male rats. Conversely, females displayed an activated stress-responsive module, promoting axon guidance and long-term potentiation, which may contribute to improved cognitive outcomes. Among the identified circRNAs, rno-Gabrg3_0001 emerged as stress-sensitive in males. This circRNA exhibited predicted miRNA binding sites and interactions with proteins involved in vesicle trafficking, forming part of a highly active module enriched in genes related to ion transport and membrane protein localization. Overall, these findings uncover sex-dependent regulatory mechanisms driving transcriptomic changes under chronic stress, deepening our understanding of ventral hippocampal molecular functions. Investigating these regulatory networks, which differentially affect the male and female ventral hippocampus, could inform the development of sex-specific therapeutic strategies for stress-related disorders.

Transcriptomic analysis after SARS-CoV-2 mRNA vaccination reveals a specific gene signature in low-responder hemodialysis patients

Introduction

Individuals with comorbidities, such as chronic kidney disease and hemodialysis patients (HDP), are particularly susceptible to severe COVID-19 and to its complications. Furthermore, their immune response to vaccines is impaired, requiring tailored vaccination strategies. In this study, we investigated through transcriptomic profiling the immune response heterogeneity of HDP vaccinated with two doses of mRNA BNT162b2 vaccine.

Methods

Transcriptomic analyses were conducted in peripheral blood mononuclear cells (PBMC) collected from HDP and healthy controls (HC) before and 7 days after each dose. The HDP were stratified into high- and low-responders based on their humoral response after the second dose.

Results

Significant differences in gene expression related to B cell abundance and regulation, CD4 T cell proliferation, and inflammation pathways were observed at baseline and day 7 between HDP-low responders and HC, while the HDP high-responders displayed an intermediate expression profile for these genes.

Discussion

Results were consistent with the known immunologic alterations occurring in HDP cohorts related to lymphopenia, chronic inflammation, and dysregulated proliferation of CD4+. Our analyses identified an early transcriptional signature correlated with a diminished immune response in HDP low-responders, highlighting the importance of conducting a characterization of immunocompromised cohorts.

Integrative immunology identified interferome signatures in uveitis and systemic disease-associated uveitis

Introduction

Uveitis accounts for up to 25% of global legal blindness and involves intraocular inflammation, classifed as infectious or non-infectious. Its complex pathophysiology includes dysregulated cytokines, particularly interferons (IFNs). However, the global signature of type I, II, and III interferon-regulated genes (Interferome) remains largely uncharacterized in uveitis.

Methods

In this study, we conducted an integrative systems biology analysis of blood transcriptome data from 169 non-infectious uveitis patients (56 isolated uveitis, 113 systemic disease-associated uveitis) and 82 healthy controls.

Results

Modular co-expression analysis identified distinct cytokine signaling networks, emphasizing interleukin and interferon pathways. A meta-analysis revealed 110 differentially expressed genes (metaDEGs) in isolated uveitis and 91 in systemic disease-associated uveitis, predominantly linked to immune responses. The Interferome database confirmed a predominance of type I and II IFN signatures in both groups. Pathway enrichment analysis highlighted inflammatory responses, including cytokine production (IL-8, IL1-β, IFN-γ, β, and α) and toll-like receptor signaling (TLR4, TLR7, TLR8, CD180). Principal component analysis emphasized the IFN signature's discriminative power, particularly in systemic disease-associated uveitis. Machine learning identified IFN-associated genes as robust predictors, while linear discriminant analysis pinpointed CCR2, CD180, GAPT, and PTGS2 as key risk factors in isolated uveitis and CA1, SIAH2, and PGS in systemic disease-associated uveitis.

Conclusion

These findings highlight IFN-driven imune dysregulation and potential molecular targets for precision therapies in uveitis.

The blood transcriptional response in patients developing intensive care unit-acquired pneumonia

BACKGROUND

Immune response dysregulation has been implicated in the development of intensive care unit (ICU)-acquired pneumonia. We aimed to determine differences in the longitudinal blood transcriptional response between patients who develop ICU-acquired pneumonia (cases) and those who do not (controls).

METHODS

We performed a case-cohort study in mechanically ventilated trauma and surgery patients with ICU stays >2 days, enrolled in 30 hospitals across Europe. We collected blood for RNA sequencing at baseline, day 7 and (in cases) the day of pneumonia diagnosis. We performed gene set enrichment analysis and analysed longitudinal gene expression changes using linear mixed models. External validation was performed using an independent trauma cohort.

RESULTS

We enrolled 113 cases and 115 controls, with similar baseline characteristics. At baseline (median 2 days after ICU admission), cases showed upregulated gene pathways relating to innate immunity, haemostasis and metabolism, and downregulated adaptive immune pathways. These changes persisted at the day of pneumonia diagnosis (median 6 days, compared to day 7 in controls). In the longitudinal comparison, cases exhibited enhanced upregulation of innate immunity, adaptive immunity and haemostasis pathways, along with enhanced downregulation of metabolism pathways, relative to controls (all p<0.00001, except haemostasis p<0.05). These findings were largely externally validated. Cases had higher quantitative sepsis response signature scores (p<0.001), reflective of immune dysregulation.

CONCLUSION

Patients developing ICU-acquired pneumonia exhibit distinct blood transcriptional responses shortly after ICU admission and in the subsequent path to pneumonia, suggestive of broad immune dysfunction with both immunosuppressive and inflammatory features.

GeneCOCOA: Detecting context-specific functions of individual genes using co-expression data

Extraction of meaningful biological insight from gene expression profiling often focuses on the identification of statistically enriched terms or pathways. These methods typically use gene sets as input data, and subsequently return overrepresented terms along with associated statistics describing their enrichment. This approach does not cater to analyses focused on a single gene-of-interest, particularly when the gene lacks prior functional characterization. To address this, we formulated GeneCOCOA, a method which utilizes context-specific gene co-expression and curated functional gene sets, but focuses on a user-supplied gene-of-interest (GOI). The co-expression between the GOI and subsets of genes from functional groups (e.g. pathways, GO terms) is derived using linear regression, and resulting root-mean-square error values are compared against background values obtained from randomly selected genes. The resulting p values provide a statistical ranking of functional gene sets from any collection, along with their associated terms, based on their co-expression with the gene of interest in a manner specific to the context and experiment. GeneCOCOA thereby provides biological insight into both gene function, and putative regulatory mechanisms by which the expression of the GOI is controlled. Despite its relative simplicity, GeneCOCOA outperforms similar methods in the accurate recall of known gene-disease associations. We furthermore include a differential GeneCOCOA mode, thus presenting the first implementation of a gene-focused approach to experiment-specific gene set enrichment analysis. GeneCOCOA is formulated as an R package for ease-of-use, available at https://github.com/si-ze/geneCOCOA.

The Silent Saboteur: How Mitochondria Shape the Long-Term Fate of the Injured Brain

Traumatic brain injury (TBI) is a major risk factor for neurodegenerative diseases, including Alzheimer's disease (AD), yet the mechanistic link remains unclear. Here, we integrated human patient-derived transcriptomics with a 3D in vitro brain injury model to dissect cell-specific mitochondrial dysfunction as a driver of injury-induced neurodegeneration. Comparative transcriptomic analysis at 6 and 48 hours post-injury revealed conserved mitochondrial impairments across excitatory neurons, interneurons, astrocytes, and microglia. Using a novel cell-specific mitochondria tracking system, we demonstrate prolonged neuronal mitochondrial fragmentation, bioenergetic failure, and metabolic instability, coinciding with the emergence of AD markers, including pTau, APP, and Aβ42/40 dysregulation. Glial mitochondria exhibited delayed but distinct metabolic dysfunctions, with astrocytes impaired metabolic support and microglia sustained chronic inflammation. These findings establish neuronal mitochondrial failure as an early trigger of injury-induced neurodegeneration, reinforcing mitochondrial dysfunction as a therapeutic target for preventing TBI-driven AD pathology.

