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Abstract The AS04-adjuvanted human papillomavirus (HPV)16/18 vaccine, an L1-based vaccine, provides strong vaccine efficacy (VE) against vaccine-targeted type infections, and partial cross-protection to phylogenetically-related types, which may be affected by variant-level heterogeneity. We compared VE against incident HPV31, 33, 35, and 45 detections between lineages and SNPs in the L1 region among 2846 HPV-vaccinated and 5465 HPV-unvaccinated women through 11-years of follow-up in the Costa Rica HPV Vaccine Trial. VE was lower against HPV31-lineage-B (VE=60.7%;95%CI = 23.4%,82.8%) compared to HPV31-lineage-A (VE=94.3%;95%CI = 83.7%,100.0%) (VE-ratio = 0.64;95%CI = 0.25,0.90). Differential VE was observed at several lineage-associated HPV31-L1-SNPs, including a nonsynonymous substitution at position 6372 on the FG-loop, an important neutralization domain. For HPV35, the only SNP-level difference was at position 5939 on the DE-loop, with significant VE against nucleotide-G (VE=65.0%;95%CI = 28.0,87.8) but not for more the common nucleotide-A (VE=7.4%;95%CI = −34.1,36.7). Because of the known heterogeneity in precancer/cancer risk across cross-protected HPV genotype variants by race and region, our results of differential variant-level AS04-adjuvanted HPV16/18 vaccine efficacy has global health implications.

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that regulates genes of drug transporters and metabolic enzymes to detoxify small molecule xenobiotics. It has a complex role in cancer biology, influencing both the progression and suppression of tumors by modulating malignant properties of tumor cells and anti-tumor immunity, depending on the specific tumor type and developmental stage. This has led to the discovery and development of selective AhR modulators, including BAY 2416964 which is currently in clinical trials. To identify small molecule anticancer agents that might be combined with AhR antagonists for cancer therapy, a high-throughput combination screen was performed using multi-cell type tumor spheroids grown from malignant cells, endothelial cells, and mesenchymal stem cells. The AhR selective antagonists BAY 2416964, GNF351, and CH-223191 were tested individually and in combination with twenty-five small molecule anticancer agents. As single agents, BAY 2416964 and CH-223191 showed minimal activity, whereas GNF351 reduced the viability of some spheroid models at concentrations greater than 1 µM. The activity of most combinations aligned well with the single agent activity of the combined agent, without apparent contributions from the AhR antagonist. All three AhR antagonists sensitized tumor spheroids to TAK-243, an E1 ubiquitin-activating enzyme inhibitor. These combinations were active in spheroids containing bladder, breast, ovary, kidney, pancreas, colon, and lung tumor cell lines. The AhR antagonists also potentiated pevonedistat, a selective inhibitor of the NEDD8-activating enzyme E1 regulatory subunit, in several tumor spheroid models. In contrast, the AhR antagonists did not enhance the cytotoxicity of the proteasome inhibitor bortezomib.

Abstract The tumor microenvironment (TME) is a highly intricate milieu, comprising a multitude of components, including immune cells and stromal cells, that exert a profound influence on tumor initiation and progression. Within the TME, angiogenesis is predominantly orchestrated by endothelial cells (ECs), which foster the proliferation and metastasis of malignant cells. The interplay between tumor and immune cells with ECs is complex and can either bolster or hinder the immune system. Thus, a comprehensive understanding of the intricate crosstalk between ECs and immune cells is essential to advance the development of immunotherapeutic interventions. Despite recent progress, the underlying molecular mechanisms that govern the interplay between ECs and immune cells remain elusive. Nevertheless, the immunomodulatory function of ECs has emerged as a pivotal determinant of the immune response. In light of this, the study of the relationship between ECs and immune checkpoints has garnered considerable attention in the field of immunotherapy. By targeting specific molecular pathways and signaling molecules associated with ECs in the TME, novel immunotherapeutic strategies may be devised to enhance the efficacy of current treatments. In this vein, we sought to elucidate the relationship between ECs, immune cells, and immune checkpoints in the TME, with the ultimate goal of identifying novel therapeutic targets and charting new avenues for immunotherapy.

