Biology subjects

Mostolizadeh, R.

Publications and source records attributed to Mostolizadeh, R..

5 recordsLinked to original sources

bioRxiv2026

First Genome-Scale Metabolic Model for Understanding HMPV-Host Interaction

BackgroundHuman Metapneumovirus (HMPV) is a major contributor to acute respiratory tract infections. Currently, no approved vaccines or specific antiviral therapies are available worldwide. Genome-scale metabolic models (GEMs), when integrated with Viral Biomass Objective Functions (VBOFs), provide a robust computational framework for identifying host metabolic dependencies essential for viral replication. This approach enables systematic prioritization of potential antiviral drug targets. ResultsThe first comprehensive VBOF for HMPV was constructed by integrating stoichiometric data from the viral genome, including structural proteins with defined copy numbers, amino acid residues per virion, envelope lipids, and glycan modifications. The reconstructed VBOF was incorporated into the human bronchial epithelial cell model, iHBEC1, to analyze metabolic changes between uninfected and infected host cells. Knockout analysis identified two selective antiviral gene targets: PGM3 (phosphoacetylglucosamine mutase) and GNPNAT1 (N-acetylglucosamine-6-phosphate acetyltransferase), as well as seven selective reaction targets primarily within the hexosamine biosynthesis and nucleotide sugar pathways. Knockout of PGM3 or GNPNAT1 completely abolished viral production while preserving complete host cell viability. Additionally, guanylate kinase (GUK1/GK1) emerged as a highly selective target for HMPV, confirming findings from Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) studies and suggesting a conserved vulnerability across respiratory viruses. ConclusionsUDP-GlcNAc, the vital end-product of the hexosamine biosynthesis pathway (HBP), is computationally predicted as a critical metabolic hub. This pathway represents the primary metabolic vulnerability of HMPV, due to the extensive glycosylation requirements of the HMPV attachment protein. The host-directed antiviral targets PGM3 and GNPNAT1 are high-priority candidates for experimental validation and may provide novel strategies to combat this respiratory infection.

Systems Biology↗

bioRxiv2025

Generative models for antimicrobial peptide design: auto-encoders and beyond

BackgroundSince the number of multi-resistant pathogens is growing rapidly, new strategies to accelerate the development of antimicrobial drugs are urgently needed. A promising candidate class for new antibiotics are antimicrobial peptides, showing lower tendency to induce antibiotic resistance. High-throughput in silico strategies for candidate mining, such as generative deep learning algorithms, have become popular over the last few years and offer novel ways for peptide discovery. MethodsThis study presents a comparative analysis of contemporary deep learning models generative performance for generating novel antimicrobial peptides. The models examined include Variational Auto-Encoders, a Wasserstein Auto-Encoder, a Recurrent Neural Network and a Language Model. The primary focus of this study is the systematic comparison and evaluation of various methods and sampling options to identify the most suitable model and sampling strategy combination for different use cases. ResultsThe findings demonstrate the models capacity to generate peptide sequences exhibiting analogous properties to those of naturally occurring active peptides, which are utilized for model training while featuring an appropriate degree of sequence diversity. Auto-encoder-based models, particularly the Wasserstein auto-encoder, have generated novel and remarkably diverse sequences compared to recurrent neural networks and language models. This model category exhibits a propensity to prioritize the frequencies of individual amino acids during the learning process, in contrast to variational auto-encoders. Furthermore, latent space models have been shown to possess the capacity to utilize diverse methodologies for generating novel peptides. However, it is imperative to note that these sampling strategies are not universally advantageous or disadvantageous; their optimal selection is contingent on the specificities of each individual use case. ConclusionThe present study investigates the strengths and weaknesses of various generative models for antimicrobial peptides and suggests which model and sampling strategy combination should be favoured for specific individual applications.

bioinformatics↗

bioRxiv2024

MCC: Automated Mass and Charge Curation at Genome-Scale Applied to C. tuberculostearicum

BackgroundFor many years, antibiotics reliably protected mankind against bacterial infections, including the respiratory tract colonizer Corynebacterium tuberculostearicum. However, the spread of antimicrobial resistance necessitates the search for new treatment options, where the microbiota may play a crucial role. One way to investigate the complex nature of bacteria and their interactions with human hosts or microbiota is through genome-scale metabolic models (GEMs). Constructing GEMs is labor-intensive and time-consuming. ResultsWe introduce the Python package Mass and Charge Curation (MCC), which implements a new automated algorithm to facilitate mass and charge balancing--one of the most time-consuming reconstruction steps. This package manipulates reconstructions by consolidating data from multiple resources and updating the notes-field with relevant changes. It also generates a visual comparison between draft and curated models, ensuring high-quality metabolic reconstructions. Using MCC, we developed a metabolic reconstruction of C. tuberculostearicum strain DSM 44922. The model was improved based on standardization policies, resulting in a functional, well-annotated, high-quality product. We also simulated the organisms growth in synthetic nasal medium 3 (SNM3). ConclusionsThe high-quality model iCTUB2024RM consistently resembles growth behavior under realistic conditions in an artificial human nasal environment, enhancing understanding of C. tuberculostearicum and its potential impact on health and disease. The curation process of this model led to the development of the MCC package, which facilitates the mass and charge balancing of arbitrary flux balance constraints (fbc) models in SBML format. AvailabilityMCC is freely available via PIP, from https://github.com/draeger-lab/MassChargeCuration/. The model iCTUB2024RM can be obtained as an SBML file wrapped in an OMEX archive from BioModels Database, accession MODEL2407310001. ImportanceThe rise of antibiotic resistance has made it essential to explore alternative treatments for bacterial infections, particularly those caused by respiratory tract colonizers like Corynebacterium tuberculostearicum. Understanding the metabolic behavior of these bacteria and their interactions with the human host or microbiota is crucial. genome-scale metabolic models (GEMs) are powerful tools for investigating these interactions, but they are time-consuming to build. Our new Python package, Mass and Charge Curation (MCC), automates a crucial step in the GEM reconstruction process-mass and charge balancing-making it more efficient and reliable. By applying this tool, we developed a high-quality, functional metabolic model for C. tuberculostearicum (iCTUB2024RM), which provides deeper insights into the organisms growth in a simulated human nasal environment. This work offers a foundation for future research into microbial communities and their role in human health.

systems biology↗

bioRxiv2024

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

Genome-scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community-driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses--Flux variability, Reaction deletion, Objective function, and Gene deletion--to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.

systems biology↗

bioRxiv2020

Model-based prediction of bacterial population dynamics in gastrointestinal infection

The complex interplay of a pathogen with the host immune response and the endogenous microbiome determines the course and outcome of gastrointestinal infection (GI). Expansion of a pathogen within the gastrointestinal tract implies an increased risk to develop systemic infection. Through computational modeling, we aimed to calculate bacterial population dynamics in GI in order to predict infection course and outcome. For the implementation and parameterization of the model, oral mouse infection experiments with Yersinia enterocolitica were used. Our model takes into account pathogen specific characteristics, such as virulence, as well as host properties, such as microbial colonization resistance or immune responses. We were able to confirm the model calculations in these scenarios by experimental mouse infections and show that it is possible to computationally predict the infection course. Far future clinical application of computational modeling of infections may pave the way for personalized treatment and prevention strategies of GI.

microbiology↗