Ping Ma

Global Assessment of Combinatorial Post-translational Modification of Core Histones in Yeast Using Contemporary Mass Spectrometry LYS4 TRIMETHYLATION CORRELATES WITH DEGREE OF ACETYLATION ON THE SAME H3 TAIL

A global view of all core histones in yeast is provided by tandem mass spectrometry of intact histones H2A, H2B, H4, and H3. This allowed detailed characterization of >50 distinct histone forms and their semiquantitative assessment in the deletion mutants gcn5Δ, spt7Δ, ahc1Δ, and rtg2Δ, affecting the chromatin remodeling complexes SAGA, SLIK, and ADA. The “top down” mass spectrometry approach detected dramatic decreases in acetylation on H3 and H2B in gcn5Δ cells versus wild type. For H3 in wild type cells, tandem mass spectrometry revealed a direct correlation between increases of Lys4 trimethylation and the 0, 1, 2, and 3 acetylation states of histone H3. The results show a wide swing from 10 to 80% Lys4 trimethylation levels on those H3 tails harboring 0 or 3 acetylations, respectively. Reciprocity between these chromatin marks was apparent, since gcn5Δ cells showed a 30% decrease in trimethylation levels on Lys4 in addition to a decrease of acetylation levels on H3 in bulk chromatin. Deletion of Set1, the Lys4 methyltransferase, was associated with the linked disappearance of both Lys4 methylation and Lys14 and Lys18 or Lys23 acetylation on H3. In sum, we have defined the “basis set” of histone forms present in yeast chromatin using a current mass spectrometric approach that both quickly profiles global changes and directly probes the connectivity of modifications on the same histone.

Optimal smoothing in nonparametric mixed-effect models

Mixed-effect models are widely used for the analysis of correlated data such as longitudinal data and repeated measures. In this article, we study an approach to the nonparametric estimation of mixed-effect models. We consider models with parametric random effects and flexible fixed effects, and employ the penalized least squares method to estimate the models. The issue to be addressed is the selection of smoothing parameters through the generalized cross-validation method, which is shown to yield optimal smoothing for both real and latent random effects. Simulation studies are conducted to investigate the empirical performance of generalized cross-validation in the context. Real-data examples are presented to demonstrate the applications of the methodology.

A dysregulated acetyl/SUMO switch of FXR promotes hepatic inflammation in obesity

Acetylation of transcriptional regulators is normally dynamically regulated by nutrient status but is often persistently elevated in nutrient‐excessive obesity conditions. We investigated the functional consequences of such aberrantly elevated acetylation of the nuclear receptor FXR as a model. Proteomic studies identified K217 as the FXR acetylation site in diet‐induced obese mice. In vivo studies utilizing acetylation‐mimic and acetylation‐defective K217 mutants and gene expression profiling revealed that FXR acetylation increased proinflammatory gene expression, macrophage infiltration, and liver cytokine and triglyceride levels, impaired insulin signaling, and increased glucose intolerance. Mechanistically, acetylation of FXR blocked its interaction with the SUMO ligase PIASy and inhibited SUMO2 modification at K277, resulting in activation of inflammatory genes. SUMOylation of agonist‐activated FXR increased its interaction with NF‐κB but blocked that with RXRα, so that SUMO2‐modified FXR was selectively recruited to and trans‐repressed inflammatory genes without affecting FXR/RXRα target genes. A dysregulated acetyl/SUMO switch of FXR in obesity may serve as a general mechanism for diminished anti‐inflammatory response of other transcriptional regulators and provide potential therapeutic and diagnostic targets for obesity‐related metabolic disorders.

Factorial microarray analysis of zebrafish retinal development

In a zebrafish recessive mutant young (yng), retinal cells are specified to distinct cell classes, but they fail to morphologically differentiate. A null mutation in a brahma-related gene 1 (brg1) is responsible for this phenotype. To identify retina-specific Brg1-regulated genes that control cellular differentiation, we conducted a factorial microarray analysis. Gene expression profiles were compared from wild-type and yng retinas and stage-matched whole embryos at 36 and 52 hours postfertilization (hpf). From our analysis, three categories of genes were identified: (i) Brg1-regulated retinal differentiation genes (731 probesets), (ii) retina-specific genes independent of Brg1 regulation (3,038 probesets), and (iii) Brg1-regulated genes outside the retina (107 probesets). Biological significance was confirmed by further analysis of components of the Cdk5 signaling pathway and Irx transcription factor family, representing genes identified in category 1. This study highlights the utility of factorial microarray analysis to efficiently identify relevant regulatory pathways influenced by both specific gene products and normal developmental events.

Penalized Clustering of Large-Scale Functional Data With Multiple Covariates

In this article we propose a penalized clustering method for large-scale data with multiple covariates through a functional data approach. In our proposed method, responses and covariates are linked together through nonparametric multivariate functions (fixed effects), which have great flexibility in modeling various function features, such as jump points, branching, and periodicity. Functional ANOVA is used to further decompose multivariate functions in a reproducing kernel Hilbert space and provide associated notions of main effect and interaction. Parsimonious random effects are used to capture various correlation structures. The mixed-effects models are nested under a general mixture model in which the heterogeneity of functional data is characterized. We propose a penalized Hendersons likelihood approach for model fitting and design a rejection-controlled EM algorithm for the estimation. Our method selects smoothing parameters through generalized cross-validation. Furthermore, Bayesian confidence intervals are used to measure the clustering uncertainty. Simulation studies and real-data examples are presented to investigate the empirical performance of the proposed method. Open-source code is available in the R package MFDA.

