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Understanding cellular engagement with its environment is essential to control and monitor metabolism. Molecular Communication theory (MC) offers a computational means to identify environmental perturbations that direct or signify cellular behaviors by quantifying the information about a molecular environment that is transmitted through a metabolic system. We developed an model that integrates conventional flux balance analysis metabolic modeling (FBA) and MC to mechanistically expand the scope of MC, and thereby uniquely blends mechanistic biology and information theory to understand how substrate consumption is captured reaction activity, metabolite excretion, and biomass growth. This is enabled by defining several channels through which environmental information transmits in a metabolic network. The information flow in bits that is calculated through this workflow further determines the maximal metabolic effect of environmental perturbations on cellular metabolism and behaviors, since FBA simulates maximal efficiency of the metabolic system. We exemplify this method on two intestinal symbionts – Bacteroides thetaiotaomicron and Methanobrevibacter smithii – and visually consolidated the results into constellation diagrams that facilitate interpretation of information flow from given environments and thereby cultivate the design of controllable biological systems. The unique confluence of metabolic modeling and information theory in this model advances basic understanding of cellular metabolism and has applied value for the Internet of Bio-Nano Things, synthetic biology, microbial ecology, and autonomous laboratories. 

Abstract The Ruby Mountains–East Humboldt Range–Wood Hills–Pequop Mountains (REWP) metamorphic core complex, northeast Nevada, exposes a record of Mesozoic contraction and Cenozoic extension in the hinterland of the North American Cordillera. The timing, magnitude, and style of crustal thickening and succeeding crustal thinning have long been debated. The Pequop Mountains, comprising Neoproterozoic through Triassic strata, are the least deformed part of this composite metamorphic core complex, compared to the migmatitic and mylonitized ranges to the west, and provide the clearest field relationships for the Mesozoic–Cenozoic tectonic evolution. New field, structural, geochronologic, and thermochronological observations based on 1:24,000-scale geologic mapping of the northern Pequop Mountains provide insights into the multi-stage tectonic history of the REWP. Polyphase cooling and reheating of the middle-upper crust was tracked over the range of <100 °C to 450 °C via novel 40Ar/39Ar multi-diffusion domain modeling of muscovite and K-feldspar and apatite fission-track dating. Important new observations and interpretations include: (1) crosscutting field relationships show that most of the contractional deformation in this region occurred just prior to, or during, the Middle-Late Jurassic Elko orogeny (ca. 170–157 Ma), with negligible Cretaceous shortening; (2) temperature-depth data rule out deep burial of Paleozoic stratigraphy, thus refuting models that incorporate large cryptic overthrust sheets; (3) Jurassic, Cretaceous, and Eocene intrusions and associated thermal pulses metamorphosed the lower Paleozoic–Proterozoic rocks, and various thermochronometers record conductive cooling near original stratigraphic depths; (4) east-draining paleovalleys with ∼1–1.5 km relief incised the region before ca. 41 Ma and were filled by 41–39.5 Ma volcanic rocks; and (5) low-angle normal faulting initiated after the Eocene, possibly as early as the late Oligocene, although basin-generating extension from high-angle normal faulting began in the middle Miocene. Observed Jurassic shortening is coeval with structures in the Luning-Fencemaker thrust belt to the west, and other strain documented across central-east Nevada and Utah, suggesting ∼100 km Middle-Late Jurassic shortening across the Sierra Nevada retroarc. This phase of deformation correlates with terrane accretion in the Sierran forearc, increased North American–Farallon convergence rates, and enhanced Jurassic Sierran arc magmatism. Although spatially variable, the Cordilleran hinterland and the high plateau that developed across it (i.e., the hypothesized Nevadaplano) involved a dynamic pulsed evolution with significant phases of both Middle-Late Jurassic and Late Cretaceous contractional deformation. Collapse long postdated all of this contraction. This complex geologic history set the stage for the Carlin-type gold deposit at Long Canyon, located along the eastern flank of the Pequop Mountains, and may provide important clues for future exploration. 

