Systems analysis of non-parenchymal cell modulation of liver repair across multiple regeneration modes

Longitudinal ultrasound imaging and network modeling in rats reveal sex-dependent suppression of liver regeneration after resection in alcoholic liver disease

Liver resection is an important surgical technique in the treatment of cancers and transplantation. We used ultrasound imaging to study the dynamics of liver regeneration following two-thirds partial hepatectomy (PHx) in male and female rats fed via Lieber-deCarli liquid diet protocol of ethanol or isocaloric control or chow for 5–7 weeks. Ethanol-fed male rats did not recover liver volume to the pre-surgery levels over the course of 2 weeks after surgery. By contrast, ethanol-fed female rats as well as controls of both sexes showed normal volume recovery. Contrary to expectations, transient increases in both portal and hepatic artery blood flow rates were seen in most animals, with ethanol-fed males showing higher peak portal flow than any other experimental group. A computational model of liver regeneration was used to evaluate the contribution of physiological stimuli and estimate the animal-specific parameter intervals. The results implicate lower metabolic load, over a wide range of cell death sensitivity, in matching the model simulations to experimental data of ethanol-fed male rats. However, in the ethanol-fed female rats and controls of both sexes, metabolic load was higher and in combination with cell death sensitivity matched the observed volume recovery dynamics. We conclude that adaptation to chronic ethanol intake has a sex-dependent impact on liver volume recovery following liver resection, likely mediated by differences in the physiological stimuli or cell death responses that govern the regeneration process. Immunohistochemical analysis of pre- and post-resection liver tissue validated the results of computational modeling by associating lack of sensitivity to cell death with lower rates of cell death in ethanol-fed male rats. Our results illustrate the potential for non-invasive ultrasound imaging to assess liver volume recovery towards supporting development of clinically relevant computational models of liver regeneration.

Predicting liver regeneration following major resection

Breakdown of synthesis, excretion and detoxification defines liver failure. Post-hepatectomy liver failure (PHLF) is specific for liver resection and a rightfully feared complication due to high lethality and limited therapeutic success. Individual cytokine and growth factor profiles may represent potent predictive markers for recovery of liver function. We aimed to investigate these profiles in post-hepatectomy regeneration. This study combined a time-dependent cytokine and growth factor profiling dataset of a training (30 patients) and a validation (14 patients) cohorts undergoing major liver resection with statistical and predictive models identifying individual pathway signatures. 2319 associations were tested. Primary hepatocytes isolated from patient tissue samples were stimulated and their proliferation was analysed through DNA content assay. Common expression trajectories of cytokines and growth factors with strong correlation to PHLF, morbidity and mortality were identified despite highly individual perioperative dynamics. Especially, dynamics of EGF, HGF, and PLGF were associated with mortality. PLGF was additionally associated with PHLF and complications. A global association-network was calculated and validated to investigate interdependence of cytokines and growth factors with clinical attributes. Preoperative cytokine and growth factor signatures were identified allowing prediction of mortality following major liver resection by regression modelling. Proliferation analysis of corresponding primary human hepatocytes showed associations of individual regenerative potential with clinical outcome. Prediction of PHLF was possible on as early as first postoperative day (POD1) with AUC above 0.75. Prediction of PHLF and mortality is possible on POD1 with liquid-biopsy based risk profiling. Further utilization of these models would allow tailoring of interventional strategies according to individual profiles.

