scholarly journals Enzymatic RNA Biotinylation for Affinity Purification and Identification of RNA-protein Interactions

2020 ◽  
Author(s):  
Kayla N. Busby ◽  
Amitkumar Fulzele ◽  
Dongyang Zhang ◽  
Eric J. Bennett ◽  
Neal K. Devaraj
Keyword(s):  
Protein Interactions ◽  
Mammalian Cells ◽  
Noncoding Rna ◽  
Affinity Purification ◽  
Functional Characterization ◽  
Selective Enrichment ◽  
Binding Partners ◽  
Nucleotide Mutation ◽  
Rna Biology ◽  
And Function

ABSTRACTThroughout their cellular lifetime, RNA transcripts are bound to proteins, playing crucial roles in RNA metabolism, trafficking, and function. Despite the importance of these interactions, identifying the proteins that interact with an RNA of interest in mammalian cells represents a major challenge in RNA biology. Leveraging the ability to site-specifically and covalently label an RNA of interest using E. Coli tRNA guanine transglycosylase and an unnatural nucleobase substrate, we establish the identification of RNA-protein interactions and the selective enrichment of cellular RNA in mammalian systems. We demonstrate the utility of this approach through the identification of known binding partners of 7SK snRNA via mass spectrometry. Through a minimal 4-nucleotide mutation of the long noncoding RNA HOTAIR, enzymatic biotinylation enables identification putative HOTAIR binding partners in MCF7 breast cancer cells that suggest new potential pathways for oncogenic function. Furthermore, using RNA sequencing and qPCR, we establish that an engineered enzyme variant achieves high levels of labeling selectivity against the human transcriptome allowing for 145-fold enrichment of cellular RNA directly from mammalian cell lysates. The flexibility and breadth of this approach suggests that this system could be routinely applied to the functional characterization of RNA, greatly expanding the toolbox available for studying mammalian RNA biology.

scholarly journals Affinity Purification of NF1 Protein–Protein Interactors Identifies Keratins and Neurofibromin Itself as Binding Partners

Genes ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 650 ◽  
Author(s):  
Rachel M. Carnes ◽  
Robert A. Kesterson ◽  
Bruce R. Korf ◽  
James A. Mobley ◽  
Deeann Wallis
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Affinity Purification ◽  
Neurofibromatosis Type ◽  
Hek293 Cells ◽  
Ras Signaling ◽  
Affinity Tag ◽  
Pathogenic Variants ◽  
Binding Partners ◽  
Keratin Expression

Neurofibromatosis Type 1 (NF1) is caused by pathogenic variants in the NF1 gene encoding neurofibromin. Definition of NF1 protein–protein interactions (PPIs) has been difficult and lacks replication, making it challenging to define binding partners that modulate its function. We created a novel tandem affinity purification (TAP) tag cloned in frame to the 3’ end of the full-length murine Nf1 cDNA (mNf1). We show that this cDNA is functional and expresses neurofibromin, His-Tag, and can correct p-ERK/ERK ratios in NF1 null HEK293 cells. We used this affinity tag to purify binding partners with Strep-Tactin®XT beads and subsequently, identified them via mass spectrometry (MS). We found the tagged mNf1 can affinity purify human neurofibromin and vice versa, indicating that neurofibromin oligomerizes. We identify 21 additional proteins with high confidence of interaction with neurofibromin. After Metacore network analysis of these 21 proteins, eight appear within the same network, primarily keratins regulated by estrogen receptors. Previously, we have shown that neurofibromin levels negatively regulate keratin expression. Here, we show through pharmacological inhibition that this is independent of Ras signaling, as the inhibitors, selumetinib and rapamycin, do not alter keratin expression. Further characterization of neurofibromin oligomerization and binding partners could aid in discovering new neurofibromin functions outside of Ras regulation, leading to novel drug targets.

scholarly journals Identification of sulfenylated cysteines in Arabidopsis thaliana proteins using a disulfide-linked peptide reporter

2020 ◽  
Author(s):  
Bo Wei ◽  
Patrick Willems ◽  
Jingjing Huang ◽  
Caiping Tian ◽  
Jing Yang ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Protein Interactions ◽  
Affinity Purification ◽  
Mass Spectrometry Analysis ◽  
Technological Advancement ◽  
Amino Acid Residues ◽  
Cysteine Oxidation ◽  
Spectrometry Analysis ◽  
Mixed Disulfide ◽  
And Function

