Network Pharmacology to Reveal the Mechanism of Fufang Banmao Capsule for Treating Unresectable Primary Liver Cancer and Clinical Data Validation.

INTRODUCTION: Fufang Banmao capsules (FFBM), a traditional Chinese medicine, has been used to treat primary liver cancer (PLC) for several years. However, the bioactive ingredients, and mechanism of FFBM for treating PLC remains unclear. Our objective is to utilize network pharmacology to investigate these aspects and subsequently validate their effectiveness through clinical data. MATERIALS AND METHODS: The FFBM ingredients were obtained from the HERB database and screened for bioactive ingredients using the SwissTargetPrediction database. The PharmMapper and GEO database were used to acquire targets and differentially expressed genes (DEGs) for FFBM and PLC, respectively. Common targets were identified using Venn diagrams, followed by enrichment and protein-protein interaction (PPI) analysis. Furthermore, the Cytoscape software was utilized to identify Hub genes and construct the ingredienttarget- pathway network. Subsequently, patients diagnosed with unresectable PLC who underwent transcatheter arterial chemoembolization (TACE) at our hospital between January 2008 and December 2019 were retrospectively collected. Finally, Cox analysis was conducted to reveal the role of FFBM in the treatment of unresectable PLC. RESULTS: FFBM had 232 targets, and PLC had 1582 DEGs. HSP90AA1 and SRC were identified as crucial targets. Alpha-santalol, glycyrrhizin, and morroniside were identified as the top three bioactive ingredients. Enrichment analysis revealed a significant connection between FFBM utilization for treating PLC and multiple pathways, such as chemical carcinogenesis, PI3K-AKT, Rap1, FoxO, MAPK, and VEGF pathway. Clinic data revealed that consuming FFBM significantly improved the prognosis of unresectable PLC with a hazard ratio of 0.69. CONCLUSION: Our study identified the bioactive ingredients of FFBM and its potential mechanisms for treating PLC. Additionally, we validated the effectiveness through clinical data.

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

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

Divergent molecular strategies drive evolutionary adaptation to competitive fitness in biofilm formation.

Biofilm is a group of heterogeneously structured and densely packed bacteria with limited access to nutrients and oxygen. These intrinsic features can allow a mono-species biofilm to diversify into polymorphic subpopulations, determining the overall community's adaptive capability to changing ecological niches. However, the specific biological functions underlying biofilm diversification and fitness adaptation are poorly demonstrated. Here, we launched and monitored the experimental evolution of Pseudomonas aeruginosa biofilms, finding that two divergent molecular trajectories were adopted for adaptation to higher competitive fitness in biofilm formation: one involved hijacking bacteriophage superinfection to aggressively inhibit kin competitors, whereas the other induced a subtle change in c-di-GMP signaling to gain a positional advantage via enhanced early biofilm adhesion. Bioinformatics analyses implicated that similar evolutionary strategies were prevalent among clinical P. aeruginosa strains, indicative of parallelism between natural and experimental evolution. Divergence in the molecular bases illustrated the adaptive values of genomic plasticity for gaining competitive fitness in biofilm formation. Finally, we demonstrated that these fitness-adaptive mutations reduced bacterial virulence. Our findings revealed how the mutations intrinsically generated from the biofilm environment influence the evolution of P. aeruginosa.

Identification of MAP1LC3A as a promising mitophagy-related gene in polycystic ovary syndrome.

Increasing evidence suggests that mitophagy is crucially involved in the progression of polycystic ovary syndrome (PCOS). Exploration of PCOS-specific biomarkers related to mitophagy is expected to provide critical insights into disease pathogenesis. In this study, we employed bioinformatic analyses and machine learning algorithms to determine novel biomarkers for PCOS that may be tied with mitophagy. A grand total of 12 differential expressed mitophagy-related genes (DE-MRGs) associated with PCOS were identified. TOMM5 and MAP1LC3A among the 12 DE-MRGs were recognized as potential marker genes by LASSO, RF and SVM-RFE algorithms. The area under the ROC curve (AUROC) of MAP1LC3A were all greater than 0.8 both in the training set and validation sets. The CIBERSORT analysis indicated a potential association between alterations in the immune microenvironment of PCOS individuals and MAP1LC3A expression. In addition, we found that MAP1LC3A was positively related to the testosterone levels of PCOS patients. Overall, MAP1LC3A was identified as optimal PCOS-specific biomarkers related to mitophagy. Our findings created a diagnostic strength and offered a perspective for investigating the mitophagy process in PCOS.

Homologous Delta-12 Fatty Acid Desaturase (FAD2) Genes Affect Gene Expression and Linoleic Acid Levels in Lentinula edodes under Heat Stress.

Delta-12 fatty acid desaturases (FAD2s) actively regulate stress responses and cell differentiation in living organisms. In this study, six homologous FAD2 genes were identified based on the genome sequence of Lentinula edodes. Then, the six FAD2 protein sequences were analyzed using bioinformatics tools, including ExPASy ProtParam, SignalP, TMHMM, and TargetP. These analyses were performed to predict the physical and chemical properties, signal peptides, and transmembrane and conserved domains of these proteins. The polypeptide sequences were aligned, and a maximum likelihood phylogenetic tree was constructed using MEGA 7.0 software to elucidate the phylogenetic relationships between homologous FAD2 sequences. The results demonstrated that the FAD2 proteins contained three conserved histidine-rich regions (HXXXH, HXXHH, and HXXHH), which included eight histidine residues. The linoleic acid content and FAD2 enzyme activity were further analyzed, and the levels in the mutagenic heat-tolerant strain 18N44 were lower than those in the wild-type strain 18. Interestingly, the expression levels of the FAD2-2 and FAD2-3 genes under heat stress in strain 18N44 were lower than those in strain 18. These findings indicated that FAD2-2 and FAD2-3 may play major roles in the synthesis of linoleic acid during heat stress.

Exploring the role of pyroptosis and immune infiltration in sepsis based on bioinformatic analysis.

PURPOSE: Sepsis is a disease that is typically treated in intensive care units with high mortality and morbidity. Pyroptosis is a newly identified type of programmed cell death and is characterized by inflammatory cytokine secretion. However, the role of pyroptosis in sepsis remains unclear. METHODS: GSE28750 and GSE134347 datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed pyroptosis genes (DEPGs) were identified between sepsis and healthy controls. Machine learning was used to further narrow the gene range. Receiver operating curves (ROC) were generated to estimate the diagnostic efficacy. Immune infiltration levels were estimated via single-sample gene set enrichment analysis (ssGSEA). A network database was used to predict the upstream transcription factors and miRNAs of DEPGs. Finally, the expression of the genes was validated by qRT-PCR between sepsis patients and healthy controls. RESULTS: We found that the pyroptosis pathway was enriched and activated in sepsis. 8 DEPGs were identified. A heatmap showed that the genes, NLRC4, NAIP, IL-18, AIM2 and ELANE, were abundant in the sepsis samples, and the genes, NLRP1, CHMP7 and TP53, were abundant in the healthy control samples. The ssGSEA results showed that the abundances of activated dendritic cells, MDSC, macrophage, plasmacytoid dendritic cells, regulatory T-cells, and Th17-cells were significantly higher, while the activated B-cell, activated CD8 T-cell, CD56 dim tural killer cell, immature B-cell, monocyte, and T follicular helper cell abundances were lower in sepsis samples compared to healthy controls. The qRT-PCR results showed that the expression levels of NAIP, IL-18, TP53, CHMP7, NLRC4, ELANE and NLRP1 were consistant with the bioinformatic analyses, while the expression level of AIM2 has no significant difference. CONCLUSION: Our study identified seven potential pyroptosis-related genes, NAIP, IL-18, TP53, CHMP7, NLRC4, ELANE and NLRP1. This study revealed that pyroptosis may promote sepsis development by activating the immune response.

Investigation on the Potential Functions of ZmEPF/EPFL Family Members in Response to Abiotic Stress in Maize.

Maize is an important crop used for food, feed, and fuel. Abiotic stress is an important factor affecting maize yield. The EPF/EPFL gene family encodes class-specific secretory proteins that play an important role in the response to abiotic stress in plants. In order to explore and utilize the EPF/EPFL family in maize, the family members were systematically identified, and their chromosomal localization, physicochemical properties, cis-acting element prediction in promoters, phylogenetic tree construction, and expression pattern analysis were carried out using bioinformatics techniques. A total of 18 ZmEPF/EPFL proteins were identified in maize, which are mostly alkaline and a small portion acidic. Subcellular localization results showed that ZmEPF6, ZmEPF12, and ZmEPFL2 are localized in the nucleus and cytoplasm. Analysis of cis-acting elements revealed that members of the ZmEPF/EPFL family contain regulatory elements such as light response, anoxic, low temperature, and hormone response regulatory elements. RT-qPCR results showed that these family members are indeed responding to cold stress and hormone treatments. These results of this study provide a theoretical basis for improving the abiotic stress resistance of maize in future research.

Revealing the novel metabolism-related genes in the ossification of the ligamentum flavum based on whole transcriptomic data.

Backgrounds: The ossification of the ligamentum flavum (OLF) is one of the major causes of thoracic myelopathy. Previous studies indicated there might be a potential link between metabolic disorder and pathogenesis of OLF. The aim of this study was to determine the potential role of metabolic disorder in the pathogenesis of OLF using the strict bioinformatic workflow for metabolism-related genes and experimental validation. Methods: A series of bioinformatic approaches based on metabolism-related genes were conducted to compare the metabolism score between OLF tissues and normal ligamentum flavum (LF) tissues using the single sample gene set enrichment analysis. The OLF-related and metabolism-related differentially expressed genes (OMDEGs) were screened out, and the biological functions of OMDEGs were explored, including the Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and protein-protein interaction. The competing endogenous RNA (ceRNA) network based on pairs of miRNA-hub OMDEGs was constructed. The correlation analysis was conducted to explore the potential relationship between metabolic disorder and immunity abnormality in OLF. In the end, the cell experiments were performed to validate the roles of GBE1 and TNF-α in the osteogenic differentiation of LF cells. Results: There was a significant difference of metabolism score between OLF tissues and normal LF tissues. Forty-nine OMDEGs were screened out and their biological functions were determined. The ceRNA network containing three hub OMDEGs and five differentially expressed miRNAs (DEmiRNAs) was built. The correlation analysis between hub OMDEGs and OLF-related infiltrating immune cells indicated that metabolic disorder might contribute to the OLF via altering the local immune status of LF tissues. The cell experiments determined the important roles of GBE1 expression and TNF-α in the osteogenic differentiation of LF cells. Conclusions: This research, for the first time, preliminarily illustrated the vital role of metabolic disorder in the pathogenesis of OLF using strict bioinformatic algorithms and experimental validation for metabolism-related genes, which could provide new insights for investigating disease mechanism and screening effective therapeutic targets of OLF in the future.

The "Dry-Lab" Side of Food Authentication: Benchmark of Bioinformatic Pipelines for the Analysis of Metabarcoding Data.

Next Generation Sequencing Technologies (NGS), particularly metabarcoding, are valuable tools for authenticating foodstuffs and detecting eventual fraudulent practices such as species substitution. This technique, mostly used for the analysis of prokaryotes in several environments (including food), is in fact increasingly applied to identify eukaryotes (e.g., fish, mammals, avian, etc.) in multispecies food products. Besides the "wet-lab" procedures (e.g., DNA extraction, PCR, amplicon purification, etc.), the metabarcoding workflow includes a final "dry-lab" phase in which sequencing data are analyzed using a bioinformatic pipeline (BP). BPs play a crucial role in the accuracy, reliability, and interpretability of the metabarcoding results. Choosing the most suitable BP for the analysis of metabarcoding data could be challenging because it might require greater informatics skills than those needed in standard molecular analysis. To date, studies comparing BPs for metabarcoding data analysis in foodstuff authentication are scarce. In this study, we compared the data obtained from two previous studies in which fish burgers and insect-based products were authenticated using a customizable, ASV-based, and command-line interface BP (BP1) by analyzing the same data with a customizable but OTU-based and graphical user interface BP (BP2). The final sample compositions were compared statistically. No significant difference in sample compositions was highlighted by applying BP1 and BP2. However, BP1 was considered as more user-friendly than BP2 with respect to data analysis streamlining, cost of analysis, and computational time consumption. This study can provide useful information for researchers approaching the bioinformatic analysis of metabarcoding data for the first time. In the field of food authentication, an effective and efficient use of BPs could be especially useful in the context of official controls performed by the Competent Authorities and companies' self-control in order to detect species substitution and counterfeit frauds.

