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Why Flies and Humans Freeze When Startled

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When faced with something unexpected and potentially ominous, like a sudden, loud noise or a threat of danger, humans often freeze before we act. This is colloquially referred to as the “deer in the headlights” phenomenon. The movie of fruit flies that you see above may help explain the ancient origins of the “startle response” and other biomechanical aspects of motion.

In this video, which shows a footrace between two flies (Drosophila melanogaster), there are no winners or losers. Their dash across the screen provides a world-class view of the biomechanics of walking in these tiny, 3 millimeter-long insects that just won’t sit still.

The fly at the top zips along at about 25 millimeters per second, the normal walking speed for Drosophila. As a six-legged hexapod, the fly walks with a “tripod gait,” alternating between its stance phase—right fore (RF), left middle (LM), and right hind (RH) —and its swing phase sequence of left fore (LF), right middle (RM), and left hind (LH).

The slowpoke at the bottom of the video clocks in at a mere 15 millimeters per second. This fly’s more-tentative gait isn’t due to an injury or a natural lack of speed. What is causing the delay is the rapid release of the chemical messenger serotonin into its nervous system, which models a startle response.

You may have already heard about serotonin because of its role in regulating mood and appetite in humans. Now, a team led by Richard S. Mann and Clare Howard, Columbia University’s Zuckerman Institute, New York, has discovered that fruit flies naturally release serotonin to turn on neural circuits that downshift and steady the speed of their gait.

As detailed recently in Current Biology [1], serotonin is active under myriad conditions to tell flies to slow things down. For example, serotonin helps flies weather the stress of extreme temperatures, conserve energy during bouts of hunger, and even walk upside down on the ceiling.

But the research team, which was supported by the NIH-led Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative, found that serotonin’s most-powerful effect came during an actual startle response, prompted by a sudden, jolting vibration. Scientists suspect the release of serotonin activates motor neurons much like an emergency brake, stiffening and locking up the fly’s leg joints. When the researchers blocked the fly’s release of serotonin, it interrupted their normal startle response.

In years past, such a detailed, high-resolution “action video” of Drosophila, one of the most-popular model organisms in biology, would have been impossible to produce. Fruit flies are tiny and possess extremely high energy.

But a few years ago, the Mann lab developed the approach used in this video to bring the hurried gait of fruit flies into tight focus [2]. Their system combines an optical touch sensor and high-speed video imaging that records the footfalls of all six of a fly’s feet.

Then, using the lab’s unique software program called FlyWalker , the researchers can extract various biomechanical parameters of walking in time and space. These include step length, footprint alignment, and, as the letters in the video show, the natural sequence of a tripod gait.

Drosophila may be a very distant relative of humans. But these ubiquitous insects that sometimes buzz around our fruit bowls contain many fundamental clues into human biology, whether the area of research is genetics, nutrition, biomechanics, or even the underlying biology of the startle response.

Reference:

[1] Serotonergic Modulation of Walking in Drosophila. Howard CE, Chen CL, Tabachnik T, Hormigo R, Ramdya P, Mann RS. Curr Biol. 2019 Nov 22.

[2] Quantification of gait parameters in freely walking wild type and sensory deprived Drosophila melanogaster. Mendes CS, Bartos I, Akay T, Márka S, Mann RS. Elife. 2013 Jan 8;2:e00231.

Links:

Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative (NIH)

Mann Lab (Columbia University’s Zuckerman Institute, New York)

MouseWalker Colored Feet (YouTube)

NIH Support: National Institute for Neurological Disorders and Stroke; National Institute of General Medical Sciences


Largest-Ever Genetic Study of Autism Yields New Insights

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Baby and DNA Strands

Anyone who’s spent time with people affected by autism spectrum disorder (ASD) can tell you that it’s a very complex puzzle. The wide variability seen among individuals with this group of developmental brain disorders, which can disrupt communication, behavior control, and social skills, has also posed a huge challenge for researchers trying to identify underlying genetic and environmental factors. So, it’s no surprise that there’s been considerable interest in the recent findings of the largest-ever genetic study of ASD.

In a landmark study that analyzed the DNA of more than 35,000 people from around the world, the NIH-funded international Autism Sequencing Consortium (ASC) identified variants in 102 genes associated with increased risk of developing ASD, up from 65 identified previously. Of the 102 genes, 60 had not been previously linked to ASD and 53 appeared to be primarily connected to ASD as opposed to other types of intellectual disability or developmental delay. It is expected that this newfound genetic knowledge will serve to improve understanding of the complex biological mechanisms involved in ASD, ultimately paving the way for new approaches to diagnosis and treatment.

The study reported in the journal Cell was led by Joseph Buxbaum, Icahn School of Medicine at Mount Sinai, New York; Stephan Sanders, University of California, San Francisco; Kathryn Roeder, Carnegie Mellon University, Pittsburgh, PA; and Mark Daly, Massachusetts General Hospital, Boston, MA and the Broad Institute of MIT and Harvard, Cambridge, MA. These researchers and their teams faced what might seem like a rather daunting task.

While common genetic variants collectively are known to contribute substantially to ASD, rare variants have been recognized individually as more major contributors to a person’s risk of developing ASD. The challenge was how to find such rare variants—whether inherited or newly arising.

To do so, the researchers needed to analyze a enormous amount of DNA data. Fortunately, they and their ASC colleagues already had assembled a vast trove of data. Over the last decade, the ASC had collected DNA samples with full consent from thousands of people with and without ASD, including unaffected siblings and parents. All were aggregated with other studies, and, at the time of this investigation, they had gathered 35,584 unique samples. Those included more than 21,000 family-based samples and almost 12,000 samples from people diagnosed with ASD.

In search of rare genetic alterations, they sequenced whole exomes, the approximately 1.5 percent of the genome that codes for proteins. Their search produced a list of 102 ASD-associated genes, including 30 that had never been implicated in any developmental brain disorder previously.

But that was just the beginning. Next, the ASC team dug deeper into this list. The researchers knew from previous work that up to half of people with ASD also have an intellectual disability or developmental delay. Many of the associated genes overlap, meaning they play roles in both outcomes. So, in one set of analyses, the team compared the list to the results of another genetic study of people diagnosed with developmental delays, including problems with learning or gross motor skills such as delayed walking.

The detailed comparison allowed them to discern genes that are more associated with features of ASD, as opposed to those that are more specific to these developmental delays. It turns out that 49 of the 102 autism-associated genes were altered more often in people with developmental delay than in those diagnosed with ASD. The other 53 were altered more often in ASD, suggesting that they may be more closely linked to this condition’s unique features.

Further study also showed that people who carried alterations in genes found predominantly in ASD also had better intellectual function. They also were more likely to have learned to walk without a developmental delay.

The 102 new genes fell primarily into one of two categories. Many play a role in the brain’s neural connections. The rest are involved primarily in switching other genes on and off in brain development. Interestingly, they are expressed both in excitatory neurons, which are active in sending signals in the brain, and in inhibitory neurons that squelch such activity. Many of these genes are also commonly expressed in the brain’s cerebral cortex, the outermost part of the brain that is responsible for many complex behaviors.

Overall, these findings underscore that ASD truly does exist on a spectrum. Indeed, there are many molecular paths to this disorder. The ASC researchers continue to collect samples, so we can expect this list of 102 genes will continue to expand in the future.

With these gene discoveries in hand, the researchers will now also turn their attention to unravelling additional details about how these genes function in the brain. The hope is that this growing list of genes will converge on a smaller number of important molecular pathways, pointing the way to new and more precise ways of treating ASD in all its complexity.

