Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning

Deciphering immune dynamics in atherosclerosis: Inflammatory mediators as biomarkers and therapeutic target

BACKGROUND

Atherosclerosis, one of the main causes of cardiovascular disease, is driven by complex interactions between lipid metabolism and immune mechanisms in the vascular system. Regulatory molecules, particularly protein fragments derived from cytokines, chemokines and other immune-related proteins, play a central role in modulating inflammation and immune responses in atherosclerotic plaques.

RESULTS

Recent advances in peptidomics have revealed the dual role of immune system-derived peptides as indicators and effectors of atherosclerotic cardiovascular disease (ASCVD). Certain subsets of immune cells, such as pro-inflammatory monocytes and regulatory T cells, contribute to this peptide-mediated regulation. New findings suggest that these peptides may serve as diagnostic biomarkers and therapeutic targets in atherosclerosis.

CONCLUSION

This review highlights the translational relevance of immune-mediated peptides in ASCVD and emphasizes their diagnostic and therapeutic potential. By integrating peptidomics with immunology research, a new framework for understanding and targeting inflammation in atherosclerosis is proposed, opening new avenues for precision medicine in cardiovascular care.

RBPsuite 2.0: an updated RNA-protein binding site prediction suite with high coverage on species and proteins based on deep learning

BACKGROUND

RNA-binding proteins (RBPs) play crucial roles in many biological processes, and computationally identifying RNA-RBP interactions provides insights into the biological mechanism of diseases associated with RBPs.

RESULTS

To make the RBP-specific deep learning-based RBP binding sites prediction methods easily accessible, we developed an updated easy-to-use webserver, RBPsuite 2.0, with an updated web interface for predicting RBP binding sites from linear and circular RNA sequences. RBPsuite 2.0 has a higher coverage on the number of supported RBPs and species compared to the original RBPsuite, supporting an increased number of RBPs from 154 to 353 and expanding the supported species from one to seven. Additionally, RBPsuite 2.0 replaces the CRIP built into RBPsuite 1.0 with iDeepC, a more accurate RBP binding site predictor for circular RNAs. Furthermore, RBPsuite 2.0 estimates the contribution score of individual nucleotides on the input sequences as potential binding motifs and links to the UCSC browser track for better visualization of the prediction results.

CONCLUSIONS

RBPsuite 2.0 is an updated, more comprehensive webserver for predicting RBP binding sites in both linear and circular RNA sequences. It supports more RBPs and species and provides more accurate predictions for circular RNAs. The tool is freely available at http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/ .

From computational models of the splicing code to regulatory mechanisms and therapeutic implications

Since the discovery of RNA splicing and its role in gene expression, researchers have sought a set of rules, an algorithm or a computational model that could predict the splice isoforms, and their frequencies, produced from any transcribed gene in a specific cellular context. Over the past 30 years, these models have evolved from simple position weight matrices to deep-learning models capable of integrating sequence data across vast genomic distances. Most recently, new model architectures are moving the field closer to context-specific alternative splicing predictions, and advances in sequencing technologies are expanding the type of data that can be used to inform and interpret such models. Together, these developments are driving improved understanding of splicing regulatory mechanisms and emerging applications of the splicing code to the rational design of RNA- and splicing-based therapeutics.

Refining the cis-regulatory grammar learned by sequence-to-activity models by increasing model resolution

Chromatin accessibility can be measured genome-wide with ATAC-seq, enabling the discovery of regulatory regions that control gene expression and determine cell type. Deep genomic sequence-to-function (S2F) models link underlying genomic sequences to the measured chromatin state and identify motifs that regulate chromatin accessibility. Previously, we developed AI-TAC, a S2F model that predicts chromatin accessibility across 81 immune cell types and identifies sequence patterns that control their differential ATAC-seq signals. While AI-TAC provided valuable insights into the regulatory patterns that govern immune cell differentiation, later research established that ATAC-seq profiles (the distribution of Tn5 cuts) contain additional information about the exact location and strength of TF binding. To make use of this additional information, we developed bpAI-TAC, a multi-task neural network which models ATAC-seq at base-pair resolution across 90 immune cell types. We show that adding ATAC-profile information consistently improves predictions of differential chromatin accessibility. We also demonstrate that simultaneous learning of related cell types through multi-task modeling leads to better predictions than single task models. We then present a systematic framework for comparing how differences in model performance can be attributed to differences in what the model has learned. To understand what additional information bpAI-TAC gleans from ATAC-profiles, we use sequence attributions and identify motifs that have different effect sizes when trained on profiles. We conclude that modeling ATAC-seq at base-pair resolution enables the model to learn a more sensitive representation of the regulatory syntax that drives differences between immunocytes, and therefore will improve predictions of variant effects.

Master regulators governing protein abundance across ten human cancer types

Protein abundance correlates only moderately with mRNA levels, and are modulated post-transcriptionally by a network of regulators including ribosomes, RNA-binding proteins (RBPs), and the proteasome. Here, we identified Master Protein abundance Regulators (MaPRs) across ten cancer types by devising a new computational pipeline that jointly analyzed transcriptomes and proteomes from 1,305 tumor samples. We identified 232 to 1,394 MaPRs per cancer type, mediating up to 79% of post-transcriptional regulatory networks. MaPRs exhibit high network connectivity, strong genetic dependency in cancer cells, and significant enrichment for RBPs. Combining tumor up-regulation, druggability, and target network analyses identified cancer-specific vulnerabilities. MaPRs predict tumor proteomic subtypes more accurately than other proteins. Finally, significant portions of RBP MaPR-target relationships were validated by experimental evidence from eCLIP binding and knockdown assays. Our findings uncover central MaPRs that govern post-transcriptional networks, highlighting diverse processes underlying human proteome regulation and identifying key regulators in cancer biology.

The splicing factor QKI inhibits metastasis by modulating alternative splicing of E-Syt2 in papillary thyroid carcinoma

Alternative splicing (AS) plays a crucial role in the hallmarks of cancer and can open new avenues for targeted therapies. However, the aberrant AS events and the metastatic cascade in papillary thyroid carcinoma (PTC) remain largely unclear. Here, we identify the splicing factor, quaking protein (QKI), which was significantly downregulated in PTC and correlated with poor survival outcomes in patients with PTC. Functional studies indicated that low expression of QKI promoted the PTC cell growth and metastasis in vitro and in vivo. Mechanistically, low QKI induced exon 14 retention of extended synaptotagmin 2 (E-Syt2) and produced a long isoform transcript (termed E-Syt2L) that acted as an important oncogenic factor of PTC metastasis. Notably, overexpression of long non-coding RNA eosinophil granule ontogeny transcript (EGOT) physically binds to QKI and suppressed its activity by inhibiting ubiquitin specific peptidase 25 (USP25) mediated deubiquitination and subsequent degradation of QKI. Collectively, these data demonstrate the novel mechanistic links between the splicing factor QKI and splicing event in PTC metastasis and support the potential utility of targeting splicing events as a therapeutic strategy for PTC.

Viral RNA Interactome: The Ultimate Researcher's Guide to RNA-Protein Interactions

RNA molecules in the cell are bound by a multitude of RNA-binding proteins (RBPs) with a variety of regulatory consequences. Often, interactions with these RNA-binding proteins are facilitated by the complex secondary and tertiary structures of RNA molecules. Viral RNAs especially are known to be heavily structured and interact with many RBPs, with roles including genome packaging, immune evasion, enhancing replication and transcription, and increasing translation efficiency. As such, the RNA-protein interactome represents a critical facet of the viral replication cycle. Characterization of these interactions is necessary for the development of novel therapeutics targeted at the disruption of essential replication cycle events. In this review, we aim to summarize the various roles of RNA structures in shaping the RNA-protein interactome, the regulatory roles of these interactions, as well as up-to-date methods developed for the characterization of the interactome and directions for novel, RNA-directed therapeutics.

Molecular insights into regulatory RNAs in the cellular machinery

It is apparent that various functional units within the cellular machinery are derived from RNAs. The evolution of sequencing techniques has resulted in significant insights into approaches for transcriptome studies. Organisms utilize RNA to govern cellular systems, and a heterogeneous class of RNAs is involved in regulatory functions. In particular, regulatory RNAs are increasingly recognized to participate in intricately functioning machinery across almost all levels of biological systems. These systems include those mediating chromatin arrangement, transcription, suborganelle stabilization, and posttranscriptional modifications. Any class of RNA exhibiting regulatory activity can be termed a class of regulatory RNA and is typically represented by noncoding RNAs, which constitute a substantial portion of the genome. These RNAs function based on the principle of structural changes through cis and/or trans regulation to facilitate mutual RNA‒RNA, RNA‒DNA, and RNA‒protein interactions. It has not been clearly elucidated whether regulatory RNAs identified through deep sequencing actually function in the anticipated mechanisms. This review addresses the dominant properties of regulatory RNAs at various layers of the cellular machinery and covers regulatory activities, structural dynamics, modifications, associated molecules, and further challenges related to therapeutics and deep learning.

RNA binding proteins in cardiovascular development and disease

Congenital heart disease (CHD) is the most common birth defect affecting>1.35 million newborn babies worldwide. CHD can lead to prenatal, neonatal, postnatal lethality or life-long cardiac complications. RNA binding protein (RBP) mutations or variants are emerging as contributors to CHDs. RBPs are wizards of gene regulation and are major contributors to mRNA and protein landscape. However, not much is known about RBPs in the developing heart and their contributions to CHD. In this chapter, we will discuss our current knowledge about specific RBPs implicated in CHDs. We are in an exciting era to study RBPs using the currently available and highly successful RNA-based therapies and methodologies. Understanding how RBPs shape the developing heart will unveil their contributions to CHD. Identifying their target RNAs in the embryonic heart will ultimately lead to RNA-based treatments for congenital heart disease.

DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning

MOTIVATION

Accurate prediction of RNA subcellular localization plays an important role in understanding cellular processes and functions. Although post-transcriptional processes are governed by trans-acting RNA binding proteins (RBPs) through interaction with cis-regulatory RNA motifs, current methods do not incorporate RBP-binding information.

RESULTS

In this article, we propose DeepLocRNA, an interpretable deep-learning model that leverages a pre-trained multi-task RBP-binding prediction model to predict the subcellular localization of RNA molecules via fine-tuning. We constructed DeepLocRNA using a comprehensive dataset with variant RNA types and evaluated it on the held-out dataset. Our model achieved state-of-the-art performance in predicting RNA subcellular localization in mRNA and miRNA. It has also demonstrated great generalization capabilities, performing well on both human and mouse RNA. Additionally, a motif analysis was performed to enhance the interpretability of the model, highlighting signal factors that contributed to the predictions. The proposed model provides general and powerful prediction abilities for different RNA types and species, offering valuable insights into the localization patterns of RNA molecules and contributing to our understanding of cellular processes at the molecular level. A user-friendly web server is available at: https://biolib.com/KU/DeepLocRNA/.