Biology subjects

Park, T.

Publications and source records attributed to Park, T..

31 records · Page 2Linked to original sources

bioRxiv2023

An autochthonous model of lung cancer in the Naked Mole-Rat (Heterocephalus glaber)

Studies on cancer resistance in the naked mole-rat (NMR) have generally failed to interrogate possible resistance mechanisms in a physiological context. Here, we provide evidence that the NMR presents as a novel model of tumor initiation. We developed an endogenous lung cancer model in NMRs, driven by an oncogenic Eml4-Alk fusion protein introduced through CRISPR- mediated genome editing. While this is sufficient to drive tumorigenesis in mice, the development of progressive disease in NMRs required the additional loss of key tumor suppressors. Our results show that tumor initiation in NMRs more closely recapitulates that of human tumors. This suggests that the proposed "resistance" of NMRs to cancer development may stem from tumor initiation events that are likely to be comparable to the mechanisms in human cells. One-Sentence SummaryTumor development in the cancer-resistant naked mole-rat more accurately represents the tumor initiation process in humans.

cancer biology↗

bioRxiv2023

Unilateral ephaptic program underlying sweetness dominance

In ephaptic coupling, physically adjacent neurons influence one anothers activity via the electric fields they generate. To date, the molecular mechanisms that mediate and modulate ephaptic couplings effects remain poorly understood. Here, we show that the hyperpolarization-activated cyclic nucleotide-gated (HCN) channel lateralizes the potentially mutual ephaptic inhibition between Drosophila gustatory receptor neurons (GRNs). While sweet-sensing GRNs (sGRNs) engage in ephaptic suppression of the adjacent bitter-sensing GRNs (bGRNs), HCN expression in sGRNs enables them to resist ephaptic suppression from the bGRNs. Such one-sided ephaptic inhibition confers sweetness dominance, facilitating ingestion of bitter-laced sweets. Flies with HCN-deficient sGRNs exhibited dramatically decreased attraction to sucrose mixed with moderate levels of caffeine, highlighting the behavioral significance that gustatory ephaptic inhibition promotes ingestion of carbohydrates buried in bitterness. Our findings indicate a role for the gating of ephaptic coding to ensure the intake of the essential nutrient despite bitter contaminants present in the feeding niche of Drosophila, as the gating establishes a hierarchy of gustatory neuron excitation. Such refinement provides a previously unappreciated mechanism for controlling the activity of a neuronal network with potential implications in the mammalian brain, given the evolutionary conservation of the HCN genes.

neuroscience↗

bioRxiv2023

A bioorthogonal antibody-based chemically-induced-dimerization switch for therapeutic application

We present the Indinavir Ligand Induced Transient Engagement switch (IDV LITE Switch), a fully synthetic Chemically Induced Dimerization (CID) system wherein two humanized antibody fragments are heterodimerized by the antiviral drug indinavir. The IDV LITE Switch represents the first CID system made from fully humanized protein components and dimerized by a clinically approved small molecule drug lacking a mammalian target, making it an ideal bioorthogonal molecular switch for application in small-molecule controlled therapeutics.

synthetic biology↗

bioRxiv2023

A meta-analysis of liver microbiome in beef and dairy cattle using metatranscriptome sequencing reads

The gut-liver axis is at the forefront of host-microbial interactions given the proximity of liver to the gut and connection via portal circulation. In recent years, many studies in human and mouse models have demonstrated the existence of a significant microbial community embedded in diverse tissue types, including blood and liver. Yet, in cattle, the rumen microbiome has been the primary focus. The liver microbiome and its metabolic role in host health and performance remain largely unexplored. While there has been considerable work focusing on the liver of diseased cattle, the objective of this study was to evaluate, through meta-analysis, the commensal liver microbiome in various cattle breeds. To our knowledge, this is the first study in which a core liver microbiome has been described in cattle without overt liver disease. We discovered abundant microbial taxa in the liver, varying by host age, species, and developmental stage. Eight bacterial phyla (Actinobacteria, Bacteroidetes, Cyanobacteria, Deinococcus-Thermus, Firmicutes, Fusobacteria, Proteobacteria, and Tenericutes) were found to be the core microbial taxa, representing almost half of the total liver bacterial population. Additionally, we identified several KEGG pathways with significant association with cattle age. This study provides a baseline knowledge of the liver microbiome as identified by metatranscriptome sequencing in cattle. Besides finding the microbial taxa previously reported by studies using DNA-based, 16S rRNA amplicon sequencing methods, this study identified several core phyla that have not been reported in cattle liver, highlighting the improved sensitivity or ability in detecting microbes by RNA-over DNA-based methods.

bioinformatics↗

bioRxiv2023

Tumour mitochondrial DNA mutations drive aerobic glycolysis to enhance checkpoint blockade

The mitochondrial genome encodes essential machinery for respiration and metabolic homeostasis but is paradoxically among the most common targets of somatic mutation in the cancer genome, with truncating mutations in respiratory complex I genes being most over-represented1. While mitochondrial DNA (mtDNA) mutations have been associated with both improved and worsened prognoses in several tumour lineages1-,3, whether these mutations are drivers or exert any functional effect on tumour biology remains controversial. Here we discovered that complex I-encoding mtDNA mutations are sufficient to remodel the tumour immune landscape and therapeutic resistance to immune checkpoint blockade. Using mtDNA base editing technology4 we engineered recurrent truncating mutations in the mtDNA-encoded complex I gene, Mt-Nd5, into murine models of melanoma. Mechanistically, these mutations promoted utilisation of pyruvate as a terminal electron acceptor and increased glycolytic flux without major effects on oxygen consumption, driven by an over-reduced NAD pool and NADH shuttling between GAPDH and MDH1, mediating a Warburg-like metabolic shift. In turn, without modifying tumour growth, this altered cancer cell-intrinsic metabolism reshaped the tumour microenvironment in both mice and humans, promoting an anti- tumour immune response characterised by loss of resident neutrophils. This subsequently sensitised tumours bearing high mtDNA mutant heteroplasmy to immune checkpoint blockade, with phenocopy of key metabolic changes being sufficient to mediate this effect. Strikingly, patient lesions bearing >50% mtDNA mutation heteroplasmy also demonstrated a >2.5-fold improved response rate to checkpoint inhibitor blockade. Taken together these data nominate mtDNA mutations as functional regulators of cancer metabolism and tumour biology, with potential for therapeutic exploitation and treatment stratification.

cancer biology↗

bioRxiv2022

A Multimodal Atlas of Tumor Metabolism Reveals the Architecture of Gene-Metabolite Co-regulation

Tumor metabolism is controlled by coordinated changes in metabolite abundance and gene expression, but simultaneous quantification of metabolites and transcripts in primary tissue is rare. To overcome this limitation and study gene-metabolite coregulation in cancer, we assembled the Cancer Atlas of Metabolic Profiles (cAMP) of metabolomic and transcriptomic data from 988 tumor/normal specimens spanning 11 cancer types. Meta-analysis of the cAMP revealed two classes of Gene-Metabolite Interactions (GMIs) that transcended cancer types. The first corresponded to a small number of gene-metabolite pairs engaged in direct enzyme-substrate interactions, identifying putative metabolite-pool-size-controlling genes. A second class of GMIs represented a small number of hub metabolites, including quinolinate and NAD+, which correlated to many genes specifically expressed on immune cell populations. These results provide evidence that gene-metabolite coregulation in human tissue arises, in part, from both mechanistic interactions between genes/metabolites, and from metabolic remodeling in specific immune microenvironments.

cancer biology↗

bioRxiv2022

Turbulent adaptive landscape shaped size evolution in modern ocean giants

Adaptive landscapes are central to evolutionary theory, forming a conceptual bridge between micro- and macro-evolution1-4. Evolution by natural selection across an adaptive landscape should drive lineages towards fitness peaks, shaping the distribution of phenotypic variation within and among clades over evolutionary timescales5. Constant shifts in selection pressures mean the peaks themselves also evolve through time4, thus a key challenge is to identify these ghosts of selection past. Here, we characterise the global and local adaptive landscape for total length in cetaceans (whales and dolphins) across their ~ 53 million year evolutionary history, using 345 living and fossil taxa. We analyse shifts in long-term mean size6 and directional changes in average trait values7 using cutting-edge phylogenetic comparative methods. We demonstrate that the global macroevolutionary adaptive landscape of cetacean body size is relatively flat, with very few peak shifts after cetaceans colonised the oceans. Local peaks represent trends along branches linked to specific adaptations such as deep diving. These results contrast with previous studies using only extant taxa8, highlighting the vital role of fossil data for understanding macroevolutionary dynamics. Our results indicate that adaptive peaks are constantly changing and are associated with subzones of local adaptations, resembling turbulent waters with waves and ripples, creating moving targets for species adaptation. In addition, we identify limits in our ability to detect some evolutionary patterns and processes, and suggest multiple approaches are required to characterise complex hierarchical patterns of adaptation in deep-time.

evolutionary biology↗

bioRxiv2022

The metabolic influence of the core ciliate Entodinium caudatum within the rumen microbiome

