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Microbiome composition through the redundancy of oxalate-degrading bacteria, critically influence oxalate metabolism, explaining variable outcomes of Oxalobacter formigenes therapy and offers insights for improving probiotic selection and clinical trial strategies.

Decades of research have made clear that host-associated microbiomes touch all facets of health. However, effective therapies that target the microbiome have been elusive given its inherent complexity. Here, we experimentally examined diet-microbe-host interactions through a complex systems framework, centered on dietary oxalate. Using multiple, independent molecular, animal, and in vitro experimental models, we found that microbiome composition influenced multiple oxalate-microbe-host interfaces. Importantly, administration of the oxalate-degrading specialist, Oxalobacter formigenes, was only effective against a poor oxalate-degrading microbiota background and gives critical new insights into why clinical intervention trials with this species exhibit variable outcomes. Data suggest that, while heterogeneity in the microbiome impacts multiple diet-host-microbe interfaces, metabolic redundancy among diverse microorganisms in specific diet-microbe axes is a critical variable that may impact the efficacy of bacteriotherapies, which can help guide patient and probiotic selection criteria in probiotic clinical trials.

Bacterial regional demonstration after global dissemination is an essential pathway for selecting distinct finesses. However, the evolution of the resistome during the transition to endemicity remains unaddressed. Using the most comprehensive whole-genome sequencing dataset of Salmonella enterica serovar Gallinarum (S. Gallinarum) collected from 15 countries, including 45 newly recovered samples from two related local regions, we established the relationship among avian-specific pathogen genetic profiles and localization patterns. Initially, we revealed the international transmission and evolutionary history of S. Gallinarum to recent endemicity through phylogenetic analysis conducted using a spatiotemporal Bayesian framework. Our findings indicate that the independent acquisition of the resistome via the mobilome, primarily through plasmids, transposons, and prophages, shapes a unique antimicrobial resistance profile among different lineages. Notably, the mobilome-resistome combination among distinct lineages exhibits a geographical-specific manner, further supporting a localized endemic mobilome-driven process. Collectively, this study elucidates resistome adaptation in the endemic transition of an avian-specific pathogen, likely driven by the localized farming style, and provides valuable insights for targeted interventions.

Clostridioides difficile is an urgent threat in hospital-acquired infections world-wide, yet the microbial composition associated with C. difficile, in particular in C. difficile infection (CDI) cases, remains poorly characterised. To investigate the gut microbiome composition in CDI patients, we analysed 534 metagenomes from 10 publicly available CDI study populations. We then tracked C. difficile on a global scale, screening 42,900 metagenomes from 253 public studies. Among the CDI cohorts, we detected C. difficile in only 30% of the stool samples from CDI patients. However, we found that multiple other toxigenic species capable of inducing CDI-like symptomatology were prevalent. In addition, the majority of the investigated studies did not adhere to the recommended guidelines for a correct CDI diagnosis.In the global survey, we found that C. difficile prevalence, abundance and biotic context were age-dependent. C. difficile is a rare taxon associated with reduced diversity in healthy adults, but common and associated with increased diversity in infants. We identified a group of species co-occurring with C. difficile exclusively in healthy infants, enriched in obligate anaerobes and in species typical of the healthy adult gut microbiome. C. difficile in healthy infants was therefore associated with multiple indicators of healthy gut microbiome maturation.Our analysis raises concerns about potential CDI overdiagnosis and suggests that C. difficile is an important commensal in infants and that its asymptomatic carriage in adults depends on microbial context.

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Methods Data availability References Decision letter Author response Article and author information Metrics Abstract Biological wastewater treatment plants (BWWTP) are considered to be hotspots for the evolution and subsequent spread of antimicrobial resistance (AMR). Mobile genetic elements (MGEs) promote the mobilization and dissemination of antimicrobial resistance genes (ARGs) and are thereby critical mediators of AMR within the BWWTP microbial community. At present, it is unclear whether specific AMR categories are differentially disseminated via bacteriophages (phages) or plasmids. To understand the segregation of AMR in relation to MGEs, we analyzed meta-omic (metagenomic, metatranscriptomic and metaproteomic) data systematically collected over 1.5 years from a BWWTP. Our results showed a core group of 15 AMR categories which were found across all timepoints. Some of these AMR categories were disseminated exclusively (bacitracin) or primarily (aminoglycoside, MLS and sulfonamide) via plasmids or phages (fosfomycin and peptide), whereas others were disseminated equally by both. Combined and timepoint-specific analyses of gene, transcript and protein abundances further demonstrated that aminoglycoside, bacitracin and sulfonamide resistance genes were expressed more by plasmids, in contrast to fosfomycin and peptide AMR expression by phages, thereby validating our genomic findings. In the analyzed communities, the dominant taxon Candidatus Microthrix parvicella was a major contributor to several AMR categories whereby its plasmids primarily mediated aminoglycoside resistance. Importantly, we also found AMR associated with ESKAPEE pathogens within the BWWTP, and here MGEs also contributed differentially to the dissemination of the corresponding ARGs. Collectively our findings pave the way toward understanding the segmentation of AMR within MGEs, thereby shedding new light on resistome populations and their mediators, essential elements that are of immediate relevance to human health. Editor's evaluation This paper reports important results regarding the presence and potential dissemination of antibiotic resistance genes in wastewaters by convincingly combining analysis of gene abundance, expression, and association with mobile genetic elements and bacterial taxa. Via systematic evaluation and implementation of multiple tools, the authors provide a valuable approach for monitoring antibiotic resistance genes in the environment and assessing their dispersal and possible risks to human health. https://doi.org/10.7554/eLife.81196.sa0 Decision letter eLife's review process Introduction Throughout human history, bacterial infections have been a major cause of both disease and mortality (Bonilla and Muniz, 2009). The discovery as well as the subsequent development and medical use of antibiotics have provided effective treatment options which limited the development and spread of bacterial pathogens. However, the use of antibiotics has exacerbated the emergence of antimicrobial resistance (AMR) in both commensal and pathogenic bacteria (Wright, 2007). As a result, AMR, as the ‘silent pandemic’, has become a prevalent threat to human health (Brogan and Mossialos, 2016; Mahoney et al., 2021; O’Neill, 2014). From a public health perspective, biological wastewater treatment plants (BWWTPs) are considered hotspots of AMR due to the convergence of antibiotics with resistant, potentially pathogenic microorganisms originating from both the general population as well as agriculture, healthcare services and industry (Alexander et al., 2020; Rodríguez-Molina et al., 2019). Additionally, the mobilization of antimicrobial resistance genes (ARGs) through rampant horizontal gene transfer (HGT) promotes the dissemination of AMR within the BWWTP microbial community (von Wintersdorff et al., 2016). Therefore, BWWTPs represent an environment exceptionally suited for the evolution and subsequent spread of AMR (Calero-Cáceres et al., 2014; Chen et al., 2013). To date, more than 32 studies have documented the role of BWWTPs as key reservoirs of AMR (Fouz et al., 2020). Furthermore, BWWTPs generally do not contain the necessary infrastructure to remove either ARGs or resistant bacteria, which are released into the receiving water via the effluent, promoting its spread in the environment at large (Alexander et al., 2020). Most often these are surface water bodies such as rivers, which contribute to the further dissemination of AMR and resistant bacteria among environmental microorganisms (Singer et al., 2016). Acquired resistance may in turn be carried over to humans and animals using these water resources. In fact, there is strong evidence suggesting that ARGs from environmental bacteria can be taken up by human-associated and pathogenic bacteria (Nadeem et al., 2020; Trinh et al., 2018). From an epidemiological and surveillance perspective, BWWTPs also provide samples representative of entire populations (Hendriksen et al., 2019). As such, BWWTPs have recently been crucial for the monitoring of SARS-CoV-2 within the human population (Herold et al., 2021). Overall, to increase our understanding of the dissemination of AMR and the underlying mechanisms as well as its general prevalence, it is necessary to map the resistome of various environments starting with biological BWWTPs because it is critical to unravel the extent to which they act as reservoirs for the dissemination of antimicrobial resistance genes (ARGs) to bacterial pathogens. Moreover, understanding the community-level overviews of the ARG potential and its expression, coupled with population-level linking, including to pathogens, may allow for efficient monitoring of pathogenic and AMR potential with broad impacts on human health. The presence of resistance genes and mobile genetic elements (MGEs) along with sub-inhibitory antibiotic selection pressures may facilitate HGT of ARGs into new hosts through the mobilome (von Wintersdorff et al., 2016). Previous work has in particular shown that antibiotic selection pressures may alter HGT processes, thereby increasing the number of resistance elements which reside on mobile DNA (Datta and Hughes, 1983). Acquisition of ARGs via MGEs primarily occurs through two mechanisms: conjugation or transduction (MacLean and San Millan, 2019). In conjugation, plasmids carrying one or more resistance genes are transferred between microorganisms (Carattoli, 2013), while in transduction bacteriophages carrying ARGs infect bacteria and integrate their genome into those of the host thereby conferring resistance (Chiang et al., 2019). Of these mechanisms, conjugation is often thought to have the greatest influence on the dissemination of ARGs, while transduction is deemed less important (von Wintersdorff et al., 2016). In general terms, studies concerning AMR and its dissemination focus either on phage (Lood et al., 2017; Strange et al., 2021) or plasmids solely (Li et al., 2019). Alternatively, the two are treated collectively (Alexander et al., 2020; Che et al., 2019) without a comprehensive comparative analysis. This circumstance has created a knowledge gap whereby the contributions of plasmids and phages as independent entities to AMR transmission within complex communities, such as those found in biological BWWTPs, is largely unknown. To shed light on the dissemination and potential segregation of AMR within MGEs in a WTTP microbial community, we leveraged longitudinal meta-omics data (metagenomics, metatranscriptomics, and metaproteomics). Samples collected for 51 consecutive weeks over a period of 1.5 years were used to characterize the resistome. We found that several bacterial orders such as Acidimicrobiales, Burkholderiales, and Pseudomonadales were associated with 29 AMR categories across all timepoints. Our longitudinal analysis suggests that MGEs are important drivers of AMR dissemination within BWWTPs. More importantly, we reveal that MGEs, that is plasmidomes and phageomes, contribute differentially to AMR dissemination. Furthermore, we observed this phenomenon in clinically-relevant taxa such as the ESKAPEE pathogens (Reza et al., 2019), for which plasmids and phages were exclusively associated with specific ARGs. Collectively, our data suggest that BWWTPs are critical reservoirs of AMR which show clear evidence for the segregation of distinct AMR genes within MGEs especially in complex microbial communities. In general, we believe that these findings may provide crucial insights into the segregation of the resistome via the mobilome in any and all reservoirs of AMR, including but not limited to animals, humans, and other environmental systems. Results Longitudinal assessment of the resistome within a BWWTP To characterize the BWWTP resistome, we sampled a municipal BWWTP on a weekly basis over a 1.5-year period (ranging from 21-03-2011 to 03-05-2012) (Herold et al., 2020; Martínez Arbas et al., 2021). Metagenomic and metatranscriptomic reads were preprocessed, and both sets of reads co-assembled using the Integrated Meta-omic Pipeline as described previously (Narayanasamy et al., 2016). Subsequently, utilizing the PathoFact pipeline (de Nies et al., 2021) on the assembled contigs (Methods), we resolved the BWWTP resistome. This analysis revealed the presence of 29 different categories of AMR within the BWWTP. Subsequent longitudinal analyses highlighted enrichments in aminoglycoside, beta-lactam and multidrug resistance genes (Figure 1a). Concomitantly, we observed specific shifts in the AMR profiles over time. For example, a transient change at two timepoints (13-05-2011, 08-02-2012) highlighted a steep increase in resistance genes corresponding to glycopeptide resistance. Other AMR categories, such as diaminopyrimidine resistance, exhibited a less drastic but more fluid change in longitudinal abundance observable over multiple timepoints. Figure 1 with 1 supplement see all Download asset Open asset Longitudinal metagenomic and metatranscriptomic assessment of AMR. (a) ARG relative abundances over time within the BWWTP. (b) AMR categories at various timepoints categorized in four distinct groups based on presence/absence: Core (all timepoints), Prevalent (>75% of timepoints), Moderate (50–75% of timepoints), and Rare (<50% of all timepoints). (c) Relative abundance levels of expressed AMR categories over time within the BWWTP. Colors of all panels correspond to the AMR categories. Additionally, AMR categories were found to persist over time within the BWWTP (Figure 1b). A core group of 15 AMR categories in total were identified and