Microbiology

The natural environment of microbial cells like bacteria and yeast is often a complex community in which growth and internal organization reflect morphogenetic processes and interactions that are dependent on spatial position and time. While most of research is performed in simple homogeneous environments (e.g., bulk liquid cultures), which cannot capture full spatiotemporal community dynamics, studying biofilms or colonies is complex and usually does not give access to the spatiotemporal dynamics at single cell level. Here, we detail a protocol for generation of a microfluidic device, the “yeast machine”, with arrays of long monolayers of yeast colonies to advance the global understanding of how intercellular metabolic interactions affect the internal structure of colonies within defined and customizable spatial dimensions. With Saccharomyces cerevisiae as a model yeast system we used the “yeast machine” to demonstrate the emergence of glucose gradients by following expression of fluorescently labelled hexose transporters. We further quantified the expression spatial patterns with intra-colony growth rates and expression of other genes regulated by glucose availability. In addition to this, we showed that gradients of amino acids also form within a colony, potentially opening similar approaches to study spatiotemporal formation of gradients of many other nutrients and metabolic waste products. This approach could be used in the future to decipher the interplay between long-range metabolic interactions, cellular development, and morphogenesis in other same species or more complex multi-species systems at single-cell resolution and timescales relevant to ecology and evolution.

Biofilms are bacterial communities in the shape of exopolysaccharide matrix-encased aggregates attached onto interphases able to resist environmental aggressions. The development of bacteria in the shape of biofilms deeply affects the performance of many industrial processes which work with fluidic systems, where bacteria may settle and prosper. As a consequence industrial equipment experiments low performance issues and substantial maintenance costs.

The study of how bacteria of industrial interest such as Pseudomonas putida spread in these fluidic systems is highly dependent on the chosen experimental system to retrieve such data, thus using scaled prototypes becomes an essential step towards the design of a more efficient system to handle biofilms, either to control them or to prevent them. This protocol describes how to assemble, operate and maintain a device to grow and monitor the biofilm spreading pattern of this bacterium (as a function of the fluid hydrodynamics) in a custom-made chamber larger than those typically used in laboratory environments, and how to analyze the information gathered from it in a straightforward fashion. Description of the protocol was thought to be used as a working template not only for the presented case study but for any other potential experiment in different contexts and diverse scales following similar design principles.

Bacteria in nature live in complex communities with multiple cell types and spatially-dependent interactions. Studying cells in well-mixed environments such as shaking culture tubes or flasks cannot capture these spatial dynamics, but cells growing in full-fledged biofilms are difficult to observe in real time. We present here a protocol for observing time-resolved, multi-species interactions at single-cell resolution. The protocol involves growing bacterial cells in a near monolayer in a microfluidic device. As a demonstration, we describe in particular observing the dynamic interactions between E. coli and Acinetobacter baylyi. In this case, the protocol is capable of observing both contact-dependent lysis of E. coli by A. baylyi via the Type VI Secretion System (T6SS) and subsequent functional horizontal gene transfer (HGT) of genes from E. coli to A. baylyi.

Besides analyzing the composition and dynamics of microbial communities, plant microbiome research aims to understanding the mechanism of plant microbiota assembly and their biological functions. Here, we describe procedures to investigate the role of bacterial interspecies interactions in root microbiome assembly and the beneficial effects of the root microbiota on hosts by using a maize root-associated simplified seven-species (Stenotrophomonas maltophilia, Ochrobactrum pituitosum, Curtobacterium pusillum, Enterobacter cloacae, Chryseobacterium indologenes, Herbaspirillum frisingense and Pseudomonas putida) synthetic bacterial community described in our previous work. Surface-sterilized maize seeds were grown in a gnotobiotic system based on double-tube growth chambers after being soaked in suspensions containing multiple species of bacteria. The dynamics of the composition of the bacterial communities colonized on maize roots were tracked by a culture-dependent method with a selective medium for each of the seven strains. The impact of bacterial interactions on the community assembly was evaluated by monitoring the changes of community structure. The plant-protection effects of the simplified seven-species community were assessed by quantifying (1) the growth of a fungal phytopathogen, Fusarium verticillioides on the surfaces of the seeds and (2) the severity of seedling blight disease the fungus causes, in the presence and absence of the bacterial community. Our protocol will serve as useful guidance for studying plant-microbial community interactions under the laboratory conditions.

Plant roots associate with a wide diversity of bacteria and archaea across the root-soil spectrum. The rhizosphere microbiota, the communities of microbes in the soil adjacent to the root, can contain up to 10 billion bacterial cells per gram of soil (Raynaud and Nunan, 2014) and can play important roles for the fitness of the host plant. Subsets of the rhizospheric microbiota can colonize the root surface (rhizoplane) and the root interior (endosphere), forming an intimate relationship with the host plant. Compositional analysis of these communities is important to develop tools in order to manipulate root-associated microbiota for increased crop productivity. Due to the reduced cost and increasing throughput of next-generation sequencing, major advances in deciphering these communities have recently been achieved, mainly through the use of amplicon sequencing of the 16S rRNA gene. Here we first present a protocol for dissecting the microbiota from various root compartments, developed using rice as a model. We next present a method for amplifying fragments of the 16S rRNA gene using a dual index approach. Finally, we present a simple workflow for analyzing the resulting sequencing data to make ecological inferences.

We propose a modified RNA-Seq method for small subunit ribosomal RNA (SSU rRNA)-based microbial community analysis that depends on the direct ligation of a 5’ adaptor to RNA before reverse-transcription. The method requires only a low-input quantity of RNA (10-100 ng) and does not require a DNA removal step. Using this method, we could obtain more 16S rRNA sequences of the same regions (variable regions V1-V2) without the interference of DNA in order to analyze OTU (operational taxonomic unit)-based microbial communities and diversity. The generated SSU rRNA sequences are also suitable for the coverage evaluation for bacterial universal primer 8F (Escherichia coli position 8 to 27), which is commonly used for bacterial 16S rRNA gene amplification. The modified RNA-Seq method will be useful to determine potentially active microbial community structures and diversity for various environmental samples, and will also be useful for identifying novel microbial taxa.

Next-generation sequencing (NGS) offers unparalleled resolution for untargeted organism detection and characterization. However, the majority of NGS analysis programs require users to be proficient in programming and command-line interfaces. EDGE bioinformatics was developed to offer scientists with little to no bioinformatics expertise a point-and-click platform for analyzing sequencing data in a rapid and reproducible manner. EDGE (Empowering the Development of Genomics Expertise) v1.0 released in January 2017, is an intuitive web-based bioinformatics platform engineered for the analysis of microbial and metagenomic NGS-based data (Li et al., 2017). The EDGE bioinformatics suite combines vetted publicly available tools, and tracks settings to ensure reliable and reproducible analysis workflows. To execute the EDGE workflow, only raw sequencing reads and a project ID are necessary. Users can access in-house data, or run analyses on samples deposited in Sequence Read Archive. Default settings offer a robust first-glance and are often sufficient for novice users. All analyses are modular; users can easily turn workflows on/off, and modify parameters to cater to project needs. Results are compiled and available for download in a PDF-formatted report containing publication quality figures. We caution that interpreting results still requires in-depth scientific understanding, however report visuals are often informative, even to novice users.

We describe a two-step PCR strategy using tagged highly degenerate primer (THDP-PCR) targeting copper-containing membrane-bound monooxygenases (CuMMO) genes for community analysis of methane- or ammonia-oxidizing bacteria. This strategy consists of a primary CuMMO gene-specific PCR followed by a secondary PCR with a tag as a single primer. This strategy remarkably increases the divergence of CuMMO gene amplicons while maintaining PCR efficiency without obvious amplification bias. This THDP-PCR strategy can be extended to other functional gene-based community analysis with design of new highly degenerate primer covering target functional gene sequences.