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Abstract 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 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 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 Background and Aim The biological functions and clinical implications of lysophosphatidylcholine acyltransferase 1 (LPCAT1) remain unclarified in gastric cancer (GC). The aim of the current study was to explore the possible clinicopathological significance of LPCAT1 and its perspective mechanism in GC tissues. Materials and Methods The protein expression and mRNA levels of LPCAT1 were detected from in‐house immunohistochemistry and public high‐throughput RNA arrays and RNA sequencing. To have a comprehensive understanding of the clinical value of LPCAT1 in GC, all enrolled data were integrated to calculate the expression difference and standard mean difference (SMD). The biological mechanism of LPCAT1 in GC was confirmed by computational biology and in vitro experiments. Migration and invasion assays were also conducted to confirm the effect of LPCAT1 in GC. Results Both protein and mRNA expression levels of LPCAT1 in GC were remarkably higher than those in noncancerous controls. Comprehensively, the SMD of LPCAT1 mRNA was 1.11 (95% CI = 0.86–1.36) in GC, and the summarized AUC was 0.85 based on 15 datasets containing 1727 cases of GC and 940 cases of non‐GC controls. Moreover, LPCAT1 could accelerate the invasion and migration of GC by boosting the neutrophil degranulation pathway and disturbing the immune microenvironment. Conclusion An increased level of LPCAT1 may promote the progression of GC.

Abstract Several bioinformatics studies have been performed on high‐throughput expression data to determine the cellular pathways and hub genes affected by Gastric cancer (GC). However, these studies differ in using a healthy tissue or normal tissue adjacent to the tumour (NAT) as calibrator tissues. This study was designed to find how using healthy or NAT tissues as calibrator tissues could affect pathway enrichment data and hub genes in GC. Two gene expression datasets with NAT tissues (GSE79973 and GSE118916) and one dataset with healthy tissues (GSE54129) were downloaded and processed by the limma package to screen the differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were performed by the Enrichr online tool. Protein‐protein interaction network construction, module analysis, and hub genes selection were performed by Cytoscape software, Molecular Complex Detection plugin, and cytoHubba plugin, respectively. The gene expression profiling interactive analysis web server was used to analyse RNA sequencing expression data from The Cancer Genome Atlas Program. The Kaplan—Meier plotter was used to perform survival analysis. Our results showed that some KEGG and GO pathways were shared between studies with NAT and the study with healthy tissues. However, some terms, especially inflammation‐related terms, were missed when NAT tissues were used as calibrator tissues. Also, only FN1 and COL1A1 are common hub genes between DEGs of the studies with NAT and healthy tissues. Since hub genes are usually extracted and suggested as candidate targets for GC diagnosis, prognosis, or treatment, selecting healthy or NAT tissues may affect the hub genes selection.

Abstract Aims The co‐morbidities contribute to the inferior prognosis of COVID‐19 patients. Recent reports suggested that the higher co‐morbidity rate between COVID‐19 and heart failure (HF) leads to increased mortality. However, the common pathogenic mechanism between them remained elusive. Here, we aimed to reveal underlying molecule mechanisms and genetic correlation between COVID‐19 and HF, providing a new perspective on current clinical management for patients with co‐morbidity. Methods The gene expression profiles of HF (GSE26887) and COVID‐19 (GSE147507) were retrieved from the GEO database. After identifying the common differentially expressed genes (|log2FC| > 1 and adjusted P

Background Abdominal aortic aneurysm (AAA) is a vascular disease with a mortality rate of >80% if ruptured. Mitochondrial dysfunction has been previously implicated in AAA pathogenesis. In this study, we aimed to characterize the mitochondrial genetic landscape in AAA. Methods and Results Whole mitochondrial genome sequencing and bioinformatics analysis were performed in comorbidity matched 48 cases without AAA and 48 cases with AAA, objectively diagnosed, and selected from a cohort of 65‐year‐old men recruited for a screening program. We identified differential mutational landscapes in men with and without AAA, with errors in mitochondrial DNA replication or repair as potential sources. Heteroplasmic insertions and overall heteroplasmy of structural rearrangements were significantly elevated in AAA cases. Three heteroplasmic variants were associated with risk factors of AAA: leukocyte concentration, plasma glucose, and cholesterol levels, respectively. Interestingly, mutations were more prevalent in regulatory part of the mitochondria, the displacement loop region, in AAA as compared with controls (P value

Abstract Due to current technology enhancement, molecular databases have exponentially grown requesting faster efficient methods that can handle these amounts of huge data. Therefore, Multi‐processing CPUs technology can be used including physical and logical processors (Hyper Threading) to significantly increase the performance of computations. Accordingly, sequence comparison and pairwise alignment were both found contributing significantly in calculating the resemblance between sequences for constructing optimal alignments. This research used the Hash Table‐NGram‐Hirschberg (HT‐NGH) algorithm to represent this pairwise alignment utilizing hashing capabilities. The authors propose using parallel shared memory architecture via Hyper Threading to improve the performance of molecular dataset protein pairwise alignment. The proposed parallel hyper threading method targeted the transformation of the HT‐NGH on the datasets decomposition for sequence level efficient utilization within the processing units, that is, reducing idle processing unit situations. The authors combined hyper threading within the multicore architecture processing on shared memory utilization remarking performance of 24.8% average speed up to 34.4% as the highest boosting rate. The benefit of this work improvement is shown preserving acceptable accuracy, that is, reaching 2.08, 2.88, and 3.87 boost‐up as well as the efficiency of 1.04, 0.96, and 0.97, using 2, 3, and 4 cores, respectively, as attractive remarkable results.

Pericytes conceivably play important roles in the tumour microenvironment of glioblastoma multiforme (GBM) by allowing for an aberrant vasculature and acting as a component in the perivascular niche that supports glioma stem‐like cells. However, a lack of specific markers has hampered in‐depth elucidation of the functional contribution of pericytes to GBM. This study provides a comprehensive computational biology approach to annotate pericyte marker genes in the GBM vasculature through integration of data from single‐cell RNA‐sequencing studies of both mouse and human tissue, as well as bulk tumour and healthy tissue gene expression data from patients with GBM. We identified distinct vascular‐ and immune‐related gene expression programmes in tumour pericytes that we assessed for association with GBM characteristics and patient survival. Most compellingly, pericyte gene signatures that were upregulated in tumours compared with normal brain tissue were indicative of progression of low‐grade gliomas into high‐grade glioma, suggested by a markedly shorter overall survival. Our results underline the functional importance of tumour pericytes in low‐grade glioma and may serve as a starting point for efforts for precision targeting of pericytes.

Background: The global prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing. The pathogenesis of NAFLD is multifaceted, and the underlying mechanisms are elusive. We conducted data mining analysis to gain a better insight into the disease and to identify the hub genes associated with the progression of NAFLD. Methods: The dataset GSE49541, containing the profile of 40 samples representing mild stages of NAFLD and 32 samples representing advanced stages of NAFLD, was acquired from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the R programming language. The Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database were used to perform the enrichment analysis and construct protein–protein interaction (PPI) networks, respectively. Subsequently, transcription factor networks and key modules were identified. The hub genes were validated in a mice model of high fat diet (HFD)-induced NAFLD and in cultured HepG2 cells by real-time quantitative PCR. Results: Based on the GSE49541 dataset, 57 DEGs were selected and enriched in chemokine activity and cellular component, including the extracellular region. Twelve transcription factors associated with DEGs were indicated from PPI analysis. Upregulated expression of five hub genes (SOX9, CCL20, CXCL1, CD24, and CHST4), which were identified from the dataset, was also observed in the livers of HFD-induced NAFLD mice and in HepG2 cells exposed to palmitic acid or advanced glycation end products. Conclusion: The hub genes SOX9, CCL20, CXCL1, CD24, and CHST4 are involved in the aggravation of NAFLD. Our results offer new insights into the underlying mechanism of NAFLD progression.

Epimedium is a traditional Chinese medicine with a wide range of clinical applications; however, there have been numerous reports of adverse reactions in recent years. The most common side effect of Epimedium is liver injury. In this study, the liquid chromatography-mass spectrometry (LC-MS) method has been established to study the components of Epimedium and to identify the components absorbed into the blood of rats. Bioinformatics was used to screen out potential toxic components, and the integrating metabolomics method was used to explore the molecular mechanism of Epimedium-induced liver injury. The chemical constituents of Epimedium were identified by LC-MS, and 62 compounds were obtained, including 57 flavonoids, four organic acids and one alkaloid. The toxicity network of "Epimedium-component-target-liver injury" was constructed using bioinformatics research methods, and then the key hepatotoxic component icaritin was identified. Integrating metabolomics was used to investigate the changes in the metabolic profile of L-02 cells with different durations of icaritin administration compared with the control group, and 106 different metabolites were obtained. A total of 14 potential biomarkers significantly associated with cell survival were screened by Pearson correlation analysis combined with the L-02 cell survival rate. Our study preliminarily revealed the mechanism of hepatotoxicity induced by Epimedium., (© 2024 John Wiley & Sons Ltd.)

EMT dysfunction is a dominant mechanisms of hypospadias. Thus, identification of EMT-related lncRNAs based on transcriptome sequencing data of hypospadias might provide novel molecular markers and therapeutic targets for hypospadias. First, the microarray data related to hypospadias were downloaded from Gene Expression Omnibus (GEO). Besides, the differentially expressed lncRNAs and messenger RNAs (mRNAs) related to EMT were screened to construct lncRNA-mRNA co-expression interaction pairs. In addition, the microRNA (miRNA) prediction analysis was performed through bioinformatics methods to construct a ceRNA network. Moreover, function prediction and function enrichment and pathway analyses were also performed. Finally, the core EMT-related lncRNAs were verified based on mRNA expression changes and cell functions. A total of 6 EMT-related lncRNAs were identified and 123 mRNA-lncRNA co-expression interaction pairs were screened in this study. Additionally, a ceRNA regulatory network comprising 17 mRNAs, 4 lncRNAs, and 28 miRNAs was constructed based on the prediction of hypospadias-related miRNAs. The validation results of the dataset GSE121712 revealed that only BEX1 was positively correlated with the expression of the lncRNA GNAS-AS1 (r = 0.874, P < 0.01), both of which had high expression. The cell experiment results demonstrated that interfering with the expression of GNAS-AS1 significantly promoted the proliferation, migration, and EMT of cells. Importantly, it was confirmed that GNAS-AS1 can serve as a ceRNA and play an important role in the EMT of hypospadias. Hence, it may be considered as a potential target in the treatment of this disease., (© 2024 The Author(s). IET Systems Biology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.)

Objective: Systemic lupus erythematosus (SLE), an autoimmune disease with incompletely understood etiology, has a strong genetic component. Although genome-wide association studies (GWASs) have revealed multiple SLE susceptibility loci and associated single-nucleotide polymorphisms (SNPs), the precise causal variants, target genes, cell types, tissues, and mechanisms of action remain largely unknown., Methods: Here, we report a comprehensive post-GWAS analysis using extensive bioinformatics, molecular modeling, and integrative functional genomic and epigenomic analyses to optimize fine-mapping. We compile and cross-reference immune cell-specific expression quantitative trait loci (cis- and trans-expression quantitative trait loci) with promoter capture high-throughput capture chromatin conformation (PCHi-C), allele-specific chromatin accessibility, and massively parallel reporter assay data to define predisposing variants and target genes. We experimentally validate a predicted locus using CRISPR/Cas9 genome editing, quantitative polymerase chain reaction, and Western blot., Results: Anchoring on 452 index SNPs, we selected 9,931 high linkage disequilibrium (r 2 > 0.8) SNPs and defined 182 independent non-human leukocyte antigen (HLA) SLE loci. The 3,746 SNPs from 143 loci were identified as regulating 564 unique genes. Target genes are enriched in lupus-related tissues and associated with other autoimmune diseases. Of these, 329 SNPs (106 loci) showed significant allele-specific chromatin accessibility and/or enhancer activity, indicating regulatory potential. Using CRISPR/Cas9, we validated reference SNP identifier 57668933 (rs57668933) as a functional variant regulating multiple targets, including SLE-risk gene ELF1 in B cells., Conclusion: We demonstrate and validate post-GWAS strategies for using multidimensional data to prioritize likely causal variants with cognate gene targets underlying SLE pathogenesis. Our results provide a catalog of significantly SLE-associated SNPs and loci, target genes, and likely biochemical mechanisms to guide experimental characterization., (© 2024 American College of Rheumatology.)

