Biomarker may lead to clinical laboratory testing that enables clinical pathologists and urologists to diagnose risk for diabetic kidney failure years before it occurs
Clinical laboratories working with nephrologists and urologists to diagnose patients experiencing urinary system difficulties know that albumin (excessive protein found in the urine) is a common biomarker used in clinical laboratory testing for kidney disease. But patients with diabetes generally have low protein in their urine due to that disease. Thus, it is difficult to diagnose early stage kidney failure in diabetic patients.
But now, researchers at the University of Texas Health Science Center at San Antonio (UT Health San Antonio) have discovered a biomarker called adenine (also found in the urine) which, they say, offers the ability to diagnose diabetic patients at risk of kidney failure significantly earlier than other biomarkers.
A UT Health San Antonio news release states, “Urine levels of adenine, a metabolite produced in the kidney, are predictive and a causative biomarker of looming progressive kidney failure in patients with diabetes, a finding that could lead to earlier diagnosis and intervention.”
The study’s senior author Kumar Sharma, MD, professor and Chief of Nephrology at UT Health San Antonio, said, “The finding paves the way for clinic testing to determine—five to 10 years before kidney failure—that a patient is at risk.”
“The study is remarkable as it could pave the way to precision medicine for diabetic kidney disease at an early stage of the disease,” said study lead Kumar Sharma, MD (above), professor and Chief of Nephrology at UT Health San Antonio, in a news release. This would be a boon to clinical laboratories and pathology groups that work with urologists to diagnose and treat diabetic patients who are at-risk for kidney failure. (Photo copyright: UT Health San Antonio.)
Completing the UT Health Study
Sharma and his team worked for five years to discover that the adenine molecule was damaging kidney tissue, News4SA reported. The research required the team to develop new methods for viewing small molecules known as metabolites.
“UT Health San Antonio is one of few centers in the US perfecting a technique called spatial metabolomics on kidney biopsies from human patients,” the news release notes. The kidney biopsies were obtained through the Kidney Precision Medicine Project (KPMP) and were gathered from various US academic centers.
“It’s a very difficult technique, and it took us several years to develop a method where we combine high resolution of the geography of the kidney with mass spectrometry analysis to look at the metabolites,” Sharma said.
Testing by the UT Health team unearthed “endogenous adenine around scarred blood vessels in the kidney and around tubular-shaped kidney cells that were being destroyed. Endogenous substances are those that naturally occur in the body,” the news release notes.
Findings Could Affect Diabetic Care
UT Health San Diego’s study findings could allow for early intervention and change the way diabetes care is managed, Sharma said.
“The study results are significant because until now, the most important marker for kidney disease has been protein (or albumin) in the urine. Up to half of diabetes patients who develop kidney failure never have much protein in their urine. As 90% of patients with diabetes (more than 37 million patients in the US) remain at increased risk despite low levels of albumin in their urine, this study has widespread consequences. It is the first study to identify these patients at an early stage by measuring this new causative marker in the urine,” the UT Health news release states.
“We’re hoping that by identifying patients early in their course, and with new therapies targeting adenine and kidney scarring, we can block kidney disease or extend the life of the kidney much longer,” Sharma said.
Getting Ahead of Kidney Disease
Though many patients recognize their risk for kidney disease, those who do not have protein in their urine may not take the risk seriously enough, Sharma noted.
“They could be feeling a false sense of security that there is no kidney disease occurring in their body, but in fact, in many cases it is progressing, and they often don’t find out until the kidney disease is pretty far advanced. And at that time, it is much harder to protect the kidneys and prevent dialysis,” he said in the new release.
“Once a patient needs dialysis, he or she must have a fistula or catheter placed and go on a dialysis machine three times a week, four hours at a time to clean the blood,” the news release states.
“The death rate is very high, especially in patients with diabetes,” Sharma added. “There is about 40% mortality within five years in patients with diabetes and kidney failure.”
