MicroRNA expression in relation to different dietary habits: a comparison in stool and plasma samples

Mutagenesis, Sep 2014

MicroRNAs (miRNAs), a class of small non-coding RNAs, are fundamental for the post-transcriptional regulation of gene expression. Altered expression of miRNAs has been detected in cancers, not only in primary tissue but also in easily obtainable specimens like plasma and stools. miRNA expression is known to be modulated by diet (micro and macronutrients, phytochemicals) and possibly by other lifestyle factors; however, such influence has not yet been exhaustively explored in humans. In the present study, we analysed the expression levels of a panel of seven human miRNAs in plasma and stool samples of a group of 24 healthy individuals characterised by different dietary habits (eight vegans, eight vegetarians and eight subjects with omnivorous diet, all groups with similar age and sex distribution). The dual aim of the study was to identify possible differences in miRNA expression due to diet (or other lifestyle factors recorded from questionnaires) and to compare results in both types of specimens. miR-92a was differentially expressed in both plasma and stool samples and with the same trend, among the three groups with different diets (P = 0.0002 and P = 0.02, respectively, with expression levels of vegans>vegetarians>omnivores). miR-92a was also associated with low body mass index (P = 0.04 and P = 0.05, respectively) in both types of specimens, and with several dietary factors. Other analysed miRNAs (miR-16, miR-21, mir-34a and miR-222) were associated with dietary and lifestyle factors, but not consistently in both stool and plasma. Our pilot study provides the first evidence of miRNA modulation by diet and other factors, that can be detected consistently in both plasma and stools samples.

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MicroRNA expression in relation to different dietary habits: a comparison in stool and plasma samples

Mutagenesis MicroRNA expression in relation to different dietary habits: a comparison in stool and plasma samples Sonia Tarallo 2 3 Barbara Pardini 1 2 3 Giuseppe Mancuso 2 3 Fabio Rosa 1 2 3 Cornelia Di Gaetano 1 2 3 Floriano Rosina 0 2 Paolo Vineis 2 3 4 Alessio Naccarati 2 3 0 Division of Gastro-Hepatology, Ospedale Gradenigo , Corso Regina Margherita 8, 10153 Turin , Italy 1 Department of Medical Sciences, University of Turin , via Santena 19, 10126 Turin , Italy 2 The Author 2014. Published by Oxford University Press on behalf of the UK Environmental Mutagen Society. All rights reserved. For permissions , please 3 Human Genetics Foundation , via Nizza 52, 10126 Turin , Italy 4 School of Public Health, Imperial College , Norfolk Place, London W2 1PG , UK †These authors contributed equally to this work. - MicroRNAs (miRNAs), a class of small non-coding RNAs, are fundamental for the post-transcriptional regulation of gene expression. Altered expression of miRNAs has been detected in cancers, not only in primary tissue but also in easily obtainable specimens like plasma and stools. miRNA expression is known to be modulated by diet (micro and macronutrients, phytochemicals) and possibly by other lifestyle factors; however, such influence has not yet been exhaustively explored in humans. In the present study, we analysed the expression levels of a panel of seven human miRNAs in plasma and stool samples of a group of 24 healthy individuals characterised by different dietary habits (eight vegans, eight vegetarians and eight subjects with omnivorous diet, all groups with similar age and sex distribution). The dual aim of the study was to identify possible differences in miRNA expression due to diet (or other lifestyle factors recorded from questionnaires) and to compare results in both types of specimens. miR-92a was differentially expressed in both plasma and stool samples and with the same trend, among the three groups with different diets (P = 0.0002 and P = 0.02, respectively, with expression levels of vegans>vegetarians>omnivores). miR-92a was also associated with low body mass index (P = 0.04 and P = 0.05, respectively) in both types of specimens, and with several dietary factors. Other analysed miRNAs (miR-16, miR-21, mir34a and miR-222) were associated with dietary and lifestyle factors, but not consistently in both stool and plasma. Our pilot study provides the first evidence of miRNA modulation by diet and other factors, that can be detected consistently in both plasma and stools samples. Introduction MicroRNAs (miRNAs) are a class of evolutionarily conserved, small non-coding RNAs of 19–24 nucleotides in length that regulate gene expression mostly at the post-transcriptional level ( 1 ). Initially, miRNAs were thought to be involved in the regulation of development and cell proliferation, but more recently it has been discovered that they participate in a broad range of processes including cell cycling, apoptosis, cell differentiation, tumour development, invasion, metastasis and angiogenesis ( 2 ). The discovery that few miRNAs are involved in sensing nutrient stress in plants ( 3 ) opens the way to new epidemiological research on their roles. In fact, they seem to be key factors in the response to environmental stress as they represent one of the mechanisms deployed to reprogram gene expression so that cells can adapt to changing environments ( 4,5 ). For these reasons the interest for miRNAs has grown in the last decades, and from being considered a curiosity of the model organism Caenorhabditis elegans, they have been recently described as promising biomarkers of disease ( 6 ). In particular, miRNAs have been observed to be aberrantly expressed in several human cancers ( 7 ) and they are promising alternative biomarkers for the detection and prognosis of cancer, and monitoring response to treatment, as well as crucial players in cancer initiation, development and metastasis ( 7,8 ). From the biological point of view, miRNAs may be better predictive and prognostic markers than DNA or messenger RNA (mRNA). A single miRNA, in fact, may regulate hundreds of target mRNAs, frequently grouped in specific biological pathways. Consequently, a miRNA signature may provide prognostic information that is several orders of magnitude greater than mRNAs. Besides, miRNAs are more stable than other biomarkers during sample processing, and are thus more suitable for analysis in plasma, urine and stool (9), which is a key point in the search for cancer markers. In the case of large scale screening for cancer detection, for instance, the direct analysis in target tissue/biopsies is often unfeasible, while the possibility of studying markers from easily accessible and inexpensive surrogate tissues like plasma and stool could be ideal, especially for repeated measurements. It has been hypothesised that the observed differential expression of miRNAs in cancer may be attributed also to exposure factors, such as diet, generally not considered in biomarker studies. Diet may modulate circulating miRNA profiles (for a review see ref. ( 10 )). The indirect influence of dietary substances on endogenous miRNAs is well established: vitamin A, zinc and various nutrients affect miRNA expression and activity ( 11–13 ). On the other hand, a connection between diet and disease has been repeatedly hypothesised for cancer ( 14 ). The present study has measured and compared the expression of six selected miRNAs in plasma and stool samples from healthy volunteers (n = 24) following different dietary habits (vegan, vegetarian and omnivorous diets). The aim was to assess whether the different regimes may induce differences in miRNA expression and whether the investigated specimens (plasma and stool) consistently reflect these differences. Additionally, we have evaluated the influence of demographic factors, specific food items and other lifestyle factors on miRNA expression levels. The panel of miRNAs was selected from previous studies in the literature reporting their detection in plasma or stool samples in relation to colorectal cancer, or indicated to be linked to diet in vivo and in vitro ( 15–22 ). Material and methods Study participants Sixteen subjects with differential dietary habits were enrolled in this study (eight vegans and eight vegetarians) between June–August 2013, through online contact with an Italian society of vegetarian nutrition, or by direct acquaintance. An additional eight volunteers with similar age and sex distribution, but with an omnivorous diet were also enrolled. Their characteristics are summarised in Table I. The subjects, all living in the Turin city area, were invited to attend a meeting where the aims of the study and the requirements to participate were briefly explained. All participants received a kit containing detailed information about the study, two questionnaires from the European Prospective Investigation into Cancer and nutrition (EPIC) study (one about the dietary habits and one about lifestyle habits, specific for men and women) ( 23,24 ), an additional short questionnaire about changes in their dietary habits, medical history and additional questions not present in the EPIC questionnaires, and finally a disposable 30 ml polystyrene screw cap container with spoon for stool collection (AsOne). A written informed consent form to participate in the study was signed by all volunteers. The study was conducted according to the guidelines in the Declaration of Helsinki. The protocol of the study was approved by the Gradenigo Hospital Ethics Committee in Turin. Collection of faecal samples Naturally evacuated stool samples were obtained at home from all patients. All participants were instructed to bring the sample to the laboratory. The faecal samples were prepared for the next step immediately after they were brought to our laboratory and excess faeces were stored at −80°C. Collection of plasma samples Plasma samples were collected according to standard phlebotomy procedures at the moment when volunteers brought the stool samples to the laboratory. Blood sample (5 ml) was collected into ethylenediaminetetraacetic acid tubes and immediately placed on ice. Tubes were centrifuged at ×1000g for 10 min at room temperature. Plasma was collected and distributed in cryovial tubes. One tube was immediately used for RNA extraction while the other aliquots were labelled and stored at −80°C. The time from sample procurement to storage at −80°C was less than 3 h. Extraction of total RNA from faeces and plasma Before extraction of total RNA, faecal samples were solved in phosphate buffered saline and then homogenised by a Silent Crusher (Heidolph Instruments, USA). Homogenised samples (500 µl) were used for RNA extraction. For plasma samples, the volume used for RNA extraction was 300 µl. Total RNA was extracted using the miRvana MiRNA Isolation Kit (Ambion) using the protocol recommended by the manufacturer. cDNA synthesis and miRNA expression analysis by quantitative real-time PCR Complementary DNA (cDNA) was synthesised using the TaqMan® MicroRNA reverse transcription Kit (Applied Biosystems, Foster City, CA, USA), in accordance with the manufacturer’s instructions. cDNA synthesis was conducted by incubation at 16°C for 30 min and 42°C for 30 min, 85°C for 5 min. The reaction mixture for the real-time PCR analysis consisted of 2 μl of template cDNA, 10 μl of TaqMan® Universal PCR Master Mix II without UNG (Applied