Biology subjects

Huang, J.

Publications and source records attributed to Huang, J..

At least 217 records · Page 12Linked to original sources

bioRxiv2025

SynSeg: Generating Synthetic Datasets for Accurate Subcellular Segmentation with U-net

Accurate segmentation of subcellular components is crucial for understanding cellular processes, but traditional methods struggle with noise and complex structures. Convolutional neural networks improve accuracy but require large, time-consuming, and biased manually annotated datasets. Here, we developed SynSeg, a pipeline that generates synthetic training data to train a U-net model for subcellular structure segmentation, eliminating the need for manual annotation. SynSeg leverages synthetic datasets with variations in intensity, morphology, and signal distribution to deliver context-aware segmentations, even in challenging imaging conditions. We demonstrate SynSegs superior performance in segmenting vesicles and cytoskeletal filaments from culture cells and live C. elegans, outperforming traditional methods such as Otsus thresholding, ILEE, and FilamentSensor 2.0. Additionally, SynSeg effectively quantified disease-associated microtubule morphology in live cells, uncovering structural defects caused by mutant Tau proteins linked to neurodegenerative diseases. These results highlight the potential of synthetic data-driven approaches to advance biological segmentation and enhance microscopy techniques. Significance StatementThis study introduces a novel approach for accurately segmenting cellular structures, such as microtubules and vesicles, using synthetic datasets and advanced deep learning techniques. By leveraging a U-Net model trained on thousands of artificially generated images, our method eliminates the need for labor-intensive experimental data and simplifies the data creation process. Importantly, it incorporates noise and variability into the training datasets to make the model more robust and biologically relevant. Our findings demonstrate that the model can successfully identify cellular components, paving the way for its application in real-world microscopy images. This innovation has the potential to accelerate discoveries in cell biology by providing an efficient, scalable tool for analyzing complex cellular structures, even in challenging imaging conditions.

bioinformatics↗

bioRxiv2025

'Trans-differentiation of neutrophils from plasmablast' is an artefact caused by over-reliance on machine algorithms in single cell RNA sequencing analysis: Lesson learnt and steps ahead

Single cell RNA sequencing (scRNA-seq) provides new opportunities to characterize gene expression for individual cells. However, the sparse nature of the scRNA-seq data with many zero counts or missing values presents a major challenge to its analysis. The presence of low-quality cells further complicates the analysis. Here, we showed that the trans-differentiation of plasmablasts (activated plasma cells) into neutrophils reported in COVID patients (Wilk et al., 2020 in Nature Medicine) was an artefact of trajectory analysis. It was caused by {bsim}30 low-quality cells linking the 2 cell populations that are of unrelated lineages in hematopoietic differentiation. Such artefacts are not readily spotted during the current practice of peer reviews as the current statistical guidelines of most journals are not catered for big data such as that of scRNA-seq. New standards of statistics and quality control measures for machine algorithms are not in place and they are urgently needed to safeguard against over-interpretation of high dimensional data. We propose a comprehensive framework to ensure reproducibility in high-dimensional data analysis, emphasizing quality checks, sensitivity analyses, alternative and multiple algorithms validation. Finally, and most importantly, a hypothesis-driven research approach should be upheld.

bioinformatics↗

bioRxiv2025

Platform-dependent effects of genetic variants on plasma APOL1 and their implications for kidney disease

Mutations in apolipoprotein L1 (APOL1) are strongly associated with protection against parasitic infections and increased risk of kidney disease in individuals of African ancestry. To better understand the mechanisms underlying APOL1-related pathologies, we examined genetic drivers of circulating APOL1 in individuals of African and European ancestry from four independent cohorts (UK Biobank, AASK, deCODE, and Health ABC) using three proteomic technologies (Olink, SomaLogic, and mass spectrometry). We found that disease-associated APOL1 G1 and G2 variants are strong cis-pQTLs for plasma APOL1 measured by Olink and SomaLogic, but not mass spectrometry. Critically, the direction of variant effects differed between proteomic platforms, being positive with Olink and negative with SomaLogic. We identified an additional APOL1 missense variant (rs2239785), common in Europeans, exhibiting the same platform-dependent directional discrepancy. Furthermore, variants in the kallikrein-kinin system (KKS), involving KLKB1, F12, and KNG1, and their genetic interactions showed strong trans-pQTL effects on APOL1 measured by Olink, but not SomaLogic. These platform-dependent discrepancies raise the possibility that both intrinsic APOL1 mutations and extrinsic KKS activity induce conformational changes in the APOL1 protein that are differentially recognized by the proteomic platforms.