ABSTRACT Analysis of the genes retained in the minimized Mycoplasma JCVI-Syn3A genome established that systems that repair or preempt metabolite damage are essential to life. Several genes known to have such functions were identified and experimentally validated, including 5-formyltetrahydrofolate cycloligase, coenzyme A (CoA) disulfide reductase, and certain hydrolases. Furthermore, we discovered that an enigmatic YqeK hydrolase domain fused to NadD has a novel proofreading function in NAD synthesis and could double as a MutT-like sanitizing enzyme for the nucleotide pool. Finally, we combined metabolomics and cheminformatics approaches to extend the core metabolic map of JCVI-Syn3A to include promiscuous enzymatic reactions and spontaneous side reactions. This extension revealed that several key metabolite damage control systems remain to be identified in JCVI-Syn3A, such as that for methylglyoxal. IMPORTANCE Metabolite damage and repair mechanisms are being increasingly recognized. We present here compelling genetic and biochemical evidence for the universal importance of these mechanisms by demonstrating that stripping a genome down to its barest essentials leaves metabolite damage control systems in place. Furthermore, our metabolomic and cheminformatic results point to the existence of a network of metabolite damage and damage control reactions that extends far beyond the corners of it that have been characterized so far. In sum, there can be little room left to doubt that metabolite damage and the systems that counter it are mainstream metabolic processes that cannot be separated from life itself. 

Metabolic engineering uses enzymes as parts to build biosystems for specified tasks. Although a part’s working life and failure modes are key engineering performance indicators, this is not yet so in metabolic engineering because it is not known how long enzymes remain functional in vivo or whether cumulative deterioration (wear-out), sudden random failure, or other causes drive replacement. Consequently, enzymes cannot be engineered to extend life and cut the high energy costs of replacement. Guided by catalyst engineering, we adopted catalytic cycles until replacement (CCR) as a metric for enzyme functional life span in vivo. CCR is the number of catalytic cycles that an enzyme mediates in vivo before failure or replacement, i.e., metabolic flux rate/protein turnover rate. We used estimated fluxes and measured protein turnover rates to calculate CCRs for ∼100–200 enzymes each from Lactococcus lactis , yeast, and Arabidopsis . CCRs in these organisms had similar ranges (<10 3 to >10 7 ) but different median values (3–4 × 10 4 in L. lactis and yeast versus 4 × 10 5 in Arabidopsis ). In all organisms, enzymes whose substrates, products, or mechanisms can attack reactive amino acid residues had significantly lower median CCR values than other enzymes. Taken with literature on mechanism-based inactivation, the latter finding supports the proposal that 1) random active-site damage by reaction chemistry is an important cause of enzyme failure, and 2) reactive noncatalytic residues in the active-site region are likely contributors to damage susceptibility. Enzyme engineering to raise CCRs and lower replacement costs may thus be both beneficial and feasible. 

The rapid acceleration of microbial genome sequencing increases opportunities to understand bacterial gene function. Unfortunately, only a small proportion of genes have been studied. Recently, TnSeq has been proposed as a cost-effective, highly reliable approach to predict gene functions as a response to changes in a cell’s fitness before-after genomic changes. However, major questions remain about how to best determine whether an observed quantitative change in fitness represents a meaningful change. To address the limitation, we develop a Gaussian mixture model framework for classifying gene function from TnSeq experiments. In order to implement the mixture model, we present the Expectation-Maximization algorithm and a hierarchical Bayesian model sampled using Stan’s Hamiltonian Monte-Carlo sampler. We compare these implementations against the frequentist method used in current TnSeq literature. From simulations and real data produced by E.coli TnSeq experiments, we show that the Bayesian implementation of the Gaussian mixture framework provides the most consistent classification results. 

Abstract For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical ‘Rosetta Stone’ to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase. 

Abstract The reconstruction of bacterial and archaeal genomes from shotgun metagenomes has enabled insights into the ecology and evolution of environmental and host-associated microbiomes. Here we applied this approach to >10,000 metagenomes collected from diverse habitats covering all of Earth’s continents and oceans, including metagenomes from human and animal hosts, engineered environments, and natural and agricultural soils, to capture extant microbial, metabolic and functional potential. This comprehensive catalog includes 52,515 metagenome-assembled genomes representing 12,556 novel candidate species-level operational taxonomic units spanning 135 phyla. The catalog expands the known phylogenetic diversity of bacteria and archaea by 44% and is broadly available for streamlined comparative analyses, interactive exploration, metabolic modeling and bulk download. We demonstrate the utility of this collection for understanding secondary-metabolite biosynthetic potential and for resolving thousands of new host linkages to uncultivated viruses. This resource underscores the value of genome-centric approaches for revealing genomic properties of uncultivated microorganisms that affect ecosystem processes. 

Abstract Over the last 25 years, biology has entered the genomic era and is becoming a science of ‘big data’. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3–4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.