Biology subjects

Yun, T.

Publications and source records attributed to Yun, T..

10 recordsLinked to original sources

bioRxiv2026

Distinct Spatial Programs of Response versus Resistance in Non-Small Cell Lung Cancer after Neoadjuvant Chemoimmunotherapy

BackgroundNeoadjuvant chemoimmunotherapy (nCIT) has become a standard treatment for locally advanced resectable non-small cell lung cancer (NSCLC), yet the spatial biology underlying treatment resistance remains poorly understood. We used spatial transcriptomics to define the microenvironmental architecture of residual cancers in patients who did not achieve major pathologic response (non-MPR) compared with those who did (MPR). MethodsSpatial transcriptomics was performed on 10 formalin-fixed paraffin-embedded (FFPE) tumor blocks (5 MPR, 5 non-MPR) obtained from 8 patients treated with nCIT. A deep learning algorithm was applied to detect viable residual cancer spots from treatment-induced fibrosis and necrosis. Spatial deconvolution, distance modeling, ligand-receptor analysis, and functional pathway scoring were integrated to characterize niche-specific programs. ResultsMPR cancer core displayed an immune-permissive remodeling environment with deep infiltration of cytotoxic CD8+ T cells, mature dendritic cells (LAMP3+, CCR7+), and active efferocytosis signaling (APOE-TREM2), alongside robust MHC class II expression. Non-MPR cancer core, by contrast, exhibited spatial immune exclusion: a dense fibroblast barrier reinforced by TIMP1-CD63 signaling and Treg-enriched boundaries physically restricted effector T cell access to the cancer core. Residual cancer cells in non-MPR samples maintained active cell cycling and independently upregulated cytochrome P450-mediated drug detoxification and DNA damage response pathways without inducing MHC class II expression -- effectively decoupling intrinsic survival from immune recognition. The non-MPR core also showed a hyper-metabolic profile, including elevated glutathione metabolism consistent with antioxidant buffering against chemotherapy-induced oxidative stress. TROP2 was broadly expressed across the non-MPR cancer core and co-localized with DNA damage response and nuclear factor erythroid 2-related factor 2 resistance signatures. ConclusionsResidual cancer cores in non-MPR tumors appear to represent evolved resistant niches sustained by structural immune exclusion, metabolic rewiring, and DNA repair proficiency. These findings highlight the spatial co-localization of epithelial anchors, such as TROP2, with intrinsic resistance pathways, providing a structural rationale for developing novel precision therapeutic strategies to bypass stromal barriers and overcome the cancer cores intrinsic repair capacity.

cancer biology↗

bioRxiv2025

Upregulation of MAM by C99 disrupts ACSL4 activity and phospholipid homeostasis in Alzheimer disease.

The structure and function of cellular and intracellular membranes are critically governed by the fatty acid (FA) composition of phospholipids (PLs), which is dynamically regulated by a network of enzymes that fine-tune lipid species according to cellular demands. In this study, we identify a mechanism through which the formation of mitochondria-associated endoplasmic reticulum (ER) membranes (MAMs) modulates the activity of the acyl-CoA synthetase long-chain family member 4 (ACSL4), an enzyme that channels polyunsaturated fatty acids (PUFAs) into phosphatidylcholine (PC) via the Lands cycle. Through integrated biochemical, proteomic, and lipidomic analyses in both cellular and animal models, we demonstrate that MAM formation enhances ACSL4 activity, promoting arachidonic acid (AA) activation and its preferential incorporation into PC in concert with the MAM-localized lysophospholipid acyltransferase 4 (LPCAT4). Our findings further uncover an unexpected link between this pathway and the pathogenesis of Alzheimers disease (AD). We show that elevated levels of C99--the {beta}-secretase cleavage product of amyloid precursor protein (APP)--induce MAM remodeling through cholesterol clustering, which in turn activates ACSL4 and alters PC composition. This effect is mirrored in AD models as well as in fibroblasts, neurons, and immune cells derived from both familial and sporadic AD patients, all of which exhibit chronically increased C99 levels, heightened ACSL4 activity, and enrichment of PUFA-containing PC species, leading to lipid imbalance and membrane dysfunction. Together, these results establish MAMs as dynamic lipid-regulatory hubs that coordinate ACSL4-dependent membrane remodeling and highlight the contribution of MAM dysregulation to lipid abnormalities observed in AD.

