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Abstract 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

Abstract The aryl hydrocarbon receptor (AHR) is a transcription factor with roles in detoxification, development, immune response, chronic kidney disease and other syndromes. It regulates the expression of drug transporters and drug metabolizing enzymes in a proposed Remote Sensing and Signaling Network involved in inter-organ communication via metabolites and signaling molecules. Here, we use integrated omics approaches to analyze its contributions to metabolism across multiple scales from the organ to the organelle. Global metabolomics analysis of Ahr −/− mice revealed the role of AHR in the regulation of 290 metabolites involved in many biochemical pathways affecting fatty acids, bile acids, gut microbiome products, antioxidants, choline derivatives, and uremic toxins. Chemoinformatics analysis suggest that AHR plays a role in determining the hydrophobicity of metabolites and perhaps their transporter-mediated movement into and out of tissues. Of known AHR ligands, indolepropionate was the only significantly altered molecule, and it activated AHR in both human and murine cells. To gain a deeper biological understanding of AHR, we employed genome scale metabolic reconstruction to integrate knockout transcriptomics and metabolomics data, which indicated a role for AHR in regulation of organic acids and redox state. Together, the results indicate a central role of AHR in metabolism and signaling between multiple organs and across multiple scales.

Abstract In vitro and in vivo studies have established the organic anion transporters OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8) among the main multi-specific “drug” transporters. They also transport numerous endogenous metabolites, raising the possibility of drug-metabolite interactions (DMI). To help understand the role of these drug transporters on metabolism across scales ranging from organ systems to organelles, a formal multi-scale analysis was performed. Metabolic network reconstructions of the omics-alterations resulting from Oat1 and Oat3 gene knockouts revealed links between the microbiome and human metabolism including reactions involving small organic molecules such as dihydroxyacetone, alanine, xanthine, and p-cresol—key metabolites in independent pathways. Interestingly, pairwise organ-organ interactions were also disrupted in the two Oat knockouts, with altered liver, intestine, microbiome, and skin-related metabolism. Compared to older models focused on the “one transporter-one organ” concept, these more sophisticated reconstructions, combined with integration of a multi-microbial model and more comprehensive metabolomics data for the two transporters, provide a considerably more complex picture of how renal “drug” transporters regulate metabolism across the organelle (e.g. endoplasmic reticulum, Golgi, peroxisome), cellular, organ, inter-organ, and inter-organismal scales. The results suggest that drugs interacting with OAT1 and OAT3 can have far reaching consequences on metabolism in organs (e.g. skin) beyond the kidney. Consistent with the Remote Sensing and Signaling Theory (RSST), the analysis demonstrates how transporter-dependent metabolic signals mediate organ crosstalk (e.g., gut-liver-kidney) and inter-organismal communication (e.g., gut microbiome-host).

Abstract 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

The “gunsight approach” was initially described as the use of overlapping snares and through- and-through puncture of the portal vein and inferior vena cava for the creation of a transcaval portosystemic shunt. This technique can be adapted for the creation of an extra-anatomic chan- nel between any 2 locations where snares can be deployed. We explain the technique, discuss finer technical points, and describe 2 cases where refractory vascular occlusions are crossed using this technique. The first case involves an extensively calcified femoral arterial chronic total occlusion where subintimal tracking past the occlusion is achieved, but luminal re-entry is ham- pered by dense calcific plaque refractory to multiple re-entry devices. The second case involves a chronic venous occlusion along the femoral vein with loss of in-line flow due to prior stenting. In both cases, the gunsight technique was successfully used as a bailout option after standard recanalization techniques were unsuccessful.

Abstract 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.

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.

Abstract Mitochondrial dysfunction and neurodegeneration underlie movement disorders such as Parkinson’s disease, Huntington’s disease and Manganism among others. As a corollary, inhibition of mitochondrial complex I (CI) and complex II (CII) by toxins 1-methyl-4-phenylpyridinium (MPP+) and 3-nitropropionic acid (3-NPA) respectively, induced degenerative changes noted in such neurodegenerative diseases. We aimed to unravel the down-stream pathways associated with CII inhibition and compared with CI inhibition and the Manganese (Mn) neurotoxicity. Genome-wide transcriptomics of N27 neuronal cells exposed to 3-NPA, compared with MPP+ and Mn revealed varied transcriptomic profile. Along with mitochondrial and synaptic pathways, Autophagy was the predominant pathway differentially regulated in the 3-NPA model with implications for neuronal survival. This pathway was unique to 3-NPA, as substantiated by in silico modelling of the three toxins. Morphological and biochemical validation of autophagy markers in the cell model of 3-NPA revealed incomplete autophagy mediated by mechanistic Target of Rapamycin Complex 2 (mTORC2) pathway. Interestingly, Brain Derived Neurotrophic Factor (BDNF), which was elevated in the 3-NPA model could confer neuroprotection against 3-NPA. We propose that, different downstream events are activated upon neurotoxin-dependent CII inhibition compared to other neurotoxins, with implications for movement disorders and regulation of autophagy could potentially offer neuroprotection.

Abstract Background Unretrievable foreign bodies are associated with high morbidity and mortality. While the majority of reported cases involve the venous circulation, intra-arterial foreign body displacement have the potential to migrate more distally with a higher risk for dissection and hemorrhagic complications during retrieval. As the number of intravascular procedures continues to increase, there is also likely to be a concomittant increase in the number of retrieval procedures, particular for fractured catheters and sheaths. Although snaring is frequently the traditional, ‘go-to’ method for retrieval, there are inherent risks of further dislodgement or fracture. Case report We describe a case that involves retrieval of a fractured sheath that originated in the common femoral artery but migrated into the popliteal artery. Different retrieval approaches were employed, however ultimately balloon assisted, over-the-wire retrieval was the successful approach. Conclusions We anticipate that over-the-wire, inline-retrieval approaches will continue to grow in popularity and use, particularly with respect to manipulation within the arterial circulation.

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.

Concurrent advancements in imaging and genomic biomarkers have created opportunities to identify non-invasive imaging surrogates of molecular phenotypes. In order to develop such imaging surrogates radiomics and radiogenomics/imaging genomics will be necessary; there has been consistent progress in these fields for primary liver cancers. In this article we evaluate the current status of the field specifically with regards to hepatocellular carcinoma and intrahepatic cholangiocarcinoma, highlighting some of the up and coming results that were presented at the annual Radiological Society of North America Conference in 2017. There are an increasing number of studies in this area with a bias towards quantitative feature measurement, which is expected to benefit reproducibility of the findings and portends well for the future development of biomarkers for diagnosis, prognosis, and treatment response assessment. We review some of the advancements and look forward to some of the exciting future applications that are anticipated as the field develops.

Summary: Understanding the mechanisms for cellular aging is a fundamental question in biology. Normal red blood cells (RBCs) survive for approximately 100 days, and their survival is likely limited by functional decline secondary to cumulative damage to cell constituents, which may be reflected in altered metabolic capabilities. To investigate metabolic changes during in vivo RBC aging, labeled cell populations were purified at intervals and assessed for abundance of metabolic intermediates using mass spectrometry. A total of 167 metabolites were profiled and quantified from cell populations of defined ages. Older RBCs maintained ATP and redox charge states at the cost of altered activity of enzymatic pathways. Time-dependent changes were identified in metabolites related to maintenance of the redox state and membrane structure. These findings illuminate the differential metabolic pathway usage associated with normal cellular aging and identify potential biomarkers to determine average RBC age and rates of RBC turnover from a single blood sample.

[This corrects the article DOI: 10.1371/journal.pone.0226634.].

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.

The Warburg effect (WE), or aerobic glycolysis, is commonly recognized as a hallmark of cancer and has been extensively studied for potential anti-cancer therapeutics development. Beyond cancer, the WE plays an important role in many other cell types involved in immunity, angiogenesis, pluripotency, and infection by pathogens (e.g., malaria). Here, we review the WE in non-cancerous context as a “hallmark of rapid proliferation.” We observe that the WE operates in rapidly dividing cells in normal and pathological states that are triggered by internal and external cues. Aerobic glycolysis is also the preferred metabolic program in the cases when robust transient responses are needed. We aim to draw attention to the potential of computational modeling approaches in systematic characterization of common metabolic features beyond the WE across physiological and pathological conditions. Identification of metabolic commonalities across various diseases may lead to successful repurposing of drugs and biomarkers.

Graphical Abstract Recent results obtained by Chindelevitch et al (2014) are challenged by Ebrahim et al, illustrating several issues in the reproducibility of computational biology methods.

Abstract Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome‐scale metabolic models have shown utility in integrating ‐omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host–pathogen interactions using in silico mass‐balanced, genome‐scale models has not been performed. We, therefore, constructed a cell‐specific alveolar macrophage model, iAB‐AMØ‐1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host–pathogen genome‐scale reconstruction, iAB‐AMØ‐1410‐Mt‐661. The integrated host–pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High‐throughput data from infected macrophages were mapped onto the host–pathogen network and were able to describe three distinct pathological states. Integrated host–pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed.

