Copper Centers in the Cryo-EM Structure of Particulate Methane Monooxygenase Reveal the Catalytic Machinery of Methane Oxidation.

The particulate methane monooxygenase (pMMO) is the first enzyme in the C1 metabolic pathway in methanotrophic bacteria. As this enzyme converts methane into methanol efficiently near room temperature, it has become the paradigm for developing an understanding of this difficult C1 chemistry. pMMO is a membrane-bound protein with three subunits (PmoB, PmoA, and PmoC) and 12-14 coppers distributed among different sites. X-ray crystal structures that have revealed only three mononuclear coppers at three sites have neither disclosed the location of the active site nor the catalytic mechanism of the enzyme. Here we report a cyro-EM structure of holo-pMMO from Methylococcus capsulatus (Bath) at 2.5 Å, and develop quantitative electrostatic-potential profiling to scrutinize the nonprotein densities for signatures of the copper cofactors. Our results confirm a mononuclear CuI at the A site, resolve two CuIs at the B site, and uncover additional CuI clusters at the PmoA/PmoC interface within the membrane (D site) and in the water-exposed C-terminal subdomain of the PmoB (E clusters). These findings complete the minimal set of copper factors required for catalytic turnover of pMMO, offering a glimpse of the catalytic machinery for methane oxidation according to the chemical principles underlying the mechanism proposed earlier.

Prostate-specific antigen doubling times in patients who have failed radical prostatectomy: correlation with histologic characteristics of the primary cancer.

OBJECTIVES: This study sought to characterize the postoperative prostate-specific antigen (PSA) doubling time and time to biochemical recurrence in patients who have failed radical prostatectomy. METHODS: Of 539 consecutive patients who underwent radical prostatectomy between 1984 and 1992, postoperative PSA levels in 80 initially became undetectable (less than 0.07 ng/mL) before eventually increasing, as evidenced by rising PSA levels above the residual cancer detection limit of the Tosoh AIA-600 immunoassay run in the ultrasensitive mode (i.e., 0.07 ng/mL or higher). The PSA doubling time and time to biochemical recurrence were calculated for each of the 80 patients and were correlated with the histopathologic variables from the operative specimen. RESULTS: Postoperative PSA doubling times were predicted by the extent of capsular penetration, percent Gleason grade 4 or 5, lymph node involvement, and tumor volume on univariate analysis and by capsular penetration, percent Gleason grade 4 or 5, lymph node involvement, and patient age on multivariate analysis. Times to recurrence were predicted by the presence of positive margins and percent Gleason grade 4 or 5 in both univariate and multivariate regression models. The PSA doubling time did not correlate with recurrence time. The median PSA doubling time for all patients was 284 days, and the median time to recurrence was 648 days. CONCLUSIONS: These results demonstrate that PSA doubling time and recurrence time are indicative of different biologic characteristics of recurrent prostate cancer: Doubling time appears to represent the aggressiveness of the original prostate cancer, whereas time to recurrence reflects the extent of residual postoperative disease. This information should aid in the selection of men who need greater vigilance during postoperative surveillance.

Detection of disease genes by use of family data. I. Likelihood-based theory.

We present a class of likelihood-based score statistics that accommodate genotypes of both unrelated individuals and families, thereby combining the advantages of case-control and family-based designs. The likelihood extends the one proposed by Schaid and colleagues (Schaid and Sommer 1993, 1994; Schaid 1996; Schaid and Li 1997) to arbitrary family structures with arbitrary patterns of missing data and to dense sets of multiple markers. The score statistic comprises two component test statistics. The first component statistic, the nonfounder statistic, evaluates disequilibrium in the transmission of marker alleles from parents to offspring. This statistic, when applied to nuclear families, generalizes the transmission/disequilibrium test to arbitrary numbers of affected and unaffected siblings, with or without typed parents. The second component statistic, the founder statistic, compares observed or inferred marker genotypes in the family founders with those of controls or those of some reference population. The founder statistic generalizes the statistics commonly used for case-control data. The strengths of the approach include both the ability to assess, by comparison of nonfounder and founder statistics, the potential bias resulting from population stratification and the ability to accommodate arbitrary family structures, thus eliminating the need for many different ad hoc tests. A limitation of the approach is the potential power loss and/or bias resulting from inappropriate assumptions on the distribution of founder genotypes. The systematic likelihood-based framework provided here should be useful in the evaluation of both the relative merits of case-control and various family-based designs and the relative merits of different tests applied to the same design. It should also be useful for genotype-disease association studies done with the use of a dense set of multiple markers.

Power of association and linkage tests when the disease alleles are unobserved.

Genomewide association studies have been advocated as a promising alternative to genomewide linkage scans for detection of small-effect genes in complex diseases. Comparisons of power and sample size between the two strategies have shown considerable advantages for the association studies. These comparisons assume that the set of markers includes the exact disease-related polymorphism. A concern, however, is that the power of an association study decreases when this is not the case, because of discrepant allele frequencies and less-than-maximum disequilibrium between the disease-related polymorphism and its nearest marker. Here, we quantify this concern by comparing the sample sizes needed by the two strategies when the markers exclude the disease-related polymorphism. For affected sib pairs and their parents, we found that incomplete disequilibrium and differing allele frequencies can have substantial negative impact on the power of association studies, resulting, in some circumstances, in little gain and even in loss of power, compared with linkage analysis. We provide some guidelines for choosing between strategies, for the detection of genes for complex diseases.

Simple, robust linkage tests for affected sibs.

Parametric-linkage analysis applied to large pedigrees with many affected individuals has helped in the identification of highly penetrant genes; but, for diseases lacking a clear Mendelian inheritance pattern or caused by several genes of low to moderate penetrance, a more robust strategy is nonparametric analysis applied to small sets of affected relatives, such as affected sib pairs. Here we show that the robustness of affected-sib-pair tests is related to the shape of the constraint set for the sibs' identity-by-descent (IBD) probabilities. We also derive a set of constraints for the IBD probabilities of affected sib triples and use common features of the shapes of the two constrain sets to introduce new nonparametric tests (called "minmax" tests) that are more robust than those in current use. Asymptotic-power computations support the robustness of the proposed minmax tests.

Detection of disease genes by use of family data. II. Application to nuclear families.

Two likelihood-based score statistics are used to detect association between a disease and a single diallelic polymorphism, on the basis of data from arbitrary types of nuclear families. The first statistic, the nonfounder statistic, extends the transmission/disequilibrium test to accommodate affected and unaffected offspring and missing parental genotypes. The second statistic, the founder statistic, compares observed or inferred parental genotypes with those of some reference population. In this comparison, the genotypes of affected parents or of those with many affected offspring are weighted more heavily than are the genotypes of unaffected parents or of those with few affected offspring. Genotypes of single unrelated cases and controls can be included in this analysis. We illustrate the two statistics by applying them to data on a polymorphism of the SDR5A2 gene in nuclear families with multiple cases of prostate cancer. We also use simulations to compare the power of the nonfounder statistic with that of the score statistic, on the basis of the conditional logistic regression of offspring genotypes.