Statistical Workflow for Feature Selection in Human Metabolomics Data.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations

Unravelling the metabolic impact of SBS-associated microbial dysbiosis: Insights from the piglet short bowel syndrome model

peer-reviewedLiver disease is a major source of morbidity and mortality in children with short bowel syndrome (SBS). SBS-associated microbial dysbiosis has recently been implicated in the development of SBS-associated liver disease (SBS-ALD), however the pathological implications of this association have not been explored. In this study high-throughput sequencing of colonic content from the well-validated piglet SBS-ALD model was examined to determine alterations in microbial communities, and concurrent metabolic alterations identified in urine samples via targeted mass spectrometry approaches (GC-MS, LC-MS, FIA-MS) further uncovered impacts of microbial disturbance on metabolic outcomes in SBS-ALD. Multi-variate analyses were performed to elucidate contributing SBS-ALD microbe and metabolite panels and to identify microbe-metabolite interactions. A unique SBS-ALD microbe panel was clearest at the genus level, with discriminating bacteria predominantly from the Firmicutes and Bacteroidetes phyla. The SBS-ALD metabolome included important alterations in the microbial metabolism of amino acids and the mitochondrial metabolism of branched chain amino acids. Correlation analysis defined microbe-metabolite clustering patterns unique to SBS-ALD and identified a metabolite panel that correlates with dysbiosis of the gut microbiome in SBS

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

Contrasting signatures of genomic divergence during sympatric speciation

Population genomic analyses of Midas cichlid fishes in young Nicaraguan crater lakes suggest that sympatric speciation is promoted by polygenic architectures. The transition from 'well-marked varieties' of a single species into 'well-defined species'-especially in the absence of geographic barriers to gene flow (sympatric speciation)-has puzzled evolutionary biologists ever since Darwin(1,2). Gene flow counteracts the buildup of genome-wide differentiation, which is a hallmark of speciation and increases the likelihood of the evolution of irreversible reproductive barriers (incompatibilities) that complete the speciation process(3). Theory predicts that the genetic architecture of divergently selected traits can influence whether sympatric speciation occurs(4), but empirical tests of this theory are scant because comprehensive data are difficult to collect and synthesize across species, owing to their unique biologies and evolutionary histories(5). Here, within a young species complex of neotropical cichlid fishes (Amphilophus spp.), we analysed genomic divergence among populations and species. By generating a new genome assembly and re-sequencing 453 genomes, we uncovered the genetic architecture of traits that have been suggested to be important for divergence. Species that differ in monogenic or oligogenic traits that affect ecological performance and/or mate choice show remarkably localized genomic differentiation. By contrast, differentiation among species that have diverged in polygenic traits is genomically widespread and much higher overall, consistent with the evolution of effective and stable genome-wide barriers to gene flow. Thus, we conclude that simple trait architectures are not always as conducive to speciation with gene flow as previously suggested, whereas polygenic architectures can promote rapid and stable speciation in sympatry.Peer reviewe

Análise estatística de dados de metabolómica: identificação dos compostos envolvidos na resposta das plantas à simbiose com fungos ectomicorrízicos

Trabalho de projeto de mestrado em Bioestatística, Universidade de Lisboa, Faculdade de Ciências, 2020A associação simbiótica entre as raízes e fungos micorrízicos do solo é extremamente importante para a nutrição e desenvolvimento das plantas. Apesar de haver já algum conhecimento sobre os genes (genoma/transcritoma) e proteínas (proteoma) envolvidos no processo de micorrização, sabe-se muito pouco sobre as alterações dos metabolitos da planta hospedeira quando esta se encontra em simbioses com o fungo micorrízico. Assim, a identificação do conjunto de metabolitos ou compostos biológicos (metaboloma) envolvidos na micorrização reveste-se de especial interesse para completar a informação fornecida pelo genoma/transcritoma e proteoma. Estes são os últimos produtos da expressão dos genes e, por isso, são extremamente informativos acerca das alterações moleculares que estão a acontecer nos organismos vivos. Sabe-se que os organismos vivos sujeitos a stresses ambientais (e.g. seca, salinidade do solo) ou durante interações com microrganismos simbióticos ou patogénicos, podem produzir metabolitos específicos ou sofrer alterações na quantidade de metabolitos já produzidos. A identificação destas alterações permite saber, a nível molecular, o que está a acontecer quando uma planta estabelece, por exemplo, uma relação simbiótica com um fungo da rizosfera. Neste estudo pretendeu-se contribuir para a identificação das alterações no metaboloma do sobreiro quando este se encontra em simbiose com um fungo micorrízico do solo. O metaboloma da raíz micorrizada do sobreiro foi extraído usando quatro frações compostas por diferentes solventes: água, metanol, acetonitrilo e clorofórmio (orgânica). Foram extraídas seis réplicas de plantas de sobreiros – sendo três micorrizadas e três utilizadas como grupo de controlo não micorrizado, para efeitos de comparação. Através da aplicação de métodos estatísticos tais como Rank Products, Modelos Lineares e Análise Discriminante por Mínimos Quadrados Parciais (PLS-DA) , foi possível a identificação dos metabolitos diferencialmente acumulados que possibilitarão a descoberta de novas informações sobre a identidade dos metabolitos envolvidos na simbiose entre o sobreiro e o fungo micorrízico, promovendo novos conhecimentos sobre as vias metabólicas implicadas na micorrização.The symbiotic association between roots and mycorrhizal fungi is extremely important for plant nutrition and development. Although there is already some knowledge about the genes and proteins involved in the mycorrhization process, very little is known about changes in the host plant metabolome when in symbiosis with the mycorrhizal fungus. Thus, the identification of the set of metabolites or biological compounds involved in mycorrhization (metabolome) is of particular interest to supplement the information provided by the transcriptome (transcript) and proteome (protein). These are the latest products of gene expression and are therefore extremely informative about the molecular changes that are happening in living organisms. It is well known that living organisms produce specific metabolites or suffer alterations in metabolite levels when subjected to environmental stresses (e.g. drought, soil salinity) or interactions with microorganisms, pathogenic or symbiotic. By identifying these changes, it is possible to know at a molecular level what is happening when a plant establishes a symbiotic relationship. The aim of this study was to contribute for the identification of changes in the metabolome of the cork oak during symbiosis with a soil mycorrhizal fungus. The mycorrhizal root metabolome was extracted using four fractions composed of different solvents: water, methanol, acetonitrile and chloroform (organic). Six replicates of cork oak plants were extracted − three mycorrhizal and three used as non-mycorrhizal control group for comparison. Through the application of statistical methods for linear models of microarray data, like Rank Products and Linear Models, and PLS-DA (Partial Least Squares Discriminant Analysis), it was possible to identify the differentially accumulated metabolites that will allow the discovery of new information about the identity of the metabolites involved in the symbiosis between cork oak and the mycorrhizal fungus, allowing new knowledge about the metabolic pathways involved in mycorrhizal symbiosis

Bruk av Liquid Array Diagnostics (LAD) som verktøy for analyse av sammensetning og funksjon av tarmens mikrobiota

The microbial species residing in the human gut exercise vital functions for the host. They produce different metabolites that are crucial for human wellbeing. A variety of such molecules mediate signalling along the gut-brain axis, regulate host gene expression, develop and maintain intestinal and blood-brain barriers, are involved in lipogenesis and gluconeogenesis, in addition to taking part in a wide range of other functions. A deviation in the intestinal flora composition is mechanistically linked to various health disorders, including inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), type 2 diabetes, Parkinson’s and Alzheimer’s disease. Such a deviation, known as dysbiosis, represents an unbalanced composition where certain microbial groups are promoted in the expense of others. These species are considered as promising biomarkers, valuable for disease diagnosis, monitoring and treatment. Of particular interest are those markers that can additionally unveil phenotypical characteristics, such as the overall level of short-chain fatty acids (SCFA) in human gut samples. The prospect of discovering additional markers is high, considering that the content of healthy human guts worldwide is not fully characterized. The field of gut microbiota is at a stage of switching focus to clinically relevant species, particularly to their rapid detection, as a means of offering simple diagnostic solutions with increased availability and accessibility. This affords putting biological findings to practical clinical use, which is often not feasible with current species identification platforms. With the intention of filling this need, the main aim of this thesis was to develop a targeted approach for rapid gut microbiota testing based on the novel Liquid Array Diagnostics (LAD) technology. LAD is adopted to target 16S rRNA gene sites unique for specific microbial groups. Requiring only commonplace qPCR instrumentation, it can detect up to 30 distinct microbial markers in a single-tube multiplex reaction within a working day. LAD’s utility in microbiome studies was validated by testing the prevalence and abundance of 15 microbial markers in 541 samples collected from mothers and their children, as reported in Paper I. Paper II, on the other hand, describes a comprehensive human gut prokaryotic genome collection, HumGut. It was built after screening thousands of human gut metagenome samples, collected from healthy people worldwide, for the presence of any high quality publicly available prokaryote genome. The main rationale for creating it was to enable functional studies through LAD-based 16S targeting. It was demonstrated that HumGut, as a reference database, aids whole genome sequencing studies by significantly increasing the number of mapped sequencing reads, thus elevating the potential for an improved taxonomic classification. However, as it is, HumGut exhibits limited practical use for 16S rRNA gene targeted approaches like LAD. This because most of the representative genomes either lack this gene, or the quality of 16S sequences is compromised (addressed in Paper III). Nonetheless, LAD was exploited to infer a segment of human gut microbiota functionality by targeting the 16S rRNA gene. This was performed based on data retrieved from 16S rDNA sequencing and short-chain fatty acid (SCFA) measurements. LAD’s value in classifying samples with disturbed SCFA ratios (namely high propionate-to-butyrate ratio) - an indication of functional dysbiosis - is presented in Paper IV. Taken together, this thesis introduces two tools, LAD and HumGut, both pointing at the direction of simplified human gut functional analysis via gut microbial composition detection.De mikrobielle artene som bor i menneskets tarm utøver vitale funksjoner for verten. De produserer forskjellige metabolitter avgjørende for menneskers helse. En rekke av disse molekylene deltar i prosesser som signaltransduksjon langs tarm-hjerne-aksen, regulering av genekspresjon, utvikling og vedlikehold av tarm- og blod-hjerne-barrieren, lipogenese og glukoneogenese, samt en rekke andre funksjoner. Avvik i tarmflorasammensetningen kan knyttes til mange ulike sykdommer og lidelser, inkludert irritabel tarm (IBS), innflammatorisk tarmsykdom (IBD), type -2 diabetes, Parkinsons og Alzheimers sykdom. Slike avvik, kjent som dysbiose, kjennetegnes av at visse mikrobielle grupper fremmes på bekostning av andre. Disse artene har potensiale som biomarkører, og kan slik være verdifulle for sykdomsdiagnose og behandling. Spesielt lovende er biomarkører i tarm som kan knyttes opp mot phenotypiske trekk, slik som kortkjedede fettsyrer (SCFA). Det antas at enda flere slike arter vil identifiseres i fremtiden, da mikrobiota-komposisjonen i sunne tarmer ikke er fullt karakterisert globalt. Mikrobiota-feltet er nå på et stadium hvor fokuset endres fra eksplorative studier til identifisering av klinisk relevante arter. Det vil da bli spesielt viktig med metoder som muliggjør rask deteksjon, da dette vil innebære enkle diagnostiske løsninger tilgjengelig for praktisk klinisk bruk, noe som ofte ikke er gjennomførbart med dagens artsidentifikasjonsplattformer. Hovedmålet med denne oppgaven var å utvikle en målrettet tilnærming for rask tarmmikrobiotatesting basert på det nye Liquid Array Diagnostics (LAD)-prinsippet. LAD er utviklet for å identifisere sekvenser i 16S rRNA-genet som er unike for spesifikke mikrobielle markører. Metoden krever kun et vanlig qPCR-instrument og kan oppdage inntil 30 forskjellige mikrobielle markører i étt enkelt test-rør i løpet av en arbeidsdag. LADs nytteverdi i mikrobiomstudier ble validert ved å teste forekomsten av 15 mikrobielle markører i 541 prøver samlet fra mødre og deres barn, som rapportert i Artikel I. Artikel II beskriver genereringen av en omfattende prokaryot genomsamling av menneskets tarm. Den ble bygget ved å screene tusenvis av metagenom fra tarmprøver samlet inn fra friske mennesker over hele verden. Metagenomene ble screenet for tilstedeværelse av alle offentlig tilgjengelige prokaryote genom. Sekvenser av dårlig kvalitet ble fjernet mens alle andre sekvenser ble samlet i én stor referansedatabase, HumGut. Hovedmålet med å lage denne referansedatabasen var å muliggjøre LAD-baserte funksjonelle studier. Det ble vist at HumGut fungerer som et nyttig verktøy for full-genoms sekvenseringsstudier ved å øke antallet artlagte sekvenseringsavlesninger betydelig, da dette gir forbedret taksonomisk klassifisering. HumGut har imidlertid begrenset nytteverdi for 16S rRNA-baserte metoder som LAD. Dette fordi de fleste genom i samlingen enten mangler dette genet fullstendig, eller har for dårlig kvalitet på 16S-sekvensene (behandlet i Artikel III). Til tross for begrensningene knyttet til 16S rRNA-genet i HumGut, ble LAD benyttet til å utvikle en 16S rDNA-basert test for måling av menneskelig tarmmikrobiotafunksjonalitet. Dette ble utført basert på data hentet fra 16S-sekvensering og målinger av kortkjedede fettsyrer (SCFA). LADs evne til å klassifisere prøver med forstyrret SCFA-forhold (nemlig høyt propionat-tilbutyrat-forhold) - en indikasjon på funksjonell dysbiose - er presentert i Artikel IV. Til sammen presenterer denne oppgaven to verktøy, LAD og HumGut, som begge peker i retning av forenklet funksjonell analyse av menneskelig tarm via deteksjon av mikrobiell sammensetning i tarmen

Dielectrophoretic discrimination of pluripotent myoblast with Raman spectroscopic analysis of the cell plasma membrane for application in Huntington's disease

Myoblasts are muscle derived mesenchymal stem cell progenitors that have great potential for use in regenerative medicine, especially for cardiomyogenesis grafts and intracardiac cell transplantation. To utilise such cells for pre -clinical and clinical applications, and especially for personalized medicine, it is essential to generate a synchronised, homogenous, population of cells that display phenotypic and genotypic homogeneity within a population of cells. This thesis demonstrates that the biomarker -free technique of dielectrophoresis (DEP) can be used to discriminate cells between stages of differentiation in the C2C12 myoblast pluripotent mouse model. Terminally differentiated myotubes were separated from C2C12 myoblasts to better than 96% purity, a result validated by flow cytometry and Western blotting. To determine the extent to which cell membrane capacitance, rather than cell size, determined the DEP response of a cell, C2C12 myoblasts were co- cultured with GFP- expressing fibroblasts of comparable size distributions (mean diameter -10 gm). A DEP sorting efficiency greater than 98% was achieved for these two cell types, a result concluded to arise from the fibroblasts possessing a larger membrane capacitance than the myoblasts. It is currently assumed that differences in membrane capacitance primarily reflect differences in the extent of folding or surface features of the membrane. However, our finding by Raman spectroscopy that the fibroblast membranes contained a smaller proportion of saturated lipids than those of the myoblasts suggests that the membrane chemistry should also be taken into account.These high levels of discrimination raised more questions about the cell plasma membrane characteristics that may be responsible for the dielectrophoretic response. This prompted to extend the work to a specific neurodegenerative disease, Huntington's disease. Several studies have been revealing the association between plasma membrane dysregulation and Huntington's disease. In particular the feasibility to use peripheral fibroblasts cells from donors affected by the disease, as a forecasting model marker for Huntington. Although there are substantial evidences about the indirect effect of the disease on the plasma membrane, a non -invasive technique that can discriminate and characterise a cell sample is not available. Raman spectroscopy with associated statistical multivariate analysis was used to characterise sub -cellular differences in extracted plasma membranes from peripheral fibroblastic cells in order to elucidate the differences between cells affect and non - affected by the disease. The results clearly showed that indeed the plasma membrane carries differences that can be attributed to the presence of the disease making the plasma membrane an amenable and novel biomarker for Huntington's diseas