BEERS2: RNA-Seq simulation through high fidelity in silico modeling.

Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully length messenger RNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in polymerase chain reaction (PCR) amplification, barcode read errors and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

Degree of Cyclooxygenase-2 Inhibition Modulates Blood Pressure Response to Celecoxib and Naproxen.

Background: Non-steroidal anti-inflammatory drugs (NSAIDs) increase the risk of adverse cardiovascular events via suppression of cyclooxygenase (COX)-2-derived prostacyclin (PGI2) formation in heart, vasculature, and kidney. The Prospective Randomized Evaluation of Celecoxib Integrated Safety versus Ibuprofen Or Naproxen (PRECISION) trial and other large clinical studies compared the cardiovascular risk of traditional NSAIDs (i.e. naproxen), which inhibit both COX isozymes, with NSAIDs selective for COX-2 (i.e. celecoxib). However, whether pharmacologically equipotent doses were used - that is, whether a similar degree of COX-2 inhibition was achieved - was not considered. We compared drug target inhibition and blood pressure response to celecoxib at the dose used by most patients in PRECISION with the lowest recommended naproxen dose for osteoarthritis, which is lower than the dose used in PRECISION. Methods: Sixteen healthy participants (19-61 years) were treated with celecoxib (100 mg every 12h), naproxen (250 mg every 12h), or placebo administered twice daily for seven days in a double-blind, crossover design randomized by order. On Day 7 when drug levels had reached steady state, the degree of COX inhibition was assessed ex vivo and in vivo. Ambulatory blood pressure was measured throughout the final 12h dosing interval. Results: Both NSAIDs inhibited COX-2 activity relative to placebo, but naproxen inhibited COX-2 activity to a greater degree (62.9±21.7%) than celecoxib (35.7±25.2%; p<0.05). Similarly, naproxen treatment inhibited PGI2 formation in vivo (48.0±24.9%) to a greater degree than celecoxib (26.7±24.6%; p<0.05). Naproxen significantly increased blood pressure compared to celecoxib (differences in least-square means of mean arterial pressure: 2.5 mm Hg (95% CI: 1.5, 3.5); systolic blood pressure: 4.0 mm Hg (95% CI: 2.9, 5.1); diastolic blood pressure: 1.8 mm Hg (95% CI: 0.8, 2.8); p<0.05 for all). The difference in systolic blood pressure relative to placebo was associated with the degree of COX-2 inhibition (p<0.05). Conclusions: Celecoxib 200 mg/day inhibited COX-2 activity to a lesser degree than naproxen 500 mg/day, resulting in a less pronounced blood pressure increase. While the PRECISION trial concluded the non-inferiority of celecoxib regarding cardiovascular risk, this is based on a comparison of doses that are not equipotent.ClinicalTrials.gov identifier: NCT02502006 (https://clinicaltrials.gov/study/NCT02502006).

Three-dimensional chromatin reorganization regulates B cell development during ageing.

The contribution of three-dimensional genome organization to physiological ageing is not well known. Here we show that large-scale chromatin reorganization distinguishes young and old bone marrow progenitor (pro-) B cells. These changes result in increased interactions at the compartment level and reduced interactions within topologically associated domains (TADs). The gene encoding Ebf1, a key B cell regulator, switches from compartment A to B with age. Genetically reducing Ebf1 recapitulates some features of old pro-B cells. TADs that are most reduced with age contain genes important for B cell development, including the immunoglobulin heavy chain (Igh) locus. Weaker intra-TAD interactions at Igh correlate with altered variable (V), diversity (D) and joining (J) gene recombination. Our observations implicate three-dimensional chromatin reorganization as a major driver of pro-B cell phenotypes that impair B lymphopoiesis with age.