Biological Inquiry Research Articles

Scalable, compressed phenotypic screening using pooled perturbations.

High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.

Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses.

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

Leveraging Descriptor Learning and Functional Map-based Shape Matching for Automatic Landmark Acquisition.

Geometric morphometrics is widely employed across the biological sciences for the quantification of morphological traits. However, the scalability of these methods to large datasets is hampered by the requisite placement of landmarks, which can be laborious and time consuming if done manually. Additionally, the selected landmarks embody a particular hypothesis regarding the critical geometry pertinent to the biological inquiry at hand. Modifying this hypothesis lacks flexibility, necessitating the acquisition of an entirely new set of landmarks on the entire dataset to reflect any theoretical adjustments. In our research, we investigate the precision and accuracy of landmarks derived from the comprehensive set of functional correspondences acquired through the functional map framework of geometry processing. We use a deep functional map network to learn shape descriptors that effectively yield functional map-based and point-to-point correspondences between the specimens in our dataset. We then interrogate these maps to identify corresponding landmarks given manually placed landmarks from the entire dataset. We assess our method by automating the landmarking process on a dataset comprising mandibles from various rodent species, comparing its efficacy against MALPACA, a cutting-edge technique for automatic landmark placement. Compared to MALPACA, our model is notably faster and maintains competitive accuracy. The Root Mean Square Error (RMSE) analysis reveals that while MALPACA generally exhibits the lowest RMSE, our models perform comparably, especially with smaller training datasets, suggesting strong generalizability. Visual evaluations confirm the precision of our landmark placements, with deviations remaining within an acceptable range. These findings underscore the potential of unsupervised learning models in anatomical landmark placement, providing a viable and efficient alternative to traditional methods.

An Analysis of Examination Question Trends in the field of Biology Education on Secondary Biology Teacher Appointment Examination from 2014 to 2024

This study aims to analyze the examination trend of questions related to the field of biology education among the questions of the secondary biology teacher appointment examination from 2014 to 2024. The analysis of examination question trends was conducted by using the contents of the evaluation area and the sub-evaluation content elements of the biology subject suggested by the Korea Institute for Curriculum Evaluation (KICE) as a framework. The results of the study are as follows. First, only 75% of the evaluation content elements were linked to the set questions, and it was found that only biology teaching-learning theory, biology teaching-learning practice, and biology teaching-learning evaluation areas had questions linked to all evaluation content elements. Second, the biology teachinglearning theory, biology teaching-learning practice, and biology inquiry areas accounted for 67% of the total questions. Third, it was found that no question was set in the biology education environment and support area. These results show that the secondary biology teacher appointment exam is biased towards certain areas and evaluation content elements. Furthermore, since the evaluation area and evaluation content elements also affect the education for the professionalism of prospective biology teachers, it shows the need to comprehensively review and improve the content of the evaluation area and evaluation content elements, considering teachers’ competency and educational factors for the successful progress of field education.

Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques

The global spread of the SARS-CoV-2 pandemic, originating in Wuhan, China, has had profound consequences on both health and the economy. Traditional alignment-based phylogenetic tree methods for tracking epidemic dynamics demand substantial computational power due to the growing number of sequenced strains. Consequently, there is a pressing need for an alignment-free approach to characterize these strains and monitor the dynamics of various variants. In this work, we introduce a swift and straightforward tool named GenoSig, implemented in C++. The tool exploits the Di and Tri nucleotide frequency signatures to delineate the taxonomic lineages of SARS-CoV-2 by employing diverse machine learning (ML) and deep learning (DL) models. Our approach achieved a tenfold cross-validation accuracy of 87.88% (± 0.013) for DL and 86.37% (± 0.0009) for Random Forest (RF) model, surpassing the performance of other ML models. Validation using an additional unexposed dataset yielded comparable results. Despite variations in architectures between DL and RF, it was observed that later clades, specifically GRA, GRY, and GK, exhibited superior performance compared to earlier clades G and GH. As for the continental origin of the virus, both DL and RF models exhibited lower performance than in predicting clades. However, both models demonstrated relatively higher accuracy for Europe, North America, and South America compared to other continents, with DL outperforming RF. Both models consistently demonstrated a preference for cytosine and guanine over adenine and thymine in both clade and continental analyses, in both Di and Tri nucleotide frequencies signatures. Our findings suggest that GenoSig provides a straightforward approach to address taxonomic, epidemiological, and biological inquiries, utilizing a reductive method applicable not only to SARS-CoV-2 but also to similar research questions in an alignment-free context.

