Explore the vast range of titles available.

Multiple Sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system. The risk of developing MS is strongly influenced by genetic predisposition, and over 100 loci have been established as associated with susceptibility. However, the biologically relevant variants underlying disease risk have not been defined for the vast majority of these loci, limiting the power of these genetic studies to define new avenues of research for the development of MS therapeutics. It is therefore crucial that candidate MS susceptibility loci are carefully investigated to identify the biological mechanism linking genetic polymorphism at a given gene to the increased chance of developing MS. MERTK has been established as an MS susceptibility gene and is part of a family of receptor tyrosine kinases known to be involved in the pathogenesis of demyelinating disease. In this study we have refined the association of MERTK with MS risk to independent signals from both common and low frequency variants. One of the associated variants was also found to be linked with increased expression of MERTK in monocytes and higher expression of MERTK was associated with either increased or decreased risk of developing MS, dependent upon HLA-DRB1*15:01 status. This discordant association potentially extended beyond MS susceptibility to alterations in disease course in established MS. This study provides clear evidence that distinct polymorphisms within MERTK are associated with MS susceptibility, one of which has the potential to alter MERTK transcription, which in turn can alter both susceptibility and disease course in MS patients.

Mutations in the profilin 1 ( PFN1 ) gene, which is crucial for the conversion of monomeric to filamentous actin, can cause familial amyotrophic lateral sclerosis, suggesting that alterations in cytoskeletal pathways contribute to disease pathogenesis. Genetics of familial amyotrophic lateral sclerosis In nearly half of the familial cases of the neurodegenerative disorder amyotrophic lateral sclerosis (ALS), the genetic basis remains unknown. These authors show that mutations in the profilin 1 ( PFN1 ) gene, which is essential for the conversion of monomeric to filamentous actin, can cause familial ALS. The available data suggest that alterations in cytoskeletal pathways contribute to the pathogenesis of ALS. The observation of PFN1 mutations in ALS has immediate implications for diagnostic testing of familial ALS cases and provides a novel potential target for the treatment of ALS. Amyotrophic lateral sclerosis (ALS) is a late-onset neurodegenerative disorder resulting from motor neuron death. Approximately 10% of cases are familial (FALS), typically with a dominant inheritance mode. Despite numerous advances in recent years 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , nearly 50% of FALS cases have unknown genetic aetiology. Here we show that mutations within the profilin 1 ( PFN1 ) gene can cause FALS. PFN1 is crucial for the conversion of monomeric (G)-actin to filamentous (F)-actin. Exome sequencing of two large ALS families showed different mutations within the PFN1 gene. Further sequence analysis identified 4 mutations in 7 out of 274 FALS cases. Cells expressing PFN1 mutants contain ubiquitinated, insoluble aggregates that in many cases contain the ALS-associated protein TDP-43. PFN1 mutants also display decreased bound actin levels and can inhibit axon outgrowth. Furthermore, primary motor neurons expressing mutant PFN1 display smaller growth cones with a reduced F/G-actin ratio. These observations further document that cytoskeletal pathway alterations contribute to ALS pathogenesis.

In this dissertation I present three significant advances in the theory and practice of systems biology research. The research presented touches upon diverse topics spanning the field, including models for biological network inference, methods for estimating the statistical significance of the results generated by these models, and computational analysis of protein network motif conservation. We conclude by presenting a novel algorithm that integrates heterogeneous genomic data sets to identify key functional regulators of human disease. First, we implement a robust, efficient and flexible web application (BNET) for the evaluation of computationally and experimentally inferred biological networks. The tool makes use of a powerful statistical network evaluation model and has applications across a broad spectrum of both computational and experimental systems biology research. BNET compares predicted network models against large-scale experimental data mined from public databases, and quantifies the level of evidentiary support for the proposed network in a statistically defensible manner. Applications include rapid evaluation of competing network inference algorithms, consistency and repeatability testing for experimental methods in systems biology, and the assessment of novel coverage achieved by newly developed experimental techniques. Next, we present a computational evolutionary analysis of protein networks in four species. Our analysis sheds new light on some of the principal assumptions underlying biomedical research in model organisms and leads to the development of an evolutionary model of conservation in protein networks that is consistent with the available network data. Our model additionally supports robust annotation transfer of protein sub-network data from model organisms to human, drastically improving data coverage in the human protein network. Finally, we develop a novel algorithm for identifying key proteins involved in regulating human disease. This algorithm uses only protein subnetwork information and is therefore able to exploit high-coverage protein networks inferred under our previously described model of sub-network conservation. Case studies on genome-wide diabetes and cancer profile data sets reveal that the algorithm identifies known key regulators of both cancer progression and diabetes. Taken together, our results represent significant advancements towards the fundamental promise of systems biology: that the development of comprehensive, detailed and robust quantitative network models can drive creation of the biological knowledge necessary to both understand and treat the most intrusive and most costly human diseases.

There is limited knowledge of the scale and impact of multimorbidity for patients who have had an acute myocardial infarction (AMI). Therefore, this study aimed to determine the extent to which multimorbidity is associated with long-term survival following AMI. This national observational study included 693,388 patients (median age 70.7 years, 452,896 [65.5%] male) from the Myocardial Ischaemia National Audit Project (England and Wales) who were admitted with AMI between 1 January 2003 and 30 June 2013. There were 412,809 (59.5%) patients with multimorbidity at the time of admission with AMI, i.e., having at least 1 of the following long-term health conditions: diabetes, chronic obstructive pulmonary disease or asthma, heart failure, renal failure, cerebrovascular disease, peripheral vascular disease, or hypertension. Those with heart failure, renal failure, or cerebrovascular disease had the worst outcomes (39.5 [95% CI 39.0-40.0], 38.2 [27.7-26.8], and 26.6 [25.2-26.4] deaths per 100 person-years, respectively). Latent class analysis revealed 3 multimorbidity phenotype clusters: (1) a high multimorbidity class, with concomitant heart failure, peripheral vascular disease, and hypertension, (2) a medium multimorbidity class, with peripheral vascular disease and hypertension, and (3) a low multimorbidity class. Patients in class 1 were less likely to receive pharmacological therapies compared with class 2 and 3 patients (including aspirin, 83.8% versus 87.3% and 87.2%, respectively; β-blockers, 74.0% versus 80.9% and 81.4%; and statins, 80.6% versus 85.9% and 85.2%). Flexible parametric survival modelling indicated that patients in class 1 and class 2 had a 2.4-fold (95% CI 2.3-2.5) and 1.5-fold (95% CI 1.4-1.5) increased risk of death and a loss in life expectancy of 2.89 and 1.52 years, respectively, compared with those in class 3 over the 8.4-year follow-up period. The study was limited to all-cause mortality due to the lack of available cause-specific mortality data. However, we isolated the disease-specific association with mortality by providing the loss in life expectancy following AMI according to multimorbidity phenotype cluster compared with the general age-, sex-, and year-matched population. Multimorbidity among patients with AMI was common, and conferred an accumulative increased risk of death. Three multimorbidity phenotype clusters that were significantly associated with loss in life expectancy were identified and should be a concomitant treatment target to improve cardiovascular outcomes. ClinicalTrials.gov NCT03037255.

Clopidogrel and aspirin are the most commonly used antiplatelet therapies for percutaneous coronary intervention (PCI). We assessed the effect of various clopidogrel and aspirin regimens in prevention of major cardiovascular events and stent thrombosis in patients undergoing PCI. The CURRENT-OASIS 7 trial was undertaken in 597 centres in 39 countries. 25 086 individuals with acute coronary syndromes and intended early PCI were randomly assigned to double-dose (600 mg on day 1, 150 mg on days 2–7, then 75 mg daily) versus standard-dose (300 mg on day 1 then 75 mg daily) clopidogrel, and high-dose (300–325 mg daily) versus low-dose (75–100 mg daily) aspirin. Randomisation was done with a 24 h computerised central automated voice response system. The clopidogrel dose comparison was double-blind and the aspirin dose comparison was open label with blinded assessment of outcomes. This prespecified analysis is of the 17 263 individuals who underwent PCI. The primary outcome was cardiovascular death, myocardial infarction, or stroke at 30 days. Analyses were by intention to treat, adjusted for propensity to undergo PCI. This trial is registered with ClinicalTrials.gov, number NCT00335452. 8560 patients were assigned to double-dose and 8703 to standard-dose clopidogrel (8558 and 8702 completed 30-day follow-up, respectively), and 8624 to high-dose and 8639 to low-dose aspirin (8622 and 8638 completed 30-day follow-up, respectively). Compared with the standard dose, double-dose clopidogrel reduced the rate of the primary outcome (330 events [3·9%] vs 392 events [4·5%]; adjusted hazard ratio 0·86, 95% CI 0·74–0·99, p=0·039) and definite stent thrombosis (58 [0·7%] vs 111 [1·3%]; 0·54 [0·39–0·74], p=0·0001). High-dose and low-dose aspirin did not differ for the primary outcome (356 [4·1%] vs 366 [4·2%]; 0·98, 0·84–1·13, p=0·76). Major bleeding was more common with double-dose than with standard-dose clopidogrel (139 [1·6%] vs 99 [1·1%]; 1·41, 1·09–1·83, p=0·009) and did not differ between high-dose and low-dose aspirin (128 [1·5%] vs 110 [1·3%]; 1·18, 0·92–1·53, p=0·20). In patients undergoing PCI for acute coronary syndromes, a 7-day double-dose clopidogrel regimen was associated with a reduction in cardiovascular events and stent thrombosis compared with the standard dose. Efficacy and safety did not differ between high-dose and low-dose aspirin. A double-dose clopidogrel regimen can be considered for all patients with acute coronary syndromes treated with an early invasive strategy and intended early PCI. Sanofi-Aventis and Bristol-Myers Squibb.

