Locations and patterns of meiotic recombination in two-generation pedigrees

<p>Abstract</p> <p>Background</p> <p>Meiotic crossovers are the major mechanism by which haplotypes are shuffled to generate genetic diversity. Previously available methods for the genome-wide, high-resolution identification of meiotic crossover sites are limited by the laborious nature of the assay (as in sperm typing).</p> <p>Methods</p> <p>Several methods have been introduced to identify crossovers using high density single nucleotide polymorphism (SNP) array technologies, although programs are not widely available to implement such analyses.</p> <p>Results</p> <p>Here we present a two-generation "reverse pedigree analysis" method (analyzing the genotypes of two children relative to each parent) and a web-accessible tool to determine and visualize inheritance differences among siblings and crossover locations on each parental gamete. This approach is complementary to existing methods and uses informative markers which provide high resolution for locating meiotic crossover sites. We introduce a segmentation algorithm to identify crossover sites, and used a synthetic data set to determine that the segmentation algorithm specificity was 92% and sensitivity was 89%. The use of reverse pedigrees allows the inference of crossover locations on the X chromosome in a maternal gamete through analysis of two sons and their father. We further analyzed genotypes from eight multiplex autism families, observing a 1.462 maternal to paternal recombination ratio and no significant differences between affected and unaffected children. Meiotic recombination results from pediSNP can also be used to identify haplotypes that are shared by probands within a pedigree, as we demonstrated with a multiplex autism family.</p> <p>Conclusion</p> <p>Using "reverse pedigrees" and defining unique sets of genotype markers within pedigree data, we introduce a method that identifies inherited allelic differences and meiotic crossovers. We implemented the method in the pediSNP software program, and we applied it to several data sets. This approach uses data from two generations to identify crossover sites, facilitating studies of recombination in disease. pediSNP is available online at <url>http://pevsnerlab.kennedykrieger.org/pediSNP</url>.</p

Increased Skin Tumor Incidence and Keratinocyte Hyper-Proliferation in a Mouse Model of Down Syndrome.

Down syndrome (DS) is a genetic disorder caused by the presence of an extra copy of human chromosome 21 (Hsa21). People with DS display multiple clinical traits as a result of the dosage imbalance of several hundred genes. While many outcomes of trisomy are deleterious, epidemiological studies have shown a significant risk reduction for most solid tumors in DS. Reduced tumor incidence has also been demonstrated in functional studies using trisomic DS mouse models. Therefore, it was interesting to find that Ts1Rhr trisomic mice developed more papillomas than did their euploid littermates in a DMBA-TPA chemical carcinogenesis paradigm. Papillomas in Ts1Rhr mice also proliferated faster. The increased proliferation was likely caused by a stronger response of trisomy to TPA induction. Treatment with TPA caused hyperkeratosis to a greater degree in Ts1Rhr mice than in euploid, reminiscent of hyperkeratosis seen in people with DS. Cultured trisomic keratinocytes also showed increased TPA-induced proliferation compared to euploid controls. These outcomes suggest that altered gene expression in trisomy could elevate a proliferation signalling pathway. Gene expression analysis of cultured keratinocytes revealed upregulation of several trisomic and disomic genes may contribute to this hyperproliferation. The contributions of these genes to hyper-proliferation were further validated in a siRNA knockdown experiment. The unexpected findings reported here add a new aspect to our understanding of tumorigenesis with clinical implications for DS and demonstrates the complexity of the tumor repression phenotype in this frequent condition

Survival, tumor initiation, and number in DMBA-TPA treated mice.

<p>Tumor number and size were measured twice a week from the initial DMBA treatment for 20 weeks. (A) WT (n = 20) survived significantly longer than Ts1Rhr (n = 21) mice. **p = 0.007 by Log-rank test. (B) The tumor initiation plotted versus time (weeks) for WT and Ts1Rhr mice. (C) Tumor number per mouse plotted versus time was not different in Ts and WT mice (error bar is SE). From week 9 on, Ts1Rhr developed significantly more tumors per individual compared to WT (ANOVA, *p<0.04).</p

Ts1Rhr mice demonstrate increased proliferation and TPA-induced hyperkeratosis <i>in vivo</i> and <i>in vitro</i>.

<p>(A) The average tumor size (mm) ± S.E. was plotted versus time in WT and Ts groups. * T-test, p<0.05. (B) Tumors showed increased relative growth rate in Ts compared to WT (*p<0.028). (C) H&E stain of WT and Ts skin without TPA treatment (WT-NT and Ts-NT). H&E stain of TPA-treated skin (WT-TPA and Ts-TPA). (D) There is no difference in thickness of the epidermis between WT (n = 12) and Ts (n = 14) without TPA treatment (NT group). The epidermis of Ts (n = 14) is significantly thicker than that of WT (n = 16) (*P = 0.023, T-test). (E) EdU staining of keratinocytes +/- TPA treatment. (F) Quantitation of cells in (2E) showed significantly more EdU positive cells in Ts cultures following TPA treatment (*p = 0.043, T-test). NT, no TPA treatment; TPA, treatment with TPA (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146570#sec002" target="_blank">Methods</a>).</p

Microarray analysis and RT-PCR validation of gene expression in keratinocytes.

<p>(A) TPA treated and untreated keratinocytes isolated from trisomic (Ts) and euploid (WT) mice are compared in this figure (n = 4 in each group). Differentially expressed genes are depicted in the Venn diagram. (B) Gene Set Enrichment Analysis (GSEA) was used to identify gene sets that exhibited significant overlaps with those gene differentially expressed between WT-NT and Ts-NT. Enrichment plot (left panel) and Heat map (right panel) for the Chr21q22 region gene set from GSEA analysis of WT-NT vs. Ts-NT keratinocytes. The relative ratios of transcripts are shown for trisomic genes. (C) <i>PigP</i>, (D) <i>Ttc3</i> and (E) <i>Ets2</i> in WT and Ts keratinocyte cultures before and after TPA treatment (*p<0.01). (F) Gene enrichment analysis found genes in the signature of head and neck squamous cell carcinoma significantly upregulated in the Ts-TPA group vs. WT-TPA (left panel). A heat map shows core genes upregulated in the Ts-TPA group (right panel).</p

Robust Classification of Small-Molecule Mechanism of Action Using a Minimalist High-Content Microscopy Screen and Multidimensional Phenotypic Trajectory Analysis.

Phenotypic screening through high-content automated microscopy is a powerful tool for evaluating the mechanism of action of candidate therapeutics. Despite more than a decade of development, however, high content assays have yielded mixed results, identifying robust phenotypes in only a small subset of compound classes. This has led to a combinatorial explosion of assay techniques, analyzing cellular phenotypes across dozens of assays with hundreds of measurements. Here, using a minimalist three-stain assay and only 23 basic cellular measurements, we developed an analytical approach that leverages informative dimensions extracted by linear discriminant analysis to evaluate similarity between the phenotypic trajectories of different compounds in response to a range of doses. This method enabled us to visualize biologically-interpretable phenotypic tracks populated by compounds of similar mechanism of action, cluster compounds according to phenotypic similarity, and classify novel compounds by comparing them to phenotypically active exemplars. Hierarchical clustering applied to 154 compounds from over a dozen different mechanistic classes demonstrated tight agreement with published compound mechanism classification. Using 11 phenotypically active mechanism classes, classification was performed on all 154 compounds: 78% were correctly identified as belonging to one of the 11 exemplar classes or to a different unspecified class, with accuracy increasing to 89% when less phenotypically active compounds were excluded. Importantly, several apparent clustering and classification failures, including rigosertib and 5-fluoro-2'-deoxycytidine, instead revealed more complex mechanisms or off-target effects verified by more recent publications. These results show that a simple, easily replicated, minimalist high-content assay can reveal subtle variations in the cellular phenotype induced by compounds and can correctly predict mechanism of action, as long as the appropriate analytical tools are used

10 most phenotypically active misclassified compounds.

<p>10 most phenotypically active misclassified compounds.</p

Classification results for all eight test compounds.

<p>Classification results for all eight test compounds.</p

Maximum Sequential Weighted Overlap.

<p><b>(A)</b> HDAC inhibitors follow a consistent, stereotypical phenotypic track, shown here in the “Cell Damage” and “Structural Breakdown” (tubulin intensity outside cell boundary, Hoescht stain outside nucleus, increased tetraploidy signal) dimensions. <b>(B)</b> Different compounds move through “Structural Breakdown” with differing potencies and sensitivities. <b>(C)</b> The optimal overlap between abexinostat and panobinostat uses the highest seven concentrations of abexinostat and a non-consecutive subset of doses of panobinostat.</p