A proteome-level view of brain tumors for a better understanding of novel diagnosis, prognosis, and therapy.

INTRODUCTION: Brain tumors are complex and heterogeneous malignancies with significant challenges in diagnosis, prognosis, and therapy. Proteomics, the large-scale study of proteins and their functions, has emerged as a powerful tool to comprehensively investigate the molecular mechanisms underlying brain tumor regulation. AREAS COVERED: This review explores brain tumors from a proteomic standpoint, highlighting recent progress and insights gained through proteomic methods. It delves into the proteomic techniques employed and underscores potential biomarkers for early detection, prognosis, and treatment planning. Recent PubMed Central proteomic studies (2017-present) are discussed, summarizing findings on altered protein expression, post-translational changes, and protein interactions. This sheds light on brain tumor signaling pathways and their significance in innovative therapeutic approaches. EXPERT OPINION: Proteomics offers immense potential for revolutionizing brain tumor diagnosis and therapy. To unlock its full benefits, further translational research is crucial. Combining proteomics with other omics data enhances our grasp of brain tumors. Validating and translating proteomic biomarkers are vital for better patient results. Challenges include tumor complexity, lack of curated proteomic databases, and the need for collaboration between researchers and clinicians. Overcoming these challenges requires investment in technology, data sharing, and translational research.

Brain tumors are complex and diverse types of cancer that present significant challenges in their diagnosis, prognosis, and treatment. Proteomics, a field that focuses on studying proteins and their functions on a large scale has emerged as a powerful tool for understanding how brain tumors work at the molecular level. In this review, we offer a detailed look into the role of proteomics in studying brain tumor regulation, discussing recent advancements and insights gained from proteomic techniques. We explore various mass spectrometry-based proteomic methods, which help uncover unique protein patterns associated with brain tumors. By analyzing changes in protein expression, modifications, interactions, and location within cells, researchers have gained important knowledge about the underlying mechanisms of brain tumors. Proteomics also plays a crucial role in identifying potential biomarkers for early detection, predicting patient outcomes, and developing targeted therapies and immunotherapies. However, there are still challenges to overcome, such as integrating data from different 'omics' fields, standardizing protocols and analysis procedures and utilizing artificial intelligence to interpret complex proteomic data. We require more robust attempts at validating and translating all these findings for patient benefit.

Integrated Meta-Omics Analysis Unveils the Pathways Modulating Tumorigenesis and Proliferation in High-Grade Meningioma.

Meningioma, a primary brain tumor, is commonly encountered and accounts for 39% of overall CNS tumors. Despite significant progress in clinical research, conventional surgical and clinical interventions remain the primary treatment options for meningioma. Several proteomics and transcriptomics studies have identified potential markers and altered biological pathways; however, comprehensive exploration and data integration can help to achieve an in-depth understanding of the altered pathobiology. This study applied integrated meta-analysis strategies to proteomic and transcriptomic datasets comprising 48 tissue samples, identifying around 1832 common genes/proteins to explore the underlying mechanism in high-grade meningioma tumorigenesis. The in silico pathway analysis indicated the roles of extracellular matrix organization (EMO) and integrin binding cascades in regulating the apoptosis, angiogenesis, and proliferation responsible for the pathobiology. Subsequently, the expression of pathway components was validated in an independent cohort of 32 fresh frozen tissue samples using multiple reaction monitoring (MRM), confirming their expression in high-grade meningioma. Furthermore, proteome-level changes in EMO and integrin cell surface interactions were investigated in a high-grade meningioma (IOMM-Lee) cell line by inhibiting integrin-linked kinase (ILK). Inhibition of ILK by administrating Cpd22 demonstrated an anti-proliferative effect, inducing apoptosis and downregulating proteins associated with proliferation and metastasis, which provides mechanistic insight into the disease pathophysiology.

A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors.

BACKGROUND: Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective. METHODS: A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins. RESULTS: A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades. CONCLUSIONS: The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.

A Proteomics Investigation of Cigarette Smoke Exposed Wistar Rats Revealed Improved Anti-Inflammatory Effects of the Cysteamine Nanoemulsions Delivered via Inhalation.

Cigarette smoking is the major cause of chronic inflammatory diseases such as chronic obstructive pulmonary disease (COPD). It is paramount to develop pharmacological interventions and delivery strategies against the cigarette smoke (CS) associated oxidative stress in COPD. This study in Wistar rats examined cysteamine in nanoemulsions to counteract the CS distressed microenvironment. In vivo, 28 days of CS and 15 days of cysteamine nanoemulsions treatment starting on 29th day consisting of oral and inhalation routes were established in Wistar rats. In addition, we conducted inflammatory and epithelial-to-mesenchymal transition (EMT) studies in vitro in human bronchial epithelial cell lines (BEAS2B) using 5% CS extract. Inflammatory and anti-inflammatory markers, such as tumor necrosis factor-alpha (TNF-α), interleukin (IL)-6, IL-1ß, IL-8, IL-10, and IL-13, have been quantified in bronchoalveolar lavage fluid (BALF) to evaluate the effects of the cysteamine nanoemulsions in normalizing the diseased condition. Histopathological analysis of the alveoli and the trachea showed the distorted, lung parenchyma and ciliated epithelial barrier, respectively. To obtain mechanistic insights into the CS COPD rat model, "shotgun" proteomics of the lung tissues have been carried out using high-resolution mass spectrometry wherein genes such as ABI1, PPP3CA, PSMA2, FBLN5, ACTG1, CSNK2A1, and ECM1 exhibited significant differences across all the groups. Pathway analysis showed autophagy, signaling by receptor tyrosine kinase, cytokine signaling in immune system, extracellular matrix organization, and hemostasis, as the major contributing pathways across all the studied groups. This work offers new preclinical findings on how cysteamine taken orally or inhaled can combat CS-induced oxidative stress.

Deep proteome investigation of high-grade gliomas reveals heterogeneity driving differential metabolism of 5-aminolevulinic acid.

Background: Fluorescence-guided surgery (FGS) using 5-aminolevulinic acid (5-ALA) as adjunct for high-grade gliomas (HGGs) has been on the rise in recent years. Despite being largely effective, we observed multiple histologically similar sub-regions of the same tumor from a few individuals with varying protoporphyrin IX (PpIX) levels. The current study aims at understanding the proteomic changes driving differential metabolism of 5-ALA in HGGs. Methods: Biopsies were histologically and biochemically assayed. Following this, a deep proteomics investigation was carried out using high resolution liquid chromatography-mass spectrometry (HR LC-MS) to identify protein expression in differentially fluorescing regions of HGGs. Results: Our analysis identified 5437 proteins with high confidence. Differential analysis in the subgroup with HGGs carrying IDH mutation (IDH mt.) revealed 93 differentially regulated proteins (raw p-value ≤ 0.05 and absolute FC ≥ 1.5). Similar analysis in the IDH wild type (IDH wt.) subgroup revealed 20 differentially regulated proteins. Gene set enrichment analysis (GSEA) identified key pathways like ion channel transport, trafficking of AMPA receptors, and regulation of heme-oxygenase-1 in the IDH wt. subgroup. Pathways such as scavenging of heme, signaling by NOTCH4, negative regulation of PI3-AKT pathway, and iron uptake and transport were observed to be differentially regulated in the IDH mt. subgroup. Conclusions: Tumor regions from the same patient exhibiting differential fluorescence following 5-ALA administration were observed to have different proteome profiles. Future studies aimed at a better molecular understanding of 5-ALA metabolism in HGGs hold the potential to increase the efficacy of FGS and the use of 5-ALA as a theragnostic tool.

Integrative Proteomic and Pharmacological Analysis of Colon Cancer Reveals the Classical Lipogenic Pathway with Prognostic and Therapeutic Opportunities.

