Functional whole-genome analysis identifies Polo-like kinase 2 and poliovirus receptor as essential for neuronal differentiation upstream of the negative regulator alphaB-crystallin.

This study aimed at identifying transcriptional changes associated to neuronal differentiation induced by six distinct stimuli using whole-genome microarray hybridization analysis. Bioinformatics analyses revealed the clustering of these six stimuli into two categories, suggesting separate gene/pathway dependence. Treatment with specific inhibitors demonstrated the requirement of both Janus kinase and microtubule-associated protein kinase activation to trigger differentiation with nerve growth factor (NGF) and dibutyryl cAMP. Conversely, activation of protein kinase A, phosphatidylinositol-3-kinase alpha, and mammalian target of rapamycin, although required for dibutyryl cAMP-induced differentiation, exerted a negative feedback on NGF-induced differentiation. We identified Polo-like kinase 2 (Plk2) and poliovirus receptor (PVR) as indispensable for NGF-driven neuronal differentiation and alphaB-crystallin (Cryab) as an inhibitor of this process. Silencing of Plk2 or PVR blocked NGF-triggered differentiation and Cryab down-regulation, while silencing of Cryab enhanced NGF-induced differentiation. Our results position both Plk2 and PVR upstream of the negative regulator Cryab in the pathway(s) leading to neuronal differentiation triggered by NGF.

Modeling stochasticity and robustness in gene regulatory networks.

MOTIVATION: Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations. RESULTS: In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs. AVAILABILITY: Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/ approximately garg/genysis.html.

Synchronous versus asynchronous modeling of gene regulatory networks.

MOTIVATION: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. RESULTS: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process. AVAILABILITY: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Analysis of circulating angiopoietin-like protein 3 and genetic variants in lipid metabolism and liver health: the DiOGenes study.

BACKGROUND: Angiopoietin-like protein 3 (ANGPTL3), a liver-derived protein, plays an important role in the lipid and lipoprotein metabolism. Using data available from the DiOGenes study, we assessed the link with clinical improvements (weight, plasma lipid, and insulin levels) and changes in liver markers, alanine aminotransferase, aspartate aminotransferase (AST), adiponectin, fetuin A and B, and cytokeratin 18 (CK-18), upon low-calorie diet (LCD) intervention. We also examined the role of genetic variation in determining the level of circulating ANGPTL3 and the relation between the identified genetic markers and markers of hepatic steatosis. METHODS: DiOGenes is a multicenter, controlled dietary intervention where obese participants followed an 8-week LCD (800 kcal/day, using a meal replacement product). Plasma ANGPTL3 and liver markers were measured using the SomaLogic (Boulder, CO) platform. Protein quantitative trait locus (pQTL) analyses assessed the link between more than four million common variants and the level of circulating ANGPTL3 at baseline and changes in levels during the LCD intervention. RESULTS: Changes in ANGPTL3 during weight loss showed only marginal association with changes in triglycerides (nominal p = 0.02) and insulin (p = 0.04); these results did not remain significant after correcting for multiple testing. However, significant association (after multiple-testing correction) were observed between changes in ANGPTL3 and AST during weight loss (p = 0.004) and between ANGPTL3 and CK-18 (baseline p = 1.03 × 10-7, during weight loss p = 1.47 × 10-13). Our pQTL study identified two loci significantly associated with changes in ANGPTL3. One of these loci (the APOA4-APOA5-ZNF259-BUD13 gene cluster) also displayed significant association with changes in CK-18 levels during weight loss (p = 0.007). CONCLUSION: We clarify the link between circulating levels of ANGPTL3 and specific markers of liver function. We demonstrate that changes in ANGPLT3 and CK-18 during LCD are under genetic control from trans-acting variants. Our results suggest an extended function of ANGPTL3 in the inflammatory state of liver steatosis and toward liver metabolic processes.

A 48-Hour Vegan Diet Challenge in Healthy Women and Men Induces a BRANCH-Chain Amino Acid Related, Health Associated, Metabolic Signature.

