Ana Laura Guzmán-Ortiz

In recent years, the use of high-throughput omics technologies has led to the rapid discovery of many candidate biomarkers. However, few of them have made the transition to the clinic. In this review, the promise of omics technologies to contribute to the process of biomarker development is described. An overview of the current state in this area is presented with examples of genomics, proteomics, transcriptomics, metabolomics and microbiomics biomarkers in the field of oncology, along with some proposed strategies to accelerate their validation and translation to improve the care of patients with neoplasms. The inherent complexity underlying neoplasms combined with the requirement of developing well-designed biomarker discovery processes based on omics technologies present a challenge for the effective development of biomarkers that may be useful in guiding therapies, addressing disease risks, and predicting clinical outcomes. En los últimos años, el uso de las tecnologías ómicas de alta densidad de datos ha permitido el rápido descubrimiento de posibles biomarcadores. Sin embargo, esto no ha tenido un impacto notable en la clínica ya que se han implementado muy pocos de esos biomarcadores. En el presente documento se describe el potencial de las tecnologías ómicas en el desarrollo de nuevos biomarcadores. Con el objetivo de dar a conocer un panorama general de la situación actual, se comentan algunos ejemplos ilustrativos de biomarcadores genómicos, transcriptómicos, proteómicos, metabolómicos y microbiómicos en el campo de la investigación en oncología. Asimismo, se señalan algunas de las recomendaciones que se han propuesto para acelerar su validación e implementación, y se comenta sobre cómo la complejidad inherente a las enfermedades se combina con la complejidad de las tecnologías ómicas, de tal modo que el desarrollo de biomarcadores predictivos, pronósticos y diagnósticos eficientes plantea retos importantes.

In recent years, the use of high-throughput omics technologies has led to the rapid discovery of many candidate biomarkers. However, few of them have made the transition to the clinic. In this review, the promise of omics technologies to contribute to the process of biomarker development is described. An overview of the current state in this area is presented with examples of genomics, proteomics, transcriptomics, metabolomics and microbiomics biomarkers in the field of oncology, along with some proposed strategies to accelerate their validation and translation to improve the care of patients with neoplasms. The inherent complexity underlying neoplasms combined with the requirement of developing well-designed biomarker discovery processes based on omics technologies present a challenge for the effective development of biomarkers that may be useful in guiding therapies, addressing disease risks, and predicting clinical outcomes. En los últimos años, el uso de las tecnologías ómicas de alta densidad de datos ha permitido el rápido descubrimiento de posibles biomarcadores. Sin embargo, esto no ha tenido un impacto notable en la clínica ya que se han implementado muy pocos de esos biomarcadores. En el presente documento se describe el potencial de las tecnologías ómicas en el desarrollo de nuevos biomarcadores. Con el objetivo de dar a conocer un panorama general de la situación actual, se comentan algunos ejemplos ilustrativos de biomarcadores genómicos, transcriptómicos, proteómicos, metabolómicos y microbiómicos en el campo de la investigación en oncología. Asimismo, se señalan algunas de las recomendaciones que se han propuesto para acelerar su validación e implementación, y se comenta sobre cómo la complejidad inherente a las enfermedades se combina con la complejidad de las tecnologías ómicas, de tal modo que el desarrollo de biomarcadores predictivos, pronósticos y diagnósticos eficientes plantea retos importantes.

The genome of the SARS-CoV-2 virus, the causal agent of the COVID-19 pandemic, has diverged due to multiple mutations since its emergence as a human pathogen in December 2019. Some mutations have defined several SARS-CoV-2 clades that seem to behave differently in terms of regional distribution and other biological features. Next-generation sequencing (NGS) approaches are used to classify the sequence variants in viruses from individual human patients. However, the cost and relative scarcity of NGS equipment and expertise in developing countries prevent studies aimed to associate specific clades and variants to clinical features and outcomes in such territories. As of March 2021, the GR clade and its derivatives, including the B.1.1.7 and B.1.1.28 variants, predominate worldwide. We implemented the post-PCR small-amplicon high-resolution melting analysis to genotype SARS-CoV-2 viruses isolated from the saliva of individual patients. This procedure was able to clearly distinguish two groups of samples of SARS-CoV-2-positive samples predicted, according to their melting profiles, to contain GR and non-GR viruses. This grouping of the samples was validated by means of amplification-refractory mutation system (ARMS) assay as well as Sanger sequencing.

Quorum sensing in P. aeruginosa controls the production of costly public goods such as exoproteases. This cooperative behavior is susceptible to social cheating by mutants that do not invest in the exoprotease production but assimilate the amino acids and peptides derived by the hydrolysis of proteins in the extracellular media. In sequential cultures with protein as the sole carbon source, these social cheaters are readily selected and often reach equilibrium with the exoprotease producers. Nevertheless, an excess of cheaters causes the collapse of population growth. In this work, using the reference strain PA14 and a clinical isolate from a burn patient, we demonstrate that the initial amount of public goods (exoprotease) that comes with the inoculum in each sequential culture is essential for maintaining population growth and that eliminating the exprotease in the inoculum leads to rapid population collapse. Therefore, our results suggest that sequential washes should be combined with public good inhibitors to more effectively combat P. aeruginosa infections.

Background: Pituitary adenomas (PA) are the second most common tumor in the central nervous system and have low counts of mutated genes. Splicing occurs in 95% of the coding RNA. There is scarce information about the spliceosome and mRNA-isoforms in PA, and therefore we carried out proteomic and transcriptomic analysis to identify spliceosome components and mRNA isoforms in PA. Methods: Proteomic profile analysis was carried out by nano-HPLC and mass spectrometry with a quadrupole time-of-flight mass spectrometer. The mRNA isoforms and transcriptomic profiles were carried out by microarray technology. With proteins and mRNA information we carried out Gene Ontology and exon level analysis to identify splicing-related events. Results: Approximately 2000 proteins were identified in pituitary tumors. Spliceosome proteins such as SRSF1, U2AF1 and RBM42 among others were found in PA. These results were validated at mRNA level, which showed up-regulation of spliceosome genes in PA. Spliceosome-related genes segregate and categorize PA tumor subtypes. The PA showed alterations in CDK18 and THY1 mRNA isoforms which could be tumor specific. Conclusions: Spliceosome components are significant constituents of the PA molecular machinery and could be used as molecular markers and therapeutic targets. Splicing-related genes and mRNA-isoforms profiles characterize tumor subtypes.