Spaceflight and Differential Gene Expression Analysis of Mice Quadriceps Exposed to Microgravity

he trip to Mars and back is planned for the next 20 years. Improvement in technology and research has allowed data analysis, at a larger scale, on spaceflight specimen. However, research involving spaceflight is decentralized, as research is spread across laboratories with different methodologies. NASA’S GeneLab is a public repository for spaceflight-related omics data and promotes centralizing spaceflight RNA-Seq studies using NASA’s RNA-Seq pipeline. Jonathan Oribello, a SJSU’s Bioinformatics graduate and now an employee at GeneLab, has implemented Nextflow to NASA’s RNA-Seq pipeline. The Nextflow RNA-Seq pipeline was ran on San Jose State University’s College of Science High Performance Computing system, using study GLDS-103 from GeneLab. The results have shown the reproducibility of Nextflow, and new insights through customizable inputs. This project has shown possible insights to the skeletal muscle system, and the effects of microgravity on the circadian rhythm

Transcriptome Analysis of Non‐Coding RNAs in Livestock Species: Elucidating the Ambiguity

The recent remarkable development of transcriptomics technologies, especially next generation sequencing technologies, allows deeper exploration of the hidden landscapes of complex traits and creates great opportunities to improve livestock productivity and welfare. Non-coding RNAs (ncRNAs), RNA molecules that are not translated into proteins, are key transcriptional regulators of health and production traits, thus, transcriptomics analyses of ncRNAs are important for a better understanding of the regulatory architecture of livestock phenotypes. In this chapter, we present an overview of common frameworks for generating and processing RNA sequence data to obtain ncRNA transcripts. Then, we review common approaches for analyzing ncRNA transcriptome data and present current state of the art methods for identification of ncRNAs and functional inference of identified ncRNAs, with emphasis on tools for livestock species. We also discuss future challenges and perspectives for ncRNA transcriptome data analysis in livestock species

RNA sequencing of non-coding RNAs in ischaemic heart disease

Ischaemic heart disease is a major cause of death worldwide and a leading cause of mortality and morbidity in New Zealand. Older adults and those of Māori and Pacific ancestry are particularly affected. Ischaemic heart disease accounts for over half of all cardiovascular disease mortality and, again, rates are more than twice as high among Māori than non-Māori. Ischaemic heart disease can lead to myocardial infarction (heart attack) which, if not fatal, can then lead to heart failure, a complex, multifactorial disease characterised by neurohormonal signalling and remodelling of the heart. Currently the natriuretic peptides are the international gold standard for diganosing heart failure and are also excellent prognostic markers in patients with heart failure. However, there is still a clinical need for early biomarkers of myocardial ischaemia (to identify people at risk of myocardial infarction) and to identify patients at risk of developing heart failure before detrimental remodelling has occurred. As sequencing technologies have evolved there has been intense research in the fields of circulating cell free DNA and RNA, especially non-coding RNA. As RNA is actively transcribed, it has the advantage of providing a ‘real time’ insight into the disease status of an individual. Recent discoveries have highlighted the regulatory roles and diseases associated with non-coding RNAs, including long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). LncRNAs have been demonstrated to have mutliple functional roles both within the nucleus and cytoplasm such as chromatin remodelling, histone modification, transcription factor recruitment, formation of subnuclear structures and control of mRNA translation and decay. CircRNA, a relative newcomer, has also been demonstrated to have functional roles such as sequestering miRNAs, binding proteins and even coding for peptides. There is great excitment for the potential utility of circRNAs as biomarkers as, due to their circular structure, they are more resistant to degradation in the circulation than their linear RNA counterparts. The overall aim of this thesis was to identify non-coding RNAs associated with ischaemic heart disease. To address this aim, a bioinformatics pipeline was developed to identify mRNAs, lncRNAs including putative novel lncRNAs, and circRNAs using short-read RNA Sequencing (RNA-Seq) data. This pipeline was tested and validated with publicly available data and used to screen for candidate mRNA and lncRNA biomarkers associated with ischaemic heart disease in human heart tissue. A whole genome network correlation approach identified several promising candidate biomarkers for myocardial ischaemia including several novel lncRNAs, which were validated with long-read Nanopore sequencing in independent samples. The sub-cellular localisation of three promising lncRNAs candidates (two annotated lncRNAs, one novel lncRNA) was identified using the in-situ hybridisation assay, RNAscope®. Next, an RNA-Seq protocol was developed to detect mRNAs, lncRNAs and circRNAs in human plasma. This protocol was applied to plasma from patients with ischaemic heart disease and healthy controls to screen for candidate mRNA, lncRNA and circRNA biomarkers for progression from ischaemic heart disease to heart failure. Although candidate biomarkers for disease progression could not be detected in these patients several additional lncRNA candidates for the presence of ischaemic heart disease were identified. In summary, this study has established a bioinformatics pipeline and methodology for identifying and validating putative novel lncRNAs and circRNAs in human tissue and plasma. This work has identified several promising candidate lncRNA biomarkers for ischaemic heart disease, which, if validated, may provide early diagnostic information in high-risk patients. The pipeline is freely available to download at https://github.com/zoeward-nz/Ph

Expresión génica en mieloma múltiple: análisis de datos de RNA-seq y microarrays en combinación con estudios de metaanálisis y predicción de respuesta al tratamiento

[ES] El mieloma múltiple (MM) es una neoplásia hematológica que presenta una gran variabilidad clínica como consecuencia de su heterogeneidad genética. Así, desde las etapas precursoras del MM conviven en la médula ósea múltiples clones celulares genéticamente distintos y por tanto, con capacidad diferente para sobrevivir y resistir a la acción de factores externos como son los tratamientos. Esta diversidad, ha conducido en los últimos 20 años al desarrollo de nuevos fármacos con mecanismos de acción muy diferentes a la quimioterapia clásica, lo que ha contribuído a prolongar notablemente la supervivencia global de las personas afectadas por esta patología. Igualmente, en las últimas décadas ha habido grandes progresos en el descubrimiento de los procesos biológicos implicados en el MM, derivados, principalmente, de la inmensa información aportada en campos como la transcriptómica gracias a tecnologías de análisis masivo de datos como los microarrays o la RNA-seq. Con estos antecedentes, en el presente trabajo se ha procedido a la búsqueda de las firmas de expresión génica de diferentes compuestos utilizados en el tratamiento del MM, así como a la determinación de genes implicados en la respuesta de estos fármacos en pacientes con esta patología. Para ello, se propuso en un primer lugar, una guía para el análisis de datos de RNA-seq mediante el desarrollo de un flujo de análisis o pipeline, en el que se determinaron los métodos y algoritmos más adecuados para el procesamiento de datos de esta tecnología. Así, se llevó a cabo la evaluación del rendimiento de 192 pipelines a nivel de expresión génica cruda y de 17 métodos a nivel de expresión génica diferencial, de manera que finalmente fueron establecidos los pipelines y algoritmos con mejores índices de precisión y exactitud a la hora de la determinación de la expresión génica a ambos niveles. En una siguiente etapa se realizó un estudio comparativo entre la RNA-seq y el microarray transcriptómico HTA 2.0 de Affymetrix, para establecer cuál de las dos tecnologías muestra un mayor rendimiento en estudios de expresión génica. Tras el establecimiento de las metodologías óptimas de análisis, se procedió a la determinación de los perfiles de expresión génica asociados a 12 fármacos antimieloma mediante técnicas de metaanálisis en líneas celulares. Esto condujo a la especificación de una firma génica para cada uno de los compuestos analizados, que fue asociada posteriormente a posibles mecanismos de acción o de quimiorresistencia. Adicionalmente, también se llevó a cabo la definición de los perfiles de expresión génica asociados a la respuesta a tres esquemas de tratamiento en pacientes con MM al momento del diagnóstico, siendo comprobada, en un último paso, la eficiencia de estos perfiles de expresión en la predicción de la respuesta propuestos a través del uso de modelos estadísticos predictivos