Regulatory Variants on the Leukocyte Immunoglobulin-Like Receptor Gene Cluster are Associated with Crohn's Disease and Interact with Regulatory Variants for TAP2.

BACKGROUND AND AIMS: Crohn's disease [CD] has a complex polygenic aetiology with high heritability. There is ongoing effort to identify novel variants associated with susceptibility to CD through a genome-wide association study [GWAS] in large Korean populations. METHODS: Genome-wide variant data from 902 Korean patients with CD and 72 179 controls were used to assess the genetic associations in a meta-analysis with previous Korean GWAS results from 1621 patients with CD and 4419 controls. Epistatic interactions between CD-risk variants of interest were tested using a multivariate logistic regression model with an interaction term. RESULTS: We identified two novel genetic associations with the risk of CD near ZBTB38 and within the leukocyte immunoglobulin-like receptor [LILR] gene cluster [p < 5 × 10-8], with highly consistent effect sizes between the two independent Korean cohorts. CD-risk variants in the LILR locus are known quantitative trait loci [QTL] for multiple LILR genes, of which LILRB2 directly interacts with various ligands including MHC class I molecules. The LILR lead variant exhibited a significant epistatic interaction with CD-associated regulatory variants for TAP2 involved in the antigen presentation of MHC class I molecules [p = 4.11 × 10-4], showing higher CD-risk effects of the TAP2 variant in individuals carrying more risk alleles of the LILR lead variant (odds ratio [OR] = 0.941, p = 0.686 in non-carriers; OR = 1.45, p = 2.51 × 10-4 in single-copy carriers; OR = 2.38, p = 2.76 × 10-6 in two-copy carriers). CONCLUSIONS: This study demonstrated that genetic variants at two novel susceptibility loci and the epistatic interaction between variants in LILR and TAP2 loci confer a risk of CD.

Oxidation of iron by giant impact and its implication on the formation of reduced atmosphere in the early Earth.

Giant impact-driven redox processes in the atmosphere and magma ocean played crucial roles in the evolution of Earth. However, because of the absence of rock records from that time, understanding these processes has proven challenging. Here, we present experimental results that simulate the giant impact-driven reactions between iron and volatiles (H2O and CO2) using x-ray free electron laser (XFEL) as fast heat pump and structural probe. Under XFEL pump, iron is oxidized to wüstite (FeO), while volatiles are reduced to H2 and CO. Furthermore, iron oxidation proceeds into formation of hydrides (γ-FeHx) and siderite (FeCO3), implying redox boundary near 300-km depth. Through quantitative analysis on reaction products, we estimate the volatile and FeO budgets in bulk silicate Earth, supporting the Theia hypothesis. Our findings shed light on the fast and short-lived process that led to reduced atmosphere, required for the emergence of prebiotic organic molecules in the early Earth.

Enrichment of Activated Fibroblasts as a Potential Biomarker for a Non-Durable Response to Anti-Tumor Necrosis Factor Therapy in Patients with Crohn's Disease.

We investigated whether the response to anti-tumor necrosis factor (anti-TNF) treatment varied according to inflammatory tissue characteristics in Crohn's disease (CD). Bulk RNA sequencing (RNA-seq) data were obtained from inflamed and non-inflamed tissues from 170 patients with CD. The samples were clustered based on gene expression profiles using principal coordinate analysis (PCA). Cellular heterogeneity was inferred using CiberSortx, with bulk RNA-seq data. The PCA results displayed two clusters of CD-inflamed samples: one close to (Inflamed_1) and the other far away (Inflamed_2) from the non-inflamed samples. Inflamed_1 was rich in anti-TNF durable responders (DRs), and Inflamed_2 was enriched in non-durable responders (NDRs). The CiberSortx results showed that the cell fraction of activated fibroblasts was six times higher in Inflamed_2 than in Inflamed_1. Validation with public gene expression datasets (GSE16879) revealed that the activated fibroblasts were enriched in NDRs over Next, we used DRs by 1.9 times pre-treatment and 7.5 times after treatment. Fibroblast activation protein (FAP) was overexpressed in the Inflamed_2 and was also overexpressed in the NDRs in both the RISK and GSE16879 datasets. The activation of fibroblasts may play a role in resistance to anti-TNF therapy. Characterizing fibroblasts in inflamed tissues at diagnosis may help to identify patients who are likely to respond to anti-TNF therapy.

RNA-Seq-Based Molecular Classification Analyses in Colorectal Cancer and Synchronous Adenoma.

Colorectal cancers (CRC) are classified into consensus molecular subtypes (CMS) based on gene expression profiles. The revised classification system iCMS was proposed by considering intrinsic epithelial status, microsatellite instability (MSI), and fibrosis. This study aimed to provide molecular evidence for the adenoma-carcinoma sequence concept by examining CRC and synchronous adenomas using iCMS. Epithelial CMS cell proportion was estimated using CiberSortx, an in silico cell fractionation method that included CMS cell types among the reference cell types. A random forest (RF) model estimated the posterior probabilities of CMS classes, which were compared with the CiberSortx results. Gene expression profiles of the published iCMS signature panel were retrieved from our dataset and subjected to heatmap clustering for classification. Bulk RNA sequencing data were collected from 29 adenocarcinomas and 11 adenoma samples. CiberSortx showed all CRC contained either CMS2 or CMS3 as the major epithelial cancer cell type. The RF model classified approximately half of the CRC as CMS4, whereas CMS4 was hardly detected by CiberSortx. Because they were enriched with myofibroblasts as per the CiberSortx classification, we tentatively designated them as iCMS2-F/iCMS3-F. iCMS coupled with the application of an in silico cell fractionation method can provide the molecular dissection of CRC and adenoma.

Fully Automated Segmentation Models of Supratentorial Meningiomas Assisted by Inclusion of Normal Brain Images.

To train an automatic brain tumor segmentation model, a large amount of data is required. In this paper, we proposed a strategy to overcome the limited amount of clinically collected magnetic resonance image (MRI) data regarding meningiomas by pre-training a model using a larger public dataset of MRIs of gliomas and augmenting our meningioma training set with normal brain MRIs. Pre-operative MRIs of 91 meningioma patients (171 MRIs) and 10 non-meningioma patients (normal brains) were collected between 2016 and 2019. Three-dimensional (3D) U-Net was used as the base architecture. The model was pre-trained with BraTS 2019 data, then fine-tuned with our datasets consisting of 154 meningioma MRIs and 10 normal brain MRIs. To increase the utility of the normal brain MRIs, a novel balanced Dice loss (BDL) function was used instead of the conventional soft Dice loss function. The model performance was evaluated using the Dice scores across the remaining 17 meningioma MRIs. The segmentation performance of the model was sequentially improved via the pre-training and inclusion of normal brain images. The Dice scores improved from 0.72 to 0.76 when the model was pre-trained. The inclusion of normal brain MRIs to fine-tune the model improved the Dice score; it increased to 0.79. When employing BDL as the loss function, the Dice score reached 0.84. The proposed learning strategy for U-net showed potential for use in segmenting meningioma lesions.

A Novel P53 Nanomedicine Reduces Immunosuppression and Augments Anti-PD-1 Therapy for Non-Small Cell Lung Cancer in Syngeneic Mouse Models.

Lung cancer is among the most common and lethal cancers and warrants novel therapeutic approaches to improving patient outcomes. Although immune checkpoint inhibitors (ICIs) have demonstrated substantial clinical benefits, most patients remain unresponsive to currently approved ICIs or develop resistance after initial response. Many ongoing clinical studies are investigating combination therapies to address the limited efficacy of ICIs. Here, we have assessed whether p53 gene therapy via a tumor-targeting nanomedicine (termed SGT-53) can augment anti-programmed cell death-1 (PD-1) immunotherapy to expand its use in non-responding patients. Using syngeneic mouse models of lung cancers that are resistant to anti-PD-1, we demonstrate that restoration of normal p53 function potentiates anti-PD-1 to inhibit tumor growth and prolong survival of tumor-bearing animals. Our data indicate that SGT-53 can restore effective immune responses against lung cancer cells by reducing immuno-suppressive cells (M2 macrophages and regulatory T cells) and by downregulating immunosuppressive molecules (e.g., galectin-1, a negative regulator of T cell activation and survival) while increasing activity of cytotoxic T cells. These results suggest that combining SGT-53 with anti-PD-1 immunotherapy could increase the fraction of lung cancer patients that responds to anti-PD-1 therapy and support evaluation of this combination particularly in patients with ICI-resistant lung cancers.