Vascular diseases are the leading cause of morbidity and mortality worldwide and are urgently in need of diagnostic biomarkers and therapeutic strategies. Circular RNAs (circRNAs) represent a unique class of RNAs characterized by a circular loop configuration and have recently been identified to possess a wide variety of biological functions. CircRNAs exhibit exceptional stability, tissue specificity, and are detectable in body fluids, thus holding promise as potential biomarkers. Their encoding function and stable gene expression also position circRNAs as an excellent alternative to gene therapy. Here, we briefly review the biogenesis, degradation, and functions of circRNAs. We summarize circRNAs discovered in major vascular diseases such as atherosclerosis and aneurysms, with a particular focus on molecular mechanisms of circRNAs identified in vascular endothelial cells and smooth muscle cells, in the hope to reveal new directions for mechanism, prognosis and therapeutic targets of vascular diseases.

ABSTRACT: Background: Epstein–Barr virus (EBV) infection contributes to cancers in a fraction of seropositive individuals, but much remains to be learned about variation in EBV-directed humoral immunity in cancer-free adults. Methods: A protein microarray was used to probe serum from 175 Taiwanese and 141 Northern European adults for immunoglobulin G (IgG) antibody responses to 115 different peptide sequences, representing protein segments or protein variants, from 45 EBV proteins. It was posited that this antibody-based approach could identify EBV peptide sequences representing immunodominant regions relevant for B-cell immunity. Results: Analyses of 45 EBV proteins with multiple protein segments or variants printed on the array identified eight EBV peptide sequences that appear to play a role in immunogenicity. This included: (1) three proteins with segments/regions associated with IgG reactivity (BALF5, LMP1, LMP2A); and (2) five proteins with sequence variants/amino acid changes associated with IgG reactivity (BDLF4, EBNA3A, EBNA3B, EBNA-LP, LF1). Conclusion: This examination of IgG antibody responses against 115 EBV peptide sequences in 316 cancer-free adults represents an important step toward identifying specific EBV protein sequences that play a role in generating B-cell immunity in humans.

There is a controversy over what causes the low robustness of some programs for predicting protein stability change upon mutation. Some researchers suggested that low-quality data and insufficiently informative features are the primary reasons, while others attributed the problem largely to a bias caused by data imbalance as there are more destabilizing mutations than stabilizing ones. In this study, a simple approach was developed to construct a balanced dataset that was then conjugated with a leave-one-protein-out approach to illustrate that the bias may not be the primary reason for poor performance. A balanced dataset with some seemly good conventional n-fold CV results should not be used as a proof that a model for predicting protein stability change upon mutations is robust. Thus, some of the existing algorithms need to be re-examined before any practical applications. Also, more emphasis should be put on obtaining high quality and quantity of data and features in future research.

Natural products remain a significant source of anticancer chemotherapeutics. The search for targeted drugs for cancer treatment includes consideration of natural products, which may provide new opportunities for antitumor cytotoxicity as single agents or in combination therapy. We examined the association of molecular genomic features in the well‐characterized NCI‐60 cancer cell line panel with in vitro response to treatment with 1302 small molecules which included natural products, semisynthetic natural product derivatives, and synthetic compounds based on a natural product pharmacophore from the Developmental Therapeutics Program of the US National Cancer Institute's database. These compounds were obtained from a variety of plant, marine, and microbial species. Molecular information utilized for the analysis included expression measures for 23059 annotated transcripts, lncRNAs, and miRNAs, and data on protein‐changing single nucleotide variants in 211 cancer‐related genes. We found associations of expression of multiple genes including SLFN11, CYP2J2, EPHX1, GPC1, ELF3, and MGMT involved in DNA damage repair, NOTCH family members, ABC and SLC transporters, and both mutations in tyrosine kinases and BRAF V600E with NCI‐60 responses to specific categories of natural products. Hierarchical clustering identified groups of natural products, which correlated with a specific mechanism of action. Specifically, several natural product clusters were associated with SLFN11 gene expression, suggesting that potential action of these compounds may involve DNA damage. The associations between gene expression or genome alterations of functionally relevant genes with the response of cancer cells to natural products provide new information about potential mechanisms of action of these identified clusters of compounds with potentially similar biological effects. This information will assist in future drug discovery and in design of new targeted cancer chemotherapy agents.

