Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype-phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.e., bibliomic data). Herein we describe the reconstruction process and demonstrate how the resulting genome-scale (or global) network can be used (i) for the discovery of missing information, (ii) for the formulation of an in silico model, and (iii) as a structured context for analyzing high-throughput biological data sets. Our comprehensive evaluation of the literature revealed many gaps in the current understanding of human metabolism that require future experimental investigation. Mathematical analysis of network structure elucidated the implications of intracellular compartmentalization and the potential use of correlated reaction sets for alternative drug target identification. Integrated analysis of high-throughput data sets within the context of the reconstruction enabled a global assessment of functional metabolic states. These results highlight some of the applications enabled by the reconstructed human metabolic network. The establishment of this network represents an important step toward genome-scale human systems biology.constraint based ͉ metabolism ͉ model ͉ systems biology A n individual's metabolism is determined by one's genetics, environment, and nutrition. With the available human genome sequence and its annotation (1-3), we can hope to define the human body's complement of metabolic enzymes. In addition, numerous metabolic genes and enzymes have been individually studied for decades, resulting in a collective knowledge base, or ''bibliome,'' that includes reaction mechanisms and well characterized interactions. Manual component-bycomponent (bottom-up) reconstruction of genomic and bibliomic data leads to a biochemically, genetically, and genomically structured (BiGG) reconstruction (4) that can be mathematically represented as an in silico model for computing allowable network states under governing chemical and genetic constraints (5). The procedure for integrating these diverse data types to form a network reconstruction and predictive model is well established for microorganisms (4) and has recently been applied to mouse hybridomas (6). Such in silico models have enabled hypothesis-driven biology, including the prediction of the outcome of adaptive evolution (7-11) and the identification and discovery of candidates for missing metabolic functions that were subsequently experimentally verified (12). Because metabolic networks are more complex in mammals than in singlecelled organisms, there is likely to be an even greater opportunity for the use of computational models to understand the basis of normal and abnormal cellular function.He...

The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.

Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are “genome-scale” and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

Background: Several strains of bacteria have sequenced and annotated genomes, which have been used in conjunction with biochemical and physiological data to reconstruct genome-scale metabolic networks. Such reconstruction amounts to a two-dimensional annotation of the genome. These networks have been analyzed with a constraint-based formalism and a variety of biologically meaningful results have emerged. Staphylococcus aureus is a pathogenic bacterium that has evolved resistance to many antibiotics, representing a significant health care concern. We present the first manually curated elementally and charge balanced genome-scale reconstruction and model of S. aureus' metabolic networks and compute some of its properties.

Ischemia-reperfusion of the intestine produces a set of inflammatory mediators, the origin of which has recently been shown to involve pancreatic digestive enzymes. Matrix metalloproteinase-9 (MMP-9) participates in a variety of inflammatory processes including myocardial, hepatic, and pancreatic ischemiareperfusion. In the present study, we explore the role of neutrophil-derived MMP-9 in acute intestinal ischemia-reperfusion and its interaction with pancreatic trypsin. Male Sprague-Dawley rats were subjected to 45 minutes of superior mesenteric arterial occlusion followed by 90 minutes of reperfusion. In situ zymography of the proximal jejunum reveals increased gelatinase activity in the intestinal wall after ischemiareperfusion. Gel electrophoresis zymography and immunofluorescence co-localization suggests that this gelatinase activity is derived from MMP-9 released from infiltrating neutrophils. The role of intraluminal trypsin in this process was investigated using an in vivo isolated jejunal loop model of intestinal ischemia-reperfusion. Trypsin increased the inflammatory response after reperfusion, with an augmented neutrophil infiltration of the intestinal wall. Furthermore, trypsin stimulated a rapid conversion of neutrophil-released proMMP-9 into the lower molecular weight enzymatically active MMP-9. This process represents a powerful in vivo pathophysiological mechanism for trypsin-induced MMP-9 activation and is likely to play a central role in the development of acute intestinal inflammation and shock. Intestinal ischemia-reperfusion (I/R) is a localized pathology that often leads to multi-organ dysfunction and shock.

Sugarcane is a major crop used for food and bioenergy production. Modern cultivars are hybrids derived from crosses between Saccharum officinarum and Saccharum spontaneum. Hybrid cultivars combine favorable characteristics from ancestral species and contain a genome that is highly polyploid and aneuploid, containing 100–130 chromosomes. These complex genomes represent a huge challenge for molecular studies and for the development of biotechnological tools that can facilitate sugarcane improvement. Here, we describe full-length enriched cDNA libraries for Saccharum officinarum, Saccharum spontaneum, and one hybrid genotype (SP803280) and analyze the set of open reading frames (ORFs) in their genomes (i.e., their ORFeomes). We found 38,195 (19%) sugarcane-specific transcripts that did not match transcripts from other databases. Less than 1.6% of all transcripts were ancestor-specific (i.e., not expressed in SP803280). We also found 78,008 putative new sugarcane transcripts that were absent in the largest sugarcane expressed sequence tag database (SUCEST). Functional annotation showed a high frequency of protein kinases and stress-related proteins. We also detected natural antisense transcript expression, which mapped to 94% of all plant KEGG pathways; however, each genotype showed different pathways enriched in antisense transcripts. Our data appeared to cover 53.2% (17,563 genes) and 46.8% (937 transcription factors) of all sugarcane full-length genes and transcription factors, respectively. This work represents a significant advancement in defining the sugarcane ORFeome and will be useful for protein characterization, single nucleotide polymorphism and splicing variant identification, evolutionary and comparative studies, and sugarcane genome assembly and annotation.

