A new Bayesian approach incorporating covariate information for heterogeneity and its comparison with HLOD

We consider a new Bayesian approach for heterogeneity that can take into account categorical covariates, if available. We use the Genetic Analysis Workshop 14 simulated data to first compare the Bayesian approach with the heterogeneity LOD, when no covariate information is used. We find that the former is more powerful, while the two approaches have comparable false-positive rates. We then include informative covariates in the Bayesian approach and find that it tends to give more precise interval estimates of the disease gene location than when covariates are not included. We had knowledge of the simulation models at the time we performed the analyses

Can differences in phosphorus uptake kinetics explain the distribution of cattail and sawgrass in the Florida Everglades?

<p>Abstract</p> <p>Background</p> <p>Cattail (<it>Typha domingensis</it>) has been spreading in phosphorus (P) enriched areas of the oligotrophic Florida Everglades at the expense of sawgrass (<it>Cladium mariscus </it>spp. <it>jamaicense</it>). Abundant evidence in the literature explains how the opportunistic features of <it>Typha </it>might lead to a complete dominance in P-enriched areas. Less clear is how <it>Typha </it>can grow and acquire P at extremely low P levels, which prevail in the unimpacted areas of the Everglades.</p> <p>Results</p> <p>Apparent P uptake kinetics were measured for intact plants of <it>Cladium </it>and <it>Typha </it>acclimated to low and high P at two levels of oxygen in hydroponic culture. The saturated rate of P uptake was higher in <it>Typha </it>than in <it>Cladium </it>and higher in low-P acclimated plants than in high-P acclimated plants. The affinity for P uptake was two-fold higher in <it>Typha </it>than in <it>Cladium</it>, and two- to three-fold higher for low-P acclimated plants compared to high-P acclimated plants. As <it>Cladium </it>had a greater proportion of its biomass allocated to roots, the overall uptake capacity of the two species at high P did not differ. At low P availability, <it>Typha </it>increased biomass allocation to roots more than <it>Cladium</it>. Both species also adjusted their P uptake kinetics, but <it>Typha </it>more so than <it>Cladium</it>. The adjustment of the P uptake system and increased biomass allocation to roots resulted in a five-fold higher uptake per plant for <it>Cladium </it>and a ten-fold higher uptake for <it>Typha</it>.</p> <p>Conclusions</p> <p>Both <it>Cladium </it>and <it>Typha </it>adjust P uptake kinetics in relation to plant demand when P availability is high. When P concentrations are low, however, <it>Typha </it>adjusts P uptake kinetics and also increases allocation to roots more so than <it>Cladium</it>, thereby improving both efficiency and capacity of P uptake. <it>Cladium </it>has less need to adjust P uptake kinetics because it is already efficient at acquiring P from peat soils (e.g., through secretion of phosphatases, symbiosis with arbuscular mycorrhizal fungi, nutrient conservation growth traits). Thus, although <it>Cladium </it>and <it>Typha </it>have qualitatively similar strategies to improve P-uptake efficiency and capacity under low P-conditions, <it>Typha </it>shows a quantitatively greater response, possibly due to a lesser expression of these mechanisms than <it>Cladium</it>. This difference between the two species helps to explain why an opportunistic species such as <it>Typha </it>is able to grow side by side with <it>Cladium </it>in the P-deficient Everglades.</p

Liver Stiffness Measurement With FibroScan: Use the Right Probe in the Right Conditions!

