Unraveling the copper-death connection: Decoding COVID-19's immune landscape through advanced bioinformatics and machine learning approaches

This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1's pivotal role validated. FDX1's high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1's connection to immune pathways offers insights into COVID-19 mechanisms and therapy.

Exploring the molecular mechanisms of asthma across multiple datasets

BACKGROUND

Asthma, a prevalent chronic respiratory disorder, remains enigmatic, notwithstanding considerable advancements in our comprehension. Continuous efforts are crucial for discovering novel molecular targets and gaining a comprehensive understanding of its pathogenesis.

MATERIALS AND METHODS

In this study, we analyzed gene expression data from 212 individuals, including asthma patients and healthy controls, to identify 267 differentially expressed genes, among which C1orf64 and C7orf26 emerged as potential key genes in asthma pathogenesis. Various bioinformatics tools, including differential gene expression analysis, pathway enrichment, drug target prediction, and single-cell analysis, were employed to explore the potential roles of the genes.

RESULTS

Quantitative PCR demonstrated differential expression of C1orf64 and C7orf26 in the asthmatic airway epithelial tissue, implying their potential involvement in asthma pathogenesis. GSEA enrichment analysis revealed significant enrichment of these genes in signaling pathways associated with asthma progression, such as ABC transporters, cell cycle, CAMs, DNA replication, and the Notch signaling pathway. Drug target prediction, based on upregulated and downregulated differential expression, highlighted potential asthma treatments, including Tyrphostin-AG-126, Cephalin, Verrucarin-a, and Emetine. The selection of these drugs was based on their significance in the analysis and their established anti-inflammatory and antiviral invasion properties. Utilizing Seurat and Celldex packages for single-cell sequencing analysis unveiled disease-specific gene expression patterns and cell types. Expression of C1orf64 and C7orf26 in T cells, NK cells, and B cells, instrumental in promoting hallmark features of asthma, was observed, suggesting their potential influence on asthma development and progression.

CONCLUSION

This study uncovers novel genetic aspects of asthma, highlighting potential therapeutic pathways. It exemplifies the power of integrative bioinformatics in decoding complex disease patterns. However, these findings require further validation, and the precise roles of C1orf64 and C7orf26 in asthma warrant additional investigation to validate their therapeutic potential.

Elevated ALOX12 in renal tissue predicts progression in diabetic kidney disease

Diabetic kidney disease (DKD) is one of the major causes of end-stage renal disease and one of the significant complications of diabetes. This study aims to identify the main differentially expressed genes in DKD from transcriptome sequencing results and analyze their diagnostic value. The present study sequenced db/m mouse and db/db mouse to determine the ALOX12 genetic changes related to DKD. After preliminary validation, ALOX12 levels were significantly elevated in the blood of DKD patients, but not during disease progression. Moreover, urine ALOX12 was increased only in macroalbuminuria patients. Therefore, to visualize the diagnostic efficacy of ALOX12 on the onset and progression of renal injury in DKD, we collected kidney tissue from patients for immunohistochemical staining. ALOX12 was increased in the kidneys of patients with DKD and was more elevated in macroalbuminuria patients. Clinical chemical and pathological data analysis indicated a correlation between ALOX12 protein expression and renal tubule injury. Further immunofluorescence double staining showed that ALOX12 was expressed in both proximal tubules and distal tubules. Finally, the diagnostic value of the identified gene in the progression of DKD was assessed using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) value for ALOX12 in the diagnosis of DKD entering the macroalbuminuria stage was 0.736, suggesting that ALOX12 has good diagnostic efficacy. During the development of DKD, the expression levels of ALOX12 in renal tubules were significantly increased and can be used as one of the predictors of the progression to macroalbuminuria in patients with DKD.

Integrated bioinformatics analysis identifies autophagy-associated genes as candidate biomarkers and reveals the immune infiltration landscape in psoriasis

Autophagy is implicated in the pathogenesis of psoriasis. We aimed to identify autophagy-related biomarkers in psoriasis via an integrated bioinformatics approach. We downloaded the gene expression profiles of GSE30999 dataset, and the "limma" package was applied to identify differentially expressed genes (DEGs). Then, differentially expressed autophagy-related genes (DEARGs) were identified via integrating autophagy-related genes with DEGs. CytoHubba plugin was used for the identification of hub genes and verified by the GSE41662 dataset. Subsequently, a series of bioinformatics analyses were employed, including protein-protein interaction network, functional enrichment, spearman correlation, receiver operating characteristic, and immune infiltration analyses. One hundred and one DEARGs were identified, and seven DEARGs were identified as hub genes and verified using the GSE41662 dataset. These validated genes had good diagnostic value in distinguishing psoriasis lesions. Immune infiltration analysis indicated that ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3 were correlated with infiltration of immune cells. Seven DEARGs, namely ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3, may be involved in the pathogenesis of psoriasis, which expanded the understanding of the development of psoriasis and provided important clinical significance for treatment of this disease.

Bioinformatics and systems-biology approach to identify common pathogenic mechanisms for COVID-19 and systemic lupus erythematosus

BACKGROUND

The Coronavirus disease 2019 (COVID-19) pandemic has brought a heavy burden to the world, interestingly, it shares many clinical symptoms with systemic lupus erythematosus (SLE). It is unclear whether there is a similar pathological process between COVID-9 and SLE. In addition, we don't know how to treat SLE patients with COVID-19. In this study, we analyse the potential similar pathogenesis between SLE and COVID-19 and explore their possible drug regimens using bioinformatics and systems biology approaches.

METHODS

The common differentially expressed genes (DEGs) were extracted from the COVID-19 datasets and the SLE datasets for functional enrichment, pathway analysis and candidate drug analysis.

RESULT

Based on the two transcriptome datasets between COVID-19 and SLE, 325 common DEGs were selected. Hub genes were identified by protein-protein interaction (PPI) analysis. few found a variety of similar functional changes between COVID-19 and SLE, which may be related to the pathogenesis of COVID-19. Besides, we explored the related regulatory networks. Then, through drug target matching, we found many candidate drugs for patients with COVID-19 only or COVID-19 combined with SLE.

CONCLUSION

COVID-19 and SLE patients share many common hub genes, related pathways and regulatory networks. Based on these common targets, we found many potential drugs that could be used in treating patient with COVID-19 or COVID-19 combined with SLE.

Exploring the molecular biology of ischemic cardiomyopathy based on ferroptosis‑related genes

Ischemic cardiomyopathy (ICM) is a serious cardiac disease with a very high mortality rate worldwide, which causes myocardial ischemia and hypoxia as the main damage. Further understanding of the underlying pathological processes of cardiomyocyte injury is key to the development of cardioprotective strategies. Ferroptosis is an iron-dependent form of regulated cell death characterized by the accumulation of lipid hydroperoxides to lethal levels, resulting in oxidative damage to the cell membrane. The current understanding of the role and regulation of ferroptosis in ICM is still limited, especially in the absence of evidence from large-scale transcriptomic data. Through comprehensive bioinformatics analysis of human ICM transcriptome data obtained from the Gene Expression Omnibus database, the present study identified differentially expressed ferroptosis-related genes (DEFRGs) in ICM. Subsequently, their potential biological mechanisms and cross-talk were analyzed, and hub genes were identified by constructing protein-protein interaction networks. Ferroptosis features such as reactive oxygen species generation, changes in ferroptosis marker proteins, iron ion aggregation and lipid oxidation, were identified in the H9c2 anoxic reoxygenation injury model. Finally, the diagnostic ability of Gap junction alpha-1 (GJA1), Solute carrier family 40 member 1 (SLC40A1), Alpha-synuclein (SNCA) were identified through receiver operating characteristic curves and the expression of DEFRGs was verified in an in vitro model. Furthermore, potential drugs (retinoic acid) that could regulate ICM ferroptosis were predicted based on key DEFRGs. The present article presents new insights into the role of ferroptosis in ICM, investigating the regulatory role of ferroptosis in the pathological process of ICM and advocating for ferroptosis as a potential novel therapeutic target for ICM based on evidence from the ICM transcriptome.

