Form and function remixed: developmental physiology in the evolution of vertebrate body plans

Situating physiology within evolutionary theory

Traditionally defined as the science of the living, or as the field that beyond anatomical structure and bodily form studies functional organization and behaviour, physiology has long been excluded from evolutionary research. The main reason for this exclusion is that physiology has a presential and futuristic outlook on life, while evolutionary theory is traditionally defined as the study of natural history. In this paper, I re-evaluate these classic science divisions and situate physiology within the history of the evolutionary sciences, as well as within debates on the Extended Evolutionary Synthesis and the need for a Third Way of Evolution. I then briefly point out how evolutionary physiology in particular contributes to research on function, causation, teleonomy, agency and cognition.

Parameterized Computational Framework for the Description and Design of Genetic Circuits of Morphogenesis Based on Contact-Dependent Signaling and Changes in Cell-Cell Adhesion

Synthetic development is a nascent field of research that uses the tools of synthetic biology to design genetic programs directing cellular patterning and morphogenesis in higher eukaryotic cells, such as mammalian cells. One specific example of such synthetic genetic programs was based on cell-cell contact-dependent signaling using synthetic Notch pathways and was shown to drive the formation of multilayered spheroids by modulating cell-cell adhesion via differential expression of cadherin family proteins in a mouse fibroblast cell line (L929). The design method for these genetic programs relied on trial and error, which limited the number of possible circuits and parameter ranges that could be explored. Here, we build a parameterized computational framework that, given a cell-cell communication network driving changes in cell adhesion and initial conditions as inputs, predicts developmental trajectories. We first built a general computational framework where contact-dependent cell-cell signaling networks and changes in cell-cell adhesion could be designed in a modular fashion. We then used a set of available in vitro results (that we call the "training set" in analogy to similar pipelines in the machine learning field) to parameterize the computational model with values for adhesion and signaling. We then show that this parameterized model can qualitatively predict experimental results from a "testing set" of available in vitro data that varied the genetic network in terms of adhesion combinations, initial number of cells, and even changes to the network architecture. Finally, this parameterized model is used to recommend novel network implementation for the formation of a four-layered structure that has not been reported previously. The framework that we develop here could function as a testing ground to identify the reachable space of morphologies that can be obtained by controlling contact-dependent cell-cell communications and adhesion with these molecular tools and in this cellular system. Additionally, we discuss how the model could be expanded to include other forms of communication or effectors for the computational design of the next generation of synthetic developmental trajectories.

The Multiple Contexts of Brain Scaling: Phenotypic Integration in Brain and Behavioral Evolution

Understanding the adaptive functions of increasing brain size have occupied scientists for decades.  Here, taking the general perspective of the Extended Evolutionary Synthesis, the question of how brains change in size will be considered in two developmental frameworks. The first framework will consider the particular developmental mechanisms that control and generate brain mass, concentrating on neurogenesis in a comparative vertebrate context. The consequences of limited adult neurogenesis in mammals, and the dominating role of duration of neurogenesis for mammalian evolution will be discussed for the particular case of the teleost versus mammalian retina, and for paths of brain evolution more generally. The second framework examines brain mass in terms of life history, particularly the features of life history that correlate highly, if imperfectly, with brain mass, including duration of development to adolescence, duration of parental care, body and range size, and longevity. This covariation will be examined in light of current work on genetic causes and consequences of covariation in craniofacial bone groupings. The eventual development of a multivariate structure for understanding brain evolution which specifically integrates formerly separate layers of analysis is the ultimate goal.

Closing the gap from transcription to the structural connectome enhances the study of connections in the human brain

The brain is composed of a complex web of networks but we have yet to map the structural connections of the human brain in detail. Diffusion MR imaging is a high-throughput method that relies on the principle of diffusion to reconstruct tracts (ie, pathways) across the brain. Although diffusion MR tractography is an exciting method to explore the structural connectivity of the brain in development and across species, the tractography has at times led to questionable interpretations. There are at present few if any alternative methods to trace structural pathways in the human brain. Given these limitations and the potential of diffusion MR imaging to map the human connectome, it is imperative that we develop new approaches to validate neuroimaging techniques. I discuss our recent studies integrating neuroimaging with transcriptional and anatomical variation across humans and other species over the course of development and in adulthood. Developing a novel framework to harness the potential of diffusion MR tractography provides new and exciting opportunities to study the evolution of developmental mechanisms generating variation in connections and bridge the gap between model systems to humans.

Physicochemical Foundations of Life that Direct Evolution: Chance and Natural Selection are not Evolutionary Driving Forces

The current framework of evolutionary theory postulates that evolution relies on random mutations generating a diversity of phenotypes on which natural selection acts. This framework was established using a top-down approach as it originated from Darwinism, which is based on observations made of complex multicellular organisms and, then, modified to fit a DNA-centric view. In this article, it is argued that based on a bottom-up approach starting from the physicochemical properties of nucleic and amino acid polymers, we should reject the facts that (i) natural selection plays a dominant role in evolution and (ii) the probability of mutations is independent of the generated phenotype. It is shown that the adaptation of a phenotype to an environment does not correspond to organism fitness, but rather corresponds to maintaining the genome stability and integrity. In a stable environment, the phenotype maintains the stability of its originating genome and both (genome and phenotype) are reproduced identically. In an unstable environment (i.e., corresponding to variations in physicochemical parameters above a physiological range), the phenotype no longer maintains the stability of its originating genome, but instead influences its variations. Indeed, environment- and cellular-dependent physicochemical parameters define the probability of mutations in terms of frequency, nature, and location in a genome. Evolution is non-deterministic because it relies on probabilistic physicochemical rules, and evolution is driven by a bidirectional interplay between genome and phenotype in which the phenotype ensures the stability of its originating genome in a cellular and environmental physicochemical parameter-depending manner.

The natural defense system and the normative self model

Infectious agents are not the only agressors, and the immune system is not the sole defender of the organism. In an enlarged perspective, the ‘normative self model’ postulates that a ‘natural defense system’ protects man and other complex organisms against the environmental and internal hazards of life, including infections and cancers. It involves multiple error detection and correction mechanisms that confer robustness to the body at all levels of its organization. According to the model, the self relies on a set of physiological norms, and NONself (meaning : Non Obedient to the Norms of the self) is anything ‘off-norms’. The natural defense system comprises a set of ‘civil defenses’ (to which all cells in organs and tissues contribute), and a ‘professional army ‘, made of a smaller set of mobile cells. Mobile and non mobile cells differ in their tuning abilities. Tuning extends the recognition capabilities of NONself by the mobile cells, which increase their defensive function. To prevent them to drift, which would compromise self/NONself discrimination, the more plastic mobile cells need to periodically refer to the more stable non mobile cells to keep within physiological standards.

Freely chosen stride frequencies during walking and running are not correlated with freely chosen pedalling frequency and are insensitive to strength training

Despite biomechanical differences between walking, running, and cycling, these types of movement are supposedly generated by shared neural networks. According to this hypothesis, we investigated relationships between movement frequencies in these tasks as well as effects of strength training on locomotion behaviour. The movement frequencies during walking, running, and cycling were 58.1±2.6 strides min(-1), 81.3±4.4 strides min(-1), and 77.2±11.5 revolutions min(-1), respectively (n=27). Stride frequencies in walking and running correlated positively (r=0.72, p<0.001) while no significant correlations were found between stride frequencies during walking and running, respectively, and pedalling frequency (r=0.16, p=0.219 and r=0.04, p=0.424). Potential changes in the freely chosen stride frequencies and stride phase characteristics were also investigated during walking and running through 4 weeks of (i) hip extension strength training (n=9), (ii) hip flexion strength training (n=9), and (iii) no intervention (n=9). Results showed that stride characteristics were unaffected by strength training. That is in contrast to previous observations of decreased pedalling frequency following strength training. In total, these results are proposed to indicate that walking and running movements are robustly generated due to an evolutionary consolidation of the interaction between the musculoskeletal system and neural networks. Further, based on the present results, and the fact that cycling is a postnatally developed task that likely results in a different pattern of descending and afferent input to rhythm generating neural networks than walking and running, we propose pedalling to be generated by neural networks mainly consolidated for locomotion.

Evolving gene regulatory networks into cellular networks guiding adaptive behavior: an outline how single cells could have evolved into a centralized neurosensory system

Understanding the evolution of the neurosensory system of man, able to reflect on its own origin, is one of the major goals of comparative neurobiology. Details of the origin of neurosensory cells, their aggregation into central nervous systems and associated sensory organs and their localized patterning leading to remarkably different cell types aggregated into variably sized parts of the central nervous system have begun to emerge. Insights at the cellular and molecular level have begun to shed some light on the evolution of neurosensory cells, partially covered in this review. Molecular evidence suggests that high mobility group (HMG) proteins of pre-metazoans evolved into the definitive Sox [SRY (sex determining region Y)-box] genes used for neurosensory precursor specification in metazoans. Likewise, pre-metazoan basic helix-loop-helix (bHLH) genes evolved in metazoans into the group A bHLH genes dedicated to neurosensory differentiation in bilaterians. Available evidence suggests that the Sox and bHLH genes evolved a cross-regulatory network able to synchronize expansion of precursor populations and their subsequent differentiation into novel parts of the brain or sensory organs. Molecular evidence suggests metazoans evolved patterning gene networks early, which were not dedicated to neuronal development. Only later in evolution were these patterning gene networks tied into the increasing complexity of diffusible factors, many of which were already present in pre-metazoans, to drive local patterning events. It appears that the evolving molecular basis of neurosensory cell development may have led, in interaction with differentially expressed patterning genes, to local network modifications guiding unique specializations of neurosensory cells into sensory organs and various areas of the central nervous system.