System model network for adipose tissue signatures related to weight changes in response to calorie restriction and subsequent weight maintenance

Discordant gene expression in subcutaneous adipose and skeletal muscle tissues in response to exercise training

Exercise has different effects on different tissues in the body, the sum of which may determine the response to exercise and the health benefits. In the present study, we aimed to investigate whether physical training regulates transcriptional network communites common to both skeletal muscle (SM) and subcutaneous adipose tissue (SAT). Eight such shared transcriptional communities were found in both tissues. Eighteen young overweight adults voluntarily participated in 7 weeks of combined strength and endurance training (five training sessions per week). Biopsies were taken from SM and SAT before and after training. Five of the network communities were regulated by training in SM but showed no change in SAT. One community involved in insulin- AMPK signaling and glucose utilization was upregulated in SM but downregulated in SAT. This diverging exercise regulation was confirmed in two independent studies and was also associated with BMI and diabetes in an independent cohort. Thus, the current finding is consistent with the differential responses of different tissues and suggests that body composition may influence the observed individual whole-body metabolic response to exercise training and help explain the observed attenuated whole-body insulin sensitivity after exercise training, even if it has significant effects on the exercising muscle.

Network Analyses Reveal Negative Link Between Changes in Adipose Tissue GDF15 and BMI During Dietary-induced Weight Loss

CONTEXT

Adipose tissue (AT) transcriptome studies provide holistic pictures of adaptation to weight and related bioclinical settings changes.

OBJECTIVE

To implement AT gene expression profiling and investigate the link between changes in bioclinical parameters and AT gene expression during 3 steps of a 2-phase dietary intervention (DI).

METHODS

AT transcriptome profiling was obtained from sequencing 1051 samples, corresponding to 556 distinct individuals enrolled in a weight loss intervention (8-week low-calorie diet (LCD) at 800 kcal/day) followed with a 6-month ad libitum randomized DI. Transcriptome profiles obtained with QuantSeq sequencing were benchmarked against Illumina RNAseq. Reverse transcription quantitative polymerase chain reaction was used to further confirm associations. Cell specificity was assessed using freshly isolated cells and THP-1 cell line.

RESULTS

During LCD, 5 modules were found, of which 3 included at least 1 bioclinical variable. Change in body mass index (BMI) connected with changes in mRNA level of genes with inflammatory response signature. In this module, change in BMI was negatively associated with changes in expression of genes encoding secreted protein (GDF15, CCL3, and SPP1). Through all phases of the DI, change in GDF15 was connected to changes in SPP1, CCL3, LIPA and CD68. Further characterization showed that these genes were specific to macrophages (with LIPA, CD68 and GDF15 expressed in anti-inflammatory macrophages) and GDF15 also expressed in preadipocytes.

CONCLUSION

Network analyses identified a novel AT feature with GDF15 upregulated with calorie restriction induced weight loss, concomitantly to macrophage markers. In AT, GDF15 was expressed in preadipocytes and macrophages where it was a hallmark of anti-inflammatory cells.

The pentose phosphate pathway constitutes a major metabolic hub in pathogenic Francisella

Metabolic pathways are now considered as intrinsic virulence attributes of pathogenic bacteria and thus represent potential targets for antibacterial strategies. Here we focused on the role of the pentose phosphate pathway (PPP) and its connections with other metabolic pathways in the pathophysiology of Francisella novicida. The involvement of the PPP in the intracellular life cycle of Francisella was first demonstrated by studying PPP inactivating mutants. Indeed, we observed that inactivation of the tktA, rpiA or rpe genes severely impaired intramacrophage multiplication during the first 24 hours. However, time-lapse video microscopy demonstrated that rpiA and rpe mutants were able to resume late intracellular multiplication. To better understand the links between PPP and other metabolic networks in the bacterium, we also performed an extensive proteo-metabolomic analysis of these mutants. We show that the PPP constitutes a major bacterial metabolic hub with multiple connections to glycolysis, the tricarboxylic acid cycle and other pathways, such as fatty acid degradation and sulfur metabolism. Altogether our study highlights how PPP plays a key role in the pathogenesis and growth of Francisella in its intracellular niche.

