@article {115259, title = {Exploring the medicinally important secondary metabolites landscape through the lens of transcriptome data in fenugreek (Trigonella foenum graecum L.).}, journal = {Sci Rep}, volume = {12}, year = {2022}, month = {2022 Aug 08}, pages = {13534}, abstract = {

Fenugreek (Trigonella foenum-graecum L.) is a self-pollinated leguminous crop belonging to the Fabaceae family. It is a multipurpose crop used as herb, spice, vegetable and forage. It is a traditional medicinal plant in India attributed with several nutritional and medicinal properties including antidiabetic and anticancer. We have performed a combined transcriptome assembly from RNA sequencing data derived from leaf, stem and root tissues. Around 209,831 transcripts were deciphered from the assembly of 92\% completeness and an N50 of 1382 bases. Whilst secondary metabolites of medicinal value, such as trigonelline, diosgenin, 4-hydroxyisoleucine and quercetin, are distributed in several tissues, we report transcripts that bear sequence signatures of enzymes involved in the biosynthesis of such metabolites and are highly expressed in leaves, stem and roots. One of the antidiabetic alkaloid, trigonelline and its biosynthesising enzyme, is highly abundant in leaves. These findings are of value to nutritional and the pharmaceutical industry.

}, keywords = {Diosgenin, Hypoglycemic Agents, Plant Extracts, Plants, Medicinal, Transcriptome, Trigonella}, issn = {2045-2322}, doi = {10.1038/s41598-022-17779-8}, author = {Naika, Mahantesha B N and Sathyanarayanan, Nitish and Sajeevan, Radha Sivarajan and Bhattacharyya, Teerna and Ghosh, Pritha and Iyer, Meenakshi S and Jarjapu, Mahita and Joshi, Adwait G and Harini, K and Shafi, K Mohamed and Kalmankar, Neha and Karpe, Snehal D and Mam, Bhavika and Pasha, Shaik Naseer and Sowdhamini, Ramanathan} } @article {78096, title = {Dataset for the combined transcriptome assembly of and functional annotation.}, journal = {Data Brief}, volume = {30}, year = {2020}, month = {2020 Jun}, pages = {105416}, abstract = {

In this paper, we present the data acquired during transcriptome analysis of the Moringa oleifera [1] from five different tissues (root, stem, leaf, flower and seed) by RNA sequencing. A total of 271 million reads were assembled with an N50 of 2094 bp. The combined transcriptome was assessed for transcript abundance across five tissues. The protein coding genes identified from the transcripts were annotated and used for orthology analysis. Further, enzymes involved in the biosynthesis of select medicinally important secondary metabolites, vitamins and ion transporters were identified and their expression levels across tissues were examined. The data generated by RNA sequencing has been deposited to NCBI public repository under the accession number PRJNA394193 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA394193).

}, issn = {2352-3409}, doi = {10.1016/j.dib.2020.105416}, author = {Shafi, K Mohamed and Joshi, Adwait G and Meenakshi, Iyer and Pasha, Shaik Naseer and Harini, K and Mahita, Jarjapu and Sajeevan, Radha Sivarajan and Karpe, Snehal D and Ghosh, Pritha and Nitish, Sathyanarayanan and Gandhimathi, A and Mathew, Oommen K and Prasanna, Subramanian Hari and Malini, Manoharan and Mutt, Eshita and Naika, Mahantesha and Ravooru, Nithin and Rao, Rajas M and Shingate, Prashant N and Sukhwal, Anshul and Sunitha, Margaret S and Upadhyay, Atul K and Vinekar, Rithvik S and Sowdhamini, Ramanathan} } @article {83641, title = {A knowledge-driven protocol for prediction of proteins of interest with an emphasis on biosynthetic pathways.}, journal = {MethodsX}, volume = {7}, year = {2020}, month = {2020}, pages = {101053}, abstract = {

This protocol describes a stepwise process to identify proteins of interest from a query proteome derived from NGS data. We implemented this protocol on transcriptome to identify proteins involved in secondary metabolite and vitamin biosynthesis and ion transport. This knowledge-driven protocol identifies proteins using an integrated approach involving sensitive sequence search and evolutionary relationships. We make use of functionally important residues (FIR) specific for the query protein family identified through its homologous sequences and literature. We screen protein hits based on the clustering with true homologues through phylogenetic tree reconstruction complemented with the FIR mapping. The protocol was validated for the protein hits through qRT-PCR and transcriptome quantification. Our protocol demonstrated a higher specificity as compared to other methods, particularly in distinguishing cross-family hits. This protocol was effective in transcriptome data analysis of as described in Pasha et al.{\textbullet}Knowledge-driven protocol to identify secondary metabolite synthesizing protein in a highly specific manner.{\textbullet}Use of functionally important residues for screening of true hits.{\textbullet}Beneficial for metabolite pathway reconstruction in any (species, metagenomics) NGS data.

