Alternative Splicing During the Chlamydomonasreinhardtii Cell Cycle.

Genome-wide analysis of transcriptome data in Chlamydomonas reinhardtii shows periodic patterns in gene expression levels when cultures are grown under alternating light and dark cycles so that G1 of the cell cycle occurs in the light phase and S/M/G0 occurs during the dark phase. However, alternative splicing, a process that enables a greater protein diversity from a limited set of genes, remains largely unexplored by previous transcriptome based studies in C. reinhardtii In this study, we used existing longitudinal RNA-seq data obtained during the light-dark cycle to investigate the changes in the alternative splicing pattern and found that 3277 genes (19.75% of 17,746 genes) undergo alternative splicing. These splicing events include Alternative 5' (Alt 5'), Alternative 3' (Alt 3') and Exon skipping (ES) events that are referred as alternative site selection (ASS) events and Intron retention (IR) events. By clustering analysis, we identified a subset of events (26 ASS events and 10 IR events) that show periodic changes in the splicing pattern during the cell cycle. About two-thirds of these 36 genes either introduce a pre-termination codon (PTC) or introduce insertions or deletions into functional domains of the proteins, which implicate splicing in altering gene function. These findings suggest that alternative splicing is also regulated during the Chlamydomonas cell cycle, although not as extensively as changes in gene expression. The longitudinal changes in the alternative splicing pattern during the cell cycle captured by this study provides an important resource to investigate alternative splicing in genes of interest during the cell cycle in Chlamydomonas reinhardtii and other eukaryotes.

Functional cis-regulatory modules encoded by mouse-specific endogenous retrovirus.

Cis-regulatory modules contain multiple transcription factor (TF)-binding sites and integrate the effects of each TF to control gene expression in specific cellular contexts. Transposable elements (TEs) are uniquely equipped to deposit their regulatory sequences across a genome, which could also contain cis-regulatory modules that coordinate the control of multiple genes with the same regulatory logic. We provide the first evidence of mouse-specific TEs that encode a module of TF-binding sites in mouse embryonic stem cells (ESCs). The majority (77%) of the individual TEs tested exhibited enhancer activity in mouse ESCs. By mutating individual TF-binding sites within the TE, we identified a module of TF-binding motifs that cooperatively enhanced gene expression. Interestingly, we also observed the same motif module in the in silico constructed ancestral TE that also acted cooperatively to enhance gene expression. Our results suggest that ancestral TE insertions might have brought in cis-regulatory modules into the mouse genome.

An improved predictive recognition model for Cys(2)-His(2) zinc finger proteins.

Cys(2)-His(2) zinc finger proteins (ZFPs) are the largest family of transcription factors in higher metazoans. They also represent the most diverse family with regards to the composition of their recognition sequences. Although there are a number of ZFPs with characterized DNA-binding preferences, the specificity of the vast majority of ZFPs is unknown and cannot be directly inferred by homology due to the diversity of recognition residues present within individual fingers. Given the large number of unique zinc fingers and assemblies present across eukaryotes, a comprehensive predictive recognition model that could accurately estimate the DNA-binding specificity of any ZFP based on its amino acid sequence would have great utility. Toward this goal, we have used the DNA-binding specificities of 678 two-finger modules from both natural and artificial sources to construct a random forest-based predictive model for ZFP recognition. We find that our recognition model outperforms previously described determinant-based recognition models for ZFPs, and can successfully estimate the specificity of naturally occurring ZFPs with previously defined specificities.

Improved models for transcription factor binding site identification using nonindependent interactions.

Identifying transcription factor (TF) binding sites is essential for understanding regulatory networks. The specificity of most TFs is currently modeled using position weight matrices (PWMs) that assume the positions within a binding site contribute independently to binding affinity for any site. Extensive, high-throughput quantitative binding assays let us examine, for the first time, the independence assumption for many TFs. We find that the specificity of most TFs is well fit with the simple PWM model, but in some cases more complex models are required. We introduce a binding energy model (BEM) that can include energy parameters for nonindependent contributions to binding affinity. We show that in most cases where a PWM is not sufficient, a BEM that includes energy parameters for adjacent dinucleotide contributions models the specificity very well. Having more accurate models of specificity greatly improves the interpretation of in vivo TF localization data, such as from chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments.