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Journal of Evolutionary Biology Jun 2021Research on the genomics of adaptation is rapidly changing. In the last few decades, progress in this area has been driven by methodological advances, not only in the...
Research on the genomics of adaptation is rapidly changing. In the last few decades, progress in this area has been driven by methodological advances, not only in the way increasingly large amounts of molecular data are generated (e.g. with high-throughput sequencing), but also in the way these data are analysed. This includes a growing appreciation and quantitative treatment of covariation among units within the same data type (e.g. genes) or across data types (e.g. genes and phenotypes). The development and adoption of more and more integrative tools have resulted in richer and more interesting empirical work. This special issue - comprising methodological, empirical, and review papers - aims to capture a 'snapshot' of this rapidly evolving field. We discuss in particular three important themes in the study of adaptation: the genetic architecture of adaptive variation, protein-coding and regulatory changes, and parallel evolution. We highlight how more traditional key themes in the study of genetic architecture (e.g. the number of loci underlying adaptive traits and the distribution of their effects) are now being complemented by other factors (e.g. how patterns of linkage and number of loci interact to affect the ability to adapt). Similarly, apart from addressing the relative importance of protein-coding and regulatory changes, we now have the tools to look in-depth at specific types of regulatory variation to gain a clearer picture of regulatory networks. Finally, parallel evolution has always been central to the study of adaptation, but now we are often able to address the question of whether - and to what extent - parallelism at the organismal or phenotypic level is matched by parallelism at the genetic level. Perhaps most importantly, we can now determine what mechanisms are driving parallelism (or lack thereof) across levels of biological organization. All these recent methodological developments open up new directions for future studies of adaptive changes across traits, levels of biological organization, demographic contexts and time scales.
Topics: Adaptation, Biological; Biological Evolution; Genetic Variation; Genomics
PubMed: 34145685
DOI: 10.1111/jeb.13871 -
Nurse Researcher Sep 2023Quantitative methods and statistical analysis are essential tools in nursing research, as they support researchers testing phenomena, illustrate their findings clearly...
BACKGROUND
Quantitative methods and statistical analysis are essential tools in nursing research, as they support researchers testing phenomena, illustrate their findings clearly and accurately, and provide explanation or generalisation of the phenomenon being investigated. The most popular inferential statistics test is the one-way analysis of variance (ANOVA), as it is the test designated for comparing the means of a study's target groups to identify if they are statistically different to the others. However, the nursing literature has identified that statistical tests are not being used correctly and findings are being reported incorrectly.
AIM
To present and explain the one-way ANOVA.
DISCUSSION
The article presents the purpose of inferential statistics and explains one-way ANOVA. It uses relevant examples to examine the steps needed to successfully apply the one-way ANOVA. The authors also provide recommendations for other statistical tests and measurements in parallel to one-way ANOVA.
CONCLUSION
Nurses need to develop their understanding and knowledge of statistical methods, to engage in research and evidence-based practice.
IMPLICATIONS FOR PRACTICE
This article enhances the understanding and application of one-way ANOVAs by nursing students, novice researchers, nurses and those engaged in academic studies. Nurses, nursing students and nurse researchers need to familiarise themselves with statistical terminology and develop their understanding of statistical concepts, to support evidence-based, quality, safe care.
Topics: Humans; Nursing Research; Analysis of Variance; Research Design; Students, Nursing; Correlation of Data
PubMed: 37317616
DOI: 10.7748/nr.2023.e1885 -
JCI Insight Jun 2023Innate and adaptive immune cells modulate the severity of autosomal dominant polycystic kidney disease (ADPKD), a common kidney disease with inadequate treatment...
Innate and adaptive immune cells modulate the severity of autosomal dominant polycystic kidney disease (ADPKD), a common kidney disease with inadequate treatment options. ADPKD has parallels with cancer, in which immune checkpoint inhibitors have been shown to reactivate CD8+ T cells and slow tumor growth. We have previously shown that in PKD, CD8+ T cell loss worsens disease. This study used orthologous early-onset and adult-onset ADPKD models (Pkd1 p.R3277C) to evaluate the role of immune checkpoints in PKD. Flow cytometry of kidney cells showed increased levels of programmed cell death protein 1 (PD-1)/cytotoxic T lymphocyte associated protein 4 (CTLA-4) on T cells and programmed cell death ligand 1 (PD-L1)/CD80 on macrophages and epithelial cells in Pkd1RC/RC mice versus WT, paralleling disease severity. PD-L1/CD80 was also upregulated in ADPKD human cells and patient kidney tissue versus controls. Genetic PD-L1 loss or treatment with an anti-PD-1 antibody did not impact PKD severity in early-onset or adult-onset ADPKD models. However, treatment with anti-PD-1 plus anti-CTLA-4, blocking 2 immune checkpoints, improved PKD outcomes in adult-onset ADPKD mice; neither monotherapy altered PKD severity. Combination therapy resulted in increased kidney CD8+ T cell numbers/activation and decreased kidney regulatory T cell numbers correlative with PKD severity. Together, our data suggest that immune checkpoint activation is an important feature of and potential novel therapeutic target in ADPKD.
Topics: Adult; Humans; Animals; Mice; Polycystic Kidney, Autosomal Dominant; B7-H1 Antigen; Polycystic Kidney Diseases; Kidney; Combined Modality Therapy; B7-1 Antigen
PubMed: 37345660
DOI: 10.1172/jci.insight.161318 -
BMC Bioinformatics May 2021Statistical geneticists employ simulation to estimate the power of proposed studies, test new analysis tools, and evaluate properties of causal models. Although there...
BACKGROUND
Statistical geneticists employ simulation to estimate the power of proposed studies, test new analysis tools, and evaluate properties of causal models. Although there are existing trait simulators, there is ample room for modernization. For example, most phenotype simulators are limited to Gaussian traits or traits transformable to normality, while ignoring qualitative traits and realistic, non-normal trait distributions. Also, modern computer languages, such as Julia, that accommodate parallelization and cloud-based computing are now mainstream but rarely used in older applications. To meet the challenges of contemporary big studies, it is important for geneticists to adopt new computational tools.
RESULTS
We present TraitSimulation, an open-source Julia package that makes it trivial to quickly simulate phenotypes under a variety of genetic architectures. This package is integrated into our OpenMendel suite for easy downstream analyses. Julia was purpose-built for scientific programming and provides tremendous speed and memory efficiency, easy access to multi-CPU and GPU hardware, and to distributed and cloud-based parallelization. TraitSimulation is designed to encourage flexible trait simulation, including via the standard devices of applied statistics, generalized linear models (GLMs) and generalized linear mixed models (GLMMs). TraitSimulation also accommodates many study designs: unrelateds, sibships, pedigrees, or a mixture of all three. (Of course, for data with pedigrees or cryptic relationships, the simulation process must include the genetic dependencies among the individuals.) We consider an assortment of trait models and study designs to illustrate integrated simulation and analysis pipelines. Step-by-step instructions for these analyses are available in our electronic Jupyter notebooks on Github. These interactive notebooks are ideal for reproducible research.
CONCLUSION
The TraitSimulation package has three main advantages. (1) It leverages the computational efficiency and ease of use of Julia to provide extremely fast, straightforward simulation of even the most complex genetic models, including GLMs and GLMMs. (2) It can be operated entirely within, but is not limited to, the integrated analysis pipeline of OpenMendel. And finally (3), by allowing a wider range of more realistic phenotype models, TraitSimulation brings power calculations and diagnostic tools closer to what investigators might see in real-world analyses.
Topics: Aged; Cloud Computing; Computer Simulation; Genetic Testing; Humans; Pedigree; Phenotype
PubMed: 33941078
DOI: 10.1186/s12859-021-04086-8 -
Science Advances Jul 2022pH controls a large repertoire of chemical and biochemical processes in water. Densely arrayed pH microenvironments would parallelize these processes, enabling their...
pH controls a large repertoire of chemical and biochemical processes in water. Densely arrayed pH microenvironments would parallelize these processes, enabling their high-throughput studies and applications. However, pH localization, let alone its arrayed realization, remains challenging because of fast diffusion of protons in water. Here, we demonstrate arrayed localizations of picoliter-scale aqueous acids, using a 256-electrochemical cell array defined on and operated by a complementary metal oxide semiconductor (CMOS)-integrated circuit. Each cell, comprising a concentric pair of cathode and anode with their current injections controlled with a sub-nanoampere resolution by the CMOS electronics, creates a local pH environment, or a pH "voxel," via confined electrochemistry. The system also monitors the spatiotemporal pH profile across the array in real time for precision pH control. We highlight the utility of this CMOS pH localizer-imager for high-throughput tasks by parallelizing pH-gated molecular state encoding and pH-regulated enzymatic DNA elongation at any selected set of cells.
PubMed: 35895813
DOI: 10.1126/sciadv.abm6815 -
Molecular Biology and Evolution Mar 2022Bioinformatic research relies on large-scale computational infrastructures which have a nonzero carbon footprint but so far, no study has quantified the environmental...
Bioinformatic research relies on large-scale computational infrastructures which have a nonzero carbon footprint but so far, no study has quantified the environmental costs of bioinformatic tools and commonly run analyses. In this work, we estimate the carbon footprint of bioinformatics (in kilograms of CO2 equivalent units, kgCO2e) using the freely available Green Algorithms calculator (www.green-algorithms.org, last accessed 2022). We assessed 1) bioinformatic approaches in genome-wide association studies (GWAS), RNA sequencing, genome assembly, metagenomics, phylogenetics, and molecular simulations, as well as 2) computation strategies, such as parallelization, CPU (central processing unit) versus GPU (graphics processing unit), cloud versus local computing infrastructure, and geography. In particular, we found that biobank-scale GWAS emitted substantial kgCO2e and simple software upgrades could make it greener, for example, upgrading from BOLT-LMM v1 to v2.3 reduced carbon footprint by 73%. Moreover, switching from the average data center to a more efficient one can reduce carbon footprint by approximately 34%. Memory over-allocation can also be a substantial contributor to an algorithm's greenhouse gas emissions. The use of faster processors or greater parallelization reduces running time but can lead to greater carbon footprint. Finally, we provide guidance on how researchers can reduce power consumption and minimize kgCO2e. Overall, this work elucidates the carbon footprint of common analyses in bioinformatics and provides solutions which empower a move toward greener research.
Topics: Algorithms; Carbon Footprint; Computational Biology; Genome-Wide Association Study; Software
PubMed: 35143670
DOI: 10.1093/molbev/msac034 -
Biochimica Et Biophysica Acta.... Jan 2021Lanthanides are relative newcomers to the field of cell biology of metals; their specific incorporation into enzymes was only demonstrated in 2011, with the isolation of... (Review)
Review
Lanthanides are relative newcomers to the field of cell biology of metals; their specific incorporation into enzymes was only demonstrated in 2011, with the isolation of a bacterial lanthanide- and pyrroloquinoline quinone-dependent methanol dehydrogenase. Since that discovery, the efforts of many investigators have revealed that lanthanide utilization is widespread in environmentally important bacteria, and parallel efforts have focused on elucidating the molecular details involved in selective recognition and utilization of these metals. In this review, we discuss the particular chemical challenges and advantages associated with biology's use of lanthanides, as well as the currently known lanthano-enzymes and -proteins (the lanthanome). We also review the emerging understanding of the coordination chemistry and biology of lanthanide acquisition, trafficking, and regulatory pathways. These studies have revealed significant parallels with pathways for utilization of other metals in biology. Finally, we discuss some of the many unresolved questions in this burgeoning field and their potentially far-reaching applications.
Topics: Alcohol Oxidoreductases; Bacteria; Bacterial Proteins; Lanthanoid Series Elements; Metals; Protein Transport
PubMed: 32979423
DOI: 10.1016/j.bbamcr.2020.118864 -
Frontiers in Plant Science 2022The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or... (Review)
Review
The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.
PubMed: 36072310
DOI: 10.3389/fpls.2022.868565 -
Frontiers in Plant Science 2022Nectaries are a promising frontier for plant evo-devo research, and are particularly fascinating given their diversity in form, position, and secretion methods across...
Nectaries are a promising frontier for plant evo-devo research, and are particularly fascinating given their diversity in form, position, and secretion methods across angiosperms. Emerging model systems permit investigations of the molecular basis for nectary development and nectar secretion across a range of taxa, which addresses fundamental questions about underlying parallelisms and convergence. Herein, we explore nectary development and nectar secretion in the emerging model taxa, (Cleomaceae), which exhibits a prominent adaxial nectary. First, we characterized nectary anatomy and quantified nectar secretion to establish a foundation for quantitative and functional gene experiments. Next, we leveraged RNA-seq to establish gene expression profiles of nectaries across three key stages of development: pre-anthesis, anthesis, and post-fertilization. We then performed functional studies on five genes that were putatively involved in nectary and nectar formation: (, (, and a highly expressed but uncharacterized transcript. These experiments revealed a high degree of functional convergence to homologues from other core Eudicots, especially . , redundantly with and , are required for nectary initiation. Concordantly, is essential for nectar formation and secretion, which indicates that the process is eccrine based in . While demonstration of conservation is informative to our understanding of nectary evolution, questions remain. For example, it is unknown which genes are downstream of the developmental initiators , , and , or what role the gene family plays in nectary initiation in this family. Further to this, we have initiated a characterization of associations between nectaries, yeast, and bacteria, but more research is required beyond establishing their presence. is an excellent model for continued research into nectary development because of its conspicuous nectaries, short generation time, and close taxonomic distance to .
PubMed: 36844906
DOI: 10.3389/fpls.2022.1085900 -
Cell Systems Jan 2022For microbiome biology to become a more predictive science, we must identify which descriptive features of microbial communities are reproducible and predictable, which...
For microbiome biology to become a more predictive science, we must identify which descriptive features of microbial communities are reproducible and predictable, which are not, and why. We address this question by experimentally studying parallelism and convergence in microbial community assembly in replicate glucose-limited habitats. Here, we show that the previously observed family-level convergence in these habitats reflects a reproducible metabolic organization, where the ratio of the dominant metabolic groups can be explained from a simple resource-partitioning model. In turn, taxonomic divergence among replicate communities arises from multistability in population dynamics. Multistability can also lead to alternative functional states in closed ecosystems but not in metacommunities. Our findings empirically illustrate how the evolutionary conservation of quantitative metabolic traits, multistability, and the inherent stochasticity of population dynamics, may all conspire to generate the patterns of reproducibility and variability at different levels of organization that are commonplace in microbial community assembly.
Topics: Microbiota; Population Dynamics; Reproducibility of Results
PubMed: 34653368
DOI: 10.1016/j.cels.2021.09.011