Chapter 12: Human Microbiome Analysis

Humans are essentially sterile during gestation, but during and after birth, every body surface, including the skin, mouth, and gut, becomes host to an enormous variety of microbes, bacterial, archaeal, fungal, and viral. Under normal circumstances, these microbes help us to digest our food and to maintain our immune systems, but dysfunction of the human microbiota has been linked to conditions ranging from inflammatory bowel disease to antibiotic-resistant infections. Modern high-throughput sequencing and bioinformatic tools provide a powerful means of understanding the contribution of the human microbiome to health and its potential as a target for therapeutic interventions. This chapter will first discuss the historical origins of microbiome studies and methods for determining the ecological diversity of a microbial community. Next, it will introduce shotgun sequencing technologies such as metagenomics and metatranscriptomics, the computational challenges and methods associated with these data, and how they enable microbiome analysis. Finally, it will conclude with examples of the functional genomics of the human microbiome and its influences upon health and disease

Clinical decision making for prediction of otitis using machine learning approach

This study investigates the relationship between autoimmune disease otitis and gut microbial community abundance by using machine learning as an aid in the medical decision-making process. Stool samples of healthy and otitis diseased infants were obtained from the curatedMetagenomicData package. Class imbalance present in the dataset was handled by oversampling a minority class. Afterwards, we built several machine learning models (support vector machine, k-nn, artificial neural networks, random forest and gradient boosting) to predict otitis from gut microbial samples. The best overall accuracy was obtained by the random forest classifier, 0.99, followed by support vector machine and gradient boosting algorithms, both achieving 0.96 accuracy. We also obtained the most informative predictors as potential microbial biomarkers for otitis disease. The obtained results showed better accuracy in prediction of otitis from microbial metagenome than previously proposed methods found in literature

Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women

The role of molecular signals from the microbiome and their coordinated interactions with those from the host in hepatic steatosis – notably in obese patients and as risk factors for insulin resistance and atherosclerosis – needs to be understood. We reveal molecular networks linking gut microbiome and host phenome to hepatic steatosis in a cohort of non diabetic obese women. Steatotic patients had low microbial gene richness and increased genetic potential for processing of dietary lipids and endotoxin biosynthesis (notably from Proteobacteria), hepatic inflammation and dysregulation of aromatic and branched-chain amino acid (AAA and BCAA) metabolism. We demonstrated that faecal microbiota transplants and chronic treatment with phenylacetic acid (PAA), a microbial product of AAA metabolism, successfully trigger steatosis and BCAA metabolism. Molecular phenomic signatures were predictive (AUC = 87%) and consistent with the gut microbiome making an impact on the steatosis phenome (>75% shared variation) and, therefore, actionable via microbiome-based therapies

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

Interactions between fecal gut microbiome, enteric pathogens, and energy regulating hormones among acutely malnourished rural Gambian children

Background: The specific roles that gut microbiota, known pathogens, and host energy-regulating hormones play in the pathogenesis of non-edematous severe acute malnutrition (marasmus SAM) and moderate acute malnutrition (MAM) during outpatient nutritional rehabilitation are yet to be explored. Methods: We applied an ensemble of sample-specific (intra- and inter-modality) association networks to gain deeper insights into the pathogenesis of acute malnutrition and its severity among children under 5 years of age in rural Gambia, where marasmus SAM is most prevalent. Findings: Children with marasmus SAM have distinct microbiome characteristics and biologically-relevant multimodal biomarkers not observed among children with moderate acute malnutrition. Marasmus SAM was characterized by lower microbial richness and biomass, significant enrichments in Enterobacteriaceae, altered interactions between specific Enterobacteriaceae and key energy regulating hormones and their receptors. Interpretation: Our findings suggest that marasmus SAM is characterized by the collapse of a complex system with nested interactions and key associations between the gut microbiome, enteric pathogens, and energy regulating hormones. Further exploration of these systems will help inform innovative preventive and therapeutic interventions. Funding: The work was supported by the UK Medical Research Council (MRC; MC-A760-5QX00) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement; Bill and Melinda Gates Foundation (OPP 1066932) and the National Institute of Medical Research (NIMR), UK. This network analysis was supported by NIH U54GH009824 [CLD] and NSF OCE-1558453 [CLD]. © 2021 The Author(s). **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Richard Bradbury" is provided in this record*

Human-microbiota interactions in health and disease :bioinformatics analyses of gut microbiome datasets

EngD ThesisThe human gut harbours a vast diversity of microbial cells, collectively known as the gut microbiota, that are crucial for human health and dysfunctional in many of the most prevalent chronic diseases. Until recently culture dependent methods limited our ability to study the microbiota in depth including the collective genomes of the microbiota, the microbiome. Advances in culture independent metagenomic sequencing technologies have since provided new insights into the microbiome and lead to a rapid expansion of data rich resources for microbiome research. These high throughput sequencing methods and large datasets provide new opportunities for research with an emphasis on bioinformatics analyses and a novel field for drug discovery through data mining. In this thesis I explore a range of metagenomics analyses to extract insights from metagenomics data and inform drug discovery in the microbiota. Firstly I survey the existing technologies and data sources available for data mining therapeutic targets. Then I analyse 16S metagenomics data combined with metabolite data from mice to investigate the treatment model of a proposed antibiotic treatment targetting the microbiota. Then I investigate the occurence frequency and diversity of proteases in metagenomics data in order to inform understanding of host-microbiota-diet interactions through protein and peptide associated glycan degradation by the gut microbiota. Finally I develop a system to facilitate the process of integrating metagenomics data for gene annotations. One of the main challenges in leveraging the scale of data availability in microbiome research is managing the data resources from microbiome studies. Through a series of analytical studies I used metagenomics data to identify community trends, to demonstrate therapeutic interventions and to do a wide scale screen for proteases that are central to human-microbiota interactions. These studies articulated the requirement for a computational framework to integrate and access metagenomics data in a reproducible way using a scalable data store. The thesis concludes explaining how data integration in microbiome research is needed to provide the insights into metagenomics data that are required for drug discovery

Use of microbiome data to explain the expression of productive traits in domestic species

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Veterinaria, leída el 11-03-2022El descubrimiento de comunidades microbianas asociadas simbióticamente a organismos eucariotas ha llevado a un cambio de paradigma en la definición de individuo biológico, que ahora se ve como una combinación codependiente del hospedador y su microbioma, u holobionte. Por tanto, el estudio de los microbiomas se ha convertido en algo fundamental para comprender la biología de los organismos vivos complejos. De hecho, se ha observado que las comunidades microbianas poseen un papel crucial en la salud, supervivencia, desarrollo y metabolismo del hospedador. Los recientes avances en secuenciación genética han supuesto un importante impulso para la investigación en microbiología, al permitir la obtención de bases de datos de secuenciación masiva que abarcan una gran parte de la diversidad presente dentro de los microbiomas. La era del next-generation sequencing ha aportado nuevos conocimientos sobre el efecto de las comunidades microbianas sobre el fenotipo del hospedador, con especial relevancia del microbioma intestinal. Para la industria ganadera este hecho ha dado lugar a importantes avances en la comprensión de los mecanismos biológicos que influyen en productividad, sostenibilidad y bienestar animal, lo que podría ser útil para afrontar los desafíos existentes en este sector...The discovery of microbial communities symbiotically associated with eukaryotic organisms has led to a paradigm shift in the definition of the biological individual, which is now seen as a co-dependent combination of the host and its microbiome, or holobiont. Thus, the study of microbiomes has become essential to understand the biology of complex living organisms. Indeed, current research points to a crucial role of microbial communities in host health, survivability, development and metabolism. Recent advances in DNA sequencing have entailed a significant boost to microbial research, allowing the generation of massive sequencing databases encompassing a large proportion of the diversity inside microbiomes. The era of next-generation sequencing has brought new knowledge about the role of microbial communities, with special significance for gut microbiomes, in host phenotype. For livestock industry, this has led to important advances in the understanding of biological mechanisms influencing animal welfare, productivity and sustainability, which could be useful to face existing challenges in animal production...Fac. de VeterinariaTRUEunpu

Understanding host-microbe interactions in maize kernel and sweetpotato leaf metagenomic profiles.

Functional and quantitative metagenomic profiling remains challenging and limits our understanding of host-microbe interactions. This body of work aims to mediate these challenges by using a novel quantitative reduced representation sequencing strategy (OmeSeq-qRRS), development of a fully automated software for quantitative metagenomic/microbiome profiling (Qmatey: quantitative metagenomic alignment and taxonomic identification using exact-matching) and implementing these tools for understanding plant-microbe-pathogen interactions in maize and sweetpotato. The next generation sequencing-based OmeSeq-qRRS leverages the strengths of shotgun whole genome sequencing and costs lower that the more affordable amplicon sequencing method. The novel FASTQ data compression/indexing and enhanced-multithreading of the MegaBLAST in Qmatey allows for computational speeds several thousand-folds faster than typical runs. Regardless of sample number, the analytical pipeline can be completed within days for genome-wide sequence data and provides broad-spectrum taxonomic profiling (virus to eukaryotes). As a proof of concept, these protocols and novel analytical pipelines were implemented to characterize the viruses within the leaf microbiome of a sweetpotato population that represents the global genetic diversity and the kernel microbiomes of genetically modified (GMO) and nonGMO maize hybrids. The metagenome profiles and high-density SNP data were integrated to identify host genetic factors (disease resistance and intracellular transport candidate genes) that underpin sweetpotato-virus interactions Additionally, microbial community dynamics were observed in the presence of pathogens, leading to the identification of multipartite interactions that modulate disease severity through co-infection and species competition. This study highlights a low-cost, quantitative and strain/species-level metagenomic profiling approach, new tools that complement the assay’s novel features and provide fast computation, and the potential for advancing functional metagenomic studies