The zebrafish gut microbiome influences benzo[a]pyrene developmental neurobehavioral toxicity.

Early-life exposure to environmental toxicants like Benzo[a]pyrene (BaP) is associated with several health consequences in vertebrates (i.e., impaired or altered neurophysiological and behavioral development). Although toxicant impacts were initially studied relative to host physiology, recent studies suggest that the gut microbiome is a possible target and/or mediator of behavioral responses to chemical exposure in organisms, via the gut-brain axis. However, the connection between BaP exposure, gut microbiota, and developmental neurotoxicity remains understudied. Using a zebrafish model, we determined whether the gut microbiome influences BaP impacts on behavior development. Embryonic zebrafish were treated with increasing concentrations of BaP and allowed to grow to the larval life stage, during which they underwent behavioral testing and intestinal dissection for gut microbiome profiling via high-throughput sequencing. We found that exposure affected larval zebrafish microbiome diversity and composition in a manner tied to behavioral development: increasing concentrations of BaP were associated with increased taxonomic diversity, exposure was associated with unweighted UniFrac distance, and microbiome diversity and exposure predicted larval behavior. Further, a gnotobiotic zebrafish experiment clarified whether microbiome presence was associated with BaP exposure response and behavioral changes. We found that gut microbiome state altered the relationship between BaP exposure concentration and behavioral response. These results support the idea that the zebrafish gut microbiome is a determinant of the developmental neurotoxicity that results from chemical exposure.

Compositional Graphical Lasso Resolves the Impact of Parasitic Infection on Gut Microbial Interaction Networks in a Zebrafish Model.

Understanding how microbes interact with each other is key to revealing the underlying role that microorganisms play in the host or environment and to identifying microorganisms as an agent that can potentially alter the host or environment. For example, understanding how the microbial interactions associate with parasitic infection can help resolve potential drug or diagnostic test for parasitic infection. To unravel the microbial interactions, existing tools often rely on graphical models to infer the conditional dependence of microbial abundances to represent their interactions. However, current methods do not simultaneously account for the discreteness, compositionality, and heterogeneity inherent to microbiome data. Thus, we build a new approach called "compositional graphical lasso" upon existing tools by incorporating the above characteristics into the graphical model explicitly. We illustrate the advantage of compositional graphical lasso over current methods under a variety of simulation scenarios and on a benchmark study, the Tara Oceans Project. Moreover, we present our results from the analysis of a dataset from the Zebrafish Parasite Infection Study, which aims to gain insight into how the gut microbiome and parasite burden covary during infection, thus uncovering novel putative methods of disrupting parasite success. Our approach identifies changes in interaction degree between infected and uninfected individuals for three taxa, Photobacterium, Gemmobacter, and Paucibacter, which are inversely predicted by other methods. Further investigation of these method-specific taxa interaction changes reveals their biological plausibility. In particular, we speculate on the potential pathobiotic roles of Photobacterium and Gemmobacter in the zebrafish gut, and the potential probiotic role of Paucibacter. Collectively, our analyses demonstrate that compositional graphical lasso provides a powerful means of accurately resolving interactions between microbiota and can thus drive novel biological discovery.

Revealing General Patterns of Microbiomes That Transcend Systems: Potential and Challenges of Deep Transfer Learning.

A growing body of research has established that the microbiome can mediate the dynamics and functional capacities of diverse biological systems. Yet, we understand little about what governs the response of these microbial communities to host or environmental changes. Most efforts to model microbiomes focus on defining the relationships between the microbiome, host, and environmental features within a specified study system and therefore fail to capture those that may be evident across multiple systems. In parallel with these developments in microbiome research, computer scientists have developed a variety of machine learning tools that can identify subtle, but informative, patterns from complex data. Here, we recommend using deep transfer learning to resolve microbiome patterns that transcend study systems. By leveraging diverse public data sets in an unsupervised way, such models can learn contextual relationships between features and build on those patterns to perform subsequent tasks (e.g., classification) within specific biological contexts.

Effects of Six Sequential Charged Particle Beams on Behavioral and Cognitive Performance in B6D2F1 Female and Male Mice.

