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11.
Park, Helen; Joachimiak, Marcin P.; Jungbluth, Sean P.; Yang, Ziming; Riehl, William J.; Canon, R. Shane; Arkin, Adam P.; Dehal, Paramvir S.
A bacterial sensor taxonomy across earth ecosystems for machine learning applications Journal Article
In: mSystems, 2023, ISSN: 2379-5077.
Abstract | Links | BibTeX | Tags: Behavior and Systematics, Biochemistry, Computer Science Applications, Ecology, Evolution, Genetics, kbase, Microbiology, Modeling and Simulation, Molecular Biology, Physiology
@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}
}
<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>
<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>
10.
Wu, Xiaoqin; Gushgari-Doyle, Sara; Lui, Lauren M.; Hendrickson, Andrew J.; Liu, Yina; Jagadamma, Sindhu; Nielsen, Torben N.; Justice, Nicholas B.; Simmons, Tuesday; Hess, Nancy J.; Joyner, Dominique C.; Hazen, Terry C.; Arkin, Adam P.; Chakraborty, Romy
Distinct Depth-Discrete Profiles of Microbial Communities and Geochemical Insights in the Subsurface Critical Zone Journal Article
In: Appl Environ Microbiol, vol. 89, no. 6, 2023, ISSN: 1098-5336.
Abstract | Links | BibTeX | Tags: Applied Microbiology and Biotechnology, Biotechnology, Ecology, Food Science
@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}
}
9.
Price, Morgan N.; Arkin, Adam P.
Interactive Analysis of Functional Residues in Protein Families Journal Article
In: mSystems, vol. 7, no. 6, 2022, ISSN: 2379-5077.
Abstract | Links | BibTeX | Tags: Behavior and Systematics, Biochemistry, Computer Science Applications, Ecology, Evolution, Genetics, Microbiology, Modeling and Simulation, Molecular Biology, Physiology
@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}
}
8.
Goff, Jennifer L.; Szink, Elizabeth G.; Thorgersen, Michael P.; Putt, Andrew D.; Fan, Yupeng; Lui, Lauren M.; Nielsen, Torben N.; Hunt, Kristopher A.; Michael, Jonathan P.; Wang, Yajiao; Ning, Daliang; Fu, Ying; Nostrand, Joy D. Van; Poole, Farris L.; Chandonia, John‐Marc; Hazen, Terry C.; Stahl, David A.; Zhou, Jizhong; Arkin, Adam P.; Adams, Michael W. W.
In: Environmental Microbiology, vol. 24, no. 11, pp. 5546–5560, 2022, ISSN: 1462-2920.
Abstract | Links | BibTeX | Tags: Behavior and Systematics, Ecology, Evolution, Microbiology
@article{Goff2022b,
title = {Ecophysiological and genomic analyses of a representative isolate of highly abundant \textit{Bacillus cereus} strains in contaminated subsurface sediments},
author = {Jennifer L. Goff and Elizabeth G. Szink and Michael P. Thorgersen and Andrew D. Putt and Yupeng Fan and Lauren M. Lui and Torben N. Nielsen and Kristopher A. Hunt and Jonathan P. Michael and Yajiao Wang and Daliang Ning and Ying Fu and Joy D. Van Nostrand and Farris L. Poole and John‐Marc Chandonia and Terry C. Hazen and David A. Stahl and Jizhong Zhou and Adam P. Arkin and Michael W. W. Adams},
doi = {10.1111/1462-2920.16173},
issn = {1462-2920},
year = {2022},
date = {2022-11-00},
journal = {Environmental Microbiology},
volume = {24},
number = {11},
pages = {5546--5560},
publisher = {Wiley},
abstract = {Abstract Bacillus cereus strain CPT56D‐587‐MTF (CPTF) was isolated from the highly contaminated Oak Ridge Reservation (ORR) subsurface. This site is contaminated with high levels of nitric acid and multiple heavy metals. Amplicon sequencing of the 16S rRNA genes (V4 region) in sediment from this area revealed an amplicon sequence variant (ASV) with 100% identity to the CPTF 16S rRNA sequence. Notably, this CPTF‐matching ASV had the highest relative abundance in this community survey, with a median relative abundance of 3.77% and comprised 20%–40% of reads in some samples. Pangenomic analysis revealed that strain CPTF has expanded genomic content compared to other B. cereus species—largely due to plasmid acquisition and expansion of transposable elements. This suggests that these features are important for rapid adaptation to native environmental stressors. We connected genotype to phenotype in the context of the unique geochemistry of the site. These analyses revealed that certain genes (e.g. nitrate reductase, heavy metal efflux pumps) that allow this strain to successfully occupy the geochemically heterogenous microniches of its native site are characteristic of the B. cereus species while others such as acid tolerance are mobile genetic element associated and are generally unique to strain CPTF.
keywords = {Behavior and Systematics, Ecology, Evolution, Microbiology},
pubstate = {published},
tppubtype = {article}
}
7.
Wood-Charlson, Elisha M.; Crockett, Zachary; Erdmann, Chris; Arkin, Adam P.; Robinson, Carly B.
Ten simple rules for getting and giving credit for data Journal Article
In: PLoS Comput Biol, vol. 18, no. 9, 2022, ISSN: 1553-7358.
Links | BibTeX | Tags: Behavior and Systematics, Cellular and Molecular Neuroscience, Computational Theory and Mathematics, Ecology, Evolution, Genetics, Modeling and Simulation, Molecular Biology
@article{Wood-Charlson2022,
title = {Ten simple rules for getting and giving credit for data},
author = {Elisha M. Wood-Charlson and Zachary Crockett and Chris Erdmann and Adam P. Arkin and Carly B. Robinson},
editor = {Russell Schwartz},
doi = {10.1371/journal.pcbi.1010476},
issn = {1553-7358},
year = {2022},
date = {2022-09-29},
journal = {PLoS Comput Biol},
volume = {18},
number = {9},
publisher = {Public Library of Science (PLoS)},
keywords = {Behavior and Systematics, Cellular and Molecular Neuroscience, Computational Theory and Mathematics, Ecology, Evolution, Genetics, Modeling and Simulation, Molecular Biology},
pubstate = {published},
tppubtype = {article}
}
6.
Tao, Xuanyu; Zhou, Aifen; Kempher, Megan L.; Liu, Jiantao; Peng, Mu; Li, Yuan; Michael, Jonathan P.; Chakraborty, Romy; Deutschbauer, Adam M.; Arkin, Adam P.; Zhou, Jizhong
Development of a Markerless Deletion Mutagenesis System in Nitrate-Reducing Bacterium Rhodanobacter denitrificans Journal Article
In: Appl Environ Microbiol, vol. 88, no. 14, 2022, ISSN: 1098-5336.
Abstract | Links | BibTeX | Tags: Applied Microbiology and Biotechnology, Biotechnology, Ecology, Food Science
@article{Tao2022,
title = {Development of a Markerless Deletion Mutagenesis System in Nitrate-Reducing Bacterium Rhodanobacter denitrificans},
author = {Xuanyu Tao and Aifen Zhou and Megan L. Kempher and Jiantao Liu and Mu Peng and Yuan Li and Jonathan P. Michael and Romy Chakraborty and Adam M. Deutschbauer and Adam P. Arkin and Jizhong Zhou},
editor = {Arpita Bose},
doi = {10.1128/aem.00401-22},
issn = {1098-5336},
year = {2022},
date = {2022-07-26},
journal = {Appl Environ Microbiol},
volume = {88},
number = {14},
publisher = {American Society for Microbiology},
abstract = {
Rhodanobacter denitrificans
is capable of denitrification and is also resistant to toxic heavy metals and low pH. Accordingly, the presence of
Rhodanobacter
species at a particular environmental site is considered an indicator of nitrate and uranium contamination.
keywords = {Applied Microbiology and Biotechnology, Biotechnology, Ecology, Food Science},
pubstate = {published},
tppubtype = {article}
}
is capable of denitrification and is also resistant to toxic heavy metals and low pH. Accordingly, the presence of
species at a particular environmental site is considered an indicator of nitrate and uranium contamination.
5.
Price, Morgan N.; Deutschbauer, Adam M.; Arkin, Adam P.
Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics Journal Article
In: PLoS Genet, vol. 18, no. 4, 2022, ISSN: 1553-7404.
Abstract | Links | BibTeX | Tags: Behavior and Systematics, Cancer Research, Ecology, Evolution, Genetics, Genetics (clinical), Molecular Biology
@article{Price2022b,
title = {Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics},
author = {Morgan N. Price and Adam M. Deutschbauer and Adam P. Arkin},
editor = {Bernhard O. Palsson},
doi = {10.1371/journal.pgen.1010156},
issn = {1553-7404},
year = {2022},
date = {2022-04-13},
journal = {PLoS Genet},
volume = {18},
number = {4},
publisher = {Public Library of Science (PLoS)},
abstract = {To discover novel catabolic enzymes and transporters, we combined high-throughput genetic data from 29 bacteria with an automated tool to find gaps in their catabolic pathways. GapMind for carbon sources automatically annotates the uptake and catabolism of 62 compounds in bacterial and archaeal genomes. For the compounds that are utilized by the 29 bacteria, we systematically examined the gaps in GapMind’s predicted pathways, and we used the mutant fitness data to find additional genes that were involved in their utilization. We identified novel pathways or enzymes for the utilization of glucosamine, citrulline, myo-inositol, lactose, and phenylacetate, and we annotated 299 diverged enzymes and transporters. We also curated 125 proteins from published reports. For the 29 bacteria with genetic data, GapMind finds high-confidence paths for 85% of utilized carbon sources. In diverse bacteria and archaea, 38% of utilized carbon sources have high-confidence paths, which was improved from 27% by incorporating the fitness-based annotations and our curation. GapMind for carbon sources is available as a web server (http://papers.genomics.lbl.gov/carbon ) and takes just 30 seconds for the typical genome. },
keywords = {Behavior and Systematics, Cancer Research, Ecology, Evolution, Genetics, Genetics (clinical), Molecular Biology},
pubstate = {published},
tppubtype = {article}
}
4.
Peng, Mu; Wang, Dongyu; Lui, Lauren M.; Nielsen, Torben; Tian, Renmao; Kempher, Megan L.; Tao, Xuanyu; Pan, Chongle; Chakraborty, Romy; Deutschbauer, Adam M.; Thorgersen, Michael P.; Adams, Michael W. W.; Fields, Matthew W.; Hazen, Terry C.; Arkin, Adam P.; Zhou, Aifen; Zhou, Jizhong
Genomic Features and Pervasive Negative Selection in Rhodanobacter Strains Isolated from Nitrate and Heavy Metal Contaminated Aquifer Journal Article
In: Microbiol Spectr, vol. 10, no. 1, 2022, ISSN: 2165-0497.
Abstract | Links | BibTeX | Tags: Cell Biology, Ecology, General Immunology and Microbiology, Genetics, Infectious Diseases, Microbiology (medical), Physiology
@article{Peng2022,
title = {Genomic Features and Pervasive Negative Selection in
\textit{Rhodanobacter}
Strains Isolated from Nitrate and Heavy Metal Contaminated Aquifer},
author = {Mu Peng and Dongyu Wang and Lauren M. Lui and Torben Nielsen and Renmao Tian and Megan L. Kempher and Xuanyu Tao and Chongle Pan and Romy Chakraborty and Adam M. Deutschbauer and Michael P. Thorgersen and Michael W. W. Adams and Matthew W. Fields and Terry C. Hazen and Adam P. Arkin and Aifen Zhou and Jizhong Zhou},
editor = {Kristen M. DeAngelis},
doi = {10.1128/spectrum.02591-21},
issn = {2165-0497},
year = {2022},
date = {2022-02-23},
journal = {Microbiol Spectr},
volume = {10},
number = {1},
publisher = {American Society for Microbiology},
abstract = {
Despite the dominance of
Rhodanobacter
species in the subsurface of the contaminated Oak Ridge Reservation (ORR) site, very little is known about the mechanisms underlying their adaptions to the various stressors present at ORR. Recently, multiple
Rhodanobacter
strains have been isolated from the ORR groundwater samples from several wells with varying geochemical properties.
keywords = {Cell Biology, Ecology, General Immunology and Microbiology, Genetics, Infectious Diseases, Microbiology (medical), Physiology},
pubstate = {published},
tppubtype = {article}
}
Despite the dominance of
species in the subsurface of the contaminated Oak Ridge Reservation (ORR) site, very little is known about the mechanisms underlying their adaptions to the various stressors present at ORR. Recently, multiple
strains have been isolated from the ORR groundwater samples from several wells with varying geochemical properties.
