Daily Consumption of Orange Juice from Citrus sinensis L. Osbeck cv

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Bioactive Constituents, Metabolites, and Functions

Daily consumption of orange juice from Citrus sinensis L. Osbeck cv. Cara Cara and cv. Bahia differently affects Gut Microbiota Profiling as unveiled by an integrated meta-omics approach Elisa Brasili, Neuza Mariko Aymoto Hassimotto, Federica Del Chierico, Frederico Marini, Andrea Quagliarello, Fabio Sciubba, Alfredo Miccheli, Lorenza Putignani, and Franco Maria Lajolo J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b05408 • Publication Date (Web): 15 Jan 2019 Downloaded from http://pubs.acs.org on January 16, 2019

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Journal of Agricultural and Food Chemistry

Daily consumption of orange juice from Citrus sinensis L. Osbeck cv. Cara Cara and cv. Bahia differently affects Gut Microbiota Profiling as unveiled by an integrated metaomics approach Elisa Brasiliab*, Neuza Mariko Aymoto Hassimottoab, Federica Del Chiericoc, Federico Marinid, Andrea Quagliarielloc, Fabio Sciubbad, Alfredo Micchelid, Lorenza Putignanice, Franco Lajoloab

aDepartment

of Food Science and Experimental Nutrition, School of Pharmaceutical Science,

University of São Paulo, São Paulo, Brazil bFood

Research Center (FoRC), CEPID-FAPESP (Research Innovation and Dissemination

Centers Sao Paulo Research Foundation), São Paulo, Brazil cUnit

of Human Microbiome, Children's Hospital and Research Institute Bambino Gesù,

Rome, Italy dDepartment

eUnit

of Chemistry, University of Rome "La Sapienza", Rome, Italy

of Parasitology, Children's Hospital and Research Institute Bambino Gesù, Rome, Italy

Corresponding Author *(E.B.) Phone: (+55) 11-30910128. Fax: (+55) 11-3815 4410. E-mail: [email protected] https://orcid.org/0000-0002-0623-2705

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ACS Paragon Plus Environment


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ABSTRACT

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We have investigated the effect of intake of two different orange juices from Citrus sinensis

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cv. ‘Cara Cara’ and cv. ‘Bahia’ on faecal microbiota and metabolome using an integrated

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meta-omics approach. Following a randomized cross-over design, healthy subjects daily

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consumed 500 mL of orange juice from Cara Cara or Bahia juices or an isocaloric control

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drink. Stools were collected at baseline (T0) and after a week (T7) of intervention.

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Operational taxonomic units (OTUs) were pyrosequenced targeting 16S rRNA and faecal

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metabolites were analysed by an untargeted metabolomics approach based on 1H-NMR-

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Spectroscopy. The major shift observed in microbiota composition after orange juice intake

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was the increased abundance of a network of Clostridia OTUs from Mogibacteriaceae,

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Tissierellaceae, Veillonellaceae, Odoribacteraceae and Ruminococcaceae families, whose

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members were differently affected by Cara Cara or Bahia juice consumption. A core of six

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metabolites such as inositol, choline, lysine, arginine, urocanic acid and formate significantly

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increased in Cara Cara compared to Bahia group.

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Keywords: ‘Cara Cara’ orange juice, ‘Bahia’ orange juice, gut microbiota, Clostridia,

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pyrosequencing, 1H-NMR-based metabolomics

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INTRODUCTION

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The gut microbiota is a metabolically active community of microorganisms that interact with

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one another and with the host organism influencing many aspects of human health.1,2

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Genetic, lifestyle and environmental factors, including diet exert the greatest influence on gut

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microbiota by modifying not only its composition but also its functionality to a largely

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unknown extent. Emerging evidence suggests that exists a high interindividual variability in

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gut microbiota composition and functions, making it difficult to predict the response of

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individual’s gut microbiota to a given dietary intervention.3,4

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Citrus sinensis L. Osbeck orange juice is an excellent dietary source of bioactive compounds

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including vitamin C, flavonoids such as hesperidin and narirutin, carotenoids, sugars and

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fibres that contribute to general health status and reduce the risk of metabolic and chronic

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diseases.5-6 There are evidences that the bioactive components present in orange juice are

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associated with the metabolism of gut microbiota.7 Most of ingested orange juice ﬂavanones

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reach the colon where are subjected to the action of the microorganisms able to hydrolyse

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ﬂavanone glycosides. A part of the released aglycones are absorbed and transformed to

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glucuronide and sulphate metabolites and a further portion is excreted in the faeces.8,9 In this

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context, Pereira-Caro et al.10, demonstrated that the aglycone hesperetin could be converted

