Biology subjects

Lin, D.

Publications and source records attributed to Lin, D..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Considerations for Cell Type Heterogeneity in Pediatric Salivary DNA Methylation Analyses: Comparison of Reference Panels & Stratification by Estimated Cell TypeProportion

Saliva is widely used in biomedical population research, including epigenetic analyses to investigate gene-environment interplay and identify biomarkers. Its minimally invasive collection procedure makes it ideal for studies in pediatric populations. Saliva is a heterogenous tissue composed of immune and buccal epithelial cells (BEC). Amongst the many epigenetic marks, DNA methylation (DNAm) is the most studied in human populations. Given DNAms integral role to cellular differentiation and maintenance, DNAm profiles are often highly cell type (CT)-specific and CT composition can drive salivary DNAm associations with environments or health as well as epigenetic age acceleration (EAA), discrepancy between chronological age and biological age derived from DNAm. To address this, reference-based CT deconvolution and statistically adjustment with estimated CT in DNAm analyses have become a common practice. However, it remains unclear how different CT reference panels--constructed from adult versus pediatric samples--affect DNAm results. Additionally, whether DNAm and EAA associations in saliva primarily originate from immune cells or BECs, or if they persist across saliva samples despite varying CT proportions, has yet to be examined. The current study used salivary DNAm samples obtained from 529 children (mean age=7.26 years, SD=0.26 years) in a community-based cohort, the Family Life Project. Our results highlighted the impact of estimated CT discrepancies across child and adult reference panels on DNAm associations. Upon stratifying the salivary DNAm samples into three subsamples--primarily BECs, primarily immune cells, and an approximately equal mix of both, we found significantly different EAAs across stratified samples when CT proportions were not accounted for. In both the context of DNAm and EAA associations, we detected stronger effects of cotinine concentrations, a tobacco smoke-exposure biomarker, in the subsample with primarily immune cells. We discussed the implications of our findings for the interpretation and replication of epigenetic research involving pediatric saliva samples.

bioinformatics↗

bioRxiv2024

Characterisation of RNA guanine-7 methyltransferase (RNMT) using a small molecule approach

The maturation of the RNA cap involving guanosine N-7 methylation, catalyzed by the HsRNMT (RNA guanine-7 methyltransferase)-RAM (RNA guanine-N7 methyltransferase activating subunit) complex, is currently under investigation as a novel strategy to combat PIK3CA mutant breast cancer. However, the development of effective drugs is hindered by a limited understanding of the enzymes mechanism and a lack of small molecule inhibitors. Following the elucidation of the HsRNMT-RAM molecular mechanism, we report the biophysical characterization of two small molecule hits. Biophysics, biochemistry and structural biology confirm that both compounds bind competitively with cap and bind effectively to HsRNMT-RAM in the presence of the co-product SAM, with a binding affinity (KD) of approximately 1 M. This stabilisation of the enzyme-product complex results in uncompetitive inhibition. Finally, we describe the properties of the cap pocket and provided suggestions for further development of the tool compounds.

biochemistry↗

bioRxiv2024

The multi-stage plasticity in the aggression circuit underlying the winner effect

Winning increases the readiness to attack and the probability of winning, a widespread phenomenon known as the "winner effect". Here, we reveal a transition from target-specific to generalized aggression enhancement over 10 days of winning in male mice, which is supported by three stages of plasticity in the ventrolateral part of the ventromedial hypothalamus (VMHvl), a critical node for aggression. Over 10-day winning, VMHvl cells experience monotonic potentiation of long-range excitatory inputs, a transient local connectivity strengthening, and a delayed excitability increase. These plasticity events are causally linked. Optogenetically coactivating the posterior amygdala (PA) terminals and VMHvl cells potentiates the PA-VMHvl pathway and triggers the cascade of plasticity events as those during repeated winning. Optogenetically blocking PA-VMHvl synaptic potentiation eliminates all winning-induced plasticity. These results reveal the complex Hebbian synaptic and excitability plasticity in the aggression circuit during winning that ultimately leads to an increase in "aggressiveness" in repeated winners.

