Browse
Submit Data
Databases
API
Help

Dataset Information

6 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Bioinformatic and Reactivity-Based Discovery of Linaridins.

ABSTRACT: Linaridins are members of the ribosomally synthesized and post-translationally modified peptide (RiPP) family of natural products. Five linaridins have been reported, which are defined by the presence of dehydrobutyrine, a dehydrated, alkene-containing amino acid derived from threonine. This work describes the development of a linaridin-specific scoring module for Rapid ORF Description and Evaluation Online (RODEO), a genome-mining tool tailored toward RiPP discovery. Upon mining publicly accessible genomes available in the NCBI database, RODEO identified 561 (382 nonredundant) linaridin biosynthetic gene clusters. Linaridin BGCs with unique gene architectures and precursor sequences markedly different from previous predictions were uncovered during these efforts. To aid in data set validation, two new linaridins, pegvadin A and B, were detected through reactivity-based screening and isolated from Streptomyces noursei and Streptomyces auratus, respectively. Reactivity-based screening involves the use of a probe that chemoselectively modifies an organic functional group present in the natural product. The dehydrated amino acids present in linaridins as α/β-unsaturated carbonyls were appropriate electrophiles for nucleophilic 1,4-addition using a thiol-functionalized probe. The data presented within significantly expand the number of predicted linaridin biosynthetic gene clusters and serve as a roadmap for future work in the area. The combination of bioinformatics and reactivity-based screening is a powerful approach to accelerate natural product discovery.

SUBMITTER: Georgiou MA

PROVIDER: S-EPMC7680433 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Bioinformatic Discovery of a Cambialistic Monooxygenase.

Project description:Dinuclear monooxygenases mediate challenging C-H bond oxidation reactions throughout nature. Many of these enzymes are presumed to exclusively utilize diiron cofactors. Herein we report the bioinformatic discovery of an orphan dinuclear monooxygenase that preferentially utilizes a heterobimetallic manganese-iron (Mn/Fe) cofactor to mediate an O2-dependent C-H bond hydroxylation reaction. Unlike the structurally similar Mn/Fe-dependent monooxygenase AibH2, the diiron form of this enzyme (SfbO) exhibits a nascent enzymatic activity. This behavior raises the possibility that many other dinuclear monooxygenases may be endowed with the capacity to harness cofactors with a variable metal content.

| S-EPMC10811679 | biostudies-literature

Bioinformatic Expansion and Discovery of Thiopeptide Antibiotics.

Project description:Thiopeptides are members of the ribosomally synthesized and post-translationally modified peptide family of natural products. Most characterized thiopeptides display nanomolar potency toward Gram-positive bacteria by blocking protein translation with several being produced at the industrial scale for veterinary and livestock applications. Employing our custom bioinformatics program, RODEO, we expand the thiopeptide family of natural products by a factor of four. This effort revealed many new thiopeptide biosynthetic gene clusters with products predicted to be distinct from characterized thiopeptides and identified gene clusters for previously characterized molecules of unknown biosynthetic origin. To further validate our data set of predicted thiopeptide biosynthetic gene clusters, we isolated and characterized a structurally unique thiopeptide featuring a central piperidine and rare thioamide moiety. Termed saalfelduracin, this thiopeptide displayed potent antibiotic activity toward several drug-resistant Gram-positive pathogens. A combination of whole-genome sequencing, comparative genomics, and heterologous expression experiments confirmed that the thioamide moiety of saalfelduracin is installed post-translationally by the joint action of two proteins, TfuA and YcaO. These results reconcile the previously unknown origin of the thioamide in two long-known thiopeptides, thiopeptin and Sch 18640. Armed with these new insights into thiopeptide chemical-genomic space, we provide a roadmap for the discovery of additional members of this natural product family.

| S-EPMC6070396 | biostudies-literature

Recurrent Implantation Failure: Bioinformatic Discovery of Biomarkers and Identification of Metabolic Subtypes