Exploring common genomic biomarkers to disclose common drugs for the treatment of colorectal cancer and hepatocellular carcinoma with type-2 diabetes through transcriptomics analysis

Type 2 diabetes (T2D) is a crucial risk factor for both colorectal cancer (CRC) and hepatocellular carcinoma (HCC). However, so far, there was no study that has investigated common drugs against HCC and CRC during their co-occurrence with T2D patients. Consequently, patients often require multiple disease-specific multiple drugs, which can lead toxicities and adverse effects to the patients due to drug-drug interactions. This study aimed to identify common genomic biomarkers (cGBs) and associated pathogenetic mechanisms underlying CRC, HCC, and T2D to uncover potential common therapeutic compounds against these three diseases. Firstly, we identified 86 common differentially expressed genes (cDEGs) capable of separating each of CRC, HCC and T2D patients from control groups based on transcriptomic profiling. Of these cDEGs, 37 genes were upregulated and 49 were downregulated. Genetic association studies based on average of Log2 fold-change (aLog2FC) of cDEGs suggested a genetic association among CRC, HCC and T2D. Subsequently, six top-ranked cDEGs (MYC, MMP9, THBS1, IL6, CXCL1, and SPP1) were identified as common genomic biomarkers (cGBs) through protein-protein interaction (PPI) network analysis. Further analysis of these cGBs with GO-terms and KEGG pathways revealed shared pathogenetic mechanisms of three diseases, including specific biological processes, molecular functions, cellular components and signaling pathways. The gene co-regulatory network analysis identified two transcription factors (FOXC1 and GATA2) and three miRNAs (hsa-mir-195-5p, hsa-mir-124a-3p, and hsa-mir-34a-5p) as crucial transcriptional and post-transcriptional regulators of the cGBs. Finally, cGBs-guided seven candidate drugs (Digitoxin, Camptosar, AMG-900, Imatinib, Irinotecan, Midostaurin, and Linsitinib) as the common treatment against T2D, CRC and HCC were identified through molecular docking, cross-validation, and ADME/T (Absorption-Distribution-Metabolism-Excretion-Toxicity) analysis. Most of these findings received support by the literature review of diseases specific individual studies. Thus, this study offers valuable insights for researchers and clinicians to improve the diagnosis and treatment of CRC and/or HCC patients during the co-occurrence of T2D.

Accelerating crop improvement via integration of transcriptome-based network biology and genome editing

MAIN CONCLUSION

Big data and network biology infer functional coupling between genes. In combination with machine learning, network biology can dramatically accelerate the pace of gene discovery using modern transcriptomics approaches and be validated via genome editing technology for improving crops to stresses. Unlike other living things, plants are sessile and frequently face various environmental challenges due to climate change. The cumulative effects of combined stresses can significantly influence both plant growth and yields. In navigating the complexities of climate change, ensuring the nourishment of our growing population hinges on implementing precise agricultural systems. Conventional breeding methods have been commonly employed; however, their efficacy has been impeded by limitations in terms of time, cost, and infrastructure. Cutting-edge tools focussing on big data are being championed to usher in a new era in stress biology, aiming to cultivate crops that exhibit enhanced resilience to multifactorial stresses. Transcriptomics, combined with network biology and machine learning, is proving to be a powerful approach for identifying potential genes to target for gene editing, specifically to enhance stress tolerance. The integration of transcriptomic data with genome editing can yield significant benefits, such as gaining insights into gene function by modifying or manipulating of specific genes in the target plant. This review provides valuable insights into the use of transcriptomics platforms and the application of biological network analysis and machine learning in the discovery of novel genes, thereby enhancing the understanding of plant responses to combined or sequential stress. The transcriptomics as a forefront omics platform and how it is employed through biological networks and machine learning that lead to novel gene discoveries for producing multi-stress-tolerant crops, limitations, and future directions have also been discussed.

Identification and validation of hub genes associated with biotic and abiotic stresses by modular gene co-expression analysis in Oryza sativa L

Rice, a staple food consumed by half of the world's population, is severely affected by the combined impact of abiotic and biotic stresses, with the former causing increased susceptibility of the plant to pathogens. Four microarray datasets for drought, salinity, tungro virus, and blast pathogen were retrieved from the Gene Expression Omnibus database. A modular gene co-expression (mGCE) analysis was conducted, followed by gene set enrichment analysis to evaluate the upregulation of module activity across different stress conditions. Over-representation analysis was conducted to determine the functional association of each gene module with stress-related processes and pathways. The protein-protein interaction network of mGCE hub genes was constructed, and the Maximal Clique Centrality (MCC) algorithm was applied to enhance precision in identifying key genes. Finally, genes implicated in both abiotic and biotic stress responses were validated using RT-qPCR. A total of 11, 12, 46, and 14 modules containing 85, 106, 253, and 143 hub genes were detected in drought, salinity, tungro virus, and blast. Modular genes in drought were primarily enriched in response to heat stimulus and water deprivation, while salinity-related genes were enriched in response to external stimuli. For the tungro virus and blast pathogen, enrichment was mainly observed in the defence and stress responses. Interestingly, RPS5, PKG, HSP90, HSP70, and MCM were consistently present in abiotic and biotic stresses. The DEG analysis revealed the upregulation of MCM under the tungro virus and downregulation under blast and drought in resistant rice, indicating its role in viral resistance. HSP70 showed no changes, while HSP90 was upregulated in susceptible rice during blast and drought. PKG increased during drought but decreased in japonica rice under salinity. RPS5 was highly upregulated during blast in both resistant and susceptible rice. The RT-qPCR analysis showed that all five hub genes were upregulated in all treatments, indicating their role in stress responses and potential for crop improvement.

Screening of common genomic biomarkers to explore common drugs for the treatment of pancreatic and kidney cancers with type-2 diabetes through bioinformatics analysis

Type 2 diabetes (T2D) is a crucial risk factor for both pancreatic cancer (PC) and kidney cancer (KC). However, effective common drugs for treating PC and/or KC patients who are also suffering from T2D are currently lacking, despite the probability of their co-occurrence. Taking disease-specific multiple drugs during the co-existence of multiple diseases may lead to adverse side effects or toxicity to the patients due to drug-drug interactions. This study aimed to identify T2D-, PC and KC-causing common genomic biomarkers (cGBs) highlighting their pathogenetic mechanisms to explore effective drugs as their common treatment. We analyzed transcriptomic profile datasets, applying weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis approaches to identify T2D-, PC-, and KC-causing cGBs. We then disclosed common pathogenetic mechanisms through gene ontology (GO) terms, KEGG pathways, regulatory networks, and DNA methylation of these cGBs. Initially, we identified 78 common differentially expressed genes (cDEGs) that could distinguish T2D, PC, and KC samples from controls based on their transcriptomic profiles. From these, six top-ranked cDEGs (TOP2A, BIRC5, RRM2, ALB, MUC1, and E2F7) were selected as cGBs and considered targets for exploring common drug molecules for each of three diseases. Functional enrichment analyses, including GO terms, KEGG pathways, and regulatory network analyses involving transcription factors (TFs) and microRNAs, along with DNA methylation and immune infiltration studies, revealed critical common molecular mechanisms linked to PC, KC, and T2D. Finally, we identified six top-ranked drug molecules (NVP.BHG712, Irinotecan, Olaparib, Imatinib, RG-4733, and Linsitinib) as potential common treatments for PC, KC and T2D during their co-existence, supported by the literature reviews. Thus, this bioinformatics study provides valuable insights and resources for developing a genome-guided common treatment strategy for PC and/or KC patients who are also suffering from T2D.

Transcriptomic insights into early mechanisms underlying post-chikungunya chronic inflammatory joint disease

Chikungunya virus (CHIKV) infection often results in a chronic joint condition known as Post-Chikungunya Chronic Inflammatory Joint Disease (pCHIKV-CIJD). This condition disrupts individuals' daily lives and contributes to increased healthcare expenditure. This study investigated the molecular mechanisms underlying pCHIKV-CIJD development by analyzing RNA transcripts, including small RNAs, of whole blood from CHIKV-infected patients. By comparing patients who evolved to pCHIKV-CIJD with those who did not, we identified molecular signatures associated with chronification in acute and post-acute disease phases. These molecules were primarily associated with an altered immune response regulation. Notably, LIFR, an immune receptor that enhanced IL-6 transcription, was down-regulated in the acute phase of pCHIKV-CIJD patients, while its inhibitor, hsa-miR-98-5p, was up-regulated in these individuals. Other downregulated genes include members of immune mechanisms whose impairment can lead to a reduction in the first line of antiviral response, thereby promoting virus persistence for a longer period in these patients. Additionally, pCHIKV-CIJD patients exhibited reduced transcript levels of MMP8, LFT, and DDIT4, genes already implicated in the pathological process of other types of inflammatory arthritis and seemingly relevant for pCHIKV-CIJD development. Overall, our findings provide insights into the early molecular mechanisms involved in the chronification and highlight potential targets for further investigation.

