Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula

ETHNOPHARMACOLOGICAL RELEVANCE

Network pharmacology is a new discipline based on systems biology theory, biological system network analysis, and multi-target drug molecule design specific signal node selection. The mechanism of action of TCM formula has the characteristics of multiple targets and levels. The mechanism is similar to the integrity, systematization and comprehensiveness of network pharmacology, so network pharmacology is suitable for the study of the pharmacological mechanism of Chinese medicine compounds.

AIM OF THE STUDY

The paper summarizes the present application status and existing problems of network pharmacology in the field of Chinese medicine formula, and formulates the research ideas, up-to-date key technology and application method and strategy of network pharmacology. Its purpose is to provide guidance and reference for using network pharmacology to reveal the modern scientific connotation of Chinese medicine.

MATERIALS AND METHODS

Literatures in this review were searched in PubMed, China National Knowledge Infrastructure (CNKI), Web of Science, ScienceDirect and Google Scholar using the keywords "traditional Chinese medicine", "Chinese herb medicine" and "network pharmacology". The literature cited in this review dates from 2002 to 2022.

RESULTS

Using network pharmacology methods to predict the basis and mechanism of pharmacodynamic substances of traditional Chinese medicines has become a trend.

CONCLUSION

Network pharmacology is a promising approach to reveal the pharmacology mechanism of Chinese medicine formula.

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.

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.. [DOI: 10.1101/2023.03.22.533772] [Cited by in Crossref: 0] [Impact Index Per Article: 0] Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain, e.g., for protein-protein-interaction (PPI) networks. Here, we present our ensemble-GNN library, which can be used to build federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplarily show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA)

Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain, e.g., for protein-protein-interaction (PPI) networks. Here, we present our ensemble-GNN library, which can be used to build federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplarily show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA).

Evaluation of the use of different cell types as a substrate pool forin vitrokinase reaction.. [DOI: 10.1101/2023.03.20.533483] [Cited by in Crossref: 0] [Impact Index Per Article: 0] We evaluated the effect of cell lines used to obtainin vitrosubstrates on phosphorylation motifs. Three kinases with representative phosphorylation motifs were evaluated in this study. And, we demonstrated that the difference of cell lines did not affect phosphorylation motifs obtained fromin vitrokinase reaction

We evaluated the effect of cell lines used to obtainin vitrosubstrates on phosphorylation motifs. Three kinases with representative phosphorylation motifs were evaluated in this study. And, we demonstrated that the difference of cell lines did not affect phosphorylation motifs obtained fromin vitrokinase reaction.

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.

CXC chemokine receptor 4 (CXCR4) blockade in cancer treatment

CXC chemokine receptor type 4 (CXCR4) is a member of the G protein-coupled receptors (GPCRs) superfamily and is specific for CXC chemokine ligand 12 (CXCL12, also known as SDF-1), which makes CXCL12/CXCR4 axis. CXCR4 interacts with its ligand, triggering downstream signaling pathways that influence cell proliferation chemotaxis, migration, and gene expression. The interaction also regulates physiological processes, including hematopoiesis, organogenesis, and tissue repair. Multiple evidence revealed that CXCL12/CXCR4 axis is implicated in several pathways involved in carcinogenesis and plays a key role in tumor growth, survival, angiogenesis, metastasis, and therapeutic resistance. Several CXCR4-targeting compounds have been discovered and used for preclinical and clinical cancer therapy, most of which have shown promising anti-tumor activity. In this review, we summarized the physiological signaling of the CXCL12/CXCR4 axis and described the role of this axis in tumor progression, and focused on the potential therapeutic options and strategies to block CXCR4.

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.

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.

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.

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.

