Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities.

Analysis of the expression level and predictive value of CLEC16A|miR-654-5p|RARA regulatory axis in the peripheral blood of patients with ischemic stroke based on biosignature analysis

Introduction

Ischemic stroke (IS) is a cerebrovascular disease that can be disabling and fatal, and there are limitations in the clinical treatment and prognosis of IS. It has been reported that changes in the expression profile of circRNAs have been found during injury in ischemic stroke, and circRNAs play an important role in the IS cascade response. However, the specific mechanisms involved in the pathogenesis of IS are not yet fully understood, and thus in-depth studies are needed.

Methods

In this study, one circRNA dataset (GSE161913), one miRNA dataset (GSE60319) and one mRNA dataset (GSE180470) were retrieved from the Gene Expression Omnibus (GEO) database and included, and the datasets were differentially expressed analyzed by GEO2R and easyGEO to get the DEcircRNA, DEmiRNA and DEmRNA, and DEmRNA was enriched using ImageGP, binding sites were predicted in the ENCORI database, respectively, and the competitive endogenous RNA (ceRNA) regulatory network was visualized by the cytoscape software, and then selected by MCC scoring in the cytoHubba plugin Hub genes. In addition, this study conducted a case-control study in which blood samples were collected from stroke patients and healthy medical examiners to validate the core network of ceRNAs constructed by biosignature analysis by real-time fluorescence quantitative qRT-PCR experiments.

Results

A total of 233 DEcircRNAs, 132 DEmiRNAs and 72 DEmRNAs were screened by bioinformatics analysis. circRNA-mediated ceRNA regulatory network was constructed, including 148 circRNAs, 43 miRNAs and 44 mRNAs. Finally, CLEC16A|miR-654-5p|RARA competitive endogenous regulatory axis was selected for validation by qRT-PCR, and the validation results were consistent with the bioinformatics analysis.

Discussion

In conclusion, the present study establishes a new axis of regulation associated with IS, providing new insights into the pathogenesis of IS.

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.

Small molecule drug discovery for glioblastoma treatment based on bioinformatics and cheminformatics approaches

Background: Glioblastoma (GBM) is a common and highly aggressive brain tumor with a poor prognosis for patients. It is urgently needed to identify potential small molecule drugs that specifically target key genes associated with GBM development and prognosis. Methods: Differentially expressed genes (DEGs) between GBM and normal tissues were obtained by data mining the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Gene function annotation was performed to investigate the potential functions of the DEGs. A protein-protein interaction (PPI) network was constructed to explore hub genes associated with GBM. Bioinformatics analysis was used to screen the potential therapeutic and prognostic genes. Finally, potential small molecule drugs were predicted using the DGIdb database and verified using chemical informatics methods including absorption, distribution, metabolism, excretion, toxicity (ADMET), and molecular docking studies. Results: A total of 429 DEGs were identified, of which 19 hub genes were obtained through PPI analysis. The hub genes were confirmed as potential therapeutic targets by functional enrichment and mRNA expression. Survival analysis and protein expression confirmed centromere protein A (CENPA) as a prognostic target in GBM. Four small molecule drugs were predicted for the treatment of GBM. Conclusion: Our study suggests some promising potential therapeutic targets and small molecule drugs for the treatment of GBM, providing new ideas for further research and targeted drug development.

Investigating the Role of SNAI1 and ZEB1 Expression in Prostate Cancer Progression and Immune Modulation of the Tumor Microenvironment

