(Image caption: An abnormal protein, left, is intercepted by the UW’s compound that can bind to the toxic protein and neutralize it, as shown at right. Image courtesy: University of Washington)

New protein structure could help treat Alzheimer’s, related diseases

There is no cure for Alzheimer’s disease and other forms of dementia, but the research community is one step closer to finding treatment.

University of Washington bioengineers have designed a peptide structure that can stop the harmful changes of the body’s normal proteins into a state that’s linked to widespread diseases such as Alzheimer’s, Parkinson’s, heart disease, Type 2 diabetes and Lou Gehrig’s disease. The synthetic molecule blocks these proteins as they shift from their normal state into an abnormally folded form by targeting a toxic intermediate phase.

The discovery of a protein blocker could lead to ways to diagnose and even treat a large swath of diseases that are hard to pin down and rarely have a cure.

“If you can truly catch and neutralize the toxic version of these proteins, then you hopefully never get any further damage in the body,” said senior author Valerie Daggett, a UW professor of bioengineering. “What’s critical with this and what has never been done before is that a single peptide sequence will work against the toxic versions of a number of different amyloid proteins and peptides, regardless of their amino acid sequence or the normal 3-D structures.”

The findings were published online this month in the journal eLife.

More than 40 illnesses known as amyloid diseases – Alzheimer’s, Parkinson’s and rheumatoid arthritis are a few – are linked to the buildup of proteins after they have transformed from their normally folded, biologically active forms to abnormally folded, grouped deposits called fibrils or plaques. This happens naturally as we age, to a certain extent – our bodies don’t break down proteins as quickly as they should, causing higher concentrations in some parts of the body.

Each amyloid disease has a unique, abnormally folded protein or peptide structure, but often such diseases are misdiagnosed because symptoms can be similar and pinpointing which protein is present usually isn’t done until after death, in an autopsy.

As a result, many dementias are broadly diagnosed as Alzheimer’s disease without definitive proof, and other diseases can go undiagnosed and untreated.

The molecular structure of an amyloid protein can be only slightly different from a normal protein and can transform to a toxic state fairly easily, which is why amyloid diseases are so prevalent. The researchers built a protein structure, called “alpha sheet,” that complements the toxic structure of amyloid proteins that they discovered in computer simulations. The alpha sheet effectively attacks the toxic middle state the protein goes through as it transitions from normal to abnormal.

The structures could be tailored even further to bind specifically with the proteins in certain diseases, which could be useful for specific therapies.

The researchers hope their designed compounds could be used as diagnostics for amyloid diseases and as drugs to treat the diseases or at least slow progression.

“For example, patients could have a broad first-pass test done to see if they have an amyloid disease and then drill down further to determine which proteins are present to identify the specific disease,” Daggett said.

Glucose ‘control switch’ in the brain key to both types of diabetes

Researchers at Yale School of Medicine have pinpointed a mechanism in part of the brain that is key to sensing glucose levels in the blood, linking it to both type 1 and type 2 diabetes. The findings are published in the July 28 issue of Proceedings of the National Academies of Sciences.

“We’ve discovered that the prolyl endopeptidase enzyme — located in a part of the hypothalamus known as the ventromedial nucleus — sets a series of steps in motion that control glucose levels in the blood,” said lead author Sabrina Diano, professor in the Departments of Obstetrics, Gynecology & Reproductive Sciences, Comparative Medicine, and Neurobiology at Yale School of Medicine. “Our findings could eventually lead to new treatments for diabetes.”

The ventromedial nucleus contains cells that are glucose sensors. To understand the role of prolyl endopeptidase in this part of the brain, the team used mice that were genetically engineered with low levels of this enzyme. They found that in absence of this enzyme, mice had high levels of glucose in the blood and became diabetic.

Diano and her team discovered that this enzyme is important because it makes the neurons in this part of the brain sensitive to glucose. The neurons sense the increase in glucose levels and then tell the pancreas to release insulin, which is the hormone that maintains a steady level of glucose in the blood, preventing diabetes.

“Because of the low levels of endopeptidase, the neurons were no longer sensitive to increased glucose levels and could not control the release of insulin from the pancreas, and the mice developed diabetes.” said Diano, who is also a member of the Yale Program in Integrative Cell Signaling and Neurobiology of Metabolism.

Diano said the next step in this research is to identify the targets of this enzyme by understanding how the enzyme makes the neurons sense changes in glucose levels. “If we succeed in doing this, we could be able to regulate the secretion of insulin, and be able to prevent and treat type 2 diabetes,” she said.

