Differentially Expressed Genes Research Articles

Exploring the predictive values of SERP4 and FRZB in dilated cardiomyopathy based on an integrated analysis

Background and objectiveThe aim of this study was to investigate potential hub genes for dilated cardiomyopathy (DCM).MethodsFive DCM-related microarray datasets were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were used for identification. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, disease ontology, gene ontology annotation and protein-protein interaction (PPI) network analysis were then performed, while a random forest was constructed to explore central genes. Artificial neural networks were used to compare with known genes and to develop new diagnostic models. 240 population blood samples were collected and expression of hub genes was verified in these samples using RT-PCR and demonstrated by Nomogram.ResultsAfter differential analysis, 33 genes were statistically significant (adjusted P < 0.05). Functional enrichment of these differential genes resulted in 85 Gene Ontology (GO) functions identified and 6 pathways enriched for the KEGG pathway. PPI networks and molecular complex assays identified 10 hub genes (adjusted P < 0.05). Random forest identified SMOC2 and SFRP4 as the most important, followed by FCER1G and FRZB. NeuraHF models (SMOC2, SFRP4, FCER1G and FRZB) were selected by artificial neural network model and had better diagnostic efficacy for the onset of DCM, compared with the traditional KG-DCM models (MYH7, ACTC1, TTN and LMNA). Finally, SFRP4 and FRZB were expressed higher in DCM verified by RT-PCR and as a factor for DCM identified by Nomogram.ConclusionsWe performed an integrated analysis and identified SFRP4 and FRZB as a new factor for DCM. But the exact mechanism still needs further experimental verification.

Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy

The early diagnosis of diabetic retinopathy (DR) is challenging, highlighting the urgent need to identify new biomarkers. Immune responses play a crucial role in DR, yet there are currently no reports of machine learning (ML) algorithms being utilized for the development of immune-related molecular markers in DR. Based on the datasets GSE102485 and GSE160306, differentially expressed genes (DEGs) were screened using Weighted Gene Co-expression Network Analysis (WGCNA). Five ML algorithms including Bayesian, Learning Vector Quantization (LVQ), Wrapper (Boruta), Random Forest (RF), and Logistic Regression were employed to select immune-related genes associated with DR (DR.Sig). Seven ML algorithms including Naive Bayes (NB), RF, Support Vector Machine (SVM), AdaBoost Classification Trees (AdaBoost), Boosted Logistic Regressions (LogitBoost), K-Nearest Neighbors (KNN), and Cancerclass were utilized to construct a predictive model for DR. The relationship between DR.Sig genes and immune cells was analyzed using single-sample Gene Set Enrichment Analysis (ssGSEA). Additionally, drug sensitivity prediction of DR.Sig genes and molecular docking were performed. Through the utilization of 5 ML algorithms, 6 immune-related biomarkers closely related to the occurrence of DR were identified, including FCGR2B, CSRP1, EDNRA, SDC2, TEK, and CIITA. The DR predictive model constructed based on these 6 DR.Sig genes using the Cancerclass algorithm demonstrated superior predictive performance compared to 4 previously published DR-related biomarkers. In vivo and in vitro experiments also provided strong validation of the expression of the 6 genes in DR. Positive correlations were observed between these genes and 22 types of immune cells. Molecular docking results revealed that CSRP1, EDNRA, and TEK exhibited the highest affinities with the small molecule compounds etoposide, FR-139317, and camptothecin, respectively. The models constructed based on various ML algorithms can effectively predict the occurrence of DR events and hold potential for targeted drug therapies, providing a basis for the early diagnosis and targeted treatment of DR.

Multi-scalar data integration decoding risk genes for chronic kidney disease

BackgroundChronic Kidney Disease (CKD) impacts over 10% of the global population, and recent advancements in high-throughput analytical technologies are uncovering the complex physiology underlying this condition. By integrating Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq/RNA array), and single-cell RNA sequencing (scRNA-seq) data, our study aimed to explore the genes and cell types relevant to CKD traits.MethodsGWAS summary data for end-stage renal failure (ESRD) and decreased eGFR (CKD) with or without diabetes and (micro)proteinuria were obtained from the GWAS Catalog and the UK Biobank (UKB) database. Two gene Expression Omnibus (GEO) transcriptome datasets were used to establish glomerular and tubular gene expression differences between CKD patients and healthy individuals. Two scRNA-seq datasets were utilized to obtain the expression of key genes at the single-cell level. The expression profile, differentially expressed genes (DEGs), gene-gene interaction, and pathway enrichment were analysed for these CKD risk genes.ResultsA total of 779 distinct SNPs were identified from GWAS across different CKD traits, involving 681 genes. While many of these risk genes are specific to the CKD traits of renal failure, decreased eGFR, and (micro)proteinuria, they share common pathways, including extracellular matrix (ECM). ECM modeling was enriched in upregulated glomerular and tubular DEGs from CKD kidneys compared to healthy controls, with the expression of relevant collagen genes, such as COL1A2, prevalent in fibroblasts/myofibroblasts. Additionally, immune responses, including T cell differentiation, were dysregulated in CKD kidneys. The late podocyte signature gene THSD7A was enriched in podocytes but downregulated in CKD. We also highlighted that the regulated risk genes of CKD are mainly expressed in tubular cells and immune cells in the kidney.ConclusionsOur integrated analysis highlight the genes, pathways, and relevant cell types associational with the pathogenesis of kidney traits, as a basis for further mechanistic studies to understand the pathogenesis of CKD.

Integration of mRNA-miRNA Reveals the Possible Role of PyCYCD3 in Increasing Branches Through Bud-Notching in Pear (Pyrus bretschneideri Rehd.).

Bud-notching in pear varieties with weak-branches enhances branch development, hormone distribution, and germination, promoting healthier growth and improving early yield. To examine the regulatory mechanisms of endogenous hormones on lateral bud germination in Pyrus spp. (cv. 'Huangguan') (Pyrus bretschneideri Rehd.), juvenile buds were collected from 2-year-old pear trees. Then, a comprehensive study, including assessments of endogenous hormones, germination and branching rates, RNA-seq analysis, and gene function analysis in these lateral buds was conducted. The results showed that there was no significant difference in germination rate between the control and bud-notching pear trees, but the long branch rate was significantly increased in bud-notching pear trees compared to the control (p < 0.05). After bud-notching, there was a remarkable increase in IAA and BR levels in the pruned section of shoots, specifically by 141% and 93%, respectively. However, the content of ABA in the lateral buds after bud-notching was not significantly different from the control. Based on RNA-seq analysis, a notable proportion of the differentially expressed genes (DEGs) were linked to the plant hormone signal transduction pathway. Notably, the brassinosteroid signaling pathway seemed to have the closest connection with the branching ability of pear with the related genes encoding BRI1 and CYCD3, which showed significant differences between lateral buds. Finally, the heterologous expression of PyCYCD3 has a positive regulatory effect on the increased Arabidopsis growth and branching numbers. Therefore, the PyCYCD3 was identified as an up-regulated gene that is induced via brassinosteroid (BR) and could act as a conduit, transforming bud-notching cues into proliferative signals, thereby governing lateral branching mechanisms in pear trees.

Transcriptome analyses of shell color and egg production traits between the uteruses of blue-green eggshell chickens and Hy-Line brown layers

Blue-green eggs exhibit unique shell color; however, compared to commercial layers, blue-green eggshell chickens have lower egg production and lack uniform shell colors. Aiming to confirm the molecular mechanisms that affect shell color and egg production, this study collected the uteruses of 12 blue-green eggshell chickens (BG group) and six Hy-Line layers (Brown group), which had significantly different shell color indexes (SCI) and egg numbers at 300 days of age (EN300). Transcriptome sequencing and comparative analyses were subsequently performed. BG hens were divided into two groups for comparative analysis (BGblue vs. BGgreen and BGlow vs. BGhigh, respectively), based on the differences in SCI and EN300, respectively. The result of weighted gene co-expression network (WGCNA) analysis showed that the sequenced and mapped 16,785 genes were clustered into 18 modules, among which six modules with a total of 4270 genes were highly correlated with SCI and EN300 traits. Five hundred and eleven differentially expressed genes (DEGs) belonged to the six key modules. Through KEGG mapping, GO enrichment, and Cytoscape network analysis, nine Hub genes were tightly associated with SCI and EN300. The up-regulated genes were CCR2, CCR8, CD40LG, IL18RAP, INHBA, and P2RY13, while the down-regulated genes were ABCA13, ADCY2, and GRM1. Co-analyses with the results of comparisons between the BG subgroups revealed that the expression of solute carrier (SLC) proteins and ABC transporters were highly related to eggshell color, while cytokine-cytokine receptor interactions and neuroactive ligand-receptor interactions were key pathways affecting egg production. The expression of extracellular cytokines and membrane receptors were significantly up-regulated in low-yield chickens. The candidate DEGs and pathways found in the study will assist in clarifying the molecular mechanisms affecting shell color and egg production, and improve the breeding of blue-green eggshell chickens.

Mining and expression analysis of color related genes in Bougainvillea glabra bracts based on transcriptome sequencing.

Bract coloration is one of the key ornamental traits in Bougainvillea, yet research has predominantly focused on phenotypic color traits and pigment composition, with limited understanding of the molecular mechanisms underlying color formation. This gap hinders the improvement and innovation in bract coloration. To elucidate the regulatory mechanisms of bract coloration in Bougainvillea and to enhance the utilization of its germplasm resources, this study employed the Illumina Novaseq 6000 sequencing platform to conduct transcriptomic sequencing on 21 samples of bracts exhibiting seven distinct phenotypes. Comparative analysis against Nr, Pfam, EggNOG, GO, and KEGG databases annotated 90,279 unigenes. The highest annotation rates were achieved with the Nr (40.13%), GO (30.44%), and EggNOG (25.64%) databases. Among the species annotated, Beta vulgaris (20.08%) and Chenopodium quinoa (14.58%) shared the highest homology with Bougainvillea bract transcriptomes. WGCNA analysis identified 12 positively correlated tissue-specific modules, of which 2 are related to bract color formation. By comparing transcriptome data and genes within these specific modules against the KEGG database, a total of 321 unigenes associated with bract color formation in Bougainvillea were discovered. Among these, 220 unigenes are involved in anthocyanin synthesis, 43 unigenes are involved in betalain synthesis, 23 unigenes are annotated as Chlorophyll a-b binding protein genes, and 35 unigenes participate in carotenoid synthesis. Quantitative real-time PCR (qRT-PCR) validation of 16 differentially expressed genes (DEGs) including PAL2, CHS1, ANS, BZ1, 6GT, CDOPA5GT, ANR, CHS2, and DOPA, revealed significant expression differences among magenta, yellow, white, and cherry-colored bracts, suggesting their potential as candidate genes for bract color development. This study not only enriches the transcriptomic data of Bougainvillea but also identifies genes associated with bract coloration, providing a valuable theoretical basis for future gene cloning, genetic engineering, and breeding efforts in Bougainvillea.

Chemical and Transcriptomic Analyses Provide New Insights into Key Genes for Ginsenoside Biosynthesis in the Rhizome of Panax japonicus C. A. Meyer.

Panax japonicus C. A. Meyer is renowned for its significant therapeutic effects and is commonly used worldwide. Its active ingredients, triterpenoid saponins, show variation in content among different tissues. The tissue-specific distribution of saponins is potentially related to the expression of vital genes in the biosynthesis pathway. In this study, the contents of five saponins (ginsenoside Ro, chikusetsusaponin IV, chikusetsusaponin IVa, ginsenoside Rg1, and ginsenoside Rb1) in three different tissues were determined by HPLC. Transcriptome sequencing analysis identified differentially expressed genes (DEGs) involved in triterpenoid saponin biosynthesis, highlighting significant correlations between saponin contents and the expression levels of 10 cytochrome p450 monooxygenase (CYP) and 3 UDP-glycosyltransferase (UGT) genes. Cloning, sequencing, and prokaryotic expression of UGT genes confirmed the molecular weights of UGT proteins. Gene sequence alignment and phylogenetic analysis provided preliminary insights into UGT gene functions. Meanwhile, the function of one UGT gene was characterized in the yeast. These findings advance our understanding of the triterpenoid saponin biosynthesis in P. japonicus and support future research in traditional Chinese medicine (TCM) and synthetic biology.

Transcriptomic analysis of rat prefrontal cortex following chronic stress induced by social isolation – Relevance to psychiatric and neurodevelopmental illness, and implications for treatment

Social isolation is an established risk factor for psychiatric illness, and became increasingly topical with the spread of SARS-CoV-2. We used RNA sequencing (RNA-Seq) to enable unbiased assessment of transcriptomic changes within the prefrontal cortex (PFC) of isolation-reared rats. To provide insight into the relevance of this manipulation for studying human illness, we compared differentially expressed genes (DEGs) and enriched biological functions against datasets involving post-mortem frontal cortical tissue from patients with psychiatric and neurodevelopmental illnesses. Sixteen male Sprague-Dawley rats were reared in groups of four or individually from weaning on postnatal day (PND) 22–24 until PFC tissue collection for RNA-Seq (PND64-66). We identified a total of 183 DEGs in isolates, of which 128 mirrored those in PFC tissue from patients with stress-related mental illnesses and/or neurodevelopmental conditions featuring social deficits. Seventy-one encode proteins classed as druggable by the gene-drug interaction database. Interestingly there are antagonists or inhibitors for the products of three of these up-regulated DEGs (Hrh3, Snca and Sod1) and agonists or activators for products of six of these down-regulated DEGs (Chrm4, Klf2, Lrrk2, Nr4a1, Nr4a3 and Prkca). Some have already undergone pre-clinical and clinical evaluation, and studies with the remainder may be warranted. Changes to Hrh3, Sod1, Chrm4, Lrrk2, Nr4a1 and Prkca were replicated in an independent cohort of sixteen male Sprague-Dawley rats via quantitative reverse transcription polymerase chain reaction (qRT-PCR). Our findings support the continued use of post-weaning isolation rearing to investigate the neurobiology of stress-related disorders and evaluate therapeutic targets.