found to be present across the 1.5-year sampling period. These included aminoglycoside, beta-lactam, and multidrug resistance genes, which contributed the most to the pool of ARGs. A further six (aminocoumarin, aminoglycoside:aminocoumarin, elfamycin, nucleoside, triclosan, and unclassified) AMR categories were found to be prevalent (>75% of all timepoints), while another three AMR categories were moderately (50–75% of all timepoints) present over time (Figure 1b). Five other categories were rarely present within the BWWTP, with resistance corresponding to acridine dye only present at six of the timepoints. Altogether, this emphasized that the BWWTP resistome varies over time, substantiating the requirement for a longitudinal analysis to obtain an accurate overview of the community’s overall resistome. Although the data thus far provided a clear overview of the BWWTP from a metagenomic perspective, it did not provide any information regarding AMR expression. We therefore utilized the corresponding metatranscriptomic dataset to investigate the expression of identified ARGs and monitor their changes, within the BWWTP, over time. In contrast to the metagenomic data, we observed a difference in AMR expression levels for several categories. Aminoglycoside, beta-lactam, and multidrug resistance identified at high levels in metagenomic information were also highly expressed within the BWWTP (Figure 1c). However, peptide resistance demonstrated the highest expression levels of all the AMR categories. We further investigated which ARG subtypes contributed to the identified peptide resistance category and found that ~90% of the expressed peptide resistance was directly contributed by a single resistance gene, YojI, which was found to be widely distributed among the major taxa comprising the BWWTP community such as the Comamonadaceae (Figure 1—figure supplement 1). YojI is typically associated with resistance to microcins by reducing the intracellular concentration of the toxic antibiotic peptide (Delgado et al., 2005). The high incidence of this gene indicates a broad adaptive strategy amongst the microbial populations in the BWWTP against these specific stressors. Microbial community and co-occurrence patterns of AMR Based on the previously identified microbial community (Herold et al., 2020), we hypothesized that the abundant and prevalent bacterial orders such as Acidimicrobiales were major contributors to the abundance in ARGs observable via metagenomics. To further investigate the contribution to AMR by the distinct microbial populations, we linked AMR genes to the contig-based taxonomic annotations of the assemblies (Methods). Herein, we identified a wide variety of taxonomic orders contributing to AMR, with multiple orders often contributing to the same resistance categories (Figure 2—figure supplement 1). Overall, taxa belonging to Acidimicrobiales, followed by Burkholderiales, were found to encode most of the ARGs (Figure 2a). Additionally, the abundance of ARGs linked to taxonomy varied over time. This was most noticeable during a five-week period (autumn: 02-11-2011 to 29-11-2011), where a decrease in abundance in ARGs linked to Acidimicrobiales and Bacteroidales was observed coinciding with an increase in ARG abundance in Pseudomonadales and Lactobacillales. Figure 2 with 2 supplements see all Download asset Open asset Microbial population-linked AMR. (a) Longitudinal ARG relative abundance levels linked to their corresponding microbial taxa (order level). Colors correspond to AMR categories. (b) Relative abundance of AMR categories linked to Candidatus Microthrix parvicella with colors corresponding to AMR categories. (c) Association network depicting co-occurrence patterns of individual antimicrobial resistance genes (ARGs) and microbial taxa on genus level. Nodes represent taxa or ARG with the node size representing the number of edges. The size of the edges represent the strength of interaction between the nodes. Since the order Acidimicrobiales was found to be linked to the highest abundance in ARGs, we further resolved the taxonomic affiliation and identified the species Candidatus Microthrix parvicella (hereafter known as M. parvicella) to be the main contributor to AMR. M. parvicella was previously found to dominate this microbial community (Martínez Arbas et al., 2021) and is a well-characterized bacterium commonly occurring in the BWWTP (Calusinska et al., 2018). Overall, aminoglycoside, beta-lactam, multidrug, and peptide resistance were found to be abundant in this species (Figure 2b), with aminoglycoside resistance demonstrating the highest expression levels as confirmed through metatranscriptomic analysis (Figure 2—figure supplement 2). Although it was not surprising to find a high abundance of ARGs linked to this species, the longitudinal variation in the abundances of these ARGs was nevertheless surprising (Figure 2b). Furthermore, coupled to a decrease in the abundance of M. parvicella itself (Martínez Arbas et al., 2021), we observed an almost complete decrease in ARGs at two timepoints (23-11-2011 and 29-11-2011). However, the M. parvicella population recovered to levels resembling the earlier timepoints in conjunction with the abundances in ARGs toward the end of the sampling period (Figure 2a, Figure 2b), underlining their overall contribution to AMR within this BWWTP. Alternatively, it is plausible that the dominance of M. parvicella is attributable to the encoded ARGs, which in turn, may confer a fitness advantage. In order to determine whether the abundances in ARGs may be directly associated with the community composition over time, co-occurrence patterns between ARG subtypes and taxa (genus level) were explored using the metagenomic data. Association network analyses (Figure 2c) demonstrated that ARGs, within or across ARG types and microbial taxa, showed clear and distinct co-occurrence patterns within the BWWTP. These patterns indicated a strong segregation of distinct, taxa-specific ARG subtypes within the BWWTP community over time. One clear example was that of M. parvicella which encoded different aminoglycoside resistance genes (Figure 2c). Thus, the abundance of this bacterium along with the aminoglycoside ARGs were highly correlated. Monitoring pathogenic microorganisms within BWWTPs In conjunction with the families observed within BWWTPs, we also found that certain ESKAPEE pathogens (Reza et al., 2019), such as Klebsiella spp. and Pseudomonas spp., demonstrated co-occurring patterns with ARGs (Figure 2c). As previously mentioned, BWWTPs represent a collection of potentially pathogenic microorganisms originating from, among others, the human population. Moreover, evidence suggests that ARGs from environmental and commensal bacteria can spread to pathogenic bacteria through HGT (MacLean and San Millan, 2019). Therefore, we assessed the presence of AMR in the extended priority list of pathogens (Table 1), characterized as such by the WHO (Tacconelli et al., 2018), using both metagenomics and metatranscriptomics. Table 1 WHO priority list for research and development of new antibiotics for antibiotics-resistant bacteria (Tacconelli et al., 2018). BacteriaPriorityOrganism detectedResistance detectedAcinetobacter baumanniiCritical++Pseudomonas aeruginosaCritical++EnterobacteriaceaeCritical++Enterococcus faeciumHigh++Staphylococcus aureusHigh++Helicobacter pyloriHigh++Campylobacter sppHigh+-Salmonella sppHigh++Neisseria gonorrhoeaeHigh+-Streptococcus