Identification of common genes of rhinovirus single/double‑stranded RNA‑induced asthma deterioration by bioinformatics analysis

Rhinovirus (RV) is the most common respiratory virus affecting humans. The majority of asthma deteriorations are triggered by RV infections. However, whether the effects of RV single- and double-stranded RNA on asthma deterioration have common target genes needs to be further studied. In the present study, two datasets (GSE51392 and GSE30326) were used to screen for common differentially expressed genes (cDEGs). The molecular function, signaling pathways, interaction networks, hub genes, key modules and regulatory molecules of cDEGs were systematically analyzed using online tools such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, STRING and NetworkAnalyst. Finally, the hub genes STAT1 and IFIH1 were verified in clinical samples using reverse transcription-quantitative PCR (RT-qPCR). A total of 85 cDEGs were identified. Function analysis revealed that cDEGs served an important role in the innate immune response to viruses and its regulation. Signal transducer and activator of transcription 1 (STAT1), interferon induced with helicase C domain 1 (IFIH1), interferon regulatory factor 7 (IRF7), DExD/H box helicase 58 (DDX58) and interferon-stimulating gene 15 (ISG15) were detected to be hub genes based on the protein-protein interactions and six topological algorithms. A key module involved in influenza A, the Toll-like receptor signaling pathway, was identified using Cytoscape software. The hub genes were regulated by GATA-binding factor 2 and microRNA-146a-5p. In addition, RT-qPCR indicated that the expression levels of the hub genes STAT1 and IFIH1 were low during asthma deterioration compared with post-treatment recovery samples. The present study enhanced the understanding of the mechanism of RV-induced asthma deterioration.

An energy-conserving reaction in amino acid metabolism catalyzed by arginine synthetase

All forms of life are presumed to synthesize arginine from citrulline via a two-step pathway consisting of argininosuccinate synthetase and argininosuccinate lyase using citrulline, adenosine 5'-triphosphate (ATP), and aspartate as substrates. Conversion of arginine to citrulline predominantly proceeds via hydrolysis. Here, from the hyperthermophilic archaeon Thermococcus kodakarensis, we identified an enzyme which we designate "arginine synthetase". In arginine synthesis, the enzyme converts citrulline, ATP, and free ammonia to arginine, adenosine 5'-diphosphate (ADP), and phosphate. In the reverse direction, arginine synthetase conserves the energy of arginine deimination and generates ATP from ADP and phosphate while releasing ammonia. The equilibrium constant of this reaction at pH 7.0 is [Cit][ATP][NH3]/[Arg][ADP][Pi] = 10.1 ± 0.7 at 80 °C, corresponding to a ΔG°' of -6.8 ± 0.2 kJ mol-1. Growth of the gene disruption strain was compared to the host strain in medium composed of amino acids. The results suggested that arginine synthetase is necessary in providing ornithine, the precursor for proline biosynthesis, as well as in generating ATP. Growth in medium supplemented with citrulline indicated that arginine synthetase can function in the direction of arginine synthesis. The enzyme is widespread in nature, including bacteria and eukaryotes, and catalyzes a long-overlooked energy-conserving reaction in microbial amino acid metabolism. Along with ornithine transcarbamoylase and carbamate kinase, the pathway identified here is designated the arginine synthetase pathway.

Tongue-Coating Microbial and Metabolic Characteristics in Halitosis

Halitosis is a common oral condition, which leads to social embarrassment and affects quality of life. Cumulative evidence has suggested the association of tongue-coating microbiome with the development of intraoral halitosis. The dynamic variations of tongue-coating microbiota and metabolites in halitosis have not been fully elucidated. Therefore, the present study aimed to determine the tongue-coating microbial and metabolic characteristics in halitosis subjects without other oral diseases using metagenomics and metabolomics analysis. The participants underwent oral examination, halitosis assessment, and tongue-coating sample collection for the microbiome and metabolome analysis. It was found that the microbiota richness and diversity were significantly elevated in the halitosis group. Furthermore, species from Actinomyces, Prevotella, Veillonella, and Solobacterium were significantly more abundant in the halitosis group. However, the Rothia and Streptococcus species exhibited opposite tendencies. Eleven Kyoto Encyclopedia of Genes and Genomes pathways were significantly enriched in the halitosis tongue coatings, including cysteine and methionine metabolism. Functional genes related to sulfur, indole, skatole, and cadaverine metabolic processes (such as serA, metH, metK and dsrAB) were identified to be more abundant in the halitosis samples. The metabolome analysis revealed that indole-3-acetic, ornithine, and L-tryptophan were significantly elevated in the halitosis samples. Furthermore, it was observed that the values of volatile sulfur compounds and indole-3-acetic abundances were positively correlated. The multiomics analysis identified the metagenomic and metabolomic characteristics to differentiate halitosis from healthy individuals using the least absolute shrinkage and selection operator logistic regression and random forest classifier. A total of 19 species and 39 metabolites were identified as features in halitosis patients, which included indole-3-acetic acid, Bacillus altitudinis, Candidatus Saccharibacteria, and Actinomyces species. In conclusion, an evident shift in microbiome and metabolome characteristics was observed in the halitosis tongue coating, which may have a potential etiological significance and provide novel insights into the mechanism for halitosis.

Comprehensive analysis of lung adenocarcinoma: Unveiling differential gene expression, survival-linked genes, subtype stratification, and immune landscape implications

This study offers a detailed exploration of lung adenocarcinoma (LUAD), addressing its heterogeneity and treatment challenges through a multi-faceted analysis that includes gene expression, genetic subtyping, pathway analysis, immune assessment, and drug sensitivity. It identifies 165 genes with significant expression differences and 46 genes associated with survival, revealing insights into oxidative stress and autophagy. LUAD samples were divided into three subtypes using consensus clustering on these 46 genes, with distinct survival outcomes. Gene Set Enrichment Analysis (GSEA) on HALLMARK gene sets indicated pathway variations with survival implications. The immune landscape, analyzed using the CIBERSORT algorithm, showed different immune cell distributions across subtypes, with the first subtype exhibiting a better immune environment and survival prospects. Advanced machine learning techniques developed a risk model from a set of four genes, effectively categorizing patients into high and low-risk groups, validated through external datasets and analyses. This model linked lower risk scores to better clinical stages, with a higher mutation rate and potential immunotherapy benefits observed in the high-risk group. Drug sensitivity assessments highlighted varied treatment responses between risk groups, suggesting avenues for personalized therapy. This comprehensive analysis enhances the understanding of LUAD's molecular and clinical nuances, offering valuable insights for tailored treatment approaches.

Bioinformatics study and experimental evaluation of miR-182, and miR-34 expression profiles in Tuberculosis and lung cancer

Background

Lung cancer is one of the most dangerous diseases among cancers and tuberculosis is one of the deadliest infectious diseases in the world. Many studies have mentioned the connection between lung cancer and tuberculosis, and also the microRNAs that play a significant role in the development of these two diseases. This study aims to use different databases to find effective miRNAs and their role on different genes on lung and tuberculosis diseases. Also determining the role of miR-34a and miR-182 in lung cancer and tuberculosis.

Methods

Using the GEO database, the influential microRNA databases were studied in two diseases. Finally, regarding bioinformatics results and literature studies, two miR-34a and miR-182 were selected. The role of these microRNAs and their target genes was carefully evaluated using bioinformatics. The expression of microRNAs in the blood plasma of patients with lung cancer and tuberculosis and healthy people were investigated.

Results

According to the GEO database, miR-34a and miR-182 are microRNAs that affect tuberculosis and lung cancer. By checking the miRBase, miRcode, Diana, miRDB, galaxy, KEGG databases, the role of these microRNAs on genes and different molecular pathways and their effect on these microRNAs were mentioned. The results of the present study showed that the expression of miR-34a and miR-182 was lower than that of healthy people. The P value amount for miR-182 was <0.0001 and for miR-34a was 0.3380.

Conclusion

Reducing the expression pattern of these microRNAs indicates their role in lung cancer and tuberculosis occurrence. Therefore, these microRNAs can be used as a biomarker for prognosis, diagnosis, and treatment methods.

In silico genome-wide analysis of homeodomain-leucine zipper transcription factors in Cannabis sativa L

HD-Zip (Homeodomain-Leucine Zipper) is a family of transcription factors unique to higher plants and plays a vital role in plant growth and development. Increasing research results show that HD-Zip transcription factors are widely involved in many life processes in plants. However, the HD-Zip transcription factor for cannabis, a valuable crop, has not yet been identified. The sequence characteristics, chromosome localization, system evolution, conservative motif, gene structure, and gene expression of the HD-Zip transcription factor in the cannabis genome were systematically studied. Real-time quantitative polymerase chain reaction (qRT-PCR) was used to verify its function. The results showed that cannabis contained 33 HD-Zip gene members. The number of amino acids is 136-849aa, the isoelectric point is 4.54-9.04, and the molecular weight is 23264.32-93147.87Da. Many cis-acting elements are corresponding to hormone and abiotic stress in the HD-Zip family promoter area of cannabis. Sequencing of the transcriptome at 5 tissue sites of hemp, stems, leaves, bracts, and seeds showed similar levels of expression of 33 members of the HD-Zip gene family at 5 tissue sites. Bioinformatics results show that HD-Zip expression is tissue-specific and may be influenced by hormones and environmental factors. This lays a foundation for further research on the gene function of HD-Zip.

NXPH4 can be used as a biomarker for pan-cancer and promotes colon cancer progression

NXPH4 promotes cancer proliferation and invasion. However, its specific role and mechanism in cancer remain unclear. Transcriptome and clinical data for pan-cancer were derived from the TCGA database. K-M survival curve and univariate Cox were used for prognostic analysis. CIBERSORT and TIMER algorithms were employed to calculate immune cell infiltration. Gene set enrichment analysis (GSEA) was employed for investigating the function of NXPH4. Western blot verified differential expression of NXPH4 in colon cancer. Functional assays (CCK-8, plate clonogenicity assay, wound healing assay, and Transwell assay) confirmed the impact of NXPH4 on proliferation, invasion, and migration of colon cancer cells. Dysregulation of NXPH4 in pan-cancer suggests its potential as a diagnostic and prognostic marker for certain cancers, including colon and liver cancer. High expression of NXPH4 in pan-cancer might be associated with the increase in copy number and hypomethylation. NXPH4 expression in pan-cancer is substantially linked to immune cell infiltration in the immune microenvironment. NXPH4 expression is associated with the susceptibility to immunotherapy and chemotherapy. Western blot further confirmed the higher expression of NXPH4 in colon cancer. Knockdown of NXPH4 significantly suppresses proliferation, invasion, and migration of colon cancer cell lines HT-29 and HCT116, as validated by functional assays.

Overexpressed FKBP5 mediates colorectal cancer progression and sensitivity to FK506 treatment via the NF-κB signaling pathway

Colorectal cancer (CRC) is a common and deadly tumor. FK506-binding protein 5 (FKBP5) is associated with some cancers, but the role of FKBP5 in CRC is not clear. The present study aimed to reveal the relationship between FKBP5 and CRC and to uncover the roles of FK506 in CRC. In total, 96 CRC patients were recruited. Survival analysis was conducted using the Kaplan-Meier method and COX regression analyses. Bioinformatics analyses were performed to explore the functions of FKBP5. The mechanisms of FKBP5 and the roles of FK506 in CRC progression were clarified by immunohistochemistry, MTS, scratch assay, transwell and flow cytometric analyses via in vitro and in vivo experiments. FKBP5 was overexpressed in 77 cancer tissues compared to that in matched normal tissues, and the overall survival rate of these patients was relatively shorter. Bioinformatics analyses showed that FKBP5 regulates proliferation, invasion, migration, epithelial-mesenchymal transition and nuclear factor-kappa B (NF-κB) signaling. The upregulation or downregulation of FKBP5 dramatically increases or decreases the proliferation, invasion and migration abilities of CRC cells. The expression of NF-κB, inhibitor B kinase α, matrix metalloproteinase-2 and metalloproteinase-9 positively correlated with FKBP5. FK506 inhibits the progression of CRC via the FKBP5/NF-κB signaling pathway. Our study identified a regulatory role for FKBP5 in CRC progression. Therefore, targeting FKBP5 may provide a novel treatment approach for CRC. FK506 can inhibit the progression of CRC by restraining the FKBP5/NF-κB signaling pathway and is expected to become a new drug for the treatment of CRC.

Draft genome of three isolates of Listeria monocytogenes isolated from Stenella coeruleoalba in Italy

Listeria monocytogenes is the etiological agent of the listeriosis. Here, we described three draft genome sequences of L. monocytogenes isolated in Italy from stranded individuals of the striped dolphin Stenella coeruleoalba. All the genomes have been molecular typed through the multilocus sequence typing to identify the phylogenetic lineage, clonal complex, sublineage, and serogroup.

PROS1 is a crucial gene in the macrophage efferocytosis of diabetic foot ulcers: a concerted analytical approach through the prisms of computer analysis

BACKGROUND

Diabetic foot ulcers (DFUs) pose a serious long-term threat because of elevated mortality and disability risks. Research on its biomarkers is still, however, very limited. In this paper, we have effectively identified biomarkers linked with macrophage excretion in diabetic foot ulcers through the application of bioinformatics and machine learning methodologies. These findings were subsequently validated using external datasets and animal experiments. Such discoveries are anticipated to offer novel insights and approaches for the early diagnosis and treatment of DFU.

METHODS

In this work, we used the Gene Expression Omnibus (GEO) database's datasets GSE68183 and GSE80178 as the training dataset to build a gene model using machine learning methods. After that, we used the training and validation sets to validate the model (GSE134431). On the model genes, we performed enrichment analysis using both gene set variant analysis (GSVA) and gene set enrichment analysis (GSEA). Additionally, the model genes were subjected to immunological association and immune function analyses.

RESULTS

In this study, PROS1 was identified as a potential key target associated with macrophage efflux in DFU by machine learning and bioinformatics approaches. Subsequently, the key biomarker status of PROS1 in DFU was also confirmed by external datasets. In addition, PROS1 also plays a key role in macrophage exudation in DFU. This gene may be associated with macrophage M1, CD4 memory T cells, naïve B cells, and macrophage M2, and affects IL-17, Rap1, hedgehog, and JAK-STAT signaling pathways.

CONCLUSIONS

PROS1 was identified and validated as a biomarker for DFU. This finding has the potential to provide a target for macrophage clearance of DFU.

Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides

Programmed cell death ligand 1 (PDL1), as an important biomarker, is quantified by immunohistochemistry (IHC) with few established histopathological patterns. Deep learning aids in histopathological assessment, yet heterogeneity and lacking spatially resolved annotations challenge precise analysis. Here, we present a weakly supervised learning approach using bulk RNA sequencing for PDL1 expression prediction from hematoxylin and eosin (H&E) slides. Our method extends the multiple instance learning paradigm with the teacher-student framework, which assigns dynamic pseudo-labels for intra-slide heterogeneity and retrieves unlabeled instances using temporal ensemble model distillation. The approach, evaluated on 12,299 slides across 20 solid tumor types, achieves a weighted average area under the curve of 0.83 on fresh-frozen and 0.74 on formalin-fixed specimens for 9 tumors with PDL1 as an established biomarker. Our method predicts PDL1 expression patterns, validated by IHC on 20 slides, offering insights into histologies relevant to PDL1. This demonstrates the potential of deep learning in identifying diverse histological patterns for molecular changes from H&E images.

Functional Investigation and Two-sample Mendelian Randomization Study of Inguinal Hernia Hub Genes Obtained by Bioinformatics Analysis

BACKGROUND

Inguinal hernia in adults is a common and frequent disease in surgery, prone to occur in the elderly or in those with a weak abdominal wall. Despite its prevalence, Molecular mechanisms underlying inguinal hernia formation are unclear.

OBJECTIVE

This study aims to identify potential gene markers for inguinal hernia and available drugs.

