Biology subjects

Zulfiqar, M.

Publications and source records attributed to Zulfiqar, M..

4 recordsLinked to original sources

bioRxiv2025

Gut bacteria generate prodrugs in situ increasing systemic drug exposure

The human gut microbiome has been increasingly recognized as a metabolic compartment that influences health and therapeutic responses. While recent studies have cataloged diverse sets of drug biotransformation reactions as a result of microbial metabolism1-3, the pharmacological relevance of the resulting drug metabolites remains mostly uncharacterized. We investigated whether certain microbial drug metabolites can act as functional prodrugs. By systematically mining 871 putative drug metabolites produced by gut bacteria1, we identified microbial drug metabolites with key physicochemical properties of prodrugs. Using bacterial culturing, a genetic gain-of-function screen coupled to mass spectrometry and comparative genomics, we uncover that Bacteroidota bacteria encode conserved methyltransferases that can generate prodrugs through carboxyl methylation. As a case in point, we demonstrate that bezafibrate gets converted to a prodrug by a distinct bacterial enzyme, increasing epithelial drug permeability in vitro and systemic drug exposure in vivo in a gnotobiotic mouse model. Additionally, we tested 170 structurally and clinically diverse drugs, and demonstrate that microbial drug-prodrug conversion is a common result of gut bacterial drug biotransformation. Altogether, our findings suggest a novel mechanism of how the gut microbiota influences an individuals pharmacokinetics, and may generally cause interpersonal differences in microbiota-host metabolic interactions.

microbiology↗

bioRxiv2023

Analysis of Metabolomics and Transcriptomics Data to Assess Interactions in Microalgal Co-culture of Skeletonema marinoi and Prymnesium parvum

In marine ecosystems, microbial communities often interact using specialised metabolites, which play a central role in shaping the dynamics of the ecological networks and maintaining the balance of the ecosystem. With metabolomics and transcriptomics analyses, this study explores the interactions between two marine microalgae, Skeletonema marinoi and Prymnesium parvum, grown in mono-cultures and non-contact co-cultures. As a growth indicator, the photosynthetic potential, measured via fluorescence, suggested chemical interaction between S. marinoi and P. parvum. Using Liquid Chromatography-Mass Spectrometry (LC-MS) data, we identified 346 and 521 differentially produced features in the endo- and exometabolome of S. marinoi and P. parvum, respectively. Despite limited tandem mass spectrometry data (MS2) for these features, we structurally annotated 14 compounds, most of which were previously under-studied specialised metabolites. Differential gene expression analysis was then performed on the transcriptomes of the microalgae, which uncovered differentially expressed genes involved in energy metabolism and cellular repair for both species. These metabolic and transcriptomics changes depict the adaptation of both species in the co-culture. However, further data acquisition and investigation will be necessary to confirm the type of interaction and the underlying mechanisms. ImportanceMarine microalgae have great ecological importance and biochemical potential. Among these microbes are the diatom Skeletonema marinoi, known for its marine biogeochemical cycling, and the haptophyte Prymnesium parvum, which poses adverse environmental consequences. Given these opposing roles for the two cosmopolitan microalgae, we designed a study using untargeted metabolomics and transcriptomics to acquire a comprehensive snapshot of their interactions, grown as mono-cultures and co-cultures. The statistical analysis of the chlorophyll a fluorescence levels, and the metabolomics and transcriptomics dataset revealed metabolic communication occurring among the two species via specialised metabolites and activated cellular repair mechanisms. These findings reveal the complexity of the interactions within marine microbial ecosystems, offering a foundation for future research to understand and harness marine ecological systems.

microbiology↗

bioRxiv2023

Untargeted Metabolomics to Expand the Chemical Space of the Marine Diatom Skeletonema marinoi

Diatoms (Bacillariophyceae) are aquatic photosynthetic microalgae with an ecological role as primary producers in the aquatic food web. They account substantially for global carbon, nitrogen, and silicon cycling. Elucidating the chemical space of diatoms is crucial to understanding their physiology and ecology. To expand the known chemical space of a cosmopolitan marine diatom, Skeletonema marinoi, we performed High-Resolution Liquid Chromatography-Tandem Mass Spectrometry (LC-MS2) for untargeted metabolomics data acquisition. The spectral data from LC-MS2 was used as input for the Metabolome Annotation Workflow (MAW) to obtain putative annotations for all measured features. A suspect list of metabolites previously identified in the Skeletonema spp. was generated to verify the results. These known metabolites were then added to the putative candidate list from LC-MS2 data to represent an expanded catalogue of 1970 metabolites estimated to be produced by S. marinoi. The most prevalent chemical superclasses, based on the ChemONT ontology in this expanded dataset, were "Organic acids and derivatives", "Organoheterocyclic compounds", "Lipids and lipid-like molecules", and "Organic oxygen compounds". The metabolic profile from this study can aid the bioprospecting of marine microalgae for medicine, biofuel production, agriculture, and environmental conservation. The proposed analysis can be applicable for assessing the chemical space of other microalgae, which can also provide molecular insights into the interaction between marine organisms and their role in the functioning of ecosystems. ImportanceDiatoms are abundant marine phytoplankton members and have great ecological importance and biochemical potential. The cosmopolitan diatom Skeletonema marinoi has become an ecological and environmental research model organism. In this study, we used untargeted metabolomics to acquire a general metabolic profile of S. marinoi to assess its chemical diversity and expand the known metabolites produced by this diatom. S. marinoi produces a chemically diverse set of secondary metabolites with potential therapeutic properties, such as anti-cancer, antioxidant, and anti-inflammatory. Such metabolites are highly significant due to their potential role in drug discovery and bioeconomy. Lipids from S. marinoi also have potential in the biofuel industry. Furthermore, the environmental fluctuations in the water bodies directly affect the production of different secondary metabolites from diatoms, which can be key indicators of climate change.

microbiology↗

bioRxiv2022

MAW - The Reproducible Metabolome Annotation Workflow for Untargeted Tandem Mass Spectrometry

Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC-MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS2) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and mawpy. The source code and documentation are available on GitHub (https://github.com/zmahnoor14/MAW). The performance of MAW is evaluated on two case studies. MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

bioinformatics↗