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into 3-(3'-hydroxy-4'-methoxyphenyl)propionic acid, 3-(3',4'-dihydroxyphenyl)propionic

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acid, and 3-(3'-hydroxyphenyl)propionic acid, while 3-(phenyl)propionic acid is the major

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end product derived from naringenin. In a recent in vitro study, it was demonstrated that fresh

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and pasteurized orange juices increased Lactobacillus spp., Enterococcus spp.,

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Bifidobacterium spp., and Clostridium spp. and reduced enterobacteria.11 These variations in

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microbiota composition were also associated to an increase in butyric, acetic, and propionic

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acid concentrations, and a decrease in ammonium production.11

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The metabolic relationships between nutrition and host-microbe co-metabolism are still

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poorly characterized. Additionally, most of the studies focused on a single bioactive

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compound and selected bacterial populations and there are not reports examining the impact

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of orange juice on the human gut microbiota composition and metabolism.

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Major advances in metabolomics and metagenomics technologies have opened a new

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scenario in the comprehension of the gut ecosystem by shedding light on its composition,

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modulation and interplay with microorganisms.12,13

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Recently, we have analyzed the chemical composition of two different Brazilian orange

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juices from Citrus sinensis L. Osbeck, namely ‘Bahia’ and ‘Cara Cara’. A higher ascorbic

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acid content was detected in Bahia as compared to Cara Cara juice.14 Total flavanones content

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as well as hesperidin and didymin levels were higher in Cara Cara with respect to Bahia

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juice. Cara Cara was also characterized by a significantly higher sucrose content and a higher

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and diversified carotenoid content compared to Bahia juice with a mixture of (Z)-isomers of

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lycopene, all-E-β-carotene, phytoene, and phytofluene isomers accounting for the highest

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carotenoid proportion. In addition, Cara Cara showed a higher content of insoluble fibre

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compared to Bahia juice, whereas a higher content of soluble fibre was found in Bahia

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compared to Cara Cara juice.14

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It should be also considered that little is known about how the gut microbiota varies across

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different countries and that Brazil presents a unique combination of large demographic

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distribution and nutrition transition.

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On account of these evidence, we investigated the effect of two different orange juices from

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Citrus sinensis L. Osbeck ‘Bahia’ and ‘Cara Cara’ on Brazilian healthy volunteer gut

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microbiota composition and activities using an integrated meta-omics approach. OTUs were

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identified by pyrosequencing of 16S ribosomal RNA (rRNA) and metabolic profiling was

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characterized by an untargeted NMR-based metabolomics approach. 4


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MATERIAL AND METHODS

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Subjects and study design

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The study protocol was approved by the Ethics Committee of University of São Paulo (CEP

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FCF 1097907) and was formally registered as clinical trial (NCT02685124).

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In this study, healthy volunteers, aged 18-45 years old and concordant for normal weight

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(BMI= 18.5-24.9 kg/m2) were included. Before enrolling in the study, participants underwent

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a health screening examination to exclude those with a history of gastrointestinal diseases,

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chronic illness, taking medicaments or nutritional supplements, eating disorder or following

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special diets, smokers and pregnant, which could interfere with the results of the study. A

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complete blood exam of all participants was performed before they started the interventional

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study.

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A convenience sample of 21 participants (n=11 men and n=10 women) who have completed

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a food-frequency questionnaire was included. All participants gave a written informed

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consent.

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Following a randomized controlled cross-over design (Figure S1), each participant consumed

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500 mL of orange juice from Citrus sinensis L. Osbeck cv. Cara Cara or Bahia or an

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isocaloric control drink containing water, sucrose and vitamin C (CTRLs), daily for a period

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of 7 days with a 1-week washout period between consecutive interventions.

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During the trial, participants recorded their dietary intake and were asked to follow a diet low

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in polyphenolic compounds by avoiding fruits and vegetables, especially tomatoes and other

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citrus fruits, chocolate, high-fibre products, and beverages, such as tea and coffee. A day

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before the beginning and the end of each intervention period, participants were given a

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standard low polyphenol meal (roasted chicken breast with potato puree). They were

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instructed to continue the low polyphenol diet throughout the intervention period. 5



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To analyse the gut microbiota composition and faecal metabolome, stool samples were

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collected from each subject in sterile plastic pots at the time 0 (T0) and after 7 days (T7) of

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intervention. Samples were stored at −80 °C until further analyses.

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Gut microbiota profiling

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The genomic DNA was isolated from faecal samples using the QIAamp DNA Stool Mini Kit

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(Qiagen, Germany) according to the protocol described by Del Chierico et al.15 Briefly, the

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amplification of the 16S rRNA V1-V3 region was performed using a 454- Junior Genome

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Sequencer (Roche 454 Life Sciences, Branford, CT). Reads were analysed with Quantitative

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Insights into Microbial Ecology (QIIME v.1.8.0)16. To characterized the taxonomical

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structure of the faecal samples, the sequences were organized into OTUs by clustering at a

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threshold of 97% similarity. OTUs were matched against Greengenes database (v. 13.8) for

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taxonomic assignment. The representative sequences were submitted to PyNAST for

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sequence alignment16, and to UCLUST for sequence clustering.17

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Faecal metabolites analysis by 1H NMR Spectroscopy

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Faecal water samples were obtained by modifying published procedures.18,19 Briefly, 500 mg

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of frozen faeces were mixed with ice-cold PBS/D2O (1.0 mL, pH 7.12, deuterium content

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99.96%, PBS 0.1 mM). After centrifugation at 10,000×g for 25 min at 4° C, supernatants

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were filtered through a cell strainer (40 μm pore size, BD bioscience) and further centrifuged.

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PBS-D2O (200 μL) containing 2 mM trimethylsilyl-sodium-deuterated propionate (TSP), as

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internal standard, and sodium azide solution 0.1% (v/v) were added to the supernatant (500

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μL). Finally, samples were transferred into 5-mm NMR tubes and analysed by high-

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resolution NMR spectroscopy. 1H-NMR spectra of faecal water were acquired at 298 K using

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a Bruker AVANCE 400 spectrometer (Bruker BioSpin GmbH, Rheinstetten, Germany) 6


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equipped with a magnet operating at 9.4 T and 400.13 MHz for 1H frequency, as described

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elsewhere.18,20 1H spectra were acquired employing the presat pulse sequence for solvent

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suppression and resonance assignments were determined by comparison with literature

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data21,22 as well as with online (Human Metabolome Database) 23 and in-house databases.

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Two-dimensional (TOCSY and HSQC) NMR experiments were carried out to confirm the

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assignments. 1H- and 2D-NMR spectra were processed and quantified as previously

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described by Calvani et al.18 The quantification of metabolites was obtained by comparison of

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the integrals of specific signals with the internal standard TSP integral. Concentrations of

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metabolites are expressed in μmol/L.

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Statistical Analysis

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Ecological diversity for each intervention group was assessed by: i) number of obtained

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OTUs; ii) Shannon index providing the entropy information of the OTU abundances and

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accounts for both richness and evenness; iii) Chao1 metric estimating species richness; iv)

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phylogenetic distance (PD_whole_tree) to assess quantitative measure of phylogenetic

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diversity; v) Good’s coverage measuring what percent of the total species is represented in a

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sample.

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To measure the differences in taxonomic abundance profiles from different samples (β-

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diversity), Bray-Curtis algorithm was used. Adonis test (R vegan package) was performed to

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test for significant differences between groups (999 permutations).

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The α and β diversity were performed using both QIIME software and phyloseq package in

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R.24 Data normality was checked using the Shapiro-Wilk test by Sigma plot (V13.0). Since

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the data distribution was not normal, nonparametric test (Mann-Whitney) was applied to

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compare the data. Significantly different OTUs were selected by false discovery rate (FDR)-

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adjusted p values. The meta-omic data from metagenomic and metabolomic were analysed 7



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using partial least squares discriminant analysis (PLS-DA). PLS-DA was performed using in-

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house routines running under Matlab R2015b environment (The MathWorks, Natick, MA,

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USA).

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In order to obtain an unbiased estimate of the prediction performances, since the available

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number of observation wouldn't allow for splitting the data into a single representative

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training/test set pair, a repeated double cross-validation procedure was used. In detail,

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cancelation groups were defined so to have an outer and an inner cross-validation loop, the

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latter for model selection and the former to evaluate the performances of the optimized model

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on a set of samples not used during the optimization stage; in order not to rely on a single

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definition of the cross-validation splits, the procedure was repeated for 30 times and the

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classification results have been averaged. Moreover, as a further validation stage, in order to

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show that the classification results were significantly higher than those which could be

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obtained by chance, the double-cross-validated predictions were compared to their respective

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distribution under the null hypothesis, which in turn were estimated non-parametrically by

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means of permutation tests with 1000 randomizations.

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The significance of variables was evaluated using the variable importance in the projection

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(VIP) method. Metabolites with VIP > 1.5 were considered significantly different and

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selected for further analysis.

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RESULTS

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Gut microbiota profiling

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A total of 63,627 16S rRNA sequencing reads were obtained from the 53 faecal samples. The

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effect of consumption of orange juice from two cultivars on faecal microbiota was assessed

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based on α-diversity indices and β-diversity (Bray-Curtis).