METHODS

Pubmed2Ensembl text mining was used to identify genes related to "inguinal hernia" keywords. The GeneCodis system was used to specify GO biological process terms and KEGG pathways defined in the Kyoto Encyclopedia of Genes and Genomes (KEGG). The STRING tool was used to construct protein-protein interaction networks, which were then visualized using Cytoscape.CytoHubba and Molecular Complex Detection were utilized to analyze the module (MCODE). A GO and KEGG analysis of gene modules was conducted using the DAVID platform database. Hub genes are those that are concentrated in prominent modules. The druggene interaction database was also used to identify potential drugs for inguinal hernia patients based on their interactions between the hub genes. Finally, a Mendelian randomization study was conducted based on genome-wide association studies to determine whether hub genes cause inguinal hernias.

RESULTS

The identification of 96 genes associated with inguinal hernia was carried out using text mining techniques. It was constructed using PPI networks with 80 nodes and 476 edges, and the sequence of the genes was performed using CytoHubba. MCODE analysis identified three gene modules. Three modules contain 37 genes clustered as hub candidate genes associated with inguinal hernia patients. The PI3K-Akt, MAPK, AGE-RAGE, and HIF-1 pathways were found to be enriched in signaling pathways. Sixteen of the 37 genes were found to be targetable by 30 existing drugs. The relationship between hub genes and inguinal hernia was examined using Mendelian randomization. The research revealed nine genes that may be connected with inguinal hernia, such as POMC, CD40LG, TFRC, VWF, LOX, IGF2, BRCA1, TNF, and HGF in the plasma. By inverse variance weighting, ALB was associated with an increased risk of inguinal hernia with an OR of 1.203 (OR [95%] = 1,04 [1.012 to 1.089], p = 0.008).

CONCLUSION

We identified potential hub genes for inguinal hernia, predicted potential drugs for inguinal hernia, and reverse-validated potential genes by Mendelian randomization. This may provide further insights into asymptomatic pre-diagnostic methods and contribute to studies to understand the molecular mechanisms of risk genes associated with inguinal hernia.

RNA-DNA triplexes: molecular mechanisms and functional relevance

Interactions of RNA with DNA are principles of gene expression control that have recently gained considerable attention. Among RNA-DNA interactions are R-loops and RNA-DNA hybrid G-quadruplexes, as well as RNA-DNA triplexes. It is proposed that RNA-DNA triplexes guide RNA-associated regulatory proteins to specific genomic locations, influencing transcription and epigenetic decision making. Although triplex formation initially was considered solely an in vitro event, recent progress in computational, biochemical, and biophysical methods support in vivo functionality with relevance for gene expression control. Here, we review the central methodology and biology of triplexes, outline paradigms required for triplex function, and provide examples of physiologically important triplex-forming long non-coding RNAs.

Microbial community structures and bacteria-Cylindrospermopsis raciborskii interactions in Yilong Lake

Cylindrospermopsis raciborskii-dominated harmful algae blooms (HABs) have been reported globally in recent years. However, our understanding of the ecology of C. raciborskii in natural conditions is still poor. In this study, we collected the water samples from a C. raciborskii-blooming lake, Yilong Lake, in Yunnan province, China, and used both culture-dependent and culture-independent approaches to investigate their microbial communities and the interactions between C. raciborskii and the other bacteria. The composition and diversity of microbial communities were revealed with 16S rRNA gene high-throughput sequencing data analysis. Microbial co-occurrences analysis suggests C. raciborskii may have complex associations with other bacteria. Based on co-inoculation tests, we obtained 14 strains of bacterial strains from the water samples that exhibited either algicidal or promoting effects on a strain of C. raciborskii. Two bacterial isolates exhibited a consistent performance between co-occurrence analysis and experimental result. Effects of these bacteria-algae interspecies interactions on the bloom event were discussed. All these results may shed new insights into the C. raciborskii-dominated blooms and how its interspecies relationships with other bacteria may influence the bloom events in eutrophic waters over the world.

Identification and validation of autophagy-related genes in SSc

Multiple organs are affected by the complex autoimmune illness known as systemic sclerosis (SSc), which has a high fatality rate. Genes linked to autophagy have been linked to the aetiology of SSc. It is yet unknown, though, whether autophagy-related genes play a role in the aetiology of SSc. After using bioinformatics techniques to examine two databases (the GSE76885 and GSE95065 datasets) and autophagy-related genes, we were able to identify 12 autophagy-related differentially expressed genes that are linked to the pathophysiology of SSc. Additional examination of the receiver operating characteristic curve revealed that SFRP4 (AUC = 0.944, P < 0.001) and CD93 (AUC = 0.904, P < 0.001) might be utilized as trustworthy biomarkers for the diagnosis of SSc. The SSc group's considerably greater CD93 and SFRP4 expression levels compared to the control group were further confirmed by qRT-PCR results. The autophagy-related genes SFRP4 and CD93 were found to be viable diagnostic indicators in this investigation. Our research sheds light on the processes by which genes linked to autophagy affect the pathophysiology of SSc.

Fit for Purpose Approach To Evaluate Detection of Amino Acid Substitutions in Shotgun Proteomics

Amino acid substitutions (AASs) alter proteins from their genome-expected sequences. Accumulation of substitutions in proteins underlies numerous diseases and antibiotic mechanisms. Accurate global detection of AASs and their frequencies is crucial for understanding these mechanisms. Shotgun proteomics provides an untargeted method for measuring AASs but introduces biases when extrapolating from the genome to identify AASs. To characterize these biases, we created a "ground-truth" approach using the similarities betweenEscherichia coli and Salmonella typhimurium to model the complexity of AAS detection. Shotgun proteomics on mixed lysates generated libraries representing ∼100,000 peptide-spectra and 4161 peptide sequences with a single AAS and defined stoichiometry. Identifying S. typhimurium peptide-spectra with only the E. coli genome resulted in 64.1% correctly identified library peptides. Specific AASs exhibit variable identification efficiencies. There was no inherent bias from the stoichiometry of the substitutions. Short peptides and AASs localized near peptide termini had poor identification efficiency. We identify a new class of "scissor substitutions" that gain or lose protease cleavage sites. Scissor substitutions also had poor identification efficiency. This ground-truth AAS library reveals various sources of bias, which will guide the application of shotgun proteomics to validate AAS hypotheses.

Genome Mining for New Enzyme Chemistry

A revolution in the field of biocatalysis has enabled scalable access to compounds of high societal values using enzymes. The construction of biocatalytic routes relies on the reservoir of available enzymatic transformations. A review of uncharacterized proteins predicted from genomic sequencing projects shows that a treasure trove of enzyme chemistry awaits to be uncovered. This Review highlights enzymatic transformations discovered through various genome mining methods and showcases their potential future applications in biocatalysis.

Role of Radiation-related Lung Function Genes in Prognosis and Immune Infiltration of Lung Adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a common malignant tumor with no obvious clinical symptoms in its early stages. Patients can be divided into radiotherapysensitive groups (RS) and radiotherapy-resistant groups (RR) due to their varying conditions. The therapeutic effect of radiotherapy is quite different between the two groups. Therefore, this paper explores the role of radiation-related lung function genes in LUAD and its immune landscape.

METHODS

Firstly, we divided LUAD samples from the TCGA cohort into RS and RR groups and analyzed differential expression to obtain differentially expressed genes (DEGs). Then, DEGs and patients' grouping information were input into the weighted co-expression network, and the genes in the radiotherapy-related modules were identified. Furthermore, after the intersection of DEGs and lung function-related genes, the prognosis-related genes were obtained through univariate Cox and Lasso-Cox analyses, respectively, and the risk model was constructed. Finally, the differences in prognosis and immunity of the samples in the risk model were explored. Additionally, we also performed a qPCR experiment on lung function-related genes.

RESULTS

In this paper, radiation-related genes of LUAD were identified through a series of bioinformatics analyses. By conducting enrichment analysis on these genes, several pathways related to LUAD radiation were identified, and DEGs associated with significant prognosis were determined. Furthermore, a radiation-related risk model of LUAD was developed. All samples were divided into high-risk and low-risk groups based on the risk score, and the differences in immune cell infiltration abundance and immune function between these groups were evaluated. The qPCR experimental results demonstrated a significant difference in the expression of genes related to lung function.

CONCLUSION

The prognosis-related genes identified in this paper and the risk model created can serve as a reference for diagnosing and treating LUAD.

