Biology subjects

Gao, S.

Publications and source records attributed to Gao, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

GPLD1 Regulates the Shedding of IZUMO1R to Block Polyspermy in Porcine Oocyte

Mammalian fertilization commences with the essential interaction between sperm IZUMO1 and its oocyte-surface receptor, JUNO. Following gamete fusion, JUNO is rapidly shed from the oocyte to establish a definitive membrane block to polyspermy, a pathological condition that remains a major hurdle in porcine in vitro fertilization (IVF). Despite its biological importance, the molecular networks driving JUNO cleavage has remained elusive. Here, by integrating proteomics, dual-color live-cell imaging, and functional perturbations, we identify the extended existence of JUNO and the GPI-specific phospholipase D1 (GPLD1) as the requisite enzyme mediating JUNO shedding in porcine oocytes. Targeted knockdown or pharmacological inhibition of GPLD1 stabilizes oocyte JUNO, prolongs the window of oocyte receptivity, and significantly exacerbates polyspermy, ultimately compromising embryonic developmental competence. Conversely, GPLD1 overexpression restricts redundant sperm adherence and enhances the efficiency of monospermic zygote formation and blastocyst development. Live-cell imaging reveals that fertilization triggers a transient, pulsed recruitment of GPLD1 in the oocyte, which precisely coincides with the biphasic kinetics of JUNO depletion. Our findings establish that the enzymatic cleavage of the GPI-anchor by GPLD1 is critical for JUNO release, defining a fundamental mechanism for the membrane-level block to polyspermy. This work provides a molecular framework for ensuring sperm-oocyte recognition and improving in vitro fertilization outcomes in mammals.

developmental biology↗

bioRxiv2026

Midgestation metabolic constraint in purine metabolism drives distinct strategies for placenta and fetal growth

Purine nucleotides are essential for mammalian development1,2. Purine monophosphates support cell signaling and proliferation and are synthesized by cells through either de novo synthesis or a salvage pathway3. We previously identified a midgestational metabolic transition in mice (gestational days gd10.5-11.5) characterized by changes in purine metabolism4. Midgestation is a period of rapid growth for placenta and embryo, yet it remains unclear how the placental tissues expand without directly competing with the embryo for biosynthetic resources. Here, we show that this midgestational metabolic transition is associated with a marked reduction in embryonic expression of purine salvage enzymes, which constrains embryonic metabolism and leads to different strategies for purine synthesis between the placenta and embryo. Midgestation embryos are unable to engage the purine salvage pathway even when de novo purine synthesis is blocked either in vivo or in ex utero embryo culture, whereas placental tissue and trophoblasts retain the capacity to use either pathway. Disruption of de novo purine synthesis in mice causes reduced embryonic growth, impaired axial elongation, and abnormal brain and placental development, which are only partially rescued by supplementation with purine salvage precursors. In human placenta, trophoblast stem cells readily switch between the de novo and salvage pathways based on nutrient availability, and syncytiotrophoblasts (STB) preferentially rely on the salvage pathway. We identified guanosine monophosphate (GMP) as a metabolic checkpoint regulating STB differentiation, with insufficient GMP levels causing degradation of the small GTPase Rheb and failure of mTOR activation. Supplementation of purine salvage substrates restored GMP synthesis and STB differentiation in humans, but not mice. Further, in vivo measurements in humans revealed that maternal circulating hypoxanthine decreases during pregnancy and is further reduced in women with clinically small placentas, highlighting the role of hypoxanthine in supporting placental growth. These results uncover compartmentalized purine salvage between the embryo and placenta as a mechanism that limits competition for biosynthetic resources and enables coordinated growth during mammalian development.

developmental biology↗

bioRxiv2026

Fluorescence anisotropy structured illumination microscopy for quantitative super-resolved mapping of cell microenvironment and cytoskeletal dynamics

The crowded intracellular milieu shapes organelle architecture and dynamics, yet nanoscale heterogeneity in its physicochemical properties remains difficult to visualize with conventional fluorescence anisotropy (FA) imaging. Here, we develop fluorescence anisotropy structured illumination microscopy (FA-SIM), which employs orthogonal-polarization structured illumination with dual-angle detection to achieve [~]100-nm resolution and quantitative FA retrieval with 0.56% relative error, representing over 20-fold higher accuracy than conventional FA imaging. With low phototoxicity, FA-SIM enables dual-color, hour-long quantitative super-resolution imaging in cells. Using viscosity standards, defined nanoparticles and small-molecule drug binding assays, we validate FA-SIM as a quantitative reporter of rotational mobility and molecular interactions. In cells, FA-SIM resolves nanoscale crowding heterogeneity, correlates anisotropy landscapes with condensate dynamics, and uncovers a radial crowding gradient across the microtubule network and mitotic spindle. Long-term dual-color imaging further resolves coordinated actin-microtubule remodeling and associated microenvironmental changes. By enabling quantitative, super-resolved mapping of intracellular physical properties in living systems, FA-SIM provides a powerful platform for investigating the physical regulation of cellular organization and dynamics in health and disease.

cell biology↗

bioRxiv2026

EasyPseudogene: an easy-to-use and multithreaded pipeline for pseudogene detection

Pseudogenes are recognized as essential components for reconstructing adaptive evolutionary trajectories and understanding genomic remodeling. However, identifying these sequences in large eukaryotic genomes remains technically challenging due to fragmented workflows, complex manual configurations, and the lack of high-performance, parallelized tools capable of processing rapidly growing data volumes. We present EasyPseudogene, an automated and multithreaded pipeline designed for the end-to-end identification of pseudogenes across diverse eukaryotic lineages. Unlike traditional self-mapping tools that often fail to detect unitary pseudogenes when functional counterparts are absent, EasyPseudogene introduces an inter-species reference-driven paradigm that utilizes high-quality proteomes as probes to scan target genomes for evolutionary relics. The pipeline employs a modular "hierarchical screening and precision detection" architecture, integrating high-speed homology searching via MMseqs2 and spliced alignments via miniprot with high-fidelity, three-frame alignments using GeneWise. Performance benchmarking on cetacean genomes demonstrates that EasyPseudogene can replicate known gene loss events, such as the functional decay of the ADRB3 gene, with 100% consistency relative to established manual workflows. By encapsulating complex comparative genomics logic into a standardized framework with interactive HTML visualization for mutation auditing at single-base resolution, EasyPseudogene provides a versatile and reproducible solution for marine ecology and evolutionary research.

bioinformatics↗

bioRxiv2026

Minimal biophysical rules are sufficient for the emergence of computational intelligence at the neuronal scale

How intelligence emerges from the brains complex microscopic physical system is a central question for neuroscience and artificial intelligence. Constrained by the genomic bottleneck that precludes synapse-by-synapse specification, we propose and validate a microscopic structure-function concise-constraint sufficiency hypothesis. We develop the Neuro-Informed Generative Connectome (NIGC) framework, and show that connectomes generated under a concise set of biophysical constraints (geometric embedding, node propensity modulation, a global energy budget and maximum-entropy selection) closely match the structural statistics of a measured mouse V1 microcircuit (similarities, 0.997). In parallel, using the generated connectome as the fixed reservoir of an echo state network (ESN), training only a linear readout achieves 90% accuracy on an auditory multi-classification task. Moreover, multidimensional biologically consistent functional phenotypes, such as hierarchical transmission delays and low-dimensional spatiotemporal trajectories, are obtained without fitting functional matrices or time courses. Further, by combining single-constraint ablations, pathological perturbations and cross-modal validation, we clarify how specific structural constraints map onto functional consequences. Together, these results delineate sufficient conditions for computational intelligence emergence at the microscopic scale, and provide an auditable benchmark for first-principles understanding of brain construction.

neuroscience↗

bioRxiv2026

Thermal Nano-Engineering of Ginger Extracellular Vesicles for Targeted Oral Therapy of Colitis

Plant-derived extracellular vesicles (PEVs) are promising candidates for oral drug delivery, yet their clinical translation is hindered by limited targeting precision and inconsistent systemic absorption. While surface engineering can enhance tissue accumulation, strategies that preserve biocompatibility and enable scalable production remain limited. Here, we introduce a simple thermal processing approach, boiling, to structurally reconfigure ginger EVs into functionally enhanced, thermally reassembled nanoparticles (B-GEVs). The surface architecture of B-GEVs is enriched with key vesicle trafficking regulators, including V-type proton ATPase subunit G, ARF1, and {beta}-adaptin-like protein. This specific composition drives their tissue-specific accumulation in the intestine and liver and potentiates clathrin-dependent cellular uptake in intestinal cells by 8.57-fold. Beyond superior intrinsic anti-inflammatory activity through NLRP3 inflammasome suppression, B-GEVs function as an efficient oral delivery platform. When loaded with TNF- siRNA, they enable a synergistic therapy that simultaneously modulates upstream inflammation and silences key downstream mediators, showing potent efficacy in colitis. Our findings position boiling as a natural strategy for enhancing the bioactivity and targeted oral delivery potential of ginger-derived EVs.

bioengineering↗

bioRxiv2026

Medea: An omics AI agent for therapeutic discovery

Therapeutic hypotheses can transfer across diseases but their relevance depends on biological context. The same target, perturbation, or treatment can produce different effects across cell types, disease states, genetic backgrounds, and patients. Therapeutic reasoning therefore requires methods that preserve context, test when evidence supports transfer, and identify where context-specific effects limit it. Although AI agents can perform therapeutic analyses, existing systems often fail to preserve biological context over long workflows, verify intermediate computational steps, or reconcile conflicting evidence across datasets and literature. Here, we present Medea, an AI agent for therapeutic reasoning across biological contexts. Medea executes multi-step analyses using biological tools, machine learning models, and literature retrieval while enforcing verification during planning, execution, and evidence synthesis. We evaluate Medea across 5,673 open-ended analyses in three domains: cell type specific therapeutic target nomination in five diseases and 29 cell types, synthetic lethality prediction in 7 cancer cell lines, and immunotherapy response prediction from multimodal patient profiles. Using a previously unpublished epistatic miniarray profiling screen performed under two DNA-damaging treatments, we evaluate Medea on predicting synthetic lethality among 238,046 gene-gene pairs in yeast. Medea predicts these experimentally measured synthetic lethal interactions, indicating that its performance reflects biological relevance rather than information leakage from benchmark datasets. Across these evaluations, Medea improves performance over large language models, reasoning models, biomedical agents, and specialized machine learning models while maintaining low failure rates and calibrated abstention. These results show that verifiable AI agents can perform therapeutic analyses across biological contexts.

bioinformatics↗

bioRxiv2026

ANKS1B in the Nucleus Accumbens controls escalated cocaine self-administration via regulating CBP-FoxO3 complex

The transition from controlled to escalated drug intake is a core feature of cocaine use disorder (CUD), yet the molecular mechanisms underlying this behavioral escalation remain poorly defined. Our prior genome-wide association study (GWAS) identified ANKS1B as a significant shared genetic risk factor for heroin, methamphetamine, and alcohol dependence, suggesting a broad role in addiction vulnerability. However, the specific function of ANKS1B in cocaine addiction and its associated neural mechanisms were unknown. Here, we found that ANKS1B expression level in the Nucleus Accumbens (NAc) was downregulated after extended cocaine use, and manipulating ANKS1B could selectively influence the escalation of cocaine intake and the subsequent cocaine-seeking behavior in the long-access cocaine self-administration rat model. Molecular experiments reveal that ANKS1B interacts with the histone acetyltransferase CBP to control H3K27 acetylation and extended cocaine intake, via epigenetically repressing the transcription factor FoxO3. Overall, these findings suggest that ANKS1B is a crucial factor influencing the escalation of cocaine use. The ANKS1B-CBP-FoxO3 signaling pathway presents a promising target for potential therapeutic interventions for controlling extended cocaine use.

neuroscience↗

bioRxiv2026

Can AI Conduct Autonomous Scientific Research? Case Studies on Two Real-World Tasks

Recent advances in artificial intelligence (AI) have prompted claims about autonomous "AI scientists," yet systematic evaluations of these capabilities remain scarce. This exploratory study investigates whether current AI frameworks can execute scientific research tasks beyond isolated demonstrations. We tested eight open-source AI frameworks (Agent Laboratory, AutoGen, BabyAGI, GPT Researcher, MOOSE-Chem2, SciAgents, SciMON, and Virtual Lab) on two tasks that aimed to reproduce research on algorithm development from recent papers in uncertainty quantification and protein interaction discovery. In our evaluation, no framework completed a full research cycle from literature understanding through computational execution to validated results and scientific paper writing. While all systems showed competence in conceptual tasks such as planning and summarization, they consistently failed at robust implementation. Every framework produced sophisticated hallucinations. Deployment proved demanding, requiring substantial debugging and technical expertise, which undermines common claims about the democratization of science with AI. Despite these limitations, the frameworks showed promise as research assistants for methodological planning and ideation under careful human supervision. Our findings suggest that the explored AI systems cannot yet autonomously conduct scientific research, but may provide real value for specific subtasks within the research workflow. We offer preliminary observations to help researchers and developers better understand the gap between advertised and actual capabilities of AI in science.

bioinformatics↗

bioRxiv2025

Pan-cancer tumor classification by a holistic tumor microenvironment atlas

The tumor microenvironment (TME) heterogeneity presents a major bottleneck to effective cancer immunotherapies. We address this by establishing a pan-cancer tumor classification system based on the holistic TME cellular components. First, using single-cell transcriptomes from 1,271 patients across 26 cancer types, we compile a TME cellular atlas, leading to the identification of recurrent cell states and multicellular modules across cancer types. Among these, we highlight a type I interferon (IFN-I)-related multicellular module, including IFIT1+ tumor-associated macrophages, as a critical contributor to immune-activation TMEs. From a holistic perspective, pan-cancer TMEs exhibit trichotomous patterns predominantly driven by T, myeloid, and stromal compartments. Leveraging fine-grained TME cellular composition, we further stratify all tumors into 10 stable groups. These groups display varying responses to immune checkpoint blockade (ICB), and each rationally selected therapeutic strategy specifically perturbs the corresponding expression signature. Our pan-cancer tumor classification scheme reveals the underlying patterns of TME heterogeneity and provides a framework for stratifying patients, guiding treatment selection, and developing novel therapeutic options.

cancer biology↗

bioRxiv2025

A global view of human centromere variation and evolution

Centromeres are essential for accurate chromosome segregation during cell division, yet their highly repetitive sequence has historically hindered their complete assembly and characterization. Consequently, the full spectrum of centromere diversity across individuals, populations, and evolutionary contexts remains largely unexplored. Here, we address this gap in knowledge by assembling and characterizing 2,110 complete human centromeres from a diverse cohort of individuals representing 5 continental and 28 population groups. By developing a novel suite of bioinformatic tools tailored for centromeric regions, we uncover previously unknown variation within centromeres, including 226 novel centromere haplotypes and 1,870 new -satellite higher-order repeat (HOR) variants. We find that mobile element insertions are present in 30% of centromeres, with chromosome 16 harboring Alu elements within the kinetochore site at an 11-fold higher frequency than expected. While most centromeres have a single kinetochore site, 6% of them have di-kinetochores, and <<1% have tri-kinetochores, which we confirm with long-read CENP-A CUT&RUN, DiMeLo-seq, and multi-generational inheritance. We further show that the position of the kinetochore is not random and is, instead, closely associated with the underlying sequence and structure of the centromere. To understand the nature of evolutionary change, we compared 2,110 complete human centromeres to 5,747 complete centromeres recently assembled from the Human Pangenome Reference Consortium. We show that centromeres have a >50-fold variation in mutation rate, with the most rapidly mutating centromeres on chromosome 1 and the slowest mutating centromeres on chromosome Y. Additionally, a subset of centromeres show evidence of introgression from archaic hominins, shaping their sequence, structure, and evolutionary history. We validate these centromere mutation rates in a four-generation family, spanning 28 family members and 483 accurately assembled centromeres, and show that the kinetochore site is the most rapidly mutating region in the centromere, with twofold more single-nucleotide variants than the rest of the centromeric -satellite HOR array on average. We propose a model that reveals an arms race between centromeric sequence and proteins, with frequent mutations within the site of the kinetochore that lead to changes in genetic and epigenetic landscapes and, ultimately, rapid evolution of these critically important regions.

genomics↗

bioRxiv2025

The Notch/snRNA negative feedback regulation and its implications in Alzheimer's disease

Alzheimers disease (AD) is a multifactorial disorder whose hallmark lesions have been recognized for more than a century, yet its molecular pathobiology remains fragmentary. During our AD research, we discovered a direct mechanistic axis that links the Notch signalling pathway -- a crucial regulator of cell differentiation and proliferation--with the small nuclear RNAs (snRNAs) that drive pre-mRNA splicing. For the first time, our discovery unifies two previously siloed paradigms -- cell signal transduction and RNA splicing, expanding the fundamental knowledge of gene expression and its regulation. The first contribution of the present study is the discovery and validation of the Notch/snRNA negative feedback regulation. More implications include: (1) it reframes splicing-related diseases as dynamic consequences of pathway dysregulation rather than static snRNA-gene mutations; (2) it explains why global splicing abnormalities appear in AD, aging, and cancer even when snRNA genes themselves are not mutated; and (3) it provides an immediate, druggable "splicing rheostat" for any Notch-hyperactive disorder, such as AD.

cell biology↗

bioRxiv2025

PRSNet-2: End-to-end genotype-to-phenotype prediction via hierarchical graph neural networks

The emergence of large-scale biobanks has opened unprecedented opportunities for the development of data-driven approaches, especially deep learning-based methods, for genotype-to-phenotype (G2P) prediction. However, designing an end-to-end framework capable of directly leveraging extremely high-dimensional genotypic data while simultaneously mitigating overfitting and ensuring robust prediction remains a significant challenge. In this study, we introduce PRSNet-2, an interpretable end-to-end deep learning framework designed to predict complex phenotypes directly from large-scale genotypic data. PRSNet-2 proposes a novel hierarchical graph neural network (GNN) architecture, which first employs a multi-kernel aggregator to map high-dimensional genotypic features to gene-level representations. Next, it models gene-gene interactions through message-passing operations and uses an attention-based readout module to generate interpretable phenotypic predictions. We further introduce significance-guided regularization strategies to boost models generalizability based on prior genetic associations. Extensive empirical evaluations across multiple complex traits and diseases demonstrate that PRSNet-2 consistently outperforms a variety of baseline methods and exhibits superior capabilities in overcoming overfitting, even when modeling around half a million single nucleotide polymorphisms (SNPs). Moreover, PRSNet-2 is shown to be easily extendable to integrate multiple genome-wide association study (GWAS) datasets into a single model, thereby enhancing predictive performance for both single-phenotype and multi-phenotype prediction tasks. The inherent interpretability of PRSNet-2 further facilitates the identification of disease-relevant genes, functional gene modules, and potential therapeutic targets. In summary, PRSNet-2 offers a powerful, versatile tool for both genetic risk stratification and the discovery of biological insights.

