​Cloning and Sequence Analysis of Transferrin Binding Protein Gene A of Field and Vaccine Strains of Pasteurella multocida B:2

Background: Pasteurella multocida is an important bacterial pathogen that causes many major diseases of which haemorrhagic septiciemia (HS) in cattle and buffalo is responsible for catastrophic epizootics in India and South Asia. In India, the disease haemorrhagic septiciemia is considered as the most dreaded bacterial disease. Various host- and pathogen- specific determinants are responsible for disease outcome. Various bacterial virulence genes (tbpA, pfhA, toxA, hgbB, hgbA, nanH, nanB, sodA, sodC, oma87 and ptfA) have been proposed to play a key role in this interaction. Methods: The present study was done to compare the gene and deduced amino acid sequence of transferrin binding protein gene (tbpA) gene of field isolates and vaccine strain of P. multocida B: 2. Result: It was observed that tbpA gene of field and vaccine strains have similar nucleotide sequence except at positions 574 and 620. The sequence of tbpA gene was used for prediction of matured TbpA protein characteristics. The deduced amino acid sequences of 242 amino acids revealed 99% homology with TbpA of P. multocida and with a variety of other TonB-dependent receptor proteins, indicating that it belongs to the family of outer membrane receptors. Deduced amino acid sequence was found to be similar in field and vaccine strains except at 207th amino acid. In field isolates Leucine was there while in vaccine strain Phenyl alanine was found. These both amino acids are hydrophobic in nature so no change in physico-chemical property of TbpA is expected. From this study it is concluded that single amino acid difference between field isolate and vaccine strain might not cause change in its binding and physico-chemical property.

DGraph Clusters Flaviviruses and β-Coronaviruses According to Their Hosts, Disease Type, and Human Cell Receptors

Motivation: There is a need for rapid and easy-to-use, alignment-free methods to cluster large groups of protein sequence data. Commonly used phylogenetic trees based on alignments can be used to visualize only a limited number of protein sequences. DGraph, introduced here, is an application developed to generate 2-dimensional (2D) maps based on similarity scores for sequences. The program automatically calculates and graphically displays property distance (PD) scores based on physico-chemical property (PCP) similarities from an unaligned list of FASTA files. Such “PD-graphs” show the interrelatedness of the sequences, whereby clusters can reveal deeper connectivities. Results: Property distance graphs generated for flavivirus (FV), enterovirus (EV), and coronavirus (CoV) sequences from complete polyproteins or individual proteins are consistent with biological data on vector types, hosts, cellular receptors, and disease phenotypes. Property distance graphs separate the tick- from the mosquito-borne FV, cluster viruses that infect bats, camels, seabirds, and humans separately. The clusters correlate with disease phenotype. The PD method segregates the β-CoV spike proteins of severe acute respiratory syndrome (SARS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and Middle East respiratory syndrome (MERS) sequences from other human pathogenic CoV, with clustering consistent with cellular receptor usage. The graphs also suggest evolutionary relationships that may be difficult to determine with conventional bootstrapping methods that require postulating an ancestral sequence.

D-graph clusters flaviviruses and β-coronaviruses according to their hosts, disease type and human cell receptors

MotivationThere is a need for rapid and easy to use, alignment free methods to cluster large groups of protein sequence data. Commonly used phylogenetic trees based on alignments can be used to visualize only a limited number of protein sequences. DGraph, introduced here, is a dynamic programming application developed to generate 2D-maps based on similarity scores for sequences. The program automatically calculates and graphically displays property distance (PD) scores based on physico-chemical property (PCP) similarities from an unaligned list of FASTA files. Such “PD-graphs” show the interrelatedness of the sequences, whereby clusters can reveal deeper connectivities.ResultsPD-Graphs generated for flavivirus (FV), enterovirus (EV), and coronavirus (CoV) sequences from complete polyproteins or individual proteins are consistent with biological data on vector types, hosts, cellular receptors and disease phenotypes. PD-graphs separate the tick- from the mosquito-borne FV, clusters viruses that infect bats, camels, seabirds and humans separately and the clusters correlate with disease phenotype. The PD method segregates the β-CoV spike proteins of SARS, SARS-CoV-2, and MERS sequences from other human pathogenic CoV, with clustering consistent with cellular receptor usage. The graphs also suggest evolutionary relationships that may be difficult to determine with conventional bootstrapping methods that require postulating an ancestral sequence.Availability and implementationDGraph is written in Java, compatible with the Java 5 runtime or newer. Source code and executable is available from the GitHub website (https://github.com/bjmnbraun/DGraph/releases). Documentation for installation and use of the software is available from the Readme.md file at (https://github.com/bjmnbraun/DGraph)[email protected] or [email protected] informationSupplementary information Table S1 and Fig. S1 are online available.