Despite recent advancements, the high mortality rate remains a concern in colon cancer (CAC). Identification of therapeutic markers could prove to be a great asset in CAC management. Multiple studies have reported hyperactivation of de novo lipogenesis (DNL), but its association with the pathology is unclear. This study aims to establish the importance as well as the prognostic and therapeutic potential of DNL in CAC. The key lipogenic enzymes fatty acid synthase along with ATP citrate lyase were quantified using an LC-MS/MS-based targeted proteomics approach in the samples along with the matched controls. The potential capacity of the proteins to distinguish between the tumor and controls was demonstrated using random forest-based class prediction analysis using the peptide intensities. Furthermore, in-depth proteomics of DNL inhibition in the CAC cell line revealed the significance of the pathway in proliferation and metastasis. DNL inhibition affected the major signaling pathways, including DNA repair, PI3K-AKT-mTOR pathway, membrane trafficking, proteasome, etc. The study revealed the upregulation of 26S proteasome machinery as a result of the treatment with subsequent induction of apoptosis. Again, in silico molecular docking-based drug repurposing was performed to find potential drug candidates. Furthermore, we have demonstrated that blocking DNL could be explored as a therapeutic option in CAC treatment.

Deep Phosphoproteome Landscape of Interhemispheric Functionality of Neuroanatomical Regions of the Human Brain.

Post-translational modifications (PTMs) are one of the compulsive and predominant biological processes that regulate the diverse molecular mechanism, modulate the onset of disease, and are the reason behind the functional diversity of proteins. Despite the widespread research findings in neuroproteomics, one of the key drawbacks has been the lack of proteome-level knowledge of hemispheric lateralization. We have investigated the proteome level expression in different neuroanatomical regions under the Human Brain Proteome Project (HBPP) and developed the global interhemispheric brain proteome map (Brainprot) earlier. Furthermore, this study has extended to decipher the phosphoproteome map of human brain interhemispheric regions through high-resolution mass spectrometry. The phosphoproteomics examination of 12 unique interhemispheric neurological brain regions using Orbitrap fusion liquid chromatography with tandem mass spectrometry provided comprehensive coverage of 996 phosphoproteins, 2010 phosphopeptides, and 3567 phosphosites. Moreover, interhemispheric phosphoproteome profiling has been categorized according to synaptic ontologies and interhemispheric expression to understand the functionality. Finally, we have integrated the phosphosites data under the PhosphoMap section in the Inter-Hemispheric Brain Proteome Map Portal (https://www.brainprot.org/) for the advancement and support of the ongoing neuroproteomics research worldwide. Data is available via ProteomeXchange with the identifier PXD031188.

Clinical Proteomics for Meningioma: An Integrated Workflow for Quantitative Proteomics and Biomarker Validation in Formalin-Fixed Paraffin-Embedded Tissue Samples.

Clinical proteomics is a rapidly emerging frontier in laboratory medicine. High-throughput proteomic investigations of biopsy tissues provide mechanistic insights into complex human diseases. For large-scale proteomics, formalin-fixed and paraffin-embedded (FFPE) tissue samples offer a viable alternative to fresh-frozen (FF) tissues that have restricted availability. In this context, meningioma is one of the most common primary brain tumors where innovation in diagnostics and therapeutic targets can benefit from clinical proteomics. We present here an integrated workflow for quantitative proteomics and biomarker validation of meningioma FFPE tissues. Applying label-free quantitative (LFQ) proteomics, we reproducibly (Pearson's correlation: 0.84-0.91) obtained an in-depth proteome coverage (nearly 4000 proteins per sample) from 120 min gradient of single unfractionated mass spectrometry run. Furthermore, building upon LFQ data and literature curated set of meningioma-associated proteins, we validated VIM, AHNAK, and CLU from FFPE tissues using selected reaction monitoring (SRM) assay and compared its performance with FF tissues. This study illustrates how knowledge from label-free proteomics can be integrated for selecting peptides for targeted validation and suggests that FFPE tissues are comparable to FF tissues for SRM assays. This quantitative clinical proteomics workflow is scalable for large-scale clinical diagnostics studies in the future, for example, utilizing the global repository of FFPE tissues in meningioma and possibly in other cancers.

