Biology subjects

Nelson, W. C.

Publications and source records attributed to Nelson, W. C..

At least 19 recordsLinked to original sources

bioRxiv2026

High-resolution HLA and MICA/MICB typing of the 1000 Genomes panel: a resource for precision medicine

High-resolution characterization of human leukocyte antigen (HLA) and MICA/MICB variation is important for transplantation, immunogenetics, and the interpretation of disease associations within the major histocompatibility complex (MHC). However, publicly available population reference datasets remain limited in their resolution and coverage of these loci. We performed high-resolution typing of the 1000 Genomes Phase 3 panel across 13 HLA and MIC loci, generating HLA genotypes for 2,486 individuals, extended HLA-A, -B, and -C allele assignments, and MICA/MICB genotypes for 2,393 individuals representing 25 populations and five continental super-populations. Concordance with previously reported HLA genotypes was 96.2-97.4% at two-field resolution, and independent retyping confirmed the present assignments for samples exhibiting reciprocal genotype discrepancies with an earlier dataset. Analysis of extended class I variation within defined assay boundaries revealed that most of the 78 common three-field HLA lineages were dominated by a single extended sequence group. However, eleven lineages exhibited substantial heterogeneity, with the predominant group accounting for less than 80% of observed copies. The distinguishing sequence differences were noncoding, involving intronic and 3' untranslated regions, and their distributions varied among ancestral populations. These findings identify recurrent, population-relevant variants for which functional consequences remain to be established. Population analyses demonstrated extensive allelic diversity at classical class I loci and DRB1, whereas the greatest between-population differentiation occurred at selected class II and DRB-linked loci. Most common alleles were shared among populations but differed substantially in frequency. Multilocus haplotypes revealed characteristic population-associated combinations of HLA and MIC alleles and provided the basis for predicting MIC genotypes from existing HLA typing. Using two-field HLA-B and HLA-C genotypes, five-fold cross-validation achieved exact genotype prediction accuracies of 77.3% for MICA and 53.6% for MICB, compared with 4.6% and 21.6%, respectively, without HLA information. Together, these results provide an extended-resolution HLA and MIC reference resource, demonstrate the limited routine utility of universal fourth-field typing while identifying specific variants for functional investigation, and establish the potential for recovering MIC variation from existing HLA-typed cohorts. The resource also supports future investigations of MHC variation underlying disease associations and transplantation outcomes.

genetics↗

bioRxiv2026

Technology solutions for immune loci: integrated high-resolution KIR genotyping reveals allelic, structural, and KIR-HLA diversity across 24 human populations

High-resolution genotyping of killer cell immunoglobulin-like receptor (KIR) genes remains challenging because of extensive sequence homology, copy-number variation, and complex haplotype structures. Although KIR allelic variation influences receptor expression and function, routine typing has largely been limited to gene presence or absence. Here, we evaluated an integrated short-read sequencing system combining a four-reaction, two-stage multiplex PCR assay (ScisGo-KIR-v3), centralized cloud-based copy-aware allele analysis, and interactive genotype review. The system was applied to 2,298 individuals representing 24 populations from the 1000 Genomes Project, with matched high-resolution HLA class I genotypes. Independent validation against complete, phased PacBio KIR sequences from 48 samples demonstrated 98.6% automated copy-number concordance and 92.8% combined copy-number and allele concordance across 768 gene-sample comparisons. Following desktop review, all evaluated assignments were concordant with the reference sequences. In a separate comparison of four computational genotyping methods involving 311 shared samples, the automated Scisco pipeline showed the lowest overall mismatch with the four-method consensus (1.62%). Population analyses identified substantial allele diversity and distinct centromeric and telomeric haplotype-motif distributions. Although exact haplotype pairs were frequently ambiguous, structural assignments became increasingly resolvable at the motif level, and motif backgrounds exhibited characteristic allele compositions. Paired KIR and HLA analysis revealed inherited receptor-ligand configurations concealed by gene-content typing. Among 1,401 KIR3DL1/HLA-B Bw4 co-carriers, 57 lacked an allele annotated for ordinary surface expression, while 457 of 1,175 KIR2DS4 co-carriers of six specified HLA-C allotypes carried only truncated receptor forms. Allele-specific KIR2DL1 and KIR2DS5 classifications further distinguished receptor properties and published ligand-binding annotations that varied among populations. Although potential receptor-ligand repertoire composition differed across populations, analyses did not establish preferential KIR-HLA genetic association after accounting for population structure and multiple testing. Together, these findings demonstrate that integrated short-read KIR genotyping can provide scalable, high-resolution allele and copy-number information while explicitly retaining uncertainty in inferred haplotype structures. Compatibility with established HLA laboratory workflows provides a practical foundation for incorporating allele-resolved KIR analysis into immunogenetic research and future clinical investigations.

genetics↗

bioRxiv2026

Mobile genetic elements are active and responsive to community context in model microbial consortium

Insertion and excision of genomic islands (GIs), chromosomally-integrated mobile genetic elements (MGEs), are major sources of microbial genome plasticity and can impact gene expression and phenotype of the host organism. GI mobilization also influences microbial communities beyond the host organism as GI excision generates MGEs that can be transferred between community members through horizontal gene transfer and induction of prophages can kill host populations, which impacts community structure. Established computational methods now enable precise GI mapping in genomes, as well as highly sensitive detection of GI excision from deep-genome sequencing data. We applied these approaches to metagenomic datasets from a defined soil microbial consortium grown on glass beads under hydration stress and compared GI activity with that observed in monoculture. Under these environmentally structured community growth conditions, GI excision was more abundant and involved a broader range of host species and GI types than under isolate growth conditions. Combined analysis with metatranscriptomic and metaproteomic data identified patterns of GI gene expression associated with induction. Three GIs showed particularly high excision together with strong transcription, numerous detected proteins, and evidence of association with potential transfer particles, including phages or vesicles. These results indicate that isolate studies can miss a substantial environmentally responsive layer of GI activity. More broadly, this work establishes a framework for mining existing community multi-omic datasets to quantify dynamic genome restructuring, identify active but poorly understood GIs, and generate mechanistic hypotheses about the processes that shape microbial genome plasticity and gene flow.

microbiology↗

bioRxiv2026

Carbon starvation of Mycobacterium abscessus induces a non-replicating state with extensive proteomic remodeling

Mycobacterium abscessus (Mab) is an opportunistic pathogen that can cause chronic, debilitating lung disease. Mab is intrinsically resistant to most antibiotics, making Mab infections challenging to manage and frequently incurable. During infection, Mab adapts to survive various stresses, including hypoxia and nutrient starvation. In vitro, these conditions drive Mab into a drug-tolerant, non-replicating state. Changes in the Mab proteome that result from entering a non-replicating state have been minimally described despite the clinical importance of this physiological state. Using Mab reference strain ATCC 19977, we collected proteomic data comparing replicating to non-replicating states using a carbon starvation (CS) model of persistence. We identified 2251 proteins overall (46% proteome coverage), and 17% of these proteins were found in only one of the two conditions. A third of identified proteins were significantly changed in abundance, indicating an extensive proteomic response to CS. The response regulator DosR and many DosRS responsive proteins were significantly more abundant under CS, suggesting that the DosRS stress response regulator plays a key role in CS-induced Mab persistence. Many aspects of cell wall biosynthesis were changed, including changes in glycolipid abundance under CS. Proteins involved in other key cellular processes such as secretion, oxidative phosphorylation, and nutrient metabolism were altered under CS. The proteomic analysis presented provides new insights and clarity into how the Mab proteome is regulated during non-replicating persistence, a key consideration for understanding Mab pathophysiology.

