Masuzawa, T. | bioRxiv Science

bioRxiv2021

Light-dependent synthesis of a nucleotide second messenger controls bacterial motility

Nucleotide second messengers are universally crucial factors for the signal transduction of various organisms. In prokaryotes, cyclic nucleotide messengers are involved in the bacterial life cycle and function, such as virulence, biofilm formation, and others mainly via gene regulation. Here we show that the swimming motility of a soil bacterium is rapidly modulated by cyclic adenosine monophosphate (cAMP) synthesized upon light exposure. Analysis of a loss-of-photoresponsivity mutant obtained by transposon random mutagenesis determined the novel sensory gene, and its expression in Escherichia coli through codon optimization revealed the light-dependent synthesis of cAMP. GFP labeling showed the localization of the photoresponsive enzyme at the cell poles where flagellar motors reside. The present findings highlight the new role of cAMP that rapidly controls the flagella-dependent bacterial motility and the global distribution of the discovered photoactivated cyclase among diverse microbial species.

microbiology↗

bioRxiv2020

A Machine Learning Approach Towards Standardizing Microscopic Agglutination Test for Diagnosis of Leptospirosis

Leptospirosis is a zoonosis caused by the pathogenic bacterium Leptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospiras, and the presence or absence of aggregation is determined under a dark-field microscope to calculate the antibody titer. Problems of the current MAT method are 1) a requirement of examining many specimens per sample, and 2) a need of distinguishing contaminants from true aggregates to accurately identify positivity. Therefore, increasing efficiency and accuracy are the key to refine MAT. It is possible to achieve efficiency and standardize accuracy at the same time by automating the decision making process. In this study, we built an automatic identification algorithm of MAT using a machine learning method to determine aggregation within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera of Leptospira- infected (positive) and non-infected (negative) hamsters, respectively. In addition to the acquired original images, wavelet-transformed images were also considered as features. We utilized a support vector machine (SVM) as a proposed decision method. We validated the trained SVMs with 210 positive and 154 negative images. When the features were obtained from original or wavelet-transformed images, all negative images were misjudged as positive, and the classification performance was very low with sensitivity of 1 and specificity of 0. In contrast, when the histograms of wavelet coefficients were used as features, the performance was greatly improved with sensitivity of 0.99 and specificity of 0.99. We confirmed that the current algorithm judges the positive or negative of agglutinations in MAT images and gives the further possibility of automatizing MAT procedure.

microbiology↗