Management of deep hypothermic (DH) cardiopulmonary bypass (CPB), a critical neuroprotective strategy, currently relies on non-invasive temperature to guide cerebral metabolic suppression during complex cardiac surgery in neonates. Considerable inter-subject variability in temperature response and residual metabolism may contribute to the persisting risk for postoperative neurological injury. To characterize and mitigate this variability, we assess the sufficiency of conventional nasopharyngeal temperature (NPT) guidance, and in the process, validate combined non-invasive frequency-domain diffuse optical spectroscopy (FD-DOS) and diffuse correlation spectroscopy (DCS) for direct measurement of cerebral metabolic rate of oxygen (CMRO2). During CPB, n = 8 neonatal swine underwent cooling from normothermia to 18℃, sustained DH perfusion for 40 min, and then rewarming to simulate cardiac surgery. Continuous non-invasive and invasive measurements of intracranial temperature (ICT) and CMRO2 were acquired. Significant hysteresis (p < 0.001) between cooling and rewarming periods in the NPT versus ICT and NPT versus CMRO2 relationships were found. Resolution of this hysteresis in the ICT versus CMRO2 relationship identified a crucial insufficiency of conventional NPT guidance. Non-invasive CMRO2 temperature coefficients with respect to NPT (Q10 = 2.0) and ICT (Q10 = 2.5) are consistent with previous reports and provide further validation of FD-DOS/DCS CMRO2 monitoring during DH CPB to optimize management.
Allelic expression (AE) imbalance between the two alleles of a gene can be used to detect cis-acting regulatory SNPs (rSNPs) in individuals heterozygous for a transcribed SNP (tSNP). In this paper, we propose three tests for AE analysis focusing on phase-unknown data and any degree of linkage disequilibrium (LD) between the rSNP and tSNP: a test based on the minimum P-value of a one-sided F test and a two-sided t test (proposed previously for phase-unknown data), a test the combines the F and t tests, and a mixture-model-based test. We compare these three tests to the F and t tests and an existing regression-based test for phase-known data. We show that the ranking of the tests based on power depends most strongly on the magnitude of the LD between the rSNP and tSNP. For phase-unknown data, we find that under a range of scenarios, our proposed tests have higher power than the F and t tests when LD between the rSNP and tSNP is moderate (∼0.2<<∼0.8). We further demonstrate that the presence of a second ungenotyped rSNP almost never invalidates the proposed tests nor substantially changes their power rankings. For detection of cis-acting regulatory SNPs using phase-unknown AE data, we recommend the F test when the rSNP and tSNP are in or near linkage equilibrium (<0.2); the t test when the two SNPs are in strong LD (<0.7); and the mixture-model-based test for intermediate LD levels (0.2<<0.7).
RNA sequencing (RNA-Seq) allows an unbiased survey of the entire transcriptome in a high-throughput manner. A major application of RNA-Seq is to detect differential isoform expression across experimental conditions, which is of great biological interest due to its direct relevance to protein function and disease pathogenesis. Detection of differential isoform expression is challenging because of uncertainty in isoform expression estimation owing to ambiguous reads and variability in precision of the estimates across samples. It is desirable to have a method that can account for these issues and is flexible enough to allow adjustment for covariates.In this paper, we present MetaDiff, a random-effects meta-regression model that naturally fits for the above purposes. Through extensive simulations and analysis of an RNA-Seq dataset on human heart failure, we show that the random-effects meta-regression approach is computationally fast, reliable, and can improve the power of differential expression analysis while controlling for false positives due to the effect of covariates or confounding variables. In contrast, several existing methods either fail to control false discovery rate or have reduced power in the presence of covariates or confounding variables. The source code, compiled JAR package and documentation of MetaDiff are freely available at https://github.com/jiach/MetaDiff.Our results indicate that random-effects meta-regression offers a flexible framework for differential expression analysis of isoforms, particularly when gene expression is influenced by other variables.
Allele-specific expression (ASE) analysis, which quantifies the relative expression of two alleles in a diploid individual, is a powerful tool for identifying cis-regulated gene expression variations that underlie phenotypic differences among individuals. Existing methods for gene-level ASE detection analyze one individual at a time, therefore failing to account for shared information across individuals. Failure to accommodate such shared information not only reduces power, but also makes it difficult to interpret results across individuals. However, when only RNA sequencing (RNA-seq) data are available, ASE detection across individuals is challenging because the data often include individuals that are either heterozygous or homozygous for the unobserved cis-regulatory SNP, leading to sample heterogeneity as only those heterozygous individuals are informative for ASE, whereas those homozygous individuals have balanced expression. To simultaneously model multi-individual information and account for such heterogeneity, we developed ASEP, a mixture model with subject-specific random effect to account for multi-SNP correlations within the same gene. ASEP only requires RNA-seq data, and is able to detect gene-level ASE under one condition and differential ASE between two conditions (e.g., pre- versus post-treatment). Extensive simulations demonstrated the convincing performance of ASEP under a wide range of scenarios. We applied ASEP to a human kidney RNA-seq dataset, identified ASE genes and validated our results with two published eQTL studies. We further applied ASEP to a human macrophage RNA-seq dataset, identified genes showing evidence of differential ASE between M0 and M1 macrophages, and confirmed our findings by results from cardiometabolic trait-relevant genome-wide association studies. To the best of our knowledge, ASEP is the first method for gene-level ASE detection at the population level that only requires the use of RNA-seq data. With the growing adoption of RNA-seq, we believe ASEP will be well-suited for various ASE studies for human diseases.
Diffuse axonal injury (DAI) is an important consequence of traumatic brain injury (TBI). At the moment of trauma, axons rarely disconnect, but undergo cytoskeletal disruption and transport interruption leading to protein accumulation within swellings. The amyloid precursor protein (APP) accumulates rapidly and the standard histological evaluation of axonal pathology relies upon its detection. APP+ swellings first appear as varicosities along intact axons, which can ultimately undergo secondary disconnection to leave a terminal "axon bulb" at the disconnected, proximal end. However, sites of disconnection are difficult to determine with certainty using standard, thin tissue sections, thus limiting the comprehensive evaluation of axon degeneration. The tissue-clearing technique, CLARITY, permits three-dimensional visualization of axons that would otherwise be out of plane in standard tissue sections. Here, we examined the morphology and connection status of APP+ swellings using CLARITY at 6 h, 24 h, 1 week and 1 month following the controlled cortical impact (CCI) model of TBI in mice. Remarkably, many APP+ swellings that appeared as terminal bulbs when viewed in standard 8-µm-thick regions of tissue were instead revealed to be varicose swellings along intact axons when three dimensions were fully visible. Moreover, the percentage of these potentially viable axon swellings differed with survival from injury and may represent the delayed onset of distinct mechanisms of degeneration. Even at 1-month post-CCI, ~10% of apparently terminal bulbs were revealed as connected by CLARITY and are thus potentially salvageable. Intriguingly, the diameter of swellings decreased with survival, including varicosities along intact axons, and may reflect reversal of, or reduced, axonal transport interruption in the chronic setting. These data indicate that APP immunohistochemistry on standard thickness tissue sections overestimates axon disconnection, particularly acutely post-injury. Evaluating cleared tissue demonstrates a surprisingly delayed process of axon disconnection and thus longer window of therapeutic opportunity than previously appreciated. Intriguingly, a subset of axon swellings may also be capable of recovery.
Abstract Background ‘Mitochondrial Myopathy’ (MM) refers to genetically‐confirmed Primary Mitochondrial Disease (PMD) that predominantly impairs skeletal muscle function. Validated outcome measures encompassing core MM domains of muscle weakness, muscle fatigue, imbalance, impaired dexterity, and exercise intolerance do not exist. The goal of this study was to validate clinically‐meaningful, quantitative outcome measures specific to MM. Methods This was a single centre study. Objective measures evaluated included hand‐held dynamometry, balance assessments, Nine Hole Peg Test (9HPT), Functional Dexterity Test (FDT), 30 second Sit to Stand (30s STS), and 6‐minute walk test (6MWT). Results were assessed as z ‐scores, with < −2 standard deviations considered abnormal. Performance relative to the North Star Ambulatory Assessment (NSAA) of functional mobility was assessed by Pearson's correlation. Results In genetically‐confirmed MM participants [ n = 59, mean age 21.6 ± 13.9 (range 7 – 64.6 years), 44.1% male], with nuclear gene aetiologies, n = 18/59, or mitochondrial (mtDNA) aetiologies, n = 41/59, dynamometry measurements demonstrated both proximal [dominant elbow flexion (−2.6 ± 2.1, mean z ‐score ± standard deviation, SD), hip flexion (−2.5 ± 2.3), and knee flexion (−2.8 ± 1.3)] and distal muscle weakness [wrist extension (−3.4 ± 1.7), palmar pinch (−2.5 ± 2.8), and ankle dorsiflexion (−2.4 ± 2.5)]. Balance [Tandem Stance (TS) Eyes Open (−3.2 ± 8.8, n = 53) and TS Eyes Closed (−2.6 ± 2.7, n = 52)] and dexterity [FDT (−5.9 ± 6.0, n = 44) and 9HPT (−8.3 ± 11.2, n = 53)] assessments also revealed impairment. Exercise intolerance was confirmed by strength‐based 30s STS test (−2.0 ± 0.8, n = 38) and mobility‐based 6MWT mean z ‐score (−2.9 ± 1.3, n = 46) with significant decline in minute distances (slope −0.9, p = 0.03, n = 46). Muscle fatigue was quantified by dynamometry repetitions with strength decrement noted between first and sixth repetitions at dominant elbow flexors (−14.7 ± 2.2%, mean ± standard error, SEM, n = 21). All assessments were incorporated in the MM‐Composite Assessment Tool (MM‐COAST). MM‐COAST composite score for MM participants was 1.3 ± 0.1 ( n = 53) with a higher score indicating greater MM disease severity, and correlated to NSAA ( r = −0.64, p < 0.0001, n = 52) to indicate clinical meaning. Test–retest reliability of MM‐COAST assessments in an MM subset ( n = 14) revealed an intraclass correlation coefficient (ICC) of 0.81 (95% confidence interval: 0.59–0.92) indicating good reliability. Conclusions We have developed and successfully validated a MM‐specific Composite Assessment Tool to quantify the key domains of MM, shown to be abnormal in a Definite MM cohort. MM‐COAST may hold particular utility as a meaningful outcome measure in future MM intervention trials.
