Bronchiectasis: a model for chronic bacterial infection inducing autoimmunity in rheumatoid arthritis.

ArticleCopyright © 2015 The Authors. Arthritis & Rheumatology is published by Wiley Periodicals, Inc. on behalf of the American College of Rheumatology.Objective: Bronchiectasis (BR) is a risk factor for rheumatoid arthritis (RA). Here we examine the potential of BR in generating rheumatoid factors (RFs) and anti-citrullinated peptide antibodies (ACPA) in patients with BR alone and in patients with BR and RA (BRRA). Methods: We studied 122 patients with BR alone, 50 BRRA, 50 RA without lung disease, with 87 asthma and 79 healthy subjects as controls. RFs were measured by an automated analyzer, and ACPA using CCP2. Fine specificities to citrullinated α-enolase (CEP-1), citrullinated vimentin (cVim) and fibrinogen (cFib) with their arginine control peptides (REP-1, Vim and Fib) measured by ELISA. Results: In the BR patients 39% were ever smokers compared to 42% of the controls. Serum samples from BR patients had an increased frequency of RF (25%; p< 0.05) and 5% to CCP2, 7% to CEP-1, 7% to cVIM (all p=ns) and 12% cFib (p <0.05). There was also a corresponding increase in antibodies to the arginine-containing control peptides in the BR patients; REP-1, 19% (p< 0.01) and Vim, 16% (p<0.05), demonstrating that the ACPA response in BR is not citrulline-specific. Lack of citrulline specificity was further confirmed by absorption studies. In BRRA all ACPA specificities were highly citrulline-specific. Conclusion: Bronchiectasis is an unusual but potent model for the induction of autoimmunity in RA by bacterial infection in the lung. Our study suggests that in the early stages of tolerance breakdown, the ACPA response is not citrulline-specific, but becomes more so in those patients with BR that develop BRRA.Arthritis Research UKEuropean UnionIMI project BTCure7th Framework Programme project Gums and Joint

Infinitesimally Robust Estimation in General Smoothly Parametrized Models

We describe the shrinking neighborhood approach of Robust Statistics, which applies to general smoothly parametrized models, especially, exponential families. Equal generality is achieved by object oriented implementation of the optimally robust estimators. We evaluate the estimates on real datasets from literature by means of our R packages ROptEst and RobLox

Light smoking at base-line predicts a higher mortality risk to women than to men; evidence from a cohort with long follow-up

BACKGROUND: There is conflicting evidence as to whether smoking is more harmful to women than to men. The UK Cotton Workers’ Cohort was recruited in the 1960s and contained a high proportion of men and women smokers who were well matched in terms of age, job and length of time in job. The cohort has been followed up for 42 years. METHODS: Mortality in the cohort was analysed using an individual relative survival method and Cox regression. Whether smoking, ascertained at baseline in the 1960s, was more hazardous to women than to men was examined by estimating the relative risk ratio women to men, smokers to never smoked, for light (1–14), medium (15–24), heavy (25+ cigarettes per day) and former smoking. RESULTS: For all-cause mortality relative risk ratios were 1.35 for light smoking at baseline (95% CI 1.07-1.70), 1.15 for medium smoking (95% CI 0.89-1.49) and 1.00 for heavy smoking (95% CI 0.63-1.61). Relative risk ratios for light smoking at baseline for circulatory system disease was 1.42 (95% CI 1.01 to 1.98) and for respiratory disease was 1.89 (95% CI 0.99 to 3.63). Heights of participants provided no explanation for the gender difference. CONCLUSIONS: Light smoking at baseline was shown to be significantly more hazardous to women than to men but the effect decreased as consumption increased indicating a dose response relationship. Heavy smoking was equally hazardous to both genders. This result may help explain the conflicting evidence seen elsewhere. However gender differences in smoking cessation may provide an alternative explanation

Differential splicing using whole-transcript microarrays

<p>Abstract</p> <p>Background</p> <p>The latest generation of Affymetrix microarrays are designed to interrogate expression over the entire length of every locus, thus giving the opportunity to study alternative splicing genome-wide. The Exon 1.0 ST (sense target) platform, with versions for Human, Mouse and Rat, is designed primarily to probe every known or predicted exon. The smaller Gene 1.0 ST array is designed as an expression microarray but still interrogates expression with probes along the full length of each well-characterized transcript. We explore the possibility of using the Gene 1.0 ST platform to identify differential splicing events.</p> <p>Results</p> <p>We propose a strategy to score differential splicing by using the auxiliary information from fitting the statistical model, RMA (robust multichip analysis). RMA partitions the probe-level data into probe effects and expression levels, operating robustly so that if a small number of probes behave differently than the rest, they are downweighted in the fitting step. We argue that adjacent poorly fitting probes for a given sample can be evidence of <it>differential </it>splicing and have designed a statistic to search for this behaviour. Using a public tissue panel dataset, we show many examples of tissue-specific alternative splicing. Furthermore, we show that evidence for putative alternative splicing has a strong correspondence between the Gene 1.0 ST and Exon 1.0 ST platforms.</p> <p>Conclusion</p> <p>We propose a new approach, FIRMAGene, to search for differentially spliced genes using the Gene 1.0 ST platform. Such an analysis complements the search for differential expression. We validate the method by illustrating several known examples and we note some of the challenges in interpreting the probe-level data.</p> <p>Software implementing our methods is freely available as an <monospace>R</monospace> package.</p

Evaluation of clustering algorithms for gene expression data

BACKGROUND: Cluster analysis is an integral part of high dimensional data analysis. In the context of large scale gene expression data, a filtered set of genes are grouped together according to their expression profiles using one of numerous clustering algorithms that exist in the statistics and machine learning literature. A closely related problem is that of selecting a clustering algorithm that is "optimal" in some sense from a rather impressive list of clustering algorithms that currently exist. RESULTS: In this paper, we propose two validation measures each with two parts: one measuring the statistical consistency (stability) of the clusters produced and the other representing their biological functional congruence. Smaller values of these indices indicate better performance for a clustering algorithm. We illustrate this approach using two case studies with publicly available gene expression data sets: one involving a SAGE data of breast cancer patients and the other involving a time course cDNA microarray data on yeast. Six well known clustering algorithms UPGMA, K-Means, Diana, Fanny, Model-Based and SOM were evaluated. CONCLUSION: No single clustering algorithm may be best suited for clustering genes into functional groups via expression profiles for all data sets. The validation measures introduced in this paper can aid in the selection of an optimal algorithm, for a given data set, from a collection of available clustering algorithms

Effects of Picture Size Reduction and Blurring on Emotional Engagement

The activity of basic motivational systems is reflected in emotional responses to arousing stimuli, such as natural pictures. The manipulation of picture properties such as size or detail allows for investigation into the extent to which separate emotional reactions are similarly modulated by perceptual changes, or, rather, may subserve different functions. Pursuing this line of research, the present study examined the effects of two types of perceptual degradation, namely picture size reduction and blurring, on emotional responses. Both manipulations reduced picture relevance and dampened affective modulation of skin conductance, possibly because of a reduced action preparation in response to degraded or remote pictures. However, the affective modulation of the startle reflex did not vary with picture degradation, suggesting that the identification of these degraded affective cues activated the neural circuits mediating appetitive or defensive motivation

Signature of multilayer growth of 2D layered Bi2Se3 through heteroatom-assisted step-edge barrier reduction

During growth of two-dimensional (2D) materials, abrupt growth of multilayers is practically unavoidable even in the case of well-controlled growth. In epitaxial growth of a quintuple-layered Bi2Se3 film, we observe that the multilayer growth pattern deduced from in situ x-ray diffraction implies nontrivial interlayer diffusion process. Here we find that an intriguing diffusion process occurs at step edges where a slowly downward-diffusing Se adatom having a high step-edge barrier interacts with a Bi adatom pre-existing at step edges. The Se???Bi interaction lowers the high step-edge barrier of Se adatoms. This drastic reduction of the overall step-edge barrier and hence increased interlayer diffusion modifies the overall growth significantly. Thus, a step-edge barrier reduction mechanism assisted by hetero adatom???adatom interaction could be fairly general in multilayer growth of 2D heteroatomic materials

