An IL-17-DUOX2 axis controls gastrointestinal colonization by Candida albicans.

Candida albicans is a ubiquitous fungus in the human gut microbiome as well as a prevalent cause of opportunistic mucosal and systemic disease. There is currently little understanding, however, as to how crosstalk between C. albicans and the host regulates colonization of this key niche. Here, we performed expression profiling on ileal and colonic tissues in germ-free mice colonized with C. albicans to define the global response to this fungus. We reveal that Duox2 and Duoxa2 , encoding dual NADPH oxidase activity, are upregulated in both the ileum and colon, and that induction requires the C. albicans yeast-hyphal transition and the hyphal-specific toxin candidalysin. Hosts lacking the IL-17 receptor failed to upregulate Duox2/Duoxa2 in response to C. albicans , while addition of IL-17A to colonoids induced these genes together with the concomitant production of hydrogen peroxide. To directly define the role of Duox2/Duoxa2 in fungal colonization, antibiotic-treated mice lacking intestinal DUOX2 activity were evaluated for C. albicans colonization and host responses. Surprisingly, loss of DUOX2 function reduced fungal colonization at extended time points (>17 days colonization) and increased the proportion of hyphal cells in the gut. IL-17A levels were also elevated in C. albicans -colonized mice lacking functional DUOX2 highlighting cross-regulation between this cytokine and DUOX2. Together, these experiments reveal novel links between fungal cells, candidalysin toxin and the host IL-17-DUOX2 axis, and that a complex interplay between these factors regulates C. albicans filamentation and colonization in the gut.

MYC-Mediated Inhibition of ARNT2 Uncovers a Key Tumor Suppressor in Glioblastoma.

Tumor initiation and progression rely on intricate cellular pathways that promote proliferation while suppressing differentiation, yet the importance of pathways inhibiting differentiation in cancer remains incompletely understood. Here, we reveal a novel mechanism centered on the repression of the neuronal-specific transcription factor ARNT2 by the MYC oncogene that governs the balance between proliferation and differentiation. We found that MYC coordinates the transcriptional repression of ARNT2 through the activity of polycomb repressive complex 2 (PRC2). Notably, ARNT2, highly and specifically expressed in the central nervous system, is diminished in glioblastoma, inversely correlating with patient survival. Utilizing in vitro and in vivo models, we demonstrate that ARNT2 knockout (KO) exerts no discernible effect on the in vitro proliferation of glioblastoma cells, but significantly enhances the growth of glioblastoma cells in vivo. Conversely, ARNT2 overexpression severely dampens the growth of fully transformed glioblastoma cells subcutaneously or orthotopically xenografted in mice. Mechanistically, ARNT2 depletion diminishes differentiation and enhances stemness of glioblastoma cells. Our findings provide new insights into the complex mechanisms used by oncogenes to limit differentiation in cancer cells and define ARNT2 as a tumor suppressor in glioblastoma.

Unconventional mechanism of action and resistance to rapalogs in renal cancer.

mTORC1 is aberrantly activated in renal cell carcinoma (RCC) and is targeted by rapalogs. As for other targeted therapies, rapalogs clinical utility is limited by the development of resistance. Resistance often results from target mutation, but mTOR mutations are rarely found in RCC. As in humans, prolonged rapalog treatment of RCC tumorgrafts (TGs) led to resistance. Unexpectedly, explants from resistant tumors became sensitive both in culture and in subsequent transplants in mice. Notably, resistance developed despite persistent mTORC1 inhibition in tumor cells. In contrast, mTORC1 became reactivated in the tumor microenvironment (TME). To test the role of the TME, we engineered immunocompromised recipient mice with a resistance mTOR mutation (S2035T). Interestingly, TGs became resistant to rapalogs in mTORS2035T mice. Resistance occurred despite mTORC1 inhibition in tumor cells and could be induced by coculturing tumor cells with mutant fibroblasts. Thus, enforced mTORC1 activation in the TME is sufficient to confer resistance to rapalogs. These studies highlight the importance of mTORC1 inhibition in nontumor cells for rapalog antitumor activity and provide an explanation for the lack of mTOR resistance mutations in RCC patients.

3D Multiparametric Photoacoustic Computed Tomography of Primary and Metastatic Tumors in Living Mice.

Photoacoustic computed tomography (PACT), an emerging imaging modality in preclinical cancer research, can provide multiparametric 3D information about structures, physiological functions, and pharmacokinetics. Here, we demonstrate the use of high-definition 3D multiparametric PACT imaging of both primary and metastatic tumors in living mice to noninvasively monitor angiogenesis, carcinogenesis, hypoxia, and pharmacokinetics. The high-definition PACT system with a 1024-element hemispherical ultrasound transducer array provides an isotropic spatial resolution of 380 µm, an effective volumetric field-of-view of 12.8 mm × 12.8 mm × 12.8 mm without scanning, and an acquisition time of <30 s for a whole mouse body. Initially, we monitor the structural progression of the tumor microenvironment (e.g., angiogenesis and vessel tortuosity) after tumor cell inoculation. Then, we analyze the change in oxygen saturation of the tumor during carcinogenesis, verifying induced hypoxia in the tumor's core region. Finally, the whole-body pharmacokinetics are photoacoustically imaged after intravenous injection of micelle-loaded IR780 dye, and the in vivo PACT results are validated in vivo and ex vivo by fluorescence imaging. By employing the premium PACT system and applying multiparametric analyses to subcutaneous primary tumors and metastatic liver tumors, we demonstrate that this PACT system can provide multiparametric analyses for comprehensive cancer research.

Microsphere-assisted hyperspectral imaging: super-resolution, non-destructive metrology for semiconductor devices.

As semiconductor devices shrink and their manufacturing processes advance, accurately measuring in-cell critical dimensions (CD) becomes increasingly crucial. Traditional test element group (TEG) measurements are becoming inadequate for representing the fine, repetitive patterns in cell blocks. Conventional non-destructive metrology technologies like optical critical dimension (OCD) are limited due to their large spot diameter of approximately 25 µm, which impedes their efficacy for detailed in-cell structural analysis. Consequently, there is a pressing need for small-spot and non-destructive metrology methods. To address this limitation, we demonstrate a microsphere-assisted hyperspectral imaging (MAHSI) system, specifically designed for small spot optical metrology with super-resolution. Utilizing microsphere-assisted super-resolution imaging, this system achieves an optical resolution of 66 nm within a field of view of 5.6 µm × 5.6 µm. This approach effectively breaks the diffraction limit, significantly enhancing the magnification of the system. The MAHSI system incorporating hyperspectral imaging with a wavelength range of 400-790 nm, enables the capture of the reflection spectrum at each camera pixel. The achieved pixel resolution, which is equivalent to the measuring spot size, is 14.4 nm/pixel and the magnification is 450X. The MAHSI system enables measurement of local uniformity in critical areas like corners and edges of DRAM cell blocks, areas previously challenging to inspect with conventional OCD methods. To our knowledge, this approach represents the first global implementation of microsphere-assisted hyperspectral imaging to address the metrology challenges in complex 3D structures of semiconductor devices.

Tryptophan fuels MYC-dependent liver tumorigenesis through indole 3-pyruvate synthesis.

Cancer cells exhibit distinct metabolic activities and nutritional dependencies compared to normal cells. Thus, characterization of nutrient demands by individual tumor types may identify specific vulnerabilities that can be manipulated to target the destruction of cancer cells. We find that MYC-driven liver tumors rely on augmented tryptophan (Trp) uptake, yet Trp utilization to generate metabolites in the kynurenine (Kyn) pathway is reduced. Depriving MYC-driven tumors of Trp through a No-Trp diet not only prevents tumor growth but also restores the transcriptional profile of normal liver cells. Despite Trp starvation, protein synthesis remains unhindered in liver cancer cells. We define a crucial role for the Trp-derived metabolite indole 3-pyruvate (I3P) in liver tumor growth. I3P supplementation effectively restores the growth of liver cancer cells starved of Trp. These findings suggest that I3P is a potential therapeutic target in MYC-driven cancers. Developing methods to target this metabolite represents a potential avenue for liver cancer treatment.