neuroscience↗

bioRxiv2024

AmygdalaGo-BOLT3D: A boundary learning transformer for tracing human amygdala

Each year, thousands of brain MRI scans are collected to study structural development in children and adolescents. However, the amygdala, a particularly small and complex structure, remains difficult to segment reliably, especially in developing populations where its volume is even smaller. To address this challenge, we developed AmygdalaGo-BOLT, a boundary-aware deep learning model tailored for human amygdala segmentation. It was trained and validated using 854 manually labeled scans from pediatric datasets, with independent samples used to ensure performance generalizability. The model integrates multiscale image features, spatial priors, and self-attention mechanisms within a compact encoder-decoder architecture to enhance boundary detection. Validation across multiple imaging centers and age groups shows that AmygdalaGo-BOLT closely matches expert manual labels, improves processing efficiency, and outperforms existing tools in accuracy. This enables robust and scalable analysis of amygdala morphology in developmental neuroimaging studies where manual tracing is impractical. To support open and reproducible science, we publicly release both the labeled datasets and the full source code. TeaserAn AI model enables accurate amygdala segmentation across ages and centers, supporting large-scale brain imaging studies.

neuroscience↗

bioRxiv2024

PINDER: The protein interaction dataset and evaluation resource

Protein-protein interactions (PPIs) are fundamental to understanding biological processes and play a key role in therapeutic advancements. As deep-learning docking methods for PPIs gain traction, benchmarking protocols and datasets tailored for effective training and evaluation of their generalization capabilities and performance across real-world scenarios become imperative. Aiming to overcome limitations of existing approaches, we introduce PINDER, a comprehensive annotated dataset that uses structural clustering to derive non-redundant interface-based data splits and includes holo (bound), apo (unbound), and computationally predicted structures. PINDER consists of 2,319,564 dimeric PPI systems (and up to 25 million augmented PPIs) and 1,955 high-quality test PPIs with interface data leakage removed. Additionally, PINDER provides a test subset with 180 dimers for comparison to AlphaFold-Multimer without any interface leakage with respect to its training set. Unsurprisingly, the PINDER benchmark reveals that the performance of existing docking models is highly overestimated when evaluated on leaky test sets. Most importantly, by retraining DiffDock-PP on PINDER interface-clustered splits, we show that interface cluster-based sampling of the training split, along with the diverse and less leaky validation split, leads to strong generalization improvements.

bioinformatics↗

bioRxiv2024

Visual quality control with CytoMDS, a Bioconductor package for low dimensional representation of cytometry sample distances

Quality Control (QC) of samples is an essential preliminary step in cytometry data analysis. Notably, identification of potential batch effects and outlying samples is paramount, to avoid mistaking these effects for true biological signal in downstream analyses. However, this task can prove to be delicate and tedious, especially for datasets with dozens of samples. Here, we present CytoMDS, a Bioconductor package implementing a dedicated method for low dimensional representation of cytometry samples composed of marker expressions for up to millions of single cells. This method allows a global representation of all samples of a study, with one single point per sample, in such a way that projected distances can be visually interpreted. CytoMDS uses Earth Movers Distance for assessing dissimilarities between multi-dimensional distributions of marker expression, and Multi Dimensional Scaling for low dimensional projection of distances. Some additional visualization tools, both for projection quality diagnosis and for user interpretation of the projection coordinates, are also provided in the package. We demonstrate the strengths and advantages of CytoMDS for QC of cytometry data on three real biological datasets, revealing the presence of low quality samples, batch effects and biological signal between sample groups.

bioinformatics↗

bioRxiv2024

Enhanced purinergic signaling in the paraventricular hypothalamus induces hyperphagic obesity and insulin resistance

Body energy homeostasis is tightly regulated by hypothalamic neural circuits. However, their remodeling upon metabolic challenges remains incompletely characterized, thus complicating the development of safe medications against the surge of metabolic diseases. Oxytocin (OXT) neurons in the paraventricular nucleus of the hypothalamus (PVH) are one of the key effectors regulating energy balance. In this work, we report that high-fat diet (HFD) feeding in mice evokes spatiotemporally selective ATP release from astrocytes (Inflares) in the PVH, accompanied by the expression of hematopoietic lineage-specific ADP/ATP receptor P2Y12 on local OXT (PVHOXT) neurons. Strikingly, increased purinergic signaling leads to impaired responsiveness of PVHOXT neurons accompanied by hyperphagic obesity and insulin resistance in mice. Conversely, loss of P2Y12 in PVHOXT neurons attenuates metabolic phenotypes, illustrating that such remodeling is both necessary and sufficient to induce metabolic phenotypes. Inflares were also induced by hyperglycemia, while emergence of P2Y12 on OXT neurons of patients with diabetes mellitus suggests an evolutionary conserved purinergic response to various metabolic challenges and its potential as a drug target. Accordingly, nasal administration of clinically approved doses of P2Y12 inhibitors counteracts obesity in mice and non-human primates, paving the way for application of these compounds in patients with metabolic disorders.

neuroscience↗

bioRxiv2024

Genomic and single-cell characterization of patient-derived tumor organoid models of head and neck squamous cell carcinoma

Head and Neck Squamous Cell Carcinoma (HNSCC) remains a significant health burden due to tumor heterogeneity and treatment resistance, emphasizing the need for improved biological understanding and tailored therapies. This study enrolled 31 HNSCC patients for the establishment of patient-derived tumor organoids (PDOs), which faithfully maintained genomic features and histopathological traits of primary tumors. Long-term culture preserved key characteristics, affirming PDOs as robust representative models. PDOs demonstrated predictive capability for cisplatin treatment responses, correlating ex vivo drug sensitivity with patient outcomes. Bulk and single-cell RNA sequencing unveiled molecular subtypes and intratumor heterogeneity (ITH) in PDOs, paralleling patient tumors. Notably, a hybrid epithelial-mesenchymal transition (hEMT)-like ITH program is associated with cisplatin resistance and poor patient survival. Functional analyses identified amphiregulin (AREG) as a potential regulator of the hybrid epithelial/mesenchymal state. Moreover, AREG contributes to cisplatin resistance via EGFR pathway activation, corroborated by clinical samples. In summary, HNSCC PDOs serve as reliable and versatile models, offer predictive insights into ITH programs and treatment responses, and uncover potential therapeutic targets for personalized medicine. One Sentence SummaryThis study establishes patient-derived tumor organoids (PDOs) from 31 Head and Neck Squamous Cell Carcinoma (HNSCC) patients, faithfully recapitulating characteristics of primary tumors and accurately predicting clinical responses to cisplatin treatment. We reveal intertumoral heterogeneity within PDOs and a hybrid epithelial-mesenchymal transition (hEMT) program conferring cisplatin resistance, highlighting amphiregulin (AREG) as a regulator of cellular plasticity and potential therapeutic target for HNSCC treatment.

cancer biology↗

bioRxiv2024

Hierarchical global and local auxin signals coordinate cellular interdigitation in Arabidopsis

The development of multicellular tissues requires both local and global coordination of cell polarization, however, the mechanisms underlying their interplay are poorly understood. In Arabidopsis, leaf epidermal pavement cells (PC) develop a puzzle-piece shape locally coordinated through apoplastic auxin signaling. Here we show auxin also globally coordinates interdigitation by activating the TIR1/AFB-dependent nuclear signaling pathway. This pathway promotes a transient maximum of auxin at the cotyledon tip, which then moves across the leaf activating local PC polarization, as demonstrated by locally uncaged auxin globally rescuing defects in tir1;afb1;afb2;afb4;afb5 mutant but not in tmk1;tmk2;tmk3;tmk4 mutants. Our findings show that hierarchically integrated global and local auxin signaling systems, which respectively depend on TIR1/AFB-dependent gene transcription in the nucleus and TMK-mediated rapid activation of ROP GTPases at the cell surface, control PC interdigitation patterns in Arabidopsis cotyledons, revealing a mechanism for coordinating a local cellular process with the development of whole tissues.

plant biology↗

bioRxiv2024

Mining the UniProtKB/Swiss-Prot database forantimicrobial peptides

The ever-growing global health threat of antibiotic resistance is compelling researchers to explore alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are emerging as a promising solution to fill this need. Naturally occurring AMPs are produced by all forms of life as part of the innate immune system. High-throughput bioinformatics tools have enabled fast and large-scale discovery of AMPs from genomic, transcriptomic, and proteomic resources of selected organisms. Public protein sequence databases, comprising over 200 million records and growing, serve as comprehensive compendia of sequences from a broad range of source organisms. Yet, large-scale in silico probing of those databases for novel AMPs has never been reported. In the present study, we propose an AMP mining workflow to predict novel AMPs from the UniProtKB/Swiss-Prot database using the AMP prediction tool, AMPlify, as its discovery engine. Using this workflow, we identified 8,008 novel putative AMPs from all eukaryotic sequences in the database. Focusing on the practical use of AMPs as suitable antimicrobial agents with applications in the poultry industry, we prioritized 40 of those AMPs based on their similarities to known chicken AMPs in predicted structures. In our tests, 13 out of the 38 successfully synthesized peptides showed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus.

bioinformatics↗

bioRxiv2024

Telomere-to-telomere phased genome assembly using error-corrected Simplex nanopore reads

Telomere-to-telomere phased assemblies have become the norm in genomics. To achieve these for diploid and even polyploid genomes, the contemporary approach involves a combination of two long-read sequencing technologies: high-accuracy long reads, e.g. Pacific Biosciences (PacBio) HiFi or Oxford Nanopore (ONT) Duplex reads, and ultra-long ONT Simplex reads. Using two different technologies increases the cost and the required amount of genomic DNA. Here, we show that comparable results are possible using error correction of ultra-long ONT Simplex reads and then assembling them using state-of-the-art de novo assembly methods. To achieve this, we have developed the deep learning-based HERRO framework, which corrects ONT Simplex reads while carefully preserving differences in related genomic sequences. Taking into account informative positions that differentiate the haplotypes or genomic repeat copies, HERRO achieves an increase of read accuracy of up to 100-fold for diploid human genomes. By combining HERRO with Verkko assembler, we achieve high contiguity on several human genomes by reconstructing many chromosomes telomere-to-telomere, including chromosomes X and Y. HERRO supports both R9.4.1 and R10.4.1 ONT Simplex reads and generalizes well to other species. These results provide an opportunity to reduce the cost of genome sequencing and use corrected ONT reads to analyze more complex genomes with different levels of ploidy or even aneuploidy.

bioinformatics↗

bioRxiv2024

Deciphering the Transcription Factor Landscape in Neuroendocrine Prostate Cancer Progression: A Novel Approach to Understand NE Transdifferentiation

Background and ObjectiveProstate cancer (PCa) is a leading cause of cancer mortality in men, with neuroendocrine prostate cancer (NEPC) representing a particularly resistant subtype. The role of transcription factors (TFs) in the progression from prostatic adenocarcinoma (PRAD) to NEPC is poorly understood. This study aims to identify and analyze lineage-specific TF profiles in PRAD and NEPC and illustrate their dynamic shifts during NE transdifferentiation. MethodsA novel algorithmic approach was developed to evaluate the weighted expression of TFs within patient samples, enabling a nuanced understanding of TF landscapes in PCa progression and TF dynamic shifts during NE transdifferentiation. Resultsunveiled TF profiles for PRAD and NEPC, identifying 126 shared TFs, 46 adenocarcinoma-TFs, and 56 NEPC-TFs. Enrichment analysis across multiple clinical cohorts confirmed the lineage specificity and clinical relevance of these lineage-TFs signatures. Functional analysis revealed that lineage-TFs are implicated in pathways critical to cell development, differentiation, and lineage determination. Novel lineage-TF candidates were identified, offering potential targets for therapeutic intervention. Furthermore, our longitudinal study on NE transdifferentiation highlighted dynamic TF expression shifts and delineated a three-phase hypothesis for the process comprised of de-differentiation, dormancy, and re-differentiation. and proposing novel insights into the mechanisms of PCa progression. ConclusionThe lineage-specific TF profiles in PRAD and NEPC reveal a dynamic shift in the TF landscape during PCa progression, highlighting three distinct phases of NE transdifferentiation.