Revealing mitf functions and visualizing allografted tumor metastasis in colorless and immunodeficient Xenopus tropicalis

Transparent immunodeficient animal models not only enhance in vivo imaging investigations of visceral organ development but also facilitate in vivo tracking of transplanted tumor cells. However, at present, transparent and immunodeficient animal models are confined to zebrafish, presenting substantial challenges for real-time, in vivo imaging studies addressing specific biological inquiries. Here, we employed a mitf−/−/prkdc−/−/il2rg−/− triple-knockout strategy to establish a colorless and immunodeficient amphibian model of Xenopus tropicalis. By disrupting the mitf gene, we observed the loss of melanophores, xanthophores, and granular glands in Xenopus tropicalis. Through the endogenous mitf promoter to drive BRAFV600E expression, we confirmed mitf expression in melanophores, xanthophores and granular glands. Moreover, the reconstruction of the disrupted site effectively reinstated melanophores, xanthophores, and granular glands, further highlighting the crucial role of mitf as a regulator in their development. By crossing mitf−/− frogs with prkdc−/−/il2rg−/− frogs, we generated a mitf−/−/prkdc−/−/il2rg−/−Xenopus tropicalis line, providing a colorless and immunodeficient amphibian model. Utilizing this model, we successfully observed intravital metastases of allotransplanted xanthophoromas and migrations of allotransplanted melanomas. Overall, colorless and immunodeficient Xenopus tropicalis holds great promise as a valuable platform for tumorous and developmental biology research.

Multivalent Aptamer-Based Lysosome-Targeting Chimeras (LYTACs) Platform for Mono- or Dual-Targeted Proteins Degradation on Cell Surface.

Selective protein degradation platforms have opened novel avenues in therapeutic development and biological inquiry. Antibody-based lysosome-targeting chimeras (LYTACs) have emerged as a promising technology that extends the scope of targeted protein degradation to extracellular targets. Aptamers offer an advantageous alternative owing to their potential for modification and manipulation toward a multivalent state. In this study, a chemically engineered platform of multivalent aptamer-based LYTACs (AptLYTACs) is established for the targeted degradation of either single or dual protein targets. Leveraging the biotin-streptavidin system as a molecular scaffold, this investigation reveals that trivalently mono-targeted AptLYTACs demonstrate optimum efficiency in degrading membrane proteins. The development of this multivalent AptLYTACs platform provides a principle of concept for mono-/dual-targets degradation, expanding the possibilities of targeted protein degradation.

A Schrödinger Equation for Evolutionary Dynamics

We establish an analogy between the Fokker–Planck equation describing evolutionary landscape dynamics and the Schrödinger equation which characterizes quantum mechanical particles, showing that a population with multiple genetic traits evolves analogously to a wavefunction under a multi-dimensional energy potential in imaginary time. Furthermore, we discover within this analogy that the stationary population distribution on the landscape corresponds exactly to the ground-state wavefunction. This mathematical equivalence grants entry to a wide range of analytical tools developed by the quantum mechanics community, such as the Rayleigh–Ritz variational method and the Rayleigh–Schrödinger perturbation theory, allowing us not only the conduct of reasonable quantitative assessments but also exploration of fundamental biological inquiries. We demonstrate the effectiveness of these tools by estimating the population success on landscapes where precise answers are elusive, and unveiling the ecological consequences of stress-induced mutagenesis—a prevalent evolutionary mechanism in pathogenic and neoplastic systems. We show that, even in an unchanging environment, a sharp mutational burst resulting from stress can always be advantageous, while a gradual increase only enhances population size when the number of relevant evolving traits is limited. Our interdisciplinary approach offers novel insights, opening up new avenues for deeper understanding and predictive capability regarding the complex dynamics of evolving populations.

