Morphological Evolution: Bioinspired Methods for Analyzing Bioinspired Robots

To fully understand the evolution of complex morphologies, analyses cannot stop at selection: It is essential to investigate the roles and interactions of multiple processes that drive evolutionary outcomes. The challenges of undertaking such analyses have affected both evolutionary biologists and evolutionary roboticists, with their common interests in complex morphologies. In this paper, we present analytical techniques from evolutionary biology, selection gradient analysis and morphospace walks, and we demonstrate their applicability to robot morphologies in analyses of three evolutionary mechanisms: randomness (genetic mutation), development (an explicitly implemented genotype-to-phenotype map), and selection. In particular, we applied these analytical techniques to evolved populations of simulated biorobots—embodied robots designed specifically as models of biological systems, for the testing of biological hypotheses—and we present a variety of results, including analyses that do all of the following: illuminate different evolutionary dynamics for different classes of morphological traits; illustrate how the traits targeted by selection can vary based on the likelihood of random genetic mutation; demonstrate that selection on two selected sets of morphological traits only partially explains the variance in fitness in our biorobots; and suggest that biases in developmental processes could partially explain evolutionary dynamics of morphology. When combined, the complementary analytical approaches discussed in this paper can enable insight into evolutionary processes beyond selection and thereby deepen our understanding of the evolution of robotic morphologies.

Habitat Adaptation Drives Speciation of a Streptomyces Species with Distinct Habitats and Disparate Geographic Origins

Both isolation by distance and isolation by environment occur in bacteria, and different diversification patterns may apply to different species. Streptomyces species, typified by producing useful natural products, are widespread in nature and possess high genetic diversity. However, the ecological processes and evolutionary mechanisms that shape their distribution are not well understood.

Genomic and Immunological Features of Micropapillary Pattern in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) usually contain heterogeneous histological subtypes, among which the micropapillary (MIP) subtype was associated with poor prognosis while the lepidic (LEP) subtype possessed the most favorable outcome. A more comprehensive analysis involving discovery and public validation cohorts on the two subtypes could better decipher the key biological and evolutionary mechanisms.Methods: We firstly retrospectively studied the survival status of 286 LUAD patients with different subtypes. MIP and LEP components were micro-dissected for whole-exome sequencing (WES). Shared and private alterations as well as genomic alternation characteristics between the two components were investigated. Four public cohorts containing LEP and MIP samples were further selected for genomic profile comparison, novel therapeutic target investigation and immune infiltration quantification.Results: LEP and MIP subtypes exhibited largest disease free survival (DFS) in our patients. A total of 2035 SNV and 2757 InDels were identified in the sequenced LEP and MIP components. EGFR was found with highest mutation frequency. Distinct biological processes or pathways were involved in the evolutionary of the two components. Besides, analyses on copy number variation (CNV) and intratumor heterogeneity further discovered the possible immunosurveillance escape, the discrepancy between mutation and CNV level ITH and the pervasive DNA Damage Response as well as WNT pathway gene alternations in MIP component. Phylogenetic analysis on 5 pairs of LEP and MIP components further confirmed the presence of ancestral EGFR mutations. Through comprehensive analysis in our samples and public cohorts, PTP4A3, NAPRT and RECQL4 were identified as novel therapeutic and diagnostic targets in MIP subtype. Immunosuppression prevalence in MIP component was finally confirmed by multi-omics data.Conclusion: We identified genetic differences responsible for variated prognosis. The subtype evolution trajectory was additionally unraveled. Novel gene targets and the immunological analyses also provided therapeutic suggestions for MIP subtype.

Covariant Fitness Clusters Reveal Structural Evolution of SARS-CoV-2 Polymerase Across the Human Population

Understanding the fitness landscape of viral mutations is crucial for uncovering the evolutionary mechanisms contributing to pandemic behavior. Here, we apply a Gaussian process regression (GPR) based machine learning approach that generates spatial covariance (SCV) relationships to construct stability fitness landscapes for the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2. GPR generated fitness scores capture on a residue-by-residue basis a covariant fitness cluster centered at the C487-H642-C645-C646 Zn2+ binding motif that iteratively evolves since the early phase pandemic. In the Alpha and Delta variant of concern (VOC), multi-residue SCV interactions in the NiRAN domain form a second fitness cluster contributing to spread. Strikingly, a novel third fitness cluster harboring a Delta VOC basal mutation G671S augments RdRp structural plasticity to potentially promote rapid spread through viral load. GPR principled SCV provides a generalizable tool to mechanistically understand evolution of viral genomes at atomic resolution contributing to fitness at the pathogen-host interface.

Exploration of Hydrogeochemical Characterization and Assessment of Organic Pollution Characteristics of Shallow Groundwater near a Chemical Plant That Discharged Sewage Illegally

Groundwater plays a significant role in domestic use and agricultural irrigation in rural areas of northern China. The untreated wastewater from the chemical plant was directly discharged into a seepage well, resulting in the pollution of groundwater. Assessing characteristics of groundwater organic pollution and identifying evolutionary mechanisms of hydrogeochemistry are beneficial for groundwater protection and sustainable management. Statistical methods (correlation analysis (CA) and principal component analysis (PCA)) combined with hydrogeochemical methods including Piper, Gibbs, Gaillardet, and ions binary diagrams and the chloride alkalinity index were employed to explore hydrogeochemical characteristics and evolutionary mechanisms. The results showed that cations were predominantly located at the Ca2+ end and anions were mostly close to the SO42− and Cl− end. The ion concentrations of groundwater were mainly affected by water–rock interactions. The weathering or dissolution of silicate (i.e., aluminosilicate minerals), evaporite (i.e., halite and gypsum), carbonate minerals (i.e., calcite and dolomite), cation exchange, and anthropogenic activities contribute to the chemical compositions of groundwater. Based on CA and PCA, the dissolution of halide minerals and the use of pesticides and fertilizers were the main factors controlling water chemistry. Additionally, the dissolution of sulfur-bearing minerals and gypsum was the key factor controlling the concentrations of Ca2+ and Mg2+. Application of mathematical statistical methods characterized that the exceedance rate of seven organic compounds with high detection rates were as follows: carbon tetrachloride (39.83%) > 1,1,2-trichloroethane (28.81%) > chloroform (10.17%) > trichloroethene (6.78%) > 1,1,2,2-tetrachloroethane (5.93%) > perchloroethylene (5.08%) > trichlorofluoromethane (0.85%). Simultaneously, pollution under the influence of volatilization and diffusion was significantly less than that in the direction of groundwater runoff.

SweepCluster: A SNP clustering tool for detecting gene-specific sweeps in prokaryotes

Background The gene-specific sweep is a selection process where an advantageous mutation along with the nearby neutral sites in a gene region increases the frequency in the population. It has been demonstrated to play important roles in ecological differentiation or phenotypic divergence in microbial populations. Therefore, identifying gene-specific sweeps in microorganisms will not only provide insights into the evolutionary mechanisms, but also unravel potential genetic markers associated with biological phenotypes. However, current methods were mainly developed for detecting selective sweeps in eukaryotic data of sparse genotypes and are not readily applicable to prokaryotic data. Furthermore, some challenges have not been sufficiently addressed by the methods, such as the low spatial resolution of sweep regions and lack of consideration of the spatial distribution of mutations. Results We proposed a novel gene-centric and spatial-aware approach for identifying gene-specific sweeps in prokaryotes and implemented it in a python tool SweepCluster. Our method searches for gene regions with a high level of spatial clustering of pre-selected polymorphisms in genotype datasets assuming a null distribution model of neutral selection. The pre-selection of polymorphisms is based on their genetic signatures, such as elevated population subdivision, excessive linkage disequilibrium, or significant phenotype association. Performance evaluation using simulation data showed that the sensitivity and specificity of the clustering algorithm in SweepCluster is above 90%. The application of SweepCluster in two real datasets from the bacteria Streptococcus pyogenes and Streptococcus suis showed that the impact of pre-selection was dramatic and significantly reduced the uninformative signals. We validated our method using the genotype data from Vibrio cyclitrophicus, the only available dataset of gene-specific sweeps in bacteria, and obtained a concordance rate of 78%. We noted that the concordance rate could be underestimated due to distinct reference genomes and clustering strategies. The application to the human genotype datasets showed that SweepCluster is also applicable to eukaryotic data and is able to recover 80% of a catalog of known sweep regions. Conclusion SweepCluster is applicable to a broad category of datasets. It will be valuable for detecting gene-specific sweeps in diverse genotypic data and provide novel insights on adaptive evolution.

