The human gut hosts an array of microbes that form a complex community beginning shortly after birth. These microbes prime the immune system, help extract nutrients from the diet and offer protection against pathogens. Decades of research have shown that individuals who suffer from inflammatory bowel diseases (IBD) or other systemic disorders tend to have far less variety of gut microbes compared to healthy individuals. Yet it remains unclear to what extent the difference in microbial diversity is the cause of the disease or a consequence of it.

In 2023, a study suggested that the usual teamwork between different kinds of microbes breaks down during disease. Many microbes depend on each other to provide certain nutrients, while others can survive on their own. It could be that people with IBD lose most of the ‘dependent’ microbes and retain those that are more self-sufficient and thus able to survive in the stressed and deteriorating gut environment.

To test this hypothesis, Veseli et al. – who are part of the research group that performed the 2023 study – developed a computer program to quantify self-sufficient gut microbes in large numbers of stool samples collected from healthy individuals and patients with IBD. This revealed that individuals with IBD had higher numbers of self-sufficient microbes, while healthy people also harbored microbes that depended on others for the provision of essential metabolites. External disruptions to the gut homeostasis, such as antibiotics, resulted in a similar selection for independent microbes.

These findings support the idea that changes in the gut microbiome are more likely a by-product of disease, rather than its cause and offer important ecological clues for microbial therapies that aim to restore gut health. While this perspective assigns a more neutral role for gut microbial communities in non-transmissible diseases, more research is needed to see if an enrichment of self-sufficient microbes could negatively influence disease progression.

The human gut is home to a diverse assemblage of microbial cells that form complex communities (Coyte et al., 2015). This gut microbial ecosystem is established almost immediately after birth and plays a lifelong role in human well-being by contributing to immune system maturation and functioning (Belkaid and Hand, 2014; Maynard et al., 2012), extracting dietary nutrients (Hijova, 2019), providing protection against pathogens (Khosravi and Mazmanian, 2013), metabolizing drugs (Zimmermann et al., 2019), and more (Knight et al., 2017). There is no universal definition of a healthy gut microbiome (Fan and Pedersen, 2021), but associations between host disease states and changes in microbial community composition have sparked great interest in the therapeutic potential of gut microbes (Cani, 2018; Sorbara and Pamer, 2022) and led to the emergence of hypotheses that directly link disruptions of the gut microbiome to noncommunicable diseases of complex etiology (Byndloss and Bäumler, 2018).

Inflammatory bowel diseases (IBDs), which describe a heterogeneous group of chronic inflammatory disorders (Shan et al., 2022), represent an increasingly common health risk around the globe (Kaplan, 2015). Understanding the role of gut microbiota in IBD has been a major area of focus in human microbiome research. Studies focusing on individual microbial taxa that typically change in relative abundance in IBD patients have proposed a range of host-microbe interactions that may contribute to disease manifestation and progression (Joossens et al., 2011; Schirmer et al., 2019; Henke et al., 2019; Machiels et al., 2014). However, even within well-constrained cohorts, a large proportion of variability in the taxonomic composition of the microbiota is unexplained, and the proportion of variability explained by disease status is low (Gevers et al., 2014; Schirmer et al., 2018; Lloyd-Price et al., 2019; Khan et al., 2019). As neither individual taxa nor broad changes in microbial community composition yield effective predictors of disease (Knox et al., 2019; Lee and Chang, 2021), the role of gut microbes in the etiology of IBD – or the extent to which they are bystanders to disease – remains unclear (Khan et al., 2019).

The marked decrease in microbial diversity in IBD is often associated with the loss of Firmicutes populations and an increased representation of a relatively small number of taxa, such as Bacteroides, Enterococcaceae, and others (Prindiville et al., 2000; Saitoh et al., 2002; Sartor, 2006; Rhodes, 2007; Devkota et al., 2012; Machiels et al., 2014; Vineis et al., 2016; Lloyd-Price et al., 2019). Why a handful of taxa that also typically occur in healthy individuals in lower abundances (Lee and Chang, 2021; Nishida et al., 2018) tend to dominate the IBD microbiome is a fundamental but open question to gain insights into the ecological underpinnings of the gut microbial ecosystem under IBD. Going beyond taxonomic summaries, a recent metagenome-wide metabolic modeling study revealed a significant loss of cross-feeding partners as a hallmark of IBD, where microbial interactions were disrupted in IBD-associated microbial communities compared to those found in healthy individuals (Marcelino et al., 2023). This observation is in line with another recent work that proposed that the extent of ‘metabolic independence’ (characterized by the genomic presence of a set of key metabolic modules for the synthesis of essential nutrients) is a determinant of microbial survival in IBD (Watson et al., 2023). It is conceivable that the disrupted metabolic interactions among microbes observed in IBD (Marcelino et al., 2023) indicate an environment that lacks the ecosystem services provided by a complex network of microbial interactions, and selects for those organisms that harness high metabolic independence (HMI) (Watson et al., 2023). This interpretation offers an ecological mechanism to explain the dominance of populations with specific metabolic features in IBD and requires further investigation.

Here, we implemented a high-throughput, taxonomy-independent strategy to estimate metabolic capabilities of microbial communities directly from metagenomes and investigate whether the enrichment of populations with HMI predicts IBD in the human gut. We benchmarked our findings using representative genomes associated with the human gut and their distribution in healthy individuals as well as those who have been diagnosed with IBD. Our results suggest that high metabolic potential (indicated by a set of 33 largely biosynthetic metabolic modules) provides enough signal to consistently distinguish gut microbiomes under stress from those that are in homeostasis, providing deeper insights into adaptive processes initiated by stress conditions that promote rare members of gut microbiota to dominance during disease.

We compiled 2893 publicly available stool metagenomes from 13 different studies, 5 of which explicitly studied the IBD gut microbiome (Supplementary file 1a–c). The average sequencing depth varied across individual datasets (4.2 to 60.3 million paired-end reads, with a median value of 21.4, Supplementary file 1c). To improve the sensitivity and accuracy of our downstream analyses that depend on metagenomic assembly, we excluded samples with less than 25 million reads, resulting in a set of 408 relatively deeply sequenced metagenomes from 10 studies (26.4 to 61.9 million paired-end reads, with a median value of 37.0, Supplementary file 1b, Appendix 1, Methods), which we de novo assembled individually. The final dataset included individuals who were healthy (n = 229), diagnosed with IBD (n = 101), or suffered from other gastrointestinal conditions (‘non-IBD’, n = 78). In accordance with previous observations of reduced microbial diversity in IBD (Kostic et al., 2014; Nagalingam and Lynch, 2012; Knox et al., 2019), the estimated number of populations based on the occurrence of bacterial single-copy core genes (SCGs) present in these metagenomes was higher in healthy individuals than those diagnosed with IBD (Appendix 1—figure 1, Supplementary file 1).

Estimating normalized copy numbers of metabolic modules from metagenomic assemblies

Gaining insights into microbial metabolism requires accurate estimates of the presence/absence and completion of metabolic modules. While a myriad of tools address this task for single genomes (Machado et al., 2018; Aziz et al., 2008; Arkin et al., 2018; Palù et al., 2022; Shaffer et al., 2020; Geller-McGrath et al., 2023; Zorrilla et al., 2021; Zhou et al., 2022; Zimmermann et al., 2021), working with complex environmental metagenomes poses additional challenges due to the large number of organisms that are present in metagenomic assemblies. A few tools can estimate community-level metabolic potential from metagenomes without relying on the reconstruction of individual population genomes or reference-based approaches (Ye and Doak, 2009; Karp et al., 2021; Supplementary file 5). These high-level summaries of module presence and redundancy in a given environment are suitable for most surveys of metabolic capacity, particularly for microbial communities of similar richness. However, since the frequency of observed metabolic modules will increase as the number of distinct microbial populations in a habitat increases, investigations of metabolic determinants of survival across environmental conditions with substantial differences in microbial richness may suffer from ambiguous observations from quantitative data. For instance, the estimated copy number of a given metabolic module may be identical between two metagenomes but its enrichment may be relatively higher in the metagenome with a lower alpha diversity, revealing its potential role in overcoming environment-specific selective pressures that influence an entire community. Working solely with raw copy numbers of metabolic modules without a normalization step that considers the microbial richness will thus shroud potentially critical insights. To quantify the differential enrichment of metabolic modules between metagenomes generated from healthy individuals and those from individuals diagnosed with IBD, we implemented a new software framework (https://anvio.org/m/anvi-estimate-metabolism) that reconstructs metabolic modules from genomes and metagenomes, and a means to calculate the per-population copy number (PPCN) of modules in metagenomes to account for potential differences in microbial richness (Methods, Appendices 1 and 2). Briefly, the PPCN estimates the proportion of microbes in a community with a particular metabolic capacity (Figure 1, Appendix 1—figure 2) by normalizing observed metabolic module copy numbers with the ‘number of microbial populations in a given metagenome’, which we estimate using the SCGs without relying on the reconstruction of individual genomes. Our validation of this method using simulated metagenomic data demonstrated that it is accurate in capturing metagenome-level metabolic capacity relative to genome-level metabolic capacity estimated from the same data (Appendix 2, Supplementary file 6).

Figure 1

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Key biosynthetic modules are enriched in microbial populations from IBD samples

To gain insight into potential metabolic determinants of microbial survival in the IBD gut environment, we assessed the distribution of metabolic modules within samples from each group (IBD and healthy) with and without using PPCN normalization. Without normalizing, module copy numbers were overall higher in healthy samples (Figure 2A) and modules exhibited weak differential occurrence between cohorts (Figure 2B and C, Appendix 1—figure 3). The application of PPCN reversed this trend, and most metabolic modules were elevated in IBD (Appendix 1—figure 5). This observation is influenced by two independent aspects of the healthy and IBD microbiota. The first one is the increased representation of microbial organisms with smaller genomes in healthy individuals (Watson et al., 2023), which increases the likelihood that the overall copy number of a given metabolic module is below the actual number of populations. In contrast, one of the hallmarks of the IBD microbiota is the generally increased representation of organisms with larger genomes (Watson et al., 2023). The second aspect is that the generally higher diversity of microbes in healthy individuals increases the denominator of the PPCN. This results in a greater reduction in the PPCN of metabolic modules that are not shared across all members of the diverse gut microbial populations in health.

Figure 2

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To go beyond this general trend and identify modules that were highly conserved in the IBD group, we first selected those that passed a relatively high statistical significance threshold in our enrichment test (Wilcoxon rank-sum test, FDR-adjusted p-value < 2e-10). We then accounted for effect size by ranking these modules according to the difference between their median PPCN in IBD samples and their median PPCN in healthy samples, and keeping only those in the top 50% (which translated to an effect size threshold of > 0.12). This stringent filtering revealed a set of 33 metabolic modules that were significantly enriched in metagenomes obtained from individuals diagnosed with IBD (Figure 2D and E), 17 of which matched the modules that were associated with HMI previously (Watson et al., 2023; Figure 2F). This result suggests that the PPCN normalization is an important step in comparative analyses of metabolisms between samples with different levels of microbial diversity.

The majority of the metabolic modules that were significantly enriched in the microbiomes of IBD patients encoded biosynthetic capabilities (23 out of 33) that resolved to amino acid metabolism (33%), carbohydrate metabolism (21%), cofactor and vitamin biosynthesis (15%), nucleotide biosynthesis (12%), lipid biosynthesis (6%), and energy metabolism (6%) (Supplementary file 2a). In contrast to previous reports based on reference genomes (Gevers et al., 2014; Morgan et al., 2012), amino acid synthesis and carbohydrate metabolism were not reduced in the IBD gut microbiome in our dataset. Rather, our results were in accordance with a more recent finding that predicted amino acid secretion potential is increased in the microbiomes of individuals with IBD (Heinken et al., 2021).

Within our set of 33 modules that were enriched in IBD, it is notable that all the biosynthesis and central carbohydrate modules are directly or indirectly linked via shared enzymes and metabolites. Each enriched module shared on average 25.6% of its enzymes and 40.2% of metabolites with the other enriched modules, and overall 18.2% of enzymes and 20.4% of compounds across these modules were shared (Supplementary file 2a). Thus, modules may be enriched not just due to the importance of their immediate end products, but also because of their role in the larger metabolic network. The few standalone modules that were enriched included the efflux pump MepA and the beta-lactam resistance system, which are associated with drug resistance. These capacities may provide an advantage since antibiotics are a common treatment for IBDs (Nitzan et al., 2016), but are not necessarily related to the systematic enrichment of biosynthesis modules that likely provide resilience to general environmental stress rather than to a specific stressor such as antibiotics.

Microbiome data generated by different groups can result in systematic biases that may outweigh biological differences between otherwise similar samples (Lozupone et al., 2013; Sinha et al., 2017; Clausen and Willis, 2022). The potential impact of such biases constitutes an important consideration for meta-analyses such as ours that analyze publicly available metagenomes from multiple sources. To account for cohort biases, we conducted an analysis of our data on a per-cohort basis. All cohorts within a given group exhibited similar distributions of PPCN values, which indicates that the trends we observed above result from an overall between-group difference in signal rather than a cohort-specific signal (Appendix 1—figure 6B and C). Another source of potential bias stems from the annotation efficiency of gene function. For instance, we noticed that, independent of the annotation strategy, a smaller proportion of genes resolved to known functions in metagenomic assemblies of samples from healthy individuals compared to the samples from individuals who were diagnosed with IBD (Appendix 1—figure 4). This highlights the possibility that samples from healthy individuals merely appear to harbor less metabolic capabilities due to missing annotations. Indeed, we found that the normalized copy numbers of most metabolic modules were reduced in the healthy group, where 84% of KEGG modules (98 out of 118) have significantly lower median copy numbers (Appendix 1—figure 5C, Appendix 1). While the presence of a bias between the two cohorts is clear, the source of this bias and its implications are not. One hypothesis that could explain this phenomenon is that the increased proportion of unknown functions in environments where populations with low metabolic independence (LMI) thrive is due to our inability to identify distant homologs of even well-studied functions in poorly studied novel genomes through public databases. If true, this would indeed impair our ability to annotate genes using state-of-the-art functional databases and bias metabolic module completion estimates. Such a limitation would warrant a careful reconsideration of common workflows and studies that rely on public resources to characterize gene function in complex environments. Another hypothesis that could explain our observation is that the general absence of microbes with smaller genomes in culture had a historical impact on the characterization of novel functions that represent a relatively larger fraction of their gene repertoire. If true, this would suggest that the unknown functions are unlikely essential for well-studied metabolic capabilities. Furthermore, HMI and LMI genomes may be indistinguishable with respect to the distribution of such novel genes, but the increased number of genes in HMI genomes that resolve to well-studied metabolisms would reduce the proportion of known functions in LMI genomes, and thus in metagenomes where they thrive. While testing these hypotheses falls outside the scope of our work, we find the latter hypothesis more likely due to examples in literature that have successfully identified genes that belong to known metabolisms in some of the most obscure organisms via annotation strategies similar to those we have used in our work (Jaffe et al., 2020; Farag et al., 2020).

Taken together, these results (1) demonstrate that the PPCN normalization is an important consideration for investigations of metabolic enrichment in complex microbial communities as a function of microbial diversity, and (2) reveal that the enrichment of HMI populations in an environment offers a high-resolution marker to resolve different levels of environmental stress.

Reference genomes with higher metabolic independence are overrepresented in the gut metagenomes of individuals with IBD

So far, our findings demonstrate an overall, metagenome-level trend of increasing HMI within gut microbial communities as a function of IBD status without considering the individual genomes that contribute to this signal. Since we can measure the extent of metabolic independence as defined in our study based on the completion of a few key metabolic modules for any given genome, we next considered a genome-based approach to further benchmark our findings by investigating whether publicly available microbial genomes that appear to have properties of HMI are more commonly found in individuals diagnosed with IBD.

To identify a set of microbial genomes that are generally associated with the human gut environment, we cast a broad net by surveying the ecology of 19,226 genomes in the Genome Taxonomy Database (GTDB) (Parks et al., 2022) that belonged to three major phyla: Bacteroidetes, Firmicutes, and Proteobacteria, which represent the vast majority of microbial diversity in the human gut environment (Woting and Blaut, 2016; Turnbaugh et al., 2009). As these phyla also include a large number of taxa that primarily occur outside of the human gut, we only kept for downstream analyses those that were detected in at least 2% of the participants of the Human Microbiome Project (HMP) (Human Microbiome Project Consortium, 2012; Appendix 1—figure 8, Methods). Of the final set of 338 reference genomes that passed our filters, 258 (76.3%) resolved to Firmicutes, 60 (17.8%) to Bacteroidetes, and 20 (5.9%) to Proteobacteria. Most of these genomes resolved to families common to the colonic microbiota, such as Lachnospiraceae (30.0%), Ruminococcaceae/Oscillospiraceae (23.1%), and Bacteroidaceae (10.1%) (Arumugam et al., 2011), while 5.9% belonged to poorly studied families with temporary code names (Supplementary file 3a). Finally, we performed a more comprehensive read recruitment analysis on this smaller set of genomes using all deeply sequenced metagenomes from cohorts that included healthy, non-IBD, and IBD samples (Figure 3). This provided us with a quantitative summary of the detection patterns of GTDB genome representatives common to the human gut across our dataset.

Figure 3

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We assumed that a given genome had HMI if its average completeness of the 33 HMI-associated metabolic modules was at least 80%, equivalent to a summed metabolic independence score of 26.4 (Methods). Given the number of ways a genome can pass or fail this threshold, this arbitrary cutoff has significant shortcomings, which was demonstrated by the fact that several species in the Bacteroides group were not classified as HMI despite their frequent dominance of the gut microbiome of individuals with IBD (Saitoh et al., 2002; Wexler, 2007; Vineis et al., 2016) (Appendix 1). That said, the genomes that were classified as HMI by this approach were consistently higher in their detection and abundance in IBD samples (Figure 3A). It is likely that there are multiple ways to have HMI which are not fully captured by the 33 IBD-enriched metabolic modules identified in this study. Across all genomes, the mean metabolic independence score was 24.0 (Q1: 19.9, Q3: 25.7). We identified 17.5% (59) of the reference genomes as HMI. HMI genomes were on average substantially larger (3.8 Mbp) than non-HMI genomes (2.9 Mbp) and encoded more genes (3634 vs 2683 genes, respectively), which is in accordance with the reduced metabolic potential of non-HMI populations (Supplementary file 3a). Our read recruitment analysis showed that HMI reference genomes were present in a significantly higher proportion of IBD samples compared to non-HMI genomes (Figure 3B, p < 1e-5, Wilcoxon rank-sum test). Similarly, the fraction of HMI populations was significantly higher within a given IBD sample compared to samples classified as ‘non-IBD’ and those from healthy individuals (Figure 3C, p < 1e-24, Kruskal-Wallis rank-sum test). In contrast, the detection of HMI populations and non-HMI populations was similar in healthy individuals (Figure 3B, p = 0.267, Wilcoxon rank-sum test). The intestinal environment of healthy individuals likely supports both HMI and non-HMI populations, wherein ‘metabolic diversity’ is maintained by metabolic interactions such as cross-feeding. Indeed, loss of cross-feeding interactions in the gut microbiome appears to be associated with a number of human diseases, including IBD (Marcelino et al., 2023). This interpretation is further supported by the fact that the top two HMI-associated modules are required for the synthesis of cobalamin from glutamate. Auxotrophy for cobalamin biosynthesis is common among gut bacteria that rely on cross-feeding for this essential cofactor (Degnan et al., 2014a; Magnúsdóttir et al., 2015; Kelly et al., 2019) (Appendix 1).

Overall, the classification of reference gut genomes as HMI and their enrichment in individuals diagnosed with IBD strongly supports the contribution of HMI to stress resilience of individual microbial populations. We note that survival in a disturbed gut environment will likely require a wide variety of additional functions that are not covered in the list of metabolic modules we consider to determine HMI status – e.g., see Degnan et al., 2014b; Martens et al., 2014; Zong et al., 2020; Feng et al., 2020; Goodman et al., 2009; Powell et al., 2016. Indeed, there may be many ways for a microbe to be metabolically independent, and our strategy likely failed to identify some HMI populations. Nonetheless, these data suggest that HMI serves as a reliable proxy for the identification of microbial populations that are particularly resilient.

HMI-associated metabolic potential predicts general stress on gut microbes

Our analysis identified HMI as an emergent property of gut microbial communities associated with individuals diagnosed with IBD. This community-level signal translates to individual microbial populations and provides insights into the microbial ecology of stressed gut environments. HMI-associated metabolic modules were enriched at the community level, and microbial populations encoding these modules were more prevalent in individuals with IBD than in healthy individuals. Furthermore, the copy number of these modules and the proportion of HMI populations reflect the severity of environmental stress and translate to host health states (Appendix 1—figure 5B, Figure 3C). The ecological implications of these observations suggest that HMI may serve as a predictor of general stress in the human gut environment.

So far, efforts to identify IBD using microbial markers have presented classifiers based on (1) taxonomy in pediatric IBD patients (Papa et al., 2012; Gevers et al., 2014), (2) community composition in combination with clinical data (Halfvarson et al., 2017), (3) untargeted metabolomics and/or species-level relative abundance from metagenomes (Franzosa et al., 2019), and (4) k-mer-based sequence variants in metagenomes that can be linked to microbial genomes associated with IBD (Reiter et al., 2022). Performance varied both between and within studies according to the target classes and data types used for training and validation of each classifier (Supplementary file 4a). For those studies reporting accuracy, a maximum accuracy of 77% was achieved based on either metabolite profiles (for prediction of IBD subtype) (Franzosa et al., 2019) or k-mer-based sequence variants (for differentiating between IBD and non-IBD samples) (Reiter et al., 2022). Some studies reported performance as area under the receiver operating characteristic curve (AUROCC), a typical measure of classifier utility describing both sensitivity (ability to correctly identify the disease) and specificity (ability to correctly identify absence of disease). For this metric the highest value was 0.92, achieved by Franzosa et al., 2019, when using metabolite profiles, with or without species abundance data, for classifying IBD vs non-IBD. However, the majority of these classifiers were trained and tested on a relatively small group of individuals that all come from the same region, i.e., clinical studies confined to a specific hospital. Though some had high performance, they either relied on data that are inaccessible to most laboratories and clinics considering that untargeted metabolomics analyses are difficult to reproduce (Koek et al., 2011; Lin et al., 2020), or they required complex k-mer-based models without the resolution to differentiate gradients in host health (Reiter et al., 2022). These classifiers thus have limited translational potential across global clinical settings and do not provide an ecological framework to explain the observed shifts in community composition and activity. For practical use as a diagnostic tool, a microbiome-based classifier for IBD should rely on an ecologically meaningful, easy to measure, and high-level signal that is robust to host variables like lifestyle, geographical location, and ethnicity. HMI could potentially fill this gap as a metric related to the ecological filtering that defines microbial community changes in the IBD gut microbiome.

We trained a logistic regression classifier to explore the applicability of HMI as a noninvasive diagnostic tool for IBD. The classifier’s predictors were the PPCNs of IBD-enriched metabolic modules in a given metagenome. Across the 330 deeply sequenced IBD and healthy samples included in this analysis, the classifier had high sensitivity and specificity (Figure 4). It correctly identified (on average) 76.8% of samples from individuals diagnosed with IBD and 89.5% of samples representing healthy individuals, for an overall accuracy of 85.6% and an average AUROCC of 0.832 (Supplementary file 4c). Our model outperforms (Gevers et al., 2014; Halfvarson et al., 2017; Reiter et al., 2022) or has comparable performance to Franzosa et al., 2019; Papa et al., 2012 the previous attempts to classify IBD from fecal samples in more restrictively defined cohorts. It also has the advantage of being a simple model, utilizing a relatively low number of features compared to the other classifiers. Determining whether such a model has broader utility as a diagnostic tool requires further research and validation; however, these results demonstrate the potential of HMI as an accessible diagnostic marker of IBD. Due to the lack of time-series studies that include individuals in the pre-diagnosis phase of IBD development, we cannot test the applicability of HMI to predict IBD onset (Lloyd-Price et al., 2019).

Figure 4

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Yet, the gradient of metabolic independence reflected by per-population module copy number and the relative increase in the number of HMI populations detected in non-IBD samples (Appendix 1—figure 5B, Figure 3C) suggests that the degree of HMI in the gut microbiome may be indicative of general gut stress, such as the stress induced by antibiotic use. Antibiotics can cause long-lasting perturbations of the gut microbiome – including reduced diversity, emergence of opportunistic pathogens, increased microbial load, and development of highly resistant strains – with potential implications for host health (Ramirez et al., 2020). We applied our metabolism classifier to a metagenomic dataset that reflects the changes in the microbiome of healthy people before, during, and up to 6 months following a 4-day antibiotic treatment (Palleja et al., 2018). The resulting pattern of sample classification corresponds to the posttreatment decline and subsequent recovery of species richness documented in the study by Palleja et al., 2018.

All pretreatment samples were classified as ‘healthy’ followed by a decline in the proportion of ‘healthy’ samples to a minimum 8 days posttreatment, and a gradual increase until 180 days posttreatment, when over 90% of samples were classified as ‘healthy’ (Figure 5, Supplementary file 4b). In other words, the increase in the HMI metric serves as an indicator of stress in the gut microbiome, regardless of whether that stress arises from the IBD condition or the application of antibiotics. These observations support the role of HMI as an ecological driver of microbial resilience during gut stress caused by a variety of environmental perturbations and demonstrate its diagnostic power in reflecting gut microbiome state.

Figure 5

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A bioinformatics workflow that further details all analyses described below and gives access to reproducible data products is available at the URL https://merenlab.org/data/ibd-gut-metabolism/.

Accession numbers for publicly available data are listed in our Supplementary Tables that are also accessible at https://doi.org/10.6084/m9.figshare.22679080. Anvi'o contigs databases of our assemblies for the 408 deeply-sequenced metagenomes, as well as assemblies of the Palleja et al. 2018 metagenomes are available at can be accessed at https://doi.org/10.5281/zenodo.7897987. Finally the URL https://doi.org/10.5281/zenodo.7883421 gives access to anvi'o contigs databases for the 338 Genome Taxonomy Database (GTDB) reference genomes that represent populations that are prevalent in human gut metagenomes.

1. 1. Veseli I
   2. Eren AM

   (2024) Figshare

   Supplementary Tables for Veseli et al. 2023.

   https://doi.org/10.6084/m9.figshare.22679080
2. 1. Veseli I

   (2023) Zenodo

   Palleja et al. 2018 Metagenome Assemblies for Veseli et al. 2023.

   https://doi.org/10.5281/zenodo.7897987
3. 1. Veseli I

   (2023) Zenodo

   GTDB Genome Contigs DBs for Veseli et al. 2023.

   https://doi.org/10.5281/zenodo.7883421

1. 1. Qin J
   2. Li Y
   3. Cai Z
   4. Li S
   5. Zhu J
   6. Zhang F
   7. Liang S

   (2012) NCBI Sequence Read Archive

   ID SRA050230. A metagenome-wide association study of gut microbiota in type 2 diabetes.

   https://www.ncbi.nlm.nih.gov/sra?term=SRA050230
2. 1. Le Chatelier E
   2. Nielsen T
   3. Qin J
   4. Prifti E
   5. Hildebrand F
   6. Falony G
   7. Almeida M

   (2013) EBI European Nucleotide Archive

   ID PRJEB4336. Richness of human gut microbiome correlates with metabolic markers.

   https://www.ebi.ac.uk/ena/browser/view/PRJEB4336
3. 1. Feng Q
   2. Liang S
   3. Jia H
   4. Stadlmayr A
   5. Tang L
   6. Lan Z
   7. Zhang D

   (2015) EBI European Nucleotide Archive

   ID PRJEB7774. Gut microbiome development along the colorectal adenoma-carcinoma sequence.

   https://www.ebi.ac.uk/ena/browser/view/PRJEB7774
4. 1. Franzosa EA
   2. Sirota-Madi A
   3. Avila-Pacheco J
   4. Fornelos N
   5. Haiser HJ
   6. Reinker S
   7. Vatanen T

   (2019) NCBI BioProject

   ID PRJNA400072. Gut microbiome structure and metabolic activity in inflammatory bowel disease.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA400072
5. 1. Lloyd-Price J
   2. Arze C
   3. Ananthakrishnan AN
   4. Schirmer M
   5. Pacheco JA
   6. Poon TW
   7. Andrews E

   (2019) NCBI BioProject

   ID PRJNA398089. Longitudinal Multi’omics of the Human Microbiome in Inflammatory Bowel Disease.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA398089
6. 1. Qin J
   2. Li Y
   3. Cai Z
   4. Li S
   5. Zhu J
   6. Zhang F
   7. Liang S

   (2012) NCBI Sequence Read Archive

   ID SRA045646. A metagenome-wide association study of gut microbiota in type 2 diabetes.

   https://www.ncbi.nlm.nih.gov/sra?term=SRA045646
7. 1. Quince C
   2. Ijaz UZ
   3. Loman N
   4. Eren AM
   5. Saulnier D
   6. Russell J
   7. Haig SJ

   (2015) NCBI BioProject

   ID PRJEB7576. Exclusive enteral nutrition modulates the faecal metagenome in paediatric Crohn’s disease not by enriching the abundance of presumably ‘beneficial’ commensals but by suppressing ‘dysbiotic’ bacteria.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJEB7576
8. 1. Raymond F
   2. Ouameur AA
   3. Déraspe M
   4. Iqbal N
   5. Gingras H
   6. Dridi B
   7. Leprohon P

   (2016) EBI European Nucleotide Archive

   ID PRJEB8094. The initial state of the human gut microbiome determines its reshaping by antibiotics.

   https://www.ebi.ac.uk/ena/browser/view/PRJEB8094
9. 1. Schirmer M
   2. Franzosa EA
   3. Lloyd-Price J
   4. McIver LJ
   5. Schwager R
   6. Poon TW
   7. Ananthakrishnan AN

   (2018) NCBI BioProject

   ID PRJNA389280. Dynamics of metatranscription in the inflammatory bowel disease gut microbiome.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA389280/
10. 1. University of Sydney

    (2016) NCBI BioProject

    ID PRJEB6092. metagenome fecal microbiota, Ilumina seq reads of 12 individuals at 2 timepoints.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJEB6092/
11. 1. Vineis JH
    2. Ringus DL
    3. Morrison HG
    4. Delmont TO
    5. Dalal S
    6. Raffals LH
    7. Antonopoulos DA

    (2016) NCBI BioProject

    ID PRJNA46881. Patient-Specific Bacteroides Genome Variants in Pouchitis.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA46881
12. 1. Wen C
    2. Zheng Z
    3. Shao T
    4. Liu L
    5. Xie Z
    6. Chatelier EL
    7. He Z

    (2017) NCBI Sequence Read Archive

    ID SRP100575. Quantitative metagenomics reveals unique gut microbiome biomarkers in ankylosing spondylitis.

    https://www.ncbi.nlm.nih.gov/sra/?term=SRP100575
13. 1. Qin N
    2. Yang F
    3. Li A
    4. Prifti E
    5. Chen Y
    6. Chatelier EL
    7. Yao J
    8. Wu L
    9. Zhou J
    10. Ni S
    11. Liu L
    12. Pons N
    13. Batto JM
    14. Kennedy SP
    15. Leonard P

    (2014) EBI European Nucleotide Archive

    ID PRJEB6337. Alterations of the human gut microbiome in liver cirrhosis.

    https://www.ebi.ac.uk/ena/browser/view/PRJEB6337
14. 1. Xie H
    2. Guo R
    3. Feng Q
    4. Lan Z
    5. Qin B
    6. Ward KJ
    7. Zhong H

    (2016) EBI European Nucleotide Archive

    ID PRJEB9584. Shotgun Metagenomics of 250 Adult Twins Reveals Genetic and Environmental Impacts on the Gut Microbiome.

    https://www.ebi.ac.uk/ena/browser/view/PRJEB9584
15. 1. Palleja A
    2. Mikkelsen KH
    3. Forslund SK
    4. Kashani A
    5. Allin KH
    6. Nielsen T
    7. Hansen TH

    (2018) EBI European Nucleotide Archive

    ID PRJEB20800. Gut resistome modulates resilience and recovery of gut microbiota after broad-spectrum antibiotic treatment.

    https://www.ebi.ac.uk/ena/browser/view/PRJEB20800
16. 1. Rampelli S
    2. Schnorr SL
    3. Consolandi C
    4. Turroni S
    5. Severgnini M
    6. peano C
    7. Brigidi P
    8. Crittenden AN
    9. Henry AG
    10. Candela M

    (2015) NCBI BioProject

    ID PRJNA278393. Metagenome Sequencing of the Hadza Hunter-Gatherer Gut Microbiota.

