Tumor suppressor genes are generally viewed as being recessive at the cellular level, so that mutation or loss of both tumor suppressor alleles is a prerequisite for tumor formation. The tumor suppressor gene, p53, is mutated in~50% of human sporadic cancers and in an inherited cancer predisposition (Li-Fraumeni syndrome). We have analyzed the status of the wildtype p53 allele in tumors taken from p53-deficient heterozygous (p53⍣/-) mice. These mice inherit a single null p53 allele and develop tumors much earlier than those mice with two functional copies of wildtype p53. We present evidence that a high proportion of the tumors from the p53⍣/-mice retain an intact, functional, wild-type p53 allele. Unlike p53⍣/-tumors which lose their wild-type allele, the tumors which retain an intact p53 allele express p53 protein that induces apoptosis following γ-irradiation, activates p21 WAF1/CIP1 and Mdm2 expression, represses PCNA expression (a negatively regulated target of wild-type p53), shows high levels of binding to oligonucleotides containing a wild-type p53 response element and prevents chromosomal instability as measured by comparative genomic hybridization. These results indicate that loss of both p53 alleles is not a prerequisite for tumor formation and that mere reduction in p53 levels may be sufficient to promote tumorigenesis.

Site-specific gene addition can allow stable transgene expression for gene therapy. When possible, this is preferred over the use of promiscuously integrating vectors, which are sometimes associated with clonal expansion1 and oncogenesis2. Site-specific endonucleases that can induce high rates of targeted genome editing are finding increasing applications in biological discovery and gene therapy3. However, two safety concerns persist: (1) endonuclease-associated adverse effects, both on4 and off-target5,6; and (2) oncogene activation caused by promoter integration, even without nucleases7. Here, we perform recombinant adeno-associated virus (rAAV) mediated promoterless gene targeting without nucleases and demonstrate amelioration of the bleeding diathesis in haemophilia B mice. In particular, we target a promoterless human coagulation factor IX (hF9) gene to the liver-expressed albumin (Alb) locus. hF9 is targeted, along with a preceding 2A-peptide coding sequence, to be integrated just upstream to the Alb stop codon. While hF9 is fused to Alb at the DNA and RNA levels, two separate proteins are synthesized by way of ribosomal skipping. Thus, hF9 expression is linked to robust hepatic albumin expression without disrupting it. We injected an AAV8-hF9 vector into neonatal and adult mice and achieved on-target integration into ~0.5% of the albumin alleles in hepatocytes. We established that hF9 was produced only from on-target integration, and ribosomal skipping was highly efficient. Stable hF9 plasma levels at 7–20% of normal were obtained, and treated factor IX deficient mice had normal coagulation times. In conclusion, transgene integration as a 2A-fusion to a highly expressed endogenous gene may obviate the requirement for nucleases and/or vector-borne promoters. This method may allow for safe and efficacious gene targeting in both infants and adults by greatly diminishing off-target effects while still providing therapeutic levels of expression from integration.

This study describes a novel three-dimensional (3D) convolutional neural networks (CNN) based method that automatically classifies crops from spatio-temporal remote sensing images. First, 3D kernel is designed according to the structure of multi-spectral multi-temporal remote sensing data. Secondly, the 3D CNN framework with fine-tuned parameters is designed for training 3D crop samples and learning spatio-temporal discriminative representations, with the full crop growth cycles being preserved. In addition, we introduce an active learning strategy to the CNN model to improve labelling accuracy up to a required threshold with the most efficiency. Finally, experiments are carried out to test the advantage of the 3D CNN, in comparison to the two-dimensional (2D) CNN and other conventional methods. Our experiments show that the 3D CNN is especially suitable in characterizing the dynamics of crop growth and outperformed the other mainstream methods.

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