AI Article Synopsis

  • The text discusses the introduction of the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), a tool designed for studying microbial ecological networks through metagenomic sequencing data.
  • iNAP 2.0 includes a four-module process for analyzing metabolic interactions, which involves preparing metabolic models, inferring pairwise interactions, constructing networks, and performing detailed analyses.
  • Key features of iNAP 2.0 include methods for quantifying metabolic complementarity, identifying transferable metabolites, and using random matrix theory for network construction; the tool is freely available online for users.

Article Abstract

With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co-occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four-module process for metabolic interaction analysis, namely: (I) Prepare genome-scale metabolic models; (II) Infer pairwise interactions of genome-scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome-assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance-adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross-feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome-assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11487609PMC
http://dx.doi.org/10.1002/imt2.235DOI Listing

Publication Analysis

Top Keywords

metabolic interaction
24
metabolic complementarity
20
metabolic
15
interaction networks
12
network analysis
8
complementarity network
8
interaction analysis
8
genome-scale metabolic
8
metabolic models
8
inap
7

Similar Publications

Quantitative Proteomics Identifies Profilin-1 as a Pseudouridine-Binding Protein.

J Am Chem Soc

January 2025

Department of Chemistry, University of California, Riverside, California 92521-0403, United States.

Pseudouridine (Ψ) is the most abundant RNA modification in nature; however, not much is known about the biological functions of this modified nucleoside. Employing an unbiased quantitative proteomics method, we identified multiple candidate reader proteins of Ψ in RNA, including a cytoskeletal protein profilin-1 (PFN1). We demonstrated that PFN1 binds directly and selectively to Ψ-containing RNA.

View Article and Find Full Text PDF

The purpose of this review was to analyse the literature regarding the correlation between the level of tryptamine, aryl hydrocarbon receptor (AHR) signalling pathway activation, and monoamine oxidase (MAO)-A and MAO-B activity in health and conditions such as neurodegenerative, neurodevelopmental, and psychiatric disorders. Tryptamine is generated through the decarboxylation of tryptophan by aromatic amino acid decarboxylase (AADC) in the central nervous system (CNS), peripheral nervous system (PNS), endocrine system, and gut bacteria. Organ-specific metabolism of tryptamine, which is mediated by different MAO isoforms, causes this trace amine to have different pharmacokinetics between the brain and periphery.

View Article and Find Full Text PDF

Apatinib, a commonly used tyrosine kinase inhibitor in cancer treatment, can cause adverse reactions such as hypertension. Hypertension, in turn, can increase the risk of certain cancers. The coexistence of these diseases makes the use of combination drugs more common in clinical practice, but the potential interactions and regulatory mechanisms in these drug combinations are poorly understood.

View Article and Find Full Text PDF

Background Severe acute pancreatitis (SAP) manifests as a critical state marked by acute abdominal symptoms, often associated with intestinal barrier dysfunction, exacerbating SAP retroactively. Ganoderic acid A (GAA) demonstrates anti-inflammatory properties in various inflammatory disorders. Nonetheless, its potential therapeutic impact on SAP and the underlying mechanisms remain unexplored.

View Article and Find Full Text PDF

This study has developed a pressure sensor array based on four functionalized DNA-nanoenzymes with catalase-like activity for multiple detections of foodborne pathogens through a portable pressure manometer. Benefiting from functionalization of 4-mercaptophenylboronic acid and β-mercaptoethylamine, the diversity of nonspecific interactions between four DNA-nanoenzymes and each of the nine bacteria leads to differences in pressure response patterns by catalyzing HO to generate exclusive "fingerprints". As effective statistical tools for processing multivariate data, principal component analysis and hierarchical clustering analysis are employed to identify nine foodborne pathogens by analyzing pressure response patterns.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!