Integrative analysis of gene expression and DNA methylation to identify biomarkers of non-genital warts induced by low-risk human papillomaviruses infection

Background

Human papillomaviruses have been shown to dysregulate the gene expression and DNA methylation profiles of their host cells over the course of infection. However, there is a lack of information on the impact of low-risk HPV infection and wart formation on host cell's expression and methylation patterns. Therefore, the objective of this study is to analyse the genome and methylome of common warts using an integrative approach.

Methods

In the present study, gene expression (GSE136347) and methylation (GSE213888) datasets of common warts were obtained from the GEO database. Identification of the differentially expressed and differentially methylated genes was carried out using the RnBeads R package and the edgeR Bioconductor package. Next, functional annotation of the identified genes was obtained using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Network construction and analyses of the gene-gene, protein-protein, and signaling interactions of the differentially expressed and differentially methylated genes was performed using the GeneMANIA web interface, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the Signaling Network Open Resource 2.0 (SIGNOR 2.0), respectively. Lastly, significant hub genes were identified using the Cytoscape application CytoHubba.

Results

A total of 276 genes were identified as differentially expressed and differentially methylated in common warts, with 52% being upregulated and hypermethylated. Functional enrichment analysis identified extracellular components as the most enriched annotations, while network analyses identified ELN, ITGB1, TIMP1, MMP2, LGALS3, COL1A1 and ANPEP as significant hub genes.

Conclusions

To the best knowledge of the authors, this is the first integrative study to be carried out on non-genital warts induced by low-risk HPV types. Future studies are required to re-validate the findings in larger populations using alternative approaches.

Regulation of MEK inhibitor selumetinib sensitivity by AKT phosphorylation in the novel BRAF L525R mutant

BACKGROUND

Oncogenic mutations in BRAF genes are found in approximately 5-10% of colorectal cancers. The majority of BRAF mutations are located within exons 11-15 of the catalytic kinase domains, with BRAF V600E accounting for more than 80% of the observed BRAF mutations. Sensitivity to BRAF- and mitogen-activated protein kinase (MEK) inhibitors varies depending on BRAF mutations and tumor cell types. Previously, we newly identified, BRAF L525R-mutation, in the activation segment of the kinase in colorectal cancer patient. Here, we characterized the function of the BRAF L525R mutation.

METHODS

HEK293 cells harboring a BRAF mutation (V600E or L525R) were first characterized and then treated with cetuximab, dabrafenib, and selumetinib. Cell viability was measured using WST-1 assay and the expression of proteins involved in the extracellular signal-regulated kinase (ERK) and protein kinase B (AKT) signaling pathways was evaluated using western blot analysis.

RESULTS

The MEK inhibitor selumetinib effectively inhibited cell proliferation and ERK phosphorylation in BRAF L525R cells but not in BRAF V600E cells. Further studies revealed that AKT phosphorylation was reduced by selumetinib in BRAF L525R cells but not in BRAF V600E cells or selumetinib-resistant BRAF L525R cells. Moreover, the AKT inhibitor overcame the selumetinib resistance.

CONCLUSIONS

We established a model system harboring BRAF L525R using HEK293 cells. BRAF L525R constitutively activated ERK. AKT phosphorylation caused sensitivity and resistance to selumetinib. Our results suggest that a comprehensive network analysis may provide insights to identify effective therapies.

Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network

Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative 'multi-modality attributes' learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.

Human papillomavirus in oral squamous cell carcinoma using p16 and its co-relationship with cervical lymph node metastasis and clinicopathological parameters

Background

Due to the increasing prevalence of oral squamous cell carcinoma (OSCC) in Iran and especially in young people, this study aimed to identify human papillomavirus (HPV) using p16 in OSCC.

Materials and Methods

In this descriptive-analytical cross-sectional study, 40 samples from the archives of the Pathology Department of Kashani Hospital were selected by a definitive diagnosis of OSCC with neck dissection. Demographic information including age, gender, location, and size of the lesion was obtained. Samples were divided into two groups based on lymph node (LN) metastasis. Immunohistochemical staining was performed for p16. Data were entered into SPSS 24 software and statistically analyzed by t-test, ANOVA, and Spearman nonparametric test. P <0.05 was statistically significant.

Results

The mean age of patients was 59.7 ± 17.11 which in terms of age and gender there was no significant difference between the two groups including with and without cervical LN metastasis (P > 0.05). There was no significant difference between the two groups based on the grade of tumor, perninural invasion, tumor size and location (P > 0.05). The only significant difference between the two groups was based on lymphovascular invasion and disease stage (P < 0.05). The p16 expression also showed a significant difference between the two groups (P < 0.05).

Conclusion

In OSCCs without cervical LN metastasis, a significant increase in p16 expression was observed compared to samples with cervical LNs metastasis. The presence of HPV was higher in samples with less LNs metastasis and possibly a better prognosis.

Is Cancer Reversible? Rethinking Carcinogenesis Models-A New Epistemological Tool

A growing number of studies shows that it is possible to induce a phenotypic transformation of cancer cells from malignant to benign. This process is currently known as "tumor reversion". However, the concept of reversibility hardly fits the current cancer models, according to which gene mutations are considered the primary cause of cancer. Indeed, if gene mutations are causative carcinogenic factors, and if gene mutations are irreversible, how long should cancer be considered as an irreversible process? In fact, there is some evidence that intrinsic plasticity of cancerous cells may be therapeutically exploited to promote a phenotypic reprogramming, both in vitro and in vivo. Not only are studies on tumor reversion highlighting a new, exciting research approach, but they are also pushing science to look for new epistemological tools capable of better modeling cancer.

Exploring the hub genes and mechanisms of Daphne altaica treating esophageal squamous cell carcinoma based on network pharmacology and bioinformatics analysis

PURPOSE

Esophageal squamous cell carcinoma (ESCC), is a frequent digestive tract malignant carcinoma with a high fatality rate. Daphne altaica (D. altaica), a medicinal plant that is frequently employed in Kazakh traditional medicine, and which has traditionally been used to cure cancer and respiratory conditions, but research on the mechanism is lacking. Therefore, we examined and verified the hub genes and mechanism of D. altaica treating ESCC.

METHODS

Active compounds and targets of D. altaica were screened by databases such as TCMSP, and ESCC targets were screened by databases such as GeneCards and constructed the compound-target network and PPI network. Meantime, data sets between tissues and adjacent non-cancerous tissues from GEO database (GSE100942, GPL570) were analyzed to obtain DEGs using the limma package in R. Hub genes were validated using data from the Kaplan-Meier plotter database, TIMER2.0 and GEPIA2 databases. Finally, AutoDock software was used to predict the binding sites through molecular docking.

RESULTS

In total, 830 compound targets were obtained from TCMSP and other databases. In addition, 17,710 disease targets were acquired based on GeneCards and other databases. In addition, we constructed the compound-target network and PPI network. Then, 127 DEGs were observed (82 up-regulated and 45 down-regulated genes). Hub genes were screened including TOP2A, NUF2, CDKN2A, BCHE, and NEK2, and had been validated with the help of several publicly available databases. Finally, molecular docking results showed more stable binding between five hub genes and active compounds.

CONCLUSIONS

In the present study, five hub genes were screened and validated, and potential mechanisms of action were predicted, which could provide a theoretical understanding of the treatment of ESCC with D. altaica.

