Posts Tagged ‘neurodegenerative disease’

Using “Cerebral Organoids” to Trace the Elemental Composition of a Developing Brain

Curator: Marzan Khan, B.Sc

A research focused on the detection of micronutrient accumulation in the developing brain has been conducted recently by a team of scientific researchers in Brazil(1). Their study was comprised of a cutting-edge technology human cerebral organoids, which are a close equivalent of the embryonic brain, in in-vitro models to identify some of the minerals essential during brain development using synchroton radiation(1). Since the majority of studies done on this matter have relied on samples from animal models, the adult brain or post-mortem tissue, this technique has been dubbed the “closest and most complete study system to date for understanding human neural development and its pathological manifestations”(2).

Cerebral organoids are three-dimensional miniature structures derived from human pluripotent stem cells that further differentiate into structures closely resembling the developing brain(2). Concentrating on two different time points during the developmental progression, the researchers illustrated the micronutrient content during an interval of high cell division marked on day 30 as well as day 40 when the organoids were starting to become mature neurons that secrete neurotransmitters, arranging into layers and forming synapses(2).

Synchrotron radiation X-ray fluorescence (SR-XRF) spectroscopy was used to discern each type of element present(2). After an incident beam of X-ray was directed at the sample, each atom emitted a distinct photon signature(2). Phosphorus (P), Potassium (P), Sulphur (S), Calcium (Ca), Iron (Fe), and Zinc (Zn) were found to be present in the samples in significant concentrations(2). Manganese (Mn), Nickel (Ni) and Copper (Cu) were also detected, but in negligible amounts, and therefore tagged as “ultratrace” elements(2). The distribution of these minerals, their concentration as well as their occurrence in pairs were examined at each interval(2).

Phosphorus was discovered to be the most abundant element in the cerebral organoid samples(3). Between the two time points at 30 days (cell proliferation) and 45 days (neuronal maturation) there was a marked decrease in P content(2). Since phosphorus is a major component of nucleotides and phospholipids, this reduction was clarified as a shift from a stage of cell division that requires the production of DNA and phospholipids, to a migratory and differentiation phase(2). Potassium levels were maintained during both phases, substantiating its role in mitotic cell division as well as cell migration over long distances(2). Sulfur levels were reportedly high at 30 days and 45 days(2). It was hypothesized that this element was responsible for the patterning of the organoids(2). Calcium, known to control transcription factors involved in neuronal differentiation and survival were detected in the micromolar range, along with zinc and iron(2). Zinc commits the differentiation of pluripotent stem cells into neuronal cells and iron is necessary for neuronal tissue expansion(2).

The cells in an embryo start to differentiate very early on- the neural plate is formed on the 16th day of contraception, which further folds and bulges out to become the nervous system (containing the brain and spinal cord regions)(3). Nutrients obtained from the mother are the primary sources of diet and energy for a developing embryo to fully differentiate and specialize into different organs(2). Lack of proper nutrition in pregnant mothers has been linked to many neurodegenerative diseases occurring in their progeny(2). Spina bifida which is characterized by the incomplete development of the brain and spinal cord, is a classic example of maternal malnutrition(2,4). Paucity of minerals in the diet of pregnant women are known to hamper learning and memory in children(2). Even Schizophrenia, Parkinson’s and Huntington’s disease have been associated to malnourishment(2). By showing the different types of elements present in statistically significant concentrations in cerebral organoids, the results of this study underscore the necessity of a healthy nourishment available to mothers during pregnancy for optimal development of the fetal brain(2).


1.Kenny Walter. 02/10/2017. Study focuses on Microcutrients in Human Minibrains. RandDMagazine.http://www.rdmag.com/article/2017/02/study-focuses-micronutrients-human-minibrains?et_cid=5825577&et_rid=461755519&type=cta&et_cid=5825577&et_rid=461755519&linkid=conten

2.Sartore RC, Cardoso SC, Lages YVM, Paraguassu JM, Stelling MP, Madeiro da Costa RF, Guimaraes MZ, Pérez CA, Rehen SK.(2017)Trace elements during primordial plexiform network formation in human cerebral organoids. PeerJ 5:e2927https://doi.org/10.7717/peerj.292

3.Fetal Development: Baby’s Nervous System and Brain; What to expect; 20/07/201. http://www.whattoexpect.com/pregnancy/fetal-brain-nervous-system/

4. Spina Bifida Fact Sheet; National Institute of Neurological Disorders and Stroke National Institutes of Health, Bethesda, MD 20892


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


Zinc-Finger Nucleases (ZFNs) and Transcription Activator–Like Effector Nucleases (TALENs)

Reporter: Larry H Bernstein, MD, FCAP



Calcium Regulation Key Mechanism Discovered: New Potential for Neuro-degenerative Diseases Drug Development

Reporter: Aviva Lev-Ari, PhD., RN



How Methionine Imbalance with Sulfur-Insufficiency Leads to Hyperhomocysteinemia

Curator: Larry H Bernstein, MD, FACP



Erythropoietin (EPO) and Intravenous Iron (Fe) as Therapeutics for Anemia in Severe and Resistant CHF: The Elevated N-terminal proBNP Biomarker

Co-Author of the FIRST Article: Larry H. Bernstein, MD, FCAP

Reviewer and Curator of the SECOND and of the THIRD Articles: Larry H. Bernstein, MD, FCAP

Article Architecture Curator: Aviva Lev-Ari, PhD., RN



The relationship of S amino acids to marasmic and kwashiorkor PEM

Larry H. Bernstein, MD, FCAP, Curator



Mutations in a Sodium-gated Potassium Channel Subunit Gene related to a subset of severe Nocturnal Frontal Lobe Epilepsy

Reporter: Aviva Lev-Ari, PhD., RN



Copper and its role on “progressive neurodegeneration” and death

Reported by: Dr. Venkat S. Karra, Ph.D.



Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics

Reviewer and Curator: Larry H. Bernstein, MD, FCAP 



Nutrition and Aging

Curator: Larry H Bernstein, MD, FCAP



The Three Parent Technique to Avoid Mitochondrial Disease in Embryo

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



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Notable Awards – 2015

Larry H. Bernstein, MD, FCAP, Curator



Breakthrough Prizes Give Top Scientists the Rock Star Treatment

“By challenging conventional thinking and expanding knowledge over the long term, scientists can solve the biggest problems of our time,” Mr. Zuckerberg said in a statement. “The Breakthrough Prize honors achievements in science and math so we can encourage more pioneering research and celebrate scientists as the heroes they truly are.”

Left, Karl Deisseroth, Stanford School of Medicine; Edward S. Boyden of the McGovern Institute for Brain Research at M.I.T.CreditLeft, Winni Wintermeyer for The New York Times; Dominick Reuter/M.I.T. News


Karl Deisseroth and Edward S. Boyden

Karl Deisseroth, a professor at Stanford University and a Howard Hughes Medical Institute investigator, and Edward S. Boyden, a professor at the Massachusetts Institute of Technology, each received $3 million for their roles in the development of optogenetics, a technique that allows scientists to use light to turn neurons and groups of neurons on and off.

The technique is transforming the study of the brain because it allows scientists to test ideas about how the brain works. It has already been used to turn a kind of aggression on and off in flies, and thirst on and off in mice, pinpointing the brain cells involved.

The technique is universally praised, but the question of who will be recognized for its development is an issue for any prize committee. Dr. Boyden, Dr. Deisseroth and three other scientists published a paper in 2005that is recognized as a breakthrough. They demonstrated how to reliably control mammalian neurons with light, making widespread use of the technique inevitable.

Their paper built on earlier work, as much of science does. Opsins, light-sensitive chemicals that are crucial to optogenetics, have been studied since the 1970s. And the fact that optogenetics could be done was demonstrated in 2002.

In 2013, the European Brain Prize recognized six scientists for work on optogenetics, including Dr. Boyden and Dr. Deisseroth.




John Hardy
Alzheimer’s research

Alzheimer’s disease was a complete mystery in the late 1980s. In autopsies, pathologists could see the ravages left in patients’ brains, but how and why did the process start? There were rare families in which the disease seemed to be inherited, though, and perhaps there was a gene mutation that might provide a clue to what goes awry. The problem was finding those families.

In the late 1980s, a woman who lived in Nottingham, England, contacted John Hardy at University College London and asked if he and his team wanted to study her family. Her father was one of 10 siblings, five of whom had developed Alzheimer’s disease, and she could trace the disease back for three generations. Their investigation led to the discovery of a gene mutation that, if inherited, always caused the disease. The gene was presenilin, and its protein was the amyloid precursor protein, or APP. Every person in that family who inherited the gene overproduced amyloid and got the disease. For the first time, scientists had a clue to what starts the horrendous destruction of brain cells in Alzheimer’s disease. And for the first time, by putting that gene mutation in mice, they could study Alzheimer’s in a lab animal, look for drugs to block the gene’s effects and finally use the tools of science to look for a cure.



Helen Hobbs
Cholesterol research

Helen Hobbs, a professor at the University of Texas Southwestern Medical Center and a Howard Hughes Medical Institute investigator, and her colleague Jonathan Cohen were intrigued when they read a short paper describing a French family with stunningly high levels of LDL cholesterol, the dangerous kind, and early deaths from heart attacks and strokes. The family members turned out to have a mutation in a gene, PCSK9, whose function was unknown. Dr. Hobbs and Dr. Cohen began to wonder: If too much PCSK9 caused heart disease, would people who made too little be protected? They scrutinized genetic data from a federal study and found that about 2.5 percent of blacks had a mutation that destroyed one copy of the gene; 3.2 percent of whites had a mutation that hobbled a copy of the gene but did not destroy it. In both cases, less PCSK9 was made and LDL levels were low. The people with the mutations seemed almost immune to heart disease, even if they had other risk factors like high blood pressure, smoking or diabetes.

What would happen if someone had both copies of PCSK9 destroyed? Dr. Hobbs found one young woman, an aerobics instructor, without PCSK9. She was healthy and fertile even though her LDL level was 14, lower than seemed possible (the average is 100). That discovery led to a race among drug companies to make cholesterol-lowering drugs that mimicked the effects of the PCSK9 mutations. The result is drugs that can make LDL levels plunge to the 30s, the 20s, even the teens. The first two such PCSK9 inhibitors were approved this year for people with high cholesterol levels who cannot get them down with statins and are at high risk of heart disease.



TED Prize Goes to Archaeologist Who Combats Looting With Satellite Technology


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Brain and Cognition

Larry H. Bernstein, MD, FCAP, Curator


Brain activity may be as unique as fingerprints

Tue, 10/13/2015 – Bill Hathaway, Yale Univ.


Image: Michael S. Helfenbeing/Shutterstock

A person’s brain activity appears to be as unique as his or her fingerprints, a new Yale Univ.-led imaging study shows. These brain “connectivity profiles” alone allow researchers to identify individuals from the fMRI images of brain activity of more than 100 people, according to the study published in Nature Neuroscience.

“In most past studies, fMRI data have been used to draw contrasts between, say, patients and healthy controls,” said Emily Finn, a PhD student in neuroscience and co-first author of the paper. “We have learned a lot from these sorts of studies, but they tend to obscure individual differences which may be important.”

Finn and co-first author Xilin Shen, under the direction of R. Todd Constable, professor of diagnostic radiology and neurosurgery at Yale, compiled fMRI data from 126 subjects who underwent six scan sessions over two days. Subjects performed different cognitive tasks during four of the sessions. In the other two, they simply rested. Researchers looked at activity in 268 brain regions: specifically, coordinated activity between pairs of regions. Highly coordinated activity implies two regions are functionally connected. Using the strength of these connections across the whole brain, the researchers were able to identify individuals from fMRI data alone, whether the subject was at rest or engaged in a task. They were also able to predict how subjects would perform on tasks.

Finn said she hopes that this ability might one day help clinicians predict or even treat neuropsychiatric diseases based on individual brain connectivity profiles.

Brain Activity Identifies Individuals

By Kerry Grens

Neural connectome patterns differ enough between people to use them as a fingerprint.

New Alzheimer’s Gene Identified

Megan Brooks


Researchers have identified a new gene involved in the immune system that increases the risk for Alzheimer’s disease (AD), providing a potential new target for prevention and treatment.

They found that older adults at risk for AD and those with the disease who carry a specific variant in the interleukin-1 receptor accessory protein (IL1RAP) had higher rates of amyloid plaque accumulation in the brain over 2 years. The effect of the variant was stronger than the well-known AD risk allele APOE ε4.

“These findings suggest that targeting the IL1RAP immune pathway may be a viable approach for promoting the clearance of amyloid deposits and fighting an important cause of progression in Alzheimer’s disease,” Andrew J. Saykin, PsyD, director of the Indiana Alzheimer Disease Center, Indianapolis, and the national Alzheimer’s Disease Neuroimaging Initiative Genetics Core, said in a statement.

The study was published in the October 1 issue of Brain.

Novel Association

The researchers conducted a genome-wide association study of longitudinal changes in brain amyloid burden measured by florbetapir positron emission tomography (PET) in nearly 500 individuals. They assessed the levels of brain amyloid deposits at an initial visit and again 2 years later.

Study participants came from the Alzheimer’s Disease Neuroimaging Initiative, the Indiana Memory and Aging Study, the Religious Orders Study, and the Rush Memory and Aging Project, all longitudinal studies of older adults representing clinical stages along the continuum from normal aging to AD.

As expected, APOE ε4 was associated with higher rates of amyloid plaque buildup. However, they also identified a novel association between a single nucleotide polymorphism in IL1RAP (rs12053868-G) and higher rates of amyloid accumulation, independent of APOE ε4.

Carriers of the IL1RAP rs12053868-G variant showed accelerated cognitive decline and were more likely to progress from mild cognitive impairment to AD. They also showed greater longitudinal atrophy of the temporal cortex, which is involved in memory and had a lower level of microglial activity as measured by PET scans, the researchers report.

“This was an intriguing finding because IL1RAP is known to play a central role in the activity of microglia, the immune system cells that act as the brain’s ‘garbage disposal system’ and the focus of heavy investigation in a variety of neurodegenerative diseases,” Vijay K. Ramanan, MD, PhD, postdoctoral researcher at the Indiana University School of Medicine, Indianapolis, who worked on the study, said in the statement.

“These results suggest a crucial role of activated microglia in limiting amyloid accumulation and nominate the IL-1/IL1RAP pathway as a potential target for modulating this process,” the investigators write.

The study was supported by the National Institute on Aging and a consortium of private partners through the Foundation for the National Institutes of Health. Several authors disclosed relationships with pharmaceutical companies. A complete list can be found with the original article.

Brain. 2015;138:3076-3088. Abstract

Cognitive Impairments in Elderly Diabetic Patients: Understanding the Risks for Better Management

Medscape Medical News from the

Visit Medscape in Hall B Booth #B13:31

Medscape Diabetes & Endocrinology


Lyse Bordier, MD


Editor’s Note: The following is an edited, translated transcript of a presentation by Professor Lyse Bordier, a diabetologist at Military Hospital Bégin, Saint-Mandé, France, summarizing her lecture at the European Association for the Study of Diabetes (EASD) 2015 AnnualMeeting in Stockholm, Sweden.

Hello. I am Professor Lyse Bordier. I work at the Bégin Military Hospital, in Saint-Mandé, France, and I had the pleasure of participating in a symposium organized by the EASD 2015 conference in Stockholm on elderly patients, specifically on cognitive impairments.

A Public Health Problem

Dementia and cognitive impairments are a major problem; Alzheimer disease accounts for 70% of all cases of dementia. The other main causes are vascular dementias and mixed dementias. They are a real public health problem; it is estimated that, in the United States, 5.2 million people have this condition, and worldwide, every 7 seconds, a new case of dementia is diagnosed.[1,2] In France, for example, it was estimated in 2010 that 750,000-850,000 people had dementia and that this figure will increase by a factor of 2.4 by the year 2050.

Diabetes is an important contributor to the development of cognitive impairments, all the way up to dementia. In Europe, it is estimated that nearly 25% of people over age 85 years have dementia. Its prevalence and incidence are higher in women than in men.[2] We know that the complications of diabetes have changed over the years and that acute metabolic complications are, in the end, much less important. With the improvement in life expectancy in our diabetic patients, who are now better treated thanks to better therapeutic management, new complications have arisen, such as renal failure, heart failure, and, of course, geriatric complications, which are, in large part, cognitive disorders.[3]

Prevalence Underestimated by Physicians

These cognitive impairments are common and largely underestimated. This was clearly shown in the GERODIAB study,[4] which included a cohort of 987 patients over the age of 70 years. At inclusion, the physicians reported that 11% of their patients had cognitive impairments and that 3% had dementia. In actual fact, 25% of the patients had impaired cognitive functions, with a Mini-Mental State Examination (MMSE) score under 25. The prevalence is therefore significantly underestimated by physicians.

Cognitive impairments are more prevalent and more severe in diabetics than in nondiabetics. It is estimated that the risk for cognitive impairments and that for dementia are 20% to 70% and 60% higher, respectively, in the presence of diabetes.[5] Furthermore, the risk for Alzheimer dementia is considerable, it being 40% higher in diabetics. As expected (given the combination of the other cardiovascular risk factors), the increase in the risk is even greater for vascular dementia, with an odds ratio of 2.38.[6]


What are the mechanisms in the development of cognitive impairments and dementia? There are many mechanisms, and they are often poorly understood. Hyperglycemia plays a very important role as a direct result of oxidative stress, of advanced glycation end-products, but also as a result of micro- and macroangiopathy, hypertension, and dyslipidemia.[7,8] Other major factors, such as hypoglycemia,[9-12]play an extremely important role in the development of cognitive impairments. As well, a great deal of literature has been published lately on the role of inflammation[13] and genetic factors. Another widely known aspect is insulin resistance, which increases the risk for dementia at a fairly early stage by 40%[14,15]; this already during the metabolic syndrome, even before the onset of type 2 diabetes.


Figure. Multiple and poorly understood mechanisms of cognitive impairments and dementia. HTA = arterial hypertension. Adapted from Buysschaert M, et al.[16]

What Are the Consequences of Cognitive Impairments?

Cognitive impairments lead to a number of complications, including a reduction in life expectancy. In the GERODIAB cohort, we found, after 2 years of follow-up, that the mortality rate was twice as high in the patients with an MMSE score <24 compared with those with an MMSE score >24. In this study, the patients with a lower MMSE score had less well-controlled diabetes, were usually treated with insulin, and had heart failure and cerebrovascular complications more often. Very surprisingly, hypoglycemia was not more prevalent in these patients, perhaps because, being less independent, they were better managed by care teams.[17]

Cognitive impairments lead to geriatric complications, such as malnutrition, falls, and a loss of autonomy. They also promote social and family isolation and iatrogenic accidents, as well as depression, which can both mask cognitive impairments and exacerbate an underlying dementia. Another important aspect is that cognitive impairments increase the risk for hypoglycemia. This has been shown very clearly in all of the studies. There is, in fact, a bidirectional link between dementia and hypoglycemia: Hypoglycemia doubles the risk for dementia, and dementia triples the risk for hypoglycemia.[18]

Screening and Management

What do we do when a patient presents with cognitive impairments? First, they should be identified so that they can be managed. We need to be vigilant for certain little signs: changes in the patient’s behavior (eg, a patient who forgets his appointments, whose personal hygiene has declined, who is less diligent in keeping his blood glucose diary, and, lastly, who has an unexplained diabetic imbalance). We should also know how to use simple tests, such as the MMSE, which provides an overall assessment of space-time orientation, cognitive functions, language functions, and calculation, and how to assess the patient’s autonomy and loss of autonomy.[19] Next, we should, as per the recommendations of the American Diabetes Association[20] and the EASD, individualize the glycemic goals, taking into account, in the most fragile, elderly patients, cognitive status, the level of autonomy, depression, nutritional status—in particular, sarcopenia, which can coexist with obesity, and the risk for hypoglycemia.[21]

We should therefore avoid overtreating the most fragile patients (those at greatest risk for hypoglycemia), but neither should we undertreat patients who have a long life expectancy and who could develop micro- and macroangiopathic complications.

One last aspect, which is very important, is the family. Help needs to be provided to prevent the patient’s loss of autonomy.[21] Lastly, I think that cognitive decline should be added to the already long list of degenerative complications of diabetes.