Prostate cancer (PCa) is an immunologically cold tumor and the molecular processes that underlie this behavior are poorly understood. In this study, we investigated a primary cohort of intermediate-risk PCa (n = 51) using two NanoString profiling panels designed to study cancer progression and immune response. We identified differentially expressed genes (DEGs) and pathways associated with biochemical recurrence (BCR) and clinical risk. Confirmatory analysis was performed using the TCGA-PRAD cohort. Noteworthy DEGs included collagens such as COL1A1, COL1A2, and COL3A1. Changes in the distribution of collagens may influence the immune activity in the tumor microenvironment (TME). In addition, immune-related DEGs such as THY1, IRF5, and HLA-DRA were also identified. Enrichment analysis highlighted pathways such as those associated with angiogenesis, TGF-beta, UV response, and EMT. Among the 39 significant DEGs, 11 (28%) were identified as EMT target genes for ZEB1 using the Harmonizome database. Elevated ZEB1 expression correlated with reduced BCR risk. Immune landscape analysis revealed that ZEB1 was associated with increased immunosuppressive cell types in the TME, such as naïve B cells and M2 macrophages. Increased expression of both ZEB1 and SNAI1 was associated with elevated immune checkpoint expression. In the future, modulation of EMT could be beneficial for overcoming immunotherapy resistance in a cold tumor, such as PCa.

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.

Assessing Engraftment Following Fecal Microbiota Transplant

Fecal Microbiota Transplant (FMT) is an FDA approved treatment for recurrent Clostridium difficile infections, and is being explored for other clinical applications, from alleviating digestive and neurological disorders, to priming the microbiome for cancer treatment, and restoring microbiomes impacted by cancer treatment. Quantifying the extent of engraftment following an FMT is important in determining if a recipient didn't respond because the engrafted microbiome didn't produce the desired outcomes (a successful FMT, but negative treatment outcome), or the microbiome didn't engraft (an unsuccessful FMT and negative treatment outcome). The lack of a consistent methodology for quantifying FMT engraftment extent hinders the assessment of FMT success and its relation to clinical outcomes, and presents challenges for comparing FMT results and protocols across studies. Here we review 46 studies of FMT in humans and model organisms and group their approaches for assessing the extent to which an FMT engrafts into three criteria: 1) Chimeric Asymmetric Community Coalescence investigates microbiome shifts following FMT engraftment using methods such as alpha diversity comparisons, beta diversity comparisons, and microbiome source tracking. 2) Donated Microbiome Indicator Features tracks donated microbiome features (e.g., amplicon sequence variants or species of interest) as a signal of engraftment with methods such as differential abundance testing based on the current sample collection, or tracking changes in feature abundances that have been previously identified (e.g., from FMT or disease-relevant literature). 3) Temporal Stability examines how resistant post-FMT recipient's microbiomes are to reverting back to their baseline microbiome. Individually, these criteria each highlight a critical aspect of microbiome engraftment; investigated together, however, they provide a clearer assessment of microbiome engraftment. We discuss the pros and cons of each of these criteria, providing illustrative examples of their application. We also introduce key terminology and recommendations on how FMT studies can be analyzed for rigorous engraftment extent assessment.

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

Cylindrospermopsis raciborskii-dominated harmful algae blooms 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 results. Effects of these bacteria-algae interspecies interactions on the bloom event are discussed. All these results may provide new insights into the C. raciborskii-dominated blooms and how its interspecies relationships with other bacteria may influence the bloom events in eutrophic waters throughout the world.

Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont

Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.

Transcriptomic analysis identifies the shared diagnostic biomarkers and immune relationship between Atherosclerosis and abdominal aortic aneurysm based on fatty acid metabolism gene set

Background

Epidemiological research has demonstrated that there is a connection between lipid metabolism disorder and an increased risk of developing arteriosclerosis (AS) and abdominal aortic aneurysm (AAA). However, the precise relationship between lipid metabolism, AS, and AAA is still not fully understood. The objective of this study was to examine the pathways and potential fatty acid metabolism-related genes (FRGs) that are shared between AS and AAA.

Methods

AS- and AAA-associated datasets were retrieved from the Gene Expression Omnibus (GEO) database, and the limma package was utilized to identify differentially expressed FRGs (DFRGs) common to both AS and AAA patients. Functional enrichment analysis was conducted on the (DFRGs), and a protein-protein interaction (PPI) network was established. The selection of signature genes was performed through the utilization of least absolute shrinkage and selection operator (LASSO) regression and random forest (RF). Subsequently, a nomogram was developed using the results of the screening process, and the crucial genes were validated in two separate external datasets (GSE28829 and GSE17901) as well as clinical samples. In the end, single-sample gene set enrichment analysis (ssGSEA) was utilized to assess the immune cell patterns in both AS and AAA. Additionally, the correlation between key crosstalk genes and immune cell was evaluated.

