Towards More Efficient Depression Risk Recognition via Gait

Depression, a highly prevalent mental illness, affects over 280 million individuals worldwide. Early detection and timely intervention are crucial for promoting remission, preventing relapse, and alleviating the emotional and financial burdens associated with depression. However, patients with depression often go undiagnosed in the primary care setting. Unlike many physiological illnesses, depression lacks objective indicators for recognizing depression risk, and existing methods for depression risk recognition are time-consuming and often encounter a shortage of trained medical professionals. The correlation between gait and depression risk has been empirically established. Gait can serve as a promising objective biomarker, offering the advantage of efficient and convenient data collection. However, current methods for recognizing depression risk based on gait have only been validated on small, private datasets, lacking large-scale publicly available datasets for research purposes. Additionally, these methods are primarily limited to hand-crafted approaches. Gait is a complex form of motion, and hand-crafted gait features often only capture a fraction of the intricate associations between gait and depression risk. Therefore, this study first constructs a large-scale gait database, encompassing over 1,200 individuals, 40,000 gait sequences, and covering six perspectives and three types of attire. Two commonly used psychological scales are provided as depression risk annotations. Subsequently, a deep learning-based depression risk recognition model is proposed, overcoming the limitations of hand-crafted approaches. Through experiments conducted on the constructed large-scale database, the effectiveness of the proposed method is validated, and numerous instructive insights are presented in the paper, highlighting the significant potential of gait-based depression risk recognition

Dixdc1 Is a Critical Regulator of DISC1 and Embryonic Cortical Development

The psychiatric illness risk gene Disrupted in Schizophrenia-1 (DISC1) plays an important role in brain development; however, it is unclear how DISC1 is regulated during cortical development. Here, we report that DISC1 is regulated during embryonic neural progenitor proliferation and neuronal migration through an interaction with DIX domain containing-1 (Dixdc1), the third mammalian gene discovered to contain a Disheveled-Axin (DIX) domain. We determined that Dixdc1 functionally interacts with DISC1 to regulate neural progenitor proliferation by co-modulating Wnt-GSK3β/β-catenin signaling. However, DISC1 and Dixdc1 do not regulate migration via this pathway. During neuronal migration, we discovered that phosphorylation of Dixdc1 by cyclin-dependent kinase 5 (Cdk5) facilitates its interaction with the DISC1-binding partner Ndel1. Furthermore, Dixdc1 phosphorylation and its interaction with DISC1/Ndel1 in vivo is required for neuronal migration. Together, these data reveal that Dixdc1 integrates DISC1 into Wnt-GSK3β/β-catenin-dependent and -independent signaling pathways during cortical development and further delineate how DISC1 contributes to neuropsychiatric disorders.Human Frontier Science Program (Strasbourg, France) (Long-Term Fellowship)Natural Sciences and Engineering Research Council of Canada (Postdoctoral Fellowship)National Alliance for Research on Schizophrenia and Depression (U.S.) (Young Investigator Award)National Institutes of Health (U.S.) (Grant NS37007

Transcriptomic Insights Into Root Development and Overwintering Transcriptional Memory of Brassica rapa L. Grown in the Field

As the only overwintering oil crop in the north area of China, living through winter is the primary feature of winter rapeseed. Roots are the only survival organ during prolonged cold exposure during winter to guarantee flowering in spring. However, little is known about its root development and overwintering memory mechanism. In this study, root collar tissues (including the shoot apical meristem) of three winter rapeseed varieties with different cold resistance, i.e., Longyou-7 (strong cold tolerance), Tianyou-4 (middle cold tolerance), and Lenox (cold-sensitive), were sampled in the pre-winter period (S1), overwintering periods (S2–S5), and re-greening stage (S6), and were used to identify the root development and overwintering memory mechanisms and seek candidate overwintering memory genes by measuring root collar diameter and RNA sequencing. The results showed that the S1–S2 stages were the significant developmental stages of the roots as root collar diameter increased slowly in the S3–S5 stages, and the roots developed fast in the strong cold resistance variety than in the weak cold resistance variety. Subsequently, the RNA-seq analysis revealed that a total of 37,905, 45,102, and 39,276 differentially expressed genes (DEGs), compared to the S1 stage, were identified in Longyou-7, Tianyou-4, and Lenox, respectively. The function enrichment analysis showed that most of the DEGs are significantly involved in phenylpropanoid biosynthesis, plant hormone signal transduction, MAPK signaling pathway, starch and sucrose metabolism, photosynthesis, amino sugar and nucleotide sugar metabolism, and spliceosome, ribosome, proteasome, and protein processing in endoplasmic reticulum pathways. Furthermore, the phenylpropanoid biosynthesis and plant hormone signal transduction pathways were related to the difference in root development of the three varieties, DEGs involved in photosynthesis and carbohydrate metabolism processes may participate in overwintering memory of Longyou-7 and Tianyou-4, and the spliceosome pathway may contribute to the super winter resistance of Longyou-7. The transcription factor enrichment analysis showed that the WRKY family made up the majority in different stages and may play an important regulatory role in root development and overwintering memory. These results provide a comprehensive insight into winter rapeseed's complex overwintering memory mechanisms. The identified candidate overwintering memory genes may also serve as important genetic resources for breeding to further improve the cold resistance of winter rapeseed

Disrupted in Schizophrenia 1 Regulates Neuronal Progenitor Proliferation via Modulation of GSK3β/β-Catenin Signaling

