Repression of developmental transcription factor networks triggers aging-associated gene expression in human glial progenitor cells.

Human glial progenitor cells (hGPCs) exhibit diminished expansion competence with age, as well as after recurrent demyelination. Using RNA-sequencing to compare the gene expression of fetal and adult hGPCs, we identify age-related changes in transcription consistent with the repression of genes enabling mitotic expansion, concurrent with the onset of aging-associated transcriptional programs. Adult hGPCs develop a repressive transcription factor network centered on MYC, and regulated by ZNF274, MAX, IKZF3, and E2F6. Individual over-expression of these factors in iPSC-derived hGPCs lead to a loss of proliferative gene expression and an induction of mitotic senescence, replicating the transcriptional changes incurred during glial aging. miRNA profiling identifies the appearance of an adult-selective miRNA signature, imposing further constraints on the expansion competence of aged GPCs. hGPC aging is thus associated with acquisition of a MYC-repressive environment, suggesting that suppression of these repressors of glial expansion may permit the rejuvenation of aged hGPCs.

Identification of master regulator genes controlling pathogenic CD4+ T cell fate in inflammatory bowel disease through transcriptional network analysis.

Inflammatory bowel diseases (IBD) are a group of chronic inflammatory conditions of the gastrointestinal tract associated with multiple pathogenic factors, including dysregulation of the immune response. Effector CD4+ T cells and regulatory CD4+ T cells (Treg) are central players in maintaining the balance between tolerance and inflammation. Interestingly, genetic modifications in these cells have been implicated in regulating the commitment of specific phenotypes and immune functions. However, the transcriptional program controlling the pathogenic behavior of T helper cells in IBD progression is still unknown. In this study, we aimed to find master transcription regulators controlling the pathogenic behavior of effector CD4+ T cells upon gut inflammation. To achieve this goal, we used an animal model of IBD induced by the transfer of naïve CD4+ T cells into recombination-activating gene 1 (Rag1) deficient mice, which are devoid of lymphocytes. As a control, a group of Rag1-/- mice received the transfer of the whole CD4+ T cells population, which includes both effector T cells and Treg. When gut inflammation progressed, we isolated CD4+ T cells from the colonic lamina propria and spleen tissue, and performed bulk RNA-seq. We identified differentially up- and down-regulated genes by comparing samples from both experimental groups. We found 532 differentially expressed genes (DEGs) in the colon and 30 DEGs in the spleen, mostly related to Th1 response, leukocyte migration, and response to cytokines in lamina propria T-cells. We integrated these data into Gene Regulatory Networks to identify Master Regulators, identifying four up-regulated master gene regulators (Lef1, Dnmt1, Mybl2, and Jup) and only one down-regulated master regulator (Foxo3). The altered expression of master regulators observed in the transcriptomic analysis was confirmed by qRT-PCR analysis and found an up-regulation of Lef1 and Mybl2, but without differences on Dnmt1, Jup, and Foxo3. These two master regulators have been involved in T cells function and cell cycle progression, respectively. We identified two master regulator genes associated with the pathogenic behavior of effector CD4+ T cells in an animal model of IBD. These findings provide two new potential molecular targets for treating IBD.

Construction of ceRNA regulatory networks for active pulmonary tuberculosis.

Delayed diagnosis in patients with pulmonary tuberculosis (PTB) often leads to serious public health problems. High throughput sequencing was used to determine the expression levels of lncRNAs, mRNAs, and miRNAs in the lesions and adjacent health lung tissues of patients with PTB. Their differential expression profiles between the two groups were compared, and 146 DElncRs, 447 DEmRs, and 29 DEmiRs were obtained between lesions and adjacent health tissues in patients with PTB. Enrichment analysis for mRNAs showed that they were mainly involved in Th1, Th2, and Th17 cell differentiation. The lncRNAs, mRNAs with target relationship with miRNAs were predicted respectively, and correlation analysis was performed. The ceRNA regulatory network was obtained by comparing with the differentially expressed transcripts (DElncRs, DEmRs, DEmiRs), then 2 lncRNAs mediated ceRNA networks were established. The expression of genes within the network was verified by quantitative real-time PCR (qRT-PCR). Flow cytometric analysis revealed that the proportion of Th1 cells and Th17 cells was lower in PTB than in controls, while the proportion of Th2 cells increased. Our results provide rich transcriptome data for a deeper investigation of PTB. The ceRNA regulatory network we obtained may be instructive for the diagnosis and treatment of PTB.

