Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis

Abstract

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

Figure 1. From Social Network to Biological Network.

The C2D annotation approach for manual annotation and curation of Mtb interactome followed by network analysis to predict potential drug targets reported at various sequence and structural level filters. (A) Illustrates the overall approach of crowd sourcing through social network implemented in C2D exercise (B)(a) Mtb Genome (b) Manual collation and sequence/structure based curation for gene annotation (c) Collation of re-annotated genome into comprehensive data structure (d) Construction of protein-protein interaction network based on the annotated data (e) Target identification using network analysis; Sequence level comparison of selected proteins with that of human homologs, human gut flora and human oral flora; systems, sequence and structure level analysis of shortlisted proteins and experimentally validated drug targets.

Figure 2

IPW interactome and comparison with existing annotation databases (a) IPW-Only protein-protein functional interaction network, (b) Comparative analysis of IPW-Only proteins and interaction with existing manually curated databases, Ring represents all interactions and proteins in IPW displaying the subsets which are obtained from other manually curated databases (b1) Comparative analysis of IPW-Only interactions to that of existing manually curated databases (b2) comparative analysis of protein as curated in IPW-Only to that of proteins presents in other manually curated databases (c) TubercuList functional class interaction relation based on the interactions as obtained from IPW-Only. The connectivity (lines) represents the interacting proteins within these classes.

Figure 3

Network parameters (a) Characteristic path length of IPW-Only network and IPWSI network. In both the graphs the x-axis represents the path length whereas the y-axis represents the frequency. 3(b) Log-Log plot of degree distribution of IPW network, the solid line was obtained by fitting the power law for γ  = 1.99 and Log-Log plot of degree distribution of IPWSI network, the solid line represents the power law fit with γ  = 2.01.

Figure 4. Illustrates the comprehensive analyses of central proteins as potential drug targets.

The various filters include comparison with validated drug targets, sequence and structural level comparison with Human proteome, gut and oral flora (a) The list of 73 central ORFs wherein Rv Ids in bold represent IPW central ORFs, Rv IDs in regular font represents IPWSI central ORFs and the italicized-bold represent common Rv Ids to both IPW and IPWSI. (b & b’) Five of the 17 IPW and six of 64 central ORFs with experimental validation as drug targets. (c) Sequence homology comparison with human proteome and human microbiome results in 62 ORFs with no significant similarity (d) Octamer analyses against human proteome and human microbiome results in one ORF with no hits (e) Comparative binding site analysis with human proteome results in 26 ORFs with no significant similarity (lists b, b’, c, d and e available in Table S2).

Figure 5. Illustration of 17 putative drug target interaction from IPW interactome depicting the cascade of how the central proteins interact with each other in a spatio-temporal manner under different conditions like growth, stress and survival in macrophages including virulence.

Under normal conditions, PknB phosphorylates RshA which inhibits SigH. However, under oxidative stress, RshA is not phosphorylated and this abolishes its binding to SigH, rendering it free. SigH in turn upregulates expression of SigE and SigB which regulates MprA (bacterial persistence regulator). MprA also regulates SigB and SigE. SigB plays important role in adaptation to stationary phase and nutritionally poor conditions and SigE is upregulated in mycobacterial growth within human macrophages and is transcribed from three different promoters under different conditions. sigB is also regulated by SigF, which regulates the expression of genes involved in the biosynthesis and structure of the mycobacterial cell envelope, including complex polysaccharides and lipids, particularly virulence- related sulfolipids and several transcription factors. Rv0516c is an anti-anti sigma factor and regulates anti-sigma factor SigF (upregulated during infection culture of human macrophages and in nutrient starvation condition; regulates transcription of genes involved in cell wall biosynthesis, sulfolipid metabolism, nucleotide metabolism, energy metabolism and several transcription factors) on getting phosphorylated by PknD which in turn is regulated by Rv0020c phosphorylated by PknB and PknE. SigF also regulates sigC and regulates hspx that is also regulated by dosR regulon. dosR regulon in turn is again regulated by PhoP which is a transcription factor for nuoG, eccCb1, esxb/cfp10 .