Allometry and Ecology of the Bilaterian Gut Microbiome

Sherrill-Mix, Scott; McCormick, Kevin; Lauder, Abigail; Bailey, Aubrey; Zimmerman, Laurie; Li, Yingying; Django, Jean-Bosco N.; Bertolani, Paco; Colin, Christelle; Hart, John; Hart, Térese B.; Georgiev, Alexander V.; Sanz, Crickette; Morgan, David; Atencia, Rebeca; Cox, Debby; Muller, Martin N.; Sommer, Volker; Piel, Alex K.; Stewart, Fiona; Speede, Sheri; Roman, Joe; Wu, Gary D.; Taylor, Josh; Bohm, Rudolf P.; Rose, Heather Marshall; Carlson, John K.; Mjungu, Deus; Schmidt, Paul S.; Gaughan, Celeste; Bushman, Joyslin I.; Schmidt, Ella; Bittinger, Kyle; Collman, Ronald G.; Hahn, Beatrice H.; Bushman, Frederic D.

doi:10.1128/mbio.00319-18

Cited by 41 publications

(47 citation statements)

References 70 publications

(118 reference statements)

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“…In reanalyzing Sherrill-Mix et al's [38] data, a similar phenomenon was also observed ( and an LOP-based nonparametric approach to fit ′ (). We will model the covariance…”

Section: Likelihood Formulationsupporting

confidence: 52%

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Mobile microbiome networks across spatiotemporal gradients

Jiang

Griffin

2020

Preprint

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Background: The microbiome, a community of microbes that co-reside in biotic or abiotic environments, underlies biogeochemical cycling, plant and animal development, and human health. Increasing evidence shows that much of the role the microbiome plays is executed through complex interactions among microbes. Thus, network reconstruction has been increasingly used as a tool to disentangle internal workings within microbial communities.Results: We developed a general framework for recovering microbial interaction networks from any design of microbial experiment. This framework represents a quasi-dynamic game model (qdGM) derived from the seamless integration of evolutionary game theory and allometric scaling laws. The qdGM can not only characterize how individual microbes act singly, but also reveal how different microbes interact with each other to govern microbial community assembly. Beyond existing approaches that can only identify a single overall microbial network from a number of samples, our framework can track and visualize how interaction networks vary from sample to sample and covert sample-specific (personalized) networks into context-specific networks. More importantly, this framework can reconstruct such mobile microbial networks from steady-state data, facilitating the widespread use of network tools to understand the impact of the microbiome on natural processes.Conclusions: As proof of concept, we used the new framework to analyze human gut microbiota data and interspecific animal-associated microbiota data. Mobile networks reconstructed from each dataset can characterize previously unknown mechanisms that drive the change of microbial interaction architecture and organization along spatiotemporal gradients. This framework provides a tool to generate process-specific microbiome networks that can be readily translated into various biotechnological applications and evolutionary studies.

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“…In reanalyzing Sherrill-Mix et al's [38] data, a similar phenomenon was also observed ( and an LOP-based nonparametric approach to fit ′ (). We will model the covariance…”

Section: Likelihood Formulationsupporting

confidence: 52%

“…We analyzed gut bacterial 16S rRNA gene sequence data collected on 265 specimens from 64 species [38]. The bacterial lineages collected were placed into phylogenetic taxa, approximately including 65 families that are common to all animal species.…”

Section: Networking the Impact Of Microbiomes On Animal Evolutionmentioning

confidence: 99%

Mobile microbiome networks across spatiotemporal gradients

Jiang

Griffin

2020

Preprint

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“…17 In addition, 16S rRNA gene qPCR was carried out on a subset of samples using primers and protocols previously described. 18 To identify bacteria (taxa) that are commonly associated with HAIs, we specifically queried sequences from practitioner stethoscope samples of sets A and C for the presence of Staphylococcus, Pseudomonas, Acinetobacter, Clostridium, Enterococcus, Stenotrophomonas, and Burkholderia genera. The 16S rRNA gene sequences assigned to these genera in the QIIME pipeline were then manually aligned to the NCBI 16S rRNA sequence database with BLAST to confirm genus identity and, if possible, to generate species-level assignment.…”

Section: Bacterial Amplification Sequencing and Analysismentioning

confidence: 99%

Molecular analysis of bacterial contamination on stethoscopes in an intensive care unit

Knecht

McGinniss

Shankar

et al. 2018

Infect. Control Hosp. Epidemiol.

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Background: Culture-based studies, which focus on individual organisms, have implicated stethoscopes as potential vectors of nosocomial bacterial transmission. However, the full bacterial communities that contaminate in-use stethoscopes have not been investigated. Methods: We used bacterial 16S rRNA gene deep-sequencing, analysis, and quantification to profile entire bacterial populations on stethoscopes in use in an intensive care unit (ICU), including practitioner stethoscopes, individual-use patient-room stethoscopes, and clean unused individual-use stethoscopes. Two additional sets of practitioner stethoscopes were sampled before and after cleaning using standardized or practitioner-preferred methods. Results: Bacterial contamination levels were highest on practitioner stethoscopes, followed by patient-room stethoscopes, whereas clean stethoscopes were indistinguishable from background controls. Bacterial communities on stethoscopes were complex, and community analysis by weighted UniFrac showed that physician and patient-room stethoscopes were indistinguishable and significantly different from clean stethoscopes and background controls. Genera relevant to healthcare-associated infections (HAIs) were common on practitioner stethoscopes, among which Staphylococcus was ubiquitous and had the highest relative abundance (6.8%-14% of contaminating bacterial sequences). Other HAI-related genera were also widespread although lower in abundance. Cleaning of practitioner stethoscopes resulted in a significant reduction in bacterial contamination levels, but these levels reached those of clean stethoscopes in only a few cases with either standardized or practitioner-preferred methods, and bacterial community composition did not significantly change. Conclusions: Stethoscopes used in an ICU carry bacterial DNA reflecting complex microbial communities that include nosocomially important taxa. Commonly used cleaning practices reduce contamination but are only partially successful at modifying or eliminating these communities.Cite this article: Knecht VR, et al. (2019). Molecular analysis of bacterial contamination on stethoscopes in an intensive care unit.

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“…1 ). In mammalian microbiome studies, there is often a strong correlation between host phylogeny and diet, making it difficult to parse apart the effects of diet and phylogeny on microbiome evolution ( 28 ). Bats provide a potential solution to this issue, because several feeding modes have evolved in parallel two or more times within the order.…”

Section: Bats Are Taxonomically and Ecologically Diversementioning

confidence: 99%