The genetic architecture of membranous nephropathy and its potential to improve non-invasive diagnosis Jingyuan Xie et al. # Membranous Nephropathy (MN) is a rare autoimmune cause of kidney failure. Here we report a genome-wide association study (GWAS) for primary MN in 3,782 cases and 9,038 controls of East Asian and European ancestries. We discover two previously unreported loci, NFKB1

Summary The green alga Chlamydomonas reinhardtii does not synthesize high‐value ketocarotenoids like canthaxanthin and astaxanthin; however, a β‐carotene ketolase (CrBKT) can be found in its genome. CrBKT is poorly expressed, contains a long C‐terminal extension not found in homologues and likely represents a pseudogene in this alga. Here, we used synthetic redesign of this gene to enable its constitutive overexpression from the nuclear genome of C. reinhardtii. Overexpression of the optimized CrBKT extended native carotenoid biosynthesis to generate ketocarotenoids in the algal host causing noticeable changes the green algal colour to reddish‐brown. We found that up to 50% of native carotenoids could be converted into astaxanthin and more than 70% into other ketocarotenoids by robust CrBKT overexpression. Modification of the carotenoid metabolism did not impair growth or biomass productivity of C. reinhardtii, even at high light intensities. Under different growth conditions, the best performing CrBKT overexpression strain was found to reach ketocarotenoid productivities up to 4.3 mg/L/day. Astaxanthin productivity in engineered C. reinhardtii shown here might be competitive with that reported for Haematococcus lacustris (formerly pluvialis) which is currently the main organism cultivated for industrial astaxanthin production. In addition, the extractability and bio‐accessibility of these pigments were much higher in cell wall‐deficient C. reinhardtii than the resting cysts of H. lacustris. Engineered C. reinhardtii strains could thus be a promising alternative to natural astaxanthin producing algal strains and may open the possibility of other tailor‐made pigments from this host.

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Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate (“A-by-G” grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.