PDGFR-ß Plays a Key Role in the Ectopic Migration of Neuroblasts in Cerebral Stroke

Hikari Sato et al.

The neuroprotective agents and induction of endogenous neurogenesis remain as the urgent issues to be established for the care of cerebral stroke. Platelet-derived growth factor receptor beta (PDGFR-ß) is mainly expressed in neural stem/progenitor cells (NSPCs), neurons and vascular pericytes of the brain; however, the role in pathological neurogenesis remains elusive. This review examined the role of PDGFR-ß in the migration and proliferation of NSPCs after stroke.

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Notable Papers in Neurosciences

Larry H. Bernstein, MD, FCAP, Curator


Notable Papers in Neurosciences

 NIH researchers’ new mouse model points to a gene therapy for eye disease

Oliver Worsley

mouse model has been established for Leber hereditary optic neuropathy (LHON), a vision disorder caused by mutations within genes in the “battery packs” of our cells–the mitochondria. And investigators at the NIH say they were able to develop a gene therapy that could be used to treat it.

Within the mitochondrion are mitochondrial DNA (mtDNA) which carries the instructions for important metabolic processes required to keep the cell topped up with energy. Mutations in genes found in mtDNA can lead to various diseases; one of these is LHON which affects around 1 in 30,000 in England.

“[Until now] there was no efficient way to get DNA into mitochondria,” said John Guy, who is a professor of ophthalmology and is lead author of this study. Their work has been published in the Proceedings of the National Academy of Sciences.

Early symptoms of the disease include blurred vision and eyesight will eventually deteriorate over time. A loss of retinal ganglion cells is at the crux of the pathology and these cells are crucial for carrying visual signals from the retina to the brain, via the optic nerve.

The most prevalent mutation responsible for LHON is in a mitochondrial gene called ND4. Dr. Guy and his lab have been attempting to develop a gene therapy approach to correct this mutation for 15 years now. But one issue with adopting the widely used viral vector is that despite its efficacy in integrating into nuclear DNA–viruses have a harder time penetrating the mitochondria.

In developing his mouse model, Dr. Guy found a way around this. He fixed a virus with the same mutation in ND4 seen in 70% of LHON patients–adding a protein that mitochondria require from outside the organelle, as they cannot produce it on their own.

In the hijacked virus they included a fluorescent tag so they could confirm the future progeny of mice which had the defective gene. The mouse model does what is seen in patients with the same disease and optic nerve atrophy, loss of retinal ganglion cells and a decline in visual response is consistently observed.

The next step was providing a gene therapy to reverse it. The researchers packaged a normal ND4 gene into the same type of virus and injected it directly into the eye–leading to marked visual improvements without any side effects from the virus itself.

Related Articles:
Study: Eyes may signal brain pathology in schizophrenia
Stem cell therapy protects vision in preclinical retinal disease study
Retinas made from embryonic stem cells implanted into mice for the first time

GEN News Highlights

Oct 7, 2015

Stem Cell Advance Brings Vision Repair in Sight


Transplantation of cones produced from stem cells could reverse macular degeneration. A new differentiation approach yields abundant cones from human embryonic stem cells. When allowed to grow to confluence, the cones spontaneously form sheets of organized retinal tissue. [G. Bernier, University of Montreal]


A dearth of cone cells means degraded vision, so perhaps cone cell numbers could be raised, if only there were a way to produce cone cells in abundance. Then, cone cells could be transplanted en masse, potentially reversing the vision losses due to age-related macular degeneration.

We are born with a fixed number of cone cells. Additional cone cells must be contrived if degradation of the retina, a condition that is accelerated in nearly one out of four people, is to be reversed. Although cone cells have been produced by means of stem cell differentiation, the output has been meager. Now, however, scientists at the University of Montreal report that they have developed an efficient technique for producing cone cells from human embryonic stem cells.

These scientists, led by Gilbert Bernier, Ph.D., essentially closed a number of signaling pathways in stem cells, leaving open a default pathway that led to photoreceptor genesis. The scientists detailed their work in the journal Development, in an article that appeared online October 1. The article—“Differentiation of human embryonic stem cells into cone photoreceptors through simultaneous inhibition of BMP, TGFβ, and Wnt signaling”—is the culmination of years of work.

Bernier has been interested in the genes that code and enable the induction of the retina during embryonic development since completing his doctorate in molecular biology in 1997. “During my post-doc at the Max-Planck Institute in Germany, I developed the idea that there was a natural molecule that must exist and be capable of forcing embryonic stem cells into becoming cones,” he said. Indeed, bioinformatic analysis led him to predict the existence of a mysterious protein: COCO, a “recombinational” human molecule that is normally expressed within photoreceptors during their development.

In 2001, Bernier launched his laboratory in Montreal and immediately isolated the molecule. But it took several years of research to demystify the molecular pathways involved in the photoreceptors development mechanism. The Bernier laboratory’s current work has established that Coco (Dand5), a member of the Cerberus gene family, is expressed in the developing and adult mouse retina.

“Upon exposure to recombinant COCO, human embryonic stem cells (hESCs) differentiated into S-cone photoreceptors, developed an inner segment-like protrusion, and could degrade cGMP when exposed to light,” Bernier and colleagues wrote in the Development article. “Addition of thyroid hormone resulted in a transition from a unique S-cone population toward a mixed M/S-cone population.”

In addition, when the COCO-exposed hESCs were cultured at confluence for a prolonged period of time, they spontaneously developed into a cellular sheet composed of polarized cone photoreceptors. “Within 45 days, the cones that we allowed to grow toward confluence spontaneously formed organized retinal tissue that was 150 microns thick,” Dr. Bernier noted. “This has never been achieved before.”

In order to verify the technique, Dr. Bernier injected clusters of retinal cells into the eyes of healthy mice. The transplanted photoreceptors migrated naturally within the retina of their host.

Although Dr. Bernier acknowledged that the transplantation of photoreceptors in clinical trials was years away, he expressed optimism that his laboratory had made a significant advance, one that could, ultimately, benefit countless patients. “Our method has the capacity to differentiate 80% of the stem cells into pure cones,” Dr. Gilbert explained. “Thanks to our simple and effective approach, any laboratory in the world will now be able to create masses of photoreceptors.”

Beyond the clinical applications, Dr. Bernier’s findings could enable the modeling of human retinal degenerative diseases through the use of induced pluripotent stem cells, offering the possibility of directly testing potential avenues for therapy on the patient’s own tissues. “Our work,” the Development article concluded, “provides a unique platform to produce human cones for developmental, biochemical, and therapeutic studies.”

Neurogenesis in the Mammalian Brain

Neuron nurseries in the adult brains of rodents and humans appear to influence cognitive function.

By Jef Akst | October 1, 2015


In rodents, there are two populations of neural stem cells in the adult brain. The majority of new neurons are born in the subventricular zone along the lateral ventricle wall and migrate through the rostral migratory stream (RMS) to the olfactory bulb. About one-tenth as many new neurons are produced in the subgranular zone of the dentate gyrus (white) of the hippocampus.

In the rodent dentate gyrus, neural stem cells differentiate into neuroblasts before maturing and integrating with hippocampal circuits important in learning and memory.

In the rodent subventricular zone, neural stem cells differentiate into neuroblasts, which make their way to the olfactory bulb, where they complete their development.

Researchers have also demonstrated that neurogenesis occurs in the adult human brain, though the locations and degree of cell proliferation appear to differ somewhat from rodents. Strong evidence now exists that new neurons are born in the dentate gyrus of the hippocampus, where they integrate into existing circuits. But so far, there is no definitive support for the migration of new neurons migrating from the subventricular zone (SVZ) of the lateral ventricle to the olfactory bulb, which is atrophied relative to the olfactory bulb of rodents and other mammals that rely more heavily on smell. However, one study did report signs of neurogenesis in an area next to the SVZ, the striatum, which is important for cognitive function and motor control.

Brain Gain

Young neurons in the adult human brain are likely critical to its function.

By Jef Akst | October 1, 2015


How the Brain Builds New Thoughts

10/06/2015 Harvard University


“One of the big mysteries of human cognition is how the brain takes ideas and puts them together in new ways to form new thoughts,” said postdoctoral fellow Steven Frankland. (Kris Snibbe/Harvard Staff Photographer)Let’s start with a simple sentence: Last week Joe Biden beat Vladimir Putin in a game of Scrabble.


It’s a strange notion to entertain, certainly, but one humans can easily make sense of, researchers say, thanks to the way the brain constructs new thoughts.

A new study, co-authored by postdoctoral fellow Steven Frankland and Professor of Psychology Joshua Greene, suggests that two adjacent brain regions allow humans to build new thoughts using a sort of conceptual algebra, mimicking the operations of silicon computers that represent variables and their changing values. The study is described in a Sept. 17 paper in the Proceedings of the National Academy of Sciences.

“One of the big mysteries of human cognition is how the brain takes ideas and puts them together in new ways to form new thoughts,” said Frankland, the lead author of the study. “Most people can understand ‘Joe Biden beat Vladimir Putin at Scrabble’ even though they’ve never thought about that situation, because, as long as you know who Putin is, who Biden is, what Scrabble is, and what it means to win, you’re able to put these concepts together to understand the meaning of the sentence. That’s a basic, but remarkable, cognitive ability.”

But how are such thoughts constructed? According to one theory, the brain does it by representing conceptual variables, answers to recurring questions of meaning such as “What was done?” and “Who did it?” and “To whom was it done?” A new thought such as “Biden beats Putin” can then be built by making “beating” the value of the action variable, “Biden” the value of the “agent” variable (“Who did it?”), and “Putin” the value of the “patient” variable (“To whom was it done?”). Frankland and Greene are the first to point to specific regions of the brain that encode such mental syntax.

“This has been a central theoretical discussion in cognitive science for a long time, and although it has seemed like a pretty good bet that the brain works this way, there’s been little direct empirical evidence for it,” Frankland said.

To identify the regions, Frankland and Greene used functional magnetic resonance imaging (fMRI) to scan students’ brains as they read a series of simple sentences such as “The dog chased the man” and “The man chased the dog.”

Equipped with that data, they then turned to algorithms to identify patterns of brain activity that corresponded with “dog” and “boy.”

“What we found is there are two regions in the left superior temporal lobe, one which is situated more toward the center of the head, that carries information about the agent, the one doing an action,” Frankland said. “An immediately adjacent region, located closer to the ear, carries information about the patient, or who the action was done to.”

Importantly, Frankland added, the brain appears to reuse the same patterns across multiple sentences, implying that these patterns function like symbols.

“So we might say ‘the dog chased the boy,’ or ‘the dog scratched the boy,’ but if we use some new verb the algorithms can still recognize the ‘dog’ pattern as the agent,” Frankland said. “That’s important because it suggests these symbols are used over and over again to compose new thoughts. And, moreover, we find that the structure of the thought is mapped onto the structure of the brain in a systematic way.”

That ability to use a series of repeatable concepts to formulate new thoughts may be part of what makes human thought unique ― and uniquely powerful.

“This paper is about language,” Greene said. “But we think it’s about more than that. There’s a more general mystery about how human thinking works.

“What makes human thinking so powerful is that we have this library of concepts that we can use to formulate an effectively infinite number of thoughts,” he continued. “Humans can engage in complicated behaviors that, for any other creature on Earth, would require an enormous amount of training. Humans can read or hear a string of concepts and immediately put those concepts together to form some new idea.”

Unlike models of perception, which put more complex representations at the top of a processing hierarchy, Frankland and Greene’s study supports a model of higher cognition that relies on the dynamic combination of conceptual building blocks to formulate thoughts.

“You can’t have a set of neurons that are there just waiting for someone to say ‘Joe Biden beat Vladimir Putin at Scrabble,’ ” Greene said. “That means there has to be some other system for forming meanings on the fly, and it has to be incredibly flexible, incredibly quick and incredibly precise.” He added, “This is an essential feature of human intelligence that we’re just beginning to understand.”

Source: Harvard Gazette

Predicting Change in the Alzheimer’s Brain

Tue, 10/06/2015 – 9:14am

Larry Hardesty, MIT News Office


MIT researchers are developing a computer system that uses genetic, demographic, and clinical data to help predict the effects of disease on brain anatomy.


In experiments, they trained a machine-learning system on MRI data from patients with neurodegenerative diseases and found that supplementing that training with other patient information improved the system’s predictions. In the cases of patients with drastic changes in brain anatomy, the additional data cut the predictions’ error rate in half, from 20 percent to 10 percent.

“This is the first paper that we’ve ever written on this,” said Polina Golland, a professor of electrical engineering and computer science at MIT and the senior author on the new paper. “Our goal is not to prove that our model is the best model to do this kind of thing; it’s to prove that the information is actually in the data. So what we’ve done is, we take our model, and we turn off the genetic information and the demographic and clinical information, and we see that with combined information, we can predict anatomical changes better.”

First author on the paper is Adrian Dalca, an MIT graduate student in electrical engineering and computer science and a member of Golland’s group at MIT’s Computer Science and Artificial Intelligence Laboratory. They’re joined by Ramesh Sridharan, another Ph.D. student in Golland’s group, and by Mert Sabuncu, an assistant professor of radiology at Massachusetts General Hospital, who was a postdoc in Golland’s group.

The researchers are presenting the paper at the International Conference on Medical Image Computing and Computer Assisted Intervention this week. The work is a project of the Neuroimage Analysis Center, which is based at Brigham and Women’s Hospital in Boston and funded by the National Institutes of Health.

Common denominator

In their experiments, the researchers used data from the Alzheimer’s Disease Neuroimaging Initiative, a longitudinal study on neurodegenerative disease that includes MRI scans of the same subjects taken months and years apart.

Each scan is represented as a three-dimensional model consisting of millions of tiny cubes, or “voxels,” the 3-D equivalent of image pixels.

The researchers’ first step is to produce a generic brain template by averaging the voxel values of hundreds of randomly selected MRI scans. They then characterize each scan in the training set for their machine-learning algorithm as a deformation of the template. Each subject in the training set is represented by two scans, taken between six months and seven years apart.

The researchers conducted two experiments: one in which they trained their system on scans of both healthy subjects and those displaying evidence of either Alzheimer’s disease or mild cognitive impairment, and one in which they trained it only on data from healthy subjects.

In the first experiment, they trained the system twice, once using just the MRI scans and the second time supplementing them with additional information. This included data on genetic markers known as single-nucleotide polymorphisms; demographic data, such as subject age, gender, marital status, and education level; and rudimentary clinical data, such as patients’ scores on various cognitive tests.

The brains of healthy subjects and subjects in the early stages of neurodegenerative disease change little over time, and indeed, in cases where the differences between a subject’s scans were slight, the system trained only on MRI data fared well. In cases where the changes were more marked, however, the addition of the supplementary data made a significant difference.


In the second experiment, the researchers trained the system just once, on both the MRI data and the supplementary data of healthy subjects. But they instead used it to predict what the brains of Alzheimer’s patients would have looked like had they not been disfigured by disease.

In this case, there are no clinical data that could validate the system’s predictions. But the researchers believe that exploring this sort of counterfactual could be scientifically useful.

“It would illuminate how changes in individual subjects — for example, with mild cognitive impairment, which is a precursor to Alzheimer’s — evolve along this trajectory of degeneration, as compared to what normal degeneration would be,” Golland said. “We think that there are very interesting research applications of this. But I have to be honest and say that the original motivation was curiosity about how much of anatomy we could predict from genetics and other non-image data.”

“It’s not surprising that clinical and genetic data would help,” said Bruce Rosen, a professor of radiology at Harvard Medical School and director of the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital. “But the fact that it did as well as it did is encouraging.”

“There are lots of ways these tools could be beneficial to the research community,” Rosen adds. “To my mind, the more challenging question is whether they could be useful clinically.”

Some promising experimental Alzheimer’s drugs require early determination of how the disease is likely to progress, Rosen said. Currently, he said, that determination relies on a combination of MRI and PET scan data. “People think MRI is expensive, but it’s only a fraction of what PET scans cost,” Rosen said. “If machine-learning tools can help avoid the need for PET scans in evaluating patients early in the disease course, that will be very impactful.”

Source: Massachusetts Institute of Technology

 Alzheimer’s: Investigators spotlight a pathway for amyloid beta clearance

By John Carroll

There are a variety of theories as to why people develop Alzheimer’s. And one of the best known is that toxic clusters of amyloid beta in the brain wipe out memories and trigger dementia in the elderly.

Now researchers at Indiana University say that they have determined that the IL1RAP immune pathway could provide a promising avenue for drug developers. And they’re quick to add that some experimental therapies that already hit this target could offer a quick way to help determine their utility against Alzheimer’s.

The team confirmed an observation that has been made before: the APOE e4 allele is associated with a significant accumulation of amyloid beta. But they were surprised to find that the IL1RAP gene–which they note codes for the immune signaling factor interleukin-1 receptor accessory protein–“showed an independent and even stronger influence on amyloid accumulation.”

They also determined that the gene was linked to a lower level of microglial activity as measured by PET scans; increased atrophy of the temporal cortex; swift cognitive decline and a “greater likelihood among study participants of progression from mild cognitive impairment to Alzheimer’s disease.”

“This was an intriguing finding because IL1RAP is known to play a central role in the activity of microglia, the immune system cells that act as the brain’s “garbage disposal system” and the focus of heavy investigation in a variety of neurodegenerative diseases,” said Dr. Vijay Ramanan, postdoctoral researcher at the IU School of Medicine.

There are already experimental anti-inflammatories and antibodies that are designed to hit this target, offering a shortcut in determining the impact on patients.

“These findings suggest that targeting the IL1RAP immune pathway may be a viable approach for promoting the clearance of amyloid deposits and fighting an important cause of progression in Alzheimer’s disease,” said Andrew Saykin, director of the Indiana Alzheimer Disease Center and the national Alzheimer’s Disease Neuroimaging Initiative Genetics Core.

It’s also useful to note that while many researchers believe that amyloid beta causes Alzheimer’s, there’s no consensus at the FDA on that point. And while many programs have been put in place to treat the disease, the vast majority have failed in the clinic, including drugs that aim at amyloid beta clearance.

Related Articles:
Mayo Clinic team renews Alzheimer’s feud, fingers tau over amyloid
Alzheimer’s study finds a molecule that might stymie critical stage of the disease
Neuroscience project tries to put the immune system to work against Alzheimer’s

An Accessible Approach to Making a Mini-brain

10/05/2015 – Brown University



A bioengineering team at Brown University can grow “mini-brains” of neurons and supporting cells that form networks and are electrically active. (Image: Hoffman-Kim lab/Brown University)


If you need a working miniature brain — say for drug testing, to test neural tissue transplants, or to experiment with how stem cells work — a new paper describes how to build one with what the Brown University authors say is relative ease and low expense. The little balls of brain aren’t performing any cogitation, but they produce electrical signals and form their own neural connections — synapses — making them readily producible testbeds for neuroscience research, the authors said.

“We think of this as a way to have a better in vitro [lab] model that can maybe reduce animal use,” said graduate student Molly Boutin, co-lead author of the new paper in the journal Tissue Engineering: Part C. “A lot of the work that’s done right now is in two-dimensional culture, but this is an alternative that is much more relevant to the in vivo [living] scenario.”

Just a small sample of living tissue from a single rodent can make thousands of mini-brains, the researchers said. The recipe involves isolating and concentrating the desired cells with some centrifuge steps and using that refined sample to seed the cell culture in medium in an agarose spherical mold.

The mini-brains, about a third of a millimeter in diameter, are not the first or the most sophisticated working cell cultures of a central nervous system, the researchers acknowledged, but they require fewer steps to make and they use more readily available materials.

“The materials are easy to get and the mini-brains are simple to make,” said co-lead author Yu-Ting Dingle, who earned her Ph.D. at Brown in May 2015. She compared them to retail 3-D printers which have proliferated in recent years, bringing that once-rare technology to more of a mass market. “We could allow all kinds of labs to do this research.”

The spheres of brain tissue begin to form within a day after the cultures are seeded and have formed complex 3-D neural networks within two to three weeks, the paper shows.

25-cent mini-brains

There are fixed costs, of course, but an approximate cost for each new mini-brain is on the order of $0.25, said study senior author Diane Hoffman-Kim, associate professor of molecular pharmacology, physiology and biotechnology and associate professor of engineering at Brown.

