Co-expression Modules Research Articles

Genetic parameters, genome-wide association study, and selection perspective on gestation length in 16 French cattle breeds

Lifetime productivity is a trait of great importance to dairy cattle populations as it combines information from production and longevity variables. Therefore, we investigated the genetic background of lifetime productivity in high-producing dairy cattle by integrating genomics and transcriptomics data sets. A total of 3,365,612 test-day milk yield records from 134,029 Chinese Holstein cows were used to define 6 lifetime productivity traits, including lifetime milk yield covering full lifespan and 5 cumulative milk yield traits covering partial lifespan. Genetic parameters were estimated based on univariate and bivariate linear animal models and the Restricted Maximum Likelihood (REML) method. Genome-wide association studies (GWAS) and weighted gene co-expression network analyses (WGCNA) were performed to identify candidate genes associated with lifetime productivity based on genomic data from 3,424 cows and peripheral blood RNA-seq data from 23 cows, respectively. Lifetime milk yield averaged 24,800.8 ± 14,396.6 kg (mean ± SD) across an average of 2.4 parities in Chinese Holstein population. The heritability estimates for lifetime productivity traits ranged from 0.05 (±0.01 for SE) to 0.10 (±0.02 for SE). The estimate of genetic correlation between lifetime milk yield and productive life is 0.88 (±0.3 for SE) while the genetic correlation with 305d milk yield in the first lactation was 0.49 (±0.08 for SE). Absolute values for most genetic correlation estimates between lifetime productivity and type traits were lower than 0.30. Moderate genetic correlations were found between udder related traits and lifetime productivity, such as with udder depth (0.33), rear udder attachment height (0.33), and udder system (0.34). Some single nucleotide polymorphisms and gene co-expression modules significantly associated with lifetime milk yield were identified based on GWAS and WGCNA analyses, respectively. Functional enrichment analyses of the candidate genes identified revealed important pathways related to immune system, longevity, energy utilization and metabolism, and FoxO signaling. The genes NTMT1, FNBP1, and S1PR1 were considered to be the most important candidate genes influencing lifetime productivity in Holstein cows. Overall, our findings indicate that lifetime productivity is heritable in Chinese Holstein cattle and important candidate genes were identified by integrating genomic and transcriptomic data sets.

Identifying transcriptomic profiles in ovine spleen after repetitive vaccination.

Aluminum hydroxide has long been employed as a vaccine adjuvant for its safety profile, although its precise mechanism of action remains elusive. In this study, we investigated the transcriptomic responses in sheep spleen following repetitive vaccination with aluminum adjuvanted vaccines and aluminum hydroxide alone. Notably, this work represents the first exploration of the sheep spleen transcriptome in such conditions. Animals were splitted in 3 treatment groups: vaccine group, adjuvant alone group and control group. A total of 18 high-depth RNA-seq libraries were sequenced, resulting in a rich dataset which also allowed isoform-level analysis. The comparisons between vaccine-treated and control groups (V vs C) as well as between vaccine-treated and adjuvant-alone groups (V vs A) revealed significant alterations in gene expression profiles, including protein coding genes and long non-coding RNAs. Among the differentially expressed genes, many were associated with processes such as endoplasmic reticulum (ER) stress, immune response and cell cycle. The analysis of co-expression modules further indicated a correlation between vaccine treatment and genes related to ER stress and unfolded protein response. Surprisingly, adjuvant-alone treatment had little impact on the spleen transcriptome. Additionally, the role of alternative splicing in the immune response was explored. We identified isoform switches in genes associated with immune regulation and inflammation, potentially influencing protein function. In conclusion, this study provides valuable insights into the transcriptomic changes in sheep spleen following vaccination with aluminum adjuvanted vaccines and aluminum hydroxide alone. These findings shed light on the molecular mechanisms underlying vaccine-induced immune responses and emphasize the significance of antigenic components in aluminum adjuvant mechanism of action. Furthermore, the analysis of alternative splicing revealed an additional layer of complexity in the immune response to vaccination in a livestock species.

Identifying key genes and functionally enriched pathways in acute myeloid leukemia by weighted gene co-expression network analysis.