Omics, bioinformatics, molecular docking, and experimental validation were used to elucidate the hepatoprotective effects, mechanisms, and active compounds of Shandougen (SDG) based on the biolabel-led research pattern. Integrated omics were used to explore the biolabels of SDG intervention in liver tissue. Subsequently, bioinformatics and molecular docking were applied to topologically analyze its therapeutic effects, mechanisms, and active compounds based on biolabels. Finally, an animal model was used to verify the biolabel analysis results. Omics, bioinformatics, and molecular docking revealed that SDG may exert therapeutic effects on liver diseases in the multicompound and multitarget synergistic modes, especially liver cirrhosis. In the validation experiment, SDG and its active compounds (betulinic acid and gallic acid) significantly improved the liver histopathological damage in the CCl 4 -induced liver cirrhosis model. Meanwhile, they also produced significant inhibitory effects on the focal adhesion pathway (integrin alpha-1, myosin regulatory light chain 2, laminin subunit gamma-1, etc.) and alleviated the associated pathological processes: focal adhesion (focal adhesion kinase 1)-extracellular matrix (collagen alpha-1(IV) chain, collagen alpha-1(VI) chain, and collagen alpha-2(VI) chain) dysfunction, carcinogenesis (alpha-fetoprotein, NH 3 , and acetylcholinesterase), inflammation (tumor necrosis factor alpha, interleukin-1 [IL-1], IL-6, and IL-10), and oxidative stress (reactive oxygen species, malonaldehyde, and superoxide dismutase). This study provides new evidence and insights for the hepatoprotective effects, mechanisms, and active compounds of SDG., (© 2024 John Wiley & Sons, Ltd.)

Abstract Fish eDNA metabarcoding is a nonintrusive, time‐ and cost‐effective way to detect biodiversity, with potential applications in ecosystem‐based management and fisheries assessment. Nevertheless, fish‐specific eDNA resources are currently not well‐developed, and many fish species are not yet sequenced in reference databases. We developed Mitohelper, a Python‐based command line tool to annotate and align fish mitochondrial sequences available in the existing MitoFish database. Mitohelper improves MitoFish annotations by adding gene names and additional taxonomic classifications. Using these improved reference datasets, Mitohelper's getrecord function searches MitoFish for available mitochondrial (cytochrome c oxidase, 12S rRNA, and others) gene sequences against a user‐provided list of fish taxonomic names. Mitohelper's getalignment function aligns (often partial) mitochondrial gene sequences to a user‐specified full‐length reference sequence for the assessment and visualization of overlapping sequencing regions. To facilitate the development of taxonomic classifiers, we combined Mitohelper's 12S rRNA dataset with SILVA’s 16S rRNA and 18S rRNA datasets in a QIIME 2‐compatible format. By providing valuable information on taxonomic and gene region coverage of currently available fish mitochondrial data, Mitohelper's functions promote informed experimental design that can guide sequencing and analysis strategies. In summary, Mitohelper improves the breadth and functionality of eDNA data resources, as well as the accuracy of taxonomic classification. Mitohelper and its reference datasets are updated approximately monthly and available at https://github.com/aomlomics/mitohelper.

Abstract Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan‐binding proteins or lectins are abound in nature. Widely used as staining and characterization reagents in cell biology and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, LectinOracle, a model combining transformer‐based representations for proteins and graph convolutional neural networks for glycans to predict their interaction, is presented. Using a curated data set of 564,647 unique protein–glycan interactions, it is shown that LectinOracle predictions agree with literature‐annotated specificities for a wide range of lectins. Using a range of specialized glycan arrays, it is shown that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. It is further demonstrated that LectinOracle can be used to improve lectin classification, accelerate lectin directed evolution, predict epidemiological outcomes in the context of influenza virus, and analyze whole lectomes in host–microbe interactions. It is envisioned that the herein presented platform will advance both the study of lectins and their role in (glyco)biology.

