DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

Application of artificial intelligence in drug design: A review

Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.

CBGDA: a manually curated resource for gene-disease associations based on genome-wide CRISPR

The field of understanding the association between genes and diseases is rapidly expanding, making it challenging for researchers to keep up with the influx of new publications and genetic datasets. Fortunately, there are now several regularly updated databases available that focus on cataloging gene-disease relationships. The development of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 system has revolutionized the field of gene editing, providing a highly efficient, accurate, and reliable method for exploring gene-disease associations. However, currently, there is no resource specifically dedicated to collecting and integrating the latest experimentally supported gene-disease association data derived from genome-wide CRISPR screening. To address this gap, we have developed the CRISPR-Based Gene-Disease Associations (CBGDA) database, which includes over 200 manually curated gene-disease association data derived from genome-wide CRISPR screening studies. Through CBGDA, users can explore gene-disease association data derived from genome-wide CRISPR screening, gaining insights into the expression patterns of genes in different diseases, associated chemical data, and variant information. This provides a novel perspective on understanding the associations between genes and diseases. What is more, CBGDA integrates data from several other databases and resources, enhancing its comprehensiveness and utility. In summary, CBGDA offers a fresh perspective and comprehensive insights into the research on gene-disease associations. It fills the gap by providing a dedicated resource for accessing up-to-date, experimentally supported gene-disease association data derived from genome-wide CRISPR screening. Database URL: http://cbgda.zhounan.org/main.

The therapeutic targets and signaling mechanisms of ondansetron in the treatment of critical illness in the ICU

Background

There is accumulating evidence regarding the benefits of the 5-HT3 receptor antagonist ondansetron for the treatment of critical illness due to its potential anti-inflammatory effect. This study attempted to determine the potential targets and molecular mechanisms of ondansetron's action against critical illnesses.

Methods

A bioinformatics analysis of network pharmacology was conducted to demonstrate screening targets and the signaling pathways of ondansetron action against the most common critical illnesses such as acute kidney injury (AKI), sepsis, and acute respiratory distress syndrome (ARDS). Experiments of LPS-stimulated rat neutrophils with ondansetron treatment were conducted to further validate the relevant hypothesis.

Results

A total of 198, 111, and 26 primary causal targets were identified from the data for the action of ondansetron against AKI, sepsis, and ARDS respectively. We found that the pathway of neutrophil extracellular traps (NETs) formation is statistically significantly involved in the action of ondansetron against these three critical illnesses. In the pathway of NETs formation, the common drug-disease intersection targets in these three critical illnesses were toll-like receptor 8 (TLR8), mitogen-activated protein kinase-14 (MAPK14), nuclear factor kappa-B1 (NFKB1), neutrophil elastase (NE), and myeloperoxidase (MPO). Considering these bioinformatics findings, we concluded that ondansetron anti-critical illness effects are mechanistically and pharmacologically implicated with suppression of neutrophils-associated inflammatory processes. It was also showed that after treatment of LPS-stimulated rat neutrophils with ondansetron, the key proteins NE, MPO, and Peptide Arginine Deaminase 4 (PAD4) in the NETs formation were significantly reduced, and the inflammatory factors IL-6, IL-1β, TNF-α, and chemokine receptor (CXCR4) were also significantly decreased.

Conclusion

The excessive formation of NETs may have important research value in the development and progression of critical illness. Ondansetron may reduce excessive inflammatory injury in critical diseases by reducing the formation of NETs via influencing the five targets: TLR8, NFKB1, MAPK14, NE, and MPO. Ondansetron and these primary predictive biotargets may potentially be used to treat critical illness in future clinical practice.

Integrative analysis reveals novel insights into juvenile idiopathic arthritis pathogenesis and shared molecular pathways with associated traits

Background

Juvenile idiopathic arthritis (JIA) is an autoimmune joint disease that frequently co-occurs with other complex phenotypes, including cancers and other autoimmune diseases. Despite the identification of numerous risk variants through genome-wide association studies (GWAS), the affected genes, their connection to JIA pathogenesis, and their role in the development of associated traits remain unclear. This study aims to address these gaps by elucidating the gene-regulatory mechanisms underlying JIA pathogenesis and exploring its potential role in the emergence of associated traits.

Methods

A two-sample Mendelian Randomization (MR) analysis was conducted to identify blood-expressed genes causally linked to JIA. A curated protein interaction network was subsequently used to identify sets of single-nucleotide polymorphisms (i.e., spatial eQTL SNPs) that regulate the expression of JIA causal genes and their protein interaction partners. These SNPs were cross-referenced against the GWAS catalog to identify statistically enriched traits associated with JIA.

Results

The two-sample MR analysis identified 52 genes whose expression changes in the blood are putatively causal for JIA. These genes (e.g., HLA, LTA, LTB, IL6ST) participate in a range of immune-related pathways (e.g., antigen presentation, cytokine signalling) and demonstrate cell type-specific regulatory patterns across different immune cell types (e.g., PPP1R11 in CD4+ T cells). The spatial eQTLs that regulate JIA causal genes and their interaction partners were statistically enriched for GWAS SNPs linked with 95 other traits, including both known and novel JIA-associated traits. This integrative analysis identified genes whose dysregulation may explain the links between JIA and associated traits, such as autoimmune/inflammatory diseases (genes at 6p22.1 locus), Hodgkin lymphoma (genes at 6p21.3 [FKBPL, PBX2, AGER]), and chronic lymphocytic leukemia (BAK1).

Conclusion

Our approach provides a significant advance in understanding the genetic architecture of JIA and associated traits. The results suggest that the burden of associated traits may differ among JIA patients, influenced by their combined genetic risk across different clusters of traits. Future experimental validation of the identified connections could pave the way for refined patient stratification, the discovery of new biomarkers, and shared therapeutic targets.

Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

Geniposide effectively safeguards HT22 cells against Aβ-induced damage by activating mitophagy via the PINK1/Parkin signaling pathway

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the significant involvement of amyloid-beta (Aβ) peptide in its pathogenesis. Geniposide, derived from the versatile medicinal of Gardenia jasminoides, is one of the active compounds studied extensively. The objective was to explore the impact of geniposide on Aβ25-35-induced damage in HT22 cells, specifically focusing on its modulation of PINK1/Parkin-mediated mitophagy. In our investigation, geniposide exhibited remarkable restorative effects by enhancing cell viability and preserving the mitochondrial membrane potential. Moreover, it effectively reduced and mitigated the oxidative stress and apoptosis rates induced by Aβ25-35. Notably, geniposide exhibited the capacity to enhance autophagic flux, upregulate LC3II and Beclin-1 expression, and downregulate the expression of p62. Furthermore, geniposide positively influenced the expression of PINK1 and Parkin proteins, with molecular docking substantiating a strong interaction between geniposide and PINK1/Parkin proteins. Intriguingly, the beneficial outcomes of geniposide on alleviating the pronounced apoptosis rates, the overproduction of reactive oxygen species, and diminished the PINK1 and Parkin expression induced by Aβ25-35 were compromised by the mitophagy inhibitor cyclosporine A (CsA). Collectively, these findings suggested that geniposide potentially shields HT22 cells against neurodegenerative damage triggered by Aβ25-35 through the activation of mitophagy. The insights contribute valuable references to the defensive consequences against neurological damage of geniposide, thereby highlighting its potential as a therapeutic intervention in AD.

The Gene Network Correlation Analysis of Obesity to Type 1 Diabetes and Cardiovascular Disorders: An Interactome-Based Bioinformatics Approach

The current study focuses on the importance of Protein-Protein Interactions (PPIs) in biological processes and the potential of targeting PPIs as a new treatment strategy for diseases. Specifically, the study explores the cross-links of PPIs network associated with obesity, type 1 diabetes mellitus (T1DM), and cardiac disease (CD), which is an unexplored area of research. The research aimed to understand the role of highly connected proteins in the network and their potential as drug targets. The methodology for this research involves retrieving genes from the NCBI online gene database, intersecting genes among three diseases (type 1 diabetes, obesity, and cardiovascular) using Interactivenn, determining suitable drug molecules using NetworkAnalyst, and performing various bioinformatics analyses such as Generic Protein-Protein Interactions, topological properties analysis, function enrichment analysis in terms of GO, and Kyoto Encyclopedia of Genes and Genomes (KEGG), gene co-expression network, and protein drug as well as protein chemical interaction network. The study focuses on human subjects. The results of this study identified 12 genes [VEGFA (Vascular Endothelial Growth Factor A), IL6 (Interleukin 6), MTHFR (Methylenetetrahydrofolate reductase), NPPB (Natriuretic Peptide B), RAC1 (Rac Family Small GTPase 1), LMNA (Lamin A/C), UGT1A1 (UDP-glucuronosyltransferase family 1 membrane A1), RETN (Resistin), GCG (Glucagon), NPPA (Natriuretic Peptide A), RYR2 (Ryanodine receptor 2), and PRKAG2 (Protein Kinase AMP-Activated Non-Catalytic Subunit Gamma 2)] that were shared across the three diseases and could be used as key proteins for protein-drug/chemical interaction. Additionally, the study provides an in-depth understanding of the complex molecular and biological relationships between the three diseases and the cellular mechanisms that lead to their development. Potentially significant implications for the therapy and management of various disorders are highlighted by the findings of this study by improving treatment efficacy, simplifying treatment regimens, cost-effectiveness, better understanding of the underlying mechanism of these diseases, early diagnosis, and introducing personalized medicine. In conclusion, the current study provides new insights into the cross-links of PPIs network associated with obesity, T1DM, and CD, and highlights the potential of targeting PPIs as a new treatment strategy for these prevalent diseases.

Bioinformatics and In Vitro Study Reveal ERα as The Potential Target Gene of Honokiol to Enhance Trastuzumab Sensitivity in HER2+ Trastuzumab-Resistant Breast Cancer Cells

Trastuzumab resistance presents a significant challenge in the treatment of HER2+ breast cancer, necessitating the investigation of combination therapies to overcome this resistance. Honokiol, a compound with broad anticancer activity, has shown promise in this regard. This study aims to discover the effect of honokiol in increasing trastuzumab sensitivity in HER2+ trastuzumab-resistant breast cancer cells HCC1954 and the underline mechanisms behind. A bioinformatics study performed to explore the most potential target hub gene for honokiol in HER2+ breast cancer. Honokiol, trastuzumab and combined treatment cytotoxicity activity was then evaluated in both parental HCC1954 and trastuzumab resistance (TR-HCC1954) cells using MTT assay. The expression levels of these hub genes were then analyzed using qRT-PCR and those that could not be analyzed were subjected to molecular docking to determine their potential. Honokiol showed a potent cytotoxicity activity with an IC50 of 41.05 μM and 69.61 μM in parental HCC1954 and TR-HCC1954 cell line respectively. Furthermore, the combination of honokiol and trastuzumab resulted in significant differences in cytotoxicity in TR-HCC1954 cells at specific concentrations. Molecular docking and the qRT-PCR showed that the potential ERα identified from the bioinformatics analysis was affected by the treatment. Our results show that honokiol has the potential to increase the sensitivity of trastuzumab in HER2+ trastuzumab resistant breast cancer cell line HCC1954 by affecting regulating estrogen receptor signaling. Further research is necessary to validate these findings.

Disease types and pathogenic mechanisms induced by PM2.5 in five human systems: An analysis using omics and human disease databases

Atmospheric fine particulate matter (PM2.5) can harm various systems in the human body. Due to limitations in the current understanding of epidemiology and toxicology, the disease types and pathogenic mechanisms induced by PM2.5 in various human systems remain unclear. In this study, the disease types induced by PM2.5 in the respiratory, circulatory, endocrine, and female and male urogenital systems have been investigated and the pathogenic mechanisms identified at molecular level. The results reveal that PM2.5 is highly likely to induce pulmonary emphysema, reperfusion injury, malignant thyroid neoplasm, ovarian endometriosis, and nephritis in each of the above systems respectively. The most important co-existing gene, cellular component, biological process, molecular function, and pathway in the five systems targeted by PM2.5 are Fos proto-oncogene (FOS), extracellular matrix, urogenital system development, extracellular matrix structural constituent conferring tensile strength, and ferroptosis respectively. Differentially expressed genes that are significantly and uniquely targeted by PM2.5 in each system are BTG2 (respiratory), BIRC5 (circulatory), NFE2L2 (endocrine), TBK1 (female urogenital) and STAT1 (male urogenital). Important disease-related cellular components, biological processes, and molecular functions are specifically induced by PM2.5. For example, response to wounding, blood vessel morphogenesis, body morphogenesis, negative regulation of response to endoplasmic reticulum stress, and response to type I interferon are the top uniquely existing biological processes in each system respectively. PM2.5 mainly acts on key disease-related pathways such as the PD-L1 expression and PD-1 checkpoint pathway in cancer (respiratory), cell cycle (circulatory), apoptosis (endocrine), antigen processing and presentation (female urogenital), and neuroactive ligand-receptor interaction (male urogenital). This study provides a novel analysis strategy for elucidating PM2.5-related disease types and is an important supplement to epidemiological investigation. It clarifies the risks of PM2.5 exposure, elucidates the pathogenic mechanisms, and provides scientific support for promoting the precise prevention and treatment of PM2.5-related diseases.

A network-based trans-omics approach for predicting synergistic drug combinations

BACKGROUND

Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses.

METHODS

The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML).

RESULTS

Here we show that SyndrumNET outperforms the previous methods in terms of high accuracy. We perform in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibits synergistic anticancer effects. Our mode-of-action analysis also reveals that the drug synergy of the top predicted combination of capsaicin and mitoxantrone is due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway.

CONCLUSIONS

The proposed method is expected to be useful for discovering synergistic drug combinations for various complex diseases.

Integrating network pharmacology and experimental models to investigate the efficacy and mechanism of Tiansha mixture on xerosis

Epidermal Growth Factor Receptor Inhibitors (EGFRIs) is a common cancer therapy, but they occasionally cause severe side effects such as xerosis. Tiansha mixture (TM), a traditional Chinese medicines formulation, is develpoed to treat xerosis. This study aims to understand mechanisms of TM on xerosis. Bio-active compounds were selected from databases (TCMSP, TCM-ID, HERB, ETCM) and removed for poor oral bioavailability and low drug likeness. Then a network-based approach filtered out potential active compounds against xerosis. KEGG enrichment analysis identified PI3K/AKT and ERK/MAPK pathways, which were further verified by molecular docking. Afterwards, the effect of TM on activation of PI3K/AKT and ERK/MAPK pathways was validated in gefitinib-induced xerosis rats, where AKT-activator SC79 and MAPK-activator CrPic were also applied. Skin damage was assessed by dorsal score and HE and Tunel stainings. the levels of inflammation factors IL-6 and TNF-α in serum and skin tissue were measured by ELISA. Western blot was used to detect protein levels in the pathways. Network pharmacology identified 111 bio-active compounds from TM and 14 potential targets. Docking simulation showed apigenin, luteolin, and quercetin bio-active compounds in TM bound to IKBKG, INSR, and RAF-1 proteins. In xerosis model rats, TM mitigated xerosis damage, decreased inflammation factors, and phosphorylation of PI3K/AKT and ERK/MAPK proteins. SC79 or CrPic or their combination reversed TM's effect. The current study identified potential targets and PI3K/AKT and ERK/MAPK pathways involved in the effect of TM on xerosis, thus providing a foundation for TM clinical application.

Transcriptome signatures of the medial prefrontal cortex underlying GABAergic control of resilience to chronic stress exposure

Analyses of postmortem human brains and preclinical studies of rodents have identified somatostatin (SST)-positive interneurons as key elements that regulate the vulnerability to stress-related psychiatric disorders. Conversely, genetically induced disinhibition of SST neurons or brain region-specific chemogenetic activation of SST neurons in mice results in stress resilience. Here, we used RNA sequencing of mice with disinhibited SST neurons to characterize the transcriptome changes underlying GABAergic control of stress resilience. We found that stress resilience of male but not female mice with disinhibited SST neurons is characterized by resilience to chronic stress-induced transcriptome changes in the medial prefrontal cortex. Interestingly, the transcriptome of non-stressed stress-resilient male mice resembled the transcriptome of chronic stress-exposed stress-vulnerable mice. However, the behavior and the serum corticosterone levels of non-stressed stress-resilient mice showed no signs of physiological stress. Most strikingly, chronic stress exposure of stress-resilient mice was associated with an almost complete reversal of their chronic stress-like transcriptome signature, along with pathway changes indicating stress-induced enhancement of mRNA translation. Behaviorally, the mice with disinhibited SST neurons were not only resilient to chronic stress-induced anhedonia - they also showed an inversed anxiolytic-like response to chronic stress exposure that mirrored the chronic stress-induced reversal of the chronic stress-like transcriptome signature. We conclude that GABAergic dendritic inhibition by SST neurons exerts bidirectional control over behavioral vulnerability and resilience to chronic stress exposure that is mirrored in bidirectional changes in expression of putative stress resilience genes, through a sex-specific brain substrate.

Relationships between nine neuropsychiatric disorders and cervical cancer: insights from genetics, causality and shared gene expression patterns

BACKGROUND

Neuropsychiatric disorders and cervical cancer exert substantial influences on women's health. Furthermore, neuropsychiatric disorders frequently manifest as common symptoms in cancer patients, potentially increasing the risk of malignant neoplasms. This study aimed to identify neuropsychiatric disorders that are genetically and causally related to cervical cancer and to investigate the molecular mechanisms underlying these associations.

METHODS

GWAS data related to nine neuropsychiatric disorders, namely, schizophrenia, bipolar disorder, autism spectrum disorder, Parkinson's disease, anxiety, Alzheimer's disease, mood disorders, depression, and alcohol dependence, were obtained to calculate heritability (h2) and genetic correlation (rg) with cervical cancer using linkage disequilibrium score regression (LDSC). Mendelian randomization (MR) analysis of the two cohorts was employed to assess the causal effects. Shared gene expression pattern analysis was subsequently conducted to investigate the molecular mechanism underlying these significant associations.

RESULTS

Anxiety, mood disorders, depression, and alcohol dependence were genetically correlated with cervical cancer (all adjusted P < 0.05). Only depression was causally related to cervical cancer in both the discovery (ORIVW: 1.41, PIVW = 0.02) and replication cohorts (ORIVW: 1.80, PIVW = 0.03) in the MR analysis. Gene expression pattern analysis revealed that 270 genes related to depression and cervical cancer, including tumour necrosis factor (TNF), were significantly upregulated in cervical cancer patients, while vascular endothelial growth factor A (VEGFA), transcription factor AP-1 (JUN), and insulin-like growth factor I (IGF-I) were associated with prognosis in cervical cancer patients (all P < 0.05). These overlapping genes implicated the involvement of multiple biological mechanisms, such as neuron death, the PI3K-Akt signalling pathway, and human papillomavirus infection.

CONCLUSIONS

Genetic, causal and molecular evidence indicates that depression increases the risk of cervical cancer. The TNF, VEGFA, JUN, and IGF-1 genes and the neuron death, PI3K-Akt, and human papillomavirus infection signalling pathways may possibly explain this association.

Integrative network analysis of miRNA-mRNA expression profiles during epileptogenesis in rats reveals therapeutic targets after emergence of first spontaneous seizure

Epileptogenesis is the process by which a normal brain becomes hyperexcitable and capable of generating spontaneous recurrent seizures. The extensive dysregulation of gene expression associated with epileptogenesis is shaped, in part, by microRNAs (miRNAs) - short, non-coding RNAs that negatively regulate protein levels. Functional miRNA-mediated regulation can, however, be difficult to elucidate due to the complexity of miRNA-mRNA interactions. Here, we integrated miRNA and mRNA expression profiles sampled over multiple time-points during and after epileptogenesis in rats, and applied bi-clustering and Bayesian modelling to construct temporal miRNA-mRNA-mRNA interaction networks. Network analysis and enrichment of network inference with sequence- and human disease-specific information identified key regulatory miRNAs with the strongest influence on the mRNA landscape, and miRNA-mRNA interactions closely associated with epileptogenesis and subsequent epilepsy. Our findings underscore the complexity of miRNA-mRNA regulation, can be used to prioritise miRNA targets in specific systems, and offer insights into key regulatory processes in epileptogenesis with therapeutic potential for further investigation.

Sertraline Alleviates Chronic Prostatitis by Regulating the TRPV1 Channel

Introduction

Although sertraline has been widely used for chronic prostatitis (CP), the mechanisms are unclear. Herein, we explored the mechanisms of sertraline in treating CP.

Methods

Network pharmacology methods were used to explore the potential targets and molecular mechanisms. LPS was used to stimulate RWPE-1 cells to construct an in vitro model of CP. An experimental autoimmune prostatitis (EAP) mice model was built. CCK-8 assay, EdU assay, BrdU detection, and Tunel assay were performed to evaluate the proliferation and apoptosis process of cells or tissues, respectively. DCFH-DA and Fluo-4 fluorescence probes were used to detect intracellular ROS and calcium concentrations. Von Frey filaments and open-field tests were utilized to evaluate pain response and depressive-like behavior of mice. Histopathology was evaluated through hematoxylin and eosin staining. RT-qPCR, Western blot, immunofluorescence, and immunohistochemistry were utilized to evaluate the transcription, expression, and location of related proteins. Molecular dynamics (MD) simulation and surface plasmon resonance (SPR) assay were performed to measure the binding capacity of sertraline and related proteins.

Results

Through a network pharmacology analysis, 27 potential targets of sertraline for CP were obtained, and 5 key targets (CHRM1, ADRA1B, HTR2B, HTR2A, and TRPV1) were finally identified. Functional experiments suggested that TRPV1 was involved in the proliferation, apoptosis inhibition, and ROS production of LPS-induced RWPE-1 cells. In vitro experiments showed that sertraline significantly inhibited cell proliferation, ROS generation, and transcription of inflammation cytokines of LPS-induced RWPE-1 cells. Additionally, sertraline markedly promoted the apoptosis level of LPS-stimulated RWPE-1 cells and elevated the expression level of BAX while reducing the expression levels of Bcl2 and Caspase-3. MD simulation and SPR assay confirmed the direct binding of sertraline to TRPV1. Moreover, sertraline significantly down-regulated the expression level of TRPV1 and inhibited calcium influx of LPS-induced RWPE-1 cells. TRPV1 agonist (Capsaicin) significantly restored the effects on proliferation, apoptosis, ROS production, and calcium influx of sertraline on LPS-induced RWPE-1 cells. Mice experiments demonstrated that sertraline treatment could reduce pain response, improve depression-like symptoms, and relieve local prostate inflammation of EAP mice, as well as down-regulated the expression level of TRPV1, inhibit the proliferation, and promote apoptosis of prostate tissues in EAP mice.

Discussion

The results revealed the anti-inflammatory effect of sertraline for RWPE-1 cells and EAP mice, and the potential mechanism was regulating the TRPV1 channel. It indicated that sertraline might serve as a complementary anti-inflammatory agent for CP.

Preclinical side effect prediction through pathway engineering of protein interaction network models

Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein-protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein-protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein-protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.

Comprehensive pan-cancer analysis reveals the versatile role of GALNT7 in epigenetic alterations and immune modulation in cancer

Cancer is a leading cause of mortality globally, characterized by intricate molecular alterations, including epigenetic changes such as glycosylation. This study presents a comprehensive pan-cancer analysis of Polypeptide N-Acetylgalactosaminyltransferase 7 (GALNT7), an enzyme involved in mucin-type O-linked protein glycosylation. GALNT7 has previously been linked to various cancers, but a unified analysis across cancer types is lacking. Leveraging data from TCGA, GTEx, and other sources, we scrutinized GALNT7's expression, prognostic relevance, links to immune-related genes, immune cell infiltration, and its involvement in tumor genetic heterogeneity across 33 cancer types. GALNT7 exhibited diverse expression patterns across cancer types, showcasing its potential as an oncogenic factor, with its expression levels linked to both positive and negative prognoses, highlighting the context-specific nature of its role in cancer progression. We delved into the intricate interplay between GALNT7 and immune genes, unveiling positive and negative correlations, underscoring complex interactions in the tumor microenvironment. GALNT7 was found to impact immune cell infiltration, which could have implications for treatment strategies. Additionally, GALNT7 displayed associations with genetic tumor aspects, encompassing genomic instability, DNA repair issues, and genetic mutations, hinting at its pivotal role in shaping the genetic landscape of diverse cancers. Enrichment analysis uncovered potential functions of GALNT7 beyond glycosylation, such as its participation in signaling pathways and its association with various diseases, notably cancer. This comprehensive analysis elucidates the multifaceted role of GALNT7 in cancer biology, underlining its potential as a therapeutic target and biomarker across various cancer types. These findings provide valuable insights for future research and the development of personalized cancer treatment strategies.

COVID-19 Vaccination in Pregnancy: Pilot Study of Plasma MicroRNAs Associated with Inflammatory Cytokines after COVID-19 mRNA Vaccination

BACKGROUND

The impact of mRNA COVID-19 vaccines on the immunological profiles of pregnant women remains a crucial area of study. This research aims to explore the specific immunological changes triggered by these vaccines in this demographic.

METHODS

In a focused investigation, we examined the effects of mRNA COVID-19 vaccination on microRNA expression in pregnant women. Key microRNAs, including miR-451a, miR-23a-3p, and miR-21-5p, were analyzed for expression changes post-vaccination. Additionally, we assessed variations in S1RBD IgG levels and specific cytokines to gauge the broader immunological response.

RESULTS

Post-vaccination, significant expression shifts in the targeted microRNAs were observed. Alongside these changes, we noted alterations in S1RBD IgG and various cytokines, indicating an adapted inflammatory response. Notably, these immunological markers displayed no direct correlation with S1RBD IgG concentrations, suggesting a complex interaction between the vaccine and the immune system in pregnant women.

CONCLUSIONS

Our pilot study provides valuable insights into the nuanced effects of the mRNA COVID-19 vaccine on immune dynamics in pregnant women, particularly emphasizing the role of microRNAs. The findings illuminate the intricate interplay between vaccines, microRNAs, and immune responses, enhancing our understanding of these relationships in the context of pregnancy. This research contributes significantly to the growing body of knowledge regarding mRNA COVID-19 vaccines and their specific impact on maternal immunology, offering a foundation for further studies in this vital area.

Network pharmacology and experimental insights into STAT3 inhibition by novel isoxazole derivatives of piperic acid in triple negative breast cancer

STAT3 is a crucial member within a family of seven essential transcription factors. Elevated STAT3 levels have been identified in various cancer types, notably in breast cancer (BC). Consequently, inhibiting STAT3 is recognized as a promising and effective strategy for therapeutic intervention against breast cancer. We herein synthesize a library of isoxazole (PAIs) from piperic acid [2E, 4E)-5-(2H-1,3-Benzodioxol-5-yl) penta-2,4-dienoic acid] on treatment with propargyl bromide followed by oxime under prescribed reaction conditions. Piperic acid was obtained by hydrolysis of piperine extracted from Piper nigrum. First, we checked the binding potential of isoxazole derivatives with breast cancer target proteins by network pharmacology, molecular docking, molecular dynamic (MD) simulation and cytotoxicity analysis as potential anti-breast cancer (BC) agents. The multi-source databases were used to identify possible targets for isoxazole derivatives. A network of protein-protein interactions (PPIs) was generated by obtaining 877 target genes that overlapped gene symbols associated with isoxazole derivatives and BC. Molecular docking and MD modelling demonstrated a strong affinity between isoxazole derivatives and essential target genes. Further, the cell viability studies of isoxazole derivatives on the human breast carcinoma cell lines showed toxicity in all breast cancer cell lines. In summary, our study indicated that the isoxazole derivative showed the significant anticancer activity. The results highlight the prospective utility of isoxazole derivatives as new drug candidates for anticancer chemotherapy, suggesting route for the continued exploration and development of drugs suitable for clinical applications.

Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer

INTRODUCTION

Cross-talk among biological pathways is essential for normal biological function and plays a significant role in cancer progression. Through integrated network analysis, this study explores the significance of pathway cross-talk in colorectal cancer (CRC) development at both the pathway and gene levels.

METHODS

In this study, we integrated the gene expression data with domain knowledge to construct state-dependent pathway cross-talk networks. The significance of the genes involved in pathway cross-talk was assessed by analyzing their association with cancer hallmarks, disease-gene relation, genetic alterations, and survival analysis. We also analyzed the gene regulatory network to identify the dysregulated genes and their role in CRC progression.

RESULTS

Cross-talk was observed between immune-related pathways and pathways associated with cell communication and signaling. The PTPRC gene was identified as a mediator, facilitating interactions within the immune system and other signaling pathways. The rewired interactions of ITGA7 were identified as influential in the epithelial-mesenchymal transition in CRC. This study also highlighted the crucial link between cell communication and vascular smooth muscle contraction pathway in CRC progression. The survival analysis of identified gene clusters showed their significant prognostic value in distinguishing high-risk from low-risk CRC groups, and L1000CDS2 revealed seven potential drug molecules in CRC. Nine dysregulated genes (CTNNB1, EP300, JUN, MYC, NFKB1, RELA, SP1, STAT1, and TP53) emerge as transcription factors acting as common regulators across various pathways.

CONCLUSIONS

This study highlights the crucial role of pathway cross-talk in CRC progression and identified the potential prognostic biomarkers and potential drug molecules.

Unlocking mitochondrial drug targets: The importance of mitochondrial transport proteins

A disturbed mitochondrial function contributes to the pathology of many common diseases. These organelles are therefore important therapeutic targets. On the contrary, many adverse effects of drugs can be explained by a mitochondrial off-target effect, in particular, due to an interaction with carrier proteins in the inner membrane. Yet this class of transport proteins remains underappreciated and understudied. The aim of this review is to provide a deeper understanding of the role of mitochondrial carriers in health and disease and their significance as drug targets. We present literature-based evidence that mitochondrial carrier proteins are associated with prevalent diseases and emphasize their potential as drug (off-)target sites by summarizing known mitochondrial drug-transporter interactions. Studying these carriers will enhance our knowledge of mitochondrial drug on- and off-targets and provide opportunities to further improve the efficacy and safety of drugs.

Analysis of gene expression in the postmortem brain of neurotypical Black Americans reveals contributions of genetic ancestry

Ancestral differences in genomic variation affect the regulation of gene expression; however, most gene expression studies have been limited to European ancestry samples or adjusted to identify ancestry-independent associations. Here, we instead examined the impact of genetic ancestry on gene expression and DNA methylation in the postmortem brain tissue of admixed Black American neurotypical individuals to identify ancestry-dependent and ancestry-independent contributions. Ancestry-associated differentially expressed genes (DEGs), transcripts and gene networks, while notably not implicating neurons, are enriched for genes related to the immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson disease and 30% of heritability for Alzheimer's disease. Ancestry-associated DEGs also show general enrichment for the heritability of diverse immune-related traits but depletion for psychiatric-related traits. We also compared Black and non-Hispanic white Americans, confirming most ancestry-associated DEGs. Our results delineate the extent to which genetic ancestry affects differences in gene expression in the human brain and the implications for brain illness risk.

Effects of chemical in vitro activation versus fragmentation on human ovarian tissue and follicle growth in culture

STUDY QUESTION

What is the effect of the chemical in vitro activation (cIVA) protocol compared with fragmentation only (Frag, also known as mechanical IVA) on gene expression, follicle activation and growth in human ovarian tissue in vitro?

SUMMARY ANSWER

Although histological assessment shows that cIVA significantly increases follicle survival and growth compared to Frag, both protocols stimulate extensive and nearly identical transcriptomic changes in cultured tissue compared to freshly collected ovarian tissue, including marked changes in energy metabolism and inflammatory responses.

WHAT IS KNOWN ALREADY

Treatments based on cIVA of the phosphatase and tensin homolog (PTEN)-phosphatidylinositol 3-kinase (PI3K) pathway in ovarian tissue followed by auto-transplantation have been administered to patients with refractory premature ovarian insufficiency (POI) and resulted in live births. However, comparable effects with mere tissue fragmentation have been shown, questioning the added value of chemical stimulation that could potentially activate oncogenic responses.

STUDY DESIGN SIZE DURATION

Fifty-nine ovarian cortical biopsies were obtained from consenting women undergoing elective caesarean section (C-section). The samples were fragmented for culture studies. Half of the fragments were exposed to bpV (HOpic)+740Y-P (Frag+cIVA group) during the first 24 h of culture, while the other half were cultured with medium only (Frag group). Subsequently, both groups were cultured with medium only for an additional 6 days. Tissue and media samples were collected for histological, transcriptomic, steroid hormone, and cytokine/chemokine analyses at various time points.

PARTICIPANTS/MATERIALS SETTING METHODS

Effects on follicles were evaluated by counting and scoring serial sections stained with hematoxylin and eosin before and after the 7-day culture. Follicle function was assessed by quantification of steroids by ultra-performance liquid chromatography tandem-mass spectrometry at different time points. Cytokines and chemokines were measured by multiplex assay. Transcriptomic effects were measured by RNA-sequencing (RNA-seq) of the tissue after the initial 24-h culture. Selected differentially expressed genes (DEGs) were validated by quantitative PCR and immunofluorescence in cultured ovarian tissue as well as in KGN cell (human ovarian granulosa-like tumor cell line) culture experiments.

MAIN RESULTS AND THE ROLE OF CHANCE

Compared to the Frag group, the Frag+cIVA group exhibited a significantly higher follicle survival rate, increased numbers of secondary follicles, and larger follicle sizes. Additionally, the tissue in the Frag+cIVA group produced less dehydroepiandrosterone compared to Frag. Cytokine measurement showed a strong inflammatory response at the start of the culture in both groups. The RNA-seq data revealed modest differences between the Frag+cIVA and Frag groups, with only 164 DEGs identified using a relaxed cut-off of false discovery rate (FDR) <0.1. Apart from the expected PI3K-protein kinase B (Akt) pathway, cIVA also regulated pathways related to hypoxia, cytokines, and inflammation. In comparison to freshly collected ovarian tissue, gene expression in general was markedly affected in both the Frag+cIVA and Frag groups, with a total of 3119 and 2900 DEGs identified (FDR < 0.001), respectively. The top enriched gene sets in both groups included several pathways known to modulate follicle growth such as mammalian target of rapamycin (mTOR)C1 signaling. Significant changes compared to fresh tissue were also observed in the expression of genes encoding for steroidogenesis enzymes and classical granulosa cell markers in both groups. Intriguingly, we discovered a profound upregulation of genes related to glycolysis and its upstream regulator in both Frag and Frag+cIVA groups, and these changes were further boosted by the cIVA treatment. Cell culture experiments confirmed glycolysis-related genes as direct targets of the cIVA drugs. In conclusion, cIVA enhances follicle growth, as expected, but the mechanisms may be more complex than PI3K-Akt-mTOR alone, and the impact on function and quality of the follicles after the culture period remains an open question.

LARGE SCALE DATA

Data were deposited in the GEO data base, accession number GSE234765. The code for sequencing analysis can be found in https://github.com/tialiv/IVA_project.

LIMITATIONS REASONS FOR CAUTION

Similar to the published IVA protocols, the first steps in our study were performed in an in vitro culture model where the ovarian tissue was isolated from the regulation of hypothalamic-pituitary-ovarian axis. Further in vivo experiments will be needed, for example in xeno-transplantation models, to explore the long-term impacts of the discovered effects. The tissue collected from patients undergoing C-section may not be comparable to tissue of patients with POI.

WIDER IMPLICATIONS OF THE FINDINGS

The general impact of fragmentation and short (24 h) in vitro culture on gene expression in ovarian tissue far exceeded the effects of cIVA. Yet, follicle growth was stimulated by cIVA, which may suggest effects on specific cell populations that may be diluted in bulk RNA-seq. Nevertheless, we confirmed the impact of cIVA on glycolysis using a cell culture model, suggesting impacts on cellular signaling beyond the PI3K pathway. The profound changes in inflammation and glycolysis following fragmentation and culture could contribute to follicle activation and loss in ovarian tissue culture, as well as in clinical applications, such as fertility preservation by ovarian tissue auto-transplantation.

STUDY FUNDING/COMPETING INTERESTS

This study was funded by research grants from European Union's Horizon 2020 Research and Innovation Programme (Project ERIN No. 952516, FREIA No. 825100), Swedish Research Council VR (2020-02132), StratRegen funding from Karolinska Institutet, KI-China Scholarship Council (CSC) Programme and the Natural Science Foundation of Hunan (2022JJ40782). International Iberian Nanotechnology Laboratory Research was funded by the European Union's H2020 Project Sinfonia (857253) and SbDToolBox (NORTE-01-0145-FEDER-000047), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund. No competing interests are declared.