microbiology↗

bioRxiv2025

Chemoproteomic elucidation of β-lactam drug targets in Mycobacterium abscessus

The pathogen Mycobacterium abscessus (Mab) can cause severe and difficult to treat chronic lung infections. Despite the rising incidence and clinical concern of Mab infections, treatment options are limited and often ineffective. Treatment is complicated by Mabs ability to persist in a non-replicative, drug-resistant state. Several {beta}-lactam antibiotics are potently bactericidal against Mab but are underutilized because their molecular mechanisms of action against Mab are incompletely understood. In the current study, we used {beta}-lactam-derived activity-based probes and chemoproteomics to report the first comprehensive list of enzymes in Mab targeted by {beta}-lactams. We compared {beta}-lactam targets across two Mab subspecies in actively replicating and non-replicative cultures, using a new carbon starvation model of persistence. We identified 17 targets that were active in every condition tested, seven of which were previously unknown to bind {beta}-lactams. Lastly, we characterized the {beta}-lactamase activity and {beta}-lactam inhibition profiles of nine Mab enzymes, demonstrating that imipenem inhibits these targets more effectively than cefoxitin. These findings provide clarity on the mechanisms of action of clinically relevant {beta}-lactams in Mab, a crucial step toward fully realizing their potential for treating infections caused by this opportunistic pathogen.

microbiology↗

bioRxiv2025

Snekmer Learn/Apply: A kmer-based vector similarity approach to proteinclassification suitable for metagenomic datasets

Advances in whole genome sequencing have led to a rapid and ongoing increase in the amount of sequence data available, but 40-50% of known genes have no functional annotation and only 25-30% have specific functional annotations. Current functional annotation approaches typically rely on computationally expensive pairwise or multiple sequence alignments, preventing rapid development of models for novel protein functions and sometimes limiting methods to one ontology. Representation of sequence in short segments (kmers) has been used in many applications for nucleotide sequence, and more recently has been applied to protein sequence as well. We previously developed Snekmer, a tool which uses kmer patterns to develop alignment-free individual protein family models. Other approaches, such as MMSeqs2 and DIAMOND, use protein kmers as a fast filter to reduce search space for subsequent sequence alignment. Here, we describe a novel addition to the Snekmer tool which builds kmer libraries for protein families and uses those libraries to map functional annotations to new sequences. We first demonstrate that our method accurately applies TIGRFAMs annotations to protein fragments and to a low-sequence identity benchmark dataset, and further use it to annotate a set of drought stress associated soil and rhizosphere metagenome sequences with higher sensitivity towards several important protein function classes than that shown by HMMs. We have incorporated this workflow into Snekmer.

bioinformatics↗

bioRxiv2025

Discovery of non-opioid receptor protein targets of fentanyl across tissues from animal models and humans using photoaffinity probes

Synthetic opioids such as fentanyl and related analogs have been widely used for pain management. However, their negative side effects, including respiratory depression and high potential for addiction, underscore the need for a deeper understanding of fentanyls interactions with proteins throughout the human body. Fentanyl analogs bind and activate opioid receptors in the central and peripheral nervous systems, triggering numerous downstream signaling pathways. Increasingly, fentanyl has been shown to interact with non-opioid receptors, and elucidation of these non-canonical fentanyl-protein interactions may provide insights into the mechanisms contributing to fentanyls adverse effects and illuminate novel countermeasure strategies. To identify proteins in mammalian tissues that may interact with fentanyl, we designed and synthesized three affinity-based probes (AfBPs) that include the fentanyl core and feature a diazirine photoaffinity group and alkyne handle for click chemistry at different positions. Molecular docking simulations predicted that these AfBPs bind the mu opioid receptor similarly to fentanyl. Affinity-based protein profiling using the FA-T1 probe in vitro in tissues from six animal species identified histamine N-methyltransferase (HNMT), endophilin-B1 (SH3GLB1), fructosamine-3-kinase (FN3K), cutA divalent cation tolerance analog (CUTA), and monoamine oxidase B (MAOB) among the top proteins that bind fentanyl in multiple species and tissue types. Molecular docking of fentanyl and remifentanil with these protein structures identified putative binding sites. The interaction of fentanyl with specific proteins was empirically assessed through protein structural analyses. These findings highlight potential fentanyl-protein interactions that may contribute to the acute and long-term impacts of fentanyl exposures.

pharmacology and toxicology↗

bioRxiv2024

A Rhodopseudomonas strain with a substantially smaller genome retains the core metabolic versatility of its genus

Rhodopseudomonas are a group of phototrophic microbes with a marked metabolic versatility and flexibility that underpins their potential use in production of value-added products, bioremediation and plant growth promotion. Members of this group have an average genome size of about 5.5 Mb, but two closely related strains have genome sizes of about 4.0 Mb. To identify the types of genes missing in a reduced genome strain, we compared strain DSM127 with other Rhodopseudomonas isolates at the genomic and phenotypic levels. We found that DSM127 can grow as well as other members of the Rhodopseudomonas genus and retains most of their metabolic versatility, but it has many fewer genes associated with high affinity transport of nutrients, iron uptake, nitrogen metabolism and biodegradation of aromatic compounds. This analysis indicates genes that can be deleted in genome reduction campaigns and suggests that DSM127 could be a favorable choice for biotechnology applications using Rhodopseudomonas or as a strain that can be engineered further to reside in a specialized natural environment. IMPORTANCERhodopseudomonas are a cohort of phototrophic bacteria with broad metabolic versatility. Members of this group are present in diverse soil and water environments and some strains are found associated with plants and have plant growth promoting activity. Motivated by the idea that it may be possible to design bacteria with reduced genomes that can survive well only in a specific environment or that may be more metabolically efficient, we compared Rhodopseudomonas strains with typical genome sizes of about 5.5 Mb to a strain with a reduced genome size of 4.0 Mb. From this we concluded that metabolic versatility is part of the identity of the Rhodopseudomonas group, but high-affinity transport genes and genes of apparent redundant function can be dispensed with.

microbiology↗

bioRxiv2024

Environmental matrix and moisture are key determinants of microbial phenotypes expressed in a reduced complexity soil-analog

Soil moisture and porosity regulate microbial metabolism by influencing factors such as redox conditions, substrate availability, and soil connectivity. However, the inherent biological, chemical, and physical heterogeneity of soil complicates laboratory investigations into microbial phenotypes that mediate community metabolism. This difficulty arises from challenges in accurately representing the soil environment and in establishing a tractable microbial community that limits confounding variables. To address these challenges in our investigation of community metabolism, we use a reduced-complexity microbial consortium grown in a soil analog using a glass-bead matrix amended with chitin. Long-read and short-read metagenomes, metatranscriptomes, metaproteomes, and metabolomes were analyzed to test the effects of soil structure and moisture on chitin degradation. Our soil structure analog system greatly altered microbial expression profiles compared to the liquid-only incubations, emphasizing the importance of incorporating environmental parameters, like pores and surfaces, for understanding microbial phenotypes relevant to soil ecosystems. These changes were mainly driven by differences in overall expression of chitin-degrading Streptomyces species and stress-tolerant Ensifer. Our findings suggest that the success of Ensifer in a structured environment is likely related to its ability to repurpose carbon via the glyoxylate shunt while potentially using polyhydroxyalkanoate granules as a C source. We also identified traits like motility, stress resistance, and biofilm formation that underlie the degradation of chitin across our treatments and inform how they may ultimately alter carbon use efficiency. Together our results demonstrate that community functions like decomposition are sensitive to environmental conditions and more complex than the multi-enzyme pathways involved in depolymerization. ImportanceSoil moisture and porosity are critical mediators of microbial metabolism by influencing factors such as redox conditions, substrate availability, and soil connectivity. However, identifying how microbial community metabolism shifts in response to varying levels of moisture and porosity remains a challenging frontier. This difficulty arises from challenges in accurately representing the soil environment and in establishing tractable microbial communities that limit confounding variables. Moreover, inferring phenotypes based on "key" genes often fails to predict complex phenotypes that arise from cellular interactions. Here, we establish a tractably complex microbial community in a soil analog system amended with chitin and leverage it to understand how microorganisms respond to changes in porosity and moisture. By using genome-resolved metagenomics, metatranscriptomics, and metaproteomics, we report on the microbial lifestyle strategies that underpin changes in community expression like carbon conservation, biofilm production, and stress response.

microbiology↗

bioRxiv2024

Improving the visualization of viruses in soil

Viruses are numerically the most abundant forms on Earth, and most are present in soil. Even though viruses are highly abundant in soil and critical to rhizosphere function, visualizing the diverse morphotypes within soil has been challenging. The difficulty is primarily due to the heterogenous nature of isolated suspensions that typically contain nanometer to micron scale debris which renders protein crystallography for structural studies unfeasible and hinders cryo-electron microscopy due to ice thickness and contrast issues. Here we employed and compared a simple spin filtration method to cleanup solutions of extracted viruses for direct observation with cryo-electron microscopy. Although relatively simple, the method employs common physical biochemical separation steps to remove large and small debris which dramatically improves image quality and preservation of structural features to permit visualizing morphotypes not typically seen with conventional negative stain approaches. In addition to tailed and non-tailed polyhedral phages, several under reported or novel morphotypes of soil viruses are directly visualized as a particle library with both 2D and 3D information.

microbiology↗

bioRxiv2023

A novel phenylpropanoid methyl esterase enables catabolism of aromatic compounds that inhibit biological nitrification

Agriculture is the largest source of greenhouse gases (GHG) production. Conversion of nitrogen fertilizers into more reduced forms by microbes through a process known as biological nitrification drives GHG production, enhances proliferation of toxic algal blooms, and increases cost of crop production. Some plants reduce biological nitrification in soils by exuding a diverse array of biological nitrification inhibitors (BNIs) that inhibit the ammonium oxidizing microbes responsible for nitrification. Applying synthetic biology to enhance and transfer BNI production into food and bioenergy crops is a promising approach to reduce nitrification but the success of this strategy is contingent upon improving our limited understanding of BNIs mechanisms-of-action and degradation in the soil. We addressed this gap by investigating the previously unknown metabolic route through which a prominent class of aromatic BNIs known as phenylpropanoid methyl esters (PPMEs) are catabolized. While neither transcriptomics (RNAseq) or high-throughput functional genomics (RB-TnSeq) methods reduced the genetic search space for pathway discovery into a tenable number of genes, the combination of narrowed the search space to a collection of 4 proteins of unknown function. Using genetic and biochemical analyses we found that two previously uncharacterized enzymes, including a novel phenylpropanoid methyl esterase, funnel PPMEs into an established phenylpropanoid catabolism pathway. Transfer of these two enzymes into bacteria capable of using other phenylpropanoids use of PPMEs as carbon sources. This work both provided insight into BNI catabolism and is the first step towards development of model in vivo plant-microbe systems for studying BNI mechanisms under well controlled conditions.

microbiology↗

bioRxiv2023

Profiling sorghum-microbe interactions with a specialized photoaffinity probe identifies key sorgoleone binders in Acinetobacter pittii

