Original liver mass after resection is restored by mature hepatocytes in the residual liver undergoing oscillating cell divisions (Miyaoka and Miyajima, 2013). The first wave of division encompasses about 60% of the hepatocytes, followed by waves of considerably less proliferation (Zou et al., 2012; Miyaoka and Miyajima, 2013). The immediate regenerative response after resection is mediated by HGF and IL-6, the so-called priming factors of liver regeneration allowing hepatocytes to re-enter the cell cycle (Fausto and Campbell, 2003). As part of the stress response of liver cells to tissue injury, the process of liver growth is highly controlled by a variety of signaling molecules involving, among others, cytokines, growth factors (Böhm et al., 2010), and hormones (Marino et al., 1992).

Substantial recovery of the liver mass occurs within 10 days, and 80 to 90% of the original liver mass is reached within 6– 12 months following 70% resection (Nadalin et al., 2004; Kele et al., 2012). In contrast, reports about the recovery of liver function are highly variable, as this depends on the specific aspect under investigation. For instance, liver biochemical parameters [bilirubin, international normalized ratio (indicator of blood coagulation)] return to normal within 10 days, whereas cholinesterase, albumin, and galactose elimination capacity recover within 90 days (Nadalin et al., 2004).

The liver accumulates lipids during regeneration (Michalopoulos, 2007; Zou et al., 2012; Miyaoka and Miyajima, 2013). These lipids derive from an increased adipose tissue lipolysis and provide energy substrates for the proliferation of hepatocytes in the liver (Farrell, 2004; Fausto, 2004; Walldorf et al., 2010). While this "physiological" post-resection steatosis is beneficial, excess lipid accumulation in hepatocytes causes hepatocyte death and impaired liver regeneration. This is of special interest after extended liver resections, because a small liver remnant has lower lipid storage capacity, and thus a higher risk of lipid overload and organ dysfunction, than a larger remnant. Since obviously the liver is unable to regulate the amount of lipid uptake in relation to its size after resection, extended resections lead to a pathophysiological shift from utilization during regeneration to excess storage (Shteyer et al., 2004; Hamano et al., 2014; Tautenhahn et al., 2016).

Ultimately, the course of liver regeneration depends on the functional capacity of hepatocytes in the liver remnant. The loss of liver tissue puts an additional stress on the residual parenchyma to take over the metabolic tasks previously accomplished by the whole liver prior to resection. This is critical in situations where hepatocyte function is already impaired by preexisting damage, like, e.g., hepatic steatosis as discussed below.

### Preexisting Diseases

Preexisting global liver diseases can increase the risk of liver surgery. Liver diseases affecting the whole organ comprise metabolic, inflammatory and autoimmune, or infectious diseases. Such diseases compromise architecture, function, and regeneration of the liver and are often associated with or may lead to steatosis, cholestasis, and fibrosis. In the following, we focus on hepatic steatosis to delineate how one exemplary liver disease may aggravate liver surgery.

Hepatic steatosis is defined as an excessive accumulation of fat in the hepatocytes. Steatosis starts with development of small droplets (microvesicular steatosis) progressing to large droplet formation (macrovesicular steatosis). Depending on the etiology, fat accumulation often starts in one specific zone, e.g., in the pericentral zone in case of ethanol-induced toxic etiology.

Patients with steatosis have a higher surgical risk than patients without steatosis (Kooby et al., 2003; Clavien et al., 2007; McCormack et al., 2007). Several reasons contribute to the risk: (a) Steatosis causes an alteration of hepatic architecture leading to an inhomogeneous impairment of perfusion and to an increase in portal pressure (Seifalian et al., 1998). Impaired perfusion is at least partially caused by swollen fatty hepatocytes and sinusoidal "capillarization" (Brock and Dorman, 2007) and reduces oxygen and nutrient supply, contributing to the impaired regenerative response (Yarbrough et al., 1991). (b) Steatosis induces metabolic impairment, which aggravates post-resection lipid overload. Preexisting steatosis is the result of the pathologic shift of lipid metabolism from utilization to storage due to regulatory impairment. This impairment is not resolved after PHx.  This extends lipotoxic exposure for each single hepatocyte, thus augmenting cell death by, e.g., ROS as described below. Hence, preexisting steatosis exacerbates the reduction of the functional capacity of the liver after resection. (c) Steatosis aggravates hepatic ischemia/reperfusion injury. The increased metabolic supply and the impaired microcirculation in the fatty liver "disrupt hepatic oxygen homeostasis," ultimately leading to local tissue hypoxia (Suzuki et al., 2014). This preoperative condition makes fat-loaded hepatocytes particularly vulnerable to ischemia/reperfusion due to an increased level of oxidative stress. Thus, aberrant lipid accumulation in hepatocytes sensitizes them against ischemia/reperfusion injury, which occurs during the surgical procedure of partial liver resection and transplantations (El-Badry et al., 2011; Kimura et al., 2016).

Taken together, flow restrictions due to excessive lipid accumulation, hepatocyte impairment of lipid metabolism in association with oxidative stress, and cell death impair liver regeneration after resection in case of preexisting fatty liver diseases. This is corroborated by clinical and experimental studies indicating that preoperative metabolic interventions improve the impaired regenerative response of the steatotic liver (Liu et al., 2013). In mice fed with a high fat diet, which induced hepatic steatosis, omega-3 polyunsaturated fatty acids given 1 h prior to operation, ameliorated liver regeneration after both two thirds and 86% partial liver resection by attenuating hepatic steatosis and ischemia/reperfusion injury (Linecker et al., 2017).

In summary, preexisting liver diseases such as hepatic steatosis increase the surgical risk for liver resection in multiple aspects. Currently, this multi-dimensional risk is difficult to quantify preoperatively for the individual patient. Therefore, tools are needed to promote an integrated risk-assessment based on different assessment modalities taking as many aspects as possible into consideration.

# COMPUTATIONAL-AIDED SURGERY FOR LIVER RESECTION

Current computational tools primarily support surgical planning and intraoperative guidance based on images of the individual patient anatomy, but do not include functional aspects (see **Figure 3**). Surgical planning needs to address questions (Hansen et al., 2014) related to (a) anatomic resectability, (b) safety margin widths around lesions, and (c) resection strategy, but also to (d) the functional capacity of the future remnant liver.

# Medical Imaging Techniques for Liver Surgery

A variety of imaging techniques is available for the detection and differential diagnosis of liver pathologies, the assessment of liver anatomy, and more lately also for the spatially resolved evaluation of liver function. The armamentarium includes ultrasonography, computed tomography (CT) and magnetic resonance imaging (MRI) as well as nuclear medical imaging modalities. The latter, for instance, play an important role in detecting microvascular invasion of carcinoma preoperatively using 18F fluorodeoxyglucose (FDG) PET-CT (Kobayashi et al., 2016), but also allow to assess hepatic perfusion and excretory function based on hepatobiliary sequence scintigraphy (Cieslak et al., 2015, 2016) using different tracers, such as 99mTc (technetium), 99mTc-galactosyl, or 99mTc-mebrofenin.

CT is a core technology for tumor staging and volumetric evaluation of the liver. It enables precise visualization of the tumor location with respect to the intrahepatic vascular anatomy. In fact, the first computational planning tools considering the individual hepatic anatomy were developed on the basis of CT imaging (Radtke et al., 2007; Lehmann et al., 2008). Currently, CT is the most common first-line imaging modality for staging and monitoring of liver diseases (Pinato et al., 2017) as well as postoperative risk prediction based on future remnant liver volume (Vauthey et al., 2002; Truant et al., 2007). Advantages

corresponding portal venous and hepatic venous territories. Interactive tools allow to perform virtual liver resections and the (perfused) volume of the future liver remnant can be calculated for the selected resection surface. The resection surface can be modified according to the width of the safety margin. The state of the art of surgical planning for liver resection is based on the assumption that all liver volume is functionally equal without any heterogeneity. Such an approach does not take functional aspects into account. The stack of CT images on the left was adapted from (Figure 1B in Chung et al., 2013), image license: CC-BY (https:// creativecommons.org/licenses/by/3.0/).

of CT include low cost, high availability, and fast scan times. With perfusion CT, functional assessment of the liver is made possible by performing dynamic CT acquisitions following intravenous administration of contrast agent to extract blood supply characteristics into the tissue (Wang et al., 2013).

More recently, liver ultrasonography (US) and MRI have gained ground with regard to their use in the detection, characterization, and assessment of the response to treatment of focal and diffuse liver diseases (van Beers et al., 2015).

Ultrasonography allows early diagnosis, treatment management, and monitoring therapy outcome (Matos et al., 2015). Recent developments in dynamic contrast-enhanced US (Lencioni et al., 2007) and US-based elastography (Serai et al., 2017; Wang et al., 2017) have facilitated dedicated and specific liver pathology assessment. Contrast-enhanced US promises great potential to evaluate tumor vascularization in real time (Rübenthaler et al., 2017b) and has meanwhile evolved to a minimally invasive imaging modality for evaluating unclear liver lesions (Bartolotta et al., 2016; Rübenthaler et al., 2017a). However, there are still several open issues concerning standardization, operator dependency, 3D capabilities, and the potential for quantitative perfusion. US-based elastography allows predicting postoperative liver failure based on the elasticity of the tissue (Shen et al., 2017).

MRI stands out for its superior soft tissue contrast and the absence of ionizing radiation. MRI makes it possible to evaluate different tissue properties, including fat content, restriction of water diffusion, or increased T2-relaxation times, all of which support lesion detection. Furthermore, in combination with a liver-specific contrast agent such as gadoxetic acid (Gd-EOB-DTPA), monitoring the perfusion dynamics and the uptake of the agent allows functional assessment of the liver (Imbriaco et al., 2017; Szklaruk et al., 2017; Zhou et al., 2017), thereby improving the detection of liver carcinoma and classification of microvascular invasion in hepatocellular carcinoma. Thus, MRI is a versatile modality for creating detailed, anatomically accurate models for computationally aided liver surgery (Oshiro and Ohkohchi, 2017; Rutkowski et al., 2017). In addition, it offers further potential in form of magnetic resonance cholangiography or contrast enhanced magnetic resonance angiography allowing comprehensive assessment of a patient's biliary and vascular status and possible complications (Boraschi et al., 2008).

Localized magnetic resonance spectroscopy is a non-invasive method to quantify the relative fat fractions of liver tissue, thus providing an elegant means to assess preexisting steatosis (Chiang et al., 2016; Di Martino et al., 2016; Krishan et al., 2016; Kramer et al., 2017). It is often used as gold standard for determining the proton density fat fraction with the potential to replace liver biopsy and takes advantage of the so-called chemical shift, which is based on magnetic field shielding by the molecules' electrons. The different chemical shifts between hydrogen bound to water and lipids can also be utilized by fatwater quantification imaging sequences (Hedderich et al., 2017; Jhaveri et al., 2017), which offer more detailed insight into the spatially inhomogeneous distribution of fat deposits in a steatotic liver (Jang et al., 2017). This way, image-based MR methods may overcome some of the limitations of magnetic resonance spectroscopy associated with restricted spatial coverage and subjective positioning of the volume of interest, which may adversely affect accuracy.

As mentioned before, nuclear medicine also offers very specific imaging methods to support liver surgery. Using the radio-fluorinated carbohydrate (Mun, 2013) 2-[(18)F]fluoro-2 deoxy-D-galactose and PET-CT detection to assess galactose clearance, improved detection of hepatocellular carcinoma has been demonstrated (Horsager et al., 2016). For patients undergoing a major resection, risk assessment and prediction of remnant and future liver function based on hepatobiliary scintigraphy using 99mTc-mebrofenin has been shown to provide better sensitivity, specificity, and positive/negative prediction values compared to conventional remnant liver volume-based risk assessments (de Graaf et al., 2010; Cieslak et al., 2016). Though this method is currently used only in explorative studies at a small number of sites, combining 99mTc scintigraphy with the liver-specific functionalization agent mebrofenin appears fairly promising for spatially resolved, accurate functional assessment of the liver.

Taken together, a diversity of imaging modalities and methods is currently available which, however, are not evenly spread and readily available at all centers for daily routine yet. While basic CT, US, and MRI are ubiquitously performed, particularly the more recently developed methods in magnetic resonance imaging and spectroscopy, contrast-enhanced US and nuclear medicine, despite being very promising, are so far largely limited to specialized centers.

# Current Virtual Resection Tools

Presently, most computational models supporting liver resection planning are based on individual patient anatomy (see **Figure 3**), in particular the spatial relationship between tumor location and hepatic vascular systems (e.g., Fishman et al., 1996; Marescaux et al., 1998; Lang et al., 2005). Accurate visualization of this spatial relationship is important for the surgical success of a liver resection (Saito et al., 2005), and can be achieved by 3D imaging and appropriate visualization techniques (e.g., Fishman et al., 1996).

More advanced approaches support the planning of the resection by virtual resection tools. HepaVision (now MeVis LiverAnalyzer; Schenk et al., 1999) and LiverPlanner (Reitinger et al., 2006) provide a patient-specific resection planning proposal and highlight different safety margins sizes and affected vascular structures as well as the remaining total and perfused liver volume. Thus, the surgeon can adjust the desired safety margin, which influences the resection proposal. Such planning software is implemented in clinical routine for extended liver resection planning.

Recent developments integrate additional biophysical properties of the liver. Liversim (Oshiro et al., 2015) is a novel virtual hepatectomy simulation software tool, which additionally captures motion and deformation of the liver caused by the intervention. A soft-tissue deformation model including hyperelasticity, porosity, and viscosity of hepatic tissue allows simulating realistic liver deformations and intrahepatic displacements in real time for surgery training (Marchesseau et al., 2010) and planning. Modern medical imaging coupled with computational fluid dynamics (CFD) modeling also facilitates predicting patient-specific alterations in hepatic hemodynamics in response to partial hepatectomy (Rutkowski et al., 2017).

## Volume-Based Risk Assessment in Clinical Routine

Optimizing the surgical planning phase by computer-assisted risk analysis can enhance surgery success. In case of hepatic cancer, liver resections can be supported by a preoperative, computerbased calculation of the remnant liver volume (Lang et al., 2005). Hepatic volume estimation by a surgical planning software tool revealed enhanced accuracy compared to the radiologist's volume estimations based on planimetry of a single CT/MR slice (DuBray et al., 2011). The ratio of pre- and postoperative liver tissue volumes, as a rough approximation of postoperative liver function, has been included in virtual surgical planning systems (e.g., Glombitza et al., 1999a,b; Simpson et al., 2014; Hallet et al., 2015; Oshiro and Ohkohchi, 2017).

The aim of liver tumor resection is the complete removal of the cancer. The surgical planning phase encompasses the determination of an optimal safety margin width around the tumor locations (Vandeweyer et al., 2009). Here, a trade-off exists between adequate remnant liver function and sufficient safety margin width. Some computer-based resection planning tools that link visualization of liver structures with an additional volume-margin function support precise operation planning (Glombitza et al., 1999b; Preim et al., 2002; Hansen et al., 2009), thereby enhancing the awareness of the surgical risk and supporting the decision for a smaller resection volume compared to surgical planning based only on conventional 2D/3D viewer application (Hansen et al., 2014).

# The Challenge of Function-Based Risk Assessment

Current surgical planning tools focus on the estimation of liver volume as a surrogate predictor of remnant liver function. The underlying assumption is that all hepatocytes contribute equally to liver function. This, however, neglects the spatial heterogeneity of liver metabolism and perfusion, potential alterations of hepatic function in the presence of a liver disease, or individual variations in metabolic function due to genetic variants, or as a consequence of lifestyle.

Consequently, accurate assessment of the preoperative risk requires improved evaluation of the individual functional capacity and prediction of this capacity for the future liver remnant. Such an improved assessment is essential for the ultimate goal of prevention and early detection of postoperative liver failure (Daylami et al., 2016). The measured changes in metabolic function associated with liver surgery and disease depends on the substance used in the function test. However, as outlined above, the liver is a multifunctional organ, for which a single functional assay only provides information about one specific aspect of hepatic function.

Only few diagnostic tools are currently available for measuring metabolic function of the liver. Information about the metabolic functional capacity can be obtained by means of dynamic quantitative liver function tests, which measure the clearance of selected substances specifically metabolized by the liver such as, e.g., the clearance of caffeine (Fuhr et al., 1996), indocyanine green (De Gasperi et al., 2016), or methacetin (LiMAx test, Jara et al., 2015). The metabolic clearance of a selected compound is hereby used to approximate global metabolic liver function as a cumulative effect. Hence, it is necessary to understand the underlying metabolism of the relevant substances and its alteration due to disease and surgery.

This approach cannot provide information about spatial heterogeneity such as, e.g., inhomogeneously distributed steatosis throughout the organ resulting in areas with higher and lower functional activity. Furthermore, such approaches cannot discriminate between the influences of cellular metabolic activity and altered perfusion or liver size after surgery. Here, novel methods are needed to accurately reflect severity, distribution, and composition of fat accumulation and, even more importantly, the resulting spatially resolved functional impairment.

A comprehensive function-based risk assessment requires consideration of all relevant clinical information. Such an assessment needs to integrate information about resection volume/amount, preoperative metabolic impairment in case of preexisting liver disease, intraoperative damage to the future liver remnant as well as metabolic and regenerative capacity of the future liver remnant. To achieve this, multi-scale computational approaches are needed for integrating all relevant processes into one comprehensive risk prediction. Currently, however, only some of the required features are already available (see section below on "Computational Modeling of Liver Diseases Relevant for Surgeries"), but not within one comprehensive risk assessment tool.

One first attempt to extend surgical planning beyond mere visualization and volume estimation has been provided recently by a model, which simulates postoperative liver regeneration in a patient-specific manner (Yamamoto et al., 2016). This model provides predictions of the duration of the postoperative recovery period and possible complications.

### COMPUTATIONAL LIVER MODELS RELEVANT FOR LIVER SURGERIES

Regulation and maintenance of liver function involves complex biological processes spanning multiple spatial and temporal scales. Spatial scales range from the intracellular level up to the level of the organism, whereas temporal scales have to reflect time periods of seconds to years (e.g., metabolism in seconds to days, regeneration over weeks, or disease progression over months). Various biological processes play a role for hepatic function in liver surgery, particularly important are the hepatic stress response, metabolic adaptations, and regeneration.

Thus, multi-scale-oriented modeling approaches are especially suited to provide a more comprehensive understanding of hepatic processes and mechanisms. Multi-scale-oriented modeling consists of developing "simple" separate models of certain sub-aspects or scales of the function of interest. Subsequent model integration links input and output variables of these separate models and leads to a more comprehensive combined model, possibly spanning multiple scales. This socalled hierarchical modeling approach (Cedersund and Strålfors, 2009; Nyman et al., 2011) allows adapting the model resolution to the corresponding research question (Kirschner et al., 2014). Current computational models can simulate a variety of selected liver functions, see **Tables 1**–**3** and reviews (Bogle et al., 2012; Hetherington et al., 2012; Sumner et al., 2012; Fisher et al., 2014; Petta et al., 2016).

The following sections present selected models/modeling approaches for addressing liver functions, which might be essential for future multi-scale models supporting liver resection: (a) the hepatic stress response following physical damage, (b) the metabolic pathways affected by surgery, as well as (c) the regeneration of liver volume and function recovery.

TABLE 1 | Selection of existing computational models to address the stress response with potential relevance for surgical planning, sorted according to spatial scale (cell to organism).

# Stress Response Induced by Physical Damage

Resection induces a hepatic stress response, which involves a modulation of signaling pathways and gene expressions. Understanding the signaling network of the liver and how the signaling affects metabolism, inflammatory processes, and regeneration is important to assess the overall hepatic stress response to resection. Signaling pathways are interconnected in a non-linear fashion, involving complex interactions as well as feedforward and feedback loops (D'Alessandro et al., 2015a). An intuitive understanding of the signaling network is impossible due to this intricate dynamic behavior. Here, mathematical modeling can be used to disentangle the complex crosstalk between signaling pathways. Based on this knowledge, further mathematical models can be developed, which connect the degree of surgical injury with liver function, inflammatory response, and regenerative capacity. Such models enable predictions of the hepatic response to surgical intervention and possible postoperative complications in regard to an impaired metabolism or regeneration based on the degree and/or location of surgical damage. Here, understanding the relation between remnant liver volume, hepatic metabolic function, and the local immune response is important to optimize liver resections planning (Schindl et al., 2005).

In the following, we provide a short overview of existing computational models of hepatic signaling pathways to illustrate the current state of knowledge. Then, we focus on ROS as important signaling molecules (Dickinson and Chang, 2011; Ray et al., 2012) and as a source of cellular damage impairing hepatic metabolism and activating inflammatory processes after surgical injury. Finally, we take a closer look at current models considering the inflammatory response and the activation of the innate immune system. A summary of selected models available to address the hepatic stress response, which might be relevant for surgical planning, is given in **Table 1**.

### Models of Signaling Pathways

A variety of mathematical models of hepatic signaling processes were developed, mostly using ordinary differential equations (ODEs). Aspects covered by such models include, e.g., the origin of zonation patterning (e.g., Wnt/β-catenin signaling pathway, Kogan et al., 2012; Benary et al., 2013), the propagation of calcium waves at the lobular scale involved in the regulation of diverse hepatic functions (Verma et al., 2016), or the link between the circadian clock and hepatic metabolism (Woller et al., 2016). These models elucidate important features in the regulation and signaling of hepatic function. One example is a fuzzy-logic based model of the GLI-code, the set of three transcription factors linking hedgehog signaling with regulation of metabolic zonation as well as lipid and drug metabolism in hepatocytes (Schmidt-Heck et al., 2015). This relation was also used to explain the link between hedgehog signaling and steatosis (Matz-Soja et al., 2016).

Mathematical models of signaling pathways relevant for liver surgery are necessary to predict, how the liver responds to interventions. One promising approach is the hybrid modeling strategy (D'Alessandro et al., 2015b), which links interaction graph modeling of the signaling network with ODEs, thus

TABLE 2 | Selection of existing computational models addressing metabolism with potential relevance for surgical planning, sorted according to spatial scale (cell to organism).

AB, Agent-based; CFD, computational fluid dynamics; FBA, flux-balance analysis; NAFLD, non-alcoholic fatty liver disease; ODE, ordinary differential equations; PDE, partial differential equations; PK/PD, pharmacokinetic/pharmacodynamic modeling.

permitting time-dependent simulations. In a first step, the minimal model structure of a signaling network is identified by interaction graphs. Then, subsequent analysis of ODE models of this minimal model structure allows the identification of the best model version. Such a modeling strategy helps to disentangle the intracellular signaling network structure and to predict the outcome of disturbances. The strategy was applied to the hepatocyte growth factor-induced signaling network and allows the prediction of the network response to interventions. An accurate and precise prediction of the response of a relevant signaling network to liver resection would allow better assessment of, e.g., course of regeneration, and thus help to optimize surgical procedures or even to decide for or against an operation.

### Models of Reactive Oxygen Species

Reactive oxygen species play a prominent role in the signaling network being active after liver resection, and influence, for example, the JNK pathway (Seki et al., 2012). During the first hours after liver resection, an increased level of ROS was observed

Integration

TABLE 3 | Selection of existing computational models addressing regeneration processes with potential relevance for surgical planning, sorted according to spatial scale (cell to organism).

AB, Agent-based; IPS, interacting particle system; ODE, ordinary differential equations; PDE, partial differential equations.

• Cells at sinusoid—IPS+ODE (Hohmann et al., 2014)

(Guerrieri et al., 1999; Lee et al., 1999). This high ROS level is involved in the initiation of regenerative (Fausto, 2000; Tormos et al., 2013) and inflammatory processes (Bhogal et al., 2010; Seki et al., 2012) in response to the injury. Moreover, these oxygenbased radicals are toxic and lead to oxidative stress, which can result in vast cell damage and decreased metabolic function.

Therefore, computational models focusing on ROS linked to relevant signaling pathways may be helpful in understanding (and predicting) the hepatic surgical stress response. Based on ODEs, several computational models have considered various aspects of the production and degradation of ROS (e.g., Selivanov et al., 2009, 2012; Gauthier et al., 2013; Markevich and Hoek, 2015). Furthermore, a mathematical model simulating the complex regulation of insulin signaling by ROS yielded insights into both protective and detrimental effects of ROS (Smith and Shanley, 2013). The comprehensive overview by Pereira et al. (2016) of the intracellular ROS crosstalk, including the previous models, provides a systems-level examination of the complexities of ROS as intracellular signal molecule and toxic compound. However, mathematical models describing ROS signaling pathways relevant for liver surgery are still missing and no specific model of the processes leading to ischemia/reperfusion injury in the liver exists.

### Models of Inflammation and the Immune Response

The stress response of the liver involves also a local inflammatory reaction. The signaling process starts with the release of so-called damage-associated molecular patterns (Zhang et al., 2010) from stressed hepatocytes. These signals activate the production of pro-inflammatory cytokines in Kupffer cells, which initiate the recruitment of leukocyte subsets to the injured site (van Golen et al., 2012). Immediately after surgery, the concentration of cytokines provides some hint of the degree of tissue damage (Badia et al., 1998; Strey et al., 2011; Friedman et al., 2012). Genome-wide gene expression measures were used to fit and refine a literature-based Boolean model of interleukin 1 and interleukin 6 signaling as a representation of hepatocellular inflammation and proliferation (Ryll et al., 2011). Novel relations between proliferation-associated processes were identified in this study, which provided better understanding of the stress response after surgery. In addition, the release of interleukin 6 and tumor necrosis factor alpha by activated Kupffer cells triggered the cell cycle entry of hepatocytes and therefore initiates liver regeneration (van Mierlo et al., 2016). An ODE model to simulate the cytokine signaling and the increased metabolic demand as triggers for regeneration has been established (Cook et al., 2015). Depending on signaling patterns, the model showed the existence of different modes of regeneration after resection and emphasized the importance of Kupffer cell cytokine signaling for the regenerative process.

Computational models can help to elucidate important links between hepatic function and the immune response. Postoperative hepatic dysfunction augments the probability to acquire an infection (Schindl et al., 2005). Thus, quantifying the relationship between liver volume, hepatic function, and the immune response is of major importance to enhance the safety of liver resections (Schindl et al., 2005). For example, the Petri net approach was used to clarify the timing and regulation of activation of hepatic stellate cells (Kuttippurathu et al., 2014), an important cell type for the modulation of the innate immune response. Relevant signaling pathways, such as NF-κB and STAT3, were coupled to the regulation of microRNAs and the model elucidated the driving regulatory factors in the process of stellate cell activation. Another modeling framework used a set of ODEs to simulate key inflammatory processes (see Clermont et al., 2004; Chow et al., 2005 for model details) initiated by surgical trauma and hemorrhagic shock to predict global damage and dysfunction as an approximation to patient survival (Lagoa et al., 2006).

### Perspective: Stress Response Models in Computational Liver Surgery

In conclusion, computational models coupling signaling and the innate immune response already exist. Their usage has greatly Christ et al. Systems Surgery

improved the understanding of the immediate hepatic stress response to physical damage. However, mathematical models linking, for example, the postoperative metabolic impairment with ROS-induced cellular damage are still missing. The cell damage caused by an increased level of ROS after an operation affects the function of the remnant liver and, therefore, is relevant for the risk assessment of postoperative liver failure. Future computer-based predictions of the remnant liver function should take into account the preoperative metabolic capacity of the liver as well as the possible postoperative impairment caused by oxidative stress. Also, computationally supported identification of patients at specific risks for developing sepsis or acquiring a serious infection after the intervention is still lacking.

The challenge for modelers in the field of hepatic signaling is now to shift the focus to a surgical perspective. Computational models are needed that incorporate the knowledge of signaling networks and the hepatic stress response, thus linking the degree of surgically caused tissue damage to impairments in metabolism and to the activation of the inflammatory response. This would enable a more precise computer-supported risk assessment before resection. It is conceivable that such a tool predicts the surgical outcome in response to the expected surgical tissue damage and guides the decision of the surgeon for or against a resection and for postoperative therapy strategy.

### Metabolism

Removal of functional liver tissue exceeding a critical cut-off leads to a compromised metabolic liver function and ultimately to liver failure. For accurate and quantitative evaluation of the remnant functional capacity, the metabolic function of the remaining volume must be determined. This function depends on alterations of metabolism, perfusion, and morphology in the acute phase after surgical intervention and during regeneration. Computational models of hepatic metabolism can provide a better understanding of the functional capacity of the healthy liver (for an overview see also Cvitanovic et al., 2017 ´ ) and the metabolic alterations occurring with disease, after liver resection, and during regeneration.

In the following, we provide an overview on computational models describing metabolic liver function with a special focus on models incorporating multiple scales and coupling liver morphology and perfusion to metabolism, followed by an outlook on the application of such models to liver surgery. A summary of selected models available to address the hepatic metabolism, which might be relevant for surgical planning, is given in **Table 2**.

### Models on the Cellular Scale

A comprehensive view of the various metabolic capabilities of the liver can be obtained via genome-scale metabolic models (GEMs) to analyze the flow of metabolites through hepatic metabolism based on steady state approaches. The most popular approach is Flux Balance Analysis (Orth et al., 2010). Multiple GEMs of the liver have been published (Gille et al., 2010; Jerby et al., 2010; Agren et al., 2014; Naik et al., 2014) and were applied to study central metabolic functions of the liver like the NH+ 4 detoxification (Gille et al., 2010), to predict metabolic fluxes across different hormonal and dietary conditions, or to simulate alterations as a consequence of gain or loss of function of single liver enzymes (Pagliarini et al., 2016). Such GEMs have proven useful as templates for the integration of omics data to understand the genotype-phenotype relationship in a mechanistic manner (Agren et al., 2014). In recent years, GEMs have been applied to stratify HCC patients (Björnson et al., 2015), to chart metabolic activity and functionality in non-alcoholic fatty liver disease (NAFLD) by integrating metabolic flux data and global transcriptomic data from human liver biopsies (Hyötyläinen et al., 2016), or to reveal alterations of metabolic pathways in NAFLD (Mardinoglu et al., 2014).

To date, GEMs have not been applied in the context of liver surgery, but coupling of omics data to analyse the global metabolic changes following liver resection and during regeneration could be an important next step.

An alternative approach is the use of kinetic pathway models based on ODEs. This approach focusses on specific metabolic functions by means of detailed mathematical description of the involved cellular processes and molecular players. Computational models of central liver functions have been developed, e.g., for the hepatic glucose homeostasis (König et al., 2012) providing insights into the switch of glucose pathways and the role of hormonal regulation. Additional examples are a minimal model of lipid metabolism in steatosis development (Schleicher et al., 2014) and a computational model of both hepatic glucose and lipid metabolism (Ashworth W. B. et al., 2016; Ashworth W. et al., 2016) yielding insight in the development of steatosis. Moreover, one possible mechanism involved in hepatic lipid deficiencies was elucidated by a detailed kinetic model of fatty acid beta-oxidation, in which an overload of substrate slowed down lipid degradation (van Eunen et al., 2013). Multiple pathways models for the detoxification of individual drugs have been published, e.g., for acetaminophen (Reddyhoff et al., 2015).

A more data-driven approach to metabolic function is to apply genome-wide omics data for phenomenological modeling of liver-related diseases. A large number of such studies exists, most of them aiming to identify key molecules, biological functions, and pathways relevant for the disease by differential omics analysis or via correlation-based networks and subsequent topological analysis. Omics-based models have been applied in the context of liver-related surgery, such as, e.g., in the analysis of pathobiochemical signatures of cholestatic liver disease after bile duct ligation in mice (Abshagen et al., 2015). Quantitative metabolomics was potentially useful to diagnose early graft dysfunction in liver transplantation (Serkova et al., 2007). Metabolomics data in orthotopic liver transplantation by consecutive liver biopsies revealed hundreds of significant metabolic differences between pre- and post-reperfusion grafts, among others increased urea production and bile acid synthesis (Hrydziuszko et al., 2010). Omics-based models will be an essential tool in understanding the alterations in liver functional capacity after resection and during regeneration.

### Models on the Sinusoidal and Lobular Scale

Kinetic pathway models, GEMs and omics approaches provide important information about metabolic functions and their alteration with disease. However, these approaches are limited, because they neither include tissue architecture nor perfusion, two important determinants of liver function especially in the context of liver surgery. Hepatic metabolism involves multiple spatial scales, ranging from metabolic pathways on the cellular scale via lobular zonation of metabolic properties and gradients of relevant compounds to metabolic heterogeneity on the organ level. Various multi-scale modeling approaches have been proposed (Diaz Ochoa et al., 2012; Kuepfer et al., 2012; Sluka et al., 2016) to represent the metabolism of the entire liver and especially the spatial heterogeneity of metabolic function on the lobule and organ scales.

One common approach of coupling metabolism to perfusion is treating the 1D porto-central axis of the sinusoid, consisting of a sinusoid surrounded by hepatocytes, as the repeating unit of the liver. Such ODE-based computational models were used to model the zonated damage and steatosis in NAFLD (Ashworth W. et al., 2016) or to analyze glucose homeostasis (Chalhoub et al., 2007; Ashworth W. B. et al., 2016), lipid metabolism (Schleicher et al., 2014, 2017), hepatic glucose and lipid metabolism (Chalhoub et al., 2007), the detoxification of xenobiotics like acetaminophen (Sluka et al., 2016), or effects of zonated damage on drug metabolism (Schwen et al., 2015, 2016). These sinusoidal unit models can be used as building blocks of whole-liver and wholebody models (for details, cf. Schwen et al., 2015; Sluka et al., 2016).

On the lobule-scale, metabolic pathway models have been integrated with agent-based models of perfusion and ammonia metabolism (Toepfer et al., 2007; Bartl et al., 2010, 2015; Schliess et al., 2014; Ghallab et al., 2016), contributing to a better understanding of how liver function depends on liver structure. In the agent-based approach, individual hepatocytes act as agents with intrinsic metabolism and behavior (like movement and proliferation). Such mathematical models have been applied to investigate the effect of liver damage on metabolic function after CCl4-induced necrosis (Schliess et al., 2014; Ghallab et al., 2016).

Alternatively, the liver lobule is modeled using homogenized continuum mechanical multiphase approaches, e.g., via the theory of porous media (Ehlers, 2002; Ricken et al., 2010, 2013, 2015; De Boer, 2012). Embedding a coupled system of ODEs in a porous medium model results in a spatio-temporal description of perfusion and metabolism. This approach was used to evaluate an anisotropic relation for the permeability of the liver lobule, the effect of outflow obstruction on liver remodeling and hepatic perfusion (Ricken et al., 2014), or the importance of vascular septa for homogeneous perfusion (Debbaut et al., 2014). Cellular glucose metabolism was coupled to the blood flow through a porous medium leading to an ODE/PDE (partial differential equations) model that helped to better understand glucose homeostasis on the lobule scale (Ricken et al., 2015).

An alternative approach for modeling perfusion is to apply Computational Fluid Dynamics (CFD) using detailed perfusion models in vessel geometries. CFD was applied to the liver to study blood flow in a segment of a lobule consisting of a resolved hepatic microvascular system (Rani et al., 2006). CFD was also used to simulate hemodynamic changes of the macro-circulation in the cirrhotic liver, a multi-scale computational model to simulate perfusion in the human liver on the organ and lobule scale (Peeters et al., 2015), and in liver cancer arterial perfusion models (Aramburu et al., 2016). A 3D multi-scale model of biliary fluid dynamics in the mouse liver lobule predicted druginduced alterations of bile flow, and demonstrated that bile flow is driven by the osmotic effects of bile secretion and bile canaliculi contractility (Meyer et al., 2017). Until now the integration of metabolic models with CFD and porous medium models is very limited, and application in the context of liver surgery is missing.

### Models on the Whole-Liver and Whole-Body Scale

Sinusoid and lobule-scale models allow to represent the entire liver by applying appropriate scaling in a simplified way. Such models are based on the assumption that the organ does not contribute additional heterogeneity (e.g., in Sluka et al., 2016), or use multiple instances of such models "in parallel" to capture organ-scale heterogeneity (e.g., in Schwen et al., 2015). The organ scale has also been addressed directly via an ODE/PDE model of perfusion in the liver vessel tree and drug metabolization (Schwen et al., 2014). Tissue and whole-liver models allow to incorporate metabolic changes due to damage and resection by suitable adaptation of model parameters. With such approaches, effects of necrosis can be simulated on the lobule scale (Schliess et al., 2014) or changes in drug clearance can be predicted in steatotic livers (Schwen et al., 2014).

The liver in the context of the whole body is typically modeled using pharmacokinetic/pharmacodynamic (PK/PD) models (Jones and Rowland-Yeo, 2013) with a model spectrum ranging from detailed physiologically based models (Willmann et al., 2012) to strongly lumped models (Pilari and Huisinga, 2010). Many simplified models of various drugs being detoxified by the liver exist, often modeled via simple one-step reactions or a few reactions in the context of such PK/PD models (e.g., glutathione and acetaminophen metabolism; Geenen et al., 2013). For the liver, GEMs have been integrated into PK/PD models (Bordbar et al., 2011; Krauss et al., 2012; Naik et al., 2014) predicting, e.g., paracetamol clearance (Krauss et al., 2012). Examples of the coupling of sinusoidal metabolic models to PK/PD models are the analysis of glucose regulation (Ashworth W. B. et al., 2016) or acetaminophen detoxification (Sluka et al., 2016).

### Perspective: Metabolic Models in Computational Liver Surgery

Computational models of metabolic functions of the liver have been developed, many of them based on multi-scale approaches and integration of perfusion and tissue architecture. However, the application of such models to liver surgery, especially on how the metabolic function is changing after resection and subsequent regeneration, is still in its infancy. By coupling metabolic models to models capable of describing the effects of perfusion and morphology on liver function, a holistic understanding of changes after liver surgery on a local (tissue) and global (organ) scale could be achieved.

Surgical planning using model-based predictions of functional liver volumes could substantially improve clinical outcome. Importantly, computational models of hepatic metabolism could provide insights into the heterogenous distribution of metabolic liver functions like the heterogeneity of fat in NAFLD and its consequences for the regional functional capacity. Multi-scale metabolic models of NAFLD/steatosis would allow to calculate hepatic functional capacity based on given fat content, tissue properties like stiffness and elasticity, and perfusion. Thereby, they would provide important insights into surgical planning. Multi-scale computational models of metabolic functions may also improve evaluation of quantitative liver function tests, like galactose elimination capacity or LiMAx. Integrated with surgical planning tools, computational models of such liver function tests could provide a more accurate prediction of metabolic function after resection and during regeneration.

Integrating omics data with metabolic models for predicting changes after liver surgery seems a promising future direction. Personalizing generic models based on individual omics data, a personalized prediction of metabolic liver function and its alteration after resection could be achieved. This personalization as well as the stratification of patients into subgroups has already been demonstrated (Björnson et al., 2015; Hyötyläinen et al., 2016). The use of omics data, however, is not yet part of clinical routine, but could be important for the prediction of the remnant liver function and thereby surgical planning in the future. For individual function predictions, computational models could be parametrized with a subset of omics data relevant for the respective model.

