Search results for “Proteomic”

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.

Effect of Drought and Salt Stress on Cereal Crop Plants and their Proteomic and Physiological Studies

The photosynthetic potential and underlying internal metabolism of a plant are some of the most commonly affected physiological functions as a direct consequence of stresses due to salt and water resulting in hindering plant growth and productivity. Under the influence of such detrimental stresses, a drastic alteration in a plant's osmotic requirements, hormonal production, shedding of leaves, and closure of stomata, along with a lessening in the diffusion and transportation of CO2 and H2O are commonly seen. This review unfolds with a description of the basic methodology involved in the proteomic analysis of various proteins involved in stress response along with a brief idea on identifying and obtaining a genomic sequence for proteomic studies. It then dives deep into understanding the impact of abiotic stresses such as salinity, drought and high temperatures on cereal crops such as rice and sorghum as well as the internal dynamics of tolerance mechanism unfolding during stresses have also been described. Extensive literature describing the proteomic and physiological responses to primary and secondary effects of salt stress in cereal crops emphasizing on ROS production and apoptosis, the role of osmolytes as ROS scavengers during osmotic stress and vacuolar antiporters in ionic stress along with the responses during drought stress such as the accumulation of LEA proteins and ABA-based signaling has been reviewed and critically discussed. The study also sheds light on some experimental proteomic studies conducted on the seedlings, root tissues, and shoots of rice cultivars.

Shotgun Label-Free Proteomic Analyses of the Oyster Parasite Perkinsus Marinus

Perkinsus marinus is an intracellular parasitic protozoan that is responsible for serious disease epizootics in marine bivalve mollusks worldwide. Despite all available information on P. marinus genomics, more baseline data is required at the proteomic level. Our aim was to study the proteome profile of in vitro cultured P. marinus isolated from oysters Crassostrea spp. using a label-free shotgun UDMSE approach. A total of 4073 non-redundant proteins were identified across three biological replicates with stringent identification. Proteins specifically related to adaptive survival, cell recognition, antioxidants, regulation of apoptosis and others were detected. Important virulence factors of P. marinus were identified including serine protease and iron-dependent superoxide dismutase. Other proteins with involvement in several pathogens invasion strategies were rhoptries, serine-threonine kinases, and protein phosphatases. Interestingly, peptides corresponding to retroviruses polyproteins were identified in all replicates. The interactomic analysis of P. marinus proteins demonstrated extensive clusters network related to biological processes. In conclusion, we provide the first comprehensive proteomic profile of P. marinus that can be useful for further investigations on Perkinsus biology and virulence mechanisms.

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.

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.

Challenger and Propose Novel Methods and Techniques for Prevention, Prognosis, Diagnosis, Imaging, Screening, Treatment and Management of Lung Cancer

Using samples of small cell lung tumors, a research team led by biologist Dr. Raymond discovered two new ways to induce tumor cell death. By activating ferroptosis, one of two subtypes of tumor cells can be targeted: first, iron-dependent cell death due to oxidative stress, and second, oxidative stress. Therefore, cell death can also be induced in a different way. Both types of cell death must be caused by drugs at the same time to eliminate the majority of the tumor mass. It is currently in clinical trials for cancer treatment. Auranofin, which inhibits the production of protective antioxidants in cancer cells, has been used to treat rheumatoid arthritis for decades. Future clinical trials using this combination therapy will determine the extent to which this targeted treatment option improves the prognosis of small cell lung cancer patients. It is currently in clinical trials for cancer treatment. Lung cancer is the leading cause of cancer death in the United States. Despite evidence of molecular abnormalities in biological specimens, progress in this disease is hampered by the lack of diagnostic markers useful for clinical practice. The majority of patients with lung cancer are still diagnosed at an advanced stage, when prognosis is poor. This article reviews new strategies being studied for the early detection of lung cancer. These strategies involve new methods of imaging (including low-dose computed tomography CT scanning), DNA analysis, and proteomic-based techniques. These strategies have not only improved our understanding of lung cancer but show promise in offering better survival to patients with this deadly disease. Of paramount importance in the search for methods of early detection is the need for the identification of the ideal population to screen, a multidisciplinary approach, and validation of promising techniques.

