Biology subjects

Rivas, E.

Publications and source records attributed to Rivas, E..

16 recordsLinked to original sources

bioRxiv2026

Tocilizumab induces significant changes in longitudinal proteomes of blood serum from patients with severe COVID-19 pneumonia

Coronavirus disease 2019 (COVID-19) shows highly variable clinical outcomes that are not fully explained by age or comorbidities, underscoring the importance of host molecular responses in determining disease severity. Proteomic and multi-omics studies have linked severe COVID-19 to profound dysregulation of immune, inflammatory, and coagulation pathways, and have shown that circulating protein signatures can predict clinical trajectories. Tocilizumab (TCZ), a monoclonal antibody targeting the interleukin-6 receptor (IL-6R), is an established therapy for IL-6-driven inflammatory diseases and can normalize aberrant molecular profiles. Here, we applied longitudinal serum proteomics to patients with severe SARS-CoV-2 pneumonia treated with TCZ to further characterize how IL-6R blockade reshapes the systemic inflammatory milieu. After TCZ administration, several clinical and inflammatory markers, including C-reactive protein (CRP), CCL5 and CXCL10, decreased. Proteomic profiling revealed that TCZ exerts a sustained effect on the serum proteome, with the most pronounced changes emerging 7 days after treatment. These changes were associated with a broad reconfiguration of the proteomic profile toward a pattern resembling a healthy physiological state, characterized by the restoration of key protein abundances to levels comparable to those observed under homeostatic conditions. Collectively, our findings support that TCZ treatment contributes to the normalization of the inflammatory state in severe COVID-19 and represents a viable therapeutic option for managing the acute inflammatory phase of the disease, while also highlighting additional pathways and biomarkers involved in this recovery process.

pharmacology and toxicology↗

bioRxiv2025

RNAcentral in 2026: Genes and literature integration

RNAcentral was founded in 2014 to serve as a comprehensive database of non-coding RNA sequences. It began by providing a single unified interface to more specialised resources, and now contains 45 million sequences. It has grown beyond providing a single interface to many specialised resources and now provides several services and analyses. These include secondary structure prediction with R2DT, sequence search, and analysis with Rfam. Since its last publication in 2021, RNAcentral has developed two major features. First, literature integration with the development of LitScan and LitSumm. LitScan automatically identifies and links relevant publications to RNA entries, while LitSumm uses natural language processing to generate functional summaries from the literature. Together, these tools address the critical challenge of connecting sequence data with scattered functional knowledge across thousands of publications. Secondly, RNAcentral has created gene level entries. Gene level entries represent a large structural change to RNAcentral. While RNAcentral previously organized data exclusively at the sequence level, we now group related transcripts into gene-centric views. This allows researchers to explore all isoforms, splice variants, and related sequences for a gene in a unified interface, better reflecting biological organization and facilitating comparative analyses. RNAcentral is freely available at: https://rnacentral.org.

bioinformatics↗

bioRxiv2025

What does it take to learn the rules of RNA base pairing? A lot less than you may think

Amidst the fast-developing trend of RNA large language models with millions of parameters, we asked what would be the minimum required to rediscover the rules of RNA canonical base pairing, mainly the Watson-Crick-Franklin A:U, G:C and the wobble G:U base pairs (the secondary structure). Here, we conclude that it does not require much at all. It does not require knowing secondary structures; it does not require aligning the sequences; and it does not require many parameters. We selected a probabilistic model of palindromes (a stochastic context-free grammar or SCFG) with a total of just 21 parameters. Using standard deep learning techniques, we estimate its parameters by implementing the generative process in an automatic differentiation (autodiff) framework and applying stochastic gradient descent (SGD). We define and minimize a loss function that does not use any structural or alignment information. Trained on as few as fifty RNA sequences, the rules of RNA base pairing emerge after only a few iterations of SGD. Crucially, the sole inputs are RNA sequences. When optimizing for sequences corresponding to structured RNAs, SGD also yields the rules of RNA base-pair aggregation into helices. Trained on shuffled sequences, the system optimizes by avoiding base pairing altogether. Trained on messenger RNAs, it reveals interactions that are different from those of structural RNAs, and specific to each mRNA. Our results show that the emergence of canonical base-pairing can be attributed to sequence-level signals that are robust and detectable even without labeled structures or alignments, and with very few parameters. Autodiff algorithms for probabilistic models, such as, but not restricted to SCFGs, have significant potential as they allow these models to be incorporated into end-to-end RNA deep learning methods for discerning transcripts of different functionalities. Availabilityrivaslab.org, https://github.com/EddyRivasLab/R-scape/tree/master/python/d-SCFG

bioinformatics↗

bioRxiv2025

RNAhub - an automated pipeline to search and align RNA homologs with secondary structure assessment

The complexity in the generation of RNA multiple sequence alignments and assessment of the accuracy of such alignments contributes to the challenges in the utilization of RNA alignments in diverse integrative methods. RNAhub is a freely available user-friendly web server for a reliable generation of RNA multiple sequence alignments and the detection of the presence of structural RNA utilizing evolutionary information. This web-based tool, developed by the integration of existing computational approaches, takes an RNA sequence as input and automatically retrieves and aligns sequences homologous to the input (query) RNA sequence through an iterative and structure-agnostic approach. Based on the alignment, this tool statistically assesses whether the query RNA sequence has a conserved RNA structure using covariation analysis. The web server allows the user to efficiently search the sequence of interest against carefully curated, ready-to-use genomic databases to produce a multiple sequence alignment. Using this alignment, our tool either detects the presence of a conserved structural RNA, finds evidence against the presence of a conserved structure, or cannot make any assessment due to a lack of sequence diversity in the alignment. The web server is freely available at https://rnahub.org. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=181 HEIGHT=200 SRC="FIGDIR/small/642701v3_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@540f6forg.highwire.dtl.DTLVardef@c15e53org.highwire.dtl.DTLVardef@117ced2org.highwire.dtl.DTLVardef@b679c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

On inputs to deep learning for RNA 3D structure prediction

Today, there are several effective deep learning models for predicting the 3D structure of proteins. Building on their success, models have been developed for predicting the 3D structure of non-coding RNAs. Unfortunately, these models are much less accurate than their protein counterparts. In this paper, we highlight differences between protein and RNA structure, and demonstrate methods for deep learning targeted at addressing those differences, with the aim of prompting discussion on these topics. We present an RNA-specific pipeline for generating structural Multiple Sequence Alignments (MSAs). Derived from the structural alignments, we introduce engineered evolutionary features that strongly inform RNA structure. Further, from the crystal structure, we derive structural features describing RNA base pairing. These evolutionary and structural features can be used in loss functions at different stages of training. Finally, we discuss different cropping strategies informed by RNA structure.

bioinformatics↗

bioRxiv2024

All-at-once RNA folding with 3D motif prediction framed by evolutionary information

Structural RNAs exhibit a vast array of recurrent short 3D elements found in loop regions involving non-Watson-Crick interactions that help arrange canonical double helices into tertiary structures. We present CaCoFold-R3D, a probabilistic grammar that predicts these RNA 3D motifs (also termed modules) jointly with RNA secondary structure over a sequence or alignment. CaCoFold-R3D uses evolutionary information present in an RNA alignment to reliably identify canonical helices (including pseudoknots) by covariation. We further introduce the R3D grammars, which also exploit helix covariation that constrains the positioning of the mostly non-covarying RNA 3D motifs. Our method runs predictions over an almost-exhaustive list of over fifty known RNA motifs (everything). Motifs can appear in any non-helical loop region (including 3-way, 4-way and higher junctions) (everywhere). All structural motifs as well as the canonical helices are arranged into one single structure predicted by one single joint probabilistic grammar (all-at-once). Our results demonstrate that CaCoFold-R3D is a valid alternative for predicting the all-residue interactions present in a RNA 3D structure. CaCoFold-R3D is fast and easily customizable for novel motif discovery and shows promising value both as a strong input for deep learning approaches to all-atom structure prediction as well as towards guiding RNA design as drug targets for therapeutic small molecules. AvailabilityThe source code can be downloaded from the website rivaslab.org, the git https://github.com/EddyRivasLab/R-scape, as well as from the supplementary materials associated to this manuscript. Supplementary informationSupplementary materials (data and code) are provided with this manuscript, and at rivaslab.org.