Reference:

[1] Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An JY, Peng M, Collins R, Grove J, Klei L, Stevens C, Reichert J, Mulhern MS, Artomov M, Gerges S, Sheppard B, Xu X, Bhaduri A, Norman U, Brand H, Schwartz G, Nguyen R, Guerrero EE, Dias C; Autism Sequencing Consortium; iPSYCH-Broad Consortium, Betancur C, Cook EH, Gallagher L, Gill M, Sutcliffe JS, Thurm A, Zwick ME, Børglum AD, State MW, Cicek AE, Talkowski ME, Cutler DJ, Devlin B, Sanders SJ, Roeder K, Daly MJ, Buxbaum JD.Cell. 2020 Jan 23. {Epub ahead of print]

Links:

Autism Spectrum Disorder (NIH/National Institute of Mental Health)

Joseph Buxbaum (Icahn School of Medicine at Mount Sinai, New York)

Sanders Lab (University of California, San Francisco)

Kathryn Roeder (Carnegie Mellon University, Pittsburgh, PA)

Mark Daly (Broad Institute of MIT and Harvard, Cambridge, MA)

Autism Sequencing Consortium (Emory University, Atlanta)

NIH Support: National Institute Mental Health; National Human Genome Research Institute


A Real-Time Look at Value-Based Decision Making

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All of us make many decisions every day. For most things, such as which jacket to wear or where to grab a cup of coffee, there’s usually no right answer, so we often decide using values rooted in our past experiences. Now, neuroscientists have identified the part of the mammalian brain that stores information essential to such value-based decision making.

Researchers zeroed in on this particular brain region, known as the retrosplenial cortex (RSC), by analyzing movies—including the clip shown about 32 seconds into this video—that captured in real time what goes on in the brains of mice as they make decisions. Each white circle is a neuron, and the flickers of light reflect their activity: the brighter the light, the more active the neuron at that point in time.

All told, the NIH-funded team, led by Ryoma Hattori and Takaki Komiyama, University of California at San Diego, La Jolla, made recordings of more than 45,000 neurons across six regions of the mouse brain [1]. Neural activity isn’t usually visible. But, in this case, researchers used mice that had been genetically engineered so that their neurons, when activated, expressed a protein that glowed.

Their system was also set up to encourage the mice to make value-based decisions, including choosing between two drinking tubes, each with a different probability of delivering water. During this decision-making process, the RSC proved to be the region of the brain where neurons persistently lit up, reflecting how the mouse evaluated one option over the other.

The new discovery, described in the journal Cell, comes as something of a surprise to neuroscientists because the RSC hadn’t previously been implicated in value-based decisions. To gather additional evidence, the researchers turned to optogenetics, a technique that enabled them to use light to inactivate neurons in the RSC’s of living animals. These studies confirmed that, with the RSC turned off, the mice couldn’t retrieve value information based on past experience.

The researchers note that the RSC is heavily interconnected with other key brain regions, including those involved in learning, memory, and controlling movement. This indicates that the RSC may be well situated to serve as a hub for storing value information, allowing it to be accessed and acted upon when it is needed.

The findings are yet another amazing example of how advances coming out of the NIH-led Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative are revolutionizing our understanding of the brain. In the future, the team hopes to learn more about how the RSC stores this information and sends it to other parts of the brain. They note that it will also be important to explore how activity in this brain area may be altered in schizophrenia, dementia, substance abuse, and other conditions that may affect decision-making abilities. It will also be interesting to see how this develops during childhood and adolescence.

Reference:

[1] Area-Specificity and Plasticity of History-Dependent Value Coding During Learning. Hattori R, Danskin B, Babic Z, Mlynaryk N, Komiyama T. Cell. 2019 Jun 13;177(7):1858-1872.e15.

Links:

Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative (NIH)

Komiyama Lab (UCSD, La Jolla)

NIH Support: National Institute of Neurological Disorders and Stroke; National Eye Institute; National Institute on Deafness and Other Communication Disorders


Artificial Intelligence Speeds Brain Tumor Diagnosis

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Real time diagnostics in the operating room
Caption: Artificial intelligence speeds diagnosis of brain tumors. Top, doctor reviews digitized tumor specimen in operating room; left, the AI program predicts diagnosis; right, surgeons review results in near real-time.
Credit: Joe Hallisy, Michigan Medicine, Ann Arbor

Computers are now being trained to “see” the patterns of disease often hidden in our cells and tissues. Now comes word of yet another remarkable use of computer-generated artificial intelligence (AI): swiftly providing neurosurgeons with valuable, real-time information about what type of brain tumor is present, while the patient is still on the operating table.

This latest advance comes from an NIH-funded clinical trial of 278 patients undergoing brain surgery. The researchers found they could take a small tumor biopsy during surgery, feed it into a trained computer in the operating room, and receive a diagnosis that rivals the accuracy of an expert pathologist.

Traditionally, sending out a biopsy to an expert pathologist and getting back a diagnosis optimally takes about 40 minutes. But the computer can do it in the operating room on average in under 3 minutes. The time saved helps to inform surgeons how to proceed with their delicate surgery and make immediate and potentially life-saving treatment decisions to assist their patients.

As reported in Nature Medicine, researchers led by Daniel Orringer, NYU Langone Health, New York, and Todd Hollon, University of Michigan, Ann Arbor, took advantage of AI and another technological advance called stimulated Raman histology (SRH). The latter is an emerging clinical imaging technique that makes it possible to generate detailed images of a tissue sample without the usual processing steps.

The SRH technique starts off by bouncing laser light rapidly through a tissue sample. This light enables a nearby fiberoptic microscope to capture the cellular and structural details within the sample. Remarkably, it does so by picking up on subtle differences in the way lipids, proteins, and nucleic acids vibrate when exposed to the light.

Then, using a virtual coloring program, the microscope quickly pieces together and colors in the fine structural details, pixel by pixel. The result: a high-resolution, detailed image that you might expect from a pathology lab, minus the staining of cells, mounting of slides, and the other time-consuming processing procedures.

To interpret the SRH images, the researchers turned to computers and machine learning. To teach a computer, it must be fed large datasets of examples in order to learn how to perform a given task. In this case, they used a special class of machine learning called deep neural networks, or deep learning. It’s inspired by the way neural networks in the human brain process information.

In deep learning, computers look for patterns in large collections of data. As they begin to recognize complex relationships, some connections in the network are strengthened while others are weakened. The finished network is typically composed of multiple information-processing layers, which operate on the data to return a result, in this case a brain tumor diagnosis.

The team trained the computer to classify tissues samples into one of 13 categories commonly found in a brain tumor sample. Those categories included the most common brain tumors: malignant glioma, lymphoma, metastatic tumors, and meningioma. The training was based on more than 2.5 million labeled images representing samples from 415 patients.

Next, they put the machine to the test. The researchers split each of 278 brain tissue samples into two specimens. One was sent to a conventional pathology lab for prepping and diagnosis. The other was imaged with SRH, and then the trained machine made a diagnosis.

Overall, the machine’s performance was quite impressive, returning the right answer about 95 percent of the time. That’s compared to an accuracy of 94 percent for conventional pathology.

Interestingly, the machine made a correct diagnosis in all 17 cases that a pathologist got wrong. Likewise, the pathologist got the right answer in all 14 cases in which the machine slipped up.

The findings show that the combination of SRH and AI can be used to make real-time predictions of a patient’s brain tumor diagnosis to inform surgical decision-making. That may be especially important in places where expert neuropathologists are hard to find.

Ultimately, the researchers suggest that AI may yield even more useful information about a tumor’s underlying molecular alterations, adding ever greater precision to the diagnosis. Similar approaches are also likely to work in supplying timely information to surgeons operating on patients with other cancers too, including cancers of the skin and breast. The research team has made a brief video to give you a more detailed look at the new automated tissue-to-diagnosis pipeline.

Reference:

[1] Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Hollon TC, Pandian B, Adapa AR, Urias E, Save AV, Khalsa SSS, Eichberg DG, D’Amico RS, Farooq ZU, Lewis S, Petridis PD, Marie T, Shah AH, Garton HJL, Maher CO, Heth JA, McKean EL, Sullivan SE, Hervey-Jumper SL, Patil PG, Thompson BG, Sagher O, McKhann GM 2nd, Komotar RJ, Ivan ME, Snuderl M, Otten ML, Johnson TD, Sisti MB, Bruce JN, Muraszko KM, Trautman J, Freudiger CW, Canoll P, Lee H, Camelo-Piragua S, Orringer DA. Nat Med. 2020 Jan 6.

