Multi-omics Dissection of Gut Microbiome Engraftment During FMT

Overview

The gut microbiota plays a key role in immunity and metabolism and contributes to diseases such as recurrent C. difficile infection (rCDI), ulcerative colitis (UC), and metabolic syndrome (MetS). Microbiota therapeutics, particularly fecal microbiota transplantation (FMT), show promise-achieving \~90% cure rates in rCDI-but demonstrate variable efficacy in chronic conditions. Microbiome engraftment appears critical for FMT success, yet consistent predictors remain lacking. A meta-analysis of 20 FMT studies by our group and the Segata Lab linked engraftment to clinical response across diseases, with taxon-specific patterns and ML-based predictability. While viral, fungal, host immune, genetic, and metabolic factors may affect engraftment, their roles are not well-defined. Key unresolved questions include the interplay among host factors, microbial strains, and metabolites, their influence on engraftment, and impact on clinical outcomes. This study aims to unravel microbiome engraftment dynamics and link them to therapeutic response.

Gut microbiota regulates key functions in humans, i.e. immunity and metabolism, and is a pathogenic pathway of many disorders, including recurrent C. difficile infection (rCDI), ulcerative colitis (UC), and metabolic syndrome (MetS). Microbiota therapeutics (MT) have raised high expectations without comparable results. Among MT, fecal microbiota transplantation (FMT), the transfer of healthy donor feces to a recipient with a microbiome-associated disease, has achieved high (nearly 90%) cure rates of rCDI but lower and less consistent results in chronic disorders, i.e. UC or MetS. Clinical and microbial features seem to be related with FMT outcomes, but consistent predictors are not available. Donor-recipient microbiome engraftment may be critical for the clinical success of FMT. In a pooled meta-analysis of 20 FMT studies in different diseases by our group and the Segata Lab , donor recipient microbiome engraftment was associated with clinical response regardless of disease, differed among bacterial taxa, and was predicted by machine learning (ML). Other factors could influence engraftment, but evidence is unclear. Virome and fungome, have been linked to FMT success, but their engraftment kinetics is unknown. Host factors, i.e. genetics, gut immunity and microbial metabolites are supposed to play a role in engraftment, but supporting data are still absent. Crucial issues of the engraftment dynamics remain still unsolved, including 1) which are the interactions among host factors, microbial strains, and products during FMT; 2)whether and how they influence engraftment and 3) clinical outcomes.Our aim is to disentangle the dynamics of microbiome engraftment and correlate them to clinical outcomes. OBJECTIVES Primary objectives - To assess the longitudinal multidomain interactions of host and microbiome variables and their influence on microbial engraftment Secondary Objectives \- To assess the longitudinal multidomain interactions of host and microbiome variables and their influence on clinical outcomes Endpoints Primary - The longitudinal evaluation of multidomain interactions of host and microbiome variables throughout a multi-omics approach at 90 days after the last FMT Secondary \- The longitudinal evaluation of multidomain interactions of host and microbiome variables throughout a multi-omics approach at 7,30,180,360 days after the last FMT Procedures: Baseline assessment At baseline enrolled patients will be evaluated by the gastroenterology staff and endocrine and metabolic Unit staff of the Fondazione Policlinico Universitario A. Gemelli IRCCS and their demographic, clinical characteristics and laboratory data will be recorded, specifically: * Disease clinical and endoscopic activity for UC patients, expressed using Mayo score * Insulin sensitivity, assessed by Matsuda index and OGIS index after an oral glucose tolerance test (OGTT), for MetS patients. * Clinical characteristic, the occurrence of diarrhea and fecal C. difficile toxin, in patients affected by recurrent CDI In addition, the following data for patients in all cohorts will be collected: * Erythrocyte Sedimentation Rate (ESR) and C Reactive Protein (CRP) serum levels. * White Blood Count, Red Blood Count, serum creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), urine analysis. At baseline (pre - FMT), a blood and stool sample, and gut (colonic) biopsy will be collected. After the baseline assessment, all patients will undergo to the FMT combined (colonoscopy + capsules) procedure. Follow-up visits All patients will undergo follow-up visits at day 7, 30, 60, 90, 180, 360 after the last FMT. At each time point, the gastroenterology staff and endocrine and metabolic unit staff of the Fondazione Policlinico Universitario A. Gemelli IRCCS, will assess the same items assessed at baseline (demographic, clinical characteristics including disease activity, and laboratory data) and the same blood and stool sample will be collected. Moreover, a stool sample will be collected after the pre-conditioning with nonabsorbable antibiotics. Gut biopsies will be collected at day 30, 90, 180 and 360 after the last FMT. Treatment Patients will receive a first donor FMT by colonoscopy, after a pre-conditioning with vancomycin and neomycin + bacitracin for 3 days, because data from our group show that pre-FMT antibiotics are associated with higher rates of microbial engraftment. Then they will receive two cycles, respectively after 3 and 7 days after colonoscopy - FMT, of frozen donor FMT capsules (15 capsules b.i.d. per 3 days). Patients will always receive feces from the same donor. Donors Recruitment Potential donor candidates will be evaluated by the gastroenterology staff of the Fondazione Policlinico Universitario A. Gemelli IRCCS, following protocols recommended by international guidelines and according to the recent recommendations imposed by the reorganization of faecal microbiota transplant during the COVID-19 pandemic. In addition, in relation to the national and international spread of human cases of monkey pox, according to the reports of the European Centre for Disease Prevention and Control (ECDC) and the FDA and as indicated in the circulars of the General Directorate for Health Prevention of the National Ministry of Health of 25/5/2022 (Prot. DGPREV 0026837), of 02/08/2022 (Prot. DGPREV 0034905) and in The National Transplants Center note of 07/06/2022 (Prot. ISS 0021745). Collection and storage of stool and blood samples Stool samples will be collected in donors and in patients at baseline and at each follow-up visit, using a Zymo buffer, to preserve feces at room temperature for up to 48 hours. Fecal samples will be stored at -80°C and assigned de-identified IDs. Blood samples will be collected in donors and in patients at each timepoint (1 mL of whole blood per sample), centrifuged at 2000 rpm for 15 minutes for serum collection, stored at a temperature of -80°C. Both stool and blood samples will be stored until the end of the clinical study (after the end of the follow-up of the last enrolled patient). Then, blood samples will be used for the analysis of human genes, associated with microbiome and beta diversity, while stool samples will be used for microbiome analysis. RNA extraction Total RNA will be prepared by the RNeasy kit (Qiagen) based on manufacturer's instructions. Samples are first lysed and then homogenized. Ethanol is added to the lysate to provide ideal binding conditions. The lysate is then loaded onto the RNeasy silica membrane and RNA binds to the silica membrane, and all contaminants are efficiently washed away. 100 ng/μl of RNA will be analyzed for RNA integrity and then interrogated by microarray using Affymetrix technology. Fold-change data will be calculated for each gene from the microarray analysis, comparing expression among different groups of samples. Data set will be trimmed to include only those genes showing at least a ±1.5-fold change relative to the C. difficile infection cohort, which will be then submitted to Ingenuity Pathway Analysis (IPA, Qiagen) for unsupervised clustering analysis. Potential gene networks will be identified by the Global Molecular Network algorithm. Genome extraction and shotgun metagenomic sequencing DNA extraction will be performed by using the Dneasy PowerSoil Pro Kit (QIAGEN, Germany) according to the manufacturer's procedures. DNA concentration will be measured with Qubit (Thermo Fisher Scientific, USA), and DNA will be then stored at - 20°C. Sequencing libraries will be prepared using the Illumina® DNA Prep (M) Tagmentation kit (Illumina, California, USA) following the manufacturer's guidelines. Shotgun metagenomic sequencing will be performed on the Illumina NovaSeq platform. A \>7.5Gb/sample of 150nt paired end reads (insert size \~150nt) will be generate. This will be sufficient to cover at 2x a 4Mb bacterial, fungal, and viral genome present at an abundance of 0.1% after accounting for human DNA reads removal, and to detect with our markerbased strategy organisms at abundances as low as 0.01%. All the above procedures have been extensively validated. As gut mycobiome is hardly detected by WGS, it will be assessed also by sequencing the ITS2 region and the 18SrRNA gene. Metagenome quality control and pre-processing Newly generated shotgun metagenomic sequences will be pre-processed and quality controlled using the pipeline available at https://github.com/SegataLab/preprocessing. Reads will be quality-controlled and those of low quality (quality score 2 ambiguous nucleotides were removed with Trim Galore. Contaminant and host DNA will be identified with Bowtie2 using the parameter -sensitive-local, allowing confident removal of the phiX 174 Illumina spike-in and human reads (hg19 human genome release). Remaining high-quality reads will be sorted and split to create forward reverse and unpaired reads output files for each metagenome. Microbiome taxonomic profiling Microbiome taxonomic profiling will be performed following the general guidelines and relying on the bioBakery computational environment. The taxonomic profiling and quantification of organisms' relative abundances of all metagenomic samples will be quantified using MetaPhlAn 3.0 (species-level profiling) and StrainPhlAn 3 (strain-level profiling). Metatranscriptomic, metabolomic and metaproteomic analysis Metatranscriptomics (MTR), metabolomics (MTB) and metaproteomics (MTP) will be performed in stool samples. For a metatrascriptomic analysis sample preparation will use the most up-to-date and validated protocol for mRNA fraction enrichment by rRNA depletion as per the