Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic adenocarcinoma (PDAC) persists as a malignancy with high morbidity and mortality that can benefit from new means to characterize and detect these tumors, such as radiogenomics. In order to address this gap in the literature, constructed a transcriptomic-CT radiogenomic (RG) map for PDAC. METHODS: In this Institutional Review Board approved study, a cohort of subjects (n = 50) with gene expression profile data paired with histopathologically confirmed resectable or borderline resectable PDAC were identified. Studies with pre-operative contrast-enhanced CT images were independently assessed for a set of 88 predefined imaging features. Microarray gene expression profiling was then carried out on the histopathologically confirmed pancreatic adenocarcinomas and gene networks were constructed using Weighted Gene Correlation Network Analysis (WCGNA) (n = 37). Data were analyzed with bioinformatics analyses, multivariate regression-based methods, and Kaplan-Meier survival analyses. RESULTS: Survival analyses identified multiple features of interest that were significantly associated with overall survival, including Tumor Height (P = 0.014), Tumor Contour (P = 0.033), Tumor-stroma Interface (P = 0.014), and the Tumor Enhancement Ratio (P = 0.047). Gene networks for these imaging features were then constructed using WCGNA and further annotated according to the Gene Ontology (GO) annotation framework for a biologically coherent interpretation of the imaging trait-associated gene networks, ultimately resulting in a PDAC RG CT-transcriptome map composed of 3 stage-independent imaging traits enriched in metabolic processes, telomerase activity, and podosome assembly (P < 0.05). CONCLUSIONS: A CT-transcriptomic RG map for PDAC composed of semantic and quantitative traits with associated biology processes predictive of overall survival, was constructed, that serves as a reference for further mechanistic studies for non-invasive phenotyping of pancreatic tumors.

A key role for the transporter OAT1 in systemic lipid metabolism.

Organic anion transporter 1 (OAT1/SLC22A6) is a drug transporter with numerous xenobiotic and endogenous substrates. The Remote Sensing and Signaling Theory suggests that drug transporters with compatible ligand preferences can play a role in "organ crosstalk," mediating overall organismal communication. Other drug transporters are well known to transport lipids, but surprisingly little is known about the role of OAT1 in lipid metabolism. To explore this subject, we constructed a genome-scale metabolic model using omics data from the Oat1 knockout mouse. The model implicated OAT1 in the regulation of many classes of lipids, including fatty acids, bile acids, and prostaglandins. Accordingly, serum metabolomics of Oat1 knockout mice revealed increased polyunsaturated fatty acids, diacylglycerols, and long-chain fatty acids and decreased ceramides and bile acids when compared with wildtype controls. Some aged knockout mice also displayed increased lipid droplets in the liver when compared with wildtype mice. Chemoinformatics and machine learning analyses of these altered lipids defined molecular properties that form the structural basis for lipid-transporter interactions, including the number of rings, positive charge/volume, and complexity of the lipids. Finally, we obtained targeted serum metabolomics data after short-term treatment of rodents with the OAT-inhibiting drug probenecid to identify potential drug-metabolite interactions. The treatment resulted in alterations in eicosanoids and fatty acids, further supporting our metabolic reconstruction predictions. Consistent with the Remote Sensing and Signaling Theory, the data support a role of OAT1 in systemic lipid metabolism.

Tissue clearing techniques for three-dimensional optical imaging of intact human prostate and correlations with multi-parametric MRI.