Epithelial-to-mesenchymal transition (EMT) has been closely linked with therapy resistance and cancer stem cells (CSCs). However, EMT pathways have proven challenging to therapeutically target. MicroRNA 145 (miR-145) targets multiple stem cell transcription factors and its expression is inversely correlated with EMT. Therefore, we hypothesized that miR-145 represents a therapeutic target to reverse snail family transcriptional repressor 1 (SNAI1)-mediated stemness and radiation resistance (RT). Stable expression of SNAI1 in DLD1 and HCT116 cells (DLD1-SNAI1; HCT116-SNAI1) increased expression of Nanog and decreased miR-145 expression compared to control cells. Using a miR-145 luciferase reporter assay, we determined that ectopic SNAI1 expression significantly repressed the miR-145 promoter. DLD1-SNAI1 and HCT116-SNAI1 cells demonstrated decreased RT sensitivity and, conversely, miR-145 replacement significantly enhanced RT sensitivity. Of the five parental colon cancer cell lines, SW620 cells demonstrated relatively high endogenous SNAI1 and low miR-145 levels. In the SW620 cells, miR-145 replacement decreased CSC-related transcription factor expression, spheroid formation, and radiation resistance. In rectal cancer patient-derived xenografts, CSC identified by EpCAM+/aldehyde dehydrogenase (ALDH)+ demonstrated high expression of SNAI1, c-Myc, and Nanog compared with non-CSCs (EpCAM+/ALDH-). Conversely, patient-derived CSCs demonstrated low miR-145 expression levels relative to non-CSCs. These results suggest that the SNAI1:miR-145 pathway represents a novel therapeutic target in colorectal cancer to overcome RT resistance.

Gray wolves (Canis lupus) were extirpated from the northern Rocky Mountains (NRM) of the United States by the 1930s. Dispersing wolves from Canada naturally recolonized Montana and first denned there in 1986. In 1995 and 1996, the United States Fish and Wildlife Service reintroduced 66 wolves into central Idaho and Yellowstone National Park. By 2008, there were ≥1,655 wolves in ≥217 packs, including 95 breeding pairs in the NRM. From 1993–2008, we captured and radio‐collared 1,681 wolves and documented 297 radio‐collared wolves dispersing as lone individuals. We monitored dispersing wolves to determine their pack characteristics (i.e., pack size and surrounding pack density) before and after dispersal, their reproductive success, and eventual fate. We calculated summary statistics for characteristics of wolf dispersal (i.e., straight‐line distance, age, time of year, sex ratio, reproduction, and survival), and we tested these characteristics for differences between sexes and age groups. Approximately, 10% of the known wolf population dispersed annually. The sex ratio of dispersals favored males (169 M, 128 F), but fewer dispersed males reproduced (28%, n = 47) than females (42%, n = 54). Fifty‐nine percent of all dispersers of known age were adults (n = 156), 37% were yearlings (n = 99), and 4% were pups (n = 10). Mean age at dispersal for males (32.8 months) was not significantly different (P = 0.88) than for females (32.1 months). Yellowstone National Park had a significant positive effect on dispersal rate. Pack density in a wolf's natal population had a negative effect on dispersal rate when the entire NRM population was considered. The mean NRM pack size (6.9) from 1993 to 2008 was smaller than the mean size of packs (10.0) from which wolves dispersed during that time period (P < 0.001); however, pack size was not in our most supported model. Dispersals occurred throughout the year but generally increased in the fall and peaked in January. The mean duration of all dispersals was 5.5 months. Radio‐collared wolves dispersed between Montana, Idaho, and Wyoming to other adjacent states, and between the United States and Canada throughout the study. Mean straight‐line distance between starting and ending points for dispersing males (98.1 km) was not significantly different than females (87.7 km; P = 0.11). Ten wolves (3.4%) dispersed distances >300 km. On average, dispersal distance decreased later in the study (P = 0.006). Sex, survival rate in the natal population, start date, dispersal distance, and direction were not significant predictors of dispersal rate or successful dispersal. Wolves that formed new packs were >11 times more likely to reproduce than those that joined packs and surrounding pack density had a negative effect on successful dispersal. Dispersal behavior seems to be innate in sexually mature wolves and thereby assures that genetic diversity will remain high and help conserve the NRM wolf population. © 2017 The Wildlife Society.