Biology subjects

Zeng, Z.

Publications and source records attributed to Zeng, Z..

At least 73 records · Page 4Linked to original sources

bioRxiv2023

Biosynthesis of H-GDGTs linked to ocean oxygen deficiency

Archaeal membrane lipids GDGTs (glycerol dialkyl glycerol tetraethers) are biomarkers used for tracking Earths historical environmental changes. Among these GDGTs, the H-shaped GDGTs (H-GDGTs, or GMGTs) represent a less-explored and often overlooked subset, with its biosynthetic pathway and geological significance remaining elusive. Here, we identified the gene responsible for biosynthesizing H-GDGTs, which encodes to a radical S-adenosyl-L-methionine (SAM) enzyme, named as H-GDGTs bridge synthase (Hbs). Heterologous expression of the gene hbs in a methanogen, as well as in vitro activity assay using the purified Hbs enzyme were performed. Additionally, we found that the genes encoding Hbs are exclusively present in obligate anaerobic archaea genomes and the metagenomes obtained from oxygen-deficient environments, but not in oxic settings. The H-GDGTs lipids were also consistently enriched in the modern oxygen-deficient environments, and remarkably accumulated in ancient sediments during oceanic anoxic event-2 (OAE-2, [~]94 million years ago) period. Our findings indicate H-GDGTs holds significant promise as a novel biomarker for studying historical ocean oxygen deficiency supported by a well-established biological basis.

microbiology↗

bioRxiv2023

SACNet: A Multiscale Diffeomorphic Convolutional Registration Network with Prior Neuroanatomical Constraints for Flexible Susceptibility Artifact Correction in Echo Planar Imaging

Susceptibility artifacts (SAs), inevitable in brain diffusion MR (dMRI) scans acquired using single-shot echo planar imaging (EPI), severely compromise the accurate detection of human brain structure. Existing SA correction (SAC) methods offer inadequate correction quality and limited applicability across diverse datasets with varied acquisition protocols. To address these challenges, we proposed SACNet, a SAC framework based on unsupervised registration convolutional networks, featuring: i) a novel diffeomorphism regularization function to avoid unnatural SAC warps, modified from a potential well function; ii) an integration with prior neuroanatomical constraints and coarse-to-fine processing strategy to enables multi-scale geometric and intensity recoveries in severe distorted areas; iii) a unified registration framework that incorporates multiple phase-encoding (PE) EPI images and structural images, ensuring compatibility with both single- and inverse-PE protocols, with or without field maps. Utilizing simulated dMRI images and over 2000 brain scans from neonatal, child, adult and traveling participants, our method consistently demonstrates state-of-the-art correction performance. Notably, SACNet effectively reduces SAs-related multicenter effects compared to existing methods. We have developed user-friendly tools using containerization techniques, hope to facilitate SAC correction quality across extensive neuroimaging studies.

neuroscience↗

bioRxiv2023

Functional connectome through the human life span

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimers disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

neuroscience↗

bioRxiv2023

CBD and PSP cell-passaged Tau Seeds Generate Heterogeneous Fibrils with A Subpopulation Adopting Disease Folds

The recent discovery by cryo-electron microscopy (cryo-EM) that the neuropathological hallmarks of different tauopathies, including Alzheimers disease, corticobasal degeneration (CBD), and progressive supranuclear palsy (PSP), are caused by unique misfolded conformations of the protein tau is among the most profound developments in neurodegenerative disease research. To capitalize on these discoveries for therapeutic development, one must achieve in vitro replication of tau fibrils that adopt the representative tauopathy disease folds, which represents a grand challenge for the field. An widely used approach is seeded propagation using pathological tau fibrils derived from post-mortem patients in biosensor cells that expresses a fragment of the tau protein, known as K18 or Tau4RD, containing mainly the microtubule-binding repeat domain of tau as the substrate. The new insight from cryo-EM raised the question whether the Tau4RD fragment is capable of adopting the specific tau folds found in CBD and PSP patient fibrils, and whether cell-passaged and amplified tau fibrils can be used as seeds to achieve cell-free assembly of recombinant 4R tau into fibrils without the addition of any cofactors. Using Double Electron Electron Resonance (DEER) spectroscopy, we discovered that cell-passaged pathological seeds generate heterogeneous fibrils that are distinct between the CBD and PSP lysate-seeded fibrils. These fibrils are also distinct from heparin-induced tau fibril populations. Moreover, the lysate-seeded fibrils contain a characteristic sub-population that resembles the disease fold corresponding to the respective starting patient sample. These findings indicate that templated propagation using CBD and PSP patient-derived fibrils is possible with a tau fragment that does not contain the entire pathological fibril core, and that additional mechanisms must be tuned to converge to a homogeneous fibril population.

biophysics↗

bioRxiv2023

DeepNeuropePred: a robust and universal tool to predict cleavage sites from neuropeptide precursors by protein language model

Neuropeptides play critical roles in many biological processes such as growth, learning, memory, metabolism, and neuronal differentiation. A few approaches have been reported for predicting neuropeptides that are cleaved from precursor protein sequences. However, these models for cleavage site prediction of precursors were developed using a limited number of neuropeptide precursor datasets and simple precursors representation models. In addition, a universal method for predicting neuropeptide cleavage sites that can be applied to all species is still lacking. In this paper, we proposed a novel deep learning method called DeepNeuropePred, using a combination of pretrained language model and Convolutional Neural Networks for feature extraction and predicting the neuropeptide cleavage sites from precursors. To demonstrate the models effectiveness and robustness, we evaluated the performance of DeepNeuropePred and four models from the NeuroPred server in the independent dataset and our model achieved the highest AUC score (0.916), which are 6.9%, 7.8%, 8.8%, and 10.9% higher than Mammalian (0.857), insects (0.850), Mollusc (0.842) and Motif (0.826), respectively. For the convenience of researchers, we provide an easy-to-install GitHub package (https://github.com/ISYSLAB-HUST/DeepNeuropePred) and a web server (http://isyslab.info/NeuroPepV2/deepNeuropePred.jsp). Key PointsO_LIDeepNeuropePred uses a deep learning algorithm based on protein language model to accurately predict neuropeptide cleavage sites from neuropeptide precursors. C_LIO_LIIndependent test experiments show that DeepNeuropePred achieves significantly better performance than existing methods. C_LIO_LIDeepNeuropePred could capture meaningful patterns between neuro-peptide and non-neuropeptide cleavage sites. C_LIO_LIWe further provide an easy-to-install GitHub package and a web server. C_LI

bioinformatics↗

bioRxiv2023

Endothelial Knockdown of the Tumor Suppressor, WWOX, Increases Inflammation in Ventilator-Induced Lung Injury

BackgroundChronic cigarette smoke exposure downregulates lung expression of WWOX, an ARDS relevant tumor suppressor. Prior work has revealed a barrier protective function of WWOX during infectious models of ARDS. Proteomic analysis of WWOX-silenced lung endothelial cells suggest involvement of WWOX in protection against mechanical stretch-induced inflammation. MethodsProtein lysates from WWOX-silenced endothelial cells (ECs) were analyzed using tandem mass tag mass spectrometry (TMT-MS) to determine the differential expression status of the proteome compared to wild type ECs. WWOX-silenced ECs as well as those isolated from endothelial Wwox knockout (EC Wwox KO) mice were subjected to cyclic stretch (18% elongation, 0.5 Hz, 4 hours). Cellular lysates and media supernatant were harvested for assays of cellular signaling, protein expression, and cytokine release. Dual silencing of WWOX and zyxin was achieved to determine the role of zyxin upregulation in IL-8 production following mechanical stretch and during WWOX knockdown. Control and EC Wwox KO mice were subjected to high tidal volume ventilation (VILI, 40ml/kg, 65 breath/min, 4hours). Bronchoalveolar lavage fluid and mouse lung tissue were harvested for cellular signaling, cytokine secretion, and histologic assays. ResultsTMT-MS revealed upregulation of zyxin expression during WWOX knockdown which predicted a heightened inflammatory response to mechanical stretch. WWOX-silenced ECs and ECs isolated from EC Wwox mice displayed significantly increased cyclic stretch-mediated secretion of various cytokines (IL-6, KC/IL-8, IL-1{beta}, and MCP-1) relative to controls. This was associated with increased ERK and JNK phosphorylation but decreased p38 MAPK phosphorylation. EC Wwox KO mice subjected to VILI sustained a greater degree of injury than corresponding controls. Silencing of zyxin during WWOX knockdown abrogated stretch-induced increases in IL-8 secretion. ConclusionLoss of WWOX function in ECs is associated with a heightened inflammatory response during mechanical stretch that is associated with increased MAPK phosphorylation and appears to be dependent on upregulation of zyxin.

