Bioinformatics Methods Research Articles

Deciphering molecular bridges: Unveiling the interplay between metabolic syndrome and Alzheimer's disease through a systems biology approach and drug repurposing.

The association between Alzheimer's disease and metabolic disorders as significant risk factors is widely acknowledged. However, the intricate molecular mechanism intertwining these conditions remains elusive. To address this knowledge gap, we conducted a thorough investigation using a bioinformatics method to illuminate the molecular connections and pathways that provide novel perspectives on these disorders' pathological and clinical features. Microarray datasets (GSE5281, GSE122063) from the Gene Expression Omnibus (GEO) database facilitated the way to identify genes with differential expression in Alzheimer's disease (141 genes). Leveraging CoreMine, CTD, and Gene Card databases, we extracted genes associated with metabolic conditions, including hypertension, non-alcoholic fatty liver disease, and diabetes. Subsequent analysis uncovered overlapping genes implicated in metabolic conditions and Alzheimer's disease, revealing shared molecular links. We utilized String and HIPPIE databases to visualize these shared genes' protein-protein interactions (PPI) and constructed a PPI network using Cytoscape and MCODE plugin. SPP1, CD44, IGF1, and FLT1 were identified as crucial molecules in the main cluster of Alzheimer's disease and metabolic syndrome. Enrichment analysis by the DAVID dataset was employed and highlighted the SPP1 as a novel target, with its receptor CD44 playing a significant role in the inflammatory cascade and disruption of insulin signaling, contributing to the neurodegenerative aspects of Alzheimer's disease. ECM-receptor interactions, focal adhesion, and the PI3K/Akt pathways may all mediate these effects. Additionally, we investigated potential medications by repurposing the molecular links using the DGIdb database, revealing Tacrolimus and Calcitonin as promising candidates, particularly since they possess binding sites on the SPP1 molecule. In conclusion, our study unveils crucial molecular bridges between metabolic syndrome and AD, providing insights into their pathophysiology for therapeutic interventions.

Characterization and efficacy of C60 nano-photosensitive drugs in colorectal cancer treatment

BackgroundFullerenes C60 shows great potential for drug transport. C60 generates large amounts of singlet oxygen upon photoexcitation, which has a significant inhibitory effect on tumor cells, so the photosensitive properties of C60 were exploited for photodynamic therapy of tumors by laser irradiation. MethodsIn this study, C60-NH2 was functionalized by introducing amino acids on the surface of C60, coupled with 5-FU to obtain C60 amino acid-derived drugs (C60AF, C60GF, C60LF), and activated photosensitive drugs (C60AFL, C60GFL, C60LFL) were obtained by laser irradiation. The C60 nano-photosensitive drugs were characterized in various ways, and the efficacy and safety of C60 nano-photosensitive drugs were verified by cellular experiments and animal experiments. Bioinformatics methods and cellular experiments were used to confirm the photosensitive drug targets and verify the therapeutic targets with C60AF. ResultsPhotosensitised tumor-targeted drug delivery effectively crosses cell membranes, leads to more apoptotic cell death, and provides higher anti-tumor efficacy and safety in vitro and in vivo colorectal cancer pharmacodynamic assays compared to free 5-FU.C60 photosensitized drug promotes tumor killing by inhibiting the colorectal cancer FLOR1 tumor protein target, with no significant toxic effects on normal organs. ConclusionC60 photosensitized drug delivery systems are expected to improve efficacy and reduce side effects in the future treatment of colorectal cancer. Further and better development and design of drugs and vectors for colorectal cancer therapy.

Single-nucleus analysis reveals microenvironment-specific neuron and glial cell enrichment in Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is a complicated neurodegenerative disease. Neuron-glial cell interactions are an important but not fully understood process in the progression of AD. We used bioinformatic methods to analyze single-nucleus RNA sequencing (snRNA-seq) data to investigate the cellular and molecular biological processes of AD.MethodsnRNA-seq data were downloaded from Gene Expression Omnibus (GEO) datasets and reprocessed to identify 240,804 single nuclei from healthy controls and patients with AD. The cellular composition of AD was further explored using Uniform Manifold Approximation and Projection (UMAP). Enrichment analysis for the functions of the DEGs was conducted and cell development trajectory analyses were used to reveal underlying cell fate decisions. iTALK was performed to identify ligand-receptor pairs among various cell types in the pathological ecological microenvironment of AD.ResultsSix cell types and multiple subclusters were identified based on the snRNA-seq data. A subcluster of neuron and glial cells co-expressing lncRNA-SNHG14, myocardin-related transcription factor A (MRTFA), and MRTFB was found to be more abundant in the AD group. This subcluster was enriched in mitogen-activated protein kinase (MAPK)-, immune-, and apoptosis-related pathways. Through molecular docking, we found that lncRNA-SNHG14 may bind MRTFA and MRTFB, resulting in an interaction between neurons and glial cells.ConclusionsThe findings of this study describe a regulatory relationship between lncRNA-SNHG14, MRTFA, and MRTFB in the six main cell types of AD. This relationship may contribute to microenvironment remodeling in AD and provide a theoretical basis for a more in-depth analysis of AD.

RGC32 promotes the progression of ccRCC by activating the NF-κB/SHP2/EGFR signaling pathway.

The role and clinical significance of the response gene to complement 32 (RGC32) in various cancers have been documented, yet its implications in clear cell Renal Cell Carcinoma (ccRCC) remain underexplored. This study investigated RGC32's diagnostic and prognostic relevance in ccRCC using bioinformatics methods with data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The impact of RGC32 on ccRCC progression was assessed through nude mouse tumor assays. Immunohistochemistry evaluated RGC32 levels in ccRCC and adjacent normal tissues, while cell proliferation, migration, and invasion capabilities were analyzed using CCK-8, monoclonal proliferation assays, Transwell, and wound healing assays, respectively. Western blotting measured relevant protein expressions. Bioinformatics analysis highlighted RGC32's significant role in ccRCC pathogenesis. Elevated RGC32 expression in ccRCC tissues was linked to disease progression. Functionally, RGC32 was found to enhance the expression of proteins such as p-PI3K, CyclinA1, CyclinD1, p-STAT3, MMP2, MMP3, MMP9, p-SMAD2/3, Snail, Slug, and N-Cadherin via the NF-κB/SHP2/EGFR pathway, while decreasing E-cadherin levels. Moreover, RGC32 facilitated ccRCC cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT). RGC32 is a pivotal factor in ccRCC development, primarily through the activation of the NF-κB/SHP2/EGFR signaling pathway.

Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens.

Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increasingly intricate genetic regulatory patterns. Hence, novel approaches are necessary for future genomic prediction. In this study, we used an illumina 50K SNP chip to genotype 4190 egg-type female Rhode Island Red chickens. Machine learning (ML) and classical bioinformatics methods were integrated to fit genotypes with 10 economic traits in chickens. We evaluated the effectiveness of ML methods using Pearson correlation coefficients and the RMSE between predicted and actual phenotypic values and compared them with rrBLUP and BayesA. Our results indicated that ML algorithms exhibit significantly superior performance to rrBLUP and BayesA in predicting body weight and eggshell strength traits. Conversely, rrBLUP and BayesA demonstrated 2-58% higher predictive accuracy in predicting egg numbers. Additionally, the incorporation of suggestively significant SNPs obtained through the GWAS into the ML models resulted in an increase in the predictive accuracy of 0.1-27% across nearly all traits. These findings suggest the potential of combining classical bioinformatics methods with ML techniques to improve genomic prediction in the future.

MiR-122-5p Promotes Cowshed Particulate Matter2.5-Induced Apoptosis in NR8383 by Targeting COL4A1.

It is well known that Particulate Matter2.5 (PM2.5) has a major adverse effect on the organism. However, the health hazards of livestock farm PM2.5 to humans and animals are not yet known, and the role of miRNAs in the cellular damage induced by livestock farm PM2.5 is also unclear. Therefore, our study used cowshed PM2.5 to stimulate rat alveolar macrophage NR8383 to construct an in vitro injury model to investigate the effect of miR-122-5p on PM2.5-induced apoptosis in the NR8383. The level of apoptosis was quantified by flow cytometry and Hoechst 33342/PI double staining. Furthermore, the potential target gene Collagen type IV alpha (COL4A1) of miR-122-5p was identified through the use of bioinformatics methods. The results demonstrated a decline in cell viability and an increase in apoptosis with rising PM2.5 concentrations and exposure durations. The transfection of miR-122-5p mimics resulted in an upregulation of the pro-apoptotic protein Bcl-xL/Bcl-2 and activation of cleaved caspase-3 while inhibiting the anti-apoptotic protein B-cell lymphoma-2. The experimental data indicate that miR-122-5p is involved in the apoptotic process by targeting COL4A1. Furthermore, the overexpression of COL4A1 was observed to enhance the PM2.5-activated PI3K/AKT/NF-κB signaling pathway, which contributed to the inhibition of apoptosis. This finding offers a promising avenue for the development of therapeutic strategies aimed at mitigating cellular damage induced by PM2.5 exposure.

Identification and Expression Analysis of the BTB/POZ Gene Family in Solanum tuberosum

The BTB/POZ (broad-complex, tramtrack, and bric-a-brac) family of proteins is widespread in plants and animals and plays important roles in growth, development, metabolism, and environmental responses. There are few reports on BTB family genes in potato. In this study, 34 sequences containing conserved BTB domains were obtained from the potato gene database, and the phylogenetic, physical, and chemical properties, gene structure, conserved motif, domain, and chromosomal localization of the potato BTB protein family were analyzed via bioinformatics methods. In addition, we used qRT-PCR to detect 12 selected StBTB genes. The results confirmed that these genes are involved in cold, ABA, salt, hydrogen peroxide (H2O2), drought, and low-nitrogen stress, which is highly important for elucidating BTB family members and studying stress response and tolerance mechanisms. This study provides a theoretical basis for the study of the function and expression of potato BTB and lays a solid foundation for further understanding the molecular mechanism of the potato BTB gene under various environmental stresses.

Whole-genome sequencing of marine water-derived Curvularia verruculosa KHW-7: a pioneering study

Marine microorganisms are renowned for being a rich source of new secondary metabolites that are significant to humans. The fungi strain KHW-7 was isolated from the seawater collected from the Gulf of Khambhat, India, and identified as Curvularia verruculosa KHW-7. On a next-generation sequencing platform, C. verruculosa KHW-7’s whole-genome sequencing (WGS) and gene annotation were carried out using several bioinformatic methods. The 31.59 MB genome size, 52.3% GC, and 158 bp mean read length were discovered using WGS. This genome also contained 9,745 protein-coding genes, including 852 secreted proteins and 2048 transmembrane proteins. The antiSMASH algorithm used to analyze genomes found 25 secondary metabolite biosynthetic gene clusters (BGCs) that are abundant in terpene, non-ribosomal peptide synthetase (NRPS), and polyketides type 1 (T1PKS). To our knowledge, this is the first whole-genome sequence report of C. verruculosa. The WGS analysis of C. verruculosa KHW-7 indicated that this marine-derived fungus could be an efficient generator of bioactive secondary metabolites and an important industrial enzyme, both of which demand further investigation and development.

KIF23 promotes cervical cancer progression via inhibiting NLRP3-mediated pyroptosis.

Cervical cancer (CC), closely linked to persistent human papillomavirus infection, represents a major health problem for women worldwide. The objective of this study is to elucidate KIF23's role in the development of CC and its regulatory mechanism. The bioinformatics methods were utilized to extract pyroptosis-associated differentially expressed genes (DEGs) and pivot genes from the GSE9750 and GSE63678 datasets, followed by immune infiltration analysis and quantification of these genes' expression. The effects of kinesin family member 23 (KIF23) were verified through functional experiments invitro and a mouse xenograft model. The NLPR3 activator, nigericin, was applied for further analyzing the potential regulatory mechanism of KIF23 in CC. A total of 8 pyroptosis-related DEGs were screened out, among which 4 candidate core genes were identified as candidate hub genes and confirmed upregulation in CC tissues and cells. These genes respectively showed a positive correlation with the infiltration of distinct immune cells or tumor purity. Downregulation of KIF23 could suppress the proliferation, migration, and invasion abilities in CC cells and tumorigenesis through enhancing pyroptosis. Conversely, KIF23 overexpression accelerated the malignant phenotypes of CC cells and inhibited pyroptosis activation, which was blocked by nigericin treatment. KIF23 may play an oncogenic role in CC progression via inhibition of the NLRP3-mediated pyroptosis pathway.

#2263 Computational drug repurposing screen targeting PLA2R antibody binding

Abstract Background and Aims Approximately 60% of patients with membranous nephropathy have antibodies against the phospholipase A2 receptor (PLA2R). The cysteine rich (CysR) epitope of PLA2R is a likely site of protein-antibody interaction. Current treatment regimens for membranous nephropathy involve maximal conservative management or systemic immunosuppression in severe cases. We have developed protocols and programs for an in silico high-throughput repurposing screen using structural modelling, docking, and molecular dynamics (MD). Uniquely, our approach uses a bespoke interface on Google Colab for graphic processing units to run the MD simulations. We describe their application to a repurposing screen aiming to identify larger licensed drug molecules capable of impeding antibody-protein interactions at the site of the CysR epitope. Method A complete model of PLA2R was obtained from I-TASSER using the canonical sequence on the UNIPROT database. This structure was equilibrated using a five nanosecond molecular dynamics (MD) simulation. The simulation was prepared using CHARMM-GUI and run on the GROMACS-ON-COLAB platform. The centroid of the MD system between three and five nanoseconds was used for docking experiments using PLANTS PLP with a study area that included the CysR epitope. A library of compounds listed in the British National Formulary was used for the docking studies. The results would be analysed by PLANTS energy and by key residue overlap score. Results Viable docking results were obtained for 891 of the 1510 compounds tested. 60 of these compounds were in contact with at least 10% of the residues in the CysR region. By restricting these results to compounds in the lowest 20% of the total sample for calculated binding energy, 22 compounds remained. 16 of these compounds have been assessed for their safety for oral or intravenous use. These sixteen compounds were then identified for further investigation by MD to assess the stability of the identified interactions. The sixteen compounds include Lymecycline, Trazodone, Mirabegron, Sulfasalazine, Methotrexate, Rosuvastatin and Montelukast. Conclusion Structural bioinformatics methods provide a relatively fast and economical option for identifying protein-drug interactions that could result in new treatment strategies. The compounds identified for investigation are used for an array of different health conditions. However, they all share the chemical properties of large molecular weight and have the potential to form multiple hydrogen bonds. If validated experimentally, these compounds could provide new treatment options for PLA2R antibody positive membranous nephropathy.

