Multi-omics of the gut microbial ecosystem in patients with microsatellite-instability-high gastrointestinal cancer resistant to immunotherapy.

Despite the encouraging efficacy of anti-PD-1/PD-L1 immunotherapy in microsatellite-instability-high/deficient mismatch repair (MSI-H/dMMR) advanced gastrointestinal cancer, many patients exhibit primary or acquired resistance. Using multi-omics approaches, we interrogate gut microbiome, blood metabolome, and cytokines/chemokines of patients with MSI-H/dMMR gastrointestinal cancer (N = 77) at baseline and during the treatment. We identify a number of microbes (e.g., Porphyromonadaceae) and metabolites (e.g., arginine) highly associated with primary resistance to immunotherapy. An independent validation cohort (N = 39) and mouse model are used to further confirm our findings. A predictive machine learning model for primary resistance is also built and achieves an accuracy of 0.79 on the external validation set. Furthermore, several microbes are pinpointed that gradually changed during the process of acquired resistance. In summary, our study demonstrates the essential role of gut microbiome in drug resistance, and this can be utilized as a preventative diagnosis tool and therapeutic target in the future.

Gut indigenous Ruminococcus gnavus alleviates constipation and stress-related behaviors in mice with loperamide-induced constipation.

Refractory constipation is the most severe form of constipation, and its etiology remains unknown. The symptoms of constipation occur repeatedly, which brings great pain to the patient's body and psychology. Accumulating studies suggest that constipation patients present a significant dysbiosis of the gut microbiota compared with healthy individuals. In this study, we analyzed the gut microbiota composition of fresh feces and accumulated feces (old feces) of patients with refractory constipation and found that there was a significant difference between them. Through a mouse model of loperamide-induced constipation, it was proved that the old feces of patients with refractory constipation could aggravate the constipation symptoms in mice, while the fresh feces could alleviate the symptoms, which is consistent with the effect of feces from healthy volunteers in a mouse model of loperamide-induced constipation. We identified an indigenous strain Ruminococcus gnavus (R. gnavus), which is highly enriched in the fresh feces of patients with refractory constipation, and found that oral administration of R. gnavus could effectively improve the constipation symptoms in mice with constipation induced by loperamide and fecal bacteria transplanted from patients with constipation and significantly improve the stress-related behaviors of mice. This result may be related to the regulation of intestinal muc2, c-kit, sert and other gene expression by R. gnavus and the control of somatostatin (SS) and motilin (MTL) production. Our results suggest that gut microbe intervention with indigenous strains such as R. gnavus is a potential and promising alternative for the treatment of constipation, especially for refractory constipation.

The gut microbiome affects response of treatments in HER2-negative advanced gastric cancer.

BACKGROUND: Common treatments for metastatic/unresectable HER2-negative gastric cancer include chemotherapy, immune checkpoint inhibitor monotherapy and chemotherapy plus immune checkpoint inhibitor. However, significant drug resistance exists regardless of the treatment regimen. METHODS: Patients with metastatic/unresectable HER2-negative gastric/gastroesophageal junction adenocarcinoma were enrolled. All patients were divided into three groups according to the treatment regimen and were further divided into responders and non-responders according to efficacy evaluation. Metagenomics sequencing were performed to analyze gut microbiome signature of patients receiving different treatments at baseline and throughout treatment. RESULTS: One hundred seventeen patients with HER2-negative advanced gastric or gastroesophageal junction adenocarcinoma receiving chemotherapy alone, anti PD-1/PD-L1 immunotherapy alone or combined regimen were included in this study. Microbiome signatures related to clinical response are distinct among the three treatment groups. Among which, 14, 8 and 13 species were significantly different between responders and non-responders in immunotherapy, immunotherapy plus chemotherapy and chemotherapy group, respectively. Patients with higher relative abundance of Lactobacillus possessed higher microbiome diversity and significantly better response to anti-PD-1/PD-L1 immunotherapy and had a trend to achieve better progression-free survival. Another cohort of 101 patients has been used as an external validation set to confirm the stability and reliability of these findings. CONCLUSIONS: Gut microbiome affects response of treatments in HER2-negative advanced gastric cancer in a treatment-specific way, immunotherapy plus chemotherapy did not equal to a simple superposition of immunotherapy and chemotherapy. Lactobacillus is expected to become a novel choice as an adjuvant agent in promoting the efficacy of immunotherapy in gastric cancer.

Correlation of the gut microbiome and immune-related adverse events in gastrointestinal cancer patients treated with immune checkpoint inhibitors.

