Studies could lead to new prognostic biomarkers and clinical laboratory diagnostics for cancer
Might fungi be involved in human cancers? Two separately published studies have found fungal DNA in various cancers in the human body. However, the researchers are unclear on how the fungi got into the cancer cells and if it is affecting the cancers’ pathology. Nevertheless, these discoveries could lead to utilizing tumor-associated fungal DNA as clinical laboratory diagnostics or prognostic biomarkers in the fight against cancer.
“The finding that fungi are commonly present in human tumors should drive us to better explore their potential effects and re-examine almost everything we know about cancer through a ‘microbiome lens,’” said Ravid Straussman, MD, PhD (above), a principal investigator at Weizmann Institute of Science and one of the authors of the study in a UCSD press release. These findings could lead to new clinical laboratory diagnostics and prognostic biomarkers. (Photo copyright: Weizmann Institute of Science.)
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Microbiome Key to Cancer Biology and Detection
To perform their research, the team examined 17,401 samples of patient tissues, blood, and plasma across 35 different types of cancers in four independent cohorts. They discovered fungal DNA and cells in low abundances in many human cancers.
“The existence of fungi in most human cancers is both a surprise and to be expected,” said biologist Rob Knight, PhD, founding Director of the Center for Microbiome Innovation and Professor of Pediatrics and Computer Science and Engineering at UC San Diego in a UCSD press release. “It is surprising because we don’t know how fungi could get into tumors throughout the body. But it is also expected because it fits the pattern of healthy microbiomes throughout the body, including the gut, mouth and skin, where bacteria and fungi interact as part of a complex community.”
The main highlights of this study include:
Fungi detected in the different cancer types were often intracellular.
Multiple fungal-bacterial-immune ecologies were detected across tumors.
Intratumoral fungi stratified clinical outcomes, including immunotherapy response.
Cell-free fungal DNA found in both healthy and cancer patients in early-stage disease.
Fungi found on the human body appear as either environmental fungi, such as yeasts and molds, and commensal fungi, which live either on or inside the body. Both are typically harmless to most healthy people and can provide some benefits, such as improving gut health, but they may also be a contributing factor in some disease.
The researchers found that there were notable parallels between specific fungi and certain factors, such as age, tumor subtypes, smoking status, immunotherapy responses, and survival measures.
“These findings validate the view that the microbiome in its entirety is a key piece of cancer biology and may present significant translational opportunities, not only in cancer detection, but also in other biotech applications related to drug development, cancer evolution, minimal residual disease, relapse, and companion diagnostics,” said Gregory Sepich-Poore, MD, PhD, one of the study’s authors and co-founder and chief analytics officer at biotechnology company Micronoma, in the UCSD press release.
New Clinical Laboratory Tests to Identify Fungal Species in Cancer
Researchers from Duke University and Cornell University uncovered compelling evidence of fungi in multiple cancer types and focused on a detected link between Candida and gastrointestinal cancers.
They found that “several Candida species were enriched in tumor samples and tumor-associated Candida DNA was predictive of decreased survival,” according to their paper.
Their analysis of multiple body sites revealed tumor-associated mycobiomes in fungal cells. The researchers found that fungal spores known as blastomyces were associated with tumor tissues in lung cancers, and that high rates of Candida were present in stomach and colon cancers.
The Duke/Cornell researchers hope their work can provide a framework to develop new tests that can distinguish fungal species in tumors and predict cancer progression and help medical professionals and patients chose the best treatment therapies.
“These findings open up a lot of exciting research directions, from the development of diagnostics and treatments to studies of the detailed biological mechanisms of fungal relationships to cancers,” said Iliyan Iliev, PhD, Associate Professor of Microbiology and Immunology in Medicine, Weill Cornell Medicine, and one of the authors of the study, in a Weill news release.
More research is needed to determine if fungal DNA plays a role in disease pathology or if its presence does not have any causal link.
“It’s plausible that some of these fungi are promoting tumor progression and metastasis, but even if they aren’t, they could be very valuable as prognostic indicators,” Iliev said.
The insights gleaned from these two studies will be of particular interest to microbiologists, clinical laboratory professionals, and anatomic pathologists. Additional research could answer questions about how and if fungi infect tumors and if such fungi is a factor that increases cancer risk and outcomes.
Proof-of-concept study ‘highlights that using AI to integrate different types of clinically informed data to predict disease outcomes is feasible’ researchers say
Artificial intelligence (AI) and machine learning are—in stepwise fashion—making progress in demonstrating value in the world of pathology diagnostics. But human anatomic pathologists are generally required for a prognosis. Now, in a proof-of-concept study, researchers at Brigham and Women’s Hospital in Boston have developed a method that uses AI models to integrate multiple types of data from disparate sources to accurately predict patient outcomes for 14 different types of cancer.
The process also uncovered “the predictive bases of features used to predict patient risk—a property that could be used to uncover new biomarkers,” according to Genetic Engineering and Biotechnology News (GEN).
Should these research findings become clinically viable, anatomic pathologists may gain powerful new AI tools specifically designed to help them predict what type of outcome a cancer patient can expect.
“Experts analyze many pieces of evidence to predict how well a patient may do. These early examinations become the basis of making decisions about enrolling in a clinical trial or specific treatment regimens,” said Faisal Mahmood, PhD (above) in a Brigham press release. “But that means that this multimodal prediction happens at the level of the expert. We’re trying to address the problem computationally,” he added. Should they be proven clinically-viable through additional studies, these findings could lead to useful tools that help anatomic pathologists and clinical laboratory scientists more accurately predict what type of outcomes cancer patient may experience. (Photo copyright: Harvard.)
AI-based Prognostics in Pathology and Clinical Laboratory Medicine
The team at Brigham constructed their AI model using The Cancer Genome Atlas (TCGA), a publicly available resource which contains data on many types of cancer. They then created a deep learning-based algorithm that examines information from different data sources.
Pathologists traditionally depend on several distinct sources of data, such as pathology images, genomic sequencing, and patient history to diagnose various cancers and help develop prognoses.
For their research, Mahmood and his colleagues trained and validated their AI algorithm on 6,592 H/E (hematoxylin and eosin) whole slide images (WSIs) from 5,720 cancer patients. Molecular profile features, which included mutation status, copy-number variation, and RNA sequencing expression, were also inputted into the model to measure and explain relative risk of cancer death.
The scientists “evaluated the model’s efficacy by feeding it data sets from 14 cancer types as well as patient histology and genomic data. Results demonstrated that the models yielded more accurate patient outcome predictions than those incorporating only single sources of information,” states a Brigham press release.
“This work sets the stage for larger healthcare AI studies that combine data from multiple sources,” said Faisal Mahmood, PhD, Associate Professor, Division of Computational Pathology, Brigham and Women’s Hospital; and Associate Member, Cancer Program, Broad Institute of MIT and Harvard, in the press release. “In a broader sense, our findings emphasize a need for building computational pathology prognostic models with much larger datasets and downstream clinical trials to establish utility.”
Future Prognostics Based on Multiple Data Sources
The Brigham researchers also generated a research tool they dubbed the Pathology-omics Research Platform for Integrative Survival Estimation (PORPOISE). This tool serves as an interactive platform that can yield prognostic markers detected by the algorithm for thousands of patients across various cancer types.
The researchers believe their algorithm reveals another role for AI technology in medical care, but that more research is needed before their model can be implemented clinically. Larger data sets will have to be examined and the researchers plan to use more types of patient information, such as radiology scans, family histories, and electronic medical records in future tests of their AI technology.
“Future work will focus on developing more focused prognostic models by curating larger multimodal datasets for individual disease models, adapting models to large independent multimodal test cohorts, and using multimodal deep learning for predicting response and resistance to treatment,” the Cancer Cell paper states.
“As research advances in sequencing technologies, such as single-cell RNA-seq, mass cytometry, and spatial transcriptomics, these technologies continue to mature and gain clinical penetrance, in combination with whole-slide imaging, and our approach to understanding molecular biology will become increasingly spatially resolved and multimodal,” the researchers concluded.
Anatomic pathologists may find the Brigham and Women’s Hospital research team’s findings intriguing. An AI tool that integrates data from disparate sources, analyzes that information, and provides useful insights, could one day help them provide more accurate cancer prognoses and improve the care of their patients.
Study also may have found relationship between atherosclerosis and cholesterol
Chinese scientists have developed a cutting-edge method for non-invasively monitoring blood cholesterol levels in humans. The innovative technology utilizes images of skin on hands and may eliminate the need for both invasive venipunctures and fasting for testing cholesterol. Given the large volumes of blood cholesterol tests currently performed by clinical laboratories, this new technology could have significant impact on cholesterol testing if further studies confirm its capabilities.
Notably, the Chinese researchers have apparently already developed a lab analyzer to perform the procedure and it is being used in clinical care. However, in the United States and other countries, this technology will require additional clinical studies and regulatory review before clinical laboratories would be able to use it in daily patient care.
The cholesterol sensing system consists of a detection reagent associated with a fluorescent group that binds to skin cholesterol, and a detection device. Cholesterol levels are easily obtained from the skin, according to the researchers, by analyzing the manner in which the skin absorbs and scatters light via a scanner.
Should this technology be validated for clinical care, it could replace other invasive clinical laboratory tests for cholesterol measurement.
The series of images above, taken from the researchers’ Lipids in Health and Disease published study, demonstrates how their non-invasive clinical laboratory test for total blood cholesterol is performed. Non-invasive clinical laboratory tests for monitoring biomarkers in the blood are always preferred by patients over veinous punctures and fasting. (Photo copyright: Hefei Institutes of Physical Science, Chinese Academy of Sciences.)
First Evidence of Relationship between Cholesterol and Atherosclerosis
“Just put your hands on, and the system will tell you the cholesterol data,” Yikun Wang, PhD, Professor, Department of Physical Sciences, Hefei Institutes of Physical Science, Chinese Academy of Sciences, and leader of the research team, told Diagnostics World. “Cholesterol is one of several types of fats (lipids) that play an important role in human body, we can track your fats in this simple way.”
To perform the testing, clinicians first clean the test site located on the fleshy edge of the palm of the hand with an alcohol swab. A patient’s non-dominant hand is used for the test as the skin on that hand is typically less abrasive and contains fewer melanocytes, which allows for more stable results. A plastic-coated annulus is then applied to the test site and the examined portion is positioned on the measuring hole of the detection system to measure the background light spectrum of the skin.
Once the background signal is ascertained, the detection reagent is added to the annulus until it is full. After 60 seconds, any excess detection reagent is removed from the annulus. A cleaning reagent is then added to the annulus for 30 seconds and removed with a sterile cotton swab. The treated portion of the skin is then placed over the measuring hole of the detection system and two spectrums of light are compared to measure the skin cholesterol, which accurately correlates to the cholesterol in the bloodstream.
“Compared to in-situ detection used in the previous clinical research, our device may offer more accurate results for we can avoid the influence of pressure and skin background differences [person to person],” Wang said. “Study results offer the first evidence of a relationship between skin cholesterol and atherosclerotic disease in a Chinese population, which may be of great significance to researchers around the world.”
Initially, 154 patients diagnosed with acute coronary syndrome (ACS) between January 2020 and April 2021 were involved in the study. However, only 121 of those patients were included in the final study with the remaining being excluded due to at least one of the following criteria:
Severe hepatic (liver) or renal (kidney) insufficiency, and
Obesity.
Clinician Use Can Affect Accuracy of Test
Developed by researchers from the Hefei Institutes of Physical Science Chinese Academy of Sciences, and the University of Science and Technology of China, the researchers noted that how clinicians operate the device can have an impact on the accuracy of the test results.
“A critical step in the [testing] process that is subject to operator variability is blotting, which requires the operator to remove an unbound detector from the palm before adding the indicator,” Wang told Diagnostics World. “Excess residual indicator solution can result in falsely increased skin cholesterol levels. Considering this, we are planning to develop a simplified and standardized blotting procedure.”
Millions of people in the US live with illness that requires regular monitoring of blood cholesterol. Normal total cholesterol should be less than 200 milligrams per deciliter (mg/dL). According to the federal Centers for Disease Control and Prevention (CDC), nearly 94 million US adults over the age of 20 have total cholesterol levels higher than 200 mg/dL and 28 million adults have total cholesterol levels higher than 240 mg/dL. In addition, 7% of children and adolescents between the ages of six and 19 have high cholesterol. For these reasons, cholesterol testing represents a substantial portion of the clinical laboratory tests performed daily in this country.
This new non-invasive technology for monitoring total blood cholesterol in humans could greatly benefit patients, especially if it eliminates the need for venipunctures and fasting prior to testing. Clinical laboratory managers and pathologists may want to follow the progress of this new cholesterol testing technology as it demonstrates its value in China and is submitted for regulatory review in this country.
