Ecology

CORRESPONDENCE
01 June 2021

French vote for river barriers defies biodiversity strategy

Simon Blanchet

 ORCID: http://orcid.org/0000-0002-3843-589X

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Pablo A. Tedesco

 ORCID: http://orcid.org/0000-0001-5972-5928

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Simon Blanchet

National Centre for Scientific Research (CNRS), Moulis, France.

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Pablo A. Tedesco

French National Research Institute for Development (IRD), Toulouse, France.

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Europe’s rivers are disrupted by more than one million artificial barriers, including small dams, weirs and fords (see, for example, B. Belleti et al. Nature 588, 436–441; 2020). There is strong scientific evidence that such obstructions can harm both hydrological and ecological systems, yet the French parliament has voted to leave them in place (see go.nature.com/3ck9mxq).By limiting the transfer of sediments and movement of organisms, these small barriers create a succession of reaches of warming, stagnant water that threatens freshwater biodiversity (M. R. Fuller et al. Ann. NY Acad. Sci. 1335, 31–51; 2015). Dismantling such small barriers is the most effective way to restore river connectivity and is now a worldwide objective (J. E. O’Connor et al. Science 348, 496–497; 2015).The French parliament’s decision flies in the face of the EU Biodiversity Strategy. It also has no economic justification. Most small barriers cannot generate hydroelectricity and those that can contribute less than 1% to France’s electricity (see go.nature.com/2rphjch).In our view, the fate of each barrier should be decided by balancing its ecological benefits and socioeconomic costs.

Nature 594, 26 (2021)
doi: https://doi.org/10.1038/d41586-021-01467-0

Competing Interests
The authors declare no competing interests.

