PENCALC: A program for penetrance estimation in autosomal dominant diseases

We present a computer program developed for estimating penetrance rates in autosomal dominant diseases by means of family kinship and phenotype information contained within the pedigrees. The program also determines the exact 95% credibility interval for the penetrance estimate. Both executable (PenCalc for Windows) and web versions (PenCalcWeb) of the software are available. The web version enables further calculations, such as heterozygosity probabilities and assessment of offspring risks for all individuals in the pedigrees. Both programs can be accessed and down-loaded freely at the home-page address http://www.ib.usp.br/~otto/software.htm

Predicting Secondary Structures, Contact Numbers, and Residue-wise Contact Orders of Native Protein Structure from Amino Acid Sequence by Critical Random Networks

Prediction of one-dimensional protein structures such as secondary structures and contact numbers is useful for the three-dimensional structure prediction and important for the understanding of sequence-structure relationship. Here we present a new machine-learning method, critical random networks (CRNs), for predicting one-dimensional structures, and apply it, with position-specific scoring matrices, to the prediction of secondary structures (SS), contact numbers (CN), and residue-wise contact orders (RWCO). The present method achieves, on average, $Q_3$ accuracy of 77.8% for SS, correlation coefficients of 0.726 and 0.601 for CN and RWCO, respectively. The accuracy of the SS prediction is comparable to other state-of-the-art methods, and that of the CN prediction is a significant improvement over previous methods. We give a detailed formulation of critical random networks-based prediction scheme, and examine the context-dependence of prediction accuracies. In order to study the nonlinear and multi-body effects, we compare the CRNs-based method with a purely linear method based on position-specific scoring matrices. Although not superior to the CRNs-based method, the surprisingly good accuracy achieved by the linear method highlights the difficulty in extracting structural features of higher order from amino acid sequence beyond that provided by the position-specific scoring matrices.Comment: 20 pages, 1 figure, 5 tables; minor revision; accepted for publication in BIOPHYSIC

Influenza d virus infection in herd of cattle, Japan

Citation: Murakami, S., Endoh, M., Kobayashi, T., Takenaka-Uema, A., Chambers, J. K., Uchida, K., . . . Horimoto, T. (2016). Influenza d virus infection in herd of cattle, Japan. Emerging Infectious Diseases, 22(8), 1517-1519. doi:10.3201/eid2208.160362Although the provisionally named influenza D virus was first isolated as an influenza C–like virus from pigs with respiratory illness in Oklahoma in 2011 (1,2), epidemiologic analyses suggested that cattle are major reservoirs of this virus (3) and the virus is potentially involved in the bovine respiratory disease complex. The high rates of illness and death related to this disease in feedlot cattle are caused by multiple factors, including several viral and bacterial co-infections. Influenza D viruses were detected in cattle and pigs with respiratory diseases (and in some healthy cattle) in China (4), France (5), Italy (6), among other countries, indicating their wide global geographic distribution. Although the influenza D virus, like the human influenza C virus, is known to use 9-O-acetylated sialic acids as the cell receptor (2,7), its zoonotic potential is undefined because of limited research (1,8). We report influenza D virus infection in a herd of cattle in Japan

Genetic characterization of H5N1 influenza viruses isolated from chickens in Indonesia in 2010

Since 2003, highly pathogenic H5N1 avian influenza viruses have caused outbreaks among poultry in Indonesia every year, producing the highest number of human victims worldwide. However, little is known about the H5N1 influenza viruses that have been circulating there in recent years. We therefore conducted surveillance studies and isolated eight H5N1 viruses from chickens. Phylogenic analysis of their hemagglutinin and neuraminidase genes revealed that all eight viruses belonged to clade 2.1.3. However, on the basis of nucleotide differences, these viruses could be divided into two groups. Other viruses genetically closely related to these two groups of viruses were all Indonesian isolates, suggesting that these new isolates have been evolving within Indonesia. Among these viruses, two distinct viruses circulated in the Kalimantan islands during the same season in 2010. Our data reveal the continued evolution of H5N1 viruses in Indonesia

Identifying Influenza Viruses with Resequencing Microarrays

Resequencing microarrays rapidly identify influenza viruses

Multi-Ancestry Genome-Wide Association Study Accounting for Gene-Psychosocial Factor Interactions Identifies Novel Loci for Blood Pressure Traits

