Citations

Abstract Motivation: Using stable isotopes in global proteome scans, labeled molecules from one sample are pooled with unlabeled molecules from another sample and subsequently subjected to mass-spectral analysis. Stable-isotope methodologies make use of the fact that identical molecules of different stable-isotope compositions are differentiated in a mass spectrometer and are represented in a mass spectrum as distinct isotopic clusters with a known mass shift. We describe two multivariable linear regression models for 16O/18O stable-isotope labeled data that jointly model pairs of resolved isotopic clusters from the same peptide and quantify the abundance present in each of the two biological samples while concurrently accounting for peptide-specific incorporation rates of the heavy isotope. The abundance measure for each peptide from the two biological samples is then used in down-stream statistical analyses, e.g. differential expression analysis. Because the multivariable regression models are able to correct for the abundance of the labeled peptide that appear as an unlabeled peptide due to the inability to exchange the natural C-terminal oxygen for the heavy isotope, they are particularly advantageous for a two-step digestion/labeling procedure. We discuss how estimates from the regression model are used to quantify the variability of the estimated abundance measures for the paired samples. Although discussed in the context of 16O/18O stable-isotope labeled data, the multivariable regression models are generalizable to other stable-isotope labeled technologies. Contact: eckel@mayo.edu

Abstract Motivation: The need for normalization in microarray experiments has been well documented in the literature. Currently, many analysis methods treat normalization and analysis as a series of steps, with summarized data carried forward to the next step. Results: We present a unified algorithm which incorporates normalization and class comparison in one analysis using probe level perfect match and mismatch data. The algorithm is based on calibration models common to most biological assays, and the resulting chip-specific parameters have a natural interpretation. We show that the algorithm fits into the statistical generalized linear models framework, describe a practical fitting strategy and present results of the algorithm applied to an example dataset as well as based on metrics used in affycomp. The algorithm ranks amongst the top third of the affycomp competitors, performing best in measures of bias. Availability: R functions are available on request from the authors. Contact: oberg.ann@mayo.edu

Abstract An abnormal serum immunoglobulin free light chain (FLC) ratio at diagnosis may identify risk of progression to myeloma in patients with solitary bone plasmacytoma (SBP). In the cohort of 116 patients, 43 have progressed to myeloma, with a median time to progression of 1.8 years. The FLC ratio was determined in all 116 patients on serum collected at time of diagnosis and was abnormal in 54 patients (47%). An abnormal FLC ratio was associated with a higher risk of progression to myeloma (P = .039). The risk of progression at 5 years was 44% in patients with an abnormal serum FLC ratio at diagnosis compared with 26% in those with a normal FLC ratio. One to 2 years following diagnosis, a persistent serum M protein level of 5 g/L (0.5 g/dL) or higher was an additional risk factor for progression. A risk stratification model was constructed using the 2 variables of FLC ratio and M protein level: patients with a normal FLC ratio at baseline and M protein level less than 5 g/L (0.5 g/dL) at 1 to 2 years following diagnosis (low risk, n = 31); with either risk factor abnormal (intermediate risk, n = 26); and with both an abnormal FLC ratio and M protein level of 5 g/L (0.5 g/dL) or higher (high risk, n = 18). The corresponding progression rates at 5 years were significantly different in the low, intermediate, and high groups: 13%, 26%, and 62%, respectively (P < .001).

Abstract Estrogen induction of progesterone receptor (PR) expression may be important to bone physiology because progesterone has been implicated in the control of bone formation and resorption. Although PR gene expression can be induced in osteoblasts by estrogen signaling through the estrogen receptor (ER) α isoform, it is unknown whether the ER-β isoform is involved in this regulation. The effect of estrogen on PR expression was examined in human fetal osteoblast (hFOB) cell lines stably transfected with either ER-α or ER-β. Estrogen treatment of hFOB/ER-α cells induced PR messenger RNA (mRNA) steady-state levels after 24 h and protein levels after 48 h, as established by competitive reverse transcriptase-polymerase chain reaction (RT-PCR) and Western blotting. Interestingly, no induction of PR expression was observed in the hFOB/ER-β cells during this period. However, PR mRNA was induced progressively after 48 h of treatment with estrogen with maximum levels achieved at 12 days posttreatment. ER protein also was increased after 12 days of treatment. Both A and B isoforms of PR (PRA and PRB) were induced by estrogen in the hFOB/ER-α cells as well as much later in hFOB/ER-β cells. The pure antiestrogen ICI 182,780 prevented PR induction by estrogen in both cell lines. An ER-β-selective antagonist R, R-tetrahydrochrysene (THC) abolished the induction of PR mRNA in hFOB/ER-β but not in hFOB/ER-α cells, verifying that the response in the former cell line was ER-β-mediated. Transient cotransfection of hFOB cells with ER-α or ER-β together with either a human PRA or PRB promoter linked to a reporter plasmid revealed that although the PRB promoter was stimulated equally by estrogen activation of either ER isoform, PRA was activated preferentially by ER-α. Together, these results show that although estrogen can up-regulate endogenous PR gene expression in osteoblasts via both ER isoforms, ER-α is the predominant inducer.

Abstract The relationship between BMD and fracture risk was estimated in a meta-analysis of data from 12 cohort studies of ∼39,000 men and women. Low hip BMD was an important predictor of fracture risk. The prediction of hip fracture with hip BMD also depended on age and z score. Introduction: The aim of this study was to quantify the relationship between BMD and fracture risk and examine the effect of age, sex, time since measurement, and initial BMD value. Materials and Methods: We studied 9891 men and 29,082 women from 12 cohorts comprising EVOS/EPOS, EPIDOS, OFELY, CaMos, Rochester, Sheffield, Rotterdam, Kuopio, DOES, Hiroshima, and 2 cohorts from Gothenburg. Cohorts were followed for up to 16.3 years and a total of 168,366 person-years. The effect of BMD on fracture risk was examined using a Poisson model in each cohort and each sex separately. Results of the different studies were then merged using weighted coefficients. Results: BMD measurement at the femoral neck with DXA was a strong predictor of hip fractures both in men and women with a similar predictive ability. At the age of 65 years, risk ratio increased by 2.94 (95% CI = 2.02-4.27) in men and by 2.88 (95% CI = 2.31-3.59) in women for each SD decrease in BMD. However, the effect was dependent on age, with a significantly higher gradient of risk at age 50 years than at age 80 years. Although the gradient of hip fracture risk decreased with age, the absolute risk still rose markedly with age. For any fracture and for any osteoporotic fracture, the gradient of risk was lower than for hip fractures. At the age of 65 years, the risk of osteoporotic fractures increased in men by 1.41 per SD decrease in BMD (95% CI = 1.33-1.51) and in women by 1.38 per SD (95% CI = 1.28-1.48). In contrast with hip fracture risk, the gradient of risk increased with age. For the prediction of any osteoporotic fracture (and any fracture), there was a higher gradient of risk the lower the BMD. At a z score of -4 SD, the risk gradient was 2.10 per SD (95% CI = 1.63-2.71) and at a z score of -1 SD, the risk was 1.73 per SD (95% CI = 1.59-1.89) in men and women combined. A similar but less pronounced and nonsignificant effect was observed for hip fractures. Data for ultrasound and peripheral measurements were available from three cohorts. The predictive ability of these devices was somewhat less than that of DXA measurements at the femoral neck by age, sex, and BMD value. Conclusions: We conclude that BMD is a risk factor for fracture of substantial importance and is similar in both sexes. Its validation on an international basis permits its use in case finding strategies. Its use should, however, take account of the variations in predictive value with age and BMD.