scholarly journals TMOD-19. INDIVIDUAL SPECIFIC HUMAN GUT MICROBE COMMUNITIES INFLUENCE RESPONSE TO IMMUNOTHERAPY IN A HUMANIZED MICROBIOME MOUSE MODEL OF GLIOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii232-ii232
Author(s):  
Kory Dees ◽  
Hyunmin Koo ◽  
J Fraser Humphreys ◽  
Joseph Hakim ◽  
David Crossman ◽  
...  
Keyword(s):  
Mouse Model ◽  
Microbial Community Composition ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Microbial Composition ◽  
Healthy Human ◽  
Fecal Transplantation ◽  
Microbiome Composition ◽  
Gut Microbe ◽  
Mouse Lines

Abstract Although immunotherapy works well in glioblastoma (GBM) pre-clinical mouse models, the therapy has not demonstrated efficacy in GBM patients. Since recent studies have linked the gut microbial composition to the success with immunotherapy for other cancers, we utilized a novel humanized microbiome (HuM) model in order 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 since it is now recognized that microbe strain level differences render individual humans with a unique microbial community composition. After the transplanted microbiomes stabilized, the mice were bred to generate 5 independent humanized mouse lines (humanized microbiome HuM1-HuM5). Analysis of shotgun metagenomic sequencing data from fecal samples revealed a unique microbiome composition with significant differences in diversity and microbial composition among HuM1-HuM5 lines. We next analyzed the growth of intracranial glioma cells in the HuM lines. All HuM mouse lines were susceptible to GBM transplantation, and exhibited similar median survival ranging from 19-26 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 non-responders to anti-PD-1 resulting in the death of the mice from the intracranial tumors, 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 HuM2 and HuM3 were closely related. Detailed taxonomic comparison analysis at the top 5 across all HuM mouse lines revealed that Bacteroides cellulosilyticus was commonly found between HuM2 and HuM3 with high abundances. The results of our study establish the utility of humanized microbiome mice as avatars to delineate features of the host interaction with gut microbe communities needed for effective immunotherapy against GBM.

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

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.

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

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.

scholarly journals TMOD-19. ELUCIDATING THE RESISTANCE TO IMMUNOTHERAPY IN BRAIN TUMORS USING A HUMANIZED MICROBIOME MOUSE MODEL

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi266-vi266
Author(s):  
Kory Dees ◽  
Hyunmin Koo ◽  
Joseph Hakim ◽  
J Fraser Humphreys ◽  
David Crossman ◽  
...  
Keyword(s):  
Mouse Model ◽  
Tumor Growth ◽  
Mouse Models ◽  
Gut Microbiome ◽  
Standard Of Care ◽  
Similar Efficacy ◽  
Healthy Human ◽  
Human Donor ◽  
Microbiome Composition ◽  
Gut Microbe

Abstract Although the immunotherapy anti-PD-1 works well in glioblastoma (GBM) pre-clinical mouse models, the therapy has not demonstrated a similar efficacy in patient clinical trials. Recent studies have linked the gut microbe composition to tumor growth and response to immunotherapy in some cancers. To date, all GBM pre-clinical studies have been done in mouse models using mouse gut microbiomes. There are significant differences between mouse and human microbial gut compositions, with up to 85% of gut bacteria found in laboratory mice not found in humans. Because it is known that the gut microbe composition can impact the immune system, we hypothesize that the non-responsiveness of GBM patients to immunotherapy may be due to the composition of the gut microbiome. Therefore, we have generated a humanized microbiome mouse model in which mice have been colonized by human donor microbes in their GI tract (two different healthy human donors (HuM1 and HuM2)). In preliminary results, we have found that HuM1 mice are resistant to anti-PD-1, while HuM2 mice are responders to anti-PD-1 in the GL261 syngeneic intracranial model. These mice are genetically identical and only differ in gut microbiome composition. Furthermore, we found that HuM2 mice exhibited a significant increase in cytotoxic CD8+T-cells producing IFN-γ and significant increased CD8+/Treg ratio in the spleen following anti-PD-1 treatment, which was not observed in the HuM1 mice. When testing the efficacy of standard of care temozolomide (TMZ) in our humanized mice, we found that TMZ significantly prolonged survival of both HuM1 and HuM2 mice with intracranial tumors. However, HuM2 mice exhibited superior efficacy (p< 0.001; 57% survival), compared to HuM1 mice (p< 0.01; 0% survival). We are extending these studies to analyze additional humanized microbiome lines as well as GBM patient donor lines to more accurately understand individual responses to tumor growth and responsiveness to therapies.

scholarly journals Compared the Microbiota Profiles between Samples from Bronchoalveolar Lavage and Endotracheal Aspirates in Severe Pneumonia: A Real-World Experience