Interaction between the gut microbiota and colonic enteroendocrine cells regulates host metabolism.

Nutrient handling is an essential function of the gastrointestinal tract. Hormonal responses of small intestinal enteroendocrine cells (EECs) have been extensively studied but much less is known about the role of colonic EECs in metabolic regulation. To address this core question, we investigated a mouse model deficient in colonic EECs. Here we show that colonic EEC deficiency leads to hyperphagia and obesity. Furthermore, colonic EEC deficiency results in altered microbiota composition and metabolism, which we found through antibiotic treatment, germ-free rederivation and transfer to germ-free recipients, to be both necessary and sufficient for the development of obesity. Moreover, studying stool and blood metabolomes, we show that differential glutamate production by intestinal microbiota corresponds to increased appetite and that colonic glutamate administration can directly increase food intake. These observations shed light on an unanticipated host-microbiota axis in the colon, part of a larger gut-brain axis, that regulates host metabolism and body weight.

DROSHA Regulates Mesenchymal Gene Expression in Wilms Tumor.

Wilms tumor, the most common pediatric kidney cancer, resembles embryonic renal progenitors. Currently, there are no ways to therapeutically target Wilms tumor driver mutations, such as in the microRNA processing gene DROSHA. In this study, we used a "multiomics" approach to define the effects of DROSHA mutation in Wilms tumor. We categorized Wilms tumor mutations into four mutational subclasses with unique transcriptional effects: microRNA processing, MYCN activation, chromatin remodeling, and kidney developmental factors. In particular, we find that DROSHA mutations are correlated with de-repressing microRNA target genes that regulate differentiation and proliferation and a self-renewing, mesenchymal state. We model these findings by inhibiting DROSHA expression in a Wilms tumor cell line, which led to upregulation of the cell cycle regulator cyclin D2 (CCND2). Furthermore, we observed that DROSHA mutations in Wilms tumor and DROSHA silencing in vitro were associated with a mesenchymal state with aberrations in redox metabolism. Accordingly, we demonstrate that Wilms tumor cells lacking microRNAs are sensitized to ferroptotic cell death through inhibition of glutathione peroxidase 4, the enzyme that detoxifies lipid peroxides. Implications: This study reveals genotype-transcriptome relationships in Wilms tumor and points to ferroptosis as a potentially therapeutic vulnerability in one subset of Wilms tumor.

Development and Validation of Deep Learning-Based Automated Detection of Cervical Lymphadenopathy in Patients with Lymphoma for Treatment Response Assessment: A Bi-institutional Feasibility Study.

The purpose is to train and evaluate a deep learning (DL) model for the accurate detection and segmentation of abnormal cervical lymph nodes (LN) on head and neck contrast-enhanced CT scans in patients diagnosed with lymphoma and evaluate the clinical utility of the DL model in response assessment. This retrospective study included patients who underwent CT for abnormal cervical LN and lymphoma assessment between January 2021 and July 2022. Patients were grouped into the development (n = 76), internal test 1 (n = 27), internal test 2 (n = 87), and external test (n = 26) cohorts. A 3D SegResNet model was used to train the CT images. The volume change rates of cervical LN across longitudinal CT scans were compared among patients with different treatment outcomes (stable, response, and progression). Dice similarity coefficient (DSC) and the Bland-Altman plot were used to assess the model's segmentation performance and reliability, respectively. No significant differences in baseline clinical characteristics were found across cohorts (age, P = 0.55; sex, P = 0.13; diagnoses, P = 0.06). The mean DSC was 0.39 ± 0.2 with a precision and recall of 60.9% and 57.0%, respectively. Most LN volumes were within the limits of agreement on the Bland-Altman plot. The volume change rates among the three groups differed significantly (progression (n = 74), 342.2%; response (n = 8), - 79.2%; stable (n = 5), - 8.1%; all P < 0.01). Our proposed DL segmentation model showed modest performance in quantifying the cervical LN burden on CT in patients with lymphoma. Longitudinal changes in cervical LN volume, as predicted by the DL model, were useful for treatment response assessment.

