Biology subjects

Griffith, D.

Publications and source records attributed to Griffith, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Evolutionary tuning of complexin-synaptotagmin fusion clamp governs regulated neurotransmitter release

Synaptic vesicles must be held in a fusion-ready but restrained state until calcium influx triggers release. Synaptotagmin-1 (Syt1) and Complexin (CPX) both contribute to this fusion clamp, yet removing either protein produces opposite effects in different species. Whether this reflects genuine divergence in their molecular roles, or species-specific tuning of a conserved mechanism, has remained unresolved. Here we address this by comparing mammalian and C. elegans orthologs in a reconstituted single-vesicle assay that isolates clamping from upstream docking and priming. We find that clamping in both species requires the combined action of CPX and Syt1, with neither sufficient on its own. CPX clamps only with its cognate SNARE complex, a specificity conferred entirely by its accessory helix. The Syt1 orthologs differ markedly in intrinsic clamping strength, which maps to C2B mediated higher-order oligomerization and sets the functional dependence on CPX in each system. These results support a dual-regulator model of exocytosis in which Syt1 and CPX act sequentially and cooperatively through discrete molecular states to control vesicle clamping and calcium-triggered release. This framework reconciles long-standing functional discrepancies across species and reveals how evolutionary divergence modulates conserved mechanisms governing synaptic fidelity.

neuroscience↗

bioRxiv2024

Metapredict enables accurate disorder prediction across the Tree of Life

Intrinsically disordered proteins and protein regions (collectively IDRs) are critical in numerous cellular processes. To understand how IDRs facilitate function, we need tools to accurately and rapidly identify them from sequence. While many methods for disorder prediction exist, we are currently limited by throughput and accuracy for evolutionary scale analyses. To bridge this gap, we developed metapredict V3, an updated version of our disorder predictor that enables evolutionary-scale disorder prediction. Metapredict V3 enables proteome-scale prediction with state-of-the-art accuracy in seconds and was developed with a focus on usability. It is distributed as a web server, Python software package, command-line interface, and Google Colab notebook. Here, we leverage the accuracy and throughput of metapredict V3 to predict disorder for over 20,000 proteomes to evaluate the prevalence of disorder across the kingdoms of life.

bioinformatics↗

bioRxiv2024

High-throughput affinity measurements of direct interactions between activation domains and co-activators

Sequence-specific activation by transcription factors is essential for gene regulation1,2. Key to this are activation domains, which often fall within disordered regions of transcription factors3,4 and recruit co-activators to initiate transcription5. These interactions are difficult to characterize via most experimental techniques because they are typically weak and transient6,7. Consequently, we know very little about whether these interactions are promiscuous or specific, the mechanisms of binding, and how these interactions tune the strength of gene activation. To address these questions, we developed a microfluidic platform for expression and purification of hundreds of activation domains in parallel followed by direct measurement of co-activator binding affinities (STAMMPPING, for Simultaneous Trapping of Affinity Measurements via a Microfluidic Protein-Protein INteraction Generator). By applying STAMMPPING to quantify direct interactions between eight co-activators and 204 human activation domains (>1,500 Kds), we provide the first quantitative map of these interactions and reveal 334 novel binding pairs. We find that the metazoan-specific co-activator P300 directly binds >100 activation domains, potentially explaining its widespread recruitment across the genome to influence transcriptional activation. Despite sharing similar molecular properties (e.g. enrichment of negative and hydrophobic residues), activation domains utilize distinct biophysical properties to recruit certain co-activator domains. Co-activator domain affinity and occupancy are well-predicted by analytical models that account for multivalency, and in vitro affinities quantitatively predict activation in cells with an ultrasensitive response. Not only do our results demonstrate the ability to measure affinities between even weak protein-protein interactions in high throughput, but they also provide a necessary resource of over 1,500 activation domain/co-activator affinities which lays the foundation for understanding the molecular basis of transcriptional activation.

biophysics↗

bioRxiv2023

Dissecting Mechanisms of Ligand Binding and Conformational Changes in the Glutamine-Binding Protein

The glutamin-binding protein GlnBP is part of an ATP-binding cassette transporter system in E. coli and uses two well-characterized conformational states, an open ligand-free and a closed-liganded state, to facilitate active amino-acid uptake. Existing literature on its ligand binding mechanism lacked sufficient evidence to univocally assign the kinetic type of binding mechanism for GlnBP: ligand binding prior to conformational change, i.e., an induced fit or the conformational selection, in which the ligand binds the matching conformation from a pre-existing ensemble. Since such mechanistic questions are relevant for our fundamental understanding of how this and other biomacromolecules regulate cellular processes, we here revisit the question for GlnBP. We present a biochemical and biophysical analysis using a combination of calorimetry, single-molecule and surface-plasmon resonance spectroscopy and molecular dynamics simulations. We found that both apo- and holo-GlnBP show no detectable exchange between open and (semi-)closed conformations on timescales between 100 ns and 10 ms and that ligand binding and conformational changes in GlnBP are correlated. A global analysis of our experimental results suggests that the conformational selection model is only compatible with GlnBP for the extreme scenario of very fast conformational exchange between the open and closed states on timescales <100 ns. In contrast all data remains compatible with an induced-fit mechanism, where the ligand binds GlnBP prior to conformational rearrangements. Importantly, our work demonstrates that it is an intricate task to identify the type of kinetic binding mechanism and that this requires not only a sufficient set of data, but also an integrative experimental and theoretical framework to address the question. Based on this concept, we propose that various protein systems, for which so far only insufficient kinetic data are available, should be revisited.

biophysics↗

bioRxiv2023

Labelizer: systematic selection of protein residues for covalent fluorophore labeling

An essential requirement for the use of fluorescent dyes in biomedicine, molecular biology, biochemistry, biophysics and optical imaging is their (covalent) attachment to biomolecules. There is, however, no systematic and automated approach for the selection of suitable labeling sites in macromolecules, which is particular problematic for proteins. Here, we present a general and quantitative strategy to identify optimal residues for protein labeling using a naive Bayes classifier. Based on a literature search and bioinformatics analysis of >100 proteins with ~400 successfully labeled residues, we identified four parameters, which we combined into a labeling score to rank residues for their suitability as a label-site. The utility of our approach for the systematic selection of single residues and of residue pairs for FRET experiments is supported by data from the literature and by new experiments on different proteins. To make the method available to a large community of researchers, we developed a python package called "labelizer", that performs an analysis of a pdb-structure (or structural models), label score calculation, and FRET assay scoring. We further provide a webserver (https://labelizer.bio.lmu.de/) to conveniently apply our approach and to build up a central open-access database of (non-)successfully labeled protein residues to continuously improve and refine the labelizer approach.

biophysics↗

bioRxiv2023

Direct prediction of intrinsically disordered protein conformational properties from sequence

Intrinsically disordered regions (IDRs) are ubiquitous across all domains of life and play a range of functional roles. While folded domains are generally well-described by a single 3D structure, IDRs exist in a collection of interconverting states known as an ensemble. This structural heterogeneity means IDRs are largely absent from the PDB, contributing to a lack of computational approaches to predict ensemble conformational properties from sequence. Here we combine rational sequence design, large-scale molecular simulations, and deep learning to develop ALBATROSS, a deep learning model for predicting IDR ensemble dimensions from sequence. ALBATROSS enables the instantaneous prediction of ensemble average properties at proteome-wide scale. ALBATROSS is lightweight, easy-to-use, and accessible as both a locally installable software package and a point-and-click style interface in the cloud. We first demonstrate the applicability of our predictors by examining the generalizability of sequence-ensemble relationships in IDRs. Then, we leverage the high-throughput nature of ALBATROSS to characterize emergent biophysical behavior of IDRs within and between proteomes. Update from previous versionO_LIThis preprint reports an updated version of the ALBATROSS network weights trained on simulations of over 42,000 sequences. C_LIO_LIIn addition, we provide new colab notebooks that enable proteome-wide IDR prediction and annotation in minutes. C_LIO_LIAll conclusions and observations made in versions 1 and 2 of this manuscript remain true and robust. C_LI

biophysics↗

bioRxiv2022

Physiological responses to drought stress and recovery reflect differences in leaf function and anatomy among grass lineages

Grasses are cosmopolitan, existing in many biome and climate types from xeric to tropical. Traits that control physiological responses to drought vary strongly among grass lineages, suggesting that tolerance strategies may differ with evolutionary history. Here, we withheld water from 12 species representing six tribes of grasses to compare how species respond to drought in different grass lineages. We measured physiological, morphological, and anatomical traits. Dominant lineages from tropical savannas, like Andropogoneae, tolerated drought due to above and belowground morphological traits (specific leaf area and root length, SLA and SRL), while temperate grasses in this study utilized conservative leaf physiology (gas exchange) and anatomy traits. Increased intrinsic water-use efficiency coincided with a larger number of stomata, resulting in greater water loss (with inherently greater carbon gain) and increased drought sensitivity. Inherent leaf and root economic strategies impacting drought response were observed in all species, resulting in either high SLA or SRL, but not both. Our results indicate that grasses subjected to severe drought were influenced by anatomical traits (e.g., number of stomata and xylem area) and similar within lineages. In addition, grasses recovered at least 50% of physiological functioning across all lineages and 92% within Andropogoneae species, illustrating how drought can influence functional responses across diverse grass lineages.

plant biology↗

bioRxiv2022

Metapredict V2: An update to metapredict, a fast, accurate, and easy-to-use predictor of consensus disorder and structure

Intrinsically disordered proteins and protein regions make up 20-40% of most eukaryotic proteomes and play essential roles in a wide gamut of cellular processes, from intracellular trafficking to epigenetic silencing. Given their importance, the ability to robustly, quickly, and easily identify IDRs within large proteins is critical. Here we present metapredict V2, an update to our deep-learning-based disorder predictor metapredict. Metapredict V2 has substantially improved accuracy, more features, and a more user-friendly interface via our web server (https://metapredict.net/), Python package, and command-line tool. To illustrate V2s improved performance we undertake a systematic analysis of human transcription factors, as well as illustrate that metapredict V2 works well for synthetic or non-natural proteins. KEY POINTSO_LIMetapredict is a fast and easy-to-use disorder predictor released in 2021. C_LIO_LIMetapredict V2 was released in March 2022 and includes improved accuracy and new features. C_LIO_LIMetapredict V2 is now the default metapredict implementation, although the original implementation is available as legacy metapredict. C_LIO_LIThis manuscript provides a summary of how we improved the accuracy of metapredict and compares the original version (legacy) to our improved version (V2) C_LIO_LIThis manuscript will not be submitted to a journal; if you use metapredict V2 please cite the original paper and make reference to the fact that V2 is being used. C_LI

bioinformatics↗

bioRxiv2022

The kinetic landscape of human transcription factors

Cell-to-cell variability is shaped by transcription dynamics because genes are transcribed in bursts interspersed with inactive periods. The stochasticity of bursting means that genes transcribed in rare bursts exhibit more heterogeneity at the single cell level than genes that burst often 1, 2. Transcription starts with the binding of Transcription Factors (TFs) to specific sequence motifs where they recruit the transcription machinery 3. In some systems, individual TF binding events temporally correlate with the firing of transcriptional bursts, defining the target genes frequency and duration 4-6. However, in the absence of methods that assess the impact of different TFs on transcription dynamics at the same genetic loci, it remains unclear whether DNA binding kinetics are the sole determinant of bursting. Here we develop an imaging-based synthetic recruitment assay, CRISPRburst, and measure how 92 human TFs impact bursting kinetics. We show that TFs recruited to chromatin under identical conditions generate diverse bursting signatures, some TFs increasing the probability of the gene turning on while others increase the number of mRNA molecules transcribed per burst. We find that the association of TFs with specific protein partners determines their bursting output, and train a model to predict the kinetic signatures of all human TFs. These kinetic signatures can be used as a TF classification system complementary to existing families based on DNA binding domains. Additionally, kinetic signatures provide a rational framework to design synthetic activators, model transcription regulation, and understand expression heterogeneity.

biophysics↗

bioRxiv2022

Hypoxia shapes the immune landscape in lung injury promoting inflammation persistence

Acute Respiratory Distress Syndrome (ARDS), an often-fatal complication of pulmonary or systemic inflammation, has no cure. Hypoxemia is a defining feature, yet its impact on inflammation is often neglected. Patients with ARDS are monocytopenic early in the onset of the disease. Endotoxin or Streptococcus pneumoniae acute lung injury (ALI) in the context of hypoxia replicates this finding, through hypoxia-driven suppression of type I interferon signalling. This results in failed lung monocyte-derived interstitial macrophages (IM) niche expansion and unchecked neutrophilic inflammation. Administration of colony stimulating factor 1 (CSF1) rescues the monocytopenia, alters the circulating classical monocyte phenotype in hypoxic endotoxin-driven ALI and enables lung IM population expansion, thus limiting lung injury in endotoxin- and virally-induced hypoxic ALI. Hypoxia directly alters immune dynamics to the detriment of the host and manipulation of this aberrant response offers new therapeutic strategies for ARDS.

cell biology↗

bioRxiv2022

Ex vivo and in vivo evidence that cigarette smoke-exposed T regulatory cells impair host immunity against Mycobacterium tuberculosis

A strong epidemiologic link exists between exposure to cigarette smoke (CS) and increased susceptibility to tuberculosis (TB). In vitro macrophage and in vivo murine studies showed that CS and nicotine impair host-protective immune cells against Mycobacterium tuberculosis (MTB) infection. However, little is known about how CS may affect immunosuppressive cells in the context of MTB infection. Thus, we investigated whether CS-exposed T regulatory cells (Tregs) could exacerbate MTB infection in co-culture with human macrophages and in the adoptive transfer of Tregs from air- and CS-exposed mice. We found that exposure of primary human Tregs to CS extract impaired the ability of human monocyte-derived macrophages to control an MTB infection by inhibiting phagosome-lysosome fusion and autophagosome formation. Neutralization of CTLA-4 on the CS extract-exposed Tregs abrogated the impaired control of MTB infection in macrophage and Treg co-cultures. In Foxp3+GFP+DTR+ (Thy1.2) mice depleted of endogenous Tregs, adoptive transfer of Tregs from donor CS-exposed B6.PL(Thy1.1) mice with subsequent MTB infection of the recipient Thy1.2 mice resulted in a greater burden of MTB in the lungs and spleens than those that received Tregs from airexposed mice. Mice that received Tregs from CS-exposed mice and then infected with MTB had modest but significantly reduced numbers of interleukin-12-positive dendritic cells and interferon-gamma-positive CD4+ T cells in the lungs and increased number of programmed cell death protein-1 positive CD4+ T cells in both the lungs and spleens.

immunology↗

bioRxiv2022

The formation of a fuzzy complex in the negative arm regulates the robustness of the circadian clock.

The circadian clock times cellular processes to the day/night cycle via a Transcription-Translation negative Feedback Loop (TTFL). However, a mechanistic understanding of the negative arm in both the timing of the TTFL and its control of output is lacking. We posited that the formation of negative-arm protein complexes was fundamental to clock regulation stemming from the negative arm. Using a modified peptide microarray approach termed Linear motif discovery using rational design (LOCATE), we characterized the interaction of the disordered negative-arm clock protein FREQUENCY to its partner protein FREQUENCY-Interacting RNA helicase. LOCATE identified a specific Short Linear Motif (SLiM) and interaction "hotspot" as well as positively charged "islands" that mediate electrostatic interactions, suggesting a model where negative arm proteins form a "fuzzy" complex essential for clock timing and robustness. Further analysis revealed that the positively charged islands were an evolutionarily conserved feature in higher eukaryotes and contributed to proper clock function.

