New gene-editing systems could provide markedly improved accuracy for DNA and RNA editing leading to new precision medicine tools and genetic therapies
In what may turn out to be a significant development in genetic engineering, researchers from three institutions have identified nearly 200 new systems that can be used for editing genes. It is believed that a number of these new systems can provide comparable or better accuracy when compared to CRISPER (Clustered Regularly Interspaced Short Palindromic Repeats), currently the most-used gene editing method.
CRISPR-Cas9 has been the standard tool for CRISPR gene editing and genetic engineering. However, publication of these new research findings are expected to give scientists better, more precise tools to edit genes. In turn, these developments could lead to new clinical laboratory tests and precision medicine therapies for patients with inherited genetic diseases.
“Best known as a powerful gene-editing tool, CRISPR actually comes from an inbuilt defense system found in bacteria and simple microbes called archaea. CRISPR systems include pairs of ‘molecular scissors’ called Cas enzymes, which allow microbes to cut up the DNA of viruses that attack them. CRISPR technology takes advantage of these scissors to cut genes out of DNA and paste new genes in,” according to Live Science.
In its article, New Atlas noted that the researchers looked to bacteria because “In nature, CRISPR is a self-defense tool used by bacteria.” They developed an algorithm—called FLSHclust—to conduct “a deep dive into three databases of bacteria, found in environments as diverse as Antarctic lakes, breweries, and dog saliva.”
In their paper, the researchers wrote, “We developed fast locality-sensitive hashing–based clustering (FLSHclust), a parallelized, deep clustering algorithm with linearithmic scaling based on locality-sensitive hashing. FLSHclust approaches MMseqs2, a gold-standard quadratic-scaling algorithm, in clustering performance. We applied FLSHclust in a sensitive CRISPR discovery pipeline and identified 188 previously unreported CRISPR-associated systems, including many rare systems.”
“In lab tests [the newfound CRISPR systems] demonstrated a range of functions, and fell into both known and brand new categories,” New Atlas reported.
“Some of these microbial systems were exclusively found in water from coal mines,” Soumya Kannan, PhD (above), a Graduate Fellow at MIT’s Zhang Lab and co-first author of the study, told New Atlas. “If someone hadn’t been interested in that, we may never have seen those systems.” These new gene-editing systems could lead to new clinical laboratory genetic tests and therapeutics for chronic diseases. (Photo copyright: MIT McGovern Institute.)
Deeper Look at Advancement
The CRISPR-Cas9 made a terrific impact when it was announced in 2012, earning a Nobel Prize in Chemistry.
Though CRISPR-Cas9 brought huge benefits to genetic research, the team noted in their Science paper that “existing methods for sequence mining lag behind the exponentially growing databases that now contain billions of proteins, which restricts the discovery of rare protein families and associations.
“We sought to comprehensively enumerate CRISPR-linked gene modules in all existing publicly available sequencing data,” the scientist continued. “Recently, several previously unknown biochemical activities have been linked to programmable nucleic acid recognition by CRISPR systems, including transposition and protease activity. We reasoned that many more diverse enzymatic activities may be associated with CRISPR systems, many of which could be of low abundance in existing [gene] sequence databases.”
Among the previously unknown gene-editing systems the researchers found were some belonging to the Type 1 CRISPR systems class. These “have longer guide RNA sequences than Cas9. They can be directed to their targets more precisely, reducing the risk of off-target edits—one of the main problems with CRISPR gene editing,” New Atlas reported.
“The authors also identified a CRISPR-Cas enzyme, Cas14, which cuts RNA precisely. These discoveries may help to further improve DNA- and RNA-editing technologies, with wide-ranging applications in medicine and biotechnology,” the Science paper noted.
Testing also showed these systems were able to edit human cells, meaning “their size should allow them to be delivered in the same packages currently used for CRISPR-Cas9,” New Atlas added.
Another newfound gene-editing system demonstrated “collateral activity, breaking down nucleic acids after binding to the target, New Atlas reported. SHERLOCK, a tool used to diagnose single samples of RNA or DNA to diagnose disease, previously utilized this system.
Additionally, New Atlas noted, “a type VII system was found to target RNA, which could unlock a range of new tools through RNA editing. Others could be adapted to record when certain genes are expressed, or as sensors for activity in cells.”
Looking Ahead
The strides in science from the CRISPR-Cas9 give a hint at what can come from the new discovery. “Not only does this study greatly expand the field of possible gene editing tools, but it shows that exploring microbial ecosystems in obscure environments could pay off with potential human benefits,” New Atlas noted.
