Transcriptome and Proteome Research in Veterinary Science: What Is Possible and What Questions Can Be Asked?

The Scientific World JOURNAL ◽

10.1100/2012/254962 ◽

2012 ◽

Vol 2012 ◽

pp. 1-14 ◽

Cited By ~ 12

Author(s):

Robert Klopfleisch ◽

Achim D. Gruber

Keyword(s):

Sequential Analysis ◽

Biomarker Discovery ◽

Expression Patterns ◽

Proteome Analysis ◽

Shotgun Proteomics ◽

Complete Analysis ◽

Biological Research ◽

Veterinary Research ◽

Gene Sets ◽

2D Gel

In recent years several technologies for the complete analysis of the transcriptome and proteome have reached a technological level which allows their routine application as scientific tools. The principle of these methods is the identification and quantification of up to ten thousands of RNA and proteins species in a tissue, in contrast to the sequential analysis of conventional methods such as PCR and Western blotting. Due to their technical progress transcriptome and proteome analyses are becoming increasingly relevant in all fields of biological research. They are mainly used for the explorative identification of disease associated complex gene expression patterns and thereby set the stage for hypothesis-driven studies. This review gives an overview on the methods currently available for transcriptome analysis, that is, microarrays, Ref-Seq, quantitative PCR arrays and discusses their potentials and limitations. Second, the most powerful current approaches to proteome analysis are introduced, that is, 2D-gel electrophoresis, shotgun proteomics, MudPIT and the diverse technological concepts are reviewed. Finally, experimental strategies for biomarker discovery, experimental settings for the identification of prognostic gene sets and explorative versus hypothesis driven approaches for the elucidation of diseases associated genes and molecular pathways are described and their potential for studies in veterinary research is highlighted.

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NoRCE: non-coding RNA sets cis enrichment tool

BMC Bioinformatics ◽

10.1186/s12859-021-04112-9 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Gulden Olgun ◽

Afshan Nabi ◽

Oznur Tastan

Keyword(s):

Expression Patterns ◽

Target Prediction ◽

Enrichment Analysis ◽

Fruit Fly ◽

Relevant Information ◽

R Package ◽

Data Repository ◽

Biologically Relevant ◽

Gene Sets ◽

Data Files

Abstract Background While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint at a functional association. Results We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. Conclusions NoRCE is a platform-independent, user-friendly, comprehensive R package that can be used to gain insight into the functional importance of a list of ncRNAs of any type. The tool offers flexibility to conduct the users’ preferred set of analyses by designing their own pipeline of analysis. NoRCE is available in Bioconductor and https://github.com/guldenolgun/NoRCE.

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Proteomics in Diagnosis of Prostate Cancer/ Протеомика Во Дијагноза На Простатниот Карцином

PRILOZI ◽

10.1515/prilozi-2015-0027 ◽

2015 ◽

Vol 36 (1) ◽

pp. 5-36 ◽

Cited By ~ 3

Author(s):

Katarina Davalieva ◽

Momir Polenakovic

Keyword(s):

Prostate Cancer ◽

Tumor Tissue ◽

Biomarker Discovery ◽

Molecular Level ◽

Specific Antigen ◽

Shotgun Proteomics ◽

Comparative Proteomics ◽

Label Free ◽

The Past ◽

Proteomics Research

Abstract Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men worldwide. The introduction of prostate specific antigen (PSA) has greatly increased the number of men diagnosed with PCa but at the same time, as a result of the low specificity, led to overdiagnosis, resulting to unnecessary biopsies and high medical cost treatments. The primary goal in PCa research today is to find a biomarker or biomarker set for clear and effecttive diagnosis of PCa as well as for distinction between aggressive and indolent cancers. Different proteomic technologies such as 2-D PAGE, 2-D DIGE, MALDI MS profiling, shotgun proteomics with label-based (ICAT, iTRAQ) and label-free (SWATH) quantification, MudPIT, CE-MS have been applied to the study of PCa in the past 15 years. Various biological samples, including tumor tissue, serum, plasma, urine, seminal plasma, prostatic secretions and prostatic-derived exosomes were analyzed with the aim of identifying diagnostic and prognostic biomarkers and developing a deeper understanding of the disease at the molecular level. This review is focused on the overall analysis of expression proteomics studies in the PCa field investigating all types of human samples in the search for diagnostics biomarkers. Emphasis is given on proteomics platforms used in biomarker discovery and characterization, explored sources for PCa biomarkers, proposed candidate biomarkers by comparative proteomics studies and the possible future clinical application of those candidate biomarkers in PCa screening and diagnosis. In addition, we review the specificity of the putative markers and existing challenges in the proteomics research of PCa.

