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Xu X, Feng Q, Zhang T, Gao Y, Cheng Q, Zhang W, Wu Q, Xu K, Li Y, Nguyen N, Taft DH, Mills DA, Lemay DG, Zhu W, Mao S, Zhang A, Xu K, Liu J. Infant age inversely correlates with gut carriage of resistance genes, reflecting modifications in microbial carbohydrate metabolism during early life. IMETA 2024; 3:e169. [PMID: 38882494 PMCID: PMC11170968 DOI: 10.1002/imt2.169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/23/2023] [Accepted: 12/06/2023] [Indexed: 06/18/2024]
Abstract
The infant gut microbiome is increasingly recognized as a reservoir of antibiotic resistance genes, yet the assembly of gut resistome in infants and its influencing factors remain largely unknown. We characterized resistome in 4132 metagenomes from 963 infants in six countries and 4285 resistance genes were observed. The inherent resistome pattern of healthy infants (N = 272) could be distinguished by two stages: a multicompound resistance phase (Months 0-7) and a tetracycline-mupirocin-β-lactam-dominant phase (Months 8-14). Microbial taxonomy explained 40.7% of the gut resistome of healthy infants, with Escherichia (25.5%) harboring the most resistance genes. In a further analysis with all available infants (N = 963), we found age was the strongest influencer on the resistome and was negatively correlated with the overall resistance during the first 3 years (p < 0.001). Using a random-forest approach, a set of 34 resistance genes could be used to predict age (R 2 = 68.0%). Leveraging microbial host inference analyses, we inferred the age-dependent assembly of infant resistome was a result of shifts in the gut microbiome, primarily driven by changes in taxa that disproportionately harbor resistance genes across taxa (e.g., Escherichia coli more frequently harbored resistance genes than other taxa). We performed metagenomic functional profiling and metagenomic assembled genome analyses whose results indicate that the development of gut resistome was driven by changes in microbial carbohydrate metabolism, with an increasing need for carbohydrate-active enzymes from Bacteroidota and a decreasing need for Pseudomonadota during infancy. Importantly, we observed increased acquired resistance genes over time, which was related to increased horizontal gene transfer in the developing infant gut microbiome. In summary, infant age was negatively correlated with antimicrobial resistance gene levels, reflecting a composition shift in the gut microbiome, likely driven by the changing need for microbial carbohydrate metabolism during early life.
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Affiliation(s)
- Xinming Xu
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
- Department of Nutrition and Food Hygiene, School of Public Health, Institute of Nutrition Fudan University Shanghai China
| | - Qingying Feng
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
- Biological Engineering Division Massachusetts Institute of Technology (MIT) Cambridge Massachusetts USA
| | - Tao Zhang
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
| | - Yunlong Gao
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
| | - Qu Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College Huazhong University of Science and Technology Wuhan China
| | - Wanqiu Zhang
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
| | - Qinglong Wu
- Department of Pathology and Immunology Baylor College of Medicine Houston Texas USA
| | - Ke Xu
- Department of Statistics University of Chicago Chicago Illinois
| | - Yucan Li
- State Key Laboratory of Genetic Engineering, Human Phenome Institute Fudan University Shanghai China
| | - Nhu Nguyen
- Department of Food Science and Technology University of California, Davis Davis California USA
| | - Diana H Taft
- Department of Food Science and Technology University of California, Davis Davis California USA
| | - David A Mills
- Department of Food Science and Technology University of California, Davis Davis California USA
- Department of Viticulture and Enology, Robert Mondavi Institute for Wine and Food Science University of California, Davis Davis California USA
| | - Danielle G Lemay
- USDA ARS Western Human Nutrition Research Center Davis California USA
| | - Weiyun Zhu
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
| | - Shengyong Mao
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
| | - Anyun Zhang
- Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences Sichuan University Chengdu China
| | - Kelin Xu
- Department of Biostatistics, Key Laboratory of Public Health Safety, NHC Key Laboratory of Health Technology Assessment, School of Public Health Fudan University Shanghai China
| | - Jinxin Liu
- Laboratory of Gastrointestinal Microbiology, College of Animal Science & Technology Nanjing Agricultural University Nanjing China