Differential expression of angiogenesis-related genes 'VEGF' and 'angiopoietin-1' in metastatic and EMAST-positive colorectal cancer patients.

Abnormal angiogenesis leads to tumor progression and metastasis in colorectal cancer (CRC). This study aimed to elucidate the association between angiogenesis-related genes, including VEGF-A, ANGPT-1, and ANGPT-2 with both metastatic and microsatellite alterations at selected tetranucleotide repeats (EMAST) subtypes of CRC. We conducted a thorough assessment of the ANGPT-1, ANGPT-2, and VEGF-A gene expression utilizing publicly available RNA sequencing and microarray datasets. Then, the experimental validation was performed in 122 CRC patients, considering their disease metastasis and EMAST+/- profile by using reverse transcription polymerase chain reaction (RT-PCR). Subsequently, a competing endogenous RNA (ceRNA) network associated with these angiogenesis-related genes was constructed and analyzed. The expression level of VEGF-A and ANGPT-2 genes were significantly higher in tumor tissues as compared with normal adjacent tissues (P-value < 0.001). Nevertheless, ANGPT-1 had a significantly lower expression in tumor samples than in normal colon tissue (P-value < 0.01). We identified a significantly increased VEGF-A (P-value = 0.002) and decreased ANGPT-1 (P-value = 0.04) expression in EMAST+ colorectal tumors. Regarding metastasis, a significantly increased VEGF-A and ANGPT-2 expression (P-value = 0.001) and decreased ANGPT-1 expression (P-value < 0.05) were established in metastatic CRC patients. Remarkably, co-expression analysis also showed a strong correlation between ANGPT-2 and VEGF-A gene expressions. The ceRNA network was constructed by ANGPT-1, ANGPT-2, VEGF-A, and experimentally validated miRNAs (hsa-miR-190a-3p, hsa-miR-374c-5p, hsa-miR-452-5p, and hsa-miR-889-3p), lncRNAs (AFAP1-AS1, KCNQ1OT1 and MALAT1), and TFs (Sp1, E2F1, and STAT3). Network analysis revealed that colorectal cancer is amongst the 82 significant pathways. We demonstrated a significant differential expression of VEGF-A and ANGPT-1 in colorectal cancer patients exhibiting the EMAST+ phenotype. This finding provides novel insights into the molecular pathogenesis of colorectal cancer, specifically in EMAST subtypes. Yet, the generalization of in silico findings to EMAST+ colorectal cancer warrants future experimental investigations. In the end, this study proposes that the EMAST biomarker could serve as an additional perspective on CMS4 biology which is well-defined by activated angiogenesis and worse overall survival.

Unraveling the immunogenetic landscape of autism spectrum disorder: a comprehensive bioinformatics approach.

Background: Autism spectrum disorder (ASD) is a disease characterized by social disorder. Recently, the population affected by ASD has gradually increased around the world. There are great difficulties in diagnosis and treatment at present. Methods: The ASD datasets were obtained from the Gene Expression Omnibus database and the immune-relevant genes were downloaded from a previously published compilation. Subsequently, we used WGCNA to screen the modules related to the ASD and immune. We also choose the best combination and screen out the core genes from Consensus Machine Learning Driven Signatures (CMLS). Subsequently, we evaluated the genetic correlation between immune cells and ASD used GNOVA. And pleiotropic regions identified by PLACO and CPASSOC between ASD and immune cells. FUMA was used to identify pleiotropic regions, and expression trait loci (EQTL) analysis was used to determine their expression in different tissues and cells. Finally, we use qPCR to detect the gene expression level of the core gene. Results: We found a close relationship between neutrophils and ASD, and subsequently, CMLS identified a total of 47 potential candidate genes. Secondly, GNOVA showed a significant genetic correlation between neutrophils and ASD, and PLACO and CPASSOC identified a total of 14 pleiotropic regions. We annotated the 14 regions mentioned above and identified a total of 6 potential candidate genes. Through EQTL, we found that the CFLAR gene has a specific expression pattern in neutrophils, suggesting that it may serve as a potential biomarker for ASD and is closely related to its pathogenesis. Conclusions: In conclusion, our study yields unprecedented insights into the molecular and genetic heterogeneity of ASD through a comprehensive bioinformatics analysis. These valuable findings hold significant implications for tailoring personalized ASD therapies.

Gene network-based and ensemble modeling-based selection of tumor-associated antigens with a predicted low risk of tissue damage for targeted immunotherapy.

BACKGROUND: Tumor-associated antigens and their derived peptides constitute an opportunity to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. A performant and rational antigen selection pipeline would lay the foundation for immunotherapy trials with the potential to enhance treatment, tremendously benefiting patients suffering from rare, understudied cancers. METHODS: We present an experimentally validated, data-driven computational pipeline that selects and ranks antigens in a multipronged approach. In addition to minimizing the risk of immune-related adverse events by selecting antigens based on their expression profile in tumor biopsies and healthy tissues, we incorporated a network analysis-derived antigen indispensability index based on computational modeling results, and candidate immunogenicity predictions from a machine learning ensemble model relying on peptide physicochemical characteristics. RESULTS: In a model study of uveal melanoma, Human Leukocyte Antigen (HLA) docking simulations and experimental quantification of the peptide-major histocompatibility complex binding affinities confirmed that our approach discriminates between high-binding and low-binding affinity peptides with a performance similar to that of established methodologies. Blinded validation experiments with autologous T-cells yielded peptide stimulation-induced interferon-γ secretion and cytotoxic activity despite high interdonor variability. Dissecting the score contribution of the tested antigens revealed that peptides with the potential to induce cytotoxicity but unsuitable due to potential tissue damage or instability of expression were properly discarded by the computational pipeline. CONCLUSIONS: In this study, we demonstrate the feasibility of the de novo computational selection of antigens with the capacity to induce an anti-tumor immune response and a predicted low risk of tissue damage. On translation to the clinic, our pipeline supports fast turn-around validation, for example, for adoptive T-cell transfer preparations, in both generalized and personalized antigen-directed immunotherapy settings.

Alzheimer's disease rewires gene coexpression networks coupling different brain regions.

Connectome studies have shown how Alzheimer's disease (AD) disrupts functional and structural connectivity among brain regions. But the molecular basis of such disruptions is less studied, with most genomic/transcriptomic studies performing within-brain-region analyses. To inspect how AD rewires the correlation structure among genes in different brain regions, we performed an Inter-brain-region Differential Correlation (Inter-DC) analysis of RNA-seq data from Mount Sinai Brain Bank on four brain regions (frontal pole, superior temporal gyrus, parahippocampal gyrus and inferior frontal gyrus, comprising 264 AD and 372 control human post-mortem samples). An Inter-DC network was assembled from all pairs of genes across two brain regions that gained (or lost) correlation strength in the AD group relative to controls at FDR 1%. The differentially correlated (DC) genes in this network complemented known differentially expressed genes in AD, and likely reflects cell-intrinsic changes since we adjusted for cell compositional effects. Each brain region used a distinctive set of DC genes when coupling with other regions, with parahippocampal gyrus showing the most rewiring, consistent with its known vulnerability to AD. The Inter-DC network revealed master dysregulation hubs in AD (at genes ZKSCAN1, SLC5A3, RCC1, IL17RB, PLK4, etc.), inter-region gene modules enriched for known AD pathways (synaptic signaling, endocytosis, etc.), and candidate signaling molecules that could mediate region-region communication. The Inter-DC network generated in this study is a valuable resource of gene pairs, pathways and signaling molecules whose inter-brain-region functional coupling is disrupted in AD, thereby offering a new perspective of AD etiology.

Identification of candidate biomarkers for GBM based on WGCNA.

Glioblastoma multiforme (GBM), the most aggressive form of primary brain tumor, poses a considerable challenge in neuro-oncology. Despite advancements in therapeutic approaches, the prognosis for GBM patients remains bleak, primarily attributed to its inherent resistance to conventional treatments and a high recurrence rate. The primary goal of this study was to acquire molecular insights into GBM by constructing a gene co-expression network, aiming to identify and predict key genes and signaling pathways associated with this challenging condition. To investigate differentially expressed genes between various grades of Glioblastoma (GBM), we employed Weighted Gene Co-expression Network Analysis (WGCNA) methodology. Through this approach, we were able to identify modules with specific expression patterns in GBM. Next, genes from these modules were performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using ClusterProfiler package. Our findings revealed a negative correlation between biological processes associated with neuronal development and functioning and GBM. Conversely, the processes related to the cell cycle, glomerular development, and ECM-receptor interaction exhibited a positive correlation with GBM. Subsequently, hub genes, including SYP, TYROBP, and ANXA5, were identified. This study offers a comprehensive overview of the existing research landscape on GBM, underscoring the challenges encountered by clinicians and researchers in devising effective therapeutic strategies.

