Secreted antigen A peptidoglycan hydrolase is essential for Enterococcus faecium cell separation and priming of immune checkpoint inhibitor therapy.

Enterococcus faecium is a microbiota species in humans that can modulate host immunity (Griffin and Hang, 2022), but has also acquired antibiotic resistance and is a major cause of hospital-associated infections (Van Tyne and Gilmore, 2014). Notably, diverse strains of E. faecium produce SagA, a highly conserved peptidoglycan hydrolase that is sufficient to promote intestinal immunity (Rangan et al., 2016; Pedicord et al., 2016; Kim et al., 2019) and immune checkpoint inhibitor antitumor activity (Griffin et al., 2021). However, the functions of SagA in E. faecium were unknown. Here, we report that deletion of sagA impaired E. faecium growth and resulted in bulged and clustered enterococci due to defective peptidoglycan cleavage and cell separation. Moreover, ΔsagA showed increased antibiotic sensitivity, yielded lower levels of active muropeptides, displayed reduced activation of the peptidoglycan pattern-recognition receptor NOD2, and failed to promote cancer immunotherapy. Importantly, the plasmid-based expression of SagA, but not its catalytically inactive mutant, restored ΔsagA growth, production of active muropeptides, and NOD2 activation. SagA is, therefore, essential for E. faecium growth, stress resistance, and activation of host immunity.

Migratory Tumor Cells Cooperate with Cancer Associated Fibroblasts in Hormone Receptor-Positive and HER2-Negative Breast Cancer.

Hormone receptor-positive and HER2-negative breast cancer (HR+/HER2-BC) is the most common type with a favorable prognosis under endocrine therapy. However, it still demonstrates unpredictable progression and recurrences influenced by high tumoral diversity and microenvironmental status. To address these heterogeneous molecular characteristics of HR+/HER2-BC, we aimed to simultaneously characterize its transcriptomic landscape and genetic architecture at the same resolution. Using advanced single-cell RNA and DNA sequencing techniques together, we defined four distinct tumor subtypes. Notably, the migratory tumor subtype was closely linked to genomic alterations of EGFR, related to the tumor-promoting behavior of IL6-positive inflammatory tumor-associated fibroblast, and contributing to poor prognosis. Our study comprehensively utilizes integrated analysis to uncover the complex dynamics of this breast cancer subtype, highlighting the pivotal role of the migratory tumor subtype in influencing surrounding cells. This sheds light on potential therapeutic targets by offering enhanced insights for HR+/HER2-BC treatment.

Prmt7 regulates the JAK/STAT/Socs3 signaling pathway in postmenopausal cardiomyopathy.

Protein arginine methyltransferases (PRMTs) modulate diverse cellular processes, including stress responses. The present study explored the role of Prmt7 in protecting against menopause-associated cardiomyopathy. Mice with cardiac-specific Prmt7 ablation (cKO) exhibited sex-specific cardiomyopathy. Male cKO mice exhibited impaired cardiac function, myocardial hypertrophy, and interstitial fibrosis associated with increased oxidative stress. Interestingly, female cKO mice predominantly exhibited comparable phenotypes only after menopause or ovariectomy (OVX). Prmt7 inhibition in cardiomyocytes exacerbated doxorubicin (DOX)-induced oxidative stress and DNA double-strand breaks, along with apoptosis-related protein expression. Treatment with 17ß-estradiol (E2) attenuated the DOX-induced decrease in Prmt7 expression in cardiomyocytes, and Prmt7 depletion abrogated the protective effect of E2 against DOX-induced cardiotoxicity. Transcriptome analysis of ovariectomized wild-type (WT) or cKO hearts and mechanical analysis of Prmt7-deficient cardiomyocytes demonstrated that Prmt7 is required for the control of the JAK/STAT signaling pathway by regulating the expression of suppressor of cytokine signaling 3 (Socs3), which is a negative feedback inhibitor of the JAK/STAT signaling pathway. These data indicate that Prmt7 has a sex-specific cardioprotective effect by regulating the JAK/STAT signaling pathway and, ultimately, may be a potential therapeutic tool for heart failure treatment depending on sex.

Machine learning model for circulating tumor DNA detection in chronic obstructive pulmonary disease patients with lung cancer.

Background: Patients with chronic obstructive pulmonary disease (COPD) have a high risk of developing lung cancer. Due to the high rates of complications from invasive diagnostic procedures in this population, detecting circulating tumor DNA (ctDNA) as a non-invasive method might be useful. However, clinical characteristics that are predictive of ctDNA mutation detection remain incompletely understood. This study aimed to investigate factors associated with ctDNA detection in COPD patients with lung cancer. Methods: Herein, 177 patients with COPD and lung cancer were prospectively recruited. Plasma ctDNA was genotyped using targeted deep sequencing. Comprehensive clinical variables were collected, including the emphysema index (EI), using chest computed tomography. Machine learning models were constructed to predict ctDNA detection. Results: At least one ctDNA mutation was detected in 54 (30.5%) patients. After adjustment for potential confounders, tumor stage, C-reactive protein (CRP) level, and milder emphysema were independently associated with ctDNA detection. An increase of 1% in the EI was associated with a 7% decrease in the odds of ctDNA detection (adjusted odds ratio =0.933; 95% confidence interval: 0.857-0.999; P=0.047). Machine learning models composed of multiple clinical factors predicted individuals with ctDNA mutations at high performance (AUC =0.774). Conclusions: ctDNA mutations were likely to be observed in COPD patients with lung cancer who had an advanced clinical stage, high CRP level, or milder emphysema. This was validated in machine learning models with high accuracy. Further prospective studies are required to validate the clinical utility of our findings.

Secreted antigen A peptidoglycan hydrolase is essential for Enterococcus faecium cell separation and priming of immune checkpoint inhibitor cancer therapy.

Enterococcus faecium is a microbiota species in humans that can modulate host immunity, but has also acquired antibiotic resistance and is a major cause of hospital-associated infections. Notably, diverse strains of E. faecium produce SagA, a highly conserved peptidoglycan hydrolase that is sufficient to promote intestinal immunity and immune checkpoint inhibitor antitumor activity. However, the functions of SagA in E. faecium were unknown. Here we report that deletion of sagA impaired E. faecium growth and resulted in bulged and clustered enterococci due to defective peptidoglycan cleavage and cell separation. Moreover, Δ sagA showed increased antibiotic sensitivity, yielded lower levels of active muropeptides, displayed reduced activation of the peptidoglycan pattern-recognition receptor NOD2, and failed to promote cancer immunotherapy. Importantly, plasmid-based expression of SagA, but not its catalytically-inactive mutant, restored Δ sagA growth, production of active muropeptides and NOD2 activation. SagA is therefore essential for E. faecium growth, stress resistance and activation of host immunity.

Spatio-Temporal Explanation of 3D-EEGNet for Motor Imagery EEG Classification Using Permutation and Saliency.

Recently, convolutional neural network (CNN)-based classification models have shown good performance for motor imagery (MI) brain-computer interfaces (BCI) using electroencephalogram (EEG) in end-to-end learning. Although a few explainable artificial intelligence (XAI) techniques have been developed, it is still challenging to interpret the CNN models for EEG-based BCI classification effectively. In this research, we propose 3D-EEGNet as a 3D CNN model to improve both the explainability and performance of MI EEG classification. The proposed approach exhibited better performances on two MI EEG datasets than the existing EEGNet, which uses a 2D input shape. The MI classification accuracies are improved around 1.8% and 6.1% point in average on the datasets, respectively. The permutation-based XAI method is first applied for the reliable explanation of the 3D-EEGNet. Next, to find a faster XAI method for spatio-temporal explanation, we design a novel technique based on the normalized discounted cumulative gain (NDCG) for selecting the best among a few saliency-based methods due to their higher time complexity than the permutation-based method. Among the saliency-based methods, DeepLIFT was selected because the NDCG scores indicated its results are the most similar to the permutation-based results. Finally, the fast spatio-temporal explanation using DeepLIFT provides deeper understanding for the classification results of the 3D-EEGNet and the important properties in the MI EEG experiments.

