Reassessment of the evolution of wheat chromosomes 4A, 5A, and 7B.

Key messageComparison of genome sequences of wild emmer wheat and Aegilops tauschii suggests a novel scenario of the evolution of rearranged wheat chromosomes 4A, 5A, and 7B. Past research suggested that wheat chromosome 4A was subjected to a reciprocal translocation T(4AL;5AL)1 that occurred in the diploid progenitor of the wheat A subgenome and to three major rearrangements that occurred in polyploid wheat: pericentric inversion Inv(4AS;4AL)1, paracentric inversion Inv(4AL;4AL)1, and reciprocal translocation T(4AL;7BS)1. Gene collinearity along the pseudomolecules of tetraploid wild emmer wheat (Triticum turgidum ssp. dicoccoides, subgenomes AABB) and diploid Aegilops tauschii (genomes DD) was employed to confirm these rearrangements and to analyze the breakpoints. The exchange of distal regions of chromosome arms 4AS and 4AL due to pericentric inversion Inv(4AS;4AL)1 was detected, and breakpoints were validated with an optical Bionano genome map. Both breakpoints contained satellite DNA. The breakpoints of reciprocal translocation T(4AL;7BS)1 were also found. However, the breakpoints that generated paracentric inversion Inv(4AL;4AL)1 appeared to be collocated with the 4AL breakpoints that had produced Inv(4AS;4AL)1 and T(4AL;7BS)1. Inv(4AS;4AL)1, Inv(4AL;4AL)1, and T(4AL;7BS)1 either originated sequentially, and Inv(4AL;4AL)1 was produced by recurrent chromosome breaks at the same breakpoints that generated Inv(4AS;4AL)1 and T(4AL;7BS)1, or Inv(4AS;4AL)1, Inv(4AL;4AL)1, and T(4AL;7BS)1 originated simultaneously. We prefer the latter hypothesis since it makes fewer assumptions about the sequence of events that produced these chromosome rearrangements

DXA Utilization Between 2006 and 2012 in Commercially Insured Younger Postmenopausal Women

Reimbursement for dual energy x-ray absorptiometry (DXA) scans in the outpatient setting has declined significantly since 2006. Research through 2011 has suggested reimbursement reductions for DXA scans have corresponded with an overall decreased utilization of DXA. This study updates utilization estimates for DXAs through 2012 in patients with commercial insurance and compares DXA rates before and after reimbursement changes

2015 American College of Rheumatology Workforce Study

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144285/1/art40432_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144285/2/art40432.pd

The prognostic contribution of clinical breast cancer subtype, age, and race among patients with breast cancer brain metastases

Brain metastases (BM) arising from Triple-negative breast cancer (TNBC) portend poor prognosis. TNBC is more common in premenopausal and African-American (AA) patients; both also confer poor prognosis. In a single institution cohort study, we sought to determine if inferior outcome of TN BCBM is more reflective of a higher-risk population or subtype itself

Effect of Cytotoxic Chemotherapy on Markers of Molecular Age in Patients With Breast Cancer

Senescent cells, which express p16 INK4a, accumulate with aging and contribute to age-related pathology. To understand whether cytotoxic agents promote molecular aging, we measured expression of p16 INK4a and other senescence markers in breast cancer patients treated with adjuvant chemotherapy

Phosphatidylinositol 3-kinase pathway activation in breast cancer brain metastases

Activation status of the phosphatidylinositol 3-kinase (PI3K) pathway in breast cancer brain metastases (BCBMs) is largely unknown. We examined expression of phospho(p)-AKT, p-S6, and phosphatase and tensin homologue (PTEN) in BCBMs and their implications for overall survival (OS) and survival after BCBMs. Secondary analyses included PI3K pathway activation status and associations with time to distant recurrence (TTDR) and time to BCBMs. Similar analyses were also conducted among the subset of patients with triple-negative BCBMs. METHODS: p-AKT, p-S6, and PTEN expression was assessed with immunohistochemistry in 52 BCBMs and 12 matched primary BCs. Subtypes were defined as hormone receptor (HR)+/HER2-, HER2+, and triple-negative (TNBC). Survival analyses were performed by using a Cox model, and survival curves were estimated with the Kaplan-Meier method. RESULTS: Expression of p-AKT and p-S6 and lack of PTEN (PTEN-) was observed in 75%, 69%, and 25% of BCBMs. Concordance between primary BCs and matched BCBMs was 67% for p-AKT, 58% for p-S6, and 83% for PTEN. PTEN- was more common in TNBC compared with HR+/HER2- and HER2+. Expression of p-AKT, p-S6, and PTEN- was not associated with OS or survival after BCBMs (all, P > 0.06). Interestingly, among all patients, PTEN- correlated with shorter time to distant and brain recurrence. Among patients with TNBC, PTEN- in BCBMs was associated with poorer overall survival. CONCLUSIONS: The PI3K pathway is active in most BCBMs regardless of subtype. Inhibition of this pathway represents a promising therapeutic strategy for patients with BCBMs, a group of patients with poor prognosis and limited systemic therapeutic options. Although expression of the PI3K pathway did not correlate with OS and survival after BCBM, PTEN- association with time to recurrence and OS (among patients with TNBC) is worthy of further study

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University