As a method of predicting the target's attention distribution, gaze estimation plays an important role in human-computer interaction. In this paper, we learn a 3D gaze estimator with adaptive weighted strategy to get the mapping from the complete images to the gaze vector. We select the both eyes, the complete face and their fusion features as the input of the regression model of gaze estimator. Considering that the different areas of the face have different contributions on the results of gaze estimation under free head movement, we design a new learning strategy for the regression net. To improve the efficiency of the regression model to a great extent, we propose a weighted network that can adjust the learning strategy of the regression net adaptively. Experimental results conducted on the MPIIGaze and EyeDiap datasets demonstrate that our method can achieve superior performance compared with other state-of-the-art 3D gaze estimation methods.

Problematic smartphone use (PSU) has been linked with stress. Higher levels of stress likely increased problematic smartphone use. We investigated relations between stress, fear of missing out, and problematic smartphone use. The aim of the current study was to analyze the mediating role of fear of missing out (FOMO) and smartphone use frequency (SUF) between stress and PSU. We surveyed a broad sample of 2,276 Chinese undergraduate students in July 2019, using the FOMO Scale, Smartphone Addiction Scale-Short Version, Smartphone Use Frequency Scale, and Depression Anxiety Stress Scale-21. The results showed that stress was associated with PSU severity. Gender differences were found in PSU severity. Furthermore, FOMO was positively associated with SUF and PSU severity. Structural equation modeling demonstrated that FOMO acted as a mediator between stress and PSU severity. FOMO and SUF acted as a chain of mediators between stress and PSU severity. SUF did not account for relations between stress and PSU severity. The study indicates that FOMO may be an important variable accounting for why some people with increased stress levels may overuse their smartphones.

Farnesoid X receptor (FXR, NR1H4) is a ligand-activated transcription factor, belonging to the nuclear receptor superfamily. FXR is highly expressed in the liver and is essential in regulating bile acid homeostasis. FXR deficiency is implicated in numerous liver diseases and mice with modulation of FXR have been used as animal models to study liver physiology and pathology. We have reported genome-wide binding of FXR in mice by chromatin immunoprecipitation - deep sequencing (ChIP-seq), with results indicating that FXR may be involved in regulating diverse pathways in liver. However, limited information exists for the functions of human FXR and the suitability of using murine models to study human FXR functions.In the current study, we performed ChIP-seq in primary human hepatocytes (PHHs) treated with a synthetic FXR agonist, GW4064 or DMSO control. In parallel, RNA deep sequencing (RNA-seq) and RNA microarray were performed for GW4064 or control treated PHHs and wild type mouse livers, respectively.ChIP-seq showed similar profiles of genome-wide FXR binding in humans and mice in terms of motif analysis and pathway prediction. However, RNA-seq and microarray showed more different transcriptome profiles between PHHs and mouse livers upon GW4064 treatment.In summary, we have established genome-wide human FXR binding and transcriptome profiles. These results will aid in determining the human FXR functions, as well as judging to what level the mouse models could be used to study human FXR functions.

The current study tests the hypothesis that peroxisome proliferator-activated receptor β (PPARβ) has a role in liver regeneration due to its effect in regulating energy homeostasis and cell proliferation. The role of PPARβ in liver regeneration was studied using two-third partial hepatectomy (PH) in Wild-type (WT) and PPARβ-null (KO) mice. In KO mice, liver regeneration was delayed and the number of Ki-67 positive cells reached the peak at 60 hr rather than at 36-48 hr after PH shown in WT mice. RNA-sequencing uncovered 1344 transcriptomes that were differentially expressed in regenerating WT and KO livers. About 70% of those differentially expressed genes involved in glycolysis and fatty acid synthesis pathways failed to induce during liver regeneration due to PPARβ deficiency. The delayed liver regeneration in KO mice was accompanied by lack of activation of phosphoinositide-dependent kinase 1 (PDK1)/Akt. In addition, cell proliferation-associated increase of genes encoding E2f transcription factor (E2f) 1-2 and E2f7-8 as well as their downstream target genes were not noted in KO livers 36-48 hr after PH. E2fs have dual roles in regulating metabolism and proliferation. Moreover, transient steatosis was only found in WT, but not in KO mice 36 hr after PH. These data suggested that PPARβ-regulated PDK1/Akt and E2f signaling that controls metabolism and proliferation is involved in the normal progression of liver regeneration.

