Inhibiting neddylation with MLN4924 potentiates hypoxia-induced apoptosis of mouse type B spermatogonia GC-2 cells

Hypoxia, an inadequate supply of tissue oxygen tension, has been reported to induce apoptosis of spermatogenic cells and is associated with male infertility. Neddylation, a post-translational modification similar to ubiquitination, has been shown to be involved in the hypoxia stress response. However, the functions of neddylation in hypoxia-induced apoptosis of spermatogenic cells and its association with male infertility remain largely unexplored. In this study, aiming to explore the role of neddylation in male infertility, we used the specific neddylation inhibitor MLN4924 for treatment in mouse type B spermatogonia GC-2 cells. Our results showed that MLN4924 had no apparent effect on GC-2 cell apoptosis under normoxia, but significantly increased apoptotic cells under hypoxia. Transcriptomic analysis and qPCR assay confirmed that MLN4924 could suppress the expression of hypoxia target genes in GC-2 cells under hypoxia. In addition, MLN4924 could enhance the induction of intracellular and mitochondrial reactive oxygen species (ROS) under hypoxia. These results indicate that the neddylation inhibitor MLN4924 potentiates hypoxia-induced apoptosis of mouse type B spermatogonia GC-2 cells, and neddylation may play an important role in promoting spermatogenic cells to adapt to hypoxia stress.

Genomic data imputation with variational auto-encoders

BACKGROUND

As missing values are frequently present in genomic data, practical methods to handle missing data are necessary for downstream analyses that require complete data sets. State-of-the-art imputation techniques, including methods based on singular value decomposition and K-nearest neighbors, can be computationally expensive for large data sets and it is difficult to modify these algorithms to handle certain cases not missing at random.

RESULTS

In this work, we use a deep-learning framework based on the variational auto-encoder (VAE) for genomic missing value imputation and demonstrate its effectiveness in transcriptome and methylome data analysis. We show that in the vast majority of our testing scenarios, VAE achieves similar or better performances than the most widely used imputation standards, while having a computational advantage at evaluation time. When dealing with data missing not at random (e.g., few values are missing), we develop simple yet effective methodologies to leverage the prior knowledge about missing data. Furthermore, we investigate the effect of varying latent space regularization strength in VAE on the imputation performances and, in this context, show why VAE has a better imputation capacity compared to a regular deterministic auto-encoder.

CONCLUSIONS

We describe a deep learning imputation framework for transcriptome and methylome data using a VAE and show that it can be a preferable alternative to traditional methods for data imputation, especially in the setting of large-scale data and certain missing-not-at-random scenarios.