Biology subjects
Publications and source records attributed to Lee, S. H..
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Dysregulated macrophage responses and changes in tissue metabolism are hallmarks of chronic, sterile inflammation. However, the metabolic cues that direct and support macrophage functions are poorly understood. Here, we show that during sterile inflammation in skin, the epidermis and macrophages uniquely depend on glycolysis and TCA cycle, respectively. This compartment separation is initiated by HIF1a stabilization and enhanced glycolysis in the epidermis. The end product of glycolysis, lactate is exported and utilized by the dermal macrophages to drive their effector functions. Notably, inhibition of lactate mediated crosstalk between the epidermis and macrophages leads to inhibition of sterile inflammation. Overall, our study identifies an essential role for the metabolite lactate in regulating macrophage response that can be effectively targeted to treat skin disorders such as psoriasis. One-Sentence SummaryEpidermis derived lactic acid drives sterile inflammation by augmenting pro-remodeling state in dermal macrophages.
Neuroimmune interactions across the pain pathway play a predominant role in the development of neuropathic pain. Previous reports demonstrated that complement driven effector systems including the C5a/C5aR1 axis contribute to these neuro-immune mechanisms. However, the cellular and molecular mechanisms underlying C5a/C5aR1 signaling-mediated neuropathic pain development remain ill-identified. Here we show that neuropathic pain following peripheral nerve injury was attenuated in C5aR1-deficient male and female mice as well as in wild type mice treated with a selective allosteric C5aR1 antagonist. Using two complementary cell-specific C5aR1 knockout mouse strains, we identified C5a/C5aR1 driven-activation of sensory neuron-associated macrophages (sNAMs) located in the sensory ganglia as the key site of peripheral nerve injury-induced neuropathic pain, whereas activation of macrophages of the local of peripheral nerve injury was not involved. Mechanistically, we uncovered IL-1b the main mediator of pain hypersensitivity in response to C5aR1 signaling in sNAMs. Our findings highlight a crucial role of C5a/C5aR1 axis activation in sNAMs for the development of neuropathic pain and identify this pathway as a promising novel target for neuropathic pain therapy.
The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method (e.g., that implemented in BLUPf90 software) requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning and scale factor in simulated as well as real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter < 1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, , which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase the prediction accuracy, in addition to blending and tuning processes, when using HBLUP. Author SummaryDespite significant advancements in genotyping technologies, the capability to predict the phenotypes of complex traits is still limited. H-matrix best linear unbiased prediction (HBLUP) method has been used to tackle this limitation to demonstrate a promising prediction accuracy. However, the performance of HBLUP depends heavily on the optimisation of hyper-parameters (e.g. blending and tuning). In this study, we introduce a scale factor (), as a new hyper-parameter in HBLUP, which accounts for the relationship between allele frequency and per-allele effect size. Using simulation and real data analysis, we investigate the impact of the hyper-parameters (blending, tuning, and scale factor) on the performance of HBLUP. In general, the blending process may not improve the prediction accuracy for simulation and cattle data although a marginally improved prediction accuracy is observed with a blending hyper-parameter = 0.86 for one of carcass traits in the cattle data. In contrast, the tuning process can increase the HBLUP accuracy particularly in simulated data. Furthermore, we observe that an optimal scale factor plays a significant role in improving the prediction accuracy in both simulated and real data, and the improvement is relatively large compared with blending and tuning processes. In this context, we propose considering the scale factor as a hyper-parameter to increase the predictive performance of HBLUP.
