Development of Inflammatory Bowel Disease Is Linked to a Longitudinal Restructuring of the Gut Metagenome in Mice

ABSTRACT IBD patients harbor distinct microbial communities with functional capabilities different from those seen with healthy people. But is this cause or effect? Answering this question requires data on changes in gut microbial communities leading to disease onset. By performing weekly metagenomic sequencing and mixed-effects modeling on an established mouse model of IBD, we identified several functional pathways encoded by the gut microbiome that covary with host immune status. These pathways are novel early biomarkers that may either enable microbes to live inside an inflamed gut or contribute to immune activation in IBD mice. Future work will validate the potential roles of these microbial pathways in host-microbe interactions and human disease. This study was novel in its longitudinal design and focus on microbial pathways, which provided new mechanistic insights into the role of gut microbes in IBD development. The gut microbiome is linked to inflammatory bowel disease (IBD) severity and altered in late-stage disease. However, it is unclear how gut microbial communities change over the course of IBD development, especially in regard to function. To investigate microbiome-mediated disease mechanisms and discover early biomarkers of IBD, we conducted a longitudinal metagenomic investigation in an established mouse model of IBD, where damped transforming growth factor β (TGF-β) signaling in T cells leads to peripheral immune activation, weight loss, and severe colitis. IBD development is associated with abnormal gut microbiome temporal dynamics, including damped acquisition of functional diversity and significant differences in abundance trajectories for KEGG modules such as glycosaminoglycan degradation, cellular chemotaxis, and type III and IV secretion systems. Most differences between sick and control mice emerge when mice begin to lose weight and heightened T cell activation is detected in peripheral blood. However, levels of lipooligosaccharide transporter abundance diverge prior to immune activation, indicating that it could be a predisease indicator or microbiome-mediated disease mechanism. Taxonomic structure of the gut microbiome also significantly changes in association with IBD development, and the abundances of particular taxa, including several species of Bacteroides, correlate with immune activation. These discoveries were enabled by our use of generalized linear mixed-effects models to test for differences in longitudinal profiles between healthy and diseased mice while accounting for the distributions of taxon and gene counts in metagenomic data. These findings demonstrate that longitudinal metagenomics is useful for discovering the potential mechanisms through which the gut microbiome becomes altered in IBD. IMPORTANCE IBD patients harbor distinct microbial communities with functional capabilities different from those seen with healthy people. But is this cause or effect? Answering this question requires data on changes in gut microbial communities leading to disease onset. By performing weekly metagenomic sequencing and mixed-effects modeling on an established mouse model of IBD, we identified several functional pathways encoded by the gut microbiome that covary with host immune status. These pathways are novel early biomarkers that may either enable microbes to live inside an inflamed gut or contribute to immune activation in IBD mice. Future work will validate the potential roles of these microbial pathways in host-microbe interactions and human disease. This study was novel in its longitudinal design and focus on microbial pathways, which provided new mechanistic insights into the role of gut microbes in IBD development.

Download Full-text

Development of Inflammatory Bowel Disease is Linked to a Longitudinal Restructuring of the Gut Metagenome in Mice

10.1101/173823 ◽

2017 ◽

Author(s):

Thomas Sharpton ◽

Svetlana Lyalina ◽

Julie Luong ◽

Joey Pham ◽

Emily M. Deal ◽

...

Keyword(s):

