# Systematic bioinformatic analysis of nutrigenomic data of flavanols in cell models of cardiometabolic disease

Tatjana Ruskovska, Marika Massaro, Maria Annunziata Carluccio, Anna Arola-Arnal, Begoña Muguerza, Wim Vanden Berghe, Ken Declerck, Francisca Isabel Bravo, Nadia Calabriso, Emilie Combet, et al.

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## To cite this version:

Tatjana Ruskovska, Marika Massaro, Maria Annunziata Carluccio, Anna Arola-Arnal, Begoña Muguerza, et al. Systematic bioinformatic analysis of nutrigenomic data of flavanols in cell models of cardiometabolic disease. Food and Function, 2020, 11 (6), pp.5040-5064. 10.1039/d0fo00701c hal-03038283

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University
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Ruskovska, T. et al. (2020) Systematic bioinformatic analysis of nutrigenomic data of flavanols in cell models of cardiometabolic disease. *Food and Function*, 11(6), pp. 5040-5064. (doi: [10.1039/D0FO00701C](https://doi.org/10.1039/D0FO00701C))

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Deposited on 28 May 2020
1 Systematic bioinformatic analysis of nutrigenomic data of flavanols in cell models of cardiometabolic disease

3 Tatjana Ruskovska¹,#, Marika Massaro²,#, Maria Annunziata Carluccio², Anna Arola Arnal³,

4 Begoña Muguerza Marquínez³, Wim Vanden Berghe⁴, Ken Declerck⁴, Francisca Isabel

5 Bravo Vázquez³, Nadia Calabriso², Emilie Combet⁵, Eileen R Gibney⁶, Andreia Gomes⁷,⁸,

6 Marie-Paule Gonthier⁹, Elena Kistanova¹⁰, Irena Krga¹¹,¹², Pedro Mena¹³, Christine

7 Morand¹², Claudia Nunes dos Santos⁷,⁸,¹⁴, Sonia de Pascual-Teresa¹⁵, Ana Rodriguez-

8 Mateos¹⁶, Egeria Scoditti², Manuel Suárez Recio³, Dragan Milenkovic¹², ¹⁷,*

¹ Faculty of Medical Sciences, Goce Delcev University, Stip, North Macedonia

² C.N.R. Institute of Clinical Physiology, Lecce, University of Salento, Ecotekne, Lecce,

Italy

³ Nutrigenomics Research Group, Departament de Bioquímica i Biotecnologia, Universitat

Rovira i Virgili, 43007 Tarragona, Spain

⁴ Laboratory of Protein Chemistry, Proteomics and Epigenetic Signaling (PPES),

Department of Biomedical Sciences, University of Antwerp, Wilrijk, Belgium

⁵ Human Nutrition, School of Medicine, College of Medical Veterinary and Life Sciences,

University of Glasgow, Glasgow, UK

⁶ UCD Institute of Food and Health, School of Agriculture and Food Science, University

College Dublin, Ireland

⁷ iBET, Instituto de Biologia Experimental e Tecnológica, Apartado 12, 2780-901, Oeiras,

Portugal

⁸ Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de

Lisboa, Av. da República, 2780-157, Oeiras, Portugal

⁹ Université de La Réunion, INSERM, UMR 1188 Diabète Athérothrombose Thérapies

Réunion Océan Indien (DéTROI), Saint-Denis de La Réunion, France
27 ¹⁰ Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences,
Sofia, Bulgaria

28 ¹¹ Centre of Research Excellence in Nutrition and Metabolism, Institute for Medical
Research, University of Belgrade, Belgrade, Serbia

31 ¹² Université Clermont Auvergne, INRAe, UNH, F-63000 Clermont-Ferrand, France

32 ¹³ The Laboratory of Phytochemicals in Physiology, Human Nutrition Unit, Department of
Food and Drug, University of Parma, Via Volturno 39, 43125 Parma, Italy

34 ¹⁴ CEDOC, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA
de Lisboa, Lisboa, Portugal

36 ¹⁵ Department of Metabolism and Nutrition, Institute of Food Science, Technology and
Nutrition (ICTAN-CSIC), Jose Antonio Novais 10, 28040 Madrid, Spain

38 ¹⁶ Department of Nutritional Sciences, School of Life Course Sciences, Faculty of Life
Sciences and Medicine, King's College London, London, UK

40 ¹⁷ Department of Internal Medicine, Division of Cardiovascular Medicine, School of
Medicine, University of California Davis, Davis, California, 95616, United States of
America

43 #: equal contribution of the authors

44

45

46

47 *Corresponding author: Dragan Milenkovic Email: dragan.milenkovic@inrae.fr

48

49

50

51

52
53 **Abbreviations**

54 ABCA1 ATP Binding Cassette Subfamily A Member 1

55 ADIPOQ Adiponectin

56 AGE Advanced Glycation Endproducts

57 APOA1 Apolipoprotein A1

58 APOB Apolipoprotein B

59 BAX BCL2 Associated X, Apoptosis Regulator

60 BCL2 BCL2 Apoptosis Regulator

61 CCL2 C-C Motif Chemokine Ligand 2

62 CEBPA CCAAT Enhancer Binding Protein Alpha

63 CRP C-Reactive Protein

64 CXCL8 C-X-C Motif Chemokine Ligand 8

65 EDN1 Endothelin 1

66 EGCG Epigallocatechin gallate

67 FOXC1 Forkhead Box C1

68 GATA2 GATA Binding Protein 2

69 GDF Growth Differentiation Factor

70 HIF Hypoxia Inducible Factor

71 HMOX1 Heme Oxygenase 1

72 IBD Inflammatory Bowel Disease

73 ICAM1 Intercellular Adhesion Molecule 1
74 IL2 Interleukin 2

75 IL4 Interleukin 4

76 IL6 Interleukin 6

77 IL10 Interleukin 10

78 ITGAM Integrin Subunit Alpha M

79 ITGB1 Integrin Subunit Beta 1

80 JUN Jun Proto-Oncogene: AP-1 Transcription Factor Subunit

81 KEGG Kyoto Encyclopedia of Genes and Genomes

82 LDL Low Density Lipoprotein

83 LDLR Low Density Lipoprotein Receptor

84 LPL Lipoprotein Lipase

85 LPS Lipopolysaccharide

86 MAPK8 Mitogen-Activated Protein Kinase 8

87 miRNA MicroRNA

88 MMP9 Matrix Metallopeptidase 9

89 MT-CO3 Mitochondrially Encoded Cytochrome C Oxidase III

90 NAFLD Non-Alcoholic Fatty Liver Disease

91 NFKB1 Nuclear Factor Kappa B Subunit 1

92 NLRP3 NLR Family Pyrin Domain Containing 3

93 NOS2 Nitric Oxide Synthase 2

94 NOS3 Nitric Oxide Synthase 3
<table>
  <tr>
    <td>95</td>
    <td>PBMC</td>
    <td>Peripheral Blood Mononuclear Cell</td>
  </tr>
  <tr>
    <td>96</td>
    <td>PECAM1</td>
    <td>Platelet and Endothelial Cell Adhesion Molecule 1</td>
  </tr>
  <tr>
    <td>97</td>
    <td>PPARA</td>
    <td>Peroxisome Proliferator Activated Receptor Alpha</td>
  </tr>
  <tr>
    <td>98</td>
    <td>PPARG</td>
    <td>Peroxisome Proliferator Activated Receptor Gamma</td>
  </tr>
  <tr>
    <td>99</td>
    <td>PPARs</td>
    <td>Peroxisome Proliferator Activated Receptors</td>
  </tr>
  <tr>
    <td>100</td>
    <td>PPI</td>
    <td>Protein-Protein Interaction</td>
  </tr>
  <tr>
    <td>101</td>
    <td>PTGS2</td>
    <td>Prostaglandin-Endoperoxide Synthase 2</td>
  </tr>
  <tr>
    <td>102</td>
    <td>RAGE</td>
    <td>Receptor for AGE</td>
  </tr>
  <tr>
    <td>103</td>
    <td>RETN</td>
    <td>Resistin</td>
  </tr>
  <tr>
    <td>104</td>
    <td>ROCK1</td>
    <td>Rho Associated Coiled-Coil Containing Protein Kinase 1</td>
  </tr>
  <tr>
    <td>105</td>
    <td>SELE</td>
    <td>Selectin E</td>
  </tr>
  <tr>
    <td>106</td>
    <td>SERPINE1</td>
    <td>Serpin Family E Member 1</td>
  </tr>
  <tr>
    <td>107</td>
    <td>SP1</td>
    <td>Sp1 Transcription Factor</td>
  </tr>
  <tr>
    <td>108</td>
    <td>SREBF1</td>
    <td>Sterol Regulatory Element Binding Transcription Factor 1</td>
  </tr>
  <tr>
    <td>109</td>
    <td>STAT1</td>
    <td>Signal Transducer and Activator of Transcription 1</td>
  </tr>
  <tr>
    <td>110</td>
    <td>STAT3</td>
    <td>Signal Transducer and Activator of Transcription 3</td>
  </tr>
  <tr>
    <td>111</td>
    <td>TGF-beta</td>
    <td>Transforming Growth Factor Beta</td>
  </tr>
  <tr>
    <td>112</td>
    <td>TLDA</td>
    <td>Taqman Low Density Array</td>
  </tr>
  <tr>
    <td>113</td>
    <td>TLR4</td>
    <td>Toll Like Receptor 4</td>
  </tr>
  <tr>
    <td>114</td>
    <td>TNF</td>
    <td>Tumor Necrosis Factor</td>
  </tr>
  <tr>
    <td>115</td>
    <td>TOLLIP</td>
    <td>Toll Interacting Protein</td>
  </tr>
</table>
116 VEGF Vascular Endothelial Growth Factor

