Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

Single-cell RNA sequencing uncovers heterogeneous transcriptional signatures in tumor-infiltrated dendritic cells in prostate cancer

Prostate cancer (PCa) is one of the two solid malignancies in which a higher T cell infiltration in the tumor microenvironment (TME) corresponds with a worse prognosis for the tumor. The inability of T cells to eliminate tumor cells despite an increase in their number reinforces the possibility of impaired antigen presentation. In this study, we investigated the TME at single-cell resolution to understand the molecular function and communication of dendritic cells (DCs) (as professional antigen-presenting cells). According to our data, tumor cells stimulate the migration of immature DCs to the tumor site by inducing inflammatory chemokines. Many signaling pathways such as TNF-α/NF-κB, IL2/STAT5, and E2F up-regulated after DCs enter the tumor location. In addition, some molecules such as GPR34 and SLCO2B1 decreased on the surface of DCs. The analysis of molecular and signaling alterations in DCs revealed some suppression mechanisms of tumors, such as removing mature DCs, reducing the DC's survival, inducing anergy or exhaustion in the effector T cells, and enhancing the differentiation of T cells to Th2 and T_regs. In addition, we investigated the cellular and molecular communication between DCs and macrophages in the tumor site and found three molecular pairs including CCR5/CCL5, CD52/SIGLEC10, and HLA-DPB1/TNFSF13B. These molecular pairs are involved in the migration of immature DCs to the TME and disrupt the antigen-presenting function of DCs. Furthermore, we presented new therapeutic targets by the construction of a gene co-expression network. These data increase our knowledge of the heterogeneity and the role of DCs in PCa TME.

Comprehensively analyzing the genetic alterations, and identifying key genes in ovarian cancer

Though significant improvements have been made in the treatment methods for ovarian cancer (OC), the prognosis for OC patients is still poor. Exploring hub genes associated with the development of OC and utilizing them as appropriate potential biomarkers or therapeutic targets is highly valuable. In this study, the differentially expressed genes (DEGs) were identified from an independent GSE69428 Gene Expression Omnibus (GEO) dataset between OC and control samples. The DEGs were processed to construct the protein-protein interaction (PPI) network using STRING. Later, hub genes were identified through Cytohubba analysis of the Cytoscape. Expression and survival profiling of the hub genes were validated using GEPIA, OncoDB, and GENT2. For exploring promoter methylation levels and genetic alterations in hub genes, MEXPRESS and cBioPortal were utilized, respectively. Moreover, DAVID, HPA, TIMER, CancerSEA, ENCORI, DrugBank, and GSCAlite were used for gene enrichment analysis, subcellular localization analysis, immune cell infiltration analysis, exploring correlations between hub genes and different diverse states, lncRNA-miRNA-mRNA co-regulatory network analysis, predicting hub gene-associated drugs, and conducting drug sensitivity analysis, respectively. In total, 8947 DEGs were found between OC and normal samples in GSE69428. After STRING and Cytohubba analysis, 4 hub genes including TTK (TTK Protein Kinase), (BUB1 mitotic checkpoint serine/threonine kinase B) BUB1B, (Nucleolar and spindle-associated protein 1) NUSAP1, and (ZW10 interacting kinetochore protein) ZWINT were selected as the hub genes. Further, it was validated that these 4 hub genes were significantly up-regulated in OC samples compared to normal controls, but overexpression of these genes was not associated with overall survival (OS). However, genetic alterations in those genes were found to be linked with OS and disease-free (DFS) survival. Moreover, this study also revealed some novel links between TTK, BUB1B, NUSAP1, and ZWINT overexpression and promoter methylation status, immune cell infiltration, miRNAs, gene enrichment terms, and various chemotherapeutic drugs. Four hub genes, including TTK, BUB1B, NUSAP1, and ZWINT, were revealed as tumor-promotive factors in OC, having the potential to be utilized as novel biomarkers and therapeutic targets for OC management.

HPV16 status predicts potential protein biomarkers and therapeutics in head and neck squamous cell carcinoma

Human papillomavirus (HPV) infection, especially HPV16, is one of the causative factors for the development of head and neck squamous cell (HNSC) carcinoma. HPV-positive and HPV-negative HNSC patients differ significantly in their molecular profiles and clinical features, so they should be evaluated differently depending on their HPV status. Given the tremendous variation in HNSC cancers depending on HPV, our goal in this study was to present biomarkers and treatment options tailored to the patient's HPV status. Gene expression levels of HPV16-positive and -negative patients were used as proxies, and the differential interactome algorithm was employed to identify the differential interacting proteins (DIPs). By assessing the prognostic capabilities and druggabilities of DIPs and their interacting partners (DIP-centered modules), we introduce eight modules as potential biomarkers specialized for either positive or negative phenotype. Finally, raloxifene was repositioned for the first time as a drug candidate for the treatment of HPV16-positive HNSC patients.

Shared molecular signatures between coronavirus infection and neurodegenerative diseases provide targets for broad-spectrum drug development

Growing evidences have suggested the association between coronavirus infection and neurodegenerative diseases. However, the molecular mechanism behind the association is complex and remains to be clarified. This study integrated human genes involved in infections of three coronaviruses including SARS-CoV-2, SARS-CoV and MERS-CoV from multi-omics data, and investigated the shared genes and molecular functions between coronavirus infection and two neurodegenerative diseases, namely Alzheimer's Disease (AD) and Parkinson's Disease (PD). Seven genes including HSP90AA1, ALDH2, CAV1, COMT, MTOR, IGF2R and HSPA1A, and several inflammation and stress response-related molecular functions such as MAPK signaling pathway, NF-kappa B signaling pathway, responses to oxidative or chemical stress were common to both coronavirus infection and neurodegenerative diseases. These genes were further found to interact with more than 20 other viruses. Finally, drugs targeting these genes were identified. The study would not only help clarify the molecular mechanism behind the association between coronavirus infection and neurodegenerative diseases, but also provide novel targets for the development of broad-spectrum drugs against both coronaviruses and neurodegenerative diseases.

Genetic correlation and gene-based pleiotropy analysis for four major neurodegenerative diseases with summary statistics

Recent genome-wide association studies suggested shared genetic components between neurodegenerative diseases. However, pleiotropic association patterns among them remain poorly understood. We here analyzed 4 major neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), and found suggestively positive genetic correlation. We next implemented a gene-centric pleiotropy analysis with a powerful method called PLACO and detected 280 pleiotropic associations (226 unique genes) with these diseases. Functional analyses demonstrated that these genes were enriched in the pancreas, liver, heart, blood, brain, and muscle tissues; and that 42 pleiotropic genes exhibited drug-gene interactions with 341 drugs. Using Mendelian randomization, we discovered that AD and PD can increase the risk of developing ALS, and that AD and ALS can also increase the risk of developing FTD, respectively. Overall, this study provides in-depth insights into shared genetic components and causal relationship among the 4 major neurodegenerative diseases, indicating genetic overlap and causality commonly drive their co-occurrence. It also has important implications on the etiology understanding, drug development and therapeutic targets for neurodegenerative diseases.

Identifying TNF and IL6 as potential hub genes and targeted drugs associated with scleritis: A bio-informative report

Background

Scleritis is a serious inflammatory eye disease that can lead to blindness. The etiology and pathogenesis of scleritis remain unclear, and increasing evidence indicates that some specific genes and proteins are involved. This study aimed to identify pivotal genes and drug targets for scleritis, thus providing new directions for the treatment of this disease.

Methods

We screened candidate genes and proteins associated with scleritis by text-mining the PubMed database using Python, and assessed their functions by using the DAVID database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to identify the functional enrichment of these genes and proteins. Then, the hub genes were identified with CytoHubba and assessed by protein-protein interaction (PPI) network analysis. And the serum from patients with active scleritis and healthy subjects were used for the validation of hub genes. Finally, the DGIdb database was used to predict targeted drugs for the hub genes for treating scleritis.

Results

A total of 56 genes and proteins were found to be linked to scleritis, and 65 significantly altered pathways were identified in the KEGG analysis (FDR < 0.05). Most of the top five pathways involved the categories “Rheumatoid arthritis,” “Inflammatory bowel disease”, “Type I diabetes mellitus,” and “Graft-versus-host disease”. TNF and IL6 were considered to be the top 2 hub genes through CytoHubba. Based on our serum samples, hub genes are expressed at high levels in active scleritis. Five scleritis-targeting drugs were found among 88 identified drugs.

Conclusions

This study provides key genes and drug targets related to scleritis through bioinformatics analysis. TNF and IL6 are considered key mediators and possible drug targets of scleritis. Five drug candidates may play an important role in the diagnosis and treatment of scleritis in the future, which is worthy of the further experimental and clinical study.

Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets

BACKGROUND

Systemic sclerosis (SSc) is a rare autoimmune disease characterized by extensive skin fibrosis. There are no effective treatments due to the severity, multiorgan presentation, and variable outcomes of the disease. Here, integrated bioinformatics was employed to discover tissue-specific expressed hub genes associated with SSc, determine potential competing endogenous RNAs (ceRNA) regulatory networks, and identify potential targeted drugs.

METHODS

In this study, four datasets of SSc were acquired. To identify the genes specific to tissues or organs, the BioGPS web database was used. For differentially expressed genes (DEGs), functional and enrichment analyses were carried out, and hub genes were screened and shown in a network of protein-protein interactions (PPI). The potential lncRNA-miRNA-mRNA ceRNA network was constructed using the online databases. The specifically expressed hub genes and ceRNA network were validated in the SSc mouse and in normal mice. We also used the receiver operating characteristic (ROC) curve to determine the diagnostic values of effective biomarkers in SSc. Finally, the Drug-Gene Interaction Database (DGIdb) identified specific medicines linked to hub genes.

RESULTS

The pooled datasets identified a total of 254 DEGs. The tissue/organ-specifically expressed genes involved in this analysis are commonly found in the hematologic/immune system and bone/muscle tissue. The enrichment analysis of DEGs revealed the significant terms such as regulation of actin cytoskeleton, immune-related processes, the VEGF signaling pathway, and metabolism. Cytoscape identified six gene cluster modules and 23 hub genes. And 4 hub genes were identified, including Serpine1, CCL2, IL6, and ISG15. Consistently, the expression of Serpine1, CCL2, IL6, and ISG15 was significantly higher in the SSc mouse model than in normal mice. Eventually, we found that MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1 may be promising RNA regulatory pathways in SSc. Besides, ten potential therapeutic drugs associated with the hub gene were identified.

CONCLUSIONS

This study revealed tissue-specific expressed genes, SERPINE1, CCL2, IL6, and ISG15, as effective biomarkers and provided new insight into the mechanisms of SSc. Potential RNA regulatory pathways, including MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1, contribute to our knowledge of SSc. Furthermore, the analysis of drug-hub gene interactions predicted TIPLASININ, CARLUMAB and BINDARIT as candidate drugs for SSc.

The oncogenic properties of the EWSR1::CREM fusion gene are associated with polyamine metabolism

The EWSR1::CREM fusion gene, caused by a chromosomal translocation t(10;22)(p11;q12), has been discovered in divergent malignancies, ranging from low-grade to highly malignant cancers. The translocation gives rise to a chimeric protein, EWSR1::CREM. The molecular mechanisms behind the oncogenic properties of the EWSR1::CREM protein have not previously been systematically characterized. In this study, we performed transcriptional profiling of the melanoma cell line CHL-1, with depletion of endogenous EWSR1::CREM protein using siRNA mediated knockdown. We found that the expression of 712 genes was altered (Log2 fold-change ≥ 2). We performed pathway analysis to identify EWSR1::CREM mediated pathways and cell studies to examine functional differences brought upon by the knockdown. Altered pathways involved cell cycle and proliferation, this was further validated by the cell studies where cell migration was affected as well. Among the target genes with the greatest downregulation, we discovered ODC1-a well-established oncogenic enzyme that can be pharmacologically inhibited and is essential for polyamine synthesis. We found that the main effects seen upon EWSR1::CREM knockdown can be reproduced by directly silencing ODC1 expression. These findings provide novel insights into pathogenesis of tumors harboring a EWSR1::CREM fusion gene, hopefully facilitating the development of novel therapeutic strategies.

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.

Identification and validation of ferroptosis-related genes and immune cell infiltration in thyroid associated ophthalmopathy

Thyroid associated ophthalmopathy (TAO) is an orbital autoimmune inflammatory disease that is commonly associated with thyroid dysfunction. Although the etiology of TAO is unclear, ROS accumulation and oxidative stress have been closely linked to the pathogenesis of TAO. Ferroptosis is an iron-dependent programmed cell death characterized by intracellular labile iron levels, excessive accumulation of reactive oxygen species (ROS) and lipid peroxidation. Currently, there are few reports regarding the role of ferroptosis in TAO. This article aimed to identify ferroptosis-related genes (FRGs) with diagnostic and therapeutic potential in TAO and explore their relationship with immune cells and lncRNAs. GSE58331 was downloaded from Gene Expression Omnibus (GEO) database. A total of 162 DEGs were identified between 27 TAO samples and 22 health samples from GSE58331, among which six FRGs (CYBB, CTSB, SLC38A1, TLR4, PEX3, and ABCC1) were obtained. The AUC of SLC38A1, TLR4, PEX3 in lacrimal gland tissues was greater than 80 which suggested high diagnostic value in TAO. The result of immune cell infiltrate analysis indicated increased infiltration of monocytes (p < 0.001), macrophages M0(p = 0.039), mast cells activated (p = 0.008), and neutrophils (p = 0.045) in orbital tissues from TAO patients. Meanwhile, mast cells resting (p = 0.043) and macrophages M2 (p = 0.02) showed reduced infiltration in TAO samples. There were no gender differences in immune cell infiltration in the TAO patients. Two differentially expressed lncRNAs, LINC01140 and ZFHX4-AS1, in TAO groups were identified as ferroptosis-related lncRNAs. CYBB-LINC01140-TLR4, CYBB- LINC01140- SLC38A1, TLR4- LINC01140- SLC38A1, and CTSB- ZFHX4-AS1- CYBB may be potential RNA regulatory pathways in TAO. Targeted drugs and transcription factors for differential expressed FRGs were also screened out in our study. In vitro, experiments revealed that CTSB, PEX3, ABCC1 and ZFHX4-AS1(lncRNA) were differentially expressed in orbital fibroblasts (OFs) between TAO groups and healthy controls at the transcriptional level.

Identification of potential molecular mechanisms and candidate drugs for radiotherapy- and chemotherapy-induced mucositis

BACKGROUND

Radiotherapy-induced oral mucositis (RIOM) and chemotherapy-induced oral mucositis (CIOM) are common complications in cancer patients, leading to negative clinical manifestations, reduced quality of life, and unsatisfactory treatment outcomes.

OBJECTIVE

The present study aimed to identify potential molecular mechanisms and candidate drugs by data mining.

METHODS

We obtained a preliminary list of genes associated with RIOM and CIOM. In-depth information on these genes was explored by functional and enrichment analyses. Then, the drug-gene interaction database was used to determine the interaction of the final enriched gene list with known drugs and analyze the drug candidates.

RESULTS AND CONCLUSION

This study identified 21 hub genes that may play an important role in RIOM and CIOM, respectively. Through our data mining, bioinformatics survey, and candidate drug selection, TNF, IL-6, and TLR9 could play an important role in disease progression and treatment. In addition, eight candidate drugs (olokizumab, chloroquine, hydroxychloroquine, adalimumab, etanercept, golimumab, infliximab, and thalidomide) were selected by the drug-gene interaction literature search additionally, as candidates for treating RIOM and CIOM.

Identification of the cuproptosis-related hub genes and therapeutic agents for sarcopenia

Background: Along with acceleration of population aging, the increasing prevalence of sarcopenia has posed a heavy burden on families as well as society. In this context, it is of great significance to diagnose and intervene sarcopenia as early as possible. Recent evidence has indicated the role of cuproptosis in the development of sarcopenia. In this study, we aimed to seek the key cuproptosis-related genes that can be used for identification and intervention of sarcopenia.

Methods: The GSE111016 dataset was retrieved from GEO. The 31 cuproptosis-related genes (CRGs) were obtained from previous published studies. The differentially expressed genes (DEGs) and Weighed gene co-expression network analysis (WGCNA) were subsequently analyzed. The core hub genes were acquired by the intersection of DEGs, WGCNA and CRGs. Through logistic regression analysis, we established a diagnostic model of sarcopenia based on the selected biomarkers and was validated in muscle samples from GSE111006 and GSE167186. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis were performed on these genes. Furthermore, the gene set enrichment analysis (GSEA), and immune cell infiltration were also conducted on the identified core genes. Finally, we screened the potential drugs targeting the potential biomarkers of sarcopenia.

Results: A total of 902 DEGs and WGCNA containing 1,281 significant genes were preliminarily selected. Intersection of DEGs, WGCNA and CRGs yielded four core genes (PDHA1, DLAT, PDHB, and NDUFC1) as potential biomarkers for the prediction of sarcopenia. The predictive model was established and validated with high AUC values. KEGG pathway and Gene Ontology biological analysis indicated these core genes may play a crucial role in energy metabolism in mitochondria, oxidation process, and aging-related degenerative diseases. In addition, the immune cells may be involved in the development of sarcopenia through mitochondrial metabolism. Finally, metformin was identified as a promising strategy of sarcopenia treatment via targeting NDUFC1.

Conclusion: The four cuproptosis-related genes PDHA1, DLAT, PDHB and NDUFC1 may be the diagnostic biomarkers for sarcopenia, and metformin holds great potential to be developed as a therapy for sarcopenia. These outcomes provide new insights for better understanding of sarcopenia and innovative therapeutic approaches.

Genome-wide analysis of genetic pleiotropy and causal genes across three age-related ocular disorders

Age-related macular degeneration (AMD), cataract, and glaucoma are leading causes of blindness worldwide. Previous genome-wide association studies (GWASs) have revealed a variety of susceptible loci associated with age-related ocular disorders, yet the genetic pleiotropy and causal genes across these diseases remain poorly understood. By leveraging large-scale genetic and observational data from ocular disease GWASs and UK Biobank (UKBB), we found significant pairwise genetic correlations and consistent epidemiological associations among these ocular disorders. Cross-disease meta-analysis uncovered seven pleiotropic loci, three of which were replicated in an additional cohort. Integration of variants in pleiotropic loci and multiple single-cell omics data identified that Müller cells and astrocytes were likely trait-related cell types underlying ocular comorbidity. In addition, we comprehensively integrated eye-specific gene expression quantitative loci (eQTLs), epigenomic profiling, and 3D genome data to prioritize causal pleiotropic genes. We found that pleiotropic genes were essential in nerve development and eye pigmentation, and targetable by aflibercept and pilocarpine for the treatment of AMD and glaucoma. These findings will not only facilitate the mechanistic research of ocular comorbidities but also benefit the therapeutic optimization of age-related ocular diseases.

Artificial intelligence for drug discovery: Resources, methods, and applications

Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the molecular representation schemes that convert data into computer-readable formats. Meanwhile, we summarized the algorithms used to develop AI-based models for drug discovery. Subsequently, we discussed the applications of AI techniques in pharmaceutical analysis including predicting drug toxicity, drug bioactivity, and drug physicochemical property. Furthermore, we introduced the AI-based models for de novo drug design, drug-target structure prediction, drug-target interaction, and binding affinity prediction. Moreover, we also highlighted the advanced applications of AI in drug synergism/antagonism prediction and nanomedicine design. Finally, we discussed the challenges and future perspectives on the applications of AI to drug discovery.

A drug repurposing approach for individualized cancer therapy based on transcriptome sequencing and virtual drug screening

RNA-sequencing has been proposed as a valuable technique to develop individualized therapy concepts for cancer patients based on their tumor-specific mutational profiles. Here, we aimed to identify drugs and inhibitors in an individualized therapy-based drug repurposing approach focusing on missense mutations for 35 biopsies of cancer patients. The missense mutations belonged to 9 categories (ABC transporter, apoptosis, angiogenesis, cell cycle, DNA damage, kinase, protease, transcription factor, tumor suppressor). The highest percentages of missense mutations were observed in transcription factor genes. The mutational profiles of all 35 tumors were subjected to hierarchical heatmap clustering. All 7 leukemia biopsies clustered together and were separated from solid tumors. Based on these individual mutation profiles, two strategies for the identification of possible drug candidates were applied: Firstly, virtual screening of FDA-approved drugs based on the protein structures carrying particular missense mutations. Secondly, we mined the Drug Gene Interaction (DGI) database (https://www.dgidb.org/) to identify approved or experimental inhibitors for missense mutated proteins in our dataset of 35 tumors. In conclusion, our approach based on virtual drug screening of FDA-approved drugs and DGI-based inhibitor selection may provide new, individual treatment options for patients with otherwise refractory tumors that do not respond anymore to standard chemotherapy.

Integrative bioinformatics analysis to identify novel biomarkers associated with non-obstructive azoospermia

Aim

This study aimed to identify autophagy-related genes (ARGs) associated with non-obstructive azoospermia and explore the underlying molecular mechanisms.

Methods

Two datasets associated with azoospermia were downloaded from the Gene Expression Omnibus database, and ARGs were obtained from the Human Autophagy-dedicated Database. Autophagy-related differentially expressed genes were identified in the azoospermia and control groups. These genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, protein–protein interaction (PPI) network, and functional similarity analyses. After identifying the hub genes, immune infiltration and hub gene–RNA-binding protein (RBP)–transcription factor (TF)–miRNA–drug interactions were analyzed.

Results

A total 46 differentially expressed ARGs were identified between the azoospermia and control groups. These genes were enriched in autophagy-associated functions and pathways. Eight hub genes were selected from the PPI network. Functional similarity analysis revealed that HSPA5 may play a key role in azoospermia. Immune cell infiltration analysis revealed that activated dendritic cells were significantly decreased in the azoospermia group compared to those in the control groups. Hub genes, especially ATG3, KIAA0652, MAPK1, and EGFR were strongly correlated with immune cell infiltration. Finally, a hub gene–miRNA–TF–RBP–drug network was constructed.

Conclusion

The eight hub genes, including EGFR, HSPA5, ATG3, KIAA0652, and MAPK1, may serve as biomarkers for the diagnosis and treatment of azoospermia. The study findings suggest potential targets and mechanisms for the occurrence and development of this disease.

Single-cell and bulk RNA sequencing identifies T cell marker genes score to predict the prognosis of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, with limited biomarkers identified to predict its prognosis and treatment response of immune checkpoint blockade (ICB). This study aimed to explore the predictive ability of T cell marker genes score (TMGS) to predict their overall survival (OS) and treatment response to ICB by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. Multi-omics data of PDAC were applied in this study. The uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification. The non-negative matrix factorization (NMF) algorithm was applied to molecular subtypes clustering. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was adopted for TMGS construction. The prognosis, biological characteristics, mutation profile, and immune function status between different groups were compared. Two molecular subtypes were identified via NMF: proliferative PDAC (C1) and immune PDAC (C2). Distinct prognoses and biological characteristics were observed between them. TMGS was developed based on 10 T cell marker genes (TMGs) through LASSO-Cox regression. TMGS is an independent prognostic factor of OS in PDAC. Enrichment analysis indicated that cell cycle and cell proliferation-related pathways are significantly enriched in the high-TMGS group. Besides, high-TMGS is related to more frequent KRAS, TP53, and CDKN2A germline mutations than the low-TMGS group. Furthermore, high-TMGS is significantly associated with attenuated antitumor immunity and reduced immune cell infiltration compared to the low-TMGS group. However, high TMGS is correlated to higher tumor mutation burden (TMB), a low expression level of inhibitory immune checkpoint molecules, and a low immune dysfunction score, thus having a higher ICB response rate. On the contrary, low TMGS is related to a favorable response rate to chemotherapeutic agents and targeted therapy. By combining scRNA-seq and bulk RNA-seq data, we identified a novel biomarker, TMGS, which has remarkable performance in predicting the prognosis and guiding the treatment pattern for patients with PDAC.

Common Key Genes in Differentiating Parathyroid Adenoma From Thyroid Adenoma

Recent studies have demonstrated the close relationship between parathyroid adenoma (PA) and thyroid follicular adenoma (FTA). However, the underlying pathogenesis remains unknown. This study focused on exploring common pathogenic genes, as well as the pathogenesis of these two diseases, through bioinformatics methods. This work obtained PA and FTA datasets from the Integrated Gene Expression Database to identify the common differentially expressed genes (DEGs) of two diseases. The functions of the genes were investigated by GO and KEGG enrichment. The program CytoHubba was used to select the hub genes, while receiver operating characteristic curves were plotted to evaluate the predictive significance of the hub genes. The DGIbd database was used to identify gene-targeted drugs. This work detected a total of 77 DEGs. Enrichment analysis demonstrated that DEGs had activities of 3',5'-cyclic AMP, and nucleotide phosphodiesterases and were associated with cell proliferation. NOS1, VWF, TGFBR2, CAV1, and MAPK1 were identified as hub genes after verification. The area under the curve of PA and FTA was>0.7, and the hub genes participated in the Relaxin Signaling Pathway, focal adhesion, and other pathways. The construction of the mRNA-miRNA interaction network yielded 11 important miRNAs, while gene-targeting drug prediction identified four targeted drugs with possible effects. This bioinformatics study demonstrated that cell proliferation and tumor suppression and the hub genes co-occurring in PA and FTA, have important effects on the occurrence and progression of two diseases, which make them potential diagnostic biomarkers and therapeutic targets.

In silico screening-based discovery of inhibitors against glycosylation proteins dysregulated in cancer

Targeting enzymes associated with the biosynthesis of aberrant glycans is an under-utilized strategy in discovering potential inhibitors or drugs against cancer. The formation of cancer-associated glycans is mainly due to the dysregulated expression of glycosyltransferases and glycosidases, which play crucial roles in maintaining cellular structure and function. We screened a database of more than 14,000 compounds consisting of natural products and drugs for inhibition against four glycosylation enzymes - Alpha1-6FucT, ST6Gal1, ERMan1, and GlcNAcT-V. The top inhibitors identified against each enzyme were subsequently analyzed for potential binding against all four enzymes. In silico screening results show several promising candidates that could potentially inhibit all four enzymes: (1) Amb20622156 (demethylwedelolactone) [ERMan1: -9.3 kcal/mol; Alpha1-6FucT: -7.3 kcal/mol; ST6Gal1: -8.4 kcal/mol; GlcNAcT-V: -7.2 kcal/mol], (2) Amb22173588 (1,2-dihydrotanshinone I) [ERMan1: -9.3 kcal/mol; Alpha1-6FucT: -6.1 kcal/mol; ST6Gal1: -9.2 kcal/mol; GlcNAcT-V: -7.9 kcal/mol], and (3) Amb22173591 (tanshinol B) [ERMan1: -9.3 kcal/mol; Alpha1-6FucT: -6.0 kcal/mol; ST6Gal1: -9.8 kcal/mol; GlcNAcT-V: -7.7 kcal/mol]. Drug-enzyme active site residue interaction analyses show that the putative inhibitors form non-covalent bonding interactions with key active site residues in each enzyme, suggesting critical target residues in the four enzymes' active sites. Furthermore, pharmacokinetic property prediction analysis using pkCSM indicates that all of these inhibitors have good ADMETox properties (i.e., log P < 5, Caco-2 permeability > 0.90, intestinal absorption > 30%, skin permeability>-2.5, CNS permeability <-3, maximum tolerated dose < 0.477, minnow toxicity<-0.3). The in silico docking approach to glycosylation enzyme inhibitor prediction could help guide and streamline the discovery of novel inhibitors against enzymes involved in aberrant protein glycosylation.Communicated by Ramaswamy H. Sarma.

Novel potential drugs for the treatment of primary open-angle glaucoma using protein-protein interaction network analysis

Glaucoma is the second leading cause of irreversible blindness, and primary open-angle glaucoma (POAG) is the most common type. Due to inadequate diagnosis, treatment is often not administered until symptoms occur. Hence, approaches enabling earlier prediction or diagnosis of POAG are necessary. We aimed to identify novel drugs for glaucoma through bioinformatics and network analysis. Data from 36 samples, obtained from the trabecular meshwork of healthy individuals and patients with POAG, were acquired from a dataset. Next, differentially expressed genes (DEGs) were identified to construct a protein-protein interaction (PPI) network. In both stages, the genes were enriched by studying the critical biological processes and pathways related to POAG. Finally, a drug-gene network was constructed, and novel drugs for POAG treatment were proposed. Genes with p < 0.01 and |log fold change| > 0.3 (1,350 genes) were considered DEGs and utilized to construct a PPI network. Enrichment analysis yielded several key pathways that were upregulated or downregulated. For example, extracellular matrix organization, the immune system, neutrophil degranulation, and cytokine signaling were upregulated among immune pathways, while signal transduction, the immune system, extracellular matrix organization, and receptor tyrosine kinase signaling were downregulated. Finally, novel drugs including metformin hydrochloride, ixazomib citrate, and cisplatin warrant further analysis of their potential roles in POAG treatment. The candidate drugs identified in this computational analysis require in vitro and in vivo validation to confirm their effectiveness in POAG treatment. This may pave the way for understanding life-threatening disorders such as cancer.

Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets

INTRODUCTION

Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the ccRCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer.

METHODS

ccRCC microarray datasets were downloaded from the Gene Expression Omnibus database, and after quality checking, normalization, and analysis using the Limma algorithm, differentially expressed genes (DEGs) were identified, considering the adjusted p value <0.049. The intensity values of the identified DEGs were introduced to the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to construct co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease-correlated module, and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA, and the GSCA server was used to compare the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub gene-related drugs.

RESULTS

Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes.

CONCLUSION

By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment.

Genome-wide identification of murine interferon genes in microglial-mediated neuroinflammation in Alzheimer's disease

Interferons play a major role in microglial-mediated neuroinflammation in Alzheimer's disease (AD). We investigated the interferon transcriptome (AD versus non-AD) using N9 and murine microglia. We identified 64 interferon-related differentially expressed genes (DEG) in LPS-stimulated N9 microglia versus control cells, 26 DEG in microglia from 5XFAD versus wild-type mice, with 13 DEG common to both datasets. Network analyses identified potential key mediators (Cxcl10, Ifit3) of the interferon response in AD. Gene-drug interaction analysis identified therapeutics targeting interferon-related genes. These data characterize the microglial interferon response in AD, providing new targets and therapeutics directed towards interferon-related neuroinflammation in AD.

Conserved gene signatures shared among MAPT mutations reveal defects in calcium signaling

Introduction: More than 50 mutations in the MAPT gene result in heterogeneous forms of frontotemporal lobar dementia with tau inclusions (FTLD-Tau). However, early pathogenic events that lead to disease and the degree to which they are common across MAPT mutations remain poorly understood. The goal of this study is to determine whether there is a common molecular signature of FTLD-Tau. Methods: We analyzed genes differentially expressed in induced pluripotent stem cell-derived neurons (iPSC-neurons) that represent the three major categories of MAPT mutations: splicing (IVS10 + 16), exon 10 (p.P301L), and C-terminal (p.R406W) compared with isogenic controls. The genes that were commonly differentially expressed in MAPT IVS10 + 16, p.P301L, and p.R406W neurons were enriched in trans-synaptic signaling, neuronal processes, and lysosomal function. Many of these pathways are sensitive to disruptions in calcium homeostasis. One gene, CALB1, was significantly reduced across the three MAPT mutant iPSC-neurons and in a mouse model of tau accumulation. We observed a significant reduction in calcium levels in MAPT mutant neurons compared with isogenic controls, pointing to a functional consequence of this disrupted gene expression. Finally, a subset of genes commonly differentially expressed across MAPT mutations were also dysregulated in brains from MAPT mutation carriers and to a lesser extent in brains from sporadic Alzheimer disease and progressive supranuclear palsy, suggesting that molecular signatures relevant to genetic and sporadic forms of tauopathy are captured in a dish. The results from this study demonstrate that iPSC-neurons capture molecular processes that occur in human brains and can be used to pinpoint common molecular pathways involving synaptic and lysosomal function and neuronal development, which may be regulated by disruptions in calcium homeostasis.

Promises and challenges in pharmacoepigenetics

Pharmacogenetics, the study of how interindividual genetic differences affect drug response, does not explain all observed heritable variance in drug response. Epigenetic mechanisms, such as DNA methylation, and histone acetylation may account for some of the unexplained variances. Epigenetic mechanisms modulate gene expression and can be suitable drug targets and can impact the action of nonepigenetic drugs. Pharmacoepigenetics is the field that studies the relationship between epigenetic variability and drug response. Much of this research focuses on compounds targeting epigenetic mechanisms, called epigenetic drugs, which are used to treat cancers, immune disorders, and other diseases. Several studies also suggest an epigenetic role in classical drug response; however, we know little about this area. The amount of information correlating epigenetic biomarkers to molecular datasets has recently expanded due to technological advances, and novel computational approaches have emerged to better identify and predict epigenetic interactions. We propose that the relationship between epigenetics and classical drug response may be examined using data already available by (1) finding regions of epigenetic variance, (2) pinpointing key epigenetic biomarkers within these regions, and (3) mapping these biomarkers to a drug-response phenotype. This approach expands on existing knowledge to generate putative pharmacoepigenetic relationships, which can be tested experimentally. Epigenetic modifications are involved in disease and drug response. Therefore, understanding how epigenetic drivers impact the response to classical drugs is important for improving drug design and administration to better treat disease.

Day-night and seasonal variation of human gene expression across tissues

Circadian and circannual cycles trigger physiological changes whose reflection on human transcriptomes remains largely uncharted. We used the time and season of death of 932 individuals from GTEx to jointly investigate transcriptomic changes associated with those cycles across multiple tissues. Overall, most variation across tissues during day-night and among seasons was unique to each cycle. Although all tissues remodeled their transcriptomes, brain and gonadal tissues exhibited the highest seasonality, whereas those in the thoracic cavity showed stronger day-night regulation. Core clock genes displayed marked day-night differences across multiple tissues, which were largely conserved in baboon and mouse, but adapted to their nocturnal or diurnal habits. Seasonal variation of expression affected multiple pathways, and it was enriched among genes associated with the immune response, consistent with the seasonality of viral infections. Furthermore, they unveiled cytoarchitectural changes in brain regions. Altogether, our results provide the first combined atlas of how transcriptomes from human tissues adapt to major cycling environmental conditions. This atlas may have multiple applications; for example, drug targets with day-night or seasonal variation in gene expression may benefit from temporally adjusted doses.

Distinct transcriptional signatures in purified circulating immune cells drive heterogeneity in disease location in IBD

OBJECTIVE

To infer potential mechanisms driving disease subtypes among patients with inflammatory bowel disease (IBD), we profiled the transcriptome of purified circulating monocytes and CD4 T-cells.

DESIGN

RNA extracted from purified monocytes and CD4 T-cells derived from the peripheral blood of 125 endoscopically active patients with IBD was sequenced using Illumina HiSeq 4000NGS. We used complementary supervised and unsupervised analytical methods to infer gene expression signatures associated with demographic/clinical features. Expression differences and specificity were validated by comparison with publicly available single cell datasets, tissue-specific expression and meta-analyses. Drug target information, druggability and adverse reaction records were used to prioritise disease subtype-specific therapeutic targets.

RESULTS

Unsupervised/supervised methods identified significant differences in the expression profiles of CD4 T-cells between patients with ileal Crohn's disease (CD) and ulcerative colitis (UC). Following a pathway-based classification (Area Under Receiver Operating Characteristic - AUROC=86%) between ileal-CD and UC patients, we identified MAPK and FOXO pathways to be downregulated in UC. Coexpression module/regulatory network analysis using systems-biology approaches revealed mediatory core transcription factors. We independently confirmed that a subset of the disease location-associated signature is characterised by T-cell-specific and location-specific expression. Integration of drug-target information resulted in the discovery of several new (BCL6, GPR183, TNFAIP3) and repurposable drug targets (TUBB2A, PRKCQ) for ileal CD as well as novel targets (NAPEPLD, SLC35A1) for UC.

CONCLUSIONS

Transcriptomic profiling of circulating CD4 T-cells in patients with IBD demonstrated marked molecular differences between the IBD-spectrum extremities (UC and predominantly ileal CD, sandwiching colonic CD), which could help in prioritising particular drug targets for IBD subtypes.

A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes

Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.

