CUSTOMER SPOTLIGHT
KATHLEEN C. BARNES, PHD

How does the methylome relate toclinical outcome?
Methylation arrays add another dimension to the study of
COVID-19 and other infectious diseases.

Dr. Kathleen Barnes describes herself as a genetic epidemiologist,
studying “how genetic factors can influence disease in populations,typically in the context of the environment, as well as factors
influencing its global distribution.” This work integrates population
with statistical genetics. “My entire research program has almost
exclusively focused on disentangling the genetic underpinnings
that contribute to health disparities,” states Dr. Barnes.

DR. KATHLEEN C. BARNES
PROFESSOR OF MEDICINE &EPIDEMIOLOGY
FOUNDING DIRECTOR, COLORADOCENTER FOR PERSONALIZEDMEDICINE
UNIVERSITY OF COLORADOANSCHUTZ MEDICAL CAMPUSSENIOR VICE PRESIDENT,
POPULATION HEALTH GENOMICSTEMPUS LABS

With a rich background in immunogenetics, Dr. Barnes was theideal candidate to develop and lead the Colorado Center for
Personalized Medicine (CCPM) at the University of Colorado (UC)Anschutz Medical Campus. “I spent 23 years as a researcher
at Johns Hopkins focused on classic genetic epidemiology and
now I’m running a personalized medicine program. I think genetic
epidemiology is highly complementary to precision medicine
and may have actually led us to where we are with personalized
medicine,” she states.
The growth of personalized medicine is driven by powerful healthinsights garnered from multiomics data. As a resource for collecting
this data, CCPM manages an extensive biobank comprising nearly
200,000 patient samples. When the COVID-19 pandemic arose in
March 2020, Dr. Barnes and the CCPM were well positioned withpatient samples to start projects related to coronavirus infection.
“On day two of the shutdown, the CCPM biobank was asked topivot and commence with COVID testing.” Dr. Barnes and herteam were also able to continue with their research activities anduncovered connections between host methylation, SARS-CoV-2
infection, and clinical outcomes.
We had the opportunity to meet with Dr. Barnes and discuss her
work at the CCPM and how the methylome may contribute to infectious disease.

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Q: What are the goals of the Colorado Center for Personalized Medicine?

The many "firsts" achieved by
the CCPM:• FIRST institutional biobank toleverage genomics data from acommercial SNP array

Kathleen Barnes (KB): The goal of CCPM is to personalize care forevery individual or patient, typically leveraging genetic information
within the CCPM. The goal is linking extensive electronic medical
record data to omics information. We’ve recruited and built up abase of faculty that have expertise in the field of genetic epidemiology to help achieve these goals.
Another goal of CCPM is to establish strategic partnerships thatcover end-to-end deployment of clinical applications towards
individualized health. This includes, but it’s not limited to, reliable,feasible, scalable testing assays, diagnostic predictive algorithms, and clinical decision support pipelines, all of which reallyenhance our existing capabilities and enable our program to reacheconomies of scale.

• FIRST program to launch clinicalintegration of pharmacogenomics through a research biobank• FIRST enterprise health data
warehouse to partner with
Google Cloud platform

Q: What is the CCPM Biobank?
KB: This is our CLIA- and CAP-certified biobank done in collaboration with our partner/healthcare system, UCHealth. We’ve
consented nearly 200,000 UCHealth patients to the biobank since
we launched in the spring of 2016. The recruitment process is all
done electronically through the UCHealth patient portal where
patients have the opportunity to consent. It may be some time
before they come back in and we have the opportunity to collecta blood sample. To date, we’ve either already extracted, or we’recurrently extracting samples from, about 80,000 participants.
*

Q: Before COVID, what was the main research focus of your lab?
KB: My entire research program has truly focused, almost exclusively, on disentangling the genetic underpinnings that contributeto health disparities. My main focus has been in complex lungdiseases and allergic diseases and other diseases of inflammation—most notably asthma, but almost exclusively focused on
populations of African ancestry.
This project is NIH supported through a grant called Consortium on Asthma Among African-ancestry Populations in theAmericas (CAAPA). In 2011, we shifted from exome sequencing towhole-genome sequencing at a reasonably affordable cost. We* CLIA, Clinical Laboratory Improvement Amendments; CAP, College of American
Pathologists; UCHealth University of Colorado Hospital

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were able to sequence whole genomes for about 1000 individualsof African ancestry, representing North/Central/South America,
the Caribbean, and Africa. The goal was to generate the African
Diaspora Genomic Catalog. We used that catalog to partner with
Illumina and develop what we called the African Power Chip. The
idea was to create a customized SNP chip that would allow us tofill in the gaps for information that wasn’t captured on a commercial
chip.
†

Illumina then decided to create the Multi-Ethnic Genotyping Array,or the MEGA chip. We partnered closely with Illumina, as well as
another NIH consortium called PAGE [Population Architectureusing Genomics and Epidemiology], to take all the content we hadcreated in the African Power Chip and include it on the MEGA Chip,
as well as clinically actionable sorts of data. We hadn’t imagined
this would happen and it was very exciting. That has been one of
the most gratifying accomplishments, of my career, but also on
behalf of CAAPA. We’re very proud of the CAAPA catalog. To thisday, it serves as an invaluable resource for the research community
at large because it enables imputation of genetic variation in
African ancestry populations at very high accuracy.
We’re also gratified that we were able to develop a GWAS chipthat captures ancestral diversity, not just of the African genome,
but of multiple non-European populations. This continues to shape
my research as we move into a phase of multiomics where we’re
fortunate to have this GWAS-level data, and even whole-genome
sequence data, as the backbone. From that backbone, we can thenlayer on transcriptomics, methylomics, proteomics, metabolomics,
etc. It's been very exciting.

"This continues to shape myresearch as we move into aphase of multiomics where
we’re fortunate to have this
GWAS-level data, and evenwhole-genome sequence data,as the backbone. From thatbackbone, we can then layer on‡transcriptomics, methylomics,
proteomics, metabolomics, etc."

Q: How did COVID impact your research plans?
KB: It was challenging when COVID happened in March 2020. I
think it especially impacted programs like CAAPA because theyrequire recruitment of patients with and without a particulardisease, and there was an absolute halt on any kind of clinicalresearch across all the sites, including our international partners
in Barbados, Brazil, and Nigeria. The CCPM Biobank was asked
to pivot and leverage our infrastructure to commence with COVID
testing. Having a fully running biobank meant that we were able tocontinue with most of our CCPM activities during that period.

† SNP, single nucleotide polymorphism‡ GWAS, genome-wide association study

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In the research lab, which we call CARGO, we were allowed tofunction at full capacity because of our focus on developing anovel, methylation-based COVID-19 diagnostic platform.

"We also customized the
MethylationEPIC platform byadding another 7800 CpGs
representing genes that wethought would be relevant, notjust for SARS-CoV-2 infection,
but for infectious disease ingeneral, and we started calling
this the MethylationEPIC+
BeadChip."

Q: What sparked your interest in looking at methylation data inthe context of infectious disease?
KB: In the months leading up to the pandemic, our team at CCPM,
along with our Illumina partners, was considering how we might
introduce clinical methylation-based testing into the CCPM
Biobank, specifically for tumor classification and diagnostics. Thiswas really inspired by the interesting work that had been publishedaround developing classifiers and diagnostics of neuro tumors andother types of cancers. This was all done on the Illumina Infinium™
MethylationEPIC BeadChip.
Immediately after the shutdown, there was a GenomeWeb story
that came out about studying the epigenetics of COVID. The
consideration of generating disease classifiers using this methylation platform was fresh in my mind so I immediately called myclosest Illumina counterpart and I said, “Alem [Taye], we could do
this.” It seemed reasonable, if we could leverage the biospecimens.
That was one of the biggest challenges in the early months of thepandemic at every institution. There was a mad scramble to accessbiospecimens from COVID patients for research. There was sort ofa feeding frenzy around these samples and, to be honest, that wasa big challenge for us in thinking about how we could stand this up.
Initially, we focused on collecting leftover samples from nasal
pharyngeal samples collected for COVID PCR testing. But wetheorized, because it’s infectious disease, we should be able todo this in blood. Surprisingly, collecting blood turned out to bemuch easier than trying to capture these leftover samples. We alsodecided that the gold standard for confirmation of disease status ofthese patients would be conventional PCR. Through our health data
warehouse, we were able to track and confirm whether samples
that came into our research lab had a paired PCR test, and if it
was positive or negative. We also customized the MethylationEPIC
platform by adding another 7800 CpGs representing genes that
we thought would be relevant, not just for SARS-CoV-2 infection,
but for infectious disease in general, and we started calling this the
MethylationEPIC+ BeadChip.

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Q: Why would DNA methylation changes be associated with
COVID-19? And what does this mean for the host?
KB: There are studies out there that demonstrated that enveloped
RNA viruses, such as SARS-CoV-2, can manipulate the hostepigenome, specifically via DNA methylation. There was a lot ofevidence at the time, and more so now, that you can develop highly
accurate and robust machine learning–based disease classifiers
using DNA methylation patterns. While there’s been a lot of work
to study infectious disease at the level of gene expression, forexample, transcriptomics and germline susceptibility at the levelof the genome, we really felt like the methylome hadn’t been fully
exploited to the extent that it could be in this space. I think an
advantage of DNA methylation profiling over RNA transcriptome
profiling in developing classifiers for disease state, is in fact thestability of the deoxyribonucleic acid over ribonucleic acid, andjust the inherent instability of RNA. In addition to that, methylation
quantification, using a tool like the EPIC chip, is actually pretty
affordable at scale. Being able to study the relationship between
methylation patterns, in the presence or absence of a disease,is potentially as informative as the other types of diagnostics
platforms and we always imagine that this could potentially be
scaled in whole communities to really address the pandemic.
What’s really unique about this platform, and really inspired us tomove forward with the study, is if you think about the other diagnostic tests that were emerging rapidly at the time of the onset ofthe pandemic, sequencing, RT-PCR, or antibody tests, the methylation platform can also create these disease classifiers that can
predict outcome of disease, not just diagnosis.
One of our major findings is that DNA methylation profiling in
conjunction with analysis using machine learning techniques can,in fact, identify a SARS-CoV-2 specific epigenetic signature. We’ve
demonstrated this in blood. We also describe the developmentof a classification algorithm and we’ve been able to demonstratehigh sensitivity and specificity in predicting an infection, and also
in-hospital clinical deterioration. Our findings also suggest that themeasurement of methylation signatures that come up during andafter SARS-CoV-2 infection can provide clinicians with the ability
to detect viral infection, as well as predict patient clinical course,which is a real differentiator that would potentially enable clinicians
downstream to triage their patients with clinical decision support.
.

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"...the methylation platform
can also create these diseaseclassifiers that can predict
outcome of disease, not justdiagnosis. For us, being able
to predict a patient’s clinicalcourse after a viral challenge
is a real differentiator, andthat would potentially enableclinicians downstream to triage
their patients with clinicaldecision support."

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Q: Why did you choose the Illumina MethylationEPIC BeadChip?

"we used EWAS results tovalidate our findings fromthe process of these diseaseclassifiers...even though thisKB: I’ve been an Illumina user for a very, very long time, going backto the GoldenGate™ days. We embraced the Infinium chemistry
platform when it first came out. Initially focused on the GWAS
chips, but, of course, it’s interchangeable for the EPIC chip. In our
research space, we’ve been working with the EPIC chip for several
years. We had the infrastructure in place and the know-how andthe expertise to work with this platform. We’re very familiar withhow the platform has been developed and the selection of CpGs,
and we feel very comfortable with that. And absolutely, with theongoing relationship with Illumina, it was a very easy pivot for us.
§

proof of concept was limited

Q: How are you analyzing the data?