Acute myeloid leukemia (AML) is characterized by the uncontrolled proliferation of myeloid leukemia cells in the bone marrow and other hematopoietic tissues and is highly heterogeneous. While with the progress of sequencing technology, understanding of the AML-related biomarkers is still incomplete. The purpose of this study is to identify potential biomarkers for prognosis of AML. Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases were employed for investigating potential biomarkers for the prognosis of AML. This study screened a total of 6153 genes by analyzing various changes in 103 acute myeloid leukemia (AML) samples, including gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases. Moreover, seven AML-related co-expression modules were mined by WGCNA analysis, and twelve biomarkers associated with the AML prognosis were identified from each top 10 genes of the seven co-expression modules. The AML samples were then classified into two subgroups, the prognosis of which is significantly different, based on the expression of these twelve genes. The differentially expressed 7 genes of two subgroups (HOXB-AS3, HOXB3, SLC9C2, CPNE8, MEG8, S1PR5, MIR196B) are mainly involved in glucose metabolism, glutathione biosynthesis, small G protein-mediated signal transduction, and the Rap1 signaling pathway. With the utilization of WGCNA mining, seven gene co-expression modules were identified from the TCGA database, and there are unreported genes that may be potential driver genes of AML and may be the direction to identify the possible molecular signatures to predict survival of AML patients and help guide experiments for potential clinical drug targets.

The relationship between Sjögren's syndrome and recurrent pregnancy loss: a bioinformatics analysis

Research QuestionSjögren's syndrome (SS) is an autoimmune disease and also an essential factor in recurrent pregnancy loss (RPL). What are the gene-related relationships between SS and RPL pathogenesis? DesignWe obtained the SS- and RPL-related gene datasets from the Gene Expression Omnibus database and identified the co-expression modules and shared differentially expressed genes (DEGs) in diseases through the weighted gene co-expression network analysis (WGCNA) and limma analysis based on sample size. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were applied to reveal the hidden biological pathways. Additionally, shared hub gene identification, gene set enrichment analysis, hub gene's association with ferroptosis and immunity, drug sensitivity analysis, single-cell RNA sequencing analysis, and construction of ceRNA network were conducted. ResultsBy intersecting the genes from the WGCNA and limma analysis, one shared hub gene (KCNN3) was derived, exhibiting upregulation in SS and RPL. There was a positive relationship between KCNN3 and the immune-related gene TLR2. The ceRNA network revealed that XIST was the most shared lncRNA and could competitively bind with 8 miRNAs to regulate the expression of KCNN3. Forty-eight drugs were found to be strongly associated with KCNN3 expression, such as Estramustine and cyclosporine. Moreover, KCNN3 exhibited high expression in RPL endothelial cells of villus tissue. ConclusionsThis research is one of the initial studies to reveal that SS shares common biological pathways with RPL. KCNN3 was identified as the one hub gene correlated with SS and RPL, and it may be a new target for mechanistic studies on SS and RPL.

Gene coexpression network analysis reveals the genes and pathways in pectoralis major muscle and liver associated with wooden breast in broilers

Wooden breast (WB) is a myopathy mainly affecting pectoralis major (PM) muscle in modern commercial broiler chickens, causing enormous economic losses in the poultry industry. Recent studies have observed hepatic and PM muscle injury in broilers affected by WB, but the relationships between WB and the 2 tissues are mostly unclear. In the current study, the RNA-seq raw data of PM muscle and liver were downloaded from GSE144000, and we constructed the gene coexpression networks of PM muscle and liver to explore the relationships between WB and the 2 tissues using the weighted gene coexpression network analysis (WGCNA) method. Six and 2 gene coexpression modules were significantly correlated with WB in the PM muscle and liver networks, respectively. TGF-beta signaling, Toll-like receptor signaling and mTOR signaling pathways were significantly enriched in the genes within the 6 gene modules of PM muscle network. Meanwhile, mTOR signaling pathway was significantly enriched in the genes within the 2 gene modules of liver network. In the consensus gene coexpression network across the 2 tissues, salmon module (r = -0.5 and p = 0.05) was significantly negatively correlated with WB, in which Toll-like receptor signaling, apoptosis, and autophagy pathways were significantly enriched. The genes related with the 3 pathways, myeloid differentiation primary response 88 (MYD88), interferon regulatory factor 7 (IRF7), mitogen-activated protein kinase 14 (MAPK14), FBJ murine osteosarcoma viral oncogene homolog (FOS), jun proto-oncogene (JUN), caspase-10, unc-51 like autophagy activating kinase 2 (ULK2) and serine/threonine kinase 11 (LKB1), were identified in salmon module. In this current study, we found that the signaling pathways related with cell inflammation, apoptosis and autophagy might influence WB across 2 tissues in broilers.

Sex-dependent APOE4 neutrophil-microglia interactions drive cognitive impairment in Alzheimer's disease.

APOE4 is the strongest genetic risk factor for Alzheimer's disease (AD), with increased odds ratios in female carriers. Targeting amyloid plaques shows modest improvement in male non-APOE4 carriers. Leveraging single-cell transcriptomics across APOE variants in both sexes, multiplex flow cytometry and validation in two independent cohorts of APOE4 female carriers with AD, we identify a new subset of neutrophils interacting with microglia associated with cognitive impairment. This phenotype is defined by increased interleukin (IL)-17 and IL-1 coexpressed gene modules in blood neutrophils and in microglia of cognitively impaired female APOE ε4 carriers, showing increased infiltration to the AD brain. APOE4 female IL-17+ neutrophils upregulated the immunosuppressive cytokines IL-10 and TGFβ and immune checkpoints, including LAG3 and PD-1, associated with accelerated immune aging. Deletion of APOE4 in neutrophils reduced this immunosuppressive phenotype and restored the microglial response to neurodegeneration, limiting plaque pathology in AD mice. Mechanistically, IL-17F upregulated in APOE4 neutrophils interacts with microglial IL-17RA to suppress the induction of the neurodegenerative phenotype, and blocking this axis supported cognitive improvement in AD mice. These findings provide a translational basis to target IL-17F in APOE ε4 female carriers with cognitive impairment.

A novel induced pluripotent stem cell model of Schwann cell differentiation reveals NF2 - related gene regulatory networks of the extracellular matrix.

Schwann cells are vital to development and maintenance of the peripheral nervous system and their dysfunction has been implicated in a range of neurological and neoplastic disorders, including NF2 -related schwannomatosis. We developed a novel human induced pluripotent stem cell (hiPSC) model to study Schwann cell differentiation in health and disease. We performed transcriptomic, immunofluorescence, and morphological analysis of hiPSC derived Schwann cell precursors (SPCs) and terminally differentiated Schwann cells (SCs) representing distinct stages of development. To validate our findings, we performed integrated, cross-species analyses across multiple external datasets at bulk and single cell resolution. Our hiPSC model of Schwann cell development shared overlapping gene expression signatures with human amniotic mesenchymal stem cell (hAMSCs) derived SCs and in vivo mouse models, but also revealed unique features that may reflect species-specific aspects of Schwann cell biology. Moreover, we identified gene co-expression modules that are dynamically regulated during hiPSC to SC differentiation associated with ear and neural development, cell fate determination, the NF2 gene, and extracellular matrix (ECM) organization. By cross-referencing results between multiple datasets, we identified new genes potentially associated with NF2 expression. Our hiPSC model further provides a tractable platform for studying Schwann cell development in the context of human disease.