Haplotype-resolved assembly of diploid and polyploid genomes using quantum computing

Precision medicine's emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the vehicle routing problem (VRP) assembler: an approach that transforms this task into a vehicle routing problem, an optimization formulation solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Google's OR-Tools, achieving a haplotype-resolved local assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to Hifiasm with phasing accuracy approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.

Elucidating the role of FOS in modulating the immune microenvironment through fibroblast and myeloid cell regulation in locoregional recurrent HNSCC

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) presents a significant clinical challenge, particularly due to its high propensity for locoregional recurrence. Current research underscores the need to unravel the complex interactions within the tumor microenvironment. This study addresses the critical gap in understanding how FOS modulates the immune landscape in HNSCC, with a focus on its influence on fibroblast and myeloid cell dynamics.

METHODS

Employing a comprehensive approach, we analyzed tissue samples from HNSCC patients and adjacent non-cancerous tissues using bulk RNA sequencing complemented by in-depth bioinformatics analyses, including gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and immune infiltration assessment. A pivotal aspect of our research involved dissecting single-cell RNA-seq data from GSE234933 to elucidate the cell-type-specific expression of FOS.

RESULTS

We found that FOS expression varies significantly in different cell populations in the HNSCC tumor microenvironment, especially in fibroblasts and myeloid cells. This expression difference may reflect the different roles of these cells in tumor progression and their impact on the tumor microenvironment.

CONCLUSION

Our results uncover a significant correlation between FOS expression and key immune and hypoxia-related pathways, suggesting its integral role in the tumor microenvironment. These findings not only enhance our understanding of HNSCC pathogenesis but also highlight FOS as a potential therapeutic target. This study marks a significant step towards addressing the urgent need for targeted interventions in HNSCC, particularly in the context of locoregional recurrence.

PyPop: a mature open-source software pipeline for population genomics

Python for Population Genomics (PyPop) is a software package that processes genotype and allele data and performs large-scale population genetic analyses on highly polymorphic multi-locus genotype data. In particular, PyPop tests data conformity to Hardy-Weinberg equilibrium expectations, performs Ewens-Watterson tests for selection, estimates haplotype frequencies, measures linkage disequilibrium, and tests significance. Standardized means of performing these tests is key for contemporary studies of evolutionary biology and population genetics, and these tests are central to genetic studies of disease association as well. Here, we present PyPop 1.0.0, a new major release of the package, which implements new features using the more robust infrastructure of GitHub, and is distributed via the industry-standard Python Package Index. New features include implementation of the asymmetric linkage disequilibrium measures and, of particular interest to the immunogenetics research communities, support for modern nomenclature, including colon-delimited allele names, and improvements to meta-analysis features for aggregating outputs for multiple populations. Code available at: https://zenodo.org/records/10080668 and https://github.com/alexlancaster/pypop.

Integrated bioinformatics analysis identified leucine rich repeat containing 15 and secreted phosphoprotein 1 as hub genes for calcific aortic valve disease and osteoarthritis

Calcific aortic valve disease (CAVD) and osteoarthritis (OA) are common diseases in the ageing population and share similar pathogenesis, especially in inflammation. This study aims to discover potential diagnostic and therapeutic targets in patients with CAVD and OA. Three CAVD datasets and one OA dataset were obtained from the Gene Expression Omnibus database. We used bioinformatics methods to search for key genes and immune infiltration, and established a ceRNA network. Immunohistochemical staining was performed to verify the expression of candidate genes in human and mice aortic valve tissues. Two key genes obtained, leucine rich repeat containing 15 (LRRC15) and secreted phosphoprotein 1 (SPP1), were further screened using machine learning and verified in human and mice aortic valve tissues. Compared to normal tissues, the infiltration of immune cells in CAVD tissues was significantly higher, and the expressions of LRRC15 and SPP1 were positively correlated with immune cells infiltration. Moreover, the ceRNA network showed extensive regulatory interactions based on LRRC15 and SPP1. The authors' findings identified LRRC15 and SPP1 as hub genes in immunological mechanisms during CAVD and OA initiation and progression, as well as potential targets for drug development.

A meta‑ and bioinformatics analysis of maspin expression levels influencing the prognosis of patients with breast cancer

Maspin is a serine protease inhibitor that is encoded by the human SERPINB5 gene. As a tumor inhibitor, it can inhibit the growth of tumor cells, increase adhesion between tumor cells and inhibit tumor angiogenesis. In the present study, a meta- and bioinformatics analysis was performed through the PubMed and China National Knowledge Infrastructure databases including entries added until up to March 20, 2023. It was found that compared with normal breast tissue, maspin expression was downregulated in breast cancer tissue. Maspin expression was negatively associated with lymph node metastasis. According to Kaplan-Meier plotter, it was found that lower maspin expression was negatively associated with the overall and distant metastasis-free survival rate of patients with estrogen receptor-positive, luminal A and grade 2 breast cancer. High expression of maspin was also positively associated with the relapse-free survival rate of patients of the luminal A subtype. Low maspin expression was positively associated with the post-progression and distant metastasis-free survival rate of the progesterone receptor-negative subtype. According to the GEPIA database, SERPINB5 mRNA expression was higher in normal than breast cancer tissues and negatively correlated with the TNM stage. High expression of maspin was also positively associated with the overall survival rate. In the UALCAN database, it was found that the mRNA and promoter methylation levels of SERPINB5 were higher in normal than in breast cancer tissues. These findings suggest that the expression of maspin may serve as a potential marker to indicate the occurrence, subsequent progression and even prognosis of breast cancer.

Osteopontin interacts with dendritic cells and macrophages in pulp inflammation: Comprehensive transcriptomic analysis and laboratory investigations

AIM

To investigate novel diagnostic markers for pulpitis and validate by clinical samples from normal and inflamed pulp. To explore the relationship between diagnostic markers and immune cells or their phenotypes during pulp inflammation.

METHODOLOGY

Two microarray datasets, GSE77459 and GSE92681, and identified differential expression genes were integrated. To understand immune features, gene functions, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) and ImmuneSigDB Gene Set Enrichment Analysis (GSEA) were analysed. For predictive purposes, machine learning techniques were applied to detect diagnostic markers. Immune infiltration in inflamed pulp was studied using CIBERSORT. The relationship between diagnostic markers and immune cells was investigated and validated their gene expression in clinical samples from the normal or inflamed pulp by qRT-PCR. Finally, the correlation between one marker, secreted phosphoprotein 1 (SPP1), encoding osteopontin (OPN), and dendritic cells (DCs)/macrophages was identified via HE staining and multiplex immunohistochemistry. An in vitro inflammatory dental pulp microenvironment model of THP-1 macrophages cocultured with dental pulp cells derived conditioned media (DPCs-CM) to investigate OPN production and macrophage phenotypes was established.

RESULTS

Analysis revealed unique immunologic features in inflamed pulp. Three diagnostic markers for pulpitis: endothelin-1 (EDN1), SPP1, and purine nucleoside phosphorylase (PNP), and validated them using qRT-PCR were predicted. Multiplex immunohistochemistry demonstrated OPN co-localized with activated DCs and M2 macrophages during pulp inflammation. In vitro experiments showed that THP-1 macrophages produced the highest levels of OPN when stimulated with DPCs-CM derived from the 20 μg/mL LPS pre-conditioned group, suggesting an M2b-like phenotype by increasing surface marker CD86 and expression of IL6, TNFα, IL10, and CCL1 but not CCL17 and MerTK. Levels of CCL1 and IL10 elevated significantly in the macrophages' supernatant from the 20 μg/mL LPS pre-conditioned CM group. OPN was proven co-localizing with CD86 in the inflamed pulp by immunofluorescence.

CONCLUSIONS

The current findings suggest that OPN can serve as a promising biomarker for pulpitis, correlated with DCs and macrophages. OPN+ macrophages in the inflamed pulp are associated with M2b-like phenotypes. These insights offer the potential for improved diagnosis and targeted therapy.