Drug Discovery Targeting Post-Translational Modifications in Response to DNA Damages Induced by Space Radiation

DNA damage in astronauts induced by cosmic radiation poses a major barrier to human space exploration. Cellular responses and repair of the most lethal DNA double-strand breaks (DSBs) are crucial for genomic integrity and cell survival. Post-translational modifications (PTMs), including phosphorylation, ubiquitylation, and SUMOylation, are among the regulatory factors modulating a delicate balance and choice between predominant DSB repair pathways, such as non-homologous end joining (NHEJ) and homologous recombination (HR). In this review, we focused on the engagement of proteins in the DNA damage response (DDR) modulated by phosphorylation and ubiquitylation, including ATM, DNA-PKcs, CtIP, MDM2, and ubiquitin ligases. The involvement and function of acetylation, methylation, PARylation, and their essential proteins were also investigated, providing a repository of candidate targets for DDR regulators. However, there is a lack of radioprotectors in spite of their consideration in the discovery of radiosensitizers. We proposed new perspectives for the research and development of future agents against space radiation by the systematic integration and utilization of evolutionary strategies, including multi-omics analyses, rational computing methods, drug repositioning, and combinations of drugs and targets, which may facilitate the use of radioprotectors in practical applications in human space exploration to combat fatal radiation hazards.

Protein Profiling in Human Papillomavirus-Associated Cervical Carcinogenesis: Cornulin as a Biomarker for Disease Progression

Nearly 90% of cervical cancers are linked to human papillomavirus (HPV). Uncovering the protein signatures in each histological phase of cervical oncogenesis provides a path to biomarker discovery. The proteomes extracted from formalin-fixed paraffin-embedded tissues of the normal cervix, HPV16/18-associated squamous intraepithelial lesion (SIL), and squamous cell carcinoma (SCC) were compared using liquid chromatography-mass spectrometry (LC-MS). A total of 3597 proteins were identified, with 589, 550, and 1570 proteins unique to the normal cervix, SIL, and SCC groups, respectively, while 332 proteins overlapped between the three groups. In the transition from normal cervix to SIL, all 39 differentially expressed proteins were downregulated, while all 51 proteins discovered were upregulated in SIL to SCC. The binding process was the top molecular function, while chromatin silencing in the SIL vs. normal group, and nucleosome assembly in SCC vs. SIL groups was the top biological process. The PI3 kinase pathway appears crucial in initiating neoplastic transformation, while viral carcinogenesis and necroptosis are important for cell proliferation, migration, and metastasis in cervical cancer development. Annexin A2 and cornulin were selected for validation based on LC-MS results. The former was downregulated in the SIL vs. normal cervix and upregulated in the progression from SIL to SCC. In contrast, cornulin exhibited the highest expression in the normal cervix and lowest in SCC. Although other proteins, such as histones, collagen, and vimentin, were differentially expressed, their ubiquitous expression in most cells precluded further analysis. Immunohistochemical analysis of tissue microarrays found no significant difference in Annexin A2 expression between the groups. Conversely, cornulin exhibited the strongest expression in the normal cervix and lowest in SCC, supporting its role as a tumor suppressor and potential biomarker for disease progression.

Phosphorylation barcodes direct biased chemokine signaling at CXCR3

G protein-coupled receptor (GPCR)-biased agonism, selective activation of certain signaling pathways relative to others, is thought to be directed by differential GPCR phosphorylation "barcodes." At chemokine receptors, endogenous chemokines can act as "biased agonists", which may contribute to the limited success when pharmacologically targeting these receptors. Here, mass spectrometry-based global phosphoproteomics revealed that CXCR3 chemokines generate different phosphorylation barcodes associated with differential transducer activation. Chemokine stimulation resulted in distinct changes throughout the kinome in global phosphoproteomics studies. Mutation of CXCR3 phosphosites altered β-arrestin 2 conformation in cellular assays and was consistent with conformational changes observed in molecular dynamics simulations. T cells expressing phosphorylation-deficient CXCR3 mutants resulted in agonist- and receptor-specific chemotactic profiles. Our results demonstrate that CXCR3 chemokines are non-redundant and act as biased agonists through differential encoding of phosphorylation barcodes, leading to distinct physiological processes.

Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASH

Nonalcoholic steatohepatitis (NASH) is the most common chronic liver disease globally and a leading cause for liver transplantation in the US. Its pathogenesis remains imprecisely defined. We combined two high-resolution modalities to tissue samples from NASH clinical trials, machine learning (ML)-based quantification of histological features and transcriptomics, to identify genes that are associated with disease progression and clinical events. A histopathology-driven 5-gene expression signature predicted disease progression and clinical events in patients with NASH with F3 (pre-cirrhotic) and F4 (cirrhotic) fibrosis. Notably, the Notch signaling pathway and genes implicated in liver-related diseases were enriched in this expression signature. In a validation cohort where pharmacologic intervention improved disease histology, multiple Notch signaling components were suppressed.

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

BACKGROUND

Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invariance of the nearest neighbour relationships between drugs or targets. In other words, they do not take into account the invariance of the topological relationships between nodes during representation learning. It may limit the performance of the DTI prediction methods.

RESULTS

Here, we propose a novel graph convolutional autoencoder-based model, named SDGAE, to predict DTIs. As the graph convolutional network cannot handle isolated nodes in a network, a pre-processing step was applied to reduce the number of isolated nodes in the heterogeneous network and facilitate effective exploitation of the graph convolutional network. By maintaining the graph structure during representation learning, the nearest neighbour relationships between nodes in the embedding space remained as close as possible to the original space.

CONCLUSIONS

Overall, we demonstrated that SDGAE can automatically learn more informative and robust feature vectors of drugs and targets, thus exhibiting significantly improved predictive accuracy for DTIs.

Intact regulation of G1/S transition renders esophageal squamous cell carcinoma sensitive to PI3Kα inhibitors

Phosphatidylinositol 3-kinase alpha (PI3Kα) inhibitors are currently evaluated for the therapy of esophageal squamous cell carcinoma (ESCC). It is of great importance to identify potential biomarkers to predict or monitor the efficacy of PI3Kα inhibitors in an aim to improve the clinical responsive rate in ESCC. Here, ESCC PDXs with CCND1 amplification were found to be more sensitive to CYH33, a novel PI3Kα-selective inhibitor currently in clinical trials for the treatment of advanced solid tumors including ESCC. Elevated level of cyclin D1, p21 and Rb was found in CYH33-sensitive ESCC cells compared to those in resistant cells. CYH33 significantly arrested sensitive cells but not resistant cells at G1 phase, which was associated with accumulation of p21 and suppression of Rb phosphorylation by CDK4/6 and CDK2. Hypo-phosphorylation of Rb attenuated the transcriptional activation of SKP2 by E2F1, which in turn hindered SKP2-mediated degradation of p21 and reinforced accumulation of p21. Moreover, CDK4/6 inhibitors sensitized resistant ESCC cells and PDXs to CYH33. These findings provided mechanistic rationale to evaluate PI3Kα inhibitors in ESCC patients harboring amplified CCND1 and the combined regimen with CDK4/6 inhibitors in ESCC with proficient Rb.

Biomarkers found in the tumor interstitial fluid may help explain the differential behavior among keratinocyte carcinomas

Basal Cell Carcinomas (BCC) and cutaneous Squamous Cell Carcinomas (SCC) are the most frequent types of cancer, and both originate from the keratinocyte transformation, giving rise to the group of tumors called keratinocyte carcinomas (KC). The invasive behavior is different in each group of KC and may be influenced by their tumor microenvironment. The principal aim of the study is to characterize the protein profile of the Tumor Interstitial Fluid (TIF) of KC to evaluate changes in the microenvironment that could be associated with their different invasive and metastatic capabilities. We obtained TIF from 27 skin biopsies and conducted a label-free quantitative proteomic analysis comparing 7 BCCs, 16 SCCs, and 4 Normal Skins. A total of 2945 proteins were identified, 511 of them quantified in more than half of the samples of each tumoral type. The proteomic analysis revealed differentially expressed TIF-proteins that could explain the different metastatic behavior in both KC. In detail, the SCC samples disclosed an enrichment of proteins related to cytoskeleton, such as Stratafin and Ladinin1. Previous studies found their up-regulation positively correlated with tumor progression. Furthermore, the TIF of SCC samples was enriched with the cytokines S100A8/S100A9. These cytokines influence the metastatic output in other tumors through the activation of NF-kB signaling. According to this, we observed a significant increase in nuclear NF-kB subunit p65 in SCCs but not in BCCs. In addition, the TIF of both tumors was enriched with proteins involved in the immune response, highlighting the relevance of this process in the composition of the tumor environment. Thus, the comparison of the TIF composition of both KC provides the discovery of a new set of differential biomarkers. Among them, secreted cytokines such as S100A9 may help explain the higher aggressiveness of SCCs, while Cornulin is a specific biomarker for BCCs. Finally, the proteomic landscape of TIF provides key information on tumor growth and metastasis, which can contribute to the identification of clinically applicable biomarkers that may be used in the diagnosis of KC, as well as therapeutic targets.

