Myosin V passing over Arp2/3 junctions: branching ratio calculated from the elastic lever arm model

Myosin V is a two-headed processive motor protein that walks in a hand-over-hand fashion along actin filaments. When it encounters a filament branch, formed by the Arp2/3 complex, it can either stay on the straight mother filament, or switch to the daughter filament. We study both probabilities using the elastic lever arm model for myosin V. We calculate the shapes and bending energies of all relevant configurations in which the trail head is bound to the actin filament before Arp2/3 and the lead head is bound either to the mother or to the daughter filament. Based on the assumption that the probability for a head to bind to a certain actin subunit is proportional to the Boltzmann factor obtained from the elastic energy, we calculate the mother/daughter filament branching ratio. Our model predicts a value of 27% for the daughter and 73% for the mother filament. This result is in good agreement with recent experimental data.Comment: 9 pages, 7 figures, to appear in Biophysical Journa

Elastic lever arm model for myosin V

We present a mechanochemical model for myosin V, a two-headed processive motor protein. We derive the properties of a dimer from those of an individual head, which we model both with a 4-state cycle (detached, attached with ADP.Pi, attached with ADP and attached without nucleotide) and alternatively with a 5-state cycle (where the power stroke is not tightly coupled to the phosphate release). In each state the lever arm leaves the head at a different, but fixed, angle. The lever arm itself is described as an elastic rod. The chemical cycles of both heads are coordinated exclusively by the mechanical connection between the two lever arms. The model explains head coordination by showing that the lead head only binds to actin after the power stroke in the trail head and that it only undergoes its power stroke after the trail head unbinds from actin. Both models (4- and 5-state) reproduce the observed hand-over-hand motion and fit the measured force-velocity relations. The main difference between the two models concerns the load dependence of the run length, which is much weaker in the 5-state model. We show how systematic processivity measurement under varying conditions could be used to distinguish between both models and to determine the kinetic parameters.Comment: 15 pages, 15 figures, to appear in Biophys.

Application of collapsing methods for continuous traits to the Genetic Analysis Workshop 17 exome sequence data

Genetic Analysis Workshop 17 used real sequence data from the 1000 Genomes Project and simulated phenotypes influenced by a large number of rare variants. Our aim is to evaluate the performance of various collapsing methods that were developed for analysis of multiple rare variants. We apply collapsing methods to continuous phenotypes Q1 and Q2 for all 200 replicates of the unrelated individuals data. Within each gene, we collapse (1) all SNPs, (2) all SNPs with minor allele frequency (MAF) < 0.05, and (3) nonsynonymous SNPs with MAF < 0.05. We consider two tests when collapsing variants: using the proportion of variants and using the presence/absence of any variant. We also compare our results to a single-marker analysis using PLINK. For phenotype Q1, the proportion test for collapsing rare nonsynonymous SNPs often performed the best. Two genes (FLT1 and KDR) had statistically significant results. A single-marker analysis using PLINK also provided statistically significant results for some SNPs within these two genes. For phenotype Q2, collapsing rare nonsynonymous SNPs performed the best, with almost no difference between proportion and presence tests. However, neither collapsing methods nor a single-marker analysis provided statistically significant results at the true genes for Q2. We also found that a large number of noncausal genes had high correlations with causal genes for Q1 and Q2, which may account for inflated false positives

Versatile and on-demand biologics co-production in yeast

Current limitations to on-demand drug manufacturing can be addressed by technologies that streamline manufacturing processes. Combining the production of two or more drugs into a single batch could not only be useful for research, clinical studies, and urgent therapies but also effective when combination therapies are needed or where resources are scarce. Here we propose strategies to concurrently produce multiple biologics from yeast in single batches by multiplexing strain development, cell culture, separation, and purification. We demonstrate proof-of-concept for three biologics co-production strategies: (i) inducible expression of multiple biologics and control over the ratio between biologic drugs produced together; (ii) consolidated bioprocessing; and (iii) co-expression and co-purification of a mixture of two monoclonal antibodies. We then use these basic strategies to produce drug mixtures as well as to separate drugs. These strategies offer a diverse array of options for on-demand, flexible, low-cost, and decentralized biomanufacturing applications without the need for specialized equipment

Genome-wide association analysis of GAW17 data using an empirical Bayes variable selection

Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible genetic effects related to rare alleles. We use a newly developed empirical Bayes variable selection algorithm to identify associations between studied traits and genetic markers. Using our novel method, we analyzed the three continuous phenotypes in the GAW17 data set across 200 replicates, with intriguing results

Understanding and modelling the economic impact of spinal cord injuries in the United Kingdom

Study design Economic modelling analysis. Objectives To determine lifetime direct and indirect costs from initial hospitalisation of all expected new traumatic and non-traumatic spinal cord injuries (SCI) over 12 months. Setting United Kingdom (UK). Methods Incidence-based approach to assessing costs from a societal perspective, including immediate and ongoing health, rehabilitation and long-term care directly attributable to SCI, as well as aids and adaptations, unpaid informal care and participation in employment. The model accounts for differences in injury severity, gender, age at onset and life expectancy. Results Lifetime costs for an expected 1270 new cases of SCI per annum conservatively estimated as £1.43 billion (2016 prices). This equates to a mean £1.12 million (median £0.72 million) per SCI case, ranging from £0.47 million (median £0.40 million) for an AIS grade D injury to £1.87 million (median £1.95 million) for tetraplegia AIS A–C grade injuries. Seventy-one percent of lifetime costs potentially are paid by the public purse with remaining costs due to reduced employment and carer time. Conclusions Despite the magnitude of costs, and being comparable with international estimates, this first analysis of SCI costs in the UK is likely to be conservative. Findings are particularly sensitive to the level and costs of long-term home and residential care. The analysis demonstrates how modelling can be used to highlight economic impacts of SCI rapidly to policymakers, illustrate how changes in future patterns of injury influence costs and help inform future economic evaluations of actions to prevent and/or reduce the impact of SCIs

A Branched Kinetic Scheme Describes the Mechanochemical Coupling of Myosin Va Processivity in Response to Substrate

Myosin Va is a double-headed cargo-carrying molecular motor that moves processively along cellular actin filaments. Long processive runs are achieved through mechanical coordination between the two heads of myosin Va, which keeps their ATPase cycles out of phase, preventing both heads detaching from actin simultaneously. The biochemical kinetics underlying processivity are still uncertain. Here we attempt to define the biochemical pathways populated by myosin Va by examining the velocity, processive run-length, and individual steps of a Qdot-labeled myosin Va in various substrate conditions (i.e., changes in ATP, ADP, and Pi) under zero load in the single-molecule total internal reflection fluorescence microscopy assay. These data were used to globally constrain a branched kinetic scheme that was necessary to fit the dependences of velocity and run-length on substrate conditions. Based on this model, myosin Va can be biased along a given pathway by changes in substrate concentrations. This has uncovered states not normally sampled by the motor, and suggests that every transition involving substrate binding and release may be strain-dependent. © 2012 Biophysical Society

Genome-wide case-control study in GAW17 using coalesced rare variants

Genome-wide association studies have successfully identified numerous loci at which common variants influence disease risks or quantitative traits of interest. Despite these successes, the variants identified by these studies have generally explained only a small fraction of the variations in the phenotype. One explanation may be that many rare variants that are not included in the common genotyping platforms may contribute substantially to the genetic variations of the diseases. Next-generation sequencing, which would better allow for the analysis of rare variants, is now becoming available and affordable; however, the presence of a large number of rare variants challenges the statistical endeavor to stably identify these disease-causing genetic variants. We conduct a genome-wide association study of Genetic Analysis Workshop 17 case-control data produced by the next-generation sequencing technique and propose that collapsing rare variants within each genetic region through a supervised dimension reduction algorithm leads to several macrovariants constructed for rare variants within each genetic region. A simultaneous association of the phenotype to all common variants and macrovariants is undertaken using a linear discriminant analysis using the penalized orthogonal-components regression algorithm. The results suggest that the proposed analysis strategy shows promise but needs further development

Development of a diabetes self-management + mHealth program: Tailoring the intervention for a pilot study in a low-income setting in Mexico

Background: Type 2 diabetes (T2D) is a public health pandemic disproportionately affecting low- and middle-income countries. The purpose of this formative research was to adapt evidence-based diabetes self-management education programs to the context of Seguro Popular clinics in Mexico. A theory-based mHealth (pictorial text messaging) component was developed. Method: Our formative research and development of the program protocol consisted of six phases: (1) interviews and focus groups with stakeholders on the challenges to T2D management, curriculum content needs, and the use of mHealth as a supplement to a DSME program; (2) review of the theoretical underpinning, curriculum, and interactive strategies of four evidence-based DSME programs and modification to meet the needs of adults with T2D and systems of care in Mexico City; (3) development of theory-based illustrated text messages; (4) evaluation of text messaging acceptability and access in adults with T2D via focus groups; (5) development of program manual; and (6) development of a training program for health care providers. Results: The ¡Sí, Yo Puedo Vivir Sano Con Diabetes! included 7 group-based weekly lessons; simple, interactive content; weekly empowerment messages; video novellas; group activities; and goal setting. Adaptations to the cultural context of Mexico included content/activities on diabetes etiology (addressing cultural misconceptions), nutrition (indigenous foods and plate method), self-blood glucose monitoring, and diabetes-related stress/coping. We used the Health Action Process Approach to guide the text message development, which posits that adoption, initiation, and maintenance of health behaviors require the development of intentions, plans, coping, and self-efficacy. Our final text message bank consisted of 181 messages. There were approximately 20-30 messages for each process of behavior change (e.g., action planning, maintenance self-efficacy) and 30 messages for each content topic (e.g., eating healthy, physical activity). There were 96 messages that were illustrated. Training materials were also developed. Discussion: We used a systematic approach, collaboration with stakeholders, and a well-established behavior change theory to develop an evidence-based intervention to an international context and system of care. Collectively, this process has the potential to enhance the feasibility, acceptability, and efficacy of the program

Two-stage study designs combining genome-wide association studies, tag single-nucleotide polymorphisms, and exome sequencing: accuracy of genetic effect estimates

Genome-wide association studies (GWAS) test for disease-trait associations and estimate effect sizes at tag single-nucleotide polymorphisms (SNPs), which imperfectly capture variation at causal SNPs. Sequencing studies can examine potential causal SNPs directly; however, sequencing the whole genome or exome can be prohibitively expensive. Costs can be limited by using a GWAS to detect the associated region(s) at tag SNPs followed by targeted sequencing to identify and estimate the effect size of the causal variant. Genetic effect estimates obtained from association studies can be inflated because of a form of selection bias known as the winner’s curse. Conversely, estimates at tag SNPs can be attenuated compared to the causal SNP because of incomplete linkage disequilibrium. These two effects oppose each other. Analysis of rare SNPs further complicates our understanding of the winner’s curse because rare SNPs are difficult to tag and analysis can involve collapsing over multiple rare variants. In two-stage analysis of Genetic Analysis Workshop 17 simulated data sets, we find that selection at the tag SNP produces upward bias in the estimate of effect at the causal SNP, even when the tag and causal SNPs are not well correlated. The bias similarly carries through to effect estimates for rare variant summary measures. Replication studies designed with sample sizes computed using biased estimates will be under-powered to detect a disease-causing variant. Accounting for bias in the original study is critical to avoid discarding disease-associated SNPs at follow up