Molecular BioSystems
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This article can be cited before page numbers have been issued, to do this please use: L. D. N. Selvan, S. K. Sreenivasamurthy, S. Kumar, S. D. Yelamanchi, A. K. Madugundu, A. K. Anil, S. Renuse, B. G. Nair, H. C. Harsha, P. P. Mathur, P. Satishchandra, A. Mahadevan, S. K. Shankar and T. S. K. Prasad, Mol. BioSyst., 2015, DOI: 10.1039/C5MB00187K.
This is an Accepted Manuscript, which has been through the Royal Society of Chemistry peer review process and has been accepted for publication. Accepted Manuscripts are published online shortly after acceptance, before technical editing, formatting and proof reading. Using this free service, authors can make their results available to the community, in citable form, before we publish the edited article. We will replace this Accepted Manuscript with the edited and formatted Advance Article as soon as it is available. You can find more information about Accepted Manuscripts in the Information for Authors. Please note that technical editing may introduce minor changes to the text and/or graphics, which may alter content. The journal’s standard Terms & Conditions and the Ethical guidelines still apply. In no event shall the Royal Society of Chemistry be held responsible for any errors or omissions in this Accepted Manuscript or any consequences arising from the use of any information it contains.
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Molecular BioSystems View Article Online
DOI: 10.1039/C5MB00187K
Published on 29 June 2015. Downloaded by University of New England on 15/07/2015 17:07:18.
Lakshmi Dhevi N. Selvan1,2,†, Sreelakshmi K. Sreenivasamurthy1,3,†, Satwant Kumar1, Soujanya D. Yelamanchi1,4, Anil K. Madugundu1,5, Abhijith K. Anil6, Santosh Renuse1,2, Bipin G. Nair2, Harsha Gowda1,4,7, Premendu P. Mathur4, Parthasarathy Satishchandra8, S. K. Shankar9,10, Anita Mahadevan9,10*, T. S. Keshava Prasad1,2,3,7,11* 1
Institute of Bioinformatics, International Technology Park, Bangalore 560066, India Amrita School of Biotechnology, Amrita University, Kollam 690525, India 3 Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India 4 School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India 5 Bioinformatics Centre, School of Life Sciences, Pondicherry University, Puducherry 605014, India 6 Armed Forces Medical College, Pune 411040, India 7 YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore 575018, India 8 Department of Neurology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India 9 Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India 10 Human Brain Tissue Repository, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore 560029, India 11 NIMHANS-IOB Proteomics and Bioinformatics Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore 560029, India 2
†
These authors contributed equally
* To whom correspondence should be addressed: Anita Mahadevan, M.D., Department of Neuropathology and Associate co-ordinator Human Brain Tissue Repository (HBTR) Neurobiology Research Centre National Institute of Mental Health and Neurosciences Bangalore 560 029, India E-mail: [email protected] Telephone: +91-80-26995137 Fax: +91-80-26564830 T. S. Keshava Prasad, Ph.D., Institute of Bioinformatics International Technology Park Bangalore 560066, India E-mail: [email protected] 1
Molecular BioSystems Accepted Manuscript
Characterization of host response to Cryptococcus neoformans through quantitative proteomic analysis of cryptococcal meningitis co-infected with HIV
Molecular BioSystems Accepted Manuscript
Published on 29 June 2015. Downloaded by University of New England on 15/07/2015 17:07:18.
