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Introduction: Compelling evidence shows that the COVID-19 pandemic has detrimental effects on the mental health of university students. However, little is known about the psychological distress experienced by students from high schools during the pandemic. This study, therefore, sought to examine the prevalence of depression, anxiety and stress and their associated factors among students from high schools in Rwanda. Methods and materials: A retrospective, cross-sectional study was conducted on 384 students randomly selected from high schools. Data were collected using standardized measures of mental disorders and their associated factors. Bivariate and multivariate analyses based on the odds ratio were used to indicate the associated factors of anxiety, depression, and stress. Results: The results indicated that slightly above half of the participants (51%, n = 195) had clinically significant symptoms of depression, 30.3% (n = 116) had stress and 67.3% (n = 259) had anxiety. Our analyses identified several key risk factors associated with increased odds of these mental disorders. These include exposure to domestic violence, COVID-19 symptoms like cough and myalgia, eating twice per day, having one of the three mental disorders, gender, with females showing higher susceptibility, and direct contact with the people who positively tested covid-19. Conversely, protective factors such as heightened awareness about Covid-19, positive mental health, social support, eating three times, belonging to the third Ubudehe category, and a high resilience emerged as significant elements mitigating the risks of these mental health challenges within our sample. Intriguingly, religious affiliation emerged as a notable factor, with students affiliated with the Witness of Jehovah and Adventist denominations exhibited lower risks for depression and anxiety. Conclusion: Our findings highlighted a high prevalence of depression, anxiety, and stress among students from secondary schools. Interestingly, this study also revealed the associated risk and protective factors of depression, anxiety, and stress in Rwandan students in high schools. Therefore, mental health interventions targeting the impact of COVID-19 on students, as young people are needed. [ABSTRACT FROM AUTHOR]

Temperature is one of the most important environmental factors for plant growth, as low-temperature freezing damage seriously affects the yield and distribution of plants. The Lanzhou lily (Lilium davidii, var. unicolor) is a famous ornamental plant with high ornamental value. Using an Illumina HiSeq transcriptome sequencing platform, sequencing was conducted on Lanzhou lilies exposed to two different temperature conditions: a normal temperature treatment at 20°C (A) and a cold treatment at −4°C (C). After being treated for 24 hours, a total of 5848 differentially expressed genes (DEGs) were identified, including 3478 significantly up regulated genes and 2370 significantly down regulated genes, accounting for 10.27% of the total number of DEGs. Quantitative real-time PCR (QRT-PCR) analysis showed that the expression trends of 10 randomly selected DEGs coincided with the results of high-throughput sequencing. In addition, genes responding to low-temperature stress were analyzed using the interaction regulatory network method. The anti-freeze pathway of Lanzhou lily was found to involve the photosynthetic and metabolic pathways, and the key freezing resistance genes were the OLEO3 gene, 9 CBF family genes, and C2H2 transcription factor c117817_g1 (ZFP). This lays the foundation for revealing the underlying mechanism of the molecular anti-freeze mechanism in Lanzhou lily. [ABSTRACT FROM AUTHOR]

Identifying essential targets in the genome-scale metabolic networks of cancer cells is a time-consuming process. The present study proposed a fuzzy hierarchical optimization framework for identifying essential genes, metabolites and reactions. On the basis of four objectives, the present study developed a framework for identifying essential targets that lead to cancer cell death and evaluating metabolic flux perturbations in normal cells that have been caused by cancer treatment. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. We applied nested hybrid differential evolution to solve the trilevel MDM problem to identify essential targets in genome-scale metabolic models for five consensus molecular subtypes (CMSs) of colorectal cancer. We used various media to identify essential targets for each CMS and discovered that most targets affected all five CMSs and that some genes were CMS-specific. We obtained experimental data on the lethality of cancer cell lines from the DepMap database to validate the identified essential genes. The results reveal that most of the identified essential genes were compatible with the colorectal cancer cell lines obtained from DepMap and that these genes, with the exception of EBP, LSS, and SLC7A6, could generate a high level of cell death when knocked out. The identified essential genes were mostly involved in cholesterol biosynthesis, nucleotide metabolisms, and the glycerophospholipid biosynthetic pathway. The genes involved in the cholesterol biosynthetic pathway were also revealed to be determinable, if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in the cholesterol biosynthetic pathway became non-essential if such a reaction was induced. Furthermore, the essential gene CRLS1 was revealed as a medium-independent target for all CMSs. [ABSTRACT FROM AUTHOR]

Flux balance analysis (FBA) is a powerful tool to study genome-scale models of the cellular metabolism, based on finding the optimal flux distributions over the network. While the objective function is crucial for the outcome, its choice, even though motivated by evolutionary arguments, has not been directly connected to related measures. Here, we used an available multi-scale mathematical model of yeast replicative ageing, integrating cellular metabolism, nutrient sensing and damage accumulation, to systematically test the effect of commonly used objective functions on features of replicative ageing in budding yeast, such as the number of cell divisions and the corresponding time between divisions. The simulations confirmed that assuming maximal growth is essential for reaching realistic lifespans. The usage of the parsimonious solution or the additional maximisation of a growth-independent energy cost can improve lifespan predictions, explained by either increased respiratory activity using resources otherwise allocated to cellular growth or by enhancing antioxidative activity, specifically in early life. Our work provides a new perspective on choosing the objective function in FBA by connecting it to replicative ageing. [ABSTRACT FROM AUTHOR]

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a highly adaptive pathogenic bacteria with a serious public health concern due to its increasing resistance to antibiotics. Therefore, identification of novel drug targets for S. Typhimurium is crucial. Here, we first created a pathogen-host integrated genome-scale metabolic network by combining the metabolic models of human and S. Typhimurium, which we further tailored to the pathogenic state by the integration of dual transcriptome data. The integrated metabolic model enabled simultaneous investigation of metabolic alterations in human cells and S. Typhimurium during infection. Then, we used the tailored pathogen-host integrated genome-scale metabolic network to predict essential genes in the pathogen, which are candidate novel drug targets to inhibit infection. Drug target prioritization procedure was applied to these targets, and pabB was chosen as a putative drug target. It has an essential role in 4-aminobenzoic acid (PABA) synthesis, which is an essential biomolecule for many pathogens. A structure based virtual screening was applied through docking simulations to predict candidate compounds that eliminate S. Typhimurium infection by inhibiting pabB. To our knowledge, this is the first comprehensive study for predicting drug targets and drug like molecules by using pathogen-host integrated genome-scale models, dual RNA-seq data and structure-based virtual screening protocols. This framework will be useful in proposing novel drug targets and drugs for antibiotic-resistant pathogens. [ABSTRACT FROM AUTHOR]

Chicken is the first sequenced avian that has a crucial role in human life for its meat and egg production. Because of various metabolic disorders, study the metabolism of chicken cell is important. Herein, the first genome-scale metabolic model of a chicken cell named iES1300, consists of 2427 reactions, 2569 metabolites, and 1300 genes, was reconstructed manually based on KEGG, BiGG, CHEBI, UNIPROT, REACTOME, and MetaNetX databases. Interactions of metabolic genes for growth were examined for E. coli, S. cerevisiae, human, and chicken metabolic models. The results indicated robustness to genetic manipulation for iES1300 similar to the results for human. iES1300 was integrated with transcriptomics data using algorithms and Principal Component Analysis was applied to compare context-specific models of the normal, tumor, lean and fat cell lines. It was found that the normal model has notable metabolic flexibility in the utilization of various metabolic pathways, especially in metabolic pathways of the carbohydrate metabolism, compared to the others. It was also concluded that the fat and tumor models have similar growth metabolisms and the lean chicken model has a more active lipid and carbohydrate metabolism. [ABSTRACT FROM AUTHOR]

