Hyperglycemia, hyperlipidemia, and insulin resistance are hallmarks of obesity-induced type 2 diabetes, which is often caused by a high-fat diet (HFD). However, the molecular mechanisms underlying HFD-induced insulin resistance have not been elucidated in detail. In this study, we established a Drosophila model to investigate the molecular mechanisms of HFD-induced diabetes. HFD model flies recapitulate mammalian diabetic phenotypes including elevated triglyceride and circulating glucose levels, as well as insulin resistance. Expression of glass bottom boat (gbb), a Drosophila homolog of mammalian transforming growth factor-β (TGF-β), is elevated under HFD conditions. Furthermore, overexpression of gbb in the fat body produced obese and insulin-resistant phenotypes similar to those of HFD-fed flies, whereas inhibition of Gbb signaling significantly ameliorated HFD-induced metabolic phenotypes. We also discovered that tribbles, a negative regulator of AKT, is a target gene of Gbb signaling in the fat body. Overexpression of tribbles in flies in the fat body phenocopied the metabolic defects associated with HFD conditions or Gbb overexpression, whereas tribbles knockdown rescued these metabolic phenotypes. These results indicate that HFD-induced TGF-β/Gbb signaling provokes insulin resistance by increasing tribbles expression.

High-throughput single-cell RNA sequencing (scRNA-Seq) identifies distinct cell populations based on cell-to-cell heterogeneity in gene expression. By examining the distribution of the density of gene expression profiles, we can observe the metabolic features of each cell population.Here, we employ the scRNA-Seq technique to reveal the entire biosynthetic pathway of a flower volatile.The corolla of the wild tobacco Nicotiana attenuata emits a bouquet of scents that are composed mainly of benzylacetone (BA). Protoplasts from the N. attenuata corolla limbs and throat cups were isolated at three different time points, and the transcript levels of > 16 000 genes were analyzed in 3756 single cells.We performed unsupervised clustering analysis to determine which cell clusters were involved in BA biosynthesis. The biosynthetic pathway of BA was uncovered by analyzing gene co-expression in scRNA-Seq datasets and by silencing candidate genes in the corolla.In conclusion, the high-resolution spatiotemporal atlas of gene expression provided by scRNA-Seq reveals the molecular features underlying cell-type-specific metabolism in a plant.

Summary Plants have developed tissue‐specific defense strategies in response to various herbivores with different feeding habits. Although defense responses to leaf‐chewing insects have been well studied, little is known about stem‐specific responses, particularly in the pith, to stem‐boring herbivores. To understand the stem‐specific defense, we first conducted a comparative transcriptomic analysis of the wild tobacco Nicotiana attenuata before and after attack by the leaf‐chewing herbivore Manduca sexta and the stem borer Trichobaris mucorea. When the stem‐boring herbivore attacked, lignin‐associated genes were upregulated specifically in the inner parenchymal cells of the stem, the pith; lignin also accumulated highly in the attacked pith. Silencing the lignin biosynthetic gene cinnamyl alcohol dehydrogenase enhanced the performance of the stem‐boring herbivore but had no effect on the growth of the leaf‐chewing herbivore. Two‐dimensional nuclear magnetic resonance results revealed that lignified pith contains feruloyltyramine as an unusual lignin component in the cell wall, as a response against stem‐boring herbivore attack. Pith‐specific lignification induced by the stem‐boring herbivore was modulated by both jasmonate and ethylene signaling. These results suggest that lignin provides a stem‐specific inducible barrier, protecting plants against stem‐boring insects.

It is important to effectively detect, mitigate, and defend against Android malware attacks, because Android malware has long represented a major threat to Android app security. Characterizing and classifying similar malicious apps into groups plays a particularly crucial role in building a secure Android app ecosystem. The classification of malware families can efficiently enhance the malware detection process and systematically elucidate malware patterns. In this paper, we propose a novel efficient deep learning network with multi-streams for Android malware family classification. We first obtain the input data for a convolutional neural network (CNN) in string format from some main files or sections contained in each Android malicious app. We then classify malware families by applying a 1-dimensional convolution filter-based network for the files or sections. Further, by using gradient analysis to visualize the important files and sections in malicious apps, we attempt to intuitively grasp which files or sections are the most significant for malware family classification. To validate the effectiveness of our approach, we conduct extensive experiments with the well-known DREBIN and AMD malware datasets, and we compare our approach with existing methods. Our experimental results show that the 1D CNN model is more accurate than the 2D CNN model, and that the code_item part in the classes.dex is the most relevant feature for malware classification, as it is more relevant than other parts such as AndroidManifest.xml and certificate. The proposed method achieves the best accuracy of 93.2% by using 1D convolution filters with multistreams for the main files and sections of the malware samples.

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