Bioinformatic analysis of the molecular targets of curcumin in colorectal cancer.

Colorectal cancer (CRC) is a major global health concern, with rising incidence and mortality rates. Conventional treatments often come with significant complications, prompting the exploration of natural compounds like curcumin as potential therapeutic agents. Using bioinformatic tools, this study investigated the role of curcumin in CRC treatment. Significant protein interactions between curcumin and target proteins were identified in the STITCH database. Differentially expressed genes (DEGs) associated with CRC were then analyzed from GEO databases. Comparing curcumin targets and CRC-related DEGs, nine significant common targets were identified: DNMT1, PCNA, CCND1, PLAU, MMP3, SOX9, FOXM1, CXCL2, and SERPINB5. Pathway enrichment analyses revealed that curcumin-targeted pathways were primarily related to p53, IL-17, NF-kappa B, TNF, and cell cycle signaling, all crucial in CRC development and progression. Further analyses using DAID and EnrichR algorithms showed that the curcumin targets exhibited greater specificity to bronchial epithelial cells and colorectal adenocarcinoma than other diseases. Analyses via the DSigDB database indicated that curcumin ranks highly among other drugs targeting the identified CRC-related genes. Docking studies revealed favorable binding interactions between curcumin and the key CRC-related proteins, suggesting potential molecular mechanisms by which curcumin may exert its effects. In summary, this study provides bioinformatic and docking evidence that curcumin may exert beneficial effects on CRC by modulating the expression or activity of multiple CRC-susceptibility genes involved in critical signaling pathways. These findings warrant further experimental validation and support the potential of curcumin as a therapeutic agent for CRC.

Differential expression of ion channel coding genes in the endometrium of women experiencing recurrent implantation failures.

Our study probed the differences in ion channel gene expression in the endometrium of women with Recurrent Implantation Failure (RIF) compared to fertile women. We analyzed the relative expression of genes coding for T-type Ca2+, ENaC, CFTR, and KCNQ1 channels in endometrial samples from 20 RIF-affected and 10 control women, aged 22-35, via microarray analysis and quantitative real-time PCR. Additionally, we examined DNA methylation in the regulatory region of KCNQ1 using ChIP real-time PCR. The bioinformatics component of our research included Gene Ontology analysis, protein-protein interaction networks, and signaling pathway mapping to identify key biological processes and pathways implicated in RIF. This led to the discovery of significant alterations in the expression of ion channel genes in RIF women's endometrium, most notably an overexpression of CFTR and reduced expression of SCNN1A, SCNN1B, SCNN1G, CACNA1H, and KCNQ1. A higher DNA methylation level of KCNQ1's regulatory region was also observed in RIF patients. Gene-set enrichment analysis highlighted a significant presence of genes involved with ion transport and membrane potential regulation, particularly in sodium and calcium channel complexes, which are vital for cation movement across cell membranes. Genes were also enriched in broader ion channel and transmembrane transporter complexes, underscoring their potential extensive role in cellular ion homeostasis and signaling. These findings suggest a potential involvement of ion channels in the pathology of implantation failure, offering new insights into the mechanisms behind RIF and possible therapeutic targets.

Transcriptome Profiling of Gene Expression in Atlantic Salmon (Salmo salar) at Early Stage of Development.

For Atlantic salmon development, the most critical phase is the early development stage from egg to fry through alevin. However, the studies investigating the early development of Atlantic salmon based on RNA-seq are scarce and focus only on one stage of development. Therefore, using the RNA-seq technology, the assessment of different gene expressions of various early development stages (egg, alevin, and fry) was performed on a global scale. Over 22 GB of clean data was generated from 9 libraries with three replicates for each stage with over 90% mapping efficiency. A total of 5534 genes were differentially expressed, among which 19, 606, and 826 genes were specifically expressed in each stage, respectively. The transcriptome analysis showed that the number of differentially expressed genes (DEGs) increased as the Atlantic salmon progressed in development from egg to fry stage. In addition, gene ontology enrichment demonstrated that egg and alevin stages are characterized by upregulation of genes involved in spinal cord development, neuron projection morphogenesis, axonogenesis, and cytoplasmic translation. At the fry stage, upregulated genes were enriched in the muscle development process (muscle cell development, striated muscle cell differentiation, and muscle tissue development), immune system (defense response and canonical NF-kappaB signal transduction), as well as epidermis development. These results suggest that the early development of Atlantic salmon is characterized by a dynamic shift in gene expression and DEGs between different stages, which provided a solid foundation for the investigation of Atlantic salmon development.

