The basis of liver regeneration: A systems biology approach

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Intestinal dysbiosis may be a determining factor for the development and progression of NAFLD/NASH. The increase in pathogenic bacteria and Gram-negative bacteria increases dietary energy extraction, leading to increased intestinal permeability and bacterial translocation, stimulating de novo fatty acid synthesis and increasing LPS expression, as well as the expression of NF-kβ and TNF-α. LPS, lipopolysaccharides; NF-kβ, nuclear factor-kappa β; TNF-α, tumor necrosis factor-alpha." ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Sebastião M.B. Duarte, Jose Tadeu Stefano, Claudia P. Oliveira" "autores" => array:3 [ 0 => array:2 [ "nombre" => "Sebastião M.B." "apellidos" => "Duarte" ] 1 => array:2 [ "nombre" => "Jose Tadeu" "apellidos" => "Stefano" ] 2 => array:2 [ "nombre" => "Claudia P." "apellidos" => "Oliveira" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S166526811930050X?idApp=UINPBA00004N" "url" => "/16652681/0000001800000003/v2_201906020907/S166526811930050X/v2_201906020907/en/main.assets" ] "en" => array:19 [ "idiomaDefecto" => true "cabecera" => "Original article" "titulo" => "The basis of liver regeneration: A systems biology approach" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "422" "paginaFinal" => "428" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Mamatha Bhat, Elisa Pasini, Cristina Baciu, Marc Angeli, Atul Humar, Sonya Macparland, Jordan Feld, Ian McGilvray" "autores" => array:8 [ 0 => array:4 [ "nombre" => "Mamatha" "apellidos" => "Bhat" "email" => array:1 [ 0 => "Mamatha.bhat@uhn.ca" ] "referencia" => array:3 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "Elisa" "apellidos" => "Pasini" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 2 => array:3 [ "nombre" => "Cristina" "apellidos" => "Baciu" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 3 => array:3 [ "nombre" => "Marc" "apellidos" => "Angeli" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 4 => array:3 [ "nombre" => "Atul" "apellidos" => "Humar" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 5 => array:3 [ "nombre" => "Sonya" "apellidos" => "Macparland" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "c" "identificador" => "aff0015" ] ] ] 6 => array:3 [ "nombre" => "Jordan" "apellidos" => "Feld" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] 1 => array:2 [ "etiqueta" => "d" "identificador" => "aff0020" ] ] ] 7 => array:3 [ "nombre" => "Ian" "apellidos" => "McGilvray" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "c" "identificador" => "aff0015" ] ] ] ] "afiliaciones" => array:4 [ 0 => array:3 [ "entidad" => "Multi Organ Transplant Program, University Health Network, Toronto, Canada" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Division of Gastroenterology and Hepatology, University Health Network and University of Toronto, Toronto, Canada" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Department of Laboratory Medicine and Pathobiology, Toronto, Canada" "etiqueta" => "c" "identificador" => "aff0015" ] 3 => array:3 [ "entidad" => "Toronto Centre for Liver Disease, University of Toronto, Ontario, Canada" "etiqueta" => "d" "identificador" => "aff0020" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author at: Division of Gastroenterology, University Health Network and University of Toronto, Toronto General Research Institute, 585 University Avenue, 11PMB-183, Canada." ] ] ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 3563 "Ancho" => 2833 "Tamanyo" => 644170 ] ] "descripcion" => array:1 [ "en" => "(A) Flow chart detailing the steps in our study, from data curation to final generation of networks based on protein-protein interactions. (B) Venn Diagram illustrating the distinct and overlapping dysregulated genes between human and mouse datasets. There were 75 dysregulated genes in humans, 502 dysregulated genes in mice, and 22 shared genes between human and mice (list of genes reported in Supplementary file 1). (C) Network analysis demonstrates the genes common to human and mouse early liver regeneration in the center, with the red triangles depicting up-modulated genes, and the green triangles depicting the down-modulated genes." ] ] ] "textoCompleto" => "1IntroductionLiver regeneration occurs in response to insults that induce inflammation, cell death and injury <a class="elsevierStyleCrossRef" href="#bib0220">[1]</a>, and can be categorized into acute versus chronic regeneration. Liver resection results in acute regeneration, with activation of signaling pathways, resulting ultimately in restoration of 100% of the original hepatic volume and the original liver-to-body weight ratio <a class="elsevierStyleCrossRef" href="#bib0225">[2]</a>. The liver is the only visceral organ with a tremendous capacity to regenerate in a time-limited manner with restoration of its original size. When other organs are subject to resection, there is limited local regeneration. Chronic liver regeneration occurs in response to ongoing inflammation in chronic liver diseases (viral, autoimmune, etc.). This persistent and ongoing hepatocyte proliferation in a genotoxic milieu results in the development of fibrosis and dysplasia over time <a class="elsevierStyleCrossRef" href="#bib0230">[3]</a>.The understanding of the molecular pathways underlying hepatic regeneration has been principally derived through findings from animal models <a class="elsevierStyleCrossRef" href="#bib0235">[4]</a>. Animal studies have demonstrated the dramatic gene expression changes in liver regeneration. The significant 3-fold increase in portal venous flow per hepatocyte induces various signaling changes <a class="elsevierStyleCrossRef" href="#bib0240">[5]</a>, particularly early phase proteins such as urokinase plasminogen activator, Notch1, and beta-catenin <a class="elsevierStyleCrossRef" href="#bib0245">[6]</a>. Extracellular matrix remodeling is stimulated, and results in activation of Hepatocyte Growth Factor (HGF). HGF, norepinephrine, Interleukin-6 (IL-6), Tumor Necrosis Factor (TNF-alpha), serotonin, and bile acids are all found in high concentrations in the blood early after hepatectomy <a class="elsevierStyleCrossRef" href="#bib0250">[7]</a>. HGF and Epidermal GrowthFactor (EGFR) stimulate mitosis of hepatocytes <a class="elsevierStyleCrossRef" href="#bib0255">[8]</a>. In turn, the Fibroblast Growth Factor (FGFR) produced by dividing hepatocytes triggers mitosis of endothelial and stellate cells <a class="elsevierStyleCrossRef" href="#bib0260">[9]</a>. Hepatocytes then enter the cell cycle, resulting in initiation of cell proliferation, which in animals, subsides by day 7 after hepatectomy. In terms of ontology, the types of activated genes include specific transcription factors, regulators of cell cycle entry, stress and inflammatory response proteins. These proteins have a greater than 2-fold change in expression following hepatic resection <a class="elsevierStyleCrossRef" href="#bib0265">[10]</a>.Given that there is currently no bridge to liver transplantation (LT), an improved understanding of what constitutes normal liver regeneration and how to safely stimulate it has the potential for clinical impact. Cirrhotic patients with worsening liver function while awaiting LT, and those with fulminant liver failure are key patient groups who would stand to benefit from therapies generated from such an understanding. Small-for-size syndrome is associated with adverse outcomes post-LT, and would improve with stimulation of normal regeneration.The aim of this systematic integrative analysis of all publicly available data (both human and animal) on liver regeneration is to obtain a more global understanding of the molecular basis and pathways that drive liver regeneration. Whereas most research studies tend to focus on a few key molecules, integrative analysis tools allow for an overarching picture of the drivers of a condition. Through this analysis, we will identify the key genes and pathways specific to regeneration. This would help appreciate potential therapeutic targets to drive this process as the clinical context demands it, as in acute or chronic liver failure and small-for-size syndrome.2Methods2.1Data collection, analysis and database compilingWe retrieved all available high-throughput microarray gene expression datasets related to liver regeneration from Gene Expression Omnibus (GEO), a public functional genomics data repository containing high-throughput array data (<a href="https://www.ncbi.nlm.nih.gov/geo">https://www.ncbi.nlm.nih.gov/geo</a>). We also interrogated Pubmed and ArrayExpress as per the methodology described below. A first search to identify datasets referring to human liver regeneration was performed using the following MeSH terms ((“liver regeneration”[MeSH Terms] <a class="elsevierStyleCrossRef" href="#bib0270">[11]</a> OR (“liver”[All Fields] AND “regeneration”[All Fields]) OR “liver regeneration”[All Fields]) AND high[All Fields] AND throughput[All Fields] AND “humans”[MeSH Terms]). We performed a second search to identify the datasets referring to animals, using the following search terms ((“liver regeneration”[MeSH Terms] OR (“liver”[All Fields] AND “regeneration”[All Fields]) OR “liver regeneration”[All Fields]) AND (“transcriptome”[All Fields] OR (“gene”[All Fields] AND “expression”[All Fields] AND “profile”[All Fields]) OR “gene expression profile”[All Fields])) AND “Mus musculus”[porgn] OR “Rattus norvegicus” [porgn] OR “Oryctolagus cuniculus”[porgn]. We included all high-throughput gene expression profiling datasets comparing liver regeneration at different time points to normal liver tissue (non-regenerating) at baseline as control in humans and mice. These datasets on GEO were analyzed using GEO2R (<a href="https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html">https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html</a>), a web tool available on the portal, to identify genes differentially expressed between samples of liver regeneration and normal liver. GEO2R compares original submitter-supplied processed data tables using the GEOquery <a class="elsevierStyleCrossRef" href="#bib0275">[12]</a> and limma package <a class="elsevierStyleCrossRef" href="#bib0280">[13]</a> from the Bioconductor project. Following the instructions available online, we collected all the dysregulated genes with an adjusted p-value p<0.05, and an expression fold-change value below 0.5 or above 1.5. The study workflow is illustrated in <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 1</a>.<elsevierMultimedia ident="fig0010"></elsevierMultimedia>2.2Regeneration core network analysisWe used 22 deregulated genes found as commonly modulated between human early regeneration and mice for the core network analysis. An overview of the study flow for the network analysis is illustrated in <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 1</a>. To investigate the interactions between the modulated genes found in common between mice and human regenerative liver, we used Ingenuity pathway analysis (IPA Ingenuity® Systems, <a href="http://www.ingenuity.com/">www.ingenuity.com</a>). IPA identifies network interactions and pathway interactions between genes based on an extensive manually curated database of published gene interactions. We uploaded the gene symbol and the associated expression value from the microarray data analyzed with GEO2R into IPA. These genes, called focus genes, were overlaid onto a global molecular network based on Ingenuity knowledge database. IPA includes a large repository of gene–phenotype associations, molecular interactions, chemical knowledge, and regulatory events, manually curated from scientific publications. The networks based on these focus genes were algorithmically created based on their direct or indirect interactions. Scores, calculated on Fisher's exact test by IPA, were obtained in order to rank networks based on their relevance to the genes initially uploaded. The score evaluates the number of focus genes from our original dataset in the network, and the size of the network to approximate how relevant this network is to the original list of focus genes. The network is then shown as a graph representing the molecular relationships/interactions as an edge (line) between genes or gene products (nodes). The connectivity of these nodes representing the genes is based on the data collected in the IPA knowledge base. The node color indicates an up-modulation (red) or down-modulation (green). Nodes are displayed using various shapes to represent the functional role or class of gene product (i.e. kinase, transcription regulator, enzyme). Edges are displayed with various colors or labels to better describe the nature of the relationship between the nodes. Common deregulated genes were mapped onto the core networks to explore their connection to biological function or disease affecting the liver. The upstream regulators significantly associated with our list of Differentially Expressed Genes (DEGs) at different time points were obtained by IPA Ingenuity's knowledge base, as well as the canonical pathways significantly associated with the genes in the input dataset. The right-tailed Fisher's exact test was used to calculate a p-value determining the probability that the association or overlap between the genes listed in the dataset and a given pathway's neighborhood was due to chance alone.Based on the modulation of the DEGs, IPA is able to calculate a z score, defined as a statistical measure of correlation between relationship direction and a given set of modulated genes, as a value to measure “the non-randomness” of directionality. Z score <−2 or > +2 is considered significant. Negative z score <−2 predicts an inhibition with high confidence and a positive score >+2 predicts activation with strong confidence. Outside these values, the prediction of activation or inhibition is less confident. In some cases, the z score cannot be calculated, if there is not enough information stored on IPA knowledge base, in which case there is no available prediction.2.3Hepatic regeneration protein–protein interaction network analysisWe merged and overlaid core networks with related “functions and diseases” to determine genes associated with specific hepatic biological and pathological processes according to the IPA knowledge base. We used the MAP (Molecular Activity Predictor) tool to predict the crosstalk relationship among our genes and their interactors based on their modulation for the networks considered. The drug database was overlaid with Core Network #1 in early regeneration to identify the possible effect of calcineurin inhibitors on liver regeneration.For later regeneration at 48h, Core Network #3 was overlaid with canonical pathways and the enriched pathway, and their relationship with the network is shown in <a class="elsevierStyleCrossRef" href="#sec0075">Supplementary Fig. 5A</a>.2.4Calculation of centrality in protein–protein interaction networksFor all the 22 DEGs in early and late regeneration, we retrieved the known interactors using the Integrated Interactions Database (IID at <a href="http://ophid.utoronto.ca/iid">http://ophid.utoronto.ca/iid</a>) <a class="elsevierStyleCrossRef" href="#bib0285">[14]</a> version 2016-03, selecting only data in humans, mice and liver tissue. Betweenness centrality was calculated using the betweenness function in the igraph package version 1.0.1 <a class="elsevierStyleCrossRef" href="#bib0290">[15]</a>, in R version 3.3.1 (<a href="https://www.r-project.org/">https://www.R-project.org/</a>).3Results3.1Datasets pertaining to liver regenerationWe retrieved 7 datasets for humans and 16 datasets for mice from GEO. However, most patient datasets were not investigating liver regeneration at all, and most mouse datasets did not include different time points. The final selected studies are listed in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>, one of which is a mouse study establishing gene expression data for both early and late liver regeneration. The other two datasets were human: one was obtained from 8 living donor liver grafts following reperfusion, the other was from 3 patients of varying ages (1.5, 42 and 81 years old) post-hepatectomy.<elsevierMultimedia ident="tbl0005"></elsevierMultimedia>3.2Significantly dysregulated genes in liver regenerationIdentification of the DEGs early post-hepatectomy that were overlapping in humans and mice, as opposed to distinct to each, was performed using a Venn diagram as shown in <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 1</a>. We focused on these overlapping genes for further analysis, given that these overlapping genes demonstrated consistency with respect to their involvement in early liver regeneration. The 22 genes in common, and their modulation are listed in <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 1</a>.Canonical pathway analysis revealed the key signaling cascades in which these genes are involved, as listed in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>, highlighting the importance of inflammation as a driver of the hepatic regenerative process. The NRF2-mediated Oxidative Stress Response was not only the most significantly enriched pathway in early regeneration, but also predicted to be activated (z score value >2) based on the modulation status of the 22 DEGs. Cytokine-associated pathways such as IL-10, IL-17 and IL-6, previously highlighted in the regeneration literature <a class="elsevierStyleCrossRef" href="#bib0295">[16]</a>, were also significantly associated with our gene list. <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> reports the key genes among the 22 involved in the most significant pathways, along with their degree of involvement according to percentage of the total number of genes in the pathway.<elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia>Additional analysis of upstream activators of the identified pathways using IPA revealed 16 molecules including 5 cytokines (TNF, IL-1B, IFN-G, IL-6, IL-1A), 3 transcriptional factors (EGR1, PDX1, NUPR1), 4 growth factors (Epidermal Growth Factor (EGF), growth hormone 1 (GH1), angiopoietin-related growth factor (AGF), hepatocyte growth factor (HGF)) and 4 kinases (p38,ERK, JNK, MAP2K1/2). The impact of the significant growth factors on the key genes and biological processes in liver regeneration are illustrated in <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 2</a>.<elsevierMultimedia ident="fig0015"></elsevierMultimedia>The predicted impact of calcineurin inhibitors is illustrated in <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 2</a>. Integration of Core Network #1 and drugs targeting calcineurin suggests that the most commonly used immunosuppressants in liver transplantation adversely affect liver regeneration.Liver regeneration at the later time point of 48h appears to be driven by cell cycle-related genes, as illustrated in <a class="elsevierStyleCrossRef" href="#sec0075">Supplementary Fig. 5A</a>.4DiscussionThe liver is unique in being capable of rapid regeneration in response to various stimuli, such as injury and hepatectomy. Although it is a key physiological process shown to be well-orchestrated based on in vivo data, liver regeneration in humans along a time continuum remains incompletely understood. In this integrative analysis of high-throughput datasets, we systematically deciphered the molecular basis of liver regeneration by studying and analyzing all publicly available high-throughput gene expression datasets. We discovered that the high-throughput data in liver regeneration has been limited to the early phase of regeneration. Particularly, the data in humans has been restricted to the first 1.5h after hepatectomy, which may be more representative of acute stress than a regenerative response. Given that liver regeneration in humans is a process that takes up to 12 weeks to complete following hepatectomy for living donation, with a percentage reconstitution of 80%±13% <a class="elsevierStyleCrossRef" href="#bib0300">[17]</a>, this focus on the early period after hepatectomy hinders our understanding of human liver regeneration along a time continuum. The mouse data was more representative of changes over a time continuum, and there were certainly overlapping genes between humans and mice in the early phase based on integration of the publicly available data.We discovered that the early phase after hepatectomy is dominated by inflammation-associated pathways, particularly nuclear factor erythroid 2-related factor 2 (Nrf2) oxidative stress response <a class="elsevierStyleCrossRef" href="#bib0305">[18]</a>. Nrf2 is a transcription factor that plays a key role in cytoprotection in the face of ongoing injury from reactive oxygen species and radicals induced by viruses, toxins, and lipid accumulation in the liver <a class="elsevierStyleCrossRef" href="#bib0310">[19]</a>. The crucial role of Nrf2 in activating liver regeneration has been supported by in vivo data <a class="elsevierStyleCrossRef" href="#bib0305">[18]</a>. Increased oxidative stress leads to insulin/Insulin Growth Factor resistance in Nrf2-deficient hepatocytes, which in turn prevents efficient regeneration <a class="elsevierStyleCrossRef" href="#bib0315">[20]</a>. It has been shown that Nrf2 protects the liver from toxin-mediated damage and decreases fibrogenesis, which is partly due to target genes regulated by Nrf2 in hepatocytes <a class="elsevierStyleCrossRef" href="#bib0320">[21]</a>. The IL-6<a class="elsevierStyleCrossRef" href="#bib0325">[22]</a> and IL-17<a class="elsevierStyleCrossRef" href="#bib0295">[16]</a> -associated pathways were also key to liver regeneration, as previously confirmed in the literature. We also identified the IL-10 pathway as a top canonical pathway, which concords with previous studies demonstrating that the anti-inflammatory IL-10 inhibits liver regeneration <a class="elsevierStyleCrossRef" href="#bib0330">[23]</a>. The role of inflammatory cells in regeneration has previously been highlighted in a systems analysis of non-parenchymal cell modulation in liver repair, where a balance of inflammatory signals and growth factors was reported <a class="elsevierStyleCrossRef" href="#bib0335">[24]</a>.The LXR/Retinoid × Receptor pathway was among the top canonical pathways identified, and has clearly been shown to play a role in liver regeneration in vivo <a class="elsevierStyleCrossRefs" href="#bib0340">[25–28]</a>. In fact, retinoid stores regulate cell cycle gene expression, and stimulate liver regeneration <a class="elsevierStyleCrossRefs" href="#bib0350">[27,28]</a>. We identified immediate early genes such as JUN and MYC as being central to the network in early regeneration after two-thirds hepatectomy, which is compatible with the literature<a class="elsevierStyleCrossRef" href="#bib0360">[29]</a>.Our assessment of the genes and pathways in peak liver regeneration was limited to a single mouse dataset due to the paucity of high-throughput data. Cell cycle genes were the most clearly implicated in late regeneration, along with Wnt/B-catenin and PI3K/Akt/mTOR pathways.A key finding in our study was the discovery of that the transcription factor JUN is a gene central to regeneration. This indicates that the presence of Jun in the protein-protein interaction networks is essential to driving hepatic regeneration. The importance of this critical growth-related gene has been previously demonstrated in stimulating hepatocyte proliferation <a class="elsevierStyleCrossRef" href="#bib0365">[30]</a>, with a liver-specific gene deletion in mice resulting in impaired liver regeneration post-hepatectomy <a class="elsevierStyleCrossRef" href="#bib0370">[31]</a>. Additionally, JUN has been shown to be critical in the early regenerative response to liver injury <a class="elsevierStyleCrossRefs" href="#bib0375">[32,33]</a> and small-for-size syndrome in vivo <a class="elsevierStyleCrossRef" href="#bib0385">[34]</a>.Our analysis of biological processes revealed an overlap of genes involved in liver regeneration and hepatic carcinogenesis. This has also been verified in the literature, where hierarchical clustering of liver regeneration and hepatocellular carcinoma-related demonstrated overlapping involvement in the cell cycle and cell death. Rapid proliferation in the context of injury carries the risk of accumulation of mutations <a class="elsevierStyleCrossRef" href="#bib0390">[35]</a>. Chronic liver regeneration in response to genotoxic liver injury also is associated with development of dysplasia <a class="elsevierStyleCrossRefs" href="#bib0395">[36,37]</a>.Given that living donor liver transplant recipients have liver regeneration in the first 3 months after transplant, we wished to investigate the potential effect of calcineurin inhibitors (the most commonly used immunosuppressants after liver transplant) on the pathways upregulated post-hepatectomy using the IPA software. Specifically, we situated the effect of the calcineurin proteins in these pathways early after hepatectomy, and saw that Nrf2-mediated oxidative stress response, the acute phase response and glucocorticoid signaling <a class="elsevierStyleCrossRef" href="#bib0405">[38]</a> would all be potentially impaired with calcineurin inhibition. It is possible that calcineurin inhibitors prevent liver regeneration in response to genotoxic injury such as viruses and lipid accumulation, and may be a link to the hastened progression of fibrosis following liver transplantation <a class="elsevierStyleCrossRef" href="#bib0410">[39]</a>. However, there have previously been reports of increased mitotic activity in the presence of tacrolimus <a class="elsevierStyleCrossRefs" href="#bib0415">[40,41]</a> and cyclosporine <a class="elsevierStyleCrossRef" href="#bib0425">[42]</a>, although regenerative capacity and the resulting liver volume have not been assessed. Therefore, the exact impact of calcineurin inhibitors on hepatic regeneration remains to be determined. The only alternative baseline immunosuppressant is sirolimus, which as an mTOR inhibitor is an antiproliferative agent and is contraindicated in the first month post-transplant due to the heightened risk of fatal hepatic artery thrombosis.The principal limitation of this integrative analysis is the paucity of publicly available high-throughput gene expression data. Given that our understanding of human regeneration is limited to the early phase post-hepatectomy or living donor liver transplant, the data is more influenced by acute stress and inflammatory processes such as ischemia-reperfusion injury. We did find animal data pertaining to what corresponds to their peak liver regeneration time point, however there was no such corresponding human data available. There was no high-throughput data available on termination of liver regeneration, therefore this phase could not be studied. It has been shown that the liver has an inherent hepatostat, regulating the organ size achieved <a class="elsevierStyleCrossRef" href="#bib0430">[43]</a>. However, the mechanisms underlying termination of regeneration are not well understood due to paucity of data. Nonetheless, limited data does indicate the key role of extracellular matrix-driven intracellular signaling involving proteins such as integrin-linked kinase, Glypican 3, C/EBPα, and HNF4α <a class="elsevierStyleCrossRef" href="#bib0430">[43]</a>.The literature on hepatic regeneration has been extensively influenced by animal data, and it is doubtful that this is an accurate representation of human regeneration, especially given that it occurs over a longer time frame. Nonetheless, we did uncover a commonality in genes expressed early after hepatectomy. Another important aspect is the likely contribution of different liver cell subsets such as hepatocytes, macrophages, and other immune cells to the regenerative process. The datasets obtained from the public literature represent gene expression in whole liver tissue, and do not enhance our understanding of how individual cell types contribute to hepatic regeneration.In summary, liver regeneration is a key biological process that occurs in various contexts, from chronic injury in liver disease, to partial hepatectomy performed for various indications, to living donor liver transplantation. Our systematic review and integrative analysis of high-throughput gene expression data in liver regeneration provides a picture of the processes involved early after hepatectomy and at peak liver regeneration. Inflammatory pathways predominate the early period after hepatectomy, and cell cycle-related genes drive regeneration at its peak, at least in animals. Additionally, JUN is a transcription factor that is central to regeneration, and is thus critical for this physiological process. Based on our centrality analysis of high-throughput data and protein–protein interactions, without the presence of Jun, the regenerative process would fail to occur. Validation of the central role of Jun is necessary to determine its importance as a therapeutic target to stimulate liver regeneration. Nonetheless, data to better understand the molecular basis of liver regeneration in humans on a time continuum is lacking. The effect of calcineurin inhibitors on liver regeneration may play a role in hastened fibrosis post-liver transplantation. Overall, our study strongly supports the need for more extensive data on liver regeneration, with the potential to significantly enhance the care of patients with acute and chronic liver failure as well as small-for-size syndrome.AbbreviationsDEGdifferential expressed genesEGFepidermal growth factorFGFRfibroblast growth factorGEOgene expression omnibusGH1growth hormone 1HGFhepatocyte growth factorIL-6interleukin 6IPAingenuity pathway analysisLTliver transplantTNF-Alphatumor necrosis factor alphaAuthors contributionsMB study design, and writing of manuscript. CB, EP and MA; data collection, analysis