Biological Patterns Research Articles

METTL5 Promotes Tumor Progression in Oral Squamous Cell Carcinoma by Activating the Myc Pathway.

It has been observed that methyltransferases-like 5 (METTL5) is a key mediator underlying tumorigenesis in humans. This study aimed to investigate the biological function, expression pattern, and clinical significance of METTL5 in oral squamous cell carcinoma (OSCC). Bioinformatics interrogations and immunohistochemical staining were utilized for determining the expression and localization of METTL5 in OSCC, and revealing the relationship between the expression of METTL5 and prognosis. Cell phenotype experiments and xenograft models were used to detect the impact of METTL5 silencing on OSCC. Gene Set Enrichment Analysis, Spearman correlation, and c-Myc overexpression plasmid was utilized for exploring the regulatory effects of METTL5 on the Myc pathway. METTL5 was overexpressed in OSCC and correlated with reduced survival. Cell proliferation, migration, and invasion were significantly inhibited by METTL5 knockdown invitro, and tumor growth was impaired invivo. Moreover, METTL5 was capable of activating the Myc pathway. The influences of knockdown of METTL5 on Cal27 cell biological behaviors were reversed by overexpression of c-Myc. Our data suggested that METTL5 may act as a putative oncogene and prognostic biomarker in OSCC. The Myc pathway appears to be involved in cell proliferation, migration, invasion, and apoptosis in OSCC mediated by METTL5.

Diffusive topology preserving manifold distances for single-cell data analysis

Manifold learning techniques have emerged as crucial tools for uncovering latent patterns in high-dimensional single-cell data. However, most existing dimensionality reduction methods primarily rely on 2D visualization, which can distort true data relationships and fail to extract reliable biological information. Here, we present DTNE (diffusive topology neighbor embedding), a dimensionality reduction framework that faithfully approximates manifold distance to enhance cellular relationships and dynamics. DTNE constructs a manifold distance matrix using a modified personalized PageRank algorithm, thereby preserving topological structure while enabling diverse single-cell analyses. This approach facilitates distribution-based cellular relationship analysis, pseudotime inference, and clustering within a unified framework. Extensive benchmarking against mainstream algorithms on diverse datasets demonstrates DTNE's superior performance in maintaining geodesic distances and revealing significant biological patterns. Our results establish DTNE as a powerful tool for high-dimensional data analysis in uncovering meaningful biological insights.

CASTER: Direct species tree inference from whole-genome alignments.

Genomes contain mosaics of discordant evolutionary histories, challenging the accurate inference of the tree of life. While genome-wide data are routinely used for discordance-aware phylogenomic analyses, due to modeling and scalability limitations, the current practice leaves out large chunks of genomes. As more high-quality genomes become available, we urgently need discordance-aware methods to infer the tree directly from a multiple genome alignment. Here, we introduce CASTER, a theoretically justified site-based method that eliminates the need to predefine recombination-free loci. CASTER is scalable to hundreds of mammalian whole genomes. We demonstrate the accuracy and scalability of CASTER in simulations that include recombination and apply CASTER to several biological datasets, showing that its per-site scores can reveal both biological and artefactual patterns of discordance across the genome.

A Deterministic Model for Harmful Algal Bloom (HAB) Patterns Under Turing’s Instability Perspective

Turing’s instability has been widely introduced to explain the formation of several biological and ecological patterns, such as the skin patterning of fish or animals, wings of butterflies, pigmentation, and labyrinth patterns of the cerebral cortex of mammals. Such a mechanism may occur in the ecosystem due to the differential diffusion dispersal that happen if one of the constituent species results in the activator or the prey, showing a tendency to undergo autocatalytic growth. The diffusion of the constituent species activator is a random mobility function called passive diffusion. If the other species in the system (the predator/inhibitor) disperses sufficiently faster than the activator, then the spatially uniform distribution of species becomes unstable, and the system will settle into a stationary state. This paper introduced Turing’s mechanism in our reaction–taxis–diffusion model to simulate the harmful algal bloom (HAB) pattern. A numerical approach, the Runge–Kutta method, was used to deal with this system of reaction–taxis–diffusion equations, and the findings were qualitatively compared to the aerial patterns obtained by a drone flying over Torment Lake in Nova Scotia (Canada) during the bloom season of September 2023.

Eosinophilic Granulomatosis With Polyangiitis: A Real-World Study of Biologics and Sinus Surgery Use for Managing Nasal Polyposis.