Microscopic image-based covariation network analysis for actin scaffold-modified insulin signaling

To infer a "live" protein network in single cells, we developed a novel Protein Localization and Modification-based Covariation Network (PLOM-CON) analysis method using a large set of quantitative data on the abundance (quantity), post-translational modification state (quality), and localization/morphological information of target proteins from microscope immunostained images. The generated network exhibited synchronized time-dependent behaviors of the target proteins to visualize how a live protein network develops or changes in cells under specific experimental conditions. As a proof of concept for PLOM-CON analysis, we applied this method to elucidate the role of actin scaffolds, in which actin fibers and signaling molecules accumulate and form membrane-associated protein condensates, in insulin signaling in rat hepatoma cells. We found that the actin scaffold in cells may function as a platform for glycogenesis and protein synthesis upon insulin stimulation.

Metabolic response of dolphins to short-term fasting reveals physiological changes that differ from the traditional fasting model

Bottlenose dolphins (Tursiops truncatus) typically feed on prey that are high in lipid and protein content and nearly devoid of carbohydrate, a dietary feature shared with other marine mammals. However, unlike fasted-adapted marine mammals that predictably incorporate fasting into their life history, dolphins feed intermittently throughout the day and are not believed to be fasting-adapted. To assess whether the physiological response to fasting in the dolphin shares features with or distinguishes them from those of fasting-adapted marine mammals, the plasma metabolomes of eight bottlenose dolphins were compared between post-absorptive and 24-h fasted states. Increases in most identified free fatty acids and lipid metabolites and reductions in most amino acids and their metabolites were consistent with the upregulation of lipolysis and lipid oxidation and the downregulation of protein catabolism and synthesis. Consistent with a previously hypothesized diabetic-like fasting state, fasting was associated with elevated glucose and patterns of certain metabolites (e.g. citrate, cis-aconitate, myristoleic acid) indicative of lipid synthesis and glucose cycling to protect endogenous glucose from oxidative disposal. Pathway analysis predicted an upregulation of cytokines, decreased cell growth and increased apoptosis including apoptosis of insulin-secreting β-cells. Metabolomic conditional mutual information networks were estimated for the post-absorptive and fasted states and 'topological modules' were estimated for each using the eigenvector approach to modularity network division. A dynamic network marker indicative of a physiological shift toward a negative energy state was subsequently identified that has the potential conservation application of assessing energy state balance in at-risk wild dolphins.

Serum Metabolic Profiles of Chinese Women With Perimenopausal Obesity Explored by the Untargeted Metabolomics Approach

By far, no study has focused on observing the metabolomic profiles in perimenopause-related obesity. This study attempted to identify the metabolic characteristics of subjects with perimenopause obesity (PO). Thirty-nine perimenopausal Chinese women, 21 with PO and 18 without obesity (PN), were recruited in this study. A conventional ultra-high-performance liquid chromatography-quadrupole time-of-flight/mass spectrometry (UHPLC-QTOF/MS) followed by principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used as untargeted metabolomics approaches to explore the serum metabolic profiles. Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaboAnalyst were used to identify the related metabolic pathways. A total of 46 differential metabolites, along with seven metabolic pathways relevant to PO were identified, which belonged to lipid, amino acids, carbohydrates, and organic acids. As for amino acids, we found a significant increase in l-arginine and d-ornithine in the positive ion (POS) mode and l-leucine, l-valine, l-tyrosine, and N-acetyl-l-tyrosine in the negative ion (NEG) mode and a significant decrease in l-proline in the POS mode of the PO group. We also found phosphatidylcholine (PC) (16:0/16:0), palmitic acid, and myristic acid, which are associated with the significant upregulation of lipid metabolism. Moreover, the serum indole lactic acid and indoleacetic acid were upregulated in the NEG mode. With respect to the metabolic pathways, the d-arginine and d-ornithine metabolisms and the arginine and proline metabolism pathways in POS mode were the most dominant PO-related pathways. The changes of metabolisms of lipid, amino acids, and indoleacetic acid provided a pathophysiological scenario for Chinese women with PO. We believe that the findings of this study are helpful for clinicians to take measures to prevent the women with PO from developing severe incurable obesity-related complications, such as cardiovascular disease and stroke.

Sample Preparation Methods for Lipidomics Approaches Used in Studies of Obesity

Obesity is associated with alterations in the composition and amounts of lipids. Lipids have over 1.7 million representatives. Most lipid groups differ in composition, properties and chemical structure. These small molecules control various metabolic pathways, determine the metabolism of other compounds and are substrates for the syntheses of different derivatives. Recently, lipidomics has become an important branch of medical/clinical sciences similar to proteomics and genomics. Due to the much higher lipid accumulation in obese patients and many alterations in the compositions of various groups of lipids, the methods used for sample preparations for lipidomic studies of samples from obese subjects sometimes have to be modified. Appropriate sample preparation methods allow for the identification of a wide range of analytes by advanced analytical methods, including mass spectrometry. This is especially the case in studies with obese subjects, as the amounts of some lipids are much higher, others are present in trace amounts, and obese subjects have some specific alterations of the lipid profile. As a result, it is best to use a method previously tested on samples from obese subjects. However, most of these methods can be also used in healthy, nonobese subjects or patients with other dyslipidemias. This review is an overview of sample preparation methods for analysis as one of the major critical steps in the overall analytical procedure.

Omics Application in Animal Science-A Special Emphasis on Stress Response and Damaging Behaviour in Pigs

Increasing stress resilience of livestock is important for ethical and profitable meat and dairy production. Susceptibility to stress can entail damaging behaviours, a common problem in pig production. Breeding animals with increased stress resilience is difficult for various reasons. First, studies on neuroendocrine and behavioural stress responses in farm animals are scarce, as it is difficult to record adequate phenotypes under field conditions. Second, damaging behaviours and stress susceptibility are complex traits, and their biology is not yet well understood. Dissecting complex traits into biologically better defined, heritable and easily measurable proxy traits and developing biomarkers will facilitate recording these traits in large numbers. High-throughput molecular technologies (“omics”) study the entirety of molecules and their interactions in a single analysis step. They can help to decipher the contributions of different physiological systems and identify candidate molecules that are representative of different physiological pathways. Here, we provide a general overview of different omics approaches and we give examples of how these techniques could be applied to discover biomarkers. We discuss the genetic dissection of the stress response by different omics techniques and we provide examples and outline potential applications of omics tools to understand and prevent outbreaks of damaging behaviours.

Systems Biology Will Direct Vascular-Targeted Therapy for Obesity

Healthy adipose tissue expansion and metabolism during weight gain require coordinated angiogenesis and lymphangiogenesis. These vascular growth processes rely on the vascular endothelial growth factor (VEGF) family of ligands and receptors (VEGFRs). Several studies have shown that controlling vascular growth by regulating VEGF:VEGFR signaling can be beneficial for treating obesity; however, dysregulated angiogenesis and lymphangiogenesis are associated with several chronic tissue inflammation symptoms, including hypoxia, immune cell accumulation, and fibrosis, leading to obesity-related metabolic disorders. An ideal obesity treatment should minimize adipose tissue expansion and the advent of adverse metabolic consequences, which could be achieved by normalizing VEGF:VEGFR signaling. Toward this goal, a systematic investigation of the interdependency of vascular and metabolic systems in obesity and tools to predict personalized treatment ranges are necessary to improve patient outcomes through vascular-targeted therapies. Systems biology can identify the critical VEGF:VEGFR signaling mechanisms that can be targeted to regress adipose tissue expansion and can predict the metabolic consequences of different vascular-targeted approaches. Establishing a predictive, biologically faithful platform requires appropriate computational models and quantitative tissue-specific data. Here, we discuss the involvement of VEGF:VEGFR signaling in angiogenesis, lymphangiogenesis, adipogenesis, and macrophage specification – key mechanisms that regulate adipose tissue expansion and metabolism. We then provide useful computational approaches for simulating these mechanisms, and detail quantitative techniques for acquiring tissue-specific parameters. Systems biology, through computational models and quantitative data, will enable an accurate representation of obese adipose tissue that can be used to direct the development of vascular-targeted therapies for obesity and associated metabolic disorders.

Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools

Gaussian Graphical Models (GGMs) are tools to infer dependencies between biological variables. Popular applications are the reconstruction of gene, protein, and metabolite association networks. GGMs are an exploratory research tool that can be useful to discover interesting relations between genes (functional clusters) or to identify therapeutically interesting genes, but do not necessarily infer a network in the mechanistic sense. Although GGMs are well investigated from a theoretical and applied perspective, important extensions are not well known within the biological community. GGMs assume, for instance, multivariate normal distributed data. If this assumption is violated Mixed Graphical Models (MGMs) can be the better choice. In this review, we provide the theoretical foundations of GGMs, present extensions such as MGMs or multi-class GGMs, and illustrate how those methods can provide insight in biological mechanisms. We summarize several applications and present user-friendly estimation software. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Whole transcriptome analysis and validation of metabolic pathways in subcutaneous adipose tissues during FGF21-induced weight loss in non-human primates

