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Zika virus (ZIKV), a mosquito‐borne Flavivirus of international concern, causes congenital microcephaly in newborns and Guillain–Barré syndrome in adults. ZIKV capsid (C) protein, one of three key structural proteins, is essential for viral assembly and encapsidation. In dengue virus, a closely related flavivirus, the homologous C protein interacts with host lipid systems, namely intracellular lipid droplets, for successful viral replication. Here, we investigate ZIKV C interaction with host lipid systems, showing that it binds host lipid droplets but, contrary to expected, in an unspecific manner. Contrasting with other flaviviruses, ZIKV C also does not bind very‐low density‐lipoproteins. Comparing with other Flavivirus, capsid proteins show that ZIKV C structure is particularly thermostable and seems to be locked into an auto‐inhibitory conformation due to a disordered N‐terminal, hence blocking specific interactions and supporting the experimental differences observed. Such distinct structural features must be considered when targeting capsid proteins in drug development. [ABSTRACT FROM AUTHOR]

The emergence of resistance to current antimalarial drugs poses a significant challenge in the fight against malaria. This study aimed to investigate the in vivo antiplasmodial potential of the aqueous extract of fresh and dried leaves of A3 in Plasmodium berghei‐infected (P. berghei) mice. A 4‐day suppressive test was conducted, with infected BALB/c mice receiving artesunate and A3 extracts. The results showed that the tested doses of A3 attenuated the elevation of parasitemia induced by P. berghei, particularly at the dose of 400 mg/kg, and improved hematological indices. Computational techniques, including molecular docking, binding free energy calculations, and ADMET predictions, identified several bioactive compounds in A3 with promising inhibitory potential against lysyl‐tRNA synthetases and Dihydrofolate reductase (DHFR), the crucial enzymes targeted by antimalarial drugs. In this paper, Friedelin, Bauerenol, Epifriedelanol, Alpha‐Amyrenone, Stigmasterol, and beta‐Amyrin acetate were top‐ranked, having docking scores from −10.6 to −9.9 kcal/mol, compared with the −9.4 and −7.1 kcal/mol demonstrated by artesunate and chloroquine, respectively, as standard ligands. Also, it was shown that docking score from the Lysyl‐tRNA protein target (4YCV) ranged from −9.5 to −7.8 kcal/mol in comparison to artesunate (8.1 kcal/mol) and chloroquine (5.6 kcal/mol). The results suggest that the identified compounds in A3 could serve as potential candidates for the development of new anti‐malarial agents. [ABSTRACT FROM AUTHOR]

This article provides recommendations for analyzing transcriptomic data in allergy research. Transcriptomic data analysis focuses on gene expression at the RNA transcript level and is important for identifying biomarkers and understanding the mechanisms of allergic diseases. The article suggests various normalization methods, techniques for addressing technical variability, statistical models for differential gene expression analysis, and methods for dimensionality reduction and feature selection. It emphasizes the need for rigorous methodology and result interpretation to support personalized medicine and improve treatment strategies. Monte Carlo methods, dimensionality reduction techniques, feature selection methods, and machine learning methods can enhance the accuracy and credibility of transcriptomic data analysis in the study of allergic diseases. [Extracted from the article]

The valorization of lignin, a currently underutilized component of lignocellulosic biomass, has attracted attention to promote a stable and circular bioeconomy. Successful approaches including thermochemical, biological, and catalytic lignin depolymerization have been demonstrated, enabling opportunities for lignino‐refineries and lignocellulosic biorefineries. Although significant progress in lignin valorization has been made, this review describes unexplored opportunities in chemical and biological routes for lignin depolymerization and thereby contributes to economically and environmentally sustainable lignin‐utilizing biorefineries. This review also highlights the integration of chemical and biological lignin depolymerization and identifies research gaps while also recommending future directions for scaling processes to establish a lignino‐chemical industry. [ABSTRACT FROM AUTHOR]

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi‐view clustering are used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi‐view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes (T2D) and Alzheimer's disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future. [ABSTRACT FROM AUTHOR]

The article discusses the development of optimized dCas9 programmable transcriptional activators (PTAs) for plants. The researchers aimed to improve the efficiency of PTAs by replacing the VP64 domain with plant-derived activation domains (ADs). They tested various ADs in protoplasts from Arabidopsis and Seteria species and found that ADs such as DREB2, AvrXa10, DOF1, AtHSFA6b, and DREB1 showed transactivation activity comparable or better than VP64. They also tested the ability of strong ADs to activate gene expression from endogenous promoters in Arabidopsis and found significant increases in expression. Overall, the study provides insights into improving gene expression in plants for crop engineering purposes. Additionally, the authors present a new PTA called MoonTag, which uses nanobody: epitope interactions to recruit ADs to the dCas9 protein. They demonstrate that the MoonTag PTA can overexpress the FT gene in transgenic plants, resulting in an early-flowering phenotype. The authors also explore the synergism between targeting sgRNAs at both a core promoter and enhancer region, showing that PTA-mediated enhancer activation can retain the original genomic architecture and chromatin state. The study highlights the potential use of PTAs in mapping enhancer regions in plant genomes. [Extracted from the article]

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan‐cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge‐mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well‐functioning, content‐rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field. [ABSTRACT FROM AUTHOR]

Darwin recognized that 'a grand and almost untrodden field of inquiry will be opened, on the causes and laws of variation.' However, because the Modern Synthesis assumes that the intrinsic probability of any individual mutation is unrelated to that mutation's potential adaptive value, attention has been focused on selection rather than on the intrinsic generation of variation. Yet many examples illustrate that the term 'random' mutation, as widely understood, is inaccurate. The probabilities of distinct classes of variation are neither evenly distributed across a genome nor invariant over time, nor unrelated to their potential adaptive value. Because selection acts upon variation, multiple biochemical mechanisms can and have evolved that increase the relative probability of adaptive mutations. In effect, the generation of heritable variation is in a feedback loop with selection, such that those mechanisms that tend to generate variants that survive recurring challenges in the environment would be captured by this survival and thus inherited and accumulated within lineages of genomes. Moreover, because genome variation is affected by a wide range of biochemical processes, genome variation can be regulated. Biochemical mechanisms that sense stress, from lack of nutrients to DNA damage, can increase the probability of specific classes of variation. A deeper understanding of evolution involves attention to the evolution of, and environmental influences upon, the intrinsic variation generated in gametes, in other words upon the biochemical mechanisms that generate variation across generations. These concepts have profound implications for the types of questions that can and should be asked, as omics databases become more comprehensive, detection methods more sensitive, and computation and experimental analyses even more high throughput and thus capable of revealing the intrinsic generation of variation in individual gametes. These concepts also have profound implications for evolutionary theory, which, upon reflection it will be argued, predicts that selection would increase the probability of generating adaptive mutations, in other words, predicts that the ability to evolve itself evolves. [ABSTRACT FROM AUTHOR]