pneumoniaeMedium++Haemophilus influenzaeMedium+-Shigella sppMedium++ Of the identified pathogens (Table 1), we found that Pseudomonas aeruginosa, both encoded and expressed the highest abundance of ARGs, followed by Acinetobacter baumannii, over time within the BWWTP (Figure 3). Moreover, an increase in ARG abundance and expression was observed in Pseudomonas aeruginosa during the time period, during which the otherwise dominant M. parvicella demonstrated reduced abundance (Figure 2b and Figure 3). Figure 3 Download asset Open asset Assessment of AMR associated with clinical pathogens. ARG relative abundances encoded and expressed by clinical pathogens over time within the BWWTP, with colors corresponding to the identified pathogens. Differential transmission of antimicrobial resistance via mobile genetic elements As previously described (Beceiro et al., 2013; Wee et al., 2020), the mobilome is a major contributor to the dissemination of AMR within a microbial community. Consequently, to understand (i) the role of MGE-mediated AMR transfer within the BWWTP, and (ii) to identify differential contribution of the mobilome to the dissemination of AMR, we identified both plasmids and phages within the metagenome and linked these to the respective ARGs. While, as expected, the majority of ARGs was found to be encoded on the bacterial chromosome (Figure 4—figure supplement 1), we also found that plasmids contributed to an average of 10.8% of all ARGs, while phage contributed to an average of 6.8% of all resistance genes, in agreement with the general hypothesis that conjugation has the greatest influence on the dissemination of ARGs (adj.p<0.05, One-way ANOVA) (MacLean and San Millan, 2019). This phenomenon, however, varied across time within the BWWTP (Figure 4a). Figure 4 with 2 supplements see all Download asset Open asset MGE-derived AMR within the BWWTP resistome. (a) Overall relative abundance of MGEs encoding ARGs. Contribution of plasmids to AMR (average of 10.8% of all ARGs) was found significantly increased compared to phages (average of 6.8% of all ARGs) (adj.p <0.05, One-way ANOVA). Colors depict the different MGE predictions (phage, plasmid, ambiguous) (b) Boxplots depicting significant (adj.p < 0.05, Two-way ANOVA) differential abundances of ARGs encoded by plasmids (blue) vs phages (green). (c) Relative abundance of the six significantly different AMR categories encoded on phages over time, with colors corresponding to AMR categories. (d) Relative abundance of the six significantly different AMR categories encoded on plasmids over time, with colors corresponding to AMR categories. When investigating the dissemination of AMR via MGEs, most reports typically focus on either phages or plasmids individually, or both as collective contributors to transmission (Slizovskiy et al., 2020). To date and to our knowledge, the respective contributions of phages and plasmids to AMR transmission have not been subjected to a comprehensive comparative analysis. To facilitate a systematic, comparative view of MGE-mediated AMR, we assessed the segregation of MGEs with respect to AMR and found that phages and plasmids contributed differentially to AMR (Figure 4—figure supplement 2). Specifically, we tested 28 AMR categories with respect to their association with MGEs and found a significant difference in six AMR categories when comparing ARGs encoded by phages and plasmids (adj.p <0.05, Two-way ANOVA)(Figure 4b). Aminoglycoside, bacitracin, MLS (i.e. macrolide, lincosamide, and streptogramin) and sulfonamide resistance were found to be primarily encoded by plasmids, whereas fosfomycin and peptide resistance were found to be associated with phages. To further understand AMR in relation to the community dynamics, we investigated the abundance and segregation of the above-mentioned significant resistance categories at different timepoints within the BWWTP. We observed ARG abundances varied over time both in phages (Figure 4c) as well as plasmids (Figure 4d). For instance, the abundance in aminoglycoside and sulfonamide resistance, which was encoded primarily by plasmids (Figure 5—figure supplement 1a), fluctuated widely over time in both phages and plasmids (Figure 4c). Additionally, plasmid-mediated sulfonamide resistance was reduced at 23-11-2011, followed by its highest abundance a week later (29-11-2011), while subsequently again decreasing. Similarly, in line with the above observations, fosfomycin and peptide resistance genes, while segregating within phages, demonstrated significant fluctuations over time (Figure 4d). In addition to the metagenome, we also contextualized the localization of the expressed ARGs within MGEs based on the metatranscriptomic information. Specifically, we found that plasmids demonstrated a significantly increased expression of aminoglycoside along with bacitracin and sulfonamide resistance genes, while the expression of glycopeptide, mupirocin and peptide resistance genes were primarily enriched in phages (Figure 5a). These observations pertaining to plasmid-mediated AMR were in line with the metagenomic findings (Figure 4b). Only peptide resistance was observed to be expressed via phages in contrast to the differential enrichment of fosfomycin resistance observable in the metagenomic data. Figure 5 with 2 supplements see all Download asset Open asset Taxonomic affiliations of MGE-derived resistance genes. (a) Boxplot depicting significant differential abundance (n=51 per group, adj.p <0.05, Two-way ANOVA) of ARGs expressed in plasmids vs phages. (b) network assessing the association of MGE-derived ARGs with the microbial taxa. of the representing potential of the AMR category to one MGE over the of the line representing which MGE the AMR is linked or phage and taxonomic orders which known clinical pathogens. (c) depicting the abundance of MGE-derived ARGs encoded expressed by clinical pathogens. Colors of all panels correspond to the MGEs and AMR categories. Taxonomic affiliations of MGE-derived resistance genes When assessing the differential contributions of MGEs to AMR, we found between plasmids and phages to the AMR categories and taxonomic affiliations (Figure For example, in the metagenomic data MGEs and were associated with the same AMR category and subsequently the same taxa. However, were observed with specific taxa associated with AMR either through plasmids or phages. For instance, MLS resistance in Bacteroidales and was mediated solely through plasmids, whereas the same resistance category was mediated by phage in segregation of AMR between taxa and As most bacteria MGEs, we whether the MGE-mediated AMR categories were linked to the abundance of of the earlier taxa. we found that peptide resistance encoded by M. parvicella was solely associated with phages, while aminoglycoside resistance was primarily with plasmids (Figure 5—figure supplement 1b). Other highly abundant taxa such as Pseudomonas and (Figure 5—figure supplement on the other were with sulfonamide resistance in addition to aminoglycoside resistance encoded on plasmids (Figure This was further within the data where in taxa such as Acidimicrobiales the expression levels of aminoglycoside resistance were solely associated with plasmids (Figure 5—figure supplement 2a). Additionally, in the peptide resistance was found to be expressed through phages (Figure 5—figure supplement 2b). We also found a clear segregation of the mobilome with respect to individual pathogens in the plasmids were exclusively associated