Biology subjects

Song, J.

Publications and source records attributed to Song, J..

At least 217 records · Page 12Linked to original sources

bioRxiv2021

LRRC15 is an inhibitory receptor blocking SARS-CoV-2 spike-mediated entry in trans

SARS-CoV-2 infection is mediated by the entry receptor ACE2. Although attachment factors and co-receptors facilitating entry are extensively studied, cellular entry factors inhibiting viral entry are largely unknown. Using a surfaceome CRISPR activation screen, we identified human LRRC15 as an inhibitory receptor for SARS-CoV-2 entry. LRRC15 directly binds to the receptor-binding domain (RBD) of spike protein with a moderate affinity and inhibits spike-mediated entry. Analysis of human lung single cell RNA sequencing dataset reveals that expression of LRRC15 is primarily detected in fibroblasts and particularly enriched in pathological fibroblasts in COVID-19 patients. ACE2 and LRRC15 are not co-expressed in the same cell types in the lung. Strikingly, expression of LRRC15 in ACE2-negative cells blocks spike-mediated viral entry in ACE2+ cell in trans, suggesting a protective role of LRRC15 in a physiological context. Therefore, LRRC15 represents an inhibitory receptor for SARS-CoV-2 regulating viral entry in trans.

microbiology↗

bioRxiv2021

Natural killer-like B cells promote Th17 cell differentiation and exacerbate rheumatoid arthritis

B cells are important participants in the pathogenesis of rheumatoid arthritis (RA). Besides classical B cells, novel B cell subsets are continually to be identified in recent years. Natural killer-like B (NKB) cells, a newly recognized B cell subset, are proved to be actively involved in the anti-infection immunity. However, their role in RA and the potential mechanism remain elusive. Here, we showed that NKB cells were expanded dramatically in collagen-induced arthritis (CIA) mice, demonstrating dynamic changes during the disease progression. These cells promoted CD4+ effector T cell proliferation and Th17 cell differentiation in vitro, while adoptive transfer of these cells exacerbated the arthritis severity of CIA mice. RNA Sequencing revealed that NKB cells displayed distinct differential gene expression profile under RA circumstance, potential perpetuating the disease progression. Moreover, the frequencies of NKB cells were significantly increased in RA patients, positively correlated with the clinical and immunological features. After effective therapy, these cells could be recovered to normal levels. Taken together, our results preliminarily revealed the pathogenic role of NKB cells in RA by promoting Th17 proinflammatory responses. Targeting these cells might provide potential therapeutic strategies for this persistent disease.

immunology↗

bioRxiv2021

Lactate Increases Stemness of CD8+ T Cells to Augment Anti-Tumor Immunity

Nutrients and metabolites play important roles in immune functions. Recent studies show lactate instead of glucose can serve as a primary carbon fuel source for most tissues. The role of lactate in tumor immunity is not well understood with immune suppressive functions reported for lactic acid, the conjugate acid form of lactate. In this study, we report lactate increases the stemness of CD8+ T cells and augments anti-tumor immunity. Subcutaneous administration of lactate but not glucose shows CD8+ T cell-dependent tumor growth inhibition. Single cell transcriptomics analysis revealed lactate treatment increased a subpopulation of stem-like TCF-1-expressing CD8+ T cells, which is further validated by ex vivo culture of CD8+ T cells from mouse splenocytes and human peripheral blood mononuclear cells. The inhibition of histone deacetylase activity by lactate increased acetylation in the histone H3K27 site at the Tcf7 super enhancer locus and increased the gene expression of Tcf7. Adoptive transfer of CD8+ T cells pretreated with lactate in vitro showed potent tumor growth inhibition in vivo. Our results elucidate the immune protective role of lactate in anti-tumor immunity without the masking effect of acid. These results may have broad implications for T cell therapy and the understanding of lactate in immune metabolism.

immunology↗

bioRxiv2021

Synthetic sphingolipid analogs and anti-cancer drugs synergistically induced human colon and pancreatic cancer cell death through inhibiting ceramide metabolism

