Oral microbial signatures associated with age and frailty in Canadian adults.

This study aimed to assess the association between the oral microbiome, age, and frailty. Data and saliva samples were obtained from male and female participants aged 35-70 years (n = 1357). Saliva samples were analysed by 16S rRNA gene sequencing and differences in microbial diversity and community compositions were examined in relation to chronological age and the frailty index (FI). Most alpha diversity measures (Richness, Shannon Diversity, Faith's Phylogenetic Diversity) showed an inverse association with frailty, whereas a positive association was observed with age and Shannon Diversity and Evenness. A further sex-stratified analysis revealed differences in measures of microbial diversity and composition. Multiple genera were detected as significantly differentially abundant with increasing frailty and age by at least two methods. With age, the relative abundance of Veillonella was reduced in both males and females, whereas increases in Corynebacterium appeared specific to males and Aggregatibacter, Fusobacterium, Neisseria, Stomatobaculum, and Porphyromonas specific to females. Beta diversity was significantly associated with multiple mental health components of the FI. This study shows age and frailty are differentially associated with measures of microbial diversity and composition, suggesting the oral microbiome may be a useful indicator of increased risk of frailty or a potential target for improving health in ageing adults.

Microbial hitchhikers harbouring antimicrobial-resistance genes in the riverine plastisphere.

BACKGROUND: The widespread nature of plastic pollution has given rise to wide scientific and social concern regarding the capacity of these materials to serve as vectors for pathogenic bacteria and reservoirs for Antimicrobial Resistance Genes (ARG). In- and ex-situ incubations were used to characterise the riverine plastisphere taxonomically and functionally in order to determine whether antibiotics within the water influenced the ARG profiles in these microbiomes and how these compared to those on natural surfaces such as wood and their planktonic counterparts. RESULTS: We show that plastics support a taxonomically distinct microbiome containing potential pathogens and ARGs. While the plastisphere was similar to those biofilms that grew on wood, they were distinct from the surrounding water microbiome. Hence, whilst potential opportunistic pathogens (i.e. Pseudomonas aeruginosa, Acinetobacter and Aeromonas) and ARG subtypes (i.e. those that confer resistance to macrolides/lincosamides, rifamycin, sulfonamides, disinfecting agents and glycopeptides) were predominant in all surface-related microbiomes, especially on weathered plastics, a completely different set of potential pathogens (i.e. Escherichia, Salmonella, Klebsiella and Streptococcus) and ARGs (i.e. aminoglycosides, tetracycline, aminocoumarin, fluoroquinolones, nitroimidazole, oxazolidinone and fosfomycin) dominated in the planktonic compartment. Our genome-centric analysis allowed the assembly of 215 Metagenome Assembled Genomes (MAGs), linking ARGs and other virulence-related genes to their host. Interestingly, a MAG belonging to Escherichia -that clearly predominated in water- harboured more ARGs and virulence factors than any other MAG, emphasising the potential virulent nature of these pathogenic-related groups. Finally, ex-situ incubations using environmentally-relevant concentrations of antibiotics increased the prevalence of their corresponding ARGs, but different riverine compartments -including plastispheres- were affected differently by each antibiotic. CONCLUSIONS: Our results provide insights into the capacity of the riverine plastisphere to harbour a distinct set of potentially pathogenic bacteria and function as a reservoir of ARGs. The environmental impact that plastics pose if they act as a reservoir for either pathogenic bacteria or ARGs is aggravated by the persistence of plastics in the environment due to their recalcitrance and buoyancy. Nevertheless, the high similarities with microbiomes growing on natural co-occurring materials and even more worrisome microbiome observed in the surrounding water highlights the urgent need to integrate the analysis of all environmental compartments when assessing risks and exposure to pathogens and ARGs in anthropogenically-impacted ecosystems. Video Abstract.

Cross-study analyses of microbial abundance using generalized common factor methods.

BACKGROUND: By creating networks of biochemical pathways, communities of micro-organisms are able to modulate the properties of their environment and even the metabolic processes within their hosts. Next-generation high-throughput sequencing has led to a new frontier in microbial ecology, promising the ability to leverage the microbiome to make crucial advancements in the environmental and biomedical sciences. However, this is challenging, as genomic data are high-dimensional, sparse, and noisy. Much of this noise reflects the exact conditions under which sequencing took place, and is so significant that it limits consensus-based validation of study results. RESULTS: We propose an ensemble approach for cross-study exploratory analyses of microbial abundance data in which we first estimate the variance-covariance matrix of the underlying abundances from each dataset on the log scale assuming Poisson sampling, and subsequently model these covariances jointly so as to find a shared low-dimensional subspace of the feature space. CONCLUSIONS: By viewing the projection of the latent true abundances onto this common structure, the variation is pared down to that which is shared among all datasets, and is likely to reflect more generalizable biological signal than can be inferred from individual datasets. We investigate several ways of achieving this, demonstrate that they work well on simulated and real metagenomic data in terms of signal retention and interpretability, and recommend a particular implementation.

Profiling novel lateral gene transfer events in the human microbiome.

Lateral gene transfer (LGT) is an important mechanism for genome diversification in microbial populations, including the human microbiome. While prior work has surveyed LGT events in human-associated microbial isolate genomes, the scope and dynamics of novel LGT events arising in personal microbiomes are not well understood, as there are no widely adopted computational methods to detect, quantify, and characterize LGT from complex microbial communities. We addressed this by developing, benchmarking, and experimentally validating a computational method (WAAFLE) to profile novel LGT events from assembled metagenomes. Applying WAAFLE to >2K human metagenomes from diverse body sites, we identified >100K putative high-confidence but previously uncharacterized LGT events (~2 per assembled microbial genome-equivalent). These events were enriched for mobile elements (as expected), as well as restriction-modification and transport functions typically associated with the destruction of foreign DNA. LGT frequency was quantifiably influenced by biogeography, the phylogenetic similarity of the involved taxa, and the ecological abundance of the donor taxon. These forces manifest as LGT networks in which hub species abundant in a community type donate unequally with their close phylogenetic neighbors. Our findings suggest that LGT may be a more ubiquitous process in the human microbiome than previously described. The open-source WAAFLE implementation, documentation, and data from this work are available at http://huttenhower.sph.harvard.edu/waafle.

Investigating the oral microbiome in retrospective and prospective cases of prostate, colon, and breast cancer.

The human microbiome has been proposed as a potentially useful biomarker for several cancers. To examine this, we made use of salivary samples from the Atlantic Partnership for Tomorrow's Health (PATH) project and Alberta's Tomorrow Project (ATP). Sample selection was divided into both a retrospective and prospective case control design examining prostate, breast, and colon cancer. In total 89 retrospective and 260 prospective cancer cases were matched to non-cancer controls and saliva samples were sequenced using 16S rRNA gene sequencing. We found no significant differences in alpha diversity. All beta diversity measures were insignificant except for unweighted UniFrac profiles in retrospective breast cancer cases and weighted UniFrac, Bray-Curtis and Robust Atchinson's distances in colon cancer after testing with age and sex adjusted MiRKAT models. Differential abundance (DA) analysis showed several taxa that were associated with previous cancer in all three groupings. Only one genus (Clostridia UCG-014) in breast cancer and one ASV (Fusobacterium periodonticum) in colon cancer was identified by more than one DA tool. In prospective cases three ASVs were associated with colon cancer, one ASV with breast cancer, and one ASV with prostate cancer. Random Forest classification showed low levels of signal in both study designs in breast and prostate cancer. Contrastingly, colon cancer did show signal in our retrospective analysis (AUC: 0.737) and in one of two prospective cohorts (AUC: 0.717). Our results indicate that it is unlikely that reliable microbial oral biomarkers for breast and prostate cancer exist.. However, further research into the oral microbiome and colon cancer could be fruitful.

Exploring the microbiome of oral epithelial dysplasia as a predictor of malignant progression.

A growing body of research associates the oral microbiome and oral cancer. Well-characterized clinical samples with outcome data are required to establish relevant associations between the microbiota and disease. The objective of this study was to characterize the community variations and the functional implications of the microbiome in low-grade oral epithelial dysplasia (OED) using 16S rRNA gene sequencing from annotated archival swabs in progressing (P) and non-progressing (NP) OED. We characterised the microbial community in 90 OED samples - 30 swabs from low-grade OED that progressed to cancer (cases) and 60 swabs from low-grade OED that did not progress after a minimum of 5 years of follow up (matched control subjects). There were small but significant differences between P and NP samples in terms of alpha diversity as well as beta diversity in conjunction with other clinical factors such as age and smoking status for both taxa and functional predictions. Across all samples, the most abundant genus was Streptococcus, followed by Haemophilus, Rothia, and Neisseria. Taxa and predicted functions were identified that were significantly differentially abundant with progression status (all Ps and NPs), when samples were grouped broadly by the number of years between sampling and progression or in specific time to progression for Ps only. However, these differentially abundant features were typically present only at low abundances. For example, Campylobacter was present in slightly higher abundance in Ps (1.72%) than NPs (1.41%) and this difference was significant when Ps were grouped by time to progression. Furthermore, several of the significantly differentially abundant functions were linked to the Campylobacteraceae family in Ps and may justify further investigation. Larger cohort studies to further explore the microbiome as a potential biomarker of risk in OED are warranted.

From defaults to databases: parameter and database choice dramatically impact the performance of metagenomic taxonomic classification tools.

In metagenomic analyses of microbiomes, one of the first steps is usually the taxonomic classification of reads by comparison to a database of previously taxonomically classified genomes. While different studies comparing metagenomic taxonomic classification methods have determined that different tools are 'best', there are two tools that have been used the most to-date: Kraken (k-mer-based classification against a user-constructed database) and MetaPhlAn (classification by alignment to clade-specific marker genes), the latest versions of which are Kraken2 and MetaPhlAn 3, respectively. We found large discrepancies in both the proportion of reads that were classified as well as the number of species that were identified when we used both Kraken2 and MetaPhlAn 3 to classify reads within metagenomes from human-associated or environmental datasets. We then investigated which of these tools would give classifications closest to the real composition of metagenomic samples using a range of simulated and mock samples and examined the combined impact of tool-parameter-database choice on the taxonomic classifications given. This revealed that there may not be a one-size-fits-all 'best' choice. While Kraken2 can achieve better overall performance, with higher precision, recall and F1 scores, as well as alpha- and beta-diversity measures closer to the known composition than MetaPhlAn 3, the computational resources required for this may be prohibitive for many researchers, and the default database and parameters should not be used. We therefore conclude that the best tool-parameter-database choice for a particular application depends on the scientific question of interest, which performance metric is most important for this question and the limit of available computational resources.

The gastrointestinal antibiotic resistome in pediatric leukemia and lymphoma patients.

Introduction: Most children with leukemia and lymphoma experience febrile neutropenia. These are treated with empiric antibiotics that include ß-lactams and/or vancomycin. These are often administered for extended periods, and the effect on the resistome is unknown. Methods: We examined the impact of repeated courses and duration of antibiotic use on the resistome of 39 pediatric leukemia and lymphoma patients. Shotgun metagenome sequences from 127 stool samples of pediatric oncology patients were examined for abundance of antibiotic resistance genes (ARGs) in each sample. Abundances were grouped by repeated courses (no antibiotics, 1-2 courses, 3+ courses) and duration (no use, short duration, long and/or mixed durationg) of ß-lactams, vancomycin and "any antibiotic" use. We assessed changes in both taxonomic composition and prevalence of ARGs among these groups. Results: We found that Bacteroidetes taxa and ß-lactam resistance genes decreased, while opportunistic Firmicutes and Proteobacteria taxa, along with multidrug resistance genes, increased with repeated courses and/or duration of antibiotics. Efflux pump related genes predominated (92%) among the increased multidrug genes. While we found ß-lactam ARGs present in the resistome, the taxa that appear to contain them were kept in check by antibiotic treatment. Multidrug ARGs, mostly efflux pumps or regulators of efflux pump genes, were associated with opportunistic pathogens, and both increased in the resistome with repeated antibiotic use and/or increased duration. Conclusions: Given the strong association between opportunistic pathogens and multidrug-related efflux pumps, we suggest that drug efflux capacity might allow the opportunistic pathogens to persist or increase despite repeated courses and/or duration of antibiotics. While drug efflux is the most direct explanation, other mechanisms that enhance the ability of opportunistic pathogens to handle environmental stress, or other aspects of the treatment environment, could also contribute to their ability to flourish within the gut during treatment. Persistence of opportunistic pathogens in an already dysbiotic and weakened gastrointestinal tract could increase the likelihood of life-threatening blood borne infections. Of the 39 patients, 59% experienced at least one gastrointestinal or blood infection and 60% of bacteremia's were bacteria found in stool samples. Antimicrobial stewardship and appropriate use and duration of antibiotics could help reduce morbidity and mortality in this vulnerable population.