genetics↗

bioRxiv2025

Mutation hot spots for clinical pathogenicity across the SLC6 transporter family

Genetic mutations of the Solute Carrier 6 (SLC6) family can lead to a diversity of clinal syndromes, such as creatine deficiency. Studying the impact of genetic mutations at the SLC6 family level is valuable not only for their medical significance but also for their conserved sequence and structural features. Within this work, we aim to link the disease-related mutations to their clinical significance from protein-protein interactions (PPIs) perspective, addressing how particular mutations may affect these critical interaction hotspots. In this study, we integrated both curated mutation data from previous work and predictive output from AlphaMissense to examine the entire SLC6 family. The mutations were mapped both onto the sequences and structures of SLC6 transporters. Thereby, a clustering of pathogenic mutations appeared on the surface regions that are likely involved in PPIs. After modeling complexes of SLC6s with potential shared interactors, we assessed these models overall and interface quality. By analyzing the complex interfaces together with the pathogenic mutations, we identified specific hotspots in the interfaces enriched with pathogenic mutations. In-depth examinations of selected PPIs offered insights into how particular mutations may affect these critical interaction hotspots. The hotspots were identified on the ECL3 and ECL4. For instance, Thr394Lys in SLC6A8 was found in the model interfaces, supported by experimental data showing the significant enrichment of the mutated proteins in the ER. Understanding these mutation hotspots can shed light on the broader structure-function relationships of SLC6 transporters and encourage therapeutic interventions targeting protein-protein interactions affected by pathogenic mutations.

bioinformatics↗

bioRxiv2025

The quantitative spatiotemporal relationship of whole brain activity of human brains revealed by fMRI

Human brain consists of many functional systems from the essential sensory, motor, attention and memory systems to higher order cognitive functions such as reasoning and language. Performing even a simple task may evoke multiple systems and cognitive functions, resulting in a whole brain activity across the entire brain. Despite the importance of studying task-evoked brain activated networks, investigating this whole brain activity may be crucial for understanding the neural bases of individual behavioral and clinical traits. BOLD-fMRI measures the four-dimensional (3 spatial and 1 temporal) neural activity across the entire brain at large-scale systems level. All local activities across the entire brain constitute the whole brain activity and each local activity is a part of that whole brain activity. Unlike a local activity that is characterized by its temporal neural activity, the whole brain activity is characterized by its spatial variation across the entire brain. We present a novel data-driven method to analyze the whole brain activity when performing tasks. The method enabled us to analyze the whole brain activity for each task trial and each individual subject with no requirement of a priori knowledge of task-evoked BOLD response. Our study revealed a quantitative spatiotemporal relationship of the whole brain activity with the local activities. The whole brain activity demonstrated a remarkable dynamic activity that varied from trial to trial when performing the same task repeatedly, showing the importance of analyzing the whole brain activity for investigating the neural bases of personal traits.

neuroscience↗

bioRxiv2025

Distinct CD8+ T-cell types Associated with COVID-19 Severity in Unvaccinated HLA-A2+ Patients