Fibroblast growth factor 21 (FGF21) induces weight loss in mouse, monkey, and human studies. In mice, FGF21 is thought to cause weight loss by stimulating thermogenesis, but whether FGF21 increases energy expenditure (EE) in primates is unclear. Here, we explore the transcriptional response and gene networks active in adipose tissue of rhesus macaques following FGF21-induced weight loss. Genes related to thermogenesis responded inconsistently to FGF21 treatment and weight loss. However, expression of gene modules involved in triglyceride (TG) synthesis and adipogenesis decreased, and this was associated with greater weight loss. Conversely, expression of innate immune cell markers was increased post-treatment and was associated with greater weight loss. A lipogenesis gene module associated with weight loss was evaluated by testing the function of member genes in mice. Overexpression of NRG4 reduced weight gain in diet-induced obese mice, while overexpression of ANGPTL8 resulted in elevated TG levels in lean mice. These observations provide evidence for a shifting balance of lipid storage and metabolism due to FGF21-induced weight loss in the non-human primate model, and do not fully recapitulate increased EE seen in rodent and in vitro studies. These discrepancies may reflect inter-species differences or complex interplay of FGF21 activity and counter-regulatory mechanisms.

Diabetes-induced Proteome Changes Throughout Development

BACKGROUND

Diabetes Mellitus (DM) is a multisystemic disease involving the homeostasis of insulin secretion by the pancreatic islet beta cells (β-cells). It is associated with hypertension, renal disease, and arterial and arteriolar vascular diseases.

DISCUSSION

The classification of diabetes is identified as type 1 (gene linked β-cell destruction in childhood) and type 2 (late onset associated with β-cell overload and insulin resistance in peripheral tissues. Type 1 diabetes is characterized by insulin deficiency, type 2 diabetes by both insulin deficiency and insulin resistance. The former is a genetically programmed loss of insulin secretion whereas the latter constitutes a disruption of the homeostatic relationship between the opposing activity of β- cell insulin and alpha cell (α-cell) glucagon of the Islets of Langerhans. The condition could also occur in pregnancy, as a prenatal occurring event, possibly triggered by the hormonal changes of pregnancy combined with β-cell overload. This review discusses the molecular basis of the biomolecular changes that occur with respect to glucose homeostasis and related diseases in DM. The underlying link between pancreatic, renal, and microvascular diseases in DM is based on oxidative stress and the Unfolded Protein Response (UPR).

CONCLUSION

Studying proteome changes in diabetes can deepen our understanding of the biomolecular basis of disease and help us acquire more efficient therapies.

Network Analyses and Data Integration of Proteomics and Metabolomics From Leaves of Two Contrasting Varieties of Sugarcane in Response to Drought

Uncovering the molecular mechanisms involved in the responses of crops to drought is crucial to understand and enhance drought tolerance mechanisms. Sugarcane (Saccharum spp.) is an important commercial crop cultivated mainly in tropical and subtropical areas for sucrose and ethanol production. Usually, drought tolerance has been investigated by single omics analysis (e.g. global transcripts identification). Here we combine label-free quantitative proteomics and metabolomics data (GC-TOF-MS), using a network-based approach, to understand how two contrasting commercial varieties of sugarcane, CTC15 (tolerant) and SP90-3414 (susceptible), adjust their leaf metabolism in response to drought. To this aim, we propose the utilization of regularized canonical correlation analysis (rCCA), which is a modification of classical CCA, and explores the linear relationships between two datasets of quantitative variables from the same experimental units, with a threshold set to 0.99. Light curves revealed that after 4 days of drought, the susceptible variety had its photosynthetic capacity already significantly reduced, while the tolerant variety did not show major reduction. Upon 12 days of drought, photosynthesis in the susceptible plants was completely reduced, while the tolerant variety was at a third of its rate under control conditions. Network analysis of proteins and metabolites revealed that different biological process had a stronger impact in each variety (e.g. translation in CTC15, generation of precursor metabolites, response to stress and energy in SP90-3414). Our results provide a reference data set and demonstrate that rCCA can be a powerful tool to infer experimentally metabolite-protein or protein-metabolite associations to understand plant biology.

JacLy: a Jacobian-based method for the inference of metabolic interactions from the covariance of steady-state metabolome data

Reverse engineering metabolome data to infer metabolic interactions is a challenging research topic. Here we introduce JacLy, a Jacobian-based method to infer metabolic interactions of small networks (<20 metabolites) from the covariance of steady-state metabolome data. The approach was applied to two different in silico small-scale metabolome datasets. The power of JacLy lies on the use of steady-state metabolome data to predict the Jacobian matrix of the system, which is a source of information on structure and dynamic characteristics of the system. Besides its advantage of inferring directed interactions, its superiority over correlation-based network inference was especially clear in terms of the required number of replicates and the effect of the use of priori knowledge in the inference. Additionally, we showed the use of standard deviation of the replicate data as a suitable approximation for the magnitudes of metabolite fluctuations inherent in the system.

A new approach of gene co-expression network inference reveals significant biological processes involved in porcine muscle development in late gestation

The integration of genetic information in the cellular and nuclear environments is crucial for deciphering the way in which the genome functions under different physiological conditions. Experimental techniques of 3D nuclear mapping, a high-flow approach such as transcriptomic data analyses, and statistical methods for the development of co-expressed gene networks, can be combined to develop an integrated approach for depicting the regulation of gene expression. Our work focused more specifically on the mechanisms involved in the transcriptional regulation of genes expressed in muscle during late foetal development in pig. The data generated by a transcriptomic analysis carried out on muscle of foetuses from two extreme genetic lines for birth mortality are used to construct networks of differentially expressed and co-regulated genes. We developed an innovative co-expression networking approach coupling, by means of an iterative process, a new statistical method for graph inference with data of gene spatial co-localization (3D DNA FISH) to construct a robust network grouping co-expressed genes. This enabled us to highlight relevant biological processes related to foetal muscle maturity and to discover unexpected gene associations between IGF2, MYH3 and DLK1/MEG3 in the nuclear space, genes that are up-regulated at this stage of muscle development.

Integrated Analysis of Proteomic and Transcriptomic Data Highlights Late Fetal Muscle Maturation Process

In pigs, the perinatal period is the most critical time for survival. Piglet maturation, which occurs at the end of gestation, is an important determinant of early survival. Skeletal muscle plays a key role in adaptation to extra-uterine life, e.g. motor function and thermoregulation. Progeny from two breeds with extreme neonatal mortality rates were analyzed at 90 and 110 days of gestation (dg). The Large White breed is a highly selected breed for lean growth and exhibits a high rate of neonatal mortality, whereas the Meishan breed is fatter and more robust and has a low neonatal mortality. Our aim was to identify molecular signatures underlying late fetal longissimus muscle development. First, integrated analysis was used to explore relationships between co-expression network models built from a proteomic data set (bi-dimensional electrophoresis) and biological phenotypes. Second, correlations with a transcriptomic data set (microarrays) were investigated to combine different layers of expression with a focus on transcriptional regulation. Muscle glycogen content and myosin heavy chain polymorphism were good descriptors of muscle maturity and were used for further data integration analysis. Using 89 identified unique proteins, network inference, correlation with biological phenotypes and functional enrichment revealed that mitochondrial oxidative metabolism was a key determinant of neonatal muscle maturity. Some proteins, including ATP5A1 and CKMT2, were important nodes in the network related to muscle metabolism. Transcriptomic data suggest that overexpression of mitochondrial PCK2 was involved in the greater glycogen content of Meishan fetuses at 110 dg. GPD1, an enzyme involved in the mitochondrial oxidation of cytosolic NADH, was overexpressed in Meishan. Thirty-one proteins exhibited a positive correlation between mRNA and protein levels in both extreme fetal genotypes, suggesting transcriptional regulation. Gene ontology enrichment and Ingenuity analyses identified PPARGC1A and ESR1 as possible transcriptional factors positively involved in late fetal muscle maturation.

Changes in the plasma membrane in metabolic disease: impact of the membrane environment on G protein-coupled receptor structure and function

Drug development targeting GPCRs often utilizes model heterologous cell expression systems, reflecting an implicit assumption that the membrane environment has little functional impact on these receptors or on their responsiveness to drugs. However, much recent data have illustrated that membrane components can have an important functional impact on intrinsic membrane proteins. This review is directed toward gaining a better understanding of the structure of the plasma membrane in health and disease, and how this organelle can influence GPCR structure, function and regulation. It is important to recognize that the membrane provides a potential mode of lateral allosteric regulation of GPCRs and can affect the effectiveness of drugs and their biological responses in various disease states, which can even vary among individuals across the population. The type 1 cholecystokinin receptor is reviewed as an exemplar of a class A GPCR that is affected in this way by changes in the plasma membrane.

