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Protocol for condition-dependent metabolite yield prediction using the TRIMER pipeline
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Engineering Escherichia coli for methanol-dependent growth on glucose for metabolite production
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Introducing “Identification Probability” for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies
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nmRanalysis: An Open-Source Web Application for Semi-automated NMR Metabolite Profiling
Though data acquisition and initial signal pre-processing of nuclear magnetic resonance (NMR) spectra have achieved high degrees of automation, downstream processing - specifically the profiling of spectra - has bottlenecked the overall NMR analysis workflow. Several efforts have been made to mitigate this bottleneck, but these solutions often trade an increase in automation for limitations elsewhere. Here, in this technical note, we introduce nmRanalysis, a user-friendly web-application that integrates the strengths of existing profiling tools for a more automated profiling workflow. nmRa-nalysis additionally incorporates novel features, including a machine-learning-driven recommender system for me-tabolite identification, further increasing the utility of nmRanalysis over the individual tools that it incorporates.
Statistically Driven Metabolite and Lipid Profiling of Patients from the Undiagnosed Diseases Network
Advancements in molecular separations coupled with mass spectrometry have enabled large-scale metabolome analyses for clinical cohorts. A population of interest for metabolome profiling are patients with rare disease for which abnormal metabolic signatures may yield clues into the genetic bases, as well as mechanistic drivers of the disease and possible treatment options. We undertook the metabolome profiling of a large cohort of patients with mysterious conditions characterized through the Un-diagnosed Diseases Network (UDN). Due to the size and enrollment procedures, collection of the metabolomes for UDN pa-tients took place over three years. We describe the study design to adjust for measurements collected over a long time-scale and how this enabled statistical analyses to summarize the metabolome of individual patients. We demonstrate the removal of time-based batch effects, overall statistical characteristics of the UDN population, and two case-studies of interest that demonstrate the utility of metabolome profiling for rare diseases.
Drought and plant litter chemistry alter microbial gene expression and metabolite production
Drought represents a significant stress to microorganisms and is known to reduce microbial activity and organic matter decomposition in Mediterranean ecosystems. However, we lack a detailed understanding of the drought stress response of microbial decomposers. Here we present metatranscriptomic and metabolomic data on the physiological response of in situ microbial communities on plant litter to long-term drought in Californian grass and shrub ecosystems. We hypothesised that drought causes greater microbial allocation to stress tolerance relative to growth pathways. In grass litter, communities from the decade-long ambient and reduced precipitation treatments had distinct taxonomic and functional profiles. The most discernable physiological signatures of drought were production or uptake of compatible solutes to maintain cellular osmotic balance, and synthesis of capsular and extracellular polymeric substances as a mechanism to retain water. The results show a clear functional response to drought in grass litter communities with greater allocation to survival relative to growth that could affect decomposition under drought. In contrast, communities on chemically more diverse and complex shrub litter had smaller physiological differences in response to long-term drought but higher investment in resource acquisition traits across precipitation treatments, suggesting that the functional response to drought is constrained by substrate quality. Our findings suggest, for the first time in a field setting, a trade off between microbial drought stress tolerance, resource acquisition and growth traits in plant litter microbial communities.
The metabolites NADP and NADPH are the targets of the circadian protein Nocturnin (Curled)
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Structural evolution of a DNA repair self-resistance mechanism targeting genotoxic secondary metabolites
Abstract Microbes produce a broad spectrum of antibiotic natural products, including many DNA-damaging genotoxins. Among the most potent of these are DNA alkylating agents in the spirocyclopropylcyclohexadienone (SCPCHD) family, which includes the duocarmycins, CC-1065, gilvusmycin, and yatakemycin. The yatakemycin biosynthesis cluster in Streptomyces sp. TP-A0356 contains an AlkD-related DNA glycosylase, YtkR2, that serves as a self-resistance mechanism against yatakemycin toxicity. We previously reported that AlkD, which is not present in an SCPCHD producer, provides only limited resistance against yatakemycin. We now show that YtkR2 and C10R5, a previously uncharacterized homolog found in the CC-1065 biosynthetic gene cluster of Streptomyces zelensis , confer far greater resistance against their respective SCPCHD natural products. We identify a structural basis for substrate specificity across gene clusters and show a correlation between in vivo resistance and in vitro enzymatic activity indicating that reduced product affinity—not enhanced substrate recognition—is the evolutionary outcome of selective pressure to provide self-resistance against yatakemycin and CC-1065.
