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Babaei, P.

Publications and source records attributed to Babaei, P..

2 recordsLinked to original sources

The RNA-binding protein RSRC2 promotes mitotic fidelity by interacting with the lncRNA C1QTNF1-AS1

Mitotic fidelity depends on proper chromosome alignment at the spindle equator, a process known as chromosome congression, driven by well-established protein networks. Whereas RNA-binding proteins and noncoding RNAs have been implicated in cell division, their interplay during this process remains unknown. Here, we discover that RSRC2, an arginine/serine-rich RNA-binding protein, plays an essential role in cell division by interacting with the long non-coding RNA C1QTNF1-AS1. The loss of either RSRC2 or C1QTNF1-AS1 results in defects in chromosome congression and mitotic progression. We show that RSRC2 interacts with distinct sets of proteins involved in splicing and centrosome biogenesis, contributing to the fidelity of cell division through two different mechanisms: one linked to the splicing of mitotic regulators and the other by localising to mitotic centrosomes for which the interaction with the C1QTNF1-AS1 RNA is required. Our study uncovers RSRC2 as a new regulator of cell division and illustrates how RNA-protein complexes promote error-free mitosis.

cell biology↗

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