bims-mirnam 2026-10-18 papers

Issue of 2026–10–18
six papers selected by
Hana Antonicka, McGill University

Wiley Interdiscip Rev RNA. 2026 Sep-Oct;17(5):17(5): e70065

RNA-Protein Reciprocity and the Limits of RBP Buffering.

RNA-binding proteins (RBPs) regulate RNA processing, localization, translation, and decay, yet similar perturbations can have different consequences across cells and tissues. RNA-protein reciprocity relates this variation to the balance between the binding sites presented by RNA and the protein available to regulate them. Regulation can remain stable while sufficient RBP is available, but become sensitive to small changes as that reserve narrows. The biological consequence depends on which transcripts lose regulation and how strongly the cell depends on their products. Examples from endogenous regulatory RNAs, repeat-expansion diseases, viral infection, and cancer show that RNA-dependent RBP redistribution can preserve cellular function or contribute to its failure. Binding affinity, partner proteins, autoregulation, and RNA-dependent assembly shape these responses. This Perspective develops the framework and proposes experiments to connect changes in local RNA demand and RBP supply with target binding and biological output. The aim is to identify when a modest molecular perturbation has a disproportionate functional effect, and when restoring the RNA-protein balance can recover regulation.

Keywords: RNA regulation; RNA‐binding proteins; RNA–protein stoichiometry; alternative polyadenylation; binding‐site competition; biomolecular condensates; post‐transcriptional regulation

DOI: https://doi.org/10.1002/wrna.70065

Cell Rep. 2026 Oct 14. pii: S2211-1247(26)01240-4. [Epub ahead of print]45(10): 118161

Mitochondrial stress induces transcription of cytosolic and mitochondrial protein synthesis components via NHR-180.

Avijit Mallick, Theresa Ramalho, Levi Ali, Yunguang Du, Duy Hoang, Rui Li, Pooja Rai, Sookyung Kim, Rakeyah Ahsan, Ala M Shaqra, Andreas Bergmann, Celia A Schiffer, Lihua Julie Zhu, Brian Kelch, Kuang Shen, Cole M Haynes.

The mitochondrial unfolded protein response (UPRmt), which promotes mitochondrial biogenesis and proteostasis, is regulated by the transcription factor ATFS-1. In C. elegans, impaired atfs-1 expression perturbs mitochondrial function and slows development. Here, using an RNAi suppressor screen, we find that inhibition of the nuclear hormone receptor NHR-180 increases mitochondrial function and developmental rate in atfs-1(null) worms. NHR-180 levels increase and become more nuclear-localized during mitochondrial dysfunction, inducing transcription of genes required for protein synthesis on cytosolic and mitochondrial ribosomes. The NHR-180 ligand-binding domain interacts with AMP, suggesting that NHR-180 may link cellular energy status to protein synthesis during mitochondrial stress. However, NHR-180-dependent activity is associated with exacerbated mitochondrial dysfunction. Consistent with these findings, inhibition of the S6 kinase homolog RSKS-1 increases mitochondrial function in atfs-1(null) worms. S6 kinase inhibition also increases respiration and mitochondrial DNA (mtDNA) content in mammalian ATF5-knockout cells and oxidative phosphorylation (OXPHOS)-deficient models.

Keywords: ATFS-1; CP: molecular biology; NHR-180; OXPHOS; RSKS-1, S6K, ATF5; mitochondria; mtDNA; protein synthesis

DOI: https://doi.org/10.1016/j.celrep.2026.118161

Front Cell Dev Biol. 2026 ;14 1855694

The role of mitochondrial DNA and RNA in promoting liver disease pathogenesis.

Sanjeevani Raj, Anjali Warhade, Nikkee Kumari, Shweta Pandey, Mrigya Babuta.

Mitochondria are a semi-autonomous organelle due to their unique structural organization and independent genetic system, and they play diverse roles in maintaining cellular homeostasis. The liver is the central metabolic organ and performs myriad functions, including energy production, metabolism, and detoxification. Liver diseases account for approximately two million mortalities per year. Mitochondrial dysfunction and damage are implicated in promoting the pathogenesis of different liver diseases. Mitochondria and mitochondrial nucleic acids, namely, mitochondrial DNA (mtDNA) and mitochondrial RNA (mtRNA), have emerged as regulators of innate immunity. Here, we first review the structural organization of mitochondria and the molecular mechanisms governing mitochondrial biogenesis, replication, and transcription. Next, we describe the mechanistic details by which mitochondrial dysfunction causes the release of mtDNA and mtRNA into the cytosol and extracellular milieu. Some of the mechanisms are through the opening of mitochondrial permeability transition pore (mPTP), voltage-dependent anion channel (VDAC) oligomerization, prohibitin (PHB1/2), mitochondria-derived vesicles (MDV), dysfunction of the mtRNA degradosomes, and senescence-associated secretory phenotype (SASP). Cell death pathways such as necrosis, necroptosis, and pyroptosis, in addition to perpetuating inflammation, also release mtDNA and mtRNA into the extracellular milieu. The released mtDNA and mtRNA act as damage associated molecular patterns (DAMPs) that engage various cytosolic DNA and RNA sensors and pattern recognition receptors (PRRs) such as cyclic guanosine-monophosphate-adenosine-monophosphate synthase (cGAS), toll-like receptor 9 (TLR9), toll-like receptor 3 (TLR3), Z-DNA-binding protein 1 (ZBP1), NOD-like receptor protein 3 (NLRP3), absent in melanoma 2 (AIM2), retinoic acid-inducible gene I (RIG-I), melanoma differentiation-associated protein 5 (MDA5), protein kinase R (PKR) and activate different signaling cascades downstream. This review provides a contemporary, comprehensive analysis of the available literature on the roles of mtDNA and mtRNA in liver diseases, including metabolic dysfunction-associated steatotic/steatohepatitis (MASLD/MASH), alcohol-associated liver disease (ALD), drug-induced liver injury (DILI), viral hepatitis, and hepatocellular carcinoma (HCC). Thus, the review highlights the potential of both mtDNA and mtRNA to serve as biomarkers of the pathological conditions of the liver, and the way the mitigation of mitochondrial dysfunction and the release of mtDNA or mtRNA can serve as potential therapeutic targets.

Keywords: DAMP; DNA-sensing receptors; MDV; MQC; Mitobiogenesis; megamitochondria; mt-dsRNA; ox-mtDNA

DOI: https://doi.org/10.3389/fcell.2026.1855694

Vet Res. 2026 Oct 11. pii: 181. [Epub ahead of print]57(1):

PRRSV infection induces mitochondrial damage and releases mitochondrial dsRNA to activate the NLRP3-dependent inflammatory response.

Jiangnan Li, Caihong Bi, Liguo Gao, Zengyu Shao, Li Huang, Changjiang Weng, Hongyang Liu.

Porcine reproductive and respiratory syndrome (PRRS), caused by the PRRS virus (PRRSV), remains one of the most economically significant infectious diseases affecting the global swine industry, resulting in annual losses exceeding US $1.2 billion in the USA alone. PRRSV infects porcine alveolar macrophages (PAMs) and induces profound mitochondrial dysfunction, leading to the release of mitochondrial DNA (mtDNA). However, the mechanisms by which PRRSV-induced mitochondrial damage acts as a damage-associated molecular pattern (DAMP) to trigger inflammatory responses have not been fully elucidated. Here, we report that highly pathogenic PRRSV (HP-PRRSV) infection upregulates interleukin-1β (IL-1β) messenger RNA (mRNA) expression and protein secretion in a dose- and time-dependent manner. Transmission electron microscopy revealed progressive mitochondrial swelling, cristae disorganization, and matrix loss during HP-PRRSV infection. Using RNAscope combined with MitoGreen staining, we discovered that mitochondrial double-stranded RNA (mtdsRNA), generated from bidirectional transcription of mitochondrial DNA, is released into the cytosol during PRRSV infection. In vitro, transcription-driven mtdsRNA or mtdsRNA isolated from PAMs directly activated the NLRP3 inflammasome, inducing caspase-1 cleavage and IL-1β secretion. Mechanistically, the PRRSV-encoded nonstructural protein 11 (nsp11) inhibited NLRP3-dependent inflammatory responses by suppressing the oligomerization of the apoptosis-associated speck-like protein containing a caspase recruitment domain (CARD) (ASC), thereby constraining excessive inflammation and establishing a delicate balance between pro-inflammatory signaling and viral immune evasion. Collectively, our findings identify mtdsRNA as a previously unrecognized mitochondrial DAMP that links PRRSV-induced mitochondrial injury to NLRP3 inflammasome activation, while also revealing a counter-regulatory role for nsp11 in modulating inflammation.