Biosystems) and 1 μl of 20× primer/probe mixture in a total reaction volume of 20  μl. Real-time PCR was conducted in the Applied Biosystems 7900HT Fast Real-Time PCR System with PCR initial activation at 50°C for 2 min, followed by enzyme activation step at 95°C for 10 min and then 40 cycles of denaturation at 95°C for 15 s and annealing/extension at 60°C for 1 min. For miRNA expression analysis, we targeted 7 miRNAs: hsa-miR-16, hsa-miR-21, hsa-miR-34, hsa-miR-92a, hsa-miR-106a, hsa-miR-146 and hsa-miR-222. For all miRNAs, the commercially available TaqMan MicroRNA Assay (Applied Biosystems) was used (reference number available upon request). Statistical analysis The miRNA expression analysis was conducted using GenEx (http://genex.genequantification.info/). A key issue in miRNA detection is the selection of endogenous controls, which are RNA or miRNAs found to be largely invariant in a sample set. Although several small RNA species (like RNU6B) have been recommended for normalising miRNA expression, they are not particularly indicated for specimens with low RNA yields such as stool or plasma, as they are rapidly degraded and the detection rate is relatively low ( 25 ). As there is no consensus on suitable controls for stool testing, quantification was performed adopting two different approaches for normalisation: (i) each miRNA expression data was normalised by the ΔΔCt method using the average of global measure of the miRNA expression data, or alternatively (ii) using miR-222 as normaliser (identified by Normfinder software ( 26 ) as the most stable miRNA among the ones investigated). Since the results were similar, we have opted for the former normalisation. Normalised data were converted into relative quantities and transformed to log2 scale. To evaluate the association with the dietary items and lifestyle factors, we modelled the data from the questionnaires both as categorical (yes/no) and as a continuous (weekly consumption) variables. Student’s t-test, analysis of variance (ANOVA) and univariate linear regression were used to compare means and to calculate bivariate correlations. Statistical significance was set at 5% level. To model the modulatory role of dietary, lifestyle and demographic factors on miRNA expression, we have adopted the structural equation model (SEM) statistical framework ( 27 ). The rationale behind SEM is that multiple linear equations are used to specify causal relationships between variables, some of which are manifest variables (i.e. observed and collected by the researchers), while others are latent variables (i.e. derived from the observed variables by specifying their relations using equations). Considering the heterogeneous source of our measures (consisting of both categorical and continuous variables), we have computed the Pearson product–moment correlations in our data to fill the heterogeneous correlation (HC) matrix on which we have based the development of the structural models. By using a cut-off of |R| > 0.5 for the values of HC, we have obtained a network representation of the relevant relations/ factors. This has allowed us to further estimate the causal relations of selected factors on individual miRNAs, both by graphical models and by conditional independence tests (do-calculus) ( 28 ). Each model was represented in the form of SEM by using the lavaan package ( 29 ) and was evaluated by taking into account multiple fitting indexes. A comparative finding index (CFI) > 0.90, a All (n = 24) Vegans (n = 8) Vegetarians (n = 8) Omnivorous (n = 8) Mean (range) Male Female Student Worker Retired Basic (<8 years) Average (8–13 years) High (>13 years) No Yes Only gastrointestinal cancer Mean ± SD Median <18.5 18.5–24.9 >25 Mean ± SD Short (<1 year) Average (1–3 years) Long (>3 years) 39 ( 21–60 ) 9 15 7 15 2 2 6 16 13 11 3 21.9 ± 2.5 21.3 1 19 4 – – – – root mean square error of approximation (RMSEA) < 0.05, suggest good fitting performances, as described in (30). All analyses were performed using R (R 2.15.2) (http://www.rproject.org). Results Study population characteristics The study was carried out in 24 healthy volunteers belonging to 3 different dietary categories, each group with similar age and sex distribution. The median age of the volunteers was 39  years (range spanning from 21 to 60  years), with vegans being slightly older than vegetarians on average (mean 42 vs. 37  years). Males represented approximately one-third of the study group. Subjects following an omnivorous diet displayed a non-significantly higher body mass index (BMI) compared to the other groups (23.2 ± 2.0 vs. 21.1 ± 2.0 and 21.5 ± 3.1 kg/m2). The duration of a specific dietary habit was shorter for vegans (3.1 ± 3.8  years) in comparison with vegetarians (7.7 ± 9.7 years). The majority of vegans had adopted the diet less than 1 year before recruitment, while the majority of vegetarians since more than 3  years before. One of the volunteers reported having been vegetarian for 30  years, while some of the vegans were former vegetarians. From the questionnaire, 13 subjects out of 16 reported to have adopted the specific diet for ethical issues, 4 also for the protection of the environment and 6 as a preventive measure. The main characteristics of the study population are summarised in Table I. Dietary and other lifestyle characteristics are described in Supplementary Tables 1 and 2, available at Mutagenesis Online. miRNA expression and different diets In the present study, we have analysed the expression levels of seven miRNAs: miR-16, miR-21, miR-34, miR-92a, miR106a, miR-146 and miR-222 in plasma and stool samples of all investigated subjects. Since qPCR for miR-146 did not provided any results in both specimens, this miRNA has not been included in the analyses. After ANOVA analyses, we observed that miR-92a was differentially expressed among different dietary groups in plasma samples (P = 0.0003) with a trend: both vegans and vegetarians showed higher levels in comparison to omnivores (P = 0.0002 and P = 0.02, respectively). Box plots for the results are reported in Figure 1A. In stool samples, we observed similar results with increased miR-92a levels in vegans as compared to vegetarians and omnivores. The trend was the same, but the difference was statistically significant only between vegans and omnivores (P = 0.04). Box plots for results are reported in Figure 1B. Among the other investigated miRNAs, no significant differences were found in either plasma or stool samples. miR-106a was differentially expressed in faeces (P = 0.04), but no clear trend was observed, while in plasma the difference was not significant. Individual data are reported in Supplementary Table 3, available at Mutagenesis Online. For each individual miRNA, expression levels were not significantly related between plasma and stool samples, or with duration of the diet (vegan and/or vegetarian). miRNA expression and demographic characteristics We further investigated the associations between miRNA expression levels and covariates included in the study. In plasma samples, we observed a decreased miR-16 expression with increasing age of the subjects (P = 0.05), and in stool miR-34 expression levels decreased with age (P = 0.05). Only miR-222 was differentially expressed according to gender stratification: women showed higher levels of this miRNA in plasma samples (P = 0.0004) but not in faeces. Interestingly, higher BMI was associated with decreased miR-92a in both plasma and stool (P = 0.04, and P = 0.05, respectively, Figure 2A and B). Conversely, in stool, higher BMI was related to higher expression levels of miR-222 (P = 0.03) (data not shown). miRNA expression and dietary characteristics From the long list of items included in the questionnaires, we have analysed only few and most representative for the dietary regime differences (consumption of meat, processed meat, fish, cheese and dairy products, bread and pasta, total vegetables and fruit consumption; Supplementary Table  4, available at Mutagenesis Online). Among the main findings, we observed that miR-92a in plasma and in stool was less expressed in meat/processed meat/fish consumers in comparison to nonconsumers (P = 0.002 in plasma and P = 0.089 in stool), reflecting the same pattern of dietary regimes. Interestingly, a decrease in miR-92a levels was inversely related to weekly consumption of meat/processed meat/fish in plasma samples (P = 0.001), while a non-significant trend was observed in stool. The same patterns can be observed when individual categories of the above foods are separately analysed in each specimen. miR-92a also decreased according to cheese consumption (vs. none: P = 0.004 in plasma, and P = 0.01 in stool) or weekly cheese consumption (significant in plasma P = 0.0093, and same trend albeit not significant in stool). In particular for dairy products, miR-92a was less expressed in both plasma and stool (P = 0.0006, P = 0.02, respectively), when comparing consumers versus non-consumers. miR-92a was also more expressed in plasma samples according to bread/pasta intake (P = 0.05). miR-21 was related to weekly consumption of vegetables (raw/cooked) in stool (P = 0.01) with the same but non-significant trend in plasma. In contrast, in stool but not in plasma miR-16 was inversely related to weekly intake of vegetables (P = 0.01). miR-34a expression levels were related to weekly consumption of fruit in stool (P = 0.005). miRNA expression and lifestyle characteristics We did not observe any differential expression of the analysed miRNAs in association with smoking habits or physical activity, as derived from the questionnaires. About alcohol consumption (categories: total alcohol consumers, wine or beer, or liquor consumers, both yes/no or weekly intake), we observed that in plasma miR-92a expression levels were lower among liquor consumers and according to weekly estimated intake. On the other hand, miR-21 was less expressed in wine/ liquor consumers, also according to weekly intake. Higher miR16 expression levels were detected in both plasma and stool of subjects consuming liquors (P = 0.0137 and P = 0.0292, Supplementary Table 4, available at Mutagenesis Online). Integrating modulatory effects of dietary, lifestyle and demographic factors on miRNA expression levels To integrate the contribution of previously identified relevant (dietary, lifestyle and anthropometric/demographic) factors on miRNA expression, we have initially performed a correlation analysis. The resulting HC matrix reporting all outcomes is shown in Figure  3: the majority of results have confirmed the previous findings from ANOVA or univariate regression analysis. All significant correlations (|R| > 0.500) among investigated factors and analysed miRNAs have been represented as a network that has provided the basis to detect causal relationships and test different models by SEM analysis. Among all investigated models, we have identified those related to miR-92a in plasma and in stool samples providing the best fitting indexes (CFI = 1, RMSEA = 0 for both). The relative path diagrams are shown in Figure 4. Discussion Genetic and epigenetic alterations may explain observed aberrant miRNA expression in cancer. Specific dietary/ environmental factors may be responsible for these alterations, since they have been shown to modulate miRNA expression and their mRNA targets in various processes (such as apoptosis, cell cycle regulation, angiogenesis and inflammation) ( 31 ). In this respect, dysregulated miRNAs detected in surrogate specimens such as plasma and stool samples may represent a powerful non-invasive and sensitive tool and might aid in the early detection of gastrointestinal cancer ( 32,33 ). However, at present, scanty information is available—in epidemiological studies—on how miRNAs are modulated by external factors that are known to be relevant for the risk of gastrointestinal diseases, such as diet and other lifestyle habits (12). In the present pilot study, we aimed at exploring the possible modulatory effect of different dietary habits on a panel of miRNAs by evaluating their expression levels in stool and plasma samples simultaneously, in order to compare in the same subjects any relationship with dietary factors. Specifically, we have recruited a group of 24 healthy individuals belonging to 3 different diet groups with comparable sex and age distribution. miRNAs were selected on the basis of their role in cancer (particularly in colorectal cancer) in previous studies ( 34,35 ). The main and novel finding is the simultaneous detection in both stool and plasma of differences between dietary groups according to miR-92a expression levels. In those specimens, subjects adopting a vegan diet had higher expression levels of this miRNA in comparison with vegetarians and people following an omnivorous diet, with a similarly decreasing trend. miR-92a is part of the miR-17–92 cluster, which is located at the 13q22 region and encodes for seven miRNAs processed from a single polycistronic primary transcript ( 36 ). The cluster expression has been linked to the pathogenesis of several malignancies, including lung cancer, leukemia, hepatocellular carcinoma and colorectal cancer ( 32,36,37 ). miR-92a is the last and least described member of the cluster. It has been observed to be frequently overexpressed in different types of tumours ( 38–40 ) and only recently it has been demonstrated to be highly expressed in endothelial cells and to regulate their angiogenic functions ( 41 ). In addition, some studies suggested that miR92a may regulate the immune functions by controlling lymphocyte and monocyte proliferation (reviewed by ref. 36) . mir-92a has been studied in stool or in plasma samples in association with colorectal cancer, where increased levels have been observed, mostly in studies on Asian populations ( 35,42 ). However, in other studies on Caucasians results were discordant, with no difference in miRNA expression levels among cancer cases and controls (43). Such contradictions may be due to differences in dietary habits, as observed in the present study. In fact, it is possible that populations with different dietary habits may have different expression profiles of miRNAs, such as miR-92a, also in relation to pathological conditions. The adoption of a vegan/vegetarian diet has been for long considered to lead to a decreased risk of cardiovascular disease and cancer ( 44,45 ). However, these diets may also be associated with an increase of other disease risks: the complete omission of meat and fish from the diet increases the risk of nutritional deficiencies and the effect of vegetarianism on the haematological system has been clearly described (46). The role of specific dietary changes in miRNA expression has been so far only investigated extensively in animals or in vitro. For instance, folate deficiency in rats has been associated with up regulation of several miRNAs and the development of hepatocarcinoma ( 47 ). Recently, to complicate the scenario, the potential of food-derived miRNAs (XenomiRs) to affect host gene expression has also been reported ( 48 ). Witwer and colleagues ( 49 ) proposed the existence of miRNAs that could be absorbed with the diet and would contribute directly or indirectly to the apparent expression of circulating miRNAs, although there is still no convincing evidence on the putative effect of food-derived miRNAs ( 50 ). We further investigated other possible associations with recorded anthropometric/demographic data as well as with the intake of selected food items and lifestyle factors. miR-92a expression was inversely related to BMI in both types of samples. BMI was only moderately associated with dietary groups: a non-significant trend was observed comparing omnivores to vegetarians and to vegans. In contrast, the expression of miR222 was associated with higher BMI, but only in stool, and with gender in plasma (increased levels in females). Despite a limited age range (20–60 years) in our study, miR-16 and miR34 showed decreased levels with age in plasma and in stool, respectively. A  general age-related down regulation of miRNAs has been repeatedly observed in blood samples in the few studies available so far with a broad age-range design ( 17,51 ), though still with non-consistent results. We have taken advantage of the food frequency questionnaire adopted in the EPIC study ( 23 ) to relate expression levels of analysed miRNAs with specific food items, that could either reflect the different specific dietary habits of each group of volunteers or, independently, those of all study participants at a global level. In this way, we could detect more subtle associations related to specific food components. Interestingly, miR-92a expression was strongly inversely related to cheese consumption (including dairy products), in both stool and plasma samples. This miRNA was also inversely related to meat (including processed meat) and fish consumption but only in plasma, while in stool the trend was consistent but did not reach statistical significance. In stool, miR-16 expression was associated with increased meat/fish consumption while miR-21 with a decreased consumption of processed meat; the opposite trend was observed in relation to weekly intake of vegetables. Moreover, miR-34 levels were related to fruit intake. Individual miRNAs can regulate many target genes while a group of miRNAs may modulate specific pathways. Thus, we additionally investigated the target genes of miR-92a (n = 36), miR-16 (n = 387) and miR-21 (n = 164) by TargetScan+ WebGestalt to understand whether any biological process was commonly enriched. Interestingly, among the several metabolic processes and molecular functions in which miRNA target genes were involved, a subset of them was involved in the metabolism of nitrogen compounds (GO: 0051  171, GO: 0051  172 and GO: 0034  641). This finding confirms that the observed differences in miRNA expression according to differential intake of meat/processed meat/fish or based on different dietary groups, reflect the effect of foods consumed by vegetarians and omnivores on the nutritional status. We have observed additional associations with alcohol and coffee consumption, but not with smoking habits and physical activity. Since only few of the subjects were active smokers we cannot draw any firm conclusion on this important exposure, that might also modulate miRNA expression in relation to cancer ( 52 ). Physical activity in relation to changes of miRNA levels in blood samples has recently received attention; however, studies were based on short-term changes in miRNA expression immediately following active aerobic exercise ( 53–55 ). The main hypothesis of our study was that the diet and other lifestyle factors of an individual may significantly influence miRNA expression levels, and this modulation is detectable in plasma and stool. This is presumably not a straightforward process and to globally elucidate causal relationships from observational studies remains challenging. Thus, we have adopted the theory behind SEM, a statistical technique for testing and estimating the complex structure of causal relations among selected relevant factors ( 27 ). The potentiality of SEM in epidemiological studies has still not been fully explored: this largely confirmatory technique may help researchers to determine whether a certain proposed model is valid ( 56 ). In our hands, the models involving dietary factors and individual’s anthropometric/demographic data on miR-92a levels in plasma and in stool have provided the best performances. This analysis has not only confirmed above reported findings, but has provided a precise framework to elucidate the role of different factors and their simultaneous effect. This represents a promising tool for evaluating complex models such as the miRNA modulation. In the present investigation, we were also particularly interested in examining if both plasma and stool reflected the same associations. In fact, miRNAs detected in these specimens may represent different environmental/physiological conditions, considering their origin. miRNAs in plasma represent circulating molecules either included in vesicles or in lipid associated molecules ( 57,58 ), with a contribution from different tissues, but mainly from blood cells. In contrast, miRNAs in stool are originated mainly from exfoliated colonocytes daily shed from colon crypts (16). In this sense, since miRNAs may also exhibit complex tissue-specific patterns of expression, different expression trends could be expected following different dietary/lifestyle habits ( 31 ). However, we have observed quite good agreement between findings in both specimens. Generally, the observed trends were consistent in both specimens (with a particular evidence for miR92a), with none of the statistically significant findings being in disagreement between the two types of samples. Our observation seems particularly interesting in view of a possible use of blood/ stool miRNA-based markers, which may consistently complement each other in detecting small but significant miRNA modulations, ultimately influencing the expression of genes relevant for healthy status. As diet has been found to modulate miRNA expression, profiling of these molecules could be useful in the assessment of the nutritional status in dietary intervention studies. We are aware of some limitations of the present study, including a small sample size and a restricted number of miRNAs analysed. On the other hand, this exploratory investigation has some strengths and peculiarities never adopted in previous studies: (i) we focused on an homogenous group of healthy volunteers matched by sex and age from the same area, recruited in the same time interval; (ii) two different samples (blood and stool) were simultaneously collected from the same individual and immediately processed, (iii) miRNA analysis was performed simultaneously on both specimens in the same experiment, (iv) the dietary regime differences secured some of the specific answers retrieved by the questionnaires giving more strength also to the analysis of food consumption. In conclusion, we reported the influence of different dietary habits on individual miRNAs from the concomitant analyses of plasma and stool samples of healthy subjects. Our findings clearly indicate the need to investigate modulating dietary and lifestyle factors, along with the clinical/diagnostic data, for the evaluation of miRNAs expression as stool/plasma-based disease markers. Supplementary data Supplementary Tables 1–4 are available at Mutagenesis Online. Funding The study was funded by Compagnia di San Paolo, Torino, Italy. Acknowledgements The authors are very thankful to all volunteers that participated with enthusiasm to the present study. Design of the study: S.T., B.P., G.M., A.N.; experiments: S.T., B.P., G.M., A.N.; analysing the data: F.Rosa, B.P., C.D.G.; formulating the research question(s): A.N., F.Rosina, G.M., P.V.; writing the article: B.P., G.M., S.T., A.N., P.V. Conflict of interest statement: None declared. 1. Bartel , D. P. ( 2009 ) MicroRNAs: target recognition and regulatory functions . Cell , 136 , 215 - 233 . 2. Friedman , R. C. , Farh , K. K. , Burge , C. B. and Bartel , D. P. ( 2009 ) Most mammalian mRNAs are conserved targets of microRNAs . Genome Res. , 19 , 92 - 105 . 3. Chiou , T. J. ( 2007 ) The role of microRNAs in sensing nutrient stress . Plant. Cell Environ ., 30 , 323 - 332 . 4. Holtz , J. and Pasquinelli , A. E. ( 2009 ) Uncoupling of lin-14 mRNA and protein repression by nutrient deprivation in Caenorhabditis elegans . RNA , 15 , 400 - 405 . 5. Leung , A. K. and Sharp , P. A. ( 2010 ) MicroRNA functions in stress responses . Mol. Cell , 40 , 205 - 215 . 6. Mendell , J. T. and Olson , E. N. ( 2012 ) MicroRNAs in stress signaling and human disease . Cell , 148 , 1172 - 1187 . 7. Cortez , M. A. , Bueso-Ramos , C. , Ferdin , J. , Lopez-Berestein , G. , Sood , A. K. and Calin , G. A. ( 2011 ) MicroRNAs in body fluids-the mix of hormones and biomarkers . Nat. Rev. Clin. Oncol. , 8 , 467 - 477 . 8. Nelson , K. M. and Weiss , G. J. ( 2008 ) MicroRNAs and cancer: past, present, and potential future . Mol. Cancer Ther., 7 , 3655 - 3660 . 9. Cortez , M. A. and Calin , G. A. ( 2009 ) MicroRNA identification in plasma and serum: a new tool to diagnose and monitor diseases . Expert Opin. Biol. Ther. , 9 , 703 - 711 . 10. García-Segura , L. , Pérez-Andrade , M. and Miranda-Ríos , J. ( 2013 ) The emerging role of MicroRNAs in the regulation of gene expression by nutrients . J. Nutrigenet. Nutrigenomics , 6 , 16 - 31 . 11. Davis , C. D. and Ross , S. A. ( 2008 ) Evidence for dietary regulation of microRNA expression in cancer cells . Nutr . Rev., 66 , 477 - 482 . 12. Ross , S. A. and Davis , C. D. ( 2011 ) MicroRNA, nutrition, and cancer prevention . Adv. Nutr ., 2 , 472 - 485 . 13. Ryu , M. S. , Langkamp-Henken , B. , Chang , S. M. , Shankar , M. N. and Cousins , R. J. ( 2011 ) Genomic analysis, cytokine expression, and microRNA profiling reveal biomarkers of human dietary zinc depletion and homeostasis . Proc. Natl. Acad. Sci . U. S. A., 108 , 20970 - 20975 . 14. Dy , S. M. , Lorenz , K. A. , Naeim , A. , Sanati , H. , Walling , A. and Asch , S. M. ( 2008 ) Evidence-based recommendations for cancer fatigue, anorexia, depression, and dyspnea . J. Clin. Oncol. , 26 , 3886 - 3895 . 15. Qian , J. , Jiang , B. , Li , M. , Chen , J. and Fang , M. ( 2013 ) Prognostic significance of microRNA-16 expression in human colorectal cancer . World J. Surg ., 37 , 2944 - 2949 . 16. Ahmed , F. E. , Jeffries , C. D. , Vos , P. W. , Flake , G. , Nuovo , G. J. , Sinar , D. R. , Naziri , W. and Marcuard , S. P. ( 2009 ) Diagnostic microRNA markers for screening sporadic human colon cancer and active ulcerative colitis in stool and tissue . Cancer Genomics Proteomics , 6 , 281 - 295 . 17. Olivieri , F. , Spazzafumo , L. , Santini , G. , et al. ( 2012 ) Age-related differences in the expression of circulating microRNAs: miR-21 as a new circulating marker of inflammaging . Mech. Ageing Dev., 133 , 675 - 685 . 18. Link , A. , Balaguer , F. , Shen , Y. , Nagasaka , T. , Lozano , J. J. , Boland , C. R. and Goel , A. ( 2010 ) Fecal MicroRNAs as novel biomarkers for colon cancer screening . Cancer Epidemiol . Biomarkers Prev., 19 , 1766 - 1774 . 19. Wu , C. W. , Ng , S. S. , Dong , Y. J. , et al. ( 2012 ) Detection of miR-92a and miR-21 in stool samples as potential screening biomarkers for colorectal cancer and polyps . Gut , 61 , 739 - 745 . 20. Kalimutho , M. , Di Cecilia , S. , Del Vecchio Blanco , G. , et  al. ( 2011 ) Epigenetically silenced miR-34b/c as a novel faecal-based screening marker for colorectal cancer . Br. J. Cancer , 104 , 1770 - 1778 . 21. Ortega , F. J. , Mercader , J. M. , Catalán , V. , et al. ( 2013 ) Targeting the circulating microRNA signature of obesity . Clin. Chem ., 59 , 781 - 792 . 22. Chartoumpekis , D. V. , Zaravinos , A. , Ziros , P. G. , Iskrenova , R. P. , Psyrogiannis , A. I. , Kyriazopoulou , V. E. and Habeos , I. G. ( 2012 ) Differential expression of microRNAs in adipose tissue after long-term high-fat diet-induced obesity in mice . PLoS One , 7 , e34872 . 23. Bingham , S. and Riboli , E. ( 2004 ) Diet and cancer-the European Prospective Investigation into Cancer and Nutrition . Nat. Rev. Cancer , 4 , 206 - 215 . 24. Vineis , P. and Riboli , E. ( 2009 ) The EPIC study: an update . Recent Results Cancer Res. , 181 , 63 - 70 . 25. Mestdagh , P. , Van Vlierberghe , P. , De Weer , A. , Muth , D. , Westermann , F. , Speleman , F. and Vandesompele , J. ( 2009 ) A novel and universal method for microRNA RT-qPCR data normalization . Genome Biol ., 10 , R64 . 26. Andersen , C. L. , Jensen , J. L. and Ørntoft , T. F. ( 2004 ) Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets . Cancer Res. , 64 , 5245 - 5250 . 27. Pearl , J. ( 1998 ) Graphs, causality, and structural equation models . Sociol. Method Res. , 27 , 226 - 284 . 28. Kalisch , M. , Machler , M. , Colombo , D. , Maathuis , M. H. and Buhlmann , P. ( 2012 ) Causal inference using graphical models with the R package pcalg . J. Stat. Softw. , 47 , 1 - 26 . 29. Rosseel , Y. ( 2012 ) lavaan: an R package for structural equation modeling . J. Stat. Softw. , 48 , 1 - 36 . 30. Nock , N. L. , Li , L. and Elston , R. C. ( 2009 ) Modeling genetic and environmental factors in biological systems using structural equation modeling: an application to energy balance . Proc. Ohio Collab. Conf. Bioinform. , 3 - 8 . 31. Shah , M. S. , Davidson , L. A. and Chapkin , R. S. ( 2012 ) Mechanistic insights into the role of microRNAs in cancer: influence of nutrient crosstalk . Front. Genet ., 3 , 305 . 