Speciation through the lens of biomechanics: locomotion,prey capture and reproductive isolation

Speciation is a multifaceted process that involves numerous aspects of the biological sciences and occurs for multiple reasons. Ecology plays a major role, including both abiotic and biotic factors. Whether populations experience similar or divergent ecological environments, they often adapt to local conditions through divergence in biomechanical traits. We investigate the role of biomechanics in speciation using fish predator–prey interactions, a primary driver of fitness for both predators and prey. We highlight specific groups of fishes, or specific species, that have been particularly valuable for understanding these dynamic interactions and offer the best opportunities for future studies that link genetic architecture to biomechanics and reproductive isolation (RI). In addition to emphasizing the key biomechanical techniques that will be instrumental, we also propose that the movement towards linking biomechanics and speciation will include (i) establishing the genetic basis of biomechanical traits, (ii) testing whether similar and divergent selection lead to biomechanical divergence, and (iii) testing whether/how biomechanical traits affect RI. Future investigations that examine speciation through the lens of biomechanics will propel our understanding of this key process.     

Biomechanics of Musculoskeletal System and Its Biomimetic Implications： A Review

Biological musculoskeletal system （MSK）, composed of numerous bones, cartilages, skeletal muscles, tendons, ligaments etc., provides form, support, movement and stability for human or animal body. As the result of million years of selection and evolution, the biological MSK evolves to be a nearly perfect mechanical mechanism to support and transport the human or animal body, and would provide enormously rich resources to inspire engineers to innovate new technology and methodology to develop robots and mechanisms as effective and economical as the biological systems. This paper provides a general review of the current status of musculoskeletal biomechanics studies using both experimental and computational methods. This includes the use of the latest three-dimensional motion analysis systems, various medical imaging modalities, and also the advanced rigid-body and continuum mechanics musculoskeletal modelling techniques. Afterwards, several representative biomimetic studies based on ideas and concepts inspired from the structures and biomechanical functions of the biological MSK are dis- cussed. Finally, the major challenges and also the future research directions in musculoskeletal biomechanics and its biomimetic studies are proposed.     

1H NMR-based metabolic profiling reveals inherent biological variation in yeast and nematode model systems

The application of metabolomics to human and animal model systems is poised to provide great insight into our understanding of disease etiology and the metabolic changes that are associated with these conditions. However, metabolomic studies have also revealed that there is significant, inherent biological variation in human samples and even in samples from animal model systems where the animals are housed under carefully controlled conditions. This inherent biological variability is an important consideration for all metabolomics analyses. In this study, we examined the biological variation in (1)H NMR-based metabolic profiling of two model systems, the yeast Saccharomyces cerevisiae and the nematode Caenorhabditis elegans. Using relative standard deviations (RSD) as a measure of variability, our results reveal that both model systems have significant amounts of biological variation. The C. elegans metabolome possesses greater metabolic variance with average RSD values of 29 and 39%, depending on the food source that was used. The S. cerevisiae exometabolome RSD values ranged from 8% to 12% for the four strains examined. We also determined whether biological variation occurs between pairs of phenotypically identical yeast strains. Multivariate statistical analysis allowed us to discriminate between pair members based on their metabolic phenotypes. Our results highlight the variability of the metabolome that exists even for less complex model systems cultured under defined conditions. We also highlight the efficacy of metabolic profiling for defining these subtle metabolic alterations.     

Morphological and biomechanical disparity of crocodile-line archosaurs following the end-Triassic extinction

Mesozoic crurotarsans exhibited diverse morphologies and feeding modes, representing considerable ecological diversity, yet macroevolutionary patterns remain unexplored. Here, we use a unique combination of morphological and biomechanical disparity metrics to quantify the ecological diversity and trophic radiations of Mesozoic crurotarsans, using the mandible as a morpho-functional proxy. We recover three major trends. First, the diverse assemblage of Late Triassic crurotarsans was morphologically and biomechanically disparate, implying high levels of ecological variation; but, following the end-Triassic extinction, disparity declined. Second, the Jurassic radiation of marine thalattosuchians resulted in very low morphological disparity but moderate variation in jaw biomechanics, highlighting a hydrodynamic constraint on mandibular form. Third, during the Cretaceous terrestrial radiations of neosuchians and notosuchians, mandibular morphological variation increased considerably. By the Late Cretaceous, crocodylomorphs evolved a range of morphologies equalling Late Triassic crurotarsans. By contrast, biomechanical disparity in the Cretaceous did not increase, essentially decoupling from morphology. This enigmatic result could be attributed to biomechanical evolution in other anatomical regions (e.g. cranium, dentition or postcranium), possibly releasing the mandible from selective pressures. Overall, our analyses reveal a complex relationship between morphological and biomechanical disparity in Mesozoic crurotarsans that culminated in specialized feeding ecologies and associated lifestyles.     

Comparison of eight published static finite element models of the intact lumbar spine: Predictive power of models improves when combined together

Finite element (FE) model studies have made important contributions to our understanding of functional biomechanics of the lumbar spine. However, if a model is used to answer clinical and biomechanical questions over a certain population, their inherently large inter-subject variability has to be considered. Current FE model studies, however, generally account only for a single distinct spinal geometry with one set of material properties. This raises questions concerning their predictive power, their range of results and on their agreement with in vitro and in vivo values.     

