Annotation Of Proteins Research Articles

ToxTeller: Predicting Peptide Toxicity Using Four Different Machine Learning Approaches.

Examining the toxicity of peptides is essential for therapeutic peptide-based drug design. Machine learning approaches are frequently used to develop highly accurate predictors for peptide toxicity prediction. In this paper, we present ToxTeller, which provides four predictors using logistic regression, support vector machines, random forests, and XGBoost, respectively. For prediction model development, we construct a data set of toxic and nontoxic peptides from SwissProt and ConoServer databases with existence evidence levels checked. We also fully utilize the protein annotation in SwissProt to collect more toxic peptides than using keyword search alone. From this data set, we construct an independent test data set that shares at most 40% sequence similarity within itself and with the training data set. From a quite comprehensive list of 28 feature combinations, we conduct 10-fold cross-validation on the training data set to determine the optimized feature combination for model development. ToxTeller's performance is evaluated and compared with existing predictors on the independent test data set. Since toxic peptides must be avoided for drug design, we analyze strategies for reducing false-negative predictions of toxic peptides and suggest selecting models by top sensitivity instead of the widely used Matthews correlation coefficient, and also suggest using a meta-predictor approach with multiple predictors.

Whole-genome sequence of a novel lytic bacteriophage infecting Clavibacter michiganensis subsp. michiganensis from Turkey.

Clavibacter michiganensis subsp. michiganensis (Cmm) is an important plant-pathogenic bacterium that causes canker and wilt diseases. Biological control of the disease with bacteriophages is an alternative to conventional methods. In this study, Phage33 infecting Cmm was characterized based on morphological and genomic properties. Morphological characteristics such as shape and size were investigated using electron microscopy. The whole genome was sequenced using the Illumina Novaseq 6000 platform and the sequence was assembled and annotated. VICTOR and VIRIDIC were used for determining the phylogeny and comparing viral genomes, respectively. Electron microscopy showed that Phage33 has an icosahedral head with a diameter of ~55 nm and a long, thin, non-contractile tail ~169 nm in length. The genome of Phage33 is 56 324 bp in size, has a GC content of 62.49 % and encodes 67 open reading frames. Thirty-seven ORFs showed high homology to functionally annotated bacteriophage proteins in the NCBI database. The remaining 30 ORFs were identified as hypothetical with unknown functions. The genome contains no antimicrobial resistance, no lysogenicity and no virulence signatures, suggesting that it is a suitable candidate for biocontrol agents. The results of a blastn search showed similarity to the previously reported Xylella phage Sano, with an average nucleotide sequence identity of 92.37 % and query coverage of 91 %. This result was verified using VICTOR and VIRIDIC analysis, and suggests that Phage33 is a new member of the genus Sanovirus under the class Caudoviricetes.

Fine-tuning protein embeddings for functional similarity evaluation.

Proteins with unknown function are frequently compared to better characterized relatives, either using sequence similarity, or recently through similarity in a learned embedding space. Through comparison, protein sequence embeddings allow for interpretable and accurate annotation of proteins, as well as for downstream tasks such as clustering for unsupervised discovery of protein families. However, it is unclear whether embeddings can be deliberately designed to improve their use in these downstream tasks. We find that for functional annotation of proteins, as represented by Gene Ontology (GO) terms, direct fine-tuning of language models on a simple classification loss has an immediate positive impact on protein embedding quality. Fine-tuned embeddings show stronger performance as representations for K-nearest neighbor classifiers, reaching stronger performance for GO annotation than even directly comparable fine-tuned classifiers, while maintaining interpretability through protein similarity comparisons. They also maintain their quality in related tasks, such as rediscovering protein families with clustering. github.com/mofradlab/go_metric.

Genome-Wide Gene Birth-Death Dynamics Are Associated with Diet Breadth Variation in Lepidoptera.

