Semi-supervised heterogeneous graph contrastive learning for drug-target interaction prediction

Identification of drug-target interactions (DTIs) is an important step in drug discovery and drug repositioning. In recent years, graph-based methods have attracted great attention and show advantages on predicting potential DTIs. However, these methods face the problem that the known DTIs are very limited and expensive to obtain, which decreases the generalization ability of the methods. Self-supervised contrastive learning is independent of labeled DTIs, which can mitigate the impact of the problem. Therefore, we propose a framework SHGCL-DTI for predicting DTIs, which supplements the classical semi-supervised DTI prediction task with an auxiliary graph contrastive learning module. Specifically, we generate representations for the nodes through the neighbor view and meta-path view, and define positive and negative pairs to maximize the similarity between positive pairs from different views. Subsequently, SHGCL-DTI reconstructs the original heterogeneous network to predict the potential DTIs. The experiments on the public dataset show that SHGCL-DTI has significant improvement in different scenarios, compared with existing state-of-the-art methods. We also demonstrate that the contrastive learning module improves the prediction performance and generalization ability of SHGCL-DTI through ablation study. In addition, we have found several novel predicted DTIs supported by the biological literature. The data and source code are available at: https://github.com/TOJSSE-iData/SHGCL-DTI.

Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder

Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negative rates observed in PPI data. To overcome this problem, a novel PPI prediction algorithm, namely PASNVGA, is proposed in this work by combining the sequence and network information of proteins via variational graph autoencoder. To do so, PASNVGA first applies different strategies to extract the features of proteins from their sequence and network information, and obtains a more compact form of these features using principal component analysis. In addition, PASNVGA designs a scoring function to measure the higher-order connectivity between proteins and so as to obtain a higher-order adjacency matrix. With all these features and adjacency matrices, PASNVGA trains a variational graph autoencoder model to further learn the integrated embeddings of proteins. The prediction task is then completed by using a simple feedforward neural network. Extensive experiments have been conducted on five PPI datasets collected from different species. Compared with several state-of-the-art algorithms, PASNVGA has been demonstrated as a promising PPI prediction algorithm.

Predicting Drug-Target Interaction Via Self-Supervised Learning

Recent advances in graph representation learning provide new opportunities for computational drug-target interaction (DTI) prediction. However, it still suffers from deficiencies of dependence on manual labels and vulnerability to attacks. Inspired by the success of self-supervised learning (SSL) algorithms, which can leverage input data itself as supervision,we propose SupDTI, a SSL-enhanced drug-target interaction prediction framework based on a heterogeneous network (i.e., drug-protein, drug-drug, and protein-protein interaction network; drug-disease, drug-side-effect, and protein-disease association network; drug-structure and protein-sequence similarity network). Specifically, SupDTI is an end-to-end learning framework consisting of five components. First, localized and globalized graph convolutions are designed to capture the nodes' information from both local and global perspectives, respectively. Then, we develop a variational autoencoder to constrain the nodes' representation to have desired statistical characteristics. Finally, a unified self-supervised learning strategy is leveraged to enhance the nodes' representation, namely, a contrastive learning module is employed to enable the nodes' representation to fit the graph-level representation, followed by a generative learning module which further maximizes the node-level agreement across the global and local views by learning the probabilistic connectivity distribution of the original heterogeneous network. Experimental results show that our model can achieve better prediction performance than state-of-the-art methods.

CACO: A Core-Attachment Method With Cross-Species Functional Ortholog Information to Detect Human Protein Complexes

Protein complexes play an essential role in living cells. Detecting protein complexes is crucial to understand protein functions and treat complex diseases. Due to high time and resource consumption of experiment approaches, many computational approaches have been proposed to detect protein complexes. However, most of them are only based on protein-protein interaction (PPI) networks, which heavily suffer from the noise in PPI networks. Therefore, we propose a novel core-attachment method, named CACO, to detect human protein complexes, by integrating the functional information from other species via protein ortholog relations. First, CACO constructs a cross-species ortholog relation matrix and transfers GO terms from other species as a reference to evaluate the confidence of PPIs. Then, a PPI filter strategy is adopted to clean the PPI network and thus a weighted clean PPI network is constructed. Finally, a new effective core-attachment algorithm is proposed to detect protein complexes from the weighted PPI network. Compared to other thirteen state-of-the-art methods, CACO outperforms all of them in terms of F-measure and Composite Score, showing that integrating ortholog information and the proposed core-attachment algorithm are effective in detecting protein complexes.

Gold nanoparticles attenuate the interferon-γ induced SOCS1 expression and activation of NF-κB p65/50 activity via modulation of microRNA-155-5p in triple-negative breast cancer cells

Objective

Triple-negative breast cancer (TNBC) is a very aggressive form of cancer that grows and spreads very fast and generally relapses. Therapeutic options of TNBC are limited and still need to be explored completely. Gold nanoparticles conjugated with citrate (citrate-AuNPs) are reported to have anticancer potential; however, their role in regulating microRNAs (miRNAs) in TNBC has never been investigated. This study investigated the potential of citrate-AuNPs against tumorigenic inflammation via modulation of miRNAs in TNBC cells.

Methods

Gold nanoparticles were chemically synthesized using the trisodium-citrate method and were characterized by UV-Vis spectrophotometry and dynamic light scattering studies. Targetscan bioinformatics was used to analyze miRNA target genes. Levels of miRNA and mRNA were quantified using TaqMan assays. The pairing of miRNA in 3'untranslated region (3'UTR) of mRNA was validated by luciferase reporter clone, containing the entire 3'UTR of mRNA, and findings were further re-validated via transfection with miRNA inhibitors.

Results

Newly synthesized citrate-AuNPs were highly stable, with a mean size was 28.3 nm. The data determined that hsa-miR155-5p is a direct regulator of SOCS1 (suppressor-of-cytokine-signaling) expression and citrate-AuNPs inhibits SOCS1 mRNA/protein expression via modulating hsa-miR155-5p expression. Transfection of TNBC MDA-MB-231 cells with anti-miR155-5p markedly increased SOCS1 expression (p<0.001), while citrate-AuNPs treatment significantly inhibited anti-miR155-5p transfection-induced SOCS1 expression (p<0.05). These findings were validated by IFN-γ-stimulated MDA-MB-231 cells. Moreover, the data also determined that citrate-AuNPs also inhibit IFN-γ-induced NF-κB p65/p50 activation in MDA-MB-231 cells transfected with anti-hsa-miR155-5p.

Conclusion

Newly generated citrate-AuNPs were stable and non-toxic to TNBC cells. Citrate-AuNPs inhibit IFN-γ-induced SOCS1 mRNA/protein expression and deactivate NF-κB p65/50 activity via negative regulation of hsa-miR155-5p. These novel pharmacological actions of citrate-AuNPs on IFN-γ-stimulated TNBC cells provide insights that AuNPs inhibit IFN-γ induced inflammation in TNBC cells by modulating the expression of microRNAs.

Poly(A)-specific ribonuclease protein promotes the proliferation, invasion and migration of esophageal cancer cells

BACKGROUND

Bioinformatics analysis showed that the expression of the poly(A)-specific ribonuclease (PARN) gene in gastric cancer, head and neck squamous cell carcinoma, melanoma, cervical cancer and lung squamous cell carcinoma tissues was significantly higher than that in normal tissues and was associated with high stage and poor prognosis. The expression of the PARN gene in esophageal cancer (EC) tissue is also significantly higher than that in normal tissues, but the effect of PARN on the proliferation, migration and invasion of EC cells remains unclear.

AIM

To investigate the relationship between PARN and the proliferation, migration and invasion of EC cells.

METHODS

The EC tissues of 91 patients after EC surgery and 63 paired precancerous healthy tissues were collected. PARN mRNA levels were measured using a tissue microarray, and the PARN expression level was evaluated using immunohistochemistry to analyze the relationship between PARN expression and clinicopathologic features as well as the survival and prognosis of patients. In addition, the effects of PARN gene knockout on tumor cell proliferation, invasion and migration were studied by using shRNA during the in vitro culture of EC cell lines Eca-109 and TE-1, and the effects of the PARN gene on tumor growth in vivo were verified by a xenotransplantation nude mice model.

RESULTS

The expression of PARN in EC tissues was higher than that in adjacent normal tissues, and the level of PARN expression was significantly positively correlated with lymphatic metastasis. Patients with high PARN levels had poor overall survival. BIM, IGFBP-5 and p21 levels were significantly increased in the PARN knockout group, while the expression levels of the antiapoptotic proteins Survivin and sTNF-R1 were significantly decreased in the apoptotic antibody array data. In addition, the expression levels of Akt, p-Akt, PIK3CA and CCND1 in the downstream signaling pathway regulating EC progression were significantly decreased. The culture of EC cell lines confirmed that the apoptosis rate of EC cells was significantly increased, the growth and proliferation of tumor cells were significantly inhibited, and the invasion and migration ability of tumor cells were significantly decreased after PARN gene knockout. In vivo experiments of BALB/c nude mice transfected with Eca-109 cells expressing control shRNA (sh-NC) and PARN shRNA (sh-PARN) showed that the tumor volume and weight of nude mice treated with sh-PARN were significantly decreased compared with those of nude mice treated with sh-NC, indicating that PARN knockdown significantly inhibited tumor growth in vivo.

CONCLUSION

PARN has antiapoptotic effects on EC cells and promotes their proliferation, invasion and migration, which is associated with the development of EC and poor patient prognosis. PARN may become a potential target for the diagnosis, prognosis prediction and treatment of EC.

Advances in targeted therapy and immunotherapy for esophageal cancer

ABSTRACT

Esophageal cancer (EC) is one of the most common aggressive malignant tumors in the digestive system with a severe epidemiological situation and poor prognosis. The early diagnostic rate of EC is low, and most EC patients are diagnosed at an advanced stage. Multiple multimodality treatments have gradually evolved into the main treatment for advanced EC, including surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy. And the emergence of targeted therapy and immunotherapy has greatly improved the survival of EC patients. This review highlights the latest advances in targeted therapy and immunotherapy for EC, discusses the efficacy and safety of relevant drugs, summarizes related important clinical trials, and tries to provide references for therapeutic strategy of EC.

Phosphatidylcholine (PCL) fortified nano-phytopharmaceuticals for improvement of therapeutic efficacy

Phytopharmaceuticals, derived from plants, are increasingly recognized for their potential therapeutic benefits. However, their effectiveness is often hindered by challenges such as poor bioavailability, stability, and targeted delivery. In this study, we aimed to address these limitations by developing PCL (phosphatidylcholine) fortified nano-phytopharmaceuticals to enhance therapeutic efficacy. PCL, a biocompatible and biodegradable polymer, was employed to encapsulate the phytopharmaceuticals, thereby improving their stability and bioavailability. The encapsulation process utilized nanoprecipitation, resulting in the formation of nanoparticles with controlled size and morphology. Various analytical techniques were employed to characterize the physicochemical properties of PCL fortified nano-phytopharmaceuticals, including dynamic light scattering, scanning electron microscopy, and Fourier-transform infrared spectroscopy. Furthermore, the release kinetics of encapsulated phytopharmaceuticals from PCL nanoparticles were evaluated, demonstrating sustained and controlled release profiles, essential for prolonged therapeutic effects. Cytotoxicity studies conducted on in vitro cell culture models confirmed the biocompatibility and non-toxic nature of the developed nano-phytopharmaceuticals. Additionally, in vivo studies were conducted to assess the therapeutic efficacy of PCL fortified nano-phytopharmaceuticals in animal models. The results showIased improved bioavailability, targeted tissue distribution, and enhanced therapeutic effects compared to free phytopharmaceuticals. Moreover, the developed nano-phytopharmaceuticals exhibited prolonged circulation time in the bloodstream, enabling improved drug delivery and reduced dosing frequency. This review highlights the promising potential of PCL fortified nano-phytopharmaceuticals as an effective approach for enhancing the therapeutic efficacy of phytopharmaceuticals. The improved stability, bioavailability, sustained release, and targeted delivery achieved through this formulation strategy offer promising opportunities for advancing plant-based therapies. See also the Graphical abstract(Fig. 1).

Ancistrocladinium A Induces Apoptosis in Proteasome Inhibitor-Resistant Multiple Myeloma Cells: A Promising Therapeutic Agent Candidate

The N,C-coupled naphthylisoquinoline alkaloid ancistrocladinium A belongs to a novel class of natural products with potent antiprotozoal activity. Its effects on tumor cells, however, have not yet been explored. We demonstrate the antitumor activity of ancistrocladinium A in multiple myeloma (MM), a yet incurable blood cancer that represents a model disease for adaptation to proteotoxic stress. Viability assays showed a potent apoptosis-inducing effect of ancistrocladinium A in MM cell lines, including those with proteasome inhibitor (PI) resistance, and in primary MM cells, but not in non-malignant blood cells. Concomitant treatment with the PI carfilzomib or the histone deacetylase inhibitor panobinostat strongly enhanced the ancistrocladinium A-induced apoptosis. Mass spectrometry with biotinylated ancistrocladinium A revealed significant enrichment of RNA-splicing-associated proteins. Affected RNA-splicing-associated pathways included genes involved in proteotoxic stress response, such as PSMB5-associated genes and the heat shock proteins HSP90 and HSP70. Furthermore, we found strong induction of ATF4 and the ATM/H2AX pathway, both of which are critically involved in the integrated cellular response following proteotoxic and oxidative stress. Taken together, our data indicate that ancistrocladinium A targets cellular stress regulation in MM and improves the therapeutic response to PIs or overcomes PI resistance, and thus may represent a promising potential therapeutic agent.

Targeted Glioblastoma Treatment via Synthesis and Functionalization of Gold Nanoparticles With De Novo-Engineered Transferrin-Like Peptides: Protocol for a Novel Method

BACKGROUND

Glioblastoma multiforme (GBM) is an aggressive brain tumor with limited treatment options due to the blood-brain barrier's (BBB's) impedance and inherent resistance to chemotherapy. Gold nanoparticles (AuNPs) functionalized with transferrin-like peptides show promise in overcoming these challenges, enhancing drug delivery to the brain, and reducing chemotherapy resistance.

OBJECTIVE

The primary goal of this study is to establish a detailed protocol for synthesizing and stabilizing AuNPs, functionalizing them with de novo-engineered transferrin-like peptides, and conjugating them with the chemotherapeutic agent temozolomide. This strategy aims to improve drug delivery across the BBB and circumvent chemotherapy resistance. The secondary objective includes an assessment of the safety and potential for in vivo use of the synthesized nanoparticle complex.

METHODS

The proposal involves multiple steps with rigorous quality control of AuNP synthesis, stabilization with surfactants, and polyethylene glycol coating. The engineered transferrin-like peptides will be synthesized and attached to the AuNPs' surface, followed by the attachment of temozolomide and O6-methylguanine-DNA methyltransferase inhibitors. The resulting complex will undergo in vitro testing to assess BBB penetration, efficacy against GBM cells, and potential toxicity.

RESULTS

Initial preliminary experiments and simulations suggest successful synthesis and stabilization of AuNPs and effective attachment of transferrin-like peptides. We propose peptide attachment verification using Fourier transform infrared spectroscopy and surface plasmon resonance. Additionally, we will conduct pH stability tests to ensure our functionalized AuNPs retain their properties in acidic brain tumor microenvironments.

CONCLUSIONS

The proposed functionalization of AuNPs with de novo-engineered transferrin-like peptides represents a novel approach to GBM treatment. Our strategy opens new avenues for drug delivery across the BBB and chemotherapy resistance reduction. While we primarily focus on in vitro studies and computational modeling at this stage, successful completion will lead to further development, including in vivo studies and nanoparticle design optimization. This proposal anticipates inspiring future research and funding in neuro-oncology, presenting a potentially innovative and effective treatment option for GBM.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/49417.

The Dual Role of Necroptosis in Pancreatic Ductal Adenocarcinoma

Pancreatic cancer (PC) is the seventh leading cause of cancer-related death. PC incidence has continued to increase by about 1% each year in both men and women. Although the 5-year relative survival rate of PC has increased from 3% to 12%, it is still the lowest among cancers. Hence, novel therapeutic strategies are urgently needed. Challenges in PC-targeted therapeutic strategies stem from the high PC heterogeneity and from the poorly understood interplay between cancer cells and the surrounding microenvironment. Signaling pathways that drive PC cell growth have been the subject of intense scrutiny and interest has been attracted by necroptosis, a distinct type of programmed cell death. In this review, we provide a historical background on necroptosis and a detailed analysis of the ongoing debate on the role of necroptosis in PC malignant progression.

Comprehensive analysis of circRNAs for N7-methylguanosine methylation modification in human oral squamous cell carcinoma

N7-methylguanosine (m7G) modification is closely related to the occurrence of tumors. However, the m7G modification of circRNAs in oral squamous cell carcinoma (OSCC) remains to be investigated. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) was used to measure the methylation levels of m7G and identify m7G sites in circRNAs in human OSCC and normal tissues. The host genes of differentially methylated and differentially expressed circRNAs were analyzed by Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, and circRNA-miRNA-mRNA networks were predicted using the miRanda and miRDB databases. The analysis identified 2348 m7G peaks in 624 circRNAs in OSCC tissues. In addition, the source of m7G-methylated circRNAs in OSCC was mainly the sense overlap region compared with normal tissues. The most conserved m7G motif in OSCC tissues was CCUGU, whereas the most conserved motif in normal tissues was RCCUG (R = G/A). Importantly, GO enrichment and KEGG pathway analysis showed that the host genes of differentially methylated and differentially expressed circRNAs were involved in many cellular biological functions. Furthermore, the significantly differentially expressed circRNAs were analyzed to predict the circRNA-miRNA-mRNA networks. This study revealed the whole profile of circRNAs of differential m7G methylation in OSCC and suggests that m7G-modified circRNAs may impact the development of OSCC.

TMEM16A partners with mTOR to influence pathways of cell survival, proliferation, and migration in cholangiocarcinoma

Expression of transmembrane protein 16 A (TMEM16A), a calcium activated chloride channel, is elevated in some human cancers and impacts tumor cell proliferation, metastasis, and patient outcome. Evidence presented here uncovers a molecular synergy between TMEM16A and mechanistic/mammalian target of rapamycin (mTOR), a serine-threonine kinase that is known to promote cell survival and proliferation in cholangiocarcinoma (CCA), a lethal cancer of the secretory cells of bile ducts. Analysis of gene and protein expression in human CCA tissue and CCA cell line detected elevated TMEM16A expression and Cl- channel activity. The Cl- channel activity of TMEM16A impacted the actin cytoskeleton and the ability of cells to survive, proliferate, and migrate as revealed by pharmacological inhibition studies. The basal activity of mTOR, too, was elevated in the CCA cell line compared with the normal cholangiocytes. Molecular inhibition studies provided further evidence that TMEM16A and mTOR were each able to influence the regulation of the other's activity or expression respectively. Consistent with this reciprocal regulation, combined TMEM16A and mTOR inhibition produced a greater loss of CCA cell survival and migration than their individual inhibition alone. Together these data reveal that the aberrant TMEM16A expression and cooperation with mTOR contribute to a certain advantage in CCA.NEW & NOTEWORTHY This study points to the dysregulation of transmembrane protein 16 A (TMEM16A) expression and activity in cholangiocarcinoma (CCA), the inhibition of which has functional consequences. Dysregulated TMEM16A exerts an influence on the regulation of mechanistic/mammalian target of rapamycin (mTOR) activity. Moreover, the reciprocal regulation of TMEM16A by mTOR demonstrates a novel connection between these two protein families. These findings support a model in which TMEM16A intersects the mTOR pathway to regulate cell cytoskeleton, survival, proliferation, and migration in CCA.

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network

MOTIVATION

The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction.

RESULTS

In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets.

AVAILABILITY AND IMPLEMENTATION

Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.

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.

Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma

Lung adenocarcinoma (LUAD) is associated with a low survival rate at advanced stages. Although the development of targeted therapies has improved outcomes in LUAD patients with identified and specific genetic alterations, such as activating mutations on the epidermal growth factor receptor gene (EGFR), the emergence of tumor resistance eventually occurs in all patients and this is driving the development of new therapies. In this paper, we present the In Silico EGFR-mutant LUAD (ISELA) model that links LUAD patients' individual characteristics, including tumor genetic heterogeneity, to tumor size evolution and tumor progression over time under first generation EGFR tyrosine kinase inhibitor gefitinib. This translational mechanistic model gathers extensive knowledge on LUAD and was calibrated on multiple scales, including in vitro, human tumor xenograft mouse and human, reproducing more than 90% of the experimental data identified. Moreover, with 98.5% coverage and 99.4% negative logrank tests, the model accurately reproduced the time to progression from the Lux-Lung 7 clinical trial, which was unused in calibration, thus supporting the model high predictive value. This knowledge-based mechanistic model could be a valuable tool in the development of new therapies targeting EGFR-mutant LUAD as a foundation for the generation of synthetic control arms.

The Influence of COVID-19 in Endocrine Research: Critical Overview, Methodological Implications and a Guideline for Future Designs

The COVID-19 pandemic has changed many aspects of people's lives, including not only individual social behavior, healthcare procedures, and altered physiological and pathophysiological responses. As a result, some medical studies may be influenced by one or more hidden factors brought about by the COVID-19 pandemic. Using the literature review method, we are briefly discussing the studies that are confounded by COVID-19 and facemask-induced partiality and how these factors can be further complicated with other confounding variables. Facemask wearing has been reported to produce partiality in studies of ophthalmology (particularly dry eye and related ocular diseases), sleep studies, cognitive studies (such as emotion-recognition accuracy research, etc.), and gender-influenced studies, to mention a few. There is a possibility that some other COVID-19 related influences remain unrecognized in medical research. To account for heterogeneity, current and future studies need to consider the severity of the initial illness (such as diabetes, other endocrine disorders), and COVID-19 infection, the timing of analysis, or the presence of a control group. Face mask-induced influences may confound the results of diabetes studies in many ways.

Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells

Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes.

Mixture-Based Screening of Focused Combinatorial Libraries by NMR: Application to the Antiapoptotic Protein hMcl-1

We report on an innovative ligand discovery strategy based on protein NMR-based screening of a combinatorial library of ∼125,000 compounds that was arranged in 96 distinct mixtures. Using sensitive solution protein NMR spectroscopy and chemical perturbation-based screening followed by an iterative synthesis, deconvolutions, and optimization strategy, we demonstrate that the approach could be useful in the identification of initial binding molecules for difficult drug targets, such as those involved in protein-protein interactions. As an application, we will report novel agents targeting the Bcl-2 family protein hMcl-1. The approach is of general applicability and could be deployed as an effective screening strategy for de novo identification of ligands, particularly when tackling targets involved in protein-protein interactions.

Aberrations in ion channels interacting with lipid metabolism and epithelial-mesenchymal transition in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is the most prevalent malignant gastrointestinal tumor. Ion channels contribute to tumor growth and progression through interactions with their neighboring molecules including lipids. The dysregulation of membrane ion channels and lipid metabolism may contribute to the epithelial-mesenchymal transition (EMT), leading to metastatic progression. Herein, transcriptome profiles of patients with ESCC were analyzed by performing differential gene expression and weighted gene co-expression network analysis to identify the altered ion channels, lipid metabolism- and EMT-related genes in ESCC. A total of 1,081 differentially expressed genes, including 113 ion channels, 487 lipid metabolism-related, and 537 EMT-related genes, were identified in patients with ESCC. Thereafter, EMT scores were correlated with altered co-expressed genes. The altered co-expressed genes indicated a correlation with EMT signatures. Interactions among 22 ion channels with 3 hub lipid metabolism- and 13 hub EMT-related proteins were determined using protein-protein interaction networks. A pathway map was generated to depict deregulated signaling pathways including insulin resistance and the estrogen receptor-Ca²⁺ signaling pathway in ESCC. The relationship between potential ion channels and 5-year survival rates in ESCC was determined using Kaplan-Meier plots and Cox proportional hazard regression analysis. Inositol 1,4,5-trisphosphate receptor type 3 (ITPR3) was found to be associated with poor prognosis of patients with ESCC. Additionally, drugs interacting with potential ion channels, including GJA1 and ITPR3, were identified. Understanding alterations in ion channels with lipid metabolism and EMT in ESCC pathophysiology would most likely provide potential targets for the better treatment of patients with ESCC.

Lipidation of a bioactive cyclotide-based CXCR4 antagonist greatly improves its pharmacokinetic profile in vivo

The CXCR4 chemokine is a key molecular regulator of many biological functions controlling leukocyte functions during inflammation and immunity, and during embryonic development. Overexpression of CXCR4 is also associated with many types of cancer where its activation promotes angiogenesis, tumor growth/survival, and metastasis. In addition, CXCR4 is involved in HIV replication, working as a co-receptor for viral entry, making CXCR4 a very attractive target for developing novel therapeutic agents. Here we report the pharmacokinetic profile in rats of a potent CXCR4 antagonist cyclotide, MCo-CVX-5c, previously developed in our group that displayed a remarkable in vivo resistance to biological degradation in serum. This bioactive cyclotide, however, was rapidly eliminated through renal clearance. Several lipidated versions of cyclotide MCo-CVX-5c showed a significant increase in the half-life when compared to the unlipidated form. The palmitoylated version of cyclotide MCo-CVX-5c displayed similar CXCR4 antagonistic activity as the unlipidated cyclotide, while the cyclotide modified with octadecanedioic (18-oxo-octadecanoic) acid exhibited a remarkable decrease in its ability to antagonize CXCR4. Similar results were also obtained when tested for its ability to inhibit growth in two cancer cell lines and HIV infection in cells. These results show that the half-life of cyclotides can be improved by lipidation although it can also affect their biological activity depending on the lipid employed.

Preclinical evaluation of a novel EGFR&c-Met bispecific near infrared probe for visualization of esophageal cancer and metastatic lymph nodes

PURPOSE

This study aimed to establish a near infrared fluorescent (NIRF) probe based on an EGFR&c-Met bispecific antibody for visualization of esophageal cancer (EC) and metastatic lymph nodes (mLNs).

METHODS

EGFR and c-Met expression were assessed by immunohistochemistry. EGFR&c-Met bispecific antibody EMB01 was labeled with IRDye800cw. The binding of EMB01-IR800 was assessed by enzyme linked immunosorbent assay, flow cytometry, and immunofluorescence. Subcutaneous tumors, orthotopic tumors, and patient-derived xenograft (PDX) were established for in vivo fluorescent imaging. PDX models using lymph nodes with or without metastasis were constructed to assess the performance of EMB01-IR800 in differential diagnosis of lymph nodes.

RESULTS

The prevalence of overexpressing EGFR or c-Met was significantly higher than single marker either in EC or corresponding mLNs. The bispecific probe EMB01-IR800 was successfully synthesized, with strong binding affinity. EMB01-IR800 showed strong cellular binding to both Kyse30 (EGFR overexpressing) and OE33 (c-Met overexpressing) cells. In vivo fluorescent imaging showed prominent EMB01-IR800 uptake in either Kyse30 or OE33 subcutaneous tumors. Likewise, EMB01-IR800 exhibited superior tumor enrichment in both thoracic orthotopic esophageal squamous cell carcinoma and abdominal orthotopic esophageal adenocarcinoma models. Moreover, EMB01-IR800 produced significantly higher fluorescence in patient-derived mLNs than in benign lymph nodes.

CONCLUSION

This study demonstrated the complementary overexpression of EGFR and c-Met in EC. Compared to single-target probes, the EGFR&c-Met bispecific NIRF probe can efficiently depict heterogeneous esophageal tumors and mLNs, which greatly increased the sensitivity of tumor and mLN identification.