Although emerging data have revealed the critical role of memory CD8+ T cells in preventing and controlling SARS-CoV-2 infection, virus-specific CD8+ T-cell responses against SARS-CoV-2 and its memory and innate-like subsets in unvaccinated COVID-19 patients with various disease manifestations in an HLA-restricted fashion remain to be understood. Here, we show the strong association of protective cellular immunity with mild COVID-19 and unique cell types against SARS-CoV-2 virus in an HLA-A2 restricted manner. ELISpot assays reveal that SARS-CoV-2-specific CD8+ T-cell responses in mild COVID-19 patients are significantly higher than in severe patients, whereas neutralizing antibody responses against SARS-CoV-2 virus significantly correlate with disease severity. Single-cell analyses of HLA-A2-restricted CD8+ T cells, which recognize highly conserved immunodominant SARS-CoV-2-specific epitopes, demonstrate divergent profiles in unvaccinated patients with mild versus severe disease. CD8+ T-cell types including cytotoxic KLRB1+ CD8 cells with innate-like T-cell signatures, IFNGhiID3hi memory cells and IL7R+ proliferative stem cell-like memory cells are preferentially observed in mild COVID-19, whereas distinct terminally-differentiated T-cell subsets are predominantly detected in severe COVID-19: highly activated FASLhi T-cell subsets and early-terminated or dysfunctional IL4R+ GATA3+ stem cell-like memory T-cell subset. In conclusion, our findings suggest that unique and contrasting SARS-CoV-2-specific CD8+ T-cell profiles may dictate COVID-19 severity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/632164v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@154796corg.highwire.dtl.DTLVardef@efca24org.highwire.dtl.DTLVardef@c01b9corg.highwire.dtl.DTLVardef@54ad83_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract.C_FLOATNO SARS-CoV-2 epitope-specific CD8+ T-cell subtypes associated with mild or severe COVID-19 patients. Potent memory CD8+ T-cell subtypes with gene signature in mild COVID-19 patients (upper) and dysfunctional CD8+ T-cell subtypes with gene signature in severe COVID-19 patients (lower). C_FIG

immunology↗

bioRxiv2025

Transfer learning applied in predicting small molecule bioactivity

Despite over a half-century of effort by computational chemists, developing accurate empirical QSAR (Quantitative Structure-Activity Relationships) models for predicting bioactivity directly from chemical structure has remained elusive. The difficulties have been especially pronounced for virtual screening, finding new active compounds substantially different from the known chemical matter used to train the models. Recent breakthroughs have been achieved by employing transfer of learning across huge numbers of bioactivity assays, greatly increasing the amount and diversity of chemical and biological information that informs each model. An early example was Profile-QSAR (pQSAR), a 2-level stacked model, where level-2 PLS (Partial Least Squares) models characterize compounds by their profile of bioactivity predictions from individual level-1 random forest regression QSAR models built on up to 10,000+ other assays. This study introduces metaNN, a meta-learner that trains deep neural networks (DNN) for each individual assay initialised from a well-generalized consensus DNN optimized across all assays. Comparison of the results suggested that while Profile-QSAR and metaNN perform similarly overall, metaNN works slightly better for smaller assays which were well-predicted by the consensus DNN; whereas pQSAR struggled more with smaller assays, due to the large number of level-1 models but was less sensitive to similarity to an overall consensus. An ensemble average of both methods combined the strengths of each, working better than either alone. The similar performance of the 2 largely orthogonal algorithms raises questions about whether we are approaching a limit of prediction accuracy in transfer learning, for this application scenario.

bioinformatics↗

bioRxiv2025

Rev-RRE activity modulates HIV-1 replication and latency reactivation: Implications for viral persistence and cure strategies

The HIV-1 Rev-RRE regulatory axis plays a crucial role in viral replication by facilitating the nucleo-cytoplasmic export and expression of viral mRNAs with retained introns. In this study, we investigated the impact of variation in Rev-RRE functional activity on HIV-1 replication kinetics and reactivation from latency. Using a novel HIV-1 clone with an interchangeable Rev cassette, we engineered viruses with different Rev functional activities and demonstrated that higher Rev-RRE activity confers greater viral replication capacity while maintaining a constant level of Nef expression. In addition, a low Rev activity virus rapidly acquired a compensatory mutation in the RRE that significantly increased Rev-RRE activity and replication. In a latency model, proviruses with differing Rev-RRE activity levels varied in the efficiency of viral reactivation, affecting both initial viral release and subsequent replication kinetics. These results demonstrate that activity differences in the Rev-RRE axis among different viral isolates have important implications for HIV replication dynamics and persistence. Importantly, our findings indicate that bolstering Rev/RRE activity could be explored as part of latency reversal strategies in HIV cure efforts. Author SummaryThe activity of the HIV-1 Rev-RRE axis is essential for viral replication and varies among primary viral isolates. However, the role of this for viral fitness, evolution, and persistence has not previously been investigated. Our results show that during in vitro replication, there is a selective fitness advantage for viruses with higher Rev-RRE activity and that HIV has the ability to fine tune this regulatory system with minimal sequence changes. Additionally, the maintenance of Nef expression in low Rev activity viruses suggests a potential mechanism for balancing immune evasion and replication capacity in different selection landscapes within a host. We also show that viruses with low Rev-RRE activity are more difficult to reverse from latency than viruses with higher Rev-RRE activity. Thus, differences in provirus Rev-RRE activity may be a barrier to developing effective latency reversal strategies. These findings provide new insights into the complex roles that the Rev/RRE axis plays in functionality, viral fitness, evolution, and persistence.

microbiology↗

bioRxiv2025

Scrutinization on Docking Against Individually Generated Target Pockets for Each Ligand

The longstanding challenge of limited exploration in protein receptor conformational space continues to constrain the precision of molecular docking. Ensemble docking, which employs methods such as molecular dynamics simulations to generate multiple receptor conformations for docking, has improved accuracy but remains limited by incomplete sampling and an inability to fully account for ligand-induced fit. To address these limitations, we introduce the concept of individual docking, a novel approach that involves docking against receptor conformations generated individually for each ligand in the docking library. This approach has only very recently become feasible due to advances in protein structure prediction, in particular end-to-end protein-ligand complex prediction technologies. In this study, we performed individual docking on 27 targets from the DUD-E dataset, using a two-step protocol that integrates NeuralPLexer or AlphaFold3 for receptor conformation sampling, followed by physics-based docking. Our results reveal that individual docking with AlphaFold3 predictions yields more than a twofold improvement in enrichment factors compared to standard docking. Furthermore, individual docking recovers distinct sets of active ligands, thereby expanding the diversity of virtual screening hits. Detailed analyses of pocket and ligand conformations suggest several potential incompatibilities between deep learning-based and physics-based virtual screening tools.

bioinformatics↗

bioRxiv2024

CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants

Genomic selection (GS) leverages genome-wide markers and phenotypes to predict breeding values, with its effectiveness largely dependent on the accuracy of genomic prediction (GP) models. However, GP methods often struggle to capture inter-individual variability and are limited by the curse of dimensionality, where the number of SNPs far exceeds the sample size. To address these challenges, we present CLCNet (Contrastive Learning and Chromosome-aware Network), a novel deep learning framework that integrates contrastive learning and chromosome-aware feature modeling. CLCNet comprises two key components: (i) a contrastive learning module that enhances the models ability to capture fine-grained, genotype-dependent phenotypic differences among individuals, and (ii) a chromosome-aware module that captures structured feature selection at both chromosome and genome levels, thereby distilling the most informative SNPs. We evaluated CLCNet across four crop species, covering ten agronomically important traits, and compared it with a diverse set of classical linear, machine learning, and deep learning models. CLCNet achieved superior prediction performance, with statistically significant improvements in Pearson correlation coefficient (PCC), ranging from 0.34% to 12.19% over baseline, together with reduced mean squared error (MSE). Performance gains were more pronounced for traits with moderate linkage disequilibrium (LD; r2 = 0.21-0.36) and high heritability (h2 > 0.66), such as those in maize, rapeseed, and soybean. For cotton traits characterized by high LD (r2 = 0.74) and lower heritability (h2 < 0.50), CLCNet maintained robust performance without degradation. Overall, these results demonstrate that CLCNet is an effective framework for improving genomic prediction accuracy and holds strong potential for practical applications in plant breeding. Short abstractCLCNet is a novel deep learning framework for genomic prediction that integrates contrastive learning with chromosome-aware feature selection. By jointly modeling inter-individual genotype-phenotype variation and chromosomal genomic structure, CLCNet improves prediction accuracy under high-dimensional, low-sample-size conditions. Across four crop species and ten agronomic traits, CLCNet consistently outperformed classical statistical, machine learning, and existing deep learning models. The framework also identified biologically relevant SNPs and candidate genes, demonstrating its potential for practical applications in genomic selection and computational plant breeding. Key pointsO_LIWe propose CLCNet, a multi-task deep learning framework that integrates contrastive learning with chromosome-aware feature selection for genomic prediction, under high-dimensional, low-sample-size conditions. C_LIO_LIThe chromosome-aware module explicitly exploits genomic structural information to select representative and informative SNPs across chromosomes. C_LIO_LIContrastive learning improves model robustness by stabilizing representation learning and reducing the influence of random effects across samples. C_LIO_LIBy complementing GWAS analyses, CLCNet provides additional insights into genotype-phenotype relationships with potential relevance for gene discovery. C_LI Biographical NoteJiangwei Huang is a PhD candidate at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. His research focuses on genomic prediction, deep learning, and computational plant breeding. Zhihan Yang is a PhD candidate at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. Her research interests include genomic prediction and bioinformatics. Rongcheng Han is an associate professor at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. His research focuses on bioinformatics and plant phenomics. Yuqiang Jiang is a professor at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. His research interests include plant genomics, plant phenomics and genetic improvement. Organization descriptionThe Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, is a leading research institute focusing on genetics, genomics, molecular breeding, bioinformatics, and systems biology in plants and animals.

genomics↗

bioRxiv2024

High-resolution mastigoneme structure reveals 5',5'-phosphodiesters stabilized glycan folding

Glycans play a crucial role in structure organization, energy metabolism, and signal transduction in living organisms. Compared with proteins and nucleic acids, glycans exhibit remarkable molecular heterogeneity, extensive conformational flexibility, and diverse linkages. These properties pose significant challenges for obtaining high-resolution structure of glycans. Here, we report a cryo-EM structure of the heavily glycosylated Chlamydomonas mastigoneme at 2.3-2.5 [A] resolutions. In addition to enabling analysis of accurate interactions for glycosyl packing, the high resolution map reveals an unprecedented 5,5-phosphodiester bond that links adjacent glycan chains attached to hydroxyproline (Hyp) residues n and n+3. Structural analysis reveals a secondary structural element for glycoconjugates, which we name the poly-Hyp (pHP) glycohelix. Our work represents an important advancement in deciphering glycan folding.

biochemistry↗

bioRxiv2024

Elucidation of gene clusters underlying withanolide biosynthesis in ashwagandha through yeast metabolic engineering

Withanolides are medicinally relevant steroidal lactones produced by Withania somnifera (ashwagandha) amongst other Solanaceae family plants. However, the biosynthetic pathway to withanolides is largely unknown, preventing scale-up and hindering pharmaceutical applications. We sequenced the genome of W. somnifera and identified two biosynthetic gene clusters exhibiting a segmented tissue-specific expression pattern. We characterized the cluster enzymes through stepwise pathway reconstitution in yeast and transient expression in Nicotiana benthamiana, leading to the identification of three cytochrome P450s (CYP87G1, CYP88C7, and CYP749B2) and a short-chain dehydrogenase that produce a lactone ring- containing intermediate when co-expressed. A fourth cytochrome P450 (CYP88C10) and a sulfotransferase convert this into an intermediate with the characteristic withanolide A-ring structure, featuring a C1 ketone and C2-C3 unsaturation. The discovery of the sulfotransferase as a core pathway enzyme challenges the conventional paradigm of sulfotransferases as tailoring enzymes. These insights pave the way for an efficient biomanufacturing process for withanolides and future development of withanolide-derived drugs.

biochemistry↗

bioRxiv2024

Global and genetic regulation of gene expression in human endothelial and vascular smooth muscle cells

BackgroundThe understanding of genetic and epigenetic regulation of gene expression in endothelial and vascular smooth muscle cells remains fragmented with limited experimental validation. MethodsChromatin conformation (Micro-C), DNA methylation (RRBS), chromatin accessibility (ATAC-seq), and transcriptome profiles (RNA-seq) were mapped in human induced pluripotent stem cell (hiPSC)-derived, isogenic endothelial and vascular smooth muscle cells (iECs and iVSMCs). CTCF and RAD21 were depleted to assess the functional relevance of chromatin architecture, and genome editing was used to evaluate the allelic effect of a blood pressure-associated single nucleotide polymorphism (SNP). ResultsSignificant correlations were identified between gene expression levels and chromatin interactions, chromatin accessibility, and DNA methylation in iECs and iVSMCs, with chromatin interactions showing the strongest association. Chromatin contact regions displayed distinct epigenetic landscapes depending on the types of regulatory element interactions involved. Perturbation of CTCF and RAD21 revealed their differential regulatory effects, particularly on the expression of genes overlapping chromatin contacts, with RAD21 exhibiting a broader regulatory impact. SNPs associated with several vascular traits were enriched in chromatin loops or accessible regions in iECs or iVSMCs. Precise genome editing demonstrated allele-dependent effects of SNP rs9833313 on the expression of SHOX2 located 247.4 kbp away but within the same chromatin loops as the SNP. ConclusionThis study provides an extensive epigenetic landscape of vascular cells that may drive novel research on the role of genetic and epigenetic mechanisms of vascular function and disease as demonstrated by our targeted experiments.

