A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic epistasis.

The contribution of epistasis (interactions among genes or genetic variants) to human complex trait variation remains poorly understood. Methods that aim to explicitly identify pairs of genetic variants, usually single nucleotide polymorphisms (SNPs), associated with a trait suffer from low power due to the large number of hypotheses tested while also having to deal with the computational problem of searching over a potentially large number of candidate pairs. An alternate approach involves testing whether a single SNP modulates variation in a trait against a polygenic background. While overcoming the limitation of low power, such tests of polygenic or marginal epistasis (ME) are infeasible on Biobank-scale data where hundreds of thousands of individuals are genotyped over millions of SNPs. We present a method to test for ME of a SNP on a trait that is applicable to biobank-scale data. We performed extensive simulations to show that our method provides calibrated tests of ME. We applied our method to test for ME at SNPs that are associated with 53 quantitative traits across ≈ 300 K unrelated white British individuals in the UK Biobank (UKBB). Testing 15, 601 trait-loci associations that were significant in GWAS, we identified 16 trait-loci pairs across 12 traits that demonstrate strong evidence of ME signals (p-value p<5×10-853). We further partitioned the significant ME signals across the genome to identify 6 trait-loci pairs with evidence of local (within-chromosome) ME while 15 show evidence of distal (cross-chromosome) ME. Across the 16 trait-loci pairs, we document that the proportion of trait variance explained by ME is about 12x as large as that explained by the GWAS effects on average (range: 0.59 to 43.89). Our results show, for the first time, evidence of interaction effects between individual genetic variants and overall polygenic background modulating complex trait variation.

Brain Protection in Aortic Arch Surgery: An Evolving Field.

Despite advances in cardiac surgery and anesthesia, the rates of brain injury remain high in aortic arch surgery requiring circulatory arrest. The mechanisms of brain injury, including permanent and temporary neurologic dysfunction, are multifactorial, but intraoperative brain ischemia is likely a major contributor. Maintaining optimal cerebral perfusion during cardiopulmonary bypass and circulatory arrest is the key component of intraoperative management for aortic arch surgery. Various brain monitoring modalities provide different information to improve cerebral protection. Electroencephalography gives crucial data to ensure minimal cerebral metabolism during deep hypothermic circulatory arrest, transcranial Doppler directly measures cerebral arterial blood flow, and near-infrared spectroscopy monitors regional cerebral oxygen saturation. Various brain protection techniques, including hypothermia, cerebral perfusion, pharmacologic protection, and blood gas management, have been used during interruption of systemic circulation, but the optimal strategy remains elusive. Although deep hypothermic circulatory arrest and retrograde cerebral perfusion have their merits, there have been increasing reports about the use of antegrade cerebral perfusion, obviating the need for deep hypothermia. With controversy and variability of surgical practices, moderate hypothermia, when combined with unilateral antegrade cerebral perfusion, is considered safe for brain protection in aortic arch surgery performed with circulatory arrest. The neurologic outcomes of brain protection in aortic arch surgery largely depend on the following three major components: cerebral temperature, circulatory arrest time, and cerebral perfusion during circulatory arrest. The optimal brain protection strategy should be individualized based on comprehensive monitoring and stems from well-executed techniques that balance the major components contributing to brain injury.

DUETT quantitatively identifies known and novel events in nascent RNA structural dynamics from chemical probing data.

MOTIVATION: RNA molecules can undergo complex structural dynamics, especially during transcription, which influence their biological functions. Recently developed high-throughput chemical probing experiments that study RNA cotranscriptional folding generate nucleotide-resolution 'reactivities' for each length of a growing nascent RNA that reflect structural dynamics. However, the manual annotation and qualitative interpretation of reactivity across these large datasets can be nuanced, laborious, and difficult for new practitioners. We developed a quantitative and systematic approach to automatically detect RNA folding events from these datasets to reduce human bias/error, standardize event discovery and generate hypotheses about RNA folding trajectories for further analysis and experimental validation. RESULTS: Detection of Unknown Events with Tunable Thresholds (DUETT) identifies RNA structural transitions in cotranscriptional RNA chemical probing datasets. DUETT employs a feedback control-inspired method and a linear regression approach and relies on interpretable and independently tunable parameter thresholds to match qualitative user expectations with quantitatively identified folding events. We validate the approach by identifying known RNA structural transitions within the cotranscriptional folding pathways of the Escherichia coli signal recognition particle RNA and the Bacillus cereus crcB fluoride riboswitch. We identify previously overlooked features of these datasets such as heightened reactivity patterns in the signal recognition particle RNA about 12 nt lengths before base-pair rearrangement. We then apply a sensitivity analysis to identify tradeoffs when choosing parameter thresholds. Finally, we show that DUETT is tunable across a wide range of contexts, enabling flexible application to study broad classes of RNA folding mechanisms. AVAILABILITY AND IMPLEMENTATION: https://github.com/BagheriLab/DUETT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploration of the nanomedicine-design space with high-throughput screening and machine learning.

Only a tiny fraction of the nanomedicine-design space has been explored, owing to the structural complexity of nanomedicines and the lack of relevant high-throughput synthesis and analysis methods. Here, we report a methodology for determining structure-activity relationships and design rules for spherical nucleic acids (SNAs) functioning as cancer-vaccine candidates. First, we identified ~1,000 candidate SNAs on the basis of reasonable ranges for 11 design parameters that can be systematically and independently varied to optimize SNA performance. Second, we developed a high-throughput method for making SNAs at the picomolar scale in a 384-well format, and used a mass spectrometry assay to rapidly measure SNA immune activation. Third, we used machine learning to quantitatively model SNA immune activation and identify the minimum number of SNAs needed to capture optimum structure-activity relationships for a given SNA library. Our methodology is general, can reduce the number of nanoparticles that need to be tested by an order of magnitude, and could serve as a screening tool for the development of nanoparticle therapeutics.

Machine Learning on Signal-to-Noise Ratios Improves Peptide Array Design in SAMDI Mass Spectrometry.

Emerging peptide array technologies are able to profile molecular activities within cell lysates. However, the structural diversity of peptides leads to inherent differences in peptide signal-to-noise ratios (S/N). These complex effects can lead to potentially unrepresentative signal intensities and can bias subsequent analyses. Within mass spectrometry-based peptide technologies, the relation between a peptide's amino acid sequence and S/N remains largely nonquantitative. To address this challenge, we present a method to quantify and analyze mass spectrometry S/N of two peptide arrays, and we use this analysis to portray quality of data and to design future arrays for SAMDI mass spectrometry. Our study demonstrates that S/N varies significantly across peptides within peptide arrays, and variation in S/N is attributable to differences of single amino acids. We apply supervised machine learning to predict peptide S/N based on amino acid sequence, and identify specific physical properties of the amino acids that govern variation of this metric. We find low peptide-S/N concordance between arrays, demonstrating that different arrays require individual characterization and that global peptide-S/N relationships are difficult to identify. However, with proper peptide sampling, this study illustrates how machine learning can accurately predict the S/N of a peptide in an array, allowing for the efficient design of arrays through selection of high S/N peptides.

A systems approach to integrative biology: an overview of statistical methods to elucidate association and architecture.

An organism's ability to maintain a desired physiological response relies extensively on how cellular and molecular signaling networks interpret and react to environmental cues. The capacity to quantitatively predict how networks respond to a changing environment by modifying signaling regulation and phenotypic responses will help inform and predict the impact of a changing global enivronment on organisms and ecosystems. Many computational strategies have been developed to resolve cue-signal-response networks. However, selecting a strategy that answers a specific biological question requires knowledge both of the type of data being collected, and of the strengths and weaknesses of different computational regimes. We broadly explore several computational approaches, and we evaluate their accuracy in predicting a given response. Specifically, we describe how statistical algorithms can be used in the context of integrative and comparative biology to elucidate the genomic, proteomic, and/or cellular networks responsible for robust physiological response. As a case study, we apply this strategy to a dataset of quantitative levels of protein abundance from the mussel, Mytilus galloprovincialis, to uncover the temperature-dependent signaling network.

Formation of nanodiamonds at near-ambient conditions via microplasma dissociation of ethanol vapour.

Clusters of diamond-phase carbon, known as nanodiamonds, exhibit novel mechanical, optical and biological properties that have elicited interest for a wide range of technological applications. Although diamond is predicted to be more stable than graphite at the nanoscale, extreme environments are typically used to produce nanodiamonds. Here we show that nanodiamonds can be stably formed in the gas phase at atmospheric pressure and neutral gas temperatures <100 °C by dissociation of ethanol vapour in a novel microplasma process. Addition of hydrogen gas to the process allows in flight purification by selective etching of the non-diamond carbon and stabilization of the nanodiamonds. The nanodiamond particles are predominantly between 2 and 5 nm in diameter, and exhibit cubic diamond, n-diamond and lonsdaleite crystal structures, similar to nanodiamonds recovered from meteoritic residues. These results may help explain the origin of nanodiamonds in the cosmos, and offer a simple and inexpensive route for the production of high-purity nanodiamonds.