Krishnakumar Menon, Ph.D.
Associate Professor, Centre for Nanosciences & Molecular Medicine, Amrita University, Kochi, India
A Far-Western Clinical Proteomics Approach to Detect Molecules of Clinical and Pathological Significance in the Neurodegenerative Disease Multiple Sclerosis.
Multiple Sclerosis (MS), an autoimmune neurodegenerative disorder of the central nervous system. The disease affects young adults at their prime age leading to severe debilitation over several years. Despite advances in MS research, the cause of the disease remains elusive. Thus, our objective is to identify novel molecules of pathological and diagnostic significance important in the understanding, early diagnosis and treatment of MS. Biological fluids such as cerebrospinal fluid (CSF), that bathe the brain serve as a potential source for the identification of pathologically significant autoantibody reactivity in MS. In this regard, we report the development of an unbiased clinical proteomics approach for the detection of reactive CSF molecules that target brain proteins from patients with MS. Proteins of myelin and myelin-axolemmal complexes were separated by two-dimensional gel electrophoresis, blotted onto membranes and probed separately with biotinylated unprocessed CSF samples. Protein spots that reacted specifically to MS-CSF’s were further analyzed by matrix assisted laser desorption ionization-time-of-flight time-of-flight mass spectrometry. In addition to previously reported proteins found in MS, we have identified several additional molecules involved in mitochondrial and energy metabolism, myelin gene expression and/or cytoskeletal organization. Among these identified molecules, the cellular expression pattern of collapsin response mediator protein-2 and ubiquitin carboxy-terminal hydrolase L1 were investigated in human chronic-active MS lesions by immunohistochemistry. The observation that in multiple sclerosis lesions phosphorylated collapsin response mediator protein-2 was increased, whereas Ubiquitin carboxy-terminal hydrolase L1 was down-regulated, not only highlights the importance of these molecules in the pathology of this disease, but also illustrates the use of our approach in attempting to decipher the complex pathological processes leading to multiple sclerosis and other neurodegenerative diseases. Further, we show that in clinicaly isolated syndrome (CIS), we could identify important molecules that could serve as an early diagnostic biomarker in MS.
Aditya Murthy, Ph.D.
Associate Professor, Centre For Neuroscience, Indian Institute of Science, Bangalore, India
Since Karl Lashley’s seminal work on the formulation of serial order, numerous models assume simultaneous representation of competitive elements of a sequence, to account for serial order effects in different types of behavior like typing, speech, etc. Such models follow two basic assumptions: (1) more than one plan representation can be simultaneously active in a planning layer; (2) the most active plan is chosen in another layer called the competitive choice layer. Using the oculomotor system I will describe behavioral and neurophysiological experiments that tests the two critical predictions of such queuing models, providing evidence that basal ganglia in monkeys and humans instantiate a form of queuing that transforms parallel movement representations into more serial representations, allowing for the expression of sequential saccadic eye movements.
Sanjeeva Srivastava, Ph.D.
Assistant Professor, Proteomics Lab, IIT-Bombay, India
Identification of Potential Early Diagnostic Biomarkers for Gliomas and Various Infectious Diseases using Proteomic Technologies
The spectacular advancements achieved in the field of proteomics research during the last decade have propelled the growth of proteomics for clinical research. Recently, comprehensive proteomic analyses of different biological samples such as serum or plasma, tissue, CSF, urine, saliva etc. have attracted considerable attention for the identification of protein biomarkers as early detection surrogates for diseases (Ray et al., 2011). Biomarkers are biomolecules that can be used for early disease detection, differentiation between closely related diseases with similar clinical manifestations as well as aid in scrutinizing disease progression. Our research group is performing in-depth analysis of alteration in human proteome in different types of brain tumors and various pathogenic infections to obtain mechanistic insight about the disease pathogenesis and host immune responses, and identification of surrogate protein markers for these fatal human diseases.
Applying 2D-DIGE in combination with MALDI-TOF/TOF MS we have analyzed the serum and tissue proteome profiles of glioblastoma multiforme; the most common and lethal adult malignant brain tumor (Gollapalli et al., 2012) (Figure 1). Results obtained were validated by employing different immunoassay-based approaches. In serum proteomic analysis we have identified some interesting proteins like haptoglobin, ceruloplasmin, vitamin-D binding protein etc. Moreover, proteomic analysis of different grades (grade-I to IV) of gliomas and normal brain tissue was performed and differential expressions of quite a few proteins such as SIRT2, GFAP, SOD, CDC42 have been identified, which have significant correlation with the tumor growth. While proteomic analysis of cerebrospinal fluid from low grade (grade I & II) vs. high grade (grade III & IV) gliomas revealed modulation of CSF levels of apolipoprotein E, dickkopf related protein 3, vitamin D binding protein and albumin in high grade gliomas. The prospective candidates identified in our studies provide a mechanistic insight of glioma pathogenesis and identification of potential biomarkers. We are also studying the role of JAK/STAT interactome and therapeutic potential of STAT3 inhibitors in gliomas using proteomics approach. Several candidates of the JAK/STAT interactome were identified with altered expression and a significant correlation was observed between STAT3 and PDK1 transcript expression level.
We have also investigated the changes in human serum proteome in different infectious diseases including falciparum and vivax malaria (Ray et al., 2012a; Ray et al., 2012b), dengue (Ray et al., 2012c) and leptospirosis (Srivastava et al., 2012). Although, quite a few serum proteins were found to be commonly altered in different infectious diseases and might be a consequence of inflammation mediated acute phase response signaling, uniquely modulated candidates were identified in each pathogenic infection indicating the some inimitable responses. Further, a panel of identified proteins consists of six candidates; serum amyloid A, hemopexin, apolipoprotein E, haptoglobin, retinol-binding protein and apolipoprotein A-I was used to build statistical sample class prediction models employing PLSDA and other classification methods to predict the clinical phenotypic classes and 91.37% overall prediction accuracy was achieved (Figure 2). ROC curve analysis was carried out to evaluate the individual performance of classifier proteins. The excellent discrimination among the different disease groups on the basis of differentially expressed proteins demonstrates the potential diagnostic implications of this analytical approach.
Keywords: Diagnostic biomarkers, Gliomas, Infectious Diseases, Proteomics, Serum proteome
Acknowledgments: This disease biomarker discovery research was supported by Department of Biotechnology, India grant (No. BT/PR14359/MED/30/916/2010), Board of Research in Nuclear Sciences (BRNS) DAE young scientist award (2009/20/37/4/BRNS) and a startup grant 09IRCC007 from the IIT Bombay. The active support from Advanced Center for Treatment Research and Education in Cancer (ACTREC), Tata Memorial Hospital (TMH), and Seth GS Medical College and KEM Hospital Mumbai, India in clinical sample collection process is gratefully acknowledged.
References :
- Ray S, Reddy PJ, Jain R, Gollapalli K. Moiyadi A, Srivastava S. Proteomic technologies for the identification of disease biomarkers in serum: advances and challenges ahead. Proteomics 11: 2139-61, 2011.
- Gollapalli K, Ray S, Srivastava R, Renu D, Singh P, Dhali S, Dikshit JB, Srikanth R, Moiyadi A, Srivastava S. Investigation of serum proteome alterations in human glioblastoma multiforme. Proteomics 12(14): 2378-90, 2012.
- Ray S, Renu D, Srivastava R, Gollapalli K, Taur S, Jhaveri T, Dhali S, Chennareddy S, Potla A, Dikshit JB, Srikanth R, Gogtay N, Thatte U, Patankar S, Srivastava S. Proteomic investigation of falciparum and vivax malaria for identification of surrogate protein markers. PLoS One 7(8): e41751, 2012a.
- Ray S, Kamath KS, Srivastava R, Raghu D, Gollapalli K, Jain R, Gupta SV, Ray S, Taur S, Dhali S, Gogtay N, Thatte U, Srikanth R, Patankar S, Srivastava S. Serum proteome analysis of vivax malaria: An insight into the disease pathogenesis and host immune response. J Proteomics 75(10): 3063-80, 2012b.
- Srivastava R, Ray S, Vaibhav V, Gollapalli K, Jhaveri T, Taur S, Dhali S, Gogtay N, Thatte U, Srikanth R, Srivastava S. Serum profiling of leptospirosis patients to investigate proteomic alterations. J Proteomics 76: 56-68, 2012.
- Ray S, Srivastava R, Tripathi K, Vaibhav V, Srivastava S. Serum proteome changes in dengue virus-infected patients from a dengue-endemic area of India: towards new molecular targets? OMICS 16(10): 527-36, 2012c.