Results

In comparison to control group, both AS and AAA patients exhibited a decrease in fatty acid metabolism score. We found 40 DFRGs overlapping in AS and AAA, with lipid and amino acid metabolism critical in their pathogenesis. PCBD1, ACADL, MGLL, BCKDHB, and IDH3G were identified as signature genes connecting AS and AAA. Their expression levels were confirmed in validation datasets and clinical samples. The analysis of immune infiltration showed that neutrophils, NK CD56dim cells, and Tem cells are important in AS and AAA development. Correlation analysis suggested that these signature genes may be involved in immune cell infiltration.

Conclusion

The fatty acid metabolism pathway appears to be linked to the development of both AS and AAA. Furthermore, PCBD1, ACADL, MGLL, BCKDHB, and IDH3G have the potential to serve as diagnostic markers for patients with AS complicated by AAA.

Exploring the roles and potential therapeutic strategies of inflammation and metabolism in the pathogenesis of vitiligo: a mendelian randomization and bioinformatics-based investigation

Introduction

Vitiligo, a common autoimmune acquired pigmentary skin disorder, poses challenges due to its unclear pathogenesis. Evidence suggests inflammation and metabolism's pivotal roles in its onset and progression. This study aims to elucidate the causal relationships between vitiligo and inflammatory proteins, immune cells, and metabolites, exploring bidirectional associations and potential drug targets.

Methods

Mendelian Randomization (MR) analysis encompassed 4,907 plasma proteins, 91 inflammatory proteins, 731 immune cell features, and 1400 metabolites. Bioinformatics analysis included Protein-Protein Interaction (PPI) network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Subnetwork discovery and hub protein identification utilized the Molecular Complex Detection (MCODE) plugin. Colocalization analysis and drug target exploration, including molecular docking validation, were performed.

Results

MR analysis identified 49 proteins, 39 immune cell features, and 59 metabolites causally related to vitiligo. Bioinformatics analysis revealed significant involvement in PPI, GO enrichment, and KEGG pathways. Subnetwork analysis identified six central proteins, with Interferon Regulatory Factor 3 (IRF3) exhibiting strong colocalization evidence. Molecular docking validated Piceatannol's binding to IRF3, indicating a stable interaction.

Conclusion

This study comprehensively elucidates inflammation, immune response, and metabolism's intricate involvement in vitiligo pathogenesis. Identified proteins and pathways offer potential therapeutic targets, with IRF3 emerging as a promising candidate. These findings deepen our understanding of vitiligo's etiology, informing future research and drug development endeavors.

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.

Corrigendum: Exploring the bi-directional relationship and shared genes between depression and stroke via NHANES and bioinformatic analysis

[This corrects the article DOI: 10.3389/fgene.2023.1004457.].

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.

Genetic prediction of causal association between serum bilirubin and hematologic malignancies: a two-sample Mendelian randomized and bioinformatics study

Introduction

An increasing number of cohort studies have shown a correlation between serum bilirubin and tumors, but no definitive causal relationship has been established between serum bilirubin and hematological malignancies.Therefore, the aim of the present study was to assess the causal relationship of serum bilirubin, including total bilirubin (TBIL) and direct bilirubin (DBIL), with hematological malignancies, including leukemia, lymphoma, and myeloma.

Methods

We used a genome-wide association study (GWAS) collection of TBIL, DBIL, and hematological malignancies data. Using two-sample Mendelian randomization(MR), we assessed the impact of TBIL and DBIL on hematological malignancies. For this study, the inverse variance weighting method (IVW) was the primary method of MR analysis. In the sensitivity analysis, the weighted median method, MR Egger regression, and MR-PRESSO test were used. To understand the mechanisms behind TBIL and DBIL, we used three different approaches based on screening single nucleotide polymorphisms (SNPs) and their associated genes, followed by bioinformatics analysis.

