Parallel or convergent evolution in human population genomic data revealed by genotype networks.

BACKGROUND: Genotype networks are representations of genetic variation data that are complementary to phylogenetic trees. A genotype network is a graph whose nodes are genotypes (DNA sequences) with the same broadly defined phenotype. Two nodes are connected if they differ in some minimal way, e.g., in a single nucleotide. RESULTS: We analyze human genome variation data from the 1,000 genomes project, and construct haploid genotype (haplotype) networks for 12,235 protein coding genes. The structure of these networks varies widely among genes, indicating different patterns of variation despite a shared evolutionary history. We focus on those genes whose genotype networks show many cycles, which can indicate homoplasy, i.e., parallel or convergent evolution, on the sequence level. CONCLUSION: For 42 genes, the observed number of cycles is so large that it cannot be explained by either chance homoplasy or recombination. When analyzing possible explanations, we discovered evidence for positive selection in 21 of these genes and, in addition, a potential role for constrained variation and purifying selection. Balancing selection plays at most a small role. The 42 genes with excess cycles are enriched in functions related to immunity and response to pathogens. Genotype networks are representations of genetic variation data that can help understand unusual patterns of genomic variation.

VCF2Networks: applying genotype networks to single-nucleotide variants data.

SUMMARY: A wealth of large-scale genome sequencing projects opens the doors to new approaches to study the relationship between genotype and phenotype. One such opportunity is the possibility to apply genotype networks analysis to population genetics data. Genotype networks are a representation of the set of genotypes associated with a single phenotype, and they allow one to estimate properties such as the robustness of the phenotype to mutations, and the ability of its associated genotypes to evolve new adaptations. So far, though, genotype networks analysis has rarely been applied to population genetics data. To help fill this gap, here we present VCF2Networks, a tool to determine and study genotype network structure from single-nucleotide variant data. AVAILABILITY AND IMPLEMENTATION: VCF2Networks is available at https://bitbucket.org/dalloliogm/vcf2networks. CONTACT: giovanni.dallolio@kcl.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Collagen matricryptin promotes cardiac function by mediating scar formation.

AIMS: A peptide mimetic of a collagen-derived matricryptin (p1159) was shown to reduce left ventricular (LV) dilation and fibrosis after 7 days delivery in a mouse model of myocardial infarction (MI). This suggested p1159 long-term treatment post-MI could have beneficial effects and reduce/prevent adverse LV remodeling. This study aimed to test the potential of p1159 to reduce adverse cardiac remodeling in a chronic MI model and to elucidate p1159 mode-of-action. MATERIALS AND METHODS: Using a permanent occlusion MI rodent model, animals received p1159 or vehicle solution up to 28 days. We assessed peptide treatment effects on scar composition and structure and on systolic function. To assess peptide effects on scar vascularization, a cohort of mice were injected with Griffonia simplicifolia isolectin-B4. To investigate p1159 mode-of-action, LV fibroblasts from naïve animals were treated with increasing doses of p1159. KEY FINDINGS: Matricryptin p1159 significantly improved systolic function post-MI (2-fold greater EF compared to controls) by reducing left ventricular dilation and inducing the formation of a compliant and organized infarct scar, which promoted LV contractility and preserved the structural integrity of the heart. Specifically, infarcted scars from p1159-treated animals displayed collagen fibers aligned parallel to the epicardium, to resist circumferential stretching, with reduced levels of cross-linking, and improved tissue perfusion. In addition, we found that p1159 increases cardiac fibroblast migration by activating RhoA pathways via the membrane receptor integrin α4. SIGNIFICANCE: Our data indicate p1159 treatment reduced adverse LV remodeling post-MI by modulating the deposition, arrangement, and perfusion of the fibrotic scar.

Ancient chicken remains reveal the origins of virulence in Marek's disease virus.

The pronounced growth in livestock populations since the 1950s has altered the epidemiological and evolutionary trajectory of their associated pathogens. For example, Marek's disease virus (MDV), which causes lymphoid tumors in chickens, has experienced a marked increase in virulence over the past century. Today, MDV infections kill >90% of unvaccinated birds, and controlling it costs more than US$1 billion annually. By sequencing MDV genomes derived from archeological chickens, we demonstrate that it has been circulating for at least 1000 years. We functionally tested the Meq oncogene, one of 49 viral genes positively selected in modern strains, demonstrating that ancient MDV was likely incapable of driving tumor formation. Our results demonstrate the power of ancient DNA approaches to trace the molecular basis of virulence in economically relevant pathogens.

Genomic imprinting and genetic effects on muscle traits in mice.

BACKGROUND: Genomic imprinting refers to parent-of-origin dependent gene expression caused by differential DNA methylation of the paternally and maternally derived alleles. Imprinting is increasingly recognized as an important source of variation in complex traits, however, its role in explaining variation in muscle and physiological traits, especially those of commercial value, is largely unknown compared with genetic effects. RESULTS: We investigated both genetic and genomic imprinting effects on key muscle traits in mice from the Berlin Muscle Mouse population, a key model system to study muscle traits. Using a genome scan, we first identified loci with either imprinting or genetic effects on phenotypic variation. Next, we established the proportion of phenotypic variation explained by additive, dominance and imprinted QTL and characterized the patterns of effects. In total, we identified nine QTL, two of which show large imprinting effects on glycogen content and potential, and body weight. Surprisingly, all imprinting patterns were of the bipolar type, in which the two heterozygotes are different from each other but the homozygotes are not. Most QTL had pleiotropic effects and explained up to 40% of phenotypic variance, with individual imprinted loci accounting for 4-5% of variation alone. CONCLUSION: Surprisingly, variation in glycogen content and potential was only modulated by imprinting effects. Further, in contrast to general assumptions, our results show that genomic imprinting can impact physiological traits measured at adult stages and that the expression does not have to follow the patterns of paternal or maternal expression commonly ascribed to imprinting effects.

Computational investigation of fibrin mechanical and damage properties at the interface between native cartilage and implant.

Scaffold-based tissue-engineered constructs as well as cell-free implants offer promising solutions to focal cartilage lesions. However, adequate mechanical stability of these implants in the lesion is required for successful repair. Fibrin is the most common clinically available adhesive for cartilage implant fixation, but fixation quality using fibrin is not well understood. The objectives of this study were to investigate the conditions leading to damage in the fibrin adhesive and to determine which adhesive properties are important in preventing delamination at the interface. An idealized finite element model of the medial compartment of the knee was created, including a circular defect and an osteochondral implant. Damage and failure of fibrin at the interface was represented by a cohesive zone model with coefficients determined from an inverse finite element method and previously published experimental data. Our results demonstrated that fibrin glue alone may not be strong enough to withstand physiologic loads in vivo while fibrin glue combined with chondrocytes more effectively prevents damage at the interface. The results of this study suggest that fibrin fails mainly in shear during off-axis loading and that adhesive materials that are stronger or more compliant than fibrin may be good alternatives due to decreased failure at the interface. The present model may be used to improve design and testing protocols of bioadhesives and give insight into the failure mechanisms of cartilage implant fixation in the knee joint.

Methods to assess in vitro wear of articular cartilage.

New orthopedic implants for focal cartilage defects replace only a portion of the articulating joint and wear against the opposing cartilage surface. The objective of this study was to investigate different methodologies to quantify cartilage wear for future use in screening potential implant materials and finishes. In determining the optimal test parameters, two different cartilage surface geometries were compared: smaller specimens had a flat surface, while larger ones made contact in the center but not at the edge owing to the curvature of the articulating surface. The cartilage wear of the two geometries was compared using three different techniques: the collagen worn from the cartilage specimens was assessed with a modified wear factor, the surface damage was made visible with Indian ink and was quantified, and the change in surface roughness was measured. To interpret the experimental results, maximum shear stresses were evaluated with sliding contact finite element models. Although the modified wear factor was considered to be the most accurate assessment of cartilage wear, surface damage was an effective, inexpensive, and quick technique to evaluate potential implant materials. Flat specimens showed excessive wear at the edges owing to a non-physiologic stress concentration, while the larger specimens wore more uniformly across the surface. These results will be applied to future studies evaluating prospective implant materials.

In silicomodeling of tibial fatigue life in physically active males and females during different exercise protocols.

Preventing bone stress injuries (BSI) requires a deep understanding of the condition's underlying causes and risk factors. Subject-specific computer modeling studies of gait mechanics, including the effect of changes in running speed, stride length, and landing patterns on tibial stress injury formation can provide essential insights into BSI prevention. This study aimed to computationally examine the effect of different exercise protocols on tibial fatigue life in male and female runners during prolonged walking and running at three different speeds. To achieve these aims, we combined subject-specific magnetic resonance imaging (MRI), gait data, finite element analysis, and a fatigue life prediction algorithm, including repair and adaptation's influence. The algorithm predicted a steep increase in the likelihood of developing a BSI within the first 40 days of activity. In five of the six subjects simulated, faster running speeds corresponded with higher tibial strains and higher probability of failure. Our simulations also showed that female subjects had a higher mean peak probability of failure in all four gait conditions than the male subjects studied. The approach used in this study could lay the groundwork for studies in larger populations and patient-specific clinical tools and decision support systems to reduce BSIs in athletes, military personnel, and other active individuals.

Towards a multi-scale computer modeling workflow for simulation of pulmonary ventilation in advanced COVID-19.

Physics-based multi-scale in silico models offer an excellent opportunity to study the effects of heterogeneous tissue damage on airflow and pressure distributions in COVID-19-afflicted lungs. The main objective of this study is to develop a computational modeling workflow, coupling airflow and tissue mechanics as the first step towards a virtual hypothesis-testing platform for studying injury mechanics of COVID-19-afflicted lungs. We developed a CT-based modeling approach to simulate the regional changes in lung dynamics associated with heterogeneous subject-specific COVID-19-induced damage patterns in the parenchyma. Furthermore, we investigated the effect of various levels of inflammation in a meso-scale acinar mechanics model on global lung dynamics. Our simulation results showed that as the severity of damage in the patient's right lower, left lower, and to some extent in the right upper lobe increased, ventilation was redistributed to the least injured right middle and left upper lobes. Furthermore, our multi-scale model reasonably simulated a decrease in overall tidal volume as the level of tissue injury and surfactant loss in the meso-scale acinar mechanics model was increased. This study presents a major step towards multi-scale computational modeling workflows capable of simulating the effect of subject-specific heterogenous COVID-19-induced lung damage on ventilation dynamics.

The genomic history and global expansion of domestic donkeys.

Donkeys transformed human history as essential beasts of burden for long-distance movement, especially across semi-arid and upland environments. They remain insufficiently studied despite globally expanding and providing key support to low- to middle-income communities. To elucidate their domestication history, we constructed a comprehensive genome panel of 207 modern and 31 ancient donkeys, as well as 15 wild equids. We found a strong phylogeographic structure in modern donkeys that supports a single domestication in Africa ~5000 BCE, followed by further expansions in this continent and Eurasia and ultimately returning to Africa. We uncover a previously unknown genetic lineage in the Levant ~200 BCE, which contributed increasing ancestry toward Asia. Donkey management involved inbreeding and the production of giant bloodlines at a time when mules were essential to the Roman economy and military.

Integration of mechanics and biology in computer simulation of bone remodeling.

Bone remodeling is a complex physiological process that spans across multiple spatial and temporal scales and is regulated by both mechanical and hormonal cues. An imbalance between bone resorption and bone formation in the process of bone remodeling may lead to various bone pathologies. One powerful and non-invasive approach to gain new insights into mechano-adaptive bone remodeling is computer modeling and simulation. Recent findings in bone physiology and advances in computer modeling have provided a unique opportunity to study the integration of mechanics and biology in bone remodeling. Our objective in this review is to critically appraise recent advances and developments and discuss future research opportunities in computational bone remodeling approaches that enable integration of mechanics and cellular and molecular pathways. Based on the critical appraisal of the relevant recent published literature, we conclude that multiscale in silico integration of personalized bone mechanics and mechanobiology combined with data science and analytics techniques offer the potential to deepen our knowledge of bone remodeling and provide ample opportunities for future research.

Implications of microscale lung damage for COVID-19 pulmonary ventilation dynamics: A narrative review.

The COVID-19 pandemic surges on as vast research is produced to study the novel SARS-CoV-2 virus and the disease state it induces. Still, little is known about the impact of COVID-19-induced microscale damage in the lung on global lung dynamics. This review summarizes the key histological features of SARS-CoV-2 infected alveoli and links the findings to structural tissue changes and surfactant dysfunction affecting tissue mechanical behavior similar to changes seen in other lung injury. Along with typical findings of diffuse alveolar damage affecting the interstitium of the alveolar walls and blood-gas barrier in the alveolar airspace, COVID-19 can cause extensive microangiopathy in alveolar capillaries that further contribute to mechanical changes in the tissues and may differentiate it from previously studied infectious lung injury. Understanding microlevel damage impact on tissue mechanics allows for better understanding of macroscale respiratory dynamics. Knowledge gained from studies into the relationship between microscale and macroscale lung mechanics can allow for optimized treatments to improve patient outcomes in case of COVID-19 and future respiratory-spread pandemics.

Bone morphogenetic protein 6 drives both osteogenesis and chondrogenesis in murine adipose-derived mesenchymal cells depending on culture conditions.

Bone morphogenetic proteins (BMPs) play a dual role as a factor in both bone and cartilage development and correspondingly have the therapeutic potential to regenerate both tissues. Given this dual nature, previous in vitro research using BMPs has relied on distinct media formulations and culture conditions to drive undifferentiated cells to the osteogenic or chondrogenic lineage. To isolate the impact of culture conditions and to explore the effect of BMP-6 on murine adipose-derived mesenchymal cells (ASCs), ASCs were seeded in either monolayer or pellets in an identical medium containing BMP-6. Results indicate that BMP-6 differentially promotes osteogenesis and chondrogenesis in ASCs depending on culture conditions. BMP-6 potently induced alkaline phosphatase activity and mineralization in ASCs cultured in monolayer conditions. In contrast, BMP-6 enhanced proteoglycan accumulation in ASCs seeded in chondrogenic pellet culture. A comparison of gene expression suggests that the differentiating effect of BMP-6 is specific to the particular culture condition. This study highlights the importance of the interactions between chemical signaling and microenvironmental cues in directing cell fate.

Applications of Computer Modeling and Simulation in Cartilage Tissue Engineering.

BACKGROUND: Advances in cartilage tissue engineering have demonstrated noteworthy potential for developing cartilage for implantation onto sites impacted by joint degeneration and injury. To supplement resource-intensive in vivo and in vitro studies required for cartilage tissue engineering, computational models and simulations can assist in enhancing experimental design. METHODS: Research articles pertinent to cartilage tissue engineering and computer modeling were identified, reviewed, and summarized. Various applications of computer modeling for cartilage tissue engineering are highlighted, limitations of in silico modeling are addressed, and suggestions for future work are enumerated. RESULTS: Computational modeling can help better characterize shear stresses generated by bioreactor fluid flow, refine scaffold geometry, customize the mechanical properties of engineered cartilage tissue, and model rates of cell growth and dynamics. Thus, results from in silico studies can help resourcefully enhance in vitro and in vivo studies; however, the limitations of these studies, such as the underlying assumptions and simplifications applied in each model, should always be addressed and justified where applicable. In silico models should also seek validation and verification when possible. CONCLUSION: Future studies may adopt similar approaches to supplement in vitro trials and further investigate effects of mechanical stimulation on chondrocyte and stem cell dynamics. Additionally, as precision medicine, machine learning, and powerful open-source software become more popular and accessible, applications of multi-scale and multiphysics computational models in cartilage tissue engineering are expected to increase.

A rule-based semantic approach for data integration, standardization and dimensionality reduction utilizing the UMLS: Application to predicting bariatric surgery outcomes.

Utilization of existing clinical data for improving patient outcomes poses a number of challenging and complex problems involving lack of data integration, the absence of standardization across inhomogeneous data sources and computationally-demanding and time-consuming exploration of very large datasets. In this paper, we will present a robust semantic data integration, standardization and dimensionality reduction method to tackle and solve these problems. Our approach enables the integration of clinical data from diverse sources by resolving canonical inconsistencies and semantic heterogeneity as required by the National Library of Medicine's Unified Medical Language System (UMLS) to produce standardized medical data. Through a combined application of rule-based semantic networks and machine learning, our approach enables a large reduction in dimensionality of the data and thus allows for fast and efficient application of data mining techniques to large clinical datasets. An example application of the techniques developed in our study is presented for the prediction of bariatric surgery outcomes.

Ancient goat genomes reveal mosaic domestication in the Fertile Crescent.

Current genetic data are equivocal as to whether goat domestication occurred multiple times or was a singular process. We generated genomic data from 83 ancient goats (51 with genome-wide coverage) from Paleolithic to Medieval contexts throughout the Near East. Our findings demonstrate that multiple divergent ancient wild goat sources were domesticated in a dispersed process that resulted in genetically and geographically distinct Neolithic goat populations, echoing contemporaneous human divergence across the region. These early goat populations contributed differently to modern goats in Asia, Africa, and Europe. We also detect early selection for pigmentation, stature, reproduction, milking, and response to dietary change, providing 8000-year-old evidence for human agency in molding genome variation within a partner species.

Effect of Population Size and Mutation Rate on the Evolution of RNA Sequences on an Adaptive Landscape Determined by RNA Folding.

The dynamics of populations evolving on an adaptive landscape depends on multiple factors, including the structure of the landscape, the rate of mutations, and effective population size. Existing theoretical work often makes ad hoc and simplifying assumptions about landscape structure, whereas experimental work can vary important parameters only to a limited extent. We here overcome some of these limitations by simulating the adaptive evolution of RNA molecules, whose fitness is determined by the thermodynamics of RNA secondary structure folding. We study the influence of mutation rates and population sizes on final mean population fitness, on the substitution rates of mutations, and on population diversity. We show that evolutionary dynamics cannot be understood as a function of mutation rate µ, population size N, or population mutation rate Nµ alone. For example, at a given mutation rate, clonal interference prevents the fixation of beneficial mutations as population size increases, but larger populations still arrive at a higher mean fitness. In addition, at the highest population mutation rates we study, mean final fitness increases with population size, because small populations are driven to low fitness by the relatively higher incidence of mutations they experience. Our observations show that mutation rate and population size can interact in complex ways to influence the adaptive dynamics of a population on a biophysically motivated fitness landscape.

Contralateral Eye Comparison of SMILE and Flap-Based Corneal Refractive Surgery: Computational Analysis of Biomechanical Impact.

PURPOSE: Computational analyses were performed to quantify and directly compare the biomechanical impact of flapless and flap-based procedures in a series of patients undergoing small incision lenticule extraction (SMILE) in one eye and flap-based femtosecond lenticule extraction in the other. METHODS: Tomographic data from 10 eyes of 5 patients undergoing femtosecond laser refractive lenticule extraction for myopic astigmatism with or without a stromal flap (femtosecond lenticule extraction in one eye, SMILE in the contralateral eye) were used to generate computational models. Inverse finite element analyses were performed at physiologic intraocular pressure followed by forward analyses at elevated intraocular pressure to assess corneal displacement and stress under differential loading. Case-specific treatment settings were incorporated. Preoperative material constants were obtained through inverse finite element analyses, and the surgically induced change in fiber stiffness within each flap was determined by minimization of the error between the simulated and actual 6-month topographic outcomes. RESULTS: Flap-based procedures produced a 49% (range: 2% to 87%) greater mean reduction in effective stromal collagen fiber stiffness within the flap region than contralateral SMILE cases. Lower stresses and deformations were observed within the residual stromal bed in SMILE cases than in flap-based cases. Stromal bed displacements and stresses were more affected by a loading increase in flap-based eyes than flapless eyes. CONCLUSIONS: Intrastromal flapless procedures had less impact on anterior stromal collagen mechanics and resulted in lower stromal bed displacements and stresses than flap-based procedures in contralateral eyes. However, biomechanical impact varied widely between individuals and this reinforces the need for individualized assessment of ectasia risk. [J Refract Surg. 2017;33(7):444-453].

Computational Biomechanical Analysis of Asymmetric Ectasia Risk in Unilateral Post-LASIK Ectasia.

PURPOSE: To develop a computational approach to corneal biomechanical risk analysis in refractive surgery and to investigate its utility in an enigmatic case of unilateral ectasia after bilateral LASIK. METHODS: Preoperative corneal elevation datasets from both eyes of a patient who developed unilateral post-LASIK ectasia were used to construct geometrically patient-specific, microstructurally motivated finite element models. Models were assessed before and after implementation of case-specific treatment parameters for interocular differences in corneal geometry and strain behavior under physiological loading conditions. RESULTS: Standard clinical predictors of post-LASIK ectasia risk were similar for the affected and contralateral eyes, and no risk factor asymmetry was identified in tomographic screening that included posterior corneal elevation analysis. However, differences in the magnitude and distribution of strain and stress were observed that are consistent with greater predisposition to biomechanical instability in the affected eye. Load testing with simulated intraocular pressure increases provoked opposite trends in curvature change in the preoperative models representing affected and unaffected eyes, with steepening in the ectatic eye and flattening in the clinically stable eye. CONCLUSIONS: Patient-specific computational analyses revealed differences in intrinsic biomechanical behaviors that may predispose a cornea to instability after refractive surgery. Strain and stress analyses elucidated differential risk not ascertained with current refractive surgery screening paradigms. This pilot study illustrates a risk analysis approach that implicitly considers the entire corneal three-dimensional geometry and can be performed a priori in a screening setting. [J Refract Surg. 2016;32(12):811-820.].

Comparison of Patient-Specific Computational Modeling Predictions and Clinical Outcomes of LASIK for Myopia.

Purpose: To assess the predictive accuracy of simulation-based LASIK outcomes. Methods: Preoperative and 3-month post-LASIK tomographic data from 20 eyes of 12 patients who underwent wavefront-optimized LASIK for myopia were obtained retrospectively. Patient-specific finite element models were created and case-specific treatment settings were simulated. Simulated keratometry (SimK) values and the mean tangential curvature of the central 3 mm (Kmean) were obtained from the anterior surfaces of the clinical tomographies, and computational models were compared. Correlations between Kmean prediction error and patient age, preoperative corneal hysteresis (CH), and corneal resistance factor (CRF) were assessed. Results: The mean difference for Kmean between simulated and actual post-LASIK cases was not statistically significant (-0.13 ± 0.36 diopters [D], P = 0.1). The mean difference between the surgically induced clinical change in Kmean and the model-predicted change was -0.11 ± 0.34 D (P = 0.2). Kmean prediction error was correlated to CH, CRF, and patient age (r = 0.63, 0.53, and 0.5, respectively, P < 0.02), and incorporation of CH values into predictions as a linear offset increased their accuracy. Simulated changes in Kmean accounted for 97% of the variance in actual spherical equivalent refractive change. Conclusions: Clinically feasible computational simulations predicted corneal curvature and manifest refraction outcomes with a level of accuracy in myopic LASIK cases that approached the limits of measurement error. Readily available preoperative biomechanical measures enhanced simulation accuracy. Patient-specific simulation may be a useful tool for clinical guidance in de novo LASIK cases.