lncRedibly versatile: biochemical and biological functions of long noncoding RNAs

Abstract Long noncoding RNAs (lncRNAs) are transcripts that do not code for proteins, but nevertheless exert regulatory effects on various biochemical pathways, in part via interactions with proteins, DNA, and other RNAs. LncRNAs are thought to regulate transcription and other biological processes by acting, for example, as guides that target proteins to chromatin, scaffolds that facilitate protein–protein interactions and complex formation, and orchestrators of phase-separated compartments. The study of lncRNAs has reached an exciting time, as recent advances in experimental and computational methods allow for genome-wide interrogation of biochemical and biological mechanisms of these enigmatic transcripts. A better appreciation for the biochemical versatility of lncRNAs has allowed us to begin closing gaps in our knowledge of how they act in diverse cellular and organismal contexts, including development and disease.

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Long Noncoding RNAs and Their Therapeutic Promise in Diabetic Nephropathy

Nephron ◽

10.1159/000515422 ◽

2021 ◽

pp. 1-11

Author(s):

Juan D. Coellar ◽

Jianyin Long ◽

Farhad R. Danesh

Keyword(s):

Diabetic Nephropathy ◽

Large Scale ◽

Kidney Diseases ◽

Noncoding Rnas ◽

Therapeutic Targets ◽

Long Noncoding Rnas ◽

Biological Processes ◽

Aberrant Expression ◽

Genome Wide

Recent advances in large-scale RNA sequencing and genome-wide profiling projects have unraveled a heterogeneous group of RNAs, collectively known as long noncoding RNAs (lncRNAs), which play central roles in many diverse biological processes. Importantly, an association between aberrant expression of lncRNAs and diverse human pathologies has been reported, including in a variety of kidney diseases. These observations have raised the possibility that lncRNAs may represent unexploited potential therapeutic targets for kidney diseases. Several important questions regarding the functionality of lncRNAs and their impact in kidney diseases, however, remain to be carefully addressed. Here, we provide an overview of the main functions and mechanisms of actions of lncRNAs, and their promise as therapeutic targets in kidney diseases, emphasizing on the role of some of the best-characterized lncRNAs implicated in the pathogenesis and progression of diabetic nephropathy.

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An Integrated Prediction Method for Identifying Protein-Protein Interactions

Current Proteomics ◽

10.2174/1570164616666190306152318 ◽

2020 ◽

Vol 17 (4) ◽

pp. 271-286

Author(s):

Chang Xu ◽

Limin Jiang ◽

Zehua Zhang ◽

Xuyao Yu ◽

Renhai Chen ◽

...

Keyword(s):

Protein Interactions ◽

Feature Vector ◽

Prediction Method ◽

Feature Representation ◽

Protein Interaction Networks ◽

Learning Approach ◽

Biological Processes ◽

Integrated Learning ◽

Protein Protein Interactions ◽

Training Process

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, viaMultivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pyloridataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiaedataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Humandataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.

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Roles of the 14-3-3 gene family in cotton flowering

BMC Plant Biology ◽

10.1186/s12870-021-02923-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Na Sang ◽

Hui Liu ◽

Bin Ma ◽

Xianzhong Huang ◽

Lu Zhuo ◽

...

Keyword(s):