    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA278393
17. 1. Parks DH
    2. Chuvochina M
    3. Rinke C
    4. Mussig AJ
    5. Chaumeil PA
    6. Hugenholtz P

    (2022) GTDB

    ID release95.0. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy.

    https://gtdb.ecogenomic.org/

1. 1. Agren R
   2. Liu L
   3. Shoaie S
   4. Vongsangnak W
   5. Nookaew I
   6. Nielsen J

   (2013) The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum

   PLOS Computational Biology 9:e1002980.

   https://doi.org/10.1371/journal.pcbi.1002980

   • PubMed
   • Google Scholar
2. 1. Agus A
   2. Planchais J
   3. Sokol H

   (2018) Gut microbiota regulation of tryptophan metabolism in health and disease

   Cell Host & Microbe 23:716–724.

   https://doi.org/10.1016/j.chom.2018.05.003

   • Google Scholar
3. 1. Aite M
   2. Chevallier M
   3. Frioux C
   4. Trottier C
   5. Got J
   6. Cortés MP
   7. Mendoza SN
   8. Carrier G
   9. Dameron O
   10. Guillaudeux N
   11. Latorre M
   12. Loira N
   13. Markov GV
   14. Maass A
   15. Siegel A

   (2018) Traceability, reproducibility and wiki-exploration for “à-la-carte” reconstructions of genome-scale metabolic models

   PLOS Computational Biology 14:e1006146.

   https://doi.org/10.1371/journal.pcbi.1006146

   • PubMed
   • Google Scholar
4. 1. Akram M

   (2014) Citric acid cycle and role of its intermediates in metabolism

   Cell Biochemistry and Biophysics 68:475–478.

   https://doi.org/10.1007/s12013-013-9750-1

   • PubMed
   • Google Scholar
5. 1. Alekshun MN
   2. Levy SB

   (2007) Molecular mechanisms of antibacterial multidrug resistance

   Cell 128:1037–1050.

   https://doi.org/10.1016/j.cell.2007.03.004

   • PubMed
   • Google Scholar
6. 1. Alkhalaf LM
   2. Ryan KS

   (2015) Biosynthetic manipulation of tryptophan in bacteria: pathways and mechanisms

   Chemistry & Biology 22:317–328.

   https://doi.org/10.1016/j.chembiol.2015.02.005

   • Google Scholar
7. 1. An D
   2. Na C
   3. Bielawski J
   4. Hannun YA
   5. Kasper DL

   (2011) Membrane sphingolipids as essential molecular signals for Bacteroides survival in the intestine

   PNAS 108 Suppl 1:4666–4671.

   https://doi.org/10.1073/pnas.1001501107

   • PubMed
   • Google Scholar
8. 1. Ananthakrishnan AN
   2. Luo C
   3. Yajnik V
   4. Khalili H
   5. Garber JJ
   6. Stevens BW
   7. Cleland T
   8. Xavier RJ

   (2017) Gut microbiome function predicts response to anti-integrin biologic therapy in inflammatory bowel diseases

   Cell Host & Microbe 21:603–610.

   https://doi.org/10.1016/j.chom.2017.04.010

   • PubMed
   • Google Scholar
9. 1. Aramaki T
   2. Blanc-Mathieu R
   3. Endo H
   4. Ohkubo K
   5. Kanehisa M
   6. Goto S
   7. Ogata H

   (2020) KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold

   Bioinformatics 36:2251–2252.

   https://doi.org/10.1093/bioinformatics/btz859

   • PubMed
   • Google Scholar
10. 1. Arkin AP
    2. Cottingham RW
    3. Henry CS
    4. Harris NL
    5. Stevens RL
    6. Maslov S
    7. Dehal P
    8. Ware D
    9. Perez F
    10. Canon S
    11. Sneddon MW
    12. Henderson ML
    13. Riehl WJ
    14. Murphy-Olson D
    15. Chan SY
    16. Kamimura RT
    17. Kumari S
    18. Drake MM
    19. Brettin TS
    20. Glass EM
    21. Chivian D
    22. Gunter D
    23. Weston DJ
    24. Allen BH
    25. Baumohl J
    26. Best AA
    27. Bowen B
    28. Brenner SE
    29. Bun CC
    30. Chandonia J-M
    31. Chia J-M
    32. Colasanti R
    33. Conrad N
    34. Davis JJ
    35. Davison BH
    36. DeJongh M
    37. Devoid S
    38. Dietrich E
    39. Dubchak I
    40. Edirisinghe JN
    41. Fang G
    42. Faria JP
    43. Frybarger PM
    44. Gerlach W
    45. Gerstein M
    46. Greiner A
    47. Gurtowski J
    48. Haun HL
    49. He F
    50. Jain R
    51. Joachimiak MP
    52. Keegan KP
    53. Kondo S
    54. Kumar V
    55. Land ML
    56. Meyer F
    57. Mills M
    58. Novichkov PS
    59. Oh T
    60. Olsen GJ
    61. Olson R
    62. Parrello B
    63. Pasternak S
    64. Pearson E
    65. Poon SS
    66. Price GA
    67. Ramakrishnan S
    68. Ranjan P
    69. Ronald PC
    70. Schatz MC
    71. Seaver SMD
    72. Shukla M
    73. Sutormin RA
    74. Syed MH
    75. Thomason J
    76. Tintle NL
    77. Wang D
    78. Xia F
    79. Yoo H
    80. Yoo S
    81. Yu D

    (2018) KBase: the united states department of energy systems biology knowledgebase

    Nature Biotechnology 36:566–569.

    https://doi.org/10.1038/nbt.4163

    • PubMed
    • Google Scholar
11. 1. Arumugam M
    2. Raes J
    3. Pelletier E
    4. Le Paslier D
    5. Yamada T
    6. Mende DR
    7. Fernandes GR
    8. Tap J
    9. Bruls T
    10. Batto J-M
    11. Bertalan M
    12. Borruel N
    13. Casellas F
    14. Fernandez L
    15. Gautier L
    16. Hansen T
    17. Hattori M
    18. Hayashi T
    19. Kleerebezem M
    20. Kurokawa K
    21. Leclerc M
    22. Levenez F
    23. Manichanh C
    24. Nielsen HB
    25. Nielsen T
    26. Pons N
    27. Poulain J
    28. Qin J
    29. Sicheritz-Ponten T
    30. Tims S
    31. Torrents D
    32. Ugarte E
    33. Zoetendal EG
    34. Wang J
    35. Guarner F
    36. Pedersen O
    37. de Vos WM
    38. Brunak S
    39. Doré J
    40. MetaHIT Consortium
    41. Antolín M
    42. Artiguenave F
    43. Blottiere HM
    44. Almeida M
    45. Brechot C
    46. Cara C
    47. Chervaux C
    48. Cultrone A
    49. Delorme C
    50. Denariaz G
    51. Dervyn R
    52. Foerstner KU
    53. Friss C
    54. van de Guchte M
    55. Guedon E
    56. Haimet F
    57. Huber W
    58. van Hylckama-Vlieg J
    59. Jamet A
    60. Juste C
    61. Kaci G
    62. Knol J
    63. Lakhdari O
    64. Layec S
    65. Le Roux K
    66. Maguin E
    67. Mérieux A
    68. Melo Minardi R
    69. M’rini C
    70. Muller J
    71. Oozeer R
    72. Parkhill J
    73. Renault P
    74. Rescigno M
    75. Sanchez N
    76. Sunagawa S
    77. Torrejon A
    78. Turner K
    79. Vandemeulebrouck G
    80. Varela E
    81. Winogradsky Y
    82. Zeller G
    83. Weissenbach J
    84. Ehrlich SD
    85. Bork P

    (2011) Enterotypes of the human gut microbiome

    Nature 473:174–180.

    https://doi.org/10.1038/nature09944

    • PubMed
    • Google Scholar
12. 1. Aziz RK
    2. Bartels D
    3. Best AA
    4. DeJongh M
    5. Disz T
    6. Edwards RA
    7. Formsma K
    8. Gerdes S
    9. Glass EM
    10. Kubal M
    11. Meyer F
    12. Olsen GJ
    13. Olson R
    14. Osterman AL
    15. Overbeek RA
    16. McNeil LK
    17. Paarmann D
    18. Paczian T
    19. Parrello B
    20. Pusch GD
    21. Reich C
    22. Stevens R
    23. Vassieva O
    24. Vonstein V
    25. Wilke A
    26. Zagnitko O

    (2008) The RAST Server: rapid annotations using subsystems technology

    BMC Genomics 9:75.

    https://doi.org/10.1186/1471-2164-9-75

    • PubMed
    • Google Scholar
13. 1. Bansal T
    2. Alaniz RC
    3. Wood TK
    4. Jayaraman A

    (2010) The bacterial signal indole increases epithelial-cell tight-junction resistance and attenuates indicators of inflammation

    PNAS 107:228–233.

    https://doi.org/10.1073/pnas.0906112107

    • PubMed
    • Google Scholar
14. 1. Bassil AK
    2. Bourdu S
    3. Townson KA
    4. Wheeldon A
    5. Jarvie EM
    6. Zebda N
    7. Abuin A
    8. Grau E
    9. Livi GP
    10. Punter L
    11. Latcham J
    12. Grimes AM
    13. Hurp DP
    14. Downham KM
    15. Sanger GJ
    16. Winchester WJ
    17. Morrison AD
    18. Moore GBT

    (2009) UDP-glucose modulates gastric function through P2Y14 receptor-dependent and -independent mechanisms

    American Journal of Physiology. Gastrointestinal and Liver Physiology 296:G923–G30.

    https://doi.org/10.1152/ajpgi.90363.2008

    • PubMed
    • Google Scholar
15. 1. Beghini F
    2. McIver LJ
    3. Blanco-Míguez A
    4. Dubois L
    5. Asnicar F
    6. Maharjan S
    7. Mailyan A
    8. Manghi P
    9. Scholz M
    10. Thomas AM
    11. Valles-Colomer M
    12. Weingart G
    13. Zhang Y
    14. Zolfo M
    15. Huttenhower C
    16. Franzosa EA
    17. Segata N

    (2021) Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

    eLife 10:e65088.

    https://doi.org/10.7554/eLife.65088

    • PubMed
    • Google Scholar
16. 1. Belkaid Y
    2. Hand TW

    (2014) Role of the microbiota in immunity and inflammation

    Cell 157:121–141.

    https://doi.org/10.1016/j.cell.2014.03.011

    • PubMed
    • Google Scholar
17. 1. Berg NO
    2. Dahlqvist A
    3. Lindberg T
    4. Lindstrand K
    5. Nordén A

    (1972) Morphology, dipeptidases and disaccharidases of small intestinal mucosa in vitamin B 12 and folic acid deficiency

    Scandinavian Journal of Haematology 9:167–173.

    https://doi.org/10.1111/j.1600-0609.1972.tb00927.x

    • PubMed
    • Google Scholar
18. 1. Blachier F
    2. Beaumont M
    3. Kim E

    (2019) Cysteine-derived hydrogen sulfide and gut health: A matter of endogenous or bacterial origin

    Current Opinion in Clinical Nutrition and Metabolic Care 22:68–75.

    https://doi.org/10.1097/MCO.0000000000000526

    • PubMed
    • Google Scholar
19. 1. Borren NZ
    2. Plichta D
    3. Joshi AD
    4. Bonilla G
    5. Peng V
    6. Colizzo FP
    7. Luther J
    8. Khalili H
    9. Garber JJ
    10. Janneke van der Woude C
    11. Sadreyev R
    12. Vlamakis H
    13. Xavier RJ
    14. Ananthakrishnan AN

    (2021) Alterations in fecal microbiomes and serum metabolomes of fatigued patients with quiescent inflammatory bowel diseases

    Clinical Gastroenterology and Hepatology 19:519–527.

    https://doi.org/10.1016/j.cgh.2020.03.013

    • PubMed
    • Google Scholar
20. 1. Boyle EI
    2. Weng S
    3. Gollub J
    4. Jin H
    5. Botstein D
    6. Cherry JM
    7. Sherlock G

    (2004) GO::termfinder--open source software for accessing gene ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes

    Bioinformatics 20:3710–3715.

    https://doi.org/10.1093/bioinformatics/bth456

    • PubMed
    • Google Scholar
21. 1. Bressenot A
    2. Pooya S
    3. Bossenmeyer-Pourie C
    4. Gauchotte G
    5. Germain A
    6. Chevaux JB
    7. Coste F
    8. Vignaud JM
    9. Guéant JL
    10. Peyrin-Biroulet L

    (2013) Methyl donor deficiency affects small-intestinal differentiation and barrier function in rats

    The British Journal of Nutrition 109:667–677.

    https://doi.org/10.1017/S0007114512001869

    • PubMed
    • Google Scholar
22. 1. Brown EM
    2. Clardy J
    3. Xavier RJ

    (2023) Gut microbiome lipid metabolism and its impact on host physiology

    Cell Host & Microbe 31:173–186.

    https://doi.org/10.1016/j.chom.2023.01.009

    • PubMed
    • Google Scholar
23. 1. Bryant DA
    2. Hunter CN
    3. Warren MJ

    (2020) Biosynthesis of the modified tetrapyrroles—the pigments of life

    Journal of Biological Chemistry 295:6888–6925.

    https://doi.org/10.1074/jbc.REV120.006194

    • Google Scholar
24. 1. Buchfink B
    2. Xie C
    3. Huson DH

    (2015) Fast and sensitive protein alignment using DIAMOND

    Nature Methods 12:59–60.

    https://doi.org/10.1038/nmeth.3176

    • PubMed
    • Google Scholar
25. 1. Byndloss MX
    2. Bäumler AJ

    (2018) The germ-organ theory of non-communicable diseases

    Nature Reviews. Microbiology 16:103–110.

    https://doi.org/10.1038/nrmicro.2017.158

    • PubMed
    • Google Scholar
26. 1. Cani PD

    (2018) Human gut microbiome: hopes, threats and promises

    Gut 67:1716–1725.

    https://doi.org/10.1136/gutjnl-2018-316723

    • PubMed
    • Google Scholar
27. 1. Capella-Gutiérrez S
    2. Silla-Martínez JM
    3. Gabaldón T

    (2009) trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses

    Bioinformatics 25:1972–1973.

    https://doi.org/10.1093/bioinformatics/btp348

    • PubMed
    • Google Scholar
28. 1. Cevallos SA
    2. Lee JY
    3. Tiffany CR
    4. Byndloss AJ
    5. Johnston L
    6. Byndloss MX
    7. Bäumler AJ

    (2019) Increased epithelial oxygenation links colitis to an expansion of tumorigenic bacteria

    mBio 10:e02244-19.

    https://doi.org/10.1128/mBio.02244-19

    • PubMed
    • Google Scholar
29. 1. Chan PP
    2. Lowe TM

    (2019) tRNAscan-SE: searching for trna genes in genomic sequences

    Methods in Molecular Biology 1962:1–14.

    https://doi.org/10.1007/978-1-4939-9173-0_1

    • PubMed
    • Google Scholar
30. 1. Chen Y
    2. Li D
    3. Dai Z
    4. Piao X
    5. Wu Z
    6. Wang B
    7. Zhu Y
    8. Zeng Z

    (2014) L-methionine supplementation maintains the integrity and barrier function of the small-intestinal mucosa in post-weaning piglets

    Amino Acids 46:1131–1142.

    https://doi.org/10.1007/s00726-014-1675-5

    • PubMed
    • Google Scholar
31. 1. Christodoulou D
    2. Link H
    3. Fuhrer T
    4. Kochanowski K
    5. Gerosa L
    6. Sauer U

    (2018) Reserve flux capacity in the pentose phosphate pathway enables Escherichia coli’s rapid response to oxidative stress

    Cell Systems 6:569–578.

    https://doi.org/10.1016/j.cels.2018.04.009

    • PubMed
    • Google Scholar
32. 1. Claud EC
    2. Keegan KP
    3. Brulc JM
    4. Lu L
    5. Bartels D
    6. Glass E
    7. Chang EB
    8. Meyer F
    9. Antonopoulos DA

    (2013) Bacterial community structure and functional contributions to emergence of health or necrotizing enterocolitis in preterm infants

    Microbiome 1:20.

    https://doi.org/10.1186/2049-2618-1-20

    • PubMed
    • Google Scholar
33. 1. Clausen DS
    2. Willis AD

    (2022) Evaluating replicability in microbiome data

    Biostatistics 23:1099–1114.

    https://doi.org/10.1093/biostatistics/kxab048

    • PubMed
    • Google Scholar
34. 1. Coburn LA
    2. Gong X
    3. Singh K
    4. Asim M
    5. Scull BP
    6. Allaman MM
    7. Williams CS
    8. Rosen MJ
    9. Washington MK
    10. Barry DP
    11. Piazuelo MB
    12. Casero RA Jr
    13. Chaturvedi R
    14. Zhao Z
    15. Wilson KT

    (2012) L-arginine supplementation improves responses to injury and inflammation in dextran sulfate sodium colitis

    PLOS ONE 7:e33546.

    https://doi.org/10.1371/journal.pone.0033546

    • PubMed
    • Google Scholar
35. 1. Constante M
    2. Fragoso G
    3. Calvé A
    4. Samba-Mondonga M
    5. Santos MM

    (2017) Dietary heme induces gut dysbiosis, aggravates colitis, and potentiates the development of adenomas in mice

    Frontiers in Microbiology 8:1809.

    https://doi.org/10.3389/fmicb.2017.01809

    • PubMed
    • Google Scholar
36. 1. Coyte KZ
    2. Schluter J
    3. Foster KR

    (2015) The ecology of the microbiome: Networks, competition, and stability

    Science 350:663–666.

    https://doi.org/10.1126/science.aad2602

    • PubMed
    • Google Scholar
37. 1. Culp EJ
    2. Goodman AL

    (2023) Cross-feeding in the gut microbiome: Ecology and mechanisms

    Cell Host & Microbe 31:485–499.

    https://doi.org/10.1016/j.chom.2023.03.016

    • PubMed
    • Google Scholar
38. 1. Degnan PH
    2. Barry NA
    3. Mok KC
    4. Taga ME
    5. Goodman AL

    (2014a) Human gut microbes use multiple transporters to distinguish vitamin B12 analogs and compete in the gut

    Cell Host & Microbe 15:47–57.

    https://doi.org/10.1016/j.chom.2013.12.007

    • Google Scholar
39. 1. Degnan PH
    2. Taga ME
    3. Goodman AL

    (2014b) Vitamin B12 as a modulator of gut microbial ecology

    Cell Metabolism 20:769–778.

    https://doi.org/10.1016/j.cmet.2014.10.002

    • PubMed
    • Google Scholar
40. 1. DeJongh M
    2. Formsma K
    3. Boillot P
    4. Gould J
    5. Rycenga M
    6. Best A

    (2007) Toward the automated generation of genome-scale metabolic networks in the SEED

    BMC Bioinformatics 8:139.

    https://doi.org/10.1186/1471-2105-8-139

    • PubMed
    • Google Scholar
41. 1. Devkota S
    2. Wang Y
    3. Musch MW
    4. Leone V
    5. Fehlner-Peach H
    6. Nadimpalli A
    7. Antonopoulos DA
    8. Jabri B
    9. Chang EB

    (2012) Dietary-fat-induced taurocholic acid promotes pathobiont expansion and colitis in Il10-/- mice

    Nature 487:104–108.

    https://doi.org/10.1038/nature11225

    • PubMed
    • Google Scholar
42. 1. Dhakan DB
    2. Maji A
    3. Sharma AK
    4. Saxena R
    5. Pulikkan J
    6. Grace T
    7. Gomez A
    8. Scaria J
    9. Amato KR
    10. Sharma VK

    (2019) The unique composition of Indian gut microbiome, gene catalogue, and associated fecal metabolome deciphered using multi-omics approaches

    GigaScience 8:giz004.

    https://doi.org/10.1093/gigascience/giz004

    • PubMed
    • Google Scholar
43. 1. Dias O
    2. Rocha M
    3. Ferreira EC
    4. Rocha I

    (2015) Reconstructing genome-scale metabolic models with merlin

    Nucleic Acids Research 43:3899–3910.

    https://doi.org/10.1093/nar/gkv294

    • PubMed
    • Google Scholar
44. 1. Eddy SR

    (2011) Accelerated Profile HMM Searches

    PLOS Computational Biology 7:e1002195.

    https://doi.org/10.1371/journal.pcbi.1002195

    • PubMed
    • Google Scholar
45. 1. Edgar RC

    (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput

    Nucleic Acids Research 32:1792–1797.

    https://doi.org/10.1093/nar/gkh340

    • PubMed
    • Google Scholar
46. 1. Eren AM
    2. Vineis JH
    3. Morrison HG
    4. Sogin ML

    (2013) A filtering method to generate high quality short reads using illumina paired-end technology

    PLOS ONE 8:e66643.

    https://doi.org/10.1371/journal.pone.0066643

    • PubMed
    • Google Scholar
47. Software

    1. Eren AM

    (2025) Reads-for-assembly, version swh:1:rev:cc5c9a22c8d26688e2c156878438e6fe0777ddbb

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:7bd125c4df26fd24a3d9df69c24bb2a16a3c51d2;origin=https://github.com/merenlab/reads-for-assembly;visit=swh:1:snp:db631f474996123dccaf9c1fb223c5e6cfeb8c0e;anchor=swh:1:rev:cc5c9a22c8d26688e2c156878438e6fe0777ddbb
48. 1. Fan Y
    2. Pedersen O

    (2021) Gut microbiota in human metabolic health and disease

    Nature Reviews. Microbiology 19:55–71.

    https://doi.org/10.1038/s41579-020-0433-9

    • PubMed
    • Google Scholar
49. 1. Fang X
    2. Lloyd CJ
    3. Palsson BO

    (2020) Reconstructing organisms in silico: genome-scale models and their emerging applications

    Nature Reviews. Microbiology 18:731–743.

    https://doi.org/10.1038/s41579-020-00440-4

    • PubMed
    • Google Scholar
50. 1. Farag IF
    2. Biddle JF
    3. Zhao R
    4. Martino AJ
    5. House CH
    6. León-Zayas RI

    (2020) Metabolic potentials of archaeal lineages resolved from metagenomes of deep Costa Rica sediments

    The ISME Journal 14:1345–1358.

    https://doi.org/10.1038/s41396-020-0615-5

    • PubMed
    • Google Scholar
51. 1. Faria JP
    2. Rocha M
    3. Rocha I
    4. Henry CS

    (2018) Methods for automated genome-scale metabolic model reconstruction

    Biochemical Society Transactions 46:931–936.

    https://doi.org/10.1042/BST20170246

    • PubMed
    • Google Scholar
52. 1. Faure M
    2. Moënnoz D
    3. Montigon F
    4. Mettraux C
    5. Breuillé D
    6. Ballèvre O

    (2005) Dietary threonine restriction specifically reduces intestinal mucin synthesis in rats

    The Journal of Nutrition 135:486–491.

    https://doi.org/10.1093/jn/135.3.486

    • PubMed
    • Google Scholar
53. 1. Faure M
    2. Mettraux C
    3. Moennoz D
    4. Godin J-P
    5. Vuichoud J
    6. Rochat F
    7. Breuillé D
    8. Obled C
    9. Corthésy-Theulaz I

    (2006) Specific amino acids increase mucin synthesis and microbiota in dextran sulfate sodium-treated rats

    The Journal of Nutrition 136:1558–1564.

    https://doi.org/10.1093/jn/136.6.1558

    • PubMed
    • Google Scholar
54. 1. Feng Q
    2. Liang S
    3. Jia H
    4. Stadlmayr A
    5. Tang L
    6. Lan Z
    7. Zhang D
    8. Xia H
    9. Xu X
    10. Jie Z
    11. Su L
    12. Li X
    13. Li X
    14. Li J
    15. Xiao L
    16. Huber-Schönauer U
    17. Niederseer D
    18. Xu X
    19. Al-Aama JY
    20. Yang H
    21. Wang J
    22. Kristiansen K
    23. Arumugam M
    24. Tilg H
    25. Datz C
    26. Wang J

    (2015) Gut microbiome development along the colorectal adenoma-carcinoma sequence

    Nature Communications 6:6528.

    https://doi.org/10.1038/ncomms7528

    • PubMed
    • Google Scholar
55. 1. Feng L
    2. Raman AS
    3. Hibberd MC
    4. Cheng J
    5. Griffin NW
    6. Peng Y
    7. Leyn SA
    8. Rodionov DA
    9. Osterman AL
    10. Gordon JI

    (2020) Identifying determinants of bacterial fitness in a model of human gut microbial succession

    PNAS 117:2622–2633.

    https://doi.org/10.1073/pnas.1918951117

    • PubMed
    • Google Scholar
56. Preprint

    1. Fithian W
    2. Sun D
    3. Taylor J

    (2014) Optimal Inference After Model Selection

    arXiv.

    https://doi.org/10.48550/arXiv.1410.2597

    • Google Scholar
57. 1. Franzosa EA
    2. Sirota-Madi A
    3. Avila-Pacheco J
    4. Fornelos N
    5. Haiser HJ
    6. Reinker S
    7. Vatanen T
    8. Hall AB
    9. Mallick H
    10. McIver LJ
    11. Sauk JS
    12. Wilson RG
    13. Stevens BW
    14. Scott JM
    15. Pierce K
    16. Deik AA
    17. Bullock K
    18. Imhann F
    19. Porter JA
    20. Zhernakova A
    21. Fu J
    22. Weersma RK
    23. Wijmenga C
    24. Clish CB
    25. Vlamakis H
    26. Huttenhower C
    27. Xavier RJ

    (2019) Gut microbiome structure and metabolic activity in inflammatory bowel disease

    Nature Microbiology 4:293–305.

    https://doi.org/10.1038/s41564-018-0306-4

    • PubMed
    • Google Scholar
58. 1. Galperin MY
    2. Makarova KS
    3. Wolf YI
    4. Koonin EV

    (2015) Expanded microbial genome coverage and improved protein family annotation in the COG database

    Nucleic Acids Research 43:D261–D9.

    https://doi.org/10.1093/nar/gku1223

    • PubMed
    • Google Scholar
59. 1. Galperin MY
    2. Wolf YI
    3. Makarova KS
    4. Vera Alvarez R
    5. Landsman D
    6. Koonin EV

    (2021) COG database update: focus on microbial diversity, model organisms, and widespread pathogens

    Nucleic Acids Research 49:D274–D281.

    https://doi.org/10.1093/nar/gkaa1018

    • PubMed
    • Google Scholar
60. 1. Garschagen LS
    2. Franke T
    3. Deppenmeier U

    (2021) An alternative pentose phosphate pathway in human gut bacteria for the degradation of C5 sugars in dietary fibers

    The FEBS Journal 288:1839–1858.

    https://doi.org/10.1111/febs.15511

    • PubMed
    • Google Scholar
61. Preprint

    1. Geller-McGrath D
    2. Konwar KM
    3. Edgcomb VP
    4. Pachiadaki M
    5. Roddy JW
    6. Wheeler TJ
    7. McDermott JE

    (2023) MetaPathPredict: A machine learning-based tool for predicting metabolic modules in incomplete bacterial genomes

    bioRxiv.

    https://doi.org/10.1101/2022.12.21.521254

    • Google Scholar
62. 1. Gevers D
    2. Kugathasan S
    3. Denson LA
    4. Vázquez-Baeza Y
    5. Van Treuren W
    6. Ren B
    7. Schwager E
    8. Knights D
    9. Song SJ
    10. Yassour M
    11. Morgan XC
    12. Kostic AD
    13. Luo C
    14. González A
    15. McDonald D
    16. Haberman Y
    17. Walters T
    18. Baker S
    19. Rosh J
    20. Stephens M
    21. Heyman M
    22. Markowitz J
    23. Baldassano R
    24. Griffiths A
    25. Sylvester F
    26. Mack D
    27. Kim S
    28. Crandall W
    29. Hyams J
    30. Huttenhower C
    31. Knight R
    32. Xavier RJ

    (2014) The treatment-naive microbiome in new-onset Crohn’s disease

    Cell Host & Microbe 15:382–392.

    https://doi.org/10.1016/j.chom.2014.02.005

    • Google Scholar
63. 1. Gobert AP
    2. Cheng Y
    3. Akhtar M
    4. Mersey BD
    5. Blumberg DR
    6. Cross RK
    7. Chaturvedi R
    8. Drachenberg CB
    9. Boucher J-L
    10. Hacker A
    11. Casero RA Jr
    12. Wilson KT

    (2004) Protective role of arginase in a mouse model of colitis

    The Journal of Immunology 173:2109–2117.

    https://doi.org/10.4049/jimmunol.173.3.2109

    • Google Scholar
64. 1. Gojda J
    2. Cahova M

    (2021) Gut microbiota as the link between elevated BCAA serum levels and insulin resistance

    Biomolecules 11:1414.

    https://doi.org/10.3390/biom11101414

    • PubMed
    • Google Scholar
65. 1. Goodman AL
    2. McNulty NP
    3. Zhao Y
    4. Leip D
    5. Mitra RD
    6. Lozupone CA
    7. Knight R
    8. Gordon JI

    (2009) Identifying genetic determinants needed to establish a human gut symbiont in its habitat

    Cell Host & Microbe 6:279–289.

    https://doi.org/10.1016/j.chom.2009.08.003

    • Google Scholar
66. Book

    1. Gruss A
    2. Borezée-Durant E
    3. Lechardeur D

    (2012) Chapter three - environmental heme utilization by heme-auxotrophic bacteria

    In: Poole RK, editors. In Advances in Microbial Physiology. Academic Press. pp. 69–124.

    https://doi.org/10.1016/B978-0-12-394423-8.00003-2

    • Google Scholar
67. 1. Gu C
    2. Kim GB
    3. Kim WJ
    4. Kim HU
    5. Lee SY

    (2019) Current status and applications of genome-scale metabolic models

    Genome Biology 20:121.

    https://doi.org/10.1186/s13059-019-1730-3

    • PubMed
    • Google Scholar
68. 1. Halfvarson J
    2. Brislawn CJ
    3. Lamendella R
    4. Vázquez-Baeza Y
    5. Walters WA
    6. Bramer LM
    7. D’Amato M
    8. Bonfiglio F
    9. McDonald D
    10. Gonzalez A
    11. McClure EE
    12. Dunklebarger MF
    13. Knight R
    14. Jansson JK

    (2017) Dynamics of the human gut microbiome in inflammatory bowel disease

    Nature Microbiology 2:17004.

    https://doi.org/10.1038/nmicrobiol.2017.4

    • PubMed
    • Google Scholar
69. 1. Hardivillé S
    2. Hart GW

    (2014) Nutrient regulation of signaling, transcription, and cell physiology by O-GlcNAcylation

    Cell Metabolism 20:208–213.

    https://doi.org/10.1016/j.cmet.2014.07.014

    • PubMed
    • Google Scholar
70. 1. Hashimoto T
    2. Perlot T
    3. Rehman A
    4. Trichereau J
    5. Ishiguro H
    6. Paolino M
    7. Sigl V
    8. Hanada T
    9. Hanada R
    10. Lipinski S
    11. Wild B
    12. Camargo SMR
    13. Singer D
    14. Richter A
    15. Kuba K
    16. Fukamizu A
    17. Schreiber S
    18. Clevers H
    19. Verrey F
    20. Rosenstiel P
    21. Penninger JM

    (2012) ACE2 links amino acid malnutrition to microbial ecology and intestinal inflammation

    Nature 487:477–481.

    https://doi.org/10.1038/nature11228

    • PubMed
    • Google Scholar
71. 1. Heinken A
    2. Hertel J
    3. Thiele I

    (2021) Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis

    NPJ Systems Biology and Applications 7:19.

    https://doi.org/10.1038/s41540-021-00178-6

    • PubMed
    • Google Scholar
72. 1. Hellmann J
    2. Ta A
    3. Ollberding NJ
    4. Bezold R
    5. Lake K
    6. Jackson K
    7. Dirksing K
    8. Bonkowski E
    9. Haslam DB
    10. Denson LA

    (2023) Patient-reported outcomes correlate with microbial community composition independent of mucosal inflammation in pediatric inflammatory bowel disease

    Inflammatory Bowel Diseases 29:286–296.

    https://doi.org/10.1093/ibd/izac175

    • PubMed
    • Google Scholar
73. 1. Henke MT
    2. Kenny DJ
    3. Cassilly CD
    4. Vlamakis H
    5. Xavier RJ
    6. Clardy J

    (2019) Ruminococcus gnavus, a member of the human gut microbiome associated with Crohn’s disease, produces an inflammatory polysaccharide

    PNAS 116:12672–12677.

    https://doi.org/10.1073/pnas.1904099116

    • PubMed
    • Google Scholar
74. 1. Henry CS
    2. DeJongh M
    3. Best AA
    4. Frybarger PM
    5. Linsay B
    6. Stevens RL

    (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models

    Nature Biotechnology 28:977–982.

    https://doi.org/10.1038/nbt.1672

    • PubMed
    • Google Scholar
75. 1. Herrmann KM
    2. Weaver LM

    (1999) The shikimate pathway

    Annual Review of Plant Physiology and Plant Molecular Biology 50:473–503.

    https://doi.org/10.1146/annurev.arplant.50.1.473

    • PubMed
    • Google Scholar
76. 1. Hijova E

    (2019) Gut bacterial metabolites of indigestible polysaccharides in intestinal fermentation as mediators of public health

    Bratislavske Lekarske Listy 120:807–812.

    https://doi.org/10.4149/BLL_2019_134

    • PubMed
    • Google Scholar
77. 1. Hossain KS
    2. Amarasena S
    3. Mayengbam S

    (2022) B vitamins and their roles in gut health

    Microorganisms 10:1168.

    https://doi.org/10.3390/microorganisms10061168

    • PubMed
    • Google Scholar
78. 1. Hove-Jensen B
    2. Andersen KR
    3. Kilstrup M
    4. Martinussen J
    5. Switzer RL
    6. Willemoës M

    (2017) Phosphoribosyl Diphosphate (PRPP): Biosynthesis, enzymology, utilization, and metabolic significance

    Microbiology and Molecular Biology Reviews 81:e00040-16.

    https://doi.org/10.1128/MMBR.00040-16

    • PubMed
    • Google Scholar
79. 1. Huergo LF
    2. Dixon R

    (2015) The emergence of 2-oxoglutarate as a master regulator metabolite

    Microbiology and Molecular Biology Reviews 79:419–435.

    https://doi.org/10.1128/MMBR.00038-15

    • PubMed
    • Google Scholar
80. 1. Human Microbiome Project Consortium

    (2012) A framework for human microbiome research

    Nature 486:215–221.

    https://doi.org/10.1038/nature11209

    • PubMed
    • Google Scholar
81. 1. Hunter JD

    (2007) Matplotlib: a 2D graphics environment

    Computing in Science & Engineering 9:90–95.

    https://doi.org/10.1109/MCSE.2007.55

    • Google Scholar
82. 1. Hyatt D
    2. Chen G-L
    3. Locascio PF
    4. Land ML
    5. Larimer FW
    6. Hauser LJ

    (2010) Prodigal: prokaryotic gene recognition and translation initiation site identification

    BMC Bioinformatics 11:119.

    https://doi.org/10.1186/1471-2105-11-119

    • PubMed
    • Google Scholar
83. 1. Ijssennagger N
    2. Belzer C
    3. Hooiveld GJ
    4. Dekker J
    5. van Mil SWC
    6. Müller M
    7. Kleerebezem M
    8. van der Meer R

    (2015) Gut microbiota facilitates dietary heme-induced epithelial hyperproliferation by opening the mucus barrier in colon

    PNAS 112:10038–10043.

    https://doi.org/10.1073/pnas.1507645112

    • PubMed
    • Google Scholar
84. 1. Jaffe AL
    2. Castelle CJ
    3. Matheus Carnevali PB
    4. Gribaldo S
    5. Banfield JF

    (2020) The rise of diversity in metabolic platforms across the Candidate Phyla Radiation

    BMC Biology 18:69.

    https://doi.org/10.1186/s12915-020-00804-5

    • PubMed
    • Google Scholar
85. 1. Jansson J
    2. Willing B
    3. Lucio M
    4. Fekete A
    5. Dicksved J
    6. Halfvarson J
    7. Tysk C
    8. Schmitt-Kopplin P

    (2009) Metabolomics reveals metabolic biomarkers of Crohn’s disease

    PLOS ONE 4:e6386.

    https://doi.org/10.1371/journal.pone.0006386

    • PubMed
    • Google Scholar
86. 1. Jodorkovsky D
    2. Young Y
    3. Abreu MT

    (2010) Clinical outcomes of patients with ulcerative colitis and co-existing Clostridium difficile infection

    Digestive Diseases and Sciences 55:415–420.

    https://doi.org/10.1007/s10620-009-0749-9

    • PubMed
    • Google Scholar
87. 1. Johansson MEV
    2. Gustafsson JK
    3. Sjöberg KE
    4. Petersson J
    5. Holm L
    6. Sjövall H
    7. Hansson GC

    (2010) Bacteria penetrate the inner mucus layer before inflammation in the dextran sulfate colitis model

    PLOS ONE 5:e12238.

    https://doi.org/10.1371/journal.pone.0012238

    • PubMed
    • Google Scholar
88. 1. Johansson MEV
    2. Hansson GC

    (2016) Immunological aspects of intestinal mucus and mucins

    Nature Reviews. Immunology 16:639–649.

    https://doi.org/10.1038/nri.2016.88

    • PubMed
    • Google Scholar
89. 1. Joossens M
    2. Huys G
    3. Cnockaert M
    4. De Preter V
    5. Verbeke K
    6. Rutgeerts P
    7. Vandamme P
    8. Vermeire S

    (2011) Dysbiosis of the faecal microbiota in patients with Crohn’s disease and their unaffected relatives

    Gut 60:631–637.

    https://doi.org/10.1136/gut.2010.223263

    • PubMed
    • Google Scholar
90. 1. Kananen K
    2. Veseli I
    3. Quiles Pérez CJ
    4. Miller SE
    5. Eren AM
    6. Bradley PH
    7. Miller S

    (2025) Adaptive adjustment of profile HMM significance thresholds improves functional and metabolic insights into microbial genomes

    Bioinformatics Advances 5:vbaf039.

    https://doi.org/10.1093/bioadv/vbaf039

    • PubMed
    • Google Scholar
91. 1. Kanehisa M
    2. Goto S
    3. Hattori M
    4. Aoki-Kinoshita KF
    5. Itoh M
    6. Kawashima S
    7. Katayama T
    8. Araki M
    9. Hirakawa M

    (2006) From genomics to chemical genomics: new developments in KEGG

    Nucleic Acids Research 34:D354–D7.

    https://doi.org/10.1093/nar/gkj102

    • PubMed
    • Google Scholar
92. 1. Kanehisa M
    2. Goto S
    3. Sato Y
    4. Furumichi M
    5. Tanabe M

    (2012) KEGG for integration and interpretation of large-scale molecular data sets

    Nucleic Acids Research 40:D109–D14.

    https://doi.org/10.1093/nar/gkr988

    • PubMed
    • Google Scholar
93. 1. Kanehisa M
    2. Furumichi M
    3. Sato Y
    4. Kawashima M
    5. Ishiguro-Watanabe M

    (2023) KEGG for taxonomy-based analysis of pathways and genomes

    Nucleic Acids Research 51:D587–D592.

    https://doi.org/10.1093/nar/gkac963

    • PubMed
    • Google Scholar
94. 1. Kaplan GG

    (2015) The global burden of IBD: from 2015 to 2025

    Nature Reviews. Gastroenterology & Hepatology 12:720–727.

    https://doi.org/10.1038/nrgastro.2015.150

    • PubMed
    • Google Scholar
95. Preprint

    1. Karp PD
    2. Latendresse M
    3. Paley SM
    4. Krummenacker M
    5. Ong QD
    6. Billington R
    7. Kothari A
    8. Weaver D
    9. Lee T
    10. Subhraveti P
    11. Spaulding A
    12. Fulcher C
    13. Keseler IM
    14. Caspi R

    (2016) Pathway tools version 19.0 update: software for pathway/genome informatics and systems biology

    arXiv.

    https://doi.org/10.48550/arXiv.1510.03964

    • Google Scholar
96. 1. Karp PD
    2. Midford PE
    3. Billington R
    4. Kothari A
    5. Krummenacker M
    6. Latendresse M
    7. Ong WK
    8. Subhraveti P
    9. Caspi R
    10. Fulcher C
    11. Keseler IM
    12. Paley SM

    (2021) Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biology

    Briefings in Bioinformatics 22:109–126.

    https://doi.org/10.1093/bib/bbz104

    • Google Scholar
97. 1. Katayama S
    2. Mine Y

    (2007) Antioxidative activity of amino acids on tissue oxidative stress in human intestinal epithelial cell model

    Journal of Agricultural and Food Chemistry 55:8458–8464.

    https://doi.org/10.1021/jf070866p

    • PubMed
    • Google Scholar
98. 1. Kelly CJ
    2. Alexeev EE
    3. Farb L
    4. Vickery TW
    5. Zheng L
    6. Eric L C
    7. Kitzenberg DA
    8. Battista KD
    9. Kominsky DJ
    10. Robertson CE
    11. Frank DN
    12. Stabler SP
    13. Colgan SP

    (2019) Oral vitamin B₁₂ supplement is delivered to the distal gut, altering the corrinoid profile and selectively depleting Bacteroides in C57BL/6 mice

    Gut Microbes 10:654–662.

    https://doi.org/10.1080/19490976.2019.1597667

    • PubMed
    • Google Scholar
99. 1. Khan I
    2. Ullah N
    3. Zha L
    4. Bai Y
    5. Khan A
    6. Zhao T
    7. Che T
    8. Zhang C

    (2019) Alteration of Gut Microbiota in Inflammatory Bowel Disease (IBD): cause or consequence? IBD treatment targeting the gut microbiome

    Pathogens 8:126.

    https://doi.org/10.3390/pathogens8030126

    • PubMed
    • Google Scholar
100. 1. Khosravi A
     2. Mazmanian SK

     (2013) Disruption of the gut microbiome as a risk factor for microbial infections

     Current Opinion in Microbiology 16:221–227.

     https://doi.org/10.1016/j.mib.2013.03.009

     • PubMed
     • Google Scholar
101. 1. Kilstrup M
     2. Hammer K
     3. Ruhdal Jensen P
     4. Martinussen J

     (2005) Nucleotide metabolism and its control in lactic acid bacteria

     FEMS Microbiology Reviews 29:555–590.

     https://doi.org/10.1016/j.femsre.2005.04.006

     • PubMed
     • Google Scholar
102. 1. Kim CJ
     2. Kovacs-Nolan JA
     3. Yang C
     4. Archbold T
     5. Fan MZ
     6. Mine Y

     (2010) L-Tryptophan exhibits therapeutic function in a porcine model of dextran sodium sulfate (DSS)-induced colitis

     The Journal of Nutritional Biochemistry 21:468–475.

     https://doi.org/10.1016/j.jnutbio.2009.01.019

     • PubMed
     • Google Scholar
103. 1. Knight R
     2. Callewaert C
     3. Marotz C
     4. Hyde ER
     5. Debelius JW
     6. McDonald D
     7. Sogin ML

     (2017) The microbiome and human biology

     Annual Review of Genomics and Human Genetics 18:65–86.

     https://doi.org/10.1146/annurev-genom-083115-022438

     • PubMed
     • Google Scholar
104. 1. Knox NC
     2. Forbes JD
     3. Van Domselaar G
     4. Bernstein CN

     (2019) The gut microbiome as a target for IBD treatment: are we there yet?

     Current Treatment Options in Gastroenterology 17:115–126.

     https://doi.org/10.1007/s11938-019-00221-w

     • PubMed
     • Google Scholar
105. 1. Koek MM
     2. Jellema RH
     3. van der Greef J
     4. Tas AC
     5. Hankemeier T

     (2011) Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

     Metabolomics 7:307–328.

     https://doi.org/10.1007/s11306-010-0254-3

     • PubMed
     • Google Scholar
106. 1. Kolios G
     2. Valatas V
     3. Ward SG

     (2004) Nitric oxide in inflammatory bowel disease: A universal messenger in an unsolved puzzle

     Immunology 113:427–437.

     https://doi.org/10.1111/j.1365-2567.2004.01984.x

     • PubMed
     • Google Scholar
107. 1. Köster J
     2. Rahmann S

     (2012) Snakemake--a scalable bioinformatics workflow engine

     Bioinformatics 28:2520–2522.

     https://doi.org/10.1093/bioinformatics/bts480

     • PubMed
     • Google Scholar
108. 1. Kostic AD
     2. Xavier RJ
     3. Gevers D

     (2014) The microbiome in inflammatory bowel disease: current status and the future ahead

     Gastroenterology 146:1489–1499.

     https://doi.org/10.1053/j.gastro.2014.02.009

     • PubMed
     • Google Scholar
109. 1. Kraus S
     2. Arber N

     (2009) Inflammation and colorectal cancer

     Current Opinion in Pharmacology 9:405–410.

     https://doi.org/10.1016/j.coph.2009.06.006

     • PubMed
     • Google Scholar
110. 1. Kronman MP
     2. Zaoutis TE
     3. Haynes K
     4. Feng R
     5. Coffin SE

     (2012) Antibiotic exposure and IBD development among children: A population-based cohort study

     Pediatrics 130:e794–e803.

     https://doi.org/10.1542/peds.2011-3886

     • PubMed
     • Google Scholar
111. 1. Kruger NJ
     2. von Schaewen A

     (2003) The oxidative pentose phosphate pathway: structure and organisation

     Current Opinion in Plant Biology 6:236–246.

     https://doi.org/10.1016/s1369-5266(03)00039-6

     • PubMed
     • Google Scholar
112. 1. Le Chatelier E
     2. Nielsen T
     3. Qin J
     4. Prifti E
     5. Hildebrand F
     6. Falony G
     7. Almeida M
     8. Arumugam M
     9. Batto J-M
     10. Kennedy S
     11. Leonard P
     12. Li J
     13. Burgdorf K
     14. Grarup N
     15. Jørgensen T
     16. Brandslund I
     17. Nielsen HB
     18. Juncker AS
     19. Bertalan M
     20. Levenez F
     21. Pons N
     22. Rasmussen S
     23. Sunagawa S
     24. Tap J
     25. Tims S
     26. Zoetendal EG
     27. Brunak S
     28. Clément K
     29. Doré J
     30. Kleerebezem M
     31. Kristiansen K
     32. Renault P
     33. Sicheritz-Ponten T
     34. de Vos WM
     35. Zucker J-D
     36. Raes J
     37. Hansen T
     38. Bork P
     39. Wang J
     40. Ehrlich SD
     41. Pedersen O
     42. MetaHIT consortium

     (2013) Richness of human gut microbiome correlates with metabolic markers

     Nature 500:541–546.

     https://doi.org/10.1038/nature12506

     • PubMed
     • Google Scholar
113. 1. Ledder Oren

     (2019) Antibiotics in inflammatory bowel diseases: do we know what we’re doing?

     Translational Pediatrics 8:42–55.

     https://doi.org/10.21037/tp.2018.11.02

     • PubMed
     • Google Scholar
114. 1. Lee H-S
     2. Han S-Y
     3. Ryu K-Y
     4. Kim D-H

     (2009) The degradation of glycosaminoglycans by intestinal microflora deteriorates colitis in mice

     Inflammation 32:27–36.

     https://doi.org/10.1007/s10753-008-9099-6

     • PubMed
     • Google Scholar
115. 1. Lee M
     2. Chang EB

     (2021) Inflammatory Bowel Diseases (IBD) and the microbiome-searching the crime scene for clues

     Gastroenterology 160:524–537.

     https://doi.org/10.1053/j.gastro.2020.09.056

     • PubMed
     • Google Scholar
116. 1. Leonardi R
     2. Zhang YM
     3. Rock CO
     4. Jackowski S

     (2005) Coenzyme A: back in action

     Progress in Lipid Research 44:125–153.

     https://doi.org/10.1016/j.plipres.2005.04.001

     • PubMed
     • Google Scholar
117. 1. Leung W
     2. Malhi G
     3. Willey BM
     4. McGeer AJ
     5. Borgundvaag B
     6. Thanabalan R
     7. Gnanasuntharam P
     8. Le B
     9. Weizman AV
     10. Croitoru K
     11. Silverberg MS
     12. Steinhart AH
     13. Nguyen GC

     (2012) Prevalence and predictors of MRSA, ESBL, and VRE colonization in the ambulatory IBD population

     Journal of Crohn’s & Colitis 6:743–749.

     https://doi.org/10.1016/j.crohns.2011.12.005

     • PubMed
     • Google Scholar
118. 1. Levy SB

     (2000)

     The future of antibiotics: facing antibiotic resistance

     Clinical Microbiology and Infection: The Official Publication of the European Society of Clinical Microbiology and Infectious Diseases 6 Suppl 3:101–106.

     • PubMed
     • Google Scholar
119. 1. Li D
     2. Liu C-M
     3. Luo R
     4. Sadakane K
     5. Lam T-W

     (2015) MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph

     Bioinformatics 31:1674–1676.

     https://doi.org/10.1093/bioinformatics/btv033

     • Google Scholar
120. 1. Lin R
     2. Liu W
     3. Piao M
     4. Zhu H

     (2017) A review of the relationship between the gut microbiota and amino acid metabolism

     Amino Acids 49:2083–2090.

     https://doi.org/10.1007/s00726-017-2493-3

     • Google Scholar
121. 1. Lin Y
     2. Caldwell GW
     3. Li Y
     4. Lang W
     5. Masucci J

     (2020) Inter-laboratory reproducibility of an untargeted metabolomics GC-MS assay for analysis of human plasma

     Scientific Reports 10:10918.

     https://doi.org/10.1038/s41598-020-67939-x

     • PubMed
     • Google Scholar
122. 1. Liu L
     2. Guo X
     3. Rao JN
     4. Zou T
     5. Xiao L
     6. Yu T
     7. Timmons JA
     8. Turner DJ
     9. Wang JY

     (2009) Polyamines regulate E-cadherin transcription through c-Myc modulating intestinal epithelial barrier function

     American Journal of Physiology. Cell Physiology 296:C801–C10.

     https://doi.org/10.1152/ajpcell.00620.2008

     • PubMed
     • Google Scholar
123. 1. Liu Y
     2. Breukink E

     (2016) The Mmembrane steps of bacterial cell wall synthesis as antibiotic tts

     Antibiotics 5:28.

     https://doi.org/10.3390/antibiotics5030028

     • PubMed
     • Google Scholar
124. 1. Liu Y
     2. Wang X
     3. Hu CAA

     (2017) Therapeutic potential of amino acids in inflammatory bowel disease

     Nutrients 9:920.

     https://doi.org/10.3390/nu9090920

     • PubMed
     • Google Scholar
125. 1. Llor C
     2. Bjerrum L

     (2014) Antimicrobial resistance: risk associated with antibiotic overuse and initiatives to reduce the problem

     Therapeutic Advances in Drug Safety 5:229–241.

     https://doi.org/10.1177/2042098614554919

     • PubMed
     • Google Scholar
126. 1. Lloyd-Price J
     2. Arze C
     3. Ananthakrishnan AN
     4. Schirmer M
     5. Avila-Pacheco J
     6. Poon TW
     7. Andrews E
     8. Ajami NJ
     9. Bonham KS
     10. Brislawn CJ
     11. Casero D
     12. Courtney H
     13. Gonzalez A
     14. Graeber TG
     15. Hall AB
     16. Lake K
     17. Landers CJ
     18. Mallick H
     19. Plichta DR
     20. Prasad M
     21. Rahnavard G
     22. Sauk J
     23. Shungin D
     24. Vázquez-Baeza Y
     25. White RA
     26. Braun J
     27. Denson LA
     28. Jansson JK
     29. Knight R
     30. Kugathasan S
     31. McGovern DPB
     32. Petrosino JF
     33. Stappenbeck TS
     34. Winter HS
     35. Clish CB
     36. Franzosa EA
     37. Vlamakis H
     38. Xavier RJ
     39. Huttenhower C
     40. IBDMDB Investigators

     (2019) Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases

     Nature 569:655–662.

     https://doi.org/10.1038/s41586-019-1237-9

     • PubMed
     • Google Scholar
127. Book

     1. Lopez MJ
     2. Mohiuddin SS

     (2023)

     Biochemistry, Essential Amino Acids

     StatPearls Publishing.

     • Google Scholar
128. 1. Lopez-Siles M
     2. Khan TM
     3. Duncan SH
     4. Harmsen HJM
     5. Garcia-Gil LJ
     6. Flint HJ

     (2012) Cultured representatives of two major phylogroups of human colonic Faecalibacterium prausnitzii can utilize pectin, uronic acids, and host-derived substrates for growth

     Applied and Environmental Microbiology 78:420–428.

     https://doi.org/10.1128/AEM.06858-11

     • PubMed
     • Google Scholar
129. 1. Lozupone CA
     2. Stombaugh J
     3. Gonzalez A
     4. Ackermann G
     5. Wendel D
     6. Vázquez-Baeza Y
     7. Jansson JK
     8. Gordon JI
     9. Knight R

     (2013) Meta-analyses of studies of the human microbiota

     Genome Research 23:1704–1714.

     https://doi.org/10.1101/gr.151803.112

     • PubMed
     • Google Scholar
130. 1. Machado D
     2. Andrejev S
     3. Tramontano M
     4. Patil KR

     (2018) Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

     Nucleic Acids Research 46:7542–7553.

     https://doi.org/10.1093/nar/gky537

     • PubMed
     • Google Scholar
131. 1. Machiels K
     2. Joossens M
     3. Sabino J
     4. De Preter V
     5. Arijs I
     6. Eeckhaut V
     7. Ballet V
     8. Claes K
     9. Van Immerseel F
     10. Verbeke K
     11. Ferrante M
     12. Verhaegen J
     13. Rutgeerts P
     14. Vermeire S

     (2014) A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis

     Gut 63:1275–1283.

     https://doi.org/10.1136/gutjnl-2013-304833

     • PubMed
     • Google Scholar
132. 1. Magnúsdóttir S
     2. Ravcheev D
     3. de Crécy-Lagard V
     4. Thiele I

     (2015) Systematic genome assessment of B-vitamin biosynthesis suggests co-operation among gut microbes

     Frontiers in Genetics 6:148.

     https://doi.org/10.3389/fgene.2015.00148

     • PubMed
     • Google Scholar
133. Preprint

     1. Marcelino VR
     2. Welsh C
     3. Diener C
     4. Gulliver EL
     5. Rutten EL
     6. Young RB
     7. Giles EM
     8. Gibbons SM
     9. Greening C
     10. Forster SC

     (2023) Disease-specific loss of microbial cross-feeding interactions in the human gut

     bioRxiv.

     https://doi.org/10.1101/2023.02.17.528570

     • Google Scholar
134. 1. Martens EC
     2. Kelly AG
     3. Tauzin AS
     4. Brumer H

     (2014) The devil lies in the details: how variations in polysaccharide fine-structure impact the physiology and evolution of gut microbes

     Journal of Molecular Biology 426:3851–3865.

     https://doi.org/10.1016/j.jmb.2014.06.022

     • PubMed
     • Google Scholar
135. 1. Martín R
     2. Rios-Covian D
     3. Huillet E
     4. Auger S
     5. Khazaal S
     6. Bermúdez-Humarán LG
     7. Sokol H
     8. Chatel JM
     9. Langella P

     (2023) Faecalibacterium: A bacterial genus with promising human health applications

     FEMS Microbiology Reviews 47:fuad039.

     https://doi.org/10.1093/femsre/fuad039

     • PubMed
     • Google Scholar
136. 1. Martin-Gallausiaux C
     2. Marinelli L
     3. Blottière HM
     4. Larraufie P
     5. Lapaque N

     (2021) SCFA: mechanisms and functional importance in the gut

     The Proceedings of the Nutrition Society 80:37–49.

     https://doi.org/10.1017/S0029665120006916

     • PubMed
     • Google Scholar
137. 1. Maynard CL
     2. Elson CO
     3. Hatton RD
     4. Weaver CT

     (2012) Reciprocal interactions of the intestinal microbiota and immune system

     Nature 489:231–241.

     https://doi.org/10.1038/nature11551

     • PubMed
     • Google Scholar
138. 1. McCormack SA
     2. Johnson LR

     (1991) Role of polyamines in gastrointestinal mucosal growth

     The American Journal of Physiology 260:G795–G806.

     https://doi.org/10.1152/ajpgi.1991.260.6.G795

     • PubMed
     • Google Scholar
139. 1. Mendoza SN
     2. Olivier BG
     3. Molenaar D
     4. Teusink B

     (2019) A systematic assessment of current genome-scale metabolic reconstruction tools

     Genome Biology 20:158.

     https://doi.org/10.1186/s13059-019-1769-1

     • PubMed
     • Google Scholar
140. 1. Mesnage R
     2. Antoniou MN

     (2020) Computational modelling provides insight into the effects of glyphosate on the shikimate pathway in the human gut microbiome

     Current Research in Toxicology 1:25–33.

     https://doi.org/10.1016/j.crtox.2020.04.001

     • PubMed
     • Google Scholar
141. 1. Metges CC
     2. El-Khoury AE
     3. Henneman L
     4. Petzke KJ
     5. Grant I
     6. Bedri S
     7. Pereira PP
     8. Ajami AM
     9. Fuller MF
     10. Young VR

     (1999) Availability of intestinal microbial lysine for whole body lysine homeostasis in human subjects

     The American Journal of Physiology 277:E597–E607.

     https://doi.org/10.1152/ajpendo.1999.277.4.E597

     • PubMed
     • Google Scholar
142. 1. Mikkola S

     (2020) Nucleotide sugars in chemistry and biology

     Molecules 25:23.

     https://doi.org/10.3390/molecules25235755

     • PubMed
     • Google Scholar
143. 1. Minárik P
     2. Tomásková N
     3. Kollárová M
     4. Antalík M

     (2002)

     Malate dehydrogenases--structure and function

     General Physiology and Biophysics 21:257–265.

     • PubMed
     • Google Scholar
144. 1. Minh BQ
     2. Schmidt HA
     3. Chernomor O
     4. Schrempf D
     5. Woodhams MD
     6. von Haeseler A
     7. Lanfear R

     (2020) IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era

     Molecular Biology and Evolution 37:1530–1534.

     https://doi.org/10.1093/molbev/msaa015

     • PubMed
     • Google Scholar
145. 1. Mistry J
     2. Chuguransky S
     3. Williams L
     4. Qureshi M
     5. Salazar GA
     6. Sonnhammer ELL
     7. Tosatto SCE
     8. Paladin L
     9. Raj S
     10. Richardson LJ
     11. Finn RD
     12. Bateman A

     (2021) Pfam: The protein families database in 2021

     Nucleic Acids Research 49:D412–D419.

     https://doi.org/10.1093/nar/gkaa913

     • PubMed
     • Google Scholar
146. 1. Morgan XC
     2. Tickle TL
     3. Sokol H
     4. Gevers D
     5. Devaney KL
     6. Ward DV
     7. Reyes JA
     8. Shah SA
     9. LeLeiko N
     10. Snapper SB
     11. Bousvaros A
     12. Korzenik J
     13. Sands BE
     14. Xavier RJ
     15. Huttenhower C

     (2012) Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment

     Genome Biology 13:R79.

     https://doi.org/10.1186/gb-2012-13-9-r79

     • PubMed
     • Google Scholar
147. 1. Moriya Y
     2. Itoh M
     3. Okuda S
     4. Yoshizawa AC
     5. Kanehisa M

     (2007) KAAS: an automatic genome annotation and pathway reconstruction server

     Nucleic Acids Research 35:W182–W5.

     https://doi.org/10.1093/nar/gkm321

     • PubMed
     • Google Scholar
148. 1. Musrati RA
     2. Kollárová M
     3. Mernik N
     4. Mikulásová D

     (1998)

     Malate dehydrogenase: distribution, function and properties

     General Physiology and Biophysics 17:193–210.

     • PubMed
     • Google Scholar
149. 1. Nagalingam NA
     2. Lynch SV

     (2012) Role of the microbiota in inflammatory bowel diseases

     Inflammatory Bowel Diseases 18:968–984.

     https://doi.org/10.1002/ibd.21866

     • PubMed
     • Google Scholar
150. 1. Nikolaus S
     2. Schulte B
     3. Al-Massad N
     4. Thieme F
     5. Schulte DM
     6. Bethge J
     7. Rehman A
     8. Tran F
     9. Aden K
     10. Häsler R
     11. Moll N
     12. Schütze G
     13. Schwarz MJ
     14. Waetzig GH
     15. Rosenstiel P
     16. Krawczak M
     17. Szymczak S
     18. Schreiber S

     (2017) Increased tryptophan metabolism is associated with activity of inflammatory bowel diseases

     Gastroenterology 153:1504–1516.

     https://doi.org/10.1053/j.gastro.2017.08.028

     • PubMed
     • Google Scholar
151. 1. Nishida A
     2. Inoue R
     3. Inatomi O
     4. Bamba S
     5. Naito Y
     6. Andoh A

     (2018) Gut microbiota in the pathogenesis of inflammatory bowel disease

     Clinical Journal of Gastroenterology 11:1–10.

     https://doi.org/10.1007/s12328-017-0813-5

     • PubMed
     • Google Scholar
152. 1. Nitzan O
     2. Elias M
     3. Peretz A
     4. Saliba W

     (2016) Role of antibiotics for treatment of inflammatory bowel disease

     World Journal of Gastroenterology 22:1078–1087.

     https://doi.org/10.3748/wjg.v22.i3.1078

     • PubMed
     • Google Scholar
153. Book

     1. Nygaard P

     (2014) Purine and pyrimidine salvage pathways

     In: Nygaard P, editors. In Bacillus subtilis and Other Gram-Positive Bacteria. ASM Press. pp. 359–378.

     https://doi.org/10.1128/9781555818388.ch26

     • Google Scholar
154. 1. Orth JD
     2. Thiele I
     3. Palsson BØ

     (2010) What is flux balance analysis?

     Nature Biotechnology 28:245–248.

     https://doi.org/10.1038/nbt.1614

     • PubMed
     • Google Scholar
155. 1. Oz HS
     2. Chen TS
     3. McClain CJ
     4. de Villiers WJS

     (2005) Antioxidants as novel therapy in a murine model of colitis

     The Journal of Nutritional Biochemistry 16:297–304.

     https://doi.org/10.1016/j.jnutbio.2004.09.007

     • PubMed
     • Google Scholar
156. 1. Pacheco AR
     2. Moel M
     3. Segrè D

     (2019) Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems

     Nature Communications 10:103.

     https://doi.org/10.1038/s41467-018-07946-9

     • PubMed
     • Google Scholar
157. 1. Palleja A
     2. Mikkelsen KH
     3. Forslund SK
     4. Kashani A
     5. Allin KH
     6. Nielsen T
     7. Hansen TH
     8. Liang S
     9. Feng Q
     10. Zhang C
     11. Pyl PT
     12. Coelho LP
     13. Yang H
     14. Wang J
     15. Typas A
     16. Nielsen MF
     17. Nielsen HB
     18. Bork P
     19. Wang J
     20. Vilsbøll T
     21. Hansen T
     22. Knop FK
     23. Arumugam M
     24. Pedersen O

     (2018) Recovery of gut microbiota of healthy adults following antibiotic exposure

     Nature Microbiology 3:1255–1265.

     https://doi.org/10.1038/s41564-018-0257-9

     • PubMed
     • Google Scholar
158. 1. Palù M
     2. Basile A
     3. Zampieri G
     4. Treu L
     5. Rossi A
     6. Morlino MS
     7. Campanaro S

     (2022) KEMET - A python tool for KEGG Module evaluation and microbial genome annotation expansion

     Computational and Structural Biotechnology Journal 20:1481–1486.

     https://doi.org/10.1016/j.csbj.2022.03.015

     • PubMed
     • Google Scholar
159. 1. Papa E
     2. Docktor M
     3. Smillie C
     4. Weber S
     5. Preheim SP
     6. Gevers D
     7. Giannoukos G
     8. Ciulla D
     9. Tabbaa D
     10. Ingram J
     11. Schauer DB
     12. Ward DV
     13. Korzenik JR
     14. Xavier RJ
     15. Bousvaros A
     16. Alm EJ

     (2012) Non-invasive mapping of the gastrointestinal microbiota identifies children with inflammatory bowel disease

     PLOS ONE 7:e39242.

     https://doi.org/10.1371/journal.pone.0039242

     • PubMed
     • Google Scholar
160. 1. Parks DH
     2. Chuvochina M
     3. Waite DW
     4. Rinke C
     5. Skarshewski A
     6. Chaumeil P-A
     7. Hugenholtz P