}, issn = {2215-0161}, doi = {10.1016/j.mex.2020.101053}, author = {Joshi, Adwait G and Harini, K and Meenakshi, Iyer and Shafi, K Mohamed and Pasha, Shaik Naseer and Mahita, Jarjapu and Sajeevan, Radha Sivarajan and Karpe, Snehal D and Ghosh, Pritha and Nitish, Sathyanarayanan and Gandhimathi, A and Mathew, Oommen K and Prasanna, Subramanian Hari and Malini, Manoharan and Mutt, Eshita and Naika, Mahantesha and Ravooru, Nithin and Rao, Rajas M and Shingate, Prashant N and Sukhwal, Anshul and Sunitha, Margaret S and Upadhyay, Atul K and Vinekar, Rithvik S and Sowdhamini, Ramanathan} } @article {57789, title = {The transcriptome enables the identification of candidate genes behind medicinal value of Drumstick tree (Moringa oleifera).}, journal = {Genomics}, volume = {112}, year = {2020}, month = {01/2020}, pages = {621-628}, abstract = {

Moringa oleifera is a plant well-known for its nutrition value, drought resistance and medicinal properties. cDNA libraries from five different tissues (leaf, root, stem, seed and flower) of M. oleifera cultivar Bhagya were generated and sequenced. We developed a bioinformatics pipeline to assemble transcriptome, along with the previously published M. oleifera genome, to predict 17,148 gene models. Few candidate genes related to biosynthesis of secondary metabolites, vitamins and ion transporters were identified. Expressions were further confirmed by real-time quantitative PCR experiments for few promising leads. Quantitative estimation of metabolites, as well as elemental analysis, was also carried out to support our observations. Enzymes in the biosynthesis of vitamins and metabolites like quercetin and kaempferol are highly expressed in leaves, flowers and seeds. The expression of iron transporters and calcium storage proteins were observed in root and leaves. In general, leaves retain the highest amount of small molecules of interest.

}, issn = {1089-8646}, doi = {10.1016/j.ygeno.2019.04.014}, author = {Pasha, Shaik Naseer and Shafi, K Mohamed and Joshi, Adwait G and Meenakshi, Iyer and Harini, K and Mahita, Jarjapu and Sajeevan, Radha Sivarajan and Karpe, Snehal D and Ghosh, Pritha and Nitish, Sathyanarayanan and Gandhimathi, A and Mathew, Oommen K and Prasanna, Subramanian Hari and Malini, Manoharan and Mutt, Eshita and Naika, Mahantesha and Ravooru, Nithin and Rao, Rajas M and Shingate, Prashant N and Sukhwal, Anshul and Sunitha, Margaret S and Upadhyay, Atul K and Vinekar, Rithvik S and Sowdhamini, Ramanathan} } @article {59713, title = {EcRBPome: a comprehensive database of all known E. coli RNA-binding proteins.}, journal = {BMC Genomics}, volume = {20}, year = {2019}, month = {2019 May 22}, pages = {403}, abstract = {

The repertoire of RNA-binding proteins (RBPs) in bacteria play a crucial role in their survival, and interactions with the host machinery, but there is little information, record or characterisation in bacterial genomes. As a first step towards this, we have chosen the bacterial model system Escherichia coli, and organised all RBPs in this organism into a comprehensive database named EcRBPome. It contains RBPs recorded from 614 complete E. coli proteomes available in the RefSeq database (as of October 2018). The database provides various features related to the E. coli RBPs, like their domain architectures, PDB structures, GO and EC annotations etc. It provides the assembly, bioproject and biosample details of each strain, as well as cross-strain comparison of occurrences of various RNA-binding domains (RBDs). The percentage of RBPs, the abundance of the various RBDs harboured by each strain have been graphically represented in this database and available alongside other files for user download. To the best of our knowledge, this is the first database of its kind and we hope that it will be of great use to the biological community.