molecular biology↗

bioRxiv2021

A Marmoset Model for Mycobacterium avium Complex Pulmonary Disease

RationaleMycobacterium avium complex, is the most common nontuberculous mycobacterial respiratory pathogen in humans. Disease mechanisms are poorly understood due to the absence of a reliable animal model for M. avium complex pulmonary disease. ObjectiveAssess the susceptibility, immunologic and histopathologic responses of the common marmoset (Callithrix jacchus) to M. avium complex pulmonary infection. Methods7 adult female marmosets underwent endobronchial inoculation with 108 colony-forming units of M. intracellulare and were monitored for 30 or 60 days. Prior to infection, chest radiograph and serum cytokines were assessed; serum cytokines were also monitored weekly for 30 days. At sacrifice 30 days (3 animals) or 60 days (4 animals) after infection, chest radiograph, serum and bronchoalveolar lavage cytokines, histopathology, and cultures of the bronchoalveolar lavage, lungs, liver, and kidney were analyzed. Measurements and Main ResultsFive of seven animals (two at 30 days and three at 60 days of infection) had positive lung cultures for M. intracellulare. Extra-pulmonary cultures were positive in three animals. All animals appeared healthy throughout the study. All five animals with positive lung cultures had radiographic changes consistent with pneumonitis. At 30 days, those with M. intracellulare lung infection showed granulomatous inflammation while at 60 days there was less inflammatory change, but bronchiectasis was noted. The cytokine response in the bronchoalveolar lavage fluid was uniformly greater in the animals with positive M. intracellulare cultures than those without a productive infection with greater levels at 30-days compared to 60-days. Similarly, serum cytokines were more elevated in the animals that had positive M. intracellulare cultures compared to those without a productive infection, peaking 14-21 days after inoculation. ConclusionEndobronchial instillation of M. intracellulare resulted in pulmonary mycobacterial infection in marmosets with a differential immune response, radiographic and histopathologic abnormalities, and an indolent course consistent with M. avium complex lung infection in humans.

microbiology↗

bioRxiv2021

metapredict: a fast, accurate, and easy-to-use cross-platform predictor of consensus disorder

Intrinsically disordered proteins and protein regions make up a substantial fraction of many proteomes where they play a wide variety of essential roles. A critical first step in understanding the role of disordered protein regions in biological function is to identify those disordered regions correctly. Computational methods for disorder prediction have emerged as a core set of tools to guide experiments, interpret results, and develop hypotheses. Given the multiple different predictors available, consensus scores have emerged as a popular approach to mitigate biases or limitations of any single method. Consensus scores integrate the outcome of multiple independent disorder predictors and provide a per-residue value that reflects the number of tools that predict a residue to be disordered. Although consensus scores help mitigate the inherent problems of using any single disorder predictor, they are computationally expensive to generate. They also necessitate the installation of multiple different software tools, which can be prohibitively difficult. To address this challenge, we developed a deep-learning-based predictor of consensus disorder scores. Our predictor, metapredict, utilizes a bidirectional recurrent neural network trained on the consensus disorder scores from 12 proteomes. By benchmarking metapredict using two orthogonal approaches, we found that metapredict is among the most accurate disorder predictors currently available. Metapredict is also remarkably fast, enabling proteome-scale disorder prediction in minutes. Importantly, metapredict is fully open source and is distributed as a Python package, a collection of command-line tools, and a web server, maximizing the potential practical utility of the predictor. We believe metapredict offers a convenient, accessible, accurate, and high-performance predictor for single-proteins and proteomes alike. Statement of SignificanceIntrinsically disordered regions are found across all kingdoms of life where they play a variety of essential roles. Being able to accurately and quickly identify disordered regions in proteins using just the amino acid sequence is critical for the appropriate design and interpretation of experiments. Despite this, performing large-scale disorder prediction on thousands of sequences is challenging using extant disorder predictors due to various difficulties including general installation and computational requirements. We have developed an accurate, high-performance and easy-to-use predictor of protein disorder and structure. Our predictor, metapredict, was designed for both proteome-scale analysis and individual sequence predictions alike. Metapredict is implemented as a collection of local tools and an online web server, and is appropriate for both seasoned computational biologists and novices alike.

bioinformatics↗

bioRxiv2021

PARROT: a flexible recurrent neural network framework for analysis of large protein datasets

The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex non-linear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.

bioinformatics↗

bioRxiv2020

The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA

The SARS-CoV-2 nucleocapsid (N) protein is an abundant RNA binding protein critical for viral genome packaging, yet the molecular details that underlie this process are poorly understood. Here we combine single-molecule spectroscopy with all-atom simulations to uncover the molecular details that contribute to N protein function. N protein contains three dynamic disordered regions that house putative transiently-helical binding motifs. The two folded domains interact minimally such that full-length N protein is a flexible and multivalent RNA binding protein. N protein also undergoes liquid-liquid phase separation when mixed with RNA, and polymer theory predicts that the same multivalent interactions that drive phase separation also engender RNA compaction. We offer a simple symmetry-breaking model that provides a plausible route through which single-genome condensation preferentially occurs over phase separation, suggesting that phase separation offers a convenient macroscopic readout of a key nanoscopic interaction.

biophysics↗

bioRxiv2020

Lineage Functional Types (LFTs): Characterizing functional diversity to enhance the representation of ecological behavior in Earth System Models

Process-based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics, but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass dominated ecosystems are broadly distributed across all vegetated continents and harbor large functional diversity, yet most Earth System Models (ESMs) summarize grasses into two generic PFTs based primarily on differences between temperate C3 grasses and (sub)tropical C4 grasses. Incorporation of species-level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in ESMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance-related) of dominant lineages to improve ESM representations. Our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage-based functional types (LFTs), situated between species-level trait data and PFT-level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.

ecology↗

bioRxiv2019

Functional diversification enabled grassy biomes to fill global climate space

Global change impacts on the Earth System are typically evaluated using biome classifications based on trees and forests. However, during the Cenozoic, many terrestrial biomes were transformed through the displacement of trees and shrubs by grasses. While grasses comprise 3% of vascular plant species, they are responsible for more than 25% of terrestrial photosynthesis. Critically, grass dominance alters ecosystem dynamics and function by introducing new ecological processes, especially surface fires and grazing. However, the large grassy component of many global biomes is often neglected in their descriptions, thereby ignoring these important ecosystem processes. Furthermore, the functional diversity of grasses in vegetation models is usually reduced to C3 and C4 photosynthetic plant functional types, omitting other relevant traits. Here, we compile available data to determine the global distribution of grassy vegetation and key traits related to grass dominance. Grassy biomes (where > 50% of the ground layer is covered by grasses) occupy almost every part of Earths vegetated climate space, characterising over 40% of the land surface. Major evolutionary lineages of grasses have specialised in different environments, but species from only three grass lineages occupy 88% of the land area of grassy vegetation, segregating along gradients of temperature, rainfall and fire. The environment occupied by each lineage is associated with unique plant trait combinations, including C3 and C4 photosynthesis, maximum plant height, and adaptations to fire and aridity. There is no single global climatic limit where C4 grasses replace C3 grasses. Instead this ecological transition varies biogeographically, with continental disjunctions arising through contrasting evolutionary histories.\n\nSignificance statementWorldviews of vegetation generally focus on trees and forests but grasses characterize the ground layer over 40% of the Earths vegetated land surface. This omission is important because grasses transform surface-atmosphere exchanges, biodiversity and disturbance regimes. We looked beneath the trees to produce the first global map of grass-dominated biomes. Grassy biomes occur in virtually every climate on Earth. However, three lineages of grasses are much more successful than others, characterizing 88% of the land area of grassy biomes. Each of these grass lineages evolved ecological specializations related to aridity, freezing and fire. Recognizing the extent and causes of grass dominance beneath trees is important because grassy vegetation plays vital roles in the dynamics of our biosphere and human wellbeing.

ecology↗