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Persian Gulf water and sediment samples along the oil pollution continuumWater and sediment samples were collected along the circulation current of the Persian Gulf from Hormuz Island [HW (SAMN12878178) and HS (SAMN12878113)), Asaluyeh area (AW (SAMN12878179) and AS (SAMN12878114)), and Khark Island (KhW (SAMN12878180) and KhS (SAMN12878115)] (Fig. 1). Physicochemical characteristics and Ionic content of the collected samples are presented in the Supplementary Table S1. The GC-FID analyses showed high TPH and polyaromatic hydrocarbon (PAH) concentrations in the Khark sediment (KhS) (Supplementary Table S2). The GC-SimDis analysis showed that C25–C38 HCs were dominant in the KhS (~ 60%), followed by  > C40 HCs (~ 14%) (Supplementary Fig. S1). Chrysene, fluoranthene, naphthalene, benzo(a)anthracene and phenanthrene were respectively the most abundant PAHs in KhS. This pollution could originate from oil spillage due to Island airstrikes during the imposed war (1980–1988), sub-sea pipeline failures, and discharge of oily wastewater or ballast water of oil tankers (ongoing for ~ 50 years)12. The TPH of other water and sediment samples was below the detection limit of our method ( 2%) representatives were enriched in KhS (Fig. 2B). The co-presence of the orders Methanosarcinals, Alteromanadales, and Thermotogae (Petrotogales) in the KhS hints at potential oil reservoir seepage around the sampling site since these taxa are expected to be present in oil reservoirs38. The main HC pollutants in the Asalouyeh are low molecular weight aromatic compounds that mainly influence the prokaryotic population in the water column and rarely precipitate into sediments hence the similarity of AS to HS microbial composition as they both experience low pollution rates.Apart from oil-degrading Proteobacteria (e.g. Alteromonadales, Rhodobacterales, and Oceanospirillales), a diversity of sulfur/ammonia-oxidizing chemolithoautotrophic Proteobacteria were present in these sediments although at lower abundances e.g., (Acidithiobacillales (KhS 1.8%), Chromatiales (HS 1.5, AS 1.1, KhS 0.85%), Ectothiorhodospirales (HS 3.75, AS 2.3, KhS 1.7%), Halothiobacillales (KhS 2.6%), Thiotrichales (HS 1.5, AS 1.1, KhS 0.3%), Thiohalorhabdales (HS 0.7, AS 1.2, KhS 0.5%), Thiomicrospirales (KhS 1.5%)) (Fig. 2B).Sulfate-reducing bacteria (SRB) in HS comprised up to 16.2% of the community (Desulfobacterales, NB1-j, Myxococcales, Syntrophobacterales, and Thermodesulfovibrionia). Similar groups along with Desulfarculales, comprised the SRB functional guild of the AS (~ 18.9%). In comparison, Desulfuromonadales and Desulfobacterales were the SRB representatives in KhS with a total abundance of only ~ 3.3%. The lower phylogenetic diversity and community contribution of SRBs in KhS hint at the potential susceptibility of some SRBs to oil pollution or that HC degraders might outcompete them (e.g., Deferribacterales). Additionally, KhS was gravel-sized sediment (particles ≥ 4 mm diameter), whereas HS and AS samples were silt and sand-sized sediments39. The higher oxygen penetration in gravel particles of KhS hampers anaerobic metabolism of sulfate/nitrate-reducing bacteria hence their lower relative abundance in this sample (Fig. 2B).Whereas in water samples, sulfur/ammonia-oxidizing chemolithoautotrophs such as Thiomicrospirales and sulfate/nitrate-reducing bacteria such as Desulfobacterales, NB1-j, Deferribacterales, Anaerolineales, Nitrosococcales, Nitrosopumilales, and Pirellulales were present in very small quantities (lower than 0.5% in each sample).Chronic exposure to oil pollution shapes similar prokaryotic communities as oil spill eventsWe analyzed the prokaryotic community composition of 41 oil-polluted marine water metagenomes (different depths in the water column) from Norway (Trondheimsfjord), Deepwater Horizon (Gulf of Mexico), the northern part of the Gulf of Mexico (dead zone) and Coal Oil Point of Santa Barbara; together with 65 oil exposed marine sediment metagenomes (beach sand, surface sediments and deep-sea sediments) originating from DWH Sediment (Barataria Bay), Municipal Pensacola Beach (USA) and a hydrothermal vent in Guaymas Basin (Gulf of California) in comparison with the PG water and sediment samples (in total 112 datasets) (Supplementary Table S3). This extensive analysis allowed us to get a comparative overview of the impact of chronic oil pollution on the prokaryotic community composition.Hydrocarbonoclastic bacteria affiliated to Oceanospirillales, Cellvibrionales (Porticoccaceae family), and Alteromonadales40 comprised a significant proportion of the prokaryotic community in samples with higher aliphatic compounds pollution e.g. DWHW.BD3 (sampled six days after the incubation of unpolluted water with Macondo oil), DWHW.he1, and DWHW.he2 (oil-polluted water samples incubated with hexadecane), DWHW.BM1, DWHW.BM2, DWHW.OV1 and DWHW.OV2 (sampled immediately after the oil spill in the Gulf of Mexico) (Fig. 3). Samples treated with Macondo oil, hexadecane, naphthalene, phenanthrene, and those taken immediately after the oil spill in the Gulf of Mexico had a significantly lower proportion of SAR11 due to the dominance of bloom formers and potential susceptibility of SAR11 to oil pollutants (Fig. 3).Figure 3The abundance of unassembled 16S rDNA reads from unassembled metagenomes of different oil-polluted water samples (41). Row names are microbial taxa at the order level. For taxa with lower frequency, the higher taxonomic level is shown (47 taxa in total). The right-hand dendrogram represents the clustering of rows based on the Pearson correlation. Columns are the name of water samples. Samples are clustered based on Pearson correlation and the color scale