@article{Berliner2024,

title = {Domains of life sciences in spacefaring: what, where, and how to get involved},

author = {Aaron J. Berliner and Spencer Zezulka and Gwyneth A. Hutchinson and Sophia Bertoldo and Charles S. Cockell and Adam P. Arkin},

doi = {10.1038/s41526-024-00354-y},

issn = {2373-8065},

year  = {2024},

date = {2024-12-00},

urldate = {2024-12-00},

journal = {npj Microgravity},

volume = {10},

number = {1},

publisher = {Springer Science and Business Media LLC},

keywords = {Agricultural and Biological Sciences (miscellaneous), Biochemistry, Genetics and Molecular Biology (miscellaneous), Materials Science (miscellaneous), Medicine (miscellaneous), Physics and Astronomy (miscellaneous), Space and Planetary Science},

pubstate = {published},

tppubtype = {article}

}

@article{Goff2024,

title = {Genomic and environmental controls on\textit{Castellaniella}biogeography in an anthropogenically disturbed subsurface},

author = {Jennifer L. Goff and Elizabeth G. Szink and Konnor L. Durrence and Lauren M. Lui and Torben N. Nielsen and Jennifer V. Kuehl and Kristopher A. Hunt and John-Marc Chandonia and Jiawen Huang and Michael P. Thorgersen and Farris L. Poole and David A. Stahl and Romy Chakraborty and Adam M. Deutschbauer and Adam P. Arkin and Michael W. W. Adams},

url = {http://biorxiv.org/lookup/doi/10.1101/2024.02.03.578758},

doi = {10.1101/2024.02.03.578758},

year  = {2024},

date = {2024-02-04},

urldate = {2024-02-04},

publisher = {Cold Spring Harbor Laboratory},

abstract = {<jats:title>ABSTRACT</jats:title><jats:p><jats:italic>Castellaniella</jats:italic>species have been isolated from a variety of mixed-waste environments including the nitrate and multiple metal contaminated subsurface at the Oak Ridge Reservation (ORR). Previous studies examining microbial community composition and nitrate removal at ORR during biostimulation efforts reported increased abundances of members of the<jats:italic>Castellaniella</jats:italic>genus concurrent to increased denitrification rates. Thus, we asked how genomic and abiotic factors control the<jats:italic>Castellaniella</jats:italic>biogeography at the site to understand how these factors may influence nitrate transformation in an anthropogenically impacted setting. ORR<jats:italic>Castellaniella</jats:italic>strains showed a higher degree of genetic diversification than those originating from non-ORR sites, which we attribute to the multitude of extreme stressors faced in the ORR subsurface. We report the isolation and characterization of several<jats:italic>Castellaniella</jats:italic>strains from the ORR subsurface. Five of these isolates match at 100% identity (at the 16S rRNA gene V4 region) to two<jats:italic>Castellaniella</jats:italic>amplicon sequence variants (ASVs), ASV1 and ASV2, that have persisted in the ORR subsurface for at least two decades. However, ASV2 has consistently higher relative abundance in samples taken from the site and was also the dominant blooming denitrifier population during a prior biostimulation effort. We found that the ASV2 representative strain has greater resistance to mixed metal stress than the ASV1 representative strains. We attribute this resistance, in part, to the large number of unique heavy metal resistance genes identified on a genomic island in the ASV2 representative genome. Additionally, we suggest that the relatively lower fitness of ASV1 may be connected to the loss of the nitrous oxide reductase (<jats:italic>nos</jats:italic>) operon (and associated nitrous oxide reductase activity) due to the insertion at this genomic locus of a mobile genetic element carrying copper resistance genes. This study demonstrates the value of integrating genomic, environmental, and phenotypic data to characterize the biogeography of key microorganisms in contaminated sites.</jats:p>},

howpublished = {bioRxiv},

keywords = {enigma},

pubstate = {published},

tppubtype = {article}

}

@article{Ning2024,

title = {Environmental stress mediates groundwater microbial community assembly},

author = {Daliang Ning and Yajiao Wang and Yupeng Fan and Jianjun Wang and Joy D. Van Nostrand and Liyou Wu and Ping Zhang and Daniel J. Curtis and Renmao Tian and Lauren Lui and Terry C. Hazen and Eric J. Alm and Matthew W. Fields and Farris Poole and Michael W. W. Adams and Romy Chakraborty and David A. Stahl and Paul D. Adams and Adam P. Arkin and Zhili He and Jizhong Zhou},

doi = {10.1038/s41564-023-01573-x},

issn = {2058-5276},

year  = {2024},

date = {2024-02-00},

urldate = {2024-02-00},

journal = {Nat Microbiol},

volume = {9},

number = {2},

pages = {490--501},

publisher = {Springer Science and Business Media LLC},

keywords = {Applied Microbiology and Biotechnology, Cell Biology, enigma, Genetics, Immunology, Microbiology, Microbiology (medical)},

pubstate = {published},

tppubtype = {article}

}

@article{Sander2023b,

title = {Eliminating genes for a two‐component system increases PHB productivity in \textit{Cupriavidus basilensis} 4G11 under PHB suppressing, nonstress conditions},

author = {Kyle Sander and Anthony J. Abel and Skyler Friedline and William Sharpless and Jeffrey Skerker and Adam Deutschbauer and Douglas S. Clark and Adam P. Arkin},

doi = {10.1002/bit.28532},

issn = {1097-0290},

year  = {2024},

date = {2024-01-00},

urldate = {2024-01-00},

journal = {Biotech & Bioengineering},

volume = {121},

number = {1},

pages = {139--156},

publisher = {Wiley},

abstract = {<jats:title>Abstract</jats:title><jats:p>Species of bacteria from the genus <jats:italic>Cupriavidus</jats:italic> are known, in part, for their ability to produce high amounts of poly‐hydroxybutyrate (PHB) making them attractive candidates for bioplastic production. The native synthesis of PHB occurs during periods of metabolic stress, and the process regulating the initiation of PHB accumulation in these organisms is not fully understood. Screening an RB‐TnSeq transposon library of <jats:italic>Cupriavidus basilensis</jats:italic> 4G11 allowed us to identify two genes of an apparent, uncharacterized two‐component system, which when omitted from the genome enable increased PHB productivity in balanced, nonstress growth conditions. We observe average increases in PHB productivity of 56% and 41% relative to the wildtype parent strain upon deleting each gene individually from the genome. The increased PHB phenotype disappears, however, in nitrogen‐free unbalanced growth conditions suggesting the phenotype is specific to fast‐growing, replete, nonstress growth. Bioproduction modeling suggests this phenotype could be due to a decreased reliance on metabolic stress induced by nitrogen limitation to initiate PHB production in the mutant strains. Due to uncertainty in the two‐component system's input signal and regulon, the mechanism by which these genes impart this phenotype remains unclear. Such strains may allow for the use of single‐stage, continuous bioreactor systems, which are far simpler than many PHB bioproduction schemes used previously, given a similar product yield to batch systems in such a configuration. Bioproductivity modeling suggests that omitting this regulation in the cells may increase PHB productivity up to 24% relative to the wildtype organism when using single‐stage continuous systems. This work expands our understanding of the regulation of PHB accumulation in <jats:italic>Cupriavidus</jats:italic>, in particular the initiation of this process upon transition into unbalanced growth regimes.</jats:p>},

keywords = {Applied Microbiology and Biotechnology, Bioengineering, Biotechnology, cubes},

pubstate = {published},

tppubtype = {article}

}

@article{Park2023b,

title = {A bacterial sensor taxonomy across earth ecosystems for machine learning applications},

author = {Helen Park and Marcin P. Joachimiak and Sean P. Jungbluth and Ziming Yang and William J. Riehl and R. Shane Canon and Adam P. Arkin and Paramvir S. Dehal},

editor = {Babak Momeni},

doi = {10.1128/msystems.00026-23},

issn = {2379-5077},

year  = {2023},

date = {2023-12-11},

urldate = {2023-12-11},

journal = {mSystems},

publisher = {American Society for Microbiology},

abstract = {<jats:title>ABSTRACT</jats:title>

          <jats:p>

            Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and “transduce” signals to adjust internal processes. We hypothesized that an ecosystem’s unique stimuli leave a sensor “fingerprint,” able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from

            <jats:italic>Host-associated</jats:italic>

            ,

            <jats:italic>Environmental</jats:italic>

            , and

            <jats:italic>Engineered</jats:italic>

            ecosystems across the globe. We extracted and clustered the collection’s nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient’s disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.