Identification of key genes and molecular mechanisms of chronic urticaria based on bioinformatics

Chronic urticaria (CU) is characterized by persistent skin hives, redness, and itching, enhanced by immune dysregulation and inflammation. Our main objective is identifying key genes and molecular mechanisms of chronic urticaria based on bioinformatics. We used the Gene Expression Omnibus (GEO) database and retrieved two GEO datasets, GSE57178 and GSE72540. The raw data were extracted, pre-processed, and analyzed using the GEO2R tool to identify the differentially expressed genes (DEGs). The samples were divided into two groups: healthy samples and CU samples. We defined cut-off values of log2 fold change ≥1 and p < .05. Analyses were performed in the Kyoto Encyclopaedia of Genes and Genomes (KEGG), the Database for Annotation, Visualization and Integrated Discovery (DAVID), Metascape, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and CIBERSOFT databases. We obtained 1613 differentially expressed genes. There were 114 overlapping genes in both datasets, out of which 102 genes were up-regulated while 12 were down-regulated. The biological processes included activation of myeloid leukocytes, response to inflammations, and response to organic substances. Moreover, the KEGG pathways of CU were enriched in the Nuclear Factor-Kappa B (NF-kB) signaling pathway, Tumor Necrosis Factor (TNF) signaling pathway, and Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway. We identified 27 hub genes that were implicated in the pathogenesis of CU, such as interleukin-6 (IL-6), Prostaglandin-endoperoxide synthase 2 (PTGS2), and intercellular adhesion molecule-1 (ICAM1). The complex interplay between immune responses, inflammatory pathways, cytokine networks, and specific genes enhances CU. Understanding these mechanisms paves the way for potential interventions to mitigate symptoms and improve the quality of life of CU patients.

Skin Microbial Composition and Genetic Mutation Analysis in Precision Medicine for Epidermolysis Bullosa

Epidermolysis bullosa (EB) is an inherited skin disease representing a spectrum of rare genetic disorders. These conditions share the common trait that causes fragile skin, resulting in the development of blisters and erosions. The inheritance follows an autosomal pattern, and the array of clinical presentations leads to significant physical suffering, considerable morbidity, and mortality. Despite EB having no cure, effectively managing EB remains an exceptional challenge due to its rarity and complexity, occasionally casting a profound impact on the lives of affected individuals. Considering that EB management requires a multidisciplinary approach, this sometimes worsens the condition of patients with EB due to inappropriate handling. Thus, more appropriate and precise treatment management of EB is essentially needed. Advanced technology in medicine and health comes into the bioinformatics era. Including treatment for skin diseases, omics-based approaches aim to evaluate and handle better disease management and treatment. In this work, we review several approaches regarding the implementation of omics-based technology, including genetics, pathogenic mutation, skin microbiomics, and metagenomics analysis for EB. In addition, we highlight recent updates on the potential of metagenomics analysis in precision medicine for EB.

RrA, an enzyme from Rhodospirillum rubrum, is a prototype of a new family of short-chain L-asparaginases

L-Asparaginases (ASNases) catalyze the hydrolysis of L-Asn to L-Asp and ammonia. Members of the ASNase family are used as drugs in the treatment of leukemia, as well as in the food industry. The protomers of bacterial ASNases typically contain 300-400 amino acids (typical class 1 ASNases). In contrast, the chain of ASNase from Rhodospirillum rubrum, reported here and referred to as RrA, consists of only 172 amino acid residues. RrA is homologous to the N-terminal domain of typical bacterial class 1 ASNases and exhibits millimolar affinity for L-Asn. In this study, we demonstrate that RrA belongs to a unique family of cytoplasmic, short-chain ASNases (scASNases). These proteins occupy a distinct region in the sequence space, separate from the regions typically assigned to class 1 ASNases. The scASNases are present in approximately 7% of eubacterial species, spanning diverse bacterial lineages. They seem to be significantly enriched in species that encode for more than one class 1 ASNase. Here, we report biochemical, biophysical, and structural properties of RrA, a member of scASNases family. Crystal structures of the wild-type RrA, both with and without bound L-Asp, as well as structures of several RrA mutants, reveal topologically unique tetramers. Moreover, the active site of one protomer is complemented by two residues (Tyr21 and Asn26) from another protomer. Upon closer inspection, these findings clearly outline scASNases as a stand-alone subfamily of ASNases that can catalyze the hydrolysis of L-Asn to L-Asp despite the lack of the C-terminal domain that is present in all ASNases described structurally to date.

Medium-Dose Formoterol Attenuated Abdominal Aortic Aneurysm Induced by EPO via β2AR/cAMP/SIRT1 Pathway

Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease but effective drugs for treatment of AAA are still lacking. Recently, erythropoietin (EPO) is reported to induce AAA formation in apolipoprotein-E knock out (ApoE-/-) mice but an effective antagonist is unknown. In this study, formoterol, a β2 adrenergic receptor (β2AR) agonist, is found to be a promising agent for inhibiting AAA. To test this hypothesis, ApoE-/- mice are treated with vehicle, EPO, and EPO plus low-, medium-, and high-dose formoterol, respectively. The incidence of AAA is 0, 55%, 35%,10%, and 55% in these 5 groups, respectively. Mechanistically, senescence of vascular smooth muscle cell (VSMC) is increased by EPO while decreased by medium-dose formoterol both in vivo and in vitro, manifested by the altered expression of senescence biomarkers including phosphorylation of H2AXserine139, senescence-associated β-galactosidase activity, and P21 protein level. In addition, expression of sirtuin 1 (SIRT1) in aorta is decreased in EPO-induced AAA but remarkably elevated by medium-dose formoterol. Knockdown of β2AR and blockage of cyclic adenosine monophosphate (cAMP) attenuate the inhibitory role of formoterol in EPO-induced VSMC senescence. In summary, medium-dose formoterol attenuates EPO-induced AAA via β2AR/cAMP/SIRT1 pathways, which provides a promising medication for the treatment of AAA.

Necroptosis pathway emerged as potential diagnosis markers in spinal cord injury

The present research focused on identifying necroptosis-related differentially expressed genes (NRDEGs) in spinal cord injury (SCI) to highlight potential therapeutic and prognostic target genes in clinical SCI. Three SCI-related datasets were downloaded, including GSE151371, GSE5296 and GSE47681. MSigDB and KEGG datasets were searched for necroptosis-related genes (NRGs). Differentially expressed genes (DEGs) and NRGs were intersected to obtain NRDEGs. The MCC algorithm was employed to select the first 10 genes as hub genes. A protein-protein interaction (PPI) network related to NRDEGs was developed utilizing STRING. Several databases were searched to predict interactions between hub genes and miRNAs, transcription factors, potential drugs, and small molecules. Immunoassays were performed to identify DEGs using CIBERSORTx. Additionally, qRT-PCR was carried out to verify NRDEGs in an animal model of SCI. Combined analysis of all datasets identified 15 co-expressed DEGs and NRGs. GO and KEGG pathway analyses highlighted DEGs mostly belonged to pathways associated with necroptosis and apoptosis. Hub gene expression analysis showed high accuracy in SCI diagnosis was associated with the expression of CHMP7 and FADD. A total of two hub genes, i.e. CHMP7, FADD, were considered potential targets for SCI therapy.

Turmeric Inhibits MDA-MB-231 Cancer Cell Proliferation, Altering miR-638-5p and Its Potential Targets

Objective

Recent research suggests curcumin extracted from the turmeric plant may inhibit the proliferation of cancer cells by controlling the expression of microRNAs (miRNAs). The effect of phenolic curcumin on miR-638-5p and potential target gene expressions in the triple negative breast cancer (TNBC) cell line MDA-MB-231 was investigated in this study.

Materials and Methods

GSE154255 and GSE40525 datasets were downloaded and analyzed using GEO2R to identify dysregulated miRNAs in TNBC. To find differently expressed genes in breast cancer (BRCA), The Cancer Genome Atlas Program data was examined. Utilizing in silico tools, KEGG, GO, and other enrichment analyses were performed. The databases miRNet, miRTarBase v8.0, and TarBase v.8 were used for miRNA and mRNA matching. Real-time quantitative reverse transcription polymerase chain reaction was used to examine the levels of miRNA and its targets in miRNA mimic transfected/curcumin-treated MDA-MB-231 cultures and controls. The cell viability detection kit-8 method was used to assess cell viability, and the scratch assay was used to conduct migration assessment.