Comparative analyses of gene birth-death dynamics have the potential to reveal gene families that played an important role in the evolution of morphological, behavioral, or physiological variation. Here, we used whole genomes of 30 species of butterflies and moths to identify gene birth-death dynamics among the Lepidoptera that are associated with specialist or generalist feeding strategies. Our work advances this field using a uniform set of annotated proteins for all genomes, investigating associations while correcting for phylogeny, and assessing all gene families rather than a priori subsets. We discovered that the sizes of several important gene families (e.g. those associated with pesticide resistance, xenobiotic detoxification, and/or protein digestion) are significantly correlated with diet breadth. We also found 22 gene families showing significant shifts in gene birth-death dynamics at the butterfly (Papilionoidea) crown node, the most notable of which was a family of pheromone receptors that underwent a contraction potentially linked with a shift to visual-based mate recognition. Our findings highlight the importance of uniform annotations, phylogenetic corrections, and unbiased gene family analyses in generating a list of candidate genes that warrant further exploration.

3D STRUCTURE AND FUNCTION ANALYSIS OF RECOMBINANT ALDII PROTEIN FROM Uncultured Acidilobus sp. USING I-TASSER

The AldII protein is a new recombinant protein produced from a novel gene obtained via a metagenome approach. Previous studies showed that this protein is strong and has the same metal binding aspect as the class II Aldolase enzyme. Aldolase is a valuable enzyme used in pharmaceuticals, food processing, and biochemistry. Further investigation is required to comprehend the structure and function of the AldII protein due to its potential. Researchers will conduct sophisticated bioinformatic analysis on the 3D shape and function prediction of AldII using the I-TASSER webserver from Zhanglab. The AldII protein is a new recombinant protein produced from a novel gene obtained via a metagenome approach. Previous studies showed that this protein is strong and has the same metal binding aspect as the class II Aldolase enzyme. Aldolase is a valuable enzyme used in pharmaceuticals, food processing, and biochemistry. Further investigation is required to comprehend the structure and function of the AldII protein due to its potential. Researchers will conduct sophisticated bioinformatic analysis on the 3D shape and function prediction of AldII using the I-TASSER webserver from Zhanglab. The I-TASSER server is an online tool for the automated prediction of protein structure and annotation of functions based on structure. Analysis of the AldII protein using the I-TASSER webserver shows that this protein has a stable structure with the closest structural homology to deoxyribose-phosphate aldolase from Bacillus thuringiensis with PDB code 6btdA. Additionally, the biological structure analysis shows that this protein shares the biological function of the enzyme L-fuculose-1-phosphate aldolase, which is part of the class II Aldolase enzyme that plays a role in the catabolism of arabinose, L-Fuculose, and Rhamnose. The results align with prior research that states the AldII protein is a stable protein with a catalytic side that is homologous to the class II Aldolase enzyme.

CViewer: a Java-based statistical framework for integration of shotgun metagenomics with other omics datasets

BackgroundShotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, the latter of which then helps retrieve their function based on annotated gene products, mRNA, protein, and metabolites. Within the context of a specific hypothesis, additional modalities are often included, to give host-microbiome interaction. For example, in human-associated microbiome projects, it has become increasingly common to include host immunology through flow cytometry. Whilst there are plenty of software approaches available, some that utilize marker-based and assembly-based approaches, for downstream statistical analyses, there is still a dearth of statistical tools that help consolidate all such information in a single platform. By virtue of stringent computational requirements, the statistical workflow is often passive with limited visual exploration.ResultsIn this study, we have developed a Java-based statistical framework (https://github.com/KociOrges/cviewer) to explore shotgun metagenomics data, which integrates seamlessly with conventional pipelines and offers exploratory as well as hypothesis-driven analyses. The end product is a highly interactive toolkit with a multiple document interface, which makes it easier for a person without specialized knowledge to perform analysis of multiomics datasets and unravel biologically relevant patterns. We have designed algorithms based on frequently used numerical ecology and machine learning principles, with value-driven from integrated omics tools which not only find correlations amongst different datasets but also provide discrimination based on case–control relationships.ConclusionsCViewer was used to analyse two distinct metagenomic datasets with varying complexities. These include a dietary intervention study to understand Crohn’s disease changes during a dietary treatment to include remission, as well as a gut microbiome profile for an obesity dataset comparing subjects who suffer from obesity of different aetiologies and against controls who were lean. Complete analyses of both studies in CViewer then provide very powerful mechanistic insights that corroborate with the published literature and demonstrate its full potential.EDLr-6-tH-9CdVCxoa6z_8Video