Introduction: Resilience has been proposed as a suitable solution to better deal with nursing students in cases of challenges but the complex and multidimensional nature of resilience has made its measurement challenging. This study aimed to develop and validate a new inventory theory-driven labeled Nursing Student Academic Resilience Inventory. Methods: This study was performed with an exploratory sequential mixed-method design. In the qualitative phase of the study, individual interviews were conducted by including 15 participants to elicit the concept of resilience through purposive sampling. In the quantitative phase, psychometric analysis of the extracted items was performed using face, content, and construct validities (exploratory and confirmatory factor analyses) on a sample size of 405 nursing students. Besides, reliability has been tested using internal consistency and test-retest methods. According to the COSMIN standards, beside two important indicators of validity and reliability, responsiveness and interpretability were also considered. Results: A 6-factor structure (optimism, communication, self-esteem/evaluation, self-awareness, trustworthiness, and self-regulation) with 24 items were extracted in terms of the derived categories from the qualitative phase. In confirmatory factor analysis, the χ2/df ratio was calculated as 2.11 for the NSARI six-factor structure. Suitable values were obtained for the goodness of fit indices (CFI = 0.904, AGFI = 0.885, IFI = 0.906, PCFI = 0.767, and RMSEA = 0.053). In the second-order factor analysis, AVE = 0.70 indicated the existence of both convergent and divergent validities. The Cronbach's alpha and omega coefficients were investigated as (0.66–0.78) and (0.66–0.80), respectively. The AIC was between 0.33 and 0.45 for all factors, which is an acceptable rate. Additionally, an intraclass correlation coefficient (ICC) was obtained as.903 for the whole instrument (CI.846-.946, P <0.0001). Conclusion: Multidimensional nature of resilience was supported through exploring its 6-factor structures in the nursing students' field. This tool also showed an acceptable validity and reliability for measuring resilience in the population of nursing students. [ABSTRACT FROM AUTHOR]

Clinical placements and supervision is an integral part of nursing education internationally. There are significant differences between students' expectations of clinical learning and their fulfillment. Few studies have focused on supervisors' perspectives on clinical placements. The objective of this study was to explore nursing supervisors' perspectives on students' preparedness for clinical placements. Methods: The study was conducted in a county in Southeastern-Norway, with 317.000 inhabitants, and within one hospital and one university college catchment area. Focus group interviews were conducted in the periode August to December 2018. Data were analyzed using Hsieh and Shannon's conventional content analysis. Results: 34 nursing supervisors participated, three intellectual disability nurses and 31 registered nurses, working in four different primary healthcare wards and four different hospital wards. Participants' age ranged from 23 to 58 years, one male only. Through the analysis we derived the category 'Shared responsibility for preparation' with subcategories a) Individual initiative, and b) University college facilitation. Conclusions: Findings indicate that there is a gap between nursing supervisors' expectations and reality regarding students' preparedness for clinical placements. Moreover, nursing supervisors did not seem to focus on their own role in student preparedness. [ABSTRACT FROM AUTHOR]

Essential gene prediction helps to find minimal genes indispensable for the survival of any organism. Machine learning (ML) algorithms have been useful for the prediction of gene essentiality. However, currently available ML pipelines perform poorly for organisms with limited experimental data. The objective is the development of a new ML pipeline to help in the annotation of essential genes of less explored disease-causing organisms for which minimal experimental data is available. The proposed strategy combines unsupervised feature selection technique, dimension reduction using the Kamada-Kawai algorithm, and semi-supervised ML algorithm employing Laplacian Support Vector Machine (LapSVM) for prediction of essential and non-essential genes from genome-scale metabolic networks using very limited labeled dataset. A novel scoring technique, Semi-Supervised Model Selection Score, equivalent to area under the ROC curve (auROC), has been proposed for the selection of the best model when supervised performance metrics calculation is difficult due to lack of data. The unsupervised feature selection followed by dimension reduction helped to observe a distinct circular pattern in the clustering of essential and non-essential genes. LapSVM then created a curve that dissected this circle for the classification and prediction of essential genes with high accuracy (auROC > 0.85) even with 1% labeled data for model training. After successful validation of this ML pipeline on both Eukaryotes and Prokaryotes that show high accuracy even when the labeled dataset is very limited, this strategy is used for the prediction of essential genes of organisms with inadequate experimentally known data, such as Leishmania sp. Using a graph-based semi-supervised machine learning scheme, a novel integrative approach has been proposed for essential gene prediction that shows universality in application to both Prokaryotes and Eukaryotes with limited labeled data. The essential genes predicted using the pipeline provide an important lead for the prediction of gene essentiality and identification of novel therapeutic targets for antibiotic and vaccine development against disease-causing parasites. [ABSTRACT FROM AUTHOR]

Motivation: Determining intracellular metabolic flux through isotope labeling techniques such as 13C metabolic flux analysis (13C-MFA) incurs significant cost and effort. Previous studies have shown transcriptomic data coupled with constraint-based metabolic modeling can determine intracellular fluxes that correlate highly with 13C-MFA measured fluxes and can achieve higher accuracy than constraint-based metabolic modeling alone. These studies, however, used validation data limited to E. coli and S. cerevisiae grown on glucose, with significantly similar flux distribution for central metabolism. It is unclear whether those results apply to more diverse metabolisms, and therefore further, extensive validation is needed. Results: In this paper, we formed a dataset of transcriptomic data coupled with corresponding 13C-MFA flux data for 21 experimental conditions in different unicellular organisms grown on varying carbon substrates and conditions. Three computational flux-balance analysis (FBA) methods were comparatively assessed. The results show when uptake rates of carbon sources and key metabolites are known, transcriptomic data provides no significant advantage over constraint-based metabolic modeling (average correlation coefficients, transcriptomic E-Flux2 0.725 and SPOT 0.650 vs non-transcriptomic pFBA 0.768). When uptake rates are unknown, however, predictions obtained utilizing transcriptomic data are generally good and significantly better than those obtained using constraint-based metabolic modeling alone (E-Flux2 0.385 and SPOT 0.583 vs pFBA 0.237). Thus, transcriptomic data coupled with constraint-based metabolic modeling is a promising method to obtain intracellular flux estimates in microorganisms, particularly in cases where uptake rates of key metabolites cannot be easily determined, such as for growth in complex media or in vivo conditions. [ABSTRACT FROM AUTHOR]

The reconstruction and application of genome-scale metabolic network models is a central topic in the field of systems biology with numerous applications in biotechnology, ecology, and medicine. However, there is no agreed upon standard for the definition of the nutritional environment for these models. The objective of this article is to provide a guideline and a clear paradigm on how to translate nutritional information into an in-silico representation of the chemical environment. Step-by-step procedures explain how to characterise and categorise the nutritional input and to successfully apply it to constraint-based metabolic models. In parallel, we illustrate the proposed procedure with a case study of the growth of Escherichia coli in a complex nutritional medium and show that an accurate representation of the medium is crucial for physiological predictions. The proposed framework will assist researchers to expand their existing metabolic models of their microbial systems of interest with detailed representations of the nutritional environment, which allows more accurate and reproducible predictions of microbial metabolic processes. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study was to assess the classification capability of Breast Imaging Reporting and Data System (BI-RADS) ultrasound feature descriptors targeting established commercial transcriptomic gene signatures that guide management of breast cancer. Materials and methods: This retrospective, single-institution analysis of 219 patients involved two cohorts using one of two FDA approved transcriptome-based tests that were performed as part of the clinical care of breast cancer patients at Harbor-UCLA Medical Center between April 2008 and January 2013. BI-RADS descriptive terminology was collected from the corresponding ultrasound reports for each patient in conjunction with transcriptomic test results. Recursive partitioning and regression trees were used to test and validate classification of the two cohorts. Results: The area under the curve (AUC) of the receiver operator curves (ROC) for the regression classifier between the two FDA approved tests and ultrasound features were 0.77 and 0.65, respectively; they employed the 'margins', 'retrotumoral', and 'internal echoes' feature descriptors. Notably, the 'retrotumoral' and mass 'margins' features were used in both classification trees. The identification of sonographic correlates of gene tests provides added value to the ultrasound exam without incurring additional procedures or testing. Conclusions: The predictive capability using structured language from diagnostic ultrasound reports (BI-RADS) was moderate for the two tests, and provides added value from ultrasound imaging without incurring any additional costs. Incorporation of additional measures, such as ultrasound contrast enhancement, with validation in larger, prospective studies may further substantiate these results and potentially demonstrate even greater predictive utility. [ABSTRACT FROM AUTHOR]

