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View Larger Image David Ussery, Ph.D.

Sept. 26, 2022 | LITTLE ROCK The rapid spread of monkeypox is unlike the virus past outbreaks and may be a result of genetic mutations identified by University of Arkansas for Medical Sciences (UAMS) researchers.

Led by UAMS David Ussery, Ph.D., the UAMS team published its findings this month in the Journal of Applied Microbiology.

The team compared the genomes of the 2022 virus to monkeypox genomes from a 2017 outbreak in Nigeria, plus sequenced genomes from localized outbreaks in 1965 and 1970. None of the previous monkeypox variants spread beyond their place of origin in Africa.

The UAMS teams bioninformatics analysis using advanced genomic sequencing methods revealed 25 mutations, 14 of which appear to change protein function and bear further research, said Ussery, a professor in the College of Medicine Department of Biomedical Informatics and director of the Arkansas Center for Genomic and Epidemiology Medicine at UAMS.

At least one of the differences we found could be responsible for why the current virus is causing a pandemic while past strains of monkeypox viruses did not, he said.

The teams article notes that the current monkeypox virus outbreak is not only the largest known outbreak to date, the infections result in much different clinical and epidemiological features compared to previous outbreaks.

In July, the World Health Organization declared the monkeypox outbreak a global health emergency.

While the virus is not usually lethal, its genetic makeup is strikingly similar to smallpox, Ussery said, so health officials and researchers are monitoring it closely. Smallpox killed an estimated 300500 million people in the 20th century before a vaccine campaign eradicated the virus by 1979.

Monkeypox is 99.5% identical to smallpox, Ussery said. It is so closely related that if you are old enough to have been vaccinated for smallpox, you are likely protected against monkeypox.

The research teams findings are a starting point for additional investigation in the lab, he said. A follow-up study will be needed to identify the changed properties of the monkeypox virus and to test which mutations are responsible for the virus increased ability to spread.

Co-authors on the publication are:

Usserys work is supported in part by the National Institutes of Health (NIH), grant 1P20GM121293; the UAMS Translational Research Institute, which is funded by the NIH National Center for Advancing Translational Sciences, award UL1 TR003107; the National Science Foundation, award OIA-1946391; and the Arkansas Research Alliance.

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UAMS Researchers Find Changes in Monkeypox Genome That May Explain Its Recent Rapid Spread - UAMS News

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Improved pea reference genome and pan-genome highlight genomic features and evolutionary characteristics - Nature.com

EQS-News: BRAIN Biotech AG / Key word(s): Personnel/Strategic Company DecisionChange in the Executive Management Board of BRAIN Biotech AG, genome editing activities established under the Akribion Genomics brand 27.09.2022 / 07:30 CET/CESTThe issuer is solely responsible for the content of this announcement.

Lukas Linnig stepping down as CFO. Michael Schneiders to become new CFO of BRAIN Biotech AG as of October 1st, 2022

Lukas Linnig and Dr. Michael Krohn to jointly lead Akribion Genomics activities

Akribion Genomics established as new brand for the proprietary BRAIN genome editing platform technology

Zwingenberg, September 27, 2022 BRAIN Biotech AG has announced that current Chief Financial Officer Lukas Linnig is stepping down as CFO to lead Akribion Genomics, the newly established brand for the highly promising genome editing platform technology of BRAIN Group. Michael Schneiders, currently Head of Investor Relations & Sustainability, will be appointed as the new Chief Financial Officer of BRAIN Biotech AG.

Michael Schneiders has joined the company in May 2020 after more than twenty years in investment banking. He has also been involved in the initial public offering of BRAIN. The personnel changes in the executive management board of BRAIN Biotech AG will become effective as of October 1st, 2022. Linnig will be joined at Akribion Genomics by Dr. Michael Krohn, currently Head R&D BRAIN Biotech. Linnig and Krohn will jointly lead the activities under the brand name to fully capitalize on the great promise that genome editing represents to BRAIN and its customers. It is BRAINs stated intention to establish a separate legal entity for Akribion in the next year and to attract significant external growth funding for this venture.

Adriaan Moelker, CEO of BRAIN Biotech AG, says: Lukas Linnig and Michael Krohn have been essential in leading our genome editing activities over the last years, driven by the great scientific progress made by the R&D team. It is only logical that they will now become the appointed leaders to drive Akribion Genomics towards its stated goals. Michael Krohn will assume his leadership role as soon as his successor takes over the R&D leadership at BRAIN. I am also looking forward to working together with Michael Schneiders on the executive board of BRAIN Biotech AG. Michael has proven IR, financial and business development skills and a well-established capital market track record.

Dr. Georg Kellinghusen, Chairman of the Supervisory Board of BRAIN Biotech AG, says: "It is a very strong sign of confidence in the business potential of Akribion Genomics that the envisaged spin-out will be led by the high caliber team of Lukas Linnig and Dr. Michael Krohn. I am also happy that we have been able to capitalize on our internal succession planning with Michael Schneiders as the designated CFO of BRAIN Biotech."

Lukas Linnig states: It has been a great honor for me to serve BRAIN as its CFO. Leading Akribion, I will be focusing my attention on accelerating one of BRAINs most important value drivers with transformational potential. I am thrilled to take on this challenge of building a successful business in the genome editing market under the Akribion Genomics brand.

Dr. Michael Krohn, adds: I am convinced about the potential of our proprietary genome editing technology, especially since our unique mode of action enables us to offer a very valuable addition to the existing genome editing toolsets.

About BRAIN

BRAIN Biotech AG (BRAIN) is a leading European specialist in industrial biotechnology with a focus on nutrition, health and the environment. As a technology and solution provider, the company supports the biologization of industry with biobased products and processes. From contract research and development with industrial partners to the development of own disruptive incubator projects and customized enzyme products: BRAIN's broad, innovative biotech know-how and its agile teams are the key to success.

The German BRAIN Biotech AG is the parent company of the international BRAIN Group, which distributes B2B specialty products, including enzymes and bioactive natural products. The BRAIN Group has its own fermentation or production facilities in continental Europe, the UK and the USA, which complete the value chain within the Group with the associated biotechnological production know-how.

As a participant in the United Nations Global Compact, BRAIN Biotech AG is committed to aligning its strategies and activities with the universal principles on human rights, labor, environment and anti-corruption, and to actively promote common social goals. Our products and services directly target at least five of the UN SDGs.

Since its IPO in 2016, BRAIN Biotech AG has been listed in the Prime Standard of the Frankfurt Stock Exchange (ISIN DE0005203947 / WKN 520394).

Contact Investor Relations

Michael Schneiders

Head of Investor Relations & Sustainability

Tel.: +49 6251 9331-86

E-mail: mis@brain-biotech.com

Contact media

Dr. Stephanie Konle

PR & Corporate Communications

Tel.: +49 6251 9331-70

E-mail: stk@brain-biotech.com

Follow @BRAINbiotech on Twitter (https://twitter.com/BRAINbiotech) and on LinkedIn (https://www.linkedin.com/company/brainbiotech)

Disclaimer

This press release contains forward-looking statements. These statements reflect the current views, expectations and assumptions of the management of BRAIN Biotech AG and are based on information currently available to management.

