Posts Tagged ‘brain’

COVID and the brain: researchers zero in on how damage occurs

Reporter: Danielle Smolyar

Research Assistant 3 – Text Analysis for 2.0 LPBI Group’s TNS #1 – 2020/2021 Academic Internship in Medical Text Analysis (MTA)

Recent evidence has indicated that coronavirus can cause brain fog and also lead to different neurological symptoms. 

Since the beginning of the pandemic, researchers have been trying to understand how the coronavirus SARS-CoV-2 affects the brain

Image Credit: Stanislav Krasilnikov/TASS/Getty

image source:https://www.nature.com/articles/d41586-021-01693-6?utm_source=Nature+Briefing

New evidence has shown how coronavirus has caused much damage to the brain. There is a new evidence that shows that COVID-19 assault on the brain I has the power to be multipronged. What this means is that it can attack on certain Brain cells such as reduce the amount of blood flow that the brain needs to the brain tissue.

Along with brain damage COVID-19 has also caused strokes and memory loss. A neurologist at yell University Serena Spudich says, “Can we intervene early to address these abnormalities so that people don’t have long-term problems?”

We’re on 80% of the people who have been hospitalized due to COVID-19 have showed brain symptoms which seem to be correlated to coronavirus.

At the start of the pandemic a group of researchers speculated that coronavirus they can damage the brain by infecting the neurons in the cells which are important in the process of transmitting information. After further studies they found out that coronavirus has a harder time getting past the brains defense system and the brain barrier and that it does not affect the neurons in anyway.

An expert in this study indicated that a way in which SARS-CoV-2 may be able to get to the brain is by going through the olfactory mucosa which is the lining of the nasal cavity. It is found that this virus can be found in the nasal cavity which is why we swab the nose one getting tested for COVID-19.

Spudich quotes, “there’s not a tonne of virus in the brain”.

Recent studies indicate that SARS-CoV-2 have ability to infect astrocytes which is a type of cell found in the brain. Astrocytes do quite a lot that supports normal brain function,” including providing nutrients to neurons to keep them working, says Arnold Kriegstein, a neurologist at the University of California, San Francisco.

Astrocytes are star-shaped cells in the central nervous system that perform many functions, including providing nutrients to neurons.

Image Credit: David Robertson, ICR/SPL

image source: https://www.nature.com/articles/d41586-021-01693-6?utm_source=Nature+Briefing

Kriegstein and his fellow colleagues have found that SARS-CoV-2 I mostly infects the astrocytes over any of the other brain cells present. In this research they expose brain organoids which is a miniature brain that are grown from stem cells into the virus.

As quoted in the article” a group including Daniel Martins-de-Souza, head of proteomics at the University of Campinas in Brazil, reported6 in a February preprint that it had analysed brain samples from 26 people who died with COVID-19. In the five whose brain cells showed evidence of SARS-CoV-2 infection, 66% of the affected cells were astrocytes.”

The infected astrocytes could indicate the reasoning behind some of the neurological symptoms that come with COVID-19. Specifically, depression, brain fog and fatigue. Kreigstein quotes, “Those kinds of symptoms may not be reflective of neuronal damage but could be reflective of dysfunctions of some sort. That could be consistent with astrocyte vulnerability.”

A study that was published on June 21 they compared eight different brands of deceased people who did have COVID-19 along with 14 brains as the control. The results of this research were that they found that there was no trace of coronavirus Brain infected but they found that the gene expression was affected in some of the astrocytes.

As a result of doing all this research and the findings the researchers want to know more about this topic and how many brain cells need to be infected for there to be neurological symptoms says Ricardo Costa.

Further evidence has also been done on how SARS-CoV-2 can affect the brain by reducing its blood flow which impairs the neurons’ function which ends up killing them.

Pericytes can be found on the small blood vessels which are called capillaries and are found all throughout the body and in the brain. In a February pre-print there was a report about how SARS-CoV-2 can infect the pericyte in the brain organoids. 

David Atwell, a neuroscientist at the University College London, along with his other colleagues had published a pre-print which has evidence to show that SARS-CoV-2 odes In fact pericytes behavior. I researchers saw that in the different part of the hamsters brain SARS-CoV-2 blocks the function of receptors on the pericytes which ultimately causes the capillaries found inside the tissues to constrict.

As stated in the article, It’s a “really cool” study, says Spudich. “It could be something that is determining some of the permanent injury we see — some of these small- vessel strokes.”

Attwell brought to the attention that the drugs that are used to treat high blood pressure may in fact be used in some cases of COVID-19. Currently there are two clinical trials that are being done to further investigate this idea.

There is further evidence showing that the neurological symptoms and damage could in fact be happening because of the bodies on immune system reacting or misfiring after having COVID-19.

Over the past 15 years it has become evident that people’s immune system’s make auto antibodies which attack their own tissues says Harald Prüss in the article who has a Neuroimmunologist at the German Center for neurogenerative Diseases in Berlin. This may cause neuromyelitis optica which is when you can experience loss of vision or weakness in limbs. Harald Prüss summarized that the autoantibodies can pass through the blood brain barrier and ultimately impact neurological disorders such as psychosis.

Prüss and his colleagues published a study last year that focused on them isolating antibodies against SARS-CoV-2 from people. They found that one was able to protect hamsters from lung damage and other infections. The purpose of this was to come up with and create new treatments. During this research they found that some of the antibodies from people. They found that one was able to protect hamsters from lung damage and other infections. The purpose of this was to come up with and create new treatments. During this research they found that some of the antibodies can bind to the brain tissue which can ultimately damage it. Prüss states, “We’re currently trying to prove that clinically and experimentally,” says Prüss.

Was published online in December including Prüss sorry the blood and cerebrospinal fluid of 11 people who were extremely sick with COVID-19. These 11 people had neurological symptoms as well. All these people were able to produce auto antibodies which combined to neurons. There is evidence that when the patients were given intravenous immunoglobin which is a type of antibody it was successful.

Astrocytes, pericytes and autoantibodies we’re not the only  pathways. However it is likely that people with COVID-19 experience article symptoms for many reasons. As stated, In the article, Prüss says a key question is what proportion of cases is caused by each of the pathways. “That will determine treatment,” he says.

SOURCE: https://www.nature.com/articles/d41586-021-01693-6?utm_source=Nature+Briefing

Original article: 

Marshall, M. (2021, July 7). COVID and the brain: researchers zero in on how damage occurs. Nature News. https://www.nature.com/articles/d41586-021-01693-6

Other related articles published on this Open Access Online Scientific Journal include the following:

Covid-19 and its implications on pregnancy

Reporter and Curator: Mr. Srinjoy Chakraborty (Junior Research Felllow) and Dr. Sudipta Saha, Ph.D.

Nir Hacohen and Marcia Goldberg, Researchers at MGH and the Broad Institute identify protein “signature” of severe COVID-19

Reporter and Curator:2012pharmaceutical

Identification of Novel genes in human that fight COVID-19 infection

Reporter and Curator: Amandeep Kaur

Comparing COVID-19 Vaccine Schedule Combinations, or “Com-COV” – First-of-its-Kind Study will explore the Impact of using eight different Combinations of Doses and Dosing Intervals for Different COVID-19 Vaccines

Reporter and Curator: 2012pharmaceutical

Early Details of Brain Damage in COVID-19 Patients

Reporter and Curator: Irina Robu, PhD

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.


Parkinson’s Disease (PD), characterized by both motor and non-motor system pathology, is a common neurodegenerative disorder affecting about 1% of the population over age 60. Its prevalence presents an increasing social burden as the population ages. Since its introduction in the 1960’s, dopamine (DA)-replacement therapy (e.g., L-DOPA) has remained the gold standard treatment. While improving PD patients’ quality of life, the effects of treatment fade with disease progression and prolonged usage of these medications often (>80%) results in side effects including dyskinesias and motor fluctuations. Since the selective degeneration of A9 mDA neurons (mDANs) in the substantia nigra (SN) is a key pathological feature of the disease and is directly associated with the cardinal motor symptoms, dopaminergic cell transplantation has been proposed as a therapeutic strategy.


Researchers showed that mammalian fibroblasts can be converted into embryonic stem cell (ESC)-like induced pluripotent stem cells (iPSCs) by introducing four transcription factors i.e., Oct4, Sox2, Klf4, and c-Myc. This was then accomplished with human somatic cells, reprogramming them into human iPSCs (hiPSCs), offering the possibility of generating patient-specific stem cells. There are several major barriers to implementation of hiPSC-based cell therapy for PD. First, probably due to the limited understanding of the reprogramming process, wide variability exists between the differentiation potential of individual hiPSC lines. Second, the safety of hiPSC-based cell therapy has yet to be fully established. In particular, since any hiPSCs that remain undifferentiated or bear sub-clonal tumorigenic mutations have neoplastic potential, it is critical to eliminate completely such cells from a therapeutic product.


In the present study the researchers established human induced pluripotent stem cell (hiPSC)-based autologous cell therapy. Researchers reported a platform of core techniques for the production of mDA progenitors as a safe and effective therapeutic product. First, by combining metabolism-regulating microRNAs with reprogramming factors, a method was developed to more efficiently generate clinical grade iPSCs, as evidenced by genomic integrity and unbiased pluripotent potential. Second, a “spotting”-based in vitro differentiation methodology was established to generate functional and healthy mDA cells in a scalable manner. Third, a chemical method was developed that safely eliminates undifferentiated cells from the final product. Dopaminergic cells thus produced can express high levels of characteristic mDA markers, produce and secrete dopamine, and exhibit electrophysiological features typical of mDA cells. Transplantation of these cells into rodent models of PD robustly restored motor dysfunction and reinnervated host brain, while showing no evidence of tumor formation or redistribution of the implanted cells.


Together these results supported the promise of these techniques to provide clinically applicable personalized autologous cell therapy for PD. It was recognized by researchers that this methodology is likely to be more costly in dollars and manpower than techniques using off-the-shelf methods and allogenic cell lines. Nevertheless, the cost for autologous cell therapy may be expected to decrease steadily with technological refinement and automation. Given the significant advantages inherent in a cell source free of ethical concerns and with the potential to obviate the need for immunosuppression, with its attendant costs and dangers, it was proposed that this platform is suitable for the successful implementation of human personalized autologous cell therapy for PD.




















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Reporter and Curator: Dr. Sudipta Saha, Ph.D.


The relationship between gut microbial metabolism and mental health is one of the most intriguing and controversial topics in microbiome research. Bidirectional microbiota–gut–brain communication has mostly been explored in animal models, with human research lagging behind. Large-scale metagenomics studies could facilitate the translational process, but their interpretation is hampered by a lack of dedicated reference databases and tools to study the microbial neuroactive potential.


Out of all the many ways, the teeming ecosystem of microbes in a person’s gut and other tissues might affect health. But, its potential influences on the brain may be the most provocative for research. Several studies in mice had indicated that gut microbes can affect behavior, and small scale studies on human beings suggested this microbial repertoire is altered in depression. Studies by two large European groups have found that several species of gut bacteria are missing in people with depression. The researchers can’t say whether the absence is a cause or an effect of the illness, but they showed that many gut bacteria could make substances that affect the nerve cell function—and maybe the mood.


Butyrate-producing Faecalibacterium and Coprococcus bacteria were consistently associated with higher quality of life indicators. Together with DialisterCoprococcus spp. was also depleted in depression, even after correcting for the confounding effects of antidepressants. Two kinds of microbes, Coprococcus and Dialister, were missing from the microbiomes of the depressed subjects, but not from those with a high quality of life. The researchers also found the depressed people had an increase in bacteria implicated in Crohn disease, suggesting inflammation may be at fault.


Looking for something that could link microbes to mood, researchers compiled a list of 56 substances important for proper functioning of nervous system that gut microbes either produce or break down. They found, for example, that Coprococcus seems to have a pathway related to dopamine, a key brain signal involved in depression, although they have no evidence how this might protect against depression. The same microbe also makes an anti-inflammatory substance called butyrate, and increased inflammation is implicated in depression.


Still, it is very much unclear that how microbial compounds made in the gut might influence the brain. One possible channel is the vagus nerve, which links the gut and brain. Resolving the microbiome-brain connection might lead to novel therapies. Some physicians and companies are already exploring typical probiotics, oral bacterial supplements, for depression, although they don’t normally include the missing gut microbes identified in the new study.