“We knew it was a relatively high-throughput system, but even we were surprised at the low cost per mini-brain when we computed it,” Hoffman-Kim said.

Hoffman-Kim’s lab collaborated with fellow biologists and bioengineers at Brown — faculty colleagues Julie Kauer, Jeffrey Morgan, and Eric Darling are all co-authors — to build the mini-brains. She wanted to develop a testbed for her lab’s basic biomedical research. She was interested, for example, in developing a model to test aspects of neural cell transplantation, as has been proposed to treat Parkinson’s disease. Boutin was interested in building working 3-D cell cultures to study how adult neural stem cells develop.

Morgan’s Providence startup company, MicroTissues Inc., makes the 3-D tissue engineering molds used in the study.

The method they developed yields mini-brains with several important properties:

  •     Diverse cell types: The cultures contain both inhibitory and excitatory neurons and several varieties of essential neural support cells called glia.
  •     Electrically active: the neurons fire and spike and form synaptic connections, producing complex networks.
  •     3-D: Cells connect and communicate within a realistic geometry, rather than merely across a flat plane as in a 2-D culture.
  •     Natural density: Experiments showed that the mini-brains have a density of a few hundred thousand cells per cubic millimeter, which is similar to a natural rodent brain.
  •     Physical structure: Cells in the mini-brain produce their own extracellular matrix, producing a tissue with the same mechanical properties (squishiness) as natural tissue. The cultures also don’t rely on foreign materials such as scaffolds of collagen.
  •     Longevity: In testing, cultured tissues live for at least a month.

Hoffman-Kim, who is affiliated with the Brown Institute for Brain Science and the Center for Biomedical Engineering, said she hopes the mini-brains might proliferate to many different labs, including those of researchers who have questions about neural tissue but not necessarily the degree of neuroscience and cell culture equipment required of other methods.

“If you are that person in that lab, we think you shouldn’t have to equip yourself with a microelectronics facility, and you shouldn’t have to do embryonic dissections in order to generate an in vitro model of the brain,” Hoffman-Kim said.

The National Science Foundation, the National Institutes of Health, the Brown Institute for Brain Science, and the U.S. Department of Education funded the research.

Source: Brown University

Rat Brain Simulation Runs Neocortical Maze



In this depiction of in silico retrograde staining, a digital reconstruction of neocortical microcircuitry, the presynaptic neurons of a layer 2/3 nest basket cell (red) are stained in blue. Only immediate neighboring presynaptic neurons are shown. [© BBP/EPFL 2015]

It’s a piece of rat brain containing about 30,000 neurons and 40 million synaptic connections, and there’s nothing remarkable about it, except that it isn’t real. It’s a digital reconstruction—a representation of a one-third cubic millimeter of rat neocortex—and it seems to work like the real thing.

Needless to say, its many creators are proud. They include 82 scientists and engineers from around the world, collaborators who are aware that their reconstruction represents the culmination of 20 years of biological experimentation and 10 years of computational science work. They are also aware that their work is controversial. It was criticized last year in an open letter. Signed by hundreds of neuroscientists, the letter argued that attempts to digitally reconstruct brain tissue were premature and represented an “overly narrow” approach that risked a misallocation of resources.

Undaunted, the investigators, led by scientists of the École Polytechnique Fédérale de Lausanne (EPFL), ran simulations on supercomputers to show that the electrical behavior of the virtual brain tissue matched the behavior of real rat neocortical tissue. Even though the digital reconstruction was not designed to reproduce any specific circuit phenomenon, a variety of experimental findings emerged. One such simulation examined how different types of neuron would respond if fibers coming into the neocortex were to convey signals encoding touch sensations. The researchers found that the responses of the different types of neurons in the digital reconstruction were very similar to those that had been previously observed in the laboratory.

These findings appeared October 8 in the journal Cell, in an article entitled, “Reconstruction and Simulation of Neocortical Microcircuitry.” This article also described how additional simulations revealed novel insights into the functioning of the neocortex.

“[We] find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms,” wrote the authors. “The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies.”

The authors even suggested that their work represents the first step toward the digital reconstruction and simulation of a whole brain. “They delivered what they promised,” said Patrick Aebischer, president of EPFL. This statement appeared in an EPFL press release that also indicated that the EPFL, together with the Swiss government, took the “bold step of funding the ambitious and controversial Blue Brain Project.”

The Blue Brain project is the simulation core of the Human Brain Project, a decade-long effort that is being allocated more than $1 billion.

“While a long way from the whole brain, the study demonstrates that it is feasible to digitally reconstruct and simulate brain tissue,” the release continued. “It is a first step and a significant contribution to Europe’s Human Brain Project, which Henry Markram founded, and where EPFL is the coordinating partner.”

Idan Segev, a senior author, sees the paper as building on the pioneering work of the Spanish anatomist, Ramon y Cajal from more than 100 years ago. “Ramon y Cajal began drawing every type of neuron in the brain by hand. He even drew in arrows to describe how he thought the information was flowing from one neuron to the next. Today, we are doing what Cajal would be doing with the tools of the day—building a digital representation of the neurons and synapses and simulating the flow of information between neurons on supercomputers. Furthermore, the digitization of the tissue allows the data to be preserved and reused for future generations.”

Now that the Blue Brain team has published the experimental results and the digital reconstruction, other scientists will be able to use the data and reconstruction to test other theories of brain function.

“The reconstruction is a first draft, it is not complete and it is not yet a perfect digital replica of the biological tissue,” explained Henry Markram. In fact, the current version explicitly leaves out many important aspects of the brain, such as glia, blood vessels, gap-junctions, plasticity, and neuromodulation. According to Sean Hill, a senior author: “The job of reconstructing and simulating the brain is a large-scale collaborative one, and the work has only just begun. The Human Brain Project represents the kind of collaboration that is required.”


Neuronal Waste Removal Gene Found to Prevent Parkinson’s


Researchers at the University of Copenhagen in Denmark say they have discovered that noninheritable Parkinson’s Disease (PD) may be caused by functional changes in the Interferon-beta (IFNβ) gene, which plays a vital role in keeping neurons healthy by regulating waste management. Treatment with IFNβ-gene therapy successfully prevented neuronal death and disease effects in an experimental model of PD.

The team’s study (“Lack of Neuronal IFN-β-IFNAR Causes Lewy Body- and Parkinson’s Disease-like Dementia”) was published in Cell.

“We found that IFNβ is essential for neurons ability to recycle waste proteins,” explained Patrick Ejlerskov, Ph.D., an assistant professor in the lab of Shohreh Issazadeh-Navikas, Ph.D., at the university’s Biotech Research and Innovation Center (BRIC) and first author on the paper. “Without this, the waste proteins accumulate in disease-associated structures called Lewy bodies and with time the neurons die.”

The scientists found that mice missing IFNβ developed Lewy bodies in parts of the brain, which control body movement and restoration of memory, and as a result they developed disease and clinical signs similar to patients with PD and dementia with Lewy bodies (DLB).

While hereditary gene mutations have long been known to play a role in familial PD, the study from BRIC offers one of the first models for so-called nonfamilial PD, which comprises the majority (90-95%) of patients suffering from PD. According to Dr. Issazadeh-Navikas, the new knowledge opens new therapeutic possibilities.

“This is one of the first genes found to cause pathology and clinical features of nonfamilial PD and DLB, through accumulation of disease-causing proteins,” she said. “It is independent of gene mutations known from familial PD and when we introduced IFNβ-gene therapy, we could prevent neuronal death and disease development. Our hope is that this knowledge will enable development of more effective treatment of PD.”

Current treatments are effective at improving the early motor symptoms of the disease. However, as the disease progress, the treatment effect is lost. The next step for the research team will be to gain a better understanding of the molecular mechanisms by which IFNβ protects neurons and thereby prevents movement disorders and dementia.

A review of heterogeneous data mining for brain disorder identification

  • Bokai Cao , Xiangnan Kong, Philip S. Yu

Brain Informatics 30 Sept 2015, pp 1-12



With rapid advances in neuroimaging techniques, the research on brain disorder identification has become an emerging area in the data mining community. Brain disorder data poses many unique challenges for data mining research. For example, the raw data generated by neuroimaging experiments is in tensor representations, with typical characteristics of high dimensionality, structural complexity, and nonlinear separability. Furthermore, brain connectivity networks can be constructed from the tensor data, embedding subtle interactions between brain regions. Other clinical measures are usually available reflecting the disease status from different perspectives. It is expected that integrating complementary information in the tensor data and the brain network data, and incorporating other clinical parameters will be potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. Many research efforts have been devoted to this area. They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, and multi-view feature analysis. In this paper, we review some recent data mining methods that are used for analyzing brain disorders.

Many brain disorders are characterized by ongoing injury that is clinically silent for prolonged periods and irreversible by the time symptoms first present. New approaches for detection of early changes in subclinical periods will afford powerful tools for aiding clinical diagnosis, clarifying underlying mechanisms, and informing neuroprotective interventions to slow or reverse neural injury for a broad spectrum of brain disorders, including bipolar disorder, HIV infection on brain, Alzheimer’s disease, Parkinson’s disease, etc. Early diagnosis has the potential to greatly alleviate the burden of brain disorders and the ever increasing costs to families and society.

As the identification of brain disorders is extremely challenging, many different diagnosis tools and methods have been developed to obtain a large number of measurements from various examinations and laboratory tests. Especially, recent advances in the neuroimaging technology have provided an efficient and noninvasive way for studying the structural and functional connectivity of the human brain, either normal or in a diseased state [1]. This can be attributed in part to advances in magnetic resonance imaging (MRI) capabilities [2]. Techniques such as diffusion MRI, also referred to as diffusion tensor imaging (DTI), produce in vivo images of the diffusion process of water molecules in biological tissues. By leveraging the fact that the water molecule diffusion patterns reveal microscopic details about tissue architecture, DTI can be used to perform tractography within the white matter and construct structural connectivity networks [37]. Functional MRI (fMRI) is a functional neuroimaging procedure that identifies localized patterns of brain activation by detecting associated changes in the cerebral blood flow. The primary form of fMRI uses the blood-oxygenation-level-dependent (BOLD) response extracted from the gray matter [810]. Another neuroimaging technique is positron emission tomography (PET). Using different radioactive tracers (e.g., fluorodeoxyglucose), PET produces a three-dimensional image of various physiological, biochemical, and metabolic processes [11].

A variety of data representations can be derived from these neuroimaging experiments, which present many unique challenges for the data mining community. Conventional data mining algorithms are usually developed to tackle data in one specific representation, a majority of which are particularly for vector-based data. However, the raw neuroimaging data are in the form of tensors, from which we can further construct brain networks connecting regions of interest (ROIs). Both of them are highly structured considering correlations between adjacent voxels in the tensor data and that between connected brain regions in the brain network data. Moreover, it is critical to explore interactions between measurements computed from the neuroimaging and other clinical experiments which describe subjects in different vector spaces. In this paper, we review some recent data mining methods for (1) mining tensor imaging data; (2) mining brain networks; and (3) mining multi-view feature vectors.

Tensor imaging analysis

For brain disorder identification, the raw data generated by neuroimaging experiments are in tensor representations [1113]. For example, in contrast to two-dimensional X-ray images, an fMRI sample corresponds to a four-dimensional array by recording the sequential changes of traceable signals in each voxel.1

Tensors are higher order arrays that generalize the concepts of vectors (first-order tensors) and matrices (second-order tensors), whose elements are indexed by more than two indices. Each index expresses amode of variation of the data and corresponds to a coordinate direction. In an fMRI sample, the first three modes usually encode the spatial information, while the fourth mode encodes the temporal information. The number of variables in each mode indicates the dimensionality of a mode. The order of a tensor is determined by the number of its modes. An mth-order tensor can be represented as X=(xi1,…,im)∈RI1×⋯×Im, where Ii is the dimension of X along the i-th mode.

Definition 1

(Tensor product) The tensor product of three vectors a∈RI1, b∈RI2, and c∈RI3, denoted by a⊗b⊗c, represents a third-order tensor with the elements (a⊗b⊗c)i1,i2,i3 = ai1bi2ci3.

Tensor product is also referred to as outer product in some literature [1112]. An mth-order tensor is a rank-one tensor if it can be defined as the tensor product of m vectors.

Definition 2

Given a third-order tensor X∈RI1×I2×I3 and an integer R, as illustrated in Fig. 1, a tensor factorization of X can be expressed as



Fig. 1

Tensor factorization of a third-order tensor


One of the major difficulties brought by the tensor data is the curse of dimensionality. The total number of voxels contained in a multi-mode tensor, say, X=(xi1,…,im)∈RI1×⋯×Im is I1×⋯×Im which is exponential to the number of modes. If we unfold the tensor into a vector, the number of features will be extremely high [14]. This makes traditional data mining methods prone to overfitting, especially with a small sample size. Both computational scalability and theoretical guarantee of the traditional models are compromised by such high dimensionality [13].

On the other hand, complex structural information is embedded in the tensor data. For example, in the neuroimaging data, values of adjacent voxels are usually correlated with each other [2]. Such spatial relationships among different voxels in a tensor image can be very important in neuroimaging applications. Conventional tensor-based approaches focus on reshaping the tensor data into matrices/vectors, and thus, the original spatial relationships are lost. The integration of structural information is expected to improve the accuracy and interpretability of tensor models.

2.1 Supervised learning

Suppose we have a set of tensor data D={(Xi,yi)}ni=1 for classification problem, where Xi∈RI1×⋯×Im is the neuroimaging data represented as an mth-order tensor and yi∈{−1,+1} is the corresponding binary class label of Xi. For example, if the i-th subject has Alzheimer’s disease, the subject is associated with a positive label, i.e., yi=+1. Otherwise, if the subject is in the control group, the subject is associated with a negative label, i.e., yi=−1.

Supervised tensor learning can be formulated as the optimization problem of support tensor machines (STMs) [15] which is a generalization of the standard support vector machines (SVMs) from vector data to tensor data. The objective of such learning algorithms is to learn a hyperplane by which the samples with different labels are divided as wide as possible. However, tensor data may not be linearly separable in the input space. To achieve a better performance on finding the most discriminative biomarkers or identifying infected subjects from the control group, in many neuroimaging applications, nonlinear transformation of the original tensor data should be considered. He et al. study the problem of supervised tensor learning with nonlinear kernels which can preserve the structure of tensor data [13]. The proposed kernel is an extension of kernels in the vector space to the tensor space which can take the multidimensional structure complexity into account. However, it cannot automatically consider the abundant and complicated information of the neuroimaging data in an integral manner. Han et al. apply a deep learning-based algorithm, the hierarchical convolutional sparse auto-encoder, to extract efficient and robust features and conserve abundant detail information for the neuroimaging classification [16].

Slightly different from classifying disease status (discrete label), another family of problems uses tensor neuroimages to predict cognitive outcome (continuous label). The problems can be formulated in a regression setup by treating clinical outcome as the real label, i.e., yi∈R, and treating tensor neuroimages as the input. However, most classical regression methods take vectors as input features. Simply reshaping a tensor into a vector is clearly an unsatisfactory solution.

Zhou et al. exploit the tensor structure in imaging data and integrate tensor decomposition within a statistical regression paradigm to model multidimensional arrays [14]. By imposing a low-rank approximation to the extremely high-dimensional complex imaging data, the curse of dimensionality is greatly alleviated, thereby allowing development of a fast estimation algorithm and regularization. Numerical analysis demonstrates its potential applications in identifying ROI in brains that are relevant to a particular clinical response. In scenarios where the objective is to predict a set of dependent variables, Cichocki et al. introduce a generalized multilinear regression model, higher order partial least squares, which projects the electrocorticogram data into a latent space and performs regression on the corresponding latent variables [1718].

2.2 Unsupervised learning

Modern imaging techniques have allowed us to study the human brain as a complex system by modeling it as a network [19]. For example, the fMRI scans consist of activations of thousands of voxels over time embedding a complex interaction of signals and noise [20], which naturally presents the problem of eliciting the underlying network from brain activities in the spatio-temporal tensor data. A brain connectivity network, also called a connectome [21], consists of nodes (gray matter regions) and edges (white matter tracts in structural networks or correlations between two BOLD time series in functional networks).

Although the anatomical atlases in the brain have been extensively studied for decades, task/subject specific networks have still not been completely explored with consideration of functional or structural connectivity information. An anatomically parcellated region may contain subregions that are characterized by dramatically different functional or structural connectivity patterns, thereby significantly limiting the utility of the constructed networks. There are usually trade-offs between reducing noise and preserving utility in brain parcellation [2]. Thus, investigating how to directly construct brain networks from tensor imaging data and understanding how they develop, deteriorate, and vary across individuals will benefit disease diagnosis [12].

Davidson et al. pose the problem of network discovery from fMRI data which involves simplifying spatio-temporal data into regions of the brain (nodes) and relationships between those regions (edges) [12]. Here the nodes represent collections of voxels that are known to behave cohesively over time; the edges can indicate a number of properties between nodes such as facilitation/inhibition (increases/decreases activity) or probabilistic (synchronized activity) relationships; and the weight associated with each edge encodes the strength of the relationship.

A tensor can be decomposed into several factors. However, unconstrained tensor decomposition results of the fMRI data may not be good for node discovery because each factor is typically not a spatially contiguous region nor does it necessarily match an anatomical region. That is to say, many spatially adjacent voxels in the same structure are not active in the same factor which is anatomically impossible. Therefore, to achieve the purpose of discovering nodes while preserving anatomical adjacency, known anatomical regions in the brain are used as masks and constraints are added to enforce that the discovered factors should closely match these masks [12].

Yang et al. investigate the inference of mouse brain networks and propose a hierarchical graphical model framework with tree-structural regularization [22]. In the hierarchical structure, voxels serve as the leaf nodes of the tree, and a node in the intermediate layer represents a region formed by voxels in the subtree rooted at that node. For edge discovery problem, Papalexakis et al. leverage control theory to model the dynamics of neuron interactions and infer the functional connectivity [23]. It is assumed that in addition to the linear influence of the input stimulus, there are hidden neuron regions of the brain, which interact with each other, causing the voxel activities. Veeriah et al. propose a deep learning algorithm for predicting if the two brain neurons are causally connected given their activation time-series data [24]. It reveals that the exploitation of the deep architecture is critical, which jointly extracts sequences of salient patterns of activation and aligns them to predict neural connections.

Overall, current research on tensor imaging analysis presents two directions: (1) supervised: for a particular brain disorder, a classifier can be trained by modeling the relationship between a set of neuroimages and their associated labels (disease status or clinical response); (2) unsupervised: regardless of brain disorders, a brain network can be discovered from a given neuroimage.

3 Brain network analysis

We have briefly introduced that brain networks can be constructed from neuroimaging data where nodes correspond to brain regions, e.g., insulahippocampusthalamus, and links correspond to the functional/structural connectivity between brain regions. The linkage structure in brain networks can encode tremendous information about the mental health of human subjects. For example, in brain networks derived from fMRI, functional connections can encode the correlations between the functional activities of brain regions. While structural links in DTI brain networks can capture the number of neural fibers connecting different brain regions. The complex structures and the lack of vector representations for the brain network data raise major challenges for data mining.

Next, we will discuss different approaches on how to conduct further analysis for constructed brain networks, which are also referred to as graphs hereafter.

Definition 3

(Binary graph) A binary graph is represented as G=(V,E), where V={v1,…,vnv} is the set of vertices, and E⊆V×V is the set of deterministic edges.

3.1 Kernel learning on graphs

In the setting of supervised learning on graphs, the target is to train a classifier using a given set of graph data D={(Gi,yi)}ni=1, so that we can predict the label y^ for a test graph G. With applications to brain networks, it is desirable to identify the disease status for a subject based on his/her uncovered brain network. Recent development of brain network analysis has made characterization of brain disorders at a whole-brain connectivity level possible, thus providing a new direction for brain disease classification.

Due to the complex structures and the lack of vector representations, graph data cannot be directly used as the input for most data mining algorithms. A straightforward solution that has been extensively explored is to first derive features from brain networks and then construct a kernel on the feature vectors.

Wee et al. use brain connectivity networks for disease diagnosis on mild cognitive impairment (MCI), which is an early phase of Alzheimer’s disease (AD) and usually regarded as a good target for early diagnosis and therapeutic interventions [2527]. In the step of feature extraction, weighted local clustering coefficients of each ROI in relation to the remaining ROIs are extracted from all the constructed brain networks to quantify the prevalence of clustered connectivity around the ROIs. To select the most discriminative features for classification, statistical t test is performed and features with p values smaller than a predefined threshold are selected to construct a kernel matrix. Through the employment of the multi-kernel SVM, Wee et al. integrate information from DTI and fMRI and achieve accurate early detection of brain abnormalities [27].

However, such strategy simply treats a graph as a collection of nodes/links, and then extracts local measures (e.g., clustering coefficient) for each node or performs statistical analysis on each link, thereby blinding the connectivity structures of brain networks. Motivated by the fact that some data in real-world applications are naturally represented by means of graphs, while compressing and converting them to vectorial representations would definitely lose structural information, kernel methods for graphs have been extensively studied for a decade [28].

A graph kernel maps the graph data from the original graph space to the feature space and further measures the similarity between two graphs by comparing their topological structures [29]. For example, product graph kernel is based on the idea of counting the number of walks in product graphs [30]; marginalized graph kernel works by comparing the label sequences generated by synchronized random walks of labeled graphs [31]; and cyclic pattern kernels for graphs count pairs of matching cyclic/tree patterns in two graphs [32].

To identify individuals with AD/MCI from healthy controls, instead of using only a single property of brain networks, Jie et al. integrate multiple properties of fMRI brain networks to improve the disease diagnosis performance [33]. Two different yet complementary network properties, i.e., local connectivity and global topological properties are quantified by computing two different types of kernels, i.e., a vector-based kernel and a graph kernel. As a local network property, weighted clustering coefficients are extracted to compute a vector-based kernel. As a topology-based graph kernel, Weisfeiler-Lehman subtree kernel [29] is used to measure the topological similarity between paired fMRI brain networks. It is shown that this type of graph kernel can effectively capture the topological information from fMRI brain networks. The multi-kernel SVM is employed to fuse these two heterogeneous kernels for distinguishing individuals with MCI from healthy controls.

3.2 Subgraph pattern mining

In brain network analysis, the ideal patterns we want to mine from the data should take care of both local and global graph topological information. Graph kernel methods seem promising, which, however, are not interpretable. Subgraph patterns are more suitable for brain networks, which can simultaneously model the network connectivity patterns around the nodes and capture the changes in local area [2].

Definition 4

(Subgraph) Let G′=(V′,E′) and G=(V,E) be two binary graphs. G′ is a subgraph of G (denoted as G′⊆G) iff V′⊆V and E′⊆E. If G′ is a subgraph of G, then G is supergraph of G′.

A subgraph pattern, in a brain network, represents a collection of brain regions and their connections. For example, as shown in Fig. 2, three brain regions should work collaboratively for normal people and the absence of any connection between them can result in Alzheimer’s disease in different degrees. Therefore, it is valuable to understand which connections collectively play a significant role in disease mechanism by finding discriminative subgraph patterns in brain networks.

Mining subgraph patterns from graph data has been extensively studied by many researchers [3437]. In general, a variety of filtering criteria are proposed. A typical evaluation criterion is frequency, which aims at searching for frequently appearing subgraph features in a graph dataset satisfying a prespecified threshold. Most of the frequent subgraph mining approaches are unsupervised. For example, Yan and Han develop a depth-first search algorithm: gSpan [38]. This algorithm builds a lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label. Based on this lexicographic order, gSpan adopts the depth-first search strategy to mine frequent connected subgraphs efficiently. Many other approaches for frequent subgraph mining have also been proposed, e.g., AGM [39], FSG [40], MoFa [41], FFSM [42], and Gaston [43].

Fig. 2

An example of discriminative subgraph patterns in brain networks


Moreover, the problem of supervised subgraph mining has been studied in recent work which examines how to improve the efficiency of searching the discriminative subgraph patterns for graph classification. Yan et al. introduce two concepts structural leap search and frequency-descending mining, and propose LEAP [37] which is one of the first work in discriminative subgraph mining. Thoma et al. propose CORK which can yield a near-optimal solution using greedy feature selection [36]. Ranu and Singh propose a scalable approach, called GraphSig, that is capable of mining discriminative subgraphs with a low-frequency threshold [44]. Jin et al. propose COM which takes into account the co-occurrences of subgraph patterns, thereby facilitating the mining process [45]. Jin et al. further propose an evolutionary computation method, called GAIA, to mine discriminative subgraph patterns using a randomized searching strategy [34]. Zhu et al. design a diversified discrimination score based on the log ratio which can reduce the overlap between selected features by considering the embedding overlaps in the graphs [46].

Conventional graph mining approaches are best suited for binary edges, where the structure of graph objects is deterministic, and the binary edges represent the presence of linkages between the nodes [2]. In fMRI brain network data, however, there are inherently weighted edges in the graph linkage structure, as shown in Fig. 3 (left). A straightforward solution is to threshold weighted networks to yield binary networks. However, such simplification will result in great loss of information. Ideal data mining methods for brain network analysis should be able to overcome these methodological problems by generalizing the network edges to positive and negative weighted cases, e.g., probabilistic weights in fMRI brain networks and integral weights in DTI brain networks.

Fig. 3

An example of fMRI brain networks (left) and all possible instantiations of linkage structures between red nodes (right) [47]. (Color figure online)


Definition 5

A weighted graph is represented as G˜=(V,E,p), where V={v1,…,vnv} is the set of vertices, and E⊆V×V is the set of nondeterministic edges. p:E→(0,1] is a function that assigns a probability of existence to each edge in E.

fMRI brain networks can be modeled as weighted graphs where each edge e∈E is associated with a probability p(e) indicating the likelihood of whether this edge should exist or not [4748]. It is assumed thatp(e) of different edges in a weighted graph are independent from each other. Therefore, by enumerating the possible existence of all edges in a weighted graph, we can obtain a set of binary graphs. For example, in Fig. 3 (right), consider the three red nodes and links between them as a weighted graph. There are 23=8binary graphs that can be implied with different probabilities. For a weighted graph G˜, the probability of G˜containing a subgraph feature G′ is defined as the probability that a binary graph G implied by G˜ contains subgraph G′. Kong et al. propose a discriminative subgraph feature selection method based on dynamic programming to compute the probability distribution of the discrimination scores for each subgraph pattern within a set of weighted graphs [48].

For brain network analysis, usually we only have a small number of graph instances [48]. In these applications, the graph view alone is not sufficient for mining important subgraphs. Fortunately, the side information is available along with the graph data for brain disorder identification. For example, in neurological studies, hundreds of clinical, immunologic, serologic, and cognitive measures may be available for each subject, apart from brain networks. These measures compose multiple side views which contain a tremendous amount of supplemental information for diagnostic purposes. It is desirable to extract valuable information from a plurality of side views to guide the process of subgraph mining in brain networks.

Fig. 4

Two strategies of leveraging side views in feature selection process for graph classification: late fusion and early fusion


Figure 4 illustrates two strategies of leveraging side views in the process of selecting subgraph patterns. Conventional graph classification approaches treat side views and subgraph patterns separately and may only combine them at the final stage of training a classifier. Obviously, the valuable information embedded in side views is not fully leveraged in the feature selection process. In order to fuse heterogeneous data sources at an early stage thereby exploring their correlations, Cao et al. introduce an effective algorithm for discriminative subgraph selection using multiple side views as guidance [49]. Side information consistency is first validated via statistical hypothesis testing which suggests that the similarity of side view features between instances with the same label should have higher probability to be larger than that with different labels. Based on such observations, it is assumed that the similarity/distance between instances in the space of subgraph features should be consistent with that in the space of a side view. That is to say, if two instances are similar in the space of a side view, they should also be close to each other in the space of subgraph features. Therefore the target is to minimize the distance between subgraph features of each pair of similar instances in each side view [49]. In contrast to existing subgraph mining approaches that focus on the graph view alone, the proposed method can explore multiple vector-based side views to find an optimal set of subgraph features for graph classification.

For graph classification, brain network analysis approaches can generally be put into three groups: (1) extracting some local measures (e.g., clustering coefficient) to train a standard vector-based classifier; (2) directly adopting graph kernels for classification; and (3) finding discriminative subgraph patterns. Different types of methods model the connectivity embedded in brain networks in different ways.

4 Multi-view feature analysis

Medical science witnesses everyday measurements from a series of medical examinations documented for each subject, including clinical, imaging, immunologic, serologic, and cognitive measures [50], as shown in Fig. 5. Each group of measures characterizes the health state of a subject from different aspects. This type of data is named as multi-view data, and each group of measures form a distinct view quantifying subjects in one specific feature space. Therefore, it is critical to combine them to improve the learning performance, while simply concatenating features from all views and transforming a multi-view data into a single-view data, as the method (a) shown in Fig. 6, would fail to leverage the underlying correlations between different views.

4.1 Multi-view learning and feature selection

Suppose we have a multi-view classification task with n labeled instances represented from m different views: D={(x(1)i,x(2)i,…,x(m)i,yi)}ni=1, where x(v)i∈RIv, Iv is the dimensionality of the v-th view, and yi∈{−1,+1} is the class label of the i-th instance.

Fig. 5

An example of multi-view learning in medical studies [51]


Representative methods for multi-view learning can be categorized into three groups: co-training, multiple kernel learning, and subspace learning [52]. Generally, the co-training style algorithm is a classic approach for semi-supervised learning, which trains in alternation to maximize the mutual agreement on different views. Multiple kernel learning algorithms combine kernels that naturally correspond to different views, either linearly [53] or nonlinearly [5455] to improve learning performance. Subspace learning algorithms learn a latent subspace, from which multiple views are generated. Multiple kernel learning and subspace learning are generalized as co-regularization style algorithms [56], where the disagreement between the functions of different views is taken as a part of the objective function to be minimized. Overall, by exploring the consistency and complementary properties of different views, multi-view learning is more effective than single-view learning.

In the multi-view setting for brain disorders, or for medical studies in general, a critical problem is that there may be limited subjects available (i.e., a small n) yet introducing a large number of measurements (i.e., a large ∑mi=1Ii). Within the multi-view data, not all features in different views are relevant to the learning task, and some irrelevant features may introduce unexpected noise. The irrelevant information can even be exaggerated after view combinations thereby degrading performance. Therefore, it is necessary to take care of feature selection in the learning process. Feature selection results can also be used by researchers to find biomarkers for brain diseases. Such biomarkers are clinically imperative for detecting injury to the brain in the earliest stage before it is irreversible. Valid biomarkers can be used to aid diagnosis, monitor disease progression, and evaluate effects of intervention [48].

Conventional feature selection approaches can be divided into three main directions: filter, wrapper, and embedded methods [57]. Filter methods compute a discrimination score of each feature independently of the other features based on the correlation between the feature and the label, e.g., information gain, Gini index, Relief [5859]. Wrapper methods measure the usefulness of feature subsets according to their predictive power, optimizing the subsequent induction procedure that uses the respective subset for classification [51,6063]. Embedded methods perform feature selection in the process of model training based on sparsity regularization [6467]. For example, Miranda et al. add a regularization term that penalizes the size of the selected feature subset to the standard cost function of SVM, thereby optimizing the new objective function to conduct feature selection [68]. Essentially, the process of feature selection and learning algorithm interact in embedded methods which means the learning part and the feature selection part cannot be separated, while wrapper methods utilize the learning algorithm as a black box.

However, directly applying these feature selection approaches to each separate view would fail to leverage multi-view correlations. By taking into account the latent interactions among views and the redundancy triggered by multiple views, it is desirable to combine multi-view data in a principled manner and perform feature selection to obtain consensus and discriminative low-dimensional feature representations.

4.2 Modeling view correlations

Recent years have witnessed many research efforts devoted to the integration of feature selection and multi-view learning. Tang et al. study multi-view feature selection in the unsupervised setting by constraining that similar data instances from each view should have similar pseudo-class labels [69]. Considering brain disorder identification, different neuroimaging features may capture different but complementary characteristics of the data. For example, the voxel-based tensor features convey the global information, while the ROI-based automated anatomical labeling (AAL) [70] features summarize the local information from multiple representative brain regions. Incorporating these data and additional nonimaging data sources can potentially improve the prediction. For Alzheimer’s disease (AD) classification, Ye et al. propose a kernel-based method for integrating heterogeneous data, including tensor and AAL features from MRI images, demographic information, and genetic information [11]. The kernel framework is further extended for selecting features (biomarkers) from heterogeneous data sources that play more significant roles than others in AD diagnosis.

Huang et al. propose a sparse composite linear discriminant analysis model for identification of disease-related brain regions of AD from multiple data sources [71]. Two sets of parameters are learned: one represents the common information shared by all the data sources about a feature, and the other represents the specific information only captured by a particular data source about the feature. Experiments are conducted on the PET and MRI data which measure structural and functional aspects, respectively, of the same AD pathology. However, the proposed approach requires the input as the same set of variables from multiple data sources. Xiang et al. investigate multi-source incomplete data for AD and introduce a unified feature learning model to handle block-wise missing data which achieves simultaneous feature-level and source-level selection [72].

For modeling view correlations, in general, a coefficient is assigned for each view, either at the view-level or feature-level. For example, in multiple kernel learning, a kernel is constructed from each view and a set of kernel coefficients are learned to obtain an optimal combined kernel matrix. These approaches, however, fail to explicitly consider correlations between features.

4.3 Modeling feature correlations

One of the key issues for multi-view classification is to choose an appropriate tool to model features and their correlations hidden in multiple views, since this directly determines how information will be used. In contrast to modeling on views, another direction for modeling multi-view data is to directly consider the correlations between features from multiple views. Since taking the tensor product of their respective feature spaces corresponds to the interaction of features from multiple views, the concept of tensor serves as a backbone for incorporating multi-view features into a consensus representation by means of tensor product, where the complex multiple relationships among views are embedded within the tensor structures. By mining structural information contained in the tensor, knowledge of multi-view features can be extracted and used to establish a predictive model.

Smalter et al. formulate the problem of feature selection in the tensor product space as an integer quadratic programming problem [73]. However, this method is computationally intractable on many views, since it directly selects features in the tensor product space resulting in the curse of dimensionality, as the method (b) shown in Fig. 6. Cao et al. propose to use a tensor-based approach to model features and their correlations hidden in the original multi-view data [51]. The operation of tensor product can be used to bringm-view feature vectors of each instance together, leading to a tensorial representation for common structure across multiple views, and allowing us to adequately diffuse relationships and encode information among multi-view features. In this manner, the multi-view classification task is essentially transformed from an independent domain of each view to a consensus domain as a tensor classification problem.

By using Xi to denote ∏mv=1⊗x(v)i, the dataset of labeled multi-view instances can be represented as D={(Xi,yi)}ni=1. Note that each multi-view instance Xi is an mth-order tensor that lies in the tensor product space RI1×⋯×Im. Based on the definitions of inner product and tensor norm, multi-view classification can be formulated as a global convex optimization problem in the framework of supervised tensor learning [15]. This model is named as multi-view SVM [51], and it can be solved with the use of optimization techniques developed for SVM.

Fig. 6

Schematic view of the key differences among three strategies of multi-view feature selection [51]


Furthermore, a dual method for multi-view feature selection is proposed in [51] that leverages the relationship between original multi-view features and reconstructed tensor product features to facilitate the implementation of feature selection, as the method (c) in Fig. 6. It is a wrapper model which selects useful features in conjunction with the classifier and simultaneously exploits the correlations among multiple views. Following the idea of SVM-based recursive feature elimination [60], multi-view feature selection is consistently formulated and implemented in the framework of multi-view SVM. This idea can extend to include lower order feature interactions and to employ a variety of loss functions for classification or regression [74].

5 Future work

The human brain is one of the most complicated biological structures in the known universe. While it is very challenging to understand how it works, especially when disorders and diseases occur, dozens of leading technology firms, academic institutions, scientists, and other key contributors to the field of neuroscience have devoted themselves to this area and made significant improvements in various dimensions.2 Data mining on brain disorder identification has become an emerging area and a promising research direction.

This paper provides an overview of data mining approaches with applications to brain disorder identification, which have attracted increasing attention in both data mining and neuroscience communities in recent years. A taxonomy is built based upon data representations, i.e., tensor imaging data, brain network data, and multi-view data, following which the relationships between different data mining algorithms and different neuroimaging applications are summarized. We briefly present some potential topics of interest in the future.

5.1 Bridging heterogeneous data representations

As introduced in this paper, we can usually derive data from neuroimaging experiments in three representations, including raw tensor imaging data, brain network data, and multi-view vector-based data. It is critical to study how to train a model on a mixture of data representations, although it is very challenging to combine data that are represented in tensor space, vector space, and graph space, respectively. There is a straightforward idea of defining different kernels on different feature spaces and combing them through multi-kernel algorithms. However, it is usually hard to interpret the results. The concept of side view has been introduced to facilitate the process of mining brain networks, which may also be used to guide supervised tensor learning. It is even more interesting if we can learn on tensors and graphs simultaneously.

5.2 Integrating multiple neuroimaging modalities

There are a variety of neuroimaging techniques available characterizing subjects from different perspectives and providing complementary information. For example, DTI contains local microstructural characteristics of water diffusion; structural MRI can be used to delineate brain atrophy; fMRI records BOLD response related to neural activity; and PET measures metabolic patterns [27]. Based on such multimodality representation, it is desirable to find useful patterns with rich semantics. For example, it is important to know which connectivity between brain regions is significant in the sense of both structure and functionality. On the other hand, by leveraging the complementary information embedded in the multimodality representation, better performance on disease diagnosis can be expected.

Fig. 7

A bioinformatics heterogeneous information network schema


5.3 Mining bioinformatics information networks

Bioinformatics network is a rich source of heterogeneous information involving disease mechanisms, as shown in Fig. 7. The problems of gene-disease association and drug-target binding prediction have been studied in the setting of heterogeneous information networks [7576]. For example, in gene-disease association prediction, different gene sequences can lead to certain diseases. Researchers would like to predict the association relationships between genes and diseases. Understanding the correlations between brain disorders and other diseases and the causality between certain genes and brain diseases can be transformative for yielding new insights concerning risk and protective relationships, for clarifying disease mechanisms, for aiding diagnostics and clinical monitoring, for biomarker discovery, for identification of new treatment targets, and for evaluating effects of intervention.


1  A voxel is the smallest three-dimensional point volume referenced in a neuroimaging of the brain.

2  http://www.whitehouse.gov/BRAIN


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Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders

Sidong Liu , Weidong Cai, Siqi Liu, Fan Zhang, Michael Fulham, Dagan Feng, Sonia Pujol, Ron Kikinis

Brain Informatics Sept 2015; 2(3): 167-180


Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research.

Neuroimaging has advanced rapidly in the past two decades. The advanced non-invasive neuroimaging techniques, e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), electroencephalography (EEG), and magnetoencephalography (MEG), have enabled the visualization and analysis of the brain function and structure in unprecedented detail and transformed the way we study the nervous system under normal and pathological conditions  [1], particularly neuropsychiatric disorders including neurological and psychiatric disorders that affect the nervous system  [24].

In the US, President Obama’s announcement of the ‘Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative’ on his state of the union address on April 2013 fueled resurgent interest in the neuroscience with a bold commitment to better understand the brain over the forthcoming decade [4]. Similar projects have been undertaken in the European Union [5] and Asia  [6].

Multimodal neuroimaging, which we declare as the summation of information from different neuroimaging modalities, has become one of the major drivers in neuroimaging research due to the recognition of the clinical benefits of multimodal data [78], and the better access to hybrid devices, e.g., PET/CT   [910], PET/MRI  [11], and PET/MRI/EEG [12]. Multimodal neuroimaging data can either be obtained from simultaneous imaging measurement (EEG/fMRI [13], PET/CT[14]), or integration of separate measurements (PET and sMRI [15], sMRI and dMRI [16], fMRI and dMRI [17]).

Multimodal neuroimaging advances neuroscience research, i.e., neurology, psychiatry, neurophysiology, and neurosurgery, by overcoming the limitation of individual modalities and by allowing a more comprehensive picture of the brain. For instance, we can jointly analyze the structure and function using the data provided by PET/CT and PET/MRI; EEG combined with functional MRI (fMRI) improves the spatiotemporal resolution that cannot be achieved by the single modality alone. Multimodal neuroimaging can also cross-validate findings from different sources and identify associations and patterns, e.g., causality of brain activity can be deduced by linking dynamics in different imaging readings. It can provide access, in an experimental setting, to determine the roles of different brain areas from multiple perspectives.

The growth of neuroimaging has spurred a parallel development of multimodal neuroimaging computing, which focuses on computational analysis of multimodal neuroimaging data, including pre-processing, feature extraction, image fusion, machine learning, visualization, and post-processing. These computational advances help to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information in images  [18].

Fig. 1

The explosive growth of multimodal neuroimaging studies over the past two decades. (Color figure online)


We conducted a search on PubMed using the keywords ‘multimodal AND neuroimaging’ up to ‘31 Dec 2014.’ There were 1461 relevant publications retrieved from the database. Figure 1 illustrates how multimodal neuroimaging in neuroscience research has rapidly expanded over the past 10 years. In 2004, there were 30 publications, and in 2014, there were close to 300 (indicated by the green area). There is a wide range of applications of multimodal neuroimaging, clinical and non-clinical, including building a brain machine interface (BMI)  [19], tracing neural activities and information pathways  [20], mapping mind and behavior to brain regions [2123], evaluating the effects of pharmacological treatments  [2425], and image-guided therapy (IGT)  [2628].

An important clinical application is the provision of functional and anatomical data for diagnosis of neuropsychiatric disorders  [34]. In another PubMed search on these 1461 publications, using the keywords ‘(multimodal AND neuroimaging) AND (neuropsychiatric OR neurological OR psychiatric),’ a substantial proportion (over 30%) of the relevant results focused on the neuropsychiatric disorders (see blue area in Fig. 1). The number of publications dramatically increased each year from 10 to 121 in the period 2004–2014.

Previous reviews mainly focused on a single neuropsychiatric disorder, and summarize the image-based findings of them. For Alzheimer’s disease (AD), for example, Perrin briefly reviewed the multimodal techniques, including PET, fMRI, structural MRI (sMRI), and biochemical examination of cerebrospinal fluid (CSF), to detect AD pathology  [29]. Ewers et al. integrated the findings on changes in cortical gray matter volume, white matter fiber tracts, and brain metabolism of patients  [30], and discussed the sequential changes in neuroimaging biomarkers during different disease stages  [31], similar to the review of Lin et al. [32]. In a more recent review, Nasrallah et al. extended a review to other forms of neurodegenerative dementia  [33]. More in-depth reviews on other neuropsychiatric disorders can be found in Sect. 3.

The goal of this review differs from those above in that our interest is to review the recent advances in multimodal neuroimaging and evaluate its applications in neuropsychiatric disorders. Such a review will provide a clearer picture of the current status and offer insights and inspiration to researchers as they design better models/methods for future research.

An extensive review of the image-based findings in neuropsychiatric disorders is beyond the scope of this paper, and we instead review recent studies with a focus on the applications of multimodal neuroimaging, and refer the readers to other reviews for the detailed findings. In Sect. 2, we provide an overview of the common multimodal neuroimaging techniques, and analyze the spatial/temporal resolution, functional/structural connectivity, sensitivity/specificity to brain changes, risks/benefits for clinical applications, computing workflows, and future potential. In Sect. 3, we discuss how these neuroimaging techniques can complement each other, and how they are applied in neuropsychiatric disorders. In Sect. 4, we outline future directions for multimodal neuroimaging in neuropsychiatric research.

An overview of neuroimaging techniques

The different neuroimaging techniques have different biophysical/biochemical mechanisms, and vary in imaging capabilities. Current neuroimaging techniques could be broadly classified into functional and structural neuroimaging. For example, sMRI reveals the detailed anatomy of the brain, and diffusion MRI (dMRI) provides information about fiber tracts. Functional modalities, including fMRI, PET, and EEG/MEG, provide data in brain metabolism and neural activity.

In the following paragraphs, we briefly summarize these neuroimaging techniques with respect to

  • spatial resolution; exploring the brain anatomy and detecting morphological changes
  • temporal resolution; monitoring neural activities and interactions, tracing information pathways
  • structural connectivity; tracing the major brain white matter pathways
  • functional connectivity; recording the neural co-activation, in the resting state
  • molecular imaging; detecting the molecular activity using agents to target specific functions
  • safety and risks
  • clinical availability, accessibility, and ease of use
  • future developments

Fig. 2

The overview of the properties of sMRI (blue), dMRI (green), fMRI (orange), PET (red), EEG (violet), and multimodal neuroimaging (gray), as indicated by the polar diagrams. Each axis in the diagram represents an attribute, and greater distance from the origin means better performance. Note the indexes in the diagrams are merely indicative and should not be interpreted in a quantitative way. (Color figure online)



Applications to neuropsychiatric disorders

Neuropsychiatric disorders represent the most disabling and costly category, based on the systematic analysis of descriptive epidemiology of 291 diseases and injuries from 1990 to 2010 for 187 countries  [58]. As shown in Fig. 3, neuropsychiatric disorders caused the largest number of years lost due to illness, disability, and early death measured by disability-adjusted life years (DALYs) in US, and the socioeconomic burden of neuropsychiatric disorders will be aggravated as people live longer.

Fig. 3

The disability-adjusted life years (DALYs) of 291 diseases and injuries based on the systematic analysis of descriptive epidemiology from 1990 to 2010 in US [58]. (Color figure online)


Neuroimaging techniques have expanded beyond a traditional diagnostic role to have a fundamental role in patient management from diagnosis, to selection and assessment of treatment and to prognosis stratification. There is a rising trend of using the multimodal neuroimaging approaches in neuropsychiatric disorders, as shown in Fig. 1. In this section, we summarize how these neuroimaging techniques can be integrated using the multimodal computing methods, and further demonstrate their applications in neuropsychiatric disorders as well as in stroke, traumatic brain injury (TBI), brain tumors, and the brain connectome (Fig. 4).

Fig. 4

The applications of the multimodal neuroimaging approaches in a variety of neuropsychiatric disorders, as well as in stroke, brain injury, brain tumor, and connectome. The color of circle indicates various neuroimaging techniques, same as in Fig. 2. The size of the circle indicates the prevalence of use the technique in specific applications. Note the sizes are only indicative and should not be interpreted in a quantitative way. (Color figure online)


These multimodal approaches can be separated into categories that include a structural–structural combination, a functional–functional combination, and a structural–functional combination. Each category has different applications, and requires different computing workflows. In brief, a structural–structural combination, e.g., sMRI-dMRI, is used to extract and fuse various morphological features and is applied to disorders that affect both gray matter and white matter, such as TBI and stroke. The functional–functional combination can be used to explore brain activation/metabolism patterns and is mainly applied to cognition and consciousness-related disorders, e.g., epilepsy and obsessive-compulsive disorder (OCD). The structural–functional combination is virtually applicable to all disorders, but more frequently used for identifying the structure–function associations in neurodegenerative disorders, neurodevelopmental disorders, multiple sclerosis, schizophrenia, bipolar disorder, brain tumors, and the brain connectome.

Structural–structural combination

sMRI-dMRI methods dominate the structural–structural category, as they take clinical benefits of sMRI and dMRI by integrating the gray matter and white matter morphometry. It has become a useful tool to detect lesions and evaluate treatments for various neuropsychiatric disorders that cause brain morphological changes. Here, we list a few examples of clinical uses of sMRI-dMRI.

Traumatic brain injury (TBI) has very high incidence, resulting in 6.8 million TBI cases every year in the US, and causes impairment of memory, information processing, attention, and executive function  [59]. Multimodal structural neuroimaging can assist neurosurgeons, intensive care specialists, neurologists, and rehabilitation specialists in the management of TBI  [60]. Conventional brain CT usually fails to detect the subtle structural abnormalities in mild TBI, and sMRI and dMRI are the methods of choice to evaluate and predict outcome in TBI. The sMRI sequences (T1, T2, FLAIR, susceptibility-weighted imaging (SWI) and gradient-recalled echo (GRE)) provide highly accurate depiction of pathological lesions, and dMRI detects the effects of TBI on brain connectivity and non-hemorrhagic diffuse axonal injury (DAI), which are not detected by CT. The sMRI-dMRI methods are widely used in TBI  [6162]. There are also some studies that have used dMRI and fMRI to validate the connectivity information in TBI patients in the recovery phase  [63,64].

The sMRI-dMRI methods have been routinely used in the assessment and treatment planning for stroke. Stroke is a leading cause of death worldwide. There are different types of stroke, and each requires a different diagnostic approach and treatment. T2*-weighted sMRI, e.g., SWI and GRE, is primarily used to detect hemorrhagic stroke, and has equal sensitivity to standard CT methods. However, dMRI is 4-5 times more sensitive in detecting acute ischemic stroke than CT. Other structural imaging techniques, such as perfusion CT (PCT), CT angiography (CTA), digital subtraction angiography (DSA), perfusion-weighted imaging (PWI), and MR angiography (MRA), can also be used to evaluate suspected vascular occlusion, edema, and cerebral infarction. Tong et al. [65] recently published a comprehensive comparison of these methods in the evaluation and management of stroke. Another review on multimodal neuroimaging in stroke is given by Copen et al.  [66].

sMRI-dMRI methods have also been used to analyze the gray and white matter alterations in schizophrenia  [67] and Autism spectrum disorders (ASDs)  [1668], neurodegeneration simulation  [69], classification of AD and frontotemporal dementia (FTD)  [70], and Parkinson’s Disease (PD) staging  [71].

Functional–functional combination

EEG-fMRI is valued in functional brain research due to the complementary nature of EEG and fMRI. EEG-fMRI can provide simultaneous cortical and subcortical recording of brain activity with high spatiotemporal resolution.

Epilepsy is one of the most prevalent neurological disorders worldwide. EEG-fMRI is increasingly used to provide clinical support for the diagnosis of epilepsy, in addition to the routinely used sMRI  [72] and PET  [1473]. Researches have used EEG-fMRI to identify a set of brain functional regions that collectively form ‘consciousness,’ including contributions from the DMN, ascending arousal systems, and the thalamus, as summarized by Bagshaw et al.  [74]. The activation of these regions and the connection of the networks are important in the evaluation of epilepsy, and together may provide a more fundamental understanding of the alterations of consciousness experienced in epilepsy. Abela et al.  [75] focused on altered network compositions in epilepsy, and identified the specific connectivity pathways that characterize the underlying epilepsy syndromes, such as mesial temporal lobe epilepsy (MTLE), lateral temporal lobe epilepsy (LTLE), frontal lobe epilepsy (FLE), idiopathic generalized epilepsy (IGE), and absence epilepsy (AE). A substantial proportion of patients have refractory epilepsy and surgery offers the potential to reduce seizure frequency. Successful surgical treatments, however, require accurate localization of the seizure onset zones and an understanding of surrounding functional cortex to avoid iatrogenic disability. PET, MRI, and intracranial EEG (iEEG) are all needed for optimal surgical planning and treatment evaluation of refractory epilepsy  [7677].

Another important application of EEG-fMRI is to evaluate patients with obsessive-compulsive disorder (OCD). OCD is a chronic and relatively common neuropsychiatric disorder that characterized by stereotyped and repetitive behaviors. Patients with OCD feel intense need to carry out these behaviors, and have impaired ability to recognize an error and to adjust future responses. OCD may result in social disability. Two neuroimaging biomarkers of error commission, the error-related negativity (ERN) and the dorsal anterior cingulate cortex activation, have been identified using EEG and fMRI, respectively  [78]. However, Agam et al.  [79] recently suggested that these biomarkers have different neural and genetic mediation. dMRI is also increasingly being used to examine the microstructural integrity of white matter in OCD patients, since white matter abnormalities have long been suspected in OCD, but the findings are inconsistent. For example, one recent study indicated that patients with OCD had decreased fractional anisotropy in the anterior cingulum bundle [80], but in another recent study, the OCD patients showed increased fractional anisotropy of the cingulum bundle [81]. Further investigation on large datasets is needed to confirm these findings.

Structural–functional combination

sMRI-dMRI-fMRI has been ubiquitously used in neuropsychiatric research largely because of high clinical availability, and partially due to its capability to link brain function, structure, and connectivity. It has been increasingly used in research in attention-deficit hyperactivity disorder (ADHD), Autism spectrum disorder (ASD), bipolar disorder, schizophrenia, and clinically in multiple Sclerosis (MS).

ADHD is one of the most commonly diagnosed childhood behavioral disorders. It is characterized by persistent inattention (ADHD-I), hyperactivity-impulsivity (ADHD-H), or a combination of both (ADHD-C). ADHD affects at least 5–11% of school-age children, and symptoms may persist into adulthood  [82]. Previous studies using sMRI have reported various findings, such as decreased total brain volume and abnormalities in specific brain regions. The task-evoked and resting-state fMRI approaches were also used in ADHD studies to detect the abnormal brain activation. The use of sMRI and fMRI was reported recently in ADHD  [8384]. It is only quite recently that dMRI has been applied to ADHD to characterize the disrupted interconnected structural networks in the brain. Shenton et al. provided a brief summary of the latest studies  [85]. For example, Hong et al. used dMRI and whole-brain tractography to investigate the altered white matter connectivity in 71 children with ADHD, and identified a single network (comprising 23 brain regions and 25 links) that differentiates the ADHD group from the normal control group  [86].

ASDs are neurodevelopmental disorders characterized by deficits in social reciprocity, impaired communication, and restricted interests and repetitive behaviors. Previous studies using sMRI have shown that infants with ASD might have excessive brain growth followed by abnormally slow or even arrested growth as compared to normal developing control infants in early childhood  [87]. Subsequent research indicated ASD affects both gray and white matter volumes. Therefore, dMRI has been exploited to describe the microstructural integrity and orientation of white matter. fMRI has enhanced the understanding of the neural circuity of ASDs by demonstrating the convergent structural and functional changes  [8889]. For example, Mueller et al. used sMRI-dMRI-fMRI approach and identified three brain areas with strong correlations between the structural and functional abnormalities: right temporoparietal junction and the left frontal lobe, bilateral superior temporal gyri, and the right temporoparietal region  [90].

MS is a demyelinating disease commonly seen in young people. The cause of MS is unknown. Symptoms and signs vary across patients and can include cognitive impairment, fatigue, vertigo, diplopia, ataxia, hemiparesis, and paraparesis in severe MS patients. Histopathologic and neuroimaging examinations suggest that both white matter and gray matter are affected. In particular, the thalamus can be affected frequently in MS  [91], which can lead to impaired cognition. sMRI can detect the thalamic atrophy; dMRI can be used to demonstrate the altered thalamocortical white matter pathways, and fMRI can be used to show the association between the resting-state thalamocortical functional connectivity and cognitive impairment. Recently, sMRI-dMRI-fMRI was jointly used in several studies  [9293].

Bipolar disorder is a psychotic disorder that characterized states of depression and mania, and sometimes with symptoms common to schizophrenia. It is therefore difficult to conceptualize bipolar disorder and its subtypes, and differentiate it from other psychiatric disorders. The multimodal MRI methods have been applied to bipolar disorder and clearly demonstrate abnormalities in brain networks associated with emotion processing, emotion regulation, and reward processing. In a recent study, Sui et al. proposed a joint analysis model for fMRI and DTI for discriminating bipolar disorder from schizophrenia  [94]. Common abnormalities were seen in dorsolateral prefrontal cortex, thalamus, and uncinate fasciculus, whereas differences were found in medial frontal and visual cortex, as well as occipitofrontal white matter tracts. Phillips and Swartz recently published an extensive review of these neuroimaging findings and further pointed out the future directions of neuroimaging research in bipolar disorder  [95].

Schizophrenia is a major psychosis that is characterized by altered perception, thought processes, and behaviors. It can be highly heritable disorder  [96], and can be triggered by a combination of genetic factors and environmental interactions  [97]. Disconnection in white matter pathways and alteration of cortex are assumed to underlie the cognitive abnormalities in schizophrenia, although this is a hypothesis and as yet there is no direct proof. The approaches used for characterizing schizophrenia are very similar to those for bipolar disorder, primarily using sMRI-dMRI-fMRI. Various findings in schizophrenia studies have been reported, based on the investigation on microstructure of white matter  [98] or gray matter  [97], or the connectivity between different brain regions  [6799].

The study of brain networks, the connectome, is the focus of intense current neuroscience research [100]. Exploration on the neural systems and brain connections is critical to advance our understanding of normal brain reaction and is one of the greatest challenges of the twenty first century. The Human Connectome Project1 is directed at tackling this challenge using the highest quality imaging data available today, predominantly MRI data, complemented by EEG and MEG. The information about brain anatomy, structural connectivity, and functional connectivity is being obtained using dMRI and resting-state fMRI. Additional information about brain function is being obtained using task-evoked fMRI, EEG, and MEG to record the brain activity.

sMRI-PET is a new structural–functional combination that is being applied to neurodegenerative diseases and brain tumors to improve the localization and targeting of diseased tissue with high accuracy and sensitivity. AD is the most common neurodegenerative disorder among aging people, and it accounts for close to 70% of all dementia cases. In AD, activities of daily living deteriorate over a number of years, ultimately leading to death. There is no cure [101]. AD neuroimaging biomarkers can detect the changes in brain structure (e.g., atrophy on sMRI) and function (e.g., hypometabolism, amyloid plaque, and NFT formation on PET) before there is cognitive impairment. As a result, sMRI and PET with 18F-FDG and amyloid tracers are being increasingly used in the evaluation of patients with early dementia in the research setting  [8102106]. These studies also demonstrated clear benefits of multimodal neuroimaging over any single technique alone. Recently, dMRI  [107108] and fMRI  [109] have also been used in the evaluation of dementia as there is evidence that suggests the functional connection between networks is disrupted  [110112]. There are many extensive reviews which summarized these imaging techniques and the image-based findings [293133].

Over 200,000 individuals are diagnosed with primary or metastatic brain tumors in the US each year  [28]. The primary use of sMRI-PET in brain tumors is to accurately localize and label the lesion, e.g., tumor and edema. PET has the potential to more accurately detect the peripheral tumor boundary than using sMRI alone  [11113]. For brain tumor surgery, dMRI is usually combined with sMRI and PET for pre-operative surgical planning and intra-operative surgical navigation. For example, Durst et al. used dMRI to predict tumor infiltration in patients with gliomas  [114]. Tempany et al. used sMRI and dMRI tractography to display a complete brain map for surgical planning  [28]. They further demonstrated how to optimize the separation between tumor and normal brain in intrinsic brain tumors with sMRI, and how to avoid inadequate resection of the tumor.

Future directions

Multimodal neuroimaging approaches have been increasingly used in detection, diagnosis, prognosis, and treatment planning of neuropsychiatric disorders. In this paper, we have briefly summarized the recent advances in neuroimaging techniques, and reviewed their applications to neuropsychiatric disorders to provide an overview of the current status. We have also outlined some future directions for multimodal neuroimaging research.

Improved neuroimaging capabilities Neuroimaging techniques will continue to advance rapidly, with higher spatial/temporal/angular resolutions, shorter scan time, and better image contrast. In particular, hybrid scanners, e.g., PET/CT and PET/MRI, will become more clinically accessible. These technologies will enable more discoveries in the neuropsychiatric disorders. The improved imaging capabilities will offer better neuroimaging biomarkers to evaluate neuropsychiatric disorders, and various subtypes or different stages of the same disorder with higher statistical power. These biomarkers will be standardized so they can be widely used clinically and evaluated in large-scale sample sets. In addition, once the biomarkers reach a satisfactory level or the treatment, appropriate clinical guidelines must be developed to support and encourage widespread clinical testing.

Enhanced neuroimaging computing models and methods The continued growth in the complexity and dimensionality of the neuroimaging data will spur the parallel advances of computation models and methods to analyze such complex data. Future neuroimaging analysis models will integrate the longitudinal information to track the long-term changes in the biomarkers [115]. This is essential for us to understand the pathology of the disorders and its degeneration trajectory. With sufficiently large longitudinal datasets, we may be able to identify the causes and detect the early signs, as well as predict the course of the disorders. Future studies will also focus on subject-centered therapy. However, no matter how large the datasets are, they cannot include the entire population, and there will always be inter-subject variations. Personalized/patient-centered care is highly demanded and is the ultimate goal of neuroimaging studies [116]. Neuroimaging computing models and methods also need to keep increasing the degree of automation, accuracy, reproducibility, and robustness, and eventually need to be integrated into the clinical workflow to facilitate clinical testing of the new neuroimaging biomarkers.

