Genome-resolved metagenomics suggests a mutualistic relationship between Mycoplasma and salmonid hosts.

Salmonids are important sources of protein for a large proportion of the human population. Mycoplasma species are a major constituent of the gut microbiota of salmonids, often representing the majority of microbiota. Despite the frequent reported dominance of salmonid-related Mycoplasma species, little is known about the phylogenomic placement, functions and potential evolutionary relationships with their salmonid hosts. In this study, we utilise 2.9 billion metagenomic reads generated from 12 samples from three different salmonid host species to I) characterise and curate the first metagenome-assembled genomes (MAGs) of Mycoplasma dominating the intestines of three different salmonid species, II) establish the phylogeny of these salmonid candidate Mycoplasma species, III) perform a comprehensive pangenomic analysis of Mycoplasma, IV) decipher the putative functionalities of the salmonid MAGs and reveal specific functions expected to benefit the host. Our data provide a basis for future studies examining the composition and function of the salmonid microbiota.

Gut Microbiota Linked with Reduced Fear of Humans in Red Junglefowl Has Implications for Early Domestication.

Domestication of animals can lead to profound phenotypic modifications within short evolutionary time periods, and for many species behavioral selection is likely at the forefront of this process. Animal studies have strongly implicated that the gut microbiome plays a major role in host behavior and cognition through the microbiome-gut-brain axis. Consequently, herein, it is hypothesized that host gut microbiota may be one of the earliest phenotypes to change as wild animals were domesticated. Here, the gut microbiome community in two selected lines of red junglefowl that are selected for either high or low fear of humans up to eight generations is examined. Microbiota profiles reveal taxonomic differences in gut bacteria known to produce neuroactive compounds between the two selection lines. Gut-brain module analysis by means of genome-resolved metagenomics identifies enrichment in the microbial synthesis and degradation potential of metabolites associated with fear extinction and reduces anxiety-like behaviors in low fear fowls. In contrast, high fear fowls are enriched in gut-brain modules from the butyrate and glutamate pathways, metabolites associated with fear conditioning. Overall, the results identify differences in the composition and functional potential of the gut microbiota across selection lines that may provide insights into the mechanistic explanations of the domestication process.

Dense sampling of bird diversity increases power of comparative genomics.

Whole-genome sequencing projects are increasingly populating the tree of life and characterizing biodiversity1-4. Sparse taxon sampling has previously been proposed to confound phylogenetic inference5, and captures only a fraction of the genomic diversity. Here we report a substantial step towards the dense representation of avian phylogenetic and molecular diversity, by analysing 363 genomes from 92.4% of bird families-including 267 newly sequenced genomes produced for phase II of the Bird 10,000 Genomes (B10K) Project. We use this comparative genome dataset in combination with a pipeline that leverages a reference-free whole-genome alignment to identify orthologous regions in greater numbers than has previously been possible and to recognize genomic novelties in particular bird lineages. The densely sampled alignment provides a single-base-pair map of selection, has more than doubled the fraction of bases that are confidently predicted to be under conservation and reveals extensive patterns of weak selection in predominantly non-coding DNA. Our results demonstrate that increasing the diversity of genomes used in comparative studies can reveal more shared and lineage-specific variation, and improve the investigation of genomic characteristics. We anticipate that this genomic resource will offer new perspectives on evolutionary processes in cross-species comparative analyses and assist in efforts to conserve species.

GC bias affects genomic and metagenomic reconstructions, underrepresenting GC-poor organisms.

BACKGROUND: Metagenomic sequencing is a well-established tool in the modern biosciences. While it promises unparalleled insights into the genetic content of the biological samples studied, conclusions drawn are at risk from biases inherent to the DNA sequencing methods, including inaccurate abundance estimates as a function of genomic guanine-cytosine (GC) contents. RESULTS: We explored such GC biases across many commonly used platforms in experiments sequencing multiple genomes (with mean GC contents ranging from 28.9% to 62.4%) and metagenomes. GC bias profiles varied among different library preparation protocols and sequencing platforms. We found that our workflows using MiSeq and NextSeq were hindered by major GC biases, with problems becoming increasingly severe outside the 45-65% GC range, leading to a falsely low coverage in GC-rich and especially GC-poor sequences, where genomic windows with 30% GC content had >10-fold less coverage than windows close to 50% GC content. We also showed that GC content correlates tightly with coverage biases. The PacBio and HiSeq platforms also evidenced similar profiles of GC biases to each other, which were distinct from those seen in the MiSeq and NextSeq workflows. The Oxford Nanopore workflow was not afflicted by GC bias. CONCLUSIONS: These findings indicate potential sources of difficulty, arising from GC biases, in genome sequencing that could be pre-emptively addressed with methodological optimizations provided that the GC biases inherent to the relevant workflow are understood. Furthermore, it is recommended that a more critical approach be taken in quantitative abundance estimates in metagenomic studies. In the future, metagenomic studies should take steps to account for the effects of GC bias before drawing conclusions, or they should use a demonstrably unbiased workflow.

31° South: The physiology of adaptation to arid conditions in a passerine bird.

Arid environments provide ideal ground for investigating the mechanisms of adaptive evolution. High temperatures and low water availability are relentless stressors for many endotherms, including birds; yet birds persist in deserts. While physiological adaptation probably involves metabolic phenotypes, the underlying mechanisms (plasticity, genetics) are largely uncharacterized. To explore this, we took an intraspecific approach that focused on a species that is resident over a mesic to arid gradient, the Karoo scrub-robin (Cercotrichas coryphaeus). Specifically, we integrated environmental (climatic and primary productivity), physiological (metabolic rates: a measure of energy expenditure), genotypic (genetic variation underlying the machinery of energy production) and microbiome (involved in processing food from where energy is retrieved) data, to infer the mechanism of physiological adaptation. We that found the variation in energetic physiology phenotypes and gut microbiome composition are associated with environmental features as well as with variation in genes underlying energy metabolic pathways. Specifically, we identified a small list of candidate adaptive genes, some of them with known ties to relevant physiology phenotypes. Together our results suggest that selective pressures on energetic physiology mediated by genes related to energy homeostasis and possibly microbiota composition may facilitate adaptation to local conditions and provide an explanation to the high avian intraspecific divergence observed in harsh environments.

A comparison of storage methods for gut microbiome studies in teleosts: Insights from rainbow trout (Oncorhynchus mykiss).

Immediate freezing is perhaps the most preferred method used for preserving gut microbial samples, but research on sample preservation has been principally based around samples from mammalian species, and little is known about the advantages or disadvantages relating to different storage methods for fish guts. Fish gut samples may pose additional challenges due to the different chemical and enzymatic profile, as well as the higher water content, which might affect the yield and purity of DNA recovered. To explore this, we took gut content and mucosal scrape samples from 10 rainbow trout (Oncorhynchus mykiss), and tested whether different preservation methods have any effect on the ability to construct high quality genomic libraries for shotgun and 16S rRNA gene sequencing. Four different storage methods were compared for the gut content samples (immediate freezing on dry ice, 96% ethanol, RNAlater and DNA/RNA shield), while two different methods were compared for mucosal scrape samples (96% ethanol and RNAlater). The samples were thereafter stored at -80 °C. Our findings concluded that 96% ethanol outperforms the other storage methods when considering DNA quantity, quality, cost and labor. Ethanol works consistently well for both gut content and mucosal scrape samples, and enables construction of DNA sequencing libraries of sufficient quantity and with a fragment length distribution suitable for shotgun sequencing. Two main conclusions from our study are i) sample storage optimisation is an important part of establishing a microbiome research program in a new species or sample type system, and ii) 96% ethanol is the preferred method for storing rainbow trout gut content and mucosal scrape samples.

Diabetic cats have decreased gut microbial diversity and a lack of butyrate producing bacteria.

Obesity and inactivity are major risk factors of feline diabetes mellitus (FDM) and human type II diabetes mellitus (T2DM). In recent years, changes in the gut microbiota have been suggested as a contributing factor to T2DM. Whether the gut microbiota (GM) composition plays a role in FDM remains unknown. The aim of the current study was firstly a cross-sectional comparison of the GM of diabetic cats, to that of lean, and of obese/overweight non-diabetic cats of a similar age. Specifically, fecal samples from 82 privately-owned cats from Denmark and Switzerland were sequenced using 16S rRNA gene amplicon metabarcoding. Secondly dietary intervention data was generated, by obtaining additional samples from a subset of cats after placing them on a high-protein diet for four weeks. The GM diversity of diabetic cats was lower than that of lean cats in the cross-sectional study, and lower compared to lean and to overweight/obese cats after diet intervention. Diabetic cats also exhibited fewer Anaerotruncus, Dialister, and unknown Ruminococcaceae than lean cats. Serum fructosamine levels correlated negatively with Prevotellaceae abundance and positively with Enterobacteriaceae abundance. In summary the intestinal microbiota of diabetic cats was characterized by decreased GM diversity and loss of butyrate producing bacterial genera.