Biology subjects

de Raad, M.

Publications and source records attributed to de Raad, M..

9 recordsLinked to original sources

bioRxiv2026

Microbial exometabolite responses to root exudates from tallgrass prairie plants

Root exudates influence soil microbial community assembly and function, yet plant species-specific effects on microbially-driven metabolic transformations remain insufficiently characterized under controlled conditions. Here, we present a multi-omic dataset of exudate-microbe interactions across six plant species, including tallgrass prairie species (Helianthus pauciflorus, Carex brevior, Eragrostis spectabilis, Astragalus canadensis, and Panicum virgatum) and a model grass (Brachypodium distachyon), generated using an in vitro incubation experiment. Sterile root exudates were used to amend a soil-mimicking medium inoculated with a native tall-grass prairie soil microbiome. The dataset includes full-length 16S rRNA gene sequencing for microbial community profiling, untargeted LC-MS/MS metabolomics for exometabolomic profiling, and optical density measurements of microbial growth. We describe the axenic plant growth protocols, experimental design, data acquisition, processing workflows, and technical validation. This dataset provides a resource for investigating microbially mediated exometabolite transformations across diverse plant species to help understand rhizosphere processes, microbiome assembly, and ecosystem function.

microbiology↗

bioRxiv2026

What microbes want: exploring microbial substrate preferences with the Web of Microbes Agent

Understanding and predicting bacterial substrate preferences has broad utility from microbial interactions to selecting prebiotics. Isolate exometabolite profiling directly measures which compounds a given microbe utilizes from an array of metabolites in the environment. However, modeling, mining, and integrating these data are challenging. Here, we introduce a Bayesian Personalized Ranking (BPR) model applied to substrate preferences which we find learns to rank compounds by a given microbes preference. It was found to outperform the other ranking models (AUC = 0.93), proved robust to ablation, showed strong within-genus isolate pairs correlation (Spearman rank = 0.78) and predictive ability for new data. BPR was then used to create the Web of Microbes (WoM) Agent by integrating it with the Phydon growth model and Large Language Model (LLM) for autonomous orchestration tool calling and analysis. The WoM Agent accurately predicted substrate consumption by existing strain grown on a novel medium and correctly identified bacteria enriched in soil metabolite spike-in experiments. Additionally, the WoM Agent can use autonomous reasoning including to predict substrates that will selectively promote the growth of one clade of bacteria over another including helping interpret results and suggest new hypotheses and experiments. We anticipate broad applications in microbial cultivation, microbiome engineering, and environmental microbiology, with the agents capabilities further extensible through the integration of additional tools and use of rapidly improving LLMs.

systems biology↗

bioRxiv2026

Metabolic blueprints of monocultures enable prediction and design of synthetic microbial consortia

Synthetic microbial ecology aims at designing communities with desired properties based on mathematical models of individual organisms. It is unclear whether simplified models harbor enough detail to predict the composition of synthetic communities in metabolically complex environments. Here, we use longitudinal exometabolite data of monocultures for 15 rhizosphere bacteria to parametrize a consumer-resource model, which we use to predict pairwise co-cultures and higher order communities. The capacity to artificially "switch off" cross-feeding interactions in the model demonstrates their importance in ecosystem structure. Leave-one-out and leave-two-out experiments demonstrate that pairwise co-cultures do not necessarily capture inter-species interactions within larger communities and broadly highlight the nonlinearity of interactions. Finally, we demonstrate that our model can be used to identify new sub-communities of three strains with high likelihood of coexistence. Our results establish hybrid mechanistic and data-driven metabolic models as a promising and extendable framework for predicting and engineering microbial communities.

systems biology↗

bioRxiv2022

Metabolic traits and the niche of bulk soil bacteria in a Mediterranean grassland

