Cysteamine Nanoemulsion Delivery by Inhalation to Attenuate Adverse Effects of Exposure to Cigarette Smoke: A Metabolomics Study in Wistar Rats.

There is a pressing need for novel pharmacological interventions and drug delivery innovations to attenuate the cigarette smoke-associated oxidative stress and lung disease. We report here on the attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) and metabolomics of Wistar rats exposed to cigarette smoke for 28 days. The animals were treated for 15 days with plain cysteamine given orally or cysteamine as nanoemulsion given orally or via inhalation. The study design also included two control groups as follows: rats exposed to cigarette smoke but did not receive a treatment (diseased control group) and rats neither exposed to cigarette smoke nor a treatment (normal control group). The targeted metabolomics using Parallel Reaction Monitoring showed that in the diseased control group, ornithine, nicotinamide, xanthine, hypoxanthine, and caprolactam were increased compared with the normal control group. In addition, (±)8(9)-DiHET, which was initially downregulated in the diseased control group, exhibited a reversal of this trend with cysteamine nanoemulsion given via inhalation. The cysteamine nanoemulsion delivered by inhalation highlighted the importance of the route of drug administration for targeting the lungs. To the best of our knowledge, this is the first work to use ATR-FTIR and metabolomics in Wistar rat lung tissues, suggesting how cysteamine nanoemulsion can potentially reduce cigarette smoke-induced oxidative damage. The metabolites reported herein have potential implications for discovery of novel theranostics and, thus, to cultivate diagnostic and therapeutic innovation for early prevention and treatment of cigarette smoke-associated lung diseases.

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.

Detection of Mutant Peptides of SARS-CoV-2 Variants by LC/MS in the DDA Approach Using an In-House Database.

Equipped with a dramatically high mutation rate, which happens to be a signature of RNA viruses, SARS-CoV-2 trampled across the globe infecting individuals of all ages and ethnicities. As the variants of concern (VOC) loomed large, definitive detection of SARS-CoV-2 strains became a matter of utmost importance in epidemiological and clinical research. Besides, unveiling the disease pathogenesis at the molecular level and deciphering the therapeutic targets became key priorities since the emergence of the pandemic. Mass spectrometry has been largely used in this regard. A critical part of mass spectrometric analyses is the proteome database required for the identification of peptides. Presently, the mutational information on proteins available on SARS-CoV-2 databases cannot be used to analyze data extracted from mass spectrometers. Hence, we developed the novel Mutant Peptide Database (MPD) for the mass spectrometry (MS)-based identification of mutated peptides, which contains information from 11 proteins of SARS-CoV-2 from a total of 21,549 SARS-CoV-2 variants across different regions of India. The database was validated using clinical samples, and its applicability was also demonstrated with the mutated peptides extracted from the literature. We believe that MPD will support broad-spectrum MS-based studies like viral detection, disease pathogenesis, and therapeutics with respect to SARS-CoV-2 and its variants.

Review of Psychotropic Agents Associated with Sialorrhoea, Except Clozapine.

Introduction: Sialorrhoea, or excessive salivation, is common in psychiatric patients. This can be distressing because of its physical and psychosocial complications. Sialorrhoea due to psychotropic drugs has been reported repeatedly in the literature. Clozapine is the antipsychotic most commonly associated with sialorrhoea. Objective: The objective of this review was to examine and discuss the existing literature on all psychotropic drugs associated with sialorrhoea, except clozapine. Methods: Google Scholar and PubMed were searched for the literature on psychotropic-induced sialorrhoea. The search terms used were sialorrhoea, antidepressants, antipsychotics, mood stabilizer, and benzodiazepines. Case reports on patients suffering from psychotropic-induced sialorrhoea except clozapine are reviewed in this study. Results: The pathophysiology behind psychotropic-induced sialorrhoea, the population susceptible to sialorrhoea, and the exact duration from the start of sialorrhoea the drug course to the onset of sialorrhoea are unknown. Also, sialorrhoea is not associated with drug toxicity and is observed even in patients receiving normal doses of psychotropic medications. Treatment involves dose reduction, discontinuation of drugs responsible for the adverse effect, or adding anticholinergic drugs. Conclusion: Sialorrhoea due to clozapine has been reported in the literature. Many other antipsychotics, antidepressants such as sertraline, and other psychotropic drugs such as lithium have also been reported to cause sialorrhoea. No increase in the risk of sialorrhoea was seen in any of the age groups, and no association was found with treatment duration. In cases of lithium-induced sialorrhoea, no relationship was observed between serum levels of lithium and sialorrhoea.

