Kayleen Williams

Mutations associated with neutropenia in dogs and humans disrupt intracellular transport of neutrophil elastase

Cyclic hematopoiesis is a stem cell disease in which the number of neutrophils and other blood cells oscillates in weekly phases. Autosomal dominant mutations of ELA2, encoding the protease neutrophil elastase, found in lysosome-like granules, cause cyclic hematopoiesis and most cases of the pre-leukemic disorder severe congenital neutropenia (SCN; ref. 3) in humans. Over 20 different mutations of neutrophil elastase have been identified, but their consequences are elusive, because they confer no consistent effects on enzymatic activity. The similar autosomal recessive disease of dogs, canine cyclic hematopoiesis, is not caused by mutations in ELA2 (data not shown). Here we show that homozygous mutation of the gene encoding the dog adaptor protein complex 3 (AP3) β-subunit, directing trans-Golgi export of transmembrane cargo proteins to lysosomes, causes canine cyclic hematopoiesis. C-terminal processing of neutrophil elastase exposes an AP3 interaction signal responsible for redirecting neutrophil elastase trafficking from membranes to granules. Disruption of either neutrophil elastase or AP3 perturbs the intracellular trafficking of neutrophil elastase. Most mutations in ELA2 that cause human cyclic hematopoiesis prevent membrane localization of neutrophil elastase, whereas most mutations in ELA2 that cause SCN lead to exclusive membrane localization.

The Gene, Environment Association Studies Consortium (GENEVA): Maximizing the Knowledge Obtained from GWAS by Collaboration Across Studies of Multiple Conditions

Genome‐wide association studies (GWAS) have emerged as powerful means for identifying genetic loci related to complex diseases. However, the role of environment and its potential to interact with key loci has not been adequately addressed in most GWAS. Networks of collaborative studies involving different study populations and multiple phenotypes provide a powerful approach for addressing the challenges in analysis and interpretation shared across studies. The Gene, Environment Association Studies (GENEVA) consortium was initiated to: identify genetic variants related to complex diseases; identify variations in gene‐trait associations related to environmental exposures; and ensure rapid sharing of data through the database of Genotypes and Phenotypes. GENEVA consists of several academic institutions, including a coordinating center, two genotyping centers and 14 independently designed studies of various phenotypes, as well as several Institutes and Centers of the National Institutes of Health led by the National Human Genome Research Institute. Minimum detectable effect sizes include relative risks ranging from 1.24 to 1.57 and proportions of variance explained ranging from 0.0097 to 0.02. Given the large number of research participants (N>80,000), an important feature of GENEVA is harmonization of common variables, which allow analyses of additional traits. Environmental exposure information available from most studies also enables testing of gene‐environment interactions. Facilitated by its sizeable infrastructure for promoting collaboration, GENEVA has established a unified framework for genotyping, data quality control, analysis and interpretation. By maximizing knowledge obtained through collaborative GWAS incorporating environmental exposure information, GENEVA aims to enhance our understanding of disease etiology, potentially identifying opportunities for intervention. Genet. Epidemiol. 34: 364–372, 2010.

A novel notch protein, N2N, targeted by neutrophil elastase and implicated in hereditary neutropenia.

ABSTRACT Mutations in ELA2, encoding the human serine protease neutrophil elastase, cause cyclic and severe congenital neutropenia, and recent evidence indicates that the mutations alter the membrane trafficking of neutrophil elastase. These disorders feature impaired bone marrow production of neutrophils along with excess monocytes—terminally differentiated lineages corresponding to the two alternative fates of myeloid progenitor cells. We utilized a modified yeast two-hybrid system and identified a new, widely expressed gene, N2N, whose product is homologous to Notch2, that interacts with neutrophil elastase. N2N is a 36-kDa protein distributed throughout the cell and secreted. Its amino-terminal sequence consists of several EGF repeats identical to those of the extracellular region of Notch2, and its carboxyl terminus contains a unique 24-residue domain required for interaction with neutrophil elastase. Neutrophil elastase cleaves N2N within EGF repeats in vitro and in living cells, but the C-terminal domain retards proteolysis. In vitro, N2N represses transcriptional activities of Notch proteins. Disease-causing mutations of neutrophil elastase disrupt the interaction with N2N, impair proteolysis of N2N and Notch2, and interfere with Notch2 signaling, suggesting defective proteolysis of an inhibitory form of Notch as an explanation for the alternate switching of cell fates characteristic of hereditary neutropenia.

Population Structure of Hispanics in the United States: The Multi-Ethnic Study of Atherosclerosis

Using ∼60,000 SNPs selected for minimal linkage disequilibrium, we perform population structure analysis of 1,374 unrelated Hispanic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), with self-identification corresponding to Central America (n = 93), Cuba (n = 50), the Dominican Republic (n = 203), Mexico (n = 708), Puerto Rico (n = 192), and South America (n = 111). By projection of principal components (PCs) of ancestry to samples from the HapMap phase III and the Human Genome Diversity Panel (HGDP), we show the first two PCs quantify the Caucasian, African, and Native American origins, while the third and fourth PCs bring out an axis that aligns with known South-to-North geographic location of HGDP Native American samples and further separates MESA Mexican versus Central/South American samples along the same axis. Using k-means clustering computed from the first four PCs, we define four subgroups of the MESA Hispanic cohort that show close agreement with self-identification, labeling the clusters as primarily Dominican/Cuban, Mexican, Central/South American, and Puerto Rican. To demonstrate our recommendations for genetic analysis in the MESA Hispanic cohort, we present pooled and stratified association analysis of triglycerides for selected SNPs in the LPL and TRIB1 gene regions, previously reported in GWAS of triglycerides in Caucasians but as yet unconfirmed in Hispanic populations. We report statistically significant evidence for genetic association in both genes, and we further demonstrate the importance of considering population substructure and genetic heterogeneity in genetic association studies performed in the United States Hispanic population.

Home and work neighbourhood environments in relation to body mass index: the Multi-Ethnic Study of Atherosclerosis (MESA)

Background Little is known about the neighbourhood characteristics of workplaces, the extent to which they are independently and synergistically correlated with residential environments, and their impact on health. Methods This study investigated cross-sectional relationships between home and workplace neighbourhood environments with body mass index (BMI) in 1503 working participants of the Multi-Ethnic Study of Atherosclerosis with mean age 59.6 (SD=7.4). Neighbourhood features were socioeconomic status (SES), social environment (aesthetic quality, safety and social cohesion) and physical environment (walking environment, recreational facilities and food stores) derived from census data, locational data on businesses and survey data. Paired t tests and correlations compared environments overall and by distance between locations. Cross-classified multilevel models estimated associations with BMI. Results Home neighbourhoods had more favourable social environments while workplaces had more favourable SES and physical environments. Workplace and home measures were correlated (0.39–0.70), and differences between home and workplaces were larger as distance increased. Associations between BMI and neighbourhood SES and recreational facilities were stronger for home environment (p≤0.05) but did not significantly differ for healthy food, safety or social cohesion. Healthy food availability at home and work appeared to act synergistically (interaction p=0.01). Conclusions Consideration of workplace environment may enhance our understanding of how place affects BMI.

Lymphoid enhancer factor-1 links two hereditary leukemia syndromes through core-binding factor alpha regulation of ELA2

Two hereditary human leukemia syndromes are severe congenital neutropenia (SCN), caused by mutations in the gene ELA2, encoding the protease neutrophil elastase, and familial platelet disorder with acute myelogenous leukemia (AML), caused by mutations in the gene AML1, encoding the transcription factor core-binding factor α (CBFα). In mice, CBFα regulates the expression of ELA2, suggesting a common link for both diseases. However, gene-targeted mouse models have failed to reproduce either human disease, thus prohibiting further in vivo studies in mice. Here we investigate CBFα regulation of the human ELA2 promoter, taking advantage of bone marrow obtained from patients with either illness. In particular, we have identified novel ELA2 promoter substitutions (-199 C to A) within a potential motif for lymphoid enhancer factor-1 (LEF-1), a transcriptional mediator of Wnt/β-catenin signaling, in SCN patients. The LEF-1 motif lies adjacent to a potential CBFα binding site that is in a different position in human compared with mouse ELA2. We find that LEF-1 and CBFα co-activate ELA2 expression. In vitro, the high mobility group domain of LEF-1 interacts with the runt DNA binding and proline-, serine-, threonine-rich activation domains of CBFα. ELA2 transcript levels are up-regulated in bone marrow of an SCN patient with the -199 C to A substitution. Conversely, a mutation of the CBFα activation domain, found in a patient with familial platelet disorder with AML, fails to stimulate the ELA2 promoter in vitro, and bone marrow correspondingly demonstrates reduced ELA2 transcript. Observations in these complementary patients indicate that LEF-1 cooperates with CBFα to activate ELA2 in vivo and also suggest the possibility that up-regulating promoter mutations can contribute to SCN. Two hereditary AML predisposition syndromes may therefore intersect via LEF-1, potentially linking them to more generalized cancer mechanisms.

Role of neutrophil elastase in bone marrow failure syndromes: molecular genetic revival of the chalone hypothesis.

Two forms of inherited deficiency of neutrophil numbers are cyclic hematopoiesis and severe congenital neutropenia. In cyclic hematopoiesis, neutrophil counts oscillate opposite monocytes in a 3-week cycle. Severe congenital neutropenia consists of static neutropenia and a predisposition to myelodysplasia and acute myelogenous leukemia. All cases of cyclic neutropenia and most cases of severe congenital neutropenia result from heterozygous germline mutations in the gene encoding neutrophil elastase, ela2. Recent work extends the list of neutropenia genes to include WASp, Gfi-1, adaptin, and tafazzin. Studies of mosaic patients suggest that ela2 mutations act in a cell-autonomous fashion. A hypothetical feedback circuit potentially interconnects these genes. Genetic dissection of signaling in model organisms along with experimental hematology implicate C/EPBε, RUNX1/AML1, Notch family members, LEF1, and Cdc42 as additional nodes in this pathway. The authors propose that neutrophil elastase acts as an inhibitor of myelopoiesis, substantiating a chalone hypothesis proposed many years ago.