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Artificial intelligence can be a useful tool to predict Alzheimer

Reporter: Irina Robu, PhD


3.3.10   Artificial intelligence can be a useful tool to predict Alzheimer, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

The Alzheimer’s Association estimate that around 5.7 million people live with Alzheimer’s disease in the United States which will rise to almost 14 million by 2050. Earlier diagnosis would not only benefit those affected, but it could also jointly save about $7.9 trillion in medical care and related costs over time. As Alzheimer’s disease progresses, it changes how brain cells use glucose. This alteration in glucose metabolism shows up in a type of PET imaging that tracks the uptake of a radioactive form of glucose called 18F-fluorodeoxyglucose. By giving instructions about what to look for, the scientists were able to train the deep learning algorithm to assess the PET images for early signs of Alzheimer’s.
The researchers from University of California San Francisco used positron-emission tomography images of 1002 people’s brain to train the deep learning algorithm they developed. They used 90 percent of images to teach the algorithm to spot features of Alzheimer’s disease and the remaining 10 percent to verify its performance. The researchers tested the algorithm on PET images of brains from 40 people, from which they were able to predict which individuals would receive a final diagnosis of Alzheimer’s. On average, the people who were tested were diagnosed with the disease more than 6 years after the scans.
According to the Radiology journal in which the research was published, the team describes how the algorithm “achieved 82 percent specificity at 100 percent sensitivity, an average of 75.8 months prior to the final diagnosis.” The researchers taught the algorithm with the help of more than 2,109 PET images of 1,002 individuals’ brains. The algorithm uses deep learning, which allows the algorithm to “teach itself” what to look for by spotting subtle differences among the thousands of images. The algorithm was as good as, if not better than, human experts at analyzing the FDG PET images.
Future advances will involve using larger data sets and additional images taken over time from people at various clinics and institutions. In the future, the algorithm could be a beneficial addition to the radiologist’s toolbox and advance opportunities for the early treatment of Alzheimer’s disease.



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Reporter and Curator: Dr. Sudipta Saha, Ph.D.


MRI-guided focused ultrasound (MRgFUS) surgery is a noninvasive thermal ablation method that uses magnetic resonance imaging (MRI) for target definition, treatment planning, and closed-loop control of energy deposition. Ultrasound is a form of energy that can pass through skin, muscle, fat and other soft tissue so no incisions or inserted probes are needed. High intensity focused ultrasound (HIFU) pinpoints a small target and provides a therapeutic effect by raising the temperature high enough to destroy the target with no damage to surrounding tissue. Integrating FUS and MRI as a therapy delivery system allows physicians to localize, target, and monitor in real time, and thus to ablate targeted tissue without damaging normal structures. This precision makes MRgFUS an attractive alternative to surgical resection or radiation therapy of benign and malignant tumors.


Hypothalamic hamartoma is a rare, benign (non-cancerous) brain tumor that can cause different types of seizures, cognitive problems or other symptoms. While the exact number of people with hypothalamic hamartomas is not known, it is estimated to occur in 1 out of 200,000 children and teenagers worldwide. In one such case at Nicklaus Children’s Brain Institute, USA the patient was able to return home the following day after FUS, resume normal regular activities and remained seizure free. Patients undergoing standard brain surgery to remove similar tumors are typically hospitalized for several days, require sutures, and are at risk of bleeding and infections.


MRgFUS is already approved for the treatment of uterine fibroids. It is in ongoing clinical trials for the treatment of breast, liver, prostate, and brain cancer and for the palliation of pain in bone metastasis. In addition to thermal ablation, FUS, with or without the use of microbubbles, can temporarily change vascular or cell membrane permeability and release or activate various compounds for targeted drug delivery or gene therapy. A disruptive technology, MRgFUS provides new therapeutic approaches and may cause major changes in patient management and several medical disciplines.














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Long noncoding RNA has role in brain development

Curator: Larry H. Bernstein, MD, FCAP



Brain Power

6/06/2016 – by University of California, Santa Barbara



Compared to other mammals, humans have the largest cerebral cortex. A sheet of brain cells that folds in on itself multiple times in order to fit inside the skull, the cortex is the seat of higher functions. It is what enables us to process everything we see and hear and think.

The expansion of the cerebral cortex sets humans apart from the rest of their fellow primates. Yet scientists have long wondered what mechanisms are responsible for this evolutionary development.

New research from the Kosik Molecular and Cellular Neurobiology Lab at UC Santa Barbara has pinpointed a specific long nocoding ribonucleic acid (lncRNA) that regulates neural development (ND). The findings appear in the journal Neuron.

“This lncND, as we’ve called it, can be found only in the branch of primates that leads to humans. It is a stretch of nucleotides that does not code a protein,” said senior author Kenneth S. Kosik, the Harriman Professor of Neuroscience Research in UCSB’s Department of Molecular, Cellular, and Developmental Biology. “We demonstrate that lncND is turned on during development and turned off when the cell matures.”

Lead author Neha Rani, a postdoctoral scholar in the Kosik Lab, idenfitied several binding sites on lncND for another type of RNA called a microRNA. One of them, called microRNA-143, binds to lncND.

“We found that lncND could sequester this microRNA and in doing so regulate the expression of Notch proteins,” Rani said. “Notch proteins are very important regulators during neuronal development. They are involved in cell differentiation and cell fate and are critical in the neural development pathway.”

Kosik describes lncND as a platform that binds these microRNAs like a sponge. “This allows Notch to do what it’s supposed to do during development,” he explained. “Then as the brain matures, levels of lncND go down and when they do, those microRNAs come flying off the platform and glom onto Notch to bring its levels down. You want Notch levels to be high while the brain is developing but not once maturation occurs. This lncND is an elegant way to change Notch levels quickly.”

To replicate these cell culture results, Rani used human stem cells to grow neurons into what is called a mini brain. In this pea-sized gob of brain tissue, she identified a subpopulation — radial glial cells (neuronal stem cells) and other neural progenitors — responsible for making lncND.

But the researchers wanted to see the radial glial cells in actual human brain tissue, so they turned to colleagues in the Developmental & Stem Cell Biology Graduate Program at the UC San Francisco School of Medicine. Using in situ hybridization, UCSF scientists found lncND in neural precursor cells but not in mature neurons.

“It was right where we thought it would be in brain tissue,” said Kosik, who is also the co-director of UCSB’s Neuroscience Research Institute. “But we still had one more thing we had to do because people would still not be satisfied that we had done everything possible to show that lncND was really doing something functionally.”

So the UCSF team introduced lncND into the fetal brain of a gestating mouse. Green fluorescent protein labeling allowed them to see the early development pattern and show that lncND, which ordinarily is not present in mice — lncND is present only in some primates including humans — had a functional effect on development.

“When we overexpressed lncND in the mouse fetus, we actually affected development in the predicted manner,” Kosik said. “The early developmental pattern was shifted toward more precursor cells, even though the mouse does not make lncND at all.”

According to Kosik, this work not only identifies a very critical gene for human brain development but also offers a clue about a component that likely contributed to brain expansion in humans. “We have shown that lncND might be an important player in human brain expansion, which is exciting in itself,” Rani said. “Another interesting aspect of this work is that lncND appears to help regulate the key developmental pathway of Notch signaling.”

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Alzheimer Disease Developments – Spring 2015

Larry H. Bernstein, MD, FCAP, Curator




Cognitive Stimulation Modulates Platelet Total Phospholipases A2 Activity in Subjects with Mild Cognitive Impairment


JNK: A Putative Link Between Insulin Signaling and VGLUT1 in Alzheimer’s Disease

Omega-3 Fatty Acid Status Enhances the Prevention of Cognitive Decline by B Vitamins in Mild Cognitive ImpairmentOpenly Available
Oulhaj, Abderrahim | Jernerén, Fredrik | Refsum, Helga | Smith, A. David | de Jager, Celeste A.

Preliminary Study of Plasma Exosomal Tau as a Potential Biomarker for Chronic Traumatic EncephalopathyOpenly Available
Stern, Robert A. | Tripodis, Yorghos | Baugh, Christine M. | Fritts, Nathan G. | Martin, Brett M. | Chaisson, Christine | Cantu, Robert C. | Joyce, James A. | Shah, Sahil | Ikezu, Tsuneya | Zhang, Jing | Gercel-Taylor, Cicek | Taylor, Douglas D

AZD3293: A Novel, Orally Active BACE1 Inhibitor with High Potency and Permeability and Markedly Slow Off-Rate KineticsOpenly Available
Eketjäll, Susanna | Janson, Juliette | Kaspersson, Karin | Bogstedt, Anna | Jeppsson, Fredrik | Fälting, Johanna | Haeberlein, Samantha Budd | Kugler, Alan R. | Alexander, Robert C. | Cebers, Gvido

Predictive Value of Cerebrospinal Fluid Visinin-Like Protein-1 Levels for Alzheimer’s Disease Early Detection and Differential Diagnosis in Patients with Mild Cognitive Impairment
Babić Leko, Mirjana | Borovečki, Fran | Dejanović, Nenad | Hof, Patrick R. | Šimić, Goran

Plasma Phospholipid and Sphingolipid Alterations in Presenilin1 Mutation Carriers: A Pilot Study
Chatterjee, Pratishtha | Lim, Wei L.F. | Shui, Guanghou | Gupta, Veer B. | James, Ian | …… | Wenk, Marcus R. | Bateman, Randall J. | Morris, John C. | Martins, Ralph N.

Cognitive reserve in ageing and Alzheimer’s disease / Stern Y / Lancet Neurol. 2012 Nov; 11(11):1006-12. PMID: 23079557.

A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline/ Jonsson T, Atwal JK, Steinberg S, Snaedal J, Jonsson PV, Bjornsson S, Stefansson H, Sulem P, Gudbjartsson D, Maloney J, et al. / Nature. 2012 Aug 2; 488(7409):96-9. PMID: 22801501.

 Propagation of tau pathology in a model of early Alzheimer’s disease / de Calignon A, Polydoro M, Suárez-Calvet M, William C, Adamowicz DH, Kopeikina KJ, Pitstick R, Sahara N, Ashe KH, Carlson GA, et al. / Neuron. 2012 Feb 23; 73(4):685-97. PMID: 22365544.

Stages of the pathologic process in Alzheimer disease: age categories from 1 to 100 years/ Braak H, Thal DR, Ghebremedhin E, Del Tredici K / J Neuropathol Exp Neurol. 2011 Nov; 70(11):960-9. PMID: 22002422.

Neuroinflammation in Alzheimer’s disease and mild cognitive impairment: a field in its infancy / McGeer EG, McGeer PL / J Alzheimers Dis. 2010; 19(1):355-61. PMID: 20061650.

Metallothioneins in Prion- and Amyloid-Related Diseases


Microglia are the immune cells of the CNS and account for approximately 10% of the CNS cellpopulation, with regional variation in density [27, 28]. During embryonic development, microglia originate from yolk sac progenitor cells that migrate into the developing CNS during early embryogenesis [29,30].Following construction of the blood-brain barrier (BBB), microglia are renewed by local turnover [31]. In the healthy brain, microglia actively support neurons through the release of insulin-like growth factor 1, nerve growth factor, ciliary neurotrophic factor, and brain-derived neurotrophic factor (BDNF) [32–34]. Microglia also provide indirect support to neurons by clearance of debris to maintain the extracellular environment, and phagocytosis of apoptotic cells to facilitate neurogenesis [35, 36]. In the adult brain, microglia coordinate much of their activity with astrocytes and activate in response to similar stimuli [37, 38]. Dysfunctional signaling between microglia and astrocytes often results in chronic inflammation, a characteristic of many neurodegenerative diseases [39, 40].

Historically, it has been thought that microglia ‘rest’ when not responding to inflammatory stimuli or damage [41, 42]. However, this notion is being increasingly recognized as inaccurate [43]. When not involved in active inflammatory signaling, microglia constantly patrol the neuropil by extension and retraction of their finely branched processes [44]. Microglial activation is often broadly classified into two states; pro-inflammatory (M1) or anti-inflammatory (M2) [36, 45], based on similar phenotypes in peripheral macrophages [46]. M1 activated microglia are characterized by increased expression of pro-inflammatory mediators and cytokines, including inducible nitric oxide synthase, tumor necrosis factor-α, and interleukin-1β, often under the control of the transcription factor nuclear factor-κB [45]. Pro-inflammatory microglia rapidly retract their processes and adopt an amoeboid morphology and often migrate closer to the site of injury [47]. Anti-inflammatory M2 activation of microglia, often referred to as alternative activation, represents the other side of microglial behavior. Anti-inflammatory activation is characterized by increased expression of cytokines including arginase 1 and interleukin-10, and is associated with increased ramification of processes [45]. The polarization of microglia into M1 or M2 throughout the brain is well characterized, especially in neurodegenerative diseases [48]. In the AD brain, microglia expressing markers of M1 activation are typically localized to brain regions such as the hippocampus that are most heavily affected in the disease [49]. However, it is important to note that M1 and M2 classifications of microglia may over-simplify microglial phenotypes and may only represent the extremes of microglial activation [50]. It has been more recently proposed that microglia likely occupy a continuum between these phenotypes [39, 51].

Do microglia have multiple roles in AD?

Classical pro-inflammatory activation of microglia has long been associated with AD [39, 49]. Samples taken from late-stage AD brains contain characteristic signs of inflammation, including amoeboid morphology of microglia, high levels of pro-inflammatory cytokines in the cerebrospinal fluid, and evidence of neuronal damage due to chronic exposure to pro-inflammatory cytokines and oxidative stress [52, 53]. The cause of this inflammation may be in response to direct toxicity of Aβ to neurons resulting in activation of nearby microglia and astrocytes [53, 54]. However, Aβ may also induce inflammatory activation of microglia and astrocytes. Activated immune cells are typically present surrounding amyloid plaques [55–57], with such peri-plaque cells exhibiting strong evidence of pro-inflammatory activation [56, 58–60]. The presence of undigested Aβ particles within these activated microglia may suggest that the Aβ peptide itself is a pro-inflammatory signal for microglia [61–64]. In vitro experiments provide supporting evidence for the in vivo studies, with Aβ promoting pro-inflammatory microglial activation [65, 66], and also acting as a potent chemotactic signal [67].

However, it is important to note that although widespread inflammation is characteristic of late-stage AD, it remains unclear what role inflammation could play in early stages of the disease. Some evidence suggests that reducing inflammation through the long-term use of some non-steroidal anti-inflammatory drugs (NSAIDs) can reduce the risk of AD [68]. However, these findings have not yet been verified in clinical trials [69, 70]. Little is understood about how NSAIDs and related compounds affect the delicate balance of pro- versus anti-inflammatory microglial activity within the brain. Although there is considerable evidence to suggest that chronic inflammation may contribute to pathology in the later stages of AD, it is important to note that inflammation normally only represents a small aspect of microglial function. The non-inflammatory functions of microglia may play a more important role in early disease; specifically, microglial functions relating to maintenance of the CNS.

Phagocytosis: A vital role of microglia that may be lost in AD    


Recently, a new function has been proposed for microglia. A number of studies have provided evidence that microglia prune synapses throughout life. Microglia are known to remove extraneous synapses during development to ensure that only meaningful connections remain [43]. It was, however, thought that differentiated astrocytes performed the majority of synaptic pruning in the adult brain [91]. The discovery that microglial processes are constantly active within the brain and are often positioned near synapses raised the question of whether microglial synaptic pruning continued throughout life [44, 47, 92–94]. This question was answered in 2014 in a study that demonstrated that microglia do prune synapses into adulthood, and that this activity is important for normal brain function [95]. These findings supported those found a year earlier in a study reporting that ablation of microglia from brain slices increases synapse density and results in abnormal firing of hippocampalneurons [96].

Altered microglial behavior may underlie altered neuronal firing in AD  

Altered neuronal activity is an early phenomenon in AD

The cause of DMN hypoactivity in AD is not yet clear; however studies performed in cohorts that are genetically predisposed to AD suggest that DMN hypoactivity is preceded by a period of hyperactivity and increased functional connectivity [123, 136], often manifesting as an absence of normal DMN deactivation during external tasks [137–140]. DMN hyperactivity may interfere with hippocampal memory encoding, leading to the memory deficits that are present in mild cognitive impairment [141, 142]. It has been proposed that hippocampal hyperexcitability in AD may develop as a protective mechanism against increased input from the DMN [142–144]. As AD progresses, the initial hyperexcitability of the DMN and hippocampus may result in hypoactivity due to exhaustion of compensatory mechanisms [123, 136]. Evidence from both transgenic AD mice and longitudinal human studies supports an exhaustion model of hyperactivation leading to later hypoactivation [143, 145–147]. Interestingly, a number of studies report a lower incidence of AD among those who regularly practice meditation which specifically ‘calms’ the DMN [148].

Our understanding of AD as a disease is changing. Historically considered to be primarily a disease of neuronal degeneration, this neurocentric view has widened to encompass non-neuronal cells such as astrocytes into our understanding of the disease process and pathogenesis. A proposed model for microglia in AD is shown in Fig. 2. Microglia perform a wide range of functions in the CNS and although this includes induction of an inflammatory reaction in response to damage, they also have critical roles for maintaining normal function in the brain. Recent evidence shows that microglia regulate neuronal activity through synaptic pruning throughout life as an extension on their normal phagocytosis behavior. The discovery of a large number of AD risk genes associated with reduced immune cell function suggests that perturbed microglial phagocytosis could lead to AD. In our model, altered microglial phagocytosis of synapses results in network dysfunction and onset of AD, occurring downstream of Aβ.

The immune system and microglia represent a novel target for intervention in AD. Importantly, a large number of anti-inflammatory drugs are already in use for other conditions. What is important to know at this stage is exactly how to best target immune cell function. The studies outlined here provide evidence that an indiscriminate dampening down of all microglial activity may result in a worse outcome for individuals by suppressing normal microglial regulatory functions. We currently do not know whether future microglial-based therapies should focus on reducing chronic inflammation or conversely, whether they should be aimed at boosting microglial phagocytosis. It is also likely that future treatment strategies may use a combination of approaches to target Aβ, immune cell phagocytosis and network activity. An increasing view in the AD field is that any drug or therapy needs to be provided very early in the disease process to maximize its beneficial effects. Although we are currently unable to effectively target those at risk of AD at such an early stage, advances in neuroimaging for subtle changes in network activity, or in assays for immune cell function, may provide new avenues for identification of early damage and risk of disease.



Selkoe DJ ((2011) ) Alzheimer’s disease. Cold Spring Harb Perspect Biol 3: , pii: a004457.


Masters CL , Simms G , Weinman NA , Multhaup G , McDonald BL , Beyreuther K ((1985) ) Amyloid plaque core protein in Alzheimer disease and Down syndrome. Proc Natl Acad Sci U S A 82: , 4245–4249.


Glenner GG , Wong CW ((1984) ) Alzheimer’s disease: Initial report of the purification and characterization of a novel cerebrovascular amyloid protein. Biochem Biophys Res Commun 120: , 885–890.


Goldgaber D , Lerman MI , McBride OW , Saffiotti U , Gajdusek DC ((1987) ) Characterization and chromosomal localization of a cDNA encoding brain amyloid of Alzheimer’s disease. Science 235: , 877–880.


Kang J , Lemaire HG , Unterbeck A , Salbaum JM , Masters CL , Grzeschik KH , Multhaup G , Beyreuther K , Muller-Hill B ((1987) ) The precursor of Alzheimer’s disease amyloid A4 protein resembles a cell-surface receptor. Nature 325: , 733–736.


Robakis NK , Ramakrishna N , Wolfe G , Wisniewski HM ((1987) ) Molecular cloning and characterization of a cDNA encoding the cerebrovascular and the neuritic plaque amyloid peptides. Proc Natl Acad Sci U S A 84: , 4190–4194.


Levy E , Carman MD , Fernandez-Madrid IJ , Power MD , Lieberburg I , van Duinen SG , Bots GT , Luyendijk W , Frangione B ((1990) ) Mutation of the Alzheimer’s disease amyloid gene in hereditary cerebral hemorrhage, Dutch type. Science 248: , 1124–1126.


Levy-Lahad E , Wasco W , Poorkaj P , Romano DM , Oshima J , Pettingell WH , Yu CE , Jondro PD , Schmidt SD , Wang K , et al ((1995) ) Candidate gene for the chromosome 1 familial Alzheimer’s disease locus. Science 269: , 973–977.


Rogaev EI , Sherrington R , Rogaeva EA , Levesque G , Ikeda M , Liang Y , Chi H , Lin C , Holman K , Tsuda T , et al ((1995) ) Familial Alzheimer’s disease in kindreds with missense mutations in a gene on chromosome 1 related to the Alzheimer’s disease type 3 gene. Nature 376: , 775–778.


Sherrington R , Rogaev EI , Liang Y , Rogaeva EA , Levesque G , Ikeda M , Chi H , Lin C , Li G , Holman K , Tsuda T , Mar L , Foncin JF , Bruni AC , Montesi MP , Sorbi S , Rainero I , Pinessi L , Nee L , Chumakov I , Pollen D , Brookes A , Sanseau P , Polinsky RJ , Wasco W , Da Silva HA , Haines JL , Perkicak-Vance MA , Tanzi RE , Roses AD , Fraser PE , Rommens JM , St George-Hyslop PH ((1995) ) Cloning of a gene bearing missense mutations in early-onset familial Alzheimer’s disease. Nature 375: , 754–760.


Late-Onset Metachromatic Leukodystrophy with Early Onset Dementia Associated with a Novel Missense Mutation in the Arylsulfatase A Gene

Microbes and Alzheimer’s DiseaseOpenly Available
Itzhaki, Ruth F. | Lathe, Richard | Balin, Brian J. | Ball, Melvyn J. | Bearer, Elaine L. | Braak, Heiko | Bullido, Maria J. | Carter, Chris | Clerici, Mario | Cosby, S. Louise | Del Tredici, Kelly | Field, Hugh | Fulop, Tamas | Grassi, Claudio | Griffin, W. Sue T. | Haas, Jürgen | Hudson, Alan P. | Kamer, Angela R. | Kell, Douglas B. | Licastro, Federico | Letenneur, Luc | Lövheim, Hugo | Mancuso, Roberta | Miklossy, Judith | Otth, Carola | Palamara, Anna Teresa | Perry, George | Preston, Christopher | Pretorius, Etheresia | Strandberg, Timo | Tabet, Naji | Taylor-Robinson, Simon D. | Whittum-Hudson, Judith A.

Longitudinal Relationships between Caloric Expenditure and Gray Matter in the Cardiovascular Health StudyOpenly Available
Raji, Cyrus A. | Merrill, David A. | Eyre, Harris | Mallam, Sravya | Torosyan, Nare | Erickson, Kirk I. | Lopez, Oscar L. | Becker, James T. | Carmichael, Owen T. | Gach, H. Michael | Thompson, Paul M. | Longstreth Jr., W.T. | Kuller, Lewis H.

Preliminary Study of Plasma Exosomal Tau as a Potential Biomarker for Chronic Traumatic EncephalopathyOpenly Available
Stern, Robert A. | Tripodis, Yorghos | Baugh, Christine M. | Fritts, Nathan G. | Martin, Brett M. | Chaisson, Christine | Cantu, Robert C. | Joyce, James A. | Shah, Sahil | Ikezu, Tsuneya | Zhang, Jing | Gercel-Taylor, Cicek | Taylor, Douglas D.

Unraveling Alzheimer’s: Making Sense of the Relationship between Diabetes and Alzheimer’s Disease1Openly Available
Schilling, Melissa A.

Pain Assessment in Elderly with Behavioral and Psychological Symptoms of DementiaOpenly Available
Malara, Alba | De Biase, Giuseppe Andrea | Bettarini, Francesco | Ceravolo, Francesco | Di Cello, Serena | Garo, Michele | Praino, Francesco | Settembrini, Vincenzo | Sgrò, Giovanni | Spadea, Fausto | Rispoli, Vincenzo

Editor’s Choice from Volume 50, Number 4 / 2016

Post Hoc Analyses of ApoE Genotype-Defined Subgroups in Clinical Trials
Kennedy, Richard E. | Cutter, Gary R. | Wang, Guoqiao | Schneider, Lon S.

Protective Effect of Amyloid-β Peptides Against Herpes Simplex Virus-1 Infection in a Neuronal Cell Culture Model
Bourgade, Karine | Le Page, Aurélie | Bocti, Christian | Witkowski, Jacek M. | Dupuis, Gilles | Frost, Eric H. | Fülöp, Tamás

Association Between Serum Ceruloplasmin Specific Activity and Risk of Alzheimer’s Disease
Siotto, Mariacristina | Simonelli, Ilaria | Pasqualetti, Patrizio | Mariani, Stefania | Caprara, Deborah | Bucossi, Serena | Ventriglia, Mariacarla | Molinario, Rossana | Antenucci, Mirca | Rongioletti, Mauro | Rossini, Paolo Maria | Squitti, Rosanna

Effects of Hypertension and Anti-Hypertensive Treatment on Amyloid-β (Aβ) Plaque Load and Aβ-Synthesizing and Aβ-Degrading Enzymes in Frontal Cortex
Ashby, Emma L. | Miners, James S. | Kehoe , Patrick G. | Love, Seth

AZD3293: A Novel, Orally Active BACE1 Inhibitor with High Potency and Permeability and Markedly Slow Off-Rate KineticsOpenly Available
Eketjäll, Susanna | Janson, Juliette | Kaspersson, Karin | Bogstedt, Anna | Jeppsson, Fredrik | Fälting, Johannad | Haeberlein, Samantha Budd | Kugler, Alan R. | Alexander, Robert C. | Cebers, Gvido

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Zika and neurone disorder

Larry H. Bernstein, MD, FCAP, Curator


Zika virus impairs growth in human neurospheres and brain organoids

Since the emergence of Zika virus (ZIKV), reports of microcephaly have increased significantly in Brazil; however, causality between the viral epidemic and malformations in fetal brains needs further confirmation. Here, we examine the effects of ZIKV infection in human neural stem cells growing as neurospheres and brain organoids. Using immunocytochemistry and electron microscopy, we show that ZIKV targets human brain cells, reducing their viability and growth as neurospheres and brain organoids. These results suggest that ZIKV abrogates neurogenesis during human brain development.

Primary microcephaly is a severe brain malformation characterized by the reduction of the head circumference. Patients display a heterogeneous range of brain impairments, compromising motor, visual, hearing and cognitive functions (1).

Microcephaly is associated with decreased neuronal production as a consequence of proliferative defects and death of cortical progenitor cells (2). During pregnancy, the primary etiology of microcephaly varies from genetic mutations to external insults. The so-called TORCHS factors (Toxoplasmosis, Rubella, Cytomegalovirus, Herpes virus, Syphilis) are the main congenital infections that compromise brain development in utero (3).

The increase in the rate of microcephaly in Brazil has been associated with the recent outbreak of Zika virus (ZIKV) (4, 5), a flavivirus that is transmitted by mosquitoes (6) and sexually (79). So far, ZIKV has been described in the placenta and amniotic fluid of microcephalic fetuses (1013), and in the blood of microcephalic newborns (11, 14). ZIKV had also been detected within the brain of a microcephalic fetus (13, 14), and recently, there is direct evidence that ZIKV is able to infect and cause death of neural stem cells (15).

Here, we used human induced pluripotent stem (iPS) cells cultured as neural stem cells (NSC), neurospheres and brain organoids to explore the consequences of ZIKV infection during neurogenesis and growth with 3D culture models. Human iPS-derived NSCs were exposed to ZIKV (MOI 0.25 to 0.0025). After 24 hours, ZIKV was detected in NSCs (Fig. 1, A to D), when viral envelope protein was shown in 10.10% (MOI 0.025) and 21.7% (MOI 0.25) of cells exposed to ZIKV (Fig. 1E). Viral RNA was also detected in the supernatant of infected NSCs (MOI 0.0025) by qRT-PCR (Fig. 1F), supporting productive infection.

Fig. 1ZIKV infects human neural stem cells.

Confocal microscopy images of iPS-derived NSCs double stained for (A) ZIKV in the cytoplasm, and (B) SOX2 in nuclei, one day after virus infection. (C) DAPI staining, (D) merged channels show perinuclear localization of ZIKV. Bar = 100 μm. (E) Percentage of ZIKV infected SOX2 positive cells (MOI 0.25 and 0.025). (F) RT-PCR analysis of ZIKV RNA extracted from supernatants of mock and ZIKV-infected neurospheres (MOI 0.0025) after 3 DIV, showing amplification only in infected cells. RNA was extracted, qPCR performed and virus production normalized to 12h post-infection controls. Data presented as mean ± SEM, n=5, Student’s t test, *p < 0.05, **p < 0.01.

To investigate the effects of ZIKV during neural differentiation, mock- and ZIKV-infected NSCs were cultured as neurospheres. After 3 days in vitro, mock NSCs generated round neurospheres. However, ZIKV-infected NSCs generated neurospheres with morphological abnormalities and cell detachment (Fig. 2B). After 6 days in vitro (DIV), hundreds of neurospheres grew under mock conditions (Fig. 2, C and E). Strikingly, in ZIKV-infected NSCs (MOI 2.5 to 0.025) only a few neurospheres survived (Fig. 2, D and E).

Fig. 2ZIKV alters morphology and halts the growth of human neurospheres.

(A) Control neurosphere displays spherical morphology after 3 DIV. (B) Infected neurosphere showed morphological abnormalities and cell detachment after 3 DIV. (C) Culture well-plate containing hundreds of mock neurospheres after 6 DIV. (D) ZIKV-infected well-plate (MOI 2.5-0.025) containing few neurospheres after 6 DIV. Bar = 250 μm in (A) and (B), and 1 cm in (C) and (D). (E) Quantification of the number of neurospheres in different MOI. Data presented as mean ± SEM, n=3, Student’s t test, ***p < 0.01.

Mock neurospheres presented expected ultrastructural morphology of nucleus and mitochondria (Fig. 3A). ZIKV-infected neurospheres revealed the presence of viral particles, similarly to those observed in murine glial and neuronal cells (16). ZIKV was bound to the membranes and observed in mitochondria and vesicles of cells within infected neurospheres (Fig. 3, B and F, arrows). Apoptotic nuclei, a hallmark of cell death, were observed in all ZIKV-infected neurospheres analyzed (Fig. 3B). Of note, ZIKV-infected cells in neurospheres presented smooth membrane structures (SMS) (Fig. 3, B and F), similarly to those previously described in other cell types infected with dengue virus (17). These results suggest that ZIKV induces cell death in human neural stem cells and thus impairs the formation of neurospheres.

Fig. 3ZIKV induces death in human neurospheres.

Ultrastructure of mock- and ZIKV-infected neurospheres after 6 days in vitro. (A) Mock-infected neurosphere showing cell processes and organelles, (B) ZIKV-infected neurosphere shows pyknotic nucleus, swollen mitochondria, smooth membrane structures and viral envelopes (arrow). Arrows point viral envelopes on cell surface (C), inside mitochondria (D), endoplasmic reticulum (E) and close to smooth membrane structures (F). Bar = 1 μm in (A) and (B) and 0.2 μm in (C) to (F). m = mitochondria; n = nucleus; sms = smooth membrane structures.

To further investigate the impact of ZIKV infection during neurogenesis, human iPS-derived brain organoids (18) were exposed with ZIKV, and followed for 11 days in vitro (Fig. 4). The growth rate of 12 individual organoids (6 per condition) was measured during this period (Fig. 4, A and D). As a result of ZIKV infection, the average growth area of ZIKV-exposed organoids was reduced by 40% when compared to brain organoids under mock conditions (0.624 mm2 ± 0.064 ZIKV-exposed organoids versus 1.051 mm2 ± 0.1084 mock-infected organoids normalized, Fig. 4E).

Fig. 4ZIKV reduces the growth rate of human brain organoids.

35 days old brain organoids were infected with (A) MOCK and (B) ZIKV for 11 days in vitro. ZIKV-infected brain organoids show reduction in growth compared with MOCK. Arrows point to detached cells. Organoid area was measured before and after 11 days exposure with (C) MOCK and (D) ZIKV in vitro. Plotted quantification represent the growth rate. (E) Quantification of the average of mock- and ZIKV-infected organoid area 11 days after infection in vitro. Data presented as mean ± SEM, n=6, Student’s ttest, *p < 0.05.

In addition to MOCK infection, we used dengue virus 2 (DENV2), a flavivirus with genetic similarities to ZIKV (11, 19), as an additional control group. One day after viral exposure, DENV2 infected human NSCs with a similar rate as ZIKV (fig. S1, A and B). However, after 3 days in vitro, there was no increase in caspase 3/7 mediated cell death induced by DENV2 with the same 0.025 MOI adopted for ZIKV infection (fig. S1, C and D). On the other hand, ZIKV induced caspase 3/7 mediated cell death in NSCs, similarly to the results described by Tang and colleagues (15). After 6 days in vitro, there is a significant difference in cell viability between ZIKV-exposed NSCs compared to DENV2-exposed NSCs (fig. S1, E and F). In addition, neurospheres exposed to DENV2 display a round morphology such as uninfected neurospheres after 6 days in vitro (fig. S1G). Finally, there was no reduction of growth in brain organoids exposed to DENV2 for 11 days compared to MOCK (1.023 mm2 ± 0.1308 DENV2-infected organoids versus 1.011 mm2 ± 0.2471 mock-infected organoids normalized, fig. S1, H and I). These results suggest that the deleterious consequences of ZIKV infection in human NSCs, neurospheres and brain organoids are not a general feature of the flavivirus family. Neurospheres and brain organoids are complementary models to study embryonic brain development in vitro (20, 21). While neurospheres present the very early characteristics of neurogenesis, brain organoids recapitulate the orchestrated cellular and molecular early events comparable to the first trimester fetal neocortex, including gene expression and cortical layering (18, 22). Our results demonstrate that ZIKV induces cell death in human iPS-derived neural stem cells, disrupts the formation of neurospheres and reduces the growth of organoids (fig. S2), indicating that ZIKV infection in models that mimics the first trimester of brain development may result in severe damage. Other studies are necessary to further characterize the consequences of ZIKV infection during different stages of fetal development.

Cell death impairing brain enlargement, calcification and microcephaly is well described in congenital infections with TORCHS (3, 23, 24). Our results, together with recent reports showing brain calcification in microcephalic fetuses and newborns infected with ZIKV (10, 14) reinforce the growing body of evidence connecting congenital ZIKV outbreak to the increased number of reports of brain malformations in Brazil.

Supplementary Materials


Materials and Methods

Figs. S1 and S2

References (2527)


  •  , Genetic causes of microcephaly and lessons for neuronal development. WIREs Dev. Biol.2, 461478 (2013). doi:10.1002/wdev.89 pmid:24014418

    E. C. GilmoreC. A. Walsh

  •  , Autosomal recessive primary microcephaly (MCPH): A review of clinical, molecular, and evolutionary findings. Am. J. Hum. Genet.76, 717728 (2005). doi:10.1086/429930pmid:15806441

    C. G. WoodsJ. BondW. Enard

  • N. NeuJ. Duchon,P. Zachariah

  • D. MussoC. RocheE. RobinT. NhanA. TeissierV. M. Cao-Lormeau

  • B. D. Foy,K. C. Kobylinski,J. L. Chilson Foy,B. J. Blitvich,A. Travassos da Rosa,A. D. Haddow,R. S. Lanciotti,R. B. Tesh

  • M. Sarno,G. A. Sacramento,R. Khouri,M. S. do Rosário,F. Costa,G. Archanjo,L. A. Santos,N. Nery Jr.,N.Vasilakis,A. I. Ko,A. R. de Almeida



Zika Virus Tied to MS-Like Brain Disorder


Scientists report that the Zika virus may be associated with an autoimmune disorder that attacks the brain’s myelin similar to multiple sclerosis (MS). The investigators will discuss the results of their research at the upcoming American Academy of Neurology’s 68th Annual Meeting in Vancouver, Canada.

“Though our study is small, it may provide evidence that in this case the virus has different effects on the brain than those identified in current studies,” said study author Maria Lucia Brito Ferreira, M.D., with Restoration Hospital in Recife, Brazil. “Much more research will need to be done to explore whether there is a causal link between Zika and these brain problems.”

For the study, researchers followed people who came to the hospital in Recife from December 2014 to June 2015 with symptoms compatible with arboviruses, the family of viruses that includes Zika, dengue, and chikungunya. Six people then developed neurologic symptoms that were consistent with autoimmune disorders and underwent exams and blood tests. The authors saw 151 cases with neurological manifestations during a period of December 2014 to December 2015.

All of the people came to the hospital with fever followed by a rash. Some also had severe itching, muscle and joint pain, and red eyes. The neurologic symptoms started right away for some people and up to 15 days later for others.

Of the six people who had neurologic problems, two of the people developed acute disseminated encephalomyelitis (ADEM), a swelling of the brain and spinal cord that attacks the myelin. In both cases, brain scans showed signs of damage to the brain’s white matter. Unlike MS, ADEM usually consists of a single attack that most people recover from within 6 months. In some cases, the disease can reoccur. Four of the people developed Guillain-Barré syndrome (GBS), a syndrome that involves myelin of the peripheral nervous system and has a previously reported association with the Zika virus.

When they were discharged from the hospital, five of the six people still had problems with motor functioning. One person had vision problems and one had problems with memory and thinking skills. Tests showed that the participants all had Zika virus. Tests for dengue and chikungunya were negative.

“This doesn’t mean that all people infected with Zika will experience these brain problems. Of those who have nervous system problems, most do not have brain symptoms,” said Dr. Ferreira. “However, our study may shed light on possible lingering effects the virus may be associated with in the brain.”

“At present, it does not seem that ADEM cases are occurring at a similarly high incidence as the GBS cases, but these findings from Brazil suggest that clinicians should be vigilant for the possible occurrence of ADEM and other immune-mediated illnesses of the central nervous system,” noted James Sejvar, M.D., with the Centers for Disease Control and Prevention in Atlanta and a member of the American Academy of Neurology. “Of course, the remaining question is ‘why’—why does Zika virus appear to have this strong association with GBS and potentially other immune/inflammatory diseases of the nervous system? Hopefully, ongoing investigations of Zika virus and immune-mediated neurologic disease will shed additional light on this important question.”

Zika Virus Structure Revealed


Team at Purdue becomes the first to determine the structure of the Zika virus, which reveals insights critical to the development of effective antiviral treatments and vaccines.

The team also identified regions within the Zika virus structure where it differs from other flaviviruses, the family of viruses to which Zika belongs that includes dengue, West Nile, yellow fever, Japanese encephalitis and tick-borne encephalitic viruses.

Any regions within the virus structure unique to Zika have the potential to explain differences in how a virus is transmitted and how it manifests as a disease, said Richard Kuhn, director of the Purdue Institute for Inflammation, Immunology and Infectious Diseases (PI4D) who led the research team with Michael Rossmann, Purdue’s Hanley Distinguished Professor of Biological Sciences.

“The structure of the virus provides a map that shows potential regions of the virus that could be targeted by a therapeutic treatment, used to create an effective vaccine or to improve our ability to diagnose and distinguish Zika infection from that of other related viruses,” said Kuhn, who also is head of Purdue’s Department of Biological Sciences. “Determining the structure greatly advances our understanding of Zika – a virus about which little is known. It illuminates the most promising areas for further testing and research to combat infection.”

The Zika virus, a mosquito-borne disease, has recently been associated with a birth defect called microcephaly that causes brain damage and an abnormally small head in babies born to mothers infected during pregnancy. It also has been associated with the autoimmune disease Guillain-Barré syndrome, which can lead to temporary paralysis. In the majority of infected individuals symptoms are mild and include fever, skin rashes and flulike illness, according to the World Health Organization.

Zika virus transmission has been reported in 33 countries. Of the countries where Zika virus is circulating 12 have reported an increased incidence of Guillain-Barré syndrome, and Brazil and French Polynesia have reported an increase in microcephaly, according to WHO. In February WHO declared the Zika virus to be “a public health emergency of international concern.”

“This breakthrough illustrates not only the importance of basic research to the betterment of human health, but also its nimbleness in quickly addressing a pressing global concern,” said Purdue President Mitch Daniels. “This talented team of researchers solved a very difficult puzzle in a remarkably short period of time, and have provided those working on developing vaccines and treatments to stop this virus a map to guide their way.”

Rossmann and Kuhn collaborated with Theodore Pierson, chief of the viral pathogenesis section of the Laboratory of Viral Diseases at the National Institutes of Health National Institute of Allergy and Infectious Diseases. Additional research team members include Purdue graduate student Devika Sirohi and postdoctoral research associates Zhenguo Chen, Lei Sun and Thomas Klose.

The team’s paper marks the first published success of the new Purdue Institute for Inflammation, Immunology and Infectious Diseases in Purdue’s Discovery Park.

The university’s recently announced $250 million investment in the life sciences funded the purchase of advanced equipment that allowed the team to do in a couple of months what otherwise would have taken years, Rossmann said.

“We were able to determine through cryo-electron microscopy the virus structure at a resolution that previously would only have been possible through X-ray crystallography,” he said. “Since the 1950s X-ray crystallography has been the standard method for determining the structure of viruses, but it requires a relatively large amount of virus, which isn’t always available; it can be very difficult to do, especially for viruses like Zika that have a lipid membrane and don’t organize accurately in a crystal; and it takes a long time. Now, we can do it through electron microscopy and view the virus in a more native state. This was unthinkable only a few years ago.”

The team studied a strain of Zika virus isolated from a patient infected during the French Polynesia epidemic and determined the structure to 3.8Å. At this near-atomic resolution key features of the virus structure can be seen and groups of atoms that form specific chemical entities, such as those that represent one of 20 naturally occurring amino acids, can be recognized, Rossmann said.

The team found the structure to be very similar to that of other flaviviruses with an RNA genome surrounded by a lipid, or fatty, membrane inside an icosahedral protein shell.

The strong similarity with other flaviviruses was not surprising and is perhaps reassuring in terms of vaccine development already underway, but the subtle structural differences are possibly key, Sirohi said.

“Most viruses don’t invade the nervous system or the developing fetus due to blood-brain and placental barriers, but the association with improper brain development in fetuses suggest Zika does,” Sirohi said. “It is not clear how Zika gains access to these cells and infects them, but these areas of structural difference may be involved. These unique areas may be crucial and warrant further investigation.”

The team found that all of the known flavivirus structures differ in the amino acids that surround a glycosylation site in the virus shell. The shell is made up of 180 copies of two different proteins. These, like all proteins, are long chains of amino acids folded into particular structures to create a protein molecule, Rossmann said.

The glycosylation site where Zika virus differs from other flaviviruses protrudes from the surface of the virus. A carbohydrate molecule consisting of various sugars is attached to the viral protein surface at this site.

In many other viruses it has been shown that as the virus projects a glycosylation site outward, an attachment receptor molecule on the surface of a human cell recognizes the sugars and binds to them, Kuhn said.

The virus is like a menacing stranger luring an unsuspecting victim with the offer of sweet candy. The human cell gladly reaches out for the treat and then is caught by the virus, which, once attached, may initiate infection of that cell.

The glycosylation site and surrounding residues on Zika virus may also be involved in attachment to human cells, and the differences in the amino acids between different flaviviruses could signify differences in the kinds of molecules to which the virus can attach and the different human cells it can infect, Rossmann said.

“If this site functions as it does in dengue and is involved in attachment to human cells, it could be a good spot to target an antiviral compound,” Rossmann said. “If this is the case, perhaps an inhibitor could be designed to block this function and keep the virus from attaching to and infecting human cells.”

The team plans to pursue further testing to evaluate the different regions as targets for treatment and to develop potential therapeutic molecules, Kuhn said.

Kuhn and Rossmann have studied flaviviruses, the family of viruses to which Zika belongs, for more than 14 years. They were the first to map the structure of any flavivirus when they determined the dengue virus structure in 2002. In 2003 they were first to determine the structure of West Nile virus and now they are the first to do so with the Zika virus.

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Art Therapy

Larry H. Bernstein, MD, FCAP, Curator



University Of Houston Brain Study Explores Intersection Of Art And Science

The theory that the brain has a positive response to art is not new to science. But a researcher at the University of Houston is using a different approach to test that belief.

This is your brain. This is your brain on art. Any questions? 🍳🤗🎨🗝 yes in fact this raises a TON of questions!

Jennifer Schwartz


When I’m at an art museum, I never know what piece will catch my eye.

On this particular visit to the University of Houston’s Blaffer Art Museum, it’s an art installation by Matthew Buckingham. It consists of is a 16-millimeter film projector on a pedestal, projecting a flickering black and white image of the numbers “1720” on a small screen suspended in mid-air. The music coming from the projector is a baroque flute sonata by Bach.

Picture of Matthew Buckingham's "1720"

Matthew Buckingham’s exhibit, “1720” (2009) is a continuous 16 mm film projection of the date on a suspended screen. A movement from Bach’s Sonata in G for Flute and Continuo plays as the soundtrack accompanied by the flickering sound of the film reel.




So, if someone could look into my head at this moment and see what’s going on in my brain, would they be able to see that I like what I’m looking at?

Dr. Jose Luis Contreras-Vidal, (better known as “Pepe”) is in the process of finding out. The University of Houston College of Engineering professor is collecting neural data from thousands of people while they engage in creative activities, whether it’s dancing, playing music, making art, or, in my case, viewing it.

“(The hypothesis is) that there will be brain patterns associated with aesthetic preference that are recruited when you perceive art and make a judgement about art,” Contreras-Vidal says.

Last October, three local artists – Dario Robleto, JoAnn Fleischhauer, and Lily Cox-Richard – took part in an event that allowed people to watch what was going on in their brains as they created art. The process involved fitting each artist with EEG caps, which look like swim caps with 64 electrodes attached. As they worked on their pieces, a screen on the wall showed their brain activity in blots of blue and yellow.

Picture of Contreras-Vidal

Contreras-Vidal at the Blaffer’s “Your Brain on Art” event in October.     Amy Bishop | Houston Public Media

To Cox-Richard, it’s a unique chance to help bridge the worlds of art and science.

“Being able to contribute and have it be a two-way street is part of what seemed like a really excellent opportunity for all of us to push this conversation forward,” she says.

It was just one of a series of similar experiments Contreras-Vidal has launched. The project is being made possible by funding from the National Science Foundation to advance science and health by studying the brain in action. Contreras-Vidal explains that, even though art is used as a form of therapy, there’s still a mystery surrounding what’s taking place up there to make it therapeutic.

While there have already been studies showing how creativity influences the brain, this one is different. What separates it from others is the fact that the brain is being monitored outside of the lab, such as while walking through a museum, creating art in a studio, or even dancing onstage.

“It’s as real as it gets,” Contreras-Vidal says. “We are not showing you pictures inside a scanner, which is a very different environment.”

Which brings me back to that art installation of the film projector at the Blaffer. While staring at it, I wonder, “What does my brain activity look like right now?”

I decided to find out. In the second part of this story, we’ll pick up with my EEG gallery stroll, followed by a visit to Contreras-Vidal’s laboratory to get the results.


As Houston Public Media Arts and Culture reporter, Amy Bishop spotlights Houston’s dynamic creative community. Her stories have brought national exposure to the local arts scene through NPR programs such as Here and Now.


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Protein binding to RNAs in brain

Larry H. Bernstein, MD, FCAP, Curator



Regulatory consequences of neuronal ELAV-like protein binding to coding and non-coding RNAs in human brain

 Claudia Scheckel, 

Neuronal ELAV-like (nELAVL) RNA binding proteins have been linked to numerous neurological disorders. We performed crosslinking-immunoprecipitation and RNAseq on human brain, and identified nELAVL binding sites on 8681 transcripts. Using knockout mice and RNAi in human neuroblastoma cells, we showed that nELAVL intronic and 3′ UTR binding regulates human RNA splicing and abundance. We validated hundreds of nELAVL targets among which were important neuronal and disease-associated transcripts, including Alzheimer’s disease (AD) transcripts. We therefore investigated RNA regulation in AD brain, and observed differential splicing of 150 transcripts, which in some cases correlated with differential nELAVL binding. Unexpectedly, the most significant change of nELAVL binding was evident on non-coding Y RNAs. nELAVL/Y RNA complexes were specifically remodeled in AD and after acute UV stress in neuroblastoma cells. We propose that the increased nELAVL/Y RNA association during stress may lead to nELAVL sequestration, redistribution of nELAVL target binding, and altered neuronal RNA splicing.

DOI: http://dx.doi.org/10.7554/eLife.10421.001


eLife digest

When a gene is active, its DNA is copied into a molecule of RNA. This molecule then undergoes a process called splicing which removes certain segments, and the resulting ‘messenger RNA’ molecule is then translated into protein. Many messenger RNAs go through alternative splicing, whereby different segments can be included or excluded from the final molecule. This allows more than one type of protein to be produced from a single gene.

Specialized RNA binding proteins associate with messenger RNAs and regulate not only their splicing, but also their abundance and location within the cell. These activities are crucially important in the brain where forming memories and learning new skills requires thousands of proteins to be made rapidly. Many members of a family of RNA binding proteins called ELAV-like proteins are unique to neurons. These proteins have also been associated with conditions such as Alzheimer’s disease, but it was not known which messenger RNAs were the targets of these proteins in the human brain.

Scheckel, Drapeau et al. have now addressed this question and used a method termed ‘CLIP’ to identify thousands of messenger RNAs that directly bind to neuronal ELAV-like proteins in the human brain. Many of these messenger RNAs coded for proteins that are important for the health of neurons, and neuronal ELAV-like proteins were shown to regulate both the alternative splicing and the abundance of these messenger RNAs.

The regulation of RNA molecules in post-mortem brain samples of people with or without Alzheimer’s disease was then compared. Scheckel, Drapeau et al. unexpectedly observed that, in the Alzheimer’s disease patients, the neuronal ELAV-like proteins were very often associated with a class of RNA molecules known as Y RNAs. These RNA molecules do not code for proteins, and are therefore classified as non-coding RNA. Moreover, massive shifts in the binding of ELAV-like proteins onto Y RNAs were observed in neurons grown in the laboratory that had been briefly stressed by exposure to ultraviolet radiation.

Scheckel, Drapeau et al. suggest that the strong tendency of neuronal ELAV-like proteins to bind to Y RNAs in conditions of short- or long-term stress, including Alzheimer’s disease, might prevent these proteins from associating with their normal messenger RNA targets. This was supported by finding that some messenger RNAs targeted by neuronal ELAV-like proteins showed altered regulation after stress. Such changes to the normal regulation of these messenger RNAs could have a large impact on the proteins that are produced from them.

Together, these findings link Y RNAs to both neuronal stress and Alzheimer’s disease, and suggest a new way that a cell can alter which messenger RNAs are expressed in response to changes in its environment. The next step is to explore what causes the shift in neuronal ELAV-like protein binding from messenger RNAs to Y RNAs and how it might contribute to disease.



RNA binding proteins (RBPs) associate with RNAs throughout their life cycle, regulating all aspects of RNA metabolism and function. More than 800 RBPs have been described in human cells (Castello et al., 2012). The unique structure and function of neurons, and the need to rapidly adapt RNA regulation in the brain both within and at sites distant from the nucleus, are consistent with specialized roles for RBPs in the brain. Indeed, mammalian neurons have developed their own system of RNA regulation (Darnell, 2013), and RBP:mRNA interactions are thought to regulate local protein translation at synapses, perhaps underlying learning and long-term memory (McKee et al., 2005).

Numerous RBPs have been linked to human neurological disorders (reviewed in Richter and Klann (2009)). For example, FUS, TDP-43 and ATXN2 mutations have been found in familial amyotrophic lateral sclerosis patients (Elden et al., 2010; Vance et al., 2009; Sreedharan et al., 2008), TDP-43has additionally been associated with frontotemporal lobar degeneration, Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) (Baloh, 2011), STEX has been linked to amyotrophic lateral sclerosis 4 (Chen et al., 2004), and spinal muscular atrophy can be caused by mutations in SMN (Clermont et al., 1995).

The neuronal ELAV-like (ELAVL) and NOVA RBPs are targeted by the immune system in paraneoplastic neurodegenerative disorders (Buckanovich et al., 1996; Szabo et al., 1991). Mammalian ELAVL proteins include the ubiquitously expressed paralog ELAVL1 (also termed HUA or HUR) and the three neuron-specific paralogs, ELAVL2, 3 and 4 (also termed HUB, C, and D, and collectively referred to as nELAVL; Ince-Dunn et al., 2012). nELAVL proteins are expressed exclusively in neurons in mice (Okano and Darnell, 1997), and they are important for neuronal differentiation and neurite outgrowth in cultured neurons (Akamatsu et al., 1999; Kasashima et al., 1999; Mobarak et al., 2000; Anderson et al., 2000; Antic et al., 1999; Aranda-Abreu et al., 1999). Redundancy between the three nELAVL isoforms complicates in vivo studies of their individual functions. Nevertheless, even haploinsuffiency of Elavl3 is sufficient to trigger cortical hypersynchronization, and Elavl3 and Elavl4 null mice display defects in motor function and neuronal maturation, respectively (Akamatsu et al., 2005; Ince-Dunn et al., 2012).

ELAVL proteins have been shown to regulate several aspects of RNA metabolism. In vitro and in tissue culture cells, nELAVL proteins have been implicated in the regulation of stabilization and/or translation of specific mRNAs, as well as in the regulation of splicing and polyadenylation of select transcripts [reviewed in Pascale et al. (2004)]. A more comprehensive approach was taken by immunoprecipitating an overexpressed isoform of ELAVL4 in mice, although such RNA immunoprecipitation experiments cannot distinguish between direct and indirect targets (Bolognani et al., 2010). Recently, direct binding of nELAVL to target RNAs in mouse brain was demonstrated by high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP; Ince-Dunn et al., 2012); these data, coupled with transcriptome profiling of Elavl3/4 KO mice, demonstrated that nELAVL directly regulates neuronal mRNA abundance and alternative splicing by binding to U-rich elements with interspersed purine residues in 3’UTRs and introns in mouse brain (Ince-Dunn et al., 2012).

While genome-wide approaches have been applied to studying nELAVL proteins in mice, the targets of nELAVL in the human brain remain largely unknown. This is of particular importance, as nELAVL proteins have been implicated in neurological disorders such as AD (Amadio et al., 2009; Kang et al., 2014) and PD (DeStefano et al., 2008; Noureddine et al., 2005). Hence, to advance our understanding of the function of nELAVL in humans and its link to human disease, we set out to investigate nELAVL:RNA interactions in the human brain.

To globally identify transcripts directly bound by nELAVL in human neurons, we generated a genome wide RNA binding map of nELAVL in human brain using CLIP. CLIP allows the identification of functional RNA-protein interactions in vivo by using UV-irradiation of intact tissues to covalently crosslink and then purify RNA-protein complexes present in vivo (Licatalosi and Darnell, 2010; Ule et al., 2003). This method has been adopted for a variety of RBPs (Darnell, 2010; 2013; Moore et al., 2014). Here, we systemically identified tens of thousands of reproducible nELAVL binding sites in human brain and showed that nELAVL binds transcripts that are important for neurological function and that have been linked to neurological diseases such as AD. We validated the functional consequences of nELAVL binding in mice and cultured human neuroblastoma cells and showed that the loss of nELAVL affected mRNA abundance and alternative splicing of hundreds of transcripts. We further investigated RNA regulation in AD brains, and found that numerous transcripts were differentially spliced in AD, which correlated with differential nELAVL binding in some cases. Remarkably, we observed the most significant increase in nELAVL binding in AD on a class of non-coding RNAs, Y RNAs. We recapitulated these findings in human neuroblastoma cells, showing that nELAVL binding is linked to Y ribonucleoprotein (RNP) remodeling acutely during UV-induced stress, and chronically in AD.



Figure 1.

Figure 1.Identification of nELAVL targets in human brain.

(A) Illustration depicting the brain area analyzed by CLIP and RNAseq. The image was generated using BodyParts3D/Anatomography service by DBCLS, Japan. (B) SDS-PAGE separation of radiolabeled nELAVL-RNP complexes. nELAVL-RNP complexes from 40 mg of human brain were specifically immunoprecipitated with Hu-antiserum, compared to control serum (compare lane #4 to #1), which is dependent on UV irradiation (compare lane #4 to #2). Wide-range nELAVL-RNP complexes collapse to a single band in the presence of high RNAse concentration (lane #3). RNAse dilutions: + 19.23 Units/µl; +++ 3846 Units/µl. As in studies of mouse nELAVL (Ince-Dunn et al., 2012), higher molecular weight bands were present in nELAVL CLIP autoradiograms, which correspond at least in part to nELAVL multimers. (C) Shown is the most enriched motif in the top 500 nELAVL peaks, determined with MEME-ChiP. (D) Pie chart of the genomic peak distribution of 75,592 nELAVL peaks (p < 0.01; present in at least 5 individuals). (E) nELAVL binding correlates with mRNA abundance. nELAVL binding (CLIP tags within binding sites per transcript) was compared to mRNA abundance (RNAseq tags per transcript). Only expressed genes with peaks are shown and the correlation coefficient is indicated. The top 1000 targets were identified as genes with highest normalized nELAVL binding (binding sites were normalized for mRNA abundance and summarized per gene). (F) Subnetwork of direct protein-protein interactions of top nELAVL targets. The 1000 top nELAVL target genes and six additional genes highly associated with AD (APP, BACE1, MAPT, PICALM, PSEN1 and PSEN2) were clustered using the organic layout algorithm in yEd. Genes with no direct interactions with other target genes were excluded, leaving 172 nodes from the top nELAVL target list (green) and 5 AD associated genes (blue) in this subnetwork. The size of the nodes is proportional to the connectivity degree. Six clusters (gray circles) containing at least 10 nodes were identified, and subjected to enrichment analysis (see Supplementary file 1F).

DOI: http://dx.doi.org/10.7554/eLife.10421.003


Figure 2.

Figure 2.nELAVL mediated regulation is conserved in mouse and human.

(A) Overlap of nELAVL targets in human and mouse. Human nELAVL targets (n = 8681) were intersected with mouse targets identified by RIP (Bolognani et al., 2010) or HITS-CLIP (Ince-Dunn et al., 2012). 538 genes were identified as nELAVL targets by RIP and were expressed in human brain. 1978 expressed genes had HITS-CLIP nELAVL clusters that were present in at least 3 samples (biological complexity (BC) ≥ 3). Both overlaps (n = 500 and n = 1835) were highly significant (p = 6.5e-74 and p = 2.3e-287; hypergeometric test), compared to expressed transcripts (n = 14,737). (B) Only few nELAVL binding sites are conserved between mice and human, which are predominantly present within 3’UTRs. The genomic distribution of all human nELAVL binding sites (total) and nELAVL binding sites conserved in mouse is shown. The number of nELAVL binding sites (n) within each category is indicated. (C) UCSC Genome Browser images illustrating the 3’UTRs of RAB6B, HCN3, and KCNMB2 and their normalized nELAVL binding profile in human brain. The maximum PeakHeight is indicated by numbers in the right corner. (D) The mRNA levels of transcripts with nELAVL 3’UTR binding decrease in Elavl3/4 knockout (KO) mice. Shown are the mRNA expression fold changes (knockout/wildtype) of RAB6B, HCN3, and KCNMB2. *p< 0.01 (two-tailed t test; Ince-Dunn et al., 2012). (E) UCSC Genome Browser images showing pink cassette exons in the DST, NRXN1, and CELF2 genes and their normalized nELAVL binding profiles in human brain. The maximum PeakHeight is indicated by numbers in the right corner. (F) nELAVL binding adjacent to a cassette exon in the DST gene prevents exon inclusion. Downstream nELAVL binding promotes the inclusion of cassette exons in the NRXN1 and CELF2 genes. The change in alternative exon inclusion (delta inclusion (ΔI): wildtype – Elavl3/4 KO) is shown. * significantly changing (analyzed by Aspire2;Ince-Dunn et al., 2012).

DOI: http://dx.doi.org/10.7554/eLife.10421.010


Figure 3.

Figure 3.nELAVL proteins regulate mRNA abundance of human brain targets.

(A) nELAVL depletion causes mRNA level changes in IMR-32 neuroblastoma cells. The mRNA abundance change was plotted against average mRNA abundance. Significantly changing transcripts (FDR < 0.05; n = 784) are colored in blue. Shown are only expressed genes (n = 12,743), and ELAVL1/2/3/4 transcripts are indicated. (B) nELAVL with exclusively 3’UTR binding decrease upon nELAVL RNAi depletion. Box plots represent the distribution of mRNA level differences between mock and nELAVL RNAi. We compared genes with exclusively 3’UTR (n = 2346) or intronic (n = 1693) binding that were expressed in IMR-32 cells. nELAVL binding was defined as CLIP tags within binding sites per transcript. Transcripts with exclusively 3’UTR binding were less abundant upon nELAVL RNAi compared to remaining transcripts (p = 3.8e-15; two-tailed t-test). In contrast, mRNA levels of transcripts with exclusively intron binding were even slightly increased compared to remaining transcripts (p = 1.7e-4; two-tailed t-test). (C) Transcripts with nELAVL 3’UTR binding decrease upon nELAVL RNAi. Cumulative fraction curves for genes with no 3’UTR nELAVL binding in human brain, 3’UTR binding, and top 3’UTR targets. Top targets were identified as 1000 genes with highest normalized nELAVL 3’UTR binding (binding sites were normalized for mRNA abundance before summarized per gene). 952 of the top 1000 targets were expressed in IMR-32 cells. A curve displacement to the left indicates a downregulation of mRNA abundance upon nELAVL RNAi. p values were calculated with a one-sided KS test, comparing (top) targets to non-targets. (D) Many transcripts that are decreasing upon nELAVL depletion are top nELAVL 3’UTR targets. The mRNA abundance change (nELAVL/mock RNAi) of transcripts expressed in IMR-32 cells and in human brain (n = 12,242) was plotted against average mRNA abundance. Significantly changing transcripts (FDR<0.05; n = 743) are colored in blue and additionally boxed if they are top nELAVL 3’UTR targets. Transcripts shown in E/F are indicated. (E) UCSC Genome Browser images illustrating the 3’UTRs of APPBP2, ATXN3, andSHANK2 and their normalized nELAVL binding profile in human brain. The maximum PeakHeight is indicated by numbers in the right corner. (F) The mRNA abundance of top nELAVL 3’UTR targets decreases upon nELAVL RNAi. Shown are the mRNA level changes (nELAVL/mock RNAi) of APPBP2, ATXN3, and SHANK2. * FDR<0.05 (derived from edgeR).

DOI: http://dx.doi.org/10.7554/eLife.10421.012


Figure 4.

Figure 4.nELAVL regulates splicing of human brain targets.

(A) Analysis of splicing changes upon nELAVL RNAi. Shown is the exon inclusion fraction of cassette exons that are expressed in IMR-32 cells and in human brain (n = 7903). Significantly changing exons (FDR<0.05 and ΔI>0.1) are colored in light blue (n = 473), and additionally boxed in dark blue if adjacent (+/- 2.5 kb) to intronic nELAVL binding sites (n = 155). Significantly changing exons shown in (B/C) are boxed in pink. The two alternative events withinPICALM correspond to the same alternative exon with two different 3’ splice sites. (B) UCSC Genome Browser images depicting cassette exons in pink in the BIN1, PICALM, and APP genes and their normalized nELAVL binding profiles in human brain. The maximum PeakHeight is indicated by numbers in the right corner. (C) nELAVL binding downstream of cassette exons in BIN1 and PICALM promotes exon inclusion, whereas intronic nELAVL binding ofAPP prevents exon inclusion downstream and upstream. The change in alternative exon inclusion (ΔI: mock –nELAVL RNAi) is shown. *FDR< 0.0005; **FDR< 1e-4; ***FDR<1e-16 (GLM likelihood ratio test). (D) Normalized nELAVL binding map of nELAVL regulated exons. Only exons that changed significantly upon nELAVL RNAi (FDR<0.05 and ΔI>0.1) and that are adjacent (+/- 2.5 kb) to intronic nELAVL binding sites (n = 155) were included. Red and blue peaks represent binding associated with nELAVL-dependent exon inclusion and exclusion, respectively.


RNA regulation changes in AD

nELAVL has previously been linked to neurological diseases and we observed that nELAVL regulated the mRNA abundance and splicing of multiple disease-associated genes. We examined nELAVL binding in a set of genes with disease associated 3’UTR single nucleotide polymorphisms (SNPs) (Bruno et al., 2012). We found that these genes were enriched among nELAVL 3’UTR targets (n = 200; p = 0.001; hypergeometric test), and that nELAVL binding sites directly overlapped with 45 disease associated SNPs, including SNPs associated with autism, schizophrenia, depression, AD, and PD (Figure 5—figure supplement 1, Supplementary file 3A).

nELAVL proteins have been implicated in AD (Amadio et al., 2009; Kang et al., 2014), and among the validated nELAVL regulated RNAs were also several AD-related transcripts, which led us to investigate additional AD-linked genes (hereafter termed AD genes; n = 96; Supplementary file 3B). Indeed, we found that the top nELAVL targets were enriched among AD genes (n = 11; p = 0.03; hypergeometric test; contained in Supplementary file 3B) as well as among AD risk loci identified in a genome-wide association study (GWAS) in AD (Naj et al., 2011) (n = 77; p = 1.7e-14; hypergeometric test; Supplementary file 3C). To investigate if nELAVL mediated regulation of AD related and other transcripts might be affected in AD, we performed nELAVL CLIP and RNAseq on AD subject brains, age-matched to control subjects (Figure 5—figure supplement 2, Supplementary file 1A/B and 3D). Importantly, ELAVL3/4 mRNA levels were similar between control and AD samples and ELAVL2 showed only a slight decrease in transcript abundance in AD brains (Supplementary file 1B), which allowed us to compare nELAVL binding profiles between control and AD brains. We did not detect many significant changes in nELAVL binding nor mRNA abundance (Figure 5A/B, Supplementary file 1B and 3D), probably due to the variation between human samples, the small sample size, and the potential heterogeneity of AD. We did however observe that 150 transcripts were differentially spliced in the 9 AD subjects (FDR<0.05 and ΔI>0.1; Figure 5C, Supplementary file 3E). Two of these transcripts, BIN1 and PTPRD, have previously been linked to AD (Tan et al., 2013; Ghani et al., 2012), suggesting that the differential splicing of these two transcripts as well as other RNAs might be linked to AD.

Figure 5.RNA regulation changes in AD.

(A) nELAVL binding changes in AD. The nELAVL peak binding change (AD/Control) was plotted against average nELAVL peak binding. Significantly changing peaks (FDR<0.05; n = 52) are colored in blue, and peaks within AD genes are colored in pink (1811 peaks within 69 genes). Shown are only peaks that are bound in control or AD brain (n = 115,393). (B) mRNA abundance changes in AD. The mRNA abundance change (AD/Control) was plotted against average mRNA abundance. Significantly changing transcripts (FDR<0.05; n = 3) are colored in blue, and AD transcripts are colored in pink (n = 89). Shown are only transcripts that are expressed in control or AD brain (n = 14,875). (C) Analysis of splicing changes in AD. Shown is the inclusion fraction of expressed cassette exons in control and AD subjects (n = 8163). Exons within AD genes are colored in pink (n = 79). Significantly changing exons (FDR<0.05 and ΔI>0.1) are colored in light blue (n = 170), and additionally boxed in pink if within AD genes (n = 2). (D) BIN1 is alternatively spliced in AD. UCSC Genome Browser image illustrating a cassette exon in the BIN1 gene and normalized nELAVL binding profiles in control and AD brain. The maximum PeakHeight is indicated by numbers in the right corner. Bar graphs depict the difference in alternative exon inclusion (ΔI: Control – AD) and nELAVL peak binding (AD/Control) in control and AD brain. Corresponding FDR values derived from edgeR are shown. The inclusion of the exon is promoted by nELAVL (see Figure 4), and exon inclusion as well as nELAVL peak binding are reduced in AD subjects.

DOI: http://dx.doi.org/10.7554/eLife.10421.015


As shown above (Figure 4), nELAVL depletion in IMR-32 cells was associated with the reduced inclusion of an alternative exon of BIN1, suggesting that nELAVL binding promotes the inclusion of this exon. Precisely this exon was differentially spliced in AD subjects, with AD subjects showing a reduced exon inclusion rate compared to control subjects (Figure 5D). Along with the differential exon inclusion, we observed that nELAVL peak binding was fourfold decreased in AD subjects (log2 fold change = -2.35; p = 0.16; Figure 5D). These results are consistent with nELAVL-mediated dysregulation of this exon in AD subjects, with decreased binding leading to decreased exon inclusion. In conclusion, while we did not detect global nELAVL binding and mRNA abundance changes in AD subjects, we observed that splicing of 150 transcripts was affected, which in some cases might be linked to nELAVL dysregulation.

Non-coding Y RNAs are bound by nELAVL in AD

The largest fold changes in nELAVL binding in AD (relative to the age-matched control population) occurred on a specific class of non-coding RNAs, Y RNAs (Wolin et al., 2013). Y RNAs are 100 nt long structured RNAs usually found in complex with RO60 (also known as TROVE2; Figure 6A; modified from Chen and Wolin, 2004). RO60 is believed to act as a sensor of RNA quality, targeting defective RNAs for degradation (Sim and Wolin, 2011). RO60 was initially identified as an autoantigen targeted in systemic lupus (Lerner et al., 1981) and some subjects with the paraneoplastic encephalopathy syndrome harbor both anti-RO and anti-nELAVL (Hu) autoantibodies (Manley et al., 1994). Four canonical Y RNAs, Y1/3/4/5, have been characterized in humans, but numerous slightly divergent copies of these Y RNAs, especially Y1 and Y3, are distributed throughout the human genome (Perreault et al., 2005).

Figure 6.

Figure 6.Non-coding Y RNAs are bound by nELAVL in AD.

(A) Secondary structures of Y1 and Y3. Binding sites of nELAVL and Ro are indicated. Modified from (Chen and Wolin, 2004). (B) The nELAVL binding motif (UUUUUU, allowing a G at any position) is enriched in nELAVL-bound Y RNAs compared to non-bound Y RNAs (p = 1.1e-7; Fisher’s exact test). Y RNAs were scanned for (T)6, allowing a G at any position. nELAVL-bound Y RNAs: nELAVL CLIP tags in at least two samples; n = 320. (C) nELAVL binding of Y RNAs increases in AD compared to control samples (p = 4.47e-51; paired one-sided Wilcoxon rank sum test). The axes depict nELAVL Y RNA binding (nELAVL CLIP tags per Y RNA) in control and AD subjects. Y RNAs with nELAVL binding motif are colored in green. (D) Y RNA levels do not change in AD. Y RNA abundance (RNAseq tags per Y RNA) in AD subjects was plotted against Y RNA abundance in control subjects.

DOI: http://dx.doi.org/10.7554/eLife.10421.018


Surprisingly, we observed nELAVL binding to a total of 320 Y RNAs, although Y RNA copies other than the canonical four Y1/3/4/5 genes had previously been considered to be non-functional and were labeled ‘pseudogenes’ (Supplementary file 3F). We found that 237 of the 320 nELAVL bound Y RNAs were Y3-like RNAs (Supplementary file 3F), and that nELAVL bound Y RNAs showed an enrichment of the nELAVL binding motif (202 Y RNAs contained UUUUUU, allowing a G at any one position), which is also present in the canonical hY3 RNA (Figure 6A/B). We examined the 118 nELAVL bound Y RNAs that did not fit this consensus in more detail. 91 of these Y RNAs (77%) contained either a 5mer version of the motif or the motif with an A or C instead of a G, and we found U/G rich stretches in the remaining 27 Y RNAs (Supplementary file 3F). In addition, some Y RNAs with a strong binding motif did not show any evidence of nELAVL binding. In general, these Y RNAs showed a lower expression compared to nELAVL bound Y RNA, which may explain the absence of detectable nELAVL binding (Figure 6—figure supplement 1).

We next explored nELAVL/Y RNA binding in AD brain. We observed a drastic increase in nELAVL/Y RNA association in AD subjects (Figure 6C), while Y RNA levels remained largely unchanged (Figure 6D). This suggests that Y RNPs undergo nELAVL-dependent remodeling in AD. Interestingly, we did observe a high variability in nELAVL/Y RNA association between AD samples (Figure 6—figure supplement 2), with three of them showing a very strong nELAVL/Y RNA association. Efforts to relate this difference to the expression of stress-related genes, post-mortem interval, age, extent of disease and cause of death were not conclusive, and the cause for the variation in nELAVL binding to Y RNAs among AD subjects remains elusive.

Y RNPs are remodeled during UV stress

The observation of increased nELAVL/Y RNA association in AD raised the possibility that Y RNP remodeling is associated with neuronal stress. Y RNP remodeling has previously been linked to UV-induced stress (Sim et al., 2009), and both bacterial (Chen et al., 2000; Wurtmann and Wolin, 2010) and mouse cells (Chen et al., 2003) show an increased sensitivity to UV stress in the absence of RO60. ELAVL binding can be modulated in response to stress in cultured cells (Bhattacharyya et al., 2006), and ELAVL proteins, which shuttle between nucleus and cytoplasm in response to environmental cues, preferentially accumulate in cytoplasmic stress granules upon stress (Gallouzi et al., 2000; Fan and Steitz, 1998b). We therefore examined the effect of acute UV stress on Y RNP remodeling in IMR-32 cells. IMR-32 cells were exposed to a low dose of UV stress (not sufficient to induce RNA:protein crosslinking) and allowed to recover for 24 h before being analyzed by nELAVL CLIP. We found that nELAVL bound 132 Y RNAs in neuroblastoma cells (Supplementary file 3F), that Y RNAs showed an enrichment of the nELAVL binding motif (Figure 7A) or at least contained a degenerate version of it (Supplementary file 3F), and that non-bound Y RNAs with a motif show a very low expression (Figure 7—figure supplement 1). Moreover, nELAVL binding on Y RNAs was dynamic and increased in UV stressed cells compared to non-stressed cells (Figure 7B and Figure 7—figure supplement 2), while their abundance did not change upon UV irradiation (Figure 7C). To assess whether Y RNA levels were affected by nELAVL, we depleted nELAVL by RNAi three days prior to the UV exposure, and analyzed Y RNA levels by RNAseq. Y RNA abundance was not affected by nELAVL depletion in UV stressed IMR-32 cells (Figures 7D). These results indicate that increased nELAVL binding to Y RNAs is not a function of Y RNA levels, and that nELAVL binding during stress is not required for Y RNA stability.


Figure 7.

Figure 7.Y RNPs are remodeled during UV stress.

(A) The nELAVL binding motif (UUUUUU, allowing a G at any position) is enriched in nELAVL-bound Y RNAs compared to non-bound Y RNAs (p = 6.2e-6; Fisher’s exact test). Y RNAs were scanned for (T)6, allowing a G at any position. nELAVL-bound Y RNAs: nELAVL CLIP tags in at least two samples; n = 132. (B) nELAVL binding of Y RNAs increases during UV stress compared to non-stressed cells (p = 8.23e-29; paired one-sided Wilcoxon rank sum test). The axes depict nELAVL Y RNA binding (nELAVL CLIP tags per Y RNA) in control and UV stressed cells. Y RNAs with nELAVL binding motif are colored in green. (C) Y RNA levels do not change upon UV stress. Y RNA abundance (RNAseq tags per Y RNA) in UV stressed cells was plotted against Y RNA abundance in non-stressed control cells. (D) nELAVL is binding is not required for Y RNA stability. Comparison of Y RNA abundance between mock andnELAVL RNAi treated UV stressed cells.

DOI: http://dx.doi.org/10.7554/eLife.10421.021


Figure 8.nELAVL/Y RNA correlates with loss of nELAVL-mediated splicing.

(A) Samples with high nELAVL/Y RNA association show decreased nELAVL binding on mRNA targets. Columns represent significantly changing nELAVL binding sites. Shown are changes in AD subjects with and without Y RNA association (AD_Y and AD_nY) and changes upon UV treatment. The number of nELAVL binding sites (n) within each category is indicated. (B) Identification of nELAVL-dependent UV-induced splicing changes. Comparison of the differential inclusion rate of expressed cassette exons upon UV stress between mock and nELAVL RNAi treated IMR-32 cells (n = 9397). Significant UV-induced splicing changes that do not change upon UV stress in nELAVL RNA treated cells are boxed in dark blue (FDR<0.05 and ΔI>0.1; n = 260). (C) Many exons that are alternatively spliced upon nELAVL RNAi treatment also change during UV stress in an nELAVL-dependent manner. Shown is the inclusion rate of expressed cassette exons in IMR-32 cells that were subjected to mock or nELAVL RNAi (n = 9397). nELAVL RNAi induced splicing changes are colored in light blue (n = 553), and are additionally boxed in dark blue if they are UV-induced in an nELAVL-dependent manner (n = 68). The plot is related to Figure 4A but contains additional cassette exons expressed in UV stressed cells. (D) nELAVL binding adjacent to exons that are alternatively spliced upon nELAVL RNAi and UV treatment decreases only in AD subjects with an increased Y RNA association. Displayed is the change in nELAVL peak binding. nELAVL peak binding changes were not significant except for CBFA2T2(boxed in pink). * FDR<0.05 (derived from edgeR). (E) UCSC Genome Browser images depicting an overview and an enlarged view of a cassette exon within the CBFA2T2 gene that is alternatively spliced in nELAVL RNAi and UV-treated IMR-32 cells. The nELAVL binding track in human brain and RNAseq tracks in mock and nELAVL RNAi treated non-stressed and UV-stressed IMR-32 cells are shown.

DOI: http://dx.doi.org/10.7554/eLife.10421.026


Figure 9.Y RNA overexpression is linked to nELAVL sequestration from mRNA targets.

