Biological Questions Research Articles

Limits on the evolutionary rates of biological traits.

This paper focuses on the maximum speed at which biological evolution can occur. I derive inequalities that limit the rate of evolutionary processes driven by natural selection, mutations, or genetic drift. These rate limits link the variability in a population to evolutionary rates. In particular, high variances in the fitness of a population and of a quantitative trait allow for fast changes in the trait's average. In contrast, low variability makes a trait less susceptible to random changes due to genetic drift. The results in this article generalize Fisher's fundamental theorem of natural selection to dynamics that allow for mutations and genetic drift, via trade-off relations that constrain the evolutionary rates of arbitrary traits. The rate limits can be used to probe questions in various evolutionary biology and ecology settings. They apply, for instance, to trait dynamics within or across species or to the evolution of bacteria strains. They apply to any quantitative trait, e.g., from species' weights to the lengths of DNA strands.

A Synergistic, Cultivator Model of De Novo Gene Origination.

Summary The origin and fixation of evolutionarily young genes is a fundamental question in evolutionary biology. However, understanding the origins of newly evolved genes arising de novo from non-coding genomic sequences is challenging. This is partly due to the low likelihood that several neutral or nearly-neutral mutations fix prior to the appearance of an important novel molecular function. This issue is particularly exacerbated in large effective population sizes where the effect of drift is small. To address this problem, we propose a regulation-focused, cultivator model for de novo gene evolution. This cultivator-focused model posits that each step in a novel variant’s evolutionary trajectory is driven by well-defined, selectively advantageous functions for the cultivator genes, rather than solely by the de novo genes, emphasizing the critical role of genome organization in the evolution of new genes.

What Is a Plant Cell Type in the Age of Single-Cell Biology? It's Complicated.

One of the fundamental questions in developmental biology is how a cell is specified to differentiate as a specialized cell type. Traditionally, plant cell types were defined based on their function, location, morphology, and lineage. Currently, in the age of single-cell biology, researchers typically attempt to assign plant cells to cell types by clustering them based on their transcriptomes. However, because cells are dynamic entities that progress through the cell cycle and respond to signals, the transcriptome also reflects the state of the cell at a particular moment in time, raising questions about how to define a cell type. We suggest that these complexities and dynamics of cell states are of interest and further consider the roles signaling, stochasticity, cell cycle, and mechanical forces play in plant cell fate specification. Once established, cell identity must also be maintained. With the wealth of single-cell data coming out, the field is poised to elucidate both the complexity and dynamics of cell states.

Physiology evolves convergently but lags behind warming in cities.

Cities, through the generation of urban heat islands, provide a venue for exploring contemporary convergent evolution to climatic warming. We quantified how repeatable the evolution of heat tolerance, cold tolerance, and body size were among diverse lineages in response to urban heat islands. Our study revealed significant shifts towards higher heat tolerance and diminished cold tolerance among urban populations. We further found that the magnitude of trait divergence was significantly and positively associated with the magnitude of the urban heat island, suggesting that temperature played a major role in the observed divergence in thermal tolerance. Despite these trends, the magnitude of trait responses lagged behind environmental warming. Heat tolerance responses exhibited a deficit of 0.84°C for every 1°C increase in warming, suggesting limits on adaptive evolution and consequent adaptational lags. Other moderators were predictive of greater divergence in heat tolerance, including lower baseline tolerance and greater divergence in body size. Although terrestrial species did not exhibit systematic shifts towards larger or smaller body size, aquatic species exhibited significant shifts towards smaller body size in urban habitats. Our study demonstrates how cities can be used to address long-standing questions in evolutionary biology regarding the repeatability of evolution. Importantly, this work also shows how cities can be used as forecasting tools by quantifying adaptational lags and by developing trait-based associations with responses to contemporary warming.

OsLCD3 interacts with OsSAMS1 to regulate grain size via ethylene/polyamine homeostasis control.