Inflammatory Bowel Disease ◽

Mouse Model ◽

Microbial Communities ◽

Immune Activation ◽

Bowel Disease ◽

Gut Microbiome ◽

Mixed Effects ◽

Metagenomic Data ◽

Early Biomarkers ◽

Inflammatory Bowel

AbstractThe gut microbiome is linked to inflammatory bowel disease (IBD) severity and altered in late stage disease. However, it is unclear how gut microbial communities change over the course of IBD development, especially in regards to function. To investigate microbiome mediated disease mechanisms and discover early biomarkers of IBD, we conducted a longitudinal metagenomic investigation in an established mouse model of IBD, where dampened TGF-β signaling in T cells leads to peripheral immune activation, weight loss, and severe colitis. IBD development is associated with abnormal gut microbiome temporal dynamics, including dampened acquisition of functional diversity and significant differences in abundance trajectories for KEGG modules such as glycosaminoglycan degradation, cellular chemotaxis, and type III and IV secretion systems. Most differences between sick and control mice emerge when mice begin to lose weight and heightened T cell activation is detected in peripheral blood. However, lipooligosaccharide transporter abundance diverges prior to immune activation, indicating that it could be a pre-disease indicator or microbiome-mediated disease mechanism. Taxonomic structure of the gut microbiome also significantly changes in association with IBD development, and the abundance of particular taxa, including several species ofBacteroides, correlate with immune activation. These discoveries were enabled by our use of generalized linear mixed effects models to test for differences in longitudinal profiles between healthy and diseased mice while accounting for the distributions of taxon and gene counts in metagenomic data. These findings demonstrate that longitudinal metagenomics is useful for discovering potential mechanisms through which the gut microbiome becomes altered in IBD.ImportanceIBD patients harbor distinct microbial communities with different functional capabilities compared to healthy people. But is this cause or effect? Answering this question requires data on changes in gut microbial communities leading up to disease onset. By performing weekly metagenomic sequencing and mixed effects modeling on an established mouse model of IBD, we identified several functional pathways encoded by the gut microbiome that covary with host immune status. These pathways are novel early biomarkers that may either enable microbes to live inside an inflamed gut or contribute to immune activation in IBD mice. Future work will validate the potential roles of these microbial pathways in host-microbe interactions and human disease. This study is novel in its longitudinal design and focus on microbial pathways, which provided new mechanistic insights into the role of gut microbes in IBD development.

Download Full-text

IMMU-09. HUMAN MICROBIOTA INFLUENCE THE EFFICACY OF IMMUNOTHERAPY IN A MOUSE MODEL OF GLIOBLASTOMA

Neuro-Oncology ◽

10.1093/neuonc/noab196.369 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi93-vi94

Author(s):

Kory Dees ◽

Hyunmin Koo ◽

James Humphreys ◽

Joseph Hakim ◽

David Crossman ◽

...

Keyword(s):

Mouse Model ◽

Microbial Communities ◽

Mouse Models ◽

Gut Microbiome ◽

Positive Response ◽

Checkpoint Inhibitors ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Microbial Composition ◽

Healthy Human

Abstract Although immunotherapy works well in glioblastoma (GBM) pre-clinical mouse models, the therapy has unfortunately not demonstrated efficacy in humans. In melanoma and other cancers, the composition of the gut microbiome has been shown to determine responsiveness or resistance to immune checkpoint inhibitors (anti-PD-1). Most pre-clinical cancer studies have been done in mouse models using mouse gut microbiomes, but there are significant differences between mouse and human microbial gut compositions. To address this inconsistency, we developed a novel humanized microbiome (HuM) model to study the response to immunotherapy in a pre-clinical mouse model of GBM. We used five healthy human donors for fecal transplantation of gnotobiotic mice. After the transplanted microbiomes stabilized, the mice were bred to generate five independent humanized mouse lines (HuM1-HuM5). Analysis of shotgun metagenomic sequencing data from fecal samples revealed a unique microbiome with significant differences in diversity and microbial composition among HuM1-HuM5 lines. Interestingly, we found that the HuM lines responded differently to anti-PD-1. Specifically, we demonstrate that HuM2 and HuM3 mice are responsive to anti-PD-1 and displayed significantly increased survival compared to isotype controls, while HuM1, HuM4, and HuM5 mice are resistant to anti-PD-1. These mice are genetically identical, and only differ in the composition of the gut microbiome. In a correlative experiment, we found that disrupting the responder HuM2 microbiome with antibiotics abrogated the positive response to anti-PD-1, indicating that HuM2 microbiota must be present in the mice to elicit the positive response to anti-PD-1 in the GBM model. The question remains of whether the “responsive” microbial communities in HuM2 and HuM3 can be therapeutically exploited and applicable in other tumor models, or if the “resistant” microbial communities in HuM1, HuM4, and HuM5 can be depleted and/or replaced. Future studies will assess responder microbial transplants as a method of enhancing immunotherapy.

Download Full-text

Human gut microbial communities dictate efficacy of anti-PD-1 therapy in a humanized microbiome mouse model of glioma

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab023 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Kory J Dees ◽

Hyunmin Koo ◽

J Fraser Humphreys ◽

Joseph A Hakim ◽

David K Crossman ◽

...

Keyword(s):

Mouse Model ◽

Microbial Communities ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Microbial Composition ◽

Healthy Human ◽

Fecal Transplantation ◽

Host Interaction ◽

Shotgun Metagenomic Sequencing ◽

Mouse Lines

Abstract Background Although immunotherapy works well in glioblastoma (GBM) preclinical mouse models, the therapy has not demonstrated efficacy in humans. To address this anomaly, we developed a novel humanized microbiome (HuM) model to study the response to immunotherapy in a preclinical mouse model of GBM. Methods We used 5 healthy human donors for fecal transplantation of gnotobiotic mice. After the transplanted microbiomes stabilized, the mice were bred to generate 5 independent humanized mouse lines (HuM1-HuM5). Results Analysis of shotgun metagenomic sequencing data from fecal samples revealed a unique microbiome with significant differences in diversity and microbial composition among HuM1-HuM5 lines. All HuM mouse lines were susceptible to GBM transplantation, and exhibited similar median survival ranging from 19 to 26 days. Interestingly, we found that HuM lines responded differently to the immune checkpoint inhibitor anti-PD-1. Specifically, we demonstrate that HuM1, HuM4, and HuM5 mice are nonresponders to anti-PD-1, while HuM2 and HuM3 mice are responsive to anti-PD-1 and displayed significantly increased survival compared to isotype controls. Bray-Curtis cluster analysis of the 5 HuM gut microbial communities revealed that responders HuM2 and HuM3 were closely related, and detailed taxonomic comparison analysis revealed that Bacteroides cellulosilyticus was commonly found in HuM2 and HuM3 with high abundances. Conclusions The results of our study establish the utility of humanized microbiome mice as avatars to delineate features of the host interaction with gut microbial communities needed for effective immunotherapy against GBM.

Download Full-text

Genome-Resolved Metagenomics of the Chicken Gut Microbiome

Frontiers in Microbiology ◽

10.3389/fmicb.2021.726923 ◽

2021 ◽

Vol 12 ◽

Author(s):

Maia Segura-Wang ◽

Nikolaus Grabner ◽

Andreas Koestelbauer ◽

Viviana Klose ◽

Mahdi Ghanbari

Keyword(s):

Gut Microbiome ◽

Animal Health ◽

Short Chain Fatty Acids ◽

Major Effect ◽

Glycoside Hydrolases ◽

Metagenomic Sequencing ◽

Gastrointestinal Microbiota ◽

Metabolic Potential ◽

Host Microbe Interactions ◽

Chicken Gut Microbiota

Increasing evidence shows that the chicken gastrointestinal microbiota has a major effect on the modulation of metabolic functions and is correlated with economic parameters, such as feed efficiency and health. Some of these effects derive from the capacity of the chicken to digest carbohydrates and produce energy-rich metabolites such as short-chain fatty acids (SCFA) and from host-microbe interactions. In this study, we utilized information from metagenomic assembled genomes (MAGs) from chicken gastrointestinal tract (GIT) samples, with detailed annotation of carbohydrate-active enzymes (CAZymes) and genes involved in SCFA production, to better understand metabolic potential at different ages. Metagenomic sequencing of 751 chicken GIT samples was performed to reconstruct 155 MAGs, representing species which belong to six phyla, primarily Firmicutes followed by Proteobacteria. MAG diversity significantly (p < 0.001) increased with age, with early domination of Lachnospiraceae, followed by other families including Oscillospiraceae. Age-dependent shifts were observed in the abundance of genes involved in CAZyme and SCFA production, exemplified by a significant increase in glycosyltransferases (GTs) and propionic acid production pathways (p < 0.05), and a lower abundance of glycoside hydrolases (GHs) (p < 0.01). Co-occurrence analysis revealed a large cluster highly interconnected by enzymes from GT2_2 and GH3 families, underscoring their importance in the community. Furthermore, several species were identified as interaction hubs, elucidating associations of key microbes and enzymes that more likely drive temporal changes in the chicken gut microbiota, and providing further insights into the structure of the complex microbial community. This study extends prior efforts on the characterization of the chicken GIT microbiome at the taxonomic and functional levels and lays an important foundation toward better understanding the broiler chicken gut microbiome helping in the identification of modulation opportunities to increase animal health and performance.