Six Fractal Codes of Biological Life Unifying ATOMS, WAVES and INFORMATION: Perspectives in Exobiology, Cancers Basic Research and Artificial Intelligence Biomimetism Decisions Making

In this theoretical discovery of a law of Life, there is MATHEMATICS (Geometry, Bits and Numbers) that UNIFY 3 universes as complementary as ATOMIC MASS, WAVES, and INFORMATION (DNA, RNA and Amino Acids). The discovery of a simple numerical formula for the projection of all the atomic mass of life-sustaining CONHSP bioatoms leads to the emergence of a set of Nested CODES unifying all the biological, genetic and genomic components by unifying them from bioatoms up to 'to whole genomes. In particular, we demonstrate the existence of a digital meta-code common to the three languages ​​of biology that are RNA, DNA and amino acid sequences. Through this meta-code, genomic and proteomic images appear almost analogous and correlated. The analysis of the textures of these images then reveals a binary code as well as an undulatory code whose analysis on the human genome makes it possible to predict the alternating bands constituting the cariotypes of the chromosomes. The application of these codes to perspectives in astrobiology, cancer, and specifically in INFORMATION THEORY with the emergence of binary codes and regions of local stability (voting process), whose fractal nature we demonstrate, is illustrated. PREFACE by Professor Luc Montagnier Addendum by Robert Friedman M.D After the discovery of the DNA double helix structure allowing both the stable storage of genetic information and its transfer through messenger RNA to protein synthesis organelles themselves structured by RNA most abundant in cells, the ribosomal. This wonder of nature exists in ALL living beings from the virus to humans and is based on two codes, the linear sequence of nucleotides and that derived from codons where three nucleotides allow with a certain flexibility - synonymous codons - the choice in the twenty amino acids. But we are missing a third CODE the one governing at multicellular beings from the rotifer to human, the stabilized modulation of gene expression in a nutshell the differentiation of cells from the single cell of the fertilized egg. It is logical to think that this program which begins as soon as fertilization is written in the DNA. We are also prone to associate it with non-coding DNA sequences although they control gene expression. I introduce here the notion developed by Jean-Claude Pérez of mathematical harmony, a higher order present in all living beings and whose existence it finds in genomes, including those of viruses. Thus the natural evolution of variants of the genome of coronavirus Covid 19 tends towards increasingly long Fibonacci series. It remains to determine the Who, the How and the Why of such developments. I will bet with my mathematician colleague that waves and fractals play a role. Luc Montagnier ADDENDUM Jean-claude has given scientists a strong new direction for research. He has identified a unified field of science guided by the Golden Ratio and Fibonacci Sequence. By identifying an overall guiding principle that makes possible fractal-like nesting at all levels of biological manifestation, future researchers can begin with the "whole" instead of the "parts". If we know that complex systems are organized at varying levels by the Golden Ratio and Fibonacci Sequence, we can look for those universal patterns first and then fill in the gaps with small details to complete the picture. It's like having an overall view of a crossword puzzle before beginning to assemble the individual pieces. Without an overarching vision and guiding principle, completing the puzzle is infinitely more difficult. Once scientists and researchers realize and begin using this "SECRET IN HIDDEN IN PLAIN SIGHT," their discoveries will be orders of magnitude more fruitful.  Robert Friedman M.D

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

Human Proteome Project and Current Bioinformatics Status in Disease Diagnosis and Treatment

Human proteome project was revolutionized about 40 years ago with purpose of summarizing whole proteomic data at one place. It was launched after human genome project to map and observe all proteins. The goal related proteomic study is to draft the entire human proteome in disease diagnosis by using bioinformatics tools. Pillars of human proteome project provide different databases related to proteins at transcriptional and translational level. Human proteome organization(HUPO) published biology disease HUPO whose aim is to measure protein and proteome by life and processes related to human diseases. Different human organ like plasma, liver, brain and diabetic base project are used to characterize human disease and health. Major data resources accumulated in databases like peptides Atlas, GPMDB and neXtProt for proteins. Matrices of human proteome project identify and characterize the protein products as Post translational modification (PTM), splice various isoforms from 20,300 proteins. Matrices related to different years make proteomes counterpart by magnify the research biomedical community with high output of instruments and specimen pre-analytical protocols. CALIPHO multidisciplinary group provides information about protein complexities, interactions, function and structure complexities after Uniport and Swissprot. Different bioinformatics tools are used for structural and functional annotations of protein, disease diagnosis and mutations due to protein. Extensive study of human proteome project has been proved helpful in disease treatment at translational and post- translational levels. In future, human proteome project along with bioinformatics will include protein profiling, biomarkers, Mass spectrophotometer technique and cross analysis of different proteome projects.