In silico discovery of repetitive elements as key sequence determinants of 3D genome folding

Natural and experimental genetic variants can modify DNA loops and insulating boundaries to tune transcription, but it is unknown how sequence perturbations affect chromatin organization genome wide. We developed a deep-learning strategy to quantify the effect of any insertion, deletion, or substitution on chromatin contacts and systematically scored millions of synthetic variants. While most genetic manipulations have little impact, regions with CTCF motifs and active transcription are highly sensitive, as expected. Our unbiased screen and subsequent targeted experiments also point to noncoding RNA genes and several families of repetitive elements as CTCF-motif-free DNA sequences with particularly large effects on nearby chromatin interactions, sometimes exceeding the effects of CTCF sites and explaining interactions that lack CTCF. We anticipate that our disruption tracks may be of broad interest and utility as a measure of 3D genome sensitivity, and our computational strategies may serve as a template for biological inquiry with deep learning.

Teachers’ views of Future-Oriented Pedagogy as part of inquiry-based molecular biology teaching in high school biology laboratories

ABSTRACT Integrating genetic engineering and molecular biology lab activities into open inquiry-based teaching is an innovative, unique process that has taken place in Israeli high schools since 2018, becoming part of biology studies for 11th- and 12th-grade students. Engaging these students in lab techniques and practices in this field enables them to deal with the often-abstract concepts of the molecular world, enabling them to address questions currently at the forefront of science and make their learning relevant to the 21st century. The study aimed to examine teachers’ views of how molecular biology inquiry activities in high school can incorporate and promote Future-Oriented Pedagogy (FOP). This pedagogy is a forward-looking model, based on six principles that inspire learning in the education system, focusing on the needs most relevant for 21st-century students. The study was conducted by analysing semi-structured interviews with teachers involved in this program and by observing teacher training days. The results indicate that this program facilitates the promotion of inquiry skills and enables the implementation of FOP principles. Our study has shown that bringing molecular technologies into the high school biology lab bears great potential in promoting FOP as part of open inquiry-based teaching.

Sometimes you ride the Pegasus, sometimes you take the road: Mitchell on laws in biology

Mitchell’s philosophical contributions are part of an ongoing conversation among philosophers and scientists about laws and unification in biology, going back at least to Darwin. This article situates Mitchell in this conversation, explains why and how she has correctly guided us away from false idols, and engages several difficult questions she leaves open. I argue that there are different epistemic roles laws (or models describing lawlike regularities) play in biological inquiry. First, they play the role of “how possibly” explanations, akin to Herschel’s characterization of Whewell’s “a priori Pegasus,” and second, they provide descriptions of empirical regularities, akin to the “plain matter of fact roadster.”

Targeted Protein Degradation Induced by HEMTACs Based on HSP90.

Targeted protein degradation (TPD) strategies open up new avenues for therapeutics and provide powerful tools for biological inquiry. Herein, we present a brand-new approach, termed heat shock protein 90 (HSP90)-mediated targeting chimeras (HEMTACs), to induce intracellular protein degradation by bridging a target protein to HSP90 to drive the downregulation of proteins. We successfully showcase HEMTACs for cyclin-dependent kinase 4 and 6 (CDK4/6) by using a flexible linker to connect the targeting warhead of CDK4/6 with the HSP90 ligand. Overall, our study delivers a series of evidence that HEMTACs can serve as a valuable addition to TPD strategies, most prominently proteolysis-targeting chimera technology.

메타버스 플랫폼을 활용한 생물 탐구 프로그램이 초등 과학영재의 정의적 영역에 미치는 영향

Objectives The purpose of this study was a development the biology inquiry program using a metaverse platform and to check the effect on the affective domain of elementary science gifted students. Methods The researcher developed a biology inquiry program in the virtual world by investigating prior studies. Also, 17 students participated in the pre and post-test of learning motivation and task commitment. Results The biology inquiry program developed using a metaverse platform could use a virtual world and mirror world, and the learning process and method were drawn by preceding research analysis. Also, the researcher tested the improvement and effect on the learning motivation and task commitment of science gifted students. Conclusions The biology inquiry program using the metaverse platform could encourage a positive effect on the affective domain of elementary science gifted students. And it would lead to reducing Zoom fatigue by providing positive recognition of online learning through the expansion of social interaction. 

Automated 2D, 2.5D, and 3D Segmentation of Coral Reef Pointclouds and Orthoprojections.

