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

Identification and prioritization of disease candidate genes using biomedical named entity recognition and random forest classification

BACKGROUND AND OBJECTIVE

The elucidation of candidate genes is fundamental to comprehending intricate diseases, vital for early diagnosis, personalized treatment, and drug discovery. Traditional Disease Gene Identification methods encounter limitations, necessitating substantial sample sizes and statistical power, particularly challenging for complex diseases. Conversely, Disease Gene Prioritization methods leverage biological knowledge but rely on computational predictions, often lacking experimental validation. Addressing existing tool challenges, this study introduces an innovative two-tier machine-learning protocol that distils Disease Gene Association details from disease-specific abstracts, incorporating diverse findings. Employing advanced text mining, the model classifies disease-gene associations from the abstracts into Positive, Negative, and Ambiguous classes.

METHODS

Leveraging Random Forest as a robust text classification tool, this study demonstrates its efficacy in navigating complexities within biomedical texts. In the developed 2-tiered protocol, the level 1 classifier categorizes information into two classes, distinguished by the presence or absence of disease-gene associations, whereas the level 2 classifier further classifies into three classes: Positive, Negative, and Ambiguous associations. The developed classifier underwent rigorous training and cross-validation on different gold standard datasets - Alzheimer's, Breast Cancer and Type 2 Diabetes. Its performance across these varied disease contexts underscores its versatility and robustness without succumbing to overfitting.

RESULTS

Achieving an average accuracy of 97.29 % and 98.14 % for level 1 and level 2 classification, the protocol successfully extracted 2769, 3220 and 740 genes associated positively with Alzheimer's, Breast Cancer and Type 2 Diabetes. From the identified positive genes, a substantial number-1008, 670, and 165 genes, respectively-were not reported in established databases, thus expanding the genetic exploration of these diseases. These identified genes offer promising opportunities for targeted interventions, while ambiguous genes warrant further investigation to unravel deeper disease associations.

CONCLUSIONS

This research significantly contributes to the understanding of genetic diseases by offering a comprehensive roadmap for their intricate exploration. Beyond the study's focus on Alzheimer's, Breast Cancer, and Type 2 Diabetes, the protocol's applicability extends to diverse biomedical landscapes, demonstrating its versatility and impactful potential for comprehensive disease exploration.

Bioinformatics analysis reveals shared molecular pathways for relationship between ulcerative colitis and primary sclerosing cholangitis

BACKGROUND

Inflammatory bowel disease (IBD) is a group of chronic inflammatory disorders, including ulcerative colitis (UC) and Crohn's disease, affecting the gastrointestinal tract and is associated with high morbidity and mortality. Accumulating evidence indicates that IBD not only impacts the gastrointestinal tract but also affects multiple extraintestinal organs, which may manifest prior to the diagnosis of IBD. Among these extraintestinal manifestations associated with IBD, primary sclerosing cholangitis (PSC) stands out as a prominent example. PSC is recognized as a progressive cholestatic disorder, characterized by the narrowing of bile ducts, eventual development of liver cirrhosis, end-stage liver disease, and the potential emergence of cholangiocarcinoma. This study aimed to identify the molecular contributors in UC-induced PSC by detecting the essential regulatory genes that are differentially expressed in both diseases.

MATERIALS AND METHODS

The common single-nucleotide polymorphisms (SNPs) and differentially expressed genes (DEGs) were detected using DisGeNET and GEO databases, respectively. Then, the top module and hub genes within the protein-protein interaction network were identified. Furthermore, the co-expression network of the top module was constructed using the HIPPIE database. Additionally, the gene regulatory network was constructed based on miRNAs and circRNAs. Finally, we searched the DGIdb database for possible interacting drugs with UC-PSC top module genes.

RESULTS

A total of 132 SNPs and their associated genes were found to be shared between UC and PSC. Gene expression analysis identified 56 common DEGs between the two diseases. Following functional enrichment analysis, 207 significant biological processes (BP), 48 molecular functions (MF), and 8 KEGG pathways, with notable enrichment in mRNA-related processes such as mRNA splicing and RNA binding, were defined. Particularly, the PTPN2 gene was the only gene common between UC and PSC at both the SNP level and the expression level. Additionally, the top cluster of PPI network analysis was consisted of PABPC1, SNRPA1, NOP56, NHP2L1, and HNRNPA2B1 genes. Finally, ceRNA network involving 4 mRNAs, 94 miRNAs, and 200 selected circRNAs was constructed.

CONCLUSION

The present study provides novel potential candidate genes that may be involved in the molecular association between ulcerative colitis and primary sclerosing cholangitis, resulting in the development of diagnostic tools and therapeutic targets to prevent the progression of PSC from UC.

An update on knowledge graphs and their current and potential applications in drug discovery

INTRODUCTION

Knowledge graphs are becoming prominent tools in computational drug discovery. They effectively integrate heterogeneous biomedical data and generate new hypotheses and knowledge.

AREAS COVERED

This article is based on a literature review using Google Scholar and PubMed to retrieve articles on existing knowledge graphs relevant to the drug discovery field. The authors compare the types of entities, relationships, and data sources they encompass. Additionally, the authors provide examples of their use in the drug discovery field and discuss potential strategies for advancing this research area.

EXPERT OPINION

Knowledge graphs are crucial in drug discovery, but their construction leads to challenges in data integration and consistency. Future research should prioritize the standardization of data sources and data modeling. More efforts are needed for the integration in knowledge graphs of diverse data types, such as chemical structures and epigenetic data, to enhance their effectiveness. Additionally, advancements in large language models should be pursued to aid the development of knowledge graphs, provide intuitive querying capabilities for non-expert users, and explain knowledge graphs -derived predictions, thereby making these tools more accessible and their insights more interpretable for a wider audience.

Going beyond ATP binding site as a novel inhibitor design strategy for tau protein kinases in the treatment of Alzheimer's disease: A review

Alzheimer's disease (AD) is among the top mortality causing diseases worldwide. The presence of extracellular β-amyloidosis, as well as intraneuronal neurofibrillary aggregates of the abnormally hyperphosphorylated tau protein are two major characteristics of AD. Targeting protein kinases that are involved in the disease pathways has been a common approach in the fight against AD. Unfortunately, most kinase inhibitors currently available target the adenosine triphosphate (ATP)- binding site, which has proven unsuccessful due to issues with selectivity and resistance. As a result, a pressing need to find other alternative sites beyond the ATP- binding site has profoundly evolved. In this review, we will showcase some case studies of inhibitors of tau protein kinases acting beyond ATP binding site which have shown promising results in alleviating AD.

Integrating network pharmacology and in vivo study to explore the anti-Alzheimer's potential of Bergenia ligulata and Nelumbo nucifera

Amyloid plaque buildup, tau protein tangles, oxidative stress, and neuronal death are the hallmarks of Alzheimer's disease (AD). Using network pharmacology, molecular docking, and in vivo experiments, this study investigated the neuroprotective potential of Bergenia ligulata (BL) and Nelumbo nucifera (NN) against aluminum chloride (AlCl₃)-induced AD. Network pharmacology focused on important biomarker proteins like acetylcholinesterase (AChE), BCL2, and caspase-3 to identify 74 bioactive targets linked to AD. The evaluation of ligand-protein interactions was done using molecular docking. Male Wistar rats were exposed to AlCl₃ to cause AD-like pathology in vivo, and a combination treatment of BL and NN at varying doses was provided. Apoptosis markers (BCL2, caspase-3), biochemical investigations (AChE activity, oxidative stress markers-GSH, SOD, catalase, and lipid peroxidation), behavioral evaluations (elevated plus maze, conditioned avoidance test), and histopathological analyses were investigated. The combination of BL and NN demonstrated substantial neuroprotection in a dose-dependent manner. Reduced AChE levels point out improved cholinergic activity. Oxidative stress indicators showed improvement, with lower levels of malondialdehyde and higher anti-oxidant levels of GSH, SOD, and catalase. Apoptotic markers showed an increase in BCL2 expression and a decrease in caspase-3, suggesting anti-apoptotic effects. Reduced neuronal degeneration in the cortex and hippocampal regions was confirmed by histopathology of the brain. The synergistic potential of BL and NN demonstrated potent neuroprotective effects by modulating AChE activity, reducing oxidative stress, increasing anti-oxidant levels, and inhibiting apoptosis. These findings highlighted the potential of BL and NN as a new therapeutic approach for the AD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-025-04274-w.

Exploration potential sepsis-ferroptosis mechanisms through the use of CETSA technology and network pharmacology

As an important self-protection response mechanism of the body, inflammation can not only remove the necrotic or even malignant cells in the body, but also take a series of targeted measures to eliminate the pathogen of foreign invasion and block the foreign substances that may affect the life and health of the body. Flavonoids have known anti-inflammatory, anti-oxidation, anti-cancer and other effects, including glycyrrhizin molecules is one of the representatives. Licochalcone D has known anti-inflammatory and antioxidant properties and is effective in the treatment of a variety of inflammatory diseases. However, the underlying mechanism for the treatment of sepsis remains unclear. In this study, the therapeutic potential of Licochalcone D for sepsis was studied by analyzing network pharmacology and molecular dynamics simulation methods. Sepsis-related genes were collected from the database to construct PPI network maps and drug-targeting network profiles. The potential mechanism of Licochalcone D in sepsis was predicted by gene ontology, KEGG and molecular dynamics simulation. Sixty drug-disease genes were subsequently validated. Go analysis showed that monomeric small molecule Licochalcone D could regulate the process of intracellular enzyme system. The KEGG pathway analysis showed that the signal pathway of the main effect was related to the calcium pathway. The results of intersections with iron death-related target genes showed that ALOX5, ALOX15B and other nine targets all had the effect of possibly improving sepsis, while GSE 54,514, GSE 95,233 and GSE 69,528 were used to analyze the survival rate and ROC curve. Five genes were screened, including ALOX5, ALOX15B, NFE2L2 and NR4A1, HIF1A. The results of molecular docking showed that ALOX5 and Licochalcone D had strong binding activity. Finally, the results of molecular dynamics simulation showed that there was good binding power between drug and target. In the present study, we utilized molecular dynamics simulation techniques to assess the binding affinity between the small-molecule ligand and the protein receptor. The simulation outcomes demonstrate that the binding interface between the ligand and receptor remains stable, with a calculated binding free energy (ΔG) of -32.47 kJ/mol. This signifies a high-affinity interaction between the ligand and receptor, suggesting the long-term stability of the small molecule under physiological conditions. These findings provide critical insights for drug development efforts. This study elucidates the therapeutic potential of Licochalcone D, a traditional Chinese medicine monomer, in improving sepsis through the regulation of ferroptosis, thereby providing a new direction and option for subsequent clinical drug development in the treatment of sepsis.

