Modern Biology Research Articles

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams' methods included quantitative genetics, machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

Photocleavable Guide crRNAs for a Light-Controllable CRISPR/Cas9 System.

The design of controllable and precise RNA-targeted CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats) systems is an important problem of modern molecular biology and genetic technology. Herein, we have designed a series of photocleavable guide CRISPR RNAs (crRNA) and their 2'-modified (2'-fluoro and locked nucleic acid) analogs containing one or two 1-(2-nitrophenyl)-1,2-ethanediol photolabile linkers (PL). We have demonstrated that these crRNAs can be destroyed by relatively mild UVA irradiation with the rate constants 0.24-0.77 min-1 and that the photocleavage markedly slows down the action of Cas9 nuclease in the model in vitro system. Two PLs provide more rapid crRNA destruction than a single linker. PLs in the crRNA structure improve the specificity of DNA cleavage by Cas9 nuclease for the fully complementary target. The application of photocleavable crRNA in CRISPR/Cas9 genome editing permits the system to be switched off in a spatiotemporally controlled manner, thus alleviating its off-target effects.

Joseph G. Gall (1928-2024): Cell biologist, naturalist, and mentor extraordinaire.

Joseph Grafton Gall (1928-2024), a founder of modern cell biology, made foundational discoveries on eukaryotic chromosomes and RNA biogenesis. His major contributions include the development of in situ hybridization (later called FISH), demonstration of one DNA double helix/chromosome, isolation of the first eukaryote gene, localization of satellite DNA to centromeric heterochromatin, determination of the first telomeric DNA sequence, and elucidating the structure and functions of Cajal bodies. He was an expert microscopist, a scholar of science history, and an avid naturalist. These attributes, together with his ready embrace of new technologies, contributed to his remarkable success. He was also an early and strong supporter of women in science. His contributions to science and mentoring were recognized by numerous awards including the American Society for Cell Biology's E.B. Wilson Medal, the Society for Developmental Biology's Lifetime Achievement Award, the Albert Lasker Special Achievement Award in Medical Research, and the AAAS Mentor Award for Lifetime Achievement.

Chromosome Engineering: Technologies, Applications, and Challenges.

Chromosome engineering is a transformative field at the cutting edge of biological science, offering unprecedented precision in manipulating large-scale genomic DNA within cells. This discipline is central to deciphering how the multifaceted roles of chromosomes-guarding genetic information, encoding sequence positional information, and influencing organismal traits-shape the genetic blueprint of life. This review comprehensively examines the technological advancements in chromosome engineering, which center on engineering chromosomal rearrangements, generating artificial chromosomes, de novo synthesizing chromosomes, and transferring chromosomes. Additionally, we introduce the application progress of chromosome engineering in basic and applied research fields, showcasing its capacity to deepen our knowledge of genetics and catalyze breakthroughs in therapeutic strategies. Finally, we conclude with a discussion of the challenges the field faces and highlight the profound implications that chromosome engineering holds for the future of modern biology and medical applications.

TMOD-16. ZUCLOPENTHIXOL REDUCES ACTIVITY-DEPENDENT GLIOBLASTOMA PROLIFERATION

Abstract A major challenge in modeling glioblastoma in the laboratory is the complex microenvironment in which tumors evolve in humans. The varied architecture and immune privilege of the brain limits the external validity of many experiments performed in modern cell biology laboratories. Organotypic human cortical slices present an opportunity to model glioblastoma and test the effect of drugs against glioma in ex-vivo human tissue. Cortical slice cultures maintain the cellular organization of the human brain, more closely approximating glioblastoma proliferation in patients. In this study, we test the effect zuclopenthixol, a repurposed antipsychotic inhibitor of glioblastoma-induced neuronal hyperexcitability, on the proliferation of glioblastoma cells in an organotypic human cortical slice culture. Primary patient-derived glioblastoma cultures were established, and human cortical tissues were emersed in oxygenated ACSF agarose gel before preparing 250 µm thick slices. Slices were either exogenously seeded with glioblastoma cells or stained for endogenous tumor infiltration. Slices with either endogenous or exogenous seeding were then treated with zuclopenthixol or regular media change and cultured for 10 days. Proliferation was measured with Ki67 staining and quantified via automated pixel summation using the Pillow library in Python. 0.1 µM zuclopenthixol treated slices had significantly lower proliferation index in both the endogenous tumor condition (0.04 vs 0.30, P < 0.05) and the exogenous seeding condition (0.0013 vs 0.028, P = 0.033). Reduced tissue integrity was observed beginning day 8 of culture, as tissue surrounding tumor seeding sites eroded. Here, we model glioblastoma proliferation in live human tissue and quantify the anti-proliferative effect of zuclopenthixol. We show that zuclopenthixol treatment significantly reduced the proliferation index. Our experimental model allowed the optimization of organotypic human cortical slice cultures for immunofluorescence studies and live-cell imaging.

