scholarly journals Fucosylated Proteome Profiling Identifies a Fucosylated, Non-Ribosomal, Stress-Responsive Species of Ribosomal Protein S3

Cells ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1310
Author(s):  
Gregory Watson ◽  
Daniel Lester ◽  
Hui Ren ◽  
Connor M. Forsyth ◽  
Elliot Medina ◽  
...  
Keyword(s):  
Ribosomal Protein ◽  
Protein Function ◽  
Posttranscriptional Regulation ◽  
Developmental Disorders ◽  
Secretory Pathway ◽  
Normal Tissues ◽  
Genes Encoding ◽  
Protein Functions ◽  
Ribosomal Protein S3 ◽  
Mapk Inhibitors

Alterations in genes encoding for proteins that control fucosylation are known to play causative roles in several developmental disorders, such as Dowling-Degos disease 2 and congenital disorder of glycosylation type IIc (CDGIIc). Recent studies have provided evidence that changes in fucosylation can contribute to the development and progression of several different types of cancers. It is therefore important to gain a detailed understanding of how fucosylation is altered in disease states so that interventions may be developed for therapeutic purposes. In this report, we find that fucosylation occurs on many intracellular proteins. This is an interesting finding, as the fucosylation machinery is restricted to the secretory pathway and is thought to predominately affect cell-membrane-bound and secreted proteins. We find that Ribosomal protein S3 (RPS3) is fucosylated in normal tissues and in cancer cells, and that the extent of its fucosylation appears to respond to stress, including MAPK inhibitors, suggesting a new role in posttranslational protein function. Our data identify a new ribosome-independent species of fucosylated RPS3 that interacts with proteins involved in posttranscriptional regulation of RNA, such as Heterogeneous nuclear ribonucleoprotein U (HNRNPU), as well as with a predominance of non-coding RNAs. These data highlight a novel role for RPS3, which, given previously reported oncogenic roles for RPS3, might represent functions that are perturbed in pathologies such as cancer. Together, our findings suggest a previously unrecognized role for fucosylation in directly influencing intracellular protein functions.

scholarly journals Autoregulation in the Biosynthesis of Ribosomes

2003 ◽  
Vol 23 (2) ◽  
pp. 699-707 ◽  
Author(s):  
Yu Zhao ◽  
Jung-Hoon Sohn ◽  
Jonathan R. Warner
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Ribosomal Protein ◽  
Secretory Pathway ◽  
Protein Gene ◽  
Ribosomal Protein Gene ◽  
Ribosomal Protein Genes ◽  
Ribosome Synthesis ◽  
Ribosomal Protein L1 ◽  
Initiation Of Translation ◽  
Genes Encoding

ABSTRACT The synthesis of ribosomes in Saccharomyces cerevisiae consumes a prodigious amount of the cell's resources and, consequently, is tightly regulated. The rate of ribosome synthesis responds not only to nutritional cues but also to signals dependent on other macromolecular pathways of the cell, e.g., a defect in the secretory pathway leads to severe repression of transcription of both rRNA and ribosomal protein genes. A search for mutants that interrupted this repression revealed, surprisingly, that inactivation of RPL1B, one of a pair of genes encoding the 60S ribosomal protein L1, almost completely blocked the repression of rRNA and ribosomal protein gene transcription that usually follows a defect in the secretory pathway. Further experiments showed that almost any mutation leading to a defect in 60S subunit synthesis had the same effect, whereas mutations affecting 40S subunit synthesis did not. Although one might suspect that this effect would be due to a decrease in the initiation of translation or to the presence of half-mers, i.e., polyribosomes awaiting a 60S subunit, our data show that this is not the case. Rather, a variety of experiments suggest that some aspect of the production of defective 60S particles or, more likely, their breakdown suppresses the signal generated by a defect in the secretory pathway that represses ribosome synthesis.

scholarly journals PPM1G promotes the progression of hepatocellular carcinoma via phosphorylation regulation of alternative splicing protein SRSF3

2021 ◽  
Vol 12 (8) ◽  
Author(s):  
Dawei Chen ◽  
Zhenguo Zhao ◽  
Lu Chen ◽  
Qinghua Li ◽  
Jixue Zou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Alternative Splicing ◽  
Protein Function ◽  
Vital Role ◽  
Normal Tissues ◽  
Mechanistic Analysis ◽  
Highly Correlated ◽  
Splicing Patterns

AbstractEmerging evidence has demonstrated that alternative splicing has a vital role in regulating protein function, but how alternative splicing factors can be regulated remains unclear. We showed that the PPM1G, a protein phosphatase, regulated the phosphorylation of SRSF3 in hepatocellular carcinoma (HCC) and contributed to the proliferation, invasion, and metastasis of HCC. PPM1G was highly expressed in HCC tissues compared to adjacent normal tissues, and higher levels of PPM1G were observed in adverse staged HCCs. The higher levels of PPM1G were highly correlated with poor prognosis, which was further validated in the TCGA cohort. The knockdown of PPM1G inhibited the cell growth and invasion of HCC cell lines. Further studies showed that the knockdown of PPM1G inhibited tumor growth in vivo. The mechanistic analysis showed that the PPM1G interacted with proteins related to alternative splicing, including SRSF3. Overexpression of PPM1G promoted the dephosphorylation of SRSF3 and changed the alternative splicing patterns of genes related to the cell cycle, the transcriptional regulation in HCC cells. In addition, we also demonstrated that the promoter of PPM1G was activated by multiple transcription factors and co-activators, including MYC/MAX and EP300, MED1, and ELF1. Our study highlighted the essential role of PPM1G in HCC and shed new light on unveiling the regulation of alternative splicing in malignant transformation.

A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods

2021 ◽  
Vol 28 ◽  
Author(s):  
Yu-He Yang ◽  
Jia-Shu Wang ◽  
Shi-Shi Yuan ◽  
Meng-Lu Liu ◽  
Wei Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Vital Role ◽  
Atp Binding ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Protein Ligand Interactions ◽  
Protein Functions ◽  
Ligand Interactions ◽  
Binding Residues

: Protein-ligand interactions are necessary for majority protein functions. Adenosine-5’-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is cost-ineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.

Optobiochemistry: Genetically Encoded Control of Protein Activity by Light

2021 ◽  
Vol 90 (1) ◽  
Author(s):  
Jihye Seong ◽  
Michael Z. Lin
Keyword(s):  
Protein Function ◽  
Living Cells ◽  
Optical Methods ◽  
Annual Review ◽  
Publication Date ◽  
Protein Activity ◽  
Spatiotemporal Resolution ◽  
Protein Functions ◽  
Protein Classes ◽  
Control Protein

Optobiochemical control of protein activities allows the investigation of protein functions in living cells with high spatiotemporal resolution. Over the last two decades, numerous natural photosensory domains have been characterized and synthetic domains engineered and assembled into photoregulatory systems to control protein function with light.Here, we review the field of optobiochemistry, categorizing photosensory domains by chromophore, describing photoregulatory systems by mechanism of action, and discussing protein classes frequently investigated using optical methods. We also present examples of how spatial or temporal control of proteins in living cells has provided new insights not possible with traditional biochemical or cell biological techniques. Expected final online publication date for the Annual Review of Biochemistry, Volume 90 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Ribosomal protein S3 regulates XIAP expression independently of the NF-κB pathway in breast cancer cells

2017 ◽  
Vol 38 (5) ◽  
pp. 3205-3210 ◽  
Author(s):  
Hisako Ono ◽  
Yosuke Iizumi ◽  
Wakana Goi ◽  
Yoshihiro Sowa ◽  
Tetsuya Taguchi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ribosomal Protein ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Xiap Expression ◽  
Ribosomal Protein S3

scholarly journals DeepGOZero: Improving protein function prediction from sequence and zero-shot learning based on ontology axioms

2022 ◽  
Author(s):  
Maxat Kulmanov ◽  
Robert Hoehndorf
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Prediction Method ◽  
Function Prediction ◽  
Training Data ◽  
Large Set ◽  
Theoretic Approach ◽  
Machine Learning Model ◽  
Protein Functions

Motivation: Protein functions are often described using the Gene Ontology (GO) which is an ontology consisting of over 50,000 classes and a large set of formal axioms. Predicting the functions of proteins is one of the key challenges in computational biology and a variety of machine learning methods have been developed for this purpose. However, these methods usually require significant amount of training data and cannot make predictions for GO classes which have only few or no experimental annotations. Results: We developed DeepGOZero, a machine learning model which improves predictions for functions with no or only a small number of annotations. To achieve this goal, we rely on a model-theoretic approach for learning ontology embeddings and combine it with neural networks for protein function prediction. DeepGOZero can exploit formal axioms in the GO to make zero-shot predictions, i.e., predict protein functions even if not a single protein in the training phase was associated with that function. Furthermore, the zero-shot prediction method employed by DeepGOZero is generic and can be applied whenever associations with ontology classes need to be predicted. Availability: http://github.com/bio-ontology-research-group/deepgozero

scholarly journals Ribosomal protein S3 is phosphorylated by Cdk1/cdc2 during G2/M phase

BMB Reports ◽  
2011 ◽  
Vol 44 (8) ◽  
pp. 529-534 ◽  
Author(s):  
In-Soo Yoon ◽  
Ji-Hyung Chung ◽  
Soo-Hyun Hahm ◽  
Min-Ju Park ◽  
You-Ri Lee ◽  
...  
Keyword(s):  
Ribosomal Protein ◽  
M Phase ◽  
Ribosomal Protein S3

scholarly journals Landscape Of HOXA Genes Methylation in Colorectal Cancer

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Muhiddin Ishak ◽  
Rashidah Baharudin ◽  
Loh Teng-Hern Tan ◽  
Learn-Han Lee ◽  
Nurul-Syakima Ab Mutalib
Keyword(s):  
Colorectal Cancer ◽  
Cancer Susceptibility ◽  
Cpg Islands ◽  
Methylation Pattern ◽  
Gene Promoters ◽  
Normal Tissues ◽  
Genes Encoding ◽  
Hoxa Genes ◽  
A Minor ◽  
Hoxa Gene

Colorectal cancer (CRC) is among the most common cancers worldwide and the second leading cause of cancer-related death in Malaysia. The HOXA gene cluster is a family of Homeobox A genes encoding transcriptional regulators that play vital roles in cancer susceptibility and progression. Dysregulated HOXA expression influences various aspects of carcinogenesis processes. Therefore, this study aims to elucidate the methylation landscape of HOXA genes in CRC. Twelve pairs of CRC — adjacent normal tissues were subjected to Infinium DNA MethyEPIC array. Differentially methylatedregions were identified using the ChAMP Bioconductor and methylation levels of HOXA genes were manually curated. We identified 100 significantly differentially methylated probes annotated to HOXA genes. HOXA3 has the highest number of differentially methylated probes (n=27), followed by HOXA2 (n=20) and HOXA4 (n=14). The majority (43%) of the probes were located at the transcription start site (TSS) 200, which is one of the gene promoters. In respect to CpG islands (CGI), the probes were equally located in the island and shore regions (47% each) while a minor percentage was in the shelf (6%). Our work gave a comprehensive assessment of the DNA methylation pattern of HOXA genes and provide the first evidence of HOXA2, HOXA3 and HOXA4 differential methylation in Malaysian CRC. The new knowledge from this study can be utilized to further increase our understanding of CRC methylomics, particularly on the homeobox A genes. The prognostic and diagnostic roles of the differentially methylated HOXA genes warrant future investigations.

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