Biology subjects

Wang, R.

Publications and source records attributed to Wang, R..

At least 325 records · Page 18Linked to original sources

bioRxiv2023

xTrimoDock: Rigid Protein Docking via Cross-Modal Representation Learning and Spectral Algorithm

Protein-protein interactions are the basis for the formation of protein complexes which are essential for almost all cellular processes. Knowledge of the structures of protein complexes is of major importance for understanding the biological function of these protein-protein interactions and designing protein drugs. Here we address the problem of rigid protein docking which assumes no deformation of the involved proteins during interactions. We develop a method called, xTrimoDock, which leverages a cross-modal representation learning to predict the protein distance map, and then uses a spectral initialization and gradient descent to obtain the roto-translation transformation for docking. We show that, on antibody heavy-chain and light-chain docking, and antibody-antigen docking, xTrimoDock consistently outperforms the state-of-the-art such as AlphaFold-Multimer and HDock, and can lead to as much as a 10% improvement in DockQ metric. xTrimoDock has been applied as a useful tool in protein drug design at BioMap.

bioinformatics↗

bioRxiv2023

PLANET: A Multi-Objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction

Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been developed in recent years to tackle this issue, but many of them merely focus on reproducing the binding affinity of known binders. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input, and it was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and intra-ligand distance matrix. To serve those tasks, a large number of decoy non-binders were selected and added to the standard PDBbind data set. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to other deep learning models that rely on 3D protein-ligand complex structures as input. It also showed notably better performance in virtual screening trials on the DUD-E and LIT-PCBA benchmark. In particular, PLANET achieved comparable accuracy on LIT-PCBA as the conventional docking program Glide. However, it only took less than 1% of the computation time required by Glide to finish the same job because it did not perform exhaustive conformational sampling. In summary, PLANET exhibited a decent performance in binding affinity prediction as well as virtual screening, which makes it potentially useful for drug discovery in practice.

bioinformatics↗

bioRxiv2023

Multifunctional ferromagnetic fiber robots for navigation, sensing, and treatment in minimally invasive surgery

Small-scale robots capable of remote active steering and navigation offer great potential for biomedical applications. However, the current design and manufacturing procedure impede their miniaturization and integration of various diagnostic and therapeutic functionalities. Here, we present a robotic fiber platform for integrating navigation, sensing, and therapeutic functions at a submillimeter scale. These fiber robots consist of ferromagnetic, electrical, optical, and microfluidic components, fabricated with a thermal drawing process. Under magnetic actuation, they can navigate through complex and constrained environments, such as artificial vessels and brain phantoms. Moreover, we utilize Langendorff mouse hearts model, glioblastoma microplatforms, and in vivo mouse models to demonstrate the capabilities of sensing electrophysiology signals and performing localized treatment. Additionally, we demonstrate that the fiber robots can serve as endoscopes with embedded waveguides. These fiber robots provide a versatile platform for targeted multimodal detection and treatment at hard-to-reach locations in a minimally invasive and remotely controllable manner.

bioengineering↗

bioRxiv2023

The complete chloroplast genome sequence of Akebia trifoliata and a comparative analysis within the Ranales clade

The complete nucleotide sequence of the Akebia trifoliata chloroplast (cp) genome was reported and characterized in this study. The cp genome is a closed circular molecule of 157 949 bp, composed of a pair of IR regions of 26 149 bp, one LSC region of 86 595 bp, and one SSC region of 19 056 bp. The GC contents of the LSC, SSC, and IR regions, and the whole cp genome are 37.11%, 33.62%, 43.08% and 38.66%, respectively. The cp genome contains 130 predicted functional genes, including 85 PCGs of 79 107 bp, 37 tRNA and 8 rRNA genes of 11 851 bp. 168 SSRs and 23 long repeats were identified in the cp genome. The results revealed that 21 891 codons characterize the coding capacity of 85 protein-coding genes in Akebia trifoliata, 10.84% and 1.20% of the codons coded for leucine and cysteine respectively. The usage of the start codon exhibited no bias in the A. trifoliata cp genome. Phylogenetic analysis suggest that A. trifoliata is most closely related to S. japonica, which then formed a cluster with N. dormestica and M. saniculifolia to form subgroup of Ranunculales. Our study provides information on mangrove plant species in coastal intertidal zones.

plant biology↗

bioRxiv2023

Preclinical Basis of the Efficacy and Pharmacodynamics of Finotonlimab, a Humanized Anti-PD-1 Monoclonal Antibody with Potent Implications for Clinical Benefit

BackgroundThe antibodies of programmed cell death protein 1 (PD-1) and its ligand (PD-L1) have dramatically changed the treatment landscapes for patients with cancer. Clinical uses of PD-1 antibodies have greatly improved the overall survival and durable responses in patients across selected tumor types. MethodsWe describe the preclinical characterization of Finotonlimab, a humanized anti-PD-1 antibody, by head to head comparison with Nivolumab or Pembrolizumab. Herein, we characterized the in vitro and in vivo efficacy, PK, PD and Fc mediated effector function of Finotonlimab. The single-agent anti-tumor activity of Finotonlimab was evaluated using humanized mouse models and a human PBMC reconstituted mouse model. Furthermore, in cynomolgus monkeys, comparative PK measurements confirmed better PK profiles of Finotonlimab than that of Pembrolizumab and Nivolumab. ResultsOur data showed Finotonlimab bind to human PD-1 with significantly high affinity and effectively inhibited its interaction with its ligands, PD-L1 and PD-L2, and thus could effectively stimulate the human T cell functions in vitro and exhibited significant antitumor efficacy in vivo. In addition, Finotonlimab showed minimal impact on Fc receptor dependent effector cell activation, which may contribute to the killing of PD-1+ T cells. In cynomolgus monkeys, Finotonlimab exhibited a non-linear pharmacokinetics (PK) profile in a dose-dependent manner, and approximately 90% of consistent receptor occupancy period was observed at 168 h after a single administration of 1 mg/kg. Following a 13-week successive administration of Finotonlimab, a pharmacodynamics study indicated a sustained mean receptor occupancy of [≥] 93% of PD-1 molecules on circulating T cells in cynomolgus monkeys up to 8 weeks even at 3 mg/kg. ConclusionsTaken together, these preclinical data are encouraging and provide a basis for the efficacy and pharmacodynamics of Finotonlimab in clinical trials.

immunology↗

bioRxiv2023

A cinnamyl alcohol dehydrogenase (CAD) like enzyme leads to a branch in the shikonin biosynthetic pathway in Arnebia euchroma

Shikonin derivatives are natural naphthoquinone compounds and the main bioactive components produced by several boraginaceous plants, such as Lithospermum erythrorhizon and Arnebia euchroma. Phytochemical researches utilizing L. erythrorhizon and A. euchroma cultured cells both indicate the existence of a competing route branching out from the shikonin biosynthetic pathway toward benzo/hydroquinones. A previous study has shown that the branch point is a putative alcohol dehydrogenase converting (Z)-3-hydroxygeranylhydroquinone [(Z)-3-OH-GHQ] to an aldehyde intermediate (E)-3-oxo-GHQ. However, the enzyme involved in the branch reaction is not characterized at the molecular level yet. In this study, we clone a candidate gene belonging to the cinnamyl alcohol dehydrogenase (CAD) family, AeHGO, through coexpression analysis of transcriptome data sets of shikonin-proficient and shikonin-deficient cell lines of A. euchroma. In biochemical assays, purified AeHGO protein reversibly oxidizes (Z)-3-OH-GHQ to produce (E)-3-oxo-GHQ followed by reversibly reducing (E)-3-oxo-GHQ to (E)-3-OH-GHQ, resulting in an equilibrium mixture of the three compounds. Time course analysis and kinetic parameters show that the reaction with (Z)-3-OH-GHQ is about twice as efficient as with (E)-3-OH-GHQ, which leads to the predominance of (E)-3-OH-GHQ and (E)-3-oxo-GHQ in the equilibrium mixture. According to a previous report, (E)-3-oxo-GHQ can be converted to deoxyshikonofuran, a hydroquinone metabolite produced by boraginaceous plants. Considering there is a competition for accumulation between shikonin derivatives and benzo/hydroquinones in both L. erythrorhizon and A. euchroma cultured cells, AeHGO is supposed to play an important role in the metabolic regulation of shikonin biosynthetic pathway.

biochemistry↗

bioRxiv2023

Fluorescence Image Data with Sparse and Low-Rank Poisson Regression

Multispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (1) as the number of fluorophores used in any experiment increases and (2) as the signal-to-noise ratio in the recorded images decreases. Further, the availability of prior knowledge regarding the expected spatial distributions of fluorophores in images of labeled cells provides an opportunity to improve the accuracy of fluorophore identification and abundance. We propose a regularized sparse and low-rank Poisson unmixing approach (SL-PRU) to deconvolve spectral images labeled with highly overlapping fluorophores which are recorded in low signal-to-noise regimes. Firstly, SL-PRU implements multi-penalty terms when pursuing sparseness and spatial correlation of the resulting abundances in small neighborhoods simultaneously. Secondly, SL-PRU makes use of Poisson regression for unmixing instead of least squares regression to better estimate photon abundance. Thirdly, we propose a method to tune the SL-PRU parameters involved in the unmixing procedure in the absence of knowledge of the ground truth abundance information in a recorded image. By validating on simulated and real-world images, we show that our proposed method leads to improved accuracy in unmixing fluorophores with highly overlapping spectra.

bioinformatics↗

bioRxiv2022

Hypothalamic supramammillary neurons that project to the medial septum control wakefulness

The hypothalamic supramammillary nucleus (SuM) plays a key role in controlling wakefulness, but the downstream target regions participating in this control process remain unknown. Here, using circuit-specific fiber photometry and single-neuron electrophysiology together with electroencephalogram, electromyogram and behavioral recordings, we find approximately half of SuM neurons that project to the medial septum (MS) are wake-active. Optogenetic stimulation of axonal terminals of SuM-MS projection induces a rapid and reliable transition to wakefulness from NREM or REM sleep, and chemogenetic activation of SuMMS projecting neurons significantly increases wakefulness time and prolongs latency to sleep. Consistently, chemogenetically inhibiting these neurons significantly reduces wakefulness time and latency to sleep. Therefore, these results identify the MS as a functional downstream target of SuM and provide evidence for a causal role for this hypothalamic-septal projection in wakefulness control.

neuroscience↗

bioRxiv2022

The necessity of considering enzymes as compartments in constraint-based genomescale metabolic models

As the most widespread and practical digital representations of living cells, metabolic network models have become increasingly precise and accurate. By integrating cellular resources and abiotic constraints, the prediction functions were significantly expanded in recent years. However, we found that if unreasonable modeling methods were adopted due to the lack of consideration of biological knowledge, the conflicts between stoichiometric and other constraints, such as thermodynamic feasibility and enzyme resource availability, would lead to distorted predictions. In this work, we investigated a prediction anomaly of EcoETM, a constraints-based metabolic network model, and introduced the idea of enzyme compartmentalization into the analysis process. Through rational combination of reactions, we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites. This allowed us to correct the pathway structures of L-serine and L-tryptophan. Specific analysis explains the application method of EcoETM-like model, demonstrating its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments. Notably, this work also reveals the trade-off between product yield and thermodynamic feasibility. Finally, we provide a preliminary comparison of the thermodynamic feasibility of ammonia and glutamine as amino donors, which revealed that the direct utilization of ammonia does not have a decisive impact on the thermodynamic feasibility of the anthranilate pathway. Our work is of great value for the structural improvement of constraints-based models.

bioinformatics↗

bioRxiv2022

Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification

Computer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource-intensive process. Recent advances in self-supervised learning (SSL) methods have proven instrumental in overcoming these obstacles, using purely unlabeled datasets to pre-train DL models. Here, we implement the popular self-supervised contrastive learning methods of NNCLR (Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels. Maize kernels are imaged using a commercial high-throughput imaging system. This image data is often used in multiple downstream applications across both production and breeding applications, for instance, sorting for oil content based on segmenting and quantifying the scutellums size and for classifying haploid and diploid kernels. We show that in both classification and segmentation problems, SSL techniques outperform their purely supervised transfer learning-based counterparts and are significantly more annotation efficient. Additionally, we show that a single SSL pre-trained model can be efficiently finetuned for both classification and segmentation, indicating good transferability across multiple downstream applications. Segmentation models with SSL-pretrained backbones produce DICE similarity coefficients of 0.81, higher than the 0.78 and 0.73 of those with ImageNet-pretrained and randomly initialized backbones, respectively. We observe that finetuning classification and segmentation models on as little as 1% annotation produces competitive results. These results show SSL provides a meaningful step forward in data efficiency with agricultural deep learning and computer vision.

plant biology↗

bioRxiv2022

Tau pathology in the dorsal raphe may be a prodromal indicator of Alzheimer's disease

Protein aggregation in brainstem nuclei is thought to occur in the early stages of Alzheimers disease (AD), but its specific role in driving prodromal symptoms and disease progression is largely unknown. The dorsal raphe nucleus (DRN) contains a large population of serotonin (5-hydroxytryptamine; 5-HT) neurons that regulate mood, reward-related behavior, and sleep, which are all disrupted in AD. We report here that tau pathology is present in the DRN of individuals 54-80 years old without a known history of dementia and was found at higher frequency than -synuclein and TDP-43. Most AD cases had tau pathology in the DRN (90%), whereas only a subset contained TDP-43 or -synuclein, but not both (30%). To evaluate how early tau pathology impacts behavior, we overexpressed human P301L-tau in the DRN of mice and observed depressive-like behaviors and hyperactivity without any deficits in spatial memory. Tau pathology was predominantly found in neurons relative to glia and colocalized with a significant proportion of Tph2-expressing neurons in the DRN. 5-HT neurons were also hyperexcitable in P301L-tauDRN mice, and there was an increase in the amplitude of excitatory post-synaptic currents (EPSCs), suggestive of increased glutamatergic transmission. Moreover, astrocytic density was elevated in the DRN and accompanied by an increase in IL-1 and Frk expression, which is indicative of inflammation. Additionally, tau pathology was detected in axonal processes in the thalamus, hypothalamus, amygdala, and caudate putamen and a significant proportion of this tau pathology colocalized with the serotonin reuptake transporter (SERT), suggesting that tau may spread in an anterograde manner to regions outside the DRN. Together these results indicate that tau pathology accumulates in the DRN in a subset of individuals over 50 years and may lead to behavioral dysregulation, 5-HT neuronal dysfunction, and activation of local astrocytes which may be prodromal indicators of AD.

neuroscience↗

bioRxiv2022

Aryl hydrocarbon receptor utilises cellular zinc signals to maintain the gut epithelial barrier

Both zinc and plant-derived ligands of the aryl hydrocarbon receptor (AHR) are dietary components which regulate intestinal epithelial barrier function and protect against Inflammatory Bowel Disease (IBD)1,2. Here, we explore whether zinc and AHR pathway are linked using a mouse IBD model with follow-on studies on human and mouse ileum organoids. Our data demonstrate that AHR regulates cellular zinc uptake, and that zinc is an integral part of AHR signalling processes. We show that dietary supplementation in mice with the plant-derived AHR ligand precursor, indole-3-carbinol (I3C), offers a high level of protection against dextran sulfate sodium induced IBD while protection fails in mice with AHR deleted in the intestinal epithelium. AHR agonist treatment is also ineffective in mice with a nutritional zinc deficiency. Experiments in the human Caco-2 cell line and ileum organoids showed that AHR activation increases total cellular zinc and cytosolic free Zn2+ concentrations through transcriptional upregulation of several SLC39 zinc importers. As a consequence, genes for tight junction (TJ) proteins were upregulated in a zinc-dependent manner involving zinc inhibition of signalling to NF-{kappa}B and attenuated degradation of TJ proteins through zinc inhibition of calpain activity. Thus, our data indicate that AHR activation by plant-derived dietary ligands improves gut barrier function via zinc-dependent cellular pathways, suggesting that combined dietary supplementation with AHR ligands and zinc might be effective in preventing and treating inflammatory gut disorders.

cell biology↗

bioRxiv2022

Effect of Nrf2 Loss on Senescence and Cognition of Tau-Based P301S Mice

Cellular senescence may contribute to chronic inflammation involved in the progression of age-related diseases such as Alzheimers Disease (AD), and its removal prevents cognitive impairment in a model of tauopathy. Nrf2, the major transcription factor for damage response pathways and regulators of inflammation, declines with age. Our previous work showed that silencing Nrf2 gives rise to premature senescence in cells and mice. Others have shown that Nrf2 ablation can exacerbate cognitive phenotypes of some AD models. In this study we aimed to understand the relationship between Nrf2 elimination, senescence, and cognitive impairment in AD, by generating a mouse model expressing a mutant human tau transgene in an Nrf2 knockout (Nrf2KO) background. We assessed senescent cell burden and cognitive decline of P301S mice in the presence and absence of Nrf2. Lastly, we administered 4.5 month-long treatments with two senotherapeutic drugs to analyze their potential to prevent senescent cell burden and cognitive decline: the senolytic drugs Dasatinib and Quercetin (DQ) and the senomorphic drug rapamycin. Nrf2 loss accelerated the onset of hind-limb paralysis in P301S mice. At 8.5 months of age, P301S mice did not exhibit memory deficits, while P301S mice without Nrf2 were significantly impaired. However, markers of senescence were not elevated by Nrf2 ablation in any of tissues that we examined. Neither drug treatment improved cognitive performance, nor did it reduce expression of senescence markers in brains of P301S mice. Contrarily, rapamycin treatment at the doses used delayed spatial learning and led to a modest decrease in spatial memory. Taken together, our data suggests that the emergence of senescence may be causally associated with onset of cognitive decline in the P301S model, indicate that Nrf2 protects brain function in a model of AD through mechanisms that may include, but do not require the inhibition of senescence, and suggest possible limitations for DQ and rapamycin as therapies for AD.

animal behavior and cognition↗

bioRxiv2022

Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries

We develop a new method, SBayesRC, that integrates GWAS summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyse 28 traits in the UK Biobank using [~]7 million common SNPs and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and by up to 33% in trans-ancestry prediction, compared to the baseline method SBayesR which does not use annotations, and outperforms state-of-the-art methods LDpred-funct, PolyPred-S and PRS-CSx by 12-15%. Investigation of factors affecting prediction accuracy identified a significant interaction between SNP density and annotation information, encouraging future use of whole-genome sequence variants for prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from non-synonymous SNPs.

genetics↗

bioRxiv2022

Increased public health threat of avian-origin H3N2 influenza virus during evolution in dogs

Influenza A viruses in animal reservoirs repeatedly cross species barrier to infect humans. Once an animal-borne virus with novel antigenicity acquired the efficient human to human transmissibility, it will become epidemic in the population. Dogs are the closest animal companions to humans and canine respiratory tract expresses both SA2,3-(avian type) and 2,6-Gal (human type) receptors. However, the role of dogs in the ecology of influenza viruses is unclear. H3N2 avian influenza viruses transmitted to dogs around 2006 and have formed stable lineages. The long-term epidemic of avian-origin H3N2 virus in canine offers the best models to investigate the effect of dogs on the evolution of influenza viruses. Here, we carried out a systematic and comparative identification of the biological characteristics of H3N2 canine influenza viruses (CIVs) isolated in the worldwide over 10 years. We found that during the adaptation of H3N2 CIVs to dogs, H3N2 CIVs became to recognize the human-like SA2,6-Gal receptor, gradually increased HA acid stability and replication ability in human airway epithelial cells, and acquired a 100% transmission rate via respiratory droplet in ferret model, which were essential hallmarks of being adapted to humans. We also identified that the frequency of substitutions related to human adaptation has gradually increased in H3N2 CIVs, and determined four cumulative molecular changes responsible for the increased airborne transmission ability in ferrets. Our results suggested that canine may serve as an intermediate for the adaptation of avian influenza virus to human. Continuous surveillance coordinated with risk assessment for CIVs is necessary.

microbiology↗

bioRxiv2022

An Integrated Physiological, Cytology and Proteomics Reveals Network of Sugarcane Protoplasts Responses to Enzymolysis

The protoplast experimental system has been becoming a powerful tool for functional genomics and cell fusion breeding. However, the physiology and molecular mechanism during enzymolysis is not completely understood and has become a major obstacle to protoplast regeneration. Our study used physiological, cytology, iTRAQ (Isobaric Tags for Relative and Absolute Quantification) -based proteomic and RT-PCR analyses to compare the young leaves of sugarcane (ROC22) and protoplasts of more than 90% viability. We found that oxidation product MDA content increased in the protoplasts after enzymolysis and several antioxidant enzymes such as POD, CAT, APX, and O2- content significantly decreased. The cytology results showed that after enzymolysis, the cell membranes were perforated to different degrees, the nuclear activity was weakened, the nucleolus structure was not obvious, and the microtubules depolymerized and formed many short rod-like structures in protoplasts. The proteomic results showed that 1,477 differential proteins were down-regulated and 810 were up-regulated after enzymolysis of sugarcane young leaves. The GO terms, KEGG and KOG enrichment analysis revealed that differentially abundant proteins were mainly involved in bioenergetic metabolism, cellular processes, osmotic stress, and redox homeostasis of protoplasts, which would allow protein biosynthesis or / degradation. The RT-PCR analysis revealed the expression of osmotic stress resistance genes such as DREB, WRKY, MAPK4, and NAC were up-regulated. Meanwhile, the expression of key regeneration genes such as CyclinD3, CyclinA, CyclinB, Cdc2, PSK, CESA and GAUT were significantly down-regulated in the protoplasts. Hierarchical clustering, identification of redox proteins and oxidation products showed that these proteins were involved in dynamic networks in response to oxidative stress after enzymolysis. We used a variety of methods to figure out how young sugarcane leaves react to enzymes.

cell biology↗

bioRxiv2022

DeepBIO is an automated and interpretable deep-learning platform for biological sequence prediction, functional annotation, and visualization analysis

Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learning architectures to answer any biological question. Specifically, given any biological sequence data, DeepBIO supports a total of 42 state-of-the-art deep-learning algorithms for model training, comparison, optimization, and evaluation in a fully automated pipeline. DeepBIO provides a comprehensive result visualization analysis for predictive models covering several aspects, such as model interpretability, feature analysis, functional sequential region discovery, etc. Additionally, DeepBIO supports 9 base-level functional annotation tasks using deep-learning architectures, with comprehensive interpretations and graphical visualizations to validate the reliability of annotated sites. Empowered by high-performance computers, DeepBIO allows ultra-fast prediction with up to million-scale sequence data in a few hours, demonstrating its usability in real application scenarios. Case study results show that DeepBIO provides an accurate, robust, and interpretable prediction, demonstrating the power of deep learning in biological sequence functional analysis. Overall, we expect DeepBIO to ensure the reproducibility of deep-learning biological sequence analysis, lessen the programming and hardware burden for biologists, and provide meaningful functional insights at both sequence-level and base-level from biological sequences alone. DeepBIO is publicly available at http://inner.wei-group.net/DeepBIO.

bioinformatics↗

bioRxiv2022

Topographically driven microclimatic gradients shape patterns of forest structure, diversity, and composition at a forest-grassland transition zone

O_LIGlobally, forests provide important ecosystem services, but anthropogenic change may shift the boundaries of forested biomes, because small-scale environmental changes govern biome transitions. This is especially true in semi-arid forests, where minor topographic and microclimatic changes influence forest functioning and transitions to open biomes such as grasslands. However, we lack quantitative descriptions of topographically driven microclimate variation and how it shapes forest structure, diversity, and composition in these transition zones. C_LIO_LILeveraging a 20.2-ha forest inventory plot (Niobrara plot) at a semi-arid forest-grassland transition zone in the Niobrara River valley (Nebraska, USA), we paired data on abundances and distributions of seedlings, saplings, and adults of woody species with topographic and microclimate data to test the hypothesis that if topographic variation causes variation in microclimate that affects forest function, then forest structure, diversity, and composition should vary significantly with topography and microclimate. C_LIO_LIMicroclimatic variation within the Niobrara plot strongly corresponded with topography, creating a sharp water availability and exposure gradient from the river floodplain to the forest-grassland transition zone. The magnitude of microclimate variation corresponded to that of regional macroclimate variation. Mean soil moisture was 10.2% lower along the higher-elevation transition zone than in the canyon bottoms, corresponding to variation across approximately 2.5 degrees of longitude. Mean air temperature increased by 2.2 {degrees}C from the canyon bottoms to upper canyon, corresponding to variation across approximately 3 degrees of latitude. C_LIO_LIForest structure, diversity, and composition correlated strongly with topographic and microclimatic gradients. More complex forest structure and higher species richness of adults and saplings occurred in moister, less exposed habitats with steeper slopes and lower elevations, whereas seedling stem density and richness were higher in higher-light, moister habitats at lower elevations. Species occupied well-defined topographic niches, promoting high beta diversity along topographic and microclimatic gradients and high species turnover from the floodplain to the transition zone. C_LIO_LISynthesis: Microclimatic and topographic variation drive patterns of structure, diversity, and composition in the forests at this forest-grassland transition zone. As the macroclimate becomes warmer and drier, topographically mediated microclimatic refuges supporting diverse, structurally complex forested ecosystems may shrink in semi-arid regions. C_LI

ecology↗