bioinformatics↗

bioRxiv2024

Rfam 15: RNA families database in 2025

The Rfam database, a widely-used repository of non-coding RNA (ncRNA) families, has undergone significant updates in release 15.0. This paper introduces major improvements, including the expansion of Rfamseq to 26, 106 genomes, a 76% increase, incorporating the latest UniProt reference proteomes and additional viral genomes. Sixty-five RNA families were enhanced using experimentally determined 3D structures, improving the accuracy of consensus secondary structures and annotations. R-scape covariation analysis was used to refine structural predictions in 26 families. Gene Ontology and Sequence Ontology annotations were comprehensively updated, increasing GO term coverage to 75% of families. The release adds 14 new Hepatitis C Virus RNA families and completes microRNA family synchronisation with miRBase, resulting in 1, 603 microRNA families. New data types, including FULL alignments, have been implemented. Integration with APICURON for improved curator attribution and multiple website enhancements further improve user experience. These updates significantly expand Rfams coverage and improve annotation quality, reinforcing its critical role in RNA research, genome annotation, and the development of machine learning models. Rfam is freely available at https://rfam.org. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/614430v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@9b1898org.highwire.dtl.DTLVardef@6bc030org.highwire.dtl.DTLVardef@16b789org.highwire.dtl.DTLVardef@16b8577_HPS_FORMAT_FIGEXP M_FIG Rfam has undergone a major update with the release of 15.0. We have increased the number of genomes in our sequence database Rfamseq by 75%, completed the synchronisation with miRBase and improved 65 families using 3D structures. C_FIG

genomics↗

bioRxiv2024

RNA3DB: a dataset for training and benchmarking deep learning models for RNA structure prediction

With advances in protein structure prediction thanks to deep learning models like AlphaFold, RNA structure prediction has recently received increased attention from deep learning researchers. RNAs introduce substantial challenges due to the sparser availability and lower structural diversity of the experimentally resolved RNA structures in comparison to protein structures. These challenges are often poorly addressed by the existing literature, many of which report inflated performance due to using training and testing sets with significant structural overlap. Further, the most recent Critical Assessment of Structure Prediction (CASP15) has shown that deep learning models for RNA structure are currently outperformed by traditional methods. In this paper we present RNA3DB, a dataset of structured RNAs, derived from the Protein Data Bank (PDB), that is designed for training and benchmarking deep learning models. The RNA3DB method arranges the RNA 3D chains into distinct groups (Components) that are non-redundant both with regard to sequence as well as structure, providing a robust way of dividing training, validation, and testing sets. Any split of these structurally-dissimilar Components are guaranteed to produce test and validations sets that are distinct by sequence and structure from those in the training set. We provide the RNA3DB dataset, a particular train/test split of the RNA3DB Components (in an approximate 70/30 ratio) that will be updated periodically. We also provide the RNA3DB methodology along with the source-code, with the goal of creating a reproducible and customizable tool for producing structurally-dissimilar dataset splits for structural RNAs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/578025v3_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@cb3aa2org.highwire.dtl.DTLVardef@7685fdorg.highwire.dtl.DTLVardef@156ca35org.highwire.dtl.DTLVardef@f58d72_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWhile there is a recent surge in applying deep learning to RNA structure prediction, domain experts have raised concerns about generalization and current trends in benchmarking. C_LIO_LIMany of the concerns primarily relate to how novel RNA families-i.e. families unseen in the training set-are benchmarked, and whether the models are effective at handling such cases. Performance on bench-marks reflective of real-world applications, such as CASP15 and RNA-Puzzles, is poor for RNA deep learning models. C_LIO_LIWe present a dataset-RNA3DB-that is designed for training and bench-marking deep learning models for RNA structure prediction. RNA3DB provides coverage of all RNA chains found in the Protein Data Bank (PDB). C_LIO_LIRNA3DB is clustered into groups that are both sequentially and structurally non-redundant, providing a robust way of creating training, validation, and testing sets for deep learning models. Along with the dataset, we also provide a transparent methodology as well as the source-code, making our tool both reproducible and customizable. C_LI

bioinformatics↗

bioRxiv2023

RNA covariation at helix-level resolution for the identification of evolutionarily conserved RNA structure

