MotilityJ: An open-source tool for the classification and segmentation of bacteria on motility images

Spread of Pseudomonas aeruginosa ST274 Clone in Different Niches: Resistome, Virulome, and Phylogenetic Relationship

Pseudomonas aeruginosa ST274 is an international epidemic high-risk clone, mostly associated with hospital settings and appears to colonize cystic fibrosis (CF) patients worldwide. To understand the relevant mechanisms for its success, the biological and genomic characteristics of 11 ST274-P. aeruginosa strains from clinical and non-clinical origins were analyzed. The extensively drug-resistant (XDR/DTR), the non-susceptible to at least one agent (modR), and the lasR-truncated (by ISPsp7) strains showed a chronic infection phenotype characterized by loss of serotype-specific antigenicity and low motility. Furthermore, the XDR/DTR and modR strains presented low pigment production and biofilm formation, which were very high in the lasR-truncated strain. Their whole genome sequences were compared with other 14 ST274-P. aeruginosa genomes available in the NCBI database, and certain associations have been primarily detected: blaOXA-486 and blaPDC-24 genes, serotype O:3, exoS+/exoU- genotype, group V of type IV pili, and pyoverdine locus class II. Other general molecular markers highlight the absence of vqsM and pldA/tleS genes and the presence of the same mutational pattern in genes involving two-component sensor-regulator systems PmrAB and CreBD, exotoxin A, quorum-sensing RhlI, beta-lactamase expression regulator AmpD, PBP1A, or FusA2 elongation factor G. The proportionated ST274-P. aeruginosa results could serve as the basis for more specific studies focused on better antibiotic stewardship and new therapeutic developments.

Engineered bacterial swarm patterns as spatial records of environmental inputs

A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility-a highly coordinated and rapid movement of bacteria powered by flagella. Engineering swarming is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye swarm patterns, to 'write' external inputs into visible spatial records. Specifically, we engineer tunable expression of swarming-related genes that modify pattern features, and we develop quantitative approaches to decoding. Next, we develop a dual-input system that modulates two swarming-related genes simultaneously, and we separately show that growing colonies can record dynamic environmental changes. We decode the resulting multicondition patterns with deep classification and segmentation models. Finally, we engineer a strain that records the presence of aqueous copper. This work creates an approach for building macroscale bacterial recorders, expanding the framework for engineering emergent microbial behaviors.

Microfeature Segmentation Algorithm for Biological Images Using Improved Density Peak Clustering

To address the problem of low precision in feature segmentation of biological images with large noise, a microfeature segmentation algorithm for biological images using improved density peak clustering was proposed. First, the center pixel and edge information of a biological image were obtained to remove some redundant information. The three-dimensional space of the image is constructed, and the coordinate system is used to describe every superpixel of the biological image. Second, the image symmetry and reversibility are used to obtain the stopping position of pixels, other adjacent points are used to obtain the current color and shape information, and more vectors are used to express the density to complete the image pretreatment. Finally, the improved density peak clustering method is used to cluster the image, and the pixels completed by clustering and the remaining pixels are evenly distributed into the space to segment the image so as to complete the microfeature segmentation of the biological image based on the improved density peak clustering method. The results show that the proposed algorithm improves the segmentation efficiency, segmentation integrity rate, and segmentation accuracy. The time consumed by the proposed biological image microfeature segmentation algorithm is always less than 2 minutes, and the segmentation integrity rate can reach more than 90%. Furthermore, the proposed algorithm can reduce the missing condition and the noise of the segmented image and improve the image feature segmentation effect.