A compilation of histidine kinase genes in the genome of a tissue macerating Pectobacterium brasiliense isolated from potato in South Africa

Background A compilation of kinase genes of Pectobacterium brasilense is presented. The genes were mined from a complete genome sequence of strain BD163 isolated from potato in Tshwane, South Africa. This work is an effort to collate knowledge on P. brasiliense which has become a worldwide pathogen of potatoes and other vegetables such as tomato. Histidine kinases of P. brasiliense can be targets of bacterial control agents to limit the growth, spread and pathogenicity of P. brasiliense. Methods The whole genome sequence of strain BD163 of P. brasiliense deposited on www.ncbi.nlm.nih.gov was downloaded, and individual kinase genes were searched and listed with their properties, namely, locus tag, protein accession number and annotation. Conclusions This is the only compilation of histidine kinase genes for P. brasiliense.

A Proteogenomic Approach to Unravel New Proteins Encoded in the Leishmania donovani (HU3) Genome.

The high-throughput proteomics data generated by increasingly more sensible mass spectrometers greatly contribute to our better understanding of molecular and cellular mechanisms operating in live beings. Nevertheless, proteomics analyses are based on accurate genomic and protein annotations, and some information may be lost if these resources are incomplete. Here, we show that most proteomics data may be recovered by interconnecting genomics and proteomics approaches (i.e., following a proteogenomic strategy), resulting, in turn, in an improvement of gene/protein models. In this study, we generated proteomics data from Leishmania donovani (HU3 strain) promastigotes that allowed us to detect 1908 proteins in this developmental stage on the basis of the currently annotated proteins available in public databases. However, when the proteomics data were searched against all possible open reading frames existing in the L. donovani genome, twenty new protein-coding genes could be annotated. Additionally, 43 previously annotated proteins were extended at their N-terminal ends to accommodate peptides detected in the proteomics data. Also, different post-translational modifications (phosphorylation, acetylation, methylation, among others) were found to occur in a large number of Leishmania proteins. Finally, a detailed comparative analysis of the L. donovani and Leishmania major experimental proteomes served to illustrate how inaccurate conclusions can be raised if proteomes are compared solely on the basis of the listed proteins identified in each proteome. Finally, we have created data entries (based on freely available repositories) to provide and maintain updated gene/protein models. Raw data are available via ProteomeXchange with the identifier PXD051920.

Excavation of Biomarker Candidates for the Diagnosis of Talaromyces marneffei Infection via Genome-Wide Prediction and Functional Annotation of Secreted Proteins.

Talaromyces marneffei is the third most common infectious pathogen in AIDS patients and leads to the highest death rate in Guangxi, China. The lack of reliable biomarkers is one of the major obstacles in current clinical diagnosis, which largely contributes to this high mortality. Here, we present a study that aimed at identifying diagnostic biomarker candidates through genome-wide prediction and functional annotation of Talaromyces marneffei secreted proteins. A total of 584 secreted proteins then emerged, including 382 classical and 202 nonclassical ones. Among them, there were 87 newly obtained functional annotations in this study. The annotated proteins were further evaluated by combining RNA profiling and a homology comparison. Three proteins were ultimately highlighted as biomarker candidates with robust expression and remarkable specificity. The predicted phosphoinositide phospholipase C and the galactomannoprotein were suggested to play an interactive immune game through metabolism of arachidonic acid. Therefore, they hold promise in developing new tools for clinical diagnosis of Talaromyces marneffei and also possibly serve as molecular targets for future therapy.

