Research-based versus clinical serum creatinine measurements and the association of acute kidney injury with subsequent kidney function: findings from the Chronic Renal Insufficiency Cohort study.

Background:Observational studies relying on clinically obtained data have shown that acute kidney injury (AKI) is linked to accelerated chronic kidney disease (CKD) progression. However, prior reports lacked uniform collection of important confounders such as proteinuria and pre-AKI kidney function trajectory, and may be susceptible to ascertainment bias, as patients may be more likely to undergo kidney function testing after AKI. Methods:We studied 444 adults with CKD who participated in the prospective Chronic Renal Insufficiency Cohort (CRIC) Study and were concurrent members of a large integrated healthcare delivery system. We estimated glomerular filtration rate (eGFR) trajectories using serum creatinine measurements from (i) the CRIC research protocol (yearly) and (ii) routine clinical care. We used linear mixed effects models to evaluate the associations of AKI with acute absolute change in eGFR and post-AKI eGFR slope, and explored whether these varied by source of creatinine results. Models were adjusted for demographic characteristics, diabetes status and albuminuria. Results:During median follow-up of 8.5 years, mean rate of eGFR loss was -0.31 mL/min/1.73 m2/year overall, and 73 individuals experienced AKI (55% Stage 1). A significant interaction existed between AKI and source of serum creatinine for acute absolute change in eGFR level after discharge; in contrast, AKI was independently associated with a faster rate of eGFR decline (mean additional loss of -0.67 mL/min/1.73 m2/year), which was not impacted by source of serum creatinine. Conclusions:AKI is independently associated with subsequent steeper eGFR decline regardless of the serum creatinine source used, but the strength of association is smaller than observed in prior studies after taking into account key confounders such as pre-AKI eGFR slope and albuminuria

Markov chain aggregation and its application to rule-based modelling

Rule-based modelling allows to represent molecular interactions in a compact and natural way. The underlying molecular dynamics, by the laws of stochastic chemical kinetics, behaves as a continuous-time Markov chain. However, this Markov chain enumerates all possible reaction mixtures, rendering the analysis of the chain computationally demanding and often prohibitive in practice. We here describe how it is possible to efficiently find a smaller, aggregate chain, which preserves certain properties of the original one. Formal methods and lumpability notions are used to define algorithms for automated and efficient construction of such smaller chains (without ever constructing the original ones). We here illustrate the method on an example and we discuss the applicability of the method in the context of modelling large signalling pathways

Augmented Sparse Reconstruction of Protein Signaling Networks

The problem of reconstructing and identifying intracellular protein signaling and biochemical networks is of critical importance in biology today. We sought to develop a mathematical approach to this problem using, as a test case, one of the most well-studied and clinically important signaling networks in biology today, the epidermal growth factor receptor (EGFR) driven signaling cascade. More specifically, we suggest a method, augmented sparse reconstruction, for the identification of links among nodes of ordinary differential equation (ODE) networks from a small set of trajectories with different initial conditions. Our method builds a system of representation by using a collection of integrals of all given trajectories and by attenuating block of terms in the representation itself. The system of representation is then augmented with random vectors, and minimization of the 1-norm is used to find sparse representations for the dynamical interactions of each node. Augmentation by random vectors is crucial, since sparsity alone is not able to handle the large error-in-variables in the representation. Augmented sparse reconstruction allows to consider potentially very large spaces of models and it is able to detect with high accuracy the few relevant links among nodes, even when moderate noise is added to the measured trajectories. After showing the performance of our method on a model of the EGFR protein network, we sketch briefly the potential future therapeutic applications of this approach.Comment: 24 pages, 6 figure

Noninvasive Urinary Monitoring of Progression in IgA Nephropathy.

Standard methods for detecting and monitoring of IgA nephropathy (IgAN) have conventionally required kidney biopsies or suffer from poor sensitivity and specificity. The Kidney Injury Test (KIT) Assay of urinary biomarkers has previously been shown to distinguish between various kidney pathologies, including chronic kidney disease, nephrolithiasis, and transplant rejection. This validation study uses the KIT Assay to investigate the clinical utility of the non-invasive detection of IgAN and predicting the progression of renal damage over time. The study design benefits from longitudinally collected urine samples from an investigator-initiated, multicenter, prospective study, evaluating the efficacy of corticosteroids versus Rituximab for preventing progressive IgAN. A total of 131 urine samples were processed for this study; 64 urine samples were collected from 34 IgAN patients, and urine samples from 64 demographically matched healthy controls were also collected; multiple urinary biomarkers consisting of cell-free DNA, methylated cell-free DNA, DMAIMO, MAMIMO, total protein, clusterin, creatinine, and CXCL10 were measured by the microwell-based KIT Assay. An IgA risk score (KIT-IgA) was significantly higher in IgAN patients as compared to healthy control (87.76 vs. 14.03, p < 0.0001) and performed better than proteinuria in discriminating between the two groups. The KIT Assay biomarkers, measured on a spot random urine sample at study entry could distinguish patients likely to have progressive renal dysfunction a year later. These data support the pursuit of larger prospective studies to evaluate the predictive performance of the KIT-IgA score in both screening for non-invasive diagnosis of IgAN, and for predicting risk of progressive renal disease from IgA and utilizing the KIT score for potentially evaluating the efficacy of IgAN-targeted therapies

High-throughput Binding Affinity Calculations at Extreme Scales

Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High- throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance. We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations. As such, HTBAC advances the state of the art of binding affinity calculations and protocols

Cohort profile: Canadian study of prediction of death, dialysis and interim cardiovascular events (CanPREDDICT)

Background: The Canadian Study of Prediction of Death, Dialysis and Interim Cardiovascular Events (CanPREDDICT) is a large, prospective, pan-Canadian, cohort study designed to improve our understanding of determinants of renal and cardiovascular (CV) disease progression in patients with chronic kidney disease (CKD). The primary objective is to clarify the associations between traditional and newer biomarkers in the prediction of specific renal and CV events, and of death in patients with CKD managed by nephrologists. This information could then be used to better understand biological variation in outcomes, to develop clinical prediction models and to inform enrolment into interventional studies which may lead to novel treatments. Methods/Designs: Commenced in 2008, 2546 patients have been enrolled with eGFR between 15 and 45 ml/min 1.73m2 from a representative sample in 25 rural, urban, academic and non academic centres across Canada. Patients are to be followed for an initial 3 years at 6 monthly intervals, and subsequently annually. Traditional biomarkers include eGFR, urine albumin creatinine ratio (uACR), hemoglobin (Hgb), phosphate and albumin. Newer biomarkers of interest were selected on the basis of biological relevance to important processes, commercial availability and assay reproducibility. They include asymmetric dimethylarginine (ADMA), N-terminal pro-brain natriuretic peptide (NT-pro-BNP), troponin I, cystatin C, high sensitivity C-reactive protein (hsCRP), interleukin-6 (IL6) and transforming growth factor beta 1 (TGFβ1). Blood and urine samples are collected at baseline, and every 6 monthly, and stored at −80°C. Outcomes of interest include renal replacement therapy, CV events and death, the latter two of which are adjudicated by an independent panel. Discussion: The baseline distribution of newer biomarkers does not appear to track to markers of kidney function and therefore may offer some discriminatory value in predicting future outcomes. The granularity of the data presented at baseline may foster additional questions. The value of the cohort as a unique resource to understand outcomes of patients under the care of nephrologists in a single payer healthcare system cannot be overstated. Systematic collection of demographic, laboratory and event data should lead to new insights. The mean age of the cohort was 68 years, 90% were Caucasian, 62% were male, and 48% had diabetes. Forty percent of the cohort had eGFR between 30–45 mL/min/1.73m2, 22% had eGFR values below 20 mL/min/1.73m2; 61% had uACR < 30. Serum albumin, hemoglobin, calcium and 25-hydroxyvitamin D (25(OH)D) levels were progressively lower in the lower eGFR strata, while parathyroid hormone (PTH) levels increased. Cystatin C, ADMA, NT-proBNP, hsCRP, troponin I and IL-6 were significantly higher in the lower GFR strata, whereas 25(OH)D and TGFβ1 values were lower at lower GFR. These distributions of each of the newer biomarkers by eGFR and uACR categories were variable

Targeting Oncogenic BRAF: Past, Present, and Future.

Identifying recurrent somatic genetic alterations of, and dependency on, the kinase BRAF has enabled a "precision medicine" paradigm to diagnose and treat BRAF-driven tumors. Although targeted kinase inhibitors against BRAF are effective in a subset of mutant BRAF tumors, resistance to the therapy inevitably emerges. In this review, we discuss BRAF biology, both in wild-type and mutant settings. We discuss the predominant BRAF mutations and we outline therapeutic strategies to block mutant BRAF and cancer growth. We highlight common mechanistic themes that underpin different classes of resistance mechanisms against BRAF-targeted therapies and discuss tumor heterogeneity and co-occurring molecular alterations as a potential source of therapy resistance. We outline promising therapy approaches to overcome these barriers to the long-term control of BRAF-driven tumors and emphasize how an extensive understanding of these themes can offer more pre-emptive, improved therapeutic strategies