Biological Insights Research Articles

A Study on Future Population Dynamics of Indigenous Earthworm Species in Golaghat District of Assam, India

This study presents a pioneering exploration into the population dynamics of indigenous earthworm species in the Golaghat district of Assam, India, through the lens of mathematical modeling. Recognizing the integral role of earthworms in enhancing soil productivity and ecosystem health, we embarked on a detailed longitudinal analysis spanning from 2018 to 2023 to assess their population trends across different seasons and subdivisions within the district. Utilizing the principles of Mathematical Biology, we employed the Malthus Growth model and the Logistic Growth model to estimate future population trajectories of earthworm species, integrating biological insights with mathematical rigor. Our methodology, combining extensive field data collection with sophisticated mathematical modeling, provides a replicable framework for similar ecological studies. This study contributes to the growing field of Mathematical Biology, offering a novel approach to understanding the complex interactions within ecosystems and the impact of environmental changes on key species. The outcomes offer valuable insights into sustainable agricultural practices and biodiversity conservation in tropical and subtropical regions, emphasizing the critical role of indigenous earthworm species in maintaining soil health and ecosystem services. Our findings reveal significant seasonal variations and highlight the resilience of these species in the face of ecological changes. The models suggest distinct population growth patterns, with the Logistic model providing a more realistic projection considering environmental constraints and resource availability. The bifurcation diagram is drawn for the Logistic map that presents the phenomena inside the chaotic region.

Understanding the genetic architecture of vitamin status biomarkers in the genome-wide association study era: biological insights and clinical significance

Understanding the genetic architecture of vitamin status biomarkers in the genome-wide association study era: biological insights and clinical significance

Neuroscience in peripheral cancers: tumors hijacking nerves and neuroimmune crosstalk.

Cancer neuroscience is anemerging field that investigates the intricate relationship between the nervous system and cancer, gaining increasing recognition for its importance. The central nervous system governs the development of the nervous system and directly affects brain tumors, and the peripheral nervous system (PNS) shapes the tumor microenvironment (TME) of peripheral tumors. Both systems are crucial in cancer initiation and progression, with recent studies revealing a more intricate role of the PNS within the TME. Tumors not only invade nerves but also persuade them through remodeling to further promote malignancy, creating a bidirectional interaction between nerves and cancers. Notably, immune cells also contribute to this communication, forming a triangular relationship that influences protumor inflammation and the effectiveness of immunotherapy. This review delves into the intricate mechanisms connecting the PNS and tumors, focusing on how various immune cell types influence nerve‒tumor interactions, emphasizing the clinical relevance of nerve‒tumor and nerve‒immune dynamics. By deepening our understanding of the interplay between nerves, cancer, and immune cells, this review has the potential to reshape tumor biology insights, inspire innovative therapies, and improve clinical outcomes for cancer patients.

A physically inspired approach to coarse-graining transcriptomes reveals the dynamics of aging.

Single-cell RNA sequencing has enabled the study of aging at a molecular scale. While substantial progress has been made in measuring age-related gene expression, the underlying patterns and mechanisms of aging transcriptomes remain poorly understood. To address this gap, we propose a physics-inspired, data-analysis approach to extract additional insights from single-cell RNA sequencing data. By considering the genome as a many-body interacting system, we leverage central idea of the Renormalization Group to construct an approach to hierarchically describe aging across a spectrum of scales for the gene expresion. This framework provides a quantitative language to study the multiscale patterns of aging transcriptomes. Overall, our study demonstrates the value of leveraging theoretical physics concepts like the Renormalization Group to gain new biological insights from complex high-dimensional single-cell data.

NAT10 functions as a pivotal regulator in gastric cancer metastasis and tumor immunity.

Gastric cancer (GC) presents a significant global health burden, with metastasis being the leading cause of treatment failure and mortality. NAT10, a regulatory protein involved in mRNA acetylation, has been implicated in various cancers. However, its role in GC, especially concerning metastasis and immune interactions, remains unclear. Utilizing multi-omics data from gastric cancer samples, we conducted comprehensive analyses to investigate NAT10 expression, its correlation with clinical parameters and immune relevance. Bioinformatics analysis and digital image processing were employed for this purpose. Furthermore, in vitro and in vivo experiments were conducted to elucidate the functional role of NAT10 in gastric cancer progression, aiming to provide deeper biological insights. Our findings reveal a significant association between NAT10 expression and various aspects of transcriptional, protein, as well as tumor immunity in GC patients. Additionally, we demonstrated that NAT10 promotes gastric cancer cell proliferation and migration, both in cellular models and in animal studies, suggesting its involvement in early tumor microvascular metastasis. NAT10 emerges as a promising molecular target, offering potential avenues for further research into molecular mechanisms and therapeutic strategies for GC.

A Comprehensive Review of Pedunculagin: Sources, Chemistry, Biological and Pharmacological Insights.

Pedunculagin is a widely abundant ellagitannin found in the plant kingdom, with a chemical structure featuring two hexahydroxydiphenoyl units linked to a glucose core. It has demonstrated various biological activities, including anti-cancer, anti-inflammatory, and anti-bacterial effects. This review aims to summarize the bioactivities, chemistry, and health-promoting properties of pedunculagin and plant preparations containing it. It is the first comprehensive summary covering pedunculagin's chemistry, sources, metabolism, and other relevant research. The search databases were Google Scholar, EBSCO Discovery Service, REAXYS Database, SCILIT, SCOPUS, PubMed, MEDLINE, Web of Science, Wiley Online Library, Science Direct/ELSEVIER, WordCat, and Taylor and Francis Online. All the databases were methodically searched for data published from 1911 until 2024. Various biological effects were proven in vitro for pedunculagin; however, due to the limited availability of the isolated compound, they have not been so far directly confirmed on more advanced in vivo and clinical models. However, its bioactivity can be deduced from studies conducted for plant preparations containing this ellagitannin as a dominant constituent, consequently indicating beneficial health effects. Further studies are needed to determine the molecular mechanism of action following topical application as well as the contribution of gut microbiota postbiotic metabolites- urolithins-being formed following the oral ingestion of preparations containing pedunculagin.

High-resolution subtyping of fibroblasts in gastric cancer reveals diversity among fibroblast subsets and an association between the MFAP5-fibroblast subset and immunotherapy.

Gastric cancer (GC) remains a global health threat due to frequent treatment failures caused by primary or acquired resistance. Although cancer-associated fibroblasts (CAFs) have been implicated in this process, it is still unclear which specific subtype(s) of CAFs hinder T-cell infiltration and promote resistance to immunotherapy. We analyzed the GC fibroblast atlas in detail by combining 63,955 single cells from 14 scRNA-seq datasets. We also performed RNA-seq data in a local GC cohort and examined 13 bulk RNA-seq datasets to understand the biological and clinical roles of different CAF subsets. Additionally, we conducted in vitro experiments to study the role of specific proteins in GC development. We identified a total of 17 fibroblast subsets in gastric cancer, nine of which did not fit into the existing CAFs classification. These subsets exhibited significant heterogeneity in distribution and biological characteristics (metabolism, cell-cell interactions, differentiation state), as well as clinical functions such as prognosis and response to immunotherapy. In particular, cluster 6 stood out for its high expression of MFAP5, CFD, and PI16; it was found to be negatively associated with both overall survival and response to immunotherapy in GC. This association was linked to an immunosuppressive microenvironment characterized by an increase in M2 macrophages but higher levels of T cell dysfunction and exclusion-a feature shared by tumors expressing MFAP5. Furthermore, the addition of human recombinant MFAP5 promoted proliferation and migration of HGC-27 cells by inducing the MFAP5/NOTCH1/HEY1 signaling pathway. We introduce a high-resolution GC fibroblast atlas. The 17 identified fibroblast clusters provide valuable opportunities for gaining deeper biological insights into the relationship between fibroblasts and GC development. Particularly, cluster 6 and its specific marker MFAP5 could serve as prognostic factors in GC and form a foundation for personalized therapeutic combinations to address primary resistance to ICIs.

Rare nonsynonymous germline and mosaic de novo variants in Japanese patients with schizophrenia.

Whole-exome sequencing (WES) studies have revealed that germline de novo variants (gDNVs) contribute to the genetic etiology of schizophrenia. However, the contribution of mosaic DNVs (mDNVs) to the risk of schizophrenia remains to be elucidated. In the present study, we systematically investigated the gDNVs and mDMVs that contribute to the genetic etiology of schizophrenia in a Japanese population. We performed deep WES (depth: 460×) of 73 affected offspring and WES (depth: 116×) of 134 parents from 67 families with schizophrenia. Prioritized rare nonsynonymous gDNV and mDNV candidates were validated using Sanger sequencing and ultra-deep targeted amplicon sequencing (depth: 71,375×), respectively. Subsequently, we performed a Gene Ontology analysis of the gDNVs and mDNVs to obtain biological insights. Lastly, we selected DNVs in known risk genes for psychiatric and neurodevelopmental disorders. We identified 62 gDNVs and 98 mDNVs. The Gene Ontology analysis of mDNVs implicated actin filament and actin cytoskeleton as candidate biological pathways. There were eight DNVs in known risk genes: splice region gDNVs in AKAP11 and CUL1; a frameshift gDNV in SHANK1; a missense gDNV in SRCAP; missense mDNVs in CTNNB1, GRIN2A, and TSC2; and a nonsense mDNV in ZFHX4. Our results suggest the potential contributions of rare nonsynonymous gDNVs and mDNVs to the genetic etiology of schizophrenia. This is the first report of the mDNVs in schizophrenia trios, demonstrating their potential relevance to schizophrenia pathology.

Harnessing biological insights: Integrating proton regulation, pH buffer and zincophility for highly stable Zn anode

Zinc anodes are severely threatened by hydrogen evolution, dendrite growth and by-products. Inspired by the mechanisms of L-carnosine (L-car) in maintaining intracellular pH stability and fostering mucosal repair in biological systems, this study innovatively proposed a comprehensive strategy integrating proton regulation, pH buffer and zincophility by introducing L-car as an electrolyte additive to achieve remarkable stability of zinc anode. Experimental validations and theoretical calculations demonstrate that the abundant N and O sites in L-car can form robust hydrogen bonds with protons, effectively impeding interfacial proton transport and substantially mitigating HER. Moreover, dual pH buffering sites of L-car facilitate dynamic modulation of proton concentration, stabilizing the pH of electrolyte, and suppressing Zn corrosion/passivation/by-product. Concurrently, the robust chelation between L-car and Zn2+ orchestrates uniform zinc ion deposition. This synergistic trifecta of L-car endows Zn ion batteries with an extraordinary cycling stability, extending to an ultralong duration of 5500 h. Furthermore, the Zn//MnO2 full battery, demonstrates remarkable stability, retaining a specific capacity of 106.1 mAh/g with 79.2 % retention after 1000 cycles at 3 A/g. This study pioneers the interdisciplinary application of L-car in electrolyte modification, presenting a novel paradigm for stabilizing zinc anodes.

Artificial Intelligence and Omics in Malignant Gliomas.

The availability of large scale multi-omics data requires development of computational models to infer valuable biological insights for the implementation of precision medicine. Artificial intelligence (AI) refers to a host of computational algorithms that is becoming a major tool capable of integrating large genomic, transcriptomic, proteomic, and metabolomic data. Machine learning (ML) is the most significant AI algorithm in health sciences have exploded, specifically due to the recent progress made by deep learning. Although the use of AI/ML tools in GBM-omics is still at an early stage, a comprehensive discussion of how AI can be used to unravel various aspects of GBM (intratumor heterogeneity, biomarker discovery, survival prediction, and treatment optimization) would be highly relevant to both researchers and clinicians. Here, we aim to review the different AI-based techniques that have been used to study GBM pathogenesis using multi-omics data over the last decade. We first summarize different types of GBM related omics resources that can be used to develop AI models. We then discuss various AI applications for multi-omics data in order to enhance GBM precision medicine. Finally, we discuss the technical and ethical challenges that limit its application and ways to improve its implementation in clinics.