Download Full-text

Strain Structure and Dynamics Revealed by Targeted Deep Sequencing of the Honey Bee Gut Microbiome

mSphere ◽

10.1128/msphere.00694-20 ◽

2020 ◽

Vol 5 (4) ◽

Author(s):

Louis-Marie Bobay ◽

Emily F. Wissel ◽

Kasie Raymann

Keyword(s):

Microbial Communities ◽

Honey Bee ◽

Deep Sequencing ◽

Gut Microbiome ◽

Cost Effective ◽

Whole Genome Shotgun ◽

Strain Level ◽

Whole Genome ◽

Metagenomic Sequencing ◽

Microbiome Composition

ABSTRACT Host-associated microbiomes can be critical for the health and proper development of animals and plants. The answers to many fundamental questions regarding the modes of acquisition and microevolution of microbiome communities remain to be established. Deciphering strain-level dynamics is essential to fully understand how microbial communities evolve, but the forces shaping the strain-level dynamics of microbial communities remain largely unexplored, mostly because of methodological issues and cost. Here, we used targeted strain-level deep sequencing to uncover the strain dynamics within a host-associated microbial community using the honey bee gut microbiome as a model system. Our results revealed that amplicon sequencing of conserved protein-coding gene regions using species-specific primers is a cost-effective and accurate method for exploring strain-level diversity. In fact, using this method we were able to confirm strain-level results that have been obtained from whole-genome shotgun sequencing of the honey bee gut microbiome but with a much higher resolution. Importantly, our deep sequencing approach allowed us to explore the impact of low-frequency strains (i.e., cryptic strains) on microbiome dynamics. Results show that cryptic strain diversity is not responsible for the observed variations in microbiome composition across bees. Altogether, the findings revealed new fundamental insights regarding strain dynamics of host-associated microbiomes. IMPORTANCE The factors driving fine-scale composition and dynamics of gut microbial communities are poorly understood. In this study, we used metagenomic amplicon deep sequencing to decipher the strain dynamics of two key members of the honey bee gut microbiome. Using this high-throughput and cost-effective approach, we were able to confirm results from previous large-scale whole-genome shotgun (WGS) metagenomic sequencing studies while also gaining additional insights into the community dynamics of two core members of the honey bee gut microbiome. Moreover, we were able to show that cryptic strains are not responsible for the observed variations in microbiome composition across bees.

Download Full-text

Comparative analysis of 16S rRNA gene and metagenome sequencing in pediatric gut microbiomes

10.1101/2021.02.20.432118 ◽

2021 ◽

Author(s):

Danielle Peterson ◽

Kevin S. Bonham ◽

Sophie Rowland ◽

Cassandra W. Pattanayak ◽

Vanja Klepac-Ceraj ◽

...

Keyword(s):

16S Rrna ◽

Microbial Communities ◽

Gut Microbiome ◽

Age Groups ◽

Alpha Diversity ◽

Sequencing Depth ◽

Human Infant ◽

Rrna Gene ◽

Metagenomic Sequencing ◽

Shotgun Metagenomic Sequencing

AbstractThe colonization of the human gut microbiome begins at birth, and, over time, these microbial communities become increasingly complex. Most of what we currently know about the human microbiome, especially in early stages of development, was described using culture-independent sequencing methods that allow us to identify the taxonomic composition of microbial communities using genomic techniques, such as amplicon or shotgun metagenomic sequencing. Each method has distinct tradeoffs, but there has not been a direct comparison of the utility of these methods in stool samples from very young children, which have different features than those of adults. We compared the effects of profiling the human infant gut microbiome with 16S rRNA amplicon versus shotgun metagenomic sequencing techniques in 130 fecal samples; younger than 15, 15-30, and older than 30 months of age. We demonstrate that observed changes in alpha-diversity and beta-diversity with age occur to similar extents using both profiling methods. We also show that 16S rRNA profiling identified a larger number of genera and we find several genera that are missed or underrepresented by each profiling method. We present the link between alpha diversity and shotgun metagenomic sequencing depth for children of different ages. These findings provide a guide for selecting an appropriate method and sequencing depth for the three studied age groups.