Hepatotoxicity of polymeric proanthocyanidins is caused by translocation of bacterial lipopolysaccharides through impaired gut epithelium.

Polymeric proanthocyanidins (P-PAC) induced hepatotoxicity in C57BL/6 mice. Mice were supplemented with P-PAC alone or with a mixture of probiotic bacteria (PB), Lactobacillus, Bifidobacterium, and Akkermansia muciniphila for 14 consecutive days. The liver tissues of sacrificed mice were analyzed by mass spectrometry to identify and quantify the P-PAC metabolites. Potential P-PAC metabolites, 2-hydroxyphenylacetic acid and pyrocatechol were detected in higher concentrations and 4-hydroxybenzoic acid was detected exclusively in the mice supplemented with P-PAC and PB. Supplementation with P-PAC alone or with PB caused no shift in the α-diversity of mice gut microbiota. P-PAC induced nonalcoholic steatohepatitis in mice through increasing liver exposure to intestinal bacterial lipopolysaccharides by reducing expression of gut epithelial tight junction proteins, claudin-3 and occludin. Lipopolysaccharide concentrations in the livers of mice supplemented with P-PAC were significantly high compared to the control mice. Furthermore, P-PAC downregulated the expressions of claudin-3 and claudin-4 tight junction proteins in cultured Caco-2 cell monolayers. PB biotransformed P-PAC into bioavailable metabolites and potentially reduced the toxicity of P-PAC. The toxicity of P-PAC and their synbiotics need to be critically evaluated for the safety of human consumption.

Personalised azithromycin+metronidazole (PAZAZ), in combination with standard induction therapy, to achieve a faecal microbiome community structure and metagenome changes associated with sustained remission in paediatric Crohn's disease (CD): protocol of a pilot study.

INTRODUCTION: Early relapse in Crohn's disease (CD) is associated with a more severe disease course. The microbiome plays a crucial role, yet strategies targeting the microbiome are underrepresented in current guidelines. We hypothesise that early manipulation of the microbiome will improve clinical response to standard-of-care (SOC) induction therapy in patients with a relapse-associated microbiome profile. We describe the protocol of a pilot study assessing feasibility of treatment allocation based on baseline faecal microbiome profiles. METHODS AND ANALYSIS: This is a 52-week, multicentre, randomised, controlled, open-label, add-on pilot study to test the feasibility of a larger multicontinent trial evaluating the efficacy of adjuvant antibiotic therapy in 20 paediatric patients with mild-to-moderate-CD (10

Changes to Soil Microbiome Resulting from Synergetic Effects of Fungistatic Compounds Pyrimethanil and Fluopyram in Lowbush Blueberry Agriculture, with Nine Fungicide Products Tested.