Identification of Atrial Fibrillation With Single-Lead Mobile ECG During Normal Sinus Rhythm Using Deep Learning.

BACKGROUND: The acquisition of single-lead electrocardiogram (ECG) from mobile devices offers a more practical approach to arrhythmia detection. Using artificial intelligence for atrial fibrillation (AF) identification enhances screening efficiency. However, the potential of single-lead ECG for AF identification during normal sinus rhythm (NSR) remains under-explored. This study introduces a method to identify AF using single-lead mobile ECG during NSR. METHODS: We employed three deep learning models: recurrent neural network (RNN), long short-term memory (LSTM), and residual neural networks (ResNet50). From a dataset comprising 13,509 ECGs from 6,719 patients, 10,287 NSR ECGs from 5,170 patients were selected. Single-lead mobile ECGs underwent noise filtering and segmentation into 10-second intervals. A random under-sampling was applied to reduce bias from data imbalance. The final analysis involved 31,767 ECG segments, including 15,157 labeled as masked AF and 16,610 as Healthy. RESULTS: ResNet50 outperformed the other models, achieving a recall of 79.3%, precision of 65.8%, F1-score of 71.9%, accuracy of 70.5%, and an area under the receiver operating characteristic curve (AUC) of 0.79 in identifying AF from NSR ECGs. Comparative performance scores for RNN and LSTM were 0.75 and 0.74, respectively. In an external validation set, ResNet50 attained an F1-score of 64.1%, recall of 68.9%, precision of 60.0%, accuracy of 63.4%, and AUC of 0.68. CONCLUSION: The deep learning model using single-lead mobile ECG during NSR effectively identified AF at risk in future. However, further research is needed to enhance the performance of deep learning models for clinical application.

Susceptible bacteria can survive antibiotic treatment in the mammalian gastrointestinal tract without evolving resistance.

Antibiotic resistance and evasion are incompletely understood and complicated by the fact that murine interval dosing models do not fully recapitulate antibiotic pharmacokinetics in humans. To better understand how gastrointestinal bacteria respond to antibiotics, we colonized germ-free mice with a pan-susceptible genetically barcoded Escherichia coli clinical isolate and administered the antibiotic cefepime via programmable subcutaneous pumps, allowing closer emulation of human parenteral antibiotic dynamics. E. coli was only recovered from intestinal tissue, where cefepime concentrations were still inhibitory. Strikingly, "some" E. coli isolates were not cefepime resistant but acquired mutations in genes involved in polysaccharide capsular synthesis increasing their invasion and survival within human intestinal cells. Deleting wbaP involved in capsular polysaccharide synthesis mimicked this phenotype, allowing increased invasion of colonocytes where cefepime concentrations were reduced. Additionally, "some" mutant strains exhibited a persister phenotype upon further cefepime exposure. This work uncovers a mechanism allowing "select" gastrointestinal bacteria to evade antibiotic treatment.

X-ray free-electron laser induced acoustic microscopy (XFELAM).

The X-ray free-electron laser (XFEL) has remarkably advanced X-ray imaging technology and enabled important scientific achievements. The XFEL's extremely high power, short pulse width, low emittance, and high coherence make possible such diverse imaging techniques as absorption/emission spectroscopy, diffraction imaging, and scattering imaging. Here, we demonstrate a novel XFEL-based imaging modality that uses the X-ray induced acoustic (XA) effect, which we call X-ray free-electron laser induced acoustic microscopy (XFELAM). Initially, we verified the XA effect by detecting XA signals from various materials, then we validated the experimental results with simulation outcomes. Next, in resolution experiments, we successfully imaged a patterned tungsten target with drilled various-sized circles at a spatial resolution of 7.8 ± 5.1 µm, which is the first micron-scale resolution achieved by XA imaging. Our results suggest that the novel XFELAM can expand the usability of XFEL in various areas of fundamental scientific research.

Induced degradation of lineage-specific oncoproteins drives the therapeutic vulnerability of small cell lung cancer to PARP inhibitors.

Although BRCA1/2 mutations are not commonly found in small cell lung cancer (SCLC), a substantial fraction of SCLC shows clinically relevant response to PARP inhibitors (PARPis). However, the underlying mechanism(s) of PARPi sensitivity in SCLC is poorly understood. We performed quantitative proteomic analyses and identified proteomic changes that signify PARPi responses in SCLC cells. We found that the vulnerability of SCLC to PARPi could be explained by the degradation of lineage-specific oncoproteins (e.g., ASCL1). PARPi-induced activation of the E3 ligase HUWE1 mediated the ubiquitin-proteasome system (UPS)-dependent ASCL1 degradation. Although PARPi induced a general DNA damage response in SCLC cells, this signal generated a cell-specific response in ASCL1 degradation, leading to the identification of HUWE1 expression as a predictive biomarker for PARPi. Combining PARPi with agents targeting these pathways markedly improved therapeutic response in SCLC. The degradation of lineage-specific oncoproteins therefore represents a previously unidentified mechanism for PARPi efficacy in SCLC.

Anticancer benzoxaboroles block pre-mRNA processing by directly inhibiting CPSF3.

A novel class of benzoxaboroles was reported to induce cancer cell death but the mechanism was unknown. Using a forward genetics platform, we discovered mutations in cleavage and polyadenylation specific factor 3 (CPSF3) that reduce benzoxaborole binding and confer resistance. CPSF3 is the endonuclease responsible for pre-mRNA 3'-end processing, which is also important for RNA polymerase II transcription termination. Benzoxaboroles inhibit this endonuclease activity of CPSF3 in vitro and also curb transcriptional termination in cells, which results in the downregulation of numerous constitutively expressed genes. Furthermore, we used X-ray crystallography to demonstrate that benzoxaboroles bind to the active site of CPSF3 in a manner distinct from the other known inhibitors of CPSF3. The benzoxaborole compound impeded the growth of cancer cell lines derived from different lineages. Our results suggest benzoxaboroles may represent a promising lead as CPSF3 inhibitors for clinical development.

Optimizing coagulant dosage using deep learning models with large-scale data.

Water treatment plants are facing challenges that necessitate transition to automated processes using advanced technologies. This study introduces a novel approach to optimize coagulant dosage in water treatment processes by employing a deep learning model. The study utilized minute-by-minute data monitored in real time over a span of five years, marking the first attempt in drinking water process modeling to leverage such a comprehensive dataset. The deep learning model integrates a one-dimensional convolutional neural network (Conv1D) and gated recurrent unit (GRU) to effectively extract features and model complex time-series data. Initially, the model predicted coagulant dosage and sedimentation basin turbidity, validated against a physicochemical model. Subsequently, the model optimized coagulant dosage in two ways: 1) maintaining sedimentation basin turbidity below the 1.0 NTU guideline, and 2) analyzing changes in sedimentation basin turbidity resulting from reduced coagulant dosage (5-20%). The findings of the study highlight the effectiveness of the deep learning model in optimizing coagulant dosage with substantial reductions in coagulant dosage (approximately 22% reduction and 21 million KRW/year). The results demonstrate the potential of deep learning models in enhancing the efficiency and cost-effectiveness of water treatment processes, ultimately facilitating process automation.

Comprehensive characterization of patient-derived xenograft models of pediatric leukemia.

Patient-derived xenografts (PDX) remain valuable models for understanding the biology and for developing novel therapeutics. To expand current PDX models of childhood leukemia, we have developed new PDX models from Hispanic patients, a subgroup with a poorer overall outcome. Of 117 primary leukemia samples obtained, successful engraftment and serial passage in mice were achieved in 82 samples (70%). Hispanic patient samples engrafted at a rate (51/73, 70%) that was similar to non-Hispanic patient samples (31/45, 70%). With a new algorithm to remove mouse contamination in multi-omics datasets including methylation data, we found PDX models faithfully reflected somatic mutations, copy-number alterations, RNA expression, gene fusions, whole-genome methylation patterns, and immunophenotypes found in primary tumor (PT) samples in the first 50 reported here. This cohort of characterized PDX childhood leukemias represents a valuable resource in that germline DNA sequencing has allowed the unambiguous determination of somatic mutations in both PT and PDX.

Evaluation of optimal methods and ancestries for calculating polygenic risk scores in East Asian population.

Polygenic risk scores (PRSs) have been studied for predicting human diseases, and various methods for PRS calculation have been developed. Most PRS studies to date have focused on European ancestry, and the performance of PRS has not been sufficiently assessed in East Asia. Herein, we evaluated the predictive performance of PRSs for East Asian populations under various conditions. Simulation studies using data from the Korean cohort, Health Examinees (HEXA), demonstrated that SBayesRC and PRS-CS outperformed other PRS methods (lassosum, LDpred-funct, and PRSice) in high fixed heritability (0.3 and 0.7). In addition, we generated PRSs using real-world data from HEXA for ten diseases: asthma, breast cancer, cataract, coronary artery disease, gastric cancer, glaucoma, hyperthyroidism, hypothyroidism, osteoporosis, and type 2 diabetes (T2D). We utilized the five previous PRS methods and genome-wide association study (GWAS) data from two biobank-scale datasets [European (UK Biobank) and East Asian (BioBank Japan) ancestry]. Additionally, we employed PRS-CSx, a PRS method that combines GWAS data from both ancestries, to generate a total of 110 PRS for ten diseases. Similar to the simulation results, SBayesRC showed better predictive performance for disease risk than the other methods. Furthermore, the East Asian GWAS data outperformed those from European ancestry for breast cancer, cataract, gastric cancer, and T2D, but neither of the two GWAS ancestries showed a significant advantage on PRS performance for the remaining six diseases. Based on simulation data and real data studies, it is expected that SBayesRC will offer superior performance for East Asian populations, and PRS generated using GWAS from non-East Asian may also yield good results.

Genetic determinants underlying the progressive phenotype of ß-lactam/ß-lactamase inhibitor resistance in Escherichia coli.

IMPORTANCE: The increased feasibility of whole-genome sequencing has generated significant interest in using such molecular diagnostic approaches to characterize difficult-to-treat, antimicrobial-resistant (AMR) infections. Nevertheless, there are current limitations in the accurate prediction of AMR phenotypes based on existing AMR gene database approaches, which primarily correlate a phenotype with the presence/absence of a single AMR gene. Our study utilized a large cohort of cephalosporin-susceptible Escherichia coli bacteremia samples to determine how increasing the dosage of narrow-spectrum ß-lactamase-encoding genes in conjunction with other diverse ß-lactam/ß-lactamase inhibitor (BL/BLI) genetic determinants contributes to progressively more severe BL/BLI phenotypes. We were able to characterize the complexity of the genetic mechanisms underlying progressive BL/BLI resistance including the critical role of ß-lactamase encoding gene amplification. For the diverse array of AMR phenotypes with complex mechanisms involving multiple genomic factors, our study provides an example of how composite risk scores may improve understanding of AMR genotype/phenotype correlations.

Loss of p53 and mutational heterogeneity drives immune resistance in an autochthonous mouse lung cancer model with high tumor mutational burden.

The role of tumor mutational burden (TMB) in shaping tumor immunity is a key question that has not been addressable using genetically engineered mouse models (GEMMs) of lung cancer. To induce TMB in lung GEMMs, we expressed an ultra-mutator variant of DNA polymerase-E (POLE)P286R in lung epithelial cells. Introduction of PoleP286R allele into KrasG12D and KrasG12D; p53L/L (KP) models significantly increase their TMB. Immunogenicity and sensitivity to immune checkpoint blockade (ICB) induced by Pole is partially dependent on p53. Corroborating these observations, survival of NSCLC patients whose tumors have TP53truncating mutations is shorter than those with TP53WT with immunotherapy. Immune resistance is in part through reduced antigen presentation and in part due to mutational heterogeneity. Total STING protein levels are elevated in Pole mutated KP tumors creating a vulnerability. A stable polyvalent STING agonist or p53 induction increases sensitivity to immunotherapy offering therapeutic options in these polyclonal tumors.