Transcriptional reprogramming post-peripheral nerve injury: A systematic review.

Neuropathic pain is characterised by periodic or continuous hyperalgesia, numbness, or allodynia, and results from insults to the somatosensory nervous system. Peripheral nerve injury induces transcriptional reprogramming in peripheral sensory neurons, contributing to increased spinal nociceptive input and the development of neuropathic pain. Effective treatment for neuropathic pain remains an unmet medical need as current therapeutics offer limited effectiveness and have undesirable effects. Understanding transcriptional changes in peripheral nerve injury-induced neuropathy might offer a path for novel analgesics. Our literature search identified 65 papers exploring transcriptomic changes post-peripheral nerve injury, many of which were conducted in animal models. We scrutinize their transcriptional changes data and conduct gene ontology enrichment analysis to reveal their common functional profile. Focusing on genes involved in 'sensory perception of pain' (GO:0019233), we identified transcriptional changes for different ion channels, receptors, and neurotransmitters, shedding light on its role in nociception. Examining peripheral sensory neurons subtype-specific transcriptional reprograming and regeneration-associated genes, we delved into downstream regulation of hypersensitivity. Identifying the temporal program of transcription regulatory mechanisms might help develop better therapeutics to target them effectively and selectively, thus preventing the development of neuropathic pain without affecting other physiological functions.

BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data.

Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratifications of predictor variables, leading to algorithms that are both more accurate and generalizable. However, the wider availability of machine learning tools capable of incorporating such biological knowledge remains limited. Addressing this gap, we introduce BioM2, a novel R package designed for biologically informed multistage machine learning. BioM2 uniquely leverages biological information to effectively stratify and aggregate high-dimensional biological data in the context of machine learning. Demonstrating its utility with genome-wide DNA methylation and transcriptome-wide gene expression data, BioM2 has shown to enhance predictive performance, surpassing traditional machine learning models that operate without the integration of biological knowledge. A key feature of BioM2 is its ability to rank predictor variables within biological categories, specifically Gene Ontology pathways. This functionality not only aids in the interpretability of the results but also enables a subsequent modular network analysis of these variables, shedding light on the intricate systems-level biology underpinning the predictive outcome. We have proposed a biologically informed multistage machine learning framework termed BioM2 for phenotype prediction based on omics data. BioM2 has been incorporated into the BioM2 CRAN package (https://cran.r-project.org/web/packages/BioM2/index.html).

A computational study of gene expression patterns in head and neck squamous cell carcinoma using TCGA data.

Aim: Head and Neck squamous cell carcinoma (HNSCC) is the second most prevalent cancer in Pakistan. Methods: Gene expression data from TCGA and GETx for normal genes to analyze Differentially Expressed Genes (DEGs). Data was further investigated using the Enrichr tool to perform Gene Ontology (GO). Results: Our analysis identified most significantly differentially expressed genes and explored their established cellular functions as well as their potential involvement in tumor development. We found that the highly expressed Keratin family and S100A9 genes. The under-expressed genes KRT4 and KRT13 provide instructions for the production of keratin proteins. Conclusion: Our study suggests that factors such as poor oral hygiene and smokeless tobacco can result in oral stress and cellular damage and cause cancer.

The Cancer Genome Atlas (TCGA) holds vast cancer data processed with powerful computers and cloud tech. This sparks new bioinformatics for better cancer diagnosis, treatment, and prevention. In Southeast Asia, Head and Neck Squamous Cell Carcinoma (HNSCC) is prevalent. We used TCGA and GETx data to study gene expression. High-expression Keratin and S100A9 genes fight cellular damage under stress, while under-expressed KRT4 and KRT13 genes shape cell structure. Poor oral care and smokeless tobacco could induce cell damage, sparking cancer mutations. Unveiling HNSCC mechanisms may guide targeted treatments and preventive strategies.

