STRING: a database of predicted functional associations between proteins

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Knowledge-guided learning methods for integrative analysis of multi-omics data

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.

Elucidating the role of MICAL1 in pan-cancer using integrated bioinformatics and experimental approaches

Molecule interacting with CasL 1 (MICAL1) is a crucial protein involved in cell motility, axon guidance, cytoskeletal dynamics, and gene transcription. This pan-cancer study analyzed MICAL1 across 33 cancer types using bioinformatics and experiments. Dysregulated expression, diagnostic potential, and prognostic value were assessed. Associations with tumor characteristics, immune factors, and drug sensitivity were explored. Enrichment analysis revealed MICAL1's involvement in metastasis, angiogenesis, metabolism, and immune pathways. Functional experiments demonstrated its impact on renal carcinoma cells. These findings position MICAL1 as a potential biomarker and therapeutic target in specific cancers, warranting further investigation into its role in cancer pathogenesis.

Identification the role of necroptosis in rheumatoid arthritis by WGCNA network

Rheumatoid arthritis (RA) is the predominant manifestation of inflammatory arthritis, distinguished by an increasing burden of morbidity and mortality. The intricate interplay of genes and signalling pathways involved in synovial inflammation in patients with RA remains inadequately comprehended. This study aimed to ascertain the role of necroptosis in RA, as along with their associations with immune cell infiltration. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify central genes for RA. In this study, identified total of 28 differentially expressed genes (DEGs) were identified in RA. Utilising WGCNA, two co-expression modules were generated, with one module demonstrating the strongest correlation with RA. Through the integration of differential gene expression analysis, a total of 5 intersecting genes were discovered. These 5 hub genes, namely fused in sarcoma (FUS), transformer 2 beta homolog (TRA2B), eukaryotic translation elongation factor 2 (EEF2), cleavage and polyadenylation specific factor 6 (CPSF6) and signal transducer and activator of transcription 3 (STAT3) were found to possess significant diagnostic value as determined by receiver operating characteristic (ROC) curve analysis. The close association between the concentrations of various immune cells is anticipated to contribute to the diagnosis and treatment of RA. Furthermore, the infiltration of immune cells mentioned earlier is likely to exert a substantial influence on the initiation of this disease.

Subclinical peripheral inflammation has systemic effects impacting central nervous system proteome in budgerigars

Regulation of neuroimmune interactions varies across avian species. Little is presently known about the interplay between periphery and central nervous system (CNS) in parrots, birds sensitive to neuroinflammation. Here we investigated the systemic and CNS responses to dextran sulphate sodium (DSS)- and lipopolysaccharide (LPS)-induced subclinical acute peripheral inflammation in budgerigar (Melopsittacus undulatus). Three experimental treatment groups differing in DSS and LPS stimulation were compared to controls. Individuals treated with DSS showed significant histological intestinal damage. Through quantitative proteomics we described changes in plasma (PL) and cerebrospinal fluid (CSF) composition. In total, we identified 180 proteins in PL and 978 proteins in CSF, with moderate co-structure between the proteomes. Between treatments we detected differences in immune, coagulation and metabolic pathways. Proteomic variation was associated with the levels of pro-inflammatory cytokine mRNA expression in intestine and brain. Our findings shed light on systemic impacts of peripheral low-grade inflammation in birds.

Cucurbitacin B targets STAT3 to induce ferroptosis in non-small cell lung cancer

Cucurbitacin B (CuB) is a compound found in plants like Cucurbitaceae that has shown promise in fighting cancer, particularly in lung cancer. However, the specific impact of CuB on ferroptosis and how it works in lung cancer cells has not been fully understood. Our research has discovered that CuB can effectively slow down the growth of non-small cell lung cancer (NSCLC) cells. Even in small amounts, it was able to inhibit the growth of various NSCLC cell lines. This inhibitory effect was reversed when ferroptosis inhibitors DFO, Lip-1 and Fer-1 were introduced. CuB was found to increase the levels of reactive oxygen species (ROS), lipid ROS, MDA, and ferrous ions within H358 lung cancer cells, leading to a decrease in GSH, mitochondrial membrane potential (MMP) and changes in ferroptosis-related proteins in a dose-dependent manner. These findings were also confirmed in A549 lung cancer cells. In A549 cells, different concentrations of CuB induced the accumulation of intracellular lipid ROS, ferrous ions and changes in ferroptosis-related indicators in a concentration-dependent manner. Meanwhile, the cytotoxic effect induced by CuB in A549 cells was counteracted by ferroptosis inhibitors DFO and Fer-1. Through network pharmacology, we identified potential targets related to ferroptosis in NSCLC cells treated with CuB, with STAT3 targets showing high scores. Further experiments using molecular docking and cell thermal shift assay (CETSA) revealed that CuB interacts with the STAT3 protein. Western blot and immunofluorescence staining demonstrated that CuB inhibits the phosphorylation of STAT3 (P-STAT3) in H358 cells. Silencing STAT3 enhanced CuB-induced accumulation of lipid ROS and iron ions, as well as the expression of ferroptosis-related proteins. On the other hand, overexpression of STAT3 reversed the effects of CuB-induced ferroptosis. The results indicate that CuB has the capability to suppress STAT3 activation, resulting in ferroptosis, and could be a promising treatment choice for NSCLC.

Machine learning, network pharmacology, and molecular dynamics reveal potent cyclopeptide inhibitors against dengue virus proteins

The dengue virus is a major global health hazard responsible for an estimated 390 million diseases yearly. This study focused on identifying cyclopeptide inhibitors for envelope structural proteins E, NS1, NS3, and NS5. Additionally, 5579 cyclopeptides were individually screened against the four target proteins using a machine learning-based quantitative structure-activity relationship model. Subsequently, the best 10 cyclopeptides from each protein were selected for molecular docking with their corresponding proteins. Moreover, the protein-peptide complexes with the highest affinity were subjected to a 100-ns molecular dynamics simulation. The protein-protein complexes exhibited superior structural stability and binding interactions. Based on the results of the MD simulation analyses, which included checking values for Root Mean Square Deviation, Root Mean Square Fluctuation, Principal Component Analysis (PCA), free energy landscapes, and energetic components, it was found that NS5-CP03714 complex is more stable and has stronger binding interactions than NS3-CP02054. PCA and free energy landscape plots have confirmed the higher conformational stability of NS5-CP03714. Analysis of the energetic components revealed that NS5-CP03714 (total binding energy = - 47.19 kcal/mol) exhibits more favorable interaction energies and overall binding energy compared to NS3-CP02054 (total binding energy = - 27.36 kcal/mol), suggesting a stronger and more stable formation of the complex. In addition, the drug-target network of two specific peptides (CP02950 and CP05582) and their associated target proteins were analyzed. This analysis revealed valuable information about their ability to target several proteins and their potential for broad-spectrum activity. Additional experimental investigations are necessary to validate these computational results and assess the efficacy of identified peptide inhibitors in biological systems.

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.

Proteomic and phosphoproteomic reveal immune-related function of milk fat globule membrane in bovine milk of different lactation periods

Information regarding protein expression and phosphorylation modifications in the bovine milk fat globule membrane is scarce, particularly throughout various lactation periods. This study employed a complete proteome and phosphoproteome between bovine colostrum and mature milk. A total of 11 proteins were seen in both protein expression and phosphorylation levels. There were 400 proteins identified in only protein expression, and 104 phosphoproteins identified in only phosphorylation levels. A total of 232 significant protein characteristics were identified within the proteome and significant phosphorylation sites within 86 phosphoproteins of the phosphoproteome. Biological activities and pathways primarily exhibited associations with the immune system. Simultaneously, a comprehensive analysis of proteins and phosphorylation sites using a multi-omics approach. Hence, the data we have obtained has the potential to expand our understanding of how the bovine milk fat globule membrane might be utilized as a beneficial component in dairy products.

Integrated analysis reveals an aspartate metabolism-related gene signature for predicting the overall survival in patients with hepatocellular carcinoma

BACKGROUND

Deregulating cellular metabolism is one of the prominent hallmarks of malignancy, with a critical role in tumor survival and growth. However, the role of reprogramming aspartate metabolism in hepatocellular carcinoma (HCC) are largely unknown.

