Deficient H2A.Z deposition is associated with genesis of uterine leiomyoma.

One in four women suffers from uterine leiomyomas (ULs)-benign tumours of the uterine wall, also known as uterine fibroids-at some point in premenopausal life. ULs can cause excessive bleeding, pain and infertility1, and are a common cause of hysterectomy2. They emerge through at least three distinct genetic drivers: mutations in MED12 or FH, or genomic rearrangement of HMGA23. Here we created genome-wide datasets, using DNA, RNA, assay for transposase-accessible chromatin (ATAC), chromatin immunoprecipitation (ChIP) and HiC chromatin immunoprecipitation (HiChIP) sequencing of primary tissues to profoundly understand the genesis of UL. We identified somatic mutations in genes encoding six members of the SRCAP histone-loading complex4, and found that germline mutations in the SRCAP members YEATS4 and ZNHIT1 predispose women to UL. Tumours bearing these mutations showed defective deposition of the histone variant H2A.Z. In ULs, H2A.Z occupancy correlated positively with chromatin accessibility and gene expression, and negatively with DNA methylation, but these correlations were weak in tumours bearing SRCAP complex mutations. In these tumours, open chromatin emerged at transcription start sites where H2A.Z was lost, which was associated with upregulation of genes. Furthermore, YEATS4 defects were associated with abnormal upregulation of bivalent embryonic stem cell genes, as previously shown in mice5. Our work describes a potential mechanism of tumorigenesis-epigenetic instability caused by deficient H2A.Z deposition-and suggests that ULs arise through an aberrant differentiation program driven by deranged chromatin, emanating from a small number of mutually exclusive driver mutations.

FUNGI: FUsioN Gene Integration toolset.

MOTIVATION: Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusion events we have developed FUsionN Gene Identification toolset (FUNGI) that uses an ensemble of fusion detection algorithms with prioritization and visualization modules. RESULTS: We applied FUNGI to an ovarian cancer dataset of 107 tumor samples from 36 patients. Ten out of 11 detected and prioritized fusion genes were validated. Many of detected fusion genes affect the PI3K-AKT pathway with potential role in treatment resistance. AVAILABILITYAND IMPLEMENTATION: FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples.

MOTIVATION: A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. RESULTS: To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments. AVAILABILITYAND IMPLEMENTATION: https://bitbucket.org/anthakki/prism. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets.

MOTIVATION: Non-parametric dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE), are the most frequently used methods in the exploratory analysis of single-cell datasets. Current implementations scale poorly to massive datasets and often require downsampling or interpolative approximations, which can leave less-frequent populations undiscovered and much information unexploited. RESULTS: We implemented a fast t-SNE package, qSNE, which uses a quasi-Newton optimizer, allowing quadratic convergence rate and automatic perplexity (level of detail) optimizer. Our results show that these improvements make qSNE significantly faster than regular t-SNE packages and enables full analysis of large datasets, such as mass cytometry data, without downsampling. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are openly available at https://bitbucket.org/anthakki/qsne/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying differentially methylated sites in samples with varying tumor purity.

Motivation: DNA methylation aberrations are common in many cancer types. A major challenge hindering comparison of patient-derived samples is that they comprise of heterogeneous collection of cancer and microenvironment cells. We present a computational method that allows comparing cancer methylomes in two or more heterogeneous tumor samples featuring differing, unknown fraction of cancer cells. The method is unique in that it allows comparison also in the absence of normal cell control samples and without prior tumor purity estimates, as these are often unavailable or unreliable in clinical samples. Results: We use simulations and next-generation methylome, RNA and whole-genome sequencing data from two cancer types to demonstrate that the method is accurate and outperforms alternatives. The results show that our method adapts well to various cancer types and to a wide range of tumor content, and works robustly without a control or with controls derived from various sources. Availability and implementation: The method is freely available at https://bitbucket.org/anthakki/dmml. Supplementary information: Supplementary data are available at Bioinformatics online.

Predictable allele frequency changes due to habitat fragmentation in the Glanville fritillary butterfly.

