The genetic contribution of the major histocompatibility complex to bleomycin-induced lung injury

Pulmonary fibrosis (PF) is the result of a variety of environmental and cancer treatment related insults and is characterized by excessive deposition of collagen. Gas exchange in the alveoli is impaired as the normal lung becomes dense and collapsed leading to a loss of lung volume. It is now accepted that lung injury and fibrosis are in part genetically regulated. ^ Bleomycin is a chemotherapeutic agent used for testicular cancer and lymphomas that induces significant pulmonary toxicity. We delivered bleomycin to mice subcutaneously via a miniosmotic pump in order to elicit lung injury (LI) and quantified the %LI morphometrically using video imaging software. We previously identified a quantitative trait loci, Blmpf-1(LOD=17.4), in the Major Histocompatibility Complex (MHC), but the exact genetic components involved have remained unknown. ^ In the current studies, Blmpf-1 was narrowed to an interval spanning 31.9-32.9Mb on Chromosome 17 using MHC Congenic mice. This region includes the MHC Class II and III genes, and is flanked by the TNF-alpha super locus and MHC Class I genes. Knockout mice of MHC Class I genes (B2mko), MHC Class II genes (Cl2ko), and TNF-alpha (TNF-/-) and its receptors (p55-/-, p75-/-, and p55/p75-/-) were treated with bleomycin in order to ascertain the role of these genes in the pathogenesis of lung injury. ^ Cl2ko mice had significantly better survival and %LI when compared to treated background BL/6 (B6, P<.05). In contrast, B2mko showed no differences in survival or %LI compared to B6. This suggests that the MHC Class II locus contains susceptibility genes for bleomycin-induced lung injury. ^ TNF-alpha, a Class III gene, was examined and it was found that TNF-/- and p55-/- mice had higher %LI and lower survival when compared to B6 (P<.05). In contrast, p75-/- mice had significantly reduced %LI when compared to TNF-/-, p55-/-, and B6 mice as well as higher survival (P<.01). These data contradict the current paradigm that TNF-alpha is a profibrotic mediator of lung injury and suggest a novel and distinct role for the p55 and p75 receptors in mediating lung injury. ^

Functional data analysis approaches for genotype-phenotype association studies from next-generation sequencing

Next-generation DNA sequencing platforms can effectively detect the entire spectrum of genomic variation and is emerging to be a major tool for systematic exploration of the universe of variants and interactions in the entire genome. However, the data produced by next-generation sequencing technologies will suffer from three basic problems: sequence errors, assembly errors, and missing data. Current statistical methods for genetic analysis are well suited for detecting the association of common variants, but are less suitable to rare variants. This raises great challenge for sequence-based genetic studies of complex diseases.^ This research dissertation utilized genome continuum model as a general principle, and stochastic calculus and functional data analysis as tools for developing novel and powerful statistical methods for next generation of association studies of both qualitative and quantitative traits in the context of sequencing data, which finally lead to shifting the paradigm of association analysis from the current locus-by-locus analysis to collectively analyzing genome regions.^ In this project, the functional principal component (FPC) methods coupled with high-dimensional data reduction techniques will be used to develop novel and powerful methods for testing the associations of the entire spectrum of genetic variation within a segment of genome or a gene regardless of whether the variants are common or rare.^ The classical quantitative genetics suffer from high type I error rates and low power for rare variants. To overcome these limitations for resequencing data, this project used functional linear models with scalar response to develop statistics for identifying quantitative trait loci (QTLs) for both common and rare variants. To illustrate their applications, the functional linear models were applied to five quantitative traits in Framingham heart studies. ^ This project proposed a novel concept of gene-gene co-association in which a gene or a genomic region is taken as a unit of association analysis and used stochastic calculus to develop a unified framework for testing the association of multiple genes or genomic regions for both common and rare alleles. The proposed methods were applied to gene-gene co-association analysis of psoriasis in two independent GWAS datasets which led to discovery of networks significantly associated with psoriasis.^

Single nucleotide polymorphisms (SNPs) associated with TGF-beta pathway and their significance in systemic sclerosis - A multilevel analysis