Circ-ATIC regulates esophageal squamous cell carcinoma growth and metastasis through miR-1294/PBX3 pathway

Esophageal squamous cell carcinoma (ESCC) is a digestive tract malignancy associated with poor clinical outcome. Growing evidence have elucidated that circular RNAs (circRNAs) play important roles in the pathological process of ESCC. However, the detailed mechanisms how circRNAs modulate the development of ESCC remain largely unknown. Our study aimed to decipher the role and mechanism of circ-ATIC (also termed as circRNA_0058063) in regulating the progression of ESCC. We found that circ-ATIC and its host gene ATIC were significantly increased in ESCC tissues and cells compared with the adjacent noncancerous tissues or normal esophagus epithelial cell. Circ-ATIC knockdown substantially reduced proliferation and the number of invaded ESCC cells and retarded EMT process, reflecting by the decreased N-cadherin and elevated E-cadherin. However, the level of host gene ATIC was not changed under circ-ATIC suppression. It was predicted that circ-ATIC could bind to miR-1294 and serve as a sponge RNA. The luciferase reporter assay and RNA immunoprecipitation (RIP) assay confirmed their relations. MiR-1294 was decreased in ESCC tissues and cells, which was reversely correlated with circ-ATIC level. Furthermore, PBX3 was predicted and proved to be a downstream direct target of miR-1294. PBX3 mRNA and protein were obviously upregulated in ESCC tumor tissues and cells. PBX3 overexpression could reverse the suppressive roles of miR-1294 mimics on ESCC proliferation and invasion. In an xenograft nude mice model, stable transfection of sh-circ-ATIC significantly retarded the growth of tumor and suppressed VEGF and Ki67. Collectively, circ-ATIC promoted ESCC proliferation and invasion by regulating miR-1294/PBX3 axis.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Global profiling of protein lysine lactylation and potential target modified protein analysis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC), the most common type of primary liver cancer, often metastasizes to the lungs. The implications of lysine lactylation (Kla), a recently identified histone post-translational modification (PTM), in the pathology of HCC remain unclear. Here, we report the first proteomic survey of this specific modification in HCC (with no metastasis during 3 years of follow-up), normal liver tissues, and lung metastasis samples of HCC. Of the 2045 modification sites detected on 960 proteins, 1438 sites on 772 proteins contained quantitative information. Subsequently, we analyzed the differentially modified proteins among the different groups. Differentially lactylated proteins were found to be involved in several biological processes, including-but not limited to-amino acid metabolism, ribosomal protein synthesis, and fatty acid metabolism. In addition, we identified numerous highly valuable lactate-modified proteins from the literature. Among them, we verified the lactate modification levels of the following two tumor-related proteins and obtained similar results: USP14 and ABCF1. These two modified proteins will be further investigated in our future studies. This paper is the first report on the lactylome of HCC and it provides a reliable foundation for further research on Kla in HCC.

DM-MOGA: a multi-objective optimization genetic algorithm for identifying disease modules of non-small cell lung cancer

BACKGROUND

Constructing molecular interaction networks from microarray data and then identifying disease module biomarkers can provide insight into the underlying pathogenic mechanisms of non-small cell lung cancer. A promising approach for identifying disease modules in the network is community detection.

RESULTS

In order to identify disease modules from gene co-expression networks, a community detection method is proposed based on multi-objective optimization genetic algorithm with decomposition. The method is named DM-MOGA and possesses two highlights. First, the boundary correction strategy is designed for the modules obtained in the process of local module detection and pre-simplification. Second, during the evolution, we introduce Davies-Bouldin index and clustering coefficient as fitness functions which are improved and migrated to weighted networks. In order to identify modules that are more relevant to diseases, the above strategies are designed to consider the network topology of genes and the strength of connections with other genes at the same time. Experimental results of different gene expression datasets of non-small cell lung cancer demonstrate that the core modules obtained by DM-MOGA are more effective than those obtained by several other advanced module identification methods.

CONCLUSIONS

The proposed method identifies disease-relevant modules by optimizing two novel fitness functions to simultaneously consider the local topology of each gene and its connection strength with other genes. The association of the identified core modules with lung cancer has been confirmed by pathway and gene ontology enrichment analysis.

