Biologic insights from single-cell studies of psoriasis and psoriatic arthritis.

INTRODUCTION: Psoriasis (PSO) and psoriatic arthritis (PSA) represent a large burden of global inflammatory disease, but sustained treatment response and early diagnosis remain challenging. Both conditions arise from complex immune cell dysregulation. Single-cell techniques, including single-cell RNA sequencing (scRNA-seq), have revolutionized our understanding of pathogenesis by illuminating heterogeneous cell populations and their interactions. AREAS COVERED: We discuss the transcriptional profiles and cellular interactions unique to PSO/PSA affecting T cells, myeloid cells, keratinocytes, innate lymphoid cells, and stromal cells. We also review advances, limitations, and future challenges associated with single-cell studies. EXPERT OPINION: Following analyses of 22 single-cell studies, several themes emerged. A small subpopulation of cells can have a large impact on disease pathogenesis. Multiple cell types identified via scRNA-seq play supporting roles in PSO pathogenesis, contrary to the traditional paradigm focusing on IL-23/IL-17 signaling among dendritic cells and T cells. Immune cell states are dynamic, with psoriatic subpopulations aberrantly re-activating and differentiating into inflammatory phenotypes depending on surrounding signaling cues. Comparison of circulating immune cells with resident skin/joint cells has uncovered specific T cell clonotypes associated with the disease. Finally, machine learning models demonstrate great promise in identifying biomarkers to diagnose clinically ambiguous rashes and PSA at earlier stages.

Identification of therapeutically potential targets and their ligands for the treatment of OSCC.

Recent advancements in cancer biology have revealed molecular changes associated with carcinogenesis and chemotherapeutic exposure. The available information is being gainfully utilized to develop therapies targeting specific molecules involved in cancer cell growth, survival, and chemoresistance. Targeted therapies have dramatically increased overall survival (OS) in many cancers. Therefore, developing such targeted therapies against oral squamous cell carcinoma (OSCC) is anticipated to have significant clinical implications. In the current work, we have identified drug-specific sensitivity-related prognostic biomarkers (BOP1, CCNA2, CKS2, PLAU, and SERPINE1) using gene expression, Cox proportional hazards regression, and machine learning in OSCC. Dysregulation of these markers is significantly associated with OS in many cancers. Their elevated expression is related to cellular proliferation and aggressive malignancy in various cancers. Mechanistically, inhibition of these biomarkers should significantly reduce cellular proliferation and metastasis in OSCC and should result in better OS. It is pertinent to note that no effective small-molecule candidate has been identified against these biomarkers to date. Therefore, a comprehensive in silico drug design strategy assimilating homology modeling, extensive molecular dynamics (MD) simulation, and ensemble molecular docking has been applied to identify potential compounds against identified targets, and potential molecules have been identified. We hope that this study will help in deciphering potential genes having roles in chemoresistance and a significant impact on OS. It will also result in the identification of new targeted therapeutics against OSCC.

Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning.

Early diagnosis of psoriatic arthritis (PSA) is important for successful therapeutic intervention but currently remains challenging due, in part, to the scarcity of non-invasive biomarkers. In this study, we performed single cell profiling of transcriptome and cell surface protein expression to compare the peripheral blood immunocyte populations of individuals with PSA, individuals with cutaneous psoriasis (PSO) alone, and healthy individuals. We identified genes and proteins differentially expressed between PSA, PSO, and healthy subjects across 30 immune cell types and observed that some cell types, as well as specific phenotypic subsets of cells, differed in abundance between these cohorts. Cell type-specific gene and protein expression differences between PSA, PSO, and healthy groups, along with 200 previously published genetic risk factors for PSA, were further used to perform machine learning classification, with the best models achieving AUROC ≥ 0.87 when either classifying subjects among the three groups or specifically distinguishing PSA from PSO. Our findings thus expand the repertoire of gene, protein, and cellular biomarkers relevant to PSA and demonstrate the utility of machine learning-based diagnostics for this disease.

Oncogenic potential of ATAD2 in stomach cancer and insights into the protein-protein interactions at its AAA + ATPase domain and bromodomain.

ATAD2 has recently been shown to promote stomach cancer. However, nothing is known about the functional network of ATAD2 in stomach carcinogenesis. This study illustrates the oncogenic potential of ATAD2 and the participation of its ATPase and bromodomain in stomach malignancy. Expression of ATAD2 in stomach cancer is analyzed by in silico and in vitro techniques including western blot and immunofluorescence microscopy of stomach cancer cells (SCCs) and tissues. The oncogenic potential of ATAD2 is examined thoroughly using genetic alterations, driver gene prediction, survival analysis, identification of interacting partners, and analysis of canonical pathways. To understand the protein-protein interactions (PPI) at residue level, molecular docking and molecular dynamics simulations (1200 ns) are performed. Enhanced expression of ATAD2 is observed in H. pylori-infected SCCs, patient biopsy tissues, and all stages and grades of stomach cancer. High expression of ATAD2 is found to be negatively correlated with the survival of stomach cancer patients. ATAD2 is a cancer driver gene with 37 mutational sites and a predictable factor for stomach cancer prognosis with high accuracy. The top canonical pathways of ATAD2 indicate its participation in stomach malignancy. The ATAD2-PPI in stomach cancer identify top-ranked partners; ESR1, SUMO2, SPTN2, and MYC show preference for the bromodomain whereas NCOA3 and HDA11 have preference for the ATPase domain of ATAD2. The oncogenic characterization of ATAD2 provides strong evidence to consider ATAD2 as a stomach cancer biomarker. These studies offer an insight for the first time into the ATAD2-PPI interface presenting a novel target for cancer therapeutics. Communicated by Ramaswamy H. Sarma.

Nimbolide-encapsulated PLGA nanoparticles induces Mesenchymal-to-Epithelial Transition by dual inhibition of AKT and mTOR in pancreatic cancer stem cells.

Pancreatic ductal adenocarcinoma (PDAC) is associated with poor prognosis and remains highly aggressive despite current advancements in therapies. Chemoresistance and high metastatic nature of PDAC is attributed to a small subset of stem-like cells within the tumor known as Cancer Stem Cells (CSCs). Here, we developed a strategy for targeting pancreatic CSCs through forceful induction of mesenchymal-to-epithelial transition driven by encapsulating a phytochemical Nimbolide in nanoparticles. Binding of Nimbolide with the key regulator proteins of CSCs were studied through molecular docking and molecular dynamic simulation studies, which revealed that it binds to AKT and mTOR with high affinity. Further, in vitro studies revealed that Nim NPs are capable of inducing forceful mesenchymal-to-epithelial transition of pancreatospheres that leads to loss of multidrug resistance and self-renewal properties of pancreatospheres. Our study gives a proof of concept that encapsulation of Nim in PLGA nanoparticles increases its therapeutic effect on pancreatospheres. Further, binding of Nim to AKT and mTOR negatively regulates their activity that ultimately leads to mesenchymal-to-epithelial transition of pancreatic CSCs.

Identification of multipotent drugs for COVID-19 therapeutics with the evaluation of their SARS-CoV2 inhibitory activity.

The SARS-CoV2 is a highly contagious pathogen that causes COVID-19 disease. It has affected millions of people globally with an average lethality of ~3%. There is an urgent need of drugs for the treatment of COVID-19. In the current studies, we have used bioinformatics techniques to screen the FDA approved drugs against nine SARS-CoV2 proteins to identify drugs for repurposing. Additionally, we analyzed if the identified molecules can also affect the human proteins whose expression in lung changed during SARS-CoV2 infection. Targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 74 molecules that can bind to various SARS-CoV2 and human host proteins. We experimentally validated our in-silico predictions using vero E6 cells infected with SARS-CoV2 virus. Interestingly, many of our predicted molecules viz. capreomycin, celecoxib, mefloquine, montelukast, and nebivolol showed good activity (IC50) against SARS-CoV2. We hope that these studies may help in the development of new therapeutic options for the treatment of COVID-19.

RRBP1 rewires cisplatin resistance in oral squamous cell carcinoma by regulating Hippo pathway.

BACKGROUND: Chemoresistance is one of the major factors for treatment failure in OSCC. Identifying key resistance triggering molecules will be useful strategy for developing novel treatment methods. METHODS: To identify the causative factors of chemoresistance, we performed RNA sequencing and global proteomic profiling of human OSCC lines presenting with sensitive, early and late cisplatin-resistance patterns. RESULTS: From the common set of dysregulated genes from both the analysis, RRBP1 was identified to be upregulated in both early and late cisplatin-resistant cells with respect to the sensitive counterpart. Analysis of OSCC patient sample indicates that RRBP1 expression is upregulated in chemotherapy-non-responder tumours as compared to chemotherapy-responder tumours. Genetic (knockout) or pharmacological (Radezolid, represses expression of RRBP1) inhibition of RRBP1 restores cisplatin-mediated cell death in chemo-resistant OSCC. Mechanistically, RRBP1 regulates Yes-associated protein1 (YAP1), a key protein in the Hippo pathway to induce chemoresistance. The PDC xenograft data suggests that knockout of RRBP1 induces cisplatin-mediated cell death and facilitates a significant reduction of tumour burden. CONCLUSION: Overall, our data suggests that (I) RRBP1 is a major driver of cisplatin-resistance in OSCC, (II) RRBP1 regulates YAP1 expression to mediate cisplatin-resistance, (III) Radezolid represses RRBP1 expression and (IV) targeting RRBP1 reverses cisplatin-induced chemoresistance in advanced OSCC.

Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC).

Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities.