Exploring the protist microbiome: the diversity of bacterial communities associated with Arcella spp. (Tubulina: Amoebozoa)

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Gomaa, F., Utter, D. R., Loo, W., Lahr, D. J. G., & Cavanaugh, C. M. Exploring the protist microbiome: the diversity of bacterial communities associated with Arcella spp. (Tubulina: Amoebozoa). European Journal of Protistology, 82, (2022): 125861, https://doi.org/10.1016/j.ejop.2021.125861.Research on protist-bacteria interactions is increasingly relevant as these associations are now known to play important roles in ecosystem and human health. Free-living amoebae are abundant in all environments and are frequent hosts for bacterial endosymbionts including pathogenic bacteria. However, to date, only a small fraction of these symbionts have been identified, while the structure and composition of the total symbiotic bacterial communities still remains largely unknown. Here, we use the testate amoeba Arcella spp. as model organisms to investigate the specificity and diversity of Arcella-associated microbial communities. High-throughput amplicon sequencing from the V4 region of the 16S rRNA gene revealed high diversity in the bacterial communities associated with the wild Arcella spp. To investigate the specificity of the associated bacterial community with greater precision, we investigated the bacterial communities of two lab-cultured Arcella species, A. hemispherica and A. intermedia, grown in two different media types. Our results suggest that Arcella-bacteria associations are species-specific, and that the associated bacterial community of lab-cultured Arcella spp. remains distinct from that of the surrounding media. Further, each host Arcella species could be distinguished based on its bacterial composition. Our findings provide insight into the understanding of eukaryotic-bacterial symbiosis.This project was funded by National Science Foundation Postdoctoral Research Fellowship in Biology to F. Gomaa, Grant Number: PRFB1611514. Support was provided to D.R.U. from the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE1745303 to D.R.U and by Harvard University’s Department of Organismic and Evolutionary Biology program

The genome of the intracellular bacterium of the coastal bivalve, Solemya velum: a blueprint for thriving in and out of symbiosis

Background: Symbioses between chemoautotrophic bacteria and marine invertebrates are rare examples of living systems that are virtually independent of photosynthetic primary production. These associations have evolved multiple times in marine habitats, such as deep-sea hydrothermal vents and reducing sediments, characterized by steep gradients of oxygen and reduced chemicals. Due to difficulties associated with maintaining these symbioses in the laboratory and culturing the symbiotic bacteria, studies of chemosynthetic symbioses rely heavily on culture independent methods. The symbiosis between the coastal bivalve, Solemya velum, and its intracellular symbiont is a model for chemosynthetic symbioses given its accessibility in intertidal environments and the ability to maintain it under laboratory conditions. To better understand this symbiosis, the genome of the S. velum endosymbiont was sequenced. Results: Relative to the genomes of obligate symbiotic bacteria, which commonly undergo erosion and reduction, the S. velum symbiont genome was large (2.7 Mb), GC-rich (51%), and contained a large number (78) of mobile genetic elements. Comparative genomics identified sets of genes specific to the chemosynthetic lifestyle and necessary to sustain the symbiosis. In addition, a number of inferred metabolic pathways and cellular processes, including heterotrophy, branched electron transport, and motility, suggested that besides the ability to function as an endosymbiont, the bacterium may have the capacity to live outside the host. Conclusions: The physiological dexterity indicated by the genome substantially improves our understanding of the genetic and metabolic capabilities of the S. velum symbiont and the breadth of niches the partners may inhabit during their lifecycle. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-924) contains supplementary material, which is available to authorized users

Finding the sources of missing heritability in a yeast cross

For many traits, including susceptibility to common diseases in humans, causal loci uncovered by genetic mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this "missing heritability" have been proposed. Here we use a large cross between two yeast strains to accurately estimate different sources of heritable variation for 46 quantitative traits and to detect underlying loci with high statistical power. We find that the detected loci explain nearly the entire additive contribution to heritable variation for the traits studied. We also show that the contribution to heritability of gene-gene interactions varies among traits, from near zero to 50%. Detected two-locus interactions explain only a minority of this contribution. These results substantially advance our understanding of the missing heritability problem and have important implications for future studies of complex and quantitative traits

Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis

BackgroundMotivated by an inconsistency between reports of high diagnosis-classification accuracies and known heterogeneity in attention-deficit/hyperactivity disorder (ADHD), this study assessed classification accuracy in studies of ADHD as a function of methodological factors that can bias results. We hypothesized that high classification results in ADHD diagnosis are inflated by methodological factors.MethodsWe reviewed 69 studies (of 95 studies identified) that used neuroimaging features to predict ADHD diagnosis. Based on reported methods, we assessed the prevalence of circular analysis, which inflates classification accuracy, and evaluated the relationship between sample size and accuracy to test if small-sample models tend to report higher classification accuracy, also an indicator of bias.ResultsCircular analysis was detected in 15.9% of ADHD classification studies, lack of independent test set was noted in 13%, and insufficient methodological detail to establish its presence was noted in another 11.6%. Accuracy of classification ranged from 60% to 80% in the 59.4% of reviewed studies that met criteria for independence of feature selection, model construction, and test datasets. Moreover, there was a negative relationship between accuracy and sample size, implying additional bias contributing to reported accuracies at lower sample sizes.ConclusionsHigh classification accuracies in neuroimaging studies of ADHD appear to be inflated by circular analysis and small sample size. Accuracies on independent datasets were consistent with known heterogeneity of the disorder. Steps to resolve these issues, and a shift toward accounting for sample heterogeneity and prediction of future outcomes, will be crucial in future classification studies in ADHD

Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis.

BackgroundMotivated by an inconsistency between reports of high diagnosis-classification accuracies and known heterogeneity in attention-deficit/hyperactivity disorder (ADHD), this study assessed classification accuracy in studies of ADHD as a function of methodological factors that can bias results. We hypothesized that high classification results in ADHD diagnosis are inflated by methodological factors.MethodsWe reviewed 69 studies (of 95 studies identified) that used neuroimaging features to predict ADHD diagnosis. Based on reported methods, we assessed the prevalence of circular analysis, which inflates classification accuracy, and evaluated the relationship between sample size and accuracy to test if small-sample models tend to report higher classification accuracy, also an indicator of bias.ResultsCircular analysis was detected in 15.9% of ADHD classification studies, lack of independent test set was noted in 13%, and insufficient methodological detail to establish its presence was noted in another 11.6%. Accuracy of classification ranged from 60% to 80% in the 59.4% of reviewed studies that met criteria for independence of feature selection, model construction, and test datasets. Moreover, there was a negative relationship between accuracy and sample size, implying additional bias contributing to reported accuracies at lower sample sizes.ConclusionsHigh classification accuracies in neuroimaging studies of ADHD appear to be inflated by circular analysis and small sample size. Accuracies on independent datasets were consistent with known heterogeneity of the disorder. Steps to resolve these issues, and a shift toward accounting for sample heterogeneity and prediction of future outcomes, will be crucial in future classification studies in ADHD

Finding the sources of missing heritability in a yeast cross.

For many traits, including susceptibility to common diseases in humans, causal loci uncovered by genetic-mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this 'missing heritability' have been proposed. Here we use a large cross between two yeast strains to accurately estimate different sources of heritable variation for 46 quantitative traits, and to detect underlying loci with high statistical power. We find that the detected loci explain nearly the entire additive contribution to heritable variation for the traits studied. We also show that the contribution to heritability of gene-gene interactions varies among traits, from near zero to approximately 50 per cent. Detected two-locus interactions explain only a minority of this contribution. These results substantially advance our understanding of the missing heritability problem and have important implications for future studies of complex and quantitative traits

Correction: Longitudinal microbiome profiling reveals impermanence of probiotic bacteria in domestic pigeons.

[This corrects the article DOI: 10.1371/journal.pone.0217804.]