The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology

Background Chemical genetics provides a powerful alternative to conventional genetics for understanding gene function. However, its application to plants has been limited by the lack of a technology that allows detailed phenotyping of whole-seedling development in the context of a high-throughput chemical screen. We have therefore sought to develop an automated micro-phenotyping platform that would allow both root and shoot development to be monitored under conditions where the phenotypic effects of large numbers of small molecules can be assessed. Results The ‘Microphenotron’ platform uses 96-well microtitre plates to deliver chemical treatments to seedlings of Arabidopsis thaliana L. and is based around four components: (a) the ‘Phytostrip’, a novel seedling growth device that enables chemical treatments to be combined with the automated capture of images of developing roots and shoots; (b) an illuminated robotic platform that uses a commercially available robotic manipulator to capture images of developing shoots and roots; (c) software to control the sequence of robotic movements and integrate these with the image capture process; (d) purpose-made image analysis software for automated extraction of quantitative phenotypic data. Imaging of each plate (representing 80 separate assays) takes 4 min and can easily be performed daily for time-course studies. As currently configured, the Microphenotron has a capacity of 54 microtitre plates in a growth room footprint of 2.1 m², giving a potential throughput of up to 4320 chemical treatments in a typical 10 days experiment. The Microphenotron has been validated by using it to screen a collection of 800 natural compounds for qualitative effects on root development and to perform a quantitative analysis of the effects of a range of concentrations of nitrate and ammonium on seedling development. Conclusions The Microphenotron is an automated screening platform that for the first time is able to combine large numbers of individual chemical treatments with a detailed analysis of whole-seedling development, and particularly root system development. The Microphenotron should provide a powerful new tool for chemical genetics and for wider chemical biology applications, including the development of natural and synthetic chemical products for improved agricultural sustainability

Bile Acid Profiles in Primary Sclerosing Cholangitis and their Ability to Predict Hepatic Decompensation

Postponed access: the file will be available after 2021-11-23Background and Aims: Altered bile acid (BA) homeostasis is an intrinsic facet of cholestatic liver diseases, but clinical usefulness of plasma BA assessment in primary sclerosing cholangitis (PSC) remains understudied. We performed BA profiling in a large retrospective cohort of patients with PSC and matched healthy controls, hypothesizing that plasma BA profiles vary among patients and have clinical utility. Approach and Results: Plasma BA profiling was performed in the Clinical Biochemical Genetics Laboratory at Mayo Clinic using a mass spectrometry based assay. Cox proportional hazard (univariate) and gradient boosting machines (multivariable) models were used to evaluate whether BA variables predict 5-year risk of hepatic decompensation (HD; defined as ascites, variceal hemorrhage, or encephalopathy). There were 400 patients with PSC and 302 controls in the derivation cohort (Mayo Clinic) and 108 patients with PSC in the validation cohort (Norwegian PSC Research Center). Patients with PSC had increased BA levels, conjugated fraction, and primary-to-secondary BA ratios relative to controls. Ursodeoxycholic acid (UDCA) increased total plasma BA level while lowering cholic acid and chenodeoxycholic acid concentrations. Patients without inflammatory bowel disease (IBD) had primary-to-secondary BA ratios between those of controls and patients with ulcerative colitis. HD risk was associated with increased concentration and conjugated fraction of many BA, whereas higher G:T conjugation ratios were protective. The machine-learning model, PSC-BA profile score (concordance statistic [C-statistic], 0.95), predicted HD better than individual measures, including alkaline phosphatase, and performed well in validation (C-statistic, 0.86). Conclusions: Patients with PSC demonstrated alterations of plasma BA consistent with known mechanisms of cholestasis, UDCA treatment, and IBD. Notably, BA profiles predicted future HD, establishing the clinical potential of BA profiling, which may be suited for use in clinical trials.acceptedVersio