Galeb Abu-Ali

Bio: Galeb Abu-Ali is an academic researcher from Harvard University. The author has contributed to research in topics: Microbiome & Metagenomics. The author has an hindex of 18, co-authored 27 publications receiving 2770 citations. Previous affiliations of Galeb Abu-Ali include Broad Institute & Center for Food Safety and Applied Nutrition.

The healthy human microbiome

Abstract: Humans are virtually identical in their genetic makeup, yet the small differences in our DNA give rise to tremendous phenotypic diversity across the human population. By contrast, the metagenome of the human microbiome—the total DNA content of microbes inhabiting our bodies—is quite a bit more variable, with only a third of its constituent genes found in a majority of healthy individuals. Understanding this variability in the “healthy microbiome” has thus been a major challenge in microbiome research, dating back at least to the 1960s, continuing through the Human Microbiome Project and beyond. Cataloguing the necessary and sufficient sets of microbiome features that support health, and the normal ranges of these features in healthy populations, is an essential first step to identifying and correcting microbial configurations that are implicated in disease. Toward this goal, several population-scale studies have documented the ranges and diversity of both taxonomic compositions and functional potentials normally observed in the microbiomes of healthy populations, along with possible driving factors such as geography, diet, and lifestyle. Here, we review several definitions of a ‘healthy microbiome’ that have emerged, the current understanding of the ranges of healthy microbial diversity, and gaps such as the characterization of molecular function and the development of ecological therapies to be addressed in the future.

Sequencing and beyond: integrating molecular 'omics' for microbial community profiling

Abstract: High-throughput DNA sequencing has proven invaluable for investigating diverse environmental and host-associated microbial communities. In this Review, we discuss emerging strategies for microbial community analysis that complement and expand traditional metagenomic profiling. These include novel DNA sequencing strategies for identifying strain-level microbial variation and community temporal dynamics; measuring multiple 'omic' data types that better capture community functional activity, such as transcriptomics, proteomics and metabolomics; and combining multiple forms of omic data in an integrated framework. We highlight studies in which the 'multi-omics' approach has led to improved mechanistic models of microbial community structure and function.

Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium

Abstract: In order for human microbiome studies to translate into actionable outcomes for health, meta-analysis of reproducible data from population-scale cohorts is needed. Achieving sufficient reproducibility in microbiome research has proven challenging. We report a baseline investigation of variability in taxonomic profiling for the Microbiome Quality Control (MBQC) project baseline study (MBQC-base). Blinded specimen sets from human stool, chemostats, and artificial microbial communities were sequenced by 15 laboratories and analyzed using nine bioinformatics protocols. Variability depended most on biospecimen type and origin, followed by DNA extraction, sample handling environment, and bioinformatics. Analysis of artificial community specimens revealed differences in extraction efficiency and bioinformatic classification. These results may guide researchers in experimental design choices for gut microbiome studies.

bioBakery: a meta'omic analysis environment.

Abstract: Summary bioBakery is a meta'omic analysis environment and collection of individual software tools with the capacity to process raw shotgun sequencing data into actionable microbial community feature profiles, summary reports, and publication-ready figures. It includes a collection of pre-configured analysis modules also joined into workflows for reproducibility. Availability and implementation bioBakery (http://huttenhower.sph.harvard.edu/biobakery) is publicly available for local installation as individual modules and as a virtual machine image. Each individual module has been developed to perform a particular task (e.g. quantitative taxonomic profiling or statistical analysis), and they are provided with source code, tutorials, demonstration data, and validation results; the bioBakery virtual image includes the entire suite of modules and their dependencies pre-installed. Images are available for both Amazon EC2 and Google Compute Engine. All software is open source under the MIT license. bioBakery is actively maintained with a support group at biobakery-users@googlegroups.com and new tools being added upon their release. Contact chuttenh@hsph.harvard.edu. Supplementary information Supplementary data are available at Bioinformatics online.

Metagenomic biomarker discovery and explanation

Abstract: This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.

Gut microbiota, metabolites and host immunity

Abstract: The microbiota - the collection of microorganisms that live within and on all mammals - provides crucial signals for the development and function of the immune system. Increased availability of technologies that profile microbial communities is facilitating the entry of many immunologists into the evolving field of host-microbiota studies. The microbial communities, their metabolites and components are not only necessary for immune homeostasis, they also influence the susceptibility of the host to many immune-mediated diseases and disorders. In this Review, we discuss technological and computational approaches for investigating the microbiome, as well as recent advances in our understanding of host immunity and microbial mutualism with a focus on specific microbial metabolites, bacterial components and the immune system.

Bifidobacteria can protect from enteropathogenic infection through production of acetate

Abstract: The human gut is colonized with a wide variety of microorganisms, including species, such as those belonging to the bacterial genus Bifidobacterium, that have beneficial effects on human physiology and pathology. Among the most distinctive benefits of bifidobacteria are modulation of host defence responses and protection against infectious diseases. Nevertheless, the molecular mechanisms underlying these effects have barely been elucidated. To investigate these mechanisms, we used mice associated with certain bifidobacterial strains and a simplified model of lethal infection with enterohaemorrhagic Escherichia coli O157:H7, together with an integrated 'omics' approach. Here we show that genes encoding an ATP-binding-cassette-type carbohydrate transporter present in certain bifidobacteria contribute to protecting mice against death induced by E. coli O157:H7. We found that this effect can be attributed, at least in part, to increased production of acetate and that translocation of the E. coli O157:H7 Shiga toxin from the gut lumen to the blood was inhibited. We propose that acetate produced by protective bifidobacteria improves intestinal defence mediated by epithelial cells and thereby protects the host against lethal infection.

Opportunities and obstacles for deep learning in biology and medicine.

Abstract: Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases.

Abstract: Inflammatory bowel diseases, which include Crohn's disease and ulcerative colitis, affect several million individuals worldwide. Crohn's disease and ulcerative colitis are complex diseases that are heterogeneous at the clinical, immunological, molecular, genetic, and microbial levels. Individual contributing factors have been the focus of extensive research. As part of the Integrative Human Microbiome Project (HMP2 or iHMP), we followed 132 subjects for one year each to generate integrated longitudinal molecular profiles of host and microbial activity during disease (up to 24 time points each; in total 2,965 stool, biopsy, and blood specimens). Here we present the results, which provide a comprehensive view of functional dysbiosis in the gut microbiome during inflammatory bowel disease activity. We demonstrate a characteristic increase in facultative anaerobes at the expense of obligate anaerobes, as well as molecular disruptions in microbial transcription (for example, among clostridia), metabolite pools (acylcarnitines, bile acids, and short-chain fatty acids), and levels of antibodies in host serum. Periods of disease activity were also marked by increases in temporal variability, with characteristic taxonomic, functional, and biochemical shifts. Finally, integrative analysis identified microbial, biochemical, and host factors central to this dysregulation. The study's infrastructure resources, results, and data, which are available through the Inflammatory Bowel Disease Multi'omics Database ( http://ibdmdb.org ), provide the most comprehensive description to date of host and microbial activities in inflammatory bowel diseases.