scholarly journals Cancer Proteomics: The State of the Art

2001 ◽  
Vol 17 (2) ◽  
pp. 49-57 ◽  
Author(s):  
Paul C. Herrmann ◽  
Lance A. Liotta ◽  
Emanuel F. Petricoin III
Keyword(s):  
Early Detection ◽  
Protein Interactions ◽  
State Of The Art ◽  
Careful Analysis ◽  
Cellular Protein ◽  
Protein Networks ◽  
New Paradigm ◽  
Detailed Understanding ◽  
Cellular Processes ◽  
Cancer Proteomics

Now that the human genome has been determined, the field of proteomics is ramping up to tackle the vast protein networks that both control and are controlled by the information encoded by the genome. The study of proteomics should yield an unparalleled understanding of cancer as well as an invaluable new target for therapeutic intervention and markers for early detection. This rapidly expanding field attempts to track the protein interactions responsible for all cellular processes. By careful analysis of these systems, a detailed understanding of the molecular causes and consequences of cancer should emerge. A brief overview of some of the cutting edge technologies employed by this rapidly expanding field is given, along with specific examples of how these technologies are employed. Soon cellular protein networks will be understood at a level that will permit a totally new paradigm of diagnosis and will allow therapy tailored to individual patients and situations.

scholarly journals easyCLIP Quantifies RNA-Protein Interactions and Characterizes Recurrent PCBP1 Mutations in Cancer

2019 ◽  
Author(s):  
Douglas F. Porter ◽  
Paul A. Khavari
Keyword(s):  
Protein Interactions ◽  
Rna Binding ◽  
Cellular Protein ◽  
Good Efficiency ◽  
Cellular Processes ◽  
Simple Protocol ◽  
Link Rate ◽  
Cross Links ◽  
Rna Interaction ◽  
Insight Into

ABSTRACTRNA-protein interactions mediate a host of cellular processes, underscoring the need for methods to quantify their occurrence in living cells. RNA interaction frequencies for the average cellular protein are undefined, however, and there is no quantitative threshold to define a protein as an RNA-binding protein (RBP). Ultraviolet (UV) cross-linking immunoprecipitation (CLIP)-sequencing, an effective and widely used means of characterizing RNA-protein interactions, would particularly benefit from the capacity to quantitate the number of RNA cross-links per protein per cell. In addition, CLIP-seq methods are difficult, have high experimental failure rates and many ambiguous analytical decisions. To address these issues, the easyCLIP method was developed and used to quantify RNA-protein interactions for a panel of known RBPs as well as a spectrum of random non-RBP proteins. easyCLIP provides the advantages of good efficiency compared to current standards, a simple protocol with a very low failure rate, troubleshooting information that includes direct visualization of prepared libraries without amplification, and a new form of analysis. easyCLIP, which uses sequential on-bead ligation of 5’ and 3’ adapters tagged with different infrared dyes, classified non-RBPs as those with a per protein RNA cross-link rate of <0.1%, with most RBPs substantially above this threshold, including Rbfox1 (18%), hnRNPC (22%), CELF1 (11%), FBL (2%), and STAU1 (1%). easyCLIP with the PCBP1L100 RBP mutant recurrently seen in cancer quantified increased RNA binding compared to wild-type PCBP1 and suggested a potential mechanism for this RBP mutant in cancer. easyCLIP provides a simple, efficient and robust method to both obtain both traditional CLIP-seq information and to define actual RNA interaction frequencies for a given protein, enabling quantitative cross-RBP comparisons as well as insight into RBP mechanisms.

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

2019 ◽  
Vol 15 (3) ◽  
pp. 216-230 ◽  
Author(s):  
Abbasali Emamjomeh ◽  
Javad Zahiri ◽  
Mehrdad Asadian ◽  
Mehrdad Behmanesh ◽  
Barat A. Fakheri ◽  
...  
Keyword(s):  
Computational Methods ◽  
State Of The Art ◽  
Noncoding Rnas ◽  
Research Field ◽  
Design Strategies ◽  
Structural Prediction ◽  
Computational Costs ◽  
Cellular Processes ◽  
Laboratory Techniques ◽  
Fast Prediction

Background:Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.Objective:The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA’s roles in cellular processes and drugs design, briefly.Method:In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.Results:The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.Conclusion:ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

scholarly journals Discovery and function exploration of microRNA-155 as a molecular biomarker for early detection of breast cancer

Breast Cancer ◽  
2021 ◽  
Author(s):  
Xuemin Liu ◽  
Qingyu Chang ◽  
Haiqiang Wang ◽  
Hairong Qian ◽  
Yikun Jiang
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Protein Interactions ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Diagnostic Biomarker ◽  
Analysis Model ◽  
Sensitivity Specificity ◽  
Function Enrichment

