A mutation that replaces a single amino acid in a potent tumor-suppressing protein turns it from saint to sinister. A new study by a coalition of Texas institutions shows why that is more damaging than previously known.
The ubiquitous p53 protein in its natural state, sometimes called “the guardian of the genome,” is a front-line protector against cancer. But the mutant form appears in 50% or more of human cancers and actively blocks cancer suppressors.
Researchers led by Peter Vekilov at the University of Houston (UH) and Anatoly Kolomeisky at Rice University have discovered the same mutant protein can aggregate into clusters. These in turn nucleate the formation of amyloid fibrils, a prime suspect in cancers as well as neurological diseases like Alzheimer’s.
The condensation of p53 into clusters is driven by the destabilization of the protein’s DNA-binding pocket when a single arginine amino acid is replaced with glutamine, they reported.
“It’s known that a mutation in this protein is a main source of cancer, but the mechanism is still unknown,” said Kolomeisky, a professor and chair of Rice’s Department of Chemistry and a professor of chemical and biomolecular engineering.
“This knowledge gap has significantly constrained attempts to control aggregation and suggest novel cancer treatments,” said Vekilov, the John and Rebecca Moores Professor of Chemical and Biomolecular Engineering and Chemistry at UH.

advertisement

The mutant p53 clusters, which resemble those discovered by Vekilov in solutions of other proteins 15 years ago, and the amyloid fibrils they nucleate prompt the aggregation of proteins the body uses to suppress cancer. “This is similar to what happens in the brain in neurological disorders, though those are very different diseases,” Kolomeisky said.
The p53 mechanism described in the Proceedings of the National Academy of Sciences may be similar to those that form functional and pathological solids like tubules, filaments, sickle cell polymers, amyloids and crystals, Vekilov said.
Researchers at UH combined 3D confocal images of breast cancer cells taken in the lab of chemical and biomolecular engineer Navin Varadarajan with light scattering and optical microscopy of solutions of the purified protein carried out in the Vekilov lab.
Transmission electron microscopy micrographs of cluster and fibril formation contributed by Michael Sherman at the University of Texas Medical Branch at Galveston (UTMB) supported the main result of the study, as did molecular simulations by Kolomeisky’s group
All confirmed the p53 mutant known as R248Q goes through a two-step process to form mesoscopic condensates. Understanding the mechanism could provide insight into treating various cancers that manipulate either p53 or its associated signaling pathways, Vekilov said.
In normal cell conditions, the concentration of p53 is relatively low, so the probability of aggregation is low, he said. But when a mutated p53 is present, the probability increases.
“Experiments show the size of these clusters is independent of the concentration of p53,” Kolomeisky said. “Mutated p53 will even take normal p53 into the aggregates. That’s one of the reasons for the phenomenon known as loss of function.”
If even a small relative fraction of the mutant is present, it’s enough to kill or lower the ability of normal, wild-type p53 to fight cancer, according to the researchers.
The Rice simulations showed normal p53 proteins are compact and easily bind to DNA. “But the mutants have a more open conformation that allows them to interact with other proteins and gives them a higher tendency to produce a condensate,” Kolomeisky said. “It’s possible that future anti-cancer drugs will target the mutants in a way that suppresses the formation of these aggregates and allows wild-type p53 to do its job.”

Story Source:
Materials provided by Rice University. Note: Content may be edited for style and length.

New treatments for Alzheimer’s disease are desperately needed, but numerous clinical trials of investigational drugs have failed to generate promising options. Now a team at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) has developed an artificial intelligence-based method to screen currently available medications as possible treatments for Alzheimer’s disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action.
“Repurposing FDA-approved drugs for Alzheimer’s disease is an attractive idea that can help accelerate the arrival of effective treatment — but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer’s disease,” explains Artem Sokolov, PhD, director of Informatics and Modeling at the Laboratory of Systems Pharmacology at HMS. “We therefore built a framework for prioritizing drugs, helping clinical studies to focus on the most promising ones.”
In an article published in Nature Communications, Sokolov and his colleagues describe their framework, called DRIAD (Drug Repurposing In Alzheimer’s Disease), which relies on machine learning — a branch of artificial intelligence in which systems are “trained” on vast amounts of data, “learn” to identify telltale patterns and augment researchers’ and clinicians’ decision-making.
DRIAD works by measuring what happens to human brain neural cells when treated with a drug. The method then determines whether the changes induced by a drug correlate with molecular markers of disease severity.
The approach also allowed the researchers to identify drugs that had protective as well as damaging effects on brain cells.
“We also approximate the directionality of such correlations, helping to identify and filter out neurotoxic drugs that accelerate neuronal death instead of preventing it,” says co-first author Steve Rodriguez, PhD, an investigator in the Department of Neurology at MGH and an instructor at HMS.
DRIAD also allows researchers to examine which proteins are targeted by the most promising drugs and if there are common trends among the targets, an approach designed by Clemens Hug, PhD, a research associate in the Laboratory of Systems Pharmacology and a co-first author.
The team applied the screening method to 80 FDA-approved and clinically tested drugs for a wide range of conditions. The analysis yielded a ranked list of candidates, with several anti-inflammatory drugs used to treat rheumatoid arthritis and blood cancers emerging as top contenders. These drugs belong to a class of medications known as Janus kinase inhibitors. The drugs work by blocking the action of inflammation-fueling Janus kinase proteins, suspected to play a role in Alzheimer’s disease and known for their role in autoimmune conditions. The team’s analyses also pointed to other potential treatment targets for further investigation.
“We are excited to share these results with the academic and pharmaceutical research communities. Our hope is that further validation by other researchers will refine the prioritization of these drugs for clinical investigation,” says Mark Albers, MD, PhD, the Frank Wilkins Jr. and Family Endowed Scholar and associate director of the Massachusetts Center for Alzheimer Therapeutic Science at MGH and a faculty member of the Laboratory of Systems Pharmacology at HMS. One of these drugs, baricitinib, will be investigated by Albers in a clinical trial for patients with subjective cognitive complaints, mild cognitive impairment, and Alzheimer’s disease that will be launching soon at MGH in Boston and at Holy Cross Health in Fort Lauderdale, Florida. “In addition, independent validation of the nominated drug targets could provide new insights into the mechanisms behind Alzheimer’s disease and lead to novel therapies,” says Albers.
This work was supported by the National Institute on Aging, the CART fund and the Harvard Catalyst Program for Faculty Development and Diversity Inclusion.

Story Source:
Materials provided by Massachusetts General Hospital. Note: Content may be edited for style and length.

New research at Washington University School of Medicine in St. Louis indicates that three new, fast-spreading variants of the virus that cause COVID-19 can evade antibodies that work against the original form of the virus that sparked the pandemic. With few exceptions, whether such antibodies were produced in response to vaccination or natural infection, or were purified antibodies intended for use as drugs, the researchers found more antibody is needed to neutralize the new variants.
The findings, from laboratory-based experiments and published March 4 in Nature Medicine, suggest that COVID-19 drugs and vaccines developed thus far may become less effective as the new variants become dominant, as experts say they inevitably will. The researchers looked at variants from South Africa, the United Kingdom and Brazil.
“We’re concerned that people whom we’d expect to have a protective level of antibodies because they have had COVID-19 or been vaccinated against it, might not be protected against the new variants,” said senior author Michael S. Diamond, MD, PhD, the Herbert S. Gasser Professor of Medicine. “There’s wide variation in how much antibody a person produces in response to vaccination or natural infection. Some people produce very high levels, and they would still likely be protected against the new, worrisome variants. But some people, especially older and immunocompromised people, may not make such high levels of antibodies. If the level of antibody needed for protection goes up tenfold, as our data indicate it does, they may not have enough. The concern is that the people who need protection the most are the ones least likely to have it.”
The virus that causes COVID-19, known as SARS-CoV-2, uses a protein called spike to latch onto and get inside cells. People infected with SARS-CoV-2 generate the most protective antibodies against the spike protein.
Consequently, spike became the prime target for COVID-19 drug and vaccine developers. The three vaccines authorized by the Food and Drug Administration (FDA) for emergency use in the U.S. — made by Pfizer/BioNTech, Moderna and Johnson & Johnson — both target spike. And potent anti-spike antibodies were selected for development into antibody-based drugs for COVID-19.
Viruses are always mutating, but for nearly a year the mutations that arose in SARS-CoV-2 did not threaten this spike-based strategy. Then, this winter, fast-spreading variants were detected in the United Kingdom, South Africa, Brazil and elsewhere. Sparking concern, the new variants all carry multiple mutations in their spike genes, which could lessen the effectiveness of spike-targeted drugs and vaccines now being used to prevent or treat COVID-19. The most worrisome new variants were given the names of B.1.1.7 (from the U.K.), B.1.135 (South Africa) and B.1.1.248, also known as P.1 (Brazil).
To assess whether the new variants could evade antibodies made for the original form of the virus, Diamond and colleagues, including first author Rita E. Chen, a graduate student in Diamond’s lab, tested the ability of antibodies to neutralize three virus variants in the laboratory.
The researchers tested the variants against antibodies in the blood of people who had recovered from SARS-CoV-2 infection or were vaccinated with the Pfizer vaccine. They also tested antibodies in the blood of mice, hamsters and monkeys that had been vaccinated with an experimental COVID-19 vaccine, developed at Washington University School of Medicine, that can be given through the nose. The B.1.1.7 (U.K.) variant could be neutralized with similar levels of antibodies as were needed to neutralize the original virus. But the other two variants required from 3.5 to 10 times as much antibody for neutralization.
Then, they tested monoclonal antibodies: mass-produced replicas of individual antibodies that are exceptionally good at neutralizing the original virus. When the researchers tested the new viral variants against a panel of monoclonal antibodies, the results ranged from broadly effective to completely ineffective.
Since each virus variant carried multiple mutations in the spike gene, the researchers created a panel of viruses with single mutations so they could parse out the effect of each mutation. Most of the variation in antibody effectiveness could be attributed to a single amino acid change in the spike protein. This change, called E484K, was found in the B.1.135 (South Africa) and B.1.1.248 (Brazil) variants, but not B.1.1.7 (U.K.). The B.1.135 variant is widespread in South Africa, which may explain why one of the vaccines tested in people was less effective in South Africa than in the U.S., where the variant is still rare, Diamond said.
“We don’t exactly know what the consequences of these new variants are going to be yet,” said Diamond, also a professor of molecular microbiology and of pathology & immunology. “Antibodies are not the only measure of protection; other elements of the immune system may be able to compensate for increased resistance to antibodies. That’s going to be determined over time, epidemiologically, as we see what happens as these variants spread. Will we see reinfections? Will we see vaccines lose efficacy and drug resistance emerge? I hope not. But it’s clear that we will need to continually screen antibodies to make sure they’re still working as new variants arise and spread and potentially adjust our vaccine and antibody-treatment strategies.”
The research team also included co-corresponding author Ali Ellebedy, PhD, an assistant professor of pathology & immunology, of medicine, and of molecular microbiology at Washington University; and co-corresponding author Pei-Yong Shi, PhD, and co-first author Xianwen Zhang, PhD, of the University of Texas Medical Branch.

Over evolutionary time scales, a single gene may acquire different roles in diverging species. However, revealing the multiple hidden roles of a gene was not possible before genome editing came along. Cold Spring Harbor Laboratory (CSHL) Professor and HHMI Investigator Zach Lippman and CSHL postdoctoral fellow Anat Hendelman collaborated with Idan Efroni, HHMI International Investigator at Hebrew University Faculty of Agriculture in Israel, to uncover this mystery. They dissected the activity of a developmental gene, WOX9, in different plants and at different moments in development. Using genome editing, they found that without changing the protein produced by the gene, they could change a plant’s traits by changing the gene’s regulation.
“Genes” are the DNA that code for proteins, but other nearby stretches of DNA regulate the activity of genes, instructing them where, when, and to what degree they should be active. With the genome-editing tool CRISPR, scientists can introduce precise mutations into DNA, including these regulatory regions. Though scientists would like to use CRISPR to fine-tune plant traits, the technique sometimes yields surprising results; some genes turn out to have functions that were previously unknown.
WOX9 is one of several “homeobox” genes that help plants and animals set borders in developing structures. While the gene plays a role in early development in arabidopsis, a weedy relative of broccoli, it influences later development — reproduction and flowering — in tomatoes. Lippman and Hendelman used CRISPR to create a series of mutations in the regulatory DNA surrounding WOX9 to reveal additional functions in tomato, groundcherry, and arabidopsis plants. Given the right regulatory sequence, the gene could induce more flowers to form in all three species. WOX9 is thus a candidate to increase yields in these and other crop plants just by changing its regulation. This discovery suggests that other genes may also have hidden multiple roles. Lippman says:
“We know about a whole bunch of genes that you might want to target with genome editing to improve crops, but there’s a whole other set of genes for which they might have really useful functions that could also help improve crops. And so by using this approach, you can expose those roles and then you can predictably fine-tune the activity of that gene for that specific role to get the desired trait modification.”
Lippman’s team published their findings in the journal Cell. Their work will make it easier to improve crop traits more predictably.