Subfunctionalization and epigenetic regulation of a biosynthetic gene cluster in Solanaceae

Biosynthetic gene clusters (BGCs) are sets of often heterologous genes that are genetically and functionally linked. Among eukaryotes, BGCs are most common in plants and fungi and ensure the coexpression of the different enzymes coordinating the biosynthesis of specialized metabolites. Here, we report the identification of a withanolide BGC in Physalis grisea (ground-cherry), a member of the nightshade family (Solanaceae). A combination of transcriptomic, epigenomic, and metabolic analyses revealed that, following a duplication event, this BGC evolved two tissue-specifically expressed subclusters, containing several pairs of paralogs that contribute to related but distinct biochemical processes; this subfunctionalization is tightly associated with epigenetic features and the local chromatin environment. The two subclusters appear strictly isolated from each other at the structural chromatin level, each forming a highly self-interacting chromatin domain with tissue-dependent levels of condensation. This correlates with gene expression in either above- or below-ground tissue, thus spatially separating the production of different withanolide compounds. By comparative phylogenomics, we show that the withanolide BGC most likely evolved before the diversification of the Solanaceae family and underwent lineage-specific diversifications and losses. The tissue-specific subfunctionalization is common to species of the Physalideae tribe but distinct from other, independent duplication events outside of this clade. In sum, our study reports on an instance of an epigenetically modulated subfunctionalization within a BGC and sheds light on the biosynthesis of withanolides, a highly diverse group of steroidal triterpenoids important in plant defense and amenable to pharmaceutical applications due to their anti-inflammatory, antibiotic, and anticancer properties.

Molecular Insights into Cell-Mediated Immunity in Atypical Non-Ulcerated Cutaneous Leishmaniasis

Leishmania (Leishmania) infantum chagasi infections range from asymptomatic (AS) to severe visceral leishmaniasis (VL). One of the manifestations is an atypical non-ulcerated cutaneous leishmaniasis (NUCL), which occurs in some locations of Central America with few cases of VL. We conducted a transcriptomic analysis of cell-mediated immunity (CMI) on blood samples from NUCL, AS, VL patients from Amapala, Honduras, and healthy controls. RNA-seq revealed a similar perturbation of gene expression in NUCL and AS. Eight gene signatures of CMI were found in NUCL involved in CD8+ T lymphocyte infiltration, reactive oxygen species generation, PD-1 receptor ligand, inflammasome assembly, chemotaxis, complement receptor and suppressor immune cell infiltration. NUCL was distinguished from VL by its up-regulation of differently expressed genes (DEGs) related to T lymphocyte exhaustion, adhesion and transmigration of leukocytes, and down-regulation of oxidative stress genes. In contrast, VL exhibited up-regulated DEGs involved in antigen cross-presentation, and similar to VL from Brazil, down-regulated DEGs involved in innate immunity. Corroborating the transcriptome findings, both the Leishmanin skin test, and the immunopathology of NUCL skin lesion defined NUCL as a proinflammatory condition, intermediate between the AS and VL clinical outcomes. That condition may be the underlying element for the benign nature of the NUCL.

Accelerated epigenetic aging in Huntington's disease involves polycomb repressive complex 1

Loss of epigenetic information during physiological aging compromises cellular identity, leading to de-repression of developmental genes. Here, we assessed the epigenomic landscape of vulnerable neurons in two reference mouse models of Huntington neurodegenerative disease (HD), using cell-type-specific multi-omics, including temporal analysis at three disease stages via FANS-CUT&Tag. We show accelerated de-repression of developmental genes in HD striatal neurons, involving histone re-acetylation and depletion of H2AK119 ubiquitination and H3K27 trimethylation marks, which are catalyzed by polycomb repressive complexes 1 and 2 (PRC1 and PRC2), respectively. We further identify a PRC1-dependent subcluster of bivalent developmental transcription factors that is re-activated in HD striatal neurons. This mechanism likely involves progressive paralog switching between PRC1-CBX genes, which promotes the upregulation of normally low-expressed PRC1-CBX2/4/8 isoforms in striatal neurons, alongside the down-regulation of predominant PRC1-CBX isoforms in these cells (e.g., CBX6/7). Collectively, our data provide evidence for PRC1-dependent accelerated epigenetic aging in HD vulnerable neurons.

Hypoxia-inducible factor 2 regulates alveolar regeneration after repetitive injury in three-dimensional cellular and in vivo models

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease in which repetitive epithelial injury and incomplete alveolar repair result in accumulation of profibrotic intermediate/transitional "aberrant" epithelial cell states. The mechanisms leading to the emergence and persistence of aberrant epithelial populations in the distal lung remain incompletely understood. By interrogating single-cell RNA sequencing (scRNA-seq) data from patients with IPF and a mouse model of repeated lung epithelial injury, we identified persistent activation of hypoxia-inducible factor (HIF) signaling in these aberrant epithelial cells. Using mouse genetic lineage-tracing strategies together with scRNA-seq, we found that these disease-emergent aberrant epithelial cells predominantly arose from airway-derived (Scgb1a1-CreER-traced) progenitors and exhibited transcriptional programs of Hif2a activation. In mice treated with repetitive intratracheal bleomycin, deletion of Epas1 (Hif2a) but not Hif1a, from airway-derived progenitors, or administration of the small-molecule HIF2 inhibitor PT-2385, using both prevention and rescue approaches, attenuated experimental lung fibrosis, reduced the appearance of aberrant epithelial cells, and promoted alveolar repair. In mouse alveolar organoids, genetic or pharmacologic inhibition of Hif2 promoted alveolar differentiation of airway-derived epithelial progenitors. In addition, treatment of human distal lung organoids with PT-2385 increased colony-forming efficiency, enhanced protein and transcriptional markers of alveolar type 2 epithelial cell maturation, and prevented the emergence of aberrant epithelial cells. Together, these studies showed that HIF2 activation drives the emergence of aberrant epithelial populations after repetitive injury and that targeted HIF2 inhibition may represent an effective therapeutic strategy to promote functional alveolar repair in IPF and other interstitial lung diseases.

Integrative multiomics reveals common endotypes across PSEN1, PSEN2, and APP mutations in familial Alzheimer's disease

BACKGROUND

PSEN1, PSEN2, and APP mutations cause Alzheimer's disease (AD) with an early age at onset (AAO) and progressive cognitive decline. PSEN1 mutations are more common and generally have an earlier AAO; however, certain PSEN1 mutations cause a later AAO, similar to those observed in PSEN2 and APP.

METHODS

We examined whether common disease endotypes exist across these mutations with a later AAO (~ 55 years) using hiPSC-derived neurons from familial Alzheimer's disease (FAD) patients harboring mutations in PSEN1A79V, PSEN2N141I, and APPV717I and mechanistically characterized by integrating RNA-seq and ATAC-seq.

RESULTS

We identified common disease endotypes, such as dedifferentiation, dysregulation of synaptic signaling, repression of mitochondrial function and metabolism, and inflammation. We ascertained the master transcriptional regulators associated with these endotypes, including REST, ASCL1, and ZIC family members (activation), and NRF1 (repression).

CONCLUSIONS

FAD mutations share common regulatory changes within endotypes with varying severity, resulting in reversion to a less-differentiated state. The regulatory mechanisms described offer potential targets for therapeutic interventions.

Uncovering NK cell sabotage in gut diseases via single cell transcriptomics

The identification of immune environments and cellular interactions in the colon microenvironment is essential for understanding the mechanisms of chronic inflammatory disease. Despite occurring in the same organ, there is a significant gap in understanding the pathophysiology of ulcerative colitis (UC) and colorectal cancer (CRC). Our study aims to address the distinct immunopathological response of UC and CRC. Using single-cell RNA sequencing datasets, we analyzed the profiles of immune cells in colorectal tissues obtained from healthy donors, UC patients, and CRC patients. The colon tissues from patients and healthy participants were visualized by immunostaining followed by laser confocal microscopy for select targets. Natural killer (NK) cells from UC patients on medication showed reduced cytotoxicity compared to those from healthy individuals. Nonetheless, a UC-specific pathway called the BAG6-NCR3 axis led to higher levels of inflammatory cytokines and increased the cytotoxicity of NCR3+ NK cells, thereby contributing to the persistence of colitis. In the context of colorectal cancer (CRC), both NK cells and CD8+ T cells exhibited significant changes in cytotoxicity and exhaustion. The GALECTIN-9 (LGALS9)-HAVCR2 axis was identified as one of the CRC-specific pathways. Within this pathway, NK cells solely communicated with myeloid cells under CRC conditions. HAVCR2+ NK cells from CRC patients suppressed NK cell-mediated cytotoxicity, indicating a reduction in immune surveillance. Overall, we elucidated the comprehensive UC and CRC immune microenvironments and NK cell-mediated immune responses. Our findings can aid in selecting therapeutic targets that increase the efficacy of immunotherapy.

Unraveling pathogenesis and potential biomarkers for autism spectrum disorder associated with HIF1A pathway based on machine learning and experiment validation

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a high social burden and limited treatments. Hypoxic condition of the brain is considered an important pathological mechanism of ASD. HIF1A is a key participant in brain hypoxia, but its contribution to the pathophysiological landscape of ASD remains unclear.

METHODS

ASD-related datasets were obtained from GEO database, and HIF1A-related genes from GeneCards. Co-expression module analysis identified module genes, which were intersected with HIF1A-related genes to identify common genes. Machine learning identified hub genes from intersection genes and PPI networks were constructed to explore relationships among hub and HIF1A. Single-cell RNA sequencing analyzed hub gene distribution across cell clusters. ASD mouse model was created by inducing maternal immune activation (MIA) with poly(I:C) injections, verified through behavioral tests. Validation of HIF1A pathway and hub genes was confirmed through Western Blot, qPCR, and immunofluorescence in ASD mice and microglia BV-2 cells.

RESULTS

Using CEMiTool and GeneCards, 45 genes associated with ASD and HIF1A pathway were identified. Machine learning identified CDKN1A, ETS2, LYN, and SLC16A3 as potential ASD diagnostic markers. Single-cell sequencing pinpointed activated microglia as key immune cells. Behavioral tests showed MIA offspring mice exhibited typical ASD-like behaviors. Immunofluorescence confirmed the activation of microglia and HIF1A pathway in frontal cortex of ASD mice. Additionally, IL-6 contributed to ASD by activating JUN/HIF1A pathway, affecting CDKN1A, LYN, and SLC16A3 expression in microglia.

CONCLUSIONS

HIF1A-related genes CDKN1A, ETS2, LYN, and SLC16A3 are strong diagnostic markers for ASD and the activation of IL-6/JUN/HIF1A pathway in microglia contributes to the pathogenesis of ASD.

Dysregulation of lipid metabolism in the liver of Tspo knockout mice

The translocator protein, TSPO, has been implicated in a wide range of cellular processes exerted from its position in the outer mitochondrial membrane from where it influences lipid metabolism and mitochondrial oxidative activity. Understanding how this protein regulates a profusion of processes requires further elucidation and to that end we have examined lipid metabolism and used an RNAseq strategy to compare transcript abundance in wildtype and Tspo knockout (KO) mouse liver. The levels of cholesterol, triglyceride and phospholipid were significantly elevated in the KO mouse liver. The expression of cholesterol homeostasis genes was markedly downregulated. Determination of the differential expression revealed that many genes were either up- or downregulated in the KO animals. However, a striking observation within the results was a decrease of transcripts for protein degradation proteins in KO animals while protease inhibitors were enriched. When the entire abundance data-set was analysed with CEMiTool, and revealed a module of proteins that were under-represented in the KO animals. These could subsequently be formed into a network comprising three interlinked clusters at the centre of which were proteins of cytoplasmic ribosomes with gene ontology terms suggesting impairment to translation. The largest cluster was dominated by proteins of lipid metabolism but also contained disparate systems of iron metabolism and behaviour. The third cluster was dominated by proteins of the electron transport chain and oxidative phosphorylation. These findings suggest that TSPO contributes to lipid metabolism, detoxification of active oxygen species and oxidative phosphorylation, and regulates mitochondrial retrograde signalling.

Emergence of power law distributions in protein-protein interaction networks through study bias

Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations, and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.

Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives

BACKGROUND

Identifying differentially expressed genes (DEGs) is a core task of transcriptome analysis, as DEGs can reveal the molecular mechanisms underlying biological processes. However, interpreting the biological significance of large DEG lists is challenging. Currently, gene ontology, pathway enrichment and protein-protein interaction analysis are common strategies employed by biologists. Additionally, emerging analytical strategies/approaches (such as network module analysis, knowledge graph, drug repurposing, cell marker discovery, trajectory analysis, and cell communication analysis) have been proposed. Despite these advances, comprehensive guidelines for systematically and thoroughly mining the biological information within DEGs remain lacking.

AIM OF REVIEW

This review aims to provide an overview of essential concepts and methodologies for the biological interpretation of DEGs, enhancing the contextual understanding. It also addresses the current limitations and future perspectives of these approaches, highlighting their broad applications in deciphering the molecular mechanism of complex diseases and phenotypes. To assist users in extracting insights from extensive datasets, especially various DEG lists, we developed DEGMiner (https://www.ciblab.net/DEGMiner/), which integrates over 300 easily accessible databases and tools.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review offers strong support and guidance for exploring DEGs, and also will accelerate the discovery of hidden biological insights within genomes.

Systems Immunology Approaches to Understanding Immune Responses in Acute Infection of Yellow Fever Patients

In the 2018 yellow fever (YF) outbreak in Brazil, we generated new transcriptomic data and combined it with clinical and immunological data to decode the pathogenesis of YF. Analyzing 79 patients, we found distinct gene expression patterns between acute YF, other viral infections, and the milder YF-17D vaccine infection. We identified a critical role for low-density, immature neutrophils in severe outcomes, marked by the downregulation of genes essential for neutrophil migration and maturation, such as PADI4, CSF3R, and ICAM1, in deceased patients. Our study also revealed complex interactions among inflammation-related genes, including increased CXCL10 and IL1R2 expression and decreased IL-1b expression in the acute phase. The diminished expression of HLA class II genes indicates impaired antigen presentation. These findings highlight the delicate balance of immune responses in YF pathogenesis and lay the groundwork for future therapeutic and diagnostic advancements.

Identifying prognostic hub genes and key pathways in pediatric adrenocortical tumors through RNA sequencing and Co-expression analysis

Pediatric adrenocortical tumors (ACTs), rare conditions with uncertain prognoses, have high incidence in southern and southeastern Brazil. Pediatric ACTs are highly heterogeneous, so establishing prognostic markers for these tumors is challenging. We have conducted transcriptomic analysis on 14 pediatric ACT samples and compared cases with favorable and unfavorable clinical outcomes to identify prognostically significant genes. This comparison showed 1257 differentially expressed genes in favorable and unfavorable cases. Among these genes, 15 out of 60 hub genes were significantly associated with five-year event-free survival (EFS), and 10 had significant diagnostic value for predicting ACT outcomes in an independent microarray dataset of pediatric adrenocortical carcinomas (GSE76019). Overexpression of N4BP2, HSPB6, JUN, APBB1IP, STK17B, CSNK1D, and KDM3A was associated with poorer EFS, whereas lower expression of ISCU, PTPR, PRKAB2, CD48, PRF1, ITGAL, KLK15, and HIST1H3J was associated with worse outcomes. Collectively, these findings underscore the prognostic significance of these hub genes and suggest that they play a potential role in pediatric ACT progression and are useful predictors of clinical outcomes.

Co-expression gene module analysis in response to attenuated cercaria vaccine reveals a critical role for NK cells in protection against Schistosoma mansoni

BACKGROUND

Despite decades of research, an effective schistosomiasis vaccine remains elusive. The radiation-attenuated (RA) cercarial vaccine remains the best model for eliciting high levels of protection. We have recently explored this model in mice to identify potentially protective pathways by examining gene expression patterns in peripheral blood mononuclear cells (PBMC).

METHODS

Herein, we reanalyzed the transcriptomic data from PBMC obtained from vaccinated and infected C57BL/6 mice in three timepoints (Days 7 and 17 after infection or vaccination and Day 7 post-challenge). In addition, we generated new data on PBMC collected 35 days after infection. Deconvolution analysis was performed to estimate immune cell composition by CIBERSORTx. Gene co-expression networks and over-representation analysis (ORA) were performed using the CEMiTool package. Protein-protein interaction networks were constructed using STRING, and the hub proteins for each module were identified using Cytoscape.