Construction of a multi-tissue compound-target interaction network of Qingfei Paidu decoction in COVID-19 treatment based on deep learning and transcriptomic analysis.

The Qingfei Paidu decoction (QFPDD) is a widely acclaimed therapeutic formula employed nationwide for the clinical management of coronavirus disease 2019 (COVID-19). QFPDD exerts a synergistic therapeutic effect, characterized by its multi-component, multi-target, and multi-pathway action. However, the intricate interactions among the ingredients and targets within QFPDD and their systematic effects in multiple tissues remain undetermined. To address this, we qualitatively characterized the chemical components of QFPDD. We integrated multi-tissue transcriptomic analysis with GraphDTA, a deep learning model, to screen for potential compound-target interactions of QFPDD in multiple tissues. We predicted 13 key active compounds, 127 potential targets and 27 pathways associated with QFPDD across six different tissues. Notably, oleanolic acid-AXL exhibited leading affinity in the heart, blood, and liver. Molecular docking and molecular dynamics simulation confirmed their strong binding affinity. The robust interaction between oleanolic acid and the AXL receptor suggests that AXL is a promising target for developing clinical intervention strategies. Through the construction of a multi-tissue compound-target interaction network, our study further elucidated the mechanisms through which QFPDD effectively combats COVID-19 in multiple tissues. Our work also establishes a framework for future investigations into the systemic effects of other Traditional Chinese Medicine (TCM) formulas in disease treatment.

iTRAQ proteomics analysis of placental tissue with gestational diabetes mellitus.

BACKGROUND: Gestational diabetes mellitus is an endocrine and metabolic disorder that appears for the first time during pregnancy and causes varying degrees of short- and/or long-term effects on the mother and child. The etiology of the disease is currently unknown and isobaric tags for relative and absolute quantitation proteomics approach, the present study attempted to identify potential proteins in placental tissues that may be involved in the pathogenesis of GDM and adverse foetal pregnancy outcomes. METHODS: Pregnant women with GDM hospitalised were selected as the experimental group, and pregnant women with normal glucose metabolism as the control group. The iTRAQ protein quantification technology was used to screen the differentially expressed proteins between the GDM group and the normal control group, and the differentially expressed proteins were analysed by GO, KEGG, PPI, etc., and the key proteins were subsequently verified by western blot. RESULTS: Based on the proteomics of iTRAQ, we experimented with three different samples of placental tissues from GDM and normal pregnant women, and the total number of identified proteins were 5906, 5959, and 6017, respectively, which were similar in the three different samples, indicating that the results were reliable. Through the Wayne diagram, we found that the total number of proteins coexisting in the three groups was 4475, and 91 differential proteins that could meet the quantification criteria were strictly screened, of which 32 proteins were up-regulated and 59 proteins were down-regulated. By GO enrichment analysis, these differential proteins are widely distributed in extracellular membrane-bounded organelle, mainly in extracellular exosome, followed by intracellular vesicle, extracellular organelle. It not only undertakes protein binding, protein complex binding, macromolecular complex binding, but also involves molecular biological functions such as neutrophil degranulation, multicellular organismal process, developmental process, cellular component organization, secretion, regulated exocytosis. Through the analysis of the KEGG signaling pathway, it is found that these differential proteins are mainly involved in HIF-1 signaling pathway, Glycolysis/Gluconeogenesis, Central carbon metabolism in cancer, AMPK signaling pathway, Proteoglycans in cancer, Protein processing in endoplasmic reticulum, Thyroid cancer, Alcoholism, Glucagon signaling pathway. DISCUSSION: This preliminary study helps us to understand the changes in the placental proteome of GDM patients, and provides new insights into the pathophysiology of GDM.

Identification of ibuprofen targeting CXCR family members to alleviate metabolic disturbance in lipodystrophy based on bioinformatics and in vivo experimental verification.

Background: Lipodystrophy is a rare disease that is poorly diagnosed due to its low prevalence and frequent phenotypic heterogeneity. The main therapeutic measures for patients with clinical lipodystrophy are aimed at improving general metabolic complications such as diabetes mellitus, insulin resistance, and hypertriglyceridemia. Therefore, there is an urgent need to find new biomarkers to aid in the diagnosis and targeted treatment of patients with congenital generalized lipodystrophy (CGL). Methods: Dataset GSE159337 was obtained via the Gene Expression Omnibus database. First, differentially expressed genes (DEGs) between CGL and control samples were yielded via differential expression analysis and were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment to explore the functional pathways. Next, protein-protein interaction analysis and the MCC algorithm were implemented to yield candidate genes, which were then subjected to receiver operating characteristic (ROC) analysis to identify biomarkers with an area under the curve value exceeding 0.8. Moreover, random forest (RF), logistic regression, and support vector machine (SVM) analyses were carried out to assess the diagnostic ability of biomarkers for CGL. Finally, the small-molecule drugs targeting biomarkers were predicted, and ibuprofen was further validated in lipodystrophy mice. Results: A total of 71 DEGs in GSE159337 were sifted out and were involved in immune receptor activity, immune response-regulating signaling pathway, and secretory granule membrane. Moreover, CXCR2, TNFSF10, NLRC4, CCR2, CEACAM3, TLR10, TNFAIP3, and JUN were considered as biomarkers by performing ROC analysis on 10 candidate genes. Meanwhile, RF, logistic regression, and SVM analyses further described that those biomarkers had an excellent diagnosis capability for CGL. Eventually, the drug-gene network included ibuprofen-CXCR1, ibuprofen-CXCR1, cenicriviroc-CCR2, fenofibrate-JUN, and other relationship pairs. Ibuprofen treatment was also validated to downregulate CXCR1 and CXCR2 in peripheral blood mononuclear cells (PBMCs) and improve glucose tolerance, hypertriglyceridemia, hepatic steatosis, and liver inflammation in lipodystrophy mice. Conclusion: Eight biomarkers, namely, CXCR2, TNFSF10, NLRC4, CCR2, CEACAM3, TLR10, TNFAIP3, and JUN, were identified through bioinformatic analyses, and ibuprofen targeting CXCR1 and CXCR2 in PBMCs was shown to improve metabolic disturbance in lipodystrophy, contributing to studies related to the diagnosis and treatment of lipodystrophy.

Novel insights into STIM1's role in store-operated calcium entry and its implications for T-cell mediated inflammation in trigeminal neuralgia.

This investigation aims to elucidate the novel role of Stromal Interaction Molecule 1 (STIM1) in modulating store-operated calcium entry (SOCE) and its subsequent impact on inflammatory cytokine release in T lymphocytes, thereby advancing our understanding of trigeminal neuralgia (TN) pathogenesis. Employing the Gene Expression Omnibus (GEO) database, we extracted microarray data pertinent to TN to identify differentially expressed genes (DEGs). A subsequent comparison with SOCE-related genes from the Genecards database helped pinpoint potential target genes. The STRING database facilitated protein-protein interaction (PPI) analysis to spotlight STIM1 as a gene of interest in TN. Through histological staining, transmission electron microscopy (TEM), and behavioral assessments, we probed STIM1's pathological effects on TN in rat models. Additionally, we examined STIM1's influence on the SOCE pathway in trigeminal ganglion cells using techniques like calcium content measurement, patch clamp electrophysiology, and STIM1- ORAI1 co-localization studies. Changes in the expression of inflammatory markers (TNF-α, IL-1ß, IL-6) in T cells were quantified using Western blot (WB) and enzyme-linked immunosorbent assay (ELISA) in vitro, while immunohistochemistry and flow cytometry were applied in vivo to assess these cytokines and T cell count alterations. Our bioinformatic approach highlighted STIM1's significant overexpression in TN patients, underscoring its pivotal role in TN's etiology and progression. Experimental findings from both in vitro and in vivo studies corroborated STIM1's regulatory influence on the SOCE pathway. Furthermore, STIM1 was shown to mediate SOCE-induced inflammatory cytokine release in T lymphocytes, a critical factor in TN development. Supportive evidence from histological, ultrastructural, and behavioral analyses reinforced the link between STIM1-mediated SOCE and T lymphocyte-driven inflammation in TN pathogenesis. This study presents novel evidence that STIM1 is a key regulator of SOCE and inflammatory cytokine release in T lymphocytes, contributing significantly to the pathogenesis of trigeminal neuralgia. Our findings not only deepen the understanding of TN's molecular underpinnings but also potentially open new avenues for targeted therapeutic strategies.

