DNMT3.1 controls trade-offs between growth, reproduction, and life span under starved conditions in Daphnia magna.

The cladoceran crustacean Daphnia has long been a model of energy allocation studies due to its important position in the trophic cascade of freshwater ecosystems. However, the loci for controlling energy allocation between life history traits still remain unknown. Here, we report CRISPR/Cas-mediated target mutagenesis of DNA methyltransferase 3.1 (DNMT3.1) that is upregulated in response to caloric restriction in Daphnia magna. The resulting biallelic mutant is viable and did not show any change in growth rate, reproduction, and longevity under nutrient rich conditions. In contrast, under starved conditions, the growth rate of this DNMT3.1 mutant was increased but its reproduction was reciprocally reduced compared to the wild type when the growth and reproduction activities competed during a period from instar 4 to 8. The life span of this mutant was significantly shorter than that of the wild type. We also compared transcriptomes between DNMT3.1 mutant and wild type under nutrient-rich and starved conditions. Consistent with the DNMT3.1 mutant phenotypes, the starved condition led to changes in the transcriptomes of the mutant including differential expression of vitellogenin genes. In addition, we found upregulation of the I am not dead yet (INDY) ortholog, which has been known to shorten the life span in Drosophila, explaining the shorter life span of the DNMT3.1 mutant. These results establish DNMT3.1 as a key regulator for life span and energy allocation between growth and reproduction during caloric restriction. Our findings reveal how energy allocation is implemented by selective expression of a DNMT3 ortholog that is widely distributed among animals. We also infer a previously unidentified adaptation of Daphnia that invests more energy for reproduction than growth under starved conditions.

Mutation of the Cytochrome P450 CYP360A8 Gene Increases Sensitivity to Paraquat in Daphnia magna.

The freshwater crustacean Daphnia magna has traditionally been a model for ecotoxicological studies owing to its sensitivity to many xenobiotics. Because it is used in many toxicity assessments, its detoxification mechanism for xenobiotics is important and requires further study. However, studies related to detoxification genes are limited to transcriptomic profiling, and there are no D. magna mutants for use in the understanding of xenobiotic metabolism in vivo. We report the generation of a D. magna CYP360A8 mutant-the gene is a cytochrome P450 (CYP) clan 3 gene. Based on RNA sequencing of adult D. magna, we found that CYP360A8 has the highest expression level among all CYP genes. At ovarian maturation, its expression level is up-regulated 6-fold compared to the juvenile stages and is maintained thereafter. Using the CRISPR/CRISPR-associated 9 (Cas9) system, we disrupted CYP360A8 by coinjecting CYP360A8-targeting guide RNA and Cas9 proteins into D. magna eggs and established one monoallelic CYP360A8 mutant line. This CYP360A8 mutant had a higher sensitivity to the herbicide paraquat compared to the wild type. We confirmed the up-regulation of CYP360A8 by paraquat. The results demonstrate the role of CYP360A8 in paraquat detoxification. The present study establishes a CYP mutant of D. magna, and this strategy can be a basic platform to document a range of CYP gene-xenobiotic relationships in this species. Environ Toxicol Chem 2021;40:1279-1288. © 2020 SETAC.

Caloric restriction upregulates the expression of DNMT3.1, lacking the conserved catalytic domain, in Daphnia magna.

DNA methylation plays an important role in many aspects of biology, including development, disease, and phenotypic plasticity. In the branchiopod crustacean, Daphnia, de novo DNA methylation has been detected in specific environmental contexts. However, fundamental information on de novo DNA methyltransferase DNMT3 orthologs, including domain organization, developmental expression, and response to environmental stimuli, is lacking. In this study, we examined two DNMT3 orthologs in Daphnia magna, DapmaDNMT3.1 and DapmaDNMT3.2. Amino acid sequence alignment revealed that DapmaDNMT3.1 and DapmaDNMT3.2 lack the conserved methyltransferase motifs of the catalytic domain and the PWWP domain, respectively. We profiled the expression of the two orthologs during embryogenesis and under various feeding levels. During embryogenesis, in contrast to the low DapmaDNMT3.1 expression, DapmaDNTM3.2 was highly expressed at specific stages, that is, in the one cell-stage and at 48 hr post ovulation. In nutrient-rich condition, both genes were lowly expressed, whereas DapmaDNMT3.1 was upregulated at the lower food levels, suggesting a potential role of DapmaDNMT3.1 in gene regulation in response to caloric restriction. These findings provide a basis for understanding the developmental stage- and stress-dependent function of DNMT3 orthologs in D. magna.

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band.

Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2⁻4.2% depending on the type of wrist activities.

Daily wrist activity classification using a smart band.

OBJECTIVE: In this letter, we propose a novel method for classifying daily wrist activities by using a smart band. APPROACH: Triaxial acceleration data are collected by built-in sensors of the smart band during experiments regarding five activities, i.e. texting, calling, placing a hand in a pocket, carrying a suitcase, and swinging a hand. We analyze patterns in the sensor signals during these activities based on three types of features, i.e. norm, norm-variance, and frequency-domain features. After extracting the significant features, a multi-class support vector machine algorithm is applied to classify these activities. MAIN RESULTS: We obtained recognition error rates of approximately 2.7% by applying the proposed method to the experimental dataset.