Page 53 - Biennial Report 2018-20 Jun 2021

genomes currently exceed more than 5K in recent years. The first part of this project was focused
on a data analytics approach, whereby computational models of antimicrobial drug-resistance
within ESKAPE pathogens were proposed to be built. To achieve this objective, large-scale
genomic information from available high-throughput biological datasets such as PATRIC was
compiled. The current data statistics led to collection of 1548, 252, 2602, 5562, 1152, 1270
genomes corresponding to antibiotic susceptibility from Enterobacter species, Enterococcus
faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii,
Pseudomonas aeruginosa, respectively. The above data was also heavily processed with
sequences merged into a single file for alignment with the reference genome.
Genomes from antibiotic resistant E. coli were analyzed with Freebayes tool to understand
variations associated with AMR. Further processing of .vcf files was done using Annovar
construction for tabulating the nature of polymorphisms (SNPs). As a result, novel mutations
were obtained and mapped to the E. coli proteome which was binned into six functional classes
i.e., Membrane proteins, secretory proteins, stress-response proteins, enzymes, ribosomal
proteins, and cytosolic proteins. This was primarily done to understand the missing link between
antibiotic-induced genomic changes with functional impact. Currently, all the proteins have been
extracted from Uniprot and manual curation is underway.

Next, ribosomes were analyzed to begin with experimental validation. Owing to its central role
in cellular function, the ribosome is also one of the most common targets of antibiotics.
Modulation of methylation patterns in 16S rRNA is now emerging as one of the intrinsic
mechanisms of drug resistance in several bacteria, through acquisition of 16S rRNA
methyltransferases. In addition to the canonical methylations in 16SrRNA, the acquired
methyltransferases related to drug resistance (ArmA, RmtA, RmtB, RmtC, RmtE, RmtF, RmtG and
RmtH), all methylate the N7-G1405 position except NpmA which methylates at N1-A1408
position. Despite the presence of Rossman fold in the 16S rRNA methyltransferase, the canonical
and acquired (‘drug resistance’) methyltransferases cluster into distinct phylogenetic branches.
In order to investigate the molecular details of interplay of acquired ribosomal (‘drug resistance’)
nucleotide methyltransferases with drug resistance, ArmA, RmtE, RmtA, RmtF and NpmA were
selected and cloned and the groundwork for further detailed structural analysis into their
functions was laid.
Membrane proteins were also focused upon, whereby 130 novel mutations were obtained, and
the highest number was obtained in VpjA, a previously uncharacterised protein. This revealed
interesting insights into how AMR is manifested across bilayer proteins. In addition to fine tuning
methods and obtaining novel mutations, an algorithm to rank the mutations on the basis of
phenotype was also developed.
.Large-scale biological information of ESKAPE organisms is proposed to be connected to precise
molecular details and how these microbial resistance mutations impair protein activities would
also be studied. Computational analytics, modelling, biomolecular simulations will reveal
proteins with atomistic regions that are putative origin of drug-resistance within ESKAPE
pathogens.

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