Colony analysis and deep learning uncover 5-hydroxyindole as an inhibitor of gliding motility and iridescence in Cellulophaga lytica

Gliding motility of a uranium-tolerant Bacteroidetes bacterium Chryseobacterium sp. strain PMSZPI: insights into the architecture of spreading colonies

Uranium-tolerant soil bacterium Chryseobacterium sp. strain PMSZPI moved over solid agar surfaces by gliding motility thereby forming spreading colonies which is a hallmark of members of Bacteroidetes phylum. PMSZPI genome harboured orthologs of all the gld and spr genes considered as core bacteroidetes gliding motility genes of which gldK, gldL, gldM and gldN were co-transcribed. Here, we present the intriguing interplay between gliding motility and cellular organization in PMSZPI spreading colonies. While nutrient deficiency enhanced colony spreading, high agar concentrations and presence of motility inhibitor like 5-hydroxyindole reduced the spreading. A detailed in situ structural analysis of spreading colonies revealed closely packed cells forming multiple layers at centre of colony while the edges showed clusters of cells periodically arranged in hexagonal lattices interconnected with each other. The cell migration within colony was visualized as branched structures wherein the cells were buried within extracellular matrix. PMSZPI colonies exhibited strong iridescence possibly as a result of periodicity within the cell population achieved through gliding motility. Presence of uranium reduced motility and iridescence and induced biofilm formation. The coordinated study of gliding motility and iridescence apparently influenced by uranium provides unique insights into the lifestyle of PMSZPI residing in uranium enriched environment.

Polysaccharide metabolism regulates structural colour in bacterial colonies

The brightest colours in nature often originate from the interaction of light with materials structured at the nanoscale. Different organisms produce such coloration with a wide variety of materials and architectures. In the case of bacterial colonies, structural colours stem for the periodic organization of the cells within the colony, and while considerable efforts have been spent on elucidating the mechanisms responsible for such coloration, the biochemical processes determining the development of this effect have not been explored. Here, we study the influence of nutrients on the organization of cells from the structurally coloured bacteria Flavobacterium strain IR1. By analysing the optical properties of the colonies grown with and without specific polysaccharides, we found that the highly ordered organization of the cells can be altered by the presence of fucoidans. Additionally, by comparing the organization of the wild-type strain with mutants grown in different nutrient conditions, we deduced that this regulation of cell ordering is linked to a specific region of the IR1 chromosome. This region encodes a mechanism for the uptake and metabolism of polysaccharides, including a polysaccharide utilization locus (PUL operon) that appears specific to fucoidan, providing new insight into the biochemical pathways regulating structural colour in bacteria.

Yields and Variability of Ozone Reaction Products from Human Skin

The skin of 20 human participants was exposed to ∼110 ppb O₃ and volatile products of the resulting chemistry were quantified in real time. Yields (ppb product emitted/ppb ozone consumed) for 40 products were quantified. Major products of the primary reaction of ozone-squalene included 6-methyl 5-hepten-2-one (6-MHO) and geranyl acetone (GA) with average yields of 0.22 and 0.16, respectively. Other major products included decanal, methacrolein (or methyl vinyl ketone), nonanal, and butanal. Yields varied widely among participants; summed yields ranged from 0.33 to 0.93. The dynamic increase in emission rates during ozone exposure also varied among participants, possibly indicative of differences in the thickness of the skin lipid layer. Factor analysis indicates that much of the variability among participants is due to factors associated with the relative abundance of (1) "fresh" skin lipid constituents (such as squalene and fatty acids), (2) oxidized skin lipids, and (3) exogenous compounds. This last factor appears to be associated with the presence of oleic and linoleic acids and could be accounted for by uptake of cooking oils or personal care products to skin lipids.

Deep learning predicts microbial interactions from self-organized spatiotemporal patterns

Microbial communities organize into spatial patterns that are largely governed by interspecies interactions. This phenomenon is an important metric for understanding community functional dynamics, yet the use of spatial patterns for predicting microbial interactions is currently lacking. Here we propose supervised deep learning as a new tool for network inference. An agent-based model was used to simulate the spatiotemporal evolution of two interacting organisms under diverse growth and interaction scenarios, the data of which was subsequently used to train deep neural networks. For small-size domains (100 µm × 100 µm) over which interaction coefficients are assumed to be invariant, we obtained fairly accurate predictions, as indicated by an average R2 value of 0.84. In application to relatively larger domains (450 µm × 450 µm) where interaction coefficients are varying in space, deep learning models correctly predicted spatial distributions of interaction coefficients without any additional training. Lastly, we evaluated our model against real biological data obtained using Pseudomonas fluorescens and Escherichia coli co-cultures treated with polymeric chitin or N-acetylglucosamine, the hydrolysis product of chitin. While P. fluorescens can utilize both substrates for growth, E. coli lacked the ability to degrade chitin. Consistent with our expectations, our model predicted context-dependent interactions across two substrates, i.e., degrader-cheater relationship on chitin polymers and competition on monomers. The combined use of the agent-based model and machine learning algorithm successfully demonstrates how to infer microbial interactions from spatially distributed data, presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Transposon mutagenesis in Mycobacterium kansasii links a small RNA gene to colony morphology and biofilm formation and identifies 9,885 intragenic insertions that do not compromise colony outgrowth