Forward-looking statements are not guarantees of future performance and involve known and unknown risks and uncertainties. The actual future results of BRAIN Biotech AG and the BRAIN Group and developments concerning BRAIN Biotech AG and the BRAIN Group may therefore differ materially from the expectations and assumptions expressed herein due to various factors. These factors include, in particular, changes in the general economic situation and the competitive situation. In addition, developments on the financial markets and exchange rate fluctuations, as well as national and international legislative changes, particularly with regard to tax regulations, and other factors may have an impact on the future results and developments of BRAIN Biotech AG.

BRAIN Biotech AG assumes no obligation to update the statements contained in this release.

27.09.2022 CET/CEST Dissemination of a Corporate News, transmitted by EQS - a service of EQS Group AG.The issuer is solely responsible for the content of this announcement.

The EQS Distribution Services include Regulatory Announcements, Financial/Corporate News and Press Releases.Archive at http://www.eqs-news.com

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Change in the Executive Management Board of BRAIN Biotech AG, genome editing activities established under the Akribion Genomics brand - Yahoo Finance...

Washington [US], September 27 (ANI): Mutations can drastically help or hurt the odds of an organism surviving and reproducing. Researchers have created a computer program called ExtRaINSIGHT that tracks the history of harmful mutations throughout human evolution. Theyve discovered several regions of the genome are especially vulnerable to mutations, meaning any mutations there could lead to severe or lethal consequences.

Siepels program is called ExtRaINSIGHT. It finds harmful mutations by looking for their absence. By random chance, every part of the human genome should have mutations but some have none. Siepel calls these places ultraselected. When they occur, the mutations there can be deadly or drastically hurt the odds of reproducing. Siepel explains:

If we look across a panel of a hundred thousand humans and we never see a mutation at a particular gene, that suggests that any mutation that did occur was so harmful, that anyone carrying that mutation died out from the population.

The team analyzed over 70,000 human genomes with ExtRaINSIGHT. They discovered that three parts of the genome have been extremely sensitive to mutations over generations. Of these, splice sites are the most sensitive. Splice sites help produce correct instructions for making proteins. Mutations here can have a huge impact on the odds of passing down genes, also known as fitness. Theyre linked to several diseases including spinal muscular atrophy, the leading genetic cause of death in infants and toddlers. Siepel says:

If you see a mutation in a splice site, you better take it seriously. That mutation alone would reduce your fitness by 1 or 2%. That doesnt sound like very much, but thats a huge fitness effect. And if you had multiple of these, pretty soon your chance of passing on your genes might be close to zero.

Molecules called miRNA and central nervous system genes are also sensitive. If you find a mutation in miRNA theres a good chance its responsible for a genetic disease, Siepel says. And because the nervous system is so complex and interconnected, it seems particularly sensitive to mutation.

The origins of many genetic diseases and conditions remain a mystery. Siepel hopes technology like ExtRaINSIGHT will help reveal their origins and guide diagnoses and future treatments. He also hopes his work will help further illustrate how mutations continue to shape the evolution of the human genome. (ANI)

This report is auto-generated from ANI news service. ThePrint holds no responsibility for its content.

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Exposing the evolutionary weak spots of the human genome: Research - ThePrint

When is a pandemic over? Did biotech over-SPAC? And what cant be CRISPRd?

We cover all that and more this week on The Readout LOUD, STATs biotech podcast. Heidi Tworek, a professor at the University of British Columbia and expert on public health communication, joins us to discuss President Bidens declaration that the pandemic is over and how leaders around the world are talking about Covid-19 as it enters its third year. Well also discuss the latest news in the life sciences, including the twilight of the SPAC boom, the coming evolution of genome editing, and the next big trial in Alzheimers disease.

For more on what we cover, heres the reaction to Bidens remarks; heres more on CRISPR; heres a look at the next Alzheimers readout; heres where you can find episodes of Color Code; heres where you can subscribe to the First Opinion Podcast;and heres our complete coverage of the Covid-19 pandemic.

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Biden's Covid declaration, twilight of the SPAC, & genome editing 2.0 - STAT

The Genome Engineering Market Industry research forecast to 20222028 offers in-depth market information to help companies develop growth strategies and make better business decisions based on forecasts and market trends. The studys marketing variables include the dynamic market structure, key players product offerings, their difficulties, technical innovation, roadblocks and hurdles, data on communication and sales, sales by country, risk, prospects, competitive landscape, growth strategy, and others. It delves extensively into the situation of the market, both now and in the future. The study examines several elements, such as levels of development, technical advances, and the various business models employed by the markets current top players.

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Global genome engineering market is estimated to be valued atUS$ 5,205.60 millionin2022and is expected to exhibit aCAGRof14.3%during the forecast period(2022-2030).

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Major Key playersare : Thermo Fisher Scientific Inc., CRISPR Therapeutics AG, Intellia Therapeutics, Inc., Editas Medicine, Inc., Sangamo Therapeutics, Inc., Bluebird Bio, Inc., Cellectis S.A., and Merck Group.

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Market Dynamics

Increasing strategic collaboration for genome engineering technologies by key players is expected to drive market growth over the forecast period. Key players in market are focusing on strategic collaborations, in order to increase their product offerings. For instance, in February 2018, Kite Pharma, Inc., a Gilead Sciences, Inc. company, collaborated with Sangamo Therapeutics Inc. for developing engineered cell therapies to treat cancer. As per the agreement, Kite Pharma, Inc. would use Sangamo Therapeutics zinc finger nuclease (ZFN) gene-editing technology for developing next-generation ex vivo cell therapies for treatment of cancer. Furthermore, in 2017, Synthego and Thermo Fisher Scientific collaborated to manufacture and distribute synthetic guide ribonucleic acid (RNA) products for CRISPR genome engineering.

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Key features of the study:

Detailed Segmentation:

Table of Contents:

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The study analyses emerging market trends as well as the likelihood that different trends will have an impact on expansion.The analysis also covers the factors, challenges, and opportunities that will significantly affect the worldwide Genome Engineering industry.Technological tools and benchmarks that mirror the projected growth of the Genome Engineering industry.In order to provide futuristic growth estimates, the research includes a detailed analysis of market statistics and historical and present growth conditions.The research includes a comprehensive analytical overview of the competitive environment, as well as highlights on fundamental capabilities and growth plans of the profiled businesses.

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The predicted market size for the Genome Engineering Market Industry at the conclusion of the forecast period is shown in this market report.The paper also analyses market sizes in the past and present.The charts show the year-over-year growth (percent) and compound annual growth rate (CAGR) for the given projected period based on a variety of metrics.The research contains a market overview, geographical breadth, segmentation, and financial performance of main competitors.The research evaluates the current situation of the industry in North America, Asia Pacific, Europe, Latin America, the Middle East, and Africa,as well as future growth opportunities.The study examines the future periods growth rate, market size, and market worth.

