Miracle-dz/UniFAR-FTUs huggingface dataset:
VectorMOD: Method for Bottom-Up Proteomic Characterization of rAAV Capsid Post-Translational Modifications and Vector Impurities

Progress in recombinant AAV gene therapy product and process development has advanced our understanding of the basic biology of this critical delivery vector. The discovery of rAAV capsid post-translational modifications (PTMs) has spurred interest in the field for detailed rAAV-specific methods for vector lot characterization by mass spectrometry given the unique challenges presented by this viral macromolecular complex. Recent concerns regarding immunogenic responses to systemically administered rAAV at high doses has highlighted the need for investigators to catalog and track potentially immunogenic vector lot components including capsid PTMs and PTMs on host cell protein impurities. Here we present a simple step-by-step guide for academic rAAV laboratories and Chemistry, Manufacturing and Control (CMC) groups in industry to perform an in-house or outsourced bottom-up mass spectrometry workflow to characterize capsid PTMs and process impurities.
A Nomogram Combining a Four-Gene Biomarker and Clinical Factors for Predicting Survival of Melanoma

Background Currently there is no effective prognostic indicator for melanoma, the deadliest skin cancer. Thus, we aimed to develop and validate a nomogram predictive model for predicting survival of melanoma. Methods Four hundred forty-nine melanoma cases with RNA sequencing (RNA-seq) data from TCGA were randomly divided into the training set I (n = 224) and validation set I (n = 225), 210 melanoma cases with RNA-seq data from Lund cohort of Lund University (available in GSE65904) were used as an external test set. The prognostic gene biomarker was developed and validated based on the above three sets. The developed gene biomarker combined with clinical characteristics was used as variables to develop and validate a nomogram predictive model based on 379 patients with complete clinical data from TCGA (Among 470 cases, 91 cases with missing clinical data were excluded from the study), which were randomly divided into the training set II (n = 189) and validation set II (n = 190). Area under the curve (AUC), concordance index (C-index), calibration curve, and Kaplan-Meier estimate were used to assess predictive performance of the nomogram model. Results Four genes, i.e., CLEC7A, CLEC10A, HAPLN3, and HCP5 comprise an immune-related prognostic biomarker. The predictive performance of the biomarker was validated using tROC and log-rank test in the training set I (n = 224, 5-year AUC of 0.683), validation set I (n = 225, 5-year AUC of 0.644), and test set I (n = 210, 5-year AUC of 0.645). The biomarker was also significantly associated with improved survival in the training set (P < 0.01), validation set (P < 0.05), and test set (P < 0.001), respectively. In addition, a nomogram combing the four-gene biomarker and six clinical factors for predicting survival in melanoma was developed in the training set II (n = 189), and validated in the validation set II (n = 190), with a concordance index of 0.736 ± 0.041 and an AUC of 0.832 ± 0.071. Conclusion We developed and validated a nomogram predictive model combining a four-gene biomarker and six clinical factors for melanoma patients, which could facilitate risk stratification and treatment planning.
An Inductive Logistic Matrix Factorization Model for Predicting Drug-Metabolite Association With Vicus Regularization

Metabolites are closely related to human disease. The interaction between metabolites and drugs has drawn increasing attention in the field of pharmacomicrobiomics. However, only a small portion of the drug-metabolite interactions were experimentally observed due to the fact that experimental validation is labor-intensive, costly, and time-consuming. Although a few computational approaches have been proposed to predict latent associations for various bipartite networks, such as miRNA-disease, drug-target interaction networks, and so on, to our best knowledge the associations between drugs and metabolites have not been reported on a large scale. In this study, we propose a novel algorithm, namely inductive logistic matrix factorization (ILMF) to predict the latent associations between drugs and metabolites. Specifically, the proposed ILMF integrates drug–drug interaction, metabolite–metabolite interaction, and drug-metabolite interaction into this framework, to model the probability that a drug would interact with a metabolite. Moreover, we exploit inductive matrix completion to guide the learning of projection matrices U and V that depend on the low-dimensional feature representation matrices of drugs and metabolites: Fm and Fd. These two matrices can be obtained by fusing multiple data sources. Thus, FdU and FmV can be viewed as drug-specific and metabolite-specific latent representations, different from classical LMF. Furthermore, we utilize the Vicus spectral matrix that reveals the refined local geometrical structure inherent in the original data to encode the relationships between drugs and metabolites. Extensive experiments are conducted on a manually curated “DrugMetaboliteAtlas” dataset. The experimental results show that ILMF can achieve competitive performance compared with other state-of-the-art approaches, which demonstrates its effectiveness in predicting potential drug-metabolite associations.
A Non-canonical Wnt Signature Correlates With Lower Survival in Gastric Cancer

Genetic evidence suggests a role for the Wnt/β-catenin pathway in gastric cancer. However, Wnt5a, regarded as a prototypical non-canonical Wnt ligand, has also been extensively associated with this disease. Therefore, the roles of the Wnt signaling pathway in gastric cancer initiation and progression, and particularly the precise mechanisms by which the non-canonical Wnt pathway might promote the development and progression of gastric cancer, are not entirely well understood. This article analyzes publicly available gene and protein expression data and reveals the existence of a WNT5A/FZD2/FZD7/ROR2 signature, which correlates with tumor-infiltrating and mesenchymal cell marker expression. High expression of FZD7 and ROR2 correlates with a shared gene and protein expression profile, which in turn correlates with poor prognosis. In summary, the findings presented in this article provide an updated view of the relative contributions of the Wnt/β-catenin and non-canonical Wnt pathways in gastric cancer.
Mutated DNA Damage Repair Pathways Are Prognostic and Chemosensitivity Markers for Resected Colorectal Cancer Liver Metastases

Deficiency of the DNA damage repair (DDR) signaling pathways is potentially responsible for genetic instability and oncogenesis in tumors, including colorectal cancer. However, the correlations of mutated DDR signaling pathways to the prognosis of colorectal cancer liver metastasis (CRLM) after resection and other clinical applications have not been fully investigated. Here, to test the potential correlation of mutated DDR pathways with survival and pre-operative chemotherapy responses, tumor tissues from 146 patients with CRLM were collected for next-generation sequencing with a 620-gene panel, including 68 genes in 7 DDR pathways, and clinical data were collected accordingly. The analyses revealed that 137 of 146 (93.8%) patients had at least one mutation in the DDR pathways. Mutations in BER, FA, HRR and MMR pathways were significantly correlated with worse overall survival than the wild-types (P < 0.05), and co-mutated DDR pathways showed even more significant correlations (P < 0.01). The number of mutated DDR pathways was also proved an independent stratifying factor of overall survival by Cox multivariable analysis with other clinical factors and biomarkers (hazard ratio = 9.14; 95% confidence interval, 1.21–68.9; P = 0.032). Additionally, mutated FA and MMR pathways were positively and negatively correlated with the response of oxaliplatin-based pre-operative chemotherapy (P = 0.0095 and 0.048, respectively). Mutated DDR signaling pathways can predict pre-operative chemotherapy response and post-operative survival in CRLM patients.
Smyd1 Orchestrates Early Heart Development Through Positive and Negative Gene Regulation

SET and MYND domain-containing protein 1 (Smyd1) is a striated muscle-specific histone methyltransferase. Our previous work demonstrated that deletion of Smyd1 in either cardiomyocytes or the outflow tract (OFT) resulted in embryonic lethality at E9.5, with cardiac structural defects such as truncation of the OFT and right ventricle and impaired expansion and proliferation of the second heart field (SHF). The cardiac phenotype was accompanied by the downregulation of ISL LIM Homeobox 1 (Isl1) and upregulation of atrial natriuretic factor (ANF). However, the mechanisms of Smyd1 regulating Isl1 and ANF during embryonic heart development remain to be elucidated. Here, we employed various biochemical and molecular biological approaches including chromatin immunoprecipitation polymerase chain reaction (ChIP-PCR), pGL3 fluorescence reporter system, and co-immunoprecipitation (CoIP) and found that Smyd1 interacted with absent small homeotic-2-like protein (ASH2L) and activated the promoter of Isl1 by trimethylating H3K4. We also found that Smyd1 associated with HDAC to repress ANF expression using trichostatin A (TSA), a deacetylase inhibitor. In conclusion, Smyd1 participates in early heart development by upregulating the expression of Isl1 and downregulating the expression of ANF.
Psychological Differences Among Healthcare Workers of a Rehabilitation Institute During the COVID-19 Pandemic: A Two-Step Study

Introduction: Healthcare workers facing the threatening COVID-19 can experience severe difficulties. Despite the need to evaluate both the psychological distress and positive protective resources, brief and reliable assessment tools are lacking. Aim: Study 1 aimed at developing a new assessment tool to measure psychological distress and esteem in healthcare workers during the COVID-19 pandemic. Study 2 aimed to explore and compare the psychological reactions of healthcare workers of the COVID-19 and the non-COVID-19 wards. Methods: In Study 1, psychologists created 25 items based on their clinical experience. A preliminary qualitative evaluation selected the best 15 items for the new tool (CPI-HP) assessing the COVID-19 psychological impact with 2 scales: psychological distress and esteem. The CPI-HP was administered to 110 healthcare professionals to study its psychometric properties and the internal structure with exploratory graph analysis and confirmatory factor analysis. Study 2 compared two groups of healthcare professionals of the COVID-19 and non-COVID-19 departments. Results: In Study 1, the CPI-HP showed satisfying psychometric properties, and the two-factor structure was confirmed with good fit indices. In Study 2, the two groups of healthcare workers showed comparable levels of psychological distress and resilient coping, but the COVID-19 group displayed significantly higher esteem and appreciation of the experience. Discussion: All operators showed high psychological distress during the emergency, but the COVID-19 group reported higher resources, probably due to stronger group cohesion and greater esteem, perceived meaning, and own work value. Conclusion: Assessing the psychological distress and resources of healthcare professionals with specific tools is important. Psychological interventions should promote their psychological health.
Seven Glycolysis-Related Genes Predict the Prognosis of Patients With Pancreatic Cancer

Objectives To identify the key glycolysis-related genes (GRGs) in the occurrence and development of pancreatic ductal carcinoma (PDAC), and to construct a glycolysis-related gene model for predicting the prognosis of PDAC patients. Methodology Pancreatic ductal carcinoma (PDAC) data and that of normal individuals were downloaded from the TCGA database and Genotype-Tissue Expression database, respectively. GSEA analysis of glycolysis-related pathways was then performed on PDAC data to identify significantly enriched GRGs. The genes were combined with other patient’s clinical information and used to construct a glycolysis-related gene model using cox regression analysis. The model was further evaluated using data from the validation group. Mutations in the model genes were subsequently identified using the cBioPortal. In the same line, the expression levels of glycolysis related model genes in PDAC were analyzed and verified using immunohistochemical images. Model prediction for PDAC patients with different clinical characteristics was then done and the relationship between gene expression level, clinical stage and prognosis further discussed. Finally, a nomogram map of the predictive model was constructed to evaluate the prognosis of patients with PDAC. Results GSEA results of the training set revealed that genes in the training set were significantly related to glycolysis pathway and iconic glycolysis pathway. There were 108 differentially expressed GRGs. Among them, 29 GRGs were closely related to prognosis based on clinical survival time. Risk regression analysis further revealed that there were seven significantly expressed glycolysis related genes. The genes were subsequently used to construct a predictive model. The model had an AUC value of more than 0.85. It was also significantly correlated with survival time. Further expression analysis revealed that CDK1, DSC2, ERO1A, MET, PYGL, and SLC35A3 were highly expressed in PDAC and CHST12 was highly expressed in normal pancreatic tissues. These results were confirmed using immunohistochemistry images of normal and diseases cells. The model could effectively evaluate the prognosis of PDAC patients with different clinical characteristics. Conclusion The constructed glycolysis-related gene model effectively predicts the occurrence and development of PDAC. As such, it can be used as a prognostic marker to diagnose patients with PDAC.
Rearrangement Strategy for the Preparation of Polymers With π-Conjugated Structures

π-Conjugated polymers are usually prepared by polymerization only. In this perspective article, typical synthesis methods of conjugated polymers are briefly summarized, and a novel strategy for preparing conjugated polymers by rearrangement is proposed. During the metalation process, many conjugated structures were generated in polybutadiene by double bond migration. The effects of reaction time, temperature, and catalyst dosage on the product structure were investigated. Moreover, the structure of the products was confirmed by FTIR, 1H NMR, and 2D HSQC NMR spectra. Thus, a possible reaction mechanism was proposed, in which polybutadiene generates allylic carbanions in the presence of n-butyllithium, and then the double bonds migrate through the carbanions rearrangement to generate many conjugated structures in the backbone chain. The method shows promise in facile and low-cost synthesis of conjugated polymers without the need for precious metal catalysts.
Music Listening as a Strategy for Managing COVID-19 Stress in First-Year University Students

The COVID-19 pandemic brought rapid changes to travel, learning environments, work conditions, and social support, which caused stress for many University students. Research with young people has revealed music listening to be among their most effective strategies for coping with stress. As such, this survey of 402 first-year Australian University students (73.9% female, Mage = 19.6; 75% domestic and 25% international) examined the effectiveness of music listening during COVID-19 compared with other stress management strategies, whether music listening for stress management was related to well-being, and whether differences emerged between domestic and international students. We also asked participants to nominate a song that helped them to cope with COVID-19 stress and analyzed its features. Music listening was among the most effective stress coping strategies, and was as effective as exercise, sleep, and changing location. Effectiveness of music listening as a coping strategy was related to better well-being but not to level of COVID-19 related stress. Although international students experienced higher levels of COVID-19 stress than domestic students, well-being was comparable in the two cohorts. Nominated songs tended to be negative in valence and moderate in energy. No correlations were found between any self-report measure and the valence and energy of nominated coping songs. These findings suggest that although domestic and international students experienced different levels of stress resulting from COVID-19, music listening remained an effective strategy for both cohorts, regardless of the type of music they used for coping.
Polyene-Producing Streptomyces spp. From the Fungus-Growing Termite Macrotermes barneyi Exhibit High Inhibitory Activity Against the Antagonistic Fungus Xylaria

Fungus-growing termites are engaged in a tripartite mutualism with intestinal microbes and a monocultivar (Termitomyces sp.) in the fungus garden. The termites are often plagued by entomopathogen (Metarhizium anisopliae) and fungus garden is always threatened by competitors (Xylaria spp.). Here, we aim to understand the defensive role of intestinal microbes, the actinomycetes which were isolated from the gut of Macrotermes barneyi. We obtained 44 antifungal isolates, which showed moderate to strong inhibition to Xylaria sp. HPLC analysis indicated that different types of polyenes (tetraene, pentene, and heptaene) existed in the metabolites of 10 strong antifungal Streptomyces strains. Two pentene macrolides (pentamycin and 1′14-dihydroxyisochainin) were firstly purified from Streptomyces strain HF10, both exhibiting higher activity against Xylaria sp. and M. anisopliae than cultivar Termitomyces. Subsequently, tetraene and heptaene related gene disruption assay showed that the mutant strains lost the ability to produce corresponding polyenes, and they also had significantly decreased activities against Xylaria sp. and M. anisopliae compared to that of wild type strains. These results indicate that polyene-producing Streptomyces from the guts of M. barneyi have strong inhibition to competitor fungus and polyenes contribute to inhibitory effects on Xylaria sp.
Differential Transcriptomes and Methylomes of Trophoblast Stem Cells From Naturally-Fertilized and Somatic Cell Nuclear-Transferred Embryos

Trophoblast stem cells (TSCs) are critical to mammalian embryogenesis by providing the cell source of the placenta. TSCs can be derived from trophoblast cells. However, the efficiency of TSC derivation from somatic cell nuclear transfer (NT) blastocysts is low. The regulatory mechanisms underlying transcription dynamics and epigenetic landscape remodeling during TSC derivation remain elusive. Here, we derived TSCs from the blastocysts by natural fertilization (NF), NT, and a histone deacetylase inhibitor Scriptaid-treated NT (SNT). Profiling of the transcriptomes across the stages of TSC derivation revealed that fibroblast growth factor 4 (FGF4) treatment resulted in many differentially expressed genes (DEGs) at outgrowth and initiated transcription program for TSC formation. We identified 75 transcription factors (TFs) that are continuously upregulated during NF TSC derivation, whose transcription profiles can infer the time course of NF not NT TSC derivation. Most DEGs in NT outgrowth are rescued in SNT outgrowth. The correct time course of SNT TSC derivation is inferred accordingly. Moreover, these TFs comprise an interaction network important to TSC stemness. Profiling of DNA methylation dynamics showed an extremely low level before FGF4 treatment and gradual increases afterward. FGF4 treatment results in a distinct DNA methylation remodeling process committed to TSC formation. We further identified 1,293 CpG islands (CGIs) whose DNA methylation difference is more than 0.25 during NF TSC derivation. The majority of these CGIs become highly methylated upon FGF4 treatment and remain in high levels. This may create a barrier for lineage commitment to restrict embryonic development, and ensure TSC formation. There exist hundreds of aberrantly methylated CGIs during NT TSC derivation, most of which are corrected during SNT TSC derivation. More than half of the aberrantly methylated CGIs before NT TSC formation are inherited from the donor genome. In contrast, the aberrantly methylated CGIs upon TSC formation are mainly from the highly methylated CGIs induced by FGF4 treatment. Functional annotation indicates that the aberrantly highly methylated CGIs play a role in repressing placenta development genes, etc., related to post-implantation development and maintaining TSC pluripotency. Collectively, our findings provide novel insights into the transcription dynamics, DNA methylation remodeling, and the role of FGF4 during TSC derivation.
Small Conductance Ca2 +-Activated K+ (SK) Channel mRNA Expression in Human Atrial and Ventricular Tissue: Comparison Between Donor, Atrial Fibrillation and Heart Failure Tissue

In search of more efficacious and safe pharmacological treatments for atrial fibrillation (AF), atria-selective antiarrhythmic agents have been promoted that target ion channels principally expressed in the atria. This concept allows one to engage antiarrhythmic effects in atria, but spares the ventricles from potentially proarrhythmic side effects. It has been suggested that cardiac small conductance Ca2+-activated K+ (SK) channels may represent an atria-selective target in mammals including humans. However, there are conflicting data concerning the expression of SK channels in different stages of AF, and recent findings suggest that SK channels are upregulated in ventricular myocardium when patients develop heart failure. To address this issue, RNA-sequencing was performed to compare expression levels of three SK channels (KCNN1, KCNN2, and KCNN3) in human atrial and ventricular tissue samples from transplant donor hearts (no cardiac disease), and patients with cardiac disease in sinus rhythm or with AF. In addition, for control purposes expression levels of several genes known to be either chamber-selective or differentially expressed in AF and heart failure were determined. In atria, as compared to ventricle from transplant donor hearts, we confirmed higher expression of KCNN1 and KCNA5, and lower expression of KCNJ2, whereas KCNN2 and KCNN3 were statistically not differentially expressed. Overall expression of KCNN1 was low compared to KCNN2 and KCNN3. Comparing atrial tissue from patients with AF to sinus rhythm samples we saw downregulation of KCNN2 in AF, as previously reported. When comparing ventricular tissue from heart failure patients to non-diseased samples, we found significantly increased ventricular expression of KCNN3 in heart failure, as previously published. The other channels showed no significant difference in expression in either disease. Our results add weight to the view that SK channels are not likely to be an atria-selective target, especially in failing human hearts, and modulators of these channels may prove to have less utility in treating AF than hoped. Whether targeting SK1 holds potential remains to be elucidated.