Mitotic DNA synthesis is caused by transcription-replication conflicts in BRCA2-deficient cells.

Aberrant replication causes cells lacking BRCA2 to enter mitosis with under-replicated DNA, which activates a repair mechanism known as mitotic DNA synthesis (MiDAS). Here, we identify genome-wide the sites where MiDAS reactions occur when BRCA2 is abrogated. High-resolution profiling revealed that these sites are different from MiDAS at aphidicolin-induced common fragile sites in that they map to genomic regions replicating in the early S-phase, which are close to early-firing replication origins, are highly transcribed, and display R-loop-forming potential. Both transcription inhibition in early S-phase and RNaseH1 overexpression reduced MiDAS in BRCA2-deficient cells, indicating that transcription-replication conflicts (TRCs) and R-loops are the source of MiDAS. Importantly, the MiDAS sites identified in BRCA2-deficient cells also represent hotspots for genomic rearrangements in BRCA2-mutated breast tumors. Thus, our work provides a mechanism for how tumor-predisposing BRCA2 inactivation links transcription-induced DNA damage with mitotic DNA repair to fuel the genomic instability characteristic of cancer cells.

Cardiomyocyte tetrahydrobiopterin synthesis regulates fatty acid metabolism and susceptibility to ischaemia-reperfusion injury.

NEW FINDINGS: What is the central question of this study? What are the physiological roles of cardiomyocyte-derived tetrahydrobiopterin (BH4) in cardiac metabolism and stress response? What is the main finding and its importance? Cardiomyocyte BH4 has a physiological role in cardiac metabolism. There was a shift of substrate preference from fatty acid to glucose in hearts with targeted deletion of BH4 synthesis. The changes in fatty-acid metabolic profile were associated with a protective effect in response to ischaemia-reperfusion (IR) injury, and reduced infarct size. Manipulating fatty acid metabolism via BH4 availability could play a therapeutic role in limiting IR injury. ABSTRACT: Tetrahydrobiopterin (BH4) is an essential cofactor for nitric oxide (NO) synthases in which its production of NO is crucial for cardiac function. However, non-canonical roles of BH4 have been discovered recently and the cell-specific role of cardiomyocyte BH4 in cardiac function and metabolism remains to be elucidated. Therefore, we developed a novel mouse model of cardiomyocyte BH4 deficiency, by cardiomyocyte-specific deletion of Gch1, which encodes guanosine triphosphate cyclohydrolase I, a required enzyme for de novo BH4 synthesis. Cardiomyocyte (cm)Gch1 mRNA expression and BH4 levels from cmGch1 KO mice were significantly reduced compared to Gch1flox/flox (WT) littermates. Transcriptomic analyses and protein assays revealed downregulation of genes involved in fatty acid oxidation in cmGch1 KO hearts compared with WT, accompanied by increased triacylglycerol concentration within the myocardium. Deletion of cardiomyocyte BH4 did not alter basal cardiac function. However, the recovery of left ventricle function was improved in cmGch1 KO hearts when subjected to ex vivo ischaemia-reperfusion (IR) injury, with reduced infarct size compared to WT hearts. Metabolomic analyses of cardiac tissue after IR revealed that long-chain fatty acids were increased in cmGch1 KO hearts compared to WT, whereas at 5 min reperfusion (post-35 min ischaemia) fatty acid metabolite levels were higher in WT compared to cmGch1 KO hearts. These results indicate a new role for BH4 in cardiomyocyte fatty acid metabolism, such that reduction of cardiomyocyte BH4 confers a protective effect in response to cardiac IR injury. Manipulating cardiac metabolism via BH4 could play a therapeutic role in limiting IR injury.

Whole Exome Sequencing Reveals the Major Genetic Contributors to Nonsyndromic Tetralogy of Fallot.

