Pandey, Akhilesh

Pancreatic ductal adenocarcinoma is a recalcitrant tumor with minimal response to conventional chemotherapeutic approaches. Oncogenic signaling by activated tyrosine kinases has been implicated in cancers resulting in activation of diverse effector signaling pathways. Thus, the discovery of aberrantly activated tyrosine kinases is of great interest in developing novel therapeutic strategies in the treatment and management of pancreatic cancer. Patient-derived tumor xenografts (PDXs) in mice serve as potentially valuable preclinical models as they maintain the histological and molecular heterogeneity of the original human tumor. Here, we employed high-resolution mass spectrometry combined with immunoaffinity purification using anti-phosphotyrosine antibodies to profile tyrosine phosphoproteome across 13 pancreatic ductal adenocarcinoma PDX models. This analysis resulted in the identification of 1199 tyrosine-phosphorylated sites mapping to 704 proteins. The mass spectrometric analysis revealed widespread and heterogeneous activation of both receptor and non-receptor tyrosine kinases. Preclinical studies confirmed ephrin type-B receptor 4 (EphB4) as a potential therapeutic target based on the efficacy of human serum albumin-conjugated soluble EphB4 in mice bearing orthotopic xenografts. Immunohistochemistry-based validation using tissue microarrays from 346 patients with PDAC showed significant expression of EphB4 in >70% of patients. In summary, we present a comprehensive landscape of tyrosine phosphoproteome with EphB4 as a promising therapeutic target in pancreatic ductal adenocarcinoma.

Abstract Background We evaluated the analytical sensitivity and specificity of 4 rapid antigen diagnostic tests (Ag RDTs) for severe acute respiratory syndrome coronavirus 2, using reverse transcription quantitative PCR (RT-qPCR) as the reference method and further characterizing samples using droplet digital quantitative PCR (ddPCR) and a mass spectrometric antigen test. Methods Three hundred fifty (150 negative and 200 RT-qPCR positive) residual PBS samples were tested for antigen using the BD Veritor lateral flow (LF), ACON LF, ACON fluorescence immunoassay (FIA), and LumiraDx FIA. ddPCR was performed on RT-qPCR–positive samples to quantitate the viral load in copies/mL applied to each Ag RDT. Mass spectrometric antigen testing was performed on PBS samples to obtain a set of RT-qPCR–positive, antigen-positive samples for further analysis. Results All Ag RDTs had nearly 100% specificity compared to RT-qPCR. Overall analytical sensitivity varied from 66.5% to 88.3%. All methods detected antigen in samples with viral load >1 500 000 copies/mL RNA, and detected ≥75% of samples with viral load of 500 000 to 1 500 000 copies/mL. The BD Veritor LF detected only 25% of samples with viral load between 50 000 to 500 000 copies/mL, compared to 75% for the ACON LF device and >80% for LumiraDx and ACON FIA. The ACON FIA detected significantly more samples with viral load <50 000 copies/mL compared to the BD Veritor. Among samples with detectable antigen and viral load <50 000 copies/mL, sensitivity of the Ag RDT varied between 13.0% (BD Veritor) and 78.3% (ACON FIA). Conclusions Ag RDTs differ significantly in analytical sensitivity, particularly at viral load <500 000 copies/mL.

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the worldwide COVID-19 pandemic which began in 2019. It has a high transmission rate and pathogenicity leading to health emergencies and economic crisis. Recent studies pertaining to the understanding of the molecular pathogenesis of SARS-CoV-2 infection exhibited the indispensable role of ion channels in viral infection inside the host. Moreover, machine learning-based algorithms are providing higher accuracy for host-SARS-CoV-2 protein-protein interactions (PPIs). In this study, predictions of PPIs of SARS-CoV-2 proteins with human ion channels (HICs) were performed using PPI-MetaGO algorithm. The PPIs were predicted with 82.71% accuracy, 84.09% precision, 84.09% sensitivity, 0.89 AUC-ROC, 65.17% MCC score and 84.09% F1 score. Thereafter, PPI networks of SARS-CoV-2 proteins with HICs were generated. Furthermore, biological pathway analysis of HICs interacting with SARS-CoV-2 proteins showed the involvement of six pathways, namely inflammatory mediator regulation of TRP channels, insulin secretion, renin secretion, gap junction, taste transduction and apelin signaling pathway. The inositol 1,4,5-trisphosphate receptor 1 (ITPR1) and transient receptor potential cation channel subfamily A member 1 (TRPA1) were identified as potential target proteins. Various FDA approved drugs interacting with ITPR1 and TRPA1 are also available. It is anticipated that targeting ITPR1 and TRPA1 may provide a better therapeutic management of infection caused by SARS-CoV-2. The study also reinforces the drug repurposing approach for the development of host directed antiviral drugs.

Shukla-Vernon syndrome (SHUVER) is an extremely rare neurodevelopmental disorder characterized by global developmental delay, intellectual disability, behavioral anomalies, and dysmorphic features. Pathogenic variants in the BCORL1 gene have been identified as the molecular cause for this disorder. The BCORL1 gene encodes for BCL-6 corepressor-like protein 1, a transcriptional corepressor that is an integral component of protein complexes involved in transcription repression. In this study, we report an Indian family with two male siblings with features of Shukla-Vernon syndrome. The patients exhibited global developmental delay, intellectual disability, kyphosis, seizures, and dysmorphic features including bushy prominent eyebrows with synophrys, sharp beaked prominent nose, protuberant lower jaw, squint, and hypoplastic ears with fused ear lobes. No behavioral abnormalities were observed. Whole exome sequencing revealed a novel potentially pathogenic arginine to cysteine substitution (p.Arg1265Cys) in the BCORL1 protein. This is the second report of Shukla-Vernon syndrome with a novel missense variant in the BCORL1 gene. Our study confirms and expands the phenotypes and genotypes described previously for this syndrome and should aid in diagnosis and genetic counselling of patients and their families.