From Seeing to Simulating: A Survey of Imaging Techniques and Spatially-Resolved Data for Developing Multiscale Computational Models of Liver Regeneration

Liver regeneration, which leads to the re-establishment of organ mass, follows a specifically organized set of biological processes acting on various time and length scales. Computational models of liver regeneration largely focused on incorporating molecular and signaling detail have been developed by multiple research groups in the recent years. These modeling efforts have supported a synthesis of disparate experimental results at the molecular scale. Incorporation of tissue and organ scale data using noninvasive imaging methods can extend these computational models towards a comprehensive accounting of multiscale dynamics of liver regeneration. For instance, microscopy-based imaging methods provide detailed histological information at the tissue and cellular scales. Noninvasive imaging methods such as ultrasound, computed tomography and magnetic resonance imaging provide morphological and physiological features including volumetric measures over time. In this review, we discuss multiple imaging modalities capable of informing computational models of liver regeneration at the organ-, tissue- and cellular level. Additionally, we discuss available software and algorithms, which aid in the analysis and integration of imaging data into computational models. Such models can be generated or tuned for an individual patient with liver disease. Progress towards integrated multiscale models of liver regeneration can aid in prognostic tool development for treating liver disease.

Optimized 3D Culture of Hepatic Cells for Liver Organoid Metabolic Assays

The liver is among the principal organs for glucose homeostasis and metabolism. Studies of liver metabolism are limited by the inability to expand primary hepatocytes in vitro while maintaining their metabolic functions. Human hepatic three-dimensional (3D) organoids have been established using defined factors, yet hepatic organoids from adult donors showed impaired expansion. We examined conditions to facilitate the expansion of adult donor-derived hepatic organoids (HepAOs) and HepG2 cells in organoid cultures (HepGOs) using combinations of growth factors and small molecules. The expansion dynamics, gluconeogenic and HNF4α expression, and albumin secretion are assessed. The conditions tested allow the generation of HepAOs and HepGOs in 3D cultures. Nevertheless, gluconeogenic gene expression varies greatly between conditions. The organoid expansion rates are limited when including the TGFβ inhibitor A8301, while are relatively higher with Forskolin (FSK) and Oncostatin M (OSM). Notably, expanded HepGOs grown in the optimized condition maintain detectable gluconeogenic expression in a spatiotemporal distribution at 8 weeks. We present optimized conditions by limiting A8301 and incorporating FSK and OSM to allow the expansion of HepAOs from adult donors and HepGOs with gluconeogenic competence. These models increase the repertoire of human hepatic cellular tools available for use in liver metabolic assays.

The basis of liver regeneration: A systems biology approach

INTRODUCTION

Liver 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.

METHODS

We 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.

RESULTS

In 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.

CONCLUSION

There 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.

Clinical translation of liver regeneration therapies: A conceptual road map

The increasing incidence of severe liver diseases worldwide has resulted in a high demand for curative liver transplantation. Unfortunately, the need for transplants by far eclipses the availability of suitable grafts leaving many waitlisted patients to face liver failure and often death. Routine use of smaller grafts (for example left lobes, split livers) from living or deceased donors could increase the number of life-saving transplants but is often limited by the graft versus recipient weight ratio defining the safety margins that minimize the risk of small for size syndrome (SFSS). SFSS is a severe complication characterized by failure of a small liver graft to regenerate and occurs when a donor graft is insufficient to meet the metabolic demand of the recipient, leading to liver failure as a result of insufficient liver mass. SFSS is not limited to transplantation but can also occur in the setting of hepatic surgical resections, where life-saving large resections of tumors may be limited by concerns of post-surgical liver failure. There are, as yet no available pro-regenerative therapies to enable liver regrowth and thus prevent SFSS. However, there is optimism around targeting factors and pathways that have been identified as regulators of liver regeneration to induce regrowth in vivo and ex vivo for clinical use. In this commentary, we propose a roadmap for developing such pro-regenerative therapy and for bringing it into the clinic. We summarize the clinical indications, preclinical models, pro-regenerative pathways and safety considerations necessary for developing such a drug.

Non-invasive Imaging and Modeling of Liver Regeneration After Partial Hepatectomy

The liver has a unique regenerative capability upon injury or partial resection. The regeneration process comprises a complex interplay between parenchymal and non-parenchymal cells and is tightly regulated at different scales. Thus, we investigated liver regeneration using multi-scale methods by combining non-invasive imaging with immunohistochemical analyses. In this context, non-invasive imaging can provide quantitative data of processes involved in liver regeneration at organ and body scale. We quantitatively measured liver volume recovery after 70% partial hepatectomy (PHx) by micro computed tomography (μCT) and investigated changes in the density of CD68⁺ macrophages by fluorescence-mediated tomography (FMT) combined with μCT using a newly developed near-infrared fluorescent probe. In addition, angiogenesis and tissue-resident macrophages were analyzed by immunohistochemistry. Based on the results, a model describing liver regeneration and the interactions between different cell types was established. In vivo analysis of liver volume regeneration over 21 days after PHx by μCT imaging demonstrated that the liver volume rapidly increased after PHx reaching a maximum at day 14 and normalizing until day 21. An increase in CD68⁺ macrophage density in the liver was detected from day 4 to day 8 by combined FMT-μCT imaging, followed by a decline towards control levels between day 14 and day 21. Immunohistochemistry revealed the highest angiogenic activity at day 4 after PHx that continuously declined thereafter, whereas the density of tissue-resident CD169⁺ macrophages was not altered. The simulated time courses for volume recovery, angiogenesis and macrophage density reflect the experimental data describing liver regeneration after PHx at organ and tissue scale. In this context, our study highlights the importance of non-invasive imaging for acquiring quantitative organ scale data that enable modeling of liver regeneration.

Single-Cell Gene Expression Analysis Identifies Chronic Alcohol-Mediated Shift in Hepatocyte Molecular States After Partial Hepatectomy

The analysis of molecular states of individual cells, as defined by their mRNA expression profiles and protein composition, has gained widespread interest in studying biological phenomena ranging from embryonic development to homeostatic tissue function and genesis and evolution of cancers. Although the molecular content of individual cells in a tissue can vary widely, their molecular states tend to be constrained within a transcriptional landscape partly described by the canonical archetypes of a population of cells. In this study, we sought to characterize the effects of an acute (partial hepatectomy) and chronic (alcohol consumption) perturbation on the molecular states of individual hepatocytes during the onset and progression of liver regeneration. We analyzed the expression of 84 genes across 233 individual hepatocytes acquired using laser capture microdissection. Analysis of the single-cell data revealed that hepatocyte molecular states can be considered as distributed across a set of four states irrespective of perturbation, with the proportions of hepatocytes in these states being dependent on the perturbation. In addition to the quiescent, primed, and replicating hepatocytes, we identified a fourth molecular state lying between the primed and replicating subpopulations. Comparison of the proportions of hepatocytes from each experimental condition in these four molecular states suggested that, in addition to aberrant priming, a slower transition from primed to replication state could contribute toward ethanol-mediated suppression of liver regenerative response to partial hepatectomy.

Model-based virtual patient analysis of human liver regeneration predicts critical perioperative factors controlling the dynamic mode of response to resection

BACKGROUND

Liver has the unique ability to regenerate following injury, with a wide range of variability of the regenerative response across individuals. Existing computational models of the liver regeneration are largely tuned based on rodent data and hence it is not clear how well these models capture the dynamics of human liver regeneration. Recent availability of human liver volumetry time series data has enabled new opportunities to tune the computational models for human-relevant time scales, and to predict factors that can significantly alter the dynamics of liver regeneration following a resection.

METHODS

We utilized a mathematical model that integrates signaling mechanisms and cellular functional state transitions. We tuned the model parameters to match the time scale of human liver regeneration using an elastic net based regularization approach for identifying optimal parameter values. We initially examined the effect of each parameter individually on the response mode (normal, suppressed, failure) and extent of recovery to identify critical parameters. We employed phase plane analysis to compute the threshold of resection. We mapped the distribution of the response modes and threshold of resection in a virtual patient cohort generated in silico via simultaneous variations in two most critical parameters.