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The Mann-Whitney test was used to highlight significant differences in the α-diversity term

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for the number of OTUs, Shannon index, and Chao1 index and the entire phylogenetic

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distance tree for all three groups (Cara Cara, Bahia and CTRLs).

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Significantly different (p< 0.05) Shannon index (Figure S2) and phylogenetic distance tree

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(Table S1) were observed in CTRLs group between T0 and T7. Conversely, the mean

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differences in the α-shannon index, Chao1 and entire phylogenetic distance tree values

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between the T0 and T7 in Cara Cara and Bahia groups were not significant.

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The β-diversity analysis highlighted the existence of a significant difference between T0 and

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T7 for both Bahia and Cara Cara groups (p-value =0.037 and 0.024, respectively), while no

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difference was detected among CTRLs samples (Figure 1).

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The most of the OTU sequences were attributed to ten main phyla: Firmicutes, Bacteroidetes,

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Cyanobacteria, Synergistetes, TM7, Fusobacteria, Tenericutes, Verrucomicrobia,

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Actinobacteria and Proteobacteria. The most abundant phyla in all the volunteers were

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Firmicutes, Bacteroidetes and Tenericutes (Figure 2). At time 0, all groups preserved the

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same proportion between Firmicutes, Bacteroidetes and Tenericutes. Cara Cara group was

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characterized by Firmicutes (67%), Bacteroidetes (29%) and Tenericutes (2%). Similarly,

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Bahia group showed Firmicutes (70%), Bacteroidetes (23%) and Tenericutes (4%) levels.

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Although the CTRLs group presented the higher Tenericutes level (5%), this one was not

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significantly different from Cara Cara and Bahia group and all the observed little variations at

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phylum level did not attain the statistical significance.

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When we compared T0 and T7 within each intervention group, a significantly increase in

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Firmicutes level was observed in CTRLs group (0.6960 versus 0.7995; p=0.0379) after intake

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of the drink containing sucrose and vitamin C (Figure 2).

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To evaluate the gut microbiota composition at the final time of each intervention period, a

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comparison among the T7 of three groups was carried out. The Proteobacteria significantly 9



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increased at T7 in Cara Cara compared to CTRLs group (0.01368 versus 0.00976; p =0.034)

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(Figure 2).

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Within the Firmicutes, the Mogibacteriaceae and Tissierellaceae families increased after

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intake of Cara Cara orange juice, whereas among Bacteroidetes, the Odoribacteraceae family

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and the Odoribacter genus decreased. The Mogibacteriaceae family statistically increased

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also after intake of Bahia orange juice. In Bahia group, the Enterococcaceae family and

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Veillonellaceae family increased while Ruminococcaceae family and the Faecalibacterium

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prausnitzii decreased. Ruminococcaceae family and Oscillospira genus as well as

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Porphyromonadaceae family and Parabacteroides distasonis significantly decreased in

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CTRLs group (Table 1).

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To highlight other differences among the treatments, we compared the final time (T7) of all

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the groups (Table 2). Among the Actinobacteria, the relative abundance of the

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Coriobacteriaceae family and Adlercreutzia genus were significantly higher in Bahia

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compared to CTRLs group. Within the Bacteroidetes, the Porphyromonadaceae family and

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Parabacteroides genus alike the Odoribacteraceae family and Butyricimonas genus were

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increased in Cara Cara compared to CTRLs group. Among Firmicutes, a significant increase

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of Enterococcaceae family and Enterococcus genus was observed in Bahia compared to

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CTRLs group. Veillonellaceae family showed a significant increase across the groups CTRLs

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< Cara Cara < Bahia. After Bahia juice intake, a significantly higher abundance of

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Clostridiaceae family and Clostridium genus as well as Ruminococcaceae family and

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Anaerotruncus genus compared to CTRLs group was also observed. In addition,

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Anaerotruncus genus appeared to decrease across the groups Bahia > Cara Cara > CTRLs.

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On the other hand, Cara Cara group showed a higher relative abundance of

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Christensenellaceae, Lachnospiraceae and Ruminococcaceae families compared to CTRLs

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group. Interestingly, at species level, only Eubacterium dolichum showed evidence of

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variation between groups, being detected exclusively in Cara Cara group.

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Regarding the Proteobacteria, a higher abundance of the Enterobacteriaceae family was

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observed in Cara Cara compared to CTRLs group, while a significant increase of

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Pasteurellaceae was observed in Bahia compared to CTRLs group (Table 2).

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1H-NMR-based

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Typical 1H NMR and TOCSY spectra of faecal water at baseline (T0) are illustrated in

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Figure S3 A and B respectively. We identified and quantified a series of faecal metabolites

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including amino acids (glycine, phenylalanine, lysine, arginine), microbial-related

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metabolites (isovalerate, acetate, butyrate, isobutyrate, propionate), alcohols (methanol,

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ethanol, isopropanol), phenolic-derived compounds (3-(4-Hydroxyphenyl)-propionic acid),

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histidine metabolism (urocanate) and others (oxoglutarate, inositol, dihydroxyacetone,

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choline, p-cresol, formate, uracil, niacin) (Table S2).

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In order to obtain a balanced data matrix for statistical analysis, we selected faecal water

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samples at each experimental time point from volunteers that completed the study.

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A preliminary analysis was conducted to characterize the differences among subjects (inter-

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individual variation) and separate it from the treatment-related (intra-individual variation). To

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investigate the effect of each treatment on participants (intra-individual variation), a PLS-DA

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classification model was constructed and validated using post-intervention-baseline values

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for pairs of classes. The relevance of individual metabolites was identified by inspecting the

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values of the variable importance in projection (VIP) index. The first PLS-DA model “Bahia

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versus Cara Cara” was built using three LVs that accounted for more than 65% of the

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variance originally present in the X block. The model allowed to correctly predict the effect

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of intake of Bahia and Cara Cara juices in 100.0% of participants in the calibration phase and

metabolic profiles of faecal water

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92.9% of participants in double cross-validation (outer loop; 100% Bahia and 87.5% Cara

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Cara). The classification capacity of the model is also evident by inspecting the projection of

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participants into the space spanned by the first three LVs of the PLS-DA model (Figure 3,

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Figure S4), which shows a clear separation between “Bahia” and “Cara Cara” groups. A total

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of six variables contributed significantly to the discrimination model, in particular choline,

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inositol, urocanate, lysine, formate and arginine as shown in Table 3. The second PLS-DA

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model “Bahia versus Control” was built using the first LV that accounted 41.46% of the

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variance originally present in the X block (Figure 4). As indicated by the cross-validation

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procedure, the model predicted the effect of Bahia juice and control drink intake in 86.7% of

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participants in calibration (100% Bahia and 77.8% Control) and 80.0% in double cross-

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validation (83.3% Bahia and 77.8% Control). Relevant variables that contributed

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significantly to the discrimination model were isovalerate, choline, p-cresol, isopropanol,

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phenylalanine + phenylacetate, formate and α-ketoglutarate (Table 3). The PLS-DA model

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for Cara Cara and Control groups did not show a clear discrimination among the classes,

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indicating an unsatisfactory model with low predictive ability (data not shown).

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Integrated profiling of the metagenomics and metabolomic results

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The data referring to OTUs and faecal metabolites were composed into a single data matrix

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and processed using multivariate analysis. To prevent the misleading results potentially due

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to the large quantity of null data, a new data matrix was assembled considering variables and

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subjects populated more than 70%. The final data set included 56 variables (32 OTUs and 24

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fecal metabolites). PLS-DA was used to identify an integrated metagenomics/metabolomics

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profile for Cara Cara, Bahia and Control group. The PLS-DA analysis showed that only the

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Cara Cara group could be significantly distinguished from CTRLs group within OTUs

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variables (Table S3, Figure S5). Based on PLS-DA model, Cara Cara group had 12


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significantly higher levels of Enterobacteriaceae, Lachnospiraceae, Lachnospira,

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Bacteroidaceae, Bacteroides ovatus, Porphyromonadaceae, Parabacteroides,

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Ruminococcaceae, Oscillospira, Erysipelotrichaceae compared to CTRLs group. The PLS-

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DA models built to compare Cara Cara versus Bahia group and Bahia versus CTRL group

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did not display significant discriminating features between groups, indicating that these PLS-

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DA models were not able to display statistically robust covariations of metagenomics and

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metabolomics variables.

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In order to explore the relationships between the bacteria and faecal metabolites after orange

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juice intake, the Spearman rank test was performed in Cara Cara and Bahia groups. The

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results are reported in Table 4.

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In Cara Cara group, the Mogibacteriaceae family showed consistent positive correlations with

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isobutyrate, isovalerate, α-ketoglutarate, inositol, choline, lysine, arginine, glycine and p-

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cresol.

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Butyrate positively correlated with Lachnospiraceae and Enterobacteriaceae. Similarly,

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propionate showed a positive correlation with Lachnospiraceae family. Conversely,

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Odoribacter genus negatively correlated with butyrate, isobutyrate and propionate in Cara

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Cara group. A series of alcohols were also inversely correlated to Odoribacter after Cara

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Cara juice intake, including ethanol, isopropanol and methanol.

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In Bahia group, butyrate, propionate and acetate negatively correlated with Bacteroides.

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Interestingly, desaminotyrosine showed consistent positive correlations with Bacteroides

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uniformis and Odoribacter and a negative correlation with Lachnospira. Faecalibacterium

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prausnitzii showed only a strong negative correlation with fumarate. Among the bacterial

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products, urocanate was positively correlated with Lachnospiraceae in Cara Cara group and

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with Coriobacteriaceae in Bahia group.

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DISCUSSION

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The most of dietary interventions in human cohorts have investigated microbial community

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variations on timescales of weeks25 to months26. A limited number of researches examined

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the effect of short-term consumption of diets on microbial community structure and

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activities.27 Further studies are needed to establish how rapidly and reproducibly the human

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gut microbiome responds to short-term diet change.

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Orange juice is an important source of dietary bioactive compounds including polyphenols,

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carbohydrates and fibres responsible for various health effects.

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The association of citrus fruit bioactive compounds to gut microbiota metabolism has been

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previously demonstrated using in vitro human cell lines and animal models.4,10,28-29 The

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clinical trials in which citrus fruit bioactive compounds were associated to gut microbiota

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community and metabolism are, however, very limited.30

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In vivo studies carried out to investigate the effect of orange juice flavanone bioavailability in

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healthy humans highlighted a large interindividual variability in the biological response to

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(poly)phenols, probably due to differences in the gut microbiota or to epigenetic differences

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between the different volunteers.31,32 Flavanone bioavailability seems to be dependent on the

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presence of specific microorganisms able to remove the rutinosides from the juice glycosides,

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which results in aglycones that are then absorbed from the gut. 29,31,33,34 In view of the above,

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It is essential to study the correlations between gut microbiota composition and the metabolic

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signature generated by citrus (poly)phenols assumption in both health and illness states.

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The impact of two distinguished cultivars of orange juice from Citrus sinensis Osbeck ‘Cara

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Cara’ and ‘Bahia’ characterized by a different content of vitamin C, flavanones and

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carbohydrates on the human faecal microbiota and metabolome was investigated.

321

We observed that daily consumption of Cara Cara or Bahia orange juices differently affects

322

the gut microbiota profiling. 14


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The major shift observed in microbiota composition after orange juice intake was the

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increased abundance of a network of Clostridia OTUs from Mogibacteriaceae,

325

Tissierellaceae, Veillonellaceae, Lachnospiraceae and Ruminococcaceae families, whose

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members were differently affected by Cara Cara or Bahia juice consumption.

327

There are evidences that hesperidin and naringenin flavanones are specifically exposed to

328

anaerobic degradation by Clostridia bacteria, since they are able to synthesize enzymes that

329

cleaved the C ring of flavonoids.35

330

Although the Mogibacteriaceae family was not characterized at genus and species level, we

331

observed that it was statistically increased both after Cara Cara and after Bahia juice intake.

332

Not much is known about the function of the Mogibacteriaceae family in the gut. However,

333

in a recent study about faecal microbiota of healthy Japanese, it was observed that the

334

abundance of Mogibacteriaceae was significantly higher in lean than in obese subjects and

335

was also negatively associated with bowel movement frequency.36

336

In addition to unclassified members of Tissierellaceae that significantly discriminated T0 and

337

T7, the intervention with ‘Cara Cara’ juice also resulted in increased numbers of

338

Lachnospiraceae, Lachnospira and Dorea, as well as numbers of Ruminococcaceae,

339

Anaerotruncus and Oscillospira. Lachnospiraceae and Ruminococcaceae are the most

340

abundant Firmicutes families in gut environment, accounting for roughly 50% and 30% of

341

phylotypes, respectively.37 The decrease in the relative abundance of Lachnospiraceae

342

members in the gut microbiota was associated with compromised health status of patients

343

suffering from colorectal cancer 38, ulcerative colitis39, type 140 and type 2 diabetes.41

344

Likewise, the importance of the members of Ruminococcaceae family for the gut health is

345

indicated by their reduced abundance in faeces of Crohn's disease42 and ulcerative colitis

346

patients.39

15



Page 16 of 38

347

Members of Lachnospiraceae and Ruminococcaceae have in common the capacity to

348

generate SCFAs, mainly butyrate from fermentation of non-digestible plant fibers.43 Butyrate

349

exerts a key role in maintaining gut homeostasis and epithelial integrity, since it is used as the

350

primary energy source by colonic epithelial cells and then is transported to the mitochondria

351

to undergo β-oxidation to acetyl-CoA that enters the TCA cycle and results in ATP

352

production.43 Interestingly, after Cara Cara juice intake consistent positive correlations were

353

observed between butyrate and Lachnospiraceae family, as well as among the most abundant

354

SCFAs present in the colon, including acetate, butyrate and propionate.

355

The consumption of Bahia juice statistically increased the relative abundance of

356

Veillonellaceae but also Enterococcaceae and Coriobacteriaceae families.

357

Veillonella spp. are an essential component of the upper-gastrointestinal microbiota44 where

358

they form a trophic chain with Streptococcus spp able to convert lactate into propionate.45 Up

359

to now, it is not clear the role of Veillonella in human health, although several studies have

360

found an increase of Veillonella in faecal samples of patients suffering from irritable bowel

361

syndrome.46,47 The relative abundance of Faecalibacterium prausnitzii was lower after Bahia

362

juice intake, although no significant change was observed comparing T7 of all groups,

363

suggesting that its level was maintained constant among the subjects independently from the

364

dietary intervention. It is well known that F. prausnitzii is able to utilize simple carbohydrates

365

as glucose and fructose, as well as complex molecules as pectin and N-acetylglucosamine to

366

synthetize butyrate, formate and lactate.48

367

F. prausnitzii has also found to exhibit anti-inflammatory properties in mice 49 and was

368

already associated with a wide range of metabolic processes in the human gastrointestinal

369

tract.50 However, in this study, we observed that the selected orange juices were not potential

370

candidate foods to stimulate F. prausnitzii growth.

16


Page 17 of 38


371

The increase of Enterococcus (Enterococcaceae) and Adlercreutzia (Coriobacteriaceae)

372

indicates that Bahia orange juice, differently from Cara Cara one, is able to modulate also

373

Actinobacteria and Proteobacteria phyla. The data on Enterococcus spp. in human health are

374

contradictory. Enterococcus spp. are widely described as opportunistic pathogens, although

375

these species can exhibit probiotic abilities.51 Adlercreutzia spp. are well known gut species

376

able to produce equol, an isoflavandiol metabolized from daidzein.52 The ability to produce

377

equol is significantly higher in Asian as compared to Caucasian gut microbiota53 probably

378

due to the adaptation of the microbiota to the higher availability of isoflavones mainly

379

derived from soy beans. However, the consumption of soy and soy-based foods is also widely

380

spread in Brazil.54

381

After the Cara Cara juice intake, a modulation of faecal members of Bacteroidetes from

382

Bacteroidaceae, Barnesiellaceae and Porphyromonadaceae families was also observed. In this

383

group, the increase of Parabacteroides genus and Bacteroides ovatus was associated to the

384

decrease of Odoribacter genus. In previous studies, it was observed that tumour-bearing mice

385

showed enrichment in OTUs associated with members of the Odoribacter genus.55 In

386

humans, Bacteroides species are able to degrade plant fibre and complex polysaccharides,

387

including pectin and xylans derived from fruit and vegetable.56 It has been demonstrated that

388

Bacteroides ovatus, B. thetaiotaomicron and B. uniformis ferment a large number of complex

389

polysaccharides. In particular, B. ovatus metabolizes plant cell components, specifically

390

hemicelluloses of which the orange juice is rich, targeting the β1,4-glucosidic linkages.57

391

The importance of Parabacteroides genus is evinced by its ability to decrease the severity of

392

intestinal inflammation in animal models of acute and chronic colitis.58 Among species of

393

Parabacteroides genus, Parabacteroides distasonis, owns the ability to reduce the intestinal

394

inflammation by inducing the anti-inflammatory cytokine IL-10 and suppressing the secretion

395

of inflammatory cytokines such as IL-17, IL-6, and IFN-γ.59 Unexpectedly, in our study a 17



Page 18 of 38

396

significant increase of this bacterium was observed after intake of control drink containing

397

vitamin C and sucrose.

398

The Bacteroides species exert also an important role in protein metabolism, since several

399

species show proteolytic activity, attributed to protease enzymes linked to the cell wall.60

400

As revealed by the PLS-DA model, a core of six host and microbial-derived metabolites

401

including choline, inositol, urocanate, lysine, arginine and formate significantly distinguished

402

Cara Cara from Bahia group. It is well known that colonic bacteria utilize the amino acids

403

including lysine, arginine, glycine as well as leucine, valine, and isoleucine (BCAA) to

404

generate a complex mixture of metabolic end products including acetate, butyrate, propionate

405

(SCFAs) and branched-chain fatty acids (BCFA; valerate, isobutyrate, and isovalerate).61

406

Importantly, these bacterial metabolites have been found to influence epithelial physiology by

407

influencing the signalling pathways in epithelial cells and by modulating the mucosal

408

immune system of the host.62 Although, no significant increase in SCFAs level was observed