An Insight into Advances in Developing Nanotechnology Based Therapeutics, Drug Delivery, Diagnostics and Vaccines: Multidimensional Applications in Tuberculosis Disease Management

Tuberculosis (TB), one of the deadliest contagious diseases, is a major concern worldwide. Long-term treatment, a high pill burden, limited compliance, and strict administration schedules are all variables that contribute to the development of MDR and XDR tuberculosis patients. The rise of multidrug-resistant strains and a scarcity of anti-TB medications pose a threat to TB control in the future. As a result, a strong and effective system is required to overcome technological limitations and improve the efficacy of therapeutic medications, which is still a huge problem for pharmacological technology. Nanotechnology offers an interesting opportunity for accurate identification of mycobacterial strains and improved medication treatment possibilities for tuberculosis. Nano medicine in tuberculosis is an emerging research field that provides the possibility of efficient medication delivery using nanoparticles and a decrease in drug dosages and adverse effects to boost patient compliance with therapy and recovery. Due to their fascinating characteristics, this strategy is useful in overcoming the abnormalities associated with traditional therapy and leads to some optimization of the therapeutic impact. It also decreases the dosing frequency and eliminates the problem of low compliance. To develop modern diagnosis techniques, upgraded treatment, and possible prevention of tuberculosis, the nanoparticle-based tests have demonstrated considerable advances. The literature search was conducted using Scopus, PubMed, Google Scholar, and Elsevier databases only. This article examines the possibility of employing nanotechnology for TB diagnosis, nanotechnology-based medicine delivery systems, and prevention for the successful elimination of TB illnesses.

Network medicine: an approach to complex kidney disease phenotypes

Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.

IL17RB and IL17REL Expression Are Associated with Improved Prognosis in HPV-Infected Head and Neck Squamous Cell Carcinomas

Changes in the cellular secretome are implicated in virus infection, malignancy, and anti-tumor immunity. We analyzed the association between transcriptional signatures (TS) from 24 different immune and stromal cell types on the prognosis of HPV-infected and HPV-free head and neck squamous carcinoma (HNSCC) patients from The Cancer Genome Atlas (TCGA) cohort. We found that HPV-positive HNSCC patients have tumors with elevated immune cell TS and improved prognosis, which was specifically associated with an increased tumor abundance of memory B and activated natural killer (NK) cell TS, compared to HPV-free HNSCC patients. HPV-infected patients upregulated many transcripts encoding secreted factors, such as growth factors, hormones, chemokines and cytokines, and their cognate receptors. Analysis of secretome transcripts and cognate receptors revealed that tumor expression of IL17RB and IL17REL are associated with a higher viral load and memory B and activated NK cell TS, as well as improved prognosis in HPV-infected HNSCC patients. The transcriptional parameters that we describe may be optimized to improve prognosis and risk stratification in the clinic and provide insights into gene and cellular targets that may potentially enhance anti-tumor immunity mediated by NK cells and memory B cells in HPV-infected HNSCC patients.

Analysis of intracellular tyrosine phosphorylation in circulating neutrophils as a rapid assay for the in vivo effect of oral tyrosine kinase inhibitors

Tyrosine kinases are crucial signaling components of diverse biological processes and are major therapeutic targets in various malignancies and immune-mediated disorders. A critical step of development of novel tyrosine kinase inhibitors is the transition from the confirmation of the in vitro effects of drug candidates to the analysis of their in vivo efficacy. To facilitate this transition, we have developed a rapid in vivo assay for the analysis of the effect of oral tyrosine kinase inhibitors on basal tyrosine phosphorylation of circulating mouse neutrophils. The assay uses a single drop of peripheral blood without sacrificing the mice. Flow cytometry using intracellular staining by fluorescently labeled anti-phosphotyrosine antibodies revealed robust basal tyrosine phosphorylation in resting circulating neutrophils. This signal was abrogated by the use of isotype control antibodies or by pre-saturation of the anti-phosphotyrosine antibodies with soluble phosphotyrosine amino acids or tyrosine-phosphorylated peptides. Basal tyrosine phosphorylation was dramatically reduced in neutrophils of triple knockout mice lacking the Src-family tyrosine kinases Hck, Fgr, and Lyn. Neutrophil tyrosine phosphorylation was also abrogated by oral administration of the Abl/Src-family inhibitor dasatinib, a clinically used anti-leukemic agent. Detailed dose-response and kinetic studies revealed half-maximal reduction of neutrophil tyrosine phosphorylation by 2.9 mg/kg dasatinib, with maximal reduction observed 2 h after inhibitor administration. Taken together, our assay allows highly efficient analysis of the in vivo effect of orally administered tyrosine kinase inhibitors, and may be used as a suitable alternative to other existing approaches.

Determining human-coronavirus protein-protein interaction using machine intelligence

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) virus spread the novel CoronaVirus −19 (nCoV-19) pandemic, resulting in millions of fatalities globally. Recent research demonstrated that the Protein-Protein Interaction (PPI) between SARS-CoV-2 and human proteins is accountable for viral pathogenesis. However, many of these PPIs are poorly understood and unexplored, necessitating a more in-depth investigation to find latent yet critical interactions. This article elucidates the host-viral PPI through Machine Learning (ML) lenses and validates the biological significance of the same using web-based tools. ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins, namely Amino Acid Composition, Pseudo Amino Acid Composition, Conjoint Triad, Dipeptide Composition, and Normalized Auto Correlation. A majority voting rule-based ensemble method composed of the Random Forest Model (RFM), AdaBoost, and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work. The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor ≥70%, validated by utilizing Gene Ontology (GO) and KEGG pathway enrichment analysis. Consequently, this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

Integrated analysis of SR-like protein kinases Sky1 and Sky2 links signaling networks with transcriptional regulation in Candida albicans

Fungal infections are a major global health burden where Candida albicans is among the most common fungal pathogen in humans and is a common cause of invasive candidiasis. Fungal phenotypes, such as those related to morphology, proliferation and virulence are mainly driven by gene expression, which is primarily regulated by kinase signaling cascades. Serine-arginine (SR) protein kinases are highly conserved among eukaryotes and are involved in major transcriptional processes in human and S. cerevisiae. Candida albicans harbors two SR protein kinases, while Sky2 is important for metabolic adaptation, Sky1 has similar functions as in S. cerevisiae. To investigate the role of these SR kinases for the regulation of transcriptional responses in C. albicans, we performed RNA sequencing of sky1Δ and sky2Δ and integrated a comprehensive phosphoproteome dataset of these mutants. Using a Systems Biology approach, we study transcriptional regulation in the context of kinase signaling networks. Transcriptomic enrichment analysis indicates that pathways involved in the regulation of gene expression are downregulated and mitochondrial processes are upregulated in sky1Δ. In sky2Δ, primarily metabolic processes are affected, especially for arginine, and we observed that arginine-induced hyphae formation is impaired in sky2Δ. In addition, our analysis identifies several transcription factors as potential drivers of the transcriptional response. Among these, a core set is shared between both kinase knockouts, but it appears to regulate different subsets of target genes. To elucidate these diverse regulatory patterns, we created network modules by integrating the data of site-specific protein phosphorylation and gene expression with kinase-substrate predictions and protein-protein interactions. These integrated signaling modules reveal shared parts but also highlight specific patterns characteristic for each kinase. Interestingly, the modules contain many proteins involved in fungal morphogenesis and stress response. Accordingly, experimental phenotyping shows a higher resistance to Hygromycin B for sky1Δ. Thus, our study demonstrates that a combination of computational approaches with integration of experimental data can offer a new systems biological perspective on the complex network of signaling and transcription. With that, the investigation of the interface between signaling and transcriptional regulation in C. albicans provides a deeper insight into how cellular mechanisms can shape the phenotype.