BACKGROUND: Tissue clearing technologies have enabled remarkable advancements for in situ characterization of tissues and exploration of the three-dimensional (3D) relationships between cells, however, these studies have predominantly been performed in non-human tissues and correlative assessment with clinical imaging has yet to be explored. We sought to evaluate the feasibility of tissue clearing technologies for 3D imaging of intact human prostate and the mapping of structurally and molecularly preserved pathology data with multi-parametric volumetric MR imaging (mpMRI). METHODS: Whole-mount prostates were processed with either hydrogel-based CLARITY or solvent-based iDISCO. The samples were stained with a nuclear dye or fluorescently labeled with antibodies against androgen receptor, alpha-methylacyl coenzyme-A racemase, or p63, and then imaged with 3D confocal microscopy. The apparent diffusion coefficient and Ktrans maps were computed from preoperative mpMRI. RESULTS: Quantitative analysis of cleared normal and tumor prostate tissue volumes displayed differences in 3D tissue architecture, marker-specific cell staining, and cell densities that were significantly correlated with mpMRI measurements in this initial, pilot cohort. CONCLUSIONS: 3D imaging of human prostate volumes following tissue clearing is a feasible technique for quantitative radiology-pathology correlation analysis with mpMRI and provides an opportunity to explore functional relationships between cellular structures and cross-sectional clinical imaging.

Mechanical thrombectomy of COVID-19 DVT with congenital heart disease leading to phlegmasia cerulea dolens: a case report.

BACKGROUND: COVID-19 and Fontan physiology have each been associated with an elevated risk of venous thromboembolism (VTE), however little is known about the risks and potential consequences of having both. CASE PRESENTATION: A 51 year old male with tricuspid atresia status post Fontan and extracardiac Glenn shunt, atrial flutter, and sinus sick syndrome presented with phlegmasia cerulea dolens (PCD) of the left lower extremity in spite of supratherapeutic INR in the context of symptomatic COVID-10 pneumonia. He was treated with single session, catheter directed mechanical thrombectomy that was well-tolerated. CONCLUSIONS: This report of acute PCD despite therapeutic anticoagulation with a Vitamin K antagonist, managed with emergent mechanical thrombectomy, calls to attention the importance of altered flow dynamics in COVID positive patients with Fontan circulation that may compound these independent risk factors for developing deep venous thrombosis with the potential for even higher morbidity.

Functional interrogation of Plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting.

Several antimalarial drugs exist, but differences between life cycle stages among malaria species pose challenges for developing more effective therapies. To understand the diversity among stages and species, we reconstructed genome-scale metabolic models (GeMMs) of metabolism for five life cycle stages and five species of Plasmodium spanning the blood, transmission, and mosquito stages. The stage-specific models of Plasmodium falciparum uncovered stage-dependent changes in central carbon metabolism and predicted potential targets that could affect several life cycle stages. The species-specific models further highlight differences between experimental animal models and the human-infecting species. Comparisons between human- and rodent-infecting species revealed differences in thiamine (vitamin B1), choline, and pantothenate (vitamin B5) metabolism. Thus, we show that genome-scale analysis of multiple stages and species of Plasmodium can prioritize potential drug targets that could be both anti-malarials and transmission blocking agents, in addition to guiding translation from non-human experimental disease models.

Receptor-based Surrogate Subtypes and Discrepancies with Breast Cancer Intrinsic Subtypes: Implications for Image Biomarker Development.

Purpose To determine the concordance and accuracy of imaging surrogates of immunohistochemical (IHC) markers and the molecular classification of breast cancer. Materials and Methods A total of 3050 patients from 17 public breast cancer data sets containing IHC marker receptor status (estrogen receptor/progesterone receptor/human epidermal growth factor receptor 2 [HER2]) and their molecular classification (basal-like, HER2-enriched, luminal A or B) were analyzed. Diagnostic accuracy and concordance as measured with the κ statistic were calculated between the IHC and molecular classifications. Simulations were performed to assess the relationship between accuracy of imaging-based IHC markers to predict molecular classification. A simulation was performed to examine effects of misclassification of molecular type on patient survival. Results Accuracies of intrinsic subtypes based on IHC subtype were 71.7% (luminal A), 53.7% (luminal B), 64.8% (HER2-enriched), and 81.7% (basal-like). The κ agreement was fair (κ = 0.36) for luminal A and HER2-enriched subtypes, good (κ = 0.65) for the basal-like subtype, and poor (κ = 0.09) for the luminal B subtypes. Introduction of image misclassification by simulation lowered image-true subtype accuracies and κ values. Simulation analysis showed that misclassification caused survival differences between luminal A and basal-like subtypes to decrease. Conclusion There is poor concordance between triple-receptor status and intrinsic molecular subtype in breast cancer, arguing against their use in the design of prognostic genomic-based image biomarkers. © RSNA, 2018 Online supplemental material is available for this article.

An Organic Anion Transporter 1 (OAT1)-centered Metabolic Network.

There has been a recent interest in the broader physiological importance of multispecific "drug" transporters of the SLC and ABC transporter families. Here, a novel multi-tiered systems biology approach was used to predict metabolites and signaling molecules potentially affected by the in vivo deletion of organic anion transporter 1 (Oat1, Slc22a6, originally NKT), a major kidney-expressed drug transporter. Validation of some predictions in wet-lab assays, together with re-evaluation of existing transport and knock-out metabolomics data, generated an experimentally validated, confidence ranked set of OAT1-interacting endogenous compounds enabling construction of an "OAT1-centered metabolic interaction network." Pathway and enrichment analysis indicated an important role for OAT1 in metabolism involving: the TCA cycle, tryptophan and other amino acids, fatty acids, prostaglandins, cyclic nucleotides, odorants, polyamines, and vitamins. The partly validated reconstructed network is also consistent with a major role for OAT1 in modulating metabolic and signaling pathways involving uric acid, gut microbiome products, and so-called uremic toxins accumulating in chronic kidney disease. Together, the findings are compatible with the hypothesized role of drug transporters in remote inter-organ and inter-organismal communication: The Remote Sensing and Signaling Hypothesis (Nigam, S. K. (2015) Nat. Rev. Drug Disc. 14, 29). The fact that OAT1 can affect many systemic biological pathways suggests that drug-metabolite interactions need to be considered beyond simple competition for the drug transporter itself and may explain aspects of drug-induced metabolic syndrome. Our approach should provide novel mechanistic insights into the role of OAT1 and other drug transporters implicated in metabolic diseases like gout, diabetes, and chronic kidney disease.

Multiregional Radiogenomic Assessment of Prostate Microenvironments with Multiparametric MR Imaging and DNA Whole-Exome Sequencing of Prostate Glands with Adenocarcinoma.

Purpose To assess the underlying genomic variation of prostate gland microenvironments of patients with prostate adenocarcinoma in the context of colocalized multiparametric magnetic resonance (MR) imaging and histopathologic assessment of normal and abnormal regions by using whole-exome sequencing. Materials and Methods Six patients with prostate adenocarcinoma who underwent robotic prostatectomy with whole-mount preservation of the prostate were identified, which enabled spatial mapping between preoperative multiparametric MR imaging and the gland. Four regions of interest were identified within each gland, including regions found to be normal and abnormal via histopathologic analysis. Whole-exome DNA sequencing (>50 times coverage) was performed on each of these spatially targeted regions. Radiogenomic analysis of imaging and mutation data were performed with hierarchical clustering, phylogenetic analysis, and principal component analysis. Results Radiogenomic multiparametric MR imaging and whole-exome spatial characterization in six patients with prostate adenocarcinoma (three patients, Gleason score of 3 + 4; and three patients, Gleason score of 4 + 5) was performed across 23 spatially distinct regions. Hierarchical clustering separated histopathologic analysis-proven high-grade lesions from the normal regions, and this reflected concordance between multiparametric MR imaging and resultant histopathologic analysis in all patients. Seventy-seven mutations involving 29 cancer-associated genes across the 23 spatially distinct prostate samples were identified. There was no significant difference in mutation load in cancer-associated genes between regions that were proven to be normal via histopathologic analysis (34 mutations per sample ± 19), mildly suspicious via multiparametric MR imaging (37 mutations per sample ± 21), intermediately suspicious via multiparametric MR imaging (31 mutations per sample ± 15), and high-grade cancer (33 mutations per sample ± 18) (P = .30). Principal component analysis resolved samples from different patients and further classified samples (regardless of histopathologic status) from prostate glands with Gleason score 3 + 4 versus 4 + 5 samples. Conclusion Multiregion spatial multiparametric MR imaging and whole-exome radiogenomic analysis of prostate glands with adenocarcinoma shows a continuum of mutations across regions that were found via histologic analysis to be high grade and normal. © RSNA, 2017 Online supplemental material is available for this article.