Statistical analysis of hard X-ray radiation at the PAL-XFEL facility performed by Hanbury Brown and Twiss interferometry.

A Hanbury Brown and Twiss interferometry experiment based on second-order correlations was performed at the PAL-XFEL facility. The statistical properties of the X-ray radiation were studied within this experiment. Measurements were performed at the NCI beamline at 10 keV photon energy under various operation conditions: self-amplified spontaneous emission (SASE), SASE with a monochromator, and self-seeding regimes at 120 pC, 180 pC and 200 pC electron bunch charge. Statistical analysis showed short average pulse duration from 6 fs to 9 fs depending on the operational conditions. A high spatial degree of coherence of about 70-80% was determined in the spatial domain for the SASE beams with the monochromator and self-seeding regime of operation. The obtained values describe the statistical properties of the beams generated at the PAL-XFEL facility.

Nanomedicine-Based Gene Delivery for a Truncated Tumor Suppressor RB94 Promotes Lung Cancer Immunity.

Because lung cancer remains the most common and lethal of cancers, novel therapeutic approaches are urgently needed. RB94 is a truncated form of retinoblastoma tumor suppressor protein with elevated anti-tumor efficacy. Our investigational nanomedicine (termed scL-RB94) is a tumor-targeted liposomal formulation of a plasmid containing the gene encoding RB94. In this research, we studied anti-tumor and immune modulation activities of scL-RB94 nanocomplex in preclinical models of human non-small cell lung cancer (NSCLC). Systemic treatment with scL-RB94 of mice bearing human NSCLC tumors significantly inhibited tumor growth by lowering proliferation and increasing apoptosis of tumor cells in vivo. scL-RB94 treatment also boosted anti-tumor immune responses by upregulating immune recognition molecules and recruiting innate immune cells such as natural killer (NK) cells. Antibody-mediated depletion of NK cells blunted the anti-tumor activity of scL-RB94, suggesting that NK cells were crucial for the observed anti-tumor activity in these xenograft models. Treatment with scL-RB94 also altered the polarization of tumor-associated macrophages by reducing immune-suppressive M2 macrophages to lower immune suppression in the tumor microenvironment. Collectively, our data suggest that the efficacy of scL-RB94 against NSCLC is due to an induction of tumor cell death as well as enhancement of innate anti-tumor immunity.

Development of a Machine Learning Model to Predict Non-Durable Response to Anti-TNF Therapy in Crohn's Disease Using Transcriptome Imputed from Genotypes.

Almost half of patients show no primary or secondary response to monoclonal anti-tumor necrosis factor α (anti-TNF) antibody treatment for inflammatory bowel disease (IBD). Thus, the exact mechanisms of a non-durable response (NDR) remain inadequately defined. We used our genome-wide genotype data to impute expression values as features in training machine learning models to predict a NDR. Blood samples from various IBD cohorts were used for genotyping with the Korea Biobank Array. A total of 234 patients with Crohn's disease (CD) who received their first anti-TNF therapy were enrolled. The expression profiles of 6294 genes in whole-blood tissue imputed from the genotype data were combined with clinical parameters to train a logistic model to predict the NDR. The top two and three most significant features were genetic features (DPY19L3, GSTT1, and NUCB1), not clinical features. The logistic regression of the NDR vs. DR status in our cohort by the imputed expression levels showed that the ß coefficients were positive for DPY19L3 and GSTT1, and negative for NUCB1, concordant with the known eQTL information. Machine learning models using imputed gene expression features effectively predicted NDR to anti-TNF agents in patients with CD.

The association of genetic alterations with response rate in newly diagnosed chronic myeloid leukemia patients.

