Mitch - A rapidly evolving component of the Ndc80 kinetochore complex required for correct chromosome segregation in Drosophila

We identified an essential kinetochore protein, Mitch, from a genetic screen in D. melanogaster. Mitch localizes to the kinetochore, and its targeting is independent of microtubules (MTs) and several other known kinetochore components. Animals carrying mutations in mitch die as late third-instar larvae; mitotic neuroblasts in larval brains exhibit high levels of aneuploidy. Analysis of fixed D. melanogaster brains and mitch RNAi in cultured cells, as well as video recordings of cultured mitch mutant neuroblasts, reveal that chromosome alignment in mitch mutants is compromised during spindle formation, with many chromosomes displaying persistent mono-orientation. These misalignments lead to aneuploidy during anaphase. Mutations in mitch also disrupt chromosome behavior during both meiotic divisions in spermatocytes: the entire chromosome complement often moves to only one spindle pole. Mutant mitotic cells exhibit contradictory behavior with respect to the spindle assembly checkpoint (SAC). Anaphase onset is delayed in untreated cells, probably because incorrect kinetochore attachment maintains the SAC. However, mutant brain cells and mitch RNAi cells treated with MT poisons prematurely disjoin their chromatids, and exit mitosis. These data suggest that Mitch participates in SAC signaling that responds specifically to disruptions in spindle microtubule dynamics. The mitch gene corresponds to the transcriptional unit CG7242, and encodes a protein that is a possible ortholog of the Spc24 or Spc25 subunit of the Ndc80 kinetochore complex. Despite the crucial role of Mitch in cell division, the mitch gene has evolved very rapidly among species in the genus Drosophila

Shared heritability and functional enrichment across six solid cancers

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (rg = 0.57, p = 4.6 × 10−8), breast and ovarian cancer (rg = 0.24, p = 7 × 10−5), breast and lung cancer (rg = 0.18, p =1.5 × 10−6) and breast and colorectal cancer (rg = 0.15, p = 1.1 × 10−4). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis

TRY plant trait database, enhanced coverage and open access

Plant traits-the morphological, ahawnatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives