Biological Questions Research Articles

TOMOMAN: a software package for large scale cryo-electron tomography data preprocessing, community data sharing, and collaborative computing.

Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) are becoming the preferred methodologies for investigating subcellular and macromolecular structures in native or near-native environments. While cryo-ET is amenable to a wide range of biological problems, these problems often have data processing requirements that need to be individually optimized, precluding the notion of a one-size-fits-all processing pipeline. Cryo-ET data processing is also becoming progressively more complex due to an increasing number of packages for each processing step. Though each package has its own strengths and weaknesses, independent development and different data formats makes them difficult to interface with one another. TOMOMAN (TOMOgram MANager) is an extensible package for streamlining the interoperability of packages, enabling users to develop project-specific processing workflows. TOMOMAN does this by maintaining an internal metadata format and wrapping external packages to manage and perform preprocessing, from raw tilt-series data to reconstructed tomograms. TOMOMAN can also export this metadata between various STA packages. TOMOMAN also includes tools for archiving projects to data repositories; allowing subsequent users to download TOMOMAN projects and directly resume processing where it was previously left off. By tracking essential metadata, TOMOMAN streamlines data sharing, which improves reproducibility of published results, reduces computational costs by minimizing reprocessing, and enables distributed cryo-ET projects between multiple groups and institutions. TOMOMAN provides a way for users to test different software packages to develop processing workflows that meet the specific needs of their biological questions and to distribute their results with the broader scientific community.

A Guided Tour of Phylogenetic Comparative Methods for Studying Trait Evolution

Phylogenetic comparative methods are important tools in biology, providing insights into the way traits evolve. There are many technical resources describing how these methods work. Our aim here is to complement these with an overview of the types of biological questions that can be addressed by different methods and to outline potential pitfalls and considerations when embarking on comparative studies. First, we introduce what comparative methods are and why they are important. Second, we outline how they can be used to understand when, where, and how frequently traits evolve. Third, we examine how the coevolution of traits within and between species can be studied, along with patterns of causality. Finally, we discuss how to approach comparative analyses and the ways in which different types of data, such as published relationships, omic, and remote sensing data, can be integrated.

Open-source, high-throughput targeted in situ transcriptomics for developmental and tissue biology.

Multiplexed spatial profiling of mRNAs has recently gained traction as a tool to explore the cellular diversity and the architecture of tissues. We propose a sensitive, open-source, simple and flexible method for the generation of in situ expression maps of hundreds of genes. We use direct ligation of padlock probes on mRNAs, coupled with rolling circle amplification and hybridization-based in situ combinatorial barcoding, to achieve high detection efficiency, high-throughput and large multiplexing. We validate the method across a number of species and show its use in combination with orthogonal methods such as antibody staining, highlighting its potential value for developmental and tissue biology studies. Finally, we provide an end-to-end computational workflow that covers the steps of probe design, image processing, data extraction, cell segmentation, clustering and annotation of cell types. By enabling easier access to high-throughput spatially resolved transcriptomics, we hope to encourage a diversity of applications and the exploration of a wide range of biological questions.

Physical modeling of embryonic transcriptomes identifies collective modes of gene expression.

Starting from one totipotent cell, complex multicellular organisms form through a series of differentiation and morphogenetic events, culminating in a multitude of cell types arranged in a functional and intricate spatial pattern. To do so, cells coordinate with each other, resulting in dynamics which follow a precise developmental trajectory, constraining the space of possible embryo-to-embryo variation. Using recent single-cell sequencing data of early ascidian embryos, we leverage natural variation together with modeling and inference techniques from statistical physics to investigate development at the level of a complete interconnected embryo - an embryonic transcriptome. After developing a robust and biophysically motivated approach to identifying distinct transcriptomic states or cell types, a statistical analysis reveals correlations within embryos and across cell types demonstrating the presence of collective variation. From these intra-embryo correlations, we infer minimal networks of cell-cell interactions, which reveal the collective modes of gene expression. Our work demonstrates how the existence and nature of spatial interactions along with the collective modes of expression that they give rise to can be inferred from single-cell gene expression measurements, opening up a wider range of biological questions that can be addressed using sequencing-based modalities.

Optogenetic control of phosphate-responsive genes using single component fusion proteins in Saccharomyces cerevisiae.

Blue light illumination can be detected by Light-Oxygen-Voltage (LOV) photosensing proteins and translated into a range of biochemical responses, facilitating the generation of novel optogenetic tools to control cellular function. Here we develop new variants of our previously described VP-EL222 light-dependent transcription factor and apply them to study the phosphate-responsive signaling (PHO) pathway in the budding yeast Saccharomyces cerevisiae, exemplifying the utilities of these new tools. Focusing first on the VP-EL222 protein itself, we quantified the tunability of gene expression as a function of light intensity and duration, and demonstrated that this system can tolerate the addition of substantially larger effector domains without impacting function. We further demonstrated the utility of several EL222-driven transcriptional controllers in both plasmid and genomic settings, using the PHO5 and PHO84 promoters in their native chromosomal contexts as examples. These studies highlight the utility of light-controlled gene activation using EL222 tethered to either artificial transcription domains or yeast activator proteins (Pho4). Similarly, we demonstrate the ability to optogenetically repress gene expression with EL222 fused to the yeast Ume6 protein. We finally investigated the effects of moving EL222 recruitment sites to different locations within the PHO5 and PHO84 promoters, as well as determining how this artificial light-controlled regulation could be integrated with the native controls dependent on inorganic phosphate (Pi) availability. Taken together, our work expands the applicability of these versatile optogenetic tools in the types of functionality they can deliver and biological questions that can be probed.

Convergent evolution of plant prickles by repeated gene co-option over deep time.

An enduring question in evolutionary biology concerns the degree to which episodes of convergent trait evolution depend on the same genetic programs, particularly over long timescales. In this work, we genetically dissected repeated origins and losses of prickles-sharp epidermal projections-that convergently evolved in numerous plant lineages. Mutations in a cytokinin hormone biosynthetic gene caused at least 16 independent losses of prickles in eggplants and wild relatives in the genus Solanum. Homologs underlie prickle formation across angiosperms that collectively diverged more than 150 million years ago, including rice and roses. By developing new Solanum genetic systems, we leveraged this discovery to eliminate prickles in a wild species and an indigenously foraged berry. Our findings implicate a shared hormone activation genetic program underlying evolutionarily widespread and recurrent instances of plant morphological innovation.

α-Mannosylated HLA-II glycopeptide antigens dominate the immunopeptidome of immortalised cells and tumour tissues.

Immunopeptides are cell surface-located protein fragments that aid our immune system to recognise and respond to pathogenic insult and malignant transformation. In this two-part communication, we firstly summarise and reflect on our recent discovery documenting that MHC-II-bound immunopeptides from immortalised cell lines prevalently carry N-glycans that differ from the cellular glycoproteome (Goodson, Front Immunol, 2023). These findings are important as immunopeptide glycosylation remains poorly understood in immunosurveillance. The study also opened up new technical and biological questions that we address in the second part of this communication. Our study highlighted that the performance of the search engines used to detect glycosylated immunopeptides from LC-MS/MS data remains untested and, importantly, that little biochemical in vivo evidence is available to document the nature of glycopeptide antigens in tumour tissues. To this end, we compared the N-glycosylated MHC-II-bound immunopeptides that were reported from tumour tissues of 14 meningioma patients in the MSFragger-HLA-Glyco database (Bedran, Nat Commun, 2023) to those we identified with the commercial Byonic software. Encouragingly, the search engines produced similar outputs supporting that N-glycosylated MHC-II-bound immunopeptides are prevalent in meningioma tumour tissues. Consistent also with in vitro findings, the tissue-derived MHC-II-bound immunopeptides were found to predominantly carry hyper-processed (paucimannosidic- and chitobiose core-type) and hypo-processed (oligomannosidic-type) N-glycans that varied in prevalence and distribution between patients. Taken together, evidence is emerging suggesting that α-mannosidic glycoepitopes abundantly decorate MHC-II-bound immunopeptides presented in both immortalised cells and tumour tissues warranting further research into their functional roles in immunosurveillance.

