Biology subjects

Jensen, K.

Publications and source records attributed to Jensen, K..

40 records · Page 3Linked to original sources

bioRxiv2019

Does Sex Modify an Association of Electrophysiological Substrate with Sudden Cardiac Death? The Atherosclerosis Risk in Communities (ARIC) Study

BackgroundSex is a well-recognized risk factor for sudden cardiac death (SCD). Sex differences in electrophysiological (EP) substrate of SCD are known. However, it remains unknown whether sex can modify an association of EP substrate with SCD.\n\nMethodsParticipants from the Atherosclerosis Risk in Communities study with analyzable ECGs (n=14,725; age, 54.2{+/-}5.8 yrs; 55% female, 74% white) were included. EP substrate was characterized by traditional 12-lead ECG (heart rate, QRS, QTc, Cornell voltage), spatial ventricular gradient (SVG) and sum absolute QRST integral (SAI QRST) metrics. Two competing outcomes were adjudicated SCD and nonSCD. Interaction of ECG metrics with sex was studied in Cox proportional hazards and Fine-Gray competing risk models. Relative hazard ratio (RHR) and relative sub-hazard ratio (RSHR) with a 95% confidence interval for SCD and nonSCD risk for women relative to men were calculated. Model 1 was adjusted for prevalent cardiovascular disease (CVD) and risk factors. Time-updated model 2 was additionally adjusted for incident non-fatal CVD.\n\nResultsOver a median follow-up of 24.4 years, there were 530 SCDs (incidence 1.72 (1.58-1.88)/1000 person-years) and 2,178 nonSCDs (incidence 7.09; (6.80-7.39)/ 1000 person-years). Women experienced a greater than men risk of SCD associated with Cornell voltage (RHR 1.18(1.06-1.32); P=0.003), SAI QRST (RHR 1.16(1.04-1.30); P=0.007), area SVG magnitude (RHR 1.24(1.05-1.45); P=0.009), and peak SVG magnitude (RHR 1.22(1.04-1.44); P=0.018), independently from incident CVD. Greater risk of SCD for women than men associated with QRS duration (RHR 1.24(1.07-1.44); P=0.004) and QTc (RSHR 1.15(1.02-1.30); P=0.025) was explained by incident CVD. Furthermore, women had greater odds of SCD associated with heart rate (RSHR 1.19(1.01-1.40); P=0.036), independently of incident CVD.\n\nConclusionsSex modifies an association of EP substrate with SCD. In women, global EP substrate is associated with up to 27% greater risk of SCD than in men. Development of sex-specific risk scores of SCD is necessary. Further studies of mechanisms behind sex differences in EP substrate of SCD are warranted.

epidemiology↗

bioRxiv2019

Adaptive laboratory evolution reveals general and specific chemical tolerance mechanisms and enhances biochemical production

Tolerance to high product concentrations is a major barrier to achieving economically viable processes for bio-based chemical production. Chemical tolerance mechanisms are often unknown, thus their rational design is not achievable. To reveal unknown tolerance mechanisms we used an automated platform to evolve Escherichia coli to grow in previously toxic concentrations of 11 chemicals that have applications as polymer precursors, chemical intermediates, or biofuels. Re-sequencing of isolates from 88 independently evolved populations, reconstruction of mutations, and cross-compound tolerance profiling was employed to uncover general and specific tolerance mechanisms. We found that: 1) the broad tolerance of strains towards chemicals varied significantly depending on the chemical stress condition under which the strain was evolved; 2) the strains that acquired high levels of NaCl tolerance also became broadly tolerant to most chemicals; 3) genetic tolerance mechanisms included alterations in regulatory, cell wall, transcriptional and translational functions, as well as more chemical-specific mechanisms related to transport and metabolism; 4) using pre-tolerized starting strains can significantly enhance subsequent production of chemicals when a production pathway is inserted; and 5) only a subset of the evolved isolates showed improved production indicating that this approach is especially useful when a large number of independently evolved isolates are screened for production. We provide a comprehensive genotype-phenotype map based on identified mutations and growth phenotypes for 224 chemical tolerant strains.

microbiology↗

bioRxiv2018

Alcohol-responsive genes identified in human iPSC-derived neural cultures

Alcohol use contributes to numerous diseases and injuries. The nervous system is affected by alcohol in diverse ways, though the molecular mechanisms of these effects are not clearly understood. Using human-induced pluripotent stem cells (iPSCs), we developed a neural cell culture model to identify the mechanisms of alcohols effects. iPSCs were generated from fibroblasts and differentiated into forebrain neural cells cultures that were treated with 50 mM alcohol or sham conditions (same media lacking alcohol) for 7 days. We analyzed gene expression using total RNA sequencing (RNA-seq) for 34 samples derived from 11 subjects and for 10 samples from 5 subjects in an independent experiment that had intermittent exposure to the same dose of alcohol. We also analyzed genetic effects on gene expression and conducted a weighted correlation network analysis. We found that differentiated neural cell cultures have the capacity to recapitulate gene regulatory effects previously observed in specific primary neural tissues and identified 226 genes that were differentially expressed (FDR< 0.1) after alcohol treatment. The effects on expression included decreases in INSIG1 and LDLR, two genes involved in cholesterol homeostasis. We also identified a module of 58 co-expressed genes that were uniformly decreased following alcohol exposure. The majority of these effects were supported in the independent alcohol exposure experiment. Enrichment analysis linked the alcohol responsive genes to cell cycle, notch signaling, and cholesterol biosynthesis pathways, which are disrupted in several neurological disorders. Our findings suggest that there is convergence between these disorders and the effects of alcohol exposure.

neuroscience↗

bioRxiv2017

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories

Computational systems biology methods enable rational design of cell factories on a genomescale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, for the majority of these methods implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knock-out, knock-in, over-expression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated website including documentation, examples, and installation instructions can be found at http://cameo.bio. Users can also give cameo a try at http://try.cameo.bio.

bioengineering↗