The associations of bone mineral density and bone turnover markers with osteoarthritis of the hand and knee in pre- and perimenopausal women

Objective To determine whether Caucasian women ages 28–48 years with newly defined osteoarthritis (OA) would have greater bone mineral density (BMD) and less bone turnover over time than would women without OA. Methods Data were derived from the longitudinal Michigan Bone Health Study. Period prevalence and 3-year incidence of OA were based on radiographs of the dominant hand and both knees, scored with the Kellgren/Lawrence (K/L) scale. OA scores were related to BMD, which was measured by dual-energy x-ray absorptiometry, and to serum osteocalcin levels, which were measured by radioimmunoassay. Results The period prevalence of OA (K/L grade ≥2 in the knees or the dominant hand) was 15.3% (92 of 601), with 8.7% for the knees and 6.7% for the hand. The 3-year incidence of knee OA was 1.9% (9 of 482) and of hand OA was 3.3% (16 of 482). Women with incident knee OA had greater average BMD (z-scores 0.3–0.8 higher for the 3 BMD sites) than women without knee OA ( P 60%; P = 0.02) or knee OA (20%; P not significant). The average change in absolute serum osteocalcin levels was not as great in women with incident hand OA or knee OA as in women without OA ( P < 0.02 and P < 0.05, respectively). Conclusion Women with radiographically defined knee OA have greater BMD than do women without knee OA and are less likely to lose that higher level of BMD. There was less bone turnover among women with hand OA and/or knee OA. These findings suggest that bone-forming cells might show a differential response in OA of the hand and knee, and may suggest a different pathogenesis of hand OA and knee OA.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34294/1/13_ftp.pd

Alternative splicing enriched cDNA libraries identify breast cancer-associated transcripts

<p>Abstract</p> <p>Background</p> <p>Alternative splicing (AS) is a central mechanism in the generation of genomic complexity and is a major contributor to transcriptome and proteome diversity. Alterations of the splicing process can lead to deregulation of crucial cellular processes and have been associated with a large spectrum of human diseases. Cancer-associated transcripts are potential molecular markers and may contribute to the development of more accurate diagnostic and prognostic methods and also serve as therapeutic targets. Alternative splicing-enriched cDNA libraries have been used to explore the variability generated by alternative splicing. In this study, by combining the use of trapping heteroduplexes and RNA amplification, we developed a powerful approach that enables transcriptome-wide exploration of the AS repertoire for identifying AS variants associated with breast tumor cells modulated by <it>ERBB2</it> (<it>HER-2/neu</it>) oncogene expression.</p> <p>Results</p> <p>The human breast cell line (C5.2) and a pool of 5 ERBB2 over-expressing breast tumor samples were used independently for the construction of two AS-enriched libraries. In total, 2,048 partial cDNA sequences were obtained, revealing 214 alternative splicing sequence-enriched tags (ASSETs). A subset with 79 multiple exon ASSETs was compared to public databases and reported 138 different AS events. A high success rate of RT-PCR validation (94.5%) was obtained, and 2 novel AS events were identified. The influence of <it>ERBB2</it>-mediated expression on AS regulation was evaluated by capillary electrophoresis and probe-ligation approaches in two mammary cell lines (Hb4a and C5.2) expressing different levels of <it>ERBB2</it>. The relative expression balance between AS variants from 3 genes was differentially modulated by <it>ERBB2</it> in this model system.</p> <p>Conclusions</p> <p>In this study, we presented a method for exploring AS from any RNA source in a transcriptome-wide format, which can be directly easily adapted to next generation sequencers. We identified AS transcripts that were differently modulated by <it>ERBB2</it>-mediated expression and that can be tested as molecular markers for breast cancer. Such a methodology will be useful for completely deciphering the cancer cell transcriptome diversity resulting from AS and for finding more precise molecular markers.</p