What our reports offer:

Market share assessments for the regional and country-level segments Strategic recommendations for the new entrants Covers market data for 2021, 2022, till 2028 Market trends (drivers, opportunities, threats, challenges, investment opportunities, and recommendations) Strategic recommendations in key business segments based on the market estimations Competitive landscaping mapping the key common trends Company profiling with detailed strategies, financials, and recent developments Supply chain trends mapping the latest technological advancements

Examine market data, tables, and figures in detail. The most recent independent research report on a wide range of market development strategies and business approaches, including product and service development, joint ventures, partnerships, mergers and acquisitions, and so on. Market company profiles comprise Business Overview, Product / Service Offerings, SWOT Analysis, Segment & Total Revenue, Gross Margin, and% Market Share in order to provide a more complete picture. This Genome Engineering study examines market definitions, an overview, a classification, and segmentation, including market type and applications, before moving on to product details, production plans, pricing schemes, raw material sourcing, and supply chain analysis.

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Genome Engineering Market Report 2022 with Leading Key Players and Regional Analysis 2028 | Thermo Fisher Scientific Inc., CRISPR Therapeutics AG,...

NEW YORK Researchers have uncovered shared genetic liability between autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD), but also genetic distinctions between the conditions.

ASD and ADHD are common neurodevelopmental disorders affecting children that may last into adulthood. They are also highly heritable and polygenic, and previous studies have suggested the two conditions are genetically correlated.

To look into just which genetic variants are shared by the conditions, an international team of researchers led by Aarhus University's Anders Brglum conducted genome-wide association studies using the Psychiatric Genomics Consortium (PGC) and the Lundbeck Foundation for Integrative Psychiatric Research (iPSYCH) cohorts. As they reported in Nature Genetics on Monday, they found seven loci shared by ASD and ADHD but also five loci that differentiate them.

"[W]e have disentangled the shared and differentiating genetic liability underlying ASD and ADHD, identifying shared and disorder-specific risk variants providing information on pathophysiology," Brglum, director of the iPSYCH program, and colleagues wrote in their paper.

The researchers conducted a combined genome-wide association study of diagnosed ASD or ADHD in a cohort of 34,462 cases and 41,201 controls. After identifying more than 260 SNPs in seven distinct loci, the researchers conducted a transcriptome-wide association study to home in on putative causal shared genes. This identified five genes or isoforms that were differentially expressed between the case and control groups. Further gene-based analysis using the tool MAGMA confirmed those TWAS findings and identified two additional shared loci, for a total of seven genetic loci shared between ASD and ADHD.

These shared loci tended to be pleiotropic and had been identified in previous GWAS of related disorders or cross-disorder studies, the researchers noted.

Meanwhile, to identify loci that differ between ASD and ADHD, the researchers performed an ADHD versus ASD GWAS using 9,315 ASD-only and 11,964 ADHD-only cases from the iPSYCH cohort. This analysis uncovered five genome-wide significant loci, three of which had not previously been tied to either ASD or ADHD.

Those loci, though, have been linked to related disorders or traits, particularly cognitive traits. Four of the loci, for instance, have been associated with cognitive ability or neuroticism, and two with educational attainment. The lead variants further exhibited opposite direction of effect in the two conditions.

In a TWAS, the top gene or isoform identified that differentiated the disorders was HIST1H2BD-201, located on chromosome 6. Deleterious or de novo variants in histone-modifying or histone-related genes have previously been linked to autism and developmental delay with features of autism. Here, the researchers found that the expression of HIST1H2BD-201 was lower in ASD compared to ADHD. Further, the ASD risk allele of the lead SNP there was linked to better educational performance and increased volume of the left globus pallidus in the brain.

The researchers proposed that the ADHD-ASD differentiating locus on chromosome 6 affects the expression of HIST1H2BD-201 and left globus pallidus volume and, in turn, traits like educational performance, social interaction, and motor impairments. This pushes the disorder presentation toward ASD or ADHD.

The researchers additionally conducted a GWAS of 2,304 individuals diagnosed with both ASD and ADHD, as well as a polygenic risk score-based analysis. They found that these comorbid cases had an ASD-PRS load similar to that of ASD-only cases as well as an ADHD-PRS load similar to that of ADHD-only cases, indicating they are "double-loaded" with genetic predispositions for both disorders. This provides support, they noted, for the recent change in diagnostic guidelines allowing for the diagnosis of both ASD and ADHD in the same individual.

"The results advance our understanding of the complex etiologic basis of ASD and ADHD and the relationship between the two disorders, toward the long-term goals of better diagnosis and treatment of these disorders," the authors wrote.

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Autism, ADHD Found to Have Overlapping and Distinct Genetic Contributions - GenomeWeb

ALAMEDA, Calif.--(BUSINESS WIRE)--Scribe Therapeutics Inc., a molecular engineering company pioneering a CRISPR by Design platform for genetic medicine, today announced a strategic collaboration with Sanofi for the use of Scribes CRISPR genome editing technologies to enable genetic modification of novel natural killer (NK) cell therapies for cancer.

The agreement grants Sanofi non-exclusive rights to Scribes proprietary CRISPR platform of wholly owned enzymes to create ex vivo NK cell therapies. Scribes suite of custom engineering genome editing and delivery tools called CasX-Editors (XE), based on novel foundations such as the CasX enzyme, will support Sanofis expanding pipeline of NK cell therapeutics for oncology.

Were pleased to provide Sanofi with access to Scribes proprietary and enhanced gene editing technologies for use in ex vivo oncology applications distinct from our current pipeline, said Benjamin Oakes, Ph.D., co-founder and CEO of Scribe. Scribe is proud to expand the use of our XE CRISPR technologies with the team at Sanofi, whose commitment to deep scientific rigor and clinical development experience will enable the rapid advancement of novel ex vivo cell therapies for patients in need.

At Sanofi, we are pushing the boundaries of science by developing a diverse range of next-generation therapies based on natural killer (NK) cells, which could have broad applications across solid tumors and blood cancers, said Frank Nestle, Global Head of Research and Chief Scientific Officer, Sanofi. This collaboration with Scribe complements our robust research efforts across the NK cell therapy spectrum and offers our scientists unique access to engineered CRISPR-based technologies as they strive to deliver off-the-shelf NK cell therapies and novel combination approaches that improve upon the first generation of cell therapies."

Deal Terms

Under the terms of the agreement, Scribe will receive $25 million in upfront payment and be eligible to potentially receive more than $1 billion in payments based on development and commercial milestones, as well as tiered royalties on net future sales on any products that may result from this research agreement.

About Scribe Therapeutics

Scribe Therapeutics is a molecular engineering company focused on creating best-in-class in vivo therapies that permanently treat the underlying cause of disease. Founded by CRISPR inventors and leading molecular engineers Benjamin Oakes, Brett Staahl, David Savage, and Jennifer Doudna, Scribe is overcoming the limitations of current genome editing technologies by developing custom engineered enzymes and delivery modalities as part of a proprietary, evergreen CRISPR by Design platform for genetic medicine. The company is backed by leading individual and institutional investors including Andreessen Horowitz, Avoro Ventures and Avoro Capital Advisors, OrbiMed Advisors, Perceptive Advisors, funds and accounts advised by T. Rowe Price Associates, Inc., funds managed by Wellington Management, RA Capital Management, and Menlo Ventures. To learn more about Scribes mission to engineer the future of genetic medicine, visit http://www.scribetx.com.

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Scribe Therapeutics Announces Research Collaboration with Sanofi to Accelerate Breakthrough CRISPR-based Cell Therapies for Cancer - Business Wire

Modelling random DNA fragmentation

To begin our study, we required a model that would accurately reflect the properties of a stochastically fragmented DNA sample. The odds that a region targeted by a PCR assay will be interrupted by a DNA breakage in randomly fragmented DNA depend on the length of the region and the size of the fragments. These odds are effectively determined by establishing two adjacent fragment-sized sliding windows (wherein the end of one fragment is the start of another) and calculating the number of times a region is fully within the first fragment window, compared to the number of times the region is situated within both windows (Fig.1).

Diagram depicting example calculation of the proportion of intact copies of a target region (4bp) given a single specified fragment length (6bp). This calculation can be viewed as the probability that a region will not be cleaved at any point along its length if a genome were broken into equal length fragments. The fragment-sized Window 1 sliding across this region depicts all possible fragmentation states for this region. The intact proportion is calculated as the number of states where the region remains entirely within the fragment window over the total number of possible fragmentation states. Window 2 demonstrates that all possible states are represented at the point before the region fully exits Window 1, as these states are then repeated in this adjacent window.

This model is represented in Eq.(1), which determines the probability that a region of DNA will remain unbroken for a given fragment length:

$${text{proportion}};{text{intact}} = frac{{{text{f }}{-}{text{ r }} + { }1}}{{text{f}}},$$

(1)

where r is the length of the DNA region and f is the length at which the DNA is fragmented. However, DNA samples do not fragment at a single length but rather as a distribution, and by incorporating size distribution profiles, which contain the concentration of DNA at each fragment length, the proportion of intact target regions within a fragmented DNA sample can be calculated, as detailed in Eq.(2):

$${text{proportion}};{text{intact }} = frac{{mathop sum nolimits_{{f = r{ }}}^{n} frac{{{text{f }}{-}{text{ r }} + { }1}}{{text{f }}}{ }C_{f} }}{{mathop sum nolimits_{{f = m{ }}}^{n} C_{f} }},$$

(2)

where n is the length of the longest fragment within the sample, m is the length of the shortest fragment, and Cf is the concentration of each fragment length (i.e., pg/l).

We next sought to design qPCR and ddPCR assays that could be used to interrogate DNA fragmentation. A major focus of this assay design was to incorporate design elements that would enable the assays to be used on clinical cancer samples, as these samples are some of the most common types to undergo stochastic fragmentation. However, cancer samples are also prone to chromosomal amplifications and deletions within the genome16,17,18, and PCR assays that intersected with frequently amplified/deleted regions would result in inaccurate measures of concentration when these copy number aberrations (CNAs) occurred (i.e., the concentration of a region that is unique in the human reference genome is assumed to correspond to the overall number of genome copies within the measured sample). To control for this, we undertook an analysis to determine the regions of the human genome that were least affected by CNAs. CNA data that had been tested for statically significant gain or loss was retrieved from the Catalogue of Somatic Mutations in Cancer (COSMIC release v78)19,20. This data was filtered to exclude cell line samples and samples missing total copy number or minor allele values. Only 27 of the 10,637 samples remaining after this filtering were not derived from The Cancer Genome Atlas (TCGA) data21. We, therefore, opted to exclusively use these 10,610 TCGA samples to better ensure a dataset with experimental and analytical consistency in determining copy number changes (S1 Table).

After filtering out regions that were not covered by Affymetrix copy number probes (e.g., centromeres) the only regions completely devoid of CNAs were telomeric and likely artefactual. Outside of telomeres the minimum CNA region contained 5 samples. To determine a reasonable threshold for low copy number variation that might provide us with enough region space to meet the requirements of our assay design, we calculated the number of samples with CNAs in commonly used copy number reference genes. We found that the Human TaqMan Copy Number Reference Assays targeting RNase P and TERT offered by Applied Biosystems had CNAs in 61 and 360 of the total 10,610 samples, respectively, and the well-established standard reference gene RPP30 had CNAs in 23 samples. Based on this we set a threshold at the bottom 10th percentile of regions, excluding those where greater than 34 samples had significant copy number variation (Fig.2A). After applying this filter, we were left with 621 megabases across 858 non-contiguous regions on 22 chromosomes.

Design and performance of PCR assays against copy-neutral regions in the genome. (A) Circos plot depicting the percent of samples that undergo copy number aberration (CNA) in cancer. Chromosomes are shown in the outermost ring and include an overlay of cytogenetic Giemsa banding and centromeres marked with a red band. The second outermost ring shows the 946,615 Affymetrix Genome-Wide Human SNP Array 6.0 copy number probes used for the detection of CNAs by the TCGA. The final layer is a histogram displaying the number of samples that underwent statistically significant CNA (either loss or gain) within each region. Each grid line represents 1% of the 10,610 total samples. Universal assays were designed to target regions in the bottom 10th percentile of CNAs, excluding regions that are not covered by the Affymetrix CNV probes (e.g., centromeres). Less than 35 of the 10,610 samples (<0.33%) have CNAs in these regions, represented on the histogram as a dotted white line (above which regions were excluded). (B, C) Standard curves estimating amplification efficiencies of universal quantitation assays in 4-plex qPCR on gDNA (B) and bisulfite-converted DNA (C). Curves are artificially offset for better visualisation. E=efficiency.

We next designed a single-tube 4-plex quantitative PCR assay targeting these CNA neutral regions, which included a variety of design considerations to maximize the utility of the assay and minimize confounding effects. First, each assay would target a separate chromosome to minimize inaccurate quantification due to the remote possibility that one of the chromosomes, or at least a large portion, may be affected by CNAs. Given the size and number of regions, the second design consideration was identifying assay regions that would be unaffected by bisulfite conversion treatment, since the bisulfite conversion process is used to examine DNA methylation and is a common application in cancer genomics but also leads to substantial sample fragmentation and loss. To address this design consideration the CNA neutral regions were further analysed to identify primer and probe regions that were cytosine-free and would, therefore, be unaffected by the bisulfite conversion process. Notably, use of the assays on bisulfite material requires an extra step in qPCR data analysis to correct for the fact that only one DNA strand is quantified, resulting in a positive shift of 1 cycle threshold when compared to the unconverted genomic DNA (gDNA) counterpart.

The third design criterion was to enable assessment of the degree of sample fragmentation using this 4-plex assay. To achieve this, two of the assays were designed to be 125bp in length, and two were designed to be 175bp long. By taking the ratio of concentrations for the long to short assays, a quantitative metric for sample fragmentation can be imputed for any sample.

Finally, we sought to establish the combination of fluorescent probe chemistries that would enable successful multiplexing quantitation using either standard qPCR or ddPCR. In qPCR four different probe fluorophores (FAM, HEX, Cy5 and Texas Red) were used, whereas ddPCR 4-plex was achieved using a method developed by Dobnik et al. (2016)22 that uses two FAM probes and two HEX probes and varies probe concentrations to alter the resulting levels of fluorescence amplitude, allowing for the detection of two targets per fluorescence channel (S1 and S2 Figs).

After all these design criteria were successfully implemented, we next undertook experiments to verify the amplification fidelity and efficiency of each of the four assays. The fidelity of the assays was established by performing standard PCR and qPCR on a variety of sample types (buffy coat DNA, cfDNA, and bisulfite-converted DNA) and analysing the PCR products by standard DNA gel electrophoresis to confirm that only a single PCR amplicon was produced in singleplex (S3 Fig), and that multiplex assays produced only two bands of the expected sizes (S4 and S5 Figs). Next, the amplification efficiencies of all assays were determined using LinRegPCR window-of-linearity analysis23, and standard titration curves; this was done for all four amplicons in both singleplex and multiplex configurations, for both genomic and bisulfite-converted DNA, using both fluorescent dye and PrimeTime qPCR probes in multiple fluorophore configurations (Fig.2B,C, Table 1). Notably, all assays demonstrated>90% amplification efficiency across all conditions, indicating robust performance. Primer and probe sequences can be found in S2 Table.

The [long]/[short] ratios of any two target region lengths can be determined by applying the following equation to fragment size distribution data (Eq.3):

$${text{[long]/[short]}} = frac{{mathop sum nolimits_{{f = b{ }}}^{n} frac{{{text{f }}{-}{text{ b }} + { }1}}{{text{f }}}{ }C_{f} }}{{mathop sum nolimits_{f = s}^{n} frac{{{text{f }}{-}{text{ s }} + { }1}}{{text{f }}}{ }C_{f} }},$$

(3)

where b is the length of the longer region and s is the length of the shorter region.

To evaluate how well our model of stochastic fragmentation fit with experimental results we compared [175bp]/[125bp] ddPCR and qPCR ratios with those derived using Eq.(3) on Agilent 2100 Bioanalyzer fragment size concentration data. This analysis was performed on seven levels of increasing fragmentation induced by the ultrasonication of pooled buffy coat gDNA. The ddPCR and qPCR [175bp]/[125bp] ratios of our sonicated samples both showed high goodness-of-fit for ratios derived using Eq.(3), with R-square values of 0.995 and 0.989 for ddPCR and qPCR, respectively (Fig.3A,B).

Modelling and quantifying randomly fragmented DNA. (A) Table showing DNA samples sonicated to different fragment lengths, their fragment distribution profiles in electropherogram and pseudo-gel form, and comparison between the theoretically (Eq.3 applied to Bioanalyzer data) and experimentally determined [175bp]/[125bp] differential amplicon length ratios. The full unedited pseudo-gel image for these sonicated samples can be found in S6 Fig. (B) Line graph plotting the [175bp]/[125bp] ratios determined by qPCR, ddPCR, and our mathematical model applied to fragment size distribution data (Bioanalyzer) on differentially fragmented DNA samples. (C) Comparison of nucleic acid quantification methods on fragmented DNA. PCR data points are averages of the two universal assays for each amplicon length per well. Fluorometric (Qubit) and absorbance (Nanodrop) spectroscopy measurements were made on each sample on three separate occasions. Spectroscopy concertation measurements are depicted in ng/l and PCR as copies/l. Axes are scaled so that 1 copy=3.5pg.

Quantification of DNA samples affects all subsequent experimental steps and can lead to costly experimental failures if this step is not performed accurately. Therefore, to further extend our study we next compared the effects of fragmentation on nucleic acid quantification techniques using our sonicated DNA samples, referred to here by their peak (modal) fragment sizes: 150, 195, 283, 694, 828, 1082, and 1504bp.

One overlooked aspect of DNA fragmentation is that it results in fewer adjacent base pairs for fluorescent DNA dyes to intercalate when dye-based fluorometric methods are used. Thus predictably, and as other studies have noted1,2, the mean DNA concentration measured by fluorescence spectroscopy (Qubit 2.0) decreased with increasing fragmentation (p<0.001; one-way ANOVA), with untreated gDNA measuring at 50.40ng/l (SD=0.72), and the most fragmented sample (150bp) at 35.27ng/l (SD=2.14), which calculate to 14,400 (SD=206) and 10,100 (SD=613) genome copies, respectively, assuming 1 genome weighs 3.5pg based on the following formula:

$$begin{aligned} {text{Amount}} left( {{text{pg}}} right) & = frac{{{text{length }}left( {{text{bp}}} right)*{text{pg}}/{text{g}}*{text{weight}};{text{of}};{text{bp}} left( {{text{g}}/{text{mole}}/{text{bp}}} right)*{text{copies }} left( {{text{molecules}}} right)}}{{{text{Avogadro's}};{text{number }} left( {{text{molecules}}/{text{mole}}} right)}} \ Amount left( {pg} right) & = frac{{3,234,830,000* 10^{12} *650*1}}{{6.022*10^{23} }} \ end{aligned}$$

(4)

For absorption spectroscopy (Nanodrop 1000), the mean measurement for intact gDNA was 68.40ng/l (SD=1.97), which calculates to 19,600 (SD=563) genome copies. Although there was no dose-dependent trend towards decreasing concentration with increasing fragmentation, a one-way ANOVA did show a significant difference in concentration (p<0.001), and a Tukey's HSD test found the concentration of intact gDNA to be significantly higher than all seven levels of fragmentation (p<0.001). The highest mean concentration measured for the fragmented gDNA was 63.10ng/l (SD=0.79; 150bp) and the lowest was 57.43ng/l (SD=0.32; 283bp), which calculate to 18,100 (SD=226) and 16,400 (SD=92) genome copies, respectively.

Both qPCR and ddPCR measured substantial downward trends in concentration with increasing fragmentation (Fig.3C). This decline in amplifiable copies with increasing fragmentation reflects an increasing number of breakages in the targeted regions, the magnitude of decline being greater for the 175bp amplicon as longer target regions are more likely to be cleaved. ddPCR on the intact gDNA measured 18,984 (SD=765) and 19,058 (SD=608) mean copies for the two 125bp assays and 18,905 (SD=308) and 19,306 (SD=246) for the two 175bp assays.

The mean absorbance spectroscopy estimate for the number of genome copies in our intact gDNA sample was only 2.8% greater than the combined mean of the four ddPCR assays (M=19,063, SD=150). Whereas, the mean number of genome copies estimate for fluorescence spectroscopy was 25% lower, suggesting this method also underestimated intact, not just fragmented, DNA concentration. Our results, therefore, show that absorbance spectroscopy is the most accurate method for quantifying overall nucleic acid concentration, regardless of the degree of fragmentation. However, this technique lacks sensitivity and becomes increasingly inaccurate at the lower end of its analytical range (15ng/l)24. Absorbance spectroscopy is also highly susceptible to reporting falsely high concentrations due to protein contamination and/or phenolic compounds that absorb UV. PCR-based quantification is highly sensitive and most accurately measures the amount of amplifiable DNA at the amplicon length used. Our universal multiplex assay and accompanying online tool Fragment Calculator, which we detail in the following section, extends this ability to estimate the amount of amplifiable DNA of any given region length, while also providing an estimate of overall concentration when working with human genomic or bisulfite-converted DNA.