Abstract Alzheimer's disease (AD) is the most common cause of dementia and is associated with serious neurologic sequelae resulting from neurodegenerative changes. Identification of markers of early-stage AD could be important for designing strategies to arrest the progression of the disease. The brain is rich in lipids because they are crucial for signal transduction and anchoring of membrane proteins. Cerebrospinal fluid (CSF) is an excellent specimen for studying the metabolism of lipids in AD because it can reflect changes occurring in the brain. We aimed to identify CSF lipidomic alterations associated with AD, using untargeted lipidomics, carried out in positive and negative ion modes. We found CSF lipids that were significantly altered in AD cases. In addition, comparison of CSF lipid profiles between persons with mild cognitive impairment (MCI) and AD showed a strong positive correlation between the lipidomes of the MCI and AD groups. The novel lipid biomarkers identified in this study are excellent candidates for validation in a larger set of patient samples and as predictive biomarkers of AD through future longitudinal studies. Once validated, the lipid biomarkers could lead to early detection, disease monitoring and the ability to measure the efficacy of potential therapeutic interventions in AD.

DKK3 is a secreted protein, which belongs to a family of Wnt antagonists and acts as a potential tumor suppressor in gallbladder cancer. To further understand its tumor suppressor functions, we overexpressed DKK3 in 3 GBC cell lines. We have employed high-resolution mass spectrometry and tandem mass tag (TMT) multiplexing technology along with immobilized metal affinity chromatography to enrich phosphopeptides to check the downstream regulators. In this study, we reported for the first time the alteration in the phosphorylation of 14 kinases upon DKK3 overexpression. In addition, we observed DKK3 induced hyper phosphorylation of 2 phosphatases: PPP1R12A and PTPRA, which have not been reported previously. Canonical pathway analysis of altered molecules indicated differential enrichment of signaling cascades upon DKK3 overexpression in all the 3 cell lines. Protein kinase A signaling, Sirtuin signaling pathway, and Cell Cycle Control of Chromosomal Replication were observed to be differentially activated in the GBC cell lines. Our study revealed, DKK3 overexpression has differential effect based on the aggressive behavior of the cell lines. This study expands the understanding of DKK3-mediated signaling events and can be used as a primary factor for understanding the complex nature of this molecule.

Acellular bronchoalveolar lavage (BAL) proteomics can partially separate lung cancer from non-lung cancer patients based on principal component analysis and multivariate analysis. Furthermore, the variance in the proteomics data sets is correlated mainly with lung cancer status and, to a lesser extent, smoking status and gender. Despite these advances BAL small and large extracellular vehicles (EVs) proteomes reveal aberrant protein expression in paracrine signaling mechanisms in cancer initiation and progression. We consequently present a case-control study of 24 bronchoalveolar lavage extracellular vesicle samples which were analyzed by state-of-the-art liquid chromatography-mass spectrometry (LC-MS). We obtained evidence that BAL EVs proteome complexity correlated with lung cancer stage 4 and mortality within two years´ follow-up (p value = 0.006). The potential therapeutic target DNMT3B complex is significantly up-regulated in tumor tissue and BAL EVs. The computational analysis of the immune and fibroblast cell markers in EVs suggests that patients who deceased within the follow-up period display higher marker expression indicative of innate immune and fibroblast cells (four out of five cases). This study provides insights into the proteome content of BAL EVs and their correlation to clinical outcomes.

Abstract A data-independent acquisition (DIA) approach is being increasingly adopted as a promising strategy for identification and quantitation of proteomes. As most DIA data sets are acquired with wide isolation windows, highly complex MS/MS spectra are generated, which negatively impacts obtaining peptide information through classical protein database searches. Therefore, the analysis of DIA data mainly relies on the evidence of the existence of peptides from prebuilt spectral libraries. Consequently, one major weakness of this method is that it does not account for peptides that are not included in the spectral library, precluding the use of DIA for discovery studies. Here, we present a strategy termed Precursor ion And Small Slice-DIA (PASS-DIA) in which MS/MS spectra are acquired with small isolation windows (slices) and MS/MS spectra are interpreted with accurately determined precursor ion masses. This method enables the direct application of conventional spectrum-centric analysis pipelines for peptide identification and precursor ion-based quantitation. The performance of PASS-DIA was observed to be superior to both data-dependent acquisition (DDA) and conventional DIA experiments with 69 and 48% additional protein identifications, respectively. Application of PASS-DIA for the analysis of post-translationally modified peptides again highlighted its superior performance in characterizing phosphopeptides (77% more), N-terminal acetylated peptides (56% more), and N-glycopeptides (83% more) as compared to DDA alone. Finally, the use of PASS-DIA to characterize a rare proteome of human fallopian tube organoids enabled 34% additional protein identifications than DDA alone and revealed biologically relevant pathways including low abundance proteins. Overall, PASS-DIA is a novel DIA approach for use as a discovery tool that outperforms both conventional DDA and DIA experiments to provide additional protein information. We believe that the PASS-DIA method is an important strategy for discovery-type studies when deeper proteome characterization is required.