Search results for “Proteomics”

Discovery and Quantification in Mass Spectrometry-Based Proteomics

Mass spectrometry (MS) has been successfully used to analyze biological samples and advances of MS-based approaches have turn MS data from largely qualitative to quantitative. These MS-based quantitative approaches using label-free, tags, or stable isotope labeling have their own strengths and limitations. The variability introduced by different methods prior to quantitative mass spectrometry should be considered, and accuracy and precision of MS measurements can also vary depending on the strategy used for MS quantification. Therefore, the development of methods for accurate protein quantitation is one of the most challenging areas of proteomics. Using these quantitative approaches, one can investigate the dynamics of proteome through differential protein expression in normal biological processes and diseases.

Proteome and Proteomics: from Single Protein to Whole Body

A primer explains how proteomics scales from single‑protein analysis to whole‑organism studies, touching on technologies, bioinformatics, and applications.

Modern Proteomics: Methods and Applications – Special Issue

Determination of the Proteomic Response to Lapatinib Treatment using a Comprehensive and Reproducible Ion-Current-Based Proteomics Strategy

Lapatinib, a small molecule tyrosine kinase inhibitor is currently used in the treatment of HER2-positive breast cancer. The aim of this study was to further understanding of lapatinib response for the development of novel treatment lapatinib-focussed treatment strategies. HER2-overexpressing SKBR3 breast cancer cells were treated with lapatinib for 12 hours and the resultant proteome analyzed by a comprehensive ion-current-based LC-MS strategy. Among the 1224 unique protein identified from SKBR3 cell lysates, 67 showed a significant change in protein abundance in response to lapatinib. Of these, CENPE a centromeric protein with increased abundance, was chosen for further validation. Knockdown and inhibition of CENPE demonstrated that CENPE enhances SKBR3 cell survival in the presence of lapatinib. Based on this study, CENPE inhibitors may warrant further investigation for use in combination with lapatinib.

Quantitative Proteomics Using 15N SILAC Mouse

In biomedical research the use of mammalian tissues is crucial to increase our understanding of complex human diseases. Mass spectrometry-based proteomic approach has become the most powerful tool of studying large-scale protein expression profiles in mammalian tissues. To perform global proteome analysis quantification of mammalian tissues, we generated 15N SILAC mice to obtain tissue-matched labeled peptide libraries for mass spectrometry-based quantitative proteomic analysis. We developed a new labeling protocol to circumvent adverse effects of introducing 15N labeled diet to mice, and showed that the new labeling scheme has no significant effect on the fertility and reproduction of C57/BL6 mice. Using labeled tissues from these mice, we compared the reproducibility of mass spectrometry-based quantification with or without 15N labeled internal standards among biological replicates of young and old brains. We found that labeled-based quantification is less susceptible to variations from instrument conditions and produces more consistent quantifications among biological replicates than label-free quantification. Lastly, we showed that over 60% of peptides from the human brain are quantifiable with internal standards from 15N labeled mouse brain and therefore present a promising alternative of quantifying human tissues that do not have existing cell lines available for SILAC labeling.

Editorial for Journal of Proteomics and Genomics Research: Second Issue

An editorial for the second issue highlights notable contributions and reaffirms peer review and openness as pillars for the field.

Editorial for Journal of Proteomics and Genomics Research

An editorial announcing the journal’s launch sets expectations for data standards, reproducibility, and integrative omics studies with clinical relevance.

Proteomic and Genomic Techniques in Medical Research: Applications in Cancer, Diagnostics, and Personalized Medicine

Advancements in proteomic and genomic technologies have transformed molecular biology by enabling comprehensive analysis of biological systems at the molecular level. This literature review explores the evolution, methodologies, and practical applications of key proteomic and genomic techniques. In proteomics, tools such as two-dimensional electrophoresis, mass spectrometry, Western blotting, Edman degradation, and functional protein microarrays have facilitated high-throughput protein identification, post-translational modification analysis, and biomarker discovery. Similarly, genomic methodologies like PCR, recombinant DNA technology, gel electrophoresis, and Southern blotting have revolutionized gene detection, manipulation, and expression profiling. The review also highlights the interdisciplinary impact of these technologies across clinical diagnostics, oncology, autoimmune disorders, infectious disease surveillance, cardiovascular research, and personalized nutrition. Integrative approaches combining proteomics and genomics are enabling the discovery of novel therapeutic targets, improving disease classification, and advancing precision medicine. Despite current limitations, such as the absence of amplification techniques for proteins and challenges in data interpretation, ongoing innovations promise to bridge these gaps. This synthesis underscores the pivotal role of molecular techniques in deepening our understanding of human biology and accelerating biomedical advancements for improved healthcare outcomes.