The Origins of Engineered Biomaterials: NSF-Funded, University of Washington Engineered Biomaterials (UWEB).

The University of Washington Engineered Biomaterials (UWEB) Engineering Research Center (ERC) was funded from 1996 to 2007 by the U.S. National Science Foundation. The mission of UWEB was to advance biomaterials by integrating modern biology with materials science. UWEB specifically focused on the healing and integration of medical implants. UWEB teamed biologists, physicians, engineers, and industry and demonstrated three paths that might advance biomaterials so they could seamlessly integrate and heal in the body. The three primary lines of investigation were precision porous scaffolds, super-non-fouling surfaces, and the control of matricellular proteins. The UWEB program set the groundwork for the modern field of immunoengineering. Also, UWEB invested significantly in training scientists/engineers who could freely integrate advances in biological sciences, state-of-the-art materials science, and medical technology. This historical summary of the UWEB program demonstrates that federal investment in interfacing forefront fields can yield dividends with benefits for society and the economy.

Culturally responsive strategies and practical considerations for live tissue studies in Māori participant cohorts.

Indigenous communities globally are inequitably affected by non-communicable diseases such as cancer and coronary artery disease. Increased focus on personalized medicine approaches for the treatment of these diseases offers opportunities to improve the health of Indigenous people. Conversely, poorly implemented approaches pose increased risk of further exacerbating current inequities in health outcomes for Indigenous peoples. The advancement of modern biology techniques, such as three-dimensional (3D) in vitro models and next generation sequencing (NGS) technologies, have enhanced our understanding of disease mechanisms and individualized treatment responses. However, current representation of Indigenous peoples in these datasets is lacking. It is crucial that there is appropriate and ethical representation of Indigenous peoples in generated datasets to ensure these technologies can be used to maximize the benefit of personalized medicine for Indigenous peoples. This project discusses the use of 3D tumor organoids and single cell/nucleus RNA sequencing to study cancer treatment responses and explore immune cell roles in coronary artery disease. Using key pillars from currently available Indigenous bioethics frameworks, strategies were developed for the use of Māori participant samples for live tissue and sequencing studies. These were based on extensive collaborations with local Māori community, scientific leaders, clinical experts, and international collaborators from the Broad Institute of MIT and Harvard. Issues surrounding the use of live tissue, genomic data, sending samples overseas and Indigenous data sovereignty were discussed. This paper illustrates a real-world example of how collaboration with community and the incorporation of Indigenous worldviews can be applied to molecular biology studies in a practical and culturally responsive manner, ensuring fair and equitable representation of Indigenous peoples in modern scientific data.

Disentangling Race from Skin Color in Modern Biology and Medicine

In this review, we examine the taxonomies used to classify people, which influenced the development of the modern disciplines of biology and medicine, including dermatology, throughout the world. Early European scientists and physicians were intertwined with the social environment that created classifications and hierarchies of skin-color-based races, which were reinforced by prevailing political systems that supported colonial economic structures and, in many cases, chattel slavery. Even after genomic analysis of diverse human DNA sequences have revealed that systems of skin color-based racial and ethnic classification lacked biological meaning and were socially constructed, these classifications persist and are reinforced by census classifications and frameworks for comparisons in biomedicine in many parts of the world. The bodies of knowledge and practices that were built on these classifications did not reflect the observable biological diversity of people but the dominant cultural institutions and economic systems of their times. We provide actions for our modern institutions to reduce the underpinnings and ramifications of racial and ethnic classifications ultimately to improve biomedical research and medical care for all patients.

Eating and Drinking Formula in the Qur’an: A Systematic Study Based on the Integration of Qur’an, Sunnah, Biology and Medicine

Non-communicable diseases; related to poor eating habits, present a significant burden on people, as well as economic resources; which in-turn, may affect the effectiveness of saying and doing in the path of Allah (S.W.T). This systematic study was designed to review the guidance of Qur’an on how to avoid this burden; by integrating its verses, ahadith, and signs in the modern Biology and Medicine. Tafsir ibn Kathir and Saheeh International Translation were used to identify the verses that are directly related to this burden. The related ahadith were searched from the six canonical books of ahadith of Ahlu Sunnah Wal-Jama’aa. The related biomedical searches were carried out in PUBMED and MEDLINE databases. Both original and review studies; that were published in the last ten years, were selected. The search terms included food, drink, nutrition, risk factors, and non-communicable diseases. Twenty-four verses, one Hadith, and 23 articles were identified. These verses have revealed the formula on eating and drinking which should be adhered in order to accomplish the mission of our creation, and if not adhered, it may lead to both physical and spiritual effects. This study may have profound contribution on the reduction of this burden; which in turn, may help our capacity building on the work that Allah (S.W.T) has commanded us to do, which is to say and do in His path. It may also rekindle the interest of other researchers to research more on this subject and build up on the formula presented in this study.

Peptide stimulation effect on organotypic culture development of neuroendocrine tissues

Introduction. One of the actual task of modern biology and medicine. Is the creation of bioregulator preparation, which can stimulate the neuroendocrine system of organism. Purpose of study. Study of effect of polypeptide complex from brain cortex (PPCbc) and from pancreas (PPCp) of calfs on the cellular proliferation in organotypic culture of nerve and endocrine of young and old rats. Methods. The adequate method of organotypic tissue culture of tissue from young and old rats was used for quick biologically activity of peptides screening. Results. The square index (SI) of nerve and endocrine tissues explants were increased statistically reliable, as compared to the control, also in young and old rats, under the polypeptides complexes effect. Conclusion. The data obtained produce the base for working and quick testing of preparations for treatment of patients with pathology in brain cortex and endocrine tissues

MATHEMATICAL METHODS OF STATISTICS IN BIOLOGICAL RESEARCH

The article examines mathematical statistical methods used in biological research to process data and detect patterns within complex biological systems. Key methods such as correlation analysis, regression analysis, analysis of variance (ANOVA), cluster analysis, and hypothesis testing are discussed. Specific examples of their application in various biological fields, including genetics, ecology, and biomedical research, are provided. These methods play a critical role in the accurate analysis of large datasets, allowing researchers to evaluate the impact of different factors on biological processes, model interactions between variables, and make reliable predictions.The proper application of statistical methods significantly enhances the accuracy of scientific conclusions and reduces the likelihood of misinterpretation of data. This is especially relevant in modern biology, where large-scale data collection, such as genomic sequencing and environmental monitoring, requires sophisticated analytical tools. The review of recent publications emphasizes the importance of statistical literacy among researchers, as incorrect use of statistical tests can lead to invalid conclusions. This highlights the need for improved training in statistical methods to ensure the reproducibility and reliability of scientific findings.Additionally, the article discusses future research prospects, particularly the integration of traditional statistical methods with modern machine learning tools. This integration offers new opportunities for the automated processing and analysis of biological data, enabling the discovery of previously unknown relationships within biological systems. Machine learning techniques, when combined with statistical methods, hold great potential for advancing the fields of genomics, proteomics, and ecological modeling, providing a more in-depth understanding of biological phenomena.

Viruses and Mitochondrial Dysfunction in Neurodegeneration and Cognition: An Evolutionary Perspective

Mitochondria, the cellular powerhouses with bacterial evolutionary origins, play a pivotal role in maintaining neuronal function and cognitive health. Several viruses have developed sophisticated mechanisms to target and disrupt mitochondrial function which contribute to cognitive decline and neurodegeneration. The interplay between viruses and mitochondria might be traced to their co-evolutionary history with bacteria and may reflect ancient interactions that have shaped modern mitochondrial biology.

Same Day Access to Folded Synthetic Proteins.

Understanding protein function is a cornerstone of modern biology. Research centers worldwide dedicate significant efforts to prepare individual proteins for study, the isolation and purification of which can take days to months. We developed a workflow that enables same-day access to functional synthetic proteins. Chemical synthesis provides access to crude protein chains in hours, but the removal of the synthetic side products is typically a days-long process. We find that chemical modifications on side products lead to significant and unpredictable changes in the folding behavior. Consistent with these findings, we discovered that approaches based on biophysical properties characteristic of the folded protein target can discriminate against chemically similar species. Confirming our protocol with nine protein targets, we show that appropriate desalting followed by different folding strategies enables isolation of functional single-domain proteins in hours instead of days. Each target was isolated in under 10 h, including some proteins with post-translational modifications, non-natural amino acids, and disulfide bonds. Rapid biological discovery requires on-demand access to proteins, and the folding pipeline described here is uniquely suited to enabling these efforts. The folding process presented here was not assessed on complex proteins, and therefore, it may require further optimization in those cases.

Novel Vibrational Proteins.

Genetically encoded green fluorescent protein (GFP) and its brighter and redder variants have tremendously revolutionized modern molecular biology and life science by enabling direct visualization of gene regulated protein functions on microscopic and nanoscopic scales. However, the current fluorescent proteins (FPs) only emit a few colors with an emission width of about 30-50 nm. Here, we engineer novel vibrational proteins (VPs) that undergo much finer vibrational transitions and emit rather narrow vibrational spectra (0.1-0.3 nm, roughly 3-10 cm-1). In response to an amber stop codon (UAG), a terminal alkyne bearing an unnatural amino acid (UAA, pEtF) is directly incorporated in place of Tyr64 in the chromophore of pr-Kaede by genetic code expansion. Essentially, the UAA64 further conjugates into a large π system with the contiguous two editable amino acid residues (His63 and Gly65), resulting in a programmable Raman resonance shift of the embedded alkyne. In the proof-of-concept experiment, we constructed a series of novel pEtF-VP mutants and observed fine Raman shifts of the alkynyl group in different chromophores. The genetically encoded novel VPs, could potentially label tens of proteins in the future.

Is metaethical naturalism sufficient? A Confucian response to problems of meaning

ABSTRACT Ethical naturalism is sometimes accused of problematic metaphysics or epistemology. Some argue that naturalists rely on concepts of ‘nature’ indefensible in the light of modern evolutionary biology. There is also an epistemological worry that has been raised recently that strong normative evaluation, such as meaning in human life, is empirically inaccessible or even in conflict with what we know in scientific contexts. While the critics have targeted Aristotelian and Neo-Aristotelian views, I will appeal to an argument from the Neo-Confucian Zhu Xi as one potential way that we can respond to skeptics. If we can know that human beings are capable of moral goodness, and it is comprehensible for us to take moral goodness as a final or unifying goal of our lives, then we can respond to the skeptical objections which allege that we cannot sustain rich normative judgments about meaning in life in the face of scientific evidence.

Albanian first-year university students’ knowledge and acceptance of evolution

Evolutionary theory provides the foundation for modern biology with evolution as a core concept and unifying principle. This research aimed to examine evolutionary knowledge and acceptance among first-year university students. The<b> </b>participants considered for analysis consisted of 87 students enrolled in the first-year bachelor’s in biology and biotechnology at the University of Tirana, Albania. The respondents’ average age was 18.95 ± 0.78 years, with a pronounced dominance of females (90.2%). The study used the standardized research instrument “evolution education questionnaire on acceptance and knowledge” to measure knowledge and acceptance about evolution in an international context. We employed descriptive statistics to analyze the collected data.<b> </b>The findings of research revealed that over half of the respondents, 61%, fall into the ‘<i>very low knowledge</i>’ category on evolution and none fall in the ‘<i>high knowledge</i>’ category. Only 60.9% accept evolution. The need for a revision of the evolutionary concepts and topics in the curricula, from primary school, is recommended coupled with the incorporation of philosophical dialogue into teaching practice.

Charting a future for entomological taxonomy in New Zealand

ABSTRACT There is an increasing need for the identification and monitoring of insects. However, funding for entomological taxonomy, which traditionally provides the knowledge and tools for insect identification is in decline. Here, I review the history of entomological taxonomy in New Zealand and argue that the funding decline is a consequence of two broad shifts in biology. First, taxonomists test hypotheses in a qualitative manner unlike much of biology which has become increasingly quantitative. Second, there has been a shift in biology away from studying patterns and towards understanding processes and mechanisms. These scientific transformations have left the practice of taxonomy intellectually isolated from much of modern biology and led to its devaluing. I advocate for broadening taxonomy beyond the Linnaean system and to include genetic and genomic methods for identifying evolutionary lineages. This New Zealand Tree of Life would be comprised of ‘preliminary species hypotheses’ that can later be robustly tested with diverse character data and incorporated into the Linnean classification if resources and capability become available. Preliminary species hypotheses as well as associated genomic, DNA barcode, and specimen image data will support portable technologies that allow anyone to identify insects for environmental monitoring and biosecurity. This democratisation of insect identification has the potential to help build support for collections and taxonomic research. A concerted effort to accelerate the digitisation of collection data, especially genomic, DNA barcode, and specimen image data, will be necessary for implementation.

Chrono-Nutrition: Understanding the Interplay Between Circadian Rhythms and Personalized Nutrition

Chrono-nutrition, a concept emerging from the integration of chronobiology and nutrition sciences, investigates the timing of food intake in alignment with the body’s circadian rhythms. This interplay between dietary patterns and biological rhythms has garnered significant attention in recent years due to its potential implications for health and disease. In this comprehensive review, we explore the key findings from studies in the last decade on chrono-nutrition, focusing on its role in cardiovascular health, diabetes management, glucose metabolism, and the influence of sex, socioeconomic factors, and cultural diversity. The intricate dance between the molecular circadian clock and the gut microbiome is a fascinating area of study in modern biology. Both systems are integral to the regulation of metabolic processes, responding to dietary inputs and environmental cues to optimize the host’s adaptability and survival. This review delves into the dynamic interplay between circadian rhythms and the gut microbiome, exploring their mutual influence and the implications for human health.

Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training.

The functional study of proteins is a critical task in modern biology, playing a pivotal role in understanding the mechanisms of pathogenesis, developing new drugs, and discovering novel drug targets. However, existing computational models for subcellular localization face significant challenges, such as reliance on known Gene Ontology (GO) annotation databases or overlooking the relationship between GO annotations and subcellular localization. To address these issues, we propose DeepMTC, an end-to-end deep learning-based multi-task collaborative training model. DeepMTC integrates the interrelationship between subcellular localization and the functional annotation of proteins, leveraging multi-task collaborative training to eliminate dependence on known GO databases. This strategy gives DeepMTC a distinct advantage in predicting newly discovered proteins without prior functional annotations. First, DeepMTC leverages pre-trained language model with high accuracy to obtain the 3D structure and sequence features of proteins. Additionally, it employs a graph transformer module to encode protein sequence features, addressing the problem of long-range dependencies in graph neural networks. Finally, DeepMTC uses a functional cross-attention mechanism to efficiently combine upstream learned functional features to perform the subcellular localization task. The experimental results demonstrate that DeepMTC outperforms state-of-the-art models in both protein function prediction and subcellular localization. Moreover, interpretability experiments revealed that DeepMTC can accurately identify the key residues and functional domains of proteins, confirming its superior performance. The code and dataset of DeepMTC are freely available at https://github.com/ghli16/DeepMTC.

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams' methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.