Systemic sclerosis (SSc) or Scleroderma is a complex disease and its etiopathogenesis remains unelucidated. Fibrosis in multiple organs is a key feature of SSc and studies have shown that transforming growth factor-β (TGF-β) pathway has a crucial role in fibrotic responses. For a complex disease such as SSc, expression quantitative trait loci (eQTL) analysis is a powerful tool for identifying genetic variations that affect expression of genes involved in this disease. In this study, a multilevel model is described to perform a multivariate eQTL for identifying genetic variation (SNPs) specifically associated with the expression of three members of TGF-β pathway, CTGF, SPARC and COL3A1. The uniqueness of this model is that all three genes were included in one model, rather than one gene being examined at a time. A protein might contribute to multiple pathways and this approach allows the identification of important genetic variations linked to multiple genes belonging to the same pathway. In this study, 29 SNPs were identified and 16 of them located in known genes. Exploring the roles of these genes in TGF-β regulation will help elucidate the etiology of SSc, which will in turn help to better manage this complex disease. ^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Ligamiento genético entre variables asociadas a calidad del fruto de tomate y polipéptidos expresados en dos estados de madurez

Los perfiles de polipéptidos proveen información sobre la constitución genética de un individuo y su expresión, y son útiles como marcadores moleculares. El objetivo del trabajo fue detectar ligamiento entre los perfiles de polipéptidos del pericarpio en dos estados de madurez y caracteres cuantitativos y de calidad de los frutos, analizando 21 genotipos de tomate. Se obtuvieron los perfiles polipéptidos en los estados verde y rojo maduro de frutos de 18 líneas endocriadas recombinantes (RILs, recombinant inbred lines), derivadas de un cruzamiento interespecífico entre el cultivar Caimanta de S. lycopersicum y la entrada LA722 de S. pimpinellifolium, que se incluyeron como testigos experimentales junto a su F1. En estos 21 genotipos se evaluaron también vida poscosecha, peso, firmeza, porcentaje de reflectancia, índice cromático, forma, pH, acidez titulable, contenido de sólidos solubles, espesor de pericarpio y número de lóculos de los frutos. Los perfiles mostraron polimorfismo entre los estados de madurez dentro de un mismo genotipo y entre genotipos para un mismo estado de madurez. Algunos polipéptidos segregaron de forma mendeliana (1:1) y, por análisis de un único punto, mostraron ligamiento con caracteres de calidad del fruto. Se detectaron loci de caracteres cuantitativos (QTLs, quantitative trait loci) asociados a número de lóculos, peso, pH, firmeza y vida poscosecha de los frutos.

Heat stress effects on crop performance and tools for tolerance breeding

Abiotic stress is one of the most common causes of crop deficit and loss and hence an important area of study. Moreover, concerns regarding global climate change over past decades mean the study of different abiotic stresses appears to be essential if its effects are to be mitigated. The current review covers the effects of heat stress on crop performance, the response crops make when subjected to this stress and the development of tools designed to breed for stress tolerant crops. Distinct levels of the problem are considered, from the morphological/anatomical, through the physiological and to the biochemical/molecular. The study of heat shock proteins (HSPs), quantitative trait loci (QTLs) identification and the relationship between metabolomics (OMICS) and heat stress are given special consideration.

The nitrogen costs of photosynthesis in a diatom under current and future pCO2

With each cellular generation, oxygenic photoautotrophs must accumulate abundant protein complexes that mediate light capture, photosynthetic electron transport and carbon fixation. In addition to this net synthesis, oxygenic photoautotrophs must counter the light-dependent photoinactivation of Photosystem II (PSII), using metabolically expensive proteolysis, disassembly, resynthesis and re-assembly of protein subunits. We used growth rates, elemental analyses and protein quantitations to estimate the nitrogen (N) metabolism costs to both accumulate the photosynthetic system and to maintain PSII function in the diatom Thalassiosira pseudonana, growing at two pCO2 levels across a range of light levels. The photosynthetic system contains c. 15-25% of total cellular N. Under low growth light, N (re)cycling through PSII repair is only c. 1% of the cellular N assimilation rate. As growth light increases to inhibitory levels, N metabolite cycling through PSII repair increases to c. 14% of the cellular N assimilation rate. Cells growing under the assumed future 750 ppmv pCO2 show higher growth rates under optimal light, coinciding with a lowered N metabolic cost to maintain photosynthesis, but then suffer greater photoinhibition of growth under excess light, coincident with rising costs to maintain photosynthesis. We predict this quantitative trait response to light will vary across taxa.

Future trends in Animal Breeding due to new genetic tecnologies

The Darwin theory of evolution by natural selection is based on three principles: (a) variation; (b) inheritance; and (c) natural selection. Here, I take these principles as an excuse to review some topics related to the future research prospects in Animal Breeding. With respect to the first principle I describe two forms of variation different from mutation that are becoming increasingly important: variation in copy number and microRNAs. With respect to the second principle I comment on the possible relevance of non-mendelian inheritance, the so-called epigenetic effects, of which the genomic imprinting is the best characterized in domestic species. Regarding selection principle I emphasize the importance of selection for social traits and how this could contribute to both productivity and animal welfare. Finally, I analyse the impact of molecular biology in Animal Breeding, the achievements and limitations of quantitative trait locus and classical marker-assisted selection and the future of genomic selection

Mapping and Introgression of QTL Involved in Fruit Shape Transgressive Segregation into 'Piel de Sapo' Melon (Cucumis melo L.)

A mapping F2 population from the cross ‘Piel de Sapo’ × PI124112 was selectively genotyped to study the genetic control of morphological fruit traits by QTL (Quantitative Trait Loci) analysis. Ten QTL were identified, five for FL (Fruit Length), two for FD (Fruit Diameter) and three for FS (Fruit Shape). At least one robust QTL per character was found, flqs8.1 (LOD = 16.85, R2 = 34%), fdqs12.1 (LOD = 3.47, R2 = 11%) and fsqs8.1 (LOD = 14.85, R2 = 41%). flqs2.1 and fsqs2.1 cosegregate with gene a (andromonoecious), responsible for flower sex determination and with pleiotropic effects on FS. They display a positive additive effect (a) value, so the PI124112 allele causes an increase in FL and FS, producing more elongated fruits. Conversely, the negative a value for flqs8.1 and fsqs8.1 indicates a decrease in FL and FS, what results in rounder fruits, even if PI124112 produces very elongated melons. This is explained by a significant epistatic interaction between fsqs2.1 and fsqs8.1, where the effects of the alleles at locus a are attenuated by the additive PI124112 allele at fsqs8.1. Roundest fruits are produced by homozygous for PI124112 at fsqs8.1 that do not carry any dominant A allele at locus a (PiPiaa). A significant interaction between fsqs8.1 and fsqs12.1 was also detected, with the alleles at fsqs12.1 producing more elongated fruits. fsqs8.1 seems to be allelic to QTL discovered in other populations where the exotic alleles produce elongated fruits. This model has been validated in assays with backcross lines along 3 years and ultimately obtaining a fsqs8.1-NIL (Near Isogenic Line) in ‘Piel de Sapo’ background which yields round melons.

Polyploid formation created unique avenues for response to selection in Gossypium (cotton)

A detailed restriction fragment length polymorphism map was used to determine the chromosomal locations and subgenomic distributions of quantitative trait loci (QTLs) segregating in a cross between cultivars of allotetraploid (AADD) Gossypium hirsutum (“Upland” cotton) and Gossypium barbadense (“Sea Island,” “Pima,” or “Egyptian” cotton) that differ markedly in the quality and quantity of seed epidermal fibers. Most QTLs influencing fiber quality and yield are located on the “D” subgenome, derived from an ancestor that does not produce spinnable fibers. D subgenome QTLs may partly account for the fact that domestication and breeding of tetraploid cottons has resulted in fiber yield and quality levels superior to those achieved by parallel improvement of “A” genome diploid cottons. The merger of two genomes with different evolutionary histories in a common nucleus appears to offer unique avenues for phenotypic response to selection. This may partly compensate for reduction in quantitative variation associated with polyploid formation and be one basis for the prominence of polyploids among extant angiosperms. These findings impel molecular dissection of the roles of divergent subgenomes in quantitative inheritance in many other polyploids and further exploration of both “synthetic” polyploids and exotic diploid genotypes for agriculturally useful variation.

Importance of epistasis as the genetic basis of heterosis in an elite rice hybrid

The genetic basis of heterosis was investigated in an elite rice hybrid by using a molecular linkage map with 150 segregating loci covering the entire rice genome. Data for yield and three traits that were components of yield were collected over 2 years from replicated field trials of 250 F2:3 families. Genotypic variations explained from about 50% to more than 80% of the total variation. Interactions between genotypes and years were small compared with the main effects. A total of 32 quantitative trait loci (QTLs) were detected for the four traits; 12 were observed in both years and the remaining 20 were detected in only one year. Overdominance was observed for most of the QTLs for yield and also for a few QTLs for the component traits. Correlations between marker heterozygosity and trait expression were low, indicating that the overall heterozygosity made little contribution to heterosis. Digenic interactions, including additive by additive, additive by dominance, and dominance by dominance, were frequent and widespread in this population. The interactions involved large numbers of marker loci, most of which individually were not detectable on single-locus basis; many interactions among loci were detected in both years. The results provide strong evidence that epistasis plays a major role as the genetic basis of heterosis.

Interleukin 9: A candidate gene for asthma

Asthma is a complex heritable inflammatory disorder of the airways associated with clinical signs of atopy and bronchial hyperresponsiveness. Recent studies localized a major gene for asthma to chromosome 5q31-q33 in humans. Thus, this segment of the genome represents a candidate region for genes that determine susceptibility to bronchial hyperresponsiveness and atopy in animal models. Homologs of candidate genes on human chromosome 5q31-q33 are found in four regions in the mouse genome, two on chromosome 18, and one each on chromosomes 11 and 13. We assessed bronchial responsiveness as a quantitative trait in mice and found it linked to chromosome 13. Interleukin 9 (IL-9) is located in the linked region and was analyzed as a gene candidate. The expression of IL-9 was markedly reduced in bronchial hyporesponsive mice, and the level of expression was determined by sequences within the qualitative trait locus (QTL). These data suggest a role for IL-9 in the complex pathogenesis of bronchial hyperresponsiveness as a risk factor for asthma.

Localization of non-Mhc collagen-induced arthritis susceptibility loci in DBA/1j mice

One approach to understanding common human diseases is to determine the genetic defects responsible for similar diseases in animal models and place those defective genes in their corresponding biochemical pathways. Our laboratory is working with an animal model for human rheumatoid arthritis called collagen-induced arthritis (CIA). We are particularly interested in determining the location of disease-predisposing loci. To that end, we performed experiments to localize susceptibility loci for CIA in an F2 cross between the highly susceptible mouse strain DBA/1j and the highly resistant mouse strain SWR/j. Specifically, a quantitative trait locus analysis was performed to localize regions of the mouse genome responsible for susceptibility/severity to CIA. One susceptibility locus, Cia1 in the major histocompatibility locus, had been identified previously. Two additional loci were detected in our analysis that contribute to CIA severity (Cia2, Cia3) on chromosomes 2 and 6. A third locus was detected that contributes to the age of onset of the disease. This locus (Cia4) was located on chromosome 2 and was linked to the same region as Cia2. Determining the identity of these loci may provide insights into the etiology of human rheumatoid arthritis.

Adaptive evolution via a major gene effect: Paedomorphosis in the Mexican axolotl

Although adaptive evolution is thought to depend primarily on mutations of small effect, major gene effects may underlie many of the important differences observed among species in nature. The Mexican axolotl (Ambystoma mexicanum) has a derived mode of development that is characterized by metamorphic failure (paedomorphosis), an adaptation for an entirely aquatic life cycle. By using an interspecific crossing design and genetic linkage analysis, a major quantitative trait locus for expression of metamorphosis was identified in a local map of amplified fragment length polymorphisms. These data are consistent with a major gene hypothesis for the evolution of paedomorphosis in A. mexicanum.

Evolution of genome–phenome diversity under environmental stress

The genomic era revolutionized evolutionary biology. The enigma of genotypic-phenotypic diversity and biodiversity evolution of genes, genomes, phenomes, and biomes, reviewed here, was central in the research program of the Institute of Evolution, University of Haifa, since 1975. We explored the following questions. (i) How much of the genomic and phenomic diversity in nature is adaptive and processed by natural selection? (ii) What is the origin and evolution of adaptation and speciation processes under spatiotemporal variables and stressful macrogeographic and microgeographic environments? We advanced ecological genetics into ecological genomics and analyzed globally ecological, demographic, and life history variables in 1,200 diverse species across life, thousands of populations, and tens of thousands of individuals tested mostly for allozyme and partly for DNA diversity. Likewise, we tested thermal, chemical, climatic, and biotic stresses in several model organisms. Recently, we introduced genetic maps and quantitative trait loci to elucidate the genetic basis of adaptation and speciation. The genome–phenome holistic model was deciphered by the global regressive, progressive, and convergent evolution of subterranean mammals. Our results indicate abundant genotypic and phenotypic diversity in nature. The organization and evolution of molecular and organismal diversity in nature at global, regional, and local scales are nonrandom and structured; display regularities across life; and are positively correlated with, and partly predictable by, abiotic and biotic environmental heterogeneity and stress. Biodiversity evolution, even in small isolated populations, is primarily driven by natural selection, including diversifying, balancing, cyclical, and purifying selective regimes, interacting with, but ultimately overriding, the effects of mutation, migration, and stochasticity.