Converged neurotechnologies Another future direction will be to combine imaging with non-imaging studies. The multidisciplinary nature of neuroimaging computing will keep bringing together clinicians, biologists, computer scientists, engineers, physicists, and other researchers. Imaging genetics is a very promising area for the future, where the aim is to identify the genetic basis of anatomical and functional abnormalities of the human brain and show how this is connected with neuropsychiatric disorders. There is a trend to use imaging findings in brain disorders to reveal the endophenotypes for various gene mutations. By converting the endophenotype data to novel genetic biomarkers, it may be possible to identify individuals at greater risk of developing brain disorders, and in the near future provide treatment options before the symptoms appear.


1 http://​www.​neuroscienceblue​print.​nih.​gov/​connectome


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Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis

Original description - :Cartoon representation...

Original description – :Cartoon representation of ubiquitin protein, highlighting the secondary structure. α-helices are coloured in blue and the β-sheet in green. The normal attachment point for a further ubiquitin molecule in polyubiquitin chain formation, lysine 48, is shown in pink. :Image was created using PyMOL (Photo credit: Wikipedia)

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis

Larry H Bernstein, MD, FACP, Curator, Reporter, AEW

The work reviewed follows a seminal contribution by two Israeli and an American molecular biologists who shared the Nobel Prize in Chemistry in 2004.

The Royal Swedish Academy of Sciences awarded the Nobel Prize in Chemistry for 2004 “for the discovery of ubiquitin-mediated protein degradation” jointly to Aaron Ciechanover Technion – Israel Institute of Technology, Haifa, Israel, Avram Hershko Technion – Israel Institute of Technology, Haifa, Israel and Irwin Rose – University of California, Irvine, USA.

Aaron Ciechanover, born 1947 (57 years) in Haifa, Israel (Israeli citizen) received a Doctor’s degree in medicine in 1975 at Hebrew University of Jerusalem, and in biology in 1982 at the Technion (Israel Institute of Technology), Haifa. He is a Distinguished Professor at the Center for Cancer and Vascular Biology, and the Rappaport Faculty of Medicine and Research Institute at the Technion, Haifa,

Avram Hershko, born 1937 (67 years) in Karcag, Hungary (Israeli citizen) earned the Doctor’s degree in medicine in 1969 at the Hadassah and the Hebrew University Medical School, Jerusalem.  He is a Distinguished Professor at the Rappaport Family Institute for Research in Medical Sciences at the Technion (Israel Institute of Technology), Haifa, Israel.

Irwin Rose, born 1926 (78 years) in New York, USA (American citizen) achieved a Doctor’s degree in 1952 at the University of Chicago, USA. Specialist at the Department of Physiology and Biophysics, College of Medicine, University of California, Irvine, USA.

Proteins labelled for destruction
Proteins build up all living things: plants, animals and therefore us humans. In the past few decades biochemistry has come a long way towards explaining how the cell produces all its various proteins. But as to the breaking down of proteins, not so many researchers were interested. Aaron Ciechanover, Avram Hershko and Irwin Rose went against the stream and at the beginning of the 1980s discovered one of the cell’s most important cyclical processes, regulated protein degradation. For this, they are being rewarded
with the 2004 Nobel Prize in Chemistry.

The label consists of a molecule called ubiquitin. This fastens to the protein to be destroyed, accompanies it to the proteasome where it is recognised as the key in a lock, and signals that a protein is on the way for disassembly. Shortly before the protein is squeezed into the proteasome, its ubiquitin label is disconnected for re-use.

Aaron Ciechanover, Avram Hershko and Irwin Rose have brought us to realise that the cell functions as a highly-efficient checking station where proteins are built up and broken down at a furious rate. The degradation is not indiscriminate but takes place through a process that is controlled in detail so that the proteins to be broken down at any given moment are given a molecular label, a ‘kiss of death’, to be dramatic. The labelled proteins are then fed into the cells’ “waste disposers”, the so called proteasomes, where they are chopped into small pieces and destroyed.

Animation (Plug in requirement: Flash Player 6)

Thanks to the work of the three Laureates it is now possible to understand at  molecular level how the cell controls a number of central processes by breaking down certain proteins and not others. Examples of processes governed by ubiquitin-mediated protein degradation are cell division, DNA repair, quality control of newly-produced proteins, and important parts of the immune defence. When the degradation does not work correctly, we fall ill. Cervical cancer and cystic fibrosis are two examples. Knowledge of
ubiquitin-mediated protein degradation offers an opportunity to develop drugs against these diseases and others.

Aaron Ciechanover and Ronen Ben-Saadon. N-terminal ubiquitination: more protein substrates join in. TRENDS in Cell Biology 2004; 14 (3):103-106.

The ubiquitin–proteasome system (UPS) is involved in selective targeting of innumerable cellular proteins through a complex pathway that plays important roles in a broad array of processes. An important step in the proteolytic cascade is specific recognition of the substrate by one of many ubiquitin ligases, E3s, which is followed by generation of the polyubiquitin degradation signal. For most substrates, it is believed that the first ubiquitin moiety is conjugated, through its C-terminal Gly76 residue, to an 1-NH2 group of an internal Lys residue. Recent findings indicate that, for several proteins, the first ubiquitin moiety is fused linearly to the a-NH2 group of the N-terminal residue.

The ubiquitin–proteasome system (UPS). Ubiquitin is first activated to a high-energy intermediate by E1. It is then transferred to a member of the E2 family of enzymes. From E2 it can be transferred directly to the substrate (S, red) that is bound specifically to a member of the ubiquitin ligase family of proteins, E3

  • (a). This occurs when the E3 belongs to the RING finger family of ligases. In the case of a HECT-domain-containing ligase
  • (b), the activated ubiquitin is transferred first to the E3 before it is conjugated to the E3-bound substrate . Additional ubiquitin moieties are added successively to the previously conjugated moiety to generate a polyubiquitin chain.
  • The polyubiquitinated substrate binds to the 26S proteasome complex (comprising 19S and 20S sub-complexes): the substrate is degraded to short peptides, and free and reusable ubiquitin is released through the activity of de-ubiquitinating enzymes (DUBs).

Ubiquitination on an internal lysine and on the N-terminal residue of the target substrate.

  • (a) The first ubiquitin moiety is conjugated, through its C-terminal Gly76 residue, to the 1-NH2 group of an internal lysine residue of the target substrate (Kn).
  • (b) The first ubiquitin moiety is conjugated to a free a-NH2 group of the N-terminal residue, X.
  • In both cases, successive addition of activated ubiquitin moieties to internal Lys48 on the previously conjugated ubiquitin moiety leads to the synthesis of a  polyubiquitin chain that serves as the degradation signal for the 26S proteasome


A UPS Autophagy Review

Summary: This discussion is another in a series discussing mitochondrial metabolism, energetics and regulatory function, and dysfunction, and the process leading to apoptosis and a larger effect on disease, with a specific targeting of neurodegeneration. Why neurological and muscle damage are more sensitive than other organs is not explained easily, but recall in the article on mitochondrial oxidation-reduction reactions and repair that there are organ specific differences in the rates of organelle mutation errors and in the rates of repair. In addition, consider the effect of iron-binding in the function of the cell, and Ca2+ binding in the creation of the mechanic work or signal transmission carried out by the neuromuscular system. We target the previously mentioned role of ubiquitin-proteosome, and interaction with autophagy, mitophagy, and disease.

Keywords: autophagy, ubiquitin-proteosome, UPS, protein degradation, defective organelle removal, selective degradation, E3, neurodegenerative disease, mitochondria, mitophagy, proteolysis, ribosomes, apoptosis, Ca++, rapamycin, TORC1, atg1p kinase, ubiqitization, trafficking pathways, unfolded protein response (UPS), p52/sequestrome, IC3, nitrogen starvation, acetaldehyde dehydrogenase (Ald6p), Ut1hp, toxisomes, Pex3/14 proteins, Bax, E3 Ligase, TRAP1, TNF-a, NFkB.

Ubiquitin-Proteosome Pathway
Three recent papers, describing three apparently independent biological processes, highlight the role of the ubiquitin-proteasome system as a major, however selective, proteolytic and regulatory pathway. Using specific inhibitors to the proteasome, Rock et al. (1994) demonstrate a role for this protease in the degradation of the major bulk of cellular proteins. They also showed that antigen processing requires the ubiquitin-activating enzyme, El. This indicates that antigen processing is both ubiquitin dependent and proteasome dependent. Furthermore, inhibitors to the proteasome prevent tumor necrosis factor a (TNFa)-induced activation of mature NFKB and its entry into the nucleus. The two studies clearly demonstrate that the ubiquitin-proteasome system is involved not only in complete destruction of its protein substrates, but also in limited proteolysis and posttranslational processing in which biologically active peptides or fragments are generated. In addition, the unstable c-Jut but not the stable v-Jun, is multiubiquitinated and degraded. The escape of the oncogenic v-Jun from ubiquitin-dependent degradation suggests a novel route to malignant transformation. Presented here is a review of the components, mechanisms of action, and cellular physiology of the ubiquitin-proteasome pathway.

Experimental evidence implicates the ubiquitin system in the degradation of

  • mitotic cyclins,
  • oncoproteins,
  • the tumor suppressor protein p53,
  • several cell surface receptors,
  • transcriptional regulators, and
  • mutated and damaged proteins.

Some of the proteolytic processes occur throughout the cell cycle, whereas others are tightly programmed and occur following cell cycle-dependent posttranslational modifications of the components involved. Signaling and degradation of other proteins (cell surface receptors, for example) may occur only following structural changes or modification(s) in the target molecule that results from ligand binding. Cell cycle-and modification-dependent degradation, as well the ability of the system to destroy completely or only partially its protein substrates, reflects the complexity involved in regulated intracellular protein degradation.

Enzymes of the System
The reaction occurs in two distinct steps:

  1. signaling of the protein by covalent attachment of multiple ubiquitin molecules and
  2. degradation of the targeted protein with the release of free and reutilizable ubiquitin.

Conjugation of ubiquitin to proteins destined for degradation proceeds, in general, in a three-step mechanism.

  1. Initially, the C-terminal Gly of ubiquitin is activated by ATP to a high energy thiol ester intermediate in a reaction catalyzed by the ubiquitin-activating enzyme, El.
  2. Following activation, E2 (ubiquitin carrier protein or ubiquitin-conjugating enzyme [USC]) transfers ubiquitin from El to the substrate that is bound to a ubiquitin-protein ligase, E3.
  3. Here an isopeptide bond is formed between the activated C-terminal Gly of ubiquitin and an c-NH2 group of a Lys residue of the substrate.

As E3 enzymes specifically synthesized by processive transfer of ubiquitin moieties to Lys-48 of the previous (and already conjugated) ubiquitin molecule. In many cases, E2 transfers activated ubiquitin directly to the protein substrate. Thus, E2 enzymes also play an important role in substrate recognition, although, in most cases, this modification is of the monoubiquitin type.

The Ubiquitin-Mediated Proteolytic Pathway
(1) Activation of ubiquitin by El and E2.
(2) Binding of the protein substrate to E3.
(3) EP dependent but EM independent monoubiquitination.
(4) EP-dependent but EM independent polyubiquitination?
(5) Ed-dependent polyubiquitination.
(6) Degradation of ubiquitin-protein conjugate by the 26s protease.
(7) “Correction” function of C-terminal hydrolase(s).
(6) Release of ubiquitin from terminal proteolytic products by &terminal hydrolase(s).

It is essential for the system that ubiquitin recycles. This function is carried out by ubiquitin C-terminal hydrolases (isopeptidases). In protein degradation, hydrolase(s) is required to release ubiquitin from isopeptide linkage with Lys residues of the protein substrate at the final stage of the proteolytic process. A ubiquitin C-terminal hydrolytic activity is also required to disassemble polyubiquitin chains linked to the protein substrate, following or during the degradative process. A “proofreading” function has been proposed for hydrolases to release free protein from “incorrectly” ubiquitinated proteins. Another possibility is that ubiquitin C-terminal hydrolases are required for trimming polyubitin chains.

Hydrolases are probably required for the processing of biosynthetic precursors of ubiquitin, since most ubiquitin genes are arranged either in linear polyubiquitin arrays or are fused to ribosomal proteins. Yet another hydrolase may be required for the removal of extra amino acid residues that are encoded by certain genes at the C-termini of some polyubiquitin molecules. Ubiquitin C-terminal hydrolases may have other functions as well. High energy El-ubiquitin and E2-ubiquitin thiol esters may react with intracellular nucleophiles (such as glutathione or polyamines). Such reactions may lead to rapid depletion of free ubiquitin unless such side products are rapidly cleaved.

Recognition of Substrates
Short-lived proteins contain a region enriched with Pro, Glu, Ser, and Thr (PEST region). However, it has not been shown that this region indeed serves as a consensus proteolysis targeting signal. An interesting problem involves the evolution of the N-end rule pathway and its physiological roles. Proteins that are derived from processing of polyproteins (Sindbis virus RNA polymerase, for example) may contain destabilizing N-termini and thus are proteolyzed via the N-end rule pathway.

Using a “synthetic lethal” screen, Ota and Varshavsky attempted to isolate a mutant that requires the N-end rule pathway for viability. They characterized an extragenic suppressor of the mutation and found that it encodes a protein with a strong correlation to protein phosphotyrosine phosphatase. The target protein or the connection between dephosphorylation of phosphotyrosine and the N-end rule pathway is still obscure. In an additional study, these researchers have shown that a missense mutation in SLNI, a member of a two-component signal transduction system in yeast, is lethal in the absence, but not in the presence, of the N-end rule pathway. Further studies are required to isolate the target protein and identify the signal transduction pathway.

Two recent studies have shed light on the role of the ubiquitin system and the proteasome in the process. Michalek et al. (1993) have shown that a mutant cell that harbors a thermolabile El cannot present peptides derived from ovalbumin following inactivation of the enzyme. In contrast, presentation of a minigene-expressed antigene peptide or presentation of exogenous similar peptide was not perturbed at the nonpermissive temperature. The important conclusion of the researchers is that the processing of the protein to peptides requires the complete ubiquitin pathway. In a complementary study, Rock et al. (1994) have shown that inhibitors that block the chymotryptic activity of the proteasome also block antigen presentation, most probably by inhibiting proteolysis of the antigen (ovalbumin). Thus, it appears that processing of MHC restricted class I antigens requires both ubiquitination and subsequent degradation by the proteasome. It is likely that the proteasome catalyzes processing of these antigens as part of the 26s protease complex.
Ciechanover A. The Ubiquitin-Proteasome Proteolytic Pathway. Cell 1994; 79:13-21.
Regulation of autophagy
The protein content of the cell is determined by the balance between protein synthesis and protein degradation. At constant intracellular protein concentration, i.e. at steady state, rates of protein synthesis and degradation are equal. Although turnover of protein results in energy dissipation, regulation at the level of protein degradation effectively controls protein levels.
Intracellular proteins to be degraded in the lysosomes can get access to these organelles by the following processes:

  • macroautophagy,
  • microautophagy,
  • crinophagy and selective,
  • chaperonin mediated, direct uptake of proteins.

Overview of the involvement of signal transduction in the regulation of macroautophagic proteolysis by amino acids and cell swelling.

  1. Amino acids (AA) stimulate a protein kinase cascade via a plasma membrane receptor.
  2. Receptor activation results in activation of PtdIns 3-kinase (PI3K), possibly via a heterotrimeric Gái3 protein.
  3. followed by activation of PKC-æ, PKB/Akt, p70S6 kinase (p70S6k) and finally phosphorylation of ribosomal protein S6 (S6P).
  4. The GDP-bound form of Gái3 is required for autophagic sequestration, whereas the GTP-bound form is inhibitory.
  5. The constitutively formed phosphatidylinositol 3-phosphate (PI3P) is also required for autophagic sequestration. Therefore,

inhibition of PtdIns 3-kinase activity by

  • wortmannin (W),
  • LY294002 (LY) or
  • 3-methyladenine (3MA) prevents autophagic sequestration.

Activation of PKC-æ and PKB/Akt is mediated by the 3,4- and 3,4,5-phosphate forms of phosphatidylinositol (PI3,4P2 and PI3,4,5P3) that are produced upon activation of PtdIns 3-kinase.

As a result of this, the first step of the macroautophagic pathway is

  • inhibited by components of the cascade that are downstream of PtdIns 3-kinase.
  • inhibition of this downstream cascade by rapamycin (RAPA) accelerates autophagic sequestration.
  • cell swelling potentiates the effect of amino acids via a change in the receptor owing to membrane stretch.

Furthermore, the site of action of the different effectors of the cytoskeleton (okadaic acid, cytochalasin, nocodazole, vinblastin and colchicine) are indicated.

  • AVi,
  • initial autophagic vacuole;
  • AVd,
  • mature degradative autophagic vacuole,
  • ER, endoplasmic reticulum.

The rate of proteolysis , an important determinant of the intracellular protein content, and part of its degradation occurs in the lysosomes and is mediated by macroautophagy. In liver, macroautophagy is very active and almost completely accounts for starvation-induced proteolysis. Factors inhibiting this process include

  • amino acids,
  • cell swelling and
  • insulin.

In the mechanisms controlling macroautophagy, protein phosphorylation plays an important role.

  • Activation of a signal transduction pathway, ultimately
  • leading to phosphorylation of ribosomal protein S6,
  • accompanies Inhibition of macroautophagy.

Components of this pathway may include

  • a heterotrimeric Gi3-protein,
  • phosphatidylinositol 3-kinase and
  • p70S6 kinase.

Selectivity of Autophagy
It has been assumed for a long time that macroautophagy is a non-selective process, in which macromolecules are randomly degraded in the same ratio as they occur in the cytoplasm . However, recent observations strongly suggest that this may not always be the case, and that macroautophagy can be selective. Lysosomal protein degradation can selectively occur via ubiquitin-dependent and -independent pathways. In the perfused liver, although autophagic breakdown of protein and RNA (mainly ribosomal RNA) is sensitive to inhibition by amino acids and insulin, glucagon accelerates proteolysis but has no effect on RNA degradation.

Another example of selective autophagy is the degradation of superfluous peroxisomes in hepatocytes from clofibrate-treated rats. When hepatocytes from these rats, in which the number of peroxisomes is greatly increased, are incubated in the absence of amino acids to ensure maximal flux through the macroautophagic pathway, peroxisomes are degraded at a relative rate that exceeds that of any other component in the liver cell. The accelerated degradation of peroxisomes was sensitive to inhibition by 3-methyladenine, a specific autophagic sequestration inhibitor. Interestingly, the accelerated removal of peroxisomes was prevented by long-chain but not short-chain fatty acids. Since long-chain fatty acids are substrates for peroxisomal â-oxidation, this indicates that these organelles are removed by autophagy when they are functionally redundant.  Our hypothesis is that acylation (palmitoylation?) of a peroxisomal membrane protein protects the peroxisome against autophagic sequestration.

Under normal conditions macroautophagy may be largely unselective and serves, for example, to produce amino acids for gluconeogenesis and the synthesis of essential proteins in starvation. When cell structures are functionally redundant or when they become damaged, the autophagic system is able to recognize this and is able to degrade the structure concerned. As yet, nothing is known about the recognition signals. A possibility is that ubiquitination of membrane proteins is required to mark the structure to be degraded for autophagic sequestration.

Ubiquitin may be involved in macroautophagy
Ubiquitin not only contributes to extralysosomal proteolysis but is also involved in autophagic protein degradation. Thus, in fibroblasts ubiquitin–protein conjugates can be found in the lysosomes, as shown by immunohistochemistry and immunogold electron microscopy. Free ubiquitin can also be found inside lysosomes. Accumulations of ubiquitin–protein conjugates in filamentous, presumably lysosomal, structures are also found in a large number of neurodegenerative diseases. Mallory bodies in the liver of alcoholics also contain ubiquitin–protein conjugates.

