Multiple stochastic pathways in forced peptide-lipid membrane detachment

We have used high resolution AFM based dynamic force spectroscopy to investigate peptide-lipid membrane interactions by measuring the detachment (last-rupture) force distribution, P(F), and the corresponding force dependent rupture rate, k(F), for two different peptides and lipid bilayers. The measured quantities, which differed considerably for different peptides, lipid-membranes, AFM tips (prepared under identical conditions), and retraction speeds of the AFM cantilever, could not be described in terms of the standard theory, according to which detachment occurs along a single pathway, corresponding to a diffusive escape process across a free energy barrier. In particular, the prominent retraction speed dependence of k(F) was a clear indication that peptide-lipid membrane dissociation occurs stochastically along several detachment pathways. Thereby, we have formulated a general theoretical approach for describing P(F) and k(F), by assuming that peptide detachment from lipid membranes occurs, with certain probability, along a few dominant diffusive pathways. This new method was validated through a consistent interpretation of the experimental data. Furthermore, we have found that for moderate retraction speeds at intermediate force values, k(F) exhibits catch-bond behavior (i.e. decreasing detachment rate with increasing force). According to the proposed model this behavior is due to the stochastic mixing of individual detachment pathways which do not convert or cross during rupture. To our knowledge, such catch-bond mechanism has not been proposed and demonstrated before for a peptide-lipid interaction

The hArtes Tool Chain

This chapter describes the different design steps needed to go from legacy code to a transformed application that can be efficiently mapped on the hArtes platform

Differentially Expressed RNA from Public Microarray Data Identifies Serum Protein Biomarkers for Cross-Organ Transplant Rejection and Other Conditions

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers

A Peripheral Blood Diagnostic Test for Acute Rejection in Renal Transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93759/1/j.1600-6143.2012.04253.x.pd

Advances of genomic science and systems biology in renal transplantation: a review

The diagnosis of rejection in kidney transplant patients is based on histologic classification of a graft biopsy. The current “gold standard” is the Banff 97 criteria; however, there are several limitations in classifying rejection based on biopsy samples. First, a biopsy involves an invasive procedure. Second, there is significant variance among blinded pathologists in the interpretation of a biopsy. And third, there is also variance between the histology and the molecular profiles of a biopsy. To increase the positive predictive value of classifiers of rejection, a Banff committee is developing criteria that integrate histologic and molecular data into a unified classifier that could diagnose and prognose rejection. To develop the most appropriate molecular criteria, there have been studies by multiple groups applying omics technologies in attempts to identify biomarkers of rejection. In this review, we discuss studies using genome-wide data sets of the transcriptome and proteome to investigate acute rejection, chronic allograft dysfunction, and tolerance. We also discuss studies which focus on genetic biomarkers in urine and peripheral blood, which will provide clinicians with minimally invasive methods for monitoring transplant patients. We also discuss emerging technologies, including whole-exome sequencing and RNA-Seq and new bioinformatic and systems biology approaches, which should increase the ability to develop both biomarkers and mechanistic understanding of the rejection process

System-level design space exploration of dynamic reconfigurable architectures

One of the major challenges of designing heterogeneous reconfigurable systems is to obtain the maximum system performance with efficient utilization of the reconfigurable logic resources. To accomplish this, it is essential to perform design space exploration (DSE) at the early design stages. System-level simulation is used to estimate the performance of the system and to make early decisions of various design parameters in order to obtain an optimal system that satisfies the given constraints. Towards this goal, in this paper, we develop a model, which can assist designers at the system-level DSE stage to explore the utilization of the reconfigurable resources and evaluate the relative impact of certain design choices. A case study of a real application shows that the model can be used to explore various design parameters by evaluating the system performance for different application-to-architecture mappings

A computational gene expression score for predicing immune injury in renal allografts

Background Whole genome microarray meta-analyses of 1030 kidney, heart, lung and liver allograft biopsies identified a common immune response module (CRM) of 11 genes that define acute rejection (AR) across different engrafted tissues. We evaluated if the CRM genes can provide a molecular microscope to quantify graft injury in acute rejection (AR) and predict risk of progressive interstitial fibrosis and tubular atrophy (IFTA) in histologically normal kidney biopsies. Methods Computational modeling was done on tissue qPCR based gene expression measurements for the 11 CRM genes in 146 independent renal allografts from 122 unique patients with AR (n = 54) and no-AR (n = 92). 24 demographically matched patients with no-AR had 6 and 24 month paired protocol biopsies; all had histologically normal 6 month biopsies, and 12 had evidence of progressive IFTA (pIFTA) on their 24 month biopsies. Results were correlated with demographic, clinical and pathology variables. Results The 11 gene qPCR based tissue CRM score (tCRM) was significantly increased in AR (5.68 ± 0.91) when compared to STA (1.29 ± 0.28; p < 0.001) and pIFTA (7.94 ± 2.278 versus 2.28 ± 0.66; p = 0.04), with greatest significance for CXCL9 and CXCL10 in AR (p <0.001) and CD6 (p<0.01), CXCL9 (p<0.05), and LCK (p<0.01) in pIFTA. tCRM was a significant independent correlate of biopsy confirmed AR (p < 0.001; AUC of 0.900; 95% CI = 0.705-903). Gene expression modeling of 6 month biopsies across 7/11 genes (CD6, INPP5D, ISG20, NKG7, PSMB9, RUNX3, and TAP1) significantly (p = 0.037) predicted the development of pIFTA at 24 months. Conclusions Genome-wide tissue gene expression data mining has supported the development of a tCRM-qPCR based assay for evaluating graft immune inflammation. The tCRM score quantifies injury in AR and stratifies patients at increased risk of future pIFTA prior to any perturbation of graft function or histology