Ceramide metabolism is a potential target for anti-cancer therapy. Studies show that chemotherapeutic agents can induce apoptosis and it is mediated by ceramide. Synthesized sphingolipid analogs can induce cell death in human lymphocytes and leukemia cells. By screening a group of synthetic sphingolipid analogs, we found that low concentrations of AD2750 and AD2646 induced cell death in human cancer cells by preventing ceramide from converting to sphingomyelin, individually or in combination with commercial cancer drugs. The combination of low concentrations of Taxol and AD2750 or AD2646 significantly increased cell death on human colon cancer cells (HT29). Co-administering low concentrations of Doxorubicin with AD2750 or AD2646 elevated cellular toxicity on human pancreatic cancer cells (CRL1687). This synergistic effect is related to the elevated cellular ceramide. Combining AD2750 or AD2646 with chemotherapy drugs can be used to manipulate ceramide and sphingomyelin metabolism, potentially to affect the growth of human cancer cells and increase the effectiveness of anti-cancer drugs on killing cancer cells.

cell biology↗

bioRxiv2021

Reference genomes of 545 silkworms enable high-throughput exploring genotype-phenotype relationships

The silkworm Bombyx mori is a domestic insect for silk production and a lepidopteran model. The currently available genomes limit a full understanding of its genetic and phenotypic diversity. Here we assembled long-read genomes of 545 domestic and wild silkworms and constructed a high-resolution pan-genome dataset. We found that the silkworm population harbors extremely variable genomes containing 7,308 new gene families, 4,260 (22%) core gene families, and 3,432,266 non-redundant SVs. We deciphered a series of causal genes and variants associated with domestication, breeding, and ecological adaptation traits, and experimentally validated two of those genes using CRISPR-Cas9 or RNA interference. This unprecedented large-scale genomic resource allows for high-throughput screening of interesting traits for functional genomic research and breeding improvement of silkworms and may serve as a guideline for traits decoding in other species.

genomics↗

bioRxiv2021

Broad ultra-potent neutralization of SARS-CoV-2 variants by monoclonal antibodies specific to the tip of RBD

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) continue to wreak havoc across the globe. Higher transmissibility and immunologic resistance of VOCs bring unprecedented challenges to epidemic extinguishment. Here we describe a monoclonal antibody, 2G1, that neutralizes all current VOCs and has surprising tolerance to mutations adjacent to or within its interaction epitope. Cryo-electron microscopy structure showed that 2G1 bound to the tip of receptor binding domain (RBD) of spike protein with small contact interface but strong hydrophobic effect, which resulted in nanomolar to sub-nanomolar affinities to spike proteins. The epitope of 2G1 on RBD partially overlaps with ACE2 interface, which gives 2G1 ability to block interaction between RBD and ACE2. The narrow binding epitope but high affinity bestow outstanding therapeutic efficacy upon 2G1 that neutralized VOCs with sub-nanomolar IC50 in vitro. In SARS-CoV-2 and Beta- and Delta-variant-challenged transgenic mice and rhesus macaque models, 2G1 protected animals from clinical illness and eliminated viral burden, without serious impact to animal safety. Mutagenesis experiments suggest that 2G1 could be potentially capable of dealing with emerging SARS-CoV-2 variants in future. This report characterized the therapeutic antibodies specific to the tip of spike against SARS-CoV-2 variants and highlights the potential clinical applications as well as for developing vaccine and cocktail therapy.

cell biology↗

bioRxiv2021

Identification of two pathways mediating protein targeting from ER to lipid droplets

Pathways localizing proteins to their sites of action within a cell are essential for eukaryotic cell organization and function. Although mechanisms of protein targeting to many organelles have been defined, little is known about how proteins, such as key metabolic enzymes, target from the ER to cellular lipid droplets (LDs). Here, we identify two distinct pathways for ER-to-LD (ERTOLD) protein targeting: early ERTOLD, occurring during LD formation, and late ERTOLD, targeting mature LDs after their formation. By using systematic, unbiased approaches, we identified specific membrane-fusion machinery, including regulators, a tether, and SNARE proteins, that are required for late ERTOLD targeting. Components of this fusion machinery localize to LD-ER interfaces and appear to be organized at ER exit sites (ERES) to generate ER-LD membrane bridges. We also identified multiple cargoes for early and late ERTOLD. Collectively, our data provide a new model for how proteins target LDs from the ER. HIGHLIGHTSO_LIProteins localize to LDs either during formation or later through ER-LD bridges C_LIO_LISpecific membrane fusion machinery localizes to LDs and mediates protein targeting C_LIO_LISpecific ER exit site proteins associate with LDs and participate in ERTOLD targeting C_LIO_LIProteomic studies reveal cargoes for early and late ERTOLD targeting C_LI

cell biology↗

bioRxiv2021

Programmable Sequences of Reflectin Proteins Provides Evidence for Their Evolution Processes, Tunable Intracellular Localization and Interaction with Cytoskeleton

Reflectin proteins are nature copolymers consist of repeated canonical domains, locating in a biophotonic system called Bragg lamellae and manipulating the dynamic structural coloration of iridocytes. Their biological functions are intriguing, but the underlying mechanism is not fully understood. In this study, reflectin A1, A2, B1 and C were found to present distinguished cyto-/nucleoplasmic localization preferences. Comparable intracellular localization can be reproduced by truncated reflectin variants, suggesting a conceivable evolutionary order among reflectin proteins. Secondly, the size-dependent access of reflectin variants into nucleus demonstrate a potential model of how reflectins get into Bragg lamellae. Thirdly, RfA1 was found to extensively interact with cytoskeleton, including its binding to actin and microtubule minus-end-directed movement. This implies cytoskeleton system plays fundamental role during the organization and transportation of reflectin proteins. Findings presented here recommend reflectins as programmable biomaterials which can be used to decipher their evolution processes, to delineate their biological mechanism, and to achieve tunable intracellular targeting as editable tags.

cell biology↗

bioRxiv2021

Liposomal Dexamethasone Reduces A/H1N1 Influenza-Associated Morbidity in Mice

Re-emerging viral threats have continued to challenge the medical and public health systems. It has become clear that a significant number of severe viral infection cases are due to an overreaction of the immune system, which leads to hyperinflammation. In this study, we aimed to demonstrate the therapeutic efficacy of the dexamethasone nanomedicine in controlling the symptoms of influenza infection. We found that the A/Wisconsin/WSLH34939/2009 (H1N1) infection induced severe pneumonia in mice with a death rate of 80%, accompanied by significant epithelial cell damage, infiltration of immune cells, and accumulation of pro-inflammatory cytokines in the airway space. Moreover, the intranasal delivery of liposomal dexamethasone during disease progression reduced the death rate by 20%. It also significantly reduced the protein level of tumor necrosis factor-alpha (TNF), interleukin1{beta} (IL-1{beta}), IL-6, and the C-X-C motif chemokine ligand 2 (CXCL2) as well as the number of infiltrated immune cells in the bronchoalveolar lavage fluids as compared to the free drug. It was found that the liposomal dexamethasone was mainly distributed into the monocyte/macrophages in the lungs, suggesting its mode of action via the specific delivery of the drug into myeloid cells as a major cell population for inducing the cytokine storm. Taken together, the intranasal delivery of liposomal dexamethasone may serve as a novel promising therapeutic strategy for the treatment of influenza A-induced pneumonia. ImportanceInfluenza A virus causes annual epidemics and sporadic pandemics of respiratory diseases. An excessive immune response, called a cytokine storm, caused by the influenza virus is the most common complication of influenza infection that is associated with high levels of morbidity and mortality. Here, we report that the liposomal dexamethasone targeting the monocytes/macrophages during disease progression reduced the death rate as well as the protein level of inflammatory cytokines and the number of immune cells. Therefore, the findings of this study may be applied to address the limitations of the current strategies for the treatment of influenza.

immunology↗

bioRxiv2021

The molecular basis of extensively drug-resistant Salmonella Typhi isolates from pediatric septicemia patients

Sepsis is a syndromic response to infections and is becoming an emerging threat to the public health sector, particularly in developing countries. Salmonella Typhi (S. Typhi), the cause of typhoid fever, is one primary cause of pediatric sepsis in typhoid endemic areas. Extensively drug-resistant (XDR) S. Typhi is more common among pediatric patients, which is responsible for over 90% of the reported XDR typhoid cases, but the majority of antibiotic resistance studies available have been carried out using S. Typhi isolates from adult patients. Here, we characterized XDR S. Typhi isolates from a medium size cohort of pediatric typhoid patients to determine their antibiotic-resistance-related gene signatures associated with common treatment options to typhoid fever patients. This study informs the molecular basis of antibiotic-resistance among recent S. Typhi isolates from pediatric septicemia patients, therefore providing insights into the development of molecular detection methods and control strategies for XDR S. Typhi.

microbiology↗

bioRxiv2021

Endogenous RNA editing of a nuclear gene BOSS triggers flowering in tomato

RNA editing is defined as the production of transcripts with RNA sequences different from those of the DNA template. Most of previous RNA editing studies have focused on organelles, while RNA editing of nuclear transcripts is largely unknown. Here, we describe the first example of nuclear transcript RNA editing in plant, the BOSS RNA editing regulates tomato flowering. The SNP (SNP1) located in the 5UTR of BOSS gene (the Balancer of SP and SFT) is associated with tomato early flowering, and two transcripts of BOSS produced by SNP1 associated RNA editing show functional differentiation, where BOSS-{beta} transcript promotes flowering while BOSS- does not. Furthermore, these two transcripts of BOSS are shown to regulate SP (anti-florigen) pathway at transcription level in the shoot apical meristem (SAM). Our findings reveal a new layer of complexity in the control of plant stem cell proliferation and provide the evidence of RNA-editing of a single gene for flowering, suggesting that molecular breeding programs to increase the RNA-editing efficiency may improve the productivity of tomatoes and other agricultural organisms.

plant biology↗

bioRxiv2021

Alpha-XIC: a deep neural network for scoring the coelution of peak groups improves peptide identification by data-independent acquisition mass spectrometry

MotivationThe peptide-centric identification methodologies of data-independent acquisition (DIA) data mainly rely on scores for the mass spectrometric signals of targeted peptides. Among these scores, the coelution scores of peak groups constructed by the chromatograms of peptide fragment ions have a significant influence on the identification. Most of the existing coelution scores are achieved by artificially designing some functions in terms of the shape similarity, retention time shift of peak groups. However, these scores cannot characterize the coelution robustly when the peak group is in the circumstance of interference. ResultsOn the basis that the neural network is more powerful to learn the implicit features of data robustly from a large number of samples, and thus minimizing the influence of data noise, in this work, we propose Alpha-XIC, a neural network-based model to score the coelution. By learning the characteristics of the coelution of peak groups derived from identified peptides, Alpha-XIC is capable of reporting robust coelution scores even for peak groups with interference. With this score appending to initial scores generated by the accompanying identification engine, the ensuing statistical validation tool can update the identification result and recover the misidentified peptides. In our evaluation of the HeLa dataset with gradient lengths ranging from 0.5h to 2h, Alpha-XIC delivered 16.7% ~ 49.1% improvements in the number of identified precursors at 1% FDR. Furthermore, Alpha-XIC was tested on LFQbench, a mixed-species dataset with known ratios, and increased the number of peptides and proteins fell within valid ratios by up to 16.6% and 13.8%, respectively, compared to the initial identification. Availability and ImplementationSource code are available at www.github.com/YuAirLab/Alpha-XIC.

bioinformatics↗

bioRxiv2021

Barcoded Competitive Clone-Initiating Cell (BC-CIC) Analysis Reveals Differences in Ovarian Cancer Cell Genotype and Niche Specific Clonal Fitness During Growth and Metastasis In Vivo

During oncogenesis, pathogenic clones develop which contain cells capable of spreading throughout the body, ultimately compromising vital organ functions and physiology. Understanding how metastatic clones develop and spread is critical for improving cancer treatments. However, our understanding of these processes has been hampered by a paucity of quantitative methodologies to comprehensively map, track and characterize such clones. To address this shortcoming, we have developed a DNA barcoding and next-generation sequencing based system-wide clonal tracking technology integrated with a computational data analysis pipeline called Clone-Initiating Cell (CIC) Calculator. The CIC Calculator interfaces with the CIC Morbus Mandala (CIC-MM) plot, a novel tool to visually comprehend and detect four distinct categories that explains their complex relationships with various tissues/organ sites. Further, we describe machine learning approaches to study CIC number, frequency, and estimate clone size and distribution demonstrating distinct growth patterns, and their inter-relationships and their routes of metastatic spread at clonal resolution. We demonstrate these methodologies, using our novel multifunctional lentiviral barcode libraries, and specifically barcoded tubal-ovarian metastatic OVCAR5 cell lines (engineered to express varying levels of metastasis promoting LRRC15 gene) and co-injected cells in a competitive CIC assay into tubal or ovarian sites in highly immunodeficient NSG mice. DNA was isolated from primary tumors, omental/bowel metastasis and system-wide anatomical site/organs. Amplicon sequencing libraries were constructed with spike-in-control barcodes (serving as internal calibration controls) to estimate absolute clone sizes. The computational pipeline CIC Calculator was then used to deconvolute and filter the data, set stringent thresholds, and generate high-quality information on CIC numbers and frequencies, clone sizes, linkages across sites and classify clones based on their extent of metastatic activity. Using of CIC-MM plot, statistical models and machine learning approaches, we generated high-resolution clonal maps of metastasis for each animal. The information generated included clone types and system-wide metastasis, similar and dissimilar clonal patterns of dominance at heterotopic sites and their routes of metastases. The data revealed previously unknown influences of cellular genotype and their implanted sites on selecting certain clones with specific system-wide clonal patterns, and identified rare LRRC15 expressor clones (classified as CIC.Toti) predisposed to exploit all sites, albeit at varying degrees of dominance. The genomic technology and computational methodology described here are tissue-agnostic. They enable rapid adoption for an investigation into various stages of system-wide metastasis and growth of transplantable malignant cells at the highest clonal resolution.

cancer biology↗

bioRxiv2021

Alpha-Frag: a deep neural network for fragment presence prediction improves peptide identification by data independent acquisition mass spectrometry

Identification of peptides in mass spectrometry-based proteomics typically relies on spectra matches. As MS/MS spectra record presence and intensity of fragment ions, the match should take both fragment presence similarity and intensity similarity into consideration. Fragment presence similarity can be calculated with the help of fragment presence prediction such as theoretical enumeration of all possible fragment ions or selecting non-zero intensity ions from the result of fragment intensity prediction, but neither of these two methods is accurate enough. In this work, we developed a deep neural network based model, Alpha-Frag, to predict precisely the fragment ions that should be present for a given peptide. Alpha-Frag modelled fragment presence prediction as a multi-label classification task and trained with ProteomeTools dataset. In terms of intersection over union (IoU), Alpha-Frag achieved an average of >0.7 and outperformed the benchmarks across the validation datasets. Furthermore, fragment presence similarity was calculated based on presence prediction and incorporated into the peptide statistical validation tools as an additional score to improve peptide identifications. Our preliminary experiments show that this score led to a maximum increase of 26.8% (FDR 0.1%) and 21.6% (FDR 1%) for the DDA and the DIA identification, respectively. Significance StatementA better prediction of fragmentation for peptides in mass spectrometry (MS) is beneficial to the peptide identification. As the MS/MS spectra record two-dimensional information of fragment ions derived from precursors, mass-to-charge ratio (m/z) and their corresponding intensities, besides the fragment intensity prediction, it is necessary to study the presence prediction. Although the presence prediction can be realized by enumerating all the possible fragmentation patterns of a peptide with equal probability or by selecting non-zero intensity fragment ions from the result of fragment intensity prediction, neither of these two methods is accurate enough. In this study, deep learning is leveraged to precisely predict the fragment ions of a given peptide. Based on the fragment presence prediction, fragment presence similarity between experimental spectra and predicted spectra can be calculated which is proved to promote the peptide detections both for DDA and for DIA data.

bioinformatics↗

bioRxiv2021

Missing Value Imputation using XGboost for Label-Free Mass Spectrometry-Based Proteomics Data

The label-free mass spectrometry-based proteomics data inevitably suffer from the problem of missing values. The existence of missing values prevents the downstream analyses which need a complete data matrix. Our motivation is to introduce the state-of-art machine learning algorithm XGboost to realize a method of imputation which can improve the accuracy of imputation. But in practical, XGboost has many parameters need to be tuned to deliver on its potential high performance. Although cross validation may find the best parameters, it is much time-consuming. Alternatively, we empirically determined the parameters to two kinds of base learners of XGboost. To explore the robustness and performance of XGboost based imputation with predetermined parameters, we conducted tests on three benchmark datasets. As a comparative, six common imputation methods were also experimented in terms of normalized root mean squared error and Pearson correlation coefficient. The comparative experimental results indicated that the XGboost based imputation method using the linear base learner is competitive to or out-performs its competitors, including the random forest based imputation, by achieving smaller imputation errors and better structure preservation under the empirical parameters for the three benchmark datasets.

bioinformatics↗

bioRxiv2021

Lipocalin 2 promotes inflammatory breast cancer tumorigenesis and skin invasion

Inflammatory breast cancer (IBC) is an aggressive form of primary breast cancer characterized by rapid onset and high risk of metastasis and poor clinical outcomes. The biological basis for the aggressiveness of IBC is still not well understood and no IBC-specific targeted therapies exist. In this study we report that lipocalin 2 (LCN2), a small secreted glycoprotein belonging to the lipocalin superfamily, is expressed at significantly higher levels in IBC versus non-IBC tumors, independently of molecular subtype. LCN2 levels were also significantly higher in IBC cell lines and in their culture media than in non-IBC cell lines. High expression was associated with poor-prognosis features and shorter overall survival in IBC patients. Depletion of LCN2 in IBC cell lines reduced proliferation, colony formation, migration, and cancer stem cell populations in vitro, and inhibited tumor growth, skin invasion, and brain metastasis in mouse models of IBC. Analysis of our proteomics data showed reduced expression of proteins involved in cell cycle and DNA repair in LCN2-silenced IBC cells. Our findings support that LCN2 promotes IBC tumor aggressiveness and offer a new potential therapeutic target for IBC.

cancer biology↗

bioRxiv2021

Structural basis of anti-SARS-CoV-2 activity of hydroxychloroquine: specific binding to NTD/CTD and disruption of LLPS of N protein

Great efforts have led to successfully developing the spike-based vaccines but challenges still exist to completely terminate the SARS-CoV-2 pandemic. SARS-CoV-2 nucleocapsid (N) protein plays the essential roles in almost all key steps of the viral life cycle, thus representing a top drug target. Almost all key functions of N protein including liquid-liquid phase separation (LLPS) depend on its capacity in interacting with nucleic acids. Therefore, only the variants with their N proteins functional in binding nucleic acids might survive and spread in evolution and indeed, the residues critical for binding nucleic acids are highly conserved. Very recently, hydroxychloroquine (HCQ) was shown to prevent the transmission in a large-scale clinical study in Singapore but so far, no specific SARS-CoV-2 protein was experimentally identified to be targeted by HCQ. Here by NMR, we unambiguously decode that HCQ specifically binds NTD and CTD of SARS-CoV-2 N protein with Kd of 112.1 and 57.1 M respectively to inhibit their interaction with nucleic acid, as well as to disrupt LLPS essential for the viral life cycle. Most importantly, HCQ-binding residues are identical in SARS-CoV-2 variants and therefore HCQ is likely effective to them all. The results not only provide a structural basis for the anti-SARS-CoV-2 activity of HCQ, but also renders HCQ to be the first known drug capable of targeting LLPS. Furthermore, the unique structure of the HCQ-CTD complex decodes a promising strategy for further design of better anti-SARS-CoV-2 drugs from HCQ. Therefore, HCQ is a promising candidate to help terminate the pandemic.

biophysics↗

bioRxiv2021

A chloride efflux transporter OsBIRG1 regulates grain size and salt tolerance in rice

Grain size is determined by the number of cells and cell size of the grain. Regulation of grain size is crucial for improving crop yield. However, the genes and underlying molecular mechanisms controlling grain size remain elusive. Here we report a member of Detoxification efflux carrier (DTX)/Multidrug and Toxic Compound Extrusion (MATE) family transporter, BIG RICE GRAIN 1 (BIRG1), negatively regulates the grain size in rice. BIRG1 is highly expressed in reproductive organs and roots. In birg1 grain, the size of the outer parenchyma layer cells of spikelet hulls is noticeably larger but the cell number is not altered compared with that in the wild-type (WT) grain. When expressed in Xenopus oocytes, BIRG1 exhibits chloride efflux activity. In line with the role of BIRG1 in mediating chloride efflux, the birg1 mutant shows reduced tolerance to salt stress under which the chloride level is toxic. Moreover, the birg1 grains contain higher level of chloride compared to WT grains when grown under normal paddy field. The birg1 roots accumulate more chloride than those of WT under saline condition. Collectively, our findings suggest that BIRG1 functions as a chloride efflux transporter regulating grain size and salt tolerance via controlling chloride homeostasis in rice.

plant biology↗