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Application of microRNA Database Mining in Biomarker Discovery and Identification of Therapeutic Targets for Complex Disease

Methods and Protocols ◽

10.3390/mps4010005 ◽

2020 ◽

Vol 4 (1) ◽

pp. 5

Author(s):

Jennifer L. Major ◽

Rushita A. Bagchi ◽

Julie Pires da Silva

Keyword(s):

Complex Disease ◽

Biomarker Discovery ◽

Ischemia Reperfusion Injury ◽

Ischemia Reperfusion ◽

Expression Patterns ◽

Therapeutic Targets ◽

Human Diseases ◽

Mirna Expression Data ◽

Artery Disease ◽

Underlying Mechanisms

Over the past two decades, it has become increasingly evident that microRNAs (miRNA) play a major role in human diseases such as cancer and cardiovascular diseases. Moreover, their easy detection in circulation has made them a tantalizing target for biomarkers of disease. This surge in interest has led to the accumulation of a vast amount of miRNA expression data, prediction tools, and repositories. We used the Human microRNA Disease Database (HMDD) to discover miRNAs which shared expression patterns in the related diseases of ischemia/reperfusion injury, coronary artery disease, stroke, and obesity as a model to identify miRNA candidates for biomarker and/or therapeutic intervention in complex human diseases. Our analysis identified a single miRNA, hsa-miR-21, which was casually linked to all four pathologies, and numerous others which have been detected in the circulation in more than one of the diseases. Target analysis revealed that hsa-miR-21 can regulate a number of genes related to inflammation and cell growth/death which are major underlying mechanisms of these related diseases. Our study demonstrates a model for researchers to use HMDD in combination with gene analysis tools to identify miRNAs which could serve as biomarkers and/or therapeutic targets of complex human diseases.

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2D-DIGE A Powerful Tool for Proteome Analysis

Protocols used in Molecular Biology ◽

10.2174/9789811439315120010010 ◽

2020 ◽

pp. 67-73

Keyword(s):

Protein Expression ◽

Gel Electrophoresis ◽

Proteome Analysis ◽

Great Sensitivity ◽

Two Dimensional Gel Electrophoresis ◽

2D Dige ◽

Protein Sample ◽

Different Types ◽

Complex Protein ◽

2D Gel

In the recent past, two dimensional gel electrophoresis has emerged as a powerful molecular biology tool for the comparative expression profiling of complex protein sample. It involves the separation as well as the resolution of diverse proteins sample on the basis of isoelectric points and molecular mass of protein in two dimension ways. In this way, it reflects the view of overall proteome status including differentiation in protein expression levels, post-translational modifications etc. Moreover, this allows the identification of novel biological signatures, which may give a particular identity of pathological background to cells or tissues associated with various types of cancers and neurological disorders. Therefore, by utilizing such tools, one can clearly investigate and compare the effects of particular drugs on cells of tissues and also one can analyze the effects of disease on the basis of variations in protein expression profile at broad spectrum. Recently, to get more error-less and accurate proteome profile, conventional 2-D gel electrophoresis has been enhanced with the inclusion of different types of protein labeling dyes which enables a more comparative analysis of diverse protein sample in a single 2-D gel. In this advanced technique (2-D-DIGE), protein samples are labeled with three different types of CyDyes (Cy2, Cy3, and Cy5) separately and combined and further resolved on the same gel. This will facilitate the more accurate spot matching on a single gel platform and will also minimize the experimental variations as commonly reported in the conventional 2D-gel electrophoresis. Therefore, in the present proteomic research era, 2D-DIGE has proved to be an extremely powerful tool with great sensitivity for up to 125 ng of proteins in clinical research volubility especially, neurological and cancer related disorders.