molecular biology↗

bioRxiv2024

The Role of Alpha Synuclein in Synucleinopathy: Impact on Lipid Regulation at Mitochondria ER Membranes

The protein alpha-synuclein (Syn) plays a critical role in the pathogenesis of synucleinopathy, which includes Parkinsons disease and multiple system atrophy, and mounting evidence suggests that lipid dyshomeostasis is a critical phenotype in these neurodegenerative conditions. Previously, we identified that Syn localizes to mitochondria-associated endoplasmic reticulum membranes (MAMs), temporary functional domains containing proteins that regulate lipid metabolism, including the de novo synthesis of phosphatidylserine. In the present study, we have analyzed the lipid composition of postmortem human samples, focusing on the substantia nigra pars compacta of Parkinsons disease and controls, as well as three less affected brain regions of Parkinsons donors. To further assess synucleinopathy-related lipidome alterations, similar analyses were performed on the striatum of multiple system atrophy cases. Our data show region-and disease-specific changes in the levels of lipid species. Specifically, our data revealed alterations in the levels of specific phosphatidylserine species in brain areas most affected in Parkinsons disease. Some of these alterations, albeit to a lesser degree, are also observed multiples system atrophy. Using induced pluripotent stem cell-derived neurons, we show that Syn contributes to regulating phosphatidylserine metabolism at MAM domains, and that Syn dosage parallels the perturbation in phosphatidylserine levels. Our results support the notion that Syn pathophysiology is linked to the dysregulation of lipid homeostasis, which may contribute to the vulnerability of specific brain regions in synucleinopathy. These findings have significant therapeutic implications. Significance StatementSynucleinopathy is a complex group of neurodegenerative disorders whose causes and underlying mechanisms remain unknown. In this work, we examined synucleinopathy postmortem brain samples and patient-derived neuron models and identified the functional impairment of the mitochondrial-associated endoplasmic reticulum membrane (MAM) domain, which facilitates lipid regulation. The protein alpha-synuclein is associated with synucleinopathy and increasing levels result in the mislocalization of this protein and the disruption of MAM domains, which, in turn, results in lipid and membrane composition alterations. Specifically, we report that increased alpha-synuclein expression impairs the regulation of phosphatidylserine synthase 2 and the levels of phosphatidylserine in cellular membranes from affected cells. Our study offers mechanistic insight tying alpha-synuclein pathology and lipid dysregulation as seminal factors in synucleinopathy, which may have pathogenic and therapeutic implications.

neuroscience↗

bioRxiv2024

Selective enrichment of Methylococcaceae versus Methylocystaceae methanotrophs via control of methane feeding scheme

Methanotrophs are crucial in keeping environmental CH4 emissions in check. However, how different groups of methanotrophs contribute to this important role in different environmental settings remain ambiguous. Here, in a simplified laboratory setting of well-mixed batch reactors fed continuous flow of CH4-containing gas, methanotrophic microbiomes were enriched from paddy soils under six different incubation conditions prepared as combinations of two different CH4 mixing ratios (0.5% and 10% v/v) and three supplemented Cu2+ concentrations (0, 2, and 10 M). Monitoring of the temporal community shifts in the reactor microbiomes observed domination of Methylocystis spp. in all three reactors fed 0.5% v/v, as further supported by the analyses of pmoCAB genes in the shotgun metagenomes of the single-point samples from the same reactors. Copper deficiency did not select for mmoXYZ-possessing methanotrophs. Instead, a cluster of mbn genes with an abundance accounting for approximately 5% of Methylocystis population was identified, suggesting a comparative ecological importance of methanobactin in Cu-deficient methanotrophy over soluble methane monooxygenases. These findings highlight the importance of Methylocystis spp. in mitigating emissions from CH4 hotspots, e.g., landfills and rice paddies, and suggest the feasibility of directed enrichment/isolation of Methylocystis spp. for utilization in, for example, methanobactin and polyhydroxybutyrate production. SynopsisThis study reports enrichment of a complex soil microbiota with 0.5% methane resulting in dominance of a specific group of methane-consuming bacteria Methylocystis, highlighting their ecological significance as CH4 sink.

microbiology↗

bioRxiv2021

DeepConsensus: Gap-Aware Sequence Transformers for Sequence Correction

Pacific BioScience (PacBio) circular consensus sequencing (CCS) generates long (10-25 kb), accurate "HiFi" reads by combining serial observations of a DNA molecule into a consensus sequence. The standard approach to consensus generation uses a hidden Markov model (pbccs). Here, we introduce DeepConsensus, which uses a unique alignment-based loss to train a gap-aware transformer-encoder (GATE) for sequence correction. Compared to pbccs, DeepConsensus reduces read errors in the same dataset by 42%. This increases the yield of PacBio HiFi reads at Q20 by 9%, at Q30 by 27%, and at Q40 by 90%. With two SMRT Cells of HG003, reads from DeepConsensus improve hifiasm assembly contiguity (NG50 4.9Mb to 17.2Mb), increase gene completeness (94% to 97%), reduce false gene duplication rate (1.1% to 0.5%), improve assembly base accuracy (Q43 to Q45), and also reduce variant calling errors by 24%.

bioinformatics↗

bioRxiv2021

DeepNull: Modeling non-linear covariate effects improves phenotype prediction and association power

Genome-wide association studies (GWAS) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n=370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average).

genetics↗

bioRxiv2021

DeepTrio: Variant Calling in Families Using Deep Learning

Every human inherits one copy of the genome from their mother and another from their father. Parental inheritance helps us understand the transmission of traits and genetic diseases, which often involve de novo variants and rare recessive alleles. Here we present DeepTrio, which learns to analyze child-mother-father trios from the joint sequence information, without explicit encoding of inheritance priors. DeepTrio learns how to weigh sequencing error, mapping error, and de novo rates and genome context directly from the sequence data. DeepTrio has higher accuracy on both Illumina and PacBio HiFi data when compared to DeepVariant. Improvements are especially pronounced at lower coverages (with 20x DeepTrio roughly equivalent to 30x DeepVariant). DeepTrio includes pre-trained models for Illumina WGS, Illumina exome, and PacBio HiFi.

bioinformatics↗

bioRxiv2021

Improving variant calling using population data and deep learning

Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In this study, we develop population-aware DeepVariant models with a new channel encoding allele frequencies from the 1000 Genomes Project. This model reduces variant calling errors, improving both precision and recall in single samples, and reduces rare homozygous and pathogenic clinvar calls cohort-wide. We assess the use of population-specific or diverse reference panels, finding the greatest accuracy with diverse panels, suggesting that large, diverse panels are preferable to individual populations, even when the population matches sample ancestry. Finally, we show that this benefit generalizes to samples with different ancestry from the training data even when the ancestry is also excluded from the reference panel.

bioinformatics↗

bioRxiv2020

Accurate, scalable cohort variant calls using DeepVariant and GLnexus

Population-scale sequenced cohorts are foundational resources for genetic analyses, but processing raw reads into analysis-ready variants remains challenging. Here we introduce an open-source cohort variant-calling method using the highly-accurate caller DeepVariant and scalable merging tool GLnexus. We optimized callset quality based on benchmark samples and Mendelian consistency across many sample sizes and sequencing specifications, resulting in substantial quality improvements and cost savings over existing best practices. We further evaluated our pipeline in the 1000 Genomes Project (1KGP) samples, showing superior quality metrics and imputation performance. We publicly release the 1KGP callset to foster development of broad studies of genetic variation.

genomics↗

bioRxiv2019

Development of group-specific nosZ quantification method targeting active nitrous oxide reducing population in complex environmental samples

Substantial N2O emission results from activated sludge nitrogen removal processes. The importance of N2O-reducers possessing NosZ-type N2O reductases have been recognized as the only N2O sink in situ key to determination of the net N2O emissions; however, reliable quantification methods for nosZ genes and transcripts have yet to be developed. Here, nosZ genes and transcripts in activated sludge tank microbiomes were analyzed with the group-specific qPCR assays designed de novo combining culture-based and computational approach. A sewage sample was enriched in a batch reactor fed continuous stream of N2 containing 20-10,000 ppmv N2O, where 14 genera of potential N2O-reducers were identified. All available amino acid sequences of NosZ affiliated to these taxa were grouped into five subgroups (two clade I and three clade II groups), and primer/probe sets exclusively and comprehensively targeting the subgroups were designed and validated with in silico PCR. Four distinct activated sludge samples from three different wastewater treatment plants in Korea were analyzed with the qPCR assays and the results were validated by comparison with the shotgun metagenome analysis results. With the validated qPCR assays, the nosZ genes and transcripts of six additional activated sludge samples were analyzed and the results of the analyses clearly indicated the dominance of two clade II nosZ subgroups (Flavobacterium-like and Dechloromonas-like) among both nosZ gene and transcript pools.

microbiology↗