The subventricular zone (SVZ) in the brain is associated with gliomagenesis and resistance to treatment in glioblastoma. In this study, we investigate the prognostic role and biological characteristics of subventricular zone (SVZ) involvement in glioblastoma. We analyzed T1-weighted, gadolinium-enhanced MR images of a retrospective cohort of 647 primary glioblastoma patients diagnosed between 2005–2013, and performed a multivariable Cox regression analysis to adjust the prognostic effect of SVZ involvement for clinical patient- and tumor-related factors. Protein expression patterns of a.o. markers of neural stem cellness (CD133 and GFAP-δ) and (epithelial-) mesenchymal transition (NF-κB, C/EBP-β and STAT3) were determined with immunohistochemistry on tissue microarrays containing 220 of the tumors. Molecular classification and mRNA expression-based gene set enrichment analyses, miRNA expression and SNP copy number analyses were performed on fresh frozen tissue obtained from 76 tumors. Confirmatory analyses were performed on glioblastoma TCGA/TCIA data. Involvement of the SVZ was a significant adverse prognostic factor in glioblastoma, independent of age, KPS, surgery type and postoperative treatment. Tumor volume and postoperative complications did not explain this prognostic effect. SVZ contact was associated with increased nuclear expression of the (epithelial-) mesenchymal transition markers C/EBP-β and phospho-STAT3. SVZ contact was not associated with molecular subtype, distinct gene expression patterns, or markers of stem cellness. Our main findings were confirmed in a cohort of 229 TCGA/TCIA glioblastomas. In conclusion, involvement of the SVZ is an independent prognostic factor in glioblastoma, and associates with increased expression of key markers of (epithelial-) mesenchymal transformation, but does not correlate with stem cellness, molecular subtype, or specific (mi)RNA expression patterns. [ABSTRACT FROM AUTHOR]

Objectives: To study the differences in the levels of nitrogen metabolites, such as ammonia and nitric oxide and the correlations existing among them in both red blood cells (RBCs) and serum, as well as the possible differences by gender in healthy subjects and patients with type 2 Diabetes Mellitus (DM). Design and methods: This cross-sectional study included 80 patients diagnosed with type 2 DM (40 female and 40 male patients) and their corresponding controls paired by gender (40 female and 40 male). We separated serum and RBC and determined metabolites mainly through colorimetric and spectrophotometric assays. We evaluated changes in the levels of the main catabolic by-products of blood nitrogen metabolism, nitric oxide (NO), and malondialdehyde (MDA). Results: Healthy female and male controls showed a differential distribution of blood metabolites involved in NO metabolism and arginine metabolism for the ornithine and urea formation. Patients with DM had increased ammonia, citrulline, urea, uric acid, and ornithine, mainly in the RBCs, whereas the level of arginine was significantly lower in men with type 2 DM. These findings were associated with hyperglycemia, glycosylated hemoglobin (Hb A1C), and levels of RBC’s MDA. Furthermore, most of the DM-induced alterations in nitrogen-related metabolites appear to be associated with a difference in the RBC capacity for the release of these metabolites, thereby causing an abrogation of the gender-related differential management of nitrogen metabolites in healthy subjects. Conclusions: We found evidence of a putative role of RBC as an extra-hepatic mechanism for controlling serum levels of nitrogen-related metabolites, which differs according to gender in healthy subjects. Type 2 DM promotes higher ammonia, citrulline, and MDA blood levels, which culminate in a loss of the differential management of nitrogen-related metabolites seen in healthy women and men. [ABSTRACT FROM AUTHOR]

The selection of optimal enzyme concentration in multienzyme cascade reactions for the highest product yield in practice is very expensive and time-consuming process. The modelling of biological pathways is a difficult process because of the complexity of the system. The mathematical modelling of the system using an analytical approach depends on the many parameters of enzymes which rely on tedious and expensive experiments. The artificial neural network (ANN) method has been successively applied in different fields of science to perform complex functions. In this study, ANN models were trained to predict the flux for the upper part of glycolysis as inferred by NADH consumption, using four enzyme concentrations i.e., phosphoglucoisomerase, phosphofructokinase, fructose-bisphosphate-aldolase, triose-phosphate-isomerase. Out of three ANN algorithms, the neuralnet package with two activation functions, “logistic” and “tanh” were implemented. The prediction of the flux was very efficient: RMSE and R2 were 0.847, 0.93 and 0.804, 0.94 respectively for logistic and tanh functions using a cross validation procedure. This study showed that a systemic approach such as ANN could be used for accurate prediction of the flux through the metabolic pathway. This could help to save a lot of time and costs, particularly from an industrial perspective. The R-code is available at: . [ABSTRACT FROM AUTHOR]

In the search for new pharmaceutical leads, especially with DNA-binding molecules or genome editing methods, the issue of side and off-target effects have always been thorny in nature. A particular case is the investigation into the off-target effects of N-methylpyrrole-N-methylimidazole polyamides, a naturally inspired class of DNA binders with strong affinity to the minor-groove and sequence specificity, but at < 20 bases, their relatively short motifs also insinuate the possibility of non-unique genomic binding. Binding at non-intended loci potentially lead to the rise of off-target effects, issues that very few approaches are able to address to-date. We here report an analytical method to infer off-target binding, via expression profiling, based on probing the relative impact to various biochemical pathways; we also proposed an accompanying side effect prediction engine for the systematic screening of candidate polyamides. This method marks the first attempt in PI polyamide research to identify elements in biochemical pathways that are sensitive to the treatment of a candidate polyamide as an approach to infer possible off-target effects. Expression changes were then considered to assess possible outward phenotypic changes, manifested as side effects, should the same PI polyamide candidate be administered clinically. We validated some of these effects with a series of animal experiments, and found agreeable corroboration in certain side effects, such as changes in aspartate transaminase levels in ICR and nude mice post-administration. [ABSTRACT FROM AUTHOR]

Many human genetic disorders and diseases are known to be related to each other through frequently observed co-occurrences. Studying the correlations among multiple diseases provides an important avenue to better understand the common genetic background of diseases and to help develop new drugs that can treat multiple diseases. Meanwhile, network science has seen increasing applications on modeling complex biological systems, and can be a powerful tool to elucidate the correlations of multiple human diseases. In this article, known disease-gene associations were represented using a weighted bipartite network. We extracted a weighted human diseases network from such a bipartite network to show the correlations of diseases. Subsequently, we proposed a new centrality measurement for the weighted human disease network (WHDN) in order to quantify the importance of diseases. Using our centrality measurement to quantify the importance of vertices in WHDN, we were able to find a set of most central diseases. By investigating the 30 top diseases and their most correlated neighbors in the network, we identified disease linkages including known disease pairs and novel findings. Our research helps better understand the common genetic origin of human diseases and suggests top diseases that likely induce other related diseases. [ABSTRACT FROM AUTHOR]

Cellular systems shift metabolic states by adjusting gene expression and enzyme activities to adapt to physiological and environmental changes. Biochemical and genetic studies are identifying how metabolic regulation affects the selection of metabolic phenotypes. However, how metabolism influences its regulatory architecture still remains unexplored. We present a new method of extreme pathway analysis (the minimal set of conically independent metabolic pathways) to deduce regulatory structures from pure pathway information. Applying our method to metabolic networks of human red blood cells and Escherichia coli, we shed light on how metabolic regulation are organized by showing which reactions within metabolic networks are more prone to transcriptional or allosteric regulation. Applied to a human genome-scale metabolic system, our method detects disease-associated reactions. Thus, our study deepens the understanding of the organizing principle of cellular metabolic regulation and may contribute to metabolic engineering, synthetic biology, and disease treatment. [ABSTRACT FROM AUTHOR]