Links:

Video: Artificial Intelligence: Collecting Data to Maximize Potential (NIH)

New Imaging Technique Allows Quick, Automated Analysis of Brain Tumor Tissue During Surgery (National Institute of Biomedical Imaging and Bioengineering/NIH)

Daniel Orringer (NYU Langone, Perlmutter Cancer Center, New York City)

Todd Hollon (University of Michigan, Ann Arbor)

NIH Support: National Cancer Institute; National Institute of Biomedical Imaging and Bioengineering


Seeing the Cytoskeleton in a Whole New Light

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It’s been 25 years since researchers coaxed a bacterium to synthesize an unusual jellyfish protein that fluoresced bright green when irradiated with blue light. Within months, another group had also fused this small green fluorescent protein (GFP) to larger proteins to make their whereabouts inside the cell come to light—like never before.

To mark the anniversary of this Nobel Prize-winning work and show off the rainbow of color that is now being used to illuminate the inner workings of the cell, the American Society for Cell Biology (ASCB) recently held its Green Fluorescent Protein Image and Video Contest. Over the next few months, my blog will feature some of the most eye-catching entries—starting with this video that will remind those who grew up in the 1980s of those plasma balls that, when touched, light up with a simulated bolt of colorful lightning.

This video, which took third place in the ASCB contest, shows the cytoskeleton of a frequently studied human breast cancer cell line. The cytoskeleton is made from protein structures called microtubules, made visible by fluorescently tagging a protein called doublecortin (orange). Filaments of another protein called actin (purple) are seen here as the fine meshwork in the cell periphery.

The cytoskeleton plays an important role in giving cells shape and structure. But it also allows a cell to move and divide. Indeed, the motion in this video shows that the complex network of cytoskeletal components is constantly being organized and reorganized in ways that researchers are still working hard to understand.

Jeffrey van Haren, Erasmus University Medical Center, Rotterdam, the Netherlands, shot this video using the tools of fluorescence microscopy when he was a postdoctoral researcher in the NIH-funded lab of Torsten Wittman, University of California, San Francisco.

All good movies have unusual plot twists, and that’s truly the case here. Though the researchers are using a breast cancer cell line, their primary interest is in the doublecortin protein, which is normally found in association with microtubules in the developing brain. In fact, in people with mutations in the gene that encodes this protein, neurons fail to migrate properly during development. The resulting condition, called lissencephaly, leads to epilepsy, cognitive disability, and other neurological problems.

Cancer cells don’t usually express doublecortin. But, in some of their initial studies, the Wittman team thought it would be much easier to visualize and study doublecortin in the cancer cells. And so, the researchers tagged doublecortin with an orange fluorescent protein, engineered its expression in the breast cancer cells, and van Haren started taking pictures.

This movie and others helped lead to the intriguing discovery that doublecortin binds to microtubules in some places and not others [1]. It appears to do so based on the ability to recognize and bind to certain microtubule geometries. The researchers have since moved on to studies in cultured neurons.

This video is certainly a good example of the illuminating power of fluorescent proteins: enabling us to see cells and their cytoskeletons as incredibly dynamic, constantly moving entities. And, if you’d like to see much more where this came from, consider visiting van Haren’s Twitter gallery of microtubule videos here:

Reference:

[1] Doublecortin is excluded from growing microtubule ends and recognizes the GDP-microtubule lattice. Ettinger A, van Haren J, Ribeiro SA, Wittmann T. Curr Biol. 2016 Jun 20;26(12):1549-1555.

Links:

Lissencephaly Information Page (National Institute of Neurological Disorders and Stroke/NIH)

Wittman Lab (University of California, San Francisco)

Green Fluorescent Protein Image and Video Contest (American Society for Cell Biology, Bethesda, MD)

NIH Support: National Institute of General Medical Sciences


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