Bioinformatics in bioscience and bioengineering: Recent advances, applications, and perspectives

Bridging the gaps through environmental DNA: A review of critical considerations for interpreting the biodiversity data in coral reef ecosystems

Coral reefs, the rainforests of the sea, are vital hotspots for marine biodiversity. However, the persistent challenge of climate change directly threatens the delicate balance of coral reef ecosystems, impacting myriad species and critical ecosystem services. Therefore, this comprehensive review critically discusses the associated challenges in assessing and preserving coral reef diversity, emphasizing the need for novel biomonitoring techniques due to the elusive and cryptic nature of many reef organisms. The review focuses on environmental DNA (eDNA) analysis as a non-invasive tool for coral species monitoring at various ecological levels. The review highlights that using eDNA in coral reef monitoring requires careful consideration of multiple factors, such as strategic assay development, optimization, and marker selection, substrate selection, and sample volume, which are critical for maximizing the probability of species detection. Moreover, integrating environmental RNA (eRNA) provides additional insights into temporal aspects advancing the coral reef biodiversity research and conservation efforts.

SDC4 protein action and related key genes in nonhealing diabetic foot ulcers based on bioinformatics analysis and machine learning

Diabetic foot ulcers (DFU) is a complication associated with diabetes characterised by high morbidity, disability, and mortality, involving chronic inflammation and infiltration of multiple immune cells. We aimed to identify the critical genes in nonhealing DFU using single-cell RNA sequencing, transcriptomic analysis and machine learning. The GSE165816, GSE134431, and GSE143735 datasets were downloaded from the GEO database. We processed and screened the datasets, and identified the cell subsets. Each cell subtype was annotated, and the predominant cell types contributing to the disease were analysed. Key genes were identified using the LASSO regression algorithm, followed by verification of model accuracy and stability. We investigated the molecular mechanisms and changes in signalling pathways associated with this disease using immunoinfiltration analysis, GSEA, and GSVA. Through scRNA-seq analysis, we identified 12 distinct cell clusters and determined that the basalKera cell type was important in disease development. A high accuracy and stability prediction model was constructed incorporating five key genes (TXN, PHLDA2, RPLP1, MT1G, and SDC4). Among these five genes, SDC4 has the strongest correlation and plays an important role in the development of DFU. Our study identified SDC4 significantly associated with nonhealing DFU development, potentially serving as new prevention and treatment strategies for DFU.

Study on the mechanism of Shenmai injection in the treatment of sepsis

Shenmai injection (SMI) is widely used in the clinical treatment of sepsis, but its mechanism is not yet clear. This study aimed to explore the molecular mechanism through network pharmacology, bioinformatics, and molecular docking technologies. The active ingredients and targets of SMI were screened through traditional Chinese medicine databases and the Swiss Target Prediction database, respectively. The disease genes were searched using GEO and GeneCards databases, and Venn mapping was used to screen potential therapeutic targets. The key targets were selected using Cytoscape 3.9.1 software. The BioGPS database was used to evaluate the expression of these targets in tissues/cells. The DAVID database is used for enrichment analysis. Molecular docking technology was used to evaluate the interaction between these targets and core active ingredients. 122 potential therapeutic targets and 28 key targets were identified. Forty-six potential therapeutic targets showed highly specific expression in 40 tissues/cells. The PI3K-AKT, RAP1, and MAPK signalling pathways are highly enriched. The molecular docking results showed good interactions. This study systematically analysed the mechanism of SMI in treating sepsis, involving multiple targets and pathways, possibly related to anti-inflammatory, anti-oxidative stress, and immune regulation, providing reference value for future basic research of sepsis.

Seasonal Variations of Sediment Fungal Community of a Shallow Lake in North China

Fungi play important roles in the process of material cycling and energy transfers in aquatic ecosystems. Yet, little is known about the fungal community in lake sediment. In this study, sediment samples from five habitat types in Baiyangdian Lake (BYD Lake) were collected across three seasons. High-throughput sequencing techniques were used to determine the compositions of fungal communities. Fungi are highly diverse in the sediment of BYD Lake, although some important fungi have not been accurately identified. The fungal diversity was highest in winter and lowest in summer, while there was no significant difference in species richness among sampling sites. The compositions of fungal community differed among seasons and habitats. Physicochemical properties of sediments were measured and the influence of the environmental factors on fungal communities were analyzed. Temperature, P, N, and heavy metals explained 48.98% of the variations of fungal communities across three seasons. Human activities have affected the species and biomass of fungi to some extent. Temperature is the most influential factor and negatively correlated to fungal diversity. Nutrients in different forms have different effects on shaping the fungal community. The effect of heavy metals is relatively low.

Structure of POU2AF1 recombinant protein and it affects the progression and treatment of liver cancer based on WGCNA and molecular docking analysis

Hepatocellular carcinoma, also referred to as HCC, is the most frequent form of primary liver cancer. It is anticipated that the discovery of the molecular pathways related with HCC would open up new possibilities for the treatment of HCC.WGCNA (Weighted gene co-expression network analysis) and molecular docking analysis were used to study the structural characteristics of POU2AF1 recombinant protein and its interaction with related proteins. Normal samples were placed in one group, and tumor samples were placed in another group inside the GEO database. We continued our investigation of the DEGs by performing an enrichment analysis using GO and KEGG. The GSCA platform is utilized in the process of doing an analysis of the connection between gene expression and medication sensitivity. In the end, the core target and the active molecule were both given the green light for a molecular docking investigation. POU2AF1 is being considered as a possible therapeutic target for HCC, and the results of our work have presented novel concepts for the treatment of HCC.