“This study introduces FLSHclust as a tool to cluster millions of sequences quickly and efficiently, with broad applications in mining large sequence databases. The CRISPR-linked systems that we discovered represent an untapped trove of diverse biochemical activities linked to RNA-guided mechanisms, with great potential for development as biotechnologies,” the researchers wrote in Science.
How these newfound gene-editing tools and the new FLSHclust algorithm will eventually lead to new clinical laboratory tests and precision medicine diagnostics is not yet clear. But the discoveries will certainly improve DNA/RNA editing, and that may eventually lead to new clinical and biomedical applications.
Dogs’ acute sense of smell can even surpass effectiveness of some clinical laboratory testing in detecting certain diseases in humans
When it comes to COVID-19 testing, a recent Italian study demonstrates that trained dogs can detect SARS-CoV-2 with accuracy comparable to rapid molecular tests used in clinical laboratories. The researchers wanted to determine if dogs could be more effective at screening people for COVID-19 at airports, schools, and other high-traffic environments as a way to detect the coronavirus and reduce the spread of this infectious disease.
Scientists at the State University of Milan in Italy conducted a study that shows dogs can be trained to accurately identify the presence of the COVID-19 infection from both biological samples and by simply smelling an individual.
For their validation study, the Italian team trained three dogs named Nala, Otto, and Helix, “to detect the presence of SARS-CoV-2 in sweat samples from infected people. At the end of the training, the dogs achieved an average sensitivity of 93% and a specificity of 99%, showing a level of accuracy highly consistent with that of the RT-PCR [reverse transcription polymerase chain reaction] used in molecular tests and a moderate to strong reproducibility over time,” Nature reported.
RT-PCR tests are the current gold-standard for SARS-CoV-2 detection. This is yet another example of scientists training dogs to smell a disease with “acceptable” accuracy. This time for COVID-19.
“We only recruited dogs that showed themselves predisposed and positively motivated to carry out this type of activity. One of the fundamental aspects was not to cause stress or anxiety in the subjects used,” Federica Pirrone, PhD (above), Associate Professor, Department of Veterinary Medicine and Animal Sciences, University of Milan, and one of the authors of the study told Lifegate. “Training always takes place using positive reinforcement of a food nature: whether it’s a particularly appetizing morsel, a biscuit, or something that associates the dog’s search with a rewarding prize.” In some instances, dogs have been shown to be as good or more effective at detecting certain diseases than clinical laboratory testing. (Photo copyright: Facebook.)
Dogs More Accurate than Rapid Antigen Testing
Nala and four other dogs (Nim, Hope, Iris and Chaos) were later trained by canine technicians from Medical Detection Dogs Italy (MDDI) to identify the existence of the SARS-CoV-2 virus by directly smelling people waiting in line in pharmacies to get a nasal swab to test for the coronavirus.
Working with their handlers, the five dogs accurately signaled the presence or absence of the virus with 89% sensitivity and 95% specificity. That rate is “well above the minimum required by the WHO [World Health Organization] for rapid swabs for SARS-CoV-2,” according to Nature.
“The results of studies published so far on the accuracy of canine smell in detecting the presence of SARS-CoV-2 in biological samples (e.g., saliva, sweat, urine, trachea-bronchial secretions) from infected people suggest that sniffer dogs might reach percentages of sensitivity and specificity comparable to, or perhaps even higher, than those of RT-PCR,” the scientists wrote in Scientific Reports.
“However, although most of these studies are of good quality, none of them provided scientific validation of canine scent detection, despite this being an important requirement in the chemical analysis practice. Therefore, further applied research in this field is absolutely justified to provide definitive validation of this biodetection method,” the researchers concluded.
Other Studies into Using Dogs for Detecting Disease
Scientists from the Division of Biological and Health Sciences, Department of Agriculture and Livestock at the University of Sonora; and the Canine Training Center Obi-K19, both in Hermosillo, Mexico, conducted the study “as part of a Frontiers of Science Project of the National Council of Science and Technology (CONACYT), in which in addition to analyzing sweat compounds, trained dogs are put to sniff the samples and make detections in people who show symptoms or could be positive for coronavirus,” Mexico Daily Post reported.
The researchers trained four dogs with sweat samples and three dogs with saliva samples of COVID-19 positive patients. The samples were obtained from a health center located in Hermosillo, Sonora, in Mexico. The dogs were restricted to spend five minutes per patient and the researchers calculated the performance of the dogs by measuring sensitivity, specificity, and their 95% confidence intervals (CI).
The researchers concluded that all four of the dogs could detect COVID-19 from either sweat or saliva samples “with sensitivity and specificity rates significantly different from random [sampling] in the field.” According to the Frontiers in Medicine study, the researchers found their results promising because, they said, it is reasonable to expect the detection rate would improve with longer exposure to the samples.
The objective of the Mexican researchers is for the dogs to ultimately reach the sensitivity range requested by WHO for the performance of an antigen test, which is at least 80% sensitivity and 97% specificity. If that goal is achieved, dogs could become important partners in the control of the COVID-19 pandemic, the scientists wrote.
Data obtained so far from these studies indicate that biosensing dogs may represent an effective method of screening for COVID-19 as well as other diseases. More studies and clinical trials are needed before the widespread use of dogs might become feasible. Nevertheless, scientists all over the world are finding that Man’s best friend can be a powerful ally in the fight against the spread of deadly diseases.
In the meantime, the gold standard in COVID-19 testing will continue to be the FDA-cleared assays used by clinical laboratories throughout the United States.
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.
Researchers say their method can trace ancestry back 100,000 years and could lay groundwork for identifying new genetic markers for diseases that could be used in clinical laboratory tests
Cheaper, faster, and more accurate genomic sequencing technologies are deepening scientific knowledge of the human genome. Now, UK researchers at the University of Oxford have used this genomic data to create the largest-ever human family tree, enabling individuals to trace their ancestry back 100,000 years. And, they say, it could lead to new methods for predicting disease.
This new database also will enable genealogists and medical laboratory scientists to track when, where, and in what populations specific genetic mutations emerged that may be involved in different diseases and health conditions.
New Genetic Markers That Could Be Used for Clinical Laboratory Testing
As this happens, it may be possible to identify new diagnostic biomarkers and genetic indicators associated with specific health conditions that could be incorporated into clinical laboratory tests and precision medicine treatments for chronic diseases.
“We have basically built a huge family tree—a genealogy for all of humanity—that models as exactly as we can the history that generated all the genetic variation we find in humans today,” said Yan Wong, DPhil, an evolutionary geneticist at the Big Data Institute (BDI) at the University of Oxford, in a news release. “This genealogy allows us to see how every person’s genetic sequence relates to every other, along all the points of the genome.”
Researchers from University of Oxford’s BDI in London, in collaboration with scientists from the Broad Institute of MIT and Harvard; Harvard University, and University of Vienna, Austria, developed algorithms for combining different databases and scaling to accommodate millions of gene sequences from both ancient and modern genomes.
“Essentially, we are reconstructing the genomes of our ancestors and using them to form a series of linked evolutionary trees that we call a ‘tree sequence,’” said geneticist Anthony Wilder Wohns, PhD (above), in the Oxford news release. Wohns, a postdoctoral researcher in statistical and population genetics at the Broad Institute, led the study. “We can then estimate when and where these ancestors lived. The power of our approach is that it makes very few assumptions about the underlying data and can also include both modern and ancient DNA samples.” The study may result in new genetic biomarkers that lead to advances in clinical laboratory diagnostics for today’s diseases. (Photo copyright: Harvard School of Engineering and Applied Sciences.)
Tracking Genetic Markers of Disease
The BDI team overcame the major obstacle to tracing the origins of human genetic diversity when they developed algorithms to handle the massive amount of data created when combining genome sequences from many different databases. In total, they compiled the genomic sequences of 3,601 modern and eight high-coverage ancient people from 215 populations in eight datasets.
The ancient genomes included three Neanderthal genomes, a Denisovan genome, and a family of four people who lived in Siberia around 4,600 years ago.
The University of Oxford researchers noted in their news release that their method could be scaled to “accommodate millions of genome sequences.”
“This structure is a lossless and compact representation of 27 million ancestral haplotype fragments and 231 million ancestral lineages linking genomes from these datasets back in time. The tree sequence also benefits from the use of an additional 3,589 ancient samples compiled from more than 100 publications to constrain and date relationships,” the researchers wrote in their published study.
Wong believes his research team has laid the groundwork for the next generation of DNA sequencing.
“As the quality of genome sequences from modern and ancient DNA samples improves, the tree will become even more accurate and we will eventually be able to generate a single, unified map that explains the descent of all the human genetic variation we see today,” he said in the news release.
Developing New Clinical Laboratory Biomarkers for Modern Diagnostics
In a video illustrating the study’s findings, evolutionary geneticist Yan Wong, DPhil, a member of the BDI team, said, “If you wanted to know why some people have some sort of medical conditions, or are more predisposed to heart attacks or, for example, are more susceptible to coronavirus, then there’s a huge amount of that described by their ancestry because they’ve inherited their DNA from other people.”
Wohns agrees that the significance of their tree-recording methods extends beyond simply a better understanding of human evolution.
“[This study] could be particularly beneficial in medical genetics, in separating out true associations between genetic regions and diseases from spurious connections arising from our shared ancestral history,” he said.
