Dataset Information

Fingerprinting cities: differentiating subway microbiome functionality.

ABSTRACT:

Background

Accumulating evidence suggests that the human microbiome impacts individual and public health. City subway systems are human-dense environments, where passengers often exchange microbes. The MetaSUB project participants collected samples from subway surfaces in different cities and performed metagenomic sequencing. Previous studies focused on taxonomic composition of these microbiomes and no explicit functional analysis had been done till now.

Results

As a part of the 2018 CAMDA challenge, we functionally profiled the available ~?400 subway metagenomes and built predictor for city origin. In cross-validation, our model reached 81% accuracy when only the top-ranked city assignment was considered and 95% accuracy if the second city was taken into account as well. Notably, this performance was only achievable if the similarity of distribution of cities in the training and testing sets was similar. To assure that our methods are applicable without such biased assumptions we balanced our training data to account for all represented cities equally well. After balancing, the performance of our method was slightly lower (76/94%, respectively, for one or two top ranked cities), but still consistently high. Here we attained an added benefit of independence of training set city representation. In testing, our unbalanced model thus reached (an over-estimated) performance of 90/97%, while our balanced model was at a more reliable 63/90% accuracy. While, by definition of our model, we were not able to predict the microbiome origins previously unseen, our balanced model correctly judged them to be NOT-from-training-cities over 80% of the time. Our function-based outlook on microbiomes also allowed us to note similarities between both regionally close and far-away cities. Curiously, we identified the depletion in mycobacterial functions as a signature of cities in New Zealand, while photosynthesis related functions fingerprinted New York, Porto and Tokyo.

Conclusions

We demonstrated the power of our high-speed function annotation method, mi-faser, by analysing ~?400 shotgun metagenomes in 2 days, with the results recapitulating functional signals of different city subway microbiomes. We also showed the importance of balanced data in avoiding over-estimated performance. Our results revealed similarities between both geographically close (Ofa and Ilorin) and distant (Boston and Porto, Lisbon and New York) city subway microbiomes. The photosynthesis related functional signatures of NYC were previously unseen in taxonomy studies, highlighting the strength of functional analysis.

SUBMITTER: Zhu C

PROVIDER: S-EPMC6822482 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Fingerprinting cities: differentiating subway microbiome functionality.

Zhu Chengsheng C Miller Maximilian M Lusskin Nick N Mahlich Yannick Y Wang Yanran Y Zeng Zishuo Z Bromberg Yana Y

Biology direct 20191030 1

<h4>Background</h4>Accumulating evidence suggests that the human microbiome impacts individual and public health. City subway systems are human-dense environments, where passengers often exchange microbes. The MetaSUB project participants collected samples from subway surfaces in different cities and performed metagenomic sequencing. Previous studies focused on taxonomic composition of these microbiomes and no explicit functional analysis had been done till now.<h4>Results</h4>As a part of the 2 ...[more]

PMID: 31666099

Similar Datasets

Project description:Anisotropic biomaterials can affect cell function by driving cell alignment, which is critical for cardiac engineered tissues. Recent work, however, has shown that pluripotent stem cell-derived cardiomyocytes may self-align over long periods of time. To determine how the degree of biomaterial substrate anisotropy impacts differentiating cardiomyocyte structure and function, we differentiated mouse embryonic stem cells to cardiomyocytes on nonaligned, semialigned, and aligned fibrous substrates and evaluated cell alignment, contractile displacement, and calcium transient synchronicity over time. Although cardiomyocyte gene expression was not affected by fiber alignment, we observed gradient- and threshold-based differences in cardiomyocyte alignment and function. Cardiomyocyte alignment increased with the degree of fiber alignment in a gradient-based manner at early time points and in a threshold-based manner at later time points. Calcium transient synchronization tightly followed cardiomyocyte alignment behavior, allowing highly anisotropic biomaterials to drive calcium transient synchronization within 8 days, while such synchronized cardiomyocyte behavior required 20 days of culture on nonaligned biomaterials. In contrast, cardiomyocyte contractile displacement had no directional preference on day 8 yet became anisotropic in the direction of fiber alignment on aligned fibers by day 20. Biomaterial anisotropy impact on differentiating cardiomyocyte structure and function is temporally dependent. Impact Statement This work demonstrates that biomaterial anisotropy can quickly drive desired pluripotent stem cell-derived cardiomyocyte structure and function. Such an understanding of matrix anisotropy's time-dependent influence on stem cell-derived cardiomyocyte function will have future applications in the development of cardiac cell therapies and in vitro cardiac tissues for drug testing. Furthermore, our work has broader implications concerning biomaterial anisotropy effects on other cell types in which function relies on alignment, such as myocytes and neurons.