Differential expression analysis identifies a prognostically significant extracellular matrix-enriched gene signature in hyaluronan-positive clear cell renal cell carcinoma.

Hyaluronan (HA) accumulation in clear cell renal cell carcinoma (ccRCC) is associated with poor prognosis; however, its biology and role in tumorigenesis are unknown. RNA sequencing of 48 HA-positive and 48 HA-negative formalin-fixed paraffin-embedded (FFPE) samples was performed to identify differentially expressed genes (DEG). The DEGs were subjected to pathway and gene enrichment analyses. The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data and DEGs were used for the cluster analysis. In total, 129 DEGs were identified. HA-positive tumors exhibited enhanced expression of genes related to extracellular matrix (ECM) organization and ECM receptor interaction pathways. Gene set enrichment analysis showed that epithelial-mesenchymal transition-associated genes were highly enriched in the HA-positive phenotype. A protein-protein interaction network was constructed, and 17 hub genes were discovered. Heatmap analysis of TCGA-KIRC data identified two prognostic clusters corresponding to HA-positive and HA-negative phenotypes. These clusters were used to verify the expression levels and conduct survival analysis of the hub genes, 11 of which were linked to poor prognosis. These findings enhance our understanding of hyaluronan in ccRCC.

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.

A Principal Components Analysis and Functional Annotation of Differentially Expressed Genes in Brain Regions of Gray Rats Selected for Tame or Aggressive Behavior.

The process of domestication, despite its short duration as it compared with the time scale of the natural evolutionary process, has caused rapid and substantial changes in the phenotype of domestic animal species. Nonetheless, the genetic mechanisms underlying these changes remain poorly understood. The present study deals with an analysis of the transcriptomes from four brain regions of gray rats (Rattus norvegicus), serving as an experimental model object of domestication. We compared gene expression profiles in the hypothalamus, hippocampus, periaqueductal gray matter, and the midbrain tegmental region between tame domesticated and aggressive gray rats and revealed subdivisions of differentially expressed genes by principal components analysis that explain the main part of differentially gene expression variance. Functional analysis (in the DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Resources database) of the differentially expressed genes allowed us to identify and describe the key biological processes that can participate in the formation of the different behavioral patterns seen in the two groups of gray rats. Using the STRING- DB (search tool for recurring instances of neighboring genes) web service, we built a gene association network. The genes engaged in broad network interactions have been identified. Our study offers data on the genes whose expression levels change in response to artificial selection for behavior during animal domestication.

Unraveling the Significance of Nanog in the Generation of Embryonic Stem-like Cells from Spermatogonia Stem Cells: A Combined In Silico Analysis and In Vitro Experimental Approach.

Embryonic stem-like cells (ES-like cells) are promising for medical research and clinical applications. Traditional methods involve "Yamanaka" transcription (OSKM) to derive these cells from somatic cells in vitro. Recently, a novel approach has emerged, obtaining ES-like cells from spermatogonia stem cells (SSCs) in a time-related process without adding artificial additives to cell cultures, like transcription factors or small molecules such as pten or p53 inhibitors. This study aims to investigate the role of the Nanog in the conversion of SSCs to pluripotent stem cells through both in silico analysis and in vitro experiments. We used bioinformatic methods and microarray data to find significant genes connected to this derivation path, to construct PPI networks, using enrichment analysis, and to construct miRNA-lncRNA networks, as well as in vitro experiments, immunostaining, and Fluidigm qPCR analysis to connect the dots of Nanog significance. We concluded that Nanog is one of the most crucial differentially expressed genes during SSC conversion, collaborating with critical regulators such as Sox2, Dazl, Pou5f1, Dnmt3, and Cdh1. This intricate protein network positions Nanog as a pivotal factor in pathway enrichment for generating ES-like cells, including Wnt signaling, focal adhesion, and PI3K-Akt-mTOR signaling. Nanog expression is presumed to play a vital role in deriving ES-like cells from SSCs in vitro. Finding its pivotal role in this path illuminates future research and clinical applications.

Signature and function of plasma exosome-derived circular RNAs in patients with hypertensive intracerebral hemorrhage.