LINKED ARTICLES

This article is part of a themed section on Molecular Pharmacology of GPCRs. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.21/issuetoc.

Type 1 Diabetes: Urinary Proteomics and Protein Network Analysis Support Perturbation of Lysosomal Function

While insulin replacement therapy restores the health and prevents the onset of diabetic complications (DC) for many decades, some T1D patients have elevated hemoglobin A1c values suggesting poor glycemic control, a risk factor of DC. We surveyed the stool microbiome and urinary proteome of a cohort of 220 adolescents and children, half of which had lived with T1D for an average of 7 years and half of which were healthy siblings. Phylogenetic analysis of the 16S rRNA gene did not reveal significant differences in gut microbial alpha-diversity comparing the two cohorts. The urinary proteome of T1D patients revealed increased abundances of several lysosomal proteins that correlated with elevated HbA1c values. In silico protein network analysis linked such proteins to extracellular matrix components and the glycoprotein LRG1. LRG1 is a prominent inflammation and neovascularization biomarker. We hypothesize that these changes implicate aberrant glycation of macromolecules that alter lysosomal function and metabolism in renal tubular epithelial cells, cells that line part of the upper urinary tract.

A Systems Genetics Approach Identified GPD1L and its Molecular Mechanism for Obesity in Human Adipose Tissue

To explore novel molecular mechanisms underlying obesity, we applied a systems genetics framework to integrate risk genetic loci from the largest body mass index (BMI) genome-wide association studies (GWAS) meta-analysis with mRNA and microRNA profiling in adipose tissue from 200 subjects. One module was identified to be most significantly associated with obesity and other metabolic traits. We identified eight hub genes which likely play important roles in obesity metabolism and identified microRNAs that significantly negatively correlated with hub genes. This module was preserved in other three test gene expression datasets, and all hub genes were consistently downregulated in obese subjects through the meta-analysis. Gene GPD1L had the highest connectivity and was identified a key causal regulator in the module. Gene GPD1L was significantly negatively correlated with the expression of miR-210, which was experimentally validated that miR-210 regulated GPD1L protein level through direct interaction with its mRNA three prime untranslated region (3'-UTR). GPD1L was found to be upregulated during weight loss and weight maintenance induced by low calorie diet (LCD), while downregulated during weight gain induced by high-fat diet (HFD). The results indicated that increased GPD1L in adipose tissue may have a significant therapeutic potential in reducing obesity and insulin resistance.

Quantifying a Systems Map: Network Analysis of a Childhood Obesity Causal Loop Diagram

Causal loop diagrams developed by groups capture a shared understanding of complex problems and provide a visual tool to guide interventions. This paper explores the application of network analytic methods as a new way to gain quantitative insight into the structure of an obesity causal loop diagram to inform intervention design. Identification of the structural features of causal loop diagrams is likely to provide new insights into the emergent properties of complex systems and analysing central drivers has the potential to identify leverage points. The results found the structure of the obesity causal loop diagram to resemble commonly observed empirical networks known for efficient spread of information. Known drivers of obesity were found to be the most central variables along with others unique to obesity prevention in the community. While causal loop diagrams are often specific to single communities, the analytic methods provide means to contrast and compare multiple causal loop diagrams for complex problems.

Diet-induced weight loss leads to a switch in gene regulatory network control in the rectal mucosa

BACKGROUND

Weight loss may decrease risk of colorectal cancer in obese individuals, yet its effect in the colorectum is not well understood. We used integrative network modeling, Passing Attributes between Networks for Data Assimilation, to estimate transcriptional regulatory network models from mRNA expression levels from rectal mucosa biopsies measured pre- and post-weight loss in 10 obese, pre-menopausal women.

RESULTS

We identified significantly greater regulatory targeting of glucose transport pathways in the post-weight loss regulatory network, including "regulation of glucose transport" (FDR=0.02), "hexose transport" (FDR=0.06), "glucose transport" (FDR=0.06) and "monosaccharide transport" (FDR=0.08). These findings were not evident by gene expression analysis alone. Network analysis also suggested a regulatory switch from NFΚB1 to MAX control of MYC post-weight loss.

CONCLUSIONS

These network-based results expand upon standard gene expression analysis by providing evidence for a potential mechanistic alteration caused by weight loss.

Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes

Worldwide population is aging, and a large part of the growing burden associated with age-related conditions can be prevented or delayed by promoting healthy lifestyle and normalizing metabolic risk factors. However, a better understanding of the pleiotropic effects of available nutritional interventions and their influence on the multiple processes affected by aging is needed to select and implement the most promising actions. New methods of analysis are required to tackle the complexity of the interplay between nutritional interventions and aging, and to make sense of a growing amount of -omics data being produced for this purpose. In this paper, we review how various systems biology-inspired methods of analysis can be applied to the study of the molecular basis of nutritional interventions promoting healthy aging, notably caloric restriction and polyphenol supplementation. We specifically focus on the role that different versions of network analysis, molecular signature identification and multi-omics data integration are playing in elucidating the complex mechanisms underlying nutrition, and provide some examples on how to extend the application of these methods using available microarray data.

Fatty Acid Composition of Adipose Tissue Triglycerides in Obese Diabetic Women After Bariatric Surgery: a 2-Year Follow up

Bariatric surgery is the most effective method in the treatment of obesity and type 2 diabetes (T2DM). The aim of this study was to evaluate the effects of different types of bariatric procedures on remission of T2DM and on the fatty acid composition in subcutaneous adipose tissue. Patients included obese diabetic women who underwent bariatric surgery: biliopancreatic diversion (BPD), n=8, laparoscopic gastric banding (LAGB), n=9 or laparoscopic greater curvature plication (LGCP), n=12. Anthropometric characteristics and fatty acid composition of adipose tissue (FA AT) were analyzed before surgery, then 6 months and 2 years after surgery. FA AT was analyzed by gas chromatography. Diabetes remission was estimated. BPD was most efficient in inducing a remission of diabetes (p=0.004). Significantly higher increases in lauric (12:0), myristoleic (14:1n-5) and palmitoleic (16:1n-7) acids and delta-9 desaturase were found two years after BPD, suggesting higher lipogenesis in adipose tissue. Docosatetraenoic acid (22:4n-6) increased significantly after BPD, while docosapentaenoic acid (22:5n-3) decreased 6 months after BPD and increased after 2 years. No changes were found after LAGB and LGCP after 2 years. Bariatric surgery led to significant changes in the fatty acid composition of subcutaneous adipose tissue in severely obese diabetic women after six months and two years, and was partly influenced by the type of surgery used.

The FAT expandability (FATe) Project: Biomarkers to determine the limit of expansion and the complications of obesity

Background

Obesity is an excessive accumulation of fat frequently, but not always, associated with health problems, mainly type 2 diabetes and cardiovascular disease. During a positive energy balance, as caused by excessive intake or sedentary lifestyle, subcutaneous adipose tissue expands and accumulates lipids as triglycerides. However, the amount of adipose tissue per se is unlikely to be the factor linking obesity and metabolic complications. The expandability hypothesis states that, if this positive energy balance is prolonged, a point is eventually reached where subcutaneous adipose tissue can not further expand and energy surplus no longer can be safely stored. Once the limit on storage capacity has been exceeded, the dietary lipids start spilling and accumulate ectopically in other organs (omentum, liver, muscle, pancreas) forming lipid byproducts toxic to cells.

Methods/Design

FATe is a multidisciplinary clinical project aimed to fill gaps that still exist in the expandability hypothesis. Imaging techniques (CT-scan), metabolomics, and transcriptomics will be used to identify the factors that set the limit expansion of subcutaneous adipose tissue in a cohort of caucasian individuals with varying degrees of adiposity. Subsequently, a set of biomarkers that inform the individual limits of expandability will be developed using computational and mathematical modeling. A different validation cohort will be used to minimize the risk of false positive rates and increase biomarkers' predictive performance.

Discussion

The work proposed here will render a clinically useful screening method to predict which obese individuals will develop metabolic derangements, specially diabetes and cardiovascular disease. This study will also provide mechanistic evidence that promoting subcutaneous fat expansion might be a suitable therapy to reduce metabolic complications associated with positive energy balance characteristic of Westernized societies.