Influence of Butyrate on Impaired Gene Expression in Colon from Patients with High Blood Pressure

Hypertension (HTN) is associated with gut dysbiosis and the depletion of butyrate-producing bacteria in animal models and people. Furthermore, fecal material transfer from donor hypertensive patients increases blood pressure in normotensive recipient animals and ameliorates HTN-associated pathophysiology. These observations have implications in the impaired interactions between the gut and gut microbiota in HTN. Although this concept is supported in animal models, little is known about human HTN. Therefore, our objective for this study was to compare gene expression with transcriptomics and its potential to influence microbiota in subjects with normal and high blood pressure (HBP). Colon samples from reference subjects with normal blood pressure (REF) and HBP were used for RNA-seq to analyze their transcriptomes. We observed the significant downregulation of gene sets governing immune responses (e.g., SGK1 and OASL), gut epithelial function (e.g., KRT20 and SLC9A3R1), gut microbiota (e.g., PPARG and CIDEC) and genes associated with cardiovascular and gut diseases (e.g., PLAUR and NLN) in HBP subjects; the expression of genes within these pathways correlated with blood pressure. Potential drug targets in the gut epithelium were identified using the Drug Gene International Database for possible use in HTN. They include peroxisome proliferator-activated receptor gamma (PPRG), active serum/glucocorticoid regulated kinase 1 (SGK1) and 3 beta-hydroxysteroid isomerase type II inhibitor (HSD3B). Finally, butyrate, a microbiota-derived short-chain fatty acid, restored the disrupted expression of certain functional genes in colonic organoids from HBP subjects. Patients with HBP exhibit a unique transcriptome that could underlie impaired gut-microbiota interactions. Targeting these interactions could provide a promising new therapeutic intervention for hypertension management.

Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs

The exploration of biologically relevant chemical space for the discovery of small bioactive molecules present in marine organisms has led not only to important advances in certain therapeutic areas, but also to a better understanding of many life processes. The still largely untapped reservoir of countless metabolites that play biological roles in marine invertebrates and microorganisms opens new avenues and poses new challenges for research. Computational technologies provide the means to (i) organize chemical and biological information in easily searchable and hyperlinked databases and knowledgebases; (ii) carry out cheminformatic analyses on natural products; (iii) mine microbial genomes for known and cryptic biosynthetic pathways; (iv) explore global networks that connect active compounds to their targets (often including enzymes); (v) solve structures of ligands, targets, and their respective complexes using X-ray crystallography and NMR techniques, thus enabling virtual screening and structure-based drug design; and (vi) build molecular models to simulate ligand binding and understand mechanisms of action in atomic detail. Marine natural products are viewed today not only as potential drugs, but also as an invaluable source of chemical inspiration for the development of novel chemotypes to be used in chemical biology and medicinal chemistry research.

Exploiting the Stemness and Chemoresistance Transcriptome of Ewing Sarcoma to Identify Candidate Therapeutic Targets and Drug-Repurposing Candidates

Outcomes for most patients with Ewing sarcoma (ES) have remained unchanged for the last 30 years, emphasising the need for more effective and tolerable treatments. We have hypothesised that using small-molecule inhibitors to kill the self-renewing chemotherapy-resistant cells (Ewing sarcoma cancer stem-like cells; ES-CSCs) responsible for progression and relapse could improve outcomes and minimise treatment-induced morbidities. For the first time, we demonstrate that ABCG1, a potential oncogene in some cancers, is highly expressed in ES-CSCs independently of CD133. Using functional models, transcriptomics and a bespoke in silico drug-repurposing pipeline, we have prioritised a group of tractable small-molecule inhibitors for further preclinical studies. Consistent with the cellular origin of ES, 21 candidate molecular targets of pluripotency, stemness and chemoresistance were identified. Small-molecule inhibitors to 13 of the 21 molecular targets (62%) were identified. POU5F1/OCT4 was the most promising new therapeutic target in Ewing sarcoma, interacting with 10 of the 21 prioritised molecular targets and meriting further study. The majority of small-molecule inhibitors (72%) target one of two drug efflux proteins, p-glycoprotein (n = 168) or MRP1 (n = 13). In summary, we have identified a novel cell surface marker of ES-CSCs and cancer/non-cancer drugs to targets expressed by these cells that are worthy of further preclinical evaluation. If effective in preclinical models, these drugs and drug combinations might be repurposed for clinical evaluation in patients with ES.

CpH methylome analysis in human cortical neurons identifies novel gene pathways and drug targets for opioid use disorder

Introduction

DNA methylation (DNAm), an epigenetic mechanism, has been associated with opioid use disorder (OUD) in preclinical and human studies. However, most of the studies have focused on DNAm at CpG sites. DNAm at non-CpG sites (mCpHs, where H indicates A, T, or C) has been recently shown to have a role in gene regulation and to be highly abundant in neurons. However, its role in OUD is unknown. This work aims to evaluate mCpHs in the human postmortem orbital frontal cortex (OFC) in the context of OUD.

Methods

A total of 38 Postmortem OFC samples were obtained from the VA Brain Bank (OUD = 12; Control = 26). mCpHs were assessed using reduced representation oxidative bisulfite sequencing in neuronal nuclei. Differential analysis was performed using the "methylkit" R package. Age, ancestry, postmortem interval, PTSD, and smoking status were included as covariates. Significant mCpHs were set at q-value < 0.05. Gene Ontology (GO) and KEGG enrichment analyses were performed for the annotated genes of all differential mCpH loci using String, ShinyGO, and amiGO software. Further, all annotated genes were analyzed using the Drug gene interaction database (DGIdb).

Results

A total of 2,352 differentially methylated genome-wide significant mCpHs were identified in OUD, mapping to 2,081 genes. GO analysis of genes with differential mCpH loci showed enrichment for nervous system development (p-value = 2.32E-19). KEGG enrichment analysis identified axon guidance and glutamatergic synapse (FDR 9E-4-2.1E-2). Drug interaction analysis found 3,420 interactions between the annotated genes and drugs, identifying interactions with 15 opioid-related drugs, including lofexidine and tizanidine, both previously used for the treatment of OUD-related symptoms.

Conclusion

Our findings suggest a role of mCpHs for OUD in cortical neurons and reveal important biological pathways and drug targets associated with the disorder.

Genome-wide CRISPR screen reveals genetic modifiers of Ca 2+ -mediated cell death

Ca 2+ is a fundamental determinant of survival in living cells. Excessive intracellular Ca 2+ causes cellular toxicity and death but the genetic pathways contributing to Ca 2+ induced cell death are incompletely understood. Here, we performed genome-wide CRISPR knock-out screening in human cells challenged with the Ca 2+ ionophore ionomycin and identified genes and pathways essential for cell death after Ca 2+ overload. We discovered 115 protective gene knockouts, 82 of which are non-essential genes and 21 of which belong to the druggable genome. Notably, members of store operated Ca 2+ entry (SOCE), very long-chain fatty acid synthesis, and SWItch/Sucrose Non-Fermentable (SWI/SNF) pathways provided marked protection against Ca 2+ toxicity. These results reveal pathways previously unknown to mediate Ca 2+ -induced cell death and provide a resource for the development of pharmacotherapies against the sequelae of Ca 2+ overload in disease.

Candidates for drug repurposing to address the cognitive symptoms in schizophrenia

In the protein-protein interactome, we have previously identified a significant overlap between schizophrenia risk genes and genes associated with cognitive performance. Here, we further studied this overlap to identify potential candidate drugs for repurposing to treat the cognitive symptoms in schizophrenia. We first defined a cognition-related schizophrenia interactome from network propagation analyses, and identified drugs known to target more than one protein within this network. Thereafter, we used gene expression data to further select drugs that could counteract schizophrenia-associated gene expression perturbations. Additionally, we stratified these analyses by sex to identify sex-specific pharmacological treatment options for the cognitive symptoms in schizophrenia. After excluding drugs contraindicated in schizophrenia, we identified 12 drug repurposing candidates, most of which have anti-inflammatory and neuroprotective effects. Sex-stratified analyses showed that out of these 12 drugs, four were identified in females only, three were identified in males only, and five were identified in both sexes. Based on our bioinformatics analyses of disease genetics, we suggest 12 candidate drugs that warrant further examination for repurposing to treat the cognitive symptoms in schizophrenia, and suggest that these symptoms could be addressed by sex-specific pharmacological treatment options.

Immune Pathways with Aging Characteristics Improve Immunotherapy Benefits and Drug Prediction in Human Cancer

(1) Background: Perturbation of immune-related pathways can make substantial contributions to cancer. However, whether and how the aging process affects immune-related pathways during tumorigenesis remains largely unexplored. (2) Methods: Here, we comprehensively investigated the immune-related genes and pathways among 25 cancer types using genomic and transcriptomic data. (3) Results: We identified several pathways that showed aging-related characteristics in various cancers, further validated by conventional aging-related gene sets. Genomic analysis revealed high mutation burdens in cytokines and cytokines receptors pathways, which were strongly correlated with aging in diverse cancers. Moreover, immune-related pathways were found to be favorable prognostic factors in melanoma. Furthermore, the expression level of these pathways had close associations with patient response to immune checkpoint blockade therapy in melanoma and non-small cell lung cancer. Applying a net-work-based method, we predicted immune- and aging-related genes in pan-cancer and utilized these genes for potential immunotherapy drug discovery. Mapping drug target data to our top-ranked genes identified potential drug targets, FYN, JUN, and SRC. (4) Conclusions: Taken together, our systematic study helped interpret the associations among immune-related pathways, aging, and cancer and could serve as a resource for promoting clinical treatment.

Anti-Epileptic Drug Target Perturbation and Intracranial Aneurysm Risk: Mendelian Randomization and Colocalization Study

BACKGROUND

In a genome-wide association study of intracranial aneurysms (IA), enrichment was found between genes associated with IA and genes encoding targets of effective anti-epileptic drugs. Our aim was to assess if this pleiotropy is driven by shared disease mechanisms that could potentially highlight a treatment strategy for IA.

METHODS

Using 2-sample inverse-variance weighted Mendelian randomization and genetic colocalization analyses we assessed: (1) if epilepsy liability in general affects IA risk, and (2) whether changes in gene- and protein-expression levels of anti-epileptic drug targets in blood and arterial tissue may causally affect IA risk.

RESULTS

We found no overall effect of epilepsy liability on IA. Expression of 21 genes and 13 proteins corresponding to anti-epileptic drug targets supported a causal effect (P<0.05) on IA risk. Of those genes and proteins, genetic variants affecting CNNM2 levels showed strong evidence for colocalization with IA risk (posterior probability>70%). Higher CNNM2 levels in arterial tissue were associated with increased IA risk (odds ratio, 3.02; [95% CI, 2.32-3.94]; P=3.39×10^-16). CNNM2 expression was best proxied by rs11191580. The magnitude of the effect of this variant was greater than would be expected if systemic blood pressure was the sole IA-causing mechanism in this locus.

CONCLUSIONS

CNNM2 is a driver of the pleiotropy between IA and anti-epileptic drug targets. Administration of the anti-epileptic drugs phenytoin, valproic acid, or carbamazepine may be expected to decrease CNNM2 levels and therefore subsequently decrease IA risk. CNNM2 is therefore an important target to investigate further for its role in the pathogenesis of IA.

Resveratrol; a Double-Edged Sword Antioxidant Agent for Preserving Platelet Cell Functions During Storage; Molecular Insights

Background

In the current study we have aimed to find the effects of Resveratrol treatment on platelet concentrates (PCs) at the dose dependent manner. We have also attempted to find the molecular mechanism of the effects.

Methods

The PCs, have received from Iranian blood transfusion organization (IBTO). Totally 10 PCs were studied. The PCs divided into 4 groups including untreated (control) and treated by different dose of Resveratrol; 10, 30 and 50 µM. Platelet aggregation and total reactive oxygen species (ROS) levels were evaluated at day 3 of PCs storage. In silico analysis was carried out to find out the potential involved mechanisms.

Results

The aggregation against collagen has fallen dramatically in all studied groups but at the same time, aggregation was significantly higher in the control versus treated groups (p<0.05). The inhibitory effect was dose dependent. The aggregation against Ristocetin did not significantly affect by Resveratrol treatment. The mean of total ROS significantly increased in all studied groups except those PCs treated with 10 µM of Resveratrol (P=0.9). The ROS level significantly increased with increasing Resveratrol concentration even more than control group (slope=11.6, P=0.0034). Resveratrol could potently interact with more than 15 different genes which, 10 of them enrolled in cellular regulation of the oxidative stress.

Conclusions

Our findings indicated that the Resveratrol affect the platelet aggregation at the dose dependent manner. Moreover, we have also found that the Resveratrol play as double-edged sword in the controlling oxidative state of the cells. Therefore, Using the optimal dose of Resveratrol is the great of importance.

Drug Discovery in Canine Pyometra Disease Identified by Text Mining and Microarray Data Analysis

Canine pyometra, which is accompanied by bacterial contamination of the dog uterus, is defined as a complex disease associated with the activation of several systems, including the immune system. This study uses text mining and microarray data analysis methods to discover some existing targeted gene drugs and expand potential new drug indications. Text mining ("canine pyometra") and microarray data analysis (GSE99877) were used to obtain a common set of genes. These genes and protein-protein interaction (PPI) networks were analyzed using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Then, the important genes clustered in the PPI network were selected for gene-drug interaction analysis to provide evidence for potential drug discovery. Through text mining and data analysis, we obtained 17,544 text mining genes (TMGs) and 399 differentially expressed genes (DEGs), respectively. There were 256 repeat genes between TMGs and DEGs, including 70 upregulated genes and 186 downregulated genes. Thirty-seven genes clustered in three significant gene modules. Eight of the 37 genes can target 23 existing drugs. In conclusion, the discovery of 8 immune response-related genes (BTK, CSF2RA, CSF2RB, ITGAL, NCF4, PLCG2, PTPRC, and TOP2A) targeting 23 existing drugs may expand the drug indications for pyometra-related diseases in dogs.

Exploring breast cancer exosomes for novel biomarkers of potential diagnostic and prognostic importance

The comprehensive bioinformatics analysis of breast cancer exosomes revealed that HSP90AA1, CCT2, and ENO1 were novel hub genes in the giant protein-protein interaction network of 110 exosomal proteins. Exosomes and their cargo such as discrete proteins, nucleic acids, and lipids are having potential role in the pathophysiology of breast cancer (BC). This study showed that the identified hub genes were particularly abundant in GO and KEGG pathways relevant to the positive regulation of telomerase. In addition, these hub genes were found to be considerably overexpressed in breast adenocarcinoma patients compared to healthy controls, and further, this overexpression is linked to the poor prognosis in BC patients. Furthermore, the ROC analysis revealed that CCT2 gene has strong diagnostic and prognostic value for BC. Additionally, this in silico analysis found that the anticancer agents and HSP90 inhibitors such as ganetespib, retaspimycin, and tanespimycin would have considerable potential in the treatment of BC. Overall, this study findings imply that HSP90AA1, a molecular chaperon and CCT2, a chaperonin would serve as diagnostic and prognostic biomarkers, respectively, for BC. However, these findings need to be further confirmed by laboratory and clinical studies for validating their potential applications.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03422-w.

Discovering KYNU as a feature gene in hidradenitis suppurativa

BACKGROUND

Hidradenitis suppurativa (HS) is a chronic auto-inflammatory skin condition characterized by nodules, abscesses, and fistulae in skin folds. The underlying pathogenesis of HS remains unclear, and effective therapeutic drugs are limited.

METHODS

We acquired mRNA expression profiles from the Gene Expression Omnibus (GEO) database and conducted differential expression analysis between control and HS samples using R software. Four machine learning algorithms (SVM, RF, ANN, and lasso) and WCGNA were utilized to identify feature genes. GO, KEGG, Metascape, and GSVA were utilized for the enrichment analysis. CIBERSORT and ssGSEA were employed to analyze immune infiltration.

RESULTS

A total of 29 DEGs were identified, with the majority showing up-regulation in HS. Enrichment analysis revealed their involvement in immune responses and cytokine activities. KEGG analysis highlighted pathways such as IL-17 signaling, rheumatoid arthritis, and TNF signaling in HS. Immune infiltration analysis revealed the predominant presence of neutrophils, monocytes, and CD8 T cells. Machine learning algorithms and WCGNA identified KYNU as a feature gene associated with HS. We have also identified 59 potential drugs for HS based on the DEGs. Additionally, ceRNA network analysis identified the MUC19_hsa-miR-382-5p_KYNU pathway as a potential regulatory pathway.

CONCLUSIONS

KYNU emerged as a feature gene associated with HS, and the ceRNA network analysis identified the MUC19_hsa-miR-382-5p_KYNU pathway as a potential regulator.

GPX3 and GSTT1 as biomarkers related to oxidative stress during renal ischemia reperfusion injuries and their relationship with immune infiltration

Background

Renal ischemia reperfusion injuries (IRIs) are very common in clinical diagnoses and treatments, which are a common cause of impaired renal functions, worsening pathological damage, affecting disease progression and hindering recovery. Renal IRIs are an inflammatory disease mediated by the adaptive and innate immune system. There is a complex interaction between oxidative stress and immune cell infiltration. Therefore, we aimed to determine biomarkers associated with oxidative stress during renal IRIs and their relationship with immune cell infiltration.

Method

A differential gene expression analysis was made based on the GSE148420 dataset from the NCBI Gene Expression Comprehensive Database (GEO) combined with 92 oxidative-stress (OS)-related genes identified in the Molecular Signatures Database. Then we identified differentially-expressed genes (DEOSGs) associated with oxidative stress, which were used for gene ontology (GO) and a Kyoto Encyclopedia of Genomes (KEGG) enrichment analysis. At the same time, we used PPI protein interaction networks and Lasso regression analysis to identify key genes, which were verified by the validation sets GSE58438 and GSE71647, as well as Western Blot detection on rat renal IRI models. At the same time, PAS staining, HE staining and immunohistochemistry were used to detect tissue damage and expression of markers related to oxidative stress during renal ischemia-reperfusion. Single-gene enrichment analysis (GSEA) was used to further clarify the underlying biological functions of key genes. Cibersort was used to analyze the immune cell infiltration during renal IRI and the correlation of key genes with immune cells. At the same time, we constructed a network of transcription-factor (TF)-Hub genes and miRNA-Hub genes. DGIDB was used to predict drugs and molecular compounds that might interact with the Hub genes.

Results

Compared with the control group, a total of 5456 differential genes (DEGs) were measured in the renal IRI group, 2486 of which were upregulated and 2970 were down-regulated. Among them, we found 30 DEGs (DEOSGs) associated with oxidative stress. The results of GO and KEGG enrichment analysis showed that these DEOSGs were mainly enriched in glutathione metabolism, the response to oxidative stress stimulation, the regulation of T cell activation and apoptosis signaling pathways. Through a protein interaction network (PPI) and a LASSO regression analysis, a total of two Hub genes were identified, namely GPX3 and GSTT1, which were validated through external validation sets and animal experiments. Through pathological methods, we found that the pathological damage of renal tissue and the expression of oxidative stress markers increased after renal ischemia-reperfusion. The results of GSEA showed that the Hub genes were related to oxidative stress pathways, apoptosis signaling pathways and immune-response-related signaling pathways. An immunoinfiltration correlation analysis showed that genes GPX3 and GSTT1 were significantly positively correlated with plasma cells and macrophage M0, while were negatively correlated with monocytes and macrophages M1 and M2. Using the Strust, Starbase and DGIDB database, we predicted that 81 transcription factors, 49 miRNAs and 13 drug or molecular compounds might interact with the Hub genes.

Conclusion

Through a comprehensive analysis of gene expression, our findings may provide new potential biomarkers for the pathogenesis of renal IRIs and a reliable basis for its early diagnosis as well as treatment.

Integrated bioinformatic analysis of mitochondrial metabolism-related genes in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is a common hematologic malignancy characterized by poor prognoses and high recurrence rates. Mitochondrial metabolism has been increasingly recognized to be crucial in tumor progression and treatment resistance. The purpose of this study was to examined the role of mitochondrial metabolism in the immune regulation and prognosis of AML.

Methods

In this study, mutation status of 31 mitochondrial metabolism-related genes (MMRGs) in AML were analyzed. Based on the expression of 31 MMRGs, mitochondrial metabolism scores (MMs) were calculated by single sample gene set enrichment analysis. Differential analysis and weighted co-expression network analysis were performed to identify module MMRGs. Next, univariate Cox regression and the least absolute and selection operator regression were used to select prognosis-associated MMRGs. A prognosis model was then constructed using multivariate Cox regression to calculate risk score. We validated the expression of key MMRGs in clinical specimens using immunohistochemistry (IHC). Then differential analysis was performed to identify differentially expressed genes (DEGs) between high- and low-risk groups. Functional enrichment, interaction networks, drug sensitivity, immune microenvironment, and immunotherapy analyses were also performed to explore the characteristic of DEGs.

Results

Given the association of MMs with prognosis of AML patients, a prognosis model was constructed based on 5 MMRGs, which could accurately distinguish high-risk patients from low-risk patients in both training and validation datasets. IHC results showed that MMRGs were highly expressed in AML samples compared to normal samples. Additionally, the 38 DEGs were mainly related to mitochondrial metabolism, immune signaling, and multiple drug resistance pathways. In addition, high-risk patients with more immune-cell infiltration had higher Tumor Immune Dysfunction and Exclusion scores, indicating poor immunotherapy response. mRNA-drug interactions and drug sensitivity analyses were performed to explore potential druggable hub genes. Furthermore, we combined risk score with age and gender to construct a prognosis model, which could predict the prognosis of AML patients.

Conclusion

Our study provided a prognostic predictor for AML patients and revealed that mitochondrial metabolism is associated with immune regulation and drug resistant in AML, providing vital clues for immunotherapies.

Molecular features and predictive models identify the most lethal subtype and a therapeutic target for osteosarcoma

Background

Osteosarcoma is the most common primary malignant bone tumor. The existing treatment regimens remained essentially unchanged over the past 30 years; hence the prognosis has plateaued at a poor level. Precise and personalized therapy is yet to be exploited.

Methods

One discovery cohort (n=98) and two validation cohorts (n=53 & n=48) were collected from public data sources. We performed a non-negative matrix factorization (NMF) method on the discovery cohort to stratify osteosarcoma. Survival analysis and transcriptomic profiling characterized each subtype. Then, a drug target was screened based on subtypes' features and hazard ratios. We also used specific siRNAs and added a cholesterol pathway inhibitor to osteosarcoma cell lines (U2OS and Saos-2) to verify the target. Moreover, PermFIT and ProMS, two support vector machine (SVM) tools, and the least absolute shrinkage and selection operator (LASSO) method, were employed to establish predictive models.

Results

We herein divided osteosarcoma patients into four subtypes (S-I ~ S-IV). Patients of S- I were found probable to live longer. S-II was characterized by the highest immune infiltration. Cancer cells proliferated most in S-III. Notably, S-IV held the most unfavorable outcome and active cholesterol metabolism. SQLE, a rate-limiting enzyme for cholesterol biosynthesis, was identified as a potential drug target for S-IV patients. This finding was further validated in two external independent osteosarcoma cohorts. The function of SQLE to promote proliferation and migration was confirmed by cell phenotypic assays after the specific gene knockdown or addition of terbinafine, an inhibitor of SQLE. We further employed two machine learning tools based on SVM algorithms to develop a subtype diagnostic model and used the LASSO method to establish a 4-gene model for predicting prognosis. These two models were also verified in a validation cohort.

Conclusion

The molecular classification enhanced our understanding of osteosarcoma; the novel predicting models served as robust prognostic biomarkers; the therapeutic target SQLE opened a new way for treatment. Our results served as valuable hints for future biological studies and clinical trials of osteosarcoma.

Meta-analysis of commonly mutated genes in leptomeningeal carcinomatosis

Background

Leptomeningeal carcinomatosis (LMC) is a rare type of cancer that settles at the meninges through metastasis of non-small cell lung cancer (NSCLC), breast cancer and melanoma. The molecular mechanism underlying LMC is not known, therefore molecular studies investigating the development of LMC are needed. Here, we aimed to identify commonly mutated genes in LMC caused by NSCLC, breast cancer, and melanoma using an in-slico approach and their interactions using integrated bioinformatic approaches/tools in this meta-analysis.

Methods

We conducted a meta-analysis using information from 16 studies that included different sequencing techniques of patients with LMC caused by three different primary cancers: breast cancer, NSCLC, and melanoma. All studies that assessed mutation information from patients with LMC were searched in PubMed, from their inception to February, 16 2022. Studies that performed NGS on LMC patients with NSCLC, breast cancer, or melanoma were included, while studies that did not apply NGS to CSF samples, did not provide information on altered genes, were reviews, editorials, or conference abstracts, or whose main goal was the detection of malignancies were all excluded. We identified commonly mutated genes in all three types of cancer. Next, we constructed a protein-protein interaction network, then performed pathway enrichment analysis. We searched National Institutes of Health (NIH) and Drug-Gene Interaction Database (DGIdb) to find candidate drugs.

Results

We found that TP53, PTEN, PIK3CA, IL7R, and KMT2D genes were commonly mutated genes in all three types of cancer via our meta-analysis that consisted out of 16 studies. Our pathway enrichment analysis showed that all five genes were primarily associated with regulation of cell communication and signaling, and cell proliferation. Other enriched pathways included regulation of apoptotic processes of leukocytes and fibroblasts, macroautophagy and growth. According to our drug search we found candidate drugs; Everolimus, Bevacizumab and Temozolomide, which interact with these five genes.

Conclusion

In conclusion, a total of 96 mutated genes in LMC were investigated via meta-analysis. Our findings suggested vital roles of TP53, PTEN, PIK3CA, KMT2D, and IL7R, which can provide insight into the molecular basis of LMC development and paving the door to the development of new targeted medicine and will encourage molecular biologists to seek biological evidence.

Unveiling Key Biomarkers and Therapeutic Drugs in Polycystic Ovary Syndrome (PCOS) Through Pathway Enrichment Analysis and Hub Gene-miRNA Networks

Background

Polycystic ovary syndrome (PCOS) affects women of reproductive age globally with an incidence rate of 5% - 26%. Growing evidence reports important roles for microRNAs (miRNAs) in the pathophysiology of granulosa cells (GCs) in PCOS.

Objectives

The objectives of this study were to identify the top differentially expressed miRNAs (DE-miRNAs) and their corresponding targets in hub gene-miRNA networks, as well as identify novel DE-miRNAs by analyzing three distinct microarray datasets. Additionally, functional enrichment analysis was performed using bioinformatics approaches. Finally, interactions between the 5 top-ranked hub genes and drugs were investigated.

Methods

Using bioinformatics approaches, three GC profiles from the gene expression omnibus (GEO), namely gene expression omnibus series (GSE)-34526, GSE114419, and GSE137684, were analyzed. Targets of the top DE-miRNAs were predicted using the multiMiR R package, and only miRNAs with validated results were retrieved. Genes that were common between the "DE-miRNA prediction results" and the "existing tissue DE-mRNAs" were designated as differentially expressed genes (DEGs). Gene ontology (GO) and pathway enrichment analyses were implemented for DEGs. In order to identify hub genes and hub DE-miRNAs, the protein-protein interaction (PPI) network and miRNA-mRNA interaction network were constructed using Cytoscape software. The drug-gene interaction database (DGIdb) database was utilized to identify interactions between the top-ranked hub genes and drugs.

Results

Out of the top 20 DE-miRNAs that were retrieved from the GSE114419 and GSE34526 microarray datasets, only 13 of them had "validated results" through the multiMiR prediction method. Among the 13 DE-miRNAs investigated, only 5, namely hsa-miR-8085, hsa-miR-548w, hsa-miR-612, hsa-miR-1470, and hsa-miR-644a, demonstrated interactions with the 10 hub genes in the hub gene-miRNA networks in our study. Except for hsa-miR-612, the other 4 DE-miRNAs, including hsa-miR-8085, hsa-miR-548w, hsa-miR-1470, and hsa-miR-644a, are novel and had not been reported in PCOS pathogenesis before. Also, GO and pathway enrichment analyses identified "pathogenic E. coli infection" in the Kyoto encyclopedia of genes and genomes (KEGG) and "regulation of Rac1 activity" in FunRich as the top pathways. The drug-hub gene interaction network identified ACTB, JUN, PTEN, KRAS, and MAPK1 as potential targets to treat PCOS with therapeutic drugs.

Conclusions

The findings from this study might assist researchers in uncovering new biomarkers and potential therapeutic drug targets in PCOS treatment.

PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology

MOTIVATION

Identifying appropriate pharmacotherapy options from genomics results is a significant challenge in personalized oncology. However, computational methods for prioritizing drugs are underdeveloped. With the hypothesis that network-based approaches can improve the performance by extending the use of potential drug targets beyond direct interactions, we devised two network-based methods for personalized pharmacotherapy prioritization in cancer.

RESULTS

We developed novel personalized drug prioritization approaches, PANACEA: PersonAlized Network-based Anti-Cancer therapy EvaluAtion. In PANACEA, initially, the protein interaction network is extended with drugs, and a driverness score is assigned to each altered gene. For scoring drugs, either (i) the 'distance-based' method, incorporating the shortest distance between drugs and altered genes, and driverness scores, or (ii) the 'propagation' method involving the propagation of driverness scores via a random walk with restart framework is performed. We evaluated PANACEA using multiple datasets, and demonstrated that (i) the top-ranking drugs are relevant for cancer pharmacotherapy using TCGA data; (ii) drugs that cancer cell lines are sensitive to are identified using GDSC data; and (iii) PANACEA can perform adequately in the clinical setting using cases with known drug responses. We also illustrate that the proposed methods outperform iCAGES and PanDrugs, two previous personalized drug prioritization approaches.

AVAILABILITY AND IMPLEMENTATION

The corresponding R package is available on GitHub. (https://github.com/egeulgen/PANACEA.git).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying Genes Associated with Alzheimer's Disease Using Gene-Based Polygenic Risk Score

BACKGROUND

Except APOE, Alzheimer's disease (AD) associated genes identified in recent large-scale genome-wide association studies (GWAS) had small effects and explained a small portion of heritability. Many AD-associated genes have even smaller effects thereby sub-threshold p-values in large-scale GWAS and remain to be identified. For some AD-associated genes, drug targeting them may have limited efficacies due to their small effect sizes.

OBJECTIVE

The purpose of this study is to identify AD-associated genes with sub-threshold p-values and prioritize drugs targeting AD-associated genes that have large efficacies.

METHODS

We developed a gene-based polygenic risk score (PRS) to identify AD genes. It was calculated using SNPs located within genes and having the same directions of effects in different study cohorts to exclude cohort-specific findings and false positives. Gene co-expression modules and protein-protein interaction networks were used to identify AD-associated genes that interact with multiple other genes, as drugs targeting them have large efficacies via co-regulation or interactions.

RESULTS

Gene-based PRS identified 389 genes with 164 of them not previously reported as AD-associated. These 389 genes explained 56.12% -97.46% SNP heritability; and they were enriched in brain tissues and 164 biological processes, most of which are related to AD and other neurodegenerative diseases. We prioritized 688 drugs targeting 64 genes that were in the same co-expression modules and/or PPI networks.

CONCLUSIONS

Gene-based PRS is a cost-effective way to identify AD-associated genes without substantially increasing the sample size. Co-expression modules and PPI networks can be used to identify drugs having large efficacies.

The shared genetic architecture of suicidal behaviour and psychiatric disorders: A genomic structural equation modelling study

Background: Suicidal behaviour (SB) refers to behaviours, ranging from non-fatal suicidal behaviour, such as suicidal ideation and attempt, to completed suicide. Despite recent advancements in genomic technology and statistical methods, it is unclear to what extent the spectrum of suicidal behaviour is explained by shared genetic aetiology. Methods: We identified nine genome-wide association statistics of suicidal behaviour (sample sizes, n, ranging from 62,648 to 125,844), ten psychiatric traits [n up to 386,533] and collectively, nine summary datasets of anthropometric, behavioural and socioeconomic-related traits [n ranging from 58,610 to 941,280]. We calculated the genetic correlation among these traits and modelled this using genomic structural equation modelling, identified shared biological processes and pathways between suicidal behaviour and psychiatric disorders and evaluated potential causal associations using Mendelian randomisation. Results: Among populations of European ancestry, we observed strong positive genetic correlations between suicide ideation, attempt and self-harm (rg range, 0.71-1.09) and moderate to strong genetic correlations between suicidal behaviour traits and a range of psychiatric disorders, most notably, major depression disorder (rg = 0.86, p = 1.62 × 10^-36). Multivariate analysis revealed a common factor structure for suicidal behaviour traits, major depression, attention deficit hyperactivity disorder (ADHD) and alcohol use disorder. The derived common factor explained 38.7% of the shared variance across the traits. We identified 2,951 genes and 98 sub-network hub genes associated with the common factor, including pathways associated with developmental biology, signal transduction and RNA degradation. We found suggestive evidence for the protective effects of higher household income level on suicide attempt [OR = 0.55 (0.44-0.70), p = 1.29 × 10^-5] and while further investigation is needed, a nominal significant effect of smoking on suicide attempt [OR = 1.24 (1.04-1.44), p = 0.026]. Conclusion: Our findings provide evidence of shared aetiology between suicidal behaviour and psychiatric disorders and indicate potential common molecular mechanisms contributing to the overlapping pathophysiology. These findings provide a better understanding of the complex genetic architecture of suicidal behaviour and have implications for the prevention and treatment of suicidal behaviour.

EGCG identified as an autophagy inducer for rosacea therapy

Background: Rosacea is a common facial skin inflammatory disease featured by hyperactivation of mTORC1 signaling in the epidermis. Due to unclear pathogenesis, the effective treatment options for rosacea remain limited. Methods: Weighted gene co-expression network analysis (WGCNA) analyzed the relationship between epidermis autophagy and mTOR pathways in rosacea, and further demonstrated it through immunofluorescence and qPCR analysis. A potential therapeutic agent for rosacea was predicted based on the key genes of the WGCNA module. In vivo and in vitro experiments were conducted to verify its therapeutic role. Drug-target prediction (TargetNet, Swiss, and Tcmsp) and molecular docking offered potential pharmacological targets. Results: WGCNA showed that epidermis autophagy was related to the activation of mTOR pathways in rosacea. Next, autophagy was downregulated in the epidermis of rosacea, which was regulated by mTOR. In addition, the in vivo experiment demonstrated that autophagy induction could be an effective treatment strategy for rosacea. Subsequently, based on the key genes of the WGCNA module, epigallocatechin-3-gallate (EGCG) was predicted as a potential therapeutic agent for rosacea. Furthermore, the therapeutic role of EGCG on rosacea was confirmed in vivo and in vitro. Finally, drug-target prediction and molecular docking revealed that AKT1/MAPK1/MMP9 could be the pharmacological targets of EGCG in rosacea. Conclusion: Collectively, our findings revealed the vital role of autophagy in rosacea and identified that EGCG, as a therapeutic agent for rosacea, attenuated rosacea-like inflammation via inducing autophagy in keratinocytes.

Stages of preadipocyte differentiation: biomarkers and pathways for extracellular structural remodeling

BACKGROUND

This study utilized bioinformatics to analyze the underlying biological mechanisms involved in adipogenic differentiation, synthesis of the extracellular matrix (ECM), and angiogenesis during preadipocyte differentiation in human Simpson-Golabi-Behmel syndrome at different time points and identify targets that can potentially improve fat graft survival.

RESULTS

We analyzed two expression profiles from the Gene Expression Omnibus and identified differentially expressed genes (DEGs) at six different time points after the initiation of preadipocyte differentiation. Related pathways were identified using Gene Ontology/Kyoto Encyclopedia of Genes and Genomes analyses and Gene Set Enrichment Analysis (GSEA). We further constructed a protein-protein interaction (PPI) network and its central genes. The results showed that upregulated DEGs were involved in cell differentiation, lipid metabolism, and other cellular activities, while downregulated DEGs were associated with angiogenesis and development, ECM tissue synthesis, and intercellular and intertissue adhesion. GSEA provided a more comprehensive basis, including participation in and positive regulation of key pathways of cell metabolic differentiation, such as the "peroxisome proliferator-activated receptor signaling pathway" and the "adenylate-activated protein kinase signaling pathway," a key pathway that negatively regulates pro-angiogenic development, ECM synthesis, and adhesion.

CONCLUSIONS

We identified the top 20 hub genes in the PPI network, including genes involved in cell differentiation, ECM synthesis, and angiogenesis development, providing potential targets to improve the long-term survival rate of fat grafts. Additionally, we identified drugs that may interact with these targets to potentially improve fat graft survival.