RESULTS

Co-expression network analysis identified a module (M2) associated with the infection process, grouping genes related to a Th2 immune response, and a second module (M6) associated with the vaccination process, displaying pathways related to a Th1 response, CD8 + T cells and NK cells. Within each module, five hub proteins were identified based on protein-protein interaction networks. The M2 infection module revealed Chil3, Il4, Cx3cr1, Emr1 and Ccl2 as hubs, while module M6, associated with vaccination, disclosed Prf1, Klrc1, IFN-γ, Ncr1 and Tbx21 as hub proteins.

CONCLUSIONS

Our data point to the potentiald role of NK cells that may contribute to the RA vaccine response through the production of IFN-γ orchestrated by the T-bet transcription factor (Tbx21).

Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches

Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This study suggested GBM-causing ten key genes (ASPM, CCNB2, CDK1, AURKA, TOP2A, CHEK1, CDCA8, SMC4, MCM10, and RAD51AP1) from nine transcriptomics datasets by combining supervised and unsupervised learning results. Differential expression patterns of key genes (KGs) between GBM and control samples were verified by different independent databases. Gene regulatory network (GRN) detected some important transcriptional and post-transcriptional regulators for KGs. The KGs-set enrichment analysis unveiled some crucial GBM-causing molecular functions, biological processes, cellular components, and pathways. The DNA methylation analysis detected some hypo-methylated CpG sites that might stimulate the GBM development. From the immune infiltration analysis, we found that almost all KGs are associated with different immune cell infiltration levels. Finally, we recommended KGs-guided four repurposable drug molecules (Fluoxetine, Vatalanib, TGX221 and RO3306) against GBM through molecular docking, drug likeness, ADMET analyses and molecular dynamics simulation studies. Thus, the discoveries of this study could serve as valuable resources for wet-lab experiments in order to take a proper treatment plan against GBM.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Integrating machine learning and bioinformatics approaches for identifying novel diagnostic gene biomarkers in colorectal cancer

This study aimed to identify diagnostic gene biomarkers for colorectal cancer (CRC) by analyzing differentially expressed genes (DEGs) in tumor and adjacent normal samples across five colon cancer gene-expression profiles (GSE10950, GSE25070, GSE41328, GSE74602, GSE142279) from the Gene Expression Omnibus (GEO) database. Intersecting identified DEGs with the module with the highest correlation to gene expression patterns of tumor samples in the gene co-expression network analysis revealed 283 overlapped genes. Centrality measures were calculated for these genes in the reconstructed STRING protein-protein interaction network. Applying LASSO logistic regression, eleven genes were ultimately recognized as candidate diagnostic genes. Among these genes, the area under the receiver operating characteristic curve (AUROC) values for nine genes (CDC25B, CDK4, IQGAP3, MMP1, MMP7, SLC7A5, TEAD4, TRIB3, and UHRF1) surpassed the threshold of 0.92 in both the training and validation sets. We evaluated the diagnostic performance of these genes with four machine learning algorithms: random forest (RF), support vector machines (SVM), artificial neural network (ANN), and gradient boosting machine (GBM). In the testing dataset (GSE21815 and GSE106582), the AUROC scores were greater than 0.95 for all of the machine learning algorithms, indicating the high diagnostic performance of the nine genes. Besides, these nine genes are also significantly correlated to twelve immune cells, namely Mast cells activated, Macrophages M0, M1, and M2, Neutrophils, T cells CD4 memory activated, T cells follicular helper, T cells CD8, T cells CD4 memory resting, B cells memory, Plasma cells, and Mast cells resting (P < 0.05). These results strongly suggest that all of the nine genes have the potential to serve as reliable diagnostic biomarkers for CRC.

N6-methyladenosine modification-tuned lipid metabolism controls skin immune homeostasis via regulating neutrophil chemotaxis

Disrupted N6-methyladenosine (m6A) modification modulates various inflammatory disorders. However, the role of m6A in regulating cutaneous inflammation remains elusive. Here, we reveal that the m6A and its methyltransferase METTL3 are down-regulated in keratinocytes in inflammatory skin diseases. Inducible deletion of Mettl3 in murine keratinocytes results in spontaneous skin inflammation and increases susceptibility to cutaneous inflammation with activation of neutrophil recruitment. Therapeutically, restoration of m6A alleviates the disease phenotypes in mice and suppresses inflammation in human biopsy specimens. We support a model in which m6A modification stabilizes the mRNA of the lipid-metabolizing enzyme ELOVL6 via the m6A reader IGF2BP3, leading to a rewiring of fatty acid metabolism with a reduction in palmitic acid accumulation and, consequently, suppressing neutrophil chemotaxis in cutaneous inflammation. Our findings highlight a previously unrecognized epithelial-intrinsic m6A modification-lipid metabolism pathway that is essential for maintaining epidermal and immune homeostasis and lay the basis for potential therapeutic targeting of m6A modulators to attenuate inflammatory skin diseases.

Inflammation of the nasal mucosa is associated with susceptibility to experimental pneumococcal challenge in older adults

Streptococcus pneumoniae colonization in the upper respiratory tract is linked to pneumococcal disease development, predominantly affecting young children and older adults. As the global population ages and comorbidities increase, there is a heightened concern about this infection. We investigated the immunological responses of older adults to pneumococcal-controlled human infection by analyzing the cellular composition and gene expression in the nasal mucosa. Our comparative analysis with data from a concurrent study in younger adults revealed distinct gene expression patterns in older individuals susceptible to colonization, highlighted by neutrophil activation and elevated levels of CXCL9 and CXCL10. Unlike younger adults challenged with pneumococcus, older adults did not show recruitment of monocytes into the nasal mucosa following nasal colonization. However, older adults who were protected from colonization showed increased degranulation of cluster of differentiation 8+ T cells, both before and after pneumococcal challenge. These findings suggest age-associated cellular changes, in particular enhanced mucosal inflammation, that may predispose older adults to pneumococcal colonization.

Unveiling differential gene co-expression networks and its effects on levodopa-induced dyskinesia

Levodopa-induced dyskinesia (LID) refers to involuntary motor movements of chronic use of levodopa in Parkinson's disease (PD) that negatively impact the overall well-being of people with this disease. The molecular mechanisms involved in LID were investigated through whole-blood transcriptomic analysis for differential gene expression and identification of new co-expression and differential co-expression networks. We found six differentially expressed genes in patients with LID, and 13 in patients without LID. We also identified 12 co-expressed genes exclusive to LID, and six exclusive hub genes involved in 23 gene-gene interactions in patients with LID. Convergently, we identified novel genes associated with PD and LID that play roles in mitochondrial dysfunction, dysregulation of lipid metabolism, and neuroinflammation. We observed significant changes in disease progression, consistent with previous findings of maladaptive plastic changes in the basal ganglia leading to the development of LID, including a chronic pro-inflammatory state in the brain.

Resolving the metabolism of monolignols and other lignin-related aromatic compounds in Xanthomonas citri

Lignin, a major plant cell wall component, has an important role in plant-defense mechanisms against pathogens and is a promising renewable carbon source to produce bio-based chemicals. However, our understanding of microbial metabolism is incomplete regarding certain lignin-related compounds like p-coumaryl and sinapyl alcohols. Here, we reveal peripheral pathways for the catabolism of the three main lignin precursors (p-coumaryl, coniferyl, and sinapyl alcohols) in the plant pathogen Xanthomonas citri. Our study demonstrates all the necessary enzymatic steps for funneling these monolignols into the tricarboxylic acid cycle, concurrently uncovering aryl aldehyde reductases that likely protect the pathogen from aldehydes toxicity. It also shows that lignin-related aromatic compounds activate transcriptional responses related to chemotaxis and flagellar-dependent motility, which might play an important role during plant infection. Together our findings provide foundational knowledge to support biotechnological advances for both plant diseases treatments and conversion of lignin-derived compounds into bio-based chemicals.

Exploring the structural landscape of DNA maintenance proteins

Evolutionary annotation of genome maintenance (GM) proteins has conventionally been established by remote relationships within protein sequence databases. However, often no significant relationship can be established. Highly sensitive approaches to attain remote homologies based on iterative profile-to-profile methods have been developed. Still, these methods have not been systematically applied in the evolutionary annotation of GM proteins. Here, by applying profile-to-profile models, we systematically survey the repertoire of GM proteins from bacteria to man. We identify multiple GM protein candidates and annotate domains in numerous established GM proteins, among other PARP, OB-fold, Macro, TUDOR, SAP, BRCT, KU, MYB (SANT), and nuclease domains. We experimentally validate OB-fold and MIS18 (Yippee) domains in SPIDR and FAM72 protein families, respectively. Our results indicate that, surprisingly, despite the immense interest and long-term research efforts, the repertoire of genome stability caretakers is still not fully appreciated.