Association between COVID-19 and Male Fertility: Systematic Review and Meta-Analysis of Observational Studies

PURPOSE

Whether COVID-19 reduces male fertility remains requires further investigation. This meta-analysis and systematic review evaluated the impact of COVID-19 on male fertility.

MATERIALS AND METHODS

The literature in PubMed, Embase, MEDLINE, Web of Science, and Cochrane Library up to January 01, 2022 was systematically searched, and a meta-analysis was conducted to investigate the effect of COVID-19 on male fertility. Totally 17 studies with a total of 1,627 patients and 1,535 control subjects were included in our meta-analysis.

RESULTS

Regarding sperm quality, COVID-19 decreased the total sperm count (p=0.012), sperm concentration (p=0.001), total motility (p=0.001), progressive sperm motility (p=0.048), and viability (p=0.031). Subgroup analyses showed that different control group populations did not change the results. It was found that during the illness stage of COVID-19, semen volume decreased, and during the recovery stage of COVID-19, sperm concentration and total motility decreased <90 days. We found that sperm concentration and total motility decreased during recovery for ≥90 days. Fever because of COVID-19 significantly reduced sperm concentration and progressive sperm motility, and COVID-19 without fever ≥90 days, the sperm total motility and progressive sperm motility decreased. Regarding disease severity, the moderate type of COVID-19 significantly reduced sperm total motility, but not the mild type. Regarding sex hormones, COVID-19 increased prolactin and estradiol. Subgroup analyses showed that during the illness stage, COVID-19 decreased testosterone (T) levels and increased luteinizing hormone levels. A potential publication bias may have existed in our meta-analysis.

CONCLUSIONS

COVID-19 in men significantly reduced sperm quality and caused sex hormone disruption. COVID-19 had long-term effects on sperm quality, especially on sperm concentration and total motility. It is critical to conduct larger multicenter studies to determine the consequences of COVID-19 on male fertility.

The pathogenesis and development of targeted drugs in acute T lymphoblastic leukaemia

Acute lymphoblastic leukaemia (ALL) is mainly classified into acute T- and B-lymphoblastic leukaemia according to the source of its lymphocytes, thymus and bone. Among them, the incidence of adult T-cell accounts for about 25% of adult acute lymphoblastic leukaemia, but the degree of malignancy is high and the treatment rate and prognosis are poor. At this stage, there are few targeted drugs and the commonly used broad-spectrum chemotherapeutic drugs have poor efficacy and many adverse drug reactions. Understanding and investigating the pathogenesis of T-acute lymphoblastic leukaemia is very important for further developing new targeting drugs and improving existing drugs. Dysregulated signalling pathways are the main aetiological factors of T-acute lymphoblastic leukaemia. They play crucial roles in promoting tumour initiation, progression, drug design and therapy responses. This is primarily because signalling pathways are indispensable for many cellular biological processes, including tumour growth, migration, invasion, metastasis and others. As a result, small molecule inhibitors targeting the major kinase components of the signalling pathway have received a lot of attention and have been developed and evaluated in preclinical models and clinical trials. Already marketed drugs are also being repurposed in combination therapies to further improve efficacy and overcome tumour cell resistance. In this review, we have aimed to examine the latest and most classical signalling pathways in the aetiology of T-acute lymphoblastic leukaemia and shed light on potential targets for novel therapeutic agents to act on.

Association between periodontitis and hypertension: cross-sectional survey from the Fourth National Oral Health Survey of China (2015-2016)

OBJECTIVES

To evaluate the association between periodontitis severity and hypertension based on Chinese epidemiological data.

DESIGN

This cross-sectional survey enrolled adults from the Fourth National Oral Health Survey of China (2015-2016).

SETTING

The data were obtained from the Fourth National Oral Health Survey of China (2015-2016).

PARTICIPANTS

The study included individuals aged 35-44 years (n=4409), 55-64 years (n=4568) and 65-74 years (n=4218).

PRIMARY AND SECONDARY OUTCOME MEASURES

Periodontal status, defined on the basis of the 2017 classification scheme, and periodontal parameters (eg, bleeding on probing (BOP)) were compared between individuals with hypertension and individuals with normotension. Smoothed scatterplots were constructed to demonstrate the associations of periodontal parameters and periodontal status with hypertension.

RESULTS

Severe periodontitis (stages III and IV) was present in 41.4% of individuals with hypertension and 28.0% of individuals with normotension, respectively (p<0.001). The prevalence of severe periodontitis was higher in individuals with hypertension than that in individuals with normotension among participants aged 35-44 years (18.0% vs 10.1%, p<0.001) and 55-64 years (40.2% vs 36.7%, p=0.035), but not in participants aged 65-74 years (46.4% vs 45.1%, p=0.429). Therefore, the difference in periodontal status between individuals with hypertension and individuals with normotension decreased with age. There were higher prevalences of BOP, probing depth (PD) ≥4 mm and PD ≥6 mm in individuals with hypertension than in individuals with normotension (52.1% vs 49.2%, 19.6% vs 14.7% and 1.8% vs 1.1%, respectively). Periodontitis severity and the proportion of teeth with PD ≥4 or ≥6 mm were positively associated with hypertension.

CONCLUSION

Periodontitis is associated with hypertension in Chinese adults. Hypertension prevalence increased with periodontitis severity, particularly among young participants. Accordingly, it is necessary to improve the education, awareness of periodontal treatment and preventive management among individuals at risk of hypertension, particularly in the younger population.

Integrative proteogenomic characterization of early esophageal cancer

Esophageal squamous cell carcinoma (ESCC) is malignant while the carcinogenesis is still unclear. Here, we perform a comprehensive multi-omics analysis of 786 trace-tumor-samples from 154 ESCC patients, covering 9 histopathological stages and 3 phases. Proteogenomics elucidates cancer-driving waves in ESCC progression, and reveals the molecular characterization of alcohol drinking habit associated signatures. We discover chromosome 3q gain functions in the transmit from nontumor to intraepithelial neoplasia phases, and find TP53 mutation enhances DNA replication in intraepithelial neoplasia phase. The mutations of AKAP9 and MCAF1 upregulate glycolysis and Wnt signaling, respectively, in advanced-stage ESCC phase. Six major tracks related to different clinical features during ESCC progression are identified, which is validated by an independent cohort with another 256 samples. Hyperphosphorylated phosphoglycerate kinase 1 (PGK1, S203) is considered as a drug target in ESCC progression. This study provides insight into the understanding of ESCC molecular mechanism and the development of therapeutic targets.

Immune Dynamics Involved in Acute and Convalescent COVID-19 Patients

COVID-19 is a viral disease that has caused millions of deaths around the world since 2020. Many strategies have been developed to manage patients in critical conditions; however, comprehension of the immune system is a key factor in viral clearance, tissue repairment, and adaptive immunity stimulus. Participation of immunity has been identified as a major factor, along with biomarkers, prediction of clinical outcomes, and antibody production after infection. Immune cells have been proposed not only as a hallmark of severity, but also as a predictor of clinical outcomes, while dynamics of inflammatory molecules can also induce worse consequences for acute patients. For convalescent patients, mild disease was related to higher antibody production, although the factors related to the specific antibodies based on a diversity of antigens were not clear. COVID-19 was explored over time; however, the study of immunological predictors of outcomes is still lacking discussion, especially in convalescent patients. Here, we propose a review using previously published studies to identify immunological markers of COVID-19 outcomes and their relation to antibody production to further contribute to the clinical and laboratorial management of patients.

Phosphorylation barcodes direct biased chemokine signaling at CXCR3

G protein-coupled receptor (GPCR) biased agonism, the activation of some signaling pathways over others, is thought to largely be due to differential receptor phosphorylation, or "phosphorylation barcodes." At chemokine receptors, ligands act as "biased agonists" with complex signaling profiles, which contributes to the limited success in pharmacologically targeting these receptors. Here, mass spectrometry-based global phosphoproteomics revealed that CXCR3 chemokines generate different phosphorylation barcodes associated with differential transducer activation. Chemokine stimulation resulted in distinct changes throughout the kinome in global phosphoproteomic studies. Mutation of CXCR3 phosphosites altered β-arrestin conformation in cellular assays and was confirmed by molecular dynamics simulations. T cells expressing phosphorylation-deficient CXCR3 mutants resulted in agonist- and receptor-specific chemotactic profiles. Our results demonstrate that CXCR3 chemokines are non-redundant and act as biased agonists through differential encoding of phosphorylation barcodes and lead to distinct physiological processes.

Association of COVID-19 with Comorbidities: An Update

Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) which was identified in Wuhan, China in December 2019 and jeopardized human lives. It spreads at an unprecedented rate worldwide, with serious and still-unfolding health conditions and economic ramifications. Based on the clinical investigations, the severity of COVID-19 appears to be highly variable, ranging from mild to severe infections including the death of an infected individual. To add to this, patients with comorbid conditions such as age or concomitant illnesses are significant predictors of the disease's severity and progression. SARS-CoV-2 enters inside the host cells through ACE2 (angiotensin converting enzyme2) receptor expression; therefore, comorbidities associated with higher ACE2 expression may enhance the virus entry and the severity of COVID-19 infection. It has already been recognized that age-related comorbidities such as Parkinson's disease, cancer, diabetes, and cardiovascular diseases may lead to life-threatening illnesses in COVID-19-infected patients. COVID-19 infection results in the excessive release of cytokines, called "cytokine storm", which causes the worsening of comorbid disease conditions. Different mechanisms of COVID-19 infections leading to intensive care unit (ICU) admissions or deaths have been hypothesized. This review provides insights into the relationship between various comorbidities and COVID-19 infection. We further discuss the potential pathophysiological correlation between COVID-19 disease and comorbidities with the medical interventions for comorbid patients. Toward the end, different therapeutic options have been discussed for COVID-19-infected comorbid patients.

Pan-Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival

Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co-regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss-of-function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity.

The protective role of the microenvironment in hairy cell leukemia treatment: Facts and perspectives

Hairy cell leukemia (HCL) is an incurable, rare lymphoproliferative hematological malignancy of mature B cAlthough first line therapy with purine analogues leads to positive results, almost half of HCL patients relapse after 5-10 years, and standard treatment may not be an option due to intolerance or refractoriness. Proliferation and survival of HCL cells is regulated by surrounding accessory cells and soluble signals present in the tumor microenvironment, which actively contributes to disease progression. In vitro studies show that different therapeutic approaches tested in HCL impact the tumor microenvironment, and that this milieu offers a protection affecting treatment efficacy. Herein we explore the effects of the tumor microenvironment to different approved and experimental therapeutic options for HCL. Dissecting the complex interactions between leukemia cells and their milieu will be essential to develop new targeted therapies for HCL patients.

Reversal of malignant ADAR1 splice isoform switching with Rebecsinib

Adenosine deaminase acting on RNA1 (ADAR1) preserves genomic integrity by preventing retroviral integration and retrotransposition during stress responses. However, inflammatory-microenvironment-induced ADAR1p110 to p150 splice isoform switching drives cancer stem cell (CSC) generation and therapeutic resistance in 20 malignancies. Previously, predicting and preventing ADAR1p150-mediated malignant RNA editing represented a significant challenge. Thus, we developed lentiviral ADAR1 and splicing reporters for non-invasive detection of splicing-mediated ADAR1 adenosine-to-inosine (A-to-I) RNA editing activation; a quantitative ADAR1p150 intracellular flow cytometric assay; a selective small-molecule inhibitor of splicing-mediated ADAR1 activation, Rebecsinib, which inhibits leukemia stem cell (LSC) self-renewal and prolongs humanized LSC mouse model survival at doses that spare normal hematopoietic stem and progenitor cells (HSPCs); and pre-IND studies showing favorable Rebecsinib toxicokinetic and pharmacodynamic (TK/PD) properties. Together, these results lay the foundation for developing Rebecsinib as a clinical ADAR1p150 antagonist aimed at obviating malignant microenvironment-driven LSC generation.

Extracellular vesicle proteomics and phosphoproteomics identify pathways for increased risk in patients hospitalized with COVID-19 and type 2 diabetes mellitus

Recent studies suggest that extracellular vesicles (EVs) play a role in the pathogenesis of SARS-CoV-2 infection and the severity of COVID-19. However, their role in the interaction between COVID-19 and type 2 diabetes (T2D) has not been addressed. Here, we characterized the circulating EV proteomic and phosphoproteomic landscape in patients with and without T2D hospitalized with COVID-19 or non-COVID-19 acute respiratory illness (RSP). We detected differentially expressed protein and phosphoprotein signatures that effectively characterized the study groups. The trio of immunomodulatory and coagulation proteins C1QA, C1QB, and C1QC appeared to be a central cluster in both the COVID-19 and T2D functional networks. PKCβ appeared to be retained in cells by being diverted from EV pathways and contribute to the COVID-19 and T2D interaction via a PKC/BTK/TEC axis. EV-shuttled CASP3 and ROCK1 appeared to be coregulated and likely contribute to disease interactions in patients with COVID-19 and T2D. Predicted activation of AMPK, MAPK, and SYK appeared to also play important roles driving disease interaction. These results suggest that activated cellular kinases (i.e., PKC, AMPK, MAPK, and SYK) and multiple EV-shuttled kinases (i.e., PKCβ, BTK, TEC, MAP2K2, and ROCK1) may play key roles in severe COVID-19, particularly in patients with comorbid diabetes.

The role of SF3B1 and NOTCH1 in the pathogenesis of leukemia

The discovery of new genes/pathways improves our knowledge of cancer pathogenesis and presents novel potential therapeutic options. For instance, splicing factor 3b subunit 1 (SF3B1) and NOTCH1 genetic alterations have been identified at a high frequency in hematological malignancies, such as leukemia, and may be related to the prognosis of involved patients because they change the nature of malignancies in different ways like mediating therapeutic resistance; therefore, studying these gene/pathways is essential. This review aims to discuss SF3B1 and NOTCH1 roles in the pathogenesis of various types of leukemia and the therapeutic potential of targeting these genes or their mutations to provide a foundation for leukemia treatment.

MicroRNAs Markedly Expressed in Apical Periodontitis Cooperatively Regulate Cytokines and Growth Factors Promoting an Anti-inflammatory Response

INTRODUCTION

MicroRNAs have been shown to play a role in the pathogenesis of apical periodontitis. Upregulation of miR-10a-5p and downregulation of miR-891a-5p were previously reported in apical periodontitis samples. This study aims to perform a functional characterization of miR-10a-5p, investigating its capacity to regulate the expression of inflammatory cytokines and growth factors, as well as a possible co-regulation mechanism with miR-891a-5p in the development of apical periodontitis.

