Bioinformatics Databases Research Articles

MiR-148a-3p/SIRT7 Axis Relieves Inflammatory-Induced Endothelial Dysfunction.

In endothelial cells, miR-148a-3p is involved in several pathological pathways, including chronic inflammatory conditions. However, the molecular mechanism of miR-148a-3p in endothelial inflammatory states is, to date, not fully elucidated. To this end, we investigated the involvement of miR-148a-3p in mitochondrial dysfunction and cell death pathways in human aortic endothelial cells (teloHAECs) treated with interleukin-6 (IL-6), a major driver of vascular dysfunction. The results showed that during IL6-activated inflammatory pathways, including increased protein levels of sirtuin 7 (SIRT7) (p < 0.01), mitochondrial stress (p < 0.001), and apoptosis (p < 0.01), a decreased expression of miR-148a-3p was observed (p < 0.01). The employment of a miR-148a mimic counteracted the IL-6-induced cytokine release (p < 0.01) and apoptotic cell death (p < 0.01), and ameliorated mitochondria redox homeostasis and respiration (p < 0.01). The targeted relationship between miR-148a-3p and SIRT7 was predicted by a bioinformatics database analysis and validated via the dual-luciferase reporter assay. Mechanistically, miR-148a-3p targets the 3' untranslated regions of SIRT7 mRNA, downregulating its expression (p < 0.01). Herein, these in vitro results support the role of the miR-148a-3p/SIRT7 axis in counteracting mitochondrial damage and apoptosis during endothelial inflammation, unveiling a novel target for future strategies to prevent endothelial dysfunction.

ANALISIS KETERKAITAN NETWORK PHAMACOLOGY SENYAWA METABOLIT SEKUNDER Abrus precatorius L. SECARA IN SILICO

Cancer remains a global health challenge, prompting extensive research efforts worldwide. Lung cancer, the second most diagnosed cancer, presents a particularly grim survival rate. In Indonesia, cancer incidence ranks significantly, with millions affected and hundreds of thousands succumbing to the disease annually. Traditional medicine persists as a preferred option among many, perceived as safer and more affordable. Abrus precatorius L., an ancient medicinal plant, holds promise in this regard, with a rich history of use and a diverse range of pharmacological activities, including anti-cancer properties. Employing in silico modeling and network pharmacology, this study explores the interaction between Abrus precatorius L. compounds and various proteins associated with cancer. Through bioinformatics tools and databases, 27 bioactive compounds are identified and their physicochemical properties assessed, ensuring adherence to pharmacological guidelines. The study predicts potential protein targets for Abrus precatorius L. compounds, revealing interactions with 453 proteins, including those implicated in cancer pathways. Further analysis using StringDB and DISEASES database establishes protein-protein interaction networks, highlighting key proteins like EGFR and TERT, pivotal in multiple cancer types. The study validates the compounds' adherence to Lipinski's Rule of Five, indicating their potential for pharmacological activity and oral absorption. False Discovery Rate (FDR) analysis confirms significant associations between Abrus precatorius L. compounds and various cancers, further underscoring their therapeutic potential. In conclusion, Abrus precatorius L. compounds, particularly targeting EGFR and TERT proteins, emerge as promising candidates for cancer treatment. Their diverse pharmacological activities and interactions with key cancer-related proteins pave the way for further exploration and development of these compounds as alternative medicinal agents. In vitro and in vivo studies are warranted to validate their efficacy, particularly in addressing the complexities of different cancer types. Ultimately, this research offers valuable insights into leveraging natural compounds for combating cancer, addressing a critical need in global healthcare.

Sevoflurane blocks KLF5-mediated transcriptional activation of ITGB2 to inhibit macrophage infiltration in hepatic ischemia/reperfusion injury.