METHODS

The multi-omics data of HCC patients were downloaded from public databases. Univariate and multivariate stepwise Cox regression were used to establish an aspartate metabolism-related gene signature (AMGS) in HCC. The Kaplan-Meier and receiver operating characteristic curve analyses were performed to evaluate the predictive ability for overall survival (OS) in HCC patients. Gene set enrichment analysis and immune infiltration analysis were operated to determine the potential mechanisms underlying the AMGS. Single-cell RNA sequencing (scRNA-seq) data of liver cancer stem cells were visualized by t-SNE algorithm. In vivo and in vitro experiments were implemented to investigate the biological function of CAD in HCC. In addition, a nomogram based on the AMGS and clinicopathologic characteristics was constructed by univariate and multivariate Cox regression analyses.

RESULTS

Patients in the high-AMGS subgroup exerted advanced tumor status and poor prognosis. Mechanistically, the high-AMGS subgroup patients had significantly enhanced proliferation and stemness-related pathways, increased infiltration of regulatory T cells and upregulated expression levels of suppressive immune checkpoints in the tumor immune microenvironment. Notably, scRNA-seq data revealed CAD, one of the aspartate metabolism-related gene, is significantly upregulated in liver cancer stem cells. Silencing CAD inhibited proliferative capacity and stemness properties of HCC cells in vitro and in vivo. Finally, a novel nomogram based on the AMGS showed an accurate prediction in HCC patients.

CONCLUSIONS

The AMGS represents a promising prognostic value for HCC patients, providing a perspective for finding novel biomarkers and therapeutic targets for HCC.

Exploration of the potential mechanism of aqueous extract of Artemisia capillaris for the treatment of non-alcoholic fatty liver disease based on network pharmacology and experimental verification

OBJECTIVES

Non-alcoholic fatty liver disease (NAFLD) is a nutritional and metabolic disease with a high prevalence today. Artemisia capillaris has anti-inflammatory, antioxidant, and other effects. However, the mechanism of A. capillaris in treating NAFLD is still poorly understood.

METHODS

This study explored the mechanism of A. capillaris in the treatment of NAFLD through network pharmacology and molecular docking, and verified the results through in vivo experiments using a high-fat diet-induced mouse model and in vitro experiments using an oleic acid-induced HepG2 cell model.

KEY FINDINGS

Aqueous extract of A. capillaris (AEAC) can reduce blood lipids, reduce liver lipid accumulation and liver inflammation in NAFLD mice, and improve NAFLD. Network pharmacology analysis revealed that 51 drug ingredients in A. capillaris correspond to 370 targets that act on NAFLD. GEO data mining obtained 93 liver differentially expressed genes related to NAFLD. In the UHPLC-MS detection results, 36 components were characterized and molecular docked with JNK. Verified in vitro and in vivo, the results show that JNK and the phosphorylation levels of IL-6, IL-1β, c-Jun, c-Fos, and CCL2 are key targets and pathways.

CONCLUSIONS

This study confirmed that AEAC reduces lipid accumulation and inflammation in the liver of NAFLD mice by inhibiting the JNK/AP-1 pathway.

Network Pharmacological Analysis Combined with Experimental Verification to Explore the Effect of Ginseng Polypeptide on the Improvement of Diabetes Symptoms in db/db Mice

Diabetes mellitus is a typical metabolic disease that has become a major threat to human health worldwide. Ginseng polypeptide (GP), a small molecule active substance isolated from ginseng, has shown positive hypoglycemic effects in preliminary studies. However, its mechanism in ameliorating multiorgan damage in db/db mice is unclear. In this study, we utilized network pharmacology, molecular docking, and animal experiments to explore the targets and biological mechanisms of GP to ameliorate multiorgan damage in T2DM. The results showed that GP improves T2DM by inhibiting inflammation and oxidative damage, thereby alleviating hyperglycemia, insulin resistance, and multiorgan damage in db/db mice. These effects are potentially mediated through the PI3K-Akt signaling pathway and the MAPK signaling pathway. This study establishes GP's efficacy in alleviating T2DM and provides a robust theoretical basis for the development of new drugs or functional foods for treating this disease.

Unveiling the double-edged sword: SOD1 trimers possess tissue-selective toxicity and bind septin-7 in motor neuron-like cells

Misfolded species of superoxide dismutase 1 (SOD1) are associated with increased death in amyotrophic lateral sclerosis (ALS) models compared to insoluble protein aggregates. The mechanism by which structurally independent SOD1 trimers cause cellular toxicity is unknown but may drive disease pathology. Here, we uncovered the SOD1 trimer interactome-a map of potential tissue-selective protein-binding partners in the brain, spinal cord, and skeletal muscle. We identified binding partners and key pathways associated with SOD1 trimers and found that trimers may affect normal cellular functions such as dendritic spine morphogenesis and synaptic function in the central nervous system and cellular metabolism in skeletal muscle. We discovered SOD1 trimer-selective enrichment of genes. We performed detailed computational and biochemical characterization of SOD1 trimer protein binding for septin-7. Our investigation highlights key proteins and pathways within distinct tissues, revealing a plausible intersection of genetic and pathophysiological mechanisms in ALS through interactions involving SOD1 trimers.

Integrative analysis reveals key lysosomal genes as potential therapeutic targets in Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder with early autophagy deficits. Our study probed the role of lysosomal-related genes (LRGs) in AD. Using the Gene Expression Omnibus (GEO) database, we analyzed differentially expressed genes (DEGs) in AD. AD-related genes and lysosomal-related genes (LRGs) were extracted from public databases. Leveraging the UpSetR package, we identified differentially expressed LRGs (DE-LRGs). Subsequently, consensus cluster analysis was used to stratify AD patients into distinct molecular subtypes based on DE-LRGs. Immune cell patterns were studied via Single-Sample Gene Set Enrichment Analysis (ssGSEA). Molecular pathways were assessed through Gene Set Variation Analysis (GSVA), while Mendelian Randomization (MR) discerned potential gene-AD causations. To reinforce our bioinformatics findings, we conducted in vitro experiments. In total, 52 DE-LRGs were identified, with LAMP1, VAMP2, and CTSB as standout hub genes. Leveraging the 52 DE-LRGs, AD patients were categorized into three distinct molecular subtypes. Interestingly, the three aforementioned hub genes exhibited significant predictive accuracy for AD differentiation across the subtypes. The ssGSEA further illuminated correlations between LAMP1, VAMP2, and CTSB with plasma cells, fibroblasts, eosinophils, and endothelial cells. GSVA analysis underscored significant associations of LAMP1, VAMP2, and CTSB with NOTCH, TGFβ, and P53 pathways. Compellingly, MR findings indicated a potential causative relationship between LAMP1, CTSB, and AD. Augmenting our bioinformatics conclusions, in vitro tests revealed that LAMP1 potentially alleviates AD progression by amplifying autophagic processes. LAMP1 and CTSB emerge as potential AD biomarkers, paving the way for innovative therapeutic interventions.

Uncovering the protective mechanism of baicalin in treatment of fatty liver based on network pharmacology and cell model of NAFLD

Excessive nonesterified fatty acids (NEFA) impair cellular metabolism and will induce fatty liver formation in dairy cows during the periparturient. Baicalin, an active flavonoid, has great potential efficacy in alleviating lipid accumulation and ameliorating the development of fatty liver disease. Nevertheless, its mechanism remains unclear. Here, the potential mechanism of baicalin on system levels was explored using network pharmacology and in vitro experiments. Firstly, the target of baicalin and fatty liver disease was predicted, and then the protein-protein interaction (PPI) network was constructed. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) (q-value) pathway enrichment is performed through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server. Finally, the results of the network analysis of the in vitro treatment of bovine hepatocytes by NEFA were confirmed. The results showed that 33 relevant targets of baicalin in the treatment of liver fatty were predicted by network pharmacology, and the top 20 relevant pathways were extracted by KEGG database. Baicalin treatment can reduce triglyceride (TAG) content and lipid droplet accumulation in NEFA-treated bovine hepatocytes, and the mechanism is related to inhibiting lipid synthesis and promoting lipid oxidation. The alleviating effect of baicalin on fatty liver may be related to the up-regulation of solute vector family member 4 (SLC2A4), Down-regulated AKT serine/threonine kinase 1 (AKT1), Peroxisome proliferator-activated receptor gamma (PPARG), Epidermal growth factor receptor (EGFR), tumor necrosis factor (TNF), Interleukin 6 (IL-6) were associated. These results suggested that baicalin may modulate key inflammatory markers, and lipogenesis processes to prevent fatty liver development in dairy cows.

