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index 047e51c..25a7862 100644
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"text": "Type-1 diabetes (T1D) is an autoimmune disease that is characterized by the destruction of the insulin producing β cells in the Islets of Langerhans of the pancreas. Currently the measurement of autoantibodies (Aabs) like islet-cell autoantibodies, protein tyrosine phosphatase, glutamic acid decarboxylase, insulin and zinc transporter Slc30A8 protein indicates the manifestation of β cells autoimmunity and increased disease risk. However, the destruction of β cells usually starts early in life and symptoms appears when 90% of the cells are destroyed. The time period of appearance of first Aabs to the onset of the clinical disease can vary from 1 month to over 10 years, moreover, not all Aab positive subjects develop T1D. Thus additional indicators of early disease process and progression are needed. To identify disease associated changes, a careful selection of study group is essential such as The Finnish Type-1 Diabetes Prediction and Prevention project DIPP has initiated in 1994. The DIPP cohort has collected blood serum samples at 3 to 6 months intervals from children with a genetically conferred T1D risk and tested for T1D associated autoantibodies. These longitudinal series of samples cover all the stages of disease progression from birth to clinical T1D and matching samples from carefully matched healthy children.\nWe utilized such unique samples from the DIPP cohort to identify early serum protein biomarkers associated with T1D using quantitative mass spectrometry based approach. The study involved LC-MS/MS analysis with both iTRAQ and label-free quantification strategy on the immunodepleted serum.\nPrevious serum proteomics biomarker studies of T1D have typically compared disease end points with control groups, i.e. the differences between patients with T1D and healthy controls. In contrast to the published reports, to our knowledge we have shown for the first time serum proteomics profile of pre-diabetic children, mapping the changes from early infancy, seroconversion and diagnosis. The main finding included lower and higher levels of APOC4 and AFAM in cases compared to controls respectively and, the combination of this two proteins classified T1D developing children from controls with 91% success rate with an area under the curve value of 0.85. Notably the levels of APOC4 were found to be lower even before seroconversion."
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- "text": "I really like Methods in Molecular Biology book series. They publish step by step protocols with detailed information on materials and methods to carry out the experiment in a reproducible manner. In particular notes section provides useful tip and troubleshooting guide.\nWe published a protocol entitled Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation. It presents a workflow detailing sample preparation with and without immunodepletion of high abundant serum/plasma proteins and, LC-MS/MS methodology for discovery and targeted measurements of candidate biomarkers."
+ "text": "Mass spectrometry based proteomics is the coolest technique to identify and characterize the proteins (including their interaction, alternative splicing, post-transnational modifications and more). Introduction and details about the technology are beyond the scope of this blog post, however, readers are recommended to follow the comprehensive overview of modern proteomics.\nTypical shotgun proteomics experiment on representative number of samples results in generation of several gigabytes of mass spectrometry data files. The analysis of such data undergoes following steps.\n\nQuality control checks.\nDatabase search and quantitative analysis.\nStatistical analysis\nFunctional annotation analysis\n\nIn this blog post, I will highlight the tools available to process the mass spectrometry data by outlining the above headings.\n\nQuality control checks: Depending on the mode of LC-MS/MS data acquisition (i.e. either DDA or DIA), there exist plethora of tools to measure QC metrics. However, for the DIA analysis, limited pipelines are available.\nOften times to use the functionality of some tools, users needs to convert the proprietary MS files into generic file format such as mzmL\nDDA analysis\n\nRawMeat: developed by Vast Scientific gives a quick overview of TIC (total ion chromatogram), charge state distribution, fill time, spray stability and target fill times. The tool is limited to use with Thermo instrument and it is no longer supported.\nRawBeans: generates an interactive html report for\nQuiC ™: Properitary software from Biognosys, supports most of data acquisition mode (SRM, PRM, DIA or DDA) but it requirs addition of iRT peptides in the samples."
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- "text": "Proteins represent the key interacting biomolecules in the complex network within the cell and their interactions are crucial in orchestrating all aspects of life at the molecular level. Most biochemical functions are not carried out by a specific protein in isolation but by the multiple protein in associations refereed as a protein-protein interactions (PPIs).\nAffinity purification-based mass spectrometry (AP-MS) is a technique of choice in discovering PPIs. These experiments are usually carried out by coupling a bait protein to the protein A or immunoglobulin G (IgG) surfaces or an affinity matrix followed by purification of tagged protein from a cell lysate. Additionally, suitable negative control replicates are mandatorily included to define the non-specific background. The composition of PPIs are then delineated by mass spectrometry analysis. These types of studies are useful in understanding the complicated interplay of proteins inside the cells for generating new hypothesis or may be helpful in placing a specific interactor in a pathway to explain observed phenotypes.\nWe used AP-MS method to study interactome of FOS related proteins (FOSL1 and FOSL2) in human Th17 cells. The fate of Th17 cells is regulated by various transcription factors such as BATF, IRF4 and STAT3. Furthermore, the members of the activator protein (AP-1) family including ATF, FOS and JUN also modulate the differentiation of Th17 cells. Of these AP-1 members, FOS related proteins regulates variety of processes such as cancer progression, embryonic development and immune cells signaling. In order to understand how FOS related proteins mediates signaling mechanism in Th17 cells, we performed their interactome analysis.\nThe analysis resulted in the identification of 163 and 67 proteins for FOSL1 and FOSL2 respectively. These interactors have passed certain criteria including mass spectrometry interaction statistics (MiST) algorithm scores with the matching IgG controls and they were mapped against in house common contaminant detected in related AP-MS experiments.\nFurthermore, we validated the interesting binding partners of FOSL1 and FOSL2 by western blotting and parallel reaction monitoring mass spectrometry. The shared interactors between FOSL1 and FOSL2 as depicted in below figure were mapped against the STRING database to construct a network using Cytoscape.\n\nThe gene ontology based molecular functional analysis were performed by ClueGO and CluePedia apps built in Cytoscape."
+ "text": "Welcome to my blog, here, you will find a collection of posts for some of the previously published articles!\n\n\n\n\n\n\n\n\n \n\n\n\n\nProteomics data analysis and visualization (no programming skills..its okay..but)\n\n\n\n\n\n\n\ndata analysis\n\n\n\n\n\n\n\n\n\n\n\nOctober 10, 2023\n\n\n\n\n\n\n \n\n\n\n\nAP-MS to Study FOSL Related Proteins Interactome\n\n\n\n\n\n\n\npublications\n\n\nT cells\n\n\n\n\n\n\n\n\n\n\n\nSeptember 16, 2021\n\n\n\n\n\n\n \n\n\n\n\nMass Spectrometry Based Serum Proteomics\n\n\n\n\n\n\n\npublications\n\n\nbook chapter\n\n\n\n\n\n\n\n\n\n\n\nNovember 18, 2020\n\n\n\n\n\n\n \n\n\n\n\nSerum Proteomics Atherosclerosis\n\n\n\n\n\n\n\npublications\n\n\nserum\n\n\n\n\n\n\n\n\n\n\n\nNovember 11, 2020\n\n\n\n\n\n\n \n\n\n\n\nSerum Proteomics Pre diabetic\n\n\n\n\n\n\n\npublications\n\n\nserum\n\n\n\n\n\n\n\n\n\n\n\nOctober 27, 2020\n\n\n\n\n\n\nNo matching items"
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"text": "I am currently employed as an associate biomedical scientist at Precision biomarker laboratories, Cedars-Sinai Medical Center, Los Angeles, CA, USA.\nDuring my postdoctoral fellowship, I worked in Prof. Martin R. Larsen’s lab at the University of Southern Denmark on developing a pipeline for identifying post-translationally modified biomarkers in clinical samples.\nI pursued my PhD from University of Turku under the joint advisorship of Prof. Riitta Lahesmaa and Dr. Robert Moulder. During the course of studies, I worked on identifying and validating the serum protein biomarkers for type 1 diabetes and carotid atherosclerosis.\nBefore enrollment into the PhD study, I gained my first level of research experience with my master thesis in the area of proteomics and mass spectrometry at National Chemical Laboratory (NCL) under the joint supervision of Drs. Mahesh J. Kulkarni and B.Santhakumari, after which I continued as a teacher in college of pharmacy and then as a research assistant again at NCL."
