An external validation study reporting poor correlation between the claims-based index for rheumatoid arthritis severity and the disease activity score

IntroductionWe conducted an external validation study to examine the correlation of a previously published claims-based index for rheumatoid arthritis severity (CIRAS) with disease activity score in 28 joints calculated by using C-reactive protein (DAS28-CRP) and the multi-dimensional health assessment questionnaire (MD-HAQ) physical function score.MethodsPatients enrolled in the Brigham and Women’s Hospital Rheumatoid Arthritis Sequential Study (BRASS) and Medicare were identified and their data from these two sources were linked. For each patient, DAS28-CRP measurement and MD-HAQ physical function scores were extracted from BRASS, and CIRAS was calculated from Medicare claims for the period of 365 days prior to the DAS28-CRP measurement. Pearson correlation coefficient between CIRAS and DAS28-CRP as well as MD-HAQ physical function scores were calculated. Furthermore, we considered several additional pharmacy and medical claims-derived variables as predictors for DAS28-CRP in a multivariable linear regression model in order to assess improvement in the performance of the original CIRAS algorithm.ResultsIn total, 315 patients with enrollment in both BRASS and Medicare were included in this study. The majority (81%) of the cohort was female, and the mean age was 70 years. The correlation between CIRAS and DAS28-CRP was low (Pearson correlation coefficient = 0.07, P = 0.24). The correlation between the calculated CIRAS and MD-HAQ physical function scores was also found to be low (Pearson correlation coefficient = 0.08, P = 0.17). The linear regression model containing additional claims-derived variables yielded model R² of 0.23, suggesting limited ability of this model to explain variation in DAS28-CRP.ConclusionsIn a cohort of Medicare-enrolled patients with established RA, CIRAS showed low correlation with DAS28-CRP as well as MD-HAQ physical function scores. Claims-based algorithms for disease activity should be rigorously tested in distinct populations in order to establish their generalizability before widespread adoption.     

Discovery of Lu AA33810: a highly selective and potent NPY5 antagonist with in vivo efficacy in a model of mood disorder

The structure-activity relationship of a series of tricyclic-sulfonamide compounds 11-32 culminating in the discovery of N-[trans-4-(4,5-dihydro-3,6-dithia-1-aza-benzo[e]azulen-2-ylamino)-cyclohexylmethyl]-methanesulfonamide (15, Lu AA33810) is reported. Compound 15 was identified as a selective and high affinity NPY5 antagonist with good oral bioavailability in mice (42%) and rats (92%). Dose dependent inhibition of feeding was observed after i.c.v. injection of the selective NPY5 agonist ([cPP(1-7),NPY(19-23),Ala(31),Aib(32),Gln(34)]-hPP). In addition, ip administration of Lu AA33810 (10 mg/kg) produced antidepressant-like effects in a rat model of chronic mild stress.     

IFN-α/β and autophagy: tug-of-war between HCV and the host

Hepatitis C virus (HCV) infects approximately 130 million people worldwide. The clinical sequelae of this chronic disease include cirrhosis, functional failure and carcinoma of the liver. HCV induces autophagy, a fundamental cellular process for maintaining homeostasis and mediating innate immune response, and also inhibits autophagic protein degradation and suppresses antiviral immunity. In addition to this ploy, the HCV serine protease composed of the viral non-structural proteins 3/4A (NS3/4A) can enzymatically digest two cellular proteins, mitochondria-associated anti-viral signaling protein (MAVS) and Toll/interleukin-1 receptor domain containing adaptor inducing IFN-β (TRIF). Since these two proteins are the adaptor molecules in the retinoic acid-inducible gene I (RIG-I) and TLR3 pathways, respectively, their cleavage has been suggested as a pivotal mechanism by which HCV blunts the IFN-α/β signaling and antiviral responses. Thus far, how HCV perturbs autophagy and copes with IFN-α/β in the liver remains unclear.     

A novel PKC-ι inhibitor abrogates cell proliferation and induces apoptosis in neuroblastoma

Protein Kinase C-iota (PKC-ι), an atypical protein kinase C isoform manifests its potential as an oncogene by targeting various aspects of cancer cells such as growth, invasion and survival. PKC-ι confers resistance to drug-induced apoptosis in cancer cells. The acquisition of drug resistance is a major obstacle to good prognosis in neuroblastoma. The focus of this research was to identify the efficacy of [4-(5-amino-4-carbamoylimidazol-1-yl)-2,3-dihydroxycyclopentyl] methyl dihydrogen phosphate (ICA-1) as a novel PKC-ι inhibitor in neuroblastoma cell proliferation and apoptosis. ICA-1 specifically inhibits the activity of PKC-ι but not that of PKC-zeta (PKC-ζ), the closely related atypical PKC family member. The IC(50) for the kinase activity assay was approximately 0.1μM which is 1000 times less than that of aurothiomalate, a known PKC-ι inhibitor. Cyclin dependent kinase 7 (Cdk7) phosphorylates cyclin dependent kinases (cdks) and promotes cell proliferation. Our data shows that PKC-ι is an in vitro Cdk7 kinase and the phosphorylation of Cdk7 by PKC-ι was potently inhibited by ICA-1. Furthermore, our data shows that neuroblastoma cells proliferate via a PKC-ι/Cdk7/cdk2 cell signaling pathway and ICA-1 mediates its antiproliferative effects by inhibiting this pathway. ICA-1 (0.1μM) inhibited the in vitro proliferation of BE(2)-C neuroblastoma cells by 58% (P=0.01). Additionally, ICA-1 also induced apoptosis in neuroblastoma cells. Interestingly, ICA-1 did not affect the proliferation of normal neuronal cells suggesting its potential as chemotherapeutic with low toxicity. Hence, our results emphasize the potential of ICA-1 as a novel PKC-ι inhibitor and chemotherapeutic agent for neuroblastoma.     

A photocrosslinking assay for reporting protein interactions in polyketide and fatty acid synthases

Understanding protein-protein interactions that occur between ACP and KS domains of polyketide synthases and fatty acid synthases is critical to improving the scope and efficiency of combinatorial biosynthesis efforts aimed at producing non-natural polyketides. Here, we report a facile strategy for rapidly reporting such ACP-KS interactions based on the incorporation of an amino acid with photocrosslinking functionality. Crucially, this photocrosslinking strategy can be applied to any polyketide or fatty acid synthase regardless of substrate specificity, and can be adapted to a high-throughput format for directed evolution studies.     

Bacteroides in the infant gut consume milk oligosaccharides via mucus-utilization pathways

Newborns are colonized with an intestinal microbiota shortly after birth, but the factors governing the retention and abundance of specific microbial lineages are unknown. Nursing infants consume human milk oligosaccharides (HMOs) that pass undigested to the distal gut, where they may be digested by microbes. We determined that the prominent neonate gut residents, Bacteroides thetaiotaomicron and Bacteroides fragilis, induce the same genes during HMO consumption that are used to harvest host mucus glycans, which are structurally similar to HMOs. Lacto-N-neotetraose, a specific HMO component, selects for HMO-adapted species such as Bifidobacterium infantis, which cannot use mucus, and provides a selective advantage to B. infantis in vivo when biassociated with B. thetaiotaomicron in the gnotobiotic mouse gut. This indicates that the complex oligosaccharide mixture within HMOs attracts both mutualistic mucus-adapted species and HMO-adapted bifidobacteria to the infant intestine that likely facilitate both milk and future solid food digestion.     

Mining relational paths in integrated biomedical data

Much life science and biology research requires an understanding of complex relationships between biological entities (genes, compounds, pathways, diseases, and so on). There is a wealth of data on such relationships in publicly available datasets and publications, but these sources are overlapped and distributed so that finding pertinent relational data is increasingly difficult. Whilst most public datasets have associated tools for searching, there is a lack of searching methods that can cross data sources and that in particular search not only based on the biological entities themselves but also on the relationships between them. In this paper, we demonstrate how graph-theoretic algorithms for mining relational paths can be used together with a previous integrative data resource we developed called Chem2Bio2RDF to extract new biological insights about the relationships between such entities. In particular, we use these methods to investigate the genetic basis of side-effects of thiazolinedione drugs, and in particular make a hypothesis for the recently discovered cardiac side-effects of Rosiglitazone (Avandia) and a prediction for Pioglitazone which is backed up by recent clinical studies.     

AGeS: a software system for microbial genome sequence annotation

BACKGROUND: The annotation of genomes from next-generation sequencing platforms needs to be rapid, high-throughput, and fully integrated and automated. Although a few Web-based annotation services have recently become available, they may not be the best solution for researchers that need to annotate a large number of genomes, possibly including proprietary data, and store them locally for further analysis. To address this need, we developed a standalone software application, the Annotation of microbial Genome Sequences (AGeS) system, which incorporates publicly available and in-house-developed bioinformatics tools and databases, many of which are parallelized for high-throughput performance. METHODOLOGY: The AGeS system supports three main capabilities. The first is the storage of input contig sequences and the resulting annotation data in a central, customized database. The second is the annotation of microbial genomes using an integrated software pipeline, which first analyzes contigs from high-throughput sequencing by locating genomic regions that code for proteins, RNA, and other genomic elements through the Do-It-Yourself Annotation (DIYA) framework. The identified protein-coding regions are then functionally annotated using the in-house-developed Pipeline for Protein Annotation (PIPA). The third capability is the visualization of annotated sequences using GBrowse. To date, we have implemented these capabilities for bacterial genomes. AGeS was evaluated by comparing its genome annotations with those provided by three other methods. Our results indicate that the software tools integrated into AGeS provide annotations that are in general agreement with those provided by the compared methods. This is demonstrated by a >94% overlap in the number of identified genes, a significant number of identical annotated features, and a >90% agreement in enzyme function predictions.