Virulence and proteomic responses of Metarhizium anisopliae against Aedes albopictus larvae

The tropical climate in Malaysia provides an ideal environment for the rapid proliferation of Aedes mosquitoes, notably Aedes aegypti and Aedes albopictus, prominent vectors of dengue fever. Alarmingly, these species are increasingly developing resistance to conventional pesticides. This study aimed to evaluate the efficacy of Metarhizium anisopliae isolate HSAH5 spores, specifically on conidia (CO) and blastospores (BL), against Ae. albopictus larvae. The study centered on evaluating their pathogenic effects and the resultant changes in protein expression. Spore suspensions with varying concentrations were prepared for larvicidal bioassays, and protein expressions were analysed using liquid chromatography-mass spectrometry. Subsequently, protein annotation and network analysis were conducted to elucidate infection mechanisms and the proteomic response. Based on the lethal concentrations and time frames, CO exhibited faster larval mortality than BL at lower concentrations. Despite this, both spore types demonstrated comparable overall pathogenic effects. Results from the proteomic profiling revealed 150 proteins with varied expressions following exposure to Ae. albopictus extract, shedding light on distinct infection strategies between the spores. Gene Ontology enrichment and network analysis illustrated the diverse metabolic adaptations of M. anisopliae and interactions with mosquito larvae. This highlighted the complexity of host-pathogen dynamics and the significance of biosynthetic processes, energy storage, and cellular interaction pathways in disease progression. The BL network, consisting 80 proteins and 74 connections, demonstrates the intricate fungal mechanisms triggered by host stimuli. Conversely, the CO network, though smaller, displayed notable interconnectivity and concentrated involvement at the cell periphery, suggesting a deliberate strategy for initial host contact. This study offers valuable insights into proteome dynamics of M. anisopliae's BL and CO for managing mosquito populations and combating disease transmission, thereby significantly advancing public health and environmental conservation efforts.

ProMENDA: an updated resource for proteomic and metabolomic characterization in depression

Depression is a prevalent mental disorder with a complex biological mechanism. Following the rapid development of systems biology technology, a growing number of studies have applied proteomics and metabolomics to explore the molecular profiles of depression. However, a standardized resource facilitating the identification and annotation of the available knowledge from these scattered studies associated with depression is currently lacking. This study presents ProMENDA, an upgraded resource that provides a platform for manual annotation of candidate proteins and metabolites linked to depression. Following the establishment of the protein dataset and the update of the metabolite dataset, the ProMENDA database was developed as a major extension of its initial release. A multi-faceted annotation scheme was employed to provide comprehensive knowledge of the molecules and studies. A new web interface was also developed to improve the user experience. The ProMENDA database now contains 43,366 molecular entries, comprising 20,847 protein entries and 22,519 metabolite entries, which were manually curated from 1370 human, rat, mouse, and non-human primate studies. This represents a significant increase (more than 7-fold) in molecular entries compared to the initial release. To demonstrate the usage of ProMENDA, a case study identifying consistently reported proteins and metabolites in the brains of animal models of depression was presented. Overall, ProMENDA is a comprehensive resource that offers a panoramic view of proteomic and metabolomic knowledge in depression. ProMENDA is freely available at https://menda.cqmu.edu.cn.

Integrating transcriptomic and proteomic data for a comprehensive molecular perspective on the association between sarcopenia and osteoporosis