Describing the evolutionary dynamics of now extinct populations is challenging, as their genetic composition before extinction is generally unknown. The Glanville fritillary butterfly has a large extant metapopulation in the Åland Islands in Finland, but declined to extinction in the nearby fragmented southwestern (SW) Finnish archipelago in the 20th century. We genotyped museum samples for 222 SNPs across the genome, including SNPs from candidate genes and neutral regions. SW Finnish populations had significantly reduced genetic diversity before extinction, and their allele frequencies gradually diverged from those in contemporary Åland populations over 80 y. We identified 15 outlier loci among candidate SNPs, mostly related to flight, in which allele frequencies have changed more than the neutral expectation. At outlier loci, allele frequencies in SW Finland shifted in the same direction as newly established populations deviated from old local populations in contemporary Åland. Moreover, outlier allele frequencies in SW Finland resemble those in fragmented landscapes as opposed to continuous landscapes in the Baltic region. These results indicate selection for genotypes associated with good colonization capacity in the highly fragmented landscape before the extinction of the populations. Evolutionary response to habitat fragmentation may have enhanced the viability of the populations, but it did not save the species from regional extinction in the face of severe habitat loss and fragmentation. These results highlight a potentially common situation in changing environments: evolutionary changes are not strong enough to fully compensate for the direct adverse effects of environmental change and thereby rescue populations from extinction.

Expressional profiling of prostate cancer risk SNPs at 11q13.5 identifies DGAT2 as a new target gene.

A total of nine non-coding variants on 11q13.5 predispose men to prostate cancer (PrCa). rs200331695 within the EMSY intron is associated with aggressive PrCa and two high linkage disequilibrium (LD) groups of single-nucleotide polymorphisms (SNPs) in the intergenic region are associated with PrCa death. Here, the cis-effect of the SNPs on gene expression using expression quantitative trait loci analysis was investigated. The regulatory potential was screened in prostate tumors (n = 41) and in whole blood (n = 99). The results were validated in a second tumor set (n = 41), in lymphoblastoid cell lines (LCLs) (n = 38), and using the GTEx Portal. The effects of haplotypes were analyzed in the whole blood. The high LD SNPs (rs143975731, rs12277366, rs2155225, and rs2155222) were associated with DGAT2 expression in both tumors sets (screening P = 0.035-0.043; validation P = 0.005-0.018). The PrCa death-associated alleles decreased the expression by two-fold. rs200331695 decreased DGAT2 expression in LCLs (P = 0.006). The findings of SNPs regulating CAPN5 (P = 0.026-0.046) and AP001189.4 (P = 0.03-0.039) in the whole blood were not observed in LCLs, but the association with AP001189.4 expression was validated via the GTEx Portal (P = 8.7 × 10(-5) to 4.3 × 10(-4) ), which suggests that the high LD intergenic SNPs exert a tissue-dependent effect on the expression of two genes. No haplotypes including the risk SNPs at 11q13.5 were associated with gene expression and PrCa. The findings indicate the functionality of the PrCa death-predisposing SNPs rs143975731, rs12277366, rs2155225, and rs2155222 as DGAT2 regulators in prostate tumors. © 2016 Wiley Periodicals, Inc.

Genomic profile of pseudomyxoma peritonei analyzed using next-generation sequencing and immunohistochemistry.

Pseudomyxoma peritonei (PMP) is a relatively rare clinical syndrome characterized by neoplastic epithelial cells growing in the peritoneal cavity and secreting mucinous ascites. Our aim was to explore the molecular events behind this fatal but under-investigated disease. We extracted DNA from 19 appendix-derived PMP tumors and nine corresponding normal tissues, and analyzed the mutational hotspot areas of 48 cancer-related genes by amplicon-based next-generation sequencing (NGS). Further, we analyzed the protein expression of V600E mutated BRAF, MLH1, MSH2, MSH6 and p53 from a larger set of PMP tumors (n = 74) using immunohistochemistry. With NGS, we detected activating somatic KRAS mutations in all of the tumors studied. GNAS was mutated in 63% of the tumors with no marked difference between low-grade and high-grade tumors. Only one (5.3%) tumor showed oncogenic PIK3CA mutation, one showed oncogenic AKT1 mutation, three (15.8%) showed SMAD4 mutations and none showed an APC mutation. P53 protein was aberrantly expressed in higher proportion of high-grade tumors as compared with low-grade ones (31.3 vs. 7.1%, respectively; p = 0.012) and aberrant expression was an independent factor for reduced overall survival (p = 0.002). BRAF V600E mutation was only found in one (1.4%) high-grade tumor by immunohistochemistry (n = 74). All the studied tumors expressed mismatch repair proteins MLH1, MSH2 and MSH6. Our results indicate that KRAS mutations are evident in all and GNAS mutations in most of the PMPs, but BRAF V600E, PIK3CA and APC mutations are rare. Aberrantly expressed p53 is associated with high-grade histology and reduced survival.

Flight-induced changes in gene expression in the Glanville fritillary butterfly.

Insect flight is one of the most energetically demanding activities in the animal kingdom, yet for many insects flight is necessary for reproduction and foraging. Moreover, dispersal by flight is essential for the viability of species living in fragmented landscapes. Here, working on the Glanville fritillary butterfly (Melitaea cinxia), we use transcriptome sequencing to investigate gene expression changes caused by 15 min of flight in two contrasting populations and the two sexes. Male butterflies and individuals from a large metapopulation had significantly higher peak flight metabolic rate (FMR) than female butterflies and those from a small inbred population. In the pooled data, FMR was significantly positively correlated with genome-wide heterozygosity, a surrogate of individual inbreeding. The flight experiment changed the expression level of 1513 genes, including genes related to major energy metabolism pathways, ribosome biogenesis and RNA processing, and stress and immune responses. Males and butterflies from the population with high FMR had higher basal expression of genes related to energy metabolism, whereas females and butterflies from the small population with low FMR had higher expression of genes related to ribosome/RNA processing and immune response. Following the flight treatment, genes related to energy metabolism were generally down-regulated, while genes related to ribosome/RNA processing and immune response were up-regulated. These results suggest that common molecular mechanisms respond to flight and can influence differences in flight metabolic capacity between populations and sexes.

Identification of sample-specific regulations using integrative network level analysis.

BACKGROUND: Histologically similar tumors even from the same anatomical position may still show high variability at molecular level hindering analysis of genome-wide data. Leveling the analysis to a gene regulatory network instead of focusing on single genes has been suggested to overcome the heterogeneity issue although the majority of the network methods require large datasets. Network methods that are able to function at a single sample level are needed to overcome the heterogeneity and sample size issues. METHODS: We present a novel network method, Differentially Expressed Regulation Analysis (DERA) that integrates expression data to biological network information at a single sample level. The sample-specific networks are subsequently used to discover samples with similar molecular functions by identification of regulations that are shared between samples or are specific for a subgroup. RESULTS: We applied DERA to identify key regulations in triple negative breast cancer (TNBC), which is characterized by lack of estrogen receptor, progesterone receptor and HER2 expression and has poorer prognosis than the other breast cancer subtypes. DERA identified 110 core regulations consisting of 28 disconnected subnetworks for TNBC. These subnetworks are related to oncogenic activity, proliferation, cancer survival, invasiveness and metastasis. Our analysis further revealed 31 regulations specific for TNBC as compared to the other breast cancer subtypes and thus form a basis for understanding TNBC. We also applied DERA to high-grade serous ovarian cancer (HGS-OvCa) data and identified several common regulations between HGS-OvCa and TNBC. The performance of DERA was compared to two pathway analysis methods GSEA and SPIA and our results shows better reproducibility and higher sensitivity in a small sample set. CONCLUSIONS: We present a novel method called DERA to identify subnetworks that are similarly active for a group of samples. DERA was applied to breast cancer and ovarian cancer data showing our method is able to identify reliable and potentially important regulations with high reproducibility. R package is available at http://csbi.ltdk.helsinki.fi/pub/czliu/DERA/.

High genetic load in an old isolated butterfly population.

We investigated inbreeding depression and genetic load in a small (N(e) ∼ 100) population of the Glanville fritillary butterfly (Melitaea cinxia), which has been completely isolated on a small island [Pikku Tytärsaari (PT)] in the Baltic Sea for at least 75 y. As a reference, we studied conspecific populations from the well-studied metapopulation in the Åland Islands (ÅL), 400 km away. A large population in Saaremaa, Estonia, was used as a reference for estimating genetic diversity and N(e). We investigated 58 traits related to behavior, development, morphology, reproductive performance, and metabolism. The PT population exhibited high genetic load (L = 1 - W(PT)/W(ÅL)) in a range of fitness-related traits including adult weight (L = 0.12), flight metabolic rate (L = 0.53), egg viability (L = 0.37), and lifetime production of eggs in an outdoor population cage (L = 0.70). These results imply extensive fixation of deleterious recessive mutations, supported by greatly reduced diversity in microsatellite markers and immediate recovery (heterosis) of egg viability and flight metabolic rate in crosses with other populations. There was no significant inbreeding depression in most traits due to one generation of full-sib mating. Resting metabolic rate was significantly elevated in PT males, which may be related to their short lifespan (L = 0.25). The demographic history and the effective size of the PT population place it in the part of the parameter space in which models predict mutation accumulation. This population exemplifies the increasingly common situation in fragmented landscapes, in which small and completely isolated populations are vulnerable to extinction due to high genetic load.

Prevalence of germline PTEN, BMPR1A, SMAD4, STK11, and ENG mutations in patients with moderate-load colorectal polyps.

BACKGROUND & AIMS: Gastrointestinal polyposis is a common clinical problem, yet there is no consensus on how to best manage patients with moderate-load polyposis. Identifying genetic features of this disorder could improve management and especially surveillance of these patients. We sought to determine the prevalence of hamartomatous polyposis-associated mutations in the susceptibility genes PTEN, BMPR1A, SMAD4, ENG, and STK11 in individuals with ≥5 gastrointestinal polyps, including at least 1 hamartomatous or hyperplastic/serrated polyp. METHODS: We performed a prospective, referral-based study of 603 patients (median age: 51 years; range, 2-89 years) enrolled from June 2006 through January 2012. Genomic DNA was extracted from peripheral lymphocytes and analyzed for specific mutations and large rearrangements in PTEN, BMPR1A, SMAD4, and STK11, as well as mutations in ENG. Recursive partitioning analysis was used to determine cutoffs for continuous variables. The prevalence of mutations was compared using Fisher's exact test. Logistic regression analyses were used to determine univariate and multivariate risk factors. RESULTS: Of 603 patients, 119 (20%) had a personal history of colorectal cancer and most (n = 461 [76%]) had <30 polyps. Seventy-seven patients (13%) were found to have polyposis-associated mutations, including 11 in ENG (1.8%), 13 in PTEN (2.2%), 13 in STK11 (2.2%), 20 in BMPR1A (3.3%), and 21 in SMAD4 (3.5%). Univariate clinical predictors for risk of having these mutations included age at presentation younger than 40 years (19% vs 10%; P = .008), a polyp burden of ≥30 (19% vs 11%; P = .014), and male sex (16% vs 10%; P = .03). Patients who had ≥1 ganglioneuroma (29% vs 2%; P < .001) or presented with polyps of ≥3 histologic types (20% vs 2%; P = .003) were more likely to have germline mutations in PTEN. CONCLUSIONS: Age younger than 40 years, male sex, and specific polyp histologies are significantly associated with risk of germline mutations in hamartomatous-polyposis associated genes. These associations could guide clinical decision making and further investigations.

Lep-MAP: fast and accurate linkage map construction for large SNP datasets.

MOTIVATION: Current high-throughput sequencing technologies allow cost-efficient genotyping of millions of single nucleotide polymorphisms (SNPs) for hundreds of samples. However, the tools that are currently available for constructing linkage maps are not well suited for large datasets. Linkage maps of large datasets would be helpful in de novo genome assembly by facilitating comprehensive genome validation and refinement by enabling chimeric scaffold detection, as well as in family-based linkage and association studies, quantitative trait locus mapping, analysis of genome synteny and other complex genomic data analyses. RESULTS: We describe a novel tool, called Lepidoptera-MAP (Lep-MAP), for constructing accurate linkage maps with ultradense genome-wide SNP data. Lep-MAP is fast and memory efficient and largely automated, requiring minimal user interaction. It uses simultaneously data on multiple outbred families and can increase linkage map accuracy by taking into account achiasmatic meiosis, a special feature of Lepidoptera and some other taxa with no recombination in one sex (no recombination in females in Lepidoptera). We demonstrate that Lep-MAP outperforms other methods on real and simulated data. We construct a genome-wide linkage map of the Glanville fritillary butterfly (Melitaea cinxia) with over 40 000 SNPs. The data were generated with a novel in-house SOLiD restriction site-associated DNA tag sequencing protocol, which is described in the online supplementary material. AVAILABILITY AND IMPLEMENTATION: Java source code under GNU general public license with the compiled classes and the datasets are available from http://sourceforge.net/users/lep-map.

Phenotypic plasticity in thermal tolerance in the Glanville fritillary butterfly.