Results

Bioinformatics analysis showed that miR-638-5p was significantly reduced in TNBC patients. Experimental results showed that miR-638-5p was upregulated in MDA-MB-231 treated with curcumin, while the potential target genes of miR-638-5p, CFL1, SIX4, MAZ, and CDH1 were downregulated. Mimic miR-638-5p transfection inhibited MDA-MB-231 cell proliferation and reduced migration and expression of CFL1, SIX4, and MAZ genes was decreased in mimic miR-638-5p transfected cells.

Conclusion

These findings suggest that curcumin exerts its anticancer effects on MDA-MB-231 cells by modulating the expression of miR-638-5p and its possible target genes.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms

Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.

Analysis of disulfidptosis- and cuproptosis-related LncRNAs in modulating the immune microenvironment and chemosensitivity in colon adenocarcinoma

The main objective was to establish a prognostic model utilising long non-coding RNAs associated with disulfidptosis and cuproptosis. The data for RNA-Sequence and clinicopathological information of Colon adenocarcinoma (COAD) were acquired from The Cancer Genome Atlas. A prognostic model was constructed using Cox regression and the Least Absolute Shrinkage and Selection Operator method. The model's predictive ability was assessed through principal component analysis, Kaplan-Meier analysis, nomogram etc. The ability of identifying the rates of overall survival, infiltration of immune cells, and chemosensitivity was also explored. In vitro experiments were conducted for the validation of differential expression and function of lncRNAs. A disulfidptosis and cuproptosis-related lncRNA prognostic model was constructed. The prognostic model exhibits excellent independent predictive capability for patient outcomes. Based on the authors' model, the high-risk group exhibited higher tumour mutation burdened worse survival. Besides, differences in immune cell infiltration and responsiveness to chemotherapeutic medications exist among patients with different risk scores. Furthermore, aberrant expressions in certain lncRNAs have been validated in HCT116 cells. In particular, FENDRR and SNHG7 could affect the proliferation and migration of colorectal cancer cells. Our study developed a novel prognostic signature, providing valuable insights into prognosis, immune infiltration, and chemosensitivity in COAD patients.

Comprehensive analysis of the interaction of antigen presentation during anti-tumour immunity and establishment of AIDPS systems in ovarian cancer

There are hundreds of prognostic models for ovarian cancer. These genes are based on different gene classes, and there are many ways to construct the models. Therefore, this paper aims to build the most stable prognostic evaluation system known to date through 101 machine learning strategies. We combined 101 algorithm combinations with 10 machine learning algorithms to create antigen presentation-associated genetic markers (AIDPS) with outstanding precision and steady performance. The inclusive set of algorithms comprises the elastic network (Enet), Ridge, stepwise Cox, Lasso, generalized enhanced regression model (GBM), random survival forest (RSF), supervised principal component (SuperPC), Cox partial least squares regression (plsRcox), survival support vector machine (Survival-SVM). Then, in the train cohort, the prediction model was fitted using a leave-one cross-validation (LOOCV) technique, which involved 101 different possible combinations of prognostic genes. Seven validation data sets (GSE26193, GSE26712, GSE30161, GSE63885, GSE9891, GSE140082 and ICGC_OV_AU) were compared and analysed, and the C-index was calculated. Finally, we collected 32 published ovarian cancer prognostic models (including mRNA and lncRNA). All data sets and prognostic models were subjected to a univariate Cox regression analysis, and the C-index was calculated to demonstrate that the antigen presentation process should be the core criterion for evaluating ovarian cancer prognosis. In a univariate Cox regression analysis, 22 prognostic genes were identified based on the expression profiles of 283 genes involved in antigen presentation and the intersection of genes (p < 0.05). AIDPS were developed by our machine learning-based integration method, which was applied to these 22 genes. One hundred and one prediction models are fitted using the LOOCV framework, and the C-index is calculated for each model across all validation sets. Interestingly, RSF + Lasso was the best model overall since it had the greatest average C-index and the highest C-index of any combination of models tested on the validated data sets. In comparing external cohorts, we found that the C-index correlated AIDPS method using the RSF + Lasso method in 101 prediction models was in contrast to other features. Notably, AIDPS outperformed the vast majority of models across all data sets. Antigen-presenting anti-tumour immune pathways can be used as a representative gene set of ovarian cancer to track the prognosis of patients with cancer. The antigen-presenting model obtained by the RSF + Lasso method has the best C-INDEX, which plays a key role in developing antigen-presenting targeted drugs in ovarian cancer and improving the treatment outcome of patients.

The circadian metabolome of atopic dermatitis

BACKGROUND

Atopic dermatitis (AD) is a chronic inflammatory skin disease characterized by dry, pruritic skin. Several studies have described nocturnal increases in itching behavior, suggesting a role for the circadian rhythm in modulating symptom severity. However, the circadian rhythm of metabolites in the skin and serum of patients with AD is yet to be described.

OBJECTIVE

We sought to assess circadian patterns of skin and serum metabolism in patients with AD.

METHODS

Twelve patients with moderate to severe AD and 5 healthy volunteers were monitored for 28 hours in a controlled environment. Serum was collected every 2 hours and tape strips every 4 hours from both lesional and nonlesional skin in participants with AD and location-, sex-, and age-matched healthy skin of controls. We then performed an untargeted metabolomics analysis, examining the circadian peaks of metabolism in patients with AD.

RESULTS

Distinct metabolic profiles were observed in AD versus control samples. When accounting for time of collection, the greatest differences in serum metabolic pathways were observed in arachidonic acid, steroid biosynthesis, and terpenoid backbone biosynthesis. We identified 42 circadian peaks in AD or control serum and 17 in the skin. Pathway enrichment and serum-skin metabolite correlation varied throughout the day. Differences were most evident in the late morning and immediately after sleep onset.

CONCLUSIONS

Although limited by a small sample size and observational design, our findings suggest that accounting for sample collection time could improve biomarker detection studies in AD and highlight that metabolic changes may be associated with nocturnal differences in symptom severity.

TIGD1 Is an Independent Prognostic Factor that Promotes the Progression of Colon Cancer

Background: Trigger transposable element-derived 1 (TIGD1) is a human-specific gene, but no studies have been conducted to determine its mechanism of action. Our aim is to ascertain the function and mode of action of TIGD1 in the development of colon cancer. Materials and Methods: The authors used bioinformatics to analyze the relationship between TIGD1 and the clinical characteristics of colon cancer, as well as its prognosis. A series of cell assays were conducted to assess the function of TIGD1 in the proliferation and migration of colon cancer, and flow cytometry was used to explore its effects on apoptosis and the cell cycle. Results: The authors discovered that the expression of TIGD1 was remarkably elevated in colon cancer. Clinical correlation analysis demonstrated that TIGD1 expression was elevated in the tissues of advanced-stage patients, and it was remarkably elevated in individuals with both lymph node and distant metastasis. Further, the authors found that individuals showing elevated TIGD1 expression levels had a shortened survival time. Univariate and multivariate Cox regression analyses revealed that TIGD1 was an independent prognostic factor. Overexpression of the TIGD1 gene remarkedly enhances the proliferation and metastasis of colon cancer cells and suppresses apoptosis. In addition, the overexpression of TIGD1 can enhance the transition of tumor cells from the G1 toward the S phase. Western blot results suggested that TIGD1 may promote the malignant activity of colon cancer cells via the Wnt/β-catenin signaling pathway, Bcl-2, N-cadherin, BAX, E-cadherin, CDK6, and CyclinD1. Conclusions: TIGD1 may be an independent prognostic factor in the advancement of colon cancer, and therefore function as a therapeutic target.

Construction of a diagnostic model based on random forest and artificial neural network for peri-implantitis

OBJECTIVES

This study aimed to reveal critical genes regulating peri-implantitis during its development and construct a diagnostic model by using random forest (RF) and artificial neural network (ANN).

METHODS

GSE-33774, GSE106090, and GSE57631 datasets were obtained from the GEO database. The GSE33774 and GSE106090 datasets were analyzed for differential expression and functional enrichment. The protein-protein interaction networks (PPI) and RF screened vital genes. A diagnostic model for peri-implantitis was established using ANN and validated on the GSE33774 and GSE57631 datasets. A transcription factor-gene interaction network and a transcription factor-micro-RNA (miRNA) regulatory network were also established.

