Citations

Renal disease variability in autosomal dominant polycystic kidney disease (ADPKD) is strongly influenced by the gene locus ( PKD1 versus PKD2 ). Recent studies identified nontruncating PKD1 mutations in approximately 30% of patients who underwent comprehensive mutation screening, but the clinical significance of these mutations is not well defined. We examined the genotype-renal function correlation in a prospective cohort of 220 unrelated ADPKD families ascertained through probands with serum creatinine ≤1.4 mg/dl at recruitment. We screened these families for PKD1 and PKD2 mutations and reviewed the clinical outcomes of the probands and affected family members. Height–adjusted total kidney volume (htTKV) was obtained in 161 affected subjects. Multivariate Cox proportional hazard modeling for renal and patient survival was performed in 707 affected probands and family members. Overall, we identified pathogenic mutations in 84.5% of our families, in which the prevalence of PKD1 truncating, PKD1 in–frame insertion/deletion, PKD1 nontruncating, and PKD2 mutations was 38.3%, 4.3%, 27.1%, and 30.3%, respectively. Compared with patients with PKD1 truncating mutations, patients with PKD1 in–frame insertion/deletion, PKD1 nontruncating, or PKD2 mutations have smaller htTKV and reduced risks (hazard ratio [95% confidence interval]) of ESRD (0.35 [0.14 to 0.91], 0.10 [0.05 to 0.18], and 0.03 [0.01 to 0.05], respectively) and death (0.31 [0.11 to 0.87], 0.20 [0.11 to 0.38], and 0.18 [0.11 to 0.31], respectively). Refined genotype-renal disease correlation coupled with targeted next generation sequencing of PKD1 and PKD2 may provide useful clinical prognostication for ADPKD.

ABSTRACTLike any foreign object, orthopaedic implants are susceptible to infection when introduced into the human body. Without additional preventative measures, the absolute number of annual prosthetic joint infections will continue to rise, and may exceed the capacity of health care systems in the near future. Bacteria are difficult to eradicate from synovial joints due to their exceptionally diverse taxonomy, complex mechanistic attachment capabilities, and tendency to evolve antibiotic resistance. When a primary orthopaedic implant fails from prosthetic joint infection, surgeons are generally challenged by limited options for intervention. In this review, we highlight the etiology and taxonomic groupings of bacteria known to cause prosthetic joint infections, and examine their key mechanisms of attachment. We propose that antimicrobial strategies should focus on the most harmful bacteria taxa within the context of occurrence, taxonomic diversity, adhesion mechanisms, and implant design. Patient‐specific identification of organisms that cause prosthetic joint infections will permit assessment of their biological vulnerabilities. The latter can be targeted using a range of antimicrobial techniques that exploit different colonization mechanisms including implant surface attachment, biofilm formation, and/or hematogenous recruitment. We anticipate that customized strategies for each patient, joint, and prosthetic component will be most effective at reducing prosthetic joint infections, including those caused by antibiotic‐resistant and polymicrobial bacteria. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 34:177–186, 2016.

ABSTRACT We report on nine draft genomes of Pseudomonas aeruginosa isolates, assembled using a hybrid paired-end and Nextera mate-pair library approach. Eight are of clinical origin, and one is the ATCC 27853 strain. We also report their multilocus sequence types.

Abstract Summary: The development of the Infinium HumanMethylation450 BeadChip enables epigenome-wide association studies at a reduced cost. One observation of the 450K data is that many CpG sites the beadchip interrogates have very large measurement errors. Including these noisy CpGs will decrease the statistical power of detecting relevant associations due to multiple testing correction. We propose to use intra-class correlation coefficient (ICC), which characterizes the relative contribution of the biological variability to the total variability, to filter CpGs when technical replicates are available. We estimate the ICC based on a linear mixed effects model by pooling all the samples instead of using the technical replicates only. An ultra-fast algorithm has been developed to address the computational complexity and CpG filtering can be completed in minutes on a desktop computer for a 450K data set of over 1000 samples. Our method is very flexible and can accommodate any replicate design. Simulations and a real data application demonstrate that our whole-sample ICC method performs better than replicate-sample ICC or variance-based method. Availability and implementation: CpGFilter is implemented in R and publicly available under CRAN via the R package ‘CpGFilter’. Contact: chen.jun2@mayo.edu or xlin@hsph.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.