Differential Expression Analysis Research Articles

Comprehensive analysis of competing endogenous RNA networks involved in the regulation of glycolysis in clear cell renal cell carcinoma

Abstract Objectives This study aims to elucidate the role of competing endogenous RNAs (ceRNAs), which are pivotal in the post-transcriptional regulation of cancer cells, in the glycolysis of clear cell renal cell carcinoma (ccRCC). Methods RNA-seq data from ccRCC samples and public datasets were subjected to differential expression analysis to identify the upregulated circular RNAs (circRNAs), long noncoding RNAs (lncRNAs), and mRNAs for constructing a ceRNA network. This network focused on the upregulated mRNAs linked to glycolysis and gluconeogenesis, which were verified at the proteome level using the CPTAC database. The ENCORI database was used to predict the lncRNA/circRNA-miRNA and miRNA-mRNA interactions, which formed a network visualized using Cytoscape. This study further examined the association of selected lncRNAs/circRNAs with biological processes and patient survival and explored lncRNA and mRNA expressions at the single-cell level. Results Three circRNAs and eight lncRNAs were identified to be regulators of four glycolysis pathway genes (HK2, LDHA, PFKP, and ALDOC) via 54 miRNAs. Notably, their expressions were aberrant at the transcriptome and proteome levels. These RNA elements were correlated with malignant and immune pathways, and several lncRNAs showed prognostic significance for ccRCC. Furthermore, single-cell RNA sequencing indicated the abnormal enrichment of these lncRNAs and mRNAs in malignant cells. Conclusions This study identified a ceRNA network that plays a critical role in the glycolysis of ccRCC. These findings highlight the importance of ceRNAs in cancer malignancy and their potential impact on patient prognosis.

In vivo effect of vaginal seminal plasma application on the human endometrial transcriptome: a randomized controlled trial.

Studies in humans and animals suggest that seminal plasma, the acellular seminal fluid component, stimulates the endometrium to promote immune tolerance and facilitate implantation. We designed a randomized, double-blinded, placebo-controlled trial to investigate changes in the endometrial transcriptomic profile after vaginal application of seminal plasma. The study participants were randomized into two groups. Five women received a vaginal application of seminal plasma, and four received a placebo application with saline solution. The application was performed two days after human chorionic gonadotrophin-triggered ovulation in an unstimulated cycle. After 5-8 days, an endometrial biopsy was collected to analyze differences in the endometrial transcriptomic profile using microarray analyses. A differential gene expression analysis and a gene set analysis were performed. The gene set enrichment analysis showed a positive enrichment of pathways associated with the immune response, cell viability, proliferation and cellular movement. Moreover, pathways involved in implantation, embryo development, oocyte maturation, and angiogenesis were positively enriched. The differential gene expression analysis, after adjusting for multiple testing, showed no significantly differentially expressed genes between the two groups. A comparative analysis was also performed with similar studies conducted in other animals or in vitro using human endometrial cells. The comparative analysis showed that the effect of seminal plasma effect on the endometrium is similar in pigs, mice and in vitro human endometrial cells. The present study provides evidence that seminal plasma might impact the endometrium during the implantation window, with potential to affect endometrial receptivity and embryo development.

Large-scale transcriptomic and genomic analyses reveal a novel functional gene SERPINB6 for chicken carcass traits

BackgroundCarcass traits are crucial indicators of meat production efficiency. However, the molecular regulatory mechanisms associated with these traits remain unclear.ResultsIn this study, we conducted comprehensive transcriptomic and genomic analyses on 399 Tiannong partridge chickens to identify key genes and variants associated with carcass traits and to elucidate the underlying regulatory mechanisms. Based on association analyses with the elastic net (EN) model, we identified 12 candidate genes (AMY1A, AP3B2, CEBPG, EEF2, EIF4EBP1, FGFR1, FOXD3, GOLM1, LOC107052698, PABPC1, SERPINB6 and TBC1D16) for 4 carcass-related traits, namely live weight, dressed weight, eviscerated weight, and breast muscle weight. SERPINB6 was identified as the only overlapping gene by 3 analyses, EN model analysis, weighted gene co-expression network analysis and differential expression analysis. Cell-level experiments confirmed that SERPINB6 promotes the proliferation of chicken DF1 cells and primary myoblasts. Further expression genome-wide association study and association analysis indicated that rs317934171 is the critical site that enhances SERPINB6 expression. Furthermore, a dual-luciferase reporter assay proved that gga-miR-1615 targets the 3′UTR of SERPINB6.ConclusionsCollectively, our findings reveal that SERPINB6 serves as a novel gene for chicken carcass traits by promoting fibroblast and myoblast proliferation. Additionally, the downstream variant rs317934171 regulates SERPINB6 expression. These results identify a new target gene and molecular marker for the molecular mechanisms of chicken carcass traits.

Thymic gene expression analysis reveals a potential link between HIF-1A and Th17/Treg imbalance in thymoma associated myasthenia gravis

Myasthenia gravis (MG) is an immune-mediated disease frequently associated with thymic changes. Increased T helper 17 (Th17) cell activity and dysfunctional regulatory T (Treg) cells have been demonstrated in subgroups of MG. On the other hand, hypoxia-inducible factor 1 (HIF-1) has been shown to regulate the Th17/Treg balance by inducing Th17 differentiation while attenuating Treg development. To identify the underlying mechanisms of different thymic pathologies in MG development, we evaluated thymic samples from thymoma-associated myasthenia gravis (TAMG), MG with hyperplasia (TFH-MG) and thymoma without MG (TOMA) patients. Differential gene expression analysis revealed that TAMG and TFH-MG cells are associated with different functional pathways. A higher RORC/FOXP3 ratio provided evidence for Th17/Treg imbalance in TAMG potentially related to increased HIF1A. The hypoxic microenvironment in thymoma may be a driver of TAMG by increasing HIF1A. These findings may lead to new therapeutic approaches targeting HIF1A in the development of TAMG.

Diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease

The purpose of this study was to explore the diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease. In this study, we first collected 161 samples from the GEO database (including 87 in the AD group and 74 in the normal group). Subsequently, through differential expression analysis and the iUUCD 2.0 database, we obtained 3450 Differentially Expressed Genes (DEGs) and 806 Ubiquitin-related genes (UbRGs). After taking the intersection, we obtained 128 UbR-DEGs. Secondly, by conducting GO and KEGG enrichment analysis on these 128 UbR-DEGs, we identified the main molecular functions and biological pathways related to AD. Furthermore, through the utilization of GSEA analysis, we have gained insight into the enrichment of functions and pathways within both the AD and normal groups. Further, using lasso regression analysis and cross-validation techniques, we identified 22 characteristic genes associated with AD. Subsequently, we constructed a logistic regression model and optimized it, resulting in the identification of 6 RUbR-DEGs: KLHL21, WDR82, DTX3L, UBTD2, CISH, and ATXN3L. In addition, the ROC result showed that the diagnostic model we built has excellent accuracy and reliability in identifying AD patients. Finally, we constructed a lncRNA-miRNA-mRNA (competing endogenous RNA, ceRNA) regulatory network for AD based on six RUbR-DEGs, further elucidating the interaction between UbRGs and lncRNA, miRNA. In conclusion, our findings will contribute to further understanding of the molecular pathogenesis of AD and provide a new perspective for AD risk prediction, early diagnosis and targeted therapy in the population.

Identification of driving genes of familial adenomatous polyposis by differential gene expression analysis and weighted gene co-expression network analysis.

Despite the advancement of new screening strategies and the advances in pharmacological therapies, the cancerization rates of familial adenomatous polyposis (FAP) are stable and even increased in the last years. Therefore, it necessitates additional research to characterize and understand the underlying mechanisms of FAP. To determine the genes that drive the pathogenesis of familial adenomatous polyposis (FAP). We performed on a cohort (GSE111156) gene profile, which consist of four group of gene expressions (the gene expressions of cancer, adenoma and normal tissue of duodenal cancer from patients with FAP were defined as Case N, Case A and Case C respectively, while that of adenoma tissue from patients with FAP who did not have duodenal cancer was Ctrl A). Tracking Tumor Immunophenotype (TIP) website was applied to reveal immune infiltration profile and signature genes of FAP. We merged the genes of key module (pink and midnight module) with signature genes to obtained the biomarkers related with FAP pathogenesis. The expression of these five biomarkers in FAP intratumoral region (IT) and tumor rim (TR) was detected with Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). In total, 220, 23 and 63 DEGs were determined in Cases C, A and N, in comparison to Ctrl A. In total, 196 and 10 DEGs were determined in Cases C and A, separately, as compared to Case N. A total of four biomarkers including CCL5, CD3G, CD2 and TLR3 were finally identified associated with pink module, while only one biomarker (KLF2) associated with midnight module was identified. All biomarkers were evidently raised in FAP IT tissues utilizing qRT-PCR. We identified five potential biomarkers for pathogenesis of FAP to understand the fundamental mechanisms of FAP progression and revealed some probable targets for the diagnosis or treatment of FAP.

Hepatitis B-related hepatocellular carcinoma: classification and prognostic model based on programmed cell death genes

InstructionHepatitis B virus (HBV) infection is a major risk factor for hepatocellular carcinoma (HCC). Programmed cell death (PCD) is a critical process in suppressing tumor growth, and alterations in PCD-related genes may contribute to the progression of HBV-HCC. This study aims to develop a prognostic model that incorporates genomic and clinical information based on PCD-related genes, providing novel insights into the molecular heterogeneity of HBV-HCC through bioinformatics analysis and experimental validation.MethodsIn this study, we analyzed 139 HBV-HCC samples from The Cancer Genome Atlas (TCGA) and validated them with 30 samples from the Gene Expression Omnibus (GEO) database. Various bioinformatics tools, including differential expression analysis, gene set variation analysis, and machine learning algorithms were used for comprehensive analysis of RNA sequencing data from HBV-HCC patients. Furthermore, among the PCD-related genes, we ultimately chose DLAT for further research on tissue chips and patient cohorts. Besides, immunohistochemistry, qRT-PCR and Western blot analysis were conducted.ResultsThe cluster analysis identified three distinct subgroups of HBV-HCC patients. Among them, Cluster 2 demonstrated significant activation in DNA replication-related pathways and tumor-related processes. Analysis of copy number variations (CNVs) of PCD-related genes also revealed distinct patterns in the three subgroups, which may be associated with differences in pathway activation and survival outcomes. DLAT in tumor tissues of HBV-HCC patients is upregulated.DiscussionBased on the PCD-related genes, we developed a prognostic model that incorporates genomic and clinical information and provided novel insights into the molecular heterogeneity of HBV-HCC. In our study, we emphasized the significance of PCD-related genes, particularly DLAT, which was examined in vitro to explore its potential clinical implications.

Retinal microglia express more MHC class I and promote greater T-cell-driven inflammation than brain microglia

IntroductionMacrophage function is determined by microenvironment and origin. Brain and retinal microglia are both derived from yolk sac progenitors, yet their microenvironments differ. Utilizing single-cell RNA sequencing (scRNA-seq) data from mice, we tested the hypothesis that retinal and brain microglia exhibit distinct transcriptional profiles due to their unique microenvironments.MethodsEyes and brains from 2-4 month wildtype mice were combined (20 eyes; 3 brains) to yield one biologically diverse sample per organ. Each tissue was digested into single cell suspensions, enriched for immune cells, and sorted for scRNA-seq. Analysis was performed in Seurat v3 including clustering, integration, and differential expression. Multi-parameter flow cytometry was used for validation of scRNA-seq results. Lymphocytic choriomeningitis virus (LCMV) Clone 13, which produces a systemic, chronic, and neurotropic infection, was used to validate scRNA-seq and flow cytometry results in vivo.ResultsCluster analysis of integrated gene expression data from eye and brain identified 6 Tmem119+P2ry12+ microglial clusters. Differential expression analysis revealed that eye microglia were enriched for more pro-inflammatory processes including antigen processing via MHC class I (14.0-fold, H2-D1 and H2-K1) and positive regulation of T-cell immunity (8.4-fold) compared to brain microglia. Multi-parameter flow cytometry confirmed that retinal microglia expressed 3.2-fold greater H2-Db and 263.3-fold more H2-Kb than brain microglia. On Day 13 and 29 after LCMV infection, CD8+ T-cell density was greater in the retina than the brain.DiscussionOur data demonstrate that the microenvironment of retina and brain differs, resulting in microglia-specific gene expression changes. Specifically, retinal microglia express greater MHC class I by scRNA-seq and multi-parameter flow cytometry, resulting in a possibly enhanced capability to stimulate CD8+ T-cell inflammation during LCMV infection. These results may explain tissue-specific differences between retina and brain during systemic viral infections and CD8+ T-cell driven autoimmune disease.

Identification of Molecular Subtypes Associated with PCD in Esophageal Squamous Cell Carcinoma and Analysis of Immune Microenvironment.

Esophageal squamous cell carcinoma (ESCC) is a highly fatal malignancy with increasing incidence, and programmed cell death (PCD) plays an important role in homeostasis. This study aimed to explore the ESCC of heterogeneity based on the PCD signatures for the diagnosis and treatment of patients. The clinical information and RNA-seq data of patients with ESCC and the PCD-related genes set were used to identify PCD signatures.The "limma" package was used to identify the differentially expressed genes (DEGs). "Clusterprofiler" package was used for function enrichment analysis, and the "ConsensusClusterPlus" package was performed for consensus clustering. Finally, the "GSVA" package and the Cibersort algorithm were used for the immune infiltration analysis. We performed differential expression analysis between ESCC and normal samples and identified 1659 DEGs, of which 124 DEGs were PCD genes. Then, the patients were divided into cluster1 and cluster2 based on the expression of 124 PCD genes. There was a significant difference in immune infiltration between the two clusters. The patients in cluster 1 had a higher immune score and more CD56dim natural killer cells, monocytes, activated CD4 T cells, eosinophil, and activated B cells infiltration, while cluster2 had a higher stromal score, more immune regulation, and immune checkpoint genes expression. We identified two clusters based on PCD gene expression and characterized their tumor microenvironment and immune checkpoint difference. Our findings may provide some new insight into the treatment of ESCC.

Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference

Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew’s correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows.

Computational analysis of circadian-rhythm dysregulated genes exhibiting undifferential expression in Oryza sativa

ABSTRACT Differential expression(DE) analysis identifies genes with differential expressions in a phenotype, but it cannot detect coordinated regulations of a gene from its transcription factors(TFs). This study selected rice diurnal rhythmic samples to train a transcriptional regulatory relationship model, which was used to predict the expression levels of target genes based on those of TFs in the other samples. The difference between the predicted and real expression levels of a target gene in a sample was defined as this gene’s model-based quantitative transcription regulation alternation (mqTrans) value. This study paid special attention to those genes with significant changes in their mqTrans values but without differential expression patterns, which were called dark biomarkers of rice circadian rhythm. The investigation revealed three dark biomarkers that were otherwise ignored by the conventional biomarker studies due to the undifferential expressions. One detected dark biomarker Os11g0135400 was crucial for rice circadian changes, but they were not on the list of published genes that were differentially expressed in circadian rhythm changes. This study captured important circadian rhythmic biomarkers ignored by traditional biological and computational methods. The proposed mqTrans analysis and the detected dark biomarkers contributed complementary information to the understanding of the rice circadian rhythmic mechanism.

Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis

BackgroundIdiopathic pulmonary fibrosis (IPF) is a fatal disease of unknown etiology with a poor prognosis, characterized by a lack of effective diagnostic and therapeutic interventions. The role of immunity in the pathogenesis of IPF is significant, yet remains inadequately understood. This study aimed to identify potential key genes in IPF and their relationship with immune cells by integrated bioinformatics analysis and verify by in vivo and in vitro experiments.MethodsGene microarray data were obtained from the Gene Expression Omnibus (GEO) for differential expression analysis. The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. Then their diagnostic significance and potential co-regulators were elucidated. We further analyzed the correlation between key genes and immune infiltrating cells via single-sample gene set enrichment analysis (ssGSEA). Subsequently, a single-cell RNA sequencing data (scRNA-seq) was used to explore which cell types expressed key genes in IPF samples. Finally, a series of in vivo and in vitro experiments were conducted to validate the expression of candidate genes by western blot (WB), quantitative real-time PCR (qRT-PCR), and immunohistochemistry (IHC) analysis.ResultsA total of 647 DEGs of IPF were identified based on two datasets, including 225 downregulated genes and 422 upregulated genes. They are closely related to biological functions such as cell migration, structural organization, immune cell chemotaxis, and extracellular matrix. CFH and FHL2 were identified as key genes with diagnostic accuracy for IPF by three machine learning algorithms. Analysis using ssGSEA revealed a significant association of both CFH and FHL2 with diverse immune cells, such as B cells and NK cells. Further scRNA-seq analysis indicated CFH and FHL2 were specifically upregulated in human IPF tissues, which was confirmed by in vitro and in vivo experiments.ConclusionIn this study, CFH and FHL2 have been identified as novel potential biomarkers for IPF, with potential diagnostic utility in future clinical applications. Subsequent investigations into the functions of these genes in IPF and their interactions with immune cells may enhance comprehension of the disease’s pathogenesis and facilitate the identification of therapeutic targets.

Transcriptional Profiling of Early Defense Response to White Pine Blister Rust Infection in Pinus albicaulis (Whitebark Pine)

Pathogen perception generates the activation of signal transduction cascades to host defense. White pine blister rust (WPBR) is caused by Cronartium ribicola J.C. Fisch and affects a number of species of Pinus. One of the most severely affected species is Pinus albicaulis Engelm (whitebark pine). WPBR resistance in the species is a polygenic and complex trait that requires an optimized immune response. We identified early responses in 2-year-old seedlings after four days of fungal inoculation and compared the underlying transcriptomic response with that of healthy non-inoculated individuals. A de novo transcriptome assembly was constructed with 56,796 high quality-annotations derived from the needles of susceptible and resistant individuals in a resistant half-sib family. Differential expression analysis identified 599 differentially expressed transcripts, from which 375 were upregulated and 224 were downregulated in the inoculated seedlings. These included components of the initial phase of active responses to abiotic factors and stress regulators, such as those involved in the first steps of flavonoid biosynthesis. Four days after the inoculation, infected individuals showed an overexpression of chitinases, reactive oxygen species (ROS) regulation signaling, and flavonoid intermediates. Our research sheds light on the first stage of infection and emergence of disease symptoms among whitebark pine seedlings. RNA sequencing (RNA-seq) data encoding hypersensitive response, cell wall modification, oxidative regulation signaling, programmed cell death, and plant innate immunity were differentially expressed during the defense response against C. ribicola.

Study on the Characteristics of Coarse Feeding Tolerance of Ding’an Pigs: Phenotypic and Candidate Genes Identification

Ding’an (DA) pig, a prominent local breed in Hainan Province, exhibits notable advantages in coarse feeding tolerance and high-quality meat. To explore the potential genetic mechanism of coarse feeding tolerance in DA pigs, 60-day-old full sibling pairs of DA and DLY (Duroc-Landrace-Yorkshire) pigs were subjected to fed normal (5%) and high (10%) crude fiber diets for 56 days, respectively. The findings showed that increasing the crude fiber level had no impact on the apparent digestibility of crude fiber, intramuscular fat, and marbling scores in DA pigs, whereas these factors were significantly reduced in DLY pigs (p < 0.05). Through differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) of the colonic mucosal transcriptome data, 65 and 482 candidate genes with coarse feeding tolerance in DA pigs were identified, respectively. Joint analysis screened four key candidate genes, including LDHB, MLC1, LSG1, and ESM1, potentially serving as key regulated genes for coarse feeding tolerance. Functional analysis revealed that the most significant pathway enriched in differential genes associated with coarse feeding tolerance in Ding’an pigs was the signaling receptor binding. The results hold substantial significance for advancing our understanding of the genetic mechanisms governing coarse feeding tolerance in Ding’an pigs.

Transcriptomics reveals age-related changes in ion transport–related factors in yak lungs

Yaks inhabit high-altitude, low-oxygen regions, where ion transport functions play a crucial role in maintaining intracellular and extracellular ionic balance and regulating pulmonary vascular tension. These functions affect pulmonary ventilation and blood flow rate, aiding tissue development and enhancing oxygen transfer efficiency, thus facilitating better adaptation to hypoxic environments. To investigate the regulatory mechanisms of ion transport-related factors on the growth and development of yak lungs, we employed RNA sequencing (RNA-seq)for sequencing the transcriptome in the lung tissues of neonatal (1-day-old), juvenile (1-year-old), and adult (4-year-old) yaks. We also performed differential gene expression and functional analyses. The results yielded 26 genes associated with ion transport, mainly enriched in the salivary and pancreatic secretion pathways. Finally, we used several methods including quantitative polymerase chain reaction (qRT-PCR), and Western blotting (WB), immunohistochemical (IHC) and immunofluorescence (IF) staining to determine the distribution of the expression of the ion transport genes FOXI1, KCNMA1, and SLC12A2 in yak lung tissues. qRT-PCR and WB results indicated that mRNA and protein relative expression levels of FOXI1 and SLC12A2 were significantly higher in neonatal yaks than in juvenile and adult yaks (all p < 0.05), whereas those of KCNMA1 were significantly higher in adult yaks than in neonatal and juvenile yaks (all p < 0.05). IHC and IF results demonstrated that FOXI1, KCNMA1, and SLC12A2 were distributed among the epithelial mucosal layers (including ciliated, goblet, and Clara cells) of the yaks’ bronchi and their branches in the lungs across different age groups of yak. Therefore, our results suggested that FOXI1, KCNMA1, and SLC12A2 may be strongly associated with the development and aging processes in yak lungs. These results provide insights into the molecular mechanisms underlying the yak’s adaptation to high-altitude environments and valuable references for further research.

Single-cell transcriptomic analysis of hematopoietic progenitor cells from patients with systemic lupus erythematosus reveals interferon-inducible reprogramming in early progenitors

IntroductionImmune cells that contribute to the pathogenesis of systemic lupus erythematosus (SLE) derive from adult hematopoietic stem and progenitor cells (HSPCs) within the bone marrow (BM). For this reason, we reasoned that fundamental abnormalities in SLE can be traced to a BM-derived HSPC inflammatory signature.MethodsBM samples from four SLE patients, six healthy controls, and two umbilical cord blood (CB) samples were used. CD34+ cells were isolated from BM and CB samples, and single-cell RNA-sequencing was performed.ResultsA total of 426 cells and 24,473 genes were used in the analysis. Clustering analysis resulted in seven distinct clusters of cell types. Mutually exclusive markers, which were characteristic of each cell type, were identified. We identified three HSPC subpopulations, one of which consisted of proliferating cells (MKI67 expressing cells), one T-like, one B-like, and two myeloid-like progenitor subpopulations. Differential expression analysis revealed i) cell cycle-associated signatures, in healthy BM of HSPC clusters 3 and 4 when compared with CB, and ii) interferon (IFN) signatures in SLE BM of HSPC clusters 3 and 4 and myeloid-like progenitor cluster 5 when compared with healthy controls. The IFN signature in SLE appeared to be deregulated following TF regulatory network analysis and differential alternative splicing analysis between SLE and healthy controls in HSPC subpopulations.DiscussionThis study revealed both quantitative—as evidenced by decreased numbers of non-proliferating early progenitors—and qualitative differences—characterized by an IFN signature in SLE, which is known to drive loss of function and depletion of HSPCs. Chronic IFN exposure affects early hematopoietic progenitors in SLE, which may account for the immune aberrancies and the cytopenias in SLE.

Deciphering novel TCF4-driven mechanisms underlying a common triplet repeat expansion-mediated disease.

Fuchs endothelial corneal dystrophy (FECD) is an age-related cause of vision loss, and the most common repeat expansion-mediated disease in humans characterised to date. Up to 80% of European FECD cases have been attributed to expansion of a non-coding CTG repeat element (termed CTG18.1) located within the ubiquitously expressed transcription factor encoding gene, TCF4. The non-coding nature of the repeat and the transcriptomic complexity of TCF4 have made it extremely challenging to experimentally decipher the molecular mechanisms underlying this disease. Here we comprehensively describe CTG18.1 expansion-driven molecular components of disease within primary patient-derived corneal endothelial cells (CECs), generated from a large cohort of individuals with CTG18.1-expanded (Exp+) and CTG 18.1-independent (Exp-) FECD. We employ long-read, short-read, and spatial transcriptomic techniques to interrogate expansion-specific transcriptomic biomarkers. Interrogation of long-read sequencing and alternative splicing analysis of short-read transcriptomic data together reveals the global extent of altered splicing occurring within Exp+ FECD, and unique transcripts associated with CTG18.1-expansions. Similarly, differential gene expression analysis highlights the total transcriptomic consequences of Exp+ FECD within CECs. Furthermore, differential exon usage, pathway enrichment and spatial transcriptomics reveal TCF4 isoform ratio skewing solely in Exp+ FECD with potential downstream functional consequences. Lastly, exome data from 134 Exp- FECD cases identified rare (minor allele frequency <0.005) and potentially deleterious (CADD>15) TCF4 variants in 7/134 FECD Exp- cases, suggesting that TCF4 variants independent of CTG18.1 may increase FECD risk. In summary, our study supports the hypothesis that at least two distinct pathogenic mechanisms, RNA toxicity and TCF4 isoform-specific dysregulation, both underpin the pathophysiology of FECD. We anticipate these data will inform and guide the development of translational interventions for this common triplet-repeat mediated disease.

Matrisome Transcriptome Dynamics during Tissue Aging

The extracellular matrix (ECM) is a complex three-dimensional network of macromolecules that provides structural support for the cells and plays a significant role in tissue homeostasis and repair. Growing evidence indicates that dysregulation of ECM remodeling contributes to various pathological conditions in the body, including age-associated diseases. In this work, gene expression data of normal human tissues obtained from the Genotype-Tissue Expression project, as well as data from MatrisomeDB 2.0, the ECM-protein knowledge database, are used to estimate the age-dependent matrisome transcriptome dynamics in the blood, heart, brain, liver, kidneys, lungs, and muscle. Differential gene expression (DE) analysis revealed dozens of matrisome genes encoding both structural elements of the ECM and ECM-associated proteins, which had a tissue-specific expression profile with age. Among common DE genes that changed expression with age in at least three tissues, COL18A1, MFAP1, IGFBP7, AEBP1, LTBP2, LTBP4, LG14, EFEMP1, PRELP, BGN, FAM20B, CTSC, CTSS, and CLEC2B were observed. The findings of the study also reveal that there are sex-specific alterations during aging in the matrisome gene expression. Taken together, the results obtained in this work may help in understanding the role of the ECM in tissue aging and might prove valuable for the future development of the field of ECM research in general.

Bioinformatics to Identify Biomarkers of Diabetic Nephropathy based on Sphingolipid Metabolism and their Molecular Mechanisms.

Diabetes mellitus (DM) frequently results in Diabetic Nephropathy (DN), which has a significant negative impact on the quality of life of diabetic patients. Sphingolipid metabolism is associated with diabetes, but its relationship with DN is unclear. Therefore, screening biomarkers related to sphingolipid metabolism is crucial for treating DN. To identify Differentially Expressed Genes (DEGs) in the GSE142153 dataset, we conducted a differential expression analysis (DN samples versus control samples). The intersection genes were obtained by overlapping DEGs and Sphingolipid Metabolism-Related Genes (SMRGs). Furthermore, The Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were used to filter biomarkers. We further analyzed the Gene Set Enrichment analysis (GSEA) and the immunoinfiltrational analysis based on biomarkers. We identified 2,186 DEGs associated with DN. Then, five SMR-DEGs were obtained. Subsequently, biomarkers associated with sphingolipid metabolism (S1PR1 and SELL) were identified by applying machine learning and expression analysis. In addition, GSEA showed that these biomarkers were correlated with cytokine cytokine receptor interaction'. Significant variations in B cells, DCs, Tems, and Th2 cells between the two groups suggested that these cells might have a role in DN. Overall, we obtained two sphingolipid metabolism-related biomarkers (S1PR1 and SELL) associated with DN, which laid a theoretical foundation for treating DN.

Transcriptomic responses to drought stress in the Patagonian southern beech Nothofagus alpina

BackgroundDeciphering the genetic architecture of drought tolerance could allow the candidate genes identification responding to water stress. In the Andean Patagonian forest, the genus Nothofagus represents an ecologically relevant species to be included in different genomic studies. These studies are scarce in South American ecosystems however represent an important source of genomic data in order to interpret future climate-change environment scenarios of these emblematic forests. Here, we achieved the assemblage of the transcriptome of N. alpina while searching for key genes of activated or suppressed metabolic pathways in response to drought stress.ResultsDe novo transcriptome assembly resulted in 104,030 transcripts. Following confirmation of drought conditions, based on reduction of leaf water potential and stomatal conductance, a differential gene expression analysis resulted in 2720 significantly expressed genes (1601 up-regulated and 1119 down-regulated). Enrichment analysis (over-representation analysis and gene set enrichment analysis) resulted in more than one hundred stress-responsive term ontologies (i.e. biological processes) and pathways. Terms such as response to abscisic acid and pathways such as plant hormone signal transduction or starch and sucrose metabolism were over-represented. Protein–protein interaction assessment resulted in networks with significantly expressed top common hub gene clusters (e.g. plant-type cell wall biogenesis among down-regulated or ABA-signalling among up-regulated). These networks evidenced important regulators at gene expression such as transcriptional factors.ConclusionsResponses of N. alpina seedlings to drought stress were evidenced by the activation of several genes linked to GO biological processes and KEGG pathways, which were mainly based on over-expression of specific protein kinases, phosphatases, synthases and transcription factors. This suggests an up-regulation of signalling pathways, triggered through plant hormones such as abscisic acid or auxin, which could counteract the osmotic stress created as a probable immediate response to drought. On the other hand, groups of carbon fixation genes related to the galactose metabolism, photosynthesis, secondary wall biogenesis, and fatty acid biosynthesis degradation were down-regulated under drought. Overall, our results provide new genomic data for understanding how non-model long-lived trees of Patagonian forests would acclimate to environmental changes.