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

Differential Expression of Three Cryptosporidium Species-Specific MEDLE Proteins

Cryptosporidium parvum and Cryptosporidium hominis share highly similar proteomes, with merely ~3% divergence in overall nucleotide sequences. Cryptosporidium-specific MEDLE family is one of the major differences in gene content between the two species. Comparative genomic analysis indicated that MEDLE family may contribute to differences in host range among Cryptosporidium spp. Previous studies have suggested that CpMEDLE-1 encoded by cgd5_4580 and CpMEDLE-2 encoded by cgd5_4590 are potentially involved in the invasion of C. parvum. In this study, we expressed in Escherichia coli, the C. hominis-specific member of the MEDLE protein family, ChMEDLE-1 encoded by chro.50507, and two C. parvum-specific members, CpMEDLE-3 encoded by cgd5_4600 and CpMEDLE-5 encoded by cgd6_5480. Quantitative PCR, immunofluorescence staining and in vitro neutralization assay were conducted to assess their biologic characteristics. The expression of the cgd5_4600 gene was high during 12–48 h of the in vitro culture, while the expression of cgd6_5480 was the highest at 2 h. ChMEDLE-1 and CpMEDLE-3 proteins were mostly located in the anterior and mid-anterior region of sporozoites and merozoites, whereas CpMEDLE-5 was expressed over the entire surface of these invasive stages. Polyclonal antibodies against MEDLE proteins had different neutralization efficiency, reaching approximately 50% for ChMEDLE-1 and 60% for CpMEDLE-3, but only 20% for CpMEDLE-5. The differences in protein and gene expression and neutralizing capacity indicated the MEDLE proteins may have different roles during Cryptosporidium invasion and growth

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

Live births after simultaneous avoidance of monogenic diseases and chromosome abnormality by next-generation sequencing with linkage analyses

In vitro fertilization (IVF), preimplantation genetic diagnosis (PGD), and preimplantation genetic screening (PGS) help patients to select embryos free of monogenic diseases and aneuploidy (chromosome abnormality). Next-generation sequencing (NGS) methods, while experiencing a rapid cost reduction, have improved the precision of PGD/PGS. However, the precision of PGD has been limited by the false-positive and false-negative single-nucleotide variations (SNVs), which are not acceptable in IVF and can be circumvented by linkage analyses, such as short tandem repeats or karyomapping. It is noteworthy that existing methods of detecting SNV/copy number variation (CNV) and linkage analysis often require separate procedures for the same embryo. Here we report an NGS-based PGD/PGS procedure that can simultaneously detect a single-gene disorder and aneuploidy and is capable of linkage analysis in a cost-effective way. This method, called "mutated allele revealed by sequencing with aneuploidy and linkage analyses" (MARSALA), involves multiple annealing and looping-based amplification cycles (MALBAC) for single-cell whole-genome amplification. Aneuploidy is determined by CNVs, whereas SNVs associated with the monogenic diseases are detected by PCR amplification of the MALBAC product. The false-positive and -negative SNVs are avoided by an NGS-based linkage analysis. Two healthy babies, free of the monogenic diseases of their parents, were born after such embryo selection. The monogenic diseases originated from a single base mutation on the autosome and the X-chromosome of the disease-carrying father and mother, respectively.Chemistry and Chemical Biolog

Crop Growth and Irrigation Interact to Influence Surface Fluxes in a Regional Climate-Cropland Model (WRF3.3‑CLM4crop)

In this study, we coupled Version 4.0 of the Community Land Model that includes crop growth and management (CLM4crop) into the Weather Research and Forecasting (WRF) model Version 3.3 to better represent interactions between climate and agriculture. We evaluated the performance of the coupled model (WRF3.3-CLM4crop) by comparing simulated crop growth and surface climate to multiple observational datasets across the continental United States. The results showed that although the model with dynamic crop growth overestimated leaf area index (LAI) and growing season length, interannual variability in peak LAI was improved relative to a model with prescribed crop LAI and growth period, which has no environmental sensitivity. Adding irrigation largely improved daily minimum temperature but the RMSE is still higher over irrigated land than non-irrigated land. Improvements in climate variables were limited by an overall model dry bias. However, with addition of an irrigation scheme, soil moisture and surface energy flux partitioning were largely improved at irrigated sites. Irrigation effects were sensitive to crop growth: the case with prescribed crop growth underestimated irrigation water use and effects on temperature and overestimated soil evaporation relative to the case with dynamic crop growth in moderately irrigated regions. We conclude that studies examining irrigation effects on weather and climate using coupled climate–land surface models should include dynamic crop growth and realistic irrigation schemes to better capture land surface effects in agricultural regions. © 2015, Springer-Verlag Berlin Heidelberg

Screening and Evaluation of Excellent Blackberry Cultivars and Strains Based on Nutritional Quality, Antioxidant Properties, and Genetic Diversity

To screen and evaluate excellent blackberry cultivars and strains, 17 indexes of plant growth and fruit horticultural and nutritional characteristics were measured, 20 simple sequence repeat (SSR) markers were analyzed, the fingerprints of 23 blackberry cultivars and strains were constructed, and the processing characteristics of 10 excellent cultivars and strains were evaluated. The results showed that ‘Chester’ and ‘Shuofeng’ had the highest plant yield (6.5 kg per plant), of which the ‘Chester’ fruit also had the highest hardness (2.78 kg/cm2). ‘Kiowa’ had the highest single fruit weight (10.43 g). ‘10-5n-2’ had the highest total anthocyanin content (225.4 mg/100 g FW) and total polyphenol content (3.24 mg/g FW), but a low plant yield. These results suggest that ‘Shuofeng’ and ‘Chester’ are the top two blackberry cultivars planted in Nanjing, with the best growth and comprehensive quality. Moreover, a total of 119 alleles were detected with an average number of 6 alleles per locus. The polymorphism information content (PIC) was 0.374~0.844, with an average of 0.739, indicating a high genetic diversity among the 23 blackberry cultivars and strains. This study provides insight into the plant growth, fruit characteristics and genetic diversity of the 23 blackberry cultivars and strains, and is thus conducive to the protection and utilization of blackberry cultivars and strains