SCOPE: Research is limited on diet challenges to improve health. A short-term, vegan protein diet regimen nutritionally balanced in macronutrient composition compared to an omnivorous diet is hypothesized to improve metabolic measurements of blood sugar regulation, blood lipids, and amino acid metabolism. METHODS AND RESULTS: This randomized, cross-over, controlled vegan versus animal diet challenge is conducted on 21 (11 female,10 male) healthy participants. Fasting plasma is measured during a 3 d diet intervention for clinical biochemistry and metabonomics. Intervention diet plans meet individual caloric needs. Meals are provided and supervised. Diet compliance is monitored. CONCLUSIONS: The vegan diet lowers triglycerides, insulin and homeostatic model assessment (HOMA-IR), bile acids, elevated magnesium levels, and changed branched-chain amino acids (BCAAs) metabolism (p < 0.05), potentiating insulin and blood sugar control after 48 h. Cholesterol control improves significantly in the vegan versus omnivorous diets. Plasma amino acid and magnesium concentrations positively correlate with dietary amino acids. Polyunsaturated fatty acids and dietary fiber inversely correlate with insulin, HOMA-IR, and triglycerides. Nutritional biochemistries, BCAAs, insulin, and HOMA-IR are impacted by sexual dimorphism. A health-promoting, BCAA-associated metabolic signature is produced from a short-term, healthy, controlled, vegan diet challenge when compared with a healthy, controlled, omnivorous diet.

Dynamic simulation of regulatory networks using SQUAD.

BACKGROUND: The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. RESULTS: We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. CONCLUSION: The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.

PromoterPlot: a graphical display of promoter similarities by pattern recognition.

PromoterPlot (http://promoterplot.fmi.ch) is a web-based tool for simplifying the display and processing of transcription factor searches using either the commercial or free TransFac distributions. The input sequence is a TransFac search (public version) or FASTA/Affymetrix IDs (local install). It uses an intuitive pattern recognition algorithm for finding similarities between groups of promoters by dividing transcription factor predictions into conserved triplet models. To minimize the number of false-positive models, it can optionally exclude factors that are known to be unexpressed or inactive in the cells being studied based on microarray or proteomic expression data. The program will also estimate the likelihood of finding a pattern by chance based on the frequency observed in a control set of mammalian promoters we obtained from Genomatix. The results are stored as an interactive SVG web page on our server.

Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects.

Background: A low-calorie diet (LCD) reduces fat mass excess, improves insulin sensitivity, and alters adipose tissue (AT) gene expression, yet the relation with clinical outcomes remains unclear.Objective: We evaluated AT transcriptome alterations during an LCD and the association with weight and glycemic outcomes both at LCD termination and 6 mo after the LCD.Design: Using RNA sequencing (RNAseq), we analyzed transcriptome changes in AT from 191 obese, nondiabetic patients within a multicenter, controlled dietary intervention. Expression changes were associated with outcomes after an 8-wk LCD (800-1000 kcal/d) and 6 mo after the LCD. Results were validated by using quantitative reverse transcriptase-polymerase chain reaction in 350 subjects from the same cohort. Statistical models were constructed to classify weight maintainers or glycemic improvers.Results: With RNAseq analyses, we identified 1173 genes that were differentially expressed after the LCD, of which 350 and 33 were associated with changes in body mass index (BMI; in kg/m2) and Matsuda index values, respectively, whereas 29 genes were associated with both endpoints. Pathway analyses highlighted enrichment in lipid and glucose metabolism. Classification models were constructed to identify weight maintainers. A model based on clinical baseline variables could not achieve any classification (validation AUC: 0.50; 95% CI: 0.36, 0.64). However, clinical changes during the LCD yielded better performance of the model (AUC: 0.73; 95% CI: 0.60, 0.87]). Adding baseline expression to this model improved the performance significantly (AUC: 0.87; 95% CI: 0.77, 0.96; Delong's P = 0.012). Similar analyses were performed to classify subjects with good glycemic improvements. Baseline- and LCD-based clinical models yielded similar performance (best AUC: 0.73; 95% CI: 0.60, 0.86). The addition of expression changes during the LCD improved the performance substantially (AUC: 0.80; 95% CI: 0.69, 0.92; P = 0.058).Conclusions: This study investigated AT transcriptome alterations after an LCD in a large cohort of obese, nondiabetic patients. Gene expression combined with clinical variables enabled us to distinguish weight and glycemic responders from nonresponders. These potential biomarkers may help clinicians understand intersubject variability and better predict the success of dietary interventions. This trial was registered at clinicaltrials.gov as NCT00390637.

Protein quantitative trait locus study in obesity during weight-loss identifies a leptin regulator.

