Epigenomics: The New Science of Functional and Evolutionary Morphology

2003 ◽  
Vol 53 (3) ◽  
pp. 225-243 ◽  
Author(s):  
Kenneth Kardong
Keyword(s):  
Natural Selection ◽  
Boundary Conditions ◽  
Biological Diversity ◽  
Gene Pools ◽  
Biological Processes ◽  
Evolutionary Morphology ◽  
New Science ◽  
Biological Hierarchy ◽  
Analytical Tools ◽  
Selection Of

AbstractOrganisms are more than the genes that look after their assembly. Chemical and mechanical inputs from the environment, epigenomic (≈epigenetic) cues, also have an effect on the final phenotype. In fact, continued environmental influences on the adult phenotype continue to affect its characteristics. Despite its importance, it is a mistake to turn then to epigenomics as a causative agent of evolutionary modification. Within a biological hierarchy, higher levels result from lower-level processes (genes up to phenotype), and lower levels result from higher-level processes (natural selection of phenotypes down to gene pools), respectively, upward and downward causation. Predictable epigenomic cues are assimilated into the genome. The evolved genome therefore incorporates epigenomic cues or the expectation of their arrival, placing the current genome in the position of determining how much epigenomic information is included, what epigenomic information is incorporated, and when epigenomic information initiates gene expression during morphogenesis of the phenotype. Consequently scientific explanations of changes in phenotypes (e.g., morphological design) are of two kinds, causes and boundary conditions. Causes are the events directly involved in producing changes in the state of a biological system; they act within limits or constraints, the boundary conditions. Confusion between these two types of explanation has misled some to equate epigenomic cues, which are boundary conditions, with natural selection, which is a causative explanation. Such confusion extends outside of biology per se where the consequences of non-equilibrium thermodynamics or chaos complexity unfortunately have been championed for their challenge to biological processes. However, because functional and evolutionary morphology employs analytical tools that describe the boundary conditions set by an integrated adaptation, the discipline is most favourably suited to providing explanations of biological diversity and evolution.

scholarly journals Evolutionary plant breeding for low input systems

2005 ◽  
Vol 143 (4) ◽  
pp. 245-254 ◽  
Author(s):  
S. L. PHILLIPS ◽  
M. S. WOLFE
Keyword(s):  
Natural Selection ◽  
Gene Pools ◽  
Heritable Variation ◽  
Low Input ◽  
Pests And Diseases ◽  
Evolutionary Origins ◽  
Crop Varieties ◽  
Pure Lines ◽  
And Control ◽  
Selection Of

Heritable variation is at the heart of the process of evolution. However, variation is restricted in breeding for uniform crop populations using the pedigree line approach. Pedigree lines are successful in agriculture because synthetic inputs are used to raise fertility and control weeds, pests and diseases. An alternative method promoted for exploring the value of variation and evolutionary fitness in crops is to create composite cross populations. Composite cross populations are formed by assembling seed stocks with diverse evolutionary origins, recombination of these stocks by hybridization, the bulking of F1 progeny, and subsequent natural selection for mass sorting of the progeny in successive natural cropping environments. Composite cross populations can provide dynamic gene pools, which in turn provide a means of conserving germplasm resources: they can also allow selection of heterogeneous crop varieties. The value of composite cross populations in achieving these aims is dependent on the outcome of mass trials by artificial and natural selection acting upon the heterogeneous mixture. There is evidence to suggest that composite cross populations may be an efficient way of providing heterogeneous crops and of selecting superior pure lines for low input systems characterized by unpredictable stress conditions.

scholarly journals Processes and patterns of interaction as units of selection: An introduction to ITSNTS thinking

2018 ◽  
Vol 115 (16) ◽  
pp. 4006-4014 ◽  
Author(s):  
W. Ford Doolittle ◽  
S. Andrew Inkpen
Keyword(s):  
Natural Selection ◽  
Biogeochemical Cycles ◽  
The Other ◽  
Units Of Selection ◽  
Biological Hierarchy ◽  
Global Biogeochemical Cycles ◽  
Evolution By Natural Selection ◽  
Definition Of ◽  
Patterns Of Interaction ◽  
Selection Of

Many practicing biologists accept that nothing in their discipline makes sense except in the light of evolution, and that natural selection is evolution’s principal sense-maker. But what natural selection actually is (a force or a statistical outcome, for example) and the levels of the biological hierarchy (genes, organisms, species, or even ecosystems) at which it operates directly are still actively disputed among philosophers and theoretical biologists. Most formulations of evolution by natural selection emphasize the differential reproduction of entities at one or the other of these levels. Some also recognize differential persistence, but in either case the focus is on lineages of material things: even species can be thought of as spatiotemporally restricted, if dispersed, physical beings. Few consider—as “units of selection” in their own right—the processes implemented by genes, cells, species, or communities. “It’s the song not the singer” (ITSNTS) theory does that, also claiming that evolution by natural selection of processes is more easily understood and explained as differential persistence than as differential reproduction. ITSNTS was formulated as a response to the observation that the collective functions of microbial communities (the songs) are more stably conserved and ecologically relevant than are the taxa that implement them (the singers). It aims to serve as a useful corrective to claims that “holobionts” (microbes and their animal or plant hosts) are aggregate “units of selection,” claims that often conflate meanings of that latter term. But ITSNS also seems broadly applicable, for example, to the evolution of global biogeochemical cycles and the definition of ecosystem function.

The Natural Selection of Minds

1998 ◽  
Vol 43 (4) ◽  
pp. 263-264
Author(s):  
Joseph F. Rychlak
Keyword(s):  
Natural Selection ◽  
Selection Of

An Insight into Oligopeptide Transporter 3 (OPT3) family proteins

2020 ◽  
Vol 27 ◽  
Author(s):  
Fırat Kurt
Keyword(s):  
Stress Responses ◽  
Chelating Agent ◽  
Biological Processes ◽  
Biotic And Abiotic Stress ◽  
Oligopeptide Transporter ◽  
Binding Residues ◽  
Fe Homeostasis ◽  
Proteins Interactions ◽  
Insight Into ◽  
Selection Of

: Oligopeptide transporter 3 (OPT3) proteins are one of the subsets of OPT clade, yet little is known about these transporters. Therefore, homolog OPT3 proteins in several plant species were investigated and characterized using bioinformatical tools. Motif and co-expression analyses showed that OPT3 proteins may be involved in both biotic and abiotic stress responses as well as growth and developmental processes. AtOPT3 usually seemed to take part in Fe homeostasis whereas ZmOPT3 putatively interacted with proteins involved in various biological processes from plant defense system to stress responses. Glutathione (GSH), as a putative alternative chelating agent, was used in the AtOPT3 and ZmOPT3 docking analyses to identify their putative binding residues. The information given in this study will contribute to the understanding of OPT3 proteins’ interactions in various pathways and to the selection of potential ligands for OPT3s.

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

scholarly journals Genetic diversity and natural selection on the thrombospondin-related adhesive protein (TRAP) gene of Plasmodium falciparum on Bioko Island, Equatorial Guinea and global comparative analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Li-Yun Lin ◽  
Hui-Ying Huang ◽  
Xue-Yan Liang ◽  
Dong-De Xie ◽  
Jiang-Tao Chen ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Plasmodium Falciparum ◽  
Natural Selection ◽  
Protein Structure ◽  
Transmembrane Protein ◽  
Fixation Index ◽  
Bioko Island ◽  
Adhesive Protein ◽  
Trap Gene ◽  
Selection Of

Abstract Background Thrombospondin-related adhesive protein (TRAP) is a transmembrane protein that plays a crucial role during the invasion of Plasmodium falciparum into liver cells. As a potential malaria vaccine candidate, the genetic diversity and natural selection of PfTRAP was assessed and the global PfTRAP polymorphism pattern was described. Methods 153 blood spot samples from Bioko malaria patients were collected during 2016–2018 and the target TRAP gene was amplified. Together with the sequences from database, nucleotide diversity and natural selection analysis, and the structural prediction were preformed using bioinformatical tools. Results A total of 119 Bioko PfTRAP sequences were amplified successfully. On Bioko Island, PfTRAP shows its high degree of genetic diversity and heterogeneity, with π value for 0.01046 and Hd for 0.99. The value of dN–dS (6.2231, p < 0.05) hinted at natural selection of PfTRAP on Bioko Island. Globally, the African PfTRAPs showed more diverse than the Asian ones, and significant genetic differentiation was discovered by the fixation index between African and Asian countries (Fst > 0.15, p < 0.05). 667 Asian isolates clustered in 136 haplotypes and 739 African isolates clustered in 528 haplotypes by network analysis. The mutations I116T, L221I, Y128F, G228V and P299S were predicted as probably damaging by PolyPhen online service, while mutations L49V, R285G, R285S, P299S and K421N would lead to a significant increase of free energy difference (ΔΔG > 1) indicated a destabilization of protein structure. Conclusions Evidences in the present investigation supported that PfTRAP gene from Bioko Island and other malaria endemic countries is highly polymorphic (especially at T cell epitopes), which provided the genetic information background for developing an PfTRAP-based universal effective vaccine. Moreover, some mutations have been shown to be detrimental to the protein structure or function and deserve further study and continuous monitoring.

scholarly journals Nanometre-Scale Visualization of Chemical Parameter Changes by T1-Weighted ODMR Imaging Using a Fluorescent Nanodiamond

Chemosensors ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 68
Author(s):  
Takahiro Fujisaku ◽  
Ryuji Igarashi ◽  
Masahiro Shirakawa
Keyword(s):  
Magnetic Resonance ◽  
Laser Pulse ◽  
Real Time ◽  
Physical Parameters ◽  
Biological Processes ◽  
Chemical Parameter ◽  
Ph Conditions ◽  
Optically Detected ◽  
Life Phenomena ◽  
Selection Of

The dynamics of physical parameters in cells is strongly related to life phenomena; thus, a method to monitor and visualize them on a single-organelle scale would be useful to reveal unknown biological processes. We demonstrate real-time nanometre-scale T1-weighted imaging using a fluorescent nanodiamond. We explored optically detected magnetic resonance (ODMR) contrast at various values of interval laser pulse (τ), showing that sufficient contrast is obtained by appropriate selection of τ. By this method, we visualized nanometre-scale pH changes using a functionalized nanodiamond whose T1 has a dependence on pH conditions.

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