on the top left represents the row Z-score. Figure was plotted using “circlize” and “ComplexHeatmap” packages in R.Full size imageFlavobacteriales and Rhodobacterales were present in relatively high abundance in almost all oil-polluted water samples except for those with recent pollution. Samples named NTW5, NTW6, NTW11, NTW12, which were incubated with MC252 oil for 32–64 days, represented similar prokaryotic composition dominating taxa that are reportedly involved in degrading recalcitrant compounds like PAHs in the middle-to-late stages of the oil degradation process (Alteromonadales, Cellvibrionales, Flavobacteriales, and Rhodobacterales). Whereas at the earlier contamination stages, samples represented a different community composition with a higher relative abundance of Oceanospirillales (e.g., NTW8, NTW9, NTW15, NTW16, and NTW17 sampled after 0–8 days incubation) (Fig. 3).The non-metric multidimensional analysis of the prokaryotic community of 106 oil-polluted water and sediment samples, together with the PG samples, is represented in Fig. 4. Water and sediment samples expectedly represented distinct community compositions. The AW sample was placed near samples treated with phenanthrene and naphthalene in the NMDS plot showing the impact of aromatic compounds on its microbial community. The KhW sample was located near NTW13 in the plot, both of which had experienced recent oil pollution.Figure 4Non-metric multidimensional scaling (NMDS) of the Persian Gulf water and sediment metagenomes along with oil-polluted marine water and sediment metagenomes based on Bray–Curtis dissimilarity of the abundance of 16S rDNA reads in unassembled metagenomes at the order level. Samples with different geographical locations are shown in different colors. PG water and sediment samples are shown in red. Water and sediment samples are displayed by triangle and square shapes, respectively. Figure was plotted using “vegan” library in R.Full size imageThe orders Oceanospirillales, Alteromonadales, and Pseudomonadales were present in relatively high abundances in all oil-polluted water samples except for HW (PG input water) and samples collected from the northern Gulf of Mexico dead zone (GOMDZ) (Fig. 3). Persian Gulf was located in the proximity of the developing oxygen minimum zone (OMZ) of the Arabian Sea that is slowly expanding towards the Gulf of Oman41. Potential water exchange with OMZ areas could be the cause of higher similarity to the GOMDZ microbial community42.Our results suggest that water samples with similar contaminants and exposure time to oil pollution enrich for similar phylogenetic diversity in their prokaryotic communities (Fig. 3). Marine prokaryotes represent vertical stratification with discrete community composition across the depth profile. According to our analyses, the prokaryotic communities of the oil-polluted areas are consistently dominated by similar taxa regardless of sampling depth or geographical location. We speculate that the high nutrient input due to crude oil intrusion into the water presumably disturbs this stratification and HC degrading microorganisms are recruited to the polluted sites where their populations flourish.The inherent heterogeneity of the sediment prokaryotic communities is retained even after exposure to oil pollution, reflected in their higher alpha diversity (Supplementary Fig. S3). However, similar taxa dominate the community in response to oil pollution (Fig. 5).Figure 5The abundance of unassembled 16S rDNA reads from unassembled metagenomes of different oil-polluted sediment samples (65). Row names are microbial taxa at the order level. For taxa with lower frequency, the higher taxonomic level is shown (77 taxa in total). The right-hand dendrogram represents the clustering of rows based on the Pearson correlation. Columns are the name of sediment samples. Samples are clustered based on Pearson correlation and the color scale on the top left represents the row Z-score. Figure was plotted using “circlize” and “ComplexHeatmap” packages in R.Full size imageIn sediment samples, Deltaproteobacteria had the highest abundance, followed by Gammaproteobacteria representatives. Ectothiorhodospirales, Rhizobiales, Desulfobacterales, Myxococcales, and Betaproteobacteriales representatives were present in almost all samples at relatively high quantities (Fig. 5). Sulfate/nitrate-reducing bacteria were major HC degraders in sediment, showing substrate specificity for anaerobic HC degradation43. Desulfobacterales and Myxococcales were ubiquitous sulfate-reducers, present in almost all oil-polluted sediment samples44. Sulfate-reducing Deltaproteobacteria play a key role in anaerobic PAH degradation, especially in sediments containing recalcitrant HC types45. Members of Rhizobiales are involved in nitrogen fixation, which accelerates the HC removal process in the sediment samples46, and therefore their abundance increase in response to oil pollution (Fig. 5).Prokaryotes involved in nitrogen/sulfur cycling of sediments are defined by factors such as trace element composition, temperature, pressure, and more importantly, depth and oxygen availability. In oil-polluted sediment samples, the simultaneous reduction of available oxygen with an accumulation of recalcitrant HCs along the depth profile complicates the organic matter removal. However, anaerobic sulfate-reducing HC degrading bacteria will cope with this complexity47. Prokaryotic communities of HS and AS samples represented similar phylogenetic diversity (Figs. 4, 5). Their prokaryotic community involved in the nitrogen and sulfur cycling resembles the community of DWHS samples. The KhS sample had a similar prokaryotic community to deeper sediment samples collected from 30 to 40 cm depth (USFS3, USFS11, and