(A) Validation of Y RNA overexpression. Shown are RNA expression fold changes of Y3wt or Y3mut infected IMR-32 cells compared to non-infected IMR-32 cells assessed by qPCR. Y RNAs expression increased while control mRNAs (ACTB, GAPDH, ELAVL4) were not affected. Error bars represent SEM. p values were calculated with a two-tailed t-test (ns: not significant; * p<0.05). (B) The expression of endogenous Y3-like Y RNAs increases upon Y3wt but not Y3mut infection. Box plots represent the distribution of endogenous Y3-like and non-Y3-like Y RNA expression fold changes upon Y3wt or Y3mut infection. Y3-like Y RNAs show a slight increase in abundance upon Y3wt compared to non-Y3-like Y RNAs (p = 0.057; one-tailed t-test). In contrast, the mRNA abundance of Y3-like Y RNAs does not change upon Y3mut infection, when compared to non-Y3 like Y RNAs (p = 0.602; one-tailed t-test). (C) Identification of Y3 dependent splicing changes. Shown is the exon inclusion fraction of cassette exons that are expressed in IMR-32 cells subjected to Y3wt or Y3mut infection (n = 10,189). Exons changing significantly between Y3wt and Y3mut infection (FDR<0.05 and ΔI>0.1) are colored in light blue (n = 191). (D) Exons that are alternatively spliced upon Y3wt infection are enriched for nELAVL bound exons. Bar graph representing total expressed exons (n = 10,189), exons that change in either Y3wt (n = 240; blue points in the left panel of Figure 9—figure supplement 4) or Y3mut (n = 151; blue points in the right panel of Figure 9—figure supplement 4) infected cells compared to non-infected cells, and exons that change in Y3wt compared to Y3mut infected cells (n = 191; blue points in Figure 9C). Exons that are alternatively spliced upon Y3wt infection compared to either non-infected (p = 0.037; hypergeometric test) or Y3mut infected cells (p = 0.069; hypergeometric test) are enriched for nELAVL bound exons.

DOI: http://dx.doi.org/10.7554/eLife.10421.029


In contrast to the mRNA abundance changes, only few splicing changes overlapped between Y3wt and Y3mut infection when compared to non-infected cells (17% of Y3wt induced changes overlapped with Y3mut induced changes). Most of the observed splicing changes are therefore likely to be specific to Y RNA overexpression. Importantly, we observed an enrichment of nELAVL bound exons and of nELAVL RNAi dependent exons among the exons that changed upon Y3wt but not Y3mut overexpression (Figure 9C/D and Figure 9—figure supplement 4, 5). The relatively small enrichment is consistent with the modest increase in total Y3-like Y RNAs. These results suggest that Y RNA overexpression results in nELAVL sequestration from some of its intronic targets and consequent splicing changes, and partially recapitulates the stress induced nELAVL sequestration due to increased nELAVL/Y RNA association seen in AD patients and UV treated IMR-32 cells.


nELAVL proteins are abundant neuron-specific RNA binding proteins which have been suggested to regulate various neurological processes and have been linked to neurodegenerative disorders including AD and PD. Yet the RNA targets of nELAVL in human brain were completely unknown. Here, we generated a comprehensive genome-wide RNA binding map of nELAVL in human brain, identifying 75,592 significant binding events within 8681 transcripts. We observed a significant overlap between these binding sites and disease-associated 3’UTR SNPs, and the potential disruption of nELAVL-mediated RNA regulation at these sites might contribute to disease manifestation. Most deleterious variants to date have been identified by exome sequencing while as many as 50% of disease-causing mutations are thought to affect splicing (Ward and Cooper, 2009). With whole genome sequencing being increasingly available, non-coding variants are also increasingly detected, some of which may be linked to disease. As the majority of nELAVL binding occurs in introns and 3’UTRs, we expect that many binding sites will overlap with prospective disease-associated non-coding variants. The overlap between deleterious variants and nELAVL binding sites, and the observation that nELAVL binding at individual sites diverged between mice and human, underscores the importance of this study and illustrates the caveat of relying solely on mouse models when studying human disease. Considering the widespread nature of nELAVL binding in human brain and that RNA dysregulation has been linked to numerous neurological disorders, we believe that this binding map will be a valuable resource for the scientific community.

To analyze the functional consequences of nELAVL binding, we used two different loss-of-function models: Elavl3/4 KO mice and nELAVL RNAi depletion in neuroblastoma cells. Due to the incomplete RNAi depletion of nELAVL in neuroblastoma cells, and potential differences in mRNA abundance and therefore nELAVL binding between the different samples, it is likely that we validated only a fraction of nELAVL-regulated transcripts. Despite these technical limitations we demonstrated that nELAVL impacts mRNA abundance and/or splicing of hundreds of targets. Among the nELAVL regulated transcripts were many transcripts implicated in human disease, including AD, which led us to investigate RNA regulation in AD subjects. Due to the relatively small sample size and the heterogeneity between these samples, likely due to both differences between individuals and sample preservation during postmortem collection, we did not detect many reproducible changes in mRNA abundance or nELAVL binding between AD and non-AD subjects. However, we found that 150 transcripts were differentially spliced in AD subjects, which in some cases coincided with differential nELAVL binding. Unexpectedly, the most significant binding change in AD was a dramatic increase in nELAVL binding to a class of non-coding RNAs, termed Y RNAs. This change was evident on a specific subset of Y RNAs harboring the nELAVL binding site. nELAVL/Y RNA binding also increased during UV stress in human neuroblastoma cells, while the abundance of Y RNAs remained constant in AD subjects and upon UV exposure. The increased nELAVL/Y RNA association correlated with decreased nELAVL binding at a subset of intronic binding sites, and was associated with similar splicing changes as induced by nELAVL depletion, suggesting that nELAVL/Y RNP remodeling during acute and chronic stress sequesters nELAVL from its mRNA targets. We provided further evidence for a Y RNA dependent nELAVL sequestration by overexpressing Y3 RNAs harboring either a wild type or mutated nELAVL binding site. Exons that were differentially spliced upon Y RNA overexpression were enriched for nELAVL bound exons, indicating nELAVL sequestration, which was dependent on an intact nELAVL binding site in the Y RNA.

nELAVL 3’UTR binding has been implicated in increasing mRNA abundance in vivo (Ince-Dunn et al., 2012). We described numerous nELAVL 3’UTR targets in brain, and were able to validate many of these targets, including disease-associated transcripts, indicating that nELAVL 3’UTR binding is important for the regulation of mRNA abundance in human brain. While ELAVL binding is frequently reported to result in an increase in mRNA abundance, we found several cases where nELAVL binding seemed to have an opposing effect. ELAVL proteins can compete or collaborate with miRNAs as well as RBPs like AUF1, CUGBP1 and TIA1 to regulate its targets (Bhattacharyya et al., 2006;Kawai et al., 2006; Lal et al., 2004; Young et al., 2009; Yu et al., 2013; Kim et al., 2009). The ultimate outcome of nELAVL 3’UTR binding might therefore vary between individual transcripts.

nELAVL has also been shown to regulate splicing in mouse brain by binding to intronic sequences (Ince-Dunn et al., 2012). We observed many instances of intronic nELAVL binding events adjacent to alternative exons in brain, and confirmed that nELAVL regulates many of these exons in mice and neuroblastoma cells. In contrast to the position-dependent splicing observed for other RBPs (Licatalosi and Darnell, 2010), we observed that upstream nELAVL binding was associated with both exon skipping and inclusion. While nELAVL binding was observed within 25-50 nucleotides upstream of skipped exons, coinciding with the branch point sequence, nELAVL binding peaked within the proximal 25 nucleotides upstream of included exons, overlapping the polypyrimidine tract. Binding of auxiliary splicing factors, including nELAVL, to the branch point sequence usually interferes with spliceosome assembly and thus leads to exon skipping (Licatalosi and Darnell, 2010). Polypyrimidine tract binding however can lead to both exon inclusion and skipping (Licatalosi et al., 2012; Wei et al., 2012), presumably depending on the recruitment of splicing enhancers or silencers. Our data indicates that upstream nELAVL binding can both interfere with the assembly of the spliceosome as well as promote splicing, most likely by recruiting splicing enhancers.

Splicing defects have been associated with many neurological diseases (Licatalosi and Darnell, 2006), and among the nELAVL-regulated transcripts we describe here are numerous transcripts related to disease, including AD. For example, intronic nELAVL binding of the gene encoding the amyloid precursor protein, APP, was associated with skipping of exons 7 and 8. Both exons have previously been shown to be alternatively spliced and encode for the Kunitz protease inhibitory (KPI) motif, a domain that has been linked to APP processing (Ben Khalifa et al., 2012). Remarkably, KPI domain containing isoforms of APP have been shown to be increased in AD (Zhang et al., 2012), indicating that APP splicing might contribute to AD pathogenesis, and that nELAVL binding in human brain might be important to regulate the inclusion of the KPI domain. nELAVL regulates the splicing of two more AD-related transcripts, PICALM and BIN1, by promoting the inclusion of alternative exons 13 and 6a, respectively. Both proteins have been implicated in APP trafficking and both exons lie within domains mediating protein-protein interactions (Tan et al., 2013; Treusch et al., 2011). Moreover, inclusion of the alternative exon 13 in PICALM has been linked to an AD-associated SNP (Parikh et al., 2014), and we observed in this study that exon 6a of BIN1 shows a higher inclusion rate in controls compared to AD subjects. Since nELAVL binding promotes the inclusion of this exon, and control subjects show higher nELAVL binding, we propose that the altered splicing of BIN1 in AD subjects might be due to differential nELAVL binding. In fact, several nELAVL-regulated exons have been shown to be differentially spliced in AD subjects, further strengthening the link between nELAVL dysregulation and AD.

While Y RNAs have not been linked to AD before, they have been implicated in various types of stress responses. The RNA binding protein RO60 usually associates with Y RNAs and is required for their stabilization (Chen et al., 2000; 2003; Labbé et al., 1999; Wolin et al., 2013; Xue et al., 2003). Besides RO60, Y RNPs contain several other RBPs such as ZBP1, MOV10, and Y-box proteins, and have been found to be remodeled upon stress (Sim et al., 2012). Our data suggests that nELAVL becomes increasingly associated with specific Y RNAs during both UV-induced stress and AD. ELAVL proteins can shuttle between nucleus and cytoplasm in response to environmental cues and preferentially accumulate in cytoplasmic stress granules upon cellular stress (Fan and Steitz, 1998a;Gallouzi et al., 2000), and ELAVL binding to the CAT-1 transcript is modulated in response to stress in cultured cells (Bhattacharyya et al., 2006). Interestingly, while we found that nELAVL specifically associates with Y RNAs during AD and acute UV stress, the nucleocytoplasmic distribution of nELAVL, RO60, and Y RNAs was not affected by UV stress. Because Y RNA levels remained constant, we propose that Y RNP complexes are specifically remodeled during AD and acute stress, which is not likely due to a change in nucleocytoplasmic protein/RNA distribution. These results are consistent with previous observations that stress induced shuttling might be limited to ELAVL1 (Burry and Smith, 2006). Our observation of Y RNP remodeling in two very different systems of neuronal stress suggests that differential nELAVL/Y RNA association may be a widespread phenomenon and a focus of future studies.

In addition to the four canonical human Y RNAs, hY1/3/4/5, hundreds of additional Y RNA genes are distributed throughout the human genome (Perreault et al., 2005). The apparent lack of promoters upstream led to a premature designation of these Y RNAs as pseudogenes. Surprisingly, we found that hundreds of these Y RNA copies are expressed in human brain and neuroblastoma cells, although it remains unclear if these Y RNAs can still associate with RO60, because the RO60 binding site in many Y RNA copies is mutated (Perreault et al., 2005). We observed that numerous Y RNA copies were more strongly associated with nELAVL in AD brain and acutely stressed cells, yet nELAVL binding did not affect their levels, indicating a function for this interaction other than Y RNA stabilization. While the outcome of nELAVL/Y RNA remains to be elucidated, our work revealed an aspect of nELAVL/Y RNA association related to stoichiometry. Hundreds of Y RNAs are bound by nELAVL in AD and UV-stress, which corresponds to up to 5% of all nELAVL CLIP tags. This shift of nELAVL binding may distort the normal stoichiometry of nELAVL interactions with its mRNA targets. Indeed, non-coding RNAs have previously been shown to affect RBP-RNA stoichiometry and therefore the biological function of other RNAs or RBPs (Borah et al., 2011; Cazalla et al., 2010;Hansen et al., 2013). Our data indicate that the binding of nELAVL to Y RNAs during stress may lead to a redistribution of nELAVL binding and/or competition of nELAVL from other RNAs. Consistently, we found that high nELAVL/Y RNA association was associated with a general decrease in nELAVL binding at a subset of binding sites, especially within introns, and consequential splicing changes were reminiscent of splicing changes provoked by nELAVL depletion. Consistently, splicing changes induced by Y RNA overexpression showed an enrichment of nELAVL binding that was dependent on the presence of the ELAVL binding motif in Y RNAs. Hence we propose that the increased association of nELAVL and Y RNAs during stress causes sequestration of nELAVL from its mRNA targets.

Taken together, our data indicate that nELAVL becomes strongly associated with Y RNAs in some AD subjects as well as in cells subjected to UV stress, and this is linked to a sequestration of nELAVL from some of its intronic targets, partially recapitulating splicing changes induced by nELAVL depletion. Our results are consistent with a hypothesis that a relatively subtle and perhaps long-term effect of Y RNA binding on normal nELAVL stoichiometry may underlie subtle and long-term changes in nELAVL biology. Perhaps analogously, the sequestration of the RBP, TDP-43, has previously been linked to neurodegenerative disorders (Lee et al., 2012). While the underlying mechanisms of TDP-43 and nELAVL sequestration are distinct, relatively subtle and long-term rearrangement of RNA:protein stoichiometry and interactions might be a recurrent theme of neurodegeneration.




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