Orchestration of Floral Initiation by APETALA1

The MADS-domain transcription factor APETALA1 (AP1) is a key regulator of Arabidopsis flower development. To understand the molecular mechanisms underlying AP1 function, we identified its target genes during floral initiation using a combination of gene expression profiling and genome-wide binding studies. Many of its targets encode transcriptional regulators, including known floral repressors. The latter genes are down-regulated by AP1, suggesting that it initiates floral development by abrogating the inhibitory effects of these genes. Although AP1 acts predominantly as a transcriptional repressor during the earliest stages of flower development, at more advanced stages it also activates regulatory genes required for floral organ formation, indicating a dynamic mode of action. Our results further imply that AP1 orchestrates floral initiation by integrating growth, patterning, and hormonal pathways

ChIP-seq Analysis in R (CSAR): An R package for the statistical detection of protein-bound genomic regions

<p>Abstract</p> <p>Background</p> <p><it>In vivo </it>detection of protein-bound genomic regions can be achieved by combining chromatin-immunoprecipitation with next-generation sequencing technology (ChIP-seq). The large amount of sequence data produced by this method needs to be analyzed in a statistically proper and computationally efficient manner. The generation of high copy numbers of DNA fragments as an artifact of the PCR step in ChIP-seq is an important source of bias of this methodology.</p> <p>Results</p> <p>We present here an R package for the statistical analysis of ChIP-seq experiments. Taking the average size of DNA fragments subjected to sequencing into account, the software calculates single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the ratio test or the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutations. Computational efficiency is achieved by implementing the most time-consuming functions in C++ and integrating these in the R package. An analysis of simulated and experimental ChIP-seq data is presented to demonstrate the robustness of our method against PCR-artefacts and its adequate control of the error rate.</p> <p>Conclusions</p> <p>The software <it>ChIP-seq Analysis in R </it>(CSAR) enables fast and accurate detection of protein-bound genomic regions through the analysis of ChIP-seq experiments. Compared to existing methods, we found that our package shows greater robustness against PCR-artefacts and better control of the error rate.</p

Practical guidelines for the comprehensive analysis of ChIP-seq data.

Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality checking, mapping, data normalization, assessment of reproducibility, peak calling, differential binding analysis, controlling the false discovery rate, peak annotation, visualization, and motif analysis. At each step in our guidelines we discuss some of the software tools most frequently used. We also highlight the challenges and problems associated with each step in ChIP-seq data analysis. We present a concise workflow for the analysis of ChIP-seq data in Figure 1 that complements and expands on the recommendations of the ENCODE and modENCODE projects. Each step in the workflow is described in detail in the following sections

Serum S100B Protein Concentrations in SGA/FGR newborns

Objectives: Fetal growth restriction is associated with chronic fetal hypoxia, poor perinatal outcome and increased perinatal mortality. There are no reliable methods to detect cell damage in the central nervous system (CNS) in these patients. The findings of increased an acidic calcium-binding protein (S100B) concentration in biological fluids of infants after brain injury have supported the use of S100B as a biochemical marker of CNS damage. The purpose of the study was to assess blood S100B concentrations in small for gestational age (SGA) and appropriate for gestational age (AGA) newborns and to evaluate the usefulness of S100B for early detection of hypoxia. Material and methods: The investigation was carried out between November 2011 and April 2014. Serum S100B protein level was assessed in cord blood collected from newborns after birth. Medical records of mothers of neonates studied were reviewed for pregnancy induced hypertension (PIH), preeclampsia, maternal smoking during pregnancy and abnormalities in umbilical artery (UA) Doppler ultrasound examination. Results: The study was carried out in 88 SGA neonates and 80 AGA neonates. The median value of S100B protein concentration in the SGA study group was significantly higher than in AGA controls (p &lt; 0.001). Cord blood serum S100B concentration in SGA neonates with prenatal normal UA Doppler ultrasound findings (n = 32) did not differ from that SGA neonates with abnormal prenatal UA Doppler findings (n = 25) (p = 0.74), but was significantly higher than in AGA newborns (p &lt; 0.001). Conclusions: Elevated S100B protein levels in cord blood collected from SGA newborns may be helpful in detecting infants at higher risk of postnatal neurologic disturbances at an early stage

Dynamics of chromatin accessibility and gene regulation by MADS-domain transcription factors in flower development.

BACKGROUND: Development of eukaryotic organisms is controlled by transcription factors that trigger specific and global changes in gene expression programs. In plants, MADS-domain transcription factors act as master regulators of developmental switches and organ specification. However, the mechanisms by which these factors dynamically regulate the expression of their target genes at different developmental stages are still poorly understood. RESULTS: We characterized the relationship of chromatin accessibility, gene expression, and DNA binding of two MADS-domain proteins at different stages of Arabidopsis flower development. Dynamic changes in APETALA1 and SEPALLATA3 DNA binding correlated with changes in gene expression, and many of the target genes could be associated with the developmental stage in which they are transcriptionally controlled. We also observe dynamic changes in chromatin accessibility during flower development. Remarkably, DNA binding of APETALA1 and SEPALLATA3 is largely independent of the accessibility status of their binding regions and it can precede increases in DNA accessibility. These results suggest that APETALA1 and SEPALLATA3 may modulate chromatin accessibility, thereby facilitating access of other transcriptional regulators to their target genes. CONCLUSIONS: Our findings indicate that different homeotic factors regulate partly overlapping, yet also distinctive sets of target genes in a partly stage-specific fashion. By combining the information from DNA-binding and gene expression data, we are able to propose models of stage-specific regulatory interactions, thereby addressing dynamics of regulatory networks throughout flower development. Furthermore, MADS-domain TFs may regulate gene expression by alternative strategies, one of which is modulation of chromatin accessibility

Constrained estimators of treatment parameters in semiparametric models

Semiparametric models are generalizations of parametric regression models. We present a method of estimation of treatment effects in a semiparametric model with one smoothing term under additional conditions on their linear functions and its application to hypothesis testing.Cubic smoothing splines Estimation Hypothesis testing Loess