Biology subjects

Pereira, C.

Publications and source records attributed to Pereira, C..

2 recordsLinked to original sources

bioRxiv2018

Self-assembly of the RZZ complex into filaments drives kinetochore expansion in the absence of microtubule attachment

The kinetochore is a dynamic multi-protein assembly that forms on each sister chromatid and interacts with microtubules of the mitotic spindle to drive chromosome segregation. In animals, kinetochores without attached microtubules expand their outermost layer into crescent and ring shapes to promote microtubule capture and spindle assembly checkpoint (SAC) signalling. Kinetochore expansion is an example of protein co-polymerization, but the mechanism is not understood. Here, we present evidence that kinetochore expansion is driven by oligomerization of the Rod-Zw10-Zwilch (RZZ) complex, an outer kinetochore component that recruits the motor dynein and the SAC proteins Mad1-Mad2. Depletion of ROD in human cells suppresses kinetochore expansion, as does depletion of Spindly, the adaptor that connects RZZ to dynein, while dynein itself is dispensable. Expansion is also suppressed by mutating ZWILCH residues implicated in Spindly binding. Conversely, supplying cells with excess ROD facilitates kinetochore expansion under otherwise prohibitive conditions. Using the C. elegans early embryo, we demonstrate that ROD-1 has a concentration-dependent propensity for oligomerizing into {micro}m-scale filaments, and we identify the ROD-1 {beta}-propeller as a key regulator of self-assembly. Finally, we show that a minimal ROD-1-Zw10 complex efficiently oligomerizes into filaments in vitro. Our results suggest that RZZs capacity for oligomerization is harnessed by kinetochores to assemble the expanded outermost domain, in which RZZ filaments serve as recruitment platforms for SAC components and microtubule-binding proteins. Thus, we propose that RZZ self-assembly into filaments underlies the adaptive change in kinetochore size that contributes to chromosome segregation fidelity.

cell biology↗

bioRxiv2017

SQANTI: extensive characterization of long read transcript sequences for quality control in full-length transcriptome identification and quantification

High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in very well annotated organisms as mice and humans. Nonetheless, there is a need for studies and tools that characterize these novel isoforms. Here we present SQANTI, an automated pipeline for the classification of long-read transcripts that computes 47 descriptors that can be used to assess the quality of the data and of the preprocessing pipelines. We applied SQANTI to a neuronal mouse transcriptome using PacBio long reads and illustrate how the tool is effective in readily describing the composition of and characterizing the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach, and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, result more frequently in novel ORFs than novel UTRs and are enriched in both general metabolic and neural specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases we find that alternative isoforms are elusive to proteogenomics detection and are variable in protein changes with respect to the principal isoform of their genes. SQANTI allows the user to maximize the analytical outcome of long read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes. SQANTI is available at https://bitbucket.org/ConesaLab/sqanti.

bioinformatics↗