Thursday, 25 May 2017

Laterosporulin10: A New Potential Anticancer Agent

Use of small antimicrobial peptides (AMPs) against cancer cells has opened a new door to optimize these AMPs in order to develop new therapeutic candidates.


By Ratneshwar Thakur Published in The Hawk



(From left to right- Dr. Suresh Korpole and Dr. Piyush Baindara)


Cancer cells are smart and the emergence of resistance in cancer cells towards existing anticancer drugs makes the situation even more critical. Ideally, anticancer drugs should specifically target cancer cells, sparing normal cells but, unfortunately, most of the available anticancer drugs display severe side effects. To reduce or eliminate these adverse effects, there is a pressing need to increase the drug arsenal to control this deadly disease.

Researchers are now turning toward naturally occurring proteins known as antimicrobial peptides (AMPs) like Bacteriocin (a protein produced by bacteria of one strain and active against those of a closely related strain), which can kill not only microbes but have another therapeutic potential as well.

Indian scientists from CSIR- Institute of Microbial Technology (IMTECH), Chandigarh, including the team of Dr. Suresh Korpole, a corresponding author of this paper, and Dr. Gajendra P.S. Raghava, Head of the Bioinformatics Centre, have reported bacteriocin laterosporulin-10 (LS-10) showing the potential of targeted killing of cancer cells.

This work was carried out in the facility of “Microbial Type Culture Collection and Gene Bank (MTCC),” at IMTECH. The study was published in the Journal “Scientific Reports”.

In the previous study, Dr. Korpole’s team had reported that LS-10 is capable of killing Mycobacterium tuberculosis (Mtb H37Rv strain) residing in the phagosomes of murine macrophages. In the present study, they have made an attempt to explore the anticancer potential of LS-10.

“This finding is exciting because LS-10 not only specifically kills human pathogen Mtb H37Rv but it also has anticancer potential,” says Dr. Piyush Baindara, first author of this paper.

Previous studies have shown that most of the AMPs have anticancer activities, however, they could not move forward in drug development pipeline because of their high hemolytic nature. However, this new study revealed that LS-10 has low cytotoxicity against normal cells and erythrocytes.

“In our study, the most interesting finding is that bacteriocin LS-10 showed a significant cytotoxicity against a variety of cancer cells but it was relatively less cytotoxic against normal cells,” says Dr. Ankur Gautam, one of the authors of this paper.

If these peptides are optimized for remedial use, the scientists envision that they could be used against many pathogenic bacteria as well as for treatment of dreaded diseases like cancer.

“We have demonstrated that peptide-like LS-10 can be used as potential anticancer molecules, though it’s very preliminary findings. Further studies are required particularly in vivo study on mice models to demonstrate the full potential of LS-10 as an anticancer molecule,” said the Investigators.

Sunday, 14 May 2017

Gene Signatures To Predict Cigarette Smoke Exposure

Gene signatures identified using Computational biology approaches to distinguish between former smokers, current smokers, and non-smokers.


By Ratneshwar Thakur Published in The Hawk


                       Blood Cells, Photo Credit: Interactive Biology


The new study, published in “Computational Toxicology”, reveals how smoke exposure influences the gene expression levels- from mice to human. These gene signatures may give us a really good clue to understanding what might have happened due to smoke exposure.

The authors of this article were part of three teams that participated in a world-wide computational challenge organized by Philip Morris International (PMI), Switzerland and were ranked at the top in this crowdsourcing initiative (https://sbvimprover.com/challenge-4) that was designed to determine whether dysregulation of blood expression of a common set of genes can classify both human and mouse subjects into cigarette smoke exposure groups.

“The common motivation to participate in the challenge was to assess our approaches to develop accurate prediction models based on high-dimensional datasets, and also to understand how translatable the response to toxicants is between mouse and human”, said the Investigators.

The ability to translate the impact of such toxicants from animals to humans is key in systems toxicology, which is enabled by the ability to profile tens of thousands of molecules (e.g. mRNAs) in biological samples.

In this challenge, participants applied their prediction models to a blinded test dataset which was obtained by expression profiling of blood samples from 27 smokers, 26 former smokers, and 28 never-smokers. The mouse training dataset was based on a 7-month cigarette smoke inhalation study conducted with mice and included three groups of animals: exposed to smoke for 7 months (equivalent to a human current smoker), exposed to smoke for 2 months followed by exposure to air (equivalent to a human former smoker) and mice continuously exposed to air (equivalent to human never-smoker).

“This study is interesting because it shows that a common gene signature predicts with good accuracy current exposure to cigarette smoke in both human and mouse, and that the microarray technology and our computational methods reached the maturity needed to obtain reliable results in other similar research settings’’, says Adi L. Tarca, Associate Professor, Wayne State University, School of Medicine.

“The publication has reported several genes that are different in the former smoker and current smoker. Additionally, it opens the industries to seek crowdsourcing solutions to their research problems”, says Dr. Sandeep Kumar Dhanda, Bioinformatics postdoc, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA

“Prediction of outcomes from high-dimensional data is a research topic with applications in many areas of biomedicine, as researchers use these types of data to develop models to predict future onset of disease or patient response to treatments. Moreover, for those working in toxicology, the data presented in this study are useful to assess the suitability of the mouse as a model organism to predict the response to toxicants in human based on observation in the mouse model”, says Prof. Tarca.

“We observed that smoking affects the gene expression profile of an individual, but this effect is minimized if a person quit smoking for more than the year. These gene expression profiles can be used as a platform to decode the underlying changes going on in the body of a smoker”, says post-doctoral researchers Dr. Sandeep K. Dhanda and Dr. Rahul Kumar.