Detection and resolution of genetic loci affecting circadian period in Brassica oleracea

Circadian rhythms regulate many aspects of plant growth, Wtness and vigour. The components and detailed mechanism of circadian regulation to date have been dissected in the reference species Arabidopsis thaliana. To determine the genetic basis and range of natural allelic variation for intrinsic circadian period in the closest crop relatives, we used an accurate and high throughput data capture system to record rhythmic cotyledon movement in two immortal segregating populations of Brassica oleracea, derived from parent lines representing diVerent crop types. Periods varied between 24.4 and 26.1 h between the parent lines, with transgressive segregation between extreme recombinant lines in both populations of »3.5 h. The additive eVect of individual QTL identiWed in each population varied from 0.17 to 0.36 h. QTL detected in one doubled haploid population were veriWed and the mapping intervals further resolved by determining circadian period in genomic substitution lines derived from the parental lines. Comparative genomic analysis based on collinearity between Brassica and Arabidopsis also allowed identiWcation of candidate orthologous genes known to regulate period in Arabidopsis, that may account for the additive circadian eVects of speciWc QTL. The distinct QTL positions detected in the two populations, and the extent of transgressive segregation suggest that there is likely to be considerable scope for modulating the range of available circadian periods in natural populations and crop species of Brassica. This may provide adaptive advantage for optimising growth and development in diVerent latitudes, seasons or climate conditions

Synchronized age-related gene expression changes across multiple tissues in human and the link to complex diseases

Aging is one of the most important biological processes and is a known risk factor for many age-related diseases in human. Studying age-related transcriptomic changes in tissues across the whole body can provide valuable information for a holistic understanding of this fundamental process. In this work, we catalogue age-related gene expression changes in nine tissues from nearly two hundred individuals collected by the Genotype-Tissue Expression (GTEx) project. In general, we find the aging gene expression signatures are very tissue specific. However, enrichment for some well-known aging components such as mitochondria biology is observed in many tissues. Different levels of cross-tissue synchronization of age-related gene expression changes are observed, and some essential tissues (e.g., heart and lung) show much stronger "co-aging" than other tissues based on a principal component analysis. The aging gene signatures and complex disease genes show a complex overlapping pattern and only in some cases, we see that they are significantly overlapped in the tissues affected by the corresponding diseases. In summary, our analyses provide novel insights to the co-regulation of age-related gene expression in multiple tissues; it also presents a tissue-specific view of the link between aging and age-related diseases.JY is supported through Berg postdoc fellowship. ZT receives financial support from Berg Pharma as a consultant. ZT JZ ES receive support from Fondation Leducq Understanding coronary artery disease genes grant. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health. Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI/SAIC-Frederick, Inc. (SAIC-F) subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171), and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to The Broad Institute, Inc. Biorepository operations were funded through an SAIC-F subcontract to Van Andel Institute (10ST1035). Additional data repository and project management were provided by SAIC-F (HHSN261200800001E). The Brain Bank was supported by supplements to University of Miami grants DA006227 & DA033684 and to contract N01MH000028. Statistical Methods development grants were made to the University of Geneva (MH090941 & MH101814), the University of Chicago (MH090951, MH090937, MH101820, MH101825), the University of North Carolina - Chapel Hill (MH090936 & MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University St Louis (MH101810), and the University of Pennsylvania (MH101822)