Data_Sheet_1_Modulation of gut microbiota: The effects of a fruits and vegetables supplement.PDF

The consumption of an optimal amount of fruits and vegetables is known to improve physical fitness and physiological body functions. Healthy eating habits, including intake of fruits and vegetables, can modify gut microbiota. This study aimed to demonstrate the effectiveness of a formulated fruit and vegetable supplement (FVS) in modulating the antioxidant capacity and the gut microbiota composition. We enrolled 30 healthy volunteer subjects, matched for age, gender, BMI, and smoking habits, and randomized them into the FVS and the placebo (PLA) groups. Among the serum vitamins, the folic acid level was significantly higher (p = 0.001) in the FVS group than in the PLA group, whereas the vitamin B2 level was significantly higher in the PLA group than in the FVS group (p = 0.028). The antioxidant capacity, measured by using the oxygen radical absorbance capacity (ORAC) method, was also slightly higher in the FVS group than in the PLA group but did not reach statistical significance. The dietary intake, assessed by 24-h recalls, did not show any significant changes after the supplementation in both the groups. The gut microbiome composition, measured by 16S rDNA sequencing, showed no difference in both alpha and beta diversities, whereas the LEfse analysis revealed a microbial shift after the treatment, with a decreased abundance of the genus Ruminococcus from the Lachnospiraceae family (p = 0.009), and the unclassified genus from the family Erysipelotrichaceae (UC36, p = 0.003) in the FVS group compared with the PLA group (confirmed by SIAMCAT analysis, AUC = 74.1%). With a minor effect, the genus Faecalibacterium and unclassified genus and family from the order Lactobacillales (UC31) were also increased in the FVS group compared with the PLA group (p = 0.0474, p = 0.0352, respectively). SCFA measurement by gas chromatography–mass spectrometry showed an increased level of 2-methylbutyrate in the FVS group compared with the PLA group (p = 0.0385). Finally, the Spearman correlation analysis showed that in the FVS group, the genus Faecalibacterium positively correlated with 2-methyl butyrate (p = 0.040). In the PLA group, none of the significant bacteria correlated with either SCFA or serum biomarkers. The network analysis confirmed the positive correlation between genus Faecalibacterium and 2-methyl butyrate. We can conclude that the FVS in healthy individuals modified the gut microbiota composition and metabolites, and it can potentially contribute to reduce the pro-inflammatory response along with the antioxidant capacity.</p

Steiner tree.

<p>Steiner tree obtained applying a Shortest Path Heuristic (SPH) algorithm. The tree has 167 nodes and 166 edges. The size of each node (i.e. gene) is proportional to its degree (i.e. the number of incoming and outgoing connections). Node colours indicate: perturbed seeds (green), non-perturbed seeds (red), perturbed connectors (blue), and non-perturbed connectors (yellow). Red edges correspond to perturbed interactions, while edge thickness is proportional to their weight (i.e. their perturbance level). A perturbed interaction has a weight <i>w</i> < 0.33 (i.e. p < 0.05 threshold over the nominal p-value). The entire network is characterized by a backbone <i>CAMK2A-TCF7L2- CTNNB1-JUN-MAK8-PRKACG</i>, where <i>CAMK2A</i> and <i>MAPK8-PRKACG</i> are the main perturbed hubs, <i>CTNNB1</i> and <i>JUN</i> represent the sinks of the entire system, and <i>TCF7L2</i> is the bottleneck connecting them.</p

ncWNT sub-network.

<p>This sub-network focuses on the module characterized by a series of receptors and enzymes regulating Calcium/cAMP homeostasis and involved in the non-canonical Ca⁺²/WNT signaling pathway. Among them, the most perturbed are the <i>EGFR</i> receptor and its target phospholipase <i>PLCB3</i>, and the routes <i>ITPR1-CAMK2A-CALM2-PPP3CC</i> and <i>ITPR1-CAMK2A-EP300-TCF7L2</i>. <i>JUN</i> is a large sink between this module and the MAPK-JNK one. Nodes and edges are labelled according to the conventions followed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185797#pone.0185797.g002" target="_blank">Fig 2</a>.</p

Data analysis workflow.

<p>Our network-based data analysis method includes three inputs: (i) a reference interactome that is used as a gene-gene interaction space; (ii) a set of seed nodes representing terminals (sources and targets) of information spreading through the interactome; and (iii) quantitative data used to build network weights. Weights are then used to generate a Steiner tree connecting seed genes through paths maximizing edge perturbation, using a weighted heuristic shortest path algorithm. The resulting Steiner tree is then converted into a Structural Equation Model (SEM) and fitted, to assess its validity. During SEM-based procedure, covariance between pairs of leaf genes (i.e., ancestral bow-free nodes) are tested and fitted using a latent variable (LV) model. The group variable <i>C</i> = {0, 1} influences a LV, modelling the unobserved cause(s) acting on the two target genes. Significant covariances are retained in the extended network, representing the final disease-network.</p

SEM goodness of fit.

<p>Goodness of fit measures for different models, fitted to multivariate data of the extracted Steiner tree. The selected model, indicated by (*), has the lowest Akaike Information Criterion (AIC = -9324.98).</p

Essential node sub-network.

<p>Node essentiality is determined by considering nodes having both degree centrality and weighted betweenness centrality over the upper-quartile. Essential nodes are placed in non-redundant portions of the network and thus cannot be removed without a deep impact on network connectivity. These genes intercept the network backbone, represented by the axis <i>TCF7L2-JUN-MAK8-PRKACG</i>, carrying the top perturbation levels, especially in proximity of the sources. Nodes and edges are labelled according to the conventions followed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185797#pone.0185797.g002" target="_blank">Fig 2</a>.</p

MAPK-JNK sub-network.

<p>This sub-network focuses on the module characterized by a series Serine/Threonine kinases involved in the MAPK-JNK signaling pathway. Among them, <i>MAPK8</i> is the one having the highest outgoing connectivity of the entire network, and the most perturber incoming interaction carried by <i>PRKACG</i>, another FTD-network hub. Other deeply perturbed interactions include <i>MAPK8-TNF</i>, <i>MAPK8-CRK</i>. <i>JUN</i> is a large sink within this module and the non-canonical WNT pathway (ncWNT) one. Nodes and edges are labelled according to the conventions followed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185797#pone.0185797.g002" target="_blank">Fig 2</a>.</p

F_st values (in bold) and genomic control inflation factor (λ_GC) (<i>in italics</i>) between Sardinian linguistic macro-areas.

<p>F_st values (in bold) and genomic control inflation factor (λ_GC) (<i>in italics</i>) between Sardinian linguistic macro-areas.</p

Map of Mediterranean basin showing the localization of Sardinia and Sardinian linguistic domains.

<p>A) Map of the Mediterranean basin showing the geographic position of Sardinia. B) The Sardinian linguistic domains: 1 = <i>Gallurese</i> (77 individuals); 2 = <i>Nuorese</i> (88); 3 = <i>Logudorese</i> (385); 4 = <i>Sassarese</i> (342); 5 = <i>Alghero</i> (87); 6 = <i>Campidanese</i> (98).</p

Mean genomic inbreeding coefficients (F_RoH %) using 0.5 and 5 Mb minimum RoH thresholds and mean sum of RoH.

<p>* <i>p-value</i> smaller than 0.05 when comparing each linguistic macro-area to peninsular Italy.</p