with AMR in six of the taxa (Figure These included Klebsiella and Furthermore, the plasmids were also associated with conferring multidrug, beta-lactam, bacitracin, aminoglycoside, and sulfonamide resistance. were exclusively associated with glycopeptide and aminoglycoside resistance in Overall, our results revealed for the time the key segregation patterns of AMR via the mobilome in taxa that are of relevance to human health and Moreover, substantiating the metagenomic data, the pathogenic bacteria and were found to ARGs solely associated with plasmids (Figure Collectively, these findings an threat to health due to the potential dissemination of resistant pathogens across of AMR abundance and expression In order to our findings with the expression analyses on the BWWTP, we further used the corresponding data to information at the protein level. to the metagenome data we found protein expression linked to aminoglycoside, beta-lactam and multidrug resistance, over time within the BWWTP (Figure supplement 1). especially those linked to multidrug resistance were found to increase over time. To further unravel AMR expression and its across time, we the protein per gene, as in the by all of the data. The demonstrated levels of aminoglycoside and multidrug resistance within the BWWTP (Figure Specifically, conferring multidrug resistance were found to increase over time, which is in line with the and Furthermore, we contextualized the conferring AMR to their localization on We identified resistance categories, that is aminoglycoside, beta-lactam, multidrug, and resistance, to be expressed through MGEs (Figure Of these categories we found that aminoglycoside resistance, in with the gene and expression was more associated with plasmids than phages (adj.p <0.05, Two-way ANOVA). We further found that the MGE-mediated AMR categories were associated with specific microbial taxa. with plasmid-mediated aminoglycoside resistance found to be associated with the previously M. parvicella (Figure the other we did not identify any associated with the ESKAPEE pathogens via Figure with 1 supplement see all Download asset Open asset assessment of AMR. (a) Metagenomic and metatranscriptomic protein levels linked to AMR within the WTTP over time. (b) network assessing the protein levels from MGEs and associated taxa. Boxplots depicting significant differential (n=51 per group, adj.p <0.05, Two-way ANOVA) abundance of aminoglycoside resistance in phage in Candidatus Microthrix parvicella as well as Colors of all panels correspond to the MGEs and AMR categories. Discussion The

Article Figures and data Abstract Editor's evaluation Introduction Materials and methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Diarrhoea remains one of the leading causes of childhood mortality globally. Recent epidemiological studies conducted in low-middle income countries (LMICs) identified Shigella spp. as the first and second most predominant agent of dysentery and moderate diarrhoea, respectively. Antimicrobial therapy is often necessary for Shigella infections; however, we are reaching a crisis point with efficacious antimicrobials. The rapid emergence of resistance against existing antimicrobials in Shigella spp. poses a serious global health problem. Methods: Aiming to identify alternative antimicrobial chemicals with activity against antimicrobial resistant Shigella, we initiated a collaborative academia-industry drug discovery project, applying high-throughput phenotypic screening across broad chemical diversity and followed a lead compound through in vitro and in vivo characterisation. Results: We identified several known antimicrobial compound classes with antibacterial activity against Shigella. These compounds included the oral carbapenem Tebipenem, which was found to be highly potent against broadly susceptible Shigella and contemporary MDR variants for which we perform detailed pre-clinical testing. Additional in vitro screening demonstrated that Tebipenem had activity against a wide range of other non-Shigella enteric bacteria. Cognisant of the risk for the development of resistance against monotherapy, we identified synergistic behaviour of two different drug combinations incorporating Tebipenem. We found the orally bioavailable prodrug (Tebipenem pivoxil) had ideal pharmacokinetic properties for treating enteric pathogens and was effective in clearing the gut of infecting organisms when administered to Shigella-infected mice and gnotobiotic piglets. Conclusions: Our data highlight the emerging antimicrobial resistance crisis and shows that Tebipenem pivoxil (licenced for paediatric respiratory tract infections in Japan) should be accelerated into human trials and could be repurposed as an effective treatment for severe diarrhoea caused by MDR Shigella and other enteric pathogens in LMICs. Funding: Tres Cantos Open Lab Foundation (projects TC239 and TC246), the Bill and Melinda Gates Foundation (grant OPP1172483) and Wellcome (215515/Z/19/Z). Editor's evaluation Through a high throughput screen, the authors identify an oral carbapenem antibiotic, Tebipenem, effective against susceptible and multi-drug resistant Shigella strains and other enteric pathogens. This antibiotic was also effective when combined with other antibiotics and thus shows promise as a repurposed alternative for the treatment for severe diarrhea. https://doi.org/10.7554/eLife.69798.sa0 Decision letter eLife's review process Introduction Humanity is currently in the midst of a global antimicrobial resistance (AMR) crisis (Theuretzbacher, 2017; Antimicrobial Resistance Collaborators, 2022). Many of the antimicrobials that we rely on to treat and/or control infections caused by common bacterial pathogens have lost or are rapidly losing their efficacy. This situation has become typical for a host of diseases and pathogens but is arguably epitomised by enteric (diarrhoeal) infections that arise in low and middle-income countries (LMICs). Annually, diarrhoea accounts for approximately 1.3 million deaths globally, with the preponderance of these occurring in young children residing in LMICs (Kotloff, 2017). Due to a lack of other scalable approaches, antimicrobials are a key tool in limiting the impact of these infections, but the trajectory of AMR describes an escalating problem, especially in vulnerable populations. Diarrhoeal diseases are notoriously complex, as a wide range of pathogens can trigger the syndrome; but of all potential aetiological agents, bacteria belonging to genus Shigella pose the greatest AMR threat. Recent estimates suggest that Shigella are associated with ~125 million diarrhoeal episodes and 200,000 deaths annually (Khalil et al., 2018). Additionally, Shigella along with enterotoxigenic Escherichia coli (ETEC), were the most predominant bacterial pathogens in the stools of paediatric diarrhoeal patients in South Asia and sub-Saharan Africa (Kotloff et al., 2013). This study also found that Shigella were the most prevalent pathogen in diarrhoeal children aged 2–5 years; a reanalysis of stool samples from this study using more sensitive molecular approaches suggested that burden of Shigella may actual be double that of previous estimates (Liu et al., 2016). Clinical evidence strongly supports the use of antimicrobials to treat severe Shigella associated diarrhoea (Kotloff et al., 2018). Appropriate antimicrobial therapy reduces the duration of fever and diarrhoea associated with Shigella by 1–2 days, which in turn limits the severity of symptoms and the risk of further complications (Vinh et al., 2000; Christopher et al., 2010). Additionally, the shedding of the pathogen in the stools is restricted by antimicrobials, which then reduce onward transmission. As there is currently no licenced vaccine for Shigella, antimicrobials are one of the only reliable mechanisms available for disease control (Levine et al., 2007). However, Shigella are highly adept at acquiring multi-drug resistance (MDR) plasmids from other member of the Enterobacteriaceae and resistance mutations are commonly associated with ‘successful’ lineages (Baker et al., 2015; Chung The et al., 2019). Consequently, there has been an ‘arms race’ between the pathogen and antimicrobials, with the WHO ultimately recommending ciprofloxacin, azithromycin, pivmecillinam, and ceftriaxone as the empirical drug choices for Shigella infections (Williams and Berkley, 2018). Inevitably, resistance to these antimicrobials has emerged and increasingly resistant organisms, such as extensively drug resistant (XDR) Shigella sonnei in Vietnam (Thanh Duy et al., 2020), are spreading across LMICs (Chung The et al., 2019), in travellers returning from LMICs (Baker et al., 2016), and in men-who-have-sex-with-men (MSM) (Baker et al., 2015). Accordingly, the US-CDC has declared AMR Shigella a serious global health threat requiring new interventions and the WHO has placed them on the global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics (The World Health Organisation, 2007). After the genomics revolution of the 1990s, the research community embarked on a series of target-based drug discovery programmes to identify novel antimicrobial compounds. This strategy, because of lack of translation from target-activity to whole-cell activity, produced largely disappointing results (Gwynn et al., 2010). Conversely, phenotypic antimicrobial screens have proven to yield a higher hit rate than target-based approaches with several successful examples in infectious diseases (Gamo et al., 2010; Ballell et al., 2013). Aiming to identify compounds with activity against MDR Shigella, we established an academic-industrial collaboration (Ballell et al., 2016), creating a platform to perform in vitro phenotypic screening of ~1.7 million compounds against antimicrobial susceptible and MDR Shigella. We interrogated a collection of compounds spanning broad chemical diversity, comprised of a combination of naive chemistry, compounds originating from previous antibacterial drug discovery programs, and currently marketed antimicrobials. Using this approach, we identified several lead chemical classes with previously reported antimicrobial activity, opting to further characterise Tebipenem (Yao et al., 2016), an oral carbapenem, that could be rapidly accelerated into clinical testing to treat infections caused by MDR Shigella and other gastrointestinal pathogens. Materials and methods High-throughput screening of compound collections Request a detailed protocol We developed a high-throughput screening (HTS) method to identify compounds that could inhibit the growth of Shigella flexneri 2457T in Müller Hinton medium (Sigma Aldrich, UK). We adapted a resazurin reduction protocol from Franzblau et al. as surrogate readout for bacterial viability because it provided a stable signal, a low interference level, and was cost effective (Franzblau et al., 1998). Prior to the day of experimentation, one colony was used to inoculate 50 mL Mueller Hinton medium, and the culture was incubated overnight at 37 °C. The next day, 500 μL of the bacterial culture were inoculated into 50 mL fresh Mueller Hinton medium and bacterial growth was measured periodically at 600 nm. When the culture reached OD = 0.4–0.6, the inoculum was diluted to 1 × 106 CFU/mL; this bacterial suspension was added to compound pre-dispensed plates for screening. The assay was performed in 1536-well polystyrene assay plates (HiBase, μClear, Greiner Bio‐One) and used to screen ~1.7 million compounds from (a) GSK naïve chemical diversity, (b) GSK antibacterial programs, and (c) marketed antimicrobial agents. All commercially available chemicals were purchased from either Sigma Aldrich, UK, AK Scientific, or Fischer Chemicals. The GSK compound screening set was sub-selected from the 8 million compound GSK collection using the principles described by Harper and co-workers (Harper et al., 2004). The process to generate the structural filters to remove compounds bearing undesirable/reactive chemotypes and/or non-developable physicochemical properties has been described previously by Chakravorty et al., 2018. The set of 4000 compounds with known antibacterial pedigree from the GSK antibacterial programs was selected from GSK compound collection using an MIC <1 µM against E. coli as cut-off value for selection. The set of marketed antibacterial agents included most commercial antimicrobial agents. For screening, plates were prepared by dispensing 50 nl of compound from master plates at 1 mM in each well using a LabCyte Echo system. The final assay volume was 5 μl and the final compound concentration was 10 µM. The 11th and 12th columns were growth controls containing 50 nL of DMSO. The 34th and 35th columns contained 50 nL of Moxifloxacin at 20 μM as negative growth controls. Five µl of bacterial inoculum was dispensed into compound pre-dispensed plates using a Multidrop Combi dispenser (Thermo Scientific). Plates were agitated for 10 s to ensure mixing and incubated at 37 °C overnight (16–17 hr). Following overnight culture, 2 µl resazurin solution (Resazurin tablets for milk testing, BDH) was added to each well and incubated at room temperature in the dark for 3 hr. Fluorescence intensity was measured using an Envision plate reader (PerkinElmer). For IC50 and IC90 determination, data were normalised by using DMSO as positive control (100% bacterial growth) and 20 μM moxifloxacin as negative control (0% bacterial growth). Data were analysed using Base software and statistical cut-off analysis was used for ‘hit’ selection following a standard GSK protocol. To calculate the statistical cut-off, percentage growth inhibition was calculated per well according to the plate controls, these values were aggregated to calculate the robust mean and standard deviation (SD) of the distribution. The robust mean was calculated starting from the median and the MAD of the distribution with an iterative process that aimed to remove outliers. Once the mean and SD were calculated, all compounds above mean + 3 SD were selected as primary hits. After the initial screen, we generated fresh samples of the primary compound hits and tested them in duplicate against S. flexneri 2457T at 10 μM to confirm activity. The confirmed hit list was narrowed by applying the following filters: a 30% cut-off value, absence of undesirable structural features (e.g. electrophiles, peroxides, and Michael acceptors) and reduced physicochemical risk (maximum five aromatic rings and PFI <8)(Leeson and Young, 2015). The resulting compounds were progressed to dose-response assays, performed in duplicate at a concentration of 100–0.00169 μM against S. flexneri 2457T. Compounds with an IC50 <1 μM were clustered and progressed to evaluation against two MDR clinical Shigella isolates from Vietnam (S. flexneri EG478 and S. sonnei 02–1182). This additional evaluation was performed in duplicate, with compounds displaying an inhibition halo at 10 μM in both isolates progressed to a dose-response