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. 

Bioinformatic Analysis of Coronary Disease Associated SNPs and Genes to Identify Proteins Potentially Involved in the Pathogenesis of Atherosclerosis

Factors that contribute to the onset of atherosclerosis may be elucidated by bioinformatic techniques applied to multiple sources of genomic and proteomic data. The results of genome wide association studies, such as the CardioGramPlusC4D study, expression data, such as that available from expression quantitative trait loci (eQTL) databases, along with protein interaction and pathway data available in Ingenuity Pathway Analysis (IPA), constitute a substantial set of data amenable to bioinformatics analysis. This study used bioinformatic analyses of recent genome wide association data to identify a seed set of genes likely associated with atherosclerosis. The set was expanded to include protein interaction candidates to create a network of proteins possibly influencing the onset and progression of atherosclerosis. Local average connectivity (LAC), eigenvector centrality, and betweenness metrics were calculated for the interaction network to identify top gene and protein candidates for a better understanding of the atherosclerotic disease process. The top ranking genes included some known to be involved with cardiovascular disease (APOA1, APOA5, APOB, APOC1, APOC2, APOE, CDKN1A, CXCL12, SCARB1, SMARCA4 and TERT), and others that are less obvious and require further investigation (TP53, MYC, PPARG, YWHAQ, RB1, AR, ESR1, EGFR, UBC and YWHAZ). Collectively these data help define a more focused set of genes that likely play a pivotal role in the pathogenesis of atherosclerosis and are therefore natural targets for novel therapeutic interventions.

Bioinformatic Resources for Diabetic Nephropathy

The number of individuals with diabetes is increasing worldwide and a large subset of those affected will develop diabetic nephropathy. Diabetic nephropathy is the leading cause of end-stage renal disease, has serious health consequences for affected individuals, and represents a major monetary cost to healthcare providers. Technological and analytical developments have enabled large-scale, collaborative studies that are revealing risk factors associated with diabetic nephropathy. However, much of the inherited predisposition and biological mechanisms underpinning risk of this disease remain to be identified. Meta-analyses and integrated pathway studies are becoming an increasingly important part of research for diabetic nephropathy including, genetic, epigenetic, transcriptomic, proteomic research, clinical observations and the development of animal models. This report highlights current bioinformatic resources and standards of reporting to maximise interdisciplinary research for diabetic nephropathy. The identification of an -Omics profile that can lead to earlier diagnosis and / or offer improved clinical evaluation of individuals with diabetes would not only provide significant health benefits to affected individuals, but may also have major utility for the efficient use of healthcare resources.

Differences in the Alveolar Macrophage Proteome in Transgenic Mice Expressing Human SP-A1 and SP-A2

Surfactant protein A (SP-A) plays a number of roles in lung host defense and innate immunity. There are two human genes, SFTPA1 and SFTPA2, and evidence indicates that the function of SP-A1 and SP-A2 proteins differ in several respects. To investigate the impact of SP-A1 and SP-A2 on the alveolar macrophage (AM) phenotype, we generated humanized transgenic (hTG) mice on the SP-A knockout (KO) background, each expressing human SP-A1 or SP-A2. Using two-dimensional difference gel electrophoresis (2D-DIGE) we studied the AM cellular proteome. We compared mouse lines expressing high levels of SP-A1, high levels of SP-A2, low levels of SP-A1, and low levels of SP-A2, with wild type (WT) and SP-A KO mice. AM from mice expressing high levels of SP-A2 were the most similar to WT mice, particularly for proteins related to actin and the cytoskeleton, as well as proteins regulated by Nrf2. The expression patterns from mouse lines expressing higher levels of the transgenes were almost the inverse of one another – the most highly expressed proteins in SP-A2 exhibited the lowest levels in the SP-A1 mice and vice versa. The mouse lines where each expressed low levels of SP-A1 or SP-A2 transgene had very similar protein expression patterns suggesting that responses to low levels of SP-A are independent of SP-A genotype, whereas the responses to higher amounts of SP-A are genotype-dependent. Together these observations indicate that in vivo exposure to SP-A1 or SP-A2 differentially affects the proteomic expression of AMs, with SP-A2 being more similar to WT.