Illumina protocols. cDNA libraries will be synthetized, and dedicated pipelines will follow. Furthermore, volatile, and nonvolatile metabolites will be assessed in stool samples to perform a metabolomic analysis. The untargeted analysis will be carried out by GC-MS coupled to solid phase microextraction (SPME) (Agilent Technologies 7890B GC, coupled to a 5977A mass selective detector), 1H-NMR (50), and by LC-MS/MS based technology by pipelines optimized in house. Data will be analyzed with MetaboAnalyst, SCIEX OS, MATLAB toolbox, and R and Phyton scripts. Metaproteomics required a specific procedure as follow, microbial cells will be purified from 300 mg of each stool sample and cells lysis will be performed adding 4% SDS (w/v) lysis buffer followed by incubation at 95 °C for 10 min with agitation and 3 rounds of sonication. After protein quantification, equal amount of each sample will be trypsin digested by FASP protocol. The tryptic peptide mixtures will be analyzed by nanoLCESI-MS/MS acquisition on a high resolution mass spectrometer. Data will be analyzed with MetaLab platform and ad-hoc Python scripts. Cytokine Multiplex Immunoassay Through Multiplex Bead analysis for the assessment of specific cytokine in biopsy samples will be perform, following manufacturer's instructions. IL-1b, IL-1Ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, Eotaxin, FGF basic, G-CSF, GM-CSF, IFN-g, IP-10, MCP-1, MIP-1a, PDGF-bb, MIP-1b, RANTES, TNF-a, VEGF will be analyzed. Flow cytometry will be also performed in colonic biopsies to identify the specific phenotype of immune cells localized in the mucosal samples. Machine learning analysis Machine learning models will be used to identify and reproducibly characterize responder and non-responder profiles. In a Python 3.9 environment using scikit-learn (ver. 0.22.1), two unsupervised ML algorithm, K-means and Agglomerative Hierarchical Clustering, will be used for creating patient clusters based on baseline microbiome, cytokine signature and clinical features, in order to assess whether ML may identify distinct subgroups of microbiome and cytokine profiles associated with clinical response. Statistical Analysis Sample size calculation This study is exploratory in nature and sample size calculation is no hypotheses-driven. A size of 30 patients in each group (total sample size 3\*30=90 patients) will be considered. As a general approach 30 patients will have an 80% power of detecting large effect size (f=0.40) at a significance level of 5%. Data Analysis Descriptive statistics will be used to analyse baseline and follow-up variables. Data will be presented as mean and standard deviation (SD) or as median and first and third quartile according to the normality of data distribution. Qualitative variables will be summarized by absolute and relative frequencies (percentage). All statistics will be tabulated according to groups (UC, rCDI, MetS) and to different timepoints. According to the data distribution an approach based on analysis of variance or Kruskal-Wallis/Friedmann test will be employed. Due to the high number of variables resulting from analyses and to the exploratory nature of this project no adjustment for multiple testing will be considered. Machine-Learning algorithms will deal with the complexity of the multidimensional data matrix. SAFETY REPORTING No specific serious adverse events are expected. Adverse events are defined as any undesirable experience occurring to a subject during the study, whether or not considered related to the surveillance protocol. All adverse events reported spontaneously by the subject or observed by the investigator or his staff will be recorded and reported to the coordinating investigator. A serious adverse event is any untoward medical occurrence or effect that at any dose: * results in death; * is life threatening (at the time of the event); * requires hospitalization or prolongation of existing inpatients' hospitalization; * results in persistent or significant disability or incapacity; * is a congenital anomaly or birth defect; * is a new event of the trial likely to affect the safety of the subjects, such as an unexpected outcome of an adverse reaction, lack of efficacy of an IMP used for the treatment of a life-threatening disease, major safety finding from a newly completed animal study, etc. All SAEs will be reported by the coordinating investigator to the ethics committee of Fondazione Policlinico "A. Gemelli" IRCCS and to the Italian National Transplant Centre SAEs that result in death or are life threatening should be reported expedited. The expedited reporting will occur not later than 7 days after the responsible investigator has first knowledge of the adverse reaction. This is for a preliminary report with another 8 days for completion of the report. All adverse events will be followed until they have abated or until a stable situation has been reached. Depending on the event, follow up may require additional tests or medical procedures as indicated, and/or referral to the general physician or a medical specialist. ETHICS The study protocol must be approved by the ethics committee CET Lazio Area 3 and by the Italian National Transplant Centre will be registered at ClinicalTrials.gov. The study will be conducted in accordance with the Consolidated Standards of Reporting Trials (CONSORT) Statement.