SPINK4 modulates inhibition of glycolysis against colorectal cancer progression.

Dysregulation of glycolysis is frequently linked to aggressive tumor activity in colorectal cancer (CRC). Although serine peptidase inhibitor, Kazal type 4 (SPINK4) has been linked to CRC, its exact linkage to glycolytic processes and gene expression remains unclear. Differentially expressed genes (DEGs) were screened from two CRC-related datasets (GSE32323 and GSE141174), followed by expression and prognostic analysis of SPINK4. In vitro techniques such as flow cytometry, western blotting, transwell assay, and quantitative real-time polymerase chain reaction (qRT-PCR) were used to assess SPINK4 expression in CRC cells. Its effects on apoptosis, glycolysis, and the cell cycle were also investigated. Finally, the impact of SPINK4 overexpression on tumor development was assessed using a xenograft model, while histological and immunohistochemical analyses characterized SPINK4 expression patterns in CRC tissues. SPINK4 expression was downregulated in CRC, correlating with poor patient prognosis. In vitro assays confirmed that overexpression of SPINK4 reduced CRC cell proliferation, invasion, and migration, while its knockdown promoted these processes and caused G1 arrest. SPINK4 also regulated apoptosis by altering caspase activation and Bcl-2 expression. Besides, SPINK4 overexpression altered glycolytic activity, reduced 2-Deoxy-D-glucose (2-DG) absorption, and controlled critical glycolytic enzymes, resulting in alterations in metabolic pathways, whereas SPINK4 knockdown reversed this effect. SPINK4 overexpression significantly reduced tumor volume in vivo, indicating its inhibitory role in carcinogenesis. Moreover, high expression of SPINK4, hexokinase 2 (HK2), glucose transporter 1 (GLUT1), lactate dehydrogenase A (LDHA), and pyruvate kinase M2 (PKM2) was observed in CRC tissues. As a key inhibitor of glycolytic metabolism in CRC, SPINK4 promises metabolic intervention in CRC therapy due to its impact on tumor growth and cell proliferation.

The Effects of swnH1 Gene Function of Endophytic Fungus Alternaria oxytropis OW 7.8 on Its Swainsonine Biosynthesis.

The swnH1 gene in the endophytic fungus Alternaria oxytropis OW 7.8 isolated from Oxytropis glabra was identified, and the gene knockout mutant ΔswnH1 was first constructed in this study. Compared with A. oxytropis OW 7.8, the ΔswnH1 mutant exhibited altered colony and mycelium morphology, slower growth rate, and no swainsonine (SW) in mycelia, indicating that the function of the swnH1 gene promoted SW biosynthesis. Five differential expressed genes (DEGs) closely associated with SW synthesis were identified by transcriptomic analysis of A. oxytropis OW 7.8 and ΔswnH1, with sac, swnR, swnK, swnN, and swnH2 down-regulating. Six differential metabolites (DEMs) closely associated with SW synthesis were identified by metabolomic analysis, with P450, PKS-NRPS, saccharopine, lipopolysaccharide kinase, L-PA, α-aminoadipic, and L-stachydrine down-regulated, while L-proline was up-regulated. The SW biosynthetic pathways in A. oxytropis OW 7.8 were predicted and refined. The results lay the foundation for in-depth exploration of the molecular mechanisms and metabolic pathways of SW synthesis in fungi and provide reference for future control of SW in locoweeds, which would benefit the development of animal husbandry and the sustainable use of grassland ecosystems.

Transcriptomic Analysis Reveals the Mechanism of Color Formation in the Peel of an Evergreen Pomegranate Cultivar 'Danruo No.1' During Fruit Development.

