School of Mathematics and Statistics - Research Publications
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ItemStatistical challenges in longitudinal microbiome data analysis.(Oxford University Press (OUP), 2022-07-18)The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.
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ItemSincast: a computational framework to predict cell identities in single-cell transcriptomes using bulk atlases as references(OXFORD UNIV PRESS, 2022-03-31)Characterizing the molecular identity of a cell is an essential step in single-cell RNA sequencing (scRNA-seq) data analysis. Numerous tools exist for predicting cell identity using single-cell reference atlases. However, many challenges remain, including correcting for inherent batch effects between reference and query data andinsufficient phenotype data from the reference. One solution is to project single-cell data onto established bulk reference atlases to leverage their rich phenotype information. Sincast is a computational framework to query scRNA-seq data by projection onto bulk reference atlases. Prior to projection, single-cell data are transformed to be directly comparable to bulk data, either with pseudo-bulk aggregation or graph-based imputation to address sparse single-cell expression profiles. Sincast avoids batch effect correction, and cell identity is predicted along a continuum to highlight new cell states not found in the reference atlas. In several case study scenarios, we show that Sincast projects single cells into the correct biological niches in the expression space of the bulk reference atlas. We demonstrate the effectiveness of our imputation approach that was specifically developed for querying scRNA-seq data based on bulk reference atlases. We show that Sincast is an efficient and powerful tool for single-cell profiling that will facilitate downstream analysis of scRNA-seq data.
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ItemAlterations in the Gut Fungal Community in a Mouse Model of Huntington's Disease(AMER SOC MICROBIOLOGY, 2022-04-01)Huntington's disease (HD) is a neurodegenerative disorder caused by a trinucleotide expansion in the HTT gene, which is expressed throughout the brain and body, including the gut epithelium and enteric nervous system. Afflicted individuals suffer from progressive impairments in motor, psychiatric, and cognitive faculties, as well as peripheral deficits, including the alteration of the gut microbiome. However, studies characterizing the gut microbiome in HD have focused entirely on the bacterial component, while the fungal community (mycobiome) has been overlooked. The gut mycobiome has gained recognition for its role in host homeostasis and maintenance of the gut epithelial barrier. We aimed to characterize the gut mycobiome profile in HD using fecal samples collected from the R6/1 transgenic mouse model (and wild-type littermate controls) from 4 to 12 weeks of age, corresponding to presymptomatic through to early disease stages. Shotgun sequencing was performed on fecal DNA samples, followed by metagenomic analyses. The HD gut mycobiome beta diversity was significantly different from that of wild-type littermates at 12 weeks of age, while no genotype differences were observed at the earlier time points. Similarly, greater alpha diversity was observed in the HD mice by 12 weeks of age. Key taxa, including Malassezia restricta, Yarrowia lipolytica, and Aspergillus species, were identified as having a negative association with HD. Furthermore, integration of the bacterial and fungal data sets at 12 weeks of age identified negative correlations between the HD-associated fungal species and Lactobacillus reuteri. These findings provide new insights into gut microbiome alterations in HD and may help identify novel therapeutic targets. IMPORTANCE Huntington's disease (HD) is a fatal neurodegenerative disorder affecting both the mind and body. We have recently discovered that gut bacteria are disrupted in HD. The present study provides the first evidence of an altered gut fungal community (mycobiome) in HD. The genomes of many thousands of gut microbes were sequenced and used to assess "metagenomics" in particular the different types of fungal species in the HD versus control gut, in a mouse model. At an early disease stage, before the onset of symptoms, the overall gut mycobiome structure (array of fungi) in HD mice was distinct from that of their wild-type littermates. Alterations of multiple key fungi species were identified as being associated with the onset of disease symptoms, some of which showed strong correlations with the gut bacterial community. This study highlights the potential role of gut fungi in HD and may facilitate the development of novel therapeutic approaches.
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ItemHost Traits and Phylogeny Contribute to Shaping Coral-Bacterial Symbioses(AMER SOC MICROBIOLOGY, 2022-03-07)The success of tropical scleractinian corals depends on their ability to establish symbioses with microbial partners. Host phylogeny and traits are known to shape the coral microbiome, but to what extent they affect its composition remains unclear. Here, by using 12 coral species representing the complex and robust clades, we explored the influence of host phylogeny, skeletal architecture, and reproductive mode on the microbiome composition, and further investigated the structure of the tissue and skeleton bacterial communities. Our results show that host phylogeny and traits explained 14% of the tissue and 13% of the skeletal microbiome composition, providing evidence that these predictors contributed to shaping the holobiont in terms of presence and relative abundance of bacterial symbionts. Based on our data, we conclude that host phylogeny affects the presence of specific microbial lineages, reproductive mode predictably influences the microbiome composition, and skeletal architecture works like a filter that affects bacterial relative abundance. We show that the β-diversity of coral tissue and skeleton microbiomes differed, but we found that a large overlapping fraction of bacterial sequences were recovered from both anatomical compartments, supporting the hypothesis that the skeleton can function as a microbial reservoir. Additionally, our analysis of the microbiome structure shows that 99.6% of tissue and 99.7% of skeletal amplicon sequence variants (ASVs) were not consistently present in at least 30% of the samples, suggesting that the coral tissue and skeleton are dominated by rare bacteria. Together, these results provide novel insights into the processes driving coral-bacterial symbioses, along with an improved understanding of the scleractinian microbiome.
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ItemNo Preview AvailableA field guide to cultivating computational biology(PUBLIC LIBRARY SCIENCE, 2021-10-01)Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.
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ItemGene-environment-gut interactions in Huntington's disease mice are associated with environmental modulation of the gut microbiome(CELL PRESS, 2022-01-21)Gut dysbiosis in Huntington's disease (HD) has recently been reported using microbiome profiling in R6/1 HD mice and replicated in clinical HD. In HD mice, environmental enrichment (EE) and exercise (EX) were shown to have therapeutic impacts on the brain and associated symptoms. We hypothesize that these housing interventions modulate the gut microbiome, configuring one of the mechanisms that mediate their therapeutic effects observed in HD. We exposed R6/1 mice to a protocol of either EE or EX, relative to standard-housed control conditions, before the onset of gut dysbiosis and motor deficits. We characterized gut structure and function, as well as gut microbiome profiling using 16S rRNA sequencing. Multivariate analysis identified specific orders, namely Bacteroidales, Lachnospirales and Oscillospirales, as the main bacterial signatures that discriminate between housing conditions. Our findings suggest a promising role for the gut microbiome in mediating the effects of EE and EX exposures, and possibly other environmental interventions, in HD mice.
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ItemInterpretation of network-based integration from multi-omics longitudinal data.(Oxford University Press (OUP), 2022-03-21)Multi-omics integration is key to fully understand complex biological processes in an holistic manner. Furthermore, multi-omics combined with new longitudinal experimental design can unreveal dynamic relationships between omics layers and identify key players or interactions in system development or complex phenotypes. However, integration methods have to address various experimental designs and do not guarantee interpretable biological results. The new challenge of multi-omics integration is to solve interpretation and unlock the hidden knowledge within the multi-omics data. In this paper, we go beyond integration and propose a generic approach to face the interpretation problem. From multi-omics longitudinal data, this approach builds and explores hybrid multi-omics networks composed of both inferred and known relationships within and between omics layers. With smart node labelling and propagation analysis, this approach predicts regulation mechanisms and multi-omics functional modules. We applied the method on 3 case studies with various multi-omics designs and identified new multi-layer interactions involved in key biological functions that could not be revealed with single omics analysis. Moreover, we highlighted interplay in the kinetics that could help identify novel biological mechanisms. This method is available as an R package netOmics to readily suit any application.
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ItemNo Preview AvailableAn integrated metagenomics and metabolomics approach implicates the microbiota-gut-brain axis in the pathogenesis of Huntington's disease(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2021-01-01)BACKGROUND: Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder with onset and severity of symptoms influenced by various environmental factors. Recent discoveries have highlighted the importance of the gastrointestinal microbiome in mediating the gut-brain-axis bidirectional communication via circulating factors. Using shotgun sequencing, we investigated the gut microbiome composition in the R6/1 transgenic mouse model of HD from 4 to 12 weeks of age (early adolescent through to adult stages). Targeted metabolomics was also performed on the blood plasma of these mice (n = 9 per group) at 12 weeks of age to investigate potential effects of gut dysbiosis on the plasma metabolome profile. RESULTS: Modelled time profiles of each species, KEGG Orthologs and bacterial genes, revealed heightened volatility in the R6/1 mice, indicating potential early effects of the HD mutation in the gut. In addition to gut dysbiosis in R6/1 mice at 12 weeks of age, gut microbiome function was perturbed. In particular, the butanoate metabolism pathway was elevated, suggesting increased production of the protective SCFA, butyrate, in the gut. No significant alterations were found in the plasma butyrate and propionate levels in the R6/1 mice at 12 weeks of age. The statistical integration of the metagenomics and metabolomics unraveled several Bacteroides species that were negatively correlated with ATP and pipecolic acid in the plasma. CONCLUSIONS: The present study revealed the instability of the HD gut microbiome during the pre-motor symptomatic stage of the disease which may have dire consequences on the host's health. Perturbation of the HD gut microbiome function prior to significant cognitive and motor dysfunction suggest the potential role of the gut in modulating the pathogenesis of HD, potentially via specific altered plasma metabolites which mediate gut-brain signaling.
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Itemmultiomics: A user-friendly multi-omics data harmonisation R pipeline(F1000 Research Ltd, 2021-07-06)Data from multiple omics layers of a biological system is growing in quantity, heterogeneity and dimensionality. Simultaneous multi-omics data integration is a growing field of research as it has strong potential to unlock information on previously hidden biological relationships leading to early diagnosis, prognosis and expedited treatments. Many tools for multi-omics data integration are being developed. However, these tools are often restricted to highly specific experimental designs, and types of omics data. While some general methods do exist, they require specific data formats and experimental conditions. A major limitation in the field is a lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation. There is an increasing demand for a generic multi-omics pipeline to facilitate general-purpose data exploration and analysis of heterogeneous data. Therefore, we present our R multiomics pipeline as an easy to use and flexible pipeline that takes unrefined multi-omics data as input, sample information and user-specified parameters to generate a list of output plots and data tables for quality control and downstream analysis. We have demonstrated application of the pipeline on two separate COVID-19 case studies. We enabled limited checkpointing where intermediate output is staged to allow continuation after errors or interruptions in the pipeline and generate a script for reproducing the analysis to improve reproducibility. A seamless integration with the mixOmics R package is achieved, as the R data object can be loaded and manipulated with mixOmics functions. Our pipeline can be installed as an R package or from the git repository, and is accompanied by detailed documentation with walkthroughs on two case studies. The pipeline is also available as Docker and Singularity containers.