* Correspondence: Dr. Sanjeeva Srivastava, Department of Biosciences and Bioengineering, IIT Bombay, Mumbai 400 076, India: E-mail: sanjeeva@iitb.ac.in; Phone: +91-22-2576-7779, Fax: +91-22-2572-3480
![Figure 1 (a) Differentially expressed proteins in GBM identified using 2D-DIGE. Representative 2D- DIGE image to compare serum proteome of HC and GBM patients. GBM and HC samples were labeled with Cy3 and Cy5 respectively, while the protein reference pool (internal standard) was labeled with Cy2. Graphical and 3D fluorescence intensity representations of few selected statistically significant (p < 0.05) differentially expressed proteins in GBM patients identified in biological variation analysis (BVA) using DeCyder 2D software. (b) Involvement of different essential physiological pathways with differentially expressed proteins in GBM. Members of multiple essential physiological processes including cell growth and proliferation, vitamin D metabolism, lipoprotein metabolism and transport, oxidative stress regulation, complement cascade, and platelet activation found to be modulated in the GBM patients (Gollapalli et al., Proteomics 2012).](http://www.amritabioquest.org/conference/2013/wp-content/uploads/sites/2/2015/02/sanjeeva.jpg)
![Figure 2 (a) Western blot analysis of haptoglobin (HP), serum amyloid A (SAA), and clusterin (CLU) from serum samples of healthy control (HC) [n = 12], falciparum malaria (FM) [n = 12], vivax malaria (VM) [n = 12], Leptospirosis (Lep) [n = 6], dengue fever [DF] [n = 6] and non infectious disease control (NIDC:GBM) [n = 12]. Representative blots of the target proteins are depicted along with their respective relative abundance volumes (volume X 104). All the data are represented as mean ± SE. (b) Discrimination of malaria from dengue, leptospirosis and GBM using PLS-DA analysis. PLS-DA scores Plot for FM (blue spheres, n = 8), VM (green spheres, n = 8), DF (red spheres, n = 6), Lep (grey spheres, n = 6) and GBM (brown spheres, n = 8) samples based on 6 differentially expressed proteins (serum amyloid A, hemopexin, apolipoprotein E, haptoglobin, retinol-binding protein and apolipoprotein A-I) identified using DIGE. The axes of the plot indicate PLSDA latent variables t0-t2.](http://www.amritabioquest.org/conference/2013/wp-content/uploads/sites/2/2015/02/sanjeeva-2.jpg)
Karmeshu, Ph.D.
Dean & Professor, School of Computer & Systems Sciences & School of Computational & Integrative Sciences, Jawaharlal Nehru University, India.
Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law
The study of interspike interval distribution of spiking neurons is a key issue in the field of computational neuroscience. A wide range of spiking patterns display unimodal, bimodal ISI patterns including power law behavior. A challenging problem is to understand the biophysical mechanism which can generate the empirically observed patterns. A neuronal model with distributed delay (NMDD) is proposed and is formulated as an integro-stochastic differential equation which corresponds to a non-markovian process. The widely studied IF and LIF models become special cases of this model. The NMDD brings out some interesting features when excitatory rates are close to inhibitory rates rendering the drift close to zero. It is interesting that NMDD model with gamma type memory kernel can also account for bimodal ISI pattern. The mean delay of the memory kernels plays a significant role in bringing out the transition from unimodal to bimodal ISI distribution. It is interesting to note that when a collection of neurons group together and fire together, the ISI distribution exhibits power law.
![Delegate Talk: Inflammation Induced Epigenetic Changes in Endothelial Cells: Role in Vascular Insulin Resistance @ Acharya Hall | Vallikavu | Kerala | India](http://www.amritabioquest.org/conference/2013/wp-content/uploads/sites/2/2015/02/gtm.jpg)
Aswath Balakrishnan, Kapaettu Satyamoorthy and Manjunath B Joshi
Introduction
Insulin resistance is a hall mark of metabolic disorders such as diabetes. Reduced insulin response in vasculature leads to disruption of IR/Akt/eNOS signaling pathway resulting in vasoconstriction and subsequently to cardiovascular diseases. Recent studies have demonstrated that inflammatory regulator interleukin-6 (IL-6), as one of the potential mediators that can link chronic inflammation with insulin resistance. Accumulating evidences suggest a significant role of epigenetic mechanisms such as DNA methylation in progression of metabolic disorders. Hence the present study aimed to understand the role of epigenetic mechanisms involved during IL-6 induced vascular insulin resistance and its consequences in cardiovascular diseases.