- Jiangsu Key Laboratory of Gastrointestinal Nutrition and Animal Health, National Center for International Research on Animal Gut Nutrition Nanjing Agricultural University Nanjing China
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Ye X, Yu F, Zhou J, Zhao C, Wu J, Ni X. Analysis of the gut microbiota in children with gastroesophageal reflux disease using metagenomics and metabolomics. Front Cell Infect Microbiol 2023; 13:1267192. [PMID: 37900308 PMCID: PMC10613033 DOI: 10.3389/fcimb.2023.1267192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/19/2023] [Indexed: 10/31/2023] Open
Abstract
Background There is no direct evidence of gut microbiota disturbance in children with gastroesophageal reflux disease (GERD). This study aimed to provide direct evidence and a comprehensive understanding of gut microbiota disturbance in children with GERD through combined metagenomic and metabolomic analysis. Methods 30 children with GERD and 30 healthy controls (HCs) were continuously enrolled, and the demographic and clinical characteristics of the subjects were collected. First, 16S rRNA sequencing was used to evaluate differences in the gut microbiota between children with GERD and HC group, and 10 children with GERD and 10 children in the HC group were selected for metagenomic analysis. Nontargeted metabolomic analysis was performed using liquid chromatography/mass spectrometry (LC/MS), and metagenomic and metabolomic data were analyzed together. Results There were significant differences in the gut microbiota diversity and composition between children with GERD and HCs. The dominant bacteria in children with GERD were Proteobacteria and Bacteroidota. At the species level, the top three core bacterial groups were Bacteroides stercoris, Bacteroides vulgatus and Alistipes putredinis. The main differential pathways were identified to be related to energy, amino acid, vitamin, carbohydrate and lipid metabolism. LC/MS detected 288 different metabolites in the positive and negative ion modes between children with GERD and HCs, which were mainly involved in arachidonic acid (AA), tyrosine, glutathione and caffeine metabolism. Conclusion This study provides new evidence of the pathogenesis of GERD. There are significant differences in the gut microbiota, metabolites and metabolic pathways between HCs and children with GERD, and the differences in metabolites are related to specific changes in bacterial abundance. In the future, GERD may be treated by targeting specific bacteria related to AA metabolism.
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Affiliation(s)
- Xiaolin Ye
- Department of Gastroenterology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Feihong Yu
- Department of Gastroenterology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Jin Zhou
- Department of Gastroenterology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Chunna Zhao
- Department of Gastroenterology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Jie Wu
- Department of Gastroenterology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Xin Ni
- National Center for Pediatric Cancer Surveillance, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
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Bai H, Liu T, Wang S, Shen L, Wang Z. Variations in gut microbiome and metabolites of dogs with acute diarrhea in poodles and Labrador retrievers. Arch Microbiol 2023; 205:97. [PMID: 36823480 DOI: 10.1007/s00203-023-03439-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/09/2023] [Accepted: 02/11/2023] [Indexed: 02/25/2023]
Abstract
For different breeds of dogs with acute diarrhea, the gut microbiota and metabolome profiles are unclear. This prospective observational study analyzed the gut microbiomes of poodles with acute diarrhea and Labrador retrievers with acute diarrhea based on 16S amplicon sequencing, with respective healthy dogs as controls. Fecal non-target metabolomics and metagenomics were performed on poodles with acute diarrhea. This study found that the diversity and structure of the microbial community differed significantly between the two breeds in cohorts of healthy dogs. Two breeds of dogs with acute diarrhea demonstrated different changes in microbial communities and metabolic functions. The metabolism of starch and sucrose was significantly decreased in dogs with acute diarrhea, which may be attributed to the reduced activity of dextran dextrinase. Non-targeted metabolomics identified 21 abnormal metabolic pathways exhibited by dogs with acute diarrhea, including starch, amino acid, bile acid metabolism, etc., and were closely related to specific intestinal flora. This study provided new insights into breed specificity and the development of dietary treatment strategy in canine gastrointestinal disease.
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Affiliation(s)
- Huasong Bai
- Nourse Centre for Pet Nutrition, Wuhu, 241200, China
| | - Tong Liu
- Nourse Centre for Pet Nutrition, Wuhu, 241200, China
| | - Songjun Wang
- Nourse Centre for Pet Nutrition, Wuhu, 241200, China
| | - Liya Shen
- Nourse Centre for Pet Nutrition, Wuhu, 241200, China
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Escudeiro P, Henry CS, Dias RP. Functional characterization of prokaryotic dark matter: the road so far and what lies ahead. CURRENT RESEARCH IN MICROBIAL SCIENCES 2022; 3:100159. [PMID: 36561390 PMCID: PMC9764257 DOI: 10.1016/j.crmicr.2022.100159] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 07/18/2022] [Accepted: 08/05/2022] [Indexed: 12/25/2022] Open
Abstract
Eight-hundred thousand to one trillion prokaryotic species may inhabit our planet. Yet, fewer than two-hundred thousand prokaryotic species have been described. This uncharted fraction of microbial diversity, and its undisclosed coding potential, is known as the "microbial dark matter" (MDM). Next-generation sequencing has allowed to collect a massive amount of genome sequence data, leading to unprecedented advances in the field of genomics. Still, harnessing new functional information from the genomes of uncultured prokaryotes is often limited by standard classification methods. These methods often rely on sequence similarity searches against reference genomes from cultured species. This hinders the discovery of unique genetic elements that are missing from the cultivated realm. It also contributes to the accumulation of prokaryotic gene products of unknown function among public sequence data repositories, highlighting the need for new approaches for sequencing data analysis and classification. Increasing evidence indicates that these proteins of unknown function might be a treasure trove of biotechnological potential. Here, we outline the challenges, opportunities, and the potential hidden within the functional dark matter (FDM) of prokaryotes. We also discuss the pitfalls surrounding molecular and computational approaches currently used to probe these uncharted waters, and discuss future opportunities for research and applications.
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Affiliation(s)
- Pedro Escudeiro
- BioISI - Instituto de Biosistemas e Ciências Integrativas, Faculdade de Ciências, Universidade de Lisboa, Lisboa 1749-016, Portugal
| | - Christopher S. Henry
- Argonne National Laboratory, Lemont, Illinois, USA,University of Chicago, Chicago, Illinois, USA
| | - Ricardo P.M. Dias
- BioISI - Instituto de Biosistemas e Ciências Integrativas, Faculdade de Ciências, Universidade de Lisboa, Lisboa 1749-016, Portugal,iXLab - Innovation for National Biological Resilience, Faculdade de Ciências, Universidade de Lisboa, Lisboa 1749-016, Portugal,Corresponding author.
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Abstract
Antimicrobial resistance (AMR) represents a significant source of morbidity and mortality worldwide, with expectations that AMR-associated consequences will continue to worsen throughout the coming decades. Since resistance to antibiotics is encoded in the microbiome, interventions aimed at altering the taxonomic composition of the gut might allow us to prophylactically engineer microbiomes that harbor fewer antibiotic resistant genes (ARGs). Diet is one method of intervention, and yet little is known about the association between diet and antimicrobial resistance. To address this knowledge gap, we examined diet using the food frequency questionnaire (FFQ; habitual diet) and 24-h dietary recalls (Automated Self-Administered 24-h [ASA24®] tool) coupled with an analysis of the microbiome using shotgun metagenome sequencing in 290 healthy adult participants of the United States Department of Agriculture (USDA) Nutritional Phenotyping Study. We found that aminoglycosides were the most abundant and prevalent mechanism of AMR in these healthy adults and that aminoglycoside-O-phosphotransferases (aph3-dprime) correlated negatively with total calories and soluble fiber intake. Individuals in the lowest quartile of ARGs (low-ARG) consumed significantly more fiber in their diets than medium- and high-ARG individuals, which was concomitant with increased abundances of obligate anaerobes, especially from the family Clostridiaceae, in their gut microbiota. Finally, we applied machine learning to examine 387 dietary, physiological, and lifestyle features for associations with antimicrobial resistance, finding that increased phylogenetic diversity of diet was associated with low-ARG individuals. These data suggest diet may be a potential method for reducing the burden of AMR. IMPORTANCE Antimicrobial resistance (AMR) represents a considerable burden to health care systems, with the public health community largely in consensus that AMR will be a major cause of death worldwide in the coming decades. Humans carry antibiotic resistance in the microbes that live in and on us, collectively known as the human microbiome. Diet is a powerful method for shaping the human gut microbiome and may be a tractable method for lessening antibiotic resistance, and yet little is known about the relationship between diet and AMR. We examined this relationship in healthy individuals who contained various abundances of antibiotic resistance genes and found that individuals who consumed diverse diets that were high in fiber and low in animal protein had fewer antibiotic resistance genes. Dietary interventions may be useful for lessening the burden of antimicrobial resistance and might ultimately motivate dietary guidelines which will consider how nutrition can reduce the impact of infectious disease.
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Mathieu A, Leclercq M, Sanabria M, Perin O, Droit A. Machine Learning and Deep Learning Applications in Metagenomic Taxonomy and Functional Annotation. Front Microbiol 2022; 13:811495. [PMID: 35359727 PMCID: PMC8964132 DOI: 10.3389/fmicb.2022.811495] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/02/2022] [Indexed: 12/12/2022] Open
Abstract
Shotgun sequencing of environmental DNA (i.e., metagenomics) has revolutionized the field of environmental microbiology, allowing the characterization of all microorganisms in a sequencing experiment. To identify the microbes in terms of taxonomy and biological activity, the sequenced reads must necessarily be aligned on known microbial genomes/genes. However, current alignment methods are limited in terms of speed and can produce a significant number of false positives when detecting bacterial species or false negatives in specific cases (virus, plasmids, and gene detection). Moreover, recent advances in metagenomics have enabled the reconstruction of new genomes using de novo binning strategies, but these genomes, not yet fully characterized, are not used in classic approaches, whereas machine and deep learning methods can use them as models. In this article, we attempted to review the different methods and their efficiency to improve the annotation of metagenomic sequences. Deep learning models have reached the performance of the widely used k-mer alignment-based tools, with better accuracy in certain cases; however, they still must demonstrate their robustness across the variety of environmental samples and across the rapid expansion of accessible genomes in databases.
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Affiliation(s)
- Alban Mathieu
- Computational Biology Laboratory, CHU de Québec - Université Laval Research Centre, Québec City, QC, Canada
| | - Mickael Leclercq
- Computational Biology Laboratory, CHU de Québec - Université Laval Research Centre, Québec City, QC, Canada
| | | | - Olivier Perin
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-Bois, France
| | - Arnaud Droit
- Computational Biology Laboratory, CHU de Québec - Université Laval Research Centre, Québec City, QC, Canada
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Blakeley-Ruiz JA, Kleiner M. Considerations for Constructing a Protein Sequence Database for Metaproteomics. Comput Struct Biotechnol J 2022; 20:937-952. [PMID: 35242286 PMCID: PMC8861567 DOI: 10.1016/j.csbj.2022.01.018] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 12/14/2022] Open
Abstract
Mass spectrometry-based metaproteomics has emerged as a prominent technique for interrogating the functions of specific organisms in microbial communities, in addition to total community function. Identifying proteins by mass spectrometry requires matching mass spectra of fragmented peptide ions to a database of protein sequences corresponding to the proteins in the sample. This sequence database determines which protein sequences can be identified from the measurement, and as such the taxonomic and functional information that can be inferred from a metaproteomics measurement. Thus, the construction of the protein sequence database directly impacts the outcome of any metaproteomics study. Several factors, such as source of sequence information and database curation, need to be considered during database construction to maximize accurate protein identifications traceable to the species of origin. In this review, we provide an overview of existing strategies for database construction and the relevant studies that have sought to test and validate these strategies. Based on this review of the literature and our experience we provide a decision tree and best practices for choosing and implementing database construction strategies.
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Affiliation(s)
- J. Alfredo Blakeley-Ruiz
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, USA
- Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Corresponding authors at: Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, USA.
| | - Manuel Kleiner
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, USA
- Corresponding authors at: Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, USA.