409

after intake of Cara Cara or Bahia juice, we detected a significant increase in isovalerate and

410

a positive correlation between Mogibacteriaceae and isovalerate, α-ketoglutarate, choline,

411

lysine, arginine and glycine in Cara Cara group suggesting a link between these bacteria and

412

their metabolic products with chemical composition of Cara Cara orange juice. As microbial

413

metabolic product, urocanate is the first intermediate of the histidine degradation pathway

414

and can be produced by the bacterial histidine-ammonia lyase enzyme, which convert

415

histidine in urocanate and ammonia. The observed positive correlation between urocanate

416

and Lachnospiraceae or Coriobacteriaceae family, respectively in Cara Cara and Bahia group,

417

suggests that the two orange juices could affect the modulation of different bacteria involved

418

in histidine degradation reactions leading to the urocanate synthesis.

419

The orange juice consumption from Citrus sinensis Osbeck ‘Cara Cara’ and ‘Bahia’ induced

420

significant changes in the gut microbiota which were mainly associated to a core of Clostridia 18


Page 19 of 38


421

OTUs potentially involved in several host physiological processes including glucose and

422

protein metabolism, immune homeostasis and body energy balance.

423

Together, these findings demonstrate that the orange juice intake could modulate the host-

424

microbe interactions heralding a promising future therapeutic approach in nutrition studies

425

targeting chronic human diseases, including inflammatory bowel disease, obesity, type 2

426

diabetes, cardiovascular disease, and cancer. In this scenario, further research using large,

427

long-term intervention studies to evaluate the effect of Cara Cara or Bahia orange juice

428

consumption on gut microbiota composition and function would be helpful in making

429

specific dietary recommendations to population. As far as we know, this is the first study of

430

the Brazilian healthy volunteer faecal microbiota and metabolome after orange juice intake.

431 432

Funding

433

Funding has been provided by the Sao Paulo Research Foundation (FAPESP) (Grant

434

2016/08796-7) and the National Counsel of Technological and Scientific Development

435

(CNPq Grant 150033/2015-0).

436 437

The authors declare no competing financial interest.

438 439

ACKNOWLEDGMENTS

440

We thank Fundecitrus company in Araraquara (São Paulo, Brazil) for providing the orange

441

juices.

442 443 444

Supporting Information description 19



Page 20 of 38

445

Figure S1. Experimental design.

446

Figure S2. Alpha diversity (Shannon index).

447

Figure S3. (A) Typical 1H-NMR spectra of faecal water at baseline (T0). (B) Typical TOCSY

448

spectrum of faecal water at baseline (T0).

449

Figure S4. PLS-DA loading plot of Bahia versus Cara Cara group obtained from NMR

450

metabolomics data.

451

Figure S5. Distribution of number of misclassifications (NMC), area under the ROC curve

452

(AUROC), and discriminant Q2 (DQ2) values under their respective null hypothesis as

453

estimated by permutation tests with 1,000 randomizations (blue histograms) and corresponding

454

values obtained by the PLS-DA model on unpermuted data (red dashed lines).

455

Table S1. Ecological diversity for each intervention group assessed by (i) number of obtained

456

OTUs; ii) Shannon index; iii) Chao1 metric estimating species richness; iv) phylogenetic

457

distance (PD_whole_tree); v) Good’s coverage.

458

Table S2. Assignments list: 1H chemical shifts of fecal metabolite signals.

459

Table S3. List of discriminant OTUs between Cara Cara and CTRLs groups as revealed by

460

PLS-DA model.

461 462 463

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60) Macfarlane, G.T., Cummings, J.H., Allison, C. Protein degradation by human intestinal bacteria. J Gen Microbiol. 1986, 132, 1647-56. 61) Dai, Z.L., Wu, G., Zhu, W.Y. Amino acid metabolism in intestinal bacteria: links between gut ecology and host health. Front Biosci (Landmark Ed). 2011, 16, 1768-86. 62) Schaible, U.E., Kaufmann, S.H. A nutritive view on the host-pathogen interplay. Trends Microbiol. 2005, 13, 373-80.

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FIGURE CAPTIONS 656

Figure 1. Metagenomics beta-diversity plots. Principal component plots of the diversity in

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the microbial communities between T0 and T7 faecal samples in Cara Cara (A) Bahia (B)

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and CTRLs (C) groups.

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Figure 2. Pie charts of OTU distribution. The distribution of the OTUs was compared

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between T0 and T7 (*) within Cara Cara (A), Bahia (B) and CTRLs (C) groups, and between

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T7 (**) of different groups (P-value

Daily Consumption of Orange Juice from Citrus sinensis L. Osbeck cv

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