127 research outputs found
Specificity of the innate immune responses to different classes of non-tuberculous mycobacteria
Mycobacterium avium is the most common nontuberculous mycobacterium (NTM) species causing infectious disease. Here, we characterized a M. avium infection model in zebrafish larvae, and compared it to M. marinum infection, a model of tuberculosis. M. avium bacteria are efficiently phagocytosed and frequently induce granuloma-like structures in zebrafish larvae. Although macrophages can respond to both mycobacterial infections, their migration speed is faster in infections caused by M. marinum. Tlr2 is conservatively involved in most aspects of the defense against both mycobacterial infections. However, Tlr2 has a function in the migration speed of macrophages and neutrophils to infection sites with M. marinum that is not observed with M. avium. Using RNAseq analysis, we found a distinct transcriptome response in cytokine-cytokine receptor interaction for M. avium and M. marinum infection. In addition, we found differences in gene expression in metabolic pathways, phagosome formation, matrix remodeling, and apoptosis in response to these mycobacterial infections. In conclusion, we characterized a new M. avium infection model in zebrafish that can be further used in studying pathological mechanisms for NTM-caused diseases
Multiple Sclerosis
The book “Multiple Sclerosis: Diagnosis and Treatment” provides a collection of selected articles published in Biomedicines as part of Volume II addressing current issues on this major inflammatory demyelinating neurological disorder. The articles describe recent advances and discoveries in the molecular and cell microenvironment contributing to neuro-inflammation in the brain and spinal cord. The role of the neurofilaments light chain as a serological biomarker of axonal and neuronal degeneration of the CNS is presented not just as a sensitive diagnostic tool, but also as an indicator of treatment responses. The new generation of therapeutic molecules belonging to the sphingosine-1-phosphate class are discussed, and their potential as disease-modifying treatment is considered. Investigations into the intimate molecular mechanisms of highly active MS, disclosed clinically and by MRI, are discussed by researchers proposing that the expression of RNAs in peripheral blood cells is a biomarker of highly active disease. The book also addresses important clinical and epidemiological aspects of pediatric MS and that in multi-ethnic cohorts
Marine Glycomics
Marine creatures are rich sources of glycoconjugate-containing glycans and have diversified structures. The advance of genomics has provided a valuable clue for their production and developments. This information will encourage breeding and engineering functional polysaccharides with slime ingredients in algae. These glycans will have the potential for applications to antioxidant, anticancer, and antimicrobial drugs in addition to health supplements and cosmetics. The combination of both biochemical and transcriptome approaches of marine creatures will lead to the opportunity to discover new activities of proteins such as glycan-relating enzymes and lectins. These proteins will also be used for experimental and medical purposes, such as diagnostics and trial studies. The topic of marine glycomics is also focusing on understanding the physiological properties of marine creatures, such as body defense against pathogens and cancers. In the competitions for natural selection, living creatures have evolved both their glycans and their recognition. They have primitive systems of immunity, and few of their mechanisms are closely related to glycans. If we are able to describe the accumulation of data of glycans of creatures living in the seashore and the oceans, we may be able to anticipate a time when we can talk about the ecosystem with glycans. That knowledge will be useful for the development of drugs that cure our diseases and for an understanding of living systems in addition to the preservation of living environments
Anti-aging Nutrients with Health Beneficial Effects
Recently, many kinds of foods and food-derived nutrients have been reported to show health-beneficial effects. In particular, some foods and food-derived nutrients have shown anti-aging effects on several organs and tissues, such as brain, muscle, skin, intestine, and so on. In some kinds of foods, the molecular basis of their functionalities (e.g., anti-brain aging, anti-sarcopenia, and anti-skin aging) and inter-tissue networks activated by these foods mediated by exosomes, cytokines, and immune cells have been clarified in detail
ClockOME: searching for oscillatory genes in early vertebrate development
Embryo development is a dynamic process regulated in space and time. Cells must
integrate biochemical and mechanical signals to generate fully functional organisms, where
oscillatory gene expression plays a key role. The embryo molecular clock (EMC) is the best
known genetic oscillator active in embryo segmentation, involving genes from the Notch, FGF,
and WNT pathways. However, the list of cyclic genes is still incomplete mostly due to the
challenges involved with studying periodic systems. Recently, such studies have become more
feasible with the development of pseudo-time ordering algorithms that search for candidate
oscillatory genes using large transcriptomics datasets sampled without explicit time
measurements.
This study aims at finding candidate oscillatory genes - ClockOME - active in early
chick embryo development.
Two Gallus gallus microarray transcriptomics datasets from Presomitic mesoderm
(PSM), and one dataset from limb segmentation were gathered from GEO and ArrayExpress.
To normalize these data from different experiments, an RData package - FrozenChicken - was
developed to apply a frozen Robust MultiArray (fRMA) normalization to the data. Next the
datasets were processed with Oscope (a pseudo-time ordering algorithm) to search for candidate
periodic genes clustered by similar oscillatory behaviour. The clusters of predicted oscillators
were then subject to functional enrichment and interaction network analyses to highlight the
biological functions associated with these genes. Oscope predicted three clusters of oscillators:
two in PSM (106 and 32 genes), and one in Limb (162 genes). Overall, the genes are associated
with regulatory, morphological, and developmental processes. Mesp2, a gene involved with the
EMC, was found in this dataset, validating the approach, however, the majority of genes are
novel oscillatory candidates, associated with chromatin and transcriptional regulation, as well
as protein and oxygen metabolism. The list of candidate oscillators represents a valuable
resource for guided experimental validation to discover additional members of the chick EMC.
Six genes have been proposed for high-priority experimental validation: SRC, PTCH1,
NOTCH2, YAP1, KDR, CTR9.O desenvolvimento embrionário é um processo dinâmico que envolve alterações
moleculares no espaço e no tempo. As células embrionárias são constantemente expostas a
estímulos bioquímicos e mecânicos, e respondem ao ambiente em que se encontram alterando
o seu programa genético. Quando corretamente integradas, estas respostas celulares culminam
com o desenvolvimento bem-sucedido de um organismo funcional. Assim, a embriogénese
envolve processos moleculares estritamente regulados, sendo a expressão oscilatória de genes
uma das formas possíveis para a regulação do comportamento das células ao longo do tempo.
O relógio molecular embrionário é um conhecido oscilador genético, e está envolvido na
segmentação do tecido paraxial embrionário. O conceito de relógio molecular foi inicialmente
proposto em 1976 por Cooke e Zeeman, ao qual chamaram o modelo Clock and Wavefront
(Relógio e Frente de Onda)1. Este modelo foi concebido para descrever teoricamente a
formação rítmica de sómitos em ambos os lados da mesoderme paraxial (PSM) nos vertebrados,
e baseia-se na existência de osciladores genéticos que regulam esse processo de segmentação
da PSM ao longo do tempo. Para além do relógio, como diz o nome, o modelo inclui a existência
de uma frente de onda, que determina espacialmente o comportamento das células presentes na
mesoderme pré-somítica (PSM). Assim, os dois mecanismos guiam a diferenciação das células
da PSM, que consequentemente sofrem transformações genéticas que precedem a formação dos
sómitos. A base deste relógio molecular consiste na expressão periódica de genes que fazem
parte das vias moleculares Notch, FGF e WNT. Contudo, a lista de genes envolvidos no relógio
embrionário ainda não se encontra completa, facto este que se deve principalmente às
dificuldades experimentais relacionadas com o estudo de sistemas periódicos quando não se
conhece de antemão a periodicidade/ritmo da expressão dos genes envolvidos.
Com o advento de novas técnicas de transcriptómica que permitem o estudo dos valores
de expressão de todos os genes simultaneamente, nomeadamente usando Microarrays, ou mais
recentemente através de métodos de sequenciação, como RNA-sequencing ou Single-Cell
RNA-sequencing, surge a oportunidade de procurar alargar a lista de genes com expressão
oscilatória. Porém, estes métodos implicam a extração do RNA das células amostradas
resultando na morte celular. Assim, este processamento inviabiliza o estudo das mesmas células
ao longo do tempo, originando dados moleculares estáticos, isto é, os níveis de expressão
obtidos representam uma única amostra temporal. Para o estudo de processos periódicos, seria
então necessário fazer uma série temporal amostrando diferentes indivíduos ao longo do tempo de desenvolvimento, aumentando grandemente o número de amostras biológicas necessárias
para resolver o ciclo de oscilação para cada gene estudado.
Assim, sem informação temporal medida explicitamente, a expressão oscilatória de
genes pode apenas ser estudada usando modelos matemáticos apropriados, nomeadamente
através da aplicação de algoritmos de ordenação pseudo-temporal. Estes métodos ordenam as
amostras ao longo do tempo de uma oscilação de forma a obter o padrão do comportamento
cíclico para todos os genes cuja expressão oscila concomitantemente. Torna-se assim possível,
bioinformaticamente, inferir o potencial oscilatório de genes medidos por estas técnicas de
transcriptómica, sem informação temporal explícita.
Deste modo, o objetivo deste estudo é encontrar novos genes oscilatórios, a que
coletivamente chamamos ClockOME, que estão ativos durante as primeiras etapas do
desenvolvimento embrionário (somitogénese) da galinha, nos tecidos da mesoderme présomítica
(PSM), e no membro superior (Limb); tecidos estes onde o relógio molecular foi
descrito, atuando como regulador temporal das alterações genéticas subjacentes.
Para tal, recolheu-se 3 conjuntos de dados (datasets) de transcriptómica obtidos por
microarray de dois repositórios de dados públicos: GEO (da instituição americana NCBI) e
ArrayExpress (da instituição europeia EMBL-EBI). Dois datasets continham dados de
mesoderme paraxial (PSM) – tecido onde ocorre a somitogénese; e um dataset de dados de
obtidos do membro superior do embrião de galinha. Com o objetivo de normalizar os três
datasets de forma a torná-los comparáveis (uma vez que são oriundos de processos
experimentais diferentes), foi desenvolvido um pacote de R denominado “FrozenChicken:
Promoting the meta-analysis of chicken microarray data” (publicado em 2021)
(https://doi.org/10.1101/2021.02.25.432894). Este pacote contém dados sumarizados de 472
datasets de microarrays de embriões de galinha, tornando possível a normalização por fRMA
(frozen Robust MultiArray) de microarrays de Gallus gallus. Após normalização e controlo de
qualidade dos valores de expressão genética, os dados da PSM e do membro foram processados
com o Oscope (algoritmo de ordenação pseudo-temporal), com o propósito de prever genes
oscilatórios. Este algoritmo avalia todas as combinações de pares de genes, agrupando aqueles
que apresentem padrões de expressão semelhantes, ou seja, cujos valores de expressão ao longo
das amostras seguem trajetórias semelhantes, indiciando um período de oscilação
potencialmente semelhante. Os clusters de genes previstos pelo Oscope foram posteriormente submetidos a uma análise de enriquecimento funcional e a uma análise de interações funcionais,
com o intuito de perceber o seu potencial papel biológico, e funções moleculares subjacentes.
O Oscope reportou três listas de genes potencialmente oscilatórios: dois grupos foram
encontrados a partir dos dados da PSM (com 106 e 32 genes cada) e o terceiro grupo de 162
genes foi encontrado nos dados do membro superior. No total, a lista de genes que
denominamos ClockOME é composta por 296 genes potencialmente oscilatórios, envolvidos
em diversos mecanismos regulatórios importantes para o desenvolvimento embrionário e para
a morfogénese. A maioria dos genes presentes nesta lista não estão descritos na literatura como
sendo oscilatórios (novel candidates), representando, portanto, uma mais-valia para a
comunidade científica que estuda o relógio molecular embrionário. Estes genes parecem estar
associados a funções como remodelação da cromatina, regulação da transcrição, metabolismo
proteico e metabolismo do oxigénio, sendo, portanto, bons candidatos para futura validação
experimental. Notavelmente, o Oscope identificou com sucesso o Mesp2, um gene oscilatório
bem descrito na literatura, mostrando assim a validade e o potencial desta abordagem teórica.
Em suma, este trabalho produziu uma lista de 296 genes potencialmente oscilatórios.
Com base na sua novidade e na função molecular anotada, foi proposta uma lista de seis genes
candidatos de particular relevância para validação experimental no futuro próximo,
nomeadamente: SRC, PTCH1, NOTCH2, YAP1, KDR, CTR9. Assim, as listas resultantes do
trabalho desta tese poderão agora guiar futuras experiências laboratoriais capazes de adicionar
novos interactores moleculares ao atual modelo do relógio molecular embrionário
Geometric, biomechanical and molecular analyses of abdominal aortic aneurysm
Background
Abdominal aortic aneurysm (AAA) is defined as a dilatation of the abdominal
aorta of 30 mm in diameter or more. Main risk factors are smoking, age and male sex.
Pathophysiological features include inflammation, smooth muscle cell loss and destruction
of the extracellular matrix. The AAA is typically asymptomatic but can expand and
eventually rupture, with a mortality of 70-80% as a result. Risk factors for rupture include a
large diameter, female sex, active smoking, high blood pressure and low body mass index
(BMI). There is no medical treatment to inhibit growth or rupture of AAA. The only measure
to prevent rupture in a large AAA is aortic surgery. This intervention carries its own
significant risk of morbidity and mortality, necessitating a risk stratification method. The
diameter is currently used to decide when to operate on an AAA and it is repeatedly
monitored until the threshold for surgery is reached. However, this measurement leaves room
for improvement, as the individual aneurysm growth rate is difficult to predict and some large
AAAs do not rupture while in other patients, small AAAs rupture during surveillance. Finite
element analysis (FEA) is a method by which biomechanical rupture risk can be estimated
based on patient characteristics and a computed tomography (CT)-derived 3D model of an
AAA. Microarray analysis allows high-throughput analyses of tissue gene expression.
Aims
The overall aim of this thesis was to explore and develop new strategies to improve,
refine and individualize management of patients with AAA, by applying geometric,
biomechanical and molecular analyses.
Methods and Results
In study I, the CTs of 146 patients with AAAs of diameters between
40 and 60 mm were analyzed with three-dimensional (3D) segmentation and FEA. Simple
and multiple regression analyses were performed. Female sex, patient height, lumen volume,
body surface area (BSA) and low BMI were shown to be associated with the biomechanical
rupture risk of AAA. Study II included 191 patients with AAAs of diameters between 40-50
mm. The AAAs were analyzed with 3D segmentation and FEA after which prediction
algorithms were developed by use of machine learning strategies. More precise diameter
measurements improved prediction of growth and four-year prognosis of small AAAs.
Biomechanical indices and lumen diameter were predictive of future rupture or symptomatic
AAA. Growth and rupture required different prediction models. In study III, 37 patients, 42
controls and a validation cohort of 51 patients were analyzed with respect to their circulating
levels of neutrophil elastase-derived fibrin degradation products (E-XDP). The results
showed that E-XDP was a sensitive marker for AAA, independently of examined
comorbidities, and its concentration in peripheral blood correlated with the AAA diameter
and the volume and mechanical stress of the intraluminal thrombus (ILT). It was further
increased by the presence of coexisting aneurysms. Study IV included 246 tissue samples,
divided into tunica media and adventitia, from 76 patients with AAA and 13 organ donor
controls, analyzed by microarrays. There were large differences between the transcriptomes
of AAA and control media and adventitia. Processes related to inflammation were transmural,
whereas the upregulation of proteolysis, angiogenesis and apoptosis along with
downregulation of smooth muscle- and differentiation-related gene sets were specific for the
aneurysm media. Active smoking increased oxidative stress in all tissues and increased
inflammation and lipid-related processes in AAA. The growth rate of the AAA diameter
correlated with adaptive immunity in media and lipid processes in adventitia.
Conclusions
In this thesis, we show that known clinical risk factors and certain geometric
properties are associated with biomechanical deterioration of AAAs. Furthermore, geometric
and biomechanical analyses can enhance prediction of outcome. Importantly, there are
differences between prediction of AAA growth and rupture. Finally, a biomarker was
discovered and the transcriptome of AAA including effects of the ILT, smoking and rapid
diameter growth rate, was mapped and we envision that the data may be used for future
biomarker and drug target discovery
Statistical approaches of gene set analysis with quantitative trait loci for high-throughput genomic studies.
Recently, gene set analysis has become the first choice for gaining insights into the underlying complex biology of diseases through high-throughput genomic studies, such as Microarrays, bulk RNA-Sequencing, single cell RNA-Sequencing, etc. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Further, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. Hence, a comprehensive overview of the available gene set analysis approaches used for different high-throughput genomic studies is provided. The analysis of gene sets is usually carried out based on gene ontology terms, known biological pathways, etc., which may not establish any formal relation between genotype and trait specific phenotype. Further, in plant biology and breeding, gene set analysis with trait specific Quantitative Trait Loci data are considered to be a great source for biological knowledge discovery. Therefore, innovative statistical approaches are developed for analyzing, and interpreting gene expression data from Microarrays, RNA-sequencing studies in the context of gene sets with trait specific Quantitative Trait Loci. The utility of the developed approaches is studied on multiple real gene expression datasets obtained from various Microarrays and RNA-sequencing studies. The selection of gene sets through differential expression analysis is the primary step of gene set analysis, and which can be achieved through using gene selection methods. The existing methods for such analysis in high-throughput studies, such as Microarrays, RNA-sequencing studies, suffer from serious limitations. For instance, in Microarrays, most of the available methods are either based on relevancy or redundancy measures. Through these methods, the ranking of genes is done on single Microarray expression data, which leads to the selection of spuriously associated, and redundant gene sets. Therefore, newer, and innovative differential expression analytical methods have been developed for Microarrays, and single-cell RNA-sequencing studies for identification of gene sets to successfully carry out the gene set and other downstream analyses. Furthermore, several methods specifically designed for single-cell data have been developed in the literature for the differential expression analysis. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to review the performance of the existing methods. Hence, a comprehensive overview, classification, and comparative study of the available single-cell methods is hereby undertaken to study their unique features, underlying statistical models and their shortcomings on real applications. Moreover, to address one of the shortcomings (i.e., higher dropout events due to lower cell capture rates), an improved statistical method for downstream analysis of single-cell data has been developed. From the users’ point of view, the different developed statistical methods are implemented in various software tools and made publicly available. These methods and tools will help the experimental biologists and genome researchers to analyze their experimental data more objectively and efficiently. Moreover, the limitations and shortcomings of the available methods are reported in this study, and these need to be addressed by statisticians and biologists collectively to develop efficient approaches. These new approaches will be able to analyze high-throughput genomic data more efficiently to better understand the biological systems and increase the specificity, sensitivity, utility, and relevance of high-throughput genomic studies
Molecular and computational approach to the link between nutrition and cancer
Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Química Física Aplicada. Fecha de lectura: 22-11-201
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