Introduction: The wide application of immune checkpoint inhibitors has significantly improved the survival expectation of cancer patients. While immunotherapy brings benefits to patients, it also results in a series of immune-related adverse events (irAEs). Increasing evidence suggests that the gut microbiome is critical for immunotherapy response and the development of irAEs. Methods: In this prospective study, we recruited 95 patients with advanced/unresectable gastrointestinal cancers treated with immunotherapy and report a comprehensive analysis of the association of the gut microbiome with irAEs. Metagenome sequencing was used to analyze the differences in bacterial composition and metabolic pathways of baseline fecal samples. Results: In summary, we identified bacterial species and metabolic pathways that might be associated with the occurrence of irAEs in gastric, esophageal, and colon cancers. Ruminococcus callidus and Bacteroides xylanisolvens were enriched in patients without severe irAEs. Several microbial metabolic pathways involved in the urea cycle, including citrulline and arginine biosynthesis, were associated with irAEs. We also found that irAEs in different cancer types and toxicity in specific organs and the endocrine system were associated with different gut microbiota profiles. These findings provide the basis for future mechanistic exploration.

Integrated multi-omics reveal important roles of gut contents in intestinal ischemia-reperfusion induced injuries in rats.

Intestinal ischemia-reperfusion (IIR) is a life-threatening clinical event with damaging signals whose origin and contents are unclear. Here we observe that IIR significantly affect the metabolic profiles of most organs by unbiased organ-wide metabolic analysis of gut contents, blood, and fifteen organs in rats (n = 29). Remarkably, correlations between gut content metabolic profiles and those of other organs are the most significant. Gut contents are also the only ones to show dynamic correlations during IIR. Additionally, according to targeted metabolomics analysis, several neurotransmitters are considerably altered in the gut during IIR, and displayed noteworthy correlations with remote organs. Likewise, metagenomics analysis (n = 35) confirm the effects of IIR on gut microbiota, and identify key species fundamental to the changes in gut metabolites, particularly neurotransmitters. Our multi-omics results establish key roles of gut contents in IIR induced remote injury and provide clues for future exploration.

GMrepo v2: a curated human gut microbiome database with special focus on disease markers and cross-dataset comparison.

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purposes are to increase the reusability and accessibility of human gut metagenomic data, and enable cross-project and phenotype comparisons. To achieve these goals, we performed manual curation on the meta-data and organized the datasets in a phenotype-centric manner. GMrepo v2 contains 353 projects and 71,642 runs/samples, which are significantly increased from the previous version. Among these runs/samples, 45,111 and 26,531 were obtained by 16S rRNA amplicon and whole-genome metagenomics sequencing, respectively. We also increased the number of phenotypes from 92 to 133. In addition, we introduced disease-marker identification and cross-project/phenotype comparison. We first identified disease markers between two phenotypes (e.g. health versus diseases) on a per-project basis for selected projects. We then compared the identified markers for each phenotype pair across datasets to facilitate the identification of consistent microbial markers across datasets. Finally, we provided a marker-centric view to allow users to check if a marker has different trends in different diseases. So far, GMrepo includes 592 marker taxa (350 species and 242 genera) for 47 phenotype pairs, identified from 83 selected projects. GMrepo v2 is freely available at: https://gmrepo.humangut.info.

Elevated Exhaustion Levels of NK and CD8+ T Cells as Indicators for Progression and Prognosis of COVID-19 Disease.

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) induced Coronavirus Disease 2019 (COVID-19) has posed a global threat to public health. The immune system is crucial in defending and eliminating the virus and infected cells. However, immune dysregulation may result in the rapid progression of COVID-19. Here, we evaluated the subsets, phenotypic and functional characteristics of natural killer (NK) and T cells in patients with COVID-19 and their associations with disease severity. Methods: Demographic and clinical data of COVID-19 patients enrolled in Wuhan Union Hospital from February 25 to February 27, 2020, were collected and analyzed. The phenotypic and functional characteristics of NK cells and T cells subsets in circulating blood and serum levels of cytokines were analyzed via flow cytometry. Then the LASSO logistic regression model was employed to predict risk factors for the severity of COVID-19. Results: The counts and percentages of NK cells, CD4+ T cells, CD8+ T cells and NKT cells were significantly reduced in patients with severe symptoms. The cytotoxic CD3-CD56dimCD16+ cell population significantly decreased, while the CD3-CD56dimCD16- part significantly increased in severe COVID-19 patients. More importantly, elevated expression of regulatory molecules, such as CD244 and programmed death-1 (PD-1), on NK cells and T cells, as well as decreased serum cytotoxic effector molecules including perforin and granzyme A, were detected in patients with COVID-19. The serum IL-6, IL-10, and TNF-α were significantly increased in severe patients. Moreover, the CD3-CD56dimCD16- cells were screened out as an influential factor in severe cases by LASSO logistic regression. Conclusions: The functional exhaustion and other subset alteration of NK and T cells may contribute to the progression and improve the prognosis of COVID-19. Surveillance of lymphocyte subsets may in the future enable early screening for signs of critical illness and understanding the pathogenesis of this disease.

Dynamic metabolomic analysis of intestinal ischemia-reperfusion injury in rats.