2022 ◽  
Vol 11 (2) ◽  
pp. 327
Author(s):  
Yeong-Nan Cheng ◽  
Wei-Chih Huang ◽  
Chen-Yu Wang ◽  
Pin-Kuei Fu
Keyword(s):  
Microbial Community ◽  
Bronchoalveolar Lavage ◽  
Microbial Community Composition ◽  
Severe Pneumonia ◽  
Metagenomic Sequencing ◽  
Microbial Composition ◽  
Icu Patients ◽  
Mechanically Ventilated ◽  
The Difference ◽  
New Onset

Lower respiratory tract sampling from endotracheal aspirate (EA) and bronchoalveolar lavage (BAL) are both common methods to identify pathogens in severe pneumonia. However, the difference between these two methods in microbiota profiles remains unclear. We compared the microbiota profiles of pairwise EA and BAL samples in ICU patients with respiratory failure due to severe pneumonia. We prospectively enrolled 50 ICU patients with new onset of pneumonia requiring mechanical ventilation. EA and BAL were performed on the first ICU day, and samples were analyzed for microbial community composition via 16S rRNA metagenomic sequencing. Pathogens were identified in culture medium from BAL samples in 21 (42%) out of 50 patients. No difference was observed in the antibiotic prescription pattern, ICU mortality, or hospital mortality between BAL-positive and BAL-negative patients. The microbiota profiles in the EA and BAL samples are similar with respect to diversity, microbial composition, and microbial community correlations. The antibiotic treatment regimen was rarely changed based on the BAL findings. The samples from BAL did not provide more information than EA in the microbiota profiles. We suggest that EA is more useful than BAL for microbiome identification in mechanically ventilated patients.

scholarly journals Fecal Microbiome Composition Does Not Predict Diet‐Induced TMAO Production in Healthy Adults

2021 ◽  
Author(s):  
Marc Ferrell ◽  
Peter Bazeley ◽  
Zeneng Wang ◽  
Bruce S. Levison ◽  
Xinmin S. Li ◽  
...  
Keyword(s):  
Global Analysis ◽  
Gene Families ◽  
Red Meat ◽  
Gene Copy ◽  
Metagenomic Sequencing ◽  
Microbial Composition ◽  
Unique Identifier ◽  
Fecal Microbiome ◽  
Gut Microbes ◽  
Microbiome Composition

Background Trimethylamine‐ N ‐oxide (TMAO) is a small molecule derived from the metabolism of dietary nutrients by gut microbes and contributes to cardiovascular disease. Plasma TMAO increases following consumption of red meat. This metabolic change is thought to be partly because of the expansion of gut microbes able to use nutrients abundant in red meat. Methods and Results We used data from a randomized crossover study to estimate the degree to which TMAO can be estimated from fecal microbial composition. Healthy participants received a series of 3 diets that differed in protein source (red meat, white meat, and non‐meat), and fecal, plasma, and urine samples were collected following 4 weeks of exposure to each diet. TMAO was quantitated in plasma and urine, while shotgun metagenomic sequencing was performed on fecal DNA. While the cai gene cluster was weakly correlated with plasma TMAO (rho=0.17, P =0.0007), elastic net models of TMAO were not improved by abundances of bacterial genes known to contribute to TMAO synthesis. A global analysis of all taxonomic groups, genes, and gene families found no meaningful predictors of TMAO. We postulated that abundances of known genes related to TMAO production do not predict bacterial metabolism, and we measured choline‐ and carnitine‐trimethylamine lyase activity during fecal culture. Trimethylamine lyase genes were only weakly correlated with the activity of the enzymes they encode. Conclusions Fecal microbiome composition does not predict systemic TMAO because, in this case, gene copy number does not predict bacterial metabolic activity. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01427855.

scholarly journals BiomeSeq: A Tool for the Characterization of Animal Microbiomes from Metagenomic Data

2021 ◽  
Author(s):  
Kelly A. Mulholland ◽  
Calvin L. Keeler
Keyword(s):  
Relative Abundance ◽  
Performance Metrics ◽  
Complete Characterization ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Microbial Composition ◽  
Additional Species ◽  
User Friendly