Weighted single-step genome-wide association study to reveal new candidate genes for productive traits of Landrace pig in Korea.

The objective of this study was to identify genomic regions and candidate genes associated with productive traits using a total of 37,099 productive records and 6,683 single nucleotide polymorphism (SNP) data obtained from five Great-Grand-Parents (GGP) farms in Landrace. The estimated of heritabilities for days to 105 kg (AGE), average daily gain (ADG), backfat thickness (BF), and eye muscle area (EMA) were 0.49, 0.49, 0.56, and 0.23, respectively. We identified a genetic window that explained 2.05%-2.34% for each trait of the total genetic variance. We observed a clear partitioning of the four traits into two groups, and the most significant genomic region for AGE and ADG were located on the Sus scrofa chromosome (SSC) 1, while BF and EMA were located on SSC 2. We conducted Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), which revealed results in three biological processes, four cellular component, three molecular function, and six KEGG pathway. Significant SNPs can be used as markers for quantitative trait loci (QTL) investigation and genomic selection (GS) for productive traits in Landrace pig.

Deciphering early responsive signature genes in rice blast disease: an integrated temporal transcriptomic study.

Rice blast disease, caused by Magnaporthe oryzae, reigns as the top-most cereal killer, jeopardizing global food security. This necessitates the timely scouting of pathogen stress-responsive genes during the early infection stages. Thus, we integrated time-series microarray (GSE95394) and RNA-Seq (GSE131641) datasets to decipher rice transcriptome responses at 12- and 24-h post-infection (Hpi). Our analysis revealed 1580 differentially expressed genes (DEGs) overlapped between datasets. We constructed a protein-protein interaction (PPI) network for these DEGs and identified significant subnetworks using the MCODE plugin. Further analysis with CytoHubba highlighted eight plausible hub genes for pathogenesis: RPL8 (upregulated) and RPL27, OsPRPL3, RPL21, RPL9, RPS5, OsRPS9, and RPL17 (downregulated). We validated the expression levels of these hub genes in response to infection, finding that RPL8 exhibited significantly higher expression compared with other downregulated genes. Remarkably, RPL8 formed a distinct cluster in the co-expression network, whereas other hub genes were interconnected, with RPL9 playing a central role, indicating its pivotal role in coordinating gene expression during infection. Gene Ontology highlighted the enrichment of hub genes in the ribosome and protein translation processes. Prior studies suggested that plant immune defence activation diminishes the energy pool by suppressing ribosomes. Intriguingly, our study aligns with this phenomenon, as the identified ribosomal proteins (RPs) were suppressed, while RPL8 expression was activated. We anticipate that these RPs could be targeted to develop new stress-resistant rice varieties, beyond their housekeeping role. Overall, integrating transcriptomic data revealed more common DEGs, enhancing the reliability of our analysis and providing deeper insights into rice blast disease mechanisms.

Comprehensive microarray analysis of severe preeclampsia placenta to identify differentially expressed genes, biological pathways, hub genes, and their related non-coding RNAs.

