Niche-Based Microbial Community Assemblage in Urban Transit Systems and the Influence of City Characteristics

ABSTRACT Microbiota residing on the urban transit systems (UTSs) can be shared by travelers and have niche-specific assemblage. However, it remains unclear how the assemblages are influenced by city characteristics, rendering city-specific and microbial-aware urban planning challenging. Here, we analyzed 3,359 UTS microbial samples collected from 16 cities around the world. We found the stochastic process dominated in all UTS microbiota assemblages, with the explanation rate (R2) of the neutral community model (NCM) higher than 0.7. Moreover, city characteristics predominantly drove such assemblage, largely responsible for the variation in the stochasticity ratio (50.1%). Furthermore, by utilizing an artificial intelligence model, we quantified the ability of UTS microbes in discriminating between cities and found that the ability was also strongly affected by city characteristics, especially climate and continent. From these, we found that although the NCM R2 of the New York City UTS microbiota was 0.831, the accuracy of the microbial-based city characteristic classifier was higher than 0.9. This is the first study to demonstrate the effects of city characteristics on the UTS microbiota assemblage, paving the way for city-specific and microbial-aware applications. IMPORTANCE We analyzed the urban transit system microbiota assemblage across 16 cities. The stochastic process was dominant in the urban transit system microbiota assemblage. The urban transit system microbe’s ability in discriminating between cities was quantified using transfer learning based on random forest (RF) methods. Certain urban transit system microbes were strongly affected by city characteristics.

idea that these systems can have characteristic or signature microbial communities is important, especially in terms of possible pathogen monitoring. I agree with the setup in the manuscript that a baseline understanding of the microbes that inhabit or persist in these spaces and the local factors that influence these assemblages is needed if a monitoring framework is to be implemented in the future. The results are comprehensive and reliable, the whole manuscript is well organized. In principle, this paper represents a timely work for the urban transit system microbiome.
Minor Issues 1. Was the FEAST model run with all default settings or were any settings changed? There is not enough detail to recreate the FEAST model runs. IF it is too detailed, then this could go to supplemental methods.
2. Figure 2 legend title is a more a result statement then a short description of what is being presented. This should be changed to be more descriptive of what data the figure displays.
Reviewer #2 (Comments for the Author): Xiong et al reported the microbial community assemblage in urban transit systems and the factors effected it by AI. The work is well-done, and I have only few questions on writing. The author should completely check the MS, especacially the usage of abbrevation.
Line 31, which characteristics can affect should be explained. Line 68， COVID-19 should be written in the full name. Line78, if there are multiple factors, it should not only be two. Line 156, line 75, and line 204, the explanations in the two parts are repeated. Line 195, 186, the abbreviation of UTS has been mentioned already.
Staff Comments:

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Comments
Microbial community profile and assemblage in urban transit systems and the influence of city characteristics is an important yet challenging topic. The study has novelty in that it used ecological theories and transfer learning. If community patterns were predictable at the local or the city scale, the manuscript suggests this understanding could facilitate a broad spectrum of applications including public health monitoring or city-specific microbial-aware urban planning. Overall, I find that the manuscript describes a thorough examination of the microbial communities in urban transit systems. The idea that these systems can have characteristic or signature microbial communities is important, especially in terms of possible pathogen monitoring. I agree with the setup in the manuscript that a baseline understanding of the microbes that inhabit or persist in these spaces and the local factors that influence these assemblages is needed if a monitoring framework is to be implemented in the future. The results are comprehensive and reliable, the whole manuscript is well organized. In principle, this paper represents a timely work for the urban transit system microbiome Minor Issues 1. Was the FEAST model run with all default settings or were any settings changed? There is not enough detail to recreate the FEAST model runs. IF it is too detailed, then this could go to supplemental methods.

Figure 2 legend title is a more a result statement then a short description of what is being
presented. This should be changed to be more descriptive of what data the figure displays.

Response to Comments from Reviewers Dear Reviewers,
We greatly appreciate the efforts of you for reviewing and providing constructive suggestions on our manuscript (Manuscript Number: Spectrum00167-23). We have studied the comments carefully and have made revision according to all comments. All of the revisions are highlighted in red in the revised manuscript. The point-by-point answers to the comments and suggestions were listed as below.
Reviewer #1: Microbial community profile and assemblage in urban transit systems and the influence of city characteristics is an important yet challenging topic. The study has novelty in that it used ecological theories and transfer learning. If community patterns were predictable at the local or the city scale, the manuscript suggests this understanding could facilitate a broad spectrum of applications including public health monitoring or city-specific microbial-aware urban planning. Overall, I find that the manuscript describes a thorough examination of the microbial communities in urban transit systems. The idea that these systems can have characteristic or signature microbial communities is important, especially in terms of possible pathogen monitoring. I agree with the setup in the manuscript that a baseline understanding of the microbes that inhabit or persist in these spaces and the local factors that influence these assemblages is needed if a monitoring framework is to be implemented in the future. The results are comprehensive and reliable, the whole manuscript is well organized. In principle, this paper represents a timely work for the urban transit system microbiome.
Answer: We thank reviewer for these insights. We have made the explanations about the urban transit system microbiome in more details and with higher clarity, as shown in main text and below answers.
Minor Issues 1. Was the FEAST model run with all default settings or were any settings changed? There is not enough detail to recreate the FEAST model runs. IF it is too detailed, then this could go to supplemental methods. Answer: We thank reviewer for this comment. The FEAST model run with all default settings. The source and sink used in source tacking analysis were described in Results part (Line 206-208: 'Microbial communities from one surface type were used as the sinks, and the samples from 13 other surface types were used as the sources.'). To make this clearer, we have updated the Method part in main text as below (Line 153-155): "To determine the association of UTS microbial communities, the FEAST (Shenhav et al., 2019) model using default parameter settings was applied for calculating the contribution of different surface microbial communities in New York City transit system." 2. Figure 2 legend title is a more a result statement then a short description of what is being presented. This should be changed to be more descriptive of what data the figure displays. Answer: We thank reviewer for this comment. We have updated Figure 2 legend title in Figure  captions part as below (Line 735-737): "The association between the stochastic ratio of urban transit system microbiome assemblage and city characteristics." Reviewer #2: Xiong et al reported the microbial community assemblage in urban transit systems and the factors effected it by AI. The work is well-done, and I have only few questions on writing. The author should completely check the MS, especially the usage of abbreviation. Answer: We thank reviewer for these insights. We have made the explanations about the urban transit system microbiome in more details and with higher clarity, as shown in main text and below answers.
Line 31, which characteristics can affect should be explained. Answer: We thank reviewer for this comment. We have updated the Abstract part in main text as below (Line 29-31): "Furthermore, by utilizing an artificial intelligence model, we quantified the ability of UTS microbes in discriminating between cities, and found that the ability was also strongly affected by city characteristics, especially climate and continent." Line 68, COVID-19 should be written in the full name. Answer: We thank reviewer for this comment. We have updated the Introduction part in main text as below (Line 68): "For example, Corona Virus Disease 2019 (COVID-19) asymptomatic subjects could mass transmit the pathogenic SARS-CoV-2 (severe acute respiratory syndrome coronavirus) in UTS by breathing (Turnbaugh et al., 2007)." Line78, if there are multiple factors, it should not only be two.