Story Source:
Materials provided by Cold Spring Harbor Laboratory. Original written by Jasmine Lee. Note: Content may be edited for style and length.

In the era of social distancing, using robots for some health care interactions is a promising way to reduce in-person contact between health care workers and sick patients. However, a key question that needs to be answered is how patients will react to a robot entering the exam room.
Researchers from MIT and Brigham and Women’s Hospital recently set out to answer that question. In a study performed in the emergency department at Brigham and Women’s, the team found that a large majority of patients reported that interacting with a health care provider via a video screen mounted on a robot was similar to an in-person interaction with a health care worker.
“We’re actively working on robots that can help provide care to maximize the safety of both the patient and the health care workforce. The results of this study give us some confidence that people are ready and willing to engage with us on those fronts,” says Giovanni Traverso, an MIT assistant professor of mechanical engineering, a gastroenterologist at Brigham and Women’s Hospital, and the senior author of the study.
In a larger online survey conducted nationwide, the researchers also found that a majority of respondents were open to having robots not only assist with patient triage but also perform minor procedures such as taking a nose swab.
Peter Chai, an assistant professor of emergency medicine at Brigham and Women’s Hospital and a research affiliate in Traverso’s lab, is the lead author of the study, which appears today in JAMA Network Open.
Triage by robot
After the Covid-19 pandemic began early last year, Traverso and his colleagues turned their attention toward new strategies to minimize interactions between potentially sick patients and health care workers. To that end, they worked with Boston Dynamics to create a mobile robot that could interact with patients as they waited in the emergency department. The robots were equipped with sensors that allow them to measure vital signs, including skin temperature, breathing rate, pulse rate, and blood oxygen saturation. The robots also carried an iPad that allowed for remote video communication with a health care provider.

advertisement

This kind of robot could reduce health care workers’ risk of exposure to Covid-19 and help to conserve the personal protective equipment that is needed for each interaction. However, the question still remained whether patients would be receptive to this type of interaction.
“Often as engineers, we think about different solutions, but sometimes they may not be adopted because people are not fully accepting of them,” Traverso says. “So, in this study we were trying to tease that out and understand if the population is receptive to a solution like this one.”
The researchers first conducted a nationwide survey of about 1,000 people, working with a market research company called YouGov. They asked questions regarding the acceptability of robots in health care, including whether people would be comfortable with robots performing not only triage but also other tasks such as performing nasal swabs, inserting a catheter, or turning a patient over in bed. On average, the respondents stated that they were open to these types of interactions.
The researchers then tested one of their robots in the emergency department at Brigham and Women’s Hospital last spring, when Covid-19 cases were surging in Massachusetts. Fifty-one patients were approached in the waiting room or a triage tent and asked if they would be willing to participate in the study, and 41 agreed. These patients were interviewed about their symptoms via video connection, using an iPad carried by a quadruped, dog-like robot developed by Boston Dynamics. More than 90 percent of the participants reported that they were satisfied with the robotic system.
“For the purposes of gathering quick triage information, the patients found the experience to be similar to what they would have experienced talking to a person,” Chai says.

advertisement

Robotic assistants
The numbers from the study suggest that it could be worthwhile to try to develop robots that can perform procedures that currently require a lot of human effort, such as turning a patient over in bed, the researchers say. Turning Covid-19 patients onto their stomachs, also known as “proning,” has been shown to boost their blood oxygen levels and make breathing easier. Currently the process requires several people to perform. Administering Covid-19 tests is another task that requires a lot of time and effort from health care workers, who could be deployed for other tasks if robots could help perform swabs.
“Surprisingly, people were pretty accepting of the idea of having a robot do a nasal swab, which suggests that potential engineering efforts could go into thinking about building some of these systems,” Chai says.
The MIT team is continuing to develop sensors that can obtain vital sign data from patients remotely, and they are working on integrating these systems into smaller robots that could operate in a variety of environments, such as field hospitals or ambulances.
Other authors of the paper include Farah Dadabhoy, Hen-wei Huang, Jacqueline Chu, Annie Feng, Hien Le, Joy Collins, Marco da Silva, Marc Raibert, Chin Hur, and Edward Boyer. The research was funded by the National Institutes of Health, the Hans and Mavis Lopater Psychosocial Foundation, e-ink corporation, the Karl Van Tassel (1925) Career Development Professorship, MIT’s Department of Mechanical Engineering, and the Brigham and Women’s Hospital Division of Gastroenterology.

