Decrease in specific gene ‘silencing’ molecules linked with pediatric brain tumors

Experimenting with lab-grown brain cancer cells, Johns Hopkins Medicine researchers have added to evidence that a shortage of specific tiny molecules that silence certain genes is linked to the development and growth of pediatric brain tumors known as low-grade gliomas.

A report of the findings was published this fall 2018 in Scientific Reports, and supports the idea of increasing levels of microRNAs as a potential means of treating these tumors.

An estimated 1,600 cases of pediatric low-grade gliomas (PLGGs) are diagnosed annually in the United States, and the vast majority of these slow-growing tumors are treatable and curable mainly by surgical removal, although in some cases surgery has the potential to damage critical nearby brain tissue, depending on tumor location. Unlike high-grade glioblastomas such as the one that took the life of Arizona Senator John McCain, PLGGs mostly affect school-age children and young adults.

“It has long been known that microRNAs play a role in controlling various tumor properties such as growth,” says Fausto Rodriguez, M.D., associate professor of pathology at the Johns Hopkins University School of Medicine and the study’s senior author.

“Our findings identified a subset of microRNAs that, in sufficient quantity, seem to decrease the growth and invasion of cancerous cells in pediatric low-grade gliomas.”

MicroRNAs are tiny molecules that, in ways similar to how an orchestra conductor controls the flow of each instrument group, command the expression of entire gene networks that make proteins by essentially silencing them, and are responsible for regulating biological processes such as nutrient intake, cell growth and cell death. Altered levels of specific microRNAs can disrupt entire biological pathways just as a misguided section of an orchestra can unsettle an entire score.

“One microRNA can target multiple genes and have a profound effect on cell processes, and the alterations are dynamic,” notes Rodriguez, who says PLGGs are good candidates for analyzing microRNA types and levels because genetically PLGGs are stable compared with other tumors. That makes it relatively easier, he says, to identify any relevant genetic abnormalities and potential targets for therapy.

For the new study, the researchers first analyzed previously gathered microRNA subtype data in two studies. They examined tumors from 125 patients with low-grade gliomas for levels of a specific microRNA, known as miR-125b, using chromogenic in situ hybridization (CISH), a technique that is applicable to routinely processed tissue in pathology and allows for identification of specific microRNAs in the cells of interest. Levels of this microRNA were lower in 43 pilocytic astrocytomas (the most common subtype of PLGG) when compared with 24 diffuse astrocytomas and normal brain tissues.

Rodriguez and the research team next looked at eight cancerous cell lines derived from brain (glial) tumors in children for levels of microRNA 125b-p using a method that can rapidly make thousands to millions of copies of a genetic sequence for easier analysis of how much of a gene is expressed. Although levels of microRNA 125b-p varied across the lab-grown cell lines, they were significantly and uniformly lower in cancerous cell lines than noncancerous cell lines, Rodriguez reports.

In further experiments designed to identify the role of these microRNAs in cell growth, the investigators increased levels of miR-125b in cancerous cell lines by introducing a DNA segment in the tumor cells using specific viruses, and saw a decrease in cell division and growth. To check whether cell death contributed to this decrease in cell growth, Rodriguez stained cells containing high levels of microRNA 125b and noted cell death in all cell lines, suggesting that increasing levels of microRNA 125b can stop the growth of PLGG.

“These findings are an example of where advances in precision medicine might take us, and show how, someday, increasing levels of specific genes and microRNAs might be a targeted treatment for PLGGs,” says Rodriguez.

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Why modest goals are so appealing: Achieving a small incremental goal is perceived as easier — and more satisfying — than maintaining the status quo

Thanks to a quirk in the way our brain evaluates goals, people feel it’s easier to achieve a small incremental goal than to maintain the status quo, when both goals are assessed in isolation. This is especially true if the context is seen as unfavourable.

This finding, which contrasts with the popular belief that no change is easier than any change, is the fruit of research led by marketing professors from INSEAD, IE Business School and Pamplin College of Business.

“When evaluating goal difficulty, our brain first considers the gap between the starting point and the desired state. Usually, the bigger the gap, the more difficult the goal. However, if there is no gap to speak of, as in the case of a status quo goal, the brain starts scanning the context, anticipating potential reasons for failure,” said study co-author Amitava Chattopadhyay, Professor of Marketing and the GlaxoSmithKline Chaired Professor of Corporate Innovation at INSEAD.

For example, if your goal is to keep the same weight this year, you may start considering the odds of you regularly eating out due to a high workload, the number of your upcoming business trips, the fact that a new donut shop has opened in your neighbourhood, etc.

“Our assessment of context is peculiar in the sense that it is greatly impacted by a negativity bias,” says Antonios Stamatogiannakis, Assistant Professor of Marketing at IE Business School. Our brain has evolved over the millennia to be more sensitive to bad news than good news. Most of us instinctively give more weight to potential reasons for failure than reasons for success.