Metabolic networks comprise thousands of enzymatic reactions functioning in a controlled manner and have been shaped by natural selection. Thanks to the genome data, the footprints of adaptive (positive) selection are detectable, and the strength of purifying selection can be measured. This has made possible to know where, in the metabolic network, adaptive selection has acted and where purifying selection is more or less strong and efficient. We have carried out a comprehensive molecular evolutionary study of all the genes involved in the human metabolism. We investigated the type and strength of the selective pressures that acted on the enzyme-coding genes belonging to metabolic pathways during the divergence of primates and rodents. Then, we related those selective pressures to the functional and topological characteristics of the pathways. We have used DNA sequences of all enzymes (956) of the metabolic pathways comprised in the HumanCyc database, using genome data for humans and five other mammalian species. We have found that the evolution of metabolic genes is primarily constrained by the layer of the metabolism in which the genes participate: while genes encoding enzymes of the inner core of metabolism are much conserved, those encoding enzymes participating in the outer layer, mediating the interaction with the environment, are evolutionarily less constrained and more plastic, having experienced faster functional evolution. Genes that have been targeted by adaptive selection are endowed by higher out-degree centralities than non-adaptive genes, while genes with high in-degree centralities are under stronger purifying selection. When the position along the pathway is considered, a funnel-like distribution of the strength of the purifying selection is found. Genes at bottom positions are highly preserved by purifying selection, whereas genes at top positions, catalyzing the first steps, are open to evolutionary changes. These results show how functional and topological characteristics of metabolic pathways contribute to shape the patterns of evolutionary pressures driven by natural selection and how pathway network structure matters in the evolutionary process that shapes the evolution of the system. [ABSTRACT FROM AUTHOR]

Adipose tissue (AT) plays a major role in metabolic adaptations in postpartum (PP) dairy cows. The endocannabinoid (eCB) system is a key regulator of metabolism and energy homeostasis; however, information about this system in ruminants is scarce. Therefore, this work aimed to assess the eCB system in subcutaneous AT, and to determine its relation to the metabolic profile in peripartum cows. Biopsies of AT were performed at 14 d prepartum, and 4 and 30 d PP from 18 multiparous peripartum cows. Cows were categorized retrospectively according to those with high body weight (BW) loss (HWL, 8.5 ± 1.7% BW loss) or low body weight loss (LWL, 2.9 ± 2.5% BW loss) during the first month PP. The HWL had higher plasma non-esterified fatty acids and a lower insulin/glucagon ratio PP than did LWL. Two-fold elevated AT levels of the main eCBs, N-arachidonoylethanolamine (AEA) and 2-arachidonoylglycerol (2-AG), were found 4 d PP compared with prepartum in HWL, but not in LWL cows. AT levels of the eCB-like molecules oleoylethanolamide, palmitoylethanolamide, and of arachidonic acid were elevated PP compared with prepartum in all cows. The abundance of monoglyceride lipase (MGLL), the 2-AG degrading enzyme, was lower in HWL vs. LWL AT PP. The relative gene expression of the cannabinoid receptors CNR1 and CNR2 in AT tended to be higher in HWL vs. LWL PP. Proteomic analysis of AT showed an enrichment of the inflammatory pathways’ acute phase signaling and complement system in HWL vs. LWL cows PP. In summary, eCB levels in AT were elevated at the onset of lactation as part of the metabolic adaptations in PP dairy cows. Furthermore, activating the eCB system in AT is most likely associated with a metabolic response of greater BW loss, lipolysis, and AT inflammation in PP dairy cows. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the uncertainty of radiomics features from contrast-enhanced breath-hold helical CT scans of non-small cell lung cancer for both manual and semi-automatic segmentation due to intra-observer, inter-observer, and inter-software reliability. Methods: Three radiation oncologists manually delineated lung tumors twice from 10 CT scans using two software tools (3D-Slicer and MIM Maestro). Additionally, three observers without formal clinical training were instructed to use two semi-automatic segmentation tools, Lesion Sizing Toolkit (LSTK) and GrowCut, to delineate the same tumor volumes. The accuracy of the semi-automatic contours was assessed by comparison with physician manual contours using Dice similarity coefficients and Hausdorff distances. Eighty-three radiomics features were calculated for each delineated tumor contour. Informative features were identified based on their dynamic range and correlation to other features. Feature reliability was then evaluated using intra-class correlation coefficients (ICC). Feature range was used to evaluate the uncertainty of the segmentation methods. Results: From the initial set of 83 features, 40 radiomics features were found to be informative, and these 40 features were used in the subsequent analyses. For both intra-observer and inter-observer reliability, LSTK had higher reliability than GrowCut and the two manual segmentation tools. All observers achieved consistently high ICC values when using LSTK, but the ICC value varied greatly for each observer when using GrowCut and the manual segmentation tools. For inter-software reliability, features were not reproducible across the software tools for either manual or semi-automatic segmentation methods. Additionally, no feature category was found to be more reproducible than another feature category. Feature ranges of LSTK contours were smaller than those of manual contours for all features. Conclusion: Radiomics features extracted from LSTK contours were highly reliable across and among observers. With semi-automatic segmentation tools, observers without formal clinical training were comparable to physicians in evaluating tumor segmentation. [ABSTRACT FROM AUTHOR]

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures. [ABSTRACT FROM AUTHOR]

In this work we aim to show how Genome Scale Metabolic Models (GSMMs) can be used as tools for drug design. By comparing the chemical structures of human metabolites (obtained using their KEGG indexes) and the compounds contained in the DrugBank database, we have observed that compounds showing Tanimoto scores higher than 0.9 with a metabolite, are 29.5 times more likely to bind the enzymes metabolizing the considered metabolite, than ligands chosen randomly. By using RNA-seq data to constrain a human GSMM it is possible to obtain an estimation of its distribution of metabolic fluxes and to quantify the effects of restraining the rate of chosen metabolic reactions (for example using a drug that inhibits the enzymes catalyzing the mentioned reactions). This method allowed us to predict the differential effects of lipoamide analogs on the proliferation of MCF7 (a breast cancer cell line) and ASM (airway smooth muscle) cells respectively. These differential effects were confirmed experimentally, which provides a proof of concept of how human GSMMs could be used to find therapeutic windows against cancer. By using RNA-seq data of 34 different cancer cell lines and 26 healthy tissues, we assessed the putative anticancer effects of the compounds in DrugBank which are structurally similar to human metabolites. Among other results it was predicted that the mevalonate pathway might constitute a good therapeutic window against cancer proliferation, due to the fact that most cancer cell lines do not express the cholesterol transporter NPC1L1 and the lipoprotein lipase LPL, which makes them rely on the mevalonate pathway to obtain cholesterol. [ABSTRACT FROM AUTHOR]

Large-scale kinetic models of metabolism are becoming increasingly comprehensive and accurate. A key challenge is to understand the biochemical basis of the dynamic properties of these models. Linear analysis methods are well-established as useful tools for characterizing the dynamic response of metabolic networks. Central to linear analysis methods are two key matrices: the Jacobian matrix (J) and the modal matrix (M-1) arising from its eigendecomposition. The modal matrix M-1 contains dynamically independent motions of the kinetic model near a reference state, and it is sparse in practice for metabolic networks. However, connecting the structure of M-1 to the kinetic properties of the underlying reactions is non-trivial. In this study, we analyze the relationship between J, M-1, and the kinetic properties of the underlying network for kinetic models of metabolism. Specifically, we describe the origin of mode sparsity structure based on features of the network stoichiometric matrix S and the reaction kinetic gradient matrix G. First, we show that due to the scaling of kinetic parameters in real networks, diagonal dominance occurs in a substantial fraction of the rows of J, resulting in simple modal structures with clear biological interpretations. Then, we show that more complicated modes originate from topologically-connected reactions that have similar reaction elasticities in G. These elasticities represent dynamic equilibrium balances within reactions and are key determinants of modal structure. The work presented should prove useful towards obtaining an understanding of the dynamics of kinetic models of metabolism, which are rooted in the network structure and the kinetic properties of reactions. [ABSTRACT FROM AUTHOR]

