Subgenomic flaviviral RNAs and human proteins: in silico exploration of anti-host defense mechanisms.

Flaviviruses pose significant global health threats, infecting over 300 million people annually. Among their evasion strategies, the production of subgenomic flaviviral RNAs (sfRNAs) from the 3' UTR of viral genomes is particularly notable. Utilizing a comprehensive in silico approach with the catRAPID algorithm, we analyzed over 300,000 interactions between sfRNAs and human proteins derived from more than 8000 flavivirus genomes, including Dengue, Zika, Yellow Fever, West Nile, and Japanese Encephalitis viruses. By providing the first extensive atlas of sfRNA interactions, we offer new insights into how flaviviruses can manipulate host cellular machinery to facilitate viral survival and persistence. Our study not only validated known interactions but also revealed novel human proteins that could be involved in sfRNA-mediated host defense evasion, including helicases, splicing factors, and chemokines. These findings significantly expand the known interactome of sfRNAs with human proteins, underscoring their role in modulating host cellular pathways. Intriguingly, we predict interaction with stress granules, a critical component of the cellular response to viral infection, suggesting a mechanism by which flaviviruses inhibit their formation to evade host defenses. Moreover, a set of highly-interacting proteins in common among the sfRNAs showed predictive power to identify sfRNA-forming regions, highlighting how protein signatures could be used to annotate viruses. This atlas not only serves as a resource for exploring therapeutic targets but also aids in the identification of sfRNA biomarkers for improved flavivirus diagnostics.

Unveiling the role of PUS7-mediated pseudouridylation in host protein interactions specific for the SARS-CoV-2 RNA genome.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a positive single-stranded RNA virus, engages in complex interactions with host cell proteins throughout its life cycle. While these interactions enable the host to recognize and inhibit viral replication, they also facilitate essential viral processes such as transcription, translation, and replication. Many aspects of these virus-host interactions remain poorly understood. Here, we employed the catRAPID algorithm and utilized the RNA-protein interaction detection coupled with mass spectrometry technology to predict and validate the host proteins that specifically bind to the highly structured 5' and 3' terminal regions of the SARS-CoV-2 RNA. Among the interactions identified, we prioritized pseudouridine synthase PUS7, which binds to both ends of the viral RNA. Using nanopore direct RNA sequencing, we discovered that the viral RNA undergoes extensive post-transcriptional modifications. Modified consensus regions for PUS7 were identified at both terminal regions of the SARS-CoV-2 RNA, including one in the viral transcription regulatory sequence leader. Collectively, our findings offer insights into host protein interactions with the SARS-CoV-2 UTRs and highlight the likely significance of pseudouridine synthases and other post-transcriptional modifications in the viral life cycle. This new knowledge enhances our understanding of virus-host dynamics and could inform the development of targeted therapeutic strategies.

Tracking the digital health gap in elderly: A study in Italian remote areas.

The Covid-19 pandemic has provided a major innovative thrust to public services regarding their digitization to continue providing an effective response to the population's needs and to reduce management costs. However, there has been a partial lack of those welfare policies that can provide an adequate response to the elderly segment of the population, which is most affected by the introduction of new technologies into the public sphere. This study analyses the digital gap in health in the elderly living in remote areas of Italy and investigates the use of digital devices for health purposes. It compares the use of digital solutions for health with people's common digital competencies and their willingness to use them. A descriptive analysis of the sample was constructed to verify the different responses of the elderly by age, gender, educational qualification, and geographic area. Furthermore, regression analyses have been conducted to test whether there is any dependent effect among the elderly's characteristics or geographic areas. The results highlight the existence of a potential digital health gap among the elderly in remote areas of Italy both due to infrastructural issues and the lack of digital skills. The latter are positively correlated with educational qualification, such that it is also possible to highlight differences between age groups analysed and shape future welfare policies to reduce digital inequality.

The PRALINE database: protein and Rna humAn singLe nucleotIde variaNts in condEnsates.

SUMMARY: Biological condensates are membraneless organelles with different material properties. Proteins and RNAs are the main components, but most of their interactions are still unknown. Here, we introduce PRALINE, a database for the interrogation of proteins and RNAs contained in stress granules, processing bodies and other assemblies including droplets and amyloids. PRALINE provides information about the predicted and experimentally validated protein-protein, protein-RNA and RNA-RNA interactions. For proteins, it reports the liquid-liquid phase separation and liquid-solid phase separation propensities. For RNAs, it provides information on predicted secondary structure content. PRALINE shows detailed information on human single-nucleotide variants, their clinical significance and presence in protein and RNA binding sites, and how they can affect condensates' physical properties. AVAILABILITY AND IMPLEMENTATION: PRALINE is freely accessible on the web at http://praline.tartaglialab.com.

A high-throughput approach to predict A-to-I effects on RNA structure indicates a change of double-stranded content in noncoding RNAs.

RNA molecules undergo a number of chemical modifications whose effects can alter their structure and molecular interactions. Previous studies have shown that RNA editing can impact the formation of ribonucleoprotein complexes and influence the assembly of membrane-less organelles such as stress granules. For instance, N6-methyladenosine (m6A) enhances SG formation and N1-methyladenosine (m1A) prevents their transition to solid-like aggregates. Yet, very little is known about adenosine to inosine (A-to-I) modification that is very abundant in human cells and not only impacts mRNAs but also noncoding RNAs. Here, we introduce the CROSSalive predictor of A-to-I effects on RNA structure based on high-throughput in-cell experiments. Our method shows an accuracy of 90% in predicting the single and double-stranded content of transcripts and identifies a general enrichment of double-stranded regions caused by A-to-I in long intergenic noncoding RNAs (lincRNAs). For the individual cases of NEAT1, NORAD, and XIST, we investigated the relationship between A-to-I editing and interactions with RNA-binding proteins using available CLIP data and catRAPID predictions. We found that A-to-I editing is linked to the alteration of interaction sites with proteins involved in phase separation, which suggests that RNP assembly can be influenced by A-to-I. CROSSalive is available at http://service.tartaglialab.com/new_submission/crossalive.

Phase Separation Drives SARS-CoV-2 Replication: A Hypothesis.

Identifying human proteins that interact with SARS-CoV-2 genome is important to understand its replication and to identify therapeutic strategies. Recent studies have unveiled protein interactions of SARS-COV-2 in different cell lines and through a number of high-throughput approaches. Here, we carried out a comparative analysis of four experimental and one computational studies to characterize the interactions of SARS-CoV-2 genomic RNA. Although hundreds of interactors have been identified, only twenty-one appear in all the experiments and show a strong propensity to bind. This set of interactors includes stress granule forming proteins, pre-mRNA regulators and elements involved in the replication process. Our calculations indicate that DDX3X and several editases bind the 5' end of SARS-CoV-2, a regulatory region previously reported to attract a large number of proteins. The small overlap among experimental datasets suggests that SARS-CoV-2 genome establishes stable interactions only with few interactors, while many proteins bind less tightly. In analogy to what has been previously reported for Xist non-coding RNA, we propose a mechanism of phase separation through which SARS-CoV-2 progressively sequesters human proteins hijacking the host immune response.

The Interplay Between Disordered Regions in RNAs and Proteins Modulates Interactions Within Stress Granules and Processing Bodies.

Condensation, or liquid-like phase separation, is a phenomenon indispensable for the spatiotemporal regulation of molecules within the cell. Recent studies indicate that the composition and molecular organization of phase-separated organelles such as Stress Granules (SGs) and Processing Bodies (PBs) are highly variable and dynamic. A dense contact network involving both RNAs and proteins controls the formation of SGs and PBs and an intricate molecular architecture, at present poorly understood, guarantees that these assemblies sense and adapt to different stresses and environmental changes. Here, we investigated the physico-chemical properties of SGs and PBs components and studied the architecture of their interaction networks. We found that proteins and RNAs establishing the largest amount of contacts in SGs and PBs have distinct properties and intrinsic disorder is enriched in all protein-RNA, protein-protein and RNA-RNA interaction networks. The increase of disorder in proteins is accompanied by an enrichment in single-stranded regions of RNA binding partners. Our results suggest that SGs and PBs quickly assemble and disassemble through dynamic contacts modulated by unfolded domains of their components.

Design and Functional Validation of a Mutant Variant of the LncRNA HOTAIR to Counteract Snail Function in Epithelial-to-Mesenchymal Transition.

HOTAIR is a lncRNA overexpressed in several epithelial cancers and strongly correlated with invasion. This lncRNA was proven a pivotal element of the epithelial-to-mesenchymal transition (EMT), a transdifferentiation process triggering metastasis. Snail, master inducer of EMT, requires HOTAIR to recruit EZH2 on specific epithelial target genes (i.e., HNF4α, E-cadherin, and HNF1α) and cause their repression. Here, we designed a HOTAIR deletion mutant form, named HOTAIR-sbid, including the putative Snail-binding domain but depleted of the EZH2-binding domain. HOTAIR-sbid acted as a dominant negative of the endogenous HOTAIR. In both murine and human tumor cells, HOTAIR-sbid impaired the ability of HOTAIR to bind Snail and, in turn, trigger H3K27me3/EZH2-mediated repression of Snail epithelial target genes. Notably, HOTAIR-sbid expression was proven to reduce cellular motility, invasiveness, anchorage-independent growth, and responsiveness to TGFß-induced EMT. These data provide evidence on a lncRNA-based strategy to effectively impair the function of a master EMT-transcriptional factor. SIGNIFICANCE: This study defines an innovative RNA-based strategy to interfere with a pivotal function of the tumor-related lncRNA HOTAIR, comprising a dominant negative mutant that was computationally designed and that impairs epithelial-to-mesenchymal transition.

Structural analysis of SARS-CoV-2 genome and predictions of the human interactome.

Specific elements of viral genomes regulate interactions within host cells. Here, we calculated the secondary structure content of >2000 coronaviruses and computed >100 000 human protein interactions with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The genomic regions display different degrees of conservation. SARS-CoV-2 domain encompassing nucleotides 22 500-23 000 is conserved both at the sequence and structural level. The regions upstream and downstream, however, vary significantly. This part of the viral sequence codes for the Spike S protein that interacts with the human receptor angiotensin-converting enzyme 2 (ACE2). Thus, variability of Spike S is connected to different levels of viral entry in human cells within the population. Our predictions indicate that the 5' end of SARS-CoV-2 is highly structured and interacts with several human proteins. The binding proteins are involved in viral RNA processing, include double-stranded RNA specific editases and ATP-dependent RNA-helicases and have strong propensity to form stress granules and phase-separated assemblies. We propose that these proteins, also implicated in viral infections such as HIV, are selectively recruited by SARS-CoV-2 genome to alter transcriptional and post-transcriptional regulation of host cells and to promote viral replication.

Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners.

Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and structures that are selectively recognised by an RBP remains challenging, since these interactions can be transient and highly dynamic, and may be mediated by unstructured regions in the protein, as in the case of many non-canonical RBPs. Numerous experimental and computational methodologies have been developed to predict, identify and verify the binding between a given RBP and potential RNA partners, but navigating across the vast ocean of data can be frustrating and misleading. In this mini-review, we propose a workflow for the identification of the RNA binding partners of putative, newly identified RBPs. The large pool of potential binders selected by in-cell experiments can be enriched by in silico tools such as catRAPID, which is able to predict the RNA sequences more likely to interact with specific RBP regions with high accuracy. The RNA candidates with the highest potential can then be analysed in vitro to determine the binding strength and to precisely identify the binding sites. The results thus obtained can furthermore validate the computational predictions, offering an all-round solution to the issue of finding the most likely RNA binding partners for a newly identified potential RBP.

Proteome-wide observation of the phenomenon of life on the edge of solubility.

To function effectively proteins must avoid aberrant aggregation, and hence they are expected to be expressed at concentrations safely below their solubility limits. By analyzing proteome-wide mass spectrometry data of Caenorhabditis elegans, however, we show that the levels of about three-quarters of the nearly 4,000 proteins analyzed in adult animals are close to their intrinsic solubility limits, indeed exceeding them by about 10% on average. We next asked how aging and functional self-assembly influence these solubility limits. We found that despite the fact that the total quantity of proteins within the cellular environment remains approximately constant during aging, protein aggregation sharply increases between days 6 and 12 of adulthood, after the worms have reproduced, as individual proteins lose their stoichiometric balances and the cellular machinery that maintains solubility undergoes functional decline. These findings reveal that these proteins are highly prone to undergoing concentration-dependent phase separation, which on aging is rationalized in a decrease of their effective solubilities, in particular for proteins associated with translation, growth, reproduction, and the chaperone system.

CROSSalive: a web server for predicting the in vivo structure of RNA molecules.

MOTIVATION: RNA structure is difficult to predict in vivo due to interactions with enzymes and other molecules. Here we introduce CROSSalive, an algorithm to predict the single- and double-stranded regions of RNAs in vivo using predictions of protein interactions. RESULTS: Trained on icSHAPE data in presence (m6a+) and absence of N6 methyladenosine modification (m6a-), CROSSalive achieves cross-validation accuracies between 0.70 and 0.88 in identifying high-confidence single- and double-stranded regions. The algorithm was applied to the long non-coding RNA Xist (17 900 nt, not present in the training) and shows an Area under the ROC curve of 0.83 in predicting structured regions. AVAILABILITY AND IMPLEMENTATION: CROSSalive webserver is freely accessible at http://service.tartaglialab.com/new_submission/crossalive. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.