Proterozoic microfossils continue to provide new insights into the rise of complex eukaryotic life.

Eukaryotes have evolved to dominate the biosphere today, accounting for most documented living species and the vast majority of the Earth's biomass. Consequently, understanding how these biologically complex organisms initially diversified in the Proterozoic Eon over 539 million years ago is a foundational question in evolutionary biology. Over the last 70 years, palaeontologists have sought to document the rise of eukaryotes with fossil evidence. However, the delicate and microscopic nature of their sub-cellular features affords early eukaryotes diminished preservation potential. Chemical biomarker signatures of eukaryotes and the genetics of living eukaryotes have emerged as complementary tools for reconstructing eukaryote ancestry. In this review, we argue that exceptionally preserved Proterozoic microfossils are critical to interpreting these complementary tools, providing crucial calibrations to molecular clocks and testing hypotheses of palaeoecology. We highlight recent research on their preservation and biomolecular composition that offers new ways to enhance their utility.

Identification of Student Misconceptions in General Biology Courses using Four Tier Multiple Choice Tests Containing High Order Thinking Skills

This research aims to identify misconceptions of biology education students in the General Biology course. The method used in this research uses quantitative descriptive. The sample used in this research was Biology education students who had taken General Biology courses consisting of students in semesters 3, 5, and 7. Data collection was carried out using a test in the form of General Biology questions in a four-tier multiple-choice diagnostic format containing high-order thinking skills, totaling 35 questions. The research results showed that the percentage of misconceptions in each class was 43.80% (semester 3), 33.93% (semester 5), and 29.46% (semester 7). The highest percentage of misconception categories among third-semester students was found in the digestive system material (57.78%), fifth-semester students in ecology material (56.67%), and seventh-semester students in ecological material (40.00%). Misconceptions in students could be caused by students not being used to working on this type of question HOTS. To be able to answer correctly, students must be able to analyze and relate the data or information in the question to existing concepts

Research Progress on the Regulating Factors of Muscle Fiber Heterogeneity in Livestock: A Review.

The type of muscle fiber plays a crucial role in the growth, development, and dynamic plasticity of animals' skeletal muscle. Additionally, it is a primary determinant of the quality of both fresh and processed meat. Therefore, understanding the regulatory factors that contribute to muscle fibers' heterogeneity is of paramount importance. Recent advances in sequencing and omics technologies have enabled comprehensive cross-verification of research on the factors affecting the types of muscle fiber across multiple levels, including the genome, transcriptome, proteome, and metabolome. These advancements have facilitated deeper exploration into the related biological questions. This review focused on the impact of individual characteristics, feeding patterns, and genetic regulation on the proportion and interconversion of different muscle fibers. The findings indicated that individual characteristics and feeding patterns significantly influence the type of muscle fiber, which can effectively enhance the type and distribution of muscle fibers in livestock. Furthermore, non-coding RNA, genes and signaling pathways between complicated regulatory mechanisms and interactions have a certain degree of impact on muscle fibers' heterogeneity. This, in turn, changes muscle fiber profile in living animals through genetic selection or environmental factors, and has the potential to modulate the quality of fresh meat. Collectively, we briefly reviewed the structure of skeletal muscle tissue and then attempted to review the inevitable connection between the quality of fresh meat and the type of muscle fiber, with particular attention to potential events involved in regulating muscle fibers' heterogeneity.

Correlated multimodal imaging in life sciences: lessons learnt

Correlated Multimodal Imaging (CMI) gathers information about the same specimen with two or more modalities that–combined–create a composite and complementary view of the sample (including insights into structure, function, dynamics and molecular composition). CMI allows one to reach beyond what is possible with a single modality and describe biomedical processes within their overall spatio-temporal context and gain a mechanistic understanding of cells, tissues, and organisms in health and disease by untangling their molecular mechanisms within their native environment. The field of CMI has grown substantially over the last decade and previously unanswerable biological questions have been solved by applying novel CMI workflows. To disseminate these workflows and comprehensively share the scattered knowledge present within the CMI community, an initiative was started to bring together imaging, image analysis, and biomedical scientists and work towards an open community that promotes and disseminates the field of CMI. This community project was funded for the last 4 years by an EU COST Action called COMULIS (COrrelated MUltimodal imaging in the LIfe Sciences). In this review we share some of the showcases and lessons learnt from the action. We also briefly look ahead at how we anticipate building on this initial initiative.

Inferring Taxonomic Affinities and Genetic Distances Using Morphological Features Extracted from Specimen Images: a Case Study with a Bivalve dataset.

Reconstructing the tree of life and understanding the relationships of taxa are core questions in evolutionary and systematic biology. The main advances in this field in the last decades were derived from molecular phylogenetics; however, for most species, molecular data are not available. Here, we explore the applicability of two deep learning methods - supervised classification approaches and unsupervised similarity learning - to infer organism relationships from specimen images. As a basis, we assembled an image dataset covering 4144 bivalve species belonging to 74 families across all orders and subclasses of the extant Bivalvia, with molecular phylogenetic data being available for all families and a complete taxonomic hierarchy for all species. The suitability of this dataset for deep learning experiments was evidenced by an ablation study resulting in almost 80% accuracy for identifications on the species level. Three sets of experiments were performed using our dataset. First, we included taxonomic hierarchy and genetic distances in a supervised learning approach to obtain predictions on several taxonomic levels simultaneously. Here, we stimulated the model to consider features shared between closely related taxa to be more critical for their classification than features shared with distantly related taxa, imprinting phylogenetic and taxonomic affinities into the architecture and training procedure. Second, we used transfer learning and similarity learning approaches for zero-shot experiments to identify the higher-level taxonomic affinities of test species that the models had not been trained on. The models assigned the unknown species to their respective genera with approximately 48% and 67% accuracy. Lastly, we used unsupervised similarity learning to infer the relatedness of the images without prior knowledge of their taxonomic or phylogenetic affinities. The results clearly showed similarities between visual appearance and genetic relationships at the higher taxonomic levels. The correlation was 0.6 for the most species-rich subclass (Imparidentia), ranging from 0.5 to 0.7 for the orders with the most images. Overall, the correlation between visual similarity and genetic distances at the family level was 0.78. However, fine-grained reconstructions based on these observed correlations, such as sister-taxa relationships, require further work. Overall, our results broaden the applicability of automated taxon identification systems and provide a new avenue for estimating phylogenetic relationships from specimen images.

Body plan evolvability: The role of variability in gene regulatory networks.

Recent computational modeling of early fruit fly (Drosophila) development has characterized the degree to which gene regulation networks can be robust to natural variability. In the first few hours of development, broad spatial gradients of maternally derived transcription factors activate embryonic gap genes. These gap patterns determine the subsequent segmented insect body plan through pair-rule gene expression. Gap genes are expressed with greater spatial precision than the maternal patterns. Computational modeling of the gap-gap regulatory interactions provides a mechanistic understanding for this robustness to maternal variability in wild-type (WT) patterning. A long-standing question in evolutionary biology has been how a system which is robust, such as the developmental program creating any particular species' body plan, is also evolvable, i.e. how can a system evolve or speciate, if the WT form is strongly buffered and protected? In the present work, we use the WT model to explore the breakdown of such Waddington-type 'canalization'. What levels of variability will push the system out of the WT form; are there particular pathways in the gene regulatory mechanism which are more susceptible to losing the WT form; and when robustness is lost, what types of forms are most likely to occur (i.e. what forms lie near the WT)? Manipulating maternal effects in several different pathways, we find a common gap 'peak-to-step' pattern transition in the loss of WT.We discuss these results in terms of the evolvability of insect segmentation, and in terms of experimental perturbations and mutations which could test the model predictions. We conclude by discussing the prospects for using continuum models of pattern dynamics to investigate a wider range of evo-devo problems.