This presence of ubiquitin–protein conjugates in filamentous inclusions in neurons and other cells can be caused by a defect in the extralysosomal ubiquitin-dependent proteolytic pathway. However, it is also possible that these filamentous inclusions represent an attempt of the cell to get rid of unwanted material (proteins, organelles) via autophagy. Direct evidence that ubiquitin may be involved in the control of macroautophagy came from experiments with CHO cells with a temperature-sensitive mutation in the ubiquitin-activating enzyme E1. Wild-type cells increased their rate of proteolysis in response to stress (amino acid depletion, increased temperature). This was prevented by the acidotropic agent ammonia or by the autophagic sequestration inhibitor 3-methyladenine, indicating that the accelerated proteolysis occurred by autophagy. In the mutant cells, there was no such increase in proteolysis in response to stress at the restrictive temperature.

Autophagy and carcinogenesis
In cancer development, cell growth is mainly induced by inhibition of protein degradation, since differences in the rate of protein synthesis between tumorigenic cells and their normal counterparts are rather small. A striking example of how reduced autophagic proteolysis can contribute to cell growth can be found in the development of liver carcinogenesis. This decrease in autophagic flux results from a decrease in the rate of autophagic sequestration and is already detectable in the early preneoplastic stage. Autophagic flux is then hardly inhibitable by amino acids nor is it inducible by catabolic stimuli
and declines in the more advanced stage of cancer development to a rate of less than 20% of that seen in normal hepatocytes. The fact that the addition of 3-methyladenine to hepatocytes from normal rats increased hepatocyte viability to the same level as observed for the tumour cells strongly suggests that the fall in autophagic proteolysis contributes to the rapid growth rate of these cells and gives them a selective advantage over the normal hepatocytes.

Underlying control mechanisms for autophagy are gradually being unravelled. It is perhaps not surprising that protein phosphorylation and signal transduction are key elements in these mechanisms. The discovery of an amino acid receptor in the plasma membrane of the hepatocyte with a signal transduction pathway coupled to it may have important repercussions, not only for the control of macroautophagy but also for the control of other pathways.

It remains to be seen whether the details of the mechanisms controlling the process in yeast are similar to those in mammalian cells. For example, it is not known whether amino acids are able to control the process as they do in mammalian cells.

Blommaart EFC, Luiken JJFP, Meijer AJ. Autophagic proteolysis: control and specificity. Histochemical Journal (1997); 29:365–385.
A Novel Type of Selective Autophagy
Eukaryotic cells use autophagy and the ubiquitin–proteasome system (UPS) as their major protein degradation pathways. Whereas the UPS is required for the rapid degradation of proteins when fast adaptation is needed, autophagy pathways selectively remove protein aggregates and damaged or excess organelles. However, little is known about the targets and mechanisms that provide specificity to this process. Here we show that mature ribosomes are rapidly degraded by autophagy upon nutrient starvation in Saccharomyces cerevisiae. Surprisingly, this degradation not only occurs by a nonselective mechanism, but also involves a novel type of selective autophagy, which we term ‘ribophagy’. A genetic screen revealed that selective degradation of ribosomes requires catalytic activity of the Ubp3p/Bre5p ubiquitin protease. Although Ubp3p and Bre5p cells strongly accumulate 60S ribosomal particles upon starvation, they are proficient in starvation sensing and in general trafficking and autophagy pathways. Moreover, ubiquitination of several ribosomal subunits and/or ribosome associated proteins was specifically enriched in Ubp3p cells, suggesting that the regulation of ribophagy by ubiquitination may be direct. Interestingly, Ubp3p cells are sensitive to rapamycin and nutrient starvation, implying that selective degradation of ribosomes is functionally important in vivo. Taken together, our results suggest a link between ubiquitination and the regulated degradation of mature ribosomes by autophagy.
Kraft C, Deplazes A, Sohrmann M,Peter M. Mature ribosomes are selectively degraded upon starvation by an autophagy pathway requiring the Ubp3p/Bre5p ubiquitin protease. Nature Cell Biology 2008; 10(5): 603-609. DOI: 10.1038/ncb1723.  www.nature.com/naturecellbiology

Mitochondrial Failure and Protein Degradation

Progressive mitochondrial failure is tightly associated with the the development of most age-related human diseases including neurodegenerative diseases, cancer, and type 2 diabetes.

This tight connection results from the double-edged sword of mitochondrial respiration, which is responsible for generating both ATP and ROS, as well as from risks that are inherent to mitochondrial biogenesis. To prevent and treat these diseases, a precise understanding of the mechanisms that maintain functional mitochondria is necessary. Mitochondrial protein quality control is one of the mechanisms that protect mitochondrial integrity, and increasing evidence implicates the cytosolic ubiquitin/proteasome system (UPS) as part of this surveillance network. In this review, we will discuss our current understanding of UPS-dependent mitochondrial protein degradation, its roles in diseases progression, and insights into future studies.

While mitochondria have their own genome, about 99% of the roughly 1000 mitochondrial proteins are encoded in the nuclear genome. Most mitochondrial proteins are therefore

  • synthesized in the cytoplasm,
  • unfolded,
  • transported across one or both mitochondrial membranes,
  • then refolded and/or assembled into complexes (Tatsuta, 2009).

Failure of this complex series of events generates unfolded or misfolded proteins within mitochondria, often disrupting critical functions.

Mitochondrial oxidative phosphorylation generates usable cellular energy in the form of ATP, but also produces reactive oxygen species (ROS) . ROS tend to react quickly, so their predominant sites of damage are mitochondrial macromolecules that are localized nearby the source of ROS production.

Exposure to oxidative stress facilitates misfolding and aggregation of these mitochondrial proteins, leading to disassembly of protein complexes and eventual loss of mitochondrial integrity.

The clearance of misfolded and aggregated proteins is constantly needed to maintain functional mitochondria.
There are several systems promoting this turnover.

  1. Mitophagy, a selective mitochondrial autophagy, mediates a bulk removal of damaged mitochondria.
  2. mitochondria intrinsically contain proteases in each of their compartments and these proteases recognize misfolded mitochondrial proteins and mediate their degradation.

Accumulating evidence shows that the ubiquitin proteasome system (UPS) plays an important role in mitochondrial protein degradation. At various cellular sites, the UPS is involved in protein degradation. With the help of ubiquitin E1–E2–E3 enzyme cascades, target proteins destined for destruction are marked by conjugation of K48-linked poly-ubiquitin chain. This poly-ubiquitinated protein is then targeted to the proteasome for degradation.

Cells treated with proteasome inhibitors exhibit elevated levels of ubiquitinated mitochondrial proteins, suggesting the potentially important roles of the proteasome on mitochondrial protein degradation. Studies have also identified mitochondrial substrates of the UPS.

  • Fzo1, an outer mitochondrial membrane (OMM) protein involved in mitochondrial fusion, is partially dependent on the proteasome for its degradation in yeast.
  • The F box protein Mdm30 mediates ubiquitination of Fzo1 by Skp1-Cullin-F-boxMdm30 ligase, which leads to proteasomal degradation.

The UPS has also been implicated in mitochondrial protein degradation in higher organisms. In mammals,

  • the OMM proteins mitofusin 1 and 2 (Mfn1/2; the mammalian orthologs of Fzo1) and Mcl1 are polyubiquitinated and degraded by the proteasome.
  • VDAC1, Tom20 and Tom70 were also suggested as targets of proteasomal degradation as they are stabilized by proteasome inhibition.
  •  inactivation of the proteasome also induces accumulation of intermembrane space (IMS) proteins and, consistent with this, the proteasome plays a role in degradation of the IMS protein, Endonuclease G.

Turnover of some inner mitochondrial membrane (IMM) proteins is also dependent upon the proteasome. Uncoupling proteins (UCPs) 2 and 3 exhibit an unusually short half-life compared with other IMM proteins, and Brand and colleagues showed that inactivation of the proteasome prevents their turnover in vivo and in a reconstituted in vitro system. Finally, mitochondrial matrix proteins can also be degraded by the proteasome.

Cdc48/p97 is involved in many cellular processes through its role in protein degradation and is targeted to different subcellular sites by adaptor proteins. For example, Cdc48/p97 is recruited to the endoplasmic reticulum with the help of two adaptor proteins, Npl4 and Ufd1. This implies the existence of specific adaptors that recruit Cdc48/p97 to mitochondria. Consistent with this notion, the authors recently identified a mitochondrial adaptor protein for Cdc48, which we named Vms1 (VCP/Cdc48-associated mitochondrial stress responsive 1). Vms1 interacts with Cdc48/p97 and Npl4, but not with Ufd1, which indicates that the Cdc48/p97–Npl4–Ufd1 complex functions in ER protein degradation while the Vms1–Cdc48/p97–Npl4 complex acts in mitochondria. In agreement with this notion, overexpression of Cdc48 or Npl4 rescues the Vms1 mutant phenotype while Ufd1 has no effect.

Normally, Vms1 is cytoplasmic. Upon mitochondrial stress, however, Vms1 recruits Cdc48 and Npl4 to mitochondria. In agreement with the role of Cdc48/p97 in OMM protein degradation, loss of the Vms1 system results in accumulation of ubiquitin-conjugated proteins in purified mitochondria as well as stabilization of Fzo1 under mitochondrial stress conditions. Accumulation of damaged and misfolded mitochondrial proteins disturbs the normal physiology of the mitochondria, leading to mitochondrial dysfunction. As expected, the Vms1 mutants progressively lose mitochondrial respiratory activity, eventually leading to cell death. The VMS1 gene is broadly conserved in eukaryotes, implying an important functional role in a wide range of organisms. The C. elegans Vms1 homolog exhibits a similar pattern of mitochondrial stress responsive translocation and is required for normal lifespan. Additionally, mammalian Vms1 also forms a stable complex with p97. Combining these observations, the authors conclude that Vms1 is a conserved component of the UPS-dependent mitochondrial protein quality control system.

The UPS regulates mitochondrial dynamics and initiation of mitophagy
The UPS regulates mitochondrial dynamics. Major proteins involved in mitochondrial fission or fusion (e.g. Mfn1/2, Drp1 and Fis1) are degraded by the UPS.  MITOL, a mitochondrial E3 ubiquitin ligase, is required for Drp1-dependent mitochondrial fission as depletion or inactivation of MITOL blocks mitochondrial fragmentation. Moreover, knockdown of USP30, an OMM-localized deubiquitinating enzyme, induces an elongated mitochondrial morphology, suggesting a defect in fission. Through this regulatory process, the UPS controls mitochondrial dynamics. Parkin, an E3 ligase involved in mitophagy, utilizes the UPS to enhance mitochondrial fission through degradation of components of the fusion machinery. By facilitating fragmentation of damaged mitochondria, which is essential for initiation of mitophagy, Parkin stimulates mitophagy. The underlying mechanisms linking the UPS to the regulation of mitochondrial dynamics remain unclear.

Accumulation of aberrant proteins and human diseases
In neurodegenerative diseases wherein aberrant pathological proteins accumulate throughout the cell, including sites in mitochondria. Amyloid precursor protein (APP), a protein associated with Alzheimer’s disease, accumulates within mitochondria and is implicated in blockade of mitochondrial protein import. A, a neurotoxic APP cleavage product, can also facilitate the formation of the mitochondrial permeability transition pore (mPTP) by binding to mPTP components VDAC1, CypD and ANT, which provokes cell death.
-Synuclein, a protein associated with the development of Parkinson’s disease, is targeted to the IMM where it binds to the mitochondrial respiratory complex I and impairs its function. -Synuclein interferes with mitochondrial dynamics as its unique interaction with the mitochondrial membrane disturbs the fusion process. Finally, in Huntington’s disease, increased association of the mutant huntingtin protein with mitochondria can impair mitochondrial trafficking. Moreover, accumulation of mutant huntingtin protein disrupts cristae structure and it facilitates mitochondrial fragmentation by activation of Drp1. These examples demonstrate the crucial importance of prompt removal of dysfunctional and/or aberrant proteins in maintaining functional mitochondria.

UPS-mediated mitochondrial protein degradation.
Misfolded and/or damaged mitochondrial proteins destined for proteasomal degradation in the cytosol are recruited to the outer mitochondrial membrane (OMM) from each mitochondrial compartment by unknown mechanisms. Upon reaching the OMM, these proteins are presented to the proteasome through a series of events. They are K48 polyubiquitinated by the cytoplasmic (e.g. Parkin) or mitochondrial ubiquitin E3 ligases. For proteasomal degradation, polyubiquitinated mitochondrial substrate proteins need to be retrotranslocated to the cytoplasm, probably, either by the proteasome per se or by the help of UPS components such as Vms1, Cdc48/p97 and Npl4. Following dislocation to the cytoplasm, these substrate proteins are degraded by the proteasome.

Treatment of diseases that arise from defects in protein quality control will depend on greater depth in our understanding of this process, which could contribute to the development of novel therapeutic approaches. For instance, both mutant SOD1, a misfolded mitochondrial protein associated with the onset of amyotrophic lateral sclerosis, and polyglutamine expanded ataxin-3, a pathogenic protein causing Machado-Joseph disease, are ubiquitinated by MITOL and then degraded by the proteasome. Facilitating the proteasomal degradation of these aberrant proteins might therefore efficiently control diseases progression and, eventually, cure the diseases. Answering these questions would partially unveil the mysterious physiology of mitochondria, which, in turn, would facilitate the development of therapeutics to prevent and cure devastating human diseases.

Heo JM, Rutter J. Ubiquitin-dependent mitochondrial protein degradation. The International Journal of Biochemistry & Cell Biology 2011; 43:1422– 1426. http://www.elsevier.com/locate/biocel
UPS Inhibitors and Apoptotic Machinery
Over the past decade, the promising results of UPSIs (UPS inhibitors) in eliciting apoptosis in various cancer cells, and the approval of the first UPSI (Bortezomib/Velcade/PS-341) for the treatment of multiple myeloma have raised interest in assessing the death program activated upon proteasomal blockage. Several reports indicate that UPSIs stimulate apoptosis in malignant cells by operating at multiple levels, possibly by inducing different types of cellular stress. Normally cellular stress signals converge on the core elements of the apoptotic machinery to trigger the cellular demise. In addition to eliciting multiple stresses, UPSIs can directly operate on the core elements of the apoptotic machinery to control their abundance. Alterations in the relative levels of anti and pro-apoptotic factors can render cancer cells more prone to die in response to other anti-cancer treatments. Aim of the present review is to discuss those core elements of the apoptotic machinery that are under the control of the UPS.

The UPS (Ubquitin-Proteasome System)
To fulfill the protein-degradation process two branches, operating at different levels, principally comprise the UPS.

  • The first branch is formed by the enzymatic activities responsible for delivering the substrate to the degradative machinery: the targeting branch.
  • The second branch is represented by the proteolytic machinery, which ultimately fragments the protein substrate into small oligopeptides.

Oligopeptides are further digested to single amino acids by cytosolic proteases.
It is important to remember that conjugation of ubiquitin to a specific protein is not sufficient to determine its degradation. In fact, mono-ubiquitination or poly-monoubiquitination and in certain cases also poly-ubiquitination of proteins are post-translational modifications related to various cellular functions including DNA repair or membrane trafficking . To deliver polypeptides for proteasomal degradation poly-ubiquitin chains of more than 4 ubiquitins must be assembled through lysine-48 linkages.

There are 3 catalytic sites for each polyubiquitin chain. These sites show specific requirements in terms of substrate specificities and catalytic activities, and they are identified as

  1. trypsin-like, which prefer to cleave after hydrophobic bonds, chymotrypsin-like, which cleave at basic residues and
  2. postglutamyl peptide hydrolase-like or
  3. caspase-like activities, which cut after acidic amino acid.

Each proteasome active site uses the side chain hydroxyl group of an NH2-terminal threonine as the catalytic nucleophile, a mechanism that distinguishes the proteasome from other cellular proteases. The presence of substrate proteolysis small size peptides ranging from 3 to 22 residues are generated. Alternative catalytic sites guarantees the efficient processing of several different substrates.

UPS Inhibitors
By UPS inhibitors (UPSI) we mean small molecules that share the ability to target and inhibit specific activities of the UPS, causing the accumulation of poly-ubiquitinated proteosomal substrates. UPSIs are heterogeneous compounds and among them bortezomib is the only one used in clinical practice.

PR-171, a modified peptide related to the natural product epoxomicin, is composed of two key elements:

  1. a peptide portion that selectively binds with high affinity in the substrate binding pocket(s) of the proteasome and
  2. an epoxyketone pharmacophore that stereospecifically interacts with the catalytic threonine residue and irreversibly inhibits enzyme activity.

In comparison to bortezomib, PR-171 exhibits equal potency, but greater selectivity, for the chymotrypsin-like activity of the proteasome. In cell culture PR-171 is more cytotoxic than bortezomib. In mice PR-171 is well tolerated and shows stronger anti-tumor activity when compared with bortezomib . Clinical studies are in progress to test the safety of PR-171 at different dose levels on some hematological cancers.

Cell Death by UPSI
In vitro experiments have unambiguously established that incubation of neoplastic cells with UPSIs including bortezomib triggers their death. Apoptosis or type I cell death relies on the timed activation of caspases, a group of cysteine proteases, which cleave selected cellular substrates after aspartic residues. Two main apoptotic pathways keep in check caspase activation.

The turnover of a large number of cellular proteins is under the control of the UPS. Thus in principle any proteosomal substrate could contribute directly or indirectly to the cell death phenotype. This is perfectly exemplified by two master regulators of cell life and death, p53 and NFkB.  UPSIs cause

  • NF-kB inhibition through reduced IkB degradation and,
  • in opposition; they promote stabilization and accumulation of p53.

c-FLIP is the most important element of the extrinsic pathway under the direct control of the UPS. Two different FLIP isoforms exist:

  1. c-FLIPL (Long) and
  2. c-FLIPS (Short).

c-FLIPL is highly homologus to caspase-8 and contains two tandem repeat Death Effector Domains (DED) and a catalytically inactive caspase-like domain. Both FLIPs can be degraded by the UPS; however they display distinct half-lives and the unique C terminus of c-FLIPS possesses a destabilizing function. The regulation of c-FLIP levels in response to UPSIs is rather controversial. Some reports indicate that UPSIs can reduce c-FLIP levels and in this manner synergize with TRAIL to promote apoptosis.

UPSIs activate multiple cellular responses and different stress signals that ultimately cause cell death. For this reason they represent broad inducers of apoptosis. In addition, since many of the available UPSIs alter the proteolytic activity of the proteasome, they represent non-specific modulators of the expression/activity of various components of the apoptotic machinery. Paradoxically they can simultaneously favor the accumulation of pro- and anti-apoptotic factors.
Brancolini C. Inhibitors of the Ubiquitin-Proteasome System and the Cell Death Machinery: How Many Pathways are Activated? Current Molecular Pharmacology, 2008; 1:24-37.

Mitochondrial Quality Control
The PINK1–Parkin pathway plays a critical role in mitochondrial quality control by selectively targeting damaged mitochondria for autophagy. The AAA-type ATPase p97 acts downstream of PINK1 and Parkin to segregate fusion-incompetent mitochondria for turnover. [Tanaka et al. (2010. J. Cell Biol. doi: 10.1083/jcb.201007013)]. p97 acts by targeting the mitochondrial fusion-promoting factor mitofusin for degradation through an endoplasmic reticulum–associated degradation (ERAD)-like mechanism.

Pallanck LJ. Culling sick mitochondria from the herd. J Cell Biol 2012;191(7):1225–1227. http://www.jcb.org/cgi/doi/10.1083/jcb.201011068

PINK1 and Parkin and Parkinson’s Disease

Studies of the familial Parkinson disease-related proteins PINK1 and Parkin have demonstrated that these factors promote the fragmentation and turnover of mitochondria following treatment of cultured cells with mitochondrial depolarizing agents. Whether PINK1 or Parkin influence mitochondrial quality control under normal physiological conditions in dopaminergic neurons, a principal cell type that degenerates in Parkinson disease, remains unclear. To address this matter, we developed a method to purify and characterize neural subtypes of interest from the adult Drosophila brain.