Genome-wide identification of YABBY gene family and its expression pattern analysis in Astragalus mongholicus

ABSTRACT During plant growth and development, the YABBY gene plays a crucial role in the morphological structure, hormone signaling, stress resistance, crop breeding, and agricultural production of plant lateral organs, leaves, flowers, and fruits. Astragalus mongholicus is a perennial herbaceous plant in the legume family, widely used worldwide due to its high medicinal and edible value. However, there have been no reports of the YABBY gene family in A. mongholicus. This study used bioinformatics methods, combined with databases and analysis websites, to systematically analyze the AmYABBY gene family in the entire genome of A. mongholicus and verified its expression patterns in different tissues of A. mongholicus through transcriptome data and qRT-PCR experiments. A total of seven AmYABBY genes were identified, which can be divided into five subfamilies and distributed on three chromosomes. Two pairs of AmYABBY genes may be involved in fragment duplication on three chromosomes. All AmYABBY proteins have a zinc finger YABBY domain, and members of the same group have similar motif composition and intron – exon structure. In the promoter region of the genes, light-responsive and MeJa-response cis-elements are dominant. AmYABBY is highly expressed in stems and leaves, especially AmYABBY1, AmYABBY2, and AmYABBY3, which play important roles in the growth and development of stems and leaves. The AmYABBY gene family regulates the growth and development of A. mongholicus. In summary, this study provides a theoretical basis for in-depth research on the function of the AmYABBY gene and new insights into the molecular response mechanism of the growth and development of the traditional Chinese medicine A. mongholicus.

Clinical implications of synovial tissue phenotypes in rheumatoid arthritis.

Autoimmune forms of inflammatory arthritis, such as Rheumatoid Arthritis (RA), are clinically heterogeneous in presentation and disease course. Treatment-related outcomes vary despite patient exposure to similar treatment strategies. It is likely that variation seen in synovial pathogenesis influences outcomes and is heterogeneous outcomes influenced by patient factors, including environmental exposures, microbiota, behaviors, timely access to therapy, and synovial cell variation. Patients' unique complex factors manifest as specific synovial phenotypes characterized by clusters of synovial cell types and states. Precision medicine aims to use such clinical and biological data to identify the right treatment for the right patient at the right time, enabling patients to achieve sustained remission. Identifying synovial targets susceptible to a given treatment, enabling the choice of effective therapy for a given patient, will realize the goals of precision medicine. Over the last 7 years, improved acquisition and processing of synovial tissue obtained by ultra-sound guided biopsy has enabled researchers to define synovial pathotypes using histologic features and predominant cell types associated with clinical manifestations. Technical advances have enabled single-cell simultaneous sequencing of proteins and gene expression that, through increasingly sophisticated bioinformatics methods, have taken transcriptional and proteomic data to identify diverse and novel cell types and states that cluster in the RA synovium to further define patient subgroups. Synovial pathotypes and endotypes are now integrated into clinical studies and trials to explain clinical heterogeneity in disease course and treatment response. Rapidly evolving clinical-translational research has linked an expanded understanding of RA synovial pathogenesis with clinically meaningful subgroups and treatment outcomes and the clinical heterogeneity in RA.

Sequencing by Binding rivals error-corrected Sequencing by Synthesis technology for accurate detection and quantification of minor (

Detecting very minor (< 1%) subpopulations using next-generation sequencing is a critical need for multiple applications including detection of drug resistant pathogens and somatic variant detection in oncology. To enable these applications, wet lab enhancements and bioinformatic error correction methods have been developed for 'sequencing by synthesis' technology to reduce its inherent sequencing error rate. A recently available sequencing approach termed 'sequencing by binding' claims to have higher base calling accuracy data "out of the box." This paper evaluates the utility of using 'sequencing by binding' for the detection of ultra-rare subpopulations down to 0.001%.

Role of ICAM-1 in triple-negative breast cancer.

Intercellular adhesion molecule-1 (ICAM-1) is related to the occurrence and development of a variety of tumors. However, the role of ICAM-1 in the regulation of growth, metastasis, and clinical prognosis of the specific molecular subtypes of breast cancer, triple-negative breast cancer (TNBC), remains to be elucidated. This study explored the role of ICAM-1 in breast cancer and its triple-negative subtypes by systematic bioinformatics methods. The results showed that the expression of ICAM-1 in breast cancer tissues was significantly higher than that in normal tissues, especially in TNBC subtypes. In breast cancer, ICAM-1 mainly activates pathways related to apoptosis and epithelial-mesenchymal transition, while its overexpression in TNBC is associated with inflammatory response, apoptosis, and other processes. TNBC patients displaying higher ICAM-1 expression demonstrate enhanced responses to immunotherapy. High ICAM-1 expression is sensitive to drugs targeting tumor cell proliferation, apoptosis, and angiogenesis. In conclusion, breast cancer is characterized by significantly high expression of ICAM-1, with TNBC subtypes expressing ICAM-1 at much higher levels than other subtypes. The diagnosis, prognosis, development, distant metastases, and immunotherapy of TNBC are correlated with high expression of ICAM-1. This research provides available data for the further study of the diagnosis and treatment of TNBC.

Analysis of Reproductive Tract Microecological Changes During the Frozen-Thawed Embryo Transfer Cycle and Clinical Pregnancy Outcomes

This study aims to analyze the relationship between reproductive tract microecological changes, metabolic differences, and pregnancy outcomes at different time points in the frozen-thawed embryo transfer cycle while patients are undergoing hormone replacement therapy, which will be a breakthrough point for improving outcomes. A total of 20 women undergoing frozen-thawed single blastocyst transfer for the first time at the Reproductive Medicine Center of Fujian Maternal and Child Health Hospital between July 2022 and January 2023 were recruited for this study. Their vaginal and cervical secretions were collected for 16S rRNA sequencing and non-targeted metabolomics analysis on days 2-5 of menstruation, day 7 after estrogen replacement therapy started, the day when progesterone was added, and the day of transplantation. The subjects were divided into different groups according to their clinical pregnancy status and the sequencing results were analyzed using bioinformatics methods. 1) The alpha-diversity index of the vaginal and cervical microbiota was higher on days 2-5 of menstruation (P<0.01), but did not differ significantly on day 7 after oral estrogen replacement therapy started, the day of progesterone administration, and the day of transplantation (P≥0.1). 2) Both the pregnant group and the non-pregnant group showed a variety of microorganisms and metabolites with significant differences in the lower reproductive tract at different time points. 3) Microbial analysis at different time points showed that there were significant differences in vaginal flora, including Peptoniphilus, Enterocloster, Finegoldia, Klebsiella, Anaerobutyricum, Agathobaculum, Sporanaerobacter, Bilophila, Prevotella, and Anaerococcus in the pregnant group (P<0.05). 4) Metabolite analysis at different time points showed that there were significant differences in 3-hydroxybenzoic acid, linatine, (R)-amphetamine, hydroxychloroquine, and L-altarate in the vaginal secretions of the pregnant group (P<0.05), and that there were significant differences in isocitric acid, quassin, citrinin, and 12(R)-HETE in the cervical secretions (P<0.05). 5) Metabolite analysis at different time points showed that, in the non-pregnant group, there were significant differences in linatine, decanoyl-L-carnitine, aspartame, sphingosine, and hydroxychloroquine in the vaginal secretions (P<0.05), and the isocitric acid, quassin, ctrinin, and 12(R)-HETE in the cervical secretions (P<0.05). 6) Combined microbiome and metabolomics analysis showed that certain metabolites were significantly associated with microbial communities, especially Klebsiella. Significant differences in the microbiota genera and metabolites at different time points were found during the frozen-embryo transfer cycle of hormone replacement therapy, which may be used as potential biomarkers to predict pregnancy outcomes of embryo transfer.

Exploration of the molecular linkage between endometriosis and Crohn disease by bioinformatics methods.

