Biology subjects

Ying, K.

Publications and source records attributed to Ying, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Organelle Aging Predicts Human Disease

Aging affects cellular organelles differently, but whether subcellular aging predicts human disease remains unknown. Here, we integrated organelle-enriched proteins in plasma and cerebrospinal fluid proteomes from 61,931 individuals to model aging of 10 organelles across the human lifespan. Organelle aging was heterogeneous with lysosomes, plasma membranes, and Golgi compartments accelerating rapidly after age 60. Individuals with four or more prematurely aged compartments exhibited up to 22-fold higher kidney disease risk and up to 8-fold higher mortality risk. Major diseases are characterized by distinct organelle aging signatures and disease risk increased with the number of accelerated organelles. APOE-{varepsilon}4 dosage was associated with lysosomal and mitochondrial aging and reduced PDP1 levels, implicating altered energy metabolism. Decomposition of organelle signature coefficients into biological programs further showed that diseases are associated with distinct subcellular patterns. These findings establish organelle-resolved aging as a measurable dimension of human biology, revealing subcellular mechanisms underlying disease susceptibility and resilience.

systems biology↗

bioRxiv2026

Systemic control and inter-organ communication of ovarian aging

Whether ovarian aging is intrinsic and unidirectional or dynamically regulated by systemic context remains an open question for fertility and women's healthspan. To address it, we developed a mouse ovarian transcriptomic aging clock (OvaAge) and applied it, together with published transcriptomic, epigenetic and pathway-resolved DNA methylation (PathAge) clocks, multi-omic analyses and reproductive outcomes, to new reciprocal heterochronic ovary transplantation and parabiosis experiments. Ovarian molecular age shifted in both directions: aged ovaries became younger in young hosts and young ovaries became older in aged hosts, and in each case the shift was partial across transcriptomic, epigenetic and pathway-resolved measures. Ovaries from 13-month-old C57BL/6J donors supported full-term pregnancies and live births at 17-18 months of age when transplanted into young hosts, but not when transplanted into old hosts, and young ovaries supported live births in 17-18-month-old recipients. Donor ovary age altered the transcriptomic age of the uterus in both directions, while young ovaries also lowered the transcriptomic age of aged liver and brain, identifying the ovary as an active node within an inter-organ aging network. Across unilateral and bilateral transplantation and parabiosis, an older environment increased inflammatory gene expression in the ovary, whereas a younger environment reduced it and increased mitochondrial and biosynthetic programs. Integration of methylation, expression and cell-type markers linked these changes to declining oocyte-associated programs and an expanding stromal compartment enriched for multinucleated giant cells (MNGCs). MNGC-rich regions formed a reversibly remodeled, focally aged niche with distinct spatial metabolic features, and a related macrophage expression signature was detected in human ovaries. Together, these findings identify ovarian aging as a modular, bidirectionally plastic process that is regulated by the systemic environment and point to tractable intervention points.

systems biology↗

bioRxiv2026

Mammalian aging involves genome-wide splicing degeneration leading to functional decline

Alternative splicing exhibits significant changes during development and aging, affecting the composition and variance in the transcriptome. However, it is unclear whether and how age-associated splicing dysregulation leads to functional consequences. Here, an integrative analysis of transcriptome data across mouse and human tissues revealed that aging is characterized by systematic deterioration of the fidelity of RNA splicing, here termed splicing degeneration, a measure of functional alteration of reading frame and domain configuration of protein products. Genes with higher aging-associated splicing degeneration were more conserved and enriched for processes such as RNA metabolism and antigen presentation. By assessing alternative splicing events associated with functional deterioration, we quantified the degree of splicing degeneration. Its level increased with age but was alleviated following calorie restriction or rapamycin treatment, indicating that it can serve as a new molecular hallmark of aging. Mechanistically, through a comprehensive meta-data analysis, we discovered that splicing degeneration is associated with age-associated changes in specific splicing factors, which in turn showed a strong association with age-related transcriptome changes. Overall, our study demonstrates the intricate relationship between aging and genome-wide splicing degeneration, revealing a promising target for aging interventions acting to reverse splicing degeneration.

Systems Biology↗

bioRxiv2026

Distinct contribution of autoreactive B cell Bruton's tyrosine kinase signaling to neuroinflammation

In multiple sclerosis (MS), autoreactive B cells play a central role in driving CD4 T cell-mediated inflammatory damage to myelin (1). Here we investigated how disrupting Brutons tyrosine kinase (BTK) signaling exclusively in B cells shapes the course of experimental autoimmune encephalomyelitis (EAE), a model for MS, through alterations in B cell development and activity. B cell-specific BTK deletion significantly ameliorated both human MOG (hMOG) induced EAE (p = 0.0087) as well as spontaneous disease in 2D2+IgHMOG mice (p = 0.0004). Additionally, MOG-specific cells were found to be more sensitive to loss of BTK than tolerant clones (p = 0.0002) and production of anti-MOG immunoglobulins was also found to be diminished (p < 0.004) while overall IgG was unchanged (p = 0.44). B cells isolated from conditional knockout mice did not upregulate expression of co-stimulatory receptors or MHC II to the same extent as controls when cultured alongside MOG-specific CD4 T cells (p < 0.005) and were inferior at driving T cell proliferation (p < 0.0001) in vitro. Lastly, while BTK deletion diminished the proliferative and survival response of B cells following mitogen stimulation, B cell trafficking to the leptomeninges and organization into ectopic lymphoid tissues (ELTs) in 2D2+IgHMOG mice continued unabated. We identified that BTK signaling regulates several features adopted by autoreactive B cells that contribute to EAE pathogenesis. This study provides mechanistic insights into the therapeutic benefits of BTK inhibitors observed in clinical trials exploring BTK as a therapeutic target in the context of MS. Significance statementAutoreactive B cells contribute to the neuroinflammation that drives multiple sclerosis (MS) and related diseases, yet the molecular mechanisms enabling their pathogenicity remain incompletely understood. This study demonstrates that B cell-specific deletion of Brutons tyrosine kinase (BTK) markedly reduces disease severity in two complementary versions of experimental autoimmune encephalomyelitis (EAE), a widely used animal model for MS. Loss of BTK impairs autoreactive B cell survival, antibody production, antigen presentation to encephalitogenic T cells, and T cell activation, while leaving meningeal ectopic lymphoid tissue formation intact. These findings provide direct mechanistic evidence that BTK signaling in B cells promotes neuroinflammatory damage and supports the therapeutic targeting of BTK to limit B cell-driven pathology in MS.

immunology↗

bioRxiv2025

Rejuvenation of white adipose tissue in a longitudinal heterochronic transplantation model

Exposure to a younger system can induce organismal rejuvenation, yet whether all tissues can be rejuvenated and by what mechanisms remains understudied. We performed heterochronic and isochronic transplantation of subcutaneous white adipose tissue (WAT) between young and old mice and longitudinally tracked changes in biological age. Transplantation accelerated tissue aging, and the molecular age of grafts shifted toward that of the host. Most importantly, old WAT was rejuvenated in a young body. Epigenetic and transcriptomic clocks revealed a reduction of predicted age, accompanied by coordinated activation of canonical and previously unrecognized thermogenic pathways. Molecular rejuvenation was further supported by architectural changes toward a youthful state, including reduced lipid droplet size and decreased cellular heterogeneity. Mitochondrial abundance and morphology remained unchanged, while collagen deposition increased. These results demonstrate that WAT biological age is partially reversible and identify molecular and cellular features underlying its rejuvenation

systems biology↗

bioRxiv2025

Robust brain complexity measurement using T2* informed multivariate sample entropy from multi-echo BOLD fMRI

Sample entropy is a useful tool to analyze resting-state fMRI data and evaluate the complexity of neural activity. However, sample entropy computed from conventional BOLD signals is not highly resistant to artifacts. In contrast, T2* may be more sensitive to certain neural-related changes, and directly incorporating T2* information enables the detection of features that are often lost in BOLD-only sample entropy due to noise influences. To further improve the robustness and reliability of complexity estimation, we introduce a multivariate sample entropy approach that incorporates T2* signals derived from multi-echo BOLD signal. We applied this method to compare a cohort of 23 elder adults and 12 younger adults, and observed significant group differences in several brain regions. Notably, the proposed method yielded more pronounced group differences than conventional sample entropy computed without T2* information. Moreover, in regions with reduced neural activity due to ischemia, as well as in ventricular areas, our approach demonstrated superior performance. These results indicate that the proposed method enhances the ability to characterize the complexity of neural activity.

biophysics↗

bioRxiv2024

Illumina Complete Long Read Assay yields contiguous bacterial genomes from human gut metagenomes

Metagenomics enables direct investigation of the gene content and potential functions of gut bacteria without isolation and culture. However, metagenome-assembled genomes are often incomplete and have low contiguity due to challenges in assembling repeated genomic elements. Long-read sequencing approaches have successfully yielded circular bacterial genomes directly from metagenomes, but these approaches are low throughput, have high DNA input requirements, and can have high error rates. Illumina has recently launched the Illumina Complete Long Read (ICLR) assay, a new approach for generating kilobase-scale reads with low DNA input requirements and high accuracy. Here, we evaluate the performance of ICLR sequencing for gut metagenomics. We sequenced a microbial mock community and ten human gut microbiome samples with standard, shotgun 2x150 paired-end sequencing, ICLR sequencing, and nanopore long-read sequencing and compared performance in read lengths, assembly contiguity, and bin quality. We find that ICLR human metagenomic assemblies have higher N50 (119.5 {+/-} 24.8 kilobases) than short read assemblies (9.9 {+/-} 4.5 kilobases; p = 0.002), and comparable N50 to nanopore assemblies (91.0 {+/-} 43.8 kilobases; p = 0.32). Additionally, we find that ICLR draft microbial genomes are more complete (94.0% {+/-} 20.6%) than nanopore draft genomes (85.9% {+/-} 23.0%; p[≤] 0.001), and that nanopore draft genomes have truncated gene lengths (924.6 {+/-} 114.7 base pairs) relative to ICLR genomes (954.6 {+/-} 71.5 base pairs; p[≤] 0.001). Overall, we find that ICLR sequencing is a promising method for high-throughput and accurate assembly of microbial genomes from gut metagenomes. ImportanceMetagenomic sequencing allows scientists to directly measure the genome content and structure of microbes residing in complex microbial communities. Traditional short-read metagenomic sequencing methods often yield fragmented genomes, whereas advanced long-read sequencing methods improve genome assembly quality but often suffer from high error rates and are logistically limited due to low throughput and high input requirements. A new method, the Illumina Complete Long Read Assay (ICLR), is capable of generating highly-accurate kilobase-scale sequencing reads with minimal input material. To evaluate the utility of ICLR in metagenomic contexts, we applied short-read, long-read, and ICLR methods to simple and complex microbial communities. We found that ICLR outperforms short-read methods, and yields comparable metagenomic assemblies to standard long-read approaches while requiring less input material. Overall, ICLR represents a high-throughput and efficient solution for assembling complete genomes from complex metagenomes.

genomics↗

bioRxiv2024

An Open Competition for Biomarkers of Aging

Open scientific competitions have successfully driven biomedical advances but remain underutilized in aging research, where biological complexity and heterogeneity require methodological innovations. Here, we present the results from Phase I of the Biomarkers of Aging Challenge, an open competition designed to drive innovation in aging biomarker development and validation. The challenge leverages a unique DNA methylation dataset and aging outcomes from 500 individuals, aged 18 to 99. Participants are asked to develop novel models to predict chronological age, mortality, and multi-morbidity. Results from the chronological age prediction phase show important advances in biomarker accuracy and innovation compared to existing models. The winning models feature improved predictive power and employ advanced machine learning techniques, innovative data preprocessing, and the integration of biological knowledge. These approaches have led to the identification of novel age-associated methylation sites and patterns. This challenge establishes a paradigm for collaborative aging biomarker development, potentially accelerating the discovery of clinically relevant predictors of aging-related outcomes. This supports personalized medicine, clinical trial design, and the broader field of geroscience, paving the way for more targeted and effective longevity interventions.

bioinformatics↗

bioRxiv2024

MethylGPT: a foundation model for the DNA methylome

DNA methylation serves as a powerful biomarker for disease diagnosis and biological age assessment. However, current analytical approaches often rely on linear models that cannot capture the complex, context-dependent nature of methylation regulation. Here we present MethylGPT, a transformer-based foundation model trained on 226,555 (154,063 after QC and deduplication) human methylation profiles spanning diverse tissue types from 5,281 datasets, curated 49,156 CpG sites, and 7.6 billion training tokens. MethylGPT learns biologically meaningful representations of CpG sites, capturing both local genomic context and higher-order chromosomal features without external supervision. The model demonstrates robust methylation value prediction (Pearson R=0.929) and maintains stable performance in downstream tasks with up to 70% missing data. Applied to age prediction across multiple tissue types, MethylGPT achieves superior accuracy compared to existing methods. Analysis of the models attention patterns reveals distinct methylation signatures between young and old samples, with differential enrichment of developmental and aging-associated pathways. When finetuned to mortality and disease prediction across 60 major conditions using 18,859 samples from Generation Scotland, MethylGPT achieves robust predictive performance and enables systematic evaluation of intervention effects on disease risks, demonstrating potential for clinical applications. Our results demonstrate that transformer architectures can effectively model DNA methylation patterns while preserving biological interpretability, suggesting broad utility for epigenetic analysis and clinical applications.

systems biology↗

bioRxiv2024

High-dimensional Ageome Representations of Biological Aging across Functional Modules

The aging process involves numerous molecular changes that lead to functional decline and increased disease and mortality risk. While epigenetic aging clocks have shown accuracy in predicting biological age, they typically provide single estimates for the samples and lack mechanistic insights. In this study, we challenge the paradigm that aging can be sufficiently described with a single biological age estimate. We describe Ageome, a computational framework for measuring the epigenetic age of thousands of molecular pathways simultaneously in mice and humans. Ageome is based on the premise that an organisms overall biological age can be approximated by the collective ages of its functional modules, which may age at different rates and have different biological ages. We show that, unlike conventional clocks, Ageome provides a high-dimensional representation of biological aging across cellular functions, enabling comprehensive assessment of aging dynamics within an individual, in a population, and across species. Application of Ageome to longevity intervention models revealed distinct patterns of pathway-specific age deceleration. Notably, cell reprogramming, while rejuvenating cells, also accelerated aging of some functional modules. When applied to human cohorts, Ageome demonstrated heterogeneity in predictive power for mortality risk, and some modules showed better performance in predicting the onset of age-related diseases, especially cancer, compared to existing clocks. Together, the Ageome framework offers a comprehensive and interpretable approach for assessing aging, providing insights into mechanisms and targets for intervention.

systems biology↗

bioRxiv2024

Imputed DNA methylation outperforms measured loci associations with smoking and chronological age

Multi-locus signatures of blood-based DNA methylation are well-established biomarkers for lifestyle and health outcomes. Here, we focus on two CpGs that are strongly associated with age and smoking behaviour. Imputing these loci via epigenome-wide CpGs results in stronger associations with outcomes in external datasets compared to directly measured CpGs. If extended epigenome-wide, CpG imputation could augment historic arrays and recently-released, inexpensive but lower-content arrays, thereby yielding better-powered association studies.

genetics↗

bioRxiv2024

Precious3GPT: Multimodal Multi-Species Multi-Omics Multi-Tissue Transformer for Aging Research and Drug Discovery

We present a multimodal multi-species multi-omics multi-tissue transformer for aging research and drug discovery capable of performing multiple tasks such as age prediction across species, target discovery, tissue, sex, and disease sample classification, drug sensitivity prediction, replication of omics response and prediction of biological and phenotypic response to compound treatment. This model combines textual, tabular, and knowledge graph-derived representations of biological experiments to provide insights into molecular-level biological processes. We demonstrate that P3GPT has developed an intuition for the interactions between compounds, pathologies, and gene regulation in the context of multiple species and tissues. In these areas, it outperforms existing LLMs and we highlight its utility in diverse case studies. P3GPT is a general model that may be used as a target identification tool, aging clock, digital laboratory, and scientific assistant. The model is intended as a community resource available open source as well as via a Discord server.

bioinformatics↗

bioRxiv2024

Transcriptomic Hallmarks of Mortality Reveal Universal and Specific Mechanisms of Aging, Chronic Disease, and Rejuvenation

