Systems level analysis and identification of pathways and networks associated with liver fibrosis

Legumain-deficient macrophages regulate inflammation and lipid metabolism in adipose tissues to protect against diet-induced obesity

Adipose tissue macrophages (ATMs) are key players in the development of obesity and associated metabolic inflammation, which contributes to systemic metabolic dysfunction, and understanding the interaction between macrophages and adipocytes is crucial for developing novel macrophage-based strategies against obesity. Here, we found that Legumain (Lgmn), a well-known lysosomal cysteine protease, is expressed mainly in the ATMs of obese mice. To further define the potential role of Lgmn-expressing macrophages in the generation of an aberrant metabolic state, LgmnF/F; LysMCre mice, which do not express Lgmn in macrophages, were maintained on a high-fat diet (HFD), and metabolic parameters were assessed. Macrophage-specific Lgmn deficiency protects mice against HFD-induced obesity, diminishes the quantity of proinflammatory macrophages in obese adipose tissues, and alleviates hepatic steatosis and insulin resistance. By analysing the transcriptome and proteome of murine visceral white adipose tissue (vWAT) after HFD feeding, we determined that macrophage Lgmn deficiency causes changes in lipid metabolism and the inflammatory response. Furthermore, the reciprocity of macrophage-derived Lgmn with integrin α5β1 in adipocytes was tested via colocalization analyses. It is further demonstrated in macrophage and adipocyte coculture system that macrophage derived Lgmn bound to integrin α5β1 in adipocytes, therefore attenuating PKA activation, downregulating lipolysis-related proteins and eventually exacerbating obesity development. Overall, our study identified Lgmn as a previously unrecognized regulator involved in the interaction between ATMs and adipocytes contributing to diet-induced obesity and suggested that Lgmn is a potential target for treating metabolic disorders.

Predictive Role of Cluster Bean (Cyamopsis tetragonoloba) Derived miRNAs in Human and Cattle Health

MicroRNAs (miRNAs) are small non-coding conserved molecules with lengths varying between 18-25nt. Plants miRNAs are very stable, and probably they might have been transferred across kingdoms via food intake. Such miRNAs are also called exogenous miRNAs, which regulate the gene expression in host organisms. The miRNAs present in the cluster bean, a drought tolerant legume crop having high commercial value, might have also played a regulatory role for the genes involved in nutrients synthesis or disease pathways in animals including humans due to dietary intake of plant parts of cluster beans. However, the predictive role of miRNAs of cluster beans for gene-disease association across kingdoms such as cattle and humans are not yet fully explored. Thus, the aim of the present study is to (i) find out the cluster bean miRNAs (cb-miRs) functionally similar to miRNAs of cattle and humans and predict their target genes' involvement in the occurrence of complex diseases, and (ii) identify the role of cb-miRs that are functionally non-similar to the miRNAs of cattle and humans and predict their targeted genes' association with complex diseases in host systems. Here, we predicted a total of 33 and 15 functionally similar cb-miRs (fs-cb-miRs) to human and cattle miRNAs, respectively. Further, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed the participation of targeted genes of fs-cb-miRs in 24 and 12 different pathways in humans and cattle, respectively. Few targeted genes in humans like LCP2, GABRA6, and MYH14 were predicted to be associated with disease pathways of Yesinia infection (hsa05135), neuroactive ligand-receptor interaction (hsa04080), and pathogenic Escherichia coli infection (hsa05130), respectively. However, targeted genes of fs-cb-miRs in humans like KLHL20, TNS1, and PAPD4 are associated with Alzheimer's, malignant tumor of the breast, and hepatitis C virus infection disease, respectively. Similarly, in cattle, targeted genes like ATG2B and DHRS11 of fs-cb-miRs participate in the pathways of Huntington disease and steroid biosynthesis, respectively. Additionally, the targeted genes like SURF4 and EDME2 of fs-cb-miRs are associated with mastitis and bovine osteoporosis, respectively. We also found a few cb-miRs that do not have functional similarity with human and cattle miRNAs but are found to target the genes in the host organisms and as well being associated with human and cattle diseases. Interestingly, a few genes such as NRM, PTPRE and SUZ12 were observed to be associated with Rheumatoid Arthritis, Asthma and Endometrial Stromal Sarcoma diseases, respectively, in humans and genes like SCNN1B associated with renal disease in cattle.

Computational prognostic evaluation of Alzheimer's drugs from FDA-approved database through structural conformational dynamics and drug repositioning approaches

Drug designing is high-priced and time taking process with low success rate. To overcome this obligation, computational drug repositioning technique is being promptly used to predict the possible therapeutic effects of FDA approved drugs against multiple diseases. In this computational study, protein modeling, shape-based screening, molecular docking, pharmacogenomics, and molecular dynamic simulation approaches have been utilized to retrieve the FDA approved drugs against AD. The predicted MADD protein structure was designed by homology modeling and characterized through different computational resources. Donepezil and galantamine were implanted as standard drugs and drugs were screened out based on structural similarities. Furthermore, these drugs were evaluated and based on binding energy (Kcal/mol) profiles against MADD through PyRx tool. Moreover, pharmacogenomics analysis showed good possible associations with AD mediated genes and confirmed through detail literature survey. The best 6 drug (darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar) further docked and analyzed their interaction behavior through hydrogen binding. Finally, MD simulation study were carried out on these drugs and evaluated their stability behavior by generating root mean square deviation and fluctuations (RMSD/F), radius of gyration (Rg) and soluble accessible surface area (SASA) graphs. Taken together, darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar displayed good lead like profile as compared with standard and can be used as possible therapeutic agent in the treatment of AD after in-vitro and in-vivo assessment.

Mutagenicity of PM2.5 and Ethnic Susceptibility in Chengdu-Chongqing Economic Circle, China

PM_2.5 seriously endangers human health, and its mutagenicity is considered an important pathogenic mechanism. However, the mutagenicity of PM_2.5 is mainly determined by traditional bioassays, which are limited in the large-scale identification of mutation sites. Single nucleoside polymorphisms (SNPs) can be used for the large-scale analysis of DNA mutation sites but have not yet been used on the mutagenicity of PM_2.5. The Chengdu-Chongqing Economic Circle is one of China's four major economic circles and five major urban agglomerations, and the relationship between the mutagenicity of PM_2.5 and ethnic susceptibility in this circle remains unclear. In this study, the representative samples are PM_2.5 from Chengdu in summer (CDSUM), Chengdu in winter (CDWIN), Chongqing in summer (CQSUM) and Chongqing in winter (CQWIN) respectively. PM_2.5 from CDWIN, CDSUM and CQSUM induce the highest levels of mutation in the regions of exon/5'Utr, upstream/splice site and downstream/3'Utr respectively. PM_2.5 from CQWIN, CDWIN and CDSUM induce the highest ratio of missense, nonsense and synonymous mutation respectively. PM_2.5 from CQWIN and CDWIN induce the highest transition and transversion mutations respectively. The ability of PM_2.5 from the four groups to induce disruptive mutation effects is similar. For ethnic susceptibility, PM_2.5 in this economic circle is more likely to induce DNA mutation in Chinese Dai from Xishuangbanna among Chinese ethnic groups. PM_2.5 from CDSUM, CDWIN, CQSUM and CQWIN may particularly tend to induce Southern Han Chinese, Dai in Xishuangbanna, Dai in Xishuangbanna and Southern Han Chinese respectively. These findings may assist in the development of a new method for analyzing the mutagenicity of PM_2.5. Moreover, this study not only promotes attention to ethnic susceptibility to PM_2.5, but also introduces public protection policies for the susceptible population.

Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a major health problem around the world. The management of this disease is complicated by the lack of noninvasive diagnostic tools and the few treatment options available. Better clinical outcomes can be achieved if HCC is detected early, but unfortunately, clinical signs appear when the disease is in its late stages. We aim to identify novel genes that can be targeted for the diagnosis and therapy of HCC. We performed a meta-analysis of transcriptomics data to identify differentially expressed genes and applied network analysis to identify hub genes. Fatty acid metabolism, complement and coagulation cascade, chemical carcinogenesis and retinol metabolism were identified as key pathways in HCC. Furthermore, we integrated transcriptomics data into a reference human genome-scale metabolic model to identify key reactions and subsystems relevant in HCC. We conclude that fatty acid activation, purine metabolism, vitamin D, and E metabolism are key processes in the development of HCC and therefore need to be further explored for the development of new therapies. We provide the first evidence that GABRP, HBG1 and DAK (TKFC) genes are important in HCC in humans and warrant further studies.

Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond

A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.

Comparison of plasminogen activator inhibitor-1 levels in chronic hepatitis B patients with hepatic cirrhosis and without hepatic cirrhosis

Introduction: One of the hepatic cirrhosis manifestations is bleeding disorders. Among all the substance that plays a pivotal role in maintaining the balance between thrombosis and thrombolysis is PAI-1, synthesized by hepatocytes. The dynamics of increase and decrease of PAI-1 is a natural response to the ongoing hepatic cirrhosis, but may not be seen in non-hepatic cirrhosis. PAI-1 levels also depends on the stage of fibrosis. Several conditions may interfere with PAI-1 levels including age, body mass index, and gender Objectives: This study aims to find out the comparison of PAI-1 levels in hepatitis B patients with hepatic cirrhosis and without hepatic cirrhosis and to compare it with every stage of hepatic cirrhosis. Patients and Methods: This study is an observational analytical study with a cross-sectional approach conducted at Wahidin Sudirohusodo hospitals, Makassar. Subjects are chronic hepatitis B patients with and without hepatic cirrhosis which meet inclusion criteria. Serum PAI-1 levels were measured by using Bender MedSystems human plasminogen activator inhibitor-1 ELISA kit (BMS2033) and using the ELISA technique. Statistical analysis was performed using the Kolmogorov Smirnov normality test as well as the Mann-Whitney method. Statistical results are considered significant if the p-value <0.05. Results: The research was conducted on 60 subjects who meet inclusion criteria, consisting of 33 men and 27 women. There were 16 patients with hepatic cirrhosis. Levels of PAI-1 in hepatic cirrhosis was significantly different which lower than non-hepatic cirrhosis patient (0.43 ng/mL Vs 1.11 ng/mL, p=0.024).  By staging of hepatic fibrosis, stage F2 hepatic fibrosis had the highest levels of PAI-1, in contrast with end-stage hepatic fibrosis which had the lowest levels. Conclusion: Levels of PAI-1 fluctuate through different stages of hepatic fibrosis. The significant difference in PAI-1 levels in hepatic cirrhosis and non-hepatic cirrhosis demonstrates a correlation between PAI-1 and hepatic cirrhosis  Keywords: Chronic hepatitis B, Hepatic cirrhosis, Plasminogen Activator Inhibitor-1

A Proteomic Approach to Study the Biological Role of Hepatitis C Virus Protein Core+1/ARFP

Hepatitis C virus is the major cause of chronic liver diseases and the only cytoplasmic RNA virus known to be oncogenic in humans. The viral genome gives rise to ten mature proteins and to additional proteins, which are the products of alternative translation initiation mechanisms. A protein-known as ARFP (alternative reading frame protein) or Core+1 protein-is synthesized by an open reading frame overlapping the HCV Core coding region in the (+1) frame of genotype 1a. Almost 20 years after its discovery, we still know little of the biological role of the ARFP/Core+1 protein. Here, our differential proteomic analysis of stable hepatoma cell lines expressing the Core+1/Long isoform of HCV-1a relates the expression of the Core+1/Long isoform with the progression of the pathology of HCV liver disease to cancer.

Probiotics counteract the expression of hepatic profibrotic genes via the attenuation of TGF-β/SMAD signaling and autophagy in hepatic stellate cells

Hepatic fibrosis is caused by the increased accumulation and improper degradation of extracellular matrix (ECM) proteins in the liver. Hepatic stellate cells (HSCs) activation is a key process in initiating hepatic fibrosis and can be ameliorated by the administration of probiotic strains. This study hypothesized that LAB strains (Lactiplantibacillus plantarum, Lactobacillus brevis, and Weissella cibaria) might attenuate pro-fibrogenic cytokine TGF-β mediated HSCs activation and induce collagen deposition, expression of other fibrogenic/inflammatory markers, autophagy, and apoptotic processes in vitro. Few studies have evaluated the probiotic effects against fibrogenesis in vitro. In this study, TGF-β exposure increased collagen deposition in LX-2 cells, but this increase was diminished when the cells were pretreated with LAB strains before TGF-β stimulation. TGF-β not only increased collagen deposition, but it also significantly upregulated the mRNA levels of Col1A1, alpha-smooth muscle actin (α-SMA), matrix metalloproteinases-2 (MMP-2), IL-6, CXCL-8, CCL2, and IL-1β in LX-2 cells. Pretreatment of the cells with LAB strains counteracted the TGF-β-induced pro-fibrogenic and inflammatory markers by modulating SMAD-dependent and SMAD-independent TGF-β signaling. In addition, LX-2 cells exposed to TGF-β induced the autophagic and apoptotic associated proteins that were also positively regulated by the LAB strains. These findings suggest that LAB can attenuate TGF-β signaling that is associated with liver fibrogenesis.

Mechanical communication in fibrosis progression

Mechanical hallmarks of fibrotic microenvironments are both outcomes and causes of fibrosis progression. Understanding how cells sense and transmit mechanical cues in the interplay with extracellular matrix (ECM) and hemodynamic forces is a significant challenge. Recent advances highlight the evolvement of intracellular mechanotransduction pathways responding to ECM remodeling and abnormal hemodynamics (i.e., low and disturbed shear stress, pathological stretch, and increased pressure), which are prevalent biomechanical characteristics of fibrosis in multiple organs (e.g., liver, lung, and heart). Here, we envisage the mechanical communication in cell-ECM, cell-hemodynamics and cell-ECM-cell crosstalk (namely paratensile signaling) during fibrosis expansion. We also provide a comprehensive overview of in vitro and in silico engineering systems for disease modeling that will aid the identification and prediction of mechano-based therapeutic targets to ameliorate fibrosis progression.

Recombinant antibodies recognize conformation-dependent epitopes of the leucine zipper of misfolding-prone myocilin

Recombinant antibodies with well-characterized epitopes and known conformational specificities are critical reagents to support robust interpretation and reproducibility of immunoassays across biomedical research. For myocilin, a protein prone to misfolding that is associated with glaucoma and an emerging player in other human diseases, currently available antibodies are unable to differentiate among the numerous disease-associated protein states. This fundamentally constrains efforts to understand the connection between myocilin structure, function, and disease. To address this concern, we used protein engineering methods to develop new recombinant antibodies that detect the N-terminal leucine zipper structural domain of myocilin and that are cross-reactive for human and mouse myocilin. After harvesting spleens from immunized mice and in vitro library panning, we identified two antibodies, 2A4 and 1G12. 2A4 specifically recognizes a folded epitope while 1G12 recognizes a range of conformations. We matured antibody 2A4 for improved biophysical properties, resulting in variant 2H2. In a human IgG1 format, 2A4, 1G12, and 2H2 immunoprecipitate full-length folded myocilin present in the spent media of human trabecular meshwork (TM) cells, and 2H2 can visualize myocilin in fixed human TM cells using fluorescence microscopy. These new antibodies should find broad application in glaucoma and other research across multiple species platforms.

A systems pharmacology approach based on oncogenic signalling pathways to determine the mechanisms of action of natural products in breast cancer from transcriptome data

BACKGROUND

Narrow spectrum of action through limited molecular targets and unforeseen drug-related toxicities have been the main reasons for drug failures at the phase I clinical trials in complex diseases. Most plant-derived compounds with medicinal values possess poly-pharmacologic properties with overall good tolerability, and, thus, are appropriate in the management of complex diseases, especially cancers. However, methodological limitations impede attempts to catalogue targeted processes and infer systemic mechanisms of action. While most of the current understanding of these compounds is based on reductive methods, it is increasingly becoming clear that holistic techniques, leveraging current improvements in omic data collection and bioinformatics methods, are better suited for elucidating their systemic effects. Thus, we developed and implemented an integrative systems biology pipeline to study these compounds and reveal their mechanism of actions on breast cancer cell lines.

METHODS

Transcriptome data from compound-treated breast cancer cell lines, representing triple negative (TN), luminal A (ER+) and HER2+ tumour types, were mapped on human protein interactome to construct targeted subnetworks. The subnetworks were analysed for enriched oncogenic signalling pathways. Pathway redundancy was reduced by constructing pathway-pathway interaction networks, and the sets of overlapping genes were subsequently used to infer pathway crosstalk. The resulting filtered pathways were mapped on oncogenesis processes to evaluate their anti-carcinogenic effectiveness, and thus putative mechanisms of action.

RESULTS

The signalling pathways regulated by Actein, Withaferin A, Indole-3-Carbinol and Compound Kushen, which are extensively researched compounds, were shown to be projected on a set of oncogenesis processes at the transcriptomic level in different breast cancer subtypes. The enrichment of well-known tumour driving genes indicate that these compounds indirectly dysregulate cancer driving pathways in the subnetworks.

CONCLUSION

The proposed framework infers the mechanisms of action of potential drug candidates from their enriched protein interaction subnetworks and oncogenic signalling pathways. It also provides a systematic approach for evaluating such compounds in polygenic complex diseases. In addition, the plant-based compounds used here show poly-pharmacologic mechanism of action by targeting subnetworks enriched with cancer driving genes. This network perspective supports the need for a systemic drug-target evaluation for lead compounds prior to efficacy experiments.

The Mechanism of Asparagine Endopeptidase in the Progression of Malignant Tumors: A Review

Asparagine endopeptidase (AEP), also called legumain, is currently the only known cysteine protease that specifically cleaves peptide bonds in asparaginyl residue in the mammalian genome. Since 2003, AEP has been reported to be widely expressed in a variety of carcinomas and is considered a potential therapeutic target. In the following years, researchers intensively investigated the substrates of AEP and the mechanism of AEP in partial tumors. With the identification of substrate proteins such as P53, integrin αvβ3, MMP-2, and MMP-9, the biochemical mechanism of AEP in carcinomas is also more precise. This review will clarify the probable mechanisms of AEP in the progression of breast carcinoma, glioblastoma, gastric carcinoma, and epithelial ovarian carcinoma. This review will also discuss the feasibility of targeted therapy with AEP inhibitor (AEPI) in these carcinomas.

On the limits of active module identification

In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein-protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.

TOXPANEL: A Gene-Set Analysis Tool to Assess Liver and Kidney Injuries

Gene-set analysis is commonly used to identify trends in gene expression when cells, tissues, organs, or organisms are subjected to conditions that differ from those within the normal physiological range. However, tools for gene-set analysis to assess liver and kidney injury responses are less common. Furthermore, most websites for gene-set analysis lack the option for users to customize their gene-set database. Here, we present the ToxPanel website, which allows users to perform gene-set analysis to assess liver and kidney injuries using activation scores based on gene-expression fold-change values. The results are graphically presented to assess constituent injury phenotypes (histopathology), with interactive result tables that identify the main contributing genes to a given signal. In addition, ToxPanel offers the flexibility to analyze any set of custom genes based on gene fold-change values. ToxPanel is publically available online at https://toxpanel.bhsai.org. ToxPanel allows users to access our previously developed liver and kidney injury gene sets, which we have shown in previous work to yield robust results that correlate with the degree of injury. Users can also test and validate their customized gene sets using the ToxPanel website.

