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4. Phosphoproteome of Cryptococcus neoformans

Author

Selvan, Lakshmi Dhevi N., Renuse, Santosh, Kaviyil, Jyothi Embekkat, Sharma, Jyoti, Pinto, Sneha M., Yelamanchi, Soujanya D., Puttamallesh, Vinuth N., Ravikumar, Raju, Pandey, Akhilesh, Prasad, T. S. Keshava, and Harsha, H.C.

Published
2014
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5. Signaling network map of the aryl hydrocarbon receptor

Author

Yelamanchi, Soujanya D., Solanki, Hitendra Singh, Radhakrishnan, Aneesha, Balakrishnan, Lavanya, Advani, Jayshree, Raja, Remya, Sahasrabuddhe, Nandini A., Mathur, Premendu Prakash, Dutta, Pinaki, Prasad, T. S. Keshava, Korbonits, Márta, Chatterjee, Aditi, Gowda, Harsha, and Mukherjee, Kanchan Kumar

Published
2016
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6. A pathway map of glutamate metabolism

Author

Yelamanchi, Soujanya D., Jayaram, Savita, Thomas, Joji Kurian, Gundimeda, Seetaramanjaneyulu, Khan, Aafaque Ahmad, Singhal, Anish, Keshava Prasad, T. S., Pandey, Akhilesh, Somani, B. L., and Gowda, Harsha

Published
2016
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8. Additional file 6 of Phosphorylation of β-catenin at Serine552 correlates with invasion and recurrence of non-functioning pituitary neuroendocrine tumours

Author

Rai, Ashutosh, Yelamanchi, Soujanya D., Radotra, Bishan D., Gupta, Sunil K., Mukherjee, Kanchan K., Tripathi, Manjul, Chhabra, Rajesh, Ahuja, Chirag K., Kumar, Narendra, Pandey, Akhilesh, Korbonits, Márta, Dutta, Pinaki, and Gaston-Massuet, Carles

Abstract

Additional File 6: Fig. S5. Gene ontology (GO) pathway analyses showing enriched (red) or depleted (green) hyperphosphorylated proteins in R NF-PitNET. a–d Phosphoproteins exclusively overphosphorylated in recurrent PitNETs were used for gene ontology analysis using FunRich (version 3.1.3) software. X axis represents fold change enrichment of GO categories in recurrent NF-PitNETs as compared to non-invasive/non-recurrent and Y axis represents the GO categories. a Cell component analysis showed endoplasmic reticular membrane proteins were most enriched followed by cell cortex, ribosome, nuclear speck, and cytoplasmic microtubules. b Graphical representation of molecular function revealed proteins with ATPase activity were most enriched while caspase activator proteins were most depleted in recurrent NF-PitNETs. c Graphical representation of biological process showed proteins involved in cell proliferation, regulation of cell proliferation and migration were most enriched. d Graphical representation of biological pathways showed glypican-3 signalling and overall high enrichment of Wnt signalling and regulation of nuclear β-catenin signalling as the most enriched pathways in recurrent NF-PitNETs. R Recurrence; GO Gene ontology

Published
2022
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9. Additional file 1 of Phosphorylation of β-catenin at Serine552 correlates with invasion and recurrence of non-functioning pituitary neuroendocrine tumours

Author

Rai, Ashutosh, Yelamanchi, Soujanya D., Radotra, Bishan D., Gupta, Sunil K., Mukherjee, Kanchan K., Tripathi, Manjul, Chhabra, Rajesh, Ahuja, Chirag K., Kumar, Narendra, Pandey, Akhilesh, Korbonits, Márta, Dutta, Pinaki, and Gaston-Massuet, Carles

Abstract

Additional file 1: Supplementary Tables.

Published
2022
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10. Additional file 4 of Phosphorylation of β-catenin at Serine552 correlates with invasion and recurrence of non-functioning pituitary neuroendocrine tumours

Author

Rai, Ashutosh, Yelamanchi, Soujanya D., Radotra, Bishan D., Gupta, Sunil K., Mukherjee, Kanchan K., Tripathi, Manjul, Chhabra, Rajesh, Ahuja, Chirag K., Kumar, Narendra, Pandey, Akhilesh, Korbonits, Márta, Dutta, Pinaki, and Gaston-Massuet, Carles

Abstract

Additional File 4: Fig. S3. Differential phosphorylation of Ser, Thr, and Tyr in recurrent NF-PitNETs and reproducibility of data. a–c Fisher’s test shows significantly high number of hyper and hypo phosphorylated Ser (a, p< 0.0001) and Thr (b, p = 0.01) phosphopeptides in R as compared to I. c No difference in number of phosphotyrosine was found. d, e Pearson’s correlation coefficient for invasive (d, r = 0.78) and recurrent (e, r = 0.73) indicate strong reproducibility among replicates (p < 0.0001). I Invasive; R Recurrent; Ser Serine; Thr Threonine; Tyr Tyrosine.

Published
2022
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11. Additional file 5 of Phosphorylation of β-catenin at Serine552 correlates with invasion and recurrence of non-functioning pituitary neuroendocrine tumours

Author

Rai, Ashutosh, Yelamanchi, Soujanya D., Radotra, Bishan D., Gupta, Sunil K., Mukherjee, Kanchan K., Tripathi, Manjul, Chhabra, Rajesh, Ahuja, Chirag K., Kumar, Narendra, Pandey, Akhilesh, Korbonits, Márta, Dutta, Pinaki, and Gaston-Massuet, Carles

Abstract

Additional File 5: Fig. S4. Increased upregulated phosphopeptides in the recurrent NF-PitNET subgroup. a, b Graphical representation showing the number of phosphopeptides with the Log2-fold change in the various NF-PitNET groups with red lines indicating hypo- and hyper-phosphorylated peptides. c Principle component (PC) analysis of NF-PitNET phosphoproteome reveal disease subgroup segregation and replicates group together. Singular value decomposition (SVD) with imputation of fold change of phosphopeptides of each NF-PitNET subgroup is used to calculate principal components. X and Y axis show principal component 1(PC1) and principal component 2 (PC2) that explain 63.3% and 82.8% of the total variance, respectively. Each grey dots represents an NF-PitNET subgroup. Abbreviations: NI/NR, non-invasive/non-recurrent; I, Invasive; R, Recurrent; PC Principle component. Green colour represents invasive (I) subgroups while blue colour represents recurrent (R).

Published
2022
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12. Additional file 2 of Phosphorylation of β-catenin at Serine552 correlates with invasion and recurrence of non-functioning pituitary neuroendocrine tumours

Author

Rai, Ashutosh, Yelamanchi, Soujanya D., Radotra, Bishan D., Gupta, Sunil K., Mukherjee, Kanchan K., Tripathi, Manjul, Chhabra, Rajesh, Ahuja, Chirag K., Kumar, Narendra, Pandey, Akhilesh, Korbonits, Márta, Dutta, Pinaki, and Gaston-Massuet, Carles

Abstract

Additional file 2: Fig. S1. Classification of the NF-PitNET’s subgroups based on the expression of transcription factors. a–d Immunohistochemistry against PIT1 (a), TPIT (b), and SF1 (c). Tumours negative for all three PIT1, TPIT, and SF1 were classified as null cell tumours (d). Positive immunostaining is shown by brown colour marked by black arrows. Scale bar in H represents 100 µm.

Published
2022
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13. Additional file 3 of Phosphorylation of β-catenin at Serine552 correlates with invasion and recurrence of non-functioning pituitary neuroendocrine tumours

Author

Rai, Ashutosh, Yelamanchi, Soujanya D., Radotra, Bishan D., Gupta, Sunil K., Mukherjee, Kanchan K., Tripathi, Manjul, Chhabra, Rajesh, Ahuja, Chirag K., Kumar, Narendra, Pandey, Akhilesh, Korbonits, Márta, Dutta, Pinaki, and Gaston-Massuet, Carles

Abstract

Additional File 3: Fig. S2. Experimental workflow of phosphoproteomic analysis of NF-PitNETs. Patients were divided into three groups: non-invasive/non-recurrent (NI/NR), invasive (I), and recurrent (R). For quantification peptides were labelled with tandem mass tags (TMT). Phosphopeptide enrichment was done by titanium dioxide (TiO2) and fractionated by basic reverse phase liquid chromatography (bRPLC) prior to LC–MS/MS analysis on two different mass spectrometrs, Orbitrap Velos and Orbitarp Fusion Tribrid mass spectrometer (ThermoScientific). Peptides identified in triplicates were further used for in-silico functional analysis and validation on large cohort (n = 200) by immunohistochemistry on tissue microarray (TMA).