Soil microorganisms have adapted to compete and exploit different metabolic niches in their physically and chemically diverse environment via evolution and acquisition of distinct physiological and biochemical traits. As the interface for most carbon and nutrient exchange between plants and microorganisms, the rhizosphere has received substantial attention. By comparison, what is commonly termed bulk-soil (soil free of living roots) represents a far greater volume and surface area throughout the season, and substantially higher taxonomic and phylogenetic diversity; the traits and activity of its inhabitants may also have a significant impact on overall soil function. We used a combination of comparative genomics and exoproteomics to identify metabolic traits of bacteria adapted to life in bulk soil and compared these with traits of bacteria living in the rhizosphere of wild oat, Avena barbata. In bulk soil bacteria, we observed: (i) greater investment in extracellular polymer-degrading enzyme production; (ii) greater potential for secretion (presence of signal peptides) of polymer-degrading enzymes; (iii) production of accessory proteins (carbohydrate binding modules) fused with glycoside hydrolases that enhance substrate affinity, stabilize, and increase reaction rates of polymer degrading enzymes; and (iv) organization of polymer degradation machinery within gene clusters that facilitate co-transcription of enzymes, transcription factors and transporters for polymer depolymerization products. Together, these findings suggest that unlike rhizosphere-adapted bacteria--which specialize on small molecules released primarily as root exudates--bulk soil-adapted bacteria have evolved to exploit plant polymers. This biochemically costly strategy may be mitigated by protein-level adaptations that enhance the efficiency of extracellular enzyme-mediated substrate acquisition. IMPORTANCEPlant-soil-microbe interactions are dynamic and complex, with significant implications for ecosystem functioning. Microbial traits, such as nutrient acquisition and growth yield, combined with soil and climate parameters, impact major biogeochemical processes and can define the future fate of soil carbon. Diverse soil microorganisms occupy different physical habitats within soil and exploit distinct niches by expressing different metabolic traits. Identifying and quantifying traits that underlie their fitness and function is key for understanding and predicting how soil carbon transformation and stabilization will change in the future or can be managed through intervention.

microbiology↗

bioRxiv2021

Biofilm Interaction Mapping and Analysis (BIMA): A tool for deconstructing interspecific interactions in co-culture biofilms

Pseudomonas species are ubiquitous in nature and include numerous medically, agriculturally and technologically beneficial strains of which the interspecific interactions are of great interest for biotechnologies. Specifically, co-cultures containing Pseudomonas stutzeri have been used for bioremediation, biocontrol, aquaculture management and wastewater denitrification. Furthermore, the use of P. stutzeri biofilms, in combination with consortia based approaches, may offer advantages for these processes. Understanding the interspecific interaction within biofilm co-cultures or consortia provides a means for improvement of current technologies. However, the investigation of biofilm based consortia has been limited. We present an adaptable and scalable method for the analysis of macroscopic interactions (colony morphology, inhibition and invasion) between colony forming bacterial strains using an automated printing method followed by analysis of the genes and metabolites involved in the interactions. Using Biofilm Interaction Mapping and Analysis (BIMA), these interactions were investigated between P. stutzeri strain RCH2, a denitrifier isolated from chromium (VI) contaminated soil, and thirteen other species of pseudomonas isolated from non-contaminated soil. The metabolites and genes associated with both active co-culture growth and inhibitory growth were investigated using mass spectrometry based metabolomics and mutant fitness profiling of a DNA-barcoded mutant library. One interaction partner, Pseudomonas fluorescens N1B4 was selected for mutant fitness profiling; with this approach four genes of importance were identified and the effects on interactions were evaluated with deletion mutants and metabolomics. IMPORTANCEThe Biofilm Interaction Mapping and Analysis (BIMA) methodology provides a way to rapidly screen for positive and negative interspecific interactions, followed by an analysis of the genes and metabolites that may be involved. Knowledge of these may offer opportunities for engineered strains with improved function in biotechnology systems. P. stutzeri, an organism with wide-spread utilization in consortia based biotechnologies, was used to demonstrate the utility of this approach. Where little is known about the factors influencing biofilm based interactions, elucidation of the genes and metabolites involved allows for better control of the system for improved function or yield.

microbiology↗

bioRxiv2021

A defined medium based on R2A for cultivation and exometabolite profiling of soil bacteria

Exometabolomics is an approach to assess how microorganisms alter their environments through the depletion and secretion of chemical compounds. Comparisons of inoculated with uninoculated media can be used to provide direct biochemical observations on depleted and secreted metabolites which can be used to predict resource competition, cross-feeding and secondary metabolite production in microbial isolates and communities. This approach is most powerful when used with defined media that enable tracking of all depleted metabolites. However, microbial growth media have traditionally been developed for the isolation and growth of microorganisms but not metabolite utilization profiling through LC-MS/MS. Here, we describe the construction of a defined medium, the Northen Lab Defined Medium (NLDM), that not only supports the growth of diverse bacteria but is defined and therefore suited for exometabolomic experiments. Metabolites included in NLDM were selected based on their presence in R2A medium and soil, elemental stoichiometry requirements, as well as knowledge of metabolite usage by different bacteria. We found that NLDM supported the growth of 53 phylogenetically diverse soil bacterial isolates and all of its metabolites were trackable through LC-MS/MS analysis. These results demonstrate the viability and utility of the constructed NLDM medium for cultivating and characterizing diverse microbial isolates and communities. Originality-Significance StatementWe build a defined medium based on the metabolite composition of R2A medium and soil, elemental stoichiometry requirements, and knowledge of metabolite usage by different bacteria. The newly formulated defined medium was evaluated on its ability to support the growth of soil isolates and its application for metabolite utilization profiling. We found that of 53 phylogenetically diverse soil bacterial isolates grew on the defined medium and all of its metabolites were trackable through LC-MS/MS analysis. This demonstrates the viability and utility of the constructed defined medium for cultivating and characterizing diverse microbial isolates and communities.

microbiology↗

bioRxiv2019

Cervicovaginal microbiome composition drives metabolic profiles in healthy pregnancy

BackgroundMicrobes and their metabolic products influence early-life immune and microbiome development, yet remain understudied during pregnancy. Vaginal microbial communities are typically dominated by one or a few well adapted microbes, which are able to survive in a narrow pH range. In comparison to other human-associated microbes, vaginal microbes are adapted to live on host-derived carbon sources, likely sourced from glycogen and mucin present in the vaginal environment. MethodsUsing 16S rRNA and ITS amplicon sequencing, we characterized the cervicovaginal microbiomes of 18 healthy women throughout the three trimesters of pregnancy. Shotgun metagenomic sequencing permitted refinement of the taxonomy established by amplicon sequencing, and identification of functional genes. Additionally, we analyzed saliva and urine metabolomes using GC-TOF and LC-MS/MS lipidomics approaches for samples from mothers and their infants through the first year of life. ResultsAmplicon sequencing revealed most women had either a simple community with one highly abundant species of Lactobacillus or a more diverse community characterized by a high abundance of Gardnerella, as has also been previously described in several independent cohorts. Integrating GC-TOF and lipidomics data with amplicon sequencing, we found metabolites that distinctly associate with particular communities. For example, cervicovaginal microbial communities dominated by Lactobacillus crispatus also have high mannitol levels, which contradicts the basic characterization of L. crispatus as a homofermentative Lactobacillus species. It may be that fluctuations in which Lactobacillus dominate a particular vaginal microbiome are dictated by the availability of host sugars, such as fructose, which is the most likely substrate being converted to mannitol. Furthermore, indole-3-lactate (ILA) was also indicative of L. crispatus specifically. ILA has immunomodulatory properties through binding the human aryl hydrocarbon receptor (AhR), which may maintain the especially low diversity of L. crispatus dominated communities. ConclusionsOverall, using a multi- omic approach, we begin to address the genetic and molecular means by which a particular vaginal microbiome becomes vulnerable to large changes in composition.

microbiology↗

bioRxiv2018

Enzyme promiscuity shapes evolutionary innovation and optimization

Evidence suggests that novel enzyme functions evolved from low-level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems-level adaptations are poorly understood. Furthermore, it remains untested whether knowledge of an organisms promiscuous reaction set ( underground metabolism) can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non-native substrates in E. coli K-12 MG1655. After as few as 20 generations, the evolving populations repeatedly acquired the capacity to grow on five predicted novel substrates-D-lyxose, D-2-deoxyribose, D-arabinose, m-tartrate, and monomethyl succinate-none of which could support growth in wild-type cells. Promiscuous enzyme activities played key roles in multiple phases of adaptation. Altered promiscuous activities not only established novel high-efficiency pathways, but also suppressed undesirable metabolic routes. Further, structural mutations shifted enzyme substrate turnover rates towards the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome-scale model simulations of metabolism with enzyme promiscuity. Computational approaches will be essential to synthesize the complex role of promiscuous activities in applied biotechnology and in models of evolutionary adaptation.

systems biology↗

bioRxiv2017

MAGI: A Bayesian-like method for metabolite, annotation, and gene integration

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, ambiguous metabolite identifications are a common challenge and biochemical interpretation is often limited by incomplete and inaccurate genome-based predictions of enzyme activities (i.e. gene annotations). Metabolite, Annotation, and Gene Integration (MAGI) generates a metabolite-gene association score using a biochemical reaction network. This is calculated by a method that emphasizes consensus between metabolites and genes via biochemical reactions. To demonstrate the potential of this method, we applied MAGI to integrate sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. Our findings suggest that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations in this organism. MAGI also made biochemical predictions for poorly annotated genes that were consistent with the extensive literature on this important organism. This limited analysis suggests that using metabolomics data has the potential to improve annotations in sequenced organisms and also provides testable hypotheses for specific biochemical functions. MAGI is freely available for academic use both as an online tool at https://magi.nersc.gov and with source code available at https://github.com/biorack/magi.

bioinformatics↗