Biomechanics and functionality of hepatocytes in liver cirrhosis

Cirrhosis is a life-threatening condition that is generally attributed to overproduction of collagen fibers in the extracellular matrix that mechanically stiffens the liver. Chronic liver injury due to causes including viral hepatitis, inherited and metabolic liver diseases and external factors such as alcohol abuse can result in the development of cirrhosis. Progression of cirrhosis leads to hepatocellular dysfunction. While extensive studies to understand the complexity underlying liver fibrosis have led to potential application of anti-fibrotic drugs, no such FDA-approved drugs are currently available. Additional studies of hepatic fibrogenesis and cirrhosis primarily have focused on the extracellular matrix, while hepatocyte biomechanics has received limited attention. The role of hepatocyte biomechanics in liver cirrhosis remains elusive, and how the cell stiffness is correlated with biological functions of hepatocytes is also unknown. In this study, we demonstrate that the biomechanical properties of hepatocytes are correlated with their functions (e.g., glucose metabolism), and that hepatic dysfunction can be restored through modulation of the cellular biomechanics. Furthermore, our results indicate the hepatocyte functionality appears to be regulated through a crosstalk between the Rho and Akt signaling. These novel findings may lead to biomechanical intervention of hepatocytes and the development of innovative tissue engineering for clinical treatment to target liver cells rather than exclusively focusing on the extracellular matrix alone in liver cirrhosis.     

Considerations for reporting finite element analysis studies in biomechanics

Simulation-based medicine and the development of complex computer models of biological structures is becoming ubiquitous for advancing biomedical engineering and clinical research. Finite element analysis (FEA) has been widely used in the last few decades to understand and predict biomechanical phenomena. Modeling and simulation approaches in biomechanics are highly interdisciplinary, involving novice and skilled developers in all areas of biomedical engineering and biology. While recent advances in model development and simulation platforms offer a wide range of tools to investigators, the decision making process during modeling and simulation has become more opaque. Hence, reliability of such models used for medical decision making and for driving multiscale analysis comes into question. Establishing guidelines for model development and dissemination is a daunting task, particularly with the complex and convoluted models used in FEA. Nonetheless, if better reporting can be established, researchers will have a better understanding of a model's value and the potential for reusability through sharing will be bolstered. Thus, the goal of this document is to identify resources and considerate reporting parameters for FEA studies in biomechanics. These entail various levels of reporting parameters for model identification, model structure, simulation structure, verification, validation, and availability. While we recognize that it may not be possible to provide and detail all of the reporting considerations presented, it is possible to establish a level of confidence with selective use of these parameters. More detailed reporting, however, can establish an explicit outline of the decision-making process in simulation-based analysis for enhanced reproducibility, reusability, and sharing.     

Verification, validation and sensitivity studies in computational biomechanics

Computational techniques and software for the analysis of problems in mechanics have naturally moved from their origins in the traditional engineering disciplines to the study of cell, tissue and organ biomechanics. Increasingly complex models have been developed to describe and predict the mechanical behavior of such biological systems. While the availability of advanced computational tools has led to exciting research advances in the field, the utility of these models is often the subject of criticism due to inadequate model verification and validation (V&V). The objective of this review is to present the concepts of verification, validation and sensitivity studies with regard to the construction, analysis and interpretation of models in computational biomechanics. Specific examples from the field are discussed. It is hoped that this review will serve as a guide to the use of V&V principles in the field of computational biomechanics, thereby improving the peer acceptance of studies that use computational modeling techniques.     

Bayesian meta-analysis models for microarray data: a comparative study

Background

With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.

Results

Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.

Conclusion

The Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.     

Biomechanics and mechanobiology in functional tissue engineering

The field of tissue engineering continues to expand and mature, and several products are now in clinical use, with numerous other preclinical and clinical studies underway. However, specific challenges still remain in the repair or regeneration of tissues that serve a predominantly biomechanical function. Furthermore, it is now clear that mechanobiological interactions between cells and scaffolds can critically influence cell behavior, even in tissues and organs that do not serve an overt biomechanical role. Over the past decade, the field of “functional tissue engineering” has grown as a subfield of tissue engineering to address the challenges and questions on the role of biomechanics and mechanobiology in tissue engineering. Originally posed as a set of principles and guidelines for engineering of load-bearing tissues, functional tissue engineering has grown to encompass several related areas that have proven to have important implications for tissue repair and regeneration. These topics include measurement and modeling of the in vivo biomechanical environment; quantitative analysis of the mechanical properties of native tissues, scaffolds, and repair tissues; development of rationale criteria for the design and assessment of engineered tissues; investigation of the effects biomechanical factors on native and repair tissues, in vivo and in vitro; and development and application of computational models of tissue growth and remodeling. Here we further expand this paradigm and provide examples of the numerous advances in the field over the past decade. Consideration of these principles in the design process will hopefully improve the safety, efficacy, and overall success of engineered tissue replacements.     