Chemical Pollutant Exposure in Neurodevelopmental Disorders: Integrating Toxicogenomic and Transcriptomic Evidence to Elucidate Shared Biological Mechanisms and Developmental Signatures

Rapid industrialization has introduced a range of chemicals into the environment, posing significant risks to fetal and child brain development. Using the Comparative Toxicogenomics Database (CTD), we constructed chemical exposome frameworks for seven neurodevelopmental disorders (NDDs) and identified chemical pollutants of epidemiological concern, including air pollutants (n = 8), toxic elements (n = 14), pesticides and related compounds (n = 18), synthetic organic chemicals (n = 16), and solvents (n = 5). Gene set enrichment analysis validated and revealed significant toxicogenomic associations between these chemical pollutants and NDDs, including autism spectrum disorder (ASD) (12 pollutants, proportional reporting ratio (PRR) 3.56-7.21) and intellectual disability (ID) (9 pollutants, PRR 3.13-5.59). Functional annotation of pollutant-specific gene sets highlighted shared biological processes, such as metabolic processes (e.g., xenobiotic metabolic process, xenobiotic catabolic process, and cytochrome P450 pathway) for ASD and cognitive processes (e.g., cognition, social behavior, and synapse assembly) for ID (Bonferroni-corrected p-values < 0.05). Time trajectory analysis of developmental transcriptomic data from the BrainSpan database for ASD (275 genes) and ID (93 genes) revealed three distinct expression patterns of chemical-pollutant-associated genes-higher prenatal, postnatal, and perinatal expression-indicating common and divergent underlying mechanisms across critical windows of chemical pollutant exposure.

DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models

Deoxyribonucleic acid (DNA) serves as fundamental genetic blueprint that governs development, functioning, growth, and reproduction of all living organisms. DNA can be altered through germline and somatic mutations. Germline mutations underlie hereditary conditions, while somatic mutations can be induced by various factors including environmental influences, chemicals, lifestyle choices, and errors in DNA replication and repair mechanisms which can lead to cancer. DNA sequence analysis plays a pivotal role in uncovering the intricate information embedded within an organism's genetic blueprint and understanding the factors that can modify it. This analysis helps in early detection of genetic diseases and the design of targeted therapies. Traditional wet-lab experimental DNA sequence analysis through traditional wet-lab experimental methods is costly, time-consuming, and prone to errors. To accelerate large-scale DNA sequence analysis, researchers are developing AI applications that complement wet-lab experimental methods. These AI approaches can help generate hypotheses, prioritize experiments, and interpret results by identifying patterns in large genomic datasets. Effective integration of AI methods with experimental validation requires scientists to understand both fields. Considering the need of a comprehensive literature that bridges the gap between both fields, contributions of this paper are manifold: It presents diverse range of DNA sequence analysis tasks and AI methodologies. It equips AI researchers with essential biological knowledge of 44 distinct DNA sequence analysis tasks and aligns these tasks with 3 distinct AI-paradigms, namely, classification, regression, and clustering. It streamlines the integration of AI into DNA sequence analysis tasks by consolidating information of 36 diverse biological databases that can be used to develop benchmark datasets for 44 different DNA sequence analysis tasks. To ensure performance comparisons between new and existing AI predictors, it provides insights into 140 benchmark datasets related to 44 distinct DNA sequence analysis tasks. It presents word embeddings and language models applications across 44 distinct DNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 39 word embeddings and 67 language models based predictive pipeline performance values as well as top performing traditional sequence encoding-based predictors and their performances across 44 DNA sequence analysis tasks.

Zika virus infection in a non-neural cell host promotes differential expression of proteins associated with neurological conditions

The Zika Virus (ZIKV) is a Flavivirus that caused a recent outbreak worldwide resulting in different neurological outcomes that are still poorly characterized and understood. Concerning this issue, in vitro and in vivo models are being applied to improve the molecular understanding of ZIKV infection. In this work, applying shotgun proteomics we revealed the differential ZIKV infection proteome in Vero cells, a non-neural cell model. A dramatic change resulting from infection was found including the differential expression of several proteins previously associated with brain diseases. The molecular alterations caused by this pathogen were further characterized through bioinformatics such as Gene Ontology and protein-protein interaction network of resulting differential proteome. Our findings identified molecular markers that were differentially expressed during ZIKV infection and had been previously linked to neurological conditions and infections caused by ZIKV and/or SARS-CoV-2. The results presented in this article highlight molecular markers associated with neurological dysfunctions, demonstrating that ZIKV infection can dysregulate neural-specific genes, even in non-neural cells.

Exploring bioactive natural products for treating neurodegenerative diseases: a computational network medicine approach targeting the estrogen signaling pathway in amyotrophic lateral sclerosis and Parkinson's disease

Amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) share overlapping molecular mechanisms, including estrogen signaling dysregulation, oxidative stress, and neuroinflammation. Standard treatments often lead to adverse effects due to unintended cross-talk with the estrogen signaling pathway. Identifying key regulatory genes and bioactive plant-derived compounds that modulate estrogen signaling without interfering with standard therapies offers a promising neuroprotective strategy. A network medicine and systems biology approach was used, beginning with the screening of 29 medicinal plants for ALS and 49 for PD, identifying 12 shared plants with neuroprotective potential. Bioactive compounds were screened for gene, protein, and pathway interactions, leading to target prediction (846 ALS-related and 690 PD-related targets) and disease association mining, which identified 93 overlapping genes (OGs). Protein-protein interaction (PPI) network analysis and MCODE clustering revealed ESR1, EGFR, and SRC as key hub-bottleneck (HB) genes, further validated via differential gene expression analysis. Gene ontology (GO) and pathway enrichment analyses revealed significant enrichment in estrogen signaling confirming the involvement of HB genes in neurodegenerative disease progression. Differential expression analysis confirmed ESR1 upregulation in ALS but downregulation in PD, suggesting a converse disease-specific regulatory pattern. Gene regulatory network (GRN) analysis identified hsa-miR-145-5p (ALS) and hsa-miR-181a-5p (PD) as key regulators, while FOXC1, GATA2, and TP53 emerged as crucial transcription factors (TFs) influencing disease progression. Molecular docking and MD simulations validated strong and stable interactions of Eupalitin (CYP19A1, -9.0 kcal/mol), Hesperetin (ESR1, -8.1 kcal/mol), and Sumatrol (PIK3CA, -8.9 kcal/mol). These phytochemicals, derived from Rosmarinus officinalis, Artemisia scoparia, Ocimum tenuiflorum, and Indigofera tinctoria, maintained stable hydrogen bonding and hydrophobic interactions for over 30% of a 25 ns simulation, supporting their therapeutic potential. The identification of ESR1, EGFR, and SRC as key targets, alongside estrogen signaling involvement, highlights the need for targeted nutraceutical interventions. These findings pave the way for safer, plant-based therapies that mitigate neurodegeneration while preserving estrogen signaling integrity, offering a promising adjuvant strategy alongside existing treatments.

G0S2 modulates normal vitreous-induced proliferation in endothelial cells

Abnormal blood vessel growth in the eye is a leading cause of vision loss globally, particularly in diseases like diabetic retinopathy where the vitreous plays a crucial but poorly understood role in disease progression. While we know the vitreous can stimulate blood vessel growth, the specific molecular mechanisms remain unclear. Here we show that a protein called G0S2 (G0/G1 switch gene 2) serves as a key regulator of blood vessel growth in response to normal vitreous. Through comprehensive gene analysis, we discovered that G0S2 levels increase significantly when blood vessel cells are exposed to normal vitreous. The importance of G0S2 is highlighted by our finding that uveal melanoma patients with higher G0S2 levels had poorer survival rates. When we removed G0S2 from blood vessel cells, they no longer responded to vitreous stimulation, confirming its critical role. Notably, we identified an existing drug that can target G0S2, potentially offering a new therapeutic approach. This discovery of G0S2's role and its potential therapeutic targeting opens new avenues for treating eye diseases characterized by abnormal blood vessel growth, while also providing a valuable biomarker for predicting disease progression in eye cancer patients.

EIF2S2 as a prognostic marker and therapeutic target in glioblastoma: insights into its role and downstream mechanisms

Glioblastoma (GBM) the most common and most aggressive primary brain tumor has a five-year survival rate of less than 5%. The onset of GBM is very complicated and has always been the focus of researchers. This study analyzed data from 637 GBM and 20 normal tissues from The Cancer Genome Atlas (TCGA), and patients were categorized into high and low EIF2S2 expression groups. The Overall survival and disease-free survival of GBM patients in low expression of EIF2S2 group were significantly higher than those in high expression of EIF2S2 group (p < 0.001), and the expression level of EIF2S2 was significantly correlated with tumor grade (p < 0.001) and tumor recurrence (p < 0.001). The study designed three different short hairpin RNA (shRNA) sequence vectors, identifying shEIF2S2-1 as the most effective. This vector significantly reduced EIF2S2 expression, cell proliferation, and migration while increasing apoptosis in SHG-44 and U251 cells (p < 0.01). By detecting SHG-44 cells infected with shEIF2S2 vector and shCtrl with human whole gene expression chip, we identified WNT5A that is a downstream target gene of EIF2S2. Interfering with WNT5A and overexpressing EIF2S2 in SHG-44 and U251 cells revealed that EIF2S2 regulates WNT5A expression. This regulation led to an increased apoptosis rate (p < 0.05) and a significant reduction in cell migration (p < 0.05) in both the EIF2S2 overexpression and shWNT5A interference groups, confirming that WNT5A is a downstream regulatory target of EIF2S2. This study revealed the key role of EIF2S2 in GBM and its potential molecular mechanism.

A comprehensive multi-omics study reveals potential prognostic and diagnostic biomarkers for colorectal cancer

BACKGROUND AND OBJECTIVE

Colorectal cancer (CRC) is a complex disease with diverse genetic alterations and causes 10 % of cancer-related deaths worldwide. Understanding its molecular mechanisms is essential for identifying potential biomarkers and therapeutic targets for its effective management.

METHODS

We integrated copy number alterations (CNA) and mutation data via their differentially expressed genes termed as candidate genes (CGs) computed using bioinformatics approaches. Then, using the CGs, we perform Weighted correlation network analysis (WGCNA) and utilise several hazard models such as Univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO) Cox and multivariate Cox to identify the key genes involved in CRC progression. We used different machine-learning models to demonstrate the discriminative power of selected hub genes among normal and CRC (early and late-stage) samples.

RESULTS

The integration of CNA with mRNA expression identified over 3000 CGs, including CRC-specific driver genes like MYC and APC. In addition, pathway analysis revealed that the CGs are mainly enriched in endocytosis, cell cycle, wnt signalling and mTOR signalling pathways. Hazard models identified four key genes, CASP2, HCN4, LRRC69 and SRD5A1, that were significantly associated with CRC progression and predicted the 1-year, 3-years, and 5-years survival times. WGCNA identified seven hub genes: DSCC1, ETV4, KIAA1549, NOP56, RRS1, TEAD4 and ANKRD13B, which exhibited strong predictive performance in distinguishing normal from CRC (early and late-stage) samples.

CONCLUSIONS

Integrating regulatory information with gene expression improved early versus late-stage prediction. The identified potential prognostic and diagnostic biomarkers in this study may guide us in developing effective therapeutic strategies for CRC management.

Bioinformatics study and cytotoxicity of several curcumin analogues in ovarian cancer

Ovarian cancer ranks as Indonesia's third-leading cause of cancer-related death, emphasising the need for innovative treatments. This study combined bioinformatics, molecular docking, and experimental assays to tackle this challenge. We identified 166 ovarian cancer-related genes, with MYC standing out as a key target. Analysis of MYC mutations revealed prevalent alterations, though no significant survival differences were observed in patients with or without the mutations. Molecular docking pinpointed compound B155 as a promising MYC inhibitor. A preliminary cytotoxicity assay revealed compound B155's notable activity, with an 87.19 % inhibition of cell viability at 50 μM. Most of the other curcumin analogues only caused more than 50 % inhibition at the same concentration. This result suggests alternative mechanisms of action, possibly antioxidant effects, warranting further exploration. In summary, this study unveiled MYC as a prime target for ovarian cancer treatment, with curcumin analogues like B155 showing potential. Nonetheless, the complex factors affecting cytotoxicity underscore the need for deeper investigation into these compounds' mechanisms in ovarian cancer cells.

Computational drug repurposing: approaches, evaluation of in silico resources and case studies

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

Revealing Lingonberry's Neuroprotective Potential in Alzheimer's Disease Through Network Pharmacology and Molecular Docking

Alzheimer's disease is a progressive neurodegenerative disorder with limited treatment options. Lingonberry (Vaccinium vitis-idaea L.) has demonstrated neuroprotective and anti-inflammatory properties, yet the underlying mechanisms remain unclear. This study employed network pharmacology, molecular docking, and molecular dynamics simulations to explore the therapeutic potential in Alzheimer's disease. Pathway analysis identified monoamine oxidase B as a key target involved in serotonergic synapse dysfunction related to Alzheimer's disease. Molecular docking revealed that ferulic acid, a major bioactive compound in lingonberry, exhibits strong binding affinity to monoamine oxidase B. Further molecular dynamics simulations confirmed the stability of this interaction, highlighting the potential inhibitory effect of ferulic acid on monoamine oxidase B. These findings provide novel insights into the neuroprotective mechanisms of lingonberry and suggest its potential as a natural therapeutic intervention for Alzheimer's disease.

A Systems Biology Approach for Prioritizing ASD Genes in Large or Noisy Datasets

Autism spectrum disorder (ASD) is a complex multifactorial neurodevelopmental disorder. Despite extensive research involving genome-wide association studies, copy number variant (CNV) testing, and genome sequencing, the comprehensive genetic landscape remains incomplete. In this context, we developed a systems biology approach to prioritize genes associated with ASD and uncover potential new candidates. A Protein-Protein Interaction (PPI) network was generated from genes associated to ASD in a public database. Leveraging gene topological properties, particularly betweenness centrality, we prioritized genes and unveiled potential novel candidates (e.g., CDC5L, RYBP, and MEOX2). To test this approach, a list of genes within CNVs of unknown significance, identified through array comparative genomic hybridization analysis in 135 ASD patients, was mapped onto the PPI network. A prioritized gene list was obtained through ranking by betweenness centrality score. Intriguingly, by over-representation analysis, significant enrichments emerged in pathways not strictly linked to ASD, including ubiquitin-mediated proteolysis and cannabinoid receptor signaling, suggesting their potential perturbation in ASD. Our systems biology approach provides a promising strategy for identifying ASD risk genes, especially in large and noisy datasets, and contributes to a deeper understanding of the disorder's complex genetic basis.

Leveraging hierarchical structures for genetic block interaction studies using the hierarchical transformer

Initially introduced in 1909 by William Bateson, classic epistasis (genetic variant interaction) refers to the phenomenon that one variant prevents another variant from a different locus from manifesting its effects. The potential effects of genetic variant interactions on complex diseases have been recognized for the past decades. Moreover, It has been studied and demonstrated that leveraging the combined SNP effects within the genetic block can significantly increase calculation power, reducing background noise, ultimately leading to novel epistasis discovery that the single SNP statistical epistasis study might overlook. However, it is still an open question how we can best combine gene structure representation modelling and interaction learning into an end-to-end model for gene interaction searching. Here, in the current study, we developed a neural genetic block interaction searching model that can effectively process large SNP chip inputs and output the potential genetic block interaction heatmap. Our model augments a previously published hierarchical transformer architecture (Liu and Lapata, 2019) with the ability to model genetic blocks. The cross-block relationship mapping was achieved via a hierarchical attention mechanism which allows the sharing of information regarding specific phenotypes, as opposed to simple unsupervised dimensionality reduction methods e.g. PCA. Results on both simulation and UK Biobank studies show our model brings substantial improvements compared to traditional exhaustive searching and neural network methods.

Effect of Rhei Radix Et Rhizome on treatment of polycystic ovary syndrome by regulating PI3K/AKT pathway and targeting EGFR/ALB in rats

ETHNOPHARMACOLOGICAL RELEVANCE

Abnormal endocrine metabolism caused by polycystic ovary syndrome (PCOS) poses a serious risk to reproductive health in females. According to Traditional Chinese Medicine (TCM) theories, the leading causes of PCOS include turbid phlegm, blood stasis and stagnation of liver Qi. Rhei Radix Et Rhizome is widely used in TCM to attack stagnation, clear damp heat, relieve fire. Rhei Radix Et Rhizome is an important part of the TCM formulas for the treatment of PCOS, which has a long history of medicinal use. However, the specific effect and mechanisms of Rhei Radix Et Rhizome on PCOS have yet to be elucidated.

AIM OF THE STUDY

The object of this study aimed to investigate the effect and its pharmacological mechanism of Rhei Radix Et Rhizome on the treatment of polycystic ovary syndrome.

METHODS

PCOS was induced in female Sprague Dawley (SD) rats by administering letrozole (1 mg/kg, per orally, p.o.) for 21 days, then treated with Rhei Radix Et Rhizome at doses of 0.6 g/kg or 1.2 g/kg. Rats weight, blood glucose and estrus period are measured, and serum hormone include free testosterone (T), luteinizing hormone (LH), follicle-stimulating hormone (FSH) and ovarian lesions were observed to determine the effects of Rhei Radix Et Rhizome. Network pharmacology and molecular docking predicted the targets of Rhei Radix Et Rhizome on PCOS. Epidermal growth factor receptor (EGFR), albumin (ALB), PI3K and P-AKT/AKT protein expression levels in ovarian tissues were assessed by Western blot.

