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The cross-species transmission of viruses into new host populations, termed virus emergence, is a significant issue in public health, agriculture, wildlife management, and related fields. Virus emergence requires overlap between host populations, alterations in virus genetics to permit infection of new hosts, and adaptation to novel hosts such that between-host transmission is sustainable, all of which are the purview of the fields of ecology and evolution. A firm understanding of the ecology of viruses and how they evolve is required for understanding how and why viruses emerge. In this paper, I address the evolutionary mechanisms of virus emergence and how they relate to virus ecology. I argue that, while virus acquisition of the ability to infect new hosts is not difficult, limited evolutionary trajectories to sustained virus between-host transmission and the combined effects of mutational meltdown, bottlenecking, demographic stochasticity, density dependence, and genetic erosion in ecological sinks limit most emergence events to dead-end spillover infections. Despite the relative rarity of pandemic emerging viruses, the potential of viruses to search evolutionary space and find means to spread epidemically and the consequences of pandemic viruses that do emerge necessitate sustained attention to virus research, surveillance, prophylaxis, and treatment. [ABSTRACT FROM AUTHOR]

This paper focuses on the relationship between the circadian system and glucose metabolism. Research across the translational spectrum confirms the importance of the circadian system for glucose metabolism and offers promising clues as to when and why these systems go awry. In particular, basic research has started to clarify the molecular and genetic mechanisms through which the circadian system regulates metabolism. The study of human behavior, especially in the context of psychiatric disorders, such as bipolar disorder and major depression, forces us to see how inextricably linked mental health and metabolic health are. We also emphasize the remarkable opportunities for advancing circadian science through big data and advanced analytics. Advances in circadian research have translated into environmental and pharmacological interventions with tremendous therapeutic potential. [ABSTRACT FROM AUTHOR]

This paper provides an overview of racial variations in health and shows that differences in socioeconomic status (SES) across racial groups are a major contributor to racial disparities in health. However, race reflects multiple dimensions of social inequality and individual and household indicators of SES capture relevant but limited aspects of this phenomenon. Research is needed that will comprehensively characterize the critical pathogenic features of social environments and identify how they combine with each other to affect health over the life course. Migration history and status are also important predictors of health and research is needed that will enhance understanding of the complex ways in which race, SES, and immigrant status combine to affect health. Fully capturing the role of race in health also requires rigorous examination of the conditions under which medical care and genetic factors can contribute to racial and SES differences in health. The paper identifies research priorities in all of these areas. [ABSTRACT FROM AUTHOR]

This paper highlights recent human neuroimaging and cross-species developmental and genetic studies that examine how fear regulation varies by age and the individual, especially during the period of adolescence, when there is a peak in the prevalence of anxiety disorders. The findings have significant implications for understanding who may be at risk for anxiety disorders and for whom, and when, an exposure-based therapy may be most effective. We provide proof of concept for targeting treatment to the individual as a function of age and genetics, inferred frommouse and human studies, and suggest optimization of treatment for nonresponders. [ABSTRACT FROM AUTHOR]

All living organisms depend on homeostasis, the complex set of interacting metabolic chemical reactions for maintaining life and well-being. This is no less true for psychiatric well-being than for physical well-being. Indeed, a focus on homeostasis forces us to see how inextricably linked mental and physical well-being are. This paper focuses on these linkages. In particular, it addresses the ways in which understanding of disturbed homeostasis may aid in creating classes of patients with mood and anxiety disorders based on such phenotypes. At the cellular level, we may be able to compensate for the inability to study living brain tissue through the study of homeostatic mechanisms in fibroblasts, pluripotent human cells, and mitochondria and determine how homeostasis is disturbed at the level of these peripheral tissues through exogenous stress. We also emphasize the remarkable opportunities for enhancing knowledge in this area that are offered by advances in technology. The study of human behavior, especially when combined with our greatly improved capacity to study unique but isolated populations, offers particularly clear windows into the relationships among genetic, environmental, and behavioral contributions to homeostasis. [ABSTRACT FROM AUTHOR]

The Endangered Species Act of 1973 is one of the nation's most significant and most controversial environmental laws. Over three-and-a-half decades, it has profoundly influenced both private and federal agency behavior. As the scope of that influence has come to be recognized, a law that is ostensibly to be guided by science has inevitably become entangled in politics. The generality of many of the law's key provisions has produced continuing uncertainty and conflict over some basic issues. Among these are what species or other taxa are potentially subject to the Act's protections, what the extent of those protections is, and whether the Act's ultimate goal of recovery is one that is being effectively achieved. New challenges face the administrators of this law, including that of incorporating climate change considerations into the decisions made under the Act, and responding to the information made available by recent advances in genetics. This paper provides a brief overview of the Endangered Species Act's history and its key provisions, and a more in-depth look at some of the current and recurrent controversies that have attended its implementation. [ABSTRACT FROM AUTHOR]

Over the last decades the burden of disease in Western countries has shifted from comparably easily treated infectious diseases to more complex diseases, such as the metabolic syndrome, cardiovascular disease, and psychiatric disorders. A common characteristic of these illnesses is the interplay of multiple genetic and nongenetic factors, which eventually results in the manifestation of disease symptoms. Large-scale epidemiological studies in humans have resulted in the identification of various environmental and genetic risk factors, which contribute to the onset, duration, and severity of disease. While tremendous progress has been made, it is still impossible to predict which combination of risk factors will result in the manifestation of a specific illness. This lack of knowledge is also frequently reflected in inadequate treatment strategies, which mainly focus on symptom reversal rather than targeting the cause of the diseases. One of the most prominent environmental risk factors described for numerous diseases is chronic exposure to stressful situations. In this paper we address clinical and preclinical evidence of chronic stress as a risk factor for disease and introduce a novel, high-throughput mouse model for chronic social stress. We can show that this model has a high degree of construct, face, and predictive validity in terms of physiological, behavioral, and gene expression changes. We further illustrate how novel animal models of chronic social stress can help to unravel the complex interaction of individual genetic vulnerability and environmental risk factors. [ABSTRACT FROM AUTHOR]