In addition to describing the fragmentation of the sample, the dual 175 and 125bp assays, combined with representative DNA samples, can also be leveraged to estimate the concentration of any other sized DNA region. To better enable this we designed the Fragment Calculator online tool to provide a more quantitative and actionable estimate of fragmentation (www.primer-suite.com/fragcalc). This tool uses measured 175bp and 125bp concentrations and the [175bp]/[125bp] ratio to estimate the average fragment length of a genomic or bisulfite-converted human DNA sample, the total number of genome copies in a measured sample, as well as the number of amplifiable (unbroken) instances of a DNA region of any length. This tool uses the fragment size distribution data of our seven sonicated DNA samples with average fragment lengths of 254, 291, 428, 493, 590, 745, and 1274bp, a highly fragmented FFPE DNA sample with an average fragment length of 92bp to represent the lower bounds of random fragmentation, and four gDNA samples with average fragment lengths of 6714, 15,422, 34,625 and 41,496bp for the upper bounds (S1 File).

The number of intact copies of an input DNA region length is estimated by taking the two [175bp]/[125bp] ratios from our representative fragment size distribution data that an input [175bp]/[125bp] ratio falls between (x1,x2), calculating the corresponding [125bp]/[input size] ratios using Eq.(3) on these size distribution data (y1, y2), determining the slope between these points to estimate the [125bp]/[input size] ratio corresponding to the input [175bp]/[125bp] ratio, and dividing the 125bp concentration by this ratio. For example, if the concentration measured for a fragmented DNA sample is 1000 copies for the 125bp amplicon and 700 copies for the 175bp amplicon, the input [175bp]/[125bp] ratio is 0.7, which falls between the [175bp]/[125bp] ratios of the 291bp (0.669) and 428bp (0.778) reference samples. To estimate the concentration of a 50bp region, for example, the corresponding [125bp]/[50bp] ratios determined using Eq.(3) are 0.585 and 0.707, for the 291bp and 428bp reference samples, respectively. The 50bp concentration is then calculated using the following linear equation:

$${text{y}} = mx + y_{0} ,$$

(5)

$$m = frac{{y_{2} - y_{1} }}{{x_{2} - x_{1} }}$$

$$m = frac{{0.707 - 0.585{ }}}{0.778 - 0.669}$$

$$[125{text{bp}}]/[50{text{bp}}] = 1.119*0.7 - 0.164,$$

$$[50{text{bp}}] = frac{{[125{text{bp}}]}}{{[125{text{bp}}]/[50{text{bp}}]}},$$

$$[50{text{bp}}] = frac{{1000 ;{text{copies}}}}{0.619},$$

$$[50{text{bp}}] = 1615 ;{text{copies}},$$

where m is the slope and y0 is the y-intercept. The number of genome copies is also estimated using this same method by dividing the input 125bp concentration by the [125bp]/[1bp] ratio. Similarly, the average fragment length is estimated using the [175bp]/[125bp] ratios from our fragment size distribution data (x1,x2) and their corresponding average fragment lengths (y1, y2) (Fig.4).

Fragment Calculator online tool with example inputs. The concentrations measured by the two amplicon sizes of our universal quantitative PCR 4-plex assay (125bp and 175bp) can be used to estimate the total concentration (i.e., the number of genomic copies), average fragment length of the sample, and the concentration of intact copies of any input region size.

Importantly, Fragment Calculator assumes fragment distributions for the samples being estimated to be similar to those of our representative samples. However, in our experience working with these assays, we have found FFPE samples do not behave like untreated DNA samples. The [175bp]/[125bp] ratio for FFPE samples is generally much lower than the ratio calculated from the size distributions of these samples using Eq.(3). This reveals that there is generally less amplifiable DNA in FFPE samples than their size distribution profiles suggest, which we hypothesise is likely due to a combination of single-stranded breaks and incomplete reversal of DNA crosslinking. Our assays are, therefore, a better indicator of the amount of amplifiable FFPE treated DNA than fragment size distribution data from microfluidic capillary electrophoresis instruments like the Agilent 2100 Bioanalyzer.

Further complicating this, however, is evidence that even regions of the same length can have substantially different concentrations of amplifiable FFPE treated DNA. Some of our routine quality control and quantification analyses of FFPE treated samples have revealed vast differences in the number of copies measured by the two 125bp assays, and these differences are consistent among numerous FFPE samples (S2 File). Despite assays having the same length amplicons, differences in the number of amplifiable copies are likely to occur at high degrees of fragmentation, for instance, due to differences in binding efficiencies among primers when their target regions are truncated. Indeed, we regularly observe statistically significant differences in the number of copies measured by assays of the same size in highly fragmented pooled buffy coat gDNA samples subjected to ultrasonication, some examples of which are forthcoming. However, these differences are relatively small in magnitude and may be due to sequence-specific biases in sonication-induced scission25,26. We hypothesise that the much greater differences we observe in FFPE samples may emerge due to differences in the degree to which crosslinking is reversed among regions, as well as potential differences in their susceptibility to DNA breakage. These differences may reflect an underlying nucleosome footprint given that formaldehyde cross-linking is more efficient in nucleosome-bound DNA, as evidenced by the FAIRE-Seq (Formaldehyde-Assisted Isolation of Regulatory Elements) technique27.

Since PCR-based assays that target both genomic and bisulfite-converted DNA provide more accurate measures of bisulfite conversion recovery than other quantification techniques28, we next assessed the performance and utility of our universal multiplex assay to compare the recovery and degree of fragmentation of three commonly used commercial bisulfite conversion kits (MethylEasy Exceed, EZ DNA Methylation-Gold, and EZ DNA Methylation-Lightning) across three starting concentrations (500, 50 and 5ng) using high molecular weight (HMW) gDNA.

A three-way ANOVA on the qPCR results found significant effects of starting concentration (p<0.001), assay (p<0.001), and conversion kit (p<0.001) on recovery (Fig.5A). Additionally, a significant interaction was found between starting concentration and kit (p<0.001), resulting from an increase in recovery with decreasing concentration in MethylEasy Exceed but a decrease in EZ DNA Methylation-Gold and EZ DNA Methylation-Lightning. Trends were similar for the 125bp and 175bp assays, except in MethylEasy Xceed where the proportional increase in mean recovery between 50 and 5ng was greater in 125bp assays (22%, SD=12 vs. 32%, SD=5) compared to the 175bp assays (16%, SD=10 vs. 20%, SD=5; Fig.5B). As for fragmentation, a two-way ANOVA found a significant effect of conversion kit on the [175bp]/[125bp] ratio (p<0.001), no significant effect of starting concentration (p=0.251), but a significant interaction between kit and concentration (p=0.027) arising from a decrease in the [175bp]/[125bp] ratio of MethylEasy Xceed with decreasing starting concentration.

Universal assay comparisons of DNA recovery and fragmentation by bisulfite conversion kits. (A) Recovery and fragmentation across different starting concentrations as measured by universal quantitation assays in 4-plex qPCR. (B) Plots comparing the recovery and fragmentation trends from qPCR data across decreasing starting concentrations. Recovery data points are averages of the two universal assays for each amplicon length and these values were divided to determine the [175bp]/[125bp] ratios. (C) Recovery and fragmentation measured by ddPCR 4-plex. Also includes results from in-house bisulfite protocol. (A, C) Each conversion was conducted in six replicates per concentration for each kit. [175bp]/[125bp] fragmentation ratios were calculated by dividing the average copies of the two 175bp assays by the average of copies the two 125bp assays. Error bars represent one standard deviation.

Due to the low starting concentration and recovery of the 5ng samples, we did not have enough sample left for ddPCR analysis and therefore only ran the 500ng and 50ng samples. In addition to the three commercial kits, we also included our in-house bisulfite conversion protocol in these ddPCR comparisons (Fig.5C). A three-way ANOVA showed similar results to the qPCR analysis, with significant effects of starting concentration (p<0.001), assay (p<0.001), and conversion kit (p<0.001) on recovery, and a significant interaction between kit and concentration (p=0.001). Similar to qPCR, this interaction resulted from declines in the mean recovery of similar proportions between 500 and 50ng in all kits except MethylEasy Xceed, which showed a mild increase (13%, SD=4 vs. 16%, SD=11). A two-way ANOVA found a slight statistically significant difference in the [175bp]/[125bp] ratios among conversion kits (p=0.033), which a Tukey's HSD test showed resulted from a significant difference (p=0.048) between DNA Methylation-Lightning (M=0.83, SD=0.05) and MethylEasy Xceed (M=0.75, SD=0.07). Our in-house method and DNA Methylation-Gold had mean ratios of 0.77 (SD=0.04) and 0.81 (SD=0.07), respectively. To estimate the absolute nucleic acid recovery and average fragment size after bisulfite conversion we used our Fragment Calculator tool on combined qPCR and ddPCR results (Table 2).

Snyder et al. (2016)11 identified nucleosome protection peaks using deep sequencing of pooled cfDNA samples. Implicit in these analyses is the fact that nucleosome position correlated with the enrichment of fragments at specific locations, which could only occur if nucleosome positions were at least somewhat conserved among people. However, it was unclear the extent to which these peaks might shift between individuals. If little movement occurs and peaks are instead universally conserved, this would have important implications for assay design. Targeting such peaks would maximise an assays sensitivity in cfDNA while failing to consider nucleosome protection could severely reduce sensitivity.

Snyder et al. (2016)11 calculated a Windowed Protection Score (WPS) for each nucleotide position within the mappable human genome by summing the number of sequenced 120180bp cfDNA fragments that wholly overlap a centred 120bp window and subtracting the number that truncate within this window. Peaks in nucleosome-mediated protection were then called by identifying contiguous regions of elevated WPS. Using the nucleosome protection peaks determined for the pooled healthy sample CH01, we designed two cfDNA assays targeting nucleosome protection peaks that could also be used for bisulfite-converted DNA material: a 95bp assay targeting chromosome 2 (cfUQ02) with an above-average WPS of 108 and maximum distance of 62bp from the local maxima, and a 100bp assay targeting chromosome 11 (cfUQ11) with a below-average WPS of 30 and a maximum distance of 56bp. The mean WPS of the nearly 13 million peaks identified in the CH01 sample is 63.7 (SD=41.4). We also designed several staggered PCR assays of varying lengths to flank each of these regions.

15 cfDNA samples isolated from the blood plasma of breast cancer patients were profiled using dye-based ddPCR to compare the number of amplifiable copies of our universal cfDNA assays along with these staggered assays. We observed that some samples displayed substantial differences in amplifiable copies among assays whereas others did not and that this appeared to coincide with the technique used for cfDNA isolation. We measured the fragmentation profiles of these samples and found 6 displayed characteristic~166 peaks with no sign of HMW contamination, which we thus classified as true cfDNA (Fig.6A), 6 had little to no cfDNA peak and were reclassified as contaminating HMW DNA (Fig.6B), and 3 had strong cfDNA peaks but also possible or likely contamination by HMW DNA and were excluded from analysis (S7 Fig). Although high levels of HMW DNA can occur in cfDNA due to non-apoptotic cell death (e.g., necrosis), we suspect the source in these samples was instead the result of poor plasma separation and extraction. Regardless of its source, we only expect to find nucleosome-mediated patterns of fragmentation in the DNA of apoptosed cells, and HMW DNA is likely to obscure these patterns.

Effects of amplicon length and distance from nucleosome protection peak on intact copies in cfDNA. (AB) Electropherograms and pseudo-gel images from Agilent 2100 Bioanalyzer with a High Sensitivity DNA Chip (2100 Expert version B.02.10.SI764). DNA samples are from plasma of breast cancer patients, except sample labelled sDNA which is a pooled buffy coat gDNA sample sonicated and gel-purified to produce a similar fragment size distribution to cfDNA. Samples classified as true cfDNA samples (A) were isolated using our in-house phenolchloroform method (14) and QIAamp Circulating Nucleic Acid Kit with EconoSpin All-In-One Mini Spin Columns (Epoch Life Sciences) instead of columns supplied with the kit (56). Samples classified as contaminating buffy coat DNA (B) were isolated using QIAamp Circulating Nucleic Acid Kit (711) and in-house phenolchloroform method (12). (CE) Plots of ratio to mean copies (assay/sample mean) against amplicon length (C) and against distance from nucleosome protection peak (D) for assays targeting chr11 nucleosome protection peak locus, and against distance from nucleosome protection peak for assays targeting chr2 locus (E). Box and whisker plots are centred above corresponding amplicon positions for each assay, along with cell line data of nucleosome signal (K652 and GM12878) from Kundaje et al. (2012)29 and nucleosome protection peak position (blue tick) from Snyder et al. (2016)11, adjoined by characteristic 146bp nucleosomal DNA length (blue) and 10bp linker DNA (red). Sample numbers for each sample type are gDNA=15, buffy coat DNA=6, breast cancer cfDNA=6, and sonicated DNA=1 (four technical replicates). (F-G) Box and whisker plots for chr11 102bp/56bp and chr2 142bp/62bp differential distance from protection peak copy count ratios (F), as well as the ratio to mean copies across the two loci for cfDNA samples (G). Sample numbers for each sample type are gDNA=20, colon cancer cfDNA=34, brain cancer cfDNA=10, and sonicated DNA=1 (four technical replicates). All four amplicons are 100bp in length. Letters above or below box and whisker plots (DG) represent homogenous subsets determined by post hoc Tukeys HSD analyses (=0.05) of one-way ANOVAs (p values on plots). The bottom line of each box represents the 25th percentile, top line the 75th percentile, and thick middle line the median. Whiskers extend up to a maximum of 1.5 times the height of the box. Any values that fall outside this range are classified as outliers (circles). Values that are greater than 3 times the height of the box are classified as extreme outliers (asterisks).