3.
McCausland, Hayley C.; Wetmore, Kelly M.; Arkin, Adam P.; Komeili, Arash
Global Analysis of Biomineralization Genes in Magnetospirillum magneticum AMB-1 Journal Article
In: mSystems, vol. 7, no. 1, 2022, ISSN: 2379-5077.
Abstract | Links | BibTeX | Tags: Behavior and Systematics, Biochemistry, Computer Science Applications, Ecology, Evolution, Genetics, Microbiology, Modeling and Simulation, Molecular Biology, Physiology
@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}
}
2.
Thorgersen, Michael P.; Xue, Jingchuan; Majumder, Erica L. W.; Trotter, Valentine V.; Ge, Xiaoxuan; Poole, Farris L.; Owens, Trenton K.; Lui, Lauren M.; Nielsen, Torben N.; Arkin, Adam P.; Deutschbauer, Adam M.; Siuzdak, Gary; Adams, Michael W. W.
Deciphering Microbial Metal Toxicity Responses via Random Bar Code Transposon Site Sequencing and Activity-Based Metabolomics Journal Article
In: Appl Environ Microbiol, vol. 87, no. 21, 2021, ISSN: 1098-5336.
Abstract | Links | BibTeX | Tags: Applied Microbiology and Biotechnology, Biotechnology, Ecology, Food Science
@article{Thorgersen2021,
title = {Deciphering Microbial Metal Toxicity Responses via Random Bar Code Transposon Site Sequencing and Activity-Based Metabolomics},
author = {Michael P. Thorgersen and Jingchuan Xue and Erica L. W. Majumder and Valentine V. Trotter and Xiaoxuan Ge and Farris L. Poole and Trenton K. Owens and Lauren M. Lui and Torben N. Nielsen and Adam P. Arkin and Adam M. Deutschbauer and Gary Siuzdak and Michael W. W. Adams},
editor = {Rebecca E. Parales},
doi = {10.1128/aem.01037-21},
issn = {1098-5336},
year = {2021},
date = {2021-10-14},
journal = {Appl Environ Microbiol},
volume = {87},
number = {21},
publisher = {American Society for Microbiology},
abstract = {
Studying microbial interactions with their environment can lead to a deeper understanding of biological molecular mechanisms. In the manuscript, two global techniques, RB-TnSeq and activity metabolomics, were successfully used to probe the interactions between a metal-resistant microorganism,
Pantoea
sp. strain MT58, and metals contaminating a site where the organism can be located.
keywords = {Applied Microbiology and Biotechnology, Biotechnology, Ecology, Food Science},
pubstate = {published},
tppubtype = {article}
}
Studying microbial interactions with their environment can lead to a deeper understanding of biological molecular mechanisms. In the manuscript, two global techniques, RB-TnSeq and activity metabolomics, were successfully used to probe the interactions between a metal-resistant microorganism,
sp. strain MT58, and metals contaminating a site where the organism can be located.
1.
Kothari, Ankita; Roux, Simon; Zhang, Hanqiao; Prieto, Anatori; Soneja, Drishti; Chandonia, John-Marc; Spencer, Sarah; Wu, Xiaoqin; Altenburg, Sara; Fields, Matthew W.; Deutschbauer, Adam M.; Arkin, Adam P.; Alm, Eric J.; Chakraborty, Romy; Mukhopadhyay, Aindrila
Ecogenomics of Groundwater Phages Suggests Niche Differentiation Linked to Specific Environmental Tolerance Journal Article
In: mSystems, vol. 6, no. 3, 2021, ISSN: 2379-5077.
Abstract | Links | BibTeX | Tags: Behavior and Systematics, Biochemistry, biodesign, Computer Science Applications, Ecology, Evolution, Genetics, Microbiology, Modeling and Simulation, Molecular Biology, Physiology
@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}
}
<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>