Recent advances in mass-spectrometry based proteomics software, tools and databases.

The field of proteomics immensely depends on data generation and data analysis which are thoroughly supported by software and databases. There has been a massive advancement in mass spectrometry-based proteomics over the last 10 years which has compelled the scientific community to upgrade or develop algorithms, tools, and repository databases in the field of proteomics. Several standalone software, and comprehensive databases have aided the establishment of integrated omics pipeline and meta-analysis workflow which has contributed to understand the disease pathobiology, biomarker discovery and predicting new therapeutic modalities. For shotgun proteomics where Data Dependent Acquisition is performed, several user-friendly software are developed that can analyse the pre-processed data to provide mechanistic insights of the disease. Likewise, in Data Independent Acquisition, pipelines are emerged which can accomplish the task from building the spectral library to identify the therapeutic targets. Furthermore, in the age of big data analysis the implications of machine learning and cloud computing are appending robustness, rapidness and in-depth proteomics data analysis. The current review talks about the recent advancement, and development of software, tools, and database in the field of mass-spectrometry based proteomics.

Deciphering the Interregional and Interhemisphere Proteome of the Human Brain in the Context of the Human Proteome Project.

This study, which performs an extensive mass spectrometry-based analysis of 19 brain regions from both left and right hemispheres, presents the first draft of the human brain interhemispheric proteome. This high-resolution proteomics data provides comprehensive coverage of 3300 experimentally measured (nonhypothetical) proteins across multiple regions, allowing the characterization of protein-centric interhemispheric differences and synapse biology, and portrays the regional mapping of specific regions for brain disorder biomarkers. In the context of the Human Proteome Project (HPP), the interhemispheric proteome data reveal specific markers like chimerin 2 (CHN2) in the cerebellar vermis, olfactory marker protein (OMP) in the olfactory bulb, and ankyrin repeat domain 63 (ANKRD63) in basal ganglia, in line with regional brain transcriptomes mapped in the Human Protein Atlas (HPA). In addition, an in silico analysis pipeline was used to predict the structure and function of the uncharacterized uPE1 protein ANKRD63, and parallel reaction monitoring (PRM) was applied to validate its region-specific expression. Finally, we have built the Interhemispheric Brain Proteome Map (IBPM) Portal (www.brainprot.org) to stimulate the scientific community's interest in the brain molecular landscape and accelerate and support research in neuroproteomics. Data are available via ProteomeXchange with identifier PXD019936.

Comprehensive proteomic analysis reveals distinct functional modules associated with skull base and supratentorial meningiomas and perturbations in collagen pathway components.