Project description:Recurrent implantation failure (RIF) is a challenging scenario from different standpoints. This study aimed to investigate its correlation with the endometrial metabolic characteristics. Transcriptomics data of 70 RIF and 99 normal endometrium tissues were retrieved from the Gene Expression Omnibus database. Common differentially expressed metabolism-related genes were extracted and various enrichment analyses were applied. Then, RIF was classified using a consensus clustering approach. Three machine learning methods were employed for screening key genes, and they were validated through the RT-qPCR experiment in the endometrium of 10 RIF and 10 healthy individuals. Receiver operator characteristic (ROC) curves were generated and validated by 20 RIF and 20 healthy individuals from Peking University People’s Hospital. We uncovered 109 RIF-related metabolic genes and proposed a novel two-subtype RIF classification according to their metabolic features. Eight characteristic genes (SRD5A1, POLR3E, PPA2, PAPSS1, PRUNE, CA12, PDE6D, and RBKS) were identified, and the area under curve (AUC) was 0.902 and the external validated AUC was 0.867. Higher immune cell infiltration levels were found in RIF patients and a metabolism-related regulatory network was constructed. Our work has explored the metabolic and immune characteristics of RIF, which paves a new road to future investigation of the related pathogenic mechanisms.

2024-09-09 | GSE243550 | GEO

Enabling synthesis in fragment-based drug discovery by reactivity mapping: photoredox-mediated cross-dehydrogenative heteroarylation of cyclic amines.

Project description:In fragment-based drug discovery (FBDD), a weakly binding fragment hit is elaborated into a potent ligand by bespoke functionalization along specific directions (growth vectors) from the fragment core in order to complement the 3D structure of the target protein. This structure-based design approach can present significant synthetic challenges, as growth vectors often originate on sp2 or sp3 ring carbons which are not the most synthetically accessible points on the fragment. To address this issue and expedite synthesis in FBDD, we established a nanogram-to-gram workflow for the development of enabling synthetic transformations, such as the direct C-H functionalization of heterocycles. This novel approach deploys high-throughput experimentation (HTE) in 1536-well microtiter plates (MTPs) facilitated by liquid handling robots to screen reaction conditions on the nanomolar scale; subsequently the reaction is upscaled in a continuous flow to generate gram-quantities of the material. In this paper, we disclose the use of this powerful workflow for the development of a photoredox-mediated cross-dehydrogenative coupling of fragments and medicinally relevant heterocyclic precursors via Minisci-type addition of α-amino radicals to electron-deficient heteroarenes. The optimized reaction conditions were employed on the milligram-scale on a diverse set of 112 substrates to map out structure-reactivity relationships (SRRs) of the transformation. The coupling exhibits excellent tolerance to a variety of functional groups and N-rich heteroarenes relevant to FBDD and was upscaled in a continuous flow to afford gram-quantities of pharmaceutically relevant sp2-sp3 privileged architectures.

| S-EPMC6385880 | biostudies-other

Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery.

Project description:BackgroundThere is an important need for the identification of novel serological biomarkers for the early detection of cancer. Current biomarkers suffer from a lack of tissue specificity, rendering them vulnerable to non-disease-specific increases. The present study details a strategy to rapidly identify tissue-specific proteins using bioinformatics.MethodsPrevious studies have focused on either gene or protein expression databases for the identification of candidates. We developed a strategy that mines six publicly available gene and protein databases for tissue-specific proteins, selects proteins likely to enter the circulation, and integrates proteomic datasets enriched for the cancer secretome to prioritize candidates for further verification and validation studies.ResultsUsing colon, lung, pancreatic and prostate cancer as case examples, we identified 48 candidate tissue-specific biomarkers, of which 14 have been previously studied as biomarkers of cancer or benign disease. Twenty-six candidate biomarkers for these four cancer types are proposed.ConclusionsWe present a novel strategy using bioinformatics to identify tissue-specific proteins that are potential cancer serum biomarkers. Investigation of the 26 candidates in disease states of the organs is warranted.

| S-EPMC3378448 | biostudies-literature

Novel targets in renal fibrosis based on bioinformatic analysis II

Project description:Renal fibrosis is a widely used pathological indicator of progressive chronic kidney disease (CKD), and renal fibrosis mediates most progressive renal diseases as a final pathway. Nevertheless, the key genes related to the host response are still unclear. In this study, the potential gene network, signaling pathways, and key genes under UUO model in mouse kidneys were investigated by integrating two transcriptional data profiles.

