Bioinformatics Databases Research Articles

TWF1 induces autophagy and accelerates malignant phenotype in lung adenocarcinoma via inhibiting the cAMP signaling pathway.

Many studies have shown that the actin cytoskeleton plays an essential role in the initiation and progression of cancer. As an actin-binding protein, Twinfilin1 (TWF1) plays an important role in regulating cytoskeleton-related functions. However, little is known about the expression and function of TWF1 in human tumors. The present study aimed to investigate the functional roles and the underlying molecular mechanisms of TWF1 in human lung adenocarcinoma (LUAD). By using bioinformatics databases and tumor tissues, TWF1 expression was found to be higher in LUAD tissues than in adjacent tissues and poor survival was predicted in patients with LUAD. In vitro and in vivo assays indicated that downregulation of TWF1 expression suppressed LUAD cells invasion and migration. Further studies revealed that TWF1 interacted with p62 and was involved in the regulation of autophagy. The molecular mechanisms underlying TWF1 were investigated by RNA-seq analysis and a series of functional experiments. The results showed that downregulation of TWF1 suppressed LUAD progression through the cAMP signaling pathway. Therefore, overexpression of TWF1 in LUAD promoted migration, invasion, and autophagy through the cAMP signaling pathway.

Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome.

Metaproteomics, a method for untargeted, high-throughput identification of proteins in complex samples, provides functional information about microbial communities and can tie functions to specific taxa. Metaproteomics often generates less data than other omics techniques, but analytical workflows can be improved to increase usable data in metaproteomic outputs. Identification of peptides in the metaproteomic analysis is performed by comparing mass spectra of sample peptides to a reference database of protein sequences. Although these protein databases are an integral part of the metaproteomic analysis, few studies have explored how database composition impacts peptide identification. Here, we used cervicovaginal lavage (CVL) samples from a study of bacterial vaginosis (BV) to compare the performance of databases built using six different strategies. We evaluated broad versus sample-matched databases, as well as databases populated with proteins translated from metagenomic sequencing of the same samples versus sequences from public repositories. Smaller sample-matched databases performed significantly better, driven by the statistical constraints on large databases. Additionally, large databases attributed up to 34% of significant bacterial hits to taxa absent from the sample, as determined orthogonally by 16S rRNA gene sequencing. We also tested a set of hybrid databases which included bacterial proteins from NCBI RefSeq and translated bacterial genes from the samples. These hybrid databases had the best overall performance, identifying 1,068 unique human and 1,418 unique bacterial proteins, ~30% more than a database populated with proteins from typical vaginal bacteria and fungi. Our findings can help guide the optimal identification of proteins while maintaining statistical power for reaching biological conclusions. IMPORTANCE Metaproteomic analysis can provide valuable insights into the functions of microbial and cellular communities by identifying a broad, untargeted set of proteins. The databases used in the analysis of metaproteomic data influence results by defining what proteins can be identified. Moreover, the size of the database impacts the number of identifications after accounting for false discovery rates (FDRs). Few studies have tested the performance of different strategies for building a protein database to identify proteins from metaproteomic data and those that have largely focused on highly diverse microbial communities. We tested a range of databases on CVL samples and found that a hybrid sample-matched approach, using publicly available proteins from organisms present in the samples, as well as proteins translated from metagenomic sequencing of the samples, had the best performance. However, our results also suggest that public sequence databases will continue to improve as more bacterial genomes are published.

PSMD8 can serve as potential biomarker and therapeutic target of the PSMD family in ovarian cancer: based on bioinformatics analysis and in vitro validation

BackgroundThe ubiquity-proteasome system is an indispensable mechanism for regulating intracellular protein degradation, thereby affecting human antigen processing, signal transduction, and cell cycle regulation. We used bioinformatics database to predict the expression and related roles of all members of the PSMD family in ovarian cancer. Our findings may provide a theoretical basis for early diagnosis, prognostic assessment, and targeted therapy of ovarian cancer.MethodsGEPIA, cBioPortal, and Kaplan–Meier Plotter databases were used to analyze the mRNA expression levels, gene variation, and prognostic value of PSMD family members in ovarian cancer. PSMD8 was identified as the member with the best prognostic value. The TISIDB database was used to analyze the correlation between PSMD8 and immunity, and the role of PSMD8 in ovarian cancer tissue was verified by immunohistochemical experiments. The relationship of PSMD8 expression with clinicopathological parameters and survival outcomes of ovarian cancer patients was analyzed. The effects of PSMD8 on malignant biological behaviors of invasion, migration, and proliferation of ovarian cancer cells were studied by in vitro experiments.ResultsThe expression levels of PSMD8/14 mRNA in ovarian cancer tissues were significantly higher than those in normal ovarian tissues, and the expression levels of PSMD2/3/4/5/8/11/12/14 mRNA were associated with prognosis. Up-regulation of PSMD4/8/14 mRNA expression was associated with poor OS, and the up-regulation of PSMD2/3/5/8 mRNA expression was associated with poor PFS in patients with ovarian serous carcinomas. Gene function and enrichment analysis showed that PSMD8 is mainly involved in biological processes such as energy metabolism, DNA replication, and protein synthesis. Immunohistochemical experiments showed that PSMD8 was mainly expressed in the cytoplasm and the expression level was correlated with FIGO stage. Patients with high PSMD8 expression had poor prognosis. Overexpression of PSMD8 significantly enhanced the proliferation, migration, and invasion abilities in ovarian cancer cells.ConclusionWe observed different degrees of abnormal expression of members of PSMD family in ovarian cancer. Among these, PSMD8 was significantly overexpressed in ovarian malignant tissue, and was associated with poor prognosis. PSMDs, especially PSMD8, can serve as potential diagnostic and prognostic biomarkers and therapeutic targets in ovarian cancer.

Smurf1: A possible therapeutic target in dry age-related macular degeneration

Smad ubiquitylation regulatory factor-1 (Smurf1) is one of C2-WW-HECT domain E3 ubiquitin ligases, it can regulate BMP pathway by mediating ubiquitylation degradation of Smad1/Smad5. Many functions about Smurf1 also are still unknown, especially in retina. This research is about to explore the role of Smurf1 in retina degeneration. Tail vein injection of sodium iodate (NaIO3) in C57BL/6J mice was the animal model of retina degeneration. In NaIO3 model, Smurf1 had more expression than normal mice. Specific Smurf1 inhibitor, A01, was injected into vitreous cavity. Results showed that inhibiting Smurf1 could alleviate acute retina injury, such as keeping a better retina structure in living imaging and histologic sections, less cell death and inflammation activation. Tert-butyl hydroperoxide (TBH) was used to establish oxidative stress injury in human retinal pigments epithelial cell line (ARPE-19). Oxidative stress injury gradually caused co-upregulation of Smurf1, TGF-β1 and phosphorylated NF-κB (pNF-κB). TGF-β1 could directly induce Smurf1 expression. Inhibiting Smurf1 had an anti-epithelial mesenchymal transition (anti-EMT) function. Similarly, A01 also could inhibit the expression of pNF-κB, NLRP3 and IL-1β. At last, after searching bioinformatics database, Smurf1 had a possible interaction with beta-transducin repeat containing E3 ubiquitin protein ligase (β-TrCP), another E3 ubiquitin ligases. β-TrCP can mediate ubiquitination degradation of p-IκBα. Lentivirus-SMURF1 was used to overexpress Smurf1, and GS143 was used to inhibit β-TrCP. The results showed Smurf1 could directly induce NF-κB, pNF-κB, and NLRP3 expression, and keep a stable β-TrCP expression. However, inhibiting β-TrCP could cause more NF-κB activation and NLRP3 expression. Therefore, β-TrCP may play a negative role in NF-κB pathway activation. In summary, Smurf1 plays a role in exacerbating oxidative stress injury and inflammation in retina and may become a potential therapeutic target in ROS injury of retina.

MiR-4482 and miR-3912 aim for 3'UTR of ERG mRNA in prostate cancer.