Published
2022
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14. Additional file 7 of Phosphorylation of β-catenin at Serine552 correlates with invasion and recurrence of non-functioning pituitary neuroendocrine tumours

Author

Rai, Ashutosh, Yelamanchi, Soujanya D., Radotra, Bishan D., Gupta, Sunil K., Mukherjee, Kanchan K., Tripathi, Manjul, Chhabra, Rajesh, Ahuja, Chirag K., Kumar, Narendra, Pandey, Akhilesh, Korbonits, Márta, Dutta, Pinaki, and Gaston-Massuet, Carles

Abstract

Additional File 7: Fig. S6. Protein-protein interaction network of phosphoproteins exclusively overphosphorylated in recurrent NFPTs showed upregulation of β-catenin signalling. Phosphoproteins only overphosphorylated in recurrent subgroup (R) were mapped using FunRich (version 3.1.3) software. Each red node represents a hyperphosphorylated protein and blue line indicates interactions. EGFR as found to be in the center of interaction hub while β-catenin was found to be part of most of the upregulated pathways. Blue arrows indicate β-catenin and MYH9, which are significantly hyperphosphorylated in R. Nodes with green circles represents proteins involved in regulation of nuclear β-catenin signaling, while nodes with yellow circles represents proteins involved in developmental pathways. PSMD2 and PSMA3 (indicated by black arrows) represents proteins involved in cell cycle regulation.

Published
2022
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16. A draft map of the human proteome

Author

Kim, Min-Sik, Pinto, Sneha M., Getnet, Derese, Nirujogi, Raja Sekhar, Manda, Srikanth S., Chaerkady, Raghothama, Madugundu, Anil K., Kelkar, Dhanashree S., Isserlin, Ruth, Jain, Shobhit, Thomas, Joji K., Muthusamy, Babylakshmi, Leal-Rojas, Pamela, Kumar, Praveen, Sahasrabuddhe, Nandini A., Balakrishnan, Lavanya, Advani, Jayshree, George, Bijesh, Renuse, Santosh, Selvan, Lakshmi Dhevi N., Patil, Arun H., Nanjappa, Vishalakshi, Radhakrishnan, Aneesha, Prasad, Samarjeet, Subbannayya, Tejaswini, Raju, Rajesh, Kumar, Manish, Sreenivasamurthy, Sreelakshmi K., Marimuthu, Arivusudar, Sathe, Gajanan J., Chavan, Sandip, Datta, Keshava K., Subbannayya, Yashwanth, Sahu, Apeksha, Yelamanchi, Soujanya D., Jayaram, Savita, Rajagopalan, Pavithra, Sharma, Jyoti, Murthy, Krishna R., Syed, Nazia, Goel, Renu, Khan, Aafaque A., Ahmad, Sartaj, Dey, Gourav, Mudgal, Keshav, Chatterjee, Aditi, Huang, Tai-Chung, Zhong, Jun, Wu, Xinyan, Shaw, Patrick G., Freed, Donald, Zahari, Muhammad S., Mukherjee, Kanchan K., Shankar, Subramanian, Mahadevan, Anita, Lam, Henry, Mitchell, Christopher J., Shankar, Susarla Krishna, Satishchandra, Parthasarathy, Schroeder, John T., Sirdeshmukh, Ravi, Maitra, Anirban, Leach, Steven D., Drake, Charles G., Halushka, Marc K., Prasad, T. S. Keshava, Hruban, Ralph H., Kerr, Candace L., Bader, Gary D., Iacobuzio-Donahue, Christine A., Gowda, Harsha, and Pandey, Akhilesh

Published
2014
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19. Proteomic Analysis of the Human Anterior Pituitary Gland

Author

Yelamanchi, Soujanya D., primary, Tyagi, Ankur, additional, Mohanty, Varshasnata, additional, Dutta, Pinaki, additional, Korbonits, Márta, additional, Chavan, Sandip, additional, Advani, Jayshree, additional, Madugundu, Anil K., additional, Dey, Gourav, additional, Datta, Keshava K., additional, Rajyalakshmi, M., additional, Sahasrabuddhe, Nandini A., additional, Chaturvedi, Abhishek, additional, Kumar, Amit, additional, Das, Apabrita Ayan, additional, Ghosh, Dhiman, additional, Jogdand, Gajendra M., additional, Nair, Haritha H., additional, Saini, Keshav, additional, Panchal, Manoj, additional, Sarvaiya, Mansi Ashwinsinh, additional, Mohanraj, Soundappan S., additional, Sengupta, Nabonita, additional, Saxena, Priti, additional, Subramani, Pradeep Annamalai, additional, Kumar, Pradeep, additional, Akkali, Rakhil, additional, Reshma, Saraswatipura Vishwabrahmachar, additional, Santhosh, Ramachandran Sarojini, additional, Rastogi, Sangita, additional, Kumar, Sudarshan, additional, Ghosh, Susanta Kumar, additional, Irlapati, Vamshi Krishna, additional, Srinivasan, Anand, additional, Radotra, Bishan Das, additional, Mathur, Premendu P., additional, Wong, G. William, additional, Satishchandra, Parthasarathy, additional, Chatterjee, Aditi, additional, Gowda, Harsha, additional, Bhansali, Anil, additional, Pandey, Akhilesh, additional, Shankar, Susarla K., additional, Mahadevan, Anita, additional, and Prasad, T.S. Keshava, additional

Published
2018
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20. Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes

Author

Prasad, T. S. Keshava, Mohanty, Ajeet Kumar, Kumar, Manish, Sreenivasamurthy, Sreelakshmi K., Dey, Gourav, Nirujogi, Raja Sekhar, Pinto, Sneha M., Madugundu, Anil K., Pati, Arun H., Advani, Jayshree, Manda, Srikanth S., Gupta, Manoj Kumar, Dwivedi, Sutopa B., Kelkar, Dhanashree S., Hall, Brantley, Jiang, Xiaofang, Peery, Ashley, Rajagopalan, Pavithra, Yelamanchi, Soujanya D., Solanki, Hitendra S., Raja, Remya, Sathe, Gajanan J., Chavan, Sandip, Verma, Renu, Patel, Krishna M., Jain, Ankit P., Syed, Nazia, Datta, Keshava K., Khan, Aafaque Ahmed, Dammalli, Manjunath, Jayaram, Savita, Radhakrishnan, Aneesha, Mitchell, Christopher J., Na, Chan-Hyun, Kumar, Nirbhay, Sinnis, Photini, Sharakhov, Igor V., Wang, Charles, Gowda, Harsha, Tu, Zhijian Jake, Kumar, Ashwani, Pandey, Akhilesh, Prasad, T. S. Keshava, Mohanty, Ajeet Kumar, Kumar, Manish, Sreenivasamurthy, Sreelakshmi K., Dey, Gourav, Nirujogi, Raja Sekhar, Pinto, Sneha M., Madugundu, Anil K., Pati, Arun H., Advani, Jayshree, Manda, Srikanth S., Gupta, Manoj Kumar, Dwivedi, Sutopa B., Kelkar, Dhanashree S., Hall, Brantley, Jiang, Xiaofang, Peery, Ashley, Rajagopalan, Pavithra, Yelamanchi, Soujanya D., Solanki, Hitendra S., Raja, Remya, Sathe, Gajanan J., Chavan, Sandip, Verma, Renu, Patel, Krishna M., Jain, Ankit P., Syed, Nazia, Datta, Keshava K., Khan, Aafaque Ahmed, Dammalli, Manjunath, Jayaram, Savita, Radhakrishnan, Aneesha, Mitchell, Christopher J., Na, Chan-Hyun, Kumar, Nirbhay, Sinnis, Photini, Sharakhov, Igor V., Wang, Charles, Gowda, Harsha, Tu, Zhijian Jake, Kumar, Ashwani, and Pandey, Akhilesh

Abstract

Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions,192 novel protein start sites,19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted "noncoding RNAs" to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.

Published
2017
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21. Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes

Author

Biochemistry, Entomology, Prasad, T. S. Keshava, Mohanty, Ajeet Kumar, Kumar, Manish, Sreenivasamurthy, Sreelakshmi K., Dey, Gourav, Nirujogi, Raja Sekhar, Pinto, Sneha M., Madugundu, Anil K., Pati, Arun H., Advani, Jayshree, Manda, Srikanth S., Gupta, Manoj Kumar, Dwivedi, Sutopa B., Kelkar, Dhanashree S., Hall, Brantley, Jiang, Xiaofang, Peery, Ashley, Rajagopalan, Pavithra, Yelamanchi, Soujanya D., Solanki, Hitendra S., Raja, Remya, Sathe, Gajanan J., Chavan, Sandip, Verma, Renu, Patel, Krishna M., Jain, Ankit P., Syed, Nazia, Datta, Keshava K., Khan, Aafaque Ahmed, Dammalli, Manjunath, Jayaram, Savita, Radhakrishnan, Aneesha, Mitchell, Christopher J., Na, Chan-Hyun, Kumar, Nirbhay, Sinnis, Photini, Sharakhov, Igor V., Wang, Charles, Gowda, Harsha, Tu, Zhijian Jake, Kumar, Ashwani, Pandey, Akhilesh, Biochemistry, Entomology, Prasad, T. S. Keshava, Mohanty, Ajeet Kumar, Kumar, Manish, Sreenivasamurthy, Sreelakshmi K., Dey, Gourav, Nirujogi, Raja Sekhar, Pinto, Sneha M., Madugundu, Anil K., Pati, Arun H., Advani, Jayshree, Manda, Srikanth S., Gupta, Manoj Kumar, Dwivedi, Sutopa B., Kelkar, Dhanashree S., Hall, Brantley, Jiang, Xiaofang, Peery, Ashley, Rajagopalan, Pavithra, Yelamanchi, Soujanya D., Solanki, Hitendra S., Raja, Remya, Sathe, Gajanan J., Chavan, Sandip, Verma, Renu, Patel, Krishna M., Jain, Ankit P., Syed, Nazia, Datta, Keshava K., Khan, Aafaque Ahmed, Dammalli, Manjunath, Jayaram, Savita, Radhakrishnan, Aneesha, Mitchell, Christopher J., Na, Chan-Hyun, Kumar, Nirbhay, Sinnis, Photini, Sharakhov, Igor V., Wang, Charles, Gowda, Harsha, Tu, Zhijian Jake, Kumar, Ashwani, and Pandey, Akhilesh

Abstract

Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions,192 novel protein start sites,19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted "noncoding RNAs" to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.

Published
2017

22. “Omics” of Food-Borne Gastroenteritis: Global Proteomic and Mutagenic Analysis ofSalmonella entericaSerovar Enteritidis

Author

Arunima, Aryashree, primary, Yelamanchi, Soujanya D., additional, Padhi, Chandrashekhar, additional, Jaiswal, Sangeeta, additional, Ryan, Daniel, additional, Gupta, Bhawna, additional, Sathe, Gajanan, additional, Advani, Jayshree, additional, Gowda, Harsha, additional, Prasad, T.S. Keshava, additional, and Suar, Mrutyunjay, additional

Published
2017
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23. Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes

Author

Prasad, T.S. Keshava, primary, Mohanty, Ajeet Kumar, additional, Kumar, Manish, additional, Sreenivasamurthy, Sreelakshmi K., additional, Dey, Gourav, additional, Nirujogi, Raja Sekhar, additional, Pinto, Sneha M., additional, Madugundu, Anil K., additional, Patil, Arun H., additional, Advani, Jayshree, additional, Manda, Srikanth S., additional, Gupta, Manoj Kumar, additional, Dwivedi, Sutopa B., additional, Kelkar, Dhanashree S., additional, Hall, Brantley, additional, Jiang, Xiaofang, additional, Peery, Ashley, additional, Rajagopalan, Pavithra, additional, Yelamanchi, Soujanya D., additional, Solanki, Hitendra S., additional, Raja, Remya, additional, Sathe, Gajanan J., additional, Chavan, Sandip, additional, Verma, Renu, additional, Patel, Krishna M., additional, Jain, Ankit P., additional, Syed, Nazia, additional, Datta, Keshava K., additional, Khan, Aafaque Ahmed, additional, Dammalli, Manjunath, additional, Jayaram, Savita, additional, Radhakrishnan, Aneesha, additional, Mitchell, Christopher J., additional, Na, Chan-Hyun, additional, Kumar, Nirbhay, additional, Sinnis, Photini, additional, Sharakhov, Igor V., additional, Wang, Charles, additional, Gowda, Harsha, additional, Tu, Zhijian, additional, Kumar, Ashwani, additional, and Pandey, Akhilesh, additional

