Performing Department
(N/A)
Non Technical Summary
Polyphenol-rich Haskap berries (Haskap) have untapped therapeutic potential to improve humanhealth, and agricultural producers in northern U.S. states are poised to increase production ifconsumer demand increases. A critical knowledge gap is that little is known about the interactionsbetween gut microbes and Haskap polyphenols to produce bioactive metabolites linked todownstream health impacts. Additionally, we need to know which Haskap varieties and harvesttiming will yield the greatest bioactive potential. The long-term goal of this project is to form apartnership linking the health impacts of Haskap varieties and management practices that maximizehealth-promoting compounds to benefit both consumers and producers. Our objectives are todetermine 1) the impact of Haskap on the gut microbiome and metabolome, 2) how gut microbiomecomposition and production of bioactive metabolites from Haskap impacts health and inflammationbiomarkers, and 3) which Haskap varieties and growing practices increase production of health-promoting compounds. A four-armed, randomized, triple-blind, placebo controlled clinical trial ofHaskap versus placebo for two separate groups with distinctly low and high metabolic syndromestatus will be completed. Participants will be assessed for health biometrics, gut microbiomecomposition, inflammation, and both the gut and serum metabolome before and after 8 weeks ofintervention. Haskap fruit from twenty varieties will primarily come from the randomized blockdesign field trial and fruit will be harvested at four stages of fruit maturity, then analyzed forpolyphenol content. This part of the study will be replicated over three growing seasons.
Animal Health Component
0%
Research Effort Categories
Basic
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Applied
100%
Developmental
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Goals / Objectives
Polyphenol-rich Haskap berries (Haskap) have untapped therapeutic potential to improvehuman health, and agricultural producers in northern U.S. states are poised to increaseproduction if consumer demand increases. This is important because inflammation andabnormal metabolism play a major role in the development and progression of manydiseases including type 2 diabetes, cardiovascular disease and cancer (1, 2). Therefore,identifying foods with bioactive components that decrease inflammation and improvemetabolism is an important strategy for reducing disease burden. Berries high inpolyphenols promote human health through a variety of mechanisms, includinginflammation lowering, that may be dependent on interactions with the gut microbiota. Weidentified gut microbiota dependent anti-inflammatory polyphenol breakdown productsfrom polyphenol rich berry juice in the blood of germ-free mice humanized with fecalmicrobial transplants from humans (Wilson et al.). Some of these bioactive polyphenolmolecules were measured in mice with a gut microbiota from a human donor with low butnot high inflammation levels, suggesting that the microbial digestion of polyphenols maycontribute, at least in part, to the difference in inflammation between the human donors.Haskap (Lonicera caerulea L.) are rich in anthocyanins and other polyphenoliccompounds (3). The long-term goal of this project is to form a partnership linking thehealth impacts of Haskap with varieties and management practices to maximizehealth-promoting compounds to benefit both consumers and producers. A criticalknowledge gap is that little is known about the interactions between gut microbes andHaskap polyphenols to produce bioactive metabolites linked to downstream healthimpacts. Additionally, we need to know which Haskap varieties and harvest timing willyield the greatest bioactive potential.To address this gap, we will investigate the interaction of bioactive components in Haskapwith gut microbiota and the resultant gut and serum metabolites, inflammation, andmetabolic health, and then couple this with analysis of berries from different Haskapvarieties and harvest times. Based on established gut microbiome differences betweenmetabolically healthy and unhealthy individuals (4, 5) and our findings that polyphenolrich juice was metabolized differently in mice humanized with a low versus highinflammation microbiota (Wilson et al.), we propose an analysis that will compare gutmicrobiota and health impacts of Haskap berry consumption between metabolicallyhealthy and unhealthy groups. One key characteristic of the metabolically unhealthygroup will be an elevated waist circumference, which corresponds with elevatedinflammation (6). This proposal will significantly advance research by 1) determining theinteraction between the gut microbiota and bioactive components of Haskap, 2)elucidating the microbiome dependent metabolic impacts of Haskap underpinningchanges in health and inflammation biomarkers, and 3) partnering with regional plantscientists to integrate metabolomic analysis of polyphenols within a variety of Haskapvarieties and growing conditions with human health outcomes to identify those with thegreatest potential health impacts. Our overarching hypothesis is that Haskap will lowerinflammation and improve metabolic health through gut microbiome dependentmechanisms. The following specific objectives are proposed:Objective 1: Determine the impact of Haskap on the gut microbiome and metabolome ina cohort of adults with both low and high risk of metabolic syndrome. Hypothesis: Theimpact of Haskap will be mediated by initial composition of the gut microbiome and thatHaskap will shift the gut microbiome and gut metabolites of the high metabolic syndromerisk adults toward those with low risk.Objective 2: Determine how gut microbiome composition and production of bioactivemetabolites from Haskap impacts serum metabolite, health, and inflammation biomarkersin a cohort of adults with both low and high risk of metabolic syndrome. This analysis willidentify the impact of individual differences in gut microbiome composition on theresponse to Haskap consumption. Hypothesis: Individual differences in response toHaskap consumption are mediated by composition of the gut microbiome and resultingmetabolite production in the gut.Objective 3: Identify Haskap varieties and growing practices that increase production ofhealth-promoting compounds. Previous research has demonstrated the concentration ofgeneral and specific polyphenols and in vivo anti-inflammatory and anti-diabetic activityin Haskap differ widely among varieties and harvest timing (fruit maturity). MSU-WesternAg Research Center will determine the effects of harvest timing on concentration andyield of health-promoting compounds in over twenty varieties of Haskap. Hypothesis:Varieties will differ in their concentration of bioactive compounds and the effects of fruitmaturity on these concentrations will be variety specific.
Project Methods
Haskap versus Placebo Intervention and Dosing. To ensure an adequate dose to induce health impacts, we propose a total polyphenol dose from Haskap dose of 0.02 g·kg-1.Juice Processing and Storage. Haskap juice will be obtained from team member Miller from berries grown at or in the region of the Western Agricultural Research Center (WARC).Preparation of Placebo. A flavor and color-matched placebo will be utilized. The recipe includes black cherry Kool-Aid™, lemon juice, and food coloring to match flavor and color profiles. In addition, the placebo will be matched for sugar content (glucose and fructose) based on Haskap juice analysis . Visit 3 (pre-intervention) and Visit 4 (post-intervention) Procedures. We will utilize the web-based Automated-Self-Administered 24-hour (ASA24®) Dietary Assessment Tool. Subjects will collect a stool sample the afternoon or evening before their visit and report to the lab after a 12-hour fast. A urine sample will be collected, anthropometric assessments will be completed, and then participants will sit for a minimum of 15 minutes before resting blood pressure is measured and a fasting blood sample is collected using a standard venipuncture technique. Stool sample collection and processing. Sterile, anaerobic phosphate buffered saline will be added to each sample to make a slurry (to preserve obligate anaerobes). This slurry will be split into aliquots and stored at -80 °C until DNA extraction.Habitual diet assessment. Our study will use the web-based Diet History Questionnaire (DHQ III) to assess habitual diet over the one-year period prior to study enrollment (Visit 3) and during the final month of the intervention (Visit 4).Analysis of Glucose, Lipids, and Inflammatory Cytokines. Plasma will be analyzed in real time for a full lipid panel plus blood glucose using a clinical chemistry analyzer (Piccolo xpress). HbA1c will be analyzed in real time using a clinical chemistry analyzer (Abbott Afinion 2). Cytokines TNFα, IL-1β, IL-6, IL-10, IFNγ, GM-CSF, IL-17 and IL-23 will be measured in each blood sample. Analysis of Gut Microbiome Composition. Extraction of bulk bacterial DNA from fecal samples will be performed using Powersoil® DNA Isolation Kit (Mo Bio Laboratories, Inc.) and bead beating. Extracted DNA will be stored at -80ºC until being shipped overnight to the University of Michigan, Michigan Microbiome Project for Illumina MiSeq amplicon sequencing of the 16S rRNA V4 region. Raw sequencing reads will be processed and curated using MOTHUR software (Version 1.35.1) following the MOTHUR standard operating procedure for the MiSeq platform (Schloss et al 2009). LCMS Analysis of stool, urine, and serum metabolite extracts. Samples will undergo LC-MS/MS analysis using a Waters I-Class UHPLC coupled to a Waters Synapt-XS Q-IMS-TOF. Separation will be achieved with a 12 to 24-minute protocol, depending on biospecimen and targets using a Waters BEH-HILIC (2.1 x 100mm) column at 40°C and a flow rate at 0.6 mL/minute. Analysis of DataObjective 1: Impact of Haskap consumption on gut microbiome composition. Alpha diversity will be calculated using phyloseq 1.38.0 (R). Beta-diversity analyses will be performed on subsampled data with filtering of OTUs less than 3 counts in at least 20% of the samples. Permutational analysis (PERMANOVA) of distance matrices with stratification by cage and 999 permutations will be performed using the adonis function in the vegan package 2.5-6 (R). Canonical correspondence analysis (CCA) will be used to assess the impact of time, donor, and juice treatment on the microbial community at the species level using phyloseq 1.38.0 (R).A framework for differential abundance analysis (LinDA) will be utilized from the MicrobiomeStat R package to determine specific taxonomic groups that distinguish between treatment groups (placebo or Haskap) and MetS phenotypes.Objective 1 and 2: Impact of Haskap consumption on fecal (Obj 1) and serum (Obj 2) metabolites. The goal is to identify metabolites induced by Haskap and establish relevance to health biomarkers. Peak intensity concentrations of fecal and serum metabolites measured at baseline (pre-intervention) and 8 weeks (post-intervention) will be identified using Metaboanalyst.To identify metabolites that have the greatest change over time in response to Haskap consumption, multivariate time course analysis will be used. Participants' metabolite data at each sample time point will be modeled with a multivariate empirical Bayes analysis of variance (MEBA) time series model. ANOVA Simultaneous Component Analysis (ASCA) will also be used for feature selection and to assess the impact of interaction of intervention with time on metabolic profiles(71). Objective 2: Impact of gut microbial taxonomic composition on metabolomic responses to Haskap consumption. Linear regression modeling will then be utilized to establish relevance of identified significant metabolites to health biomarkers of interest. Metabolomic features identified from the MEBA and ASCA analyses will be assessed as predictor variables of identified changes in blood biomarkers (inflammation, lipid and glucose metabolism as described in Health Biomarker Assessment) from pre- to post- intervention. Multiple testing correction, such as FDR, will be applied to correct for the multiple two-sided, t-test comparisons utilized in regression analysis. Linear regression modeling will be utilized to assess predictive variables (inflammation phenotype, microbial taxonomic composition and diversity) of the identified Haskap metabolomic signature. A multiple testing FDR correction will be applied to correct for multiple two-sided, t-test comparisons utilizing in this analysis. Log-transformed microbiome abundance of the 100 most abundant OTUs will be fit to univariate outcome variables, e.g. change in inflammation using Cox proportional hazards to identify positive and negative correlations for individual microbial taxa.?Methods for Objective 3Haskap fruit from twenty varieties will primarily come from the randomized block design field trial at MSU-WARC that was planted in 2015, with additional varieties harvested from mature plantings at MSU-WARC and a neighboring commercial Haskap farm. Fruit will be harvested at four stages of fruit maturity: 50% veraison (when half of the fruit is fully blue), 100% veraison, two weeks after veraison, and four weeks after veraison. Each fruit sample will consist of 90 berries harvested from 3 plants (30 berries per plant) and each variety and fruit maturity stage will be represented by three field replicates. LCMS and NMR Whole Berry and Juice Analysis. Concentrations of bioactive components as well as sugar content analysis will be measured in the MSU Proteomics, Metabolomics, and Mass Spectrometry Facility and the MSU Nuclear Magnetic Resonance Core Facility, respectively as described below. In addition to measuring the polyphenol content in whole berries for Objective 3, this analysis will be utilized to identify the correct daily dose of Haskap juice and to match sugar content of the placebo juice to the Haskap juice for the human clinical trial.During analysis, juice samples will be diluted in MilliQ water (1:100) and dried using a Speed Vac. Samples will be reconstituted using MeOH:H20 (50:50) and placed in clean mass spectrometry vials. LCMS analysis will be completed on a Waters I-Class UHPLC coupled to a Waters Synapt-XS Q-IMS-TOF. LC methodology will follow the same 12-minute protocol outlined for metabolomic LCMS analysis. Analysis of juice carbohydrate content will be accomplished by mixing juice samples with a deuterated NMR buffer (50:50) before NMR analysis using a Bruker 300MHz machine. Carbohydrates will be identified and quantified using Chemonx software. The effects of variety and fruit maturity on the fruit chemical composition will be analyzed with repeated measures ANOVA.?