Sevoflurane (Sevo) preconditioning and postconditioning play a protective role against injury induced by hepatic ischemia/reperfusion (I/R). At the same time, the involvement of macrophage infiltration in this process and the precise mechanisms are unclear. Here, we designed this research to elucidate the protective effects of Sevo against hepatic I/R injury and the molecules involved. The alleviating effect of Sevo on the liver injury was analyzed by liver function analysis, hematoxylin and eosin staining, Masson trichrome staining, terminal deoxynucleotidyl transferase-mediated 2'-deoxyuridine 5'-triphosphate nick end labeling, western blot analysis and an enzyme-linked immunosorbent assay. An in vitro cell model was developed using alpha mouse liver 12 (AML12) cells, and the cell model was treated with oxygen-glucose deprivation and reoxygenation and Sevo. Multiple bioinformatics databases were used to screen transcriptional regulators related to hepatic I/R injury and the targets of Krueppel-like factor 5 (KLF5). KLF5 expression was artificially upregulated alone or with integrin beta-2 (ITGB2) knockdown to substantiate their involvement in Sevo-mediated hepatoprotection. Sevo protected the liver against I/R injury by reducing cell apoptosis and inflammatory response. KLF5 was upregulated in liver tissues following I/R injury, whereas KLF5 overexpression aggravated macrophage infiltration and liver injury induced by I/R injury. KLF5 bound to the promoter of ITGB2 to enhance ITGB2 transcription. Knockdown of ITGB2 reversed the aggravation of injury caused by KLF5 overexpression in mice and AML12 cells. Sevo blocked KLF5-mediated transcriptional activation of ITGB2, thereby inhibiting macrophage infiltration in hepatic I/R injury.

CK1δ/ε kinases regulate TDP-43 phosphorylation and are therapeutic targets for ALS-related TDP-43 hyperphosphorylation

Hyperphosphorylated TAR DNA-binding protein 43 (TDP-43) aggregates in the cytoplasm of neurons is the neuropathological hallmark of amyotrophic lateral sclerosis (ALS) and a group of neurodegenerative diseases collectively referred to as TDP-43 proteinopathies that includes frontotemporal dementia, Alzheimer's disease, and limbic onset age-related TDP-43 encephalopathy. The mechanism of TDP-43 phosphorylation is poorly understood. Previously we reported casein kinase 1 epsilon gene (CSNK1E gene encoding CK1ε protein) as being tightly correlated with phosphorylated TDP-43 (pTDP-43) pathology. Here we pursued studies to investigate in cellular models and in vitro how CK1ε and CK1δ (a closely related family sub-member) mediate TDP-43 phosphorylation in disease. We first validated the binding interaction between TDP-43 and either CK1δ and CK1ε using kinase activity assays and predictive bioinformatic database. We utilized novel inducible cellular models that generated translocated phosphorylated TDP-43 (pTDP-43) and cytoplasmic aggregation. Reducing CK1 kinase activity with siRNA or small molecule chemical inhibitors resulted in significant reduction of pTDP-43, in both soluble and insoluble protein fractions. We also established CK1δ and CK1ε are the primary kinases that phosphorylate TDP-43 compared to CK2α, CDC7, ERK1/2, p38α/MAPK14, and TTBK1, other identified kinases that have been implicated in TDP-43 phosphorylation. Throughout our studies, we were careful to examine both the soluble and insoluble TDP-43 protein fractions, the critical protein fractions related to protein aggregation diseases. These results identify CK1s as critical kinases involved in TDP-43 hyperphosphorylation and aggregation in cellular models and in vitro, and in turn are potential therapeutic targets by way of CK1δ/ε inhibitors.

LncRNA NEAT1 promotes MPP+ induced injury of PC12 cells and accelerates the progression of Parkinson's disease in mice through FUS mediated inhibition of PI3K/AKT/mTOR signalling pathway

Long noncoding RNA nuclear-enriched abundant transcript 1 (NEAT1) is involved in the progression of Parkinson's disease (PD), but the specific regulatory role needs further exploration. This study showed that the expression of NEAT1 was upregulated in the cerebrospinal fluid (CSF) and peripheral blood of patients with different stages of PD. 1-Methyl-4-phenylpyridine (MPP)-treated PC 12 cells were transfected with si-NEAT1, and MPP treatment promoted cell apoptosis, oxidative stress and inflammatory factor secretion. Si-NEAT1 reversed the effects of MPP. NEAT1 silencing eliminated the effect of MPP on the protein expression levels of LC3-II and p62/SQSTM1. By using an online bioinformatics database, Fused in Sarcoma (FUS) was confirmed to be an RNA binding protein of NEAT1, and it was highly expressed in the CSF and peripheral blood of patients with PD. Si-FUS was transfected into MPP-treated PC 12 cells to detect cell apoptosis, oxidative stress, inflammatory factor secretion and autophagy, and the results were the same as those of transfection of si-NEAT1. Furthermore, MPP treatment reduced the phosphorylation levels of PI3K, Akt and mTOR, whereas si-FUS reversed the effects of MPP. In vivo, compared with the model group, the PD mice showed reduced NEAT1 and FUS expression levels and activated PI3K pathway after being injected with si-NEAT1. The brain tissue of NEAT1-silenced PD mice had decreased inflammatory infiltration and apoptosis and increased neurological scores. In conclusion, NEAT1 is involved in PD progression through FUS-mediated inhibition of the PI3K/AKT/mTOR signalling pathway.

Genome-scale annotation of protein binding sites via language model and geometric deep learning.

Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven't fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.

Genome-scale annotation of protein binding sites via language model and geometric deep learning

Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.

Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes.

Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU.

The possibility of Polygonum cuspidatum Against Osteoarthritis Based on Network Pharmacology and Molecular Docking.

Polygonum cuspidatum（PC）, a widely used Chinese herbal medicine (CHM), plays an important role in treating various diseases including osteoarthritis (OA). Yet, the multicomponent and multitarget characteristics of PC make deciphering the pharmacological mechanisms difficult. The purpose of this study is to identify the core molecular mechanisms of PC against OA. The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to search the active ingredients of PC. GeneCards was then screened to establish relevant databases for OA. A visual interactive network diagram of the relationship between the active ingredient, action target, and disease was built using Uniprot. Finally, we used STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database to explain the interaction network of proteins and to further analyze the relationships between related proteins. PC was screened for nine potentially effective active compounds that can be used to treat OA: 6,8-Dihydroxy-7-methoxyxanthone, rhein, physovenine, beta-sitosterol, picralinal, quercetin, luteolin, catechin, and resveratrol. Using GeneCards database and TCMSP database, we obtained 149 OA-related genes after taken the intersection of OA and PC targets. Moreover, eight core target proteins were calculated by CytoNCA plugin, which is used for network centrality analysis. The enrichment analysis of the common target genes shared by PC and OA unraveled the main biological processes, such as responses to lipopolysaccharide, chemical stress, and reactive oxygen species. Previous research has been demonstrated that signaling pathways related to apoptosis, inflammation, and cartilage protection are involved with those core target genes we found, like TNF and PI3K-Akt signaling pathways. The outcome of the molecular docking experiments illustrates that some PC's active components, including luteolin, resveratrol, and 6,8-Dihydroxy-7-methoxyxanthone, share strong molecular docking as well as binding ability with the core common target protein. The results bring that PC similarly has the potential to treat OA. The main purpose of this study is to screen the active ingredients and most important target molecules of PC in treating OA. This was achieved using bioinformatic tools and databases to investigate molecular docking technology. The findings provide a theoretical foundation and potential new treatment plan for OA using PC.

Bioinformatics database and production layout visual analysis platform of medicinal plants:a case study of Dao-di medicinal materials in Sichuan province

In the new stage of trans-omics and trans-subjects for medicinal plants, it is an urgent need to integrate big data, provide interactive applications, and form a unified and multi-level research system and big data platform. Dao-di medicinal material, as an important source of medicinal plants, is a unique quality concept and comprehensive standard of tranditional Chinese medicine(TCM). Several databases have been developed in China and abroad, such as the Encyclopedia of Traditional Chinese Medicine(ETCM) and the Global Pharmacopoeia Genome Database(GPGD). Yet, most databases do not provide multi-dimensional data, including geographic data, phenotype data, compound data, and genetic data. Sichuan, known as the hometown of TCM therapies and the treasure trove of TCM, is the most representative region of medicinal plant diversity in China. According to the latest data of the fourth national survey of TCM resources, there are more than 8 000 TCM and 86 Dao-di medicinal materials in Sichuan province. Based on resource census data and relevant achievements, this study constructed the bioinformatics database of medicinal plants and the visual analysis platform of production layout by taking the Dao-di medicinal materials in Sichuan province as an example, covering geographic data, phenotype data, compound data, and genetic data. It effectively integrates multi-dimensional data of Dao-di medicinal materials and provides different levels of data interaction applications. The platform is the first large-scale multi-dimensional database and visual platform of Dao-di medicinal materials in Sichuan province, which serves as an essential resource for germplasm resources identification, decomposition of biosynthetic pathways, molecular breeding of varieties and provides medicinal plant resource information and data support for development and utilization of medicinal plants in China and abroad.

A two-step Bayesian network approach to identify key SNPs associated to multiple phenotypic traits in four purebred laying hen lines.

When purebred laying hen chicks hatch, they remain at a rearing farm until approximately 17 weeks of age, after which they are transferred to a laying farm. Chicks or pullets are removed from the flocks during these 17 weeks if they display any rearing abnormality. The aim of this study was to investigate associations between single nucleotide polymorphisms (SNPs) and rearing success of 4 purebred White Leghorns layer lines by implementing a Bayesian network approach. Phenotypic traits and SNPs of four purebred genetic White Leghorn layer lines were available for 23,000 rearing batches obtained between 2010 and 2020. Associations between incubation traits (clutch size, embryo mortality), rearing traits (genetic line, first week mortality, rearing abnormalities, natural death, rearing success, pullet flock age, and season) and SNPs were analyzed, using a two-step Bayesian Network (BN) approach. Furthermore, the SNPs were connected to their corresponding genes, which were further explored in bioinformatics databases. BN analysis revealed a total of 28 SNPs associated with some of the traits: ten SNPs were associated with clutch size, another 10 with rearing abnormalities, a single SNP with natural death, and seven SNPs with first week mortality. Exploration via bioinformatics databases showed that one of the SNPs (ENAH) had a protein predicted network composed of 11 other proteins. The major hub of this SNP was CDC42 protein, which has a role in egg production and reproduction. The results highlight the power of BNs in knowledge discovery and how their application in complex biological systems can help getting a deeper understanding of functionality underlying genetic variation of rearing success in laying hens. Improved welfare and production might result from the identified SNPs. Selecting for these SNPs through breeding could reduce stress and increase livability during rearing.

Rapid multiple protein sequence search by parallel and heterogeneous computation.

Protein sequence database search and multiple sequence alignment generation is a fundamental task in many bioinformatics analyses. As the data volume of sequences continues to grow rapidly, there is an increasing need for efficient and scalable multiple sequence query algorithms for super-large databases without expensive time and computational costs. We introduce Chorus, a novel protein sequence query system that leverages parallel model and heterogeneous computation architecture to enable users to query thousands of protein sequences concurrently against large protein databases on a desktop workstation. Chorus achieves over 100× speedup over BLASTP without sacrificing sensitivity. We demonstrate the utility of Chorus through a case study of analyzing a ∼1.5-TB large-scale metagenomic datasets for novel CRISPR-Cas protein discovery within 30 min. Chorus is open-source and its code repository is available at https://github.com/Bio-Acc/Chorus.