Mycobacterium kansasii (Mk) is a resilient opportunistic human pathogen that causes tuberculosis‐like chronic pulmonary disease and mortality stemming from comorbidities and treatment failure. The standard treatment of Mk infections requires costly, long‐term, multidrug courses with adverse side effects. The emergence of drug‐resistant isolates further complicates the already challenging drug therapy regimens and threatens to compromise the future control of Mk infections. Despite the increasingly recognized global burden of Mk infections, the biology of this opportunistic pathogen remains essentially unexplored. In particular, studies reporting gene function or generation of defined mutants are scarce. Moreover, no transposon (Tn) mutagenesis tool has been validated for use in Mk, a situation limiting the repertoire of genetic approaches available to accelerate the dissection of gene function and the generation of gene knockout mutants in this poorly characterized pathogen. In this study, we validated the functionality of a powerful Tn mutagenesis tool in Mk and used this tool in conjunction with a forward genetic screen to establish a previously unrecognized role of a conserved mycobacterial small RNA gene of unknown function in colony morphology features and biofilm formation. We also combined Tn mutagenesis with next‐generation sequencing to identify 12,071 Tn insertions that do not compromise viability in vitro. Finally, we demonstrated the susceptibility of the Galleria mellonella larva to Mk, setting the stage for further exploration of this simple and economical infection model system to the study of this pathogen.

Selective Cu-Catalyzed Intramolecular Annulation of 3-Aryl/Heteryl-2-(diazoacetyl)-1H-pyrroles: Synthesis of Benzo/Furo/Thieno[e]-Fused 1H-Indol-7-oles and Their Transformations

The Co(III)-catalyzed reaction of 1,3-dicarbonyl compounds with 2-(diazoacetyl)-2H-azirines, prepared by a simplified procedure from 2H-azirin-2-carbonyl chlorides, led in high yields to the formation of 2-(diazoacetyl)pyrroles, while leaving the diazoacetyl function intact. The intramolecular aromatic substitution reaction of 2-(diazoacetyl)pyrroles, catalyzed by Cu(OTf)₂, provided selectively previously unknown benzo[e]- and hetero[e]-fused indol-7-oles in good yields. Formylation of benzo[e]indol-4-ol led selectively to the 5-formyl derivative, which is a good precursor for an unusual salen ligand and its Ni-complex. Triflates prepared from benzo[e]indol-4-oles gave various 4-substituted benzo[e]indoles carrying aryl, 2-thienyl, 2-pyridyl, and alkynyl groups, in excellent yields using cross-coupling reactions. 4-(2-Pyridyl)benzo[e]indoles, upon treatment with BF₃·Et₂O/Et₃N, afforded a new type of fluorescent boron complexes with large Stokes shifts.

Algal Morphological Identification in Watersheds for Drinking Water Supply Using Neural Architecture Search for Convolutional Neural Network

An excessive increase in algae often has various undesirable effects on drinking water supply systems, thus proper management is necessary. Algal monitoring and classification is one of the fundamental steps in the management of algal blooms. Conventional microscopic methods have been most widely used for algal classification, but such approaches are time-consuming and labor-intensive. Thus, the development of alternative methods for rapid, but reliable algal classification is essential where an advanced machine learning technique, known as deep learning, is considered to provide a possible approach for rapid algal classification. In recent years, one of the deep learning techniques, namely the convolutional neural network (CNN), has been increasingly used for image classification in various fields, including algal classification. However, previous studies on algal classification have used CNNs that were arbitrarily chosen, and did not explore possible CNNs fitting algal image data. In this paper, neural architecture search (NAS), an automatic approach for the design of artificial neural networks (ANN), is used to find a best CNN model for the classification of eight algal genera in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis sp., Oscillatoria sp., and Anabaena sp.), three diatoms (Fragilaria sp., Synedra sp., and two green algae (Staurastrum sp. and Pediastrum sp.). The developed CNN model effectively classified the algal genus with an F1-score of 0.95 for the eight genera. The results indicate that the CNN models developed from NAS can outperform conventional CNN development approaches, and would be an effective tool for rapid operational responses to algal bloom events. In addition, we introduce a generic framework that provides a guideline for the development of the machine learning models for algal image analysis. Finally, we present the experimental results from the real-world environments using the framework and NAS.