New insights into PSAT1 as a therapeutic target for myelodysplastic syndrome (MDS)

The metabolomic landscape in myelodysplastic syndrome (MDS) is highly deregulated and presents promising avenues for understanding disease pathogenesis and potential molecular dependencies. Here, we evaluated the transcriptomic landscape in MDS in multiple independent studies focusing more on metabolomics pathways. Identifying molecular dependencies will pave the way for a more precise disease stratification as well as the development of novel personalized treatment strategies. The study adopted a retrospective, cross-sectional approach, utilizing transcriptomic data from multiple MDS studies. The transcriptomic data were then subjected to comprehensive analyses, including differential gene expression, gene enrichment analysis, gene co-expression analysis, protein-protein interaction analyses, and survival analyses. PSAT1 showed a significant upregulation profile in MDS patients. This observed upregulation is correlated with the deregulation of immune-related pathways in MDS samples. This observation suggests a novel role for PSAT1 in immune modulation and potentially in augmenting immune evasion, which may lead to poor prognosis. This was evident in other tumors in the TCGA database, where cancer patients with high PSAT1 expression have a shorter overall survival. This study unveils a novel potential therapeutic avenue in MDS. Identifying the role of the PSAT1 gene sheds light on the disease's intricate biology, highlighting the ongoing cross-talk between metabolism and immune regulation, which may pave the way for innovative treatment modalities.

A 5-Hydroxymethylcytosine-Based Noninvasive Model for Early Detection of Colorectal Carcinomas and Advanced Adenomas: The METHOD-2 Study

PURPOSE

Detection of colorectal carcinomas at a time when there are more treatment options is associated with better outcomes. This prospective case-control study assessed the 5-hydroxymethylcytosine (5hmC) biomarkers in circulating cell-free DNA (cfDNA) for early detection of colorectal carcinoma and advanced adenomas (AA).

EXPERIMENTAL DESIGN

Plasma cfDNA samples from 2,576 study participants from the multicenter METHOD-2 study (NCT03676075) were collected, comprising patients with newly diagnosed colorectal carcinoma (n = 1,074), AA (n = 356), other solid tumors (n = 80), and non-colorectal carcinoma/AA controls (n = 1,066), followed by genome-wide 5hmC profiling using the 5hmC-Seal technique and the next-generation sequencing. A weighted diagnostic model for colorectal carcinoma (stage I-III) and AA was developed using the elastic net regularization in a discovery set and validated in independent samples.

RESULTS

Distribution of 5hmC in cfDNA reflected gene regulatory relevance and tissue of origin. Besides being confirmed in internal validation, a 96-gene model achieved an area under the curve (AUC) of 90.7% for distinguishing stage I-III colorectal carcinoma from controls in 321 samples from multiple centers for external validation, regardless of primary location or mutation status. This model also showed cancer-type specificity as well as high capacity for distinguishing AA from controls with an AUC of 78.6%. Functionally, differential 5hmC features associated with colorectal carcinoma and AA demonstrated relevance to colorectal carcinoma biology, including pathways such as calcium and MAPK signaling.

CONCLUSIONS

Genome-wide mapping of 5hmC in cfDNA shows promise as a highly sensitive and specific noninvasive blood test to be integrated into screening programs for improving early detection of colorectal carcinoma and high-risk AA.

Neuronal A2A receptor exacerbates synapse loss and memory deficits in APP/PS1 mice

Early pathological upregulation of adenosine A2A receptors (A2ARs), one of the caffeine targets, by neurons is thought to be involved in the development of synaptic and memory deficits in Alzheimer's disease (AD) but mechanisms remain ill-defined. To tackle this question, we promoted a neuronal upregulation of A2AR in the hippocampus of APP/PS1 mice developing AD-like amyloidogenesis. Our findings revealed that the early upregulation of A2AR in the presence of an ongoing amyloid pathology exacerbates memory impairments of APP/PS1 mice. These behavioural changes were not linked to major change in the development of amyloid pathology but rather associated with increased phosphorylated tau at neuritic plaques. Moreover, proteomic and transcriptomic analyses coupled with quantitative immunofluorescence studies indicated that neuronal upregulation of the receptor promoted both neuronal and non-neuronal autonomous alterations, i.e. enhanced neuroinflammatory response but also loss of excitatory synapses and impaired neuronal mitochondrial function, presumably accounting for the detrimental effect on memory. Overall, our results provide compelling evidence that neuronal A2AR dysfunction, as seen in the brain of patients, contributes to amyloid-related pathogenesis and underscores the potential of A2AR as a relevant therapeutic target for mitigating cognitive impairments in this neurodegenerative disorder.

The microRNA expression in crypt-top and crypt-bottom colonic epithelial cell populations demonstrates cell-type specificity and correlates with endoscopic activity in ulcerative colitis

BACKGROUND AND AIMS

Colonic epithelial barrier dysfunction is one of the early events in ulcerative colitis (UC) and microRNAs (miRNAs) participate in its regulation. However, cell type-specific miRNome during UC is still unknown. Thus, we aimed to explore miRNA expression patterns in colon tissue and epithelial cells at active and quiescent UC.

METHODS

Small RNA-sequencing in colon tissue, crypt-bottom (CD44+), and crypt-top (CD66a+) colonic epithelial cells from two cohorts of UC patients (n=74) and healthy individuals (n=50) was performed. Data analysis encompassed differential expression, weighted gene co-expression network, correlation, gene-set enrichment analyses.

RESULTS

Differentially expressed colonic tissue miRNAs showed potential involvement in regulation of interleukin-4 and interleukin-13 signalling during UC. As this pathway plays role in intestinal barrier regulation, consecutive analysis of spatially distinct colonic epithelial cell populations was performed. Cell-type (crypt-top and crypt-bottom) specific miRNA expression patterns were identified in both active and quiescent UC. Target genes of differentially expressed epithelial miRNAs at different disease activity were overrepresented in epithelial cell migration and therefore intestinal barrier integrity regulation. The pro-inflammatory miRNA co-expression module M1 correlated with endoscopic disease activity and successfully distinguished active and quiescent UC not only in both epithelial cell populations, but also in the colon tissue. The anti-inflammatory module M2 was specific to crypt-bottom cells and significantly enriched in the quiescent UC patients.

CONCLUSIONS

miRNA expression was specific to colonic epithelial cell populations and UC state, reflecting endoscopic disease activity. Irrespective of the UC state, deregulated epithelial miRNAs were associated with regulation of intestinal barrier integrity.

Temporal changes in mouse hippocampus transcriptome after pilocarpine-induced seizures

Introduction

Status epilepticus (SE) is a seizure lasting more than 5 min that can have lethal consequences or lead to various neurological disorders, including epilepsy. Using a pilocarpine-induced SE model in mice we investigated temporal changes in the hippocampal transcriptome.

Methods

We performed mRNA-seq and microRNA-seq analyses at various times after drug treatment.

Results

At 1 h after the start of seizures, hippocampal cells upregulated transcription of immediate early genes and genes involved in the IGF-1, ERK/MAPK and RNA-PolII/transcription pathways. At 8 h, we observed changes in the expression of genes associated with oxidative stress, overall transcription downregulation, particularly for genes related to mitochondrial structure and function, initiation of a stress response through regulation of ribosome and translation/EIF2 signaling, and upregulation of an inflammatory response. During the middle of the latent period, 36 h, we identified upregulation of membrane components, cholesterol synthesis enzymes, channels, and extracellular matrix (ECM), as well as an increased inflammatory response. At the end of the latent period, 120 h, most changes in expression were in genes involved in ion transport, membrane channels, and synapses. Notably, we also elucidated the involvement of novel pathways, such as cholesterol biosynthesis pathways, iron/BMP/ferroptosis pathways, and circadian rhythms signaling in SE and epileptogenesis.

Discussion

These temporal changes in metabolic reactions indicate an immediate response to injury followed by recovery and regeneration. CREB was identified as the main upstream regulator. Overall, our data provide new insights into molecular functions and cellular processes involved at different stages of seizures and offer potential avenues for effective therapeutic strategies.