Genetic differences may be associated with the response to tyrosine kinase inhibitor (TKI) in patients with chronic myeloid leukemia (CML). In this study, we identified genetic alterations between rapid and slow responders (BCR/ABL1 International Scale at 6 months: ≤0.1 % vs. > 0.1 %) of TKI treatment in chronic phase CML patients. Our analyses involved single nucleotide polymorphism (SNP), a Genome Wide Association Study and a Network-wide Association Study (NetWAS). Seventy-two patients from 16 institutions were enrolled and treated with a TKI, nilotinib. Gene Set Analysis identified genetic alterations in pathways related to the differentiation, proliferation, and activity of various innate immune cells. The NetWAS analysis found that genes associated with natural killer (NK) cells (PTPRCAP, BLNK, HCK, ARHGEF11, GPR183, TRPV2, SHKBP1, CD2) showed significant differences between rapid and slow responders of nilotinib. However, we found no significantly different genetic alterations according to the response in the SNP analysis. In conclusion, we found that rapidity of response to TKI was associated with pathway-associated genetic alterations in immune cells, particularly with respect to NK cell activity. These results suggested that the innate immune system at initial diagnosis had an important role in treatment response in patients with CML.

Reconstruction of paralyzed arm function in patients with hemiplegia through contralateral seventh cervical nerve cross transfer: a multicenter study and real-world practice guidance.

BACKGROUND: A previous randomized controlled trial showed contralateral seventh cervical nerve (CC7) cross transfer to be safe and effective in restoring the arm function of spastic arm paralysis patients in a specified population. Guidance on indications, safety and expected long-term improvements of the surgery are needed for clinical practice. METHODS: This is a retrospective, multicenter, propensity score-matched cohort study. All patients registered between 2013 and 2019 with unilateral spastic arm paralysis over 1 year who were registered at one of five centers in China and South Korea were included. Patients received CC7 cross transfer or rehabilitation treatment in each center. Primary outcome was the change in the upper-extremity Fugl-Meyer (UEFM) score from baseline to 2-year follow-up; larger increase indicated better functional improvements. FINDINGS: The analysis included 425 eligible patients. After propensity score matching, 336 patients who were 1:1 matched into surgery and rehabilitation groups. Compared to previous trial, patient population was expanded on age (< 12 and > 45 years old), duration of disease (< 5 years) and severity of paralysis (severe disabled patients with UEFM < 20 points). In matched patients, the overall increases of UEFM score from preoperative evaluation to 2-year follow-up were 15.14 in the surgery group and 2.35 in the rehabilitation group (difference, 12.79; 95% CI: 12.02-13.56, p < 0.001). This increase was 16.58 at 3-year and 18.42 at 5-year follow-up compared with the surgery group baseline. Subgroup analysis revealed substantial increase on UEFM score in each subgroup of age, duration of disease, severity of paralysis and cause of injury. No severe complication or disabling sequela were reported in the surgery group. INTERPRETATION: This study showed that CC7 cross transfer can provide effective, safe and stable functional improvements in long-term follow-up, and provided evidences for expanding the indications of the surgery to a wider population of patients with hemiplegia.

Development of a Machine Learning Model to Distinguish between Ulcerative Colitis and Crohn's Disease Using RNA Sequencing Data.

Crohn's disease (CD) and ulcerative colitis (UC) can be difficult to differentiate. As differential diagnosis is important in establishing a long-term treatment plan for patients, we aimed to develop a machine learning model for the differential diagnosis of the two diseases using RNA sequencing (RNA-seq) data from endoscopic biopsy tissue from patients with inflammatory bowel disease (n = 127; CD, 94; UC, 33). Biopsy samples were taken from inflammatory lesions or normal tissues. The RNA-seq dataset was processed via mapping to the human reference genome (GRCh38) and quantifying the corresponding gene models that comprised 19,596 protein-coding genes. An unsupervised learning model showed distinct clusters of four classes: CD inflammatory, CD normal, UC inflammatory, and UC normal. A supervised learning model based on partial least squares discriminant analysis was able to distinguish inflammatory CD from inflammatory UC after pruning the strong classifiers of normal CD vs. normal UC. The error rate was minimal and affected only two components: 20 and 50 genes for the first and second components, respectively. The corresponding overall error rate was 0.147. RNA-seq analysis of tissue and the two components revealed in this study may be helpful for distinguishing CD from UC.

Ligand-Field Effects in a Ruthenium(II) Polypyridyl Complex Probed by Femtosecond X-ray Absorption Spectroscopy.