Intracerebral hemorrhage (ICH) is one of the major causes of death and disability, and hypertensive ICH (HICH) is the most common type of ICH. Currently, the outcomes of HICH patients remain poor after treatment, and early prognosis prediction of HICH is important. However, there are limited effective clinical treatments and biomarkers for HICH patients. Although circRNA has been widely studied in diseases, the role of plasma exosomal circRNAs in HICH remains unknown. The present study was conducted to investigate the characteristics and function of plasma exosomal circRNAs in six HICH patients using circRNA microarray and bioinformatics analysis. The results showed that there were 499 differentially expressed exosomal circRNAs between the HICH patients and control subjects. According to GO annotation and KEGG pathway analyses, the targets regulated by differentially expressed exosomal circRNAs were tightly related to the development of HICH via nerve/neuronal growth, neuroinflammation and endothelial homeostasis. And the differentially expressed exosomal circRNAs could mainly bind to four RNA-binding proteins (EIF4A3, FMRP, AGO2 and HUR). Moreover, of differentially expressed exosomal circRNAs, hsa_circ_00054843, hsa_circ_0010493 and hsa_circ_00090516 were significantly associated with bleeding volume and Glasgow Coma Scale score of the subjects. Our findings firstly revealed that the plasma exosomal circRNAs are significantly involved in the progression of HICH, and could be potent biomarkers for HICH. This provides the basis for further research to pinpoint the best biomarkers and illustrate the mechanism of exosomal circRNAs in HICH.

Role of N6-methyladenosine-related lncRnas in pseudoexfoliation glaucoma.

To explore the role of lncRNA m6A methylation modification in aqueous humour (AH) of patients with pseudoexfoliation glaucoma (PXG). Patients with open-angle PXG under surgery from June 2021 to December 2021 were selected. Age- and gender-matched patients with age-related cataract (ARC) were chosen as control. Patients underwent detailed ophthalmic examinations. 0.05-0.1 ml AH were extracted during surgery for MeRIP-Seq and RNA-Seq. Joint analysis was used to screen lncRNAs with differential m6A methylation modification and expression. Online software tools were used to draw lncRNA-miRNA-mRNA network (ceRNA). Expression of lncRNAs and mRNAs was confirmed using quantitative real-time PCR. A total of 4151 lncRNAs and 4386 associated m6A methylation modified peaks were identified in the PXG group. Similarly, 2490 lncRNAs and 2595 associated m6A methylation modified peaks were detected in the control. Compared to the ARC group, the PXG group had 234 hypermethylated and 402 hypomethylated m6A peaks, with statistically significant differences (| Fold Change (FC) |≥2, p < 0.05). Bioinformatic analysis revealed that these differentially methylated lncRNA enriched in extracellular matrix formation, tight adhesion, TGF- ß signalling pathway, AMPK signalling pathway, and MAPK signalling pathway. Joint analysis identified 10 lncRNAs with differential m6A methylation and expression simultaneously. Among them, the expression of ENST000000485383 and ROCK1 were confirmed downregulated in the PXG group by RT-qPCR. m6A methylation modification may affect the expression of lncRNA and participate in the pathogenesis of PXG through the ceRNA network. ENST000000485383-hsa miR592-ROCK1 May be a potential target pathway for further investigation in PXG m6A methylation.

Co-expression network analysis and identification of core genes in the interaction between wheat and Puccinia striiformis f. sp. tritici.

The epidemic of stripe rust, caused by the pathogen Puccinia striiformis f. sp. tritici (Pst), would reduce wheat (Triticum aestivum) yields seriously. Traditional experimental methods are difficult to discover the interaction between wheat and Pst. Multi-omics data analysis provides a new idea for efficiently mining the interactions between host and pathogen. We used 140 wheat-Pst RNA-Seq data to screen for differentially expressed genes (DEGs) between low susceptibility and high susceptibility samples, and carried out Gene Ontology (GO) enrichment analysis. Based on this, we constructed a gene co-expression network, identified the core genes and interacted gene pairs from the conservative modules. Finally, we checked the distribution of Nucleotide-binding and leucine-rich repeat (NLR) genes in the co-expression network and drew the wheat NLR gene co-expression network. In order to provide accessible information for related researchers, we built a web-based visualization platform to display the data. Based on the analysis, we found that resistance-related genes such as TaPR1, TaWRKY18 and HSP70 were highly expressed in the network. They were likely to be involved in the biological processes of Pst infecting wheat. This study can assist scholars in conducting studies on the pathogenesis and help to advance the investigation of wheat-Pst interaction patterns.