          </jats:p>

          <jats:sec>

            <jats:title>IMPORTANCE</jats:title>

            <jats:p>Microbes infect, colonize, and proliferate due to their ability to sense and respond quickly to their surroundings. In this research, we extract the sensory proteins from a diverse range of environmental, engineered, and host-associated metagenomes. We trained machine learning classifiers using sensors as features such that it is possible to predict the ecosystem for a metagenome from its sensor profile. We use the optimized model’s feature importance to identify the most impactful and predictive sensors in different environments. We next use the sensor profile from human gut metagenomes to classify their disease states and explore which sensors can explain differences between diseases. The sensors most predictive of environmental labels here, most of which correspond to uncharacterized proteins, are a useful starting point for the discovery of important environment signals and the development of possible diagnostic interventions.</jats:p>

          </jats:sec>},

keywords = {Behavior and Systematics, Biochemistry, Computer Science Applications, Ecology, Evolution, Genetics, kbase, Microbiology, Modeling and Simulation, Molecular Biology, Physiology},

pubstate = {published},

tppubtype = {article}

}

@article{Bernstein2023,

title = {Evaluating \textit{E. coli} genome‐scale metabolic model accuracy with high‐throughput mutant fitness data},

author = {David B Bernstein and Batu Akkas and Morgan N Price and Adam P Arkin},

doi = {10.15252/msb.202311566},

issn = {1744-4292},

year  = {2023},

date = {2023-12-06},

journal = {Molecular Systems Biology},

volume = {19},

number = {12},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractThe Escherichia coli genome‐scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision–recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene‐protein‐reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high‐throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.},

keywords = {Applied Mathematics, Computational Theory and Mathematics, General Agricultural and Biological Sciences, General Biochemistry, General Immunology and Microbiology, Genetics and Molecular Biology, Information Systems},

pubstate = {published},

tppubtype = {article}

}

@article{Piya2023,

title = {Systematic and scalable genome-wide essentiality mapping to identify nonessential genes in phages},

author = {Denish Piya and Nicholas Nolan and Madeline L. Moore and Luis A. Ramirez Hernandez and Brady F. Cress and Ry Young and Adam P. Arkin and Vivek K. Mutalik},

editor = {Paula Jauregui},

doi = {10.1371/journal.pbio.3002416},

issn = {1545-7885},

year  = {2023},

date = {2023-12-04},

journal = {PLoS Biol},

volume = {21},

number = {12},

publisher = {Public Library of Science (PLoS)},

abstract = {Phages are one of the key ecological drivers of microbial community dynamics, function, and evolution. Despite their importance in bacterial ecology and evolutionary processes, phage genes are poorly characterized, hampering their usage in a variety of biotechnological applications. Methods to characterize such genes, even those critical to the phage life cycle, are labor intensive and are generally phage specific. Here, we develop a systematic gene essentiality mapping method scalable to new phage–host combinations that facilitate the identification of nonessential genes. As a proof of concept, we use an arrayed genome-wide CRISPR interference (CRISPRi) assay to map gene essentiality landscape in the canonical coliphages λ and P1. Results from a single panel of CRISPRi probes largely recapitulate the essential gene roster determined from decades of genetic analysis for lambda and provide new insights into essential and nonessential loci in P1. We present evidence of how CRISPRi polarity can lead to false positive gene essentiality assignments and recommend caution towards interpreting CRISPRi data on gene essentiality when applied to less studied phages. Finally, we show that we can engineer phages by inserting DNA barcodes into newly identified inessential regions, which will empower processes of identification, quantification, and tracking of phages in diverse applications.},

keywords = {General Agricultural and Biological Sciences, General Biochemistry, General Immunology and Microbiology, General Neuroscience, Genetics and Molecular Biology},

pubstate = {published},

tppubtype = {article}

}

@article{Thorgersen2023,

title = {Mixed nitrate and metal contamination influences operational speciation of toxic and essential elements},

author = {Michael P. Thorgersen and Jennifer L. Goff and Farris L. Poole and Kathleen F. Walker and Andrew D. Putt and Lauren M. Lui and Terry C. Hazen and Adam P. Arkin and Michael W.W. Adams},

