BioSciences Publications

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https://hdl.handle.net/1911/78269

BioSciences faculty publications. For works published before Summer 2014, please see the Biochemistry & Cell Biology and Ecology & Evolutionary Biology collections.

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Ecological shifts underlie parallels between ontogenetic and evolutionary allometries in parrotfishes
(Royal Society, 2024) Neves, Mayara P.; Hugi, April; Chan, Howan; Arnold, Kaleigh; Titus, Kara; Westneat, Mark W.; Zelditch, Miriam L.; Brandl, Simon; Evans, Kory M.
During ontogeny, animals often undergo significant shape and size changes, coinciding with ecological shifts. This is evident in parrotfishes (Eupercaria: Labridae), which experience notable ecological shifts during development, transitioning from carnivorous diets as larvae and juveniles to herbivorous and omnivorous diets as adults, using robust beaks and skulls for feeding on coral skeletons and other hard substrates. These ontogenetic shifts mirror their evolutionary history, as parrotfishes are known to have evolved from carnivorous wrasse ancestors. Parallel shifts at ontogenetic and phylogenetic levels may have resulted in similar evolutionary and ontogenetic allometric trajectories within parrotfishes. To test this hypothesis, using micro-computed tomography (μCT) scanning and three-dimensional geometric morphometrics, we analyse the effects of size on the skull shape of the striped parrotfish Scarus iseri and compare its ontogenetic allometry to the evolutionary allometries of 57 parrotfishes and 162 non-parrotfish wrasses. The young S. iseri have skull shapes resembling non-parrotfish wrasses and grow towards typical adult parrotfish forms as they mature. There was a significant relationship between size and skull shapes and strong evidence for parallel ontogenetic and evolutionary slopes in parrotfishes. Our findings suggest that morphological changes associated with the ecological shift characterizing interspecific parrotfish evolution are conserved in their intraspecific ontogenies.
Invasive plants and their root traits are linked to the homogenization of soil microbial communities across the United States
(National Academy of Sciences, 2024) Nunez-Mir, Gabriela C.; McCary, Matthew A.
Although the impacts of invasive plants on soil ecosystems are widespread, the role and impacts of invader root traits in structuring microbial communities remain poorly understood. Here, we present a macroecological study investigating how plant invaders and their root traits affect soil microbial communities, spanning data from 377 unique plots across the United States sampled multiple times, totaling 632 sampling events and 94 invasive plant species. We found that native and invasive plants harbor different root traits on average, with invasive plants possessing higher specific root lengths and native plants having higher root tissue density. We also show that soil microbial communities experiencing heavy plant invasions were more similar to each other in composition across ecosystem types and geographical regions than plots with higher proportions of native plants, which displayed highly variable microbial communities across the continent. Root traits of invasive plants in highly invaded plots explained two times more variation in microbial composition than native plants. This work represents an important step toward understanding macroscale and cross-scale patterns of the relationship between plant invasions, root traits, and soil microbial composition. Our findings provide insights into how invasive plants may impact ecosystem functioning at the macroscale via their homogenizing influence on microbial communities.
Evolutionary innovation accelerates morphological diversification in pufferfishes and their relatives
(Oxford University Press, 2024) Troyer, Emily M; Evans, Kory M; Goatley, Christopher H R; Friedman, Matt; Carnevale, Giorgio; Nicholas, Benjamin; Kolmann, Matthew; Bemis, Katherine E; Arcila, Dahiana
Evolutionary innovations have played an important role in shaping the diversity of life on Earth. However, how these innovations arise and their downstream effects on patterns of morphological diversification remain poorly understood. Here, we examine the impact of evolutionary innovation on trait diversification in tetraodontiform fishes (pufferfishes, boxfishes, ocean sunfishes, and allies). This order provides an ideal model system for studying morphological diversification owing to their range of habitats and divergent morphologies, including the fusion of the teeth into a beak in several families. Using three-dimensional geometric morphometric data for 176 extant and fossil species, we examine the effect of skull integration and novel habitat association on the evolution of innovation. Strong integration may be a requirement for rapid trait evolution and facilitating the evolution of innovative structures, like the tetraodontiform beak. Our results show that the beak arose in the presence of highly conserved patterns of integration across the skull, suggesting that integration did not limit the range of available phenotypes to tetraodontiforms. Furthermore, we find that beaks have allowed tetraodontiforms to diversify into novel ecological niches, irrespective of habitat. Our results suggest that general rules pertaining to evolutionary innovation may be more nuanced than previously thought.
