Biology subjects

Zheng, K.

Publications and source records attributed to Zheng, K..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

A comprehensive RNA virome from molluscan transcriptomes reveals extensive diversity and modular genome evolution

Mollusca is the second-largest animal phylum and an important marine food resource for humans. While DNA viruses that threaten molluscan aquaculture have received much attention, molluscan RNA viromes remain poorly explored. Here, based on 223 molluscan metatranscriptomes covering eight animal classes, we identified 80 species-level RNA viruses spanning three viral phyla and nine viral families. Phylogenetic result combined structural modeling found a major increase in the number of Pisuviricota-related lineages. Extensive modular evolution in viral genomes was observed including gene rearrangements and co-evolution of capsid and RdRP genes. Host prediction linked 78% of the RNA viruses to a diverse range of eukaryotes, including vertebrates, invertebrates, and algae. These findings expand the known diversity of RNA viruses in molluscs and shed light on their phylogenetic relationships, highlighting molluscs as valuable models for RNA virus evolution. Graphical abstractRNA virus diversity within 223 molluscan transcriptome samples were analyzed based on RdRP identification. Viruses belonging to the phylum Pisoviricota were the most abundant; co-evolution between RdRP and capsid proteins of molluscan Picornavirales was revealed, and a complex virus-host interaction network was constructed. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC="FIGDIR/small/673676v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1ce8fc0org.highwire.dtl.DTLVardef@3f9068org.highwire.dtl.DTLVardef@121cc2forg.highwire.dtl.DTLVardef@1e431a0_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIComprehensive metatranscriptomic analysis found extensive RNA viral diversity in molluscs. C_LIO_LIPisuviricota dominate the molluscan RNA virome, substantially broadening known viral lineages. C_LIO_LIModular genome evolution involving gene rearrangements and conserved structural features characterize molluscan RNA viruses. C_LIO_LIMolluscs potentially serve as major ecological reservoirs for diverse RNA viruses infecting various eukaryotic hosts. C_LI

bioinformatics↗

bioRxiv2025

Viruses associated with Nitrososphaerota archaea: diversity, evolution and horizontal gene transfers

Nitrososphaeria, an archaeal class containing all ammonium-oxidizing archaea (AOA) and their closely related clades (e.g. Ca. Caldarchaeales), is a widely distributed archaeal lineage across diverse ecosystems. Due to the difficulty of isolation and cultivation, the viral diversity and evolution associated with non-marine AOA lineages and Ca. Caldarchaeales remain largely uncharacterized. Here, based on globally sampled genomes and metagenomes, we assembled a genomic catalogue associated with AOA, including 283 species-level viruses and 415 species-level mobile genetic elements (MGEs). Notably, 259 previously unrecognized species-level viruses or MGEs associated with Ca. Caldarchaeales were identified. Across the two major Nitrososphaeria lineages, we uncovered an expansion of archaeal viral families: 11 families associated with AOA, including seven new head-tailed lineages, one of which comprises a mega archaeal virus with a genome size exceeding 220 kb; ten families associated with Ca. Caldarchaeales, including four novel head-tailed groups and two previously unknown rod-shaped family (Demeterviridae and Gaiaviridae). Phylogenetic inference showed that rod-shaped viruses associated with Ca. Caldarchaeales and methanogens are phylogenetically intertwined and likely share a common ancestor with Hoswirudivirus, a Sulfolobales-associated rod-shaped virus characterized by two copies of the coat gene. Through structural modelling, a rare fusion of the coat protein in Demeterviridae was observed, in which a single open reading frame within a gene island adjacent to a glycosyl transferase appears capable of forming a complete capsomer. This provides new insights into the origin and evolution of Adnaviria viral realm. Functional genomics analyses further revealed that these novel rod-shaped viruses encode virus-specific rare quinone biosynthesis genes and arsenic response transcriptional regulator genes, providing potential clues to their mechanisms of adaptation to hyperthermal environments. Collectively, our work uncovers the hidden genomic, evolution and functional diversity of Nitrososphaeria viromes for the first time and closes a long-standing gap in archaeal virome research. HighlightsO_LIFirst genomic catalogue of viruses and MGEs linked to Nitrososphaeria. C_LIO_LIDiscovery of diverse novel archaeal viral families. C_LIO_LINew insights into rod-shaped virus evolution and archaeal virus-host adaptation. C_LI

bioinformatics↗

bioRxiv2025

TopoLa: A Universal Framework to Enhance Cell Representations for Single-cell and Spatial Omics through Topology-encoded Latent Hyperbolic Geometry

Recent advances in cellular research demonstrate that scRNA-seq characterizes cellular heterogeneity, while spatial transcriptomics reveals the spatial distribution of gene expression. Cell representation is the fundamental issue in the two fields. Here, we propose Topology-encoded Latent Hyperbolic Geometry (TopoLa), a computational framework enhancing cell representations by capturing fine-grained intercellular topological relationships. The framework introduces a new metric, TopoLa distance (TLd), which quantifies the geometric distance between cells within latent hyperbolic space, capturing the networks topological structure more effectively. With this framework, the cell representation can be enhanced considerably by performing convolution on its neighboring cells. Performance evaluation across seven biological tasks, including scRNA-seq data clustering and spatial transcriptomics domain identification, shows that TopoLa significantly improves the performance of several state-of-the-art models. These results underscore the generalizability and robustness of TopoLa, establishing it as a valuable tool for advancing both biological discovery and computational methodologies.

bioinformatics↗

bioRxiv2025

ODFormer: a Virtual Organoid for Predicting Personalized Therapeutic Responses in Pancreatic Cancer

Pancreatic cancer (PC) patient-derived organoids (PDOs) faithfully recapitulate therapeutic responses but face clinical translation barriers, including high costs and technical complexity. To address these problems and the lack of frameworks for PDO-based drug-response assays, we developed ODFormer, a computational framework that simulates PC PDOs to predict clinically actionable, patient-specific drug responses by integrating transcriptomic and mutational profiles. ODFormer first employed two encoders, pretrained on 30,000 pan-cancer bulk transcriptomics and 1 million PC single-cell profiles respectively, to distil tissue-and organoid-specific representations. Then, trained on our curated 14,000 PDO drug-response assay (across 183 PDOs and 98 drugs) using a transformer-augmented hybrid contrastive network, ODFormer significantly outperformed state-of-the-art methods, notably achieving a PCC >0.9 in predicting standardized drug response. Multi-cohort retrospective analyses further demonstrated that ODFormer-guided personalized therapy significantly improves clinical outcomes, without requiring physical organoid assays. Furthermore, ODFormer identified novel clinico-biological PC subtypes and revealed therapy resistance biomarkers by stratifying predicted responders and non-responders. These were validated using independent datasets including TCGA-PDAC. Notably, ODFormer-guided treatment efficacy showed high concordance with prospective clinical responses by CA19-9.

bioinformatics↗

bioRxiv2025

Spatio-temporal mass spectrometry in living cells reveals regulators of cuproptosis

Cuproptosis is a unique form of regulated cell death triggered by excessive copper accumulation in mitochondria. To decipher the mechanisms of cuproptosis, we develop Spatio-Temporal Mass Spectrometry (STMS) technique to characterize dynamic subcellular proteomic changes during its early stage. STMS analysis reveals distinct subcellular proteome alterations and unique trafficking patterns over time. Through a genetic functional screen, we discover that copper stress reduces OXA1L in mitochondria, disrupting MRPL20 import and impairing oxidative phosphorylation complex I synthesis. This mitochondrial dysfunction promotes MRPL20 nuclear translocation, triggering the mitochondrial unfolded protein response as an adaptive mechanism to restore mitochondrial proteostasis during early cuproptosis. Meanwhile, AMPK phosphorylates ISCU at serine 29, stabilizing ISCU protein, and increasing the abundance of Fe-S cluster-containing proteins, which, in turn, enhances cells susceptible to cuproptosis. Building on this mechanistic insight, we develop AMPK-SPARK, a phase separation-based EGFP reporter for real-time visualization of endogenous AMPK activity in living cells during cuproptosis. Additionally, we identify COMMD4 as a cuproptosis sensor and establish a GFP-tagged COMMD4 cell model for high-content drug screening. Using this platform, we identify three FDA-approved drugs including Plerixafor, Dithranol, and Bleomycin as potential cuproptosis inhibitors. Overall, our work advances the understanding of cuproptosis regulation and provides valuable molecular tools and data resource for future research in this area.

cell biology↗

bioRxiv2025

Stiffness sensing fuels matrix-driven metabolic reboot for kidney repair and regeneration

Kidney repair after acute kidney injury (AKI) relies on a finely tuned extracellular matrix (ECM) that provides structural integrity and mechanical cues. As primary ECM architects, fibroblasts and pericytes rapidly mobilize to the injury site post-AKI, yet the ECM-driven repair mechanisms remain incompletely defined. Here, leveraging tissue engineering, genetic and pharmacological models, and multi-omics, we profiled the proteome landscape of decellularized kidney matrix scaffold post-AKI and highlighted microfibrillar-associated protein 2 (Mfap2) as a key core matrisome component primarily sourced from fibroblasts and pericytes. Mfap2 loss disrupted kidney architecture and metabolism, aggravating AKI. Global proteomics revealed that Mfap2 deficiency suppressed tubular 3-hydroxy-3-methylglutaryl-CoA synthase 2 (Hmgcs2) expression via estrogen receptor 2 (Esr2)-mediated transcriptional repression and increased protein succinylation. Phosphoproteomics and spatial transcriptomics further demonstrated a shift in mechanical signaling, with Mfap2 loss hyperactivating mitogen-activated protein kinases and upregulating large tumor suppressor kinase 1 (Lats1) in tubular cells without altering integrin receptor activities. In turn, Lats1 suppressed Esr2 transcription independent of its canonical Yap/Taz effectors, without affecting ubiquitin-mediated Esr2 degradation. Therapeutically, Esr2 agonists restored kidney function in Mfap2-deficient models. These findings position Mfap2 as a key regulator of ECM dynamics and mechanosignaling, linking tissue stiffness to metabolic reprogramming required for kidney repair.

cell biology↗

bioRxiv2025

Advanced illumination-imaging reveals photosynthesis-triggered pH, ATP and NAD redox signatures across plant cell compartments

Photosynthesis provides energy and organic substrates to most life. In plants, photosynthesis dominates chloroplast physiology but represents only a fraction of the tightly interconnected metabolic network that spans the entire cell. Here, we explore how photosynthetic activity affects energy physiology within and beyond the chloroplast. We developed a new standard for the live-monitoring of subcellular energy physiology by combining confocal imaging of genetically encoded fluorescent protein biosensors with advanced on-stage illumination technology to investigate pH, MgATP2- and NADH/NAD+ dynamics at dark-light transitions in Arabidopsis mesophyll cells. Our findings reveal a stromal alkalinization signature induced by photosynthetic proton pumping, extending to the cytosol and mitochondria as an alkalinization wave. Photosynthesis leads to increased MgATP2- levels in both the stroma and cytosol. Additionally, we observed reduction of the NAD pool driven by photosynthesis-derived electron export. Arabidopsis lines defective in chloroplast NADP- and mitochondrial NAD-dependent malate dehydrogenases show more reduced cytosolic NAD redox status even in darkness, highlighting the involvement of chloroplasts and mitochondria in shaping cytosolic redox metabolism via malate metabolism. Our study sets a novel methodological standard for precision live-monitoring of photosynthetic cell physiology. Applying this technology reveals signatures of photosynthetic physiology within and beyond the chloroplast with unprecedented resolution. Those signatures link photosynthetic activity and the fundamental biochemical functions of phototrophic cells. Significance statementBy applying novel live microscopy monitoring using fluorescent protein biosensors in plant cells, we reveal that dark-light transitions trigger profound re-orchestration of subcellular pH, ATP and NAD redox physiology not limited to chloroplasts but extending into the cytosol and the mitochondria.

plant biology↗

bioRxiv2025

LSH-mediated resolution of R-loops mitigates transcription-replication conflicts to preserve genomic stability in prostate cancer cells

Prostate cancer cells exhibit heightened transcriptional activity to sustain their aggressive phenotype, yet this creates DNA topological stress, leading to RNA: DNA hybrids (R-loops) and DNA damage that compromise genomic stability. LSH, an SNF2-family chromatin remodeler, is frequently dysregulated in cancers and linked to tumor progression, but its mechanistic role in genome maintenance remains unclear. Here, we demonstrate that LSH resolves R-loops in an ATP-dependent manner, mitigating transcription- replication (TR) conflicts in prostate cancer cells. LSH depletion caused R- loop accumulation due to reduced FANCD2 recruitment, exacerbating DNA damage and impairing Rad51 filament formation. Strikingly, LSH deficiency also increased R-loops at promoters of MYC and E2F target genes, stalling RNA polymerase II elongation and disrupting transcriptional programs critical for proliferation. Our findings reveal LSH is a key guardian of genome integrity, resolving R-loops to prevent TR conflicts and maintaining DNA repair fidelity. By linking LSH to R-loop resolution and transcriptional regulation, this study reveals its dual role in prostate cancer pathogenesis-- supporting malignant proliferation while safeguarding genomic stability. These insights nominate LSH as a potential therapeutic target to disrupt oncogenic transcription and induce synthetic lethality in prostate cancer.

cancer biology↗

bioRxiv2025

Polyphenol oxidase silencing avoids protein crosslinking and enzymatic browning in Nicotiana benthamiana leaf extracts

Oxidation in extracts from agroinfiltrated leaves leads to browning and protein precipitation. Here we show that silencing of polyphenol oxidase (PPO) in Nicotiana benthamiana suppresses enzymatic browning and avoids crosslinking of endogenous proteins such as RuBisCo. PPO silencing does not impact plant growth or development and may also enhance transient gene expression. These results show that depletion of PPO activity could be highly beneficial for plant science applications and molecular pharming.

plant biology↗

bioRxiv2025

A novel virus vB_PshM_Y4 with low similarity to both cultured and uncultured viruses

Viruses are the abundant and diverse biological entities on the Earth. During past decades, our knowledge about genomic diversity of viruses has been greatly expanded through high-throughput sequencing. However, whether traditional isolation method could detect virus that could not be identified by high-throughput sequencing is still unknown. In this study, a novel virus, vB_PshM_Y4, infecting Pseudoalteromonas shioyasakiensis, which is an opportunistic pathogen of economically important marine species, was isolated. After comparing vB_PshM_Y4 with the over 15 million genomes of both cultivated and uncultivated viruses in the NCBI and IMG/VR v4 datasets, no similar genome was identified. This suggests that viral isolation methods can detect novel viruses that may not be identified through high-throughput sequencing. Transmission electron microscopy showed that vB_PshM_Y4 exhibits the myoviral morphology. The virus possesses a double-stranded DNA genome that is 158,231 bp in length, with a G+C content of 30.92%, and contains 226 open reading frames (ORFs). Six ORFs were identified as putative auxiliary metabolic genes (AMGs). Phylogenetic tree and gene content-based network analyses indicated that vB_PshM_Y4 may represent a novel viral species within an unknown family of the Caudoviricetes class. Additionally, biogeographical distribution analysis showed that vB_PshM_Y4 were mainly detected in the Arctic and temperate and tropical epipelagic zones with low abundance. ImportanceThis study suggests that viral isolation could detect novel virus that could not be identified through high-throughput sequencing, contributed to a deeper understanding of the genetic diversity and distribution of Pseudoalteromonas viruses, and offered valuable insights into the prevention and control of opportunistic pathogenic bacteria affecting economically important marine organisms.

microbiology↗

bioRxiv2025

LRTM: Left-Right Transition Matrices for Molecular Interaction Prediction

Molecular interactions are central to most biological processes. The discovery and identification of potential associations between molecules can provide insights into biological exploration, diagnostic and therapeutic interventions, and drug development. So far many relevant computational methods have been proposed, but most of them are usually limited to specific domains and rely on complex preprocessing procedures, which restricts the models ability to be applied to other tasks. Therefore, it remains a challenge to explore a generalized approach to accurately predicting potential associations. In this study, We propose Left-Right Transition Matrices (LRTM) for molecular interaction prediction. From the perspective on the diffusion model, we construct two transition matrices to model undirected graph information propagation. This allows modeling the transition probabilities of links, which facilitates link prediction in molecular bipartite networks. The extensive experimental results show that the proposed LRTM algorithm performs better than the compared methods. Also, the proposed algorithm has the potential for cross-task prediction. Furthermore, case studies show that LRTM is a powerful tool that can be effectively applied to practical applications.

bioinformatics↗

bioRxiv2024

An estrogen receptor signaling transcriptional program linked to immune evasion in human hormone receptor-positive breast cancer

T cells are generally sparse in hormone receptor-positive (HR+) breast cancer, potentially due to limited antigen presentation, but the driving mechanisms of low T cell abundance remains unclear. Therefore, we defined and investigated programs ( gene modules), related to estrogen receptor signaling (ERS) and immune signaling using bulk and single-cell transcriptome and multiplexed immunofluorescence of breast cancer tissues from multiple clinical sources and human cell lines. The ERS gene module, dominantly expressed in cancer cells, was negatively associated with immune-related gene modules TNF/NF-{kappa}B signaling and type-I interferon (IFN-I) response, which were expressed in distinct stromal and immune cell types, but also, in part, expressed and preserved as a cancer cell-intrinsic mechanisms. Spatial analysis revealed that ERS strongly correlated with reduced T cell infiltration, potentially due to its association with suppression of TNF/NF-{kappa}B-induced angiogenesis and IFN-I-induced HLA expression in macrophages. Preoperative endocrine therapy in ER+/HER2-breast cancer patients produced better responses in ERS-high patients, with TNF/NF-{kappa}B expression associated with reduced ERS. Targeting these pathways may enhance T cell infiltration in HR+ breast cancer patients. Statement of SignificanceThis study elucidates the immunosuppressive role of ER signaling in breast cancer, highlighting a complex interplay between cancer, stromal, and immune cells and reveals potential approaches to enhance immunogenicity in HR+ breast cancer. These findings offer crucial insights into immune evasion in breast cancer and identify strategies to enhance T cell abundance.

cancer biology↗

bioRxiv2024

Low RT-based Genome Editing Fidelity in Mouse Hepatocytes: Challenges and Solutions

Abstract/SummaryIntegrase-mediated Programmable Genomic Integration (I-PGI) uses a Cas9 nickase (nCas9) with a reverse transcriptase (RT), to write a large serine integrase (LSI) target site (attB/P, here called "beacon") in a programmed location. Co-delivery of the LSI and a DNA template containing the cognate recognition site results in precise integration of the template in a specific genomic location. While we were able to achieve high-fidelity beacon placement in a range of primate cycling and non-dividing cells, when translating our technology into an in vivo rodent model (liver) we surprisingly observed very low beacon fidelity, with the vast majority of beacons being unsuitable for integration. This phenomenon was independent of mouse strain, but was specific to non-dividing cells, as a cycling mouse hepatocyte cell line (Hepa1-6) demonstrated very high levels of fidelity. To address this issue we utilized neonatal mice, which have a much higher proportion of proliferating hepatocytes than adult mice. This resulted in a significant increase in the placement of high-fidelity beacons, and achieved functional gene expression after I-PGI in a therapeutically relevant target site. In an alternate approach, we engineered transgenic mice with intact beacons placed in specific genomic locations, allowing us to optimize integrase and DNA template dosing and kinetics. In summary, we have identified a previously undescribed challenge when using RT-based editing to write long sequences (~40 bp) in non-dividing rodent hepatocytes. This phenomenon was specific to rodents and was not observed in primate dividing or non-dividing cells. This previously unidentified challenge using RTs will limit the use of I-PGI in mouse models, however here we describe two methods that address this issue.

bioengineering↗

bioRxiv2024

Revolutionizing large-scale DNA synthesis with microchip-based massive in parallel synthesis system

DNA synthesis serves as the fundamental enabling technology of engineering biology, aiming to provide DNA molecules of designed composition, length, and complexity at scale and low cost. Current high-throughput DNA synthesis technologies rely on intricate chip manufacturing and microfluidic systems to provide large-scale synthetic oligonucleotides, at the expense of low concentration and limited compatibility in the processing of longer DNA constructs assembly. Here, we report a microchip-based massive in parallel synthesis (mMPS), pioneering an "identification-sorting-synthesis-recycling" iteration mechanism to microchips for high throughput DNA synthesis. In comparison to microarray-based methods, we demonstrate that our method can increase the DNA product concentration by 4 magnitudes (to picomole-scale per sequence) and greatly simplifies the downstream processes for large-scale gene synthesis construction. By the construction of 1.97 million-diversity variant libraries that cover 1,254 human protein domains, we demonstrated the uniformity of the constructed variant libraries using mMPS-derived oligos is greatly improved, with amino acid distribution highly consistent as designed. In addition, by synthesizing 285 1kb-to-3kb genes with varying degrees of sequence complexity from previously reported strains A501 and 3DAC, potential ancestor of early archaea and bacteria, our result shows that the overall gene assembly success rate using mMPS-derived oligos is increased by 10-fold in comparison to other methods. Our mMPS technology holds the potential to close the gap between the quality and cost of writing DNA in increasing demand across many sectors of research and industrial activities.

synthetic biology↗

bioRxiv2024

Breaking Free: Development of Circular AAV Cargos for Targeted Seamless Integration in the Liver

Recent advancements in gene insertion have shifted from DNA repair-dependent mechanisms to more precise approaches, enhancing safety and predictability for editing outcomes. Integrase-mediated programmable genomic integration (I-PGI) utilizes a DNA cargo to insert transgenes in a targeted, unidirectional manner. In vivo, where nuclear delivery of DNA is challenging, adeno-associated virus (AAV) can act as the cargo vector. While I-PGI does not require DNA double-stranded breaks (DSBs) for activity, linear DNA cargo, like AAV, stimulates DNA end joining activity after integration. To mitigate potential risks from DSBs with linear viral cargo, we developed two circular genome types capable of seamless gene insertion in non-dividing cells. We first harnessed the orthogonal property of large serine integrases to produce circle-AAV (cAAV) from linear viral genomes in cells. cAAV demonstrated faithful seamless cargo integration in primary human hepatocytes (PHH) and robust DSB-free insertion structures in vivo. We then investigated the delivery of packaged circular AAV cargo (AAV.AD), which eliminates the need for enzymatic manipulation in the cell. AAV.AD proved to be a viable cargo for I-PGI, exhibiting functional integration in PHH and in vivo, that resulted in seamless insertion structures. Together, these findings provide the first reported evidence of DSB-free programmable genomic integration using integrase and AAV cargo, addressing a previously unrecognized challenge in the field.

bioengineering↗

bioRxiv2024

A unified framework for drug-target interaction prediction by semantic-guided meta-path method

Drug-target interaction (DTI) prediction plays a crucial role in drug development, impacting areas such as virtual screening, drug repurposing, and the identification of potential drug side effects. Despite significant efforts dedicated to improving DTI prediction, existing methods still struggle with the challenges posed by the high sparsity of DTI datasets and the complexity of capturing heterogeneous information in biological networks. To address these challenges, we propose a unified framework for DTI prediction based on a semantics-guided meta-path walk. Specifically, we first pre-train drug and protein embeddings to capture their semantic information. This semantic information is then leveraged to guide a meta-path-based random walk on the biological heterogeneous network, generating sequences of interactions. These sequences are used to compute embedding features via a heterogeneous skip-gram model, which are subsequently fed into downstream tasks to predict DTIs. SGMDTI achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, it excels in the cold-start scenario, which is often a challenging case in DTI prediction. These results indicate the effectiveness of our approach in predicting drug-target interactions.Experimental datasets and experimental codes can be found in https://github.com/HYLPRC/SGMDTI

bioinformatics↗

bioRxiv2024

Burning glass effect of water droplets triggers an ER-derived calcium response in the chloroplast stroma of Arabidopsis thaliana leaves

Plants require water and light for photosynthesis, but light, when focused by water droplets on leaves, can create high light intensity spots that are harmful to plants. As excessive light intensity can reduce growth or even induce cell death, it is vital for plants to detect and react to changes in light exposure and acclimate to high light stress. Ca2+ signaling was previously implicated in high light acclimation. However, the dynamics of free Ca2+ concentration in the chloroplast, the primary site of photosynthesis, or in the nucleus and in the cytoplasm, where transcription and translation for long-term acclimation occurs, remain unknown. Here we studied the dynamics and mechanism of the Ca2+ response to high light exposure. Focusing light through a glass bead to mimic water droplets triggered an increase of the free Ca2+ concentration in the chloroplast stroma of Arabidopsis thaliana. This finding was corroborated using established and newly developed genetically encoded calcium indicators, which revealed a biphasic increase in the stromal free Ca2+ concentration when exposed to varying intensities and qualities of light. Among photosynthetic by-products, reactive oxygen and lipophilic species in particular, have been implicated in high light stress acclimation. A H2O2 signature was induced, albeit with different dynamics than the Ca2+ response, while chemical inhibition of the photosynthetic electron transport points towards singlet oxygen as a potential culprit of the high light-induced increase in stromal free Ca2+ concentration. The observed dynamics differed from those of a heat-shock induced Ca2+ signature, although temperature had a positive effect on the Ca2+ response. Based on Ca2+ inhibitor treatments and the free Ca2+ concentration dynamics, we suggest that the high light-induced stromal Ca2+ is derived from the endoplasmic reticulum rather than from the cytoplasm. In conclusion, inspired by the burning glass effect of water droplets on leaves, we uncovered a Ca2+ response that implicates a novel mechanism for plants to acclimate to high light stress--a process that will become increasingly relevant in a changing climate.

plant biology↗

bioRxiv2024

MOL-AE: Auto-Encoder Based Molecular Representation Learning With 3D Cloze Test Objective

3D molecular representation learning has gained tremendous interest and achieved promising performance in various downstream tasks. A series of recent approaches follow a prevalent framework: an encoder-only model coupled with a coordinate denoising objective. However, through a series of analytical experiments, we prove that the encoderonly model with coordinate denoising objective exhibits inconsistency between pre-training and downstream objectives, as well as issues with disrupted atomic identifiers. To address these two issues, we propose MO_SCPLOWOLC_SCPLOW-AE for molecular representation learning, an auto-encoder model using positional encoding as atomic identifiers. We also propose a new training objective named 3D Cloze Test to make the model learn better atom spatial relationships from real molecular substructures. Empirical results demonstrate that MO_SCPLOWOLC_SCPLOW-AE achieves a large margin performance gain compared to the current state-of-the-art 3D molecular modeling approach. The source codes of MO_SCPLOWOLC_SCPLOW-AE are publicly available at https://github.com/yjwtheonly/MolAE.

bioinformatics↗