Findings could lead to new clinical laboratory involvement in diagnostics targeted at overweight patients
Does the SARS-CoV-2 coronavirus make us fat so it can better take over our bodies? It sounds like the plot for a science fiction horror movie! But a team of scientists in the Pacific Northwest say that is exactly what the virus does, and their findings could lead to clinical laboratories playing a role in evaluating how the virus highjacks fat cells to aid in its invasion of humans.
They found that certain types of lipids support replication of the COVID-19 virus. Their study illustrates how lipids may play a more important role in the human body than scientists previously understood.
“This is exciting work, but it’s the start of a very long journey,” said Fikadu Tafesse, PhD (left), Assistant Professor of Molecular Microbiology and Immunology, OHSU School of Medicine and corresponding author of the study in an OHSU press release. “We have an interesting observation, but we have a lot more to learn about the mechanisms of this disease.” Clinical laboratories may eventually be part of a new diagnostic process for overweight COVID-19 patients. (Photo copyright: Oregon Health and Science University.)
Does Obesity Promote COVID-19 Infection?
The OHSU and PNNL scientists performed their research by examining the effect of SARS-CoV-2 on more than 400 lipids in two different cell lines. They observed that individuals with a high body mass index (BMI) appear to be more sensitive to the COVID-19 virus.
The researchers discovered there is a tremendous shift in lipid levels in those cell lines when the virus was present, with some fats increasing by a massive 64 times! Nearly 80% of the fats in one cell line were changed by the virus and more than half of the fats were altered in the other cell line.
The lipids that were most affected by the COVID-19 virus were triglycerides which are critical to human health. Triglycerides are basically tiny bundles of fat that allow the body to store energy and maintain healthy cell membranes. When a body needs energy, these fat parcels are broken up into useful, raw materials to provide the required energy.
“Lipids are an important part of every cell. They literally hold us together by keeping our cells intact, and they’re a major source of energy storage for our bodies,” said Jennifer Kyle, PhD, in the OHSU press release. Kyle is a research scientist at PNNL who specializes in all stages of lipidomic research. “They are an attractive target for a virus,” she noted.
Stopping SARS-CoV-2 Replication
The scientists discovered that SARS-CoV-2 alters our fat-processing system by boosting the number of triglycerides in our cells and changing the body’s ability to utilize stored fat as fuel. The team also analyzed the effects of lipid levels in 24 of the virus’ 29 proteins. They identified several proteins that had a strong influence on triglyceride levels.
The team then searched databases and identified several compounds that interfered with the body’s fat-processing system by cutting off the flow of fatty fuel. They found that several of these compounds were successful at stopping the SARS-CoV-2 virus from replicating.
A synthetic organic compound known as GSK2194069, which selectively and potently inhibits fatty acid synthase (FAS), and a weight-loss medication called Orlistat, were both able to stop viral replication in the lab.
Although the scientists believe their work is an important step in understanding the SARS-CoV-2 coronavirus, they also note that their results occurred in cell culture (in vitro) and not in people (in vivo). Therefore, more research is needed to determine if the compounds will work in the same manner in human trials.
“As the virus replicates, it needs a continuous supply of energy. More triglycerides could provide that energy in the form of fatty acids. But we don’t know exactly how the virus uses these lipids to its advantage,” Tafesse said in the press release.
“Our findings fill an important gap in our understanding of host dependency factors of coronavirus infection. … In light of the evolving nature of SARS-CoV-2, it is critical that we understand the basic biology of its life cycle in order to illuminate additional avenues for protection and therapy against this global pandemic pathogen, which spreads quickly and mutates with ease,” the OHSU/PNNL scientists wrote in Nature Communications.
More research is needed to validate the findings of this study and to better understand the dynamic between lipids and SARS-CoV-2 infection. However, it is reasonable to assume that, in the future, some COVID-19 patients may require a clinical laboratory work-up to determine how the coronavirus may be hijacking their fat cells to exacerbate the illness.
Two studies show the accuracy of perception-based systems in detecting disease biomarkers without needing molecular recognition elements, such as antibodies
Researchers from multiple academic and research institutions have collaborated to develop a non-conventional machine learning-based technology for identifying and measuring biomarkers to detect ovarian cancer without the need for molecular identification elements, such as antibodies.
Traditional clinical laboratory methods for detecting biomarkers of specific diseases require a “molecular recognition molecule,” such as an antibody, to match with each disease’s biomarker. However, according to a Lehigh University news release, for ovarian cancer “there’s not a single biomarker—or analyte—that indicates the presence of cancer.
“When multiple analytes need to be measured in a given sample, which can increase the accuracy of a test, more antibodies are required, which increases the cost of the test and the turnaround time,” the news release noted.
Unveiled in two sequential studies, the new method for detecting ovarian cancer uses machine learning to examine spectral signatures of carbon nanotubes to detect and recognize the disease biomarkers in a very non-conventional fashion.
“Carbon nanotubes have interesting electronic properties,” said Daniel Heller, PhD (above), in the Lehigh University news release. “If you shoot light at them, they emit a different color of light, and that light’s color and intensity can change based on what’s sticking to the nanotube. We were able to harness the complexity of so many potential binding interactions by using a range of nanotubes with various wrappings. And that gave us a range of different sensors that could all detect slightly different things, and it turned out they responded differently to different proteins.” This method differs greatly from traditional clinical laboratory methods for identifying disease biomarkers. (Photo copyright: Memorial Sloan-Kettering Cancer Center.)
Perception-based Nanosensor Array for Detecting Disease
In the Science Advances paper, the researchers described their development of “a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids.
“Perception-based machine learning (ML) platforms, modeled after the complex olfactory system, can isolate individual signals through an array of relatively nonspecific receptors. Each receptor captures certain features, and the overall ensemble response is analyzed by the neural network in our brain, resulting in perception,” the researchers wrote.
“This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements,” the researchers concluded.
In the Nature Biomedical Engineering paper, the researchers described a fined-tuned toolset that could accurately differentiate ovarian cancer biomarkers from biomarkers in individuals who are cancer-free.
“Here we show that a ‘disease fingerprint’—acquired via machine learning from the spectra of near-infrared fluorescence emissions of an array of carbon nanotubes functionalized with quantum defects—detects high-grade serous ovarian carcinoma in serum samples from symptomatic individuals with 87% sensitivity at 98% specificity (compared with 84% sensitivity at 98% specificity for the current best [clinical laboratory] screening test, which uses measurements of cancer antigen 125 and transvaginal ultrasonography,” the researchers wrote.
“We demonstrated that a perception-based nanosensor platform could detect ovarian cancer biomarkers using machine learning,” said Yoona Yang, PhD, a postdoctoral research associate in Lehigh’s Department of Chemical and Biomolecular Engineering and co-first author of the Science Advances article, in the news release.
How Perception-based Machine Learning Platforms Work
According to Yang, perception-based sensing functions like the human brain.
“The system consists of a sensing array that captures a certain feature of the analytes in a specific way, and then the ensemble response from the array is analyzed by the computational perceptive model. It can detect various analytes at once, which makes it much more efficient,” Yang said.
“SWCNTs have unique optical properties and sensitivity that make them valuable as sensor materials. SWCNTS emit near-infrared photoluminescence with distinct narrow emission bands that are exquisitely sensitive to the local environment,” the researchers wrote in Science Advances.
“Carbon nanotubes have interesting electronic properties,” said Daniel Heller, PhD, Head of the Cancer Nanotechnology Laboratory at Memorial Sloan Kettering Cancer Center and Associate Professor in the Department of Pharmacology at Weill Cornell Medicine of Cornell University, in the Lehigh University news release.
“If you shoot light at them, they emit a different color of light, and that light’s color and intensity can change based on what’s sticking to the nanotube. We were able to harness the complexity of so many potential binding interactions by using a range of nanotubes with various wrappings. And that gave us a range of different sensors that could all detect slightly different things, and it turned out they responded differently to different proteins,” he added.
The researchers put their technology to practical test in the second study. The wanted to learn if it could differentiate symptomatic patients with high-grade ovarian cancer from cancer-free individuals.
The research team used 269 serum samples. This time, nanotubes were bound with a specific molecule providing “an extra signal in terms of data and richer data from every nanotube-DNA combination,” said Anand Jagota PhD, Professor, Bioengineering and Chemical and Biomolecular Engineering, Lehigh University, in the news release.
This year, 19,880 women will be diagnosed with ovarian cancer and 12,810 will die from the disease, according to American Cancer Society data. While more research and clinical trials are needed, the above studies are compelling and suggest the possibility that one day clinical laboratories may detect ovarian cancer faster and more accurately than with current methods.
Screening and analysis of ocean samples also identified a possible missing link in how the RNA viruses evolved
An international team of scientists has used genetic screening and machine learning techniques to identify more than 5,500 previously unknown species of marine RNA viruses and is proposing five new phyla (biological groups) of viruses. The latter would double the number of RNA virus phyla to 10, one of which may be a missing link in the early evolution of the microbes.
Though the newly-discovered viruses are not currently associated with human disease—and therefore do not drive any current medical laboratory testing—for virologists and other microbiologists, “a fuller catalog of these organisms is now available to advance scientific understanding of how viruses evolve,” said Dark Daily Editor-in-Chief Robert Michel.
“While scientists have cataloged hundreds of thousands of DNA viruses in their natural ecosystems, RNA viruses have been relatively unstudied,” wrote four microbiologists from Ohio State University (OSU) who participated in the study in an article they penned for The Conversation.
The OSU study authors included:
Ahmed Zayed, PhD, research scientist, Dept. of Microbiology, OSU.
Matthew Sullivan, PhD, Professor of Microbiology and Director of the Center of Microbiome Science at OSU.
“RNA viruses are clearly important in our world, but we usually only study a tiny slice of them—the few hundred that harm humans, plants and animals,” explained Matthew Sullivan, PhD (above), Director, Center of Microbiome Science, in an OSU news story. Sullivan led the OSU research team. “We wanted to systematically study them on a very big scale and explore an environment no one had looked at deeply, and we got lucky because virtually every species was new, and many were really new,” he added. (Photo copyright: University of Ohio.)
RNA versus DNA Viruses
In contrast to the better-understood DNA virus, an RNA virus contains RNA instead of DNA as its genetic material, according to Samanthi Udayangani, PhD, in an article she penned for Difference Between. Examples of RNA viruses include:
One major difference, she explains, is that RNA viruses mutate at a higher rate than do DNA viruses.
The OSU scientists identified the new species by analyzing a database of RNA sequences from plankton collected during a series of ocean expeditions aboard a French schooner owned by the Tara Ocean Foundation.
“Plankton are any aquatic organisms that are too small to swim against the current,” the authors explained in The Conversation. “They’re a vital part of ocean food webs and are common hosts for RNA viruses.”
The team’s screening process focused on the RNA-dependent RNA polymerase (RdRp) gene, “which has evolved for billions of years in RNA viruses, and is absent from other viruses or cells,” according to the OSU news story.
“RdRp is supposed to be one of the most ancient genes—it existed before there was a need for DNA,” Zayed said.
The RdRp gene “codes for a particular protein that allows a virus to replicate its genetic material. It is the only protein that all RNA viruses share because it plays an essential role in how they propagate themselves. Each RNA virus, however, has small differences in the gene that codes for the protein that can help distinguish one type of virus from another,” the study authors explained.
The screening “ultimately identified over 44,000 genes that code for the virus protein,” they wrote.
Identifying Five New Phyla
The researchers then turned to machine learning to organize the sequences and identify their evolutionary connections based on similarities in the RdRp genes.
“The more similar two genes were, the more likely viruses with those genes were closely related,” they wrote.
The technique classified many of the sequences within the five previously known phyla of RNA viruses:
But the researchers also identified five new phyla—including two dubbed “Taraviricota” and “Arctiviricota”—that “were particularly abundant across vast oceanic regions,” they wrote. Taraviricota is named after the Tara expeditions and Arctiviricota gets its name from the Arctic Ocean.
They speculated that Taraviricota “might be the missing link in the evolution of RNA viruses that researchers have long sought, connecting two different known branches of RNA viruses that diverged in how they replicate.”
In addition to the five new phyla, the researchers are proposing at least 11 new classes of RNA viruses, according to the OSU story. The scientists plan to issue a formal proposal to the International Committee on Taxonomy of Viruses (ICTV), the body responsible for classification and naming of viruses.
Studying RNA Viruses Outside of Disease Environments
“As the COVID-19 pandemic has shown, RNA viruses can cause deadly diseases. But RNA viruses also play a vital role in ecosystems because they can infect a wide array of organisms, including microbes that influence environments and food webs at the chemical level,” wrote the four study authors in The Conversation. “Mapping out where in the world these RNA viruses live can help clarify how they affect the organisms driving many of the ecological processes that run our planet. Our study also provides improved tools that can help researchers catalog new viruses as genetic databases grow.”
This remarkable study, which was partially funded by the US National Science Foundation, will be most intriguing to virologists and microbiologists. However, clinical laboratories also should be interested in the fact that the catalog of known viruses has just expanded by 5,500 types of RNA viruses.