We employ femtosecond X-ray absorption spectroscopy of [Ru(m-bpy)3]2+ (m-bpy = 6-methyl-2,2'-bipyridine) to elucidate the time evolution of the spin and charge density upon metal-to-ligand charge-transfer (MLCT) excitation. The core-level transitions at the Ru L3-edge reveal a very short MLCT lifetime of 0.9 ps and relaxation to the lowest triplet metal-centered state (3MC) which exhibits a lifetime of about 300 ps. Time-dependent density functional theory relates ligand methylation to a lower ligand field strength that stabilizes the 3MC state. A quarter of the 3MLCT population appears to be trapped which may be attributed to intramolecular vibrational relaxation or further electron transfer to the solvent. Our results demonstrate that small changes in the ligand field allow control of the photophysical properties. Moreover, this study underscores the high information content of femtosecond L-edge spectroscopy as a probe of valence charge density and spin-state in 4d transition metals.

Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn's Disease: Results from the IMPACT Study.

Early intestinal resection in patients with Crohn's disease (CD) is necessary due to a severe and complicating disease course. Herein, we aim to predict which patients with CD need early intestinal resection within 3 years of diagnosis, according to a tree-based machine learning technique. The single-nucleotide polymorphism (SNP) genotype data for 337 CD patients recruited from 15 hospitals were typed using the Korea Biobank Array. For external validation, an additional 126 CD patients were genotyped. The predictive model was trained using the 102 candidate SNPs and seven sets of clinical information (age, sex, cigarette smoking, disease location, disease behavior, upper gastrointestinal involvement, and perianal disease) by employing a tree-based machine learning method (CatBoost). The importance of each feature was measured using the Shapley Additive Explanations (SHAP) model. The final model comprised two clinical parameters (age and disease behavior) and four SNPs (rs28785174, rs60532570, rs13056955, and rs7660164). The combined clinical-genetic model predicted early surgery more accurately than a clinical-only model in both internal (area under the receiver operating characteristic (AUROC), 0.878 vs. 0.782; n = 51; p < 0.001) and external validation (AUROC, 0.836 vs. 0.805; n = 126; p < 0.001). Identification of genetic polymorphisms and clinical features enhanced the prediction of early intestinal resection in patients with CD.

Deep Neural Network-Based Prediction of the Risk of Advanced Colorectal Neoplasia.

Background/Aims: Risk prediction models using a deep neural network (DNN) have not been reported to predict the risk of advanced colorectal neoplasia (ACRN). The aim of this study was to compare DNN models with simple clinical score models to predict the risk of ACRN in colorectal cancer screening. Methods: Databases of screening colonoscopy from Kangbuk Samsung Hospital (n=121,794) and Kyung Hee University Hospital at Gangdong (n=3,728) were used to develop DNN-based prediction models. Two DNN models, the Asian-Pacific Colorectal Screening (APCS) model and the Korean Colorectal Screening (KCS) model, were developed and compared with two simple score models using logistic regression methods to predict the risk of ACRN. The areas under the receiver operating characteristic curves (AUCs) of the models were compared in internal and external validation databases. Results: In the internal validation set, the AUCs of DNN model 1 and the APCS score model were 0.713 and 0.662 (p<0.001), respectively, and the AUCs of DNN model 2 and the KCS score model were 0.730 and 0.667 (p<0.001), respectively. However, in the external validation set, the prediction performances were not significantly different between the two DNN models and the corresponding APCS and KCS score models (both p>0.1). Conclusions: Simple score models for the risk prediction of ACRN are as useful as DNN-based models when input variables are limited. However, further studies on this issue are warranted to predict the risk of ACRN in colorectal cancer screening because DNN-based models are currently under improvement.

Subnanosecond phase transition dynamics in laser-shocked iron.