cancer biology↗

bioRxiv2024

A multi-track landscape of haematopoiesis informed by cellular barcoding and agent-based modelling

In classic ball-and-stick models of haematopoiesis the implicit assumption is that all cells within each defined stem or progenitor cell population are equivalent in their fate. Instead, more recent models suggest a haematopoietic stem and progenitor cell (HSPC) continuum of lineage bias and commitment, which is largely inferred through snapshot analysis of single cell gene expression or clonal fate. However, the dynamic assessment of lineage commitment of specific HSPC populations and their clonal output over time in vivo is still lacking but is essential to fully inform accurate models of haematopoiesis. Here, using cellular barcoding we compare the single cell output of long-term haematopoietic stem cells (LT-HSCs), short-term HSCs (ST-HSCs), multipotent progenitors (LMPPs), common myeloid progenitors (CMPs), common lymphoid progenitors (CLPs), and macrophage/dendritic cell progenitors (MDPs). Each population was assessed for their output to multiple haematopoietic cell types spanning a subset of time points from 9 to 112 days of haematopoiesis after transplantation. These analyses revealed a wide range of clonal fate patterns that were inconsistent with their eponymous labels, i.e. stem and multipotent progenitors were rarely multi- or equipotent, and common progenitors were often highly restricted in their fate. To better describe how these clonal patterns integrate into a revised landscape, a novel agent-based mathematical modelling approach that explicitly accounts for haematopoiesis at a clonal level was developed to allow the simulation of growth, timing and branching of clonal trajectories that underlie the process. Rather than a continuum, the proposed model is suggestive of multiple tracks down which clonal trajectories progress, and where fate can branch to a track of lower potency at multiple points down the entire cascade of haematopoiesis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/587126v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@b09137org.highwire.dtl.DTLVardef@de064org.highwire.dtl.DTLVardef@10705e3org.highwire.dtl.DTLVardef@139b6f_HPS_FORMAT_FIGEXP M_FIG C_FIG In a multi-track model, while some HSPCs are multipotent and branch into daughters with committed fate (left trajectory), other HSPCs may already committed or biased to a certain lineage such that their daughters inherit and maintain it in subsequent divisions during haematopoiesis. However, this commitment/fate bias is not evident through current phenotypic definitions of HSPC subsets (background colour) but occurs through putative expression of transcription factors, epigenetic programming or other factor that is currently unresolved (as indicated by the coloured fate potential triangles inside cells).

immunology↗

bioRxiv2024

Bridging Domains in Chronic Lower Back Pain: Large Language Models and Ontology-driven Strategies for Knowledge Graph Construction

Link prediction and entity resolution play pivotal roles in uncovering hidden relationships within networks and ensuring data quality in the era of heterogeneous data integration. This paper explores the utilization of large language models to enhance link prediction, particularly through knowledge graphs derived from transdisciplinary literature. Investigating zero-shot entity resolution techniques, we examine the impact of ontology-based and large language model approaches on the stability of link prediction results. Through a case study focusing on chronic lower back pain research, we analyze workflow decisions and their influence on prediction outcomes. Our research underscores the importance of robust methodologies in improving predictive accuracy and data integration across diverse domains.

bioinformatics↗

bioRxiv2024

Gut microbes of the cecum versus the colon drive more severe lethality and multi-organ damage

An intestinal perforation or puncture leads to a high risk of sepsis-associated morbidity. A perforation initiates the transfer of the intestinal contents (ICs) to the peritoneal cavity, leading to abdominal infections and varying with different prognoses. However, the mechanisms associated with different perforations in the cecum and colon remain unknown. We sought to examine how different gut flora contribute to prognoses in different intestinal perforation sites. We compared the microbiome of the ICs in the cecum and colon in a fecal-induced peritonitis mouse model. The results showed that cecum ICs developed more severe sepsis than colon ICs, including a shorter median survival time, increased biochemical indicators, more pathological changes in multiple organs and overwhelmed systematic inflammation. Moreover, our results demonstrated that cecum ICs hold more bacterial burden in unit weight than colon ICs, and the microbial communities differed between the ICs from the cecum and colon. A more detailed comparison of the two microbiome groups showed that the abundance of potentially pathogenic bacteria increased in the cecum ICs. Our data suggest that the sepsis severity developed by perforation was associated with bacterial burden and increased abundance of potentially pathogenic bacteria in the cecum. Our findings first compared the differences in the lethality associated with the ICs of the cecum and colon, which pointed out that the site of perforation could help providers predict the severity of sepsis.

immunology↗

bioRxiv2024

Leptin Activated Hypothalamic BNC2 Neurons Acutely Suppress Food Intake

Leptin is an adipose tissue hormone that maintains homeostatic control of adipose tissue mass by regulating the activity of specific neural populations controlling appetite and metabolism1. Leptin regulates food intake by inhibiting orexigenic agouti-related protein (AGRP) neurons and activating anorexigenic pro-opiomelanocortin (POMC) neurons2. However, while AGRP neurons regulate food intake on a rapid time scale, acute activation of POMC neurons has only a minimal effect3-5. This has raised the possibility that there is a heretofore unidentified leptin-regulated neural population that suppresses appetite on a rapid time scale. Here, we report the discovery of a novel population of leptin-target neurons expressing basonuclin 2 (Bnc2) that acutely suppress appetite by directly inhibiting AGRP neurons. Opposite to the effect of AGRP activation, BNC2 neuronal activation elicited a place preference indicative of positive valence in hungry but not fed mice. The activity of BNC2 neurons is finely tuned by leptin, sensory food cues, and nutritional status. Finally, deleting leptin receptors in BNC2 neurons caused marked hyperphagia and obesity, similar to that observed in a leptin receptor knockout in AGRP neurons. These data indicate that BNC2-expressing neurons are a key component of the neural circuit that maintains energy balance, thus filling an important gap in our understanding of the regulation of food intake and leptin action.

neuroscience↗

bioRxiv2024

Multi-omic characterization of allele-specific regulatory variation in hybrid pigs

Genetic variation in the regulation of gene expression contributes substantially to phenotypic variation. Understanding how variation in DNA sequences and epigentic modifications leads to gene expression variation remains a challenging task. In hybrid animals where cellular environments are homogeneous, differences between the expression of paternal and maternal alleles must be due to cis sequence or epigenic differences. Therefore, hybrid mapping is a powerful approach to efficiently identify and characterize genes under regulation through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we performed a comprehensive multi-omic characterization of regulatory variation across brain, liver, muscle, and placenta in four developmental stages. We produced one of the largest multi-omic datasets to date in pigs, including 16 whole genome sequenced genomes, 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We developed a novel read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We showed that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identified 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. By integrating methylation, chromatin accessibility, and gene expression data, many of these allele specific expression can be explained by allele specific methylation and/or chromatin accessibility. This study provides one of the most comprehensive characterizations of regulatory variation across multiple tissues and developmental stages in pigs and offers new opportunity of genetic improvement for this important food animal species.

developmental biology↗

bioRxiv2023

Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network

Distributed hypothalamic-midbrain neural circuits orchestrate complex behavioral responses during social interactions. How population-averaged neural activity measured by multi-fiber photometry (MFP) for calcium fluorescence signals correlates with social behaviors is a fundamental question. We propose a state-space analysis framework to characterize mouse MFP data based on dynamic latent variable models, which include continuous-state linear dynamical system (LDS) and discrete-state hidden semi-Markov model (HSMM). We validate these models on extensive MFP recordings during aggressive and mating behaviors in male-male and male-female interactions, respectively. Our results show that these models are capable of capturing both temporal behavioral structure and associated neural states. Overall, these analysis approaches provide an unbiased strategy to examine neural dynamics underlying social behaviors and reveals mechanistic insights into the relevant networks.

neuroscience↗