Ets-related gene (ERG) is overexpressed as a fusion protein in prostate cancer. During metastasis, the pathological role of ERG is associated with cell proliferation, invasion, and angiogenesis. Here, we hypothesized that miRNAs regulate ERG expression through its 3'UTR. Several bioinformatics tools were used to identify miRNAs and their binding sites on 3'UTR of ERG. The selected miRNAs expression was analyzed in prostate cancer samples by qPCR. The miRNAs overexpression was induced in prostate cancer cells (VCaP) to analyze ERG expression. Reporter gene assay was performed to evaluate the ERG activity in response to selected miRNAs. The expression of ERG downstream target genes was also investigated through qPCR after miRNAs overexpression. To observe the effects of selected miRNAs on cell proliferation and migration, scratch assay was performed to calculate the cell migration rate. miR-4482 and miR-3912 were selected from bioinformatics databases. miR-4482 and -3912 expression were decreased in prostate cancer samples, as compared to controls (p<0.05 and p<0.001), respectively. Overexpression of miR-4482 and miR-3912 significantly reduced ERG mRNA (p<0.001 and p<0.01), respectively) and protein (p<0.01) in prostate cancer cells. The transcriptional activity of ERG was significantly reduced (p<0.01) in response to miR-4482 and-3912. ERG angiogenic targets and cell migration rate was also reduced significantly (p<0.001) after miR-4482 and -3912 over-expression. This study indicates that miR-4482 and -3912 can suppress the ERG expression and its target genes, thereby, halt prostate cancer progression. These miRNAs may be employed as a potential therapeutic target for the miRNA-based therapy against prostate cancer.

Development of an Online Genome Sequence Comparison Resource for Bacillus cereus sensu lato Strains Using the Efficient Composition Vector Method.

An automated method was developed for differentiating closely related B. cereus sensu lato (s.l.) species, especially biopesticide Bacillus thuringiensis, from other human pathogens, B. anthracis and B. cereus sensu stricto (s.s.). In the current research, four typing methods were initially compared, including multi-locus sequence typing (MLST), single-copy core genes phylogenetic analysis (SCCGPA), dispensable genes content pattern analysis (DGCPA) and composition vector tree (CVTree), to analyze the genomic variability of 23 B. thuringiensis strains from aizawai, kurstaki, israelensis, thuringiensis and morrisoni serovars. The CVTree method was the best option to be used for typing B. thuringiensis strains since it proved to be the fastest method, whilst giving high-resolution data about the strains. In addition, CVTree agrees well with ANI-based method, revealing the relationship between B. thuringiensis and other B. cereus s.l. species. Based on these data, an online genome sequence comparison resource was built for Bacillus strains called the Bacillus Typing Bioinformatics Database to facilitate strain identification and characterization.

Leucyl and Cystinyl Aminopeptidase as a Prognostic-Related Biomarker in OV Correlating with Immune Infiltrates.

It was indicated that tumor intrinsic heterogeneity and the tumor microenvironment (TME) of ovarian cancer (OV) influence immunotherapy efficacy and patient outcomes. Leucyl and cystinyl aminopeptidase (LNPEP) encodes a zinc-dependent aminopeptidase, which has been proved to participant in the vesicle-mediated transport and class I MHC mediated antigen processing and presentation. However, the function of LNPEP in TME of OV and its potential molecular mechanisms have not been determined. Therefore, we aimed to investigate a prognostic biomarker which may be helpful in identifying TME heterogeneity of ovarian cancer. In this study, bioinformatics databases were used to explore the expression profile and immune infiltration of LNPEP. Bioinformatics analyses of survival data and interactors of LNPEP were conducted to predict the prognostic value of LNPEP in OV. The protein levels of LNPEP were validated by Western blot and immunohistochemistry. Based on the TCGA data, our data displayed that the mRNA expression of LNPEP was markedly down-regulated in ovarian cancer than that in para-cancer tissues, contrary to the protein level. Importantly, high LNPEP expression was associated with poor prognosis in patients with OV. Furthermore, Cox regression analysis showed that LNPEP was an independent prognostic factor in OV. GO and KEGG pathway analyses indicated the co-expressed genes of LNPEP were mainly related to a variety of immune-related pathways, including Th1 and Th2 cell differentiation, Th17 cell differentiation, and immunoregulatory interaction. Our data also demonstrated that the expression of LNPEP was strongly correlated with immune infiltration levels, immunomodulators, chemokines and chemokine receptors. In our study, we identified and established a prognostic signature of immune-related LNPEP in OV, which will be of great value in predicting the prognosis of clinical trials and may become a new therapeutic target for immunological research and potential prognostic biomarker in OV.

Mistle: bringing spectral library predictions to metaproteomics with an efficient search index.

Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics. In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes. Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle.

LncRNA SH3BP5-AS1 Regulates the Proliferation and Cell Cycle of Non-Small Cell Lung Cancer Cells

Background: Non-small cell lung cancer (NSCLC) consists of a class of heterogeneous diseases. Objective: LncRNAs are exceedingly implicated in the pathogenesis of NSCLC. Herein, the current study set out to illustrate the molecular mechanism of SH3BP5-AS1 in NSCLC cells. Methods: SH3BP5-AS1 expression in clinical NSCLC tissues and its impact on prognosis were analyzed by bioinformatics database. SH3BP5-AS1 expression patterns in NSCLC cell lines (A549/H1299/H1975/H460) and human normal lung epithelial cell lines (BEAS-2B) were examined by RT-qPCR. SH3BP5-AS1 was overexpressed in A549 or silenced in H1975 cells through transfection to assess its effect on proliferation, cell cycle distribution, and apoptosis, apoptosisrelated protein (Cleaved Caspase-3, Bax, Bcl-2) levels, invasive, migratory, and healing capacity through CCK-8, colony formation assay, flow cytometry, Western blot, Transwell, and cell scratch test. Results: SH3BP5-AS1 was under-expressed in NSCLC clinical tissues, and NSCLC patients with low SH3BP5-AS1 expression showed poor prognosis. A549/H1299/H1975/H460 cells had reduced levels of SH3BP5-AS1, with the relative level lowest/highest expression in A549/H1975 cells, respectively. SH3BP5-AS1 overexpression repressed A549 cell proliferation, slowed down cell cycle progression, enhanced apoptosis, elevated Cleared Caspase-3, Bax, suppressed Bcl-2 protein levels, and inhibited migratory, invasive, and scratch healing capacities, while SH3BP5-AS1 silencing brought about the opposite results in H1975 cells. Conclusion: SH3BP5-AS1 could suppress NSCLC cell proliferation, slow down cell cycle progression, stimulate apoptosis, and limit invasion and migration.

NPM3 as a novel oncogenic factor and poor prognostic marker contributes to cell proliferation and migration in lung adenocarcinoma

BackgroundLung cancer is the leading cause of cancer-related deaths worldwide, and despite recent advances in targeted therapies and immunotherapies, the clinical benefit remains limited. Therefore, there is an urgent need to further investigate the molecular mechanisms underlying lung cancer. The aim of this study was to investigate the expression and function of NPM3 in the tumor microenvironment of lung adenocarcinoma (LUAD).MethodsWe utilized bioinformatics tools and databases, including UALCAN, GEPIA2, HPA, and Sangerbox, to analyze NPM3 expression in LUAD samples and its association with prognosis and mutational landscape. NPM3 expression in various cell types was assessed at the single cell level using the TISCH database. We also used algorithms such as TIMER and EPIC to explore the crosstalk between NPM3 expression and immune features. KEGG enrichment analysis was performed to identify potential signaling pathways of NPM3. Finally, we employed siRNA knockdown strategy to investigate the effect of NPM3 on LUAD cell proliferation and migration in vitro.ResultsNPM3 was significantly upregulated in LUAD tissues and was strongly associated with poor prognosis and TP53 gene mutations. Single-cell sequencing analysis revealed that NPM3 was expressed in immune cells (dendritic cells and monocytes/macrophages) in the tumor microenvironment. Moreover, NPM3 expression was negatively associated with immune B cell and CD4 T cell infiltration, as well as with several immune-related genes (including CCL22, CXCR2, CX3CR1, CCR6, HLA-DOA, HLA-DQA2). KEGG enrichment analysis indicated that NPM3 expression was associated with cell cycle, CAMs, and NSCLC pathway genes. Finally, in vitro experiments showed that NPM3 knockdown inhibited LUAD cell proliferation and migration in NCI-H1299 and SPC-A1 cells, and suppressed the expression of CCNA2 and MAD2L1.ConclusionElevated NPM3 expression predicts poor clinical outcome and an immunosuppressive microenvironment in LUAD tissues. NPM3 promotes LUAD progression by promoting cell proliferation and migration, and targeting NPM3 may represent a novel therapeutic strategy for LUAD.Graphical

LEM Domain Containing 1 Acts as a Novel Oncogene and Therapeutic Target for Triple-Negative Breast Cancer.

Breast cancer is the most common deadly malignancy in women worldwide. In particular, triple-negative breast cancer (TNBC) exhibits the worst prognosis among four subtypes of breast cancer due to limited treatment options. Exploring novel therapeutic targets holds promise for developing effective treatments for TNBC. Here, we demonstrated for the first time that LEMD1 (LEM domain containing 1) is highly expressed in TNBC and contributes to reduced survival in TNBC patients, through analysis of both bioinformatic databases and collected patient samples. Furthermore, LEMD1 silencing not only inhibited the proliferation and migration of TNBC cells in vitro, but also abolished tumor formation of TNBC cells in vivo. Knockdown of LEMD1 enhanced the sensitivity of TNBC cells to paclitaxel. Mechanistically, LEMD1 promoted the progress of TNBC by activating the ERK signaling pathway. In summary, our study revealed that LEMD1 may act as a novel oncogene in TNBC, and targeting LEMD1 may be exploited as a promising therapeutic approach to enhance the efficacy of chemotherapy against TNBC.

Advances in Computational and Bioinformatics Tools and Databases for Designing and Developing a Multi-Epitope-Based Peptide Vaccine.

A vaccine is defined as a biologic preparation that trains the immune system, boosts immunity, and protects against a deadly microbial infection. They have been used for centuries to combat a variety of contagious illnesses by means of subsiding the disease burden as well as eradicating the disease. Since infectious disease pandemics are a recurring global threat, vaccination has emerged as one of the most promising tools to save millions of lives and reduce infection rates. The World Health Organization reports thatimmunization protects three million individuals annually. Currently, multi-epitope-based peptide vaccines are a unique concept in vaccine formulation. Epitope-based peptide vaccines utilize small fragments of proteins or peptides (parts of the pathogen), called epitopes, that trigger an adequate immune response against a particular pathogen. However, conventional vaccine designing and development techniques are too cumbersome, expensive, and time-consuming. With the recent advancement in bioinformatics, immunoinformatics, and vaccinomicsdiscipline, vaccine science has entered a new era accompanying a modern, impressive, and more realistic paradigm in designing and developing next-generation strong immunogens. In silico designing and developing a safe and novel vaccine construct involves knowledge of reverse vaccinology, various vaccine databases, and high throughput techniques. The computational tools and techniques directly associated with vaccine research are extremely effective, economical, precise, robust, and safe for human use. Many vaccine candidates have entered clinical trials instantly and are available prior to schedule. In light of this, the present article provides researchers with up-to-date information on various approaches, protocols, and databases regarding the computational designing and development of potent multi-epitope-based peptide vaccines that can assist researchers in tailoring vaccines more rapidly and cost-effectively.

Current trends in RNA virus detection through metatranscriptome sequencing data.

With advances in sequencing technology, metatranscriptome sequencing from a variety of environmental and biological sources has revealed the existence of various previously unknown RNA viruses. This review presents recent major RNA virome studies sampled from invertebrate and vertebrate species as well as aquatic environments. In particular, we focus on severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and related RNA virus identification through metatranscriptome sequencing analyses. Recently developed bioinformatics software and databases for RNA virus identification are introduced. A relationship between newly identified RNA viruses and endogenous viral elements in host genomes is also discussed.

SeqWiz: a modularized toolkit for next-generation protein sequence database management and analysis