Reentrant DNA shells tune polyphosphate condensate size.

The ancient, inorganic biopolymer polyphosphate (polyP) occurs in all three domains of life and affects myriad cellular processes. An intriguing feature of polyP is its frequent proximity to chromatin, and in the case of many bacteria, its occurrence in the form of magnesium-enriched condensates embedded in the nucleoid, particularly in response to stress. The physical basis of the interaction between polyP and DNA, two fundamental anionic biopolymers, and the resulting effects on the organization of both the nucleoid and polyP condensates remain poorly understood. Given the essential role of magnesium ions in the coordination of polymeric phosphate species, we hypothesized that a minimal system of polyP, magnesium ions, and DNA (polyP-Mg2+-DNA) would capture key features of the interplay between the condensates and bacterial chromatin. We find that DNA can profoundly affect polyP-Mg2+ coacervation even at concentrations several orders of magnitude lower than found in the cell. The DNA forms shells around polyP-Mg2+ condensates and these shells show reentrant behavior, primarily forming in the concentration range close to polyP-Mg2+ charge neutralization. This surface association tunes both condensate size and DNA morphology in a manner dependent on DNA properties, including length and concentration. Our work identifies three components that could form the basis of a central and tunable interaction hub that interfaces with cellular interactors. These studies will inform future efforts to understand the basis of polyP granule composition and consolidation, as well as the potential capacity of these mesoscale assemblies to remodel chromatin in response to diverse stressors at different length and time scales.

Parkinson's disease kinase LRRK2 coordinates a cell-intrinsic itaconate-dependent defence pathway against intracellular Salmonella.

Cell-intrinsic defences constitute the first line of defence against intracellular pathogens. The guanosine triphosphatase RAB32 orchestrates one such defence response against the bacterial pathogen Salmonella, through delivery of antimicrobial itaconate. Here we show that the Parkinson's disease-associated leucine-rich repeat kinase 2 (LRRK2) orchestrates this defence response by scaffolding a complex between RAB32 and aconitate decarboxylase 1, which synthesizes itaconate from mitochondrial precursors. Itaconate delivery to Salmonella-containing vacuoles was impaired and Salmonella replication increased in LRRK2-deficient cells. Loss of LRRK2 also restored virulence of a Salmonella mutant defective in neutralizing this RAB32-dependent host defence pathway in mice. Cryo-electron tomography revealed tether formation between Salmonella-containing vacuoles and host mitochondria upon Salmonella infection, which was significantly impaired in LRRK2-deficient cells. This positions LRRK2 centrally within a host defence mechanism, which may have favoured selection of a common familial Parkinson's disease mutant allele in the human population.

Information about immune cell proportions and tumor stage improves the prediction of recurrence in patients with colorectal cancer.

Predicting cancer recurrence is essential to improving the clinical outcomes of patients with colorectal cancer (CRC). Although tumor stage information has been used as a guideline to predict CRC recurrence, patients with the same stage show different clinical outcomes. Therefore, there is a need to develop a method to identify additional features for CRC recurrence prediction. Here, we developed a network-integrated multiomics (NIMO) approach to select appropriate transcriptome signatures for better CRC recurrence prediction by comparing the methylation signatures of immune cells. We validated the performance of the CRC recurrence prediction based on two independent retrospective cohorts of 114 and 110 patients. Moreover, to confirm that the prediction was improved, we used both NIMO-based immune cell proportions and TNM (tumor, node, metastasis) stage data. This work demonstrates the importance of (1) using both immune cell composition and TNM stage data and (2) identifying robust immune cell marker genes to improve CRC recurrence prediction.