Though measuring adenine in urine is a challenge, Sharma and his team developed a method that can be performed at UT Health San Antonio on at-risk patients with a doctor’s order. The test results go back to the patient’s doctor.
“The test is being approved for clinical use and right now it is an experimental test, but we expect it to be available for all patients in the near future.” Sharma told News4SA.
“What we’re hoping is that by identifying patients early in their course, and with new therapies targeting adenine and kidney scarring, we can block kidney disease or extend the life of the kidney much longer,” Sharma said in the news release.
And so, thanks to the UT Health researchers, pathologists and clinical laboratories may soon see a new diagnostic test biomarker that will help urologists identify diabetic patients at-risk for kidney failure years earlier than previously possible.
The ASBMB story notes that nanopore technology depends on differences in charges on either side of the membrane to force DNA or RNA through the hole. This is one reason why proteins pose such a challenge.
“Think of a cell as a miniature city, with proteins as its inhabitants. Each protein-resident has a unique identity, its own characteristics, and function. If there was a database cataloging the fingerprints, job profiles, and talents of the city’s inhabitants, such a database would undoubtedly be invaluable!” said Behzad Mehrafrooz, PhD (above), Graduate Research Assistant at University of Illinois at Urbana-Champaign in an article he penned for the university website. This research should be of interest to the many clinical laboratories that do protein testing. (Photo copyright: University of Illinois.)
How the Maglia Process Works
In a Groningen University news story, Maglia said protein is “like cooked spaghetti. These long strands want to be disorganized. They do not want to be pushed through this tiny hole.”
His technique, developed in collaboration with researchers at the University of Rome Tor Vergata, uses electrically charged ions to drag the protein through the hole.
“We didn’t know whether the flow would be strong enough,” Maglia stated in the news story. “Furthermore, these ions want to move both ways, but by attaching a lot of charge on the nanopore itself, we were able to make it directional.”
The researchers tested the technology on what Maglia described as a “difficult protein” with many negative charges that would tend to make it resistant to flow.
“Previously, only easy-to-thread proteins were analyzed,” he said in the news story. “But we gave ourselves one of the most difficult proteins as a test. And it worked!”
Maglia now says that he intends to commercialize the technology through a new startup called Portal Biotech.
Detecting Post-Translational Modifications in the UK
In another recent study, researchers at the University of Oxford reported that they have adapted nanopore technology to detect post-translational modifications (PTMs) in protein chains. The term refers to changes made to proteins after they have been transcribed from DNA, explained an Oxford news story.
“The ability to pinpoint and identify post-translational modifications and other protein variations at the single-molecule level holds immense promise for advancing our understanding of cellular functions and molecular interactions,” said contributing author Hagan Bayley, PhD, Professor of Chemical Biology at University of Oxford, in the news story. “It may also open new avenues for personalized medicine, diagnostics, and therapeutic interventions.”
Bayley is the founder of Oxford Nanopore Technologies, a genetic sequencing company in the UK that develops and markets nanopore sequencing products.
The news story notes that the new technique could be integrated into existing nanopore sequencing devices. “This could facilitate point-of-care diagnostics, enabling the personalized detection of specific protein variants associated with diseases including cancer and neurodegenerative disorders,” the story states.
In another recent study, researchers at the University of Washington reported that they have developed their own method for protein sequencing with nanopore technology.
“This opens up the possibility for barcode sequencing at the protein level for highly multiplexed assays, PTM monitoring, and protein identification!” Motone wrote.
Single-cell proteomics, enabled by nanopore protein sequencing technology, “could provide higher sensitivity and wider throughput, digital quantification, and novel data modalities compared to the current gold standard of protein MS [mass spectrometry],” they wrote. “The accessibility of these tools to a broader range of researchers and clinicians is also expected to increase with simpler instrumentation, less expertise needed, and lower costs.”
There are approximately 20,000 human genes. However, there are many more proteins. Thus, there is strong interest in understanding the human proteome and the role it plays in health and disease.
Technology that makes protein testing faster, more accurate, and less costly—especially with a handheld analyzer—would be a boon to the study of proteomics. And it would give clinical laboratories new diagnostic tools and bring some of that testing to point-of-care settings like doctor’s offices.
New artificial intelligence model agrees with interpretations of human medical technologists and microbiologists with extraordinary accuracy
Microbiology laboratories will be interested in news from Brescia University in Italy, where researchers reportedly have developed a deep learning model that can visually identify and analyze bacterial species in culture plates with a high level of agreement with interpretations made by medical technologists.
They initially trained and tested the system to digitally identify pathogens associated with urinary tract infections (UTIs). UTIs are the source for a large volume of clinical laboratory microbiological testing.
The system, known as DeepColony, uses hierarchical artificial intelligence technology. The researchers say hierarchical AI is better suited to complex decision-making than other approaches, such as generative AI.
In their Nature paper, the researchers explained that microbiologists use conventional methods to visually examine culture plates that contain bacterial colonies. The scientists hypothesize which species of bacteria are present, after which they test their hypothesis “by regrowing samples from each colony separately and then employing mass spectroscopy techniques,” to confirm their hypotheses.
However, DeepColony—which was designed for use with clinical laboratory automation systems—looks at high-resolution digital scans of cultured plates and attempts to identify the bacterial strains and analyze them in much the same way a microbiologist would. For example, it can identify species based on their appearance and determine which colonies are suitable for analysis, the researchers explained.
“Working on a large stream of clinical data, and a complete set of 32 pathogens, the proposed system is capable of effectively assisting plate interpretation with a surprising degree of accuracy in the widespread and demanding framework of urinary tract infections,” the study authors wrote. “Moreover, thanks to the rich species-related generated information, DeepColony can be used for developing trustworthy clinical decision support services in laboratory automation ecosystems from local to global scale.”
“Compared to the most common solutions based on single convolutional neural networks (CNN), multi-network architectures are attractive in our case because of their ability to fit into contexts where decision-making processes are stratified into a complex structure,” wrote the study’s lead author Alberto Signoroni, PhD (above), Associate Professor of Computer Science, University of Brescia, and his researcher team in their Nature paper. “The system must be designed to generate useful and easily interpretable information and to support expert decisions according to safety-by-design and human-in-the-loop policies, aiming at achieving cost-effectiveness and skill-empowerment respectively.” Microbiologists and clinical laboratory managers will want to follow the further development of this technology. (Photo copyright: University of Brescia.)
How Hierarchical AI Works
Writing in LinkedIn, patent attorney and self-described technology expert David Cain, JD, of Hauptman Ham, LLP, explained that hierarchical AI systems “are structured in layers, each with its own distinct role yet interconnected in a way that forms a cohesive whole. These systems are significant because they mirror the complexity of human decision-making processes, incorporating multiple levels of analysis and action. This multi-tiered approach allows for nuanced problem-solving and decision-making, akin to a seasoned explorer deftly navigating through a multifaceted terrain.”
DeepColony, the researchers wrote, consists of multiple convolutional neural networks (CNNs) that exchange information and cooperate with one another. The system is structured into five levels—labeled 0 through 4—each handling a different part of the analysis:
At level 0, the system determines the number of bacterial colonies and their locations on the plate.
At level 1, the system identifies “good colonies,” meaning those suitable for further identification and analysis.
At level 2, the system assigns each good colony to a bacterial species “based on visual appearance and growth characteristics,” the researchers wrote, referring to the determination as being “pathogen aware, similarity agnostic.”
The CNN used at this stage was trained by using images of 26,213 isolated colonies comprising 32 bacterial species, the researchers wrote in their paper. Most came from clinical laboratories, but some were obtained from the American Type Culture Collection (ATCC), a repository of biological materials and information resources available to researchers.