RESULTS

Analysis of the responses to resection with individual parameter variations showed that the response mode and extent of recovery following resection were most sensitive to variations in two perioperative factors, metabolic load and cell death post partial hepatectomy. Phase plane analysis identified two steady states corresponding to recovery and failure, with a threshold of resection separating the two basins of attraction. The size of the basin of attraction for the recovery mode varied as a function of metabolic load and cell death sensitivity, leading to a change in the multiplicity of the system in response to changes in these two parameters.

CONCLUSIONS

Our results suggest that the response mode and threshold of failure are critically dependent on the metabolic load and cell death sensitivity parameters that are likely to be patient-specific. Interventions that modulate these critical perioperative factors may be helpful to drive the liver regenerative response process towards a complete recovery mode.

Cellular network modeling and single cell gene expression analysis reveals novel hepatic stellate cell phenotypes controlling liver regeneration dynamics

Background

Recent results from single cell gene and protein regulation studies are starting to uncover the previously underappreciated fact that individual cells within a population exhibit high variability in the expression of mRNA and proteins (i.e., molecular variability). By combining cellular network modeling, and high-throughput gene expression measurements in single cells, we seek to reconcile the high molecular variability in single cells with the relatively low variability in tissue-scale gene and protein expression and the highly coordinated functional responses of tissues to physiological challenges. In this study, we focus on relating the dynamic changes in distributions of hepatic stellate cell (HSC) functional phenotypes to the tightly regulated physiological response of liver regeneration.

Results

We develop a mathematical model describing contributions of HSC functional phenotype populations to liver regeneration and test model predictions through isolation and transcriptional characterization of single HSCs. We identify and characterize four HSC transcriptional states contributing to liver regeneration, two of which are described for the first time in this work. We show that HSC state populations change in vivo in response to acute challenges (in this case, 70% partial hepatectomy) and chronic challenges (chronic ethanol consumption). Our results indicate that HSCs influence the dynamics of liver regeneration through steady-state tissue preconditioning prior to an acute insult and through dynamic control of cell state balances. Furthermore, our modeling approach provides a framework to understand how balances among cell states influence tissue dynamics.

Conclusions

Taken together, our combined modeling and experimental studies reveal novel HSC transcriptional states and indicate that baseline differences in HSC phenotypes as well as a dynamic balance of transitions between these phenotypes control liver regeneration responses.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0605-7) contains supplementary material, which is available to authorized users.

Modeling the Dynamics of Human Liver Failure Post Liver Resection

Liver resection is an important clinical intervention to treat liver disease. Following liver resection, patients exhibit a wide range of outcomes including normal recovery, suppressed recovery, or liver failure, depending on the regenerative capacity of the remnant liver. The objective of this work is to study the distinct patient outcomes post hepatectomy and determine the processes that are accountable for liver failure. Our model based approach shows that cell death is one of the important processes but not the sole controlling process responsible for liver failure. Additionally, our simulations showed wide variation in the timescale of liver failure that is consistent with the clinically observed timescales of post hepatectomy liver failure scenarios. Liver failure can take place either instantaneously or after a certain delay. We analyzed a virtual patient cohort and concluded that remnant liver fraction is a key regulator of the timescale of liver failure, with higher remnant liver fraction leading to longer time delay prior to failure. Our results suggest that, for a given remnant liver fraction, modulating a combination of cell death controlling parameters and metabolic load may help shift the clinical outcome away from post hepatectomy liver failure towards normal recovery.

Modeling Dynamics and Function of Bone Marrow Cells in Mouse Liver Regeneration

In rodents and humans, the liver can efficiently restore its mass after hepatectomy. This is largely attributed to the proliferation and cell cycle re-entry of hepatocytes. On the other hand, bone marrow cells (BMCs) migrate into the liver after resection. Here, we find that a block of BMC recruitment into the liver severely impairs its regeneration after the surgery. Mobilized hematopoietic stem and progenitor cells (HSPCs) in the resected liver can fuse with hepatocytes, and the hybrids proliferate earlier than the hepatocytes. Genetic ablation of the hybrids severely impairs hepatocyte proliferation and liver mass regeneration. Mathematical modeling reveals a key role of bone marrow (BM)-derived hybrids to drive proliferation in the regeneration process, and predicts regeneration efficiency in experimentally non-testable conditions. In conclusion, BM-derived hybrids are essential to trigger efficient liver regeneration after hepatectomy.

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Bone marrow cell migration after liver hepatectomy is key for liver regeneration

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Migrated bone marrow cells fuse with hepatocytes

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Hybrids are essential for liver regeneration

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Mathematical modeling unveils the hybrid function for liver regeneration

Computational Modeling in Liver Surgery

The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.

A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation

Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension). We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.

Complex diseases such as hypertension often involve maladaptive autonomic nervous system control over the cardiovascular, renal, hepatic, immune, and endocrine systems. We studied the pathogenesis of physiological homeostasis by examining the temporal dynamics of gene expression levels from multiple organs in an animal model of autonomic dysfunction characterized by cardiovascular disease, metabolic dysregulation, and immune system aberrations. We employed a data-driven modeling approach to jointly predict continuous gene expression dynamics and gene regulatory interactions across organs in the disease and control phenotypes. We combined our analyses of multi-organ gene regulatory network dynamics and connectivity with bioinformatic analyses of genetic mutations that could regulate gene expression. Our multi-organ modeling approach to investigate the mechanisms of complex disease pathogenesis revealed novel candidates for therapeutic interventions against the development and progression of complex diseases involving autonomic nervous system dysfunction.

Loss of pericyte smoothened activity in mice with genetic deficiency of leptin

BACKGROUND

Obesity is associated with multiple diseases, but it is unclear how obesity promotes progressive tissue damage. Recovery from injury requires repair, an energy-expensive process that is coupled to energy availability at the cellular level. The satiety factor, leptin, is a key component of the sensor that matches cellular energy utilization to available energy supplies. Leptin deficiency signals energy depletion, whereas activating the Hedgehog pathway drives energy-consuming activities. Tissue repair is impaired in mice that are obese due to genetic leptin deficiency. Tissue repair is also blocked and obesity enhanced by inhibiting Hedgehog activity. We evaluated the hypothesis that loss of leptin silences Hedgehog signaling in pericytes, multipotent leptin-target cells that regulate a variety of responses that are often defective in obesity, including tissue repair and adipocyte differentiation.

RESULTS

We found that pericytes from liver and white adipose tissue require leptin to maintain expression of the Hedgehog co-receptor, Smoothened, which controls the activities of Hedgehog-regulated Gli transcription factors that orchestrate gene expression programs that dictate pericyte fate. Smoothened suppression prevents liver pericytes from being reprogrammed into myofibroblasts, but stimulates adipose-derived pericytes to become white adipocytes. Progressive Hedgehog pathway decay promotes senescence in leptin-deficient liver pericytes, which, in turn, generate paracrine signals that cause neighboring hepatocytes to become fatty and less proliferative, enhancing vulnerability to liver damage.

CONCLUSIONS

Leptin-responsive pericytes evaluate energy availability to inform tissue construction by modulating Hedgehog pathway activity and thus, are at the root of progressive obesity-related tissue pathology. Leptin deficiency inhibits Hedgehog signaling in pericytes to trigger a pericytopathy that promotes both adiposity and obesity-related tissue damage.