Knowledge Enhanced Representation Learning for Drug Discovery

Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases of molecule SMILES and protein sequences. While these representations have significantly enhanced the predictions, they are usually based on a limited set of modalities, and they do not exploit available knowledge about existing relations among molecules and proteins. Our study reveals that enhanced representations, derived from multimodal knowledge graphs describing relations among molecules and proteins, lead to state-of-the-art results in well-established benchmarks (first place in the leaderboard for Therapeutics Data Commons benchmark ``Drug-Target Interaction Domain Generalization Benchmark", with an improvement of 8 points with respect to previous best result). Moreover, our results significantly surpass those achieved in standard benchmarks by using conventional pre-trained representations that rely only on sequence or SMILES data. We release our multimodal knowledge graphs, integrating data from seven public data sources, and which contain over 30 million triples. Pretrained models from our proposed graphs and benchmark task source code are also released.

Abstract 290: The role of tumor stromal topography-regulated genes in modulating the cellular landscape of tumor stroma in breast cancer

Abstract The tumor remodels the surrounding extracellular matrix fibers to become topographically aligned at the tumor front, further facilitating cancer cell migration. Our recent studies show that aligned tumor stromal topography promotes the detachment of breast cancer cells from the main tumor, then, in the following cell migration, upregulating genes such as genes encoding the cytochrome P450 family 1 subfamily A member 1 (CYP1) and inhibitor of DNA binding (ID) proteins. In this study, we further explored the role of topography-regulated genes in modulating the cellular landscape of tumor stroma. By analyzing the correlation of topography-regulated genes with stromal cell infiltration in bioinformatics databases, we found significant positive correlations between the RNA expression levels of most topography-regulated genes and the infiltration levels of cancer-associated fibroblasts and endothelial cells. Notably, the strongest correlation was between the expression level of ID3 and the infiltration levels of cancer-associated fibroblasts (r = 0.509) and endothelial cells (r = 0.506). Additionally, ID3 remained highly expressed in circulating tumor cells but exhibited low expression at distant metastatic sites. These findings suggest that genes regulated by tumor stromal topography, especially ID3, may play a role in neovascularization and fibrosis of the tumor microenvironment and the survival adaptation of cancer cells in circulation, thereby promoting metastasis. Citation Format: Chia-Yi Su. The role of tumor stromal topography-regulated genes in modulating the cellular landscape of tumor stroma in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 290.

Identify CTBP1-DT as an immunological biomarker that promotes lipid synthesis and apoptosis resistance in KIRC

Recently, mounting evidence has highlighted the essential function of the C-terminal binding protein-1 divergent transcript (CTBP1-DT) in malignancies. However, its role in kidney renal clear cell carcinoma (KIRC) remains largely unknown. Our study aimed to identify the potential function of CTBP1-DT in KIRC. RT-qPCR, Kaplan-Meier survival analysis, Cox regression analysis, and nomogram analysis were utilized to determine the expression and effects of CTBP1-DT on survival. The subcellular localization of CTBP1-DT was determined using RNA fluorescence in situ hybridization (FISH). To investigate the functions of CTBP1-DT in regulating KIRC cell proliferation, migration, invasion, lipid synthesis, and apoptosis, we conducted CCK8, EdU, Transwell, and Oil Red O staining and cell apoptosis staining assays. The relationships between CTBP1-DT and the tumor microenvironment were investigated with multiple bioinformatics analysis algorithms and databases, including CYBERSORT, TIMER2, Spearman correlation test, tumor mutation burden (TMB), microsatellite instability (MSI), and immunophenoscore (IPS). According to our results, CTBP1-DT is a lncRNA located in the nucleus that is significantly upregulated in KIRC and is correlated with better clinical outcomes. Downregulating CTBP1-DT inhibited cell viability, migration, invasion, and lipid synthesis but triggered cell apoptosis. Additionally, we explored the potential effect of CTBP1-DT in regulating immune cell infiltration in KIRC and other malignancies. Furthermore, CTBP1-DT could be used to predict the effectiveness of targeted drugs and immune checkpoint inhibitors. In conclusion, we identified CTBP1-DT as a potential immunological biomarker and discovered the potential role of CTBP1-DT in regulating lipid synthesis and apoptosis resistance.

Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

BackgroundMicrobiota are closely associated with human health and disease. Metaproteomics can provide a direct means to identify microbial proteins in microbiota for compositional and functional characterization. However, in-depth and accurate metaproteomics is still limited due to the extreme complexity and high diversity of microbiota samples. It is generally recommended to use metagenomic data from the same samples to construct the protein sequence database for metaproteomic data analysis. Although different metagenomics-based database construction strategies have been developed, an optimization of gene taxonomic annotation has not been reported, which, however, is extremely important for accurate metaproteomic analysis.ResultsHerein, we proposed an accurate taxonomic annotation pipeline for genes from metagenomic data, namely contigs directed gene annotation (ConDiGA), and used the method to build a protein sequence database for metaproteomic analysis. We compared our pipeline (ConDiGA or MD3) with two other popular annotation pipelines (MD1 and MD2). In MD1, genes were directly annotated against the whole bacterial genome database; in MD2, contigs were annotated against the whole bacterial genome database and the taxonomic information of contigs was assigned to the genes; in MD3, the most confident species from the contigs annotation results were taken as reference to annotate genes. Annotation tools, including BLAST, Kaiju, and Kraken2, were compared. Based on a synthetic microbial community of 12 species, it was found that Kaiju with the MD3 pipeline outperformed the others in the construction of protein sequence database from metagenomic data. Similar performance was also observed with a fecal sample, as well as in silico mixed datasets of the simulated microbial community and the fecal sample.ConclusionsOverall, we developed an optimized pipeline for gene taxonomic annotation to construct protein sequence databases. Our study can tackle the current taxonomic annotation reliability problem in metagenomics-derived protein sequence database and can promote the in-depth metaproteomic analysis of microbiome. The unique metagenomic and metaproteomic datasets of the 12 bacterial species are publicly available as a standard benchmarking sample for evaluating various analysis pipelines. The code of ConDiGA is open access at GitHub for the analysis of microbiota samples.6yn7yKvS5c7jYK96vohJFUVideo

ThermoPCD: a database of molecular dynamics trajectories of antibody-antigen complexes at physiologic and fever-range temperatures.

Progression of various cancers and autoimmune diseases is associated with changes in systemic or local tissue temperatures, which may impact current therapies. The role of fever and acute inflammation-range temperatures on the stability and activity of antibodies relevant for cancers and autoimmunity is unknown. To produce molecular dynamics (MD) trajectories of immune complexes at relevant temperatures, we used the Research Collaboratory for Structural Bioinformatics (RCSB) database to identify 50 antibody:antigen complexes of interest, in addition to single antibodies and antigens, and deployed Groningen Machine for Chemical Simulations (GROMACS) to prepare and run the structures at different temperatures for 100-500ns, in single or multiple random seeds. MD trajectories are freely available. Processed data include Protein Data Bank outputs for all files obtained every 50ns, and free binding energy calculations for some of the immune complexes. Protocols for using the data are also available. Individual datasets contain unique DOIs. We created a web interface, ThermoPCD, as a platform to explore the data. The outputs of ThermoPCD allow the users to relate thermally-dependent changes in epitopes:paratopes interfaces to their free binding energies, or against own experimentally derived binding affinities. ThermoPCD is a free to use database of immune complexes' trajectories at different temperatures that does not require registration and allows for all the data to be available for download. Database URL: https://sites.google.com/view/thermopcd/home.

Landscape of FLT3 Variations Associated with Structural and Functional Impact on Acute Myeloid Leukemia: A Computational Study.

Acute myeloid leukemia (AML) is hallmarked by the clonal proliferation of myeloid blasts. Mutations that result in the constitutive activation of the fms-like tyrosine kinase 3 (FLT3) gene, coding for a class III receptor tyrosine kinase, are significantly associated with this heterogeneous hematologic malignancy. The fms-related tyrosine kinase 3 ligand binds to the extracellular domain of the FLT3 receptor, inducing homodimer formation in the plasma membrane, leading to autophosphorylation and activation of apoptosis, proliferation, and differentiation of hematopoietic cells in bone marrow. In the present study, we evaluated the association of FLT3 as a significant biomarker for AML and tried to comprehend the effects of specific variations on the FLT3 protein's structure and function. We also examined the effects of I836 variants on binding affinity to sorafenib using molecular docking. We integrated multiple bioinformatics tools, databases, and resources such as OncoDB, UniProt, COSMIC, UALCAN, PyMOL, ProSA, Missense3D, InterProScan, SIFT, PolyPhen, and PredictSNP to annotate the structural, functional, and phenotypic impact of the known variations associated with FLT3. Twenty-nine FLT3 variants were analyzed using in silico approaches such as DynaMut, CUPSAT, AutoDock, and Discovery Studio for their impact on protein stability, flexibility, function, and binding affinity. The OncoDB and UALCAN portals confirmed the association of FLT3 gene expression and its mutational status with AML. A computational structural analysis of the deleterious variants of FLT3 revealed I863F mutants as destabilizers of the protein structure, possibly leading to functional changes. Many single-nucleotide variations in FLT3 have an impact on its structure and function. Thus, the annotation of FLT3 SNVs and the prediction of their deleterious pathogenic impact will facilitate an insight into the tumorigenesis process and guide experimental studies and clinical implications.

Ten common issues with reference sequence databases and how to mitigate them.

Metagenomic sequencing has revolutionized our understanding of microbiology. While metagenomic tools and approaches have been extensively evaluated and benchmarked, far less attention has been given to the reference sequence database used in metagenomic classification. Issues with reference sequence databases are pervasive. Database contamination is the most recognized issue in the literature; however, it remains relatively unmitigated in most analyses. Other common issues with reference sequence databases include taxonomic errors, inappropriate inclusion and exclusion criteria, and sequence content errors. This review covers ten common issues with reference sequence databases and the potential downstream consequences of these issues. Mitigation measures are discussed for each issue, including bioinformatic tools and database curation strategies. Together, these strategies present a path towards more accurate, reproducible and translatable metagenomic sequencing.

HELLS Knockdown Inhibits the Malignant Progression of Lung Adenocarcinoma Via Blocking Akt/CREB Pathway by Downregulating KIF11.

Lung adenocarcinoma (LUAD) is a malignant tumor with the characteristics of progressive advancement and high mortality rate worldwide. We aimed to explore the role and mechanism of helicase Lymphoid-Specific (HELLS) in LUAD. Bioinformatics databases were applied to predict HELLS and kinesin family member (KIF)11 expression in LUAD tissues. The expressions of HELLS and KIF11 before and after HELLS knockdown were detected by RT-qPCR and western blot. After HELLS was knocked down, the proliferative, migratory, and invasive capabilities of A549 cells were evaluated. Cell apoptotic level was assessed using TUNEL. Western blot was employed to evaluate the expressions of Akt/CREB pathway-related proteins. The interaction between HELLS and KIF11 was analyzed using bioinformatics databases, and testified by Co-IP assay. Results revealed that HELLS and KIF11 expressions were significantly upregulated in LUAD cells and tissues. High HELLS and KIF11 expression was correlated with the poor prognosis of patients with LUAD. Additionally, HELLS knockdown suppressed the capabilities of LUAD cells to proliferate, migrate, and invade whereas promoted the cell apoptotic level. Moreover, HELLS could interact with KIF11 and had positive correlation with KIF11. Furthermore, KIF11 overexpression partially counteracted the impacts of HELLS knockdown on cell proliferative, migratory, invasive capabilities, and apoptotic level in LUAD cells. Besides, Akt/CREB pathway was blocked by HELLS silencing, which was restored by KIF11 overexpression. Collectively, HELLS knockdown blocked Akt/CREB pathway by downregulating KIF11 expression, thereby inhibiting LUAD cell proliferation, invasion, migration, and promoting apoptosis.