DisGeNET: a Cytoscape plugin to visualize, integrate, search and analyze gene-disease networks

Investigating the molecular mechanism of purslane‑based vitiligo treatment using network pharmacology, molecular docking and in vitro analyses

Purslane is a traditional Chinese medicine with a long‑standing history of efficacy in the management of dermatological conditions such as vitiligo. However, the molecular mechanisms underlying its therapeutic effects on vitiligo remain unclear. Therefore, the present study explored these mechanisms using network pharmacology, molecular docking and in vitro experiments. Following the screening process, seven principal active components were identified, namely kaempferol, hesperetin, luteolin, quercetin, arachidonic acid, cycloartenol and β‑sitosterol. In addition, six key targets, namely AKT1, tumor protein p53, peroxisome proliferator‑activated receptor γ (PPARG), estrogen receptor 1, prostaglandin‑endoperoxidase synthase 2 and mitogen‑activated protein kinase 1, and eight pathways in purslane‑based vitiligo treatment were identified. Network pharmacology and molecular docking demonstrated that flavonoids are the key components of purslane likely to mitigate oxidative stress damage in vitiligo. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that the phosphatidylinositol 3‑kinase (PI3K)/AKT, p53 and PPARG signaling pathways are associated with purslane components and vitiligo. In vitro experiments revealed that purslane total flavones (PTF) increased cell viability, decreased ROS levels and increased antioxidant enzyme activities in H2O2‑induced B16F10 cells. In addition, PTF activated the PI3K/AKT signaling pathway in H2O2‑induced B16F10 cells, and the antioxidant effect of PTF was attenuated by a PI3K/AKT inhibitor. In conclusion, the findings of the present study suggest that the flavonoids of purslane contribute, at least in part, to its therapeutic effectiveness in vitiligo by mitigating oxidative stress in melanocytes through the PI3K/AKT signaling pathway.

Measurement of Disease Comorbidity Using Semantic Profiling of Disease Genes

The identification of overlapping disease genes between different diseases is the first step in the elucidation of the biological mechanism of disease comorbidity; however, in the absence of common genes, it is difficult to determine the mechanism of comorbidity even if clinical evidence of disease co-occurrence exists. In this research, a gene-set-based measurement of the comorbidity of diseases (GS.CoMoD) was proposed. The underlying assumption of GS.CoMoD is that if the p-value vectors obtained from the enrichment analyses of different disease gene lists indicate similarity, the diseases are possibly comorbid. Therefore, comorbidity can be detected even without overlapping genes. A simulation analysis showed that GS.CoMoD yielded higher scores for comorbid disease pairs vs. random disease pairs. Moreover, comparison analyses revealed that GS.CoMoD outperformed the pre-existing methods for detecting comorbidity.

Genome-wide association study on color-image-based convolutional neural networks

Background

Convolutional neural networks have excellent modeling abilities to complex large-scale datasets and have been applied to genomics. It requires converting genotype data to image format when employing convolutional neural networks to genome-wide association studies. Existing studies converting the data into grayscale images have shown promising. However, the grayscale image may cause the loss of information of the genotype data.

Methods

In order to make full use of the information, we proposed a new method, color-image-based convolutional neural networks, by converting the data into color images.

Results

The experiments on simulation and real data show that our method outperforms the existing methods proposed by Yue and Chen for converting data into grayscale images, in which the model accuracy is improved by an average of 7.61%, and the ratio of disease risk genes is increased by an average of 18.91%. The new method has better robustness and generalized performance.

Inspecting Biological Deregulation, Putative Markers, and Therapeutic Targets for Neurodegenerative Diseases Through an Integrative Bioinformatics Analysis of the Human Cerebrospinal Fluid Proteome: A Tutorial

Cerebrospinal fluid (CSF) is a source of valuable information concerning brain disorders. The technical advances of high throughput omics platforms to analyze body fluids can generate a huge amount of data, whose translation of the biological meaning can be a challenge. Several bioinformatics tools have emerged to help handle this data from a systems biology perspective. Herein, we describe a step-by-step tutorial for CSF proteome data analysis in the set of neurodegenerative diseases using: (i) ShinyGO webtool to perform functional enrichment analysis envisioning the characterization of the biological pathways and processes deregulated in neurodegenerative diseases including Alzheimer's and Parkinson's diseases; (ii) Cytoscape to map disease-specific proteins based on evidence from proteomics; (iii) DisGeNET to identify the proteins more strongly and more specifically associated with neurodegenerative diseases to date; (iv) STRING to identify putative therapeutic targets through a combined protein-protein interaction and network topological analyses. This step-by-step guide might help researchers to better characterize disease pathogenesis and to identify putative disease biomarkers and therapeutic targets.

Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives

Gastric cancer (GC) represents a significant global health burden, ranking as the fifth most common malignancy and the fourth leading cause of cancer-related death worldwide. Despite recent advancements in GC treatment, the five-year survival rate for advanced-stage GC patients remains low. Consequently, there is an urgent need to identify novel drug targets and develop effective therapies. However, traditional drug discovery approaches are associated with high costs, time-consuming processes, and a high failure rate, posing challenges in meeting this critical need. In recent years, there has been a rapid increase in the utilization of artificial intelligence (AI) algorithms and big data in drug discovery, particularly in cancer research. AI has the potential to improve the drug discovery process by analyzing vast and complex datasets from multiple sources, enabling the prediction of compound efficacy and toxicity, as well as the optimization of drug candidates. This review provides an overview of the latest AI algorithms and big data employed in drug discovery for GC. Additionally, we examine the various applications of AI in this field, with a specific focus on therapeutic discovery. Moreover, we discuss the challenges, limitations, and prospects of emerging AI methods, which hold significant promise for advancing GC research in the future.

An in-silico investigation and network pharmacology based approach to explore the anti-breast-cancer potential of Tecteria coadunata (Wall.) C. Chr

Uncontrolled cell proliferation is a common definition of cancer. After lung carcinoma, breast neoplasm is the second-most prevalent kind of cancer. The majority of breast cancer cells and healthy breast cells both have receptors for circulating oestrogen and progesterone. In order to promote the development and division of cancer cells, oestrogen and progesterone bind to the receptors and may collaborate with growth factors (such as oncogenes and mutant tumour suppressor genes). As per the literature, Tecteria coadunata (Wall.) C. Chr. has anticancer, antioxidant and anti-inflammatory potential. After the hydroalcoholic extraction of this rhizome, total of 200 phytochemicals were retrieved from HR-LCMS analysis. In this current study, Network pharmacology was carried out to explore the rationale of Tecteria coadunata (Wall.) C. Chr. by using different database using Cytoscape software. The network depicted the interaction of Bioactives with their targets and their association with several disease, especially breast cancer. Tecteria coadunata (Wall.) C. Chr. has offered new relationship with variety of genes and its applications in different types of breast cancers. Further Gene Ontology was carried out and it showed key targets were TP53, BRCA2, PGR and CHEK 2. Further Signalling pathways were also enriched. Flex-X software was used for molecular docking studies, and it verified that Dopaxanthin, Dantrolene and Orotidin shows the highest binding affinities with key targets. Additionally, Pharmacokinetic analysis revealed that all top three lead compounds which follows the Lipinski Rule (Rule of three) without interrupting the conditions of bioavailability with minimal toxicity.Communicated by Ramaswamy H. Sarma.

Patient-centric knowledge graphs: a survey of current methods, challenges, and applications

Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information holistically and multi-dimensionally. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health, enabling more personalized and effective care. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs, focusing on their role in integrating disparate healthcare data and enhancing patient care through a unified health perspective. In addition, this review also discusses the complexities of PCKG development, including ontology design, data integration techniques, knowledge extraction, and structured representation of knowledge. It highlights advanced techniques such as reasoning, semantic search, and inference mechanisms essential in constructing and evaluating PCKGs for actionable healthcare insights. We further explore the practical applications of PCKGs in personalized medicine, emphasizing their significance in improving disease prediction and formulating effective treatment plans. Overall, this review provides a foundational perspective on the current state-of-the-art and best practices of PCKGs, guiding future research and applications in this dynamic field.