METHODS

miR-10a-5p mimics/controls and miR-891a-5p inhibitors/controls were introduced to human K-562 cells in the presence or absence of lipopolysaccharide. Total RNA was extracted from cell lysates, and target genes were examined via quantitative reverse transcription-polymerase chain reaction. Cell lysates were also subjected to proteomics analysis. Furthermore, mimics of miR-10a-5p and inhibitors of miR-891a-5p were co-transfected into K-562 cells. RNA sequencing and quantitative reverse transcription-polymerase chain reaction were carried out to examine their target genes.

RESULTS

Overexpression of miR-10a-5p led to downregulation of tumor necrosis factor-alpha and interleukin-1 beta mRNA and upregulation of transforming growth factor-beta 1 (TGFB1) mRNA expression, whereas interleukin 3 and TGF-β1 proteins were upregulated. Simultaneous overexpression of miR-10a-5p and inhibition of miR-891a-5p further increased TGFB1 mRNA transcript levels. RNA sequencing revealed that genes co-regulated by miR-10a-5p and miR-891a-5p may be involved in apical periodontitis-related pathways such as tumor necrosis factor, transient receptor potential, and vascular endothelial growth factor signaling pathways.

CONCLUSIONS

miR-10a-5p may modulate the expression of multiple inflammatory cytokines and growth factors such as tumor necrosis factor-alpha, IL-1β, interleukin 3, and TGF-β1. In addition, miR-10a-5p and miR-891a-5p cooperatively regulate TGFB1 gene expression, and the gene network of this co-regulation is integrated with many pathways in apical periodontitis.

The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection

Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.

ESCCdb: A Comprehensive Database and Key Regulator Exploring Platform Based on Cross Dataset Comparisons for Esophageal Squamous Cell Carcinoma

Esophageal cancer is the seventh most prevalent and the sixth most lethal cancer. Esophageal squamous cell carcinoma (ESCC) is one of the major esophageal cancer subtypes that accounts for 87 % of the total cases. However, its molecular mechanism remains unclear. Here, we present an integrated database for ESCC called ESCCdb, which includes a total of 56 datasets and published studies from the GEO, Xena or SRA databases and related publications. It helps users to explore a particular gene with multiple graphical and interactive views with one click. The results comprise expression changes across 20 datasets, copy number alterations in 11 datasets, somatic mutations from 12 papers, related drugs derived from DGIdb, related pathways, and gene correlations. ESCCdb enables directly cross-dataset comparison of a gene's mutations, expressions and copy number changes in multiple datasets. This allows users to easily assess the alterations in ESCC. Furthermore, survival analysis, drug-gene relationships, and results from whole-genome CRISPR/Cas9 screening can help users determine the clinical relevance, derive functional inferences, and identify potential drugs. Notably, ESCCdb also enables the exploration of the correlation structure and identification of potential key regulators for a process. Finally, we identified 789 consistently differential expressed genes; we summarized recurrently mutated genes and genes affected by significant copy number alterations. These genes may be stable biomarkers or important players during ESCC development. ESCCdb fills the gap between massive omics data and users' needs for integrated analysis and can promote basic and clinical ESCC research. The database is freely accessible at http://cailab.labshare.cn/ESCCdb.

Multianalyte Serum Biomarker Panel for Early Detection of Pancreatic Adenocarcinoma

PURPOSE

We determined whether a large, multianalyte panel of circulating biomarkers can improve detection of early-stage pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

We defined a biologically relevant subspace of blood analytes on the basis of previous identification in premalignant lesions or early-stage PDAC and evaluated each in pilot studies. The 31 analytes that met minimum diagnostic accuracy were measured in serum of 837 subjects (461 healthy, 194 benign pancreatic disease, and 182 early-stage PDAC). We used machine learning to develop classification algorithms using the relationship between subjects on the basis of their changes across the predictors. Model performance was subsequently evaluated in an independent validation data set from 186 additional subjects.

RESULTS

A classification model was trained on 669 subjects (358 healthy, 159 benign, and 152 early-stage PDAC). Model evaluation on a hold-out test set of 168 subjects (103 healthy, 35 benign, and 30 early-stage PDAC) yielded an area under the receiver operating characteristic curve (AUC) of 0.920 for classification of PDAC from non-PDAC (benign and healthy controls) and an AUC of 0.944 for PDAC versus healthy controls. The algorithm was then validated in 146 subsequent cases presenting with pancreatic disease (73 benign pancreatic disease and 73 early- and late-stage PDAC cases) and 40 healthy control subjects. The validation set yielded an AUC of 0.919 for classification of PDAC from non-PDAC and an AUC of 0.925 for PDAC versus healthy controls.

CONCLUSION

Individually weak serum biomarkers can be combined into a strong classification algorithm to develop a blood test to identify patients who may benefit from further testing.

Complex Disease Genes Identification Using a Heterogeneous Network Embedding Approach

Finding the causal relation between a gene and a disease using experimental approaches is a time-consuming and expensive task. However, computational approaches are cost-efficient methods for identifying candidate genes. This article proposes a new heterogeneous biological network embedding approach, named NetEM, to identify disease-associated genes. To evaluate NetEM, we examine six complex diseases, including peroxisomal disorders, sarcoma, grave's disease, lysosomal storage diseases, blood coagulation disorders, and cardiomyopathy hypertrophic. Our experiments indicate that NetEM outperforms three well-known state-of-the-art algorithms: Cardigan, DIAMOnD and GeneWanderer, in identifying disease genes. We examine TCGA data of Invasive Lobular Breast Cancer and CPTAC data of human glioblastoma as other case studies to evaluate NetEM using real data. This evaluation also indicates the validity of the method. The source codes of NetEM and data are available in the supplementary of this article.

Effective Reversal of Macrophage Polarization by Inhibitory Combinations Predicted by a Boolean Protein–Protein Interaction Model

Background: The function and polarization of macrophages has a significant impact on the outcome of many diseases. Targeting tumor-associated macrophages (TAMs) is among the greatest challenges to solve because of the low in vitro reproducibility of the heterogeneous tumor microenvironment (TME). To create a more comprehensive model and to understand the inner workings of the macrophage and its dependence on extracellular signals driving polarization, we propose an in silico approach. Methods: A Boolean control network was built based on systematic manual curation of the scientific literature to model the early response events of macrophages by connecting extracellular signals (input) with gene transcription (output). The network consists of 106 nodes, classified as 9 input, 75 inner and 22 output nodes, that are connected by 217 edges. The direction and polarity of edges were manually verified and only included in the model if the literature plainly supported these parameters. Single or combinatory inhibitions were simulated mimicking therapeutic interventions, and output patterns were analyzed to interpret changes in polarization and cell function. Results: We show that inhibiting a single target is inadequate to modify an established polarization, and that in combination therapy, inhibiting numerous targets with individually small effects is frequently required. Our findings show the importance of JAK1, JAK3 and STAT6, and to a lesser extent STK4, Sp1 and Tyk2, in establishing an M1-like pro-inflammatory polarization, and NFAT5 in creating an anti-inflammatory M2-like phenotype. Conclusions: Here, we demonstrate a protein–protein interaction (PPI) network modeling the intracellular signalization driving macrophage polarization, offering the possibility of therapeutic repolarization and demonstrating evidence for multi-target methods.

Challenges and Perspectives in Target Identification and Mechanism Illustration for Chinese Medicine

Chinese medicine (CM) is an important resource for human life understanding and discovery of drugs. However, due to the unclear pharmacological mechanism caused by unclear target, research and international promotion of many active components have made little progress in the past decades of years. CM is mainly composed of multi-ingredients with multi-targets. The identification of targets of multiple active components and the weight analysis of multiple targets in a specific pathological environment, that is, the determination of the most important target is the main obstacle to the mechanism clarification and thus hinders its internationalization. In this review, the main approach to target identification and network pharmacology were summarized. And BIBm (Bayesian inference modeling), a powerful method for drug target identification and key pathway determination was introduced. We aim to provide a new scientific basis and ideas for the development and international promotion of new drugs based on CM.