In silico evaluation of the role of lisdexamfetamine on attention-deficit/hyperactivity disorder common psychiatric comorbidities: mechanistic insights on binge eating disorder and depression

Attention-deficit/hyperactivity disorder (ADHD) is a psychiatric condition well recognized in the pediatric population that can persist into adulthood. The vast majority of patients with ADHD present psychiatric comorbidities that have been suggested to share, to some extent, the pathophysiological mechanism of ADHD. Lisdexamfetamine (LDX) is a stimulant prodrug approved for treating ADHD and, in the US, also for binge eating disorder (BED). Herein, we evaluated, through a systems biology-based in silico method, the efficacy of a virtual model of LDX (vLDX) as ADHD treatment to improve five common ADHD psychiatric comorbidities in adults and children, and we explored the molecular mechanisms behind LDX's predicted efficacy. After the molecular characterization of vLDX and the comorbidities (anxiety, BED, bipolar disorder, depression, and tics disorder), we created a protein-protein interaction human network to which we applied artificial neural networks (ANN) algorithms. We also generated virtual populations of adults and children-adolescents totaling 2,600 individuals and obtained the predicted protein activity from Therapeutic Performance Mapping System models. The latter showed that ADHD molecular description shared 53% of its protein effectors with at least one studied psychiatric comorbidity. According to the ANN analysis, proteins targeted by vLDX are predicted to have a high probability of being related to BED and depression. In BED, vLDX was modeled to act upon neurotransmission and neuroplasticity regulators, and, in depression, vLDX regulated the hypothalamic-pituitary-adrenal axis, neuroinflammation, oxidative stress, and glutamatergic excitotoxicity. In conclusion, our modeling results, despite their limitations and although requiring in vitro or in vivo validation, could supplement the design of preclinical and potentially clinical studies that investigate treatment for patients with ADHD with psychiatric comorbidities, especially from a molecular point of view.

Potential Applications of Nanoparticles in Improving the Outcome of Lung Cancer Treatment

Lung cancer is managed using conventional therapies, including chemotherapy, radiation therapy, or a combination of both. Each of these therapies has its own limitations, such as the indiscriminate killing of normal as well as cancer cells, the solubility of the chemotherapeutic drugs, rapid clearance of the drugs from circulation before reaching the tumor site, the resistance of cancer cells to radiation, and over-sensitization of normal cells to radiation. Other treatment modalities include gene therapy, immunological checkpoint inhibitors, drug repurposing, and in situ cryo-immune engineering (ICIE) strategy. Nanotechnology has come to the rescue to overcome many shortfalls of conventional therapies. Some of the nano-formulated chemotherapeutic drugs, as well as nanoparticles and nanostructures with surface modifications, have been used for effective cancer cell killing and radio sensitization, respectively. Nano-enabled drug delivery systems act as cargo to deliver the sensitizer molecules specifically to the tumor cells, thereby enabling the radiation therapy to be more effective. In this review, we have discussed the different conventional chemotherapies and radiation therapies used for inhibiting lung cancer. We have also discussed the improvement in chemotherapy and radiation sensitization using nanoparticles.

CXCL12-CXCR4/CXCR7 Axis in Cancer: from Mechanisms to Clinical Applications

Cancer is a multi-step disease caused by the accumulation of genetic mutations and/or epigenetic changes, and is the biggest challenge around the world. Cytokines, including chemokines, exhibit expression changes and disorders in all human cancers. These cytokine abnormalities can disrupt homeostasis and immune function, and make outstanding contributions to various stages of cancer development such as invasion, metastasis, and angiogenesis. Chemokines are a superfamily of small molecule chemoattractive cytokines that mediate a variety of cellular functions. Importantly, the interactions of chemokine members CXCL12 and its receptors CXCR4 and CXCR7 have a broad impact on tumor cell proliferation, survival, angiogenesis, metastasis, and tumor microenvironment, and thus participate in the onset and development of many cancers including leukemia, breast cancer, lung cancer, prostate cancer and multiple myeloma. Therefore, this review aims to summarize the latest research progress and future challenges regarding the role of CXCL12-CXCR4/CXCR7 signaling axis in cancer, and highlights the potential of CXCL12-CXCR4/CXCR7 as a biomarker or therapeutic target for cancer, providing essential strategies for the development of novel targeted cancer therapies.

Cross-Kingdom Interaction of miRNAs and Gut Microbiota with Non-Invasive Diagnostic and Therapeutic Implications in Colorectal Cancer

Colorectal cancer (CRC) has one of the highest incidences among all types of malignant diseases, affecting millions of people worldwide. It shows slow progression, making it preventable. However, this is not the case due to shortcomings in its diagnostic and management procedure and a lack of effective non-invasive biomarkers for screening. Here, we discuss CRC-associated microRNAs (miRNAs) and gut microbial species with potential as CRC diagnostic and therapy biomarkers. We provide rich evidence of cross-kingdom miRNA-mediated interactions between the host and gut microbiome. miRNAs have emerged with the ability to shape the composition and dynamics of gut microbiota. Intestinal microbes can uptake miRNAs, which in turn influence microbial growth and provide the ability to regulate the abundance of various microbial species. In the context of CRC, targeting miRNAs could aid in manipulating the balance of the microbiota. Our findings suggest the need for correlation analysis between the composition of the gut microbiome and the miRNA expression profile.

Molecular Characterization of Esophageal Squamous Cell Carcinoma Using Quantitative Proteomics

Esophageal squamous cell carcinoma (ESCC) is a heterogeneous cancer associated with a poor prognosis in advanced stages. In India, it is the sixth most common cause of cancer-related mortality. In this study, we employed high-resolution mass spectrometry-based quantitative proteomics to characterize the differential protein expression pattern associated with ESCC. We identified several differentially expressed proteins including PDPN, TOP2A, POSTN and MMP2 that were overexpressed in ESCC. In addition, we identified downregulation of esophagus tissue-enriched proteins such as SLURP1, PADI1, CSTA, small proline-rich proteins such as SPRR3, SPRR2A, SPRR1A, KRT4, and KRT13, involved in squamous cell differentiation. We identified several overexpressed proteins mapped to the 3q24-29 chromosomal region, aligning with CNV alterations in this region reported in several published studies. Among these, we identified overexpression of SOX2, TP63, IGF2BP2 and RNF13 that are encoded by genes in the 3q26 region. Functional enrichment analysis revealed proteins involved in cell cycle pathways, DNA replication, spliceosome, and DNA repair pathways. We identified the overexpression of multiple proteins that play a major role in alleviating ER stress, including SYVN1 and SEL1L. The SYVN1/SEL1L complex is an essential part of the ER quality control machinery clearing misfolded proteins from the ER. SYVN1 is an E3 ubiquitin ligase that ubiquitinates ER-resident proteins. Interestingly, there are also other non-canonical substrates of SYVN1 which are known to play a crucial role in tumor progression. Thus, SYVN1 could be a potential therapeutic target in ESCC.

A Facile Synthesis of Flower-like Iron Oxide Nanoparticles and Its Efficacy Measurements for Antibacterial, Cytotoxicity and Antioxidant Activity

The objective of this study was to investigate the rhombohedral-structured, flower-like iron oxide (Fe₂O₃) nanoparticles that were produced using a cost-effective and environmentally friendly coprecipitation process. The structural and morphological characteristics of the synthesized Fe₂O₃ nanoparticles were analyzed using XRD, UV-Vis, FTIR, SEM, EDX, TEM, and HR-TEM techniques. Furthermore, the cytotoxic effects of Fe₂O₃ nanoparticles on MCF-7 and HEK-293 cells were evaluated using in vitro cell viability assays, while the antibacterial activity of the nanoparticles against Gram-positive and Gram-negative bacteria (Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae) was also tested. The results of our study demonstrated the potential cytotoxic activity of Fe₂O₃ nanoparticles toward MCF-7 and HEK-293 cell lines. The antioxidant potential of Fe₂O₃ nanoparticles was evidenced by the 1,1-diphenyl-2-picrylhydrazine (DPPH) and nitric oxide (NO) free radical scavenging assays. In addition, we suggested that Fe₂O₃ nanoparticles could be used in various antibacterial applications to prevent the spread of different bacterial strains. Based on these findings, we concluded that Fe₂O₃ nanoparticles have great potential for use in pharmaceutical and biological applications. The effective biocatalytic activity of Fe₂O₃ nanoparticles recommends its use as one of the best drug treatments for future views against cancer cells, and it is, therefore, recommended for both in vitro and in vivo in the biomedical field.

Proteomic applications in identifying protein-protein interactions

There are many things that can be used to characterize a protein. Size, isoelectric point, hydrophobicity, structure (primary to quaternary), and subcellular location are just a few parameters that are used. The most important feature of a protein, however, is its function. While there are many experiments that can indicate a protein's role, identifying the molecules it interacts with is probably the most definitive way of determining its function. Owing to technology limitations, protein interactions have historically been identified on a one molecule per experiment basis. The advent of high throughput multiplexed proteomic technologies in the 1990s, however, made identifying hundreds and thousands of proteins interactions within single experiments feasible. These proteomic technologies have dramatically increased the rate at which protein-protein interactions (PPIs) are discovered. While the improvement in mass spectrometry technology was an early driving force in the rapid pace of identifying PPIs, advances in sample preparation and chromatography have recently been propelling the field. In this chapter, we will discuss the importance of identifying PPIs and describe current state-of-the-art technologies that demonstrate what is currently possible in this important area of biological research.

NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool

The nuclear receptor (NR) superfamily includes phylogenetically related ligand-activated proteins, which play a key role in various cellular activities. NR proteins are subdivided into seven subfamilies based on their function, mechanism, and nature of the interacting ligand. Developing robust tools to identify NR could give insights into their functional relationships and involvement in disease pathways. Existing NR prediction tools only use a few types of sequence-based features and are tested on relatively similar independent datasets; thus, they may suffer from overfitting when extended to new genera of sequences. To address this problem, we developed Nuclear Receptor Prediction Tool (NRPreTo), a two-level NR prediction tool with a unique training approach where in addition to the sequence-based features used by existing NR prediction tools, six additional feature groups depicting various physiochemical, structural, and evolutionary features of proteins were utilized. The first level of NRPreTo allows for the successful prediction of a query protein as NR or non-NR and further subclassifies the protein into one of the seven NR subfamilies in the second level. We developed Random Forest classifiers to test on benchmark datasets, as well as the entire human protein datasets from RefSeq and Human Protein Reference Database (HPRD). We observed that using additional feature groups improved the performance. We also observed that NRPreTo achieved high performance on the external datasets and predicted 59 novel NRs in the human proteome. The source code of NRPreTo is publicly available at https://github.com/bozdaglab/NRPreTo.

MFI2 upregulation promotes malignant progression through EGF/FAK signaling in oral cavity squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is the predominant histological type of the head and neck squamous cell carcinoma (HNSCC). By comparing the differentially expressed genes (DEGs) in OSCC-TCGA patients with copy number variations (CNVs) that we identify in OSCC-OncoScan dataset, we herein identified 37 dysregulated candidate genes. Among these potential candidate genes, 26 have been previously reported as dysregulated proteins or genes in HNSCC. Among 11 novel candidates, the overall survival analysis revealed that melanotransferrin (MFI2) is the most significant prognostic molecular in OSCC-TCGA patients. Another independent Taiwanese cohort confirmed that higher MFI2 transcript levels were significantly associated with poor prognosis. Mechanistically, we found that knockdown of MFI2 reduced cell viability, migration and invasion via modulating EGF/FAK signaling in OSCC cells. Collectively, our results support a mechanistic understanding of a novel role for MFI2 in promoting cell invasiveness in OSCC.

Network medicine framework identified drug-repurposing opportunities of pharmaco-active compounds of Angelica acutiloba (Siebold & Zucc.) Kitag. for skin aging

Increasing incidence of skin aging has highlighted the importance of identifying effective drugs with repurposed opportunities for skin aging. We aimed to identify pharmaco-active compounds with drug-repurposing opportunities for skin aging from Angelica acutiloba (Siebold & Zucc.) Kitag. (AAK). The proximity of network medicine framework (NMF) firstly identified 8 key AAK compounds with repurposed opportunities for skin aging, which may exert by regulating 29 differentially expressed genes (DGEs) of skin aging, including 13 up-regulated targets and 16 down-regulated targets. Connectivity MAP (cMAP) analysis revealed 8 key compounds were involved in regulating the process of cell proliferation and apoptosis, mitochondrial energy metabolism and oxidative stress of skin aging. Molecular docking analysis showed that 8 key compounds had a high docked ability with AR, BCHE, HPGD and PI3, which were identified as specific biomarker for the diagnosis of skin aging. Finally, the mechanisms of these key compounds were predicted to be involved in inhibiting autophagy pathway and activating Phospholipase D signaling pathway. In conclusion, this study firstly elucidated the drug-repurposing opportunities of AAK compounds for skin aging, providing a theoretical reference for identifying repurposing drugs from Chinese medicine and new insights for our future research.

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.

DEDTI versus IEDTI: efficient and predictive models of drug-target interactions

Drug repurposing is an active area of research that aims to decrease the cost and time of drug development. Most of those efforts are primarily concerned with the prediction of drug-target interactions. Many evaluation models, from matrix factorization to more cutting-edge deep neural networks, have come to the scene to identify such relations. Some predictive models are devoted to the prediction's quality, and others are devoted to the efficiency of the predictive models, e.g., embedding generation. In this work, we propose new representations of drugs and targets useful for more prediction and analysis. Using these representations, we propose two inductive, deep network models of IEDTI and DEDTI for drug-target interaction prediction. Both of them use the accumulation of new representations. The IEDTI takes advantage of triplet and maps the input accumulated similarity features into meaningful embedding corresponding vectors. Then, it applies a deep predictive model to each drug-target pair to evaluate their interaction. The DEDTI directly uses the accumulated similarity feature vectors of drugs and targets and applies a predictive model on each pair to identify their interactions. We have done a comprehensive simulation on the DTINet dataset as well as gold standard datasets, and the results show that DEDTI outperforms IEDTI and the state-of-the-art models. In addition, we conduct a docking study on new predicted interactions between two drug-target pairs, and the results confirm acceptable drug-target binding affinity between both predicted pairs.

Mechanistic insights into the interactions between cancer drivers and the tumour immune microenvironment

BACKGROUND

The crosstalk between cancer and the tumour immune microenvironment (TIME) has attracted significant interest in the latest years because of its impact on cancer evolution and response to treatment. Despite this, cancer-specific tumour-TIME interactions and their mechanistic insights are still poorly understood.

METHODS

Here, we compute the significant interactions occurring between cancer-specific genetic drivers and five anti- and pro-tumour TIME features in 32 cancer types using Lasso regularised ordinal regression. Focusing on head and neck squamous cancer (HNSC), we rebuild the functional networks linking specific TIME driver alterations to the TIME state they associate with.

RESULTS

The 477 TIME drivers that we identify are multifunctional genes whose alterations are selected early in cancer evolution and recur across and within cancer types. Tumour suppressors and oncogenes have an opposite effect on the TIME and the overall anti-tumour TIME driver burden is predictive of response to immunotherapy. TIME driver alterations predict the immune profiles of HNSC molecular subtypes, and perturbations in keratinization, apoptosis and interferon signalling underpin specific driver-TIME interactions.

CONCLUSIONS

Overall, our study delivers a comprehensive resource of TIME drivers, gives mechanistic insights into their immune-regulatory role, and provides an additional framework for patient prioritisation to immunotherapy. The full list of TIME drivers and associated properties are available at http://www.network-cancer-genes.org .

The Versatility of Plectin in Cancer: A Pan-Cancer Analysis on Potential Diagnostic and Prognostic Impacts of Plectin Isoforms

Plectin, encoded by PLEC, is a cytoskeletal and scaffold protein with a number of unique isoforms that act on various cellular functions such as cell adhesion, signal transduction, cancer cell invasion, and migration. While plectin has been shown to display high expression and mislocalization in tumor cells, our knowledge of the biological significance of plectin and its isoforms in tumorigenesis remain limited. In this study, we first performed pathway enrichment analysis to identify cancer hallmark proteins associated with plectin. Then, a pan-cancer analysis was performed using RNA-seq data collected from the Cancer Genome Atlas (TCGA) to detect the mRNA expression levels of PLEC and its transcript isoforms, and the prognostic as well as diagnostic significance of the transcript isoforms was evaluated considering cancer stages. We show here that several tissue specific PLEC isoforms are dysregulated in different cancer types and stages but not the expression of PLEC. Among them, PLEC 1d and PLEC 1f are potential biomarker candidates and call for further translational and personalized medicine research. This study makes a contribution as a stride to unravel the molecular mechanisms underpinning plectin isoforms in cancer development and progression by revealing the potent plectin isoforms in different stages of cancer as potential early cancer detection biomarkers. Importantly, uncovering how plectin isoforms guide malignancy and particular cancer types by comprehensive functional studies might open new avenues toward novel cancer therapeutics.

Online bias-aware disease module mining with ROBUST-Web

SUMMARY

We present ROBUST-Web which implements our recently presented ROBUST disease module mining algorithm in a user-friendly web application. ROBUST-Web features seamless downstream disease module exploration via integrated gene set enrichment analysis, tissue expression annotation, and visualization of drug-protein and disease-gene links. Moreover, ROBUST-Web includes bias-aware edge costs for the underlying Steiner tree model as a new algorithmic feature, which allow to correct for study bias in protein-protein interaction networks and further improves the robustness of the computed modules.

AVAILABILITY AND IMPLEMENTATION

Web application: https://robust-web.net. Source code of web application and Python package with new bias-aware edge costs: https://github.com/bionetslab/robust-web, https://github.com/bionetslab/robust_bias_aware.

Characterization of a Potent and Orally Bioavailable Lys-Covalent Inhibitor of Apoptosis Protein (IAP) Antagonist

We have recently reported on the use of aryl-fluorosulfates in designing water- and plasma-stable agents that covalently target Lys, Tyr, or His residues in the BIR3 domain of the inhibitor of the apoptosis protein (IAP) family. Here, we report further structural, cellular, and pharmacological characterizations of this agent, including the high-resolution structure of the complex between the Lys-covalent agent and its target, the BIR3 domain of X-linked IAP (XIAP). We also compared the cellular efficacy of the agent in two-dimensional (2D) and three-dimensional (3D) cell cultures, side by side with the clinical candidate reversible IAP inhibitor LCL161. Finally, in vivo pharmacokinetic studies indicated that the agent was long-lived and orally bioavailable. Collectively our data further corroborate that aryl-fluorosulfates, when incorporated correctly in a ligand, can result in Lys-covalent agents with pharmacodynamic and pharmacokinetic properties that warrant their use in the design of pharmacological probes or even therapeutics.

How Driver Oncogenes Shape and Are Shaped by Alternative Splicing Mechanisms in Tumors

The development of RNA sequencing methods has allowed us to study and better understand the landscape of aberrant pre-mRNA splicing in tumors. Altered splicing patterns are observed in many different tumors and affect all hallmarks of cancer: growth signal independence, avoidance of apoptosis, unlimited proliferation, invasiveness, angiogenesis, and metabolism. In this review, we focus on the interplay between driver oncogenes and alternative splicing in cancer. On one hand, oncogenic proteins-mutant p53, CMYC, KRAS, or PI3K-modify the alternative splicing landscape by regulating expression, phosphorylation, and interaction of splicing factors with spliceosome components. Some splicing factors-SRSF1 and hnRNPA1-are also driver oncogenes. At the same time, aberrant splicing activates key oncogenes and oncogenic pathways: p53 oncogenic isoforms, the RAS-RAF-MAPK pathway, the PI3K-mTOR pathway, the EGF and FGF receptor families, and SRSF1 splicing factor. The ultimate goal of cancer research is a better diagnosis and treatment of cancer patients. In the final part of this review, we discuss present therapeutic opportunities and possible directions of further studies aiming to design therapies targeting alternative splicing mechanisms in the context of driver oncogenes.

Stereochemical Control of Splice Modulation in FD-895 Analogues

Highly functionalized skeletons of macrolide natural products gain access to rare spatial arrangements of atoms, where changes in stereochemistry can have a profound impact on the structure and function. Spliceosome modulators present a unique consensus motif, with the majority targeting a key interface within the SF3B spliceosome complex. Our recent preparative-scale synthetic campaign of 17S-FD-895 provided unique access to stereochemical analogues of this complex macrolide. Here, we report on the preparation and systematic activity evaluation of multiple FD-895 analogues. These studies examine the effects of modifications at specific stereocenters within the molecule and highlight future directions for medicinal chemical optimization of spliceosome modulators.

GePI: large-scale text mining, customized retrieval and flexible filtering of gene/protein interactions

We present GePI, a novel Web server for large-scale text mining of molecular interactions from the scientific biomedical literature. GePI leverages natural language processing techniques to identify genes and related entities, interactions between those entities and biomolecular events involving them. GePI supports rapid retrieval of interactions based on powerful search options to contextualize queries targeting (lists of) genes of interest. Contextualization is enabled by full-text filters constraining the search for interactions to either sentences or paragraphs, with or without pre-defined gene lists. Our knowledge graph is updated several times a week ensuring the most recent information to be available at all times. The result page provides an overview of the outcome of a search, with accompanying interaction statistics and visualizations. A table (downloadable in Excel format) gives direct access to the retrieved interaction pairs, together with information about the molecular entities, the factual certainty of the interactions (as verbatim expressed by the authors), and a text snippet from the original document that verbalizes each interaction. In summary, our Web application offers free, easy-to-use, and up-to-date monitoring of gene and protein interaction information, in company with flexible query formulation and filtering options. GePI is available at https://gepi.coling.uni-jena.de/.

FLONE: fully Lorentz network embedding for inferring novel drug targets

Motivation

To predict drug targets, graph-based machine-learning methods have been widely used to capture the relationships between drug, target and disease entities in drug-disease-target (DDT) networks. However, many methods cannot explicitly consider disease types at inference time and so will predict the same target for a given drug under any disease condition. Meanwhile, DDT networks are usually organized hierarchically carrying interactive relationships between involved entities, but these methods, especially those based on Euclidean embedding cannot fully utilize such topological information, which might lead to sub-optimal results. We hypothesized that, by importing hyperbolic embedding specifically for modeling hierarchical DDT networks, graph-based algorithms could better capture relationships between aforementioned entities, which ultimately improves target prediction performance.

Results

We formulated the target prediction problem as a knowledge graph completion task explicitly considering disease types. We proposed FLONE, a hyperbolic embedding-based method based on capturing hierarchical topological information in DDT networks. The experimental results on two DDT networks showed that by introducing hyperbolic space, FLONE generates more accurate target predictions than its Euclidean counterparts, which supports our hypothesis. We also devised hyperbolic encoders to fuse external domain knowledge, to make FLONE enable handling samples corresponding to previously unseen drugs and targets for more practical scenarios.

Availability and implementation

Source code and dataset information are at: https://github.com/arantir123/DDT_triple_prediction.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Functional and Clinical Proteomic Exploration of Pancreatic Cancer

Pancreatic cancer, most cases being pancreatic ductal adenocarcinoma (PDAC), is one of the most lethal cancers with a median survival time of less than 6 months. Therapeutic options are very limited for PDAC patients, and surgery is still the most effective treatment, making improvements in early diagnosis critical. One typical characteristic of PDAC is the desmoplastic reaction of its stroma microenvironment, which actively interacts with cancer cells to orchestrate key components in tumorigenesis, metastasis, and chemoresistance. Global exploration of cancer-stroma crosstalk is essential to decipher PDAC biology and design intervention strategies. Over the past decade, the dramatic improvement of proteomics technologies has enabled profiling of proteins, post-translational modifications (PTMs), and their protein complexes at unprecedented sensitivity and dimensionality. Here, starting with our current understanding of PDAC characteristics, including precursor lesions, progression models, tumor microenvironment, and therapeutic advancements, we describe how proteomics contributes to the functional and clinical exploration of PDAC, providing insights into PDAC carcinogenesis, progression, and chemoresistance. We summarize recent achievements enabled by proteomics to systematically investigate PTMs-mediated intracellular signaling in PDAC, cancer-stroma interactions, and potential therapeutic targets revealed by these functional studies. We also highlight proteomic profiling of clinical tissue and plasma samples to discover and verify useful biomarkers that can aid early detection and molecular classification of patients. In addition, we introduce spatial proteomic technology and its applications in PDAC for deconvolving tumor heterogeneity. Finally, we discuss future prospects of applying new proteomic technologies in comprehensively understanding PDAC heterogeneity and intercellular signaling networks. Importantly, we expect advances in clinical functional proteomics for exploring mechanisms of cancer biology directly by high-sensitivity functional proteomic approaches starting from clinical samples.

Downregulation of HHLA2 inhibits ovarian cancer progression via the NF-κB signaling pathway and suppresses the expression of CA9

HHLA2 has been recently demonstrated to play multifaceted roles in several types of cancers. However, its underlying mechanism in the progression of human ovarian cancer (OC) remains largely unexplored. In the present study, we aimed to determine whether downregulation of HHLA2 inhibited malignant phenotypes of human OC cells and explore its specific mechanism. Our results revealed that downregulation of HHLA2 by transfection with a lentiviral vector significantly suppressed the viability, invasion, and migration of OC cells. Interaction study showed that downregulation of HHLA2 in OC cells reduced the expression of CA9 and increased the expressions of p-IKKβ and p-RelA. Conversely, the viability, invasion, and migration of HHLA2-depleted OC cells were increased when CA9 was upregulated. In vivo, we found that downregulation of HHLA2 significantly inhibited tumor growth, which was reversed by CA9 overexpression. In addition, downregulation of HHLA2 inhibited the OC progression via activating the NF-κB signaling pathway and decreasing the expression of CA9. Collectively, our data suggested a link between HHLA2 and NF-κB axis in the pathogenesis of OC, and these findings might provide valuable insights into the development of novel potential therapeutic targets for OC.

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

BACKGROUND

There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings.

METHODS

This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. It focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the biomedical domain. For this, we retrieved and analyzed 42 studies focusing on emerging architectural and methodological advances, the encoding of biological domain knowledge and the integration of explainability methods.

RESULTS

We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction networks) and interpretability. This represents a fundamental functional shift towards models which can integrate mechanistic and statistical inference aspects. We introduce a concept of bio-centric interpretability and according to its taxonomy, we discuss representational methodologies for the integration of domain prior knowledge in such models.

CONCLUSIONS

The paper provides a critical outlook into contemporary methods for explainability and interpretability used in DL for cancer. The analysis points in the direction of a convergence between encoding prior knowledge and improved interpretability. We introduce bio-centric interpretability which is an important step towards formalisation of biological interpretability of DL models and developing methods that are less problem- or application-specific.

Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson's disease

BACKGROUND

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been recognized as genetic risk factors for Parkinson's disease (PD). However, compared to cancer, fewer genetic mutations contribute to the cause of PD, propelling the search for protein biomarkers for early detection of the disease.

METHODS

Utilizing 138 urine samples from four groups, healthy individuals (control), healthy individuals with G2019S mutation in the LRRK2 gene (non-manifesting carrier/NMC), PD individuals without G2019S mutation (idiopathic PD/iPD), and PD individuals with G2019S mutation (LRRK2 PD), we applied a proteomics strategy to determine potential diagnostic biomarkers for PD from urinary extracellular vesicles (EVs).

RESULTS

After efficient isolation of urinary EVs through chemical affinity followed by mass spectrometric analyses of EV peptides and enriched phosphopeptides, we identify and quantify 4476 unique proteins and 2680 unique phosphoproteins. We detect multiple proteins and phosphoproteins elevated in PD EVs that are known to be involved in important PD pathways, in particular the autophagy pathway, as well as neuronal cell death, neuroinflammation, and formation of amyloid fibrils. We establish a panel of proteins and phosphoproteins as novel candidates for disease biomarkers and substantiate the biomarkers using machine learning, ROC, clinical correlation, and in-depth network analysis. Several putative disease biomarkers are further partially validated in patients with PD using parallel reaction monitoring (PRM) and immunoassay for targeted quantitation.

CONCLUSIONS

These findings demonstrate a general strategy of utilizing biofluid EV proteome/phosphoproteome as an outstanding and non-invasive source for a wide range of disease exploration.

Identification of a druggable pocket of the calcium-activated chloride channel TMEM16A in its open state

The calcium-activated chloride channel TMEM16A is a potential drug target to treat hypertension, secretory diarrhea, and several cancers. However, all reported TMEM16A structures are either closed or desensitized, and direct inhibition of the open state by drug molecules lacks a reliable structural basis. Therefore, revealing the druggable pocket of TMEM16A exposed in the open state is important for understanding protein-ligand interactions and facilitating rational drug design. Here, we reconstructed the calcium-activated open conformation of TMEM16A using an enhanced sampling algorithm and segmental modeling. Furthermore, we identified an open state druggable pocket and screened a potent TMEM16A inhibitor, etoposide, which is a derivative of a traditional herbal monomer. Molecular simulations and site-directed mutagenesis showed that etoposide binds to the open state of TMEM16A, thereby blocking the ion conductance pore of the channel. Finally, we demonstrated that etoposide can target TMEM16A to inhibit the proliferation of prostate cancer PC-3 cells. Together, these findings provide a deep understanding of the TMEM16A open state at an atomic level and identify pockets for the design of novel inhibitors with broad applications in chloride channel biology, biophysics, and medicinal chemistry.

Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery

Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.

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.