Complete genome of a Puumala virus strain from Central Europe

Puumala virus (PUUV) is one of the predominant hantavirus species in Europe causing mild to moderate cases of haemorrhagic fever with renal syndrome. Parts of Lower Saxony in north-western Germany are endemic for PUUV infections. In this study, the complete PUUV genome sequence of a bank vole-derived tissue sample from the 2007 outbreak was determined by a combined primer-walking and RNA ligation strategy. The S, M and L genome segments were 1,828, 3,680 and 6,550 nucleotides in length, respectively. Sliding-window analyses of the nucleotide sequences of all available complete PUUV genomes indicated a non-homogenous distribution of variability with hypervariable regions located at the 3′-ends of the S and M segments. The overall similarity of the coding genome regions to the other PUUV strains ranged between 80.1 and 84.7 % at the level of the nucleotide sequence and between 89.5 and 98.1 % for the deduced amino acid sequences. In comparison to the phylogenetic trees of the complete coding sequences, trees based on partial segments revealed a general drop in phylogenetic support and a lower resolution. The Astrup strain S and M segment sequences showed the highest similarity to sequences of strains from geographically close sites in the Osnabrück Hills region. In conclusion, a primer-walking-mediated strategy resulted in the determination of the first complete nucleotide sequence of a PUUV strain from Central Europe. Different levels of variability along the genome provide the opportunity to choose regions for analyses according to the particular research question, e.g., large-scale phylogenetics or within-host evolution.

Complete Genome Sequence of KPC-3- and CTX-M-15-Producing Klebsiella pneumoniae Sequence Type 307.

Klebsiella pneumoniaesequence type (ST) 307, carryingblaKPC-3,blaCTX-M-15,blaOXA-1,aac(6')-Ib-cr, andqnrB1 genes, is replacing the predominant hyperepidemic ST258 clone in Italy. Whole-genome and complete plasmid sequencing of one ST307 strain was performed and new features were identified.

Genome characterization, prevalence and distribution of a Macula-like virus from Apis mellifera and Varroa destructor

Around 14 distinct virus species-complexes have been detected in honeybees, each with one or more strains or sub-species. Here we present the initial characterization of an entirely new virus species-complex discovered in honeybee (Apis mellifera L.) and varroa mite (Varroa destructor) samples from Europe and the USA. The virus has a naturally poly-adenylated RNA genome of about 6500 nucleotides with a genome organization and sequence similar to the Tymoviridae (Tymovirales; Tymoviridae), a predominantly plant-infecting virus family. Literature and laboratory analyses indicated that the virus had not previously been described. The virus is very common in French apiaries, mirroring the results from an extensive Belgian survey, but could not be detected in equally-extensive Swedish and Norwegian bee disease surveys. The virus appears to be closely linked to varroa, with the highest prevalence found in varroa samples and a clear seasonal distribution peaking in autumn, coinciding with the natural varroa population development. Sub-genomic RNA analyses show that bees are definite hosts, while varroa is a possible host and likely vector. The tentative name of Bee Macula-like virus (BeeMLV) is therefore proposed. A second, distantly related Tymoviridae-like virus was also discovered in varroa transcriptomes, tentatively named Varroa Tymo-like virus (VTLV).

The Apis mellifera filamentous virus genome

A complete reference genome of the Apis mellifera Filamentous virus (AmFV) was determined using Illumina Hiseq sequencing. The AmFV genome is a double stranded DNA molecule of approximately 498,500 nucleotides with a GC content of 50.8%. It encompasses 247 non-overlapping open reading frames (ORFs), equally distributed on both strands, which cover 65% of the genome. While most of the ORFs lacked threshold sequence alignments to reference protein databases, twenty-eight were found to display significant homologies with proteins present in other large double stranded DNA viruses. Remarkably, 13 ORFs had strong similarity with typical baculovirus domains such as PIFs (per os infectivity factor genes: pif-1, pif-2, pif-3 and p74) and BRO (Baculovirus Repeated Open Reading Frame). The putative AmFV DNA polymerase is of type B, but is only distantly related to those of the baculoviruses. The ORFs encoding proteins involved in nucleotide metabolism had the highest percent identity to viral proteins in GenBank. Other notable features include the presence of several collagen-like, chitin-binding, kinesin and pacifastin domains. Due to the large size of the AmFV genome and the inconsistent affiliation with other large double stranded DNA virus families infecting invertebrates, AmFV may belong to a new virus family.

TAC102 Is a Novel Component of the Mitochondrial Genome Segregation Machinery in Trypanosomes

Trypanosomes show an intriguing organization of their mitochondrial DNA into a catenated network, the kinetoplast DNA (kDNA). While more than 30 proteins involved in kDNA replication have been described, only few components of kDNA segregation machinery are currently known. Electron microscopy studies identified a high-order structure, the tripartite attachment complex (TAC), linking the basal body of the flagellum via the mitochondrial membranes to the kDNA. Here we describe TAC102, a novel core component of the TAC, which is essential for proper kDNA segregation during cell division. Loss of TAC102 leads to mitochondrial genome missegregation but has no impact on proper organelle biogenesis and segregation. The protein is present throughout the cell cycle and is assembled into the newly developing TAC only after the pro-basal body has matured indicating a hierarchy in the assembly process. Furthermore, we provide evidence that the TAC is replicated de novo rather than using a semi-conservative mechanism. Lastly, we demonstrate that TAC102 lacks an N-terminal mitochondrial targeting sequence and requires sequences in the C-terminal part of the protein for its proper localization.

RNA-Guided Genome Editing

Ciliates have evolved highly complex and intricately controlled pathways to ensure the precise and complete removal of all genomic sequences not required for vegetative growth. At the same time, they retain a reference copy of all their genetic information for future generations. This chapter describes how different ciliates use RNA-mediated DNA comparison processes to form new somatic nuclei from germline nuclei. While these processes vary in their precise mechanisms, they all use RNA to target genomic DNA sequences—either for retention or elimination. They also all consist of more than one individual pathway acting cooperatively—the two subsets of small RNAs in Paramecium and the guide RNAs and Piwi-interacting RNAs in Oxytricha—to ensure a strong belt-and-braces approach to consistent and precise somatic nucleus development. Nonetheless, this genome comparison approach to somatic nucleus development provides an elegant method for trans-generational environmental adaptation. Conceptually, it is easy to imagine how somatic changes that occur during vegetative growth could be transferred to meiotic offspring, while an unaltered germline genome is retained. Further research in this area will have far-reaching implications for the trans-generational adaptation of more distantly related eukaryotes, such as humans.

Familial aggregation, candidate genes and genome scans: Analyzing the role of genetics in stroke

The genetic etiology of stroke likely reflects the influence of multiple loci with small effects, each modulating different pathophysiological processes. This research project utilized three analytical strategies to address the paucity of information related to the identification and characterization of genetic variation associated with stroke in the general population. ^ First, the general contribution of familial factors to stroke susceptibility was evaluated in a population-based sample of unrelated individuals. Increased risk of subclinical cerebral infarction was observed among individuals with a positive parental history of stroke. This association did not appear to be mediated by established stroke risk factors, specifically blood pressure levels or hypertension status. ^ The need to identify specific gene variation associated with stroke in the general population was addressed by evaluating seven candidate gene polymorphisms in a population-based sample of unrelated individuals. Three polymorphisms were significantly associated with increased subclinical cerebral infarction or incident clinical ischemic stroke risk. These relationships include the G-protein β3 subunit 825C/T polymorphism and clinical stroke in Whites, the lipoprotein lipase S/X447 polymorphism and subclinical and clinical stroke in men, and the angiotensin I-converting enzyme Ins/Del polymorphism and subclinical stroke in White men. These associations did not appear to be obfuscated by the stroke risk factors adjusted for in the analysis models specifically blood pressure levels or anti-hypertensive medication use. ^ The final research strategy considered, on a genome-wide scale, the idea that genetic variation may contribute to the occurrence of hypertension or stroke through a common etiologic pathway. Genomic regions were identified for which significant evidence of heterogeneity was observed among hypertensive sibpairs stratified by family history of stroke information. Regions identified on chromosome 15 in African Americans, and chromosome 13 in Whites and African Americans, suggest the presence of genes influencing hypertension and stroke susceptibility. ^ Insight into the role of genetics in stroke is useful for the potential early identification of individuals at increased risk for stroke and improved understanding of the etiology of the disease. The ultimate goal of these endeavors is to guide the development of therapeutic intervention and informed prevention to provide a lasting and positive impact on public health. ^

GCN5 maintains genome stability during development

The histone acetyltransferase, GCN5, is essential for survival of mice during embryogenesis. GCN5 null embryos die early during development due to increased apoptosis. We have demonstrated that the increased apoptosis in associated with increased p53 protein levels. Loss of p53 rescues the embryonic apoptosis in the GCN5 null embryos. These results raised the question of what molecular trigger leads to p53 stabilization and cell death in the absence of GCN5. p53 is generally referred to as the gatekeeper of the cell, monitoring cellular responses to DNA damage, genotoxic stress, and other unfavorable conditions in the cell. Therefore, we examined individual cells in wild type and mutant embryos for gross chromosomal aberrations that might trigger a genome integrity checkpoint. Karyotype analysis indicates that approximately 30% of the cells in an E8.5 GCN5 null embryo display chromosomal aberrations, predominantly chromosomal end adhesions and associations. In wild type E8.5 embryos, only 6% of the cells have chromosomal aberrations. Recent data using telomeric FISH demonstrates that cells from GCN5 null embryos have a decreased telomeric signal. Telomere maintenance is essential for maintaining genome integrity. Telomeric defects are associated with loss of chromosomes and chromosomal rearrangements that can lead to detrimental gene fusions involved in many types of cancers. Little is known about the chromatin structures present near the telomeric ends, or whether any of the telomere-associated proteins are subject to post-translational modification such as acetylation. Our results are the first data to demonstrate the involvement of a histone acetyltransferase, GCN5, in maintaining genome integrity through telomere maintenance and/or capping. ^

Whole genome analysis of Fusobacterium nucleatum, Rickettsia typhi and Francisella tularensis

The genomes of Fusobacterium nucleatum subspecies polymorphum strain ATCC 10953, Rickettsia typhi strain Wilmington, and Francisella tularensis subspecies holarctica strain OSU18 were sequenced, annotated, and analyzed. Each genome was then compared to the sequenced genomes of closely related bacteria. The genome of F. nucleatum ATCC 10953 was compared to two additional F. nucleatum subspecies, subspecies nucleatum and subspecies vincentii. This analysis revealed substantial evidence of horizontal gene transfer along with considerable genetic diversity within the species of F. nucleatum. R. typhi was compared to R. prowazekii and R. conorii. This analysis uncovered a hotspot for chromosomal rearrangements in the Spotted Fever Group but not the Typhus Group Rickettsia and revealed the close genetic relationship between the Typhus Group rickettsial species. F. tularensis OSU18 was compared to two additional F. tularensis strains. These comparisons uncovered significant chromosomal rearrangements between F. tularensis subspecies due to recombination between insertion sequence elements. ^

Identification of genetic variation influencing apolipoprotein E levels: Follow-up of genome-wide linkage scans in the GENOA study

Apolipoprotein E (ApoE) plays a major role in the metabolism of high density and low density lipoproteins (HDL and LDL). Its common protein isoforms (E2, E3, E4) are risk factors for coronary artery disease (CAD) and explain between 16 to 23% of the inter-individual variation in plasma apoE levels. Linkage analysis has been completed for plasma apoE levels in the GENOA study (Genetic Epidemiology Network of Atherosclerosis). After stratification of the population by lipoprotein levels and body mass index (BMI) to create more homogeneity with regard to biological context for apoE levels, Hispanic families showed significant linkage on chromosome 17q for two strata (LOD=2.93 at 104 cM for a low cholesterol group, LOD=3.04 at 111 cM for a low cholesterol, high HDLC group). Replication of 17q linkage was observed for apoB and apoE levels in the unstratified Hispanic and African-American populations, and for apoE levels in African-American families. Replication of this 17q linkage in different populations and strata provides strong support for the presence of gene(s) in this region with significant roles in the determination of inter-individual variation in plasma apoE levels. Through a positional and functional candidate gene approach, ten genes were identified in the 17q linked region, and 62 polymorphisms in these genes were genotyped in the GENOA families. Association analysis was performed with FBAT, GEE, and variance-component based tests followed by conditional linkage analysis. Association studies with partial coverage of TagSNPs in the gene coding for apolipoprotein H (APOH) were performed, and significant results were found for 2 SNPs (APOH_20951 and APOH_05407) in the Hispanic low cholesterol strata accounting for 3.49% of the inter-individual variation in plasma apoE levels. Among the other candidate genes, we identified a haplotype block in the ACE1 gene that contains two major haplotypes associated with apoE levels as well as total cholesterol, apoB and LDLC levels in the unstratified Hispanic population. Identifying genes responsible for the remaining 60% of inter-individual variation in plasma apoE level, will yield new insights into the understanding of genetic interactions involved in the lipid metabolism, and a more precise understanding of the risk factors leading to CAD. ^

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Identification of diabetic retinopathy genes through a genome-wide association study among Mexican-Americans from Starr County, Texas

To identify genetic susceptibility loci for severe diabetic retinopathy, 286 Mexican-Americans with type 2 diabetes from Starr County, Texas completed detailed physical and ophthalmologic examinations including fundus photography for diabetic retinopathy grading. 103 individuals with moderate-to-severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy were defined as cases for this study. DNA samples extracted from study subjects were genotyped using the Affymetrix GeneChip® Human Mapping 100K Set, which includes 116,204 single nucleotide polymorphisms (SNPs) across the whole genome. Single-marker allelic tests and 2- to 8-SNP sliding-window Haplotype Trend Regression implemented in HelixTreeTM were first performed with these direct genotypes to identify genes/regions contributing to the risk of severe diabetic retinopathy. An additional 1,885,781 HapMap Phase II SNPs were imputed from the direct genotypes to expand the genomic coverage for a more detailed exploration of genetic susceptibility to diabetic retinopathy. The average estimated allelic dosage and imputed genotypes with the highest posterior probabilities were subsequently analyzed for associations using logistic regression and Fisher's Exact allelic tests, respectively. To move beyond these SNP-based approaches, 104,572 directly genotyped and 333,375 well-imputed SNPs were used to construct genetic distance matrices based on 262 retinopathy candidate genes and their 112 related biological pathways. Multivariate distance matrix regression was then used to test hypotheses with genes and pathways as the units of inference in the context of susceptibility to diabetic retinopathy. This study provides a framework for genome-wide association analyses, and implicated several genes involved in the regulation of oxidative stress, inflammatory processes, histidine metabolism, and pancreatic cancer pathways associated with severe diabetic retinopathy. Many of these loci have not previously been implicated in either diabetic retinopathy or diabetes. In summary, CDC73, IL12RB2, and SULF1 had the best evidence as candidates to influence diabetic retinopathy, possibly through novel biological mechanisms related to VEGF-mediated signaling pathway or inflammatory processes. While this study uncovered some genes for diabetic retinopathy, a comprehensive picture of the genetic architecture of diabetic retinopathy has not yet been achieved. Once fully understood, the genetics and biology of diabetic retinopathy will contribute to better strategies for diagnosis, treatment and prevention of this disease.^

Genome-wide gene-gene interaction analysis for cardiovascular disease

Numerous studies have been carried out to try to better understand the genetic predisposition for cardiovascular disease. Although it is widely believed that multifactorial diseases such as cardiovascular disease is the result from effects of many genes which working alone or interact with other genes, most genetic studies have been focused on identifying of cardiovascular disease susceptibility genes and usually ignore the effects of gene-gene interactions in the analysis. The current study applies a novel linkage disequilibrium based statistic for testing interactions between two linked loci using data from a genome-wide study of cardiovascular disease. A total of 53,394 single nucleotide polymorphisms (SNPs) are tested for pair-wise interactions, and 8,644 interactions are found to be significant with p-values less than 3.5×10-11. Results indicate that known cardiovascular disease susceptibility genes tend not to have many significantly interactions. One SNP in the CACNG1 (calcium channel, voltage-dependent, gamma subunit 1) gene and one SNP in the IL3RA (interleukin 3 receptor, alpha) gene are found to have the most significant pair-wise interactions. Findings from the current study should be replicated in other independent cohort to eliminate potential false positive results.^

Genome-wide algorithm for detecting CNV associations with diseases

SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). The quality of the inferences about copy number can be affected by many factors including batch effects, DNA sample preparation, signal processing, and analytical approach. Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP genotyping data. However, these algorithms lack specificity to detect small CNVs due to the high false positive rate when calling CNVs based on the intensity values. Association tests based on detected CNVs therefore lack power even if the CNVs affecting disease risk are common. In this research, by combining an existing Hidden Markov Model (HMM) and the logistic regression model, a new genome-wide logistic regression algorithm was developed to detect CNV associations with diseases. We showed that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than an existing popular algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.^

Genome-wide association study of LDL-cholesterol over ApoB ratio

Genome-Wide Association Study analytical (GWAS) methods were applied in a large biracial sample of individuals to investigate variation across the genome for its association with a surrogate low-density lipoprotein (LDL) particle size phenotype, the ratio of LDL-cholesterol level over ApoB level. Genotyping was performed on the Affymetrix 6.0 GeneChip with approximately one million single nucleotide polymorphisms (SNPs). The ratio of LDL cholesterol to ApoB was calculated, and association tests used multivariable linear regression analysis with an additive genetic model after adjustment for the covariates sex, age and BMI. Association tests were performed separately in African Americans and Caucasians. There were 9,562 qualified individuals in the Caucasian group and 3,015 qualified individuals in the African American group. Overall, in Caucasians two statistically significant loci were identified as being associated with the ratio of LDL-cholesterol over ApoB: rs10488699 (p<5 x10-8, 11q23.3 near BUD13) and the SNP rs964184 (p<5 x10-8 11q23.3 near ZNF259). We also found rs12286037 ((p<4x10-7) (11q23.3) near APOA5/A4/C3/A1 with suggestive associate in the Caucasian sample. In exploratory analyses, a difference in the pattern of association between individuals taking and not taking LDL-cholesterol lowering medications was observed. Individuals who were not taking medications had smaller p-value than those taking medication. In the African-American group, there were no significant (p<5x10-8) or suggestive associations (p<4x10-7) with the ratio of LDL-cholesterol over ApoB after adjusting for age, BMI, and sex and comparing individuals with and without LDL-cholesterol lowering medication. Conclusions: There were significant and suggestive associations between SNP genotype and the ratio of LDL-cholesterol to ApoB in Caucasians, but these associations may be modified by medication treatment.^