Biophysics Concept Inventory Survey: An Assessment in Biophysical Undergraduate Education

ABSTRACT Since the Force Concept Inventory in 1992, many concept inventories have been developed to cover classical scientific fields. However, there is a lack of concept inventories for interdisciplinary fields, such as biophysics. We introduce a Biophysics Concept Inventory Survey (BCIS), a 20-question, multiple-choice survey to measure student gains in biophysical concepts. The BCIS contains 5 question classifications: remember, understand, apply, analyze, and create, as well as question concepts divided into primarily physics or primarily biology questions. We administered the BCIS to 3 cohorts of students over 4 years. Each cohort participated in a 10-week summer Research Experience for Undergraduates (REU) in biophysics. We compared the presurvey (before REU) and postsurvey (after REU) scores to determine the fraction of the maximum possible gain or loss realized. Our analysis of the results suggests that the BCIS shows no biases based on sex or ethnicity. Further, we used the BCIS to show that 69% of the REU participants showed gains in biophysics concepts, with most of the total participant mean of gain occurring at higher levels of Bloom’s taxonomy: create and analyze. Overall, participants obtain slightly higher scores in physics (8% increase) than biology (5% increase) when comparing the pre- and postscores. The coronavirus disease 2019 pandemic allows a splitting of prepandemic and postpandemic cohorts, with the postpandemic cohort showing significantly larger gains than the prepandemic students. These results show that the BCIS, with question classifications and concepts, probes the students’ ability to apply knowledge to various biophysical science topics without underlying biases and enables instructors to obtain answers to important questions about the effectiveness of the educational programs. The BCIS fills a gap for interdisciplinary concept inventories.

Image-based identification and isolation of micronucleated cells to dissect cellular consequences.

Recent advances in isolating cells based on visual phenotypes have transformed our ability to identify the mechanisms and consequences of complex traits. Micronucleus (MN) formation is a frequent outcome of genome instability, triggers extensive disease-associated changes in genome structure and signaling coincident with MN rupture, and is almost exclusively defined by visual analysis. Automated MN detection in microscopy images has proved extremely challenging, limiting unbiased discovery of the mechanisms and consequences of MN formation and rupture. In this study we describe two new MN segmentation modules: a rapid and precise model for classifying micronucleated cells and their rupture status (VCS MN), and a robust model for accurate MN segmentation (MNFinder) from a broad range of microscopy images. As a proof-of-concept, we define the transcriptome of non-transformed human cells with intact or ruptured MN after inducing chromosome missegregation by combining VCS MN with photoactivation-based cell isolation and RNASeq. Surprisingly, we find that neither MN formation nor rupture triggers a unique transcriptional response. Instead, transcriptional changes are correlated with increased aneuploidy in these cell classes. Our MN segmentation modules overcome a significant challenge to reproducible MN quantification, and, joined with visual cell sorting, enable the application of powerful functional genomics assays, including pooled CRISPR screens and time-resolved analyses of cellular and genetic consequences, to a wide-range of questions in MN biology.

Using Microfluidics to Align Matrix Architecture and Generate Chemokine Gradients Promotes Directional Branching in a Model of Epithelial Morphogenesis.

The mechanical cue of fiber alignment plays a key role in the development of various tissues in the body. The ability to study the effect of these stimuli in vitro has been limited previously. Here, we present a microfluidic device capable of intrinsically generating aligned fibers using the microchannel geometry. The device also features tunable interstitial fluid flow and the ability to form a morphogen gradient. These aspects allow for the modeling of complex tissues and to differentiate cell response to different stimuli. To demonstrate the abilities of our device, we incorporated luminal epithelial cysts into our device and induced growth factor stimulation. We found the mechanical cue of fiber alignment to play a dominant role in cell elongation and the ability to form protrusions was dependent on cadherin-3. Together, this work serves as a springboard for future potential with these devices to answer questions in developmental biology and complex diseases such as cancers.

PDX1+ cell budding morphogenesis in a stem cell-derived islet spheroid system

Remarkable advances in protocol development have been achieved to manufacture insulin-secreting islets from human pluripotent stem cells (hPSCs). Distinct from current approaches, we devised a tunable strategy to generate islet spheroids enriched for major islet cell types by incorporating PDX1+ cell budding morphogenesis into staged differentiation. In this process that appears to mimic normal islet morphogenesis, the differentiating islet spheroids organize with endocrine cells that are intermingled or arranged in a core-mantle architecture, accompanied with functional heterogeneity. Through in vitro modelling of human pancreas development, we illustrate the importance of PDX1 and the requirement for EphB3/4 signaling in eliciting cell budding morphogenesis. Using this new approach, we model Mitchell-Riley syndrome with RFX6 knockout hPSCs illustrating unexpected morphogenesis defects in the differentiation towards islet cells. The tunable differentiation system and stem cell-derived islet models described in this work may facilitate addressing fundamental questions in islet biology and probing human pancreas diseases.