RESULTS

Rhei Radix Et Rhizome reduce abnormal weight and fasting blood glucose induced by letrozole (n = 5, p < 0.01), and improve the disturbed estrus cycle, reduce T, LH levels and LH/FSH ratio of PCOS rats (n = 4, p < 0.01). In addition, it alleviates the polycystic changes of ovaries in PCOS rats and reduces ovarian histopathological damage (n = 4, p < 0.01). Additionally, the core active components of Rhei Radix Et Rhizome for PCOS include Sennoside D_qt, Procyanidin B-5,3'-O-gallate, and Mutatochrome, which strongly bind to core therapeutic targets ALB and EGFR. Furthermore, the treatment reduces the increase of EGFR and ALB induced by letrozole (n = 4, p < 0.01). KEGG pathway enrichment analysis highlights endocrine resistance and prolactin signaling pathway, in both of which the PI3K/AKT pathway plays a crucial role. Our results show Rhei Radix Et Rhizome rescue the abnormal expression of PI3K/AKT pathway in PCOS rats (n = 4, p < 0.01). However, no significant dose-dependent relationship was observed in the tested dose range for the above experiments.

CONCLUSION

These findings suggest that Rhei Radix Et Rhizome can regulate the PI3K/AKT pathway and target EGFR and ALB to treat polycystic ovary syndrome in rats. This study provides a scientific basis for the use of Rhei Radix Et Rhizome in the treatment of PCOS and highlights its potential mechanism through modulation of the PI3K/AKT pathway.

EndoMAP.v1, a Structural Protein Complex Landscape of Human Endosomes

Early/sorting endosomes are dynamic organelles that play key roles in proteome control by triaging plasma membrane proteins for either recycling or degradation in the lysosome1,2,3. These events are coordinated by numerous transiently-associated regulatory complexes and integral membrane components that contribute to organelle identity during endosome maturation4. While a subset of the several hundred protein components and cargoes known to associate with endosomes have been studied at the biochemical and/or structural level, interaction partners and higher order molecular assemblies for many endosomal components remain unknown. Here, we combine cross-linking and native gel mass spectrometry5-8 of purified early endosomes with AlphaFold9,10 and computational analysis to create a systematic human endosomal structural interactome. We present dozens of structural models for endosomal protein pairs and higher order assemblies supported by experimental cross-links from their native subcellular context, suggesting structural mechanisms for previously reported regulatory processes. Using induced neurons, we validate two candidate complexes whose interactions are supported by crosslinks and structural predictions: TMEM230 as a subunit of ATP8/11 lipid flippases11 and TMEM9/9B as subunits of CLCN3/4/5 chloride-proton antiporters12. This resource and its accompanying structural network viewer provide an experimental framework for understanding organellar structural interactomes and large-scale validation of structural predictions.

Non-canonical lysosomal lipolysis drives mobilization of adipose tissue energy stores with fasting

Physiological adaptations to fasting enable humans to survive for prolonged periods without food and involve molecular pathways that may drive life-prolonging effects of dietary restriction in model organisms. Mobilization of fatty acids and glycerol from adipocyte lipid stores by canonical neutral lipases, including the rate limiting adipose triglyceride lipase (Pnpla2/ATGL), is critical to the adaptive fasting response. Here we discovered an alternative mechanism of lipolysis in adipocytes involving a lysosomal program. We functionally tested lysosomal lipolysis with pharmacological and genetic approaches in mice and in murine and human adipocyte and adipose tissue explant culture, establishing dependency on lysosomal acid lipase (LIPA/LAL) and the microphthalmia/transcription factor E (MiT/TFE) family. Our study establishes a model whereby the canonical pathway is critical for rapid lipolytic responses to adrenergic stimuli operative in the acute stage of fasting, while the alternative lysosomal pathway dominates with prolonged fasting.

Network Toxicology Analysis Reveals Molecular Mechanisms Associated with Noise Exposure to Multiple Diseases

Noise pollution is recognized as an environmental stressor that affects various biological processes beyond auditory functions, mainly through stress hormones release. This work explored the biological processes, diseases attributable to noise-regulated targets, and the main targets involved in each disease, employing a network toxicology approach. Through various databases and bioinformatics analysis, a total of 577 targets were identified as potential candidates implicated in diseases related to noise exposure, 10 from the GEO database and the rest from other databases. Noise pollution was found to regulate processes such as hormone response, cellular response to cytokines, and circulatory system functions, contributing to the development of the pathological manifestations related to the diseases like hypertension, ischemia, atherosclerosis, and cirrhosis. Hub targets for ischemia included IL-6, CASP3, AKT1, and TNF-α, while NOS3 was related to hypertension, and NOS3, TNF-α, AGT, and IL-1B to atherosclerosis. The targets were found to be linked to vascular regulation and inflammation in cardio- and cerebrovascular diseases. Molecular docking studies indicated stress hormones released by noise exposure regulates these diseases through signaling pathways, without implicating its direct binding to hub targets. The results indicate that individuals with vascular diseases are more vulnerable to the effects of prolonged noise exposure.

Elucidating genetic intersections: Co-differentially expressed genes in myasthenia gravis and idiopathic inflammatory myopathies and their role in comorbid pathogenesis

Background

Myasthenia gravis (MG) and idiopathic inflammatory myopathies (IIM) are autoimmune disorders that can co-occur, complicating diagnosis and treatment. The molecular mechanisms underlying this comorbidity are not well understood.

Objective

This study aims to identify common differentially expressed genes (co-DEGs) between MG and IIM to elucidate shared pathogenic pathways and potential therapeutic targets.

Methods

Transcriptomic data from the Gene Expression Omnibus (GEO) were analyzed using the "limma" package in RStudio. Functional enrichment analyses were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. A nomogram prediction model was developed, and receiver operating characteristic (ROC) analysis was used to evaluate its diagnostic potential.

Results

Four co-DEGs were identified between MG and IIM, associated with neurotransmitter transport and ion channel regulation. The nomogram model, incorporating three of these co-DEGs, showed high predictive accuracy for MG with IIM complications, with an area under the ROC curve of 0.94. Immune infiltration analysis revealed distinct patterns in MG and IIM, particularly involving gamma delta T cells and activated mast cells.

Conclusion

The study identifies key genetic intersections between MG and IIM, providing insights into their shared pathogenesis and highlighting potential diagnostic and therapeutic targets. Further experimental validation is required to confirm these findings.

Study on mechanism of iridoid glycosides derivatives from Fructus Gardeniae in treatment of hepatic encephalopathy by network pharmacology and molecular docking technology

BACKGROUND

This study aims to explore the mechanism of the iridoid glycosides from Fructus Gardeniae (IGFG) in treating hepatic encephalopathy (HE) by combining network pharmacology and molecular docking technology.

METHODS

Firstly, we collected the targets of IGFG and HE. The targets of IGFG were predicted through the CTD, SWISS and TCMSP database and the targets of HE were screened through the DisGeNET database. Then the targets of IGFG and HE were mapped to attain the common target of IGFG in treating HE. Then, chemicals-target-disease network was constructed. Secondly, we constructed protein-protein interactions (PPI) network using STRING database and Cytoscape software. Moreover, we screened the core targets according to the degree value. Thirdly, the mechanism of IGFG in treating HE was revealed by Gene ontology and KEGG enrichment analysis. Meanwhile, chemicals-target-pathway network was constructed. Finally, to further verify the analysis results, molecular docking study was conducted.

RESULTS

Network pharmacology indicates that there are 12 common targets between IGFG and HE. Eleven core targets were identified by the construction of PPI network. Association for core targets, and related pathways was analyzed, implying that core targets related to these pathways are AKT1, tumor necrosis factor, MTOR, CHUK, PPP2CA, IKBKB, AKT2, IKBKG, IL1B, NFKBIA, and CASP8. The main mechanism of IGFG in treating HE is closely related to inhibit inflammatory reaction, regulate immunity, promote hepatocyte regeneration, reduce hepatocyte apoptosis, maintain liver function homeostasis and antiviral function. Finally, the results of molecular docking showed that the binding free energy of geniposide with the core target was less than -5 kJ/mol, which indicated that geniposide could spontaneously bind to the receptor protein and had strong binding force.

CONCLUSION

IGFG can achieve simultaneous intervention of HE by inhibit inflammatory reaction, regulate immunity, promote hepatocyte regeneration, reduce hepatocyte apoptosis, maintain liver function homeostasis and antiviral function. It presents the network regulation mechanism of mutual influence and complex correlation. This study provides a scientific basis for IGFG in the treatment of patients with HE.

Data Mining Approach to Melatonin Treatment in Alzheimer's Disease: New Gene Targets MMP2 and NR3C1

Melatonin is a hormone released by the pineal gland that regulates the sleep-wake cycle. It has been widely studied for its therapeutic effects on Alzheimer's disease (AD), particularly through the amyloidosis, oxidative stress, and neuroinflammation pathways. Nevertheless, the mechanisms through which it exerts its neuroprotective effects in AD are still largely unknown. Data mining was used to identify potential gene targets that link melatonin's effects to AD pathways, yielding a comprehensive view of the underlying molecular mechanisms. We identified 3397 genes related to AD from DisGeNet and 329 melatonin gene targets from ChEMBL, which revealed 223 overlapping genes and the potential shared pathways. These genes were used to construct a protein-protein interaction (PPI) network comprising 143 nodes and 823 edges, which demonstrated significant PPI enrichment. A cluster analysis highlighted two key clusters centered on MMP2 and NR3C1, with both genes playing crucial roles in steroid hormone signaling, apoptosis, and monoamine neurotransmission. Gene Ontology (GO) enrichment and KEGG pathway analyses further elucidated their involvement in critical pathways, for instance, steroid hormone signaling and apoptosis regulation, significantly influencing AD pathology through mechanisms such as extracellular matrix remodeling, epigenetic modifications, and neuroinflammation. Our findings emphasize MMP2 and NR3C1 as important gene targets for future research on melatonin treatment in AD, paving the way for further investigations into their roles in AD pathophysiology.

Drug Repurposing: Insights into Current Advances and Future Applications

Drug development is a complex and expensive process that involves extensive research and testing before a new drug can be approved for use. This has led to a limited availability of potential therapeutics for many diseases. Despite significant advances in biomedical science, the process of drug development remains a bottleneck, as all hypotheses must be tested through experiments and observations, which can be timeconsuming and costly. To address this challenge, drug repurposing has emerged as an innovative strategy for finding new uses for existing medications that go beyond their original intended use. This approach has the potential to speed up the drug development process and reduce costs, making it an attractive option for pharmaceutical companies and researchers alike. It involves the identification of existing drugs or compounds that have the potential to be used for the treatment of a different disease or condition. This can be done through a variety of approaches, including screening existing drugs against new disease targets, investigating the biological mechanisms of existing drugs, and analyzing data from clinical trials and electronic health records. Additionally, repurposing drugs can lead to the identification of new therapeutic targets and mechanisms of action, which can enhance our understanding of disease biology and lead to the development of more effective treatments. Overall, drug repurposing is an exciting and promising area of research that has the potential to revolutionize the drug development process and improve the lives of millions of people around the world. The present review provides insights on types of interaction, approaches, availability of databases, applications and limitations of drug repurposing.

Citronellol Induces Apoptosis via Differential Regulation of Caspase-3, NF-κB, and JAK2 Signaling Pathways in Glioblastoma Cell Line

Citronellol (CT) is a naturally occurring lipophilic monoterpenoid which has shown anticancer effects in numerous cancerous cell lines. This study was, therefore, designed to examine CT's potential as an anticancer agent against glioblastoma (GBM). Network pharmacology analysis was employed to identify potential anticancer targets of CT. A comprehensive data mining was carried out to assess CT and GBM-associated target genes. Protein-protein interaction network was constructed to identify hub genes and later GO and KEGG enrichment analysis was performed to elucidate the possible mechanism. Human glioblastoma cell line "SF767" was used to confirm in silico findings. MTT, crystal violet, and trypan blue assays were performed to assess the cytotoxic effects of various concentrations of CT. Subsequently, ELISA and qPCR were performed to analyze the effects of CT on proapoptotic and inflammatory mediators. In silico findings indicated that CT differentially regulated proapoptotic and inflammatory pathways by activating caspase-3 and 8 and inhibiting nuclear factor-kappa B (NF-κB), tumor necrosis factor-α, Janus kinase 2 (JAK2). Molecular docking also demonstrated strong binding affinities of CT with the above-mentioned mediators when compared to 5-fluorouracil or temozolomide. In SF767 cell line, CT displayed dose-dependent cytotoxic and antioxidant effects, and upregulation of annexin-V, caspase-3, and 8 along with downregulation of inflammatory modulators. In a nutshell, it can be concluded from these findings that CT possesses robust anticancer activity which is mediated via differential regulation of caspase-3, JAK2, and NF-κB pathways.

Exploring the Molecular Mechanism by which Kaempferol Attenuates Sepsis-related Acute Respiratory Distress Syndrome Based on Network Pharmacology and Experimental Verification

BACKGROUND

Sepsis-related acute respiratory distress syndrome (ARDS) is a fatal disease without effective therapy. Kaempferol is a flavonoid compound extracted from natural plant products; it exerts numerous pharmacological effects. Kaempferol attenuates sepsis-related ARDS; however, the underlying protective mechanism has not been elucidated completely.

OBJECTIVES

This study aimed to use network pharmacology and experimental verification to investigate the mechanisms by which kaempferol attenuates sepsis-related ARDS.

METHODS

We screened the targets of kaempferol by PharMapper, Swiss Target Prediction, and CTD database. We identified the targets of sepsis-related ARDS by GeneCards, DisGeNet, OMIM, and TTD. The Weishengxin platform was used to map the targets of both kaempferol and sepsis-related ARDS. We created a Venn diagram to identify the intersection targets. We constructed the "component-intersection targets-disease" network diagram using Cytoscape 3.9.1 software. The intersection targets were imported into the STRING database for developing the protein-protein interaction network. Metascape was used for the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. We selected the leading 20 KEGG pathways to establish the KEGG relationship network. Finally, we performed experimental verification to confirm our prediction results.

RESULTS

Through database screening, we obtained 502, 360, and 78 kaempferol targets, disease targets of sepsis-related ARDS, and intersection targets, respectively. The core targets consisted of tumor necrosis factor-alpha (TNF-α), interleukin (IL)-6, albumin (ALB), IL-1β, and AKT serine/ threonine kinase (AKT)1. GO enrichment analysis identified 426 items, which were principally involved in response to lipopolysaccharide, regulation of inflammatory response, inflammatory response, positive regulation of cell migration, positive regulation of cell adhesion, positive regulation of protein phosphorylation, response to hormone, regulation of reactive oxygen species (ROS) metabolic process, negative regulation of apoptotic signaling pathway, and response to decreased oxygen levels. KEGG enrichment analysis identified 151 pathways. After eliminating the disease and generalized pathways, we obtained the hypoxia-inducible factor 1 (HIF-1), nuclear factor κB (NF-κB), and phosphoinositide 3-kinase (PI3K)-Akt signaling pathways. Our experimental verification confirmed that kaempferol blocked the HIF-1, NFκκB, and PI3K-Akt signaling pathways, diminished TNF-α, IL-1β, and IL-6 expressions, suppressed ROS production, and inhibited apoptosis in lipopolysaccharide (LPS)-induced murine alveolar macrophage (MH-S) cells.

CONCLUSION

Kaempferol can reduce inflammatory response, ROS production, and cell apoptosis by acting on the HIF-1, NF-κB, and PI3K-Akt signaling pathways, thereby alleviating sepsis- related ARDS.

BACH1 as a key driver in rheumatoid arthritis fibroblast-like synoviocytes identified through gene network analysis

RNA-sequencing and differential gene expression studies have significantly advanced our understanding of pathogenic pathways underlying rheumatoid arthritis (RA). Yet, little is known about cell-specific regulatory networks and their contributions to disease. In this study, we focused on fibroblast-like synoviocytes (FLS), a cell type central to disease pathogenesis and joint damage in RA. We used a strategy that computed sample-specific gene regulatory networks to compare network properties between RA and osteoarthritis FLS. We identified 28 transcription factors (TFs) as key regulators central to the signatures of RA FLS. Six of these TFs are new and have not been previously implicated in RA through ex vivo or in vivo studies, and included BACH1, HLX, and TGIF1. Several of these TFs were found to be co-regulated, and BACH1 emerged as the most significant TF and regulator. The main BACH1 targets included those implicated in fatty acid metabolism and ferroptosis. The discovery of BACH1 was validated in experiments with RA FLS. Knockdown of BACH1 in RA FLS significantly affected the gene expression signatures, reduced cell adhesion and mobility, interfered with the formation of thick actin fibers, and prevented the polarized formation of lamellipodia, all required for the RA destructive behavior of FLS. This study establishes BACH1 as a central regulator of RA FLS phenotypes and suggests its potential as a therapeutic target to selectively modulate RA FLS.

A step towards quantifying, modelling and exploring uncertainty in biomedical knowledge graphs

OBJECTIVE

This study aims at automatically quantifying and modelling the uncertainty of facts in biomedical knowledge graphs (BKGs) based on their textual supporting evidence using deep learning techniques.

MATERIALS AND METHODS

A sentence transformer is employed to extract deep features of sentences used to classify sentence factuality using a naive Bayes classifier. For each fact and its supporting evidence in a source KG, the deep feature extractor and the classifier are used to quantify the factuality of each sentence which are then transformed to numerical values in [0,1] before being averaged to get the confidence score of the fact.

RESULTS

The fact classification feature extractor enhances the separability of classes in the embedding space. This helped the fact classification model to achieve a better performance than existing factuality classification with hand-crafted features. Uncertainty quantification and modelling were demonstrated on SemMedDB by creating USemMedDB, showing KGB2U's ability to process large BKGs. A subset of USemMedDB facts is modelled to demonstrate the correlation between the structure of the uncertain BKG and the confidence scores. The best-trained model is used to predict confidence scores of existing and unseen facts. The top-ranked unseen facts were grounded using scientific evidence showing KGB2U's ability to discover new knowledge.

CONCLUSION

Supporting literature of BKG facts can be used to automatically quantify their uncertainty. Additionally, the resulting uncertain biomedical KGs can be used for knowledge discovery. BKG2U interface and source code are available at http://biofunk.datanets.org/ and https://github.com/BahajAdil/KBG2U respectively.

Network pharmacology analysis and clinical verification of Panax notoginseng saponins in deep venous thrombosis prevention

In the present study, the mechanism of Panax notoginseng saponins (PNS), the extract of Panax notoginseng, against deep vein thrombosis (DVT) was explored by networks pharmacology and its effect was demonstrated through clinical data. PNS includes 5 main active components, which have 101 targets. A total of 1,342 DVT-related targets were obtained, 55 of which were the common targets of PNS and DVT. AKT1, TNF, IL1B, EGFR, VEGFA and MAPK3 were selected as hub genes from the protein-protein interaction network. The potential anti-DVT mechanism of PNS may involve the AGE-RAGE signaling pathway and the PI3K-Akt signaling pathway. Molecular docking presented a total of 10 binding interactions, with all molecules showing good binding ability with PNS-DVT common hub target genes (all binding energy <-6 kcal/mol). Analysis of clinical data showed that the combined use of PNS significantly reduced the incidence of postoperative DVT in patients undergoing orthopedic surgery compared with the use of low-molecular-weight heparin alone, which is the most commonly used clinical anticoagulant.

Exploring microRNA signatures in pediatric non-infectious uveitis: meta-analysis and molecular profiling of patient samples

To find a distinct non-coding RNA characteristic for idiopathic uveitis in the pediatric population. To explore the autoimmune-related miRNA expression profile in pediatric patients with idiopathic uveitis (IU) and juvenile idiopathic arthritis-associated uveitis (JIA-AU) and find a common molecular background for idiopathic uveitis and other autoimmune diseases. The expression levels of miRNAs were analyzed by quantitative real-time PCR using serum samples from patients with idiopathic uveitis (n = 8), juvenile idiopathic arthritis-associated uveitis (n = 7), and healthy controls. We selected the most promising miRNAs from the original research papers: miR-16-5p, miR-26a-5p, miR-145-5p, and miR-451a as markers for juvenile idiopathic arthritis; miR-23a-3p, miR-29a-3p, miR-140-5p, miR-193a-5p, and miR-491-5p for uveitis in the adult population; and miR-125a-5p, miR-146a-5p, miR-155-5p, miR-223-5p, and miR-223-3p characteristic for both diseases and confirm their expression changes in serum from children with idiopathic uveitis. We comprehensively reviewed the literature enrolling the papers that met the inclusion criteria (miRNA and non-infectious uveitis/juvenile idiopathic arthritis) and performed target prediction analysis of appoint miRNAs. It additionally confirmed that altered miRNAs target the immunologically involved genes. Immunological-involved miRNAs such as miR-146a-5p and miR-155-5p show diverse expression levels in different patients as they interact with multiple targets. miR-204-5p is downregulated in both patient groups compared to healthy controls. miR-204-5p and miR-155-5p are candidates for molecular markers of autoimmune uveitis. We did not identify the miRNAs specific only to idiopathic uveitis, but for the first time in the pediatric population, we confirmed that this disease entity shares a molecular basis with other autoimmune diseases. Further studies are required to elucidate the molecular interactions among miRNAs, cytokines, and transcription factors within the intricate immune response, particularly in the eye.

Neuromolecular and behavioral effects of cannabidiol on depressive-associated behaviors and neuropathic pain conditions in mice

BACKGROUND AND AIMS

Neuropathic pain (NP) has a high incidence in the general population, is closely related to anxiety disorders, and has a negative impact on the quality of life. Cannabidiol (CBD), as a natural product, has been extensively studied for its potential therapeutic effects on symptoms such as pain and depression (DP). However, the mechanism of CBD in improving NP with depression is not fully understood.

METHODS

First, we used bioinformatics tools to deeply mine the intersection genes associated with NP, DP, and CBD. Secondly, the core targets were screened by Protein-protein interaction network, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, molecular docking and molecular dynamics simulation. Next, the effects of CBD intervention on pain and depressive behaviors in the spinal nerve ligation (SNL) mouse model were evaluated using behavioral tests, and dose-response curves were plotted. After the optimal intervention dose was determined, the core targets were verified by Western blot (WB) and Quantitative Polymerase Chain Reaction (qPCR). Finally, we investigated the potential mechanism of CBD by Nissl staining, Immunofluorescence (IF) and Transmission Electron Microscopy (TEM).

RESULTS

A total of five core genes of CBD most associated with NP and DP were screened by bioinformatics analysis, including PTGS2, GPR55, SOD1, CYP1A2 and NQO1. Behavioral test results showed that CBD by intraperitoneal administration 5 mg/kg can significantly improve the pain behavior and depressive state of SNL mice. WB, qPCR, IF, and TEM experiments further confirmed the regulatory effects of CBD on key molecules.

CONCLUSION

In this study, we found five targets of CBD in the treatment of NP with DP. These findings provide further theoretical and experimental basis for CBD as a potential therapeutic agent.

Computational dissection through network pharmacology and structure-based analysis unravels mechanistic actions of bioactive compounds in a hepatoprotective herb, Picrorhiza kurroa for the treatment of NAFLD and NASH

Non-Alcoholic fatty liver disease has become a silent pandemic worldwide with no authorized medicine available. Picrorhiza kurroa is a traditional hepatoprotective herb wherein extracts provide therapeutic efficacy but not the individual compounds. Hence, the aim of the study is exploration of active molecules in P. kurroa extracts and identification of mechanistic actions to pinpoint potential leads towards drug development. We employed network pharmacology to identify the significance of combinatorial effect of compounds on multiple targets. The NAFLD/NASH associated genes encoding protein targets overlapped with the predicted protein targets of P. kurroa compounds. Then, overlapping targets were considered further to capture the interactive targets from Protein-Protein-Interaction network of NAFLD and NASH. The networks were generated to capture the role of proteins in different signaling pathways, diseases, and effective compounds as therapeutics. Furthermore, structural, and biophysical analysis was performed for significant complexes. We observed that the compounds like astragalin, Picroside-I, Vernicoside, Rutin, Quercetin, Kaempferol, Gallic acid, Ellagic acid in P. kurroa acted synergistically by enhancing the bioavailability of active compounds and affecting various morbidities of NAFLD through involvement in different signaling and disease pathways such as oxidative phosphorylation, FoxO signaling, inflammation, several cancerous and diabetic pathways. The network pharmacology revealed the interactive behavior of proteins involved in NAFLD treated by P. kurroa compounds. Furthermore, molecular docking and molecular dynamic simulation study showed potential candidates in therapeutics. Overall, the study suggested multi-target drug discovery for treating complex diseases by providing leads in herbal extracts as potential therapeutic botanicals.

Toxicogenomic assessment of in vitro macrophages exposed to profibrotic challenge reveals a sustained transcriptomic immune signature

Immune signalling is a crucial component in the progression of fibrosis. However, approaches for the safety assessment of potentially profibrotic substances, that provide information on mechanistic immune responses, are underdeveloped. This study aimed to develop a novel framework for assessing the immunotoxicity of fibrotic compounds. We exposed macrophages in vitro to multiple sublethal concentrations of the profibrotic agent bleomycin, over multiple timepoints, and generated RNA sequencing data. Using a toxicogenomic approach, we performed dose-dependent analysis to discover genes dysregulated by bleomycin exposure in a dose-responsive manner. A subset of immune genes displayed a sustained dose-dependent and differential expression response to profibrotic challenge. An immunoassay revealed cytokines and proteinases responding to bleomycin exposure that closely correlated to transcriptomic alterations, underscoring the integration between transcriptional immune response and external immune signalling activity. This study not only increases our understanding of the immunological mechanisms of fibrosis, but also offers an innovative framework for the toxicological evaluation of substances with potential fibrogenic effects on macrophage signalling. Our work brings a new immunotoxicogenomic direction for hazard assessment of fibrotic compounds, through the implementation of a time and resource efficient in vitro methodology.

Identification of molecular biomarkers associated with non-small-cell lung carcinoma (NSCLC) using whole-exome sequencing

ObjectivesSignificant progress has been made in the treatment of patients with pulmonary adenocarcinoma (ADCA) based on molecular profiling. However, no such molecular target exists for squamous cell carcinoma (SQCC). An exome sequence may provide new markers for personalized medicine for lung cancer patients of all subtypes. The current study aims to discover new genetic markers that can be used as universal biomarkers for non-small cell lung cancer (NSCLC).MethodsWES of 19 advanced NSCLC patients (10 ADCA and 9 SQCC) was performed using Illumina HiSeq 2000. Variant calling was performed using GATK HaplotypeCaller and then the impacts of variants on protein structure or function were predicted using SnpEff and ANNOVAR. The clinical impact of somatic variants in cancer was assessed using cancer archives. Somatic variants were further prioritized using a knowledge-driven variant interpretation approach. Sanger sequencing was used to validate functionally important variants.ResultsWe identified 24 rare single-nucleotide variants (SNVs) including 17 non-synonymous SNVs, and 7 INDELs in 18 genes possibly linked to lung carcinoma. Variants were classified as known somatic (n = 10), deleterious (n = 8), and variant of uncertain significance (n = 6). We found TBP and MPRIP genes exclusively associated with ADCA subtypes, FBOX6 with SQCC subtypes and GPRIN2, KCNJ18 and TEKT4 genes mutated in all the patients. The Sanger sequencing of 10 high-confidence somatic SNVs showed 100% concordance in 7 genes, and 80% concordance in the remaining 3 genes.ConclusionsOur bioinformatics analysis identified KCNJ18, GPRIN2, TEKT4, HRNR, FOLR3, ESSRA, CTBP2, MPRIP, TBP, and FBXO6 may contribute to progression in NSCLC and could be used as new biomarkers for the treatment. The mechanism by which GPRIN2, KCNJ12, and TEKT4 contribute to tumorigenesis is unclear, but our results suggest they may play an important role in NSCLC and it is worth investigating in future.

Anti-depression molecular mechanism elucidation of the phytochemicals in edible flower of Hemerocallis citrina Baroni

The edible flower of Hemerocallis citrina Baroni, commonly known as "Huang Huacai" in China, has anti-depressant effects. However, targets and molecular mechanisms of Hemerocallis citrina Baroni edible flowers (HEF) in depression treatment are still unclear. The potential anti-depression targets in HEF were identified by the intersecting results from typical drug databases. The network construction and Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis were carried out for core targets. The molecular docking was conducted to predict the binding affinity between the active components and the central targets. The intersecting results indicated that there were 24 active components in HEF, with 449 anti-depression targets identified. After screening through degree centrality (DC), betweenness centrality (BC), and closeness centrality (CC), 166 core targets were determined. Tumor protein 53 (TP53) and interleukin 6 (IL-6) had the highest degree values. The results of GO enrichment analysis associated with anti-depression revealed that the biological processes were negative regulation of osteoclast differentiation and positive regulation of phosphorus metabolic process. KEGG enrichment analysis results revealed that pathways, such as the phosphatidylinositol 3‑kinase-protein kinase B (PI3K-Akt) signaling pathway and mitogen-activated protein kinase (MAPK) signaling pathway, were primarily associated with anti-depression. Molecular docking results indicated that the top 10 active ingredients in HEF could bind to the central targets. This study applied network pharmacology to unveil the potential anti-depressive mechanisms of HEF, providing a theoretical basis for further exploration of the effective components in H. citrina edible flower parts.

Integrated systems pharmacology, molecular docking, and MD simulations investigation elucidating the therapeutic mechanisms of BHD in Alzheimer's disease treatment

Alzheimer's disease (AD) poses a longstanding health challenge, prompting a century-long exploration into its etiology and progression. Despite significant advancements in medical science, current AD treatments provide only symptomatic relief, urging a shift towards innovative paradigms. This study, departing from the amyloid hypothesis, integrates Systems Pharmacology, Molecular Docking and Molecular Dynamic Simulations to investigate a polyherbal phytoformulation (US 7,273,626 B2) rooted in Ayurveda for AD, consisting of Bacopa monnieri, Hippophae rhamnoides, and Dioscorea bulbifera (BHD). Diosgenin emerges as a crucial compound, aligning with previous studies, yet recognizing its limitations in explaining BHD's mechanism, this research delves into the intricate network of interactions. Protein-Protein Interaction (PPI) network analysis identifies hub genes (ALOX5, GSK3B, ACHE, SRC, AKT1, EGFR, PIK3R1, ESR1 and APP), suggesting a systems-level modulation of AD. Enrichment analyses unveil 370 AD-associated genes and key terms like "Cellular Response to Chemical Stimulus" and "Regulation of Biological Quality." KEGG pathway analysis underscores BHD's potential in Alzheimer's disease pathway (hsa05010), Endocrine resistance (hsa01522), and PI3K-Akt signaling (hsa04151). Molecular docking, carefully selecting compounds (Kaempferol, Quercetin, Myricetin, Isorhamnetin, Beta-Sitosterol, Stigmasterol, Emodin and Diosgenin) and top modulated targets, validates interactions with high dock scores, providing promising therapeutic avenues. Two core targets, Acetylcholinesterase (AChE) and Estrogen Receptor 1 (ESR1), were identified for further investigation due to their critical roles in Alzheimer's disease. To validate the molecular docking results, Molecular Dynamics (MD) simulations were performed on the AChE complexes with Myricetin, Beta-Sitosterol, and Stigmasterol, as well as the ESR1 complexes with Emodin, Diosgenin, and Beta-Sitosterol. These simulations were then compared to the interactions observed with the marketed drugs Donepezil and Estradiol, which are commonly used in Alzheimer's treatment. The MD simulations provided detailed insights into the stability and behavior of these complexes over time. The findings indicated that Myricetin and Emodin not only maintained stable interactions with AChE and ESR1 but also exhibited greater stability than Donepezil and Estradiol at specific time points and protein regions, as demonstrated by lower RMSD and RMSF values. These results suggest that natural compounds hold promise as potential therapeutic agents in the treatment of Alzheimer's disease, offering new avenues for drug development, while the formulation BHD shows potential as an adjuvant in integrative medicine alongside standard Alzheimer's treatments, effectively targeting related pathways and genes.

Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping

High-throughput transcriptomics (HTTr) uses gene expression profiling to characterize the biological activity of chemicals in in vitro cell-based test systems. As an extension of a previous study testing 44 chemicals, HTTr was used to screen an additional 1,751 unique chemicals from the EPA's ToxCast collection in MCF7 cells using 8 concentrations and an exposure duration of 6 h. We hypothesized that concentration-response modeling of signature scores could be used to identify putative molecular targets and cluster chemicals with similar bioactivity. Clustering and enrichment analyses were conducted based on signature catalog annotations and ToxPrint chemotypes to facilitate molecular target prediction and grouping of chemicals with similar bioactivity profiles. Enrichment analysis based on signature catalog annotation identified known mechanisms of action (MeOAs) associated with well-studied chemicals and generated putative MeOAs for other active chemicals. Chemicals with predicted MeOAs included those targeting estrogen receptor (ER), glucocorticoid receptor (GR), retinoic acid receptor (RAR), the NRF2/KEAP/ARE pathway, AP-1 activation, and others. Using reference chemicals for ER modulation, the study demonstrated that HTTr in MCF7 cells was able to stratify chemicals in terms of agonist potency, distinguish ER agonists from antagonists, and cluster chemicals with similar activities as predicted by the ToxCast ER Pathway model. Uniform manifold approximation and projection (UMAP) embedding of signature-level results identified novel ER modulators with no ToxCast ER Pathway model predictions. Finally, UMAP combined with ToxPrint chemotype enrichment was used to explore the biological activity of structurally related chemicals. The study demonstrates that HTTr can be used to inform chemical risk assessment by determining in vitro points of departure, predicting chemicals' MeOA and grouping chemicals with similar bioactivity profiles.

Constructing the biomolecular networks associated with diabetic nephropathy and dissecting the effects of biomolecule variation underlying pathogenesis

Diabetic nephropathy (DN) is a common and serious complication of diabetes, contributing significantly to patient mortality. Complication of DN (CDN) ranks as the second leading cause of end-stage renal disease globally. To address this, understanding the genetic regulation underlying DN is crucial for personalized treatment strategies. In this study, we identified genes and lncRNAs associated with diabetes and diabetic nephropathy constructing a DN-related lncRNA-mRNA network (DNLMN). This network, characterized by scale-free biomolecular properties, generated through the study of topological properties, elucidates key regulatory interactions. Enrichment analysis of important network modules revealed critical biological processes and pathways involved in DN pathogenesis. In the second step, we investigated the differential expression and co-expression of hub nodes in diseased and normal individuals, identifying lncRNA-mRNA relationships implicated in disease regulation. Finally, we gathered DN-related single nucleotide polymorphisms (SNPs) and lncRNAs from the LincSNP 3.0 database. The DNLMN encompasses SNP-associated lncRNAs, and transcription factors (TFs) linked to differentially expressed lncRNAs between diseased and normal samples. These results underscore the significance of biomolecular networks in disease progression and highlighting the role of biomolecular variability contributes to personalized disease phenotyping and treatment.

Integrating Network Pharmacology, Molecular Docking and Experimental Validation to Explore the Pharmacological Mechanisms of Quercetin Against Diabetic Wound

The chronic non-healing diabetic wound (DW) has remained a challenge to both the society and individuals. Previous studies suggested dietary moderate consumption of quercetin (QCT) are beneficial in preventing diabetic complications, including non-healing DW. However, there were few studies that have investigated QCT-related underlying molecular mechanisms against DW. In the present study, we for the first-time combined network pharmacology with molecular docking and experimental validation to investigate QCT-related therapeutic targets and mechanisms for treating DW. Finally, 191 QCT-related targets and 1750 DW-related pathogenetic targets were obtained from online databases. After removing duplicates, a total of 90 potential therapeutic targets of quercetin for treating DW were ultimately identified. Furthermore, 7 targets with higher degree including IL-6, EGFR, SRC, TNF, AKT1, JUN and MMP9 were predicted as central therapeutic targets of QCT for treating DW. Functional enrichment analysis demonstrated that QCT exerted strong levels of multitargeting regulatory activity. In addition, the KEGG enrichment analysis indicated that several signaling pathways including AGE-RAGE signaling pathway in diabetic complications, IL-17, PI3k-AKT, TNF, HIF-1, VEGF were predicted as key regulators of QCT for treating DW. Molecular docking results suggested that QCT had strong binding activity with the predicted targets. In addition, verification experiments suggested that QCT could significantly attenuated the expression of inflammatory cytokines and the regulation of PI3K-AKT signaling pathway was probably a vital mechanism involved in the pharmacological mechanism of QCT for treating DW. Taken together, combined network pharmacological with experimental validation, we for the first time systematically investigated associated-therapeutic targets and potential pathways of QCT for DW treatment. Our study might provide theoretical basis for DW treatment.

Assessment of pulmonary fibrosis using weighted gene co-expression network analysis

For many industrial chemicals toxicological data is sparse regarding several regulatory endpoints, so there is a high and often unmet demand for NAMs that allow for screening and prioritization of these chemicals. In this proof of concept case study we propose multi-gene biomarkers of compounds' ability to induce lung fibrosis and demonstrate their application in vitro. For deriving these biomarkers we used weighted gene co-expression network analysis to reanalyze a study where the time-dependent pulmonary gene-expression in mice treated with bleomycin had been documented. We identified eight modules of 58 to 273 genes each which were particularly activated during the different phases (inflammatory; acute and late fibrotic) of the developing fibrosis. The modules' relation to lung fibrosis was substantiated by comparison to known markers of lung fibrosis from DisGenet. Finally, we show the modules' application as biomarkers of chemical inducers of lung fibrosis based on an in vitro study of four diketones. Clear differences could be found between the lung fibrosis inducing diketones and other compounds with regard to their tendency to induce dose-dependent increases of module activation as determined using a previously proposed differential activation score and the fraction of differentially expressed genes in the modules. Accordingly, this study highlights the potential use of composite biomarkers mechanistic screening for compound-induced lung fibrosis.