BackgroundCurrent proteomic technologies are fast-evolving to uncover the complex features of sequence processes, variations and modifications. Thus, protein sequence database and the corresponding softwares should also be improved to solve this issue.ResultsWe developed a state-of-the-art toolkit (SeqWiz) for constructing next-generation sequence databases and performing proteomic-centric sequence analyses. First, we proposed two derived data formats: SQPD (a well-structured and high-performance local sequence database based on SQLite), and SET (an associated list of selected entries based on JSON). The SQPD format follows the basic standards of the emerging PEFF format, which also aims to facilitate the search of complex proteoform. The SET format is designed for generating subsets with with high-efficiency. These formats are shown to greatly outperform the conventional FASTA or PEFF formats in time and resource consumption. Then, we mainly focused on the UniProt knowledgebase and developed a collection of open-source tools and basic modules for retrieving species-specific databases, formats conversion, sequence generation, sequence filter, and sequence analysis. These tools are implemented by using the Python language and licensed under the GNU General Public Licence V3. The source codes and distributions are freely available at GitHub (https://github.com/fountao/protwiz/tree/main/seqwiz).ConclusionsSeqWiz is designed to be a collection of modularized tools, which is friendly to both end-users for preparing easy-to-use sequence databases as well as bioinformaticians for performing downstream sequence analysis. Besides the novel formats, it also provides compatible functions for handling the traditional text based FASTA or PEFF formats. We believe that SeqWiz will promote the implementing of complementary proteomics for data renewal and proteoform analysis to achieve precision proteomics. Additionally, it can also drive the improvement of proteomic standardization and the development of next-generation proteomic softwares.

A systems biology approach for discovering the cellular and molecular aspects of psychogenic non-epileptic seizure.

Psychogenic non-epileptic seizure (PNES) is the most common non-epileptic disorder in patients referring to epilepsy centers. Contrary to common beliefs about the disease's harmlessness, the death rate of PNES patients is similar to drug-resistant epilepsy. Meanwhile, the molecular pathomechanism of PNES is unknown with very limited related research. Thus, the aim of this in silico study was to find different proteins and hormones associated with PNES via a systems biology approach. Different bioinformatics databases and literature review were used to find proteins associated with PNES. The protein-hormone interaction network of PNES was constructed to discover its most influential compartments. The pathways associated with PNES pathomechanism were found by enrichment analysis of the identified proteins. Besides, the relationship between PNES-related molecules and psychiatric diseases was discovered, and the brain regions that could express altered levels of blood proteins were discovered. Eight genes and three hormones were found associated with PNES through the review process. Proopiomelanocortin (POMC), neuropeptide Y (NPY), cortisol, norepinephrine, and brain-derived neurotrophic factor (BDNF) were identified to have a high impact on the disease pathogenesis network. Moreover, activation of Janus kinase-signaling transducer and activator of transcription (JAK-STAT) and JAK, as well as signaling of growth hormone receptor, phosphatidylinositol 3-kinase /protein kinase B (PI3K/AKT), and neurotrophin were found associated with PNES molecular mechanism. Several psychiatric diseases such as depression, schizophrenia, and alcohol-related disorders were shown to be associated with PNES predominantly through signaling molecules. This study was the first to gather the biochemicals associated with PNES. Multiple components and pathways and several psychiatric diseases associated with PNES, and some brain regions that could be altered during PNES were suggested, which should be confirmed in further studies. Altogether, these findings could be used in future molecular research on PNES patients.

The EcoCyc Database (2023).

EcoCyc is a bioinformatics database available online at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on the regulation of gene expression, E. coli gene essentiality, and nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for the analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed online. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. Data generated from a whole-cell model that is parameterized from the latest data on EcoCyc are also available. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.

Exploring the molecular mechanism of Huangqin-Jinyinhua couplet medicines for the treatment of hand-foot and mouth disease using network pharmacology, molecular docking and bioinformatics databases

BackgroundHuangqin-Jinyinhua couplet medicines (HQJYH) were often used to treat hand-foot and mouth disease (HFMD), although its mechanism remains unclear. This study investigated the active ingredients in HQJYH and their mechanism when treating HFMD by network pharmacology and molecular docking. MethodsThe TCMSP database obtained the principal active ingredients found in HQJYH. The GeneCards, CTD, PharmGkb and DisGeNet databases were used to obtain the main targets involved in HFMD, and the merged targets were obtained by R software and the Venn package. The DAVID database performed GO and KEGG enrichment analyses on the intersection targets. We also used Cytoscape software to construct an “HQJYH-Active Ingredients-Targets” network and used the STRING platform to conduct protein–protein interaction (PPI) analyses on the intersection targets. Molecular docking of core active ingredients-core targets interactions were modeled using AutoDock Vina software. Results56 active ingredients were found in HQJYH, corresponding to 212 targets, 5323 HFMD targets, and 156 intersection targets. KEGG enrichment analysis found that genes were mainly enriched in the PI3K-Akt signaling pathway, MAPK signaling pathway and other pathways. Cytoscape showed that the core active ingredients were quercetin, luteolin, kaempferol, beta-sitosterol, stigmasterol, wogonin, baicalein and acacetin. The PPI network showed that the core targets involved were TP53, CASP3, AKT1, IL6, MAPK14, EGFR, and HIF1A. The molecular docking results indicated key binding activity between Baicalein-AKT1, quercetin-AKT1, wogonin-AKT1, kaempferol-AKT and wogonin-MAPK14. ConclusionThis study was based on network pharmacology and revealed the potential molecular mechanisms involved in treating HFMD by HQJYH.