Molecular BioSystems View Article Online
2
Page 2 of 27
DOI: 10.1039/C5MB00187K
Telephone: +91-80-28416140 Fax: +91-80-28416132
Page 3 of 27
Molecular BioSystems View Article Online
DOI: 10.1039/C5MB00187K
Cryptococcal meningitis is the most common opportunistic fungal infection causing morbidity and mortality (>60%) in HIV-associated immunocompromised individuals caused by Cryptococcus neoformans. Molecular mechanisms of cryptococcal infection in brain have been studied using experimental animal models and cell lines. There are limited studies for the molecular understanding of cryptococcal meningitis in human brain. The proteins involved in the process of invasion and infection in human brain still remains obscure. To this end we carried out mass spectrometry-based quantitative proteomics of frontal lobe brain tissues from cryptococcal meningitis patients and controls to identify host proteins that are associated with the pathogenesis of cryptococcal meningitis. We identified 317 proteins to be differentially expressed (≥ 2-fold) from a total of 3,423 human proteins. We found proteins involved in immune response and signal transduction to be differentially expressed in response to cryptococcal infection in human brain. Immune response proteins including complement factors, major histocompatibility proteins, proteins previously known to be involved in fungal invasion to brain such as caveolin 1, actin were identified to be differentially expressed in cryptococcal meningitis brain tissues co-infected with HIV. We also validated the expression status of 5 proteins using immunohistochemistry. Overexpression of major histocompatibility complex, class I, B (HLA-B), actin alpha 2 smooth muscle aorta (ACTA2) and Caveolin 1 (CAV1) and downregulation of Peripheral myelin protein 2 (PMP2) and Alpha crystallin B chain (CRYAB) in cryptococcal meningitis were confirmed by IHC-based validation experiments. This study provides brain proteome profile of cryptococcal meningitis co-infected with HIV for better understanding of the host response associated with the disease. Keywords: Neuroinfection, chronic meningitis, mass spectrometry, pathogenesis, synaptic vesicle pathway, central nervous system AIDS, Caveolin 1 protein,
3
Molecular BioSystems Accepted Manuscript
Published on 29 June 2015. Downloaded by University of New England on 15/07/2015 17:07:18.
Abstract
Molecular BioSystems
Page 4 of 27 View Article Online
DOI: 10.1039/C5MB00187K
Cryptococcal meningitis is the inflammation of the meninges caused by inhalation of aerosolized Cryptococcus yeast forms followed by hematogenous spread of infection from lungs to the brain. Immunosuppressed individuals, most commonly secondary to HIV/AIDS, recipients of solid organ transplants and patients under long term steroids or other immunosuppressive therapy are at risk of developing cryptococcal infection. HIV-associated cryptococcal meningitis accounts for the majority of these cases, with up to 80% of reported cases in sub-Saharan Africa being HIV positive 1. In an autopsy study from India, cryptococcal meningitis was the most common opportunistic infection of the central nervous system in patients with AIDS 2. Cryptococcosis is one of the leading infections in 20% of solid organ transplant recipients, and about 40% of these patients progress to develop meningitis 3, 4. Cryptococcal meningitis is diagnosed by microscopic observation of yeast cells in CSF, detection of cryptococcal antigen (CrAg) by latex agglutination or enzyme immunoassay (EIA) in CSF and culture of CSF. Although CSF culture is the current gold standard, all of these tests have to be performed to rule out false negative results and to arrive at a final confirmation for Cryptococcus infection 1, 5. Cryptococcal infection is mainly caused by the haploid yeasts Cryptococcus neoformans, including varieties grubii (serotype A) and neoformans (serotype D), and Cryptococcus gattii 1. Inhalation of the spores is the most common route of infection which later colonizes the host pulmonary alveolar spaces. In immunocompetent individuals, activated alveolar macrophages phagocytose cryptococci and form granulomas at the site of infection. In case of immunosuppressed patients, the latent form converts into active form and disseminates to the bloodstream either extracellularly or intracellularly in parasitized macrophages into the brain capillary bed to cause meningitis. The fungi can also spread into the brain parenchyma to form pseudocysts 6, 7. Cryptococcus crosses the blood brain barrier to spread infection in brain through three mechanisms. Cryptococcus can use macrophages as transporter to cross the BBB, following which the pathogen is expelled leaving the phagocytes unaffected. In transcellular pathway, it has been shown that Cryptococcus traverses the BBB through adhesion and endocytosis of pathogen by human brain microvascular endothelial cells (HBMEC). Third, the paracellular mechanism allows the entry of the pathogen into the brain by weakening and damaging the tight junctions between the BMEC 6, 8. Thus far, studies to understand the mechanism of cryptococcal invasion have been carried out by live cell imaging in cell line and animal models 9-12. However, proteins involved in the process of cryptococcal infection in human brain are yet to be determined. Finding the protein changes occurring in response to cryptococcal infection in human brain would lead to better understanding of cryptococcal meningitis. We have carried out quantitative proteomics of human brain tissues obtained from cryptococcal meningitis patients to complement the findings from cell lines and animal models in humans. Mass spectrometry-based quantitative proteomics is increasingly being employed for studying disease proteomics. Quantitative proteomic approaches have been used to identify altered levels of proteins in response to disease progression and also to elucidate the efficacy of drug treatment 4
Molecular BioSystems Accepted Manuscript
Published on 29 June 2015. Downloaded by University of New England on 15/07/2015 17:07:18.
Introduction
Page 5 of 27
Molecular BioSystems View Article Online
Published on 29 June 2015. Downloaded by University of New England on 15/07/2015 17:07:18.
and safety13-16. In vitro labeling methods such as iTRAQ labeling are being extensively used for identifying biomarkers in tissue samples in various disease contexts 17 In this study, we used an iTRAQ-based quantitative proteomic approach to compare the proteome profile of cryptococcal meningitis and relatively normal control brain tissues. Proteins identified to be differentially expressed in cryptococcal meningitis brain tissues include both novel and previously reported candidate proteins, a few of which were validated by immunohistochemical labeling. Materials and methods Sample collection Brain tissue samples from the frontal cortex with overlying meninges were collected from five confirmed cases of cryptococcal meningitis during autopsy. These patients were also found to be infected with HIV. Five uninfected control brain tissues from subjects of matched age and sex group were collected from victims of road traffic accidents within 10-19 h post mortem during medicolegal autopsy. These brain tissues were archived at the Human Brain Tissue Repository (National Research Facility), Department of Neuropathology, NIMHANS, Bangalore, India. The control tissues were confirmed to be negative for HIV-1 and cryptococcal infection. All the tissues were collected with informed consent of the close relatives and the study is approved by Institutional Scientific Ethics committee. Sample preparation and iTRAQ labeling An overview of the experimental design and labeling strategy is provided in the Figure 1. Brain tissue samples from frontal lobe were lysed in 0.5% SDS, and homogenized by crushing with mortar and pestle in liquid nitrogen. Lysates were centrifuged at 13,000 rpm for 10 min at 4°C. Supernatant was collected and protein quantitation was carried out by bicinchoninic acid (BCA) assay (Pierce, ThermoScientific). About 160 µg of protein sample from each condition was used for the experiment. Protein samples were subjected to reduction and alkylation of cysteine residues prior to trypsin digestion. Reduction was carried out by treatment of protein samples with 4 µl of reducing agent (tris (2-carboxyethyl) phosphine (TCEP)) at 60 °C for 1 h followed by alkylation with 2 µl of cysteine blocking reagent, methyl methanethiosulfonate (MMTS) for 10 min at room temperature. Reduced and alkylated samples were subjected to trypsin digestion (Sequencing Grade Modified Trypsin, Promega Cat#:V511A) in the enzyme and substrate ratio of 1:20 (w/w) at 37°C for 16 h. Peptide samples from each condition was split into equal halves (80µg each) to obtain technical replicates and labeled with iTRAQ 4-plex reagents (catalog # 4352135, Applied Biosystems, Foster City, CA, USA) as per manufacturer’s protocol. Peptides from control samples were labeled with 114 and 115 iTRAQ labels, while peptides from cryptococcal meningitis were labeled with 116 and 117 labels. The samples were pooled following iTRAQ labeling. SCX fractionation and LC – MS/MS analysis Pooled iTRAQ labeled peptides were subjected to fractionation by strong cation exchange chromatography on PolySULFOETHYL A column (200 x 2.1mm; 5µm; 200Å PolyLC, Columbia, MD) using Agilent’s 1200 series HPLC system. The peptides were reconstituted in 5
Molecular BioSystems Accepted Manuscript
DOI: 10.1039/C5MB00187K
Molecular BioSystems
Page 6 of 27 View Article Online
DOI: 10.1039/C5MB00187K