2023-01-06 | GSE217654 | GEO

Novel targets in renal fibrosis based on bioinformatic analysis I

2023-01-06 | GSE217650 | GEO

Significance of circulating microRNAs in diabetes mellitus type 2 and platelet reactivity: bioinformatic analysis and review.

Project description:In the light of growing global epidemic of type 2 diabetes mellitus (T2DM), significant efforts are made to discover next-generation biomarkers for early detection of the disease. Multiple mechanisms including inflammatory response, abnormal insulin secretion and glucose metabolism contribute to the development of T2DM. Platelet activation, on the other hand, is known to be one of the underlying mechanisms of atherosclerosis, which is a common T2DM complication that frequently results in ischemic events at later stages of the disease. Available data suggest that platelets contain large amounts of microRNAs (miRNAs) that are found in circulating body fluids, including the blood. Since miRNAs have been illustrated to play an important role in metabolic homeostasis through regulation of multiple genes, they attracted substantial scientific interest as diagnostic and prognostic biomarkers in T2DM. Various miRNAs, as well as their target genes are implicated in the complex pathophysiology of T2DM. This article will first review the different miRNAs studied in the context of T2DM and platelet reactivity, and subsequently present original results from bioinformatic analyses of published reports, identifying a common gene (PRKAR1A) linked to glucose metabolism, blood coagulation and insulin signalling and targeted by miRNAs in T2DM. Moreover, miRNA-target gene interaction networks built upon Gene Ontology information from electronic databases were developed. According to our results, miR-30a-5p, miR-30d-5p and miR-30c-5p are the most widely regulated miRNAs across all specified ontologies, hence they are the most promising biomarkers of T2DM to be investigated in future clinical studies.

| S-EPMC6716825 | biostudies-literature

Discovery of Novel Type II Bacteriocins Using a New High-Dimensional Bioinformatic Algorithm.

Project description:Antimicrobial compounds first arose in prokaryotes by necessity for competitive self-defense. In this light, prokaryotes invented the first host defense peptides. Among the most well-characterized of these peptides are class II bacteriocins, ribosomally-synthesized polypeptides produced chiefly by Gram-positive bacteria. In the current study, a tensor search protocol-the BACIIα algorithm-was created to identify and classify bacteriocin sequences with high fidelity. The BACIIα algorithm integrates a consensus signature sequence, physicochemical and genomic pattern elements within a high-dimensional query tool to select for bacteriocin-like peptides. It accurately retrieved and distinguished virtually all families of known class II bacteriocins, with an 86% specificity. Further, the algorithm retrieved a large set of unforeseen, putative bacteriocin peptide sequences. A recently-developed machine-learning classifier predicted the vast majority of retrieved sequences to induce negative Gaussian curvature in target membranes, a hallmark of antimicrobial activity. Prototypic bacteriocin candidate sequences were synthesized and demonstrated potent antimicrobial efficacy in vitro against a broad spectrum of human pathogens. Therefore, the BACIIα algorithm expands the scope of prokaryotic host defense bacteriocins and enables an innovative bioinformatics discovery strategy. Understanding how prokaryotes have protected themselves against microbial threats over eons of time holds promise to discover novel anti-infective strategies to meet the challenge of modern antibiotic resistance.

| S-EPMC7494827 | biostudies-literature

Array-based bioinformatic analysis on pediatric primary central nervous system germ cell tumors

Project description:This SuperSeries is composed of the following subset Series: GSE19347: microRNA expression data from pediatric primary intracranial germ cell tumor GSE19348: expression data from pediatric primary intracranial germ cell tumor GSE19349: Genotyping and analysis of chromosome copy number variation (CNV) from pediatric primary intracranial germ cell tumor Refer to individual Series

2010-06-24 | E-GEOD-19350 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data