Humanization of wildlife gut microbiota in urban environments

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    Evaluation Summary:

    Urbanization has broad impacts for macroecology but its consequences for wildlife microbial ecology remain unclear. Dillard et al. hypothesize that humans living in an urban setting may transfer their microbes to wildlife with potential adverse effects. They analyze 16S rRNA gene sequencing data from humans, crested anoles, and coyotes, leading to the discovery of multiple bacteria that fit the pattern of urbanization and inter-species transfer.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)

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Urbanization is rapidly altering Earth’s environments, demanding investigation of the impacts on resident wildlife. Here, we show that urban populations of coyotes ( Canis latrans ), crested anole lizards ( Anolis cristatellus ), and white-crowned sparrows ( Zonotrichia leucophrys ) acquire gut microbiota constituents found in humans, including gut bacterial lineages associated with urbanization in humans. Comparisons of urban and rural wildlife and human populations revealed significant convergence of gut microbiota among urban populations relative to rural populations. All bacterial lineages overrepresented in urban wildlife relative to rural wildlife and differentially abundant between urban and rural humans were also overrepresented in urban humans relative to rural humans. Remarkably, the bacterial lineage most overrepresented in urban anoles was a Bacteroides sequence variant that was also the most significantly overrepresented in urban human populations. These results indicate parallel effects of urbanization on human and wildlife gut microbiota and suggest spillover of bacteria from humans into wildlife in cities.

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  1. Author Response:

    Reviewer #1 (Public Review):

    Concerns: Robustness is often mentioned but is not precisely defined. Operationally robustness seems in this paper to stand for robustness to 1) activity regime change under parameter variation, 2) stability of burst characteristics with parameter variation, and 3) slow-wave amplitude, spiking strength (spike frequency), and symmetry of bursting. These are three very different things and should be clearly differentiated in the text so that when robustness is mentioned, the type of robustness is made clear. Perhaps robustness should be limited to the first, activity regime, and some other terms used for the other two.

    We added a full paragraph to the Introduction describing how we define circuit robustness and challenges associated with establishing which features are central to robustness. We revisited instances in the paper, in which we refer to robustness, and clarified whether we are talking about the change in the qualitative state of the circuit and/or sensitivity of certain features of the circuit output that might bring the circuit closer to the transition to another qualitative state.

    On several occasion in the text the authors refer to irregularity in bursting of the hybrid HCOs, but this is not quantified beyond displaying exemplars that seem to have irregular bursting. Pooled data should be analyzed in the different modes and manipulations and analyzed for statistical difference in the CoV of cycle frequency (or period) and burst duration. Similarly, the authors cite changes in symmetry in bursting in exemplars but do not present pooled quantitative data in support of the claim, just visual inspection of exemplars.

    As suggested, we analyzed pooled data for irregularity and asymmetry in different modes and conditions and presented these data in supplementary figures to Figures 4, 5 and 8. Particularly, we quantified the irregularity of the rhythms by calculating the CV of cycle frequency of the circuits operating with different mechanisms (Figure 4 – Figure Supplement 1A). We calculated the CV of cycle and spike frequencies of escape and release circuits at different temperatures (Figure 5 – Figure Supplement 1). Finally, we calculated the CV of cycle frequency of circuits operating with a mixture of mechanisms in control and with addition of the neuromodulatory current (Figure 8 – Figure Supplement 1A).

    To quantify the asymmetry in bursting in different conditions, we calculated the difference in the burst durations between neurons in the circuits with different synaptic thresholds (Figure 4 – Figure Supplement 1B), ERQ values for each neuron independently (Figure 4 C,D, Figure 7 – Figure Supplement 1), and the difference in the number of spikes per burst between neurons in a circuit in control and with the addition of IMI (Figure 8 – Figure Supplement 1B).

    In the stomatogastric networks, synaptic transmission is largely graded (based on release mediated by the slow wave of oscillation) and not so much spike-mediated, so it is reasonable that synaptic threshold should be a control variable in this system. Moreover, spikes, recorded in the cell bodies are not reflective of their amplitude at the SIZ. In other system transmission can be largely mediated by spikes. At the beginning of the paper (Figure 1), it is clear that release mode in their hybrid HCOs depends on spike-mediated transmission because synaptic threshold is above the slow-wave depolarization, thus spike frequency is a key feature determining the mechanism of oscillation. However, in escape mode the transmission is purely graded because synaptic threshold is so low that transmission is saturated by the slow-wave depolarization and spikes contribute little if anything, thus spike frequency is immaterial to the mechanism of oscillation. This situation should be addressed at the beginning of the paper in reference to Figure 1. How this spike-mediated vs. graded balance plays out in the mixed mechanism modes remains to be explored.

    We added a description of graded vs spike-mediated transmission in escape and release modes to the beginning of the results section

    In Figure 1C, the authors show convincingly that there is a vast landscape where their hybrid HCO operate in a mixed mechanistic mode somewhere between escape and release corresponding to synaptic thresholds in the middle range. This mixed mode is addressed only with a single exemplar in Figure 8B as a case for how modulation affects mixed mode circuits. The Discussion should reflect plainly that this mixed mode is likely common in biological circuits and may go hand-in-hand with significant reliance on spike-mediated transmission.

    We added a paragraph to the Discussion section reflecting that a mixture of mechanisms is common in biological systems and discussing that there is a continuum of mechanisms that can exist in rhythmic circuits. We show in the paper that the balance of the mechanistic operations is sensitive to parameter variations and perturbations and can be biased towards one or the other mechanism on the vast landscape between escape and release.

    In this paper we mostly focused on describing the behavior of the system operating at the extremes of this continuum, the synaptic escape and release mechanisms, because they are more identifiable mechanisms. However, we do describe the properties of the circuits operating in mixed modes and the transition in the mechanisms at multiple instances. Figure 2 shows how characteristics of the circuits change as they transition through the mixtures of mechanisms. Figure 4 shows how output characteristics of the circuits operating in a mixed mode depend on the changes in conductances. We also added the analysis of the pooled data from the circuits operating in the mixture of mechanisms in the presence of IMI and added these data to the supplement of Figure 8.

    The authors state "The modulatory current (IMI) restores oscillations in release circuits but has little effect in escape circuits." but this is supported by a single exemplar (Figure 8E) and no pooled data is presented.

    We performed 4 experiments, in which oscillations of the circuits with a release mechanism were lost at high temperature and restored by adding IMI to both neurons. We added a statement to the text of the manuscript that the oscillations were restored in 4/4 circuits with the addition of IMI (line 699). We have provided a single example trace in the paper, because the effect of IMI was consistent across all the preparations. We provide additional examples of IMI rescue in response to the question #35.

  2. Evaluation Summary:

    Urbanization has broad impacts for macroecology but its consequences for wildlife microbial ecology remain unclear. Dillard et al. hypothesize that humans living in an urban setting may transfer their microbes to wildlife with potential adverse effects. They analyze 16S rRNA gene sequencing data from humans, crested anoles, and coyotes, leading to the discovery of multiple bacteria that fit the pattern of urbanization and inter-species transfer.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1, Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)

  3. Reviewer #1 (Public Review):

    Dillard et al. sought to identify signatures of urbanization using paired analyses of the human, coyote, and crested anole gut microbiota using 16S rRNA gene sequencing. They report increased similarity to the human gut microbiota in coyotes and anoles living in an urban environment, leading to the identification of multiple amplicon sequence variants shared between species.

    The major strengths include the innovative use of two very distinct model systems to study urbanization coupled to robust methods for 16S amplicon analysis. The study is also conceptually innovative, proposing a testable hypothesis about microbial transfer from humans into the surrounding wildlife.

    A major weakness is the reliance on 16S profiling to assess interspecies transfer events, which (as the authors point out) lacks sufficient resolution. Metagenomics would be a more reliable way to assess if the same strains are shared across species.

    There's also a conceptual flaw in that the authors interpret any overlap in bacteria between host species to be a sign of human to wildlife transfer. The reverse direction is also possible, as is colonization of both species from a shared environmental reservoir. Furthermore, the reported trends are modest in effect size and the consequences of these events for wildlife health and disease are not established. This raises the question as to whether rare bacterial transfer events would matter given the numerous other factors that shape the microbial ecology and health status of wildlife.

    Given these caveats, the hypothesis for human to wildlife transfer is not definitively established and warrants additional data.

  4. Reviewer #2 (Public Review):

    Dillard et al. compared urban and rural populations of coyotes, anoles, and humans to ask whether urbanization impacts on the gut microbiota are similar across species and in particular whether urban, rather than rural, animal populations have a more similar gut microbiota to humans. They find that overall composition of the gut microbiota in urban and rural populations do differ for both animal species and that the urban individuals have greater similarity to humans, and specifically urban humans. In addition they show that some microbial taxa enriched in urban animals were also at greater abundance in urban humans. The authors cannot distinguish whether the observed convergence between animals and humans in urban settings is due to transmission from humans to animals or due to similar ecology (most likely diets) in urban settings. These data expand previous single species assessments of urbanization impacts on the gut microbiota by showing effects in multiple host species and explicitly assessing whether human-associated taxa are overrepresented in the gut microbiota of urban wildlife.

    The analyses conducted in this paper are generally robust, however there are some aspects of study design that weaken their interpretations.

    1. The human gut microbiota data is drawn from a study not intended to test urbanization impacts directly. The three populations represented (US, Venezuela, and Malawi) differ in numerous ways beyond whether the population is urbanized (US) or not (Venezuela and Malawi). Urban animals are more similar to all human populations, not just the urban US population, indicating that the urban context alone may not explain the dynamics.
    2. The human populations represented, as noted above, are not from the localities where wildlife samples were collected. As humans within a lifestyle group (e.g. urban or rural) can still vary considerably in their gut microbiota composition (at both the ASV and strain level), this greatly diminishes the likelihood of sharing ASVs between humans and wildlife in different localities. The authors rightly note that strain level data would be necessary to demonstrate horizontal transmission, but even with such data it is unlikely such a signal would be present in this dataset.
    3. The urbanization impacts in the two wildlife species shown do not parallel one another. They move in distinct, arguably perpendicular ways in ordination space (Fig. 1), and have non-overlapping microbial taxa that recapitulate trends in humans. Urbanization impacts need not lead to a singular urban microbiome, but what it means to have a significant but totally distinct influence between animal hosts is not clear.

  5. Reviewer #3 (Public Review):

    I found this paper to be well written and well presented. The background and hypotheses are clearly outline in the introduction, leading towards simple predictions and presentation of results showing that urban animals have more similarity and overlap with the gut microbiomes of humans. While reviewing this paper, there were several instances where I wrote down thoughts or questions that the authors then answered or addressed in following paragraphs, and so this paper was a pleasure to read.

    However, there are several places where the data could be discussed in a bit more detail to get a better sense of the magnitude of these effects. The microbiome field is now pressed to understand the relative importance of various ecological and evolutionary factors (e.g. diet versus evolutionary history), and so some discussion as to whether these changes appear to be considerably drastic or relatively minor in relation to other effects would benefit the paper.

    Additionally, the authors could discuss predictions regarding how these changes in community structure might lead to changes to host performance and fitness (since these ideas are invoked in the introduction). Note, I believe that even small changes to the community structure could still yield large biological changes - but I would like to see that discussed. Within here, I request the authors to be sure to use careful language to explicitly signal where they are speculating versus concluding from their data. But, these requests are just more interpretation and context for these results, and do not detract from my interest in these data.