Killer Immunoglobulin-Like Receptor <b>(</b>KIR<b>)</b> Centromeric-AA Haplotype Is Associated with Ethnicity and Tuberculosis Disease in a Canadian First Nations Cohort

<div><p>Killer immunoglobulin-like receptors (KIR) on natural killer (NK) cells interact with other immune cells to monitor the immune system and combat infectious diseases, such as tuberculosis (TB). The balance of activating and inhibiting KIR interactions helps determine the NK cell response. In order to examine the enrichment or depletion of KIRs as well as to explore the association between TB status and inhibitory/stimulatory KIR haplotypes, we performed KIR genotyping on samples from 93 Canadian First Nations (Dene, Cree, and Ojibwa) individuals from Manitoba with active, latent, or no TB infection, and 75 uninfected Caucasian controls. There were significant differences in KIR genes between Caucasians and First Nations samples and also between the First Nations ethnocultural groups (Dene, Cree, and Ojibwa). When analyzing ethnicity and tuberculosis status in the study population, it appears that the KIR profile and centromeric haplotype are more predictive than the presence or absence of individual genes. Specifically, the decreased presence of haplotype B centromeric genes and increased presence of centromeric-AA haplotypes in First Nations may contribute to an inhibitory immune profile, explaining the high rates of TB in this population.</p></div

Study population demographics.

<p>Study population demographics.</p

Schematic of KIR gene haplotypes A and B.

<p>white – framework genes, grey – activating KIR, black – inhibitory KIR; note that KIR2DP1 and KIR3DP1 are pseudogenes, and that KIR2DL2/2DL3 as well as KIR3DL1/3DS1 represent the same locus.</p

Data_Sheet_1.DOC

<p>This paper describes in silico models developed using a wide range of peptide features for predicting antifungal peptides (AFPs). Our analyses indicate that certain types of residue (e.g., C, G, H, K, R, Y) are more abundant in AFPs. The positional residue preference analysis reveals the prominence of the particular type of residues (e.g., R, V, K) at N-terminus and a certain type of residues (e.g., C, H) at C-terminus. In this study, models have been developed for predicting AFPs using a wide range of peptide features (like residue composition, binary profile, terminal residues). The support vector machine based model developed using compositional features of peptides achieved maximum accuracy of 88.78% on the training dataset and 83.33% on independent or validation dataset. Our model developed using binary patterns of terminal residues of peptides achieved maximum accuracy of 84.88% on training and 84.64% on validation dataset. We benchmark models developed in this study and existing methods on a dataset containing compositionally similar antifungal and non-AFPs. It was observed that binary based model developed in this study preforms better than any model/method. In order to facilitate scientific community, we developed a mobile app, standalone and a user-friendly web server ‘Antifp’ (http://webs.iiitd.edu.in/raghava/antifp).</p

THPdb: Database of FDA Approved Peptide and Protein Therapeutics

THPdb is a manually curated database of Food and Drug Administration (FDA) approved therapeutic peptide and proteins. The text file contain all the data curated in THPdb

Schematic representation of THPdb architecture.

<p>Schematic representation of THPdb architecture.</p

Overview of the function/mode of activity based classification of therapeutic proteins and peptides.

<p>Overview of the function/mode of activity based classification of therapeutic proteins and peptides.</p

The flow diagram explains the work and design of THPdb mobile application.

<p>The flow diagram explains the work and design of THPdb mobile application.</p

A schematic representation of distribution of therapeutic peptides and proteins based on disease, function/ mode of activity, route of administration, and pharmacological class.

<p>A schematic representation of distribution of therapeutic peptides and proteins based on disease, function/ mode of activity, route of administration, and pharmacological class.</p

Representative screen shots of THPdb demonstrating the flow of information after submission of a query in simple search page.

<p>Representative screen shots of THPdb demonstrating the flow of information after submission of a query in simple search page.</p