Meningiomas are brain tumors that originate from the meninges and has been primarily classified into three grades by the current WHO guidelines. Although widely prevalent and can be managed by surgery there are instances when the tumors are located in difficult regions. This results in considerable challenges for complete surgical resection and further clinical management. While the genetic signature of the skull base tumors is now known to be different from the non-skull base tumors, there is a lack of information at the functional aspects of these tumors at the proteomic level. Thus, the current study thereby aims to obtain mechanistic insights between the two radiologically distinct groups of meningiomas, namely the skull base & supratentorial (non-skull base-NSB) regions. We have employed a comprehensive mass spectrometry-based label-free quantitative proteomic analysis in Skull base and supratentorial meningiomas. Further, we have used an Artificial Neural Networking employing a sparse Multilayer perceptron (MLP) architecture to predict protein concordance. A patient-derived spectral library has been employed for a novel peptide-level validation of proteins that are specific to the radiological regions using the SRM assay based targeted proteomics approach. The comprehensive proteomics enabled the identification of nearly 4000 proteins with high confidence (1%FDR ≥ 2 unique peptides) among which 170 proteins were differentially abundant in Skull base vs Supratentorial tumors (p-value ≤0.05). In silico analysis enabled mapping of the major alterations and hinted towards an overall perturbation of extracellular matrix and collagen biosynthesis components in the non-skull base meningiomas and a prominent perturbation of molecular trafficking in the skull base meningiomas. Therefore, this study has yielded novel insights into the functional association of the proteins that are differentially abundant in the two radiological subgroups. SIGNIFICANCE: In the current study, we have performed label-free proteomic analysis on fresh frozen tissue of 14 Supratentorial (NSB) and 7 Skull base meningiomas to assess perturbations in the global proteome, we have further employed an in-depth in silico analysis to map the pathways that have enabled functional mapping of the differentially abundant proteins in the Skull base and Supratentorial tumors. The findings from the above were also subjected to a machine learning-based neural networking to find out the proteins that have the most concordance of occurrence to determine the most influential proteins of the network. We further validated the differential abundance of identified protein markers in a larger patient cohort of Skull base and Supratentorial employing targeted proteomics approach to validate key protein candidates emerging from ours and other recent studies. The previous studies that have explored the skull base and convexity meningiomas have been able to reveal alterations in the genetic mutations in these tumor types. However, there are not many studies that have explored the functional aspects of these tumors, especially at the proteome level. We have attempted for the first time to map the functional modules associated with altered proteins in these tumors and have been able to identify that there is a possibility that the Skull base meningiomas to be considerably different from the Non-skull base (NSB) tumors in terms of the perturbed pathways. Our study employed global as well as targeted proteomics to examine the proteomic alterations in these two tumor groups. The study indicates that proteins that were more abundant in Skull base tumors were part of molecular transport components, non-skull base proteins majorly mapped to the components of extracellular matrix remodeling pathways. In conclusion, this study substantiates the distinction in the proteomic signatures in the skull base and supratentorial meningiomas paving way for further investigation of the identified markers for determining if some of these proteins can be used for therapeutic interventions for cases that pose considerable challenges for complete resection.

Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential.

The pestilential pathogen SARS-CoV-2 has led to a seemingly ceaseless pandemic of COVID-19. The healthcare sector is under a tremendous burden, thus necessitating the prognosis of COVID-19 severity. This in-depth study of plasma proteome alteration provides insights into the host physiological response towards the infection and also reveals the potential prognostic markers of the disease. Using label-free quantitative proteomics, we performed deep plasma proteome analysis in a cohort of 71 patients (20 COVID-19 negative, 18 COVID-19 non-severe, and 33 severe) to understand the disease dynamics. Of the 1200 proteins detected in the patient plasma, 38 proteins were identified to be differentially expressed between non-severe and severe groups. The altered plasma proteome revealed significant dysregulation in the pathways related to peptidase activity, regulated exocytosis, blood coagulation, complement activation, leukocyte activation involved in immune response, and response to glucocorticoid biological processes in severe cases of SARS-CoV-2 infection. Furthermore, we employed supervised machine learning (ML) approaches using a linear support vector machine model to identify the classifiers of patients with non-severe and severe COVID-19. The model used a selected panel of 20 proteins and classified the samples based on the severity with a classification accuracy of 0.84. Putative biomarkers such as angiotensinogen and SERPING1 and ML-derived classifiers including the apolipoprotein B, SERPINA3, and fibrinogen gamma chain were validated by targeted mass spectrometry-based multiple reaction monitoring (MRM) assays. We also employed an in silico screening approach against the identified target proteins for the therapeutic management of COVID-19. We shortlisted two FDA-approved drugs, namely, selinexor and ponatinib, which showed the potential of being repurposed for COVID-19 therapeutics. Overall, this is the first most comprehensive plasma proteome investigation of COVID-19 patients from the Indian population, and provides a set of potential biomarkers for the disease severity progression and targets for therapeutic interventions.

Multiple Reaction Monitoring-Based Targeted Assays for the Validation of Protein Biomarkers in Brain Tumors.

