Human cytomegalovirus infection impairs neural differentiation via repressing sterol regulatory element binding protein 2-mediated cholesterol biosynthesis.

Congenital human cytomegalovirus (HCMV) infection is a major cause of abnormalities and disorders in the central nervous system (CNS) and/or the peripheral nervous system (PNS). However, the complete pathogenesis of neural differentiation disorders caused by HCMV infection remains to be fully elucidated. Stem cells from human exfoliated deciduous teeth (SHEDs) are mesenchymal stem cells (MSCs) with a high proliferation and neurogenic differentiation capacity. Since SHEDs originate from the neural crest of the early embryonic ectoderm, SHEDs were hypothesized to serve as a promising cell line for investigating the pathogenesis of neural differentiation disorders in the PNS caused by congenital HCMV infection. In this work, SHEDs were demonstrated to be fully permissive to HCMV infection and the virus was able to complete its life cycle in SHEDs. Under neurogenic inductive conditions, HCMV infection of SHEDs caused an abnormal neural morphology. The expression of stem/neural cell markers was also disturbed by HCMV infection. The impairment of neural differentiation was mainly due to a reduction of intracellular cholesterol levels caused by HCMV infection. Sterol regulatory element binding protein-2 (SREBP2) is a critical transcription regulator that guides cholesterol synthesis. HCMV infection was shown to hinder the migration of SREBP2 into nucleus and resulted in perinuclear aggregations of SREBP2 during neural differentiation. Our findings provide new insights into the prevention and treatment of nervous system diseases caused by congenital HCMV infection.

Integration of protein sequence and protein-protein interaction data by hypergraph learning to identify novel protein complexes.

Protein-protein interactions (PPIs) are the basis of many important biological processes, with protein complexes being the key forms implementing these interactions. Understanding protein complexes and their functions is critical for elucidating mechanisms of life processes, disease diagnosis and treatment and drug development. However, experimental methods for identifying protein complexes have many limitations. Therefore, it is necessary to use computational methods to predict protein complexes. Protein sequences can indicate the structure and biological functions of proteins, while also determining their binding abilities with other proteins, influencing the formation of protein complexes. Integrating these characteristics to predict protein complexes is very promising, but currently there is no effective framework that can utilize both protein sequence and PPI network topology for complex prediction. To address this challenge, we have developed HyperGraphComplex, a method based on hypergraph variational autoencoder that can capture expressive features from protein sequences without feature engineering, while also considering topological properties in PPI networks, to predict protein complexes. Experiment results demonstrated that HyperGraphComplex achieves satisfactory predictive performance when compared with state-of-art methods. Further bioinformatics analysis shows that the predicted protein complexes have similar attributes to known ones. Moreover, case studies corroborated the remarkable predictive capability of our model in identifying protein complexes, including 3 that were not only experimentally validated by recent studies but also exhibited high-confidence structural predictions from AlphaFold-Multimer. We believe that the HyperGraphComplex algorithm and our provided proteome-wide high-confidence protein complex prediction dataset will help elucidate how proteins regulate cellular processes in the form of complexes, and facilitate disease diagnosis and treatment and drug development. Source codes are available at https://github.com/LiDlab/HyperGraphComplex.

The quality of life of patients with chronic obstructive pulmonary disease: a bibliometric analysis.