Statistical assessment of the global regulatory role of histone acetylation in Saccharomyces cerevisiae

BackgroundHistone acetylation plays important but incompletely understood roles in gene regulation. A comprehensive understanding of the regulatory role of histone acetylation is difficult because many different histone acetylation patterns exist and their effects are confounded by other factors, such as the transcription factor binding sequence motif information and nucleosome occupancy.ResultsWe analyzed recent genomewide histone acetylation data using a few complementary statistical models and tested the validity of a cumulative model in approximating the global regulatory effect of histone acetylation. Confounding effects due to transcription factor binding sequence information were estimated by using two independent motif-based algorithms followed by a variable selection method. We found that the sequence information has a significant role in regulating transcription, and we also found a clear additional histone acetylation effect. Our model fits well with observed genome-wide data. Strikingly, including more complicated combinatorial effects does not improve the models performance. Through a statistical analysis of conditional independence, we found that H4 acetylation may not have significant direct impact on global gene expression.ConclusionDecoding the combinatorial complexity of histone modification requires not only new data but also new methods to analyze the data. Our statistical analysis confirms that histone acetylation has a significant effect on gene transcription rates in addition to that attributable to upstream sequence motifs. Our analysis also suggests that a cumulative effect model for global histone acetylation is justified, although a more complex histone code may be important at specific gene loci. We also found that the regulatory roles among different histone acetylation sites have important differences.

Methylated DNA is over-represented in whole-genome bisulfite sequencing data

The development of whole-genome bisulfite sequencing (WGBS) has resulted in a number of exciting discoveries about the role of DNA methylation leading to a plethora of novel testable hypotheses. Methods for constructing sodium bisulfite-converted and amplified libraries have recently advanced to the point that the bottleneck for experiments that use WGBS has shifted to data analysis and interpretation. Here we present empirical evidence for an over-representation of reads from methylated DNA in WGBS. This enrichment for methylated DNA is exacerbated by higher cycles of PCR and is influenced by the type of uracil-insensitive DNA polymerase used for amplifying the sequencing library. Future efforts to computationally correct for this enrichment bias will be essential to increasing the accuracy of determining methylation levels for individual cytosines. It is especially critical for studies that seek to accurately quantify DNA methylation levels in populations that may segregate for allelic DNA methylation states.

Statistical Analysis of Zebrafish Locomotor Response

Zebrafish larvae display rich locomotor behaviour upon external stimulation. The movement can be simultaneously tracked from many larvae arranged in multi-well plates. The resulting time-series locomotor data have been used to reveal new insights into neurobiology and pharmacology. However, the data are of large scale, and the corresponding locomotor behavior is affected by multiple factors. These issues pose a statistical challenge for comparing larval activities. To address this gap, this study has analyzed a visually-driven locomotor behaviour named the visual motor response (VMR) by the Hotelling’s T-squared test. This test is congruent with comparing locomotor profiles from a time period. Different wild-type (WT) strains were compared using the test, which shows that they responded differently to light change at different developmental stages. The performance of this test was evaluated by a power analysis, which shows that the test was sensitive for detecting differences between experimental groups with sample numbers that were commonly used in various studies. In addition, this study investigated the effects of various factors that might affect the VMR by multivariate analysis of variance (MANOVA). The results indicate that the larval activity was generally affected by stage, light stimulus, their interaction, and location in the plate. Nonetheless, different factors affected larval activity differently over time, as indicated by a dynamical analysis of the activity at each second. Intriguingly, this analysis also shows that biological and technical repeats had negligible effect on larval activity. This finding is consistent with that from the Hotelling’s T-squared test, and suggests that experimental repeats can be combined to enhance statistical power. Together, these investigations have established a statistical framework for analyzing VMR data, a framework that should be generally applicable to other locomotor data with similar structure.

Fast and Stable Multiple Smoothing Parameter Selection in Smoothing Spline Analysis of Variance Models With Large Samples

The current parameterization and algorithm used to fit a smoothing spline analysis of variance (SSANOVA) model are computationally expensive, making a generalized additive model (GAM) the preferred method for multivariate smoothing. In this article, we propose an efficient reparameterization of the smoothing parameters in SSANOVA models, and a scalable algorithm for estimating multiple smoothing parameters in SSANOVAs. To validate our approach, we present two simulation studies comparing our reparameterization and algorithm to implementations of SSANOVAs and GAMs that are currently available in R. Our simulation results demonstrate that (a) our scalable SSANOVA algorithm outperforms the currently used SSANOVA algorithm, and (b) SSANOVAs can be a fast and reliable alternative to GAMs. We also provide an example with oceanographic data that demonstrates the practical advantage of our SSANOVA framework. Supplementary materials that are available online can be used to replicate the analyses in this article.

Data analysis tools for uncertainty quantification of inverse problems

We present exploratory data analysis methods to assess inversion estimates using examples based on l2- and l1-regularization. These methods can be used to reveal the presence of systematic errors such as bias and discretization effects, or to validate assumptions made on the statistical model used in the analysis. The methods include bounds on the performance of randomized estimators of a large matrix, confidence intervals and bounds for the bias, resampling methods for model validation and construction of training sets of functions with controlled local regularity.