INTRODUCTION: FibroScan\u27s M and XL probes give significantly different results, which could lead to misevaluation of liver fibrosis if the correct probe is not chosen. According to the manufacturer, the M probe should be used when the skin-liver capsule distance (SCD) is &lt;25 mm, and the XL probe should be used when SCD is ≥25 mm. We aimed at validating this recommendation and defining the conditions of use for FibroScan probes in clinical practice. METHODS: Four hundred thirty-nine patients with biopsy-proven chronic liver disease were included. Of them, 382 had successful examinations with both M and XL probes. Advanced fibrosis was defined as Nonalcoholic Steatohepatitis Clinical Research Network (NASH CRN) F ≥3 or Metavir F ≥2. RESULTS: In a same patient, XL probe results were significantly lower than M probe results: 7.9 (5.6-11.7) vs 9.5 (6.7-14.6) kPa, respectively (P &lt; 0.001). After matching for age, sex, liver fibrosis, and serum transaminases, M probe results in patients with SCD &lt;25 mm and XL probe results in those with SCD ≥25 mm did not significantly differ: 8.8 (6.0-12.0) vs 9.1 (6.7-12.8) kPa, respectively (P = 0.175). Of note, 81.4% of patients with body mass index (BMI) &lt;32 kg/m had SCD &lt;25 mm, and 77.7% of patients with BMI ≥32 kg/m had SCD ≥25 mm. A practical algorithm using BMI first and then the FibroScan Automatic Probe Selection tool was proposed to help physicians accurately choose which probe to use in clinical practice. CONCLUSIONS: There is no significant difference in results between M and XL probes when they are used in the right conditions. In clinical practice, the probe should be selected according to the BMI and the Automatic Probe Selection tool

A Dinucleotide Deletion in CD24 Confers Protection against Autoimmune Diseases

It is generally believed that susceptibility to both organ-specific and systemic autoimmune diseases is under polygenic control. Although multiple genes have been implicated in each type of autoimmune disease, few are known to have a significant impact on both. Here, we investigated the significance of polymorphisms in the human gene CD24 and the susceptibility to multiple sclerosis (MS) and systemic lupus erythematosus (SLE). We used cases/control studies to determine the association between CD24 polymorphism and the risk of MS and SLE. In addition, we also considered transmission disequilibrium tests using family data from two cohorts consisting of a total of 150 pedigrees of MS families and 187 pedigrees of SLE families. Our analyses revealed that a dinucleotide deletion at position 1527∼1528 (P1527(del)) from the CD24 mRNA translation start site is associated with a significantly reduced risk (odds ratio = 0.54 with 95% confidence interval = 0.34–0.82) and delayed progression (p = 0.0188) of MS. Among the SLE cohort, we found a similar reduction of risk with the same polymorphism (odds ratio = 0.38, confidence interval = 0.22–0.62). More importantly, using 150 pedigrees of MS families from two independent cohorts and the TRANSMIT software, we found that the P1527(del) allele was preferentially transmitted to unaffected individuals (p = 0.002). Likewise, an analysis of 187 SLE families revealed the dinucleotide-deleted allele was preferentially transmitted to unaffected individuals (p = 0.002). The mRNA levels for the dinucleotide-deletion allele were 2.5-fold less than that of the wild-type allele. The dinucleotide deletion significantly reduced the stability of CD24 mRNA. Our results demonstrate that a destabilizing dinucleotide deletion in the 3′ UTR of CD24 mRNA conveys significant protection against both MS and SLE

Identifying hypermethylated CpG islands using a quantile regression model

<p>Abstract</p> <p>Background</p> <p>DNA methylation has been shown to play an important role in the silencing of tumor suppressor genes in various tumor types. In order to have a system-wide understanding of the methylation changes that occur in tumors, we have developed a differential methylation hybridization (DMH) protocol that can simultaneously assay the methylation status of all known CpG islands (CGIs) using microarray technologies. A large percentage of signals obtained from microarrays can be attributed to various measurable and unmeasurable confounding factors unrelated to the biological question at hand. In order to correct the bias due to noise, we first implemented a quantile regression model, with a quantile level equal to 75%, to identify hypermethylated CGIs in an earlier work. As a proof of concept, we applied this model to methylation microarray data generated from breast cancer cell lines. However, we were unsure whether 75% was the best quantile level for identifying hypermethylated CGIs. In this paper, we attempt to determine which quantile level should be used to identify hypermethylated CGIs and their associated genes.</p> <p>Results</p> <p>We introduce three statistical measurements to compare the performance of the proposed quantile regression model at different quantile levels (95%, 90%, 85%, 80%, 75%, 70%, 65%, 60%), using known methylated genes and unmethylated housekeeping genes reported in breast cancer cell lines and ovarian cancer patients. Our results show that the quantile levels ranging from 80% to 90% are better at identifying known methylated and unmethylated genes.</p> <p>Conclusions</p> <p>In this paper, we propose to use a quantile regression model to identify hypermethylated CGIs by incorporating probe effects to account for noise due to unmeasurable factors. Our model can efficiently identify hypermethylated CGIs in both breast and ovarian cancer data.</p

Conjugating uncoupler compounds with hydrophobic hydrocarbon chains to achieve adipose tissue selective drug accumulation

One potential approach for treating obesity is to increase energy expenditure in brown and white adipose tissue. Here we aimed to achieve this outcome by targeting mitochondrial uncoupler compounds selectively to adipose tissue, thus avoiding side effects from uncoupling in other tissues. Selective drug accumulation in adipose tissue has been observed with many lipophilic compounds and dyes. Hence, we explored the feasibility of conjugating uncoupler compounds with a lipophilic C8-hydrocarbon chain via an ether bond. We found that substituting the trifluoromethoxy group in the uncoupler FCCP with a C8-hydrocarbon chain resulted in potent uncoupling activity. Nonetheless, the compound did not elicit therapeutic effects in mice, likely as a consequence of metabolic instability resulting from rapid ether bond cleavage. A lipophilic analog of the uncoupler compound 2,6-dinitrophenol, in which a C8-hydrocarbon chain was conjugated via an ether bond in the para-position (2,6-dinitro-4-(octyloxy)phenol), exhibited increased uncoupling activity compared to the parent compound. However, in vivo pharmacokinetics studies suggested that 2,6-dinitro-4-(octyloxy)phenol was also metabolically unstable. In conclusion, conjugation of a hydrophobic hydrocarbon chain to uncoupler compounds resulted in sustained or improved uncoupling activity. However, an ether bond linkage led to metabolic instability, indicating the need to conjugate lipophilic groups via other chemical bonds

Preprocessing differential methylation hybridization microarray data

<p>Abstract</p> <p>Background</p> <p>DNA methylation plays a very important role in the silencing of tumor suppressor genes in various tumor types. In order to gain a genome-wide understanding of how changes in methylation affect tumor growth, the differential methylation hybridization (DMH) protocol has been developed and large amounts of DMH microarray data have been generated. However, it is still unclear how to preprocess this type of microarray data and how different background correction and normalization methods used for two-color gene expression arrays perform for the methylation microarray data. In this paper, we demonstrate our discovery of a set of internal control probes that have log ratios (M) theoretically equal to zero according to this DMH protocol. With the aid of this set of control probes, we propose two LOESS (or LOWESS, locally weighted scatter-plot smoothing) normalization methods that are novel and unique for DMH microarray data. Combining with other normalization methods (global LOESS and no normalization), we compare four normalization methods. In addition, we compare five different background correction methods.</p> <p>Results</p> <p>We study 20 different preprocessing methods, which are the combination of five background correction methods and four normalization methods. In order to compare these 20 methods, we evaluate their performance of identifying known methylated and un-methylated housekeeping genes based on two statistics. Comparison details are illustrated using breast cancer cell line and ovarian cancer patient methylation microarray data. Our comparison results show that different background correction methods perform similarly; however, four normalization methods perform very differently. In particular, all three different LOESS normalization methods perform better than the one without any normalization.</p> <p>Conclusions</p> <p>It is necessary to do within-array normalization, and the two LOESS normalization methods based on specific DMH internal control probes produce more stable and relatively better results than the global LOESS normalization method.</p

Identifying differentially methylated genes using mixed effect and generalized least square models