to a single RNA enveloped

KB: Machine learning is something near and dear to our hearts inthe clinical informatics space. We have quite a bit of expertise in
the center with faculty who routinely leverage machine learning,again around clinical informatics primarily. We use algorithms toparse data and we learn from it and make a determination, or we
make a prediction about the future state of any new data sets. So,
instead of the old-fashioned way of doing things, with a programmer hand-coding software with a specific set of instructions, inmachine learning the machines train using large amounts of data
and algorithms and that then gives it the ability to learn how to
perform the task. The programmer codes the algorithm used totrain the network instead of coding expert rules. And then thealgorithms, of course, improve their performance as the quantity
and the quality of the data are available and learning increases. It
builds a model from sample inputs and it uses that model to makepredictions based on subsequent data. We very much looked to thesuccess of leveraging the EPIC chip in the cancer world for creating
disease classifiers to differentiate tumors.

infectious agent, that is SARSCoV-2, it is certainly reasonable
that this technology could
be leveraged across otherrespiratory infectious diseases."

Q: How do you see the different omics being combined as wemove forward with infectious disease research?
KB: In thinking about the work that we’ve done with COVID,
certainly combining the methylation array with GWAS arrays has
proven to be a cost-efficient method. Combining the genomic datawith any of the other omics, through QTL mapping, is a powerful¶§ Including previous versions of the MethylationEPIC BeadChip, eg, the Infinium 450K
and 850K BeadChips¶ QTL, quantitative trait locus

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approach. In fact, in our paper1, we used EWAS results to validate
our findings from the process of these disease classifiers. You’relooking for an epigenetic signature. And I think, even though thisproof of concept was limited to a single RNA enveloped infectious agent, that is SARS-CoV-2, it is certainly reasonable that thistechnology could be leveraged across other respiratory infectious
diseases.
**

Q: What are the next steps for you?
KB: We want to expand the COVID methylation project to develop
disease classifiers that can identify asymptomatic individuals.
And we’re really focusing on our capabilities around differentiating SARS-CoV-2 from other respiratory viruses. We’ll continue tobuild our biospecimen repository. As this data set grows, there
will be a need to expand our partnerships. There’s a limit to what
an academic center can accomplish around really robust machinelearning, and artificial intelligence in general. We recognize theneed for industry partnerships and working with companies thathave built robust machine learning platforms already.

"Ideally, we will use this EPIC+
chip across our entire CCPM
biobank population, reallyfocusing on infectious disease.
We think this is somethingfeasible and novel, and wehave existing GWAS data on the
population already."

In terms of the next step for the center, we’d been having discussions about how we could implement MethylationEPIC into ourclinical biobank in the space of oncology, but now we’re thinking
much bigger. Ideally, we will use this EPIC+ chip across our entire
CCPM biobank population, really focusing on infectious disease.
We think this is something feasible and novel, and we have existing
GWAS data on the population already.

Reference
1.

Konigsberg IR, Barnes B, Campbell M, et al. Host methylation predicts SARSCoV-2 infection and clinical outcome. Preprint at www.researchsquare.com/article/rs-334297/v1. Posted March 22, 2021. Accessed September 8, 2021.

** EWAS, epigenome-wide association study

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Read the paper
Konigsberg IR, Barnes B, Campbell M, et al. Host methylation
predicts SARS-CoV-2 infection and clinical outcome. Preprint at
www.researchsquare.com/article/rs-334297/v1. Posted March 22,2021. Accessed September 8, 2021.

Learn more
Infinium MethylationEPIC BeadChip, www.illumina.com/products/
by-type/microarray-kits/infinium-methylation-epic.html

Methylation changes and COVID, www.illumina.com/content/dam/
illumina-marketing/documents/icommunity/methylation-covidbarnes-case-study-m-gl-00167.pdf

COVID-19 host risk and response, www.illumina.com/areas-of-interest/microbiology/infectious-diseases/host-genetics-immune-response.html

1.800.809.4566 toll-free (US) | +1.858.202.4566 tel | techsupport@illumina.com | www.illumina.com© 2021 Illumina, Inc. All rights reserved. All trademarks are the property of Illumina, Inc. or their respective owners.
For specific trademark information, see www.illumina.com/company/legal.html.
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