Matzenschulz1411

Asparagine synthetase (ASNS) catalyses the ATP-dependent conversion of aspartate to asparagine. However, both the regulation and biological functions of asparagine in tumour cells remain largely unknown. Here, we report that p53 suppresses asparagine synthesis through the transcriptional downregulation of ASNS expression and disrupts asparagine-aspartate homeostasis, leading to lymphoma and colon tumour growth inhibition in vivo and in vitro. Moreover, the removal of asparagine from culture medium or the inhibition of ASNS impairs cell proliferation and induces p53/p21-dependent senescence and cell cycle arrest. Mechanistically, asparagine and aspartate regulate AMPK-mediated p53 activation by physically binding to LKB1 and oppositely modulating LKB1 activity. Thus, we found that p53 regulates asparagine metabolism and dictates cell survival by generating an auto-amplification loop via asparagine-aspartate-mediated LKB1-AMPK signalling. Our findings highlight a role for LKB1 in sensing asparagine and aspartate and connect asparagine metabolism to the cellular signalling transduction network that modulates cell survival.Phonons are the main source of relaxation in molecular nanomagnets, and different mechanisms have been proposed in order to explain the wealth of experimental findings. However, very limited experimental investigations on phonons in these systems have been performed so far, yielding no information about their dispersions. Here we exploit state-of-the-art single-crystal inelastic neutron scattering to directly measure for the first time phonon dispersions in a prototypical molecular qubit. Both acoustic and optical branches are detected in crystals of [VO(acac)[Formula see text]] along different directions in the reciprocal space. Using energies and polarisation vectors calculated with state-of-the-art Density Functional Theory, we reproduce important qualitative features of [VO(acac)[Formula see text]] phonon modes, such as the presence of low-lying optical branches. Moreover, we evidence phonon anti-crossings involving acoustic and optical branches, yielding significant transfers of the spin-phonon coupling strength between the different modes.Cancers converge onto shared patterns that arise from constraints placed by the biology of the originating cell lineage and microenvironment on programs driven by oncogenic events. Here we define consistent expression modules reflecting this structure in colon and breast cancer by exploiting expression data resources and a new computationally efficient approach that we validate against other comparable methods. This approach, Parsimonious Gene Correlation Network Analysis (PGCNA), allows comparison of network structures between these cancer types identifying shared modules of gene co-expression reflecting cancer hallmarks, functional and structural gene batteries, copy number variation and biology of originating lineage. These networks along with the mapping of outcome data at gene and module level provide an interactive resource that generates context for relationships between genes within and between such modules. Assigning module expression values (MEVs) provides a tool to summarize network level gene expression in individual cases illustrating potential utility in classification and allowing analysis of linkage between module expression and mutational state. Exploiting TCGA data thus defines both recurrent patterns of association between module expression and mutation at data-set level, and exemplifies the polarization of mutation patterns with the leading edge of module expression at individual case level. We illustrate the scalable nature of the approach within immune response related modules, which in the context of breast cancer demonstrates the selective association of immune subsets, in particular mast cells, with the underlying mutational pattern. Together our analyses provide evidence for a generalizable framework to enhance molecular stratification in cancer.Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. UMs are usually initiated by a mutation in GNAQ or GNA11, unlike cutaneous melanomas, which usually harbour a BRAF or NRAS mutation. The annual incidence in Europe and the USA is ~6 per million population per year. learn more Risk factors include fair skin, light-coloured eyes, congenital ocular melanocytosis, ocular melanocytoma and the BAP1-tumour predisposition syndrome. Ocular treatment aims at preserving the eye and useful vision and, if possible, preventing metastases. Enucleation has largely been superseded by various forms of radiotherapy, phototherapy and local tumour resection, often administered in combination. Ocular outcomes are best with small tumours not extending close to the optic disc and/or fovea. Almost 50% of patients develop metastatic disease, which usually involves the liver, and is usually fatal within 1 year. Although UM metastases are less responsive than cutaneous melanoma to chemotherapy or immune checkpoint inhibitors, encouraging results have been reported with partial hepatectomy for solitary metastases, with percutaneous hepatic perfusion with melphalan or with tebentafusp. Better insight into tumour immunology and metabolism may lead to new treatments.An amendment to this paper has been published and can be accessed via a link at the top of the paper.Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.