Genetic Risk and Atrial Fibrillation in Patients with Heart Failure

Aims: To study the association between an atrial fibrillation (AF) genetic risk score with prevalent AF and all-cause mortality in patients with heart failure. Methods and results: An AF genetic risk score was calculated in 3759 European ancestry individuals (1783 with sinus rhythm, 1976 with AF) from the BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) by summing 97 single nucleotide polymorphism (SNP) alleles (ranging from 0–2) weighted by the natural logarithm of the relative SNP risk from the latest AF genome-wide association study. Further, we assessed AF risk variance explained by additive SNP variation, and performance of clinical or genetic risk factors, and the combination in classifying AF prevalence. AF was classified as AF or atrial flutter (AFL) at baseline electrocardiogram and/or a history of AF or AFL. The genetic risk score was associated with AF after multivariable adjustment. Odds ratio for AF prevalence per 1-unit increase genetic risk score was 2.12 (95% confidence interval 1.84–2.45, P = 2.15 × 10−24) in the total cohort, 2.08 (1.72–2.50, P = 1.30 × 10−14) in heart failure with reduced ejection fraction (HFrEF) and 2.02 (1.37–2.99, P = 4.37 × 10−4) in heart failure with preserved ejection fraction (HFpEF). AF-associated loci explained 22.9% of overall AF SNP heritability. Addition of the genetic risk score to clinical risk factors increased the C-index by 2.2% to 0.721. Conclusions: The AF genetic risk score was associated with increased AF prevalence in HFrEF and HFpEF. Genetic variation accounted for 22.9% of overall AF SNP heritability. Addition of genetic risk to clinical risk improved model performance in classifying AF prevalence

A Computational and Experimental Study of the Regulatory Mechanisms of the Complement System

The complement system is key to innate immunity and its activation is necessary for the clearance of bacteria and apoptotic cells. However, insufficient or excessive complement activation will lead to immune-related diseases. It is so far unknown how the complement activity is up- or down- regulated and what the associated pathophysiological mechanisms are. To quantitatively understand the modulatory mechanisms of the complement system, we built a computational model involving the enhancement and suppression mechanisms that regulate complement activity. Our model consists of a large system of Ordinary Differential Equations (ODEs) accompanied by a dynamic Bayesian network as a probabilistic approximation of the ODE dynamics. Applying Bayesian inference techniques, this approximation was used to perform parameter estimation and sensitivity analysis. Our combined computational and experimental study showed that the antimicrobial response is sensitive to changes in pH and calcium levels, which determines the strength of the crosstalk between CRP and L-ficolin. Our study also revealed differential regulatory effects of C4BP. While C4BP delays but does not decrease the classical complement activation, it attenuates but does not significantly delay the lectin pathway activation. We also found that the major inhibitory role of C4BP is to facilitate the decay of C3 convertase. In summary, the present work elucidates the regulatory mechanisms of the complement system and demonstrates how the bio-pathway machinery maintains the balance between activation and inhibition. The insights we have gained could contribute to the development of therapies targeting the complement system.Singapore. Ministry of Education (Grant T208B3109)Singapore. Agency for Science, Technology and Research (BMRC 08/1/21/19/574)Singapore-MIT Alliance (Computational and Systems Biology Flagship Project)Swedish Research Counci

Design and implementation of a generalized laboratory data model

<p>Abstract</p> <p>Background</p> <p>Investigators in the biological sciences continue to exploit laboratory automation methods and have dramatically increased the rates at which they can generate data. In many environments, the methods themselves also evolve in a rapid and fluid manner. These observations point to the importance of robust information management systems in the modern laboratory. Designing and implementing such systems is non-trivial and it appears that in many cases a database project ultimately proves unserviceable.</p> <p>Results</p> <p>We describe a general modeling framework for laboratory data and its implementation as an information management system. The model utilizes several abstraction techniques, focusing especially on the concepts of inheritance and meta-data. Traditional approaches commingle event-oriented data with regular entity data in <it>ad hoc </it>ways. Instead, we define distinct regular entity and event schemas, but fully integrate these via a standardized interface. The design allows straightforward definition of a "processing pipeline" as a sequence of events, obviating the need for separate workflow management systems. A layer above the event-oriented schema integrates events into a workflow by defining "processing directives", which act as automated project managers of items in the system. Directives can be added or modified in an almost trivial fashion, i.e., without the need for schema modification or re-certification of applications. Association between regular entities and events is managed via simple "many-to-many" relationships. We describe the programming interface, as well as techniques for handling input/output, process control, and state transitions.</p> <p>Conclusion</p> <p>The implementation described here has served as the Washington University Genome Sequencing Center's primary information system for several years. It handles all transactions underlying a throughput rate of about 9 million sequencing reactions of various kinds per month and has handily weathered a number of major pipeline reconfigurations. The basic data model can be readily adapted to other high-volume processing environments.</p

Local Inflammation Induces Complement Crosstalk Which Amplifies the Antimicrobial Response

By eliciting inflammatory responses, the human immunosurveillance system notably combats invading pathogens, during which acute phase proteins (CRP and cytokines) are elevated markedly. However, the Pseudomonas aeruginosa is a persistent opportunistic pathogen prevalent at the site of local inflammation, and its acquisition of multiple antibiotic-resistance factors poses grave challenges to patient healthcare management. Using blood samples from infected patients, we demonstrate that P. aeruginosa is effectively killed in the plasma under defined local infection-inflammation condition, where slight acidosis and reduced calcium levels (pH 6.5, 2 mM calcium) typically prevail. We showed that this powerful antimicrobial activity is provoked by crosstalk between two plasma proteins; CRP∶L-ficolin interaction led to communication between the complement classical and lectin pathways from which two amplification events emerged. Assays for C4 deposition, phagocytosis, and protein competition consistently proved the functional significance of the amplification pathways in boosting complement-mediated antimicrobial activity. The infection-inflammation condition induced a 100-fold increase in CRP∶L-ficolin interaction in a pH- and calcium-sensitive manner. We conclude that the infection-induced local inflammatory conditions trigger a strong interaction between CRP∶L-ficolin, eliciting complement-amplification pathways which are autonomous and which co-exist with and reinforce the classical and lectin pathways. Our findings provide new insights into the host immune response to P. aeruginosa infection under pathological conditions and the potential development of new therapeutic strategies against bacterial infection

RNA-seq Analysis Reveals That an ECF σ Factor, AcsS, Regulates Achromobactin Biosynthesis in Pseudomonas syringae pv. syringae B728a

Iron is an essential micronutrient for Pseudomonas syringae pv. syringae strain B728a and many other microorganisms; therefore, B728a has evolved methods of iron acquirement including the use of iron-chelating siderophores. In this study an extracytoplasmic function (ECF) sigma factor, AcsS, encoded within the achromobactin gene cluster is shown to be a major regulator of genes involved in the biosynthesis and secretion of this siderophore. However, production of achromobactin was not completely abrogated in the deletion mutant, implying that other regulators may be involved such as PvdS, the sigma factor that regulates pyoverdine biosynthesis. RNA-seq analysis identified 287 genes that are differentially expressed between the AcsS deletion mutant and the wild type strain. These genes are involved in iron response, secretion, extracellular polysaccharide production, and cell motility. Thus, the transcriptome analysis supports a role for AcsS in the regulation of achromobactin production and the potential activity of both AcsS and achromobactin in the plant-associated lifestyle of strain B728a

Radiation and breast cancer: a review of current evidence

This paper summarizes current knowledge on ionizing radiation-associated breast cancer in the context of established breast cancer risk factors, the radiation dose–response relationship, and modifiers of dose response, taking into account epidemiological studies and animal experiments. Available epidemiological data support a linear dose–response relationship down to doses as low as about 100 mSv. However, the magnitude of risk per unit dose depends strongly on when radiation exposure occurs: exposure before the age of 20 years carries the greatest risk. Other characteristics that may influence the magnitude of dose-specific risk include attained age (that is, age at observation for risk), age at first full-term birth, parity, and possibly a history of benign breast disease, exposure to radiation while pregnant, and genetic factors

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care