Pomegranate (Punica granatum L.) is an ancient fruit crop that has been cultivated worldwide and is known for its attractive appearance and functional metabolites. Fruit color is an important index of fruit quality, but the color formation pattern in the peel of evergreen pomegranate and the relevant molecular mechanism is still unknown. In this study, the contents of pigments including anthocyanins, carotenoids, and chlorophyll in the peel of 'Danruo No. 1' pomegranate fruit during three developmental stages were measured, and RNA-seq was conducted to screen key genes regulating fruit color formation. The results show that pomegranate fruit turned from green to red during development, with a dramatic increase in a* value, indicating redness and anthocyanins concentration, and a decrease of chlorophyll content. Moreover, carotenoids exhibited a decrease-increase accumulation pattern. Through RNA-seq, totals of 30, 18, and 17 structural genes related to anthocyanin biosynthesis, carotenoid biosynthesis and chlorophyll metabolism were identified from differentially expressed genes (DEGs), respectively. Transcription factors (TFs) such as MYB, bHLH, WRKY and AP2/ERF were identified as key candidates regulating pigment metabolism by K-means analysis and weighted gene co-expression network analysis (WGCNA). The results provide an insight into the theory of peel color formation in evergreen pomegranate fruit.

Identification and Analysis of Potential Biomarkers Associated with Neutrophil Extracellular Traps in Cervicitis.

Early diagnosis of cervicitis is important. Previous studies have found that neutrophil extracellular traps (NETs) play pro-inflammatory and anti-inflammatory roles in many diseases, suggesting that they may be involved in the inflammation of the uterine cervix and NETs-related genes may serve as biomarkers of cervicitis. However, what NETs-related genes are associated with cervicitis remains to be determined. Transcriptome analysis was performed using samples of exfoliated cervical cells from 15 patients with cervicitis and 15 patients without cervicitis as the control group. First, the intersection of differentially expressed genes (DEGs) and neutrophil extracellular trap-related genes (NETRGs) were taken to obtain genes, followed by functional enrichment analysis. We obtained hub genes through two machine learning algorithms. We then performed Artificial Neural Network (ANN) and nomogram construction, confusion matrix, receiver operating characteristic (ROC), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Moreover, we constructed ceRNA network, mRNA-transcription factor (TF) network, and hub genes-drug network. We obtained 19 intersecting genes by intersecting 1398 DEGs and 136 NETRGs. 5 hub genes were obtained through 2 machine learning algorithms, namely PKM, ATG7, CTSG, RIPK3, and ENO1. Confusion matrix and ROC curve evaluation ANN model showed high accuracy and stability. A nomogram containing the 5 hub genes was established to assess the disease rate in patients. The correlation analysis revealed that the expression of ATG7 was synergistic with RIPK3. The GSEA showed that most of the hub genes were related to ECM receptor interactions. It was predicted that the ceRNA network contained 2 hub genes, 3 targeted miRNAs, and 27 targeted lnRNAs, and that 5 mRNAs were regulated by 28 TFs. In addition, 36 small molecule drugs that target hub genes may improve the treatment of cervicitis. In this study, five hub genes (PKM, ATG7, CTSG, RIPK3, ENO1) provided new directions for the diagnosis and treatment of patients with cervicitis.

Single Nuclei Transcriptomics Reveals Obesity-Induced Endothelial and Neurovascular Dysfunction: Implications for Cognitive Decline.

Obesity confers risk for cardiovascular disease and vascular dementia. However, genomic alterations modulated by obesity in endothelial cells in the brain and their relationship to other neurovascular unit (NVU) cells are unknown. We performed single nuclei RNA sequencing (snRNAseq) of the NVU (endothelial cells, astrocytes, microglia, and neurons) from the hippocampus of obese (ob/ob) and wild-type (WT) male mice to characterize obesity-induced transcriptomic changes in a key brain memory center and assessed blood-brain barrier permeability (BBB) by gadolinium-enhanced magnetic resonance imaging (MRI). Ob/ob mice displayed obesity, hyperinsulinemia, and impaired glucose tolerance. snRNAseq profiled 14 distinct cell types and 32 clusters within the hippocampus of ob/ob and WT mice and uncovered differentially expressed genes (DEGs) in all NVU cell types, namely, 4462 in neurons, 1386 in astrocytes, 125 in endothelial cells, and 154 in microglia. Gene ontology analysis identified important biological processes such as angiogenesis in endothelial cells and synaptic trafficking in neurons. Cellular pathway analysis included focal adhesion and insulin signaling, which were common to all NVU cell types. Correlation analysis revealed significant positive correlations between endothelial cells and other NVU cell types. Differentially expressed long non-coding RNAs (lncRNAs) were observed in cells of the NVU-affecting pathways such as TNF and mTOR. BBB permeability showed a trend toward increased signal intensity in ob/ob mice. Taken together, our study provides in-depth insight into the molecular mechanisms underlying cognitive dysfunction in obesity and may have implications for therapeutic gene targeting.