and compiling. AH, SM, JF, IM and MB input into study design and final manuscript.Conflict of interestThere is no conflict of interest concerning this study." "textoCompletoSecciones" => array:1 [ "secciones" => array:10 [ 0 => array:3 [ "identificador" => "xres1199032" "titulo" => "Abstract" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1117472" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 3 => array:3 [ "identificador" => "sec0010" "titulo" => "Methods" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Data collection, analysis and database compiling" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Regeneration core network analysis" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Hepatic regeneration protein–protein interaction network analysis" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Calculation of centrality in protein–protein interaction networks" ] ] ] 4 => array:3 [ "identificador" => "sec0035" "titulo" => "Results" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0040" "titulo" => "Datasets pertaining to liver regeneration" ] 1 => array:2 [ "identificador" => "sec0045" "titulo" => "Significantly dysregulated genes in liver regeneration" ] ] ] 5 => array:2 [ "identificador" => "sec0050" "titulo" => "Discussion" ] 6 => array:2 [ "identificador" => "sec0055" "titulo" => "Abbreviations" ] 7 => array:2 [ "identificador" => "sec0060" "titulo" => "Authors contributions" ] 8 => array:2 [ "identificador" => "sec0065" "titulo" => "Conflict of interest" ] 9 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2018-01-19" "fechaAceptado" => "2018-07-01" "PalabrasClave" => array:1 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1117472" "palabras" => array:3 [ 0 => "Liver Regeneration" 1 => "Hepatectomy" 2 => "Transplant integrative analysis" ] ] ] ] "tieneResumen" => true "resumen" => array:1 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "IntroductionLiver regeneration is a normal response to liver injury. The aim of this study was to determine the molecular basis of liver regeneration, through an integrative analysis of high-throughput gene expression datasets. MethodsWe identified and curated datasets pertaining to liver regeneration from the Gene Expression Omnibus, where regenerating liver tissue was compared to healthy liver samples. The key dysregulated genes and pathways were identified using Ingenuity Pathway Analysis software. There were three eligible datasets in total. ResultsIn the early phase after hepatectomy, inflammatory pathways such as Nrf2 oxidative stress-mediated response and cytokine signaling were significantly upregulated. At peak regeneration, we discovered that cell cycle genes were predominantly expressed to promote cell proliferation. Using the Betweenness centrality algorithm, we discovered that Jun is the key central gene in liver regeneration. Calcineurin inhibitors may inhibit liver regeneration, based on predictive modeling. ConclusionThere is a paucity of human literature in defining the molecular mechanisms of liver regeneration along a time continuum. Nonetheless, using an integrative computational analysis approach to the available high-throughput data, we determine that the oxidative stress response and cytokine signaling are key early after hepatectomy, whereas cell cycle control is important at peak regeneration. The transcription factor Jun is central to liver regeneration and a potential therapeutic target. Future studies of regeneration in humans along a time continuum are needed to better define the underlying mechanisms, and ultimately enhance care of patients with acute and chronic liver failure while awaiting transplant." "secciones" => array:4 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Introduction" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Results" ] 3 => array:2 [ "identificador" => "abst0020" "titulo" => "Conclusion" ] ] ] ] "apendice" => array:1 [ 0 => array:1 [ "seccion" => array:1 [ 0 => array:4 [ "apendice" => "The following are the supplementary data to this article:<elsevierMultimedia ident="fig0005"></elsevierMultimedia>" "etiqueta" => "Appendix A" "titulo" => "Supplementary data" "identificador" => "sec0075" ] ] ] ] "multimedia" => array:6 [ 0 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 3563 "Ancho" => 2833 "Tamanyo" => 644170 ] ] "descripcion" => array:1 [ "en" => "(A) Flow chart detailing the steps in our study, from data curation to final generation of networks based on protein-protein interactions. (B) Venn Diagram illustrating the distinct and overlapping dysregulated genes between human and mouse datasets. There were 75 dysregulated genes in humans, 502 dysregulated genes in mice, and 22 shared genes between human and mice (list of genes reported in Supplementary file 1). (C) Network analysis demonstrates the genes common to human and mouse early liver regeneration in the center, with the red triangles depicting up-modulated genes, and the green triangles depicting the down-modulated genes." ] ] 1 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 2758 "Ancho" => 3167 "Tamanyo" => 1139360 ] ] "descripcion" => array:1 [ "en" => "(A) The 4 significant growth factors (Epidermal Growth Factor (EGF), growth hormone 1 (GH1), angiopoietin-related growth factor (AGF), Hepatocyte growth factor (HGF)) and their impact on the key genes and biological processes in liver regeneration are illustrated. The biological processes (represented by diamond) as elucidated by the Ingenuity Pathway Analysis software include hepatocyte proliferation, liver regeneration, hepatic stellate cell proliferation, but can also promote carcinogenesis (pathological consequences are represented by cross). (B) Predicted effect of calcineurin inhibitors tacrolimus and cyclosporine on Liver REGENERATION. Based on knowledge of protein-protein interactions with the Calcineurin protein(s), depicted in Core Network#1 we can predict the inhibitory effect of calcineurin inhibitors on the downstream pathways in the acute phase after hepatectomy including NRF2-mediated oxidative stress response, glucocorticoid receptor signaling, and acute phase response signaling." ] ] 2 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Dataset \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">GEO \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Published (PMID) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Species \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Platform \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Data selection \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Comparison \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Liver regeneration stage \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GSE12720 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">19353763 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Homo sapiens \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GPL570 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Living donors only and HCV negative donors only were considered \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Baseline: biopsy no manipulation/cryo preservation vs after reperfusion (1.5h) \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Early \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GSE15239 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">N/A \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Homo sapiens \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GPL570 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">All patients were included \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Baseline: T0 vs 1.5h PHx \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Early \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3a \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GSE20427 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21719609 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mus musculus(5–6 months) CB6F1 mice \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GPL81 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Only young mice early AND late regeneration was considered \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Baseline T0 vs 0.5h PHxBaseline T0 vs 1.0h PHxBaseline T0 vs 2.0h PHxBaseline T0 vs 4.0h PHx \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Early \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3b \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GSE20427 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">21719609 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Mus musculus(5–6 months) CB6F1 mice \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">GPL1261 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Only young mice early AND late regeneration was considered \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Baseline T0 vs 24.h PHxBaseline T0 vs 38.h PHxBaseline T0 vs 48.h PHx \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Late \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2047829.png" ] ] ] ] "descripcion" => array:1 [ "en" => "List of datasets included in our integrative analysis." ] ] 3 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Canonical pathway \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">z score \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-value \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NRF2-mediated oxidative stress response \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.236068 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0259 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Acute phase response signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0237 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IL-10 signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0441 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LXR/RXR activation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0248 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IL-17A signaling in fibroblasts \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0571 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">PCP pathway \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0317 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">ErbB2-ErbB3 signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0290 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Toll-like receptor signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0270 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Glucocorticoid receptor signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0105 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">TGF-ß signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0230 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Role of macrophages, fibroblasts and endothelial cells in rheumatoid arthritis \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0097 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">PPAR signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0215 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">ErbB signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0204 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">TR/RXR activation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0204 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Cholecystokinin/Gastrin-mediated signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0198 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Corticotropin releasing hormone Signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0180 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IL-6 signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NaN \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.0157 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2047827.png" ] ] ] ] "descripcion" => array:1 [ "en" => "Canonical pathway analysis of the genes common to human and mouse regeneration resulted in a list of significantly enriched pathways (p-value <0.05), the activation status of each pathways is based on the z score (−2<z score=inhibition-high confidence, −2<z score<0=inhibition-less confidence; 0>z score<+2=activation-less confidence; z score<+2=strong activation-high confidence; NaN=no prediction possible)." ] ] 4 => array:8 [ "identificador" => "tbl0015" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at3" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Top canonical pathways \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">p-value \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Overlap \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Genes \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NRF2-mediated oxidative stress response \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.40E−06 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.6% 5/193 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HMOX1, JUN, JUND, MAFF, MAFK \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Acute phase response signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.58E−05 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.4% 4/169 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HMOX1, JUN, IL1RN, SERPINE1 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IL-10 signaling \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.70E−05 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">4.4% 3/68 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">HMOX1, JUN, IL1RN \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">LXR/RXR activation \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.61E−04 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">2.5% 3/121 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">APOA5, LDLR, IL1RN \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">IL-17A signaling in fibroblasts \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">6.03E−04 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">5.7% 2/35 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CEBPD, JUN \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2047828.png" ] ] ] ] "descripcion" => array:1 [ "en" => "The top canonical pathways associated with genes common to human and mouse regeneration, with p-value and gene overlap expressed in percentage and ratio of number of genes to the total elements of each pathway." ] ] 5 => array:5 [ "identificador" => "fig0005" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => false "mostrarDisplay" => true "figura" => array:1 [ 0 => array:4 [ "imagen" => "mmc1.jpeg" "Alto" => 1411 "Ancho" => 1750 "Tamanyo" => 250581 ] ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:43 [ 0 => array:3 [ "identificador" => "bib0220" "etiqueta" => "[1]" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Integrative genomics: liver regeneration and hepatocellular carcinoma" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:2 [ 0 => "Z. 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Original article

Mamatha Bhata,b,

Corresponding author

Mamatha.bhat@uhn.ca

Corresponding author at: Division of Gastroenterology, University Health Network and University of Toronto, Toronto General Research Institute, 585 University Avenue, 11PMB-183, Canada.

, Elisa Pasinia, Cristina Baciua, Marc Angelia, Atul Humara, Sonya Macparlanda,c, Jordan Feldb,d, Ian McGilvraya,c

a Multi Organ Transplant Program, University Health Network, Toronto, Canada

b Division of Gastroenterology and Hepatology, University Health Network and University of Toronto, Toronto, Canada

c Department of Laboratory Medicine and Pathobiology, Toronto, Canada

d Toronto Centre for Liver Disease, University of Toronto, Ontario, Canada

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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">1</span><span class="elsevierStyleSectionTitle" id="sect0030">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Liver regeneration occurs in response to insults that induce inflammation&#44; cell death and injury <a class="elsevierStyleCrossRef" href="#bib0220">&#91;1&#93;</a>&#44; and can be categorized into acute versus chronic regeneration&#46; Liver resection results in acute regeneration&#44; with activation of signaling pathways&#44; resulting ultimately in restoration of 100&#37; of the original hepatic volume and the original liver-to-body weight ratio <a class="elsevierStyleCrossRef" href="#bib0225">&#91;2&#93;</a>&#46; The liver is the only visceral organ with a tremendous capacity to regenerate in a time-limited manner with restoration of its original size&#46; When other organs are subject to resection&#44; there is limited local regeneration&#46; Chronic liver regeneration occurs in response to ongoing inflammation in chronic liver diseases &#40;viral&#44; autoimmune&#44; etc&#46;&#41;&#46; This persistent and ongoing hepatocyte proliferation in a genotoxic milieu results in the development of fibrosis and dysplasia over time <a class="elsevierStyleCrossRef" href="#bib0230">&#91;3&#93;</a>&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">The understanding of the molecular pathways underlying hepatic regeneration has been principally derived through findings from animal models <a class="elsevierStyleCrossRef" href="#bib0235">&#91;4&#93;</a>&#46; Animal studies have demonstrated the dramatic gene expression changes in liver regeneration&#46; The significant 3-fold increase in portal venous flow per hepatocyte induces various signaling changes <a class="elsevierStyleCrossRef" href="#bib0240">&#91;5&#93;</a>&#44; particularly early phase proteins such as urokinase plasminogen activator&#44; <span class="elsevierStyleItalic">Notch1</span>&#44; and beta-catenin <a class="elsevierStyleCrossRef" href="#bib0245">&#91;6&#93;</a>&#46; Extracellular matrix remodeling is stimulated&#44; and results in activation of Hepatocyte Growth Factor &#40;<span class="elsevierStyleItalic">HGF</span>&#41;&#46; <span class="elsevierStyleItalic">HGF</span>&#44; norepinephrine&#44; Interleukin-6 &#40;IL-6&#41;&#44; Tumor Necrosis Factor &#40;TNF-alpha&#41;&#44; serotonin&#44; and bile acids are all found in high concentrations in the blood early after hepatectomy <a class="elsevierStyleCrossRef" href="#bib0250">&#91;7&#93;</a>&#46; <span class="elsevierStyleItalic">HGF</span> and Epidermal Growth</p><p id="par0015" class="elsevierStylePara elsevierViewall">Factor &#40;<span class="elsevierStyleItalic">EGFR</span>&#41; stimulate mitosis of hepatocytes <a class="elsevierStyleCrossRef" href="#bib0255">&#91;8&#93;</a>&#46; In turn&#44; the Fibroblast Growth Factor &#40;<span class="elsevierStyleItalic">FGFR</span>&#41; produced by dividing hepatocytes triggers mitosis of endothelial and stellate cells <a class="elsevierStyleCrossRef" href="#bib0260">&#91;9&#93;</a>&#46; Hepatocytes then enter the cell cycle&#44; resulting in initiation of cell proliferation&#44; which in animals&#44; subsides by day 7 after hepatectomy&#46; In terms of ontology&#44; the types of activated genes include specific transcription factors&#44; regulators of cell cycle entry&#44; stress and inflammatory response proteins&#46; These proteins have a greater than 2-fold change in expression following hepatic resection <a class="elsevierStyleCrossRef" href="#bib0265">&#91;10&#93;</a>&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">Given that there is currently no bridge to liver transplantation &#40;LT&#41;&#44; an improved understanding of what constitutes normal liver regeneration and how to safely stimulate it has the potential for clinical impact&#46; Cirrhotic patients with worsening liver function while awaiting LT&#44; and those with fulminant liver failure are key patient groups who would stand to benefit from therapies generated from such an understanding&#46; Small-for-size syndrome is associated with adverse outcomes post-LT&#44; and would improve with stimulation of normal regeneration&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">The aim of this systematic integrative analysis of all publicly available data &#40;both human and animal&#41; on liver regeneration is to obtain a more global understanding of the molecular basis and pathways that drive liver regeneration&#46; Whereas most research studies tend to focus on a few key molecules&#44; integrative analysis tools allow for an overarching picture of the drivers of a condition&#46; Through this analysis&#44; we will identify the key genes and pathways specific to regeneration&#46; This would help appreciate potential therapeutic targets to drive this process as the clinical context demands it&#44; as in acute or chronic liver failure and small-for-size syndrome&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2</span><span class="elsevierStyleSectionTitle" id="sect0035">Methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;1</span><span class="elsevierStyleSectionTitle" id="sect0040">Data collection&#44; analysis and database compiling</span><p id="par0030" class="elsevierStylePara elsevierViewall">We retrieved all available high-throughput microarray gene expression datasets related to liver regeneration from Gene Expression Omnibus &#40;GEO&#41;&#44; a public functional genomics data repository containing high-throughput array data &#40;<a href="https://www.ncbi.nlm.nih.gov/geo">https&#58;&#47;&#47;www&#46;ncbi&#46;nlm&#46;nih&#46;gov&#47;geo</a>&#41;&#46; We also interrogated Pubmed and ArrayExpress as per the methodology described below&#46; A first search to identify datasets referring to human liver regeneration was performed using the following MeSH terms &#40;&#40;&#8220;liver regeneration&#8221;&#91;MeSH Terms&#93; <a class="elsevierStyleCrossRef" href="#bib0270">&#91;11&#93;</a> OR &#40;&#8220;liver&#8221;&#91;All Fields&#93; AND &#8220;regeneration&#8221;&#91;All Fields&#93;&#41; OR &#8220;liver regeneration&#8221;&#91;All Fields&#93;&#41; AND high&#91;All Fields&#93; AND throughput&#91;All Fields&#93; AND &#8220;humans&#8221;&#91;MeSH Terms&#93;&#41;&#46; We performed a second search to identify the datasets referring to <span class="elsevierStyleItalic">animals</span>&#44; using the following search terms &#40;&#40;&#8220;liver regeneration&#8221;&#91;MeSH Terms&#93; OR &#40;&#8220;liver&#8221;&#91;All Fields&#93; AND &#8220;regeneration&#8221;&#91;All Fields&#93;&#41; OR &#8220;liver regeneration&#8221;&#91;All Fields&#93;&#41; AND &#40;&#8220;transcriptome&#8221;&#91;All Fields&#93; OR &#40;&#8220;gene&#8221;&#91;All Fields&#93; AND &#8220;expression&#8221;&#91;All Fields&#93; AND &#8220;profile&#8221;&#91;All Fields&#93;&#41; OR &#8220;gene expression profile&#8221;&#91;All Fields&#93;&#41;&#41; AND &#8220;Mus musculus&#8221;&#91;porgn&#93; OR &#8220;Rattus norvegicus&#8221; &#91;porgn&#93; OR &#8220;Oryctolagus cuniculus&#8221;&#91;porgn&#93;&#46; We included all high-throughput gene expression profiling datasets comparing liver regeneration at different time points to normal liver tissue &#40;non-regenerating&#41; at baseline as control in humans and mice&#46; These datasets on GEO were analyzed using GEO2R &#40;<a href="https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html">https&#58;&#47;&#47;www&#46;ncbi&#46;nlm&#46;nih&#46;gov&#47;geo&#47;info&#47;geo2r&#46;html</a>&#41;&#44; a web tool available on the portal&#44; to identify genes differentially expressed between samples of liver regeneration and normal liver&#46; GEO2R compares original submitter-supplied processed data tables using the GEOquery <a class="elsevierStyleCrossRef" href="#bib0275">&#91;12&#93;</a> and limma package <a class="elsevierStyleCrossRef" href="#bib0280">&#91;13&#93;</a> from the Bioconductor project&#46; Following the instructions available online&#44; we collected all the dysregulated genes with an adjusted <span class="elsevierStyleItalic">p</span>-value <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#44; and an expression fold-change value below 0&#46;5 or above 1&#46;5&#46; The study workflow is illustrated in <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 1</a>&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;2</span><span class="elsevierStyleSectionTitle" id="sect0045">Regeneration core network analysis</span><p id="par0035" class="elsevierStylePara elsevierViewall">We used 22 deregulated genes found as commonly modulated between human early regeneration and mice for the core network analysis&#46; An overview of the study flow for the network analysis is illustrated in <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 1</a>&#46; To investigate the interactions between the modulated genes found in common between mice and human regenerative liver&#44; we used Ingenuity pathway analysis &#40;IPA Ingenuity<span class="elsevierStyleSup">&#174;</span> Systems&#44; <a href="http://www.ingenuity.com/">www&#46;ingenuity&#46;com</a>&#41;&#46; IPA identifies network interactions and pathway interactions between genes based on an extensive manually curated database of published gene interactions&#46; We uploaded the gene symbol and the associated expression value from the microarray data analyzed with GEO2R into IPA&#46; These genes&#44; called focus genes&#44; were overlaid onto a global molecular network based on Ingenuity knowledge database&#46; IPA includes a large repository of gene&#8211;phenotype associations&#44; molecular interactions&#44; chemical knowledge&#44; and regulatory events&#44; manually curated from scientific publications&#46; The networks based on these focus genes were algorithmically created based on their direct or indirect interactions&#46; Scores&#44; calculated on Fisher&#39;s exact test by IPA&#44; were obtained in order to rank networks based on their relevance to the genes initially uploaded&#46; The score evaluates the number of focus genes from our original dataset in the network&#44; and the size of the network to approximate how relevant this network is to the original list of focus genes&#46; The network is then shown as a graph representing the molecular relationships&#47;interactions as an edge &#40;line&#41; between genes or gene products &#40;nodes&#41;&#46; The connectivity of these nodes representing the genes is based on the data collected in the IPA knowledge base&#46; The node color indicates an up-modulation &#40;red&#41; or down-modulation &#40;green&#41;&#46; Nodes are displayed using various shapes to represent the functional role or class of gene product &#40;i&#46;e&#46; kinase&#44; transcription regulator&#44; enzyme&#41;&#46; Edges are displayed with various colors or labels to better describe the nature of the relationship between the nodes&#46; Common deregulated genes were mapped onto the core networks to explore their connection to biological function or disease affecting the liver&#46; The upstream regulators significantly associated with our list of Differentially Expressed Genes &#40;DEGs&#41; at different time points were obtained by IPA Ingenuity&#39;s knowledge base&#44; as well as the canonical pathways significantly associated with the genes in the input dataset&#46; The right-tailed Fisher&#39;s exact test was used to calculate a p-value determining the probability that the association or overlap between the genes listed in the dataset and a given pathway&#39;s neighborhood was due to chance alone&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">Based on the modulation of the DEGs&#44; IPA is able to calculate a <span class="elsevierStyleItalic">z</span> score&#44; defined as a statistical measure of correlation between relationship direction and a given set of modulated genes&#44; as a value to measure &#8220;the non-randomness&#8221; of directionality&#46; <span class="elsevierStyleItalic">Z</span> score &#60;&#8722;2 or &#62; &#43;2 is considered significant&#46; Negative <span class="elsevierStyleItalic">z</span> score &#60;&#8722;2 predicts an inhibition with high confidence and a positive score &#62;&#43;2 predicts activation with strong confidence&#46; Outside these values&#44; the prediction of activation or inhibition is less confident&#46; In some cases&#44; the <span class="elsevierStyleItalic">z</span> score cannot be calculated&#44; if there is not enough information stored on IPA knowledge base&#44; in which case there is no available prediction&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;3</span><span class="elsevierStyleSectionTitle" id="sect0050">Hepatic regeneration protein&#8211;protein interaction network analysis</span><p id="par0045" class="elsevierStylePara elsevierViewall">We merged and overlaid core networks with related &#8220;functions and diseases&#8221; to determine genes associated with specific hepatic biological and pathological processes according to the IPA knowledge base&#46; We used the MAP &#40;Molecular Activity Predictor&#41; tool to predict the crosstalk relationship among our genes and their interactors based on their modulation for the networks considered&#46; The drug database was overlaid with Core Network &#35;1 in early regeneration to identify the possible effect of calcineurin inhibitors on liver regeneration&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">For later regeneration at 48<span class="elsevierStyleHsp" style=""></span>h&#44; Core Network &#35;3 was overlaid with canonical pathways and the enriched pathway&#44; and their relationship with the network is shown in <a class="elsevierStyleCrossRef" href="#sec0075">Supplementary Fig&#46; 5A</a>&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">2&#46;4</span><span class="elsevierStyleSectionTitle" id="sect0055">Calculation of centrality in protein&#8211;protein interaction networks</span><p id="par0055" class="elsevierStylePara elsevierViewall">For all the 22 DEGs in early and late regeneration&#44; we retrieved the known interactors using the Integrated Interactions Database &#40;IID at <a href="http://ophid.utoronto.ca/iid">http&#58;&#47;&#47;ophid&#46;utoronto&#46;ca&#47;iid</a>&#41; <a class="elsevierStyleCrossRef" href="#bib0285">&#91;14&#93;</a> version 2016-03&#44; selecting only data in humans&#44; mice and liver tissue&#46; Betweenness centrality was calculated using the betweenness function in the igraph package version 1&#46;0&#46;1 <a class="elsevierStyleCrossRef" href="#bib0290">&#91;15&#93;</a>&#44; in R version 3&#46;3&#46;1 &#40;<a href="https://www.r-project.org/">https&#58;&#47;&#47;www&#46;R-project&#46;org&#47;</a>&#41;&#46;</p></span></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3</span><span class="elsevierStyleSectionTitle" id="sect0060">Results</span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3&#46;1</span><span class="elsevierStyleSectionTitle" id="sect0065">Datasets pertaining to liver regeneration</span><p id="par0060" class="elsevierStylePara elsevierViewall">We retrieved 7 datasets for humans and 16 datasets for mice from GEO&#46; However&#44; most patient datasets were not investigating liver regeneration at all&#44; and most mouse datasets did not include different time points&#46; The final selected studies are listed in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#44; one of which is a mouse study establishing gene expression data for both early and late liver regeneration&#46; The other two datasets were human&#58; one was obtained from 8 living donor liver grafts following reperfusion&#44; the other was from 3 patients of varying ages &#40;1&#46;5&#44; 42 and 81 years old&#41; post-hepatectomy&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">3&#46;2</span><span class="elsevierStyleSectionTitle" id="sect0070">Significantly dysregulated genes in liver regeneration</span><p id="par0065" class="elsevierStylePara elsevierViewall">Identification of the DEGs early post-hepatectomy that were overlapping in humans and mice&#44; as opposed to distinct to each&#44; was performed using a Venn diagram as shown in <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 1</a>&#46; We focused on these overlapping genes for further analysis&#44; given that these overlapping genes demonstrated consistency with respect to their involvement in early liver regeneration&#46; The 22 genes in common&#44; and their modulation are listed in <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 1</a>&#46;</p><p id="par0070" class="elsevierStylePara elsevierViewall">Canonical pathway analysis revealed the key signaling cascades in which these genes are involved&#44; as listed in <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#44; highlighting the importance of inflammation as a driver of the hepatic regenerative process&#46; The NRF2-mediated Oxidative Stress Response was not only the most significantly enriched pathway in early regeneration&#44; but also predicted to be activated &#40;<span class="elsevierStyleItalic">z</span> score value &#62;2&#41; based on the modulation status of the 22 DEGs&#46; Cytokine-associated pathways such as <span class="elsevierStyleItalic">IL-10</span>&#44; <span class="elsevierStyleItalic">IL-17</span> and <span class="elsevierStyleItalic">IL-6</span>&#44; previously highlighted in the regeneration literature <a class="elsevierStyleCrossRef" href="#bib0295">&#91;16&#93;</a>&#44; were also significantly associated with our gene list&#46; <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> reports the key genes among the 22 involved in the most significant pathways&#44; along with their degree of involvement according to percentage of the total number of genes in the pathway&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0075" class="elsevierStylePara elsevierViewall">Additional analysis of upstream activators of the identified pathways using IPA revealed 16 molecules including 5 cytokines &#40;<span class="elsevierStyleItalic">TNF&#44; IL-1B&#44; IFN-G&#44; IL-6&#44; IL-1A</span>&#41;&#44; 3 transcriptional factors &#40;<span class="elsevierStyleItalic">EGR1&#44; PDX1&#44; NUPR1</span>&#41;&#44; 4 growth factors &#40;Epidermal Growth Factor &#40;<span class="elsevierStyleItalic">EGF</span>&#41;&#44; growth hormone 1 &#40;<span class="elsevierStyleItalic">GH1</span>&#41;&#44; angiopoietin-related growth factor &#40;AGF&#41;&#44; hepatocyte growth factor &#40;<span class="elsevierStyleItalic">HGF</span>&#41;&#41; and 4 kinases &#40;<span class="elsevierStyleItalic">p38&#44;</span><span class="elsevierStyleItalic">ERK&#44; JNK&#44; MAP2K1&#47;2</span>&#41;&#46; The impact of the significant growth factors on the key genes and biological processes in liver regeneration are illustrated in <a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 2</a>&#46;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0080" class="elsevierStylePara elsevierViewall">The predicted impact of calcineurin inhibitors is illustrated in <a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 2</a>&#46; Integration of Core Network &#35;1 and drugs targeting calcineurin suggests that the most commonly used immunosuppressants in liver transplantation adversely affect liver regeneration&#46;</p><p id="par0085" class="elsevierStylePara elsevierViewall">Liver regeneration at the later time point of 48<span class="elsevierStyleHsp" style=""></span>h appears to be driven by cell cycle-related genes&#44; as illustrated in <a class="elsevierStyleCrossRef" href="#sec0075">Supplementary Fig&#46; 5A</a>&#46;</p></span></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleLabel">4</span><span class="elsevierStyleSectionTitle" id="sect0075">Discussion</span><p id="par0090" class="elsevierStylePara elsevierViewall">The liver is unique in being capable of rapid regeneration in response to various stimuli&#44; such as injury and hepatectomy&#46; Although it is a key physiological process shown to be well-orchestrated based on in vivo data&#44; liver regeneration in humans along a time continuum remains incompletely understood&#46; In this integrative analysis of high-throughput datasets&#44; we systematically deciphered the molecular basis of liver regeneration by studying and analyzing all publicly available high-throughput gene expression datasets&#46; We discovered that the high-throughput data in liver regeneration has been limited to the early phase of regeneration&#46; Particularly&#44; the data in humans has been restricted to the first 1&#46;5<span class="elsevierStyleHsp" style=""></span>h after hepatectomy&#44; which may be more representative of acute stress than a regenerative response&#46; Given that liver regeneration in humans is a process that takes up to 12 weeks to complete following hepatectomy for living donation&#44; with a percentage reconstitution of 80&#37;<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>13&#37; <a class="elsevierStyleCrossRef" href="#bib0300">&#91;17&#93;</a>&#44; this focus on the early period after hepatectomy hinders our understanding of human liver regeneration along a time continuum&#46; The mouse data was more representative of changes over a time continuum&#44; and there were certainly overlapping genes between humans and mice in the early phase based on integration of the publicly available data&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">We discovered that the early phase after hepatectomy is dominated by inflammation-associated pathways&#44; particularly nuclear factor erythroid 2-related factor 2 &#40;<span class="elsevierStyleItalic">Nrf2</span>&#41; oxidative stress response <a class="elsevierStyleCrossRef" href="#bib0305">&#91;18&#93;</a>&#46; <span class="elsevierStyleItalic">Nrf2</span> is a transcription factor that plays a key role in cytoprotection in the face of ongoing injury from reactive oxygen species and radicals induced by viruses&#44; toxins&#44; and lipid accumulation in the liver <a class="elsevierStyleCrossRef" href="#bib0310">&#91;19&#93;</a>&#46; The crucial role of <span class="elsevierStyleItalic">Nrf2</span> in activating liver regeneration has been supported by in vivo data <a class="elsevierStyleCrossRef" href="#bib0305">&#91;18&#93;</a>&#46; Increased oxidative stress leads to insulin&#47;Insulin Growth Factor resistance in <span class="elsevierStyleItalic">Nrf2</span>-deficient hepatocytes&#44; which in turn prevents efficient regeneration <a class="elsevierStyleCrossRef" href="#bib0315">&#91;20&#93;</a>&#46; It has been shown that Nrf2 protects the liver from toxin-mediated damage and decreases fibrogenesis&#44; which is partly due to target genes regulated by Nrf2 in hepatocytes <a class="elsevierStyleCrossRef" href="#bib0320">&#91;21&#93;</a>&#46; The <span class="elsevierStyleItalic">IL-6</span><a class="elsevierStyleCrossRef" href="#bib0325">&#91;22&#93;</a> and <span class="elsevierStyleItalic">IL-17</span><a class="elsevierStyleCrossRef" href="#bib0295">&#91;16&#93;</a> -associated pathways were also key to liver regeneration&#44; as previously confirmed in the literature&#46; We also identified the <span class="elsevierStyleItalic">IL-10</span> pathway as a top canonical pathway&#44; which concords with previous studies demonstrating that the anti-inflammatory <span class="elsevierStyleItalic">IL-10</span> inhibits liver regeneration <a class="elsevierStyleCrossRef" href="#bib0330">&#91;23&#93;</a>&#46; The role of inflammatory cells in regeneration has previously been highlighted in a systems analysis of non-parenchymal cell modulation in liver repair&#44; where a balance of inflammatory signals and growth factors was reported <a class="elsevierStyleCrossRef" href="#bib0335">&#91;24&#93;</a>&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall">The <span class="elsevierStyleItalic">LXR</span>&#47;Retinoid &#215; Receptor pathway was among the top canonical pathways identified&#44; and has clearly been shown to play a role in liver regeneration in vivo <a class="elsevierStyleCrossRefs" href="#bib0340">&#91;25&#8211;28&#93;</a>&#46; In fact&#44; retinoid stores regulate cell cycle gene expression&#44; and stimulate liver regeneration <a class="elsevierStyleCrossRefs" href="#bib0350">&#91;27&#44;28&#93;</a>&#46; We identified immediate early genes such as <span class="elsevierStyleItalic">JUN</span> and <span class="elsevierStyleItalic">MYC</span> as being central to the network in early regeneration after two-thirds hepatectomy&#44; which is compatible with the literature<a class="elsevierStyleCrossRef" href="#bib0360">&#91;29&#93;</a>&#46;</p><p id="par0105" class="elsevierStylePara elsevierViewall">Our assessment of the genes and pathways in peak liver regeneration was limited to a single mouse dataset due to the paucity of high-throughput data&#46; Cell cycle genes were the most clearly implicated in late regeneration&#44; along with <span class="elsevierStyleItalic">Wnt</span>&#47;B-catenin and <span class="elsevierStyleItalic">PI3K&#47;Akt&#47;mTOR</span> pathways&#46;</p><p id="par0110" class="elsevierStylePara elsevierViewall">A key finding in our study was the discovery of that the transcription factor <span class="elsevierStyleItalic">JUN</span> is a gene central to regeneration&#46; This indicates that the presence of Jun in the protein-protein interaction networks is essential to driving hepatic regeneration&#46; The importance of this critical growth-related gene has been previously demonstrated in stimulating hepatocyte proliferation <a class="elsevierStyleCrossRef" href="#bib0365">&#91;30&#93;</a>&#44; with a liver-specific gene deletion in mice resulting in impaired liver regeneration post-hepatectomy <a class="elsevierStyleCrossRef" href="#bib0370">&#91;31&#93;</a>&#46; Additionally&#44; <span class="elsevierStyleItalic">JUN</span> has been shown to be critical in the early regenerative response to liver injury <a class="elsevierStyleCrossRefs" href="#bib0375">&#91;32&#44;33&#93;</a> and small-for-size syndrome in vivo <a class="elsevierStyleCrossRef" href="#bib0385">&#91;34&#93;</a>&#46;</p><p id="par0115" class="elsevierStylePara elsevierViewall">Our analysis of biological processes revealed an overlap of genes involved in liver regeneration and hepatic carcinogenesis&#46; This has also been verified in the literature&#44; where hierarchical clustering of liver regeneration and hepatocellular carcinoma-related demonstrated overlapping involvement in the cell cycle and cell death&#46; Rapid proliferation in the context of injury carries the risk of accumulation of mutations <a class="elsevierStyleCrossRef" href="#bib0390">&#91;35&#93;</a>&#46; Chronic liver regeneration in response to genotoxic liver injury also is associated with development of dysplasia <a class="elsevierStyleCrossRefs" href="#bib0395">&#91;36&#44;37&#93;</a>&#46;</p><p id="par0120" class="elsevierStylePara elsevierViewall">Given that living donor liver transplant recipients have liver regeneration in the first 3 months after transplant&#44; we wished to investigate the potential effect of calcineurin inhibitors &#40;the most commonly used immunosuppressants after liver transplant&#41; on the pathways upregulated post-hepatectomy using the IPA software&#46; Specifically&#44; we situated the effect of the calcineurin proteins in these pathways early after hepatectomy&#44; and saw that <span class="elsevierStyleItalic">Nrf2</span>-mediated oxidative stress response&#44; the acute phase response and glucocorticoid signaling <a class="elsevierStyleCrossRef" href="#bib0405">&#91;38&#93;</a> would all be potentially impaired with calcineurin inhibition&#46; It is possible that calcineurin inhibitors prevent liver regeneration in response to genotoxic injury such as viruses and lipid accumulation&#44; and may be a link to the hastened progression of fibrosis following liver transplantation <a class="elsevierStyleCrossRef" href="#bib0410">&#91;39&#93;</a>&#46; However&#44; there have previously been reports of increased mitotic activity in the presence of tacrolimus <a class="elsevierStyleCrossRefs" href="#bib0415">&#91;40&#44;41&#93;</a> and cyclosporine <a class="elsevierStyleCrossRef" href="#bib0425">&#91;42&#93;</a>&#44; although regenerative capacity and the resulting liver volume have not been assessed&#46; Therefore&#44; the exact impact of calcineurin inhibitors on hepatic regeneration remains to be determined&#46; The only alternative baseline immunosuppressant is sirolimus&#44; which as an mTOR inhibitor is an antiproliferative agent and is contraindicated in the first month post-transplant due to the heightened risk of fatal hepatic artery thrombosis&#46;</p><p id="par0125" class="elsevierStylePara elsevierViewall">The principal limitation of this integrative analysis is the paucity of publicly available high-throughput gene expression data&#46; Given that our understanding of human regeneration is limited to the early phase post-hepatectomy or living donor liver transplant&#44; the data is more influenced by acute stress and inflammatory processes such as ischemia-reperfusion injury&#46; We did find animal data pertaining to what corresponds to their peak liver regeneration time point&#44; however there was no such corresponding human data available&#46; There was no high-throughput data available on termination of liver regeneration&#44; therefore this phase could not be studied&#46; It has been shown that the liver has an inherent hepatostat&#44; regulating the organ size achieved <a class="elsevierStyleCrossRef" href="#bib0430">&#91;43&#93;</a>&#46; However&#44; the mechanisms underlying termination of regeneration are not well understood due to paucity of data&#46; Nonetheless&#44; limited data does indicate the key role of extracellular matrix-driven intracellular signaling involving proteins such as integrin-linked kinase&#44; Glypican 3&#44; C&#47;EBP&#945;&#44; and HNF4&#945; <a class="elsevierStyleCrossRef" href="#bib0430">&#91;43&#93;</a>&#46;</p><p id="par0130" class="elsevierStylePara elsevierViewall">The literature on hepatic regeneration has been extensively influenced by animal data&#44; and it is doubtful that this is an accurate representation of human regeneration&#44; especially given that it occurs over a longer time frame&#46; Nonetheless&#44; we did uncover a commonality in genes expressed early after hepatectomy&#46; Another important aspect is the likely contribution of different liver cell subsets such as hepatocytes&#44; macrophages&#44; and other immune cells to the regenerative process&#46; The datasets obtained from the public literature represent gene expression in whole liver tissue&#44; and do not enhance our understanding of how individual cell types contribute to hepatic regeneration&#46;</p><p id="par0135" class="elsevierStylePara elsevierViewall">In summary&#44; liver regeneration is a key biological process that occurs in various contexts&#44; from chronic injury in liver disease&#44; to partial hepatectomy performed for various indications&#44; to living donor liver transplantation&#46; Our systematic review and integrative analysis of high-throughput gene expression data in liver regeneration provides a picture of the processes involved early after hepatectomy and at peak liver regeneration&#46; Inflammatory pathways predominate the early period after hepatectomy&#44; and cell cycle-related genes drive regeneration at its peak&#44; at least in animals&#46; Additionally&#44; <span class="elsevierStyleItalic">JUN</span> is a transcription factor that is central to regeneration&#44; and is thus critical for this physiological process&#46; Based on our centrality analysis of high-throughput data and protein&#8211;protein interactions&#44; without the presence of Jun&#44; the regenerative process would fail to occur&#46; Validation of the central role of Jun is necessary to determine its importance as a therapeutic target to stimulate liver regeneration&#46; Nonetheless&#44; data to better understand the molecular basis of liver regeneration in humans on a time continuum is lacking&#46; The effect of calcineurin inhibitors on liver regeneration may play a role in hastened fibrosis post-liver transplantation&#46; Overall&#44; our study strongly supports the need for more extensive data on liver regeneration&#44; with the potential to significantly enhance the care of patients with acute and chronic liver failure as well as small-for-size syndrome&#46;</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Abbreviations</span><p id="par0140" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleDefList"><span class="elsevierStyleDefTerm">DEG</span><span class="elsevierStyleDefDescription"><p id="par0145" class="elsevierStylePara elsevierViewall">differential expressed genes</p></span><span class="elsevierStyleDefTerm">EGF</span><span class="elsevierStyleDefDescription"><p id="par0150" class="elsevierStylePara elsevierViewall">epidermal growth factor</p></span><span class="elsevierStyleDefTerm">FGFR</span><span class="elsevierStyleDefDescription"><p id="par0155" class="elsevierStylePara elsevierViewall">fibroblast growth factor</p></span><span class="elsevierStyleDefTerm">GEO</span><span class="elsevierStyleDefDescription"><p id="par0160" class="elsevierStylePara elsevierViewall">gene expression omnibus</p></span><span class="elsevierStyleDefTerm">GH1</span><span class="elsevierStyleDefDescription"><p id="par0165" class="elsevierStylePara elsevierViewall">growth hormone 1</p></span><span class="elsevierStyleDefTerm">HGF</span><span class="elsevierStyleDefDescription"><p id="par0170" class="elsevierStylePara elsevierViewall">hepatocyte growth factor</p></span><span class="elsevierStyleDefTerm">IL-6</span><span class="elsevierStyleDefDescription"><p id="par0175" class="elsevierStylePara elsevierViewall">interleukin 6</p></span><span class="elsevierStyleDefTerm">IPA</span><span class="elsevierStyleDefDescription"><p id="par0180" class="elsevierStylePara elsevierViewall">ingenuity pathway analysis</p></span><span class="elsevierStyleDefTerm">LT</span><span class="elsevierStyleDefDescription"><p id="par0185" class="elsevierStylePara elsevierViewall">liver transplant</p></span><span class="elsevierStyleDefTerm">TNF-Alpha</span><span class="elsevierStyleDefDescription"><p id="par0190" class="elsevierStylePara elsevierViewall">tumor necrosis factor alpha</p></span></span></p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Authors contributions</span><p id="par0195" class="elsevierStylePara elsevierViewall">MB study design&#44; and writing of manuscript&#46; CB&#44; EP and MA&#59; data collection&#44; analysis and compiling&#46; AH&#44; SM&#44; JF&#44; IM and MB input into study design and final manuscript&#46;</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Conflict of interest</span><p id="par0200" class="elsevierStylePara elsevierViewall">There is no conflict of interest concerning this study&#46;</p></span></span>"
    "textoCompletoSecciones" => array:1 [
      "secciones" => array:10 [
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          "identificador" => "xres1199032"
          "titulo" => "Abstract"
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              "identificador" => "abst0005"
              "titulo" => "Introduction"
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            1 => array:2 [
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              "titulo" => "Methods"
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              "titulo" => "Conclusion"
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        ]
        1 => array:2 [
          "identificador" => "xpalclavsec1117472"
          "titulo" => "Keywords"
        ]
        2 => array:2 [
          "identificador" => "sec0005"
          "titulo" => "Introduction"
        ]
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          "titulo" => "Methods"
          "secciones" => array:4 [
            0 => array:2 [
              "identificador" => "sec0015"
              "titulo" => "Data collection&#44; analysis and database compiling"
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            1 => array:2 [
              "identificador" => "sec0020"
              "titulo" => "Regeneration core network analysis"
            ]
            2 => array:2 [
              "identificador" => "sec0025"
              "titulo" => "Hepatic regeneration protein&#8211;protein interaction network analysis"
            ]
            3 => array:2 [
              "identificador" => "sec0030"
              "titulo" => "Calculation of centrality in protein&#8211;protein interaction networks"
            ]
          ]
        ]
        4 => array:3 [
          "identificador" => "sec0035"
          "titulo" => "Results"
          "secciones" => array:2 [
            0 => array:2 [
              "identificador" => "sec0040"
              "titulo" => "Datasets pertaining to liver regeneration"
            ]
            1 => array:2 [
              "identificador" => "sec0045"
              "titulo" => "Significantly dysregulated genes in liver regeneration"
            ]
          ]
        ]
        5 => array:2 [
          "identificador" => "sec0050"
          "titulo" => "Discussion"
        ]
        6 => array:2 [
          "identificador" => "sec0055"
          "titulo" => "Abbreviations"
        ]
        7 => array:2 [
          "identificador" => "sec0060"
          "titulo" => "Authors contributions"
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        8 => array:2 [
          "identificador" => "sec0065"
          "titulo" => "Conflict of interest"
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        9 => array:1 [
          "titulo" => "References"
        ]
      ]
    ]
    "pdfFichero" => "main.pdf"
    "tienePdf" => true
    "fechaRecibido" => "2018-01-19"
    "fechaAceptado" => "2018-07-01"
    "PalabrasClave" => array:1 [
      "en" => array:1 [
        0 => array:4 [
          "clase" => "keyword"
          "titulo" => "Keywords"
          "identificador" => "xpalclavsec1117472"
          "palabras" => array:3 [
            0 => "Liver Regeneration"
            1 => "Hepatectomy"
            2 => "Transplant integrative analysis"
          ]
        ]
      ]
    ]
    "tieneResumen" => true
    "resumen" => array:1 [
      "en" => array:3 [
        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Liver regeneration is a normal response to liver injury&#46; The aim of this study was to determine the molecular basis of liver regeneration&#44; through an integrative analysis of high-throughput gene expression datasets&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">We identified and curated datasets pertaining to liver regeneration from the Gene Expression Omnibus&#44; where regenerating liver tissue was compared to healthy liver samples&#46; The key dysregulated genes and pathways were identified using Ingenuity Pathway Analysis software&#46; There were three eligible datasets in total&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">In the early phase after hepatectomy&#44; inflammatory pathways such as Nrf2 oxidative stress-mediated response and cytokine signaling were significantly upregulated&#46; At peak regeneration&#44; we discovered that cell cycle genes were predominantly expressed to promote cell proliferation&#46; Using the Betweenness centrality algorithm&#44; we discovered that Jun is the key central gene in liver regeneration&#46; Calcineurin inhibitors may inhibit liver regeneration&#44; based on predictive modeling&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Conclusion</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">There is a paucity of human literature in defining the molecular mechanisms of liver regeneration along a time continuum&#46; Nonetheless&#44; using an integrative computational analysis approach to the available high-throughput data&#44; we determine that the oxidative stress response and cytokine signaling are key early after hepatectomy&#44; whereas cell cycle control is important at peak regeneration&#46; The transcription factor Jun is central to liver regeneration and a potential therapeutic target&#46; Future studies of regeneration in humans along a time continuum are needed to better define the underlying mechanisms&#44; and ultimately enhance care of patients with acute and chronic liver failure while awaiting transplant&#46;</p></span>"
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            "titulo" => "Introduction"
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            "titulo" => "Methods"
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          2 => array:2 [
            "identificador" => "abst0015"
            "titulo" => "Results"
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          3 => array:2 [
            "identificador" => "abst0020"
            "titulo" => "Conclusion"
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      0 => array:1 [
        "seccion" => array:1 [
          0 => array:4 [
            "apendice" => "<p id="par0210" class="elsevierStylePara elsevierViewall">The following are the supplementary data to this article&#58;<elsevierMultimedia ident="fig0005"></elsevierMultimedia></p>"
            "etiqueta" => "Appendix A"
            "titulo" => "Supplementary data"
            "identificador" => "sec0075"
          ]
        ]
      ]
    ]
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      0 => array:7 [
        "identificador" => "fig0010"
        "etiqueta" => "Fig&#46; 1"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
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          0 => array:4 [
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        "descripcion" => array:1 [
          "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">&#40;A&#41; Flow chart detailing the steps in our study&#44; from data curation to final generation of networks based on protein-protein interactions&#46; &#40;B&#41; Venn Diagram illustrating the distinct and overlapping dysregulated genes between human and mouse datasets&#46; There were 75 dysregulated genes in humans&#44; 502 dysregulated genes in mice&#44; and 22 shared genes between human and mice &#40;list of genes reported in Supplementary file 1&#41;&#46; &#40;C&#41; Network analysis demonstrates the genes common to human and mouse early liver regeneration in the center&#44; with the red triangles depicting up-modulated genes&#44; and the green triangles depicting the down-modulated genes&#46;</p>"
        ]
      ]
      1 => array:7 [
        "identificador" => "fig0015"
        "etiqueta" => "Fig&#46; 2"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
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        "descripcion" => array:1 [
          "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">&#40;A&#41; The 4 significant growth factors &#40;Epidermal Growth Factor &#40;EGF&#41;&#44; growth hormone 1 &#40;GH1&#41;&#44; angiopoietin-related growth factor &#40;AGF&#41;&#44; Hepatocyte growth factor &#40;HGF&#41;&#41; and their impact on the key genes and biological processes in liver regeneration are illustrated&#46; The biological processes &#40;represented by diamond&#41; as elucidated by the Ingenuity Pathway Analysis software include hepatocyte proliferation&#44; liver regeneration&#44; hepatic stellate cell proliferation&#44; but can also promote carcinogenesis &#40;pathological consequences are represented by cross&#41;&#46; &#40;B&#41; Predicted effect of calcineurin inhibitors tacrolimus and cyclosporine on Liver REGENERATION&#46; Based on knowledge of protein-protein interactions with the Calcineurin protein&#40;s&#41;&#44; depicted in Core Network&#35;1 we can predict the inhibitory effect of calcineurin inhibitors on the downstream pathways in the acute phase after hepatectomy including NRF2-mediated oxidative stress response&#44; glucocorticoid receptor signaling&#44; and acute phase response signaling&#46;</p>"
        ]
      ]
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          0 => array:3 [
            "identificador" => "at1"
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                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Dataset&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">GEO&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Published &#40;PMID&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Species&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Platform&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Data selection&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Comparison&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Liver regeneration stage&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">GSE12720&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleInterRef" id="intr0005" href="pmid:19353763">19353763</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Homo sapiens&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">GPL570&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Living donors only and HCV negative donors only were considered&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Baseline&#58; biopsy no manipulation&#47;cryo preservation vs after reperfusion &#40;1&#46;5<span class="elsevierStyleHsp" style=""></span>h&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Early&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">GSE15239&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">N&#47;A&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Homo sapiens&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">GPL570&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">All patients were included&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Baseline&#58; T0 vs 1&#46;5<span class="elsevierStyleHsp" style=""></span>h PHx&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Early&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3a&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">GSE20427&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleInterRef" id="intr0010" href="pmid:21719609">21719609</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mus musculus&#40;5&#8211;6 months&#41; CB6F1 mice&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">GPL81&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Only young mice early AND late regeneration was considered&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Baseline T0 vs 0&#46;5<span class="elsevierStyleHsp" style=""></span>h PHxBaseline T0 vs 1&#46;0<span class="elsevierStyleHsp" style=""></span>h PHxBaseline T0 vs 2&#46;0<span class="elsevierStyleHsp" style=""></span>h PHxBaseline T0 vs 4&#46;0<span class="elsevierStyleHsp" style=""></span>h PHx&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Early&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3b&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">GSE20427&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleInterRef" id="intr0015" href="pmid:21719609">21719609</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mus musculus&#40;5&#8211;6 months&#41; CB6F1 mice&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">GPL1261&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Only young mice early AND late regeneration was considered&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Baseline T0 vs 24&#46;h PHxBaseline T0 vs 38&#46;h PHxBaseline T0 vs 48&#46;h PHx&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Late&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab2047829.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">List of datasets included in our integrative analysis&#46;</p>"
        ]
      ]
      3 => array:8 [
        "identificador" => "tbl0010"
        "etiqueta" => "Table 2"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at2"
            "detalle" => "Table "
            "rol" => "short"
          ]
        ]
        "tabla" => array:1 [
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Canonical pathway&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">z</span> score&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">p</span>-value&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NRF2-mediated oxidative stress response&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2&#46;236068&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0259&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Acute phase response signaling&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0237&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">IL-10 signaling&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0441&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">LXR&#47;RXR activation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0248&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">IL-17A signaling in fibroblasts&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0571&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">PCP pathway&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0317&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">ErbB2-ErbB3 signaling&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0290&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Toll-like receptor signaling&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0270&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Glucocorticoid receptor signaling&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0105&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">TGF-&#223; signaling&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0230&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Role of macrophages&#44; fibroblasts and endothelial cells in rheumatoid arthritis&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0097&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">PPAR signaling&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0215&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">ErbB signaling&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0204&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">TR&#47;RXR activation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0204&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Cholecystokinin&#47;Gastrin-mediated signaling&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0198&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Corticotropin releasing hormone Signaling&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;0180&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">IL-6 signaling&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">NaN&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;0157&nbsp;\t\t\t\t\t\t\n
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          "en" => "<p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Canonical pathway analysis of the genes common to human and mouse regeneration resulted in a list of significantly enriched pathways &#40;<span class="elsevierStyleItalic">p</span>-value &#60;0&#46;05&#41;&#44; the activation status of each pathways is based on the <span class="elsevierStyleItalic">z</span> score &#40;&#8722;2<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">z</span> score<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>inhibition-high confidence&#44; &#8722;2<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">z</span> score<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>inhibition-less confidence&#59; 0<span class="elsevierStyleHsp" style=""></span>&#62;<span class="elsevierStyleHsp" style=""></span><span class="elsevierStyleItalic">z</span> score<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>2<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>activation-less confidence&#59; <span class="elsevierStyleItalic">z</span> score<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>2<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>strong activation-high confidence&#59; NaN<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>no prediction possible&#41;&#46;</p>"
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                  \t\t\t\t">Acute phase response signaling&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2&#46;58E&#8722;05&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">HMOX1&#44; JUN&#44; IL1RN&#44; SERPINE1&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">4&#46;70E&#8722;05&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">2&#46;61E&#8722;04&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">IL-17A signaling in fibroblasts&nbsp;\t\t\t\t\t\t\n
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                          "autores" => array:2 [
                            0 => "Z&#46; Coban"
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                    0 => array:2 [
                      "doi" => "10.1002/jcb.24104"
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                        "tituloSerie" => "J Cell Biochem"
                        "fecha" => "2012"
                        "volumen" => "113"
                        "paginaInicial" => "2179"
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                  "contribucion" => array:1 [
                    0 => array:2 [
                      "titulo" => "Hepatostat&#58; liver regeneration and normal liver tissue maintenance"
                      "autores" => array:1 [
                        0 => array:2 [
                          "etal" => false
                          "autores" => array:1 [
                            0 => "G&#46;K&#46; Michalopoulos"
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                        "fecha" => "2013"
                        "volumen" => "3"
                        "paginaInicial" => "485"
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Article information