Intestinal ischemia-reperfusion injury (IIR) is a life-threatening abdominal emergency. Compared to traditional steady-state works, we profiled the blood of rats over 72 hr (15 time points) and examined dynamic changes in molecular pathways during IIR. Using a series of methods designed for dynamic datasets analysis (batch effects corrections, metabolomics data reduction, and different features selection), we identified 39 significant different metabolites and discovered the trends of these molecules. Four main patterns were uncovered by a longitudinal pattern recognition method. Furthermore, pathway networks were explored to uncover the possible mechanisms of IIR. We found that IIR is a complex physiological process involved in multiple pathways, such as biosynthesis of amino acids, 2-oxocarboxylic acid metabolism, arginine-related metabolism, and glutathione metabolism. Among which, metabolites related with phenylalanine tyrosine and tryptophan metabolism reached a peak during the early stage of reperfusion, while molecules in biosynthesis of unsaturated fatty acids metabolism declined. Our work provides a feasible scheme to understand dynamic molecule variation and will provide new explications about the effect of intestinal ischemia reperfusion from a dynamic perspective.

GMrepo: a database of curated and consistently annotated human gut metagenomes.

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purpose is to facilitate the reusability and accessibility of the rapidly growing human metagenomic data. This is achieved by consistently annotating the microbial contents of collected samples using state-of-art toolsets and by manual curation of the meta-data of the corresponding human hosts. GMrepo organizes the collected samples according to their associated phenotypes and includes all possible related meta-data such as age, sex, country, body-mass-index (BMI) and recent antibiotics usage. To make relevant information easier to access, GMrepo is equipped with a graphical query builder, enabling users to make customized, complex and biologically relevant queries. For example, to find (1) samples from healthy individuals of 18 to 25 years old with BMIs between 18.5 and 24.9, or (2) projects that are related to colorectal neoplasms, with each containing >100 samples and both patients and healthy controls. Precomputed species/genus relative abundances, prevalence within and across phenotypes, and pairwise co-occurrence information are all available at the website and accessible through programmable interfaces. So far, GMrepo contains 58 903 human gut samples/runs (including 17 618 metagenomes and 41 285 amplicons) from 253 projects concerning 92 phenotypes. GMrepo is freely available at: https://gmrepo.humangut.info.

Metabolic Dependencies Underlie Interaction Patterns of Gut Microbiota During Enteropathogenesis.

In recent decades, increasing evidence has strongly suggested that gut microbiota play an important role in many intestinal diseases including inflammatory bowel disease (IBD) and colorectal cancer (CRC). The composition of gut microbiota is thought to be largely shaped by interspecies competition for available resources and also by cooperative interactions. However, to what extent the changes could be attributed to external factors such as diet of choice and internal factors including mutual relationships among gut microbiota, respectively, are yet to be elucidated. Due to the advances of high-throughput sequencing technologies, flood of (meta)-genome sequence information and high-throughput biological data are available for gut microbiota and their association with intestinal diseases, making it easier to gain understanding of microbial physiology at the systems level. In addition, the newly developed genome-scale metabolic models that cover significant proportion of known gut microbes enable researchers to analyze and simulate the system-level metabolic response in response to different stimuli in the gut, providing deeper biological insights. Using metabolic interaction network based on pair-wise metabolic dependencies, we found the same interaction pattern in two IBD datasets and one CRC datasets. We report here for the first time that the growth of significantly enriched bacteria in IBD and CRC patients could be boosted by other bacteria including other significantly increased ones. Conversely, the growth of probiotics could be strongly inhibited from other species, including other probiotics. Therefore, it is very important to take the mutual interaction of probiotics into consideration when developing probiotics or "microbial based therapies." Together, our metabolic interaction network analysis can predict majority of the changes in terms of the changed directions in the gut microbiota during enteropathogenesis. Our results thus revealed unappreciated interaction patterns between species could underlie alterations in gut microbiota during enteropathogenesis, and between probiotics and other microbes. Our methods provided a new framework for studying interactions in gut microbiome and their roles in health and disease.

Time-resolved metabolomics analysis of individual differences during the early stage of lipopolysaccharide-treated rats.

Lipopolysaccharide (LPS) can lead to uncontrollable cytokine production and eventually cause fatal sepsis syndrome. Individual toxicity difference of LPS has been widely reported. In our study we observed that two thirds of the rats (24/36) died at a given dose of LPS, while the rest (12/36) survived. Tracking the dynamic metabolic change in survival and non-survival rats in the early stage may reveal new system information to understand the inter-individual variation in response to LPS. As the time-resolved datasets are very complex and no single method can elucidate the problem clearly and comprehensively, the static and dynamic metabolomics methods were employed in combination as cross-validation. Intriguingly, some common results have been observed. Lipids were the main different metabolites between survival and non-survival rats in pre-dose serum and in the early stage of infection with LPS. The LPS treatment led to S-adenosly-methionine and total cysteine individual difference in early stage, and subsequent significant perturbations in energy metabolism and oxidative stress. Furthermore, cytokine profiles were analyzed to identify potential biological associations between cytokines and specific metabolites. Our collective findings may provide some heuristic guidance for elucidating the underlying mechanism of individual difference in LPS-mediated disease.