Integrating Network Pharmacology and Molecular Docking Approach to Elucidate the Mechanism of Commiphora wightii for the Treatment of Rheumatoid Arthritis

Background

Rheumatoid arthritis (RA) is considered a notable prolonged inflammatory condition with no proper cure. Synovial inflammation and synovial pannus are crucial in the onset of RA. The "tumor-like" invading proliferation of new arteries is a keynote of RA. Commiphora wightii (C wightii) is a perennial, deciduous, and trifoliate plant used in several areas of southeast Asia to cure numerous ailments, including arthritis, diabetes, obesity, and asthma. Several in vitro investigations have indicated C wightii's therapeutic efficacy in the treatment of arthritis. However, the precise molecular action is yet unknown.

Material and methods

In this study, a network pharmacology approach was applied to uncover potential targets, active therapeutic ingredients and signaling pathways in C wightii for the treatment of arthritis. In the groundwork of this research, we examined the active constituent-compound-target-pathway network and evaluated that (Guggulsterol-V, Myrrhahnone B, and Campesterol) decisively donated to the development of arthritis by affecting tumor necrosis factor (TNF), PIK3CA, and MAPK3 genes. Later on, docking was employed to confirm the active components' efficiency against the potential targets.

Results

According to molecular-docking research, several potential targets of RA bind tightly with the corresponding key active ingredient of C wightii. With the aid of network pharmacology techniques, we conclude that the signaling pathways and biological processes involved in C wightii had an impact on the prevention of arthritis. The outcomes of molecular docking also serve as strong recommendations for future research. In the context of this study, network pharmacology combined with molecular docking analysis showed that C wightii acted on arthritis-related signaling pathways to exhibit a promising preventive impact on arthritis.

Conclusion

These results serve as the basis for grasping the mechanism of the antiarthritis activity of C wightii. However, further in vivo/in vitro study is needed to verify the reliability of these targets for the treatment of arthritis.

Bioinformatics-based analysis of the dialog between COVID-19 and RSA

Pregnant women infected with SARS-CoV-2 in early pregnancy may face an increased risk of miscarriage due to immune imbalance at the maternal-fetal interface. However, the molecular mechanisms underlying the crosstalk between COVID-19 infection and recurrent spontaneous abortion (RSA) remain poorly understood. This study aimed to elucidate the transcriptomic molecular dialog between COVID-19 and RSA. Based on bioinformatics analysis, 307 common differentially expressed genes were found between COVID-19 (GSE171110) and RSA (GSE165004). Common DEGs were mainly enriched in ribosome-related and cell cycle-related signaling pathways. Using degree algorithm, the top 10 hub genes (RPS27A, RPL5, RPS8, RPL4, RPS2, RPL30, RPL23A, RPL31, RPL26, RPL37A) were selected from the common DEGs based on their scores. The results of the qPCR were in general agreement with the results of the raw letter analysis. The top 10 candidate drugs were also selected based on P-values. In this study, we provide molecular markers, signaling pathways, and small molecule compounds that may associate COVID-19. These findings may increase the accurate diagnosis and treatment of COVID-19 patients.

Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus

Schizophrenia is a complex neuropsychiatric disorder with sexually dimorphic features, including differential symptomatology, drug responsiveness, and male incidence rate. Prior large-scale transcriptome analyses for sex differences in schizophrenia have focused on the prefrontal cortex. Analyzing BrainSeq Consortium data (caudate nucleus: n = 399, dorsolateral prefrontal cortex: n = 377, and hippocampus: n = 394), we identified 831 unique genes that exhibit sex differences across brain regions, enriched for immune-related pathways. We observed X-chromosome dosage reduction in the hippocampus of male individuals with schizophrenia. Our sex interaction model revealed 148 junctions dysregulated in a sex-specific manner in schizophrenia. Sex-specific schizophrenia analysis identified dozens of differentially expressed genes, notably enriched in immune-related pathways. Finally, our sex-interacting expression quantitative trait loci analysis revealed 704 unique genes, nine associated with schizophrenia risk. These findings emphasize the importance of sex-informed analysis of sexually dimorphic traits, inform personalized therapeutic strategies in schizophrenia, and highlight the need for increased female samples for schizophrenia analyses.

Multi-technology integrated network pharmacology-based study on phytochemicals, active metabolites, and molecular mechanism of Psoraleae Fructus to promote melanogenesis

ETHNOPHARMACOLOGICAL RELEVANCE

According to the Compendium of Materia Medica (Shizhen Li, Ming dynasty) and Welfare Pharmacy (Song dynasty), Psoraleae Fructus (PF), a traditional Chinese medicine (TCM) has a bitter taste and warm nature, which has the effect of treating spleen and kidney deficiency and skin disease. Although PF has been widely used since ancient times and has shown satisfactory efficacy in treating vitiligo, the active substances and the mechanism of PF in promoting melanogenesis remain unclear.

AIM OF THE STUDY

To explore the active substances and action mechanisms of PF in promoting melanogenesis.

MATERIALS AND METHODS

Firstly, UPLC-UV-Q-TOF/MS was used to characterize the components in PF extract and identify the absorption components and metabolites of PF after oral administration at usual doses in rats. Secondly, the active substances and related targets and pathways were predicted by network pharmacology and molecular docking. Finally, pharmacodynamic and molecular biology experiments were used to verify the prediction results.

RESULTS

The experimental results showed that 15 compounds were identified in PF extract, and 44 compounds, consisting of 8 prototype components and 36 metabolites (including isomers) were identified in rats' plasma. Promising action targets (MAPK1, MAPK8, MAPK14) and signaling pathways (MAPK signaling pathway) were screened and refined to elucidate the mechanism of PF against vitiligo based on network pharmacology. Bergaptol and xanthotol (the main metabolites of PF), psoralen (prototype drug), and PF extract significantly increased melanin production in zebrafish embryos. Furthermore, bergaptol could promote the pigmentation of zebrafish embryos more than psoralen and PF extract. Bergaptol significantly increased the protein expression levels of p-P38 and decreased ERK phosphorylation in B16F10 cells, which was also supported by the corresponding inhibitor/activator combination study. Moreover, bergaptol increased the mRNA expression levels of the downstream microphthalmia-associated transcription factor (MITF) and tyrosinase in B16F10 cells. Our data elucidate that bergaptol may promote melanogenesis by regulating the p-P38 and p-ERK signaling pathway.

CONCLUSIONS

This study will lay a foundation for discovering potential new drugs for treating vitiligo and provide feasible ideas for exploring the mechanism of traditional Chinese medicine.

LSD2 Is an Epigenetic Player in Multiple Types of Cancer and Beyond

Histone demethylases, enzymes responsible for removing methyl groups from histone proteins, have emerged as critical players in regulating gene expression and chromatin dynamics, thereby influencing various cellular processes. LSD2 and LSD1 have attracted considerable interest among these demethylases because of their associations with cancer. However, while LSD1 has received significant attention, LSD2 has not been recognized to the same extent. In this study, we conduct a comprehensive comparison between LSD2 and LSD1, with a focus on exploring LSD2's implications. While both share structural similarities, LSD2 possesses unique features as well. Functionally, LSD2 shows diverse roles, particularly in cancer, with tissue-dependent roles. Additionally, LSD2 extends beyond histone demethylation, impacting DNA methylation, cancer cell reprogramming, E3 ubiquitin ligase activity and DNA damage repair pathways. This study underscores the distinct roles of LSD2, providing insights into their contributions to cancer and other cellular processes.

Genome-wide association study and fine-mapping on Korean biobank to discover renal trait-associated variants

BACKGROUND

Chronic kidney disease is a significant health burden worldwide, with increasing incidence. Although several genome- wide association studies (GWAS) have investigated single nucleotide polymorphisms (SNP) associated with kidney trait, most studies were focused on European ancestry.

METHODS

We utilized clinical and genetic information collected from the Korean Genome and Epidemiology Study (KoGES).

RESULTS

More than five million SNPs from 58,406 participants were analyzed. After meta-GWAS, 1,360 loci associated with estimated glomerular filtration rate (eGFR) at a genome-wide significant level (p = 5 × 10-8) were identified. Among them, 399 loci were validated with at least one other biomarker (blood urea nitrogen [BUN] or eGFRcysC) and 149 loci were validated using both markers. Among them, 18 SNPs (nine known ones and nine novel ones) with 20 putative genes were found. The aggregated effect of genes estimated by MAGMA gene analysis showed that these significant genes were enriched in kidney-associated pathways, with the kidney and liver being the most enriched tissues.

CONCLUSION

In this study, we conducted GWAS for more than 50,000 Korean individuals and identified several variants associated with kidney traits, including eGFR, BUN, and eGFRcysC. We also investigated functions of relevant genes using computational methods to define putative causal variants.

Network pharmacology analysis of Chandraprabha Vati: A new hope for the treatment of Metabolic Syndrome

BACKGROUND

Drug research is increasingly using Network Pharmacology (NP) to tackle complex conditions like Metabolic Syndrome (MetS), which is characterized by obesity, hyperglycemia, and dyslipidemia. Single-action drugs are inadequate to treat MetS, which is marked by a range of complications including glucose intolerance, hyperlipidemia, mitochondrial dysfunction, and inflammation.

OBJECTIVES

To analyze Chandraprabha vati using Network Pharmacology to assess its potential in alleviating MetS-related complications.