Background: Nutrient deprivation during early development has been associated with the predisposition to metabolic disorders in adulthood. Considering its interaction with metabolism, appetite and behavior, the endocannabinoid (eCB) system represents a promising target of developmental programming. Methods: By cross-fostering and variation of litter size, early postnatal nutrition of CB6F1-hybrid mice was controlled during the lactation period (3, 6, or 10 pups/mother). After weaning and redistribution at P21, all pups received standard chow ad libitum. Gene expression analyses (liver, visceral fat, hypothalamus) were performed at P50, eCB concentrations were determined in liver and visceral fat. Locomotor activity and social behavior were analyzed by means of computer-assisted videotracking. Results: Body growth was permanently altered, with differences for length, weight, body mass index and fat mass persisting beyond P100 (all 3>6>10,p<0.01). This was paralleled by differences in hepatic IGF-I expression (p<0.01). Distinct gene expression patterns for key enzymes of the eCB system were observed in fat (eCB-synthesis: 3>6>10 (DAGLα p<0.05; NAPE-PLD p = 0.05)) and liver (eCB-degradation: 3>6>10 (FAAH p<0.05; MGL p<0.01)). Concentrations of endocannabinoids AEA and 2-AG in liver and visceral fat were largely comparable, except for a borderline significance for higher AEA (liver, p = 0.049) in formerly overfed mice and, vice versa, tendencies (p<0.1) towards lower AEA (fat) and 2-AG (liver) in formerly underfed animals. In the arcuate nucleus, formerly underfed mice tended to express more eCB-receptor transcripts (CB1R p<0.05; CB2R p = 0.08) than their overfed fellows. Open-field social behavior testing revealed significant group differences, with formerly underfed mice turning out to be the most sociable animals (p<0.01). Locomotor activity did not differ. Conclusion: Our data indicate a developmental plasticity of somatic growth, behavior and parameters of the eCB system, with long-lasting impact of early postnatal nutrition. Developmental programming of the eCB system in metabolically active tissues, as shown here for liver and fat, may play a role in the formation of the adult cardiometabolic risk profile following perinatal malnutrition in humans. [ABSTRACT FROM AUTHOR]

Objectives: Radiomics utilizes quantitative image features (QIFs) to characterize tumor phenotype. In practice, radiological images are obtained from different vendors’ equipment using various imaging acquisition settings. Our objective was to assess the inter-setting agreement of QIFs computed from CT images by varying two parameters, slice thickness and reconstruction algorithm. Materials and Methods: CT images from an IRB-approved/HIPAA-compliant study assessing thirty-two lung cancer patients were included for the analysis. Each scan’s raw data were reconstructed into six imaging series using combinations of two reconstruction algorithms (Lung[L] and Standard[S]) and three slice thicknesses (1.25mm, 2.5mm and 5mm), i.e., 1.25L, 1.25S, 2.5L, 2.5S, 5L and 5S. For each imaging-setting, 89 well-defined QIFs were computed for each of the 32 tumors (one tumor per patient). The six settings led to 15 inter-setting comparisons (combinatorial pairs). To reduce QIF redundancy, hierarchical clustering was done. Concordance correlation coefficients (CCCs) were used to assess inter-setting agreement of the non-redundant feature groups. The CCC of each group was assessed by averaging CCCs of QIFs in the group. Results: Twenty-three non-redundant feature groups were created. Across all feature groups, the best inter-setting agreements (CCCs>0.8) were 1.25S vs 2.5S, 1.25L vs 2.5L, and 2.5S vs 5S; the worst (CCCs<0.51) belonged to 1.25L vs 5S and 2.5L vs 5S. Eight of the feature groups related to size, shape, and coarse texture had an average CCC>0.8 across all imaging settings. Conclusions: Varying degrees of inter-setting disagreements of QIFs exist when features are computed from CT images reconstructed using different algorithms and slice thicknesses. Our findings highlight the importance of harmonizing imaging acquisition for obtaining consistent QIFs to study tumor imaging phonotype. [ABSTRACT FROM AUTHOR]

Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications. [ABSTRACT FROM AUTHOR]

Flux balance analysis (FBA) is an increasingly useful approach for modeling the behavior of metabolic systems. However, standard FBA modeling of genetic knockouts cannot predict drug combination synergies observed between serial metabolic targets, even though such synergies give rise to some of the most widely used antibiotic treatments. Here we extend FBA modeling to simulate responses to chemical inhibitors at varying concentrations, by diverting enzymatic flux to a waste reaction. This flux diversion yields very similar qualitative predictions to prior methods for single target activity. However, we find very different predictions for combinations, where flux diversion, which mimics the kinetics of competitive metabolic inhibitors, can explain serial target synergies between metabolic enzyme inhibitors that we confirmed in Escherichia coli cultures. FBA flux diversion opens the possibility for more accurate genome-scale predictions of drug synergies, which can be used to suggest treatments for infections and other diseases. [ABSTRACT FROM AUTHOR]

The functional crosstalk between angiotensin II (Ang II) and tumor necrosis factor (TNF)-α has been shown to cause adverse left ventricular remodeling and hypertrophy in hypertension. Previous studies from our lab showed that mice lacking TNF-α (TNF-α-/-) have attenuated hypertensive response to Ang II; however, the signaling mechanisms involved are not known. In this study, we investigated the signaling pathways involved in the Ang II and TNF-α interaction. Chronic Ang II infusion (1μg/kg/min, 14 days) significantly increased cardiac collagen I, collagen III, CTGF and TGF-β mRNA and protein expression in wild-type (WT) mice, whereas these changes were decreased in TNF-α-/- mice. TNF-α-/- mice with Ang II infusion showed reduced myocardial perivascular and interstitial fibrosis compared to WT mice with Ang II infusion. In WT mice, Ang II infusion increased reactive oxygen species formation and the expression of NADPH oxidase subunits, indicating increased oxidative stress, but not in TNF-α-/- mice. In addition, treatment with etanercept (8 mg/kg, every 3 days) for two weeks blunted the Ang II-induced hypertension (133±4 vs 154±3 mmHg, p<0.05) and cardiac hypertrophy (heart weight to body weight ratio, 4.8±0.2 vs 5.6±0.3, p<0.05) in WT mice. Furthermore, Ang II-induced activation of NF-κB, p38 MAPK, and JNK were reduced in both TNF-α-/- mice and mice treated with etanercept. Together, these findings indicate that TNF-α contributes to Ang II-induced hypertension and adverse cardiac remodeling, and that these effects are associated with changes in the oxidative stress dependent MAPK/TGF-β/NF-κB pathway. These results may provide new insight into the mechanisms of Ang II and TNF-α interaction. [ABSTRACT FROM AUTHOR]