Calcific aortic valve disease (CAVD) and osteoarthritis (OA) are common diseases in the ageing population and share similar pathogenesis, especially in inflammation. This study aims to discover potential diagnostic and therapeutic targets in patients with CAVD and OA. Three CAVD datasets and one OA dataset were obtained from the Gene Expression Omnibus database. We used bioinformatics methods to search for key genes and immune infiltration, and established a ceRNA network. Immunohistochemical staining was performed to verify the expression of candidate genes in human and mice aortic valve tissues. Two key genes obtained, leucine rich repeat containing 15 (LRRC15) and secreted phosphoprotein 1 (SPP1), were further screened using machine learning and verified in human and mice aortic valve tissues. Compared to normal tissues, the infiltration of immune cells in CAVD tissues was significantly higher, and the expressions of LRRC15 and SPP1 were positively correlated with immune cells infiltration. Moreover, the ceRNA network showed extensive regulatory interactions based on LRRC15 and SPP1. The authors' findings identified LRRC15 and SPP1 as hub genes in immunological mechanisms during CAVD and OA initiation and progression, as well as potential targets for drug development., (© 2024 The Authors. IET Systems Biology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.)

Abstract Background Down syndrome (DS) is characterized by the presence of an extra full or partial human chromosome 21 (Hsa21). An invaluable model to define genotype‐phenotype correlations in DS is the study of the extremely rare cases of partial (segmental) trisomy 21 (PT21), the duplication of only a delimited region of Hsa21 associated or not to DS. A systematic retrospective reanalysis of 125 PT21 cases described up to 2015 allowed the creation of the most comprehensive PT21 map and the identification of a 34‐kb highly restricted DS critical region (HR‐DSCR) as the minimal region whose duplication is shared by all PT21 subjects diagnosed with DS. We reanalyzed at higher resolution three cases previously published and we accurately searched for any new PT21 reports in order to verify whether HR‐DSCR limits could prospectively be confirmed and possibly refined. Methods Hsa21 partial duplications of three PT21 subjects were refined by adding array‐based comparative genomic hybridization data. Seven newly described PT21 cases fulfilling stringent cytogenetic and clinical criteria have been incorporated into the PT21 integrated map. Results The PT21 map now integrates fine structure of Hsa21 sequence intervals of 132 subjects onto a common framework fully consistent with the presence of a duplicated HR‐DSCR, on distal 21q22.13 sub‐band, only in DS subjects and not in non‐DS individuals. No documented exception to the HR‐DSCR model was found. Conclusions The findings presented here further support the association of the HR‐DSCR with the diagnosis of DS, representing an unbiased validation of the original model. Further studies are needed to identify and characterize genetic determinants presumably located in the HR‐DSCR and functionally associated to the critical manifestations of DS.

In recent times, it has been realized that novel vaccines are required to combat emerging disease outbreaks, and faster optimization is required to respond to global vaccine demands. Although, fed-batch operations offer better productivity, experiment-based optimization of a new fed-batch process remains expensive and time-consuming. In this context, we propose a novel computational framework that can be used for process optimization and control of a fed-batch baculovirus-insect cell system. Since the baculovirus expression vector system (BEVS) is known to be widely used platforms for recombinant protein/vaccine production, we chose this system to demonstrate the identification of optimal profile. Toward this, first, we constructed a mathematical model that captures the time course of cell and virus growth in a baculovirus-insect cell system. Second, the proposed model was used for numerical analysis to determine the optimal operating profiles of control variables such as culture media, cell density, and oxygen based on a multiobjective optimal control formulation. Third, a detailed comparison between batch and fed-batch culture was perfromed along with a comparison between various alternatives of fed-batch operation. Finally, we demonstrate that a model-based quantification of controlled feed addition in fed-batch culture is capable of providing better productivity as compared to a batch culture. The proposed framework can be utilized for the estimation of optimal operating regions of different control variables to achieve maximum infected cell density and virus yield while minimizing the substrate/media, uninfected cell, and oxygen consumption., (© 2023 Wiley Periodicals LLC.)

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease worldwide. Basement membranes (BMs) are ubiquitous extracellular matrices which are affected in many diseases including DKD. Here, the authors aimed to identify BM-related markers in DKD and explored the immune cell infiltration in this process. The expression profiles of three datasets were downloaded from the Gene Expression Omnibus database. BM-related differentially expression genes (DEGs) were identified and Kyoto encyclopaedia of genes and genomes pathway enrichment analysis were applied to biological functions. Immune cell infiltration and immune function in the kidneys of patients with DKD and healthy controls were evaluated and compared using the ssGSEA algorithm. The association of hub genes and immune cells and immune function were explored. A total of 30 BM-related DEGs were identified. The functional analysis showed that BM-related DEGs were notably associated with basement membrane alterations. Crucially, BM-related hub genes in DKD were finally identified, which were able to distinguish patients with DKD from controls. Moreover, the authors observed that laminin subunit gamma 1(LAMC1) expression was significantly high in HK2 cells treated with high glucose. Immunohistochemistry results showed that, compared with those in db/m mouse kidneys, the levels of LAMC1 in db/db mouse kidneys were significantly increased. The biomarkers genes may prove crucial for DKD treatment as they could be targeted in future DKD treatment protocols., (© 2023 The Authors. IET Systems Biology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.)

The coronavirus disease 2019 (COVID-19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS-CoV-2 infection, deserves attention. As COVID-19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID-19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID-19. A risk prediction model was developed to assess the prognosis of patients infected with SARS-CoV-2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS-CoV-2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID-19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID-19. With the increasing availability of COVID-19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines., (© 2023 The Authors. IET Systems Biology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.)

Hepatocellular carcinoma (HCC) is a fatal disease with poor clinical outcomes. T cells play a vital role in the crosstalk between the tumour microenvironment and HCC. Single-cell RNA sequencing data were downloaded from the GSE149614 dataset. The T-cell-related prognostic signature (TRPS) was developed with the integrative procedure including 10 machine learning algorithms. The TRPS was established using 7 T-cell-related markers in the Cancer Genome Atlas cohort with 1-, 2- and 3-year area under curve values of 0.820, 0.725 and 0.678, respectively. TRPS acted as an independent risk factor for HCC patients. HCC patients with a high TRPS-based risk score had a higher Tumour Immune Dysfunction and Exclusion score, lower PD1 and CTLA4 immunophenoscore and lower level of immunoactivated cells, including CD8 + T cells and NK cells. The response rate was significantly higher in patients with low-risk scores in immunotherapy cohorts, including IMigor210 and GSE91061. The TRPS-based nomogram had a relatively good predictive value in evaluating the mortality risk at 1, 3 and 5 years in HCC. Overall, this study develops a TRPS by integrated bioinformatics analysis. This TRPS acted as an independent risk factor for the OS rate of HCC patients. It can screen for HCC patients who might benefit from immunotherapy, chemotherapy and targeted therapy., (© 2023 The Authors. IET Systems Biology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.)

Spatial biology is a rapidly developing field which enables the visualization of protein and transcriptomic data while preserving tissue context and architecture. Initially used in discovery, there is growing promise for translational and diagnostic assay developments. Immediate applications are in precision medicine, such as being able to match patients to optimal therapies through better understanding the tumor microenvironment. However, it also has ramifications for many other disciplines (e.g. immunology, cancer, infectious disease and digital pathology). With increasingly massive data sets being generated, data storage, curation, analysis and sharing require more computational approaches and artificial intelligence-powered tools to fully utilize spatial tools. Here, we discuss spatial biology as an important convergent science approach to tackling complex global challenges in areas such as health., (© 2023 the Australian and New Zealand Society for Immunology, Inc.)