Using theoretical models of bacterial metabolism and reproduction, scientists can predict the type of resistance that bacteria will develop when they are exposed to antibiotics. This has now been shown by an Uppsala University research team, in collaboration with colleagues in Cologne, Germany. The study is published in the journal Nature Ecology and Evolution.
In medical and pharmaceutical research, there is keen interest in finding the answer to how fast, and through which mechanisms, bacteria develop antibiotic resistance. Another goal is to understand how this resistance, in turn, affects bacterial growth and pathogenicity.
“This kind of knowledge would enable better tracking and slowing of the emergence of resistance, and thereby lengthen the period for which antibiotics are viable as effective treatments of bacterial infections. It would also create potential for new types of antibiotics and therapeutic methods that entail a lower risk of resistance development,” says Dan I. Andersson, Professor of Medical Bacteriology at Uppsala University.
When genetically adapting to a new environment, an organism undergoes mutations that modify its traits. Other recent studies have shown the difficulty of predicting which mutations will arise when bacteria adapt to new living conditions. For example, if a bacterium migrates to new surroundings with very low nutrient levels, its response will presumably be to evolve towards improved use of the limited resources. On the other hand, predicting the kinds of mutations that bring about this adaptation is much more difficult.
In the new study, the scientists generated a theoretical model that links both the degree and the type of resistance developed by the bacterium to its capacity to grow and divide. In their experiments, the researchers were then able to see that the more resistant the bacterium became, the more its ability of nutrient uptake deteriorated. This previously unobserved connection enabled them to predict which kinds of mutation would arise and how much resistance they would confer when the mutated bacteria were exposed to various levels of antibiotics. The results showed that a low antibiotic dose caused a particular sort of mutation to appear, while a high concentration resulted in changes of another kind.
“Our work is the first step towards developing models that connect bacterial metabolism and growth with mechanisms underlying resistance. That would pave the way for predicting ways in which bacteria change when they are exposed to antibiotics. The results also demonstrate the importance of combining theoretical models with experimental analyses to understand how bacterial metabolism is optimised under various growth conditions,” Andersson says.

Story Source:
Materials provided by Uppsala University. Note: Content may be edited for style and length.

COVID-19 is only the latest infectious disease to have had an outsized impact on human life. A new study employing ancient human DNA reveals how tuberculosis has affected European populations over the past 2,000 years, specifically the impact that disease has had on the human genome. This work, which publishes March 4 in the American Journal of Human Genetics, has implications for studying not only evolutionary genetics, but also how genetics can influence the immune system.
“Present-day humans are the descendants of those who have survived many things — climate changes and big epidemics, including the Black Death, Spanish flu, and tuberculosis,” says senior author Lluis Quintana-Murci of the Institut Pasteur in France. “This work uses population genetics to dissect how natural selection has acted on our genomes.”
This research focused on a variant of the gene TYK2, called P1104A, which first author Gaspard Kerner had previously found to be associated with an increased risk of becoming ill after infection with Mycobacterium tuberculosis when the variant is homozygous. (TYK2 has been implicated in immune function through its effect on interferon signaling pathways.) Kerner, a PhD student studying genetic diseases at the Imagine Institute of Paris University, began collaborating with Quintana-Murci, an expert in evolutionary genomics, to study the genetic determinants of human tuberculosis in the context of evolution and natural selection.
Using a large dataset of more than 1,000 European ancient human genomes, the investigators found that the P1104A variant first emerged more than 30,000 years ago. Further analysis revealed that the frequency of the variant drastically decreased about 2,000 years ago, around the time that present-day forms of infectious Mycobacterium tuberculosis strains became prevalent. The variant is not associated with other infectious bacteria or viruses.
“If you carry two copies of this variant in your genome and you encounter Mycobacterium tuberculosis, you are very likely to become sick,” Kerner says. “During the Bronze Age, this variant was much more frequent, but we saw that it started to be negatively selected at a time that correlated with the start of the tuberculosis epidemic in Europe.”
“The beauty of this work is that we’re using a population genetics approach to reconstruct the history of an epidemic,” Quintana-Murci explains. “We can use these methods to try to understand which immune gene variants have increased the most over the last 10,000 years, indicating that they are the most beneficial, and which have decreased the most, due to negative selection.”
He adds that this type of research can be complementary to other types of immunology studies, such as those performed in the laboratory. Moreover, both researchers say these tools can be used to study the history and implications of many different genetic variants for multiple infectious diseases.

Story Source:
Materials provided by Cell Press. Note: Content may be edited for style and length.