Abstract Background MicroRNA-155 (miR-155) may function as a diagnostic biomarker of breast cancer (BC). Nevertheless, the available evidence is controversial. Therefore, we performed this study to summarize the global predicting role of miR-155 for early detection of BC and preliminarily explore the functional roles of miR-155 in BC. Methods We first collected published studies and applied the bivariate meta-analysis model to generate the pooled diagnostic parameters of miR-155 in diagnosing BC such as sensitivity, specificity and area under curve (AUC). Then, we applied function enrichment and protein–protein interactions (PPI) analyses to explore the potential mechanisms of miR-155. Results A total of 21 studies were finally included. The results indicated that miR-155 allowed for the discrimination between BC patients and healthy controls with a sensitivity of 0.87 (95% CI 0.78–0.93), specificity of 0.82 (0.72–0.89), and AUC of 0.91 (0.88–0.93). In addition, the overall sensitivity, specificity and AUC for circulating miR-155 were 0.88 (0.76–0.95), 0.83 (0.72–0.90), and 0.92 (0.89–0.94), respectively. Function enrichment analysis revealed several vital ontologies terms and pathways associated with BC occurrence and development. Furthermore, in the PPI network, ten hub genes and two significant modules were identified to be involved in some important pathways associated with the pathogenesis of BC. Conclusions We demonstrated that miR-155 has great potential to facilitate accurate BC detection and may serve as a promising diagnostic biomarker for BC. However, well-designed cohort studies and biological experiments should be implemented to confirm the diagnostic value of miR-155 before it can be applied to routine clinical procedures.

scholarly journals InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dominik Jens Elias Waibel ◽  
Sayedali Shetab Boushehri ◽  
Carsten Marr
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Specific Problem ◽  
State Of The Art ◽  
Image Data ◽  
Semantic Segmentation ◽  
Parameter Tuning ◽  
Cellular Processes ◽  
Minimal Effort ◽  
Instance Segmentation

Abstract Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

scholarly journals Proteomic Adaptation of Clostridioides difficile to Treatment with the Antimicrobial Peptide Nisin

Cells ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 372
Author(s):  
Sandra Maaß ◽  
Jürgen Bartel ◽  
Pierre-Alexander Mücke ◽  
Rabea Schlüter ◽  
Thomas Sura ◽  
...  
Keyword(s):  
State Of The Art ◽  
Biomedical Application ◽  
Time Resolved ◽  
Antibiotic Resistant ◽  
Cellular Processes ◽  
Clostridioides Difficile ◽  
Antibiotic Associated Diarrhea ◽  
Life Threatening ◽  
Sublethal Concentrations ◽  
Interesting Alternative

Clostridioides difficile is the leading cause of antibiotic-associated diarrhea but can also result in more serious, life-threatening conditions. The incidence of C. difficile infections in hospitals is increasing, both in frequency and severity, and antibiotic-resistant C. difficile strains are advancing. Against this background antimicrobial peptides (AMPs) are an interesting alternative to classic antibiotics. Information on the effects of AMPs on C. difficile will not only enhance the knowledge for possible biomedical application but may also provide insights into mechanisms of C. difficile to adapt or counteract AMPs. This study applies state-of-the-art mass spectrometry methods to quantitatively investigate the proteomic response of C. difficile 630∆erm to sublethal concentrations of the AMP nisin allowing to follow the cellular stress adaptation in a time-resolved manner. The results do not only point at a heavy reorganization of the cellular envelope but also resulted in pronounced changes in central cellular processes such as carbohydrate metabolism. Further, the number of flagella per cell was increased during the adaptation process. The potential involvement of flagella in nisin adaptation was supported by a more resistant phenotype exhibited by a non-motile but hyper-flagellated mutant.

Structure, Function, Involvement in Diseases and Targeting of 14-3-3 Proteins: An Update

2018 ◽  
Vol 25 (1) ◽  
pp. 5-21 ◽  
Author(s):  
Ylenia Cau ◽  
Daniela Valensin ◽  
Mattia Mori ◽  
Sara Draghi ◽  
Maurizio Botta
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Physiological Role ◽  
Specific Protein ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Protein Partners ◽  
High Sequence Homology ◽  
Small Molecule Modulators