A fundamental question in developmental biology is how to regulate grain size to improve crop yields. Despite this, little is still known about the genetics and molecular mechanisms regulating grain size in crops. Here, we provide evidence that a putative protein kinase-like (OsLCD3) interacts with the S-adenosyl-L-methionine synthetase 1 (OsSAMS1) and determines the size and weight of grains. OsLCD3 mutation (lcd3) significantly increased grain size and weight by promoting cell expansion in spikelet hull, whereas its overexpression caused negative effects, suggesting that grain size was negatively regulated by OsLCD3. Importantly, lcd3 and OsSAMS1 overexpression (SAM1OE) led to large and heavy grains, with increased ethylene and decreased polyamines production. Based on genetic analyses, it appears that OsLCD3 and OsSAMS1 control rice grain size in part by ethylene/polyamine homeostasis. The results of this study provide a genetic and molecular understanding of how the OsLCD3-OsSAMS1 regulatory module regulates grain size, suggesting that ethylene/polyamine homeostasis is an appropriate target for improving grain size and weight.

Incorporation of Three Different Optical Trains into the IR-MALDESI Mass Spectrometry Imaging Platform to Characterize Artemisia annua.

Artemisinin is the leading medication for the treatment of malaria and is only produced naturally in Artemisia annua. The localization of artemisinin in both the glandular and non-glandular trichomes of the plant makes it an ideal candidate for mass spectrometry imaging (MSI) as a model system for method development. Infrared matrix-assisted laser desorption electrospray ionization MSI (IR-MALDESI-MSI) has the capability to detect hundreds to thousands of analytes simultaneously, providing abundance information in conjunction with species localization throughout a sample. The development of several new optical trains and their application to the IR-MALDESI-MSI platform has improved data quality in previous proof-of-concept experiments but has not yet been applied to analysis of native biological samples, especially the MSI analysis of plants. This study aimed to develop a workflow and optimize MSI parameters, specifically the laser optical train, for the analysis of Artemisia annua with the NextGen IR-MALDESI platform coupled to an Orbitrap Exploris 240 mass spectrometer. Two laser optics were compared to the conventional set up, of which include a Schwarzschild-like reflective objective and a diffractive optical element (DOE). These optics, respectively, enhance the spatial resolution of imaging experiments or create a square spot shape for top-hat imaging. Ultimately, we incorporated and characterized three different optical trains into our analysis of Artemisia annua to study metabolites in the artemisinin pathway. These improvements in our workflow, resulted in high spatial resolution and improved ion abundance from previous work, which will allow us to address many different questions in plant biology beyond this model system.

Computational methods for processing and interpreting mass spectrometry-based metabolomics.

Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.

Lions & sea lions & bears, oh my: utilizing museum specimens to study the ossification sequence of carnivoran taxa

BackgroundMammalian skeletons are largely formed before birth. Heterochronic changes in skeletal formation can be investigated by comparing the order of ossification for different elements of the skeleton. Due to the challenge of collecting prenatal specimens in viviparous taxa, opportunistically collected museum specimens provide the best material for studying prenatal skeletal development across many mammalian species. Previous studies have investigated ossification sequence in a range of mammalian species, but little is known about the pattern of bone formation in Carnivora. Carnivorans have diverse ecologies, diets, and biomechanical specializations and are well-suited for investigating questions in evolutionary biology. Currently, developmental data on carnivorans is largely limited to domesticated species. To expand available data on carnivoran skeletal development, we used micro-computed tomography (micro-CT) to non-invasively evaluate the degree of ossification in all prenatal carnivoran specimens housed in the Harvard Museum of Comparative Zoology. By coding the presence or absence of bones in each specimen, we constructed ossification sequences for each species. Parsimov-based genetic inference (PGi) was then used to identify heterochronic shifts between carnivoran lineages and reconstruct the ancestral ossification sequence of Carnivora.ResultsWe used micro-CT to study prenatal ossification sequence in six carnivora species: Eumetopias jubatus (Steller sea lion, n = 6), Herpestes javanicus (small Indian mongoose, n = 1), Panthera leo (lion, n = 1), Urocyon cinereoargenteus (gray fox, n = 1), Ursus arctos arctos (Eurasian brown bear, n = 1), and Viverricula indica (small Indian civet, n = 5). Due to the relatively later stage of collection for the available specimens, few heterochronic shifts were identified. Ossification sequences of feliform species showed complete agreement with the domestic cat. In caniforms, the bear and fox ossification sequences largely matched the dog, but numerous heterochronic shifts were identified in the sea lion.ConclusionsWe use museum specimens to generate cranial and postcranial micro-CT data on six species split between the two major carnivoran clades: Caniformia and Feliformia. Our data suggest that the ossification sequence of domestic dogs and cats are likely good models for terrestrial caniforms and feliforms, respectively, but not pinnipeds.

Machine learning enables identification of an alternative yeast galactose utilization pathway

How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the yeast subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. We used a random forest algorithm trained on these genomic, metabolic, and environmental data to predict growth on several carbon sources with high accuracy. Known structural genes involved in assimilation of these sources and presence/absence patterns of growth in other sources were important features contributing to prediction accuracy. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.2%) or growth data (82.6%) but not from isolation environment data (65.6%). Prediction accuracy was even higher (93.3%) when we combined genomic and growth data. After the GALactose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol, raising the hypothesis that several species in two orders, Serinales and Pichiales (containing the emerging pathogen Candida auris and the genus Ogataea, respectively), have an alternative galactose utilization pathway because they lack the GAL genes. Growth and biochemical assays confirmed that several of these species utilize galactose through an alternative oxidoreductive D-galactose pathway, rather than the canonical GAL pathway. Machine learning approaches are powerful for investigating the evolution of the yeast genotype-phenotype map, and their application will uncover novel biology, even in well-studied traits.

Implications of gene tree heterogeneity on downstream phylogenetic analyses: A case study employing the Fair Proportion index.

Many questions in evolutionary biology require the specification of a phylogeny for downstream phylogenetic analyses. However, with the increasingly widespread availability of genomic data, phylogenetic studies are often confronted with conflicting signal in the form of genomic heterogeneity and incongruence between gene trees and the species tree. This raises the question of determining what data and phylogeny should be used in downstream analyses, and to what extent the choice of phylogeny (e.g., gene trees versus species trees) impacts the analyses and their outcomes. In this paper, we study this question in the realm of phylogenetic diversity indices, which provide ways to prioritize species for conservation based on their relative evolutionary isolation on a phylogeny, and are thus one example of downstream phylogenetic analyses. We use the Fair Proportion (FP) index, also known as the evolutionary distinctiveness score, and explore the variability in species rankings based on gene trees as compared to the species tree for several empirical data sets. Our results indicate that prioritization rankings among species vary greatly depending on the underlying phylogeny, suggesting that the choice of phylogeny is a major influence in assessing phylogenetic diversity in a conservation setting. While we use phylogenetic diversity conservation as an example, we suspect that other types of downstream phylogenetic analyses such as ancestral state reconstruction are similarly affected by genomic heterogeneity and incongruence. Our aim is thus to raise awareness of this issue and inspire new research on which evolutionary information (species trees, gene trees, or a combination of both) should form the basis for analyses in these settings.

A Concept of Complementarity Between Complexity and Redundancy can Account for Kant's Biological Teleology and Unify Mechanistic and Finalistic Biology.

Over 160years after Darwin and 70years after the discovery of DNA, two fundamental questions of biology remain unanswered: What differentiates the living from the nonliving? How can mechanistic and finalistic or holistic biology be unified? Niels Bohr introduced a concept of complementarity in quantum physics and based on the paradox of light as a simultaneous wave and particle, conjectured that a similar concept might exist in biology that would solve the paradox of life originating from the nonliving. Bohr proposed that two mutually exclusive-independent observations may be necessary to explain a phenomenon and provided support to Immanuel Kant's idea that the "purposive" behaviour of organisms could only be explained in teleological terms and that mechanical and teleological approaches were necessary and complementary to explain biology. We present a concept of complementarity whereby biochemical pathways or cellular channels for the flow of information are simultaneously complex and redundant and complexity and redundancy complement each other. The postulates of biological complementarity are that (1) it was an essential condition in the origin of life; (2) it provided physiological flexibility that allowed organisms to mount self-protection response and complexity to evolve in the face of deleterious mutations before the evolution of bi-parental sex; (3) it laid the foundation for the evolution of a choice of response when confronted with threat; and (4) it applies to all levels of biological organizations and, thus, can serve as a basis for the unification of mechanistic and holistic biology. It is proposed that teleology is simultaneously constitutive and heuristic: constitutive because organisms' "purposive" behaviours are adaptive and are grounded in mechanism (complexity and redundancy), and heuristic because with our finite cognition and our goal-oriented (humans alone are aware of "tomorrow") and anthropomorphic pre-disposition, teleology will remain useful as a guide to our making sense of the world, even how to ask a meaningful question.

Spatial transcriptomics techniques and its applications in plant research

The heterogeneity of gene expression in plant cells plays a crucial role in determining the functional differences among tissues. Recent advancements in spatial transcriptome (ST) technology have significantly contributed to the study of specific biological questions in plants. This technology has been successfully applied to examine cell development, identification, and stress resistance. This review aims to explore the application of ST technology in plants by reviewing three aspects: the development of ST technology, its current application in plants, and future research directions. The review provides a systematic description of the development process of ST technology, with a focus on analyzing its progress in studying plant cell growth and differentiation, plant cell identification, and stress resistance. In addition, the challenges faced by ST technology in plant applications are summarized, along with proposed future directions for plant research, including the advantages of combining other omics technologies with ST technology to tackle scientific challenges in the field of plants.

Transmissible cancers, the genomes that don't melt down.

Evolutionary theory predicts that accumulation of deleterious mutations in asexually reproducing organisms should lead to genomic decay. Clonally reproducing cell lines, i.e., transmissible cancers, when cells are transmitted as allografts/xenografts, break these rules, and survive for centuries and millennia. The currently known 11 transmissible cancer lineages occur in dogs (Canine Venereal Tumour Disease, CTVT), in Tasmanian devils (Devil Facial Tumour Diseases, DFT 1 and DFT2) and in bivalves (bivalve transmissible neoplasia, BTN). Despite the mutation loads of these cell lines being much higher than observed in human cancers, they have not been eliminated in space and time. Here we provide potential explanations how these fascinating cell lines may have overcome the fitness decline due to the progressive accumulation of deleterious mutations and propose that the high mutation load may carry an indirect positive fitness outcome. We offer ideas on how these host-pathogen systems could be used to answer outstanding questions in evolutionary biology. The recent studies on the evolution of these clonal pathogens reveal key mechanistic insight into transmissible cancer genomes, information that is essential for future studies investigating how these contagious cancer cell lines can repeatedly evade immune recognition, evolve, and survive in the landscape of highly diverse hosts.

Julian Hart Lewis. 12 August 1946 — 30 April 2014

Julian Hart Lewis was an outstanding embryologist and mathematical biologist whose breadth of interests and skills helped re-establish the importance of quantitative thinking to modern molecular developmental biology. He was most interested in the fundamental question of how the single-celled fertilized egg generates many different types of cells (e.g. nerves, skin, muscle) at the correct time and arranged in the right place. This remarkable generation of complexity is fundamental to an understanding of how animals develop and how this is orchestrated by their genes. Julian was exceptional in combining a ‘feel’ for biological questions and the ability to utilize rigorous and revealing mathematical models. He was also an outstanding mentor, writer and polymath, who was ahead of his time in breaching historical barriers between different scientific disciplines.

Multiplex Digital Spatial Profiling in Breast Cancer Research: State-of-the-Art Technologies and Applications across the Translational Science Spectrum.

While RNA sequencing and multi-omic approaches have significantly advanced cancer diagnosis and treatment, their limitation in preserving critical spatial information has been a notable drawback. This spatial context is essential for understanding cellular interactions and tissue dynamics. Multiplex digital spatial profiling (MDSP) technologies overcome this limitation by enabling the simultaneous analysis of transcriptome and proteome data within the intact spatial architecture of tissues. In breast cancer research, MDSP has emerged as a promising tool, revealing complex biological questions related to disease evolution, identifying biomarkers, and discovering drug targets. This review highlights the potential of MDSP to revolutionize clinical applications, ranging from risk assessment and diagnostics to prognostics, patient monitoring, and the customization of treatment strategies, including clinical trial guidance. We discuss the major MDSP techniques, their applications in breast cancer research, and their integration in clinical practice, addressing both their potential and current limitations. Emphasizing the strategic use of MDSP in risk stratification for women with benign breast disease, we also highlight its transformative potential in reshaping the landscape of breast cancer research and treatment.

DNA Damage, Genome Stability, and Adaptation: A Question of Chance or Necessity?

DNA damage causes the mutations that are the principal source of genetic variation. DNA damage detection and repair mechanisms therefore play a determining role in generating the genetic diversity on which natural selection acts. Speciation, it is commonly assumed, occurs at a rate set by the level of standing allelic diversity in a population. The process of speciation is driven by a combination of two evolutionary forces: genetic drift and ecological selection. Genetic drift takes place under the conditions of relaxed selection, and results in a balance between the rates of mutation and the rates of genetic substitution. These two processes, drift and selection, are necessarily mediated by a variety of mechanisms guaranteeing genome stability in any given species. One of the outstanding questions in evolutionary biology concerns the origin of the widely varying phylogenetic distribution of biodiversity across the Tree of Life and how the forces of drift and selection contribute to shaping that distribution. The following examines some of the molecular mechanisms underlying genome stability and the adaptive radiations that are associated with biodiversity and the widely varying species richness and evenness in the different eukaryotic lineages.

Identification and Functional Characterization of Alternative Transcripts of LncRNA HNF1A-AS1 and Their Impacts on Cell Growth, Differentiation, Liver Diseases, and in Response to Drug Induction.

The long non-coding RNA (lncRNA) hepatocyte nuclear factor-1 alpha (HNF1A) antisense RNA 1 (HNF1A-AS1) is an important lncRNA for liver growth, development, cell differentiation, and drug metabolism. Like many lncRNAs, HNF1A-AS1 has multiple annotated alternative transcripts in the human genome. Several fundamental biological questions are still not solved: (1) How many transcripts really exist in biological samples, such as liver samples and liver cell lines? (2) What are the expression patterns of different alternative HNF1A-AS1 transcripts at different conditions, including during cell growth and development, after exposure to xenobiotics (such as drugs), and in disease conditions, such as metabolic dysfunction-associated steatotic liver disease (MASLD), alcohol-associated liver disease (ALD) cirrhosis, and obesity? (3) Does the siRNA used in previous studies knock down one or multiple transcripts? (4) Do different transcripts have the same or different functions for gene regulation? The presented data confirm the existence of several annotated HNF1A-AS1 transcripts in liver samples and cell lines, but also identify some new transcripts, which are not annotated in the Ensembl genome database. Expression patterns of the identified HNF1A-AS1 transcripts are highly correlated with the cell differentiation of matured hepatocyte-like cells from human embryonic stem cells (hESC), growth and differentiation of HepaRG cells, in response to rifampicin induction, and in various liver disease conditions. The expression levels of the HNF1A-AS1 transcripts are also highly correlated to the expression of cytochrome P450 enzymes, such as CYP3A4, during HepaRG growth, differentiation, and in response to rifampicin induction.

Classification and counting of cells in brightfield microscopy images: an application of convolutional neural networks

Microscopy is integral to medical research, facilitating the exploration of various biological questions, notably cell quantification. However, this process's time-consuming and error-prone nature, attributed to human intervention or automated methods usually applied to fluorescent images, presents challenges. In response, machine learning algorithms have been integrated into microscopy, automating tasks and constructing predictive models from vast datasets. These models adeptly learn representations for object detection, image segmentation, and target classification. An advantageous strategy involves utilizing unstained images, preserving cell integrity and enabling morphology-based classification—something hindered when fluorescent markers are used. The aim is to introduce a model proficient in classifying distinct cell lineages in digital contrast microscopy images. Additionally, the goal is to create a predictive model identifying lineage and determining optimal quantification of cell numbers. Employing a CNN machine learning algorithm, a classification model predicting cellular lineage achieved a remarkable accuracy of 93%, with ROC curve results nearing 1.0, showcasing robust performance. However, some lineages, namely SH-SY5Y (78%), HUH7_mayv (85%), and A549 (88%), exhibited slightly lower accuracies. These outcomes not only underscore the model's quality but also emphasize CNNs' potential in addressing the inherent complexities of microscopic images.

Evolutionary graph theory beyond single mutation dynamics: on how network structured populations cross fitness landscapes.

Spatially-resolved datasets are revolutionizing knowledge in molecular biology, yet are under-utilized for questions in evolutionary biology. To gain insight from these large-scale datasets of spatial organization, we need mathematical representations and modeling techniques that can both capture their complexity, but also allow for mathematical tractability. Evolutionary graph theory utilizes the mathematical representation of networks as a proxy for heterogeneous population structure and has started to reshape our understanding of how spatial structure can direct evolutionary dynamics. However, previous results are derived for the case of a single new mutation appearing in the population and the role of network structure in shaping fitness landscape crossing is still poorly understood. Here we study how network structured populations cross fitness landscapes and show that even a simple extension to a two-mutational landscape can exhibit complex evolutionary dynamics that cannot be predicted using previous single-mutation results. We show how our results can be intuitively understood through the lens of how the two main evolutionary properties of a network, the amplification and acceleration factors, change the expected fate of the intermediate mutant in the population and further discuss how to link these models to spatially-resolved datasets of cellular organization.

Probabilistic association of differentially expressed genes with cis-regulatory elements.

Differential gene expression in response to perturbations is mediated at least in part by changes in binding of transcription factors (TFs) and other proteins at specific genomic regions. Association of these cis-regulatory elements (CREs) with their target genes is a challenging task that is essential to address many biological and mechanistic questions. Many current approaches rely on chromatin conformation capture techniques or single-cell correlational methods to establish CRE-to-gene associations. These methods can be effective but have limitations, including resolution, gaps in detectable association distances, and cost. As an alternative, we have developed DegCre, a nonparametric method that evaluates correlations between measurements of perturbation-induced differential gene expression and differential regulatory signal at CREs to score possible CRE-to-gene associations. It has several unique features, including the ability to use any type of CRE activity measurement, yield probabilistic scores for CRE-to-gene pairs, and assess CRE-to-gene pairings across a wide range of sequence distances. We apply DegCre to six data sets, each using different perturbations and containing a variety of regulatory signal measurements, including chromatin openness, histone modifications, and TF occupancy. To test their efficacy, we compare DegCre associations to Hi-C loop calls and CRISPR-validated CRE-to-gene associations, establishing good performance by DegCre that is comparable or superior to competing methods. DegCre is a novel approach to the association of CREs to genes from a perturbation-differential perspective, with strengths that are complementary to existing approaches and allow for new insights into gene regulation.