grant

Determinants of inception of inflammation in inflammatory bowel diseases

Organization MASSACHUSETTS GENERAL HOSPITALLocation BOSTON, UNITED STATESPosted 24 Aug 2021Deadline 31 May 2027
NIHUS FederalResearch GrantFY2025Active Follow-upAffectAutoimmune DiseasesBacteroidesBiologicalBiological MarkersBiopsyBlood SampleBlood SerumBlood specimenBody TissuesCSIFCSIF-10CarnitineCell BodyCellsCharacteristicsClinicalColonComplexConnectionist ModelsConsumptionCrohn diseaseCrohn'sCrohn's diseaseCrohn's disorderCytokine Synthesis Inhibitory FactorDataDevelopmentDietDirect CostsDiseaseDisease remissionDisorderDrug KineticsDrugsEnvironmentEnvironmental ExposureExpression SignatureFecesFoodFunctional MetagenomicsFutureGI microbiomeGene Expression ProfileGene TranscriptionGenesGenetic TranscriptionGranulomatous EnteritisHealthIL-10IL10IL10AImmuneImmunesIndividualInflammationInflammatoryInflammatory Bowel DiseasesInflammatory Bowel DisorderInfluentialsInstitutionIntakeInterleukin 10 PrecursorInterleukin-10InterventionMachine LearningMaintenanceMeasurementMediatingMedicationMetagenomicsModelingMolecularMorbidityMorbidity - disease rateMucosaMucosal TissueMucous MembraneNeural Network ModelsNeural Network SimulationPathogenesisPathway interactionsPatient RecruitmentsPatientsPerceptronsPharmaceutical PreparationsPharmacokineticsPhysical activityPilot ProjectsPredicting RiskPrediction of Response to TherapyProteomeProteomicsRNA ExpressionRecurrent diseaseRegistriesRelapseRelapsed DiseaseRemissionResolutionRoleSamplingSerumSmokingSphingolipidsStressTechnologyTimeTissuesTrainingTranscriptionUlcerated ColitisUlcerative ColitisUnited StatesUpregulationWorkactive followupautoimmune conditionautoimmune disorderautoimmunity diseasebio-markersbiologicbiologic markerbiomarkercohortcomputer based predictiondeep learningdeep learning algorithmdeep learning methoddeep learning strategydefined contributiondevelop therapydevelopmentaldietsdigestive tract microbiomedisease preventiondisorder preventiondrug/agentdysbacteriosisdysbiosisdysbioticeleocolitisenteric microbiomefecal metabolomefecal microbiomefollow upfollow-upfollowed upfollowupforecasting riskgastrointestinal microbiomegene expression patterngene expression signatureglobal gene expressionglobal transcription profilegut microbiomegut-associated microbiomehealthy volunteerinflammatory disease of the intestineinflammatory disorder of the intestineinsightintervention developmentintestinal autoinflammationintestinal biomeintestinal microbiomemachine based learningmachine learning based modelmachine learning based prediction modelmachine learning based predictive modelmachine learning modelmachine learning predictionmachine learning prediction modelmetabolism measurementmetabolomemetabolomicsmetabonomemetabonomicsmicrobialmicrobial imbalancemicrobiomenew drug treatmentsnew drugsnew pharmacological therapeuticnew therapeuticsnew therapynext generation therapeuticsnovel drug treatmentsnovel drugsnovel pharmaco-therapeuticnovel pharmacological therapeuticnovel therapeuticsnovel therapyoutcome predictionparticipant recruitmentpathwaypilot studypredict riskpredict riskspredict therapeutic responsepredict therapy responsepredicted riskpredicted riskspredicting riskspredictive modelingpredictive riskpredicts riskpreventpreventingprospectiverecruitrecurrent neural networkregional enteritisrelapse predictionrelapse riskresistance to therapyresistant to therapyresolutionsrisk predictionrisk predictionsscRNA sequencingscRNA-seqsingle cell RNA-seqsingle cell RNAseqsingle cell expression profilingsingle cell transcriptomic profilingsingle-cell RNA sequencingsocial rolesoluble fiberstoolstool microbiomestool-associated microbiometargeted drug therapytargeted drug treatmentstargeted therapeutictargeted therapeutic agentstargeted therapytargeted treatmenttherapeutic resistancetherapeutic targettherapy developmenttherapy optimizationtherapy predictiontherapy resistanttooltranscriptional profiletranscriptional signaturetranscriptometranscriptomicstreatment developmenttreatment optimizationtreatment predictiontreatment resistancetreatment response prediction
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Full Description

Project Summary
Crohn’s disease (CD) and ulcerative colitis (UC) affect over 2 million individuals in the United States and are

associated with considerable morbidity. Existing treatments achieve remission in fewer than 50% of patients.

Further, despite achieving endoscopic remission, up to 30% of patients with CD or UC will relapse over the

subsequent three years. Pathophysiologic mechanisms leading to relapse have not been well established. The

central premise of our proposal is that despite endoscopic, there exists a local pro-inflammatory microbial milieu

and transcriptional profile that favors disease relapse. We hypothesize that current clinical tools do not have

sufficient resolution to capture this state. Existing cohorts, by recruiting patients in a heterogeneous state of

active inflammation, cannot be used to infer mechanisms of loss of remission and inception of inflammation. A

targeted effort that comprehensively and longitudinally profiles a homogeneous cohort of patients in deep

remission is essential to define the dynamic relationship between microbial alterations, metabolomic,

transcriptional, and proteomic perturbations, and onset of inflammation. Identifying deficient components

favoring relapse also allows the development of intervention to replace these deficiencies, thereby extending

remission. They will also provide clues and serve as starting points for development of novel therapies. In the

first aim, we will recruit 300 patients with IBD in clinical and endoscopic remission and prospectively,

systematically follow them for 3 years. We will comprehensively characterize such patients through serial

sampling of mucosal and fecal microbiome, serum and fecal metabolome, and proteome in addition to detailed

environmental exposure assessment and measurement of drug pharmacokinetics. We will determine the

dynamic predictive utility of each of these parameters in defining future relapse from a state of quiescence. In

the second aim, we will define the role of pro-inflammatory changes at the cellular level by performing single cell

transcriptomic analysis from colonic and ileal biopsies in patients with quiescent CD and UC recruited as above.

This will provide important insights into loss of control of inflammation at the tissue level that determines future

clinical activity. The final study aim will train and validate a machine-learning predictive model to define the

contribution of each additional biologic layer to inception of inflammation and to identify more robust biomarkers

of a state of sustained remission. Defining the molecular basis of future relapse in patients in deep remission will

provide insights into the ‘pre-disease’ state, allowing for identification of immune pathways of relevance in

preventing disease. Defining the fundamental mechanisms through which disease inception occurs from

quiescence is critically important to inform key steps in the pathogenesis of these complex diseases, which in

turn, will offer opportunities for targeted mechanism-driven interventions to aid durable maintenance of remission

and health. The approaches and analyses outlined also have broad applicability to other autoimmune diseases.

Grant Number: 5R01DK127171-05
NIH Institute/Center: NIH

Principal Investigator: Ashwin Ananthakrishnan

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