A RAD51-ADP double filament structure unveils the mechanism of filament dynamics in homologous recombination

ATP-dependent RAD51 recombinases play an essential role in eukaryotic homologous recombination by catalyzing a four-step process: 1) formation of a RAD51 single-filament assembly on ssDNA in the presence of ATP, 2) complementary DNA strand-exchange, 3) ATP hydrolysis transforming the RAD51 filament into an ADP-bound disassembly-competent state, and 4) RAD51 disassembly to provide access for DNA repairing enzymes. Of these steps, filament dynamics between the ATP- and ADP-bound states, and the RAD51 disassembly mechanism, are poorly understood due to the lack of near-atomic-resolution information of the ADP-bound RAD51-DNA filament structure. We report the cryo-EM structure of ADP-bound RAD51-DNA filaments at 3.1 Å resolution, revealing a unique RAD51 double-filament that wraps around ssDNA. Structural analysis, supported by ATP-chase and time-resolved cryo-EM experiments, reveals a collapsing mechanism involving two four-protomer movements along ssDNA for mechanical transition between RAD51 single- and double-filament without RAD51 dissociation. This mechanism enables elastic change of RAD51 filament length during structural transitions between ATP- and ADP-states.

Cryo-EM Analyses Permit Visualization of Structural Polymorphism of Biological Macromolecules

The functions of biological macromolecules are often associated with conformational malleability of the structures. This phenomenon of chemically identical molecules with different structures is coined structural polymorphism. Conventionally, structural polymorphism is observed directly by structural determination at the density map level from X-ray crystal diffraction. Although crystallography approach can report the conformation of a macromolecule with the position of each atom accurately defined in it, the exploration of structural polymorphism and interpreting biological function in terms of crystal structures is largely constrained by the crystal packing. An alternative approach to studying the macromolecule of interest in solution is thus desirable. With the advancement of instrumentation and computational methods for image analysis and reconstruction, cryo-electron microscope (cryo-EM) has been transformed to be able to produce “in solution” structures of macromolecules routinely with resolutions comparable to crystallography but without the need of crystals. Since the sample preparation of single-particle cryo-EM allows for all forms co-existing in solution to be simultaneously frozen, the image data contain rich information as to structural polymorphism. The ensemble of structure information can be subsequently disentangled through three-dimensional (3D) classification analyses. In this review, we highlight important examples of protein structural polymorphism in relation to allostery, subunit cooperativity and function plasticity recently revealed by cryo-EM analyses, and review recent developments in 3D classification algorithms including neural network/deep learning approaches that would enable cryo-EM analyese in this regard. Finally, we brief the frontier of cryo-EM structure determination of RNA molecules where resolving the structural polymorphism is at dawn.

Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D classification

2D classification plays a pivotal role in analyzing single particle cryo-electron microscopy images. Here, we introduce a simple and loss-less pre-processor that incorporates a fast dimension-reduction (2SDR) de-noiser to enhance 2D classification. By implementing this 2SDR pre-processor prior to a representative classification algorithm like RELION and ISAC, we compare the performances with and without the pre-processor. Tests on multiple cryo-EM experimental datasets show the pre-processor can make classification faster, improve yield of good particles and increase the number of class-average images to generate better initial models. Testing on the nanodisc-embedded TRPV1 dataset with high heterogeneity using a 3D reconstruction workflow with an initial model from class-average images highlights the pre-processor improves the final resolution to 2.82 Å, close to 0.9 Nyquist. Those findings and analyses suggest the 2SDR pre-processor, of minimal cost, is widely applicable for boosting 2D classification, while its generalization to accommodate neural network de-noisers is envisioned.

The maximum of a function of a Markov chain and application to linkage analysis

One method of linkage analysis in humans is based on identity-by-descent of pairs of relatives who share a phenotype of interest (for example, a particular disease). We replace the convenient assumption of continuous specification of regions of identity by descent by the more realistic, although still artificially simple, assumption of data from a discrete set of equally spaced infinitely polymorphic markers. We generalize the continuous time Markov chain analysis of Feingold (1993b) and compare the accuracy of the new approximation with that of the simpler Gaussian approximation of Feingold, Brown and Siegmund (1993) under a variety of assumptions about the composition of the pedigrees to be studied. We also suggest a perturbation of the Gaussian approximation as a compromise to achieve reasonable accuracy with minimal computational effort.

Toward automated denoising of single molecular Förster resonance energy transfer data

A wide-field two-channel fluorescence microscope is a powerful tool as it allows for the study of conformation dynamics of hundreds to thousands of immobilized single molecules by Förster resonance energy transfer (FRET) signals. To date, the data reduction from a movie to a final set containing meaningful single-molecule FRET (smFRET) traces involves human inspection and intervention at several critical steps, greatly hampering the efficiency at the post-imaging stage. To facilitate the data reduction from smFRET movies to smFRET traces and to address the noise-limited issues, we developed a statistical denoising system toward fully automated processing. This data reduction system has embedded several novel approaches. First, as to background subtraction, high-order singular value decomposition (HOSVD) method is employed to extract spatial and temporal features. Second, to register and map the two color channels, the spots representing bleeding through the donor channel to the acceptor channel are used. Finally, correlation analysis and likelihood ratio statistic for the change point detection (CPD) are developed to study the two channels simultaneously, resolve FRET states, and report the dwelling time of each state. The performance of our method has been checked using both simulation and real data.

