Ample evidence indicates that insulin resistance (IR) is closely related to white adipose tissue (WAT), but the underlying mechanisms of IR pathogenesis are still unclear. Using 352 microarray datasets from seven independent studies, we identified a meta-signature which comprised of 1,413 genes. Our meta-signature was also enriched in overall WAT in in vitro and in vivo IR models. Only 12 core enrichment genes were consistently enriched across all IR models. Among the meta-signature, we identified a drug signature made up of 211 genes with expression levels that were co-regulated by thiazolidinediones and metformin using cross-species analysis. To confirm the clinical relevance of our drug signature, we found that the expression levels of 195 genes in the drug signature were significantly correlated with both homeostasis model assessment 2-IR score and body mass index. Finally, 18 genes from the drug signature were identified by protein-protein interaction network cluster. Four core enrichment genes were included in 18 genes and the expression levels of selected 8 genes were validated by quantitative PCR. These findings suggest that our signatures provide a robust set of genetic markers which can be used to provide a starting point for developing potential therapeutic targets in improving IR in WAT.

As major components of spider venoms, neurotoxic peptides exhibit structural diversity, target specificity, and have great pharmaceutical potential. Deep learning may be an alternative to the laborious and time-consuming methods for identifying these peptides. However, the major hurdle in developing a deep learning model is the limited data on neurotoxic peptides. Here, we present a peptide data augmentation method that improves the recognition of neurotoxic peptides via a convolutional neural network model. The neurotoxic peptides were augmented with the known neurotoxic peptides from UniProt database, and the models were trained using a training set with or without the generated sequences to verify the augmented data. The model trained with the augmented dataset outperformed the one with the unaugmented dataset, achieving accuracy of 0.9953, precision of 0.9922, recall of 0.9984, and F1 score of 0.9953 in simulation dataset. From the set of all RNA transcripts of Callobius koreanus spider, we discovered neurotoxic peptides via the model, resulting in 275 putative peptides of which 252 novel sequences and only 23 sequences showing homology with the known peptides by Basic Local Alignment Search Tool. Among these 275 peptides, four were selected and shown to have neuromodulatory effects on the human neuroblastoma cell line SH-SY5Y. The augmentation method presented here may be applied to the identification of other functional peptides from biological resources with insufficient data.

Atopic dermatitis (AD) is one of the most common inflammatory skin diseases, which significantly impact the quality of life. Transcriptome-wide association study (TWAS) was conducted to estimate both transcriptomic and genomic features of AD and detected significant associations between 31 expression quantitative loci and 25 genes. Our results replicated well-known genetic markers for AD, as well as 4 novel associated genes. Next, transcriptome meta-analysis was conducted with 5 studies retrieved from public databases and identified 5 additional novel susceptibility genes for AD. Applying the connectivity map to the results from TWAS and meta-analysis, robustly enriched perturbations were identified and their chemical or functional properties were analyzed. Here, we report the first research on integrative approaches for an AD, combining TWAS and transcriptome meta-analysis. Together, our findings could provide a comprehensive understanding of the pathophysiologic mechanisms of AD and suggest potential drug candidates as alternative treatment options.

Much remains to be understood about how a group of cells break symmetry and differentiate into distinct cell types. The simple eukaryote Dictyostelium discoideum is an excellent model system for studying questions such as cell type differentiation. Dictyostelium cells grow as single cells. When the cells starve, they aggregate to develop into a multicellular structure with only two main cell types: spore and stalk. There has been a longstanding controversy as to how a cell makes the initial choice of becoming a spore or stalk cell. In this review, we describe how the controversy arose and how a consensus developed around a model in which initial cell type choice in Dictyostelium is dependent on the cell cycle phase that a cell happens to be in at the time that it starves.

Two bacterial strains, designated MJB4^T and SJ7^T, were isolated from water samples collected from Jeongbang Falls on Jeju Island, Republic of Korea. Phylogenetic analysis of 16S rRNA gene sequences indicated that the two strains belonged to the genera Nocardioides and Hyunsoonleella , owing to their high similarities to Nocardioides jensenii DSM 29641^T (97.5 %) and Hyunsoonleella rubra FA042 ^T (96.3 %), respectively. These values are much lower than the gold standard for bacterial species (98.7 %). The average nucleotide identity values between strains MJB4^T, SJ7^T and the reference strains, Nocardioides jensenii DSM 29641^T, Nocardioides daejeonensis MJ31^T and Hyunsoonleella flava T58^T were 77.2, 75.9 and 75.4 %, respectively. Strains MJB4^T and SJ7^T and the type strains of the species involved in system incidence have average nucleotide identity and average amino acid threshold values of 60.1–82.6 % for the species boundary (95–96 %), which confirms that strains MJB4^T and SJ7^T represent two new species of genus Nocardioides and Hyunsoonleella , respectively. Based on phylogenetic and phenotypic data, strains MJB4^T and SJ7^T are considered to represent novel species of the genus Nocardioides and Hyunsoonleella , respectively, for which the names Nocardioides donggukensis sp. nov. (type strain MJB4^T=KACC 21724^T=NBRC 114402^T) and Hyunsoonleella aquatilis sp. nov., (type strain SJ7^T=KACC 21715^T=NBRC 114486^T) have been proposed.