At level 3, the system attempts to improve accuracy by looking at the larger context of the plate. The goal here is to “determine if observed colonies are similar (pure culture) or different (mixed cultures),” the researchers wrote, describing this step as “similarity aware, pathogen agnostic.” This enables the system to recognize variants of the same strain, the researchers noted, and has the effect of reducing the number of strains identified by the system.
At this level, the system uses two “Siamese CNNs,” which were trained with a dataset of 200,000 image pairs.
Then, at level 4, the system “assesses the clinical significance of the entire plate,” the researchers added. Each plate is labeled as:
“Positive” (significant bacterial growth),
“No significant growth” (negative), or
“Contaminated,” meaning it has three or more “different colony morphologies without a particular pathogen that is prevalent over the others,” the researchers wrote.
If a plate is labeled as “positive,” it can be “further evaluated for possible downstream steps,” using MALDI-TOF mass spectrometry or tests to determine susceptibility to antimicrobial measures, the researchers stated.
“This decision-making process takes into account not only the identification results but also adheres to the specific laboratory guidelines to ensure a proper supportive interpretation in the context of use,” the researchers wrote.
Nearly 100% Agreement with Medical Technologists
To gauge DeepColony’s accuracy, the researchers tested it on a dataset of more than 5,000 urine cultures from a US laboratory. They then compared its analyses with those of human medical technologists who had analyzed the same samples.
Agreement was 99.2% for no-growth cultures, 95.6% for positive cultures, and 77.1% for contaminated or mixed growth cultures, the researchers wrote.
The lower agreement for contaminated cultures was due to “a deliberately precautionary behavior, which is related to ‘safety by design’ criteria,” the researchers noted.
Lead study author Alberto Signoroni, PhD, Associate Professor of Computer Science, University of Brescia, wrote in Nature that many of the plates identified by medical technologists as “contaminated” were labeled as “positive” by DeepColony. “We maximized true negatives while allowing for some false positives, so that DeepColony [can] focus on the most relevant or critical cases,” he said.
Will DeepColony replace medical technologists in clinical laboratories any time soon? Not likely. But the Brescia University study indicates the direction AI in healthcare is headed, with high accuracy and increasing speed. The day may not be far off when pathologists and microbiologists regularly employ AI algorithms to diagnose disease.
Similar health monitoring devices have been popular with chronic disease patients and physicians who treat them; this technology may give clinical laboratories a new diagnostic tool
There is an ever-increasing number of companies working to develop lab testing technologies that would be used outside of the traditional clinical laboratory. One such example is Nutromics, an Australia-based medical technology company which recently announced it has raised US $14 million to fund its new lab-on-a-patch platform, according to a company press release.
Nutromics’ lab-on-a-patch device “uses DNA sensor technology to track multiple targets in the human body, including disease biomarkers and hard-to-dose drugs,” according to MobiHealthNews. Notably, Nutromics’ technology uses interstitial fluid as the sample source.
Nutromics raised $4 million last year to support a manufacturing facility and an initial human clinical trial of its “continuous molecular monitoring (CMM) platform technology that is able to track multiple targets in the human body via a single wearable sensor. The platform provides real-time, continuous molecular-level insights for remote patient monitoring and hospital-at-home systems,” MobiHealthNews reported.
“We are aiming to cause a paradigm shift in diagnostic healthcare by essentially developing a lab-on-a-patch. A lack of timely and continuous diagnostic insights can strongly impact outcomes when dealing with critical disease states. With this strategic industry and VC (venture capital) investment in us, we see more confidence in our technology and hope to accelerate our growth,” said entrepreneur and chemical engineer Peter Vranes (above), co-founder and CEO of Nutromics, in a press release. Clinical laboratory leaders have watched similar biometric monitoring devices come to fruition. (Photo copyright: Nutromics.)