Results

The IVW test results showed evidence of effects of TBIL (odds ratio [OR]: 4.47, 95% confidence interval [CI]: 1.58-12.62) and DBIL (OR: 3.31, 95% CI: 1.08-10.18) on the risk of acute myeloid leukemia (AML).The findings from bioinformatics indicated that TBIL could potentially undergo xenobiotic metabolism through cytochrome P450 and contribute to chemical carcinogenesis.

Discussion

In this study, two-sample MR analysis revealed a causal relationship between TBIL, DBIL, and AML.

Corrigendum: Identification and validation of differentially expressed genes for targeted therapy in NSCLC using integrated bioinformatics analysis

[This corrects the article DOI: 10.3389/fonc.2023.1206768.].

Exploring research hotspots and future directions in neural tube defects field by bibliometric and bioinformatics analysis

Background

Neural tube defects (NTDs) is the most common birth defect of the central nervous system (CNS) which causes the death of almost 88,000 people every year around the world. Much efforts have been made to investigate the reasons that contribute to NTD and explore new ways to for prevention. We trawl the past decade (2013-2022) published records in order to get a worldwide view about NTDs research field.

Methods

7,437 records about NTDs were retrieved from the Web of Science (WOS) database. Tools such as shell scripts, VOSviewer, SCImago Graphica, CiteSpace and PubTator were used for data analysis and visualization.

Results

Over the past decade, the number of publications has maintained an upward trend, except for 2022. The United States is the country with the highest number of publications and also with the closest collaboration with other countries. Baylor College of Medicine has the closest collaboration with other institutions worldwide and also was the most prolific institution. In the field of NTDs, research focuses on molecular mechanisms such as genes and signaling pathways related to folate metabolism, neurogenic diseases caused by neural tube closure disorders such as myelomeningocele and spina bifida, and prevention and treatment such as folate supplementation and surgical procedures. Most NTDs related genes are related to development, cell projection parts, and molecular binding. These genes are mainly concentrated in cancer, Wnt, MAPK, PI3K-Akt and other signaling pathways. The distribution of NTDs related SNPs on chromosomes 1, 3, 5, 11, 14, and 17 are relatively concentrated, which may be associated with high-risk of NTDs.

Conclusion

Bibliometric analysis of the literature on NTDs field provided the current status, hotspots and future directions to some extant. Further bioinformatics analysis expanded our understanding of NTDs-related genes function and revealed some important SNP clusters and loci. This study provided some guidance for further studies. More extensive cooperation and further research are needed to overcome the ongoing challenge in pathogenesis, prevention and treatment of NTDs.

Unveiling the hub genes in the SIGLECs family in colon adenocarcinoma with machine learning

Background

Despite the recognized roles of Sialic acid-binding Ig-like lectins (SIGLECs) in endocytosis and immune regulation across cancers, their molecular intricacies in colon adenocarcinoma (COAD) are underexplored. Meanwhile, the complicated interactions between different SIGLECs are also crucial but open questions.

Methods

We investigate the correlation between SIGLECs and various properties, including cancer status, prognosis, clinical features, functional enrichment, immune cell abundances, immune checkpoints, pathways, etc. To fully understand the behavior of multiple SIGLECs' co-evolution and subtract its leading effect, we additionally apply three unsupervised machine learning algorithms, namely, Principal Component Analysis (PCA), Self-Organizing Maps (SOM), K-means, and two supervised learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO) and neural network (NN).

Results

We find significantly lower expression levels in COAD samples, together with a systematic enhancement in the correlations between distinct SIGLECs. We demonstrate SIGLEC14 significantly affects the Overall Survival (OS) according to the Hazzard ratio, while using PCA further enhances the sensitivity to both OS and Disease Free Interval (DFI). We find any single SIGLEC is uncorrelated to the cancer stages, which can be significantly improved by using PCA. We further identify SIGLEC-1,15 and CD22 as hub genes in COAD through Differentially Expressed Genes (DEGs), which is consistent with our PCA-identified key components PC-1,2,5 considering both the correlation with cancer status and immune cell abundance. As an extension, we use SOM for the visualization of the SIGLECs and show the similarities and differences between COAD patients. SOM can also help us define subsamples according to the SIGLECs status, with corresponding changes in both immune cells and cancer T-stage, for instance.