Genomic Adequacy from Solid Tumor Core Needle Biopsies of ex Vivo Tissue and in Vivo Lung Masses: Prospective Study.

Purpose To identify the variables and factors that affect the quantity and quality of nucleic acid yields from imaging-guided core needle biopsy. Materials and Methods This study was approved by the institutional review board and compliant with HIPAA. The authors prospectively obtained 232 biopsy specimens from 74 patients (177 ex vivo biopsy samples from surgically resected masses were obtained from 49 patients and 55 in vivo lung biopsy samples from computed tomographic [CT]-guided lung biopsies were obtained from 25 patients) and quantitatively measured DNA and RNA yields with respect to needle gauge, number of needle passes, and percentage of the needle core. RNA quality was also assessed. Significance of correlations among variables was assessed with analysis of variance followed by linear regression. Conditional probabilities were calculated for projected sample yields. Results The total nucleic acid yield increased with an increase in the number of needle passes or a decrease in needle gauge (two-way analysis of variance, P < .0001 for both). However, contrary to calculated differences in volume yields, the effect of needle gauge was markedly greater than the number of passes. For example, the use of an 18-gauge versus a 20-gauge biopsy needle resulted in a 4.8-5.7 times greater yield, whereas a double versus a single pass resulted in a 2.4-2.8 times greater yield for 18- versus 20-gauge needles, respectively. Ninety-eight of 184 samples (53%) had an RNA integrity number of at least 7 (out of a possible score of 10). Conclusion With regard to optimizing nucleic acid yields in CT-guided lung core needle biopsies used for genomic analysis, there should be a preference for using lower gauge needles over higher gauge needles with more passes. ©RSNA, 2016 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 21, 2016.

Radiogenomic Analysis Demonstrates Associations between (18)F-Fluoro-2-Deoxyglucose PET, Prognosis, and Epithelial-Mesenchymal Transition in Non-Small Cell Lung Cancer.

Purpose To investigate whether non-small cell lung cancer (NSCLC) tumors that express high normalized maximum standardized uptake value (SUVmax) are associated with a more epithelial-mesenchymal transition (EMT)-like phenotype. Materials and Methods In this institutional review board-approved study, a public NSCLC data set that contained fluorine 18 ((18)F) fluoro-2-deoxyglucose positron emission tomography (PET) and messenger RNA expression profile data (n = 26) was obtained, and patients were categorized on the basis of measured normalized SUVmax values. Significance analysis of microarrays was then used to create a radiogenomic signature. The prognostic ability of this signature was assessed in a second independent data set that consisted of clinical and messenger RNA expression data (n = 166). Signature concordance with EMT was evaluated by means of validation in a publicly available cell line data set. Finally, by establishing an in vitro EMT lung cancer cell line model, an attempt was made to substantiate the radiogenomic signature with quantitative polymerase chain reaction, and functional assays were performed, including Western blot, cell migration, glucose transporter, and hexokinase assays (paired t test), as well as pharmacologic assays against chemotherapeutic agents (half-maximal effective concentration). Results Differential expression analysis yielded a 14-gene radiogenomic signature (P < .05, false discovery rate [FDR] < 0.20), which was confirmed to have differences in disease-specific survival (log-rank test, P = .01). This signature also significantly overlapped with published EMT cell line gene expression data (P < .05, FDR < 0.20). Finally, an EMT cell line model was established, and cells that had undergone EMT differentially expressed this signature and had significantly different EMT protein expression (P < .05, FDR < 0.20), cell migration, glucose uptake, and hexokinase activity (paired t test, P < .05). Cells that had undergone EMT also had enhanced chemotherapeutic resistance, with a higher half-maximal effective concentration than that of cells that had not undergone EMT (P < .05). Conclusion Integrative radiogenomic analysis demonstrates an association between increased normalized (18)F fluoro-2-deoxyglucose PET SUVmax, outcome, and EMT in NSCLC. (©) RSNA, 2016 Online supplemental material is available for this article.

The radiogenomic risk score stratifies outcomes in a renal cell cancer phase 2 clinical trial.

OBJECTIVES: To characterize a radiogenomic risk score (RRS), a previously defined biomarker, and to evaluate its potential for stratifying radiological progression-free survival (rPFS) in patients with metastatic renal cell carcinoma (mRCC) undergoing pre-surgical treatment with bevacizumab. METHODOLOGY: In this IRB-approved study, prospective imaging analysis of the RRS was performed on phase II clinical trial data of mRCC patients (n = 41) evaluating whether patient stratification according to the RRS resulted in groups more or less likely to have a rPFS to pre-surgical bevacizumab prior to cytoreductive nephrectomy. Survival times of RRS subgroups were analyzed using Kaplan-Meier survival analysis. RESULTS: The RRS is enriched in diverse molecular processes including drug response, stress response, protein kinase regulation, and signal transduction pathways (P < 0.05). The RRS successfully stratified rPFS to bevacizumab based on pre-treatment computed tomography imaging with a median progression-free survival of 6 versus >25 months (P = 0.005) and overall survival of 25 versus >37 months in the high and low RRS groups (P = 0.03), respectively. Conventional prognostic predictors including the Motzer and Heng criteria were not predictive in this cohort (P > 0.05). CONCLUSIONS: The RRS stratifies rPFS to bevacizumab in patients from a phase II clinical trial with mRCC undergoing cytoreductive nephrectomy and pre-surgical bevacizumab. KEY POINTS: • The RRS SOMA stratifies patient outcomes in a phase II clinical trial. • RRS stratifies subjects into prognostic groups in a discrete or continuous fashion. • RRS is biologically enriched in diverse processes including drug response programs.

Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis.

PURPOSE: To perform a radiogenomic analysis of women with breast cancer to study the multiscale relationships among quantitative computer vision-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging phenotypes, early metastasis, and long noncoding RNA (lncRNA) expression determined by means of high-resolution next-generation RNA sequencing. MATERIALS AND METHODS: In this institutional review board-approved study, an automated image analysis platform extracted 47 computational quantitative features from DCE MR imaging data in a training set (n = 19) to screen for MR imaging biomarkers indicative of poor metastasis-free survival (MFS). The lncRNA molecular landscape of the candidate feature was defined by using an RNA sequencing-specific negative binomial distribution differential expression analysis. Then, this radiogenomic biomarker was applied prospectively to a validation set (n = 42) to allow prediction of MFS and lncRNA expression by using quantitative polymerase chain reaction analysis. RESULTS: The quantitative MR imaging feature, enhancing rim fraction score, was predictive of MFS in the training set (P = .007). RNA sequencing analysis yielded an average of 55.7 × 10(6) reads per sample and identified 14 880 lncRNAs from a background of 189 883 transcripts per sample. Radiogenomic analysis allowed identification of three previously uncharacterized and five named lncRNAs significantly associated with high enhancing rim fraction, including Homeobox transcript antisense intergenic RNA (HOTAIR) (P < .05), a known predictor of poor MFS in patients with breast cancer. Independent validation confirmed the association of the enhancing rim fraction phenotype with both MFS (P = .002) and expression of four of the top five differentially expressed lncRNAs (P < .05), including HOTAIR. CONCLUSION: The enhancing rim fraction score, a quantitative DCE MR imaging lncRNA radiogenomic biomarker, is associated with early metastasis and expression of the known predictor of metastatic progression, HOTAIR.

The Radiogenomic Risk Score: Construction of a Prognostic Quantitative, Noninvasive Image-based Molecular Assay for Renal Cell Carcinoma.

PURPOSE: To evaluate the feasibility of constructing radiogenomic-based surrogates of molecular assays (SOMAs) in patients with clear-cell renal cell carcinoma (CCRCC) by using data extracted from a single computed tomographic (CT) image. MATERIALS AND METHODS: In this institutional review board approved study, gene expression profile data and contrast material-enhanced CT images from 70 patients with CCRCC in a training set were independently assessed by two radiologists for a set of predefined imaging features. A SOMA for a previously validated CCRCC-specific supervised principal component (SPC) risk score prognostic gene signature was constructed and termed the radiogenomic risk score (RRS). It uses the microarray data and a 28-trait image array to evaluate each CT image with multiple regression of gene expression analysis. The predictive power of the RRS SOMA was then prospectively validated in an independent dataset to confirm its relationship to the SPC gene signature (n = 70) and determination of patient outcome (n = 77). Data were analyzed by using multivariate linear regression-based methods and Cox regression modeling, and significance was assessed with receiver operator characteristic curves and Kaplan-Meier survival analysis. RESULTS: Our SOMA faithfully represents the tissue-based molecular assay it models. The RRS scaled with the SPC gene signature (R = 0.57, P < .001, classification accuracy 70.1%, P < .001) and predicted disease-specific survival (log rank P < .001). Independent validation confirmed the relationship between the RRS and the SPC gene signature (R = 0.45, P < .001, classification accuracy 68.6%, P < .001) and disease-specific survival (log-rank P < .001) and that it was independent of stage, grade, and performance status (multivariate Cox model P < .05, log-rank P < .001). CONCLUSION: A SOMA for the CCRCC-specific SPC prognostic gene signature that is predictive of disease-specific survival and independent of stage was constructed and validated, confirming that SOMA construction is feasible.

Illuminating radiogenomic characteristics of glioblastoma multiforme through integration of MR imaging, messenger RNA expression, and DNA copy number variation.

PURPOSE: To perform a multilevel radiogenomics study to elucidate the glioblastoma multiforme (GBM) magnetic resonance (MR) imaging radiogenomic signatures resulting from changes in messenger RNA (mRNA) expression and DNA copy number variation (CNV). MATERIALS AND METHODS: Radiogenomic analysis was performed at MR imaging in 23 patients with GBM in this retrospective institutional review board-approved HIPAA-compliant study. Six MR imaging features-contrast enhancement, necrosis, contrast-to-necrosis ratio, infiltrative versus edematous T2 abnormality, mass effect, and subventricular zone (SVZ) involvement-were independently evaluated and correlated with matched genomic profiles (global mRNA expression and DNA copy number profiles) in a significant manner that also accounted for multiple hypothesis testing by using gene set enrichment analysis (GSEA), resampling statistics, and analysis of variance to gain further insight into the radiogenomic signatures in patients with GBM. RESULTS: GSEA was used to identify various oncogenic pathways with MR imaging features. Correlations between 34 gene loci were identified that showed concordant variations in gene dose and mRNA expression, resulting in an MR imaging, mRNA, and CNV radiogenomic association map for GBM. A few of the identified gene-to-trait associations include association of the contrast-to-necrosis ratio with KLK3 and RUNX3; association of SVZ involvement with Ras oncogene family members, such as RAP2A, and the metabolic enzyme TYMS; and association of vasogenic edema with the oncogene FOXP1 and PIK3IP1, which is a member of the PI3K signaling network. CONCLUSION: Construction of an MR imaging, mRNA, and CNV radiogenomic association map has led to identification of MR traits that are associated with some known high-grade glioma biomarkers and association with genomic biomarkers that have been identified for other malignancies but not GBM. Thus, the traits and genes identified on this map highlight new candidate radiogenomic biomarkers for further evaluation in future studies.

Multispecific drug transporter Slc22a8 (Oat3) regulates multiple metabolic and signaling pathways.

Multispecific drug transporters of the solute carrier and ATP-binding cassette families are highly conserved through evolution, but their true physiologic role remains unclear. Analyses of the organic anion transporter 3 (OAT3; encoded by Slc22a8/Oat3, originally Roct) knockout mouse have confirmed its critical role in the renal handling of common drugs (e.g., antibiotics, antivirals, diuretics) and toxins. Previous targeted metabolomics of the knockout of the closely related Oat1 have demonstrated a central metabolic role, but the same approach with Oat3 failed to reveal a similar set of endogenous substrates. Nevertheless, the Oat3 knockout is the only Oat described so far with a physiologically significant phenotype, suggesting the disturbance of metabolic or signaling pathways. Here we analyzed global gene expression in Oat3 knockout tissue, which implicated OAT3 in phase I and phase II metabolism (drug metabolizing enzymes or DMEs), as well as signaling pathways. Metabolic reconstruction with the recently developed "mouse Recon1" supported the involvement of Oat3 in the aforementioned pathways. Untargeted metabolomics were used to determine whether the predicted metabolic alterations could be confirmed. Many significant changes were observed; several metabolites were tested for direct interaction with mOAT3, whereas others were supported by published data. Oat3 thus appears critical for the handling of phase I (hydroxylation) and phase II (glucuronidation) metabolites. Oat3 also plays a role in bioenergetic pathways (e.g., the tricarboxylic acid cycle), as well as those involving vitamins (e.g., folate), steroids, prostaglandins, gut microbiome products, uremic toxins, cyclic nucleotides, amino acids, glycans, and possibly hyaluronic acid. The data seemingly consistent with the Remote Sensing and Signaling Hypothesis (Ahn and Nigam, 2009; Wu et al., 2011), also suggests that Oat3 is essential for the handling of dietary flavonoids and antioxidants.

Loss of the Kidney Urate Transporter, Urat1, Leads to Disrupted Redox Homeostasis in Mice.

High uric acid is associated with gout, hypertension, metabolic syndrome, cardiovascular disease, and kidney disease. URAT1 (SLC22A12), originally discovered in mice as Rst, is generally considered a very selective uric acid transporter compared to other closely-related kidney uric acid transporters such as OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8). While the role of URAT1 in regulating human uric acid is well-established, in recent studies the gene has been linked to redox regulation in flies as well as progression of renal cell carcinoma. We have now identified over twenty metabolites in the Urat1 knockout that are generally distinct from metabolites accumulating in the Oat1 and Oat3 knockout mice, with distinct molecular properties as revealed by chemoinformatics and machine learning analysis. These metabolites are involved in seemingly disparate aspects of cellular metabolism, including pyrimidine, fatty acid, and amino acid metabolism. However, through integrative systems metabolic analysis of the transcriptomic and metabolomic data using a human metabolic reconstruction to build metabolic genome-scale models (GEMs), the cellular response to loss of Urat1/Rst revealed compensatory processes related to reactive oxygen species handling and maintaining redox state balances via Vitamin C metabolism and cofactor charging reactions. These observations are consistent with the increasingly appreciated role of the antioxidant properties of uric acid. Collectively, the results highlight the role of Urat1/Rst as a transporter strongly tied to maintaining redox homeostasis, with implications for metabolic side effects from drugs that block its function.

Multi-Modality, Multi-Dimensional Characterization of Pediatric Non-Alcoholic Fatty Liver Disease.

Non-alcoholic fatty liver disease is a multifaceted disease that progresses through multiple phases; it involves metabolic as well as structural changes. These alterations can be measured directly or indirectly through blood, non-invasive imaging, and/or tissue analyses. While some studies have evaluated the correlations between two sets of measurements (e.g., histopathology with cross-sectional imaging or blood biomarkers), the interrelationships, if any, among histopathology, clinical blood profiles, cross-sectional imaging, and metabolomics in a pediatric cohort remain unknown. We created a multiparametric clinical MRI-histopathologic NMR network map of pediatric NAFLD through multimodal correlation networks, in order to gain insight into how these different sets of measurements are related. We found that leptin and other blood markers were correlated with many other measurements; however, upon filtering out the blood biomarkers, the network was decomposed into three independent hubs centered around histopathological features, each with associated MRI and plasma metabolites. These multi-modality maps could serve as a framework for characterizing disease status and progression and could potentially guide medical interventions.

Linkage of organic anion transporter-1 to metabolic pathways through integrated "omics"-driven network and functional analysis.

The main kidney transporter of many commonly prescribed drugs (e.g. penicillins, diuretics, antivirals, methotrexate, and non-steroidal anti-inflammatory drugs) is organic anion transporter-1 (OAT1), originally identified as NKT (Lopez-Nieto, C. E., You, G., Bush, K. T., Barros, E. J., Beier, D. R., and Nigam, S. K. (1997) J. Biol. Chem. 272, 6471-6478). Targeted metabolomics in knockouts have shown that OAT1 mediates the secretion or reabsorption of many important metabolites, including intermediates in carbohydrate, fatty acid, and amino acid metabolism. This observation raises the possibility that OAT1 helps regulate broader metabolic activities. We therefore examined the potential roles of OAT1 in metabolic pathways using Recon 1, a functionally tested genome-scale reconstruction of human metabolism. A computational approach was used to analyze in vivo metabolomic as well as transcriptomic data from wild-type and OAT1 knock-out animals, resulting in the implication of several metabolic pathways, including the citric acid cycle, polyamine, and fatty acid metabolism. Validation by in vitro and ex vivo analysis using Xenopus oocyte, cell culture, and kidney tissue assays demonstrated interactions between OAT1 and key intermediates in these metabolic pathways, including previously unknown substrates, such as polyamines (e.g. spermine and spermidine). A genome-scale metabolic network reconstruction generated some experimentally supported predictions for metabolic pathways linked to OAT1-related transport. The data support the possibility that the SLC22 and other families of transporters, known to be expressed in many tissues and primarily known for drug and toxin clearance, are integral to a number of endogenous pathways and may be involved in a larger remote sensing and signaling system (Ahn, S. Y., and Nigam, S. K. (2009) Mol. Pharmacol. 76, 481-490, and Wu, W., Dnyanmote, A. V., and Nigam, S. K. (2011) Mol. Pharmacol. 79, 795-805). Drugs may alter metabolism by competing for OAT1 binding of metabolites.

A pipeline for automated deep learning liver segmentation (PADLLS) from contrast enhanced CT exams.

Multiple studies have created state-of-the-art liver segmentation models using Deep Convolutional Neural Networks (DCNNs) such as the V-net and H-DenseUnet. Oversegmentation however continues to be a problem. We set forth to address these limitations by developing a an automated workflow that leverages the strengths of different DCNN architectures, resulting in a pipeline that enables fully automated liver segmentation. A Pipeline for Automated Deep Learning Liver Segmentation (PADLLS) was developed and implemented that cascades multiple DCNNs that were trained on more than 200 CT scans. First, a V-net is used to create a rough liver, spleen, and stomach mask. After stomach and spleen pixels are removed using their respective masks and ascites is removed using a morphological algorithm, the scan is passed to a H-DenseUnet to yield the final segmentation. The segmentation accuracy of the pipleline was compared to the H-DenseUnet and the V-net using the SLIVER07 and 3DIRCADb datasets as benchmarks. The PADLLS Dice score for the SLIVER07 dataset was calculated to be 0.957 ± 0.033 and was significantly better than the H-DenseUnet's score of 0.927 ± 0.044 (p = 0.0219) and the V-net's score of 0.872 ± 0.121 (p = 0.0067). The PADLLS Dice score for the 3DIRCADb dataset was 0.965 ± 0.016 and was significantly better than the H-DenseUnet's score of 0.930 ± 0.041 (p = 0.0014) the V-net's score of 0.874 ± 0.060 (p < 0.001). In conclusion, our pipeline (PADLLS) outperforms existing liver segmentation models, serves as a valuable tool for image-based analysis, and is freely available for download and use.