genomics↗

bioRxiv2024

A single-cell map of hypertension

Hypertension is a leading risk factor for disease burden and death worldwide. Several organ systems are involved in the development of hypertension, which contributes to stroke, heart disease, and kidney disease. Despite the broad health relevance, our understanding of the molecular landscape in hypertension is limited and lags other major diseases. Here we report an extensive analysis of the molecular landscape in hypertension and its end-organ damage and uncover novel mechanisms linking human genetic variants to the development of these diseases. We obtained single-nucleus RNA-seq (612,984 nuclei), single-nucleus ATAC-seq (179,637 nuclei), or spatial transcriptome data from five organs (hypothalamus, kidney, heart, 3rd order mesenteric artery, middle cerebral artery) in three mouse and rat models under twelve experimental conditions. More than one third of all hypertension research in animal models involves these three models. We identified both model-specific and convergent responses in cell types, genes, and pathways. By integrating our data with human genomic data, we partitioned the blood pressure and end-organ damage traits into cell type-specific transcriptional contributions and cell types common across multiple traits. Using genomic editing in animal models and human induced pluripotent stem cells, we extended key findings and identified new mechanisms linking human genetic variants to the development of hypertension and related renal injury. We anticipate that our rich data sets and findings will broadly drive forward the research of hypertension and hypertensive end-organ damage. Our approach of integrating multi-model and multi-tissue single-cell analysis with human genetic data and in vivo and in vitro genome editing can be applied to investigate other complex traits.

physiology↗

bioRxiv2024

Kun-peng: an ultra-memory-efficient, fast, and accurate pan-domain taxonomic classifier for all

Comprehensive metagenomic sequence classification of diverse environmental samples faces significant computing memory challenges due to exponentially expanding genome databases. Here, we present Kun-peng, featuring a unique ordered 4GB block database design for ultra-efficient resource management, faster processing, and higher accuracy. When benchmarked on mock communities (Amos HiLo, Mixed, and NIST) against Kraken2, Centrifuge, and Sylph. Kun-peng matched Sylph, achieving the highest precision and lowest false-positive rates while demonstrating superior time and memory efficiency among all tested tools. Furthermore, Kun-pengs efficient database architecture enables the practical utilization of large-scale reference databases that were previously computationally prohibitive. In comprehensive testing across 586 air, water, soil, and human metagenomic samples using an expansive pan-domain database (204,477 genomes, 4.3TB), Kun-peng classified 69.78-94.29% of reads, achieving 38-43% higher classification rates than Kraken2 with the standard database. Unexpectedly, Sylph failed to classify any reads in air samples and left > 99.85% of reads unclassified in water and soil samples. In terms of computational efficiency, Kun-peng processed each sample in 0.2[~]11.2 minutes using only 4.0[~]35.4GB peak memory. Remarkably, these processing times were comparable to Kraken2 using the standard database (81GB, 5% of the pan-domain database). Memory-wise, Kun-peng required only 35.4GB peak memory with the pan-domain database, representing a 473-fold reduction compared to Kraken2. When compared to Sylph, Kun-peng processes samples up to 46.3 times faster while using up to 20.6 times less memory. Overall, Kun-peng offers an ultra-memory-efficient, fast, and accurate solution for pan-domain metagenomic classifications.

bioinformatics↗

bioRxiv2024

Binding items to contexts through conjunctive neural representations with the Method of Loci

Schematic prior knowledge can provide a powerful scaffold for episodic memories, yet the neural mechanisms underlying this scaffolding process are still poorly understood. A crucial step of the scaffolding process is the way in which details of a new episode are connected to an existing schema, forming a robust memory representation that can be easily accessed in the future. A unique testbed for studying this binding process is a mnemonic technique called the Method of Loci (MoL), in which people meaningfully connect items to be remembered with a well-learned list of imagined loci. We collected fMRI data from participants in 3 longitudinal sessions while they were enrolled in a month-long MoL training course, all of whom showed dramatic improvements in their ability to remember lists of 20 or 40 words. We obtained neural patterns when the loci and objects are presented by themselves, when they are combined into an integrated representation at encoding, and when the integrated representation was subsequently retrieved, as well as verbal descriptions from the participants about the way they associated each item to each locus. We found that in default mode network regions, including medial prefrontal cortex (mPFC), the combined representations of loci and items are highly conjunctive: the unified locus-item representation was substantially different from a linear combination of the isolated locus and item representation, reflecting the addition of new integrative details specific to each combined pair. The conjunctive component of the representation reflected the particular creative details generated by individual participants and increased over time as participants gained expertise in MoL. Our findings reveal a critical role for the default mode network in creating meaningful connections between new information and well-learned schematic contexts.

neuroscience↗

bioRxiv2024

PlateletBase: A Comprehensive Knowledgebase for Platelet Research and Disease Insights

Platelets are vital in many pathophysiological processes, yet there is a lack of a comprehensive resource dedicated specifically to platelet research. To fill this gap, we have developed PlateletBase, a knowledge base aimed at enhancing the understanding and study of platelets and related diseases. Our team retrieved information from various public databases, specifically extracting and analyzing RNA-seq data from 3,711 samples across 41 different conditions available on NCBI. PlateletBase offers six analytical and visualization tools, enabling users to perform gene similarity analysis, pair correlation, multi-correlation, expression ranking, clinical information association, and gene annotation for platelets. The current version of PlateletBase includes 10,278 genomic entries, 31,758 transcriptomic entries, 4,869 proteomic entries, 2,614 omics knowledge entries, 1,833 drugs, 97 platelet resources, 438 diseases/traits, and six analysis modules. Each entry has been carefully curated and supported by experimental evidence. Additionally, PlateletBase features a user-friendly interface designed for efficient querying, manipulation, browsing, visualization, and analysis of detailed platelet protein and gene information. Case study results, such as those from gray platelet syndrome and angina pectoris, demonstrate that this tool can aid in identifying diagnostic biomarkers and exploring disease mechanisms, significantly advancing research in platelet functionality and its applications. PlateletBase is accessible at http://plateletbase.clinlabomics.org.cn/.

bioinformatics↗

bioRxiv2024

A Novel Bispecific Antibody CVL006 Superior to AK112 for Dual Targeting of PD-L1 and VEGF in Cancer Therapy

Both preclinical and clinical studies have shown that combining anti-VEGF/VEGFR drugs with immune checkpoint inhibitors (ICIs) significantly enhances anticancer efficacy. Currently, PDL-1/ VEGF bispecific antibodies demonstrate superior antitumor activity compared to monotherapy or even the combination of PD-L1 inhibitors with anti-VEGF antibodies. This enhanced efficacy results from the simultaneous blockade of the PD-1/PD-L1 pathway and the inhibition of VEGF-driven angiogenesis. In this study, we developed a novel bispecific antibody, CVL006, by fusing an anti-PDL1 VHH domain with a humanized IgG1 anti-VEGF monoclonal antibody while retaining antibody-dependent cellular cytotoxicity (ADCC) functionality. CVL006 demonstrated high affinity and specificity for both human PD-L1 and VEGF, effectively blocking both the VEGF/VEGFR signaling pathway and the PD-L1/PD-1 axis. This dual blockade not only suppressed VEGF-induced angiogenesis but also reactivated T cells, increasing the secretion of cytokines essential for immune response. In vivo studies further indicated that CVL006 achieved superior antitumor efficacy compared to the PD-L1 inhibitor atezolizumab in mouse models, with greater tumor growth inhibition and reduced angiogenesis. To compare with approved bispecific antibody PD-1/VEGF AK112 (ivonescimab), CVL006 showed superior antitumor efficacy in vivo. These findings underscore the therapeutic potential of CVL006, which integrates immune checkpoint inhibition with disruption of tumor vascularization, offering a robust and comprehensive anticancer strategy.

pharmacology and toxicology↗