Identification of circRNAs expression profiles and functional networks in parotid gland of type 2 diabetes mouse.

BACKGROUND: Circular RNAs (circRNAs) are a novel kind of non-coding RNAs proved to play crucial roles in the development of multiple diabetic complications. However, their expression and function in diabetes mellitus (DM)-impaired salivary glands are unknown. RESULTS: By using microarray technology, 663 upregulated and 999 downregulated circRNAs companied with 813 upregulated and 525 downregulated mRNAs were identified in the parotid glands (PGs) of type2 DM mice under a 2-fold change and P < 0.05 cutoff criteria. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis of upregulated mRNAs showed enrichments in immune system process and peroxisome proliferator-activated receptor (PPAR) signaling pathway. Infiltration of inflammatory cells and increased inflammatory cytokines were observed in diabetic PGs. Seven differently expressed circRNAs validated by qRT-PCR were selected for coding-non-coding gene co-expression (CNC) and competing endogenous RNA (ceRNA) networks analysis. PPAR signaling pathway was primarily enriched through analysis of circRNA-mRNA networks. Moreover, the circRNA-miRNA-mRNA networks highlighted an enrichment in the regulation of actin cytoskeleton. CONCLUSION: The inflammatory response is elevated in diabetic PGs. The selected seven distinct circRNAs may attribute to the injury of diabetic PG by modulating inflammatory response through PPAR signaling pathway and actin cytoskeleton in diabetic PGs.

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

Exploration of shared gene signatures and molecular mechanisms between type 2 diabetes and osteoporosis.

Type 2 diabetes mellitus (T2D) and osteoporosis (OP) are systemic metabolic diseases and often coexist. The mechanism underlying this interrelationship remains unclear. We downloaded microarray data for T2D and OP from the Gene Expression Omnibus (GEO) database. Using weighted gene co-expression network analysis (WGCNA), we identified co-expression modules linked to both T2D and OP. To further investigate the functional implications of these associated genes, we evaluated enrichment using ClueGO software. Additionally, we performed a biological process analysis of the genes unique in T2D and OP. We constructed a comprehensive miRNA-mRNA network by incorporating target genes and overlapping genes from the shared pool. Through the implementation of WGCNA, we successfully identified four modules that propose a plausible model that elucidates the disease pathway based on the associated and distinct gene profiles of T2D and OP. The miRNA-mRNA network analysis revealed co-expression of PDIA6 and SLC16A1; their expression was upregulated in patients with T2D and islet ß-cell lines. Remarkably, PDIA6 and SLC16A1 were observed to inhibit the proliferation of pancreatic ß cells and promote apoptosis in vitro, while downregulation of PDIA6 and SLC16A1 expression led to enhanced insulin secretion. This is the first study to reveal the significant roles of PDIA6 and SLC16A1 in the pathogenesis of T2D and OP, thereby identifying additional genes that hold potential as indicators or targets for therapy.

GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks.

We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based causal implicit generative model for simulating single-cell RNA-seq data, in silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on six experimental reference datasets, we show that our model captures non-linear TF-gene dependencies and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. GRouNdGAN can synthesize cells under new conditions to perform in silico TF knockout experiments. Benchmarking various GRN inference algorithms reveals that GRouNdGAN effectively bridges the existing gap between simulated and biological data benchmarks of GRN inference algorithms, providing gold standard ground truth GRNs and realistic cells corresponding to the biological system of interest.

Multi-omics integration of scRNA-seq time series data predicts new intervention points for Parkinson's disease.

Parkinson's disease (PD) is a complex neurodegenerative disorder without a cure. The onset of PD symptoms corresponds to 50% loss of midbrain dopaminergic (mDA) neurons, limiting early-stage understanding of PD. To shed light on early PD development, we study time series scRNA-seq datasets of mDA neurons obtained from patient-derived induced pluripotent stem cell differentiation. We develop a new data integration method based on Non-negative Matrix Tri-Factorization that integrates these datasets with molecular interaction networks, producing condition-specific "gene embeddings". By mining these embeddings, we predict 193 PD-related genes that are largely supported (49.7%) in the literature and are specific to the investigated PINK1 mutation. Enrichment analysis in Kyoto Encyclopedia of Genes and Genomes pathways highlights 10 PD-related molecular mechanisms perturbed during early PD development. Finally, investigating the top 20 prioritized genes reveals 12 previously unrecognized genes associated with PD that represent interesting drug targets.