32. Di Leva , G. and Croce , C. M. ( 2013 ) miRNA profiling of cancer . Curr. Opin. Genet . Dev., 23 , 3 - 11 . 33. Neerincx , M. , Buffart , T. E. , Mulder , C. J. , Meijer , G. A. and Verheul , H. M. ( 2013 ) The future of colorectal cancer: implications of screening . Gut , 62 , 1387 - 1389 . 34. Ahmed , F. E. , Ahmed , N. C. , Vos , P. W. , et al. ( 2013 ) Diagnostic microRNA markers to screen for sporadic human colon cancer in stool: I.  Proof of principle . Cancer Genomics Proteomics , 10 , 93 - 113 . 35. Ng , E. K. , Chong , W. W. , Jin , H. , Lam , E. K. , Shin , V. Y. , Yu , J. , Poon , T. C. , Ng , S. S. and Sung , J. J. ( 2009 ) Differential expression of microRNAs in plasma of patients with colorectal cancer: a potential marker for colorectal cancer screening . Gut , 58 , 1375 - 1381 . 36. Bonauer , A. and Dimmeler , S. ( 2009 ) The microRNA- 17 -92 cluster: still a miRacle? Cell Cycle , 8 , 3866 - 3873 . 37. Tsuchida , A. , Ohno , S. , Wu , W. , Borjigin , N. , Fujita , K. , Aoki , T. , Ueda , S. , Takanashi , M. and Kuroda , M. ( 2011 ) miR-92 is a key oncogenic component of the miR-17-92 cluster in colon cancer . Cancer Sci. , 102 , 2264 - 2271 . 38. He , L. , Thomson , J. M. , Hemann , M. T. , et al. ( 2005 ) A microRNA polycistron as a potential human oncogene . Nature , 435 , 828 - 833 . 39. Takakura , S. , Mitsutake , N. , Nakashima , M. , et  al. ( 2008 ) Oncogenic role of miR-17-92 cluster in anaplastic thyroid cancer cells . Cancer Sci. , 99 , 1147 - 1154 . 40. Northcott , P. A. , Fernandez-L , A., Hagan , J. P. , et al. ( 2009 ) The miR-17/92 polycistron is up-regulated in sonic hedgehog-driven medulloblastomas and induced by N-myc in sonic hedgehog-treated cerebellar neural precursors . Cancer Res. , 69 , 3249 - 3255 . 41. Bonauer , A. , Carmona , G. , Iwasaki , M. , et al. ( 2009 ) MicroRNA-92a controls angiogenesis and functional recovery of ischemic tissues in mice . Science , 324 , 1710 - 1713 . 42. Yamada , N. , Nakagawa , Y. , Tsujimura , N. , Kumazaki , M. , Noguchi , S. , Mori , T. , Hirata , I. , Maruo , K. and Akao , Y. ( 2013 ) Role of intracellular and extracellular microRNA-92a in colorectal cancer . Transl. Oncol. , 6 , 482 - 492 . 43. Faltejskova , P. , Svoboda , M. , Srutova , K. , et al. ( 2012 ) Identification and functional screening of microRNAs highly deregulated in colorectal cancer . J. Cell. Mol. Med ., 16 , 2655 - 2666 . 44. Hänninen , O. , Rauma , A. L. , Kaartinen , K. and Nenonen , M. ( 1999 ) Vegan diet in physiological health promotion . Acta Physiol. Hung. , 86 , 171 - 180 . 45. Rajaram , S. and Sabaté , J. ( 2000 ) Health benefits of a vegetarian diet . Nutrition , 16 , 531 - 533 . 46. McEvoy , C. T. , Temple , N. and Woodside , J. V. ( 2012 ) Vegetarian diets, low-meat diets and health: a review . Public Health Nutr ., 15 , 2287 - 2294 . 47. Kutay , H. , Bai , S. , Datta , J. , Motiwala , T. , Pogribny , I. , Frankel , W. , Jacob , S. T. and Ghoshal , K. ( 2006 ) Downregulation of miR-122 in the rodent and human hepatocellular carcinomas . J. Cell. Biochem. , 99 , 671 - 678 . 48. Zhang , L. , Hou , D. , Chen , X. , et al. ( 2012 ) Exogenous plant MIR168a specifically targets mammalian LDLRAP1: evidence of cross-kingdom regulation by microRNA . Cell Res ., 22 , 107 - 126 . 49. Witwer , K. W. ( 2012 ) XenomiRs and miRNA homeostasis in health and disease: evidence that diet and dietary miRNAs directly and indirectly influence circulating miRNA profiles . RNA Biol ., 9 , 1147 - 1154 . 50. Witwer , K. W. , McAlexander , M. A. , Queen , S. E. and Adams , R. J. ( 2013 ) Real-time quantitative PCR and droplet digital PCR for plant miRNAs in mammalian blood provide little evidence for general uptake of dietary miRNAs: limited evidence for general uptake of dietary plant xenomiRs . RNA Biol ., 10 , 1080 - 1086 . 51. ElSharawy , A., Keller, A., Flachsbart , F. , et  al. ( 2012 ) Genome-wide miRNA signatures of human longevity . Aging Cell , 11 , 607 - 616 . 52. Momi , N. , Kaur , S. , Rachagani , S. , Ganti , A. K. and Batra , S. K. ( 2014 ) Smoking and microRNA dysregulation: a cancerous combination . Trends Mol. Med ., 20 , 36 - 47 . 53. Bye , A. , Røsjø , H. , Aspenes , S. T. , Condorelli , G. , Omland , T. and Wisløff , U. ( 2013 ) Circulating microRNAs and aerobic fitness-the HUNT-Study . PLoS One , 8 , e57496 . 54. Mooren , F. C. , Viereck , J. , Krüger , K. and Thum , T. ( 2014 ) Circulating microRNAs as potential biomarkers of aerobic exercise capacity . Am. J. Physiol. Heart Circ. Physiol. , 306 , H557 - H563 . 55. Radom-Aizik , S. , Zaldivar , F. P. , Jr , Haddad, F. and Cooper , D. M. ( 2014 ) Impact of brief exercise on circulating monocyte gene and microRNA expression: Implications for atherosclerotic vascular disease . Brain Behav . Immun., 39 , 121 - 129 . 56. Tu , Y. K. ( 2009 ) Commentary: Is structural equation modelling a step forward for epidemiologists? Int . J. Epidemiol., 38 , 549 - 551 . 57. Turchinovich , A. , Weiz , L. and Burwinkel , B. ( 2012 ) Extracellular miRNAs: the mystery of their origin and function . Trends Biochem. Sci. , 37 , 460 - 465 . 58. Vickers , K. C. , Palmisano , B. T. , Shoucri , B. M. , Shamburek , R. D. and Remaley , A. T. ( 2011 ) MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins . Nat. Cell Biol ., 13 , 423 - 433 .


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Sonia Tarallo, Barbara Pardini, Giuseppe Mancuso, Fabio Rosa, Cornelia Di Gaetano, Floriano Rosina, Paolo Vineis, Alessio Naccarati. MicroRNA expression in relation to different dietary habits: a comparison in stool and plasma samples, Mutagenesis, 2014, 385-391, DOI: 10.1093/mutage/geu028