Periostin derived from cancer-associated fibroblasts promotes esophageal squamous cell carcinoma progression via ADAM17 activation

Periostin (POSTN) is a matricellular protein that was originally identified in osteoblasts. Past studies have shown that POSTN is also preferentially expressed in cancer-associated fibroblasts (CAFs) in various types of cancer. We previously demonstrated that the increased expression of POSTN in stromal tissues is associated with an unfavorable clinical outcome in esophageal squamous cell carcinoma (ESCC) patients. In this study, we aimed to elucidate the role of POSNT in ESCC progression and its underlying molecular mechanism. We found that POSTN is predominantly produced by CAFs in ESCC tissues, and that CAFs-cultured media significantly promoted the migration, invasion, proliferation, and colony formation of ESCC cell lines in a POSTN-dependent manner. In ESCC cells, POSTN increased the phosphorylation of ERK1/2 and stimulated the expression and activity of a disintegrin and metalloproteinase 17 (ADAM17), which is critically involved in tumorigenesis and tumor progression. The effects of POSTN on ESCC cells were suppressed by interfering with the binding of POSTN to integrin αvβ3 or αvβ5 using neutralizing antibody against POSTN. Taken together, our data show that CAFs-derived POSTN stimulates ADAM17 activity through activation of the integrin αvβ3 or αvβ5-ERK1/2 pathway and thereby contributes to the progression of ESCC.

EXOC3L1: A Novel Prognostic Biomarker Correlated with Immune Infiltration in Esophageal Squamous Cell Carcinoma

BACKGROUND Exocyst complex component 3-like 1 (EXOC3L1) is ubiquitously present in multiple organs. However, its role in esophageal squamous cell carcinoma (ESCC) remains unknown. The aim of this study was to explore the relationship between EXOC3L1 and ESCC. MATERIAL AND METHODS A total of 652 normal samples and 82 ESCC samples obtained from the University of California Santa Cruz (UCSC) Xena were applied to detect the expression difference of EXOC3L1. GSE53625 with 179 paired samples and GSE161533 with 28 paired samples were used for validation. The correlation between clinicopathological features and EXOC3L1 expression was calculated. Kaplan-Meier method was employed to assess the prognostic value of EXOC3L1 in ESCC. Univariate and multivariate Cox regression analyses were carried out to screen the factors contributing to the prognosis of ESCC. In addition, functional enrichment analysis, protein-protein interaction (PPI) network analysis, and immune infiltration analysis were conducted to identify the significantly involved functions of EXOC3L1. RESULTS EXOC3L1 was significantly overexpressed in ESCC compared to normal samples. High expression of EXOC3L1 was associated with worse prognosis, and univariate and multivariate Cox regression analysis demonstrated that EXOC3L1 was an independent prognostic predictor of ESCC. Functional enrichment analysis and immune infiltration analysis disclosed that the expression of EXOC3L1 was correlated with the abundance of several types of immune cells. CONCLUSIONS EXOC3L1 plays a crucial role in the prognosis of ESCC, and it may serve as a reliable biomarker for predicting the survival and a potential therapeutic target for ESCC.

The role of Transmembrane Protein 16A (TMEM16A) in pulmonary hypertension

Transmembrane protein 16A (TMEM16A), a member of the TMEM16 family, is the molecular basis of Ca2+-activated chloride channels (CaCCs) and is involved in a variety of physiological and pathological processes. Previous studies have focused more on respiratory-related diseases and tumors. However, recent studies have identified an important role for TMEM16A in cardiovascular diseases, especially in pulmonary hypertension. TMEM16A is expressed in both pulmonary artery smooth muscle cells and pulmonary artery endothelial cells and is involved in the development of pulmonary hypertension. This paper presents the structure and function of TMEM16A, the pathogenesis of pulmonary hypertension, and highlights the role and mechanism of TMEM16A in pulmonary hypertension, summarizing the controversies in this field and taking into account hypertension and portal hypertension, which have similar pathogenesis. It is hoped that the unique role of TMEM16A in pulmonary hypertension will be illustrated and provide ideas for research in this area.

ANO4 Expression Is a Potential Prognostic Biomarker in Non-Metastasized Clear Cell Renal Cell Carcinoma

Background: Over the past decade, transcriptome profiling has elucidated many pivotal pathways involved in oncogenesis. However, a detailed comprehensive map of tumorigenesis remains an enigma to solve. Propelled research has been devoted to investigating the molecular drivers of clear cell renal cell carcinoma (ccRCC). To add another piece to the puzzle, we evaluated the role of anoctamin 4 (ANO4) expression as a potential prognostic biomarker in non-metastasized ccRCC. Methods: A total of 422 ccRCC patients with the corresponding ANO4 expression and clinicopathological data were obtained from The Cancer Genome Atlas Program (TCGA). Differential expression across several clinicopathological variables was performed. The Kaplan-Meier method was used to assess the impact of ANO4 expression on the overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS). Univariate and multivariate Cox logistic regression analyses were conducted to identify independent factors modulating the aforementioned outcomes. Gene set enrichment analysis (GSEA) was used to discern a set of molecular mechanisms involved in the prognostic signature. Tumor immune microenvironment was estimated using xCell. Results: ANO4 expression was upregulated in tumor samples compared to normal kidney tissue. Albeit the latter finding, low ANO4 expression is associated with advanced clinicopathological variables such as tumor grade, stage, and pT. In addition, low ANO4 expression is linked to shorter OS, PFI, and DSS. Multivariate Cox logistic regression analysis identified ANO4 expression as an independent prognostic variable in OS (HR: 1.686, 95% CI: 1.120-2.540, p = 0.012), PFI (HR: 1.727, 95% CI: 1.103-2.704, p = 0.017), and DSS (HR: 2.688, 95% CI: 1.465-4.934, p = 0.001). GSEA identified the following pathways to be enriched within the low ANO4 expression group: epithelial-mesenchymal transition, G2-M checkpoint, E2F targets, estrogen response, apical junction, glycolysis, hypoxia, coagulation, KRAS, complement, p53, myogenesis, and TNF-α signaling via NF-κB pathways. ANO4 expression correlates significantly with monocyte (ρ = -0.1429, p = 0.0033) and mast cell (ρ = 0.1598, p = 0.001) infiltration. Conclusions: In the presented work, low ANO4 expression is portrayed as a potential poor prognostic factor in non-metastasized ccRCC. Further experimental studies should be directed to shed new light on the exact molecular mechanisms involved.

STC2 activates PRMT5 to induce radioresistance through DNA damage repair and ferroptosis pathways in esophageal squamous cell carcinoma

Radioresistance is the major reason for the failure of radiotherapy in esophageal squamous cell carcinoma (ESCC). Previous evidence indicated that stanniocalcin 2 (STC2) participates in various biological processes of malignant tumors. However, researches on its effect on radioresistance in cancers are limited. In this study, STC2 was screened out by RNA-sequencing and bioinformatics analyses as a potential prognosis predictor of ESCC radiosensitivity and then was determined to facilitate radioresistance. We found that STC2 expression is increased in ESCC tissues compared to adjacent normal tissues, and a higher level of STC2 is associated with poor prognosis. Also, STC2 mRNA and protein expression levels were higher in radioresistant cells than in their parental cells. Further investigation revealed that STC2 could interact with protein methyltransferase 5 (PRMT5) and activate PRMT5, thus leading to the increased expression of symmetric dimethylation of histone H4 on Arg 3 (H4R3me2s). Mechanistically, STC2 can promote DDR through the homologous recombination and non-homologous end joining pathways by activating PRMT5. Meanwhile, STC2 can participate in SLC7A11-mediated ferroptosis in a PRMT5-dependent manner. Finally, these results were validated through in vivo experiments. These findings uncovered that STC2 might be an attractive therapeutic target to overcome ESCC radioresistance.