### Regeneration

The liver is capable of regenerating both volume and function after physical damage induced by medical interventions. This includes damage at the lobule scale induced by intoxication with CCl4 (Weber et al., 2003) or damage at the organ scale due to surgical interventions (Riehle et al., 2011), as well as spatial and functional graft adaptation after transplantation (Taki-Eldin et al., 2012). Once the liver is damaged, loss of hepatic mass leads to an increase in portal blood flow per unit mass followed by metabolic overload in the remaining tissue and an increase in diverse signaling molecules including IL-6, TNFα, HGF, and EGF (Michalopoulos, 2010). These signaling molecules, as well as Hedgehog signaling (Matz-Soja, 2017), jointly orchestrate the tightly controlled process of hepatocellular proliferation. This process is composed of three phases: priming (initiation), proliferation, and termination (Fausto, 2000). Mathematical modeling of the involved biological processes in the different phases of regeneration has the potential to aid in understanding the underlying molecular mechanisms.

In this section, we review existing phenomenological models of biological tissue growth, followed by mechanistic models, which include relations and interactions between the involved biological processes specifically during liver regeneration. A summary of selected models available to address regenerative processes in the liver, which might be relevant for surgical planning, is given in **Table 3**.

### Phenomenological Models of Liver Volume Regeneration

Different types of models have been developed to simulate biological growth (see, e.g., the reviews Ambrosi et al., 2011; Jones and Chapman, 2012) and its regulation (Chara et al., 2014), in particular continuum mechanics models of growth (Skalak et al., 1982; Lubarda and Hoger, 2002), for soft tissues (Rodriguez et al., 1994; Garikipati et al., 2004; Himpel et al., 2005), or tumors (Greenspan, 1976). Such models are able to calculate the mechanically induced volumetric growth of tissue without explicitly resolving the underlying biological structures and mechanisms.

A model for volumetric growth of organs including quantitative characteristics and geometric shape of the liver (Shestopaloff and Sbalzarini, 2014) was used to quantitatively estimate patient-specific optimal size and shape of liver transplants. Volume recovery computed from 3D image data, such as shown in Haga et al. (2008), is a typical way of quantifying regeneration and can be used to either calibrate or validate the models and their predictions.

A model predicting postoperative liver volume regeneration from individual quantitative clinical data was recently developed (Yamamoto et al., 2016). This phenomenological model predicted, whether liver size would recover or remain irreversibly reduced, based on preoperative physiological and functional parameters as well as parameters of the surgical procedure.

### Mechanistic Models of Liver Volume Regeneration **Temporal Models**

Several studies aimed to mathematically model liver regeneration based on known interactions between regeneration-associated biological processes and representative molecules. These models are based on ODEs or delayed differential equations, and thus focus on the temporal scale of the process of regeneration without resolving spatial processes, in particular assuming spatial homogeneity. A model reflecting the interplay of cytokines and growth factors involved in initiating and terminating liver regeneration (Furchtgott et al., 2009) was used to derive different hypotheses for the improvement of liver regeneration. This model was later transferred to modeling of human liver regeneration in living liver transplant donors (Periwal et al., 2014). Further extensions of the model by Furchtgott et al. (2009) was used to emphasize the role of bone marrow cell migration into the liver after resection in mice (Pedone et al., 2017), and to integrate cell growth and its regulation, also in case of model diseases (Cook et al., 2015). The aforementioned models assume that the metabolic overload induces regeneration. However, studies also hypothesize that the increased portal flow per mass unit initiates the process of liver regeneration. The two hypotheses were assessed by comparison of two models reflecting liver regeneration as a consequence of hemodynamic changes or the metabolic overload (Hohmann et al., 2014).

### **Spatio-Temporal Models**

A number of studies also focused on including spatial properties of liver regeneration. Already half a century ago, an ODE-based model for cells at the sinusoidal scale was presented (Sendov and Tsanev, 1968), involving proteins, ribosomes, DNA, and mechanisms predicting cellular death and division. Such models could be used to identify hepatocyte-specific triggers when applying general cell cycle models (see e.g., Kriete et al., 2014). A mechanistic model at the cellular and lobular scale for liver regeneration after CCl4-induced damage at the lobule scale in mice was used to show that not only hepatocyte proliferation but also coordinated cell orientation as well as cell polarity are critical aspects ensuring restoration of the lobular micro-architecture (Höhme et al., 2007; Hoehme et al., 2010).

One common approach for spatio-temporal continuum models of regeneration is using mixture theory (multiphasic approaches) embedded into a biomechanical framework. This allows the integration of underlying biological mechanisms, see among others (Humphrey, 2003; Amar and Goriely, 2005; Ambrosi et al., 2017). The field of growth and remodeling in biomechanics is covered by one-phasic (Menzel and Kuhl, 2012) as well as bi-phasic (Ricken and Bluhm, 2009) approaches. Remodeling processes are presented in Ricken et al. (2010), where a mechanical biphasic model was developed and the effect of outflow obstruction on liver remodeling and hepatic perfusion was studied. Dealing with coupled solid-fluid interaction, a mixture framework using the finite element method was presented (Ricken et al., 2015). This approach allows calculating tissue growth depending on nutrient supply, e.g., diet high of free fatty acids (Waschinsky et al., 2016).

### **Network Models**

Furthermore, omics-based network models are commonly used for the initial identification of genes and proteins involved in liver regeneration and are thus used to identify keymolecules to be considered in mechanistic models. However, few studies have employed mathematical modeling based on genome-wide transcriptomics data for the identification of liver regeneration-associated molecular mechanisms and biological pathways. A correlation-based model was inferred from genome-wide transcriptomics data for the identification of molecular mechanisms underlying regeneration induced by partial hepatectomy (Zhou et al., 2014). This identified de-regulation of several genes associated with hepatocyte proliferation, inflammation, and DNA replication processes.

### Models of Liver Function Recovery

Only few models of recovery of liver function have been reported, most of them being phenomenological. Liver function (in particular the lack thereof) has mostly been addressed in terms of postoperative liver failure. Well-known risk factors for postoperative liver failure are, e.g., preexisting disease, age, nutrition (Hammond et al., 2011). The risk of liver failure can be predicted partly by preoperative tests and risk-defined score models (Clavien et al., 2007) and additionally by postoperative parameters (Yamanaka et al., 1984).

A mechanistic model (Schliess et al., 2014; Ghallab et al., 2016) of function recovery on the tissue scale deals with the recovery of ammonia detoxification and amino acid metabolism during regeneration after CCl4-induced pericentral necrosis. This model included two selected aspects of liver function and regeneration from damage pattern clearly different from those encountered in surgery, but could be used as a starting point for bridging the cellular and organ scale in regeneration modeling in computational liver surgery.

### Perspective: Regeneration Models in Computational Liver Surgery

To our knowledge, currently no mechanistic mathematical model addresses liver regeneration after hepatic surgery. Future models supporting the prediction of regeneration could be integrated in surgical risk assessment and help preventing postoperative complications. The existing predictions of liver failure could be extended to predicting the recovery of liver function based on more advanced and more mechanistic models. The tissue-scale function recovery model (Schliess et al., 2014; Ghallab et al., 2016) could form the basis for a model describing changes in lobular architecture and its impact on more generic function recovery after resection. The main challenge for modeling recovery of liver function is to link tissue regeneration to metabolism, as already described in the previous subsection. Moreover, correlation of volume and function recovery for different diseases (Yamanaka et al., 1993) could be used for phenomenological models of functional recovery.

# VISION: SYSTEMS SURGERY OF THE LIVER

Future integrated models of liver metabolism and regeneration should provide function-based risk assessment. Such models need to be accessible via a usable tool for surgery planning. To achieve a more accurate and comprehensive prediction of the functional capacity for Systems Surgery, several medical and computational challenges have to be resolved (Belghiti, 2016).

These challenges involve (a) precise determination of the preoperative state and functional capacity of the liver, taking preexisting disease into account (model input data), (b) estimation of the extent of surgical damage inflicted on the liver during the resection, and (c) prediction of the impact of hepatic tissue loss and surgical damage on the functional capacity of the (diseased) future liver remnant and its recovery process (model output data).

# Integrated Planning Tool for Liver Resection

Future surgical planning software should include a workflow for function-based risk assessment. Input data data comprise in addition to the liver anatomical architecture also spatially resolved data assessing hepatic perfusion and function as well as clinical data, e.g., quantitative dynamical liver function tests, and information about existing liver disease as summarized graphically in **Figure 4**. Multi-scale computational models of the liver based on animal models and clinical data will enable to predict in silico function and regeneration after resection in respect to variation of resection surface and safety margins.

function-based risk assessment includes in addition to the assessment of liver geometry, also the spatially resolved assessment of hepatic perfusion and hepatic function as well as clinical data, e.g., quantitative dynamical liver function tests, and information about existing liver disease. Additional output of the future surgical planning tool includes prediction of selected functions after resection, (e.g., hepatic perfusion, metabolic parameters) and their recovery in respect to variation of resection surface and safety margins. CT image stack adapted from (Figure 1B in Chung et al., 2013), image license: CC-BY (https://creativecommons.org/licenses/ by/3.0/).

Functional predictive models need to be integrated into the existing individual 3D planning of the liver representing the vascular structure of the liver and the location of the tumor.

Such an integrated planning tool will improve individualized risk prediction for hepatic surgery and provide important information about the expected liver function after surgical intervention and during subsequent liver regeneration. This tool will ultimately support surgeons in their decision about a patient's operability and the choice of a suitable intervention, but will also make them aware of possible postoperative complications allowing therapy adjustments after resection.

The integrated tool will support risk assessment depending on preexisting liver disease and damage of the liver. This requires integrating underlying pathophysiologic conditions and preexisting risk states on an individual basis. For example, steatosis and other chronic liver diseases (such as cirrhosis already impairing liver function) substantially impact the function of the future liver remnant and its regeneration, and hence increase the risk of postoperative complications and liver failure. Surgeons and patients will benefit from more comprehensive risk predictions taking functional aspects into account without the need of own expertise in multi-scale computational modeling in the implementation.

### Future Developments

Further development of such an integrated liver model can be envisioned to obtain better disease- or cohort-specific predictions and to enhance the prognostic power for the individual patient.

Reaching better disease- or cohort -specific predictions would call for including further cohort-specific data to tune the integrated model according to the specific aspect in question. Doing so will contribute to getting a better insight into disease progression and curation. However, this will require to generate considerably more animal experimental data of the specific disease and of course to collect a substantial amount of additional cohort-specific clinical data.

Such data are needed to generate probabilistic disease models, which have to be integrated into the proposed "liver resection and regeneration" model.

Enhancing the prognostic power for the individual patient could be achieved by extending the knowledge-based selection of relevant patient-specific pre-, peri-, and postoperative data considered to be relevant. Additional input data regarding the activity and severity of the complicating liver disease as well as data regarding the general patient condition (e.g., cardiovascular condition) appears extremely useful for this purpose. Similarly, additional outcome data would be necessary, requiring an detailed follow-up of the patient to collect data regarding extra-hepatic surgical and general complications [e.g., abscess formation, postoperative infections and grade their severity (Clavien-Dindo classification)] and reflecting the recovery of the patient's general condition (e.g., days in ICU and in hospital). However, increasing the number of entry variables would call for a higher number of outcome observations.

Alternatively, this could also be achieved using a "big data" approach by focussing on creating an interface with the currently used hospital information systems to have access to all patients and all patient-specific information. Following this approach, a rather large number of patients would be needed to reflect the high data variability as presented in true patient cohorts.

### Medical Challenges

Determining the preoperative state of the liver and the expected alterations after surgery must be improved to optimize surgical planning and reduce the probability of postoperative liver failure. This involves several challenges.

### Improving Preoperative Diagnostics

Here, the key point is to improve spatial resolution, which will benefit the assessment of morphological and structural alterations due to the underlying preexisting hepatic disease, the assessment of hepatic perfusion, and most importantly, the quantitative assessment of hepatic function.

### Identifying Prognostically Relevant Aspects of Hepatic Function and their Spatially Resolved Assessment

Identifying meaningful and relevant diagnostic assays from the multitude of available assays is a major challenge. These assays should be non-invasive and serve as a basis for valid predictions regarding surgical complications, surgical outcome, and changes in liver functions following liver surgery.

### Estimating Surgically Induced Damage

It is not sufficient to only quantify the loss of liver volume due to tissue removal, but also necessary to quantify the volume of liver tissue at risk due to alterations of hepatic perfusion. The key challenge is to estimate the loss of functional tissue with respect to the extent of resection, the resection surface, and the resection technique. In addition, preexisting global liver diseases impair hepatic function in a spatially heterogenous way (cf. the section "Hepatic Diseases"), which has to be taken into account during the surgical planning phase.

### Predicting Postoperative Function of the Liver Remnant

The functional capacity of the remnant liver should be predicted based on the preoperative disease state and the predicted loss of liver tissue and liver function by resection.

## Modeling Challenges

Building a comprehensive model for the prediction of the hepatic functional capacity after resection faces many challenges.

### Identifying, Understanding, and Modeling Relevant Processes in Liver Surgery

A prerequisite for building a comprehensive model of functional prediction is the availability of high quality models reflecting those aspects that are important for liver surgery, such as liver function depending on perfusion, liver volume regeneration in case of preexisting damage, or recovery of hepatic metabolic function after resection. The key processes and mechanisms of all these aspects must be understood in sufficient detail and transferred to a suitable mathematical formalism. Part of the challenge is to extend compatible model components and to develop interfaces to bring these building blocks together.

### Improving Data Availability and Quality for Computational Models

Besides understanding the processes, further key steps for model building are parameterizing and subsequently validating parametrized models. A key requirement for these steps is the availability of high-quality experimental and clinical data.

Many existing studies have only looked at a single aspect of liver surgery, such as regeneration, liver function, or changes in perfusion. Assembling data from different sources is difficult, since the experimental and clinical conditions are in general vastly different. A multitude of experimental resection studies has been performed in rodents under controlled conditions and with various liver diseases, but using a variety of experimental conditions and read-out parameters. Consequently, comparability is often limited and data integration into a single model questionable. A similar problem is the extrapolation from clinical data measured in one cohort to another cohort with different characteristics, e.g., data from young subjects to old subjects. For similar reasons, translation of results from animal studies to the human situation is even more challenging.

One recurring problem is the quality of experimental and clinical data, e.g., inaccurate or high-variance data, with model predictions strongly depending on the accuracy of the measured patient-specific data. The aforementioned issues with experimental data require new comprehensive and targeted data sets to ensure all needed input data were generated under the same conditions. One key aspect is to perform targeted experiments and studies to collect the information for model parameterization and validation. Alternatively (or in addition), analysis of the effects of the underlying datasets on model predictions and quantification of the resulting uncertainties in the predictions must be performed in order to analyze sensitivity.

### Integrating Data with Computational Models

One major challenge is the integration of different types of data (e.g., concentrations, tension, elasticity, image data, omics data, etc.) and to handle the heterogeneity within similar datasets (e.g., from different laboratories, different readers) with computational models. Standardization of data formats and models for simple and reproducible integration of the different data sets into the models is important (König et al., 2016). Especially with the perspective of routine application of such models in Systems Surgery of the liver, standardization of models and experimental data sets will be a major challenge and facilitator.

### Developing Large Multi-scale Models

Multi-scale models and models coupling distinct modeling approaches are often not easy to compute. Reasons are that such large computational models require substantial computational resources (e.g., agent-based, porous media, CFD), and that coupling of different modeling is often not supported in simulation software and difficult to implement. Multi-scale computational modeling requires connecting models via clearly defined interfaces between the different scales and sub-models. General challenges of computational modeling like parameter fitting/overfitting, model selection, parameter selection, or parameter identifiability are also major challenges in multi-scale models, often aggravated due to the large number of parameters in models spanning multiple scales.

### Performing Model Reduction

Often, model reduction is necessary for efficient model simulation (e.g., integration of a system of ODEs for metabolism in a meso- or macroscale model of whole-liver perfusion) and reduction of the parameter space for analysis. The overall goal is to reduce complexity without compromising the aspects relevant for the question at hand. Different approaches of model reduction have been applied in the field of liver simulations, e.g., representative sinusoids (Schwen et al., 2015), method of proper orthogonal decomposition (Fink and Ehlers, 2015), or the use of an energy function (Holzapfel et al., 2000; Humphrey, 2003; Balzani et al., 2006).

### Improving Model Quality and Validating Predictions

Further important challenges are the evaluation of model quality and validation of model predictions, which are two requirements for application of such models in surgical support systems. Validation of models for Systems Surgery of the liver will require prospective clinical trials, which compare the model predictions of liver function after resection and during regeneration with clinical trial data. In the surgical setting, the availability of postoperative data (invasive methods for data measurements are not feasible) limits model validation, so this will need to be done mostly in animal models.

### Quantifying Uncertainty and Robustness

Important questions to be answered in the context of model validation are (a) What is the uncertainty in input data and model parameters? and (b) How sensitive is the overall system? Together, this can quantify how robust model predictions are against uncertainty in the generic model parameters and individualized input data. There are various sources of uncertainty, e.g., direct or indirect measurement of biochemical and biophysical parameters, clinically measured physiological and systemic functional parameters, limited resolution, and noise in imaging. An analytic assessment of the sensitivity is only feasible for sub-models of limited complexity. Quantifying the robustness of an integrated multi-scale model will require thorough parameter studies to quantify the sensitivity against uncertainty in individual parameters and, e.g., Monte-Carlo simulations to determine confidence ranges of model predictions under combined parameter uncertainty.

## Implementation Challenges

Addressing these clinical and modeling challenges to achieve such model-assisted risk predictions requires a truly multidisciplinary approach involving basic and applied, clinical and computational scientists and engineers. On the one hand, anatomical and physiological phenomena, as well as clinical diagnostic and surgical procedures, need to be accurately described and translated to suitable, improved or novel, computational models. On the other hand, such models must be made available in the form of interactive and user-friendly software and thus usable not only by domain experts in systems biology. Practical usability requires user interactivity, easy and quick handling, automation, minimum of editing, and expert input in the final usage, model adaptation to work on standard workstations available in the clinics, etc. Moreover, interfaces have to be developed, which allow integration of computational models with widely used hospital information systems, e.g., using patient data for the personalization of models and adding model-based risk evaluation to electronic patient records.

# CONCLUSION

Already today, patients benefit from computational support in the planning of liver resections. This is, however, limited to an assessment of remnant liver volume and taking into account a number of risk factors for postoperative liver failure. A prediction of liver function recovery is currently not included, but would be particularly useful in case of preexisting liver disease.

Basic biological processes involved in liver metabolism, disease, and regeneration are well-understood, and various computational models for these aspects are available. However, no comprehensive model integrating all these effects on different scales has been presented yet.

With increasing knowledge of disease mechanisms, availability of experimental and clinical data as input for model-based predictions, and expertise in design and integration of computational models, the next logical step is to develop a comprehensive model for predicting liver function and regeneration. This type of outcome prediction will be an indispensable part of a strategy for a patient-tailored optimization of intervention and therapy after liver surgery.

# AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

# FUNDING

MK and LS were supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver [LiSyM, grant numbers 031L0054 (MK) and 031L0040 (LS)]. JS and UD acknowledge financial support by the German Research Foundation [grant numbers SCHL 2130/1-1 (JS) and DA 251/10-1 (UD)]. The authors moreover acknowledge support from the German Research Foundation (DFG) and Leipzig University within the program of Open Access Publishing.

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### ACKNOWLEDGMENTS

The authors would like to thank Andrea Schenk for her support with 3D liver visualizations.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Christ, Dahmen, Herrmann, König, Reichenbach, Ricken, Schleicher, Schwen, Vlaic and Waschinsky. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Discovery of a Potential Plasma Protein Biomarker Panel for Acute-on-Chronic Liver Failure Induced by Hepatitis B Virus

Ni Zhou1†, Kuifeng Wang1†, Shanhua Fang2, 3†, Xiaoyu Zhao1 , Tingting Huang1 , Huazhong Chen1 , Fei Yan1 , Yongzhi Tang1 , Hu Zhou2, 3 \* and Jiansheng Zhu1 \*

1 Department of Infectious Diseases, Affiliated Taizhou Hospital of Wenzhou Medical University, Taizhou, China, 2 E-Institute of Shanghai Municipal Education Committee, Shanghai University of Traditional Chinese Medicine, Shanghai, China, 3 Department of Analytical Chemistry and CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

### Edited by:

Kai Breuhahn, Universität Heidelberg, Germany

### Reviewed by:

Xiao Han, Biotechnology Research Institute (CAAS), China Caroline Evans, University of Sheffield, United Kingdom

### \*Correspondence:

Hu Zhou<EMAIL_ADDRESS>Jiansheng Zhu<EMAIL_ADDRESS>
† These authors have contributed equally to this work.

### Specialty section:

This article was submitted to Systems Biology, a section of the journal Frontiers in Physiology

Received: 10 September 2017 Accepted: 21 November 2017 Published: 06 December 2017

### Citation:

Zhou N, Wang K, Fang S, Zhao X, Huang T, Chen H, Yan F, Tang Y, Zhou H and Zhu J (2017) Discovery of a Potential Plasma Protein Biomarker Panel for Acute-on-Chronic Liver Failure Induced by Hepatitis B Virus. Front. Physiol. 8:1009. doi: 10.3389/fphys.2017.01009 Hepatitis B virus (HBV)-associated acute-on-chronic liver failure (HBV-ACLF), characterized by an acute deterioration of liver function in the patients with chronic hepatitis B (CHB), is lack of predicting biomarkers for prognosis. Plasma is an ideal sample for biomarker discovery due to inexpensive and minimally invasive sampling and good reproducibility. In this study, immuno-depletion of high-abundance plasma proteins followed by iTRAQ-based quantitative proteomic approach was employed to analyze plasma samples from 20 healthy control people, 20 CHB patients and 20 HBV-ACLF patients, respectively. As a result, a total of 427 proteins were identified from these samples, and 42 proteins were differentially expressed in HBV-ACLF patients as compared to both CHB patients and healthy controls. According to bioinformatics analysis results, 6 proteins related to immune response (MMR), inflammatory response (OPN, HPX), blood coagulation (ATIII) and lipid metabolism (APO-CII, GP73) were selected as biomarker candidates. Further ELISA analysis confirmed the significant up-regulation of GP73, MMR, OPN and down-regulation of ATIII, HPX, APO-CII in HBV-ACLF plasma samples (p < 0.01). Moreover, receiver operating characteristic (ROC) curve analysis revealed high diagnostic value of these candidates in assessing HBV-ACLF. In conclusion, present quantitative proteomic study identified 6 novel HBV-ACLF biomarker candidates and might provide fundamental information for development of HBV-ACLF biomarker.

Keywords: HBV-ACLF, CHB, iTRAQ, proteomics, biomarker

### INTRODUCTION

Acute-on-chronic liver failure (ACLF) is increasingly recognized as an acute deterioration of liver function combining with liver and multi-organ failures in patients with pre-existing chronic liver disease, although there is no consensus about its definition (Bernal et al., 2015; Anand and Dhiman, 2016; Arroyo and Jalan, 2016). Hepatitis B virus (HBV) associated ACLF, as a subtype of ACLF, can develop at any stage in the progression of chronic hepatitis B (CHB) (Zamora Nava et al., 2014). It is estimated that about 70% of liver failure is caused by HBV infections in the Eastern countries

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(Sarin et al., 2009), and there are approximately 97 million people suffering from HBV infection in China alone (Cui and Jia, 2013). HBV-ACLF has a poor prognosis with high mortality rate (>70%), if emergency liver transplantation is not available (Marrero et al., 2003). Therefore, it is believed that predicting and stopping the progression of CHB to ACLF at an early stage may be the most effective strategy of reducing the mortality of patients with HBV-ACLF.

Despite a number of scoring systems such as Child-Pugh score have been used for diagnosis of end-stage liver diseases, not all of these scores are specifically designed for HBV-ACLF. So far, various biochemical molecules (e.g., Prealbumin, Serum ferritin), cytokines (e.g., Interleukin 17, Fibroleukin) and chemokines (e.g., Macrophage inflammatory protein-3α) have been evaluated to be novel indicators for HBV-ACLF as reviewed by Chen et al. (2015). Wan et al. (2015) performed a particleenhanced immunonephelometry assay on serum samples from ACLF patients, and they found that the level of cystain C (CysC) was significantly higher in the ACLF with kidney failure group than those in the healthy controls and CHB patients. Their results suggested that CysC could be considered as a biomarker for renal dysfunction in ACLF patients. However, no protein biomarker has reached the clinical setting yet. Considering the complexity and heterogeneity of HBV-ACLF pathology, it has been suggested that integrated panel of biomarkers with specific and complementary functions rather than a single biomarker be useful in diagnosis of patients with HBV-ACLF.

Unbiased proteomic analysis of plasma samples holds the promise to discover clinically effective disease biomarkers. Plasma proteomics is an appealing concept in medicine due to inexpensive and minimally invasive sampling and good reproducibility (Harel et al., 2015). Plasma proteins comprise not only actual plasma proteins that maintain physiological homeostasis but also low abundance "leakage" proteins from damaged tissues, which may provide direct information about the pathology of disease and may serve as clinical biomarkers for diagnosis and treatment (Lv et al., 2007). Several studies have successfully applied this strategy to identify biomarkers of liver disease, including Hepatitis B (He et al., 2003), Hepatitis C (HCV) associated hepatic fibrosis (Yang et al., 2011) and HBV-associated hepatocellular carcinoma (HCC) (Niu et al., 2010).

With regard to HBV-ACLF, a study using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) approach showed that protein profiling was markedly changed during the progression of CHB to liver failure and suggested that these dynamic changes can distinguish different stages of the CHB (Han et al., 2010). Another study employed two-dimensional gel electrophoresis (2-DE) MS/MS approach to compared serum samples collected from normal individuals, CHB patients and HBV-ACLF patients, which proposed that Alpha-1-acid glycoprotein (A1-AGF) might be a potential biomarker of ACLF diagnosis for CHB patients (Ren et al., 2010). Currently, with the advent of quantitative proteomic technology, isobaric tagging for relative and absolute quantitation (iTRAQ) technology makes it possible to quantify several proteins in a single experiment with improved accuracy and reproducibility of quantitation (Pierce et al., 2008). Using this technology, Peng et al. (2013) has identified total of 16 significantly differential proteins in serum from patients with CHB and patients with HBV-ACLF compared to healthy controls, and suggested five of those proteins were potentially associated with progression of hepatitis B and ACLF.

In this study, iTRAQ coupled with LC-MS/MS approach was utilized to construct the plasma proteome in healthy controls, patients with CHB and HBV-ACLF to explore disease-associated alterations of plasma proteins. In addition, we sought to validate several potential biomarkers that could distinguish ACLF from both CHB and healthy control by ELISA analysis and subsequent receiver operating characteristic (ROC) curves analysis. The six candidates identified in present study can aid clinical biomarker discovery for HBV-ACLF.

# MATERIALS AND METHODS

### Human Plasma Sample Collection

Blood samples from healthy people (CON), patients with chronic hepatitis B (CHB) and patients with HBV induced acute-on-chronic liver failure (HBV-ACLF) were provided by Department of Infectious Diseases of Taizhou Hospital of Zhejiang Province, China (n = 20 per group). The study was approved by scientific ethics committee (Taizhou hospital of Zhejiang Province, China). Written informed consent was given from all participants and legal guardians before commencement of this study. The diagnoses of HBV-ACLF were based on criteria previously described (Sarin et al., 2009). Exclusion criteria included: pregnant or lactating women; liver cancer or suspected liver cancer; recent infection; use of immune-suppressive agents; anti-viral therapy, immune disease or malignant tumor; other types of hepatitis infection and HIV infection patients. Rejection criteria included: died within 7 days after enrollment, liver transplantation after enrollment. Information of clinical and demographic characteristics of patients with CHB, HBV-ACLF and healthy controls were shown in **Table 1**. HBV-ACLF samples were referred to as ACLF in the figure captions. The collected blood samples were then centrifuged at 3,000 rpm for 10 min at room temperature to remove any cells and debris. Twenty clarified plasma samples of each group were pooled into 4 samples that contained equal volume of 5 individual plasma samples from each group. As a result, a total of 60 samples were randomly pooled into 12 pooled samples.

### Plasma Sample Preparation for Proteomic Analysis

Since disease biomarkers in the plasma are usually covered by high-abundance proteins, and their signals are weak in the mass spectrum, removal of high-abundant proteins was performed using Agilent High-Capacity Human-14 Multiple Affinity Removal System (MARS Human-14, Agilent, USA) according

**Abbreviations:** HBV-ACLF, HBV induced acute-on-chronic liver failure; MMR, mannose receptor; OPN, osteopontin; HPX, hemopexin; GP73, golgi membrane protein 1; ATIII, antithrombin-III; APO-CII, apolipoprotein CII; CHB, chronic hepatitis B; CHC, Chronic hepatitis C.

TABLE 1 | Clinical and demographic characteristics of subjects enrolled in this study.

ALT, alanine transaminase; AST, glutamic-oxalacetic transaminase; TB, total bilirubin; ND, not determined.

to the manufacturer's instructions. Protein concentrations were determined by tryptophan fluorescence emission at 350 nm using an excitation wave length of 295 nm (Geiger et al., 2010). Removal effect was verified by Coomassie-stained gel. Then 100 µg of protein from each pooled sample was processed by the Filter Assisted Sample Preparation (FASP) method as previously described (Wi´sniewski et al., 2010). Briefly, each sample was transferred to a 10 kDa filter (Millipore Corporation) and centrifuged at 14,000 g for 40 min at 20◦C. Then, 200 µL of urea buffer (8 M urea, 0.1 M Tris-HCl, pH 8.5) was added and followed by another centrifugation at 15,000 g for 40 min. This step was repeated one more time. The concentrate was then mixed with 100 µL of 50 mM iodoacetamide (IAA) in urea buffer and incubated for an additional 40 min at room temperature in darkness. After that, IAA was removed by centrifugation at 14,000 g for 40 min. Next, the sample was diluted with 200 µL of urea buffer and centrifuged two more times. Then, 200 µL of 50 mM tetraethyl ammonium bromide (TEAB) was added and the sample was centrifuged at 14,000 g for 40 min. This step was repeated twice. Finally, samples were digested with trypsin (1:50, enzyme to protein in 50 mM TEAB) by incubating at 37◦C for 16 h.

### iTRAQ Labeling of Plasma Samples

Peptides were labeled with iTRAQ reagents according to the manufacturer's instructions (AB Sciex, Foster City, CA). To quantify 12 samples, 2 batches of 8-plex iTRAQ labeling experiment were performed, with a mixture of 12 samples in equal amount as a bridge for comparison among different batches. Each aliquot (50 µg of peptide equivalent) was reacted with one tube of iTRAQ reagent. After the sample was dissolved in 15 µL of 0.5 M TEAB solution, pH 8.5, the iTRAQ reagent was dissolved in 50 µL of isopropanol. The mixture was incubated at room temperature for 2 h. The 8-plex labeled samples in the same experiment branch was pooled together and lyophilized.

# High pH Reverse Phase Fractionation (HPRP)

iTRAQ-labeled peptides mixture was fractionated using a Waters XBridge BEH130 C18 3.5µm 2.1 × 150 mm column on a Agilent 1260 HPLC operating at 0.2 mL/min. Buffer A consisted of 10 mM ammonium formate and buffer B consisted of 10 mM ammonium formate with 90% acetonitrile; both buffers were adjusted to pH 10 with ammonium hydroxide as described previously (Wang et al., 2011). A CBS-B programed multifunction automatic fraction collecting instrument (Huxi instrument, Shanghai, China) was coupled to the HPLC and used to collect eluted peptides. A total of 28 fractions were collected for each peptides mixture, and then concatenated to 14 (pooling equal interval RPLC fractions). The fractions were dried for nano LC-MS/MS analysis.

### LC-MS/MS Analysis

The reverse phase high-performance liquid chromatography (RP-HPLC) separation was achieved on the Easy nano-LC system (Thermo Fisher Scientific) using a self-packed column (75µm × 150 mm; 3µm ReproSil-Pur C18 beads, 120 Å, Dr. Maisch GmbH, Ammerbuch, Germany) at a flow rate of 300 nL/min. The mobile phase A of RP-HPLC was 0.1% formic acid in water, and B was 0.1% formic acid in acetonitrile. The peptides were eluted using a gradient (2–90% mobile phase B) over a 90 min period into a nano-ESI Orbitrap Elite mass spectrometer (Thermo Fisher Scientific). The mass spectrometer was operated in data-dependent mode with each full MS scan (m/z 300– 1,500) followed by MS/MS for the 12 most intense ions with the parameters: ≥ +2 precursor ion charge, 2 Da precursor ion isolation window, 80 first mass and 38 normalized collision energy of HCD. Dynamic ExclusionTM was set for 30 s. The full mass and the subsequent MS/MS analyses were scanned in the Orbitrap analyzer with R = 60,000 and R = 15,000, respectively.

### Database Searching and Analysis

Data were processed by search against the UniProt/SwissProt Human database (IPI.human.v3.87) using Maxquant (version <IP_ADDRESS>), with default settings including the allowance of one missed cleavage and 8-plex iTRAQ fixed modifications. Minimum 7 amino acids for peptide, >2 peptides were required per protein. For peptide and protein identification, false discovery rate (FDR) was set to 1%. iTRAQ reporter ion intensity were used for quantification. By setting the median of intensity for each channel to equal and matching the distributions of each treatment iTRAQ reporter group (114, 115, 116, 118, 119, and 121) to those of the control iTRAQ reporter group (113, which corresponded to the mixture sample), we able to make consistent comparisons across different samples obtained from different iTRAQ 8-plex experiments. The ratio was restored to the intensity by multiplying the median of intensity of the first channel 113 (MIX1).

### Bioinformatics Analysis

Functional enrichment analysis of Gene Ontology (GO) of biological process, molecular function, and cellular component was performed using DAVID Bioinformatics Resources version 6.7. The protein-protein interaction (PPI) network analysis of differentially expressed proteins was performed using STRING (https://www.string-db.org/). And the PPI network was further processed by Cytoscape software.

### Elisa Assay

The expression levels of selected biomarkers were measured in plasma samples from 20 healthy controls, 45 CHB patients and 23 HBV-ACLF patients using ELISA quantitation kits (APO-CII, GP73, OPN, MMR, HPX purchased from RayBiotech; ATIII purchased from R&D systems, UK). The experimental methods were carried out according to the manufacturer's instructions.

### Evaluation of the Diagnostic Accuracy

Mathematical models for separation of HBV-ACLF from CHB patients were performed on ELISA results of 6 candidate biomarkers using SPSS 19.0 software (Chicago, IL, USA). The diagnostic score of CHB patient was set as "0," while that of ALCF patient was set as "1." The forward stepwise multivariate regression analysis was conducted to determine which proteins should be included or excluded from the diagnostic model. The global performances of the model and individual biomarkers were evaluated by constructing receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC) values.

### Statistical Analysis

One way analysis of variance (ANOVA) and Tukey's honestly significant difference (HSD) test was performed with language R. p-value <0.05 was defined as statistically significant. Clinical chemistry data are expressed as mean ± SEM. Hierarchical clustering of proteins was performed on logarithmized data, using Euclidean distances and Ward clustering method using Package of "pheatmap" in language R. Correlation between samples was analyzed using Spearman's rank correlation coefficient.

# RESULTS

### Identification of Significantly Changed Proteins in CHB and HBV-ACLF Groups

In this study, plasma samples from healthy control, CHB and HBV-ACLF patients were subjected to LC-MS/MS analysis following removal of high abundance protein, FASP preparation, tryptic digestion and iTRAQ labeling. The experimental workflow is illustrated in **Figure 1**. iTRAQ 113 and 117 were used to label the mixture of all samples as a reference pool in different sets, thus allowing for cross-set comparison (Song et al., 2008).

With the false discovery rate (FDR) <1%, 397 and 396 proteins were identified in the 8-plex iTRAQ data set 1 and 2, respectively, resulting in a total of 427 proteins identified (Table S1). Of which, 364 non-redundant proteins were commonly identified across all samples by these two iTRAQ experiments. The quality of the proteomic dataset and instrumental reproducibility was evaluated. As shown in **Figure 2A**, the box plot analysis showed that the log2 protein intensity medians of all 12 pooled samples were about 1.25, almost at the same levels across all the samples, suggesting that there were no biases toward any samples. In addition, correlation analysis was performed on intensities between biological replicates inside each cohort or between different cohorts. **Figure 2B** showed that all of correlation coefficients between each two samples were higher than 0.86, demonstrating good reproducibility of biological replicates. Taken together, these results suggest that the iTRAQ-MS/MS analysis yielded a high quality reproducible dataset.

Filtering the iTRAQ data set using criteria of p-value < 0.05 and fold change >2, we identified a total of 149 significantly changed proteins through comparing each two groups. A full list of all significantly differential proteins is given in Table S2.

A heatmap analysis of 149 differential proteins using an unsupervised hierarchical clustering and correlation distance metric was generated to depict the change of expression level in different groups (**Figure 2C**). As the dendrogram indicated, CON, CHB and HBV-ACLF group samples formed three distinct clusters and the individuals within each group displayed the closest relationship.

Venn diagrams displayed unique and overlapping differential proteins in CHB and HBV-ACLF as compared to CON. As shown in **Figure 2D**, 3 overlapping differential proteins were identified by comparison of each two groups. There were 143 proteins differentially expressed between HBV-ACLF and CON, of which 42 were significantly changed when HBV-ACLF was compared to both CON and CHB. There were only 13 proteins differentially expressed between CHB and CON.

# Bioinformatics Analysis of Differentially Expressed Proteins

To understand biological significance regarding to differentially expressed proteins in HBV-ACLF patients, the cellular component, molecular function and biological process of the 143 proteins were explored by Gene Ontology (GO) annotation (Table S3). In the cellular component category of GO, the most over-represented term is high-density lipoprotein particle (**Figure 3A**) and the most significant molecular function is endopeptidase inhibitor activity (**Figure 3B**). The top 3 biological processes terms were regulation of endopeptidase activity, platelet degranulation and regulation of complement activation. Other important biological processes such as regulation of fibrinolysis, complement activation, immune response and inflammatory response were also over-represented (**Figure 3C**).

To understand functional relationship among the 42 differential proteins between CHB and HBV-ACLF groups, protein protein interaction (PPI) network based on STRING action scores was illustrated. The annotations of biological processes based on GO analysis were also indicated in this view. PPI analysis showed a complex network with several distinct biological subgroups that contained highly connected proteins. As shown in **Figure 3D**, proteins involved in immune response, inflammatory response, blood coagulation and lipid metabolism were highly connected with each other, indicating that functional network of these processes contribute to

logistic regression analysis and ROC curve analysis.

HBV-ACLF pathophysiology. Based on promising reports from literature, 6 proteins antithrombin-III (ATIII), mannose receptor (MMR), golgi membrane protein 1 (GP73), osteopontin (OPN), apolipoprotein CII (APO-CII), hemopexin (HPX) involved in biological processes mentioned above were selected for further verification.

# Evaluation of Six Selected Proteins as Biomarker Candidates

To verify whether alterations of 6 selected candidates are reliably presented in clinical samples, we performed an ELISA assay to measure protein levels in plasma samples from healthy controls (CON, n = 20), CHB patients (CHB, n = 45), HBV-ACLF patients (ACLF, n = 23). The results showed significant elevation of GP73, MMR, and OPN (p < 0.01) and significant reduction of ATIII, HPX, APO-CII expression levels (p < 0.01) in the HBV-ACLF group as compared to both CHB and CON groups. In addition, significant differences in GP73 and MMR levels were also observed between the CHB and CON groups (p < 0.01). These results are consistent with the data obtained from the proteomic studies (**Figure 4A**).

Subsequently, the diagnostic values of 6 candidates were analyzed by forward stepwise multivariate regression. The result showed that MMR and ATIII were included in this logistic regression model as below (e is the mathematical constant and base value of natural logarithms):

P = e −0.0496+0.006MMR−0.176ATIII/1 + e −0.0496+0.006MMR−0.176ATIII

Odds ratios of MMR and ATIII in the diagnostic model were 1.006 and 0.839 respectively. Furthermore, receiver operating

characteristic (ROC) curve was exploited based on the results of the area under the curve (AUC), sensitivity and specificity. **Figures 4B,C** and **Table 2** showed the results of ROC analysis of individual biomarkers and the combined biomarker model for discriminating liver failure patients from CHB patients. The AUC of the combined biomarker model ATIII+MMR was 0.993, higher than any other individual biomarkers, indicating the combination of ATIII and MMR can effectively discriminate the HBV-ACLF patients from CHB patients.

### DISCUSSION

To our knowledge, there are two representative ACLF definitions proposed by the Asia-Pacific Association for the Study of the Liver (APASL) and the American Association for the Study of Liver Disease and the European Association for the Study of the Liver (AASLD/EASL) (Kim and Kim, 2013). The APASL focused on the occurrence of complication such as ascites and encephalopathy within 4 weeks in patients with chronic liver disease (Sarin et al., 2009), whereas the other one emphasized the occurrence of multi-organ failure and 3 months mortality (Olson and Kamath, 2011). However, most researchers agree that the concept of ACLF should include: acute deterioration of preexisting chronic liver disease, multi-system organ failure and with a mortality ≥15% at day 28 (Kim and Kim, 2013; Blasco-Algora et al., 2015). Unfortunately, there is a lack of biomarker highly sensitive and minimally invasive to predict ACLF in CHB patients. In this study, plasma proteome profiling of healthy controls, CHB patients and HBV-ACLF patients was established by iTRAQ-based proteomic analysis, aiming to search novel diagnostic biomarkers of HBV-ACLF. We identified 6 candidates with strong biological relevance to HBV-ACLF pathogenesis and further confirmed their change of plasma levels in 68 subjects using ELISA assay.

Demographic information exhibited that approximately 60% patients are male in either CHB or ACLF group as shown in **Table 1**. This result was consistent with the study from Rifai et al. (2012). They found that significantly more males than females

underwent liver transplantation for CHB. There may be a gender difference with more men susceptible to HBV infection and developing to end-stage liver disease, which could be attribute to sex hormone effects on HBV transcription and immune response to HBV infection (Wang et al., 2015).

Individual variations among patients make a big challenge for applications of conventional proteomics. This issue has been addressed in plasma proteomic studies (Zhou et al., 2012). In the present study, particularly, each 5 plasma samples in the same group were randomly pooled to minimize the individual variations (Schisterman and Vexler, 2008). Plasma has been widely used in proteomic study for biomarker discover. However, the large dynamic range of protein concentrations in plasma samples exceeds the analytical capabilities of traditional proteomic methods, making those lower abundance plasma proteins undetectable (Pernemalm and Lehtiö, 2014). Therefore, we firstly conducted removal of high-abundance proteins (such as Albumin and IgG) using immune affinity-based depletion method to improve depth of detection in plasma sample. In doing so, a total of 427 proteins were identified across all samples. We found that more extensive molecular response was occurred in progression of ACLF (143 altered proteins as compared to healthy control) than in that of CHB (13 altered proteins as compared to healthy control).

Accurate diagnostic prediction is critical for distinguishing CHB patients who require transplantation from those who will survive following intensive medical care alone. Current Venn diagram revealed that expression of 42 proteins were changed significantly when HBV-ACLF group were compared with both healthy control and CHB groups, indicating these proteins may be helpful in identifying biomarkers for discriminating HBV-ACLF from CHB patients. Analysis of the protein-protein interaction of 42 proteins revealed that these proteins connected each other to regulate distinct biological process, including immune response, inflammatory response, blood coagulation, and lipid metabolic process.

It is well accepted that ACLF is an exaggerated systemic inflammatory response in context of immune dysregulation.

FIGURE 4 | Evaluation of plasma levels of 6 candidate proteins in healthy controls, CHB patients and HBV-ACLF patients using ELISA assay. (A) Plasma levels of six candidates (ATIII, HPX, APO-CII, GP73, MMR, and OPN) in different groups were analysis by ELISA assay. Median values were shown with a horizontal line. \*p < 0.01, Upper panel indicates protein intensity of each candidate obtained from iTRAQ-proteomic analysis. (B) ROC curve analysis of the 6 individual biomarkers. (C) ROC curve analysis of the combination of ATIII and MMR.

TABLE 2 | ROC analysis of individual biomarkers and combined diagnostic model.

The inflammation may also result in the unbalance of prothrombotic and anti-thrombotic states that may be manifested by either bleeding or thrombotic complications (Blasco-Algora et al., 2015). Since these biological processes are closely related to ACLF pathology, we considered 6 proteins (MMR, OPN, HPX, GP73, ATIII, and APO-CII) involved in these processes as potential biomarkers for diagnosis of HBV-ACLF and the clinical relevance of these proteins was further confirmed by ELISA assay. Subsequent ROC analysis indicated that these candidates, especially combination of MMR and ATIII, have good sensitivity and specificity in predicting HBV-ACLF.

Mannose receptor (MMR) locates on the surface of various cell types such as macrophages and dendritic cells (Martinez-Pomares, 2012). As a pattern recognition receptor, MMR binds and internalizes the glycoproteins from various pathogens (e.g., virus, bacteria and parasites) (Stahl and Ezekowitz, 1998), thus playing an important role in innate and adaptive immune response (Apostolopoulos and McKenzie, 2001). Another function of the MMR is to eliminate inflammatory agents released into the circulation during the inflammatory response (Lee et al., 2002). It has been reported that the concentration of soluble MMR (sMR) in serum from CHC patients with cirrhosis was higher than that with mild hepatic fibrosis patients (Andersen et al., 2014). Similarly, our study showed that levels of plasma MMR in HBV-ACLF patients were higher than CHB patients and healthy controls. It can be speculated that MMRmediated immune and inflammatory response was dramatically triggered in context of HBV-ACLF.

Osteopontin (OPN), as a phosphorylated integrin-binding protein, has been implicated in many distinct pathophysiological processes including wound healing, bone turnover and tumorigenesis. Particularly, its roles in immune response and inflammation have been extensively studied (Rittling and Singh, 2015). OPN contributes to development of immune-mediated and inflammatory disease by promoting inflammatory cells recruitment (Apte et al., 2005), enhancing B cell proliferation (Wang and Denhardt, 2008) and suppressing apoptosis of immune cells (Denhardt et al., 2001). Several studies have shown that expression level of OPN was positively associated with CHB, CHC, alcoholic liver disease, fibrosis and HCC (Nagoshi, 2014; Fouad et al., 2015; Duarte-Salles et al., 2016). Recent studies reported remarkable elevation of serum OPN concentration in fulminant hepatic failure (FHF) patients and acute liver failure patients (Arai et al., 2006; Srungaram et al., 2015). Consistent with these findings, this study showed that level of plasma OPN was significantly elevated in HBV-ACLF groups as compared to CHB and healthy control groups, and this up-regulation of OPN may aggravate hepatic inflammation of CHB patients in progression to ACLF.

Hemopexin (HPX), as an acute phase glycoprotein, can bind heme with high affinity (Paoli et al., 1999), and the resultant Heme-HPX complex can be taken up by liver, protecting the body against free heme-induced oxidative damage (Hvidberg et al., 2005). Recent study reported anti-inflammatory function of HPX through its ability to regulate the pro-inflammatory cytokines and infiltration of Th17 cell (Liang et al., 2009). Xu et al. (2014) concluded that serum HPX concentration is negatively associated with severity of rat acute rejection after liver allograft. In addition, decrease of of HPX level was also observed in rat model of liver fibrosis induced by carbon tetrachloride (CCl4) (Zhang et al., 2015). In present study, plasma HPX level was significantly reduced in HBV-ACLF patients compared to CHB patients and healthy controls. Low HPX level may be attribute to impaired function of hepatocyte that is principal site of HPX synthesis, and decreased HPX may further aggravate liver damage. However, the role of HPX in pathogenesis of HBV-ACLF remains elusive to contradictory results reported by Lu et al. (2010), where up-regulation of HPX level was observed in plasma sample from HBV infected patients with liver fibrosis.

Golgi membrane protein 1 (GP73), as a type II Golgi membrane protein with unknown function, mainly presents in biliary epithelial cell and is rarely expressed in normal hepatocytes (Ba et al., 2012). However, serum GP73 levels are dramatically elevated in context of various types of liver disease such as viral infection (HBV, HCV), alcohol-induced liver disease (Kladney et al., 2002), cirrhosis (Iftikhar et al., 2004), or HCC (Gao et al., 2015; Sai et al., 2015; Zhang et al., 2016). Wei et al. (2014) revealed that expression level of serum GP73 was significantly up-regulated in patient with HBV-ACLF compared to HCC patients, CHB patients, and healthy controls, supporting our present result. Biological significance of GP73 elevation requires further study.

One of the typical clinical characteristics of liver failure is coagulation dysfunction because of the dysregulated production of coagulation factors and anti-coagulation factors. Antithrombin-III (ATIII), exclusively synthesized by hepatocytes, is a natural anticoagulant that inactivates several enzymes of coagulation system (Castelino and Salem, 1997). It was reported that ATIII levels are reduced in various liver disorders, such as cirrhosis, hepatitis and the fatty liver of pregnancy (Castelino and Salem, 1997). Tischendorf et al. (2016) suggested that reduced activity of ATIII was independent predictors of hepatic encephalopathy in patients with liver cirrhosis. Evaluating 158 HCC patients subjected to hepatectomy, Mizuguchi et al. (2012) demonstrated the decrease of serum ATIII as a useful predictor for postoperative liver dysfunction post hepatectomy. Similar conclusion was also yielded by Kuroda et al. (2015). In line with these findings, plasma ATIII level was significantly reduced in HBV-ACLF patients group.

Liver is the primary site of production for apolipoproteins that is responsible for the maintenance of lipoproteins and lipid metabolism (Bell, 1979). Apolipoprotein CII (APO-CII), together with APO-CI and APO-CIII are constituents of chylomicrons, very low-density lipoprotein (VLDL), and high-density lipoprotein (HDL) in blood circulation. APO-CII regulates triglyceride metabolism through interact with lipoprotein lipase (LPL), a enzyme for hydrolysis and clearance of triglycerides from VLDL and chylomicrons (Jong et al., 1999). However, both an excess and a lack of APO-CII inhibit LPL activity and thus result in hypertriglyceridemia (Kei et al., 2012). Song et al. (2012) concluded that reduced serum APO-CII and APO-CIII were associated with aberrant biliary cycle, and considered APO-CII and APO-CIII as potential biomarker for diagnosis of biliary atresia. Trieb et al. (2016) found that decrease of serum APO-CIII level was associated with cirrhosis mortality. However, the relationship between APO-CII and liver damage such as ACLF has not been reported. Previous study has suggested that APO-CII levels would not be affected in most patients with liver disease, despite a down-regulation of APO-CIII levels (Koga et al., 1984). Our study showed that the plasma concentration of APO-CII was reduced in HBV-ACLF patients compared to both CHB patients and healthy controls, which may reflect impairment of lipid metabolism in HBV-ACLF disease.

According to the previous proteomic study reported by Peng et al. (2013), there were 16 proteins differentially expressed in CHB and ACLF patients. We did the comparison of our differentially expressed proteins with their 16 proteins, and three proteins were overlapped, including vitronectin (VTN), Creactive protein (CRP) and platelet factor 4 (PF4). In their study, vitronectin (VTN) showed 1.23- and 2.14-fold down-regulation in CHB and ACLF patients, respectively, and this protein was also down-regulated in our dataset with the ratio of CHB/CON = 0.73 in CHB patients and the ratio of ACLF/CON = 0.48 in ACLF patients (Table S2). As a cell adhesion and spreading factor found in serum and tissues, VTN was reported that its plasma level dramatically decreased in chronic liver disease (Tomihira, 1991; Kobayashi et al., 1994). In Peng et al.'s study, pro-inflammatory protein C-reactive protein (CRP) was 2.46 fold down-regulated and 4.59-fold up-regulated in CHB and ACLF patients, respectively, and our data demonstrated that CRP was up-regulated with the ratio of CHB/CON = 1.97 in CHB patients and the ratio of ACLF/CON = 5.43 in ACLF patients. Platelet factor 4 (PF4) was down-regulated with 1.15 and 1.87-fold in CHB and ACLF patients respectively, and our data is consistent with theirs with the ratio of CHB/CON = 0.21 in CHB patients and the ratio of ACLF/CON = 0.12 in ACLF patients. Thus, the similar tendency of these three proteins between Peng et al.'s study and ours suggested that the potential clinical application of these proteins for HBV-ACLF diagnosis can be further investigated.

In summary, this study employed an iTRAQ-based quantitative proteomic approach to identify plasma biomarkers

### REFERENCES

for HBV-ACLF diagnosis. Based on protein-protein interaction analysis, we focused on 6 differentially expressed proteins involved in inflammation, immune response, blood coagulation and lipid metabolism. And the following ELISA analysis of plasma samples from patient cohorts further confirmed the up-regulation of GP73, MMR, OPN and the down-regulation of ATIII, HPX, APO-CII in HBV-ACLF patients. These proteins were involved in the key pathological processes on acute occurrence of complication or multi-organ failure in the progression of ACLF. So we believed that these proteins can be considered as the potential biomarkers for HBV-ACLF diagnosis. However, more confirmatory studies are required with hope that theses candidate biomarkers can be applied to routine clinical practice.

# AUTHOR CONTRIBUTIONS

NZ, KW, SF, HZ and JZ designed the experiments and wrote the manuscript. NZ, KW and SF performed the experiments with the support and help from FY and YT. NZ and XZ performed the clinic sample collection and ELISA experiments and statistical data analysis with the assistance of TH and HC. All authors critically reviewed content and approved final version for publication.

# FUNDING

This work was financially supported by the Zhejiang Province Major Science and Technology Programs (No. 2012C13018-3), Zhejiang Provincial Natural Science Foundation (No. LY15H030003), National Natural Science Foundation of China (Grant No. 21375138 and 81500469) and by the Strategic Priority Research Program of the Chinese Academy of Science, "Personalized Medicines-Molecular Signature-based Drug Discovery and Development" (No. XDA12030203).

# ACKNOWLEDGMENTS

We thank Dr. Jing Gao and Han He for their technical assistance. The authors declare no competing financial interest. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD007975 (http://www. ebi.ac.uk/pride).

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys. 2017.01009/full#supplementary-material

Andersen, E. S., Rødgaard-Hansen, S., Moessner, B., Christensen, P. B., Møller, H. J., and Weis, N. (2014). Macrophage-related serum biomarkers soluble CD163 (sCD163) and soluble mannose receptor (sMR) to differentiate mild liver fibrosis from cirrhosis in patients with chronic hepatitis C: a pilot study.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Zhou, Wang, Fang, Zhao, Huang, Chen, Yan, Tang, Zhou and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Ion Imbalance Is Involved in the Mechanisms of Liver Oxidative Damage in Rats Exposed to Glyphosate

Juan Tang1 , Ping Hu1 , Yansen Li 1 , Tin-Tin Win-Shwe2 and Chunmei Li 1 \*

1 Jiangsu Province Key Laboratory of Gastrointestinal Nutrition and Animal Health, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China, 2 Health Effect Assessment Section Center for Health and Environmental Risk Research, National Institute for Environmental Studies, Tsukuba, Japan

### Edited by:

Andreas Teufel, Medical Faculty Manheim, University of Heidelberg, Germany

### Reviewed by:

Simona Bertoni, Università degli Studi di Parma, Italy Viktória Venglovecz, University of Szeged, Hungary

> \*Correspondence: Chunmei Li<EMAIL_ADDRESS><EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Gastrointestinal Sciences, a section of the journal Frontiers in Physiology

Received: 20 April 2017 Accepted: 08 December 2017 Published: 19 December 2017

### Citation:

Tang J, Hu P, Li Y, Win-Shwe T-T and Li C (2017) Ion Imbalance Is Involved in the Mechanisms of Liver Oxidative Damage in Rats Exposed to Glyphosate. Front. Physiol. 8:1083. doi: 10.3389/fphys.2017.01083 Glyphosate (N-phosphonomethyl-glycine, GLP) is the most popular herbicide used worldwide. This study aimed to investigate the effects of glyphosate on rats' liver function and induction of pathological changes in ion levels and oxidative stress in hepatic tissue. Sprague-Dawley rats were treated orally with 0, 5, 50, and 500 mg/kg body weight of the GLP. After 5 weeks of treatment, blood and liver samples were analyzed for biochemical and histomorphological parameters. The various mineral elements content in the organs of the rats were also measured. Significant decreases were shown in the weights of body, liver, kidney and spleen between the control and treatment groups. Changes also happened in the histomorphology of the liver and kidney tissue of GLP-treated rats. The GLP resulted in an elevated level of glutamic-oxalacetic transaminase (GOT), glutamic-pyruvic transaminase (GPT) and IL-1β in the serum. Besides, decreased total superoxide dismutase (T-SOD) activity and increased malondialdehyde (MDA) contents in the serum, liver, and kidney indicated the presence of oxidative stress. Moreover, increase of hydrogen peroxide (H2O2) level and catalase (CAT) activity in the serum and liver and decrease of glutathione (GSH) and lutathione peroxidase (GSH-Px) activity in the kidney tissue further confirmed the occurrence of oxidative stress. The results of RT-PCR showed that the mRNA expressions of IL-1α, IL-1β, IL-6, MAPK3, NF-κB, SIRT1, TNF-α, Keap1, GPX2, and Caspase-3 were significantly increased in the GLP-treated groups compared to the control group. Furthermore, PPARα, DGAT, SREBP1c, and SCD1 mRNA expressions were also remarkably increased in the GLP-treated groups compared to the control group. In addition, aluminum (Al), iron (Fe), copper (Cu), zinc (Zn), and magnesium (Mg) levels were showed a significant difference reduction or increase in rat liver, kidney, spleen, lung, heart, muscle, brain, and fat tissues. These results suggested that glyphosate caused obvious damage to rats' liver and caused various mineral elements content imbalances in various organs of rats. Ion imbalance could weaken antioxidant capacity and involve in the mechanism of liver oxidative damage caused by GLP.

Keywords: ion, oxidative stress, liver, glyphosate, rat

# INTRODUCTION

Glyphosate (GLP) is a non-selective, post-emergence herbicide used for weed control in various crops, especially in rice, maize and soybean (Coutinho et al., 2005). Eighty percent of genetically modified crops were GLP-resistant plants, such as corn, soy, cotton and canola and so on (Williams et al., 2000). American farmers have widely used anti-GLP crops since 1996 (Frisvold et al., 2010). It means there will be much more glyphosate in soil and water environment. Study has reported that GLP and its metabolite such as aminomethylphosphonic acid (AMPA) and formaldehyde were found in the soil and rivers (Temple and Smith, 1992). It has been extensively demonstrated that exposure to GLP leads to oxidative stress in several tissue, including the livers and kidneys (Beuret et al., 2005; El-Shenawy, 2009; Modesto and Martinez, 2010; Larsen et al., 2012; Cattani et al., 2014).

GLP can chelate the iron (Fe) and aluminum (Al), which interferes with ion assimilation in the plant (Eker et al., 2006; Bellaloui et al., 2009). GLP also change the ion levels in fish by chelated with them (Ayoola, 2008; Samsel and Seneff, 2013). Al is widespread in soil, water, and air, and is also the most widely used metal by humans (Kumar and Gill, 2009). Al is mainly absorbed by the gastrointestinal tract and easily accumulates in liver cells and organelles (e.g., macrophages and lysosomes) (Krewski et al., 2007; Kumar and Gill, 2009). Some scholars believe that Al accumulation does not causes significant hepatotoxicity, because it can be eliminated by hepatocytes (Li et al., 2011). However, most studies reported that Al causes central nervous system toxicity, hepatotoxicity, nephrotoxicity, cardiotoxicity and osteoporosis to body tissue (Crisponi et al., 2013; Geyikoglu et al., 2013). Iron (Fe) is not only an important micronutrient, but also a redox reaction of the biocatalyst, and when the transition metal reaches the transition level, is conducive to the production of reactive oxygen species (Aust et al., 1985). Zinc (Zn) as an antioxidant, involved in cell membrane stabilization, copper/zinc superoxide dismutase (Cu/Zn SOD) structure and metallothionein induction. Zn deficiency can damage the oxidant defense system and cause oxidative damage to cells or tissue (Oteiza et al., 1999). Therefore, it is important to study whether GLP can effects the ion content in the liver and other organs of rats.

The aim of this study was designed to evaluate liver histomorphological changes, oxidant/antioxidant status, levels of inflammatory markers, lipid metabolism factors, and to investigate ion levels of Al, Fe, Cu, Zn, and Mg ion levels in GLPexposed rats' liver tissues. The specific mechanism between liver and other organs damage and ion imbalance need to be further studied.

# MATERIALS AND METHODS

### Chemicals

Glyphosate, N-(phosphonomethy) glycine (GLP), was purchased from Shanghai Ryon Biological Technology Co. Ltd (Shanghai, China).

# Animals and Ethic Statement

Eight week-of-age male Sprague-Dawley rats weighting 180–220 g were purchased from the Nanjing Qinglongshan Experimental Animal Center (Nanjing, China). Prior to experiment, all rats were allowed to acclimate for at least 1 week. All rats were housed in separate cages under environmental conditions (23 ± 2 ◦C, 50 ± 10% relative humidity, 12-h light: dark cycle) and had unrestricted access to food and water throughout the period of the study. Animal care and use were conducted in accordance with the National Institute of Health Guidelines for Animal Care and the Committee of Animal Research Institute, Nanjing Agricultural University, China. At the same time, the study also received ethical approval from the committee.

### Animal Treatment and Sample Collection

Rats were randomly assigned to 4 groups (n = 8/group). The rats were orally administered with glyphosate (5, 50, and 500 mg/kg body weight) daily for 35 days at 9 AM. Glyphosate dose selection was according to GLP no-observed adverse effect level (NOAEL) of 1,000 mg/kg/day for developmental toxicity (Williams et al., 2000) and equivalent to 1/1,000, 1/100, and 1/10 of LD 50 in rats (Larini, 1999; Benedetti et al., 2004). GLP was orally administered at a volume of 0.5 ml/kg. Rats orally administered with distilled water were used as the control group. Twenty four hours after the last gavage, rats were weighed and decapitated. Blood samples were collected from the jugular vein and placed at 37◦C for 1 h before being centrifuged (3,500 rpm, 15 min, 4◦C) for biochemical assays. The liver, kidney, spleen, heart, lungs, brain, adrenal glands, muscle and fat tissue were collected, rinsed twice in phosphate-buffered saline (PBS pH 7.4), use the filter paper to dry the PBS and then accurately weigh and weighed for further examinations. One piece of liver and right kidney was used for morphometric analysis and another piece was used to prepare homogenates for analyses of tissue oxidative indexes, or frozen in liquid nitrogen for subsequent qualitative reverse transcription polymerase chain reaction (RT-PCR). The organ index is calculated as follows:

Organ index (g/gBW)

= Organ absolute weight (g)/Body weight (g) × 100%

### Histological Preparation

Samples of tissue (livers and kidneys) were obtained from the animals and fixed in 4% formaldehyde solution for 24 h then dehydrated in an ascending series of alcohol, clarified using xylene, and embedded in paraffin. Paraffin were sectioned into 5µm slices and stained with hematoxylin-eosin (HE) for microscopic examination. The score system was used to evaluate the hepatic and renal damages (Ishak et al., 1995; Zheng et al., 2005; Klopfleisch, 2013). Briefly, the scores of liver sections graded on a 0–4 scale for lobular inflammation, focal necrosis and mononuclear cell infiltration, and kidney graded on a 0–4 scale for proximal and distal tubular necrosis, glomerular cellularity, and glomerular necrosis (where 0 represents no abnormality, and 1, 2, 3, and 4 represent mild, moderate, moderately severe, and severe abnormalities, respectively).

### Biochemical Evaluation

For enzymes determination, the suspension of liver, kidney and the blood samples were centrifuged at 3,500 rpm for 15 min. The homogenate and serum were collected and used for liver function assessment including measurements of the enzymes glutamicoxalacetic transaminase (GOT), glutamic-pyruvic transaminase (GPT), total superoxide dismutase (T-SOD), malondialdehyde (MDA), hydrogen peroxide (H2O2), catalase (CAT), glutathione (GSH), glutathione peroxidase (GSH-Px). The activities of SOD, H2O2, CAT, GSH, GSH-Px, and the content of MDA were assayed using commercial reagent kits obtained from the Institute of Biological Engineering of Nanjing Jiancheng (Nanjing, China) following the manufacturer's instructions. All operations were done at 4◦C.

Analyses of the SOD activity was based on SOD-mediated inhibition of nitrite formation from hydroxyammonium in the presence of O2−generators (xanthine/xanthine oxidase) (Elstner and Heupel, 1976). The total SOD activity expressed as U/mg protein. MDA was evaluated by thiobarbituric acid reactive substances method (TBARS) and expressed as nmol/mg protein (Draper and Hadley, 1990). GSH-PX activity was estimated by the analysis of reduced GSH in the enzymatic reaction (Sedlak and Lindsay, 1968). GSH-PX activity was expressed as U/mg protein. CAT activity was assayed by the method developed by Aebi (Aebi, 1984), and calculated as nM H2O2 consumed/min/mg of tissue protein. Protein concentrations in the supernatant were measured according to the Coomassie Brilliant Blue method. The activity of serum GOT and GPT was assayed according to the method that usually used in clinical examination (Reitman and Frankel, 1957).

### Serum Cytokine Measures

Serum levels of IL-1β and IL-6 were determined using a commercially available enzyme-linked immunosorbent assay (ELISA) kit purchased from R&D Systems (Shanghai, China). The results were expressed as pg/mL.

## Quantitative RT-PCR (qRT-PCR) Analysis