Thousands of genetic variants have been associated with complex traits through genome-wide association studies. However, the functional variants or mechanistic consequences remain elusive. Intermediate traits such as gene expression or protein levels are good proxies of the metabolic state of an organism. Proteome analysis especially can provide new insights into the molecular mechanisms of complex traits like obesity. The role of genetic variation in determining protein level variation has not been assessed in obesity. To address this, we design a large-scale protein quantitative trait locus (pQTL) analysis based on a set of 1129 proteins from 494 obese subjects before and after a weight loss intervention. This reveals 55 BMI-associated cis-pQTLs and trans-pQTLs at baseline and 3 trans-pQTLs after the intervention. We provide evidence for distinct genetic mechanisms regulating BMI-associated proteins before and after weight loss. Finally, by functional analysis, we identify and validate FAM46A as a trans regulator for leptin.

Single Nucleotide Polymorphism Clustering in Systemic Autoimmune Diseases.

Systemic Autoimmune Diseases, a group of chronic inflammatory conditions, have variable symptoms and difficult diagnosis. In order to reclassify them based on genetic markers rather than clinical criteria, we performed clustering of Single Nucleotide Polymorphisms. However naive approaches tend to group patients primarily by their geographic origin. To reduce this "ancestry signal", we developed SNPClust, a method to select large sources of ancestry-independent genetic variations from all variations detected by Principal Component Analysis. Applied to a Systemic Lupus Erythematosus case control dataset, SNPClust successfully reduced the ancestry signal. Results were compared with association studies between the cases and controls without or with reference population stratification correction methods. SNPClust amplified the disease discriminating signal and the ratio of significant associations outside the HLA locus was greater compared to population stratification correction methods. SNPClust will enable the use of ancestry-independent genetic information in the reclassification of Systemic Autoimmune Diseases. SNPClust is available as an R package and demonstrated on the public Human Genome Diversity Project dataset at https://github.com/ThomasChln/snpclust.

Efficient computation of minimal perturbation sets in gene regulatory networks.

In the last few decades, technological and experimental advancements have enabled a more precise understanding of the mode of action of drugs with respect to human cell signaling pathways and have positively influenced the design of new drug compounds. However, as the design of compounds has become increasingly target-specific, the overall effects of a drug on adjacent cellular signaling pathways remain difficult to predict because of the complexity of the interactions involved. Off-target effects of drugs are known to influence their efficacy and safety. Similarly, drugs which are more target-specific also suffer from lack of efficacy because their scope might be too limited in the context of cellular signaling. Even in situations where the signaling pathways targeted by a drug are known, the presence of point mutations in some of the components of the pathways can render a therapy ineffective in a considerable target subpopulation. Some of these issues can be addressed by predicting Minimal Intervention Sets (MIS) of elements of the signaling pathways that when perturbed give rise to a pre-defined cellular phenotype. These minimal gene perturbation sets can then be further used to screen a library of drug compounds in order to discover effective drug therapies. This manuscript describes algorithms that can be used to discover MIS in a gene regulatory network that can lead to a defined cellular phenotype. Algorithms are implemented in our Boolean modeling toolbox, GenYsis. The software binaries of GenYsis are available for download from http://www.vital-it.ch/software/genYsis/.

Absence of nucleolar disruption after impairment of 40S ribosome biogenesis reveals an rpL11-translation-dependent mechanism of p53 induction.

Impaired ribosome biogenesis is attributed to nucleolar disruption and diffusion of a subset of 60S ribosomal proteins, particularly ribosomal protein (rp)L11, into the nucleoplasm, where they inhibit MDM2, leading to p53 induction and cell-cycle arrest. Previously, we demonstrated that deletion of the 40S rpS6 gene in mouse liver prevents hepatocytes from re-entering the cell cycle after partial hepatectomy. Here, we show that this response leads to an increase in p53, which is recapitulated in culture by rpS6-siRNA treatment and rescued by the simultaneous depletion of p53. However, disruption of biogenesis of 40S ribosomes had no effect on nucleolar integrity, although p53 induction was mediated by rpL11, leading to the finding that the cell selectively upregulates the translation of mRNAs with a polypyrimidine tract at their 5'-transcriptional start site (5'-TOP mRNAs), including that encoding rpL11, on impairment of 40S ribosome biogenesis. Increased 5'-TOP mRNA translation takes place despite continued 60S ribosome biogenesis and a decrease in global translation. Thus, in proliferative human disorders involving hypomorphic mutations in 40S ribosomal proteins, specific targeting of rpL11 upregulation would spare other stress pathways that mediate the potential benefits of p53 induction.