ZBTB2 links p53 deficiency to HIF-1-mediated hypoxia signaling to promote cancer aggressiveness.

Aberrant activation of the hypoxia-inducible transcription factor HIF-1 and dysfunction of the tumor suppressor p53 have been reported to induce malignant phenotypes and therapy resistance of cancers. However, their mechanistic and functional relationship remains largely unknown. Here, we reveal a mechanism by which p53 deficiency triggers the activation of HIF-1-dependent hypoxia signaling and identify zinc finger and BTB domain-containing protein 2 (ZBTB2) as an important mediator. ZBTB2 forms homodimers via its N-terminus region and increases the transactivation activity of HIF-1 only when functional p53 is absent. The ZBTB2 homodimer facilitates invasion, distant metastasis, and growth of p53-deficient, but not p53-proficient, cancers. The intratumoral expression levels of ZBTB2 are associated with poor prognosis in lung cancer patients. ZBTB2 N-terminus-mimetic polypeptides competitively inhibit ZBTB2 homodimerization and significantly suppress the ZBTB2-HIF-1 axis, leading to antitumor effects. Our data reveal an important link between aberrant activation of hypoxia signaling and loss of a tumor suppressor and provide a rationale for targeting a key mediator, ZBTB2, to suppress cancer aggressiveness.

Single-cell transcriptome atlas of Drosophila gastrula 2.0.

During development, positional information directs cells to specific fates, leading them to differentiate with their own transcriptomes and express specific behaviors and functions. However, the mechanisms underlying these processes in a genome-wide view remain ambiguous, partly because the single-cell transcriptomic data of early developing embryos containing accurate spatial and lineage information are still lacking. Here, we report a single-cell transcriptome atlas of Drosophila gastrulae, divided into 77 transcriptomically distinct clusters. We find that the expression profiles of plasma-membrane-related genes, but not those of transcription-factor genes, represent each germ layer, supporting the nonequivalent contribution of each transcription-factor mRNA level to effector gene expression profiles at the transcriptome level. We also reconstruct the spatial expression patterns of all genes at the single-cell stripe level as the smallest unit. This atlas is an important resource for the genome-wide understanding of the mechanisms by which genes cooperatively orchestrate Drosophila gastrulation.

Model-based prediction of spatial gene expression via generative linear mapping.

Decoding spatial transcriptomes from single-cell RNA sequencing (scRNA-seq) data has become a fundamental technique for understanding multicellular systems; however, existing computational methods lack both accuracy and biological interpretability due to their model-free frameworks. Here, we introduce Perler, a model-based method to integrate scRNA-seq data with reference in situ hybridization (ISH) data. To calibrate differences between these datasets, we develop a biologically interpretable model that uses generative linear mapping based on a Gaussian mixture model using the Expectation-Maximization algorithm. Perler accurately predicts the spatial gene expression of Drosophila embryos, zebrafish embryos, mammalian liver, and mouse visual cortex from scRNA-seq data. Furthermore, the reconstructed transcriptomes do not over-fit the ISH data and preserved the timing information of the scRNA-seq data. These results demonstrate the generalizability of Perler for dataset integration, thereby providing a biologically interpretable framework for accurate reconstruction of spatial transcriptomes in any multicellular system.