ABSTRACTIn proteins, hydrogen peroxide (H2O2) reacts with redox-sensitive cysteines to form cysteine sulfenic acid, also known as S-sulfenylation. These cysteine oxidation events can steer diverse cellular processes by altering protein interactions, trafficking, conformation, and function. Previously, we had identified S-sulfenylated proteins by using a tagged proteinaceous probe based on the yeast AP-1–like (Yap1) transcription factor that specifically reacts with sulfenic acids and traps them through a mixed disulfide bond. However, the identity of the S-sulfenylated amino acid residues remained enigmatic. Here, we present a technological advancement to identify in situ sulfenylated cysteines directly by means of the transgenic Yap1 probe. In Arabidopsis thaliana cells, after an initial affinity purification and a tryptic digestion, we further enriched the mixed disulfide-linked peptides with an antibody targeting the YAP1C-derived peptide (C598SEIWDR) that entails the redox-active cysteine. Subsequent mass spectrometry analysis with pLink 2 identified 1,745 YAP1C cross-linked peptides, indicating sulfenylated cysteines in over 1,000 proteins. Approximately 55% of these YAP1C-linked cysteines had previously been reported as redox-sensitive cysteines (S-sulfenylation, S-nitrosylation, and reversibly oxidized cysteines). The presented methodology provides a noninvasive approach to identify sulfenylated cysteines in any species that can be genetically modified.

scholarly journals Dysregulation of Long Non-coding RNA (lncRNA) Genes and Predicted lncRNA-Protein Interactions during Zika Virus Infection

2016 ◽  
Author(s):  
Arunachalam Ramaiah ◽  
Deisy Contreras ◽  
Vineela Gangalapudi ◽  
Masumi Sameer Padhye ◽  
Jie Tang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Zika Virus ◽  
Mammalian Cells ◽  
Noncoding Rna ◽  
Target Genes ◽  
Vero Cells ◽  
Viral Pathogens ◽  
Developmental Abnormalities ◽  
Non Coding Rna ◽  
Cellular Factors

ABSTRACTZika Virus (ZIKV) is a causative agent for poor pregnancy outcome and fetal developmental abnormalities, including microcephaly and eye defects. As a result, ZIKV is now a confirmed teratogen. Understanding host-pathogen interactions, specifically cellular perturbations caused by ZIKV, can provide novel therapeutic targets. In order to complete viral replication, viral pathogens control the host cellular machineries and regulate various factors, including long noncoding RNA (lncRNA) genes, at transcriptional levels. The role of lncRNA genes in the pathogenesis of ZIKV-mediated microcephaly and eye defects is currently unknown. To gain additional insights, we focused on profiling the differentially expressed lncRNA genes during ZIKV infection in mammalian cells. For this study, we employed a contemporary clinical Zika viral isolate, PRVABC59, of Asian genotype. We utilized an unbiased RNA sequencing approach to profile the lncRNA transcriptome in ZIKV infected Vero cells. We identified a total of 121 lncRNA genes that are differentially regulated at 48 hours post-infection. The majority of these genes are independently validated by reverse-transcription qPCR. A notable observation was that the lncRNAs, MALAT1 (Metastasis Associated Lung Adenocarcinoma Transcript 1) and NEAT1 (Nuclear Paraspeckle Assembly Transcript 1), are down-regulated upon Zika viral infection. MALAT1 and NEAT1 are known as nuclear localized RNAs that regulate gene expression and cell proliferation. Protein-lncRNA interaction maps revealed that MALAT1 and NEAT1 share common interacting partners and form a larger network comprising of 71 cellular factors. ZIKV-mediated dysregulation of these two regulatory lncRNAs can alter the expression of respective target genes and associated biological functions, an important one being cell division. In conclusion, this investigation is the first to provide insight into the biological connection of lncRNAs and ZIKV which can be further explored for developing antiviral therapy and understanding fetal developmental processes.

scholarly journals Capturing RNA–protein interaction via CRUIS

2020 ◽  
Vol 48 (9) ◽  
pp. e52-e52 ◽  
Author(s):  
Ziheng Zhang ◽  
Weiping Sun ◽  
Tiezhu Shi ◽  
Pengfei Lu ◽  
Min Zhuang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Dna Damage ◽  
Protein Interactions ◽  
Noncoding Rna ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Living Cells ◽  
Innovative Methods ◽  
Rna Biology

Abstract No RNA is completely naked from birth to death. RNAs function with and are regulated by a range of proteins that bind to them. Therefore, the development of innovative methods for studying RNA–protein interactions is very important. Here, we developed a new tool, the CRISPR-based RNA-United Interacting System (CRUIS), which captures RNA–protein interactions in living cells by combining the power of CRISPR and PUP-IT, a novel proximity targeting system. In CRUIS, dCas13a is used as a tracker to target specific RNAs, while proximity enzyme PafA is fused to dCas13a to label the surrounding RNA-binding proteins, which are then identified by mass spectrometry. To identify the efficiency of CRUIS, we employed NORAD (Noncoding RNA activated by DNA damage) as a target, and the results show that a similar interactome profile of NORAD can be obtained as by using CLIP (crosslinking and immunoprecipitation)-based methods. Importantly, several novel NORAD RNA-binding proteins were also identified by CRUIS. The use of CRUIS facilitates the study of RNA–protein interactions in their natural environment, and provides new insights into RNA biology.

scholarly journals Comprehensive analysis of the host-virus interactome of SARS-CoV-2