INTRODUCTION: Preeclampsia (PE) is a serious pregnancy-related complication caused by high blood pressure in pregnant women. The severe form has more devastating effects. According to the growing evidence, the placenta is a crucial component in the pathogenesis of PE, and eliminating it will alleviate symptoms. METHODS: GEO's severe preeclampsia placenta microarray datasets; GSE147776, GSE66273, GSE102897, and GSE10588, were chosen to identify differentially expressed genes (DEGs) in different biological pathways. The analysis of hub genes and related non-coding RNAs was done as well. RESULTS: A total of 347 DEGs with adj p-value <0.05 and ǀlog2FoldChangeǀ> 0.5 were discovered between severe PEs and healthy pregnancies, including 204 over-expressed genes and 143 under-expressed genes. The MCC method identified ISG15, IFI44L, MX2, OAS2, MX1, FN1, LDHA, ITGB3, TKT, HK2 genes as the top ten hub genes. Interactions between hub genes and noncoding RNAs were also conducted. The most enriched pathways were as follows; HIF-1 signaling pathway; Pathways in cancer; Alanine, aspartate and glutamate metabolism; Arginine biosynthesis; Human papillomavirus infection; Glycolysis/Gluconeogenesis; Central carbon metabolism in cancer; Valine, leucine and isoleucine degradation; Cysteine and methionine metabolism; and Galactose metabolism. DISCUSSION: This is a secondary data analysis conducted on severe preeclampsia placenta to identify differentially expressed genes, biological pathways, hub-genes, and related noncoding RNAs. Functional studies are crucial to understanding the precise role of these genes in the pathogenesis of PE. Also, accepting a gene as a diagnostic or prognostic marker for early diagnosis and management of PE requires multiple lines of evidence.

Bioinformatics reveals the potential mechanisms and biomarkers of necroptosis in neuroblastoma.

Background: Neuroblastoma (NB) is a malignant tumor primarily found in children, presenting significant challenges in its development and prognosis. The role of necroptosis in the pathogenesis of NB has been acknowledged as crucial for treatment. This study aimed to investigate the key genes and functional pathways associated with necroptosis, as well as immune infiltration analysis, in NB. Furthermore, we aimed to evaluate the diagnostic significance of these genes for prognostic assessment and explore their potential immunological characteristics. Methods: The NB dataset (GSE19274, GSE73517, and GSE85047) was obtained from the Gene Expression Omnibus (GEO) database, and genes associated with necroptosis were collected from GeneCards and previous literature. First, we conducted differential expression analysis and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We employed gene set enrichment analysis (GSEA) to identify overlapping enriched functional pathways from the NB dataset. In addition, we constructed a protein-protein interaction (PPI) network, predicting relevant microRNAs (miRNAs) and transcription factors (TFs), as well as their corresponding drug predictions. Furthermore, the diagnostic value was assessed using receiver operating characteristic (ROC) curves. Finally, an immune infiltration analysis was performed. Results: We identified six necroptosis-related differentially expressed genes (NRDEGs) closely associated with necroptosis in NB. They were enriched in Tuberculosis, Apoptosis-multiple species, Salmonella infection, legionellosis, and platinum drug resistance. GSEA and PPI network analyses, along with mRNA-drug interaction network, revealed 38 potential drugs corresponding to BIRC2, CAMK2G, CASP3, and IL8. ROC curve analysis showed that in GSE19274, FLOT2 with area under the ROC curve (AUC) of 0.850 and DAPK1 with AUC of 0.789. Conclusions: Our study elucidates the key genes and functional pathways associated with necroptosis in NB, offering valuable insights to enhance our comprehension of the pathogenesis of NB, and improve prognosis assessment.

Comparative proteomic analysis of PK-15 cells infected with wild-type strain and its EP0 gene-deleted mutant strain of pseudorabies virus.

IMPORTANCE: As one of the main etiologic agents of infectious diseases in pigs, pseudorabies virus (PRV) infections have caused enormous economic losses worldwide. EP0, one of the PRV early proteins (EP) plays a vital role in PRV infections, but the mechanisms are unclear. OBJECTIVE: This study examined the function of EP0 to provide a direction for its in-depth analysis. METHODS: In this study, the EP0-deleted PRV mutant was obtained, and Tandem Mass Tag-based proteomic analysis was used to screen the differentially expressed proteins (DEPs) quantitatively in EP0-deleted PRV- or wild-type PRV-infected porcine kidney 15 cells. RESULTS: This study identified 7,391 DEPs, including 120 and 21 up-regulated and down-regulated DEPs, respectively. Western blot analysis confirmed the changes in the expression of the selected proteins, such as speckled protein 100. Comprehensive analysis revealed 141 DEPs involved in various biological processes and molecular functions, such as transcription regulator activity, biological regulation, and localization. CONCLUSIONS AND RELEVANCE: These results holistically outlined the functions of EP0 during a PRV infection and might provide a direction for more detailed function studies of EP0 and the stimulation of lytic PRV infections.