Metabolic pathways are increasingly postulated to be vital in programming cell fate, including stemness, differentiation, proliferation, and apoptosis. The commitment to meiosis is a critical fate decision for mammalian germ cells, and requires a metabolic derivative of vitamin A, retinoic acid (RA). Recent evidence showed that a pulse of RA is generated in the testis of male mice thereby triggering meiotic commitment. However, enzymes and reactions that regulate this RA pulse have yet to be identified. We developed a mouse germ cell-specific metabolic network with a curated vitamin A pathway. Using this network, we implemented flux balance analysis throughout the initial wave of spermatogenesis to elucidate important reactions and enzymes for the generation and degradation of RA. Our results indicate that primary RA sources in the germ cell include RA import from the extracellular region, release of RA from binding proteins, and metabolism of retinal to RA. Further, in silico knockouts of genes and reactions in the vitamin A pathway predict that deletion of Lipe, hormone-sensitive lipase, disrupts the RA pulse thereby causing spermatogenic defects. Examination of other metabolic pathways reveals that the citric acid cycle is the most active pathway. In addition, we discover that fatty acid synthesis/oxidation are the primary energy sources in the germ cell. In summary, this study predicts enzymes, reactions, and pathways important for germ cell commitment to meiosis. These findings enhance our understanding of the metabolic control of germ cell differentiation and will help guide future experiments to improve reproductive health. [ABSTRACT FROM AUTHOR]

Background: Sampling methods have proven to be a very efficient and intuitive method to understand properties of complicated spaces that cannot easily be computed using deterministic methods. Therefore, sampling methods became a popular tool in the applied sciences. Results: Here, we show that sampling methods are not an appropriate tool to analyze qualitative properties of complicated spaces unless RP = NP. We illustrate these results on the example of the thermodynamically feasible flux space of genome-scale metabolic networks and show that with artificial centering hit and run (ACHR) not all reactions that can have variable flux rates are sampled with variables flux rates. In particular a uniform sample of the flux space would not sample the flux variabilities completely. Conclusion: We conclude that unless theoretical convergence results exist, qualitative results obtained from sampling methods should be considered with caution and if possible double checked using a deterministic method. [ABSTRACT FROM AUTHOR]

Purpose: To determine the relationship between the subventricular zone (SVZ) and astrocytoma based on magnetic resonance imaging (MRI) and whether SVZ involvement can be used to distinguish solitary cerebral metastases (SCMs) from astrocytomas. Methods: This retrospective study involved 154 patients with solitary low-grade astrocytoma (LGA), high-grade astrocytoma (HGA), and SCM, who underwent T1-weighted imaging (T1WI), Gd-DTPA–enhanced T1WI, and T2-weighted imaging (T2WI) or fluid-attenuated inversion recovery (FLAIR) T2WI. The spatial relationship between the tumor and SVZ was classified as “involvement” or “segregation” on contrast-enhanced T1WI for enhanced tumors and T2WI/FLAIR T2WI for non-enhanced tumors. Patient-based SVZ-contact rates were compared between the LGA, HGA, and SCM groups. The frequencies of involvement of various lateral ventricle regions by astrocytoma were compared. The correlation between SVZ involvement and tumor necrosis was analyzed. Results: Patient-based SVZ-contact rates in SCM, LGA, and HGA were 24.1%, 68.8%, and 85.4%, respectively. Univariate analysis showed that the SVZ-contact rate was significantly different between SCM and astrocytoma (24.1% vs. 75.2% P < 0.001), also between LGA and HGA (68.1% vs. 85.4% P=0.037). After the tumor volume was adjusted as a covariate, SVZ-contact rates still differed between SCMs and astrocytomas (Odds ratio [OR]: 4.58, 95% Confidence interval [CI]: 1.65 to 12.8, P=0.004). Tumor volume differed between LGA and HGA (P< 0.001), and influenced the association between SVZ involvement and astrocytoma grade (P = 0.05). Among the lateral ventricle regions, the frontal horn was the most frequently involved by astrocytomas. SVZ-contact rates were higher in necrosis group compared with non-necrosis groups (83.9% vs. 50.0%, P < 0.001) among astrocytoma patients. Necrosis positively correlated with SVZ involvement in astrocytomas (rs = 0.342, P < 0.001), but did not correlate with SVZ involvement in SCMs (P = 0.193). Conclusions: Compared to SCMs, solitary cerebral astrocytomas exhibited spatial proximity to the SVZ, which might distinguish the supratentorial astrocytomas from SCMs. [ABSTRACT FROM AUTHOR]

Genome-scale metabolic models have proven highly valuable in investigating cell physiology. Recent advances include the development of methods to extract context-specific models capable of describing metabolism under more specific scenarios (e.g., cell types). Yet, none of the existing computational approaches allows for a fully automated model extraction and determination of a flux distribution independent of user-defined parameters. Here we present RegrEx, a fully automated approach that relies solely on context-specific data and ℓ1-norm regularization to extract a context-specific model and to provide a flux distribution that maximizes its correlation to data. Moreover, the publically available implementation of RegrEx was used to extract 11 context-specific human models using publicly available RNAseq expression profiles, Recon1 and also Recon2, the most recent human metabolic model. The comparison of the performance of RegrEx and its contending alternatives demonstrates that the proposed method extracts models for which both the structure, i.e., reactions included, and the flux distributions are in concordance with the employed data. These findings are supported by validation and comparison of method performance on additional data not used in context-specific model extraction. Therefore, our study sets the ground for applications of other regularization techniques in large-scale metabolic modeling. [ABSTRACT FROM AUTHOR]

Intra-cellular fluctuations, mainly triggered by gene expression, are an inevitable phenomenon observed in living cells. It influences generation of phenotypic diversity in genetically identical cells. Such variation of cellular components is beneficial in some contexts but detrimental in others. To quantify the fluctuations in a gene product, we undertake an analytical scheme for studying few naturally abundant linear as well as branched chain network motifs. We solve the Langevin equations associated with each motif under the purview of linear noise approximation and derive the expressions for Fano factor and mutual information in close analytical form. Both quantifiable expressions exclusively depend on the relaxation time (decay rate constant) and steady state population of the network components. We investigate the effect of relaxation time constraints on Fano factor and mutual information to indentify a time scale domain where a network can recognize the fluctuations associated with the input signal more reliably. We also show how input population affects both quantities. We extend our calculation to long chain linear motif and show that with increasing chain length, the Fano factor value increases but the mutual information processing capability decreases. In this type of motif, the intermediate components act as a noise filter that tune up input fluctuations and maintain optimum fluctuations in the output. For branched chain motifs, both quantities vary within a large scale due to their network architecture and facilitate survival of living system in diverse environmental conditions. [ABSTRACT FROM AUTHOR]

Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC≥0.897; dynamic range, DR≥0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma. [ABSTRACT FROM AUTHOR]

Recent progresses in data-driven analysis methods, including network-based approaches, are revolutionizing many classical disciplines. These techniques can also be applied to food and nutrition, which must be studied to design healthy diets. Using nutritional information from over 1,000 raw foods, we systematically evaluated the nutrient composition of each food in regards to satisfying daily nutritional requirements. The nutrient balance of a food was quantified and termed nutritional fitness; this measure was based on the food’s frequency of occurrence in nutritionally adequate food combinations. Nutritional fitness offers a way to prioritize recommendable foods within a global network of foods, in which foods are connected based on the similarities of their nutrient compositions. We identified a number of key nutrients, such as choline and α-linolenic acid, whose levels in foods can critically affect the nutritional fitness of the foods. Analogously, pairs of nutrients can have the same effect. In fact, two nutrients can synergistically affect the nutritional fitness, although the individual nutrients alone may not have an impact. This result, involving the tendency among nutrients to exhibit correlations in their abundances across foods, implies a hidden layer of complexity when exploring for foods whose balance of nutrients within pairs holistically helps meet nutritional requirements. Interestingly, foods with high nutritional fitness successfully maintain this nutrient balance. This effect expands our scope to a diverse repertoire of nutrient-nutrient correlations, which are integrated under a common network framework that yields unexpected yet coherent associations between nutrients. Our nutrient-profiling approach combined with a network-based analysis provides a more unbiased, global view of the relationships between foods and nutrients, and can be extended towards nutritional policies, food marketing, and personalized nutrition. [ABSTRACT FROM AUTHOR]