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Pellitier PT, Zak DR. Ectomycorrhizal fungal decay traits along a soil nitrogen gradient. THE NEW PHYTOLOGIST 2021; 232:2152-2164. [PMID: 34533216 DOI: 10.1111/nph.17734] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 08/16/2021] [Indexed: 06/13/2023]
Abstract
The extent to which ectomycorrhizal (ECM) fungi decay soil organic matter (SOM) has implications for accurately predicting forest ecosystem response to climate change. Investigating the distribution of gene traits associated with SOM decay among ectomycorrhizal fungal communities could improve understanding of SOM dynamics and plant nutrition. We hypothesized that soil inorganic nitrogen (N) availability structures the distribution of ECM fungal genes associated with SOM decay and, specifically, that ECM fungal communities occurring in inorganic N-poor soils have greater SOM decay potential. To test this hypothesis, we paired amplicon and shotgun metagenomic sequencing of 60 ECM fungal communities associating with Quercus rubra along a natural soil inorganic N gradient. Ectomycorrhizal fungal communities occurring in low inorganic N soils were enriched in gene families involved in the decay of lignin, cellulose, and chitin. Ectomycorrhizal fungal community composition was the strongest driver of shifts in metagenomic estimates of fungal decay potential. Our study simultaneously illuminates the identity of key ECM fungal taxa and gene families potentially involved in the decay of SOM, and we link rhizomorphic and medium-distance hyphal morphologies with enhanced SOM decay potential. Coupled shifts in ECM fungal community composition and community-level decay gene frequencies are consistent with outcomes of trait-mediated community assembly processes.
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Affiliation(s)
- Peter T Pellitier
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Donald R Zak
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, 48109, USA
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Allen LH, Hampel D, Shahab-Ferdows S, Andersson M, Barros E, Doel AM, Eriksen KG, Christensen SH, Islam M, Kac G, Keya FK, Michaelsen KF, de Barros Mucci D, Njie F, Peerson JM, Moore SE. The Mothers, Infants, and Lactation Quality (MILQ) Study: A Multi-Center Collaboration. Curr Dev Nutr 2021; 5:nzab116. [PMID: 34712893 PMCID: PMC8546155 DOI: 10.1093/cdn/nzab116] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 09/02/2021] [Accepted: 09/08/2021] [Indexed: 12/19/2022] Open
Abstract
Little valid information is available on human milk nutrient concentrations, especially for micronutrients (MNs), and there are no valid reference values (RVs) across lactation. In this multi-center collaborative study, RVs will be established for human milk nutrients across the first 8.5 mo postpartum. Well-nourished, unsupplemented women in Bangladesh, Brazil, Denmark, and The Gambia (n = 250/site) were recruited during the third trimester of pregnancy. Milk, blood, saliva, urine, and stool samples from mothers and their infants are collected identically at 3 visits (1-3.49, 3.5-5.99, 6.0-8.49 mo postpartum). Milk analyses include macronutrients, selected vitamins, trace elements and minerals, iodine, metabolomics, amino acids, human milk oligosaccharides, and bioactive peptides. We measure milk volume; maternal and infant diets, anthropometry, and morbidity; infant development, maternal genome, and the infant and maternal microbiome. RVs will be constructed based on methods for the WHO Child Growth Standards and the Intergrowth-21st Project. This trial was registered at clinical trials.gov as NCT03254329.
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Affiliation(s)
- Lindsay H Allen
- USDA, Agricultural Research Service (ARS) Western Human Nutrition Research Center, Davis, CA, USA
- Department of Nutrition, University of California, Davis, CA, USA
| | - Daniela Hampel
- USDA, Agricultural Research Service (ARS) Western Human Nutrition Research Center, Davis, CA, USA
- Department of Nutrition, University of California, Davis, CA, USA
| | - Setareh Shahab-Ferdows
- USDA, Agricultural Research Service (ARS) Western Human Nutrition Research Center, Davis, CA, USA
- Department of Nutrition, University of California, Davis, CA, USA
| | - Maria Andersson
- Nutrition Research Unit, University Children's Hospital Zurich, Zurich, Switzerland
| | - Erica Barros
- Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Kamilla Gehrt Eriksen
- Department of Nutrition, Exercise, and Sports, University of Copenhagen, Copenhagen, Denmark
| | | | - Munirul Islam
- Nutrition and Clinical Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Gilberto Kac
- Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Farhana Khanam Keya
- Nutrition and Clinical Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Kim F Michaelsen
- Department of Nutrition, Exercise, and Sports, University of Copenhagen, Copenhagen, Denmark
| | | | - Fanta Njie
- Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia, West Africa
| | - Janet M Peerson
- USDA, Agricultural Research Service (ARS) Western Human Nutrition Research Center, Davis, CA, USA
| | - Sophie E Moore
- Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia, West Africa
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Pellitier PT, Ibáñez I, Zak DR, Argiroff WA, Acharya K. Ectomycorrhizal access to organic nitrogen mediates CO 2 fertilization response in a dominant temperate tree. Nat Commun 2021; 12:5403. [PMID: 34518539 PMCID: PMC8438073 DOI: 10.1038/s41467-021-25652-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 08/19/2021] [Indexed: 01/04/2023] Open
Abstract
Plant–mycorrhizal interactions mediate plant nitrogen (N) limitation and can inform model projections of the duration and strength of the effect of increasing CO2 on plant growth. We present dendrochronological evidence of a positive, but context-dependent fertilization response of Quercus rubra L. to increasing ambient CO2 (iCO2) along a natural soil nutrient gradient in a mature temperate forest. We investigated this heterogeneous response by linking metagenomic measurements of ectomycorrhizal (ECM) fungal N-foraging traits and dendrochronological models of plant uptake of inorganic N and N bound in soil organic matter (N-SOM). N-SOM putatively enhanced tree growth under conditions of low inorganic N availability, soil conditions where ECM fungal communities possessed greater genomic potential to decay SOM and obtain N-SOM. These trees were fertilized by 38 years of iCO2. In contrast, trees occupying inorganic N rich soils hosted ECM fungal communities with reduced SOM decay capacity and exhibited neutral growth responses to iCO2. This study elucidates how the distribution of N-foraging traits among ECM fungal communities govern tree access to N-SOM and subsequent growth responses to iCO2. Root-mycorrhizal interactions could help explain the heterogeneity of plant responses to CO2 fertilisation and nutrient availability. Here the authors combine tree-ring and metagenomic data to reveal that tree growth responses to increasing CO2 along a soil nutrient gradient depend on the nitrogen foraging traits of ectomycorrhizal fungi.
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Affiliation(s)
- Peter T Pellitier
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA. .,Department of Biology, Stanford University, Stanford, CA, USA.
| | - Inés Ibáñez
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA
| | - Donald R Zak
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA.
| | - William A Argiroff
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA
| | - Kirk Acharya
- School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA
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Queirós P, Delogu F, Hickl O, May P, Wilmes P. Mantis: flexible and consensus-driven genome annotation. Gigascience 2021; 10:6291114. [PMID: 34076241 PMCID: PMC8170692 DOI: 10.1093/gigascience/giab042] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 03/22/2021] [Accepted: 05/14/2021] [Indexed: 12/22/2022] Open
Abstract
Background The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation can be described as the identification of regions of interest (i.e., domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, challenges remain in terms of speed, flexibility, and reproducibility. In the big data era, it is also increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, and thus overcoming some limitations in overly relying on computationally generated data from single sources. Results We implemented a protein annotation tool, Mantis, which uses database identifiers intersection and text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for the customization of reference data and execution parameters, and is reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which significantly improved annotation performance compared to sequence-wide annotation. The parallelized implementation of Mantis results in short runtimes while also outputting high coverage and high-quality protein function annotations. Conclusions Mantis is a protein function annotation tool that produces high-quality consensus-driven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license at https://github.com/PedroMTQ/mantis.
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Affiliation(s)
- Pedro Queirós
- Systems Ecology, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
| | - Francesco Delogu
- Systems Ecology, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
| | - Oskar Hickl
- Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
| | - Patrick May
- Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
| | - Paul Wilmes
- Systems Ecology, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367 Esch-sur-Alzette, Luxembourg
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