@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{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{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{McCausland2022,

title = {Global Analysis of Biomineralization Genes in

            \textit{Magnetospirillum magneticum}

            AMB-1},

author = {Hayley C. McCausland and Kelly M. Wetmore and Adam P. Arkin and Arash Komeili},

editor = {Sarah Glaven},

doi = {10.1128/msystems.01037-21},

issn = {2379-5077},

year  = {2022},

date = {2022-02-22},

journal = {mSystems},

volume = {7},

number = {1},

publisher = {American Society for Microbiology},

abstract = {Magnetotactic bacteria (MTB) are a group of bacteria that can form nano-sized crystals of magnetic minerals. MTB are likely an important part of their ecosystems, because they can account for up to a third of the microbial biomass in an aquatic habitat and consume large amounts of iron, potentially impacting the iron cycle.},

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

pubstate = {published},

tppubtype = {article}

}

@article{Kothari2021,

title = {Ecogenomics of Groundwater Phages Suggests Niche Differentiation Linked to Specific Environmental Tolerance},

author = {Ankita Kothari and Simon Roux and Hanqiao Zhang and Anatori Prieto and Drishti Soneja and John-Marc Chandonia and Sarah Spencer and Xiaoqin Wu and Sara Altenburg and Matthew W. Fields and Adam M. Deutschbauer and Adam P. Arkin and Eric J. Alm and Romy Chakraborty and Aindrila Mukhopadhyay},

editor = {Ileana M. Cristea},

doi = {10.1128/msystems.00537-21},

issn = {2379-5077},

year  = {2021},

date = {2021-06-29},

urldate = {2021-06-29},

journal = {mSystems},

volume = {6},

number = {3},

publisher = {American Society for Microbiology},

abstract = {<jats:p>To our knowledge, this is the first study to identify the bacteriophage distribution in a groundwater ecosystem shedding light on their prevalence and distribution across metal-contaminated and background sites. Our study is uniquely based on selective sequencing of solely the extrachromosomal elements of a microbiome followed by analysis for viral signatures, thus establishing a more focused approach for phage identifications.</jats:p>},

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

pubstate = {published},

tppubtype = {article}

}