High attenuation areas on chest computed tomography in community-dwelling adults: the MESA study

Evidence suggests that lung injury, inflammation and extracellular matrix remodelling precede lung fibrosis in interstitial lung disease (ILD). We examined whether a quantitative measure of increased lung attenuation on computed tomography (CT) detects lung injury, inflammation and extracellular matrix remodelling in community-dwelling adults sampled without regard to respiratory symptoms or smoking. We measured high attenuation areas (HAA; percentage of lung voxels between −600 and −250 Hounsfield Units) on cardiac CT scans of adults enrolled in the Multi-Ethnic Study of Atherosclerosis. HAA was associated with higher serum matrix metalloproteinase-7 (mean adjusted difference 6.3% per HAA doubling, 95% CI 1.3–11.5), higher interleukin-6 (mean adjusted difference 8.8%, 95% CI 4.8–13.0), lower forced vital capacity (FVC) (mean adjusted difference −82 mL, 95% CI −119–−44), lower 6-min walk distance (mean adjusted difference −40 m, 95% CI −1–−80), higher odds of interstitial lung abnormalities at 9.5 years (adjusted OR 1.95, 95% CI 1.43–2.65), and higher all cause-mortality rate over 12.2 years (HR 1.58, 95% CI 1.39–1.79). High attenuation areas are associated with biomarkers of inflammation and extracellular matrix remodelling, reduced lung function, interstitial lung abnormalities, and a higher risk of death among community-dwelling adults. Increased lung attenuation on CT may identify subclinical lung injury and inflammation in community-dwelling adults http://ow.ly/97k3300tvKX

Phenotype harmonization and cross-study collaboration in GWAS consortia: the GENEVA experience.

Genome‐wide association study (GWAS) consortia and collaborations formed to detect genetic loci for common phenotypes or investigate gene‐environment (G*E) interactions are increasingly common. While these consortia effectively increase sample size, phenotype heterogeneity across studies represents a major obstacle that limits successful identification of these associations. Investigators are faced with the challenge of how to harmonize previously collected phenotype data obtained using different data collection instruments which cover topics in varying degrees of detail and over diverse time frames. This process has not been described in detail. We describe here some of the strategies and pitfalls associated with combining phenotype data from varying studies. Using the Gene Environment Association Studies (GENEVA) multi‐site GWAS consortium as an example, this paper provides an illustration to guide GWAS consortia through the process of phenotype harmonization and describes key issues that arise when sharing data across disparate studies. GENEVA is unusual in the diversity of disease endpoints and so the issues it faces as its participating studies share data will be informative for many collaborations. Phenotype harmonization requires identifying common phenotypes, determining the feasibility of cross‐study analysis for each, preparing common definitions, and applying appropriate algorithms. Other issues to be considered include genotyping timeframes, coordination of parallel efforts by other collaborative groups, analytic approaches, and imputation of genotype data. GENEVAs harmonization efforts and policy of promoting data sharing and collaboration, not only within GENEVA but also with outside collaborations, can provide important guidance to ongoing and new consortia. Genet. Epidemiol. 35:159‐173, 2011.© 2011 Wiley‐Liss, Inc.

Analysis of family- and population-based samples in cohort genome-wide association studies

Cohort studies typically sample unrelated individuals from a population, although family members of index cases may also be recruited to investigate shared familial risk factors. Recruitment of family members may be incomplete or ancillary to the main cohort, resulting in a mixed sample of independent family units, including unrelated singletons and multiplex families. Multiple methods are available to perform genome-wide association (GWA) analysis of binary or continuous traits in families, but it is unclear whether methods known to perform well on ascertained pedigrees, sibships, or trios are appropriate in analysis of a mixed unrelated cohort and family sample. We present simulation studies based on Multi-Ethnic Study of Atherosclerosis (MESA) pedigree structures to compare the performance of several popular methods of GWA analysis for both quantitative and dichotomous traits in cohort studies. We evaluate approaches suitable for analysis of families, and combined the best performing methods with population-based samples either by meta-analysis, or by pooled analysis of family- and population-based samples (mega-analysis), comparing type 1 error and power. We further assess practical considerations, such as availability of software and ability to incorporate covariates in statistical modeling, and demonstrate our recommended approaches through quantitative and binary trait analysis of HDL cholesterol (HDL-C) in 2,553 MESA family- and population-based African-American samples. Our results suggest linear modeling approaches that accommodate family-induced phenotypic correlation (e.g., variance-component model for quantitative traits or generalized estimating equations for dichotomous traits) perform best in the context of combined family- and population-based cohort GWAS.