Abstract The biological community is now awash in high‐throughput data sets and is grappling with the challenge of integrating disparate data sets. Such integration has taken the form of statistical analysis of large data sets, or through the bottom–up reconstruction of reaction networks. While progress has been made with statistical and structural methods, large‐scale systems have remained refractory to dynamic model building by traditional approaches. The availability of annotated genomes enabled the reconstruction of genome‐scale networks, and now the availability of high‐throughput metabolomic and fluxomic data along with thermodynamic information opens the possibility to build genome‐scale kinetic models. We describe here a framework for building and analyzing such models. The mathematical analysis challenges are reflected in four foundational properties, (i) the decomposition of the Jacobian matrix into chemical, kinetic and thermodynamic information, (ii) the structural similarity between the stoichiometric matrix and the transpose of the gradient matrix, (iii) the duality transformations enabling either fluxes or concentrations to serve as the independent variables and (iv) the timescale hierarchy in biological networks. Recognition and appreciation of these properties highlight notable and challenging new in silico analysis issues.

Rationale and Objectives. Accurate signal to tracer concentration maps are critical to quantitative MRI. The purpose of this study was to evaluate and optimize spoiled gradient echo (SPGR) MR sequences for the use of gadolinium (Gd-DTPA) as a kinetic tracer. Methods. Water-gadolinium phantoms were constructed for a physiologic range of gadolinium concentrations. Observed and calculated SPGR signal to concentration curves were generated. Using a percentage error determination, optimal pulse parameters for signal to concentration mapping were obtained. Results. The accuracy of the SPGR equation is a function of the chosen MR pulse parameters, particularly the time to repetition (TR) and the flip angle (FA). At all experimental values of TR, increasing FA decreases the ratio between observed and calculated signals. Conversely, for a constant FA, increasing TR increases this ratio. Using optimized pulse parameter sets, it is possible to achieve excellent accuracy (approximately 5%) over a physiologic range of concentration tracer concentrations. Conclusion. Optimal pulse parameter sets exist and their use is essential for deriving accurate signal to concentration curves in quantitative MRI.

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a "middle-out" strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from "-omics" signatures are identified as key elements of a successful systems biology modeling approach in nutrition research--one that integrates physiological mechanisms and data at multiple space and time scales.

Metabolic network reconstructions represent valuable scaffolds for '-omics' data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA). Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron-sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1) quantitatively integrate gene expression data as reaction constraints and (2) compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of '-omics' datasets and thus the study of the mechanistic principles underlying the genotype-phenotype relationship.

Recent advances in reconstruction and analytical methods for signaling networks have spurred the development of large-scale models that incorporate fully functional and biologically relevant features. An extended reconstruction of the human Toll-like receptor signaling network is presented herein. This reconstruction contains an extensive complement of kinases, phosphatases, and other associated proteins that mediate the signaling cascade along with a delineation of their associated chemical reactions. A computational framework based on the methods of large-scale convex analysis was developed and applied to this network to characterize input-output relationships. The input-output relationships enabled significant modularization of the network into ten pathways. The analysis identified potential candidates for inhibitory mediation of TLR signaling with respect to their specificity and potency. Subsequently, we were able to identify eight novel inhibition targets through constraint-based modeling methods. The results of this study are expected to yield meaningful avenues for further research in the task of mediating the Toll-like receptor signaling network and its effects.

The study of dynamic functions of large-scale biological networks has intensified in recent years. A critical component in developing an understanding of such dynamics involves the study of their hierarchical organization. We investigate the temporal hierarchy in biochemical reaction networks focusing on: (1) the elucidation of the existence of "pools" (i.e., aggregate variables) formed from component concentrations and (2) the determination of their composition and interactions over different time scales. To date the identification of such pools without prior knowledge of their composition has been a challenge. A new approach is developed for the algorithmic identification of pool formation using correlations between elements of the modal matrix that correspond to a pair of concentrations and how such correlations form over the hierarchy of time scales. The analysis elucidates a temporal hierarchy of events that range from chemical equilibration events to the formation of physiologically meaningful pools, culminating in a network-scale (dynamic) structure-(physiological) function relationship. This method is validated on a model of human red blood cell metabolism and further applied to kinetic models of yeast glycolysis and human folate metabolism, enabling the simplification of these models. The understanding of temporal hierarchy and the formation of dynamic aggregates on different time scales is foundational to the study of network dynamics and has relevance in multiple areas ranging from bacterial strain design and metabolic engineering to the understanding of disease processes in humans.