evaluation at compound concentrations 10–0.078 μM. Compounds that exhibited an MIC <10 μM in both isolates were selected. Three GSK control compound sets were used to validate our HTS approach (Coma et al., 2009). Firstly, a 10,000-compound validation set was used to fine tune Z´and S/B ratio values in HTS conditions. The 0.65% hit rate obtained in this test was aligned with the rate expected for whole cell screenings. Secondly, we used a 1000-compound nuisance set containing compounds selected for their high Inhibition frequency index (IFI; defined as the % of HTS campaigns in which a compound has shown >50% inhibition at 10 μM) to identify assays with high susceptibility to nuisance mechanisms, which can lead to identification of a high rate of false positive compounds. We obtained a hit rate of 0.39 with this set, corresponding to a low false positive rate. Lastly, an antibacterial tool compound set contained an ad hoc set of marketed antimicrobials with different modes of action. This set was evaluated using the two prioritised readout systems (resazurin and XTT) and was used to mitigate the risk of biasing the hit identification by the mechanism of action as both readouts displayed similar IC50 values in all compound families. Antimicrobial susceptibility testing and time kill curves performed in clinical isolates Request a detailed protocol Selected compounds, including Tebipenem, were profiled against a panel of 338 clinical isolates of enteric pathogens comprised of Shigella (Vietnam and Kenya; n = 135), non-typhoidal Salmonella (Vietnam; n = 117), Campylobacter (Vietnam; n = 75), and various pathogenic E. coli species (Vietnam; n = 7) (Butcher et al., 2014). The antimicrobial susceptibility profile of these organisms is described in Supplementary file 1. Compound screening and dose response assays have been described previously (Van Voorhis et al., 2016; Wiegand et al., 2008). Briefly, the selected bacteria were cultured on the Luria-Bertani agar overnight at 37 °C (Campylobacter isolates were grown in 2.5% Laked Horse Blood cation-adjusted Mueller Hinton medium (CAMHB, Sigma Aldrich) and incubated in microaerophilic conditions (10% CO2, 5% O2 and 85% N2) at 42 °C); colonies were added to sterile PBS to form 0.5 McFarland solutions. All chemical testing was performed at an initial concentration of 10 μM. Bacterial suspensions were evenly spread on Mueller-Hinton agar by sterile cotton swabs (Medical Wire – MWE). To test the chemicals, 5 μl of 10 μM solutions were pipetted onto the surface of the media and 5 μl of 10 μM of ciprofloxacin/meropenem was used as a positive control and sterile PBS as a negative control. Plates were incubated overnight at 37o C. The activity of the chemical was recorded as being active (yes) or non-active (no) for each of the tested chemicals. A non-active result was recorded when there was no evidence of bacterial inhibition, and the culture area was indistinguishable from the negative control. An active result was recorded when there was an obvious zone of inhibition of bacterial growth and the observed zone was comparable to that of the positive control, that is, an area without bacterial growth where the media could be observed. MICs were evaluated using the micro dilution assay in Müller Hinton broth using 1 mM chemical stock solutions, and a final concentration of 5 × 105 CFU/mL bacteria. Twofold serial dilutions were prepared in 96-well plates at concentration ranging from 1 μM to 7.8 nM. 100 μL bacteria were added and incubated at 37o C overnight; 10 μl of each solution was dropped onto Luria-Bertani agar and incubated overnight at 37 °C for the detection of bacterial growth. Results were interpreted as minimal concentration necessary to inhibit growth. Time-kill curve assays were performed in 20 mL volume in 50 mL tubes by culturing 5 × 105 CFU/mL Shigella spp. in Müller Hinton broth in the presence of four antimicrobial concentrations in doubling dilutions ranging from 0.5xMIC to 4xMIC. Bacteria were grown with agitation at 200 rpm at 37 °C and monitored over a time-course of 24 hr (0, 2, 4, 6, 8, and 24 hr). For each concentration and time point, 100 μL of bacterial culture were diluted and used to inoculate Luria-Bertani agar plates that were incubated at 37 °C overnight. Next, bacterial colonies were enumerated. Spontaneous resistant mutants Request a detailed protocol The frequency of spontaneous resistant mutants (FoR) was determined for Tebipenem, Moxifloxacin, ciprofloxacin, ampicillin, mecillinam, ceftriaxone, and rifampicin. Bacterial inoculum without drug was used as positive control. The bacteria inoculum (1 × 105 CFU/mL) for the assay was prepared by diluting a log phase incubated at 37 °C for 3–4 hr. Mueller Hinton agar plates were prepared containing 4xMIC and 10xMIC and inoculated with different concentrations of bacteria (1 × 109, 1 × 108, and 1 × 107 CFU/mL). Plates were prepared in duplicate and incubated overnight at 37 °C. The frequency of spontaneous resistant mutants was calculated by using the formula: FoR = resistant CFUs/inoculum CFUs. The mean of the two plates was reported. Synergistic assays Request a detailed protocol Before initiating drug combination assays, the MIC values for both compounds were determined against Shigella (Sf2457T), Salmonella (St14028), and E. coli (EcDH5a) isolates. Standard checkerboard titration method using isobolograms was used to study the in vitro interaction between Tebipenem and azithromycin, ciprofloxacin, ceftriaxone, mecillinam and an LpxC inhibitor. 20 μM moxifloxacin was used as negative control (100% bacterial growth inhibition) in one column and DMSO as positive control (100% bacterial growth). A total of 25 µl of 1 × 106 CFU/mL bacterial culture was added to each well of a 384-well plate (μClear; Greiner Bio‐One). Plates were incubated overnight at 37 °C. Next, 10 µl of resazurin were added and plates were incubated at room temperature for 3 hr. Bacterial growth inhibition was measured by fluorescence in a Envision plate reader (PerkinElmer). All assays were performed in duplicate. Combination studies with clinical isolates were performed in 96-well plates (Greiner) adding 200 μl of 1 × 106 CFU/mL bacterial culture and following the same checkboard titration method as described above. The range of final working concentrations of each antimicrobial was adjusted based on the MIC of each selected organism. MICs were determined for drug A and drug B alone and in combination. The MIC of both drugs in combination were expressed as fractions of the MIC of the drug alone normalised to 1, which represented the Fractional Inhibitory Concentration (FIC). The sum of FIC was expressed using the following equation: FIC = (MIC of drug A in combination / MIC of drug A alone) + (MIC of drug B in combination / MIC of drug B alone) (Le Minh et al., 2015; Petersen et al., 2006; Hall et al., 1983; European Committee for Antimicrobial Susceptibility Testing (EUCAST) of the European Society of Clinical Microbiology and Infectious Dieases (ESCMID), 2000). Extrapolation of orapenem doses approved in paediatric population to other animal species Request a detailed protocol Pharmacokinetic parameters of Orapenem in paediatric population at the approved doses (4–6 mg/kg, bid) were obtained from Sato et al. and pharmacokinetic parameters in rats were generated in house (Sato et al., 2008). Information regarding Orapenem oral bioavailability was from et al. (Sato et al., 2008). For the of Tebipenem