Genome Tunisia Project: paving the way for precision medicine in North Africa

BACKGROUND

Key discoveries and innovations in the field of human genetics have led to the foundation of molecular and personalized medicine. Here, we present the Genome Tunisia Project, a two-phased initiative (2022-2035) which aims to deliver the reference sequence of the Tunisian Genome and to support the implementation of personalized medicine in Tunisia, a North African country that represents a central hub of population admixture and human migration between African, European, and Asian populations. The main goal of this initiative is to develop a healthcare system capable of incorporating omics data for use in routine medical practice, enabling medical doctors to better prevent, diagnose, and treat patients.

METHODS

A multidisciplinary partnership involving Tunisian experts from different institutions has come to discern all requirements that would be of high priority to fulfill the project's goals. One of the most urgent priorities is to determine the reference sequence of the Tunisian Genome. In addition, extensive situation analysis and revision of the education programs, community awareness, appropriate infrastructure including sequencing platforms and biobanking, as well as ethical and regulatory frameworks, have been undertaken towards building sufficient capacity to integrate personalized medicine into the Tunisian healthcare system.

RESULTS

In the framework of this project, an ecosystem with all engaged stakeholders has been implemented including healthcare providers, clinicians, researchers, pharmacists, bioinformaticians, industry, policymakers, and advocacy groups. This initiative will also help to reinforce research and innovation capacities in the field of genomics and to strengthen discoverability in the health sector.

CONCLUSIONS

Genome Tunisia is the first initiative in North Africa that seeks to demonstrate the major impact that can be achieved by Human Genome Projects in low- and middle-income countries to strengthen research and to improve disease management and treatment outcomes, thereby reducing the social and economic burden on healthcare systems. Sharing this experience within the African scientific community is a chance to turn a major challenge into an opportunity for dissemination and outreach. Additional efforts are now being made to advance personalized medicine in patient care by educating consumers and providers, accelerating research and innovation, and supporting necessary changes in policy and regulation.

Identification of prognostic RNA editing profiles for clear cell renal carcinoma

Objective

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer and currently lacks effective biomarkers. This research aims to analyze and identify RNA editing profile associated with ccRCC prognosis through bioinformatics and in vitro experiments.

Methods

Transcriptome data and clinical information for ccRCC were retrieved from the TCGA database, and RNA editing files were obtained from the Synapse database. Prognostic models were screened, developed, and assessed using consistency index analysis and independent prognostic analysis, etc. Internal validation models were also constructed for further evaluation. Differential genes were investigated using GO, KEGG, and GSEA enrichment analyses. Furthermore, qPCR was performed to determine gene expression in human renal tubular epithelial cells HK-2 and ccRCC cells A-498, 786-O, and Caki-2.

Results

An RNA editing-based risk score, that effectively distinguishes between high and low-risk populations, has been identified. It includes CHD3| chr17:7815229, MYO19| chr17:34853704, OIP5-AS1| chr15:41590962, MRI1| chr19:13883962, GBP4| chr1:89649327, APOL1| chr22:36662830, FCF1| chr14:75203040 edited sites or genes and could serve as an independent prognostic factor for ccRCC patients. qPCR results showed significant up-regulation of CHD3, MYO19, MRI1, APOL1, and FCF1 in A-498, 786-O, and Caki-2 cells, while the expression of OIP5-AS1 and GBP4 was significantly down-regulated.

Conclusion

RNA editing site-based prognostic models are valuable in differentiating between high and low-risk populations. The seven identified RNA editing sites may be utilized as potential biomarkers for ccRCC.

Mining and exploration of appendicitis nursing targets: An observational study

Appendicitis is an inflammation caused by obstruction of the appendiceal lumen or termination of blood supply leading to appendiceal necrosis followed by secondary bacterial infection. The relationship between TYROBP gene and the nursing of appendicitis remains unclear. The appendicitis dataset GSE9579 profile was downloaded from the gene expression omnibus database generated from GPL571. Differentially expressed genes were screened, followed by weighted gene co-expression network analysis, functional enrichment analysis, gene set enrichment analysis, construction and analysis of protein-protein interaction network, Comparative Toxicogenomics Database analysis, and immune infiltration analysis. Heatmaps of gene expression levels were plotted. A total of 1570 differentially expressed genes were identified. According to gene ontology analysis, they were mainly enriched in organic acid metabolic process, condensed chromosome kinetochore, oxidoreductase activity. In Kyoto Encyclopedia of Gene and Genome analysis, they mainly concentrated in metabolic pathways, P53 signaling pathway, PPAR signaling pathway. The soft threshold power in weighted gene co-expression network analysis was set to 12. Through the construction and analysis of protein-protein interaction network, 5 core genes (FCGR2A, IL1B, ITGAM, TLR2, TYROBP) were obtained. Heatmap of core gene expression levels revealed high expression of TYROBP in appendicitis samples. Comparative Toxicogenomics Database analysis found that core genes (FCGR2A, IL1B, ITGAM, TLR2, TYROBP) were closely related to abdominal pain, gastrointestinal dysfunction, fever, and inflammation occurrence. TYROBP gene is highly expressed in appendicitis, and higher expression of TYROBP gene indicates worse prognosis. TYROBP may serve as a molecular target for appendicitis and its nursing.