2021 ◽  
Author(s):  
Zhen Chen ◽  
Chao Wang ◽  
Xu Feng ◽  
Litong Nie ◽  
Mengfan Tang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Affinity Purification ◽  
Functional Characterization ◽  
Viral Proteins ◽  
Host Cells ◽  
Virus Protein ◽  
N Protein ◽  
Protein Protein Interaction ◽  
Human Coronaviruses

Host-virus protein-protein interaction is the key component of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lifecycle. We conducted a comprehensive interactome study between the virus and host cells using tandem affinity purification and proximity labeling strategies and identified 437 human proteins as the high-confidence interacting proteins. Functional characterization and further validation of these interactions elucidated how distinct SARS-CoV-2 viral proteins participate in its lifecycle, and discovered potential drug targets to the treatment of COVID-19. The interactomes of two key SARS-CoV-2 encoded viral proteins, NSP1 and N protein, were compared with the interactomes of their counterparts in other human coronaviruses. These comparisons not only revealed common host pathways these viruses manipulate for their survival, but also showed divergent protein-protein interactions that may explain differences in disease pathology. This comprehensive interactome of coronavirus disease-2019 provides valuable resources for understanding and treating this disease.

scholarly journals Regulation of ryanodine receptor RyR2 by protein-protein interactions: prediction of a PKA binding site on the N-terminal domain of RyR2 and its relation to disease causing mutations

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 29 ◽  
Author(s):  
Belinda Nazan Walpoth ◽  
Burak Erman
Keyword(s):  
Binding Site ◽  
Protein Interactions ◽  
Elastic Net ◽  
Coarse Grained ◽  
Protein Protein Interactions ◽  
Analysis Model ◽  
Terminal Domain ◽  
Binding Partners ◽  
Atomistic Calculations ◽  
And Function

Protein-protein interactions are the key processes responsible for signaling and function in complex networks. Determining the correct binding partners and predicting the ligand binding sites in the absence of experimental data require predictive models. Hybrid models that combine quantitative atomistic calculations with statistical thermodynamics formulations are valuable tools for bioinformatics predictions. We present a hybrid prediction and analysis model for determining putative binding partners and interpreting the resulting correlations in the yet functionally uncharacterized interactions of the ryanodine RyR2 N-terminal domain. Using extensive docking calculations and libraries of hexameric peptides generated from regulator proteins of the RyR2 channel, we show that the residues 318-323 of protein kinase A, PKA, have a very high affinity for the N-terminal of RyR2. Using a coarse grained Elastic Net Model, we show that the binding site lies at the end of a pathway of evolutionarily conserved residues in RyR2. The two disease causing mutations are also on this path. The program for the prediction of the energetically responsive residues by the Elastic Net Model is freely available on request from the corresponding author.

scholarly journals Biochemical and Functional Characterization of the Ebola Virus VP24 Protein: Implications for a Role in Virus Assembly and Budding

2003 ◽  
Vol 77 (3) ◽  
pp. 1793-1800 ◽  
Author(s):  
Ziying Han ◽  
Hani Boshra ◽  
J. Oriol Sunyer ◽  
Susan H. Zwiers ◽  
Jason Paragas ◽  
...  
Keyword(s):  
Lipid Membranes ◽  
Mammalian Cells ◽  
Matrix Protein ◽  
Ebola Virus ◽  
Virus Assembly ◽  
Functional Characterization ◽  
Minor Component ◽  
Structural Features ◽  
Structure And Function ◽  
And Function

ABSTRACT The VP24 protein of Ebola virus is believed to be a secondary matrix protein and minor component of virions. In contrast, the VP40 protein of Ebola virus is the primary matrix protein and the most abundant virion component. The structure and function of VP40 have been well characterized; however, virtually nothing is known regarding the structure and function of VP24. Wild-type and mutant forms of VP24 were expressed in mammalian cells to gain a better understanding of the biochemical and functional nature of this viral protein. Results from these experiments demonstrated that (i) VP24 localizes to the plasma membrane and perinuclear region in both transfected and Ebola virus-infected cells, (ii) VP24 associates strongly with lipid membranes, (iii) VP24 does not contain N-linked sugars when expressed alone in mammalian cells, (iv) VP24 can oligomerize when expressed alone in mammalian cells, (v) progressive deletions at the N terminus of VP24 resulted in a decrease in oligomer formation and a concomitant increase in the formation of high-molecular-weight aggregates, and (vi) VP24 was present in trypsin-resistant virus like particles released into the media covering VP24-transfected cells. These data indicate that VP24 possesses structural features commonly associated with viral matrix proteins and that VP24 may have a role in virus assembly and budding.

scholarly journals Survey of Natural Language Processing Techniques in Bioinformatics

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiqiang Zeng ◽  
Hua Shi ◽  
Yun Wu ◽  
Zhiling Hong
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Protein Interactions ◽  
Noncoding Rna ◽  
Structure And Function ◽  
Protein Protein Interactions ◽  
And Function ◽  
Processing Techniques

Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

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