Plasma TREM2 Levels, Alcohol Consumption, and Liver Enzymes in Patients with Alcohol use Disorder: A Sex-Dependent Relationship Involving MS4A6A Genetic Polymorphism

Alcohol use disorder (AUD) is the most prevalent substance use disorder. Excessive alcohol consumption leads to a range of health issues. We set out to identify inflammatory markers linked to alcohol consumption, which might ultimately offer novel insight into genetic underpinnings and have implications for alcohol-associated disease. Alcohol consumption and blood-based multi-omics data were collected by The Mayo Clinic Center for Individualized Treatment of Alcohol Dependence study. Plasma samples from patients with AUD were used for proteomics analysis using the OLINK “Explore Inflammation” panel (n=410). Liver enzymes were also measured. A genome-wide association study (GWAS) was performed to explore the relationship between genetic variants and plasma TREM2 levels. Our findings show thatplasma triggering receptor expressed on myeloid cells 2 (TREM2), a key gene associated with neurodegenerative disease, was the most significant signal correlated with alcohol consumption, and has also been associated with liver enzyme levels in patients with AUD. We identified the rs7232 single nucleotide polymorphism (SNP) in MS4A6A as a key genetic variant associated with plasma TREM2 levels, with the minor allele (A) linked to higher TREM2 levels and increased alcohol consumption, particularly in men. Furthermore, MA4A6A is an ethanol-responsive gene in a SNP-dependent manner, and the variant genotype of the rs7232 SNP was associated with lower expression for MA4A6A due to proteasome-mediated protein degradation. In summary, this study provides insight into the relationship between plasma TREM2 levels, alcohol consumption, and liver function in AUD patients, shedding light on genetic factors underlying alcohol-related diseases.

The Emerging Role of Bioinformatics in Biotechnology

Bioinformatic tools is widely used to manage the enormous genomic and proteomic data involving DNA/protein sequences management, drug designing, homology modelling, motif/domain prediction ,docking, annotation and dynamic simulation etc. Bioinformatics offers a wide range of applications in numerous disciplines such as genomics. Proteomics, comparative genomics, nutrigenomics, microbial genome, biodefense, forensics etc. Thus it offers promising future to accelerate scientific research in biotechnology

Ovarian Cancer Identification Based on Feature Weighting for High-Throughput Mass Spectrometry Data

An important use of proteomics data from Mass Spectrometry (MS) is the classification of tumor types with respect to peptides in specific cancer types. It is highly critical to find an optimal set of markers among specific cancer peptides whose expression can be clinically utilized to build assays for the diagnosis or to track the progression of specific cancer types. A number of feature selection algorithms have been proposed to obtain the classification of MS data. In this article, we proposed an improved feature selection algorithm based on feature weighting. Relief algorithm can calculate the weight of different features according to the correlation between their characteristics and categories. F-score is a simple filter-based feature selection method by evaluating how two sets of real numbers discriminate from each other. The main goal of this paper is to introduce a new feature weighting selection algorithm combining score from f-value and weight from relief, which is more accurate when classifying high-resolution MALDI-TOF (matrix-assisted laser desorption and ionization time-of-flight) MS data. We have developed a four-step strategy for data processing based on: (1) Align the study sets by binning of raw MS data, (2) local maximum search(LMS) peak detection, (3) a new combination feature weighting selection algorithm and (4) support vector machines achieve a satisfactory performance of identifying cancer and the healthy. The best parameter set for LMS were achieved with control variable method, which achieve an average accuracy of 97.4167% (sd = 0.0146) and the best accuracy of 98.6111% in 1000 independent 10 -fold cross validations.