Integrated multi-omics analysis of zinc-finger proteins uncovers roles in RNA regulation

RNA interactome studies have revealed that hundreds of zinc-finger proteins (ZFPs) are candidate RNA-binding proteins (RBPs), yet their RNA substrates and functional significance remain largely uncharacterized. Here, we present a systematic multi-omics analysis of the DNA- and RNA-binding targets and regulatory roles of more than 100 ZFPs representing 37 zinc-finger families. We show that multiple ZFPs are previously unknown regulators of RNA splicing, alternative polyadenylation, stability, or translation. The examined ZFPs show widespread sequence-specific RNA binding and preferentially bind proximal to transcription start sites. Additionally, several ZFPs associate with their targets at both the DNA and RNA levels. We highlight ZNF277, a C2H2 ZFP that binds thousands of RNA targets and acts as a multi-functional RBP. We also show that ZNF473 is a DNA/RNA-associated protein that regulates the expression and splicing of cell cycle genes. Our results reveal diverse roles for ZFPs in transcriptional and post-transcriptional gene regulation.

Farrerol alleviates insulin resistance and hepatic steatosis of metabolic associated fatty liver disease by targeting PTPN1

Metabolic associated fatty liver disease (MAFLD) is the most common chronic liver disease worldwide, characterized by excess lipid deposition. Insulin resistance (IR) serves as a fundamental pathogenic factor in MAFLD. However, currently, there are no approved specific agents for its treatment. Farrerol, a novel compound with antioxidant and anti-inflammatory effects, has garnered significant attention in recent years due to its hepatoprotective properties. Despite this, the precise underlying mechanisms of action remain unclear. In this study, a network pharmacology approach predicted protein tyrosine phosphatase non-receptor type 1 (PTPN1) as a potential target for farrerol's action in the liver. Subsequently, the administration of farrerol improved insulin sensitivity and glucose tolerance in MAFLD mice. Furthermore, farrerol alleviated lipid accumulation by binding to PTPN1 and reducing the dephosphorylation of the insulin receptor (INSR) in HepG2 cells and MAFLD mice. Thus, the phosphoinositide 3-kinase/serine/threonine-protein kinases (PI3K/AKT) signalling pathway was active, leading to downstream protein reduction. Overall, the study demonstrates that farrerol alleviates insulin resistance and hepatic steatosis of MAFLD by targeting PTPN1.

Mechanism of YJKL Decoction in Treating of PCOS Infertility by Integrative Approach of Network Pharmacology and Experimental Verification

Purpose

Currently, there is still no clear treatment for polycystic ovary syndrome (PCOS). YJKL has better therapeutic effects and lower toxic side effects for PCOS type infertility. This study aims to clarify the potential mechanism of YJKL Decoction in the treatment of PCOS based on network pharmacology and experiments verification.

Patients and Methods

Network pharmacology and experimental validation approach were used to investigate the bioactive ingredients, critical targets and potential mechanisms of YJKL Decoction against PCOS. Firstly, we use network pharmacology methods to collect core targets, and then validate their effects on diseases through experiments.

Results

Five core targets were screened, Threonine kinase 1 (AKT1), Cellular tumor antigen p53 (TP53), Tumor necrosis factor (TNF), Albumin (ALB) and Vascular endothelial growthfactor A (VEGFA). KEGG analysis showed that YJKL treatment for PCOS mainly include AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway and HIF-1 signaling pathway. The molecular docking results showed that compounds have higher affinity with targets. Finally, experimental results had shown that YJKL Decoction had an better therapeutic effects in the treatment of PCOS.

Conclusion

Based on a systematic network pharmacology approach and experimental verification, our results comprehensively illustrated the active ingredients, potential targets, and molecular mechanism of YJKL for application to PCOS and helps to illustrate mechanism of action on a comprehensive level.

SexAnnoDB, a knowledgebase of sex-specific regulations from multi-omics data of human cancers

BACKGROUND

Sexual differences across molecular levels profoundly impact cancer biology and outcomes. Patient gender significantly influences drug responses, with divergent reactions between men and women to the same drugs. Despite databases on sex differences in human tissues, understanding regulations of sex disparities in cancer is limited. These resources lack detailed mechanistic studies on sex-biased molecules.

METHODS

In this study, we conducted a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, delving into sex-biased effects. Our analyses encompassed sex-biased competitive endogenous RNA networks, regulatory networks involving sex-biased RNA binding protein-exon skipping events, sex-biased transcription factor-gene regulatory networks, as well as sex-biased expression quantitative trait loci, sex-biased expression quantitative trait methylation, sex-biased splicing quantitative trait loci, and the identification of sex-biased cancer therapeutic drug target genes. All findings from these analyses are accessible on SexAnnoDB ( https://ccsm.uth.edu/SexAnnoDB/ ).

RESULTS

From these analyses, we defined 126 cancer therapeutic target sex-associated genes. Among them, 9 genes showed sex-biased at both the mRNA and protein levels. Specifically, S100A9 was the target of five drugs, of which calcium has been approved by the FDA for the treatment of colon and rectal cancers. Transcription factor (TF)-gene regulatory network analysis suggested that four TFs in the SARC male group targeted S100A9 and upregulated the expression of S100A9 in these patients. Promoter region methylation status was only associated with S100A9 expression in KIRP female patients. Hypermethylation inhibited S100A9 expression and was responsible for the downregulation of S100A9 in these female patients.

CONCLUSIONS

Comprehensive network and association analyses indicated that the sex differences at the transcriptome level were partially the result of corresponding sex-biased epigenetic and genetic molecules. Overall, SexAnnoDB offers a discipline-specific search platform that could potentially assist basic experimental researchers or physicians in developing personalized treatment plans.

Network Medicine: A Potential Approach for Virtual Drug Screening

Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.

Prediction of the Signaling Pathway in Polycystic Ovary Syndrome Using an Integrated Bioinformatics Approach

OBJECTIVES

The purpose of this study was to define the underlying biological mechanisms of polycystic ovarian syndrome (PCOS) utilizing the protein-protein interaction networks (PPINs) that were constructed based on the putative disease-causing genes for PCOS.

DESIGN

No animals were used in this research because this is an in silico study that mainly uses software and online analysis tools. Participants/Materials, Settings: Gene datasets related to PCOS were obtained from Genecards.

METHODS

The PPINs of PCOS were created using the String Database after genes related to PCOS were obtained from Genecards. After that, we performed an analysis of the hub-gene clusters extracted from the PPIN using the ShinyGO algorithm. In the final step of this research project, functional enrichment analysis was used to investigate the primary biological activities and signaling pathways that were associated with the hub clusters.

RESULTS

The Genecards database provided the source for the identification of a total of 1,072 potential genes related to PCOS. The PPIN that was generated by using the genes that we collected above contained a total of 82 genes and three different types of cluster interaction interactions. In addition, after conducting research on the PPIN with the shinyGO plug-in, 19 of the most important gene clusters were discovered. The primary biological functions that were enriched in the key clusters that were developed were ovarian steroidogenesis, the breast cancer pathway, regulation of lipid and glucose metabolism by the AMPK pathway, and ovarian steroidogenesis. The integrated analysis that was performed in the current study demonstrated that these hub clusters and their connected genes are closely associated with the pathogenesis of PCOS.

LIMITATIONS

Several of the significant genes that were identified in this study, such as ACVR1, SMAD5, BMP6, SMAD3, SMAD4, and anti-mullerian hormone. It is necessary to do additional research using large samples, several centers, and multiple ethnicities in order to verify these findings.

CONCLUSIONS

The integrated analysis that was performed in the current study demonstrated that these hub clusters and their connected genes are closely associated with the pathogenesis of PCOS. This information may possibly bring unique insights for the treatment of PCOS as well as the investigation of its underlying pathogenic mechanism.

NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders

Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1518 GO biological processes terms and KEGG pathways and all GTEx samples. NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. NetActivity is publicly available in Bioconductor and GitHub.

Exploring the mechanism of agarwood moxa smoke in treating sleep disorders based on GC-MS and network pharmacology

Background

Agarwood moxibustion is a folk therapy developed by individuals of the Li nationality in China. There is evidence that agarwood moxa smoke (AMS) generated during agarwood moxibustion therapy can treat sleep disorders via traditional Chinese medicines' multiple target and pathway characteristics. However, the specific components and mechanisms involved have yet to be explored.

Objective

GC-MS (Gas Chromatography-Mass Spectrometry) and network pharmacology were used to investigate AMS's molecular basis and mechanism in treating sleep deprivation.

Method

GC-MS was used to determine the chemical composition of AMS; component target information was collected from TCMSP (Traditional Chinese Medicine Systems Pharmacology), PubChem (Public Chemical Database), GeneCards (Human Gene Database), and DisGeNet (Database of Genes and Diseases) were used to identify disease targets, and JVenn (Joint Venn) was used to identify the common targets of AMS and sleep disorders. STRING was used to construct a protein interaction network, Cytoscape 3.9.1 was used to build a multilevel network diagram of the "core components-efficacy targets-action pathways," the targets were imported into Metascape and DAVID for GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses and Autodock was used for molecular docking. This research used a network pharmacology methodology to investigate the therapeutic potential of Agarwood Moxa Smoke (AMS) in treating sleep problems. Examining the target genes and chemical constituents of AMS offers insights into the molecular processes and targets of the disease.

Result

Nine active ingredients comprising anti-inflammatory substances and antioxidants, such as caryophyllene and p-cymene, found seven sleep-regulating signaling pathways and eight targets linked to sleep disorders. GC-MS was used to identify the 94 active ingredients in AMS, and the active ingredients had strong binding with the key targets. Key findings included active components with known medicinal properties, such as p-cymene, eucalyptol, and caryophyllene. An investigation of network pharmacology revealed seven signaling pathways for sleep regulation and eight targets linked to sleep disorders, shedding light on AMS's effectiveness in enhancing sleep quality.

Conclusion

AMS may alleviate sleep disorders by modulating cellular and synaptic signaling, controlling hormone and neurotransmitter pathways, etc. Understanding AMS's material basis and mechanism of action provides a foundation for future research on treating sleep disorders with AMS. According to the study, Agarwood Moxa Smoke (AMS) may improve sleep quality by modifying cellular and synaptic signaling pathways for those who suffer from sleep problems. This might lead to the development of innovative therapies with fewer side effects.

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

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

SIMarker: Cellular similarity detection and its application to diagnosis and prognosis of liver cancer

BACKGROUND

The emergence of single-cell technology offers a unique opportunity to explore cellular similarity and heterogeneity between precancerous diseases and solid tumors. However, there is lacking a systematic study for identifying and characterizing similarities at single-cell resolution.

METHODS

We developed SIMarker, a computational framework to detect cellular similarities between precancerous diseases and solid tumors based on gene expression at single-cell resolution. Taking hepatocellular carcinoma (HCC) as a case study, we quantified the cellular and molecular connections between HCC and cirrhosis. Core analysis modules of SIMarker is publicly available at https://github.com/xmuhuanglab/SIMarker ("SIM" means "similarity" and "Marker" means "biomarkers).

RESULTS

We found PGA5+ hepatocytes in HCC showed cirrhosis-like characteristics, including similar transcriptional programs and gene regulatory networks. Consequently, the genes constituting the gene expression program of these cirrhosis-like subpopulations were designated as cirrhosis-like signatures (CLS). Strikingly, our utilization of CLS enabled the development of diagnosis and prognosis biomarkers based on within-sample relative expression orderings of gene pairs. These biomarkers achieved high precision and concordance compared with previous studies.

CONCLUSIONS

Our work provides a systematic method to investigate the clinical translational significance of cellular similarities between HCC and cirrhosis, which opens avenues for identifying similar paradigms in other categories of cancers and diseases.

Libertellenone C attenuates oxidative stress and neuroinflammation with the capacity of NLRP3 inhibition

Neurodegenerative diseases (NDs) are common chronic diseases arising from progressive damage to the nervous system. Here, in-house natural product database screening revealed that libertellenone C (LC) obtained from the fermentation products of Arthrinium arundinis separated from the gut of a centipede collected in our Tongji campus, showed a remarkable neuroprotective effect. Further investigation was conducted to clarify the specific mechanism. LC dose-dependently reversed glutamate-induced decreased viability, accumulated reactive oxygen species, mitochondrial membrane potential loss, and apoptosis in SH-SY5Y cells. Network pharmacology analysis predicted that the targets of LC were most likely directly related to oxidative stress and the regulation of inflammatory factor-associated signaling pathways. Further study demonstrated that LC attenuated nitrite, TNF-α, and IL-1β production and decreased inducible nitric oxide synthase and cyclooxygenase expression in lipopolysaccharide-induced BV-2 cells. LC could directly inhibit NLRP3 inflammasome activation by decreasing the expression levels of NLRP3, ASC, cleaved Caspase-1, and NF-κB p65. Our results provide a new understanding of how LC inhibits the NLRP3 inflammasome in microglia, providing neuroprotection. These findings might guide the development of effective LC-based therapeutic strategies for NDs.

Exploring the Therapeutic Potential of Triptonide in Salivary Adenoid Cystic Carcinoma: A Comprehensive Approach Involving Network Pharmacology and Experimental Validation

BACKGROUND

Salivary Adenoid Cystic Carcinoma (ACC) is characterized by a highly invasive and slow-growing pattern, and its etiology remains unidentified. Triptonide (TN) has demonstrated efficacy as a pharmacotherapeutic agent against ACC. Nonetheless, the specific targets and mechanism of molecular action underlying the effectiveness of TN in treating ACC have not been elucidated.

OBJECTIVES

By integrating network pharmacology within laboratory experiments, this research delves into the prospective targets and molecular mechanisms associated with the application of TN in treating ACC.

METHODS

Initially, pertinent targets associated with TN against ACC were acquired from public databases. Subsequently, a combination of network pharmacology and bioinformatics analysis was utilized to screen the top 10 hub targets and key signal pathways of TN-treating ACC. Finally, in vitro experiments involving various molecular assays were conducted to evaluate the biological phenotypes of cells following TN treatment, encompassing assessments of apoptosis levels, plate migration, and other parameters, thereby validating pivotal genes and pathways.

RESULTS

A total of 23 pertinent targets for TN in relation to ACC were identified, with the top 10 hub genes being MAPK8, PTGS2, RELA, MAPK14, NR3C1, HDAC1, PPARG, NFKBIA, AR, and PGR. There was a significant correlation between the TNF signaling pathway and the treatment of ACC with TN. In vitro experiments demonstrated that TN treatment elevated RELA phosphorylation while concurrently reducing MAPK14 phosphorylation and inducing G2/M arrest. TN exhibited the ability to enhance the apoptosis rate through increased caspase-3 activity, elevated levels of Reactive Oxygen Species (ROS), mitochondrial dysfunction, and inhibition of cell migration.

CONCLUSION

There is a potential therapeutic role for TN in the treatment of ACC through the activation of the TNF signaling pathway. Among the identified candidates, MAPK8, HDAC1, PTGS2, RELA, NR3C1, PPARG, NFKBIA, AR, and PGR emerge as the most pertinent therapeutic targets for TN in the context of ACC treatment.

Prediction of Drug-Disease Associations Based on Multi-Kernel Deep Learning Method in Heterogeneous Graph Embedding

Computational drug repositioning can identify potential associations between drugs and diseases. This technology has been shown to be effective in accelerating drug development and reducing experimental costs. Although there has been plenty of research for this task, existing methods are deficient in utilizing complex relationships among biological entities, which may not be conducive to subsequent simulation of drug treatment processes. In this article, we propose a heterogeneous graph embedding method called HMLKGAT to infer novel potential drugs for diseases. More specifically, we first construct a heterogeneous information network by combining drug-disease, drug-protein and disease-protein biological networks. Then, a multi-layer graph attention model is utilized to capture the complex associations in the network to derive representations for drugs and diseases. Finally, to maintain the relationship of nodes in different feature spaces, we propose a multi-kernel learning method to transform and combine the representations. Experimental results demonstrate that HMLKGAT outperforms six state-of-the-art methods in drug-related disease prediction, and case studies of five classical drugs further demonstrate the effectiveness of HMLKGAT.

Emerging Trends in Big Data Analysis in Computational Biology and Bioinformatics in Health Informatics: A Case Study on Epilepsy and Seizures

Advanced technology innovations allow cost-effective, high-throughput profiling of biological systems. It enabled genome sequencing in days using advanced technologies (e.g., next-generation sequencing, microarrays, and mass spectrometry). Since technology has been developed, massive biological data (e.g., genomics, proteomics) has been produced cheaply, allowing the "big data" era to create new opportunities to solve medical and biological complications in many disciplines-preventive medicine, biology, Personalized Medicine, gene sequencing, healthcare, and industry. Computational biology and bioinformatics are interdisciplinary fields that develop and apply computational methods (e.g., analytical methods, mathematical modeling, and simulation) to analyze large collections of biological data, such as genetic sequences, cell populations, or protein samples, to make new predictions or discover new biology. Biological data storage, mining, and analysis have challenges because data is much more heterogeneous. In this study, the big data resources of genomics, proteomics, and metabolomics have been explored to solve biological problems using big data analysis approaches. The goal is to build a network of relationship-based gene-disease associations to prioritize phenotypes common to epilepsy and seizure disease. Through network analysis, The 10 seed genes, 22 associated genes, 132 microRNAs, and 38 transcription factors have been identified that have a direct effect on all forms of epilepsy and seizures. The majority of seed genes, according to the results of a functional analysis of seed genes, are involved in the acetylcholine-gated channel complex (10%) and the heterotrimeric G-protein complex (10%) pathways related to cellular components, followed by a role in the regulation of action potential (20%) and positive regulation of vascular endothelial growth factor production (20%) in Epilepsy and Seizures pathways related to biological processes. This study might provide insight into the workings of the disease and shows the importance of continued research into epilepsy and other conditions that can trigger seizure activity.

Identifying diseases associated with Post-COVID syndrome through an integrated network biology approach

A growing body of research shows that COVID-19 is now recognized as a multi-organ disease with a wide range of manifestations that can have long-lasting repercussions, referred to as post-COVID-19 syndrome. It is unknown why the vast majority of COVID-19 patients develop post-COVID-19 syndrome, or why patients with pre-existing disorders are more likely to experience severe COVID-19. This study used an integrated network biology approach to obtain a comprehensive understanding of the relationship between COVID-19 and other disorders. The approach involved building a PPI network with COVID-19 genes and identifying highly interconnected regions. The molecular information contained within these subnetworks, as well as the pathway annotations, were used to reveal the link between COVID-19 and other disorders. Using Fisher's exact test and disease-specific gene information, significant COVID-19-disease associations were discovered. The study discovered diseases that affect multiple organs and organ systems, thus proving the theory of multiple organ damage caused by COVID-19. Cancers, neurological disorders, hepatic diseases, cardiac disorders, pulmonary diseases, and hypertensive diseases are just a few of the conditions linked to COVID-19. Pathway enrichment analysis of shared proteins revealed the shared molecular mechanism of COVID-19 and these diseases. The findings of the study shed new light on the major COVID-19-associated disease conditions and how their molecular mechanisms interact with COVID-19. The novelty of studying disease associations in the context of COVID-19 provides new insights into the management of rapidly evolving long-COVID and post-COVID syndromes, which have significant global implications.Communicated by Ramaswamy H. Sarma.

Epigenome-wide analysis identifies methylome profiles linked to obsessive-compulsive disorder, disease severity, and treatment response

Obsessive-compulsive disorder (OCD) is a prevalent mental disorder affecting ~2-3% of the population. This disorder involves genetic and, possibly, epigenetic risk factors. The dynamic nature of epigenetics also presents a promising avenue for identifying biomarkers associated with symptom severity, clinical progression, and treatment response in OCD. We, therefore, conducted a comprehensive case-control investigation using Illumina MethylationEPIC BeadChip, encompassing 185 OCD patients and 199 controls recruited from two distinct sites in Germany. Rigorous clinical assessments were performed by trained raters employing the Structured Clinical Interview for DSM-IV (SCID-I). We performed a robust two-step epigenome-wide association study that led to the identification of 305 differentially methylated CpG positions. Next, we validated these findings by pinpointing the optimal set of CpGs that could effectively classify individuals into their respective groups. This approach identified a subset comprising 12 CpGs that overlapped with the 305 CpGs identified in our EWAS. These 12 CpGs are close to or in genes associated with the sweet-compulsive brain hypothesis which proposes that aberrant dopaminergic transmission in the striatum may impair insulin signaling sensitivity among OCD patients. We replicated three of the 12 CpGs signals from a recent independent study conducted on the Han Chinese population, underscoring also the cross-cultural relevance of our findings. In conclusion, our study further supports the involvement of epigenetic mechanisms in the pathogenesis of OCD. By elucidating the underlying molecular alterations associated with OCD, our study contributes to advancing our understanding of this complex disorder and may ultimately improve clinical outcomes for affected individuals.

Improving Genetic Association Studies with a Novel Methodology that Unveils the Hidden Complexity of All-Cause Heart Failure

Motivation

Genome-Wide Association Studies (GWAS) commonly assume phenotypic and genetic homogeneity that is not present in complex conditions. We designed Transformative Regression Analysis of Combined Effects (TRACE), a GWAS methodology that better accounts for clinical phenotype heterogeneity and identifies gene-by-environment (GxE) interactions. We demonstrated with UK Biobank (UKB) data that TRACE increased the variance explained in All-Cause Heart Failure (AHF) via the discovery of novel single nucleotide polymorphism (SNP) and SNP-by-environment (i.e. GxE) interaction associations. First, we transformed 312 AHF-related ICD10 codes (including AHF) into continuous low-dimensional features (i.e., latent phenotypes) for a more nuanced disease representation. Then, we ran a standard GWAS on our latent phenotypes to discover main effects and identified GxE interactions with target encoding. Genes near associated SNPs subsequently underwent enrichment analysis to explore potential functional mechanisms underlying associations. Latent phenotypes were regressed against their SNP hits and the estimated latent phenotype values were used to measure the amount of AHF variance explained.

Results

Our method identified over 100 main GWAS effects that were consistent with prior studies and hundreds of novel gene-by-smoking interactions, which collectively accounted for approximately 10% of AHF variance. This represents an improvement over traditional GWAS whose results account for a negligible proportion of AHF variance. Enrichment analyses suggested that hundreds of miRNAs mediated the SNP effect on various AHF-related biological pathways. The TRACE framework can be applied to decode the genetics of other complex diseases.

Availability

All code is available at https://github.com/EpistasisLab/latent_phenotype_project.

Study on the mechanism of action of colchicine in the treatment of coronary artery disease based on network pharmacology and molecular docking technology

Objective: This is the first study to explore the mechanism of colchicine in treating coronary artery disease using network pharmacology and molecular docking technology, aiming to predict the key targets and main approaches of colchicine in treating coronary artery disease. It is expected to provide new ideas for research on disease mechanism and drug development. Methods: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Swiss Target Prediction and PharmMapper databases were used to obtain drug targets. GeneCards, Online Mendelian Inheritance in Man (OMIM), Therapeutic Target Database (TTD), DrugBank and DisGeNET databases were utilized to gain disease targets. The intersection of the two was taken to access the intersection targets of colchicine for the treatment of coronary artery disease. The Sting database was employed to analyze the protein-protein interaction network. Gene Ontology (GO) functional enrichment analysis was performed using Webgestalt database. Reactom database was applied for Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Molecular docking was simulated using AutoDock 4.2.6 and PyMOL2.4 software. Results: A total of 70 intersecting targets of colchicine for the treatment of coronary artery disease were obtained, and there were interactions among 50 targets. GO functional enrichment analysis yielded 13 biological processes, 18 cellular components and 16 molecular functions. 549 signaling pathways were obtained by KEGG enrichment analysis. The molecular docking results of key targets were generally good. Conclusion: Colchicine may treat coronary artery disease through targets such as Cytochrome c (CYCS), Myeloperoxidase (MPO) and Histone deacetylase 1 (HDAC1). The mechanism of action may be related to the cellular response to chemical stimulus and p75NTR-mediated negative regulation of cell cycle by SC1, which is valuable for further research exploration. However, this research still needs to be verified by experiments. Future research will explore new drugs for treating coronary artery disease from these targets.

Peptide-Based Vectors: A Biomolecular Engineering Strategy for Gene Delivery

From the first clinical trial by Dr. W.F. Anderson to the most recent US Food and Drug Administration-approved Luxturna (Spark Therapeutics, 2017) and Zolgensma (Novartis, 2019), gene therapy has revamped thinking and practice around cancer treatment and improved survival rates for adult and pediatric patients with genetic diseases. A major challenge to advancing gene therapies for a broader array of applications lies in safely delivering nucleic acids to their intended sites of action. Peptides offer unique potential to improve nucleic acid delivery based on their versatile and tunable interactions with biomolecules and cells. Cell-penetrating peptides and intracellular targeting peptides have received particular focus due to their promise for improving the delivery of gene therapies into cells. We highlight key examples of peptide-assisted, targeted gene delivery to cancer-specific signatures involved in tumor growth and subcellular organelle-targeting peptides, as well as emerging strategies to enhance peptide stability and bioavailability that will support long-term implementation.

Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.

Interactions between phenolic constituents of Scutellaria salviifolia and key targets associated with inflammation: network pharmacology, molecular docking analysis and in vitro assays

Scutellaria salviifolia Benth. (SS), an endemic plant for Turkey, is used for gastric ailments as folk medicine. In this study, we aimed to uncover the underlying molecular mechanisms with the help of network pharmacology and molecular docking analysis in the inflammation processes of gastric ailments. Gene enrichment analysis and target screening were carried out. Experimental validation was performed via cytokines of nitric oxide (NO) and interleukin-6 (IL-6) in LPS stimulated RAW 264.7 cells. Furthermore, antioxidant activity studies were performed by radical scavenging effects on different radicals. A total of 144 targets were listed for the isolated compounds where 26 of them were related to selected inflammation targets. According to the gene enrichment analysis, HIF1 signaling pathway and TNF signaling pathway were found to be involved in inflammation. We also defined AKT1, TNF, EGFR, and COX2 as key targets due to the protein-protein interactions of 26 common targets. The extract inhibited NO and IL-6 production at 100 and 200 µg/mL, while flavonoid-rich fraction possessed significant anti-inflammatory activity at the concentration of 50 and 100 µg/mL via NO and IL-6 production, respectively. It is thought that the anti-inflammatory effects of extracts, fractions and pure compounds were achieved by reducing NO and IL-6 levels via regulating the NF-κB pathway or reducing NO production by suppressing iNOS through the HIF-1 pathway when evaluated together with the results of network analysis and literature. Anti-inflammatory activities of the extract and fractions were promising and comparably with S. baicalensis, commonly used for its anti-inflammatory activity.

Studying the connection between SF3B1 and four types of cancer by analyzing networks constructed based on published research

Splicing factor 3B subunit 1 (SF3B1) is the largest component of SF3b protein complex which is involved in the pre-mRNA splicing mechanism. Somatic mutations of SF3B1 were shown to be associated with aberrant splicing, producing abnormal transcripts that drive cancer development and/or prognosis. In this study, we focus on the relationship between SF3B1 and four types of cancer, namely myelodysplastic syndrome (MDS), acute myeloid leukemia (AML), and chronic lymphocytic leukemia (CLL) and breast cancer (BC). For this purpose, we identified from the Pubmed library only articles which mentioned SF3B1 in connection with the investigated types of cancer for the period 2007 to 2018 to reveal how the connection has developed over time. We left out all published articles which mentioned SF3B1 in other contexts. We retrieved the target articles and investigated the association between SF3B1 and the mentioned four types of cancer. For this we utilized some of the publicly available databases to retrieve gene/variant/disease information related to SF3B1. We used the outcome to derive and analyze a variety of complex networks that reflect the correlation between the considered diseases and variants associated with SF3B1. The results achieved based on the analyzed articles and reported in this article illustrated that SF3B1 is associated with hematologic malignancies, such as MDS, AML, and CLL more than BC. We found that different gene networks may be required for investigating the impact of mutant splicing factors on cancer development based on the target cancer type. Additionally, based on the literature analyzed in this study, we highlighted and summarized what other researchers have reported as the set of genes and cellular pathways that are affected by aberrant splicing in cancerous cells.

The mitigative effect of lotus root (Nelumbo nucifera Gaertn) extract on acute alcoholism through activation of alcohol catabolic enzyme, reduction of oxidative stress, and protection of liver function

Objectives

Lotus root (Nelumbo nucifera Gaertn) is a common medicinal-food dual-use vegetable. In this study, the effects of lotus root extract on acute alcoholism were investigated.

Methods

The Walle-Hoch method was used to determine the ADH activity of lotus root extracts in vitro. Lotus root methanol extract were identified by UPLC-QTOF-MS/MS based metabolomics analysis. Then 109 active ingredients with achievable oral doses and drug-like properties were explored using the TCMSP platform. SwissTargetPrediction Database to predict lotus root treatment targets for acute alcoholismSTRING database (https://www.string-db.org/) was used to construct protein-protein interaction network graphs. Gene ontology (GO) functional, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of genes common to lotus root and alcoholism by Metascap database. Molecular docking simulations were performed using AutoDock 1.5.6 software. Animal experiments verified the relieving effect of lotus root extract on acute alcoholism after intervention.

Results

Results indicated the methanol extract of lotus root showed the highest activation rate of ethanol dehydrogenase in vitro (18.87%). The 433 compounds of lotus root methanol extract were identified by UPLC-QTOF-MS/MS based metabolomics analysis. Bioinformatics analysis indicate that there were 224 intersectioning targets between lotus root extract and acute alcoholism. KEGG enrichment analysised shows that lotus root extract may play a role in treating acute alcoholism by intervening with the neuroactive ligand-receptor interaction pathway. The protein-protein interaction network (PPI) analysis found that HSP90AA1, MAPK1 and STAT3 played a key role in lotus root extract-modulated PPI networks. Molecular docking showed that (7R, 8S)-dihydrodihydrodipine cypressol had the best binding ability with MAPK1. Experiments in mice indicate that lotus root extract improved the activity of liver alcohol dehydrogenase (ADH), acetaldehyde dehydrogenase (ALDH), catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidase (GSH-PX), increase glutathione (GSH) and reduce malondialdehyde (MDA) levels, decrease glutamate transaminase (AST), alanine transaminase (ALT) and alkaline phosphatase (AKP) in the serum of mice with acute alcoholism, and accelerate the metabolic rate of alcohol after drinking. This study reveals the mechanism of lotus root to alleviate acute alcoholism, which provides a basis for further research on functional foods using lotus root and offers new possibilities for the treatment of acute alcoholism.

Conclusions

The results of the current study showed that the methanolic extract of lotus root had the highest activation rate of ethanol dehydrogenase. Network pharmacology results suggest that lotus root extract may play a role in the treatment of alcoholism by regulating signaling pathways, such as neuroactive ligand-receptor interactions, as well as biological processes, such as regulation of secretion, regulation of ion transport, response to lipopolysaccharides, and response to alcohol. Animal experiments confirmed the therapeutic effect of lotus root on acute alcoholism mechanistically through activation of alcohol catabolic enzyme, reduction of oxidative stress and protection of liver function.

Rare variant analyses across multiethnic cohorts identify novel genes for refractive error

Refractive error, measured here as mean spherical equivalent (SER), is a complex eye condition caused by both genetic and environmental factors. Individuals with strong positive or negative values of SER require spectacles or other approaches for vision correction. Common genetic risk factors have been identified by genome-wide association studies (GWAS), but a great part of the refractive error heritability is still missing. Some of this heritability may be explained by rare variants (minor allele frequency [MAF] ≤ 0.01.). We performed multiple gene-based association tests of mean Spherical Equivalent with rare variants in exome array data from the Consortium for Refractive Error and Myopia (CREAM). The dataset consisted of over 27,000 total subjects from five cohorts of Indo-European and Eastern Asian ethnicity. We identified 129 unique genes associated with refractive error, many of which were replicated in multiple cohorts. Our best novel candidates included the retina expressed PDCD6IP, the circadian rhythm gene PER3, and P4HTM, which affects eye morphology. Future work will include functional studies and validation. Identification of genes contributing to refractive error and future understanding of their function may lead to better treatment and prevention of refractive errors, which themselves are important risk factors for various blinding conditions.

Dichotomous cis-regulatory motifs mediate the maturation of the neuromuscular junction by retrograde BMP signaling

Retrograde bone morphogenetic protein (BMP) signaling at the Drosophila neuromuscular junction (NMJ) has served as a paradigm to study TGF-β-dependent synaptic function and maturation. Yet, how retrograde BMP signaling transcriptionally regulates these functions remains unresolved. Here, we uncover a gene network, enriched for neurotransmission-related genes, that is controlled by retrograde BMP signaling in motor neurons through two Smad-binding cis-regulatory motifs, the BMP-activating (BMP-AE) and silencer (BMP-SE) elements. Unpredictably, both motifs mediate direct gene activation, with no involvement of the BMP derepression pathway regulators Schnurri and Brinker. Genome editing of candidate BMP-SE and BMP-AE within the locus of the active zone gene bruchpilot, and a novel Ly6 gene witty, demonstrated the role of these motifs in upregulating genes required for the maturation of pre- and post-synaptic NMJ compartments. Our findings uncover how Smad-dependent transcriptional mechanisms specific to motor neurons directly orchestrate a gene network required for synaptic maturation by retrograde BMP signaling.

DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update)

DAVID is a popular bioinformatics resource system including a web server and web service for functional annotation and enrichment analyses of gene lists. It consists of a comprehensive knowledgebase and a set of functional analysis tools. Here, we report all updates made in 2021. The DAVID Gene system was rebuilt to gain coverage of more organisms, which increased the taxonomy coverage from 17 399 to 55 464. All existing annotation types have been updated, if available, based on the new DAVID Gene system. Compared with the last version, the number of gene-term records for most annotation types within the updated Knowledgebase have significantly increased. Moreover, we have incorporated new annotations in the Knowledgebase including small molecule-gene interactions from PubChem, drug-gene interactions from DrugBank, tissue expression information from the Human Protein Atlas, disease information from DisGeNET, and pathways from WikiPathways and PathBank. Eight of ten subgroups split from Uniprot Keyword annotation were assigned to specific types. Finally, we added a species parameter for uploading a list of gene symbols to minimize the ambiguity between species, which increases the efficiency of the list upload and eliminates confusion for users. These current updates have significantly expanded the Knowledgebase and enhanced the discovery power of DAVID.

XGBG: A Novel Method for Identifying Ovarian Carcinoma Susceptible Genes Based on Deep Learning

Ovarian carcinomas (OCs) represent a heterogeneous group of neoplasms consisting of several entities with pathogenesis, molecular profiles, multiple risk factors, and outcomes. OC has been regarded as the most lethal cancer among women all around the world. There are at least five main types of OCs classified by the fifth edition of the World Health Organization of tumors: high-/low-grade serous carcinoma, mucinous carcinoma, clear cell carcinoma, and endometrioid carcinoma. With the improved knowledge of genome-wide association study (GWAS) and expression quantitative trait locus (eQTL) analyses, the knowledge of genomic landscape of complex diseases has been uncovered in large measure. Moreover, pathway analyses also play an important role in exploring the underlying mechanism of complex diseases by providing curated pathway models and information about molecular dynamics and cellular processes. To investigate OCs deeper, we introduced a novel disease susceptible gene prediction method, XGBG, which could be used in identifying OC-related genes based on different omics data and deep learning methods. We first employed the graph convolutional network (GCN) to reconstruct the gene features based on both gene feature and network topological structure. Then, a boosting method is utilized to predict OC susceptible genes. As a result, our model achieved a high AUC of 0.7541 and an AUPR of 0.8051, which indicates the effectiveness of the XGPG. Based on the newly predicted OC susceptible genes, we gathered and researched related literatures to provide strong support to the results, which may help in understanding the pathogenesis and mechanisms of the disease.

Serum proteomic analysis reveals the cardioprotective effects of Shexiang Baoxin Pill and Suxiao Jiuxin Pill in a rat model of acute myocardial infarction

ETHNOPHARMACOLOGICAL RELEVANCE

Shexiang Baoxin Pill (SBP) and Suxiao Jiuxin Pill (SJP) are traditional Chinese medicines used to treat cardiovascular disease (CVD) in China. However, the mechanism of their therapeutic effect on CVD has not been clearly elucidated yet.

AIMS

The aim of this study is to investigate the cardioprotective effect of SBP and SJP in the treatment of acute myocardial infarction (AMI) model rats by applying serum proteomic approach.

MATERIALS AND METHODS

The rat model of AMI was generated by ligating the left anterior descending coronary artery. 42 rats were randomly divided into four groups: sham-operating (Sham, n = 10) group, model (Mod, n = 8) group, Shexiang Baoxin pills pretreatment (SBP, n = 12) group and Suxiao Jiuxin pills pretreatment (SJP, n = 12) group. Data Independent Acquisition (DIA) proteomic approach was utilized to investigate the serum proteome from the rat individuals. The differentially expressed proteins were subsequently obtained with bioinformatic analysis.

RESULTS

DIA-MS identified 415 proteins within 42 samples, and 84 differentially expressed proteins may contribute to the therapeutic effects of SBP and SJP. GOBP and KEGG pathway analysis of 84 differentially expressed proteins revealed that the proteins were mainly involved in platelet activation and adhesion processes. All 84 differentially expressed proteins presented the same changing tendency in the SBP and SJP groups when compared with the Mod group. Among these 84 proteins, 25 proteins were found to be related to CVD. Among these 25 proteins, ACTB, ACTG1, FGA, FGB, FGG, PF4 and VWF were found to be involved in platelet aggregation and activation. FN1, HSPA5 and YWHAZ were associated with adhesion.

CONCLUSIONS

The results of our study suggest that the cardioprotective effects of SBP and SJP are achieved through the modulation of focal adhesion, platelet activation pathways.

The mid-secretory endometrial transcriptomic landscape in endometriosis: a meta-analysis

STUDY QUESTION

Do women with endometriosis have a different endometrial gene expression profile at the time of embryo implantation than women without endometriosis?

SUMMARY ANSWER

The endometrial gene expression profile of women with endometriosis differs from that of women without endometriosis at the mid-secretory phase, although the differences are small.

WHAT IS KNOWN ALREADY

About 50% of women with endometriosis suffer infertility. Several molecular studies have suggested impaired endometrial receptivity in women with endometriosis, while others have detected no dysregulation of endometrial receptivity. Nevertheless, the previous endometrial transcriptome studies comparing women with and without endometriosis have been performed in small sample size with limited statistical power. We set out to systematically search and compile data of endometrial gene expression signatures at the receptive phase in women with endometriosis versus control women. Based on the obtained data, we conducted a meta-analysis of differentially expressed genes in order to raise the power of the analysis for identifying the molecular profiles of receptive phase endometria in endometriosis.

STUDY DESIGN, SIZE, DURATION

A systematic literature search was conducted up to February 2022 following PRISMA criteria and included PubMed, Cochrane and Web of Science databases. For the systematic search, the term ‘endometriosis’ was paired with the terms ‘transcriptomics’, ‘transcriptome’, ‘gene expression’, ‘RNA-seq’, ‘sequencing’ and ‘array’, by using the Boolean operator ‘AND’ to connect them. Articles written in English were screened and interrogated for data extraction.

PARTICIPANTS/MATERIALS, SETTING, METHODS

A meta-analysis was performed on the selected studies to extract the differentially expressed genes described at the mid-secretory phase in women with endometriosis versus women without endometriosis in natural cycles, using the robust rank aggregation method. In total, transcriptome data of 125 women (78 patients and 47 controls) were meta-analysed, with a special focus on endometrial receptivity-specific genes based on commercial endometrial receptivity tests.

MAIN RESULTS AND THE ROLE OF CHANCE

In total, 8 studies were eligible for the quantitative meta-analysis, gathering transcriptome data from the mid-secretory phase endometria of 125 women. A total of 7779 differentially expressed transcripts between the study groups were retrieved (3496 up-regulated and 4283 down-regulated) and were meta-analysed. After stringent multiple correction, there was no differential expression of any single molecule in the endometrium of women with endometriosis versus controls, while enrichment analysis detected that the pathways of chemotaxis and locomotion are dysregulated in endometriosis. Further analysis of endometrial receptivity-specific genes highlighted dysregulation of C4BPA, MAOA and PAEP and enrichment of immune and defence pathways in women with endometriosis.

LIMITATIONS, REASONS FOR CAUTION

Most of the studies included into the meta-analysis were relatively small and had different study designs, which might have contributed to a bias.

WIDER IMPLICATIONS OF THE FINDINGS

The current meta-analysis supports the hypothesis that endometrial receptivity is altered in women with endometriosis, although the changes are small. The molecules and pathways identified could serve as future biomarkers and therapeutical targets in detecting and treating endometriosis-associated infertility.

