Fly wing evolution explained by a neutral model with mutational pleiotropy

To what extent the speed of mutational production of phenotypic variation determines the rate of long‐term phenotypic evolution is a central question. Houle et al. recently addressed this question by studying the mutational variances, additive genetic variances, and macroevolution of locations of vein intersections on fly wings, reporting very slow phenotypic evolution relative to the rates of mutational input, high phylogenetic signals, and a strong, linear relationship between the mutational variance of a trait and its rate of evolution. Houle et al. found no existing model of phenotypic evolution to be consistent with all these observations, and proposed the improbable scenario of equal influence of mutational pleiotropy on all traits. Here, we demonstrate that the purported linear relationship between mutational variance and evolutionary divergence is artifactual. We further show that the data are explainable by a simple model in which the wing traits are effectively neutral at least within a range of phenotypic values but their evolutionary rates are differentially reduced because mutations affecting these traits are purged owing to their different pleiotropic effects on other traits that are under stabilizing selection. Thus, the evolutionary patterns of fly wing morphologies are explainable under the existing theoretical framework of phenotypic evolution.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162712/3/evo14076.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162712/2/evo14076-sup-0001-SuppMat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162712/1/evo14076_am.pd

Mechanisms of Phenotypic Evolution at Different Levels of Biological Organization: From Molecules to Organisms

Understanding the origin of the vast phenotypic diversity among organisms is a central goal of evolutionary biology. Phenotypic traits evolve in face of mutations, genetic drift, and selection, which, alongside genetic architectures of traits, shape patterns of phenotypic variation within and between species. While the relative contributions of these processes to phenotypic evolution is of interest, teasing apart their effects has been challenging. In this dissertation, I take advantage of publicly available large-scale phenomic data to investigate mechanisms underlying the evolution of multiple phenotypic traits that represent different levels of biological organization. Combining computational and theoretical approaches, I test models of phenotypic evolution and evaluate the extent to which each trait of interest is influenced by selection. These analyses cover pre-transcriptional (Chapter 2) and post-transcriptional (Chapter 3) molecular processes as well as morphological traits (Chapter 4 and 5), representing different levels of biological organization. In Chapter 2, I focus on interaction between the transcription factor and the transcription factor binding sites and ask how selection that enhances their interaction can be detected without being confounded by intrinsically biased data. In Chapter 3, I focus on A-to-I RNA editing and show that the high abundance of nonsynonymous editing in coleoid cephalopods, which appeared to be adaptive, is instead better explained by a nonadaptive error-permitting model. In Chapter 4, I investigate the relationship between mutational variance and evolutionary rate among a set of wing morphological traits in drosophilid flies and show a formerly proposed interpretation resulted from a biased method; with scaling between mutational variance and evolutionary rate re-estimated with a corrected method, I also propose a mechanic model that explains the observations. In Chapter 5, I study correlated evolution of different phenotypic traits and ask how frequently correlation in long-term evolution differs from correlation resulting from mutational covariances; by analyzing cell morphological traits of yeasts and wing morphological traits of flies, I identify pairs of traits that are likely subject to multivariate selection for allometric relationship between traits. I conclude that nonadaptive processes, which used to be overlooked, can play a remarkable role in shaping patterns of phenotypic variation.PHDEcology and Evolutionary BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/174542/1/rexjiang_1.pd

Detecting natural selection in trait-trait coevolution

Abstract No phenotypic trait evolves independently of all other traits, but the cause of trait-trait coevolution is poorly understood. While the coevolution could arise simply from pleiotropic mutations that simultaneously affect the traits concerned, it could also result from multivariate natural selection favoring certain trait relationships. To gain a general mechanistic understanding of trait-trait coevolution, we examine the evolution of 220 cell morphology traits across 16 natural strains of the yeast Saccharomyces cerevisiae and the evolution of 24 wing morphology traits across 110 fly species of the family Drosophilidae, along with the variations of these traits among gene deletion or mutation accumulation lines (a.k.a. mutants). For numerous trait pairs, the phenotypic correlation among evolutionary lineages differs significantly from that among mutants. Specifically, we find hundreds of cases where the evolutionary correlation between traits is strengthened or reversed relative to the mutational correlation, which, according to our population genetic simulation, is likely caused by multivariate selection. Furthermore, we detect selection for enhanced modularity of the yeast traits analyzed. Together, these results demonstrate that trait-trait coevolution is shaped by natural selection and suggest that the pleiotropic structure of mutation is not optimal. Because the morphological traits analyzed here are chosen largely because of their measurability and thereby are not expected to be biased with regard to natural selection, our conclusion is likely general

SnO2/Diatomite Composite Prepared by Solvothermal Reaction for Low-Cost Photocatalysts

Abundant contaminants in wastewater have a negative effect on the natural environment and ecology. Developing highly efficient photocatalysts is a practical strategy to solve the pollution issue. In order to prevent the agglomeration of SnO2 nanoparticles and improve the photocatalytic efficiency, porous diatomite is adopted as a low-cost template to load monodispersed SnO2 nanoparticles by solvothermal reaction and sintering method. Through adjusting the mass of reactants, monodispersed SnO2 nanoparticles (~15 nm) generated on diatomite template achieved the maximum specific surface area of 23.53 m2·g−1. When served as a photocatalyst for degrading rhodamine B (Rh B) and methylene blue (MB), the composite presents an excellent photocatalytic activity close to pure SnO2, and achieves the fast degradation of Rh B and MB dye in 60 min. The degradation process is in well agreement with the first-order kinetic equation. The superior photocatalytic performance of SnO2/diatomite composite is attributed to the physical adsorption of dye molecules on the pores of diatomite, and the superior photocatalytic activity of monodispersed SnO2 nanoparticles. Due to the low-cost of diatomite and the easy preparation of SnO2 nanoparticles, the SnO2/diatomite composite has a promising application prospect, even better than pure SnO2 photocatalyst

HK3 stimulates immune cell infiltration to promote glioma deterioration

Abstract Background Glioma is the most common and lethal type of brain tumor, and it is characterized by unfavorable prognosis and high recurrence rates. The reprogramming of energy metabolism and an immunosuppressive tumor microenvironment (TME) are two hallmarks of tumors. Complex and dynamic interactions between neoplastic cells and the surrounding microenvironment can generate an immunosuppressive TME, which can accelerate the malignant progression of glioma. Therefore, it is crucial to explore associations between energy metabolism and the immunosuppressive TME and to identify new biomarkers for glioma prognosis. Methods In our work, we analyzed the co-expression relationship between glycolytic genes and immune checkpoints based on the transcriptomic data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) and found the correlation between HK3 expression and glioma tumor immune status. To investigate the biological role of HK3 in glioma, we performed bioinformatics analysis and established a mouse glioblastoma (GBM) xenograft model. Results Our study showed that HK3 significantly stimulated immune cell infiltration into the glioma TME. Tissue samples with higher HK3 expressive level showed increasing levels of immune cells infiltration, including M2 macrophages, neutrophils, and various subtypes of activated memory CD4+ T cells. Furthermore, HK3 expression was significantly increasing along with the elevated tumor grade, had a higher level in the mesenchymal subtype compared with those in other subtypes of GBM and could independently predict poor outcomes of GBM patients. Conclusion The present work mainly concentrated on the biological role of HK3 in glioma and offered a novel insight of HK3 regulating the activation of immune cells in the glioma microenvironment. These findings could provide a new theoretical evidence for understanding the metabolic molecular within the glioma microenvironment and identifying new therapeutic targets

Sensing of autoinducer-2 by functionally distinct receptors in prokaryotes

The small molecule AI-2 acts as a quorum sensing signal, mediating communication within and between many bacterial species. Here, the authors identify a new type of AI-2 receptor, consisting of a dCACHE domain that is present in many bacterial and archaeal proteins

Using integrated analysis from multicentre studies to identify RNA methylation-related lncRNA risk stratification systems for glioma

Abstract Background N6-methyladenosine (m6A), 5-methylcytosine (m5C) and N1-methyladenosine (m1A) are the main RNA methylation modifications involved in the progression of cancer. However, it is still unclear whether RNA methylation-related long noncoding RNAs (lncRNAs) affect the prognosis of glioma. Methods We summarized 32 m6A/m5C/m1A-related genes and downloaded RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify differentially expressed (DE-) RNA methylation-related lncRNAs in order to construct a prognostic signature of glioma and in order to determine their correlation with immune function, immune therapy and drug sensitivity. In vitro and in vivo assays were performed to elucidate the effects of RNA methylation-related lncRNAs on glioma. Results A total of ten RNA methylation-related lncRNAs were used to construct a survival and prognosis signature, which had good independent prediction ability for patients. It was found that the high-risk group had worse overall survival (OS) than the low-risk group in all cohorts. In addition, the risk group informed the immune function, immunotherapy response and drug sensitivity of patients with glioma in different subgroups. Knockdown of RP11-98I9.4 and RP11-752G15.8 induced a more invasive phenotype, accelerated cell growth and apparent resistance to temozolomide (TMZ) both in vitro and in vivo. We observed significantly elevated global RNA m5C and m6A levels in glioma cells. Conclusion Our study determined the prognostic implication of RNA methylation-related lncRNAs in gliomas, established an RNA methylation-related lncRNA prognostic model, and elucidated that RP11-98I9.4 and RP11-752G15.8 could suppress glioma proliferation, migration and TMZ resistance. In the future, these RNA methylation-related lncRNAs may become a new choice for immunotherapy of glioma