genetics↗

bioRxiv2025

R-loops orchestrate the maternal-to-zygotic transition by harnessing RNA polymerase II pause release

R-loops, ubiquitous three-stranded nucleic acid structures abundant in the mammalian genome, play pivotal roles in diverse biological processes1-3. However, the dynamics and functions of R-loops during mammalian preimplantation embryonic development remain poorly understood. Here we optimized an innovative, ultra-sensitive RIAN-seq method capable of mapping genome-wide R-loop landscape using ultra-low cell numbers to investigate R-loop dynamics in mouse gametes and early embryos. Our findings reveal the widespread presence of AT-rich R-loops during early mouse embryonic development and highlight their critical roles in orchestrating transcription machinery during zygotic genome activation (ZGA). We demonstrate that the stability and inheritance of R-loops through developmental stages are governed by their sequence composition, with GC-rich R-loops exhibiting greater stability. Notably, R-loops, particularly AT-rich R-loops, inhibit DDX21 helicase activity on the 7SK/HEXIM1 snRNP complex, thereby reducing CDK9 release. This, in turn, modulates the phosphorylation of Ser2 at C-terminal domain (CTD) of RNA polymerase II (RNAPIIS2p) at the promoters of major ZGA and maternal genes. Furthermore, R-loops promote RNAPII accumulation at major ZGA gene promoters, preventing RNAPII from premature release and ensuring timely activation of major ZGA genes. Conversely, R-loops enhance the retention of stalled RNAPII at maternal gene promoters, effectively repressing their expression and facilitating the maternal-to-zygotic transition. In summary, our study unveils a key role for R-loops in regulating the maternal-to-zygotic transcription transition and early embryonic development in mice.

developmental biology↗

bioRxiv2025

De Novo Design of Anti-Aging Peptides

Aging, a universal biological process in complex organisms, is increasingly recognized to be driven by progressive loss of epigenetic information, as proposed in the Information Theory of Aging (ITOA). However, research on anti-aging peptides remains scarce, with most existing efforts confined to derivatives of natural proteins, while systematic design attempts are virtually absent. This limitation not only restricts discovery within the evolutionary sequence space but also hampers the identification of candidates with novel mechanisms and improved efficacy. Here, we present ElixirSeeker2, the first computational framework for de novo design of anti-aging peptides. By integrating modeling of known anti-aging peptides, activity scoring from the IC50 database, and penalty constraints from toxic peptides, ElixirSeeker2 enables large-scale virtual screening and identification of novel peptide candidates. Several lead peptides demonstrated significant effects in delaying cellular senescence, restoring cellular functions in vitro and in enhancing locomotor activity of aged Caenorhabditis elegans. This study not only validates the feasibility of de novo design in anti-aging interventions but also establishes a strategy for the development of next-generation biologics.

bioinformatics↗

bioRxiv2025

Prolonged Starvation Drives Epigenetic Remodeling: Insights from DNA Methylation Profiling in the Aquatic Pathogen Flavobacterium columnare

Nutrient scarcity is a common environmental stressor encountered by bacteria. Despite its significance in bacterial virulence and physiology, the underlying epigenetic mechanisms of bacterial adaptation to prolonged starvation remain poorly understood. In this study, we utilized the latest Nanopore R10.4.1 sequencing technology to comprehensively characterize the genome-wide methylation landscape of the freshwater fish pathogen Flavobacterium columnare following long-term starvation at different temperatures. Our results revealed significant methylation plasticity under starvation conditions, characterized by distinct motif-dependent patterns. Notably, demethylation of the 6mA-modified CAYNNNNNRTG motif emerged as a robust epigenetic signature of starvation adaptation, with functional enrichment of genes involved in pathways for translation and metabolism, suggesting the role of this methylase in regulating essential cellular functions under starvation stress. Additionally, another 6mA-modified GCAGA motif exhibited temperature-dependent variation, indicating its potential role in temperature-specific response. Together, this study provides novel insights into the epigenetic mechanisms underlying the bacterial adaptation to nutrient deprivation and establishes a valuable methodological reference for bacterial epigenetics analysis utilizing advanced Nanopore sequencing technologies.

microbiology↗

bioRxiv2025

Coordination of nuclear RNA processing by speckle-localized kinase TAOK2

Nuclear speckles are membraneless organelles that act as active splicing hubs especially at sites of high transcription. Emerging views of this dynamic subnuclear structure place it as a hub of RNA processing, impacting steps from transcription to export. To manage this complex microcosm of RNA metabolism, nuclear speckles also require kinases and phosphorylation to execute their functions. The nuclear speckle-localized kinase, TAOK2, mediates the splicing and export of viral transcripts at the nuclear speckle, but its role in the processing of cellular transcripts was unknown. We used siRNA knockdown of TAOK2 and assessed RNA transcripts in both whole cell and nucleocytoplasmic fractions to characterize the complete endogenous effects of TAOK2. We found that TAOK2 knockdown impacts over 10% of the transcriptome, through changes in alternative splicing, export and transcript abundance. Cellular and biochemical phosphoproteomics further revealed nuclear speckle scaffolding proteins SRRM1 and SRRM2 as potential direct phosphorylation targets of TAOK2, mediating its large effects on speckle integrity and speckle-localized splicing. Indeed, knockdown of TAOK2 perturbs almost all speckle-resident serine/arginine (SR)-rich proteins while leaving heterogeneous ribonucleoproteins unperturbed. Altogether, we propose that phosphorylation of SRRM1/2 by TAOK2 plays a structural maintenance role that impacts SR protein-driven exon inclusion at the nuclear speckle.

molecular biology↗

bioRxiv2025

DDHD2 possesses both lipase and transacylase capacities that remodel triglyceride acyl chains

Hereditary spastic paraplegia subtype SPG54 is a genetic neurological disorder caused by mutations in the DDHD2 gene. Excessive lipid droplet accumulation is observed in the brains of SPG54 patients and DDHD2 knockout mice, consistent with DDHD2s reported neutral lipase activity. Here, we find recombinant human DDHD2 preferentially hydrolyzes diacylglycerol (DAG) over phospholipids, with a slight preference for DAG over triacylglycerol (TAG). DDHD2 also exhibits transacylase activity, which enables transfer of acyl chains from triacylglycerols to diacylglycerols and monoacylglycerols to remodel the acyl chains of triglycerides. A predicted hydrophobic amphipathic helix on DDHD2 is essential for lipid droplet binding in vitro and in cells, and its lack reduces the enzymatic activity and triglyceride acyl chain remodeling. Adipose triglyceride lipase (ATGL), but not hormone sensitive lipase (HSL), also has transacylation activity and can remodel triglyceride acyl chains, but to a lesser extent than DDHD2. Taken together, this provides evidence that DDHD2 is a neutral lipid lipase and transacylase whose broad specificity enables triglyceride acyl-chain remodeling. SIGNIFICANCE STATEMENTTriglycerides (TAGs), the primary form of long-term energy storage, have acyl chain compositions crucial for diverse cellular processes. Lipases typically hydrolyze TAGs into free fatty acids. Here, we reveal a novel function for the neutral lipid lipase DDHD2: a transacylase activity. Instead of releasing fatty acids, DDHD2 transfers them between neutral lipids, altering TAG acyl chain composition. This transacylation requires the unique oil environment of lipid droplets (LDs), which excludes water from DDHD2s lipolytic active site, favoring transacylation over hydrolysis. DDHD2s lipase and transacylase activities enable TAG acyl-chain remodeling, demonstrating the possibility that a single enzyme can catalyze TAG cycling. This finding has implications for understanding lipid metabolism, LD dynamics, and specific motor neuron diseases implicating DDHD2.

biochemistry↗