Tandem mass spectrometric analysis of the iTRAQ labeled peptides were carried out using LTQOrbitrap Velos mass spectrometer (Thermo Scientific, Bremen, Germany) interfaced with Easy nanoLC II (previously Proxeon, Thermo Scientific, Bremen, Germany). The LC system consisted of an enrichment column (3 cm × 75µ, Magic AQ C18 material 5µ particle size, 100 Å pore size) and an analytical column (10 cm × 75µ, Magic AQ C18 material C18 material 5µ particle size, 100 Å pore size) packed using pressure injection cell at 800 psi. Electrospray ionization source was fitted with an emitter tip 10 µm (New Objective, Woburn, MA) and maintained at 2000 V ion spray voltage. Peptide samples were loaded onto an enrichment column in 0.1% formic acid, 5% ACN for 15 min and peptide separation carried out using a linear gradient of 7-35% solvent B (90% ACN in 0.1% formic acid) for 60 minutes at a constant flow rate of 350 nl/min. Data was acquired using Xcalibur 2.1 (Thermo Scientific, Bremen, Germany). The MS spectra were acquired in a data-dependent manner in the m/z range of 350 to 1800 and survey scans were acquired in Orbitrap mass analyzer at a mass resolution of 60,000 at 400 m/z. The MS/MS data was acquired in Orbitrap mass analyzer at a resolution of 15,000 at 400 m/z by targeting top 20 most abundant ions for fragmentation using higher energy collisional dissociation (HCD) at 39% normalized collision energy. Single and unassigned charge state precursor ions were rejected. The dynamic exclusion option was enabled during data acquisition with exclusion duration of 60 seconds. Lock mass option was enabled for real time calibration using polycyclodimethylsiloxane (m/z, 415.12) ions. Database searches and iTRAQ data analysis: Proteome Discoverer Beta Version 1.4 (Thermo Fisher Scientific Inc., Bremen, Germany) was used for database searches. A precursor mass range of 350-8000 Da and a signal to noise of 1.5 were used. SEQUEST search was done using the Proteome Discoverer suite (Version 1.4.0.288, Thermo Scientific, Bremen, Germany) against the NCBI Human RefSeq database 52 containing 30,082 entries with known contaminants. Search parameters included trypsin as the enzyme with maximum 1 missed cleavage allowed; modifications such as oxidation of methionine was set as variable while alkylation at cysteine and iTRAQ modification at N-terminus of the peptide and lysine were set as fixed modifications. Mass deviation up to 20 ppm and 0.1 Da were allowed for precursor and fragment mass tolerance, respectively. Peptide and protein data were fetched using high peptide confidence and rank one peptide match filters. Reporter ion quantitation node was used for relative expression pattern of proteins based on the relative intensities of reporter ions for the corresponding peptides. The raw data obtained was searched against decoy database to calculate 1% false discovery rate cut-off score. Spectra that matched to the contaminants and those that did not pass the 1% FDR threshold were not considered for analysis. We have submitted results of this analysis to public repositories including PRIDE (http://www.ebi.ac.uk/pride) 18 and Human Proteinpedia (http://www.humanproteinpedia.org) 19, 20 .
6
Molecular BioSystems Accepted Manuscript
Published on 29 June 2015. Downloaded by University of New England on 15/07/2015 17:07:18.
SCX solvent A (10 mM potassium phosphate, 20% acetonitrile, pH 2.8) and loaded on SCX column. Fractionation of peptides was carried out by a linear gradient of solvent B (350 mM KCl in solvent A) for 70 min at a flow rate of 200 µl per minute. Fractions were collected every minute using a fraction collector. The fractions were dried in speedvac, reconstituted in 10 µl of 0.1% TFA and desalted using C18 stage tips prior to LC-MS/MS analysis.
Page 7 of 27
Molecular BioSystems View Article Online
DOI: 10.1039/C5MB00187K
Identified differentially expressed proteins were categorized based on biological processes, cellular component and molecular function by carrying out bioinformatics analysis using annotation in Human Protein Reference Database (HPRD, http://www.hprd.org) 21, which is in compliance with Gene Ontology (GO) standards. In addition, we performed Gene Ontologybased functional enrichment analysis using Gene Ontology PANTHER classification system. Biological Processes and Molecular Functions with p-values
View Article Online View Journal
Accepted Manuscript
This article can be cited before page numbers have been issued, to do this please use: L. D. N. Selvan, S. K. Sreenivasamurthy, S. Kumar, S. D. Yelamanchi, A. K. Madugundu, A. K. Anil, S. Renuse, B. G. Nair, H. C. Harsha, P. P. Mathur, P. Satishchandra, A. Mahadevan, S. K. Shankar and T. S. K. Prasad, Mol. BioSyst., 2015, DOI: 10.1039/C5MB00187K.
This is an Accepted Manuscript, which has been through the Royal Society of Chemistry peer review process and has been accepted for publication. Accepted Manuscripts are published online shortly after acceptance, before technical editing, formatting and proof reading. Using this free service, authors can make their results available to the community, in citable form, before we publish the edited article. We will replace this Accepted Manuscript with the edited and formatted Advance Article as soon as it is available. You can find more information about Accepted Manuscripts in the Information for Authors. Please note that technical editing may introduce minor changes to the text and/or graphics, which may alter content. The journal’s standard Terms & Conditions and the Ethical guidelines still apply. In no event shall the Royal Society of Chemistry be held responsible for any errors or omissions in this Accepted Manuscript or any consequences arising from the use of any information it contains.
www.rsc.org/molecularbiosystems
Page 1 of 27
Molecular BioSystems View Article Online
DOI: 10.1039/C5MB00187K
Published on 29 June 2015. Downloaded by University of New England on 15/07/2015 17:07:18.
Lakshmi Dhevi N. Selvan1,2,†, Sreelakshmi K. Sreenivasamurthy1,3,†, Satwant Kumar1, Soujanya D. Yelamanchi1,4, Anil K. Madugundu1,5, Abhijith K. Anil6, Santosh Renuse1,2, Bipin G. Nair2, Harsha Gowda1,4,7, Premendu P. Mathur4, Parthasarathy Satishchandra8, S. K. Shankar9,10, Anita Mahadevan9,10*, T. S. Keshava Prasad1,2,3,7,11* 1
Institute of Bioinformatics, International Technology Park, Bangalore 560066, India Amrita School of Biotechnology, Amrita University, Kollam 690525, India 3 Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India 4 School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India 5 Bioinformatics Centre, School of Life Sciences, Pondicherry University, Puducherry 605014, India 6 Armed Forces Medical College, Pune 411040, India 7 YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore 575018, India 8 Department of Neurology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India 9 Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India 10 Human Brain Tissue Repository, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore 560029, India 11 NIMHANS-IOB Proteomics and Bioinformatics Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore 560029, India 2
†
These authors contributed equally
* To whom correspondence should be addressed: Anita Mahadevan, M.D., Department of Neuropathology and Associate co-ordinator Human Brain Tissue Repository (HBTR) Neurobiology Research Centre National Institute of Mental Health and Neurosciences Bangalore 560 029, India E-mail: [email protected] Telephone: +91-80-26995137 Fax: +91-80-26564830 T. S. Keshava Prasad, Ph.D., Institute of Bioinformatics International Technology Park Bangalore 560066, India E-mail: [email protected] 1
Molecular BioSystems Accepted Manuscript
Characterization of host response to Cryptococcus neoformans through quantitative proteomic analysis of cryptococcal meningitis co-infected with HIV
Molecular BioSystems Accepted Manuscript
Published on 29 June 2015. Downloaded by University of New England on 15/07/2015 17:07:18.
