Validation of S. Pombe sequence assembly by microarray hybridization

We describe a method to make physical maps of genomes using correlative hybridization patterns of probes to random pools of BACs. We derive thereby an estimated distance between probes, and then use this estimated distance to order probes. To test the method, we used BAC libraries from Schizzosaccharomyces pombe. We compared our data to the known sequence assembly, in order to assess accuracy. We demonstrate a small number of significant discrepancies between our method and the map derived by sequence assembly. Some of these discrepancies may arise because genome order within a population is not stable; imposing a linear order on a population may not be biologically meaningful

Gene expression in large pedigrees: analytic approaches.

BackgroundWe currently have the ability to quantify transcript abundance of messenger RNA (mRNA), genome-wide, using microarray technologies. Analyzing genotype, phenotype and expression data from 20 pedigrees, the members of our Genetic Analysis Workshop (GAW) 19 gene expression group published 9 papers, tackling some timely and important problems and questions. To study the complexity and interrelationships of genetics and gene expression, we used established statistical tools, developed newer statistical tools, and developed and applied extensions to these tools.MethodsTo study gene expression correlations in the pedigree members (without incorporating genotype or trait data into the analysis), 2 papers used principal components analysis, weighted gene coexpression network analysis, meta-analyses, gene enrichment analyses, and linear mixed models. To explore the relationship between genetics and gene expression, 2 papers studied expression quantitative trait locus allelic heterogeneity through conditional association analyses, and epistasis through interaction analyses. A third paper assessed the feasibility of applying allele-specific binding to filter potential regulatory single-nucleotide polymorphisms (SNPs). Analytic approaches included linear mixed models based on measured genotypes in pedigrees, permutation tests, and covariance kernels. To incorporate both genotype and phenotype data with gene expression, 4 groups employed linear mixed models, nonparametric weighted U statistics, structural equation modeling, Bayesian unified frameworks, and multiple regression.Results and discussionRegarding the analysis of pedigree data, we found that gene expression is familial, indicating that at least 1 factor for pedigree membership or multiple factors for the degree of relationship should be included in analyses, and we developed a method to adjust for familiality prior to conducting weighted co-expression gene network analysis. For SNP association and conditional analyses, we found FaST-LMM (Factored Spectrally Transformed Linear Mixed Model) and SOLAR-MGA (Sequential Oligogenic Linkage Analysis Routines -Major Gene Analysis) have similar type 1 and type 2 errors and can be used almost interchangeably. To improve the power and precision of association tests, prior knowledge of DNase-I hypersensitivity sites or other relevant biological annotations can be incorporated into the analyses. On a biological level, eQTL (expression quantitative trait loci) are genetically complex, exhibiting both allelic heterogeneity and epistasis. Including both genotype and phenotype data together with measurements of gene expression was found to be generally advantageous in terms of generating improved levels of significance and in providing more interpretable biological models.ConclusionsPedigrees can be used to conduct analyses of and enhance gene expression studies

Telomeres cluster de novo before the initiation of synapsis: a three-dimensional spatial analysis of telomere positions before and during meiotic prophase.

We have analyzed the progressive changes in the spatial distribution of telomeres during meiosis using three-dimensional, high resolution fluorescence microscopy. Fixed meiotic cells of maize (Zea mays L.) were subjected to in situ hybridization under conditions that preserved chromosome structure, allowing identification of stage-dependent changes in telomere arrangements. We found that nuclei at the last somatic prophase before meiosis exhibit a nonrandom, polarized chromosome organization resulting in a loose grouping of telomeres. Quantitative measurements on the spatial arrangements of telomeres revealed that, as cells passed through premeiotic interphase and into leptotene, there was an increase in the frequency of large telomere-to-telomere distances and a decrease in the bias toward peripheral localization of telomeres. By leptotene, there was no obvious evidence of telomere grouping, and the large, singular nucleolus was internally located, nearly concentric with the nucleus. At the end of leptotene, telomeres clustered de novo at the nuclear periphery, coincident with a displacement of the nucleolus to one side. The telomere cluster persisted throughout zygotene and into early pachytene. The nucleolus was adjacent to the cluster at zygotene. At the pachytene stage, telomeres rearranged again by dispersing throughout the nuclear periphery. The stage-dependent changes in telomere arrangements are suggestive of specific, active telomere-associated motility processes with meiotic functions. Thus, the formation of the cluster itself is an early event in the nuclear reorganizations associated with meiosis and may reflect a control point in the initiation of synapsis or crossing over

Common DNA sequence variation influences 3-dimensional conformation of the human genome.

BACKGROUND:The 3-dimensional (3D) conformation of chromatin inside the nucleus is integral to a variety of nuclear processes including transcriptional regulation, DNA replication, and DNA damage repair. Aberrations in 3D chromatin conformation have been implicated in developmental abnormalities and cancer. Despite the importance of 3D chromatin conformation to cellular function and human health, little is known about how 3D chromatin conformation varies in the human population, or whether DNA sequence variation between individuals influences 3D chromatin conformation. RESULTS:To address these questions, we perform Hi-C on lymphoblastoid cell lines from 20 individuals. We identify thousands of regions across the genome where 3D chromatin conformation varies between individuals and find that this variation is often accompanied by variation in gene expression, histone modifications, and transcription factor binding. Moreover, we find that DNA sequence variation influences several features of 3D chromatin conformation including loop strength, contact insulation, contact directionality, and density of local cis contacts. We map hundreds of quantitative trait loci associated with 3D chromatin features and find evidence that some of these same variants are associated at modest levels with other molecular phenotypes as well as complex disease risk. CONCLUSION:Our results demonstrate that common DNA sequence variants can influence 3D chromatin conformation, pointing to a more pervasive role for 3D chromatin conformation in human phenotypic variation than previously recognized

Computational Approaches for the Analysis of Chromosome Conformation Capture Data and Their Application to Study Long-Range Gene Regulation: A Dissertation

Over the last decade, development and application of a set of molecular genomic approaches based on the chromosome conformation capture method (3C), combined with increasingly powerful imaging approaches have enabled high resolution and genome-wide analysis of the spatial organization of chromosomes. The aim of this thesis is two-fold; 1), to provide guidelines for analyzing and interpreting data obtained from genome-wide 3C methods such as Hi-C and 3C-seq and 2), to leverage the 3C technology to solve genome function, structure, assembly, development and dosage problems across a broad range of organisms and disease models. First, through the introduction of cWorld, a toolkit for manipulating genome structure data, I accelerate the pace at which *C experiments can be performed, analyzed and biological insights inferred. Next I discuss a set of practical guidelines one should consider while planning an experiment to study the structure of the genome, a simple workflow for data processing unique to *C data and a set of considerations one should be aware of while attempting to gain insights from the data. Next, I apply these guidelines and leverage the cWorld toolkit in the context of two dosage compensation systems. The first is a worm condensin mutant which shows a reduction in dosage compensation in the hermaphrodite X chromosomes. The second is an allele-specific study consisting of genome wide Hi-C, RNA-Seq and ATAC-Seq which can measure the state of the active (Xa) and inactive (Xi) X chromosome. Finally I turn to studying specific gene – enhancer looping interactions across a panel of ENCODE cell-lines. These studies, when taken together, further our understanding of how genome structure relates to genome function

Late Pleistocene human genome suggests a local origin for the first farmers of central Anatolia

Anatolia was home to some of the earliest farming communities. It has been long debated whether a migration of farming groups introduced agriculture to central Anatolia. Here, we report the first genome-wide data from a 15,000-year-old Anatolian hunter-gatherer and from seven Anatolian and Levantine early farmers. We find high genetic continuity (~80–90%) between the hunter-gatherers and early farmers of Anatolia and detect two distinct incoming ancestries: an early Iranian/Caucasus related one and a later one linked to the ancient Levant. Finally, we observe a genetic link between southern Europe and the Near East predating 15,000 years ago. Our results suggest a limited role of human migration in the emergence of agriculture in central Anatolia

Unbiased analysis of the impact of micropatterned biomaterials on macrophage behaviour provides insights beyond pre-defined polarisation states

Macrophages are master regulators of immune responses towards implanted biomaterials. The activation state adopted by macrophages in response to biomaterials determines their own phenotype and function as well as those of other resident and infiltrating immune and non-immune cells in the area. A wide spectrum of macrophage activation states exists, with M1 (pro-inflammatory) and M2 (anti-inflammatory) representing either ends of the spectrum. In biomaterials research, cellinstructive surfaces that favour or induce M2 macrophages have been considered as beneficial due to the anti-inflammatory and pro-regenerative properties of these cells. In this study, we used a gelatin methacryloyl (GelMA) hydrogel platform to determine whether micropatterned surfaces can modulate the phenotype and function of human macrophages. The effect of microgrooves/ridges and micropillars on macrophage phenotype, function, and gene expression profile were assessed using conventional methods (morphology, cytokine profile, surface marker expression, phagocytosis) and gene microarrays. Our results demonstrated that micropatterns did induce distinct gene expression profiles in human macrophages cultured on microgrooves/ridges and micropillars. Significant changes were observed in genes related to primary metabolic processes such as transcription, translation, protein trafficking, DNA repair and cell survival. However, interestingly conventional phenotyping methods, relying on surface marker expression and cytokine profile, were not able to distinguish between the different conditions, and indicated no clear shift in cell activation towards an M1 or M2 phenotypes. This highlights the limitations of studying the effect of different physicochemical conditions on macrophages by solely relying on conventional markers that are primarily developed to differentiate between cytokine polarised M1 and M2 macrophages. We therefore, propose the adoption of unbiased screening methods in determining macrophage responses to biomaterials. Our data clearly shows that the exclusive use of conventional markers and methods for determining macrophage activation status could lead to missed opportunities for understanding and exploiting macrophage responses to biomaterials