Native electrospray-ionization mass spectrometry (native MS) measures biomolecules under conditions that preserve most aspects of protein tertiary and quaternary structure, enabling direct characterization of large intact protein assemblies. However, native spectra derived from these assemblies are often partially obscured by low signal-to-noise as well as broad peak shapes due to residual solvation and adduction after the electrospray process. The wide peak widths together with the fact that sequential charge state series from highly charged ions are closely spaced means that native spectra containing multiple species often suffer from high degrees of peak overlap or else contain highly interleaved charge envelopes. This situation presents a challenge for peak detection, correct charge state and charge envelope assignment, and ultimately extracting the relevant underlying mass values of the non-covalent assemblages being investigated. In this report we describe a comprehensive algorithm developed for addressing peak detection, peak overlap, and charge state assignment in native mass spectra, called PeakSeeker. Overlapped Peaks are detected by examination of the second derivative of the raw mass spectrum. Charge state distributions of the molecular species are determined by fitting linear combinations of charge envelopes to the overall experimental mass spectrum. This current software is capable of deconvoluting heterogeneous, complex, and noisy native mass spectra of large protein assemblies as demonstrated by analysis of (a) synthetic mononucleosomes containing severely overlapping peaks, (b) an RNA polymerase II/a-amanitin complex with many closely interleaved ion signals, and (c) human TriC complex containing high levels of background noise.
The COVID-19 pandemic has laid bare the inadequacy of the U.S. healthcare system to deliver timely and resilient care. According to the American Hospital Association, the pandemic has created a $202 billion loss across the healthcare industry, forcing health care systems to lay off workers and making hospitals scramble to minimize supply chain costs. However, as the demand for personal protective equipment (PPE) grows, hospitals have sacrificed sustainable solutions for disposable options that, although convenient, will exacerbate supply strains, financial burden, and waste. We advocate for reusable gowns as a means to lower health care costs, address climate change, and improve resilience while preserving the safety of health care workers. Reusable gowns' polyester material provides comparable capacity to reduce microbial cross-transmission and liquid penetration. In addition, previous hospitals have reported a 50% cost reduction in gown expenditures after adopting reusable gowns; given the current 2000% price increase in isolation gowns during COVID-19, reusable gown use will build both healthcare resilience and security from price fluctuations. Finally, with the United States' medical waste stream worsening, reusable isolation gowns show promising reductions in energy and water use, solid waste, and carbon footprint. The gowns are shown to withstand laundering 75–100 times in contrast to the single-use disposable gown. The circumstances of the pandemic forewarn the need to shift our single-use PPE practices to standardized reusable applications. Ultimately, sustainable forms of protective equipment can help us prepare for future crises that challenge the resilience of the healthcare system.
Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31, 945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS.
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