USFS12) which could be due to our sampling method using a grab sampling device.Our results show that the polluted sediments’ sampling depth (surface or subsurface) defines the dominant microbial populations. Hydrocarbon degrading microbes had the ubiquitous distribution in almost all oil-polluted water and sediment samples including Oceanospirillales, Cellvibrionales, Alteromonadales, Flavobacteriales, Pseudomonadales, and Rhodobacterales. Mentioned orders along with Ectothiorhodospirales, Rhizobiales, Desulfobacterales, Myxococcales, and Betaproteobacteriales and also representatives of Deltaproteobacteria phylum dominated in sediment samples. However, their order of frequency varies depending on the type of oil pollution present at the sampling location and the exposure time.Genome-resolved metabolic analysis of the Persian Gulf’s prokaryotic community along the pollution continuumA total of 82 metagenome-assembled genomes (MAGs) were reconstructed from six sequenced metagenomes of the PG (completeness ≥ 40% and contamination ≤ 5%). Amongst them, eight MAGs belonged to domain Archaea and 74 to domain bacteria. According to GTDB-tk assigned taxonomy (release89) (https://data.gtdb.ecogenomic.org/releases/release89/), reconstructed MAGs were affiliated to Gammaproteobacteria (36.6%), Alphaproteobacteria (12.2%), Flavobacteriaceae (9.7%), Thermoplasmatota (5%) together with some representatives of other phyla (MAG stats in Supplementary Table S4).A collection of reported enzymes involved in the degradation of different aromatic and aliphatic HCs under both aerobic and anaerobic conditions was surveyed in the annotated MAGs of this study43,48,49,50. The KEGG orthologous accession numbers (KOs) of genes involved in HC degradation were collected, and the distribution of KEGG orthologues detected at least in one MAG (n = 76 genes) is represented in Fig. 6.Figure 6Hydrocarbon degrading enzymes present in recovered MAGs from the PG water and sediment metagenomes. Row names represent the taxonomy of recovered MAGs and their completeness is provided as a bar plot on the right side. The color indicates the MAG origin. The size of dots indicates the presence or absence of each enzyme in each recovered MAG. Columns indicate the type of hydrocarbon and in the parenthesis is the name of the enzyme hydrolyzing this compound followed by its corresponding KEGG orthologous accession number. Figure was plotted using “reshape2” and “ggplot2” packages in R.Full size imageA combination of different enzymes runs the oil degradation process. Mono- or dioxygenases are the main enzymes triggering the HC degradation process under aerobic conditions. Under anaerobic conditions, degradation is mainly started by the addition of fumarate or in some cases, by carboxylation of the substrate. Therefore, bacteria containing these genes will potentially initiate the degradation process that will be continued by other heterotrophs. Enzymes such as decarboxylase, hydroxylase, dehydrogenase, hydratase, and isomerases act on the products of initiating enzymes mentioned above through a series of oxidation/reduction reactions.Various microorganisms cooperate to cleave HCs into simpler compounds that could enter common metabolic pathways. Mono- or dioxygenases which are involved in the degradation of alkane (alkane 1-monooxygenase, alkB/alkM), cyclododecane (cyclododecanone monooxygenase, cddA), Biphenyl (Biphenyl 2, 3-dioxygenase subunit alpha/beta, bphA1/A2, Biphenyl-2, 3-diol 1, 2-dioxygenase, bphC), phenol (phenol 2-monooxygenase, pheA), toluene (benzene 1, 2-dioxygenase subunit alpha/beta todC1/C2, hydroxylase component of toluene-4-monooxygenase, todE), xylene (toluate/benzoate 1,2-dioxygenase subunit alpha/beta/electron transport component, xylX/Y/Z, hydroxylase component of xylene monooxygenase, xylM) and naphthalene/phenanthrene (catechol 1,2 dioxygenase, catA, a shared enzyme between naphthalene/phenanthrene /phenol degradation) were detected in recovered MAGs of the PG.The key enzymes including Alkylsuccinate synthase (I)/(II) (assA1/A2), benzylsuccinate synthase (BssA)/benzoyl-CoA reductase (BcrA), ethylbenzene dehydrogenase (EbdA), and 6-oxo-cyclohex-1-ene-carbonyl-CoA hydrolase (BamA) that are responsible for initiating the degradation of alkane, toluene, ethylbenzene and benzoate exclusively under anaerobic conditions were not detected in reconstructed MAGs of this study. Consequently, recovered MAGs of this study are not initiating anaerobic degradation via known pathways while they have the necessary genes to continue the degradation process started by other microorganisms.The MAG KhS_63 affiliated to Immundisolibacter contained various types of mono- or dioxygenases and had the potential to degrade a diverse range of HCs such as alkane, cyclododecane, toluene, and xylene (Fig. 6). Members of this genus have been reported to degrade high molecular weight PAHs51.Lutimaribacter representatives have been isolated from seawater and reported to be capable of degrading cyclohexylacetate52. We also detected enzymes responsible for alkane, cycloalkane (even monooxygenase enzymes), and naphthalene degradation under aerobic conditions and alkane, ethylbenzene, toluene, and naphthalene degradation under anaerobic conditions in KhS_39 affiliated to this genus (Fig. 6).The MAGs KhS_15 and KhS_26 affiliated to Roseovarius had the enzymes for degrading alkane (alkane monooxygenase, aldehyde dehydrogenase), cycloalkane, naphthalene, and phenanthrene under aerobic and toluene and naphthalene under anaerobic condition. PAHs degradation has been reported for other representatives of this taxa as well53.The MAGs KhS_11 (a representative of Rhodobacteraceae) and KhS_53 (Marinobacter) had alkB/alkM, KhS_27 (GCA-2701845), KhS_29 (UBA5862) and KhS_40 (from Porticoccaceae family) had cddA, KhS_13 and KhS_21 (UBA5335) and KhS_38 (Oleibacter) had both alkB/alkM and xylM genes. They were among microbes that were initiating the degradation of alkane, cycloalkane and xylene compounds. Other MAGs recovered from Khark sediment were involved in the continuation of the degradation pathway. For example, KhS_1 was affiliated to the genus Halomonas and had different enzymes to degrade intermediate compounds. Halomonas representatives have been frequently isolated from oil-polluted environments54. The phylum Krumholzibacteria has been first introduced in 2019 and reported to contain heterotrophic nitrite reducers55. Two MAGs, KhS_5 and KhS_10, were affiliated to this phylum and contained enzymes involved in the anaerobic degradation of toluene, phenol, and naphthalene (Fig. 6).The MAGs KhS_12 and KhW_31 affiliated to the genus Flexistipes, in Deferribacterales order, were reconstructed from both KhW and KhS samples. Deferribacterales are reported to be present in the medium to high-temperature oil reservoirs with HC degradation activity and also in high-temperature oil-degrading consortia56. The type strain of this species was isolated from environments with a minimum salinity of 3% and a temperature of 45–50 °C57. The presence of this genus in KhS could be due to natural oil seepage from the seabed as PG reservoirs mainly have medium to high temperature and high salinity. Enzymes involved in the degradation of alkane, phenol, toluene and naphthalene under anaerobic conditions were present in MAGs KhS_12 and KhW_31.As mentioned earlier, Flavobacteriales are potent marine indigenous HC degraders that bloom in response to oil pollution58. Flavobacteriales affiliated MAGs (KhW_2, KhW_3, AW_21, and AW_33) were recovered from KhW and AW and mostly contained enzymes that participate in the degradation of aromatic compounds under anaerobic conditions. KhW_2 and KhW_3 also had both alkB/M (alkane monooxygenase) and xylM enzyme, which initiates the alkane and xylene bioremediation in Khark water. Among other recovered MAGs from KhW sample, KhW_18 (UBA724), KhW_24 (clade SAR86), KhW_43 (UBA3478) had alkB/M, and xylM, KhW_24 (clade SAR86) had alkB/M and cddA, and KhW_28 (from Rhodobacteraceae family) had alkB/M and pheA genes in their genome to initiate the degradation process (Fig. 6).Marinobacter (KhW_15) was another MAG reconstructed from KhW sample. This genus is one of the main cultivable genera that play a crucial role in the bioremediation of a wide range of oil derivatives in polluted marine ecosystems54.Marine Group II (MGII) and Poseidonia representatives of Thermoplasmatota that have been reported to be nitrate-reducing Archaea59, were recovered from AW sample (AW_40, AW_45) and contained several enzymes contributing in alkane (alkane monooxygenase, aldehyde dehydrogenase) and naphthalene/phenanthrene/phenol/xylene degradation (decarboxylase) under aerobic conditions. The HC degradation potential of representatives of this phylum has been previously reported60.In the Asalouyeh water sample, MAGs AW_25 (UBA4421) and AW_38 (UBA8337) had cddA, AW_21 (UBA8444) had catA, AW_11 (Poseidonia) and AW_17 (from Rhizobiales order) had both alkB/M and xylM, and AW_4 (UBA8337) had catA and pheA genes and had potential to trigger the breakdown of their corresponding oil derivatives.Other recovered genomes had the potential to metabolize the product of initiating enzymes. For instance, AW_23 contained enzymes involved in the degradation of naphthalene, phenol and cyclododecane and was affiliated to the genus Alteromonas (Fig. 6).Three recovered MAGs of HW affiliated to Pseudomonadales (HW_23), Poseidoniales (HW_24), and Flavobacteriales (HW_30) contained some initiating enzymes to degrade cyclododecane/biphenyl/toluene, alkane/xylene, and alkane/xylene/naphthalene/phenanthrene, respectively. A representative of Heimdallarchaeia that are mainly recovered from sediment samples was reconstructed from the Hormuz water sample (HW_28). It had a completeness of 81% and contained enzymes involved in anaerobic degradation of alkanes. This archaeon could potentially be an input from the neighboring OMZ as this phyla include representatives adopted to microoxic niches61. Containing genes with the potential to initiate the oil derivative degradation in the input water with no oil exposure reiterates the intrinsic ability of marine microbiota for HC degradations and oil bioremediation.While 16S rRNA provides an overview of the community, MAGs provide the possibility to inspect the metabolic capability of the microbiota. We decided to provide both in this manuscript as we believe they are complementary. Having the full picture provided by the combination of these analyses allows for a better understanding of the community structure and their metabolic capabilities. This is even more evident for sediment samples as they are highly diverse, and reconstructing MAGs from sediment metagenomes is still a bottlenecks. In this case, we rely more on the 16S rRNA to provide an overall view of the community composition.This said, we see similar taxonomic distribution in the MAGs and 16S rRNA e.g., the prevalence of Flavobacteriales and Rhodobacterales in KhW and KhS, Synechococcales, and Desulfobacteriales and Flavobacteriales in HW, HS and AW samples, respectively.Additionally, some rare microbiota representatives were recovered among reconstructed MAGs. For example, the Immundisolibacterales showed an abundance of only 0.8% in the KhS sample based on 16S rRNA but the recovered KhS_63 MAG was affiliated to this taxon. Notably, this MAG contained many genes involved in hydrocarbon degradation having the highest potential in hydrocarbon degradation. More

Model-guided procedure guides the exploration of butyrate production landscapesWe aimed to explore the butyrate production landscape as a function of community composition to decipher microbial interactions shaping butyrate production. Exploring the butyrate production functional landscape is a major challenge because the number of sub-communities increases exponentially with the number of species43. To investigate the landscape, we developed a modeling framework to guide the iterative design of informative experiments (Fig. 1a, b). Microbial interactions can impact growth or metabolite production by influencing the availability of ecological niches or facilitating metabolite degradation. To capture these two types of interactions, we implemented a two-stage modeling framework to determine the contributions of microbial interactions to species growth and community assembly or metabolite production. In the first stage, a dynamic ecological model, referred to as the generalized Lotka–Volterra model (gLV), predicts community assembly. The second stage predicts metabolite production as a function of the resulting community composition (Fig. 1b). The gLV model is a set of coupled ordinary differential equations that capture the temporal change in species abundances due to monospecies growth parameters and inter-species growth interactions (see the “Methods” section)16. To estimate parameters for the gLV model, we use Bayesian parameter inference techniques to determine the uncertainty in our parameters based on biological and technical variability in the experimental data44.Fig. 1: Iterative modeling framework to predict microbial community assembly and metabolic function.a Two-stage modeling framework for predicting community assembly and function. The generalized Lotka–Volterra model (gLV) represents community dynamics. A Bayesian Inference approach was used to determine parameter uncertainties due to biological and technical variability. A linear regression model with interactions maps assembled community composition to metabolite concentration. Combining these two models enables prediction of a probability distribution of metabolite concentration from initial species abundances. b Design–Test–Learn cycle for model development. First, we use our model to explore the design space of possible experiments (i.e. different initial conditions of species presence/absence) and design communities that span a desired range of metabolite concentrations. Next, we use high-throughput experiments to measure species abundance and metabolite concentration. Finally, we evaluate the model’s predictive capability and infer an updated set of parameters based on the new experimental measurements. c Phylogenetic tree of the synthetic human gut microbiome composed of 25 highly prevalent and diverse species. Branch color indicates phylum and underlined species denote butyrate producers.Full size imageOur metabolite production model consists of a linear regression model with interaction terms mapping community composition (i.e. abundance of each species) at a specific time point to the concentration of an output metabolite at that time. This model was based on a phenomenological model of metabolite production used in bioprocess engineering expanded to microbial communities (see the “Methods” section). In the regression model, the first-order terms capture the monospecies production per unit biomass and the interaction terms represent the impact of inter-species interactions on metabolite production per unit biomass (i.e. deviations from constant metabolite production per unit biomass19). To estimate parameters for the regression model, we use Lasso regression to identify the most impactful interactions. Altogether, the composite gLV and regression model predicts the probability distribution of the metabolite concentration given an initial condition of species abundances (Fig. 1b, see the “Methods” section).In metabolic and protein engineering, a design–test–learn cycle (DTL) has been used to design biomolecules45 or metabolic pathways46 with properties that satisfy desired performance specifications. We hypothesized that this engineering-inspired approach could be used to explore community design spaces and understand the composition–function mapping for butyrate production. Each cycle consisted of: (1) a design phase wherein we used our model informed by experimental observations to simulate a vast number of potential community compositions to identify sub-communities that satisfied biological objectives (i.e. desired butyrate concentrations), (2) a test phase wherein the selected sub-communities were assembled and species abundance and butyrate concentration were measured, and (3) a learn phase wherein patterns in our experimental data were used to estimate model parameters and to extract information about the key microbial interactions influencing community assembly and butyrate production.Two-stage model enables efficient exploration of low richness community design spaceTo develop a system of microbes representing major metabolic functions in the gut, we selected 25 prevalent bacterial species from all major phyla in the human gut microbiome47 (Fig. 1c, Supplementary Data 1). This community contained five butyrate-producing Firmicutes which have been shown to play important roles in human health and protection from diseases (Fig. 1c, Supplementary Data 1). Due to the lack of a defined medium that universally supports the growth of gut microbes, most in vitro studies use undefined media, making it difficult to interrogate the effects of unknown components on community behaviors48. To maximize our knowledge of the substrates available to the communities, we developed a chemically defined medium to grow the synthetic communities (see the “Methods” section).Based on previous studies using pairwise communities to predict higher richness community behaviors16,18,49, we hypothesized that training our model on single and pairwise community measurements would provide an informative starting point for mapping composition–function relationships determining butyrate production. To do so, we first measured time-resolved growth of single species