The amino acid composition of a protein influences its expression

The quantity of each protein in a cell only is only partially correlated with its gene transcription rate. Independent influences on protein synthesis levels include mRNA sequence motifs, amino acyl-tRNA synthesis levels, elongation factor action, and protein susceptibility to degradation. Here we report that the amino acid composition of a protein can also influence its expression level in two distinct ways. The nutritional classification of amino acids in animals reflects their potential for scarcity-essential amino acids (EAA) are reliant on dietary supply, non-essential amino acids (NEAA) from internal biosynthesis, and conditionally essential amino acids (CEAA) from both. Accessing public proteomic datasets, we demonstrate that a protein's CEAA sequence composition is inversely correlated with expression-a correlation enhanced during rapid cellular proliferation-suggesting CEAA availability can limit translation. Similarly, proteins with the most extreme compositions of EAA are generally reduced in abundance. These latter proteins participate in biological systems such as taste and food-seeking behaviour, oxidative phosphorylation, and chemokine function, and so linking their expression to EAA availability may act as a homeostatic response to malnutrition. Protein composition can also influence general human phenotypes and disease susceptibility: stature proteins are enriched in CEAAs, and a curated dataset of over 700 cancer proteins is significantly under-represented in EAAs. We also show that individual amino acids can influence protein expression across all kingdoms of life and that this effect appears to be rooted in the unchanging structural and mRNA encoding features of each amino acid. Species-specific environmental survival pathways are shown to be enriched in proteins with individual amino acid compositions favouring higher expression. These two forms of amino acid-driven protein expression regulation promise new insights into systems biology, evolutionary studies, experimental research design, and public health intervention.

Shikonin, a natural naphthoquinone phytochemical, exerts anti-leukemia effects in human CBF-AML cell lines and zebrafish xenograft models

Core binding factor acute myeloid leukemia (CBF-AML) stands out as the most common type of adult AML, characterized by specific chromosomal rearrangements involving CBF genes, particularly t(8;21). Shikonin (SHK), a naphthoquinone phytochemical widely employed as a food colorant and traditional Chinese herbal medicine, exhibits antioxidant, anti-inflammatory, and anti-cancer activities. In this study, we aim to investigate the antileukemic effects of SHK and its underlying mechanisms in human CBF-AML cells and zebrafish xenograft models. Our study revealed that SHK reduced the viability of CBF-AML cells. SHK induced cell cycle arrest, promoted cell apoptosis, and induced differentiation in Kasumi-1 cells. Additionally, SHK downregulated the gene expression of AML1-ETO and c-KIT in Kasumi-1 cells. In animal studies, SHK showed no toxic effects in zebrafish and markedly inhibited the growth of leukemia cells in zebrafish xenografts. Transcriptomic analysis showed that differentially expressed genes (DEGs) altered by SHK are linked to key biological processes like DNA repair, replication, cell cycle regulation, apoptosis, and division. Furthermore, KEGG pathways associated with cell growth, such as the cell cycle and p53 signaling pathway, were significantly enriched by DEGs. Analysis of AML-associated genes in response to SHK treatment using DisGeNET and the STRING database indicated that SHK downregulates the expression of cell division regulators regarding AML progression. Finally, we found that SHK combined with cytarabine synergistically reduced the viability of Kasumi-1 cells. In conclusion, our findings provide novel insights into the mechanisms of SHK in suppressing leukemia cell growth, suggesting its potential as a chemotherapeutic agent for human CBF-AML.

p27 Cell Cycle Inhibitor and Survival in Luminal-Type Breast Cancer: Gene Ontology, Machine Learning, and Drug Screening Analysis

PURPOSE

A widely distributed cell cycle inhibitor, p27, regulates cyclin-dependent kinase-cyclin complexes. Although the prognostic value of p27 has been established for various types of carcinomas, its role in luminal breast cancer remains poorly understood. This study aimed to explore the functional enrichment of p27 and identify potential drug targets in patients with luminal-type breast cancer.

METHODS

Clinicopathological data were collected from 868 patients with luminal-type breast cancer. Additionally, publicly available data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset (1,500 patients) and the Gene Expression Omnibus database (855 patients) were included in the analysis. Immunohistochemical staining for p27, differential gene expression analysis, disease ontology analysis, survival prediction modeling using machine learning (ML), and in vitro drug screening were also performed.

RESULTS

Low p27 expression correlated with younger age, advanced tumor stage, estrogen receptor/progesterone receptor negativity, decreased cluster of differentiation 8+ T cell count, and poorer survival outcomes in luminal-type breast cancer. The METABRIC data revealed that reduced cyclin-dependent kinase inhibitor 1B (CDKN1B) expression (encoding p27) was associated with cell proliferation-related pathways and epigenetic polycomb repressive complex 2. Using ML, p27 emerged as the second most significant survival factor after N stage, thereby enhancing survival model performance. Additionally, luminal-type breast cancer cell lines with low CDKN1B expression demonstrated increased sensitivity to specific anticancer drugs such as voxtalisib and serdemetan, implying a potential therapeutic synergy between CDKN1B-targeted approaches and these drugs.

CONCLUSION

The integration of ML and bioinformatic analyses of p27 has the potential to enhance risk stratification and facilitate personalized treatment strategies for patients with breast cancer.

High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells

New approach methodologies (NAMs) aim to accelerate the pace of chemical risk assessment while simultaneously reducing cost and dependency on animal studies. High Throughput Transcriptomics (HTTr) is an emerging NAM in the field of chemical hazard evaluation for establishing in vitro points-of-departure and providing mechanistic insight. In the current study, 1201 test chemicals were screened for bioactivity at eight concentrations using a 24-h exposure duration in the human- derived U-2 OS osteosarcoma cell line with HTTr. Assay reproducibility was assessed using three reference chemicals that were screened on every assay plate. The resulting transcriptomics data were analyzed by aggregating signal from genes into signature scores using gene set enrichment analysis, followed by concentration-response modeling of signatures scores. Signature scores were used to predict putative mechanisms of action, and to identify biological pathway altering concentrations (BPACs). BPACs were consistent across replicates for each reference chemical, with replicate BPAC standard deviations as low as 5.6 × 10-3 μM, demonstrating the internal reproducibility of HTTr-derived potency estimates. BPACs of test chemicals showed modest agreement (R2 = 0.55) with existing phenotype altering concentrations from high throughput phenotypic profiling using Cell Painting of the same chemicals in the same cell line. Altogether, this HTTr based chemical screen contributes to an accumulating pool of publicly available transcriptomic data relevant for chemical hazard evaluation and reinforces the utility of cell based molecular profiling methods in estimating chemical potency and predicting mechanism of action across a diverse set of chemicals.

Crosstalk between MIR-96 and IRS/PI3K/AKT/VEGF cascade in hRPE cells; A potential target for preventing diabetic retinopathy

Regulation of visual system function demands precise gene regulation. Dysregulation of miRNAs, as key regulators of gene expression in retinal cells, contributes to different eye disorders such as diabetic retinopathy (DR), macular edema, and glaucoma. MIR-96, a member of the MIR-183 cluster family, is widely expressed in the retina, and its alteration is associated with neovascular eye diseases. MIR-96 regulates protein cascades in inflammatory and insulin signaling pathways, but further investigation is required to understand its potential effects on related genes. For this purpose, we identified a series of key target genes for MIR-96 based on gene and protein interaction networks and utilized text-mining resources. To examine the MIR-96 impact on candidate gene expression, we overexpressed MIR-96 via adeno-associated virus (AAV)-based plasmids in human retinal pigment epithelial (RPE) cells. Based on Real-Time PCR results, the relative expression of the selected genes responded differently to overexpressed MIR-96. While the expression levels of IRS2, FOXO1, and ERK2 (MAPK1) were significantly decreased, the SERPINF1 gene exhibited high expression simultaneously. pAAV-delivered MIR-96 had no adverse effect on the viability of human RPE cells. The data showed that changes in insulin receptor substrate-2 (IRS2) expression play a role in disrupted retinal insulin signaling and contribute to the development of diabetic complications. Considered collectively, our findings suggest that altered MIR-96 and its impact on IRS/PI3K/AKT/VEGF axis regulation contribute to DR progression. Therefore, further investigation of the IRS/PI3K/AKT/VEGF axis is recommended as a potential target for DR treatment.

Meta-analysis of set-based multiple phenotype association test based on GWAS summary statistics from different cohorts

Genome-wide association studies (GWAS) have emerged as popular tools for identifying genetic variants that are associated with complex diseases. Standard analysis of a GWAS involves assessing the association between each variant and a disease. However, this approach suffers from limited reproducibility and difficulties in detecting multi-variant and pleiotropic effects. Although joint analysis of multiple phenotypes for GWAS can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits, most of the multiple phenotype association tests are designed for a single variant, resulting in much lower power, especially when their effect sizes are small and only their cumulative effect is associated with multiple phenotypes. To overcome these limitations, set-based multiple phenotype association tests have been developed to enhance statistical power and facilitate the identification and interpretation of pleiotropic regions. In this research, we propose a new method, named Meta-TOW-S, which conducts joint association tests between multiple phenotypes and a set of variants (such as variants in a gene) utilizing GWAS summary statistics from different cohorts. Our approach applies the set-based method that Tests for the effect of an Optimal Weighted combination of variants in a gene (TOW) and accounts for sample size differences across GWAS cohorts by employing the Cauchy combination method. Meta-TOW-S combines the advantages of set-based tests and multi-phenotype association tests, exhibiting computational efficiency and enabling analysis across multiple phenotypes while accommodating overlapping samples from different GWAS cohorts. To assess the performance of Meta-TOW-S, we develop a phenotype simulator package that encompasses a comprehensive simulation scheme capable of modeling multiple phenotypes and multiple variants, including noise structures and diverse correlation patterns among phenotypes. Simulation studies validate that Meta-TOW-S maintains a desirable Type I error rate. Further simulation under different scenarios shows that Meta-TOW-S can improve power compared with other existing meta-analysis methods. When applied to four psychiatric disorders summary data, Meta-TOW-S detects a greater number of significant 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.