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Longitudinal linear combination test for gene set analysis

BMC Bioinformatics ◽

10.1186/s12859-019-3221-7 ◽

2019 ◽

Vol 20 (1) ◽

Author(s):

Elham Khodayari Moez ◽

Morteza Hajihosseini ◽

Jeffrey L. Andrews ◽

Irina Dinu

Keyword(s):

Linear Combination ◽

Pathway Analysis ◽

Time Course ◽

Expression Patterns ◽

Gene Sets ◽

Wide Range ◽

Combination Test ◽

Microarray Studies ◽

Study Designs ◽

Linear Combination Test

Abstract Background Although microarray studies have greatly contributed to recent genetic advances, lack of replication has been a continuing concern in this area. Complex study designs have the potential to address this concern, though they remain undervalued by investigators due to the lack of proper analysis methods. The primary challenge in the analysis of complex microarray study data is handling the correlation structure within data while also dealing with the combination of large number of genetic measurements and small number of subjects that are ubiquitous even in standard microarray studies. Motivated by the lack of available methods for analysis of repeatedly measured phenotypic or transcriptomic data, herein we develop a longitudinal linear combination test (LLCT). Results LLCT is a two-step method to analyze multiple longitudinal phenotypes when there is high dimensionality in response and/or explanatory variables. Alternating between calculating within-subjects and between-subjects variations in two steps, LLCT examines if the maximum possible correlation between a linear combination of the time trends and a linear combination of the predictors given by the gene expressions is statistically significant. A generalization of this method can handle family-based study designs when the subjects are not independent. This method is also applicable to time-course microarray, with the ability to identify gene sets that exhibit significantly different expression patterns over time. Based on the results from a simulation study, LLCT outperformed its alternative: pathway analysis via regression. LLCT was shown to be very powerful in the analysis of large gene sets even when the sample size is small. Conclusions This self-contained pathway analysis method is applicable to a wide range of longitudinal genomics, proteomics, metabolomics (OMICS) data, allows adjusting for potentially time-dependent covariates and works well with unbalanced and incomplete data. An important potential application of this method could be time-course linkage of OMICS, an attractive possibility for future genetic researchers. Availability: R package of LLCT is available at: https://github.com/its-likeli-jeff/LLCT

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Maintenance of neural activities in torpid Rhinolophus ferrumequinum bats revealed by 2D gel-based proteome analysis

Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics ◽

10.1016/j.bbapap.2017.04.006 ◽

2017 ◽

Vol 1865 (8) ◽

pp. 1004-1019 ◽

Cited By ~ 3

Author(s):

Qiuyuan Yin ◽

Yijian Zhang ◽

Dong Dong ◽

Ming Lei ◽

Shuyi Zhang ◽

...

Keyword(s):

Proteome Analysis ◽

Rhinolophus Ferrumequinum ◽

Neural Activities ◽

2D Gel

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Sequential analysis of transcript expression patterns improves survival prediction in multiple cancers

BMC Cancer ◽

10.1186/s12885-020-06756-x ◽

2020 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Jordan Mandel ◽

Raghunandan Avula ◽

Edward V. Prochownik

Keyword(s):

Sequential Analysis ◽

Expression Patterns ◽

Survival Prediction ◽

Transcript Expression ◽

Multiple Cancers

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Gel-Based Proteomics of Clinical Samples Identifies Potential Serological Biomarkers for Early Detection of Colorectal Cancer

International Journal of Molecular Sciences ◽

10.3390/ijms20236082 ◽

2019 ◽

Vol 20 (23) ◽

pp. 6082 ◽

Cited By ~ 3

Author(s):

Stine Thorsen ◽

Irina Gromova ◽

Ib Christensen ◽

Simon Fredriksson ◽

Claus Andersen ◽

...

