Search PubMedSearch

SEARCH · Search PubMed

Results for “ecosystem connectivity”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

28 records · Page 2Linked to original sources

Depth-dependent multi-kingdom microbial interactions and biogeochemical cycling genes in eutrophic shallow lake sediments.

Microorganisms are pivotal to lake ecosystem biogeochemical cycles, yet existing research often focuses on single microbial kingdoms or surface sediments, neglecting multi-kingdom interactions and depth-resolved dynamics. To address these gaps, we used metagenomic sequencing to characterize microbial communities and their functional associations across overlying water and 0-45 cm sediments in four shallow lakes of the middle Yangtze River basin, China. Despite increasing bacterial and fungal diversity with depth, the 0-9 cm surface sediments exhibited the strongest multi-kingdom network connectivity and the greatest microbial stability. Functional genes exhibited clear depth-dependent patterns: nitrogen cycling genes, including those involved in dissimilatory nitrate reduction to ammonium, were most enriched in the upper 0-9 cm of sediment; methane cycling genes were positively correlated with depth; phosphorus cycling genes and some sulfur cycling genes, such as assimilatory sulphate reduction, declined with depth. Sediment microbial assembly was dominated by deterministic processes, in which the vertical distribution of functional genes was primarily dictated by heavy metals and conventional environmental indicators. These findings highlight depth-specific multi-kingdom microbial interactions and their associations with biogeochemical cycling, advancing lacustrine microbial ecology understanding and providing references for lake conservation under environmental change.

Lakes

Integrative multi-omics profiling of insomnia-related molecular features reveals microbiome, immune, and therapy-relevant heterogeneity in colorectal cancer.

Emerging evidence implicates insomnia as a potential risk factor in carcinogenesis, potentially involving systemic inflammation, circadian disruption, and microbiome alterations. However, the molecular associations linking insomnia-related features to colorectal cancer (CRC), particularly with respect to tumor biology, immune microenvironmental states, and therapy-relevant phenotypes, remain largely unexplored. Multi-omics integration of genomic, transcriptomic, and microbiome data from 3,026 CRC patients across seven independent cohorts, including a large, well-annotated Clinical Omics study of Colorectal Cancer in China (COCC) cohort, enabled insomnia-based molecular classification through unsupervised non-negative matrix factorization (NMF) clustering. The insomnia subtype (IS) was biologically characterized via pathway enrichment, immune deconvolution, microbial profiling, and single-cell transcriptomics. Furthermore, an insomnia score (ISscore) was developed and validated in multiple cohorts for risk stratification and assessment of treatment-response-related indicators in CRC. Unsupervised clustering revealed two distinct molecular subtypes (IS1/IS2), with IS2 demonstrating significantly poorer survival. IS2 exhibited marked activation of EMT/angiogenesis pathways versus cell cycle activation in IS1. The IS2 microenvironment showed increased immunosuppression-related infiltration and exhausted T cell signatures, together with intratumoral microbiome variation characterized by depletion of Ruminococcaceae UCG-002 and enrichment of Hungatella/Selenomonas. The ISscore system stratified survival risk and was associated with computational indicators of immunotherapy response. Single-cell analysis nominated PPIA-BSG as a potential cell-cell communication signal involving high-ISscore tumor cells, CXCL12+ endothelial cells, and CLEC9A+ dendritic cell subsets. This multi-omics characterization of insomnia-CRC interplay suggests that insomnia-related molecular features are associated with an immunologically distinct and microbiome-altered tumor ecosystem. The ISscore provides a reproducible framework for capturing insomnia-related molecular heterogeneity, supporting risk stratification and future evaluation of therapy-relevant phenotypes.IMPORTANCEChronic insomnia affects millions, but it is not typically considered a cancer risk factor. Our study, analyzing vast biological data from over 3,000 colorectal cancer patients, uncovers a potential link between a person's predisposition to insomnia and their risk of developing this disease. This suggests that the biological pathways related to sleep may play a role in cancer development. Understanding this connection opens up new avenues for identifying individuals at higher risk and developing novel prevention strategies for colorectal cancer.

colorectal cancer

Beyond Morphology: Reframing Lymph-Node Metastasis Prediction Through Clonal Ecology-Decades-Long Genomic Instability and Polyclonal-to-Monoclonal Transitions as the Missing Dimension in Cancer.

Recent whole-genome, lineage-tracing, single-cell, and spatial studies have reshaped our understanding of tumor evolution, revealing that cancers can arise from polyclonal populations, undergo decades-long genomic instability before clinical detection, and progress through dynamic changes in subclonal composition, cellular state, and ecological organization. These findings challenge the assumption underlying morphology-based prediction models that metastatic risk can be inferred from static histological features alone. Here, we revisit lymph-node metastasis prediction in colorectal cancer through clonal ecology, integrating computational pathology with evolutionary oncology. Drawing on the subclonal switchboard model proposed in 2012 and subsequent artificial intelligence (AI)-enabled approaches for tracking dominant and dormant subclones, we synthesize evidence that metastatic potential reflects clonal ancestry, evolutionary timing, spatial niche architecture, cellular plasticity, intercellular interactions, dormancy, and treatment-driven shifts in subclonal fitness. We define five complementary methodological pillars for operationalizing clonal ecology: single-cell transcriptomics for resolving rare subclones, evolutionary trajectories, and adaptive cell states; lineage tracing and phylogenetics for reconstructing clonal ancestry and divergence; spatial transcriptomics and genomics for mapping subclonal geography and tumor-stromal-immune interactions; longitudinal liquid biopsy surveillance for monitoring residual disease, clonal turnover, and emerging resistance; and AI-enabled multimodal integration for connecting histopathology, genomics, spatial biology, and longitudinal data into predictive ecological-state models. Multiple-instance learning and pathology foundation models provide scalable computational foundations for evolution-aware prediction. Translationally, dormant subclones represent actionable reservoirs of recurrence. A longitudinal clinical and experimental study of KMT2A-rearranged acute myeloid leukemia further supports central predictions of the subclonal switchboard framework by demonstrating treatment-associated shifts in subclonal dominance, persistence of cryptic adaptive programs, and ecological rewiring during resistance and relapse. We propose clonal ecology as a measurable dimension for extending morphology-driven prediction toward integrative models that anticipate evolutionary transitions, identify therapeutic windows, and proactively constrain adaptive tumor ecosystems before resistant or metastatic subclones achieve clinical dominance.

Humans

Barcoded mutant library enables high-throughput functional genomics in a filamentous fungus.

Advances in sequencing technology enabling rapid and inexpensive whole-genome sequencing highlight how few genes are functionally characterized. This problem is particularly acute in filamentous fungi, where even in the best studied organisms upward of half of genes are poorly characterized or unannotated. High-throughput tools to identify gene function exist for single-celled organisms, like yeast and bacteria. However, filamentous fungi present challenges to high-throughput gene characterization, including low transformation efficiency and multinucleate cells. Filamentous fungi are critical components of nutrient cycling in ecosystems, form symbioses with plants that improve nutrient uptake, and are devastating human, plant, and animal pathogens causing millions of deaths and substantial crop loss each year. Thus, it is critical to overcome challenges to rapid gene characterization in filamentous fungi. We generated a library of hundreds of millions of uniquely barcoded plasmids containing a broad host-range drug resistance marker for ectopic insertion into filamentous fungal genomes by Agrobacterium tumefaciens. We then optimized A. tumefaciens mediated transformation of the biocontrol agent Trichoderma atroviride and made an insertional mutagenesis library containing 83,311 barcoded insertions, disrupting 5,331 of 11,863 predicted genes. This library enables high-throughput screens to rapidly connect genotype to phenotype. Quantifying relative barcode abundance in the pooled library before and after exposure to experimental conditions identified candidate genes and recovered known pathway components in amino acid biosynthetic, fructose utilization, and xylose utilization pathways. This resource establishes a scalable platform for high-throughput functional genomics in filamentous fungi, enabling investigations of fungal biology to improve medical outcomes, biotechnology, and sustainable agriculture.

Genomics

Reimagining research papers as interactive and reliable AI agents.

Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents. Paper2Agent transforms research output from passive artefacts into active systems that accelerate use and discovery. Conventional research papers require readers to understand and adapt the paper's code, data and methods to their work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by converting a paper into an AI agent that functions as a virtual corresponding author, exposing its manuscript, supplementary materials, datasets, code and workflows as active, agent-native knowledge rather than static text. It analyses the paper and codebase using multiple agents to construct a model context protocol (MCP) server, then generates and runs tests to refine and increase robustness of the MCP. These paper MCPs can be connected to a chat agent (such as Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the paper. We demonstrate Paper2Agent's effectiveness through case studies. Paper2Agent created an agent that leveraged AlphaGenome1 to interpret genomic variants and agents based on Scanpy2 and TISSUE (transcript imputation with spatial single-cell uncertainty estimation)3 to conduct single-cell and spatial transcriptomics analyses. We validate that these agents reproduce the results of the original papers and carry out novel user queries. Paper2Agent created multiple agents that collaborate to prioritize a causal gene for psoriasis. By turning static papers into interactive AI agents, Paper2Agent introduces a paradigm for knowledge dissemination and a collaborative ecosystem of AI co-scientists.

Journal Article

Time-lagged genomic erosion and future environmental risks in a bird on the brink of extinction.

Global biodiversity is rapidly declining due to habitat degradation and genomic erosion, highlighting the urgent need to monitor endangered species and their genetic health. Temporal genomics and ecological modelling offer finer resolution than single-time-point measurements, providing a comprehensive view of species' recent and future trajectories. We investigated genomic erosion and environmental suitability in the critically endangered regent honeyeater (Anthochaera phrygia) by sequencing whole genomes of historical and modern specimens and building multi-temporal species distribution models (SDMs) across the last century. The species has declined from hundreds of thousands of individuals to fewer than 300 over the past 100 years. SDMs correctly predicted known patterns of local extinction in southeast Australia. Our demographic reconstructions revealed a gradual population decline from 2000 to 2500 years ago, sharply accelerating in the last 500 years due to climate variability and habitat loss. Despite this substantial demographic collapse, the regent honeyeater has lost only 9% of its genetic diversity, with no evidence of inbreeding or connectivity loss. Also, it exhibits higher diversity than many other threatened bird species. Forward-in-time genomic simulations indicate that this time lag between population decline and genetic diversity loss conceals the risk of ongoing genomic erosion into a future of rapidly degrading environmental suitability. Our work underscores the need for targeted conservation efforts and continuous genetic monitoring to prevent species extinction.

Animals

Cyclin-dependent kinase 4 and 6 inhibitors and the breast cancer immune ecosystem: immune remodeling, resistance, and therapeutic reprogramming.

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6 inhibitors) combined with endocrine therapy have become a therapeutic backbone for hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer, yet durable disease control is frequently limited by intrinsic and acquired resistance. Canonical tumor-cell mechanisms, including retinoblastoma-pathway escape, cyclin E-cyclin-dependent kinase 2 (CDK2) activation, endocrine adaptation, and phosphoinositide 3-kinase (PI3K)-AKT-mechanistic target of rapamycin (mTOR) signaling, explain only part of this failure because they do not fully capture dynamic immune and stromal remodeling. Preclinical and translational studies indicate that early CDK4/6 inhibition can enhance antigen presentation, activate interferon-related programs, restrain regulatory T cells, and promote a T-cell-inflamed state. These effects are conditional and may not persist during prolonged treatment. Sustained therapy can instead drive heterogeneous resistant niches characterized by stromal remodeling, myeloid recruitment, checkpoint adaptation, and T-cell dysfunction. This immune-state dependence provides a rationale for immune checkpoint blockade, although clinical combinations have shown mixed efficacy and clinically relevant hepatic, pulmonary, and hematologic toxicities. Sequential or lead-in strategies therefore warrant prospective evaluation. Oxidative phosphorylation (OXPHOS) and redox adaptation may sustain selected resistant states and expose context-dependent ferroptotic vulnerabilities. Ferroptosis may connect tumor-cell killing with immune regulation, whereas nanomedicine may improve tumor-selective delivery. Both strategies remain largely preclinical and require further evaluation of pharmacokinetics, biodistribution, toxicity, manufacturability, and immune-cell safety. This Review distinguishes intrinsic from acquired resistance across interpatient, intratumoral, spatial, and temporal dimensions. It integrates tumor-cell escape with cytokine, immune, stromal, vascular, and metabolic remodeling and summarizes emerging therapeutic strategies. We further propose a candidate biomarker-informed framework that integrates genomic profiling, spatial immune architecture, circulating biomarkers, T-cell receptor (TCR) dynamics, transcriptomic and single-cell analyses, artificial intelligence (AI)-assisted multimodal integration, and longitudinal sampling. This framework is intended to support biomarker development and prospective trial design rather than current clinical decision-making, providing a translational basis for testing state-informed and sequence-aware therapeutic strategies.

Humans

Environmental Stresses Constrain Soil Microbial Community Functions by Regulating Deterministic Assembly and Niche Width.

Increasing evidence indicates that the loss of soil microbial α-diversity triggered by environmental stress negatively impacts microbial functions; however, the effects of microbial α-diversity on community functions under environmental stress are poorly understood. Here, we investigated the changes in bacterial and fungal α- diversity along gradients of five natural stressors (temperature, precipitation, plant diversity, soil organic C and pH) across 45 grasslands in China and evaluated their connection with microbial functional traits. By quantifying the five environmental stresses into an integrated stress index, we found that the bacterial and fungal α-diversity declined under high environmental stress across three soil layers (0-20 cm, 20-40 cm and 40-60 cm). Metagenomic-based analyses showed that the diversity of functional genes decreased along the stress gradients. High stress enhanced the abundance of genes associated with broad functional categories (e.g., glycolysis/gluconeogenesis, TCA cycle, DNA replication/repair and cell growth/death) but reduced the abundance of genes linked to specialised functional categories (e.g., C, N, S and methane metabolism). Phylogenetic null models and niche analyses indicated that stochastic assembly processes predominated in high-diversity communities, in which bacterial and fungal taxa had a narrow ecological niche. However, in low-diversity communities, deterministic assembly processes were dominant, and taxa had wide niches, correlating with the reduction in gene abundance observed for broad and specialised functional categories. Given the essential role of the microbiome in regulating ecosystem functions, our findings suggest that low-diversity-induced deterministic community assembly processes and a wide niche under high environmental stress may regulate microbial functions. These findings emphasise the ecological mechanisms through which microbial biodiversity regulates terrestrial ecosystem functioning.

Soil Microbiology

Climate Gradients and Habitat Discontinuity Structure Genetic Variation in a Spring-Specialist Plant.

BACKGROUND AND AIMS: Groundwater-dependent ecosystems support disproportionate biodiversity in arid regions, yet the population genetics of spring-specialist plants remains poorly understood. Here, we present the first species-wide genetic dataset for crimson monkeyflower (Mimulus verbenaceus, Phrymaceae), a spring-specialist plant distributed in seeps, springs, and associated riparian areas across desert regions of North America. We aim to relate landscape features and climate gradients to the spatial genetic structuring within this system. METHODS: Using genome-wide reduced representation sequencing data consisting of 10,760 SNPs from 175 individuals across 17 populations, we characterized the patterns of genetic differentiation and diversity. Population structure was assessed using ADMIXTURE and Principal Component Analysis. We examined the contributions of climate to range-wide genetic variation in crimson monkeyflower using redundancy analysis. KEY RESULTS: Patterns of genetic differentiation were more consistent with those of spring-specialist animal taxa than those of upland plants or generalist riparian plants. We found strong population structure at both broad regional scales and at fine local scales. While geographic and spatial structuring was a primary driver of genetic structure across all scales, riparian connectivity influenced local patterns of diversity, and adaptation to local climatic variation was more influential at regional scales, with temperature, relative humidity, and a monsoon-driven climate gradient contributing to genetic differentiation. CONCLUSIONS: Our findings highlight the distinctive association with isolated perennial groundwater sources, as well as climate gradients, with genetic variation in this spring-specialist plant. These findings suggest that spring-specialist plants deserve special consideration in ecological theory, management, and conservation.

Mimulus

Habitat Specialisation Impacts Clownfish Demographic Resilience to Pleistocene Sea-Level Fluctuations.

Habitat fragmentation and loss are key threats to biodiversity, yet their impacts on marine species remain poorly understood. Clownfishes, which rely on sea anemones for shelter and reproduction, provide an interesting model to explore how ecological specialisation mediates species responses to habitat perturbations. We used whole-genome data from 382 individuals across 10 species with varying host specialisations to reconstruct demographic histories and infer spatial genetic structure to assess the impact of Pleistocene sea-level fluctuations. Generalist species, associated with multiple hosts, maintained stable effective population sizes () and population connectivity during habitat fragmentation, reflecting resilience to environmental instability. In contrast, specialists experienced severedeclines and genetic structuring, driven by their dependence on specific hosts, without signs of population recovery following habitat reconnection. Spatial genomic analyses identified the Indonesian Through-Flow as a key dispersal corridor and the Coral Triangle as a critical hub of genetic diversity, while continental shelves and extensive open ocean regions appeared as barriers to gene flow. Our findings reveal how host specialisation shapes clownfish population dynamics, emphasising the importance of incorporating ecological dependencies into conservation assessments and deepening our understanding of species responses to ecological constraints and environmental changes over evolutionary timescales.

Animals