Canonical and non-canonical functions of NLRP3

BACKGROUND

Since its discovery, NLRP3 is almost never separated from its major role in the protein complex it forms with ASC, NEK7 and Caspase-1, the inflammasome. This key component of the innate immune response mediates the secretion of proinflammatory cytokines IL-1β and IL-18 involved in immune response to microbial infection and cellular damage. However, NLRP3 has also other functions that do not involve the inflammasome assembly nor the innate immune response. These non-canonical functions have been poorly studied. Nevertheless, NLRP3 is associated with different kind of diseases probably through its inflammasome dependent function as through its inflammasome independent functions.

AIM OF THE REVIEW

The study and understanding of the canonical and non-canonical functions of NLRP3 can help to better understand its involvement in various pathologies. In parallel, the description of the mechanisms of action and regulation of its various functions, can allow the identification of new therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF THE REVIEW

NLRP3 functions have mainly been studied in the context of the inflammasome, in myeloid cells and in totally deficient transgenic mice. However, for several year, the work of different teams has proven that NLRP3 is also expressed in other cell types where it has functions that are independent of the inflammasome. If these studies suggest that NLRP3 could play different roles in the cytoplasm or the nucleus of the cells, the mechanisms underlying NLRP3 non-canonical functions remain unclear. This is why we propose in this review an inventory of the canonical and non-canonical functions of NLRP3 and their impact in different pathologies.

RNA-seq preprocessing and sample size considerations for gene network inference

Background

Gene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to RNA-seq data is relatively recent. As the characteristics of RNA-seq data are different from microarray data, it is an unanswered question what preprocessing methods for RNA-seq data should be applied prior to GNI to attain optimal performance, or what the required sample size for RNA-seq data is to obtain reliable GNI estimates.

Results

We ran 9144 analysis of 7 different RNA-seq datasets to evaluate 300 different preprocessing combinations that include data transformations, normalizations and association estimators. We found that there was no single best performing preprocessing combination but that there were several good ones. The performance varied widely over various datasets, which emphasized the importance of choosing an appropriate preprocessing configuration before GNI. Two preprocessing combinations appeared promising in general: First, Log-2 TPM (transcript per million) with Variance-stabilizing transformation (VST) and Pearson Correlation Coefficient (PCC) association estimator. Second, raw RNA-seq count data with PCC. Along with these two, we also identified 18 other good preprocessing combinations. Any of these algorithms might perform best in different datasets. Therefore, the GNI performances of these approaches should be measured on any new dataset to select the best performing one for it. In terms of the required biological sample size of RNA-seq data, we found that between 30 to 85 samples were required to generate reliable GNI estimates.

Conclusions

This study provides practical recommendations on default choices for data preprocessing prior to GNI analysis of RNA-seq data to obtain optimal performance results.

The Molecular Characterization of Genetic Abnormalities in Esophageal Squamous Cell Carcinoma May Foster the Development of Targeted Therapies

Esophageal cancer is among the most common tumors in the world and is associated with poor outcomes, with a 5-year survival rate of about 10-20%. Two main histological subtypes are observed: esophageal squamous cell carcinoma (ESCC), more frequent among Asian populations, and esophageal adenocarcinoma (EAC), the predominant type in Western populations. The development of molecular analysis techniques has led to the definition of the molecular alterations observed in ESCC, consistently differing from those observed in EAC. The genetic alterations observed are complex and heterogeneous and involve gene mutations, gene deletions and gene amplifications. However, despite the consistent progress in the definition of the molecular basis of ESCC, precision oncology for these patients is still virtually absent. The recent identification of molecular subtypes of ESCC with clinical relevance may foster the development of new therapeutic strategies. It is estimated that about 40% of the genetic alterations observed in ESCC are actionable. Furthermore, the recent introduction of solid tumor immunotherapy with immune checkpoint inhibitors (ICIs) showed that a minority of ESCC patients are responsive, and the administration of ICIs, in combination with standard chemotherapy, significantly improves overall survival over chemotherapy in ESCC patients with advanced disease.