Many biologically important RNAs fold into specific 3D structures conserved through evolution. Knowing when an RNA sequence includes a conserved RNA structure that could lead to new biology is not trivial and depends on clues left behind by conservation in the form of covariation and variation. For that purpose, the R-scape statistical test was created to identify from alignments of RNA sequences, the base pairs that significantly covary above phylogenetic expectation. R-scape treats base pairs as independent units. However, RNA base pairs do not occur in isolation. The Watson-Crick (WC) base pairs stack together forming helices that constitute the scaffold that facilitates the formation of the non-WC base pairs, and ultimately the complete 3D structure. The helix-forming WC base pairs carry most of the covariation signal in an RNA structure. Here, I introduce a new measure of statistically significant covariation at helix-level by aggregation of the covariation significance and covariation power calculated at base-pair-level resolution. Performance benchmarks show that helix-level aggregated covariation increases sensitivity in the detection of evolutionarily conserved RNA structure without sacrificing specificity. This additional helix-level sensitivity reveals an artifact that results from using covariation to build an alignment for a hypothetical structure and then testing the alignment for whether its covariation significantly supports the structure. Helix-level reanalysis of the evolutionary evidence for a selection of long non-coding RNAs (lncRNAs) reinforces the evidence against these lncRNAs having a conserved secondary structure. AvailabilityHelix aggregated E-values are integrated in the R-scape software package (version 2.0.0.p and higher). The R-scape web server eddylab.org/R-scape includes a link to download the source code. Contactelenarivas@fas.harvard.edu Supplementary informationSupplementary data and code are provided with this manuscript at rivaslab.org.

bioinformatics↗

bioRxiv2022

Fitness Functions for RNA Structure Design

AO_SCPLOWBSTRACTC_SCPLOWAn RNA design algorithm takes a target RNA structure and finds a sequence that folds into that structure. This is fundamentally important for engineering therapeutics using RNA. Computational RNA design algorithms are guided by fitness functions, but not much research has been done on the merits of these functions. We survey current RNA design approaches with a particular focus on the fitness functions used. We experimentally compare the most widely used fitness functions in RNA design algorithms on both synthetic and natural sequences. It has been almost 20 years since the last comparison was published, and we find similar results with a major new result: maximizing probability outperforms minimizing ensemble defect. The probability is the likelihood of a structure at equilibrium and the ensemble defect is the weighted average number of incorrect positions in the ensemble. Also, we observe that many recently published approaches minimize structure distance to the minimum free energy prediction, which we find to be a poor fitness function.

bioinformatics↗

bioRxiv2021

A dynamic equilibrium between TTP and CPEB4 controls mRNA stability and inflammation resolution

Temporal control of inflammation is critical to avoid pathological developments, and is largely defined through the differential stabilities of mRNAs. While TTP-directed mRNA deadenylation is known to destabilize ARE-containing mRNAs, this mechanism alone cannot explain the variety of mRNA expression kinetics observed during inflammation resolution. Here we show that inflammation resolution requires CPEB4 expression, in vitro and in vivo. Our results identify that CPEB4-directed polyadenylation and TTP-mediated deadenylation compete during the resolutive phase of the LPS response to uncouple the degradation of pro-inflammatory mRNAs from the sustained expression of anti-inflammatory mRNAs. The outcome of this equilibrium is quantitatively defined by the relative number of CPEs and AREs in each mRNA, and further shaped by the coordinated regulation by the MAPK signalling pathway of the levels and activities of their trans-acting factors, CPEB4 and TTP. Altogether, we describe a temporal- and transcript-specific regulatory network controlling the extent of the inflammatory response.

molecular biology↗

bioRxiv2021

Discovery of 17 conserved structural RNAs in fungi

Many non-coding RNAs with known functions are structurally conserved: their intramolecular secondary and tertiary interactions are maintained across evolutionary time. Consequently, the presence of conserved structure in multiple sequence alignments can be used to identify candidate functional non-coding RNAs. Here, we present a bioinformatics method that couples iterative homology search with covariation analysis to assess whether a genomic region has evidence of conserved RNA structure. We used this method to examine all unannotated regions of five well-studied fungal genomes (Saccharomyces cerevisiae, Candida albicans, Neurospora crassa, Aspergillus fumigatus, and Schizosaccharomyces pombe). We identified 17 novel structurally conserved non-coding RNA candidates, which include 4 H/ACA box small nucleolar RNAs, 4 intergenic RNAs, and 9 RNA structures located within the introns and untranslated regions (UTRs) of mRNAs. For the two structures in the 3' UTRs of the metabolic genes GLY1 and MET13, we performed experiments that provide evidence against them being eukaryotic riboswitches.

bioinformatics↗

bioRxiv2020

Response to Tavares et al., "Covariation analysis with improved parameters reveals conservation in lncRNA structures"