Despite the substantial knowledge on the antidiabetic, antiobesity and antihypertensive actions of tungstate, information on its primary target/s is scarce. Tungstate activates both the ERK1/2 pathway and the vascular voltage- and Ca2+-dependent large-conductance BKαβ1 potassium channel, which modulates vascular smooth muscle cell (VSMC) proliferation and function, respectively. Here, we have assessed the possible involvement of BKαβ1 channels in the tungstate-induced ERK phosphorylation and its relevance for VSMC proliferation. Western blot analysis in HEK cell lines showed that expression of vascular BKαβ1 channels potentiates the tungstate-induced ERK1/2 phosphorylation in a Gi/o protein-dependent manner. Tungstate activated BKαβ1 channels upstream of G proteins as channel activation was not altered by the inhibition of G proteins with GDPβS or pertussis toxin. Moreover, analysis of Gi/o protein activation measuring the FRET among heterologously expressed Gi protein subunits suggested that tungstate-targeting of BKαβ1 channels promotes G protein activation. Single channel recordings on VSMCs from wild-type and β1-knockout mice indicated that the presence of the regulatory β1 subunit was essential for the tungstate-mediated activation of BK channels in VSMCs. Moreover, the specific BK channel blocker iberiotoxin lowered tungstate-induced ERK phosphorylation by 55% and partially reverted (by 51%) the tungstate-produced reduction of platelet-derived growth factor (PDGF)-induced proliferation in human VSMCs. Our observations indicate that tungstate-targeting of BKαβ1 channels promotes activation of PTX-sensitive Gi proteins to enhance the tungstate-induced phosphorylation of ERK, and inhibits PDGF-stimulated cell proliferation in human vascular smooth muscle. [ABSTRACT FROM AUTHOR]

Metaboloepigenetics is a newly coined term in biological sciences that investigates the crosstalk between epigenetic modifications and metabolism. The reciprocal relation between biochemical transformations and gene expression regulation has been experimentally demonstrated in cancers and metabolic syndromes. In this study, we explored the metabolism-histone modifications crosstalk by topological analysis and constraint-based modeling approaches in the budding yeast. We constructed nine models through the integration of gene expression data of four mutated histone tails into a genome-scale metabolic model of yeast. Accordingly, we defined the centrality indices of the lowly expressed enzymes in the undirected enzyme-centric network of yeast by CytoHubba plug-in in Cytoscape. To determine the global effects of histone modifications on the yeast metabolism, the growth rate and the range of possible flux values of reactions, we used constraint-based modeling approach. Centrality analysis shows that the lowly expressed enzymes could affect and control the yeast metabolic network. Besides, constraint-based modeling results are in a good agreement with the experimental findings, confirming that the mutations in histone tails lead to non-lethal alterations in the yeast, but have diverse effects on the growth rate and reveal the functional redundancy. [ABSTRACT FROM AUTHOR]

Background: Non-alcoholic fatty liver disease (NAFLD) has a broad spectrum of disease states ranging from mild steatosis characterized by an abnormal retention of lipids within liver cells to steatohepatitis (NASH) showing fat accumulation, inflammation, ballooning and degradation of hepatocytes, and fibrosis. Ultimately, steatohepatitis can result in liver cirrhosis and hepatocellular carcinoma. Methodology and Results: In this study we have analyzed three different mouse strains, A/J, C57BL/6J, and PWD/PhJ, that show different degrees of steatohepatitis when administered a 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) containing diet. RNA-Seq gene expression analysis, protein analysis and metabolic profiling were applied to identify differentially expressed genes/proteins and perturbed metabolite levels of mouse liver samples upon DDC-treatment. Pathway analysis revealed alteration of arachidonic acid (AA) and S-adenosylmethionine (SAMe) metabolism upon other pathways. To understand metabolic changes of arachidonic acid metabolism in the light of disease expression profiles a kinetic model of this pathway was developed and optimized according to metabolite levels. Subsequently, the model was used to study in silico effects of potential drug targets for steatohepatitis. Conclusions: We identified AA/eicosanoid metabolism as highly perturbed in DDC-induced mice using a combination of an experimental and in silico approach. Our analysis of the AA/eicosanoid metabolic pathway suggests that 5-hydroxyeicosatetraenoic acid (5-HETE), 15-hydroxyeicosatetraenoic acid (15-HETE) and prostaglandin D2 (PGD2) are perturbed in DDC mice. We further demonstrate that a dynamic model can be used for qualitative prediction of metabolic changes based on transcriptomics data in a disease-related context. Furthermore, SAMe metabolism was identified as being perturbed due to DDC treatment. Several genes as well as some metabolites of this module show differences between A/J and C57BL/6J on the one hand and PWD/PhJ on the other. [ABSTRACT FROM AUTHOR]

Background: Metabolomics has attracted the interest of the medical community for its potential in predicting early derangements from a healthy to a diseased metabolic phenotype. One key issue is the diversity observed in metabolic profiles of different healthy individuals, commonly attributed to the variation of intrinsic (such as (epi)genetic variation, gut microbiota, etc.) and extrinsic factors (such as dietary habits, life-style and environmental conditions). Understanding the relative contributions of these factors is essential to establish the robustness of the healthy individual metabolic phenotype. Methods: To assess the relative contribution of intrinsic and extrinsic factors we compared multilevel analysis results obtained from subjects of Homo sapiens and Macaca mulatta, the latter kept in a controlled environment with a standardized diet by making use of previously published data and results. Results: We observed similarities for the two species and found the diversity of urinary metabolic phenotypes as identified by nuclear magnetic resonance (NMR) spectroscopy could be ascribed to the complex interplay of intrinsic factors and, to a lesser extent, of extrinsic factors in particular minimizing the role played by diet in shaping the metabolic phenotype. Moreover, we show that despite the standardization of diet as the most relevant extrinsic factor, a clear individual and discriminative metabolic fingerprint also exists for monkeys. We investigate the metabolic phenotype both at the static (i.e., at the level of the average metabolite concentration) and at the dynamic level (i.e., concerning their variation over time), and we show that these two components sum up to the overall phenotype with different relative contributions of about 1/4 and 3/4, respectively, for both species. Finally, we show that the great degree diversity observed in the urinary metabolic phenotype of both species can be attributed to differences in both the static and dynamic part of their phenotype. [ABSTRACT FROM AUTHOR]

Accumulating evidence links numerous abnormalities in cerebral metabolism with the progression of Alzheimer's disease (AD), beginning in its early stages. Here, we integrate transcriptomic data from AD patients with a genome-scale computational human metabolic model to characterize the altered metabolism in AD, and employ state-of-the-art metabolic modelling methods to predict metabolic biomarkers and drug targets in AD. The metabolic descriptions derived are first tested and validated on a large scale versus existing AD proteomics and metabolomics data. Our analysis shows a significant decrease in the activity of several key metabolic pathways, including the carnitine shuttle, folate metabolism and mitochondrial transport. We predict several metabolic biomarkers of AD progression in the blood and the CSF, including succinate and prostaglandin D2. Vitamin D and steroid metabolism pathways are enriched with predicted drug targets that could mitigate the metabolic alterations observed. Taken together, this study provides the first network wide view of the metabolic alterations associated with AD progression. Most importantly, it offers a cohort of new metabolic leads for the diagnosis of AD and its treatment. [ABSTRACT FROM AUTHOR]

Identification of key metabolites for complex diseases is a challenging task in today's medicine and biology. A special disease is usually caused by the alteration of a series of functional related metabolites having a global influence on the metabolic network. Moreover, the metabolites in the same metabolic pathway are often associated with the same or similar disease. Based on these functional relationships between metabolites in the context of metabolic pathways, we here presented a pathway-based random walk method called PROFANCY for prioritization of candidate disease metabolites. Our strategy not only takes advantage of the global functional relationships between metabolites but also sufficiently exploits the functionally modular nature of metabolic networks. Our approach proved successful in prioritizing known metabolites for 71 diseases with an AUC value of 0.895. We also assessed the performance of PROFANCY on 16 disease classes and found that 4 classes achieved an AUC value over 0.95. To investigate the robustness of the PROFANCY, we repeated all the analyses in two metabolic networks and obtained similar results. Then we applied our approach to Alzheimer's disease (AD) and found that a top ranked candidate was potentially related to AD but had not been reported previously. Furthermore, our method was applicable to prioritize the metabolites from metabolomic profiles of prostate cancer. The PROFANCY could identify prostate cancer related-metabolites that are supported by literatures but not considered to be significantly differential by traditional differential analysis. We also developed a freely accessible web-based and R-based tool at http://bioinfo.hrbmu.edu.cn/PROFANCY. [ABSTRACT FROM AUTHOR]

