Cell-Cycle-Dependent Chromatin Dynamics at Replication Origins

Bipartite networks represent causality better than simple networks: evidence, algorithms, and applications

A network, whose nodes are genes and whose directed edges represent positive or negative influences of a regulatory gene and its targets, is often used as a representation of causality. To infer a network, researchers often develop a machine learning model and then evaluate the model based on its match with experimentally verified "gold standard" edges. The desired result of such a model is a network that may extend the gold standard edges. Since networks are a form of visual representation, one can compare their utility with architectural or machine blueprints. Blueprints are clearly useful because they provide precise guidance to builders in construction. If the primary role of gene regulatory networks is to characterize causality, then such networks should be good tools of prediction because prediction is the actionable benefit of knowing causality. But are they? In this paper, we compare prediction quality based on "gold standard" regulatory edges from previous experimental work with non-linear models inferred from time series data across four different species. We show that the same non-linear machine learning models have better predictive performance, with improvements from 5.3% to 25.3% in terms of the reduction in the root mean square error (RMSE) compared with the same models based on the gold standard edges. Having established that networks fail to characterize causality properly, we suggest that causality research should focus on four goals: (i) predictive accuracy; (ii) a parsimonious enumeration of predictive regulatory genes for each target gene g; (iii) the identification of disjoint sets of predictive regulatory genes for each target g of roughly equal accuracy; and (iv) the construction of a bipartite network (whose node types are genes and models) representation of causality. We provide algorithms for all goals.

A transcriptional roadblock protects yeast centromeres

Centromeres are the chromosomal loci essential for faithful chromosome segregation during cell division. Although centromeres are transcribed and produce non-coding RNAs (cenRNAs) that affect centromere function, we still lack a mechanistic understanding of how centromere transcription is regulated. Here, using a targeted RNA isoform sequencing approach, we identified the transcriptional landscape at and surrounding all centromeres in budding yeast. Overall, cenRNAs are derived from transcription readthrough of pericentromeric regions but rarely span the entire centromere and are a complex mixture of molecules that are heterogeneous in abundance, orientation, and sequence. While most pericentromeres are transcribed throughout the cell cycle, centromere accessibility to the transcription machinery is restricted to S-phase. This temporal restriction is dependent on Cbf1, a centromere-binding transcription factor, that we demonstrate acts locally as a transcriptional roadblock. Cbf1 deletion leads to an accumulation of cenRNAs at all phases of the cell cycle which correlates with increased chromosome mis-segregation that is partially rescued when the roadblock activity is restored. We propose that a Cbf1-mediated transcriptional roadblock protects yeast centromeres from untimely transcription to ensure genomic stability.

The STAR/GSG family protein rSLM-2 regulates the selection of alternative splice sites

We identified the rat Sam68-like mammalian protein (rSLM-2), a member of the STAR (signal transduction and activation of RNA) protein family as a novel splicing regulatory protein. Using the yeast two-hybrid system, coimmunoprecipitations, and pull-down assays, we demonstrate that rSLM-2 interacts with various proteins involved in the regulation of alternative splicing, among them the serine/arginine-rich protein SRp30c, the splicing-associated factor YT521-B and the scaffold attachment factor B. rSLM-2 can influence the splicing pattern of the CD44v5, human transformer-2beta and tau minigenes in cotransfection experiments. This effect can be reversed by rSLM-2-interacting proteins. Employing rSLM-2 deletion variants, gel mobility shift assays, and linker scan mutations of the CD44 minigene, we show that the rSLM-2-dependent inclusion of exon v5 of the CD44 pre-mRNA is dependent on a short purine-rich sequence. Because the related protein of rSLM-2, Sam68, is believed to play a role as an adapter protein during signal transduction, we postulate that rSLM-2 is a link between signal transduction pathways and pre-mRNA processing.