Eosinophilic granulomatosis with polyangiitis (EGPA) is a multisystemic disease associated with nasal polyposis. Multiple biologics are used for managing EGPA, including some approved for nasal polyps (NP). This study investigated real-world biologic prescription patterns for EGPA and their impact on NP and endoscopic sinus surgery (ESS) use. EGPA patients with NP treated with a biologic at any Mayo Clinic site (January 2010 to January 2024) were identified by querying the unified electronic medical record. Patterns of biologic therapy, clinical course, impact on NP, and performance of ESS were studied. Eighty patients were identified. Overall, 71 of 80 (88.75%) patients underwent ESS, with 62 of 80 (77.5%) undergoing 131 ESS procedures prior to biologic therapy. ESS for recalcitrant NP (47 episodes) was performed on 38 of 80 (47.5%) patients on biologics. Biologic monotherapy was used in 90% (72) of patients; mepolizumab (81.9%) was the most common, followed by rituximab (23.6%), benralizumab (18.1%), and dupilumab (12.5%). Switching of biologics was observed in 28 of 80 patients. Concurrent dual-biologic therapy was used in eight (10%) patients. For patients on single-agent biologic therapy, ESS was performed on 52.5% of patients on mepolizumab, 23.5% on rituximab, 42.8% on benralizumab, and 22.2% on dupilumab. Multidisciplinary multi-modality treatment with biologics and ESS appeared to be the mainstay of controlling NP in EGPA. Mepolizumab was the most frequently used biologic. Dual biologic therapy was necessary in 10% of patients. Overall, 71 of 80 (88.75%) patients had ESS, with almost half the study population (47.5%) undergoing ESS after initiating biologic treatment.

Artificial intelligence in molecular biology.

In recent years, computational methods and artificial intelligence approaches have proven uniquely suited forstudying patterns in molecular biology. In this focus issue, we spoke with researchers about using these tools to address various biological questions and explore both current implications and future possibilities.

Assessment of Different Conventional and Biofortified Wheat Genotypes Based on Biology and Damage Pattern of Rhyzopertha dominica and Trogoderma granarium.

The lesser grain borer, Rhyzopertha dominica (F.) (Coleoptera: Bostrichidae) and khapra beetle, Trogoderma granarium E. (Coleoptera: Dermestidae) are primary stored-grain insect pests. Differences in certain biological and physical parameters of both pest species and wheat genotypes were investigated under laboratory conditions. Zinc (Zn)-biofortified (Zincol-2016 and Akbar-2019) and conventional (Arooj-2022, Nawab-2021, Dilkash-2021, Bhakkar Star-2019) wheat genotypes were used in this study. Zn-biofortified genotypes outperformed the conventional ones, with significant differences observed in fecundity, percent adult emergence, total developmental duration, percent grain damage, and weight loss of both insect species. The results further revealed that the fecundity of R. dominica and T. granarium were lowest on Akbar-2019 in both the free-choice test (42.50 and 33.17) and no-choice test (35.50 and 32.50), respectively. Similarly, percent adult emergence of both insect species was also lowest on Akbar-2019 in both the free-choice test (69.78 and 70.28%) and no-choice test (67.38 and 70.71%). The total developmental period also showed significant variation among the tested genotypes. The longest developmental period was recorded in Akbar-2019, i.e., 44.33 and 58.83 days, for R. dominica and T. granarium, respectively. Similarly, percent grain damage (13.23 and 10.33%) and weight loss (3.62 and 2.12%) were found to be minimum in Akbar-2019 for both pest species, respectively. Additionally, a positive correlation was observed between grain moisture content and damage parameters, suggesting that the higher moisture content may aggravate the percent grain damage and weight loss. These findings indicate that the nutritional qualities of Zn-biofortified wheat genotypes negatively affected the development of both insect species; thus, it can be an efficacious approach not only for ensuring food security but also for protecting grains against storage pests.

Dynamics of resistance to immunotherapy and TKI in patients with advanced renal cell carcinoma.

Immune-based combinations are the cornerstone of the first-line treatment of metastatic renal cell carcinoma patients, leading to outstanding outcomes. Nevertheless, primary resistance and disease progression is a critical clinical challenge. To properly address this issue, it is pivotal to understand the mechanisms of resistance to immunotherapy and tyrosine kinase inhibitors, that tumor eventually develop under treatment. In this review of the literature, we aim at exploring resistance mechanisms arising in patients treated with first-line immune-based combinations in order to understand the biological pattern that should be investigated to overcome them. In more detail, mechanisms of resistance to nivolumab and pembrolizumab are divided into intrinsic to cancer cells and extrinsic (stromal or immune cells). Regarding axitinib, the increased expression of Nuclear protein 1 (NUPR1) or decreased levels of insulin receptor (INSR) characterize resistant cells. The secretion of non-VEGF pro-angiogenic factors, such as PDGF-BB, IL-1β, MMP-9, Gro-α, IL-8, IL-6, and CCL-2, can lead to resistance to cabozantinib. The reactivation of pathways previously targeted by lenvatinib or the activation of alternative pathways, such as EGFR-PAK2-ERK pathway, underlie the development of resistance to lenvatinib. Exploring resistance mechanism that arise during first-line therapy can lead to the development of treatment strategy able to overcome them in order to improve duration of response and patients outcomes.

Data-driven discovery and parameter estimation of mathematical models in biological pattern formation.

Mathematical modeling has been utilized to explain biological pattern formation, but the selections of models and parameters have been made empirically. In the present study, we propose a data-driven approach to validate the applicability of mathematical models. Specifically, we developed methods to automatically select the appropriate mathematical models based on the patterns of interest and to estimate the model parameters. For model selection, we employed Contrastive Language-Image Pre-training (CLIP) for zero-shot feature extraction, mapping the given pattern images to latent space and specifying the appropriate model. For parameter estimation, we developed a novel technique that rapidly performs approximate Bayesian inference based on Natural Gradient Boosting (NGBoost). This method allows for parameter estimation under minimal constraints; i.e., it does not require time-series data or initial conditions and is applicable to various types of mathematical models. We tested the method with Turing patterns and demonstrated its high accuracy and correspondence to analytical features. Our strategy enables efficient validation of mathematical models using spatial patterns.

Identification of a Ubiquitination‑Related Signature for Ovarian Cancer Based on 101 Combinations of Machine Learning Methods

Background: Ovarian cancer is the third most common gynecological cancer worldwide. The majority of ovarian cancer instances are identified at later stages of progression, resulting in a significantly elevated mortality rate. Additionally, the absence of reliable prognostic biomarkers significantly limits the effectiveness of prediction and personalized treatment approaches. Ubiquitination, a protein modification process intricately associated with diverse tumor-related biological pathways, occupies a central position within the tumor microenvironment (TME). However, its specific mechanisms in ovarian cancer remain inadequately understood. Methods: In this study, we explored ubiquitination-related genes at both single-cell and bulk transcriptome levels utilizing algorithms, such as AddModuleScore, single-sample Gene Set Enrichment Analysis (ssGSEA), and Weighted Gene Co-Expression Network Analysis (WGCNA). We then designed a novel machine learning framework featuring 10 algorithms and 101 combinations to establish a Ubiquitination-Related Signature (URS), which was validated in both training and external datasets. Additionally, we developed a comprehensive nomogram for clinical prognosis based on the URS. To thoroughly investigate prognostic characteristics, we applied multi-omics data. This analysis provided a deep understanding of the prognostic signature. Furthermore, we assessed the response of different risk subgroups to immunotherapy and identified suitable drugs for specific risk profiles, thereby advancing personalized medicine. Results: In this research, we discovered 35 ubiquitination-related genes significantly linked to the prognosis of ovarian cancer. Our univariate and multivariate assessments indicated that the low-risk cohort exhibited a better survival rate than the high-risk cohort, suggesting the potential of the model as an independent prognostic marker. The nomogram based on the URS offers a quantitative tool for clinical applications and can be effectively utilized in healthcare settings. Moreover, we observed significant differences in biological functions, mutation patterns, and immune cell infiltration within the tumor microenvironment between the high-risk and low-risk groups. Additionally, we highlighted potential drugs tailored for specific risk categories. Conclusion: We developed and validated a signature related to ubiquitination for ovarian cancer, which shows potential as a reliable predictor of prognosis. Investigating ubiquitination paves the way for innovative clinical strategies and novel anti-tumor therapies.

Bilateral cellular flows display asymmetry prior to left-right organizer formation in amniote gastrulation.

Bilaterians are defined by a bilaterally symmetrical body plan. Vertebrates exhibit external bilateral symmetry but display left-right (LR) asymmetry in their internal organs. Amniote embryos switch the patterning of internal organs from bilateral symmetry to LR-asymmetry. Using chick embryos as a model system, here we examined the initiation of LR symmetry breaking. Our biophysical approaches to quantify cellular flows inferred that LR symmetry breaking occurs before the formation of Hensen's node, a LR organizer, which serves as a signaling center for LR patterning-gene programs. Our work demonstrates that quantitative biophysical parameters can help unravel the initiation of LR symmetry breaking, suggesting an involvement of physical mechanisms in this critical biological patterning process.

Siglec15 in blood system diseases: from bench to bedside.

Inhibiting the PD-1/PD-L1 pathway using immunomodulators has demonstrated promising outcomes in clinics. Immunomodulators can effectively target immune checkpoints with a strong preference for the tumor microenvironment (TME). Besides, immunomodulators specifically target the recently discovered inhibitory immune checkpoint, sialic acid-binding immunoglobulin-like lectin (Siglec-15). Distinctive in its molecular composition, Siglec-15 has a unique molecular composition and been shown to be highly prevalent in numerous solid tumor tissues and tumor-associated macrophages (TAMs) in human subjects. Notably, Siglec-15 is up-regulated across various cancer types. As a result, Siglec-15 has attracted significant attention due to its exclusive nature concerning PD-L1 expression, suggesting its role in immune evasion in patients lacking PD-L1. Siglec-15 predominantly appears in certain populations and can promote tumor development by repressing T lymphocyte activation and proliferation, thereby facilitating tumor cell immune escape. Furthermore, Siglec-15 is implicated in osteoclast differentiation and bone remodeling, indicating that it is a promising target for next-generation cancer immunotherapies. Additionally, Siglec-15 can modulate immune responses to microbial infections. The current treatment strategies for hematological conditions predominantly include conventional intensive chemotherapy and transplantation methods. However, emerging immunotherapeutic approaches are increasingly recognized for their overall effectiveness, indicating that specific molecular targets should be identified. The expression of Siglec-15 within tumor cells may indicate a novel pathway for treating hematological malignancies. In this study, the biological attributes, expression patterns, and pathogenic mechanisms of Siglec-15 across various diseases were reviewed. The role of Siglec-15 in the pathogenesis and laboratory diagnosis of hematological disorders was also evaluated.

MAN2B1 in immune system-related diseases, neurodegenerative disorders and cancers: functions beyond α-mannosidosis.

Glycosylation modifications of proteins and glycan hydrolysis are critical for protein function in biological processes. Aberrations in glycosylation enzymes are linked to lysosomal storage disorders (LSDs), immune interactions, congenital disorders and tumour progression. Mannosidase alpha class 2B member 1 (MAN2B1) is a lysosomal hydrolase from the α-mannosidase family. Dysfunction of MAN2B1 has been implicated as causative factors in mannosidosis, a lysosomal storage disorder characterised by cognitive impairment, hearing loss and immune system and skeletal anomalies. Despite decades of research, its role in pathogenic infections, autoimmune conditions, cancers and neurodegenerative pathologies is highly ambiguous. Future studies are required to shed more light on the intricate functioning of MAN2B1. To this end, we review the biological functions, expression patterns, enzymatic roles and potential implications of MAN2B1 across various cell types and disease contexts. Additionally, the novel insights presented in this review may aid in understanding the role of MAN2B1 in immune cells, thereby paving the way for targeted therapeutic interventions in immune-related disorders.

Biological traits and population dynamics for sustainable harvesting of Carcinus maenas

Research focusing on the biological patterns and population dynamics of Carcinus maenas has not been conducted for the purpose of fishery management along the European coastal systems. This has led to the implementation of fisheries management policies without scientific considerations, adversely affecting fishery profitability. To addrees this gap, we studied the crab species’ population dynamics, reproductive biology, and growth patterns across different Portuguese lagoons and estuaries on a monthly basis from 2019 to 2021. Surveys were performed in the Southern (Ria Formosa lagoon and Ria Alvor estuary), Central (Sado river/estuary) and Northern regions (Ria Aveiro estuary) of Portugal. Monthly biological data was used to analyse size-frequency distributions, sex ratios, spawning seasons, recruitment pulses, estimate carapace width at first maturaty and biological growth parameters. It was observed that spawning occurs almost year-round in all systems, with a peak in the colder months, between September and March. In the southern regions of the Portuguese coast, the spawning period starts earlier than in the central and northern systems, with a higher sex ratio recorded for females in all systems. The carapace width at which 50 % (CW50) of individuals reach maturity is similar for both sexes, around 30 mm, a value below to the minimum landing size enforced in Portugal. The analysis of von Bertalanffy growth curves revealed a continuous recruitment with a peak during the colder months, with individuals reaching the size at maturation after six months. The fast growth and continuous recruitment leds to the existence of between four and six growth cohorts for both sexes across all system. The findings of this study can contribute to more effective fisheries management policies for C. maenas in Portugal, such as a reduction of the minimum landing size.

The Soil and Water Conservation Effects of Different Plant Communities and Biological Soil Crust Symbiosis Patterns in the Ecologically Fragile Area of Central Ningxia

Biological soil crusts are complex biological soil layers formed by mosses, lichens, cyanobacteria, and the underlying soil, which together with plants affect rainfall infiltration, surface runoff, soil evaporation, and water movement in the soil. The soil desertification and soil erosion in the ecologically fragile areas of central Ningxia are serious problems, and the ecological environment is extremely fragile. Effective ecological restoration technologies are urgently needed. This study took the grassland in the ecologically fragile area of central Ningxia as the object and investigated the impact of three plant communities and symbiotic patterns of biological soil crusts on soil erosion through field simulated rainfall experiments. The results showed that: (1) At a rainfall intensity of 90 mm h−1, the initial runoff time of each slope was significantly positively correlated with plant community type and biological soil crust coverage, and prolonged with the increase of plant community type and biological soil crust coverage. (2) With the extension of rainfall duration, the cumulative runoff on each slope exhibited an increasing trend. (3) The sediment concentration in runoff on slopes under different plant community and biological soil crust symbiotic patterns was significantly different, with the sediment concentration decreasing as the type of plant community and the coverage of biological soil crusts increased. (4) With the increase in the diversity of plant communities and the coverage of biological crusts, there was a gradual reduction in the volume of accumulated sediment. This study offers scientific management strategies and practical guidance for soil and water conservation efforts in the ecologically vulnerable areas of central Ningxia, highlighting the importance of promoting these symbiotic models within the region.

A three-node Turing gene circuit forms periodic spatial patterns in bacteria.

Turing patterns are self-organizing systems that can form spots, stripes, or labyrinths. Proposed examples in tissue organization include zebrafish pigmentation, digit spacing, and many others. The theory of Turing patterns in biology has been debated because of their stringent fine-tuning requirements, where patterns only occur within a small subset of parameters. This has complicated the engineering of synthetic Turing gene circuits from first principles, although natural genetic Turing networks have been identified. Here, we engineered a synthetic genetic reaction-diffusion system where three nodes interact according to a non-classical Turing network with improved parametric robustness. The system reproducibly generated stationary, periodic, concentric stripe patterns in growing E.coli colonies. A partial differential equation model reproduced the patterns, with a Turing parameter regime obtained by fitting to experimental data. Our synthetic Turing system can contribute to nanotechnologies, such as patterned biomaterial deposition, and provide insights into developmental patterning programs. A record of this paper's transparent peer review process is included in the supplemental information.

A comprehensive study on the bioactive flavonoids in Paulownia flowers: Uncovering metabolic pathways, effective components, and regulatory genes for industrial applications

The study explores Paulownia flowers as an emerging resource in medicine, food, health supplements, cosmetics, and animal feed, despite a lack of characterization of their secondary metabolite accumulation mechanisms. We conducted a comprehensive investigation into the biological activities, metabolite profiles, and gene expression patterns of flavonoids in Paulownia flowers. Our results, obtained using ABTS/FRAP/DPPH assays, MTT assays, and colorimetric analyses, demonstrate the potent antioxidant, antibacterial, hypolipidemic, and antiproliferative activities of Paulownia flower flavonoid (PFF) extracts. Notably, the Paulownia elongata tetraploid (M4) exhibited particularly notable effects. HPLC fingerprinting, molecular docking, and density functional theory calculations led to the identification of rhoifolin and luteolin as key bioactive flavonoids. UV-Vis, fluorescence, and circular dichroism spectroscopy confirmed PFF's role as a reversible and mixed inhibitor of xanthine oxidase (XOD). Transcriptome and metabolomic analyses further revealed substantial enrichment in phenylpropanoid, flavonol, and flavonoid biosynthetic pathways, with PfPP2C, PfPYR, PfPYL, PfNPR1, PfMYBs, and PfbHLHs identified as crucial regulators. This work provides a foundational understanding of flavonoid biosynthesis in Paulownia flowers, paving the way for targeted genetic modification and biotechnological interventions aimed at enhancing flavonoid accumulation for industrial applications.

Стохастическая модель процесса распространения препаратов платины в опухолевых тканях

Purpose: The study of the kinetics of the spatio-temporal distribution of cisplatin, as an agent capable of increasing the effectiveness of chemo-radiation therapy of solid tumors. Material and methods: The material was the data of literature on the results of experiments of various groups of authors who studied the content of platinum in tissues on biopsy materials. The method of simulation mathematical modeling (in silico) is used, based on the biological patterns of tumor growth, morphofunctional changes in the structure of tumor cells and tissues. Model is based on reaction-diffusion equation system, coefficients of which are random functions of space and time. The model takes into account the sequence of processes from intravenous administration of cisplatin and its spatial distribution in various tissues, until the excretion of its addicts from the cell. Results: The heterogeneity of the distribution of the concentration of platinum atoms in the tumor is shown. Perhaps this is due to the varying degree of vascularization of tissues, the rate of metabolism of the tumor occurring in the outer layer and in its hypoxic core. These processes lead to significant errors when evaluating the biopsy data obtained in the experiments and the analysis of platinum content in the biopsy. Conclusions: The calculation assessments of the distribution of platinum atoms in the tumor are consistent with the literature on the concentration of cisplatin in biopsy samples for carcinoma. The results of the calculations on the described model are fair, taking into account the morphological type, the size of the tumor and the plan of the administration of the drug. For other conditions (cancer types, treatment regimens, etc.), new calculations are necessary. To increase the efficiency of combined chemo-radiation therapy, cisplatin should be introduced at least 72 hours before the start of radiation therapy.

M2ara: unraveling metabolomic drug responses in whole-cell MALDI mass spectrometry bioassays.

Fast computational evaluation and classification of concentration responses for hundreds of metabolites represented by their mass-to-charge (m/z) ratios is indispensable for unraveling complex metabolomic drug actions in label-free, whole-cell Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) bioassays. In particular, the identification of novel pharmacodynamic biomarkers to determine target engagement, potency, and potential polypharmacology of drug-like compounds in high-throughput applications requires robust data interpretation pipelines. Given the large number of mass features in cell-based MALDI MS bioassays, reliable identification of true biological response patterns and their differentiation from any measurement artefacts that may be present is critical. To facilitate the exploration of metabolomic responses in complex MALDI MS datasets, we present a novel software tool, M2ara. Implemented as a user-friendly R-based shiny application, it enables rapid evaluation of Molecular High Content Screening (MHCS) assay data. Furthermore, we introduce the concept of Curve Response Score (CRS) and CRS fingerprints to enable rapid visual inspection and ranking of mass features. In addition, these CRS fingerprints allow direct comparison of cellular effects among different compounds. Beyond cellular assays, our computational framework can also be applied to MALDI MS-based (cell-free) biochemical assays in general. The software tool, code, and examples are available at https://github.com/CeMOS-Mannheim/M2ara and https://dx.doi.org/10.6084/m9.figshare.25736541.

QGO: a novel method for quantifying the diversity of mitochondrial genome organization

Quantifying the features of mitochondrial genome structural variation is crucial for understanding its contribution to complexity. Accurate quantification and interpretation of organizational diversity can help uncover biological evolutionary laws and patterns. The current qMGR approach accumulates the changes in two adjacent genes to calculate the rearrangement frequency RF of each single gene and the rearrangement score RS for specific taxa in the mitogenomes of a given taxonomic group. However, it may introduce bias, as it assigns scores to adjacent genes rather than to rearranged genes. To overcome this limitation, we propose a novel statistical method called qGO to quantify the diversity of gene organization. The qGO method, which is based on the homology of gene order, provides a more accurate representation of genome organizational diversity by partitioning gene strings and individually assigning weights to genes spanning different regions. Additionally, a comprehensive approach is employed for distance computation, generating an extensive matrix of rearrangement distances. Through experiments on more than 5500 vertebrate mitochondrial genomes, we demonstrated that the qGO method outperforms existing methods in terms of accuracy and interpretability. This method improves the comparability of genomes and allows a more accurate comparison of the diversity of mitochondrial genome organization across taxa. These findings have significant implications for unraveling genome evolution, exploring genome function, and investigating the process of molecular evolution.