SPAG5 and ASPM play important roles in gastric cancer: An observational study

Gastric cancer typically originates from the abnormal proliferation of normal cells within the gastric mucosa, eventually forming tumors. The roles of sperm-associated antigen 5 (SPAG5) and abnormal spindle-like microcephaly (ASPM) associated genes in gastric cancer are not yet clear. Gastric cancer datasets GSE51575 and GSE36076 profiles were downloaded from the GPL13607 and GPL570-generated gene expression omnibus database. The analysis included filtering for differentially expressed genes, weighted gene co-expression network analysis, functional enrichment analysis, gene set enrichment analysis, immune infiltration analysis, construction and analysis of the protein-protein interaction network, survival analysis, and Comparative Toxicogenomics Database analysis. Heatmaps of gene expression were also created. A total of 1457 differentially expressed genes were identified. According to gene ontology analysis, they are primarily enriched in the metabolic processes of organic acids, condensed chromosome centromere regions, and oxidoreductase activity. Kyoto Encyclopedia of Gene and Genome analysis showed they are mainly involved in metabolic pathways, P53 signaling pathway, and PPAR signaling pathway. The soft threshold power for weighted gene co-expression network analysis was set to 8. Three core genes (CENPE, SPAG5, and ASPM) were identified. Heatmaps of core gene expression revealed that SPAG5 and ASPM are highly expressed in gastric cancer samples and low in normal samples. Comparative Toxicogenomics Database analysis indicated that the core genes (CENPE, SPAG5, and ASPM) are associated with gastric tumors, gastric diseases, gastritis, gastric ulcers, tumors, inflammation, and necrosis. The SPAG5 and ASPM genes are overexpressed in gastric cancer tissues, and higher expression levels are associated with worse prognosis, may serve as potential prognostic markers.

Unveiling Immune-related feature genes for Alzheimer's disease based on machine learning

The identification of diagnostic and therapeutic biomarkers for Alzheimer's Disease (AD) remains a crucial area of research. In this study, utilizing the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we identified RHBDF2 and TNFRSF10B as feature genes associated with AD pathogenesis. Analyzing data from the GSE33000 dataset, we revealed significant upregulation of RHBDF2 and TNFRSF10B in AD patients, with correlations to age and gender. Interestingly, their expression profile in AD differs notably from that of other neurodegenerative conditions. Functional analysis unveiled their involvement in immune response and various signaling pathways implicated in AD pathogenesis. Furthermore, our study demonstrated the potential of RHBDF2 and TNFRSF10B as diagnostic biomarkers, exhibiting high discrimination power in distinguishing AD from control samples. External validation across multiple datasets confirmed the robustness of the diagnostic model. Moreover, utilizing molecular docking analysis, we identified dinaciclib and tanespimycin as promising small molecule drugs targeting RHBDF2 and TNFRSF10B for potential AD treatment. Our findings highlight the diagnostic and therapeutic potential of RHBDF2 and TNFRSF10B in AD management, shedding light on novel strategies for precision medicine in AD.

Tsc22d3 promotes morphine tolerance in mice through the GPX4 ferroptosis pathway

BACKGROUND

Morphine tolerance refers to gradual reduction in response to drug with continuous or repeated use of morphine, requiring higher doses to achieve same effect.

METHODS

The morphine tolerance dataset GSE7762 profiles, obtained from gene expression omnibus (GEO) database, were used to identify differentially expressed genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) was applied to explore core modules of DEGs related to morphine tolerance. Core genes were input into Comparative Toxicogenomics Database (CTD). Animal experiments were performed to validate role of Tsc22d3 in morphine tolerance and its relationship with ferroptosis-related pathway.

RESULTS

500 DEGs were identified. DEGs were primarily enriched in negative regulation of brain development, neuronal apoptosis processes, and neurosystem development. Core gene was identified as Tsc22d3. Tsc22d3 gene-associated miRNAs were mmu-miR-196b-5p and mmu-miR-196a-5p. Compared to Non-morphine tolerant group, Tsc22d3 expression was significantly upregulated in Morphine tolerant group. Tsc22d3 expression was upregulated in Morphine tolerant+Tsc22d3_OE, expression of HIF-1alpha, GSH, GPX4 in GPX4 ferroptosis-related pathway showed a more pronounced decrease. As Tsc22d3 expression was downregulated in Morphine tolerant+Tsc22d3_KO, expression of HIF-1alpha, GSH, GPX4 in GPX4 ferroptosis-related pathway exhibited a more pronounced increase. Upregulation of Tsc22d3 in Morphine tolerant+Tsc22d3_OE led to a more pronounced increase in expression of apoptosis proteins (P53, Caspase-3, Bax, SMAC, FAS). The expression of inflammatory factors (IL6, TNF-alpha, CXCL1, CXCL2) showed a more pronounced increase with upregulated Tsc22d3 expression in Morphine tolerant+Tsc22d3_OE.

CONCLUSIONS

Tsc22d3 is highly expressed in brain tissue of morphine-tolerant mice, activating ferroptosis pathway, enhancing apoptosis, promoting inflammatory responses in brain cells.

USP22 as a key regulator of glycolysis pathway in osteosarcoma: insights from bioinformatics and experimental approaches

Background

Osteosarcoma is the most common primary malignant bone tumor, but its pathogenesis remains unclear. Ubiquitin-specific processing peptidase 22 (USP22) is reported to be highly expressed and associated with tumor malignancy and prognosis in cancers. However, the role and mechanism of USP22 in osteosarcoma is not fully understood. This study aims to investigate the function and potential mechanism of USP22 in osteosarcoma using bioinformatics analysis combined with experimental validation.

Methods

We first integrated transcriptomic datasets and clinical information of osteosarcoma from GEO and TCGA databases to assess the expression and prognostic value of USP22 in osteosarcoma. Then, differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify USP22-related co-expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the biological functions and signaling pathways of USP22 co-expressed genes. To validate the accuracy of bioinformatics analyses, we downregulated USP22 expression in osteosarcoma cell line Sao-2 using siRNA and assessed its effect on cell proliferation, migration, invasion, apoptosis, and regulation of key signaling pathways.

Results

We found that USP22 was highly expressed in osteosarcoma tissues and correlated with poor prognosis in osteosarcoma patients. USP22 also showed potential as a diagnostic marker for osteosarcoma. In addition, 344 USP22-related co-expressed genes were identified, mainly involved in signaling pathways such as glycolysis, oxidative phosphorylation, spliceosome, thermogenesis, and cell cycle. The in vitro experiments confirmed the accuracy and reliability of bioinformatics analyses. We found that downregulation of USP22 could inhibit Sao-2 cell proliferation, migration, invasion, and induce apoptosis. Furthermore, downregulation of USP22 significantly reduced aerobic glycolysis levels in Sao-2 cells and inhibited the expression of key enzymes and transporters in aerobic glycolysis pathways such as HK2, PKM2, and GLUT1.

Conclusions

USP22 plays a critical role in the occurrence, development, and prognosis of osteosarcoma. USP22 could influence Sao-2 cell proliferation, apoptosis, migration, and invasion by regulating the glycolysis pathway, thereby promoting osteosarcoma progression. Therefore, USP22 may be a potential therapeutic target for the treatment of osteosarcoma.

NR1I2 as a core biological target in chronic venous ulcer tissues treated with ultrasound therapy

Ultrasound therapy is a method of applying ultrasonic energy to the stimulation produced by human body to change the function and tissue state of the body in order to achieve the purpose of treating diseases. Chronic venous ulcer is a common chronic skin ulcer. GSE222503 for ultrasound therapy of chronic venous ulcers was downloaded from gene expression omnibus database, which were used to identify differentially expressed genes. Weighted gene co-expression network analysis, functional enrichment analysis, gene set enrichment analysis, immune infiltration analysis and construction and analysis of protein-protein interaction network were performed. Draw gene expression heatmaps. Comparative toxicogenomics database analysis was performed. Two hundred thirty-five differentially expressed genes were obtained. According to gene ontology analysis, in biological process analysis, they were mainly enriched in positive regulation of cellular biosynthetic process, reproductive cell development, vasculogenesis, vascular morphogenesis, and inflammatory response. In cellular component analysis, they were mainly enriched in leading edge of growing cell, extracellular matrix binding organelle, F-actin capping protein complex. In molecular function analysis, they were mainly concentrated in receptor ligand activity, cytokine receptor binding. In Kyoto encyclopedia of genes and genomes analysis, they were mainly enriched in cytokine-cytokine receptor interaction, PI3K-Akt signaling pathway, HIF-1 signaling pathway, heme biosynthesis. In weighted gene co-expression network analysis, the soft threshold power was set to 9. Thirty modules were generated. PF4, NR1I2, TTC16, H3C12, KLRB1, CYP21A2 identified by 4 algorithms (MCC, EPC, closeness, stress). Heatmap of core gene expression showed that H3C12, KLRB1, PF4, NR1I2 were all underexpressed in samples of ultrasound-treated chronic venous ulcers and overexpressed in samples of untreated chronic venous ulcers. Comparative toxicogenomics database analysis showed that H3C12, KLRB1, PF4, NR1I2 are associated with thrombophlebitis, phlebitis, vascular malformations, metabolic syndrome, ulcers, and inflammation. In samples of chronic venous ulcer tissue treated with ultrasound, NR1I2 shows low expression, while in samples of chronic venous ulcer tissue without ultrasound treatment, it shows high expression. This finding suggests a potential role of NR1I2 in the process of ultrasound therapy for chronic venous ulcers, which may be related to the therapeutic effect of ultrasound therapy on chronic venous ulcers.

MYC and NCAPG2 as molecular targets of colorectal cancer and gastric cancer in nursing

Colorectal cancer is a common malignant tumor in intestinal tract, the early symptoms are not obvious. Gastric cancer is a malignant tumor originating from the gastric mucosal epithelium. However, the role of MYC and non-SMC condensin II complex subunit G2 (NCAPG2) in colorectal cancer and gastric cancer remains unclear. The colorectal cancer datasets GSE49355 and gastric cancer datasets GSE19826 were downloaded from gene expression omnibus database. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis (WGCNA) was performed. Functional enrichment analysis, gene set enrichment analysis (GSEA) and immune infiltration analysis was performed. Construction and analysis of protein-protein interactions (PPI) network. Survival analysis and comparative toxicogenomics database (CTD) were performed. A heat map of gene expression was drawn. A total of 751 DEGs were obtained. According to the gene ontology (GO) analysis, in Biological process (BP) analysis, they are mainly enriched in cell differentiation, cartilage development, and skeletal development. In cellular component (CC) analysis, they are mainly enriched in the cytoskeleton of muscle cells and actin filaments. In molecular function (MF) analysis, they are mainly concentrated in Rho GTPase binding, DNA binding, and fibronectin binding. In Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, they are mainly enriched in the MAPK signaling pathway, apoptosis, and cancer pathways. The soft threshold power for WGCNA analysis was set to 9, resulting in the generation of 40 modules. Ultimately, 2 core genes (MYC and NCAPG2) were identified. The heatmap of core gene expression showed high expression of MYC and NCAPG2 in colorectal cancer tissue samples and low expression in normal tissue samples, while they were core molecules in gastric cancer. Survival analysis indicated that MYC and NCAPG2 were risk factors, showing an upregulation trend with increasing risk scores. CTD analysis revealed associations of MYC and NCAPG2 with colorectal cancer, gastric cancer, inflammation, and immune system diseases. MYC and NCAPG2 are highly expressed in colorectal cancer. The higher the expression of MYC and NCAPG2, the worse the prognosis. MYC and NCAPG2 are core molecules in gastric cancer.

Identification of bio-active secondary metabolites from Actinobacteria as potential drug targets against Porphyromonas gingivalis in oral squamous cell carcinoma using molecular docking and dynamics study

Chronic periodontitis caused by the bacteria Porphyromonas gingivalis is thought to be a risk factor for the advancement of oral squamous cell carcinoma (OSCC). Virulence factors of P. gingivalis include gingipains, outer membrane surface lipoproteins, and fimbriae contribute to the activation of oncogenic pathways in OSCC by up-regulating different cytokines. Gingipains (Arg and Lys) proteases have an important role in the activation of proMMP-9, which promotes cellular invasion and metastatic ability of OSCC. Thus gingipains and MMP-9 were actively investigated as potential therapeutic targets in OSCC therapy. Various natural bioactive compounds from Actinobacteria have been explored for their anticancer potential in a variety of cancers, but very few studies have been reported in OSCC. Therefore, the current study is focused to identify potential actinobacterial compounds that can be considered as a therapeutic target against gingipains and inflammatory proteins in OSCC through high-throughput virtual screening, Molecular Docking (MD), and Molecular Dynamics Simulation (MDS) approaches. A total of 179 bioactive secondary metabolites of Actinobacteria were explored for their binding affinity against six virulence proteins of P. gingivalis. The Molecular Docking studies revealed that among 179 metabolites screened, Actinosporin G showed a highly acceptable binding affinity of -7.9 kcal/mol with RgpB (1CVR), and exhibited multi-protein targeting and drug-likeness property and passed level of toxicity. Comprehensive docking interaction of the best top-ranked Actinosporin G with OSCC-related protein targets illustrated high binding affinity towards MMP-9 and JAK-1 proteins among all targeted receptor proteins. The molecular dynamic (MD) simulation has been executed for the metabolite Actinosporin G for both bacterial gingipain (RgpB) and MMP-9 & JAK-1 showed stable intermolecular binding with both hydrogen and hydrophobic interactions. In conclusion, this work suggests that the bioactive secondary metabolite of Actinosporin G from Actinobacteria genera may serve as a promising therapy for P. gingivalis-induced OSCC.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00209-0.

Exploration of the causality of frailty index on psoriasis: A Mendelian randomization study

BACKGROUND

Frailty is associated with a variety of diseases, but the relationship between frailty and psoriasis remains unclear.

METHODS

First, we conducted a two-sample Mendelian randomization based on genome-wide association studies (GWAS) to investigate genetic causality between frailty index and common diseases in dermatology. Inverse variance weighted was used to estimate causality. Second, expression quantitative trait locus (eQTLs) analysis was conducted to identify the genes affected by Single nucleotide polymorphisms (SNPs). Third, we performed function and pathway enrichment, transcriptome-wide association studies (TWAS) analysis based on eQTLs.

RESULTS

It was shown that the rise of frailty index could increase the risk of psoriasis (IVW, beta = 0.916, OR = 2.500, 95%CI:1.418-4.408, p = 0.002) through Mendelian randomization (MR), and there was no heterogeneity and pleiotropy. There was no causality between the frailty index and other common diseases in dermatology. We found 31 eQTLs based on strongly correlated SNPs in the causality. TWAS analysis found that the expressions of four genes were closely related to psoriasis, including HLA-DQA1, HLA-DQA2, HLA-DRB1 and HLA-DQB1.

CONCLUSION

It suggested that the frailty index had a significant positive causality on the risk of psoriasis, which was well documented by combined genomic, transcriptome, and proteome analyses.

The high expression of TOP2A and MELK induces the occurrence of psoriasis

BACKGROUND

Psoriasis is a chronic inflammatory skin disease. However, the influence of the TOP2A and MELK genes on psoriasis remains unclear.

METHODS

Psoriasis datasets GSE166388 and GSE181318 were downloaded from the Gene Expression Omnibus (GEO) database generated from GPL570 and GPL22120. Differential gene expression (DEGs) was identified. Functional enrichment analysis, gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and immune infiltration analysis were conducted. The protein-protein interaction (PPI) network was constructed and analyzed. Gene expression heat map was generated. The most relevant diseases associated with core genes were determined through comparison with the Comparative Toxicogenomics Database (CTD) website. TargetScan was used to select miRNAs regulating central DEGs.

RESULTS

A total of 773 DEGs were identified. According to Gene Ontology (GO) analysis, they were mainly enriched in mitochondrial gene expression, oxidative phosphorylation, mitochondrial envelope, mitochondria and ribosome. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that target cells were mainly enriched in metabolic pathways, proteasome, and oxidative phosphorylation. Seven core genes (TOP2A, NUF2, MELK, ASPM, DLGAP5, CCNA2, DEPDC1B) were obtained. The gene expression heatmap showed high expression of core genes (TOP2A, MELK) in psoriasis samples, while DEPDC1B, CCNA2, DLGAP5, NUF2, ASPM were lowly expressed in psoriasis samples. CTD analysis found that TOP2A and MELK were related to skin neoplasms, skin diseases, psoriasis, erythema, dermatitis, and infections.

CONCLUSION

TOP2A and MELK genes are highly expressed in psoriasis, and higher expression of TOP2A and MELK genes is associated with poorer prognosis.

Identifying Acss1, Mtfp1 and Oxct1 as key regulators and promising biomarkers of sarcopenia in various models

Mitochondrial dysfunction plays a critical role in muscular homeostasis, but the molecular mechanism underlying mitochondrial dynamics and sarcopenia awaits to be uncovered. We all know that malnutrition, cachexia, and type 2 diabetes are significant contributors to the development of sarcopenia.Therefore, we analyzed a bioinformatic analysis on cathectic differentially expressed genes (cDEGs), fasted differentially genes (fDEGs) and mitochondria-related genes. The overlapping genes identified were then validated by RT-qPCR and Western blotting experiments in various sarcopenia mice models and used to predict aging-related muscle loss in humans. First, the correlation analysis and PPI network indicated 6 overlapping candidates (Bdh1, Gdap1, Acss1, Mtfp1, Idh2, Oxct1) may constitute a regulatory effect in mitochondrial dynamics and muscle wasting. Next, we successfully established fasted, Lewis lung carcinoma (LLC) and Diabetes Mellitus (DM) induced sarcopenia mice models and verified that Acss1, Mtfp1 and Oxct1 shared common and significant variation tendency in these sarcopenia mice models. Further-more, Pearson correlation analysis showed that Acss1 was negatively related to the weight of gastrocnemius while Mtfp1 and Oxct1 displayed a significantly positive correlation with gastrocnemius weight in sarcopenic mice model induced by LLC, fasting and DM. What's more, ROC analysis based on human aging-related datasets indicated Acss1, Mtfp1, Oxct1 had outstanding diagnostic capabilities for sarcopenia. In general, we identified three hub genes (Acss1, Mtfp1 and Oxct1) that are strongly associated with mitochondrial dysfunction in sarcopenia and may provide novel and reliable indicators for screening, diagnosis, and prognosis, as well as potential therapeutic targets for patients with sarcopenia.

LCN2 and ELANE overexpression induces sepsis

Sepsis is a syndrome characterized by a systemic inflammatory response due to the invasion of pathogenic microorganisms. The relationship between Lipocalin-2 (LCN2), elastase, neutrophil expressed (ELANE) and sepsis remains unclear. The sepsis datasets GSE137340 and GSE154918 profiles were downloaded from gene expression omnibus generated from GPL10558. Batch normalization, differentially expressed Genes (DEGs) screening, weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, construction and analysis of protein-protein interaction (PPI) networks, Comparative Toxicogenomics Database (CTD) analysis were performed. Gene expression heatmaps were generated. TargetScan was used to screen miRNAs of DEGs. 328 DEGs were identified. According to Gene Ontology (GO), in the Biological Process analysis, they were mainly enriched in immune response, apoptosis, inflammatory response, and immune response regulation signaling pathways. In cellular component analysis, they were mainly enriched in vesicles, cytoplasmic vesicles, and secretory granules. In Molecular Function analysis, they were mainly concentrated in hemoglobin binding, Toll-like receptor binding, immunoglobulin binding, and RAGE receptor binding. In Kyoto Encyclopedia of Genes and Genomes (KEGG), they were mainly enriched in NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, TNF signaling pathway, P53 signaling pathway, and legionellosis. Seventeen modules were generated. The PPI network identified 4 core genes (MPO, ELANE, CTSG, LCN2). Gene expression heatmaps revealed that core genes (MPO, ELANE, CTSG, LCN2) were highly expressed in sepsis samples. CTD analysis found that MPO, ELANE, CTSG and LCN2 were associated with sepsis, peritonitis, meningitis, pneumonia, infection, and inflammation. LCN2 and ELANE are highly expressed in sepsis and may serve as molecular targets.

Unveiling the role of miR-137-3p/miR-296-5p/SERPINA3 signaling in colorectal cancer progression: integrative analysis of gene expression profiles and in vitro studies

BACKGROUND

Colorectal cancer (CRC) is a prevalent malignancy worldwide, with increasing incidence and mortality rates. Although treatment options have improved, CRC remains a leading cause of death due to metastasis. Early intervention can significantly improve patient outcomes, making it crucial to understand the molecular mechanisms underlying CRC metastasis. In this study, we performed bioinformatics analysis to identify potential genes associated with CRC metastasis.

METHODS

We downloaded and integrated gene expression datasets (GSE89393, GSE100243, and GSE144259) from GEO database. Differential expression analysis was conducted, followed by Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub gene SERPINA3 was selected for further in vitro functional studies. Additionally, the role of miR-137-3p/miR-296-5p/ Serpin family A member 3 (SERPINA3) in CRC cell function was investigated using in vitro assays.

RESULTS

Analysis of the gene expression datasets revealed differentially expressed genes (DEGs) associated with CRC metastasis. GO analysis showed enrichment in biological processes such as blood coagulation regulation and wound healing. Cellular component analysis highlighted extracellular matrix components and secretory granules. Molecular function analysis identified activities such as serine-type endopeptidase inhibition and lipoprotein receptor binding. KEGG analysis revealed involvement in pathways related to complement and coagulation cascades, cholesterol metabolism, and immune responses. The common DEGs among the datasets were further investigated. We identified SERPINA3 as a hub gene associated with CRC metastasis. SERPINA3 exerted enhanced effects on migration, proliferation and epithelial-mesenchymal transition (EMT) and inhibitory effects on caspase-3/-9 activities in HT29 and SW620 cells. MiR-137-3p overexpression increased activities of caspase-3/-9, decreased migration and proliferation, and also repressed EMT in HT29 cells, which were obviously attenuated by SERPINA3 enforced overexpression. Consistently, SERPINA3 enforced overexpression also largely reversed miR-296-5p mimics-induced increased in activities of caspase-3/-9, decrease in migration, proliferation and EMT in HT29 cells.

CONCLUSION

Through bioinformatics analysis, we identified potential genes associated with CRC metastasis. The functional studies focusing on SERPINA3/miR-137-3p/miR-296-5p further consolidated its role in regulating CRC progression. Our findings provide insights into novel mechanisms underlying CRC metastasis and might contribute to the development of effective treatment strategies. However, the role of SERPINA3/miR-137-3p/miR-296-5p signaling in CRC still requires further investigation.

Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence

With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.

Celastrol as a candidate drug for silicosis: From bioinformatics and network pharmacology to experimental validation

Silicosis, a highly lethal occupational respiratory disease characterized by irreversible pulmonary fibrosis, remains challenging to treat due to its unclear pathogenesis. In this study, bioinformatics, network pharmacology, and experimental validation were combined to explore potential mechanisms and therapeutic drugs for silicosis. First, the differentially expressed genes（DEGs）and pathway enrichment in pulmonary fibrosis were identified by GO and KEGG analysis. Next, the differential genes were submitted to cMap database for drug prediction and celastrol stood out as the most promising candidate drug. Then, network pharmacology analysis identified pharmacological targets of celastrol and demonstrated that celastrol could regulate JAK-STAT, MAPK, and Toll-like receptor signaling pathways. Finally, we verified the therapeutic role and mechanism of celastrol on silicosis. In vivo, celastrol significantly ameliorated CS-induced inflammation and fibrosis in silicosis mice, including inflammatory cell infiltration, collagen fiber and extracellular matrix deposition, fibroblast activation and related factor expression. Moreover, it dramatically improved lung respiratory function of silicosis mice. In vitro, celastrol suppressed CS-induced cytokine expression, apoptosis of macrophages and activation of Stat3 and Erk1/2 signals. Overall, our research identified and verified celastrol as a novel and promising candidate drug for silicosis.

