The role of the protein-RNA recognition code in neurodegeneration

The evolution of mammalian Rem2: unraveling the impact of purifying selection and coevolution on protein function, and implications for human disorders

Rad And Gem-Like GTP-Binding Protein 2 (Rem2), a member of the RGK family of Ras-like GTPases, is implicated in Huntington's disease and Long QT Syndrome and is highly expressed in the brain and endocrine cells. We examine the evolutionary history of Rem2 identified in various mammalian species, focusing on the role of purifying selection and coevolution in shaping its sequence and protein structural constraints. Our analysis of Rem2 sequences across 175 mammalian species found evidence for strong purifying selection in 70% of non-invariant codon sites which is characteristic of essential proteins that play critical roles in biological processes and is consistent with Rem2's role in the regulation of neuronal development and function. We inferred epistatic effects in 50 pairs of codon sites in Rem2, some of which are predicted to have deleterious effects on human health. Additionally, we reconstructed the ancestral evolutionary history of mammalian Rem2 using protein structure prediction of extinct and extant sequences which revealed the dynamics of how substitutions that change the gene sequence of Rem2 can impact protein structure in variable regions while maintaining core functional mechanisms. By understanding the selective pressures, protein- and gene - interactions that have shaped the sequence and structure of the Rem2 protein, we gain a stronger understanding of its biological and functional constraints.

Comprehensive analysis of the functions, prognostic and diagnostic values of RNA binding proteins in head and neck squamous cell carcinoma

BACKGROUND

Accumulating evidence has suggested that RNA binding protein (RBP) dysregulation plays an essential role during tumorigenesis. Here, we sought to explore the potential biological functions and clinical significance of RBP and develop diagnostic and prognostic signatures based on RBP in patients with head and neck squamous cell carcinoma (HNSCC).

METHODS

The differently expressed RBPs between HNSCC samples and their normal counterparts were identified using the Limma package. The immunohistochemistry (IHC) images of several RBPs were collected from the Human Protein Atlas database. The diagnostic signature based on RBP was built by LASSO-logistic regression and random forest. The prognostic signature based on RBP was constructed by LASSO and stepwise Cox regression analysis in the training cohort and validated in the validation cohort.

RESULTS

Eighty-four aberrantly expressed RBPs were obtained, comprising 41 up-regulated and 43 down-regulated RBPs. Seven RBP genes (CPEB3, PDCD4, ENDOU, PARP12, DNMT3B, IGF2BP1, EXO1) were identified as diagnostic-related hub genes. They were used to establish a diagnostic RBP signature risk score (DRBPS) model by the coefficients in least absolute shrinkage and selection operator (LASSO)-logistic regression analysis and showed high specificity and sensitivity in the training (area under the receiver operating characteristic curve (AUC) = 0.998), and in all validation cohorts (AUC > 0.95 for all). Similarly, seven RBP genes (MKRN3, ZC3H12D, EIF5A2, AFF3, SIDT1, RBM24, and NR0B1) were identified as prognosis-associated hub genes by LASSO and stepwise multiple Cox regression analyses and were used to construct the prognostic model named as PRBPS. The AUC of the time-dependent receiver operator characteristic curve of the prognostic model was 0.664 at 3 years and 0.635 at 5 years in the training cohort and 0.720, 0.777 in the validation cohort, showing a favorable predictive efficacy for prognosis in HNSCC.

CONCLUSIONS

Our results demonstrate the value of consideration of RBP in the diagnosis and prognosis for HNSCC and provide a novel insight into understanding the potential role of dysregulated RBP in HNSCC.

Protein misfolding and amyloid nucleation through liquid-liquid phase separation

Liquid-liquid phase separation (LLPS) is an emerging phenomenon in cell physiology and diseases. The weak multivalent interaction prerequisite for LLPS is believed to be facilitated through intrinsically disordered regions, which are prevalent in neurodegenerative disease-associated proteins. These aggregation-prone proteins also exhibit an inherent property for phase separation, resulting in protein-rich liquid-like droplets. The very high local protein concentration in the water-deficient confined microenvironment not only drives the viscoelastic transition from the liquid to solid-like state but also most often nucleate amyloid fibril formation. Indeed, protein misfolding, oligomerization, and amyloid aggregation are observed to be initiated from the LLPS of various neurodegeneration-related proteins. Moreover, in these cases, neurodegeneration-promoting genetic and environmental factors play a direct role in amyloid aggregation preceded by the phase separation. These cumulative recent observations ignite the possibility of LLPS being a prominent nucleation mechanism associated with aberrant protein aggregation. The present review elaborates on the nucleation mechanism of the amyloid aggregation pathway and the possible early molecular events associated with amyloid-related protein phase separation. It also summarizes the recent advancement in understanding the aberrant phase transition of major proteins contributing to neurodegeneration focusing on the common disease-associated factors. Overall, this review proposes a generic LLPS-mediated multistep nucleation mechanism for amyloid aggregation and its implication in neurodegeneration.

1-L Transcription of SARS-CoV-2 Spike Protein S1 Subunit

The COVID-19 pandemic prompted rapid research on SARS-CoV-2 pathogenicity. Consequently, new data can be used to advance the molecular understanding of SARS-CoV-2 infection. The present bioinformatics study discusses the "spikeopathy" at the molecular level and focuses on the possible post-transcriptional regulation of the SARS-CoV-2 spike protein S1 subunit in the host cell/tissue. A theoretical protein-RNA recognition code was used to check the compatibility of the SARS-CoV-2 spike protein S1 subunit with mRNAs in the human transcriptome (1-L transcription). The principle for this method is elucidated on the defined RNA binding protein GEMIN5 (gem nuclear organelle-associated protein 5) and RNU2-1 (U2 spliceosomal RNA). Using the method described here, it was shown that 45% of the genes/proteins identified by 1-L transcription of the SARS-CoV-2 spike protein S1 subunit are directly linked to COVID-19, 39% are indirectly linked to COVID-19, and 16% cannot currently be associated with COVID-19. The identified genes/proteins are associated with stroke, diabetes, and cardiac injury.

1-L Transcription in Alzheimer's Disease

Alzheimer’s disease is a very complex disease and better explanations and models are needed to understand how neurons are affected and microglia are activated. A new model of Alzheimer’s disease is presented here, the β-amyloid peptide is considered an important RNA recognition/binding peptide. 1-L transcription revealed compatible sequences with AAUAAA (PAS signal) and UUUC (class III ARE rich in U) in the Aβ peptide, supporting the peptide–RNA regulatory model. When a hypothetical model of fibril selection with the prionic character of amyloid assemblies is added to the peptide-RNA regulatory model, the downregulation of the PI3K-Akt pathway and the upregulation of the PLC-IP3 pathway are well explained. The model explains why neurons are less protected from inflammation and why microglia are activated; why mitochondria are destabilized; why the autophagic flux is destabilized; and why the post-transcriptional attenuation of the axonal signal “noise” is interrupted. For example, the model suggests that Aβ peptide may post-transcriptionally control ELAVL2 (ELAV-like RNA binding protein 2) and DCP2 (decapping mRNA protein 2), which are known to regulate RNA processing, transport, and stability.

A Machine Learning Perspective on DNA and RNA G-quadruplexes

Abstract:

G-quadruplexes (G4s) are particular structures found in guanine-rich DNA and RNA sequences that exhibit a wide diversity of three-dimensional conformations and exert key functions in the control of gene expression. G4s are able to interact with numerous small molecules and endogenous proteins, and their dysregulation can lead to a variety of disorders and diseases. Characterization and prediction of G4-forming sequences could elucidate their mechanism of action and could thus represent an important step in the discovery of potential therapeutic drugs. In this perspective, we propose an overview of G4s, discussing the state of the art of methodologies and tools developed to characterize and predict the presence of these structures in genomic sequences. In particular, we report on machine learning (ML) approaches and artificial neural networks (ANNs) that could open new avenues for the accurate analysis of quadruplexes, given their potential to derive informative features by learning from large, high-density datasets.

Transcription of the Envelope Protein by 1-L Protein–RNA Recognition Code Leads to Genes/Proteins That Are Relevant to the SARS-CoV-2 Life Cycle and Pathogenesis

The theoretical protein–RNA recognition code was used in this study to research the compatibility of the SARS-CoV-2 envelope protein (E) with mRNAs in the human transcriptome. According to a review of the literature, the spectrum of identified genes showed that the virus post-transcriptionally promotes or represses the genes involved in the SARS-CoV-2 life cycle. The identified genes/proteins are also involved in adaptive immunity, in the function of the cilia and wound healing (EMT and MET) in the pulmonary epithelial tissue, in Alzheimer’s and Parkinson’s disease and in type 2 diabetes. For example, the E-protein promotes BHLHE40, which switches off the IL-10 inflammatory “brake” and inhibits antiviral T_Hαβ cells. In the viral cycle, E supports the COPII-SCAP-SREBP-HSP90α transport complex by the lowering of cholesterol in the ER and by the repression of insulin signaling, which explains the positive effect of HSP90 inhibitors in COVID-19 (geldanamycin), and E also supports importin α/β-mediated transport to the nucleus, which explains the positive effect of ivermectin, a blocker of importins α/β. In summary, transcription of the envelope protein by the 1-L protein–RNA recognition code leads to genes/proteins that are relevant to the SARS-CoV-2 life cycle and pathogenesis.

miRNA Delivery by Nanosystems: State of the Art and Perspectives

MicroRNAs (miRNAs) are short (~21-23 nucleotides), non-coding endogenous RNA molecules that modulate gene expression at the post-transcriptional level via the endogenous RNA interference machinery of the cell. They have emerged as potential biopharmaceuticals candidates for the treatment of various diseases, including cancer, cardiovascular and metabolic diseases. However, in order to advance miRNAs therapeutics into clinical settings, their delivery remains a major challenge. Different types of vectors have been investigated to allow the delivery of miRNA in the diseased tissue. In particular, non-viral delivery systems have shown important advantages such as versatility, low cost, easy fabrication and low immunogenicity. Here, we present a general overview of the main types of non-viral vectors developed for miRNA delivery, with their advantages, limitations and future perspectives.

Identification of Prognosis-Related RNA-Binding Proteins to Reveal the Role of RNA-Binding Proteins in the Progression and Prognosis of Colon Cancer

Background

RNA binding proteins (RBPs) are now under discussion as novel promising bio-markers for patients with colon cancer. The purpose of our study is to identify several RBPs related to the progression and prognosis of colon cancer and to further investigate the mechanism of their influence on tumor progression.

Methods

The transcriptome data of colon cancer and clinical characteristics were downloaded from The Cancer Genome Atlas (TCGA) database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA) were performed to elucidate the gene functions and relative pathways. Cox and Lasso regression analyses were used to analyze the effect of immune genes on the prognosis of colon cancer. An immune risk scoring model was constructed based on the statistical correlation between hub immune genes and survival. Meanwhile, multivariate Cox regression analysis was utilized to investigate whether the immune gene risk score model was an independent factor for predicting the prognosis of colon cancer. A nomogram was constructed to comprehensively predict the survival rate of colon cancer. P < 0.05 was considered statistically significant.

Results

The results showed that 473 RBPs exhibited differential expression between normal and colon cancer tissues (P < 0.05). Univariate Cox regression analysis revealed 25 RBPs statistically correlated with colon cancer-related survival risk (P < 0.05). In addition, a 10-RBPs based risk scoring model was constructed through multivariate Cox regression analysis. A K–M curve indicated that high-risk patients were associated with poor outcomes (P < 0.001). A ROC curve indicated that the immune risk score model was reliable in predicting survival risk (5-year overall survival (OS), area under curve (AUC) = 0.782). Our model showed satisfying AUC and survival correlation in the validation dataset (5-year OS, AUC = 0.744). Furthermore, multivariate Cox regression analysis confirmed that the immune risk score model was an independent factor for predicting the prognosis of colon cancer. Finally, we found that 10-RBPs and risk scores were significantly associated with clinical factors and prognosis and were involved in multiple oncogenic pathways.

Conclusion

Collectively, RBPs play an essential role in the progression and prognosis of colon cancer by regulating multiple biological pathways. Furthermore, the RBP risk score was an independent predictive factor of colon cancer, indicating poor survival.

Theoretical Analysis of S, M and N Structural Proteins by the Protein-RNA Recognition Code Leads to Genes/proteins that Are Relevant to the SARS-CoV-2 Life Cycle and Pathogenesis

In this conceptual review, based on the protein-RNA recognition code, some theoretical sequences were detected in the spike (S), membrane (M) and capsid (N) proteins that may post-transcriptionally regulate the host genes/proteins in immune homeostasis, pulmonary epithelial tissue homeostasis, and lipid homeostasis. According to the review of literature, the spectrum of identified genes/proteins shows that the virus promotes IL1α/β-IL1R1 signaling (type 1 immunity) and immunity defense against helminths and venoms (type 2 immunity). In the alteration of homeostasis in the pulmonary epithelial tissue, the virus blocks the function of cilia and the molecular programs that are involved in wound healing (EMT and MET). Additionally, the protein-RNA recognition method described here identifies compatible sequences in the S1A-domain for the post-transcriptional promotion of PIKFYVE, which is one of the critical factors for SARS-CoV-2 entry to the host cell, and for the post-transcriptional repression of xylulokinase XYLB. A decrease in XYLB product (Xu5P) in plasma was proposed as one of the potential metabolomics biomarkers of COVID-19. In summary, the protein-RNA recognition code leads to protein genes relevant to the SARS-CoV-2 life cycle and pathogenesis.

New RNA-Based Breakthroughs in Alzheimer's Disease Diagnosis and Therapeutics

Dementia is described as the fifth leading cause of death worldwide and Alzheimer’s disease (AD) is recognized as the most common, causing a huge impact on health costs and quality of patients’ lives. The main hallmarks that are commonly associated with the pathologic process are amyloid deposition, pathologic Tau phosphorylation and neurodegeneration. It is still unclear how these events are linked to the disease progression, due to the complex pathologic mechanisms. Nevertheless, several hypotheses have been proposed for a better understanding of AD. The AD diagnosis is performed by using a combination of several tools to detect β-amyloid peptide (Aβ) deposits and modifications in cognitive performance, sometimes being expensive and invasive. In the treatment field, there is still an absence of effective treatments to delay or stop the progression of the disease, with most of the approved drugs used to relieve symptoms, and all of them with significant adverse side effects. Considering all limitations, the need to establish new and more effective diagnostic and therapeutic strategies becomes clear. This review aims not only to describe the disease and its impact but also to collect the currently available diagnostic and therapeutic strategies, highlighting new promising RNA-based strategies for AD.

A Novel Prognostic Model for Oral Squamous Cell Carcinoma: The Functions and Prognostic Values of RNA-Binding Proteins

PURPOSE

The biological roles and clinical significance of RNA-binding proteins (RBPs) in oral squamous cell carcinoma (OSCC) are not fully understood. We investigated the prognostic value of RBPs in OSCC using several bioinformatic strategies.

MATERIALS AND METHODS

OSCC data were obtained from a public online database, the Limma R package was used to identify differentially expressed RBPs, and functional enrichment analysis was performed to elucidate the biological functions of the above RBPs in OSCC. We performed protein-protein interaction (PPI) network and Cox regression analyses to extract prognosis-related hub RBPs. Next, we established and validated a prognostic model based on the hub RBPs using Cox regression and risk score analyses.

RESULTS

We found that the differentially expressed RBPs were closely related to the defense response to viruses and multiple RNA processes. We identified 10 prognosis-related hub RBPs (ZC3H12D, OAS2, INTS10, ACO1, PCBP4, RNASE3, PTGES3L-AARSD1, RNASE13, DDX4, and PCF11) and effectively predicted the overall survival of OSCC patients. The area under the receiver operating characteristic (ROC) curve (AUC) of the risk score model was 0.781, suggesting that our model exhibited excellent prognostic performance. Finally, we built a nomogram integrating the 10 RBPs. The internal validation cohort results showed a reliable predictive capability of the nomogram for OSCC.

CONCLUSION

We established a novel 10-RBP-based model for OSCC that could enable precise individual treatment and follow-up management strategies in the future.

MicroRNAs in Prion Diseases-From Molecular Mechanisms to Insights in Translational Medicine

MicroRNAs (miRNAs) are small non-coding RNA molecules able to post-transcriptionally regulate gene expression via base-pairing with partially complementary sequences of target transcripts. Prion diseases comprise a singular group of neurodegenerative conditions caused by endogenous, misfolded pathogenic (prion) proteins, associated with molecular aggregates. In humans, classical prion diseases include Creutzfeldt-Jakob disease, fatal familial insomnia, Gerstmann-Sträussler-Scheinker syndrome, and kuru. The aim of this review is to present the connections between miRNAs and prions, exploring how the interaction of both molecular actors may help understand the susceptibility, onset, progression, and pathological findings typical of such disorders, as well as the interface with some prion-like disorders, such as Alzheimer's. Additionally, due to the inter-regulation of prions and miRNAs in health and disease, potential biomarkers for non-invasive miRNA-based diagnostics, as well as possible miRNA-based therapies to restore the levels of deregulated miRNAs on prion diseases, are also discussed. Since a cure or effective treatment for prion disorders still pose challenges, miRNA-based therapies emerge as an interesting alternative strategy to tackle such defying medical conditions.

Non-coding RNAs: Emerging from the discovery to therapeutic applications

The knowledge about non-coding RNAs (ncRNAs) is rapidly increasing with new data continuously emerging, regarding their diverse types, applications, and roles. Particular attention has been given to ncRNA with regulatory functions, which may have a critical role both in biological and pathological conditions. As a result of the diversity of ncRNAs and their ubiquitous involvement in several biologic processes, ncRNA started to be considered in the biomedical field, with immense potential to be exploited either as biomarkers or as therapeutic agents in certain pathologies. Indeed, ncRNA-based therapeutics have been proposed in many disorders and some even reached clinical trials. However, to prepare an RNA product suitable for pharmacological applications, certain criteria must be fulfilled, and it has to be guaranteed RNA purity, stability, and bioactivity. So, in this review, the different types of ncRNAs are identified and characterized, by describing their biogenesis, functions, and applications. A perspective on the main challenges and innovative approaches for the future and broad therapeutic application of RNA is also presented.

