Molecular probes designed to spot minute amounts of pathogens in biological samples may aid clinical laboratories’ speed-to-answer
Driven to find a better way to isolate minute samples of pathogens from among high-volumes of other biological organisms, researchers at Canada’s McMaster University in Hamilton, Ontario, have unveiled a bioinformatics algorithm which they claim shortens time-to-answer and speeds diagnosis of deadly diseases.
Two disease pathogens the researchers specifically targeted in their study are responsible for sepsis and SARS-CoV-2, the coronavirus causing COVID-19. Clinical laboratories would welcome a technology which both shortens time-to-answer and improves diagnostic accuracy, particularly for pathogens such as sepsis and SARS-CoV-2.
Their design of molecular probes that target the genomic sequences of specific pathogens can enable diagnosticians and clinical laboratories to spot extremely small amounts of viral and bacterial pathogens in patients’ biological samples, as well as in the environment and wildlife.
“There are thousands of bacterial pathogens and being able to determine which one is present in a patient’s blood sample could lead to the correct treatment faster when time is very important,” Zachery Dickson, a lead author of the study, told Brighter World. Dickson is a bioinformatics PhD candidate in the Department of Biology at McMaster University. “The probe makes identification much faster, meaning we could potentially save people who might otherwise die,” he added.
Sepsis is a life-threatening response to infection that leads to organ failure, tissue damage, and death in hospitals worldwide. According to Sepsis Alliance, about 30% of people diagnosed with severe sepsis will die without quick and proper treatment. Thus, a “shortcut” to identifying sepsis in its early stages may well save many lives, the McMaster researchers noted.
And COVID-19 has killed millions. Such a tool that identifies sepsis and SARS-CoV-2 in minute biological samples would be a boon to hospital medical laboratories worldwide.
“We currently need faster, cheaper, and more succinct ways to detect pathogens in human and environmental samples that democratize the hunt, and this pipeline does exactly that,” Hendrik Poinar, PhD (above), McMaster Professor of Anthropology and a lead author of the study, told Brighter World. Poinar is Director of the McMaster University Ancient DNA Center. Hospital medical laboratories could help save many lives if sepsis and COVID-19 could be detected earlier. (Graphic copyright: McMaster University.)
Is Bioinformatics ‘Shortcut’ Faster than PCR Testing?
The researchers say their probes enable a shortcut to detection—even in an infection’s early stages—by “targeting, isolating, and identifying the DNA sequences specifically and simultaneously.”
The probes’ design makes possible simultaneous targeted capture of diverse metagenomics targets, Biocompare explained.
But is it faster than PCR (polymerase chain reaction) testing?
The McMaster scientists were motivated by the “challenges of low signal, high background, and uncertain targets that plague many metagenomic sequencing efforts,” they noted in their paper.
They pointed to challenges posed by PCR testing, a popular technique used for detection of sepsis pathogens as well as, more recently, for SARS-CoV-2, the coronavirus causing COVID-19.
“The (PCR) technique relies on primers that bind to nucleic acid sequences specific to an organism or group of organisms. Although capable of sensitive, rapid detection and quantification of a particular target, PCR is limited when multiple loci are targeted by primers,” the researchers wrote in Cell Reports Methods.
According to LabMedica, “A wide array of metagenomic study efforts are hampered by the same challenge: low concentrations of targets of interest combined with overwhelming amounts of background signal. Although PCR or naive DNA capture can be used when there are a small number of organisms of interest, design challenges become untenable for large numbers of targets.”
Detecting Pathogens Faster, Cheaper, and More Accurately
As part of their study, researchers tested two probe sets:
one to target bacterial pathogens linked to sepsis, and
another to detect coronaviruses including SARS-CoV-2.
They were successful in using the probes to capture a variety of pathogens linked to sepsis and SARS-CoV-2.
“We validated HUBDesign by generating probe sets targeting the breadth of coronavirus diversity, as well as a suite of bacterial pathogens often underlying sepsis. In separate experiments demonstrating significant, simultaneous enrichment, we captured SARS-CoV-2 and HCoV-NL63 [Human coronavirus NL 63] in a human RNA background and seven bacterial strains in human blood. HUBDesign has broad applicability wherever there are multiple organisms of interest,” the researchers wrote in Cell Reports Methods.
The findings also have implications to the environment and wildlife, the researchers noted.
Of course, more research is needed to validate the tool’s usefulness in medical diagnostics. The McMaster University researchers intend to improve HUBDesign’s efficiency but note that probes cannot be designed for unknown targets.
Nevertheless, the advanced application of novel technologies to diagnose of sepsis, which causes 250,000 deaths in the US each year, according to the federal Centers for Disease Control and Prevention, is a positive development worth watching.
The McMaster scientists’ discoveries—confirmed by future research and clinical studies—could go a long way toward ending the dire effects of sepsis as well as COVID-19. That would be a welcome development, particularly for hospital-based laboratories.
Using animal blood, the researchers hope to improve the accuracy of AI driven diagnostic technology
What does a cheetah, a tortoise, and a Humboldt penguin have
in common? They are zoo animals helping scientists at Saarland University in
Saarbrücken, Germany, find biomarkers that can help computer-assisted diagnoses
of diseases in humans at early stages. And they are not the only animals
lending a paw or claw.
In their initial research, the scientists used blood samples
that had been collected during routine examinations of 21 zoo animals between
2016 and 2018, said a news
release. The team of bioinformatics
and human genetics experts
worked with German zoos Saarbrücken and Neunkircher for the study. The project
progresses, and thus far, they’ve studied the blood of 40 zoo animals, the
release states.
This research work may eventually add useful biomarkers and
assays that clinical
laboratories can use to support physicians as they diagnose patients,
select appropriate therapies, and monitor the progress of their patients. As medical
laboratory scientists know, for many decades, the animal kingdom has been
the source of useful insights and biological materials that have been
incorporated into laboratory assays.
“Measuring the molecular blood profiles of animals has never
been done before this way,” said Andreas
Keller, PhD, Saarland University Bioinformatics Professor and Chair for
Clinical Bioinformatics, in the news release. The Saarland researchers published
their findings in Nucleic Acids
Research, an Oxford
Academic journal.
“Studies on sncRNAs [small non-coding RNAs] are often largely based on homology-based information, relying on genomic sequence similarity and excluding actual expression data. To obtain information on sncRNA expression (including miRNAs, snoRNAs, YRNAs and tRNAs), we performed low-input-volume next-generation sequencing of 500 pg of RNA from 21 animals at two German zoological gardens,” the article states.
Can Animals Improve the Accuracy of AI to Detect Disease
in Humans?
However, the researchers perceived an inability for AI and machine learning to
discern real biomarker patterns from those that just seemed to fit.
“The machine learning methods recognize the typical
patterns, for example for a lung tumor or Alzheimer’s disease. However, it is
difficult for artificial intelligence to learn which biomarker patterns are
real and which only seem to fit the respective clinical picture. This is where
the blood samples of the animals come into play,” Keller states in the news
release.
“If a biomarker is evolutionarily conserved, i.e. also
occurs in other species in similar form and function, it is much more likely
that it is a resilient biomarker,” Keller explained. “The new findings are now
being incorporated into our computer models and will help us to identify the
correct biomarkers even more precisely in the future.”
Andreas Keller, PhD (left), and zoo director Richard Francke (right), hold a pair of radiated tortoises that participated in the Saarland University study. (Photo copyright: Oliver Dietze/Saarland University.)
“Because blood can be obtained in a standardized manner and
miRNA expression patterns are technically very stable, it is easy to accurately
compare expression between different animal species. In particular, dried blood
spots or microsampling devices appear to be well suited as containers for
miRNAs,” the researchers wrote in Nucleic Acids Research.
Animal species that participated in the study include:
Additionally, human volunteers contributed blood specimens
for a total of 19 species studied. The scientists reported success in capturing
data from all of the species. They are integrating the information into their
computer models and have developed a public database of their
findings for future research.
“With our study, we provide a large collection of small RNA
NGS expression data of species that have not been analyzed before in great
detail. We created a comprehensive publicly available online resource for
researchers in the field to facilitate the assessment of evolutionarily
conserved small RNA sequences,” the researchers wrote in their paper.
Clinical Laboratory Research and Zoos: A Future
Partnership?
This novel involvement of zoo animals in research aimed at improving
the ability of AI driven diagnostics to isolate and identify human disease is
notable and worth watching. It is obviously pioneering work and needs much
additional research. At the same time, these findings give evidence that there
is useful information to be extracted from a wide range of unlikely sources—in
this case, zoo animals.
Also, the use of artificial intelligence to search for
useful patterns in the data is a notable part of what these researchers
discovered. It is also notable that this research is focused on sequencing DNA
and RNA of the animals involved with the goal of identifying sequences that are
common across several species, thus demonstrating the common, important
functions they serve.
In coming years, those clinical laboratories doing genetic
testing in support of patient care may be incorporating some of this research
group’s findings into their interpretation of certain gene sequences.
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