Published
2016
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24. Characterization of human pineal gland proteome

Author

Yelamanchi, Soujanya D., primary, Kumar, Manish, additional, Madugundu, Anil K., additional, Gopalakrishnan, Lathika, additional, Dey, Gourav, additional, Chavan, Sandip, additional, Sathe, Gajanan, additional, Mathur, Premendu P., additional, Gowda, Harsha, additional, Mahadevan, Anita, additional, Shankar, Susarla K., additional, and Prasad, T. S. Keshava, additional

Published
2016
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25. A pathway map of glutamate metabolism

Author

Yelamanchi, Soujanya D., primary, Jayaram, Savita, additional, Thomas, Joji Kurian, additional, Gundimeda, Seetaramanjaneyulu, additional, Khan, Aafaque Ahmad, additional, Singhal, Anish, additional, Keshava Prasad, T. S., additional, Pandey, Akhilesh, additional, Somani, B. L., additional, and Gowda, Harsha, additional

Published
2015
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26. Characterization of host response to Cryptococcus neoformans through quantitative proteomic analysis of cryptococcal meningitis co-infected with HIV

Author

Selvan, Lakshmi Dhevi N., primary, Sreenivasamurthy, Sreelakshmi K., additional, Kumar, Satwant, additional, Yelamanchi, Soujanya D., additional, Madugundu, Anil K., additional, Anil, Abhijith K., additional, Renuse, Santosh, additional, Nair, Bipin G., additional, Gowda, Harsha, additional, Mathur, Premendu P., additional, Satishchandra, Parthasarathy, additional, Shankar, S. K., additional, Mahadevan, Anita, additional, and Keshava Prasad, T. S., additional

Published
2015
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27. 'Omics' of Food-Borne Gastroenteritis: Global Proteomic and Mutagenic Analysis of Salmonella enterica Serovar Enteritidis.

Author

Arunima, Aryashree, Yelamanchi, Soujanya D., Padhi, Chandrashekhar, Jaiswal, Sangeeta, Ryan, Daniel, Gupta, Bhawna, Sathe, Gajanan, Advani, Jayshree, Gowda, Harsha, Prasad, T.S. Keshava, and Suar, Mrutyunjay

Subjects
*FOODBORNE diseases, *GASTROENTERITIS, *ENTEROBACTERIACEAE, *MASS spectrometers, *IMMUNITY, *MEMBRANE proteins
Abstract

Salmonella Enteritidis causes food-borne gastroenteritis by the two type three secretion systems (TTSS). TTSS-1 mediates invasion through intestinal lining, and TTSS-2 facilitates phagocytic survival. The pathogens' ability to infect effectively under TTSS-1-deficient background in host's phagocytes is poorly understood. Therefore, pathobiological understanding of TTSS-1-defective nontyphoidal Salmonellosis is highly important. We performed a comparative global proteomic analysis of the isogenic TTSS-1 mutant of Salmonella Enteritidis (M1511) and its wild-type isolate P125109. Our results showed 43 proteins were differentially expressed. Functional annotation further revealed that differentially expressed proteins belong to pathogenesis, tRNA and ncRNA metabolic processes. Three proteins, tryptophan subunit alpha chain, citrate lyase subunit alpha, and hypothetical protein 3202, were selected for in vitro analysis based on their functional annotations. Deletion mutants generated for the above proteins in the M1511 strain showed reduced intracellular survival inside macrophages in vitro. In sum, this study provides mass spectrometry-based evidence for seven hypothetical proteins, which will be subject of future investigations. Our study identifies proteins influencing virulence of Salmonella in the host. The study complements and further strengthens previously published research on proteins involved in enteropathogenesis of Salmonella and extends their role in noninvasive Salmonellosis. [ABSTRACT FROM AUTHOR]