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+ "text": "Proteins represent the key interacting biomolecules in the complex network within the cell and their interactions are crucial in orchestrating all aspects of life at the molecular level. Most biochemical functions are not carried out by a specific protein in isolation but by the multiple protein in associations refereed as a protein-protein interactions (PPIs).\nAffinity purification-based mass spectrometry (AP-MS) is a technique of choice in discovering PPIs. These experiments are usually carried out by coupling a bait protein to the protein A or immunoglobulin G (IgG) surfaces or an affinity matrix followed by purification of tagged protein from a cell lysate. Additionally, suitable negative control replicates are mandatorily included to define the non-specific background. The composition of PPIs are then delineated by mass spectrometry analysis. These types of studies are useful in understanding the complicated interplay of proteins inside the cells for generating new hypothesis or may be helpful in placing a specific interactor in a pathway to explain observed phenotypes.\nWe used AP-MS method to study interactome of FOS related proteins (FOSL1 and FOSL2) in human Th17 cells. The fate of Th17 cells is regulated by various transcription factors such as BATF, IRF4 and STAT3. Furthermore, the members of the activator protein (AP-1) family including ATF, FOS and JUN also modulate the differentiation of Th17 cells. Of these AP-1 members, FOS related proteins regulates variety of processes such as cancer progression, embryonic development and immune cells signaling. In order to understand how FOS related proteins mediates signaling mechanism in Th17 cells, we performed their interactome analysis.\nThe analysis resulted in the identification of 163 and 67 proteins for FOSL1 and FOSL2 respectively. These interactors have passed certain criteria including mass spectrometry interaction statistics (MiST) algorithm scores with the matching IgG controls and they were mapped against in house common contaminant detected in related AP-MS experiments.\nFurthermore, we validated the interesting binding partners of FOSL1 and FOSL2 by western blotting and parallel reaction monitoring mass spectrometry. The shared interactors between FOSL1 and FOSL2 as depicted in below figure were mapped against the STRING database to construct a network using Cytoscape.\n\nThe gene ontology based molecular functional analysis were performed by ClueGO and CluePedia apps built in Cytoscape."
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"section": "",
- "text": "Welcome to my blog, here, you will find a collection of posts for some of the previously published articles!\n\n\n\n\n\n\n\n\n \n\n\n\n\nAP-MS to Study FOSL Related Proteins Interactome\n\n\n\n\n\n\n\npublications\n\n\nT cells\n\n\n\n\n\n\n\n\n\n\n\nSeptember 16, 2021\n\n\n\n\n\n\n \n\n\n\n\nMass Spectrometry Based Serum Proteomics\n\n\n\n\n\n\n\npublications\n\n\nbook chapter\n\n\n\n\n\n\n\n\n\n\n\nNovember 18, 2020\n\n\n\n\n\n\n \n\n\n\n\nSerum Proteomics Atherosclerosis\n\n\n\n\n\n\n\npublications\n\n\nserum\n\n\n\n\n\n\n\n\n\n\n\nNovember 11, 2020\n\n\n\n\n\n\n \n\n\n\n\nSerum Proteomics Pre diabetic\n\n\n\n\n\n\n\npublications\n\n\nserum\n\n\n\n\n\n\n\n\n\n\n\nOctober 27, 2020\n\n\n\n\n\n\nNo matching items"
+ "text": "I really like Methods in Molecular Biology book series. They publish step by step protocols with detailed information on materials and methods to carry out the experiment in a reproducible manner. In particular notes section provides useful tip and troubleshooting guide.\nWe published a protocol entitled Mass Spectrometry-Based Serum Proteomics for Biomarker Discovery and Validation. It presents a workflow detailing sample preparation with and without immunodepletion of high abundant serum/plasma proteins and, LC-MS/MS methodology for discovery and targeted measurements of candidate biomarkers."
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