Validating the temporal performance of genetic biomarkers in an animal model of recurrence/ non-recurrence myocardial infarction persuades by bioinformatics tools.

With a global towering prevalence of index acute myocardial infarction (nonrecurrent MI, NR-MI), a high incidence of recurrent MI (R-MI) has emerged in recent decades. Despite the extensive occurrence, the promising predictors of R-MI have been elusive within the cohort of survivors. This study investigates and validates the involvement of distinct gene expressions in R-MI and NR-MI. Bioinformatics tools were used to identify DEGs from the GEO dataset, functional annotation, pathway enrichment analysis, and the PPI network analysis to find hub genes. The validation of proposed genes was conceded by qRT-PCR and Western Blot analysis in experimentally induced NR-MI and R-MI models on a temporal basis. The temporal findings based on RT-PCR consequences reveal a significant and constant upregulation of the UBE2N in the NR-MI model out of the proposed three DEGs (UBE2N, UBB, and TMEM189), while no expression was reported in the R-MI model. Additionally, the proteomics study proposed five DEGs (IL2RB, NKG7, GZMH, CXCR6, and GZMK) for the R-MI model since IL2RB was spotted for significant and persistent downregulation with different time points. Further, Western Blot analysis validated these target genes' expressions temporally. I/R-induced NR-MI and R-MI models were confirmed by the biochemical parameters (CKMB, LDH, cTnI, serum nitrite/nitrate concentration, and inflammatory cytokines) and histological assessments of myocardial tissue. These results underscore the importance of understanding genetic mechanisms underlying MI and highlight the potential of UBE2N and IL2RB as biomarkers for non-recurrent and recurrent MI, respectively.

Identification and experimental validation of m7G-related molecular subtypes, immune signature, and feature genes in Alzheimer's disease.

Background: Studies has shown that N7-methylguanosine (m7G) modification plays a critical role in neurological diseases. However, the exact role and association of m7G with the immune microenvironment in Alzheimer's disease (AD) remain largely unknown and unexplored. Methods: The study datasets comprised 667 AD samples and 503 control samples selected from eight datasets in the Gene Expression Omnibus database; m7G regulator genes were obtained from previous literature. The AD subtypes were identified by consensus clustering analysis according to m7G regulator genes. The clinical characteristics, immune infiltration, and biological functions of the AD subgroups were evaluated. A combination of different types of machine-learning algorithms were used for the identification of AD genes. We also assessed and validated the diagnostic performance of the identified genes via qRT-PCR, immunofluorescence, and immunohistochemical analyses. Results: Two AD distinct subgroups, namely cluster A and cluster B, were identified. Cluster A had poor pathological progression and immune infiltration, representing a high-risk subgroup for AD. The differentially expressed genes of cluster A were enriched in immune and synapse-related pathways, suggesting that these genes probably contribute to AD progression by regulating immune-related pathways. Additionally, five feature genes (AEBP1, CARTPT, AK5, NPTX2, and COPG2IT1) were identified, which were used to construct a nomogram model with good ability to predict AD. The animal experiment analyses further confirmed that these feature genes were associated with AD development. Conclusion: To the best of our knowledge, this is the first study to reveal close correlations among m7G RNA modification, the immune microenvironment, and the pathogenesis of AD. We also identified five feature genes associated with AD, further contributing to our understanding of the underlying mechanisms and potential therapeutic targets for AD.

Putative Pharmacological Depression and Anxiety-Related Targets of Calcitriol Explored by Network Pharmacology and Molecular Docking.

Depression and anxiety disorders, prevalent neuropsychiatric conditions that frequently coexist, limit psychosocial functioning and, consequently, the individual's quality of life. Since the pharmacological treatment of these disorders has several limitations, the search for effective and secure antidepressant and anxiolytic compounds is welcome. Vitamin D has been shown to exhibit neuroprotective, antidepressant, and anxiolytic properties. Therefore, this study aimed to explore new molecular targets of calcitriol, the active form of vitamin D, through integrated bioinformatic analysis. Calcitriol targets were predicted in SwissTargetPrediction server (2019 version). The disease targets were collected by the GeneCards database searching the keywords "depression" and "anxiety". Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the intersections of targets. Network analyses were carried out using GeneMania server (2023 version) and Cytoscape (V. 3.9.1.) software. Molecular docking predicted the main targets of the network and Ligplot predicted the main intermolecular interactions. Our study showed that calcitriol may interact with multiple targets. The main targets found are the vitamin D receptor (VDR), histamine H3 receptor (H3R), endocannabinoid receptors 1 and 2 (CB1 and CB2), nuclear receptor NR1H3, patched-1 (PTCH1) protein, opioid receptor NOP, and phosphodiesterase enzymes PDE3A and PDE5A. Considering the role of these targets in the pathophysiology of depression and anxiety, our findings suggest novel putative mechanisms of action of vitamin D as well as new promising molecular targets whose role in these disorders deserves further investigation.

Advanced Analysis and Validation of a microRNA Signature for Fanconi Anemia.

Some years ago, we reported the generation of a Fanconi anemia (FA) microRNA signature. This study aims to develop an analytical strategy to select a smaller and more reliable set of molecules that could be tested for potential benefits for the FA phenotype, elucidate its biochemical and molecular mechanisms, address experimental activity, and evaluate its possible impact on FA therapy. In silico analyses of the data obtained in the original study were thoroughly processed and anenrichment analysis was employed to identify the classes of genes that are over-represented in the FA-miRNA population under study. Primary bone marrow mononuclear cells (MNCs) from sixFA patients and sixhealthy donors as control samples were employed in the study. RNAs containing the small RNA fractions were reverse-transcribed and real-time PCR was performed in triplicate using the specific primers. Experiments were performed in triplicate.The in-silico analysis reported six miRNAs as likely contributors to the complex pathological spectrum of FA. Among these, three miRNAs were validated by real-time PCR. Primary bone marrow mononuclear cells (MNCs) reported a significant reduction in the expression level of miRNA-1246 and miRNA-206 in the FA samples in comparison to controls.This study highlights several biochemical pathways as culprits in the phenotypic manifestations and the pathophysiological mechanisms acting in FA. A relatively low number of miRNAs appear involved in all these different phenotypes, demonstrating the extreme plasticity of the gene expression modulation. This study further highlights miR-206 as a pivotal player in regulatory functions and signaling in the bone marrow mesenchymal stem cell (BMSC) process in FA. Due to this evidence, the activity of miR-206 in FA deserves specific experimental scrutiny. The results, here presented, might be relevant in the management of FA.

Identification of Hub Genes for Psoriasis and Cancer by Bioinformatic Analysis.

Psoriasis increases the risk of developing various cancers, including colon cancer. The pathogenesis of the co-occurrence of psoriasis and cancer is not yet clear. This study is aimed at analyzing the pathogenesis of psoriasis combined with cancer by bioinformatic analysis. Skin tissue data from psoriasis (GSE117239) and intestinal tissue data from colon cancer (GSE44076) were downloaded from the GEO database. One thousand two hundred ninety-six common differentially expressed genes and 688 common shared genes for psoriasis and colon cancer were determined, respectively, using the limma R package and weighted gene coexpression network analysis (WGCNA) methods. The results of the GO and KEGG enrichment analyses were mainly related to the biological processes of the cell cycle. Thirteen hub genes were selected, including AURKA, DLGAP5, NCAPG, CCNB1, NDC80, BUB1B, TTK, CCNB2, AURKB, TOP2A, ASPM, BUB1, and KIF20A. These hub genes have high diagnostic value, and most of them are positively correlated with activated CD4 T cells. Three hub transcription factors (TFs) were also predicted: E2F1, E2F3, and BRCA1. These hub genes and hub TFs are highly expressed in various cancers. Furthermore, 251 drugs were predicted, and some of them overlap with existing therapeutic drugs for psoriasis or colon cancer. This study revealed some genetic mechanisms of psoriasis and cancer by bioinformatic analysis. These hub genes, hub TFs, and predicted drugs may provide new perspectives for further research on the mechanism and treatment.

Genomic insights of a native bacterial consortium for wheat production sustainability.

The use of plant growth-promoting bacteria as bioinoculants is a powerful tool to increase crop yield and quality and to improve nitrogen use efficiency (NUE) from fertilizers in plants. This study aimed to bioprospecting a native bacterial consortium (Bacillus cabrialesii subsp. cabrialesii TE3T, Priestia megaterium TRQ8, and Bacillus paralicheniformis TRQ65), through bioinformatic analysis, and to quantify the impact of its inoculation on NUE (measured through 15N-isotopic techniques), grain yield, and grain quality of durum wheat variety CIRNO C2008 grown under three doses of urea (0, 120, and 240 kg N ha-1) during two consecutive agricultural cycles in the Yaqui Valley, Mexico. The inoculation of the bacterial consortium (BC) to the wheat crop, at a total N concentration of 123-225 kg N ha-1 increased crop productivity and maintained grain quality, resulting in a yield increase of 1.1 ton ha-1 (6.0 vs. 7.1 ton ha-1, 0 kg N ha-1 added, 123 kg N ha-1 in the soil) and of 2.0 ton ha-1 (5.9 vs. 7.9 ton ha-1, 120 kg N ha-1 added, 104 kg N ha-1 in the soil) compared to the uninoculated controls at the same doses of N. The genomic bioinformatic analysis of the studied strains showed a great number of biofertilization-related genes regarding N and Fe acquisition, P assimilation, CO2 fixation, Fe, P, and K solubilization, with important roles in agroecosystems, as well as genes related to the production of siderophores and stress response. A positive effect of the BC on NUE at the studied initial N content (123 and 104 kg N ha-1) was not observed. Nevertheless, increases of 14 % and 12.5 % on NUE (whole plant) were observed when 120 kg N ha-1 was applied compared to when wheat was fully fertilized (240 kg N ha-1). This work represents a link between bioinformatic approaches of a native bacterial inoculant and the quantification of its impact on durum wheat.

Prognostic significance of exportin-5 in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. As liver cancer often presents no noticeable symptoms in its early stages, most patients are diagnosed at an advanced stage, complicating treatment. Therefore, the identification of new biomarkers is crucial for the early detection and treatment of HCC. Research on exportin-5 (XPO5) could offer new avenues for early diagnosis and improve treatment strategies. AIM: To explore the role of XPO5 in HCC progression and its potential as a prognostic biomarker. METHODS: This study assessed XPO5 mRNA expression in HCC using The Cancer Genome Atlas, TIMER, and International Cancer Genome Consortium databases, correlating it with clinical profiles and disease progression. We performed in vitro experiments to examine the effect of XPO5 on liver cell growth. Gene Set Enrichment Analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology were used to elucidate the biological roles and signaling pathways. We also evaluated XPO5's impact on immune cell infiltration and validated its prognostic potential using machine learning. RESULTS: XPO5 was significantly upregulated in HCC tissues, correlating with tumor grade, T-stage, and overall survival, indicating poor prognosis. Enrichment analyses linked high XPO5 expression with tumor immunity, particularly CD4 T cell memory activation and macrophage M0 infiltration. Drug sensitivity tests identified potential therapeutic agents such as MG-132, paclitaxel, and WH-4-023. Overexpression of XPO5 in HCC cells, compared to normal liver cells, was confirmed by western blotting and quantitative real-time polymerase chain reaction. The lentiviral transduction-mediated knockdown of XPO5 significantly reduced cell proliferation and metastasis. Among the various machine learning algorithms, the C5.0 decision tree algorithm achieved accuracy rates of 95.5% in the training set and 92.0% in the validation set. CONCLUSION: Our analysis shows that XPO5 expression is a reliable prognostic indicator for patients with HCC and is significantly associated with immune cell infiltration.