Protozoa comprise a major fraction of the microbial biomass in the rumen microbiome, of which the entodiniomorphs (order: Entodiniomorphida) and holotrichs (order: Vestibuliferida) are consistently observed to be dominant across a diverse genetic and geographical range of ruminant hosts. Despite the apparent core role that protozoal species exert, their major biological and metabolic contributions to rumen function remain largely undescribed in vivo. Here, we have leveraged (meta)genome-centric metaproteomes from rumen fluid samples originating from both cattle and goats fed diets with varying inclusion levels of lipids and starch, to detail the specific metabolic niches that protozoa occupy in the context of their microbial co-habitants. Initial proteome estimations via total protein counts and label-free quantification highlight that entodiniomorph species Entodinium and Epidinium as well as the holotrichs Dasytricha and Isotricha comprise an extensive fraction of the total rumen metaproteome. Proteomic detection of protozoal metabolism such as hydrogenases (Dasytricha, Isotricha, Epidinium, Enoploplastron), carbohydrate-active enzymes (Epidinium, Diplodinium, Enoploplastron, Polyplastron), microbial predation (Entodinium) and volatile fatty acid production (Entodinium and Epidinium) was observed at increased levels in high methane-emitting animals. Despite certain protozoal species having well-established reputations for digesting starch, they were unexpectedly less detectable in low methane emitting-animals fed high starch diets, which were instead dominated by propionate/succinate-producing bacterial populations suspected of being resistant to predation irrespective of host. Finally, we reaffirmed our abovementioned observations in geographically independent datasets, thus illuminating the substantial metabolic influence that under-explored eukaryotic populations have in the rumen, with greater implications for both digestion and methane metabolism.

microbiology↗

bioRxiv2021

Microscope-Cockpit: Python-based bespoke microscopy for bio-medical science

We have developed "Microscope-Cockpit" (Cockpit), a highly adaptable open source user-friendly Python-based GUI environment for precision control of both simple and elaborate bespoke microscope systems. The user environment allows next-generation near-instantaneous navigation of the entire slide landscape for efficient selection of specimens of interest and automated acquisition without the use of eyepieces. Cockpit uses "Python-Microscope" (Microscope) for high-performance coordinated control of a wide range of hardware devices using open source software. Microscope also controls complex hardware devices such as deformable mirrors for aberration correction and spatial light modulators for structured illumination via abstracted device models. We demonstrate the advantages of the Cockpit platform using several bespoke microscopes, including a simple widefield system and a complex system with adaptive optics and structured illumination. A key strength of Cockpit is its use of Python, which means that any microscope built with Cockpit is ready for future customisation by simply adding new libraries, for example machine learning algorithms to enable automated microscopy decision making while imaging. HighlightsO_LIUser-friendly setup and use for simple to complex bespoke microscopes. C_LIO_LIFacilitates collaborations between biomedical scientists and microscope technologists. C_LIO_LITouchscreen for near-instantaneous navigation of specimen landscape. C_LIO_LIUses Python-Microscope, for abstracted open source hardware device control. C_LIO_LIWell-suited for user training of AI-algorithms for automated microscopy. C_LI

biophysics↗

bioRxiv2020

Modeling and Simulation of a Fully-glycosylated Full-length SARS-CoV-2 Spike Protein in a Viral Membrane

This technical study describes all-atom modeling and simulation of a fully-glycosylated full-length SARS-CoV-2 spike (S) protein in a viral membrane. First, starting from PDB:6VSB and 6VXX, full-length S protein structures were modeled using template-based modeling, de-novo protein structure prediction, and loop modeling techniques in GALAXY modeling suite. Then, using the recently-determined most occupied glycoforms, 22 N-glycans and 1 O-glycan of each monomer were modeled using Glycan Reader & Modeler in CHARMM-GUI. These fully-glycosylated full-length S protein model structures were assessed and further refined against the low-resolution data in their respective experimental maps using ISOLDE. We then used CHARMM-GUI Membrane Builder to place the S proteins in a viral membrane and performed all-atom molecular dynamics simulations. All structures are available in CHARMM-GUI COVID-19 Archive (http://www.charmm-gui.org/docs/archive/covid19), so researchers can use these models to carry out innovative and novel modeling and simulation research for the prevention and treatment of COVID-19.

biophysics↗

bioRxiv2020

Spike protein binding prediction with neutralizing antibodies of SARS-CoV-2

Coronavirus disease 2019 (COVID-19) is a new emerging human infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2, also previously known as 2019-nCoV), originated in Wuhan seafood and animal market, China. Since December 2019, more than 69,000 cases of COVID-19 have been confirmed in China and quickly spreads to other counties. Currently, researchers put their best efforts to identify effective drugs for COVID-19. The neutralizing antibody, which binds to viral capsid in a manner that inhibits cellular entry of virus and uncoating of the genome, is the specific defense against viral invaders. In this study, we investigate to identify neutralizing antibodies that can bind to SARS-CoV-2 Sipke (S) protein and interfere with the interaction between viral S protein and a host receptor by bioinformatic methods. The sequence analysis of S protein showed two major differences in the RBD region of the SARS-CoV-2 S protein compared to SARS-CoV and SARS-CoV related bat viruses (btSARS-CoV). The insertion regions were close to interacting residues with the human ACE2 receptor. Epitope analysis of neutralizing antibodies revealed that SARS-CoV neutralizing antibodies used conformational epitopes, whereas MERS-CoV neutralizing antibodies used a common linear epitope region, which contributes to form the {beta}-sheet structure in MERS-CoV S protein and deleted in SARS-CoV-2 S protein. To identify effective neutralizing antibodies for SARS-CoV-2, the binding affinities of neutralizing antibodies with SARS-CoV-2 S protein were predicted and compared by antibody-antigen docking simulation. The result showed that CR3022 neutralizing antibody from human may have higher binding affinity with SARS-CoV-2 S protein than SARS-CoV S protein. We also found that F26G19 and D12 mouse antibodies could bind to SARS-CoV S protein with high affinity. Our findings provide crucial clues towards the development of antigen diagnosis, therapeutic antibody, and the vaccine against SARS-CoV-2.

bioinformatics↗

bioRxiv2019

Structural equation modeling for hypertension and type 2 diabetes based on multiple SNPs and multiple phenotypes

Genome-wide association studies (GWAS) have been successful in identifying genetic variants associated with complex diseases. However, association analyses between genotypes and phenotypes are not straightforward due to the complex relationships between genetic and environmental factors. Moreover, multiple correlated phenotypes further complicate such analyses.\n\nTo resolve this complexity, we present an analysis using structural equation modeling (SEM). Unlike current methods that focus only on identifying direct associations between diseases and genetic variants such as single-nucleotide polymorphisms (SNPs), our method introduces the effects of intermediate phenotypes, which are related phenotypes distinct from the target, into the systematic genetic study of diseases. Moreover, we consider multiple diseases simultaneously in a single model. The procedure can be summarized in four steps: 1) selection of informative SNPs, 2) extraction of latent variables from the selected SNPs, 3) investigation of the relationships among intermediate phenotypes and diseases, and 4) construction of an SEM. As a result, a quantitative map can be drawn that simultaneously shows the relationship among multiple SNPs, phenotypes, and diseases.\n\nIn this study, we considered two correlated diseases, hypertension and type 2 diabetes (T2D), which are known to have a substantial overlap in their disease mechanism and have significant public health implications. As intermediate phenotypes for these diseases, we considered three obesity-related phenotypes--subscapular skin fold thickness, body mass index, and waist circumference--as traits representing subcutaneous adiposity, overall adiposity, and abdominal adiposity, respectively. Using GWAS data collected from the Korea Association Resource (KARE) project, we applied the proposed SEM process. Among 327,872 SNPs, 24 informative SNPs were selected in the first step (p<1.0E-05). Ten latent variables were generated in step 2. After an exploratory analysis, we established a path diagram among phenotypes and diseases in step 3. Finally, in step 4, we produced a quantitative map with paths moving from specific SNPs to hypertension through intermediate phenotypes and T2D. The resulting model had high goodness-of fit measures ({chi}2= 536.52, NFI=0.997, CFI=0.998).

bioinformatics↗

bioRxiv2018

RUNX3 regulates cell cycle-dependent chromatin dynamics by functioning as a pioneer factor of the restriction point

The cellular decision regarding whether to undergo proliferation or death is made at the restriction (R)-point, which is disrupted in nearly all tumors. The identity of the molecular mechanisms that govern the R-point decision is one of the fundamental issues in cell biology. We found that early after mitogenic stimulation, RUNX3 bound to its target loci, where it opened chromatin structure by sequential recruitment of Trithorax group proteins and cell-cycle regulators to drive cells to the R-point. Soon after, RUNX3 closed these loci by recruiting Polycomb repressor complexes, causing the cell to pass through the R-point toward S phase. If the RAS signal was constitutively activated, RUNX3 inhibited cell cycle progression by maintaining R-point-associated genes in an open structure. Our results identify RUNX3 as a pioneer factor for the R-point and reveal the molecular mechanisms by which appropriate chromatin modifiers are selectively recruited to target loci for appropriate R-point decisions.

cell biology↗