BackgroundOsteoporosis and sarcopenia are common age-related conditions characterized by the progressive loss of bone density and muscle mass, respectively. Their co-occurrence, often referred to as osteosarcopenia, presents significant challenges in elderly care due to increased fragility and functional impairment. Existing studies have identified shared pathological mechanisms between these conditions, including inflammation, hormonal imbalances, and metabolic dysregulation, but a comprehensive understanding of their molecular interplay remains incomplete. ObjectiveThis study aims to deepen our understanding of the molecular interactions between sarcopenia and osteoporosis through an integrated omics approach, revealing potential therapeutic targets and biomarkers. MethodsEmploying a combination of proteomics and transcriptomics analyses, this study analyzed bone and muscle tissue samples from patients diagnosed with osteoporosis and osteosarcopenia. Techniques included high-throughput sequencing and label-free proteomics, supported by advanced bioinformatics tools for data analysis and functional annotation of genes and proteins. ResultsThe study found marked differences in gene and protein expressions between osteoporosis and osteosarcopenia tissues. Specifically, genes like PDIA5, TUBB1, and CYFIP2 in bone, along with MYH7 and NCAM1 in muscle, exhibited differential expression at both mRNA and protein levels. Pathway analyses revealed the significance of oxidative-reduction balance, cellular metabolism, and immune response in the progression of these conditions. Importantly, the study pinpointed osteoclast differentiation and NF-kappa B signaling pathways as critical in the molecular dynamics of osteosarcopenia, suggesting potential targets for therapy. ConclusionsThis study utilized transcriptomics and proteomics to identify key genes and proteins impacting sarcopenia and osteoporosis, employing advanced network tools to delineate interaction networks and crucial signaling pathways. It highlighted genes like PDIA5 and TUBB1, consistently expressed in both analyses, involved in pathways such as osteoclast differentiation and cytokine interactions. These insights enhance understanding of the molecular interplay in bone and muscle degeneration with aging, suggesting directions for future research into therapeutic interventions and prevention strategies for age-related degenerative diseases.

In silico characterization, homology modeling and structure-based functional annotation of Nile tilapia (Oreochromis niloticus) HSP70 and HSC70 proteins

In silico characterization, homology modeling and structure-based functional annotation of Nile tilapia (Oreochromis niloticus) HSP70 and HSC70 proteins

Abstract 132: Immunosenescence-related Biomarkers And Potential Therapeutic Targets For Coronary Artery Disease Identified Via Plasma Proteome And Immune Cell Traits

There is a pressing need to identify novel biomarkers and therapeutic targets to address the epidemiological burden of coronary artery disease (CAD). Mendelian Randomization analysis (MR) leverages Genome-Wide Association Study (GWAS) data to infer the causal mechanism underlying the phenotype of interest. The proteome is the central mediator of the etiology and pathophysiology of multifactorial diseases such as CAD. Here, we screened for potential biomarkers and therapeutic targets for CAD through MR by using genetically predicted levels of plasma proteome followed by immune cell traits and transcriptomic analysis. The main workflow of our study is outlined in Figure 1. Briefly, MR analysis was performed with protein quantitative trait loci (pQTL) and meta-analysis of four independent GWASs. Sensitivity analysis was performed to ensure the robustness of identified proteins. Pathway Enrichment analysis on whole-blood transcriptome was performed with GSEA. Secondary MR explored the causal effect of 731 immune cell traits on CAD risk. In total, 11 overlapping candidate proteins (P<0.05, OR>1.0) were identified from the plasma proteome meta-analysis, including LPA, PLA2G7, and PCSK9, substantiating the validity of our MR analysis. Functional annotation of candidate proteins identified LCAT and CA11 as promising therapeutic targets and biomarkers. LCAT suppression is protective against atherosclerosis in mice models, while the functional significance of CA11 remains to be elucidated. Transcriptomic analysis shows enrichment in IL-2 signaling pathways reflective of the exhaustion of CD28 on cytotoxic T cells identified via secondary MR using immune-cell traits. Together, these results are indicative of immunosenescence as a risk factor for CAD. Overall, our results revealed that an altered immune landscape increased the risk for CAD and unraveled an encouraging avenue for early diagnosis and treatment of CAD.

Network pharmacology and molecular docking-based systemic approach to investigate the mechanism and molecular targets of phloridzin against Alzheimer’s disease

To explore the potential targets of phloridzin against Alzheimer's disease using network pharmacology and bioinformatic approach, Phloridzin's target genes have been retrieved from public databases (PharmMapper, Swiss Target Prediction, Targetnet and SuperPred). Alzheimer's disease targets were retrieved from Genecards and DisGeNet, a disease database. Using STRING and Cytoscape, protein-protein interaction networks (PPI) were generated and hub genes were found using topological analysis and MCODE. Target protein annotation was done using functional enrichment analysis from the Kyoto Encyclopedia and Genome Database (KEGG) and Gene Ontology (GO). The Discovery studio visualizer was used to further visualize the molecular docking of phloridzin with the hub genes using Autodock tools. Phloridzin's efficacy in treating Alzheimer's has been associated with thirty-eight targets. GSR, GAPDH, ACE, NOS3, HMOX1, GSK3B, ESR1, MAPK1, BACE1 and PPARG were key targets. 500 biological processes (BP), 65 cellular components (CC) and 142 molecular functions (MF) were identified from the results of the GO functional enrichment analysis. KEGG pathway enrichment analysis discovered 117 signaling pathways (P<0.05) including highly enriched pathways like the "AGE-RAGE signaling pathway" and the "HIF-1 signaling pathway". Molecular docking of phloridzin with hub genes revealed a highly stable structure with lower binding affinity values. Phloridzin can regulate various signaling pathways and have a therapeutic effect against AD.

Proteome reorganization and amino acid metabolism during germination and seedling establishment in Lupinus albus.

During germination plants rely entirely on their seed storage compounds to provide energy and precursors for the synthesis of macromolecular structures until the seedling has emerged from the soil and photosynthesis can be established. Lupin seeds use proteins as their major storage compounds, accounting for up to 40% of the seed dry weight. Lupins are therefore a valuable complement to soy as a source of plant protein for human and animal nutrition. The aim of this study was to elucidate how storage protein metabolism is coordinated with other metabolic processes to meet the requirements of the growing seedling. In a quantitative approach, we analysed seedling growth, as well as alterations in biomass composition, the proteome, and metabolite profiles during germination and seedling establishment in Lupinus albus. The reallocation of nitrogen resources from seed storage proteins to functional seed proteins was mapped based on a manually curated functional protein annotation database. Although classified as a protein crop, Lupinus albus does not use amino acids as a primary substrate for energy metabolism during germination. However, fatty acid and amino acid metabolism may be integrated at the level of malate synthase to combine stored carbon from lipids and proteins into gluconeogenesis.

Revealing the microbiome diversity and biocontrol potential of field Aedes ssp.: Implications for disease vector management.

The mosquito Aedes spp. holds important relevance for human and animal health, as it serves as a vector for transmitting multiple diseases, including dengue and Zika virus. The microbiome's impact on its host's health and fitness is well known. However, most studies on mosquito microbiomes have been conducted in laboratory settings. We explored the mixed microbial communities within Aedes spp., utilizing the 16S rRNA gene for diversity analysis and shotgun metagenomics for functional genomics. Our samples, which included Ae. aegypti and Ae. albopictus, spanned various developmental stages-eggs, larvae, and adults-gathered from five semiurban areas in Mexico. Our findings revealed a substantial diversity of 8,346 operational taxonomic units (OTUs), representing 967 bacterial genera and 126,366 annotated proteins. The host developmental stage was identified as the primary factor associated with variations in the microbiome composition. Subsequently, we searched for genes and species involved in mosquito biocontrol. Wolbachia accounted for 9.6% of the 16S gene sequences. We observed a high diversity (203 OTUs) of Wolbachia strains commonly associated with mosquitoes, such as wAlb, with a noticeable increase in abundance during the adult stages. Notably, we detected the presence of the cifA and cifB genes, which are associated with Wolbachia's cytoplasmic incompatibility, a biocontrol mechanism. Additionally, we identified 221 OTUs related to Bacillus, including strains linked to B. thuringiensis. Furthermore, we discovered multiple genes encoding insecticidal toxins, such as Cry, Mcf, Vip, and Vpp. Overall, our study contributes to the understanding of mosquito microbiome biodiversity and metabolic capabilities, which are essential for developing effective biocontrol strategies against this disease vector.