Abstract BackgroundThe complete characterization of a microbiome is critical in elucidating the complex ecology of the microbial composition within healthy and diseased animals. Many microbiome studies characterize only the bacterial component, for which there are several well-developed sequencing methods, bioinformatics tools and databases available. The lack of comprehensive bioinformatics workflows and databases have limited efforts to characterize the other components existing in a microbiome. BiomeSeq is a tool for the analysis of the complete animal microbiome using metagenomic sequencing data. With its comprehensive workflow and customizable parameters and microbial databases, BiomeSeq can rapidly quantify the viral, fungal, bacteriophage and bacterial components of a sample and produce informative tables for analysis. ResultsSimulated datasets were constructed, which contained known abundances of microbial sequences, and several performance metrics were analyzed, including correlation of predicted abundance with known abundance, root mean square error and rate of speed. BiomeSeq demonstrated high precision (average of 99.52%) and sensitivity (average of 93.01%). BiomeSeq was employed in detecting and quantifying the respiratory microbiome of a commercial poultry broiler flock throughout its grow-out cycle from hatching to processing and successfully processed 780 million reads. For each microbial species detected, BiomeSeq calculated the normalized abundance, percent relative abundance, and coverage as well as the diversity for each sample. Rate of speed for each step in the pipeline, precision and accuracy were calculated to examine BiomeSeq’s performance using in silico sequencing datasets. When compared to bacterial results generated by the commonly used 16S rRNA sequencing method, BiomeSeq detected the same most abundant bacteria, including Gallibacterium, Corynebacterium and Staphylococcus, as well as several additional species. ConclusionsBiomeSeq provides for the detection and quantification of the microbiome from next-generation metagenomic sequencing data. This tool is implemented into a user-friendly container that requires one command and generates a table containing taxonomical information for each microbe detected. It also determines normalized abundance, percent relative abundance, genome coverage and sample diversity calculations for each sample.

scholarly journals A large-scale metagenomic survey dataset of the post-weaning piglet gut lumen

GigaScience ◽  
2021 ◽  
Vol 10 (6) ◽  
Author(s):  
Daniela Gaio ◽  
Matthew Z DeMaere ◽  
Kay Anantanawat ◽  
Graeme J Eamens ◽  
Michael Liu ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Gut Microbiota ◽  
Antibiotic Treatment ◽  
Large Scale ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Sample Collection ◽  
Microbial Composition ◽  
Intensive Farming ◽  
Wide Range

Abstract Background Early weaning and intensive farming practices predispose piglets to the development of infectious and often lethal diseases, against which antibiotics are used. Besides contributing to the build-up of antimicrobial resistance, antibiotics are known to modulate the gut microbial composition. As an alternative to antibiotic treatment, studies have previously investigated the potential of probiotics for the prevention of postweaning diarrhea. In order to describe the post-weaning gut microbiota, and to study the effects of two probiotics formulations and of intramuscular antibiotic treatment on the gut microbiota, we sampled and processed over 800 faecal time-series samples from 126 piglets and 42 sows. Results Here we report on the largest shotgun metagenomic dataset of the pig gut lumen microbiome to date, consisting of &gt;8 Tbp of shotgun metagenomic sequencing data. The animal trial, the workflow from sample collection to sample processing, and the preparation of libraries for sequencing, are described in detail. We provide a preliminary analysis of the dataset, centered on a taxonomic profiling of the samples, and a 16S-based beta diversity analysis of the mothers and the piglets in the first 5 weeks after weaning. Conclusions This study was conducted to generate a publicly available databank of the faecal metagenome of weaner piglets aged between 3 and 9 weeks old, treated with different probiotic formulations and intramuscular antibiotic treatment. Besides investigating the effects of the probiotic and intramuscular antibiotic treatment, the dataset can be explored to assess a wide range of ecological questions with regards to antimicrobial resistance, host-associated microbial and phage communities, and their dynamics during the aging of the host.

scholarly journals An enhanced characterization of the human skin microbiome: a new biodiversity of microbial interactions

2020 ◽  
Author(s):  
Akintunde Emiola ◽  
Wei Zhou ◽  
Julia Oh
Keyword(s):  
Human Skin ◽  
De Novo ◽  
Microbial Interactions ◽  
Skin Microbiome ◽  
Metagenomic Sequencing ◽  
Interspecies Interactions ◽  
Microbial Composition ◽  
Healthy Human ◽  
Skin Site ◽  
Cutibacterium Acnes

ABSTRACTThe healthy human skin microbiome is shaped by skin site physiology, individual-specific factors, and is largely stable over time despite significant environmental perturbation. Studies identifying these characteristics used shotgun metagenomic sequencing for high resolution reconstruction of the bacteria, fungi, and viruses in the community. However, these conclusions were drawn from a relatively small proportion of the total sequence reads analyzable by mapping to known reference genomes. ‘Reference-free’ approaches, based on de novo assembly of reads into genome fragments, are also limited in their ability to capture low abundance species, small genomes, and to discriminate between more similar genomes. To account for the large fraction of non-human unmapped reads on the skin—referred to as microbial ‘dark matter’—we used a hybrid de novo and reference-based approach to annotate a metagenomic dataset of 698 healthy human skin samples. This approach reduced the overall proportion of uncharacterized reads from 42% to 17%. With our refined characterization, we revisited assumptions about the skin microbiome, and demonstrated higher biodiversity and lower stability, particularly in dry and moist skin sites. To investigate hypotheses underlying stability, we examined growth dynamics and interspecies interactions in these communities. Surprisingly, even though most skin sites were relatively stable, many dominant skin microbes, including Cutibacterium acnes and staphylococci, were actively growing in the skin, with poor or no relationship between growth rate and relative abundance, suggesting that host selection or interspecies competition may be important factors maintaining community homeostasis. To investigate other mechanisms facilitating adaptation to a specific skin site, we identified Staphylococcus epidermidis genes that are likely involved in stress response and provide mechanisms essential for growth in oily sites. Finally, horizontal gene transfer—another mechanism of competition by which strains may swap antagonistic or virulent coding regions—was relatively limited in healthy skin, but suggested exchange of different metabolic and environmental tolerance pathways. Altogether, our findings underscore the value of a combined reference-based and de novo approach to provide significant new insights into microbial composition, physiology, and interspecies interactions to maintain community homeostasis in the healthy human skin microbiome.

Associations between plasma cytokine levels and gut microbiota composition in metastatic renal cell carcinoma (mRCC).

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 351-351
Author(s):  
Alex Chehrazi-Raffle ◽  
Nazli Dizman ◽  
Paulo Gustavo Bergerot ◽  
Misagh Karimi ◽  
Joann Hsu ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Kinase Inhibitor ◽  
Metagenomic Sequencing ◽  
Salmonella Spp ◽  
Microbial Composition ◽  
Protein Assay ◽  
Microbiome Composition ◽  
Shotgun Metagenomic Sequencing ◽  
Cytokine Levels ◽  
Eligibility Requirements

351 Background: Plasma cytokines and the gut microbiome have been shown separately to influence the response to systemic therapy in mRCC. We sought associations between serum cytokines and gut microbial composition in patients (pts) with mRCC. Methods: Eligibility requirements included histologically proven mRCC and an intent to receive either vascular endothelial growth factor-tyrosine kinase inhibitor (VEGF-TKI) or immune checkpoint inhibitor (ICI). Blood samples were collected prior to treatment initiation and immunologic profiles were evaluated using a Human Cytokine 30-plex protein assay (Invitrogen). Stool was collected at baseline and shotgun metagenomic sequencing was performed to quantify gut microbial populations using previously published methods (Salgia et al Eur Urol 2020). Results: A total of 50 pts were studied (36:14 M:F) with a median age of 67 (range, 32-85). Twenty pts and 30 pts had subsequent initiation of VEGF-TKI and ICI therapy, respectively. Levels of Akkermansia spp were significantly higher in pts who were IL-6 low (P = 0.023). In contrast, pts who were IL-6 high had higher levels of enteric pathogens, including Salmonella spp and Enterococcus spp. Both Akkermansia spp and Bacteroides spp levels were higher in pts who were IL-8 low. Associations between cytokine levels, microbiome composition, and treatment response will be presented. Conclusions: Given studies suggesting the role of Akkermansia spp in enhancing ICI response (Routy et al Science 2018), our data provide a critical link between the gut microbiome and systemic immunomodulation.

scholarly journals Quantification of discrete gut bacterial strains following fecal transplantation for recurrent Clostridioides difficile infection demonstrates long-term stable engraftment in non-relapsing recipients

2020 ◽  
Author(s):  
Varun Aggarwala ◽  
Ilaria Mogno ◽  
Zhihua Li ◽  
Chao Yang ◽  
Graham J. Britton ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Fecal Microbiota Transplantation ◽  
Fecal Microbiota ◽  
Systematic Evaluation ◽  
Late Relapse ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Bacterial Strains ◽  
Fecal Transplantation ◽  
Clostridioides Difficile

AbstractFecal Microbiota Transplantation (FMT), while successful for the treatment of recurrent Clostridioides difficile (rCDI) infection, lacks a quantitative identification of the discrete bacterial strains that transmit and stably engraft in recipients, and their association with clinical outcomes. Using >1,000 unique bacterial strains isolated and sequenced from a combination of 22 FMT donors and recipients, we develop a statistical approach Strainer to detect and track sequenced bacterial strains from low depth metagenomic sequencing data. On application to 14 FMT interventions, we detect stable and high engraftment of ∼71% of gut microbiota strains in recipients at even 5-years post-transplant, a remarkably durable therapeutic from a single administration. We found differential transmission and engraftment efficacy across bacterial taxonomic groups over short and long-time scales. Although ∼80% of the original pre-FMT recipient strains were eliminated by the FMT, those strains that remain persist even 5 years later, along with newer strains acquired from the environment. The precise quantification of donor bacterial strains in recipients independently explained the clinical outcomes of early and late relapse. Our framework identifies the consistently engrafting discrete bacterial strains for use in Live Biotherapeutic Products (LBP) as a safer, scalable alternative to FMT and enables systematic evaluation of different FMT and LBP study designs.

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