Targeting PERK and GRP78 in colorectal cancer: Genetic insights and novel therapeutic approaches

Colorectal cancer (CRC) ranks among the leading causes of cancer-related deaths worldwide. Enhancing CRC diagnosis and prognosis requires the development of improved biomarkers and therapeutic targets. Emerging evidence suggests that the unfolded protein response (UPR) plays a pivotal role in CRC progression, presenting new opportunities for diagnosis, treatment, and prevention. This study hypothesizes that genetic variants in endoplasmic reticulum (ER) stress response genes influence CRC susceptibility. We examined the frequencies of SNPs in PERK (rs13045) and GRP78/BiP (rs430397) within a South Iranian cohort. We mapped the cellular and molecular features of PERK and GRP78 genes in colorectal cancer, observing their differential expressions in tumor and metastatic tissues. We constructed co-expression and protein-protein interaction networks and performed gene set enrichment analysis, highlighting autophagy as a significant pathway through KEGG. Furthermore, the study included 64 CRC patients and 60 control subjects. DNA extraction and genotyping were conducted using high-resolution melting (HRM) analysis. Significant differences in PERK and GRP78 expressions were observed between CRC tissues and controls. Variations in PERK and GRP78 genotypes were significantly correlated with CRC risk. Utilizing a Multi-Target Directed Ligands approach, a dual PERK/GRP78 inhibitor was designed and subjected to molecular modeling studies. Docking experiments indicated high-affinity binding between the proposed inhibitor and both genes, PERK and GRP78, suggesting a novel therapy for CRC. These findings highlight the importance of understanding genetic backgrounds in different populations to assess CRC risk. Polymorphisms in UPR signaling pathway elements may serve as potential markers for predicting CRC susceptibility, paving the way for personalized therapeutic strategies.

IGF1 and CXCR4 Respectively Related With Inhibited M1 Macrophage Polarization in Keloids

PURPOSE

The pathophysiology of keloid remains unclear. Exploring the immune heterogeneity and new biomarkers of keloids can help design new therapeutic targets for keloid treatments and prevention.

METHODS

The authors performed single-cell RNA sequencing analysis and bulk data differential gene expression analysis of public datasets(GSE92566 and GSE163973). They used Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and immune infiltration analysis to identify the function of the differential expressed genes. Besides, the authors performed qt-PCR on keloid tissue and adjacent normal tissues from 3 patients for further verification.

RESULTS

M2 macrophage increased in keloid samples than M1 macrophage. The authors identified 2 potential novel biomarkers of keloid, IGF1 and CXCR4, which could inhibit M1 macrophage polarization. The potential mechanism could be inhibiting immune responses and anti-inflammatory activities through INF signaling and E2F targeting. The differential expression of the 2 genes was verified by clinical samples.

CONCLUSIONS

The authors identified 2 immune signaling molecules associated with keloid formation (IGF1 and CXCR4) and analyzed their potential pathogenic mechanisms.

Screening chondrocyte necroptosis-related genes in the diagnosis and treatment of osteoarthritis

Background

Osteoarthritis (OA) is the most common form of joint diseases, with hallmark of cartilage degeneration. Recent studies have shown that the pathogenesis of OA is associated with chondrocyte necroptosis.

Methods

In this study, we used single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data to analyze necroptosis regulation in OA chondrocytes. We performed enrichment analysis, carried out experimental validation, constructed machine learning models, and docked drug molecules.

Results

After least absolute shrinkage and selection operator (LASSO) algorithm screening, 4 hub genes (RIPK3, CYBB, HSP90AB1, and TRAF5) with diagnostic characteristics were obtained. Following the comparison of multiple models, the Bayesian model with an average area under curve (AUC) value of 0.944 was finally selected. We found that nimesulide exhibited strong binding affinity to CYBB and HSP90AB1, and experimentally verified that nimesulide reduced the expression of RIPK3 and CYBB, suggesting its potential as an inhibitor of chondrocyte necroptosis. Furthermore, scRNA-seq results showed that necroptosis in OA was significantly upregulated on regulatory chondrocytes (RegC) compared to other chondrocyte subtypes.

Conclusions

The results indicate that nimesulide might be used to treat OA by inhibiting chondrocyte necroptosis through down-regulation of RIK3 and CYBB genes. This study reveals the role of chondrocyte necroptosis in OA, and suggests a potential therapeutic strategy by regulating necroptosis with nimesulide.

Defective mitochondrial COX1 translation due to loss of COX14 function triggers ROS-induced inflammation in mouse liver

Mitochondrial oxidative phosphorylation (OXPHOS) fuels cellular ATP demands. OXPHOS defects lead to severe human disorders with unexplained tissue specific pathologies. Mitochondrial gene expression is essential for OXPHOS biogenesis since core subunits of the complexes are mitochondrial-encoded. COX14 is required for translation of COX1, the central mitochondrial-encoded subunit of complex IV. Here we describe a COX14 mutant mouse corresponding to a patient with complex IV deficiency. COX14M19I mice display broad tissue-specific pathologies. A hallmark phenotype is severe liver inflammation linked to release of mitochondrial RNA into the cytosol sensed by RIG-1 pathway. We find that mitochondrial RNA release is triggered by increased reactive oxygen species production in the deficiency of complex IV. Additionally, we describe a COA3Y72C mouse, affected in an assembly factor that cooperates with COX14 in early COX1 biogenesis, which displays a similar yet milder inflammatory phenotype. Our study provides insight into a link between defective mitochondrial gene expression and tissue-specific inflammation.

Rapid Revealing of Quorum Sensing (QS)-Regulated PLA, Biofilm and Lysine Targets of Lactiplantibacillus plantarum L3

Quorum sensing (QS) can regulate the production of multiple functional factors in bacteria, but the process of identifying its regulatory targets is very complex and labor-intensive. In this study, an efficient and rapid method to find QS targets through prediction was used. The genome of Lactiplantibacillus plantarum (L. plantarum) L3 was sequenced and characterized, and then linked the L. plantarum L3 genome to the STRING database for QS system regulatory target prediction. A total of 3,167,484 base pairs (bps) were examined from the genome of L. plantarum L3, and 30 QS-related genes were discovered (including luxS). The STRING database prediction indicated that the 30 QS-related genes are mainly involved in the regulation of nine metabolic pathways. Furthermore, metE, metK, aroB, cysE, and birA1 were predicted to be regulatory targets of the LuxS/AI-2 QS system, and these five targets were validated based on quantitative real-time PCR and content determination. Successful elucidation of the LuxS/AI-2 QS system's key targets and regulation mechanism in L. plantarum L3 demonstrated the effectiveness of the new approach for predicting QS targets and provides a scientific basis for future work on improving regulation of functional factor production.

Comprehensive microarray analysis of severe preeclampsia placenta to identify differentially expressed genes, biological pathways, hub genes, and their related non-coding RNAs

INTRODUCTION

Preeclampsia (PE) is a serious pregnancy-related complication caused by high blood pressure in pregnant women. The severe form has more devastating effects. According to the growing evidence, the placenta is a crucial component in the pathogenesis of PE, and eliminating it will alleviate symptoms.

METHODS

GEO's severe preeclampsia placenta microarray datasets; GSE147776, GSE66273, GSE102897, and GSE10588, were chosen to identify differentially expressed genes (DEGs) in different biological pathways. The analysis of hub genes and related non-coding RNAs was done as well.

RESULTS

A total of 347 DEGs with adj p-value <0.05 and ǀlog2FoldChangeǀ> 0.5 were discovered between severe PEs and healthy pregnancies, including 204 over-expressed genes and 143 under-expressed genes. The MCC method identified ISG15, IFI44L, MX2, OAS2, MX1, FN1, LDHA, ITGB3, TKT, HK2 genes as the top ten hub genes. Interactions between hub genes and noncoding RNAs were also conducted. The most enriched pathways were as follows; HIF-1 signaling pathway; Pathways in cancer; Alanine, aspartate and glutamate metabolism; Arginine biosynthesis; Human papillomavirus infection; Glycolysis/Gluconeogenesis; Central carbon metabolism in cancer; Valine, leucine and isoleucine degradation; Cysteine and methionine metabolism; and Galactose metabolism.

DISCUSSION

This is a secondary data analysis conducted on severe preeclampsia placenta to identify differentially expressed genes, biological pathways, hub-genes, and related noncoding RNAs. Functional studies are crucial to understanding the precise role of these genes in the pathogenesis of PE. Also, accepting a gene as a diagnostic or prognostic marker for early diagnosis and management of PE requires multiple lines of evidence.

A python library for the fast and scalable computation of biologically meaningful individual specific networks

Individual Specific Networks (ISNs) are a tool used in computational biology to infer Individual Specific relationships between biological entities from omics data. ISNs provide insights into how the interactions among these entities affect their respective functions. To address the scarcity of solutions for efficiently computing ISNs on large biological datasets, we present ISN-tractor, a data-agnostic, highly optimized Python library to build and analyse ISNs. ISN-tractor demonstrates superior scalability and efficiency in generating Individual Specific Networks (ISNs) when compared to existing methods such as LionessR, both in terms of time and memory usage, allowing ISNs to be used on large datasets. We show how ISN-tractor can be applied to real-life datasets, including The Cancer Genome Atlas (TCGA) and HapMap, showcasing its versatility. ISN-tractor can be used to build ISNs from various -omics data types, including transcriptomics, proteomics, and genotype arrays, and can detect distinct patterns of gene interactions within and across cancer types. We also show how Filtration Curves provided valuable insights into ISN characteristics, revealing topological distinctions among individuals with different clinical outcomes. Additionally, ISN-tractor can effectively cluster populations based on genetic relationships, as demonstrated with Principal Component Analysis on HapMap data.

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis

Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. This study introduces DREAMER, a novel network-based method for exploring the mechanisms underlying ADRs and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes. Applying DREAMER to 649 ADRs, we identified proteins associated with the mechanism of action for 67 ADRs across multiple organ systems. In particular, DREAMER highlights the importance of GABAergic signaling and proteins of the coagulation pathways for personality disorders and intracranial hemorrhage, respectively. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes.

Distance-depending transcriptome changes of pancreatic stellate cells in paracrine pancreatic ductal adenocarcinoma co-culture models

Pancreatic stellate cells (PSC) are one source of cancer-associated fibroblasts (CAF) and play, therefore, an essential role in pancreatic ductal adenocarcinoma (PDA). Paracrine signalling between PDA cells and CAF has been widely studied, yet external influences on paracrine crosstalk are poorly understood. This study aimed to gain a deeper insight into the communication of PSC and cancer cells under different co-culture conditions via analysis of PSC gene expression profiles. Two contactless co-culture models with tumor cells from the p48-Cre; lox-stop-lox-KrasG12D/+; lox-stop-lox-Trp53R172H/+ mouse model (KPC) and murine PSC separated through a microporous membrane and grown in different compartments (standard co-culture) or on different sides of the same membrane (inverse co-culture), were established. RNA-Sequencing analysis of PSC mRNA was performed 24 h and 72 h after co-culture with KPC cells. For selected genes, results were confirmed by quantitative RT-PCR and immunocytochemistry. Standard co-culture displayed 19 differentially expressed genes (DEG) at 24 h and 52 DEG at 72 h. In inverse co-culture, 800 DEG at 24 h and 2213 DEG at 72 h were enriched. PSC showed great heterogeneity in their gene expression profiles; however, mutually regulated genes of both co-cultures, such as VCAN and CHST11, could be identified. VCAN-protein-protein interaction-network analysis revealed several shared genes between co-culture models, such as SDC4 and FN1. In conclusion, PSC show a varying susceptibility to cancer cell signals depending on the co-culture method, with intensified transcriptome changes with closer proximity.

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples

Protein-protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks.

Identification of shared immune infiltration characteristic molecules in dermatomyositis and nasopharyngeal carcinoma using bioinformatics: Traits in dermatomyositis and nasopharyngeal cancer

BACKGROUND

Dermatomyositis (DM) is a kind of dermatologically associated autoimmune disease that is notably associated with an increased risk of concurrent malignancies, although the underlying mechanisms remain to be fully elucidated. The purpose of this investigation was to examine the immunological parallels between DM and nasopharyngeal carcinoma (NPC), with the aim of identifying pivotal biomarkers that could facilitate a deeper understanding and enhance the predictive capabilities of NPC in DM patients.

METHOD

Data for DM and NPC were sourced from the Gene Expression Omnibus (GEO) database. Immune infiltration was analyzed using the "cibersort" R package, differentially expressed genes (DEGs) were identified with the "limma" package, and functional pathways were investigated through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Characteristic genes were determined by Utilizing Protein-Protein Interaction (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO), and their features were validated using the GSE53819 dataset.

RESULTS

In comparison to normal samples, significant infiltration of macrophage M1 was observed in both DM and NPC. The analysis revealed 77 DEGs in DM and 1051 DEGs in NPC, with 22 genes found to be co-DEGs. Following PPI and LASSO analysis, six distinctive genes were retained. Notably, CCL8, IFIH1, CXCL10, and CXCL11 exhibited optimal diagnostic efficacy for NPC and displayed significant correlation with macrophage M1 infiltration within the carcinoma.

CONCLUSION

Four characteristic genes, CCL8, IFIH1, CXCL10, and CXCL11 are risk factors for both DM and NPC. They exhibit a robust correlation with the incidence of NPC and offer a commendable diagnostic efficacy. Furthermore, they may serve as prospective predictive biomarkers for the emergence of NPC in DM.

Milk transcriptome biomarker identification to enhance feed efficiency and reduce nutritional costs in dairy ewes

In recent years, rising prices for high-quality protein-based feeds have significantly increased nutrition costs. Consequently, investigating strategies to reduce these expenses and improve feed efficiency (FE) have become increasingly important for the dairy sheep industry. This research investigates the impact of nutritional protein restriction (NPR) during prepuberty and FE on the milk transcriptome of dairy Assaf ewes (sampled during the first lactation). To this end, we first compared transcriptomic differences between NPR and control ewes. Subsequently, we evaluated gene expression differences between ewes with divergent FE, using feed conversion ratio (FCR), residual feed intake (RFI), and consensus classifications of high- and low-FE animals for both indices. Lastly, we assess milk gene expression as a predictor of FE phenotype using random forest. No effect was found for the prepubertal NPR on milk performance or FE. Moreover, at the milk transcriptome level, only one gene, HBB, was differentially expressed between the NPR (n = 14) and the control group (n = 14). Further, the transcriptomic analysis between divergent FE sheep revealed 114 differentially expressed genes (DEGs) for RFI index (high-FERFI = 10 vs low-FERFI = 10), 244 for FCR (high-FEFCR = 10 vs low-FEFCR = 10), and 1 016 DEGs between divergent consensus ewes for both indices (high-FEconsensus = 8 vs low-FEconsensus = 8). These results underscore the critical role of selected FE indices for RNA-Seq analyses, revealing that consensus divergent animals for both indices maximise differences in transcriptomic responses. Genes overexpressed in high-FEconsensus ewes were associated with milk production and mammary gland development, while low-FEconsensus genes were linked to higher metabolic expenditure for tissue organisation and repair. The best prediction accuracy for FE phenotype using random forest was obtained for a set of 44 genes consistently differentially expressed across lactations, with Spearman correlations of 0.37 and 0.22 for FCR and RFI, respectively. These findings provide insights into potential sustainability strategies for dairy sheep, highlighting the utility of transcriptomic markers as FE proxies.

Dysregulated expression of cholesterol biosynthetic genes in Alzheimer's disease alters epigenomic signatures of hippocampal neurons

Aging is the main risk factor of cognitive neurodegenerative diseases such as Alzheimer's disease, with epigenome alterations as a contributing factor. Here, we compared transcriptomic/epigenomic changes in the hippocampus, modified by aging and by tauopathy, an AD-related feature. We show that the cholesterol biosynthesis pathway is severely impaired in hippocampal neurons of tauopathic but not of aged mice pointing to vulnerability of these neurons in the disease. At the epigenomic level, histone hyperacetylation was observed at neuronal enhancers associated with glutamatergic regulations only in the tauopathy. Lastly, a treatment of tau mice with the CSP-TTK21 epi-drug that restored expression of key cholesterol biosynthesis genes counteracted hyperacetylation at neuronal enhancers and restored object memory. As acetyl-CoA is the primary substrate of both pathways, these data suggest that the rate of the cholesterol biosynthesis in hippocampal neurons may trigger epigenetic-driven changes, that may compromise the functions of hippocampal neurons in pathological conditions.

Heparanase Stimulation of Physiologic Cardiac Hypertrophy Is Suppressed After Chronic Diabetes, Resulting in Cardiac Remodeling and Dysfunction

In addition to controlling smooth muscle tone in coronary vessels, endothelial cells also influence subjacent cardiomyocyte growth. Because heparanase, with exclusive expression in endothelial cells, enables extracellular matrix remodeling, angiogenesis, metabolic reprogramming, and cell survival, it is conceivable that it could also encourage development of cardiac hypertrophy. Global heparanase overexpression resulted in physiologic cardiac hypertrophy, likely an outcome of HSPG clustering and activation of hypertrophic signaling. The heparanase autocrine effect of releasing neuregulin-1 could have also contributed to this overexpression. Hyperglycemia induced by streptozotocin-induced diabetes sensitized the heart to flow-induced release of heparanase and neuregulin-1. Despite this excess secretion, progression of diabetes caused significant gene expression changes related to mitochondrial metabolism and cell death that led to development of pathologic hypertrophy and heart dysfunction. Physiologic cardiac hypertrophy was also observed in rats with cardiomyocyte-specific vascular endothelial growth factor B overexpression. When perfused, hearts from these animals released significantly higher amounts of both heparanase and neuregulin-1. However, subjecting these animals to diabetes triggered robust transcriptome changes related to metabolism and a transition to pathologic hypertrophy. Our data suggest that in the absence of mechanisms that support cardiac energy generation and prevention of cell death, as seen after diabetes, there is a transition from physiologic to pathologic cardiac hypertrophy and a decline in cardiac function.

ARTICLE HIGHLIGHTS

Involvement of metabotropic glutamate receptors in regulation of immune response in the Pacific oyster Crassostrea gigas

Metabotropic glutamate receptors (mGluRs) play a pivotal role in the neuroendocrine-immune regulation. In this study, eight mGluRs were identified in the Pacific Oyster Crassostrea gigas, which were classified into three subfamilies based on genetic similarity. All CgmGluRs harbor variable numbers of PBP1 domains at the N-terminus. The sequence and structural features of CgmGluRs are highly similar to mGluRs in other species. A uniformly upregulated expression of CgmGluRs was observed during D-shaped larval stage compared to early D-shaped larval stage. The transcripts of CgmGluRs were detectable in various tissues of oyster. Different CgmGluR exhibited diverse expression patterns response against different PAMP stimulations, among which CgmGluR5 was significantly downregulated under these stimulations, reflecting its sensitivity and broad-spectrum responsiveness to microbes. Following LPS stimulation, the mRNA expression of CgmGluR5 and CgCALM1 in haemocytes was suppressed within 6 h and returned to normal levels by 12 h. Inhibition of CgmGluR5 activity resulted in a significant reduction in CgCALM1 expression after 12 h. Further KEGG enrichment analysis suggested that CgmGluR5 might modulate calcium ion homeostasis and metabolic pathways by regulating CgCALM1. This research delivers the systematic analysis of mGluR in the Pacific Oyster, offering insights into evolutionary characteristics and immunoregulatory function of mGluR in mollusks.

Risk factor prediction and immune correlation analysis of cuproptosis-related gene in osteoarthritis

Osteoarthritis (OA) is a widespread inflammatory joint disease with significant global disability burden. Cuproptosis, a newly identified mode of cell death, has emerged as a crucial factor in various pathological conditions, including OA. In this context, our study aims to investigate the intrinsic relationship between cuproptosis-related genes (CRGs) and OA, and assess their potential as biomarkers for OA diagnosis and treatment. Datasets from the GEO databases were analysed the differential expression of CRGs, leading to the identification of 10 key CRGs (CDKN2A, DLD, FDX1, GLS, LIAS, LIPT1, MTF1, PDHA1, DLAT and PDHB). A logistic regression analysis and calibration curves were used to show excellent diagnostic accuracy. Consensus clustering revealed two CRG patterns, with Cluster 1 indicating a closer association with OA progression. RT-PCR confirmed a significant increase in the expression levels of these nine key genes in IL-1β-induced C28/i2 cells, and the expression of CDKN2A and FDX1 were also elevated in conditioned monocytes, while the expression of GLS and MTF1 were significantly decreased. In vitro experiments demonstrated that the expression levels of these 7/10 CRGs were significantly increased in chondrocytes induced by IL-1β, and upon stimulation with cuproptosis inducers, chondrocyte apoptosis was exacerbated, accompanied by an increase in the expression of cuproptosis-related proteins. These further substantiated our research findings and indicated that the nine selected cuproptosis genes have high potential for application in the diagnosis of OA.

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

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

Identification of steroid-induced osteonecrosis of the femoral head biomarkers based on immunization and animal experiments

BACKGROUND

Steroid-induced osteonecrosis of femoral head (SONFH) is a severe health risk, and this study aims to identify immune-related biomarkers and pathways associated with the disease through bioinformatics analysis and animal experiments.

METHOD

Using SONFH-related datasets obtained from the GEO database, we performed differential expression analysis and weighted gene co-expression network analysis (WGCNA) to extract SONFH-related genes. A protein-protein interaction (PPI) network was then constructed, and core sub-network genes were identified. Immune cell infiltration and clustering analysis of SONFH samples were performed to assess differences in immune cell populations. WGCNA analysis was used to identify module genes associated with immune cells, and hub genes were identified using machine learning. Internal and external validation along with animal experiments were conducted to confirm the differential expression of hub genes and infiltration of immune cells in SONFH.

RESULTS

Differential expression analysis revealed 502 DEGs. WGCNA analysis identified a blue module closely related to SONFH, containing 1928 module genes. Intersection analysis between DEGs and blue module genes resulted in 453 intersecting genes. The PPI network and MCODE module identified 15 key targets enriched in various signaling pathways. Analysis of immune cell infiltration showed statistically significant differences in CD8 + t cells, monocytes, macrophages M2 and neutrophils between SONFH and control samples. Unsupervised clustering classified SONFH samples into two clusters (C1 and C2), which also exhibited significant differences in immune cell infiltration. The hub genes (ICAM1, NR3C1, and IKBKB) were further identified using WGCNA and machine learning analysis. Based on these hub genes, a clinical prediction model was constructed and validated internally and externally. Animal experiments confirmed the upregulation of hub genes in SONFH, with an associated increase in immune cell infiltration.

CONCLUSION

This study identified ICAM1, NR3C1, and IKBKB as potential immune-related biomarkers involved in immune cell infiltration of CD8 + t cells, monocytes, macrophages M2, neutrophils and other immune cells in the pathogenesis of SONFH. These biomarkers act through modulation of the chemokine signaling pathway, Toll-like receptor signaling pathway, and other pathways. These findings provide valuable insights into the disease mechanism of SONFH and may aid in future drug development efforts.

Unveiling Potential Biomarkers for Urinary Tract Infection: An Integrated Bioinformatics Approach

Background

Urinary tract infections (UTIs) are a widespread health concern with high recurrence rates and substantial economic impact, and they can increase the prevalence of antibiotic resistance. This study employed an integrated bioinformatics approach to identify key genes associated with UTI development, offering potential targets for interventions.

Materials and Methods

For this study, the microarray dataset GSE124917 from the Gene Expression Omnibus (GEO) database was selected and reanalyzed. The differentially expressed genes (DEGs) between UTIs and healthy samples were identified using the LIMMA package in R software. In this section, Enrichr database was utilized to perform functional enrichment analysis of DEGs. Subsequently, the protein-protein interaction (PPI) network of the DEGs was constructed and visualized through Cytoscape, utilizing the STRING online database. The identification of hub genes was performed using Cytoscape's cytoHubba plug-in employing various methods. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic accuracy of hub genes.

Results

Among the outcomes of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, the tumor necrosis factor (TNF) signaling pathway was identified as one of the notable pathways. The PPI network of the DEGs was successfully established and visualized in Cytoscape with the aid of the STRING online database. Using cytoHubba with different methods, we identified seven hub genes (STAT1, IL6, IFIT1, IFIT3, IFIH1, MX1, and IRF7). Based on the ROC analysis, all hub genes showed high diagnostic value.

Conclusion

These findings provide a valuable baseline for future research aimed at unraveling the intricate molecular mechanisms behind UTI.

BCAT1 promotes cell proliferation, migration, and invasion via the PI3K-Akt signaling pathway in oral squamous cell carcinoma

OBJECTIVES

To analyze the expression, biological function of branched chain amino-acid transaminase 1 (BCAT1) in oral squamous cell carcinoma (OSCC).

MATERIALS AND METHODS

Real-time PCR and immunohistochemistry were used to analyze the expression of BCAT1 protein in OSCC and normal oral tissues. Based on the clinicopathological information of patients, the relationship between the expression of BCAT1 protein and other clinicopathological factors was analyzed. Real-time PCR and western blot assays were used to analyze the expression of BCAT1 gene and protein in normal human oral keratinocytes (HOK) and human OSCC cells, respectively. After BCAT1 overexpression or knockdown, the proliferation, cell cycle, migration, and invasion of human OSCC cells were analyzed by CCK8, flow cytometry, wound healing, and transwell invasion assays, respectively. After adding the BCAT1 inhibitor EGR240 to OSCC cells, the changes in cell proliferation, migration, and invasion ability in OSCC cells were analyzed. Based on the TCGA database, the involved signal pathway in BCAT1-related and BCAT1-binding genes was obtained for Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, verified by western blot assays. After inhibiting PI3K, the effect of BCAT1 on the expression of the downstream phosphorylated protein of the PI3K-Akt signaling pathway was analyzed by western blot assays. The relationship between the expression of BCAT1 and EMT-related protein of OSCC cells was also analyzed.

RESULTS

The expression of BCAT1 gene and protein were upregulated in OSCC tissue, which positively correlated with the pathological grade of patients with OSCC. Compared with normal oral keratinocytes, BCAT1 gene and protein were upregulated in OSCC cells. BCAT1 overexpression promoted the proliferation, migration, and invasion of OSCC cells. BCAT1 knockdown or inhibition could reduce the proliferation, migration, and invasion abilities of OSCC cells. The results of bioinformatics analysis and Western bolt showed that BCAT1 could regulate the activation of PI3K-Akt signaling pathway, and promote epithelial-mesenchymal transition (EMT) of OSCC cells.

CONCLUSIONS

BCAT1 could promote the proliferation, migration, and invasion of OSCC cells via PI3K-Akt signaling pathway, which is a potential therapeutic target for OSCC.

Insights into the radiation and oxidative stress mechanisms in genus Deinococcus

Deinococcus species, noted for their exceptional resistance to DNA-damaging environmental stresses, have piqued scientists' interest for decades. This study dives into the complex mechanisms underpinning radiation resistance in the Deinococcus genus. We have examined the genomes of 82 Deinococcus species and classified radiation-resistance proteins manually into five unique curated categories: DNA repair, oxidative stress defense, Ddr and Ppr proteins, regulatory proteins, and miscellaneous resistance components. This classification reveals important information about the various molecular mechanisms used by these extremophiles which have been less explored so far. We also investigated the presence or lack of these proteins in the context of phylogenetic relationships, core, and pan-genomes, which offered light on the evolutionary dynamics of radiation resistance. This comprehensive study provides a deeper understanding of the genetic underpinnings of radiation resistance in the Deinococcus genus, with potential implications for understanding similar mechanisms in other organisms using an interactomics approach. Finally, this study reveals the complexities of radiation resistance mechanisms, providing a comprehensive understanding of the genetic components that allow Deinococcus species to flourish under harsh environments. The findings add to our understanding of the larger spectrum of stress adaption techniques in bacteria and may have applications in sectors ranging from biotechnology to environmental research.

Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network

Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and minimal invasiveness in diagnostic procedures. Machine learning (ML) methods have been applied to enhance accuracy of the biomarker discovery. However, previous ML-based studies often overlook interactions between proteins, which are crucial in complex disorders like dementia. While protein-protein interactions (PPIs) have been used in network models, these models often fail to fully capture the diverse properties of PPIs due to their local awareness. This drawback increases the chance of neglecting critical components and magnifying the impact of noisy interactions. In this study, we propose a novel graph-based ML model for dementia subtype diagnosis, the graph propagational network (GPN). By propagating the independent effect of plasma proteins on PPI network, the GPN extracts the globally interactive effects between proteins. Experimental results showed that the interactive effect between proteins yielded to further clarify the differences between dementia subtype groups and contributed to the performance improvement where the GPN outperformed existing methods by 10.4% on average.

Impact of Extracellular Matrix-Related Genes on the Tumor Microenvironment and Prognostic Indicators in Esophageal Cancer: A Comprehensive Analytical Study

Esophageal cancer is a major global health challenge with a poor prognosis. Recent studies underscore the extracellular matrix (ECM) role in cancer progression, but the full impact of ECM-related genes on patient outcomes remains unclear. Our study utilized next-generation sequencing and clinical data from esophageal cancer patients provided by The Cancer Genome Atlas, employing the R package in RStudio for computational analysis. This analysis identified significant associations between patient survival and various ECM-related genes, including IBSP, LINGO4, COL26A1, MMP12, KLK4, RTBDN, TENM1, GDF15, and RUNX1. Consequently, we developed a prognostic model to predict patient outcomes, which demonstrated clear survival differences between high-risk and low-risk patient groups. Our comprehensive review encompassed clinical correlations, biological pathways, and variations in immune response among these risk categories. We also constructed a nomogram integrating clinical information with risk assessment. Focusing on the TENM1 gene, we found it significantly impacts immune response, showing a positive correlation with T helper cells, NK cells, and CD8+ T cells, but a negative correlation with neutrophils and Th17 cells. Gene Set Enrichment Analysis revealed enhanced pathways related to pancreatic beta cells, spermatogenesis, apical junctions, and muscle formation in patients with high TENM1 expression. This research provides new insights into the role of ECM genes in esophageal cancer and informs future research directions.

Interaction proteomics analysis to provide insight into TFAMoplex-mediated transfection

In an earlier investigation, our group introduced the TFAMoplex, a transfection agent based on the mitochondrial transcription factor A (TFAM) protein, which complexes DNA into nanoparticles. The original TFAMoplex further contained a bacterial phospholipase to achieve endosomal escape, and the vaccinia-related kinase 1 (VRK1), which significantly boosted the transfection efficiency of the system by an unknown mechanism. This study aims at replacing VRK1 within the TFAMoplex with dynein light chain proteins, specifically RP3, to directly tether the complexes to the dynein motor complex for enhanced cytosolic transport. To confirm the interaction between the dynein complex and the resulting fusion protein, we examined the binding kinetics of TFAM-RP3 to the dynein intermediate chains 1 and 2. Furthermore, we established a proteomics-based assay to compare cytosolic protein interactions of different TFAMoplex variants, including the RP3-modified version and the original VRK1-containing system. In the group of the VRK1-containing TFAMoplex, significant shifts of protein interactors were observed, especially for nucleolar proteins. Leveraging this knowledge, we incorporated one of these nuclear proteins, leucine-rich repeat-containing protein 59 (LRRC59), into the TFAMoplex, resulting in a significant improvement of transfection properties compared to the RP3-modified system and comparable levels versus the original, VRK1-containing version. This study not only advances our comprehension of the TFAMoplex system but also offers broader insights into the potential of protein engineering for designing effective gene delivery systems.

Fibroblast activation protein (FAP) as a prognostic biomarker in multiple tumors and its therapeutic potential in head and neck squamous cell carcinoma

Background

Fibroblast activation protein (FAP), a cell surface serine protease, plays roles in tumor invasion and immune regulation. However, there is currently no pan-cancer analysis of FAP. Objective: We aimed to assess the pan-cancer expression profile of FAP, its molecular function, and its potential role in head and neck squamous cell carcinoma (HNSC).

Methods

We analyzed gene expression, survival status, immune infiltration, and molecular functional pathways of FAP in The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) tumors. Furthermore, to elucidate the role of FAP in HNSC, we performed proliferation, migration, and invasion assays post-FAP overexpression or knock-down.

Results

FAP expression was elevated in nine tumor types and was associated with poor survival in eight of them. In the context of immune infiltration, FAP expression negatively correlated with CD8+ T-cell infiltration in five tumor types and positively with regulatory T-cell infiltration in four tumor types. Our enrichment analysis highlighted FAP's involvement in the PI3K-Akt signaling pathway. In HNSC cells, FAP overexpression activated the PI3K-Akt pathway, promoting tumor proliferation, migration, and invasion. Conversely, FAP knockdown showed inhibitory effects.

Conclusion

Our study unveils the association of FAP with poor tumor prognosis across multiple cancers and highlights its potential as a therapeutic target in HNSC.

Comprehensive analysis of Alfin-like transcription factors associated with drought and salt stresses in wheat (Triticum aestivum L.)

