Unrestrained markerless trait stacking in Nannochloropsis gaditana through combined genome editing and marker recycling technologies.

Robust molecular tool kits in model and industrial microalgae are key to efficient targeted manipulation of endogenous and foreign genes in the nuclear genome for basic research and, as importantly, for the development of algal strains to produce renewable products such as biofuels. While Cas9-mediated gene knockout has been demonstrated in a small number of algal species with varying efficiency, the ability to stack traits or generate knockout mutations in two or more loci are often severely limited by selectable agent availability. This poses a critical hurdle in developing production strains, which require stacking of multiple traits, or in probing functionally redundant gene families. Here, we combine Cas9 genome editing with an inducible Cre recombinase in the industrial alga Nannochloropsis gaditana to generate a strain, NgCas9+Cre+, in which the potentially unlimited stacking of knockouts and addition of new genes is readily achievable. Cre-mediated marker recycling is first demonstrated in the removal of the selectable marker and GFP reporter transgenes associated with the Cas9/Cre construct in NgCas9+Cre+ Next, we show the proof-of-concept generation of a markerless knockout in a gene encoding an acyl-CoA oxidase (Aco1), as well as the markerless recapitulation of a 2-kb insert in the ZnCys gene 5'-UTR, which results in a doubling of wild-type lipid productivity. Finally, through an industrially oriented process, we generate mutants that exhibit up to ∼50% reduction in photosynthetic antennae size by markerless knockout of seven genes in the large light-harvesting complex gene family.

Phylogenetic Distribution of CRISPR-Cas Systems in Antibiotic-Resistant Pseudomonas aeruginosa.

UNLABELLED: Pseudomonas aeruginosa is an antibiotic-refractory pathogen with a large genome and extensive genotypic diversity. Historically, P. aeruginosa has been a major model system for understanding the molecular mechanisms underlying type I clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-associated protein (CRISPR-Cas)-based bacterial immune system function. However, little information on the phylogenetic distribution and potential role of these CRISPR-Cas systems in molding the P. aeruginosa accessory genome and antibiotic resistance elements is known. Computational approaches were used to identify and characterize CRISPR-Cas systems within 672 genomes, and in the process, we identified a previously unreported and putatively mobile type I-C P. aeruginosa CRISPR-Cas system. Furthermore, genomes harboring noninhibited type I-F and I-E CRISPR-Cas systems were on average ~300 kb smaller than those without a CRISPR-Cas system. In silico analysis demonstrated that the accessory genome (n = 22,036 genes) harbored the majority of identified CRISPR-Cas targets. We also assembled a global spacer library that aided the identification of difficult-to-characterize mobile genetic elements within next-generation sequencing (NGS) data and allowed CRISPR typing of a majority of P. aeruginosa strains. In summary, our analysis demonstrated that CRISPR-Cas systems play an important role in shaping the accessory genomes of globally distributed P. aeruginosa isolates. IMPORTANCE: P. aeruginosa is both an antibiotic-refractory pathogen and an important model system for type I CRISPR-Cas bacterial immune systems. By combining the genome sequences of 672 newly and previously sequenced genomes, we were able to provide a global view of the phylogenetic distribution, conservation, and potential targets of these systems. This analysis identified a new and putatively mobile P. aeruginosa CRISPR-Cas subtype, characterized the diverse distribution of known CRISPR-inhibiting genes, and provided a potential new use for CRISPR spacer libraries in accessory genome analysis. Our data demonstrated the importance of CRISPR-Cas systems in modulating the accessory genomes of globally distributed strains while also providing substantial data for subsequent genomic and experimental studies in multiple fields. Understanding why certain genotypes of P. aeruginosa are clinically prevalent and adept at horizontally acquiring virulence and antibiotic resistance elements is of major clinical and economic importance.

Determination of fetal DNA fraction from the plasma of pregnant women using sequence read counts.

OBJECTIVE: This study introduces a novel method, referred to as SeqFF, for estimating the fetal DNA fraction in the plasma of pregnant women and to infer the underlying mechanism that allows for such statistical modeling. METHODS: Autosomal regional read counts from whole-genome massively parallel single-end sequencing of circulating cell-free DNA (ccfDNA) from the plasma of 25 312 pregnant women were used to train a multivariate model. The pretrained model was then applied to 505 pregnant samples to assess the performance of SeqFF against known methodologies for fetal DNA fraction calculations. RESULTS: Pearson's correlation between chromosome Y and SeqFF for pregnancies with male fetuses from two independent cohorts ranged from 0.932 to 0.938. Comparison between a single-nucleotide polymorphism-based approach and SeqFF yielded a Pearson's correlation of 0.921. Paired-end sequencing suggests that shorter ccfDNA, that is, less than 150 bp in length, is nonuniformly distributed across the genome. Regions exhibiting an increased proportion of short ccfDNA, which are more likely of fetal origin, tend to provide more information in the SeqFF calculations. CONCLUSION: SeqFF is a robust and direct method to determine fetal DNA fraction. Furthermore, the method is applicable to both male and female pregnancies and can greatly improve the accuracy of noninvasive prenatal testing for fetal copy number variation.

Detection of fetal subchromosomal abnormalities by sequencing circulating cell-free DNA from maternal plasma.

BACKGROUND: The development of sequencing-based noninvasive prenatal testing (NIPT) has been largely focused on whole-chromosome aneuploidies (chromosomes 13, 18, 21, X, and Y). Collectively, they account for only 30% of all live births with a chromosome abnormality. Various structural chromosome changes, such as microdeletion/microduplication (MD) syndromes are more common but more challenging to detect. Recently, several publications have shown results on noninvasive detection of MDs by deep sequencing. These approaches demonstrated the proof of concept but are not economically feasible for large-scale clinical applications. METHODS: We present a novel approach that uses low-coverage whole genome sequencing (approximately 0.2×) to detect MDs genome wide without requiring prior knowledge of the event's location. We developed a normalization method to reduce sequencing noise. We then applied a statistical method to search for consistently increased or decreased regions. A decision tree was used to differentiate whole-chromosome events from MDs. RESULTS: We demonstrated via a simulation study that the sensitivity difference between our method and the theoretical limit was <5% for MDs ≥9 Mb. We tested the performance in a blinded study in which the MDs ranged from 3 to 40 Mb. In this study, our algorithm correctly identified 17 of 18 cases with MDs and 156 of 157 unaffected cases. CONCLUSIONS: The limit of detection for any given MD syndrome is constrained by 4 factors: fetal fraction, MD size, coverage, and biological and technical variability of the event region. Our algorithm takes these factors into account and achieved 94.4% sensitivity and 99.4% specificity.

Seven new loci associated with age-related macular degeneration.

Age-related macular degeneration (AMD) is a common cause of blindness in older individuals. To accelerate the understanding of AMD biology and help design new therapies, we executed a collaborative genome-wide association study, including >17,100 advanced AMD cases and >60,000 controls of European and Asian ancestry. We identified 19 loci associated at P < 5 × 10(-8). These loci show enrichment for genes involved in the regulation of complement activity, lipid metabolism, extracellular matrix remodeling and angiogenesis. Our results include seven loci with associations reaching P < 5 × 10(-8) for the first time, near the genes COL8A1-FILIP1L, IER3-DDR1, SLC16A8, TGFBR1, RAD51B, ADAMTS9 and B3GALTL. A genetic risk score combining SNP genotypes from all loci showed similar ability to distinguish cases and controls in all samples examined. Our findings provide new directions for biological, genetic and therapeutic studies of AMD.

Genome-wide methylation profiles reveal quantitative views of human aging rates.

The ability to measure human aging from molecular profiles has practical implications in many fields, including disease prevention and treatment, forensics, and extension of life. Although chronological age has been linked to changes in DNA methylation, the methylome has not yet been used to measure and compare human aging rates. Here, we build a quantitative model of aging using measurements at more than 450,000 CpG markers from the whole blood of 656 human individuals, aged 19 to 101. This model measures the rate at which an individual's methylome ages, which we show is impacted by gender and genetic variants. We also show that differences in aging rates help explain epigenetic drift and are reflected in the transcriptome. Moreover, we show how our aging model is upheld in other human tissues and reveals an advanced aging rate in tumor tissue. Our model highlights specific components of the aging process and provides a quantitative readout for studying the role of methylation in age-related disease.

Hair cortisol level as a biomarker for altered hypothalamic-pituitary-adrenal activity in female adolescents with posttraumatic stress disorder after the 2008 Wenchuan earthquake.