Key gene modules and hub genes associated with pyrethroid and organophosphate resistance in Anopheles mosquitoes: a systems biology approach

Indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are the main methods used to control mosquito populations for malaria prevention. The efficacy of these strategies is threatened by the spread of insecticide resistance (IR), limiting the success of malaria control. Studies of the genetic evolution leading to insecticide resistance could enable the identification of molecular markers that can be used for IR surveillance and an improved understanding of the molecular mechanisms associated with IR. This study used a weighted gene co-expression network analysis (WGCNA) algorithm, a systems biology approach, to identify genes with similar co-expression patterns (modules) and hub genes that are potential molecular markers for insecticide resistance surveillance in Kenya and Benin. A total of 20 and 26 gene co-expression modules were identified via average linkage hierarchical clustering from Anopheles arabiensis and An. gambiae, respectively, and hub genes (highly connected genes) were identified within each module. Three specific genes stood out: serine protease, E3 ubiquitin-protein ligase, and cuticular proteins, which were top hub genes in both species and could serve as potential markers and targets for monitoring IR in these malaria vectors. In addition to the identified markers, we explored molecular mechanisms using enrichment maps that revealed a complex process involving multiple steps, from odorant binding and neuronal signaling to cellular responses, immune modulation, cellular metabolism, and gene regulation. Incorporation of these dynamics into the development of new insecticides and the tracking of insecticide resistance could improve the sustainable and cost-effective deployment of interventions.

Insight into Mantle Cell Lymphoma Pathobiology, Diagnosis, and Treatment Using Network-Based and Drug-Repurposing Approaches.

Mantle cell lymphoma (MCL) is a rare, incurable, and aggressive B-cell non-Hodgkin lymphoma (NHL). Early MCL diagnosis and treatment is critical and puzzling due to inter/intra-tumoral heterogeneity and limited understanding of the underlying molecular mechanisms. We developed and applied a multifaceted analysis of selected publicly available transcriptomic data of well-defined MCL stages, integrating network-based methods for pathway enrichment analysis, co-expression module alignment, drug repurposing, and prediction of effective drug combinations. We demonstrate the "butterfly effect" emerging from a small set of initially differentially expressed genes, rapidly expanding into numerous deregulated cellular processes, signaling pathways, and core machineries as MCL becomes aggressive. We explore pathogenicity-related signaling circuits by detecting common co-expression modules in MCL stages, pointing out, among others, the role of VEGFA and SPARC proteins in MCL progression and recommend further study of precise drug combinations. Our findings highlight the benefit that can be leveraged by such an approach for better understanding pathobiology and identifying high-priority novel diagnostic and prognostic biomarkers, drug targets, and efficacious combination therapies against MCL that should be further validated for their clinical impact.

Transcriptomic and Metabolomic Correlates of Increased Colonic Permeability in Postinfection Irritable Bowel Syndrome

Background and aimPost-infection irritable bowel syndrome (PI-IBS) is well-known epidemiologically; however, its physiological and molecular characteristics are not well studied. We aimed to determine the physiological phenotypes, colonic transcriptome, fecal microbiome, and metabolome in PI-IBS. MethodsFifty-one Rome III Campylobacter PI-IBS patients and 39 healthy volunteers (HV) were enrolled. Participants completed questionnaires, in vivo intestinal permeability, gastrointestinal transit, and rectal sensation. Fecal samples were collected for shotgun metagenomics, untargeted metabolomics, and sigmoid colonic biopsies for bulk RNAseq. Differential gene expression, differences in microbiota composition and metabolite abundance were determined. Gene and metabolite clusters were identified via weighted gene correlation network analysis and correlations with clinical and physiological parameters determined. ResultsPI-IBS (59% F, 46±2 yrs.) and HV (64% F, 42±2 yrs.) demographics were comparable. Mean IBS-symptom severity score was 227; 94% were non-constipation. 2-24h lactulose excretion was increased in PI-IBS, suggesting increased colonic permeability (4.4±0.5 mg vs. 2.6±0.3 mg, p=0.01). Colonic transit and sensory thresholds were similar between the two groups. Overall, expression of 2036 mucosal genes and 223 fecal metabolites were different, with changes more prominent in females. Fecal N-acetylputrescine was increased in PI-IBS and associated with colonic permeability, worse diarrhea, and negatively correlated with abundance of Collinsella aerofaciens. Histamine and N-acetyl histamine positively associated with 2-24 hr lactulose excretion. Eight weighted gene coexpression modules significantly correlated with phenotypes (sex, stool frequency, colonic permeability, transit). ConclusionsCampylobacter PI-IBS patients demonstrate higher colonic permeability which associated with changes in polyamine and histamine metabolites. Female patients demonstrated greater molecular changes.

Mate choice in the brain: species differ in how male traits 'turn on' gene expression in female brains.

Mate choice plays a fundamental role in speciation, yet we know little about the molecular mechanisms that underpin this crucial decision-making process. Stickleback fish differentially adapted to limnetic and benthic habitats are reproductively isolated and females of each species use different male traits to evaluate prospective partners and reject heterospecific males. Here, we integrate behavioural data from a mate choice experiment with gene expression profiles from the brains of females actively deciding whether to mate. We find substantial gene expression variation between limnetic and benthic females, regardless of behavioural context, suggesting general divergence in constitutive gene expression patterns, corresponding to their genetic differentiation. Intriguingly, female gene co-expression modules covary with male display traits but in opposing directions for sympatric populations of the two species, suggesting male displays elicit a dynamic neurogenomic response that reflects known differences in female preferences. Furthermore, we confirm the role of numerous candidate genes previously implicated in female mate choice in other species, suggesting evolutionary tinkering with these conserved molecular processes to generatedivergent mate preferences. Taken together, our study adds important new insights to our understanding of the molecular processes underlying female decision-making critical for generating sexual isolation and speciation.

Weighted gene coexpression network analysis and machine learning for the determination of tfh cell and B cell infiltrating biomarkers in thymoma-associated myasthenia gravis

Patients with thymoma (THYM)-associated myasthenia gravis (MG) typically have a poor prognosis and recurring illness. This study aimed to discover important biomarkers associated with immune cell infiltration and THYM-associated MG (THYM-MG) development. Gene expression microarray data were downloaded from The Cancer Genome Atlas website (TCGA) and Gene Expression Omnibus (GEO). A total of 102 differentially expressed genes were investigated. According to the immune infiltration data, the distribution of Tfh cells, B cells, and CD4 T cells differed significantly between the THYM-MG and THYM-NMG groups. WGCNA derived 25 coexpression modules; one hub module (the blue module) strongly correlated with Tfh cells. Combining differential genes revealed 21 intersecting genes. LASSO analysis subsequently revealed 16 hub genes as potential THYM-MG biomarkers. ROC curve analysis of the predictive model revealed moderate diagnostic value. The association between the 16 hub genes and infiltrating immune cells was further evaluated in TIMER2.0 and the validation dataset. Draggability analysis identified the therapeutic target genes PTGS2 and ALB, along with significant drugs including Firocoxib, Alclofenac, Pyridostigmine, and Stavudine. This was validated through MD simulation, PCA, and MM-GBSA analyses. The interaction between numerous activated B cells and follicular helper T cells is closely associated with THYM-MG pathogenesis from a bioinformatics perspective. Hub genes (including SP6, SCUBE3, B3GNT7, and MAGEL2) may be downregulated in immune cells in THYM-MG and associated with progression.

SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer

Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.

Convergent and divergent transcriptional reprogramming of motor and sensory neurons underlying response to peripheral nerve injury

IntroductionMotor neurons differ from sensory neurons in aspects including origins and surrounding environment. Understanding the similarities and differences in molecular response to peripheral nerve injury (PNI) and regeneration between sensory and motor neurons is crucial for developing effective drug targets for CNS regeneration. However, genome-wide comparisons of molecular changes between sensory and motor neurons following PNI remains limited. ObjectivesThis study aims to investigate genome-wide convergence and divergence of injury response between sensory and motor neurons to identify novel drug targets for neural repair. MethodsWe analyzed two large-scale RNA-seq datasets of in situ captured sensory neurons (SNs) and motoneurons (MNs) upon PNI, retinal ganglion cells and spinal cord upon CNS injury. Additionally, we integrated these with other related single-cell level datasets. Bootstrap DESeq2 and WGCNA were used to detect and explore co-expression modules of differentially expressed genes (DEGs). ResultsWe found that SNs and MNs exhibited similar injury states, but with a delayed response in MNs. We identified a conserved regeneration-associated module (cRAM) with 274 shared DEGs. Of which, 47% of DEGs could be changed in injured neurons supported by single-cell resolution datasets. We also identified some less-studied candidates in cRAM, including genes associated with transcription, ubiquitination (Rnf122), and neuron-immune cells cross-talk. Further in vitro experiments confirmed a novel role of Rnf122 in axon growth. Analysis of the top 10% of DEGs with a large divergence suggested that both extrinsic (e.g., immune microenvironment) and intrinsic factors (e.g., development) contributed to expression divergence between SNs and MNs following injury. ConclusionsThis comprehensive analysis revealed convergent and divergent injury response genes in SNs and MNs, providing new insights into transcriptional reprogramming of sensory and motor neurons responding to axonal injury and subsequent regeneration. It also identified some novel regeneration-associated candidates that may facilitate the development of strategies for axon regeneration.

Loss of Inpp5d has disease-relevant and sex-specific effects on glial transcriptomes.

Inpp5d is genetically associated with Alzheimer's disease risk. Loss of Inpp5d alters amyloid pathology in models of amyloidosis. Inpp5d is expressed predominantly in microglia but its function in brain is poorly understood. We performed single-cell RNA sequencing to study the effect of Inpp5d loss on wild-type mouse brain transcriptomes. Loss of Inpp5d has sex-specific effects on the brain transcriptome. Affected genes are enriched for multiple neurodegeneration terms. Network analyses reveal a gene co-expression module centered around Inpp5d in female mice. Inpp5d loss alters Pleotrophin (PTN), Prosaposin (PSAP), and Vascular Endothelial Growth Factor A (VEGFA) signaling probability between cell types. Our data suggest that the normal function of Inpp5d is entangled with mechanisms involved in neurodegeneration. We report the effect of Inpp5d loss without pathology and show that this has dramatic effects on gene expression. Our study provides a critical reference for researchers of neurodegeneration, allowing separation of disease-specific changes mediated by Inpp5d in disease from baseline effects of Inpp5d loss. Loss of Inpp5d has different effects in male and female mice. Genes dysregulated by Inpp5d loss relate to neurodegeneration. Total loss of Inpp5d in female mice collapses a conserved gene co-expression module. Loss of microglial Inpp5d affects the transcriptome of other cell types.

Identification of diagnostic biomarkers and potential therapeutic targets for biliary atresia via WGCNA and machine learning methods.

Biliary atresia (BA) is a severe and progressive biliary obstructive disease in infants that requires early diagnosis and new therapeutic targets. This study employed bioinformatics methods to identify diagnostic biomarkers and potential therapeutic targets for BA. Our analysis of mRNA expression from Gene Expression Omnibus datasets revealed 3,273 differentially expressed genes between patients with BA and those without BA (nBA). Weighted gene coexpression network analysis determined that the turquoise gene coexpression module, consisting of 298 genes, is predominantly associated with BA. The machine learning method then filtered out the top 2 important genes, CXCL8 and TMSB10, from the turquoise module. The area under receiver operating characteristic curves for TMSB10 and CXCL8 were 0.961 and 0.927 in the training group and 0.819 and 0.791 in the testing group, which indicated a high diagnostic value. Besides, combining TMSB10 and CXCL8, a nomogram with better diagnostic performance was built for clinical translation. Several studies have highlighted the potential of CXCL8 as a therapeutic target for BA, while TMSB10 has been shown to regulate cell polarity, which was related to BA progression. Our analysis with qRT PCR and immunohistochemistry also confirmed the upregulation of TMSB10 at mRNA and protein levels in BA liver samples. These findings highlight the sensitivity of CXCL8 and TMSB10 as diagnostic biomarkers and their potential as therapeutic targets for BA.

Identification of differentially expressed genes of blood leukocytes for Schizophrenia.

Schizophrenia (SCZ) is a severe neurodevelopmental disorder with brain dysfunction. This study aimed to use bioinformatic analysis to identify candidate blood biomarkers for SCZ. The study collected peripheral blood leukocyte samples of 9 SCZ patients and 20 healthy controls for RNA sequencing analysis. Bioinformatic analyses included differentially expressed genes (DEGs) analysis, pathway enrichment analysis, and weighted gene co-expression network analysis (WGCNA). This study identified 1,205 statistically significant DEGs, of which 623 genes were upregulated and 582 genes were downregulated. Functional enrichment analysis showed that DEGs were mainly enriched in cell chemotaxis, cell surface, and serine peptidase activity, as well as involved in Natural killer cell-mediated cytotoxicity. WGCNA identified 16 gene co-expression modules, and five modules were significantly correlated with SCZ (p < 0.05). There were 106 upregulated genes and 90 downregulated genes in the five modules. The top ten genes sorted by the Degree algorithm were RPS28, BRD4, FUS, PABPC1, PCBP1, PCBP2, RPL27A, RPS21, RAG1, and RPL27. RAG1 and the other nine genes belonged to the turquoise and pink module respectively. Pathway enrichment analysis indicated that these 10 genes were mainly involved in processes such as Ribosome, cytoplasmic translation, RNA binding, and protein binding. This study finds that the gene functions in key modules and related enrichment pathways may help to elucidate the molecular pathogenesis of SCZ, and the potential of key genes to become blood biomarkers for SCZ warrants further validation.

The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons.

The outstanding human cognitive capacities are computed in the cerebral cortex, a mammalian-specific brain region and the place of massive biological innovation. Long noncoding RNAs have emerged as gene regulatory elements with higher evolutionary turnover than mRNAs. The many long noncoding RNAs identified in neural tissues make them candidates for molecular sources of cerebral cortex evolution and disease. Here, we characterized the genomic and cellular shifts that occurred during the evolution of the long noncoding RNA repertoire expressed in the developing cerebral cortex and explored putative roles for these long noncoding RNAs in the evolution of the human brain. Using transcriptomics and comparative genomics, we comprehensively annotated the cortical transcriptomes of humans, rhesus macaques, mice, and chickens and classified human cortical long noncoding RNAs into evolutionary groups as a function of their predicted minimal ages. Long noncoding RNA evolutionary groups showed differences in expression levels, splicing efficiencies, transposable element contents, genomic distributions, and transcription factor binding to their promoters. Furthermore, older long noncoding RNAs showed preferential expression in germinative zones, outer radial glial cells, and cortical inhibitory (GABAergic) neurons. In comparison, younger long noncoding RNAs showed preferential expression in cortical excitatory (glutamatergic) neurons, were enriched in primate and human-specific gene co-expression modules, and were dysregulated in neurodevelopmental disorders. These results suggest different evolutionary routes for older and younger cortical long noncoding RNAs, highlighting old long noncoding RNAs as a possible source of molecular evolution of conserved developmental programs; conversely, we propose that the de novo expression of primate- and human-specific young long noncoding RNAs is a putative source of molecular evolution and dysfunction of cortical excitatory neurons, warranting further investigation.

Identifying Core Genes Related to Low-Temperature Stress Resistance in Quinoa Seedlings Based on WGCNA.

Quinoa is a nutritious crop that is tolerant to extreme environmental conditions; however, low-temperature stress can affect quinoa growth, development, and quality. Considering the lack of molecular research on quinoa seedlings under low-temperature stress, we utilized a Weighted Gene Co-Expression Network Analysis to construct weighted gene co-expression networks associated with physiological indices and metabolites related to low-temperature stress resistance based on transcriptomic data. We screened 11 co-expression modules closely related to low-temperature stress resistance and selected 12 core genes from the two modules that showed the highest associations with the target traits. Following the functional annotation of these genes to determine the key biological processes and metabolic pathways involved in low-temperature stress, we identified four important transcription factors involved in resistance to low-temperature stress: gene-LOC110731664, gene-LOC110736639, gene-LOC110684437, and gene-LOC110720903. These results provide insights into the molecular genetic mechanism of quinoa under low-temperature stress and can be used to breed lines with tolerance to low-temperature stress.

EQTLs identify regulatory networks and drivers of variation in the individual response to sepsis

Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene-expression-based patient subgroups (sepsis response signatures [SRS]) informative for outcome and underlying pathophysiology. Here, we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-sequencing data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection.