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Marino-Buslje, C.

Publications and source records attributed to Marino-Buslje, C..

8 recordsLinked to original sources

Multivalent ubiquitin recognition regulates multiple stages of Tau self-assembly

Liquid-liquid phase separation (LLPS) has emerged as an important intermediate in the pathological self-assembly of Tau, yet the molecular mechanisms that regulate this process remain incompletely understood. Although ubiquitin is a prominent component of pathological inclusions in tauopathies and other neurodegenerative diseases, its presence has largely been linked to targeting aberrant proteins for proteasomal or autophagic clearance. Here, we identify Tau as a ubiquitin-binding protein and demonstrate that multivalent, non-covalent ubiquitin recognition regulates multiple stages of Tau self-assembly. Biochemical, biophysical, structural, and computational analyses identified four ubiquitin-binding elements within the Tau microtubule-binding repeat region that engage the canonical hydrophobic patch of ubiquitin, providing a structural basis for multivalent recognition of mono- and poly-ubiquitin species. Functionally, mono- and poly-ubiquitin suppress Tau condensate formation while enhancing the liquid-like properties of condensates that persist. In contrast, mono-ubiquitin selectively enhances aggregation of the disease-linked Tau-P301L variant under conditions without detectable LLPS, demonstrating that ubiquitin exerts distinct effects depending on the molecular context and stage of Tau self-assembly. Together, these findings establish non-covalent ubiquitin recognition as a previously unrecognized mechanism regulating Tau phase behavior and aggregation. More broadly, they expand the role of ubiquitin beyond protein quality control, with ubiquitin architecture providing an additional layer of regulation over Tau condensate formation, dynamics, maturation, and aggregation.

biophysics↗

MLOsMetaDB, a meta-database to centralize the information on Liquid-liquid phase separation proteins and Membraneless organelles.

Over the past few years, there has been a focus on proteins that create separate liquid phases in the intracellular liquid environment, known as membraneless organelles (MLO). These organelles allow for the spatiotemporal associations of macromolecules that dynamically exchange within the cellular milieu. They provide a form of compartmentalization crucial for organizing key functions in many cells. Metabolic processes and signaling pathways in both the cytoplasm and nucleus are among the functions performed by MLOs, which are facilitated by diverse combinations of proteins and nucleic acids. However, disruptions in these liquid-liquid phase separation processes (LLPS) may lead to several diseases, such as neurodegenerative disorders and cancer, among others. To foster the study of this process and MLOs function, we present MLOsMetaDB (http://mlos.leloir.org.ar), a comprehensive resource of information on MLO and LLPS related proteins. Our database integrates and centralizes available information for every protein involved in MLOs, which is otherwise disseminated across a plethora of different databases. Our manuscript outlines the development and features of MLOsMetaDB which provides an interactive and user-friendly environment with modern biological visualizations and easy and quick access to proteins based on LLPS role, MLO location, and organisms. Additionally, it offers an advanced search for making complex queries to generate customized information. Furthermore, MLOsMetaDB provides evolutionary information by collecting the orthologs of every protein in the same database. This is a crucial resource for transferring knowledge to other species because the LLPS protein databases are highly biased towards human proteins and, to a lesser extent, a few other well-studied or model organisms. Overall, MLOsMetaDB is a valuable resource as a starting point for researchers studying the many processes driven by LLPS proteins and membraneless organelles.

bioinformatics↗

Pipeline for transferring annotations between proteins beyond globular domains

BackgroundDisProt is the primary repository of Intrinsically Disordered Proteins. This database is manually curated and the annotations there have strong experimental support. Currently DisProt contains a relatively small number of proteins highlighting the importance of transferring verified disorder and other annotations, in such a way as to increase the number of proteins that could benefit from this valuable information. While the principles and practicalities of homology transfer are well-established for globular proteins, these are largely lacking for disordered proteins. MethodsWe used DisProt to evaluate the transferability of the annotation terms to orthologous proteins. For each protein, we looked for their orthologs, with the assumption that they will have a similar function. Then, for each protein and their orthologs we made multiple sequence alignments (MSAs). Global and regional quality of the MSAs was evaluated with the NorMD score. ResultsWe have designed a pipeline to obtain good quality MSAs and to transfer annotations from any protein to their orthologs. Applying the pipeline to DisProt proteins, from the 1931 entries with 5,623 annotations we can reach 97,555 orthologs and transfer a total of 301,190 terms by homology. We also provide a web server for consulting the results of DisProt proteins and execute the pipeline for any other protein. The server Homology Transfer IDP (HoTIDP) is accessible at http://hotidp.leloir.org.ar.

bioinformatics↗

DisPhaseDB, an integrative database of diseases related variations in liquid-liquid phase separation proteins