Materials and Methods
Human umbilical vein endothelial cells (HUVEC) and Human dermal microvascular endothelial cells (HDMEC) were used for this study. Endothelial cells were treated in presence or absence of IL-6 (20ng/ml) for 36 hours and followed by insulin (100nM) stimulation for 15 minutes. Using immunoblotting, cell lysates were stained for phosphor- and total Akt levels to measure insulin resistance. To investigate changes in DNA methylation, cells were treated with or without neutrophil conditioned medium (NCM) as a physiological source of inflammation or IL-6 (at various concentrations) for 36 hours. Genomic DNA was processed for HPLC analysis for methyl cytosine content and cell lysates were analyzed for DNMT1 (DNA (cytosine-5)-methyltransferase 1) and DNMT3A (DNA (cytosine-5)-methyltransferase 3A) levels using immunoblotting.
Results
Endothelial cells stimulated with insulin exhibited an increase in phosphorylation of Aktser 473 in serum free conditions but such insulin response was not observed in cells treated with IL-6, suggesting chronic exposure of endothelial cells to IL-6 leads to insulin resistance. HPLC analysis for global DNA methylation resulted in decreased levels of 5-methyl cytosine in cells treated with pro-inflammatory molecules (both by NCM and IL-6) as compared to untreated controls. Subsequently, analysis in cells treated with IL-6 showed a significant decrease in DNMT1 levels but not in DNMT3A. Other pro-inflammatory marker such as TNF-α did not exhibit such changes.
Conclusion
Our study suggests: a) Chronic treatment of endothelial cells with IL-6 results in insulin resistance b) Neutrophil conditioned medium and IL-6 decreases methyl cytosine levels c) DNMT1 but not DNMT3a levels are reduced in cells treated with IL-6.
![Delegate Talk: Proteomic profiling of gallbladder cancer secretome – a source for circulatory biomarker discovery @ Amriteshwari Hall | Vallikavu | Kerala | India](http://www.amritabioquest.org/conference/2013/wp-content/uploads/sites/2/2015/02/bioanalt.jpg)
Tejaswini Subbannayya, Nandini A. Sahasrabuddhe, Arivusudar Marimuthu, Santosh Renuse, Gajanan Sathe, Srinivas M. Srikanth, Mustafa A. Barbhuiya, Bipin Nair, Juan Carlos Roa, Rafael Guerrero-Preston, H. C. Harsha, David Sidransky, Akhilesh Pandey, T. S. Keshava Prasad and Aditi Chatterjee
Proteomic profiling of gallbladder cancer secretome – a source for circulatory biomarker discovery
Gallbladder cancer (GBC) is the fifth most common cancer of the gastrointestinal tract and one of the common malignancies that occur in the biliary tract (Misra et al. 2006; Lazcano-Ponce et al. 2001). It has a poor prognosis with survival of less than 5 years in 90% of the cases (Misra et al. 2003). The etiology is ill-defined. Several risk factors have been reported including cholelithiasis, obesity, female gender and exposure to carcinogens (Eslick 2010; Kumar et al. 2006). Poor prognosis in GBC is mainly due to late presentation of the disease and lack of reliable biomarkers for early diagnosis. This emphasizes the need to identify and characterize cancer biomarkers to aid in the diagnosis and prognosis of GBC. Secreted proteins are an important class of molecules which can be detected in body fluids and has been targeted for biomarker discovery. There are challenges faced in the proteomic interrogation of body fluids especially plasma such as low abundance of tumor secreted proteins, high complexity and high abundance of other proteins that are not released by the tumor cells (Tonack et al. 2009). Profiling of conditioned media from the cancer cell lines can be used as an alternate means to identify secreted proteins from tumor cells (Kashyap et al. 2010; Marimuthu et al. 2012). We analyzed the invasive property of 7 GBC cell lines (SNU-308, G-415, GB-d1, TGBC2TKB, TGBC24TKB, OCUG-1 and NOZ). Four cell lines were selected for analysis of the cancer secretome based on the invasive property of the cells. We employed isobaric tags for relative and absolute quantitation (iTRAQ) labeling technology coupled with high resolution mass spectrometry to identify and characterize secretome from the panel of 4GBC cancer cells mentioned above. In total, we have identified around 2,000 proteins of which 175 were secreted at differential abundance across all the four cell lines. This secretome analysis will act as a reservoir of candidate biomarkers. Currently, we are investigating and validating these candidate markers from GBC cell secretome. Through this study, we have shown mass spectrometry-based quantitative proteomic analysis as a robust approach to investigate secreted proteins in cancer cells.