Endometriosis (EMT) is a common disease in reproductive-age woman and Crohn disease (CD) is a chronic inflammatory disorder in gastrointestinal tract. Previous studies reported that patients with EMT had an increased risk of CD. However, the linkage between EMT and CD remains unclear. In this study, we aimed to investigate the potential molecular mechanism of EMT and CD. The microarray data of EMT and CD were downloaded from Gene Expression Omnibus. Common genes of EMT and CD were obtained to perform the Gene Ontology and Kyoto Encyclopedia of Gene Genomes enrichments. The protein-protein interaction network was constructed by Cytoscape software and the hub genes were identified by CytoHubba plug-in. Finally we predicted the transcription factors (TFs) of hub genes and constructed a TFs-hub genes regulation network. A total of 50 common genes were identified. Kyoto Encyclopedia of Gene Genomes enrichment showed that the common genes mainly enriched in MAPK pathway, VEGF pathway, Wnt pathway, TGF-beta pathway, and Ras pathway. Fifteen hub genes were collected from the protein-protein interaction network, including FMOD, FRZB, CPE, SST, ISG15, EFEMP1, KDR, ADRA2A, FZD7, AQP1, IGFBP5, NAMPT, PLUA, FGF9, and FHL2. Among them, FGF9, FZD7, IGFBP5, KDR, and NAMPT were both validated in the other 2 datasets. Finally TFs-hub genes regulation network were constructed. Our findings firstly revealed the linkage between EMT and CD, including inflammation, angiogenesis, immune regulation, and cell behaviors, which may lead to the risk of CD in EMT. FGF9, FZD7, IGFBP5, KDR, and NAMPT may closely relate to the linkage.

An integrated in-silico approach for drug target identification in human pathogen Shigella dysenteriae.

Shigella dysenteriae, is a Gram-negative bacterium that emerged as the second most significant cause of bacillary dysentery. Antibiotic treatment is vital in lowering Shigella infection rates, yet the growing global resistance to broad-spectrum antibiotics poses a significant challenge. The persistent multidrug resistance of S. dysenteriae complicates its management and control. Hence, there is an urgent requirement to discover novel therapeutic targets and potent medications to prevent and treat this disease. Therefore, the integration of bioinformatics methods such as subtractive and comparative analysis provides a pathway to compute the pan-genome of S. dysenteriae. In our study, we analysed a dataset comprising 27 whole genomes. The S. dysenteriae strain SD197 was used as the reference for determining the core genome. Initially, our focus was directed towards the identification of the proteome of the core genome. Moreover, several filters were applied to the core genome, including assessments for non-host homology, protein essentiality, and virulence, in order to prioritize potential drug targets. Among these targets were Integration host factor subunit alpha and Tyrosine recombinase XerC. Furthermore, four drug-like compounds showing potential inhibitory effects against both target proteins were identified. Subsequently, molecular docking analysis was conducted involving these targets and the compounds. This initial study provides the list of novel targets against S. dysenteriae. Conclusively, future in vitro investigations could validate our in-silico findings and uncover potential therapeutic drugs for combating bacillary dysentery infection.

BIOINFORMATICS AND MOLECULAR ANALYSES IDENTIFIED THE CONTROL REGION AS THE MOST POWERFUL MITOGENOMIC MARKER FOR DISTINGUISHING THE MAIN MATERNAL HAPLOGROUPS IN GOATS

Identification of genetic markers to distinguish animals within and between species demands extensive genomic and bioinformatics investigation. Previous studies have not carefully taken into consideration the effect of mitogenomic components on the genetic differentiation of the maternal lineages in goats. As a precaution, the complete goat mitogenome was downloaded from the NCBI database and used in the current study to assess the effects of the choice of mitogenomic fragments on phylogenetic studies and to identify any potential polymorphic region by which the main maternal haplogroups of goats can be classified. Phylogenetic results confirmed that all 13 individual mitochondrial protein-coding genes and 2 ribosomal genes are not applicable to differentiate the maternal lineages. Instead, a single novel polymorphic region with a length of 756 bp within the control region was successfully amplified by newly designed primers. Both phylogenetic analysis and principal components analysis of the sequenced mitogenomic region of the mtDNA control region efficiently differentiated the main maternal haplogroups in goats. Higher numbers of polymorphic sites were found in the control region and the mitogenomic marker region. Highly significant correlations were discovered between the polymorphic sites and the length of each individual mitogenomic component. Our results demonstrate useful guidance and cautionary notes for researchers who are interested in the investigation of genetic diversity in animal species using mtDNA sequences. The bioinformatics and molecular methods used herein can be powerful in selecting a minimum amount of data using PCR amplification when the entire sequences of the mitogenome are unavailable.