MotivationProteins involved in liquid-liquid phase separation (LLPS) and membraneless organelles (MLOs) are recognized to be decisive for many biological processes and also responsible for several diseases. The recent explosion of research in the area still lacks tools for the analysis and data integration among different repositories. Currently, there is not a comprehensive and dedicated database that collects all disease-related variations in combination with the protein location, biological role in the MLO and all the metadata available for each protein and disease. Disease related protein variants and additional features are dispersed and the user has to navigate many databases, with different focus, formats and often not user friendly. ResultsWe present DisPhaseDB, a database dedicated to disease related variants of LLPS proteins and/or are involved in MLOs. It integrates 10 databases, contains 5.741 proteins, 1.660.059 variants and 4.051 disease terms. It also offers intuitive navigation and an informative display. It constitutes a pivotal starting point for further analysis, encouraging the development of new computational tools. Availability and ImplementationThe database is freely available at http://disphasedb.leloir.org.ar. Contactjiserte@leloir.org.ar and cmb@leloir.org.ar Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=169 SRC="FIGDIR/small/479026v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@b4e706org.highwire.dtl.DTLVardef@ab75bforg.highwire.dtl.DTLVardef@bf70cborg.highwire.dtl.DTLVardef@8622e3_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

Characterization of Intellectual disability and Autism comorbidity through gene panel sequencing

Intellectual disability (ID) and autism spectrum disorder (ASD) are clinically and genetically heterogeneous diseases. Recent whole exome sequencing studies indicated that genes associated with different neurological diseases are shared across disorders and converge on common functional pathways. Using the Ion Torrent platform, we developed a low-cost next generation sequencing (NGS) gene panel that has been transferred into clinical practice, replacing single disease gene analyses for the early diagnosis of individuals with ID/ASD. The gene panel was designed using an innovative in silico approach based on disease networks and mining data from public resources to score disease-gene associations. We analyzed 150 unrelated individuals with ID and/or ASD and a confident diagnosis has been reached in 26 cases (17%). Likely pathogenic mutations have been identified in another 15 patients, reaching a total diagnostic yield of 27%. Our data also support the pathogenic role of genes recently proposed to be involved in ASD. Although many of the identified variants need further investigation to be considered disease-causing, our results indicate the efficiency of the targeted gene panel on the identification of novel and rare variants in patients with ID and ASD.

genetics↗

How is structural divergence related to evolutionary information?

Conservation and covariation measures, as other evolutionary analysis, require a high number of distant homologous sequences, therefore a lot of structural divergence can be expected in such divergent alignments. However, most works linking evolutionary and structural information use a single structure ignoring the structural variability inside a protein family. That common practice seems unrealistic to the light of this work.\n\nIn this work we studied how structural divergence affects conservation and covariation estimations. We uncover that, within a protein family, ~51% of multiple sequence alignment columns change their exposed/buried status between structures. Also, ~53% of residue pairs that are in contact in one structure are not in contact in another structure from the same family. We found out that residue conservation is not directly related to the relative solvent accessible surface area of a single protein structure. Using information from all the available structures rather than from a single representative structure gives more confidence in the structural interpretation of the evolutionary signals. That is particularly important for diverse multiple sequence alignments, where structures can drastically differ. High covariation scores tend to indicate residue contacts that are conserved in the family, therefore, are not suitable to find protein/conformer specific contacts.\n\nOur results suggest that structural divergence should be considered for a better understanding of protein function, to transfer annotation by homology and to model protein evolution.

bioinformatics↗

Co-mutation and exclusion analysis in human tumors, a means for cancer biology studies and treatment design

BackgroundMalignant tumors originate from genomic and epigenomic alterations, which lead to loss of control of the cellular circuitry. These alterations relate with each other in patterns of mutual exclusion and co-occurrence that affect prognosis and treatment response and highlight the need for multitargeted therapy. However, to the best of our knowledge, there are no systematic reports in the literature of co-dependent and mutually exclusive mutations across all types of cancer. In addition, the studies reported so far generally deal with whole genes instead of specific mutations, ignoring the fact that different alterations in the same gene can have widely different effects.\n\nResultsHere we present a systematic analysis of co-dependencies of somatic mutations across all cancer types. Combining multi testing with conditional and expected mutational probabilities, we have found pairs and networks of co-mutations and exclusions, some of them in particular types of cancer and others widespread. We have also determined that driver loci are present in more types of cancer than non driver loci, that they tend to pair within a single gene and that those pairs are more often exclusions than co-mutations.\n\nConclusionsBased on this properties, we propose new drivers that warrant experimental validation. Our analysis is potentially relevant for cancer biology and classification, as well as for the rational selection of multitargeted therapeutic approaches.

cancer biology↗

Homology Modeling In A Dynamical World

A key concept in template-based modeling is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity.\n\nIn this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair Template-based modelling (TMB) results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.\n\nAuthor summaryTemplate-based modelling (TBM) is the most reliable and fastest approach to obtain protein structural models. TBM relies in the high correlation between sequence and structural divergence, with the practical consequence that proteins that are similar at the sequence level will also be similar at the structural level, allowing in this way the selection of the better template to obtain the 3D model of the target sequence. However, protein native state could be described by a collection of conformers in equilibrium where their structural differences are called conformational diversity.\n\nIn this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We firstly found that the extent of conformational diversity can be as high as the maximum structural differences reached by families differing in their sequences. In these proteins with higher conformational diversity levels, the well-established correlation between sequence and structural divergence is nosier than previously suggested due to the presence of structural change without sequence variation. This lack of correlation could impair TBM results due to the uncertainty in the correct template selection. Finally, we also found that the presence of order/disorder can provide useful beforehand information for better TBM performance.

bioinformatics↗