The Disrupted in Schizophrenia 1 (DISC1) gene is disrupted by a balanced chromosomal translocation (1; 11) (q42; q14.3) in a Scottish family with a high incidence of major depression, schizophrenia, and bipolar disorder. Subsequent studies provided indications that DISC1 plays a role in brain development. Here, we demonstrate that suppression of DISC1 expression reduces neural progenitor proliferation, leading to premature cell cycle exit and differentiation. Several lines of evidence suggest that DISC1 mediates this function by regulating GSK3β. First, DISC1 inhibits GSK3β activity through direct physical interaction, which reduces β-catenin phosphorylation and stabilizes β-catenin. Importantly, expression of stabilized β-catenin overrides the impairment of progenitor proliferation caused by DISC1 loss of function. Furthermore, GSK3 inhibitors normalize progenitor proliferation and behavioral defects caused by DISC1 loss of function. Together, these results implicate DISC1 in GSK3β/β-catenin signaling pathways and provide a framework for understanding how alterations in this pathway may contribute to the etiology of psychiatric disorders.National Alliance for Research on Schizophrenia and Depression (U.S.) (Young Investigator Award)Natural Sciences and Engineering Research Council of Canada (Postdoctoral Award)Human Frontier Science Program (Strasbourg, France) (Fellowship)Singleton FellowshipNational Institutes of Health (U.S.) (Grant NS37007

Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance

Genomic selection (GS) is a powerful tool for improving genetic gain in maize breeding. However, its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms. Although sequencing-based and array-based genotyping platforms have been used for GS, few studies have compared prediction performance among platforms. In this study, we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing (GBS), a 40K SNP array, and target sequence capture (TSC) using eight GS models. The GBS marker dataset yielded the highest predictabilities for all traits, followed by TSC and SNP array datasets. We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs, and BayesB, GBLUP, and RKHS performed well, while XGBoost performed poorly in most cases. We also selected significant SNP subsets using genome-wide association study (GWAS) analyses in three panels to predict hybrid performance. GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost, but depended heavily on the GWAS panel. We conclude that there is still room for optimization of the existing SNP array, and using genotyping by target sequencing (GBTS) techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays

Developmental phosphoproteomics identifies the kinase CK2 as a driver of Hedgehog signaling and a therapeutic target in medulloblastoma

A major limitation of targeted cancer therapy is the rapid emergence of drug resistance, which often arises through mutations at or downstream of the drug target or through intrinsic resistance of subpopulations of tumor cells. Medulloblastoma (MB), the most common pediatric brain tumor, is no exception, and MBs that are driven by sonic hedgehog (SHH) signaling are particularly aggressive and drug-resistant. To find new drug targets and therapeutics for MB that may be less susceptible to common resistance mechanisms, we used a developmental phosphoproteomics approach in murine granule neuron precursors (GNPs), the developmental cell of origin of MB. The protein kinase CK2 emerged as a driver of hundreds of phosphorylation events during the proliferative, MB-like stage of GNP growth, including the phosphorylation of three of the eight proteins commonly amplified in MB. CK2 was critical to the stabilization and activity of the transcription factor GLI2, a late downstream effector in SHH signaling. CK2 inhibitors decreased the viability of primary SHH-type MB patient cells in culture and blocked the growth of murine MB tumors that were resistant to currently available Hh inhibitors, thereby extending the survival of tumor-bearing mice. Because of structural interactions, one CK2 inhibitor (CX-4945) inhibited both wild-type and mutant CK2, indicating that this drug may avoid at least one common mode of acquired resistance. These findings suggest that CK2 inhibitors may be effective for treating patients with MB and show how phosphoproteomics may be used to gain insight into developmental biology and pathology

Genomic prediction of drought tolerance during seedling stage in maize using low-cost molecular markers

Drought tolerance in maize is a complex and polygenic trait, especially in the seedling stage. In plant breeding, complex genetic traits can be improved by genomic selection (GS), which has become a practical and effective breeding tool. In the present study, a natural maize population named Northeast China core population (NCCP) consisting of 379 inbred lines were genotyped with diversity arrays technology (DArT) and genotyping-by-sequencing (GBS) platforms. Target traits of seedling emergence rate (ER), seedling plant height (SPH), and grain yield (GY) were evaluated under two natural drought stress environments in northeast China. Adequate genetic variations were observed for all the target traits, but they were divergent across environments. Similarly, the heritability of the target trait also varied across years and environments, the heritabilities in 2019 (0.88, 0.82, 0.85 for ER, SPH, GY) were higher than those in 2020 (0.65, 0.53, 0.33) and cross-2-years (0.32, 0.26, 0.33). In total, three marker datasets, 11,865 SilicoDArT markers obtained from the DArT-seq platform, 7837 SNPs obtained from the DArT-seq platform, and 91,003 SNPs obtained from the GBS platform, were used for GS analysis after quality control. The results of phylogenetic trees showed that broad genetic diversity existed in the NCCP population. Genomic prediction results showed that the average prediction accuracies estimated using the DArT SNP dataset under the two-fold cross-validation scheme were 0.27, 0.19, and 0.33, for ER, SPH, and GY, respectively. The result of SilicoDArT is close to the SNPs from DArT-seq, those were 0.26, 0.22, and 0.33. For the trait with lower heritability, the prediction accuracy can be improved using the dataset filtered by linkage disequilibrium. For the same trait, the prediction accuracies estimated with two DArT marker datasets were consistently higher than that estimated with the GBS SNP dataset under the same genotyping cost. The prediction accuracy was improved by controlling population structure and marker quality, even though the marker density was reduced. The prediction accuracies were improved by more than 30% using the significant-associated SNPs. Due to the complexity of drought tolerance under the natural stress environments, multiple years of data need to be accumulated to improve prediction accuracy by reducing genotype-by-environment interaction. Modeling genotype-by-environment interaction into genomic prediction needs to be further developed for improving drought tolerance in maize. The results obtained from the present study provides valuable pathway for improving drought tolerance in maize using GS