Biomechanics in anthropology

Biomechanics is the set of tools that explain organismal movement and mechanical behavior and links the organism to the physicality of the world. As such, biomechanics can relate behaviors and culture to the physicality of the organism. Scale is critical to biomechanical analyses, as the constitutive equations that matter differ depending on the scale of the question. Within anthropology, biomechanics has had a wide range of applications, from understanding how we and other primates evolved to understanding the effects of technologies, such as the atlatl, and the relationship between identity, society, culture, and medical interventions, such as prosthetics. Like any other model, there is great utility in biomechanical models, but models should be used primarily for hypothesis testing and not data generation except in the rare case where models can be robustly validated. The application of biomechanics within anthropology has been extensive, and holds great potential for the future.     

Environmental quality and evolutionary potential: lessons from wild populations

An essential requirement to determine a population's potential for evolutionary change is to quantify the amount of genetic variability expressed for traits under selection. Early investigations in laboratory conditions showed that the magnitude of the genetic and environmental components of phenotypic variation can change with environmental conditions. However, there is no consensus as to how the expression of genetic variation is sensitive to different environmental conditions. Recently, the study of quantitative genetics in the wild has been revitalized by new pedigree analyses based on restricted maximum likelihood, resulting in a number of studies investigating these questions in wild populations. Experimental manipulation of environmental quality in the wild, as well as the use of naturally occurring favourable or stressful environments, has broadened the treatment of different taxa and traits. Here, we conduct a meta-analysis on recent studies comparing heritability in favourable versus unfavourable conditions in non-domestic and non-laboratory animals. The results provide evidence for increased heritability in more favourable conditions, significantly so for morphometric traits but not for traits more closely related to fitness. We discuss how these results are explained by underlying changes in variance components, and how they represent a major step in our understanding of evolutionary processes in wild populations. We also show how these trends contrast with the prevailing view resulting mainly from laboratory experiments on Drosophila. Finally, we underline the importance of taking into account the environmental variation in models predicting quantitative trait evolution.     

Measuring and modeling the motor system with machine learning

The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experimental work, and in this review we discuss the growing use of machine learning: from pose estimation, kinematic analyses, dimensionality reduction, and closed-loop feedback, to its use in understanding neural correlates and untangling sensorimotor systems. We also give our perspective on new avenues, where markerless motion capture combined with biomechanical modeling and neural networks could be a new platform for hypothesis-driven research.     

Effects of genotypic variation in stem solidity on parasitism of a grass-mining insect

Host plant traits can play a significant role in influencing the importance, direction and intensity of tri-trophic interactions by both direct and indirect pathways. A major goal in applied tri-trophic research has been to determine whether breeding for host plant resistance traits can be combined with biological control to develop a more comprehensive control strategy. An important component of developing such a strategy is understanding how host resistance traits affect natural enemy–prey interactions for important pest insects. Here we examine the influence of genotypic variation in stem solidity, the primary trait conferring resistance against the wheat stem sawfly, Cephus cinctus, on parasitism of this major pest of wheat by its native braconid parasitoids. To do so, we conducted a field experiment in which we established replicate plots of 23 wheat genotypes that varied in levels of stem solidity, and quantified herbivore abundance and levels of parasitism across three sites in two years. Increasing stem solidity was associated with an approximately four-fold reduction in average parasitism rates, both across experimental plots and across wheat genotypes. Our analyses suggest that these effects were primarily direct, rather than indirectly mediated via effects of stem solidity on herbivore infestation levels or density. Interestingly, wheat genotype also had a significant influence on levels of parasitism, independent of its effects on stem solidity. Overall, our results suggest that although increasing stem solidity generally reduces parasitism, significant genotypic variability in average parasitism levels exist within solidity categories. Thus it may be possible to select resistant solid stemmed genotypes that also maintain relatively high parasitism levels. To our knowledge, our study is among the first to demonstrate a strong direct effect of genotypic variation in stem solidity on parasitism of grass mining insects, with important applied implications.     

Genetics of adiponectin

Anti-inflammatory, anti-atherogenic and anti-diabetic properties of adiponectin make this adipokine an attractive target in the metabolism research. Given its biological role, genetic variation in adiponectin affecting its function might consequently play a role in the pathophysiology of various metabolic disorders. In this light, genetic aspects of adiponectin including its gene structure, heritability of serum concentrations and the role of genetic variation have been addressed in multiple genetic studies. Here, we provide a brief summary of adiponectin genetics with focus on gene structure and genetic variation controlling circulating adiponectin levels. We summarize the main findings from genome-wide linkage and association studies that have revealed the major genetic determinants of serum adiponectin. Beside genetic variants in the adiponectin gene, several other genes/loci (ARL15, CDH13, KNG1, FER, ETV5) contributing to the variability in circulating adiponectin have been identified. The majority of these variants are significantly associated with metabolic phenotypes relevant to metabolic diseases (e.g. obesity or type 2 diabetes (T2D)). Considering the protective properties of adiponectin in diseases such as T2D, comprehensive analyses of genetic variants including rare as well as frequent polymorphisms might provide insights on the specific role of adiponectin in the pathophysiology of metabolic diseases.