Improvements in viral gene annotation using large language models and soft alignments

BackgroundThe annotation of protein sequences in public databases has long posed a challenge in molecular biology. This issue is particularly acute for viral proteins, which demonstrate limited homology to known proteins when using alignment, k-mer, or profile-based homology search approaches. A novel methodology employing Large Language Models (LLMs) addresses this methodological challenge by annotating protein sequences based on embeddings.ResultsCentral to our contribution is the soft alignment algorithm, drawing from traditional protein alignment but leveraging embedding similarity at the amino acid level to bypass the need for conventional scoring matrices. This method not only surpasses pooled embedding-based models in efficiency but also in interpretability, enabling users to easily trace homologous amino acids and delve deeper into the alignments. Far from being a black box, our approach provides transparent, BLAST-like alignment visualizations, combining traditional biological research with AI advancements to elevate protein annotation through embedding-based analysis while ensuring interpretability. Tests using the Virus Orthologous Groups and ViralZone protein databases indicated that the novel soft alignment approach recognized and annotated sequences that both blastp and pooling-based methods, which are commonly used for sequence annotation, failed to detect.ConclusionThe embeddings approach shows the great potential of LLMs for enhancing protein sequence annotation, especially in viral genomics. These findings present a promising avenue for more efficient and accurate protein function inference in molecular biology.

A compilation of histidine kinase genes in the genome of a tissue macerating Pectobacterium brasiliense isolated from potato in South Africa

Background: A compilation of kinase genes of Pectobacterium brasilense is presented. The genes were mined from a complete genome sequence of strain BD163 isolated from potato in Tshwane, South Africa. This work is an effort to collate knowledge on P. brasiliense which has become a worldwide pathogen of potatoes and other vegetables such as tomato. Histidine kinases of P. brasiliense can be targets of bacterial control agents to limit the growth, spread and pathogenicity of P. brasiliense. Methods: The whole genome sequence of strain BD163 of P. brasiliense deposited on www.ncbi.nlm.nih.gov was downloaded and individual kinase genes were searched and listed with their properties, namely, locus tag, protein accession number and annotation. Conclusions: This is the only compilation of histidine kinase genes for P. brasiliense.

Gentisic acid exerts neuroprotective effects in neurotoxin-induced Parkinson's disease model in zebrafish: Cross-talk between pathways related with neurodegeneration in the gut-brain axis

Given that global prevalence of Parkinson's disease (PD) is expected to rise over the next few decades, understanding the mechanisms and causes of PD is critical. With emphasis on gut-brain axis, we sought to assess the impact of gentisic acid (GA), a diphenolic compound generated from benzoic acid, in rotenone (Rot) induced PD model in zebrafish. For thirty days, adult zebrafish were exposed to GA and rotenone. Tox-Track program was used to analyze locomotor behaviors in the control, GA, Rot, and Rot + GA groups. LC-MS/MS was performed in brain and intestinal tissues. Proteome Discoverer 2.4 was used to analyze raw files, peptide lists were searched against Danio rerio proteins. Protein interactions or annotations were obtained from STRING database. Tyrosine hydroxylase (Th) staining was performed immunohistochemically in the brain. PD-related gene expressions were determined by RT-PCR. Lipid peroxidation, nitric oxide, superoxide dismutase, glutathione S-transferase, and acetylcholinesterase were measured spectrophotometrically. Improved locomotor behaviors were observed by GA treatment in Rot group as evidenced by increased average speed, exploration rate, and total distance. 5214 proteins were identified in intestinal tissues, 4114 proteins were identified in brain by LC-MS/MS. Rotenone exposure altered protein expressions related to oxidative phosphorylation in brain and intestines. Protein expressions involved in ferroptis and actin cytoskeleton changed in brain and intestines. Altered protein expressions were improved by GA. GA ameliorated Th-immunoreactivity in brain, improved park2, park7, pink1, and lrrk2 expressions. Our results show that GA may be a candidate agent to be evaluated for its potential protective effect for PD.