A computational map of the human-SARS-CoV-2 protein-RNA interactome predicted at single-nucleotide resolution

The Role of TDP-43 in SARS-CoV-2-Related Neurodegenerative Changes

The coronavirus disease 2019 (COVID-19) pandemic has been linked to long-term neurological effects with multifaceted complications of neurodegenerative diseases. Several studies have found that pathological changes in transactive response DNA-binding protein of 43 kDa (TDP-43) are involved in these cases. This review explores the causal interactions between severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and TDP-43 from multiple perspectives. Some viral proteins of SARS-CoV-2 have been shown to induce pathological changes in TDP-43 through its cleavage, aggregation, and mislocalization. SARS-CoV-2 infection can cause liquid-liquid phase separation and stress granule formation, which accelerate the condensation of TDP-43, resulting in host RNA metabolism disruption. TDP-43 has been proposed to interact with SARS-CoV-2 RNA, though its role in viral replication remains to be fully elucidated. This interaction potentially facilitates viral replication, while viral-induced oxidative stress and protease activity accelerate TDP-43 pathology. Evidence from both clinical and experimental studies indicates that SARS-CoV-2 infection may contribute to long-term neurological sequelae, including amyotrophic lateral sclerosis-like and frontotemporal dementia-like features, as well as increased phosphorylated TDP-43 deposition in the central nervous system. Biomarker studies further support the link between TDP-43 dysregulation and neurological complications of long-term effects of COVID-19 (long COVID). In this review, we presented a novel integrative framework of TDP-43 pathology, bridging a gap between SARS-CoV-2 infection and mechanisms of neurodegeneration. These findings underscore the need for further research to clarify the TDP-43-related neurodegeneration underlying SARS-CoV-2 infection and to develop therapeutic strategies aimed at mitigating long-term neurological effects in patients with long COVID.

Association between FTO polymorphism and COVID-19 mortality among older adults: A population-based cohort study

OBJECTIVES

COVID-19 caused a global pandemic with millions of deaths. Fat mass and obesity-associated gene (FTO) (alias m6A RNA demethylase) and its functional rs17817449 polymorphism are candidates to influence COVID-19-associated mortality since methylation status of viral nucleic acids is an important factor influencing viral viability.

METHODS

We tested a population-based cohort of 5233 subjects (aged 63-87 years in 2020) where 70 persons died from COVID-19 and 394 from other causes during the pandemic period.

RESULTS

The frequency of GG homozygotes was higher among those who died from COVID-19 (34%) than among survivors (19%) or deaths from other causes (20%), P <0.005. After multiple adjustments, GG homozygotes had a higher risk of death from COVID-19 with odds ratio = 2.01 (95% confidence interval; 1.19-3.41, P <0.01) compared with carriers of at least one T allele. The FTO polymorphism was not associated with mortality from other causes.

CONCLUSIONS

Our results suggest that FTO variability is a significant predictor of COVID-19-associated mortality in Caucasians.

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

We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and miCLIP assays, outperforming state-of-the-art classifiers. RBPNet performs bias correction by modeling the raw signal as a mixture of the protein-specific and background signal. Through model interrogation via Integrated Gradients, RBPNet identifies predictive sub-sequences that correspond to known and novel binding motifs and enables variant-impact scoring via in silico mutagenesis. Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions.