Differences in microbiome populations following hematopoietic stem cell transplantation in Crohn?s disease patients

Dataset overview

Participants 37

Samples 158

Reuse readiness 9.4/10 evidence-backed score

Multi-omic modelling of inflammatory bowel disease with regularized canonical correlation analysis.

Revilla L, Mayorgas A, Corraliza AM, Masamunt MC, Metwaly A, Haller D, Tristán E, Carrasco A, et al. · PloS one · 2021

Abstract

<h4>Background</h4>Personalized medicine requires finding relationships between variables that influence a patient's phenotype and predicting an outcome. Sparse generalized canonical correlation analysis identifies relationships between different groups of variables. This method requires establishing a model of the expected interaction between those variables. Describing these interactions is challenging when the relationship is unknown or when there is no pre-established hypothesis. Thus, our aim was to develop a method to find the relationships between microbiome and host transcriptome data and the relevant clinical variables in a complex disease, such as Crohn's disease.<h4>Results</h4>We present here a method to identify interactions based on canonical correlation analysis. We show that the model is the most important factor to identify relationships between blocks using a dataset of Crohn's disease patients with longitudinal sampling. First the analysis was tested in two previously published datasets: a glioma and a Crohn's disease and ulcerative colitis dataset where we describe how to select the optimum parameters. Using such parameters, we analyzed our Crohn's disease data set. We selected the model with the highest inner average variance explained to identify relationships between transcriptome, gut microbiome and clinically relevant variables. Adding the clinically relevant variables improved the average variance explained by the model compared to multiple co-inertia analysis.<h4>Conclusions</h4>The methodology described herein provides a general framework for identifying interactions between sets of omic data and clinically relevant variables. Following this method, we found genes and microorganisms that were related to each other independently of the model, while others were specific to the model used. Thus, model selection proved crucial to finding the existing relationships in multi-omics datasets.

doi:10.1371/journal.pone.0246367 ↗ PMID 33556098 ↗ PMC7870068 ↗

Study facts

Organism: Homo sapiens
Platform: MiSeq (Illumina)
Age group: —
Disease groups: Crohn's disease, Non-IBD controls
Anatomical sites: —

Data availability

Analysis code

Strengths & limitations for reuse

Strengths

Raw reads are advertised
Feature/OTU tables are advertised
Taxonomic tables are advertised
Analysis code is available
Participant-to-sample mapping is available
Participant counts are documented
Sample counts are documented

Extraction evidence & provenance

Each extracted field is shown with the source excerpt and location used to resolve it.

Assay

Field	Value	Evidence
`assay.paired_end`	True	sequencing was performed with pooled samples in paired-end modus (PE275) Section `Methods > High throughput 16S ribosomal RNA (rRNA) gene sequencing`, offset 9500
`assay.platform`	MiSeq (Illumina)	sequencing was performed... using a MiSeq system (Illumina, Inc.) Section `High throughput 16S ribosomal RNA (rRNA) gene sequencing`, offset —
`assay.primers_reported`	True	using forward and reverse primers 341F-785R Section `Methods > High throughput 16S ribosomal RNA (rRNA) gene sequencing`, offset 9200
`assay.read_length`	275	paired-end modus (PE275) Section `Methods > High throughput 16S ribosomal RNA (rRNA) gene sequencing`, offset 9500
`assay.sequencing_type`	amplicon_16s	High throughput 16S ribosomal RNA (rRNA) gene sequencing Section `Methods > High throughput 16S ribosomal RNA (rRNA) gene sequencing`, offset 8700
`assay.target_region`	V3-V4	the V3-V4 regions of 16S rRNA gene were amplified Section `Methods > High throughput 16S ribosomal RNA (rRNA) gene sequencing`, offset 9200

Cohort

Field	Value	Evidence
`cohort.crohns_disease_participants`	18	The HSCT CD cohort involved 158 samples ... from 18 CD patients undergoing HSCT Section `Methods—Datasets`, offset 16500
`cohort.disease_activity_metadata_available`	True	segmental simple endoscopic score for Crohn’s disease (SES-CD) ... were collected Section `Methods—Datasets`, offset 16850
`cohort.non_ibd_controls`	19	biopsies were taken from the ileum and colon regions of 19 non-IBD controls Section `Methods—Patients and biopsies processing`, offset 4300
`cohort.study_design`	longitudinal	Patients were followed-up for 4 years and biopsies were collected every six or twelve months after HSCT. Section `Methods—Patients and biopsies processing`, offset 3300
`cohort.total_participants`	37 computed	The HSCT CD cohort involved 158 samples ... from 18 CD patients undergoing HSCT ... and 19 non-IBD controls Section `Methods—Datasets`, offset 16500
`cohort.treatment_exposure_documented`	True	clinical information such as age, sex, treatment, years since disease diagnosis, prior surgery Section `Methods—Datasets`, offset 16850
`cohort.treatment_response_metadata_available`	True	time of the HSCT and response to treatment were collected Section `Methods—Datasets`, offset 16850

Data_Assets

Field	Value	Evidence
`data_assets.analysis_code`	True	Analysis code of our HSCT CD dataset available at: https://github.com/llrs/TRIM Section `Data Availability`, offset 47200
`data_assets.environment_or_container_info`	True	Analysis was performed using R (version 3.6.1) and Bioconductor (Version 3.10) on Ubuntu 18.04. Section `Methods > Mucosal transcriptome`, offset 7600
`data_assets.feature_or_otu_table`	True	The resulting OTUs table was normalized using edgeR (Version 3.28). Section `Methods > Microbial profiling`, offset 10700
`data_assets.open_access`	True from source	isOpenAccess: Y Section `publication metadata`, offset —
`data_assets.pipeline_or_tool_versions`	True	using the IMNGS (version 1.0 Build 2007) pipeline based on the UPARSE approach Section `Methods > Microbial profiling`, offset 9900
`data_assets.qc_or_negative_controls_reported`	True	Sequences with less than 300 and more than 600 nucleotides and paired reads with an expected error >3 were excluded from the analysis. Section `Microbial profiling`, offset 9900
`data_assets.raw_reads`	True	Processing of raw-reads was performed by using the IMNGS Section `Microbial profiling`, offset 8400
`data_assets.sample_metadata`	True	clinical information such as age, sex, treatment ... location of the biopsies ... and response to treatment were collected. Section `Methods > Datasets`, offset 13300
`data_assets.taxonomic_table`	True	Taxonomy assignment was performed at 80% confidence level using the RDP classifier Section `Methods—Microbial profiling`, offset 12400

Specimens

Field	Value	Evidence
`specimens.body_site`	colon and ileum	Colonic and ileal biopsies were obtained at several time points Section `Patients and biopsies processing`, offset —
`specimens.inflamed_status_available`	True	Samples were obtained when possible from both uninvolved and involved areas. Section `Patients and biopsies processing`, offset 2400
`specimens.longitudinal_sampling`	True	Patients were followed-up for 4 years and biopsies were collected every six or twelve months after HSCT. Section `Methods > Patients and biopsies processing`, offset 5000
`specimens.number_of_samples`	158	The HSCT CD cohort involved 158 samples (both host RNA and microbial DNA) Section `Methods—Datasets`, offset 16500
`specimens.participant_to_sample_mapping_available`	True	the HSCT CD dataset included the following variables: patient ID, sex, age Section `Models used`, offset 23700
`specimens.sample_type`	mucosal_biopsy	Colonic and ileal biopsies were obtained at several time points during ileocolonoscopy. Section `Methods—Patients and biopsies processing`, offset 2700