MATERIAL AND METHODS

The genes related to MetS, inflammation, and the target genes of the CPV components were identified using network pharmacology tools like DisgNET and BindingDB. Followed by mapping of the CPV target genes with the genes implicated in MetS and inflammation to identify putative potential targets. Gene ontology, pathway enrichment analysis, and STRING database were employed for further exploration. Furthermore, drug-target-protein interactions network were visualized using Cytoscape 3.9.1.

RESULTS

The results showed that out of the 225 target genes of the CPV components, 33 overlapping and 19 non-overlapping genes could be potential targets for MetS. Similarly, 14 overlapping and 7 non-overlapping genes could be potential targets for inflammation. The CPV bioactives target genes were found to be involved in lipid and insulin homeostasis via several pathways revealed by the pathway analysis. The importance of CPV in treating MetS was supported by GO enrichment data; this could be due to its potential to influence pathways linked to metabolism, ER stress, mitochondrial dysfunction, oxidative stress, and inflammation.

CONCLUSIONS

These results offer a promising approach to developing treatment and repurposing CPV for complex conditions such as MetS.

Network Pharmacology Approach to Identify the Calotropis Phytoconstituents' Potential Epileptic Targets and Evaluation of Molecular Docking, MD Simulation, and MM-PBSA Performance

Epilepsy originates from unusual electrical rhythm within brain cells, causes seizures. Calotropis species have been utilized to treat a wide spectrum of ailments since antiquity. Despite chemical and biological investigations, there have been minimal studies on their anticonvulsant activity, and the molecular targets of this plant constituents are unexplored. This study aimed to investigate the plausible epileptic targets of Calotropis phytoconstituents through network pharmacology, and to evaluate their binding strength and stability with the identified targets. In detail, 125 phytoconstituents of the Calotropis plant (C. procera and C. gigantea) were assessed for their drug-likeness (DL), blood-brain-barrier (BBB) permeability and oral bioavailability (OB). Network analysis revealed that targets PTGS2 and PPAR-γ were ranked first and fourth, respectively, among the top ten hub genes significantly linked with antiepileptic drug targets. Additionally, docking, molecular dynamic (MD) simulation, and Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) were employed to validate the compound-gene interactions. Docking studies suggested ergost-5-en-3-ol, stigmasterol and β-sitosterol exhibit stronger binding affinity and favorable interactions than co-crystallized ligands with both the targets. Furthermore, both MD simulations and MM-PBSA calculations substantiated the docking results. Combined data revealed that Calotropis phytoconstituents ergost-5-en-3-ol, stigmasterol, and β-sitosterol might be the best inhibitors of both PTGS2 and PPAR-γ.

Identification of potential key ferroptosis- and autophagy-related genes in myelomeningocele through bioinformatics analysis

Myelomeningocele is a common congenital anomaly associated with polygenic disorders worldwide. However, the intricate molecular mechanisms underlying myelomeningocele remain elusive. To investigate whether ferroptosis and ferritinophagy contribute to the pathomechanism of myelomeningocele, differentially expressed genes (DEGs) were identified as novel biomarker and potential treatment agents. The GSE101141 dataset from Gene Expression Omnibus (GEO) was analyzed using GEO2R web tool to obtain DEGs based on |log2 fold change (FC)|≥1.5 and p < 0.05. Two datasets from the Ferroptosis Database (481 genes) and Autophagy Database (551 genes) were intersected with the DEGs from the GSE101141 dataset to identify ferroptosis- and autophagy-related DEGs using Venn diagrams. Functional and pathway enrichment, protein-protein interaction (PPI) network analyses were performed, and candidate genes were selected. Transcription factors (TFs), microRNAs (miRNAs), diseases and chemicals interacting with the candidate genes were identified. Receiver operating characteristic (ROC) curve analysis was performed to validate the diagnostic value of the candidate genes. Sixty ferroptosis-related and 74 autophagy-related DEGs were identified. These DEGs are involved in FoxO signaling pathway. Six candidate genes (EGFR, KRAS, IL1B, SIRT1, ATM, and MAPK8) were selected. miRNAs such as hsa-miR-27a-3p, hsa-miR-877-5p, and hsa-miR-892b, and TFs including P53, POU3F2, TATA are involved in regulation of candidate genes. Diseases such as schizophrenia, fibrosis, and neoplasms are the most relevant to the candidate genes. Chemicals, such as resveratrol, curcumin, and quercetin may have significant implications in the treatment of myelomeningocele. The candidate genes, especially MAPK8, also showed a high diagnostic value for myelomeningocele. These results help to shed light on the molecular mechanism of myelomeningocele and may provide new insights into diagnostic biomarker in the amniotic fluid and potential therapeutic agents of myelomeningocele.

A critical assessment of Traditional Chinese Medicine databases as a source for drug discovery

Traditional Chinese Medicine (TCM) has been used for thousands of years to treat human diseases. Recently, many databases have been devoted to studying TCM pharmacology. Most of these databases include information about the active ingredients of TCM herbs and their disease indications. These databases enable researchers to interrogate the mechanisms of action of TCM systematically. However, there is a need for comparative studies of these databases, as they are derived from various resources with different data processing methods. In this review, we provide a comprehensive analysis of the existing TCM databases. We found that the information complements each other by comparing herbs, ingredients, and herb-ingredient pairs in these databases. Therefore, data harmonization is vital to use all the available information fully. Moreover, different TCM databases may contain various annotation types for herbs or ingredients, notably for the chemical structure of ingredients, making it challenging to integrate data from them. We also highlight the latest TCM databases on symptoms or gene expressions, suggesting that using multi-omics data and advanced bioinformatics approaches may provide new insights for drug discovery in TCM. In summary, such a comparative study would help improve the understanding of data complexity that may ultimately motivate more efficient and more standardized strategies towards the digitalization of TCM.

Dysfunction of the adhesion G protein-coupled receptor latrophilin 1 (ADGRL1/LPHN1) increases the risk of obesity

Obesity is one of the diseases with severe health consequences and rapidly increasing worldwide prevalence. Understanding the complex network of food intake and energy balance regulation is an essential prerequisite for pharmacological intervention with obesity. G protein-coupled receptors (GPCRs) are among the main modulators of metabolism and energy balance. They, for instance, regulate appetite and satiety in certain hypothalamic neurons, as well as glucose and lipid metabolism and hormone secretion from adipocytes. Mutations in some GPCRs, such as the melanocortin receptor type 4 (MC4R), have been associated with early-onset obesity. Here, we identified the adhesion GPCR latrophilin 1 (ADGRL1/LPHN1) as a member of the regulating network governing food intake and the maintenance of energy balance. Deficiency of the highly conserved receptor in mice results in increased food consumption and severe obesity, accompanied by dysregulation of glucose homeostasis. Consistently, we identified a partially inactivating mutation in human ADGRL1/LPHN1 in a patient suffering from obesity. Therefore, we propose that LPHN1 dysfunction is a risk factor for obesity development.

The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research

BACKGROUND

As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs.

OBJECTIVE

We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics.

METHODS

We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base.

RESULTS

AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones.

CONCLUSIONS

AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

The Genomic Intersection of Oligodendrocyte Dynamics in Schizophrenia and Aging Unravels Novel Pathological Mechanisms and Therapeutic Potentials

Schizophrenia is a significant worldwide health concern, affecting over 20 million individuals and contributing to a potential reduction in life expectancy by up to 14.5 years. Despite its profound impact, the precise pathological mechanisms underlying schizophrenia continue to remain enigmatic, with previous research yielding diverse and occasionally conflicting findings. Nonetheless, one consistently observed phenomenon in brain imaging studies of schizophrenia patients is the disruption of white matter, the bundles of myelinated axons that provide connectivity and rapid signalling between brain regions. Myelin is produced by specialised glial cells known as oligodendrocytes, which have been shown to be disrupted in post-mortem analyses of schizophrenia patients. Oligodendrocytes are generated throughout life by a major population of oligodendrocyte progenitor cells (OPC), which are essential for white matter health and plasticity. Notably, a decline in a specific subpopulation of OPC has been identified as a principal factor in oligodendrocyte disruption and white matter loss in the aging brain, suggesting this may also be a factor in schizophrenia. In this review, we analysed genomic databases to pinpoint intersections between aging and schizophrenia and identify shared mechanisms of white matter disruption and cognitive dysfunction.

Unveiling the Nexus: Cellular Metabolomics Unravels the Impact of Estrogen on Nicotinamide Metabolism in Mitigating Rheumatoid Arthritis Pathogenesis

Rheumatoid arthritis (RA) is a metabolic joint disorder influenced by hormonal regulation, notably estrogen, which plays a cytoprotective role against inflammation. While estrogen's impact on RA pathogenesis has been studied, the altered metabolite expression under estrogen's influence remains unexplored. This study investigated the changes in the metabolome of synovial fibroblasts isolated from RA patients under 17β-estradiol (E2) using the liquid chromatography with tandem mass spectrometry (LC-MS/MS) approach followed by multivariate and biological pathway analysis along with in vitro validation. Results identified 3624 m/z, among which eight metabolites were significant (p < 0.05). Nicotinate and nicotinamide metabolism was found to be highly correlated with the treatment of E2, with metabolites NAD+ and 1-methynicotinamide (1-MNA) upregulated by E2 induction in RA-FLS. PharmMapper analysis identified potential gene targets of 1-MNA, which were further matched with RA gene targets, and thus, STAT1, MAPK14, MMP3, and MMP9 were concluded to be the common targets. E2 treatment affected the expression of these gene targets and ameliorated the development of oxidative stress associated with RA inflammation, which can be attributed to increased concentration of 1-MNA. Thus, an LC-MS/MS-based metabolomics study revealed the prominent role of estrogen in preventing inflammatory progression in RA by altering metabolite concentration, which can support its therapeutic capacity in remitting RA.