and observed a wide variety of growth dynamics within each phylum, including disparate growth rates and carrying capacities (Supplementary Fig. 1). We assembled each pairwise community containing at least one butyrate producer (the focal species of our system50) and measured species abundance and the concentrations of organic acid fermentation products (including butyrate, lactate, succinate, and acetate) after 48 h. The pairwise consortia displayed a broad range of butyrate concentrations of 0–50 mM (Fig. 2a).Fig. 2: Exploring the predicted butyrate production of 3–5 member communities with a model trained on 1–2 species communities.a Categorical scatter plot of butyrate production in 1–2 species and 24–25 species communities. Solid datapoints indicate the mean of the biological replicates which are represented by transparent datapoints connected to the mean with transparent lines. The colors indicate which butyrate producer was present in the community with green indicating the presence of multiple butyrate producers. DP− and AC− indicate the 24-species communities lacking Desulfovibrio piger (DP) and Anaerostipes caccae (AC), respectively. b Predicted medians (black line) and 60 percent confidence intervals (gray bars) of butyrate concentration for all 3–5 member communities containing at least one butyrate producer (46,591 community predictions). Colored lines indicate median and 60 percent confidence interval of butyrate production of communities chosen for the experimental design with the color indicating the number of species in the community (156 communities). Subplots separate groups of communities based on the identities of the combination of butyrate producers specified. c Scatter plot of measured butyrate versus predicted butyrate for 3–5 species communities. Colors indicate which butyrate producer was present in the community as in a. Biological replicates (n = 1–5, depending on the community, exact values in source data) are indicated by transparent squares connected to the corresponding mean, which is represented by the large data point. Prediction error bars (x-axis) indicate the 60% confidence interval of the predicted butyrate distribution as in b, with the center being the median prediction. Dashed line indicates the linear regression between the mean measured butyrate and the median predicted butyrate. Indicated statistics are for Pearson correlation (two-sided). Source data are available in the Source Data file.Full size imageSingle-species deletion communities have been used to investigate the contributions of individual species to a community function13,16. Therefore, we characterized the full 25-species community and each single-species deletion sub-community (i.e. 24-member consortia). In stark contrast to the pairwise communities, the 24- and 25-species communities exhibited similar low butyrate production (~2–22 mM Butyrate). The absence of only two species Desulfovibrio piger (DP) (~22 mM Butyrate) and Anaerostipes caccae (AC) (~2 mM Butyrate) resulted in a significant increase or decrease in butyrate concentration compared to the remaining 24-member and 25-member communities (Fig. 2a, Supplementary Fig. 2a). In addition, the concentrations of all measured organic acids spanned a much smaller range in the 24 and 25-member communities than the single and pairwise consortia (Supplementary Fig. 2b). These results suggest that high richness communities may trend towards a similar low butyrate-producing state that is difficult to change by the deletion of most single species and motivates a model-guided design strategy for exploring how community richness shapes butyrate production.To determine whether individual and pairwise communities could predict community composition and butyrate production of low richness communities (i.e. 3–5 species), we estimated the parameters of our model based on experimental measurements. Our initial model was informed only by pairwise communities that contained at least one butyrate producer (Supplementary Data 2, M1) and was thus naïve to all interactions between non-butyrate producers. We assumed that the unobserved growth interactions could be predicted based on trends in measured interactions across phylogenetic relatedness (see the “Methods” section)16. However, the resulting model was unable to predict butyrate production in the 24-and 25-member communities (Supplementary Fig. 3), which we attributed to missing information about non-butyrate producer interactions in our training data. Thus, we used our model to explore a low richness design space of 3–5 species communities based on the assumption that pairwise interactions would be more observable in low than high richness (i.e. >10 species) communities to identify an improved parameter estimate for non-butyrate producer interactions.We used our initial M1 model to predict the probability distributions of butyrate production for all 3–5 species communities containing at least one butyrate producer (46,591 communities). The predicted butyrate production varied substantially based on the combination of butyrate producers present in each community (Fig. 2b). In addition, we observed variations in the shapes of the probability distributions based on how the uncertainty in growth prediction propagated through the regression model. For instance, the butyrate concentration in the AC, Roseburia intestinalis (RI) pairwise community was lower than the AC monoculture, even though RI was low abundance, resulting in a high magnitude negative parameter in the regression model for a production interaction between AC and RI (Supplementary Data 3). Due to the uncertainty in the growth parameters, the model predicted that RI would grow substantially in a subset of the 3–5 member simulations containing both AC and RI. The variability in predicted RI growth combined with the high magnitude negative interaction parameter between AC and RI resulted in distributions where the median butyrate concentration was high (i.e. for simulations where RI did not grow substantially), and the 60 percent confidence interval extended to 0 mM butyrate (i.e. when RI grew substantially) (Fig. 2b). In sum, these results demonstrate that the