The Drosophila Nesprin-1 homolog MSP300 is required for muscle autophagy and proteostasis
(The Company of Biologists, 2024) van der Graaf, Kevin; Srivastav, Saurabh; Nishad, Rajkishor; Stern, Michael; McNew, James A.
Nesprin proteins, which are components of the linker of nucleoskeleton and cytoskeleton (LINC) complex, are located within the nuclear envelope and play prominent roles in nuclear architecture. For example, LINC complex proteins interact with both chromatin and the cytoskeleton. Here, we report that the Drosophila Nesprin MSP300 has an additional function in autophagy within larval body wall muscles. RNAi-mediated MSP300 knockdown in larval body wall muscles resulted in defects in the contractile apparatus, muscle degeneration and defective autophagy. In particular, MSP300 knockdown caused accumulation of cytoplasmic aggregates that contained poly-ubiquitylated cargo, as well as the autophagy receptor ref(2)P (the fly homolog of p62 or SQSTM) and Atg8a. Furthermore, MSP300 knockdown larvae expressing an mCherry–GFP-tagged Atg8a transgene exhibited aberrant persistence of the GFP signal within these aggregates, indicating failure of autophagosome maturation. These autophagy deficits were similar to those exhibited by loss of the endoplasmic reticulum (ER) fusion protein Atlastin (Atl), raising the possibility that Atl and MSP300 might function in the same pathway. In support of this possibility, we found that a GFP-tagged MSP300 protein trap exhibited extensive localization to the ER. Alteration of ER-directed MSP300 might abrogate important cytoskeletal contacts necessary for autophagosome completion.
The physical and evolutionary energy landscapes of devolved protein sequences corresponding to pseudogenes
(National Academy of Sciences, 2024) Jaafari, Hana; Bueno, Carlos; Schafer, Nicholas P.; Martin, Jonathan; Morcos, Faruck; Wolynes, Peter G.; Center for Theoretical Biophysics
Protein evolution is guided by structural, functional, and dynamical constraints ensuring organismal viability. Pseudogenes are genomic sequences identified in many eukaryotes that lack translational activity due to sequence degradation and thus over time have undergone “devolution.” Previously pseudogenized genes sometimes regain their protein-coding function, suggesting they may still encode robust folding energy landscapes despite multiple mutations. We study both the physical folding landscapes of protein sequences corresponding to human pseudogenes using the Associative Memory, Water Mediated, Structure and Energy Model, and the evolutionary energy landscapes obtained using direct coupling analysis (DCA) on their parent protein families. We found that generally mutations that have occurred in pseudogene sequences have disrupted their native global network of stabilizing residue interactions, making it harder for them to fold if they were translated. In some cases, however, energetic frustration has apparently decreased when the functional constraints were removed. We analyzed this unexpected situation for Cyclophilin A, Profilin-1, and Small Ubiquitin-like Modifier 2 Protein. Our analysis reveals that when such mutations in the pseudogene ultimately stabilize folding, at the same time, they likely alter the pseudogenes’ former biological activity, as estimated by DCA. We localize most of these stabilizing mutations generally to normally frustrated regions required for binding to other partners.
Ten Years of the Synthetic Biology Summer Course at Cold Spring Harbor Laboratory
(American Chemical Society, 2024) Haynes, Karmella A.; Andrews, Lauren B.; Beisel, Chase L.; Chappell, James; Cuba Samaniego, Christian E.; Dueber, John E.; Dunlop, Mary J.; Franco, Elisa; Lucks, Julius B.; Noireaux, Vincent; Savage, David F.; Silver, Pamela A.; Smanski, Michael; Young, Eric
The Cold Spring Harbor Laboratory (CSHL) Summer Course on Synthetic Biology, established in 2013, has emerged as a premier platform for immersive education and research in this dynamic field. Rooted in CSHL’s rich legacy of biological discovery, the course offers a comprehensive exploration of synthetic biology’s fundamentals and applications. Led by a consortium of faculty from diverse institutions, the course structure seamlessly integrates practical laboratory sessions, exploratory research rotations, and enriching seminars by leaders in the field. Over the years, the curriculum has evolved to cover essential topics such as cell-free transcription–translation, DNA construction, computational modeling of gene circuits, engineered gene regulation, and CRISPR technologies. In this review, we describe the history, development, and structure of the course, and discuss how elements of the course might inform the development of other short courses in synthetic biology. We also demonstrate the course’s impact beyond the lab with a summary of alumni contributions to research, education, and entrepreneurship. Through these efforts, the CSHL Summer Course on Synthetic Biology remains at the forefront of shaping the next generation of synthetic biologists.
Native Plant Diversity Generates Microbial Legacies That Either Promote or Suppress Non-Natives, Depending on Drought History
(Wiley, 2024) Tao, Zhibin; Zhang, Kaoping; Callaway, Ragan M.; Siemann, Evan; Liu, Yanjie; Huang, Wei
Diverse native plant communities resist non-native plants more than species-poor communities, in part through resource competition. The role of soil biota in diversity–invasibility relationships is poorly understood, although non-native plants interact with soil biota during invasions. We tested the responses of non-native plants to soil biota generated by different native plant diversities. We applied well-watered and drought treatments in both conditioning and response phases to explore the effects of ‘historical’ and ‘contemporary’ environmental stresses. When generated in well-watered soils, the microbial legacies from higher native diversity inhibited non-native growth in well-watered conditions. In contrast, when generated in drought-treated soils, the microbial legacies from higher native diversity facilitated non-native growth in well-watered conditions. Contemporary drought eliminated microbial legacy effects on non-native growth. We provide a new understanding of mechanisms behind diversity–invasibility relationships and demonstrate that temporal variation in environmental stress shapes relationships among native plant diversity, soil biota and non-native plants.
Expansion of a neural crest gene signature following ectopic MYCN expression in sympathoadrenal lineage cells in vivo
(Public Library of Science, 2024) Ibarra-García-Padilla, Rodrigo; Nambiar, Annika; Hamre, Thomas A.; Singleton, Eileen W.; Uribe, Rosa A.
Neural crest cells (NCC) are multipotent migratory stem cells that originate from the neural tube during early vertebrate embryogenesis. NCCs give rise to a variety of cell types within the developing organism, including neurons and glia of the sympathetic nervous system. It has been suggested that failure in correct NCC differentiation leads to several diseases, including neuroblastoma (NB). During normal NCC development, MYCN is transiently expressed to promote NCC migration, and its downregulation precedes neuronal differentiation. Overexpression of MYCN has been linked to high-risk and aggressive NB progression. For this reason, understanding the effect overexpression of this oncogene has on the development of NCC-derived sympathoadrenal progenitors (SAP), which later give rise to sympathetic nerves, will help elucidate the developmental mechanisms that may prime the onset of NB. Here, we found that overexpressing human EGFP-MYCN within SAP lineage cells in zebrafish led to the transient formation of an abnormal SAP population, which displayed expanded and elevated expression of NCC markers while paradoxically also co-expressing SAP and neuronal differentiation markers. The aberrant NCC signature was corroborated with in vivo time-lapse confocal imaging in zebrafish larvae, which revealed transient expansion of sox10 reporter expression in MYCN overexpressing SAPs during the early stages of SAP development. In these aberrant MYCN overexpressing SAP cells, we also found evidence of dampened BMP signaling activity, indicating that BMP signaling disruption occurs following elevated MYCN expression. Furthermore, we discovered that pharmacological inhibition of BMP signaling was sufficient to create an aberrant NCC gene signature in SAP cells, phenocopying MYCN overexpression. Together, our results suggest that MYCN overexpression in SAPs disrupts their differentiation by eliciting abnormal NCC gene expression programs, and dampening BMP signaling response, having developmental implications for the priming of NB in vivo.
Sugar ring alignment and dynamics underline cytarabine and gemcitabine inhibition on Pol η catalyzed DNA synthesis
(Elsevier, 2024) Chang, Caleb; Zhou, Grace; Lee Luo, Christie; Eleraky, Sarah; Moradi, Madeline; Gao, Yang
Nucleoside analogue drugs are pervasively used as antiviral and chemotherapy agents. Cytarabine and gemcitabine are anti-cancer nucleoside analogue drugs that contain C2′ modifications on the sugar ring. Despite carrying all the required functional groups for DNA synthesis, these two compounds inhibit DNA extension once incorporated into DNA. It remains unclear how the C2′ modifications on cytarabine and gemcitabine affect the polymerase active site during substrate binding and DNA extension. Using steady-state kinetics, static and time-resolved X-ray crystallography with DNA polymerase η (Pol η) as a model system, we showed that the sugar ring C2′ chemical groups on cytarabine and gemcitabine snugly fit within the Pol η active site without occluding the steric gate. During DNA extension, Pol η can extend past gemcitabine but with much lower efficiency past cytarabine. The Pol η crystal structures show that the -OH modification in the β direction on cytarabine locks the sugar ring in an unfavorable C2′-endo geometry for product formation. On the other hand, the addition of fluorine atoms on gemcitabine alters the proper conformational transition of the sugar ring for DNA synthesis. Our study illustrates mechanistic insights into chemotherapeutic drug inhibition and resistance and guides future optimization of nucleoside analogue drugs.
Polyphest: fast polyploid phylogeny estimation
(Oxford University Press, 2024) Yan, Zhi; Cao, Zhen; Nakhleh, Luay
Despite the widespread occurrence of polyploids across the Tree of Life, especially in the plant kingdom, very few computational methods have been developed to handle the specific complexities introduced by polyploids in phylogeny estimation. Furthermore, methods that are designed to account for polyploidy often disregard incomplete lineage sorting (ILS), a major source of heterogeneous gene histories, or are computationally very demanding. Therefore, there is a great need for efficient and robust methods to accurately reconstruct polyploid phylogenies.We introduce Polyphest (POLYploid PHylogeny ESTimation), a new method for efficiently and accurately inferring species phylogenies in the presence of both polyploidy and ILS. Polyphest bypasses the need for extensive network space searches by first generating a multilabeled tree based on gene trees, which is then converted into a (uniquely labeled) species phylogeny. We compare the performance of Polyphest to that of two polyploid phylogeny estimation methods, one of which does not account for ILS, namely PADRE, and another that accounts for ILS, namely MPAllopp. Polyphest is more accurate than PADRE and achieves comparable accuracy to MPAllopp, while being significantly faster. We also demonstrate the application of Polyphest to empirical data from the hexaploid bread wheat and confirm the allopolyploid origin of bread wheat along with the closest relatives for each of its subgenomes.Polyphest is available at https://github.com/NakhlehLab/Polyphest.
RACER-m leverages structural features for sparse T cell specificity prediction
(AAAS, 2024) Wang, Ailun; Lin, Xingcheng; Chau, Kevin Ng; Onuchic, José N.; Levine, Herbert; George, Jason T.; Center for Theoretical Biological Physics
Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.
CrysFormer: Protein structure determination via Patterson maps, deep learning, and partial structure attention
(AIP Publishing LLC, 2024) Pan, Tom; Dun, Chen; Jin, Shikai; Miller, Mitchell D.; Kyrillidis, Anastasios; Phillips, George N., Jr.
Determining the atomic-level structure of a protein has been a decades-long challenge. However, recent advances in transformers and related neural network architectures have enabled researchers to significantly improve solutions to this problem. These methods use large datasets of sequence information and corresponding known protein template structures, if available. Yet, such methods only focus on sequence information. Other available prior knowledge could also be utilized, such as constructs derived from x-ray crystallography experiments and the known structures of the most common conformations of amino acid residues, which we refer to as partial structures. To the best of our knowledge, we propose the first transformer-based model that directly utilizes experimental protein crystallographic data and partial structure information to calculate electron density maps of proteins. In particular, we use Patterson maps, which can be directly obtained from x-ray crystallography experimental data, thus bypassing the well-known crystallographic phase problem. We demonstrate that our method, CrysFormer, achieves precise predictions on two synthetic datasets of peptide fragments in crystalline forms, one with two residues per unit cell and the other with fifteen. These predictions can then be used to generate accurate atomic models using established crystallographic refinement programs.
Breakdown of Boltzmann-type models for the alignment of self-propelled rods
(Elsevier, 2024) Murphy, Patrick; Perepelitsa, Misha; Timofeyev, Ilya; Lieber-Kotz, Matan; Islas, Brandon; Igoshin, Oleg A.; Center for Theoretical Biological Physics
Studies in the collective motility of organisms use a range of analytical approaches to formulate continuous kinetic models of collective dynamics from rules or equations describing agent interactions. However, the derivation of these kinetic models often relies on Boltzmann’s “molecular chaos” hypothesis, which assumes that correlations between individuals are short-lived. While this assumption is often the simplest way to derive tractable models, it is often not valid in practice due to the high levels of cooperation and self-organization present in biological systems. In this work, we illustrated this point by considering a general Boltzmann-type kinetic model for the alignment of self-propelled rods where rod reorientation occurs upon binary collisions. We examine the accuracy of the kinetic model by comparing numerical solutions of the continuous equations to an agent-based model that implements the underlying rules governing microscopic alignment. Even for the simplest case considered, our comparison demonstrates that the kinetic model fails to replicate the discrete dynamics due to the formation of rod clusters that violate statistical independence. Additionally, we show that introducing noise to limit cluster formation helps improve the agreement between the analytical model and agent simulations but does not restore the agreement completely. These results highlight the need to both develop and disseminate improved moment-closure methods for modeling biological and active matter systems.
Observing one-divalent-metal-ion-dependent and histidine-promoted His-Me family I-PpoI nuclease catalysis in crystallo
(eLife Sciences Publications Ltd, 2024) Chang, Caleb; Zhou, Grace; Gao, Yang
Metal-ion-dependent nucleases play crucial roles in cellular defense and biotechnological applications. Time-resolved crystallography has resolved catalytic details of metal-ion-dependent DNA hydrolysis and synthesis, uncovering the essential roles of multiple metal ions during catalysis. The histidine-metal (His-Me) superfamily nucleases are renowned for binding one divalent metal ion and requiring a conserved histidine to promote catalysis. Many His-Me family nucleases, including homing endonucleases and Cas9 nuclease, have been adapted for biotechnological and biomedical applications. However, it remains unclear how the single metal ion in His-Me nucleases, together with the histidine, promotes water deprotonation, nucleophilic attack, and phosphodiester bond breakage. By observing DNA hydrolysis in crystallo with His-Me I-PpoI nuclease as a model system, we proved that only one divalent metal ion is required during its catalysis. Moreover, we uncovered several possible deprotonation pathways for the nucleophilic water. Interestingly, binding of the single metal ion and water deprotonation are concerted during catalysis. Our results reveal catalytic details of His-Me nucleases, which is distinct from multi-metal-ion-dependent DNA polymerases and nucleases.
Machine learning in biological physics: From biomolecular prediction to design
(National Academy of Sciences, 2024) Martin, Jonathan; Lequerica Mateos, Marcos; Onuchic, José N.; Coluzza, Ivan; Morcos, Faruck; Center for Theoretical Biological Physics
Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is “learnable” and propose its future use in the generation of unique sequences that can fold into a target structure.
Multimodal illumination platform for 3D single-molecule super-resolution imaging throughout mammalian cells
(Optica Publishing Group, 2024) Nelson, Tyler; Vargas-Hernández, Sofía; Freire, Margareth; Cheng, Siyang; Gustavsson, Anna-Karin; Smalley-Curl Institute;Institute of Biosciences & Bioengineering;Center for Nanoscale Imaging Sciences
Single-molecule super-resolution imaging is instrumental in investigating cellular architecture and organization at the nanoscale. Achieving precise 3D nanometric localization when imaging structures throughout mammalian cells, which can be multiple microns thick, requires careful selection of the illumination scheme in order to optimize the fluorescence signal to background ratio (SBR). Thus, an optical platform that combines different wide-field illumination schemes for target-specific SBR optimization would facilitate more precise 3D nanoscale studies of a wide range of cellular structures. Here, we demonstrate a versatile multimodal illumination platform that integrates the sectioning and background reduction capabilities of light sheet illumination with homogeneous, flat-field epi- and TIRF illumination. Using primarily commercially available parts, we combine the fast and convenient switching between illumination modalities with point spread function engineering to enable 3D single-molecule super-resolution imaging throughout mammalian cells. For targets directly at the coverslip, the homogenous intensity profile and excellent sectioning of our flat-field TIRF illumination scheme improves single-molecule data quality by providing low fluorescence background and uniform fluorophore blinking kinetics, fluorescence signal, and localization precision across the entire field of view. The increased contrast achieved with LS illumination, when compared with epi-illumination, makes this illumination modality an excellent alternative when imaging targets that extend throughout the cell. We validate our microscopy platform for improved 3D super-resolution imaging by two-color imaging of paxillin – a protein located in the focal adhesion complex – and actin in human osteosarcoma cells.
Nucleosomes play a dual role in regulating transcription dynamics
(National Academy of Sciences, 2024) Brahmachari, Sumitabha; Tripathi, Shubham; Onuchic, José N.; Levine, Herbert; Center for Theoretical Biological Physics
Transcription has a mechanical component, as the translocation of the transcription machinery or RNA polymerase (RNAP) on DNA or chromatin is dynamically coupled to the chromatin torsion. This posits chromatin mechanics as a possible regulator of eukaryotic transcription, however, the modes and mechanisms of this regulation are elusive. Here, we first take a statistical mechanics approach to model the torsional response of topology-constrained chromatin. Our model recapitulates the experimentally observed weaker torsional stiffness of chromatin compared to bare DNA and proposes structural transitions of nucleosomes into chirally distinct states as the driver of the contrasting torsional mechanics. Coupling chromatin mechanics with RNAP translocation in stochastic simulations, we reveal a complex interplay of DNA supercoiling and nucleosome dynamics in governing RNAP velocity. Nucleosomes play a dual role in controlling the transcription dynamics. The steric barrier aspect of nucleosomes in the gene body counteracts transcription via hindering RNAP motion, whereas the chiral transitions facilitate RNAP motion via driving a low restoring torque upon twisting the DNA. While nucleosomes with low dissociation rates are typically transcriptionally repressive, highly dynamic nucleosomes offer less of a steric barrier and enhance the transcription elongation dynamics of weakly transcribed genes via buffering DNA twist. We use the model to predict transcription-dependent levels of DNA supercoiling in segments of the budding yeast genome that are in accord with available experimental data. The model unveils a paradigm of DNA supercoiling-mediated interaction between genes and makes testable predictions that will guide experimental design.
Microbial symbionts buffer hosts from the demographic costs of environmental stochasticity
(Wiley, 2024) Fowler, Joshua C.; Ziegler, Shaun; Whitney, Kenneth D.; Rudgers, Jennifer A.; Miller, Tom E. X.
Species' persistence in increasingly variable climates will depend on resilience against the fitness costs of environmental stochasticity. Most organisms host microbiota that shield against stressors. Here, we test the hypothesis that, by limiting exposure to temporally variable stressors, microbial symbionts reduce hosts' demographic variance. We parameterized stochastic population models using data from a 14-year symbiont-removal experiment including seven grass species that host Epichloë fungal endophytes. Results provide novel evidence that symbiotic benefits arise not only through improved mean fitness, but also through dampened inter-annual variance. Hosts with “fast” life-history traits benefited most from symbiont-mediated demographic buffering. Under current climate conditions, contributions of demographic buffering were modest compared to benefits to mean fitness. However, simulations of increased stochasticity amplified benefits of demographic buffering and made it the more important pathway of host–symbiont mutualism. Microbial-mediated variance buffering is likely an important, yet cryptic, mechanism of resilience in an increasingly variable world.
Interactive effects of soils, local environmental conditions and herbivores on secondary chemicals in tallow tree
(Oxford University Press, 2024) Xiao, Li; Huang, Wei; Carrillo, Juli; Ding, Jianqing; Siemann, Evan
Plants produce secondary chemicals that may vary along with latitude due to changing abiotic and biotic stress gradients and local environmental conditions. Teasing apart the individual and combined effects of these different abiotic, such as soil nutrients, and biotic factors, such as soil biota and herbivores, on secondary chemicals is critical for understanding plant responses to changing environments. We conducted an experiment at different latitudes in China, using tallow tree (Triadica sebifera) seedlings sourced from a population at 31° N. These seedlings were cultivated in gardens located at low, middle and high latitudes, with either local soil or soil from the original seed collection site (origin soil). The seedlings were exposed to natural levels of aboveground herbivores or had them excluded. Plant secondary chemicals (both foliar and root), aboveground herbivores and soil characteristics were measured. Results showed that most leaf and root secondary metabolites depended on the interaction of the experimental site and soil type. Leaf and root phenolic and tannin concentrations were higher at the middle latitude site, especially in the origin soil. Root and foliar flavonoid concentrations increased when aboveground herbivores were excluded. Microbial communities depended strongly on soil treatment. The different responses of tannins versus flavonoids suggest that these two chemical classes differ in their responses to the varying abiotic and biotic factors in these sites along latitudes. Taken together, our results emphasize the importance of considering the interactive effects of local environmental conditions, soil properties and herbivory in regulating plant chemical defenses.
Geographic differences in body size distributions underlie food web connectance of tropical forest mammals
(Springer Nature, 2024) Beaudrot, Lydia; Acevedo, Miguel A.; Gorczynski, Daniel; Harris, Nyeema C.; Program in Ecology and Evolutionary Biology
Understanding variation in food web structure over large spatial scales is an emerging research agenda in food web ecology. The density of predator–prey links in a food web (i.e., connectance) is a key measure of network complexity that describes the mean proportional dietary breadth of species within a food web. Connectance is a critical component of food web robustness to species loss: food webs with lower connectance have been shown to be more susceptible to secondary extinctions. Identifying geographic variation in food web connectance and its drivers may provide insight into community robustness to species loss. We investigated the food web connectance of ground-dwelling tropical forest mammal communities in multiple biogeographic regions to test for differences among regions in food web connectance and to test three potential drivers: primary productivity, contemporary anthropogenic pressure, and variation in mammal body mass distributions reflective of historical extinctions. Mammal communities from fifteen protected forests throughout the Neo-, Afro-, and Asian tropics were identified from systematic camera trap arrays. Predator–prey interaction data were collected from published literature, and we calculated connectance for each community as the number of observed predator–prey links relative to the number of possible predator–prey links. We used generalized linear models to test for differences among regions and to identify the site level characteristics that best predicted connectance. We found that mammal food web connectance varied significantly among continents and that body size range was the only significant predictor. More possible predator–prey links were observed in communities with smaller ranges in body size and therefore sites with smaller body size ranges had higher mean proportional dietary breadth. Specifically, mammal communities in the Neotropics and in Madagascar had significantly higher connectance than mammal communities in Africa. This geographic variation in contemporary mammalian food web structure may be the product of historical extinctions in the Late Quaternary, which led to greater losses of large-bodied species in the Neotropics and Madagascar thus contributing to higher average proportional dietary breadth among the remaining smaller bodied species in these regions.

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