Advancing membrane-associated protein docking with improved sampling and scoring in Rosetta.

The oligomerization of protein macromolecules on cell membranes plays a fundamental role in regulating cellular function. From modulating signal transduction to directing immune response, membrane proteins (MPs) play a crucial role in biological processes and are often the target of many pharmaceutical drugs. Despite their biological relevance, the challenges in experimental determination have hampered the structural availability of membrane proteins and their complexes. Computational docking provides a promising alternative to model membrane protein complex structures. Here, we present Rosetta-MPDock, a flexible transmembrane (TM) protein docking protocol that captures binding-induced conformational changes. Rosetta-MPDock samples large conformational ensembles of flexible monomers and docks them within an implicit membrane environment. We benchmarked this method on 29 TM-protein complexes of variable backbone flexibility. These complexes are classified based on the root-mean-square deviation between the unbound and bound states (RMSDUB) as: rigid (RMSDUB <1.2 Å), moderately-flexible (RMSDUB ∈ [1.2, 2.2) Å), and flexible targets (RMSDUB > 2.2 Å). In a local docking scenario, i.e. with membrane protein partners starting ≈10 Å apart embedded in the membrane in their unbound conformations, Rosetta-MPDock successfully predicts the correct interface (success defined as achieving 3 near-native structures in the 5 top-ranked models) for 67% moderately flexible targets and 60% of the highly flexible targets, a substantial improvement from the existing membrane protein docking methods. Further, by integrating AlphaFold2-multimer for structure determination and using Rosetta-MPDock for docking and refinement, we demonstrate improved success rates over the benchmark targets from 64% to 73%. Rosetta-MPDock advances the capabilities for membrane protein complex structure prediction and modeling to tackle key biological questions and elucidate functional mechanisms in the membrane environment. The benchmark set and the code is available for public use at github.com/Graylab/MPDock.

Mimicking classical noise in ion channels by quantum decoherence

The mechanism of selectivity in ion channels is still an open question in biology. Recent studies suggest that the selectivity filter may exhibit quantum coherence, which could help explain how ions are selected and conducted. However, environmental noise causes decoherence and loss of quantum effects. It is hoped that the effect of classical noise on ion channels can be modeled using the framework provided by quantum decoherence theory. In this paper, the behavior of the ion channel system was simulated using two models: the Spin–Boson model and the stochastic Hamiltonian model under classical noise. Additionally, using a different approach, the system’s evolution was modeled as a two-level Spin–Boson model with tunneling, interacting with a bath of harmonic oscillators, based on decoherence theory. We investigated under what conditions the decoherence model approaches and deviates from the noise model. Specifically, we examined Gaussian noise and Ornstein-Uhlenbeck noise in our model. Gaussian noise shows a very good agreement with the decoherence model. By examining the results, it was found that the Spin–Boson model at a high hopping rate of potassium ions can simulate the behavior of the system in the classical noise approach for Gaussian noise.

Local genetic adaptation to habitat in wild chimpanzees.

How populations adapt to their environment is a fundamental question in biology. Yet we know surprisingly little about this process, especially for endangered species such as non-human great apes. Chimpanzees, our closest living relatives, are particularly interesting because they inhabit diverse habitats, from rainforest to woodland-savannah. Whether genetic adaptation facilitates such habitat diversity remains unknown, despite having wide implications for evolutionary biology and conservation. Using 828 newly generated exomes from wild chimpanzees, we find evidence of fine-scale genetic adaptation to habitat. Notably, adaptation to malaria in forest chimpanzees is mediated by the same genes underlying adaptation to malaria in humans. This work demonstrates the power of non-invasive samples to reveal genetic adaptations in endangered populations and highlights the importance of adaptive genetic diversity for chimpanzees.