Keywords: ASC; IL-1β; NLRP3; PRRSV; inflammation; mitochondrial DAMP; mtdsRNA; nsp11

DOI: https://doi.org/10.1186/s13567-026-01854-3

Brief Funct Genomics. 2026 01 09. pii: elag011. [Epub ahead of print]25

A comprehensive review on RNA subcellular localization prediction.

Cece Zhang, Xuehuan Zhu, Nick Peterson, Jieqiong Wang, Shibiao Wan.

The subcellular localization of RNAs, including long non-coding RNAs, messenger RNAs, microRNAs and other smaller RNAs, plays a critical role in determining their biological functions. Understanding RNA localization sheds light on processes like gene expression regulation with spatial and temporal precision. However, traditional wet lab methods for determining RNA localization, such as in situ hybridization, are often time-consuming, resource-demanding, and costly. To overcome these challenges, computational methods leveraging artificial intelligence (AI) and machine learning (ML) have emerged as powerful alternatives, enabling large-scale prediction of RNA subcellular localization. This paper provides a comprehensive review of the latest advancements in AI-based approaches for RNA subcellular localization prediction, covering various RNA types and focusing on sequence-based, image-based, and hybrid methodologies that combine both data types. We highlight the potential of these methods to accelerate RNA research, uncover molecular pathways, and guide targeted disease treatments. Furthermore, we critically discuss the challenges in AI/ML approaches for RNA subcellular localization, such as data scarcity and lack of benchmarks, and opportunities to address them. This review aims to serve as a valuable resource for researchers seeking to develop innovative solutions in the field of RNA subcellular localization and beyond.

Keywords: RNA subcellular localization; artificial intelligence; lncRNA; mRNA; machine learning; miRNA

DOI: https://doi.org/10.1093/bfgp/elag011

Methods Mol Biol. 2027 ;3081 19-44

Predicting RNA and Protein:RNA Complexes with AlphaFold: Possibilities, Limitations, and Practical Guidelines.

Marcin J Suskiewicz.

AlphaFold 3 (AF3) extends deep learning-based structural prediction beyond proteins to other biomolecules, but its reliability for RNA and protein:RNA complexes remains uncertain. This chapter examines AF3's applicability to these targets from both theoretical and practical perspectives. RNA differs fundamentally from proteins in ways that challenge AF3's framework: RNA structures are more conformationally heterogeneous and more condition-dependent. Furthermore, RNA-related training data in the Protein Data Bank are scarce and lack the diversity of protein data, limiting both structural prediction itself and the reliability of AF3's self-assessment confidence metrics. Benchmarking studies and individual case analyses suggest that, in the RNA realm, AF3 may function primarily through memorization of known structural motifs and homology-based modeling rather than genuine global de novo prediction; nevertheless, it performs competitively with established RNA prediction tools and can serve as an attractive hypothesis generator. Practical guidelines are offered for using AF3 with RNA-containing systems, emphasizing the importance of careful interpretation of confidence metrics, visual inspection, and experimental validation. Future improvements will likely require expanded RNA structural data, better exploitation of evolutionary information, and potentially alternative representations of RNA structure beyond rigid 3D models.

Keywords: AlphaFold; AlphaFold3; Protein–RNA interactions; Protein–RNA structure prediction; RNA; RNA structure prediction; RNA-binding proteins

DOI: https://doi.org/10.1007/978-1-0716-5571-9_2