Project description:BackgroundMass transit environments, such as subways, are uniquely important for transmission of microbes among humans and built environments, and for their ability to spread pathogens and impact large numbers of people. In order to gain a deeper understanding of microbiome dynamics in subways, we must identify variables that affect microbial composition and those microorganisms that are unique to specific habitats.MethodsWe performed high-throughput 16S rRNA gene sequencing of air and surface samples from 16 subway stations in Oslo, Norway, across all four seasons. Distinguishing features across seasons and between air and surface were identified using random forest classification analyses, followed by in-depth diversity analyses.ResultsThere were significant differences between the air and surface bacterial communities, and across seasons. Highly abundant groups were generally ubiquitous; however, a large number of taxa with low prevalence and abundance were exclusively present in only one sample matrix or one season. Among the highly abundant families and genera, we found that some were uniquely so in air samples. In surface samples, all highly abundant groups were also well represented in air samples. This is congruent with a pattern observed for the entire dataset, namely that air samples had significantly higher within-sample diversity. We also observed a seasonal pattern: diversity was higher during spring and summer. Temperature had a strong effect on diversity in air but not on surface diversity. Among-sample diversity was also significantly associated with air/surface, season, and temperature.ConclusionsThe results presented here provide the first direct comparison of air and surface bacterial microbiomes, and the first assessment of seasonal variation in subways using culture-independent methods. While there were strong similarities between air and surface and across seasons, we found both diversity and the abundances of certain taxa to differ. This constitutes a significant step towards understanding the composition and dynamics of bacterial communities in subways, a highly important environment in our increasingly urbanized and interconnect world. Video abstract.

Project description:BackgroundThe COVID-19 pandemic has highlighted the extent to which the public transportation environment, such as in subways, may be important for the transmission of potential pathogenic microbes among humans, with the possibility of rapidly impacting large numbers of people. For these reasons, sanitation procedures, including massive use of chemical disinfection, were mandatorily introduced during the emergency and remain in place. However, most chemical disinfectants have temporary action and a high environmental impact, potentially enhancing antimicrobial resistance (AMR) of the treated microbes. By contrast, a biological and eco-sustainable probiotic-based sanitation (PBS) procedure was recently shown to stably shape the microbiome of treated environments, providing effective and long-term control of pathogens and AMR spread in addition to activity against SARS-CoV-2, the causative agent of COVID-19. Our study aims to assess the applicability and impact of PBS compared with chemical disinfectants based on their effects on the surface microbiome of a subway environment.ResultsThe train microbiome was characterized by both culture-based and culture-independent molecular methods, including 16S rRNA NGS and real-time qPCR microarray, for profiling the train bacteriome and its resistome and to identify and quantify specific human pathogens. SARS-CoV-2 presence was also assessed in parallel using digital droplet PCR. The results showed a clear and significant decrease in bacterial and fungal pathogens (p < 0.001) as well as of SARS-CoV-2 presence (p < 0.01), in the PBS-treated train compared with the chemically disinfected control train. In addition, NGS profiling evidenced diverse clusters in the population of air vs. surface while demonstrating the specific action of PBS against pathogens rather than the entire train bacteriome.ConclusionsThe data presented here provide the first direct assessment of the impact of different sanitation procedures on the subway microbiome, allowing a better understanding of its composition and dynamics and showing that a biological sanitation approach may be highly effective in counteracting pathogens and AMR spread in our increasingly urbanized and interconnected environment. Video Abstract.

Dataset Information

Fingerprinting cities: differentiating subway microbiome functionality.

Background

Results

Conclusions

Publications

Fingerprinting cities: differentiating subway microbiome functionality.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets