Biology subjects

Yuan, H.

Publications and source records attributed to Yuan, H..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Multi-domain O-GlcNAcase structures reveal allosteric regulatory mechanisms

Nucleocytoplasmic protein O-GlcNAcylation is an essential modification catalysed by O-GlcNAc transferase (OGT) and reversed by O-GlcNAc hydrolase (OGA), a multi-domain enzyme that also contains a C-terminal pseudo-histone acetyltransferase (pHAT) domain. OGA and OGT are tightly regulated using O-GlcNAc-dependent feedback mechanisms that are largely unknown. Although the structure of the OGA homodimeric catalytic domain has been reported, the structure and function of the pHAT domain remains poorly understood. We describe a crystal structure of the Trichoplax adhaerens pHAT domain and cryo-EM data of the multi-domain T. adhaerens and human OGAs, together with surface plasmon resonance and small-angle X-ray scattering studies. Our findings show that the eukaryotic OGA pHAT domains affect O-GlcNAc homeostasis, and form catalytically incompetent, symmetric homodimers, projecting a partially conserved putative peptide binding site available for interactions with binding partners. While there is evidence for symmetric OGA multi-domain dimers in solution, interactions between the linkers to the pHAT domains allow these to adopt a limited range of positions. In hOGA, the positions of the pHAT domains determine the wider active site environment through a key conformational change involving a tryptophan in a flexible arm region. Taken together, these multi-domain OGA structures reveal allosteric mechanisms of regulation.

molecular biology↗

bioRxiv2025

RamEx: An R package for high-throughput microbial ramanome analyses with accurate quality assessment

Microbial single-cell Raman spectroscopy (SCRS) has emerged as a powerful tool for label-free phenotyping, enabling rapid characterization of microbial diversity, metabolic states, and functional interactions within complex communities. However, high-throughput SCRS datasets often contain spectral anomalies from noise and fluorescence interference, which obscure microbial signatures and hinder accurate classification. Robust algorithms for outlier detection and microbial ramanome analysis remain underdeveloped. Here, we introduce RamEx, an R package specifically designed for high-throughput microbial ramanome analyses with robust quality control and phenotypic classification. At the core of RamEx is the Iterative Convolutional Outlier Detection (ICOD) algorithm, which dynamically detects spectral anomalies without requiring predefined thresholds. Benchmarking on both simulated and real microbial datasets--including pathogenic bacteria, probiotic strains, and yeast fermentation populations--demonstrated that ICOD achieves an F1 score of 0.97 on simulated datasets and 0.74 on real datasets, outperforming existing approaches by at least 19.8%. Beyond anomaly detection, RamEx provides a modular and scalable workflow for microbial phenotype differentiation, taxonomic marker identification, metabolic fingerprinting, and intra-population heterogeneity analysis. It integrates Raman-based species-specific biomarkers, enabling precise classification of microbial communities and facilitating functional trait mapping at the single-cell level. To support large-scale studies, RamEx incorporates C++ acceleration, GPU parallelization, and optimized memory management, enabling the rapid processing of over one million microbial spectra within an hour. By bridging the gap between high-throughput Raman-based microbial phenotyping and computational analysis, RamEx provides a comprehensive toolkit for exploring microbial ecology, metabolic interactions, and antibiotic susceptibility at the single-cell resolution. RamEx is freely available under the MIT license at https://github.com/qibebt-bioinfo/RamEx.

bioinformatics↗

bioRxiv2025

Dynamic early recruitment of GAK-Hsc70 regulates coated pit maturation

Clathrin-mediated endocytosis (CME) is the process by which clathrin assembles on the plasma membrane to form clathrin-coated pits (CCPs), which then invaginate, accumulate cargo and are released by fission from the membrane to form clathrin-coated vesicles (CCVs). A transition of nascent CCPs from flat-to-curved has been observed by various methods. However, what drives this transition remains unknown and controversial. GAK and its chaperone protein, Hsc70, are well-known to mediate clathrin release from CCVs and several studies have observed a late burst of GAK recruitment as CCVs form. Other studies have proposed that early recruitment of GAK-Hsc70 could function to provide the necessary energy source to remodel nascent flat clathrin lattices, replacing hexagons with pentagons and enabling a gain of curvature and invagination of the growing CCP; however, direct functional evidence is lacking. Here we show that GAK knockdown inhibits CCP formation and invagination. Furthermore, mutations in the J domain of GAK that abolish Hsc70 recruitment to and activation at CCPs, lead to the accumulation of GAK at CCPs, hinder CCP stabilization and invagination and result in a striking increase in the proportion of short-lived, abortive CCPs. These findings support the hypothesis that GAK-Hsc70 promotes the turnover and remodeling of nascent clathrin assemblies required for curvature development during CME. Significance StatementGAK and its chaperone protein, Hsc70, are known to be recruited to clathrin-coated vesicles (CCVs) to mediate clathrin uncoating. Previous studies have proposed that early recruitment of GAK-Hsc70 to CCPs could function to remodel nascent flat clathrin lattices, replacing hexagons with pentagons and enabling a gain of curvature of the assembling polymeric coat. However, there are conflicting views, and direct functional evidence is lacking. Here we show that GAK knockdown inhibits CCP formation and invagination. A detailed domain-specific mutational analysis of GAK pinpointed the importance of J domain-Hsc70 interactions in regulating crucial early steps of CME including CCP stabilization and invagination. These findings support a hypothesis that GAK-Hsc70 promotes turnover of clathrin at nascent CCPs required for curvature development.

cell biology↗

bioRxiv2025

Targeting Non-Catalytic Sites of SRC Sensitizes the Efficacy of SRC Kinase Inhibitors in Solid Tumors

The non-receptor tyrosine kinase SRC promotes the progression of hematologic malignancies and numerous solid tumors. Several SRC kinase inhibitors have been approved for the treatment of hematologic malignancies. However, drug resistance to sole ATP pocket-targeted kinase inhibition, coupled with low potency for solid tumors and insufficient selectivity, has limited the clinical application of SRC kinase inhibitors. The non-catalytic functions of kinases have provided crucial mechanisms underlying kinase inhibitor resistance, thereby presenting new opportunities for discovery SRC-targeted strategies. In this study, we discovered that upon abrogation of phosphorylation by SRC kinase inhibitors, non-catalytic SRC promotes the transcription of the oncogenes TRIB3 and SPC24 transcription by binding to their promoter sequence. Simultaneously, it interacts with TRIB3 and prevents their proteasome-mediated degradation by the E3 ligase CHIP, ultimately inducing resistance to SRC kinase inhibitors. The peptide TS1-2, blocking SRC/TRIB3 interaction in non-catalytic regions, inhibits the tumor progression of pancreatic ductal adenocarcinoma (PDAC), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), and breast invasive carcinoma (BRCA), and enhances the efficacy of SRC kinase inhibitors in solid tumors. This study provides the insights of non-catalytic functions of SRC, validates the tumor-promoting function and mechanism of nuclear accumulation of SRC/TRIB3 following SRC kinase inhibitor treatment, identifies SRC/TRIB3 interaction as a potential non-catalytic target, and offers the candidate peptide TS1-2 as a potential combination strategy for overcoming SRC kinase inhibitor resistance. SignificanceThe scaffolding functions of SRC reduced the efficacy of SRC kinase inhibitors, and targeting SRC/TRIB3 non-catalytic functions synergizes the therapeutic effects of SRC kinase inhibitors in SRC-dependent solid tumors. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/638389v2_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@17aa618org.highwire.dtl.DTLVardef@12e86eeorg.highwire.dtl.DTLVardef@19c8d82org.highwire.dtl.DTLVardef@10e6c46_HPS_FORMAT_FIGEXP M_FIG C_FIG SRC kinase inhibitors block myristoylation-dependent membrane location and catalytic phosphorylation of SRC. Phosphorylated SRC activates STAT1 and thereby represses TRIB3 transcription. Accumulated non-catalytic SRC promotes TRIB3, SPC24 transcription. Upregulated TIRB3 interacts with SRC to reduce CHIP-mediated ubiquitin-proteasomal degradation. The peptide TS1-2 disrupts TRIB3/SRC complex by targeting Non-kinase regions of SRC, consequently enhancing the therapeutic efficacy of SRC kinase inhibitors.

cancer biology↗

bioRxiv2025

Preclinical Evaluation of P4B-2412 as Phosphodiesterase 4B Radioligand for Positron Emission Tomography Imaging

Phosphodiesterase 4B (PDE4B) plays a critical role in cAMP hydrolysis and is highly expressed in brain regions associated with neuroinflammation and neurodegenerative disorders. Selective PDE4B radioligands hold significant potential for elucidating disease mechanisms and enabling target occupancy measurements. In this study, we developed [18F]P4B-2412, a novel PDE4B-selective radioligand, and evaluated its utility for PET imaging. [18F]P4B-2412 was synthesized with high radiochemical yield (27.2%), excellent radiochemical purity (99%), and favorable molar activity (66.2 {+/-} 2.5 GBq/mol). In vitro autoradiography revealed high PDE4B binding, while dynamic PET imaging demonstrated high in vivo specificity for PDE4B in rodent brain regions. Notably, [18F]P4B-2412 exhibited excellent metabolic stability in vitro and in vivo. These findings establish [18F]P4B-2412 as a promising PET probe for imaging PDE4B in vivo, offering a valuable tool for investigating neuroinflammation and advancing CNS drug development.

neuroscience↗

bioRxiv2024

A single cell and spatial genomics atlas of human skin fibroblasts in health and disease

Fibroblasts are critical cells that shape the architecture and cellular ecosystems in multiple tissues. Understanding fibroblast heterogeneity and their spatial context in health and disease has enormous clinical relevance. In this study, we constructed a spatially-resolved atlas of human skin fibroblasts from healthy skin and 23 skin disorders. We define 6 major skin fibroblast populations in health and a further three skin disease-specific fibroblast subtypes, and demonstrate the fibroblast composition in different types of skin disease. We characterise a human-specific fibroblastic reticular cell (FRC)-like subtype in the skin perivascular niche and postulate their origin from prenatal skin lymphoid tissue organiser (LTo)-like cells. We also show that inflammatory myofibroblasts (IL11+MMP1+CXCL5+IL7R+) are a conserved fibroblast subtype in inflammatory disorders and cancers across multiple human tissues. We provide a harmonised nomenclature for skin fibroblasts that integrates previous findings from human skin and other tissues.

cell biology↗

bioRxiv2024

Direct Feature Identification from Raman Spectra and Precise Data-driven Classification of Phytopathogens at Single Conidium-Species Level

Conidia can cause outbreaks of fungal plant diseases, resulting in significant losses in crop yield and economy. Traditional diagnosis of phytopathogenic conidia based on morphology and molecular biology is time-consuming, labor-intensive, and often fails to differentiate fungal conidia. To overcome these challenges, a new classification approach was developed in this study by integrating Raman spectroscopy with data-driven modeling. Eight fungal species were selected and characterized using Raman spectra. Three characteristic Raman wavenumbers at 1003 to 1005 cm-1, 1153 to 1157 cm-1, and 1515 to 1522 cm-1 shared a consistent pattern across species and could be attributed to carotenoids. Clustering of the Raman spectra using principal component analysis (PCA) showed substantial overlap, indicating inaccurate classification of conidia. Three data-driven models, support vector machines (SVMs), decision trees (DTs), and eXtreme Gradient Boosting Forest (XGBoost) were trained with three categories of features (number of peaks, maximum peak, and curve roughness) identified within eight characteristic wavenumber ranges, The optimal SVM, DT, and XGBoost determined by hyperparameter tuning achieved prediction precision of 0.88, 0.88, and 0.96, respectively. PCA-XGBoost trained by feeding principal components of PCA to XGBoost achieved prediction precision of 0.94, suggesting that features extracted from the raw datasets outperformed those extracted with PCA in terms of data-driven classification. This study has demonstrated the great potential of Raman spectroscopy combined with data-driven modeling for classification of phytopathogenic conidia.

microbiology↗

bioRxiv2024

Morphological brain alterations and morphological brain network disorganizations in heroin and methamphetamine abstinent patients

Heroin and methamphetamine are the two common types of drugs abused, which poses significant health risks. However, the neural mechanisms underlying the effects of drug addiction on human brain are unclear. In this study, we collected T1-weighted magnetic resonance imaging data from 26 heroin abstinent (HA) patients, 24 methamphetamine abstinent (MA) patients and 32 healthy controls. Four surface-based morphological features including cortical thickness (CT), fractal dimension (FD), gyrification index (GI), and sulcal depth (SD) were calculated, and further used to construct the morphological brain networks. We observed the common CT reductions of the right TE 1.0 and TE 1.2 and SD reductions of the right intermediate lateral area 20 for HA and MA patients, HA-specific CT reductions in the left area 2, and the MA-specific GI reductions in the left medial area 6 and right dorsomedial parietooccipital sulcus. For the morphological brain networks, HA patients exhibited the global disorganizations (higher shortest path length) in CT-based networks, whereas MA patients showed the disrupted nodal efficiency of the left medial area 38 in CT-based networks, the right caudal area 7 in GI-based networks, and the right inferior occipital gyrus in SD-based networks. Furthermore, the altered SD of HA patients and disrupted nodal efficiency of MA patients were associated with drug abuse-related clinical variables. Our findings suggest the morphological index-dependent effects of drug addiction on human brain morphology, and indicate the differential neural mechanism underlying heroin and methamphetamine abuses which attack the global and local information transfer of morphological brain networks, respectively.

neuroscience↗

bioRxiv2024

Selective Enhancement of the Interneuron Network and Gamma-Band Power via GluN2C/GluN2D NMDA Receptor Potentiation

N-methyl-D-aspartate receptors (NMDARs) comprise a family of ligand-gated ionotropic glutamate receptors that mediate a slow, calcium-permeable component to excitatory neurotransmission. The GluN2D subunit is enriched in GABAergic inhibitory interneurons in cortical tissue. Diminished levels of GABAergic inhibition contribute to multiple neuropsychiatric conditions, suggesting that enhancing inhibition may have therapeutic utility, thus making GluN2D modulation an attractive drug target. Here, we describe the actions of a GluN2C/GluN2D-selective positive allosteric modulator (PAM), (+)-EU1180-453, which has improved drug-like properties such as increased aqueous solubility compared to the first-in-class GluN2C/GluN2D-selective prototypical PAM (+)-CIQ. (+)-EU1180-453 doubles the NMDAR response at lower concentrations (< 10 {micro}M) compared to (+)-CIQ, and produces a greater degree of maximal potentiation at 30 {micro}M. Using in vitro electrophysiological recordings, we show that (+)-EU1180-453 potentiates triheteromeric NMDARs containing at least one GluN2C or GluN2D subunit, and is active at both exon5-lacking and exon5-containing GluN1 splice variants. (+)-EU1180-453 increases glutamate efficacy for GluN2C/GluN2D-containing NMDARs by both prolonging the deactivation time and potentiating the peak response amplitude. We show that (+)-EU1180-453 selectively increases synaptic NMDAR-mediated charge transfer onto P11-15 CA1 stratum radiatum hippocampal interneurons, but is without effect on CA1 pyramidal cells. This increased charge transfer enhances inhibitory output from GABAergic interneurons onto CA1 pyramidal cells in a GluN2D-dependent manner. (+)-EU1180-453 also shifts excitatory-to-inhibitory coupling towards increased inhibition and produces enhanced gamma band power from carbachol-induced field potential oscillations in hippocampal slices. Thus, (+)-EU1180-453 can enhance overall circuit inhibition, which could prove therapeutically useful for the treatment of anxiety, depression, schizophrenia, and other neuropsychiatric disorders. Significance StatementInterneuron dysfunction and diminished GABAergic inhibition in neocortical and hippocampal circuits remains a prominent molecular hypothesis for neuropsychiatric diseases including anxiety, depression, and schizophrenia. Pharmacological agents that boost GABA receptor function have shown utility in various forms of depression and treating symptoms of schizophrenia. Cortical GABAergic interneurons, unlike their excitatory pyramidal cell counterparts, are enriched for the GluN2D subunit of the NMDA receptor. Thus, GluN2D subunit-selective modulation could be a useful therapeutic tool to enhance local inhibition, improving the prognosis for neuropsychiatric diseases for which interneuron dysfunction is prominent and causal to circuit aberration.

neuroscience↗

bioRxiv2024

A global resource constrained model to predict metabolic flux dynamics in fluctuating environments

Environmental changes often induce global variations on bacterial gene expression, frequently accompanied by alterations in growth rates. Integrating non-metabolic cellular processes, such as gene expression and macromolecule synthesis, with metabolic modeling remains a significant challenge in systems biology due to the lack of mechanistic representation of gene regulation. Here, we introduce a novel constraint-based modeling framework called dynamic Constrained Allocation Flux Balance Analysis (dCAFBA), which comprehensively integrates metabolism, cellular resource allocation, and gene regulation. We employ a quasi-steady-state assumption, positing that reaction fluxes achieve balance at each time step, adapting more rapidly than protein synthesis and growth dilution. This approach enables the prediction of reaction flux dynamics and protein allocation necessary to achieve cellular objectives, such as optimizing growth during various growth shifts (including carbon, amino acid, and transcriptional signal), without detailing molecular gene regulations. Our model offers a new method for interpreting proteome allocation and cellular metabolism in complex and transient environments, providing mechanistic insights valuable for metabolic engineering.