Background and Objectives: Changes in RHD generate variations in protein structure that lead to antigenic variants. The classical model divides them into quantitative (weak and Del) and qualitative (partial D). There are two types of protein antigens: linear and conformational. Computational biology analyses the theoretical assembly of tertiary protein structures and allows us to identify the 'topological' differences between isoforms. Our aim was to determine the theoretical antigenic differences between weak RhD variants compared with normal RhD based on structural analysis using bioinformatic techniques. Materials and Methods: We analysed the variations in secondary structures and hydrophobicity of RHD*01, RHD*01W.1, W2, W3, RHD*09.03.01, RHD*09.04, RHD*11, RHD*15 and RHD*21. We then modelled the tertiary structure and calculated their probable antigenic regions, intra‐protein interactions, displacement and membrane width and compared them with Rhce. Results: The 10 proteins are similar in their secondary structure and hydrophobicity, with the main differences observed in the exofacial coils. We identified six potential antigenic regions: one that is unique to RhD (R3), one that is common to all D (R6), three that are highly variable among RhD isoforms (R1, R2 and R4), one that they share with Rhce (R5) and two that are unique to Rhce (Ra and Rbc). Conclusion: The alloimmunization capacity of these subjects could be explained by the variability of the antigen pattern, which is not necessarily recognized or recognized with lower intensity by the commercially available antibodies, and not because they have a lower protein concentration in the membrane. [ABSTRACT FROM AUTHOR]

Prokaryotic DNA binding proteins (DBPs) play pivotal roles in governing gene regulation, DNA replication, and various cellular functions. Accurate computational models for predicting prokaryotic DBPs hold immense promise in accelerating the discovery of novel proteins, fostering a deeper understanding of prokaryotic biology, and facilitating the development of therapeutics targeting for potential disease interventions. However, existing generic prediction models often exhibit lower accuracy in predicting prokaryotic DBPs. To address this gap, we introduce ProkDBP, a novel machine learning‐driven computational model for prediction of prokaryotic DBPs. For prediction, a total of nine shallow learning algorithms and five deep learning models were utilized, with the shallow learning models demonstrating higher performance metrics compared to their deep learning counterparts. The light gradient boosting machine (LGBM), coupled with evolutionarily significant features selected via random forest variable importance measure (RF‐VIM) yielded the highest five‐fold cross‐validation accuracy. The model achieved the highest auROC (0.9534) and auPRC (0.9575) among the 14 machine learning models evaluated. Additionally, ProkDBP demonstrated substantial performance with an independent dataset, exhibiting higher values of auROC (0.9332) and auPRC (0.9371). Notably, when benchmarked against several cutting‐edge existing models, ProkDBP showcased superior predictive accuracy. Furthermore, to promote accessibility and usability, ProkDBP (https://iasri-sg.icar.gov.in/prokdbp/) is available as an online prediction tool, enabling free access to interested users. This tool stands as a significant contribution, enhancing the repertoire of resources for accurate and efficient prediction of prokaryotic DBPs. [ABSTRACT FROM AUTHOR]

The omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) characterized by 30 mutations in its spike protein, has rapidly spread worldwide since November 2021, significantly exacerbating the ongoing COVID‐19 pandemic. In order to investigate the relationship between these mutations and the variant's high transmissibility, we conducted a systematic analysis of the mutational effect on spike–angiotensin‐converting enzyme‐2 (ACE2) interactions and explored the structural/energy correlation of key mutations, utilizing a reliable coarse‐grained model. Our study extended beyond the receptor‐binding domain (RBD) of spike trimer through comprehensive modeling of the full‐length spike trimer rather than just the RBD. Our free‐energy calculation revealed that the enhanced binding affinity between the spike protein and the ACE2 receptor is correlated with the increased structural stability of the isolated spike protein, thus explaining the omicron variant's heightened transmissibility. The conclusion was supported by our experimental analyses involving the expression and purification of the full‐length spike trimer. Furthermore, the energy decomposition analysis established those electrostatic interactions make major contributions to this effect. We categorized the mutations into four groups and established an analytical framework that can be employed in studying future mutations. Additionally, our calculations rationalized the reduced affinity of the omicron variant towards most available therapeutic neutralizing antibodies, when compared with the wild type. By providing concrete experimental data and offering a solid explanation, this study contributes to a better understanding of the relationship between theories and observations and lays the foundation for future investigations. [ABSTRACT FROM AUTHOR]

DNA‐based data storage is a new technology in computational and synthetic biology, that offers a solution for long‐term, high‐density data archiving. Given the critical importance of medical data in advancing human health, there is a growing interest in developing an effective medical data storage system based on DNA. Data integrity, accuracy, reliability, and efficient retrieval are all significant concerns. Therefore, this study proposes an Effective DNA Storage (EDS) approach for archiving medical MRI data. The EDS approach incorporates three key components (i) a novel fraction strategy to address the critical issue of rotating encoding, which often leads to data loss due to single base error propagation; (ii) a novel rule‐based quaternary transcoding method that satisfies bio‐constraints and ensure reliable mapping; and (iii) an indexing technique designed to simplify random search and access. The effectiveness of this approach is validated through computer simulations and biological experiments, confirming its practicality. The EDS approach outperforms existing methods, providing superior control over bio‐constraints and reducing computational time. The results and code provided in this study open new avenues for practical DNA storage of medical MRI data, offering promising prospects for the future of medical data archiving and retrieval. [ABSTRACT FROM AUTHOR]

Background: Our study aims to delineate the miRSNP–microRNA–gene–pathway interactions in the context of hypertrophic scars (HS) and keloids. Materials and Methods: We performed a computational biology study involving differential expression analysis to identify genes and their mRNAs in HS and keloid tissues compared to normal skin, identifying key hub genes and enriching their functional roles, comprehensively analyzing microRNA‐target genes and related signaling pathways through bioinformatics, identifying MiRSNPs, and constructing a pathway‐based network to illustrate miRSNP‐miRNA‐gene‐signaling pathway interactions. Results: Our results revealed a total of 429 hub genes, with a strong enrichment in signaling pathways related to proteoglycans in cancer, focal adhesion, TGF‐β, PI3K/Akt, and EGFR tyrosine kinase inhibitor resistance. Particularly noteworthy was the substantial crosstalk between the focal adhesion and PI3K/Akt signaling pathways, making them more susceptible to regulation by microRNAs. We also identified specific miRNAs, including miRNA‐1279, miRNA‐429, and miRNA‐302e, which harbored multiple SNP loci, with miRSNPs rs188493331 and rs78979933 exerting control over a significant number of miRNA target genes. Furthermore, we observed that miRSNP rs188493331 shared a location with microRNA302e, microRNA202a‐3p, and microRNA20b‐5p, and these three microRNAs collectively targeted the gene LAMA3, which is integral to the focal adhesion signaling pathway. Conclusions: The study successfully unveils the complex interactions between miRSNPs, miRNAs, genes, and signaling pathways, shedding light on the genetic factors contributing to HS and keloid formation. [ABSTRACT FROM AUTHOR]

This document contains abstracts from various scientific studies covering a range of topics in biology and related disciplines. The studies investigate maternal overnutrition and its potential link to liver disease and cancer in offspring, the inhibition of NLRP1 inflammasome activation for neuroprotection, the effects of a nicotinic intermediate on Alzheimer's disease, the use of artificial thymic organoids to study T-cell development, the inhibition of siderophore biosynthesis for Mycobacterium abscessus infections, machine learning for early prediction of ovarian cancer, transcriptomic study of nicotine catabolism, natural variation and cellular morphology patterns in epithelial cysts, new treatments for cell quiescence in acute myeloid leukemia, the impact of endocrine disruptors on cortical development, and more. These summaries provide a brief overview of the research findings and can serve as a starting point for library patrons conducting research in these areas. [Extracted from the article]

This review article aims to significantly advance the scientific community's efforts to develop effective nucleoside‐based drugs for treating severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and other emerging infectious diseases. This study concentrates on the main viral protease (Mpro) and explores nucleoside‐based compounds as potential therapeutic agents. This investigation investigated the impact of acylation‐induced modifications on the nucleoside hydroxyl group and subsequent properties. Nucleoside analogs, which are recognized for their diverse biochemical properties, were synthesized and rigorously screened to evaluate their antimicrobial efficacy. In the domain of pharmaceutical research, computational pharmacokinetics has emerged as a critical tool, especially in the pursuit of nucleoside analogs as potential therapeutics. In silico methods aid in predicting pharmacokinetic traits, interactions with crucial enzymes, and the stability of these analogs in biological environments, thereby streamlining drug design and reducing experimental costs. Concurrently, computational studies revealed the intricate interactions between the analogs and the active site of the main protease. The amalgamation of experimental screening and computational insights underscores the emergence of potent nucleoside candidates with inhibitory activity against SARS‐CoV‐2 Mpro. Additionally, this review integrates computational studies that provide valuable insights into the interactions between nucleoside analogs and the main protease of SARS‐CoV‐2. [ABSTRACT FROM AUTHOR]

A study published in the New Phytologist journal examines the genetic diversity of grapevines and their ability to withstand extreme heatwaves caused by climate change. The researchers conducted experiments on a grapevine diversity panel in South France during a record heatwave in 2019. They discovered that certain genomic regions were linked to heat tolerance, suggesting that genetic diversity could be used to breed fruit crops that can withstand heatwaves. The study also investigated the role of leaf size, leaf mass per area, and evaporative cooling in heat tolerance. The researchers identified candidate genes that may contribute to heat tolerance in grapevines. [Extracted from the article]

Topicals and chemical peels are the standard of care for management of facial hyperpigmentation. However, traditional therapies have come under recent scrutiny, such as topical hydroquinone (HQ) has some regulatory restrictions, and high concentration trichloroacetic acid (TCA) peel pose a risk in patients with skin of colour. The objective of our research was to identify, investigate and elucidate the mechanism of action of a novel TCA‐ and HQ‐free professional‐use chemical peel to manage common types of facial hyperpigmentation. Using computational modelling and in vitro assays on tyrosinase, we identified proprietary multi‐acid synergistic technology (MAST). After a single application on human skin explants, MAST peel was found to be more effective than a commercial HQ peel in inhibiting melanin (histochemical imaging and gene expression). All participants completed the case study (N = 9) without any adverse events. After administration of the MAST peel by a dermatologist, the scoring and VISIA photography reported improvements in hyperpigmentation, texture and erythema, which could be linked to underlying pathophysiological changes in skin after peeling, visualized by non‐invasive optical biopsy of face. Using reflectance confocal microscopy (VivaScope®) and multiphoton tomography (MPTflex™), we observed reduction in melanin, increase in metabolic activity of keratinocytes, and no signs of inflammatory cells after peeling. Subsequent swabbing of the cheek skin found no microbiota dysbiosis resulting from the chemical peel. The strong efficacy with minimum downtime and no adverse events could be linked to the synergistic action of the ingredients in the novel HQ‐ and TCA‐free professional peel technology. [ABSTRACT FROM AUTHOR]

The road from transcription to protein synthesis is paved with many obstacles, allowing for several modes of post-transcriptional regulation of gene expression. A fundamental player in mRNA biology is DDX3X, an RNA binding protein that canonically regulates mRNA translation. By monitoring dynamics of mRNA abundance and translation following DDX3X depletion, we observe stabilization of translationally suppressed mRNAs. We use interpretable statistical learning models to uncover GC content in the coding sequence as the major feature underlying RNA stabilization. This result corroborates GC content-related mRNA regulation detectable in other studies, including hundreds of ENCODE datasets and recent work focusing on mRNA dynamics in the cell cycle. We provide further evidence for mRNA stabilization by detailed analysis of RNA-seq profiles in hundreds of samples, including a Ddx3x conditional knockout mouse model exhibiting cell cycle and neurogenesis defects. Our study identifies a ubiquitous feature underlying mRNA regulation and highlights the importance of quantifying multiple steps of the gene expression cascade, where RNA abundance and protein production are often uncoupled. Synopsis: Monitoring the dynamics of mRNA changes after DDX3X depletion indicates translation suppression followed by mRNA stabilization. GC content in the CDS is a strong predictor of mRNA stabilization and it is detectable in multiple transcriptomics datasets, with a potential link to cell-cycle regulation. RNA-seq and Ribo-seq on a time course of DDX3X depletion show regulation of translation and mRNA levels. Intron-exon count modeling and SLAM-seq demonstrate post-transcriptional mRNA stabilization. Random Forest and Lasso show GC content in the CDS (GCcds) as a main predictor of mRNA stabilization. ENCODE RBP knockdowns and in vivo datasets show widespread GCcds-dependent mRNA stabilization. Monitoring the dynamics of mRNA changes after DDX3X depletion indicates translation suppression followed by mRNA stabilization. GC content in the CDS is a strong predictor of mRNA stabilization and it is detectable in multiple transcriptomics datasets, with a potential link to cell-cycle regulation. [ABSTRACT FROM AUTHOR]

This article examines the impact of aboveground insect herbivory on the diversity and composition of arbuscular mycorrhizal (AM) fungal communities in plant roots. The study suggests that herbivory can affect the assembly of AM fungal communities, but the specific effects vary. While herbivory did not significantly reduce AM fungal richness, it did increase community evenness. The study also found that herbivory altered the composition and structure of AM fungal communities, leading to increased phylogenetic diversity. Additionally, plants with herbivores benefited more from AM fungi in terms of phosphorus acquisition compared to herbivore-free plants. These findings highlight the potential influence of aboveground herbivory on plant performance and nutrient acquisition. [Extracted from the article]