Metabolism expresses the phenotype of living cells and understanding it is crucial for different applications in biotechnology and health. With the increasing availability of metabolomic, proteomic and, to a larger extent, transcriptomic data, the elucidation of specific metabolic properties in different scenarios and cell types is a key topic in systems biology. Despite the potential of the elementary flux mode (EFM) concept for this purpose, its use has been limited so far, mainly because their computation has been infeasible for genome-scale metabolic networks. In a recent work, we determined a subset of EFMs in human metabolism and proposed a new protocol to integrate gene expression data, spotting key 'characteristic EFMs' in different scenarios. Our approach was successfully applied to identify metabolic differences among several human healthy tissues. In this article, we evaluated the performance of our approach in clinically interesting situation. In particular, we identified key EFMs and metabolites in adenocarcinoma and squamous-cell carcinoma subtypes of non-small cell lung cancers. Results are consistent with previous knowledge of these major subtypes of lung cancer in the medical literature. Therefore, this work constitutes the starting point to establish a new methodology that could lead to distinguish key metabolic processes among different clinical outcomes. [ABSTRACT FROM AUTHOR]

Computational models using metabolic reconstructions for in silico simulation of metabolic disorders such as type 2 diabetes mellitus (T2DM) can provide a better understanding of disease pathophysiology and avoid high experimentation costs. There is a limited amount of computational work, using metabolic reconstructions, performed in this field for the better understanding of T2DM. In this study, a new algorithm for generating tissue-specific metabolic models is presented, along with the resulting multi-confidence level (MCL) multi-tissue model. The effect of T2DM on liver, muscle, and fat in MKR mice was first studied by microarray analysis and subsequently the changes in gene expression of frank T2DM MKR mice versus healthy mice were applied to the multi-tissue model to test the effect. Using the first multi-tissue genome-scale model of all metabolic pathways in T2DM, we found out that branched-chain amino acids' degradation and fatty acids oxidation pathway is downregulated in T2DM MKR mice. Microarray data showed low expression of genes in MKR mice versus healthy mice in the degradation of branched-chain amino acids and fatty-acid oxidation pathways. In addition, the flux balance analysis using the MCL multi-tissue model showed that the degradation pathways of branched-chain amino acid and fatty acid oxidation were significantly downregulated in MKR mice versus healthy mice. Validation of the model was performed using data derived from the literature regarding T2DM. Microarray data was used in conjunction with the model to predict fluxes of various other metabolic pathways in the T2DM mouse model and alterations in a number of pathways were detected. The Type 2 Diabetes MCL multi-tissue model may explain the high level of branched-chain amino acids and free fatty acids in plasma of Type 2 Diabetic subjects from a metabolic fluxes perspective. [ABSTRACT FROM AUTHOR]

Bacterial infections remain a threat to human and animal health worldwide, and there is an urgent need to find novel targets for intervention. In the current study we used a computer model of the metabolic network of Salmonella enterica serovar Typhimurium and identified pairs of reactions (cut sets) predicted to be required for growth in vivo. We termed such cut sets synthetic auxotrophic pairs. We tested whether these would reveal possible combined targets for new antibiotics by analyzing the performance of selected single and double mutants in systemic mouse infections. One hundred and two cut sets were identified. Sixty-three of these included only pathways encoded by fully annotated genes, and from this sub-set we selected five cut sets involved in amino acid or polyamine biosynthesis. One cut set (asnA/asnB) demonstrated redundancy in vitro and in vivo and showed that asparagine is essential for S. Typhimurium during infection. trpB/trpA as well as single mutants were attenuated for growth in vitro, while only the double mutant was a cut set in vivo, underlining previous observations that tryptophan is essential for successful outcome of infection. speB/speF,speC was not affected in vitro but was attenuated during infection showing that polyamines are essential for virulence apparently in a growth independent manner. The serA/glyA cut-set was found to be growth attenuated as predicted by the model. However, not only the double mutant, but also the glyA mutant, were found to be attenuated for virulence. This adds glycine production or conversion of glycine to THF to the list of essential reactions during infection. One pair (thrC/kbl) showed true redundancy in vitro but not in vivo demonstrating that threonine is available to the bacterium during infection. These data add to the existing knowledge of available nutrients in the intra-host environment, and have identified possible new targets for antibiotics. [ABSTRACT FROM AUTHOR]

Dynamics of body weight and food intake can be studied by temporally perturbing food availability. This perturbation can be obtained by modifying the amount of available food over time while keeping the overall food quantity constant. To describe food intake dynamics, we developed a mathematical model that describes body weight, fat mass, fat-free mass, energy expenditure and food intake dynamics in rats. In addition, the model considers regulation of food intake by leptin, ghrelin and glucose. We tested our model on rats experiencing temporally variable food availability. Our model is able to predict body weight and food intake variations by taking into account energy expenditure dynamics based on a memory of the previous food intake. This model allowed us to estimate this memory lag to approximately 8 days. It also explains how important variations in food availability during periods longer than these 8 days can induce body weight gains. [ABSTRACT FROM AUTHOR]

Growth rate has long been considered one of the most valuable phenotypes that can be measured in cells. Aside from being highly accessible and informative in laboratory cultures, maximal growth rate is often a prime determinant of cellular fitness, and predicting phenotypes that underlie fitness is key to both understanding and manipulating life. Despite this, current methods for predicting microbial fitness typically focus on yields [e.g., predictions of biomass yield using GEnome-scale metabolic Models (GEMs)] or notably require many empirical kinetic constants or substrate uptake rates, which render these methods ineffective in cases where fitness derives most directly from growth rate. Here we present a new method for predicting cellular growth rate, termed SUMEX, which does not require any empirical variables apart from a metabolic network (i.e., a GEM) and the growth medium. SUMEX is calculated by maximizing the SUM of molar EXchange fluxes (hence SUMEX) in a genome-scale metabolic model. SUMEX successfully predicts relative microbial growth rates across species, environments, and genetic conditions, outperforming traditional cellular objectives (most notably, the convention assuming biomass maximization). The success of SUMEX suggests that the ability of a cell to catabolize substrates and produce a strong proton gradient enables fast cell growth. Easily applicable heuristics for predicting growth rate, such as what we demonstrate with SUMEX, may contribute to numerous medical and biotechnological goals, ranging from the engineering of faster-growing industrial strains, modeling of mixed ecological communities, and the inhibition of cancer growth. [ABSTRACT FROM AUTHOR]

Metabolic networks attempt to describe the complete suite of biochemical reactions available to an organism. One notable feature of these networks in mammals is the large number of distinct proteins that catalyze the same reaction. While the existence of these isoenzymes has long been known, their evolutionary significance is still unclear. Using a phylogenetically-aware comparative genomics approach, we infer enzyme orthology networks for sixteen mammals as well as for their common ancestors. We find that the pattern of isoenzymes copy-number alterations (CNAs) in these networks is suggestive of natural selection acting on the retention of certain gene duplications. When further analyzing these data with a machine-learning approach, we found that that the pattern of CNAs is also predictive of several important phenotypic traits, including milk composition and geographic range. Integrating tools from network analyses, phylogenetics and comparative genomics both allows the prediction of phenotypes from genetic data and represents a means of unifying distinct biological disciplines. [ABSTRACT FROM AUTHOR]

Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine. [ABSTRACT FROM AUTHOR]