shape of the predicted probability distributions can provide information about the uncertainty in species growth based on experimental observations.Based on the simulations, we selected 156 communities that spanned a broad range of predicted butyrate concentrations across the butyrate producer groups to evaluate experimentally (Fig. 2b). The model prediction exhibited good agreement with the rank order of butyrate production (Spearman rho = 0.84, p = 9*10−43) (Fig. 2c) and species abundance (Spearman rho = 0.76, p = 3*10−122) (Supplementary Fig. 4a–d), demonstrating that our initial model could predict a wide range of butyrate production in low richness communities.Composition–function landscape predicts contributions of growth and production interactionsEncouraged by our model’s predictive ability, we sought to explore composition–function relationships in higher richness communities (i.e. >10 species) using a model with updated parameters based on measurements of the 3–5 member communities (Supplementary Data 2, M2). Since the human gut microbiome exhibits functional redundancy in butyrate pathways51, we first used model M2 to simulate the assembly of all communities containing all five butyrate producers (5-butyrate producer or 5BP, 1,048,576 total) to map the composition–function landscape for butyrate production (Fig. 3a). In addition, we simulated the assembly of all communities containing the four butyrate producers excluding AC (4-butyrate producer or 4BP, 1,048,576 total) to understand how the composition–function landscape changes in the absence of the most productive butyrate producer (Fig. 3b). The majority of 5BP communities were predicted to have higher butyrate concentration than any of the 4BP communities (Fig. 3a, b), consistent with the substantial decrease in butyrate in the AC deletion community observed previously (Fig. 2a).Fig. 3: Community composition–function landscapes reveal key role of production interactions on A. caccae and negative impact of D. piger on butyrate production.a Scatter plot of predicted total butyrate producer abundance versus predicted butyrate concentration for all possible communities in which all five butyrate producers are present (1,048,576 communities). Histograms indicate the butyrate concentration distribution across the given axis. Communities are colored according to the presence (red) or absence (blue) of D. piger (DP). Blue and red dashed lines indicate the linear regression of communities with (red, y = −2.3x + 25.8, r = −0.34) or without (blue, y = 3.1x + 28.0, r = 0.76) DP. The white star indicates the full 25-member community and black star indicates the community of five butyrate producers alone. Large data points indicate communities chosen for experimental validation. Black triangles indicate leave-one-out communities, black circles indicate designed communities, and gray squares indicate random communities, with open/closed symbols indicate the absence/presence of DP. b Scatter plot of predicted total butyrate producer abundance versus predicted butyrate concentration for all possible communities in which all four butyrate producers excluding AC are present (1,048,576 communities). Histograms indicate the butyrate concentration distribution. Gray dashed line indicates the mean predicted butyrate concentration across all communities. Black dashed line indicates the linear regression of all communities (y = 13.2x−0.1, r = 0.64). The white star indicates the full 24-member community and the black star indicates the four butyrate producers alone. Large data points indicate communities chosen for experimental validation. c, d Scatter plots of experimental measurements of total butyrate producer abundance versus butyrate concentration for communities with (c) and without (d) AC. Data point shapes correspond to the legends in (a) and (b) and represent the mean of biological replicates, which are shown as small datapoints connected to the corresponding mean with lines (n = 2 except for 5 BP, 24 and 25 species communities where n = 5–8). Dashed line in (d) indicates the linear regression (y = 8.9x−0.5). e Comparison of butyrate concentration in communities from a and b with and without DP for both the designed and random experimental sets for the 5BP communities and designed set for the 4BP communities. Data point shapes correspond to the legends in a and b and represent the mean of biological replicates, which are shown as small datapoints connected to the corresponding mean with lines. Box and whisker plots represent the median (center line), quartiles (box), and range (whiskers) of the mean butyrate concentration for each community, excluding outliers (points outside 1.5 times the interquartile range). Indicated p-values are from a Mann–Whitney U test (5BP Designed: n = 28 for DP+ and n = 54 for DP−; 5BP Random: n = 27 for DP+ and n = 55 for DP−; 4BP: n = 42 for DP+ and n = 42 for DP−). f Butyrate concentration per unit biomass as a function of sulfide concentration after 24 h of growth. Butyrate concentration per biomass was normalized to the no sulfide condition. Circles indicate the mean of biological replicates, with individual replicates shown as transparent squares (n = 4). Inset: Butyrate concentration per biomass (mM OD600−1) for AC with and without the addition of 1.6 mM sulfide across time (n = 3). Endpoint sulfide concentrations were higher in the data shown in the inset than in the main figure (Supplementary Fig. 6). Source data are available in the Source Data file.Full size imageThe relationship between butyrate producer abundance and butyrate can provide insight into the contributions of growth and production interactions in the presence and absence of AC (Fig. 3a, b). If butyrate producer abundance correlates with butyrate, then growth interactions drive butyrate production, whereas the contributions of production interactions would reduce the strength of this correlation. The 5BP communities were predicted to have a large contribution of production interactions as evidenced by a weak correlation between butyrate concentration and butyrate producer abundance (Spearman rho = 0.17, p  More

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