ISSN: 16652681
Original language: English

DOI:10.1016/j.aohep.2018.07.003

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Year/Month	Html	Pdf	Total

2024 November	6	0	6
2024 October	67	9	76
2024 September	78	4	82
2024 August	55	6	61
2024 July	79	8	87
2024 June	102	11	113
2024 May	95	8	103
2024 April	66	14	80
2024 March	52	6	58
2024 February	59	9	68
2024 January	70	9	79
2023 December	81	6	87
2023 November	78	11	89
2023 October	111	18	129
2023 September	53	5	58
2023 August	51	8	59
2023 July	57	6	63
2023 June	63	2	65
2023 May	109	13	122
2023 April	84	1	85
2023 March	59	9	68
2023 February	57	7	64
2023 January	57	6	63
2022 December	76	11	87
2022 November	83	11	94
2022 October	70	13	83
2022 September	47	14	61
2022 August	45	10	55
2022 July	101	21	122
2022 June	38	15	53
2022 May	33	16	49
2022 April	49	15	64
2022 March	48	19	67
2022 February	31	4	35
2022 January	61	13	74
2021 December	30	9	39
2021 November	61	10	71
2021 October	52	17	69
2021 September	41	10	51
2021 August	22	9	31
2021 July	44	11	55
2021 June	29	8	37
2021 May	42	11	53
2021 April	116	12	128
2021 March	82	27	109
2021 February	69	20	89
2021 January	67	24	91
2020 December	58	23	81
2020 November	49	25	74
2020 October	21	12	33
2020 September	23	16	39
2020 August	27	9	36
2020 July	28	6	34
2020 June	38	8	46
2020 May	89	25	114
2020 April	26	12	38
2020 March	37	17	54
2020 February	23	8	31
2020 January	30	16	46
2019 December	27	14	41
2019 November	16	13	29
2019 October	41	17	58
2019 September	38	10	48
2019 August	20	9	29
2019 July	18	11	29
2019 June	46	36	82
2019 May	66	56	122
2019 April	0	2	2

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