     (2018) A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life

     Nature Biotechnology 36:996–1004.

     https://doi.org/10.1038/nbt.4229

     • PubMed
     • Google Scholar
161. 1. Parks DH
     2. Chuvochina M
     3. Chaumeil PA
     4. Rinke C
     5. Mussig AJ
     6. Hugenholtz P

     (2020) A complete domain-to-species taxonomy for Bacteria and Archaea

     Nature Biotechnology 38:1079–1086.

     https://doi.org/10.1038/s41587-020-0501-8

     • PubMed
     • Google Scholar
162. 1. Parks DH
     2. Chuvochina M
     3. Rinke C
     4. Mussig AJ
     5. Chaumeil P-A
     6. Hugenholtz P

     (2022) GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy

     Nucleic Acids Research 50:D785–D794.

     https://doi.org/10.1093/nar/gkab776

     • PubMed
     • Google Scholar
163. 1. Peng Y
     2. Leung HCM
     3. Yiu SM
     4. Chin FYL

     (2012) IDBA-UD: A de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth

     Bioinformatics 28:1420–1428.

     https://doi.org/10.1093/bioinformatics/bts174

     • PubMed
     • Google Scholar
164. 1. Pierzynowski S
     2. Pierzynowska K

     (2022) Alpha-ketoglutarate, a key molecule involved in nitrogen circulation in both animals and plants

     Adv Med Sci 67:142–147.

     https://doi.org/10.1016/j.advms.2022.02.004

     • Google Scholar
165. 1. Porrini C
     2. Guérin C
     3. Tran SL
     4. Dervyn R
     5. Nicolas P
     6. Ramarao N

     (2021) Implication of a key region of six Bacillus cereus genes involved in siroheme synthesis, nitrite reductase production and iron cluster repair in the bacterial response to nitric oxide stress

     International Journal of Molecular Sciences 22:5079.

     https://doi.org/10.3390/ijms22105079

     • PubMed
     • Google Scholar
166. 1. Powell JE
     2. Leonard SP
     3. Kwong WK
     4. Engel P
     5. Moran NA

     (2016) Genome-wide screen identifies host colonization determinants in a bacterial gut symbiont

     PNAS 113:13887–13892.

     https://doi.org/10.1073/pnas.1610856113

     • PubMed
     • Google Scholar
167. 1. Prindiville TP
     2. Sheikh RA
     3. Cohen SH
     4. Tang YJ
     5. Cantrell MC
     6. Silva J Jr

     (2000) Bacteroides fragilis enterotoxin gene sequences in patients with inflammatory bowel disease

     Emerging Infectious Diseases 6:171–174.

     https://doi.org/10.3201/eid0602.000210

     • PubMed
     • Google Scholar
168. 1. Qin J
     2. Li Y
     3. Cai Z
     4. Li S
     5. Zhu J
     6. Zhang F
     7. Liang S
     8. Zhang W
     9. Guan Y
     10. Shen D
     11. Peng Y
     12. Zhang D
     13. Jie Z
     14. Wu W
     15. Qin Y
     16. Xue W
     17. Li J
     18. Han L
     19. Lu D
     20. Wu P
     21. Dai Y
     22. Sun X
     23. Li Z
     24. Tang A
     25. Zhong S
     26. Li X
     27. Chen W
     28. Xu R
     29. Wang M
     30. Feng Q
     31. Gong M
     32. Yu J
     33. Zhang Y
     34. Zhang M
     35. Hansen T
     36. Sanchez G
     37. Raes J
     38. Falony G
     39. Okuda S
     40. Almeida M
     41. LeChatelier E
     42. Renault P
     43. Pons N
     44. Batto J-M
     45. Zhang Z
     46. Chen H
     47. Yang R
     48. Zheng W
     49. Li S
     50. Yang H
     51. Wang J
     52. Ehrlich SD
     53. Nielsen R
     54. Pedersen O
     55. Kristiansen K
     56. Wang J

     (2012) A metagenome-wide association study of gut microbiota in type 2 diabetes

     Nature 490:55–60.

     https://doi.org/10.1038/nature11450

     • PubMed
     • Google Scholar
169. 1. Quince C
     2. Ijaz UZ
     3. Loman N
     4. Eren AM
     5. Saulnier D
     6. Russell J
     7. Haig SJ
     8. Calus ST
     9. Quick J
     10. Barclay A
     11. Bertz M
     12. Blaut M
     13. Hansen R
     14. McGrogan P
     15. Russell RK
     16. Edwards CA
     17. Gerasimidis K

     (2015) Extensive modulation of the fecal metagenome in children with crohn’s disease during exclusive enteral nutrition

     The American Journal of Gastroenterology 110:1718–1729.

     https://doi.org/10.1038/ajg.2015.357

     • PubMed
     • Google Scholar
170. Book

     1. Ralevic V

     (2015)

     UDP-glucose

     In: Ralevic V, editors. Reference Module in Biomedical Sciences. Elsevier. pp. 1–4.

     • Google Scholar
171. 1. Ramirez J
     2. Guarner F
     3. Bustos Fernandez L
     4. Maruy A
     5. Sdepanian VL
     6. Cohen H

     (2020) Antibiotics as Mmajor disruptors of gut microbiotajor Disruptors of Gut Microbiota

     Frontiers in Cellular and Infection Microbiology 10:572912.

     https://doi.org/10.3389/fcimb.2020.572912

     • PubMed
     • Google Scholar
172. 1. Rampelli S
     2. Schnorr SL
     3. Consolandi C
     4. Turroni S
     5. Severgnini M
     6. Peano C
     7. Brigidi P
     8. Crittenden AN
     9. Henry AG
     10. Candela M

     (2015) Metagenome sequencing of the hadza hunter-gatherer gut microbiota

     Current Biology 25:1682–1693.

     https://doi.org/10.1016/j.cub.2015.04.055

     • PubMed
     • Google Scholar
173. 1. Raymond F
     2. Ouameur AA
     3. Déraspe M
     4. Iqbal N
     5. Gingras H
     6. Dridi B
     7. Leprohon P
     8. Plante P-L
     9. Giroux R
     10. Bérubé È
     11. Frenette J
     12. Boudreau DK
     13. Simard J-L
     14. Chabot I
     15. Domingo M-C
     16. Trottier S
     17. Boissinot M
     18. Huletsky A
     19. Roy PH
     20. Ouellette M
     21. Bergeron MG
     22. Corbeil J

     (2016) The initial state of the human gut microbiome determines its reshaping by antibiotics

     The ISME Journal 10:707–720.

     https://doi.org/10.1038/ismej.2015.148

     • PubMed
     • Google Scholar
174. 1. Rehman T
     2. Shabbir MA
     3. Inam‐Ur‐Raheem M
     4. Manzoor MF
     5. Ahmad N
     6. Liu Z
     7. Ahmad MH
     8. Siddeeg A
     9. Abid M
     10. Aadil RM

     (2020) Cysteine and homocysteine as biomarker of various diseases

     Food Science & Nutrition 8:4696–4707.

     https://doi.org/10.1002/fsn3.1818

     • Google Scholar
175. Preprint

     1. Reiter TE
     2. Irber L
     3. Gingrich AA
     4. Haynes D
     5. Pierce-Ward NT
     6. Brooks PT
     7. Mizutani Y
     8. Moritz D
     9. Reidl F
     10. Willis AD
     11. Sullivan BD
     12. Brown CT

     (2022) Meta-analysis of metagenomes via machine learning and assembly graphs reveals strain switches in Crohn’s disease

     bioRxiv.

     https://doi.org/10.1101/2022.06.30.498290

     • Google Scholar
176. 1. Rhodes JM

     (2007) The role of Escherichia coli in inflammatory bowel disease

     Gut 56:610–612.

     https://doi.org/10.1136/gut.2006.111872

     • PubMed
     • Google Scholar
177. 1. Rigottier-Gois L

     (2013) Dysbiosis in inflammatory bowel diseases: the oxygen hypothesis

     The ISME Journal 7:1256–1261.

     https://doi.org/10.1038/ismej.2013.80

     • PubMed
     • Google Scholar
178. 1. Roager HM
     2. Licht TR

     (2018) Microbial tryptophan catabolites in health and disease

     Nature Communications 9:3294.

     https://doi.org/10.1038/s41467-018-05470-4

     • PubMed
     • Google Scholar
179. 1. Roy A
     2. Lichtiger S

     (2016) Clostridium difficile infection: a rarity in patients receiving chronic antibiotic treatment for Crohn’s disease

     Inflammatory Bowel Diseases 22:648–653.

     https://doi.org/10.1097/MIB.0000000000000641

     • PubMed
     • Google Scholar
180. 1. Ryczko MC
     2. Pawling J
     3. Chen R
     4. Abdel Rahman AM
     5. Yau K
     6. Copeland JK
     7. Zhang C
     8. Surendra A
     9. Guttman DS
     10. Figeys D
     11. Dennis JW

     (2016) Metabolic reprogramming by hexosamine biosynthetic and golgi N-glycan branching pathways

     Scientific Reports 6:23043.

     https://doi.org/10.1038/srep23043

     • PubMed
     • Google Scholar
181. 1. Saitoh S
     2. Noda S
     3. Aiba Y
     4. Takagi A
     5. Sakamoto M
     6. Benno Y
     7. Koga Y

     (2002) Bacteroides ovatus as the predominant commensal intestinal microbe causing a systemic antibody response in inflammatory bowel disease

     Clinical and Diagnostic Laboratory Immunology 9:54–59.

     https://doi.org/10.1128/cdli.9.1.54-59.2002

     • PubMed
     • Google Scholar
182. 1. Sartor RB

     (2006) Mechanisms of disease: pathogenesis of Crohn’s disease and ulcerative colitis

     Nature Clinical Practice. Gastroenterology & Hepatology 3:390–407.

     https://doi.org/10.1038/ncpgasthep0528

     • PubMed
     • Google Scholar
183. 1. Schirmer M
     2. Franzosa EA
     3. Lloyd-Price J
     4. McIver LJ
     5. Schwager R
     6. Poon TW
     7. Ananthakrishnan AN
     8. Andrews E
     9. Barron G
     10. Lake K
     11. Prasad M
     12. Sauk J
     13. Stevens B
     14. Wilson RG
     15. Braun J
     16. Denson LA
     17. Kugathasan S
     18. McGovern DPB
     19. Vlamakis H
     20. Xavier RJ
     21. Huttenhower C

     (2018) Dynamics of metatranscription in the inflammatory bowel disease gut microbiome

     Nature Microbiology 3:337–346.

     https://doi.org/10.1038/s41564-017-0089-z

     • PubMed
     • Google Scholar
184. 1. Schirmer M
     2. Garner A
     3. Vlamakis H
     4. Xavier RJ

     (2019) Microbial genes and pathways in inflammatory bowel disease

     Nature Reviews. Microbiology 17:497–511.

     https://doi.org/10.1038/s41579-019-0213-6

     • PubMed
     • Google Scholar
185. 1. Scrivens M
     2. Dickenson JM

     (2005) Functional expression of the P2Y14 receptor in murine T-lymphocytes

     British Journal of Pharmacology 146:435–444.

     https://doi.org/10.1038/sj.bjp.0706322

     • PubMed
     • Google Scholar
186. Software

     1. Seemann TN

     (2018) Barrnap: bacterial ribosomal RNA predictor, version version 3

     Github.

     https://github.com/tseemann/barrnap
187. 1. Seetharam B
     2. Alpers DH

     (1982) Absorption and transport of cobalamin (vitamin B12)

     Annual Review of Nutrition 2:343–369.

     https://doi.org/10.1146/annurev.nu.02.070182.002015

     • PubMed
     • Google Scholar
188. 1. Sen P
     2. Orešič M

     (2019) Metabolic modeling of human gut microbiota on a genome scale: an overview

     Metabolites 9:22.

     https://doi.org/10.3390/metabo9020022

     • PubMed
     • Google Scholar
189. 1. Shaffer M
     2. Borton MA
     3. McGivern BB
     4. Zayed AA
     5. La Rosa SL
     6. Solden LM
     7. Liu P
     8. Narrowe AB
     9. Rodríguez-Ramos J
     10. Bolduc B
     11. Gazitúa MC
     12. Daly RA
     13. Smith GJ
     14. Vik DR
     15. Pope PB
     16. Sullivan MB
     17. Roux S
     18. Wrighton KC

     (2020) DRAM for distilling microbial metabolism to automate the curation of microbiome function

     Nucleic Acids Research 48:8883–8900.

     https://doi.org/10.1093/nar/gkaa621

     • PubMed
     • Google Scholar
190. 1. Shah YM

     (2016) The role of hypoxia in intestinal inflammation

     Molecular and Cellular Pediatrics 3:301.

     https://doi.org/10.1186/s40348-016-0030-1

     • PubMed
     • Google Scholar
191. 1. Shaiber A
     2. Willis AD
     3. Delmont TO
     4. Roux S
     5. Chen L-X
     6. Schmid AC
     7. Yousef M
     8. Watson AR
     9. Lolans K
     10. Esen ÖC
     11. Lee STM
     12. Downey N
     13. Morrison HG
     14. Dewhirst FE
     15. Mark Welch JL
     16. Eren AM

     (2020) Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome

     Genome Biology 21:292.

     https://doi.org/10.1186/s13059-020-02195-w

     • PubMed
     • Google Scholar
192. 1. Shan Y
     2. Lee M
     3. Chang EB

     (2022) The gut microbiome and inflammatory bowel diseases

     Annual Review of Medicine 73:455–468.

     https://doi.org/10.1146/annurev-med-042320-021020

     • PubMed
     • Google Scholar
193. 1. Sharpton T
     2. Lyalina S
     3. Luong J
     4. Pham J
     5. Deal EM
     6. Armour C
     7. Gaulke C
     8. Sanjabi S
     9. Pollard KS

     (2017) Development of inflammatory bowel disease is linked to a longitudinal restructuring of the gut metagenome in mice

     mSystems 2:e00036-17.

     https://doi.org/10.1128/mSystems.00036-17

     • PubMed
     • Google Scholar
194. 1. Shaw SY
     2. Blanchard JF
     3. Bernstein CN

     (2011) Association between the use of antibiotics and new diagnoses of Crohn’s disease and ulcerative colitis

     The American Journal of Gastroenterology 106:2133–2142.

     https://doi.org/10.1038/ajg.2011.304

     • PubMed
     • Google Scholar
195. 1. Sherrill C
     2. Fahey RC

     (1998) Import and metabolism of glutathione by Streptococcus mutans

     Journal of Bacteriology 180:1454–1459.

     https://doi.org/10.1128/JB.180.6.1454-1459.1998

     • PubMed
     • Google Scholar
196. 1. Singh K
     2. Gobert AP
     3. Coburn LA
     4. Barry DP
     5. Allaman M
     6. Asim M
     7. Luis PB
     8. Schneider C
     9. Milne GL
     10. Boone HH
     11. Shilts MH
     12. Washington MK
     13. Das SR
     14. Piazuelo MB
     15. Wilson KT

     (2019) Dietary arginine regulates severity of experimental colitis and affects the colonic microbiome

     Frontiers in Cellular and Infection Microbiology 9:66.

     https://doi.org/10.3389/fcimb.2019.00066

     • PubMed
     • Google Scholar
197. 1. Sinha R
     2. Abu-Ali G
     3. Vogtmann E
     4. Fodor AA
     5. Ren B
     6. Amir A
     7. Schwager E
     8. Crabtree J
     9. Ma S
     10. Abnet CC
     11. Knight R
     12. White O
     13. Huttenhower C
     14. Microbiome Quality Control Project Consortium

     (2017) Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium

     Nature Biotechnology 35:1077–1086.

     https://doi.org/10.1038/nbt.3981

     • PubMed
     • Google Scholar
198. 1. Skelton L
     2. Cooper M
     3. Murphy M
     4. Platt A

     (2003) Human immature monocyte-derived dendritic cells express the G protein-coupled receptor GPR105 (KIAA0001, P2Y14) and increase intracellular calcium in response to its agonist, uridine diphosphoglucose

     Journal of Immunology 171:1941–1949.

     https://doi.org/10.4049/jimmunol.171.4.1941

     • PubMed
     • Google Scholar
199. 1. Soderberg Tim

     (2005) Biosynthesis of ribose-5-phosphate and erythrose-4-phosphate in archaea: A phylogenetic analysis of archaeal genomes

     Archaea 1:347–352.

     https://doi.org/10.1155/2005/314760

     • PubMed
     • Google Scholar
200. 1. Sonnhammer EL
     2. Eddy SR
     3. Durbin R

     (1997) Pfam: A comprehensive database of protein domain families based on seed alignments

     Proteins 28:405–420.

     https://doi.org/10.1002/(sici)1097-0134(199707)28:3<405::aid-prot10>3.0.co;2-l

     • PubMed
     • Google Scholar
201. 1. Sorbara MT
     2. Pamer EG

     (2022) Microbiome-based therapeutics

     Nature Reviews. Microbiology 20:365–380.

     https://doi.org/10.1038/s41579-021-00667-9

     • PubMed
     • Google Scholar
202. 1. Soufli I
     2. Toumi R
     3. Rafa H
     4. Touil-Boukoffa C

     (2016) Overview of cytokines and nitric oxide involvement in immuno-pathogenesis of inflammatory bowel diseases

     World Journal of Gastrointestinal Pharmacology and Therapeutics 7:353.

     https://doi.org/10.4292/wjgpt.v7.i3.353

     • Google Scholar
203. 1. Sprong RC
     2. Schonewille AJ
     3. van der Meer R

     (2010) Dietary cheese whey protein protects rats against mild dextran sulfate sodium-induced colitis: role of mucin and microbiota

     Journal of Dairy Science 93:1364–1371.

     https://doi.org/10.3168/jds.2009-2397

     • PubMed
     • Google Scholar
204. 1. Spry C
     2. Kirk K
     3. Saliba KJ

     (2008) Coenzyme A biosynthesis: an antimicrobial drug target

     FEMS Microbiology Reviews 32:56–106.

     https://doi.org/10.1111/j.1574-6976.2007.00093.x

     • PubMed
     • Google Scholar
205. 1. Steinegger M
     2. Söding J

     (2017) MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets

     Nature Biotechnology 35:1026–1028.

     https://doi.org/10.1038/nbt.3988

     • PubMed
     • Google Scholar
206. 1. Teng H
     2. Wang Y
     3. Sui X
     4. Fan J
     5. Li S
     6. Lei X
     7. Shi C
     8. Sun W
     9. Song M
     10. Wang H
     11. Dong D
     12. Geng J
     13. Zhang Y
     14. Zhu X
     15. Cai Y
     16. Li Y
     17. Li B
     18. Min Q
     19. Wang W
     20. Zhan Q

     (2023) Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer

     Cancer Cell 41:124–138.

     https://doi.org/10.1016/j.ccell.2022.11.013

     • PubMed
     • Google Scholar
207. 1. Tepe N
     2. Shimko LA
     3. Duran M

     (2006) Toxic effects of thiol-reactive compounds on anaerobic biomass

     Bioresource Technology 97:592–598.

     https://doi.org/10.1016/j.biortech.2005.03.029

     • PubMed
     • Google Scholar
208. 1. Tong BC
     2. Barbul A

     (2004) Cellular and physiological effects of arginine

     Mini Reviews in Medicinal Chemistry 4:823–832.

     https://doi.org/10.2174/1389557043403305

     • PubMed
     • Google Scholar
209. 1. Treem WR
     2. Ahsan N
     3. Shoup M
     4. Hyams JS

     (1994) Fecal short-chain fatty acids in children with inflammatory bowel disease

     Journal of Pediatric Gastroenterology and Nutrition 18:159–164.

     https://doi.org/10.1097/00005176-199402000-00007

     • PubMed
     • Google Scholar
210. 1. Turnbaugh PJ
     2. Hamady M
     3. Yatsunenko T
     4. Cantarel BL
     5. Duncan A
     6. Ley RE
     7. Sogin ML
     8. Jones WJ
     9. Roe BA
     10. Affourtit JP
     11. Egholm M
     12. Henrissat B
     13. Heath AC
     14. Knight R
     15. Gordon JI

     (2009) A core gut microbiome in obese and lean twins

     Nature 457:480–484.

     https://doi.org/10.1038/nature07540

     • PubMed
     • Google Scholar
211. 1. Ungaro R
     2. Bernstein CN
     3. Gearry R
     4. Hviid A
     5. Kolho K-L
     6. Kronman MP
     7. Shaw S
     8. Van Kruiningen H
     9. Colombel J-F
     10. Atreja A

     (2014) Antibiotics associated with increased risk of new-onset Crohn’s disease but not ulcerative colitis: A meta-analysis

     The American Journal of Gastroenterology 109:1728–1738.

     https://doi.org/10.1038/ajg.2014.246

     • PubMed
     • Google Scholar
212. Website

     1. University of Sydney

     (2022) metagenome fecal microbiota, Ilumina seq reads of 12 individuals at 2 timepoints

     Accessed September 23, 2022.

     https://www.ncbi.nlm.nih.gov/bioproject/PRJEB6092
213. 1. Vacca M
     2. Celano G
     3. Calabrese FM
     4. Portincasa P
     5. Gobbetti M
     6. De Angelis M

     (2020) The controversial role of human gut lachnospiraceae

     Microorganisms 8:573.

     https://doi.org/10.3390/microorganisms8040573

     • PubMed
     • Google Scholar
214. 1. Vaisman A
     2. Pivovarov K
     3. McGeer A
     4. Willey B
     5. Borgundvaag B
     6. Porter V
     7. Gnanasuntharam P
     8. Wei Y
     9. Nguyen GC

     (2013) Prevalence and incidence of antimicrobial-resistant organisms among hospitalized inflammatory bowel disease patients

     The Canadian Journal of Infectious Diseases & Medical Microbiology = Journal Canadien Des Maladies Infectieuses et de La Microbiologie Medicale 24:e117–e21.

     https://doi.org/10.1155/2013/609230

     • PubMed
     • Google Scholar
215. 1. van Dam V
     2. Olrichs N
     3. Breukink E

     (2009) Specific labeling of peptidoglycan precursors as a tool for bacterial cell wall studies

     Chembiochem 10:617–624.

     https://doi.org/10.1002/cbic.200800678

     • PubMed
     • Google Scholar
216. 1. Vanni C
     2. Schechter MS
     3. Acinas SG
     4. Barberán A
     5. Buttigieg PL
     6. Casamayor EO
     7. Delmont TO
     8. Duarte CM
     9. Eren AM
     10. Finn RD
     11. Kottmann R
     12. Mitchell A
     13. Sánchez P
     14. Siren K
     15. Steinegger M
     16. Gloeckner FO
     17. Fernàndez-Guerra A

     (2022) Unifying the known and unknown microbial coding sequence space

     eLife 11:e67667.

     https://doi.org/10.7554/eLife.67667

     • PubMed
     • Google Scholar
217. 1. Vich Vila A
     2. Imhann F
     3. Collij V
     4. Jankipersadsing SA
     5. Gurry T
     6. Mujagic Z
     7. Kurilshikov A
     8. Bonder MJ
     9. Jiang X
     10. Tigchelaar EF
     11. Dekens J
     12. Peters V
     13. Voskuil MD
     14. Visschedijk MC
     15. van Dullemen HM
     16. Keszthelyi D
     17. Swertz MA
     18. Franke L
     19. Alberts R
     20. Festen EAM
     21. Dijkstra G
     22. Masclee AAM
     23. Hofker MH
     24. Xavier RJ
     25. Alm EJ
     26. Fu J
     27. Wijmenga C
     28. Jonkers D
     29. Zhernakova A
     30. Weersma RK

     (2018) Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome

     Science Translational Medicine 10:eaap8914.

     https://doi.org/10.1126/scitranslmed.aap8914

     • PubMed
     • Google Scholar
218. 1. Vineis JH
     2. Ringus DL
     3. Morrison HG
     4. Delmont TO
     5. Dalal S
     6. Raffals LH
     7. Antonopoulos DA
     8. Rubin DT
     9. Eren AM
     10. Chang EB
     11. Sogin ML

     (2016) Patient-specific bacteroides genome variants in pouchitis

     mBio 7:e01713-16.

     https://doi.org/10.1128/mBio.01713-16

     • PubMed
     • Google Scholar
219. 1. Walker MY
     2. Pratap S
     3. Southerland JH
     4. Farmer-Dixon CM
     5. Lakshmyya K
     6. Gangula PR

     (2018) Role of oral and gut microbiome in nitric oxide-mediated colon motility

     Nitric Oxide 73:81–88.

     https://doi.org/10.1016/j.niox.2017.06.003

     • Google Scholar
220. 1. Watson AR
     2. Füssel J
     3. Veseli I
     4. DeLongchamp JZ
     5. Silva M
     6. Trigodet F
     7. Lolans K
     8. Shaiber A
     9. Fogarty E
     10. Runde JM
     11. Quince C
     12. Yu MK
     13. Söylev A
     14. Morrison HG
     15. Lee STM
     16. Kao D
     17. Rubin DT
     18. Jabri B
     19. Louie T
     20. Eren AM

     (2023) Metabolic independence drives gut microbial colonization and resilience in health and disease

     Genome Biology 24:78.

     https://doi.org/10.1186/s13059-023-02924-x

     • PubMed
     • Google Scholar
221. 1. Wen C
     2. Zheng Z
     3. Shao T
     4. Liu L
     5. Xie Z
     6. Le Chatelier E
     7. He Z
     8. Zhong W
     9. Fan Y
     10. Zhang L
     11. Li H
     12. Wu C
     13. Hu C
     14. Xu Q
     15. Zhou J
     16. Cai S
     17. Wang D
     18. Huang Y
     19. Breban M
     20. Qin N
     21. Ehrlich SD

     (2017) Quantitative metagenomics reveals unique gut microbiome biomarkers in ankylosing spondylitis

     Genome Biology 18:142.

     https://doi.org/10.1186/s13059-017-1271-6

     • PubMed
     • Google Scholar
222. 1. Wexler HM

     (2007) Bacteroides: the good, the bad, and the nitty-gritty

     Clinical Microbiology Reviews 20:593–621.

     https://doi.org/10.1128/CMR.00008-07

     • PubMed
     • Google Scholar
223. Book

     1. Wickham H

     (2016)

     Ggplot2: Elegant Graphics for Data Analysis

     Springer.

     • Google Scholar
224. 1. Wolfe AJ

     (2005) The acetate switch

     Microbiology and Molecular Biology Reviews 69:12–50.

     https://doi.org/10.1128/MMBR.69.1.12-50.2005

     • PubMed
     • Google Scholar
225. 1. Wong CC
     2. Fong W
     3. Yu J

     (2023) Gut microbes promote chemoradiotherapy resistance via metabolic cross-feeding

     Cancer Cell 41:12–14.

     https://doi.org/10.1016/j.ccell.2022.11.017

     • PubMed
     • Google Scholar
226. 1. Woting A
     2. Blaut M

     (2016) The Intestinal microbiota in metabolic disease

     Nutrients 8:202.

     https://doi.org/10.3390/nu8040202

     • PubMed
     • Google Scholar
227. 1. Wu W
     2. Sun M
     3. Chen F
     4. Cao AT
     5. Liu H
     6. Zhao Y
     7. Huang X
     8. Xiao Y
     9. Yao S
     10. Zhao Q
     11. Liu Z
     12. Cong Y

     (2017) Microbiota metabolite short-chain fatty acid acetate promotes intestinal IgA response to microbiota which is mediated by GPR43

     Mucosal Immunology 10:946–956.

     https://doi.org/10.1038/mi.2016.114

     • PubMed
     • Google Scholar
228. 1. Wu L
     2. Tang Z
     3. Chen H
     4. Ren Z
     5. Ding Q
     6. Liang K
     7. Sun Z

     (2021) Mutual interaction between gut microbiota and protein/amino acid metabolism for host mucosal immunity and health

     Animal Nutrition 7:11–16.

     https://doi.org/10.1016/j.aninu.2020.11.003

     • PubMed
     • Google Scholar
229. 1. Xie H
     2. Guo R
     3. Zhong H
     4. Feng Q
     5. Lan Z
     6. Qin B
     7. Ward KJ
     8. Jackson MA
     9. Xia Y
     10. Chen X
     11. Chen B
     12. Xia H
     13. Xu C
     14. Li F
     15. Xu X
     16. Al-Aama JY
     17. Yang H
     18. Wang J
     19. Kristiansen K
     20. Wang J
     21. Steves CJ
     22. Bell JT
     23. Li J
     24. Spector TD
     25. Jia H

     (2016) Shotgun Metagenomics of 250 adult twins reveals genetic and environmental impacts on the gut microbiome

     Cell Systems 3:572–584.

     https://doi.org/10.1016/j.cels.2016.10.004

     • PubMed
     • Google Scholar
230. 1. Yang J
     2. Kalhan SC
     3. Hanson RW

     (2009) What is the metabolic role of phosphoenolpyruvate carboxykinase?

     The Journal of Biological Chemistry 284:27025–27029.

     https://doi.org/10.1074/jbc.R109.040543

     • PubMed
     • Google Scholar
231. 1. Yang Y
     2. Zhang Y
     3. Xu Y
     4. Luo T
     5. Ge Y
     6. Jiang Y
     7. Shi Y
     8. Sun J
     9. Le G

     (2019) Dietary methionine restriction improves the gut microbiota and reduces intestinal permeability and inflammation in high-fat-fed mice

     Food & Function 10:5952–5968.

     https://doi.org/10.1039/C9FO00766K

     • Google Scholar
232. 1. Ye Y
     2. Doak TG

     (2009) A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes

     PLOS Computational Biology 5:e1000465.

     https://doi.org/10.1371/journal.pcbi.1000465

     • PubMed
     • Google Scholar
233. 1. Yusufu I
     2. Ding K
     3. Smith K
     4. Wankhade UD
     5. Sahay B
     6. Patterson GT
     7. Pacholczyk R
     8. Adusumilli S
     9. Hamrick MW
     10. Hill WD
     11. Isales CM
     12. Fulzele S

     (2021) A tryptophan-deficient diet induces gut microbiota dysbiosis and increases systemic inflammation in aged mice

     International Journal of Molecular Sciences 22:5005.

     https://doi.org/10.3390/ijms22095005

     • PubMed
     • Google Scholar
234. 1. Zhang Z
     2. Zhang H
     3. Chen T
     4. Shi L
     5. Wang D
     6. Tang D

     (2022) Regulatory role of short-chain fatty acids in inflammatory bowel disease

     Cell Communication and Signaling 20:64.

     https://doi.org/10.1186/s12964-022-00869-5

     • PubMed
     • Google Scholar
235. 1. Zhao H
     2. Chen J
     3. Li X
     4. Sun Q
     5. Qin P
     6. Wang Q

     (2019) Compositional and functional features of the female premenopausal and postmenopausal gut microbiota

     FEBS Letters 593:2655–2664.

     https://doi.org/10.1002/1873-3468.13527

     • PubMed
     • Google Scholar
236. 1. Zhou Z
     2. Tran PQ
     3. Breister AM
     4. Liu Y
     5. Kieft K
     6. Cowley ES
     7. Karaoz U
     8. Anantharaman K

     (2022) METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks

     Microbiome 10:33.

     https://doi.org/10.1186/s40168-021-01213-8

     • PubMed
     • Google Scholar
237. 1. Zimmermann M
     2. Zimmermann-Kogadeeva M
     3. Wegmann R
     4. Goodman AL

     (2019) Mapping human microbiome drug metabolism by gut bacteria and their genes

     Nature 570:462–467.

     https://doi.org/10.1038/s41586-019-1291-3

     • Google Scholar
238. 1. Zimmermann J
     2. Kaleta C
     3. Waschina S

     (2021) gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

     Genome Biology 22:81.

     https://doi.org/10.1186/s13059-021-02295-1

     • PubMed
     • Google Scholar
239. 1. Zong X
     2. Fu J
     3. Xu B
     4. Wang Y
     5. Jin M

     (2020) Interplay between gut microbiota and antimicrobial peptides

     Animal Nutrition 6:389–396.

     https://doi.org/10.1016/j.aninu.2020.09.002

     • PubMed
     • Google Scholar
240. 1. Zorrilla F
     2. Buric F
     3. Patil KR
     4. Zelezniak A

     (2021) metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

     Nucleic Acids Research 49:e126.

     https://doi.org/10.1093/nar/gkab815

     • PubMed
     • Google Scholar