Background: The quality of life (QOL) of patients with chronic obstructive pulmonary disease (COPD) is garnering increasing attention. However, faced with thousands of relevant clinical literature, it is becoming increasingly difficult for researchers and institutions to identify impactful research. Bibliometrics can help researchers quickly and methodically analyze the impact and hot trends of clinical research, strengthen teamwork, and solve related challenges. Therefore, we used bibliometrics to analyze and visualize data on the QOL of patients with COPD over the past 31 years to understand the key authors, research areas, and future trends. Methods: We searched the Web of Science Core Collection for literature published since the establishment of the database. The main subject terms used were "chronic obstructive pulmonary disease", "quality of life" and their different combinations. Articles were selected and exported in plain text format along with citation information. Bibliometric analysis and data visualization were performed using the R package "bibliometrix" and by incorporating statistical indicators such as the number of publications, citations and outputs of core authors, author collaborations, major journals, major research countries and collaborations, and key research themes. Results: The bibliometric analysis included 9,219 articles. Document type is unlimited. All publications were published between 1992 and 2022, and the number of published articles increased consistently each year over the past decade, with periodic fluctuations. The European Respiratory Journal and the International Journal of Chronic Obstructive Pulmonary Disease emerged as the most frequently cited journals within this domain. Key authors contributing to this field include Wedzicha JA, Jones PW, Singh D, Holland AE, and Wouters EFM. The United States and the United Kingdom exhibited a high volume of publications, high citation rates, and relatively intense international collaboration in related areas, followed by China, Spain, Canada, and Australia in these metrics. Notably, prominent topics within this field included emphysema, pulmonary rehabilitation, dyspnea, acute exacerbation, living status, and mortality, among others. Future research in this field will focus on microorganisms, particulate matter, family rehabilitation, and Tai Chi. Conclusions: This bibliometric analysis highlights the growing importance of QOL research in the field of COPD, which can inform clinicians, researchers, and policymakers to prioritize areas for future investigation in order to develop comprehensive, patient-centered strategies. At the same time, it is suggested that researchers should pay more attention to the core authors, strengthen international collaboration and team exchanges, actively explore characteristic clinical featured treatment measures such as Tai Chi and family rehabilitation, carry out clinical research on the integration of traditional Chinese and Western medicine and self-management, focus more on the QOL, mental health and economic and social burden of patients, and ultimately enhance the well-being of individuals with chronic respiratory diseases.

Spatiotemporal orchestration of macrophage activation trajectories by Vγ4 T cells during skin wound healing.

Dysregulated macrophage polarization from pro-inflammatory M1 to anti-inflammatory M2 phenotypes underlies impaired cutaneous wound healing. This study reveals Vγ4+ γδ T cells spatiotemporally calibrate macrophage trajectories during skin repair via sophisticated interferon-γ (IFN-γ) conditioning across multiple interconnected tissues. Locally within wound beds, infiltrating Vγ4+ γδ T cells directly potentiate M1 activation and suppress M2 polarization thereby prolonging local inflammation. In draining lymph nodes, infiltrated Vγ4+ γδ T cells expand populations of IFN-γ-competent lymphocytes which disseminate systemically and infiltrate into wound tissues, further enforcing M1 macrophages programming. Moreover, Vγ4+γδ T cells flushed into bone marrow stimulate increased IFN-γ production, which elevates the output of pro-inflammatory Ly6C+monocytes. Mobilization of these monocytes continually replenishes the M1 macrophage pool in wounds, preventing phenotypic conversion to M2 activation. Thus, multi-axis coordination of macrophage activation trajectories by trafficking Vγ4+ γδ T cells provides a sophisticated immunological mechanism regulating inflammation timing and resolution during skin repair.

Probiotic characterization of Bacillus smithii: Research advances, concerns, and prospective trends.

Bacillus smithii is a thermophilic Bacillus that can be isolated from white wine, hot spring soil, high-temperature compost, and coffee grounds, with various biofunctions and wide applications. It is resistant to both gastric acid and high temperature, which makes it easier to perform probiotic effects than traditional commercial probiotics, so it can maintain good vitality during food processing and has great application prospects. This paper starts with the taxonomy and genetics and focuses on aspects, including genetic transformation, functional enzyme production, waste utilization, and application in the field of food science as a potential probiotic. According to available studies during the past 30 years, we considered that B. smithii is a novel class of microorganisms with a wide range of functional enzymes such as hydrolytic enzymes and hydrolases, as well as resistance to pathogenic bacteria. It is available in waste degradation, organic fertilizer production, the feed and chemical industries, the pharmaceutical sector, and food fortification. Moreover, B. smithii has great potentials for applications in the food industry, as it presents high resistance to the technological processes that guarantee its health benefits. It is also necessary to systematically evaluate the safety, flavor, and texture of B. smithii and explore its biological mechanism of action, which is of great value for further application in multiple fields, especially in food and medicine.

Novel porphyrin derivative containing cations as new photodynamic antimicrobial agent with high efficiency.

Bacterial infections from chronic wounds affect about 175 million people each year and are a significant clinical problem. Through the integration of photodynamic therapy (PDT) and chemotherapy, a new photosensitizer consisting of ammonium salt N,N-bis-(2-hydroxyethyl)-N-(6-(4-(10,15,20-trimesitylporphyrin-5-yl) phenoxy) hexane)-N-methanaminium bromide, TMP(+) was successfully synthesized with a total reaction yield of 10%. The novel photosensitizer consists of two parts, a porphyrin photosensitizer part and a quaternary ammonium salt part, to achieve the synergistic effect of photodynamic and chemical antibacterial activity. With the increase of TMP(+) concentration, the diameter of the PCT fiber membranes (POL/COL/TMP(+); POL, polycaprolactone; COL, collagen) gradually increased, which was caused by the charge of the quaternary ammonium salt. At the same time, the antibacterial properties were gradually improved. We finally selected the PCT 0.5% group for the antibacterial experiment, with excellent performance in fiber uniformity, hydrophobicity and biosafety. The antibacterial experiment showed that the modified porphyrin TMP(+) had a better antibacterial effect than others. In vivo chronic wound healing experiments proved that the antibacterial and anti-inflammatory effect of the PCTL group was the best, further confirmed by H&E histological analysis, immunofluorescence and immunohistochemistry mechanism experiments. This research lays the foundation for the manufacture of novel molecules that combine chemical and photodynamic strategies.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment.

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins.

Traditional Chinese medicine (TCM) is increasingly recognized and utilized worldwide. However, the complex ingredients of TCM and their interactions with the human body make elucidating molecular mechanisms challenging, which greatly hinders the modernization of TCM. In 2016, we developed BATMAN-TCM 1.0, which is an integrated database of TCM ingredient-target protein interaction (TTI) for pharmacology research. Here, to address the growing need for a higher coverage TTI dataset, and using omics data to screen active TCM ingredients or herbs for complex disease treatment, we updated BATMAN-TCM to version 2.0 (http://bionet.ncpsb.org.cn/batman-tcm/). Using the same protocol as version 1.0, we collected 17 068 known TTIs by manual curation (with a 62.3-fold increase), and predicted ∼2.3 million high-confidence TTIs. In addition, we incorporated three new features into the updated version: (i) it enables simultaneous exploration of the target of TCM ingredient for pharmacology research and TCM ingredients binding to target proteins for drug discovery; (ii) it has significantly expanded TTI coverage; and (iii) the website was redesigned for better user experience and higher speed. We believe that BATMAN-TCM 2.0, as a discovery repository, will contribute to the study of TCM molecular mechanisms and the development of new drugs for complex diseases.

Ferroelectric-Gated All 2D Field-Effect Transistors with Sub-60 mV/dec Subthreshold Swing.

With the continuous scaling down of the modern integrated circuits, conventional metal-oxide-semiconductor field effect transistors are becoming inefficient due to various nonideal effects such as enhanced short-channel effects. Recently, emerging two-dimensional (2D) ferroelectrics have demonstrated their ability to maintain ferroelectricity at the nanoscale and have shown superior properties compared to three-dimensional ferroelectrics. Here, we report a ferroelectric field effect transistor composed of all 2D van der Waals (vdWs) heterostructures and provide a comprehensive study of the modulation of ferroelectric polarization on the carrier transport properties. Remarkably, the ferroelectric polarization allowed for achieving an ultralow subthreshold swing of just 26 mV/dec and a high carrier mobility of up to 72.3 cm2/(V s) at a smaller drain voltage of 10 mV. These impressive characteristics offer new insights into evaluating the regulatory effect of ferroelectric polarization on the electrical properties of all 2D vdWs heterostructures.

DCABM-TCM: A Database of Constituents Absorbed into the Blood and Metabolites of Traditional Chinese Medicine.

Traditional Chinese medicine (TCM) not only maintains the health of Asian people but also provides a great resource of active natural products for modern drug development. Herein, we developed a Database of Constituents Absorbed into the Blood and Metabolites of TCM (DCABM-TCM), the first database systematically collecting blood constituents of TCM prescriptions and herbs, including prototypes and metabolites experimentally detected in the blood, together with the corresponding detailed detection conditions through manual literature mining. The DCABM-TCM has collected 1816 blood constituents with chemical structures of 192 prescriptions and 194 herbs and integrated their related annotations, including physicochemical, absorption, distribution, metabolism, excretion, and toxicity properties, and associated targets, pathways, and diseases. Furthermore, the DCABM-TCM supported two blood constituent-based analysis functions, the network pharmacology analysis for TCM molecular mechanism elucidation, and the target/pathway/disease-based screening of candidate blood constituents, herbs, or prescriptions for TCM-based drug discovery. The DCABM-TCM is freely accessible at http://bionet.ncpsb.org.cn/dcabm-tcm/. The DCABM-TCM will contribute to the elucidation of effective constituents and molecular mechanism of TCMs and the discovery of TCM-derived drug-like compounds that are both bioactive and bioavailable.

Early-phase rotator training impairs tissue repair and functional recovery after spinal cord injury.

Spinal cord injury (SCI) is a devastating disorder that often results in severe sensorimotor function impairment with limited recovery of function. In recent years, rehabilitation training for spinal cord injury has gradually emerged, and some of them play an important role in the repair of spinal cord injury However, the optimal training regimen for SCI remains to be determined. In this study, we explore the effects of rotarod training (began at 7 days post-injury) on the recovery of motor function after SCI, as well as its possible repair mechanism from the aspects of function and histopathological changes, the behaviors of specific trophic factors and cytokines, and the expression profile of specific genes. Multiple functional assessments showed that rotarod training initiated at 7 days post-injury is unsuitable for promoting neuro-electrophysiological improvement and trunk stability, but impaired functional coordination and motor recovery. In addition, rotarod training has negative effects on spinal cord repair after SCI, which is manifested as an increase of lesion area, a decrease in neuronal viability, a deterioration in immuno-microenvironment and remyelination, a significant reduction in the expression of trophic factors and an increase in the expression of pro-inflammatory factors. RNA sequencing suggested that the genes associated with angiogenesis and synaptogenesis were significantly downregulated and the PI3K-AKT pathway was inhibited, which was detrimental to spinal cord repair and impeded nerve regeneration. These results indicate that immediate rotarod training after SCI is currently unsuitable for rehabilitation in mice.

An integrated computational strategy to predict personalized cancer drug combinations by reversing drug resistance signatures.

Drug resistance currently poses the greatest barrier to cancer treatments. To overcome drug resistance, drug combination therapy has been proposed as a promising treatment strategy. Herein, we present Re-Sensitizing Drug Prediction (RSDP), a novel computational strategy, for predicting the personalized cancer drug combination A + B by reversing the resistance signature of drug A. The process integrates multiple biological features using a robust rank aggregation algorithm, including Connectivity Map, synthetic lethality, synthetic rescue, pathway, and drug target. Bioinformatics assessments revealed that RSDP achieved a relatively accurate prediction performance for identifying personalized combinational re-sensitizing drug B against cell line-specific intrinsic resistance, cell line-specific acquired resistance, and patient-specific intrinsic resistance to drug A. In addition, we developed the largest resource of cell line-specific cancer drug resistance signatures, including intrinsic and acquired resistance, as a byproduct of the proposed strategy. The findings indicate that personalized drug resistance signature reversal is a promising strategy for identifying personalized drug combinations, which may guide future clinical decisions regarding personalized medicine.

The mutual regulation between γδ T cells and macrophages during wound healing.

Macrophages are the main cells shaping the local microenvironment during wound healing. As the prime T cells in the skin, γδ T cells participate in regulating microenvironment construction, determining their mutual regulation helps to understand the mechanisms of wound healing, and explore innovative therapeutic options for wound repair. This review introduced their respective role in wound healing firstly, and then summarized the regulatory effect of γδ T cells on macrophages, including chemotaxis, polarization, apoptosis and pyroptosis. Lastly, the retrograde regulation on γδ T cells by macrophages was also discussed. The main purpose is to excavate novel interventions for treating wound, and provide new thought for further research.

DLP-printed GelMA-PMAA scaffold for bone regeneration through endochondral ossification.

Intramembranous ossification (IMO) and endochondral ossification (ECO) are two pathways of bone regeneration. The regeneration of most bone, such as limb bone, trunk bone, and skull base bone, mainly occurs in the form of endochondral ossification, which has also become one of the effective ways for bone tissue engineering. In this work, we prepared a well-structured and biocompatible methacrylated gelatin/polymethacrylic acid (GelMA/PMAA) hydrogel by digital light processing (DLP) printing technology, which could effectively chelate iron ions and continuously activate the hypoxia-inducible factor-1 alpha (HIF-1α) signaling pathway to promote the process of endochondral ossification and angiogenesis. The incorporation of PMAA endowed the hydrogel with remarkable viscoelasticity and high efficacy in chelation of iron ions, giving rise to the activation of HIF-1α signaling pathway, improving chondrogenic differentiation in the early stage, and facilitating vascularization in the later stage and bone remodeling. Therefore, the findings have significant implications on DLP printing technology of endochondral osteogenesis induced by the iron-chelating property of biological scaffold, which will provide an effective way in the development of novel bone regeneration.

Robust Variable Selection with Exponential Squared Loss for the Spatial Durbin Model.