The emergence of omics technologies over the last decade has helped in advancement of research and our understanding of complex diseases like brain cancers. However, barring genomics, no other omics technology has been able to find utility in clinical settings. The recent advancements in mass spectrometry instrumentation have resulted in proteomics technologies becoming more sensitive and reliable. Targeted proteomics, a relatively new branch of mass spectrometry-based proteomics has shown immense potential in addressing the shortcomings of the standard molecular biology-based techniques like Western blotting and Immunohistochemistry. In this study we demonstrate the utility of Multiple reaction monitoring (MRM), a targeted proteomics approach, in quantifying peptides from proteins like Apolipoprotein A1 (APOA1), Apolipoprotein E (APOE), Prostaglandin H2 D-Isomerase (PTGDS), Vitronectin (VTN) and Complement C3 (C3) in cerebrospinal fluid (CSF) collected from Glioma and Meningioma patients. Additionally, we also report transitions for peptides from proteins - Vimentin (VIM), Cystatin-C (CST3) and Clusterin (CLU) in surgically resected Meningioma tissues; Annexin A1 (ANXA1), Superoxide dismutase (SOD2) and VIM in surgically resected Glioma tissues; and Microtubule associated protein-2 (MAP-2), Splicing factor 3B subunit 2 (SF3B2) and VIM in surgically resected Medulloblastoma tissues. To our knowledge, this is the first study reporting the use of MRM to validate proteins from three types of brain malignancies and two different bio-specimens. Future studies involving a large cohort of samples aimed at accurately detecting and quantifying peptides of proteins with roles in brain malignancies could potentially result in a panel of proteins showing ability to classify and grade tumors. Successful application of these techniques could ultimately offer alternative strategies with increased accuracy, sensitivity and lower turnaround time making them translatable to the clinics.

Proteomic investigation reveals dominant alterations of neutrophil degranulation and mRNA translation pathways in patients with COVID-19.

The altered molecular proteins and pathways in response to COVID-19 infection are still unclear. Here, we performed a comprehensive proteomics-based investigation of nasopharyngeal swab samples from patients with COVID-19 to study the host response by employing simple extraction strategies. Few of the host proteins such as interleukin-6, L-lactate dehydrogenase, C-reactive protein, Ferritin, and aspartate aminotransferase were found to be upregulated only in COVID-19-positive patients using targeted multiple reaction monitoring studies. The most important pathways identified by enrichment analysis were neutrophil degranulation, interleukin-12 signaling pathways, and mRNA translation of proteins thus providing the detailed investigation of host response in COVID-19 infection. Thus, we conclude that mass spectrometry-detected host proteins have a potential for disease severity progression; however, suitable validation strategies should be deployed for the clinical translation. Furthermore, the in silico docking of potential drugs with host proteins involved in the interleukin-12 signaling pathway might aid in COVID-19 therapeutic interventions.

The proteomic analysis shows enrichment of RNA surveillance pathways in adult SHH and extensive metabolic reprogramming in Group 3 medulloblastomas.

Medulloblastoma, a common malignant brain tumor in children, comprises four molecular subgroups WNT, SHH, Group 3, and Group 4. In the present study, we performed a deep proteome-based investigation of SHH, Group 3 and Group 4 tumors. The adult SHH medulloblastomas were found to have a distinct proteomic profile. Several RNA metabolism-related pathways including mRNA splicing, 5' to 3' RNA decay, 3' to 5' RNA decay by the RNA exosome, and the N6-methyladenosine modification of RNA were enriched in adult SHH tumors. The heightened expression of the RNA surveillance pathways is likely to be essential for the viability of adult SHH subgroup medulloblastomas, which carry mutations in U1snRNA encoding gene and thus could be a vulnerability of these tumors. Group 3 and Group 4 medulloblastomas, on the other hand, are known to have an overlap in their expression profiles and underlying genetic alterations. Group 3 proteome was found to be distinctively enriched in several metabolic pathways including glycolysis, gluconeogenesis, glutamine anabolism, glutathione-mediated anti-oxidant pathway, and drug metabolism pathway suggests that the extensive metabolic rewiring is likely to be responsible for the aggressive clinical behavior of Group 3 tumors. This comprehensive proteomic analysis has provided valuable insight into the biology of Group 3 and adult SHH medulloblastomas, which could be further explored for effective treatment of these tumors.