Analysis of serum calcium, sodium, potassium, zinc, and iron in patients with pre-eclampsia in Bangladesh: A case-control study

Background and Aims

Pre-eclampsia is a particular type of pregnancy condition. Although the primary etiology of pre-eclampsia is unclear, it hypothesizes that the alteration of trace elements and macro-minerals may play a crucial function in the pathogenesis of Pre-eclampsia. Therefore, our research sought to ascertain the serum level of trace elements (zinc, iron) and macro-minerals (sodium, calcium, potassium) and their possible association with pre-eclampsia.

Methods

The present study was conducted with 74 pre-eclampsia pregnant women (case) and 118 pregnant women having normal blood pressure (controls). Atomic Absorption Spectroscopy determined the serum level of trace components and electrolytes.

Results

The researchers discovered notable differences in maternal age, gestational period, body mass index, systolic and diastolic blood pressure, hemoglobin, and creatinine level. Results of serum analysis revealed that calcium (52.06 ± 3.71 mg/L vs. 65.93 ± 2.57 mg/L, p < 0.05) and potassium (63.44 ± 5.33 mg/L vs. 102.54 ± 4.25 mg/L, p < 0.001) concentrations were substantially lower in the patient group than in control. Serum zinc (0.34 ± 0.02 mg/L vs. 0.52 ± 0.02 mg/L, p < 0.001) and iron (0.38 ± 0.03 mg/L vs. 0.46 ± 0.02 mg/L, p < 0.05) concentration were also considerably decreased in pre-eclampsia participants compared with a pregnant normotensive group. Pearson's correlation research results in the patient group revealed a connection between trace elements or macro minerals. In addition, the systolic blood pressure was positively correlated with sodium (r = 0.392, p < 0.01) and negatively correlated with potassium (r = -0.257, p < 0.05) in the control group.

Conclusions

This study concludes that calcium, potassium, iron, and zinc levels were lower, whereas sodium levels were higher in Bangladeshi pre-eclampsia patients compared to controls. These findings with Pearson's correlation and the inter-element relationship between the patient and a control subject results can act as critical indication factors for patients with pre-eclampsia in Bangladesh and, as a result, may require a higher intake of calcium, potassium, iron, and zinc for effective therapeutic intervention and reduce the intake of sodium.

Dissection of hubs and bottlenecks in a protein-protein interaction network

Analysis of degree centrality in conjunction with betweenness centrality of proteins in a human protein-protein interaction network revealed three categories of centrally important proteins: a) proteins with high degree and betweenness (hub-bottlenecks denoted as MX), b) proteins with high betweenness and low degree (non-hub-bottlenecks/pure bottlenecks denoted as PB) and c) proteins with high degree and low betweenness (hub-non-bottlenecks/pure hubs denoted as PH). When subjected to a detailed statistical analysis of their molecular-level properties, the proteins belonging to each of these categories were found to be associated with distinct canonical molecular properties, i.e., "molecular markers". The MX proteins are a) conformationally versatile, mainly comprising of essential proteins, b) the targets for interactions by the proteins of viral and bacterial pathogens, c) evolutionally constrained, involved in multiple pathways, enriched with disease genes and d) involved in the functions such as protein stabilization, phosphorylation, and mRNA slicing processes. PB proteins are a) enriched with extracellular and cancer-related proteins, b) enriched with the approved drug targets and c) involved in cell-cell signaling processes. Finally, PH are a) structurally versatile, b) enriched with essential proteins primarily involved in housekeeping processes (transcription and replication). The fact that the proteins belonging to these three categories form three distinct sets in terms of their molecular properties reveals the existence of trichotomy among hubs and bottlenecks, and this knowledge is of paramount importance while prioritizing protein targets for further studies such as drug design and disease association studies based on their network centrality values.

Exploring the macromolecules for secretory pathway in cancer disease

Secretory proteins play an important role in the tumor microenvironment and are widely distributed throughout tumor tissues. Tumor cells secrete a protein that mediates communication between tumor cells and stromal cells, thereby controlling tumor growth and affecting the success of cancer treatments in the clinic. The cancer secretome is produced by various secretory pathways and has a wide range of applications in oncoproteomics. Secretory proteins are involved in cancer development and tumor cell migration, and thus serve as biomarkers or effective therapeutic targets for a variety of cancers. Several proteomic strategies have recently been used for the analysis of cancer secretomes in order to gain a better understanding and elaborate interpretation. For instance, the development of exosome proteomics, degradomics, and tumor-host cell interaction provide clear information regarding the mechanism of cancer pathobiology. In this chapter, we emphasize the recent advances in secretory protein and the challenges in the field of secretome analysis and their clinical applications.

Prognostic model construction and validation of esophageal cancer cellular senescence-related genes and correlation with immune infiltration

Introduction

Cellular senescence is a cellular response to stress, including the activation of oncogenes, and is characterized by irreversible proliferation arrest. Restricted studies have provided a relationship between cellular senescence and immunotherapy for esophageal cancer.

Methods

In the present study, we obtained clinical sample of colon cancer from the TCGA database and cellular senescence-related genes from MSigDB and Genecard datasets. Cellular senescence-related prognostic genes were identified by WGCNA, COX, and lasso regression analysis, and a cellular senescence-related risk score (CSRS) was calculated. We constructed a prognostic model based on CSRS. Validation was performed with an independent cohort that GSE53625. Three scoring systems for immuno-infiltration analysis were performed, namely ssGSEA analysis, ESTIMATE scores and TIDE scores.

Result

Five cellular senescence-related genes, including H3C1, IGFBP1, MT1E, SOX5 and CDHR4 and used to calculate risk score. Multivariate regression analysis using cox regression model showed that cellular senescence-related risk scores (HR=2.440, 95% CI=1.154-5.159, p=0.019) and pathological stage (HR=2.423, 95% CI=1.119-5.249, p=0.025) were associated with overall survival (OS). The nomogram model predicts better clinical benefit than the American Joint Committee on Cancer (AJCC) staging for prognosis of patients with esophageal cancer with a five-year AUC of 0.946. Patients with high CSRS had a poor prognosis (HR=2.93, 95%CI=1.74-4.94, p<0.001). We observed differences in the distribution of CSRS in different pathological staging and therefore performed a subgroup survival analysis finding that assessment of prognosis by CSRS independent of pathological staging. Comprehensive immune infiltration analysis and functional enrichment analysis suggested that patients with high CSRS may develop immunotherapy resistance through mechanisms of deacetylation and methylation.

Discussion

In summary, our study suggested that CSRS is a prognostic risk factor for esophageal cancer. Patients with high CSRS may have worse immunotherapy outcomes.

Metapath-aggregated heterogeneous graph neural network for drug-target interaction prediction

Drug-target interaction (DTI) prediction is an essential step in drug repositioning. A few graph neural network (GNN)-based methods have been proposed for DTI prediction using heterogeneous biological data. However, existing GNN-based methods only aggregate information from directly connected nodes restricted in a drug-related or a target-related network and are incapable of capturing high-order dependencies in the biological heterogeneous graph. In this paper, we propose a metapath-aggregated heterogeneous graph neural network (MHGNN) to capture complex structures and rich semantics in the biological heterogeneous graph for DTI prediction. Specifically, MHGNN enhances heterogeneous graph structure learning and high-order semantics learning by modeling high-order relations via metapaths. Additionally, MHGNN enriches high-order correlations between drug-target pairs (DTPs) by constructing a DTP correlation graph with DTPs as nodes. We conduct extensive experiments on three biological heterogeneous datasets. MHGNN favorably surpasses 17 state-of-the-art methods over 6 evaluation metrics, which verifies its efficacy for DTI prediction. The code is available at https://github.com/Zora-LM/MHGNN-DTI.