Ambient temperature is an ubiquitous environmental factor affecting all organisms. Global climate change increases temperature variation and the frequency of extreme temperatures, which may pose challenges to ectotherms. Here, we examine phenotypic plasticity to temperature and genotypic effects on thermal tolerance in the Glanville fritillary butterfly (Melitaea cinxia). We found no significant difference in heat or cold tolerance in populations originating from a continental climate in China and from Finland with moderate temperature variation. Acclimation to large-amplitude temperature variation increased heat tolerance in both populations, but decreased cold tolerance and increased hsp70-2 expression in the Chinese population only. The latter result indicates a genotypic effect in the response to temperature variation. In the Finnish population, a non-synonymous SNP in the phosphoglucose isomerase (Pgi) gene was associated with heat knock-down time.

Genome-wide somatic mutation analysis of sinonasal adenocarcinoma with and without wood dust exposure.

BACKGROUND: Sinonasal adenocarcinoma is a rare cancer, encompassing two different entities, the intestinal-type sinonasal adenocarcinoma (ITAC) and the non-intestinal-type sinonasal adenocarcinoma (non-ITAC). Occurrence of ITAC is strongly associated with exposure to hardwood dusts. In countries with predominant exposure to softwood dust the occurrence of sinonasal adenocarcinomas is lower and the relative amount of non-ITACs to ITACs is higher. The molecular mechanisms behind the tumorigenic effects of wood dust remain largely unknown. METHODS: We carried out whole-genome sequencing of formalin-fixed paraffin-embedded (FFPE) samples of sinonasal adenocarcinomas from ten wood dust-exposed and six non-exposed individuals, with partial tobacco exposure data. Sequences were analyzed for the presence of mutational signatures matching COSMIC database signatures. Driver mutations and CN variant regions were characterized. RESULTS: Mutation burden was higher in samples of wood dust-exposed patients (p = 0.016). Reactive oxygen species (ROS) damage-related mutational signatures were almost exclusively identified in ITAC subtype samples (p = 0.00055). Tobacco smoke mutational signatures were observed in samples of patients with tobacco exposure or missing information, but not in samples from non-exposed patients. A tetraploidy copy number (CN) signature was enriched in ITAC subtype (p = 0.042). CN variation included recurrent gains in COSMIC Cancer Gene Census genes TERT, SDHA, RAC1, ETV1, PCM1, and MYC. Pathogenic variants were observed most frequently in TP53, NF1, CHD2, BRAF, APC, and LRP1B. Driver mutations and copy number gains did not segregate by subtype. CONCLUSIONS: Our analysis identified distinct mutational characteristics in ITAC and non-ITAC. Mutational signature analysis may eventually become useful for documentation of occupation-related cancer, while the exact mechanisms behind wood dust-driven carcinogenesis remain elusive. The presence of homologous recombination deficiency signatures implies a novel opportunity for treatment, but further studies are needed.

PIK3R1 fusion drives chemoresistance in ovarian cancer by activating ERK1/2 and inducing rod and ring-like structures.

Gene fusions are common in high-grade serous ovarian cancer (HGSC). Such genetic lesions may promote tumorigenesis, but the pathogenic mechanisms are currently poorly understood. Here, we investigated the role of a PIK3R1-CCDC178 fusion identified from a patient with advanced HGSC. We show that the fusion induces HGSC cell migration by regulating ERK1/2 and increases resistance to platinum treatment. Platinum resistance was associated with rod and ring-like cellular structure formation. These structures contained, in addition to the fusion protein, CIN85, a key regulator of PI3K-AKT-mTOR signaling. Our data suggest that the fusion-driven structure formation induces a previously unrecognized cell survival and resistance mechanism, which depends on ERK1/2-activation.

Deciphering cancer genomes with GenomeSpy: a grammar-based visualization toolkit.

BACKGROUND: Visualization is an indispensable facet of genomic data analysis. Despite the abundance of specialized visualization tools, there remains a distinct need for tailored solutions. However, their implementation typically requires extensive programming expertise from bioinformaticians and software developers, especially when building interactive applications. Toolkits based on visualization grammars offer a more accessible, declarative way to author new visualizations. Yet, current grammar-based solutions fall short in adequately supporting the interactive analysis of large datasets with extensive sample collections, a pivotal task often encountered in cancer research. FINDINGS: We present GenomeSpy, a grammar-based toolkit for authoring tailored, interactive visualizations for genomic data analysis. By using combinatorial building blocks and a declarative language, users can implement new visualization designs easily and embed them in web pages or end-user-oriented applications. A distinctive element of GenomeSpy's architecture is its effective use of the graphics processing unit in all rendering, enabling a high frame rate and smoothly animated interactions, such as navigation within a genome. We demonstrate the utility of GenomeSpy by characterizing the genomic landscape of 753 ovarian cancer samples from patients in the DECIDER clinical trial. Our results expand the understanding of the genomic architecture in ovarian cancer, particularly the diversity of chromosomal instability. CONCLUSIONS: GenomeSpy is a visualization toolkit applicable to a wide range of tasks pertinent to genome analysis. It offers high flexibility and exceptional performance in interactive analysis. The toolkit is open source with an MIT license, implemented in JavaScript, and available at https://genomespy.app/.