Development of prognostic signature based on RNA binding proteins related genes analysis in clear cell renal cell carcinoma

RNA binding proteins (RBPs) play significant roles in the development of tumors. However, a comprehensive analysis of the biological functions of RBPs in clear cell renal cell carcinoma (ccRCC) has not been performed. Our study aimed to construct an RBP-related risk model for prognosis prediction in ccRCC patients. First, RNA sequencing data of ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database. Three RBP genes (EIF4A1, CARS, and RPL22L1) were validated as prognosis-related hub genes by univariate and multivariate Cox regression analyses and were integrated into a prognostic model by least absolute shrinkage and selection operator (LASSO) Cox regression analysis. According to this model, patients with high risk scores displayed significantly worse overall survival (OS) than those with low risk scores. Moreover, the multivariate Cox analysis results indicated that risk score, tumor grade, and tumor stage were significantly correlated with patient OS. A nomogram was constructed based on the three RBP genes and showed a good ability to predict outcomes in ccRCC patients. In conclusion, this study identified a three-RBP gene risk model for predicting the prognosis of patients, which is conducive to the identification of novel diagnostic and prognostic molecular markers.

Heterogeneous Nuclear Ribonucleoproteins: Implications in Neurological Diseases

Heterogenous nuclear ribonucleoproteins (hnRNPs) are a complex and functionally diverse family of RNA binding proteins with multifarious roles. They are involved, directly or indirectly, in alternative splicing, transcriptional and translational regulation, stress granule formation, cell cycle regulation, and axonal transport. It is unsurprising, given their heavy involvement in maintaining functional integrity of the cell, that their dysfunction has neurological implications. However, compared to their more established roles in cancer, the evidence of hnRNP implication in neurological diseases is still in its infancy. This review aims to consolidate the evidences for hnRNP involvement in neurological diseases, with a focus on spinal muscular atrophy (SMA), Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), multiple sclerosis (MS), congenital myasthenic syndrome (CMS), and fragile X-associated tremor/ataxia syndrome (FXTAS). Understanding more about hnRNP involvement in neurological diseases can further elucidate the pathomechanisms involved in these diseases and perhaps guide future therapeutic advances.

Integrated Analysis of the Functions and Prognostic Values of RNA Binding Proteins in Lung Squamous Cell Carcinoma

Lung cancer is the leading cause of cancer-related deaths worldwide. Dysregulation of RNA binding proteins (RBPs) has been found in a variety of cancers and is related to oncogenesis and progression. However, the functions of RBPs in lung squamous cell carcinoma (LUSC) remain unclear. In this study, we obtained gene expression data and corresponding clinical information for LUSC from The Cancer Genome Atlas (TCGA) database, identified aberrantly expressed RBPs between tumors and normal tissue, and conducted a series of bioinformatics analyses to explore the expression and prognostic value of these RBPs. A total of 300 aberrantly expressed RBPs were obtained, comprising 59 downregulated and 241 upregulated RBPs. Functional enrichment analysis indicated that the differentially expressed RBPs were mainly associated with mRNA metabolic processes, RNA processing, RNA modification, regulation of translation, the TGF-beta signaling pathway, and the Toll-like receptor signaling pathway. Nine RBP genes (A1CF, EIF2B5, LSM1, LSM7, MBNL2, RSRC1, TRMU, TTF2, and ZCCHC5) were identified as prognosis-associated hub genes by univariate, least absolute shrinkage and selection operator (LASSO), Kaplan–Meier survival, and multivariate Cox regression analyses, and were used to construct the prognostic model. Further analysis demonstrated that high risk scores for patients were significantly related to poor overall survival according to the model. The area under the time-dependent receiver operator characteristic curve of the prognostic model was 0.712 at 3 years and 0.696 at 5 years. We also developed a nomogram based on nine RBP genes, with internal validation in the TCGA cohort, which showed a favorable predictive efficacy for prognosis in LUSC. Our results provide novel insights into the pathogenesis of LUSC. The nine-RBP gene signature showed predictive value for LUSC prognosis, with potential applications in clinical decision-making and individualized treatment.