Lowbush blueberries (Vaccinium spp.) are a crop of economic significance to Atlantic Canada, Quebec, and Maine. The fruit is produced by the management of naturally occurring plant populations. The plants have an intimate relationship with the soil microbiome and depend on it for their health and productivity. Fungicides are an important tool in combatting disease pressure but pose a potential risk to soil health. In this study, amplicon sequencing was used to determine the effects of six fungistatic compounds both alone and in combination via nine commercially available fungicide products on the bacterial and fungal microbiomes associated with lowbush blueberries and to study whether these effects are reflected in crop outcomes and plant phenotypes. One fungicide, Luna Tranquility, a combination of fluopyram and pyrimethanil, was found to impart significant effects to fungal and bacterial community structure, fungal taxonomic abundances, and bacterial functions relative to control. The two fungicides which contained fluopyram and pyrimethanil as single ingredients (Velum Prime and Scala, respectively) did not induce significant changes in any of these regards. These results suggest the possibility that these microbiome changes are the result of the synergistic effect of fluopyram and pyrimethanil on soil microbiomes. While these results suggest a significant disruption to the soil microbiome, no corresponding changes to crop development and outcomes were noted. Ultimately, the majority of the fungicides analysed in this trial did not produce significant changes to the soil microbiome relative to the untreated group (UTG). However, one of the fungicide treatments, Luna Tranquility, did produce significant changes to the soil ecosystem that could have longer-term effects on soil health and its future use may merit additional investigation onto its ecotoxicological properties.

Efficient computation of contributional diversity metrics from microbiome data with FuncDiv.

MOTIVATION: Microbiome datasets with taxa linked to the functions (e.g. genes) they encode are becoming more common as metagenomics sequencing approaches improve. However, these data are challenging to analyze due to their complexity. Summary metrics, such as the alpha and beta diversity of taxa contributing to each function (i.e. contributional diversity), represent one approach to investigate these data, but currently there are no straightforward methods for doing so. RESULTS: We addressed this gap by developing FuncDiv, which efficiently performs these computations. Contributional diversity metrics can provide novel insights that would be impossible to identify without jointly considering taxa and functions. AVAILABILITY AND IMPLEMENTATION: FuncDiv is distributed under a GNU Affero General Public License v3.0 and is available at https://github.com/gavinmdouglas/FuncDiv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrating phylogenetic and functional data in microbiome studies.

MOTIVATION: Microbiome functional data are frequently analyzed to identify associations between microbial functions (e.g. genes) and sample groups of interest. However, it is challenging to distinguish between different possible explanations for variation in community-wide functional profiles by considering functions alone. To help address this problem, we have developed POMS, a package that implements multiple phylogeny-aware frameworks to more robustly identify enriched functions. RESULTS: The key contribution is an extended balance-tree workflow that incorporates functional and taxonomic information to identify functions that are consistently enriched in sample groups across independent taxonomic lineages. Our package also includes a workflow for running phylogenetic regression. Based on simulated data we demonstrate that these approaches more accurately identify gene families that confer a selective advantage compared with commonly used tools. We also show that POMS in particular can identify enriched functions in real-world metagenomics datasets that are potential targets of strong selection on multiple members of the microbiome. AVAILABILITY AND IMPLEMENTATION: These workflows are freely available in the POMS R package at https://github.com/gavinmdouglas/POMS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Antibiotic and antifungal use in pediatric leukemia and lymphoma patients are associated with increasing opportunistic pathogens and decreasing bacteria responsible for activities that enhance colonic defense.

Due to decreased immunity, both antibiotics and antifungals are regularly used in pediatric hematologic-cancer patients as a means to prevent severe infections and febrile neutropenia. The general effect of antibiotics on the human gut microbiome is profound, yielding decreased diversity and changes in community structure. However, the specific effect on pediatric oncology patients is not well-studied. The effect of antifungal use is even less understood, having been studied only in mouse models. Because the composition of the gut microbiome is associated with regulation of hematopoiesis, immune function and gastrointestinal integrity, changes within the patient gut can have implications for the clinical management of hematologic malignancies. The pediatric population is particularly challenging because the composition of the microbiome is age dependent, with some of the most pronounced changes occurring in the first three years of life. We investigated how antibiotic and antifungal use shapes the taxonomic composition of the stool microbiome in pediatric patients with leukemia and lymphoma, as inferred from both 16S rRNA and metagenome data. Associations with age, antibiotic use and antifungal use were investigated using multiple analysis methods. In addition, multivariable differential abundance was used to identify and assess specific taxa that were associated with multiple variables. Both antibiotics and antifungals were linked to a general decline in diversity in stool samples, which included a decrease in relative abundance in butyrate producers that play a critical role in host gut physiology (e.g., Faecalibacterium, Anaerostipes, Dorea, Blautia),. Furthermore, antifungal use was associated with a significant increase in relative abundance of opportunistic pathogens. Collectively, these findings have important implications for the treatment of leukemia and lymphoma patients. Butyrate is important for gastrointestinal integrity; it inhibits inflammation, reinforces colonic defense, mucosal immunity. and decreases oxidative stress. The routine use of broad-spectrum anti-infectives in pediatric oncology patients could simultaneously contribute to a decline in gastrointestinal integrity and colonic defense while promoting increases in opportunistic pathogens within the patient gut. Because the gut microbiome has been linked to both short-term clinical outcomes, and longer-lasting health effects, systematic characterization of the gut microbiome in pediatric patients during, and beyond, treatment is warranted.

