PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting.

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. By learning from data, PHACTboost outperforms PHACT. Furthermore, the results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, metapredictors, and deep learning-based approaches as well as more recent tools such as AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost is available at https://github.com/CompGenomeLab/PHACTboost. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.

PHACT: Phylogeny-Aware Computing of Tolerance for Missense Mutations.

Evolutionary conservation is a fundamental resource for predicting the substitutability of amino acids and the loss of function in proteins. The use of multiple sequence alignment alone-without considering the evolutionary relationships among sequences-results in the redundant counting of evolutionarily related alteration events, as if they were independent. Here, we propose a new method, PHACT, that predicts the pathogenicity of missense mutations directly from the phylogenetic tree of proteins. PHACT travels through the nodes of the phylogenetic tree and evaluates the deleteriousness of a substitution based on the probability differences of ancestral amino acids between neighboring nodes in the tree. Moreover, PHACT assigns weights to each node in the tree based on their distance to the query organism. For each potential amino acid substitution, the algorithm generates a score that is used to calculate the effect of substitution on protein function. To analyze the predictive performance of PHACT, we performed various experiments over the subsets of two datasets that include 3,023 proteins and 61,662 variants in total. The experiments demonstrated that our method outperformed the widely used pathogenicity prediction tools (i.e., SIFT and PolyPhen-2) and achieved a better predictive performance than other conventional statistical approaches presented in dbNSFP. The PHACT source code is available at https://github.com/CompGenomeLab/PHACT.

Investigating the influence of collagen cross-linking on mechanical properties of thoracic aortic tissue.

Vascular diseases, such as abdominal aortic aneurysms, are associated with tissue degeneration of the aortic wall, resulting in variations in mechanical properties, such as tissue ultimate stress and a high slope. Variations in the mechanical properties of tissues may be associated with an increase in the number of collagen cross-links. Understanding the effect of collagen cross-linking on tissue mechanical properties can significantly aid in predicting diseased aortic tissue rupture and improve the clarity of decisions regarding surgical procedures. Therefore, this study focused on increasing the density of the aortic tissue through cross-linking and investigating the mechanical properties of the thoracic aortic tissue in relation to density. Uniaxial tensile tests were conducted on the porcine thoracic aorta in four test regions (anterior, posterior, distal, and proximal), two loading directions (circumferential and longitudinal), and density increase rates (0%-12%). As a result, the PPC (Posterior/Proximal/Circumferential) group experienced a higher ultimate stress than the PDC (Posterior/Distal/Circumferential) group. However, this relationship reversed when the specimen density exceeded 3%. In addition, the ultimate stress of the ADC (Anterior/Distal/Circumferential) and PPC group was greater than that of the APC (Anterior/Proximal/Circumferential) group, while these findings were reversed when the specimen density exceeded 6% and 9%, respectively. Finally, the high slope of the PDL (Posterior/Distal/Longitudinal) group was lower than that of the ADL (Anterior/Distal/Longitudinal) group, but the high slope of the PDL group appeared larger due to the stabilization treatment. This highlights the potential impact of density variations on the mechanical properties of specific specimen groups.

Defining a master curve of abdominal aortic aneurysm growth and its potential utility of clinical management.

OBJECTIVE: The maximum diameter measurement of an abdominal aortic aneurysm (AAA), which depends on orthogonal and axial cross-sections or maximally inscribed spheres within the AAA, plays a significant role in the clinical decision making process. This study aims to build a total of 21 morphological parameters from longitudinal CT scans and analyze their correlations. Furthermore, this work explores the existence of a "master curve" of AAA growth, and tests which parameters serve to enhance its predictability for clinical use. METHODS: 106 CT scan images from 25 Korean AAA patients were retrospectively obtained. We subsequently computed morphological parameters, growth rates, and pair-wise correlations, and attempted to enhance the predictability of the growth for high-risk aneurysms using non-linear curve fitting and least-square minimization. RESULTS: An exponential AAA growth model was fitted to the maximum spherical diameter, as the best representative of the growth among all parameters (r-square: 0.94) and correctly predicted to 15 of 16 validation scans based on a 95% confidence interval. AAA volume expansion rates were highly correlated (r=0.75) with thrombus accumulation rates. CONCLUSIONS: The exponential growth model using spherical diameter provides useful information about progression of aneurysm size and enables AAA growth rate extrapolation during a given surveillance period.

Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference.

OBJECTIVE: For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter. METHODS: 106 CT scans from 25 Korean AAA patients were retrospectively obtained. A two-step approach based on Bayesian calibration was used, and an exponential abdominal aortic aneurysm growth model (population-based) was specified according to each individual patient's growth (patient-specific) and morphologic characteristics of the aneurysm sac (enhanced). The distribution estimates were obtained using a Markov Chain Monte Carlo (MCMC) sampler. RESULTS: The follow-up diameters were predicted satisfactorily (i.e. the true follow-up diameter was in the 95% prediction interval) for 79% of the scans using the population-based growth model, and 83% of the scans using the patient-specific growth model. Among the evaluated geometric measurements, centerline tortuosity was a significant (p = 0.0002) predictor of growth for AAAs with accelerated and stable expansion rates. Using the enhanced prediction model, 86% of follow-up scans were predicted satisfactorily. The average prediction errors of population-based, patient-specific, and enhanced models were ±2.67, ±2.61 and ± 2.79 mm, respectively. CONCLUSION: A computational framework using patient-oriented growth models provides useful tools for per-patient basis treatment and enables better prediction of AAA growth.