The Yin-Yang of the Green Fluorescent Protein: Impact on Saccharomyces cerevisiae stress resistance.

Although fluorescent proteins are widely used as biomarkers (Yin), no study focuses on their influence on the microbial stress response. Here, the Green Fluorescent Protein (GFP) was fused to two proteins of interest in Saccharomyces cerevisiae. Pab1p and Sur7p, respectively involved in stress granules structure and in Can1 membrane domains. These were chosen since questions remain regarding the understanding of the behavior of S. cerevisiae facing different heat kinetics or oxidative stresses. The main results showed that Pab1p-GFP fluorescent mutant displayed a higher resistance than that of the wild type under a heat shock. Moreover, fluorescent mutants exposed to oxidative stresses displayed changes in the cultivability compared to the wild type strain. In silico approaches showed that the presence of the GFP did not influence the structure and so the functionality of the tagged proteins meaning that changes in yeast resistance were certainly related to GFP ROS-scavenging ability (Yang).

Prediction of Genetic Groups within Brettanomyces bruxellensis through Cell Morphology Using a Deep Learning Tool.

Brettanomyces bruxellensis is described as a wine spoilage yeast with many mainly strain-dependent genetic characteristics, bestowing tolerance against environmental stresses and persistence during the winemaking process. Thus, it is essential to discriminate B. bruxellensis isolates at the strain level in order to predict their stress resistance capacities. Few predictive tools are available to reveal intraspecific diversity within B. bruxellensis species; also, they require expertise and can be expensive. In this study, a Random Amplified Polymorphic DNA (RAPD) adapted PCR method was used with three different primers to discriminate 74 different B. bruxellensis isolates. High correlation between the results of this method using the primer OPA-09 and those of a previous microsatellite analysis was obtained, allowing us to cluster the isolates among four genetic groups more quickly and cheaply than microsatellite analysis. To make analysis even faster, we further investigated the correlation suggested in a previous study between genetic groups and cell polymorphism using the analysis of optical microscopy images via deep learning. A Convolutional Neural Network (CNN) was trained to predict the genetic group of B. bruxellensis isolates with 96.6% accuracy. These methods make intraspecific discrimination among B. bruxellensis species faster, simpler and less costly. These results open up very promising new perspectives in oenology for the study of microbial ecosystems.

Increased xerotolerance of Saccharomyces cerevisiae during an osmotic pressure ramp over several generations.

Although mechanisms involved in response of Saccharomyces cerevisiae to osmotic challenge are well described for low and sudden stresses, little is known about how cells respond to a gradual increase of the osmotic pressure (reduced water activity; aw ) over several generations as it could encounter during drying in nature or in food processes. Using glycerol as a stressor, we propagated S. cerevisiae through a ramp of the osmotic pressure (up to high molar concentrations to achieve testing-to-destruction) at the rate of 1.5 MPa day-1 from 1.38 to 58.5 MPa (0.990-0.635 aw ). Cultivability (measured at 1.38 MPa and at the harvest osmotic pressure) and glucose consumption compared with the corresponding sudden stress showed that yeasts were able to grow until about 10.5 MPa (0.926 aw ) and to survive until about 58.5 MPa, whereas glucose consumption occurred until 13.5 MPa (about 0.915 aw ). Nevertheless, the ramp conferred an advantage since yeasts harvested at 10.5 and 34.5 MPa (0.778 aw ) showed a greater cultivability than glycerol-shocked cells after a subsequent shock at 200 MPa (0.234 aw ) for 2 days. FTIR analysis revealed structural changes in wall and proteins in the range 1.38-10.5 MPa, which would be likely to be involved in the resistance at extreme osmotic pressure.

Automatic Counting of Intra-Cellular Ribonucleo-Protein Aggregates in Saccharomyces cerevisiae Using a Textural Approach.

In the context of microbiology, recent studies show the importance of ribonucleo-protein aggregates (RNPs) for the understanding of mechanisms involved in cell responses to specific environmental conditions. The assembly and disassembly of aggregates is a dynamic process, the characterization of the stage of their evolution can be performed by the evaluation of their number. The aim of this study is to propose a method to automatically determine the count of RNPs. We show that the determination of a precise count is an issue by itself and hence, we propose three textural approaches: a classical point of view using Haralick features, a frequency point of view with generalized Fourier descriptors, and a structural point of view with Zernike moment descriptors (ZMD). These parameters are then used as inputs for a supervised classification in order to determine the most relevant. An experiment using a specific Saccharomyces cerevisiae strain presenting a fusion between a protein found in RNPs (PAB1) and the green fluorescent protein was performed to benchmark this approach. The fluorescence was observed with two-photon fluorescence microscopy. Results show that the textural approach, by mixing ZMD with Haralick features, allows for the characterization of the number of RNPs.

Automatic Biological Cell Counting Using a Modified Gradient Hough Transform.

We present a computational method for pseudo-circular object detection and quantitative characterization in digital images, using the gradient accumulation matrix as a basic tool. This Gradient Accumulation Transform (GAT) was first introduced in 1992 by Kierkegaard and recently used by Kaytanli & Valentine. In the present article, we modify the approach by using the phase coding studied by Cicconet, and by adding a "local contributor list" (LCL) as well as a "used contributor matrix" (UCM), which allow for accurate peak detection and exploitation. These changes help make the GAT algorithm a robust and precise method to automatically detect pseudo-circular objects in a microscopic image. We then present an application of the method to cell counting in microbiological images.

Reliable Detection and Smart Deletion of Malassez Counting Chamber Grid in Microscopic White Light Images for Microbiological Applications.

In biology, hemocytometers such as Malassez slides are widely used and are effective tools for counting cells manually. In a previous work, a robust algorithm was developed for grid extraction in Malassez slide images. This algorithm was evaluated on a set of 135 images and grids were accurately detected in most cases, but there remained failures for the most difficult images. In this work, we present an optimization of this algorithm that allows for 100% grid detection and a 25% improvement in grid positioning accuracy. These improvements make the algorithm fully reliable for grid detection. This optimization also allows complete erasing of the grid without altering the cells, which eases their segmentation.

A robust generic method for grid detection in white light microscopy Malassez blade images in the context of cell counting.

In biology, cell counting is a primary measurement and it is usually performed manually using hemocytometers such as Malassez blades. This work is tedious and can be automated using image processing. An algorithm based on Fourier transform filtering and the Hough transform was developed for Malassez blade grid extraction. This facilitates cell segmentation and counting within the grid. For the present work, a set of 137 images with high variability was processed. Grids were accurately detected in 98% of these images.