Identification of bacteria associated with Dinoflagellates (Dinophyceae) Alexandrium spp. using tyramide signal amplification fluorescent in situ hybridization and confocal microscopy

In the marine environment, phytoplankton and bacterioplankton can be physically associated. Such association has recently been hypothesized to be involved in the toxicity of the dinoflagellate genus Alexandrium. However, the methods, which have been used so far to identify, localize, and quantify bacteria associated with phytoplankton, are either destructive, time consuming, or lack precision. In the present study we combined tyramide signal amplification–fluorescent in situ hybridization (TSA-FISH) with confocal microscopy to determine the physical association of dinoflagellate cells with bacteria. Dinoflagellate attached microflora was successfully identified with TSA-FISH, whereas FISH using monolabeled probes failed to detect bacteria, because of the dinoflagellate autofluorescence. Bacteria attached to entire dinoflagellates were further localized and distinguished from those attached to empty theca, by using calcofluor and DAPI, two fluorochromes that stain dinoflagellate theca and DNA, respectively. The contribution of specific bacterial taxa of attached microflora was assessed by double hybridization. Endocytoplasmic and endonuclear bacteria were successfully identified in the nonthecate dinoflagellate Gyrodinium instriatum. In contrast, intracellular bacteria were not observed in either toxic or nontoxic strains of Alexandrium spp. Finally, the method was successfully tested on natural phytoplankton assemblages, suggesting that this combination of techniques could prove a useful tool for the simultaneous identification, localization, and quantification of bacteria physically associated with dinoflagellates and more generally with phytoplankton.

Quantifying diet-induced metabolic changes of the human gut microbiome

The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.