The underlying methods developed by Wohns’ team could have widespread applications in medical research and lay the groundwork for identifying genetic predictors of disease risk, including future pandemics.
Clinical laboratory scientists will also note that those genetic indicators may become new biomarkers for clinical laboratory diagnostics for all sorts of diseases currently plaguing mankind.
Japanese scientists who developed the detection method hope to use it to create ‘easy testing kits that anyone can use’
What do ostriches and humans have in common during the current COVID-19 pandemic? The unexpected answer is that ostrich antibodies can be used to identify humans infected with COVID-19. If proven viable in healthcare settings, the possibility exists that new clinical laboratory tests could be developed based on wearable diagnostics technologies that pathologists would interpret for doctors and patients.
According to Study Finds, scientists at Kyoto Prefectural University in Japan have created a removable mask filter that, when sprayed with a fluorescent dye coated with antibodies extracted from ostrich eggs, will glow under UV light when COVID-19 is detected. The discovery by Yasuhiro Tsukamoto, PhD (above), President of Kyoto Prefectural University, and his researchers could lead to development of low-cost at home COVID-19 testing kits using the same ostrich-antibody-based technique. (Photo copyright: Kyoto Prefectural University/Reuters.)
The KPU scientists conducted a small study with 32 COVID-19 patients over a 10-day span. The surgical-style masks they wore later glowed around the nose and mouth areas but became dimmer over time as their viral load decreased.
“The ostrich antibody for corona placed on the mouth filter of the mask captures the coronavirus in coughing, sneezing, and water,” the researchers explained in Study Finds.
Tsukamoto himself learned he had contracted COVID-19 after wearing a prototype mask and noticing it glowed under UV light. A PCR test later confirmed his diagnosis, Kyodo News reported.
The KPU team “hopes to further develop the masks so they will glow automatically, without special lighting, if the [COVID-19] virus is detected.” Reuters noted in its coverage of the ostrich-antibody masks.
Making Medicine from Ostrich Antibodies
A profile in Audubon noted that Tsukamoto, who also serves as a veterinary medicine professor at Kyoto Prefectural University, made ostriches the focus of his research since the 1990s as he looked for ways to harness the dinosaur-like bird’s properties to fight human infections. He maintains a flock of 500 captive ostriches. Each female ostrich can produce 50 to 100 eggs/year over a 50-year life span.
Tsukamoto’s research focuses on customizing the antibodies in ostrich eggs by injecting females with inactive viruses, allergens, and bacteria, and then extracting the antibodies to develop medicines for humans. Antibodies form in the egg yolks in about six weeks and can be collected without harming the parent or young.
“The idea of using ostrich antibodies for therapeutics in general is a very interesting concept, particularly because of the advantages of producing the antibodies from eggs,” Ashley St. John, PhD, an Associate Professor in Immunology, at Duke-NUS Medical School in Singapore, told Audubon.
While more clinical studies will be needed before ostrich-antibody masks reach the commercial marketplace, Tsukamoto’s team is planning to expand their experiment to 150 participants with a goal of receiving Japanese government approval to begin selling the glowing COVID-detection masks as early as 2022. But they believe the ostrich-antibody technique ultimately may lead to development of an inexpensive COVID-19 testing kit.
“We can mass-produce antibodies from ostriches at a low cost. In the future, I want to make this into an easy testing kit that anyone can use,” Tsukamoto told Kyodo News.
Harvard, MIT Also Working on COVID-19 Detecting Facemask
According to Fast Company, the MIT/Harvard COVID-19-detecting masks use the same core technology as previous paper tests for Ebola and Zika that utilize proteins and nucleic acids embedded in paper that react to target molecules.
Fast Company explained that the mask wearer launches a test by pushing a button to release a small water reservoir embedded in the mask (above). Droplets from their breath are than analyzed by the sensors in the masks, which could be adapted to test for new COVID variants or other respiratory pathogens. In addition to eliminating the use of a nasal swab, the mask-based testing system may compete with clinical laboratory-based results. (Photo copyright: Felice Frankel/MIT.)
“They would especially be useful in situations where local variant outbreaks are occurring, allowing people to conveniently test themselves at home multiple times a day,” he told Fast Company.
“It’s on par specificity and sensitivity that you will get in a state-of-the-art [medical] laboratory, but with no one there,” Luis Ruben Soenksen, PhD, Venture Builder in Artificial Intelligence and Healthcare at MIT and one of the co-authors of the Nature Biotechnology study, told Fast Company.
As the definition of “wearable diagnostic technology” broadens, pathologists and clinical laboratory scientists may see their roles expand to include helping consumers interpret data collected by point-of-care testing technology as well as performing, evaluating, and interpreting laboratory test results that come from non-traditional sources.