During the past two decades, mindfulness meditation has gone from being a fringe topic of scientific investigation to being an occasional replacement for psychotherapy, tool of corporate well-being, widely implemented educational practice, and “key to building more resilient soldiers.” Yet the mindfulness movement and empirical evidence supporting it have not gone without criticism. Misinformation and poor methodology associated with past studies of mindfulness may lead public consumers to be harmed, misled, and disappointed. Addressing such concerns, the present article discusses the difficulties of defining mindfulness, delineates the proper scope of research into mindfulness practices, and explicates crucial methodological issues for interpreting results from investigations of mindfulness. For doing so, the authors draw on their diverse areas of expertise to review the present state of mindfulness research, comprehensively summarizing what we do and do not know, while providing a prescriptive agenda for contemplative science, with a particular focus on assessment, mindfulness training, possible adverse effects, and intersection with brain imaging. Our goals are to inform interested scientists, the news media, and the public, to minimize harm, curb poor research practices, and staunch the flow of misinformation about the benefits, costs, and future prospects of mindfulness meditation.

Identifying tumor antigen-specific T cells from cancer patients has important implications for immunotherapy diagnostics and therapeutics. Here, we show that CD103 + CD39 + tumor-infiltrating CD8 T cells (CD8 TIL) are enriched for tumor-reactive cells both in primary and metastatic tumors. This CD8 TIL subset is found across six different malignancies and displays an exhausted tissue-resident memory phenotype. CD103 + CD39 + CD8 TILs have a distinct T-cell receptor (TCR) repertoire, with T-cell clones expanded in the tumor but present at low frequencies in the periphery. CD103 + CD39 + CD8 TILs also efficiently kill autologous tumor cells in a MHC-class I-dependent manner. Finally, higher frequencies of CD103 + CD39 + CD8 TILs in patients with head and neck cancer are associated with better overall survival. Our data thus describe an approach for detecting tumor-reactive CD8 TILs that will help define mechanisms of existing immunotherapy treatments, and may lead to future adoptive T-cell cancer therapies. Identifying and enumerating tumor-specific CD8 T cells are important for assessing cancer prognosis and therapy efficacy. Here the authors show that CD39 and CD103 mark a subset of tumor-infiltrating CD8 T cells that are tumor-reactive and exhibit characteristics of exhausted or tissue-resident memory T cells.

Introduction Since 2014, the global public health community has recognized a set of targets for human immunodeficiency virus (HIV) known as “90-90-90,” an ambitious plan that called for the diagnosis of 90% of people living with HIV (PLHIV), antiretroviral therapy(ART) for 90% of those diagnosed HIV–positive, and viral suppression in 90% of those receiving ART by 2020 [1]. Population-level control of HIV will remain out of reach if many people initiating treatment disengage from ART for long periods of time, as they will have increased opportunity for viral load failure, morbidity, mortality [7], development of drug resistance, and viral transmission [8–11]. Clinical outcomes, such as viral load suppression, are not explicitly included in the cascade as long-term retention in care, the fourth stage in our cascade, is highly correlated with suppressed viral load [7,27], and our intention is to better understand PLHIV behavior as it relates to patterns of engagement, not the biological results of treatment. [...]the proposed cascade does not identify the facility at which people return to care or indicate how long they were disengaged.