Molecular BioSystems View Article Online
2
Page 2 of 27
DOI: 10.1039/C5MB00187K
Telephone: +91-80-28416140 Fax: +91-80-28416132
Page 3 of 27
Molecular BioSystems View Article Online
DOI: 10.1039/C5MB00187K
Cryptococcal meningitis is the most common opportunistic fungal infection causing morbidity and mortality (>60%) in HIV-associated immunocompromised individuals caused by Cryptococcus neoformans. Molecular mechanisms of cryptococcal infection in brain have been studied using experimental animal models and cell lines. There are limited studies for the molecular understanding of cryptococcal meningitis in human brain. The proteins involved in the process of invasion and infection in human brain still remains obscure. To this end we carried out mass spectrometry-based quantitative proteomics of frontal lobe brain tissues from cryptococcal meningitis patients and controls to identify host proteins that are associated with the pathogenesis of cryptococcal meningitis. We identified 317 proteins to be differentially expressed (≥ 2-fold) from a total of 3,423 human proteins. We found proteins involved in immune response and signal transduction to be differentially expressed in response to cryptococcal infection in human brain. Immune response proteins including complement factors, major histocompatibility proteins, proteins previously known to be involved in fungal invasion to brain such as caveolin 1, actin were identified to be differentially expressed in cryptococcal meningitis brain tissues co-infected with HIV. We also validated the expression status of 5 proteins using immunohistochemistry. Overexpression of major histocompatibility complex, class I, B (HLA-B), actin alpha 2 smooth muscle aorta (ACTA2) and Caveolin 1 (CAV1) and downregulation of Peripheral myelin protein 2 (PMP2) and Alpha crystallin B chain (CRYAB) in cryptococcal meningitis were confirmed by IHC-based validation experiments. This study provides brain proteome profile of cryptococcal meningitis co-infected with HIV for better understanding of the host response associated with the disease. Keywords: Neuroinfection, chronic meningitis, mass spectrometry, pathogenesis, synaptic vesicle pathway, central nervous system AIDS, Caveolin 1 protein,
3
Molecular BioSystems Accepted Manuscript
Published on 29 June 2015. Downloaded by University of New England on 15/07/2015 17:07:18.
Abstract
Molecular BioSystems
Page 4 of 27 View Article Online
DOI: 10.1039/C5MB00187K
Cryptococcal meningitis is the inflammation of the meninges caused by inhalation of aerosolized Cryptococcus yeast forms followed by hematogenous spread of infection from lungs to the brain. Immunosuppressed individuals, most commonly secondary to HIV/AIDS, recipients of solid organ transplants and patients under long term steroids or other immunosuppressive therapy are at risk of developing cryptococcal infection. HIV-associated cryptococcal meningitis accounts for the majority of these cases, with up to 80% of reported cases in sub-Saharan Africa being HIV positive 1. In an autopsy study from India, cryptococcal meningitis was the most common opportunistic infection of the central nervous system in patients with AIDS 2. Cryptococcosis is one of the leading infections in 20% of solid organ transplant recipients, and about 40% of these patients progress to develop meningitis 3, 4. Cryptococcal meningitis is diagnosed by microscopic observation of yeast cells in CSF, detection of cryptococcal antigen (CrAg) by latex agglutination or enzyme immunoassay (EIA) in CSF and culture of CSF. Although CSF culture is the current gold standard, all of these tests have to be performed to rule out false negative results and to arrive at a final confirmation for Cryptococcus infection 1, 5. Cryptococcal infection is mainly caused by the haploid yeasts Cryptococcus neoformans, including varieties grubii (serotype A) and neoformans (serotype D), and Cryptococcus gattii 1. Inhalation of the spores is the most common route of infection which later colonizes the host pulmonary alveolar spaces. In immunocompetent individuals, activated alveolar macrophages phagocytose cryptococci and form granulomas at the site of infection. In case of immunosuppressed patients, the latent form converts into active form and disseminates to the bloodstream either extracellularly or intracellularly in parasitized macrophages into the brain capillary bed to cause meningitis. The fungi can also spread into the brain parenchyma to form pseudocysts 6, 7. Cryptococcus crosses the blood brain barrier to spread infection in brain through three mechanisms. Cryptococcus can use macrophages as transporter to cross the BBB, following which the pathogen is expelled leaving the phagocytes unaffected. In transcellular pathway, it has been shown that Cryptococcus traverses the BBB through adhesion and endocytosis of pathogen by human brain microvascular endothelial cells (HBMEC). Third, the paracellular mechanism allows the entry of the pathogen into the brain by weakening and damaging the tight junctions between the BMEC 6, 8. Thus far, studies to understand the mechanism of cryptococcal invasion have been carried out by live cell imaging in cell line and animal models 9-12. However, proteins involved in the process of cryptococcal infection in human brain are yet to be determined. Finding the protein changes occurring in response to cryptococcal infection in human brain would lead to better understanding of cryptococcal meningitis. We have carried out quantitative proteomics of human brain tissues obtained from cryptococcal meningitis patients to complement the findings from cell lines and animal models in humans. Mass spectrometry-based quantitative proteomics is increasingly being employed for studying disease proteomics. Quantitative proteomic approaches have been used to identify altered levels of proteins in response to disease progression and also to elucidate the efficacy of drug treatment 4
Molecular BioSystems Accepted Manuscript
Published on 29 June 2015. Downloaded by University of New England on 15/07/2015 17:07:18.
