Biology subjects

Xiong, D.

Publications and source records attributed to Xiong, D..

18 recordsLinked to original sources

bioRxiv2026

Acetylation of NDUFA13 at K7 Senses OXPHOS Dysfunction to Disrupt Prohibitin Scaffolds and Drive Cristae Remodelling in Heart Failure

Mitochondrial cristae disintegration is a hallmark of heart failure, yet how oxidative phosphorylation (OXPHOS) dysfunction triggers this structural collapse remains unclear. Here we identify acetylation of the complex I subunit NDUFA13 at lysine 7 (K7) as a metabolic sensor coupling OXPHOS dysfunction to cristae remodelling. OXPHOS inhibition drove NDUFA13-K7 hyperacetylation, inducing conformational compaction and translocation to the intermembrane space. Acetylated NDUFA13 competitively engaged PHB2 through its coiled-coil domain, displacing PHB1 and disassembling prohibitin scaffolds. The acetylation-mimetic NDUFA13-K7Q induced cristae fragmentation in vitro, and cardiomyocyte-specific K7Q knock-in mice showed accelerated mortality and cristae loss under pressure overload, accompanied by impaired TCA cycle flux, defective fatty acid oxidation and selective depletion of polyunsaturated cardiolipins. These findings establish NDUFA13-K7 acetylation as a reversible switch converting bioenergetic failure into pathological cristae remodelling and metabolic reprogramming, highlighting therapeutic potential for targeted deacetylation.

cell biology↗

bioRxiv2026

Lack of ancestral SARS-CoV-2 imprinting promotes BA.3.2.2 infection in children

The ongoing evolution of SARS-CoV-2 is heavily constrained by population-level immune imprinting. Recent genomic surveillance reveals an unexpected demographic shift: the highly mutated BA.3.2.2 sublineage is significantly enriched in paediatric populations globally, unlike concurrent variants XFG and NB.1.8.1. Here, the evidence demonstrates that this paediatric susceptibility arises from the absence of ancestral Wuhan-strain immune imprinting. Serological analyses reveal a profound failure to neutralise BA.3.2.2 in young children with no ancestral strain exposure. Concurrently, single-B-cell sequencing analyses demonstrate that BA.3.2.2 completely evades Omicron-specific Class 1/4 neutralising antibodies, encoded by IGHV2-5 and IGHV5-51, which dominate the repertoires of weakly imprinted individuals such as young children. Conversely, BA.3.2.2 remains uniquely susceptible to broadly cross-reactive Class 1 antibodies, encoded by IGHV3-53/66, typically enriched in adults with strong ancestral imprinting, such as mRNA vaccine recipients. Critically, sustained transmission of BA.3.2.2 in paediatric populations may catalyse the emergence of secondary variants that combine the paediatric-evading features of BA.3.2.2 with adult-evading Class 1 mutations. This could allow the lineage to breach the imprinted immunity of the adult population, potentially driving widespread global transmission. Together, these findings show that legacy immune imprinting paradoxically provides superior protection against a highly divergent saltation variant, directly explaining the age-biased transmission of BA.3.2.2, and informing future paediatric vaccination strategies.

immunology↗

bioRxiv2025

Integrating structural homology with deep learning to achieve highly accurate protein-protein interface prediction for the human interactome

A significant portion of disease-causing mutations occur at protein-protein interfaces however, the number of structurally resolved multi-protein complexes is extremely small. Here we present a computational pipeline, PIONEER2, that integrates 3D structural similarity with geometric deep learning to accurately predict protein binding partner-specific interfacial residues. We compare the performance of PIONEER2 to that of AlphaFold3 and found, using a test set of PDB structures, that their performance is quite similar. However, about 20% of AlphaFold3 predictions for protein-protein complexes in the PDB have AlphaFold3 ranking scores below 0.5, which indicates an uncertain model. For these structures, PIONEER2 outperforms AlphaFold3 at discriminating interfacial from non-interfacial residues. Further, about half of the AlphaFold3 ranking scores on high confidence protein-protein interactions (PPIs) not associated with a PDB structure are below 0.5 indicating that PIONEER2 offers superior interface prediction for a large number of PPIs for which structures are not available. We created a comprehensive 3D structurally informed interactome encompassing all 352,124 experimentally detected binary human PPIs in the current literature and made PIONEER2 interface predictions for each. We experimentally validated these predictions by generating 1,866 mutations and testing their disruptive impact on 5,010 mutation-interaction pairs. PIONEER2-predicted interfaces are found to be comparable to PDB structures in their ability to predict disruptive mutations while AlphaFold3 performance is reduced. Similarly, PIONEER2-predicted interfaces outperform AlphaFold3 in accounting for the depletion of non-deleterious common population variants and the enrichment of disease-related mutations on protein surfaces. Overall, our results suggest that PIONEER2-predicted interfaces provide a valuable tool for studying disease etiology, advancing personalized medicine and for fundamental research. We further implemented PIONEER2 as a user-friendly web server (https://pioneer2.yulab.org) platform for users to explore our 3D interactome models and conduct genome-wide functional genomics studies.

bioinformatics↗

bioRxiv2025

Comprehensive Atomic-Scale 3D Viral-Host Protein Interactomes Enable Dissection of Key Mechanisms and Evolutionary Processes Underlying Viral Pathogenesis

Viral-human protein interactions are critical for viral replication and modulation of the host immune response. Structural modeling of these interactions is vital for developing effective antiviral therapies and vaccines. However, 99% of experimentally determined binary host-viral interactions currently lack structural information. We aimed to address this gap by leveraging computational protein structure prediction methods. Using extensive benchmarking, we found AlphaFold to be the most accurate structure prediction model for host-pathogen protein interactions. We then predicted the structures of 11,666 binary protein interactions across 33 viral families and created the most comprehensive atomic-scale 3D viral-host protein interactomes till date (https://3d-viralhuman.yulab.org). By integrating these interactomes with genetic variation data, we identified population-specific signatures of selection on variants coding for interfaces of viral-human interactions. We also found that viral interaction interfaces were less conserved than non-interface regions, a striking trend that is opposite to what is observed for host interfaces, suggesting different evolutionary pressures. Systematic analyses of interface sharing between host and viral proteins binding to the same host protein revealed mutation rate-dependent differences in interface mimicry. Similar mutation rate-dependent differences were seen in the interface sharing between viral proteins binding to a host protein. We also found that the patterns of E6 protein binding to KPNA2 differed between high- and low-risk oncogenic human papillomaviruses (HPVs), and clustering based on these binding patterns allowed the classification of HPVs with unknown oncogenic risk. Our interface mimicry analyses also unveiled a novel mechanism by which herpes simplex virus-1 UL37 suppresses the antiviral immune response through disruption of the TRAF6-MAVS signalosome interaction. Overall, our comprehensive 3D viral interactomes provide a resource at unprecedented scale and resolution that will enable researchers to explore how variation and signatures of selection influence viral interactions and disease progression. This tool also facilitates the identification of conserved and unique interaction patterns across viruses, empowering researchers to generate testable hypotheses and ultimately accelerate the discovery of novel therapeutic targets and intervention strategies.

systems biology↗

bioRxiv2025

STIM1 and Endoplasmic Reticulum-Plasma Membrane Contact Sites Oscillate Independently of Calcium-Induced Calcium Release

Calcium (Ca{superscript 2}) release from intracellular stores, Ca{superscript 2} entry across the plasma membrane, and their coordination via store-operated Ca{superscript 2} entry (SOCE) are critical for receptor-activated Ca{superscript 2} oscillations. However, the precise mechanism of Ca{superscript 2} oscillations and whether their control loop resides at the plasma membrane or intracellularly remain unresolved. By examining the dynamics of stromal interaction molecule 1 (STIM1)--an endoplasmic reticulum (ER)-localized Ca{superscript 2} sensor that activates the Orai1 channel on the plasma membrane for SOCE--and in mast cells, we found that a significant proportion of cells exhibited STIM1 oscillations with the same periodicity as Ca{superscript 2} oscillations. These cortical oscillations, occurring in the cells cortical region and shared with ER-plasma membrane (ER-PM) contact sites proteins, were only detectable using total internal reflection fluorescence microscopy (TIRFM). Notably, STIM1 oscillations could occur independently of Ca{superscript 2} oscillations. Simultaneous imaging of cytoplasmic Ca{superscript 2} and ER Ca{superscript 2} with SEPIA-ER revealed that receptor activation does not deplete ER Ca{superscript 2}, whereas receptor activation without extracellular Ca{superscript 2} influx induces cyclic ER Ca{superscript 2} depletion. However, under such nonphysiological conditions, cyclic ER Ca{superscript 2} oscillations lead to sustained STIM1 recruitment, indicating that oscillatory Ca{superscript 2} release is neither necessary nor sufficient for STIM1 oscillations. Using optogenetic tools to manipulate ER-PM contact site dynamics, we found that persistent ER-PM contact sites reduced the amplitude of Ca{superscript 2} oscillations without alteration of oscillation frequency. Together, these findings suggest an active cortical mechanism governs the rapid dissociation of ER-PM contact sites, thereby control amplitude of oscillatory Ca{superscript 2} dynamics during receptor-induced Ca{superscript 2} oscillations.

cell biology↗

bioRxiv2024

Alzheimer Disease Knowledge Graph Enhances Knowledge Discovery and Disease Prediction

BackgroundAlzheimers disease (AD), a progressive neurodegenerative disorder, continues to increase in prevalence without any effective treatments to date. In this context, knowledge graphs (KGs) have emerged as a pivotal tool in biomedical research, offering new perspectives on drug repurposing and biomarker discovery by analyzing intricate network structures. Our study seeks to build an AD-specific knowledge graph, highlighting interactions among AD, genes, variants, chemicals, drugs, and other diseases. The goal is to shed light on existing treatments, potential targets, and diagnostic methods for AD, thereby aiding in drug repurposing and the identification of biomarkers. ResultsWe annotated 800 PubMed abstracts and leveraged GPT-4 for text augmentation to enrich our training data for named entity recognition (NER) and relation classification. A comprehensive data mining model, integrating NER and relationship classification, was trained on the annotated corpus. This model was subsequently applied to extract relation triplets from unannotated abstracts. To enhance entity linking, we utilized a suite of reference biomedical databases and refine the linking accuracy through abbreviation resolution. As a result, we successfully identified 3,199,276 entity mentions and 633,733 triplets, elucidating connections between 5,000 unique entities. These connections were pivotal in constructing a comprehensive Alzheimers Disease Knowledge Graph (ADKG). We also integrated the ADKG constructed after entity linking with other biomedical databases. The ADKG served as a training ground for Knowledge Graph Embedding models with the high-ranking predicted triplets supported by evidence, underscoring the utility of ADKG in generating testable scientific hypotheses. Further application of ADKG in predictive modeling using the UK Biobank data revealed models based on ADKG outperforming others, as evidenced by higher values in the areas under the receiver operating characteristic (ROC) curves. ConclusionThe ADKG is a valuable resource for generating hypotheses and enhancing predictive models, highlighting its potential to advance ADs disease research and treatment strategies.

bioinformatics↗

bioRxiv2024

Strategy for unlimited cycles of scarless oligonucleotide directed gene editing in Chlamydomonas reinhardtii

CRISPR/Cas9 gene editing in the model green alga Chlamydomonas reinhardtii relies on the use of selective marker genes to enrich for non-selectable target mutations. This becomes challenging when many sequential modifications are required in a single cell line, as useful markers are limited. Here we demonstrate a cyclical selection process which only requires a single marker gene to identify an almost infinite sequential series of CRISPR-based target gene modifications. The NIA1 (Nit1, NR; nitrate reductase) gene was this selectable marker. In the forward stage of the cycle, a stop codon was engineered into the NIA1 gene at the CRISPR target location. Cells retaining the wild type NIA1 gene were killed by chlorate, while NIA1 knockout mutants survived. In the reverse phase of the cycle, the stop codon engineered into the NIA1 gene during the forward phase was edited back to the wild type sequence. Using nitrate as the sole nitrogen source, here only the reverted wild type cells survived. By using CRISPR to specifically deactivate and reactivate the NIA1 gene, a marker system was established that flipped back and forth between chlorate- and auxotrophic (nitrate) based selection. This provided a scarless cyclical marker system that enabled an indefinite series of CRISPR edits in other, non-selectable genes. Here, we demonstrate that this Sequential CRISPR via Recycling Endogenous Auxotrophic Markers (SCREAM) technology enables an essentially limitless series of genetic modifications to be introduced to a single cell lineage of C. reinhardtii in a fast and efficient manner to complete complex genetic engineering.

plant biology↗

bioRxiv2024

Single-Nuclei RNA Sequencing Identifies Type C Low-Threshold Mechanoreceptors as Key Players in Paclitaxel-Induced Peripheral Neuropathy

Neuropathic pain triggered by chemotherapy poses a significant clinical challenge. Investigating cell type-specific alterations through single-cell transcriptome analysis holds promise in understanding symptom development and pathogenesis. In this study, we performed single nuclei RNA (snRNA) sequencing of dorsal root ganglions (DRG) to explore the molecular mechanism underlying paclitaxel-induced neuropathic pain. Mouse exposed to repeated paclitaxel doses developed persistent pain hypersensitivity lasting at least 21 days. The snRNA sequencing unveiled seven major cell types within DRGs, with neurons further subdivided into 12 distinct subclusters using known markers. Notably, type C low-threshold mechanoreceptors (C_LTMR) exhibited the most pronounced transcriptomic changes post-paclitaxel administration. Differential gene expression and Gene Ontology (GO) analysis highlighted suppressed potassium-related currents, microtubule transport, and mitochondrial functions in C_LTMR following paclitaxel treatment. Meanwhile, Gene Set Enrichment Analysis (GSEA) suggested increased Interleukin 17 production in C_LTMR after paclitaxel exposure. Pseudo-time analysis uncovered nine distinct states (state 1 to 9) of C_LTMR. State 1 exhibits higher prevalence in paclitaxel-treated mice and altered neurotransmission properties, likely contributing to paclitaxel-induced pain hypersensitivity. This comprehensive exploration sheds light on the molecular mechanisms driving paclitaxel-induced neuropathic pain, offering potential avenues for therapeutic intervention.

neuroscience↗

bioRxiv2023

Chromosome-level genome assembly for hucho bleekeri reveals the extraordinary tandem repeat proportions and its persistent population shrinkage

Salmonid fishes are globally renowned and valuable, with most members of the Salmonidae family living in seawater and exhibiting migratory behavior. In contrast, huchonid fishes (Hucho spp.and Brachymystax spp.), an ancient evolutionary branch within Salmoninae, are entirely landlocked. The Sichuan taimen (Hucho bleekeri Kimura) is a critically endangered fish that has attracted widespread concern and is one of eleven national first-level protected fishes in China. However, genome resources for all Hucho spp., including H. bleekeri, are scarce, and the genomic characteristics of this ancient evolutionary lineage remain unclear, hindering conservation biology efforts. Here, we present the first chromosome-level genome for the Sichuan taimen, with a final genome size of approximately 3.45 Gb across 44 chromosomes. The Sichuan taimen genome contains 44.15% tandem repetitive sequences, exceeding those of all sequenced salmonid fishes. We also identified 44 Ss4R homeologous block pairs in the assembled genome. Genome synteny analysis suggested a [~]5 fold tandem repeat expansion in the Sichuan taimen compared to the Salmonidae ancestor Northern pike. Phylogenetic analysis estimated the divergence time between huchonid and other Salmoninae fishes at approximately 33.29 million years ago (Mya). The divergence time between Siberia taimen and Sichuan taimen was estimated at around 2.29 Mya, with their effective population size declining from around 1 Mya. The genomic resource provided in this article will promote the protection of the Sichuan taimen and evolutionary genetics studies of salmonids.

genomics↗

bioRxiv2023

3D structural human interactome reveals proteome-wide perturbations by disease mutations

Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from [~]60,000 germline exomes and [~]36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of [~]11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institutes Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

bioinformatics↗

bioRxiv2022

Variations of photosynthetic N-use efficiency through plant phylogeny in relation to mesophyll cell wall thickness

Leaf photosynthetic nitrogen-use efficiency (PNUE) diversified significantly among C3 species. However, morpho-physiological mechanisms and interrelationships forming PNUE remain unclear on the evolutionary time scale. In this study, we compiled a novel extensive matrix of morpho-anatomical and physiological traits of leaf in 679 C3 species ranging from bryophytes to angiosperms to understand the intricacy of interrelationships underlying the variations in PNUE. We found that LMA, mesophyll cell wall thickness (Tcwm), Rubisco N allocation fraction (PR), and mesophyll conductance (gm) together interpreted 83% of variations in PNUE, with PR and gm accounting for 65% of those variations. However, the PR effects were species-dependent on gm; that is, the contribution of PR on PNUE was extensively significant in high-gm species in comparison to low-gm species. Standard major analysis (SMA) and path analysis suggested a weak correlation between PNUE and LMA, whereas the SMA correlation for PNUE-Tcwm was strong. The PR was inversely proportional to Tcwm, which was similar to the relationship between gm and Tcwm (p-value < 0.01), so that the internal CO2 drawdown from intercellular airspace to carboxylaton sites was relatively conservative over a wide range of Tcwm. Collectively, the coordination of changes in PR and gm connecting Tcwm suggested the complex physiological mechanisms mediated by Tcwm modulating PNUE across contrasting plant groups.

plant biology↗

bioRxiv2022

Constitutive, calcium-independent endoplasmic reticulum-plasma membrane contact site oscillations and its implications in store-operated calcium entry

The endoplasmic reticulum and plasma membrane (ER-PM) contact site dynamics plays a central role for store-operated calcium entry (SOCE). ER localized calcium sensor STIM1 translocates to the contact sites, interacts with Orai and mediates calcium influx from the extracellular environment. Different species of phosphoinositides (PIPs) have been reported to be involved in contact site dynamics as well as STIM1 translocation. However, most of the studies were based on loss-of-function experiments or conditions that generate massive calcium store depletion. The kinetics of ER-PM contact site dynamics during physiological stimuli -induced calcium oscillations are not well understood. Using total internal reflection fluorescence microscopy (TIRFM), we investigated the relationship between dynamics of STIM1 as well as cortical ER (cER) proteins and calcium oscillations in rat basophilic leukemia (RBL) mast cells. Surprisingly, a significant percentage of cells displayed cyclic STIM1 and cER dynamics that were calcium-independent. Using specific lipid sensors, we showed that cyclic ER-PM contact site assembly was in phase with PI(4)P oscillation, but preceded phases of PI(4,5)P2 or PI(3,4,5)P3 oscillation. Optogenetic recruitment of the phosphoinositide 5-phosphatase from INPP5E, which decreased PI(4,5)P2 and increased PI(4)P levels on the plasma membrane, stimulated the translocation of STIM1 and inhibited calcium oscillations. Interestingly, prolonged stable translocation of STIM1 to the plasma membrane had an inhibitory effect on calcium oscillations. Collectively, our findings suggest that ER-PM contact sites formation is PI(4)P-dependent. In addition, reversibility of ER-PM contact sites dynamics and intermediate strength of ER-PM contact are needed for calcium oscillation.

cell biology↗

bioRxiv2021

Developmental integration cannot explain major features of stomatal anatomical evolution in seed plants

Functional and developmental constraints on phenotypic variation may cause traits to covary over millions of years and slow populations from reaching their adaptive optima. Alternatively, trait covariation may result from selective constraint if some trait combinations are generally maladaptive. Quantifying the relative contribution of functional, developmental, and selective constraints on phenotypic variation is a longstanding goal of macroevolution, but it is often difficult to distinguish different types of constraints. The anatomy of leaves with stomata on both surfaces (amphistomatous) present a unique opportunity to test the importance of functional and developmental constraints on phenotypyic evolution. The key insight is that stomata on each leaf surface encounter the same functional and developmental constraints, but potentially different selective constraints because of leaf asymmetry in light capture, gas exchange, and other features. Independent evolution of stomatal traits on each surface imply that functional and developmental constraints alone likely do not explain trait covariance. Packing limits on how many stomata can fit into a finite epidermis and cell-size-mediated developmental integration are hypothesized to constrain variation in stomatal anatomy. The simple geometry of the planar leaf surface and knowledge of stomatal development make it possible to derive equations for phenotypic (co)variance caused by these constraints and compare them with data. We analyzed evolutionary covariance between stomatal density and length in amphistomatous leaves from 236 phylogenetically independent contrasts using a robust Bayesian model. Stomatal anatomy on each surface diverges partially independently, meaning that packing limits and developmental integration are not sufficient to explain phenotypic (co)variation. Hence, selective constraints, which require an adaptive explanation, likely contribute to (co)variation in ecologically important traits like stomata. We show how it is possible to evaluate the contribution of different constraints by deriving expected patterns of (co)variance and testing them using similar but separate tissues, organs, or sexes.

plant biology↗

bioRxiv2020

Common variants contribute to intrinsic functional architecture of human brain

The human brain remains active in the absence of explicit tasks and forms networks of correlated activity. Resting-state functional magnetic resonance imaging (rsfMRI) measures brain activity at rest, which has been linked with both cognitive and clinical outcomes. The genetic variants influencing human brain function are largely unknown. Here we utilized rsfMRI from 44,190 individuals of multiple ancestries (37,339 in the UK Biobank) to discover and validate the common genetic variants influencing intrinsic brain activity. We identified hundreds of novel genetic loci associated with intrinsic functional signatures (P < 2.8 x 10-11), including associations to the central executive, default mode, and salience networks involved in the triple network model of psychopathology. A number of intrinsic brain activity associated loci colocalized with brain disorder GWAS (e.g., Alzheimers disease, Parkinsons disease, schizophrenia) and cognition, such as 19q13.32, 17q21.31, and 2p16.1. Particularly, we detected a colocalization between one (rs429358) of the two variants in the APOE {varepsilon}4 locus and function of the default mode, central executive, attention, and visual networks. Genetic correlation analysis demonstrated shared genetic influences between brain function and brain structure in the same regions. We also detected significant genetic correlations with 26 other complex traits, such as ADHD, major depressive disorder, schizophrenia, intelligence, education, sleep, subjective well-being, and neuroticism. Common variants associated with intrinsic brain activity were enriched within regulatory element in brain tissues.

genetics↗

bioRxiv2020

The comprehensive genetic architecture of brain white matter

Brain regions communicate with each other via tracts of myelinated axons, commonly referred to as white matter. White matter microstructure can be measured in the living human brain using diffusion based magnetic resonance imaging (dMRI), and has been found to be altered in patients with neuropsychiatric disorders. Although under strong genetic control, few genetic variants influencing white matter microstructure have ever been identified. Here we identified common genetic variants influencing white matter microstructure using dMRI in 42,919 individuals (35,741 in the UK Biobank). The dMRIs were summarized into 215 white matter microstructure traits, including 105 measures from tract-specific functional principal component analysis. Genome-wide association analysis identified many novel white matter microstructure associated loci (P < 2.3 x 10-10). We identified shared genetic influences through genetic correlations between white matter tracts and 62 other complex traits, including stroke, neuropsychiatric disorders (e.g., ADHD, bipolar disorder, major depressive disorder, schizophrenia), cognition, neuroticism, chronotype, as well as non-brain traits. Common variants associated with white matter microstructure alter the function of regulatory elements in glial cells, particularly oligodendrocytes. White matter associated genes were enriched in pathways involved in brain disease pathogenesis, neurodevelopment process, and repair of white matter damage (P < 1.5 x 10-8). In summary, this large-scale tract-specific study provides a big step forward in understanding the genetic architecture of white matter and its genetic links to a wide spectrum of clinical outcomes.

genetics↗

bioRxiv2019

Rapid phylogenetic analysis of African swine fever virus from metagenomic sequences

African swine fever virus (ASFV) has devastating impacts on swine health and the world economy. Rapid and accurate phylogenetic analysis of ASFV causing outbreaks is important to reveal diversity and evolutionary of ASFV. Because it is time-consuming and needs biosafety laboratories to isolate ASFV, here we present a new way to perform rapid genome-wide phylogenetic analysis of ASFV using an allele calling based on gene by gene approach directly from genome drafts assembled from metagenomic sequences. Using open-accessed chewBBACA software, 41 publicly available ASFV genomes were analyzed to optimize the parameters and find the alleles. Alleles as many as 94 were found for building the phylogenetic trees, which covered more than 56% of the whole genome. Based on the alleles, current ASFV isolates could be divided into two major clades and a few subclades. Then the method is used to analyze two ASFV genome drafts assembled from two metagenomic sequences of a swine whole blood and a swine spleen tissue collected in Wuhan, China. It shows that the two ASFV genomes showed highest similarity to that of Pig/HLJ/2018 strain and DB/LN/2018 strain, which isolated recently in China. This proved that the ASFV in Wuhan originate from the same source causing the earlier outbreaks in Helongjiang and Liaoning province of China. This method makes it possible to analyze phylogenetic analysis of ASFV draft genomes flexibly without the need of ASFV isolation. Furthermore, because it is based on Allele calling, the ASFV-specific genetic markers found could be translated into clinical diagnostics or can be used broadly to identify conserved putative therapeutic candidates.

bioinformatics↗

bioRxiv2019

Why high yield QTLs did not succeed in preventing yield stagnation in rice?

Rice plays a vital role in global food security, and its yield needs to be increased to meet escalating demand. Although many high yield quantitative trait loci (QTLs) have been identified in the last decades, rice grain yield in the main rice-producing countries is stagnating since the middle of the 1990s. By summarizing the yield performance of high-yielding QTL lines, we found that almost all the high-yielding QTL introduced lines had no practical usage in current high yield breeding programs, due mainly to their low absolute grain yield. Further analysis showed that scientists primarily focused on spikelets number per panicle alone rather than other yield traits, and, in most of the studies, the yield increase was referenced to very old cultivars. By analyzing the yield traits correlations across cultivars in both field and pot conditions, and yield traits correlations across different eco-sites using the same cultivars, we demonstrated that the rice high yield will be rarely achieved by using single-trait approaches due to the traits trade-offs. Building on this, several recommendations are provided to the next generation of biotechnological breeding in rice.

plant biology↗

bioRxiv2017

Diffusional conductance to CO2 is the key limitation to photosynthesis in salt-stressed leaves of rice (Oryza sativa)

Salinity significantly limits leaf photosynthesis but the photosynthetic limiting factors in salt- stressed leaves remain unclear. In the present work, photosynthetic and biochemical traits were investigated in four rice genotypes under two NaCl (0 and 150 mM) concentration to assess the stomatal, mesophyll and biochemical contributions to reduced photosynthetic rate (A) in salt stressed leaves. Our results indicated that salinity led to a decrease in A, leaf osmotic potential, electron transport rate and CO2 concentrations in the chloroplasts (Cc) of rice leaves. Decreased A in salt-stressed leaves was mainly attributable to low Cc, which was determined by stomatal and mesophyll conductance. The increased stomatal limitation was mainly related to the low leaf osmotic potential caused by soil salinity. However, the increased mesophyll limitation in salt stressed leaves was related to both osmotic stress and ion stress. These findings highlight the importance of considering mesophyll conductance when developing salinity-tolerant rice cultivars.\n\nAbbreviations

plant biology↗