A small RNA that cooperatively senses two stacked metabolites in one pocket for gene control
Riboswitches are structured non-coding RNAs often located upstream of essential genes in bacterial messenger RNAs. Such RNAs regulate expression of downstream genes by recognizing a specific cellular effector. Although nearly 50 riboswitch classes are known, only a handful recognize multiple effectors. Here, we report the 2.60-Å resolution co-crystal structure of a class I type I preQ 1 -sensing riboswitch that reveals two effectors stacked atop one another in a single binding pocket. These effectors bind with positive cooperativity in vitro and both molecules are necessary for gene regulation in bacterial cells. Stacked effector recognition appears to be a hallmark of the largest subgroup of preQ 1 riboswitches, including those from pathogens such as Neisseria gonorrhoeae. We postulate that binding to stacked effectors arose in the RNA World to closely position two substrates for RNA-mediated catalysis. These findings expand known effector recognition capabilities of riboswitches and have implications for antimicrobial development.
Microbiome–metabolite linkages drive greenhouse gas dynamics over a permafrost thaw gradient
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Metabolite-driven mechanisms reveal chemical ecology of Lehmann Lovegrass (Eragrostis lehmanniana) invasion in North American semi-arid ecosystems
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The role of secondary metabolites in the production of CuO nanoparticles by fungi: a physiological and metabolic approach
Previous studies have found that the production of nanoparticles by various fungi is related to the release of NADPH/NADH into the growth medium.
Toward industrial C8 production: oxygen intrusion drives renewable n -caprylate production from ethanol and acetate via intermediate metabolite production
A simplified open culture chain elongation process with ethanol and acetate to produce renewable n -caprylate without adding yeast extract showed a trophic hierarchy between aerobic, facultative anaerobic, and fully anaerobic microbial species.
Gut microbiota and microbial metabolites for osteoporosis
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Transport of metformin metabolites by guanidinium exporters of the small multidrug resistance family
Proteins from the small multidrug resistance (SMR) family are frequently associated with horizontally transferred multidrug resistance gene arrays found in bacteria from wastewater and the human-adjacent biosphere. Recent studies suggest that a subset of SMR transporters might participate in the metabolism of the common pharmaceutical metformin by bacterial consortia. Here, we show that both genomic and plasmid-associated transporters of the SMRGdx functional subtype export byproducts of microbial metformin metabolism, with particularly high export efficiency for guanylurea. We use solid-supported membrane electrophysiology to evaluate the transport kinetics for guanylurea and native substrate guanidinium by four representative SMRGdx homologs. Using an internal reference to normalize independent electrophysiology experiments, we show that transport rates are comparable for genomic and plasmid-associated SMRGdx homologs, and using a proteoliposome-based transport assay, we show that 2 proton:1 substrate transport stoichiometry is maintained. Additional characterization of guanidinium and guanylurea export properties focuses on the structurally characterized homolog, Gdx-Clo, for which we examined the pH dependence and thermodynamics of substrate binding and solved an x-ray crystal structure with guanylurea bound. Together, these experiments contribute in two main ways. By providing the first detailed kinetic examination of the structurally characterized SMRGdx homolog Gdx-Clo, they provide a functional framework that will inform future mechanistic studies of this model transport protein. Second, this study casts light on a potential role for SMRGdx transporters in microbial handling of metformin and its microbial metabolic byproducts, providing insight into how native transport physiologies are co-opted to contend with new selective pressures.