Elucidating the therapeutic mechanism of betanin in Alzheimer's Disease treatment through network pharmacology and bioinformatics analysis.

Betanin, a natural compound with anti-inflammatory and antioxidant properties, has shown promise in mitigating Alzheimer's disease (AD) by reducing amyloid plaque production. Employing network pharmacology, this study aimed to elucidate betanin's therapeutic mechanism in AD treatment. Through integrated analyses utilizing SwissTargetPrediction, STITCH, BindingDB, Therapeutic Target Database (TTD), and OMIM databases, potential protein targets of betanin in AD were predicted. Gene ontology analysis facilitated the identification of 49 putative AD targets. Subsequent gene enrichment and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis revealed associations between these targets and AD. Network pharmacology techniques and molecular docking aided in prioritizing essential genes, with APP, CASP7, ITPR1, CASP8, CASP3, ITPR3, and NF-KB1 emerging as top candidates. The results provide novel insights into betanin's therapeutic efficacy, shedding light on its potential clinical application in AD treatment. By targeting key genes implicated in AD pathology, betanin demonstrates promise as a valuable addition to existing therapeutic strategies. This holistic approach emphasizes the relevance of network pharmacology and bioinformatics analysis in understanding natural chemical disease therapy processes.

Cultivation driven transcriptomic changes in the wild-type and mutant strains of Rhodospirillum rubrum.

Purple photosynthetic bacteria (PPB) are versatile microorganisms capable of producing various value-added chemicals, e.g., biopolymers and biofuels. They employ diverse metabolic pathways, allowing them to adapt to various growth conditions and even extreme environments. Thus, they are ideal organisms for the Next Generation Industrial Biotechnology concept of reducing the risk of contamination by using naturally robust extremophiles. Unfortunately, the potential of PPB for use in biotechnology is hampered by missing knowledge on regulations of their metabolism. Although Rhodospirillum rubrum represents a model purple bacterium studied for polyhydroxyalkanoate and hydrogen production, light/chemical energy conversion, and nitrogen fixation, little is known regarding the regulation of its metabolism at the transcriptomic level. Using RNA sequencing, we compared gene expression during the cultivation utilizing fructose and acetate as substrates in case of the wild-type strain R. rubrum DSM 467T and its knock-out mutant strain that is missing two polyhydroxyalkanoate synthases PhaC1 and PhaC2. During this first genome-wide expression study of R. rubrum, we were able to characterize cultivation-driven transcriptomic changes and to annotate non-coding elements as small RNAs.

Interpreting Gene Ontology Annotations Derived from Sequence Homology Methods.

The Gene Ontology (GO) project describes the functions of the gene products of organisms from all kingdoms of life in a standardized way, enabling powerful analyses of experiments involving genome-wide analysis. The scientific literature is used to convert experimental results into GO annotations that systematically classify gene products' functions. However, to address the fact that only a minor fraction of all genes has been characterized experimentally, multiple predictive methods to assign GO annotations have been developed since the inception of GO. Sequence homologies between novel genes and genes with known functions help to approximate the roles of these non-characterized genes. Here we describe the main sequence homology methods to produce annotations: pairwise comparison (BLAST), protein profile models (InterPro), and phylogenetic-based annotation (PAINT). Some of these methods can be implemented with genome analysis pipelines (BLAST and InterPro2GO), while PAINT is curated by the GO consortium.

A large-scale assessment of sequence database search tools for homology-based protein function prediction.