117 VCAM1 Vascular Cell Adhesion Molecule 1

118 YY1 Yin Yang 1 Transcription Factor

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133
**Abstract:** Flavanols intake positively influences several cardiometabolic risk factors in humans. However, the specific molecular mechanisms of action of flavanols, in terms of gene regulation, in the cell types relevant to cardiometabolic disease have never been systematically addressed. On this basis, we conducted a systematic literature review and a comprehensive bioinformatic analysis of genes which expression is affected by flavanols in cells defining the cardiometabolic health: hepatocytes, adipocytes, endothelial, smooth muscle and immune cells. A systematic literature search was performed using the following pre-defined criteria: treatment with pure compounds and metabolites (no extracts), at low concentrations that are close to their plasma concentrations. Differentially expressed genes were analyzed using bioinformatics tools to identify gene ontologies, networks, cellular pathways and interactions, as well as transcriptional and post-transcriptional regulators. The systematic literature search identified 54 differentially expressed genes at mRNA level in *in vitro* models of cardiometabolic disease exposed to flavanols and their metabolites. Global bioinformatic analysis revealed that these genes are predominantly involved in inflammation, leukocyte adhesion and transendothelial migration, and lipid metabolism. We observed that, although the investigated cells responded differentially to flavanol exposure, the involvement of anti-inflammatory responses is a common mechanism of flavanol action. We also identified potential transcriptional regulators of gene expression: transcriptional factors, such as GATA2, NFKB1, FOXC1 or PPARG, and post-transcriptional regulators: miRNAs, such as mir-335-5p, let-7b-5p, mir-26b-5p or mir-16-5p. In parallel, we analyzed the nutrigenomic effects of flavanols in intestinal cells and demonstrated their predominant involvement in the metabolism of circulating lipoproteins. In conclusion, the results of this systematic analysis of the nutrigenomic effects of flavanols provides a more comprehensive picture of their molecular mechanisms of action and will support the future setup of genetic studies to pave the way for individualized dietary recommendations.
159 **Keywords:** flavanols; cardiometabolic; gene expression; in vitro; bioinformatics; cell

160 signaling

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176
177 **1. Introduction**

Cardiometabolic disease is a cluster of metabolic dysfunctions including insulin
resistance, impaired glucose tolerance, dyslipidemia, hypertension and central adiposity that,
over time, may translate in type 2 diabetes and cardiovascular disease [1]. Unhealthy eating
habits leading to overweight and obesity have been recognized as key determinants in the
development of cardiometabolic disease [2]. Since dietary factors interfere with
cardiometabolic disease progression in connection to individual genetic setting [3], the
understanding of the impact of nutrients and bioactives on the complex networking of human
genes has been long envisaged as a recommended research goal [4]. Even though this
research focus has produced novel results to date, the recent application of bioinformatics
and molecular biology tools to nutritional science has produced a large body of new exciting
evidence on how food and food bioactives may interact with the genome to control health
and wellness [5].

190

Among plant food bioactives, the most impressive advancements have been achieved in
the field of polyphenols [6]. Polyphenols are secondary plant metabolites, which are
classified into flavonoids and non-flavonoid compounds. The main subclasses of flavonoids
include flavanols (flavan-3-ols), flavonols, flavones, flavanones, isoflavonoids, and
anthocyanins [7]. Flavanols, the focus of our study, are among the most abundant
polyphenols in the human diet [8] with main dietary sources in green tea, cocoa, apples and
grapes. From a chemical point of view, flavanols represent a complex subclass of flavonoids,
which encompass a variety of monomeric, oligomeric and polymeric compounds. The main
monomeric forms include: (+)-catechin, (-)-epicatechin, (+)-gallocatechin, (-)-
epigallocatechin, (-)-epicatechin-3-O-gallate and (-)-epigallocatechin-3-O-gallate.
Proanthocyanidins (also known as condensed tannins) are oligomers or polymers of
202 flavanols, whereas polymers composed exclusively of catechin or epicatechin are called
203 procyanidins. In foods, flavanols exist predominantly as aglycones [9].

204

205 The metabolism of dietary flavanols in the human body includes series of biochemical
206 transformations that involve both host-microbiome interactions in the large intestine and
207 microbiome independent routes. Flavanol absorption largely depends on their
208 physicochemical properties; monomers can be absorbed in the small intestine but most of
209 ingested flavanols reach intact the large intestine [10,11]. In enterocytes, most of the absorbed
210 monomers are subjected to initial phase II metabolism, which include conjugation reactions
211 such as glucuronidation, sulfatation and methylation. Exception are (-)-epicatechin-3-O-
212 gallate and (-)-epigallocatechin-3-O-gallate [12], where 3-O-galloyl moiety is considered to
213 interfere the enzymes of phase II metabolism [9], and as such they reach the circulation as
214 parent compounds. Some of the phase II metabolites are transported back from the
215 enterocytes to the intestinal lumen, whereas the others are transported to the liver, where their
216 metabolism by phase II enzymes continues [13]. Since conjugation reactions facilitate the
217 excretion of flavanol derivatives, the plasma concentrations and half-life of flavanol phase II
218 metabolites result to be very low: their maximal plasma concentrations are usually found in
219 the range of nanomolar to low micromolar [14], which are reached approximately two hours
220 post-ingestion and followed by a rapid elimination [12]. A small number of dimeric
221 compounds are also absorbed in the small intestine. Most of the ingested flavanols reach the
222 large intestine where, together with the residual products of intestinal and liver phase II
223 metabolism, they are catabolized by the microbiome. Small phenolic and aromatic acids, such
224 as phenyl-γ-valerolactones, are generated through the biochemical transformations of
225 flavanols by gut microbiota. These metabolites can be absorbed and further subjected to
226 phase II metabolism before their elimination from the human body [9,15]. Therefore, besides
227 epicatechin-3-O-gallate and (-)-epigallocatechin-3-O-gallate that appear in the systemic
circulation as parent compounds, several flavanol glucuronidated, sulfatated and methylated metabolites, and phenolic acids represent the most common forms traceable in the systemic circulation and are those that likely mediate the beneficial health effects of their parent compounds. These metabolites are chemically and, in many instances, functionally distinct from the parent dietary forms, and such features determine their biological effectiveness [16]. In particular, conjugated forms of flavonoids were shown to have a significantly lower capacity for donating hydrogens and scavenging free radicals compared to the parent compounds [17].

Growing evidence from cohort studies and randomized trials indicate that higher dietary intake of polyphenols reduces the risk of cardiovascular mortality [18] and positively influences some of the key cardiometabolic risk factors, such as blood glucose, blood lipids, blood pressure, endothelial dysfunction and arterial stiffness [19-21]. Despite the large body of clinical and experimental data [22], evidence regarding the role of polyphenols in cardiometabolic protection remains not entirely consistent. This inconsistency can be explained by differences in study designs and polyphenols tested [23,24]. However, recent findings are also pinpointing role of sex, age, gut microbiome, life-style but also genotype and more recently epigenetic variations as potential factors contributing to heterogeneity in the individual response to the consumption of polyphenols [25-27].

Although cardiovascular benefits of polyphenols have been in the past attributed to their antioxidant properties (as free radical scavengers) [28], this view was not in agreement with available knowledge about their bioavailability and *in vivo* metabolism [29]. Complementary evidence suggests that their protective activities may mainly occur through genomic effects, by interfering with the expression of genes [29]. Nutrigenomics can be defined as approach to elucidate the diet-gene interaction by assessing gene or protein expression and gene
regulation [30,31]. The capacity of polyphenols to modulate gene expression has been identified in different cell types and for different families of polyphenols. For example, in endothelial cells, flavanone metabolites have been shown to affect the expression of a number of genes related to atherogenesis and especially those involved in cell adhesion, cytoskeleton organization, inflammation, and chemotaxis [32]. Similarly, the exposure of endothelial cells to curcumin before applying a pro-inflammatory stress, induced positive changes in the expression of genes involved in the control of cytoskeleton and endothelial junction dynamics, and in the pro-inflammatory redox-sensitive transcription factor NF-kappa B [33]. In a complementary fashion, the adoption of untargeted approaches has shown that plasma epicatechin metabolites affect the expression of more than two hundred of genes, some of them involved in endothelial permeability and interaction with immune cells, thus demonstrating a multi-targeted mode of action for flavanols [34]. Together with in vitro investigations, nutrigenomic modifications of polyphenols have also been demonstrated in several in vivo models of cardiometabolic disease. Curcumin [35] and naringin [36] modulate, in an anti-atherogenic manner, the gene expression profile in the aorta of mice model of atherosclerosis. Naringin is also able to modulate the expression of genes related to lipid metabolism, inflammation and insulin signaling in the liver of mice fed a high-fat diet [37]. Finally, in rats, quercetin was shown to affect the expression of genes involved in fatty acids metabolism in lung tissue [38]. In humans, several studies have confirmed the capacity of many of these food bioactives, including flavanols [39] and flavanones [40] to exert nutrigenomic regulation. However, most nutrigenomic findings with polyphenols are from in vitro studies focusing on expression of few target genes (targeted approaches), and using non-physiologically relevant conditions, that is high concentrations of non-circulating compounds for long period of time, conditions that do not take into account the bioavailability and metabolism of polyphenols following their intake. For these reasons we decided to work only on studies that were performed in physiologically relevant conditions,
that is use of circulating forms and right concentrations, studies that provided findings that are possible to happen in vivo. Furthermore, several studies reported opposite effects depending on concentrations used, for example significant effect at physiologically relevant concentrations on prevention of monocyte adhesion to endothelial cells, which is not observable at higher concentrations [22].

On this background, experts involved in the COST POSITIVe network (https://www6.inrae.fr/cost-positive) [41] aimed to identify the most significant target genes and cellular pathways of flavanols underlying their cardiometabolic health properties by performing systematic bioinformatic analyses of available nutrigenomic data. To this aim, we conducted a systematic literature search for gene expressions modulated by flavanols in cellular models of cardiometabolic disease. We included hepatocytes, adipocytes, endothelial, smooth muscle and immune cells, selecting only studies adopting research protocols testing monomeric or dimeric compounds or related metabolites at concentrations in the range of those found in the plasma after flavanol intake. The identified differentially expressed genes were then subjected to a comprehensive and integrative bioinformatic analysis among the different cell models to decipher and characterize key target genes and mechanisms of action of flavanols within a new, more holistic perspective. In parallel, we also analyzed the nutrigenomic effects of flavanols in intestinal cells exposed to high concentrations of extracts or oligomeric compounds, as occurring after the ingestion of flavanols rich sources. The results of these analyses will pave the way for the identification of genes and pathways underlying the health effects of flavanols. This knowledge will allow us to identify potential genes which polymorphisms can be investigated in humans with the aim to better explain some aspects of the inter-individual variability in response to consumption of flavanols. It will also guide the setup of future nutrigenetic studies aiming to
identify flavanol responsive genotypes, whereby flavanol intake will be optimized to reduce
the disease risk.

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## 2. Methods

### 2.1. Data sources and search strategy

Literature searches were performed using two main scientific repositories, PubMed (https://www.ncbi.nlm.nih.gov/pubmed) and Web of Science (https://www.webofknowledge.com). Both databases were searched for all relevant studies published until January 23, 2018. Search terms included, as “plant food bioactives”, catechin OR epicatechin OR epigallocatechin OR procyanidin OR proanthocyanidin AND, as “cells”, endothelial OR endothelial cells OR endothelium OR pancreatic OR pancreatic cells OR adipose OR adipose cells OR adipocyte OR intestinal OR intestinal cells OR intestinal enteroendocrine cells OR monocytoid OR monocytoid cells OR monocytes OR macrophagic OR macrophagic cells OR macrophage OR hepatic OR hepatic cells OR liver cell OR hepatocyte OR smooth muscle cell OR muscle cells OR caco-2 OR PBMC AND, as “gene/gene expression”, gene expression OR miRNA OR transcript OR nutrigenomic OR TLDA OR microarray OR genomic OR mRNA.

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### 2.2. Study selection and data extraction

To be eligible, the studies had to meet the following criteria: (1) published in English; (2) assess the effects of flavanols in *in vitro* cell models suitable to study cardiometabolic dysfunction, including endothelium, adipocytes, monocytes/macrophages, pancreatic, smooth muscle, hepatic and intestinal cells, as primary cells or cell lines; (3) show lack of toxicity at the tested concentrations; (4) evaluate data on gene expression in terms of mRNA
and miRNA modulation, but not proteins; (5) assess cardio-metabolic health outcomes. The exclusion criteria were the following: (1) treatment of the cells with bioactive compounds at concentrations higher than 10 µM (except for the intestinal cells); (2) studies performed using extracts (again with the exception of the intestinal cells); (3) redundant publications; (4) incomplete information; (5) insufficient or insignificant statistical analysis, (6) outcomes unrelated to the study objectives; (7) lack of appropriate controls; (8) studies in animal models, in humans and reviews. Also, we aimed to identify papers that showed an effect on cellular function together with changes in the expression of genes to associate genomic modifications with potential health impact. The initial lists of titles, as retrieved from PubMed and Web of Science, were merged by using EndNote X6 reference manager software, and duplicates were discarded. The resulting list of papers was screened twice, by two different co-authors, to identify those that fulfilled the predefined criteria. Data were extracted using a standardized template. The template was pilot-tested on a small subset of studies to identify and reduce misinterpretations. Extracted data from the eligible studies included: name of the first author, title, year of publication, accession number, cell type with detailed description, type of challenge, associated disease, cell function evaluated, bioactive compounds (if single or mixed; if pure or extract) and their concentrations, number of genes studied, number of differentially expressed genes, modulation (up/down), official gene symbols and full names of the differentially expressed genes, and species. Data were extracted only for those genes that were identified as modulated by flavanols exposure with a p-value <0.05. Extracted data were then further crosschecked by two co-authors; in case of doubts and/or disagreement, a third co-author was consulted.

351

2.3. *Bioinformatic analysis*
To identify gene ontologies of the differentially expressed genes extracted from *in vitro* studies, David database has been used (https://david.ncifcrf.gov) [42,43], and the identified gene ontologies were plotted in treemap plot using Revigo tool (http://revigo.irb.hr/) [44]. Gene network analyses were searched using a text-mining algorithm of MetaCore software from Clarivate Analytics (https://portal.genego.com). To identify pathways that are significantly associated with the genes, we used the web tool GeneTrail2 (https://genetrail2.bioinf.uni-sb.de/) [45], version 1.6, as a platform to access **Kyoto Encyclopedia of Genes and Genomes** (KEGG) and BioCarta databases, using the following settings: over-representation analysis; null hypothesis (for p-value computation) – two-sided; method to adjust p-values – Benjamini-Yekutieli; significance level – 0.05. Interactions between functional groups of genes were searched using the online tool Metascape (http://metascape.org), using the option “Multiple Gene List” [46]. The network obtained was further visualized using Cytoscape platform for molecular interaction networks visualization (https://cytoscape.org/) [47]. Bioinformatic analysis on **protein-protein interaction** (PPI) between the proteins that are coded by the differentially expressed genes, including their neighboring proteins, was conducted using the database STRING, version 10.5 (https://string-db.org/) [48]. For protein-protein interaction in adipocytes, hepatocytes, immune, smooth muscle and endothelial cells we applied the following settings: confidence; text-mining, experiments, databases, co-expression; high confidence – 0.700; no more than 20 interactions in the first shell and no more than 20 interactions in the second shell, without clustering. STRING settings for the intestinal cells were the following: confidence; text-mining, experiments, databases, co-expression; high confidence – 0.700; no more than 15 interactions in the first shell and no more than 15 interactions in the second shell. The resulting protein network was organized in two clusters. For integrated functional analyses of identified genes and their associated transcription factors and potential miRNAs involved in their post-transcriptional regulation, we used OmicsNet online tool from MetaboAnalyst
379 (https://www.omicsnet.ca/faces/home.xhtml) [49,50]. miRNet 2.0 was used for identification
380 of potential miRNAs (https://www.mirnet.ca). For identification of official names and
381 symbols of flavanol modulated genes, we used GeneCards (https://www.genecards.org/) [51]
382 and NCBI (https://www.ncbi.nlm.nih.gov/) databases.

383

384 **3. Results**

385 **3.1. Literature search and characteristics of papers selected for bioinformatic analysis**

386 The initial systematic search in PubMed and Web of Science using the pre-defined words
387 identified more than 1500 publications. Publications that were out of scope or in duplicate
388 were removed. The remaining 658 papers were distributed among the co-authors for
389 screening. The screening based on title and abstract retrieved 79 papers as eligible for data
390 extraction. Following a detailed analysis of the full text, 41 papers were considered for
391 bioinformatic analysis (Table 1 and supplemental Table S1), that is *in vitro* studies in which
392 cells have been exposed to flavanols (from tea, cocoa, apple or grape seed), at concentrations
393 lower than 10 µM (intestinal cells were an exception), and for which expression of genes at
394 mRNA level had been analyzed. The flow diagram of the literature search and data extraction
395 is summarized in Figure 1.
396

396

397 The majority of the studies, 26 out of 41 (63.4%), were conducted on cells of human
398 origin, and 15 (36.6%) of studies were conducted on rodent cells, 10 from mouse and 5 from
399 rat. Out of 41 studies, 37 reported results from *in vitro* studies using different cell models
400 related to cardiometabolic disease: adipocytes, hepatocytes, immune, smooth muscle, and
401 endothelial cells, and 5 used intestinal cells (in one paper both hepatocytes and intestinal cells
402 were used [52]). Although pancreatic cells were included in the search criteria, we were not
403 able to identify any eligible study conducted on this type of cells. As shown in Table 1, within
the 37 papers, the majority of experiments were conducted on cells that were challenged with dysmetabolic or pro-inflammatory stimuli, while the others examined the effects of flavanols under resting (basal) conditions. Most of these studies were carried out on endothelial cells (37.8%), followed by immune cells (27%), adipocytes (13.5%), smooth muscle cells (13.5%), and finally hepatocytes (8.1%). About half of the studies were conducted using primary cells, while the others used cell lines. Flavanols that were used for treatment of the cells include monomers, such as catechin, epicatechin, epicatechin gallate, epigallocatechin and epigallocatechin gallate (EGCG), the dimer procyanidin B2, and various flavanol metabolites. As shown in Table 1, flavanol metabolites were analyzed only in a small number of studies. Concentrations of flavanols and their metabolites varied from 0.1 to 10 µmol/L, in average 5 µmol/L, and the cells were treated for a period from 3 hours to over 24 hours.

In experiments conducted on intestinal cells, Caco-2 cells were used as an exclusive cell model. In these experiments, cells were exposed to grape seed extract or oligomeric compounds, most often at high concentrations (Table 1), which is out of our pre-established inclusion criteria for the other cell types. However, because these experimental conditions resemble physiological conditions for the intestinal cells, these papers were included in our study, but the differentially expressed genes were analyzed separately.

## 3.2. Identification of differentially expressed genes in cell models of cardiometabolic disease

Most of the retrieved studies adopted a targeted approach, analyzing the expression of a selection of targeted genes at the mRNA level. Only two studies adopted an untargeted (holistic) approach, using microarray methods [22,53]. However, for these studies, only RT-PCR data, used to validate microarray data, have been extracted and used for global systematic analysis.
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Detailed analysis of human and rodent cell models of cardiometabolic disease (adipocytes, hepatocytes, immune, smooth muscle, and endothelial cells) exposed to flavanols (monomers, dimers, or their metabolites) identified 92 differentially expressed genes at the mRNA level. An overview of data extracted from the papers reporting experiments on human and rodent cell models of cardiometabolic disease is given in Table 1, while more detailed information can be found in the supplemental Table S1. We observed that some genes had been studied more frequently than others, which results in their more frequent identification as differentially expressed. For example, *CCL2* has been identified as differentially expressed by flavanols in seven different studies, *APOA1* in five experiments, *TNF* in four different studies, whereas *MMP9*, *IL6*, *LDLR*, *APOB*, *ABCA1*, *PPARG* and *CRP* were identified as differentially expressed three times each (Figure 2A). After removal of the duplicates, a total number of genes whose expression was modulated by flavanols was 54, which were subjected to bioinformatic processing. Of these 54 genes, 42 genes were identified as having expression modulated by flavanols using human cells, 14 in mouse cell models, and 3 in cells of rat origin (Figure 2B). The analysis of papers examining the effects of flavanols in intestinal cells identified 15 differentially expressed genes (Table 1 and supplemental Table S1), i.e., 14 genes after removal of one duplicate, which were analyzed through a separate bioinformatic analysis.