Enabled by advancing technology, coral reef researchers increasingly prefer use of image-based surveys over approaches depending solely upon in situ observations, interpretations, and recordings of divers. The images collected, and derivative products such as orthographic projections and 3D models, allow researchers to study a comprehensive digital twin of their field sites. Spatio-temporally located twins can be compared and annotated, enabling researchers to virtually return to sites long after they have left them. While these new data expand the variety and specificity of biological investigation that can be pursued, they have introduced the much-discussed Big Data Problem: research labs lack the human and computational resources required to process and analyze imagery at the rate it can be collected. The rapid development of unmanned underwater vehicles suggests researchers will soon have access to an even greater volume of imagery and other sensor measurements than can be collected by diver-piloted platforms, further exacerbating data handling limitations. Thoroughly segmenting (tracing the extent of and taxonomically identifying) organisms enables researchers to extract the information image products contain, but is very time-consuming. Analytic techniques driven by neural networks offer the possibility that the segmentation process can be greatly accelerated through automation. In this study, we examine the efficacy of automated segmentation on three different image-derived data products: 3D models, and 2D and 2.5D orthographic projections thereof; we also contrast their relative accessibility and utility to different avenues of biological inquiry. The variety of network architectures and parameters tested performed similarly, ∼80% IoU for the genus Porites, suggesting that the primary limitations to an automated workflow are 1) the current capabilities of neural network technology, and 2) consistency and quality control in image product collection and human training/testing dataset generation.

Proximity labeling for investigating protein-protein interactions.

The study of protein complexes and protein-protein interactions is of great importance due to their fundamental roles in cellular function. Proximity labeling, often coupled with mass spectrometry, has become a powerful and versatile tool for studying protein-protein interactions by enriching and identifying proteins in the vicinity of a specified protein-of-interest. Here, we describe and compare traditional approaches to investigate protein-protein interactions to current day state-of-the-art proximity labeling methods. We focus on the wide array of proximity labeling strategies and underscore studies using diverse model systems to address numerous biological questions. In addition, we highlight current advances in mass spectrometry-based technology that exhibit promise in improving the depth and breadth of the data acquired in proximity labeling experiments. In all, we show the diversity of proximity labeling strategies and emphasize the broad range of applications and biological inquiries that can be addressed using this technology.

Ethanol resistance in Drosophila melanogaster has increased in parallel cold-adapted populations and shows a variable genetic architecture within and between populations.

Understanding the genetic properties of adaptive trait evolution is a fundamental crux of biological inquiry that links molecular processes to biological diversity. Important uncertainties persist regarding the genetic predictability of adaptive trait change, the role of standing variation, and whether adaptation tends to result in the fixation of favored variants. Here, we use the recurrent evolution of enhanced ethanol resistance in Drosophila melanogaster during this species’ worldwide expansion as a promising system to add to our understanding of the genetics of adaptation. We find that elevated ethanol resistance has evolved at least three times in different cooler regions of the species’ modern range—not only at high latitude but also in two African high‐altitude regions. Applying a bulk segregant mapping framework, we find that the genetic architecture of ethanol resistance evolution differs substantially not only between our three resistant populations, but also between two crosses involving the same European population. We then apply population genetic scans for local adaptation within our quantitative trait locus regions, and we find potential contributions of genes with annotated roles in spindle localization, membrane composition, sterol and alcohol metabolism, and other processes. We also apply simulation‐based analyses that confirm the variable genetic basis of ethanol resistance and hint at a moderately polygenic architecture. However, these simulations indicate that larger‐scale studies will be needed to more clearly quantify the genetic architecture of adaptive evolution and to firmly connect trait evolution to specific causative loci.

Exploring Biological Literacy: A Systematic Literature Review of Biological Literacy

<p style="text-align:justify">A large number of articles in the field of science education reflect on scientific literacy as the main goal of science education (SE), although often with varying viewpoints. Nevertheless, researchers have begun to highlight subject-specific teaching practices that are expected to specifically enhance science subject teaching, including biology. The main aim of this theoretical article is to come on consensus and to conceptualise the term biological literacy (BL) more clearly and to present a theoretical concept of BL, composed on the basis of systematically analysed articles. This theoretical concept includes two dimensions of BL: (1) cognitive (cognitive skills, conceptual understanding, biological inquiry) and (2) affective dimension, based on systematic literature review (SLR). This theoretical concept also includes in addition four dimensions of BL: (3) sustainability; (4) interdisciplinarity, (5) career awareness and (6) nature of biology (NOB), based on literature review (LR) of recent decades, that was conducted to write theoretical overview of this research.</p>

ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules.

The investigation of allosteric effects in biomolecular structures is of great current interest in diverse areas, from fundamental biological enquiry to drug discovery. Here we present ProteinLens, a user-friendly and interactive web application for the investigation of allosteric signalling based on atomistic graph-theoretical methods. Starting from the PDB file of a biomolecule (or a biomolecular complex) ProteinLens obtains an atomistic, energy-weighted graph description of the structure of the biomolecule, and subsequently provides a systematic analysis of allosteric signalling and communication across the structure using two computationally efficient methods: Markov Transients and bond-to-bond propensities. ProteinLens scores and ranks every bond and residue according to the speed and magnitude of the propagation of fluctuations emanating from any site of choice (e.g. the active site). The results are presented through statistical quantile scores visualised with interactive plots and adjustable 3D structure viewers, which can also be downloaded. ProteinLens thus allows the investigation of signalling in biomolecular structures of interest to aid the detection of allosteric sites and pathways. ProteinLens is implemented in Python/SQL and freely available to use at: www.proteinlens.io.

Analysis Pipeline of PIWI‐Interacting RNA (piRNA) in an Inflammatory Parasitic Infectious Disease

Small noncoding RNAs (sncRNA) have been implicated as major contributors to the pathogenesis of various diseases. PIWI-Interacting RNA (piRNA) are a type of sncRNA. with several regulatory roles across the genome. They are primarily expressed from piRNA clusters within the genome, where they go on to regulate and maintain genomic stability. They are capable of silencing transposable elements and inducing epigenetic changes. Some studies also suggest that piRNAs can regulate gene transcription. This gene regulatory mechanism is thought to be similar to that of miRNAs. These sncRNA play important roles in regulating gene expression by forming complexes with Argonaute proteins to recognize specific target sequences. The specific goal of this project was to develop a method of computationally analyzing piRNA in the context of their diverse functions and relate them to biological responses occurring in disease. To accomplish this, we created a pipeline to analyze dysregulation of piRNA expression and predict how these changes contribute to Trypanosoma cruzi-induced cardiac pathology. We used primary human cardiac myocytes (PHCM) challenged by T. cruzi infection and computationally assessed the impact of piRNA during inflammatory response. Small RNA from PHCM challenged by T. cruzi infection was extracted and sequenced. The piRNA annotation program Piano was used to predict known piRNAs via a support vector machine algorithm with transposon interaction informatics. The NOISeq method was used to determine differentially expressed piRNAs. Prediction of binding interactions between coding transcripts and piRNAs was done by adapting the miRanda and RNA22 algorithms using strict pairing thresholds. Predicted target transcripts were then mapped to enriched KEGG pathways using WEBGESTALT. Mapped genes were then queried for biological interaction network construction with the GeneMANIA algorithm. In combination, these analyses were used to produce biological interaction networks involving piRNAs and predicted target genes related to inflammation, specifically pro-inflammatory AP-1 transcription factor genes. We demonstrate a method of ascertaining the dynamics of changes in piRNA expression using this disease model, from which more directed biological inquiries can subsequently be made.

LYTACs that engage the asialoglycoprotein receptor for targeted protein degradation.

Selective protein degradation platforms have afforded new development opportunities for therapeutics and tools for biological inquiry. The first lysosome targeting chimeras (LYTACs) targeted extracellular and membrane proteins for degradation by bridging a target protein to the cation-independent mannose-6-phosphate receptor (CI-M6PR). Here, we developed LYTACs that engage the asialoglycoprotein receptor (ASGPR), a liver-specific lysosomal targeting receptor, to degrade extracellular proteins in a cell type-specific manner. We conjugated binders to a tri-GalNAc motif that engages ASGPR to drive downregulation of proteins. Degradation of EGFR by GalNAc-LYTAC attenuated EGFR signaling compared to inhibition with an antibody. Furthermore, we demonstrated that a LYTAC comprising a 3.4 kDa peptide binder linked to a tri-GalNAc ligand degrades integrins and reduces cancer cell proliferation. Degradation with a single tri-GalNAc ligand prompted site-specific conjugation on antibody scaffolds, which improved the pharmacokinetic profile of GalNAc-LYTACs in vivo. GalNAc-LYTACs thus represent an avenue for cell-type restricted protein degradation.