STUDY FUNDING/COMPETING INTEREST(S)

The authors declare no competing interests. This work was supported by the Spanish Ministry of Education, Culture and Sport [grant FPU15/01193] and the Margarita Salas program for the Requalification of the Spanish University system [grant UJAR01MS]; Spanish Ministry of Economy, Industry and Competitiveness (MINECO) and European Regional Development Fund (FEDER): grants RYC-2016-21199 and ENDORE SAF2017-87526-R; Programa Operativo FEDER Andalucía (B-CTS-500-UGR18; A-CTS-614-UGR20); the Junta de Andalucía [BIO-302; and PAIDI P20_00158]; the University of Jaén [PAIUJA-EI_CTS02_2017]; the University of Granada, Plan Propio de Investigación 2016, Excellence actions: Units of Excellence; Unit of Excellence on Exercise and Health (UCEES), and by the Junta de Andalucía, Consejería de Conocimiento, Investigación y Universidades and European Regional Development Fund (ERDF), ref. SOMM17/6107/UGR; the Estonian Research Council (grant PRG1076); Horizon 2020 innovation (ERIN, grant no. EU952516) of the European Commission and Enterprise Estonia (grant EU48695).

TRIAL REGISTRATION NUMBER

The systematic review was registered at PROSPERO (identifier: CRD42020122054).

Human Immune System Diseasome Networks and Female Oviductal Microenvironment: New Horizons to be Discovered

Human hypofertility and infertility are two worldwide conditions experiencing nowadays an alarming increase due to a complex ensemble of events. The immune system has been suggested as one of the responsible for some of the etiopathogenic mechanisms involved in these conditions. To shed some light into the strong correlation between the reproductive and immune system, as can be inferred by the several and valuable manuscripts published to date, here we built a network using a useful bioinformatic tool (DisGeNET), in which the key genes involved in the sperm-oviduct interaction were linked. This constitutes an important event related with Human fertility since this interaction, and specially the spermatozoa, represents a not-self entity immunotolerated by the female. As a result, we discovered that some proteins involved in the sperm-oviduct interaction are implicated in several immune system diseases while, at the same time, some immune system diseases could interfere by using different pathways with the reproduction process. The data presented here could be of great importance to understand the involvement of the immune system in fertility reduction in Humans, setting the basis for potential immune therapeutic tools in the near future.

New Potential of Roxatidine Acetate Hydrochloride on Atopic Dermatitis Mouse Model, Human Keratinocytes, and Human Skin Equivalent Model

Atopic dermatitis (AD) is a complex inflammatory skin disorder, characterized by a complicated pathophysiology and a wide range of clinical phenotypes. Roxatidine acetate chloride (RXA) is a precursor of Roxatidine and a histamine H₂ receptor antagonist, used for the treatment of gastric ulcers. In this study, we aimed to examine whether RXA had anti-AD effects and determine the underlying molecular mechanism of RXA. The anti-AD effects were examined in Dermatophagoides farinae body (Dfb)-induced AD mouse model, tumor necrosis factor (TNF)-α/interferon (IFN)-γ-stimulated HaCaT keratinocytes, and human skin equivalent model using ELISA, histological analysis, immunohistochemistry, Western blot, and immunofluorescence. Results showed that RXA treatment significantly alleviated Dfb-induced AD skin symptoms and clinical severity in mice by decreasing the levels of immunoglobulin E, histamine, and inflammatory cytokines. RXA effectively inhibited the expression of adhesive molecules and recovered the filaggrin expression in Dfb-induced AD skin lesions and TNF-α/IFN-γ-stimulated HaCaT keratinocytes. Additionally, RXA significantly upregulated the expression of aryl hydrocarbon receptor and sirtuin1. The anti-AD effects of RXA were associated with suppressed nuclear factor kappa cascade. Overall, our results suggest that RXA may be a potential anti-AD candidate owing to its inhibitory effect against skin inflammation and protection of the skin barrier function in AD.

Network pharmacology-based approach to understand the effect and mechanism of Danshen against anemia

ETHNOPHARMACOLOGICAL RELEVANCE

Danshen, the dried rhizome of Salvia miltiorrhiza Bge., is widely used to treat cardio-cerebrovascular diseases in China. However, its role in nourishing blood, which has been detailed in historical literature for thousands of years, is perpetually disputed in the academic field. Moreover, there is no systematic research on Danshen in treating anemia. This research aimed to investigate the effects and mechanisms of Danshen on anemia in a zebrafish model based on the results of a network pharmacology study.

MATERIALS AND METHODS

The network pharmacology study was based on the screening of chemical components and related targets from TCMSP and SwissADME database. The genes associated with anemia were obtained from DisgeNet database, and the genes with the intersection of Danshen target genes were screened out. The Cytoscape 3.7.2 software package was used to construct the "ingredient-target-pathway" network. The exploration of target interaction by String system and the enrichment analysis by Metascape system, was used to discover the possible anti-anemia action mechanism of Danshen. Then, a zebrafish anemia model was induced by vinorelbine followed by the administration of aqueous/ethanol extract of Danshen in contrast to SiWu Decoction (SWD), which is generally acknowledged as a positive drug for tonifying blood. Afterward, the red blood cell signal, cardiac output, and blood flow velocity were detected to evaluate their blood-enriching effects. Quantitative real-time polymerase chain reaction (qPCR) was used to analyze the mRNA levels of hematopoietic-related factors, which were predicted in network pharmacology.

RESULTS

Compounds and target screening hinted that 115 chemical targets from Danshen were related to anemia, KEGG pathway enrichment results suggested that the mechanism of Danshen in treating anemia was significantly related to the Jak-STAT signaling pathway. Pharmacodynamic results showed that aqueous extract of Danshen (DSAE) and ethanol extract of Danshen (DSEE) markedly enhanced the number of red blood cells, cardiac output, and blood flow velocity. Compared with DSAE, DSEE exerted anti-anemia effects at a lower dose; however, along with higher toxicity. PCR data demonstrated that DSAE and DSEE treatment both upregulated the mRNA expression of erythroid hematopoiesis-related factors in the Epo-JAK-STAT signaling pathway, such as Gata-1, Epo, EpoR, Jak2, STAT3, and STAT5. In general, DSAE exhibited higher activation of this signaling than DSEE.

CONCLUSIONS

These results indicated that DSAE and DSEE both possess blood-enriching functions related with their ability to promote Jak-STAT signaling. DSAE exerted lower toxicity and attenuated anemia over a wider dose range than DSEE, which suggests that DSAE may be more suitable for the treatment for anemia. These results presented experimental evidence for the clinical use of Danshen as an intervention for anemia, especially in chemotherapy-induced anemia.

Higher-Order Proximity-Based MiRNA-Disease Associations Prediction

MiRNA-disease association prediction plays an important role in identifying human disease-related miRNAs. This approach is helpful not only to formulate individualized diagnosis schemes, but also to understand the pathogenesis of diseases. Many studies have focused on enhancing the prediction performance using explicit side information, such as miRNA functional similarity and disease semantic similarity. The existing approaches, however, often ignore the higher-order implicit proximity among miRNAs and diseases. To this end, in this paper, we first propose a novel approach HOP_MDA (Higher-Order Proximity based MiRNA and Disease Association Prediction) for predicting potential association between miRNA and disease. Both explicit interaction information and implicit higher-order proximity information between miRNA and disease are encoded with different order proximity matrices which are weightily combined into a parameterized prediction matrix. A supervised learning approach based on the known miRNAs-disease associations is proposed to determine the optimal weight parameters. The prediction matrix is then used to achieve effective prediction. Additionally, a higher-order proximity approximation technique (HOPA_MDA) is presented to make more efficient predictions. 5-fold cross validation is used to evaluate the performance of our proposed method. The average AUC values of HOPA_MDA for two real datasets are 0.921+/-0.002 and 0.944+/-0.0015, respectively. Our method can also predict potential miRNAs specific to new diseases with no known related miRNAs.

Explore the potential molecular mechanism of polycystic ovarian syndrome by protein-protein interaction network analysis

Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders prevailing in reproductive age women, present in 3-15% population of women worldwide. Although there are many studies on PCOS, its underlying mechanism remains to be determined. The present study was to construct protein-protein interaction networks based on the potential disease-causing genes for PCOS and characterize the underlying molecular mechanisms of PCOS using the networks. PCOS-associated genes were extracted from DisGeNet and the protein-protein interaction networks (PPIN) of PCOS were constructed using the String Database. Then we utilized MCODE algorithm to analyse the hub-gene modules from the PPIN. Finally, the major biological functions and signaling pathways involved in the hub modules were explored by functional enrichment analysis. A total of 522 candidate genes associated to PCOS were extracted from DisGeNET database. The PPIN constructed using the genes we have collected above included 488 genes and 2767 interaction relationships. Moreover, seven major gene modules were obtained after analyzing the PPIN with the use of MCODE plug-in. The major modules generated were enriched in certain biological functions such as cancer and cell proliferation and apoptosis, regulation of lipid and glucose metabolism, cell cycle and so on. The integrated analysis performed in the current study revealed that these hub modules and their related genes are closely associated to the pathogenesis of PCOS, which may probably provide novel insights for the treatment of PCOS and the study of its latent pathogenic mechanism. The relationship between several of the key genes including ALB, TOP2A, PTGER3, NPB and BRD2 in the modules and PCOS has not been investigated previously and it remains to be verified by further research of large sample, multi-center and multi-ethnic.

Systematic analysis of Long non-coding RNAs reveals diagnostic biomarkers and potential therapeutic drugs for intervertebral disc degeneration

Long non-coding RNAs (lncRNAs) are related to a variety of human diseases. However, little is known about the role of lncRNA in intervertebral disc degeneration (IDD). LncRNA expression profile of human IDD were downloaded from Gene Expression Omnibus (GEO) database. Potential biomarkers and therapeutic drugs for IDD were analyzed by weighted gene co-expression network analysis (WGCNA), R software package Limma, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 1455 differentially expressed genes and 423 differentially expressed lncRNAs. Twenty-six co-expression modules were obtained, among them, the tan, brown, and turquoise modules were most closely related to IDD. The turquoise module contained a large number of differential expressed lncRNAs and genes, these genes were mainly enriched in the MAPK signaling pathway, TGF-beta signaling pathway. Furthermore, we obtained 11,857 LmiRM-Degenerated, these lncRNAs and genes showed higher differential expression multiples and higher expression correlation. After constructing a disease-gene interaction network, 25 disease-specific genes and 9 disease-specific lncRNAs were identified. Combined with the drug-target gene interaction network, three drugs, namely, Calcium citrate, Calcium Phosphate, and Calcium phosphate dihydrate, which may have curative effects on IDD, were determined. Finally, a genetic diagnosis model and lncRNA diagnosis model with 100% diagnostic performance in both the training data set and the validation data set were established based on these genes and lncRNA. This study provided new diagnostic features for IDD and could help design personalized treatment of IDD.

TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery

Target identification and prioritisation are prominent first steps in modern drug discovery. Traditionally, individual scientists have used their expertise to manually interpret scientific literature and prioritise opportunities. However, increasing publication rates and the wider routine coverage of human genes by omic-scale research make it difficult to maintain meaningful overviews from which to identify promising new trends. Here we propose an automated yet flexible pipeline that identifies trends in the scientific corpus which align with the specific interests of a researcher and facilitate an initial prioritisation of opportunities. Using a procedure based on co-citation networks and machine learning, genes and diseases are first parsed from PubMed articles using a novel named entity recognition system together with publication date and supporting information. Then recurrent neural networks are trained to predict the publication dynamics of all human genes. For a user-defined therapeutic focus, genes generating more publications or citations are identified as high-interest targets. We also used topic detection routines to help understand why a gene is trendy and implement a system to propose the most prominent review articles for a potential target. This TrendyGenes pipeline detects emerging targets and pathways and provides a new way to explore the literature for individual researchers, pharmaceutical companies and funding agencies.

Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data

Gastric cancer (GC) is one of the most common causes of cancer-related deaths in the world. This cancer has been regarded as a biological and genetically heterogeneous disease with a poorly understood carcinogenesis at the molecular level. Thousands of biomarkers and susceptible loci have been explored via experimental and computational methods, but their effects on disease outcome are still unknown. Genome-wide association studies (GWAS) have identified multiple susceptible loci for GC, but due to the linkage disequilibrium (LD), single-nucleotide polymorphisms (SNPs) may fall within the non-coding region and exert their biological function by modulating the gene expression level. In this study, we collected 1,091 cases and 410,350 controls from the GWAS catalog database. Integrating with gene expression level data obtained from stomach tissue, we conducted a machine learning-based method to predict GC-susceptible genes. As a result, we identified 787 novel susceptible genes related to GC, which will provide new insight into the genetic and biological basis for the mechanism and pathology of GC development.

Integrating Biomaterials and Genome Editing Approaches to Advance Biomedical Science

The recent discovery and subsequent development of the CRISPR-Cas9 (clustered regularly interspaced short palindromic repeat-CRISPR-associated protein 9) platform as a precise genome editing tool have transformed biomedicine. As these CRISPR-based tools have matured, multiple stages of the gene editing process and the bioengineering of human cells and tissues have advanced. Here, we highlight recent intersections in the development of biomaterials and genome editing technologies. These intersections include the delivery of macromolecules, where biomaterial platforms have been harnessed to enable nonviral delivery of genome engineering tools to cells and tissues in vivo. Further, engineering native-like biomaterial platforms for cell culture facilitates complex modeling of human development and disease when combined with genome engineering tools. Deeper integration of biomaterial platforms in these fields could play a significant role in enabling new breakthroughs in the application of gene editing for the treatment of human disease.

DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data

Endocrinology is the study focusing on hormones and their actions. Hormones are known as chemical messengers, released into the blood, that exert functions through receptors to make an influence in the target cell. The capacity of the mammalian organism to perform as a whole unit is made possible based on two principal control mechanisms, the nervous system and the endocrine system. The endocrine system is essential in regulating growth and development, tissue function, metabolism, and reproductive processes. Endocrine diseases such as diabetes mellitus, Grave's disease, polycystic ovary syndrome, and insulin-like growth factor I deficiency (IGFI deficiency) are classical endocrine diseases. Endocrine dysfunction is also an increasing factor of morbidity in cancer and other dangerous diseases in humans. Thus, it is essential to understand the diseases from their genetic level in order to recognize more pathogenic genes and make a great effort in understanding the pathologies of endocrine diseases. In this study, we proposed a deep learning method named DeepGP based on graph convolutional network and convolutional neural network for prioritizing susceptible genes of five endocrine diseases. To test the performance of our method, we performed 10-cross-validations on an integrated reported dataset; DeepGP obtained a performance of the area under the curve of ∼83% and area under the precision-recall curve of ∼65%. We found that type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) share most of their associated genes; therefore, we should pay more attention to the rest of the genes related to T1DM and T2DM, respectively, which could help in understanding the pathogenesis and pathologies of these diseases.

Mechanism of protective effect of xuan-bai-cheng-qi decoction on LPS-induced acute lung injury based on an integrated network pharmacology and RNA-sequencing approach

Xuan-bai-cheng-qi decoction (XCD), a traditional Chinese medicine (TCM) prescription, has been widely used to treat a variety of respiratory diseases in China, especially to seriously infectious diseases such as acute lung injury (ALI). Due to the complexity of the chemical constituent, however, the underlying pharmacological mechanism of action of XCD is still unclear. To explore its protective mechanism on ALI, firstly, a network pharmacology experiment was conducted to construct a component-target network of XCD, which identified 46 active components and 280 predicted target genes. Then, RNA sequencing (RNA-seq) was used to screen differentially expressed genes (DEGs) between ALI model rats treated with and without XCD and 753 DEGs were found. By overlapping the target genes identified using network pharmacology and DEGs using RNA-seq, and subsequent protein–protein interaction (PPI) network analysis, 6 kernel targets such as vascular epidermal growth factor (VEGF), mammalian target of rapamycin (mTOR), AKT1, hypoxia-inducible factor-1α (HIF-1α), and phosphoinositide 3-kinase (PI3K) and gene of phosphate and tension homology deleted on chromsome ten (PTEN) were screened out to be closely relevant to ALI treatment. Verification experiments in the LPS-induced ALI model rats showed that XCD could alleviate lung tissue pathological injury through attenuating proinflammatory cytokines release such as tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1β. Meanwhile, both the mRNA and protein expression levels of PI3K, mTOR, HIF-1α, and VEGF in the lung tissues were down-regulated with XCD treatment. Therefore, the regulations of XCD on PI3K/mTOR/HIF-1α/VEGF signaling pathway was probably a crucial mechanism involved in the protective mechanism of XCD on ALI treatment.