### 3.3. Global gene enrichment and functional annotation analysis of differentially expressed genes

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In order to identify biological functions of the genes differentially expressed by flavanols in adipocytes, hepatocytes, immune, smooth muscle and endothelial cells, we first performed a global gene ontology analysis. As shown in Figure 3, the analysis suggests that these genes
are involved in the regulation of different biological processes, including cell signal transduction, biosynthesis, immune response, cell adhesion, and cell proliferation/death.

Aiming to deepen the identification of biological processes in which these genes are involved in, we performed gene network analysis using a text-mining approach. We used the list of differentially expressed genes identified in different studies to construct gene-gene networks. The networks were grouped in clusters representing specific biological processes, which are presented in the pie slice (Figure 4). As shown in Figure 4, flavanol modulated genes are involved in processes regulating inflammation, immune response, cell adhesion, apoptosis and cell signaling. Within the inflammation network cluster are pathways that include IL-2, 4, 6 **signaling**, chemotaxis, or IL-10 anti-inflammatory response. Within the signal transduction network cluster are pathways involved in insulin signaling, nitric oxide signaling or TGF-beta, GDF and activin signaling. The cell adhesion network cluster includes processes regulating cell junctions, integrin-mediated cell-matrix adhesion, leucocyte chemotaxis, or platelet-endothelium-leucocyte interactions. Overall, this analysis suggests that flavanols can modulate the expression of genes identified from different cell models of cardiometabolic disease that are collectively implicated in the regulation of inflammation, cell adhesion and metabolic processes.

To further investigate the functional role of flavanol modulated genes, we aimed to search for cellular pathways in which these genes are involved using the online platform GeneTrail2, which allows accesses to KEGG and BioCarta databases. Of 54 genes that were found differentially expressed at mRNA level in adipocytes, hepatocytes, immune, smooth muscle and endothelial cells, 53 genes were mapped in GeneTrail2, whereas *MT-CO3* failed the identification. The enquiring of KEGG database revealed that the differentially expressed genes are placed within pathways related to both cellular processes and human diseases.
Among the top pathways related to cellular processes are those involved in cell signaling and endothelial cell permeability, including cell adhesion, regulation of cytoskeleton organization, or focal adhesion (Figure 5). The top five KEGG pathways related to cellular processes are all involved in cell signaling and include “TNF signaling pathway”, which encompasses eleven differentially expressed genes (*CCL2*, *EDN1*, *ICAM1*, *IL6*, *JUN*, *MMP9*, *NFKB1*, *PTGS2*, *SELE*, *TNF* and *VCAM1*), “NF-kappa B signaling pathway” encompassing eight genes (*BCL2*, *CXCL8*, *ICAM1*, *NFKB1*, *PTGS2*, *TLR4*, *TNF* and *VCAM1*), “HIF-1 signaling pathway”, also with eight genes (*BCL2*, *EDN1*, *HMOX1*, *IL6*, *NFKB1*, *NOS2*, *SERPINE1* and *TLR4*), “Toll-like receptor signaling pathway” with seven genes (*CXCL8*, *IL6*, *JUN*, *NFKB1*, *TLR4*, *TNF* and *TOLLIP*) and “NOD-like receptor signaling pathway” with six genes (*CCL2*, *CXCL8*, *IL6*, *NFKB1*, *NLRP3* and *TNF*). Among pathways related to regulation of the endothelial cell permeability, the highest number of encompassed genes modulated by flavanols have been found in “leukocyte transendothelial migration” (six genes: *ICAM1*, *ITGAM*, *ITGB1*, *MMP9*, *ROCK1* and *VCAM1*) and “cell adhesion molecules” (five genes: *ICAM1*, *ITGAM*, *ITGB1*, *SELE* and *VCAM1*). Among top KEGG pathways related to human diseases, infectious diseases were predominant, but **non-alcoholic fatty liver disease** (NAFLD), which is a consequence of complex metabolic dysfunctions, was also present encompassing nine genes (*ADIPOQ*, *BAX*, *CEBPA*, *CXCL8*, *IL6*, *JUN*, *NFKB1*, *SREBF1* and *TNF*).

Accordingly, the enquiring of BioCarta database returned pathways involved in inflammation, lipid metabolism and cell signaling (Figure 5). Top BioCarta pathways related to inflammation include “cells and molecules involved in local acute inflammatory response”, which encompasses six differentially expressed genes (*CXCL8*, *ICAM1*, *IL6*, *ITGB1*, *TNF* and *VCAM1*), “monocyte and its surface molecules”, encompassing four genes (*ICAM1*, *ITGAM*, *ITGB1* and *SELE*), “adhesion and diapedesis of granulocytes” (*CXCL8*, *TNF*).
ICAM1, ITGAM and TNF), and "adhesion and diapedesis of lymphocytes" (CXCL8, ICAM1, ITGB1 and VCAM1), also encompassing four genes each. Top BioCarta pathways related to lipid metabolism are the following: "visceral fat deposits and the metabolic syndrome", encompassing five genes (ADIPOQ, LPL, PPARG, RETN and TNF), "mechanism of gene regulation by PPARA", encompassing six genes (APOA1, JUN, LPL, NOS2, PTGS2 and TNF) and "LDL pathway during atherogenesis", with four genes (CCL2, IL6, LDLR and LPL).

Together with the identification of cellular pathways in which the genes are involved in, to facilitate their biological interpretation, we also performed network meta-analysis of interactions between functional groups of genes using text-mining approach implemented in the Metascape online tool. This analysis reveals not only the list of functions of the genes but also functional interaction between them in different cellular processes. This analysis has been performed using the option "Multiple Gene List", that is lists of genes identified as modulated by flavanols in different cell types: adipocytes, smooth muscle cells, immune cells, endothelial cells and hepatocytes, allowing us to identify which functions are specific to which cell types. Global analysis has shown that flavanol modulated genes are involved in processes regulating lipid metabolism, inflammatory response, cellular response to TNF, AGE-RAGE pathway in diabetes, or regulation of binding. Some of the functions are common to all cell types studied, such as inflammatory response and cellular response to TNF. Functions such as steroid metabolic response are more specific to hepatocytes, or HIF-1 signaling to endothelial cells (Figure 6). These analyses showed that exposure of cells to flavanols could modulate different cellular processes that are interacted at the cellular level.

For analysis of functional links between proteins coded by the differentially expressed genes extracted from the literature and their neighboring proteins, we used the database STRING. All 54 differentially expressed genes were identified as valid by STRING software.
The network obtained consists of 94 nodes (proteins) having 515 edges (interactions) with PPI enrichment value <1.0e-16 (Figure 7). Notably, some of the proteins have more interactions with other proteins within the network than others, indicating their key role in the cellular response to flavanols. For example, TNF, IL6, JUN, TLR, NFKB1, and MAPK8 are on the top of the list with ≥ 30 interactions (Table 2).

### 3.4. Transcriptional and post-transcriptional regulation of gene expression by flavanols

Our next step of analyses aimed to identify potential transcriptional and post-transcriptional regulators involved in the observed modulation of gene expression by flavanols. Expression of genes can be regulated at the transcriptional level by the activity of transcription factors or post-transcriptionally by non-coding RNAs such as miRNAs. Using the bioinformatics tool OmicsNet, we first searched for protein-protein interactions followed by the evaluation of potential transcription factors and then potential miRNAs that could bind to mRNA of the identified protein-protein network to exert post-transcriptional regulations. Top 20 transcription factors and miRNAs, with the highest number of interactions in adipocytes, hepatocytes, immune, smooth muscle and endothelial cells are presented in Table 3. Among the most significant transcription factors identified are GATA2, NFKB1, FOXC1, or PPARG. The miRNAs identified to interact with flavanol modulated genes identified are mir-335-5p, let-7b-5p, mir-26b-5p or mir-16-5p. Visualization of the interaction between the proteins of protein-protein interaction network with miRNAs and transcription factors is presented in a 3-layer 3D mode in Figure 8. These analyses showed a “dense” interaction between proteins and the regulatory elements, with each miRNA being able to regulate several proteins and one protein being potentially regulated by several miRNAs. The same is observed for transcription factors. This analysis revealed potential regulators of gene expression whose activity or level might be affected by flavanols, which determines the observed nutrigenomic modifications.
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Fifteen differentially expressed genes have been identified in the intestinal cells, i.e., 14 different genes, after removal of one duplicate (Table S1; Table 1). Bioinformatic analysis demonstrated that these genes are most significantly associated with “PPAR signaling pathway”, which encompasses seven of 14 differentially expressed genes, and the “adipocytokine signaling pathway”, encompassing four of 14 genes. Other KEGG pathways that are significantly related to the differentially expressed genes in the intestinal cells include “fat digestion and absorption”, “fatty acid degradation”, “fatty acid metabolism”, “bile secretion” and “peroxisome”, all of them encompassing 3 different genes, as well as “vitamin digestion and absorption”, encompassing two genes. The enquiring of BioCarta database revealed only “mechanism of gene regulation by peroxisome proliferators via PPARA” (Figure 9A). By analyzing the protein-protein interactions using the STRING database, two protein clusters were identified for the intestinal cells. One of them includes proteins that are mostly involved in the metabolism of circulating lipoproteins. Proteins that belong to this cluster are shown in red. The second cluster is connected to the previous one through NOS2 and NOS3 and covers mainly proteins that are involved in calcium signaling. Proteins that belong to this cluster are shown in green (Figure 9B). Proteins that have the highest number of interactions within the clusters are lipoprotein lipase, apolipoproteins, and calmodulins (Table S2).

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Transcriptional and post-transcriptional regulation of flavanol modulated genes in the intestinal cells was also analyzed using the bioinformatics tool OmicsNet. This analysis revealed that master regulators of proteins that belong to the protein-protein interaction network emerging from the differentially expressed genes extracted from the literature
include SP1, NFKB1, STAT3, PPARG or STAT1 among the transcription factors, and mir-335-5p, mir-26b-5p, mir-16-5p, mir-124-3p or mir-92a-3p among the miRNAs. A 3-Layer 3D presentation of this regulatory network is given in Figure 9C.

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## 4. Discussion

Facing an unprecedented increase of cardiometabolic, neurodegenerative and other non-communicable diseases, contemporary science strives to find effective strategies for their prevention and treatment. In this context, there is a growing body of scientific evidence about the role of diet in general, as well as of various food constituents, including bioactives, as important modulators of the cardiometabolic risk. In this review, we have systematically examined the effects of flavanols in terms of modulation of gene expressions relevant to the pathogenesis of cardiometabolic disease and identified potential pleiotropic pathways and cellular and molecular mechanisms underlying their protective actions.

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Living in the era of personalized medicine, we are witnessing an enhanced awareness of the need for a personalized approach to dietary recommendations. This applies to the general population in terms of good health preservation, and secondary prevention in patients with various non-communicable diseases. As recently reviewed, variability in the cardiometabolic response to consumption of plant food bioactives, including polyphenols, is considered as one major cause of inconsistency in the results of human intervention studies [26]. This variability is determined by a number of factors, among which a central role is ascribed to the genetic variability beside to gut microbiome composition and functionality, sex, age, lifestyle and various comorbidities (overweight and obesity, diabetes, hypertension, dyslipidemia, etc.). Aiming to take the pioneering step towards the ultimate goal - identify genetic variants in the human population underlying the individual metabolic response to the
consumption of dietary flavanols - we conducted this systematic literature search to identify
target genes involved in the protective effect of these compounds and which polymorphism
expressions may explain the inter-individual variability in response to flavanols
consumption. This is the first-ever systematic analysis of nutrigenomic data about the effects
of flavanols in cell models relevant for cardiometabolic health. In order to provide
physiologically relevant data, we applied rigorous criteria for inclusion/exclusion of the
studies, resulting in the retrieval of relatively small number of relevant papers and
differentially expressed genes.

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The complex pathogenesis of cardiometabolic disease development, in terms of many
different cell types and cellular processes involved, makes the choice of relevant *in vitro*
models to be assessed rather challenging. Indeed, one single cell model would not be able to
replicate the entire pathogenesis of the disease and/or may not be sufficient to intercept the
therapeutic potential of a given product. Rather, taking into account different cell models,
evaluated together, was needed to cover the wide spectrum of different cellular processes.
Thus, to obtain comprehensive assessment of the genomic effects of flavanols, we extracted
gene expression data from intestinal cells besides to five cell types known for their major
contribution in cardiometabolic dysfunction, such as adipocytes, hepatocytes, endothelial
cells, immune cells and smooth muscle cells. We examined results from cells exposed to
flavanols in the presence or absence of dysmetabolic and/or pro-inflammatory stimuli (such
as lipopolysaccharide (LPS), glucose or cytokines), classically used to better simulate the *in
vivo* dysmetabolic conditions, and processed the gene dataset retrieved by integrated
functional analysis tools. The assessment of the flavanol effects in these cell models of
cardiometabolic disease allow circumventing several important confounding factors inherent
to *in vivo* studies, such as age, diet, use of drugs, and chronobiological variations. For this
reason, cell models are useful to unveil all those metabolic alterations induced by a treatment
with flavanol that might not be revealed in studies using animal models or human subjects,
due to biological sample complexity. Notwithstanding, these *in vitro* models present some
limitations, particularly the fact that cultured cells fail to reproduce the complex cell-cell and
cell-matrix interactions recognized as a key determinant in the definition of the final cell
homeostasis. In the attempt to interpret the data extracted in a more complex cell networking
and circumvent the use of monotype cell models, data were also subjected to an integrated
bioinformatic analysis among different cell models. Nevertheless, the findings obtained from
these *in vitro* studies need confirmation and validation in animal models and human studies.

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To understand the biological role of the differentially expressed genes extracted from the literature, they were subjected to a global bioinformatic analysis. By integrating the relatively small amount of data scattered across different cell models on the one hand, and applying the powerful bioinformatics tools driven by a large amount of information on the other, we have been able to obtain a broader and more complex insight into the molecular effects of flavanols on the cardiometabolic health. This strategy allowed us to overcome the limitation of the targeted-approach (i.e., analysis of a selected, limited and predefined target genes) featuring most of the studies selected. The global analysis using the bioinformatics tools allowed us to identify, quantify and describe their role in the cellular functions. Furthermore, by integrating data from different cell types, the derived model could mimic, to some extent, the whole organism, which is particularly important for the cardiometabolic disease where several organs and tissues are implicated, connected with complex causal links.

656

This systematic review has identified 37 *in vitro* studies with 54 different genes up- or
down-regulated by flavanol exposure in adipocytes, hepatocytes, immune, smooth muscle,
and endothelial cells. Global bioinformatic analysis of differentially expressed genes
extracted from literature has demonstrated that flavanols primarily modulate different
661 pathogenic aspects of cardiometabolic disease particularly processes of inflammation, cell
662 adhesion and transendothelial migration, or lipid metabolism (Figure 10).
663

664 Low-grade inflammation is a risk factor that induces endothelial dysfunction in medium-
665 and large-sized arterial blood vessels [54]. Dysfunctional endothelium is characterized by an
666 increased permeability to atherogenic lipoproteins [54] and circulating immune cells [55].
667 Under such conditions, endothelial cells increase the expression of leukocyte adhesion
668 molecules on their surface [55]. In particular, ICAM1 and VCAM1, along with a plethora of
669 adhesion molecules and ligands, play major roles in the process of adhesion and
670 transendothelial migration of circulating monocytes, which includes a series of complex
671 sequential events, such as capture, slow rolling, firm adhesion, adhesion strengthening,
672 intraluminal crawling and finally, the transendothelial migration [55]. Flavanols have been
673 shown to decrease the expression of leukocyte adhesion biomarkers in humans [56], as well
674 as the leukocyte rolling over endothelium in an animal model of inflammation [34]. However,
675 a more in-depth analysis of molecular mechanisms underlying the protective effects of
676 flavanols on the arterial endothelium has been made only recently, demonstrating a high level
677 of modulation of pathways defining cell adhesion and transendothelial migration [34].
678 Concordantly, we also identified several regulators of cell adhesion, such as the “platelet-
679 endothelium-leucocyte interaction” and “cell adhesion molecules”, including *ICAM1*,
680 *ITGAM*, *ITGB1*, *SELE* and *VCAM1* genes as primarily affected by flavanols. The interaction
681 between immune and endothelial cells requires the attraction of immune cells to endothelium.
682 This process is regulated by several chemokines, which are involved in “leucocyte
683 chemotaxis” and “chemokine signaling” pathways. In line with previous results, these
684 pathways have also been recognized to be affected by flavanols. Upon adhesion to
685 endothelium, immune cells migrate in sub-endothelial space, predominantly following
686 paracellular routes [55]. Paracellular transendothelial migration requires the reorganization
687 of endothelial cytoskeleton, which is mediated by several genes, including ROCK1 [57].
688 Interestingly, our bioinformatic analyses identified pathways and gene networks regulating
689 the monocyte transmigration, such as “leukocyte transendothelial migration pathway”,
690 “regulation of actin cytoskeleton”, “focal adhesion” or “cell junctions”. “Leukocyte
691 transendothelial migration pathway” exhibited the highest statistical significance among the
692 pathways defining the endothelial cell function and include the following genes extracted
693 from in vitro studies: ICAM1, ITGAM, ITGB1, MMP9, ROCK1 and VCAM1. Concordantly,
694 bioinformatic analysis of protein-protein interactions of extracted genes that are placed in the
695 modulated cellular pathways responsible for endothelial cell function, demonstrated that
696 TNF, MAPK8 and NFKB1 are central to the network of protein-protein interactions, also
697 revealing the role of inflammation as a common underlying mechanism of cardiometabolic
698 disease. Taken together, these systematic bioinformatic analyses showed that regulation of
699 endothelium by flavanols is one of the key molecular mechanisms of these bioactives
700 underlying their health properties. Genes regulating this function present potential candidates
701 for further analyses of their importance for the inter-individual variability in response to
702 consumption of dietary flavanols.
703

704 The enquiring of BioCarta database identified pathways linked to lipid metabolism
705 including “visceral fat deposits and the metabolic syndrome”, “mechanism of gene regulation
706 by peroxisome proliferators via PPARA” and “LDL pathway during atherogenesis”. It is well
707 known that adipose tissue exerts immune-metabolic functions. Besides functioning as an
708 energy storage tissue (storing energy in the form of lipid) and controlling the lipid
709 mobilization and distribution in the body, it acts as an active endocrine organ by releasing a
710 cluster of active molecules, named adipokines with autocrine and paracrine functions and
711 modulating a range of metabolic pathways [58]. It is now widely recognized that adipose
712 tissue dysfunction, as in terms of adipose hypertrophy and deregulated release of adipokines,
713 plays a prominent role in the development of obesity and its related disorders such as insulin
714 resistance or cardiovascular disease [59]. Visceral fat accumulation, linked with levels of
715 some adipokines, induces chronic inflammation and metabolic disorders, including glucose
716 intolerance, hyperlipidemia, and arterial hypertension. Together, these conditions contribute
717 to a diagnosis of metabolic syndrome, directly associated with the onset of cardiovascular
718 disease [60]. Our data suggest that flavanols significantly interfere with the pathway related
719 to "visceral fat deposits and the metabolic syndrome" regulating the expression of five
720 interesting genes within this pathway: **PPARG**, **LPL**, **TNF**, **RETN** and **ADIPOQ**. Several
721 epidemiological and experimental studies have shown robust hypolipidemic and anti-
722 obesogenic effects by flavanols [61,62]. Regulation of **peroxisome proliferator-activated
723 receptors** (PPARs) activity and expression by these compounds has been largely suggested
724 as the primary mechanism of hypolipidemic and anti-obesogenic effects exerted by most
725 flavanols [63]. PPARs are nuclear hormone receptors that function as transcription factors
726 [64]. Up to now, three PPARs have been identified, PPARA, D/B, and G with different tissue
727 distribution and pharmacological ligand activation profile [64]. Among them, PPARG is
728 abundantly expressed in adipose tissue and muscle cells whereas it mediates the expression
729 of genes associated with adipogenesis and insulin sensitivity [65], thus making it a molecular
730 target of choice for the development of therapeutic treatments of both synthetic and natural
731 origin.

732

733 Bioinformatic analyses of the extracted nutrigenomic data were not focused only to gene
734 ontology analysis and identification of cellular pathways significantly associated to
735 differentially expressed genes, but also to the gene network analyses, analysis of interactions
736 between functional groups of genes and protein-protein interactions. Furthermore, we have
737 also taken a step forward by analyzing the transcriptional (transcription factors) and post-
738 transcriptional (miRNAs) regulation of differentially expressed genes. Among the most
739 significant transcription factors identified, we recognized PPARG and GATA2. Previous
740 studies demonstrated that in addition to its role in hematopoietic stem cell development [66],
741 GATA2 also has an important role in mediating cardiovascular disease development [67]. It
742 is abundantly expressed in vascular endothelial cells and regulates endothelial-specific genes,
743 such as VCAM1, P-selectin and PECAM1, involved in endothelial activation and dysfunction
744 that can lead to development of atherosclerosis and cardiovascular disease [67]. It has also
745 been observed that inactivation of GATA2 decreases the expression of cell adhesion
746 molecules, and that it plays an essential role in endothelial cell activation by acting together
747 with NF-kappa B, which is a critical factor in the molecular pathogenesis of atherosclerosis
748 [67]. Our results, suggesting a role for flavanols in modulating GATA2, reveal a new potential
749 regulatory site for flavanol effects. The PPARs modulate several biological processes that
750 are perturbed in obesity, including inflammation, lipid and glucose metabolism and overall
751 energy homeostasis. PPARs agonists have some efficacy in reducing cardiovascular risk in
752 patients with type 2 diabetes who also have pro-atherogenic dyslipidemia [68]. Use of PPARs
753 agonists, such as aleglitazar, was shown to improve insulin sensitivity, glucose control and
754 lipid levels in people with type 2 diabetes [69]. Interestingly, two studies have suggested that
755 polyphenols could act as PPARs agonists and prevent risk factors for obesity-related
756 metabolic disorders and cardiovascular disease, such as polyphenols from plum [70] or grape
757 seeds [71]. Together with these 2 transcription factors, our systematic bioinformatic analyses
758 also identified other ones that present key players in the genomic response to flavanol intake,
759 like YY1, FOXC1 or NFKB1.

760

761 Along with the identification of transcriptional regulators, we also searched for potential
762 post-transcriptional regulators, particularly miRNAs. miRNAs are endogenous small non-
763 coding RNAs that can interact with mRNAs, in this way exerting post-transcriptional
764 regulation activities [72]. It has been shown that they play an important role in the regulation
of lipid metabolism, endothelial function, and consequently, in the development of chronic diseases such as cardiometabolic disorders [72] or cancer. Our bioinformatic analysis identified the mir-335-5p as the most significant miRNAs affected by flavanols. It has been shown that mir-335-5p plays a role in regulating endothelial function [73], insulin secretion and diabetes development [74], and in suppressing lower extremity deep venous thrombosis [75]. Concordantly to our results, in mouse models of atherosclerosis catechins, hesperidin, quercetin, curcumin, or anthocyanins were shown to modulate the expression of this miRNA [76]. Among the other miRNAs identified by our bioinformatic analysis, there is the mir-16-5p. mir-16-5p has been interestingly suggested to be associated with insulin sensitivity and cardiometabolic risk factors in humans [77]. Capacity of polyphenols to regulate the expression of this miRNA has been described in a few studies, such as with epigallocatechin gallate and quercetin [78,79]. For let-7b-5p or mir-193b-3p, no role has been reported before in regulation of cardiometabolic disorders, whereas mir-26b-5p is involved in the regulation of inflammation in myocardial infarction [80]. Taken together, this systematic analysis of genomic data of flavanols related to cardiometabolic effects revealed potential transcriptional and post-transcriptional regulators involved in the genomic modifications of flavanols and therefore novel mechanisms of action and key players in the observed effects.

Conducting this systematic bioinformatic analysis of published nutrigenomic data about the effects of flavanols in cellular models of relevance for cardiometabolic health, such as adipocytes, hepatocytes, immune, smooth muscle and endothelial cells, we demonstrated that only in a small number of studies that were identified as eligible for inclusion in our analysis, the cells were treated with flavanol metabolites (Table 1). Given the growing scientific evidence that flavanol phase II and gut microbiota metabolites represent the main circulating forms of the majority of these bioactives and mediate the effects of their parent compounds at cellular level [9], this finding identifies a major gap in the literature limiting the power of
791 the available *in vitro* studies to demonstrate the true molecular effects of flavanols. This gap
792 in the literature should be addressed in future.

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794 Intestinal cells are not only mediators of macro- and micronutrients absorption, but they
795 also exhibit various functions that may affect the cardiometabolic health. By synthesizing
796 triglycerides [129] and apolipoproteins [52], intestinal cells actively contribute to the
797 regulation of plasma lipoprotein pools. Noteworthy, an increased atherogenic risk features
798 patients with inflammatory bowel disease (IBD) [81]. A recent literature review has indeed
799 suggested that patients with IBD may be at an increased risk of cardiovascular diseases
800 [82,83]. Several studies have shown that chronic systemic inflammation in IBD can lead to
801 endothelial dysfunction and increased platelet activation, conditions preceding the
802 development of atherosclerotic vascular disease [84] or favoring its clinical manifestations.
803 High levels of **tumor necrosis factor (TNF)**, **C-reactive protein (CRP)** and vascular
804 endothelial growth factor (VEGF) are characteristic of IBD and may therefore contribute to
805 endothelial dysfunction and atherogenesis [85]. Furthermore, in both cardiovascular disease
806 and IBD pro-inflammatory angiogenesis is recognized as a common trait sustaining both
807 atherosclerotic plaque growth and intestinal inflammation [86-88]. Finally, during IBD
808 flares, the adhesion of circulating monocytes to the intestinal microvascular endothelial cells,
809 as well as their infiltration and transformation into macrophages occurs, in tight analogy with
810 what happens in the early phases of arterial atherosclerosis [89]. Results of our bioinformatic
811 analysis suggest that flavanols may reduce cardiovascular risk also affecting the intestinal
812 homeostasis. For example, our data suggest that flavanols affect the expression of genes
813 involved in PPAR signaling pathway. Beside to adipose tissue and muscle cells, PPARG is
814 also abundantly expressed in colonic epithelial cells whereas it seems to play important anti-
815 inflammatory and anti-carcinogenic effects [90]. In experimental animal model of IBD, the
816 activation of PPARG by synthetic agonist rosiglitazone was shown to reduce the expression
817 of inflammatory genes by interfering with the activation of NF-kappa B transcription factor
818 [91]. Several experimental evidences suggest that dietary polyphenols possess both
819 protective and therapeutic effects in the management of IBD [92]. However, further
820 preclinical and clinical studies are needed in order to understand the efficacy of dietary
821 polyphenols in IBD patients.
822

823 Although cellular models do not reflect the variability across individuals within
824 population, in this work, by integrating the mechanistic *in vitro* data, we gain insights on
825 which genes or proteins are of major importance in mediating the anti-inflammatory and
826 vasoprotective effects of flavanols. Our integrative bioinformatic meta-analyses of the
827 existing genomic data from the literature allow us to better identify molecular mechanisms
828 underlying cardiometabolic health properties of flavanols and identify major molecular
829 pathways and target genes involved. Nevertheless, from the data here presented, as well as
830 from the data in the literature, there is no doubt that **TNF and IL6** are among the key gene
831 players in mediating flavanol anti-inflammatory activity, since their polymorphisms have
832 already been associated with lifestyle dependent cardiometabolic risk factors [93]. Our data
833 confirm and suggest the need to systematically investigate flavanol effects in relation to **TNF**
834 and **IL6** polymorphic expressions. Deeper analyses of our data and the data from the literature
835 may also identify other potential key target genes and polymorphisms that are worth further
836 studying in the context of inter-individual variability of the effects of flavanols on
837 cardiometabolic health. In conclusion, integrative biology approaches allow to identify
838 potential key players of flavanols involved in cardiometabolic disease prevention associated
839 to gene-protein-miRNA networks, which can be exploited for personalized nutritional
840 recommendations in cardiometabolic disease prevention.
841
843 **Figure legends**

844

845 **Figure 1. Data collection flowchart. For search criteria, see Methods section.**

846

847 **Figure 2. A) Number of genes repeated in studies conducted on adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols. B) Number of differentially expressed genes extracted from the studies on adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols.**

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852 **Figure 3. Gene ontology for adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols.** Each rectangle is a single cluster representative, and they are joined into 'superclusters' of related terms, represented with different colors. Size of the rectangles reflects the p-value of the GO.

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857 **Figure 4. Gene network pie chart for adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols.**

859

860 **Figure 5. BioCarta and KEGG pathways related to cellular processes in adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols.** *: KEGG; **: BioCarta.

863

864 **Figure 6. Functional enrichment and interactome meta-analysis based on gene lists for different cell types exposed to flavanols.** Enrichment network visualization of the results
866 from the lists of genes identified for adipocytes, smooth muscle cells, immune cells,
867 endothelial cells and hepatocytes. Nodes are functional groups represented by pie charts
868 indicating their associations with each cell type. Cluster labels were added manually. Color
869 code represents the identities of gene lists (adipocytes: red, endothelial cells: blue,
870 hepatocytes: green, immune cells: violet) and size of each color is proportional to the
871 percentage of the genes from different types of cells.

872

873 **Figure 7. Protein-protein interactions in adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols.** Colored nodes: query proteins and first shell of interactors; white nodes: second shell of interactors; filled nodes: some 3D structure is known or predicted; empty nodes: proteins of unknown 3D structure.

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878 **Figure 8. Regulation of protein-protein interaction network by transcription factors and miRNAs in adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols.**

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882 **Figure 9.** A) KEGG and BioCarta (marked with *) pathways for the intestinal cells exposed to flavanols. B) Protein-protein interactions for the intestinal cells exposed to flavanols. Protein network is organized in two clusters: in red – proteins that are mostly involved in the metabolism of circulating lipoproteins; in green – proteins that are mainly involved in calcium signaling. C) Regulation of protein-protein interaction network by transcription factors and miRNAs in the intestinal cells exposed to flavanols.

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890 **Figure 10.** Summary of identified differentially expressed genes modulated by flavanol and related to cardiometabolic health.

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911
912 **Acknowledgments:** This article is based upon work from COST Action FA1403 POSITIVe (Interindividual variation in response to consumption of plant food bioactives and determinants involved) supported by COST (European Cooperation in Science and Technology; www_cost.eu). The authors also acknowledge all the partners involved in working group 2 of the COST Action POSITIVe. A.A.A and F.I.B are Serra Húnter Fellows and thank the Serra Húnter Programme (Generalitat de Catalunya) for the academic positions with reference numbers URV-AG-587 and URV-LE-621, respectively.

919

920 **Funding:** COST (European Cooperation in Science and Technology) Action FA1403.

921

922 **Conflict of interest**

923 The authors do not have conflict of interest

924

925 **Contribution of authors**

926 All authors contributed to conceptualization, methodology, data extraction and validation of the last version of the manuscript. TR, MM, DM contributed to writing, reviewing and editing of the manuscript and preparation, creation and/or presentation of the published work.

929

930

931

932
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<table><caption>**Table 1.** Overview of data extraction for cell models exposed to different flavanols or flavanol metabolites at physiological concentrations.</caption><thead><tr><th>Flavanol tested</th><th>Concentration</th><th>Challenge</th><th>Differentially expressed genes; p&lt;0.05</th><th>Reference</th></tr></thead><tbody><tr><td colspan="5"><em>Adipocytes</em></td></tr><tr><td>EGCG</td><td>10 µM</td><td>adipogenic cocktail</td><td>CEBPA, PPARG</td><td>[94]</td></tr><tr><td>Epicatechin</td><td>0.5 – 10 µM</td><td>TNF</td><td>IL6, CCL2, RETN, TNF</td><td>[95]</td></tr><tr><td>EGCG</td><td>1 – 5 µM</td><td>dexamethasone</td><td>ADIPOQ, RETN</td><td>[96]</td></tr><tr><td>Catechin</td><td>10 µM</td><td>adipogenic cocktail</td><td>ADIPOQ, FABP4, LPL, PPARG</td><td>[97]</td></tr><tr><td>EGCG</td><td>1 µM</td><td>adipogenic cocktail</td><td>CFD</td><td>[98]</td></tr><tr><td colspan="5"><em>Endothelial cells</em></td></tr><tr><td>EGCG</td><td>10 µM</td><td>phorbol-12-myristate-13-acetate</td><td>CCL2</td><td>[99]</td></tr><tr><td>Catechin</td><td>0.1 – 10 µM</td><td>no challenge</td><td>SERPINE1</td><td>[100]</td></tr><tr><td>Catechin</td><td>10 µM</td><td>homocysteine</td><td>NRF1, TFAM, MT-CO3</td><td>[101]</td></tr><tr><td>EGCG</td><td>2.5 – 10 µM</td><td>no challenge</td><td>EDN1, HMOX1</td><td>[102]</td></tr><tr><td>EGCG</td><td>10 µM</td><td>no challenge</td><td>EDN1</td><td>[103]</td></tr><tr><td>EGCG</td><td>0.5 – 10 µM</td><td>vascular endothelial growth factor</td><td>CXCL8</td><td>[104]</td></tr><tr><td>EGCG</td><td>10 µM</td><td>TNF</td><td>CCL2</td><td>[105]</td></tr><tr><td>EGCG</td><td>10 µM</td><td>no challenge</td><td>ICAM1, CCL2</td><td>[106]</td></tr><tr><td>EGCG</td><td>10 µM</td><td>TNF</td><td>ICAM1, VCAM1, CCL2, BCL2, BAX, CASP9</td><td>[107]</td></tr><tr><td>Procyanidin B2</td><td>1 – 2 µM</td><td>LPS and ATP</td><td>NLRP3</td><td>[108]</td></tr><tr><td>EGCG</td><td>10 µM</td><td>glucose</td><td>VCAM1</td><td>[109]</td></tr><tr><td>EGCG</td><td>10 µM</td><td>no challenge</td><td>PIM1</td><td>[110]</td></tr><tr><td>Epicatechin,</td><td>1 – 10 µM</td><td>no challenge</td><td>ARG2</td><td>[111]</td></tr><tr><td>Flavanol metabolites</td><td>0.4 – 7.8 µM</td><td>no challenge</td><td></td><td></td></tr></tbody></table>
<table><thead><tr><th>Flavanol metabolites</th><th>1 µM</th><th>TNF</th><th>CALD1, TJP1, ARHGEF7, CASK, NFKB1, SELE, CCL2, ITGB1, ROCK1</th><th>[22]</th></tr></thead><tbody><tr><td colspan="5"><strong>Hepatocytes</strong></td></tr><tr><td>Epicatechin, Catechin, Procyanidin B2</td><td>0.1 – 10 µM</td><td>no challenge</td><td>APOA1, APOB, LDLR, ABCA1, SREBF1, SCARB1, SCAP</td><td>[52]</td></tr><tr><td>Epicatechin, Flavanol metabolites</td><td>10 µM</td><td>no challenge</td><td>APOA1, FOXA2</td><td>[112]</td></tr><tr><td>EGCG</td><td>1 – 10 µM</td><td>angiotensin II</td><td>AGTR1, PPARG</td><td>[113]</td></tr><tr><td colspan="5"><strong>Immune cells</strong></td></tr><tr><td>EGCG</td><td>3 – 10 µM</td><td>phorbol-12-myristate-13-acetate</td><td>S1PR2</td><td>[114]</td></tr><tr><td>EGCG</td><td>3 µM</td><td>phorbol-12-myristate-13-acetate</td><td>MMP9, PTGS2</td><td>[115]</td></tr><tr><td>Epicatechin</td><td>2 µg/mL</td><td>LPS</td><td>NOS2, PTGS2</td><td>[116]</td></tr><tr><td>Epicatechin gallate</td><td>3 µM</td><td>no challenge</td><td>ITGAM</td><td>[117]</td></tr><tr><td>Catechin</td><td>10 µM</td><td>LPS</td><td>IL6, TNF</td><td>[118]</td></tr><tr><td>EGCG</td><td>10 µM</td><td>phorbol-12-myristate-13-acetate</td><td>MMP9, BSG</td><td>[119]</td></tr><tr><td>EGCG</td><td>2.5 µM</td><td>no challenge</td><td>TOLLIP</td><td>[120]</td></tr><tr><td>EGCG, (-)-epigallocatechin-3-O-(3-O-methyl)-gallate</td><td>5 µM<br>1 µM</td><td>no challenge; palmitic acid</td><td>RNF216, TNF</td><td>[121]</td></tr><tr><td>EGCG</td><td>1 µM</td><td>LPS</td><td>MMP9, CCL2</td><td>[122]</td></tr><tr><td>EGCG</td><td>1 µM</td><td>LPS; no challenge</td><td>TNF, IL6, TLR4, TOLLIP</td><td>[53]</td></tr><tr><td colspan="5"><strong>Smooth muscle cells</strong></td></tr><tr><td>EGCG</td><td>0.1 – 10 µM</td><td>no challenge</td><td>TIMP2</td><td>[123]</td></tr></tbody></table>
<table><tbody><tr><td>EGCG</td><td>10 µM</td><td>basic fibroblast growth factor</td><td>JUN</td><td>[124]</td></tr><tr><td>EGCG</td><td>3 – 10 µM</td><td>IL-6;</td><td>CRP</td><td>[125]</td></tr><tr><td>EGCG</td><td>1 – 10 µM</td><td>angiotensin II</td><td>CRP</td><td>[126]</td></tr><tr><td>EGCG</td><td>3 – 10 µM</td><td>endothelin 1</td><td>CRP</td><td>[126]</td></tr><tr><td>Epigallocatechin</td><td>10 µM</td><td>serum</td><td>JUN</td><td>[127]</td></tr><tr><td colspan="5"><b>Intestinal cells</b></td></tr><tr><td>Hexameric procyanidins</td><td>20 µM</td><td>TNF</td><td>NOS2</td><td>[128]</td></tr><tr><td>Grape seed extract</td><td>100 mg/L<br>25 – 100 mg/L</td><td>fasted state medium;<br>postprandial state medium</td><td>ACSL5, ACSL3, FABP2, PPARA,<br>CPT1A</td><td>[129]</td></tr><tr><td>Cinnamtannin A2</td><td>1 – 10 µM</td><td>no challenge</td><td>APOA1, APOB</td><td>[52]</td></tr><tr><td>Grape seed extract</td><td>100 mg/L</td><td>no challenge</td><td>DPP4</td><td>[130]</td></tr><tr><td>Grape seed extract</td><td>20 – 100 mg/L</td><td>chenodeoxycholic acid</td><td>SLC10A2, FABP6, FGF19, SLC51A,<br>SLC51B</td><td>[131]</td></tr></tbody></table>
<table><caption>**Table 2.** Proteins with the highest number of interactions within the network (≥ 15).</caption><thead><tr><th>Symbol</th><th>Name</th><th>Number of interactions</th></tr></thead><tbody><tr><td>TNF</td><td>Tumor necrosis factor</td><td>40</td></tr><tr><td>IL6</td><td>Interleukin-6</td><td>39</td></tr><tr><td>JUN</td><td>Transcription factor AP-1</td><td>37</td></tr><tr><td>TLR4</td><td>Toll-like receptor 4</td><td>30</td></tr><tr><td>NFKB1</td><td>Nuclear factor NF-kappa-B p105 subunit</td><td>30</td></tr><tr><td>MAPK8</td><td>Mitogen-activated protein kinase 8</td><td>30</td></tr><tr><td>IL8</td><td>Interleukin-8</td><td>26</td></tr><tr><td>CCL2</td><td>C-C motif chemokine 2</td><td>24</td></tr><tr><td>MMP9</td><td>Matrix metalloproteinase-9</td><td>23</td></tr><tr><td>PPARG</td><td>Peroxisome proliferator-activated receptor gamma</td><td>22</td></tr><tr><td>BCL2</td><td>Apoptosis regulator Bcl-2</td><td>22</td></tr><tr><td>MMP2</td><td>72 kDa type IV collagenase</td><td>21</td></tr><tr><td>CYCS</td><td>Cytochrome c</td><td>21</td></tr><tr><td>FOS</td><td>Proto-oncogene c-Fos</td><td>21</td></tr><tr><td>ICAM1</td><td>Intercellular adhesion molecule 1</td><td>20</td></tr><tr><td>CRP</td><td>C-reactive protein</td><td>19</td></tr><tr><td>PTGS2</td><td>Prostaglandin G/H synthase 2</td><td>19</td></tr><tr><td>ADIPOQ</td><td>Adiponectin</td><td>19</td></tr><tr><td>CASP3</td><td>Caspase-3</td><td>18</td></tr><tr><td>NOS3</td><td>Nitric oxide synthase, endothelial</td><td>17</td></tr><tr><td>BCL2L1</td><td>Bcl-2-like protein 1</td><td>17</td></tr><tr><td>MYD88</td><td>Myeloid differentiation primary response protein MyD88</td><td>16</td></tr><tr><td>XIAP</td><td>E3 ubiquitin-protein ligase XIAP</td><td>16</td></tr><tr><td>VCAM1</td><td>Vascular cell adhesion protein 1</td><td>16</td></tr><tr><td>BAX</td><td>Apoptosis regulator BAX</td><td>15</td></tr><tr><td>EDN1</td><td>Endothelin-1</td><td>15</td></tr><tr><td>ITGAM</td><td>Integrin alpha-M</td><td>15</td></tr></tbody></table>

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Table 3: Top 20 transcription factors and miRNAs that regulate the protein-protein interaction network in adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols.