Exome sequencing reveals germline NPAT mutation as a candidate risk factor for Hodgkin lymphoma.

A strong clustering of Hodgkin lymphoma in certain families has been long acknowledged. However, the genetic factors in the background of familial Hodgkin lymphoma are largely unknown. We have studied a family of 4 cousins with a rare subtype of the disease, nodular lymphocyte predominant Hodgkin lymphoma. We applied exome sequencing together with genome-wide linkage analysis to this family and identified a truncating germline mutation in nuclear protein, ataxia-telangiectasia locus (NPAT) gene, which segregated in the family. We also studied a large number of samples from other patients with Hodgkin lymphoma, and a germline variation leading to the deletion of serine 724 was found in several cases suggesting an elevated risk for the disease (odds ratio = 4.11; P = .018). NPAT is thus far the first gene implicated in nodular lymphocyte predominant Hodgkin lymphoma predisposition.

Plastic larval development in a butterfly has complex environmental and genetic causes and consequences for population dynamics.

1. In insects, the length of larval development time typically influences adult body size and individual fitness, and hence development time can be expected to respond in an adaptive manner to variation in environmental conditions. In the wild, larval growth may be influenced by individual condition, which can be affected by population-level parameters such as population density and abundance and quality of resources. 2. We sampled larvae of the Glanville fritillary butterfly (Melitaea cinxia) from 514 local populations across a large metapopulation before the winter diapause and reared the larvae in common garden conditions after diapause. Here, we report that small post-diapause larvae prolonged their development via an extra larval instar, apparently to compensate for their 'bad start' after diapause. The number of instars was additionally a plastic response to environmental conditions, as the frequency of the extra instar increased under cooler thermal conditions. 3. The benefit of the extra instar is clear, as it allows individuals to develop into larger adults, but the cost is delayed adult eclosion, which is likely to select against the extra instar especially in males, in which early eclosion is critical for mating success. In support of this, the frequency of the extra instar was significantly lower in males (7%) than in females (42%). 4. Polymorphisms in three genes, serpin-1, vitellin-degrading protease precursor and phosphoglucose isomerase, which are known to influence development in insects, were associated with the occurrence of the extra instar. 5. At the level of local populations, the frequency of the extra instar was higher in newly established populations than that in old local ones, possibly reflecting maternal effects, as new populations are often established by females with heavy investment in dispersal. The frequency of the extra instar in turn correlated with the change in population size over 1 year and the risk of local extinction in the natural metapopulation of the Glanville fritillary. 6. Our results highlight the importance of the physiological condition of individuals in shaping subsequent life-history events and even population dynamics.

Germline mutations in FH predispose to dominantly inherited uterine fibroids, skin leiomyomata and papillary renal cell cancer.

Uterine leiomyomata (fibroids) are common and clinically important tumors, but little is known about their etiology and pathogenesis. We previously mapped a gene that predisposes to multiple fibroids, cutaneous leiomyomata and renal cell carcinoma to chromosome 1q42.3-q43 (refs 4-6). Here we show, through a combination of mapping critical recombinants, identifying individuals with germline mutations and screening known and predicted transcripts, that this gene encodes fumarate hydratase, an enzyme of the tricarboxylic acid cycle. Leiomyomatosis-associated mutations are predicted to result in absent or truncated protein, or substitutions or deletions of highly conserved amino acids. Activity of fumarate hydratase is reduced in lymphoblastoid cells from individuals with leiomyomatosis. This enzyme acts as a tumor suppressor in familial leiomyomata, and its measured activity is very low or absent in tumors from individuals with leiomyomatosis. Mutations in FH also occur in the recessive condition fumarate hydratase deficiency, and some parents of people with this condition are susceptible to leiomyomata. Thus, heterozygous and homozygous or compound heterozygous mutants have very different clinical phenotypes. Our results provide clues to the pathogenesis of fibroids and emphasize the importance of mutations of housekeeping and mitochondrial proteins in the pathogenesis of common types of tumor.