molecular biology↗

bioRxiv2023

Predicting the impact of rare variants on RNA splicing in CAGI6

BackgroundVariants which disrupt splicing are a frequent cause of rare disease that have been under-ascertained clinically. Accurate and efficient methods to predict a variants impact on splicing are needed to interpret the growing number of variants of unknown significance (VUS) identified by exome and genome sequencing. Here we present the results of the CAGI6 Splicing VUS challenge, which invited predictions of the splicing impact of 56 variants ascertained clinically and functionally validated to determine splicing impact. ResultsThe performance of 12 prediction methods, along with SpliceAI and CADD, was compared on the 56 functionally validated variants. The maximum overall accuracy achieved was 82% from two different approaches, one weighting SpliceAI scores by minor allele frequency, and one applying the recently published Splicing Prediction Pipeline (SPiP). SPiP performed optimally in terms of sensitivity, while an ensemble method combining multiple prediction tools and information from databases exceeded all others for specificity. ConclusionsSeveral challenge methods equalled or exceeded the performance of SpliceAI, with ultimate choice of prediction method likely to depend on experimental or clinical aims. One quarter of the variants were incorrectly predicted by at least 50% of the methods, highlighting the need for further improvements to splicing prediction methods for successful clinical application.

bioinformatics↗

bioRxiv2023

Speckle rheological spectroscopy reveals wideband viscoelastic spectra of biological tissues

Mechanical transformation of tissue is not merely a symptom but a decisive driver in pathological processes. Comprising intricate network of cells, fibrillar proteins, and interstitial fluid, tissues exhibit distinct solid- (elastic) and liquid-like (viscous) behaviours that span a wide band of frequencies. Yet, characterization of wideband viscoelastic behaviour in whole tissue has not been investigated, leaving a vast knowledge gap in the higher frequency range that is linked to fundamental intracellular processes and microstructural dynamics. Here, we present wideband Speckle rHEologicAl spectRoScopy (SHEARS) to address this need. We demonstrate, for the first time, analysis of frequency-dependent elastic and viscous moduli up to the sub-MHz regime in biomimetic scaffolds and tissue specimens of blood clots, breast tumours, and bone. By capturing previously inaccessible viscoelastic behaviour across the wide frequency spectrum, our approach provides distinct and comprehensive mechanical signatures of tissues that may provide new mechanobiological insights and inform novel disease prognostication.

biophysics↗

bioRxiv2023

OmicVerse: A single pipeline for exploring the entire transcriptome universe

Single-cell sequencing is frequently marred by "interruptions" due to limitations in sequencing throughput, yet bulk RNA-seq may harbor these ostensibly "interrupted" cells. In response, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping community. This approach proficiently interpolates and restores the continuity of "interrupted" cells within single-cell RNA sequencing dataset. Furthermore, OmicVerse provides an extensive toolkit for bulk and single cell RNA-seq analysis, offering uniform access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of novel biological insights to advance scientific research.

bioinformatics↗

bioRxiv2023

Modeling kidney development, disease, and plasticity with clonal expandable nephron progenitor cells and nephron organoids

Nephron progenitor cells (NPCs) self-renew and differentiate into nephrons, the functional units of the kidney. Here we report manipulation of p38 and YAP activity creates a synthetic niche that allows the long-term clonal expansion of primary mouse and human NPCs, and induced NPCs (iNPCs) from human pluripotent stem cells. Cultured iNPCs resemble closely primary human NPCs, generating nephron organoids with abundant distal convoluted tubule cells, which are not observed in published kidney organoids. The synthetic niche reprograms differentiated nephron cells into NPC state, recapitulating the plasticity of developing nephron in vivo. Scalability and ease of genome-editing in the cultured NPCs allow for genome-wide CRISPR screening, identi-fying novel genes associated with kidney development and disease. A rapid, efficient, and scala-ble organoid model for polycystic kidney disease was derived directly from genome-edited NPCs, and validated in drug screen. These technological platforms have broad applications to kidney development, disease, plasticity, and regeneration.

developmental biology↗

bioRxiv2023

AnnoPRO: an innovative strategy for protein function annotation based on image-like protein representation and multimodal deep learning

Protein function annotation has been one of the longstanding issues, which is key for discovering drug targets and understanding physiological or pathological process. A variety of computational methods have therefore been constructed to facilitate the research developments in this particular direction. However, the annotation of protein function based on computational methods has been suffering from the serious "long-tail problem", and it remains extremely challenging for existing methods to improve the prediction accuracies for protein families in tail label levels. In this study, an innovative strategy, entitled AnnoPRO, for protein function annotation was thus constructed. First, a novel method enabling image-like protein representations was proposed. This method is unique in capturing the intrinsic correlations among protein features, which can greatly favor the application of the state-of-the-art deep learning methods popular in image classification. Second, a multimodal framework integrating multichannel convolutional neural network and long short-term memory neural network was constructed to realize a deep learning-based protein functional annotation. Since this framework was inspired by a reputable method used in image classification for dealing with its long-tail problem, our AnnoPRO was expected to significantly improve the annotation performance of the protein families in tail label level. Multiple case studies based on benchmark were also conducted, which confirmed the superior performance of AnnoPRO among the existing methods. All source codes and models of AnnoPRO were freely available to all users at https://github.com/idrblab/AnnoPRO, and would be essential complement to existing methods.

bioinformatics↗

bioRxiv2023

Chromatin and Gene Regulatory Dynamics of Optic Nerve Invasion in Retinoblastoma at Single-Cell Resolution

Retinoblastoma (RB), the most aggressive pediatric intraocular malignancy, urgently requires mechanistic insights to overcome limitations of current clinical interventions. Through integrated single-cell multi-omics analysis, we constructed a comprehensive epigenetic-transcriptomic atlas of photoreceptor subpopulations in RB. Pseudotemporal trajectory analysis revealed the differentiation cascade from retinal progenitor cells (RPCs) to malignant cone-like tumor cells (Cone-T), orchestrated by dynamic regulation of tumor suppressors (FEZ1) and oncogenic drivers (CITED2) within the MAPK pathway. Notably, we identified three photoreceptor subtype-specific transcription factors (EBF1, SOX15, NFIL3) exhibiting concordant overexpression and chromatin accessibility. Cell-cell communication analysis uncovered PPIA-BSG ligand-receptor interactions potentially driving tumor progression, while drug screening prioritized nine therapeutic candidates targeting transitional proliferative photoreceptors (P-p). Our findings establish a mechanistic framework for photoreceptor transformation and provide actionable targets for precision therapies.

cancer biology↗

bioRxiv2023

Long-B prokaryotic Argonaute systems employ various effectors to confer immunity via abortive infection

Argonaute proteins (Agos) bind short nucleic acids as guides and are directed by them to recognize target complementary nucleic acids. Prokaryotic Agos (pAgos) are extremely diverse, with potential functions in microbial defense. The functions and mechanisms of a group of full-length yet inactive pAgos, long-B pAgos, remain enigmatic. Here, we show that most long-B pAgos constitute cell suicide systems together with their various associated proteins, including nucleases, Sir2-domain-containing proteins and trans-membrane proteins, respectively. Among them, the long-B pAgo-nuclease system utilizes an RNA-programmed and target-recognition-activated collateral DNA cleavage activity to sense invaders and kill the infected cells. This results in depletion of the invading plasmid from the cell population. Together, our data indicate that the long-B pAgo systems induce cell death with various effector proteins after recognition of invading nucleic acids, corresponding to an immune response via abortive infection.

microbiology↗

bioRxiv2023

Phosphoproteomics of cellular mechanosensing reveals NFATC4 as a regulator of myofibroblast activity

Feedback connections between tissue stiffness and cellular contractile forces can instruct cell identity and activity via a process referred to as mechanosensing. Specific phosphoproteome changes during mechanosensing are poorly characterized. In this work, we chart the global phosphoproteome dynamics of primary human lung fibroblasts sensing the stiffness of injury relevant fibronectin coated Poly(dimethylsiloxane) substrates. We discovered a key signaling threshold at a Youngs modulus of eight kPa stiffness, above which cells activated a large number of pathways including RhoA, CK2A1, PKA, AMPK, AKT1, and Hippo-YAP1/TAZ mediated signaling. Time-resolved phosphoproteomics of cell spreading on stiff substrates revealed the temporal dynamics of these stiffness-sensitive signaling pathways. ECM substrate stiffness above eight kPA induced fibroblast contractility, cytoskeletal rearrangements, ECM secretion, and a fibroblast to myofibroblast transition. Our data indicate that phosphorylation of the transcriptional regulator NFATC4 at S213/S217 enhances myofibroblast activity, which is the key hallmark of fibrotic diseases. NFATC4 knock down cells display reduced stiffness induced collagen secretion, cell contractility, nuclear deformation and invasion, suggesting NFATC4 as a novel target for antifibrotic therapy. SynopsisHow tissue stiffness regulates identity and activity of tissue fibroblasts is unclear. Mass spectrometry based analysis of tissue stiffness dependent phosphoproteome changes reveals how primary lung fibroblasts sense the mechanical properties of their environment and identifies NFATC4 as a novel regulator of the stiffness dependent transition of fibroblasts to ECM secreting myofibroblasts. O_LIMass spectrometry analysis reveals the signaling landscape of fibroblast mechanosensing C_LIO_LITime-resolved phosphoproteomic analysis of cell spreading on fibronectin C_LIO_LINFATC4 regulates myofibroblast collagen secretion, cell contractility and invasion C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=38 SRC="FIGDIR/small/528335v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@329c68org.highwire.dtl.DTLVardef@ba7513org.highwire.dtl.DTLVardef@15c32c4org.highwire.dtl.DTLVardef@90b96_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

Lung tumor-infiltrating Treg have divergent transcriptional profiles and function linked to checkpoint blockade response

Regulatory T cells (Treg) are conventionally viewed to suppress endogenous and therapyinduced anti-tumor immunity; however, their role in modulating responses to immune checkpoint blockade (ICB) is unclear. In this study, we integrated single-cell RNAseq/TCRseq of >73,000 tumor-infiltrating Treg (TIL-Treg) from anti-PD-1-treated and treatment naive non-small cell lung cancers (NSCLC) with single cell analysis of tumor-associated antigen (TAA)-specific Treg derived from a murine tumor model. We identified 10 subsets of human TIL-Treg, most of which have high concordance with murine TIL-Treg subsets. Notably, one subset selectively expresses high levels of OX40 and GITR, whose engangement by cognate ligand mediated proliferative programs and NF-kB activation, as well as multiple genes involved in Treg suppression, in particular LAG3. Functionally, the OX40hiGITRhi subset in the most highly suppressive ex vivo and Treg expression of OX40, GITR and LAG3, correlated with resistance to PD-1 blockade. Surprisingly, in the murine tumor model, we found that virtually all TIL-Treg expressing T cell receptors that are specific for TAA fully develop a distinct Th1-like signature over a two-week period after entry into the tumor, down-regulating FoxP3 and up-regulating expression of TBX21 (Tbet), IFN{gamma} and certain pro-inflammatory granzymes. Application of a gene score from the murine TAA-specific Th1-like Treg subset to the human single-cell dataset revealed a highly analogous subcluster that was enriched in anti-PD-1 responding tumors. These findings demonstrate that TIL-Treg partition into multiple distinct transcriptionally-defined subsets with potentially opposing effects on ICB-induced anti-tumor immunity and suggest that TAA-specific TIL-Treg may positively contribute to anti-tumor responses. One-Sentence SummaryWe define 10 subsets of lung cancer-infiltrating regulatory T cells, one of which is highly suppressive and enriched in anti-PD-1 non-responders and the other is Th1-like and is enriched in PD-1 responders.

immunology↗

bioRxiv2022

Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence

Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Using neuroimaging, connectome, transcriptome, and computational modeling, we mapped cortical thinning patterns primarily located in lateral frontal and parietal heteromodal nodes during childhood and adolescence, which is structurally constrained by white matter network architecture and is particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible while using another independent dataset. Our findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.

neuroscience↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2022

SapBase (Sapinaceae Genomic DataBase): a central portal for functional and comparative genomics of Sapindaceae species

Sapindaceae is a family of flowering plants, also known as the soapberry family, comprising 141 genera and about 1900 species (Pedro et al., 2010). Most of them are distributed in tropical and subtropical regions, including trees, shrubs, also woody or herbaceous vines. Some are dioecious, while others are monoecious. Many Sapindaceae species possess great economic value; some furnish delicious fruits, like lychee (Litchi chinensis), longan (Dimocarpus longan), rambutan (Nephelium lappaceum); and ackee (Blighia sapida) - the national fruit of Jamaica; some produce abundance secondary metabolites, like saponin from soapberry (Sapindus mukorossi), and seed oil from yellowhorn (Xanthoceras sorbifolium); some yield valuable timber including maple (Acer spp.) and buckeye (Aesculus glabra); and some are of great herbal medicinal value, like balloon-vine (Cardiospermum halicacabum). In the last decade, with the rocketing of next generation sequencing (NGS) and genomic technologies, the full genome sequences of several Sapindaceae plants have been resolved (Lin et al., 2017; Liang et al., 2019; Yang et al., 2019; Zhang et al., 2021; Hu et al., 2022; Xue et al., 2022). Among them, our recent publication of the lychee genome attracted broad attention (Edger, 2022; Hu et al., 2022; Lyu, 2022). Now the post-genome era arrives for Sapindaceae, however, there is no public genomic database available for any Sapindaceae species, let alone an integrative database for the whole Sapindaceae family. A unified data platform is in urgent need to collect, manage and share relevant data resources. Therefore, we integrated our home-brew NGS data with all publicly available data for seven Sapindaceae plants and constructed the Sapinaceae Genomic DataBase, named SapBase (www.sapindaceae.com), in order to provide genomic resources and an online powerful analytic platform for scientific research on Sapinaceae species and comparative studies with other plants.

genomics↗