Mitigating the BFL1-mediated antiapoptotic pathway in diffuse large B cell lymphoma by inhibiting HDACs

Endogenous BFL1 expression renders diffuse large B-cell lymphoma (DLBCL) cells insensitive to B-cell lymphoma 2 (BCL2) and/or MCL1 inhibitors. Considering the difficulties in developing a direct BFL1 inhibitor, we intended to inhibit histone deacetylase (HDAC) to mitigate the biological role of BFL1 by modulating WT1 and NOXA. Cells expressing high BFL1 exhibited enhanced sensitivity to pan-HDAC inhibitor compared to low BFL1 expressing cells, mainly attributable to the difference in the amount of apoptosis. HDAC inhibitors decreased BFL1 and WT1 expressions while increasing NOXA levels. The BFL1 knockdown experiment demonstrated that HDAC inhibitor's sensitivity depends on the BFL1 expression in DLBCL cells. Furthermore, we found that the specific HDAC class was expected to play a critical role in BFL1 inhibition by comparing the effects of several HDAC inhibitors. Thus, our study provides a rationale for using HDAC inhibitors to induce apoptosis in DLBCL patients using BFL1 as a predictive biomarker.

Pepsinogen/Proton Pump Co-Expression in Barrett's Esophageal Cells Induces Cancer-Associated Changes

EDUCATIONAL OBJECTIVE

At the conclusion of this presentation, participants should better understand the carcinogenic potential of pepsin and proton pump expression in Barrett's esophagus.

OBJECTIVE

Barrett's esophagus (BE) is a well-known risk factor for esophageal adenocarcinoma (EAC). Gastric H⁺ /K⁺ ATPase proton pump and pepsin expression has been demonstrated in some cases of BE; however, the contribution of local pepsin and proton pump expression to carcinogenesis is unknown. In this study, RNA sequencing was used to examine global transcriptomic changes in a BE cell line ectopically expressing pepsinogen and/or gastric H⁺ /K⁺ ATPase proton pumps.

STUDY DESIGN

In vitro translational.

METHODS

BAR-T, a human BE cell line devoid of expression of pepsinogen or proton pumps, was transduced by lentivirus-encoding pepsinogen (PGA5) and/or gastric proton pump subunits (ATP4A, ATP4B). Changes relative to the parental line were assessed by RNA sequencing.

RESULTS

Top canonical pathways associated with protein-coding genes differentially expressed in pepsinogen and/or proton pump expressing BAR-T cells included those involved in the tumor microenvironment and epithelial-mesenchymal transition. Top upstream regulators of coding transcripts included TGFB1 and ERBB2, which are associated with the pathogenesis and prognosis of BE and EAC. Top upstream regulators of noncoding transcripts included p300-CBP, I-BET-151, and CD93, which have previously described associations with EAC or carcinogenesis. The top associated disease of both coding and noncoding transcripts was cancer.

CONCLUSIONS

These data support the carcinogenic potential of pepsin and proton pump expression in BE and reveal molecular pathways affected by their expression. Further study is warranted to investigate the role of these pathways in carcinogenesis associated with BE.

LEVEL OF EVIDENCE

NA Laryngoscope, 133:59-69, 2023.

Peptide-based covalent inhibitors of protein-protein interactions

Protein-protein interactions (PPI) are involved in all cellular processes and many represent attractive therapeutic targets. However, the frequently rather flat and large interaction areas render the identification of small molecular PPI inhibitors very challenging. As an alternative, peptide interaction motifs derived from a PPI interface can serve as starting points for the development of inhibitors. However, certain proteins remain challenging targets when applying inhibitors with a competitive mode of action. For that reason, peptide-based ligands with an irreversible binding mode have gained attention in recent years. This review summarizes examples of covalent inhibitors that employ peptidic binders and have been tested in a biological context.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.