Gene Expression Thresholds Derived From Short-term Exposures Identify Rat Liver Tumorigens

Traditional methods for cancer risk assessment are resource-intensive, retrospective, and not feasible for the vast majority of environmental chemicals. In this study, we investigated whether quantitative genomic data from short-term studies may be used to set protective thresholds for potential tumorigenic effects. We hypothesized that gene expression biomarkers measuring activation of the key early events in established pathways for rodent liver cancer exhibit cross-chemical thresholds for tumorigenesis predictive for liver cancer risk. We defined biomarker thresholds for 6 major liver cancer pathways using training sets of chemicals with short-term genomic data (3-29 days of exposure) from the TG-GATES (n = 77 chemicals) and DrugMatrix (n = 86 chemicals) databases and then tested these thresholds within and between datasets. The 6 pathway biomarkers represented genotoxicity, cytotoxicity, and activation of xenobiotic, steroid, and lipid receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Thresholds were calculated as the maximum values derived from exposures without detectable liver tumor outcomes. We identified clear response values that were consistent across training and test sets. Thresholds derived from the TG-GATES training set were highly predictive (97%) in a test set of independent chemicals, whereas thresholds derived from the DrugMatrix study were 96%-97% predictive for the TG-GATES study. Threshold values derived from an abridged gene list (2/biomarker) also exhibited high predictive accuracy (91%-94%). These findings support the idea that early genomic changes can be used to establish threshold estimates or "molecular tipping points" that are predictive of later-life health outcomes.

Elucidating Potential Profibrotic Mechanisms of Emerging Biomarkers for Early Prognosis of Hepatic Fibrosis

Hepatic fibrosis has been associated with a series of pathophysiological processes causing excessive accumulation of extracellular matrix proteins. Several cellular processes and molecular mechanisms have been implicated in the diseased liver that augments fibrogenesis, fibrogenic cytokines and associated liver complications. Liver biopsy remains an essential diagnostic tool for histological evaluation of hepatic fibrosis to establish a prognosis. In addition to being invasive, this methodology presents with several limitations including poor cost-effectiveness, prolonged hospitalizations, and risks of peritoneal bleeding, while the clinical use of this method does not reveal underlying pathogenic mechanisms. Several alternate noninvasive diagnostic strategies have been developed, to determine the extent of hepatic fibrosis, including the use of direct and indirect biomarkers. Immediate diagnosis of hepatic fibrosis by noninvasive means would be more palatable than a biopsy and could assist clinicians in taking early interventions timely, avoiding fatal complications, and improving prognosis. Therefore, we sought to review some common biomarkers of liver fibrosis along with some emerging candidates, including the oxidative stress-mediated biomarkers, epigenetic and genetic markers, exosomes, and miRNAs that needs further evaluation and would have better sensitivity and specificity. We also aim to elucidate the potential role of cardiotonic steroids (CTS) and evaluate the pro-inflammatory and profibrotic effects of CTS in exacerbating hepatic fibrosis. By understanding the underlying pathogenic processes, the efficacy of these biomarkers could allow for early diagnosis and treatment of hepatic fibrosis in chronic liver diseases, once validated.

A toxicogenomic approach to assess kidney injury induced by mercuric chloride in rats

Kidney injury caused by disease, trauma, environmental exposures, or drugs may result in decreased renal function, chronic kidney disease, or acute kidney failure. Diagnosis of kidney injury using serum creatinine levels, a common clinical test, only identifies renal dysfunction after the kidneys have undergone severe damage. Other indicators sensitive to kidney injury, such as the level of urine kidney injury molecule-1 (KIM-1), lack the ability to differentiate between injury phenotypes. To address early detection as well as detailed categorization of kidney-injury phenotypes in preclinical animal or cellular studies, we previously identified eight sets (modules) of co-expressed genes uniquely associated with kidney histopathology. Here, we used mercuric chloride (HgCl₂)-a model nephrotoxicant-to chemically induce kidney injuries as monitored by KIM-1 levels in Sprague Dawley rats at two doses (0.25 or 0.50 mg/kg) and two exposure lengths (10 or 34 h). We collected whole transcriptome RNA-seq data derived from five animals at each dose and time point to perform a toxicogenomics analysis. Consistent with documented injury phenotypes for HgCl₂ toxicity, our kidney-injury-module approach identified the onset of necrosis and dilation as early as 10 h after a dose of 0.50 mg/kg that produced only mild injury as judged by urinary KIM-1 excretion. The results of these animal studies highlight the potential of the kidney-injury-module approach to provide a sensitive and histopathology-specific readout of renal toxicity.

Asparaginyl endopeptidase may promote liver sinusoidal endothelial cell angiogenesis via PI3K/Akt pathway

BACKGROUND AND AIMS

pathological angiogenesis plays an important role in the progression of chronic liver diseases. Asparaginyl endopeptidase (AEP) participates in tumor angiogenesis and was recently shown to be associated with liver fibrosis. This study aimed to explore the effect of AEP on liver sinusoidal endothelial cell (LSECs) angiogenesis and determine the underlying mechanism.

METHODS

cultured LSECs were infected with lentiviruses in order to suppress AEP expression (AEP-KD1, AEP-KD2). The effect of AEP on LSECs proliferation, apoptosis and migration were subsequently determined by a CCK8 assay, flow cytometry and wound-healing and Transwell assays, respectively, in AEP knocked-down and control LSECs. The expression of the endothelial cell surface markers CD31, CD34 and von Willebrand factor (vWF) were detected by immunofluorescence assay and western blot. The angiogenic factors, vascular endothelial growth factor receptor 2 (VEGFR2) and interleukin 8 (IL 8) were detected by real-time PCR and western blot. The effect of AEP on vessel tube formation by LSECs was examined by Matrigel™ tube-formation assay. Phosphoinositide 3-kinase (PI3K)/Akt expression and phosphorylation were detected by western blot.

RESULTS

AEP was effectively knocked down by lentivirus infection in LSECs. Down-regulation of AEP expression significantly decreased proliferation and migration and increased apoptosis of LSECs. Moreover, expression levels of the endothelial cell surface markers CD31, CD34 and vWF, as well as angiogenic factors VEGFR2 and IL 8, were also reduced after AEP was knocked-down. The vessel tube formation abilities of AEP-KD1 and AEP-KD2 LSECs were significantly inhibited compared with LSECs without AEP knocked-down. Down-regulation of AEP also inhibited the phosphorylation of PI3K and Akt.

CONCLUSION

AEP promotes LSECs angiogenesis in vitro, possibly via the PI3K/Akt pathway. AEP may therefore be a potential therapeutic target for preventing the progression of liver fibrosis.

Primary Alcohol-Activated Human and Mouse Hepatic Stellate Cells Share Similarities in Gene-Expression Profiles

Alcoholic liver disease (ALD) is a leading cause of cirrhosis in the United States, which is characterized by extensive deposition of extracellular matrix proteins and formation of a fibrous scar. Hepatic stellate cells (HSCs) are the major source of collagen type 1 producing myofibroblasts in ALD fibrosis. However, the mechanism of alcohol-induced activation of human and mouse HSCs is not fully understood. We compared the gene-expression profiles of primary cultured human HSCs (hHSCs) isolated from patients with ALD (n = 3) or without underlying liver disease (n = 4) using RNA-sequencing analysis. Furthermore, the gene-expression profile of ALD hHSCs was compared with that of alcohol-activated mHSCs (isolated from intragastric alcohol-fed mice) or CCl₄-activated mouse HSCs (mHSCs). Comparative transcriptome analysis revealed that ALD hHSCs, in addition to alcohol-activated and CCl₄-activated mHSCs, share the expression of common HSC activation (Col1a1 [collagen type I alpha 1 chain], Acta1 [actin alpha 1, skeletal muscle], PAI1 [plasminogen activator inhibitor-1], TIMP1 [tissue inhibitor of metalloproteinase 1], and LOXL2 [lysyl oxidase homolog 2]), indicating that a common mechanism underlies the activation of human and mouse HSCs. Furthermore, alcohol-activated mHSCs most closely recapitulate the gene-expression profile of ALD hHSCs. We identified the genes that are similarly and uniquely up-regulated in primary cultured alcohol-activated hHSCs and freshly isolated mHSCs, which include CSF1R (macrophage colony-stimulating factor 1 receptor), PLEK (pleckstrin), LAPTM5 (lysosmal-associated transmembrane protein 5), CD74 (class I transactivator, the invariant chain), CD53, MMP9 (matrix metallopeptidase 9), CD14, CTSS (cathepsin S), TYROBP (TYRO protein tyrosine kinase-binding protein), and ITGB2 (integrin beta-2), and other genes (compared with CCl₄-activated mHSCs). Conclusion: We identified genes in alcohol-activated mHSCs from intragastric alcohol-fed mice that are largely consistent with the gene-expression profile of primary cultured hHSCs from patients with ALD. These genes are unique to alcohol-induced HSC activation in two species, and therefore may become targets or readout for antifibrotic therapy in experimental models of ALD.

Assessing Chemical-Induced Liver Injury In Vivo From In Vitro Gene Expression Data in the Rat: The Case of Thioacetamide Toxicity

Consumers are exposed to thousands of chemicals with potentially adverse health effects. However, these chemicals will never be tested for toxicity because of the immense resources needed for animal-based (in vivo) toxicological studies. Today, there are no viable in vitro alternatives to these types of animal studies. To develop an in vitro approach, we investigated whether we could predict in vivo organ injuries in rats with the use of RNA-seq data acquired from tissues early in the development of toxicant-induced injury, by comparing gene expression data from RNA isolated from these rat tissues with those obtained from in vitro exposure of primary liver and kidney cells. We collected RNA-seq data from the liver and kidney tissues of Sprague-Dawley rats 8 or 24 h after exposing them to vehicle (control), low (25 mg/kg), or high (100 mg/kg) doses of thioacetamide, a known liver toxicant that promotes fibrosis; we used these doses and exposure times to cause only mild toxicant-induced injury. For the in vitro study, we treated two cell types from Sprague-Dawley rats, primary hepatocytes (vehicle; low, 0.025 mM; or high, 0.125 mM dose), and renal tube epithelial cells (vehicle; low, 0.125 mM; or high, 0.500 mM) dose) with the thioacetamide metabolite, thioacetamide-S-oxide, selecting in vitro doses and exposure times to recreate the early-stage toxicant-induced injury model that we achieved in vivo. RNA-seq data were collected 9 or 24 h after application of vehicle or thioacetamide-S-oxide. We found that our modular approach for the analysis of gene expression data derived from in vivo RNA-seq strongly correlated (R² > 0.6) with the in vitro results at two different dose levels of thioacetamide/thioacetamide-S-oxide after 24 h of exposure. The top-ranked liver injury modules in vitro correctly identified the ensuing development of liver fibrosis.

Mining Public Toxicogenomic Data Reveals Insights and Challenges in Delineating Liver Steatosis Adverse Outcome Pathways

Exposure to chemicals contributes to the development and progression of fatty liver, or steatosis, a process characterized by abnormal accumulation of lipids within liver cells. However, lack of knowledge on how chemicals cause steatosis has prevented any large-scale assessment of the 80,000+ chemicals in current use. To address this gap, we mined a large, publicly available toxicogenomic dataset associated with 18 known steatogenic chemicals to assess responses across assays (in vitro and in vivo) and species (i.e., rats and humans). We identified genes that were differentially expressed (DEGs) in rat in vivo, rat in vitro, and human in vitro studies in which rats or in vitro primary cell lines were exposed to the chemicals at different doses and durations. Using these DEGs, we performed pathway enrichment analysis, analyzed the molecular initiating events (MIEs) of the steatosis adverse outcome pathway (AOP), and predicted metabolite changes using metabolic network analysis. Genes indicative of oxidative stress were among the DEGs most frequently observed in the rat in vivo studies. Nox4, a pro-fibrotic gene, was down-regulated across these chemical exposure conditions. We identified eight genes (Cyp1a1, Egr1, Ccnb1, Gdf15, Cdk1, Pdk4, Ccna2, and Ns5atp9) and one pathway (retinol metabolism), associated with steatogenic chemicals and whose response was conserved across the three in vitro and in vivo systems. Similarly, we found the predicted metabolite changes, such as increases of saturated and unsaturated fatty acids, conserved across the three systems. Analysis of the target genes associated with the MIEs of the current steatosis AOP did not provide a clear association between these 18 chemicals and the MIEs, underlining the multi-factorial nature of this disease. Notably, our overall analysis implicated mitochondrial toxicity as an important and overlooked MIE for chemical-induced steatosis. The integrated toxicogenomics approach to identify genes, pathways, and metabolites based on known steatogenic chemicals, provide an important mean to assess development of AOPs and gauging the relevance of new testing strategies.

Unsupervised identification of disease states from high-dimensional physiological and histopathological profiles

The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic-induced injury. Xenobiotic-induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine-learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin-induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole-body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.