Conclusion

We conclude SIGLEC-1,15 and CD22 as the most promising hub genes in the SIGLECs family in treating COAD. PCA offers significant enhancement in the prognosis and clinical analyses, while using SOM further unveils the transition phases or potential subtypes of COAD.

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.

Analysis of weighted gene co-expression networks and clinical validation identify hub genes and immune cell infiltration in the endometrial cells of patients with recurrent implantation failure

Background

About 10% of individuals undergoing in vitro fertilization encounter recurrent implantation failure (RIF), which represents a worldwide social and economic concern. Nevertheless, the critical genes and genetic mechanisms underlying RIF are largely unknown.

Methods

We first obtained three comprehensive microarray datasets "GSE58144, GSE103465 and GSE111974". The differentially expressed genes (DEGs) evaluation, enrichment analysis, as well as efficient weighted gene co-expression network analysis (WGCNA), were employed for distinguishing RIF-linked hub genes, which were tested by RT-qPCR in our 30 independent samples. Next, we studied the topography of infiltration of 22 immune cell subpopulations and the association between hub genes and immune cells in RIF using the CIBERSORT algorithm. Finally, a novel ridge plot was utilized to exhibit the potential function of core genes.

Results

The enrichment of GO/KEGG pathways reveals that Herpes simplex virus 1 infection and Salmonella infection may have an important role in RIF. After WGCNA, the intersected genes with the previous DEGs were obtained using both variance and association. Notably, the subsequent nine hub genes were finally selected: ACTL6A, BECN1, SNRPD1, POLR1B, GSK3B, PPP2CA, RBBP7, PLK4, and RFC4, based on the PPI network and three different algorithms, whose expression patterns were also verified by RT-qPCR. With in-depth analysis, we speculated that key genes mentioned above might be involved in the RIF through disturbing endometrial microflora homeostasis, impairing autophagy, and inhibiting the proliferation of endometrium. Furthermore, the current study revealed the aberrant immune infiltration patterns and emphasized that uterine NK cells (uNK) and CD4+ T cells were substantially altered in RIF endometrium. Finally, the ridge plot displayed a clear and crucial association between hub genes and other genes and key pathways.

Conclusion

We first utilized WGCNA to identify the most potential nine hub genes which might be associated with RIF. Meanwhile, this study offers insights into the landscape of immune infiltration status to reveal the underlying immune pathogenesis of RIF. This may be a direction for the next study of RIF etiology. Further studies would be required to investigate the involved mechanisms.

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.

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.

Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes

Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU.

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.

Transcriptional and functional predictors of potato virus Y-induced tuber necrosis in potato (Solanum tuberosum)

Introduction

Potato (Solanum tuberosum L.), the fourth most important food crop in the world, is affected by several viral pathogens with potato virus Y (PVY) having the greatest economic impact. At least nine biologically distinct variants of PVY are known to infect potato. These include the relatively new recombinant types named PVY-NTN and PVYN-Wi, which induce tuber necrosis in susceptible cultivars. To date, the molecular plant-virus interactions underlying this pathogenicity have not been fully characterized. We hypothesized that this necrotic behavior is supported by transcriptional and functional signatures that are unique to PVY-NTN and PVYN-Wi.

Methods

To test this hypothesis, transcriptional responses of cv. Russet Burbank, a PVY susceptible cultivar, to three PVY strains PVY-O, PVY-NTN, and PVYN-Wi were studied using mRNA-Seq. A haploid-resolved genome assembly for tetraploid potato was used for bioinformatics analysis.