Presence of PD-1 similarity genes in monocytes may promote the development of type 1 diabetes mellitus and poor prognosis of pancreatic cancer

IntroductionTo identify proteins and corresponding genes that share sequential and structural similarity with programmed cell death protein-1 (PD-1) in patients with type 1 diabetes mellitus (T1DM) via bioinformatics analysis.Research design...

Elsholtzia bodinieri Vaniot ameliorated acute lung injury in mice by regulating pyroptosis, inflammation, oxidative stress and macrophage polarization.

Elsholtzia bodinieri Vaniot, perennial herbs, a traditional Yunnan Chinese herbal medicine. Its whole herb can be used as commonly used herbs to cure fever, headache, inflammation, indigestion etc., and its tender tip can also be used as tea in Yunnan of China. However, the protective mechanism of Elsholtzia bodinieri Vaniot on acute lung injury (ALI) still needs to be explored. ALI is characterized by acute respiratory inflammation, which remains a significant source of morbidity and mortality. The current study with the aim of determining the therapeutic the efficacy of E. bodinieri Vaniot on lipopolysaccharide-induced ALI, moreover uncovered the underlying gene-regulated framework, so E. bodinieri Vaniot might serve as functional food for adjuvant therapy or therapeutic agent. These potential pharmacological targets of E. bodinieri Vaniot against ALI were analyzed by multiple bioinformatics databases. E. bodinieri Vaniot methanol extract (EBE) was obtained by ultrasonic-assisted extraction method, and detected by UHPLC-ESI-HRMS/MS. These pyroptosis, inflammation and oxidative stress associated factors were measured using ELISA assay, western blotting, and histopathological examination to assess the effects of EBE. EcoTyper and immunofluorescence staining were employed to estimate macrophage polarization states in ALI lungs tissue. In ALI lung tissues, EBE treatment could increase B cell leukemia/lymphoma 2 (BCL2) to inhibit pyroptosis, downregulate prostaglandin-endoperoxide synthase 2 (PTGS2) to attenuate inflammation, upregulating NAD(P)H dehydrogenase, quinone 1 (NQO1) to alleviate oxidative stress and induce macrophage polarization toward the M2 phenotype. E. bodinieri Vaniot ameliorated ALI thought regulating pyroptosis, inflammation, oxidative stress and macrophage polarization, as well as could be a promising source for therapeutic agent.

Integrated transcriptome, proteome and single-cell sequencing uncover the prognostic and immunological features of colony-stimulating factor 3 receptor in pan-cancer.

Colony-stimulating factor 3 receptor (CSF3R) has been demonstrated to be associated with various hematological tumors, especially chronic neutrophilic leukemia; however, the detailed roles of CSF3R in other cancers remain to be explored. In the present study, we systematically analyzed the expression profiles of CSF3R in pan-cancer by comprehensive bioinformatics databases, such as Tumor Immune Estimation Resource, version 2 (TIMER2.0), Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2.0), etc. GEPIA2.0 was also used to analyze the relationship between CSF3R expression and patients' survival prognosis. We found that the high expression of CSF3R was associated with a poor prognosis in the brain tumor patients, such as brain lower grade glioma and glioblastoma multiforme. In addition, we further investigated the genetic mutation and DNA methylation level of CSF3R in multiple cancers. Immune infiltration analysis showed that CSF3R expression was positively correlated with a variety of tumor-infiltrating immune cells in most cancers. Single cell sequencing indicated that CSF3R levels were correlated with several cancer-associated pathways, such as DNA damage, cell invasion, and stemness. Taken together, the role of CSF3R in multiple cancers might reveal its potential as a novel prognostic biomarker and therapeutic target for cancer patients.