Sequence database searches followed by homology-based function transfer form one of the oldest and most popular approaches for predicting protein functions, such as Gene Ontology (GO) terms. These searches are also a critical component in most state-of-the-art machine learning and deep learning-based protein function predictors. Although sequence search tools are the basis of homology-based protein function prediction, previous studies have scarcely explored how to select the optimal sequence search tools and configure their parameters to achieve the best function prediction. In this paper, we evaluate the effect of using different options from among popular search tools, as well as the impacts of search parameters, on protein function prediction. When predicting GO terms on a large benchmark dataset, we found that BLASTp and MMseqs2 consistently exceed the performance of other tools, including DIAMOND-one of the most popular tools for function prediction-under default search parameters. However, with the correct parameter settings, DIAMOND can perform comparably to BLASTp and MMseqs2 in function prediction. Additionally, we developed a new scoring function to derive GO prediction from homologous hits that consistently outperform previously proposed scoring functions. These findings enable the improvement of almost all protein function prediction algorithms with a few easily implementable changes in their sequence homolog-based component. This study emphasizes the critical role of search parameter settings in homology-based function transfer and should have an important contribution to the development of future protein function prediction algorithms.

Evaluation of Near-Infrared Laser Effects on 143B Cells: A System Biology Approach.

Introduction: Photothermal therapy (PTT) by using a near-infrared (NIR) laser, as a successful treatment of cancer, has attracted extensive attention of researchers. Its advantages as a noninvasive and suitable method have been confirmed. Discovery of the NIR laser molecular mechanism at the cellular level via system biology assessment to identify the crucial targeted genes is the aim of this study. Methods: RNA-seq series of six samples were retrieved from Gene Expression Omnibus (GEO) and pre-evaluated by the GEO2R program for more analysis. The significant differentially expressed genes (DEGs) were determined and studied via gene expression analysis, protein-protein interaction (PPI) network assessment, action map evaluation, and gene ontology enrichment. Results: HSPA5, DDIT3, TRIB3, PTGS2, HMOX1, ASNS, GDF15, SLC7A11, and SQSTM1 were identified as central genes. Comparing the central genes and the determined crucial genes via gene expression analysis, actin map results, and gene ontology enrichment led to the introduction of HSPA5, DDIT3, PTGS2, HMOX1, and GDF15 as critical genes in response to the NIR laser. Conclusion: The results indicated that the principle biological process "Endoplasmic reticulum unfolded protein response" and HSPA5, DDIT3, PTGS2, HMOX1, and GDF15 are the crucial targets of the NIR laser. The results also showed that the NIR laser induces stress conditions in the irradiated cells.

Unraveling the intricate physiological processes dysregulated in CHD-affected and Dan-Lou tablet-treated individuals.

Coronary heart disease (CHD), a multifactorial cardiovascular condition, arises from the accumulation of atherosclerotic plaque in the coronary arteries, resulting in compromised blood flow to the heart and complications such as angina, myocardial infarction, or heart failure. Addressing global prevalence, risk factors, and genetics is crucial for effective management. The current study aims to identify molecular biomarkers for CHD by scrutinizing the expression patterns of differentially expressed genes (DEGs), utilizing various bioinformatic tools. In this investigation, a total of 24 samples underwent examination using the GEO2R tool. These included eight samples from individuals before treatment (GSM5434123-30), eight samples from patients after Dan-Lou tablet treatment (GSM5434131-38), and eight samples from healthy control subjects (GSM5434139-46). A suite of bioinformatics tools was used to detect enriched genes within the network, namely, Cytoscape (v3.10.1) and Molecular Complex Detection (MCODE). Functional analysis of the DEGs was conducted via clusterProfiler, a R-based package, and ClueGO. 182 and 174 DEGs corresponding to untreated and treated patient sample groups were functionally annotated for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. ARF6 gene dysregulation was implicated in the myeloid cell apoptotic process (GO:0033028), regulation of actin cytoskeleton (hsa:04810), and other vital cellular functions. The myeloid cell apoptotic process (GO:0033028) was also observed to be regulated by the differential expression of the STAT5B gene. Additionally, STAT5B was found to be associated with the regulation of erythrocyte differentiation (GO:0045646). Providing targeted therapy based on the patient's idiosyncratic gene expression profiles could lead to the curing of various disorders in the near future.