When a status quo goal is directly compared to one that involves a modest improvement, objectivity prevails: The absence of a gap makes the status quo goal seems easier, as logic would dictate. Nevertheless, in such a direct comparison scenario, study participants still preferred to pursue a small incremental goal over a “maintenance” goal, as they expected this achievement to be more satisfying.

These results are described in “Attainment versus Maintenance Goals: Perceived Difficulty and Impact on Goal Choice,” a paper co-authored by Chattopadhyay, Stamatogiannakis and Dipankar Chakravarti, Professor of Marketing at Pamplin College of Business. Their paper was published in the November 2018 issue of Organizational Behavior and Human Decision Processes.

A two-step process

Across six studies, Chattopadhyay and his study co-authors showed that the brain assesses goal difficulty using a two-step process. First comes the size of the gap to be bridged. But if that gap is zero, the brain defaults to the second step, which is the context in which the goal is to be achieved. Context assessment usually triggers negativity bias, which is why, when judged in isolation, a maintenance goal is deemed more difficult than one involving a small increment.

In the first studies, participants were split into groups that each evaluated the difficulty of a particular goal type. While the difficulty of the goal was generally correlated to the gap size, goals that involved a modest increment were rated as easier than those involving the status quo (rated separately). When asked to explain their ratings, participants evaluating status quo goals were quick to mention all the obstacles that could crop up. In later studies, participants were more interested in pursuing a modest-attainment goal than to maintain the status quo, even when real money was in play.

Implications

Managers setting goals such as sales quotas should be aware that status quo goals are less attractive than ones involving a slight increment. This may be especially true if the economy is in a downturn, as a status quo goal will precisely draw the staff’s attention to the negative context and have a demoralising effect.

“Marketing-wise, promotions requiring consumers to achieve modest attainment goals, such as a small increase in a customer’s account balance in the case of a bank, may prove more popular than promotions involving no such goal,” says Chattopadhyay.

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New deep knowledge AI system could resolve bottlenecks in drug research

Researchers at the University of Waterloo have developed a new system that could significantly speed up the discovery of new drugs and reduce the need for costly and time-consuming laboratory tests.

The new technology called Pattern to Knowledge (P2K) can predict the binding of biosequences in seconds and potentially reduce bottlenecks in drug research.

P2K uses artificial intelligence (AI) to leverage deep knowledge from data instead of relying solely on classical machine learning.

“P2K is a game changer given its ability to reveal subtle protein associations entangled in complex physiochemical environments and powerfully predict interactions based only on sequence data,” said Andrew Wong, professor, Systems Design Engineering, and Founding Director, Centre for Pattern Analysis and Machine Intelligence (CPAMI). “The ability to access this deep knowledge from proven scientific results will shift biological research going forward. P2K has the power to transform how data could be used in the future.”

Although a large amount of biological sequence data has been collected, extracting meaningful and useful knowledge hasn’t been easy. P2K algorithms tackle this challenge by disentangling multiple associations to identify and predict amino acid bindings that govern protein interactions. Since P2K is much faster than existing biosequence analysis software with almost 30 per cent better prediction accuracy, it could significantly speed up the discovery of new drugs. By drawing information from databases in the Cloud, P2K could predict how tumour proteins and potential cancer treatments would interact.

Although still in the early prototype stage, Professor Wong and his team have made the online P2K system available publicly to researchers to start identifying new bio-sequence interactions.

“Putting this AI technology in the hands of biomedical researchers will generate immediate results, which could be used for future scientific discoveries,” said Antonio Sze-To, research associate, Systems Design Engineering, and co-inventor of P2K.

Since it analyzes sequential data, the applicability of P2K isn’t limited to biomedical research. P2K could benefit the financial industry by making useful associations and predictions for smart trading or the cybersecurity sector by predicting the likelihood of a potential cyber attack.

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Artificial intelligence predicts Alzheimer’s years before diagnosis

Artificial intelligence (AI) technology improves the ability of brain imaging to predict Alzheimer’s disease, according to a study published in the journal Radiology.

Timely diagnosis of Alzheimer’s disease is extremely important, as treatments and interventions are more effective early in the course of the disease. However, early diagnosis has proven to be challenging. Research has linked the disease process to changes in metabolism, as shown by glucose uptake in certain regions of the brain, but these changes can be difficult to recognize.

“Differences in the pattern of glucose uptake in the brain are very subtle and diffuse,” said study co-author Jae Ho Sohn, M.D., from the Radiology & Biomedical Imaging Department at the University of California in San Francisco (UCSF). “People are good at finding specific biomarkers of disease, but metabolic changes represent a more global and subtle process.”

The study’s senior author, Benjamin Franc, M.D., from UCSF, approached Dr. Sohn and University of California, Berkeley, undergraduate student Yiming Ding through the Big Data in Radiology (BDRAD) research group, a multidisciplinary team of physicians and engineers focusing on radiological data science. Dr. Franc was interested in applying deep learning, a type of AI in which machines learn by example much like humans do, to find changes in brain metabolism predictive of Alzheimer’s disease.

The researchers trained the deep learning algorithm on a special imaging technology known as 18-F-fluorodeoxyglucose positron emission tomography (FDG-PET). In an FDG-PET scan, FDG, a radioactive glucose compound, is injected into the blood. PET scans can then measure the uptake of FDG in brain cells, an indicator of metabolic activity.

The researchers had access to data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a major multi-site study focused on clinical trials to improve prevention and treatment of this disease. The ADNI dataset included more than 2,100 FDG-PET brain images from 1,002 patients. Researchers trained the deep learning algorithm on 90 percent of the dataset and then tested it on the remaining 10 percent of the dataset. Through deep learning, the algorithm was able to teach itself metabolic patterns that corresponded to Alzheimer’s disease.

Finally, the researchers tested the algorithm on an independent set of 40 imaging exams from 40 patients that it had never studied. The algorithm achieved 100 percent sensitivity at detecting the disease an average of more than six years prior to the final diagnosis.

“We were very pleased with the algorithm’s performance,” Dr. Sohn said. “It was able to predict every single case that advanced to Alzheimer’s disease.”

Although he cautioned that their independent test set was small and needs further validation with a larger multi-institutional prospective study, Dr. Sohn said that the algorithm could be a useful tool to complement the work of radiologists—especially in conjunction with other biochemical and imaging tests—in providing an opportunity for early therapeutic intervention.

“If we diagnose Alzheimer’s disease when all the symptoms have manifested, the brain volume loss is so significant that it’s too late to intervene,” he said. “If we can detect it earlier, that’s an opportunity for investigators to potentially find better ways to slow down or even halt the disease process.”

Future research directions include training the deep learning algorithm to look for patterns associated with the accumulation of beta-amyloid and tau proteins, abnormal protein clumps and tangles in the brain that are markers specific to Alzheimer’s disease, according to UCSF’s Youngho Seo, Ph.D., who served as one of the faculty advisors of the study.

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Traumatic brain injuries can lead to long-term neurological and psychiatric disorders

Traumatic brain injury is a leading cause of morbidity and mortality in children, and rates of injury have increased over the past decade. According to a study being presented at the 2018 American Academy of Pediatrics National Conference & Exhibition, these injuries have long-term consequences; researchers found children who experience traumatic brain injury are at higher risk of developing headache, depression, and mental or intellectual disorders up to five years after the event.

For the study abstract, “Long-term Outcomes Following Traumatic Brain Injury (TBI) in Children,” researchers obtained diagnostic codes from medical records in the Military Health System Data Repository to analyze clinical data on children. They compared patients diagnosed with traumatic brain injury to those who suffered orthopedic injury, matching patients by age, gender and injury severity score.

In the study population, 55 percent had mild injury severity score, 41 percent had moderate injury severity score, and 4 percent had severe injury severity score. Among children who sustained traumatic brain injury, 39 percent of children developed neuropsychiatric symptoms as follows:

  • Headaches — 15 percent
  • Mental disorder — 15 percent
  • Intellectual disability — 13 percent
  • Depression/anxiety — 5 percent
  • Seizure — 4 percent
  • Brain damage — 4 percent

Researchers found that 16 percent of children who experienced orthopedic injury also developed neuropsychiatric symptoms including:

  • Intellectual disability — 8 percent
  • Mental disorder — 4 percent
  • Depression/anxiety — 3 percent
  • Headaches — 2 percent
  • Seizure — less than 1 percent
  • Brain damage — less than 1 percent

“With the incidence of concussion and traumatic brain injury rising in this nation’s children, it is vital that we continue to evaluate mechanisms for prevention and treatment,” said Lindsey Armstrong, MD MPH, surgical critical care and research fellow, Boston Children’s Hospital, Boston, Mass. “These data provide evidence to support close monitoring of injured children, even years after the event”

Researchers examined how injuries affected children up to five years later. They found that only 59 percent of children with traumatic brain injury could expect to be symptom-free in 5 years, versus 80 percent of those with orthopedic injuries.

“While primary prevention is most important, early recognition and education are essential to ensure the best possible outcome for these children,” Armstrong said. “Neuropsychiatric diagnosis following traumatic brain injury can cause impairment in cognitive function thus affected children may experience difficulty in school or with personal relationships. It’s our hope that data we are presenting will help clinicians identify children at increased risk, resulting in improved follow-up and care.”

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