Published
2017
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28. Host response profile of human brain proteome in toxoplasma encephalitis co-infected with HIV

Author

Sahu, Apeksha, primary, Kumar, Satwant, additional, Sreenivasamurthy, Sreelakshmi K, additional, Selvan, Lakshmi Dhevi N, additional, Madugundu, Anil K, additional, Yelamanchi, Soujanya D, additional, Puttamallesh, Vinuth N, additional, Dey, Gourav, additional, Anil, Abhijith K, additional, Srinivasan, Anand, additional, Mukherjee, Kanchan K, additional, Gowda, Harsha, additional, Satishchandra, Parthasarathy, additional, Mahadevan, Anita, additional, Pandey, Akhilesh, additional, Prasad, Thottethodi Subrahmanya Keshava, additional, and Shankar, Susarla Krishna, additional

Published
2014
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29. Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes

Author

Prasad, T.S. Keshava, Mohanty, Ajeet Kumar, Kumar, Manish, Sreenivasamurthy, Sreelakshmi K., Dey, Gourav, Nirujogi, Raja Sekhar, Pinto, Sneha M., Madugundu, Anil K., Patil, Arun H., Advani, Jayshree, Manda, Srikanth S., Gupta, Manoj Kumar, Dwivedi, Sutopa B., Kelkar, Dhanashree S., Hall, Brantley, Jiang, Xiaofang, Peery, Ashley, Rajagopalan, Pavithra, Yelamanchi, Soujanya D., Solanki, Hitendra S., Raja, Remya, Sathe, Gajanan J., Chavan, Sandip, Verma, Renu, Patel, Krishna M., Jain, Ankit P., Syed, Nazia, Datta, Keshava K., Khan, Aafaque Ahmed, Dammalli, Manjunath, Jayaram, Savita, Radhakrishnan, Aneesha, Mitchell, Christopher J., Na, Chan-Hyun, Kumar, Nirbhay, Sinnis, Photini, Sharakhov, Igor V., Wang, Charles, Gowda, Harsha, Tu, Zhijian, Kumar, Ashwani, and Pandey, Akhilesh

Abstract

Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted “noncoding RNAs” to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anophelinegenomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.

Published
2017
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30. Proteomic Analysis of the Human Anterior Pituitary Gland.

Author

Yelamanchi SD, Tyagi A, Mohanty V, Dutta P, Korbonits M, Chavan S, Advani J, Madugundu AK, Dey G, Datta KK, Rajyalakshmi M, Sahasrabuddhe NA, Chaturvedi A, Kumar A, Das AA, Ghosh D, Jogdand GM, Nair HH, Saini K, Panchal M, Sarvaiya MA, Mohanraj SS, Sengupta N, Saxena P, Subramani PA, Kumar P, Akkali R, Reshma SV, Santhosh RS, Rastogi S, Kumar S, Ghosh SK, Irlapati VK, Srinivasan A, Radotra BD, Mathur PP, Wong GW, Satishchandra P, Chatterjee A, Gowda H, Bhansali A, Pandey A, Shankar SK, Mahadevan A, and Prasad TSK

Subjects
Chromatography, Liquid, Humans, Mass Spectrometry, Pituitary Gland, Anterior metabolism, Proteome metabolism, Proteomics methods
Abstract

The pituitary function is regulated by a complex system involving the hypothalamus and biological networks within the pituitary. Although the hormones secreted from the pituitary have been well studied, comprehensive analyses of the pituitary proteome are limited. Pituitary proteomics is a field of postgenomic research that is crucial to understand human health and pituitary diseases. In this context, we report here a systematic proteomic profiling of human anterior pituitary gland (adenohypophysis) using high-resolution Fourier transform mass spectrometry. A total of 2164 proteins were identified in this study, of which 105 proteins were identified for the first time compared with high-throughput proteomic-based studies from human pituitary glands. In addition, we identified 480 proteins with secretory potential and 187 N-terminally acetylated proteins. These are the first region-specific data that could serve as a vital resource for further investigations on the physiological role of the human anterior pituitary glands and the proteins secreted by them. We anticipate that the identification of previously unknown proteins in the present study will accelerate biomedical research to decipher their role in functioning of the human anterior pituitary gland and associated human diseases.

Published
2018
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