Localization of phosphoserine residues in the alpha subunit of rabbit skeletal muscle phosphorylase kinase

The alpha subunit of skeletal muscle phosphorylase kinase, as isolated, carries phosphate at the serine residues 1018, 1020 and 1023. Employing the S-ethyl-cysteine method, these residues are found to be phosphorylated partially, i.e. differently phosphorylated species exist in muscle. Serine 1018 is a site which can be phosphorylated by the cyclic-AMP-dependent protein kinase. The serine residues 972, 985 and 1007 are phosphorylated by phosphorylase kinase itself when its activity is stimulated by micromolar concentrations of Ca2+. These phosphorylation sites are not identical to those found to be phosphorylated already in the enzyme as prepared from freshly excised muscle. A 'multiphosphorylation loop' uniquely present in this but not in the homologous beta subunit contains all the phosphoserine residues so far identified in the alpha subunit.     

Energy profile of maltooligosaccharide permeation through maltoporin as derived from the structure and from a statistical analysis of saccharide-protein interactions.

The crystal structure of the maltodextrin-specific porin from Salmonella typhimurium ligated with a maltotrioside at the pore eyelet is known at 2.4 A resolution. The three glucose units assume a conformation close to the natural amylose helix. The pore eyelet fits exactly the cross-section of a maltooligosaccharide chain and thus functions as a constraining orifice. The oligomer permeates the membrane by screwing along the amylose helix through this orifice. Because each glucose glides along the given helix, its interactions can be sampled at any point along the pathway. The interactions are mostly hydrogen bonds, but also contacts to aromatic rings at one side of the pore. We have derived the energy profile of a gliding maltooligosaccharide by following formation and breakage of hydrogen bonds and by assessing the saccharide-aromatics interactions from a statistical analysis of saccharide binding sites in proteins. The resulting profile indicates smooth permeation despite extensive hydrogen bonding at the orifice.     

Multimeric BLM is dissociated upon ATP hydrolysis and functions as monomers in resolving DNA structures

Bloom (BLM) syndrome is an autosomal recessive disorder characterized by an increased risk for many types of cancers. Previous studies have shown that BLM protein forms a hexameric ring structure, but its oligomeric form in DNA unwinding is still not well clarified. In this work, we have used dynamic light scattering and various stopped-flow assays to study the active form and kinetic mechanism of BLM in DNA unwinding. It was found that BLM multimers were dissociated upon ATP hydrolysis. Steady-state and single-turnover kinetic studies revealed that BLM helicase always unwound duplex DNA in the monomeric form under conditions of varying enzyme and ATP concentrations as well as 3′-ssDNA tail lengths, with no sign of oligomerization being discerned. Measurements of ATPase activity further indicated that BLM helicase might still function as monomers in resolving highly structured DNAs such as Holliday junctions and D-loops. These results shed new light on the underlying mechanism of BLM-mediated DNA unwinding and on the molecular and functional basis for the phenotype of heterozygous carriers of BLM syndrome.     

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.     

Social Cognition Deficits and Psychopathic Traits in Young People Seeking Mental Health Treatment

Antisocial behaviours and psychopathic traits place an individual at risk for criminality, mental illness, substance dependence, and psychosocial dysfunction. Social cognition deficits appear to be associated with psychopathic traits and are believed to contribute to interpersonal dysfunction. Most research investigating the relationship of these traits with social cognition has been conducted either in children or adult forensic settings. We investigated whether psychopathic traits were associated with social cognition in 91 young people presenting for mental healthcare (aged between 15 and 25 years). Participants completed symptom severity measures, neuropsychological tests, the Reading the Mind in the Eyes Test of social cognition (RMET), and the Antisocial Process Screening Device (APSD) to assess psychopathic personality traits. Correlation analyses showed poorer social cognition was associated with greater psychopathic traits (r = −.36, p = .01). Interestingly, social cognition performance predicted unique variance in concurrent psychopathic personality traits above gender, IQ sustained attention, and working memory performance. These findings suggest that social cognitive impairments are associated with psychopathic tendencies in young people presenting for community mental healthcare. Research is needed to establish the directionality of this relationship and to determine whether social cognition training is an effective treatment amongst young people with psychopathic tendencies.