Gene Family ◽

Protein Interactions ◽

Leucine Zipper ◽

Expression Patterns ◽

Bimolecular Fluorescence Complementation ◽

Structural Motif ◽

Virus Induced Gene Silencing ◽

Protein Protein Interactions ◽

Basic Leucine Zipper ◽

Genome Wide

Abstract Background In plants, 14-3-3 proteins, also called GENERAL REGULATORY FACTORs (GRFs), encoded by a large multigene family, are involved in protein–protein interactions and play crucial roles in various physiological processes. No genome-wide analysis of the GRF gene family has been performed in cotton, and their functions in flowering are largely unknown. Results In this study, 17, 17, 31, and 17 GRF genes were identified in Gossypium herbaceum, G. arboreum, G. hirsutum, and G. raimondii, respectively, by genome-wide analyses and were designated as GheGRFs, GaGRFs, GhGRFs, and GrGRFs, respectively. A phylogenetic analysis revealed that these proteins were divided into ε and non-ε groups. Gene structural, motif composition, synteny, and duplicated gene analyses of the identified GRF genes provided insights into the evolution of this family in cotton. GhGRF genes exhibited diverse expression patterns in different tissues. Yeast two-hybrid and bimolecular fluorescence complementation assays showed that the GhGRFs interacted with the cotton FLOWERING LOCUS T homologue GhFT in the cytoplasm and nucleus, while they interacted with the basic leucine zipper transcription factor GhFD only in the nucleus. Virus-induced gene silencing in G. hirsutum and transgenic studies in Arabidopsis demonstrated that GhGRF3/6/9/15 repressed flowering and that GhGRF14 promoted flowering. Conclusions Here, 82 GRF genes were identified in cotton, and their gene and protein features, classification, evolution, and expression patterns were comprehensively and systematically investigated. The GhGRF3/6/9/15 interacted with GhFT and GhFD to form florigen activation complexs that inhibited flowering. However, GhGRF14 interacted with GhFT and GhFD to form florigen activation complex that promoted flowering. The results provide a foundation for further studies on the regulatory mechanisms of flowering.

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Genome-Wide Identification and Characterization of Long Noncoding RNAs Involved in Chinese Wheat Mosaic Virus Infection of Nicotiana benthamiana

Biology ◽

10.3390/biology10030232 ◽

2021 ◽

Vol 10 (3) ◽

pp. 232

Author(s):

Weiran Zheng ◽

Haichao Hu ◽

Qisen Lu ◽

Peng Jin ◽

Linna Cai ◽

...

Keyword(s):

Virus Infection ◽

Mosaic Virus ◽

Nicotiana Benthamiana ◽

Noncoding Rnas ◽

Long Noncoding Rnas ◽

Differentially Expressed ◽

Genome Wide ◽

Chinese Wheat

Recent studies have shown that a large number of long noncoding RNAs (lncRNAs) can regulate various biological processes in animals and plants. Although lncRNAs have been identified in many plants, they have not been reported in the model plant Nicotiana benthamiana. Particularly, the role of lncRNAs in plant virus infection remains unknown. In this study, we identified lncRNAs in N. benthamiana response to Chinese wheat mosaic virus (CWMV) infection by RNA sequencing. A total of 1175 lncRNAs, including 65 differentially expressed lncRNAs, were identified during CWMV infection. We then analyzed the functions of some of these differentially expressed lncRNAs. Interestingly, one differentially expressed lncRNA, XLOC_006393, was found to participate in CWMV infection as a precursor to microRNAs in N. benthamiana. These results suggest that lncRNAs play an important role in the regulatory network of N. benthamiana in response to CWMV infection.

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A genome-wide screen for differentially methylated long noncoding RNAs identified that lncAC007255.8 is regulated by promoter DNA methylation in Beas-2B cells malignantly transformed by NNK

Toxicology Letters ◽

10.1016/j.toxlet.2021.04.013 ◽

2021 ◽

Author(s):

Enzhao Chen ◽

Jiaxin Zhou ◽

Enwu Xu ◽

Cheng Zhang ◽

Jiayu Liu ◽

...

Keyword(s):

Dna Methylation ◽

Noncoding Rnas ◽

Long Noncoding Rnas ◽

Genome Wide ◽

A Genome ◽

Promoter Dna Methylation ◽

Promoter Dna

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Genome-wide analysis of vaccinia virus protein-protein interactions

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.080078197 ◽

2000 ◽

Vol 97 (9) ◽

pp. 4879-4884 ◽

Cited By ~ 193

Author(s):

S. McCraith ◽

T. Holtzman ◽

B. Moss ◽

S. Fields

Keyword(s):

Vaccinia Virus ◽

Protein Interactions ◽

Virus Protein ◽

Protein Protein Interactions ◽

Genome Wide Analysis ◽

Genome Wide

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Coiled-coil networking shapes cell molecular machinery

Molecular Biology of the Cell ◽

10.1091/mbc.e12-05-0396 ◽

2012 ◽

Vol 23 (19) ◽

pp. 3911-3922 ◽

Cited By ~ 44

Author(s):

Yongqiang Wang ◽

Xinlei Zhang ◽

Hong Zhang ◽

Yi Lu ◽

Haolong Huang ◽

...

Keyword(s):

Protein Interactions ◽

Critical Role ◽

Coiled Coil ◽

Coiled Coils ◽

Protein Protein Interactions ◽

Full Spectrum ◽

Kinetochore Assembly ◽

Genome Wide ◽

Molecular Machinery ◽

Systematic Understanding

The highly abundant α-helical coiled-coil motif not only mediates crucial protein–protein interactions in the cell but is also an attractive scaffold in synthetic biology and material science and a potential target for disease intervention. Therefore a systematic understanding of the coiled-coil interactions (CCIs) at the organismal level would help unravel the full spectrum of the biological function of this interaction motif and facilitate its application in therapeutics. We report the first identified genome-wide CCI network in Saccharomyces cerevisiae, which consists of 3495 pair-wise interactions among 598 predicted coiled-coil regions. Computational analysis revealed that the CCI network is specifically and functionally organized and extensively involved in the organization of cell machinery. We further show that CCIs play a critical role in the assembly of the kinetochore, and disruption of the CCI network leads to defects in kinetochore assembly and cell division. The CCI network identified in this study is a valuable resource for systematic characterization of coiled coils in the shaping and regulation of a host of cellular machineries and provides a basis for the utilization of coiled coils as domain-based probes for network perturbation and pharmacological applications.

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MicroRNAs: Key Regulators in Lung Cancer

MicroRNA ◽

10.2174/2211536610666210527102522 ◽

2021 ◽

Vol 10 ◽

Author(s):

Younes El Founini ◽

Imane Chaoui ◽

Hind Dehbi ◽

Mohammed El Mzibri ◽

Roger Abounader ◽

...

Keyword(s):

Lung Cancer ◽

Messenger Rna ◽

Recent Literature ◽

Immune Surveillance ◽

Rna Stability ◽

Noncoding Rnas ◽

Therapy Resistance ◽

Biological Processes ◽

Genome Wide ◽

Stable Forms

: Noncoding RNAs have emerged as key regulators of the genome upon gene expression profiling and genome-wide sequencing. Among these noncoding RNAs, microRNAs are short noncoding RNAs that regulate a plethora of functions, biological processes and human diseases by targeting the messenger RNA stability through 3’UTR binding, leading to either mRNA cleavage or translation repression, depending on microRNA-mRNA complementarity degree. Additionally, strong evidence has suggested that dysregulation of miRNAs contribute to the etiology and progression of human cancers, such as lung cancer, the most common and deadliest cancer worldwide. Indeed, by acting as oncogenes or tumor suppressors, microRNAs control all aspects of lung cancer malignancy, including cell proliferation, survival, migration, invasion, angiogenesis, cancer stem cells, immune-surveillance escape, and therapy resistance; and their expressions are often associated with clinical parameters. Moreover, several deregulated microRNAs in lung cancer are carried by exosomes, microvesicles and secreted in body fluids, mainly the circulation where they conserve their stable forms. Subsequently, seminal efforts have been focused on extracellular microRNAs levels as noninvasive diagnostic and prognostic biomarkers in lung cancer. In this review, focusing on recent literature, we summarize the deregulation, mechanisms of action, functions and highlight clinical applications of miRNAs for better management and design of future lung cancer targeted therapies.

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