Aberrant lipid metabolism disrupts calcium homeostasis causing liver endoplasmic reticulum stress in obesity.

The endoplasmic reticulum (ER) is the main site of protein and lipid synthesis, membrane biogenesis, xenobiotic detoxification and cellular calcium storage, and perturbation of ER homeostasis leads to stress and the activation of the unfolded protein response. Chronic activation of ER stress has been shown to have an important role in the development of insulin resistance and diabetes in obesity. However, the mechanisms that lead to chronic ER stress in a metabolic context in general, and in obesity in particular, are not understood. Here we comparatively examined the proteomic and lipidomic landscape of hepatic ER purified from lean and obese mice to explore the mechanisms of chronic ER stress in obesity. We found suppression of protein but stimulation of lipid synthesis in the obese ER without significant alterations in chaperone content. Alterations in ER fatty acid and lipid composition result in the inhibition of sarco/endoplasmic reticulum calcium ATPase (SERCA) activity and ER stress. Correcting the obesity-induced alteration of ER phospholipid composition or hepatic Serca overexpression in vivo both reduced chronic ER stress and improved glucose homeostasis. Hence, we established that abnormal lipid and calcium metabolism are important contributors to hepatic ER stress in obesity.

VARIABLE SELECTION FOR HIGH DIMENSIONAL MULTIVARIATE OUTCOMES.

We consider variable selection for high-dimensional multivariate regression using penalized likelihoods when the number of outcomes and the number of covariates might be large. To account for within-subject correlation, we consider variable selection when a working precision matrix is used and when the precision matrix is jointly estimated using a two-stage procedure. We show that under suitable regularity conditions, penalized regression coefficient estimators are consistent for model selection for an arbitrary working precision matrix, and have the oracle properties and are efficient when the true precision matrix is used or when it is consistently estimated using sparse regression. We develop an efficient computation procedure for estimating regression coefficients using the coordinate descent algorithm in conjunction with sparse precision matrix estimation using the graphical LASSO (GLASSO) algorithm. We develop the Bayesian Information Criterion (BIC) for estimating the tuning parameter and show that BIC is consistent for model selection. We evaluate finite sample performance for the proposed method using simulation studies and illustrate its application using the type II diabetes gene expression pathway data.

The Dantzig Selector for Censored Linear Regression Models.

The Dantzig variable selector has recently emerged as a powerful tool for fitting regularized regression models. To our knowledge, most work involving the Dantzig selector has been performed with fully-observed response variables. This paper proposes a new class of adaptive Dantzig variable selectors for linear regression models when the response variable is subject to right censoring. This is motivated by a clinical study to identify genes predictive of event-free survival in newly diagnosed multiple myeloma patients. Under some mild conditions, we establish the theoretical properties of our procedures, including consistency in model selection (i.e. the right subset model will be identified with a probability tending to 1) and the optimal efficiency of estimation (i.e. the asymptotic distribution of the estimates is the same as that when the true subset model is known a priori). The practical utility of the proposed adaptive Dantzig selectors is verified via extensive simulations. We apply our new methods to the aforementioned myeloma clinical trial and identify important predictive genes.

Parallelism, uniqueness, and large-sample asymptotics for the Dantzig selector.

The Dantzig selector (Candès and Tao, 2007) is a popular ℓ1-regularization method for variable selection and estimation in linear regression. We present a very weak geometric condition on the observed predictors which is related to parallelism and, when satisfied, ensures the uniqueness of Dantzig selector estimators. The condition holds with probability 1, if the predictors are drawn from a continuous distribution. We discuss the necessity of this condition for uniqueness and also provide a closely related condition which ensures uniqueness of lasso estimators (Tibshirani, 1996). Large sample asymptotics for the Dantzig selector, i.e. almost sure convergence and the asymptotic distribution, follow directly from our uniqueness results and a continuity argument. The limiting distribution of the Dantzig selector is generally non-normal. Though our asymptotic results require that the number of predictors is fixed (similar to (Knight and Fu, 2000)), our uniqueness results are valid for an arbitrary number of predictors and observations.

Increased power for the analysis of label-free LC-MS/MS proteomics data by combining spectral counts and peptide peak attributes.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics provides a wealth of information about proteins present in biological samples. In bottom-up LC-MS/MS-based proteomics, proteins are enzymatically digested into peptides prior to query by LC-MS/MS. Thus, the information directly available from the LC-MS/MS data is at the peptide level. If a protein-level analysis is desired, the peptide-level information must be rolled up into protein-level information. We propose a principal component analysis-based statistical method, ProPCA, for efficiently estimating relative protein abundance from bottom-up label-free LC-MS/MS data that incorporates both spectral count information and LC-MS peptide ion peak attributes, such as peak area, volume, or height. ProPCA may be used effectively with a variety of quantification platforms and is easily implemented. We show that ProPCA outperformed existing quantitative methods for peptide-protein roll-up, including spectral counting methods and other methods for combining LC-MS peptide peak attributes. The performance of ProPCA was validated using a data set derived from the LC-MS/MS analysis of a mixture of protein standards (the UPS2 proteomic dynamic range standard introduced by The Association of Biomolecular Resource Facilities Proteomics Standards Research Group in 2006). Finally, we applied ProPCA to a comparative LC-MS/MS analysis of digested total cell lysates prepared for LC-MS/MS analysis by alternative lysis methods and show that ProPCA identified more differentially abundant proteins than competing methods.

Outcomes of a Culturally Tailored Partially Randomized Patient Preference Controlled Trial to Increase Physical Activity Among Low-Income Immigrant Latinas.

INTRODUCTION: Almost half (47.8%) of adult Latinas report they never engage in any leisure time physical activity (PA) which is an independent risk factor for the development of cardiovascular disease and other chronic illnesses. There is a pressing need to develop and test PA interventions among Latinas. Therefore, the purpose of this study was to evaluate the effects of a PA Intervention for Latinas, a culturally tailored, promotora-facilitated 12-week PA intervention. It was hypothesized that at the completion of the intervention, participants would have (a) higher daily PA levels; (b) improved aerobic fitness, muscle strength, and flexibility; and (c) lower body mass index and percentage of body fat. METHODOLOGY: A partially randomized patient preference trial design with lag group was used to test the intervention. Participants ( N = 76) attended twice weekly, low-impact aerobic/Latin dance PA classes taught by laywomen trained as promotoras. RESULTS: Significant improvements were measured in aerobic fitness, muscle strength and flexibility, and daily PA levels ( p < .001). Sixty percent of the participants attended at least 60% of the PA sessions. DISCUSSION: Findings suggest laywomen trained as promotoras can successfully facilitate the delivery of an intervention to increase PA among immigrant Latinas.

Measuring the Severity of Neonatal Seizures: Temporal-Spatial Burden.

PURPOSE: The American Clinical Neurophysiology Society recommends measuring neonatal seizures' severity by their frequency (number of seizures-anywhere per hour), burden (percentage of time with seizures-anywhere), or on a region-by-region, temporal-spatial basis. This study compares two reduced-channel montages for temporal-spatial seizure burden analyses and examines the agreement of seizures' quantification among these three methodologies. METHODS: A convenience sample of 10 neonatal electroencephalograms was annotated for the beginnings and ends of seizures, which appeared anywhere in the full neonatal montage, then repeated on a more precise, region-by-region basis using 2 reduced-channel montages A and B. Seizure severity was measured by seizures-anywhere frequency, seizures-anywhere burden, and temporal-spatial seizure burdens using montages A and B. The results were compared by measuring their correlation and by linear regression modeling. RESULTS: Seizures-anywhere frequency was correlated with seizures-anywhere burden (ρ = 0.77). However, a narrow range of seizures-anywhere frequencies corresponded with a broad range of seizures-anywhere burdens. Although there was high correlation between seizures-anywhere burdens and temporal-spatial seizure burdens (ρ = 0.92 montage A, ρ = 0.90 montage B), seizures-anywhere burdens were insensitive to variations in the spatial aspects of seizures, which were highly prevalent even in this small sample set. After adjusting for intrareader variability, the temporal-spatial seizure burdens measured by montages A and B were not significantly different (P = 0.56). CONCLUSIONS: The severity of neonatal seizures is poorly represented by simple measures such as seizures-anywhere frequencies or burdens. The use of temporal-spatial seizure burden measurements is supported in work where great precision in quantifying neonatal seizures is required.

Agreement between long-term neonatal background classification by conventional and amplitude-integrated EEG.

Conventional EEG (CEEG) in neonates is considered the gold standard for evaluating EEG background and detecting electrographic seizures. However, CEEG is expensive and cumbersome for long-term monitoring. A simplified method, amplitude-integrated EEG (AEEG) has been rapidly adopted to accomplish the same goals. The purpose of this study was to measure the agreement between the methods of classification in long-term EEG background assessments by CEEG and AEEG. Infants underwent CEEG monitoring after cardiac surgery and the background during four 12-hour epochs classified as "normal" or "mildly," "moderately," or "markedly" abnormal. CEEGs were converted to a single-channel AEEG and independently interpreted as "normal," "moderately abnormal," or "markedly abnormal" by standard amplitude criteria. The distributions of CEEG and AEEG interpretations were statistically compared, and the associations between CEEG and AEEG interpretations were measured. Generalized estimating equations were used to measure the effects of seizures and patient age on the agreement between AEEG and CEEG scores. Paired CEEGs and AEEGs were available for 637 epochs recorded from 179 infants. The distribution of CEEG backgrounds included 60% normal, 22% mildly abnormal, 13% moderately abnormal, and 5% markedly abnormal. The distribution of AEEG backgrounds was significantly different from CEEG and included 22% normal, 73% moderately abnormal, and 5% markedly abnormal. Nevertheless, the two techniques exhibited a significant, moderate positive association. Generalized estimating equations focusing on those with moderately abnormal AEEGs showed that younger patients with seizures were significantly more likely to have moderately or markedly abnormal CEEGs than older patients without seizures. Although there was overall significant moderate agreement between the two techniques, the distribution of backgrounds assigned by AEEG was significantly different from CEEG. Most moderately abnormal AEEGs were associated with normal or mildly abnormal CEEGs. However, the ability of moderately abnormal AEEGs to correctly predict moderately or markedly abnormal CEEG was significantly associated with the knowledge of the patient's age and the presence of seizures on CEEG.