Colon Adenocarcinoma Quantitative Proteomics Reveals Dysregulation in Key Cancer Signaling Pathways and a Candidate Protein Marker Panel.

Colorectal cancer (CRC) is reportedly the second leading cause of cancer death worldwide. By the end of the decade, there will likely be more than one million fatalities worldwide from this cancer, with an estimated 2.2 million additional cases. We need new ways of thinking about cancer research. One approach is to deploy systems science using quantitative proteomics to obtain postgenomic and functional insights into cancer. The present study compares the tissue proteome of CRC (n = 10) with the matched peritumoral controls (n = 10) in samples obtained from the Indian subcontinent. When compared with the controls, a list of 22 substantially altered protein candidates was identified, which were associated with the growth, survival, and metastasis of the tumor. A list of the unique peptides from top significant proteins, including olfactomedin-4, alanyl aminopeptidase, and grancalcin was further validated using a parallel reaction monitoring-based targeted proteomics approach. In addition, biological pathway analysis showed perturbation in key biological processes, including dysregulation in purine metabolism, MYC targets in cancer, DNA repair, and replication, and leukocyte transendothelial migration, among others. The protein panel reported herein is also shown to be dysregulated in CRC and warrants further research toward understanding pathobiology, diagnostics, and therapeutics development in CRC.

SARS-CoV-2 severity classification from plasma sample using confocal Raman spectroscopy.

The world is on the brink of facing coronavirus's (COVID-19) fourth wave as the mutant forms of viruses are escaping neutralizing antibodies in spite of being vaccinated. As we have already witnessed that it has encumbered our health system, with hospitals swamped with infected patients observed during the viral outbreak. Rapid triage of patients infected with SARS-CoV-2 is required during hospitalization to prioritize and provide the best point of care. Traditional diagnostics techniques such as RT-PCR and clinical parameters such as symptoms, comorbidities, sex and age are not enough to identify the severity of patients. Here, we investigated the potential of confocal Raman microspectroscopy as a powerful tool to generate an expeditious blood-based test for the classification of COVID-19 disease severity using 65 patients plasma samples from cohorts infected with SARS-CoV-2. We designed an easy manageable blood test where we used a small volume (8 µl) of inactivated whole plasma samples from infected patients without any extra solvent usage in plasma processing. Raman spectra of plasma samples were acquired and multivariate exploratory analysis PC-LDA (principal component based linear discriminant analysis) was used to build a model, which segregated the severe from the non-severe COVID-19 group with a sensitivity of 83.87%, specificity of 70.60% and classification efficiency of 76.92%. Among the bands expressed in both the cohorts, the study led to the identification of Raman fingerprint regions corresponding to lipids (1661, 1742), proteins amide I and amide III (1555, 1247), proteins (Phe) (1006, 1034), and nucleic acids (760) to be differentially expressed in severe COVID-19 patient's samples. In summary, the current study exhibits the potential of confocal Raman to generate simple, rapid, and less expensive blood tests to triage the severity of patients infected with SARS-CoV-2.

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.

The First Pituitary Proteome Landscape From Matched Anterior and Posterior Lobes for a Better Understanding of the Pituitary Gland.