Memory relies on astrocytes, the brain’s lesser known cells

When you’re expecting something—like the meal you’ve ordered at a restaurant—or when something captures your interest, unique electrical rhythms sweep through your brain.

These waves are called gamma oscillations and they reflect a symphony of cells—both excitatory and inhibitory—playing together in an orchestrated way. Though their role has been debated, gamma waves have been associated with higher-level brain function, and disturbances in the patterns have been tied to schizophrenia, Alzheimer’s disease, autism, epilepsy and other disorders.

Now, new research from the Salk Institute shows that little known supportive cells in the brain known as astrocytes may in fact be major players that control these waves.

In a study published July 28 in the Proceedings of the National Academy of Sciences, Salk researchers report a new, unexpected strategy to turn down gamma oscillations, by disabling not neurons but astrocytes—cells type traditionally thought to provide more of a support role in the brain. In the process, the team showed that astrocytes, and the gamma oscillations they help shape, are critical for some forms of memory.

"This is what could be called a smoking gun," says co-author Terrence Sejnowski, head of the Computational Neurobiology Laboratory at the Salk Institute for Biological Sciences and a Howard Hughes Medical Institute investigator. "There are hundreds of papers linking gamma oscillations with attention and memory, but they are all correlational. This is the first time we have been able to do a causal experiment, where we selectively block gamma oscillations and show that it has a highly specific impact on how the brain interacts with the world."

A collaboration among the labs of Salk professors Sejnowski, Inder Verma and Stephen Heinemann found that activity in the form of calcium signaling in astrocytes immediately preceded gamma oscillations in the brains of mice. This suggested that astrocytes, which use many of the same chemical signals as neurons, could be influencing these oscillations.

To test their theory, the group used a virus carrying tetanus toxin to disable the release of chemicals released selectively from astrocytes, effectively eliminating the cells’ ability to communicate with neighboring cells. Neurons were unaffected by the toxin.

After adding a chemical to trigger gamma waves in the animals’ brains, the researchers found that brain tissue with disabled astrocytes produced shorter gamma waves than in tissue containing healthy cells. And after adding three genes that would allow the researchers to selectively turn on and off the tetanus toxin in astrocytes at will, they found that gamma waves were dampened in mice whose astrocytes were blocked from signaling. Turning off the toxin reversed this effect.

The mice with the modified astrocytes seemed perfectly healthy. But after several cognitive tests, the researchers found that they failed in one major area: novel object recognition. A healthy mouse spent more time with a new item placed in its environment than it did with familiar items, as expected.

In contrast, the group’s new mutant mouse treated all objects the same. “That turned out to be a spectacular result in the sense that novel object recognition memory was not just impaired, it was gone—as if we were deleting this one form of memory, leaving others intact,” Sejnowski says.

The results were surprising, in part because astrocytes operate on a seconds- or longer timescale whereas neurons signal far faster, on the millisecond scale. Because of that slower speed, no one suspected astrocytes were involved in the high-speed brain activity needed to make quick decisions.

"What I thought quite unique was the idea that astrocytes, traditionally considered only guardians and supporters of neurons and other cells, are also involved in the processing of information and in other cognitive behavior," says Verma, a professor in the Laboratory of Genetics and American Cancer Society Professor.

It’s not that astrocytes are quick—they’re still slower than neurons. But the new evidence suggests that astrocytes are actively supplying the right environment for gamma waves to occur, which in turn makes the brain more likely to learn and change the strength of its neuronal connections.

Sejnowski says that the behavioral result is just the tip of the iceberg. “The recognition system is hugely important,” he says, adding that it includes recognizing other people, places, facts and things that happened in the past. With this new discovery, scientists can begin to better understand the role of gamma waves in recognition memory, he adds.

Scientists find 6 new genetic risk factors for Parkinson’s

Using data from over 18,000 patients, scientists have identified more than two dozen genetic risk factors involved in Parkinson’s disease, including six that had not been previously reported. The study, published in Nature Genetics, was partially funded by the National Institutes of Health (NIH) and led by scientists working in NIH laboratories.

"Unraveling the genetic underpinnings of Parkinson’s is vital to understanding the multiple mechanisms involved in this complex disease, and hopefully, may one day lead to effective therapies," said Andrew Singleton, Ph.D., a scientist at the NIH’s National Institute on Aging (NIA) and senior author of the study.

Dr. Singleton and his colleagues collected and combined data from existing genome-wide association studies (GWAS), which allow scientists to find common variants, or subtle differences, in the genetic codes of large groups of individuals. The combined data included approximately 13,708 Parkinson’s disease cases and 95,282 controls, all of European ancestry.

The investigators identified potential genetic risk variants, which increase the chances that a person may develop Parkinson’s disease. Their results suggested that the more variants a person has, the greater the risk, up to three times higher, for developing the disorder in some cases.

"The study brought together a large international group of investigators from both public and private institutions who were interested in sharing data to accelerate the discovery of genetic risk factors for Parkinson’s disease," said Margaret Sutherland, Ph.D., a program director at the National Institute of Neurological Disorders and Stroke (NINDS), part of NIH. "The advantage of this collaborative approach is highlighted in the identification of pathways and gene networks that may significantly increase our understanding of Parkinson’s disease."

To obtain the data, the researchers collaborated with multiple public and private organizations, including the U.S. Department of Defense, the Michael J. Fox Foundation, 23andMe and many international investigators.

Affecting millions of people worldwide, Parkinson’s disease is a degenerative disorder that causes movement problems, including trembling of the hands, arms, or legs, stiffness of limbs and trunk, slowed movements and problems with posture. Over time, patients may have difficulty walking, talking, or completing other simple tasks. Although nine genes have been shown to cause rare forms of Parkinson’s disease, scientists continue to search for genetic risk factors to provide a complete genetic picture of the disorder.

The researchers confirmed the results in another sample of subjects, including 5,353 patients and 5,551 controls. By comparing the genetic regions to sequences on a state-of-the-art gene chip called NeuroX, the researchers confirmed that 24 variants represent genetic risk factors for Parkinson’s disease, including six variants that had not been previously identified. The NeuroX gene chip contains the codes of approximately 24,000 common genetic variants thought to be associated with a broad spectrum of neurodegenerative disorders.

"The replication phase of the study demonstrates the utility of the NeuroX chip for unlocking the secrets of neurodegenerative disorders," said Dr. Sutherland. "The power of these high tech, data-driven genomic methods allows scientists to find the needle in the haystack that may ultimately lead to new treatments."

Some of the newly identified genetic risk factors are thought to be involved with Gaucher’s disease, regulating inflammation and the nerve cell chemical messenger dopamine as well as alpha-synuclein, a protein that has been shown to accumulate in the brains of some cases of Parkinson’s disease. Further research is needed to determine the roles of the variants identified in this study.

(Image caption: Techniques known as dimensionality reduction can help find patterns in the recorded activity of thousands of neurons. Rather than look at all responses at once, these methods find a smaller set of dimensions — in this case three — that capture as much structure in the data as possible. Each trace in these graphics represents the activity of the whole brain during a single presentation of a moving stimulus, and different versions of the analysis capture structure related either to the passage of time (left) or the direction of the motion (right). The raw data is the same in both cases, but the analyses finds different patterns. Credit: Jeremy Freeman, Nikita Vladimirov, Takashi Kawashima, Yu Mu, Nicholas Sofroniew, Davis Bennett, Joshua Rosen, Chao-Tsung Yang, Loren Looger, Philipp Keller, Misha Ahrens)

New Tools Help Neuroscientists Analyze Big Data

In an age of “big data,” a single computer cannot always find the solution a user wants. Computational tasks must instead be distributed across a cluster of computers that analyze a massive data set together. It’s how Facebook and Google mine your web history to present you with targeted ads, and how Amazon and Netflix recommend your next favorite book or movie. But big data is about more than just marketing.

New technologies for monitoring brain activity are generating unprecedented quantities of information. That data may hold new insights into how the brain works – but only if researchers can interpret it. To help make sense of the data, neuroscientists can now harness the power of distributed computing with Thunder, a library of tools developed at the Howard Hughes Medical Institute’s Janelia Research Campus.

Thunder speeds the analysis of data sets that are so large and complex they would take days or weeks to analyze on a single workstation – if a single workstation could do it at all. Janelia group leaders Jeremy Freeman, Misha Ahrens, and other colleagues at Janelia and the University of California, Berkeley, report in the July 27, 2014, issue of the journal Nature Methods that they have used Thunder to quickly find patterns in high-resolution images collected from the brains of active zebrafish and mice with multiple imaging techniques.

Importantly, they have used Thunder to analyze imaging data from a new microscope that Ahrens and colleagues developed to monitor the activity of nearly every individual cell in the brain of a zebrafish as it behaves in response to visual stimuli. That technology is described in a companion paper published in the same issue of Nature Methods.

Thunder can run on a private cluster or on Amazon’s cloud computing services. Researchers can find everything they need to begin using the open source library of tools at http://freeman-lab.github.io/thunder

New microscopes are capturing images of the brain faster, with better spatial resolution, and across wider regions of the brain than ever before. Yet all that detail comes encrypted in gigabytes or even terabytes of data. On a single workstation, simple calculations can take hours. “For a lot of these data sets, a single machine is just not going to cut it,” Freeman says.

It’s not just the sheer volume of data that exceeds the limits of a single computer, Freeman and Ahrens say, but also its complexity. “When you record information from the brain, you don’t know the best way to get the information that you need out of it. Every data set is different. You have ideas, but whether or not they generate insights is an open question until you actually apply them,” says Ahrens.

Neuroscientists rarely arrive at new insights about the brain the first time they consider their data, he explains. Instead, an initial analysis may hint at a more promising approach, and with a few adjustments and a new computational analysis, the data may begin to look more meaningful. “Being able to apply these analyses quickly — one after the other — is important. Speed gives a researcher more flexibility to explore and get new ideas.”

That’s why trying to analyze neuroscience data with slow computational tools can be so frustrating. “For some analyses, you can load the data, start it running, and then come back the next day,” Freeman says. “But if you need to tweak the analysis and run it again, then you have to wait another night.” For larger data sets, the lag time might be weeks or months.

Distributed computing was an obvious solution to accelerate analysis while exploring the full richness of a data set, but many alternatives are available. Freeman chose to build on a new platform called Spark. Developed at the University of California, Berkeley’s AMPLab, Spark is rapidly becoming a favored tool for large-scale computing across industry, Freeman says. Spark’s capabilities for data caching eliminates the bottleneck of loading a complete data set for all but the initial step, making it well-suited for interactive, exploratory analysis, and for complex algorithms requiring repeated operations on the same data. And Spark’s elegant and versatile application programming interfaces (APIs) help simplify development. Thunder uses the Python API, which Freeman hopes will make it particularly easy for others to adopt, given Python’s increasing use in neuroscience and data science.

To make Spark suitable for analyzing a broad range of neuroscience data – information about connectivity and activity collected from different organisms and with different techniques – Freeman first developed standardized representations of data that were amenable to distributed computing. He then worked to express typical neuroscience workflows into the computational language of Spark.

From there, he says, the biological questions that he and his colleagues were curious about drove development. “We started with our questions about the biology, then came up with the analyses and developed the tools,” he says.

The result is a modular set of tools that will expand as the Janelia team — and the neuroscience community — add new components. “The analyses we developed are building blocks,” says Ahrens. “The development of new analyses for interpreting large-scale recording is an active field and goes hand-in-hand with the development of resources for large-scale computing and imaging. The algorithms in our paper are a starting point.”

Using Thunder, Freeman, Ahrens, and their colleagues analyzed images of the brain in minutes, interacting with and revising analyses without the lengthy delays associated with previous methods. In images taken of a mouse brain with a two-photon microscope, for example, the team found cells in the brain whose activity varied with running speed.

For analyzing much larger data sets, tools such as Thunder are not just helpful, they are essential, the scientists say. This is true for the information collected by the new microscope that Ahrens and colleagues developed for monitoring whole-brain activity in response to visual stimuli.

Last year, Ahrens and Janelia group leader Phillip Keller used high-speed light-sheet imaging to engineer a microscope that captures neuronal activity cell by cell across nearly the entire brain of a larval zebrafish. That microscope produced stunning images of neurons in the zebrafish brain firing while the fish was inactive. But Ahrens wanted to use the technology to study the brain’s activity in more complex situations. Now, the team has combined their original technology with a virtual-reality swim simulator that Ahrens previously developed to provide fish with visual feedback that simulates movement.

In a light sheet microscope, a sheet of laser light scans across a sample, illuminating a thin section at a time. To enable a fish in the microscope to see and respond to its virtual-reality environment, Ahrens’ team needed to protect its eyes. So they programmed the laser to quickly shut off when its light sheet approaches the eye and restart once the area is cleared. Then they introduced a second laser that scans the sample from a different angle to ensure that the region of the brain behind the eyes is imaged. Together, the two lasers image the brain with nearly complete coverage without interfering with the animal’s vision.

Combining these two technologies lets Ahrens monitor activity throughout the brain as a fish adjusts its behavior based on the sensory information it receives. The technique generates about a terabyte of data in an hour – presenting a data analysis challenge that helped motivate the development of Thunder. When Freeman and Ahrens applied their new tools to the data, patterns quickly emerged. As examples, they identified cells whose activity was associated with movement in particular directions and cells that fired specifically when the fish was at rest, and were able to characterize the dynamics of those cells’ activities. Example analyses like these, and example data sets, are available at the website http://research.janelia.org/zebrafish/.

Ahrens now plans to explore more complex questions using the new technology, and both he and Freeman foresee expansion of Thunder. “At every level, this is really just the beginning,” Freeman says.