systems biology↗

bioRxiv2024

Maintenance of neuronal TDP-43 expression requires axonal lysosome transport

TDP-43 mislocalization and pathology occurs across a range of neurodegenerative diseases, but the pathways that modulate TDP-43 in neurons are not well understood. We generated a Halo-TDP-43 knock-in iPSC line and performed a genome-wide CRISPR interference FACS-based screen to identify modifiers of TDP-43 levels in neurons. A meta-analysis of our screen and publicly available screens identified both specific hits and pathways present across multiple screens, the latter likely responsible for generic protein level maintenance. We identified BORC, a complex required for anterograde lysosome transport, as a specific modifier of TDP-43 protein, but not mRNA, levels in neurons. BORC loss led to longer half-life of TDP-43 and other proteins, suggesting lysosome location is required for proper protein turnover. As such, lysosome location and function are crucial for maintaining TDP-43 protein levels in neurons.

neuroscience↗

bioRxiv2024

Yatakemycin biosynthesis requires two deoxyribonucleases for toxin self-resistance

The highly active natural product yatakemycin (YTM) from Streptomyces sp. TP-A0356 is a potent DNA damaging agent with antimicrobial and antitumor properties. The YTM biosynthesis gene cluster (ytk) contains several toxin self-resistance genes. Of these, ytkR2 encodes a DNA glycosylase that is important for YTM production and host survival by excising lethal YTM-adenine lesions from the genome, presumably initiating a base excision repair (BER) pathway. However, the genes involved in repair of the resulting apurinic/apyrimidinic (AP) site as the second BER step have not been identified. Here, we show that ytkR4 and ytkR5 are essential for YTM production and encode deoxyribonucleases related to other known DNA repair nucleases. Purified YtkR4 and YtkR5 exhibit AP endonuclease activity specific for YtkR2-generated AP sites, providing a basis for BER of the toxic AP intermediate produced from YTM-adenine excision and consistent with co-evolution of ytkR2, ytkR4, and ytkR5. YtkR4 and YtkR5 also exhibit 3'-5' exonuclease activity with differing substrate specificities. The YtkR5 exonuclease is capable of digesting through a YTM-DNA lesion and may represent an alternative repair mechanism to BER. We also show that ytkR4 and ytkR5 homologs are often clustered together in putative gene clusters related to natural product production, consistent with non-redundant roles in repair of other DNA adducts derived from genotoxic natural products.

biochemistry↗

bioRxiv2024

Grin1Y647S/+ Mice: A Preclinical Model of GRIN1-Related Neurodevelopmental Disorder

GRIN1-related neurodevelopmental disorder (GRIN1-NDD) is characterized by clinically significant variation in the GRIN1 gene, which encodes the obligatory GluN1 subunit of N-methyl-D-aspartate receptors (NMDARs). The identified p.Tyr647Ser (Y647S) variant is carried by a 34-year-old female with seizures and intellectual disability. This study builds upon initial in vitro investigations of the functional impacts of this variant in the SYTANLAAF domain of the GluN1 M3 helix and examines its in vivo consequences in a mouse model. To investigate in vitro functional impacts of NMDARs containing GluN1-Y647S variant subunits, GluN1-Y647S was co-expressed with wildtype GluN2A or GluN2B subunits in Xenopus laevis oocytes and HEK cells. Grin1Y647S/+ mice were created by CRISPR-Cas9 endonuclease-mediated transgenesis and the molecular, electrophysiological, and behavioural consequences of the variant were examined. Additionally, de-identified patient data were collected to examine the representative nature of Grin1Y647S/+ mice in modelling specific aspects of patient symptomology. In vitro, NMDARs containing GluN1-Y647S showed altered sensitivity to endogenous agonists and negative allosteric modulators, and reduced cell surface trafficking. Ex vivo, Grin1Y647S/+ mice displayed a reduction in whole brain GluN1 levels and a deficiency in NMDAR-mediated synaptic transmission in the hippocampus. Behaviourally, Grin1Y647S/+ mice exhibited altered vocalizations, muscle strength, sociability, and problem-solving, as well as spontaneous convulsions that were ameliorated with supplementation of {beta}-hydroxybutyrate (BHB), an endogenously produced ketone body. The Y647S variant confers a complex in vivo phenotype, which reflects largely diminished properties of NMDAR function. As a result, Grin1Y647S/+ mice display atypical behaviour in domains relevant to the clinical characteristics of GRIN1-NDD and the individual carrying the variant, which allowed for the identification of BHB supplementation as a potential anti-convulsant treatment. Ultimately, the characterization of Grin1Y647S/+ mice accomplished in the present work, expands our understanding of the mechanisms underlying GRIN1-NDD and provides a foundation for the continued development of novel therapeutics.

neuroscience↗

bioRxiv2024

GDC: An Integrated Resource to Explore the Pathogenesis of Hearing Loss through Genetics and Genomics

Effective research and clinical application in audiology and hearing loss (HL) often require the integration of diverse data. However, the absence of a dedicated database impeded understanding and insight extraction in HL. To address this, the Genetic Deafness Commons (GDC) was developed by consolidating extensive genetic and genomic data from 51 public databases and the Chinese Deafness Genetics Consortium, encompassing 5,983,613 variants across 201 HL genes. This comprehensive dataset detailed the genetic landscape of HL, identifying six novel mutational hotspots within DNA binding domains of transcription factor genes, which were eligible for evidence-based variant pathogenicity classification. Comparative phenotypic analyses highlighted considerable disparities between human and mouse models, with only 130 human HL genes exhibiting hearing abnormality in mice. Moreover, gene expression analyses in the cochleae of mice and rhesus macaques demonstrated a notable correlation (R2 = 0.76). Utilizing gene expression, function, pathway, and phenotype data, a SMOTE-Random Forest model identified 18 candidate HL genes, including TBX2 and ERCC2, newly confirmed as HL genes. The GDC, as a comprehensive and unified repository, significantly advances audiology research and clinical practice by enhancing data accessibility and usability, thereby facilitating deeper insights into hearing disorders.

genetics↗

bioRxiv2024

Building Predictive Understanding of Microbial Ecology by Bridging Microbial Growth Kinetics and Microbial Population Dynamics

Modeling microbial communities can provide predictive insights into microbial ecology, but current modeling approaches suffer from inherent limitations. In this study, a novel modeling approach was proposed to address those limitations based on the intrinsic connection between the growth kinetics of guilds and the dynamics of individual microbial populations. To implement the modeling approach, 466 samples from four full-scale activated sludge systems were retrieved from the literature. The raw samples were processed using a data transformation method that not only increased the dataset size by three times but also enabled quantification of population dynamics. Most of the 42 family-level core populations showed overall dynamics close to zero within the sampling period, explaining their resilience to environmental perturbation. Bayesian networks built with environmental factors, perturbation, historical abundance, population dynamics, and mechanistically derived microbial kinetic parameters classified the core populations into heterotrophic and autotrophic guilds. Topological data analysis was applied to identify keystone populations and their time-dependent interactions with other populations. The data-driven inferences were validated directly using the Microbial Database for Activated Sludge (MiDAS) and indirectly by predicting population abundance and community structure using artificial neural networks. The Bray-Curtis similarity between predicted and observed communities was significantly higher with microbial kinetic parameters than without parameters (0.70 vs. 0.66), demonstrating the accuracy of the modeling approach. Implemented based on engineered systems, this modeling approach can be generalized to natural systems to gain predictive understandings of microbial ecology.

microbiology↗

bioRxiv2024

The Neurolipid Atlas: a lipidomics resource for neurodegenerative diseases uncovers cholesterol as a regulator of astrocyte reactivity impaired by ApoE4

Lipid changes in the brain have been implicated in many neurodegenerative diseases including Alzheimers Disease (AD), Parkinsons disease and Amyotrophic Lateral Sclerosis. To facilitate comparative lipidomic research across brain-diseases we established a data commons named the Neurolipid Atlas, that we have pre-populated with novel human, mouse and isogenic induced pluripotent stem cell (iPSC)-derived lipidomics data for different brain diseases. We show that iPSC-derived neurons, microglia and astrocytes display distinct lipid profiles that recapitulate in vivo lipotypes. Leveraging multiple datasets, we show that the AD risk gene ApoE4 drives cholesterol ester (CE) accumulation in human astrocytes recapitulating CE accumulation measured in the human AD brain. Multi-omic interrogation of iPSC-derived astrocytes revealed that cholesterol plays a major role in astrocyte interferon-dependent pathways such as the immunoproteasome and major histocompatibility complex (MHC) class I antigen presentation. We show that through enhanced cholesterol esterification ApoE4 suppresses immune activation of astrocytes. Our novel data commons, available at neurolipidatlas.com, provides a user-friendly tool and knowledge base for a better understanding of lipid dyshomeostasis in neurodegenerative diseases.

neuroscience↗

bioRxiv2024

Annotating publicly-available samples and studies using interpretable modeling of unstructured metadata

Reusing massive collections of publicly available biomedical data can significantly impact knowledge discovery. However, these public samples and studies are typically described using unstructured plain text, hindering the findability and further reuse of the data. To combat this problem, we propose txt2onto 2.0, a general-purpose method based on natural language processing and machine learning for annotating biomedical unstructured metadata to controlled vocabularies of diseases and tissues. Compared to the previous version (txt2onto 1.0), which uses numerical embeddings as features, this new version uses words as features, resulting in improved interpretability and performance, especially when few positive training instances are available. Txt2onto 2.0 uses embeddings from a large language model during prediction to deal with unseen-yet-relevant words related to each disease and tissue term being predicted from the input text, thereby explaining the basis of every annotation. We demonstrate the generalizability of txt2onto 2.0 by accurately predicting disease annotations for studies from independent datasets, using proteomics and clinical trials as examples. Overall, our approach can annotate biomedical text regardless of experimental types or sources. Code, data, and trained models are available at https://github.com/krishnanlab/txt2onto2.0. Key pointsO_LIWe developed txt2onto 2.0, a computational method that combines language models and machine learning to annotate public samples and studies with standardized tissue and disease terms, with a focus on interpretability and explainability. C_LIO_LITxt2onto 2.0 uses word/phrase occurrence statistics to represent sample/study metadata, train machine learning models, and predict terms in controlled vocabularies to annotate each sample and study. This approach allows the model to keep track of predictive words related to model decisions and easily separate informative from uninformative words. C_LIO_LITxt2onto 2.0 outperforms its predecessor, txt2onto 1.0, in tissue and disease annotation, especially when training data is limited. C_LIO_LIThe predictive features learned by txt2onto 2.0 are highly interpretable. These features not only include explicit mentions of the actual disease or tissue terms but also related biomedical concepts, including words that are unseen by the model during training. C_LIO_LIAlthough trained on metadata of transcriptomes, txt2onto 2.0 is capable of annotating disease and tissue for any kind of biomedical metadata, making it a versatile tool for sample and study annotation. C_LI

bioinformatics↗

bioRxiv2024

PRR adjuvants restrain high stability peptides presentation on APCs

Adjuvants can affect APCs function and boost adaptive immune responses post-vaccination. However, whether they modulate the specificity of immune responses, particularly immunodominant epitope responses, and the mechanisms of regulating antigen processing and presentation remain poorly defined. Here, using overlapping synthetic peptides, we screened the dominant epitopes of Th1 responses in mice post-vaccination with different adjuvants and found that the adjuvants altered the antigen-specific CD4+ T-cell immunodominant epitope hierarchy. MHC-II immunopeptidomes demonstrated that the peptide repertoires presented by APCs were significantly altered by the adjuvants. Unexpectedly, no novel peptide presentation was detected after adjuvant treatment, whereas peptides with high binding stability for MHC-II presented in the control group were missing after adjuvant stimulation, particularly in the MPLA- and CpG-stimulated groups. The low-stability peptide present in the adjuvant groups effectively elicited robust T-cell responses and formed immune memory. Collectively, our results suggest that adjuvants (MPLA and CpG) inhibit high-stability peptide presentation instead of revealing cryptic epitopes, which may alter the specificity of CD4+ T-cell-dominant epitope responses. The capacity of adjuvants to modify peptide-MHC (pMHC) stability and antigen-specific T-cell immunodominant epitope responses has fundamental implications for the selection of suitable adjuvants in the vaccine design process and epitope vaccine development.

immunology↗