<table><thead><tr><th>Symbol</th><th>Name</th><th>Number of hits</th></tr></thead><tbody><tr><td colspan="3"><strong>Transcription factor</strong></td></tr><tr><td>FOXC1</td><td>Forkhead box protein C1</td><td>362</td></tr><tr><td>GATA2</td><td>Endothelial transcription factor GATA-2</td><td>266</td></tr><tr><td>YY1</td><td>Transcriptional repressor protein YY1</td><td>186</td></tr><tr><td>E2F1</td><td>Transcription factor E2F1</td><td>160</td></tr><tr><td>FOXL1</td><td>Forkhead box protein L1</td><td>149</td></tr><tr><td>USF2</td><td>Upstream stimulatory factor 2</td><td>141</td></tr><tr><td>RELA</td><td>Transcription factor p65</td><td>138</td></tr><tr><td>PPARG</td><td>Peroxisome proliferator-activated receptor gamma</td><td>137</td></tr><tr><td>NFKB1</td><td>Nuclear factor NF-kappa-B p105 subunit</td><td>136</td></tr><tr><td>CREB1</td><td>Cyclic AMP-responsive element-binding protein 1</td><td>134</td></tr><tr><td>TFAP2A</td><td>Transcription factor AP-2-alpha</td><td>131</td></tr><tr><td>TP53</td><td>Cellular tumor antigen p53</td><td>127</td></tr><tr><td>NFIC</td><td>Nuclear factor 1 C-type</td><td>123</td></tr><tr><td>POU2F2</td><td>POU domain, class 2, transcription factor 2</td><td>115</td></tr><tr><td>SRF</td><td>Serum response factor</td><td>115</td></tr><tr><td>HINFP</td><td>Histone H4 transcription factor</td><td>114</td></tr><tr><td>JUN</td><td>Transcription factor AP-1</td><td>113</td></tr><tr><td>SREBF1</td><td>Sterol regulatory element-binding protein 1</td><td>106</td></tr><tr><td>STAT3</td><td>Signal transducer and activator of transcription 3</td><td>106</td></tr><tr><td>MEF2A</td><td>Myocyte-specific enhancer factor 2A</td><td>92</td></tr><tr><td colspan="3"><strong>micro RNA</strong></td></tr><tr><td>mir-335-5p</td><td>microRNA-335-5p</td><td>105</td></tr><tr><td>mir-16-5p</td><td>microRNA-16-5p</td><td>83</td></tr><tr><td>mir-124-3p</td><td>microRNA-124-3p</td><td>80</td></tr><tr><td>mir-26b-5p</td><td>microRNA-26b-5p</td><td>79</td></tr><tr><td>mir-17-5p</td><td>microRNA-17-5p</td><td>77</td></tr><tr><td>let-7b-5p</td><td>let-7b-5p</td><td>74</td></tr><tr><td>mir-155-5p</td><td>microRNA-155-5p</td><td>70</td></tr><tr><td>mir-92a-3p</td><td>microRNA-92a-3p</td><td>70</td></tr><tr><td>mir-93-5p</td><td>microRNA-93-5p</td><td>66</td></tr><tr><td>mir-20a-5p</td><td>microRNA-20a-5p</td><td>64</td></tr><tr><td>mir-106b-5p</td><td>microRNA-106b-5p</td><td>61</td></tr><tr><td>mir-1-3p</td><td>microRNA-1-3p</td><td>53</td></tr><tr><td>let-7c-5p</td><td>let-7c-5p</td><td>52</td></tr></tbody></table>
<table><tr><td>mir-193b-3p</td><td>microRNA-193b-3p</td><td>51</td></tr><tr><td>mir-20b-5p</td><td>microRNA-20b-5p</td><td>51</td></tr><tr><td>mir-34a-5p</td><td>microRNA-34a-5p</td><td>51</td></tr><tr><td>mir-615-3p</td><td>microRNA-615-3p</td><td>50</td></tr><tr><td>mir-218-5p</td><td>microRNA-218-5p</td><td>49</td></tr><tr><td>mir-519d-3p</td><td>microRNA-519d-3p</td><td>49</td></tr><tr><td>mir-21-5p</td><td>microRNA-21-5p</td><td>48</td></tr></table>

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<figure><img src="image_4.png" /><figcaption>**Figure 1. Data collection flowchart.** For search criteria, see Methods section.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 2A.** Number of genes repeated in studies conducted on adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 2B.** Number of differentially expressed genes extracted from the studies on adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Gene ontology for adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols. Each rectangle is a single cluster representative, and they are joined into ‘superclusters’ of related terms, represented with different colors. Size of the rectangles reflects the p-value of the GO.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Gene network pie chart for adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Figure 5.** BioCarta and KEGG pathways related to cellular processes in adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols. *: KEGG; **: BioCarta.</figcaption></figure>
<figure><img src="image_10.png" /></figure>

**Figure 6.** Functional enrichment and interactome meta-analysis based on gene lists for different cell types exposed to flavanols. Enrichment network visualization of the results from the lists of genes identified for adipocytes, smooth muscle cells, immune cells, endothelial cells and hepatocytes. Nodes are functional groups represented by pie charts indicating their associations with each cell type. Cluster labels were added manually. Color code represents the identities of gene lists (adipocytes: red, endothelial cells: blue, hepatocytes: green, immune cells: violet) and size of each color is proportional to the percentage of the genes from different types of cells.
<figure><img src="image_11.png" /><figcaption>**Figure 7.** Protein-protein interactions in adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols. Colored nodes: query proteins and first shell of interactors; white nodes: second shell of interactors; filled nodes: some 3D structure is known or predicted; empty nodes: proteins of unknown 3D structure.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Figure 8.** Regulation of protein-protein interaction network by transcription factors and miRNAs in adipocytes, hepatocytes, immune, smooth muscle and endothelial cells exposed to flavanols.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure 9A.** KEGG and BioCarta (marked with *) pathways for the intestinal cells exposed to flavanols.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>**Figure 9B.** Protein-protein interactions for the intestinal cells exposed to flavanols.</figcaption></figure>

Protein network is organized in two clusters: in red – proteins that are mostly involved in the metabolism of circulating lipoproteins; in green – proteins that are mainly involved in calcium signaling.
<figure><img src="image_15.png" /><figcaption>**Figure 9C.** Regulation of protein-protein interaction network by transcription factors and miRNAs in the intestinal cells exposed to flavanols.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>**Figure 10.** Summary of identified differentially expressed genes modulated by flavanol and related to cardiometabolic health.</figcaption></figure>
<table><caption>Table S2: Proteins with the highest number of interactions within the network (≥ 7), for the intestinal cells.</caption><thead><tr><td>Gene symbol</td><td>Name</td><td>Number of interactions</td></tr></thead><tbody><tr><td>LPL</td><td>Lipoprotein lipase</td><td>16</td></tr><tr><td>APOA1</td><td>Apolipoprotein A-I</td><td>13</td></tr><tr><td>APOA2</td><td>Apolipoprotein A-II</td><td>12</td></tr><tr><td>APOB</td><td>Apolipoprotein B-100</td><td>11</td></tr><tr><td>APOE</td><td>Apolipoprotein E</td><td>10</td></tr><tr><td>APOC3</td><td>Apolipoprotein C-III</td><td>10</td></tr><tr><td>NCOA1</td><td>Nuclear receptor coactivator 1</td><td>10</td></tr><tr><td>ABCA1</td><td>ATP-binding cassette sub-family A member 1</td><td>9</td></tr><tr><td>CETP</td><td>Cholesteryl ester transfer protein</td><td>9</td></tr><tr><td>LDLR</td><td>Low-density lipoprotein receptor</td><td>9</td></tr><tr><td>PPARA</td><td>Peroxisome proliferator-activated receptor alpha</td><td>9</td></tr><tr><td>LPA</td><td>Apolipoprotein(a)</td><td>8</td></tr><tr><td>CPT1A</td><td>Carnitine O-palmitoyltransferase 1, liver isoform</td><td>7</td></tr><tr><td>CALM3</td><td>Calmodulin-3</td><td>15</td></tr><tr><td>CALM1</td><td>Calmodulin-1</td><td>15</td></tr><tr><td>CALM2</td><td>Calmodulin-2</td><td>15</td></tr><tr><td>SCN5A</td><td>Sodium channel protein type 5 subunit alpha</td><td>11</td></tr><tr><td>CAMK2B</td><td>Calcium/calmodulin-dependent protein kinase type II subunit beta</td><td>10</td></tr><tr><td>NOS3</td><td>Nitric oxide synthase, endothelial</td><td>10</td></tr><tr><td>CAMK2G</td><td>Calcium/calmodulin-dependent protein kinase type II subunit gamma</td><td>8</td></tr></tbody></table>
<table><tr><td>CAMK2A</td><td>Calcium/calmodulin-dependent protein kinase type II subunit alpha</td><td>8</td></tr><tr><td>CAMK2D</td><td>Calcium/calmodulin-dependent protein kinase type II subunit delta</td><td>8</td></tr></table>