Comprehensive analysis of 19q12 amplicon in human gastric cancers

Amplification at 19q12 has been observed in multiple tumor types, while cyclin E1 (CCNE1) has been considered to be the key oncogene within this amplicon. We have previously applied cDNA microarray analysis to systematically characterize gene expression patterns of gastric tumor and nontumor samples. We identified a cluster of five tightly coregulated genes all located at chromosome 19q12, including CCNE1. We found that the 19q12 gene cluster is highly expressed in gastric tumors compared to nontumor gastric samples. Array based comparative genomic hybridization and real-time PCR was used to define the boundary of the 19q12 amplicon to a region of approximately 200 kb. Interestingly, we found that in some cases amplification at 19q12 was not associated with DNA copy number gain at CCNE1, suggesting that some other genes within the 19q12 amplicon may also have important function during gastric tumorigenesis. We found high expression of the 19q12 gene cluster to be statistically correlated with the cell proliferation gene signature. Using the SAM software, we identified a set of 577 genes whose expression levels positively correlated with the 19q12 gene cluster. GO term analysis revealed that this genelist is enriched with genes involved in cell cycle regulation and cell proliferation. In conclusion, expression array analysis combined with array comparative genomic hybridization and real-time PCR provides a new and powerful tool to identify clusters of genes which may be regulated by genomic DNA aberrations. In addition, our study indicates that amplification at 19q12 is associated with cell proliferation in vivo.

A method for detecting and correcting feature misidentification on expression microarrays

Background

Much of the microarray data published at Stanford is based on mouse and human arrays produced under controlled and monitored conditions at the Brown and Botstein laboratories and at the Stanford Functional Genomics Facility (SFGF). Nevertheless, as large datasets based on the Stanford Human array began to accumulate, a small but significant number of discrepancies were detected that required a serious attempt to track down the original source of error. Due to a controlled process environment, sufficient data was available to accurately track the entire process leading to up to the final expression data. In this paper, we describe our statistical methods to detect the inconsistencies in microarray data that arise from process errors, and discuss our technique to locate and fix these errors.

Results

To date, the Brown and Botstein laboratories and the Stanford Functional Genomics Facility have together produced 40,000 large-scale (10–50,000 feature) cDNA microarrays. By applying the heuristic described here, we have been able to check most of these arrays for misidentified features, and have been able to confidently apply fixes to the data where needed. Out of the 265 million features checked in our database, problems were detected and corrected on 1.3 million of them.

Conclusion

Process errors in any genome scale high throughput production regime can lead to subsequent errors in data analysis. We show the value of tracking multi-step high throughput operations by using this knowledge to detect and correct misidentified data on gene expression microarrays.

Gene expression patterns in ovarian carcinomas

We used DNA microarrays to characterize the global gene expression patterns in surface epithelial cancers of the ovary. We identified groups of genes that distinguished the clear cell subtype from other ovarian carcinomas, grade I and II from grade III serous papillary carcinomas, and ovarian from breast carcinomas. Six clear cell carcinomas were distinguished from 36 other ovarian carcinomas (predominantly serous papillary) based on their gene expression patterns. The differences may yield insights into the worse prognosis and therapeutic resistance associated with clear cell carcinomas. A comparison of the gene expression patterns in the ovarian cancers to published data of gene expression in breast cancers revealed a large number of differentially expressed genes. We identified a group of 62 genes that correctly classified all 125 breast and ovarian cancer specimens. Among the best discriminators more highly expressed in the ovarian carcinomas were PAX8 (paired box gene 8), mesothelin, and ephrin-B1 (EFNB1). Although estrogen receptor was expressed in both the ovarian and breast cancers, genes that are coregulated with the estrogen receptor in breast cancers, including GATA-3, LIV-1, and X-box binding protein 1, did not show a similar pattern of coexpression in the ovarian cancers.

Phospholipase A2 group IIA expression in gastric adenocarcinoma is associated with prolonged survival and less frequent metastasis

We analyzed gene expression patterns in human gastric cancers by using cDNA microarrays representing approximately equal 30,300 genes. Expression of PLA2G2A, a gene previously implicated as a modifier of the Apc(Min/+) (multiple intestinal neoplasia 1) mutant phenotype in the mouse, was significantly correlated with patient survival. We confirmed this observation in an independent set of patient samples by using quantitative RT-PCR. Beyond its potential diagnostic and prognostic significance, this result suggests the intriguing possibility that the activity of PLA2G2A may suppress progression or metastasis of human gastric cancer.

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.

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.

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.

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.