RATIONALE: Familial recurrence studies provide strong evidence for a genetic component to the predisposition to sporadic, nonsyndromic Tetralogy of Fallot (TOF), the most common cyanotic congenital heart disease phenotype. Rare genetic variants have been identified as important contributors to the risk of congenital heart disease, but relatively small numbers of TOF cases have been studied to date. OBJECTIVE: We used whole exome sequencing to assess the prevalence of unique, deleterious variants in the largest cohort of nonsyndromic TOF patients reported to date. METHODS AND RESULTS: Eight hundred twenty-nine TOF patients underwent whole exome sequencing. The presence of unique, deleterious variants was determined; defined by their absence in the Genome Aggregation Database and a scaled combined annotation-dependent depletion score of ≥20. The clustering of variants in 2 genes, NOTCH1 and FLT4, surpassed thresholds for genome-wide significance (assigned as P<5×10-8) after correction for multiple comparisons. NOTCH1 was most frequently found to harbor unique, deleterious variants. Thirty-one changes were observed in 37 probands (4.5%; 95% CI, 3.2%-6.1%) and included 7 loss-of-function variants 22 missense variants and 2 in-frame indels. Sanger sequencing of the unaffected parents of 7 cases identified 5 de novo variants. Three NOTCH1 variants (p.G200R, p.C607Y, and p.N1875S) were subjected to functional evaluation, and 2 showed a reduction in Jagged1-induced NOTCH signaling. FLT4 variants were found in 2.4% (95% CI, 1.6%-3.8%) of TOF patients, with 21 patients harboring 22 unique, deleterious variants. The variants identified were distinct to those that cause the congenital lymphoedema syndrome Milroy disease. In addition to NOTCH1, FLT4 and the well-established TOF gene, TBX1, we identified potential association with variants in several other candidates, including RYR1, ZFPM1, CAMTA2, DLX6, and PCM1. CONCLUSIONS: The NOTCH1 locus is the most frequent site of genetic variants predisposing to nonsyndromic TOF, followed by FLT4. Together, variants in these genes are found in almost 7% of TOF patients.

Identification of grapevine clones via high-throughput amplicon sequencing: a proof-of-concept study.

Wine cultivars are available to growers in multiple clonal selections with agronomic and enological differences. Phenotypic differences between clones originated from somatic mutations that accrued over thousands of asexual propagation cycles. Genetic diversity between grape cultivars remains unexplored, and tools to discriminate unequivocally clones have been lacking. This study aimed to uncover genetic variations among a group of clonal selections of 4 important Vitis vinifera cultivars: Cabernet sauvignon, Sauvignon blanc, Chardonnay, and Merlot, and use this information to develop genetic markers to discriminate the clones of these cultivars. We sequenced with short-read sequencing technology the genomes of 18 clones, including biological replicates for a total of 46 genomes. Sequences were aligned to their respective cultivar's reference genome for variant calling. We used reference genomes of Cabernet sauvignon, Chardonnay, and Merlot and developed a de novo genome assembly of Sauvignon blanc using long-read sequencing. On average, 4 million variants were detected for each clone, with 74.2% being single nucleotide variants and 25.8% being small insertions or deletions (InDel). The frequency of these variants was consistent across all clones. From these variants, we validated 46 clonal markers using high-throughput amplicon sequencing for 77.7% of the evaluated clones, most of them small InDel. These results represent an advance in grapevine genotyping strategies and will benefit the viticulture industry for the characterization and identification of the plant material.

Evaluation of computational methods for human microbiome analysis using simulated data.

BACKGROUND: Our understanding of the composition, function, and health implications of human microbiota has been advanced by high-throughput sequencing and the development of new genomic analyses. However, trade-offs among alternative strategies for the acquisition and analysis of sequence data remain understudied. METHODS: We assessed eight popular taxonomic profiling pipelines; MetaPhlAn2, metaMix, PathoScope 2.0, Sigma, Kraken, ConStrains, Centrifuge and Taxator-tk, against a battery of metagenomic datasets simulated from real data. The metagenomic datasets were modeled on 426 complete or permanent draft genomes stored in the Human Oral Microbiome Database and were designed to simulate various experimental conditions, both in the design of a putative experiment; read length (75-1,000 bp reads), sequence depth (100K-10M), and in metagenomic composition; number of species present (10, 100, 426), species distribution. The sensitivity and specificity of each of the pipelines under various scenarios were measured. We also estimated the relative root mean square error and average relative error to assess the abundance estimates produced by different methods. Additional datasets were generated for five of the pipelines to simulate the presence within a metagenome of an unreferenced species, closely related to other referenced species. Additional datasets were also generated in order to measure computational time on datasets of ever-increasing sequencing depth (up to 6 × 107). RESULTS: Testing of eight pipelines against 144 simulated metagenomic datasets initially produced 1,104 discrete results. Pipelines using a marker gene strategy; MetaPhlAn2 and ConStrains, were overall less sensitive, than other pipelines; with the notable exception of Taxator-tk. This difference in sensitivity was largely made up in terms of runtime, significantly lower than more sensitive pipelines that rely on whole-genome alignments such as PathoScope2.0. However, pipelines that used strategies to speed-up alignment between genomic references and metagenomic reads, such as kmerization, were able to combine both high sensitivity and low run time, as is the case with Kraken and Centrifuge. Absent species genomes in the database mostly led to assignment of reads to the most closely related species available in all pipelines. Our results therefore suggest that taxonomic profilers that use kmerization have largely superseded those that use gene markers, coupling low run times with high sensitivity and specificity. Taxonomic profilers using more time-consuming read reassignment, such as PathoScope 2.0, provided the most sensitive profiles under common metagenomic sequencing scenarios. All the results described and discussed in this paper can be visualized using the dedicated R Shiny application (https://github.com/microgenomics/HumanMicrobiomeAnalysis). All of our datasets, pipelines and results are made available through the GitHub repository for future benchmarking.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies.

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

In-Depth Genomic and Phenotypic Characterization of the Antarctic Psychrotolerant Strain Pseudomonas sp. MPC6 Reveals Unique Metabolic Features, Plasticity, and Biotechnological Potential.

We obtained the complete genome sequence of the psychrotolerant extremophile Pseudomonas sp. MPC6, a natural Polyhydroxyalkanoates (PHAs) producing bacterium able to rapidly grow at low temperatures. Genomic and phenotypic analyses allowed us to situate this isolate inside the Pseudomonas fluorescens phylogroup of pseudomonads as well as to reveal its metabolic versatility and plasticity. The isolate possesses the gene machinery for metabolizing a variety of toxic aromatic compounds such as toluene, phenol, chloroaromatics, and TNT. In addition, it can use both C6- and C5-carbon sugars like xylose and arabinose as carbon substrates, an uncommon feature for bacteria of this genus. Furthermore, Pseudomonas sp. MPC6 exhibits a high-copy number of genes encoding for enzymes involved in oxidative and cold-stress response that allows it to cope with high concentrations of heavy metals (As, Cd, Cu) and low temperatures, a finding that was further validated experimentally. We then assessed the growth performance of MPC6 on glycerol using a temperature range from 0 to 45°C, the latter temperature corresponding to the limit at which this Antarctic isolate was no longer able to propagate. On the other hand, the MPC6 genome comprised considerably less virulence and drug resistance factors as compared to pathogenic Pseudomonas strains, thus supporting its safety. Unexpectedly, we found five PHA synthases within the genome of MPC6, one of which clustered separately from the other four. This PHA synthase shared only 40% sequence identity at the amino acid level against the only PHA polymerase described for Pseudomonas (63-1 strain) able to produce copolymers of short- and medium-chain length PHAs. Batch cultures for PHA synthesis in Pseudomonas sp. MPC6 using sugars, decanoate, ethylene glycol, and organic acids as carbon substrates result in biopolymers with different monomer compositions. This indicates that the PHA synthases play a critical role in defining not only the final chemical structure of the biosynthesized PHA, but also the employed biosynthetic pathways. Based on the results obtained, we conclude that Pseudomonas sp. MPC6 can be exploited as a bioremediator and biopolymer factory, as well as a model strain to unveil molecular mechanisms behind adaptation to cold and extreme environments.

Genome sequence of two members of the chloroaromatic-degrading MT community: Pseudomonas reinekei MT1 and Achromobacter xylosoxidans MT3.

We describe the genome sequence of Pseudomonas reinekei MT1 and Achromobacter xylosoxidans MT3, the most abundant members of a bacterial community capable of degrading chloroaromatic compounds. The MT1 genome contains open reading frames encoding enzymes responsible for the catabolism of chlorosalicylate, methylsalicylate, chlorophenols, phenol, benzoate, p-coumarate, phenylalanine, and phenylacetate. On the other hand, the MT3 strain genome possesses no ORFs to metabolize chlorosalicylates; instead the bacterium is capable of metabolizing nitro-phenolic and phenolic compounds, which can be used as the only carbon and energy source by MT3. We also confirmed that MT3 displays the genetic machinery for the metabolism of chlorocathecols and chloromuconates, where the latter are toxic compounds secreted by MT1 when degrading chlorosalicylates. Altogether, this work will advance our fundamental understanding of bacterial interactions.

Computational Methods for Human Microbiome Analysis.

As the field of microbiomics advances, the burden of computational work that scientists need to perform in order to extract biological insight has grown accordingly. Likewise, while human microbiome analyses are increasingly shifting toward a greater integration of various high-throughput data types, a core number of methods form the basis of nearly every study. In this unit, we present step-by-step protocols for five core stages of human microbiome research. The protocols presented in this unit provide a base case for human microbiome analysis, complete with sufficient detail for researchers to tailor certain aspects of the protocols to the specificities of their data. © 2017 by John Wiley & Sons, Inc.