Cancer new treatment series: Comparison immune changes of HCC by scRNA-Seq following HELC treatment one case study

Immunotherapy has emerged as a novel treatment strategy for many types of cancers, among them, liver cancer. The major advances and achievements in recent years is the use of programmed death-1 (PD-1) blockers for cancer treatment. Since patients’ immune systems are already weak following concurrent surgery plus chemo or radiotherapy, it is necessary to restore and awake their immune cells to maximize the effect for immune therapy. Methods: Liver cancers were treated twice with hapten enhanced local chemotherapy (HELC) like tumor lysates vaccine and following PD-1. Single-cell RNA sequencing (scRNA-seq) was used to analyze the changes of immune responses prior and after treatments. Results: We observed upregulation of cytotoxicity-related genes of CD8 effector T cells and NK cells in untreated tumor; Both Bmem and Naive B cells in untreated tumor showed a significant increase in MHC II signaling pathway-related genes, while MHC I-related genes was upregulated in plasma cells. Significant tumor size shrinkage was observed in both treated and untreated tumors following the HELC+PD-1 therapy. Conclusions: This study provides new biological insights into the abscopal effect at the single-cell level related to the composition of T and B cells in untreated liver cancers before and after major primary tumors treated by HELC. Our data showed that intra-tumor HELC can kill tumor cells and induced immune activity, which is likely vital in modifying tumor associate antigens (TAAs) into neo TAAs. It induces immune response just like vaccine can wake up immune cells and therefore increasing the efficacy of PD1 therapy.

Correlative microscopy - illuminating the endomembrane system of plant seeds.

The endomembrane system of cereal seed endosperm is a highly plastic and dynamic system reflecting the high degree of specialization of this tissue. It is capable of coping with high levels of storage protein synthesis and undergoes rapid changes to accommodate these storage proteins in newly formed storage organelles such as endoplasmic reticulum-derived protein bodies or protein storage vacuoles. The study of endomembrane morphology in cereal endosperm is challenging due to the amount of starch that cereal seeds accumulate and the progressive desiccation of the tissue. Here, we present a comprehensive study of the endomembrane system of developing barley endosperm cells, complemented by correlative light and electron microscopy (CLEM) imaging. The use of genetically fused fluorescent protein tags in combination with the high resolution of electron microscopy brings ultrastructural research to a new level and can be used to generate novel insights in cell biology in general and in cereal seed research in particular.

The biologic landscape and therapeutic implications of upper tract urothelial cancer.

Management of upper tract urothelial cancer (UTUC) has been largely extrapolated from bladder cancer due to its rarity; however, unique biological and clinical differences between UTUC and bladder cancer have been uncovered. The purpose of this review is to present the current therapeutic landscape of UTUC with an emphasis on biologically driven rationale. Prospective trials for patients with high-risk localized UTUC have shown improved outcomes with adjuvant and neoadjuvant platinum-based chemotherapy. However, the timing of therapy relative to nephroureterectomy may impact platinum eligibility due to renal functional decline following surgery. In recent years, emerging therapeutic classes including immune checkpoint inhibition, antibody drug conjugates, and targeted therapies have emerged as tolerable alternatives to platinum-based chemotherapy in treating metastatic disease. Biomarker-selected therapies, including those targeting HER2 and FGFR3, have shown encouraging results and are relevant to UTUC based on increased expressions of these targets; however, no prospective study to date has been powered to assess the effect of these modern treatments on patients with UTUC specifically. Unique biological insights into UTUC pathogenesis and risk factors have expanded the therapeutic landscape for these patients beyond conventional platinum-based chemotherapeutic approaches. Novel therapeutic classes have emerged to guide more precise approaches in treating patients with urothelial cancer, with a need for further trials powered specifically to the UTUC population.

Vispro improves imaging analysis for Visium spatial transcriptomics.

Spatial transcriptomics (ST) is a cutting-edge technology that enables the comprehensive analysis of gene expression while preserving the spatial context of tissues. Within this framework, histological images serve a crucial role, providing spatially cohesive information that is often challenging to capture through gene expression alone. However, the imaging process in ST data presents inherent challenges, such as variability in image quality, artifacts from fiducial markers, and difficulty in distinguishing tissue regions. These challenges can significantly impair the accuracy and effectiveness of downstream analyses. To address these limitations, we developed Vispro, an end-to-end, fully automated image processing tool tailored for ST data. Vispro integrates four key modules: fiducial marker detection, marker removal and image restoration, tissue region detection, and segmentation of disconnected tissue areas. These modules systematically enhance the quality of ST images, reducing artifacts and improving tissue segmentation accuracy. Furthermore, by improving the integrity of image data, we demonstrate that Vispro facilitates downstream analyses such as cell segmentation and image registration, empowering researchers to extract more detailed and accurate biological insights from complex tissue architectures.

ASO Author Reflections: Unraveling the Disappearance of Signet Ring Cell Gastric Cancer: Surveillance and Biological Insights.

ASO Author Reflections: Unraveling the Disappearance of Signet Ring Cell Gastric Cancer: Surveillance and Biological Insights.

Generating Protein Structures for Pathway Discovery Using Deep Learning.

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today. One common aspect of this challenge is pathway discovery, where the start and end states of a scientific phenomenon are known or can be approximated, but the mechanistic details in between are unknown. Here, we propose a representation-learning-based solution that uses interpolation and extrapolation in an abstract representation space to synthesize potential transition states, which are automatically validated using MD simulations. The new simulations of the synthesized transition states are subsequently incorporated into the representation learning, leading to an iterative framework for targeted path sampling. Our approach is demonstrated by recovering the transition of a RAS-RAF protein domain (CRD) from membrane-free to interacting with the membrane using coarse-grain MD simulations.

Neoadjuvant sunitinib plus exemestane in post-menopausal women with hormone receptor-positive/HER2-negative early-stage breast cancer (SUT_EXE-08): a phase I/II trial

Neoadjuvant endocrine therapy (NET) for hormone receptor-positive (HR+) breast cancer might be as effective as chemotherapy, with a better toxicity profile. Blocking a crucial process such as angiogenesis with sunitinib may have a synergistic effect with NET. We aimed to assess the efficacy and safety of neoadjuvant sunitinib plus exemestane in early-stage HR+/HER2-negative breast cancer. In this phase I/II study, postmenopausal women with HR+/HER2− stage II-III breast cancer received neoadjuvant exemestane at conventional dose of 25mg plus sunitinib in a 3 + 3 design at 25mg (3/1weeks scheme) or 37.5mg continuous dose, for 6 months. Coprimary endpoints were the recommended dose of sunitinib combined with exemestane and objective response. Secondary endpoints included safety and biomarkers of early response. For 15 months, 18 patients were enrolled, 15 at sunitinib 25mg and 3 at 37.5mg. Median age was 73, 77% of patients had T2 tumors and 67% node-positive disease. The most common grade 2 toxicity was asthenia (44%), as was hypertension (22%) for grade 3. No grade 4–5 were reported. Twelve patients (66%) achieved an objective response. VEGFR-2 levels significantly decreased after one month of treatment. Differential gene expression analysis showed downregulation of ESR1, PGR and NAT1 in post-treatment samples and upregulation of EGFR, MYC, SFRP1, and FOXC1. PAM50 analysis on 83% of patients showed a prevalence of luminal A subtype, both in pre-treatment (63.6%) and post-treatment tumors (54.5%). Sunitinib plus exemestane was associated with substantial yet reversible toxicities, providing safety, efficacy and biological impact insights of combining an antiangiogenic drug with hormone therapy in early-stage breast cancer.Trial registration: Registered with ClinicalTrials.gov, NCT00931450. 02/07/2009

Optimizing cancer classification: a hybrid RDO-XGBoost approach for feature selection and predictive insights

The identification of relevant biomarkers from high-dimensional cancer data remains a significant challenge due to the complexity and heterogeneity inherent in various cancer types. Conventional feature selection methods often struggle to effectively navigate the vast solution space while maintaining high predictive accuracy. In response to these challenges, we introduce a novel feature selection approach that integrates Random Drift Optimization (RDO) with XGBoost, specifically designed to enhance the performance of cancer classification tasks. Our proposed framework not only improves classification accuracy but also offers valuable insights into the underlying biological mechanisms driving cancer progression. Through comprehensive experiments conducted on real-world cancer datasets, including Central Nervous System (CNS), Leukemia, Breast, and Ovarian cancers, we demonstrate the efficacy of our method in identifying a smaller subset of unique and relevant genes. This selection results in significantly improved classification efficiency and accuracy. When compared with popular classifiers such as Support Vector Machine, K-Nearest Neighbor, and Naive Bayes, our approach consistently outperforms these models in terms of both accuracy and F-measure metrics. For instance, our framework achieved an accuracy of 97.24% in the CNS dataset, 99.14% in Leukemia, 95.21% in Ovarian, and 87.62% in Breast cancer, showcasing its robustness and effectiveness across different types of cancer data. These results underline the potential of our RDO-XGBoost framework as a promising solution for feature selection in cancer data analysis, offering enhanced predictive performance and valuable biological insights.

BIT: Bayesian Identification of Transcriptional Regulators from Epigenomics-Based Query Region Sets.

Transcriptional regulators (TRs) are master controllers of gene expression and play a critical role in both normal tissue development and disease progression. However, existing computational methods for identification of TRs regulating specific biological processes have significant limitations, such as relying on distance on a linear chromosome or binding motifs that have low specificity. Many also use statistical tests in ways that lack interpretability and rigorous confidence measures. We introduce BIT, a novel Bayesian hierarchical model for in-silico TR identification. Leveraging a comprehensive library of TR ChIP-seq data, BIT offers a fully integrated Bayesian approach to assess genome-wide consistency between user-provided epigenomic profiling data and the TR binding library, enabling the identification of critical TRs while quantifying uncertainty. It avoids estimation and inference in a sequential manner or numerous isolated statistical tests, thereby enhancing accuracy and interpretability. BIT successfully identified critical TRs in perturbation experiments, functionally essential TRs in various cancer types, and cell-type-specific TRs within heterogeneous cell populations, offering deeper biological insights into transcriptional regulation.

Sex-specific Genetic Determinants of Right Ventricular Structure and Function.

While sex differences in right heart phenotypes have been observed, the molecular drivers remain unknown. To provide biological insights into sex differences in the structure and function of the right ventricle (RV) using common genetic variation. RV phenotypes were obtained from cardiac magnetic resonance imaging in 18,156 women and 16,171 men from the UK Biobank. Observational analyses and sex-stratified genome-wide association studies were performed. Candidate female-specific loci were evaluated against invasively measured cardiac performance in 479 female patients with idiopathic or heritable pulmonary arterial hypertension (PAH), recruited to the UK NIHR BioResource Rare Diseases study. Sex was associated with differences in RV volumes and ejection fraction in models adjusting for left heart counterparts, blood pressure, lung function and sex hormone levels. Six genome-wide significant loci (13%) revealed heterogeneity of allelic effects between women and men, and significant sex-by-genotype interaction. These included two sex-specific candidate loci present in women only: a locus for RV ejection fraction in BMPR1A and a locus for RV end-systolic volume near DMRT2. Epigenetic data in RV tissue indicate that variation at the BMPR1A locus likely alters transcriptional regulation. In female patients with PAH, a variant located in the promoter of BMPR1A was significantly associated with cardiac index (effect size 0.16 l/min/m2), despite similar RV afterload. BMPR1A has emerged as a biologically plausible candidate gene for female-specific genetic determination of RV function, showing associations with cardiac performance under chronically increased afterload in female patients with PAH.