Sorghum (Sorghum bicolor) is a major food and bioenergy grass species cultivated worldwide. To promote more robust and sustainable growth of this important crop, we need a deeper understanding of the plant-microbe interactions between sorghum and soil microbial communities that benefit plant host resiliency and enhance nutrient acquisition. The release of specific metabolites from plant roots, or root exudation, drives these plant-microbe interactions, but the molecular pathways by which root exudates shape the sorghum rhizosphere microbiome require further elucidation. To investigate these complex interkingdom interactions in the sorghum rhizosphere, we developed a photoaffinity probe based on sorgoleone, a hydrophobic secondary metabolite and allelochemical produced in sorghum seedling root exudates. Here, we apply a new synthetic sorgoleone diazirine alkyne photoaffinity probe (SoDA-PAL) to the identification of sorgoleone-binding proteins in Acinetobacter pittii SO1, a potential plant growth promoting microbe derived from Sorghum bicolor rhizosphere soil. Competitive photoaffinity labeling of A. pittii whole cell lysates with SoDA-PAL identified 137 statistically enriched proteins that were complementary to a previously identified gene cluster involved in sorgoleone catabolism. Proteins identified by SoDA-PAL included a select set of putative transporters, transcription regulators, and a subset of proteins with lipid and secondary metabolic activities. We confirm binding of SoDA-PAL to a putative hydrolase in the /{beta} fold family (OH685_09420) through structural bioinformatics and in-vitro recombinant protein analysis. This photoaffinity labeling approach using metabolite-based probes can be extended in the future to proteomic profiling of complex rhizosphere microbiomes to discover genes that can be leveraged to promote beneficial plant-microbe interactions. ImportanceHere we demonstrate a photoaffinity-based chemical probe modeled after sorgoleone, a known secondary metabolite released from the roots of sorghum, can be used to dissect complicated plant-microbe interactions. Applying this probe to the sorghum-associated bacterium Acinetobacter pittii identified diverse proteins that directly interact with sorgoleone. We show that probe labeling is dose-dependent and is sensitive to competition with purified sorgoleone, demonstrating the probe is selective for protein targets that directly interact with sorgoleone. By using the probe to broadly profile proteins that interact with sorgoleone, we identified bacterial catabolic pathways, unintuitive transcriptional regulation pathways, and vital exchange mechanisms involving transporters that may be involved in sorgoleone utilization and cellular response toward this plant metabolite. We envision that this workflow will expand our understanding of the sorghum root exudate interactome and elucidate the molecular mechanisms by which specific metabolites shape the sorghum rhizosphere microbiome.

biochemistry↗

bioRxiv2023

Sorgoleone degradation by sorghum-associated bacteria; an opportunity for enforcing plant growth promotion

Metabolite exchange between plant roots and their associated rhizosphere microbiomes underpins plant growth promotion by microbes. Sorghum bicolor is a cereal crop that feeds animals and humans and is used for bioethanol production. Its root tips exude large amounts of a lipophilic benzoquinone called sorgoleone. Sorgoleone is an allelochemical that suppresses the growth of competing plant seedlings and is mineralized by microbes in soil. As an avenue to understand how sorghum and its root microbiome may be connected through root exudates, we identified the molecular determinants of microbial sorgoleone degradation and the distribution of this trait among microbes. We isolated and studied from sorghum-associated soils, three bacterial strains classified as Acinetobacter, Burkholderia, and Pseudomonas species that grow with sorgoleone as a sole carbon and energy source. The genomes of these strains were sequenced and subjected to transcriptomic and gene fitness analyses to identify candidate sorgoleone degradation genes. Follow up mutational analysis showed that sorgoleone catabolism is dependent on four contiguous genes that are conserved among the species we sequenced. Phylogenetic analysis of the sorgoleone degradation gene cluster showed that sorgoleone catabolism is enriched in sorghum-associated Streptomyces strains. The discovery of bacteria that grow on a compound like sorgoleone that is plant specific and not widely distributed in the environment, provides an opportunity to study how a plant exudate can enforce the development of a rhizosphere specific microbiome for the mutual benefit of plant and microbe. SignificanceThe grain crop sorghum exudes an herbicidal compound called sorgoleone from its root tips, which inhibits the growth of other plants. We isolated bacteria that grow on sorogleone and identified a cluster of bacterial genes required for sorogleone degradation that can be used as a biomarker for this trait. An approach to improve the production of crops in stressful conditions such as drought, is to encourage their association with plant growth promoting bacteria. Our discovery of sorgoleone degradation genes opens the door to engineering bacteria that receive benefit from sorghum in the form of a plant-specific growth substrate, and in return promote the growth of this crop.

microbiology↗

bioRxiv2023

Coupling Flux Balance Analysis with Reactive Transport Modeling through Machine Learning for Rapid and Stable Simulation of Microbial Metabolic Switching

Integration of genome-scale metabolic networks with reactive transport models (RTMs) is an advanced simulation technique that enables predicting the changes of microbial growth and metabolism in space and time. Despite promising demonstrations in the past, computational inefficiency has been pointed out as a critical issue to overcome because it requires repeated implementation of linear programming (LP) to get flux balance analysis (FBA) solutions in every time step and every spatial grid. To address this challenge, we propose a new simulation method where we train/validate artificial neural networks (ANNs) using FBA solutions randomly sampled and incorporate the resulting reduced-order FBA model (represented as algebraic equations) into RTMs as source/sink terms. We demonstrate the efficiency of our method via a case study of Shewanella oneidensis MR-1 strain. During the aerobic growth on lactate, S. oneidensis produces metabolic byproducts (such as pyruvate and acetate), which are subsequently consumed as alternative carbon sources when the preferred ones are depleted. Simulating such intricate dynamics posed a considerable challenge, which we overcame by adopting the cybernetic approach that describes metabolic switches as the outcome of dynamic competition among multiple growth options. In both zero-dimensional batch and one-dimensional column configurations, the ANN-based reduced-order models achieved substantial reduction of computational time by several orders of magnitude compared to the original LP-based FBA models. Importantly, the ANN models produced robust solutions without any special measures to prevent numerical instability. These developments significantly promote our ability to utilize genome-scale networks in complex, multi-physics, and multi-dimensional ecosystem modeling.

systems biology↗

bioRxiv2021

Fungal Mineral Weathering Mechanisms Revealed Through Direct Molecular Visualization

Soil fungi facilitate the translocation of inorganic nutrients from soil minerals to other microorganisms and plants. This ability is particularly advantageous in impoverished soils, because fungal mycelial networks can bridge otherwise spatially disconnected and inaccessible nutrient hotspots. However, the molecular mechanisms underlying fungal mineral weathering and transport through soil remains poorly understood. Here, we addressed this knowledge gap by directly visualizing nutrient acquisition and transport through fungal hyphae in a mineral doped soil micromodel using a multimodal imaging approach. We observed that Fusarium sp. DS 682, a representative of common saprotrophic soil fungi, exhibited a mechanosensory response (thigmotropism) around obstacles and through pore spaces ([~]12 {micro}m) in the presence of minerals. The fungus incorporated and translocated potassium (K) from K-rich mineral interfaces, as evidenced by visualization of mineral derived nutrient transport and unique K chemical moieties following fungal induced mineral weathering. Specific membrane transport proteins were expressed in the presence of minerals, including those involved in oxidative phosphorylation pathways and transmembrane transport of small molecular weight organic acids. This study establishes the significance of fungal biology and nutrient translocation mechanisms in maintaining fungal growth under water and nutrient limitations in a soil-like microenvironment.

microbiology↗

bioRxiv2020

Resource reallocation in engineered Escherichia coli strains with reduced genomes

A major challenge in synthetic biology is properly balancing evolved and engineered functions without compromising microbial fitness. Many microbial proteins are not required for growth in regular laboratory conditions, but it is unclear what fraction of the proteome can be eliminated to increase bioproduction and maintain fitness. Here, we investigated the effects of massive genome reduction in E. coli on the expression level and evolutionary stability of a model biosynthetic pathway to produce the pigment protodeoxyviolacein (PDV). We identified an amino acid metabolism imbalance and compromised growth that were correlated with elimination of genes associated with significant proteome fraction. Proteomic profiling suggested that increased amino acid pools are responsible for an alleviation of fitness defects associated with PDV expression. In addition, all strains with genome reductions that significantly affected the proteome exhibited decreased stability of PDV production compared to the wild-type strain under persistent PDV expression conditions despite the alleviation of fitness defects. These findings exhibit the importance of balancing evolved functions with engineered ones to achieve an optimal balance of fitness and bioproduction.

bioengineering↗

bioRxiv2020

Representing Organic Matter Thermodynamics in Biogeochemical Reactions via Substrate-Explicit Modeling

Predictive biogeochemical modeling requires data-model integration that enables explicit representation of the sophisticated roles of microbial processes that transform substrates. Data from high-resolution organic matter (OM) characterization are increasingly available and can serve as a critical resource for this purpose, but their incorporation into biogeochemical models is often prohibited due to an over-simplified description of reaction networks. To fill this gap, we proposed a new concept of biogeochemical modeling--termed substrate-explicit modeling--that enables parameterizing OM-specific oxidative degradation pathways and reaction rates based on the thermodynamic properties of OM pools. The resulting kinetic models are characterized by only two parameters regardless of the complexity of OM profiles, which can greatly facilitate the integration with reactive transport models for ecosystem simulations by alleviating the difficulty in parameter identification. For every detected organic molecule in a given sample, our approach provides a systematic way to formulate reaction kinetics from chemical formula, which enables the evaluation of the impact of OM character on biogeochemical processes across conditions. In a case study of two sites with distinct OM thermodynamics, our method not only predicted oxidative degradation to be primarily driven by thermodynamic efficiency of OM consistent with experimental rate measurements, but also revealed previously unknown critically important aspects of biogeochemical reactions, including their condition-specific response to carbon and/or oxygen limitations. Lastly, we showed that the proposed substrate-explicit modeling approach can be synergistically combined with enzyme-explicit approach to provide improved predictions. This result led us to present integrative biogeochemical modeling as a unifying framework that can ideally describe the dynamic interplay among microbes, enzymes, and substrates to address advanced questions and hypotheses in future studies. Altogether, the new modeling concept we propose in this work provides a foundational platform for unprecedented predictions of biogeochemical and ecosystem dynamics through enhanced integration with diverse experimental data and extant modeling approaches.

systems biology↗

bioRxiv2019

Temporal dynamics of nitrogen cycle gene diversity in a hyporheic microbiome

Biodiversity is thought to prevent decline in community function in response to changing environmental conditions through replacement of organisms with similar functional capacity but different optimal growth characteristics. We examined how this concept translates to the within-gene level by exploring seasonal dynamics of within-gene diversity for genes involved in nitrogen cycling in hyporheic zone communities. Nitrification genes displayed low richness-- defined as the number of unique within-gene phylotypes--across seasons. Conversely, denitrification genes varied in both richness and the degree to which phylotypes were recruited or lost. These results demonstrate that there is not a universal mechanism for maintaining community functional potential for nitrogen cycling activities, even across seasonal environmental shifts to which communities would be expected to be well adapted. As such, extreme environmental changes could have very different effects on the stability of the different nitrogen cycle activities. These outcomes suggest a need to modify existing conceptual models that link biodiversity to microbiome function to incorporate within-gene diversity. Specifically, we suggest an expanded conceptualization that (1) recognizes component steps (genes) with low diversity as potential bottlenecks influencing pathway-level function, and (2) includes variation in both the number of entities (e.g. species, phylotypes) that can contribute to a given process and the turnover of those entities in response to shifting conditions. Building these concepts into process-based ecosystem models represents an exciting opportunity to connect within-gene-scale ecological dynamics to ecosystem-scale services.

ecology↗