BACKGROUND: The present study evaluated the accumulated changes in hair cortisol levels of patients with posttraumatic stress disorder (PTSD) attributed to the 2008 Wenchuan earthquake in China. METHODS: Sixty-four female adolescents from two townships who experienced the earthquake were recruited 7 months after the disaster, including 32 subjects with PTSD (PTSD group) and 32 subjects without PTSD (non-PTSD group). Twenty matched adolescents were recruited from an area that was not affected significantly by the earthquake as the control group. Hair cortisol concentrations were measured by the electrochemiluminescence immunoassay in each 3-cm segment of hair sample from the scalp. RESULTS: There was no significant difference at the baseline hair cortisol level in the three groups before the traumatic event (p > .6). Hair cortisol levels changed over time and differed among groups (p = .0042). The hair cortisol levels among the PTSD and non-PTSD subjects were elevated, suggesting increasing levels in response to stress. However, these two groups differed in their response. The non-PTSD subjects showed a significantly higher cortisol level than the PTSD group between month 2 and month 4 (p = .0137) and also between month 5 and month 7 (p = .0438) after the traumatic event. CONCLUSIONS: This study revealed a blunted response curve to the disaster among PTSD subjects compared with subjects without PTSD. These findings suggest that hair cortisol level could be used to assess the integrated hypothalamic-pituitary-adrenal activity over a period of months after traumatic events and be used to serve as a biomarker in patients with PTSD.

High temperature requirement factor A1 (HTRA1) gene regulates angiogenesis through transforming growth factor-ß family member growth differentiation factor 6.

Genome-wide association study (GWAS) has identified genetic variants in the promoter region of the high temperature requirement factor A1 (HTRA1) gene associated with age-related macular degeneration (AMD). As a secreted serine protease, HTRA1 has been reported to interact with members of the transforming growth factor-ß (TGF-ß) family and regulate their signaling pathways. Growth differentiation factor 6 (GDF6), a member of the TGF-ß family, is involved in ectoderm patterning and eye development. Mutations in GDF6 have been associated with abnormal eye development that may result in microphthalmia and anophthalmia. In this report, we identified a single nucleotide polymorphism (SNP) rs6982567 A/G near the GDF6 gene that is significantly associated with AMD (p value = 3.54 × 10(-8)). We demonstrated that the GDF6 AMD risk allele (rs6982567 A) is associated with decreased expression of the GDF6 and increased expression of HTRA1. Similarly, the HTRA1 AMD risk allele (rs10490924 T) is associated with decreased GDF6 and increased HTRA1 expression. We observed decreased vascular development in the retina and significant up-regulation of GDF6 gene in the RPE layer, retinal and brain tissues in HTRA1 knock-out (htra1(-/-)) mice as compared with the wild-type counterparts. Furthermore, we showed enhanced SMAD signaling in htra1(-/-) mice. Our data suggests a critical role of HTRA1 in the regulation of angiogenesis via TGF-ß signaling and identified GDF6 as a novel disease gene for AMD.

Assembling global maps of cellular function through integrative analysis of physical and genetic networks.

To take full advantage of high-throughput genetic and physical interaction mapping projects, the raw interactions must first be assembled into models of cell structure and function. PanGIA (for physical and genetic interaction alignment) is a plug-in for the bioinformatics platform Cytoscape, designed to integrate physical and genetic interactions into hierarchical module maps. PanGIA identifies 'modules' as sets of proteins whose physical and genetic interaction data matches that of known protein complexes. Higher-order functional cooperativity and redundancy is identified by enrichment for genetic interactions across modules. This protocol begins with importing interaction networks into Cytoscape, followed by filtering and basic network visualization. Next, PanGIA is used to infer a set of modules and their functional inter-relationships. This module map is visualized in a number of intuitive ways, and modules are tested for functional enrichment and overlap with known complexes. The full protocol can be completed between 10 and 30 min, depending on the size of the data set being analyzed.

Genome-wide association data reveal a global map of genetic interactions among protein complexes.

This work demonstrates how gene association studies can be analyzed to map a global landscape of genetic interactions among protein complexes and pathways. Despite the immense potential of gene association studies, they have been challenging to analyze because most traits are complex, involving the combined effect of mutations at many different genes. Due to lack of statistical power, only the strongest single markers are typically identified. Here, we present an integrative approach that greatly increases power through marker clustering and projection of marker interactions within and across protein complexes. Applied to a recent gene association study in yeast, this approach identifies 2,023 genetic interactions which map to 208 functional interactions among protein complexes. We show that such interactions are analogous to interactions derived through reverse genetic screens and that they provide coverage in areas not yet tested by reverse genetic analysis. This work has the potential to transform gene association studies, by elevating the analysis from the level of individual markers to global maps of genetic interactions. As proof of principle, we use synthetic genetic screens to confirm numerous novel genetic interactions for the INO80 chromatin remodeling complex.

Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox.

The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.