}, issn = {1471-2164}, doi = {10.1186/s12864-019-5755-5}, author = {Ghosh, Pritha and Joshi, Adwait and Guita, Niang and Offmann, Bernard and Sowdhamini, R} } @article {53182, title = {PASS2 version 6: a database of structure-based sequence alignments of protein domain superfamilies in accordance with SCOPe.}, journal = {Database (Oxford)}, volume = {2019}, year = {2019}, month = {2019 Jan 01}, abstract = {

The number of protein structures is increasing due to the individual initiatives and rapid development of structure determination techniques. Structure-based sequence alignments of distantly related proteins enable the investigation of structural, evolutionary and functional relationships between proteins and their domains leading to their common evolutionary origin. Protein Alignments organized as Structural Superfamilies (PASS2) is a database that provides such alignments of members of protein domain superfamilies of known structure and with less than 40\% sequence identity. PASS2 has been continuously updated in accordance to Structural Classification of Proteins (SCOP), and now Structural Classification of Proteins - extended (SCOPe). The current update directly corresponds to SCOPe 2.06, dealing with 2006 domain superfamilies of known structure and about 14\ 000 domains. Alignments have been augmented by features such as hidden Markov models, highly conserved residues, structural motifs and gene ontology terms, which are available for download. In this update, we introduce the concepts of {\textquoteright}extreme structural outliers{\textquoteright} and {\textquoteright}split superfamilies{\textquoteright} as well.

}, issn = {1758-0463}, doi = {10.1093/database/baz028}, author = {Ghosh, Pritha and Bhattacharyya, Teerna and Mathew, Oommen K and Sowdhamini, Ramanathan} } @article {22812, title = {Bioinformatics comparisons of RNA-binding proteins of pathogenic and non-pathogenic Escherichia coli strains reveal novel virulence factors.}, journal = {BMC Genomics}, volume = {18}, year = {2017}, month = {2017 Aug 24}, pages = {658}, abstract = {

BACKGROUND: Pathogenic bacteria have evolved various strategies to counteract host defences. They are also exposed to environments that are undergoing constant changes. Hence, in order to survive, bacteria must adapt themselves to the changing environmental conditions by performing regulations at the transcriptional and/or post-transcriptional levels. Roles of RNA-binding proteins (RBPs) as virulence factors have been very well studied. Here, we have used a sequence search-based method to compare and contrast the proteomes of 16 pathogenic and three non-pathogenic E. coli strains as well as to obtain a global picture of the RBP landscape (RBPome) in E. coli.

RESULTS: Our results show that there are no significant differences in the percentage of RBPs encoded by the pathogenic and the non-pathogenic E. coli strains. The differences in the types of Pfam domains as well as Pfam RNA-binding domains, encoded by these two classes of E. coli strains, are also insignificant. The complete and distinct RBPome of E. coli has been established by studying all known E. coli strains till date. We have also identified RBPs that are exclusive to pathogenic strains, and most of them can be exploited as drug targets since they appear to be non-homologous to their human host proteins. Many of these pathogen-specific proteins were uncharacterised and their identities could be resolved on the basis of sequence homology searches with known proteins. Detailed structural modelling, molecular dynamics simulations and sequence comparisons have been pursued for selected examples to understand differences in stability and RNA-binding.

CONCLUSIONS: The approach used in this paper to cross-compare proteomes of pathogenic and non-pathogenic strains may also be extended to other bacterial or even eukaryotic proteomes to understand interesting differences in their RBPomes. The pathogen-specific RBPs reported in this study, may also be taken up further for clinical trials and/or experimental validations.

}, issn = {1471-2164}, doi = {10.1186/s12864-017-4045-3}, author = {Ghosh, Pritha and Sowdhamini, Ramanathan} } @article {3554, title = {Genome-wide survey of putative RNA-binding proteins encoded in the human proteome.}, journal = {Mol Biosyst}, volume = {12}, year = {2016}, month = {2016 Jan 26}, pages = {532-40}, abstract = {

RNA-binding proteins (RBPs) are involved in various post-transcriptional gene regulatory processes and are also functionally important members of the ribosome and the spliceosome. However, RBPs and their interactions with RNA are less well-studied in comparison to DNA-binding proteins. We have classified the existing RBP structures, available in complexes with RNA and RNA/DNA hybrids, into different structural families and created Hidden Markov Models (HMMs). These structure-centric family HMMs, along with the sequence-centric family HMMs, were used as a primary database to systematically search the human proteome for the presence of putative RBPs. We have found more than 2600 gene products with RBP signatures in humans, of which around 28\% are likely to bind to RNA but not DNA, whereas 9\% might bind to both RNA and DNA. 11\% of them do not contain an explicit functional annotation yet. Nearly 30\% of the putative RBPs are exclusively nuclear, 15\% have known disease associations and around 30\% are enzymes. Around 40\% of the proteins identified in this study are novel and have not been reported by recent large-scale studies on human RBPs.

}, issn = {1742-2051}, doi = {10.1039/c5mb00638d}, author = {Ghosh, Pritha and Sowdhamini, R} } @article {3558, title = {PASS2 database for the structure-based sequence alignment of distantly related SCOP domain superfamilies: update to version 5 and added features.}, journal = {Nucleic Acids Res}, volume = {44}, year = {2016}, month = {2016 Jan 4}, pages = {D410-4}, abstract = {

Structure-based sequence alignment is an essential step in assessing and analysing the relationship of distantly related proteins. PASS2 is a database that records such alignments for protein domain superfamilies and has been constantly updated periodically. This update of the PASS2 version, named as PASS2.5, directly corresponds to the SCOPe 2.04 release. All SCOPe structural domains that share less than 40\% sequence identity, as defined by the ASTRAL compendium of protein structures, are included. The current version includes 1977 superfamilies and has been assembled utilizing the structure-based sequence alignment protocol. Such an alignment is obtained initially through MATT, followed by a refinement through the COMPARER program. The JOY program has been used for structural annotations of such alignments. In this update, we have automated the protocol and focused on inclusion of new features such as mapping of GO terms, absolutely conserved residues among the domains in a superfamily and inclusion of PDBs, that are absent in SCOPe 2.04, using the HMM profiles from the alignments of the superfamily members and are provided as a separate list. We have also implemented a more user-friendly manner of data presentation and options for downloading more features. PASS2.5 version is available at http://caps.ncbs.res.in/pass2/.

}, issn = {1362-4962}, doi = {10.1093/nar/gkv1205}, author = {Gandhimathi, Arumugam and Ghosh, Pritha and Hariharaputran, Sridhar and Mathew, Oommen K and Sowdhamini, R} } @article {8756, title = {RStrucFam: a web server to associate structure and cognate RNA for RNA-binding proteins from sequence information.}, journal = {BMC Bioinformatics}, volume = {17}, year = {2016}, month = {2016 Oct 7}, pages = {411}, abstract = {

BACKGROUND: RNA-binding proteins (RBPs) interact with their cognate RNA(s) to form large biomolecular assemblies. They are versatile in their functionality and are involved in a myriad of processes inside the cell. RBPs with similar structural features and common biological functions are grouped together into families and superfamilies. It will be useful to obtain an early understanding and association of RNA-binding property of sequences of gene products. Here, we report a web server, RStrucFam, to predict the structure, type of cognate RNA(s) and function(s) of proteins, where possible, from mere sequence information.

RESULTS: The web server employs Hidden Markov Model scan (hmmscan) to enable association to a back-end database of structural and sequence families. The database (HMMRBP) comprises of 437 HMMs of RBP families of known structure that have been generated using structure-based sequence alignments and 746 sequence-centric RBP family HMMs. The input protein sequence is associated with structural or sequence domain families, if structure or sequence signatures exist. In case of association of the protein with a family of known structures, output features like, multiple structure-based sequence alignment (MSSA) of the query with all others members of that family is provided. Further, cognate RNA partner(s) for that protein, Gene Ontology (GO) annotations, if any and a homology model of the protein can be obtained. The users can also browse through the database for details pertaining to each family, protein or RNA and their related information based on keyword search or RNA motif search.

CONCLUSIONS: RStrucFam is a web server that exploits structurally conserved features of RBPs, derived from known family members and imprinted in mathematical profiles, to predict putative RBPs from sequence information. Proteins that fail to associate with such structure-centric families are further queried against the sequence-centric RBP family HMMs in the HMMRBP database. Further, all other essential information pertaining to an RBP, like overall function annotations, are provided. The web server can be accessed at the following link: http://caps.ncbs.res.in/rstrucfam .

}, issn = {1471-2105}, doi = {10.1186/s12859-016-1289-x}, author = {Ghosh, Pritha and Mathew, Oommen K and Sowdhamini, Ramanathan} } @article {1029, title = {A tale of two paralogs: human Transformer2 proteins with differential RNA-binding affinities.}, journal = {J Biomol Struct Dyn}, volume = {34}, year = {2016}, month = {2015 Nov 26}, pages = {1979-86}, chapter = {1979}, abstract = {

The Transformer2 (Tra2) proteins in humans are homologues of the Drosophila Tra2 protein. One of the two RNA-binding paralogs, Tra2β, has been very well-studied over the past decade, but not much is known about Tra2α. It was very recently shown that the two proteins demonstrate the phenomenon of paralog compensation. Here, we provide a structural basis for this genetic backup circuit, using molecular modelling and dynamics studies. We show that the two proteins display similar binding specificities, but differential affinities to a short GAA-rich RNA stretch. Starting from the 6-nucleotide RNA in the solution structure, close to 4000 virtual mutations were modelled on RNA and the domain-RNA interactions were studied after energy minimisation to convergence. Separately, another known 13-nucleotide stretch was docked and the domain-RNA interactions were observed through a 100-ns dynamics trajectory. We have also demonstrated the {\textquoteright}compensatory{\textquoteright} mechanism at the level of domains in one of the domain repeat-containing RNA-binding proteins.

}, issn = {1538-0254}, doi = {10.1080/07391102.2015.1100551}, author = {Ghosh, Pritha and Grellscheid, Sushma Nagaraja and Sowdhamini, R} } @article {161, title = {Genome sequencing of herb Tulsi (Ocimum tenuiflorum) unravels key genes behind its strong medicinal properties.}, journal = {BMC Plant Biol}, volume = {15}, year = {2015}, month = {2015}, pages = {212}, abstract = {

BACKGROUND: Krishna Tulsi, a member of Lamiaceae family, is a herb well known for its spiritual, religious and medicinal importance in India. The common name of this plant is {\textquoteright}Tulsi{\textquoteright} (or {\textquoteright}Tulasi{\textquoteright} or {\textquoteright}Thulasi{\textquoteright}) and is considered sacred by Hindus. We present the draft genome of Ocimum tenuiflurum L (subtype Krishna Tulsi) in this report. The paired-end and mate-pair sequence libraries were generated for the whole genome sequenced with the Illumina Hiseq 1000, resulting in an assembled genome of 374~Mb, with a genome coverage of 61~\% (612~Mb estimated genome size). We have also studied transcriptomes (RNA-Seq) of two subtypes of O. tenuiflorum, Krishna and Rama Tulsi and report the relative expression of genes in both the varieties.

RESULTS: The pathways leading to the production of medicinally-important specialized metabolites have been studied in detail, in relation to similar pathways in Arabidopsis thaliana and other plants. Expression levels of anthocyanin biosynthesis-related genes in leaf samples of Krishna Tulsi were observed to be relatively high, explaining the purple colouration of Krishna Tulsi leaves. The expression of six important genes identified from genome data were validated by performing q-RT-PCR in different tissues of five different species, which shows the high extent of urosolic acid-producing genes in young leaves of the Rama subtype. In addition, the presence of eugenol and ursolic acid, implied as potential drugs in the cure of many diseases including cancer was confirmed using mass spectrometry.

CONCLUSIONS: The availability of the whole genome of O.tenuiflorum and our sequence analysis suggests that small amino acid changes at the functional sites of genes involved in metabolite synthesis pathways confer special medicinal properties to this herb.

}, issn = {1471-2229}, doi = {10.1186/s12870-015-0562-x}, author = {Upadhyay, Atul K and Chacko, Anita R and Gandhimathi, A and Ghosh, Pritha and Harini, K and Joseph, Agnel P and Joshi, Adwait G and Karpe, Snehal D and Kaushik, Swati and Kuravadi, Nagesh and Lingu, Chandana S and Mahita, J and Malarini, Ramya and Malhotra, Sony and Malini, Manoharan and Mathew, Oommen K and Mutt, Eshita and Naika, Mahantesha and Nitish, Sathyanarayanan and Pasha, Shaik Naseer and Raghavender, Upadhyayula S and Rajamani, Anantharamanan and Shilpa, S and Shingate, Prashant N and Singh, Heikham Russiachand and Sukhwal, Anshul and Sunitha, Margaret S and Sumathi, Manojkumar and Ramaswamy, S and Gowda, Malali and Sowdhamini, Ramanathan} }