Genomics and Proteomic Techniques.

Although general anesthesia induced by inhaled anesthetics produces definitive phenotypes (e.g., loss of mobility, amnesia, analgesia), the underlying targets of these drugs are still not clear. Genomics and proteomic techniques are discussed for measurement of global transcriptional and translational changes after inhaled anesthetic exposures. The current discussion focuses primarily on the genomic and proteomic technical methodology. We also include a discussion of network and pathway analyses for data interpretation after identification of the targets.

Integrated Venomics and Venom Gland Transcriptome Analysis of Juvenile and Adult Mexican Rattlesnakes Crotalus simus, C. tzabcan, and C. culminatus Revealed miRNA-modulated Ontogenetic Shifts.

Adult rattlesnakes within genus Crotalus express one of two distinct venom phenotypes, type I (hemorrhagic) and type II (neurotoxic). In Costa Rican Central American rattlesnake, ontogenetic changes in the concentration of miRNAs modulate venom type II to type I transition. Venomics and venom gland transcriptome analyses showed that adult C. simus and C. tzabcan expressed intermediate patterns between type II and type I venoms, whereas C. culminatus had a canonical type I venom. Neonate/juvenile and adult Mexican rattlesnakes showed notable inter- and intraspecific variability in the number, type, abundance and ontogenetic shifts of the transcriptional and translational venom gland activities. These results support a role for miRNAs in the ontogenetic venom compositional changes in the three congeneric Mexican rattlesnakes. It is worth noting the finding of dual-action miRNAs, which silence the translation of neurotoxic heterodimeric PLA2 crotoxin and acidic PLA2 mRNAs while simultaneously up-regulating SVMP-targeting mRNAs. Dual transcriptional regulation potentially explains the existence of mutually exclusive crotoxin-rich (type-II) and SVMP-rich (type-I) venom phenotypic dichotomy among rattlesnakes. Our results support the hypothesis that alterations of the distribution of miRNAs, modulating the translational activity of venom gland toxin-encoding mRNAs in response to an external cue, may contribute to the mechanism generating adaptive venom variability.

Proteogenomic Tools and Approaches to Explore Protein Coding Landscapes of Eukaryotic Genomes.

Proteogenomic strategies aim to refine genome-wide annotations of protein coding features by using actual protein level observations. Most of the currently applied proteogenomic approaches include integrative analysis of multiple types of high-throughput omics data, e.g., genomics, transcriptomics, proteomics, etc. Recent efforts towards creating a human proteome map were primarily targeted to experimentally detect at least one protein product for each gene in the genome and extensively utilized proteogenomic approaches. The 14 year long wait to get a draft human proteome map, after completion of similar efforts to sequence the genome, explains the huge complexity and technical hurdles of such efforts. Further, the integrative analysis of large-scale multi-omics datasets inherent to these studies becomes a major bottleneck to their success. However, recent developments of various analysis tools and pipelines dedicated to proteogenomics reduce both the time and complexity of such analysis. Here, we summarize notable approaches, studies, software developments and their potential applications towards eukaryotic genome annotation and clinical proteogenomics.

Proteogenomics: Key Driver for Clinical Discovery and Personalized Medicine.

Proteogenomics is a multi-omics research field that has the aim to efficiently integrate genomics, transcriptomics and proteomics. With this approach it is possible to identify new patient-specific proteoforms that may have implications in disease development, specifically in cancer. Understanding the impact of a large number of mutations detected at the genomics level is needed to assess the effects at the proteome level. Proteogenomics data integration would help in identifying molecular changes that are persistent across multiple molecular layers and enable better interpretation of molecular mechanisms of disease, such as the causal relationship between single nucleotide polymorphisms (SNPs) and the expression of transcripts and translation of proteins compared to mainstream proteomics approaches. Identifying patient-specific protein forms and getting a better picture of molecular mechanisms of disease opens the avenue for precision and personalized medicine. Proteogenomics is, however, a challenging interdisciplinary science that requires the understanding of sample preparation, data acquisition and processing for genomics, transcriptomics and proteomics. This chapter aims to guide the reader through the technology and bioinformatics aspects of these multi-omics approaches, illustrated with proteogenomics applications having clinical or biological relevance.

Next Generation Sequencing Data and Proteogenomics.

The field of proteogenomics has been driven by combined advances in next-generation sequencing (NGS) and proteomic methods. NGS technologies are now both rapid and affordable, making it feasible to include sequencing in the clinic and academic research setting. Alongside the improvements in sequencing technologies, methods in high throughput proteomics have increased the depth of coverage and the speed of analysis. The integration of these data types using continuously evolving bioinformatics methods allows for improvements in gene and protein annotation, and a more comprehensive understanding of biological systems.

Using Proteomics Bioinformatics Tools and Resources in Proteogenomic Studies.

Proteogenomic studies ally the omic fields related to gene expression into a combined approach to improve the characterization of biological samples. Part of this consists in mining proteomics datasets for non-canonical sequences of amino acids. These include intergenic peptides, products of mutations, or of RNA editing events hypothesized from genomic, epigenomic, or transcriptomic data. This approach poses new challenges for standard peptide identification workflows. In this chapter, we present the principles behind the use of peptide identification algorithms and highlight the major pitfalls of their application to proteogenomic studies.

Mutant Proteogenomics.

Identification of mutant proteins in biological samples is one of the emerging areas of proteogenomics. Despite the fact that only a limited number of studies have been published up to now, it has the potential to recognize novel disease biomarkers that have unique structure and desirably high specificity. Such properties would identify mutant proteoforms related to diseases as optimal drug targets useful for future therapeutic strategies. While mass spectrometry has demonstrated its outstanding analytical power in proteomics, the most frequently applied bottom-up strategy is not suitable for the detection of mutant proteins if only databases with consensus sequences are searched. It is likely that many unassigned tandem mass spectra of tryptic peptides originate from single amino acid variants (SAAVs). To address this problem, a couple of protein databases have been constructed that include canonical and SAAV sequences, allowing for the observation of mutant proteoforms in mass spectral data for the first time. Since the resulting large search space may compromise the probability of identifications, a novel concept was proposed that included identification as well as verification strategies. Together with transcriptome based approaches, targeted proteomics appears to be a suitable method for the verification of initial identifications in databases and can also provide quantitative insights to expression profiles, which often reflect disease progression. Important applications in the field of mutant proteoform identification have already highlighted novel biomarkers in large-scale investigations.

Proteogenomics for the Comprehensive Analysis of Human Cellular and Serum Antibody Repertoires.

The vast repertoire of immunoglobulins produced by the immune system is a consequence of the huge amount of antigens to which we are exposed every day. The diversity of these immunoglobulins is due to different mechanisms (including VDJ recombination, somatic hypermutation, and antigen selection). Understanding how the immune system is capable of generating this diversity and which are the molecular bases of the composition of immunoglobulins are key challenges in the immunological field. During the last decades, several techniques have emerged as promising strategies to achieve these goals, but it is their combination which appears to be the fruitful solution for increasing the knowledge about human cellular and serum antibody repertoires.In this chapter, we address the diverse strategies focused on the analysis of immunoglobulin repertoires as well as the characterization of the genomic and peptide sequences. Moreover, the advantages of combining various -omics approaches are discussed through review different published studies, showing the benefits in clinical areas.

Precision medicine: what's all the fuss about?

Precision medicine is now recognized globally as a major new era in medicine. It is being driven by advances in genomics and other 'omics' but also by the desire on the part of both health systems and governments to offer more targeted and cost-effective care. However, it faces a number of challenges, from the economics of developing more expensive companion diagnostics to the need to educate patients and the public on the advantages for them. New models of both R&D and care delivery are needed to capture the scientific, clinical and economic benefits of precision medicine.