SGCP: a spectral self-learning method for clustering genes in co-expression networks

BACKGROUND

A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules.

RESULTS

We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines.

CONCLUSION

Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

A meta-analysis approach for annotation and identification of lncRNAs controlling perirenal fat deposition in suckling lambs

Long non-coding RNAs (lncRNAs) are being studied in farm animals due to their association with traits of economic interest, such as fat deposition. Based on the analysis of perirenal fat transcriptomes, this research explored the relevance of these regulatory elements to fat deposition in suckling lambs. To that end, meta-analysis techniques have been implemented to efficiently characterize and detect differentially expressed transcripts from two different RNA-seq datasets, one including samples of two sheep breeds that differ in fat deposition features, Churra and Assaf (n = 14), and one generated from Assaf suckling lambs with different fat deposition levels (n = 8). The joint analysis of the 22 perirenal fat RNA-seq samples with the FEELnc software allowed the detection of 3953 novel lncRNAs. After the meta-analysis, 251 differentially expressed genes were identified, 21 of which were novel lncRNAs. Additionally, a co-expression analysis revealed that, in suckling lambs, lncRNAs may play a role in controlling angiogenesis and thermogenesis, processes highlighted in relation to high and low fat deposition levels, respectively. Overall, while providing information that could be applied for the improvement of suckling lamb carcass traits, this study offers insights into the biology of perirenal fat deposition regulation in mammals.

Pan-transcriptome reveals a large accessory genome contribution to gene expression variation in yeast

Gene expression is an essential step in the translation of genotypes into phenotypes. However, little is known about the transcriptome architecture and the underlying genetic effects at the species level. Here we generated and analyzed the pan-transcriptome of ~1,000 yeast natural isolates across 4,977 core and 1,468 accessory genes. We found that the accessory genome is an underappreciated driver of transcriptome divergence. Global gene expression patterns combined with population structure showed that variation in heritable expression mainly lies within subpopulation-specific signatures, for which accessory genes are overrepresented. Genome-wide association analyses consistently highlighted that accessory genes are associated with proportionally more variants with larger effect sizes, illustrating the critical role of the accessory genome on the transcriptional landscape within and between populations.

Understanding the (epi)genetic dysregulation in Parkinson's disease through an integrative brain competitive endogenous RNA network

Parkinson's disease (PD) is a rapidly growing neurodegenerative disorder characterized by dopaminergic neuron loss in the substantia nigra pars compacta (SN) and aggregation of α-synuclein. Its aetiology involves a multifaceted interplay among genetic, environmental, and epigenetic factors. We integrated brain gene expression data from PD patients to construct a comprehensive regulatory network encompassing messenger RNAs (mRNAs), microRNAs (miRNAs), circular RNAs (circRNAs) and, for the first time, RNA binding proteins (RBPs). Expression data from the SN of PD patients and controls were systematically selected from public databases to identify combined differentially expressed genes (DEGs). Brain co-expression analysis revealed modules comprising significant DEGs that function cooperatively. The relationships among co-expressed DEGs, miRNAs, circRNAs, and RBPs revealed an intricate competitive endogenous RNA (ceRNA) network responsible for post-transcriptional dysregulation in PD. Many genes in the ceRNA network, including the TOMM20 and HMGCR genes, overlap with the most relevant genes in our previous Alzheimer's disease-associated ceRNA network, suggesting common underlying mechanisms between both conditions. Moreover, in the ceRNA subnetwork, the RBP Aly/REF export factor (ALYREF), which acts as an RNA 5-methylcytosine(m5C)-binding protein, stood out. Our data sheds new light on the potential role of brain ceRNA networks in PD pathogenesis.

SAA1-dependent reprogramming of adipocytes by tumor cells is associated with triple negative breast cancer aggressiveness

Triple negative breast cancers (TNBC) are characterized by a poor prognosis and a lack of targeted treatments. Their progression depends on tumor cell intrinsic factors, the tumor microenvironment and host characteristics. Although adipocytes, the primary stromal cells of the breast, have been determined to be plastic in physiology and cancer, the tumor-derived molecular mediators of tumor-adipocyte crosstalk have not been identified yet. In this study, we report that the crosstalk between TNBC cells and adipocytes in vitro beyond adipocyte dedifferentiation, induces a unique transcriptional profile that is characterized by inflammation and pathways that are related to interaction with the tumor microenvironment. Accordingly, increased cancer stem-like features and recruitment of pro-tumorigenic immune cells are induced by this crosstalk through CXCL5 and IL-8 production. We identified serum amyloid A1 (SAA1) as a regulator of the adipocyte reprogramming through CD36 and P2XR7 signaling. In human TNBC, SAA1 expression was associated with cancer-associated adipocyte infiltration, inflammation, stimulated lipolysis, stem-like properties, and a distinct tumor immune microenvironment. Our findings constitute evidence that the interaction between tumor cells and adipocytes through the release of SAA1 is relevant to the aggressiveness of TNBC.

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Serum and Tissue Biomarkers Associated With Composite of Relevant Endpoints for Sjögren Syndrome (CRESS) and Sjögren Tool for Assessing Response (STAR) to B Cell-Targeted Therapy in the Trial of Anti-B Cell Therapy in Patients With Primary Sjögren Syndrome (TRACTISS)

OBJECTIVE

This study aimed to identify peripheral and salivary gland (SG) biomarkers of response/resistance to B cell depletion based on the novel concise Composite of Relevant Endpoints for Sjögren Syndrome (cCRESS) and candidate Sjögren Tool for Assessing Response (STAR) composite endpoints.

METHODS

Longitudinal analysis of peripheral blood and SG biopsies was performed pre- and post-treatment from the Trial of Anti-B Cell Therapy in Patients With Primary Sjögren Syndrome (TRACTISS) combining flow cytometry immunophenotyping, serum cytokines, and SG bulk RNA sequencing.

RESULTS

Rituximab treatment prevented the worsening of SG inflammation observed in the placebo arm, by inhibiting the accumulation of class-switched memory B cells within the SG. Furthermore, rituximab significantly down-regulated genes involved in immune-cell recruitment, lymphoid organization alongside antigen presentation, and T cell co-stimulatory pathways. In the peripheral compartment, rituximab down-regulated immunoglobulins  and auto-antibodies together with pro-inflammatory cytokines and chemokines. Interestingly, patients classified as responders  according to STAR displayed significantly higher baseline levels of C-X-C motif chemokine ligand-13 (CXCL13), interleukin (IL)-22, IL-17A, IL-17F, and tumor necrosis factor-α (TNF-α), whereas a longitudinal analysis of serum T cell-related cytokines showed a selective reduction in both STAR and cCRESS responder patients. Conversely, cCRESS response was better associated with biomarkers of SG immunopathology, with cCRESS-responders showing a significant decrease in SG B cell infiltration and reduced expression of transcriptional gene modules related to T cell costimulation, complement activation, and Fcγ-receptor engagement. Finally, cCRESS and STAR response were associated with a significant improvement in SG exocrine function linked to transcriptional evidence of SG epithelial and metabolic restoration.

CONCLUSION

Rituximab modulates both peripheral and SG inflammation, preventing the deterioration of exocrine function with functional and metabolic restoration of the glandular epithelium. Response assessed by newly developed cCRESS and STAR criteria was associated with differential modulation of peripheral and SG biomarkers, emerging as novel tools for patient stratification.

Shared genetic aetiology of Alzheimer's disease and age-related macular degeneration by APOC1 and APOE genes

Background

Alzheimer's disease (AD) and age-related macular degeneration (AMD) share similar pathological features, suggesting common genetic aetiologies between the two. Investigating gene associations between AD and AMD may provide useful insights into the underlying pathogenesis and inform integrated prevention and treatment for both diseases.

Methods

A stratified quantile-quantile (QQ) plot was constructed to detect the pleiotropy among AD and AMD based on genome-wide association studies data from 17 008 patients with AD and 30 178 patients with AMD. A Bayesian conditional false discovery rate-based (cFDR) method was used to identify pleiotropic genes. UK Biobank was used to verify the pleiotropy analysis. Biological network and enrichment analysis were conducted to explain the biological reason for pleiotropy phenomena. A diagnostic test based on gene expression data was used to predict biomarkers for AD and AMD based on pleiotropic genes and their regulators.

Results

Significant pleiotropy was found between AD and AMD (significant leftward shift on QQ plots). APOC1 and APOE were identified as pleiotropic genes for AD-AMD (cFDR <0.01). Network analysis revealed that APOC1 and APOE occupied borderline positions on the gene co-expression networks. Both APOC1 and APOE genes were enriched on the herpes simplex virus 1 infection pathway. Further, machine learning-based diagnostic tests identified that APOC1, APOE (areas under the curve (AUCs) >0.65) and their upstream regulators, especially ZNF131, ADNP2 and HINFP, could be potential biomarkers for both AD and AMD (AUCs >0.8).

Conclusion

In this study, we confirmed the genetic pleiotropy between AD and AMD and identified APOC1 and APOE as pleiotropic genes. Further, the integration of multiomics data identified ZNF131, ADNP2 and HINFP as novel diagnostic biomarkers for AD and AMD.

Genetic variation is a key determinant of chromatin accessibility and drives differences in the regulatory landscape of C57BL/6J and 129S1/SvImJ mice

Most common genetic variants associated with disease are located in non-coding regions of the genome. One mechanism by which they function is through altering transcription factor (TF) binding. In this study, we explore how genetic variation is connected to differences in the regulatory landscape of livers from C57BL/6J and 129S1/SvImJ mice fed either chow or a high-fat diet. To identify sites where regulatory variation affects TF binding and nearby gene expression, we employed an integrative analysis of H3K27ac ChIP-seq (active enhancers), ATAC-seq (chromatin accessibility) and RNA-seq (gene expression). We show that, across all these assays, the genetically driven (i.e. strain-specific) differences in the regulatory landscape are more pronounced than those modified by diet. Most notably, our analysis revealed that differentially accessible regions (DARs, N = 29635, FDR < 0.01 and fold change > 50%) are almost always strain-specific and enriched with genetic variation. Moreover, proximal DARs are highly correlated with differentially expressed genes. We also show that TF binding is affected by genetic variation, which we validate experimentally using ChIP-seq for TCF7L2 and CTCF. This study provides detailed insights into how non-coding genetic variation alters the gene regulatory landscape, and demonstrates how this can be used to study the regulatory variation influencing TF binding.

Identification of IGF2 promotes skin wound healing by co-expression analysis

Oral mucosa is an ideal model for studying scarless wound healing. Researchers have shown that the key factors which promote scarless wound healing already exist in basal state of oral mucosa. Thus, to identify the other potential factors in basal state of oral mucosa will benefit to skin wound healing. In this study, we identified eight gene modules enriched in wound healing stages of human skin and oral mucosa through co-expression analysis, among which the module M8 was only module enriched in basal state of oral mucosa, indicating that the genes in module M8 may have key factors mediating scarless wound healing. Through bioinformatic analysis of genes in module M8, we found IGF2 may be the key factor mediating scarless wound healing of oral mucosa. Then, we purified IGF2 protein by prokaryotic expression, and we found that IGF2 could promote the proliferation and migration of HaCaT cells. Moreover, IGF2 promoted wound re-epithelialization and accelerated wound healing in a full-thickness skin wound model. Our findings identified IGF2 as a factor to promote skin wound healing which provide a potential target for wound healing therapy in clinic.

A Highly Sensitive and Specific Non-Invasive Test through Genome-Wide 5-Hydroxymethylation Mapping for Early Detection of Lung Cancer

Low-dose computed tomography screening can increase the detection for non-small-cell lung cancer (NSCLC). To improve the diagnostic accuracy of early-stage NSCLC detection, ultrasensitive methods are used to detect cell-free DNA (cfDNA) 5-hydroxymethylcytosine (5hmC) in plasma. Genome-wide 5hmC is profiled in 1990 cfDNA samples collected from patients with non-small cell lung cancer (NSCLC, n = 727), healthy controls (HEA, n = 1,092), as well as patients with small cell lung cancer (SCLC, n = 41), followed by sample randomization, differential analysis, feature selection, and modeling using a machine learning approach. Differentially modified features reflecting tissue origin. A weighted diagnostic model comprised of 105 features is developed to compute a detection score for each individual, which showed an area under the curve (AUC) range of 86.4%-93.1% in the internal and external validation sets for distinguishing lung cancer from HEA controls, significantly outperforming serum biomarkers (p < 0.001). The 5hmC-based model detected high-risk pulmonary nodules (AUC: 82%)and lung cancer of different subtypes with high accuracy as well. A highly sensitive and specific blood-based test is developed for detecting lung cancer. The 5hmC biomarkers in cfDNA offer a promising blood-based test for lung cancer.

Multi-omics data elucidate parasite-host-microbiota interactions and resistance to Haemonchus contortus in sheep

BACKGROUND

The integration of molecular data from hosts, parasites, and microbiota can enhance our understanding of the complex biological interactions underlying the resistance of hosts to parasites. Haemonchus contortus, the predominant sheep gastrointestinal parasite species in the tropics, causes significant production and economic losses, which are further compounded by the diminishing efficiency of chemical control owing to anthelmintic resistance. Knowledge of how the host responds to infection and how the parasite, in combination with microbiota, modulates host immunity can guide selection decisions to breed animals with improved parasite resistance. This understanding will help refine management practices and advance the development of new therapeutics for long-term helminth control.

METHODS

Eggs per gram (EPG) of feces were obtained from Morada Nova sheep subjected to two artificial infections with H. contortus and used as a proxy to select animals with high resistance or susceptibility for transcriptome sequencing (RNA-seq) of the abomasum and 50 K single-nucleotide genotyping. Additionally, RNA-seq data for H. contortus were generated, and amplicon sequence variants (ASV) were obtained using polymerase chain reaction amplification and sequencing of bacterial and archaeal 16S ribosomal RNA genes from sheep feces and rumen content.

RESULTS

The heritability estimate for EPG was 0.12. GAST, GNLY, IL13, MGRN1, FGF14, and RORC genes and transcripts were differentially expressed between resistant and susceptible animals. A genome-wide association study identified regions on chromosomes 2 and 11 that harbor candidate genes for resistance, immune response, body weight, and adaptation. Trans-expression quantitative trait loci were found between significant variants and differentially expressed transcripts. Functional co-expression modules based on sheep genes and ASVs correlated with resistance to H. contortus, showing enrichment in pathways of response to bacteria, immune and inflammatory responses, and hub features of the Christensenellaceae, Bacteroides, and Methanobrevibacter genera; Prevotellaceae family; and Verrucomicrobiota phylum. In H. contortus, some mitochondrial, collagen-, and cuticle-related genes were expressed only in parasites isolated from susceptible sheep.

CONCLUSIONS

The present study identified chromosome regions, genes, transcripts, and pathways involved in the elaborate interactions between the sheep host, its gastrointestinal microbiota, and the H. contortus parasite. These findings will assist in the development of animal selection strategies for parasite resistance and interdisciplinary approaches to control H. contortus infection in sheep.

Cell-specific housekeeping role of lncRNAs in COVID-19-infected and recovered patients

A plethora of studies have demonstrated the roles of lncRNAs in modulating disease severity and outcomes during infection. However, the spatio-temporal expression of these lncRNAs is poorly understood. In this study, we used single-cell RNA-seq to understand the spatio-temporal expression dynamics of lncRNAs across healthy, SARS-CoV-2-infected, and recovered individuals and their functional role in modulating the disease and recovery. We identified 203 differentially expressed lncRNAs, including cell type-specific ones like MALAT1, NEAT1, ZFAS1, SNHG7, SNHG8, and SNHG25 modulating immune function in classical monocyte, NK T, proliferating NK, plasmablast, naive, and activated B/T cells. Interestingly, we found invariant lncRNAs (no significant change in expression across conditions) regulating essential housekeeping functions (for example, HOTAIR, NRAV, SNHG27, SNHG28, and UCA1) in infected and recovered individuals. Despite similar repeat element abundance, variant lncRNAs displayed higher Alu content, suggesting increased interactions with proximal and distal genes, crucial for immune response modulation. The comparable repeat abundance but distinct expression levels of variant and invariant lncRNAs highlight the significance of investigating the regulatory mechanisms of invariant lncRNAs. Overall, this study offers new insights into the spatio-temporal expression patterns and functional roles of lncRNAs in SARS-CoV-2-infected and recovered individuals while highlighting the importance of invariant lncRNAs in the disease context.