Generative AI is rapidly transforming the frontier of research in computational structural biology. Indeed, recent successes have substantially advanced protein design and drug discovery. One of the key methodologies underlying these advances is diffusion models (DM). Diffusion models originated in computer vision, rapidly taking over image generation and offering superior quality and performance. These models were subsequently extended and modified for uses in other areas including computational structural biology. DMs are well equipped to model high dimensional, geometric data while exploiting key strengths of deep learning. In structural biology, for example, they have achieved state-of-the-art results on protein 3D structure generation and small molecule docking. This review covers the basics of diffusion models, associated modeling choices regarding molecular representations, generation capabilities, prevailing heuristics, as well as key limitations and forthcoming refinements. We also provide best practices around evaluation procedures to help establish rigorous benchmarking and evaluation. The review is intended to provide a fresh view into the state-of-the-art as well as highlight its potentials and current challenges of recent generative techniques in computational structural biology. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Structure and Mechanism > Molecular Structures Software > Molecular Modeling [ABSTRACT FROM AUTHOR]

Summary: Stomatal movement involves ion transport across the plasma membrane (PM) and vacuolar membrane (VM) of guard cells. However, the coupling mechanisms of ion transporters in both membranes and their interplay with Ca2+ and pH changes are largely unclear.Here, we investigated transporter networks in tobacco guard cells and mesophyll cells using multiparametric live‐cell ion imaging and computational simulations.K+ and anion fluxes at both, PM and VM, affected H+ and Ca2+, as changes in extracellular KCl or KNO3 concentrations were accompanied by cytosolic and vacuolar pH shifts and changes in [Ca2+]cyt and the membrane potential. At both membranes, the K+ transporter networks mediated an antiport of K+ and H+. By contrast, net transport of anions was accompanied by parallel H+ transport, with differences in transport capacity for chloride and nitrate. Guard and mesophyll cells exhibited similarities in K+/H+ transport but cell type‐specific differences in [H+]cyt and pH‐dependent [Ca2+]cyt signals. Computational cell biology models explained mechanistically the properties of transporter networks and the coupling of transport across the PM and VM.Our integrated approach indicates fundamental principles of coupled ion transport at membrane sandwiches to control H+/K+ homeostasis and points to transceptor‐like Ca2+/H+‐based ion signaling in plant cells. [ABSTRACT FROM AUTHOR]

Subcutaneous adipose tissue (SAT), a vital energy reservoir and endocrine organ for maintaining systemic glucose, lipid, and energy homeostasis, undergoes significant changes with age. However, among the existing aging‐related markers, only few genes are associated with SAT aging. In this study, weighted gene co‐expression network analysis was used on a transcriptome of SAT obtained from the Genotype‐Tissue Expression portal to identify biologically relevant, SAT‐specific, and age‐related marker genes. We found modules that exhibited significant changes with age and identified GYG2 as a novel key aging associated gene. The link between GYG2 and mitochondrial function as well as brown/beige adipocytes was supported using additional bioinformatics and experimental analyses. Additionally, we identified PPARG as the transcription factor of GYG2 expression. The newly discovered GYG2 marker can be used to not only determine the age of SAT but also uncover new mechanisms underlying SAT aging. [ABSTRACT FROM AUTHOR]

BACKGROUND: Streptococcus thermophilus is an important strain widely used in dairy fermentation, with distinct urea metabolism characteristics compared to other lactic acid bacteria. The conversion of urea by S. thermophilus has been shown to affect the flavor and acidification characteristics of milk. Additionally, urea metabolism has been found to significantly increase the number of cells and reduce cell damage under acidic pH conditions, resulting in higher activity. However, the physiological role of urea metabolism in S. thermophilus has not been fully evaluated. A deep understanding of this metabolic feature is of great significance for its production and application. Genome‐scale metabolic network models (GEMs) are effective tools for investigating the metabolic network of organisms using computational biology methods. Constructing an organism‐specific GEM can assist us in comprehending its characteristic metabolism at a systemic level. RESULTS: In the present study, we reconstructed a high‐quality GEM of S. thermophilus S‐3 (iCH492), which contains 492 genes, 608 metabolites and 642 reactions. Growth phenotyping experiments were employed to validate the model both qualitatively and quantitatively, yielding satisfactory predictive accuracy (95.83%), sensitivity (93.33%) and specificity (100%). Subsequently, a systematic evaluation of urea metabolism in S. thermophilus was performed using iCH492. The results showed that urea metabolism reduces intracellular hydrogen ions and creates membrane potential by producing and transporting ammonium ions. This activation of glycolytic fluxes and ATP synthase produces more ATP for biomass synthesis. The regulation of fluxes of reactions involving NAD(P)H by urea metabolism improves redox balance. CONCLUSION: Model iCH492 represents the most comprehensive knowledge‐base of S. thermophilus to date, serving as a potent tool. The evaluation of urea metabolism led to novel insights regarding the role of urease. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Objective: Hepatic overexpression of the thrombospondin 2 gene (THBS2) and elevated levels of circulating thrombospondin 2 (TSP2) have been observed in patients with chronic liver disease. This study aimed to identify the specific cells expressing THBS2/TSP2 in non-alcoholic fatty liver disease (NAFLD) and investigate the underlying mechanism behind THBS2/TSP2 upregulation. Design: Comprehensive NAFLD liver gene datasets, including single-cell RNA sequencing (scRNA-seq), in-house NAFLD liver tissue, and LX-2 cells derived from human hepatic stellate cells (HSCs), were analysed using a combination of computational biology, genetic, immunological, and pharmacological approaches. Results: Analysis of the genetic dataset revealed the presence of 1433 variable genes in patients with advanced fibrosis NAFLD, with THBS2 ranked among the top 2 genes. Quantitative polymerase chain reaction (qPCR) examination of NAFLD livers showed a significant correlation between THBS2 expression and fibrosis stage (r = .349, p < .001). In support of this, scRNA-seq data and in situ hybridization demonstrated that the THBS2 gene was highly expressed in HSCs of NAFLD patients with advanced fibrosis. Pathway analysis of the gene dataset revealed THBS2 expression to be associated with the transforming growth factor beta (TGFβ) pathway and collagen gene activation. Moreover, the activation of LX-2 cells with TGFβ increased THBS2/TSP2 and collagen expression independently of the TGFβ-SMAD2/3 pathway. THBS2 gene knockdown significantly decreased collagen expression in LX-2 cells. Conclusions: THBS2/TSP2 is highly expressed in HSCs and plays a role in regulating fibrogenesis in NAFLD patients. THBS2/TSP2 may therefore represent a potential target for anti-fibrotic therapy in NAFLD. [ABSTRACT FROM AUTHOR]

The class I HLA genotype has been widely recognized as a factor influencing HIV disease progression in treatment‐naïve subjects. However, little is known regarding its role in HIV disease course and how it influences the size of the viral reservoir once anti‐retroviral therapy (ART) is started. Here, leveraging on cutting‐edge bioinformatic tools, we explored the relationship between HLA class I and the HIV reservoir in a cohort of 90 people living with HIV (PLWH) undergoing ART and who achieved viral suppression. Analysis of HLA allele distribution among patients with high and low HIV reservoir allowed us to document a predominant role of HLA‐B and ‐C genes in regulating the size of HIV reservoir. We then focused on the analysis of HIV antigen (Ag) repertoire, by investigating immunogenetic parameters such as the degree of homozygosity, HLA evolutionary distance and Ag load. In particular, we used two different bioinformatic algorithms, NetMHCpan and MixMHCpred, to predict HLA presentation of immunogenic HIV‐derived peptides and identified HLA‐B*57:01 and HLA‐B*58:01 among the highest ranking HLAs in terms of total load, suggesting that their previously reported protective role against HIV disease progression might be linked to a more effective viral recognition and presentation to Cytotoxic T lymphocytes (CTLs). Further, we speculated that some peptide‐HLA complexes, including those produced by the interaction between HLA‐B*27 and the HIV Gag protein, might be particularly relevant for the efficient regulation of HIV replication and containment of the HIV reservoir. Last, we provide evidence of a possible synergistic effect between the CCR5 ∆32 mutation and Ag load in controlling HIV reservoir. [ABSTRACT FROM AUTHOR]

This paper aims to present a novel study on the dynamics of a fractional predator–prey–pathogen model to investigate the existence of the chaos in the model. We applied the Atangana–Baleanu fractional operator to the predator–prey–pathogen model, and new results are presented. Furthermore, the stability of the equilibrium points of the proposed model is investigated. The convergence and uniqueness of the solution for the model are also studied. Few numerical simulations have been performed for both predator, and prey populations. Some interesting chaotic behaviors of predator and prey populations of the model are also obtained by using an effective numerical scheme. Furthermore, corresponding numerical simulations were achieved for the various values of the fractional derivative. [ABSTRACT FROM AUTHOR]

Background: A prognostic model of bladder cancer was constructed based on costimulatory molecules, and its stability and accuracy were verified in different datasets. Method: The expression profile of bladder cancer RNA and the corresponding clinical data in The Cancer Genome Atlas (TCGA) database were analyzed employing computational biology, and a prognostic model was constructed for costimulating molecule‐related genes. The model was applied in GSE160693, GSE176307, Xiangya_Cohort, GSE13507, GSE19423, GSE31684, GSE32894, GSE48075, GSE69795 and GSE70691 in TCGA dataset and Gene Expression Omnibus database. The role of costimulating molecules in bladder cancer tumor subtypes was also explored. By consistent cluster analysis, bladder cancer in the TCGA dataset was categorized into two subtypes: C1 and C2. The C1 subtype exhibited a poor prognosis, high levels of immune cell infiltration and significant enrichment of natural killer cells, T cells and dendritic cells in the C1 subtype. In addition, the ImmuneScore calculated by the ESTIMATE algorithm differed greatly between the two subtypes, and the ImmuneScore of the C1 subtype was greater than the C2 subtype in a significant manner. Results: This study also assessed the relationship between costimulating molecules and immunotherapy response. The high‐risk group responded poorly to immunotherapy, with significant differences in the amount of most immune cells between the two groups. Further, three indices of the ESTIMATE algorithm and 22 immune cells of the CIBERSORT algorithm were significantly correlated with risk values. These findings suggest the potential value of costimulating molecules in predicting immunotherapy response. Conclusion: A costimulatory molecule‐based prognostic model for bladder cancer was established and validated across multiple datasets. This model introduces a novel mode for tailoring treatments to each individual with bladder cancer, and offers valuable insights for informed clinical choices. Simultaneously, this research also delved into the significance of costimulating molecules within distinct bladder cancer subtypes, shedding novel insights into improving immunotherapy strategies for the treatment of bladder cancer. [ABSTRACT FROM AUTHOR]

Most proteins found in the outer membrane of gram‐negative bacteria share a common domain: the transmembrane β‐barrel. These outer membrane β‐barrels (OMBBs) occur in multiple sizes and different families with a wide range of functions evolved independently by amplification from a pool of homologous ancestral ββ‐hairpins. This is part of the reason why predicting their three‐dimensional (3D) structure, especially by homology modeling, is a major challenge. Recently, DeepMind's AlphaFold v2 (AF2) became the first structure prediction method to reach close‐to‐experimental atomic accuracy in CASP even for difficult targets. However, membrane proteins, especially OMBBs, were not abundant during their training, raising the question of how accurate the predictions are for these families. In this study, we assessed the performance of AF2 in the prediction of OMBBs and OMBB‐like folds of various topologies using an in‐house‐developed tool for the analysis of OMBB 3D structures, and barrOs. In agreement with previous studies on other membrane protein classes, our results indicate that AF2 predicts transmembrane β‐barrel structures at high accuracy independently of the use of templates, even for novel topologies absent from the training set. These results provide confidence on the models generated by AF2 and open the door to the structural elucidation of novel transmembrane β‐barrel topologies identified in high‐throughput OMBB annotation studies or designed de novo. [ABSTRACT FROM AUTHOR]

The article discusses the use of RNA spike-ins as a normalization method for RNA sequencing (RNAseq) experiments. The authors explain that biases can be introduced in the sample preparation and sequencing process, leading to variations in gene expression. Commonly used tools for analyzing differential expression normalize for read depth, assuming that most genes do not change between samples. However, experimental conditions can affect total RNA abundance, leading to inaccurate results. The authors demonstrate that the use of RNA spike-ins improves the accuracy, specificity, and sensitivity of calling differentially expressed genes. They tested this method on sorghum samples under normal and chilling stress conditions at different times of the day, and found that spike-in normalization identified more differentially expressed genes and revealed time-specific functions and cellular processes that were missed by traditional methods. The authors suggest that RNA spike-ins could be particularly valuable for single-cell RNAseq experiments. [Extracted from the article]

Aims: Sevoflurane is an ether-based inhalational anaesthetic that induces and maintains general anaesthesia. Our study aimed to detect sevoflurane-induced nephrogenic diabetes insipidus using data mining algorithms (DMAs) and molecular docking. The FAERS database was analysed using OpenVigil 2.1 for disproportionality analysis., Methods: We analysed FAERS data from 2004 to 2022 to determine the incidence of nephrogenic diabetes insipidus associated with sevoflurane. Reporting odds ratios (RORs) and proportional reporting ratios (PRRs) with 95% confidence intervals were calculated. We also used molecular docking with AutoDock Vina to examine sevoflurane's binding affinity to relevant receptors., Results: A total of 554 nephrogenic diabetes insipidus cases were reported in FAERS, of which 2.5% (14 cases) were associated with sevoflurane. Positive signals were observed for sevoflurane with ROR of 76.012 (95% CI: 44.67-129.35) and PRR of 75.72 (χ 2 : 934.688). Of the 14 cases, 50% required hospitalization, 14% resulted in death, and the remaining cases were categorized as other outcomes. Molecular docking analysis showed that sevoflurane exhibited high binding affinity towards AQP2 (4NEF) and AVPR2 (6U1N) with docking scores of -4.9 and -5.3, respectively., Conclusions: Sevoflurane use is significantly associated with the incidence of nephrogenic diabetes insipidus. Healthcare professionals should be cautious when using this medication and report any adverse events to regulatory agencies. Further research is needed to validate these findings and identify risk factors while performing statistical adjustments to prevent false-positives. Clinical monitoring is crucial to validate potential adverse effects of sevoflurane., (© 2023 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Dr. Rocío Trueba‐Gómez and her team from Mexico City, Mexico, have been awarded the Vox Sanguinis Best Paper Prize 2023 for their paper titled "Use of computational biology to compare the theoretical tertiary structures of the most common forms of RhCE and RhD." This prestigious prize is given annually for the best original paper published in Vox Sanguinis the previous year and includes a certificate and a cash prize of 5000 Euros. The authors will receive the award during the opening ceremony of the 38th International Congress of the International Society of Blood Transfusion in Barcelona. Congratulations to the authors on their achievement. [Extracted from the article]

The Finkel-Biskis-Jinkins Osteosarcoma (c-Fos; encoded by FOS) plays an important role in several cardiovascular diseases, including atherosclerosis and stroke. However, the relationship between FOS and venous thromboembolism (VTE) remains unknown. We identified differentially expressed genes in Gene Expression Omnibus dataset, GSE48000, comprising VTE patients and healthy individuals, and analysed them using CIBERSORT and weighted co-expression network analysis (WGCNA). FOS and CD46 expressions were significantly downregulated (FOS p = 2.26E-05, CD64 p = 8.83E-05) and strongly linked to neutrophil activity in VTE. We used GSE19151 and performed PCR to confirm that FOS and CD46 had diagnostic potential for VTE; however, only FOS showed differential expression by PCR and ELISA in whole blood samples. Moreover, we found that hsa-miR-144 which regulates FOS expression was significantly upregulated in VTE. Furthermore, FOS expression was significantly downregulated in neutrophils of VTE patients (p = 0.03). RNA sequencing performed on whole blood samples of VTE patients showed that FOS exerted its effects in VTE via the leptin-mediated adipokine signalling pathway. Our results suggest that FOS and related genes or proteins can outperform traditional clinical markers and may be used as diagnostic biomarkers for VTE., (© 2024 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Idiopathic pulmonary fibrosis (IPF) is a debilitating interstitial lung disease characterized by progressive fibrosis and poor prognosis. Despite advancements in treatment, the pathophysiological mechanisms of IPF remain elusive. Herein, we conducted an integrated bioinformatics analysis combining clinical data and carried out experimental validations to unveil the intricate molecular mechanism of IPF. Leveraging three IPF datasets, we identified 817 upregulated and 560 downregulated differentially expressed genes (DEGs). Of these, 14 DEGs associated with copper metabolism were identified, shedding light on the potential involvement of disrupted copper metabolism in IPF progression. Immune infiltration analysis revealed dysregulated immune cell infiltration in IPF, with a notable correlation between copper metabolism-related genes and immune cells. Weighted gene co-expression network analysis (WGCNA) identified a central module correlated with IPF-associated genes, among which STEAP2 emerged as a key hub gene. Subsequent in vivo and in vitro studies confirmed the upregulation of STEAP2 in IPF model. Knockdown of STEAP2 using siRNA alleviated fibrosis in vitro, suggesting potential pathway related to copper metabolism in the pathophysiological progression of IPF. Our study established a novel link between immune cell infiltration and dysregulated copper metabolism. The revelation of intracellular copper overload and upregulated STEAP2 unravelled a potential therapeutic option. These findings offer valuable insights for future research and therapeutic interventions targeting STEAP2 and associated pathways in IPF., (© 2024 The Author(s). Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Cisplatin-based chemotherapy is frequently employed as the primary therapeutic approach for advanced lung cancer. Nevertheless, a significant proportion of patients may develop resistance to cisplatin, leading to diminished efficacy of chemotherapy. Through analysis of Gene Expression Omnibus databases, TSPAN6 has been identified as a key factor in conferring resistance to cisplatin, attributed to its activation of the NF-κB signaling pathway. Knockdown of TSPAN6 using siRNA resulted in decreased expression levels of NF-κB in A549 cells. This indicates that TSPAN6 may have dual effects on lung cancer cisplatin resistance and could serve as a promising therapeutic target for individuals with cisplatin resistance., (© 2024 John Wiley & Sons A/S.)

Helicobacter pylori (H. pylori) strain is the most genetically diverse pathogenic bacterium and now alarming serious human health concern ranging from chronic gastritis to gastric cancer and human death all over the world. Currently, the majority of commercially available diagnostic assays for H. pylori is a challenging task due to the heterogeneity of virulence factors in various geographical regions. In this concern, designing of universal multi-epitope immunogenic biomarker targeted for all H. pylori strains would be crucial to successfully immunodiagnosis assay and vaccine development for H. pylori infection. Hence, the present study aimed to explore the potential immunogenic epitopes of PSA D15 and Cag11 proteins of H. pylori, using immunoinformatics web tools in order to design novel immune-reactive multi-epitope antigens for enhanced immunodiagnosis in humans. Through an in silico immunoinformatics approach, high-ranked B-cell, MHC-I, and MHC-II epitopes of PSA D15 and Cag11 proteins were predicted, screened, and selected. Subsequently, a novel multi-epitope PSA D15 and Cag11 antigens were designed by fused the high-ranked B-cell, MHC-I, and MHC-II epitopes and 50S ribosomal protein L7/L12 adjuvant using linkers. The antigenicity, solubility, physicochemical properties, secondary and tertiary structures, 3D model refinement, and validations were carried. Furthermore, the designed multi-epitope antigens were subjected to codon adaptation and in silico cloning, immune response simulation, and molecular docking with receptor molecules. A novel, stable multi-epitope PSA D15 and Cag11 H. pylori antigens were developed and immune simulation of the designed antigens showed desirable levels of immunological response. Molecular docking of designed antigens with immune receptors (B-cell, MHC-I, MHC-II, and TLR-2/4) revealed robust interactions and stable binding affinity to the receptors. The codon optimized and in silico cloned showed that the designed antigens were successfully expressed (CAI value of 0.95 for PSA D15 and 1.0 for Cag11) after inserted into pET-32ba (+) plasmid of the E. coli K12 strain. In conclusion, this study revealed that the designed multi-epitope antigens have a huge immunological potential candidate biomarker and useful in developing immunodiagnostic assays and vaccines for H. pylori infection., (© 2024 John Wiley & Sons Ltd.)

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., (© 2024 John Wiley & Sons Ltd.)

Background and Objective: As a chronic inflammatory disease, periodontitis threatens oral health and is a risk factor for Alzheimer's disease (AD). There is growing evidence that these two diseases are closely related. However, current research is still incomplete in understanding the common genes and common mechanisms between periodontitis and AD. In this study, we aimed to identify common genes in periodontitis and AD and analyze the relationship between crucial genes and immune cells to provide new therapeutic targets for clinical treatment., Materials and Methods: We evaluated differentially expressed genes (DEGs) specific to periodontitis and AD. Co-expressed genes were identified by obtaining gene expression profile data from the Gene Expression Omnibus (GEO) database. Using the STRING database, protein-protein interaction (PPI) networks were constructed, and essential genes were identified. We also used four algorithms to identify critical genes and constructed regulatory networks. The association of crucial genes with immune cells and potential therapeutic effects was also assessed., Results: PDGFRB, VCAN, TIMP1, CHL1, EFEMP2, and IGFBP5 were obtained as crucial common genes. Immune infiltration analysis showed that Natural killer cells and Myeloid-derived suppressor cells were significantly differentially expressed in patients with PD and AD compared with the normal group. FOXC1 and GATA2 are important TFs for PD and AD. MiR-23a, miR-23b, miR-23a, and miR-23b were associated with AD and PD. Finally, the hub genes retrieved from the DSigDB database indicate multiple drug molecule and drug-target interactions., Conclusion: This study reveals commonalities in common hub genes and immune infiltration between periodontitis and AD, and the analysis of six hub genes and immune cells may provide new insights into potential therapeutic directions for the pathogenesis of periodontitis complicated by AD., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

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., (© 2024 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

This study aimed to identify genes shared by metabolic dysfunction-associated fatty liver disease (MASH) and diabetic nephropathy (DN) and the effect of extracellular matrix (ECM) receptor interaction genes on them. Datasets with MASH and DN were downloaded from the Gene Expression Omnibus (GEO) database. Pearson's coefficients assessed the correlation between ECM-receptor interaction genes and cross talk genes. The coexpression network of co-expression pairs (CP) genes was integrated with its protein-protein interaction (PPI) network, and machine learning was employed to identify essential disease-representing genes. Finally, immuno-penetration analysis was performed on the MASH and DN gene datasets using the CIBERSORT algorithm to evaluate the plausibility of these genes in diseases. We found 19 key CP genes. Fos proto-oncogene (FOS), belonging to the IL-17 signalling pathway, showed greater centrality PPI network; Hyaluronan Mediated Motility Receptor (HMMR), belonging to ECM-receptor interaction genes, showed most critical in the co-expression network map of 19 CP genes; Forkhead Box C1 (FOXC1), like FOS, showed a high ability to predict disease in XGBoost analysis. Further immune infiltration showed a clear positive correlation between FOS/FOXC1 and mast cells that secrete IL-17 during inflammation. Combining the results of previous studies, we suggest a FOS/FOXC1/HMMR regulatory axis in MASH and DN may be associated with mast cells in the acting IL-17 signalling pathway. Extracellular HMMR may regulate the IL-17 pathway represented by FOS through the Mitogen-Activated Protein Kinase 1 (ERK) or PI3K-Akt-mTOR pathway. HMMR may serve as a signalling carrier between MASH and DN and could be targeted for therapeutic development., (© 2024 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Background: Perinatal exposure to maternal obesity predisposes offspring to develop obesity later in life. Immune dysregulation in the hypothalamus, the brain center governing energy homeostasis, is pivotal in obesity development. This study aimed to identify key candidate genes associated with the risk of offspring obesity in maternal obesity., Methods: We obtained obesity-related datasets from the Gene Expression Omnibus (GEO) database. GSE135830 comprises gene expression data from the hypothalamus of mouse offspring in a maternal obesity model induced by a high-fat diet model (maternal high-fat diet (mHFD) group and maternal chow (mChow) group), while GSE127056 consists of hypothalamus microarray data from young adult mice with obesity (high-fat diet (HFD) and Chow groups). We identified differentially expressed genes (DEGs) and module genes using Limma and weighted gene co-expression network analysis (WGCNA), conducted functional enrichment analysis, and employed a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to pinpoint candidate hub genes for diagnosing obesity-associated risk in offspring of maternal obesity. We constructed a nomogram receiver operating characteristic (ROC) curve to evaluate the diagnostic value. Additionally, we analyzed immune cell infiltration to investigate immune cell dysregulation in maternal obesity. Furthermore, we verified the expression of the candidate hub genes both in vivo and in vitro., Results: The GSE135830 dataset revealed 2868 DEGs between the mHFD offspring and the mChow group and 2627 WGCNA module genes related to maternal obesity. The overlap of DEGs and module genes in the offspring with maternal obesity in GSE135830 primarily enriched in neurodevelopment and immune regulation. In the GSE127056 dataset, 133 DEGs were identified in the hypothalamus of HFD-induced adult obese individuals. A total of 13 genes intersected between the GSE127056 adult obesity DEGs and the GSE135830 maternal obesity module genes that were primarily enriched in neurodevelopment and the immune response. Following machine learning, two candidate hub genes were chosen for nomogram construction. Diagnostic value evaluation by ROC analysis determined Sytl4 and Kncn2 as hub genes for maternal obesity in the offspring. A gene regulatory network with transcription factor-miRNA interactions was established. Dysregulated immune cells were observed in the hypothalamus of offspring with maternal obesity. Expression of Sytl4 and Kncn2 was validated in a mouse model of hypothalamic inflammation and a palmitic acid-stimulated microglial inflammation model., Conclusion: Two candidate hub genes (Sytl4 and Kcnc2) were identified and a nomogram was developed to predict obesity risk in offspring with maternal obesity. These findings offer potential diagnostic candidate genes for identifying obesity-associated risks in the offspring of obese mothers., (© 2024 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

The study aimed to identify the biomarkers for predicting coronary atherosclerotic lesions progression in patients with inflammatory bowel disease (IBD). Related transcriptome datasets were seized from Gene Expression Omnibus database. IBD-related modules were identified via Weighted Gene Co-expression Network Analysis. The 'Limma' was applied to screen differentially expressed genes between stable coronary artery disease (CAD) and acute myocardial infarction (AMI). Subsequently, we employed protein-protein interaction (PPI) network and three machine-learning strategies to further screen for candidate hub genes. Application of the receiver operating characteristics curve to quantitatively evaluate candidates to determine key diagnostic biomarkers, followed by a nomogram construction. Ultimately, we performed immune landscape analysis, single-gene GSEA and prediction of target-drugs. 3227 IBD-related module genes and 570 DEGs accounting for AMI were recognized. Intersection yielded 85 shared genes and mostly enriched in immune and inflammatory pathways. After filtering through PPI network and multi-machine learning algorithms, five candidate genes generated. Upon validation, CTSD, CEBPD, CYP27A1 were identified as key diagnostic biomarkers with a superior sensitivity and specificity (AUC > 0.8). Furthermore, all three genes were negatively correlated with CD4 + T cells and positively correlated with neutrophils. Single-gene GSEA highlighted the importance of pathogen invasion, metabolism, immune and inflammation responses during the pathogenesis of AMI. Ten target-drugs were predicted. The discovery of three peripheral blood biomarkers capable of predicting the risk of CAD proceeding into AMI in IBD patients. These identified biomarkers were negatively correlated with CD4 + T cells and positively correlated with neutrophils, indicating a latent therapeutic target., (© 2024 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are the second most common cancers in women aged 20-39. While HPV screening can help with early detection of cervical cancer, many patients are already in the medium to late stages when they are identified. As a result, searching for novel biomarkers to predict CESC prognosis and propose molecular treatment targets is critical. TGFA is a polypeptide growth factor with a high affinity for the epidermal growth factor receptor. Several studies have shown that TGFA can improve cancer growth and progression, but data on its impact on the occurrence and advancement of CESC is limited. In this study, we used clinical data analysis and bioinformatics techniques to explore the relationship between TGFA and CESC. The results showed that TGFA was highly expressed in cervical cancer tissues and cells. TGFA knockdown can inhibit the proliferation, migration and invasion of cervical cancer cells. In addition, after TGFA knockout, the expression of IL family and MMP family proteins in CESC cell lines was significantly reduced. In conclusion, TGFA plays an important role in the occurrence and development of cervical cancer. Therefore, TGFA may become a new target for cervical cancer treatment., (© 2023 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