The radiation environment astronauts are exposed to in deep space includes galactic cosmic radiation (GCR) with different proportions of all naturally occurring ions. To assist NASA with assessment of risk to the brain following exposure to a mixture of ions broadly representative of the GCR, we assessed the behavioral and cognitive performance of female and male C57BL/6J × DBA2/J F1 (B6D2F1) mice two months following rapidly delivered, sequential 6 beam irradiation with protons (1 GeV, LET = 0.24 keV, 50%), 4He ions (250 MeV/n, LET = 1.6 keV/µm, 20%), 16O ions (250 MeV/n, LET = 25 keV/µm 7.5%), 28Si ions (263 MeV/n, LET = 78 keV/µm, 7.5%), 48Ti ions (1 GeV/n, LET = 107 keV/µm, 7.5%), and 56Fe ions (1 GeV/n, LET = 151 keV/µm, 7.5%) at 0, 25, 50, or 200 cGy) at 4-6 months of age. When the activity over 3 days of open field habituation was analyzed in female mice, those irradiated with 50 cGy moved less and spent less time in the center than sham-irradiated mice. Sham-irradiated female mice and those irradiated with 25 cGy showed object recognition. However, female mice exposed to 50 or 200 cGy did not show object recognition. When fear memory was assessed in passive avoidance tests, sham-irradiated mice and mice irradiated with 25 cGy showed memory retention while mice exposed to 50 or 200 cGy did not. The effects of radiation passive avoidance memory retention were not sex-dependent. There was no effect of radiation on depressive-like behavior in the forced swim test. There was a trend toward an effect of radiation on BDNF levels in the cortex of males, but not for females, with higher levels in male mice irradiated with 50 cGy than sham-irradiated. Finally, sequential 6-ion irradiation impacted the composition of the gut microbiome in a sex-dependent fashion. Taxa were uncovered whose relative abundance in the gut was associated with the radiation dose received. Thus, exposure to sequential six-beam irradiation significantly affects behavioral and cognitive performance and the gut microbiome.

Genetic Influences of the Microbiota on the Life Span of Drosophila melanogaster.

To better understand how associated microorganisms ("microbiota") influence organismal aging, we focused on the model organism Drosophila melanogaster We conducted a metagenome-wide association (MGWA) as a screen to identify bacterial genes associated with variation in the D. melanogaster life span. The results of the MGWA predicted that bacterial cysteine and methionine metabolism genes influence fruit fly longevity. A mutant analysis, in which flies were inoculated with Escherichia coli strains bearing mutations in various methionine cycle genes, confirmed a role for some methionine cycle genes in extending or shortening fruit fly life span. Initially, we predicted these genes might influence longevity by mimicking or opposing methionine restriction, an established mechanism for life span extension in fruit flies. However, follow-up transcriptome sequencing (RNA-seq) and metabolomic experiments were generally inconsistent with this conclusion and instead implicated glucose and vitamin B6 metabolism in these influences. We then tested if bacteria could influence life span through methionine restriction using a different set of bacterial strains. Flies reared with a bacterial strain that ectopically expressed bacterial transsulfuration genes and lowered the methionine content of the fly diet also extended female D. melanogaster life span. Taken together, the microbial influences shown here overlap with established host genetic mechanisms for aging and therefore suggest overlapping roles for host and microbial metabolism genes in organismal aging.IMPORTANCE Associated microorganisms ("microbiota") are intimately connected to the behavior and physiology of their animal hosts, and defining the mechanisms of these interactions is an urgent imperative. This study focuses on how microorganisms influence the life span of a model host, the fruit fly Drosophila melanogaster First, we performed a screen that suggested a strong influence of bacterial methionine metabolism on host life span. Follow-up analyses of gene expression and metabolite abundance identified stronger roles for vitamin B6 and glucose than methionine metabolism among the tested mutants, possibly suggesting a more limited role for bacterial methionine metabolism genes in host life span effects. In a parallel set of experiments, we created a distinct bacterial strain that expressed life span-extending methionine metabolism genes and showed that this strain can extend fly life span. Therefore, this work identifies specific bacterial genes that influence host life span, including in ways that are consistent with the expectations of methionine restriction.

The decoration of specialized metabolites influences stylar development.

Plants produce many different specialized (secondary) metabolites that function in solving ecological challenges; few are known to function in growth or other primary processes. 17-Hydroxygeranylinalool diterpene glycosides (DTGs) are abundant herbivory-induced, structurally diverse and commonly malonylated defense metabolites in Nicotiana attenuata plants. By identifying and silencing a malonyltransferase, NaMaT1, involved in DTG malonylation, we found that DTG malonylation percentages are normally remarkably uniform, but when disrupted, result in DTG-dependent reduced floral style lengths, which in turn result from reduced stylar cell sizes, IAA contents, and YUC activity; phenotypes that could be restored by IAA supplementation or by silencing the DTG pathway. Moreover, the Nicotiana genus-specific JA-deficient short-style phenotype also results from alterations in DTG malonylation patterns. Decorations of plant specialized metabolites can be tuned to remarkably uniform levels, and this regulation plays a central but poorly understood role in controlling the development of specific plant parts, such as floral styles.

Draft Genome Sequence of Lactobacillus paracasei DmW181, a Bacterium Isolated from Wild Drosophila.

The draft genome sequence of Lactobacillus paracasei DmW181, an anaerobic bacterium isolate from wild Drosophila flies, is reported here. Strain DmW181 possesses genes for sialic acid and mannose metabolism. The assembled genome is 3,201,429 bp, with 3,454 predicted genes.