The potential use of algae in biofuels applications is receiving significant attention. However, none of the current algal model species are competitive production strains. Here we present a draft genome sequence and a genetic transformation method for the marine microalga Nannochloropsis gaditana CCMP526. We show that N. gaditana has highly favourable lipid yields, and is a promising production organism. The genome assembly includes nuclear (~29 Mb) and organellar genomes, and contains 9,052 gene models. We define the genes required for glycerolipid biogenesis and detail the differential regulation of genes during nitrogen-limited lipid biosynthesis. Phylogenomic analysis identifies genetic attributes of this organism, including unique stramenopile photosynthesis genes and gene expansions that may explain the distinguishing photoautotrophic phenotypes observed. The availability of a genome sequence and transformation methods will facilitate investigations into N. gaditana lipid biosynthesis and permit genetic engineering strategies to further improve this naturally productive alga.

BackgroundTumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification of patients with cancer most likely to respond to immune checkpoint inhibitors. TMB is optimally calculated by whole exome sequencing (WES), but next-generation sequencing targeted panels provide TMB estimates in a time-effective and cost-effective manner. However, differences in panel size and gene coverage, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories. By directly comparing panel-based TMB estimates from participating laboratories, this study aims to characterize the theoretical variability of panel-based TMB estimates, and provides guidelines on TMB reporting, analytic validation requirements and reference standard alignment in order to maintain consistency of TMB estimation across platforms.MethodsEleven laboratories used WES data from The Cancer Genome Atlas Multi-Center Mutation calling in Multiple Cancers (MC3) samples and calculated TMB from the subset of the exome restricted to the genes covered by their targeted panel using their own bioinformatics pipeline (panel TMB). A reference TMB value was calculated from the entire exome using a uniform bioinformatics pipeline all members agreed on (WES TMB). Linear regression analyses were performed to investigate the relationship between WES and panel TMB for all 32 cancer types combined and separately. Variability in panel TMB values at various WES TMB values was also quantified using 95% prediction limits.ResultsStudy results demonstrated that variability within and between panel TMB values increases as the WES TMB values increase. For each panel, prediction limits based on linear regression analyses that modeled panel TMB as a function of WES TMB were calculated and found to approximately capture the intended 95% of observed panel TMB values. Certain cancer types, such as uterine, bladder and colon cancers exhibited greater variability in panel TMB values, compared with lung and head and neck cancers.ConclusionsIncreasing uptake of TMB as a predictive biomarker in the clinic creates an urgent need to bring stakeholders together to agree on the harmonization of key aspects of panel-based TMB estimation, such as the standardization of TMB reporting, standardization of analytical validation studies and the alignment of panel-based TMB values with a reference standard. These harmonization efforts should improve consistency and reliability of panel TMB estimates and aid in clinical decision-making.

We conducted a genome-wide DNA methylation analysis in CD19 (+) B-cells from chronic lymphocytic leukemia (CLL) patients and normal control samples using reduced representation bisulfite sequencing (RRBS). The methylation status of 1.8-2.3 million CpGs in the CLL genome was determined; about 45% of these CpGs were located in more than 23,000 CpG islands (CGIs). While global CpG methylation was similar between CLL and normal B-cells, 1764 gene promoters were identified as being differentially methylated in at least one CLL sample when compared with normal B-cell samples. Nineteen percent of the differentially methylated genes were involved in transcriptional regulation. Aberrant hypermethylation was found in all HOX gene clusters and a significant number of WNT signaling pathway genes. Hypomethylation occurred more frequently in the gene body including introns, exons, and 3'-UTRs in CLL. The NFATc1 P2 promoter and first intron was found to be hypomethylated and correlated with upregulation of both NFATc1 RNA and protein expression levels in CLL suggesting that an epigenetic mechanism is involved in the constitutive activation of NFAT activity in CLL cells. This comprehensive DNA methylation analysis will further our understanding of the epigenetic contribution to cellular dysfunction in CLL.