pharmacokinetic behaviour in different animal species and and distribution volume across the different animal species were A pharmacokinetic was used for using software et al., 2018). The doses in the different animal species were calculated using Tebipenem drug as comparable vivo pharmacokinetic analysis in mice Request a detailed protocol All mice were performed in mice obtained from and in in of with and ad were for to the of the the mice between 20 and Tebipenem was in (Sigma 5% DMSO (Sigma Aldrich) in solution (Sigma Aldrich) at 0.5 for and in (Sigma Aldrich) in at 5 for oral Tebipenem pivoxil was in (Sigma Aldrich) in at 5 for oral For two µl of were per time and per animal by at 2, 4, 6, and 24 hr for and 1, 2, 4, 6, and 24 hr for oral Blood samples were and with µl sterile and at °C to To generate the the samples were at temperature and 10 were with and analysed by An to a was used for the detection of Tebipenem The was performed at in a column × at °C with (Sigma Aldrich) and as curves were included to with a of of and a high of of the several control samples were included medium, and high in all the deviation of was than were from the pharmacokinetic parameters were calculated using vivo pharmacokinetic analysis in Request a detailed protocol Blood samples for analysis of orally administered Tebipenem pivoxil in gnotobiotic were from the same in the study described Tebipenem pivoxil was in (Sigma Aldrich) in at 5 for oral whole samples and were from the Tebipenem pivoxil gnotobiotic piglets. Blood was from the the piglets. Blood was at 1, 2, and hr of the first dose of Tebipenem Blood samples were at for 10 at °C. was and at °C. Pharmacokinetic analysis was performed as above Orapenem distribution studies Request a detailed protocol To the distribution of Tebipenem pivoxil and in the different of the Tebipenem pivoxil was orally at 50 to four mice in animal was at 1, and hr and of the and were and at °C. For analysis of samples were at with and by 20 of were with 200 µl of and using plate analysis was performed as described in previous For 50 of the of each were and in a on with a solution of (Sigma Aldrich, UK). Next, of were obtained using a at °C and in for After the was obtained a for was Prior to samples for were in a The was conducted in a which As solution (Sigma Aldrich) in containing at was data were obtained using a Standard solution of Tebipenem and Tebipenem at were in control mice to the analysis compounds were analysed in positive using a For Tebipenem, compound was and the detection between were the most For Tebipenem the was We used a at 85% with a 50 100 at in were used for the data for Tebipenem was and for Tebipenem pivoxil All were displayed with a of A all by the mean of all data was is a the of is to the intensity with the in each For were and with (Sigma Aldrich) following protocol and in a vivo studies Request a detailed protocol The in vivo of Tebipenem Tebipenem and was evaluated using a previously of using S. flexneri et al., 2019). S. flexneri a bacterial as previously described and was from S. flexneri purchased from the of S. flexneri were grown at 37 °C in broth with agitation at For infections, an overnight S. flexneri broth culture containing of and of Shigella et al., was diluted and grown to hr). bacteria were and the resulting was in without and (Thermo Scientific). Bacteria were and was in to the concentration of 5 × 107 S. dose of bacteria was confirmed with using an for S. flexneri mice aged were and with 5 × 107 n = 10 per were in oral and mice were administered antimicrobials or control at and hr was used as positive control and administered at of were 24 hr and the and were was in 1 and on agar plates with to S. flexneri CFUs. were performed in Data was analysed for the and using an analysis of a log active to control with a hoc at the of of or were at a analysis which that values the The data from the which were all was vivo gnotobiotic studies Request a detailed protocol The primary of drug was determined by a in symptoms in with and Additional parameters of were the of bacterial in and results of bacterial and at the of the day For S. flexneri 2457T was grown overnight on agar plate from which a colony was further grown in broth overnight. Bacterial was measured and adjusted to The bacterial

Microbial enzymes catalyzing a variety of different chemical reactions contribute significantly in modern industry by improving existing processes. In addition, the use of enzymes in replacement of traditional chemical or mechanical processes helps to reduce energy demands and environmental pollution. However, enzymes must compete commercially with relatively inexpensive traditional industrial technologies. Meeting economical and commercial feasibility criteria depends on a number of enzymatic properties including the specificity to the substrate, stability in industrial enzymatic reaction conditions and catalytic efficiency. The microorganism used as an enzyme production host should be suitable for industrial scale fermentation. Filamentous fungi are being developed as one of the best enzyme production systems due to their ability to secrete high quantities of enzymes suitable for industrial applications. The industrial importance of filamentous fungi also includes the development and commercialization of new products derived from genetically engineered fungal strains expressing modified homologous and/or heterologous enzymes. In this chapter we will discuss the challenges and biotechnological approaches used in development of novel or improved enzymes for industrial applications as well as fungal strains with increased enzyme production and secretion capacity.

ABSTRACTSoil microbial diversity represents the largest global reservoir of novel microorganisms and enzymes. In this study, we coupled functional metagenomics and DNA stable-isotope probing (DNA-SIP) using multiple plant-derived carbon substrates and diverse soils to characterize active soil bacterial communities and their glycoside hydrolase genes, which have value for industrial applications. We incubated samples from three disparate Canadian soils (tundra, temperate rainforest, and agricultural) with five native carbon (12C) or stable-isotope-labeled (13C) carbohydrates (glucose, cellobiose, xylose, arabinose, and cellulose). Indicator species analysis revealed high specificity and fidelity for many uncultured and unclassified bacterial taxa in the heavy DNA for all soils and substrates. Among characterized taxa, Actinomycetales (Salinibacterium), Rhizobiales (Devosia), Rhodospirillales (Telmatospirillum), and Caulobacterales (Phenylobacterium and Asticcacaulis) were bacterial indicator species for the heavy substrates and soils tested. Both Actinomycetales and Caulobacterales (Phenylobacterium) were associated with metabolism of cellulose, and Alphaproteobacteria were associated with the metabolism of arabinose; members of the order Rhizobiales were strongly associated with the metabolism of xylose. Annotated metagenomic data suggested diverse glycoside hydrolase gene representation within the pooled heavy DNA. By screening 2,876 cloned fragments derived from the 13C-labeled DNA isolated from soils incubated with cellulose, we demonstrate the power of combining DNA-SIP, multiple-displacement amplification (MDA), and functional metagenomics by efficiently isolating multiple clones with activity on carboxymethyl cellulose and fluorogenic proxy substrates for carbohydrate-active enzymes.