BACKGROUND

Alfin-like proteins are a kind of plant-specific transcription factors, and play vital roles in plant growth, development and stress responses.

RESULTS

In this study, a total of 27 Alfin-like transcription factors were identified in wheat. TaAL genes were unevenly distributed on chromosome. Phylogenetic analysis showed TaAL genes were divided into AL-B and AL-C subfamilies, and TaALs with closer evolutionary relationships generally shared more similar exon-intron structures and conserved motifs. The cis-acting element analysis showed MBS, ABRE and CGTCA-motif were the most common in TaAL promoters. The interacting proteins and downstream target genes of TaAL genes were also investigated in wheat. The transcriptome data and real-time PCR results indicated TaAL genes were differentially expressed under drought and salt stresses, and TaAL1-B was significantly up-regulated in response to drought stress. In addition, association analysis revealed that TaAL1-B-Hap-I allelic variation had significantly higher survival rate compared to TaAL1-B-Hap-II under drought stress.

CONCLUSIONS

These results will provide vital information to increase our understanding of the Alfin-like gene family in wheat, and help us in breeding better wheat varieties in the future.

The active components of Erzhi wan and their anti-Alzheimer's disease mechanisms determined by an integrative approach of network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulation

Erzhi Wan (EZW), a classic Traditional Chinese Medicine formula, has shown promise as a potential therapeutic option for Alzheimer's disease (AD), yet its mechanism remains elusive. Herein, we employed an integrative in-silico approach to investigate the active components and their mechanisms against AD. We screened four active components with blood-brain barrier permeabilities from TCMSP, along with 307 corresponding targets predicted by SwissTargetPrediction, PharmMapper, and TCMbank websites. Then, we retrieved 2260 AD-related targets from Genecards, OMIM, and NCBI databases. Furthermore, we constructed the protein-protein interaction (PPI) network of the intersected targets via the STRING database and performed the GO and KEGG enrichment analyses using the "clusterProfiler" R package. The results showed that the intersected targets were intimately related to the p53/PI3K/Akt signaling pathway, serotonergic synapse, and response to oxygen level. Subsequently, 25 core targets were found differentially expressed in brain regions by bioinformatics analyses of GEO datasets of clinical samples from the Alzdata database. The binding sites and stabilities between the active components and the core targets were investigated by the molecular docking approach using Autodock 4.2.6 software, followed by pocket detection and druggability assessment via the DoGSiteScorer server. The results showed that acacetin, β-sitosterol, and 3-O-acetyldammarenediol-II strongly interacted with the druggable pockets of AR, CASP8, POLB, and PREP. Eventually, the docking results were further cross-referenced with the literature research and validated by 100 ns of molecular dynamics simulations using GROMACS software. Binding free energies were calculated via MM/PBSA strategy combined with interaction entropy. The simulation results indicated stable bindings between four docking pairs including acacetin-AR, acacetin-CASP8, β-sitosterol-POLB, and 3-O-acetyldammarenediol-II-PREP. Overall, our study demonstrated a theoretical basis for how three active components of EZW confer efficacy against AD. It provides a promising reference for subsequent research regarding drug discoveries and clinical applications.

The protective effect of ginsenoside Rg1 against sepsis-induced lung injury through PI3K-Akt pathway: insights from molecular dynamics simulation and experimental validation

Sepsis-induced acute lung injury (SALI) poses a significant threat with high incidence and mortality rates. Ginsenoside Rg1 (GRg1), derived from Ginseng in traditional Chinese medicine, has been found to reduce inflammation and protect lung epithelial cells against tissue damage. However, the specific roles and mechanisms by which GRg1 mitigates SALI have yet to be fully elucidated. In this context, we employed a relevant SALI mouse model, alongside network pharmacology, molecular docking, and molecular dynamics simulation to pinpoint GRg1's action targets, complemented by in vitro assays to explore the underlying mechanisms. Our research shows that GRg1 alleviates CLP-induced SALI, decreasing lung tissue damage and levels of serum proinflammatory factor IL-6, TNF-α, and IL-1β, also enhancing the survival rate of CLP mice. A total of 116 common targets between GRg1 and ALI, with specific core targets including AKT1, VEGFA, SRC, IGF1, ESR1, STAT3, and ALB. Further in vitro experiments assessed GRg1's intervention effects on MLE-12 cells exposed to LPS, with qRT-PCR analysis and molecular dynamics simulations confirming AKT1 as the key target with the favorable binding activity for GRg1. Western blot results indicated that GRg1 increased the Bcl-2/Bax protein expression ratio to reduce apoptosis and decreased the high expression of cleaved caspase-3 in LPS-induced MLE-12 cells. More results showed significant increases in the phosphorylation of PI3K and AKT1. Flow cytometric analysis using PI and Annexin-V assays further verified that GRg1 decreased the apoptosis rate in LPS-stimulated MLE-12 cells (from 14.85 to 6.54%, p < 0.05). The employment of the AKT1 inhibitor LY294002 confirmed these trends, indicating that AKT1's inhibition negates GRg1's protective effects on LPS-stimulated MLE-12 cells. In conclusion, our research highlights GRg1's potential as an effective adjunct therapy for SALI, primarily by inhibiting apoptosis in alveolar epithelial cells and reducing pro-inflammatory cytokine secretion, thus significantly enhancing the survival rates of CLP mice. These beneficial effects are mediated through targeting AKT1 and activating the PI3K-AKT pathway.

Novel Diagnostic Biomarkers Related to Necroptosis and Immune Infiltration in Coronary Heart Disease

Purpose

Necroptosis, a monitored form of inflammatory cell death, contributes to coronary heart disease (CHD) progression. This study examined the potential of using necroptosis genes as diagnostic markers for CHD and sought to elucidate the underlying roles.

Methods

Through bioinformatic analysis of GSE20680 and GSE20681, we first identified the differentially expressed genes (DEGs) related to necroptosis in CHD. Hub genes were identified using least absolute shrinkage and selection operator (LASSO) regression and random forest analysis after studying immune infiltration and transcription factor-miRNA interaction networks according to the DEGs. Quantitative polymerase chain reaction and immunohistochemistry were used to further investigate hub gene expression in vivo, for which a diagnostic model was constructed and the predictive efficacy was validated. Finally, the CHD group was categorized into high- and low-score groups in accordance with the single-sample gene set enrichment analysis (ssGSEA) score of the necroptosis genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, GSEA, and further immune infiltration analyses were performed on the two groups to explore the possible roles of hub genes.

Results

Based on the results of the LASSO regression and random forest analyses, four genes were used to construct a diagnostic model to establish a nomogram. Additionally, an extensive analysis of all seventeen necroptosis genes revealed notable distinctions in expression between high-risk and low-risk groups. Evaluation of immune infiltration revealed that neutrophils, monocytes, B cells, and activated dendritic cells were highly distributed in the peripheral blood of patients with CHD. Specifically, the high CHD score group exhibited greater neutrophil and monocyte infiltration. Conversely, the high-score group showed lower infiltration of M0 and M2 macrophages, CD8+ T, plasma, and resting mast cells.

Conclusion

TLR3, MLKL, HMGB1, and NDRG2 may be prospective biomarkers for CHD diagnosis. These findings offer plausible explanations for the role of necroptosis in CHD progression through immune infiltration and inflammatory response.

Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto's thyroiditis through bioinformatics analysis and machine learning

There is a growing body of evidence suggesting that Hashimoto's thyroiditis (HT) may contribute to an increased risk of papillary thyroid carcinoma (PTC). However, the exact relationship between HT and PTC is still not fully understood. The objective of this study was to identify potential common biomarkers that may be associated with both PTC and HT. Three microarray datasets from the GEO database and RNA-seq dataset from TCGA database were collected to identify shared differentially expressed genes (DEGs) between HT and PTC. A total of 101 genes was identified as common DEGs, primarily enriched inflammation- and immune-related pathways through GO and KEGG analysis. We performed protein-protein interaction analysis and identified six significant modules comprising a total of 29 genes. Subsequently, tree hub genes (CD53, FCER1G, TYROBP) were selected using random forest (RF) algorithms for the development of three diagnostic models. The artificial neural network (ANN) model demonstrates superior performance. Notably, CD53 exerted the greatest influence on the ANN model output. We analyzed the protein expressions of the three genes using the Human Protein Atlas database. Moreover, we observed various dysregulated immune cells that were significantly associated with the hub genes through immune infiltration analysis. Immunofluorescence staining confirmed the differential expression of CD53, FCER1G, and TYROBP, as well as the results of immune infiltration analysis. Lastly, we hypothesise that benzylpenicilloyl polylysine and aspirinmay be effective in the treatment of HT and PTC and may prevent HT carcinogenesis. This study indicates that CD53, FCER1G, and TYROBP play a role in the development of HT and PTC, and may contribute to the progression of HT to PTC. These hub genes could potentially serve as diagnostic markers and therapeutic targets for PTC and HT.

Metabolomic Approach to Identify the Potential Metabolites from Alpinia malaccensis for Treating SARS-CoV-2 Infection

The advent of the new coronavirus, leading to the SARS-CoV-2 pandemic, has presented a substantial worldwide health hazard since its inception in the latter part of 2019. The severity of the current pandemic is exacerbated by the occurrence of re-infection or co-infection with SARS-CoV-2. Hence, comprehending the molecular process underlying the pathophysiology of sepsis and discerning possible molecular targets for therapeutic intervention holds significant importance. For the first time, 31 metabolites were tentatively identified by GC-MS analysis from Alpinia malaccensis. On the other hand, five phenolic compounds were identified and quantified from the plant in HPLC-DAD analysis, including (-) epicatechin, rutin hydrate, rosmarinic acid, quercetin, and kaempferol. Nine GC-MS and five HPLC-identified metabolites had shown interactions with 45 and 30 COVID-19-associated human proteins, respectively. Among the proteins, PARP1, FN1, PRKCA, EGFR, ALDH2, AKR1C3, AHR, and IKBKB have been found as potential therapeutic targets to mitigate SARS-CoV-2 infection. KEGG pathway analysis also showed a strong association of FN1, EGFR, and IKBKB genes with SARS-CoV-2 viral replication and cytokine overexpression due to viral infection. Protein-protein interaction (PPI) analysis also showed that TP53, MMP9, FN1, EGFR, and NOS2 proteins are highly related to the genes involved in COVID-19 comorbidity. These proteins showed interaction with the plant phytoconstituents as well. As the study offers a robust network-based procedure for identifying biomolecules relevant to COVID-19 disease, A. malaccensis could be a good source of effective therapeutic agents against COVID-19 and related viral diseases.

Crape myrtle LiGAoxs displaying activities of gibberellin oxidases respond to branching architecture

In the realm of ornamental horticulture, crape myrtle (Lagerstroemia indica) stands out for its aesthetic appeal, attributed largely to its vibrant flowers and distinctive branching architecture. This study embarked on a comprehensive exploration of the gibberellin oxidase (GAox) gene family in crape myrtle, illuminating its pivotal role in regulating GA levels, a key determinant of plant developmental processes. We identified and characterized 36 LiGAox genes, subdivided into GA2ox, GA3ox, GA20ox, and GAox-like subgroups, through genomic analyses. These genes' evolutionary trajectories were delineated, revealing significant gene expansions attributed to segmental duplication events. Functional analyses highlighted the divergent expression patterns of LiGAox genes across different crape myrtle varieties, associating them with variations in flower color and branching architecture. Enzymatic activity assays on selected LiGA2ox enzymes exhibited pronounced GA2 oxidase activity, suggesting a potential regulatory role in GA biosynthesis. Our findings offered a novel insight into the molecular underpinnings of GA-mediated growth and development in L. indica, providing a foundational framework for future genetic enhancements aimed at optimizing ornamental traits.

Cucurbitacin B induces ferroptosis in oral leukoplakia via the SLC7A11/mitochondrial oxidative stress pathway

BACKGROUND

Oral leukoplakia (OLK), characterized by abnormal epithelial hyperplasia, is the most common precancerous oral mucosa lesion and is closely related to oxidative stress. Cucurbitacin B (CuB), a tetracyclic triterpenoid molecule derived from plants, has shown promising anti-proliferative and antioxidant effects in preclinical studies. However, whether CuB can play an antiproliferative role in OLK by regulating oxidative stress remains elusive.

PURPOSE

To investigate the role of CuB in inhibiting the malignant progression of oral leukoplakia and to further explore its underlying mechanisms of action.

METHODS

In vitro, the effect of CuB on the proliferation, migration, apoptosis, and cell cycle of OLK cells DOK was detected. The core genes and key pathways of OLK and CuB were analyzed in the transcriptome database, by using immunofluorescence, qRT-PCR, and Western blot to evaluate the expression levels of the ferroptosis markers ROS, GSH, MDA, Fe2+, and marker genes SLC7A11, GPX4, and FTH1. Immunohistochemistry of human tissue was performed to investigate the expression of the SLC7A11. In vivo, the model of OLK was established in C57BL/6 mice and the biosafety of CuB treatment for OLK was further evaluated.

RESULTS

CuB substantially suppressed the proliferation of DOK cells. Bioinformatics analysis showed that the core targets of OLK crossing with CuB include SLC7A11 and that the essential pathways involve ROS and ferroptosis. In vitro experiments indicated that CuB might promote ferroptosis by down-regulating the expression of SLC7A11. We observed a gradual increase in SLC7A11 expression levels during the progression from normal oral mucosa to oral leukoplakia with varying degrees of epithelial dysplasia. In vivo experiments demonstrated that CuB inhibited the malignant progression of OLK by promoting ferroptosis in OLK mice and exhibited a certain level of biosafety.

CONCLUSION

This study demonstrated for the first time that CuB could effectively inhibit the malignant progression of OLK by inducing ferroptosis via activating the SLC7A11/ mitochondrial oxidative stress pathway. These findings indicate that CuB could serve as the lead compound for the future development of anti-oral leukoplakia drugs.

Evaluation of Cordyceps militaris steroids as anti-inflammatory agents to combat the Covid-19 cytokine storm: a bioinformatics and structure-based drug designing approach

Since the SARS-CoV-2 epidemic, researchers have been working on figuring out ways to tackle multi-organ failure and hyperinflation, which are brought on by a cytokine storm. Angiotensin-converting enzyme 2 (ACE2), a SARS-CoV-2 spike glycoprotein's cellular receptor, is involved in complicated molecular processes that result in hyperinflammation. Cordyceps militaris is one of the traditional Chinese medicines that is used as an immune booster, and it has exhibited efficacy in lowering blood glucose levels, seminal emissions, and infertility. In the current study, we explored the potential of Cordyceps militaris steroids as key agents in managing the anger of cytokine storm in Covid-19 using network ethnopharmacological techniques and structure-based drug designing approaches. The steroids present in Cordyceps militaris were initially screened against the targets involved in inflammatory pathways. The results revealed that out of 16 steroids, 5 may be effective against 17 inflammatory pathways by targeting 11 pathological proteins. Among the five steroids, beta-sitosterol, Cholest-5-en-3β-ol, 3β, and 7α-Dihydroxycholest-5-ene were found to interact with thrombin (F2), an important protein reported to reduce the severity of inflammatory mediators and Cholest-4-en-3-one was found to target Glucocorticoid receptor (NR3C1). The top docked steroid displayed key interactions with both targets, which retained key interactions throughout the 100 ns simulation period. These compounds were also shown high binding free energy scores in water swap studies. Based on obtained results the current study suggests the use of Cordyceps militaris as an add-on therapy that may reduce the progression of inflammatory co-morbidities among patients infected with SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Gene networks governing the response of a calcareous sponge to future ocean conditions reveal lineage-specific XBP1 regulation of the unfolded protein response

Marine sponges are predicted to be winners in the future ocean due to their exemplary adaptive capacity. However, while many sponge groups exhibit tolerance to a wide range of environmental insults, calcifying sponges may be more susceptible to thermo-acidic stress. To describe the gene regulatory networks that govern the stress response of the calcareous sponge, Leucetta chagosensis (class Calcarea, order Clathrinida), individuals were subjected to warming and acidification conditions based on the climate models for 2100. Transcriptome analysis and gene co-expression network reconstruction revealed that the unfolded protein response (UPR) was activated under thermo-acidic stress. Among the upregulated genes were two lineage-specific homologs of X-box binding protein 1 (XBP1), a transcription factor that activates the UPR. Alternative dimerization between these XBP1 gene products suggests a clathrinid-specific mechanism to reversibly sequester the transcription factor into an inactive form, enabling the rapid regulation of pathways linked to the UPR in clathrinid calcareous sponges. Our findings support the idea that transcription factor duplication events may refine evolutionarily conserved molecular pathways and contribute to ecological success.