Whole-genome comparison between Photorhabdus strains to identify genomic regions involved in the specificity of nematode interaction

The bacterium Photorhabdus establishes a highly specific association with Heterorhabditis, its nematode host. Photorhabdus strains associated with Heterorhabditis bacteriophora or Heterorhabditis megidis were compared using a Photorhabdus DNA microarray. We describe 31 regions belonging to the Photorhabdus flexible gene pool. Distribution analysis of regions among the Photorhabdus genus identified loci possibly involved in nematode specificity.     

Use of single nucleotide polymorphisms and haplotypes to identify genomic regions associated with protein content and water-soluble protein content in soybean

Key message

Four major SPC-specific loci were identified, and these accounted for 8.5–15.1 % of the phenotypic variation, thus explaining why certain soybean varieties have a high PC but a low SPC.

Abstract

Water-soluble protein content (SPC) is a critical factor in both food quality and the production of isolated soybean proteins. However, few data are available regarding the genetic control and the mechanisms contributing to elevated SPC. In this study, a soybean collection of 192 accessions from a wide geographic range was used to identify genomic regions associated with soybean protein content (PC) and SPC using an association mapping approach employing 1,536 SNP makers and 232 haplotypes. The diverse panel revealed a large genetic variation in PC and SPC. Association mapping was performed using three methods to minimize false-positive associations. This resulted in 4/8 SNPs and 3/6 haplotypes that were significantly associated with soybean PC/SPC in two or more environments based on the mixed model. An SNP that was highly significantly associated with PC, BARC-021267-04016, was localized 0.28 cM away from a published glycinin gene, G7, and was detected across all four environments. Four major SPC-specific loci, BARC-029149-06088, BARC-018023-02499, BARC-041663-08059 and haplotype 15 (hp15), were stably identified on chromosomes five and eight and explained 8.5–15.1 % of the phenotypic variation. Moreover, a glutelin type-B 2-like gene was identified on chromosome eight and may be related to soybean protein solubility. These markers, which are located in previously reported QTL, reconfirmed previous findings and may be important targets for the identification of protein-related genes. These novel SNPs and haplotypes are important for further understanding the genetic basis of PC and SPC. In addition, by comparing the correlation and genetic loci between PC and SPC, we provide new insights into why certain soybean varieties have a high protein content but a low SPC.     

CRISPR-Cas9 enrichment,a new strategy in microbial metagenomics to investigate complex genomic regions: The case of an environmental integron

Environmental integrons are ubiquitous in natural microbial communities, but they are mostly uncharacterized and their role remains elusive. Thus far, research has been hindered by methodological limitations. Here, we successfully used an innovative approach combining CRISPR-Cas9 enrichment with long-read nanopore sequencing to target, in a complex microbial community, a putative adaptive environmental integron, InOPS, and to unravel its complete structure and genetic context. A contig of 20 kb was recovered containing the complete integron from the microbial metagenome of oil-contaminated coastal sediments. InOPS exhibited typical integron features. The integrase, closely related to integrases of marine Desulfobacterota, possessed all the elements of a functional integron integrase. The gene cassettes harboured mostly unknown functions hampering inferences about their ecological importance. Moreover, the putative InOPS host, likely a hydrocarbonoclastic marine bacteria, raises questions as to the adaptive potential of InOPS in response to oil contamination. Finally, several mobile genetic elements were intertwined with InOPS highlighting likely genomic plasticity, and providing a source of genetic novelty. This case study showed the power of CRISPR-Cas9 enrichment to elucidate the structure and context of specific DNA regions for which only a short sequence is known. This method is a new tool for environmental microbiologists working with complex microbial communities to target low abundant, large or repetitive genetic structures that are difficult to obtain by classical metagenomics. More precisely, here, it offers new perspectives to comprehensively assess the eco-evolutionary significance of environmental integrons.     

Regional enrichment of local assemblages is robust to variation in local productivity, abiotic gradients, and heterogeneity

Theory predicts that the effects of regional richness on the richness of local communities may depend on the productivity, resource availability, and/or heterogeneity of local sites. Using the wetland plant communities of 50 independent streams as 'regions', we tested whether: (1) local richness in 1-m² quadrats and 50-m stream segments was positively related to regional richness, even after environmental influences were considered; and (2) the effect of regional richness would interact with the effects of biomass, soil moisture, and/or heterogeneity on local richness. In models that explained up to 88% of variation in local richness, we found that richness at both local scales was positively related to regional richness, and that regional richness did not interact with any of the environmental gradients that also shaped local richness. We conclude that species availability from the regional pool may consistently enrich local communities, even while other constraints on local richness operate.     

Standing genomic variation within coding and regulatory regions contributes to the adaptive capacity to climate in a foundation tree species

Global climate is rapidly changing, and the ability for tree species to adapt is dependent on standing genomic variation; however, the distribution and abundance of functional and adaptive variants are poorly understood in natural systems. We test key hypotheses regarding the genetics of adaptive variation in a foundation tree: genomic variation is associated with climate, and genomic variation is more likely to be associated with temperature than precipitation or aridity. To test these hypotheses, we used 9,593 independent, genomic single‐nucleotide polymorphisms (SNPs) from 270 individuals sampled from Corymbia calophylla's entire distribution in south‐western Western Australia, spanning orthogonal temperature and precipitation gradients. Environmental association analyses returned 537 unique SNPs putatively adaptive to climate. We identified SNPs associated with climatic variation (i.e., temperature [458], precipitation [75] and aridity [78]) across the landscape. Of these, 78 SNPs were nonsynonymous (NS), while 26 SNPs were found within gene regulatory regions. The NS and regulatory candidate SNPs associated with temperature explained more deviance (27.35%) than precipitation (5.93%) and aridity (4.77%), suggesting that temperature provides stronger adaptive signals than precipitation. Genes associated with adaptive variants include functions important in stress responses to temperature and precipitation. Patterns of allelic turnover of NS and regulatory SNPs show small patterns of change through climate space with the exception of an aldehyde dehydrogenase gene variant with 80% allelic turnover with temperature. Together, these findings provide evidence for the presence of adaptive variation to climate in a foundation species and provide critical information to guide adaptive management practices.     

Subset clustering of binary sequences, with an application to genomic abnormality data

This article develops a model-based approach to clustering multivariate binary data, in which the attributes that distinguish a cluster from the rest of the population may depend on the cluster being considered. The clustering approach is based on a multivariate Dirichlet process mixture model, which allows for the estimation of the number of clusters, the cluster memberships, and the cluster-specific parameters in a unified way. Such a clustering approach has applications in the analysis of genomic abnormality data, in which the development of different types of tumors may depend on the presence of certain abnormalities at subsets of locations along the genome. Additionally, such a mixture model provides a nonparametric estimation scheme for dependent sequences of binary data.     

Structural attributes of nucleotide sequences in promoter regions of supercoiling-sensitive genes: How to relate microarray expression data with genomic sequences

The level of supercoiling in the chromosome can affect gene expression. To clarify the basis of supercoiling sensitivity, we analyzed the structural features of nucleotide sequences in the vicinity of promoters for the genes with expression enhanced and decreased in response to loss of chromosomal supercoiling in Escherichia coli. Fourier analysis of promoter sequences for supercoiling-sensitive genes reveals the tendency in selection of sequences with helical periodicities close to 10 nt for relaxation-induced genes and to 11 nt for relaxation-repressed genes. The helical periodicities in the subsets of promoters recognized by RNA polymerase with different sigma factors were also studied. A special procedure was developed for the study of correlations between the intensities of periodicities in promoter sequences and the expression levels of corresponding genes. Significant correlations of expression with the AT content and with AT periodicities about 10, 11, and 50 nt indicate their role in regulation of supercoiling-sensitive genes.     

SVachra: a tool to identify genomic structural variation in mate pair sequencing data containing inward and outward facing reads

Background

Characterization of genomic structural variation (SV) is essential to expanding the research and clinical applications of genome sequencing. Reliance upon short DNA fragment paired end sequencing has yielded a wealth of single nucleotide variants and internal sequencing read insertions-deletions, at the cost of limited SV detection. Multi-kilobase DNA fragment mate pair sequencing has supplemented the void in SV detection, but introduced new analytic challenges requiring SV detection tools specifically designed for mate pair sequencing data. Here, we introduce SVachra – Structural Variation Assessment of CHRomosomal Aberrations, a breakpoint calling program that identifies large insertions-deletions, inversions, inter- and intra-chromosomal translocations utilizing both inward and outward facing read types generated by mate pair sequencing.

Results

We demonstrate SVachra’s utility by executing the program on large-insert (Illumina Nextera) mate pair sequencing data from the personal genome of a single subject (HS1011). An additional data set of long-read (Pacific BioSciences RSII) was also generated to validate SV calls from SVachra and other comparison SV calling programs. SVachra exhibited the highest validation rate and reported the widest distribution of SV types and size ranges when compared to other SV callers.

Conclusions

SVachra is a highly specific breakpoint calling program that exhibits a more unbiased SV detection methodology than other callers.

     

Modeling genomic data with type attributes, balancing stability and maintainability

Background

The computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes. While previous prediction approaches focused merely on differences between methylated and unmethylated DNA sequences, recent experimental results have shown the presence of much more complex patterns of methylation across tissues and time in the human genome. These patterns are only partially described by a binary model of DNA methylation. In this work we propose a novel approach, based on profile analysis of tissue-specific methylation that uncovers significant differences in the sequences of CpG islands (CGIs) that predispose them to a tissue- specific methylation pattern.

Results

We defined CGI methylation profiles that separate not only between constitutively methylated and unmethylated CGIs, but also identify CGIs showing a differential degree of methylation across tissues and cell-types or a lack of methylation exclusively in sperm. These profiles are clearly distinguished by a number of CGI attributes including their evolutionary conservation, their significance, as well as the evolutionary evidence of prior methylation. Additionally, we assess profile functionality with respect to the different compartments of protein coding genes and their possible use in the prediction of DNA methylation.