Multiplexed quantitative proteomics provides mechanistic cues for malaria severity and complexity.

Management of severe malaria remains a critical global challenge. In this study, using a multiplexed quantitative proteomics pipeline we systematically investigated the plasma proteome alterations in non-severe and severe malaria patients. We identified a few parasite proteins in severe malaria patients, which could be promising from a diagnostic perspective. Further, from host proteome analysis we observed substantial modulations in many crucial physiological pathways, including lipid metabolism, cytokine signaling, complement, and coagulation cascades in severe malaria. We propose that severe manifestations of malaria are possibly underpinned by modulations of the host physiology and defense machinery, which is evidently reflected in the plasma proteome alterations. Importantly, we identified multiple blood markers that can effectively define different complications of severe falciparum malaria, including cerebral syndromes and severe anemia. The ability of our identified blood markers to distinguish different severe complications of malaria may aid in developing new clinical tests for monitoring malaria severity.

Comprehending Meningioma Signaling Cascades Using Multipronged Proteomics Approaches & Targeted Validation of Potential Markers.

Meningiomas are one of the most prevalent primary brain tumors. Our study aims to obtain mechanistic insights of meningioma pathobiology using mass spectrometry-based label-free quantitative proteome analysis to identifying druggable targets and perturbed pathways for therapeutic intervention. Label-free based proteomics study was done from peptide samples of 21 patients and 8 non-tumor controls which were followed up with Phosphoproteomics to identify the kinases and phosphorylated components of the perturbed pathways. In silico approaches revealed perturbations in extracellular matrix remodeling and associated cascades. To assess the extent of influence of Integrin and PI3K-Akt pathways, we used an Integrin Linked Kinase inhibitor on patient-derived meningioma cell line and performed a transcriptomic analysis of the components. Furthermore, we designed a Targeted proteomics assay which to the best of our knowledge for very first-time enables identification of peptides from 54 meningioma patients via SRM assay to validate the key proteins emerging from our study. This resulted in the identification of peptides from CLIC1, ES8L2, and AHNK many of which are receptors and kinases and are difficult to be characterized using conventional approaches. Furthermore, we were also able to monitor transitions for proteins like NEK9 and CKAP4 which have been reported to be associated with meningioma pathobiology. We believe, this study can aid in designing peptide-based validation assays for meningioma patients as well as IHC studies for clinical applications.

Palliative Role of Aqueous Ginger Extract on N-Nitroso-N-Methylurea-Induced Gastric Cancer.

Ginger (Zingiber officinale) is a spice and also an herbal medicine used worldwide for managing GI tract disturbances. However, its role in gastric cancer is sparingly known. This study ensures the standardization of gastric cancer by the induction of N-nitroso N-methyl Urea (MNU) and to determine the role of the aqueous extract of ginger (AGE) in MNU-induced gastric cancer in albino Wistar rats. Accordingly, the anticancer potential of AGE and its possible mode of action were assessed on rats exposed to MNU, by various biochemical and molecular assays. As evidenced by the extent of lipid peroxidation, gastrin levels and histopathological sections in MNU-induced cancerous lesions at 8 wk which was stabilized at 16 wk confirming the induction of gastric carcinoma by the chemical carcinogen. Further, results revealed that AGE alleviated the oxidative stress as evidenced by the stomach antioxidant enzymes (SOD, catalase, GPx, and GR), markers of oxidative stress (TRx, GRx) and Gastrin, a specific marker for gastric cancer and a decreased level of pro-inflammatory markers (NF-kB, TNF-α, IL-6, PGE2) which was further confirmed by histopathological analysis. AGE is responsible to mitigate oxidative stress and inflammation related to gastric cancer and could be used as a potential dietary intervention in gastric cancer therapy.