Physiological, biochemical, and transcriptomic alterations in Castor (Ricinus communis L.) under polyethylene glycol-induced oxidative stress

BackgroundCastor is an important industrial raw material. Drought-induced oxidative stress leads to slow growth and decreased yields in castor. However, the mechanisms of drought-induced oxidative stress in castor remain unclear. Therefore, in this study, physiological, biochemical, and RNA-seq analyses were conducted on the roots of castor plants under PEG-6000 stress for 3 d and 7 d followed by 4 d of hydration.ResultsThe photosynthetic rate of castor leaves was inhibited under PEG-6000 stress for 3 and 7 d. Biochemical analysis of castor roots stressed for 3 d and 7 d, and rehydrated for 4 d revealed that the activities of APX and CAT were highest after only 3 d of stress, whereas the activities of POD, GR, and SOD peaked after 7 d of stress. RNA-seq analysis revealed 2926, 1507, and 111 differentially expressed genes (DEGs) in the roots of castor plants under PEG-6000 stress for 3 d and 7 d and after 4 d of rehydration, respectively. GO analysis of the DEGs indicated significant enrichment in antioxidant activity. Furthermore, KEGG enrichment analysis of the DEGs revealed significantly enriched metabolic pathways, including glutathione metabolism, fatty acid metabolism, and plant hormone signal transduction. WGCNA identified the core genes PP2C39 and GA2ox4 in the navajowhite1 module, which was upregulated under PEG-6000 stress. On the basis of these results, we propose a model for the response to drought-induced oxidative stress in castor.ConclusionsThis study provides valuable antioxidant gene resources, deepening our understanding of antioxidant regulation and paving the way for further molecular breeding of castor plants.

Machine Learning Analysis of RNA-Seq Data Identifies Key Gene Signatures and Pathways in Mpox Virus-Induced Gastrointestinal Complications Using Colon Organoid Models.

Mpox, caused by the Mpox virus (MPXV), emerged globally in 2022 with the Clade IIb strain, presenting a critical public health challenge. While MPXV is primarily characterized by fever and rash, gastrointestinal (GI) complications, such as diarrhea and proctitis, have also been observed. This study is a reanalysis of GSE219036 without own data and focuses on the impact of MPXV infection on the colon, using human-induced pluripotent stem cell-derived colon organoids as a model. We applied a tailored statistical framework for RNA-seq data, Generalized Linear Models with Quasi-Likelihood F-tests and Relaxed Magnitude-Altitude Scoring (GLMQL-RMAS), to identify differentially expressed genes (DEGs) across MPXV clades: MPXV I (Zr-599 Congo Basin), MPXV IIa (Liberia), and MPXV IIb (2022 MPXV). Through a novel methodology called Cross-RMAS, we ranked genes by integrating statistical significance and biological relevance across all clades. Machine learning analysis using the genes identified by Cross-RMAS, demonstrated 100% accuracy in differentiating between the different MPXV strains and mock samples. Furthermore, our findings reveal that MPXV Clade I induces the most extensive alterations in gene expression, with significant upregulation of stress response genes, such as HSPA6 and FOS, and downregulation of genes involved in cytoskeletal organization and vesicular trafficking, such as PSAP and CFL1. In contrast, Clade IIb shows the least impact on gene expression. Through Gene Ontology (GO) analysis, we identified pathways involved in protein folding, immune response, and epithelial integrity that are disrupted in infected cells, suggesting mechanisms by which MPXV may contribute to GI symptoms.

Long-Term Neurotoxic Effects and Alzheimer's Disease Risk of Early EHDPP Exposure in Zebrafish: Insights from Molecular Mechanisms to Adult Pathology.

2-Ethylhexyl diphenyl phosphate (EHDPP), ubiquitously monitored in environmental media, is highly bioaccumulative and may pose long-term risks, even after short-term exposure. In this investigation, larval zebrafish were exposed to 0.05, 0.5, and 5.0 μg/L EHDPP from 4 to 120 h postfertilization (hpf) to examine the long-term neurotoxicity effects of early exposure. Exposure to 5.0 μg/L EHDPP yielded hyperactive locomotor behavior, which was characterized by increased swimming speed, larger turning angles, and heightened sensitivity to light-dark stimulation. The predicted targets of EHDPP (top 100 potential macromolecules) were primarily associated with brain diseases like Alzheimer's disease (AD). Comparisons of differentially expressed genes (DEGs) from AD patients (GSE48350) and RNA-seq data from EHDPP-exposed zebrafish confirmed consistently abnormal regulatory pathways. EHDPP's interaction with M1 and M5 muscarinic acetylcholine receptors likely disrupted calcium homeostasis, leading to mitochondrial dysfunction and neurotransmitter imbalance as well as abnormal locomotor behavior. Especially, 5.0 μg/L EHDPP exposure during early development (4-120 hpf) triggered early- and midstage AD-like symptoms in adulthood (180 dpf), characterized by cognitive confusion, aggression, blood-brain barrier disruption, and mitochondrial damage in brains. These findings provide deep insights into the long-term neurotoxicity effects and Alzheimer's disease risks of early EHDPP exposure at extremely low dosages.