To date, very few mass spectrometry (MS)-based proteomics studies are available on the anterior and posterior lobes of the pituitary. In the past, MS-based investigations have focused exclusively on the whole pituitary gland or anterior pituitary lobe. In this study, for the first time, we performed a deep MS-based analysis of five anterior and five posterior matched lobes to build the first lobe-specific pituitary proteome map, which documented 4090 proteins with isoforms, mostly mapped into chromosomes 1, 2, and 11. About 1446 differentially expressed significant proteins were identified, which were studied for lobe specificity, biological pathway enrichment, protein-protein interaction, regions specific to comparison of human brain and other neuroendocrine glands from Human Protein Atlas to identify pituitary-enriched proteins. Hormones specific to each lobe were also identified and validated with parallel reaction monitoring-based target verification. The study identified and validated hormones, growth hormone and thyroid-stimulating hormone subunit beta, exclusively to the anterior lobe whereas oxytocin-neurophysin 1 and arginine vasopressin to the posterior lobe. The study also identified proteins POU1F1 (pituitary-specific positive transcription factor 1), POMC (pro-opiomelanocortin), PCOLCE2 (procollagen C-endopeptidase enhancer 2), and NPTX2 (neuronal pentraxin-2) as pituitary-enriched proteins and was validated for their lobe specificity using parallel reaction monitoring. In addition, three uPE1 proteins, namely THEM6 (mesenchymal stem cell protein DSCD75), FSD1L (coiled-coil domain-containing protein 10), and METTL26 (methyltransferase-like 26), were identified using the NeXtProt database, and depicted tumor markers S100 proteins having high expression in the posterior lobe. In summary, the study documents the first matched anterior and posterior pituitary proteome map acting as a reference control for a better understanding of functional and nonfunctional pituitary adenomas and extrapolating the aim of the Human Proteome Project towards the investigation of the proteome of life.

Metabolomics Profiling of Pituitary Adenomas by Raman Spectroscopy, Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy, and Mass Spectrometry of Serum Samples.

To date, no studies are available in which pituitary adenomas (PAs) have been studied using techniques like confocal Raman spectroscopy, attenuated total reflection-Fourier transform infrared (FT-IR), and liquid chromatography-tandem mass spectrometry (LC-MS/MS) in the same serum samples. To understand the metabolomics fingerprint, Raman spectra of 16 acromegaly, 19 Cushing's, and 33 nonfunctional PA (NFPA) and ATR-FTIR spectral acquisition of 16 acromegaly, 18 Cushing's, and 22 NFPA patient's serum samples were acquired. Next, Principal component-based linear discriminant analysis (PC-LDA) models were developed, Raman spectral analysis classified acromegaly with an accuracy of 79.17%, sensitivity of 75%, and specificity of 81.25%, Cushing's with an accuracy of 66.67%, sensitivity of 100%, and specificity of 52.63%, and NFPA with an accuracy of 73.17%, sensitivity of 75%, and specificity of 72.73%. ATR-FTIR spectral analysis classified acromegaly with an accuracy of 95.83%, sensitivity of 100%, and specificity of 93.75%, Cushing's with an accuracy of 65.38%, sensitivity of 87.5%, and specificity of 55.56%, and NFPA with an accuracy of 70%, sensitivity of 87.5%, and specificity of 43.75%. In either of the cases, healthy individual cohorts were clearly segregated from the disease cohort, which identified differential regulated regions of nucleic acids, lipids, amides, phosphates, and polysaccharide/C-C residue α helix regions. Furthermore, LC-MS/MS-based analysis of sera samples resulted in the identification of various sphingosine, lipids, acylcarnitines, amino acids, ethanolamine, choline, and their derivatives that differentially regulated in each tumor cohort. We believe cues obtained from the study may be used to generate the metabolite-based test to diagnose PAs from serum in addition to conventional techniques and also to understand disease biology for better disease management, point of care, and improving quality of life in PA patients.

A qualitative analysis of literature reporting and linking psychosis to COVID-19 infection. Findings from a postgraduate journal club.

Acute exacerbations of psychosis have been reported with COVID-19 infection and medications used for its treatment. Terms "psychosis", "psychotic", "COVID-19″ and "coronavirus" were searched on "PubMed" and "GOOGLE SCHOLAR", yielding 84 articles. 14 case reports were selected based on pre-defined criteria and analyzed. Among selected articles,10 attributed psychosis to COVID-19 infection. In 3 articles, psychosis was diagnosed despite concurrent delirium. In 8 and 3 articles respectively, a clear temporal demarcation of psychosis and COVID-19 infection and steroid use was not described. Psychosis can occur secondary to GMC, or exposure to medication. Due process should be followed to ascertain the same. INTRODUCTION: Neurotropic coronavirus infection is associated with numerous neurological and neuropsychiatric manifestations. Such presentations before, during and after the infection have been reported. Among these presentations, acute exacerbations of psychosis have been reportedly linked with COVID-19 infection and medications used for its treatment. METHODOLOGY: Search engines "PubMed" AND "GOOGLE SCHOLAR" were searched using specific search terms during June 2021. Out of 84 articles that came up, we selected 14 articles based on pre-determined inclusion and exclusion criteria. Selected articles were analysed and discussed in the departmental journal club. RESULTS: In 10 articles, diagnosis of psychosis was attributed to COVID-19 infection. In 3 of those articles, despite reporting concurrent delirium like presentation, diagnosis was still reported as psychosis. In 8 articles, the temporal correlation between onset of psychosis, onset of COVID-19 was not clearly demarcated. In 3 articles, clear demarcation between psychosis associated with steroid use and with a general medical condition (COVID-19) was not clearly presented. Only 2 articles did mention using a structured diagnostic system. In patients (3/17) with prior history of psychiatric illness, diagnosis was reported as relapse of psychosis (2/17), without specifying the criteria used for diagnosing a relapse. CONCLUSION: Acute exacerbation of psychosis can occur secondary to a general medical condition (GMC), or after exposure to a medication. However, due process should be followed to ascertain that the psychosis is indeed secondary to a GMC, or a medication, and not a de-novo presentation, or delirium.

Efficacy of a suicide sensitisation and prevention (SSP) workshop for medical students on their attitudes, beliefs, and factual knowledge on suicide: A pilot study.

Context: Myths and stigma about suicide and mental health among doctors are widely prevalent in India. Didactic methods of teaching alone may not be adequate to bridge the knowledge gap. Methods: Fifty-seven MBBS students participated voluntarily by accepting an invitation. They were examined pre-intervention with the Suicide Opinion Questionnaire (SOQ) for their attitudes and beliefs about suicide. Students then underwent a custom-made extracurricular Suicide Sensitization and Prevention workshop. It consisted of a 120-min session that included a didactic session on the bio-psycho-social model of suicide and two role-plays demonstrating Suicide Prevention Early Intervention Communication (SPEIC). Students were examined again after seven days with SOQ, and with the SPEIC checklist. Results: Fifty students completed the study protocol. There was a 9.5% increase in SOQ scores post-intervention indicating a change toward positive attitudes/beliefs about suicide. The emotional perturbation subscale showed the highest degree of improvement, whereas the acceptability subscale showed the least improvement. Students were able to recall 40% and 60% from the Do-Checklist and the Do Not Checklist from the SPEIC after seven days from the workshop. There was no difference in SOQ performances pre- and post-intervention in students who knew someone with psychiatric illness, or with a history of an attempt or death by suicide. Conclusion: Using role-plays and interactive teaching methods can be effective in teaching psychiatry and mental health issues to medical students. The results indicate not only better understanding of subject matter but also recall after a week from the intervention.

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.

A proteogenomic approach to target neoantigens in solid tumors.

INTRODUCTION: Proteogenomic techniques find applications in identifying novel cancer-specific peptides called neoantigens; they are non-self peptides derived from tumor-specific non-synonymous mutations. These peptides with MHCs are recognized by the T cells and induce an antitumor response. Due to their selective expression of tumor cells, neoantigens are considered attractive targets for cancer immunotherapy. AREAS COVERED: In this review, we have discussed the proteogenomic strategies to identify neoantigens. We have also provided a neoantigen identification pipeline using data from whole-exome sequencing, RNA sequencing, and MHC peptidomics. Further, we have reviewed recent tools for neoantigen discovery. EXPERT COMMENTARY: The limitations in instrument sensitivity and availability of bioinformatics tools have restricted the identification of neoantigens from tumor samples. Nonetheless, the recent improvement in genome sequencing, mass spectrometry technologies, and the development of reliable algorithms for epitope prediction provide hope for efficient identification of neoantigens. Translating this workflow on patient samples would represent a massive advancement in neoantigen identification methods, leading to the constitution of novel personalized neoantigen cancer vaccines.