Fgf2 and Ptpn11 play a role in cerebral injury caused by sevoflurane anesthesia

Sevoflurane is a new inhaled anesthetic, which has better physical properties than the existing inhalational anesthetics, rapid induction, less tissue uptake, and faster recovery. Sevoflurane can directly dilators cerebral blood vessels and increase cerebral blood flow, but it also reduces cerebral oxygen metabolism rate, thereby reducing cerebral blood flow. However, the role of Fgf2 and Ptpn11 in cerebral injury caused by sevoflurane anesthesia remains unclear. The sevoflurane anesthesia brain tissue datasets GSE139220 and GSE141242 were downloaded from gene expression omnibus (GEO). Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis (WGCNA) was performed. Construction and analysis of protein-protein interaction (PPI) Network. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG), comparative toxicogenomics database (CTD) were performed. A heat map of gene expression was drawn. TargetScan was used to screen miRNAs regulating DEGs. 500 DEGs were identified. According to GO, in Biological Process analysis, they were mainly enriched in response to hypoxia, blood vessel development, inner ear development, neural tube closure, and aging. In Cellular Component (CC), they were mainly enriched in plasma membrane, integral component of membrane, and basal lamina. In Molecular Function (MF), they were mainly associated with protein binding, Wnt-activated receptor activity, and organic anion transmembrane transporter activity. In the KEGG analysis, they were mainly enriched in proteoglycans in cancer, pathways in cancer, transcriptional misregulation in cancer, basal cell carcinoma, thyroid hormone signaling pathway. In the Metascape enrichment analysis, the GO enrichment items revealed upregulated regulation of vascular endothelial cell proliferation, platelet-derived growth factor receptor signaling pathway, inner ear development, and response to hypoxia. A total of 20 modules were generated. Gene Expression Heatmap showed that the core genes (Fgf2, Pdgfra, Ptpn11, Slc2a1) were highly expressed in sevoflurane anesthesia brain tissue samples. CTD Analysis showed that the 4 core genes (Fgf2, Pdgfra, Ptpn11, Slc2a1) were associated with neurodegenerative diseases, brain injuries, memory disorders, cognitive disorders, neurotoxicity, drug-induced abnormalities, neurological disorders, developmental disorders, and intellectual disabilities. Fgf2 and Ptpn11 are highly expressed in brain tissue after sevoflurane anesthesia, higher the expression level of Fgf2 and Ptpn11, worse the prognosis.

Bioinformatics analysis reveals lipid metabolism may play an important role in the SiO2-stimulated rat model

Silicosis is a progressive and irreversible common occupational disease caused by long-term inhalation of a large amount of free silica dust. Its pathogenesis is complex, and the existing prevention and treatment methods can not effectively improve silicosis injury. To uncover potential differential genes in silicosis, SiO₂-stimulated rats and their control original transcriptomic data sets GSE49144, GSE32147 and GSE30178 were downloaded for further bioinformatics analysis. We used R packages to extract and standardize transcriptome profiles, then screened differential genes, and enriched GO and KEGG pathways through clusterProfiler packages. In addition, we investigated the role of lipid metabolism in the progression of silicosis by qRT-PCR validation and transfection with si-CD36. A total of 426 differential genes were identified in this study. Based on GO and KEGG enrichment analysis, it was found that lipid and atherosclerosis were significantly enriched. qRT-PCR was used to detect the relative expression level of differential genes in this signaling pathway of silicosis rat models. mRNA levels of Abcg1, Il1b, Sod2, Cyba, Cd14, Cxcl2, Ccl3, Cxcl1, Ccl2 and CD36 increased, mRNA levels of Ccl5, Cybb and Il18 decreased. In addition, at the cellular level, SiO₂-stimulated lead to lipid metabolism disorder in NR8383, and silencing CD36 inhibited SiO₂-induced lipid metabolism disorder. These results indicate that lipid metabolism plays an important role in the progression of silicosis, and the genes and pathways reported in this study may provide new ideas for the pathogenesis of silicosis.

Exploration of the underlying comorbidity mechanism in psoriasis and periodontitis: a bioinformatics analysis

BACKGROUND

Increasing evidence indicates that psoriasis (PSO) and periodontitis (PD) are likely to occur together, however, the underlying mechanism remains unclear.

MATERIALS AND METHODS

The expression profiles of PSO (lesion vs non-lesion, GSE30999, GSE14905) and PD (affected vs unaffected gingival tissue, GSE16134, GSE10334) were downloaded from the GEO database. First, we investigated the common differentially expressed genes (DEGs) of PSO and PD. Then, GO and KEGG enrichment analysis, protein interaction network (PPI) construction, and hub gene identification analysis were carried out. Finally, GO and KEGG enrichment analysis, miRNA interaction analysis, and transcription factors (TFs) interaction analysis for hub genes were performed.

RESULTS

Eighteen DEGs were identified for further analysis, including 15 up-regulated genes and 3 down-regulated genes. 9 hub genes were then identified via Cytohubba, including IL1B, CXCL1, CXCL8, MMP12, CCL18, SELL, CXCL13, FCGR3B, and SELE. Their functions are mainly enriched in two aspects: neutrophil chemotaxis and migration, chemokine activation and interaction. The enriched signaling pathways includes three categories: host defense, inflammation-related signaling pathways, and disease-related pathways. 9 common miRNAs based on experimental evidence and 10 common TFs were further identified in both PSO and PD.

CONCLUSION

Our study revealed possible comorbidity mechanisms in PSO and PD from the perspective of bioinformatics tentatively. The data can present new insight for joint prevention and treatment of in PSO and PD, as well as provide data support for further prospective studies.