Set2 and H3K36 regulate the Drosophila male X chromosome in a context-specific manner, independent from MSL complex spreading.

Dosage compensation in Drosophila involves upregulating male X-genes two-fold. This process is carried out by the MSL (male-specific lethal) complex, which binds high-affinity sites and spreads to surrounding genes. Current models of MSL spreading focus on interactions betwen MSL3 (male-specific lethal 3) and Set2-dependent histone marks like trimethylated H3 lysine-36 (H3K36me3). However, Set2 could affect DC via another target, or there could be redundancy between canonical H3.2 and variant H3.3 histones. Furthermore, it is important to parse male-specific effects from those that are X-specific. To discriminate among these possibilities, we employed genomic approaches in H3K36 'residue' and Set2 'writer' mutants. The results confirm a role for Set2 in X-gene regulation, but show that expression trends in males are often mirrored in females. Instead of global, male-specific reduction of X-genes in Set2 or H3K36 mutants, we observe heterogeneous effects. Interestingly, we identified groups of differentially expressed genes (DEGs) whose changes were in opposite directions following loss of H3K36 or Set2, suggesting that H3K36me states have reciprocal functions. In contrast to H4K16R controls, differential expression analysis of combined H3.2K36R/H3.3K36R mutants showed neither consistent reduction in X-gene expression, nor correlation with MSL3 binding. Motif analysis of the DEGs implicated BEAF-32 and other insulator proteins in Set2/H3K36-dependent regulation. Overall, the data are inconsistent with the prevailing model wherein H3K36me3 is essential for spreading the MSL complex to genes along the male X. Rather, we propose that Set2 and H3K36 support DC indirectly, via processes that are utilized by MSL but common to both sexes.

Transcription factor ASCL1 targets SLC6A13 to inhibit the progression of hepatocellular carcinoma via the glycine-inflammasome signaling.

Hepatocellular carcinoma (HCC), the most common primary liver cancer, typically arises from chronic liver conditions such as hepatitis, cirrhosis, or other chronic liver diseases, and is characterized by its aggressive nature and poor prognosis. The purpose of this research was to clarify the function of achaete-scute family bHLH transcription factor 1 (ASCL1) and solute carrier family 6 member 13 (SLC6A13) in influencing tumor cell behavior, inflammatory responses, and the regulation of inflammasomes. We analyzed the differentially expressed genes (DEGs) in the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) database, as well as in the GSE14520 and GSE67764 datasets, to identify the expression changes of SLC6A13 in liver cancer. The prognostic significance of SLC6A13 in LIHC was assessed through Kaplan-Meier survival curve analysis. Transcriptional regulation of SLC6A13 by ASCL1 was explored using the Joint Annotation of the Human Genome and other species by the Systematic Pipeline for the Annotation of Regulatory Regions (JASPAR) database and dual-luciferase assays. In vitro experiments investigated the impact of ASCL1 and SLC6A13 overexpression on HCC cell growth. Additionally, the effects of ethanol treatment and glycine modulation on the inflammatory response in HCC cell lines were evaluated. HCC samples showed reduced levels of SLC6A13, which correlates with a better prognosis for liver metastases. Elevated SLC6A13 expression correlated with improved overall survival (OS), progression-free survival (PFS), recurrence-free survival (RFS), and disease-specific survival (DSS). ASCL1 upregulated SLC6A13 and inhibited proliferation, migration, and invasion of HCC cells. Ethanol induced the production of inflammatory cytokines, which was enhanced by overexpression of SLC6A13 but counteracted by glycine. This study highlighted elevated expression of SLC6A13 in LIHC which has been correlated with improved OS, PFS, RFS, and DSS. Overexpression of SLC6A13 and ASCL1 in HCC cells enhanced inflammasome activation, which was exacerbated by ethanol and attenuated by glycine.

Multi-omics PGT: re-evaluation of euploid blastocysts for implantation potential based on RNA sequencing.

In addition to chromosomal euploidy, can the transcriptome of blastocysts be used as a novel predictor of embryo implantation potential? This retrospective analysis showed that based on differentially expressed genes (DEGs) between euploid blastocysts which resulted and did not result in a clinical pregnancy, machine learning models could help improve implantation rates by blastocyst optimization. Embryo implantation is a multifaceted process, with implantation loss and pregnancy failure related not only to blastocyst euploidy but also to the intricate dialog between blastocyst and endometrium. Although in vitro studies have revealed the characteristics of trophectoderm (TE) differentiation in implanted blastocysts and the function of TE placentation at the implantation site, the precise molecular mechanisms of embryo implantation and their clinical application remain to be fully elucidated. This study involved 102 patients who underwent 111 cycles for preimplantation genetic testing for aneuploidies (PGT-A) between March 2022 and July 2023. The study included 412 blastocysts biopsied at Day 5 [D5] or Day 6 [D6] for patients who underwent PGT-A. The biopsy lysates were split and subjected to DNA and RNA sequencing (DNA- and RNA-seq). One part was used for PGT-A to detect DNA copy number variations, whereas the other part was assessed simultaneously by RNA-seq to determine the transcriptome characteristics. To validate the reliability and accuracy of RNA-seq obtained from this strategy, we initially analyzed the transcriptome of blastocysts with chromosomal aneuploidies. Subsequently, we compared the transcriptomic features of blastocysts with different rates of formation (D5 vs D6) and investigated the network of interactions between key blastulation genes and the receptive endometrium. Then to evaluate the implantation potential of euploid blastocysts, we identified DEGs between euploid blastocysts that resulted in clinical pregnancy (defined as the presence of a gestational sac detected by ultrasound after 5 weeks) and those that did not. These DEGs were then employed to construct a predictive model for optimizing blastocyst selection. The successful detection rate of PGT-A was remarkably high at 99.8%. The RNA data may infer aneuploidy for both trisomy and monosomy. Between the euploid blastocysts that formed on D5 and D6, 187 DEGs were predominantly involved in cell differentiation for embryonic placenta development, the PPAR signaling pathway, and the Notch signaling pathway. These D5/D6 DEGs also exhibited a functional dialog with the receptive phase endometrium-specific genes through protein-protein interaction networks, indicating that the embryo undergoes further differentiation for post-implantation development. Furthermore, a modeling strategy using 280 DEGs between blastocysts leading to successful clinical pregnancies or failing to produce clinical pregnancies was implemented to refine the euploid embryo optimization, achieving areas under the curves of 0.88, 0.71, and 0.84 for the random forest (RF), support vector machine, and linear discriminant analysis models, respectively. Finally, a retrospective analysis of 83 transferred euploid blastocysts using the RF model identified three types of euploid embryos with a decreasing trend in implantation potential. Notably, the implantation rate of the good group was significantly higher than that of the moderate group (88.6% vs 50.0% P = 0.001) and that of the moderate group was higher than that of the poor group (50.0% vs 20.8%, P = 0.035). The sample size was insufficient; thus, a prospective study is needed to verify the clinical effectiveness of the above model. Because we did not analyze blastocysts that led only to biochemical pregnancies but failed clinical pregnancies separately, our classification system still must be modified to screen these embryos. Transcriptomic analysis of blastocysts offers a novel approach for predicting embryo implantation potential, which can be utilized to optimize clinical embryo selection. The ranking system may be effective in reducing the times and costs involved in achieving a clinical pregnancy. This study was funded by the "Pioneer" and "Leading Goose" R&D Program of Zhejiang (No. 2023C03034), the National Natural Science Foundation of China (82101709), and the National Key Research and Development Program for Young Scientists of China (No. 2022YFC2702300). The authors state no competing interests. N/A.

A Reproducibility Focused Meta-Analysis Method for Single-Cell Transcriptomic Case-Control Studies Uncovers Robust Differentially Expressed Genes.

Here we systematically studied the reproducibility of DEGs in previously published Alzheimer's Disease (AD), Parkinson's Disease (PD), and COVID-19 scRNA-seq studies. We found that while transcriptional scores created from differentially expressed genes (DEGs) in individual PD and COVID-19 datasets had moderate predictive power for the case control status of other datasets (mean AUC=0.77 and 0.75, respectively), genes from individual AD datasets had poor predictive power (mean AUC=0.68). We developed a non-parametric meta-analysis method, SumRank, based on reproducibility of relative differential expression ranks across datasets. The meta-analysis genes had improved predictive power (AUCs of 0.88, 0.91, and 0.78, respectively). By multiple other metrics, specificity and sensitivity of these genes were substantially higher than those discovered by dataset merging and inverse variance weighted p-value aggregation methods. The DEGs revealed known and novel biological pathways, and we validate the BCAT1 gene as down-regulated in oligodendrocytes in an AD mouse model. Our analyses show that for heterogeneous diseases, DEGs of individual studies often have low reproducibility, but combining information across multiple datasets promotes the rigorous discovery of reproducible DEGs.