Comparative characterization of supragingival plaque microbiomes in malocclusion adult female patients undergoing orthodontic treatment with removable aligners or fixed appliances: a descriptive cross-sectional study.

This study aimed to explore the effects of removable aligners and fixed appliances on the supragingival bacterial communities in adult female patients undergoing orthodontic treatment. Supragingival plaque samples from 48 female individuals underwent microbiome analysis (16S rRNA gene sequencing) using PacBio Sequel sequencing. The study included 13 adults without orthodontic treatment needs as the control group (Group C), and 35 patients with comparable initial orthodontic conditions who received treatment at a university clinic in Beijing, China. The treatment involved either traditional fixed brackets (Group B, n = 17) or Invisalign® aligners (Group AT, n = 18). Bioinformatics methods were used for data analysis. From the 48 plaque samples, a total of 334,961 valid reads were obtained, averaging 6,978 sequences per sample. The 16S rDNA sequences were classified into 25,727 amplicon sequence variants (ASVs). Significant variances in alpha and beta diversity among the groups were noted. Group B microbiome exhibited an increased presence of Gram-negative bacteria. At the phylum level, Actinobacteriota was significantly more prevalent in Group C samples, while Bacteroidota was enriched in Group B samples. Family-level relative abundance analysis showed a notable increase in Saccharibacteria (formerly TM7) and Prevotellaceae in Group B. Genus-level analysis revealed a significant rise in Lautropia in Group AT. Fixed orthodontic appliances were linked to oral microbiome changes, notably an enhanced relative abundance of anaerobes, including periodontal pathogens. The observation points to the impact of orthodontic appliance on the oral microbial community, highlighting the difference between traditional braces (Group B) and clear aligners (Group AT)in terms of the predominance of anaerobic and gram negative bacteria. This emphasizes the importance of considering the microbiological effects when choosing orthodontic appliance and underscores the need for tailored oral hygiene practices for individuals undergoing these treatments. This research might provide insights that could assist in the development of innovative cleaning techniques and antibacterial materials.

Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancer.

The rate at which the anticancer drug paclitaxel is cleared from the body markedly impacts its dosage and chemotherapy effectiveness. Importantly, paclitaxel clearance varies among individuals, primarily because of genetic polymorphisms. This metabolic variability arises from a nonlinear process that is influenced by multiple single nucleotide polymorphisms (SNPs). Conventional bioinformatics methods struggle to accurately analyze this complex process and, currently, there is no established efficient algorithm for investigating SNP interactions. We developed a novel machine-learning approach called GEP-CSIs data mining algorithm. This algorithm, an advanced version of GEP, uses linear algebra computations to handle discrete variables. The GEP-CSI algorithm calculates a fitness function score based on paclitaxel clearance data and genetic polymorphisms in patients with nonsmall cell lung cancer. The data were divided into a primary set and a validation set for the analysis. We identified and validated 1184 three-SNP combinations that had the highest fitness function values. Notably, SERPINA1, ATF3 and EGF were found to indirectly influence paclitaxel clearance by coordinating the activity of genes previously reported to be significant in paclitaxel clearance. Particularly intriguing was the discovery of a combination of three SNPs in genes FLT1, EGF and MUC16. These SNPs-related proteins were confirmed to interact with each other in the protein-protein interaction network, which formed the basis for further exploration of their functional roles and mechanisms. We successfully developed an effective deep-learning algorithm tailored for the nuanced mining of SNP interactions, leveraging data on paclitaxel clearance and individual genetic polymorphisms.