Health is strongly affected by aging and lifespan-modulating interventions, but the molecular mechanisms of mortality regulation remain unclear. Here, we conducted an RNA-seq analysis of mice subjected to 20 compound treatments in the Interventions Testing Program (ITP). By integrating it with the data from over 4,000 rodent tissues representing aging and responses to genetic, pharmacological, and dietary interventions with established survival data, we developed robust multi-tissue transcriptomic biomarkers of mortality, capable of quantifying aging and change in lifespan in both short-lived and long-lived models. These tools were further extended to single-cell and human data, demonstrating common mechanisms of molecular aging across cell types and species. Via a network analysis, we identified and annotated 26 co-regulated modules of aging and longevity across tissues, and developed interpretable module-specific clocks that capture aging- and mortality-associated phenotypes of functional components, including, among others, inflammatory response, mitochondrial function, lipid metabolism, and extracellular matrix organization. These tools captured and characterized acceleration of biological age induced by progeria models and chronic diseases in rodents and humans. They also revealed rejuvenation induced by heterochronic parabiosis, early embryogenesis, and cellular reprogramming, highlighting universal signatures of mortality, shared across models of rejuvenation and age-related disease. They included Cdkn1a and Lgals3, whose human plasma levels further demonstrated a strong association with all-cause mortality, disease incidence and risk factors, such as obesity and hypertension. Overall, this study uncovers molecular hallmarks of mammalian mortality shared across organs, cell types, species and models of disease and rejuvenation, exposing fundamental mechanisms of aging and longevity.

systems biology↗

bioRxiv2024

Integrative epigenetics and transcriptomics identify aging genes in human blood

Recent epigenome-wide studies have identified a large number of genomic regions that consistently exhibit changes in their methylation status with aging across diverse populations, but the functional consequences of these changes are largely unknown. On the other hand, transcriptomic changes are more easily interpreted than epigenetic alterations, but previously identified age-related gene expression changes have shown limited replicability across populations. Here, we develop an approach that leverages high-resolution multi-omic data for an integrative analysis of epigenetic and transcriptomic age-related changes and identify genomic regions associated with both epigenetic and transcriptomic age-dependent changes in blood. Our results show that these "multi-omic aging genes" in blood are enriched for adaptive immune functions, replicate more robustly across diverse populations and are more strongly associated with aging-related outcomes compared to the genes identified using epigenetic or transcriptomic data alone. These multi-omic aging genes may serve as targets for epigenetic editing to facilitate cellular rejuvenation.

systems biology↗

bioRxiv2024

The 18S rRNA Methyltransferase DIMT-1 Regulates Lifespan in the Germline Later in Life

Ribosome heterogeneity has emerged as an important regulatory control feature for determining which proteins are synthesized, however, the influence of age on ribosome heterogeneity is not fully understood. Whether mRNA transcripts are selectively translated in young versus old cells and whether dysregulation of this process drives organismal aging is unknown. Here we examined the role of ribosomal RNA (rRNA) methylation in maintaining appropriate translation as organisms age. In a directed RNAi screen, we identified the 18S rRNA N6-dimethyl adenosine (m6,2A) methyltransferase, dimt-1, as a regulator of C. elegans lifespan and stress resistance. Lifespan extension induced by dimt-1 deficiency required a functional germline and was dependent on the known regulator of protein translation, the Rag GTPase, raga- 1, which links amino acid sensing to the mechanistic target of rapamycin complex (mTORC)1. Using an auxin-inducible degron tagged version of dimt-1, we demonstrate that DIMT-1 functions in the germline after mid-life to regulate lifespan. We further found that knock-down of dimt-1 leads to selective translation of transcripts important for stress resistance and lifespan regulation in the C. elegans germline in mid-life including the cytochrome P450 daf-9, which synthesizes a steroid that signals from the germline to the soma to regulate lifespan. We found that dimt-1 induced lifespan extension was dependent on the daf-9 signaling pathway. This finding reveals a new layer of proteome dysfunction, beyond protein synthesis and degradation, as an important regulator of aging. Our findings highlight a new role for ribosome heterogeneity, and specific rRNA modifications, in maintaining appropriate translation later in life to promote healthy aging.

genetics↗

bioRxiv2023

Biolearn, an open-source library for biomarkers of aging

Aging biomarkers are essential for understanding and quantifying the aging process and developing targeted longevity interventions. However, validation of these tools has been hindered by the lack of standardized approaches for cross-population validation, disparate biomarker designs, and inconsistencies in dataset structures. To address these challenges, we developed Biolearn, an open-source library that provides a unified framework for the curation, harmonization, and systematic evaluation of aging biomarkers. Leveraging Biolearn, we conducted a comprehensive evaluation of various aging biomarkers across multiple datasets. Our systematic approach involved three key steps: (1) harmonizing existing and novel aging biomarkers in standardized formats; (2) unifying public datasets to ensure coherent structuring and formatting; and (3) applying computational methodologies to assess the harmonized biomarkers against the unified datasets. This evaluation yielded valuable insights into the performance, robustness, and generalizability of aging biomarkers across different populations and datasets. The Biolearn python library, which forms the foundation of this systematic evaluation, is freely available at https://Bio-Learn.github.io. Our work establishes a unified framework for the curation and evaluation of aging biomarkers, paving the way for more efficient and effective clinical validation and application in the field of longevity research.

bioinformatics↗

bioRxiv2023

ClockBase: a comprehensive platform for biological age profiling in human and mouse

Decades of publicly available molecular studies have generated millions of samples testing diverse interventions, yet these datasets were rarely analyzed for their effects on aging. Aging clocks now enable biological age estimation and life outcome prediction from molecular data, creating an opportunity to systematically mine this untapped resource. We developed ClockBase Agent, a publicly accessible platform that reanalyzes millions of human and mouse methylation and RNA-seq samples by integrating them with over 40 aging clock predictions. ClockBase Agent employs specialized AI agents that autonomously generate aging-focused hypotheses, evaluate intervention effects on biological age, conduct literature reviews, and produce scientific reports across all datasets. Reanalyzing 43,602 intervention-control comparisons through multiple aging biomarkers revealed thousands of age-modifying effects missed by original investigators, including over 500 interventions that significantly reduce biological age (e.g., ouabain, KMO inhibitor, fenofibrate, and NF1 knockout). Large-scale systematic analysis reveals fundamental patterns: significantly more interventions accelerate rather than decelerate aging, disease states predominantly accelerate biological age, and loss-of-function genetic approaches systematically outperform gain-of-function strategies in decelerating aging. As validation, we show that identified interventions converge on canonical longevity pathways and with strong concordance to independent lifespan databases. We further experimentally validated ouabain, a top-scoring AI-identified candidate, demonstrating reduced frailty progression, decreased neuroinflammation, and improved cardiac function in aged mice. ClockBase Agent establishes a paradigm where specialized AI agents systematically reanalyze all prior research to identify age-modifying interventions autonomously, transforming how we extract biological insights from existing data to advance human healthspan and longevity.

bioinformatics↗

bioRxiv2022

Taming the data deluge: a novel end-to-end deep learning framework for classifying marine biological and environmental images

Marine underwater imaging facilitates non-destructive sampling of species at frequencies, durations, and accuracies that are unattainable by conventional sampling methods. These systems necessitate complex automated processes to identify organisms efficiently, however, current frameworks struggle to disentangle ecological foreground components from their dispensable background content. Underwater image processing relies on common architecture: namely image binarization for segmenting potential targets, prior to information extraction and classification by deep learning models. While intuitive, this infrastructure underperforms as it has difficulty in handling: high concentrations of biotic and abiotic particles, rapid changes in dominant taxa, and target sizes that vary by several orders of magnitude. To overcome these issues, a new framework is presented that begins with a scene classifier to capture large within-image variation, such as disparities in particle concentration and dominant taxa. Following scene classification, scene-specific regional convolutional neural network (Mask R-CNN) models were trained to separate target objects into different taxonomic groups. The procedure allows information to be extracted from different image types, while minimizing potential bias for commonly occurring features. Using in situ coastal PlanktonScope images, we compared the scene-specific models to the Mask R-CNN model including all scene categories without scene classification, defined as the full model, and found that the scene-specific approach outperformed the full model with >20% accuracy in noisy images. The full model missed up to 78% of the dominant taxonomic groups, such as Lyngbya, Noctiluca, and Phaeocystis colonies. This performance improvement is due to the scene classifier, which reduces the variation among images and allows an improved match between the observed taxonomic groups and the taxonomic groups in pre-trained models. We further tested the framework on images from a benthic video camera and an imaging sonar system. Results demonstrate that the procedure is applicable to different types of underwater images and achieves significantly more accurate results than the full model. Given that the unified framework is neither instrument nor ecosystem-specific, the proposed model facilitates deployment throughout the marine biome.

ecology↗