Static magnetic field regulates Arabidopsis root growth via auxin signaling.

Static magnetic field (SMF) plays important roles in biological processes of many living organisms. In plants, however, biological significance of SMF and molecular mechanisms underlying SMF action remain largely unknown. To address these questions, we treated Arabidopsis young seedlings with different SMF intensities and directions. Magnetic direction from the north to south pole was adjusted in parallel (N0) with, opposite (N180) and perpendicular to the gravity vector. We discovered that root growth is significantly inhanced by 600 mT treatments except for N180, but not by any 300 mT treatments. N0 treatments lead to more active cell division of the meristem, and higher auxin content that is regulated by coordinated expression of PIN3 and AUX1 in root tips. Consistently, N0-promoted root growth disappears in pin3 and aux1 mutants. Transcriptomic and gene ontology analyses revealed that in roots 85% of the total genes significantly down-regulated by N0 compared to untreatment are enriched in plastid biological processes, such as metabolism and chloroplast development. Lastly, no difference in root length is observed between N0-treated and untreated roots of the double cryptochrome mutant cry1 cry2. Taken together, our data suggest that SMF-regulated root growth is mediated by CRY and auxin signaling pathways in Arabidopsis.

Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma.

High-risk neuroblastoma is a very aggressive disease, with excessive tumor growth and poor outcomes. A proper stratification of the high-risk patients by prognostic outcome is important for treatment. However, there is still a lack of survival stratification for the high-risk neuroblastoma. To fill the gap, we adopt a deep learning algorithm, Autoencoder, to integrate multi-omics data, and combine it with K-means clustering to identify two subtypes with significant survival differences. By comparing the Autoencoder with PCA, iCluster, and DGscore about the classification based on multi-omics data integration, Autoencoder-based classification outperforms the alternative approaches. Furthermore, we also validated the classification in two independent datasets by training machine-learning classification models, and confirmed its robustness. Functional analysis revealed that MYCN amplification was more frequently occurred in the ultra-high-risk subtype, in accordance with the overexpression of MYC/MYCN targets in this subtype. In summary, prognostic subtypes identified by deep learning-based multi-omics integration could not only improve our understanding of molecular mechanism, but also help the clinicians make decisions.

Whole-Genome Sequencing Identifies a Novel Variation of WAS Gene Coordinating With Heterozygous Germline Mutation of APC to Enhance Hepatoblastoma Oncogenesis.

Hepatoblastoma (HB), a leading primary hepatic malignancy in children, originates from primitive hepatic stem cells. This study aimed to uncover the genetic variants that are responsible for HB oncogenesis. One family, which includes the healthy parents, and two brothers affected by HB, was recruited. Whole-genome sequencing (WGS) of germline DNA from all the family members identified two maternal variants, located within APC gene and X-linked WAS gene, which were harbored by the two brothers. The mutation of APC (rs137854573, c.C1606T, p.R536X) could result in HB carcinogenesis by activating Wnt signaling. The WAS variant (c.G3T, p.M1-P5del) could promote HB cell proliferation and inhibit T-cell-based immunity by activating PLK1 signaling and inactivating TCR signaling. Further analysis reflected that WAS deficiency might affect the antitumor activity of natural killer and dendritic cells. In summary, the obtained results imply that an APC mutant together with an X-linked WAS mutant, could lead to HB tumorigenesis by activating Wnt and PLK1 signaling, inhibiting TCR signaling, and reducing the antitumor activity of natural killer and dendritic cells.