An expression-directed linear mixed model discovering low-effect genetic variants

Detecting genetic variants with low-effect sizes using a moderate sample size is difficult, hindering downstream efforts to learn pathology and estimating heritability. In this work, by utilizing informative weights learned from training genetically predicted gene expression models, we formed an alternative approach to estimate the polygenic term in a linear mixed model. Our linear mixed model estimates the genetic background by incorporating their relevance to gene expression. Our protocol, expression-directed linear mixed model, enables the discovery of subtle signals of low-effect variants using moderate sample size. By applying expression-directed linear mixed model to cohorts of around 5,000 individuals with either binary (WTCCC) or quantitative (NFBC1966) traits, we demonstrated its power gain at the low-effect end of the genetic etiology spectrum. In aggregate, the additional low-effect variants detected by expression-directed linear mixed model substantially improved estimation of missing heritability. Expression-directed linear mixed model moves precision medicine forward by accurately detecting the contribution of low-effect genetic variants to human diseases.

Deciphering the molecular regulatory of RAB32/GPRC5A axis in chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a significant public health problem characterized by persistent airflow limitation. Despite previous research into the pathogenesis of COPD, a comprehensive understanding of the cell-type-specific mechanisms in COPD remains lacking. Recent studies have implicated Rab GTPases in regulating chronic immune response and inflammation via multiple pathways. In this study, the molecular regulating mechanism of RAB32 in COPD was investigated by multiple bioinformatics mining and experimental verification.

METHODS

We collected lung tissue surgical specimens from Zhongshan Hospital, Fudan University, and RT-qPCR and western blotting were used to detect the expression of Rabs in COPD lung tissues. Four COPD microarray datasets from the Gene Expression Omnibus (GEO) were analyzed. COPD-related epithelial cell scRNA-seq data was obtained from the GSE173896 dataset. Weighted gene co-expression network analysis (WGCNA), mfuzz cluster, and Spearman correlation analysis were combined to obtain the regulatory network of RAB32 in COPD. The slingshot algorithm was used to identify the regulatory molecule, and the co-localization of RAB32 and GPRC5A was observed with immunofluorescence.

RESULTS

WGCNA identified 771 key module genes significantly associated with the occurrence of COPD, including five Rab genes. RAB32 was up-regulated in lung tissues from subjects with COPD as contrast to those without COPD on both mRNA and protein levels. Integrating the results of WGCNA, Mfuzz clusters, and Spearman analysis, nine potential interacting genes with RAB32 were identified. Among these genes, GPRC5A exhibited a similar molecular expression pattern to RAB32. Co-expression density analysis at the cell level demonstrated that the co-expression density of RAB32 and GPRC5A was higher in type I alveolar epithelial cells (AT1s) than in type II alveolar epithelial cells (AT2s). The immunofluorescence also confirmed the co-localization of RAB32 and GPRC5A, and the Pearson correlation analysis found the relationship between RAB32 and GPRC5A was significantly stronger in the COPD lungs (r = 0.65) compared to the non-COPD lungs (r = 0.33).

CONCLUSIONS

Our study marked endeavor to delineate the molecular regulatory axis of RAB32 in COPD by employing diverse methods and identifying GPRC5A as a potential interacting molecule with RAB32. These findings offered novel perspectives on the mechanism of COPD.

Exposure to Airborne PM2.5 Water-Soluble Inorganic Ions Induces a Wide Array of Reproductive Toxicity

Water-soluble inorganic ions (WSIIs, primarily NH4+, SO42-, and NO3-) are major components in ambient PM2.5, but their reproductive toxicity remains largely unknown. An animal study was conducted where parental mice were exposed to PM2.5 WSIIs or clean air during preconception and the gestational period. After delivery, all maternal and offspring mice lived in a clean air environment. We assessed reproductive organs, gestation outcome, birth weight, and growth trajectory of the offspring mice. In parallel, we collected birth weight and placenta transcriptome data from 150 mother-infant pairs from the Rhode Island Child Health Study. We found that PM2.5 WSIIs induced a broad range of adverse reproductive outcomes in mice. PM2.5 NH4+, SO42-, and NO3- exposure reduced ovary weight by 24.22% (p = 0.005), 14.45% (p = 0.048), and 16.64% (p = 0.022) relative to the clean air controls. PM2.5 SO42- exposure reduced the weight of testicle by 5.24% (p = 0.025); further, mice in the PM2.5 SO42- exposure group had 1.81 (p = 0.027) fewer offspring than the control group. PM2.5 NH4+, SO42-, and NO3- exposure all led to lower birth than controls. In mice, 557 placenta genes were perturbed by exposure. Integrative analysis of mouse and human data suggested hypoxia response in placenta as an etiological mechanism underlying PM2.5 WSII exposure's reproductive toxicity.

A single-nuclei paired multiomic analysis of the human midbrain reveals age- and Parkinson's disease-associated glial changes

Age is the primary risk factor for Parkinson's disease (PD), but how aging changes the expression and regulatory landscape of the brain remains unclear. Here we present a single-nuclei multiomic study profiling shared gene expression and chromatin accessibility of young, aged and PD postmortem midbrain samples. Combined multiomic analysis along a pseudopathogenesis trajectory reveals that all glial cell types are affected by age, but microglia and oligodendrocytes are further altered in PD. We present evidence for a disease-associated oligodendrocyte subtype and identify genes lost over the aging and disease process, including CARNS1, that may predispose healthy cells to develop a disease-associated phenotype. Surprisingly, we found that chromatin accessibility changed little over aging or PD within the same cell types. Peak-gene association patterns, however, are substantially altered during aging and PD, identifying cell-type-specific chromosomal loci that contain PD-associated single-nucleotide polymorphisms. Our study suggests a previously undescribed role for oligodendrocytes in aging and PD.

Proteome changes in larval zebrafish (Danio rerio) and fathead minnow (Pimephales promelas) exposed to (±) anatoxin-a

Anatoxin-a and its analogues are potent neurotoxins produced by several genera of cyanobacteria. Due in part to its high toxicity and potential presence in drinking water, these toxins pose threats to public health, companion animals and the environment. It primarily exerts toxicity as a cholinergic agonist, with high affinity at neuromuscular junctions, but molecular mechanisms by which it elicits toxicological responses are not fully understood. To advance understanding of this cyanobacteria, proteomic characterization (DIA shotgun proteomics) of two common fish models (zebrafish and fathead minnow) was performed following  (±) anatoxin-a exposure. Specifically, proteome changes were identified and quantified in larval fish exposed for 96 h (0.01-3 mg/L (±) anatoxin-a and caffeine (a methodological positive control) with environmentally relevant treatment levels examined based on environmental exposure distributions of surface water data. Proteomic concentration - response relationships revealed 48 and 29 proteins with concentration - response relationships curves for zebrafish and fathead minnow, respectively. In contrast, the highest number of differentially expressed proteins (DEPs) varied between zebrafish (n = 145) and fathead minnow (n = 300), with only fatheads displaying DEPs at all treatment levels. For both species, genes associated with reproduction were significantly downregulated, with pathways analysis that broadly clustered genes into groups associated with DNA repair mechanisms. Importantly, significant differences in proteome response between the species was also observed, consistent with prior observations of differences in response using both behavioral assays and gene expression, adding further support to model specific differences in organismal sensitivity and/or response. When DEPs were read across from humans to zebrafish, disease ontology enrichment identified diseases associated with cognition and muscle weakness consistent with the prior literature. Our observations highlight limited knowledge of how (±) anatoxin-a, a commonly used synthetic racemate surrogate, elicits responses at a molecular level and advances its toxicological understanding.

Analysis of the Association between Copy Number Variation and Ventricular Fibrillation in ST-Elevation Acute Myocardial Infarction

Sudden cardiac death due to ventricular fibrillation (VF) during ST-elevation acute myocardial infarction (STEAMI) significantly contributes to cardiovascular-related deaths. Although VF has been linked to genetic factors, variations in copy number variation (CNV), a significant source of genetic variation, have remained largely unexplored in this context. To address this knowledge gap, this study performed whole exome sequencing analysis on a cohort of 39 patients with STEAMI who experienced VF, aiming to elucidate the role of CNVs in this pathology. The analysis revealed CNVs in the form of duplications in the PARP2 and TTC5 genes as well as CNVs in the form of deletions in the MUC15 and PPP6R1 genes, which could potentially serve as risk indicators for VF during STEAMI. The analysis also underscores notable CNVs with an average gene copy number equal to or greater than four in DEFB134, FCGR2C, GREM1, PARM1, SCG5, and UNC79 genes. These findings provide further insight into the role of CNVs in VF in the context of STEAMI.

Intrahepatic Transcriptomics Differentiate Advanced Fibrosis and Clinical Outcomes in Adults With Fontan Circulation

BACKGROUND

The molecular mechanisms underlying Fontan-associated liver disease (FALD) remain largely unknown.