Mammalian DNA folds into 3D structures that facilitate and regulate genetic processes such as transcription, DNA repair, and epigenetics. Several insights derive from chromosome capture methods, such as Hi-C, which allow researchers to construct contact maps depicting 3D interactions among all DNA segment pairs. These maps show a complex cross-scale organization spanning megabase-pair compartments to short-ranged DNA loops. To better understand the organizing principles, several groups analyzed Hi-C data assuming a Russian-doll-like nested hierarchy where DNA regions of similar sizes merge into larger and larger structures. Apart from being a simple and appealing description, this model explains, e.g., the omnipresent chequerboard pattern seen in Hi-C maps, known as A/B compartments, and foreshadows the co-localization of some functionally similar DNA regions. However, while successful, this model is incompatible with the two competing mechanisms that seem to shape a significant part of the chromosomes 3D organization: loop extrusion and phase separation. This paper aims to map out the chromosomes actual folding hierarchy from empirical data. To this end, we take advantage of Hi-C experiments and treat the measured DNA-DNA interactions as a weighted network. From such a network, we extract 3D communities using the generalized Louvain algorithm. This algorithm has a resolution parameter that allows us to scan seamlessly through the community size spectrum, from A/B compartments to topologically associated domains (TADs). By constructing a hierarchical tree connecting these communities, we find that chromosomes are more complex than a perfect hierarchy. Analyzing how communities nest relative to a simple folding model, we found that chromosomes exhibit a significant portion of nested and non-nested community pairs alongside considerable randomness. In addition, by examining nesting and chromatin types, we discovered that nested parts are often associated with active chromatin. These results highlight that crossscale relationships will be essential components in models aiming to reach a deep understanding of the causal mechanisms of chromosome folding.
The coefficient of determination (R2) is a well-established measure to indicate the predictive ability of polygenic scores (PGS). However, the sampling variance of R2 is rarely considered so that 95% confidence intervals (CI) are not usually reported. Moreover, when comparisons are made between PGS based on different discovery samples, the sampling covariance of R2 is necessary to test the difference between them. Here, we show how to estimate the variance and covariance of R2 values to assess the 95% CI and p-value of the R2 difference. We apply this approach to real data to predict into 28,880 European participants using UK Biobank (UKBB) and Biobank Japan (BBJ) GWAS summary statistics for cholesterol and BMI. We quantify the significantly higher predictive ability of UKBB PGS compared to BBJ PGS (p-value 7.6e-31 for cholesterol and 1.4e-50 for BMI). A joint model of UKBB and BBJ PGS significantly improves the predictive ability, compared to a model of UKBB PGS only (p-value 3.5e-05 for cholesterol and 1.3e-28 for BMI). The proposed approach can also be applied to testing a significant difference between R2 values across different p-value thresholds. We also show that the predictive ability of regulatory SNPs is significantly enriched than non-regulatory SNPs for cholesterol (p-value 2.6e-19 for UKBB and 8.7e-08 for BBJ). We suggest that the proposed approach (available in R package r2redux) should be used to test the statistical significance of difference between pairs of PGS, which may help to draw a correct conclusion about the predictive ability of PGS.
Flow cytometry is a standard technology in life science and clinical laboratories used to characterize the phenotypes and functional status of cells, especially immune cells. Recent advances in immunology and immuno-oncology as well as drug and vaccine discovery have increased the demand to measure more parameters. However, the overlap of fluorophore emission spectra and one-time measurement nature of flow cytometry are major barriers to meeting the need. Here, we present multi-pass flow cytometry, in which cells are tracked and measured repeatedly through barcoding with infrared laser-emitting microparticles. We demonstrate the benefits of this approach on several pertinent assays with human peripheral blood mononuclear cells (PBMCs). First, we demonstrate unprecedented time-resolved flow characterization of T cells before and after stimulation. Second, we show 33-marker deep immunophenotyping of PBMCs, analyzing the same cells in 3 back-to-back cycles. This workflow allowed us to use only 10-13 fluorophores in each cycle, significantly reducing spectral spillover and simplifying panel design. Our results open a new avenue in multi-dimensional single-cell analysis based on optical barcoding of individual cells.
In vitro fertilization (IVF) has resulted in the birth of over 8 million children. Although most of IVF-conceived children are healthy, several studies suggest an increased risk of altered growth rate, cardiovascular dysfunction, and glucose intolerance in this population compared to naturally conceived children. However, a clear understanding of how embryonic metabolism is affected by culture condition and how embryos reprogram their metabolism is unknown. Here, we studied oxidative stress and metabolic alteration in blastocysts conceived by natural mating or by IVF and culture in physiologic (5%) or atmospheric (20%) oxygen. We found that IVF-generated blastocyst manifest increased reactive oxygen species, oxidative damage to DNA/lipid/proteins, and reduction in glutathione. Metabolic analysis revealed IVF-generated blastocysts display decreased mitochondria respiration and increased glycolytic activity suggestive of enhanced Warburg metabolism. These findings were corroborated by altered intracellular and extracellular pH and increased intracellular lactate levels in IVF-generated embryos. Comprehensive proteomic analysis and targeted immunofluorescence showed reduction of LDH-B and MCT1, enzymes involved in lactate metabolism. Importantly, these enzymes remained downregulated in tissues of adult IVF-conceived mice, suggesting that metabolic alterations in IVF-generated embryos may result in alteration in lactate metabolism. These findings suggest that alterations in lactate metabolism is a likely mechanism involved in genomic reprogramming and could be involved in the developmental origin of health and disease.
Carbon-negative synthesis of biochemical products has the potential to mitigate global CO2 emissions. An attractive route to do this is the reverse {beta}-oxidation (r-BOX) pathway coupled to the Wood-Ljungdahl pathway. Here, we optimized and implemented r-BOX for the synthesis of C4-C6 acids and alcohols. With a high-throughput in vitro prototyping workflow, we screened 762 unique pathway combinations using cell-free extracts tailored for r-BOX to identify enzyme sets for enhanced product selectivity. Implementation of these pathways into Escherichia coli generated designer strains for the selective production of butanoic acid (4.9 {+/-}0.1 gL-1), hexanoic acid (3.06 {+/-} 0.03 gL-1) and 1-hexanol (1.0 {+/-} 0.1 gL-1) at the best performance reported to date in this bacterium. We also generated Clostridium autoethanogenum strains able to produce 1-hexanol from syngas, achieving a titer of 0.26 gL-1 in a 1.5-L continuous fermentation. Our strategy enables optimization of rBOX derived products for biomanufacturing and industrial biotechnology.
Membrane budding entails forces to transform flat membrane into vesicles essential for cell survival. Accumulated studies have identified coat-proteins (e.g., clathrin) as potential budding factors. However, forces mediating many non-coated membrane buddings remain unclear. By visualizing proteins in mediating endocytic budding in live neuroendocrine cells, performing in vitro protein reconstitution and physical modelling, we discovered how non-coated-membrane budding is mediated: actin filaments and dynamin generate a pulling force transforming flat membrane into {Lambda}-shape; subsequently, dynamin helices surround and constrict {Lambda}-profiles base, transforming {Lambda}- to {Omega}-profile, and then constrict {Omega}-profiles pore, converting {Omega}-profiles to vesicles. These mechanisms control budding speed, vesicle size and number, generating diverse endocytic modes differing in these parameters. Their impact is widespread beyond secretory cells, as the unexpectedly powerful functions of dynamin and actin, previously thought to mediate fission and overcome tension, respectively, may contribute to many dynamin/actin-dependent non- coated-membrane buddings, coated-membrane buddings, and other membrane remodelling processes.
Altered levels of intracellular calcium (Ca2+) is a highly prevalent feature in different forms of cardiac injury, producing changes in contractility, arrhythmias, and mitochondrial dysfunction. In cardiac ischemia-reperfusion injury, mitochondrial Ca2+ overload leads to pathological production of reactive oxygen species (ROS), activates the permeability transition, and cardiomyocyte death. Here we investigated the cardiac phenotype caused by deletion of EF-hand domain-containing protein D1 (Efhd1-/-), a Ca2+-binding mitochondrial protein whose function is poorly understood. Efhd1-/- mice are viable and have no adverse cardiac phenotypes. They feature reductions in basal ROS levels and mitoflash events, both important precursors for mitochondrial injury, though cardiac mitochondria have normal susceptibility to Ca2+ overload. Notably, we also find that Efhd1-/- mice and their cardiomyocytes are resistant to hypoxic injury.
