A Predictive Model of the Oxygen and Heme Regulatory Network in Yeast

Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included

Unveiling Integrated Functional Pathways Leading to Enhanced Respiratory Disease Associated With Inactivated Respiratory Syncytial Viral Vaccine

Respiratory syncytial virus (RSV) infection is a severe threat to young children and the elderly. Despite decades of research, no vaccine has been approved. Notably, instead of affording protection, a formalin-inactivated RSV vaccine induced severe respiratory disease including deaths in vaccinated children in a 1960s clinical trial; however, recent studies indicate that other forms of experimental vaccines can also induce pulmonary pathology in pre-clinical studies. These findings suggest that multiple factors/pathways could be involved in the development of enhanced respiratory diseases. Clearly, a better understanding of the mechanisms underlying such adverse reactions is critically important for the development of safe and efficacious vaccines against RSV infection, given the exponential growth of RSV vaccine clinical trials in recent years. By employing an integrated systems biology approach in a pre-clinical cotton rat model, we unraveled a complex network of pulmonary canonical pathways leading to disease development in vaccinated animals upon subsequent RSV infections. Cytokines including IL-1, IL-6 GRO/IL-8, and IL-17 in conjunction with mobilized pulmonary inflammatory cells could play important roles in disease development, which involved a wide range of host responses including exacerbated pulmonary inflammation, oxidative stress, hyperreactivity, and homeostatic imbalance between coagulation and fibrinolysis. Moreover, the observed elevated levels of MyD88 implicate the involvement of this critical signal transduction module as the central node of the inflammatory pathways leading to exacerbated pulmonary pathology. Finally, the immunopathological consequences of inactivated vaccine immunization and subsequent RSV exposure were further substantiated by histological analyses of these key proteins along with inflammatory cytokines, while hypercoagulation was supported by increased pulmonary fibrinogen/fibrin accompanied by reduced levels of plasma D-dimers. Enhanced respiratory disease associated with inactivated RSV vaccine involves a complex network of host responses, resulting in significant pulmonary lesions and clinical manifestations such as tachypnea and airway obstruction. The mechanistic insight into the convergence of different signal pathways and identification of biomarkers could help facilitate the development of safe and effective RSV vaccine and formulation of new targeted interventions

Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia

We conducted a genome-wide association study (GWAS) with replication in 36,180 Chinese individuals and performed further transancestry meta-analyses with data from the Psychiatry Genomics Consortium (PGC2). Approximately 95% of the genome-wide significant (GWS) index alleles (or their proxies) from the PGC2 study were overrepresented in Chinese schizophrenia cases, including ∼50% that achieved nominal significance and ∼75% that continued to be GWS in the transancestry analysis. The Chinese-only analysis identified seven GWS loci; three of these also were GWS in the transancestry analyses, which identified 109 GWS loci, thus yielding a total of 113 GWS loci (30 novel) in at least one of these analyses. We observed improvements in the fine-mapping resolution at many susceptibility loci. Our results provide several lines of evidence supporting candidate genes at many loci and highlight some pathways for further research. Together, our findings provide novel insight into the genetic architecture and biological etiology of schizophrenia

Immediate causality network of stock markets

A financial system contains many elements networked by their relationships. Extensive works show that topological structure of the network stores rich information on evolutionary behaviors of the system such as early warning signals of collapses and/or crises. Existing works focus mainly on the network structure within a single stock market, while a collapse/crisis occurs in a macro-scale covering several or even all markets in the world. This mismatch of scale leads to unacceptable noise to the topological structure, and lack of information stored in relationships between different markets. In this work by using the transfer entropy we reconstruct the influential network between ten typical stock markets distributed in the world. Interesting findings include, before a financial crisis the connection strength reaches a maxima, which can act as an early warning signal of financial crises; The markets in America are mono-directionally and strongly influenced by that in Europe and act as the center; Some strongly linked pairs have also close correlations. The findings are helpful in understanding the evolution and modelling the dynamical process of the global financial system.Comment: 7pages, 5figure

Lack of exercise leads to significant and reversible loss of scale invariance in both aged and young mice

In healthy humans and other animals, behavioral activity exhibits scale invariance over multiple timescales from minutes to 24 h, whereas in aging or diseased conditions, scale invariance is usually reduced significantly. Accordingly, scale invariance can be a potential marker for health. Given compelling indications that exercise is beneficial for mental and physical health, we tested to what extent a lack of exercise affects scale invariance in young and aged animals. We studied six or more mice in each of four age groups (0.5, 1, 1.5, and 2 y) and observed an age-related deterioration of scale invariance in activity fluctuations. We found that limiting the amount of exercise, by removing the running wheels, leads to loss of scale-invariant properties in all age groups. Remarkably, in both young and old animals a lack of exercise reduced the scale invariance in activity fluctuations to the same level. We next showed that scale invariance can be restored by returning the running wheels. Exercise during the active period also improved scale invariance during the resting period, suggesting that activity during the active phase may also be beneficial for the resting phase. Finally, our data showed that exercise had a stronger influence on scale invariance than the effect of age. The data suggest that exercise is beneficial as revealed by scale-invariant parameters and that, even in young animals, a lack of exercise leads to strong deterioration in these parameters