Biology subjects

Revuelta, M. V.

Publications and source records attributed to Revuelta, M. V..

5 recordsLinked to original sources

bioRxiv2025

BUB1B MITOTIC KINASE DRIVES THERAPY RESISTANT PROSTATE CANCER

Castration-resistant prostate cancer (CRPC) that progresses despite treatment with potent AR antagonists such as enzalutamide is a major clinical problem. Through an unbiased systems biology approach, we previously identified a therapeutically relevant seven gene set that drives CRPC. The mitotic checkpoint kinase, BUB1B (BUBR1) is a key member of this gene set, and we report here that BUB1B is a tractable and promising new therapeutic target in aggressive, treatment-resistant PC. We found that high BUB1B expression is correlated with PC progression and aggressiveness. In established CRPC cells, BUB1B depletion blocked cell proliferation through cell cycle arrest and mitosis delay. Ectopic expression of BUB1B, at levels found in CRPC, conferred castration-resistant growth of androgen-dependent PC cells in vitro and in vivo. We showed that BUB1B kinase activity was essential for CRPC progression, as only wild type (wt) BUB1B and neither of two kinase-dead mutants promoted castration-resistant growth of androgen-dependent PC cells. Rescue experiments with wt or kinase-dead mutants showed further that BUB1B kinase activity was also required to maintain proliferation of established CRPC cells. While persistent androgen receptor (AR) signaling is a mechanism of CRPC progression, BUB1B promotion of CRPC was not dependent on AR as assessed through AR knockdown. Consistent with an AR-bypass mechanism, ectopic expression of BUB1B rendered PC cells resistant to enzalutamide in vitro and in vivo. Our data points to BUB1B as a key driver of CRPC progression and enzalutamide resistance and suggests that targeting BUB1B kinase is a promising therapeutic approach for lethal, treatment-resistant disease. Statement of significanceWe document a novel role for BUB1B kinase as a critical driver of castration- and enzalutamide-resistant prostate cancer, highlighting the therapeutic potential of BUB1B kinase inhibition to overcome lethal treatment-resistant disease.

cancer biology↗

bioRxiv2021

The eukaryotic translation initiation factor eIF4E reprogrammes the splicing machinery anddrives alternative splicing

Aberrant RNA splicing contributes to the pathogenesis of many malignancies including Acute Myeloid Leukemia (AML). While mutation is the best described mechanism underpinning aberrant splicing, recent studies show that predictions based on mutations alone likely underestimate the extent of this dysregulation. Here, we show that elevation of the eukaryotic translation initiation factor eIF4E reprogrammes splicing of nearly a thousand RNAs in model cell lines. Further, in AML patient specimens which did not harbour known splice factor mutations, [~]4000 transcripts were differentially spliced based on eIF4E levels and this was associated with poor prognosis. Inhibition of eIF4E in cell lines reverted the eIF4E-dependent splice events examined. Splicing targets of eIF4E act in biological processes consistent with its role in malignancy. This altered splicing program likely arose from eIF4E-dependent increases in the production of spliceosome components including SF3B1 and U2AF1 which are frequently mutated in AML. Notably, eIF4E did not drive mutation of these factors, only their production. eIF4E also physically associated with many splice factors including SF3B1, U2AF1, and UsnRNAs. Interestingly, eIF4E interacted with both pre-mRNA splicing substrates as well as the resulting product RNAs suggesting that eIF4E could chaperone RNAs throughout splicing as well as other aspects of post-splicing transcript regulation. Importantly, many eIF4E-dependent splice events differed from those arising from splice factor mutation and were more extensive highlighting that these splicing profiles arise from distinct, but potentially overlapping, mechanisms. In all, our studies provide a paradigm for how dysregulation of a single factor, eIF4E, can reprogramme splicing.

cancer biology↗

bioRxiv2020

HSP90 facilitates oncogenic alterations of metabolism in B-cell lymphomas

HSP90 is critical for maintenance of the cellular proteostasis. In cancer cells, HSP90 also becomes a nucleating site for the stabilization of multiprotein complexes including signaling pathways and transcription complexes. Here, we described a novel role of HSP90 in the cytosolic compartmentalization of metabolic pathways in proliferating cancer cells. We found that HSP90 assists in the organization of metabolic enzymes into non-membrane-bound functional compartments termed metabosomes. Under experimental conditions that conserved the cellular proteostasis, we demonstrated that the compartmentalizing activity of HSP90 is critical to sustain the coordinated synthesis of multiple metabolites required for energy production, maintenance of the cellular biomass and secretion of immunometabolites. Conversely, inhibition of the nucleating capacity of HSP90 modified the topology of cytosolic metabosomes before protein degradation was apparent decreasing the efficiency of MYC-driven metabolic pathways. Inhibition of HSP90 decreases cancer metabolism in B-cell lymphoma cells and patients providing a novel mechanism of activity for this class of drugs.

cancer biology↗

bioRxiv2017

Substrate binding and specificity appear as major forces in the functional diversification of eqolisins.

BackgroundEqolisins are rare acid proteases found in archaea, bacteria and fungi. Certain fungi secrete acids as part of their lifestyle and interestingly these also have many eqolisin paralogs, up to nine paralogs have been recorded. This suggests functional redundancy and diversification, which was the subject of the research we performed and describe here.\n\nResultsWe identified eqolisin homologs by means of iterative HMMER analysis of the NR database. The identified sequences were scrutinized for which we defined novel hallmarks, identified by molecular dynamics simulations of mutants of highly conserved positions, using the structure of an eqolisin that was crystallized in the presence of a transition state inhibitor. Four conserved glycines were shown to be required for functionality. A substitution of W67F is shown to be accompanied by the L105W substitution. Molecular dynamics shows that the W67 binds to the substrate via a {pi}-{pi} stacking and a salt bridge, the latter being stronger in a virtual W67F/L105W double mutant of the resolved structure of Scytalido-carboxyl peptidase-B (PDB ID: 2IFW)). Additional likely fatal mutants are discussed.\n\nUpon sequence scrutiny we obtained a set of 233 sequences that in all likelihood lack false positives. This was used to reconstruct a Bayesian phylogenetic tree. We identified 14 putative specificity determining positions (SDPs) of which four are explained by mere structural explanations and nine seem to correspond to functional diversification related wit substrate binding ans specificity. A first sub-network of SDPs is related to substrate specificity whereas the second sub-network seems to affect the dynamics of three loops that are involved in substrate binding.\n\nHighlightsEqolisins are acid proteases found in prokaryotes and fungi only.\n\nThe recently co-evolved W67F-L105W substitutions promote substrate binding\n\nTwo Specificity Determining Networks, SDN1 and 2, were identified\n\nSDN1 has four Specificity Determining Positions involved in substrate specificity\n\nSDN2 has five Specificity Determining Positions involved in loop-substrate dynamics

bioinformatics↗

bioRxiv2017

HMMER Cut-off Threshold Tool (HMMERCTTER): Supervised Classification of Superfamily Protein Sequences with a reliable Cut-off Threshold

Protein superfamilies can be divided into subfamilies of proteins with different functional characteristics. Their sequences can be classified hierarchically, which is part of sequence function assignation. Typically, there are no clear subfamily hallmarks that would allow pattern-based function assignation by which this task is mostly achieved based on the similarity principle. This is hampered by the lack of a score cut-off that is both sensitive and specific.\n\nHMMER Cut-off Threshold Tool (HMMERCTTER) adds a reliable cut-off threshold to the popular HMMER. Using a high quality superfamily phylogeny, it clusters a set of training sequences such that the cluster-specific HMMER profiles show 100% precision and recall (P&R), thereby generating a specific threshold as inclusion cut-off. Profiles and threshold are then used as classifiers to screen a target dataset. Iterative inclusion of novel sequences to groups and the corresponding HMMER profiles results in high sensitivity while specificity is maintained by imposing 100% P&R. In three presented case studies of protein superfamilies, classification of large datasets with 100% P&R was achieved with over 95% coverage. Limits and caveats are presented and explained.\n\nHMMERCTTER is a promising protein superfamily sequence classifier provided high quality training datasets are used. It provides a decision support system that aids in the difficult task of sequence function assignation in the twilight zone of sequence similarity. A package containing source code and full dataset will be deposited at Github and is available for reviewers at: https://www.dropbox.com/s/aacao6ggcak30bg/Repo.tar.gz?dl=0\n\nAuthor summaryThe enormous amount of genome sequences made available in the last decade provide new challenges for scientists. An important step in genome sequence processing is function assignation of the encoded protein sequences, typically based on the similarity principle: The more similar sequences are, the more likely they encode the same function. However, evolution generated many protein superfamilies that consist of various subfamilies with different functional characteristics, such as substrate specificity, optimal activity conditions or the catalyzed reaction. The classification of superfamily sequences to their respective subfamilies can be performed based on similarity but since the different subfamilies also remain similar, it requires a reliable similarity score cut-off.\n\nWe present a tool that clusters training sequences and describes them in profiles that identify cluster members with higher similarity scores than non-cluster members, i.e. with 100% precision and recall. This defines a score cut-off threshold. Profiles and thresholds are then used to classify other sequences. Classified sequences are included in the profiles in order to improve sensitivity while maintaining specificity by imposing 100% precision and recall. Results on three case studies show that the tool can correctly classify complex superfamilies with over 95% coverage.\n\nHMMERCTTER is meant as a decision support system for the expert biologist rather than the computational biologist.

bioinformatics↗