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      The gut microbiome: a key player in the complexity of amyotrophic lateral sclerosis (ALS)

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          Abstract

          Background

          Much progress has been made in mapping genetic abnormalities linked to amyotrophic lateral sclerosis (ALS), but the majority of cases still present with no known underlying cause. Furthermore, even in families with a shared genetic abnormality there is significant phenotypic variability, suggesting that non-genetic elements may modify pathogenesis. Identification of such disease-modifiers is important as they might represent new therapeutic targets. A growing body of research has begun to shed light on the role played by the gut microbiome in health and disease with a number of studies linking abnormalities to ALS.

          Main body

          The microbiome refers to the genes belonging to the myriad different microorganisms that live within and upon us, collectively known as the microbiota. Most of these microbes are found in the intestines, where they play important roles in digestion and the generation of key metabolites including neurotransmitters. The gut microbiota is an important aspect of the environment in which our bodies operate and inter-individual differences may be key to explaining the different disease outcomes seen in ALS. Work has begun to investigate animal models of the disease, and the gut microbiomes of people living with ALS, revealing changes in the microbial communities of these groups. The current body of knowledge will be summarised in this review. Advances in microbiome sequencing methods will be highlighted, as their improved resolution now enables researchers to further explore differences at a functional level. Proposed mechanisms connecting the gut microbiome to neurodegeneration will also be considered, including direct effects via metabolites released into the host circulation and indirect effects on bioavailability of nutrients and even medications.

          Conclusion

          Profiling of the gut microbiome has the potential to add an environmental component to rapidly advancing studies of ALS genetics and move research a step further towards personalised medicine for this disease. Moreover, should compelling evidence of upstream neurotoxicity or neuroprotection initiated by gut microbiota emerge, modification of the microbiome will represent a potential new avenue for disease modifying therapies. For an intractable condition with few current therapeutic options, further research into the ALS microbiome is of crucial importance.

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          Most cited references98

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          An obesity-associated gut microbiome with increased capacity for energy harvest.

          The worldwide obesity epidemic is stimulating efforts to identify host and environmental factors that affect energy balance. Comparisons of the distal gut microbiota of genetically obese mice and their lean littermates, as well as those of obese and lean human volunteers have revealed that obesity is associated with changes in the relative abundance of the two dominant bacterial divisions, the Bacteroidetes and the Firmicutes. Here we demonstrate through metagenomic and biochemical analyses that these changes affect the metabolic potential of the mouse gut microbiota. Our results indicate that the obese microbiome has an increased capacity to harvest energy from the diet. Furthermore, this trait is transmissible: colonization of germ-free mice with an 'obese microbiota' results in a significantly greater increase in total body fat than colonization with a 'lean microbiota'. These results identify the gut microbiota as an additional contributing factor to the pathophysiology of obesity.
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            Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

            Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.
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              A human gut microbial gene catalogue established by metagenomic sequencing.

              To understand the impact of gut microbes on human health and well-being it is crucial to assess their genetic potential. Here we describe the Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals. The gene set, approximately 150 times larger than the human gene complement, contains an overwhelming majority of the prevalent (more frequent) microbial genes of the cohort and probably includes a large proportion of the prevalent human intestinal microbial genes. The genes are largely shared among individuals of the cohort. Over 99% of the genes are bacterial, indicating that the entire cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species, which are also largely shared. We define and describe the minimal gut metagenome and the minimal gut bacterial genome in terms of functions present in all individuals and most bacteria, respectively.
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                Author and article information

                Contributors
                c.j.mcdermott@sheffield.ac.uk
                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                1741-7015
                20 January 2021
                20 January 2021
                2021
                : 19
                : 13
                Affiliations
                [1 ]GRID grid.11835.3e, ISNI 0000 0004 1936 9262, Sheffield Institute for Translational Neuroscience, University of Sheffield, ; Sheffield, UK
                [2 ]GRID grid.168010.e, ISNI 0000000419368956, Stanford Center for Genomics and Personalized Medicine, , Stanford University School of Medicine, ; Stanford, USA
                [3 ]GRID grid.13992.30, ISNI 0000 0004 0604 7563, Department of Computer Science and Applied Mathematics, , Weizmann Institute of Science, ; Rehovot, Israel
                [4 ]GRID grid.13992.30, ISNI 0000 0004 0604 7563, Department of Immunology, , Weizmann Institute of Science, ; Rehovot, Israel
                [5 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, Division of Cancer-Microbiome Research, , DKFZ, ; Heidelberg, Germany
                [6 ]GRID grid.5884.1, ISNI 0000 0001 0303 540X, Centre for Behavioural Science and Applied Psychology, , Sheffield Hallam University, ; Sheffield, UK
                Article
                1885
                10.1186/s12916-020-01885-3
                7816375
                33468103
                d20bf0eb-4aaa-4873-8e42-5f8ded983565
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 13 August 2020
                : 9 December 2020
                Funding
                Funded by: NIHR Sheffield Biomedical Research Centre (GB)
                Funded by: NIHR Oxford Biomedical Research Centre (GB)
                Funded by: FundRef http://dx.doi.org/10.13039/100010269, Wellcome Trust;
                Funded by: NIH Human Microbiome Project
                Award ID: 1U54DE02378901
                Funded by: Crown Human Genome Center
                Funded by: European Research Council
                Funded by: Israel Science Foundation
                Funded by: Bill & Melinda Gates Foundation
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;
                Funded by: Medical Research Council (GB)
                Funded by: UK Research and Innovation (GB)
                Funded by: University of Sheffield (GB)
                Categories
                Review
                Custom metadata
                © The Author(s) 2021

                Medicine
                amyotrophic lateral sclerosis,als,microbiome,disease modifiers,microbial metabolites,microbial

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