To normalise among samples of the same category the concentration measured for each assay was divided by the mean concentration of all assays within each region (chr11 or chr2), giving a ratio to mean copies (assay/sample mean). For ddPCR on HMW gDNA, all assays specific for unique regions should measure the same number of copies within the same sample. Therefore, the ratio of copies measured for a single assay to the mean copies of all assays should be 1:1 for intact gDNA, regardless of proximity to nucleosome peaks. Consistent with this, a one-way ANOVA on samples classified as contaminating HMW DNA found no statistical difference in ratio to mean copies among assays in the chr11 (p=0.100) and chr2 (p=0.239) regions. HMW gDNA samples extracted from the blood of 15 healthy individuals were also used as negative controls and similarly showed little variation in ratio to mean copies among assays. No significant difference was found among assays in the chr2 region (p=0.084). However, a significant difference was detected in the chr11 region (p=0.004), resulting from a minor effect of amplicon length on the number of amplifiable copies (Fig.6C). A similar trend appears to exist in the contaminating HMW DNA; however, its effects likely did not reach statistical significance due to the smaller sample size (6 vs. 15).

In contrast, the ratio to mean copies for cfDNA decreased with increasing distance from the nucleosome peak, with the highest ratio for each region being our universal cfDNA assays (cfUQ11 and cfUQ2). However, given that cfDNA is highly fragmented, differential amplicons sizes are likely to result in differences in the number of amplifiable copies, therefore confounding the effects of nucleosome protection. To control for this we used ultrasonication and gel purification to produce a blood pooled gDNA sample with a similar level of fragmentation as cfDNA, which we measured in four technical replicates for each assay to compare the effects of random fragmentation on the number of amplifiable copies. In the chr11 region, which had the greatest variance in amplicon size among assays, similar ratios were observed in the sonicated DNA and cfDNA for each assay tested (Fig.6D). A two-way ANOVA comparing these two sample types found a significant difference among assays (p<0.001) but no statistically significant interaction between sample type and assay, signifying that only the cfDNA level of fragmentation, and not nucleosome protection, was affecting the number of amplifiable copies (p=0.637). These results show that even small differences in amplicon length can have a significant impact on the number of amplifiable copies at such high levels of fragmentation but proximity to the nucleosome protection peak is likely providing little to no differential protection within this region.

Conversely, the assays targeting the chr2 region were far less variable in length and showed little difference in ratio to mean copies in the sonicated DNA, especially when compared to the cfDNA. A one-way ANOVA on the sonicated samples within this region did find significant differences in concentration ratios among assays (p=0.001); however, the magnitudes of these differences were small, they did not track with differences in amplicon length, and they appear to result from a positional effect, perhaps resulting from a sequence-specific bias in fragmentation within this region. Unlike the chr11 assays, the ratio to mean copies for the chr2 assays tracked the distance from the nucleosome peak in cfDNA, rather than the amplicon length. A two-way ANOVA comparing the sonicated and cfDNA samples found a significant difference among assays (p<0.001) as well as a significant interaction between assay and sample (p<0.001), which supports cfDNA having an effect on the number of amplifiable copies in this region beyond that caused by its level of fragmentation on differently sized amplicons (Fig.6E). Notably, a one-way ANOVA on the cfDNA samples showed no significant difference (p=0.495) in ratio to mean copies (M=1.00, SD=0.10 vs. M=0.97, SD=0.13) for the two assays with the most similar maximum distances from the nucleosome peak (92 and 99bp) and only 1bp difference in length (100 vs. 99bp.). Whereas, the 50bp distance (92 vs. 142bp) separating the two 100bp amplicons resulted in a significant decrease (M=1.00, SD=0.10 vs. M=0.78, SD=0.07; p<0.001), and the 95bp universal cfDNA assay with the smallest maximum distance from the nucleosome peak (62bp) had a significantly higher ratio (M=1.25, SD=0.06) than each of the other three assays (p<0.001; Tukeys HSD). Despite HMW contamination, the three samples with substantial cfDNA size peaks excluded from this analysis also revealed differences in copies among assays that match a nucleosome-mediated fragmentation pattern in the chr2 region (S3 File).

To further explore and confirm these results we designed probes for one flanking assay per region (in addition to the probes already designed for the cfUQ11 and cfUQ02 universal cfDNA assays), selecting those with the greatest difference between the sonicated and cfDNA samples. Where necessary, the forward or reverse primer for each assay was redesigned to normalise all amplicons to 100bp while maintaining the same maximum distance from the nucleosome peak. We ran these assays in duplex ddPCR on cfDNA samples extracted from the blood plasma of 34 patients with colorectal cancer and 10 patients with brain cancer, as well as gDNA samples from the blood of 20 healthy donors and four technical replicates of the sonicated gDNA. We then calculated the ratio of copies for the assay furthest to the assay closest to the nucleosome peak (chr11=[102bp]/[56bp] and chr2=[142bp]/[62bp]) and compared the four sample types for each region. For the chr11 region, a one-way ANOVA found no significant difference between the ratios of the colorectal (M=0.95, SD=0.11) or brain cancer (M=0.98, SD=0.11) cfDNA, gDNA (M=1.00, SD=0.06), or sonicated DNA (M=1.07, SD=0.03) samples (p=0.081). Although not significant, these differences tended towards a slight nucleosome-mediated protective effect (Fig.6F).

Conversely, a one-way ANOVA found a significant difference (p<0.001) among sample types for the chr2 region. A post hoc Tukey HSD test showed this difference was due to a drop in the [142bp]/[62bp] ratio in cfDNA, with gDNA (M=1.00, SD=0.09) and sonicated DNA (M=1.00, SD=0.03) being placed in one homogenous subset, and colorectal (M=0.67, SD=0.10) and brain cancer (M=0.62, SD=0.12) cfDNA placed in another (p<0.001). These results strongly reinforce our previous findings, showing that, unlike the chr11 nucleosome peak, the chr2 peak provides substantial and consistent protection from fragmentation among individuals. Furthermore, comparison across these two regions revealed that the stronger chr2 protection peak resulted not only in greater protection than the weaker chr11 peak but greater degradation in the adjacent valley (Fig.6G). A two-way ANOVA found significant differences (p<0.001) in the ratio to mean copies between the four assays, and no significant interaction (p=0.189) between the colorectal and brain cancer samples, indicating that the differences between assays were similar for these two cohorts. A Tukey HSD test showed significant differences between all four assays, with the chr2:142bp (M=0.81, SD=0.09), chr11:102bp (M=0.95, SD=0.06), chr11:56bp (M=1.00, SD=0.08), and chr2:62bp (M=1.24, SD=0.10) assays each being placed into separate homogenous subsets (=0.025). These results are consistent with cfDNA protection peaks being the result of nucleosome occupancy. As predicted, the protection peak with a low WPS provided weaker but more even protection within its occupied region and the peak with a high WPS provided greater but more narrow protection, thus validating the WPS metric that Snyder et al. (2016)11 applied in their analyses.

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Modelling clinical DNA fragmentation in the development of universal PCR-based assays for bisulfite-converted, formalin-fixed and cell-free DNA sample...