Hi My name is Parul Sharma and through this video I would like to share my project in
which I look at pathogen identification up to strain level from metagenomic sequences.
Pathogen identification is important because whenever there is an outbreak the first and
most important thing is to find out the pathogen causing the disease, as this information will
help us prevent the disease from spreading further.
A perfect example of how fast pathogen detection can lead to effective containment of the disease
is illustrated through the recent extensive spread of novel coronavirus (covid'19) for
which the need of extensive testing is repeatedly made clear to help decrease the spread.
Over the years there have been a lot of technologies that focus on pathogen identification but
these technologies have low resolution and are limited to detecting only certain pathogen
species.
Sequencing technologies overcome these issues and can also detect non-cultural pathogens.
Even one step ahead is metagenomic sequencing which allows us to sequence any environmental
samples such as soil or water and therefore provides a more comprehensive picture of the
community in any sample and to achieve this we have used the Oxford Nanopore Sequencing
device called the MinION- which is a small portable device that performs sequencing in
real time.
With this in mind we designed our study to include laboratory and field samples.
For our lab samples we used a single pathogen and a mixed pathogen sample and sprayed both
samples on separate plants and when the symptoms appeared, we sequenced the whole plant sample
using MinION.
We also included 2 controls- one was just a plant as it is and the other was the mix
of pathogens without the plant.
Aside from the lab samples we also had 8 field infected samples from the Eastern Shore facility.
To analyze the sequencing data we got from MinION, we used various read based method
such as WIMP which stands for What's In My Pot! and is included in the software package
of MinION and also some other popular classification tools.
For our results, we saw that using the default databases we were correctly able to identify
the pathogen at the species level.
For the L-K40 sample we saw that most reads were assigned to Pseudomonas syringae tomato
pathogen as K40 belongs to that species.
For L-mix sample which contained an equal mixture of 2 pathogenic Pseudomonas syringae
strains and 2 nonpathogenic strains; we again saw that most reads were assigned to Pseudomonas
syringae species, which is Ok because we were not expecting the nonpathogenic strains to
grow on the plant.
For L-mock we did not observe any pathogenic species an expected and for the L-culture-mix
we saw an almost equal split between the pathogenic and the nonpathogenic strains as expected.
We then looked at the strain level using a custom database composed of known pathogenic
strains.
Here we saw that our approach worked fine in differentiating the 2 pathogenic strains
in the L-mix sample and although we were expecting an equal composition of the 2 pathogens, possible
reasons for this disparity is the fact that DC3000 and K40 are very closely related and
therefore maybe the software could not differentiate between the two.
Another reason could be that perhaps K40 is a better colonizer of tomato plants than DC3000.
We then moved on to analyze our field samples for which a visual diagnosis indicated a Xanthomonas
infection.
Through our analysis we were able to confirm the pathogen to be Xanthomonas performs.
But for sample F-5 we could not detect any pathogens as the visual diagnosis also indicated
a Septoria infection and since of septoria is a fungal pathogen, we could not detect
it with our bacterial database.
Another interesting case was the F-6 sample which was included as a control as a healthy
sample but our analysis found some amount of pathogenic reads to the sample as well.
Therefore we further investigated our samples to find the pathogenic strains and we were
able to find the pathogen to be Xanthomonas perforans group 2 strain and we found the
same strain in our F6 sample as well.
Since all our samples came from the same field we can say that through the approach we were
able to identify not just the pathogenic strain but also the asymptomatic plant which still
appeared healthy.
Therefore the key take-home points from this presentation is that MinION sequencing can
be used to identify pathogens in real time.
This technique also proves efficient in identification of asymptomatic plants.
Using third party software this technique can almost obtain strain level resolution.
With that I will like to acknowledge all the collaborators and the members of my lab who
help me throughout the course of this project.
This project is also recently published in the Phytopathology journal.
Also very thank you for listening to my presentation.