Keyword(s):

Colorectal Cancer ◽

Early Detection ◽

Biomarker Discovery ◽

Real Life ◽

Clinical Samples ◽

Plasma Samples ◽

Healthy Individuals ◽

Protein Biomarkers ◽

2D Gel ◽

Cancer Diseases

The burden of colorectal cancer (CRC) is considerable—approximately 1.8 million people are diagnosed each year with CRC and of these about half will succumb to the disease. In the case of CRC, there is strong evidence that an early diagnosis leads to a better prognosis, with metastatic CRC having a 5-year survival that is only slightly greater than 10% compared with up to 90% for stage I CRC. Clearly, biomarkers for the early detection of CRC would have a major clinical impact. We implemented a coherent gel-based proteomics biomarker discovery platform for the identification of clinically useful biomarkers for the early detection of CRC. Potential protein biomarkers were identified by a 2D gel-based analysis of a cohort composed of 128 CRC and site-matched normal tissue biopsies. Potential biomarkers were prioritized and assays to quantitatively measure plasma expression of the candidate biomarkers were developed. Those biomarkers that fulfilled the preset criteria for technical validity were validated in a case-control set of plasma samples, including 70 patients with CRC, adenomas, or non-cancer diseases and healthy individuals in each group. We identified 63 consistently upregulated polypeptides (factor of four-fold or more) in our proteomics analysis. We selected 10 out of these 63 upregulated polypeptides, and established assays to measure the concentration of each one of the ten biomarkers in plasma samples. Biomarker levels were analyzed in plasma samples from healthy individuals, individuals with adenomas, CRC patients, and patients with non-cancer diseases and we identified one protein, tropomyosin 3 (Tpm3) that could discriminate CRC at a significant level (p = 0.0146). Our results suggest that at least one of the identified proteins, Tpm3, could be used as a biomarker in the early detection of CRC, and further studies should provide unequivocal evidence for the real-life clinical validity and usefulness of Tpm3.

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Codon Usage Optimization in the Prokaryotic Tree of Life: How Synonymous Codons Are Differentially Selected in Sequence Domains with Different Expression Levels and Degrees of Conservation

mBio ◽

10.1128/mbio.00766-20 ◽

2020 ◽

Vol 11 (4) ◽

Author(s):

José Luis López ◽

Mauricio Javier Lozano ◽

María Laura Fabre ◽

Antonio Lagares

Keyword(s):

Codon Usage ◽

Expression Patterns ◽

Relative Weight ◽

Tree Of Life ◽

Mutational Bias ◽

Expression Levels ◽

Gene Sets ◽

Wide Range ◽

Highly Expressed Genes ◽

Gc Contents

ABSTRACT Prokaryote genomes exhibit a wide range of GC contents and codon usages, both resulting from an interaction between mutational bias and natural selection. In order to investigate the basis underlying specific codon changes, we performed a comprehensive analysis of 29 different prokaryote families. The analysis of core gene sets with increasing ancestries in each family lineage revealed that the codon usages became progressively more adapted to the tRNA pools. While, as previously reported, highly expressed genes presented the most optimized codon usage, the singletons contained the less selectively favored codons. The results showed that usually codons with the highest translational adaptation were preferentially enriched. In agreement with previous reports, a C bias in 2- to 3-fold pyrimidine-ending codons, and a U bias in 4-fold codons occurred in all families, irrespective of the global genomic GC content. Furthermore, the U biases suggested that U3-mRNA–U34-tRNA interactions were responsible for a prominent codon optimization in both the most ancestral core and the highly expressed genes. A comparative analysis of sequences that encode conserved (cr) or variable (vr) translated products, with each one being under high (HEP) and low (LEP) expression levels, demonstrated that the efficiency was more relevant (by a factor of 2) than accuracy to modeling codon usage. Finally, analysis of the third position of codons (GC3) revealed that in genomes with global GC contents higher than 35 to 40%, selection favored a GC3 increase, whereas in genomes with very low GC contents, a decrease in GC3 occurred. A comprehensive final model is presented in which all patterns of codon usage variations are condensed in four distinct behavioral groups. IMPORTANCE The prokaryotic genomes—the current heritage of the most ancient life forms on earth—are comprised of diverse gene sets, all characterized by varied origins, ancestries, and spatial-temporal expression patterns. Such genetic diversity has for a long time raised the question of how cells shape their coding strategies to optimize protein demands (i.e., product abundance) and accuracy (i.e., translation fidelity) through the use of the same genetic code in genomes with GC contents that range from less than 20 to more than 80%. Here, we present evidence on how codon usage is adjusted in the prokaryotic tree of life and on how specific biases have operated to improve translation. Through the use of proteome data, we characterized conserved and variable sequence domains in genes of either high or low expression level and quantitated the relative weight of efficiency and accuracy—as well as their interaction—in shaping codon usage in prokaryotes.

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