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Pandhal, J.

Publications and source records attributed to Pandhal, J..

3 recordsLinked to original sources

Metaproteomics Reveals Functional Rewiring and Metabolic Limits in Synthetic Bacterial Consortia Degrading Complex Waste Oils

Elucidating the metabolic mechanisms underpinning complex hydrocarbon biodegradation by microbial consortia benefits from integration of community-level genomic and proteomic data. Here, we deployed a combined metagenomics and metaproteomic analysis to characterize functional dynamics within bacterial consortia enriched from hydrocarbon-contaminated soil, using military waste oil as the sole carbon source. Metagenomic profiling revealed a consortium dominated by Stenotrophomonas maltophilia alongside established hydrocarbon-degrading taxa including Pseudomonas fluorescens, Pseudomonas putida, and Delftia acidovorans. Quantitative label-free metaproteomics uncovered dynamic taxonomic restructuring and metabolic reorientation, with Delftia and Achromobacter proteins enriched during active degradation, indicating niche advantages under chronic contamination. Enzymatic mapping resolved expression of degradation pathways targeting aliphatic and aromatic substrates, with one specific consortium, BPS4, exhibiting preferential catabolism of catechol intermediates channeled toward the tricarboxylic acid cycle, supported by elevated protein abundance of oxidoreductases, dehydrogenases, and {beta}-oxidation enzymes. Gene ontology enrichment revealed heightened nitrogen cycling capacity, including upregulation of nitric oxide biosynthesis and nitrate reductase, suggesting adaptive nitrogen metabolism coupled to hydrocarbon mineralization. In-vitro batch-fed adaptation studies demonstrated limited improvements in degradation of recalcitrant fractions, particularly branched aliphatics and organophosphates, revealing intrinsic metabolic constraints within the consortium. These findings underscore the complementary utility of metaproteomic interrogation for resolving both population-level dynamics and functional metabolic capabilities, while highlighting fundamental limitations in expanding the degradative spectrum of enriched consortia through conventional adaptation strategies. This work establishes a foundation for rational ecological engineering of hydrocarbon-remediating consortia informed by metaproteomics. ImportanceWaste oils from transport and industry are complex and toxic mixtures that are difficult and expensive to treat using conventional chemical methods. Microorganisms offer a sustainable alternative, but their performance depends on how different species work together and where their metabolic limits lie. In this study, we show how advanced protein-based analysis using quantitative metaproteomics, can reveal microbial community activities whilst breaking down real-world waste oils. We enriched bacteria from contaminated soils and then identified the enzymes driving effective degradation. We investigated how communities reorganise under chemical stress, and why certain oil components remain resistant even after long-term adaptation. By uncovering both strengths and limitations of naturally enriched microbial consortia, this work provides practical insights for designing more reliable biological treatments for oil-based wastes and even contaminated environments, supporting greener approaches to pollution management and industrial waste recycling.

microbiology↗

Meta-exoproteomics identifies active plant-microbe interactions operating in the rhizosphere

The advance of DNA sequencing technologies has drastically changed our perception of the complexity and structure of the plant microbiome and its role in augmenting plant health. By comparison, our ability to accurately identify the metabolically active fraction of soil microbiota and their specific functional role is relatively limited. Here, we combined our recently developed protein extraction method and an iterative bioinformatics pipeline to enable the capture and identification of extracellular proteins (meta-exoproteomics) expressed in the rhizosphere of Brassica spp. First, we validated our method in the laboratory by successfully identifying proteins related to the host plant (Brassica rapa) and a bacterial inoculant, Pseudomonas putida BIRD-1, revealing the latter expressed numerous rhizosphere specific proteins linked to the acquisition of plant-derived nutrients. Next, we analysed natural field-soil microbial communities associated with Brassica napus L (Oil Seed rape). By combining deep-sequencing metagenomics with meta-exoproteomics, a total of 1882 proteins were identified in bulk and rhizosphere samples. Importantly, meta-exoproteomics identified a clear shift (p<0.001) in the metabolically active fraction of the soil microbiota responding to the presence of B. napus roots that was not apparent in the composition of the total microbial community (metagenome). This metabolic shift was associated with the stimulation of rhizosphere-specialised bacteria, such as Gammaproteobacteria, Betaproteobacteria and Flavobacteriia and the upregulation of plant beneficial functions related to phosphorus and nitrogen mineralisation. By providing the first meta-proteomic level assessment of the active plant microbiome at the field-scale, this study demonstrates the importance of moving past a genomic assessment of the plant microbiome in order to determine ecologically important plant: microbe interactions driving plant growth.

microbiology↗

Designing synthetic bacterial consortia for landfill leachate treatment based on community matrices and regression tree analysis

The performance of microbial communities exploited by industry are largely optimised by manipulating process parameters, such as flow rates, growth conditions, and reactor parameters. Conversely, the composition of microorganisms used are often viewed as a "black box". This is mostly due to the relatively high costs and technical expertise required to identify and quantify the microbial consortia, as well as limited tools to create functional assemblages. Unknown details about the interactions among species may impose a limit on how much microbial function can be optimised for industrial purposes. Here, a new workflow was developed for studying microbial consortia using high throughput, species and community specific measurements of growth rates and yields. Growth rate and yield among all single, pairwise, triple, quadruples, quintuple and sextuple combinations of six bacterial isolates on landfill leachate were evaluated. Additive, antagonistic (e.g. competitive) or synergistic (+/-) interactions can be inferred from the rate and yield data. We found that antagonistic interactions, which hinder growth and yield, were the dominant interaction type, with only a few synergistic interactions observed. Mixed effects models were used to investigate the relationship between interaction type and species richness (biodiversity). Community identity was found to be a more important factor in predicting yield determining interactions but not rate determining interactions. Species richness was a good predictor of rate determining interactions, with the most positive interactions happening at a low species richness. Regression tree analysis identified Lysinibacillus sp. as a keystone species, a genus previously associated with bioremediation. Its presence led to a drastic change in the function of the synthetic ecosystem, with both positive yield and rate determining interactions. We were able to infer interactions about specific pairs of species, and the competitive/synergistic tendencies of single species from only basic top-down growth measurements. In this way, we have demonstrated how factorial experiments using isolated microorganisms can be used to ultimately design synthetic consortia with desirable traits for industry. Funding statementThis research was funded by the EPSRC (Vacation Bursary Award), BBSRC (Impact Acceleration Award), Department of Chemical and Biological Engineering, The University of Sheffield (Summer Undergraduate Research Fellowship) and Viridor Ltd. Ethics statements(Authors are required to state the ethical considerations of their study in the manuscript, including for cases where the study was exempt from ethical approval procedures) Does the study presented in the manuscript involve human or animal subjects: No Data availability statementGenerated Statement: This manuscript contains previously unpublished data. The name of the repository and accession number are not available.

microbiology↗