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How Nutromics’ Lab-on-a-Patch Works
“Our technology is, in fact, two technologies coming together—a marker and needle. What that does is give us access to fluid under your skin called interstitial fluid. If you’re going to measure something continuously, that’s a really good fluid [to measure],” Vranes told Outcomes Rocket.
Vranes calls the system’s aptamer-based sensor platform technology the “jewel in the crown.” An aptamer is a short sequence of artificial DNA or RNA that binds a specific target molecule. Nutromics’ aptamer sensor, Vranes said, enables targeting of analytes, unlike continuous glucose monitors (CGMs).
“[CGMs] are limited to metabolites—things that are already in the body like glucose and lactate. We’re not limited to those. We can do a whole range of different targets. And what that gives us is a ‘blue ocean’ opportunity to go in and solve problems in areas that other technologies just can’t solve,” Vranes said.
Nutromics plans to develop multiple aptamer-based sensors that measure a variety of analytes in interstitial fluid, Medtech Insight noted.
Nutromics’ wearable DNA sensor lab-on-a-patch technology (above) enables monitoring of multiple targets, including disease biomarkers and some medications, MobiHealthNews explained. The wearable patch contains microneedles that painlessly access interstitial fluid under the skin. Collected data is wirelessly transmitted to a software application and integrates with consumer health software and provider platforms, according to Nutromics. Medical laboratories could have a role in collecting this data and adding it other test results from patients using the wearable patch. (Photo copyright: Nutromics.)
Initial Launch Will Include Antibiotic Monitoring
Nutromics expects to initially launch therapeutic monitoring of vancomycin, a glycopeptide antibiotic medication used to treat various bacterial infections. The company says 60% of doses for this prescription antibiotic are not within therapeutic range.
“Interstitial fluid originates in the blood and then leaks out of capillaries to bring nutrients to cells in the body’s tissues. Because interstitial fluid is in direct communication with the cells, it should have information about the tissues themselves beyond what can be measured from testing the blood,” said Mark Prausnitz, PhD, Regents Professor and J. Erskine Love Jr. Chair, Georgia Tech School of Chemical and Biomolecular Engineering, in a 2020 news release announcing results of human trials of microneedle-based ISF sampling.
“We sampled interstitial fluid from 21 human participants and identified clinically relevant and sometimes distinct biomarkers in interstitial fluid when compared to companion plasma samples based on mass spectrometry analysis,” the scientists wrote.
Clinical laboratory leaders and pathologists will find it useful to monitor the development of diagnostics for use outside the lab. Nutromics is an example of a company developing wearable health technology that painlessly gathers data for lab tests to be conducted in point-of-care and near-patient settings.
An assay using mass spectrometry could go to clinical trial within two years
Dark Daily has regularly observed that humans generate a variety of volatile substances—particularly in breath—which can be used for diagnostic purposes. But what if people, like certain trained animals, could smell the presence of disease before the onset of symptoms? What types of clinical laboratory testing biomarkers could be developed based on human-generated volatile organic compounds?
Researchers at the University of Manchester (UM) in the United Kingdom (UK) say their “breakthrough” test to diagnose Parkinson’s disease “can diagnose disease from skin swabs in three minutes,” according to a university press release.
Perdita Barran, PhD (right), head of the University of Manchester research team that developed the mass spectrometry Parkinson’s test, is shown above with Joy Milne (left), the retired nurse from Scotland who inspired Barran’s team to develop a new Parkinson’s biomarker and method for identifying it. “We are tremendously excited by these results which take us closer to making a diagnostic test for Parkinson’s Disease that could be used in clinic,” she said in a press release. A viable clinical laboratory test for Parkinson’s disease is greatly needed, as more than 10 million people worldwide currently live with the neurodegenerative disorder. (Photo copyright: University of Manchester.)
Using Mass Spectrometry to Analyze Sebum
The UM scientists hypothesized that the smell could be due to sebum, a light oily substance on skin that was going through a chemical change due to the Parkinson’s disease, Hull Daily Mail explained.
Increased sebum, which is produced by the sebaceous glands, is a hallmark of Parkinson’s, the researchers noted.
Their new method involves analysis of sebum using mass spectrometry, according to the JACS AU paper. The method, the researchers claim, makes it possible to diagnose Parkinson’s disease from skin swabs in three minutes.
“There are no cures for Parkinson’s, but a confirmatory diagnosis would allow [Parkinson’s patients] to get the right treatment and get the drugs that will help to alleviate their symptoms,” Perdita Barran, PhD, told the Hull Daily Mail. Barran is Chair of Mass Spectrometry in the Department of Chemistry and Director of the Michael Barber Centre for Collaborative Mass Spectrometry at UM’s Manchester Institute of Biotechnology. “What we are now doing is seeing if (hospital laboratories) can do what we’ve done in a research lab in a hospital lab,” she added.
Sebum Analyzed with Mass Spectrometry
Parkinson’s disease—the world’s fastest growing neurodegenerative disorder—needs “robust biomarkers” that could advance detection and head off onset of motor symptoms such as tremor, rigidity, and postural instability, the researchers note in their paper.
Their recent study builds on earlier 2019 findings they published in ACS Central Science about volatile compounds in sebum possibly being used as Parkinson’s biomarkers.
“Sebum is an underexplored biofluid, which is readily obtained from non-invasive skin swabs, which primarily consists of a mixture of triglycerides, cholesterol, free fatty acids, waxy esters, and squalene,” the researchers explained in their JACS AU paper.
The scientists sought, “to develop a method to analyze sebum in its native state to facilitate rapid assessment of the Parkinson’s disease status. Paper spray ionization mass spectrometry, which allows the direct analysis of compounds from paper, has previously been demonstrated to detect small molecules from unprocessed biofluids, such as blood and urine, but not to date with sebum,” they wrote.
The UM researchers used mass spectrometry to analyze sebum collected on cotton swabs from the backs of 79 people with Parkinson’s and 71 healthy individuals, BBC Scotland News reported.
Depanjan Sarkar, PhD, Research Associate, University of Manchester, further explained the technique in the UM news release:
Sebum is taken from the swab to filter paper cut in a triangle.
Using a solvent and voltage, sebum compounds transfer into the mass spectrometer.
“When we did this, we found more than 4,000 unique compounds of which 500 are different between people with Parkinson’s compared to the control participants,” Sarkar said.
Fatty Acids Make Assay Possible
Could fatty acids pave the way to an assay? The UM researchers believe so.
“We have identified two classes of lipids, namely [triglycerides] and diglycerides, as components of human sebum that are significantly differentially expressed in PD,” the researchers wrote in JACS AU. “Non-invasive sampling followed by PS-IM-MS [paper spray-ion mobility–mass spectrometry] analysis targeting these compounds could provide an inexpensive assay to support clinical phenotyping for the confirmatory diagnosis of Parkinson’s disease.”
A clinical trial for their test, which costs about $20, may be done within two years in Manchester area, the Daily Mail reported.
When Dark Daily reported in 2020 on Joy Milne’s unique ability to smell her husband’s Parkinson’s disease before it was formally diagnosed, we predicted a diagnostic test for Parkinson’s may be years away. And here it is, albeit with regulatory clearance needed following clinical trials.
It may in fact be possible to leverage sebum analysis to detect other diseases, the UM researchers noted.
For diagnostics developers, this story of Joy Milne and her husband Les Milne is a useful example of how, in tracking the life of a specific patient with a specific disease and close family members, researchers were able to identify a new class of biomarkers that could be used in a diagnostic assay.
It will be interesting to follow the University of Manchester researchers in their quest for a diagnostic mass spectrometry clinical laboratory test for Parkinson’s disease. According to Parkinson’s Foundation statistics, about 10 million people worldwide live with the neurodegenerative disorder. Such a new diagnostic test could be vitally important to medical laboratory care, and to patients and their families.
