Individualized proteomics

High-throughput proteomics: a methodological mini-review

Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other 'omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery.

Novel approaches to liver disease diagnosis and modeling

Lack of a prompt and accurate diagnosis remains on top of the list of challenges faced by patients with rare liver diseases. Although rare liver diseases affect a significant percentage of the population as a group, when taken singularly they represent unique diseases and the approaches used for diagnosis of common liver diseases are insufficient. However, the development of new methods for the acquisition of molecular and clinical data (i.e., genomic, proteomics, metabolomics) and computational tools for their analysis and integration, together with advances in modeling diseases using stem cell-based technology [i.e., induced pluripotent stem cells (iPSCs) and tissue organoids] represent a promising and powerful tool to improve the clinical management of these patients. This is the goal of precision medicine, a novel approach of modern medicine that aims at delivering a specific treatment based on disease-specific biological insights and individual profile. This review will discuss the application and advances of these technologies and how they represent a new opportunity in hepatology.

Emerging Diagnostic and Therapeutic Potentials of Human Hair Proteomics

The use of noninvasive human substrates to interrogate pathophysiological conditions has become essential in the post- Human Genome Project era. Due to its high turnover rate, and its long term capability to incorporate exogenous and endogenous substances from the circulation, hair testing is emerging as a key player in monitoring long term drug compliance, chronic alcohol abuse, forensic toxicology, and biomarker discovery, among other things. Novel high-throughput 'omics based approaches like proteomics have been underutilized globally in comprehending human hair morphology and its evolving use as a diagnostic testing substrate in the era of precision medicine. There is paucity of scientific evidence that evaluates the difference in drug incorporation into hair based on lipid content, and very few studies have addressed hair growth rates, hair forms, and the biological consequences of hair grooming or bleaching. It is apparent that protein-based identification using the human hair proteome would play a major role in understanding these parameters akin to DNA single nucleotide polymorphism profiling, up to single amino acid polymorphism resolution. Hence, this work seeks to identify and discuss the progress made thus far in the field of molecular hair testing using proteomic approaches, and identify ways in which proteomics would improve the field of hair research, considering that the human hair is mostly composed of proteins. Gaps in hair proteomics research are identified and the potential of hair proteomics in establishing a historic medical repository of normal and disease-specific proteome is also discussed.

Utilização de biomarcadores de genotoxicidade e expressão gênica na avaliação de trabalhadores de postos de combustíveis expostos a vapores de gasolina

Resumo Introdução: a avaliação de uma exposição mensura sua intensidade, frequência e duração, podendo detectar danos precoces que, se ignorados, podem evoluir para um quadro nocivo. Nos campos da saúde ambiental e ocupacional, os biomarcadores de genotoxicidade tem sido largamente utilizados para essa avaliação. Objetivo: identificar, descrever e discutir os principais bioindicadores de genotoxicidade e seu uso conjunto com técnicas de avaliação de expressão gênica em estudos de exposição ocupacional ao benzeno em postos de revenda de combustíveis (PRC). Métodos: revisão bibliográfica de trabalhos publicados entre 1995 e 2015. Resultados: as técnicas identificadas foram: ensaio cometa, estresse oxidativo, micronúcleos, aberrações cromossômicas, polimorfismos, adutos de DNA e proteínas, fatores epigenéticos e expressão gênica. Foi observado que testes de danos genéticos e epigenéticos são utilizados em frentistas de PRC que participam de programas de saúde do trabalhador ou de pesquisas, embora um baixo número de publicações sobre o tema tenha sido identificado. Esse fato talvez possa ser explicado pelos poucos países onde a profissão persiste e pelas limitações para o desenvolvimento de pesquisas nesses países. Conclusão: os bioindicadores de genotoxicidade e as técnicas de expressão gênica são úteis na detecção de dano precoce desta exposição ocupacional e devem ser avaliados em conjunto.

Forward Individualized Medicine from Personal Genomes to Interactomes

When considering the variation in the genome, transcriptome, proteome and metabolome, and their interaction with the environment, every individual can be rightfully considered as a unique biological entity. Individualized medicine promises to take this uniqueness into account to optimize disease treatment and thereby improve health benefits for every patient. The success of individualized medicine relies on a precise understanding of the genotype-phenotype relationship. Although omics technologies advance rapidly, there are several challenges that need to be overcome: Next generation sequencing can efficiently decipher genomic sequences, epigenetic changes, and transcriptomic variation in patients, but it does not automatically indicate how or whether the identified variation will cause pathological changes. This is likely due to the inability to account for (1) the consequences of gene-gene and gene-environment interactions, and (2) (post)transcriptional as well as (post)translational processes that eventually determine the concentration of key metabolites. The technologies to accurately measure changes in these latter layers are still under development, and such measurements in humans are also mainly restricted to blood and circulating cells. Despite these challenges, it is already possible to track dynamic changes in the human interactome in healthy and diseased states by using the integration of multi-omics data. In this review, we evaluate the potential value of current major bioinformatics and systems biology-based approaches, including genome wide association studies, epigenetics, gene regulatory and protein-protein interaction networks, and genome-scale metabolic modeling. Moreover, we address the question whether integrative analysis of personal multi-omics data will help understanding of personal genotype-phenotype relationships.