>> Welcome to Module 1.2, the SARS-CoV-2 Genome.
This presentation is part
of the COVID-19 Genome Epidemiology Toolkit
from the CDC's Office of Advanced Molecular Detection.
My name is Dr. Shatavia Morrison,
and I'm a Bioinformatics Unit lead with the CDC.
This is Module 1.2,
which provides a very basic introduction to the genome
of SARS-CoV-2, the virus that causes COVID-19.
Please be sure to check out the toolkit's other modules,
which include a combination of case studies
and training material to help you get started supplementing
epidemiological data with genome sequencing data.
Microbial pathogens are diverse and come
from all domains of life.
They include many taxa such as viruses, bacteria, and fungi.
Fundamental to our discussion is
that almost all microbial pathogens encode their genetic
material in genomes.
Those genomes can be comprised of either RNA,
shown on the left, or DNA, shown on the right.
Both consist of building blocks called nucleotides.
You can think of both RNA and DNA genomes simply
as long polymers of these four nucleotides denoted
in the image, which we typically abbreviate
with single-letter notation, repeated and organized
in very specific combinations.
In the same way that words, sentences, and paragraphs
in a book are written in a long string of alphabetical letters,
all the information necessary for the life
of the organism is encoded in that genome.
Using high-throughput sequencing and bioinformatic methods,
we routinely study the entire genome sequences
of microbial pathogens.
Such tools make it possible to investigate the presence
of toxins, antimicrobial resistance,
and other known factors related to virulence or pathogenicity.
Also, these data can be used to aid in the investigation
of potential outbreaks or for source attribution
in epidemiology investigations.
Genome size varies greatly, spanning many orders
of magnitude, with viruses and viroids at the small end
and higher eukaryotes at the larger end of the spectrum.
There is a general, inverse relationship between genome size
and mutation rate, with smaller genomes typically mutating
more rapidly.
SARS-CoV-2 has an RNA genome and is marked here
with a red star among other RNA viruses.
It has an approximate genome size of 30,000 nucleotides
and an estimated substitution rate somewhere between the range
of 10 to the 4th to 10 to the 3rd.
For SARS-CoV-2, that equates
to approximately two mutations per month.
In Module 1.3, you will learn more
about how substitution rates
and mutations impact phylogenetic analyses.
Viruses as a group are themselves very diverse.
Viruses come in lots of shapes and sizes,
as seen in some examples in the diagram on the right.
Viral genomes are very compact, typically comprised of tens
of thousands of nucleotides encoded as either RNA or DNA.
As we saw in previous slides,
viral genomes are highly variable in size.
This is a simple schematic view of the SARS-CoV-2 genome.
The varied colors indicate some of the different genes.
As already mentioned, the genome is encoded
on a linear single strand of roughly 30,000 RNA nucleotides.
The genome includes 11 genes, which encode the instructions
for approximately 12 products.
For example, you likely heard about the spike protein,
whose gene is marked here in red.
A single copy of this linear RNA molecule is packaged inside
of every SARS-CoV-2 viral particle.
Viral genomes randomly mutate at a certain rate.
Mutations can be introduced into the genome
by various different mechanisms.
Those mutations leave behind genetic fingerprints
that can be used to infer ancestral relationships,
like a family tree, among strains.
Identification of the ancestral relationship is helpful
in depicting the genetic relatedness
between the strain sequence.
Phylogenetics is a central concept for genomic epidemiology
that will be covered more in-depth in Module 1.3
as we explore how to read phylogenetic trees.
This process of random mutation accumulations creates the
technical divisions within the global circulating population
for any microbial, even one
as recently emerged such as SARS-CoV-2.
Genomic researchers tracking population dynamics
of SARS-CoV-2 have developed a few different schemas
to describe the divisions
within the global phylogeny using standardized nomenclature.
This figure summarizes the overlaps between a few
of the popular clade nomenclature schemas, namely,
the Pangolin lineages, Nextstrain clades,
and the GISAID clades.
A clade is a group of organisms that include a single ancestor
and all of its descendants.
The Nextstrain and GISAID nomenclatures aim
to provide generic categorization
of globally circulating diversity,
while Pangolin is meant to correspond to outbreaks.
More details about each of these nomenclature schemas are
included in the additional resources for this module.
Importantly, there's currently no evidence to conclude
that subgroups defined by any of these schemas correspond
to differences in clinical characteristics.
Following this basic introduction
to SARS-CoV-2 genome, we can reiterate some key reasons
for sequencing genomes of strains recovered
from infected patients during the current pandemic.
Sequencing can aid public health officials by monitoring trends
and emergence and spread of viral strains at regional,
state, or national levels, which become more informative
as vaccination becomes widespread.
But sequencing also adds value
to local epidemiological activities
to investigate disease clusters and different scenarios,
like those listed here, which are highlighted
in the case study module in this toolkit.
To summarize, SARS-CoV-2 is a single-stranded RNA virus
with a genome of approximately 30,000 nucleotides.
Genomic analysis to date indicates that the mutation rate
in the genome is close to a rate of two mutations per month.
These mutations permit genomic fingerprinting
and phylogenetic analyses that can be used
to classify circulating SARS-CoV-2 into clades
or lineages with standardized nomenclature
but also provide the fundamental data for genomic epidemiology,
the primary topic of this training toolkit.
Because this module only offers a basic introduction
to the SARS-CoV-2 genome, additional,
more detailed references are included
in the resources material.
This concludes Module 1.2.
Part 2 of this toolkit contains case studies that review the use
of genomics in response to COVID-19.
Please visit COVID-19 Genomic Epidemiology Toolkit page
where you can find further reading on this topic.
On the Toolkit page, you can also subscribe
to our mailing list and receive announcements as new modules
and materials are released.
Thank you.