Embracing the Dark Side: Computational Approaches to Unveil the Functionality of Genes Lacking Biological Annotation in Drug-Induced Liver Injury

In toxicogenomics, functional annotation is an important step to gain additional insights into genes with aberrant expression that drive pathophysiological mechanisms. Nevertheless, there exists a gap on annotation of these genes which often hampers the interpretation of results and limits their applicability in translational medicine. In this study, we evaluated the coverage of functional annotations of differentially expressed genes (DEGs) induced by 10 selected compounds from the TG-GATEs database identified as high- or no-risk in causing drug-induced liver injury (most-DILI or no-DILI, respectively) using in vitro human data. Functional roles of DEGs not present in the most common biological annotation databases – termed “dark genes” – were unveiled via literature mining and via the identification of shared regulatory transcription factors or signaling pathways. Our results demonstrated that there were approximately 13% of dark genes induced by these compounds in vitro and we were able to obtain additional relevant information for up to 76% of those. Using interactome data from several sources, we have uncovered genes such as LRBA, and WDR26 as highly connected in the protein network that play roles in drug response. Genes such as MALAT1, H19, and MIR29C – whose links to hepatotoxicity have been confirmed – were identified as markers for the most-DILI group and appeared as top hits across all literature-based mining methods. Furthermore, we investigated the potential impact of dark genes on liver toxicity by identifying their rat orthologs in combination with their correlation to drug-induced liver pathologies observed in vivo following chemical exposure. We identified a set of important regulatory transcription factors of dark genes for all most-DILI compounds including E2F1 and JUND with supporting evidences in literature and we found Magee1 correlated with chemically induced bile duct hyperplasia and adverse responses at 29 days in rats in vivo. In conclusion, in this study we show the potential role of these poorly annotated genes in mechanisms underlying hepatotoxicity and offer a number of computational approaches that may help to minimize current gaps in gene annotation and highlight their values as potential biomarkers in toxicological studies.

Network and Pathway Analysis of Toxicogenomics Data

Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data

The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

Monocytes with Oncogenic Mutation JAK2 V617F as a Tool for Studies of the Pathogenic Mechanisms of Myelofibrosis

We analyzed previously generated stable monocyte-derived cell line carrying mutation JAK2 V617F. Evaluation of the expression of pro- and antifibrotic factors revealed changes in the production of MMPs and their inhibitors, growth factors, galectin-3, and pentraxin 3 in cells carrying mutation JAK2 in comparison with control non-modified cells.

Comparative Proteomic Analysis of Liver Steatosis and Fibrosis after Oral Hepatotoxicant Administration in Sprague-Dawley Rats

The past decade has seen an increase in the development and clinical use of biomarkers associated with histological features of liver disease. Here, we conduct a comparative histological and global proteomics analysis to identify coregulated modules of proteins in the progression of hepatic steatosis or fibrosis. We orally administered the reference chemicals bromobenzene (BB) or 4,4'-methylenedianiline (4,4'-MDA) to male Sprague-Dawley rats for either 1 single administration or 5 consecutive daily doses. Livers were preserved for histopathology and global proteomics assessment. Analysis of liver sections confirmed a dose- and time-dependent increase in frequency and severity of histopathological features indicative of lipid accumulation after BB or fibrosis after 4,4'-MDA. BB administration resulted in a dose-dependent increase in the frequency and severity of inflammation and vacuolation. 4,4'-MDA administration resulted in a dose-dependent increase in the frequency and severity of periportal collagen accumulation and inflammation. Pathway analysis identified a time-dependent enrichment of biological processes associated with steatogenic or fibrogenic initiating events, cellular functions, and toxicological states. Differentially expressed protein modules were consistent with the observed histology, placing physiologically linked protein networks into context of the disease process. This study demonstrates the potential for protein modules to provide mechanistic links between initiating events and histopathological outcomes.

A simple in silico strategy identifies candidate biomarkers for the diagnosis of liver fibrosis in morbidly obese subjects

BACKGROUND & AIMS

Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disorder, tightly associated with obesity. The histological spectrum of the disease ranges from simple steatosis to steatohepatitis, with different stages of fibrosis, and fibrosis stage is the most significant predictor of mortality in NAFLD. Liver biopsy continues to be the gold standard for its diagnosis and reliable non-invasive diagnostic tools are unavailable. We investigated the accuracy of candidate proteins, identified by an in silico approach, as biomarkers for diagnosis of fibrosis.

METHODS

Seventy-one morbidly obese (MO) subjects with biopsy-proven NAFLD were enrolled, and the cohort was subdivided according to minimal (F0/F1) or moderate (F2/F3) fibrosis. The plasmatic level of CD44 antigen (CD44), secreted protein acidic and rich in cysteine (SPARC), epidermal growth factor receptor (EGFR) and insulin-like growth factor 2 (IGF2) were determined by ELISA. Significant associations between plasmatic levels and histological fibrosis were determined by correlation analysis and the diagnostic accuracy by the area under receiver operating characteristic curves (AUROC).

RESULTS

Eighty-two percentage of the subjects had F0/F1 and 18% with F2/F3 fibrosis. Plasmatic levels of IGF2, EGFR and their ratio (EGFR/IGF2) were associated with liver fibrosis, correlating inversely for IGF2 (P < .006) and directly (P < .018; P < .0001) for EGFR and EGFR/IGF2 respectively. The IGF2 marker had the best diagnostic accuracy for moderate fibrosis (AUROC 0.83), followed by EGFR/IGF2 ratio (AUROC 0.79) and EGFR (AUROC 0.71).

CONCLUSIONS

Our study supports the potential utility of IGF2 and EGFR as non-invasive diagnostic biomarkers for liver fibrosis in morbidly obese subjects.

Accelerating Adverse Outcome Pathway Development Using Publicly Available Data Sources

The adverse outcome pathway (AOP) concept links molecular perturbations with organism and population-level outcomes to support high-throughput toxicity (HTT) testing. International efforts are underway to define AOPs and store the information supporting these AOPs in a central knowledge base; however, this process is currently labor-intensive and time-consuming. Publicly available data sources provide a wealth of information that could be used to define computationally predicted AOPs (cpAOPs), which could serve as a basis for creating expert-derived AOPs in a much more efficient way. Computational tools for mining large datasets provide the means for extracting and organizing the information captured in these public data sources. Using cpAOPs as a starting point for expert-derived AOPs should accelerate AOP development. Coupling this with tools to coordinate and facilitate the expert development efforts will increase the number and quality of AOPs produced, which should play a key role in advancing the adoption of HTT testing, thereby reducing the use of animals in toxicity testing and greatly increasing the number of chemicals that can be tested.

Mining kidney toxicogenomic data by using gene co-expression modules

Background

Acute kidney injury (AKI) caused by drug and toxicant ingestion is a serious clinical condition associated with high mortality rates. We currently lack detailed knowledge of the underlying molecular mechanisms and biological networks associated with AKI. In this study, we carried out gene co-expression analyses using DrugMatrix—a large toxicogenomics database with gene expression data from rats exposed to diverse chemicals—and identified gene modules associated with kidney injury to probe the molecular-level details of this disease.

Results

We generated a comprehensive set of gene co-expression modules by using the Iterative Signature Algorithm and found distinct clusters of modules that shared genes and were associated with similar chemical exposure conditions. We identified two module clusters that showed specificity for kidney injury in that they 1) were activated by chemical exposures causing kidney injury, 2) were not activated by other chemical exposures, and 3) contained known AKI-relevant genes such as Havcr1, Clu, and Tff3. We used the genes in these AKI-relevant module clusters to develop a signature of 30 genes that could assess the potential of a chemical to cause kidney injury well before injury actually occurs. We integrated AKI-relevant module cluster genes with protein-protein interaction networks and identified the involvement of immunoproteasomes in AKI. To identify biological networks and processes linked to Havcr1, we determined genes within the modules that frequently co-express with Havcr1, including Cd44, Plk2, Mdm2, Hnmt, Macrod1, and Gtpbp4. We verified this procedure by showing that randomized data did not identify Havcr1 co-expression genes and that excluding up to 10 % of the data caused only minimal degradation of the gene set. Finally, by using an external dataset from a rat kidney ischemic study, we showed that the frequently co-expressed genes of Havcr1 behaved similarly in a model of non-chemically induced kidney injury.

Conclusions

Our study demonstrated that co-expression modules and co-expressed genes contain rich information for generating novel biomarker hypotheses and constructing mechanism-based molecular networks associated with kidney injury.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3143-y) contains supplementary material, which is available to authorized users.

Connection Map for Compounds (CMC): A Server for Combinatorial Drug Toxicity and Efficacy Analysis

Drug discovery and development is a costly and time-consuming process with a high risk for failure resulting primarily from a drug's associated clinical safety and efficacy potential. Identifying and eliminating inapt candidate drugs as early as possible is an effective way for reducing unnecessary costs, but limited analytical tools are currently available for this purpose. Recent growth in the area of toxicogenomics and pharmacogenomics has provided with a vast amount of drug expression microarray data. Web servers such as CMap and LTMap have used this information to evaluate drug toxicity and mechanisms of action independently; however, their wider applicability has been limited by the lack of a combinatorial drug-safety type of analysis. Using available genome-wide drug transcriptional expression profiles, we developed the first web server for combinatorial evaluation of toxicity and efficacy of candidate drugs named "Connection Map for Compounds" (CMC). Using CMC, researchers can initially compare their query drug gene signatures with prebuilt gene profiles generated from two large-scale toxicogenomics databases, and subsequently perform a drug efficacy analysis for identification of known mechanisms of drug action or generation of new predictions. CMC provides a novel approach for drug repositioning and early evaluation in drug discovery with its unique combination of toxicity and efficacy analyses, expansibility of data and algorithms, and customization of reference gene profiles. CMC can be freely accessed at http://cadd.tongji.edu.cn/webserver/CMCbp.jsp .

Six-SOMAmer Index Relating to Immune, Protease and Angiogenic Functions Predicts Progression in IPF

RATIONALE

Biomarkers in easily accessible compartments like peripheral blood that can predict disease progression in idiopathic pulmonary fibrosis (IPF) would be clinically useful regarding clinical trial participation or treatment decisions for patients. In this study, we used unbiased proteomics to identify relevant disease progression biomarkers in IPF.

METHODS

Plasma from IPF patients was measured using an 1129 analyte slow off-rate modified aptamer (SOMAmer) array, and patient outcomes were followed over the next 80 weeks. Receiver operating characteristic (ROC) curves evaluated sensitivity and specificity for levels of each biomarker and estimated area under the curve (AUC) when prognostic biomarker thresholds were used to predict disease progression. Both logistic and Cox regression models advised biomarker selection for a composite disease progression index; index biomarkers were weighted via expected progression-free days lost during follow-up with a biomarker on the unfavorable side of the threshold.

RESULTS

A six-analyte index, scaled 0 to 11, composed of markers of immune function, proteolysis and angiogenesis [high levels of ficolin-2 (FCN2), cathepsin-S (Cath-S), legumain (LGMN) and soluble vascular endothelial growth factor receptor 2 (VEGFsR2), but low levels of inducible T cell costimulator (ICOS) or trypsin 3 (TRY3)] predicted better progression-free survival in IPF with a ROC AUC of 0.91. An index score ≥ 3 (group ≥ 2) was strongly associated with IPF progression after adjustment for age, gender, smoking status, immunomodulation, forced vital capacity % predicted and diffusing capacity for carbon monoxide % predicted (HR 16.8, 95% CI 2.2-126.7, P = 0.006).

CONCLUSION

This index, derived from the largest proteomic analysis of IPF plasma samples to date, could be useful for clinical decision making in IPF, and the identified analytes suggest biological processes that may promote disease progression.

Systems toxicology of chemically induced liver and kidney injuries: histopathology-associated gene co-expression modules

Organ injuries caused by environmental chemical exposures or use of pharmaceutical drugs pose a serious health risk that may be difficult to assess because of a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific histopathology outcomes via biomarkers will provide a foundation for designing precise and robust diagnostic tests. We identified co-expressed genes (modules) specific to injury endpoints using the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) - a toxicogenomics database containing organ-specific gene expression data matched to dose- and time-dependent chemical exposures and adverse histopathology assessments in Sprague-Dawley rats. We proposed a protocol for selecting gene modules associated with chemical-induced injuries that classify 11 liver and eight kidney histopathology endpoints based on dose-dependent activation of the identified modules. We showed that the activation of the modules for a particular chemical exposure condition, i.e., chemical-time-dose combination, correlated with the severity of histopathological damage in a dose-dependent manner. Furthermore, the modules could distinguish different types of injuries caused by chemical exposures as well as determine whether the injury module activation was specific to the tissue of origin (liver and kidney). The generated modules provide a link between toxic chemical exposures, different molecular initiating events among underlying molecular pathways and resultant organ damage. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Journal of Applied Toxicology published by John Wiley & Sons, Ltd.

PAI-1 4G-4G and MTHFR 677TT in non-hepatitis C virus/hepatitis B virus-related liver cirrhosis

AIM: To evaluate the different roles of thrombophilia in patients with and without viral etiology. The thrombophilic genetic factors (THRGFs), PAI-1 4G-4G, MTHFR 677TT, V Leiden 506Q and prothrombin 20210A, were studied as risk factors in 1079 patients with liver cirrhosis (LC), enrolled from January 2000 to January 2014.

METHODS: All Caucasian LC patients consecutively observed in a fourteen-year period were included; the presence of portal vein thrombosis (PVT) and Budd Chiari syndrome (BCS) was registered. The differences between the proportions of each THRGF with regard to the presence or absence of viral etiology and the frequencies of the THRGF genotypes with those predicted in a population by the Hardy-Weinberg equilibrium were registered.

RESULTS: Four hundred and seventeen/one thousand and seventy-six patients (38.6%) showed thrombophilia: 217 PAI-1 4G-4G, 176 MTHFR C677TT, 71 V Leiden factor and 41 prothrombin G20210 A, 84 with more than 1 THRGF; 350 presented with no viral liver cirrhosis (NVLC) and 729 with, called viral liver cirrhosis (VLC), of whom 56 patients were hepatitis C virus + hepatitis B virus. PAI-1 4G-4G, MTHFR C677TT, the presence of at least one TRHGF and the presence of > 1 THRGF, were statistically more frequent in patients with NVLC vs patients with VLC: All χ² > 3.85 and P < 0.05. Patients with PVT and/or BCS with at least one TRHGF were 189/352 (53.7%). The Hardy-Weinberg of PAI-1 and MTHFR 677 genotypes deviated from that expected from a population in equilibrium in patients with NVLC (respectively χ² = 39.3; P < 0.000 and χ² = 27.94; P < 0.05), whereas the equilibrium was respected in VLC.

CONCLUSION: MTHFR 677TT was nearly twofold and PAI-1 4G-4G more than threefold more frequently found in NVLC vs patients with VLC; the Hardy-Weinberg equilibrium of these two polymorphisms confirms this data in NVLC. We suggest that PAI-1 4G-4G and MTHFR 677TT could be considered as factors of fibrosis and thrombosis mechanisms, increasing the inflammation response, and causing the hepatic fibrosis and augmented intrahepatic vascular resistance typical of LC. PAI-1 4G-4G and MTHFR 677TT screening of LC patients could be useful, mainly in those with NVLC, to identify patients in which new drug therapies based on the attenuation of the hepatic stellate cells activation or other mechanisms could be more easily evaluated.

Mining for novel tumor suppressor genes using a shortest path approach

Cancer, being among the most serious diseases, causes many deaths every year. Many investigators have devoted themselves to designing effective treatments for this disease. Cancer always involves abnormal cell growth with the potential to invade or spread to other parts of the body. In contrast, tumor suppressor genes (TSGs) act as guardians to prevent a disordered cell cycle and genomic instability in normal cells. Studies on TSGs can assist in the design of effective treatments against cancer. In this study, we propose a computational method to discover potential TSGs. Based on the known TSGs, a number of candidate genes were selected by applying the shortest path approach in a weighted graph that was constructed using protein-protein interaction network. The analysis of selected genes shows that some of them are new TSGs recently reported in the literature, while others may be novel TSGs.

Mining for Candidate Genes Related to Pancreatic Cancer Using Protein-Protein Interactions and a Shortest Path Approach

Pancreatic cancer (PC) is a highly malignant tumor derived from pancreas tissue and is one of the leading causes of death from cancer. Its molecular mechanism has been partially revealed by validating its oncogenes and tumor suppressor genes; however, the available data remain insufficient for medical workers to design effective treatments. Large-scale identification of PC-related genes can promote studies on PC. In this study, we propose a computational method for mining new candidate PC-related genes. A large network was constructed using protein-protein interaction information, and a shortest path approach was applied to mine new candidate genes based on validated PC-related genes. In addition, a permutation test was adopted to further select key candidate genes. Finally, for all discovered candidate genes, the likelihood that the genes are novel PC-related genes is discussed based on their currently known functions.

Temporal changes in rat liver gene expression after acute cadmium and chromium exposure

U.S. Service Members and civilians are at risk of exposure to a variety of environmental health hazards throughout their normal duty activities and in industrial occupations. Metals are widely used in large quantities in a number of industrial processes and are a common environmental toxicant, which increases the possibility of being exposed at toxic levels. While metal toxicity has been widely studied, the exact mechanisms of toxicity remain unclear. In order to further elucidate these mechanisms and identify candidate biomarkers, rats were exposed via a single intraperitoneal injection to three concentrations of CdCl₂ and Na₂Cr₂O₇, with livers harvested at 1, 3, or 7 days after exposure. Cd and Cr accumulated in the liver at 1 day post exposure. Cd levels remained elevated over the length of the experiment, while Cr levels declined. Metal exposures induced ROS, including hydroxyl radical (•OH), resulting in DNA strand breaks and lipid peroxidation. Interestingly, ROS and cellular damage appeared to increase with time post-exposure in both metals, despite declines in Cr levels. Differentially expressed genes were identified via microarray analysis. Both metals perturbed gene expression in pathways related to oxidative stress, metabolism, DNA damage, cell cycle, and inflammatory response. This work provides insight into the temporal effects and mechanistic pathways involved in acute metal intoxication, leading to the identification of candidate biomarkers.

Effects of valproic acid and dexamethasone administration on early bio-markers and gene expression profile in acute kidney ischemia-reperfusion injury in the rat

Renal ischemia-reperfusion (IR) causes acute kidney injury (AKI) with high mortality and morbidity. The objective of this investigation was to ameliorate kidney IR injury and identify novel biomarkers for kidney injury and repair. Under general anesthesia, left renal ischemia was induced in Wister rats by occluding renal artery for 45 minutes, followed by reperfusion and right nephrectomy. Thirty minutes prior to ischemia, rats (n = 8/group) received Valproic Acid (150 mg/kg; VPA), Dexamethasone (3 mg/kg; Dex) or Vehicle (saline) intraperitoneally. Animals were sacrificed at 3, 24 or 120 h post-IR. Plasma creatinine (mg/dL) at 24 h was reduced (P<0.05) in VPA (2.7±1.8) and Dex (2.3±1.2) compared to Vehicle (3.8±0.5) group. At 3 h, urine albumin (mg/mL) was higher in Vehicle (1.47±0.10), VPA (0.84±0.62) and Dex (1.04±0.73) compared to naïve (uninjured/untreated control) (0.14±0.26) group. At 24 h post-IR urine lipocalin-2 (μg/mL) was higher (P<0.05) in VPA, Dex and Vehicle groups (9.61–11.36) compared to naïve group (0.67±0.29); also, kidney injury molecule-1 (KIM-1; ng/mL) was higher (P<0.05) in VPA, Dex and Vehicle groups (13.7–18.7) compared to naïve group (1.7±1.9). Histopathology demonstrated reduced (P<0.05) ischemic injury in the renal cortex in VPA (Grade 1.6±1.5) compared to Vehicle (Grade 2.9±1.1). Inflammatory cytokines IL1β and IL6 were downregulated and anti-apoptotic molecule BCL2 was upregulated in VPA group. Furthermore, kidney DNA microarray demonstrated reduced injury, stress, and apoptosis related gene expression in the VPA administered rats. VPA appears to ameliorate kidney IR injury via reduced inflammatory cytokine, apoptosis/stress related gene expression, and improved regeneration. KIM-1, lipocalin-2 and albumin appear to be promising early urine biomarkers for the diagnosis of AKI.