In the general framework of the weighted gene co-expression network analysis (WGCNA), a hierarchical clustering algorithm is commonly used to module definition. However, hierarchical clustering depends strongly on the topological overlap measure. In other words, this algorithm may assign two genes with low topological overlap to different modules even though their expression patterns are similar. Here, a novel gene module clustering algorithm for WGCNA is proposed. We develop a gene module clustering network (gmcNet), which simultaneously addresses single-level expression and topological overlap measure. The proposed gmcNet includes a "co-expression pattern recognizer" (CEPR) and "module classifier". The CEPR incorporates expression features of single genes into the topological features of co-expressed ones. Given this CEPR-embedded feature, the module classifier computes module assignment probabilities. We validated gmcNet performance using 4,976 genes from 20 native Korean cattle. We observed that the CEPR generates more robust features than single-level expression or topological overlap measure. Given the CEPR-embedded feature, gmcNet achieved the best performance in terms of modularity (0.261) and the differentially expressed signal (27.739) compared with other clustering methods tested. Furthermore, gmcNet detected some interesting biological functionalities for carcass weight, backfat thickness, intramuscular fat, and beef tenderness of Korean native cattle. Therefore, gmcNet is a useful framework for WGCNA module clustering. Author summaryA graph neural network is a good alternative algorithm for WGCNA module clustering. Even though the graph-based learning methods have been widely applied in bioinformatics, most studies on WGCNA did not use graph neural network for module clustering. In addition, existing methods depend on topological overlap measure of gene pairs. This can degrade similarity of expression not only between modules, but also within module. On the other hand, the proposed gmcNet, which works similar to message-passing operation of graph neural network, simultaneously addresses single-level expression and topological overlap measure. We observed the higher performance of gmcNet comparing to existing methods for WGCNA module clustering. To adopt gmcNet as clustering algorithm of WGCNA, it remains future research issues to add noise filtering and optimal k search on gmcNet. This further research will extend our proposed method to be a useful module clustering algorithm in WGCNA. Furthermore, our findings will be of interest to computational biologists since the studies using graph neural networks to WGCNA are still rare.
Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups for a complex trait. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is diverse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific causal effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to six anthropometric traits from the UK Biobank data across 5 ancestry groups. One of our findings is that for obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestry groups.
The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas12a system is composed of a Cas12a effector that acts as a deoxyribonucleic acid (DNA)-cleaving endonuclease and a crispr ribonucleic acid (crRNA) that guides the effector to the target DNA. It is considered a key molecule for inducing target-specific gene editing in various living systems. Here, we improved the efficiency and specificity of the CRISPR-Cas12a system through protein and crRNA engineering. In particular, to optimize the CRISPR-Cas12a system at the molecular level, we used a chimeric DNA-RNA guide chemically similar to crRNA to maximize target sequence specificity. Compared to the wild type (wt)-Cas12a system, when using enhanced Cas12a system (en-Cas12a), the efficiency and target specificity improved on average by 7.41 and 7.60 times respectively. In our study, when the chimeric DNA-RNA guided en-Cas12a effector was used, the gene editing efficiency and accuracy were simultaneously increased. These findings could contribute to highly accurate genome editing, such as human gene therapy, in the near future.
The expression of TERT (telomerase reverse transcriptase) has been implicated in stem and progenitor cells, which are essential for tissue homeostasis and regeneration. However, the roles of TERT-expressing cells in the pancreas remain elusive. Employing genetically engineered Tert knock-in mouse model, herein, we located a rare population of Tert+ acinar cells. While Tert+ cells are quiescent in normal conditions, acinar cell injury leads to mitotic activation of Tert+ cells and subsequent generation of new acinar cells. Moreover, the genetic ablation of Tert+ cells impairs pancreatic regeneration. We further found that Yap/Taz activation is required for the expansion of Tert+ acinar cells. Our results identified Tert+ acinar cells as a distinct subset of acinar cells, which contributes to pancreatic regeneration via Yap/Taz activation.
PurposeThis study aimed to explore the prognostic impact of spatial distribution of tumor infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole slide images stained with hematoxylin and eosin in patients with colorectal cancer (CRC). MethodsThe prognostic impact of spatial distributions of TILs in patients with CRC was explored in the Yonsei cohort (n=180) and validated in the TCGA cohort (n=268). Concurrently, two experienced pathologists manually measured TILs at the most invasive margin as 0-3 by the Klintrup-Makinen (KM) grading method and compared to DL approaches. Interobserver agreement for TILs was measured using Cohens kappa coefficient. ResultsOn multivariate analysis of spatial TILs features derived by DL approaches and clinicopathological variables including tumor stage, Microsatellite instability, and KRAS mutations, TILs densities within 200 m of the invasive margin (f_im200) was remained as the most significant prognostic factor for progression-free survival (PFS) (HR 0.004 [95% CI, 0.0001-0.1502], p=.002) in the Yonsei cohort. On multivariate analysis using the TCGA dataset, f_im200 retained prognostic significance for PFS (HR 0.031, [95% CI 0.001-0.645], p=.024). Interobserver agreement of manual KM grading based on Cohens kappa coefficient was insignificant in the Yonsei ({kappa}=.109) and the TCGA ({kappa}=.121), respectively. The survival analysis based on KM grading showed statistically significant different PFS from the TCGA cohort, but not the Yonsei cohort. ConclusionsAutomatic quantification of TILs at the invasive margin based on DL approaches showed a prognostic utility to predict PFS, and could provide robust and reproducible TILs density measurement in patients with CRC. Data and Code AvailabilitySource code and data used for this study is available at the following link: https://github.com/hwanglab/TILs_Analysis
Unilateral spatial neglect (USN) is common after stroke and associated with poor functional recovery. Prism adaptation (PA) is one of the most supported modality able to ameliorate USN but underapplied due to several issues. Using immersive virtual reality and depth-sensing camera, we developed the virtual prism adaptation therapy (VPAT) to overcome the limitations in conventional PA. In this study, we investigated whether VPAT can induce behavioral adaptations and which cortical area is most significantly activated. Fourteen healthy subjects participated in this study. The experiment consisted of four sequential phases (pre-VAPT, VPAT-10{degrees}, VPAT-20{degrees}, and post-VPAT) with functional near-infrared spectroscopy recordings. Each phase consisted of alternating target pointing and resting (or clicking) blocks. To find out the most significantly activated area during pointing in different phases (VPAT-10{degrees}, VPAT-20{degrees}, and Post-VPAT) in contrast to pointing during the pre-VPAT phase, we analyzed changes in oxyhemoglobin concentration during pointing. The pointing errors of the virtual hand deviated to the right-side during early pointing blocks in the VPAT-10{degrees} and VPAT-20{degrees} phases. There was a left-side deviation of the real hand to the target in the post-VPAT phase. The most significantly activated channels were all located in the right hemisphere, and possible corresponding cortical areas included the dorsolateral prefrontal cortex and frontal eye field. In conclusion, VPAT may induce behavioral adaptation with modulation of the dorsal attentional network. Future clinical trials using multiple sessions of a high degree of rightward deviation VPAT over a more extended period are required in stroke patients with unilateral spatial neglect.
Over the last 20 years increased funding for malaria research has resulted in very significant technical advances to study the biology of Plasmodium species. High throughput phenotypic assays have been developed to screen millions of compounds and identify small molecules with antiparasitic activity. At the same time, advances in malaria genetic have greatly facilitated the generation of genetically modified parasites, and whole genome genetic screens are now feasible in Plasmodium species. Finally, there has been an increased interest to study malaria parasites at the population level, in particular in the area of drug resistance. Drug resistant field isolates have been collected around the world, and drug resistant strains are routinely generated in the lab to study the mechanisms of drug resistance. As a result, one of the current bottlenecks in malaria research is our ability to quickly characterize the phenotype associated with compound treatment or genetic modification, or to quickly compare differences in intracellular development between strains. Here, we present a high content/high throughput phenotypic assay that combines highly selective RNA, DNA, and RBC membrane dyes to provide hourly resolution of the full erythrocytic cycle for both P. falciparum and P. knowlesi. A flow cytometry assay allows the analysis of samples in a 384-well format and a quick way to determine the parasite developmental stage. On the other hand, the fluorescence microscopy format allows for a detailed visualization of parasite morphology. Finally, using open source software we have developed protocols for the automated cluster analysis of microscopy images. This assay can be applied to any Plasmodium species, requires very little amount of sample, is performed with fixed cells, and is easily scalable. Overall, we believe this assay will be a great tool for the malaria community to study Plasmodium species.