RESULTS

A total of 124 differentially expressed genes (DEGs) involved in the regulation of peri-implantitis were screened. Enrichment analysis showed that DEGs were mainly associated with immune receptor activity and cytokine receptor activity and were mainly involved in processes such as leukocyte and neutrophil migration. The PPI and RF screened six essential genes, namely, CD38, CYBB, FCGR2A, SELL, TLR4, and CXCL8. The receiver operating characteristic curve (ROC) indicated that the ANN model had an excellent diagnostic performance. FOXC1, GATA2, and NF-κB1 may be essential transcription factors in peri-implantitis, and hsa-miR-204 may be a key miRNA.

CONCLUSIONS

The diagnostic model of peri-implantitis constructed by RF and ANN has high confidence, and CD38, CYBB, FCGR2A, SELL, TLR4, and CXCL8 are potential diagnostic markers. FOXC1, GATA2, and NF-κB1 may be essential transcription factors in peri-implantitis, and hsa-miR-204 plays a vital role as a critical miRNA.

Computational approaches to identify sites of phosphorylation

Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.

Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

Exploring the impact of TGF-β family gene mutations and expression on skin wound healing and tissue repair

Transforming Growth Factor-Beta (TGF-β) signalling pathway is of paramount importance in the processes of wound healing, epidermal integrity maintenance and development of skin cancer. The objective of this research endeavour was to clarify the impact of gene mutations and variations in expression within TGF-β family on mechanisms of tissue repair, as well as to identify potential targets for therapeutic purposes in non-melanoma skin cancer (NMSC). The methods utilized in this study involved obtaining RNA-seq data from 224 NMSC patients and paired normal skin tissues from the PRJNA320473 and PRJEB27606 databases. The purpose of the differential gene expression analysis was to identify genes whose expression had changed significantly. In order to evaluate the effects and interrelationships of identified gene variants, structural analysis with AlphaFold and PDB data and network analysis with the STRING database were both utilized. Critical gene expression was externally validated through the utilization of the GEPIA database. Tumour tissues exhibited a notable upregulation of genes associated with the TGF-β pathway, specifically MMP1, MMP3, MMP9, EGF, COL3A1 and COL1A2, in comparison with normal tissues. As indicated by the central node status of these genes in the network analysis, they play a crucial role in the progression of NMSCs. The results of the structural analysis suggested that mutations might cause functional disruptions. External validation of the upregulation confirmed the expression trends and emphasized the biomarker potential of the upregulated genes. In conclusion, this research offered thorough examination of molecular modifications that occur in TGF-β family genes, which are linked to cutaneous wound healing and NMSC. The modified expression of the identified hub genes may represent innovative targets for therapeutic intervention.

Exploration of SERPINA family functions and prognostic value in breast cancer based on transcriptome and in vitro analysis

Breast cancer poses a significant risk to women worldwide, yet specific role of SERPINA gene family in breast cancer remains unclarified. Data were collected from online databases. SERPINA family gene expression was presented, and prognosis value was evaluated. Multi-omics methods were employed to explore the SERPINA-related biological processes, followed by comprehensive analyses of their roles in breast cancer. Single-cell data were analyzed to characterize the SERPINA family gene expression in different cell clusters. We selected SERPINA5 as the target gene. Via pan-cancer analysis, SERPINA5 was also investigated in various cancers. The experimental validation was conducted in MDA-MB-231 cell line eventually. SERPINA family showed differential expression in breast cancer, which were mainly expressed in myeloid cells, epithelial cells, and dendritic cells. SERPINA5 expression was upregulated in breast cancer, which was associated with a better prognosis. Immune infiltration illustrated the positive correlativity between SERPINA5 intensity and eosinophilic recruitment. Pan-cancer analysis indicated the function of SERPINA5 as a potential biomarker in other cancers. Finally, experimental validation demonstrated that SERPINA5 contributes to lower invasion and metastatic potential of breast cancer cells. With bioinformatics analysis, the significant role SERPINA family genes functioned in breast cancer was comprehensively explored, with SERPINA5 emerging as a key gene in suppressing breast cancer progression.

A single-cell atlas enables mapping of homeostatic cellular shifts in the adult human breast

Here we use single-cell RNA sequencing to compile a human breast cell atlas assembled from 55 donors that had undergone reduction mammoplasties or risk reduction mastectomies. From more than 800,000 cells we identified 41 cell subclusters across the epithelial, immune and stromal compartments. The contribution of these different clusters varied according to the natural history of the tissue. Age, parity and germline mutations, known to modulate the risk of developing breast cancer, affected the homeostatic cellular state of the breast in different ways. We found that immune cells from BRCA1 or BRCA2 carriers had a distinct gene expression signature indicative of potential immune exhaustion, which was validated by immunohistochemistry. This suggests that immune-escape mechanisms could manifest in non-cancerous tissues very early during tumor initiation. This atlas is a rich resource that can be used to inform novel approaches for early detection and prevention of breast cancer.

Construction of a novel mRNA-miRNA-lncRNA/circRNA triple subnetwork associated with immunity and aging in intervertebral disc degeneration

OBJECTIVE

Intervertebral disk degeneration (IVDD) is one of the most common causes of low back pain. However, in the etiology of IVDD, the specific method by which nucleus pulposus (NP) cell senescence and the immune response induce disease is uncertain.

METHODS

Gene Expression Omnibus database was used to find differentially expressed genes (DEGs), differentially expressed miRNAs (DE miRNAs), differentially expressed lncRNAs (DE lncRNAs), and differentially expressed circRNAs (DE circRNAs). Functional enrichment analysis was performed through Enrichr database. Potential regulatory miRNAs, lncRNAs and circRNAs of mRNAs were predicted by ENCORI and circBank, respectively.

RESULTS

We identified 198 upregulated and 131 downregulated genes, 39 upregulated and 22 downregulated miRNAs, 2152 upregulated and 564 downregulated lncRNAs, and 352 upregulated and 279 downregulated circRNAs as DEGs, DE miRNAs, DE lncRNAs, DE circRNAs, respectively. Functional enrichment analysis revealed that they were significantly enriched in Toll-like receptor signaling route and the NF-kappa B signaling pathway. An mRNA-miRNA-lncRNA/circRNA network linked to the pathogenesis of NP cells in IVDD was constructed based on node degree and differential expression level. Eight immune-related DEGs (6 upregulated and 2 downregulated genes) and five aging-related DEGs (3 upregulated and 2 downregulated genes) were identified in the critical network.

CONCLUSION

We established a novel immune-related and aging-related triple regulatory network of mRNA-miRNA-lncRNA/circRNA ceRNA, among which all RNAs may be utilized as the pathogenesis biomarker of NP cells in IVDD.

CB-0821, a novel CC chemokine receptor 5 (CCR5) inhibitor with improved binding efficacy proposed as anti-HIV candidate: Computational and in vitro approach

The CC chemokine receptor 5 (CCR5) serves a pivotal role in human immunodeficiency virus 1 (HIV-1) infection by acting as a co-receptor and facilitating the binding of the viral envelope glycoprotein (env). Maraviroc (MVC), a Food and Drug Administration-approved monocarboxylic acid amide, is one of the CCR5 inhibitors employed in HIV treatment. Despite the existence of approved drugs, the emergence of drug resistance underscores the necessity for novel compounds to combat resistance and enhance therapeutic efficacy. In this study, CB-0821, identified from the ChemBridge library, emerged as a promising CCR5 inhibitor. Molecular dynamics simulations indicate comparable dynamic properties for CB-0821 and MVC. In silico comparisons with other CCR5 inhibitors emphasize CB-0821's superior binding affinity, positioning it as a potential lead compound. Evaluations of the dissociation constant (Ki) and absorption, distribution, metabolism, and excretion predictions suggest CB-0821 as a well-tolerated drug. Furthermore, the dose-dependent inhibition of CCR5 by CB-0821 in Peripheral blood mononuclear cells (PBMCs) (ranging from 10 to 200 nM) demonstrates efficacy, coupled with nontoxicity to Vero cells at concentrations up to 500 nM. These results underscore the potential of CB-0821 in HIV antiviral therapy, calling for additional preclinical validations before advancing to clinical considerations.