Biology subjects

Chen, S. H.

Publications and source records attributed to Chen, S. H..

16 recordsLinked to original sources

bioRxiv2025

Genome assembly of Spinifex sericeus provides insights into repetitive content and haplotype variation in a dioecious coastal grass

Spinifex sericeus (beach spinifex) is a perennial, dioecious grass native to coastal ecosystem of Oceania that has an important role in dune stability. We present a near telomere-to-telomere, haplotype-resolved nuclear genome alongside complete organelle genomes of S. sericeus. The nuclear genome is highly repeat-rich, dominated by long terminal repeat (LTR) retrotransposons. Comparative analysis of the two haplotypes reveals extensive structural variation and high levels of duplication that are suggestive of active and expanding LTRs that may drive genome evolution and expansion in this species. The chloroplast genome exhibits heteroplasmy, having two distinct circular configurations spanning 138 kbp each. The complex mitochondrial genome consists of a large linear component spanning 1.85 Mbp and a small circular 134 kbp component. These genomic resources provide a foundation for advancing research on sex determination and stress adaptation in grasses, and well as practical applications in crop improvement and genetically informed coastal dune restoration. Significance statementThe reference genome of Spinifex sericeus provides a foundational resource for understanding adaptations to the harsh coastal dune environment, including saline conditions, extreme temperatures, and drought. The genome enables comparative studies within Poaceae, where current reference genomes are dominated by crop species, and supports conservation and restoration efforts for coastal ecosystem management.

genomics↗

bioRxiv2025

Genomic prediction of disease resistance provides a path to marker assisted restoration in a wetland foundation tree species

Tree species worldwide are under threat from non-native pathogens that impact forests and the ecosystem services they provide. Myrtle rust, caused by Austropuccinia psidii, is one example, first detected in Australia in 2010. This fungal pathogen infects immature tissue from a wide range of Myrtaceae hosts, including the wetland foundation species Melaleuca quinquenervia. Durable restoration action for this species would preferentially incorporate disease resistant individuals. Our aim for this study was to provide a molecular assay to identify and select resistant individuals and seed lots for restoration. We conducted artificial inoculation of a panel of seedlings and measured their immune responses to myrtle rust. We then performed whole genome sequencing (3.2M common SNPs) and conducted a Genome Wide Association Study (N = 492), which revealed clusters of significantly associated SNPs in three chromosomal regions, including clusters of putative R genes. Associated SNPs were filtered to a panel of 1,049 for a highly accurate genomic prediction model (R = 0.83). This provides a relatively inexpensive approach to identifying resistant individuals or seed lots for restoration and a template for managing myrtle rust impacts while maintaining population genetic diversity.

plant biology↗

bioRxiv2025

Enhancing Protein Binding Site Residue Prediction with Graph Neural Networks: Impacts of Cutoff Distance and Feature Selection

Identifying protein binding site residues is critical for understanding molecular interactions but challenging due to incomplete knowledge of features that determine binding. The increasing volume and accessibility of protein sequences and structures presents an opportunity to systematically decipher their intricate relationships with binding. Here, we investigate the effect of cutoff distance between residues and the importance of sequence-based and structure-based features on binding in four eukaryotic model organisms with graph neural networks. Our results indicate that sparse graphs generated by an 8 [A] cutoff are most effective for binding site residue prediction, and that increasing cutoff distance introduces noise from non-binding site residues, impairing model performance. Yet, using hybrid cutoffs that enrich connectivity between predicted binding site residues has the potential for enhancing performance. In addition, sequence-based features learned by a pre-trained protein language model carry substantial information to binding. In contrast, structure-based features derived from protein backbone geometry are inadequate due to overprediction of binding site residues. These findings are consistent for the proteins across the four organisms, providing evidence for evolutionary conservation of binding site residues. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

biophysics↗

bioRxiv2025

Coil and flow diverting stents as drug delivery platforms for cerebral aneurysm treatment

Cerebral aneurysm occlusion with coils and flow diverting stents has become the first line treatment for both unruptured and ruptured cerebral aneurysms. As these technologies have advanced, there have been changes in device shape and surface coating to enhance aneurysm embolization while reducing stent thrombogenicity. Drug eluting stents have been used with great success in the targeted delivery of rapamycin, a mTOR complex 1 inhibitor to prevent restenosis in coronary and peripheral artery disease. However, few studies have investigated the use of coils and stents as delivery platforms for sustained drug release to cerebral aneurysm tissue. In this study, we used the bio-compatible and degradable polymers, gelatin and PLGA and a simple evaporative coating technique to investigate the release of rapamycin over time from coated platinum coils and Pipeline flow diverting stents. Rapamycin coated coils were incubated with human vascular endothelial cells in vitro to confirm therapeutic levels of rapamycin release. The rate of rapamycin release was similar in both gelatin and PLGA coated coils and was sustained for more than three weeks. Rapamycin was bioactive, at a therapeutic dose and inhibited mTOR complex 1 in human brain endothelial cells treated with a rapamycin coated coil. The relative degree of mTOR complex 1 inhibition was greater in PLGA compared to gelatin coated coils. Coating flow diverting stents with a rapamycin-PLGA coating demonstrated continuous rapamycin release over a 35 day period. Reducing the percent PLGA polymer concentration caused a robust and sustainable release of rapamycin. The PLGA coating was resilient enough to allow device recapturing without affecting rapamycin eluting rates or device deployment and expansion. This work provides a simple, feasible and tunable method to coat occlusion devices for preclinical studies investigating targeted drug delivery for improved parent vessel healing and aneurysm obliteration.

bioengineering↗

bioRxiv2025

PhyloControl: a phylogeny visualisation platform for risk analysis in weed biological control

O_LIPhylogenetic distance is a key measure used to develop species lists for host specificity tests that delimit the fundamental and realised host range of candidate weed biocontrol agents to meet the assumptions of the centrifugal phylogenetic method. Plant pathogens and insects, even those with broad host ranges, exhibit some degree of phylogenetic conservatism in their host plant associations. Thorough host-specificity testing is crucial to minimise the risk of off-target damage by biocontrol agents to native and economically important plant species. To facilitate this, host test lists need to be developed from an understanding of evolutionary relationships, usually visualised as a phylogenetic tree generated from genetic data, together with plant functional traits and geospatial information. Currently, the process of obtaining a host test list is not standardised, and the manual steps are time-consuming and challenging. C_LIO_LIWe introduce a user-friendly visualisation tool called PhyloControl to aid researchers in their decision-making during biocontrol risk analysis. PhyloControl integrates taxonomic data, molecular data, spatial data, and plant traits in an intuitive interactive interface, empowering biocontrol practitioners to summarise, visualise and analyse data efficiently. C_LIO_LIComprehensively sampled phylogenetic trees are often unavailable, and older published phylogenies often lack branch resolution and support, which increases uncertainty. PhyloControl includes a workflow implemented through Quarto notebooks in R that allows users to download publicly available DNA sequences and perform phylogenetic analyses. The modular workflow also incorporates species distribution modelling to predict the current and potential extent of target weed species. C_LIO_LIPhyloControl will streamline the development of biocontrol host tests lists to support risk analysis and decision making in classical weed biological control. C_LI

plant biology↗

bioRxiv2024

Identification and characterization of substrate- and product-selective nylon hydrolases

Enzymes have evolved to rapidly and selectively hydrolyze diverse natural and anthropogenic polymers, but only a limited group of related enzymes have been shown to hydrolyze synthetic polyamides. In this work, we synthesized and characterized a panel of 95 diverse enzymes from the N-terminal nucleophile hydrolase superfamily with 30-50% pairwise amino acid identity. We found that nearly 40% of the enzymes had substantial nylon hydrolase activity, in many cases comparable to that of the best-characterized nylon hydrolase, NylC. There was no relationship between phylogeny and activity, nor any evidence of prior selection for nylon hydrolase activity. Several newly-identified hydrolases showed significant substrate selectivity, generating up to 20-fold higher product titers with Nylon 6,6 versus Nylon 6. Finally, we determined the crystal structure and oligomerization state of a Nylon 6,6-selective hydrolase to elucidate structural factors that could affect activity and selectivity. These new enzymes provide insights into the widespread potential for nylon hydrolase evolution and opportunities for analysis and engineering of improved hydrolases. SignificanceNylons are common industrial polyamides with few recycling options. As an alternative to mechanical or chemical recycling, enzymes may provide a selective and energy-efficient route to deconstruct nylons from mixed waste. Several nylon hydrolases have been identified, most notably NylC, but these enzymes are all closely related and demonstrated similar activity and substrate range. In this work, we investigated a diverse set of enzymes and showed that nylon hydrolase activity is common, providing new insights into the evolution of microbial nylon hydrolysis. Unlike NylC, several enzymes demonstrated unprecedented substrate selectivity, preferentially hydrolyzing Nylon 6,6 compared to Nylon 6. These enzymes can be used to understand substrate selectivity in nylon hydrolysis and to engineer enzymes for nylon recycling from mixed waste.

biochemistry↗

bioRxiv2024

Genomics and resistance assays inform the management of two tree species being devastated by the invasive myrtle rust pathogen

Myrtle rust is a plant disease caused by the invasive fungal pathogen Austropuccinia psidii (G. Winter) Beenken, which has a global host list of 480 species. It was detected in Australia in 2010 and has caused the rapid decline of native Myrtaceae species, including rainforest trees Rhodamnia rubescens (Benth.) Miq. (scrub turpentine) and Rhodomyrtus psidioides (G.Don) Benth. (native guava). Ex situ collections of these species have been established, with the goal of preserving remaining genetic variation. Analysis of reduced representation sequencing (DArTseq; n = 444 for R. rubescens and n = 301 for R. psidioides) showed genetic diversity is distributed along a latitudinal gradient across the range of each species. A panel of samples of each species (n = 27 for R. rubescens and n = 37 for R. psidioides) were resequenced at genome scale, revealing large historical e]ective population sizes, and little variation among individuals in inferred levels of deleterious load. In Rhodamnia rubescens, experimental assays (n = 297) identified individuals that are putatively resistant to myrtle rust. This highlights two important points: there are tangible pathways to recovery for species that are highly susceptible to rust via a genetically informed breeding program, and there is a critical need to act quickly before more standing diversity is lost.

genomics↗

bioRxiv2024

Small but mitey: long-read assembly of a streamlined mite genome from contaminated host plant sequencing data

Technological advances have propelled DNA sequencing of non-model organisms, making sequencing more accessible and cost effective, which has also increased the availability of raw data in public repositories. However, contamination is a significant concern, and the use and reuse of sequencing data requires quality control and curation. A reference genome for the Australian native rainforest tree Rhodamnia argentea Benth. (malletwood) was assembled from Oxford Nanopore Technologies (ONT) long-reads, 10x Genomics Chromium linked-reads, and Hi-C data (N50 = 32.3 Mbp and BUSCO completeness 98.0%) with 99.0% of the 347 Mbp assembly anchored to 11 chromosomes (2n = 22). The R. argentea genome will inform conservation efforts for Myrtaceae species threatened by the global spread of the fungal disease myrtle rust. We observed contamination in the sequencing data and further investigation revealed an arthropod source. Here, we demonstrate the feasibility of assembling a high-quality gapless telomere-to-telomere mite genome using contaminated host plant sequencing data. The mite exhibits genome streamlining and has a 35 Mbp genome (68.6% BUSCO completeness) on two chromosomes, capped with a novel TTTGG telomere sequence. Phylogenomic analysis suggests that it is a previously unsequenced eriophyoid mite. Despite its unknown identity, this complete nuclear genome provides a valuable resource to investigate invertebrate genome reduction. This study emphasises the importance of checking sequencing data for contamination, especially when working with non-model organisms. It also enhances our understanding of two species, including a tree that faces substantial conservation challenges, contributing to broader biodiversity initiatives. SignificanceThe genomes of Rhodamnia argentea and an associated eriophyoid mite, which contaminated the tree raw sequencing data, were assembled for the first time. We generated valuable chromosome-level genomic resources for the conservation of myrtle rust impacted tree species, pest genomics, and understanding genome streamlining. The research underscores the growing prevalence of sequencing experiments in non-model organisms while emphasising the importance of quality control and curation of sequencing data.

genomics↗

bioRxiv2023

Reduced glutamate decarboxylase 1 underlies morphine-promoted lung metastasis of triple-negative breast cancer in mice

IntroductionMorphine is commonly used for cancer-related pain management. Long-term morphine use is not only addictive but also has been associated with risk factor for cancer. MethodsWe intraperitoneally administered morphine to mice for 14 days and then implanted EO771 cells, triple negative breast cancer cells, into their mammary fat pad. After primary tumors were removed on 38th day, a subset of mice were continuously giving saline or morphine until the 68th day. Tumor size, organ metastasis, and tumor RNA expression were analyzed. ResultsOur results revealed that long-term morphine treatment increased lung metastasis in the triple-negative breast cancer mouse model. To determine cellular pathways responsible for morphine-mediated metastasis, we performed RNA sequencing analysis to compare transcriptional profiles during metastasis. Transcriptional analysis revealed a significant number of genes down-regulated by morphine treatment. Based on pathway analysis, we focused on the novel effect of morphine on down-regulating taurine/hypotaurine biosynthesis. Considering that morphine, droperidol (dopamine receptor antagonist), and naloxone (opioid receptor antagonist) may act through opioid receptor or dopamine receptor, we further demonstrated that taurine reduced EO771 cell invasion caused by morphine, but not by droperidol, or naloxone treatment. In addition, morphine treatment significantly reduced the expression of GAD1, one of the enzymes required for biosynthesis of taurine, whereas droperidol and naloxone did not. ConclusionThese novel findings of morphine reduces GAD1 level and taurine reverses invasion suggest that taurine could potentially be employed as a supplement for triple negative breast cancer patients using morphine as pain management. Key MessagesO_LIMorphine usage has been implicated in modulating immune system and affecting cancer progression. C_LIO_LILong-term usage of morphine promotes metastasis of triple negative breast cancer through reducing taurine biosynthesis. C_LIO_LIThese novel findings of morphine reduces GAD1 level and taurine reverses invasion suggest that taurine could potentially be employed as a supplement for triple negative breast cancer patients using morphine as pain management. C_LI

cancer biology↗

bioRxiv2023

A high-quality pseudo-phased genome for Melaleuca quinquenervia shows allelic diversity of NLR-type resistance genes

BackgroundThe coastal wetland tree species Melaleuca quinquenervia (Cav.) S.T.Blake (Myrtaceae), commonly named the broad-leaved paperbark, is a foundation species in eastern Australia, Indonesia, Papua New Guinea, and New Caledonia. The species has been widely grown as an ornamental, becoming invasive in areas such as Florida in the United States. Long-lived trees must respond to a wide range pests and pathogens throughout their lifespan, and immune receptors encoded by the nucleotide- binding domain and leucine-rich repeat containing (NLR) gene family play a key role in plant stress responses. Expansion of this gene family is driven largely by tandem duplication, resulting in a clustering arrangement on chromosomes. Due to this clustering and their highly repetitive domain structure, comprehensive annotation of NLR encoding genes within genomes has been difficult. Additionally, as many genomes are still presented in their haploid, collapsed state, the full allelic diversity of the NLR gene family has not been widely published for outcrossing tree species. ResultsWe assembled a chromosome-level pseudo-phased genome for M. quinquenervia and describe the full allelic diversity of plant NLRs using the novel FindPlantNLRs pipeline. Analysis reveals variation in the number of NLR genes on each haplotype, differences in clusters and in the types and numbers of novel integrated domains. ConclusionsWe anticipate that the high quality of the genome for M. quinquenervia will provide a new framework for functional and evolutionary studies into this important tree species. Our results indicate a likely role for maintenance of NLR allelic diversity to enable response to environmental stress, and we suggest that this allelic diversity may be even more important for long-lived plants.

genomics↗

bioRxiv2022

Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing

Characterizing the structural ensembles of intrinsically disordered proteins (IDPs) is essential for studying structure-function relationships as conformational dynamics govern proteins biological functions. Due to the notable difference between the neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, the experimental timescale typically results in measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a protein complex formed by two IDP domains, the NCBD/ACTR complex, using data from selective labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. By incorporating structural metrics derived from the SANS experiments as constraints in MD structure classification, we describe a structural ensemble that captures the experimental SANS and, in addition, NMR data better than MD ensembles generated by one single force field. Based on structural similarity, DL reveals three clusters of distinct conformations in the ensemble. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.

biophysics↗

bioRxiv2021

Chromosome-level de novo genome assembly of Telopea speciosissima (New South Wales waratah) using long-reads, linked-reads and Hi-C

Telopea speciosissima, the New South Wales waratah, is an Australian endemic woody shrub in the family Proteaceae. Waratahs have great potential as a model clade to better understand processes of speciation, introgression and adaptation, and are significant from a horticultural perspective. Here, we report the first chromosome-level genome for T. speciosissima. Combining Oxford Nanopore long-reads, 10x Genomics Chromium linked-reads and Hi-C data, the assembly spans 823 Mb (scaffold N50 of 69.0 Mb) with 97.8 % of Embryophyta BUSCOs complete. We present a new method in Diploidocus (https://github.com/slimsuite/diploidocus) for classifying, curating and QC-filtering scaffolds, which combines read depths, k-mer frequencies and BUSCO predictions. We also present a new tool, DepthSizer (https://github.com/slimsuite/depthsizer), for genome size estimation from the read depth of single copy orthologues and estimate the genome size to be approximately 900 Mb. The largest 11 scaffolds contained 94.1 % of the assembly, conforming to the expected number of chromosomes (2n = 22). Genome annotation predicted 40,158 protein-coding genes, 351 rRNAs and 728 tRNAs. We investigated CYCLOIDEA (CYC) genes, which have a role in determination of floral symmetry, and confirm the presence of two copies in the genome. Read depth analysis of 180 Duplicated BUSCO genes suggest almost all are real duplications, increasing confidence in protein family analysis using annotated protein-coding genes, and highlighting a possible need to revise the BUSCO set for this lineage. The chromosome-level T. speciosissima reference genome (Tspe_v1) provides an important new genomic resource of Proteaceae to support the conservation of flora in Australia and further afield.

genomics↗

bioRxiv2021

Towards guided mutagenesis: Gaussian process regression predicts MHC class II antigen mutant binding

Antigen-specific immunotherapies (ASI) require successful loading and presentation of antigen peptide into the major histocompatibility complex (MHC) binding cleft. One route of ASI design is to mutate native antigens for either stronger or weaker binding interaction to MHC. Exploring all possible mutations is costly both experimentally and computationally. To reduce experimental and computational expense, here we investigate the minimal amount of prior data required to accurately predict the relative binding affinity of point mutations for peptide-MHC class II (pMHCII) binding. Using data from different residue subsets, we interpolate pMHCII mutant binding affinities by Gaussian process (GP) regression of residue volume and hydrophobicity. We apply GP regression to an experimental dataset from the Immune Epitope Database, and theoretical datasets from NetMHCIIpan and Free Energy Perturbation calculations. We find that GP regression can predict binding affinities of 9 neutral residues from a 6-residue subset with an average R2 coefficient of determination value of 0.62 {+/-} 0.04 ({+/-}95% CI), average error of 0.09 {+/-} 0.01 kcal/mol ({+/-}95% CI), and with an ROC AUC value of 0.92 for binary classification of enhanced or diminished binding affinity. Similarly, metrics increase to an R2 value of 0.69 {+/-} 0.04, average error of 0.07 {+/-} 0.01 kcal/mol, and an ROC AUC value of 0.94 for predicting 7 neutral residues from an 8-residue subset. Our work finds that prediction is most accurate for neutral residues at anchor residue sites without register shift. This work holds relevance to predicting pMHCII binding and accelerating ASI design.

biophysics↗

bioRxiv2020

Distinct Structural Flexibility within SARS-CoV-2 Spike Protein Reveals Potential Therapeutic Targets

The emergence and rapid worldwide spread of the novel coronavirus disease, COVID-19, has prompted concerted efforts to find successful treatments. The causative virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), uses its spike (S) protein to gain entry into host cells. Therefore, the S protein presents a viable target to develop a directed therapy. Here, we deployed an integrated artificial intelligence with molecular dynamics simulation approach to provide new details of the S protein structure. Based on a comprehensive structural analysis of S proteins from SARS-CoV-2 and previous human coronaviruses, we found that the protomer state of S proteins is structurally flexible. Without the presence of a stabilizing beta sheet from another protomer chain, two regions in the S2 domain and the hinge connecting the S1 and S2 subunits lose their secondary structures. Interestingly, the region in the S2 domain was previously identified as an immunodominant site in the SARS-CoV-1 S protein. We anticipate that the molecular details elucidated here will assist in effective therapeutic development for COVID-19.

biophysics↗

bioRxiv2018

MITO-Tag Mice enable rapid isolation and multimodal profiling of mitochondria from specific cell types in vivo

Mitochondria are metabolic organelles that are essential for mammalian life, but the dynamics of mitochondrial metabolism within mammalian tissues in vivo remains incompletely understood. While whole-tissue metabolite profiling has been useful for studying metabolism in vivo, such an approach lacks resolution at the cellular and subcellular level. In vivo methods for interrogating organellar metabolites in specific cell-types within mammalian tissues have been limited. To address this, we built on prior work in which we exploited a mitochondrially-localized 3XHA epitope-tag (\"MITO-Tag\") for the fast isolation of mitochondria from cultured cells to now generate \"MITO-Tag Mice.\" Affording spatiotemporal control over MITO-Tag expression, these transgenic animals enable the rapid, cell-type-specific immunoisolation of mitochondria from tissues, which we verified using a combination of proteomic and metabolomic approaches. Using MITO-Tag Mice and targeted and untargeted metabolite profiling, we identified changes during fasted and refed conditions in a diverse array of mitochondrial metabolites in hepatocytes and found metabolites that behaved differently at the mitochondrial versus whole-tissue level. MITO-Tag Mice should have utility for studying mitochondrial physiology and our strategy should be generally applicable for studying other mammalian organelles in specific cell-types in vivo.

molecular biology↗

bioRxiv2016

Recurring functional interactions predict network architecture of interictal and ictal states in neocortical epilepsy

Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 hours of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (i) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (ii) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (iii) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that the epileptic network drives dysfunction by controlling dynamics of functional interactions between brain regions that generate seizures and those that underlie normal function.

neuroscience↗