This study investigated the underlying comorbidity mechanism between type 2 diabetes mellitus (T2DM) and osteoarthritis (OA), while also assessing the therapeutic potential of quercetin for early intervention and treatment of these two diseases. The shared genes were obtained through GEO2R, limma and weighted gene co-expression network analysis (WGCNA), and validated using clinical databases and the area under the curves (ROC). Functional enrichment analysis was conducted to elucidate the underlying mechanisms of comorbidity between T2DM and OA. The infiltration of immune cells was analysed using the CIBERSORT algorithm in conjunction with ESTIMATE algorithm. Subsequently, transcriptional regulation analysis, potential chemical prediction, gene-disease association, relationships between the shared genes and ferroptosis as well as immunity-related genes were investigated along with molecular docking. We identified the 12 shared genes (EPHA3, RASIP1, PENK, LRRC17, CEBPB, EFEMP2, UBAP1, PPP1R15A, SPEN, MAFF, GADD45B and KLF4) across the four datasets. Our predictions suggested that targeting these shared genes could potentially serve as therapeutic interventions for both T2DM and OA. Specifically, they are involved in key signalling pathways such as p53, IL-17, NF-kB and MAPK signalling pathways. Furthermore, the regulation of ferroptosis and immunity appears to be interconnected in both diseases. Notably, in this context quercetin emerges as a promising drug candidate for treating T2DM and OA by specifically targeting the shared genes. We conducted a bioinformatics analysis to identify potential therapeutic targets, mechanisms and drugs for T2DM and OA, thereby offering novel insights into molecular therapy for these two diseases., (© 2024 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Background: The role of CIA-II has been clarified in several types of tumors; however, whether dysregulated CIA-II expression is also involved in the pathophysiology of lower-grade glioma (LGG) remains undisclosed., Methods: A comprehensive pan-cancer analysis of the expression patterns and prognostic significance of CIA-II in miscellaneous tumors was undertaken. Subsequently, a detailed bioinformatics analysis was executed to identify putative correlations between CIA-II expression and clinical features, prognosis, biological functions, immunological characteristics, genomic alterations, and chemotherapeutics in LGG. In vitro studies were implemented to examine the potential roles of CIA-II in LGG., Results: CIA-II expression was found to be abnormally elevated in a variety of tumors, including LGG. Additionally, patients with LGG with higher CIA-II expression owned worse prognosis. Importantly, the results declared that CIA-II expression was an independent prognostic indicator for LGG. Moreover, the expression of CIA-II was tightly interrelated with immune cell infiltration, gene mutations, and chemotherapeutics in LGG. In vitro studies revealed that CIA-II was increased and strongly related to the cell proliferation in LGG., Conclusion: CIA-II may be an independent prognostic factor and a serviceable therapeutic target in LGG., (© 2023 The Authors. CNS Neuroscience & Therapeutics Published by John Wiley & Sons Ltd.)

Background and Objective: Atrial fibrillation (AF) and periodontitis, both classified under chronic inflammatory diseases, share common etiologies, including genetic factors and immune pathways. However, the exact mechanisms are still poorly understood. This study aimed to explore the potential common genes and immune characteristics between AF and periodontitis., Methods: Gene expression datasets for AF and periodontitis were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was used to identify common genes in the training set. Functional analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, were conducted to elucidate the underlying mechanisms. Hub genes were further screened based on expression levels, receiver operating characteristic (ROC) curves, and least absolute shrinkage and selection operator (LASSO) regression. Then, based on the expression levels and ROC values of the hub genes in the validation set, the target genes were identified. Finally, immune cell infiltration analysis was performed on the AF and periodontitis datasets in the training set using the "CIBERSORT" R package. The relationships between target genes, infiltrating immune cells, and inflammatory factors were also investigated. In addition, AF susceptibility, atrial fibrosis, inflammatory infiltration, and RGS1 protein expression in rat models of periodontitis were assessed through in vivo electrophysiology experiments, Masson's trichrome staining, hematoxylin-eosin staining, immunohistochemistry, and western blotting, respectively., Results: A total of 21 common genes were identified between AF and periodontitis among the differentially expressed genes. After evaluating gene expression levels, ROC curves, and LASSO analysis, four significant genes between AF and periodontitis were identified, namely regulator of G-protein signaling 1 (RGS1), annexin A6 (ANXA6), solute carrier family 27 member 6 (SLC27A6), and ficolin 1 (FCN1). Further validation confirmed that RGS1 was the optimal shared target gene for AF and periodontitis. Immune cell infiltration analysis revealed that neutrophils and T cells play an important role in the pathogenesis of both diseases. RGS1 showed a significant positive correlation with activated memory CD4 T cells and gamma-delta T cells and a negative correlation with CD8 T cells and regulatory T cells in both training sets. Moreover, RGS1 was positively correlated with classical pro-inflammatory cytokines IL1β and IL6. In periodontitis rat models, AF susceptibility, atrial fibrosis, and inflammatory infiltration were significantly increased, and RGS1 expression in the atrial tissue was upregulated., Conclusion: A common gene between AF and periodontitis, RGS1 appears central in linking the two conditions. Immune and inflammatory responses may underlie the interaction between AF and periodontitis., (© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

This research aimed to find important genes and pathways related to cellular senescence (CS) in diabetic foot ulcers (DFU) and to estimate the possible pathways through which CS affects diabetic foot healing. The GSE80178 dataset was acquired from the Gene Expression Omnibus (GEO) database, containing six DFU and three diabetic foot skin (DFS) samples. The limma package was used to identify differentially expressed genes (DEGs). At the same time, DEGs associated with CS (CS-DEGs) were found using the CellAge database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the CS-DEGs. A protein-protein interaction (PPI) network was built using the String database, and the cytoHubba plug-in within Cytoscape helped identify hub genes. Lastly, the miRNA-TF-mRNA regulatory network for these hub genes was established. In total, 66 CS-DEGs were obtained. These genes mainly focus on CS, Kaposi sarcoma-associated herpesvirus infection and Toll-like receptor signalling pathway. Eight hub genes were identified to regulate cell senescence in DFU, including TP53, SRC, SIRT1, CCND1, EZH2, CXCL8, AR and CDK4. According to miRNA-TF-mRNA regulatory network, hsa-mir-132-3p/SIRT1/EZH2 axis is involved in senescence cell accumulation in DFU., (© 2023 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling., (© 2024 The Authors. Microbial Biotechnology published by Applied Microbiology International and John Wiley & Sons Ltd.)