One of the main features of Alzheimer's disease (AD) is the severe reduction of the cerebral metabolic rate for glucose (CMRglc). In vivo imaging using positron emission tomography with 2-[18F]fluoro-2-deoxy-D-glucose (FDG–PET) demonstrates consistent and progressive CMRglc reductions in AD patients, the extent and topography of which correlate with symptom severity. Increasing evidence suggests that CMRglc reductions occur at the preclinical stages of AD. CMRglc reductions were observed on FDG–PET before the onset of disease in several groups of at-risk individuals, including patients with mild cognitive impairment (MCI), often a prodrome to AD; presymptomatic individuals carrying mutations responsible for early-onset familial AD; cognitively normal elderly individuals followed for several years until they declined to MCI and eventually to AD; normal, middle-aged individuals who expressed subjective memory complaints and were carriers of the apolipoprotein E epsilon-4 allele, a strong genetic risk factor for late-onset AD. However, the causes of the early metabolic dysfunction forerunning the onset of AD are not known. An increasing body of evidence indicates a deficient or altered energy metabolism that could change the overall oxidative microenvironment for neurons during the pathogenesis and progression of AD, leading to alterations in mitochondrial enzymes and in glucose metabolism in AD brain tissue. The present paper reviews findings that implicate hypometabolism and oxidative stress as crucial players in the initiation and progression of synaptic pathology in AD. [ABSTRACT FROM AUTHOR]

The reverse engineering of biochemical networks is a central problem in systems biology. In recent years several methods have been developed for this purpose, using techniques from a variety of fields. A systematic comparison of the different methods is complicated by their widely varying data requirements, making benchmarking difficult. Also, because of the lack of detailed knowledge about most real networks, it is not easy to use experimental data for this purpose. This paper contains a comparison of four reverse-engineering methods using data from a simulated network. The network is sufficiently realistic and complex to include many of the challenges that data from real networks pose. Our results indicate that the two methods based on genetic perturbations of the network outperform the other methods, including dynamic Bayesian networks and a partial correlation method. [ABSTRACT FROM AUTHOR]

Inferring gene regulatory networks from high-throughput genomic data is one of the central problems in computational biology. In this paper, we describe a predictive modeling approach for studying regulatory networks, based on a machine learning algorithm called MEDUSA. MEDUSA integrates promoter sequence, mRNA expression, and transcription factor occupancy data to learn gene regulatory programs that predict the differential expression of target genes. Instead of using clustering or correlation of expression profiles to infer regulatory relationships, MEDUSA determines condition-specific regulators and discovers regulatory motifs that mediate the regulation of target genes. In this way, MEDUSA meaningfully models biological mechanisms of transcriptional regulation. MEDUSA solves the problem of predicting the differential (up/down) expression of target genes by using boosting, a technique from statistical learning, which helps to avoid overfitting as the algorithm searches through the high-dimensional space of potential regulators and sequence motifs. Experimental results demonstrate that MEDUSA achieves high prediction accuracy on held-out experiments (test data), that is, data not seen in training. We also present context-specific analysis of MEDUSA regulatory programs for DNA damage and hypoxia, demonstrating that MEDUSA identifies key regulators and motifs in these processes. A central challenge in the field is the difficulty of validating reverse-engineered networks in the absence of a gold standard. Our approach of learning regulatory programs provides at least a partial solution for the problem: MEDUSA's prediction accuracy on held-out data gives a concrete and statistically sound way to validate how well the algorithm performs. With MEDUSA, statistical validation becomes a prerequisite for hypothesis generation and network building rather than a secondary consideration. [ABSTRACT FROM AUTHOR]

Wegener granulomatosis (WG) is a disease considered infrequent among non-Caucasians. Few data exist on its characteristics in Latin America, except for isolated case reports and a paper on clinical features from a Chilean group. In this article we present the clinical characteristics of our cohort plus a preliminary evaluation of the risk factors for death. In comparison to large series from countries with a high WG prevalence, we observed similar age and gender distribution, disease category (localized or generalized), PR3-ANCA positivity, remission, relapse frequency, mortality, and death causes. These last ones are equally distributed, but infections account for an important proportion. Factors related with mortality were mainly those associated with side effects of treatment. We are currently expanding the data on this item as in other series, death is mainly related to the disease itself. The pending agenda includes knowing the prevalence and incidence of WG in Mexico, as well as other characteristics, such as genetic factors, that may influence its behavior in our country. [ABSTRACT FROM AUTHOR]

This paper characterizes the distribution of telomere length on individual chromosome arms in humans. By fluorescent in situ hybridization (FISH), followed by computer-assisted analysis of digital images, it is shown that the distribution of telomere length on individual chromosome arms is not random, but that humans have a common telomere profile. This profile exists in lymphocytes, amniocytes and fibroblasts, and seems to be conserved during life. A closer look at the overall pattern of the profile shows that the length of the telomeres in general follows the total chromosome length. In addition to the common profile, it is found that each person has specific characteristics, which are also conserved throughout life. Studying both twins and families we have obtained indications that these individual characteristics are at least partly inherited. Altogether, our results suggest that the length of individual telomeres might occasionally play a role in the heritability of life span. [ABSTRACT FROM AUTHOR]