An improved bacterial mRNA enrichment strategy in dual RNA sequencing to unveil the dynamics of plant-bacterial interactions.

BACKGROUND: Dual RNA sequencing is a powerful tool that enables a comprehensive understanding of the molecular dynamics underlying plant-microbe interactions. RNA sequencing (RNA-seq) poses technical hurdles in the transcriptional analysis of plant-bacterial interactions, especially in bacterial transcriptomics, owing to the presence of abundant ribosomal RNA (rRNA), which potentially limits the coverage of essential transcripts. Therefore, to achieve cost-effective and comprehensive sequencing of the bacterial transcriptome, it is imperative to devise efficient methods for eliminating rRNA and enhancing the proportion of bacterial mRNA. In this study, we modified a strand-specific dual RNA-seq method with the goal of enriching the proportion of bacterial mRNA in the bacteria-infected plant samples. The enriched method involved the sequential separation of plant mRNA by poly A selection and rRNA removal for bacterial mRNA enrichment followed by strand specific RNA-seq library preparation steps. We assessed the efficiency of the enriched method in comparison to the conventional method by employing various plant-bacterial interactions, including both host and non-host resistance interactions with pathogenic bacteria, as well as an interaction with a beneficial rhizosphere associated bacteria using pepper and tomato plants respectively. RESULTS: In all cases of plant-bacterial interactions examined, an increase in mapping efficiency was observed with the enriched method although it produced a lower read count. Especially in the compatible interaction with Xanthmonas campestris pv. Vesicatoria race 3 (Xcv3), the enriched method enhanced the mapping ratio of Xcv3-infected pepper samples to its own genome (15.09%; 1.45-fold increase) and the CDS (8.92%; 1.49-fold increase). The enriched method consistently displayed a greater number of differentially expressed genes (DEGs) than the conventional RNA-seq method at all fold change threshold levels investigated, notably during the early stages of Xcv3 infection in peppers. The Gene Ontology (GO) enrichment analysis revealed that the DEGs were predominantly enriched in proteolysis, kinase, serine type endopeptidase and heme binding activities. CONCLUSION: The enriched method demonstrated in this study will serve as a suitable alternative to the existing RNA-seq method to enrich bacterial mRNA and provide novel insights into the intricate transcriptomic alterations within the plant-bacterial interplay.

Identification of Differentially Expressed Genes and Protein-Protein Interaction in Patients With COVID-19 and Diabetes Peripheral Neuropathy: A Bioinformatics and System Biology Approach.

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact globally, resulting in a higher death toll and persistent health issues for survivors, particularly those with pre-existing medical conditions. Numerous studies have demonstrated a strong correlation between catastrophic COVID-19 results and diabetes. To gain deeper insights, we analysed the transcriptome dataset from COVID-19 and diabetic peripheral neuropathic patients. Using the R programming language, differentially expressed genes (DEGs) were identified and classified based on up and down regulations. The overlaps of DEGs were then explored between these groups. Functional annotation of those common DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Bio-Planet, Reactome, and Wiki pathways. A protein-protein interaction (PPI) network was created with bioinformatics tools to understand molecular interactions. Through topological analysis of the PPI network, we determined hub gene modules and explored gene regulatory networks (GRN). Furthermore, the study extended to suggesting potential drug molecules for the identified mutual DEG based on the comprehensive analysis. These approaches may contribute to understanding the molecular intricacies of COVID-19 in diabetic peripheral neuropathy patients through insights into potential therapeutic interventions.

[Advances in bioinformatics-based protein function prediction].

With the increasing of computer power and rapid expansion of biological data, the application of bioinformatics tools has become the mainstream approach to address biological problems. The accurate identification of protein function by bioinformatics tools is crucial for both biomedical research and drug discovery, making it a hot topic of research. In this paper, we categorize bioinformatics-based protein function prediction methods into three categories: protein sequence-based methods, protein structure-based methods, and protein interaction networks-based methods. We further analyze these specific algorithms, highlighting the latest research advancements and providing valuable references for the application of bioinformatics-based protein function prediction in biomedical research and drug discovery.