Keemei: cloud-based validation of tabular bioinformatics file formats in Google Sheets.

BackgroundBioinformatics software often requires human-generated tabular text files as input and has specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support the concurrent editing of a single spreadsheet by different users working on different platforms. Most of the researchers who enter data are not familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis.Main textWe present Keemei, a Google Sheets Add-on, for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Google's Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports the validation of two widely used tabular bioinformatics formats, the Quantitative Insights into Microbial Ecology (QIIME) sample metadata mapping file format and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others.ConclusionsKeemei will save researchers time and frustration by providing a convenient interface for tabular bioinformatics file format validation. By allowing everyone involved with data entry for a project to easily validate their data, it will reduce the validation and formatting bottlenecks that are commonly encountered when human-generated data files are first used with a bioinformatics system. Simplifying the validation of essential tabular data files, such as sample metadata, will reduce common errors and thereby improve the quality and reliability of research outcomes

Ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses.

BackgroundFungi play critical roles in many ecosystems, cause serious diseases in plants and animals, and pose significant threats to human health and structural integrity problems in built environments. While most fungal diversity remains unknown, the development of PCR primers for the internal transcribed spacer (ITS) combined with next-generation sequencing has substantially improved our ability to profile fungal microbial diversity. Although the high sequence variability in the ITS region facilitates more accurate species identification, it also makes multiple sequence alignment and phylogenetic analysis unreliable across evolutionarily distant fungi because the sequences are hard to align accurately. To address this issue, we created ghost-tree, a bioinformatics tool that integrates sequence data from two genetic markers into a single phylogenetic tree that can be used for diversity analyses. Our approach starts with a "foundation" phylogeny based on one genetic marker whose sequences can be aligned across organisms spanning divergent taxonomic groups (e.g., fungal families). Then, "extension" phylogenies are built for more closely related organisms (e.g., fungal species or strains) using a second more rapidly evolving genetic marker. These smaller phylogenies are then grafted onto the foundation tree by mapping taxonomic names such that each corresponding foundation-tree tip would branch into its new "extension tree" child.ResultsWe applied ghost-tree to graft fungal extension phylogenies derived from ITS sequences onto a foundation phylogeny derived from fungal 18S sequences. Our analysis of simulated and real fungal ITS data sets found that phylogenetic distances between fungal communities computed using ghost-tree phylogenies explained significantly more variance than non-phylogenetic distances. The phylogenetic metrics also improved our ability to distinguish small differences (effect sizes) between microbial communities, though results were similar to non-phylogenetic methods for larger effect sizes.ConclusionsThe Silva/UNITE-based ghost tree presented here can be easily integrated into existing fungal analysis pipelines to enhance the resolution of fungal community differences and improve understanding of these communities in built environments. The ghost-tree software package can also be used to develop phylogenetic trees for other marker gene sets that afford different taxonomic resolution, or for bridging genome trees with amplicon trees.Availabilityghost-tree is pip-installable. All source code, documentation, and test code are available under the BSD license at https://github.com/JTFouquier/ghost-tree

Lactobacillus reuteri V3401 Reduces Inflammatory Biomarkers and Modifies the Gastrointestinal Microbiome in Adults with Metabolic Syndrome: The PROSIR Study

The following are available online at https://www.mdpi.com/2072-6643/11/8/1761/s1: Figure S1, Rarefaction curves; Figure S2, Alpha diversity measured by means of the Shannon index (H); Figure S3, Bacterial beta diversity; Table S1, Gastrointestinal microbiome normalized dataThis paper will be part of Carmen Tenorio Jimenez’s doctorate, which is being completed as part of Programa de Doctorado en Seguridad de los Alimentos at the University of Jaén, Spain. We also wish to thank all the participants who generously contributed to the study.Previous studies have reported that probiotics may improve clinical and inflammatory parameters in patients with obesity and metabolic syndrome (MetS). Lactobacillus (L.) reuteri V3401 has shown promising results on the components of MetS in animal studies. We aimed to evaluate the effects of L. reuteri V3401 together with healthy lifestyle recommendations on adult patients with MetS. Methods: We carried out a randomized, crossover, placebo-controlled, single-center trial in which we included 53 adult patients newly diagnosed with MetS. Patients were block randomly allocated by body mass index (BMI) and sex to receive a capsule containing either the probiotic L. reuteri V3401 (5 × 109 colony-forming units) or a placebo once daily for 12 weeks. Anthropometric variables, biochemical and inflammatory biomarkers, as well as the gastrointestinal microbiome composition were determined. Results: There were no differences between groups in the clinical characteristics of MetS. However, we found that interleukin-6 (IL-6) and soluble vascular cell adhesion molecule 1 (sVCAM-1) diminished by effect of the treatment with L. reuteri V3401. Analysis of the gastrointestinal microbiome revealed a rise in the proportion of Verrucomicrobia. Conclusions: Consumption of L. reuteri V3401 improved selected inflammatory parameters and modified the gastrointestinal microbiome. Further studies are needed to ascertain additional beneficial effects of other probiotic strains in MetS as well as the mechanisms by which such effects are exerted.This study is part of the grant entitled “Guía para la sustanciación de declaraciones de salud en alimentos: funciones inmune, cognitiva y síndrome metabólico”, funded by the company Biosearch life (Granada, Spain), reference 3006, managed by Fundación General Universidad de Granada, Spain

Almond Snacking for 8 wk Increases Alpha-Diversity of the Gastrointestinal Microbiome and Decreases Bacteroides fragilis Abundance Compared with an Isocaloric Snack in College Freshmen.

BackgroundChanges in gut microbiota are associated with cardiometabolic disorders and are influenced by diet. Almonds are a rich source of fiber, unsaturated fats, and polyphenols, all nutrients that can favorably alter the gut microbiome.ObjectivesThe aim of this study was to examine the effects of 8 wk of almond snacking on the gut (fecal) microbiome diversity and abundance compared with an isocaloric snack of graham crackers in college freshmen.MethodsA randomized, controlled, parallel-arm, 8-wk intervention in 73 college freshmen (age: 18-19 y; 41 women and 32 men; BMI: 18-41 kg/m2) with no cardiometabolic disorders was conducted. Participants were randomly allocated to either an almond snack group (56.7 g/d; 364 kcal; n = 38) or graham cracker control group (77.5 g/d; 338 kcal/d; n = 35). Stool samples were collected at baseline and 8 wk after the intervention to assess primary microbiome outcomes, that is, gut microbiome diversity and abundance.ResultsAlmond snacking resulted in 3% greater quantitative alpha-diversity (Shannon index) and 8% greater qualitative alpha-diversity (Chao1 index) than the cracker group after the intervention (P < 0.05). Moreover, almond snacking for 8 wk decreased the abundance of the pathogenic bacterium Bacteroides fragilis by 48% (overall relative abundance, P < 0.05). Permutational multivariate ANOVA showed significant time effects for the unweighted UniFrac distance and Bray-Curtis beta-diversity methods (P < 0.05; R 2 ≤ 3.1%). The dietary and clinical variables that best correlated with the underlying bacterial community structure at week 8 of the intervention included dietary carbohydrate (percentage energy), dietary fiber (g), and fasting total and HDL cholesterol (model Spearman rho = 0.16; P = 0.01).ConclusionsAlmond snacking for 8 wk improved alpha-diversity compared with cracker snacking. Incorporating a morning snack in the dietary regimen of predominantly breakfast-skipping college freshmen improved the diversity and composition of the gut microbiome. This trial was registered at clinicaltrials.gov as NCT03084003

Faecal microbiota and antibiotic resistance genes in migratory waterbirds with contrasting habitat use.

Migratory birds may have a vital role in the spread of antimicrobial resistance across habitats and regions, but empirical data remain scarce. We investigated differences in the gut microbiome composition and the abundance of antibiotic resistance genes (ARGs) in faeces from four migratory waterbirds wintering in South-West Spain that differ in their habitat use. The white stork Ciconia ciconia and lesser black-backed gull Larus fuscus are omnivorous and opportunistic birds that use highly anthropogenic habitats such as landfills and urban areas. The greylag goose Anser anser and common crane Grus grus are herbivores and use more natural habitats. Fresh faeces from 15 individuals of each species were analysed to assess the composition of bacterial communities using 16S rRNA amplicon-targeted sequencing, and to quantify the abundance of the Class I integron integrase gene (intI1) as well as genes encoding resistance to sulfonamides (sul1), beta-lactams (blaTEM, blaKPC and blaNDM), tetracyclines (tetW), fluoroquinolones (qnrS), and colistin (mcr-1) using qPCR. Bacterial communities in gull faeces were the richest and most diverse. Beta diversity analysis showed segregation in faecal communities between bird species, but those from storks and gulls were the most similar, these being the species that regularly feed in landfills. Potential bacterial pathogens identified in faeces differed significantly between bird species, with higher relative abundance in gulls. Faeces from birds that feed in landfills (stork and gull) contained a significantly higher abundance of ARGs (sul1, blaTEM, and tetW). Genes conferring resistance to last resort antibiotics such as carbapenems (blaKPC) and colistin (mcr-1) were only observed in faeces from gulls. These results show that these bird species are reservoirs of antimicrobial resistant bacteria and suggest that waterbirds may disseminate antibiotic resistance across environments (e.g., from landfills to ricefields or water supplies), and thus constitute a risk for their further spread to wildlife and humans. Copyright © 2021 Elsevier B.V. All rights reserved

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities

Mineral phosphorus drives glacier algal blooms on the Greenland Ice Sheet

Melting of the Greenland Ice Sheet is a leading cause of land-ice mass loss and cryosphere-attributed sea level rise. Blooms of pigmented glacier ice algae lower ice albedo and accelerate surface melting in the ice sheet’s southwest sector. Although glacier ice algae cause up to 13% of the surface melting in this region, the controls on bloom development remain poorly understood. Here we show a direct link between mineral phosphorus in surface ice and glacier ice algae biomass through the quantification of solid and fluid phase phosphorus reservoirs in surface habitats across the southwest ablation zone of the ice sheet. We demonstrate that nutrients from mineral dust likely drive glacier ice algal growth, and thereby identify mineral dust as a secondary control on ice sheet melting.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

American Gut: an Open Platform for Citizen Science Microbiome Research

McDonald D, Hyde E, Debelius JW, et al. American Gut: an Open Platform for Citizen Science Microbiome Research. mSystems. 2018;3(3):e00031-18

A reasonable correlation between cloacal and caecal microbiomes in broiler chickens

Gut microbiota play an important role in animal health. For livestock, an understanding of the effect of husbandry interventions on gut microbiota helps develop methods that increase sustainable productivity, animal welfare and food safety. Poultry microbiota of the mid- and hind-gut can only be investigated post-mortem; however, samples from the terminal cloaca may be collected from live animals. This study tests whether cloacal microbiota reflect caecal microbiota in European broiler poultry by evaluating total and paired caecal and cloacal microbiomes from 47 animals. 16S amplicon libraries were constructed and sequenced with a MiSeq 250bp PE read metric. The composition of cloaca and caecal microbiomes were significantly affected by the age and location of animals, but the effect was very small. Bacilli were relatively more abundant in caeca, and Clostridia in cloaca. There was an overlap of 99.5% for the abundances and 59% for the types of taxa between cloacal and caecal communities, but the small fraction of rare non-shared taxa were sufficient to produce a signal for differentiation between caecal and cloacal communities. There was a significant positive correlation between specific taxa abundances in cloacal and caecal communities (Rho = 0.66, P = 2x10-16). Paired analyses revealed that cloacal communities were more closely related to caecal communities from the same individual than expected by chance. This study is in line with the only other study to evaluate the relationship between caecal and cloacal microbiomes in broiler poultry, but it extends previous findings by analysing paired caecal-cloacal samples from the same birds and reveals that abundant bacterial taxa in caeca may be reasonably inferred by sampling cloaca. Together, the findings from Europe and Australasia demonstrate that sampling cloaca shows promise as a method to estimate caecal microbiota, and especially abundant taxa, from live broiler poultry in a manner which reduces cost and increases welfare for husbandry and research purposes