The evolution of partner specificity in mutualisms

Mutualistic species vary in their level of partner specificity, which has important evolutionary, ecological, and management implications. Yet, the evolutionary mechanisms which underpin partner specificity are not fully understood. Most work on specialization focuses on the trade-off between generalism and specialism, where specialists receive more benefits from preferred partners at the expense of benefits from non-preferred partners, while generalists receive similar benefits from all partners. Because all mutualisms involve some degree of both cooperation and conflict between partners, we highlight that specialization to a mutualistic partner can be cooperative, increasing benefit to a focal species and a partner, or antagonistic, increasing resource extraction by a focal species from a partner. We devise an evolutionary game theoretic model to assess the evolutionary dynamics of cooperative specialization, antagonistic specialization, and generalism. Our model shows that cooperative specialization leads to bistability: stable equilibria with a specialist host and its preferred partner excluding all others. We also show that under cooperative specialization with spatial effects, generalists can thrive at the boundaries between differing specialist patches. Under antagonistic specialization, generalism is evolutionarily stable. We provide predictions for how a cooperation-antagonism continuum may determine the patterns of partner specificity that develop within mutualistic relationships.

Comparative genomics and evolution of avian specialized traits

Genomic data are important for understanding the origin and evolution of traits. Under the context of rapidly developing sequencing technologies and more widely available genome sequences, researchers are able to study evolutionary mechanisms of traits via comparative genomic methods. Compared with other vertebrates, bird genomes are relatively small and exhibit conserved synteny with few repetitive elements, which makes them suitable for evolutionary studies. Increasing genomic progress has been reported on the evolution of powered flight, body size variation, beak morphology, plumage coloration, high-elevation colonization, migration, and vocalization. By summarizing previous studies, we demonstrate the genetic bases of trait evolution, highlighting the roles of small-scale sequence variation, genomic structural variation, and changes in gene interaction networks. We suggest that future studies should focus on improving the quality of reference genomes, exploring the evolution of regulatory elements and networks, and combining genomic data with morphological, ecological, behavioral, and developmental biology data.

Overcoming “The Language Rubicon": Versions, Steps and Evolutionary Mechanisms

The most plausible hypotheses explaining the breakthrough of our distant ancestors to articulate speech and consciousness include: rituals of solidarity (singing, grooming, facial expressions, gestures), the role of "labor", teaching children, changes in gender relations, recruiting (mobilization for collective action), laryngeal transformation, brain growth, mirror neurons, neuronal overlap; dominance of coalitions over loners, self-domestication, the need to coordinate decisions; joint intentionality, development of normativity. The concretization of the principle of "cultural drive" leads to the following concept: new environmental, social and communicative challenges and concerns lead to multiple trials; successful behavior is consolidated in group practices, mental structures of participants, and hereditary inclinations with multilevel selection and change of many generations. Overcoming the "language Rubicon" took place in several steps, or stages, in which the structures that have developed to provide concerns lead to new concerns that require new structures. Such concerns as pacifying social order in the group, signaling general discontent, coordinating actions, etc., the following structures emerged: egalitarian coalitions with collective practices of intimidation, sound signals uttered in unison, training and self-training, self-domestication, shared intentionality, and normative rituals. The most plausible versions supplement this concept. Сhoral singing as a ritual of solidarity promoted the ability to imitate and articulate. Mirror neurons and neural overlaps have played the role of providing mechanisms for imitation and association processes. The connection between "labor" and speech was complex and interdependent, mediated by normativity. Recruiting as signals of mobilization for collective action also made its contribution but only under the already established normative order. Social learning is in fact a special ritual of constant trial and adjustment which has grown out of a normative ritual. The establishment of a new order of sexual relations also played a role: under the prohibition of violence and rape it was necessary to involve partners. Speech replaced grooming, and those who spoke clearly, convincingly and/or sang beautifully received benefits in sexual selection. The totality of all these processes reinforced the concerns of mutual understanding which were provided by the rituals of naming with the following multiplication of protowords and further growth of linguistic complexity.