Microbiome differential abundance methods produce different results across 38 datasets.

Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.

Investigation of the impact of commonly used medications on the oral microbiome of individuals living without major chronic conditions.

BACKGROUND: Commonly used medications produce changes in the gut microbiota, however, the impact of these medications on the composition of the oral microbiota is understudied. METHODS: Saliva samples were obtained from 846 females and 368 males aged 35-69 years from a Canadian population cohort, the Atlantic Partnership for Tomorrow's Health (PATH). Samples were analyzed by 16S rRNA gene sequencing and differences in microbial community compositions between nonusers, single-, and multi-drug users as well as the 3 most commonly used medications (thyroid hormones, statins, and proton pump inhibitors (PPI)) were examined. RESULTS: Twenty-six percent of participants were taking 1 medication and 21% were reported taking 2 or more medications. Alpha diversity indices of Shannon diversity, Evenness, Richness, and Faith's phylogenetic diversity were similar among groups, likewise beta diversity as measured by Bray-Curtis dissimilarity (R2 = 0.0029, P = 0.053) and weighted UniFrac distances (R2 = 0.0028, P = 0.161) were non-significant although close to our alpha value threshold (P = 0.05). After controlling for covariates (sex, age, BMI), six genera (Saprospiraceae uncultured, Bacillus, Johnsonella, Actinobacillus, Stenotrophomonas, and Mycoplasma) were significantly different from non-medication users. Thyroid hormones, HMG-CoA reductase inhibitors (statins) and PPI were the most reported medications. Shannon diversity differed significantly among those taking no medication and those taking only thyroid hormones, however, there were no significant difference in other measures of alpha- or beta diversity with single thyroid hormone, statin, or PPI use. Compared to participants taking no medications, the relative abundance of eight genera differed significantly in participants taking thyroid hormones, six genera differed in participants taking statins, and no significant differences were observed with participants taking PPI. CONCLUSION: The results from this study show negligible effect of commonly used medications on microbial diversity and small differences in the relative abundance of specific taxa, suggesting a minimal influence of commonly used medication on the salivary microbiome of individuals living without major chronic conditions.

Reporting guidelines for human microbiome research: the STORMS checklist.

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.

Gut bacterial gene changes following pegaspargase treatment in pediatric patients with acute lymphoblastic leukemia.

Treatment of pediatric acute lymphoblastic leukemia (ALL) with pegaspargase exploits ALL cells dependency on asparagine. Pegaspargase depletes asparagine, consequentially affecting aspartate, glutamine and glutamate. The gut as a confounding source of these amino acids (AAs) and the role of gut microbiome metabolism of AAs has not been examined. We examined asparagine, aspartate, glutamine and glutamate in stool samples from patients over pegaspargase treatment. Microbial gene-products, which interact with these AAs were identified. Stool asparagine declined significantly, and 31 microbial genes changed over treatment. Changes were complex, and included genes involved in AA metabolism, nutrient sensing, and pathways increased in cancers. While we identified changes in a gene (iaaA) with limited asparaginase activity, it lacked significance after correction leaving open other mechanisms for asparagine decline, possibly including loss from gut to blood. Understanding pathways that change AA availability, including by microbes in the gut, could be useful in optimizing pegaspargase therapy.