Psychological and social factors are known to influence blood pressure (BP) and risk of hypertension and associated cardiovascular diseases. To identify novel BP loci, we carried out genome-wide association meta-analyses of systolic, diastolic, pulse, and mean arterial BP, taking into account the interaction effects of genetic variants with three psychosocial factors: depressive symptoms, anxiety symptoms, and social support. Analyses were performed using a two-stage design in a sample of up to 128,894 adults from five ancestry groups. In the combined meta-analyses of stages 1 and 2, we identified 59 loci (p value \u3c 5e−8), including nine novel BP loci. The novel associations were observed mostly with pulse pressure, with fewer observed with mean arterial pressure. Five novel loci were identified in African ancestry, and all but one showed patterns of interaction with at least one psychosocial factor. Functional annotation of the novel loci supports a major role for genes implicated in the immune response (PLCL2), synaptic function and neurotransmission (LIN7A and PFIA2), as well as genes previously implicated in neuropsychiatric or stress-related disorders (FSTL5 and CHODL). These findings underscore the importance of considering psychological and social factors in gene discovery for BP, especially in non-European populations

Antibody Pressure by a Human Monoclonal Antibody Targeting the 2009 Pandemic H1N1 Virus Hemagglutinin Drives the Emergence of a Virus with Increased Virulence in Mice

In 2009, a novel H1N1 influenza A virus (2009 pH1N1) emerged and caused a pandemic. A human monoclonal antibody (hMAb; EM4C04), highly specific for the 2009 pH1N1 virus hemagglutinin (HA), was isolated from a severely ill 2009 pH1N1 virus-infected patient. We postulated that under immune pressure with EM4C04, the 2009 pH1N1 virus would undergo antigenic drift and mutate at sites that would identify the antibody binding site. To do so, we infected MDCK cells in the presence of EM4C04 and generated 11 escape mutants, displaying 7 distinct amino acid substitutions in the HA. Six substitutions greatly reduced MAb binding (K123N, D131E, K133T, G134S, K157N, and G158E). Residues 131, 133, and 134 are contiguous with residues 157 and 158 in the globular domain structure and contribute to a novel pH1N1 antibody epitope. One mutation near the receptor binding site, S186P, increased the binding affinity of the HA to the receptor. 186P and 131E are present in the highly virulent 1918 virus HA and were recently identified as virulence determinants in a mouse-passaged pH1N1 virus. We found that pH1N1 escape variants expressing these substitutions enhanced replication and lethality in mice compared to wild-type 2009 pH1N1 virus. The increased virulence of these viruses was associated with an increased affinity for α2,3 sialic acid receptors. Our study demonstrates that antibody pressure by an hMAb targeting a novel epitope in the Sa region of 2009 pH1N1 HA is able to inadvertently drive the development of a more virulent virus with altered receptor binding properties. This broadens our understanding of antigenic drift

VIGOR, an annotation program for small viral genomes

<p>Abstract</p> <p>Background</p> <p>The decrease in cost for sequencing and improvement in technologies has made it easier and more common for the re-sequencing of large genomes as well as parallel sequencing of small genomes. It is possible to completely sequence a small genome within days and this increases the number of publicly available genomes. Among the types of genomes being rapidly sequenced are those of microbial and viral genomes responsible for infectious diseases. However, accurate gene prediction is a challenge that persists for decoding a newly sequenced genome. Therefore, accurate and efficient gene prediction programs are highly desired for rapid and cost effective surveillance of RNA viruses through full genome sequencing.</p> <p>Results</p> <p>We have developed VIGOR (Viral Genome ORF Reader), a web application tool for gene prediction in influenza virus, rotavirus, rhinovirus and coronavirus subtypes. VIGOR detects protein coding regions based on sequence similarity searches and can accurately detect genome specific features such as frame shifts, overlapping genes, embedded genes, and can predict mature peptides within the context of a single polypeptide open reading frame. Genotyping capability for influenza and rotavirus is built into the program. We compared VIGOR to previously described gene prediction programs, ZCURVE_V, GeneMarkS and FLAN. The specificity and sensitivity of VIGOR are greater than 99% for the RNA viral genomes tested.</p> <p>Conclusions</p> <p>VIGOR is a user friendly web-based genome annotation program for five different viral agents, influenza, rotavirus, rhinovirus, coronavirus and SARS coronavirus. This is the first gene prediction program for rotavirus and rhinovirus for public access. VIGOR is able to accurately predict protein coding genes for the above five viral types and has the capability to assign function to the predicted open reading frames and genotype influenza virus. The prediction software was designed for performing high throughput annotation and closure validation in a post-sequencing production pipeline.</p