OBJECTIVES

This study aimed to assess intrahepatic transcriptomic differences among patients with FALD according to the degree of liver fibrosis and clinical outcomes.

METHODS

This retrospective cohort study included adults with the Fontan circulation. Baseline clinical, laboratory, imaging, and hemodynamic data as well as a composite clinical outcome (CCO) were extracted from medical records. Patients were classified into early or advanced fibrosis. RNA was isolated from formalin-fixed paraffin-embedded liver biopsy samples; RNA libraries were constructed with the use of an rRNA depletion method and sequenced on an Illumina Novaseq 6000. Differential gene expression and gene ontology analyses were performed with the use of DESeq2 and Metascape.

RESULTS

A total of 106 patients (48% male, median age 31 years [IQR: 11.3 years]) were included. Those with advanced fibrosis had higher B-type natriuretic peptide levels and Fontan, mean pulmonary artery, and capillary wedge pressures. The CCO was present in 23 patients (22%) and was not predicted by advanced liver fibrosis, right ventricular morphology, presence of aortopulmonary collaterals, or Fontan pressures on multivariable analysis. Samples with advanced fibrosis had 228 upregulated genes compared with early fibrosis. Samples with the CCO had 894 upregulated genes compared with those without the CCO. A total of 136 upregulated genes were identified in both comparisons and were enriched in cellular response to cytokine stimulus or oxidative stress, VEGFA-VEGFR2 signaling pathway, TGF-β signaling pathway, and vasculature development.

CONCLUSIONS

Patients with FALD and advanced fibrosis or the CCO exhibited upregulated genes related to inflammation, congestion, and angiogenesis.

Keystone pathobionts associated with colorectal cancer promote oncogenic reprograming

Fusobacterium nucleatum (Fn) and enterotoxigenic Bacteroides fragilis (ETBF) are two pathobionts consistently enriched in the gut microbiomes of patients with colorectal cancer (CRC) compared to healthy counterparts and frequently observed for their direct association within tumors. Although several molecular mechanisms have been identified that directly link these organisms to features of CRC in specific cell types, their specific effects on the epithelium and local immune compartment are not well-understood. To fill this gap, we leveraged single-cell RNA sequencing (scRNA-seq) on wildtype mice and mouse model of CRC. We find that Fn and ETBF exacerbate cancer-like transcriptional phenotypes in transit-amplifying and mature enterocytes in a mouse model of CRC. We also observed increased T cells in the pathobiont-exposed mice, but these pathobiont-specific differences observed in wildtype mice were abrogated in the mouse model of CRC. Although there are similarities in the responses provoked by each organism, we find pathobiont-specific effects in Myc-signaling and fatty acid metabolism. These findings support a role for Fn and ETBF in potentiating tumorigenesis via the induction of a cancer stem cell-like transit-amplifying and enterocyte population and the disruption of CTL cytotoxic function.

Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond

BACKGROUND

Biological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank.

RESULTS

We first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics.

CONCLUSION

Overall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.

rvTWAS: identifying gene-trait association using sequences by utilizing transcriptome-directed feature selection

Toward the identification of genetic basis of complex traits, transcriptome-wide association study (TWAS) is successful in integrating transcriptome data. However, TWAS is only applicable for common variants, excluding rare variants in exome or whole-genome sequences. This is partly because of the inherent limitation of TWAS protocols that rely on predicting gene expressions. Our previous research has revealed the insight into TWAS: the 2 steps in TWAS, building and applying the expression prediction models, are essentially genetic feature selection and aggregations that do not have to involve predictions. Based on this insight disentangling TWAS, rare variants' inability of predicting expression traits is no longer an obstacle. Herein, we developed "rare variant TWAS," or rvTWAS, that first uses a Bayesian model to conduct expression-directed feature selection and then uses a kernel machine to carry out feature aggregation, forming a model leveraging expressions for association mapping including rare variants. We demonstrated the performance of rvTWAS by thorough simulations and real data analysis in 3 psychiatric disorders, namely schizophrenia, bipolar disorder, and autism spectrum disorder. We confirmed that rvTWAS outperforms existing TWAS protocols and revealed additional genes underlying psychiatric disorders. Particularly, we formed a hypothetical mechanism in which zinc finger genes impact all 3 disorders through transcriptional regulations. rvTWAS will open a door for sequence-based association mappings integrating gene expressions.

Epigenetic drug screening for trophoblast syncytialization reveals a novel role for MLL1 in regulating fetoplacental growth

BACKGROUND

Abnormal placental development is a significant factor contributing to perinatal morbidity and mortality, affecting approximately 5-7% of pregnant women. Trophoblast syncytialization plays a pivotal role in the establishment and maturation of the placenta, and its dysregulation is closely associated with several pregnancy-related disorders, including preeclampsia and intrauterine growth restriction. However, the underlying mechanisms and genetic determinants of syncytialization are largely unknown.

METHODS

We conducted a systematic drug screen using an epigenetic compound library to systematically investigate the epigenetic mechanism essential for syncytialization, and identified mixed lineage leukemia 1 (MLL1), a histone 3 lysine 4 methyltransferase, as a crucial regulator of trophoblast syncytialization. BeWo cells were utilized to investigate the role of MLL1 during trophoblast syncytialization. RNA sequencing and CUT&Tag were further performed to search for potential target genes and the molecular pathways involved. Human placenta tissue was used to investigate the role of MLL1 in TEA domain transcription factor 4 (TEAD4) expression and the upstream signaling during syncytialization. A mouse model was used to examine whether inhibition of MLL1-mediated H3K4me3 regulated placental TEAD4 expression and fetoplacental growth.

RESULTS

Genetic knockdown of MLL1 or pharmacological inhibition of the MLL1 methyltransferase complex (by MI-3454) markedly enhanced syncytialization, while overexpression of MLL1 inhibited forskolin (FSK)-induced syncytiotrophoblast formation. In human placental villous tissue, MLL1 was predominantly localized in the nuclei of cytotrophoblasts. Moreover, a notable upregulation in MLL1 expression was observed in the villus tissue of patients with preeclampsia compared with that in the control group. Based on RNA sequencing and CUT&Tag analyses, depletion of MLL1 inhibited the Hippo signaling pathway by suppressing TEAD4 expression by modulating H3K4me3 levels on the TEAD4 promoter region. TEAD4 overexpression significantly reversed the FSK-induced or MLL1 silencing-mediated trophoblast syncytialization. Additionally, decreased hypoxia-inducible factor 1A (HIF1A) enrichment at the MLL1 promoter was observed during syncytialization. Under hypoxic conditions, HIF1A could bind to and upregulate MLL1, leading to the activation of the MLL1/TEAD4 axis. In vivo studies demonstrated that the administration of MI-3454 significantly enhanced fetal vessel development and increased the thickness of the syncytial layer, thereby supporting fetoplacental growth.

CONCLUSIONS

These results revealed a novel epigenetic mechanism underlying the progression of syncytialization with MLL1, and suggest potential avenues for identifying new therapeutic targets for pregnancy-related disorders.

A case of mosaic deletion of paternally-inherited PLAGL1 and two cases of upd(6)mat add to evidence for PLAGL1 under-expression as a cause of growth restriction

PLAGL1 is one of a group of imprinted genes, whose altered expression causes imprinting disorders impacting growth, development, metabolism, and behavior. PLAGL1 over-expression causes transient neonatal diabetes mellitus (TNDM type 1) and, based on murine models, under-expression would be expected to cause growth restriction. However, only some reported individuals with upd(6)mat have growth restriction, giving rise to uncertainty about the role of PLAGL1 in human growth. Here we report three individuals investigated for growth restriction, two with upd(6)mat and one with a mosaic deletion of the paternally-inherited allele of PLAGL1. These cases add to evidence of its involvement in pre- and early post-natal human growth.

A Novel High-Dimensional Kernel Joint Non-Negative Matrix Factorization With Multimodal Information for Lung Cancer Study

Judging and identifying biological activities and biomarkers inside tissues from imaging features of diseases is challenging, so correlating pathological image data with genes inside organisms is of great significance for clinical diagnosis. This paper proposes a high-dimensional kernel non-negative matrix factorization (NMF) method based on muti-modal information fusion. This algorithm can project RNA gene expression data and pathological images (WSI) into a common feature space, where the heterogeneous variables with the largest coefficient in the same projection direction form a co-module. In addition, the miRNA-mRNA and miRNA-lncRNA interaction networks in the ceRNA network are added to the algorithm as a priori information to explore the relationship between the images and the internal activities of the gene. Furthermore, the radial basis kernel function is used to calculate the feature proportion between different kinds of genes mapped in the high-dimensional feature space and projected into the common feature space to explore the gene interaction in the high-dimensional situation. The original feature matrix is regularized to improve biological correlation, and the feature factors are sparse by orthogonal constraints to reduce redundancy. Experimental results show that the proposed NMF method is better than the traditional NMF method in stability, decomposition accuracy, and robustness. Through data analysis applied to lung cancer, genes related to tissue morphology are found, such as COL7A1, CENPF and BIRC5. In addition, gene pairs with a correlation degree exceeding 0.8 are found, and potential biomarkers of significant correlation with survival are obtained such as CAPN8. It has potential application value for the clinical diagnosis of lung cancer.