doi = {10.1016/j.envpol.2023.122674},

issn = {0269-7491},

year  = {2023},

date = {2023-12-00},

journal = {Environmental Pollution},

volume = {338},

publisher = {Elsevier BV},

keywords = {General Medicine, Health, Pollution, Toxicology, Toxicology and Mutagenesis},

pubstate = {published},

tppubtype = {article}

}

@article{Carlson2023,

title = {Geochemical constraints on bacteriophage infectivity in terrestrial environments},

author = {Hans K. Carlson and Denish Piya and Madeline L. Moore and Roniya T. Magar and Nathalie H. Elisabeth and Adam M. Deutschbauer and Adam P. Arkin and Vivek K. Mutalik},

doi = {10.1038/s43705-023-00297-7},

issn = {2730-6151},

year  = {2023},

date = {2023-12-00},

journal = {ISME COMMUN.},

volume = {3},

number = {1},

publisher = {Oxford University Press (OUP)},

abstract = {AbstractLytic phages can be potent and selective inhibitors of microbial growth and can have profound impacts on microbiome composition and function. However, there is uncertainty about the biogeochemical conditions under which phage predation modulates microbial ecosystem function, particularly in terrestrial systems. Ionic strength is critical for infection of bacteria by many phages, but quantitative data is limited on the ion thresholds for phage infection that can be compared with environmental ion concentrations. Similarly, while carbon composition varies in the environment, we do not know how this variability influences the impact of phage predation on microbiome function. Here, we measured the half-maximal effective concentrations (EC50) of 80 different inorganic ions for the infection of E. coli with two canonical dsDNA and ssRNA phages, T4 and MS2, respectively. Many alkaline earth metals and alkali metals enabled lytic infection but the ionic strength thresholds varied for different ions between phages. Additionally, using a freshwater nitrate-reducing microbiome, we found that the ability of lytic phages to influence nitrate reduction end-products depended upon the carbon source as well as ionic strength. For all phage:host pairs, the ion EC50s for phage infection exceeded the ion concentrations found in many terrestrial freshwater systems. Thus, our findings support a model where phages most influence terrestrial microbial functional ecology in hot spots and hot moments such as metazoan guts, drought influenced soils, or biofilms where ion concentration is locally or transiently elevated and nutrients are available to support the growth of specific phage hosts.},

keywords = {Automotive Engineering},

pubstate = {published},

tppubtype = {article}

}

@article{Averesch2023,

title = {Microbial biomanufacturing for space-exploration—what to take and when to make},

author = {Nils J. H. Averesch and Aaron J. Berliner and Shannon N. Nangle and Spencer Zezulka and Gretchen L. Vengerova and Davian Ho and Cameran A. Casale and Benjamin A. E. Lehner and Jessica E. Snyder and Kevin B. Clark and Lewis R. Dartnell and Craig S. Criddle and Adam P. Arkin},

doi = {10.1038/s41467-023-37910-1},

issn = {2041-1723},

year  = {2023},

date = {2023-12-00},

journal = {Nat Commun},

volume = {14},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractAs renewed interest in human space-exploration intensifies, a coherent and modernized strategy for mission design and planning has become increasingly crucial. Biotechnology has emerged as a promising approach to increase resilience, flexibility, and efficiency of missions, by virtue of its ability to effectively utilize in situ resources and reclaim resources from waste streams. Here we outline four primary mission-classes on Moon and Mars that drive a staged and accretive biomanufacturing strategy. Each class requires a unique approach to integrate biomanufacturing into the existing mission-architecture and so faces unique challenges in technology development. These challenges stem directly from the resources available in a given mission-class—the degree to which feedstocks are derived from cargo and in situ resources—and the degree to which loop-closure is necessary. As mission duration and distance from Earth increase, the benefits of specialized, sustainable biomanufacturing processes also increase. Consequentially, we define specific design-scenarios and quantify the usefulness of in-space biomanufacturing, to guide techno-economics of space-missions. Especially materials emerged as a potentially pivotal target for biomanufacturing with large impact on up-mass cost. Subsequently, we outline the processes needed for development, testing, and deployment of requisite technologies. As space-related technology development often does, these advancements are likely to have profound implications for the creation of a resilient circular bioeconomy on Earth.},

keywords = {cubes, General Biochemistry, General Chemistry, General Physics and Astronomy, Genetics and Molecular Biology, Multidisciplinary},

pubstate = {published},

tppubtype = {article}

}

@article{Ascensao2023,

title = {Quantifying the local adaptive landscape of a nascent bacterial community},

author = {Joao A. Ascensao and Kelly M. Wetmore and Benjamin H. Good and Adam P. Arkin and Oskar Hallatschek},

doi = {10.1038/s41467-022-35677-5},

issn = {2041-1723},

year  = {2023},

date = {2023-12-00},

journal = {Nat Commun},

volume = {14},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractThe fitness effects of all possible mutations available to an organism largely shape the dynamics of evolutionary adaptation. Yet, whether and how this adaptive landscape changes over evolutionary times, especially upon ecological diversification and changes in community composition, remains poorly understood. We sought to fill this gap by analyzing a stable community of two closely related ecotypes (“L” and “S”) shortly after they emerged within the E. coli Long-Term Evolution Experiment (LTEE). We engineered genome-wide barcoded transposon libraries to measure the invasion fitness effects of all possible gene knockouts in the coexisting strains as well as their ancestor, for many different, ecologically relevant conditions. We find consistent statistical patterns of fitness effect variation across both genetic background and community composition, despite the idiosyncratic behavior of individual knockouts. Additionally, fitness effects are correlated with evolutionary outcomes for a number of conditions, possibly revealing shifting patterns of adaptation. Together, our results reveal how ecological and epistatic effects combine to shape the adaptive landscape in a nascent ecological community.},

keywords = {General Biochemistry, General Chemistry, General Physics and Astronomy, Genetics and Molecular Biology, Multidisciplinary},

pubstate = {published},

tppubtype = {article}

}

@article{Adams2023,

title = {Engineering osmolysis susceptibility in Cupriavidus necator and Escherichia coli for recovery of intracellular products},

author = {Jeremy David Adams and Kyle B. Sander and Craig S. Criddle and Adam P. Arkin and Douglas S. Clark},

doi = {10.1186/s12934-023-02064-8},

issn = {1475-2859},

year  = {2023},

date = {2023-12-00},

journal = {Microb Cell Fact},

volume = {22},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {Abstract

                Background

                Intracellular biomacromolecules, such as industrial enzymes and biopolymers, represent an important class of bio-derived products obtained from bacterial hosts. A common key step in the downstream separation of these biomolecules is lysis of the bacterial cell wall to effect release of cytoplasmic contents. Cell lysis is typically achieved either through mechanical disruption or reagent-based methods, which introduce issues of energy demand, material needs, high costs, and scaling problems. Osmolysis, a cell lysis method that relies on hypoosmotic downshock upon resuspension of cells in distilled water, has been applied for bioseparation of intracellular products from extreme halophiles and mammalian cells. However, most industrial bacterial strains are non-halotolerant and relatively resistant to hypoosmotic cell lysis. 

              

                Results

                To overcome this limitation, we developed two strategies to increase the susceptibility of non-halotolerant hosts to osmolysis using Cupriavidus necator, a strain often used in electromicrobial production, as a prototypical strain. In one strategy, C. necator was evolved to increase its halotolerance from 1.5% to 3.25% (w/v) NaCl through adaptive laboratory evolution, and genes potentially responsible for this phenotypic change were identified by whole genome sequencing. The evolved halotolerant strain experienced an osmolytic efficiency of 47% in distilled water following growth in 3% (w/v) NaCl. In a second strategy, the cells were made susceptible to osmolysis by knocking out the large-conductance mechanosensitive channel (mscL) gene in C. necator. When these strategies were combined by knocking out the mscL gene from the evolved halotolerant strain, greater than 90% osmolytic efficiency was observed upon osmotic downshock. A modified version of this strategy was applied to E. coli BL21 by deleting the mscL and mscS (small-conductance mechanosensitive channel) genes. When grown in medium with 4% NaCl and subsequently resuspended in distilled water, this engineered strain experienced 75% cell lysis, although decreases in cell growth rate due to higher salt concentrations were observed.

              

                Conclusions

                Our strategy is shown to be a simple and effective way to lyse cells for the purification of intracellular biomacromolecules and may be applicable in many bacteria used for bioproduction.

              },

keywords = {Applied Microbiology and Biotechnology, Bioengineering, Biotechnology, cubes},

pubstate = {published},

tppubtype = {article}

}

@article{Wu2023,

title = {Distinct Depth-Discrete Profiles of Microbial Communities and Geochemical Insights in the Subsurface Critical Zone},

author = {Xiaoqin Wu and Sara Gushgari-Doyle and Lauren M. Lui and Andrew J. Hendrickson and Yina Liu and Sindhu Jagadamma and Torben N. Nielsen and Nicholas B. Justice and Tuesday Simmons and Nancy J. Hess and Dominique C. Joyner and Terry C. Hazen and Adam P. Arkin and Romy Chakraborty},

editor = {Jennifer B. Glass},

doi = {10.1128/aem.00500-23},

issn = {1098-5336},

year  = {2023},

date = {2023-06-28},

journal = {Appl Environ Microbiol},

volume = {89},

number = {6},

publisher = {American Society for Microbiology},

abstract = {In this study, we explored the links between geochemical parameters, microbial community structure and metabolic potential across the depth of sediment, including the shallow subsurface, vadose zone, capillary fringe, and saturated zone. Our results revealed that microbes in the terrestrial subsurface can be highly localized, with communities rarely being interconnected along the depth.},

keywords = {Applied Microbiology and Biotechnology, Biotechnology, Ecology, Food Science},

pubstate = {published},

tppubtype = {article}

}

@article{Adler2023,

title = {Multicopy suppressor screens reveal convergent evolution of single-gene lysis proteins},

author = {Benjamin A. Adler and Karthik Chamakura and Heloise Carion and Jonathan Krog and Adam M. Deutschbauer and Ry Young and Vivek K. Mutalik and Adam P. Arkin},

doi = {10.1038/s41589-023-01269-7},

issn = {1552-4469},

year  = {2023},

date = {2023-06-00},

journal = {Nat Chem Biol},

volume = {19},

number = {6},

pages = {759--766},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractSingle-strand RNA (ssRNA) Fiersviridae phages cause host lysis with a product of single gene (sgl for single-gene lysis; product Sgl) that induces autolysis. Many different Sgls have been discovered, but the molecular targets of only a few have been identified. In this study, we used a high-throughput genetic screen to uncover genome-wide host suppressors of diverse Sgls. In addition to validating known molecular mechanisms, we discovered that the Sgl of PP7, an ssRNA phage of Pseudomonas aeruginosa, targets MurJ, the flippase responsible for lipid II export, previously shown to be the target of the Sgl of coliphage M. These two Sgls, which are unrelated and predicted to have opposite membrane topology, thus represent a case of convergent evolution. We extended the genetic screens to other uncharacterized Sgls and uncovered a common set of multicopy suppressors, suggesting that these Sgls act by the same or similar mechanism.},

keywords = {Cell Biology, Molecular Biology},

pubstate = {published},

tppubtype = {article}

}

@article{Trotter2023,

title = {Large-scale genetic characterization of the model sulfate-reducing bacterium, Desulfovibrio vulgaris Hildenborough},

author = {Valentine V. Trotter and Maxim Shatsky and Morgan N. Price and Thomas R. Juba and Grant M. Zane and Kara B. De León and Erica L.-W. Majumder and Qin Gui and Rida Ali and Kelly M. Wetmore and Jennifer V. Kuehl and Adam P. Arkin and Judy D. Wall and Adam M. Deutschbauer and John-Marc Chandonia and Gareth P. Butland},

doi = {10.3389/fmicb.2023.1095191},

issn = {1664-302X},

year  = {2023},

date = {2023-03-31},

journal = {Front. Microbiol.},

volume = {14},

publisher = {Frontiers Media SA},

abstract = {Sulfate-reducing bacteria (SRB) are obligate anaerobes that can couple their growth to the reduction of sulfate. Despite the importance of SRB to global nutrient cycles and their damage to the petroleum industry, our molecular understanding of their physiology remains limited. To systematically provide new insights into SRB biology, we generated a randomly barcoded transposon mutant library in the model SRB Desulfovibrio vulgaris Hildenborough (DvH) and used this genome-wide resource to assay the importance of its genes under a range of metabolic and stress conditions. In addition to defining the essential gene set of DvH, we identified a conditional phenotype for 1,137 non-essential genes. Through examination of these conditional phenotypes, we were able to make a number of novel insights into our molecular understanding of DvH, including how this bacterium synthesizes vitamins. For example, we identified DVU0867 as an atypical L-aspartate decarboxylase required for the synthesis of pantothenic acid, provided the first experimental evidence that biotin synthesis in DvH occurs via a specialized acyl carrier protein and without methyl esters, and demonstrated that the uncharacterized dehydrogenase DVU0826:DVU0827 is necessary for the synthesis of pyridoxal phosphate. In addition, we used the mutant fitness data to identify genes involved in the assimilation of diverse nitrogen sources and gained insights into the mechanism of inhibition of chlorate and molybdate. Our large-scale fitness dataset and RB-TnSeq mutant library are community-wide resources that can be used to generate further testable hypotheses into the gene functions of this environmentally and industrially important group of bacteria.},

keywords = {Microbiology, Microbiology (medical)},

pubstate = {published},

tppubtype = {article}

}

@article{Makrygiorgos2023,

title = {Data‐driven flow‐map models for data‐efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems},

author = {Georgios Makrygiorgos and Aaron J. Berliner and Fengzhe Shi and Douglas S. Clark and Adam P. Arkin and Ali Mesbah},

doi = {10.1002/bit.28295},

issn = {1097-0290},

year  = {2023},

date = {2023-03-00},

journal = {Biotech & Bioengineering},

volume = {120},

number = {3},

pages = {803--818},

publisher = {Wiley},

abstract = {AbstractComputational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow‐map (de)compositions to present a framework that can address both of these challenges via learning data‐driven models useful for capturing the dynamical behavior of biochemical systems. Data‐driven flow‐map models seek to directly learn the integration operators of the governing differential equations in a black‐box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast‐to‐evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long‐term system dynamics via experimental observations. We present a data‐efficient approach to data‐driven flow‐map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data‐driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.},

keywords = {Applied Microbiology and Biotechnology, Bioengineering, Biotechnology, cubes},

pubstate = {published},

tppubtype = {article}

}

@article{Chivian2022,

title = {Metagenome-assembled genome extraction and analysis from microbiomes using KBase},

author = {Dylan Chivian and Sean P. Jungbluth and Paramvir S. Dehal and Elisha M. Wood-Charlson and Richard S. Canon and Benjamin H. Allen and Mikayla M. Clark and Tianhao Gu and Miriam L. Land and Gavin A. Price and William J. Riehl and Michael W. Sneddon and Roman Sutormin and Qizhi Zhang and Robert W. Cottingham and Chris S. Henry and Adam P. Arkin},

doi = {10.1038/s41596-022-00747-x},

issn = {1750-2799},

year  = {2023},

date = {2023-01-00},

journal = {Nat Protoc},

volume = {18},

number = {1},

pages = {208--238},

publisher = {Springer Science and Business Media LLC},

keywords = {General Biochemistry, Genetics and Molecular Biology},

pubstate = {published},

tppubtype = {article}

}

@article{Price2022,

title = {Interactive Analysis of Functional Residues in Protein Families},

author = {Morgan N. Price and Adam P. Arkin},

editor = {Marnix Medema},

doi = {10.1128/msystems.00705-22},

issn = {2379-5077},

year  = {2022},

date = {2022-12-20},

journal = {mSystems},

volume = {7},

number = {6},

publisher = {American Society for Microbiology},

abstract = {For most microbes of interest, a genome sequence is available, but the function of its proteins is not known. Instead, proteins' functions are predicted from their similarity to other protein sequences.},

keywords = {Behavior and Systematics, Biochemistry, Computer Science Applications, Ecology, Evolution, Genetics, Microbiology, Modeling and Simulation, Molecular Biology, Physiology},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Data-driven flow-map models for data-efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems},

author = {Makrygiorgos, G.

Berliner, A. J.

Shi, F.

Clark, D. S.

Arkin, A. P.

Mesbah, A.},

doi = {10.1002/bit.28295},

issn = {1097-0290 },

year  = {2022},

date = {2022-12-02},

urldate = {2022-12-02},

journal = {Biotechnol Bioeng},

volume = {120},

issue = {3},

pages = {803-818},

abstract = {Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-to-evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.},

keywords = {cubes},

pubstate = {published},

tppubtype = {article}

}