Download Full-text

Comparative Analysis of 16S rRNA Gene and Metagenome Sequencing in Pediatric Gut Microbiomes

Frontiers in Microbiology ◽

10.3389/fmicb.2021.670336 ◽

2021 ◽

Vol 12 ◽

Author(s):

Danielle Peterson ◽

Kevin S. Bonham ◽

Sophie Rowland ◽

Cassandra W. Pattanayak ◽

Vanja Klepac-Ceraj ◽

...

Keyword(s):

16S Rrna ◽

Microbial Communities ◽

Gut Microbiome ◽

Age Groups ◽

Alpha Diversity ◽

Sequencing Depth ◽

Human Infant ◽

Rrna Gene ◽

Metagenomic Sequencing ◽

Shotgun Metagenomic Sequencing

The colonization of the human gut microbiome begins at birth, and over time, these microbial communities become increasingly complex. Most of what we currently know about the human microbiome, especially in early stages of development, was described using culture-independent sequencing methods that allow us to identify the taxonomic composition of microbial communities using genomic techniques, such as amplicon or shotgun metagenomic sequencing. Each method has distinct tradeoffs, but there has not been a direct comparison of the utility of these methods in stool samples from very young children, which have different features than those of adults. We compared the effects of profiling the human infant gut microbiome with 16S rRNA amplicon vs. shotgun metagenomic sequencing techniques in 338 fecal samples; younger than 15, 15–30, and older than 30 months of age. We demonstrate that observed changes in alpha-diversity and beta-diversity with age occur to similar extents using both profiling methods. We also show that 16S rRNA profiling identified a larger number of genera and we find several genera that are missed or underrepresented by each profiling method. We present the link between alpha diversity and shotgun metagenomic sequencing depth for children of different ages. These findings provide a guide for selecting an appropriate method and sequencing depth for the three studied age groups.

Download Full-text

Methotrexate-Induced Shifts in the Human Gut Microbiome Decrease Immune Activation

SSRN Electronic Journal ◽

10.2139/ssrn.3578147 ◽

2020 ◽

Author(s):

Renuka R. Nayak ◽

Margaret Alexander ◽

Ishani Deshpande ◽

Kye Stapleton-Grey ◽

Carles Ubeda ◽

...

Keyword(s):

Immune Activation ◽

Gut Microbiome ◽

Human Gut ◽

Human Gut Microbiome

Download Full-text

Neuroimaging, Urinary, and Plasma Biomarkers of Treatment Response in Huntington’s Disease: Preclinical Evidence with the p75NTR Ligand LM11A-31

Neurotherapeutics ◽

10.1007/s13311-021-01023-8 ◽

2021 ◽

Author(s):

Danielle A. Simmons ◽

Brian D. Mills ◽

Robert R. Butler III ◽

Jason Kuan ◽

Tyne L. M. McHugh ◽

...

Keyword(s):

Mouse Model ◽

Huntington's Disease ◽

Huntington’S Disease ◽

Treatment Response ◽

Diffusion Tensor ◽

Disease Onset ◽

Therapeutic Effects ◽

Plasma Cytokine ◽

Cytokine Levels ◽

Pharmacodynamic Biomarkers

AbstractHuntington’s disease (HD) is caused by an expansion of the CAG repeat in the huntingtin gene leading to preferential neurodegeneration of the striatum. Disease-modifying treatments are not yet available to HD patients and their development would be facilitated by translatable pharmacodynamic biomarkers. Multi-modal magnetic resonance imaging (MRI) and plasma cytokines have been suggested as disease onset/progression biomarkers, but their ability to detect treatment efficacy is understudied. This study used the R6/2 mouse model of HD to assess if structural neuroimaging and biofluid assays can detect treatment response using as a prototype the small molecule p75NTR ligand LM11A-31, shown previously to reduce HD phenotypes in these mice. LM11A-31 alleviated volume reductions in multiple brain regions, including striatum, of vehicle-treated R6/2 mice relative to wild-types (WTs), as assessed with in vivo MRI. LM11A-31 also normalized changes in diffusion tensor imaging (DTI) metrics and diminished increases in certain plasma cytokine levels, including tumor necrosis factor-alpha and interleukin-6, in R6/2 mice. Finally, R6/2-vehicle mice had increased urinary levels of the p75NTR extracellular domain (ecd), a cleavage product released with pro-apoptotic ligand binding that detects the progression of other neurodegenerative diseases; LM11A-31 reduced this increase. These results are the first to show that urinary p75NTR-ecd levels are elevated in an HD mouse model and can be used to detect therapeutic effects. These data also indicate that multi-modal MRI and plasma cytokine levels may be effective pharmacodynamic biomarkers and that using combinations of these markers would be a viable and powerful option for clinical trials.

Download Full-text

673 Precision microbiome mapping identifies a microbiome signature predictive of Immune checkpoint inhibitor response across multiple research study cohorts

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0673 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A711-A711

Author(s):

Matthew Robinson ◽

Kevin Vervier ◽

Simon Harris ◽

David Adams ◽

Doreen Milne ◽

...

Keyword(s):

Gut Microbiome ◽

Immune Checkpoint ◽

Immune Checkpoint Inhibitor ◽

Checkpoint Inhibitor ◽

Advanced Melanoma ◽

List Type ◽

Metagenomic Sequencing ◽

Genome Database ◽

Sequencing Platform ◽

Melanoma Patients

BackgroundThe gut microbiome of cancer patients appears to be associated with response to Immune Checkpoint Inhibitor (ICIs) treatment.1–4 However, the bacteria linked to response differ between published studies.MethodsLongitudinal stool samples were collected from 69 patients with advanced melanoma receiving approved ICIs in the Cambridge (UK) MELRESIST study. Pretreatment samples were analysed by Microbiotica, using shotgun metagenomic sequencing. Microbiotica’s sequencing platform comprises the world’s leading Reference Genome Database and advanced Microbiome Bioinformatics to give the most comprehensive and precise mapping of the gut microbiome. This has enabled us to identify gut bacteria associated with ICI response missed using public reference genomes. Published microbiome studies in advanced melanoma,1–3renal cell carcinoma (RCC) and non-small cell lung cancer (NSCLC)4 were reanalysed with the same platform.ResultsAnalysis of the MELRESIST samples showed an overall change in the microbiome composition between advanced melanoma patients and a panel of healthy donor samples, but not between patients who subsequently responded or did not respond to ICIs. However, we did identify a discrete microbiome signature which correlated with response. This signature predicted response with an accuracy of 93% in the MELRESIST cohort, but was less predictive in the published melanoma cohorts.1–3 Therefore, we developed a bioinformatic analytical model, incorporating an interactive random forest model and the MELRESIST dataset, to identify a microbiome signature which was consistent across all published melanoma studies. This model was validated three times by accurately predicting the outcome of an independent cohort. A final microbiome signature was defined using the validated model on MELRESIST and the three published melanoma cohorts. This was very accurate at predicting response in all four studies combined (91%), or individually (82–100%). This signature was also predictive of response in a NSCLC study and to a lesser extent in RCC. The core of this signature is nine bacteria significantly increased in abundance in responders.ConclusionsAnalysis of the MELRESIST study samples, precision microbiome profiling by the Microbiotica Platform and a validated bioinformatic analysis, have enabled us to identify a unique microbiome signature predictive of response to ICI therapy in four independent melanoma studies. This removes the challenge to the field of different bacteria apparently being associated with response in different studies, and could represent a new microbiome biomarker with clinical application. Nine core bacteria may be driving response and hold potential for co-therapy with ICIs.Ethics ApprovalThe study was approved by Newcastle & North Tyneside 2 Research Ethics Committee, approval number 11/NE/0312.ReferencesMatson V, Fessler J, Bao R, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 2018;359(6371):104–108.Gopalakrishnan V, Spencer CN, Nezi L, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018;359(6371):97–103.Frankel AE, Coughlin LA, Kim J, et al. Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia 2017;19(10):848–855.Routy B, Le Chatelier E, Derosa L, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 2018;359(6371):91–97.

Download Full-text