Tavares conclusions depend on an assumption that the statistic they use (RAFS) is an appropriate measure of RNA base pair covariation, but RAFS was not designed to measure covariation alone. RAFS detects positive signals in common patterns of primary sequence conservation in absence of any covariation. To illustrate the severity of the problem, we show that Tavares analysis reports "significantly covarying base pairs" in 100% identical sequence alignments with no variation or covariation. We use Tavares sequence alignment of HOTAIR domain 1 as an example to show that the base pairs they identify as significantly covarying actually arise from primary sequence conservation patterns. Their analysis still reports similar numbers of "significant covarying" base pairs in a negative control in which we permute residues in independent alignment columns to destroy covariation. There remains no significant covariation support for evolutionarily conserved RNA structure in the HOTAIR lncRNA or other lncRNA structures and alignments we have analyzed.

bioinformatics↗

bioRxiv2020

RNA structure prediction using positive and negative evolutionary information

Knowing the structure of conserved structural RNAs is important to elucidate their function and mechanism of action. However, predicting a conserved RNA structure remains unreliable, even when using a combination of thermodynamic stability and evolutionary covariation information. Here we present a method to predict a conserved RNA structure that combines the following three features. First, it uses significant covariation due to RNA structure and removes spurious covariation due to phylogeny. Second, it uses negative evolutionary information: basepairs that have variation but no significant covariation are prevented from occurring. Lastly, it uses a battery of probabilistic folding algorithms that incorporate all positive covariation into one structure. The method, named CaCoFold (Cascade variation/covariation Constrained Folding algorithm), predicts a nested structure guided by a maximal subset of positive basepairs, and recursively incorporates all remaining positive basepairs into alternative helices. The alternative helices can be compatible with the nested structure such as pseudoknots, or overlapping such as competing structures, base triplets, or other 3D non-antiparallel interactions. We present evidence that CaCoFold predictions are consistent with structures modeled from crystallography. Author SummaryThe availability of deeper comparative sequence alignments and recent advances in statistical analysis of RNA sequence covariation have made it possible to identify a reliable set of conserved base pairs, as well as a reliable set of non-basepairs (positions that vary without covarying). Predicting an overall consensus secondary structure consistent with a set of individual inferred pairs and non-pairs remains a problem. Current RNA structure prediction algorithms that predict nested secondary structures cannot use the full set of inferred covarying pairs, because covariation analysis also identifies important non-nested pairing interactions such as pseudoknots, base triples, and alternative structures. Moreover, although algorithms for incorporating negative constraints exist, negative information from covariation analysis (inferred non-pairs) has not been systematically exploited. Here I introduce an efficient approximate RNA structure prediction algorithm that incorporates all inferred pairs and excludes all non-pairs. Using this, and an improved visualization tool, I show that the method correctly identifies many non-nested structures in agreement with known crystal structures, and improves many curated consensus secondary structure annotations in RNA sequence alignment databases.

bioinformatics↗

bioRxiv2019

Estimating the power of sequence covariation for detecting conserved RNA structure

Pairwise sequence covariations are a signal of conserved RNA secondary structure. We describe a method for distinguishing when lack of covariation signal can be taken as evidence against a conserved RNA structure, as opposed to when a sequence alignment merely has insufficient variation to detect covariations. We find that alignments for several long noncoding RNAs previously shown to lack covariation support do have adequate covariation detection power, providing additional evidence against their proposed conserved structures.

genomics↗

bioRxiv2019

TET2 regulates the neuroinflammatory response in microglia

Epigenetic mechanisms regulate distinct aspects of the inflammatory response in various immune cell types. Despite the central role for microglia, the resident macrophages of the brain, in neuroinflammation and neurodegeneration little is known about their epigenetic regulation of the inflammatory response. Here, we show that Ten-eleven translocation 2 (TET2) methylcytosine dioxygenase expression is increased in microglia upon stimulation with various inflammogens through a NF-{kappa}B-dependent pathway. We found that TET2 regulates early gene transcriptional changes that lead to early metabolic alterations, as well as a later inflammatory response independently of its 5mC oxidation activity at the affected genes. We further show that TET2 regulates the proinflammatory response in microglia induced by intraperitoneal injection of LPS in vivo. We observed that microglia associated to amyloid {beta} plaques, recently defined as disease-associated microglia, expressed TET2 in brain tissue from individuals with Alzheimers disease (AD) and in 5xFAD mice. Collectively, our findings show that TET2 plays an important role in the microglial inflammatory response, and suggest TET2 as a potential target to combat neurodegenerative brain disorders.

neuroscience↗