Results

The study revealed 36 GO terms and nine KEGG 24 pathways that overlapped across the three PVY strains, making them generic features of PVY susceptibility in potato. Ten GO terms and three KEGG pathways enriched for PVY-NTN and PVYN-Wi only, which made them candidate functional signatures associated with PVY-induced tuber necrosis in potato. In addition, five other pathways were enriched for PVYNTN or PVYN-Wi. One carbon pool by folate was enriched exclusively in response to PVY-NTN infection; PVYN-Wi infection specifically impacted cutin, suberine and wax biosynthesis, phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and monoterpenoid biosynthesis.

Discussion

Results suggest that PVYN-Wi-induced necrosis may be mechanistically distinguishable from that of PVY-NTN. Our study provides a basis for understanding the mechanism underlying the development of PVY-induced tuber necrosis in potato.

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.

Approaches for Mapping and Analysis of R-loops

R-loops are nucleic acid structures composed of a DNA:RNA hybrid with a displaced non-template single-stranded DNA. Current approaches to identify and map R-loop formation across the genome employ either an antibody targeted against R-loops (S9.6) or a catalytically inactivated form of RNase H1 (dRNH1), a nuclease that can bind and resolve DNA:RNA hybrids via RNA exonuclease activity. This overview article outlines several ways to map R-loops using either methodology, explaining the differences and similarities among the approaches. Bioinformatic analysis of R-loops involves several layers of quality control and processing before visualizing the data. This article provides resources and tools that can be used to accurately process R-loop mapping data and explains the advantages and disadvantages of the resources as compared to one another. © 2024 Wiley Periodicals LLC.

Mechanistic insights into targeting caspase-3 activation and alveolar macrophage pyroptosis by Ephedra and bitter almond compounds for treating pediatric pneumonia via network pharmacology and bioinformatics

This study investigates the molecular mechanism of Ma Huang-Ku Xing Ren, a traditional Chinese medicine formula, in treating pediatric pneumonia. The focus is on the regulation of caspase-3 activation and reduction of alveolar macrophage necrosis through network pharmacology and bioinformatics analyses of Ephedra and bitter almond components. Active compounds and targets from ephedrine and bitter almond were obtained using TCMSP, TCMID, and GeneCards databases, identifying pediatric pneumonia-related genes. A protein-protein interaction (PPI) network was constructed, and core targets were screened. GO and KEGG pathway enrichment analyses identified relevant genes and pathways. An acute pneumonia mouse model was created using the lipopolysaccharide (LPS) inhalation method, with caspase-3 overexpression induced by a lentivirus. The mice were treated with Ephedra and bitter almond through gastric lavage. Lung tissue damage, inflammatory markers (IL-18 and IL-1β), and cell death-related gene activation were assessed through H&E staining, ELISA, western blot, flow cytometry, and immunofluorescence. The study identified 128 active compounds and 121 gene targets from Ephedra and bitter almond. The PPI network revealed 13 core proteins, and pathway analysis indicated involvement in inflammation, apoptosis, and cell necrosis, particularly the caspase-3 pathway. In vivo results showed that Ephedra and bitter almond treatment significantly mitigated LPS-induced lung injury in mice, reducing lung injury scores and inflammatory marker levels. It also decreased caspase-3 activity and cell death in alveolar macrophages. In conclusion, the active ingredients of Ma Huang-Ku Xing Ren, particularly targeting caspase-3, may effectively treat pediatric pneumonia by reducing apoptosis in alveolar macrophages, as demonstrated by both network pharmacology, bioinformatics analyses, and experimental data.

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.

pXg: Comprehensive Identification of Noncanonical MHC-I-Associated Peptides From De Novo Peptide Sequencing Using RNA-Seq Reads

Discovering noncanonical peptides has been a common application of proteogenomics. Recent studies suggest that certain noncanonical peptides, known as noncanonical major histocompatibility complex-I (MHC-I)-associated peptides (ncMAPs), that bind to MHC-I may make good immunotherapeutic targets. De novo peptide sequencing is a great way to find ncMAPs since it can detect peptide sequences from their tandem mass spectra without using any sequence databases. However, this strategy has not been widely applied for ncMAP identification because there is not a good way to estimate its false-positive rates. In order to completely and accurately identify immunopeptides using de novo peptide sequencing, we describe a unique pipeline called proteomics X genomics. In contrast to current pipelines, it makes use of genomic data, RNA-Seq abundance and sequencing quality, in addition to proteomic features to increase the sensitivity and specificity of peptide identification. We show that the peptide-spectrum match quality and genetic traits have a clear relationship, showing that they can be utilized to evaluate peptide-spectrum matches. From 10 samples, we found 24,449 canonical MHC-I-associated peptides and 956 ncMAPs by using a target-decoy competition. Three hundred eighty-seven ncMAPs and 1611 canonical MHC-I-associated peptides were new identifications that had not yet been published. We discovered 11 ncMAPs produced from a squirrel monkey retrovirus in human cell lines in addition to the two ncMAPs originating from a complementarity determining region 3 in an antibody thanks to the unrestricted search space assumed by de novo sequencing. These entirely new identifications show that proteomics X genomics can make the most of de novo peptide sequencing's advantages and its potential use in the search for new immunotherapeutic targets.

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.

Transcriptome-based deep learning analysis identifies drug candidates targeting protein synthesis and autophagy for the treatment of muscle wasting disorder

Sarcopenia, the progressive decline in skeletal muscle mass and function, is observed in various conditions, including cancer and aging. The complex molecular biology of sarcopenia has posed challenges for the development of FDA-approved medications, which have mainly focused on dietary supplementation. Targeting a single gene may not be sufficient to address the broad range of processes involved in muscle loss. This study analyzed the gene expression signatures associated with cancer formation and 5-FU chemotherapy-induced muscle wasting. Our findings suggest that dimenhydrinate, a combination of 8-chlorotheophylline and diphenhydramine, is a potential therapeutic for sarcopenia. In vitro experiments demonstrated that dimenhydrinate promotes muscle progenitor cell proliferation through the phosphorylation of Nrf2 by 8-chlorotheophylline and promotes myotube formation through diphenhydramine-induced autophagy. Furthermore, in various in vivo sarcopenia models, dimenhydrinate induced rapid muscle tissue regeneration. It improved muscle regeneration in animals with Duchenne muscular dystrophy (DMD) and facilitated muscle and fat recovery in animals with chemotherapy-induced sarcopenia. As an FDA-approved drug, dimenhydrinate could be applied for sarcopenia treatment after a relatively short development period, providing hope for individuals suffering from this debilitating condition.

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.

Knockdown of TOP2A suppresses IL-17 signaling pathway and alleviates the progression of ulcerative colitis

BACKGROUND

Ulcerative colitis (UC) is a chronic inflammatory disease of the colonic mucosa, with a gradually increasing incidence. Therefore, it is necessary to actively seek targets for the treatment of UC.

METHODS

Common differentially expressed genes (DEGs) were screened from two microarray data sets related to UC. Protein-protein interaction network was constructed to find the hub genes. The UC mouse model and cell model were induced by dextran sulfate sodium (DSS). The pathological changes of colon tissue were observed by hematoxylin-eosin staining. Immunohistochemistry and immunofluorescence were performed to detect the expressions of Ki67 and Claudin-1. The performance of mice was observed by disease activity index (DAI). The effect of TOP2A on proliferation, inflammation, oxidative stress, and interleukin-17 (IL-17) signaling pathway in UC model was measured by cell counting kit-8, enzyme-linked immunosorbent assay, and western blot.

RESULTS

Through bioinformatics analysis, 295 common DEGs were screened, and the hub gene TOP2A was selected. In UC model, there was obvious inflammatory cell infiltration in the colon and less goblet cells, while si-TOP2A lessened it. More Ki67 positive cells and less Claudin-1 positive cells were observed in UC model mice. Furthermore, knockdown of TOP2A increased the body weight and colon length of UC mice, while the DAI was decreased. Through in vivo and in vitro experiments, knockdown of TOP2A also inhibited inflammation and IL-17 signaling pathway, and promoted proliferation in DSS-induced NCM460 cells.

CONCLUSION

Knockdown of TOP2A alleviated the progression of UC by suppressing inflammation and inhibited IL-17 signaling pathway.

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.