Conclusion

Our approach provides new insights into the biological features that determine if a CGI has a functional role in the epigenetic control of gene expression and the features associated with CGI methylation susceptibility. Moreover, we show that the ability to predict CGI methylation is based primarily on the quality of the biological information used and the relationships uncovered between different sources of knowledge. The strategy presented here is able to predict, besides the constitutively methylated and unmethylated classes, two more tissue specific methylation classes conserving the accuracy provided by leading binary methylation classification methods.     

Modeling genomic data with type attributes, balancing stability and maintainability

Background  

Molecular biology (MB) is a dynamic research domain that benefits greatly from the use of modern software technology in preparing experiments, analyzing acquired data, and even performing "in-silico" analyses. As ever new findings change the face of this domain, software for MB has to be sufficiently flexible to accommodate these changes. At the same time, however, the efficient development of high-quality and interoperable software requires a stable model of concepts for the subject domain and their relations. The result of these two contradictory requirements is increased complexity in the development of MB software.     

Regular distribution of length heterogeneities within non-transcribed spacer regions of cloned and genomic rDNA of Saccharomyces cerevisiae.

A length difference of about 50 bp in the EcoRI fragment B of the rDNA from two different strains of Saccharomyces cerevisiae has been mapped in detail by sequencing of cloned fragments. This 2.4 kb EcoRI fragment contains the start of the 35S rRNA gene at one end and the 5S rRNA gene in the middle flanked by non-transcribed spacers, NTS1 and NTS2. The difference appeared as short deletions or insertions in five regularly spaced regions within the 1 kb NTS1, 3' to the 5S rRNA gene. The same regions of heterogeneities were displayed when all available sequence data of the NTS1 were compared. Four of the variable regions are located 160-170 bp apart, indicating that they might represent linker sequences between phased nucleosomes. Two variant clones, differing in the length of one subfragment of NTS1, were isolated for each strain. In both cases these represented the major variants among chromosomal NTS1 as revealed by sequencing of genomic fragments.     

Marginal analysis of longitudinal ordinal data with misclassification in both response and covariates

Marginal methods have been widely used for the analysis of longitudinal ordinal and categorical data. These models do not require full parametric assumptions on the joint distribution of repeated response measurements but only specify the marginal or even association structures. However, inference results obtained from these methods often incur serious bias when variables are subject to error. In this paper, we tackle the problem that misclassification exists in both response and categorical covariate variables. We develop a marginal method for misclassification adjustment, which utilizes second‐order estimating functions and a functional modeling approach, and can yield consistent estimates and valid inference for mean and association parameters. We propose a two‐stage estimation approach for cases in which validation data are available. Our simulation studies show good performance of the proposed method under a variety of settings. Although the proposed method is phrased to data with a longitudinal design, it also applies to correlated data arising from clustered and family studies, in which association parameters may be of scientific interest. The proposed method is applied to analyze a dataset from the Framingham Heart Study as an illustration.     

Locally efficient estimation of the quality-adjusted lifetime distribution with right-censored data and covariates

Zhao and Tsiatis (1997) consider the problem of estimation of the distribution of the quality-adjusted lifetime when the chronological survival time is subject to right censoring. The quality-adjusted lifetime is typically defined as a weighted sum of the times spent in certain states up until death or some other failure time. They propose an estimator and establish the relevant asymptotics under the assumption of independent censoring. In this paper we extend the data structure with a covariate process observed until the end of follow-up and identify the optimal estimation problem. Because of the curse of dimensionality, no globally efficient nonparametric estimators, which have a good practical performance at moderate sample sizes, exist. Given a correctly specified model for the hazard of censoring conditional on the observed quality-of-life and covariate processes, we propose a closed-form one-step estimator of the distribution of the quality-adjusted lifetime whose asymptotic variance attains the efficiency bound if we can correctly specify a lower-dimensional working model for the conditional distribution of quality-adjusted lifetime given the observed quality-of-life and covariate processes. The estimator remains consistent and asymptotically normal even if this latter submodel is misspecified. The practical performance of the estimators is illustrated with a simulation study. We also extend our proposed one-step estimator to the case where treatment assignment is confounded by observed risk factors so that this estimator can be used to test a treatment effect in an observational study.     

Integrated analysis of genetic, genomic and proteomic data

The rapid expansion of methods for measuring biological data ranging from DNA sequence variations to mRNA expression and protein abundance presents the opportunity to utilize multiple types of information jointly in the study of human health and disease. Organisms are complex systems that integrate inputs at myriad levels to arrive at an observable phenotype. Therefore, it is essential that questions concerning the etiology of phenotypes as complex as common human diseases take the systemic nature of biology into account, and integrate the information provided by each data type in a manner analogous to the operation of the body itself. While limited in scope, the initial forays into the joint analysis of multiple data types have yielded interesting results that would not have been reached had only one type of data been considered. These early successes, along with the aforementioned theoretical appeal of data integration, provide impetus for the development of methods for the parallel, high-throughput analysis of multiple data types. The idea that the integrated analysis of multiple data types will improve the identification of biomarkers of clinical endpoints, such as disease susceptibility, is presented as a working hypothesis.