Biological Questions Research Articles

In Vivo Polymer Mechanochemistry with Polynucleotides.

Polymer mechanochemistry utilizes mechanical force to activate latent functionalities in macromolecules and widely relies on ultrasonication techniques. Fundamental constraints of frequency and power intensity have prohibited the application of the polymer mechanochemistry principles in a biomedical context up to now, although medical ultrasound is a clinically established modality. Here, a universal polynucleotide framework is presented that allows the binding and release of therapeutic oligonucleotides, both DNA- and RNA-based, as cargo by biocompatible medical imaging ultrasound. It is shown that the high molar mass, colloidal assembly, and a distinct mechanochemical mechanism enable the force-induced release of cargo and subsequent activation of biological function in vitro and in vivo. Thereby, this work introduces a platform for the exploration of biological questions and therapeutics development steered by mechanical force.

Estimating maximum oxygen uptake of fishes during swimming and following exhaustive chase - different results, biological bases and applications.

The maximum rate at which animals take up oxygen from their environment (ṀO2,max) is a crucial aspect of their physiology and ecology. In fishes, ṀO2,max is commonly quantified by measuring oxygen uptake either during incremental swimming tests or during recovery from an exhaustive chase. In this Commentary, we compile recent studies that apply both techniques to the same fish and show that the two methods typically yield different mean estimates of ṀO2,max for a group of individuals. Furthermore, within a group of fish, estimates of ṀO2,max determined during swimming are poorly correlated with estimates determined during recovery from chasing (i.e. an individual's ṀO2,max is not repeatable across methods). One explanation for the lack of agreement is that these methods measure different physiological states, each with their own behavioural, anatomical and biochemical determinants. We propose that these methods are not directly interchangeable but, rather, each is suited to address different questions in fish biology. We suggest that researchers select the method that reflects the biological contexts of their study, and we advocate for the use of accurate terminology that acknowledges the technique used to elevate ṀO2(e.g. peak ṀO2,swim or peak ṀO2,recovery). If the study's objective is to estimate the 'true' ṀO2,max of an individual or species, we recommend that pilot studies compare methods, preferably using repeated-measures designs. We hope that these recommendations contribute new insights into the causes and consequences of variation in ṀO2,max within and among fish species.

BioGPT: The ChatGPT of Life Sciences

Abstract: In the medical field, pictures of the body are really important. But answering questions about these images to help diagnose problems is tricky. We made a new model called CGMVQA to help with this. It's good at sorting out different types of questions and finding answers. We use special techniques to understand written questions and make images clearer. We also made the model work faster by changing some settings. Our model is really good! It's the best one yet at answering medical image questions, like figuring out what's in a picture or matching words to images. This means doctors can use it to help with diagnoses. BioGPT is another helpful tool. It's great for scientists and teachers in biology. It gives quick and accurate answers to complex biology questions. As technology gets better, BioGPT will help us learn more about life and speed up biological research

Klumpy: A tool to evaluate the integrity of long-read genome assemblies and illusive sequence motifs.

The improvement and decreasing costs of third-generation sequencing technologies has widened the scope of biological questions researchers can address with de novo genome assemblies. With the increasing number of reference genomes, validating their integrity with minimal overhead is vital for establishing confident results in their applications. Here, we present Klumpy, a tool for detecting and visualizing both misassembled regions in a genome assembly and genetic elements (e.g. genes) of interest in a set of sequences. By leveraging the initial raw reads in combination with their respective genome assembly, we illustrate Klumpy's utility by investigating antifreeze glycoprotein (afgp) loci across two icefishes, by searching for a reported absent gene in the northern snakehead fish, and by scanning the reference genomes of a mudskipper and bumblebee for misassembled regions. In the two former cases, we were able to provide support for the noncanonical placement of an afgp locus in the icefishes and locate the missing snakehead gene. Furthermore, our genome scans were able identify an unmappable locus in the mudskipper reference genome and identify a putative repetitive element shared among several species of bees.

FLiPPR: A Processor for Limited Proteolysis (LiP) Mass Spectrometry Data Sets Built on FragPipe.

Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a data merging scheme and a protein-centric multiple hypothesis correction scheme, enabling processed LiP-MS data sets to be more robust and less redundant. These improvements strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.

Dirk Pette, remembered for his pioneering muscle research.

It is with great sadness that we learned of the passing of Prof. Dr. Dr. h.c. Dirk Pette. He passed away suddenly and unexpectedly on June 4, 2022. Dirk was an outstanding professor of biochemistry at the University of Konstanz, Germany and an internationally renowned researcher in the field of skeletal muscle biology. His research on electrical stimulation has had a profound impact on our understanding of myofiber type specification and the enormous adaptive potential of skeletal muscle. Under Dirk's leadership, new biological questions in the field of neuromuscular biology have developed into multidisciplinary approaches using advanced physiological, cell biological, and biochemical techniques. Dirk's research laboratory was frequently visited by a large number of national and international collaborators who familiarized themselves with the technically demanding stimulation protocols and bioanalytical techniques to study the intricate details of the highly complex process of fast-to-slow muscle transitions. Importantly, fundamental studies on the physiological effects of changes in innervation patterns on muscle phenotype have provided the scientific evidence base for a variety of innovative clinical applications. The skeletal muscle research community has lost one of its leading figures and an outstanding teacher of protein biochemistry. He leaves an inspiring legacy in the field of basic and applied myology. Dirk will be missed by his colleagues and by many students of neuromuscular biology and beyond.

Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data.

Numerous single-cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single-cell RNA sequencing (scRNA-seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA-seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning-based method for batch effect correction, non-linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial-based autoencoder with dual Kullback-Leibler divergence loss functions, aligning cell points from different batches within a consistent low-dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple-batch scRNA-seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA-seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell-specific differentially expressed genes.

Response of wild songbirds to songs synthesized with a low-dimensional model.

In this work, we used a dynamical system derived from an avian vocal production model to generate synthetic songs that mimic the Zonotrichia capensis songs. We confirmed that these synthetic renditions elicited behavioral responses similar to those evoked by real songs in wild songbirds of the same species. Specifically, we observed an increase in the singing rate of individual birds when a playback device was introduced into their territories. The success of our approach instills confidence in the hypotheses underpinning the model and provides a valuable tool for investigating a wide range of biological questions.

Anterior-posterior Wnt signaling network conservation between indirect developing sea urchin and hemichordate embryos.

How animal body plans evolved and diversified is a major question in evolutionary developmental biology. To address this question, it is important to characterize the exact molecular mechanisms that establish the major embryonic axes which give rise to the adult animal body plan. The anterior-posterior (AP) axis is the first axis to be established in most animal embryos, and in echinoderm sea urchin embryos its formation is governed by an integrated network of three different Wnt signaling pathways: Wnt/β-catenin, Wnt/JNK, and Wnt/PKC pathway. The extent to which this embryonic patterning mechanism is conserved among deuterostomes, or more broadly in metazoans, is an important open question whose answers could lead to a deeper appreciation of the evolution of the AP axis. Because Ambulacrarians (echinoderms and hemichordates) reside in a key phylogenetic position as the sister group to chordates, studies in these animals can help inform on how chordate body plans may have evolved. Here, we assayed the spatiotemporal gene expression of a subset of sea urchin AP Wnt patterning gene orthologs in the hemichordate, Schizocardium californicum. Our results show that positioning of the anterior neuroectoderm (ANE) to a territory around the anterior pole during early AP formation is spatially and temporally similar between indirect developing hemichordates and sea urchins. Furthermore, we show that the expression of wnt8 and frizzled5/8, two known drivers of ANE patterning in sea urchins, is similar in hemichordate embryos. Lastly, our results highlight divergence in embryonic expression of several early expressed Wnt genes (wnt1, wnt2 and wnt4). These results suggest that expression of the sea urchin AP Wnt signaling network is largely conserved in indirect developing hemichordates setting the foundation for future functional studies in S. californicum.

Wiring Between Close Nodes in Molecular Networks Evolves More Quickly Than Between Distant Nodes.

As species diverge, a wide range of evolutionary processes lead to changes in protein-protein interaction (PPI) networks and metabolic networks. The rate at which molecular networks evolve is an important question in evolutionary biology. Previous empirical work has focused on interactomes from model organisms to calculate rewiring rates, but this is limited by the relatively small number of species and sparse nature of network data across species. We present a proxy for variation in network topology: variation in drug-drug interactions (DDIs), obtained by studying drug combinations (DCs) across taxa. Here, we propose the rate at which DDIs change across species as an estimate of the rate at which the underlying molecular network changes as species diverge. We computed the evolutionary rates of DDIs using previously published data from a high-throughput study in gram-negative bacteria. Using phylogenetic comparative methods, we found that DDIs diverge rapidly over short evolutionary time periods, but that divergence saturates over longer time periods. In parallel, we mapped drugs with known targets in PPI and cofunctional networks. We found that the targets of synergistic DDIs are closer in these networks than other types of DCs and that synergistic interactions have a higher evolutionary rate, meaning that nodes that are closer evolve at a faster rate. Future studies of network evolution may use DC data to gain larger-scale perspectives on the details of network evolution within and between species.

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.

Multimodal Learning for Mapping the Genotype-Phenotype Dynamics.

How complex phenotypes emerge from intricate gene expression patterns is a fundamental question in biology. Quantitative characterization of this relationship, however, is challenging due to the vast combinatorial possibilities and dynamic interplay between genotype and phenotype landscapes. Integrating high-content genotyping approaches such as single-cell RNA sequencing and advanced learning methods such as language models offers an opportunity for dissecting this complex relationship. Here, we present a computational integrated genetics framework designed to analyze and interpret the high-dimensional landscape of genotypes and their associated phenotypes simultaneously. We applied this approach to develop a multimodal foundation model to explore the genotype-phenotype relationship manifold for human transcriptomics at the cellular level. Analyzing this joint manifold showed a refined resolution of cellular heterogeneity, enhanced precision in phenotype annotating, and uncovered potential cross-tissue biomarkers that are undetectable through conventional gene expression analysis alone. Moreover, our results revealed that the gene networks are characterized by scale-free patterns and show context-dependent gene-gene interactions, both of which result in significant variations in the topology of the gene network, particularly evident during aging. Finally, utilizing contextualized embeddings, we investigated gene polyfunctionality which illustrates the multifaceted roles that genes play in different biological processes, and demonstrated that for VWF gene in endothelial cells. Overall, this study advances our understanding of the dynamic interplay between gene expression and phenotypic manifestation and demonstrates the potential of integrated genetics in uncovering new dimensions of cellular function and complexity.

A Synergistic, Cultivator Model of De Novo Gene Origination.

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 noncoding 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.

Enhancing Density Maps by Removing the Majority of Particles in Single Particle Cryogenic Electron Microscopy Final Stacks.

Over the past decade, advancements in technology and methodology within the field of cryogenic electron microscopy (cryo-EM) single-particle analysis (SPA) have substantially improved our capacity for high-resolution structural examination of biological macromolecules. This advancement has ushered in a new era of molecular insights, replacing X-ray crystallography as the dominant method and providing answers to longstanding questions in biology. Since cryo-EM does not depend on crystallization, which is a significant limitation of X-ray crystallography, it captures particles of varying quality. Consequently, the selection of particles is crucial, as the quality of the selected particles directly influences the resolution of the reconstructed density map. An innovative iterative approach for particle selection, termed CryoSieve, significantly improves the quality of reconstructed density maps by effectively reducing the number of particles in the final stack. Experimental evidence shows that this method can eliminate the majority of particles in final stacks, resulting in a notable enhancement in the quality of density maps. This article outlines the detailed workflow of this approach and showcases its application on a real-world dataset.

Physiology Evolves Convergently but Lags Behind Warming inCities.

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 was among diverse lineages in response to urban heat islands. Our study revealed significant shifts toward 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 toward larger or smaller body size, aquatic species exhibited significant shifts toward 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.

Invasive freshwater snails are less sensitive to population density than native conspecifics.

Understanding how and why some species or lineages become invasive is critically important for effectively predicting and mitigating biological invasions. Here, we address an important unanswered question in invasion biology: do key life-history traits of invasive versus native lineages within a species differ in response to key environmental stressors? We focus on the environmental factor of population density, which is a fundamental characteristic of all populations, and investigate how changes in density affect native versus invasive Potamopyrgus antipodarum (New Zealand mudsnail). P. antipodarum has invaded 39 countries and detrimentally affects invaded environments. Previous studies of native and invasive populations and from laboratory experiments have demonstrated that growth and reproduction of P. antipodarum is sensitive to population density, though whether and how this sensitivity varies across native versus invasive lineages remains uncharacterized. We quantified individual growth rate and reproduction in P. antipodarum from multiple distinct native and invasive lineages across three different population density treatments. The growth of native but not invasive lineages decreased as density increased. There was no differential effect of density treatment on embryo production of invasive versus native snails, but a significantly higher proportion of snails were reproductive in high density compared to intermediate density for invasive lineages. In native lineages, there were no significant differences in the relative frequency of reproductive snails across density treatments. While the extent to which these results from our laboratory study can be extrapolated to the more complex natural world remain unclear, our findings are consistent with a scenario where differential sensitivity to population density could help explain why some lineages become successful invaders. Our findings also align with previous studies that show that invasive P. antipodarum lineages exhibit a relatively wide range of tolerance to environmental stressors.

Student Performance and Stress in Histology and Cell Biology in a Redesigned Preclinical Undergraduate Medical Curriculum

In the legacy curriculum at TTUHSC School of Medicine, histology and cell biology were taught in dedicated units in Block 2: Biology of Cells & Tissues (BCT). In Fall 2021 (Class of 2025), a new compressed organ-based integrated curriculum with restructured delivery of histology and cell biology and permanent change to pass/fail grading was implemented. Therein, basic histology was integrated into Unit 1 of Block 1: Anatomy, Histology & Embryology (AHE). Cell biology was divided into AHE and the second unit of Block 2: General Principles (GPX). Our objective was to evaluate performance in histology and cell biology and influential factors, such as student stress and academic and personal concerns in the new organ-based integrated curriculum. For AHE and GPX students completed pre- and post-block surveys regarding stress and academic and personal concerns. Deidentified scores for performance on the 10 most diffcult histology questions on the fall 2021 AHE Exam 1 were compared to the same exam questions in BCT in fall 2020. Scores for the 10 most diffcult cell biology questions on the fall 2021 GPX Exam 2 were compared to those for same exam questions in BCT in fall 2020. Biochemistry performance also was evaluated, as its instruction remained similar to that in the legacy curriculum. Fall 2021 histology scores were markedly lower than scores in fall 2020 (p = 0.002); however, end of block NBME exam scores for histology were comparable to scores in fall 2020 (p = 0.445). Cell biology scores in fall 2021 were moderately lower than in fall 2020 (p = 0.0035). Surprisingly, scores for the 13 most diffcult biochemistry topics in fall 2021 were lower than in fall 2020 (p = 0.0051). AHE pre- and post-block surveys indicated students anticipated dedicating 12% of their time studying histology, but actually allocated 10% of their time. At the start of AHE, the top concern was the amount of material in the block; by the end, it was learning histology. GPX post-surveys indicated student stress was comparable or even less than anticipated. Top concerns were curriculum design, biochemistry, and amount of material. Over 50% of students felt overwhelmed by the amount of material in GPX. In the new curriculum, student performance in histology on the unit exam was attenuated, but NBME exam scores were on par with the legacy curriculum. Student acclimation to the rigors of school and honing of time management skills may explain improved performance at the end of AHE. Lower performance in cell biology and biochemistry might be due in part to inclusion of medical genetics and pharmacology that were not taught in the analogous legacy block. Whether such trends in student performance in these foundational blocks persist in subsequent iterations of the curriculum and potential influence of student perception on performance are being evaluated. Funded through TTUHSC-SOM. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

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.