Biallelic germline mutations in MAD1L1 induce a syndrome of aneuploidy with high tumor susceptibility.

Germline mutations leading to aneuploidy are rare, and their tumor-promoting properties are mostly unknown at the molecular level. We report here novel germline biallelic mutations in MAD1L1, encoding the spindle assembly checkpoint (SAC) protein MAD1, in a 36-year-old female with a dozen of neoplasias. Functional studies demonstrated lack of full-length protein and deficient SAC response, resulting in ~30 to 40% of aneuploid blood cells. Single-cell RNA analysis identified mitochondrial stress accompanied by systemic inflammation with enhanced interferon and NFκB signaling both in aneuploid and euploid cells, suggesting a non-cell autonomous response. MAD1L1 mutations resulted in specific clonal expansions of γδ T cells with chromosome 18 gains and enhanced cytotoxic profile as well as intermediate B cells with chromosome 12 gains and transcriptomic signatures characteristic of leukemia cells. These data point to MAD1L1 mutations as the cause of a new variant of mosaic variegated aneuploidy with systemic inflammation and unprecedented tumor susceptibility.

Protein residues determining interaction specificity in paralogous families.

MOTIVATION: Predicting the residues controlling a protein's interaction specificity is important not only to better understand its interactions but also to design mutations aimed at fine-tuning or swapping them as well. RESULTS: In this work, we present a methodology that combines sequence information (in the form of multiple sequence alignments) with interactome information to detect that kind of residues in paralogous families of proteins. The interactome is used to define pairwise similarities of interaction contexts for the proteins in the alignment. The method looks for alignment positions with patterns of amino-acid changes reflecting the similarities/differences in the interaction neighborhoods of the corresponding proteins. We tested this new methodology in a large set of human paralogous families with structurally characterized interactions, and discuss in detail the results for the RasH family. We show that this approach is a better predictor of interfacial residues than both, sequence conservation and an equivalent 'unsupervised' method that does not use interactome information. AVAILABILITY AND IMPLEMENTATION: http://csbg.cnb.csic.es/pazos/Xdet/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.