<p>Abstract</p> <p>Background</p> <p>DNA methylation plays an important role in the process of tumorigenesis. Identifying differentially methylated genes or CpG islands (CGIs) associated with genes between two tumor subtypes is thus an important biological question. The methylation status of all CGIs in the whole genome can be assayed with differential methylation hybridization (DMH) microarrays. However, patient samples or cell lines are heterogeneous, so their methylation pattern may be very different. In addition, neighboring probes at each CGI are correlated. How these factors affect the analysis of DMH data is unknown.</p> <p>Results</p> <p>We propose a new method for identifying differentially methylated (DM) genes by identifying the associated DM CGI(s). At each CGI, we implement four different mixed effect and generalized least square models to identify DM genes between two groups. We compare four models with a simple least square regression model to study the impact of incorporating random effects and correlations.</p> <p>Conclusions</p> <p>We demonstrate that the inclusion (or exclusion) of random effects and the choice of correlation structures can significantly affect the results of the data analysis. We also assess the false discovery rate of different models using CGIs associated with housekeeping genes.</p

Criteria to Determine Reliability of Noninvasive Assessment of Liver Fibrosis With Virtual Touch Quantification

BACKGROUND &amp; AIMS: Virtual Touch Quantification (VTQ) evaluates liver fibrosis in patients with chronic liver diseases by measuring shear wave speed in the liver. We aimed to determine the reliability criteria of VTQ examination. METHODS: We performed a prospective study of 1094 patients with chronic liver disease from November 2009 through October 2016 at Angers University Hospital, and between April 2010 and May 2015 at Bordeaux University Hospital, in France. All patients underwent liver biopsy analysis (reference standard), and VTQ examination was made by experienced operators on the same day, or no more than 3 months before or afterward. Advanced liver fibrosis was defined as fibrosis stage F ≥ 3 according to the scoring system of the Nonalcoholic Steatohepatitis Clinical Research Network, or fibrosis stage F ≥ 2 according to the Metavir scoring system. The diagnostic accuracy of VTQ in detection of advanced fibrosis or cirrhosis was assessed using the area under the receiver operating characteristic (AUROC) and the rate of correctly classified patients. Reliability criteria were defined from the intrinsic characteristics of VTQ examination, which were shown to influence the diagnostic accuracy. RESULTS: VTQ identified patients with advanced fibrosis with an AUROC of 0.773 ± 0.014 and correctly classified 72.0% of patients using a diagnostic cut-off value of 1.37 m/s. VTQ identified patients with cirrhosis with an AUROC value of 0.839 ± 0.014 and correctly classified 78.4% of patients using a cut-off value of 1.87 m/s. The reliability of VTQ decreased with an increasing ratio of interquartile range/median (IQR/M) in patients with intermediate-high VTQ results. We defined 3 reliability categories for VTQ: unreliable (IQR/M ≥0.35 with VTQ result ≥1.37 m/s), reliable (IQR/M ≥0.35 with VTQ result &lt;1.37 m/s or IQR/M 0.15-0.34), and very reliable (IQR/M &lt;0.15). For advanced fibrosis, VTQ correctly classified 57.8% of patients in the unreliable group, 73.7% of patients in the reliable group, and 80.9% of patients in the very reliable group (P &lt; .001); for cirrhosis, these values were 50.0%, 83.4%, and 92.6%, respectively (P &lt; .001). Of the VTQ examinations made, 21.4% were unreliable, 55.0% were reliable, and 23.6% were very reliable. The skin-liver capsule distance was independently associated with an unreliable VTQ examination, which occurred in 52.7% of patients with a distance of 30 mm or more. CONCLUSIONS: In a study to determine the reliability of VTQ findings, compared with results from biopsy analysis, we assigned VTQ examinations to 3 categories (unreliable, reliable, and very reliable). VTQ examinations with IQR/M ≥0.35 and ≥1.37 m/s had very low diagnostic accuracy. Our reliability criteria for liver fibrosis assessment with VTQ will help physicians to accurately evaluate the severity of chronic liver diseases and monitor their progression