DNA methylation biomarkers distinguishing early-stage prostate cancer from benign prostatic hyperplasia.

Background: DNA methylation markers are considered robust diagnostic features in various cancer types, as epigenetic marks are commonly altered during cancer progression. Differentiation between benign prostatic hyperplasia (BPH) and early-stage prostate cancer (PCa) is clinically difficult, relying on the information of the patient's symptoms or levels of prostate-specific antigen. Methods: A total of 42 PCa patients and 11 BPH patients were recruited. Genomic DNA was purified from tissues and used for the library preparation of the target-enriched methylome with enzymatic conversion and a Twist 85 Mbp EM-seq panel. Paired-end sequencing (150 bp) was performed using NovaSeq 6000 or NextSeq 550. After quality control, including adapter trimming and de-duplication of raw sequencing data, differential methylation patterns were analyzed between the BPH and PCa groups. Results: We report DNA methylation patterns existing between BPH and PCa. The major finding is that broad hypermethylation occurred at genic loci in PCa tissues as compared to the BPH. Gene ontology analysis suggested that hypermethylation of genic loci involved in chromatin and transcriptional regulation is involved in cancer progression. We also compared PCa tissues with high Gleason scores to tissues with low Gleason scores. The high-Gleason PCa tissues showed hundreds of focal differentially methylated CpG sites corresponding to genes functioning in cancer cell proliferation or metastasis. This suggests that dissecting early-to-advanced-grade cancer stages requires an in-depth analysis of differential methylation at the single CpG site level. Conclusions: Our study reports that enzymatic methylome sequencing data can be used to distinguish PCa from BPH and advanced PCa from early-stage PCa. The stage-specific methylation patterns in this study will be valuable resources for diagnostic purposes as well as further development of liquid biopsy approaches for the early detection of PCa.

Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients.

BACKGROUND: Previous investigations of transcriptomic signatures of cancer patient survival and post-therapy relapse have focused on tumor tissue. In contrast, here we show that in colorectal cancer (CRC) transcriptomes derived from normal tissues adjacent to tumors (NATs) are better predictors of relapse. RESULTS: Using the transcriptomes of paired tumor and NAT specimens from 80 Korean CRC patients retrospectively determined to be in recurrence or nonrecurrence states, we found that, when comparing recurrent with nonrecurrent samples, NATs exhibit a greater number of differentially expressed genes (DEGs) than tumors. Training two prognostic elastic net-based machine learning models-NAT-based and tumor-based in our Samsung Medical Center (SMC) cohort, we found that NAT-based model performed better in predicting the survival when the model was applied to the tumor-derived transcriptomes of an independent cohort of 450 COAD patients in TCGA. Furthermore, compositions of tumor-infiltrating immune cells in NATs were found to have better prognostic capability than in tumors. We also confirmed through Cox regression analysis that in both SMC-CRC as well as in TCGA-COAD cohorts, a greater proportion of genes exhibited significant hazard ratio when NAT-derived transcriptome was used compared to when tumor-derived transcriptome was used. CONCLUSIONS: Taken together, our results strongly suggest that NAT-derived transcriptomes and immune cell composition of CRC are better predictors of patient survival and tumor recurrence than the primary tumor.

Corrigendum: Elevated IFNA1 and suppressed IL12p40 associated with persistent hyperinflammation in COVID-19 pneumonia.

[This corrects the article DOI: 10.3389/fimmu.2023.1101808.].

Elevated IFNA1 and suppressed IL12p40 associated with persistent hyperinflammation in COVID-19 pneumonia.

Introduction: Despite of massive endeavors to characterize inflammation in COVID-19 patients, the core network of inflammatory mediators responsible for severe pneumonia stillremain remains elusive. Methods: Here, we performed quantitative and kinetic analysis of 191 inflammatory factors in 955 plasma samples from 80 normal controls (sample n = 80) and 347 confirmed COVID-19 pneumonia patients (sample n = 875), including 8 deceased patients. Results: Differential expression analysis showed that 76% of plasmaproteins (145 factors) were upregulated in severe COVID-19 patients comparedwith moderate patients, confirming overt inflammatory responses in severe COVID-19 pneumonia patients. Global correlation analysis of the plasma factorsrevealed two core inflammatory modules, core I and II, comprising mainly myeloid cell and lymphoid cell compartments, respectively, with enhanced impact in a severity-dependent manner. We observed elevated IFNA1 and suppressed IL12p40, presenting a robust inverse correlation in severe patients, which was strongly associated with persistent hyperinflammation in 8.3% of moderate pneumonia patients and 59.4% of severe patients. Discussion: Aberrant persistence of pulmonary and systemic inflammation might be associated with long COVID-19 sequelae. Our comprehensive analysis of inflammatory mediators in plasmarevealed the complexity of pneumonic inflammation in COVID-19 patients anddefined critical modules responsible for severe pneumonic progression.

Circulating Tumor DNA-Based Genotyping and Monitoring for Predicting Disease Relapses of Patients with Peripheral T-Cell Lymphomas.

PURPOSE: Plasma circulating tumor DNA (ctDNA) could reflect the genetic alterations present in tumor tissues. However, there is little information about the clinical relevance of cell-free DNA genotyping in peripheral T-cell lymphoma (PTCL). MATERIALS AND METHODS: After targeted sequencing plasma cell-free DNA of patients with various subtypes of PTCL (n=94), we analyzed the mutation profiles of plasma ctDNA samples and their predictive value of dynamic ctDNA monitoring for treatment outcomes. RESULTS: Plasma ctDNA mutations were detected in 53 patients (56%, 53/94), and the detection rate of somatic mutations was highest in angioimmunoblastic T-cell lymphoma (24/31, 77%) and PTCL, not otherwise specified (18/29, 62.1%). Somatic mutations were detected in 51 of 66 genes that were sequenced, including the following top 10 ranked genes: RHOA, CREBBP, KMT2D, TP53, IDH2, ALK, MEF2B, SOCS1, CARD11, and KRAS. In the longitudinal assessment of ctDNA mutation, the difference in ctDNA mutation volume after treatment showed a significant correlation with disease relapse or progression. Thus, a ≥ 1.5-log decrease in genome equivalent (GE) between baseline and the end of treatment showed a significant association with better survival outcomes than a < 1.5-log decrease in GE. CONCLUSION: Our results suggest the clinical relevance of plasma ctDNA analysis in patients with PTCL. However, our findings should be validated by a subsequent study with a larger study population and using a broader gene panel.

The impact of COVID-19 on the economic performance of Wuhan, China (2019-2021).

This paper attempts to evaluate the impact of massive infectious and contagious diseases and its final impact on the economic performance anywhere and anytime. We are considering to evaluate the case of Wuhan, China. We are taking in consideration the case of COVID-19 to be evaluated under a domestic, national, and international level impact. In this paper, we also propose a new simulator to evaluate the impact of massive infections and contagious diseases on the economic performance subsequently. This simulator is entitled "The Impact of Pandemics on the Economic Performance Simulator (IPEP-Simulator)" Hence, this simulator tries to show a macro and micro analysis with different possible scenarios simultaneously. Finally, the IPEP-Simulator was applied to the case of Wuhan-China respectively.

Determinants of the Elderly Share of Population: A Cross-Country Empirical Analysis.

COVID-19 has had a disproportionate impact on the elderly, who are over-represented among those who suffered severe illness or death. The obvious implication is that the share of the elderly in the population significantly affects the impact of COVID-19 on the overall health of a country. More generally, the elderly share has far-reaching economic and social ramifications. In this paper, we perform empirical analysis of cross-country data from 1970 to 2018 to identify the determinants of the share of the elderly-i.e., those aged 65 and over-in a country's population. We find that the quality of health care, life expectancy, and female labor participation increases the elderly share while higher fertility and female education attainment lower the elderly share. In addition, we find that the share is higher for high income countries and countries in Europe and Central Asia.

Deconvolution of bulk tumors into distinct immune cell states predicts colorectal cancer recurrence.

Predicting colorectal cancer recurrence after tumor resection is crucial because it promotes the administration of proper subsequent treatment or management to improve the clinical outcomes of patients. Several clinical or molecular factors, including tumor stage, metastasis, and microsatellite instability status, have been used to assess the risk of recurrence, although their predictive ability is limited. Here, we predicted colorectal cancer recurrence based on cellular deconvolution of bulk tumors into two distinct immune cell states: cancer-associated (tumor-infiltrating immune cell-like) and noncancer-associated (peripheral blood mononuclear cell-like). Prediction model performed significantly better when immune cells were deconvoluted into two states rather than a single state, suggesting that the difference in cancer recurrence was better explained by distinct states of immune cells. It indicates the importance of distinguishing immune cell states using cellular deconvolution to improve the prediction of colorectal cancer recurrence.

Developmental Transitions Coordinate Assembly of the Coxiella burnetii Dot/Icm Type IV Secretion System.

Coxiella burnetii is an obligate intracellular bacterial pathogen that has evolved a unique biphasic developmental cycle. The infectious form of C. burnetii is the dormant small cell variant (SCV), which transitions to a metabolically active large cell variant (LCV) that replicates inside the lysosome-derived host vacuole. A Dot/Icm type IV secretion system (T4SS), which can deliver over 100 effector proteins to host cells, is essential for the biogenesis of the vacuole and intracellular replication. How the distinct C. burnetii life cycle impacts the assembly and function of the Dot/Icm T4SS has remained unknown. Here, we combine advanced cryo-focused ion beam (cryo-FIB) milling and cryo-electron tomography (cryo-ET) imaging to visualize all developmental transitions and the assembly of the Dot/Icm T4SS in situ. Importantly, assembled Dot/Icm machines were not present in the infectious SCV. The appearance of the assembled Dot/Icm machine correlated with the transition of the SCV to the LCV intracellularly. Furthermore, temporal characterization of C. burnetii morphological changes revealed regions of the inner membrane that invaginate to form tightly packed stacks during the LCV-to-SCV transition at late stages of infection, which may enable the SCV-to-LCV transition that occurs upon infection of a new host cell. Overall, these data establish how C. burnetii developmental transitions control critical bacterial processes to promote intracellular replication and transmission.

Super-Linear-Threshold-Switching Selector with Multiple Jar-Shaped Cu-Filaments in the Amorphous Ge3 Se7 Resistive Switching Layer in a Cross-Point Synaptic Memristor Array.

The learning and inference efficiencies of an artificial neural network represented by a cross-point synaptic memristor array can be achieved using a selector, with high selectivity (Ion /Ioff ) and sufficient death region, stacked vertically on a synaptic memristor. This can prevent a sneak current in the memristor array. A selector with multiple jar-shaped conductive Cu filaments in the resistive switching layer is precisely fabricated by designing the Cu ion concentration depth profile of the CuGeSe layer as a filament source, TiN diffusion barrier layer, and Ge3 Se7 switching layer. The selector performs super-linear-threshold-switching with a selectivity of > 107 , death region of -0.70-0.65 V, holding time of 300 ns, switching speed of 25 ns, and endurance cycle of > 106 . In addition, the mechanism of switching is proven by the formation of conductive Cu filaments between the CuGeSe and Ge3 Se7 layers under a positive bias on the top Pt electrode and an automatic rupture of the filaments after the holding time. Particularly, a spiking deep neural network using the designed one-selector-one-memory cross-point array improves the Modified National Institute of Standards and Technology classification accuracy by ≈3.8% by eliminating the sneak current in the cross-point array during the inference process.