Introduction
Page 5 of 27
Molecular BioSystems View Article Online
Published on 29 June 2015. Downloaded by University of New England on 15/07/2015 17:07:18.
and safety13-16. In vitro labeling methods such as iTRAQ labeling are being extensively used for identifying biomarkers in tissue samples in various disease contexts 17 In this study, we used an iTRAQ-based quantitative proteomic approach to compare the proteome profile of cryptococcal meningitis and relatively normal control brain tissues. Proteins identified to be differentially expressed in cryptococcal meningitis brain tissues include both novel and previously reported candidate proteins, a few of which were validated by immunohistochemical labeling. Materials and methods Sample collection Brain tissue samples from the frontal cortex with overlying meninges were collected from five confirmed cases of cryptococcal meningitis during autopsy. These patients were also found to be infected with HIV. Five uninfected control brain tissues from subjects of matched age and sex group were collected from victims of road traffic accidents within 10-19 h post mortem during medicolegal autopsy. These brain tissues were archived at the Human Brain Tissue Repository (National Research Facility), Department of Neuropathology, NIMHANS, Bangalore, India. The control tissues were confirmed to be negative for HIV-1 and cryptococcal infection. All the tissues were collected with informed consent of the close relatives and the study is approved by Institutional Scientific Ethics committee. Sample preparation and iTRAQ labeling An overview of the experimental design and labeling strategy is provided in the Figure 1. Brain tissue samples from frontal lobe were lysed in 0.5% SDS, and homogenized by crushing with mortar and pestle in liquid nitrogen. Lysates were centrifuged at 13,000 rpm for 10 min at 4°C. Supernatant was collected and protein quantitation was carried out by bicinchoninic acid (BCA) assay (Pierce, ThermoScientific). About 160 µg of protein sample from each condition was used for the experiment. Protein samples were subjected to reduction and alkylation of cysteine residues prior to trypsin digestion. Reduction was carried out by treatment of protein samples with 4 µl of reducing agent (tris (2-carboxyethyl) phosphine (TCEP)) at 60 °C for 1 h followed by alkylation with 2 µl of cysteine blocking reagent, methyl methanethiosulfonate (MMTS) for 10 min at room temperature. Reduced and alkylated samples were subjected to trypsin digestion (Sequencing Grade Modified Trypsin, Promega Cat#:V511A) in the enzyme and substrate ratio of 1:20 (w/w) at 37°C for 16 h. Peptide samples from each condition was split into equal halves (80µg each) to obtain technical replicates and labeled with iTRAQ 4-plex reagents (catalog # 4352135, Applied Biosystems, Foster City, CA, USA) as per manufacturer’s protocol. Peptides from control samples were labeled with 114 and 115 iTRAQ labels, while peptides from cryptococcal meningitis were labeled with 116 and 117 labels. The samples were pooled following iTRAQ labeling. SCX fractionation and LC – MS/MS analysis Pooled iTRAQ labeled peptides were subjected to fractionation by strong cation exchange chromatography on PolySULFOETHYL A column (200 x 2.1mm; 5µm; 200Å PolyLC, Columbia, MD) using Agilent’s 1200 series HPLC system. The peptides were reconstituted in 5
Molecular BioSystems Accepted Manuscript
DOI: 10.1039/C5MB00187K
Molecular BioSystems
Page 6 of 27 View Article Online
DOI: 10.1039/C5MB00187K
Tandem mass spectrometric analysis of the iTRAQ labeled peptides were carried out using LTQOrbitrap Velos mass spectrometer (Thermo Scientific, Bremen, Germany) interfaced with Easy nanoLC II (previously Proxeon, Thermo Scientific, Bremen, Germany). The LC system consisted of an enrichment column (3 cm × 75µ, Magic AQ C18 material 5µ particle size, 100 Å pore size) and an analytical column (10 cm × 75µ, Magic AQ C18 material C18 material 5µ particle size, 100 Å pore size) packed using pressure injection cell at 800 psi. Electrospray ionization source was fitted with an emitter tip 10 µm (New Objective, Woburn, MA) and maintained at 2000 V ion spray voltage. Peptide samples were loaded onto an enrichment column in 0.1% formic acid, 5% ACN for 15 min and peptide separation carried out using a linear gradient of 7-35% solvent B (90% ACN in 0.1% formic acid) for 60 minutes at a constant flow rate of 350 nl/min. Data was acquired using Xcalibur 2.1 (Thermo Scientific, Bremen, Germany). The MS spectra were acquired in a data-dependent manner in the m/z range of 350 to 1800 and survey scans were acquired in Orbitrap mass analyzer at a mass resolution of 60,000 at 400 m/z. The MS/MS data was acquired in Orbitrap mass analyzer at a resolution of 15,000 at 400 m/z by targeting top 20 most abundant ions for fragmentation using higher energy collisional dissociation (HCD) at 39% normalized collision energy. Single and unassigned charge state precursor ions were rejected. The dynamic exclusion option was enabled during data acquisition with exclusion duration of 60 seconds. Lock mass option was enabled for real time calibration using polycyclodimethylsiloxane (m/z, 415.12) ions. Database searches and iTRAQ data analysis: Proteome Discoverer Beta Version 1.4 (Thermo Fisher Scientific Inc., Bremen, Germany) was used for database searches. A precursor mass range of 350-8000 Da and a signal to noise of 1.5 were used. SEQUEST search was done using the Proteome Discoverer suite (Version 1.4.0.288, Thermo Scientific, Bremen, Germany) against the NCBI Human RefSeq database 52 containing 30,082 entries with known contaminants. Search parameters included trypsin as the enzyme with maximum 1 missed cleavage allowed; modifications such as oxidation of methionine was set as variable while alkylation at cysteine and iTRAQ modification at N-terminus of the peptide and lysine were set as fixed modifications. Mass deviation up to 20 ppm and 0.1 Da were allowed for precursor and fragment mass tolerance, respectively. Peptide and protein data were fetched using high peptide confidence and rank one peptide match filters. Reporter ion quantitation node was used for relative expression pattern of proteins based on the relative intensities of reporter ions for the corresponding peptides. The raw data obtained was searched against decoy database to calculate 1% false discovery rate cut-off score. Spectra that matched to the contaminants and those that did not pass the 1% FDR threshold were not considered for analysis. We have submitted results of this analysis to public repositories including PRIDE (http://www.ebi.ac.uk/pride) 18 and Human Proteinpedia (http://www.humanproteinpedia.org) 19, 20 .
6
Molecular BioSystems Accepted Manuscript
Published on 29 June 2015. Downloaded by University of New England on 15/07/2015 17:07:18.
SCX solvent A (10 mM potassium phosphate, 20% acetonitrile, pH 2.8) and loaded on SCX column. Fractionation of peptides was carried out by a linear gradient of solvent B (350 mM KCl in solvent A) for 70 min at a flow rate of 200 µl per minute. Fractions were collected every minute using a fraction collector. The fractions were dried in speedvac, reconstituted in 10 µl of 0.1% TFA and desalted using C18 stage tips prior to LC-MS/MS analysis.
Page 7 of 27
Molecular BioSystems View Article Online
DOI: 10.1039/C5MB00187K
Identified differentially expressed proteins were categorized based on biological processes, cellular component and molecular function by carrying out bioinformatics analysis using annotation in Human Protein Reference Database (HPRD, http://www.hprd.org) 21, which is in compliance with Gene Ontology (GO) standards. In addition, we performed Gene Ontologybased functional enrichment analysis using Gene Ontology PANTHER classification system. Biological Processes and Molecular Functions with p-values