Pattern analysis uncovers a chronic ethanol-induced disruption of the switch-like dynamics of C/EBP-β and C/EBP-α genome-wide binding during liver regeneration

Chronic ethanol intake impairs liver regeneration through a system-wide alteration in the regulatory networks driving the response to injury. Our study focused on the initial phase of response to 2/3rd partial hepatectomy (PHx) to investigate how adaptation to chronic ethanol intake affects the genome-wide binding profiles of the transcription factors C/EBP-β and C/EBP-α. These factors participate in complementary and often opposing functions for maintaining cellular differentiation, regulating metabolism, and governing cell growth during liver regeneration. We analyzed ChIP-seq data with a comparative pattern count (COMPACT) analysis, which exhaustively enumerates temporal patterns of discretized binding profiles to identify dominant as well as subtle patterns that may not be apparent from conventional clustering analyses. We found that adaptation to chronic ethanol intake significantly alters the genome-wide binding profile of C/EBP-β and C/EBP-α before and following PHx. A subset of these ethanol-induced changes include C/EBP-β binding to promoters of genes involved in the profibrogenic transforming growth factor-β pathway, and both C/EBP-β and C/EBP-α binding to promoters of genes involved in the cell cycle, apoptosis, homeostasis, and metabolic processes. The shift in C/EBP binding loci, coupled with an ethanol-induced increase in C/EBP-β binding at 6 h post-resection, indicates that ethanol adaptation may change both the amount and nature of C/EBP binding postresection. Taken together, our results suggest that chronic ethanol consumption leads to a spatially and temporally reorganized activity at many genomic loci, resulting in a shift in the dynamic balance and coordination of cellular processes underlying regenerative response.

Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model

Although the capacity of the liver to recover its size after resection has enabled extensive liver resection, post-hepatectomy liver failure remains one of the most lethal complications of liver resection. Therefore, it is clinically important to discover reliable predictive factors after resection. In this study, we established a novel mathematical framework which described post-hepatectomy liver regeneration in each patient by incorporating quantitative clinical data. Using the model fitting to the liver volumes in series of computed tomography of 123 patients, we estimated liver regeneration rates. From the estimation, we found patients were divided into two groups: i) patients restored the liver to its original size (Group 1, n = 99); and ii) patients experienced a significant reduction in size (Group 2, n = 24). From discriminant analysis in 103 patients with full clinical variables, the prognosis of patients in terms of liver recovery was successfully predicted in 85-90% of patients. We further validated the accuracy of our model prediction using a validation cohort (prediction = 84-87%, n = 39). Our interdisciplinary approach provides qualitative and quantitative insights into the dynamics of liver regeneration. A key strength is to provide better prediction in patients who had been judged as acceptable for resection by current pragmatic criteria.

A novel comparative pattern analysis approach identifies chronic alcohol mediated dysregulation of transcriptomic dynamics during liver regeneration

BACKGROUND

Liver regeneration is inhibited by chronic ethanol consumption and this impaired repair response may contribute to the risk for alcoholic liver disease. We developed and applied a novel data analysis approach to assess the effect of chronic ethanol intake in the mechanisms responsible for liver regeneration. We performed a time series transcriptomic profiling study of the regeneration response after 2/3rd partial hepatectomy (PHx) in ethanol-fed and isocaloric control rats.

RESULTS

We developed a novel data analysis approach focusing on comparative pattern counts (COMPACT) to exhaustively identify the dominant and subtle differential expression patterns. Approximately 6500 genes were differentially regulated in Ethanol or Control groups within 24 h after PHx. Adaptation to chronic ethanol intake significantly altered the immediate early gene expression patterns and nearly completely abrogated the cell cycle induction in hepatocytes post PHx. The patterns highlighted by COMPACT analysis contained several non-parenchymal cell specific markers indicating their aberrant transcriptional response as a novel mechanism through which chronic ethanol intake deregulates the integrated liver tissue response.

CONCLUSIONS

Our novel comparative pattern analysis revealed new insights into ethanol-mediated molecular changes in non-parenchymal liver cells as a possible contribution to the defective liver regeneration phenotype. The results revealed for the first time an ethanol-induced shift of hepatic stellate cells from a pro-regenerative phenotype to that of an anti-regenerative state after PHx. Our results can form the basis for novel interventions targeting the non-parenchymal cells in normalizing the dysfunctional repair response process in alcoholic liver disease. Our approach is illustrated online at http://compact.jefferson.edu .