Iron is one of the most studied chemical elements due to its sociotechnological and planetary importance; hence, understanding its structural transition dynamics is of vital interest. By combining a short pulse optical laser and an ultrashort free electron laser pulse, we have observed the subnanosecond structural dynamics of iron from high-quality x-ray diffraction data measured at 50-ps intervals up to 2500 ps. We unequivocally identify a three-wave structure during the initial compression and a two-wave structure during the decaying shock, involving all of the known structural types of iron (α-, γ-, and ε-phase). In the final stage, negative lattice pressures are generated by the propagation of rarefaction waves, leading to the formation of expanded phases and the recovery of γ-phase. Our observations demonstrate the unique capability of measuring the atomistic evolution during the entire lattice compression and release processes at unprecedented time and strain rate.

Single cell transcriptome analysis of developing arcuate nucleus neurons uncovers their key developmental regulators.

Despite the crucial physiological processes governed by neurons in the hypothalamic arcuate nucleus (ARC), such as growth, reproduction and energy homeostasis, the developmental pathways and regulators for ARC neurons remain understudied. Our single cell RNA-seq analyses of mouse embryonic ARC revealed many cell type-specific markers for developing ARC neurons. These markers include transcription factors whose expression is enriched in specific neuronal types and often depleted in other closely-related neuronal types, raising the possibility that these transcription factors play important roles in the fate commitment or differentiation of specific ARC neuronal types. We validated this idea with the two transcription factors, Foxp2 enriched for Ghrh-neurons and Sox14 enriched for Kisspeptin-neurons, using Foxp2- and Sox14-deficient mouse models. Taken together, our single cell transcriptome analyses for the developing ARC uncovered a panel of transcription factors that are likely to form a gene regulatory network to orchestrate fate specification and differentiation of ARC neurons.

A tumor-targeting nanomedicine carrying the p53 gene crosses the blood-brain barrier and enhances anti-PD-1 immunotherapy in mouse models of glioblastoma.

Despite its anticipated clinical potential, anti-PD-1 immunotherapy has only yielded poor outcomes in recent clinical trials for glioblastoma patients. Strategies combining anti-PD-1 antibody with other treatment modalities are being explored to alter the immunosuppressive microenvironment that appears to characterize these anti-PD-1-insensitive tumors. Here, we evaluated whether introducing wild-type p53 gene via a tumor-targeting nanomedicine (termed SGT-53) could provide immune stimulation and augment anti-PD-1 therapy in mouse syngeneic GL261 tumor models (either subcutaneous or intracranial). In both models, anti-PD-1 monotherapy had no demonstrable therapeutic effect. However, combining anti-PD-1 with our investigational nanomedicine SGT-53 was very effective in inhibiting tumor growth, inducing tumor cell apoptosis and increasing intratumoral T-cell infiltration. A significant survival benefit was observed in mice bearing intracranial glioblastoma receiving combination treatment. Importantly, SGT-53 upregulated PD-L1 expression both in vitro and in vivo. Transcriptome analysis revealed modulation of genes linked to either cancer progression or immune activation after combination treatment. Our data suggest that SGT-53 can boost antitumor immunity and sensitize glioblastoma to anti-PD-1 therapy by converting immunologically "cold" tumors into "hot" tumors. Combining SGT-53 with anti-PD-1 might benefit more patients from anti-PD-1 immunotherapy and our data support evaluation of this combination in patients with glioblastoma.

Computational Drug Repositioning for Gastric Cancer using Reversal Gene Expression Profiles.

Treatment of gastric cancer (GC) often produces poor outcomes. Moreover, predicting which GC treatments will be effective remains challenging. Computational drug repositioning using public databases is a promising and efficient tool for discovering new uses for existing drugs. Here we used a computational reversal of gene expression approach based on effects on gene expression signatures by GC disease and drugs to explore new GC drug candidates. Gene expression profiles for individual GC tumoral and normal gastric tissue samples were downloaded from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) in GC were determined with a meta-signature analysis. Profiles drug activity and drug-induced gene expression were downloaded from the ChEMBL and the LINCS databases, respectively. Candidate drugs to treat GC were predicted using reversal gene expression score (RGES). Drug candidates including sorafenib, olaparib, elesclomol, tanespimycin, selumetinib, and ponatinib were predicted to be active for treatment of GC. Meanwhile, GC-related genes such as PLOD3, COL4A1, UBE2C, MIF, and PRPF5 were identified as having gene expression profiles that can be reversed by drugs. These findings support the use of a computational reversal gene expression approach to identify new drug candidates that can be used to treat GC.