Retinoid x receptor α (RXRα) is abundantly expressed in the liver and is essential for the function of other nuclear receptors. Using chromatin immunoprecipitation sequencing and mRNA profiling data generated from wild type and RXRα-null mouse livers, the current study identifies the bona-fide hepatic RXRα targets and biological pathways. In addition, based on binding and motif analysis, the molecular mechanism by which RXRα regulates hepatic genes is elucidated in a high-throughput manner.Close to 80% of hepatic expressed genes were bound by RXRα, while 16% were expressed in an RXRα-dependent manner. Motif analysis predicted direct repeat with a spacer of one nucleotide as the most prevalent RXRα binding site. Many of the 500 strongest binding motifs overlapped with the binding motif of specific protein 1. Biological functional analysis of RXRα-dependent genes revealed that hepatic RXRα deficiency mainly resulted in up-regulation of steroid and cholesterol biosynthesis-related genes and down-regulation of translation- as well as anti-apoptosis-related genes. Furthermore, RXRα bound to many genes that encode nuclear receptors and their cofactors suggesting the central role of RXRα in regulating nuclear receptor-mediated pathways.This study establishes the relationship between RXRα DNA binding and hepatic gene expression. RXRα binds extensively to the mouse genome. However, DNA binding does not necessarily affect the basal mRNA level. In addition to metabolism, RXRα dictates the expression of genes that regulate RNA processing, translation, and protein folding illustrating the novel roles of hepatic RXRα in post-transcriptional regulation.

Protein acidostability is a common problem in biopharmaceutical and other industries. However, it remains a great challenge to engineer proteins for enhanced acidostability because our knowledge of protein acidostabilization is still very limited. In this paper, we present a comparative study of proteins from bacteria with acidic (AP) and neutral cytoplasms (NP) using an integrated statistical and machine learning approach. We construct a set of 393 non-redundant AP-NP ortholog pairs and calculate a total of 889 sequence based features for these proteins. The pairwise alignments of these ortholog pairs are used to build a residue substitution propensity matrix between APs and NPs. We use Gini importance provided by the Random Forest algorithm to rank the relative importance of these features. A scoring function using the 10 most significant features is developed and optimized using a hill climbing algorithm. The accuracy of the score function is 86.01% in predicting AP-NP ortholog pairs and is 76.65% in predicting non-ortholog AP-NP pairs, suggesting that there are significant differences between APs and NPs which can be used to predict relative acidostability of proteins. The overall trends uncovered in the study can be used as general guidelines for designing acidostable proteins. To best of our knowledge, this work represents the first systematic comparative study of the acidostable proteins and their non-acidostable orthologs.

The ability to improve protein thermostability via protein engineering is of great scientific interest and also has significant practical value. In this report we present PROTS-RF, a robust model based on the Random Forest algorithm capable of predicting thermostability changes induced by not only single-, but also double- or multiple-point mutations. The model is built using 41 features including evolutionary information, secondary structure, solvent accessibility and a set of fragment-based features. It achieves accuracies of 0.799,0.782, 0.787, and areas under receiver operating characteristic (ROC) curves of 0.873, 0.868 and 0.862 for single-, double- and multiple- point mutation datasets, respectively. Contrary to previous suggestions, our results clearly demonstrate that a robust predictive model trained for predicting single point mutation induced thermostability changes can be capable of predicting double and multiple point mutations. It also shows high levels of robustness in the tests using hypothetical reverse mutations. We demonstrate that testing datasets created based on physical principles can be highly useful for testing the robustness of predictive models.