Li-Fraumeni Syndrome (LFS) is a hereditary cancer predisposition syndrome caused by germline mutations in the
TP53
tumour suppressor gene, which encodes the multifunctional transcription factor p53. p53 is the most commonly mutated protein in human cancer, with the majority occurring within the DNA-binding domain, often disrupting transcriptional activity and resulting in a loss-of-function. Here, we characterise the novel p53
N263Tfs*7
truncated mutant, identified as a germline mutation from a patient with LFS who developed breast cancer. Functional assays revealed a partial loss-of-function across key cellular processes, including proliferation, cell death, cell motility, and transcriptional transactivation. This mutant lacked a dominant-negative effect, distinguishing it from common DNA-binding domain missense mutations. Our findings demonstrate that oncogenesis in LFS can be driven by partial impairment of functional p53, rather than dominant-negative or gain-of-function mutations alone. This underscores the clinical significance of recognising subtle
TP53
variants for the refined molecular classification and clinical prediction of cancer risk in
TP53
mutation carriers.
Francesca M. Wright, Mariela Vasileva-Slaveva, A. Yordanov et al.· BMC Cancer· 0 citations
Bioengineered neuronal systems are increasingly explored as living computational substrates, yet their end-to-end communication properties remain poorly characterized beyond global activity statistics. We model a mechanosensitive spiking neuronal network as a noisy multiple-input multiple-output (MIMO) communication system to quantify how topology and substrate mechanics shape information transfer and computation. Using a mechanosensitive Izhikevich network with time-varying stiffness, we compare random, clustered, and disintegrated topologies under matched stimulation and observation noise. Communication is quantified using transfer entropy, achievable mutual information rate from binned spike observations, and information redundancy. Computation is evaluated using a receiver-level separability metric posed as a transmitter-identification task. Across noise levels, clustered networks more consistently maintain higher achievable rates and exhibit more concentrated transmitter–receiver separability than random and disintegrated substrates. These results show that topology and mechanical state jointly regulate communication and computational separability in bioengineered neuronal networks, providing design-relevant metrics for bioengineered intelligence systems.
Joshua Mullett, A. Mohr, Reinhold Scherer et al.· IEEE Transactions on Molecul...· 1 citation