14-3-3 is a class of proteins able to interact with a multitude of targets by establishing protein-protein interactions (PPIs). They are usually found in all eukaryotes with a conserved secondary structure and high sequence homology among species. 14-3-3 proteins are involved in many physiological and pathological cellular processes either by triggering or interfering with the activity of specific protein partners. In the last years, the scientific community has collected many evidences on the role played by seven human 14-3-3 isoforms in cancer or neurodegenerative diseases. Indeed, these proteins regulate the molecular mechanisms associated to these diseases by interacting with (i) oncogenic and (ii) pro-apoptotic proteins and (iii) with proteins involved in Parkinson and Alzheimer diseases. The discovery of small molecule modulators of 14-3-3 PPIs could facilitate complete understanding of the physiological role of these proteins, and might offer valuable therapeutic approaches for these critical pathological states.

scholarly journals Akt2 Regulates Cardiac Metabolism and Cardiomyocyte Survival

2006 ◽  
Vol 281 (43) ◽  
pp. 32841-32851 ◽  
Author(s):  
Brian DeBosch ◽  
Nandakumar Sambandam ◽  
Carla Weinheimer ◽  
Michael Courtois ◽  
Anthony J. Muslin
Keyword(s):  
Pressure Overload ◽  
Cardiac Metabolism ◽  
Cellular Protein ◽  
Growth Responses ◽  
Infarct Zone ◽  
Targeted Disruption ◽  
Cellular Processes ◽  
Ligand Stimulation ◽  
And Function

The Akt family of serine-threonine kinases participates in diverse cellular processes, including the promotion of cell survival, glucose metabolism, and cellular protein synthesis. All three known Akt family members, Akt1, Akt2 and Akt3, are expressed in the myocardium, although Akt1 and Akt2 are most abundant. Previous studies demonstrated that Akt1 and Akt3 overexpression results in enhanced myocardial size and function. Yet, little is known about the role of Akt2 in modulating cardiac metabolism, survival, and growth. Here, we utilize murine models with targeted disruption of the akt2 or the akt1 genes to demonstrate that Akt2, but not Akt1, is required for insulin-stimulated 2-[3H]deoxyglucose uptake and metabolism. In contrast, akt2-/- mice displayed normal cardiac growth responses to provocative stimulation, including ligand stimulation of cultured cardiomyocytes, pressure overload by transverse aortic constriction, and myocardial infarction. However, akt2-/- mice were found to be sensitized to cardiomyocyte apoptosis in response to ischemic injury, and apoptosis was significantly increased in the peri-infarct zone of akt2-/- hearts 7 days after occlusion of the left coronary artery. These results implicate Akt2 in the regulation of cardiomyocyte metabolism and survival.

scholarly journals Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ying Li ◽  
Hang Sun ◽  
Shiyao Feng ◽  
Qi Zhang ◽  
Siyu Han ◽  
...  
Keyword(s):  
Protein Interactions ◽  
State Of The Art ◽  
Recognition Performance ◽  
Feature Learning ◽  
Biological Processes ◽  
Multimodal Features ◽  
Learning Architectures ◽  
Motif Information ◽  
Experimental Comparisons ◽  
Better Than

Abstract Background Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. Results We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. Conclusions This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver (http://csbg-jlu.site/lpc/predict) is developed to be convenient for users.

Visual detection of binary, ternary, and quaternary protein interactions in fission yeast by Pil1 co-tethering assay

2021 ◽  
Author(s):  
Zhong-Qiu Yu ◽  
Xiao-Man Liu ◽  
Dan Zhao ◽  
Dan-Dan Xu ◽  
Li-Lin Du
Keyword(s):  
Fission Yeast ◽  
Protein Interactions ◽  
Visual Detection ◽  
Protein Protein Interactions ◽  
Phosphatidylinositol 3 Kinase ◽  
Basic Form ◽  
Bait Protein ◽  
Cellular Processes ◽  
Prey Protein ◽  
Versatile Tool

Protein-protein interactions are vital for executing nearly all cellular processes. To facilitate the detection of protein-protein interactions in living cells of the fission yeast Schizosaccharomyces pombe, here we present an efficient and convenient method termed the Pil1 co-tethering assay. In its basic form, we tether a bait protein to mCherry-tagged Pil1, which forms cortical filamentary structures, and examine whether a GFP-tagged prey protein colocalizes with the bait. We demonstrate that this assay is capable of detecting pairwise protein-protein interactions of cytosolic proteins and nuclear proteins. Furthermore, we show that this assay can be used for detecting not only binary protein-protein interactions, but also ternary and quaternary protein-protein interactions. Using this assay, we systematically characterized the protein-protein interactions in the Atg1 complex and in the phosphatidylinositol 3-kinase (PtdIns3K) complexes and found that Atg38 is incorporated into the PtdIns3K complex I via an Atg38-Vps34 interaction. Our data show that this assay is a useful and versatile tool and should be added to the routine toolbox of fission yeast researchers.

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