With the continuous application of spatial dependent data in various fields, spatial econometric models have attracted more and more attention. In this paper, a robust variable selection method based on exponential squared loss and adaptive lasso is proposed for the spatial Durbin model. Under mild conditions, we establish the asymptotic and "Oracle" properties of the proposed estimator. However, in model solving, nonconvex and nondifferentiable programming problems bring challenges to solving algorithms. To solve this problem effectively, we design a BCD algorithm and give a DC decomposition of the exponential squared loss. Numerical simulation results show that the method is more robust and accurate than existing variable selection methods when noise is present. In addition, we also apply the model to the 1978 housing price dataset in the Baltimore area.

3D-Printed GelMA/PEGDA/F127DA Scaffolds for Bone Regeneration.

Tissue-engineered scaffolds are an effective method for the treatment of bone defects, and their structure and function are essential for bone regeneration. Digital light processing (DLP) printing technology has been widely used in bone tissue engineering (BTE) due to its high printing resolution and gentle printing process. As commonly used bioinks, synthetic polymers such as polyethylene glycol diacrylate (PEGDA) and Pluronic F127 diacrylate (F127DA) have satisfactory printability and mechanical properties but usually lack sufficient adhesion to cells and tissues. Here, a compound BTE scaffold based on PEGDA, F127DA, and gelatin methacrylate (GelMA) was successfully prepared using DLP printing technology. The scaffold not only facilitated the adhesion and proliferation of cells, but also effectively promoted the osteogenic differentiation of mesenchymal stem cells in an osteoinductive environment. Moreover, the bone tissue volume/total tissue volume (BV/TV) of the GelMA/PEGDA/F127DA (GPF) scaffold in vivo was 49.75 ± 8.50%, higher than the value of 37.10 ± 7.27% for the PEGDA/F127DA (PF) scaffold and 20.43 ± 2.08% for the blank group. Therefore, the GPF scaffold prepared using DLP printing technology provides a new approach to the treatment of bone defects.

Identification and characterization of human cytomegalovirus-encoded circular RNAs.

Circular RNA (circRNA) exists extensively and plays essential roles in serving as microRNA (miRNA) or protein sponges and protein scaffolding in many organisms. However, the profiles and potential functions of the virus-encoded circRNA, including human cytomegalovirus (HCMV)-encoded circular RNAs, remain unclear. In the present study, HCMV-encoded circRNAs profile in human embryonic lung fibroblasts (HELF) with lytic infection was investigated using RNA deep sequencing and bioinformatics analysis. In total, 629 HCMV-encoded circRNAs were identified with various expression patterns in our results. The full sequences and alternative splicings of circUS12, circUL55, and circUL89 were verified by reverse transcriptase-PCR (RT-PCR) with divergent primers followed and Sanger sequencing. Transcription of circUL89 was validated by Northern blot. The HCMV-encoded circRNA-miRNA network analyses revealed the potential function of HCMV-encoded circRNAs during HCMV infection in HELFs. Collectively, HCMV infection deduced abundant HCMV-associated circRNAs during infection, and the HCMV-encoded circRNAs might play important roles in benefiting HCMV infection.

CPDR: An R Package of Recommending Personalized Drugs for Cancer Patients by Reversing the Individual's Disease-Related Signature.

Due to cancer heterogeneity, only some patients can benefit from drug therapy. The personalized drug usage is important for improving the treatment response rate of cancer patients. The value of the transcriptome of patients has been recently demonstrated in guiding personalized drug use, and the Connectivity Map (CMAP) is a reliable computational approach for drug recommendation. However, there is still no personalized drug recommendation tool based on transcriptomic profiles of patients and CMAP. To fill this gap, here, we proposed such a feasible workflow and a user-friendly R package-Cancer-Personalized Drug Recommendation (CPDR). CPDR has three features. 1) It identifies the individual disease signature by using the patient subgroup with transcriptomic profiles similar to those of the input patient. 2) Transcriptomic profile purification is supported for the subgroup with high infiltration of non-cancerous cells. 3) It supports in silico drug efficacy assessment using drug sensitivity data on cancer cell lines. We demonstrated the workflow of CPDR with the aid of a colorectal cancer dataset from GEO and performed the in silico validation of drug efficacy. We further assessed the performance of CPDR by a pancreatic cancer dataset with clinical response to gemcitabine. The results showed that CPDR can recommend promising therapeutic agents for the individual patient. The CPDR R package is available at https://github.com/AllenSpike/CPDR.