Using this method, we find that dopaminergic neurons from Drosophila parkin mutants accumulate enlarged, depolarized mitochondria, and that genetic perturbations that promote mitochondrial fragmentation and turnover rescue the mitochondrial depolarization and neurodegenerative phenotypes of parkin mutants. In contrast, cholinergic neurons from parkin mutants accumulate enlarged depolarized mitochondria to a lesser extent than dopaminergic neurons, suggesting that a higher rate of mitochondrial damage, or a deficiency in alternative mechanisms to repair or eliminate damaged mitochondria explains the selective vulnerability of dopaminergic neurons in Parkinson disease.

Our study validates key tenets of the model that PINK1 and Parkin promote the fragmentation and turnover of depolarized mitochondria in dopaminergic neurons. Moreover, our neural purification method provides a foundation to further explore the pathogenesis of Parkinson disease, and to address other neurobiological questions requiring the analysis of defined neural cell types.

Burmana JL, Yua S, Poole AC, Decala RB , Pallanck L. Analysis of neural subtypes reveals selective mitochondrial dysfunction in dopaminergic neurons from parkin mutants.

Autophagy in Parkinson’s Disease.
Parkinson’s disease is a common neurodegenerative disease in the elderly. To explore the specific role of autophagy and the ubiquitin-proteasome pathway in apoptosis, a specific proteasome inhibitor and macroautophagy inhibitor and stimulator were selected to investigate pheochromocytoma (PC12) cell lines transfected with human mutant (A30P) and wildtype (WT) -synuclein.

The apoptosis ratio was assessed by flow cytometry. LC3, heat shock protein 70 (hsp70) and caspase-3 expression in cell culture were determined by Western blot. The hallmarks of apoptosis and autophagy were assessed with transmission electron microscopy. Compared to the control group or the rapamycin (autophagy stimulator) group, the apoptosis ratio in A30P and WT cells was significantly higher after treatment with inhibitors of the proteasome and macroautophagy. The results of Western blots for caspase-3 expression were similar to those of flow cytometry; hsp70 protein was significantly higher in the proteasome inhibitor group than in control, but in the autophagy inhibitor and stimulator groups, hsp70 was similar to control. These findings show that inhibition of the proteasome and autophagy promotes apoptosis, and the macroautophagy stimulator rapamycin reduces the apoptosis ratio. And inhibiting or stimulating autophagy has less impact on hsp70 than the proteasome pathway.

In conclusion, either stimulation or inhibition of macroautophagy, has less impact on hsp70 than on the proteasome pathway. This study found that rapamycin decreased apoptotic cells in A30P cells independent of caspase-3 activity. Although several lines of evidence recently demonstrated crosstalk between autophagy and caspase-independent apoptosis, we could not confirm that autophagy activation protects cells from caspase-independent cell death. Undoubtedly, there are multiple connections between the apoptotic and autophagic processes.

Inhibition of autophagy may subvert the capacity of cells to remove damaged organelles or to remove misfolded proteins, which would favor apoptosis. However, proteasome inhibition activated macroautophagy and accelerated apoptosis. A likely explanation is inhibition of the proteasome favors oxidative reactions that trigger apoptosis, presumably through

1. a direct effect on mitochondria, and
2. the absence of NADPH2 and ATP

which may deinhibit the activation of caspase-2 or MOMP. Another possibility is that aggregated proteins induced by proteasome inhibition increase apoptosis.

Yang F, Yanga YP, Maoa CJ, Caoa BY, et al. Role of autophagy and proteasome degradation pathways in apoptosis of PC12 cells overexpressing human -synuclein. Neuroscience Letters 2009; 454:203–208. doi:10.1016/j.neulet.2009.03.027. http://www.elsevier.com/locate/neulet

Parkin-dependent Ubiquitination of Endogenous Bax 

Autosomal recessive loss-of-function mutations within the PARK2 gene functionally inactivate the E3 ubiquitin ligase parkin, resulting in neurodegeneration of catecholaminergic neurons and a familial form of Parkinson disease. Current evidence suggests both a mitochondrial function for parkin and a neuroprotective role, which may in fact be interrelated. The antiapoptotic effects of Parkin have been widely reported, and may involve fundamental changes in the threshold for apoptotic cytochrome c release, but the substrate(s) involved in Parkin dependent protection had not been identified. Here, we demonstrate the Parkin-dependent ubiquitination of endogenous Bax comparing primary cultured neurons from WT and Parkin KO mice and using multiple Parkin-overexpressing cell culture systems. The direct ubiquitination of purified Bax was also observed in vitro following incubation with recombinant parkin. The authors found that Parkin prevented basal and apoptotic stress induced translocation of Bax to the mitochondria. Moreover, an engineered ubiquitination-resistant form of Bax retained its apoptotic function, but Bax KO cells complemented with lysine-mutant Bax did not manifest the antiapoptotic effects of Parkin that were observed in cells expressing WT Bax. These data suggest that Bax is the primary substrate responsible for the antiapoptotic effects of Parkin, and provide mechanistic insight into at least a subset of the mitochondrial effects of Parkin.

Johnson BN, Berger AK, Cortese GP, and LaVoie MJ. The ubiquitin E3 ligase Parkin regulates the proapoptotic function of Bax. PNAS 2012, pp 6. http://www.pnas.org/cgi/doi/10.1073/pnas.1113248109
Parkin Promotes Mitochondrial Loss in Autophagy
Parkin, an E3 ubiquitin ligase implicated in Parkinson’s disease, promotes degradation of dysfunctional mitochondria by autophagy. Using proteomic and cellular approaches, we show that upon translocation to mitochondria, Parkin activates the ubiquitin–proteasome system (UPS) for widespread degradation of outer membrane proteins. This is evidenced by an increase in K48-linked polyubiquitin on mitochondria, recruitment of the 26S proteasome and rapid degradation of multiple outer membrane proteins. The degradation of proteins by the UPS occurs independently of the autophagy pathway, and inhibition of the 26S proteasome completely abrogates Parkin-mediated mitophagy in HeLa, SH-SY5Y and mouse cells. Although the mitofusins Mfn1 and Mfn2 are rapid degradation targets of Parkin, degradation of additional targets is essential for mitophagy. These results indicate that remodeling of the mitochondrial outer membrane proteome is important for mitophagy, and reveal a causal link between the UPS and autophagy, the major pathways for degradation of intracellular substrates.

Chan NC, Salazar AM, Pham AH, Sweredoski MJ, et al. Broad activation of the ubiquitin–proteasome system by Parkin is critical for mitophagy. Human Molecular Genetics 2011; 20(9): 1726–1737. doi:10.1093/hmg/ddr048.

TRAP1 and TBP7 Interaction in Refolding of Damaged Proteins
TRAP1 is a mitochondrial antiapoptotic heat shock protein. The information available on the TRAP1 pathway describes just a few well-characterized functions of this protein in mitochondria. However, our group’s use of mass spectrometry analysis identified TBP7, an AAA-ATPase of the 19S proteasomal subunit, as a putative TRAP1-interacting protein. Surprisingly, TRAP1 and TBP7 co-localize in the endoplasmic reticulum (ER), as demonstrated by biochemical and confocal/electron microscopy analyses, and directly interact, as confirmed by FRET analysis. This is the first demonstration of TRAP1 presence in this cellular compartment. TRAP1 silencing by shRNAs, in cells exposed to thapsigargin-induced ER stress, correlates with up-regulation of BiP/Grp78, thus suggesting a role of TRAP1 in the refolding of damaged proteins and in ER stress protection. Consistently, TRAP1 and/or TBP7 interference enhanced stress-induced cell death and increased intracellular protein ubiquitination. These experiments led us to hypothesize an involvement of TRAP1 in protein quality control for mistargeted/misfolded mitochondria-destined proteins, through interaction with the regulatory proteasome protein TBP7. Remarkably, the expression of specific mitochondrial proteins decreased upon TRAP1 interference as a consequence of increased ubiquitination. The proposed TRAP1 network has an impact in vivo, since it is conserved in human colorectal cancers, is controlled by ER-localized TRAP1 interacting with TBP7 and provides a novel model of ER-mitochondria crosstalk.


VMS1 and Mitochondrial Protein Degradation
We show that Ydr049 (renamed VCP/Cdc48-associated mitochondrial stress-responsive—Vms1), a member of an unstudied pan-eukaryotic protein family, translocates from the cytosol to mitochondria upon mitochondrial stress. Cells lacking Vms1 show progressive mitochondrial failure, hypersensitivity to oxidative stress, and decreased chronological life span. Both yeast and mammalian Vms1 stably interact with Cdc48/VCP/p97, a component of the ubiquitin/proteasome system with a well-defined role in endoplasmic reticulum-associated protein degradation (ERAD), wherein misfolded ER proteins are degraded in the cytosol. We show that oxidative stress triggers mitochondrial localization of Cdc48 and this is dependent on Vms1. When this system is impaired by mutation of Vms1,

  • ubiquitin-dependent mitochondrial protein degradation,
  • mitochondrial respiratory function,and
  • cell viability are compromised.

We demonstrate that Vms1 is a required component of an evolutionarily conserved system for mitochondrial protein degradation, which is
necessary to maintain

  • mitochondrial,
  • cellular, and
  • organismal viability.

Heo JM, Livnat-Levanon N, Taylor EB, Jones KT. A Stress-Responsive System
for Mitochondrial Protein Degradation. Molecular Cell 2010; 40:465–480.
DOI 10.1016/j.molcel.2010.10.021

Mitochondrial Protein Degradation
The biogenesis of mitochondria and the maintenance of mitochondrial functions depends on an autonomous proteolytic system in the organelle which is highly conserved throughout evolution. Components of this system include processing

  • peptidases and
  • ATP-dependent proteases, as well as
  • molecular chaperone proteins and
  • protein complexes with apparently regulatory functions.

While processing peptidases mediate maturation of nuclear-encoded mitochondrial preproteins, quality control within various subcompartments of mitochondria is ensured by ATP-dependent proteases which selectively remove non-assembled or misfolded polypeptides. Moreover, these proteases appear to control the activity- or steady-state levels of specific regulatory proteins and thereby ensure mitochondrial genome integrity, gene expression and protein assembly.

Kaser M and Langer T. Protein degradation in mitochondria. CELL & DEVELOPMENTAL BIOLOGY 2000; 11:181–190. doi: 10.1006/10.1006/scdb.2000.0166.

RING finger E3s

Ubiquitin-ligases or E3s are components of the ubiquitin proteasome system (UPS) that coordinate the transfer of ubiquitin to the target protein. A major class of ubiquitin-ligases consists of RING-finger domain proteins that include the substrate recognition sequences in the same polypeptide; these are known as single-subunit RING finger E3s. We are studying a particular family of RING finger E3s, named ATL, that contain a transmembrane domain and the RING-H2 finger domain; none of the member of the family contains any other previously described domain. Although the study of a few members in A. thaliana and O. sativa has been reported, the role of this family in the life cycle of a plant is still vague.

To provide tools to advance on the functional analysis of this family we have undertaken a phylogenetic analysis of ATLs in twenty-four plant genomes. ATLs were found in all the 24 plant species analyzed, in numbers ranging from 20–28 in two basal species to 162 in soybean. Analysis of ATLs arrayed in tandem indicates that sets of genes are expanding in a species-specific manner. To
get insights into the domain architecture of ATLs we generated 75 pHMM LOGOs from 1815 ATLs, and unraveled potential protein-protein interaction regions by means of yeast two-hybrid assays. Several ATLs were found to interact with DSK2a/ubiquilin through a region at the amino-terminal end, suggesting that this is a widespread interaction that may assist in the mode of action of ATLs; the region was traced to a distinct sequence LOGO. Our analysis provides significant observations on the evolution and expansion of the ATL family in addition to information on the domain structure of this class of ubiquitin-ligases that may be involved in plant adaptation to environmental stress.

Aguilar-Hernandez V, Aguilar-Henonin L, Guzman P. Diversity in the Architecture of ATLs, a Family of Plant Ubiquitin-Ligases, Leads to Recognition and Targeting of Substrates in Different Cellular Environments. PLoS ONE 2011; 6(8): e23934. doi:10.1371/journal.pone.0023934
UPS Proteolytic Function Inadequate in Proteinopathies
Proteinopathies are a family of human disease caused by toxic aggregation-prone proteins and featured by the presence of protein aggregates in the affected cells. The ubiquitin-proteasome system (UPS) and autophagy are two major intracellular protein degradation pathways. The UPS mediates the targeted degradation of most normal proteins after performing their normal functions as well as the removal of abnormal, soluble proteins. Autophagy is mainly responsible for degradation of defective organelles and the bulk degradation of cytoplasm during starvation. The collaboration between the UPS and autophagy appears to be essential to protein quality control in the cell.

UPS proteolytic function often becomes inadequate in proteinopathies which may lead to activation of autophagy, striving to remove abnormal proteins especially the aggregated forms. HADC6, p62, and FoxO3 may play an important role in mobilizing this proteolytic consortium. Benign measures to enhance proteasome function are currently lacking; however, enhancement of autophagy via pharmacological intervention and/or lifestyle change has shown great promise in alleviating bona fide proteinopathies in the cell and animal models. These pharmacological interventions are expected to be applied clinically to treat human proteinopathies in the near future.

Zheng Q, Li J, Wang X. Interplay between the ubiquitin-proteasome system and
autophagy in proteinopathies. Int J Physiol Pathophysiol Pharmacol 2009;1:127-142. http://www.ijppp.org/IJPPP904002

Ubiquitin-associated Protein-Protein Interactions

Applicability of in vitro biotinylated ubiquitin for evaluation of endogenous ubiquitin conjugation and analysis of ubiquitin-associated protein-protein interactions has been investigated. Incubation of rat brain mitochondria with biotinylated ubiquitin followed by affinity chromatography on avidin-agarose, intensive washing, tryptic digestion of proteins bound to the affinity sorbent and their mass spectrometry analysis resulted in reliable identification of 50 proteins belonging to mitochondrial and extramitochondrial compartments. Since all these proteins were bound to avidin-agarose only after preincubation of the mitochondrial fraction with biotinylated ubiquitin, they could therefore be referred to as specifically bound proteins. A search for specific
ubiquitination signature masses revealed several extramitochondrial and intramitochondrial ubiquitinated proteins representing about 20% of total number of proteins bound to avidin-agarose. The interactome analysis suggests that the identified non-ubiquitinated proteins obviously form tight complexes either with ubiquitinated proteins or with their partners and/or mitochondrial membrane components. Results of the present study demonstrate that the use of biotinylated ubiquitin may be considered as the method of choice for in vitro evaluation of endogenous ubiquitin-conjugating machinery in particular
subcellular organelles and changes in ubiquitin/organelle associated interactomes. This may be useful for evaluation of changes in interactomes induced by protein ubiquitination.

Buneeva OA, Medvedeva MV, Kopylov AT, Zgoda VG, Medvedev AE. Use of Biotinylated Ubiquitin for Analysis of Rat Brain Mitochondrial Proteome and Interactome. Int J Mol Sci 2012; 13: 11593-11609; doi:10.3390/ijms130911593
IL-6 regulation on mitochondrial remodeling/dysfunction

Muscle protein turnover regulation during cancer cachexia is being rapidly defined, and skeletal muscle mitochondria function appears coupled to processes regulating muscle wasting. Skeletal muscle oxidative capacity and the expression of proteins regulating mitochondrial biogenesis and dynamics are disrupted in severely cachectic ApcMin/+ mice. It has not been determined if these changes occur at the onset of cachexia and are necessary for the progression of muscle wasting. Exercise and anti-cytokine therapies have proven effective in preventing cachexia development in tumor bearing mice, while their effect on mitochondrial content, biogenesis and dynamics is not well understood.

The purposes of this study were to

1) determine IL-6 regulation on mitochondrial remodeling/dysfunction during the progression of cancer cachexia and
2) to determine if exercise training can attenuate mitochondrial dysfunction and the induction of proteolytic pathways during IL-6 induced cancer cachexia.

ApcMin/+ mice were examined during the progression of cachexia, after systemic interleukin (IL)-6r antibody treatment, or after IL-6 over-expression with or without exercise. Direct effects of IL-6 on mitochondrial remodeling were examined in cultured C2C12 myoblasts.

Mitochondrial content was not reduced during the initial development of cachexia, while muscle PGC-1α and fusion (Mfn1, Mfn2) protein expression was repressed.

With progressive weight loss mitochondrial content decreased, PGC-1α and fusion proteins were further suppressed, and fission protein (FIS1) was induced.

IL-6 receptor antibody administration after the onset of cachexia

  • improved mitochondrial content,
  • PGC-1α,
  • Mfn1/Mfn2 and
  • FIS1 protein expression.

IL-6 over-expression in pre-cachectic mice

  • accelerated body weight loss and muscle wasting, without reducing mitochondrial content,
  • while PGC-1α and Mfn1/Mfn2 protein expression was suppressed
  • and FIS1 protein expression induced.

Exercise normalized these IL-6 induced effects. C2C12 myotubes administered IL-6 had

  • increased FIS1 protein expression,
  • increased oxidative stress, and
  • reduced PGC-1α gene expression
  • without altered mitochondrial protein expression.

Altered expression of proteins regulating mitochondrial biogenesis and fusion are early events in the initiation of cachexia regulated by IL-6, which precede the loss of muscle mitochondrial content. Furthermore, IL-6 induced mitochondrial remodeling and proteolysis can be rescued with moderate exercise training even in the presence of high circulating IL-6 levels.

White JP, Puppa MJ, Sato S, Gao S. IL-6 regulation on skeletal muscle mitochondrial remodeling during cancer cachexia in the ApcMin/+ mouse. Skeletal Muscle 2012; 2:14-30.

Starvation-induced Autophagy
Upon starvation cells undergo autophagy, a cellular degradation pathway important in the turnover of whole organelles and long lived proteins. Starvation-induced protein degradation has been regarded as an unspecific bulk degradation process. We studied global protein dynamics during amino acid starvation-induced autophagy by quantitative mass spectrometry and were able to record nearly 1500 protein profiles during 36 h of starvation. Cluster analysis of the recorded protein profiles revealed that cytosolic proteins were degraded rapidly, whereas proteins annotated to various complexes and organelles were degraded later at different time periods. Inhibition of protein degradation pathways identified the lysosomal/autophagosomal system as the main degradative route.

Thus, starvation induces degradation via autophagy, which appears to be selective and to degrade proteins in an ordered fashion and not completely arbitrarily as anticipated so far.

Kristensen AR, Schandorff S, Høyer-Hansen M, Nielsen MO, et al. Ordered Organelle Degradation during Starvation-induced Autophagy. Molecular & Cellular Proteomics 2008; 7:2419–2428.

Skeletal Muscle Macroautophagy
Skeletal muscles are the agent of motion and one of the most important tissues responsible for the control of metabolism. Coordinated movements are allowed by the highly organized structure of the cytosol of muscle fibers (or myofibers), the multinucleated and highly specialized cells of skeletal muscles involved in contraction. Contractile proteins are assembled into repetitive structures, the basal unit of which is the sarcomere, that are well packed into the myofiber cytosol. Myonuclei are located at the edge of the myofibers, whereas the various organelles such as mitochondria and sarcoplasmic reticulum are embedded among the myofibrils. Many different changes take place in the cytosol of myofibers during catabolic conditions:

  • proteins are mobilized
  • organelles networks are reorganized for energy needs
  • the setting of myonuclei can be modified.


  • strenuous physical activity,
  • improper dietary regimens and
  • aging

lead to mechanical and metabolic damages of myofiber organelles, especially mitochondria, and contractile proteins. During aging the protein turnover is slowed down, therefore it is easier to accumulate aggregates of dysfunctional proteins. Therefore, a highly dynamic tissue such as skeletal muscle requires a rapid and efficient system for the removal of altered organelles, the elimination of protein aggregates, and the disposal of toxic products.

The two major proteolytic systems in muscle are the ubiquitin-proteasome and the autophagy-lysosome pathways. The proteasome system requires

  • the transcription of the two ubiquitin ligases (atrogin-1 and MuRF1) and
  • the ubiquitination of the substrates.

Therefore, the ubiquitin-proteasome system can provide the rapid elimination of single proteins or small aggregates. Conversely, the autophagic system is able to degrade entire organelles and large proteins aggregates. In the autophagy-lysosome system, double-membrane vesicles named autophagosomes are able to engulf a portion of the cytosol and fuse with lysosomes, where their content is completely degraded by lytic enzymes.

The autophagy flux can be biochemicaly monitored following LC3 lipidation and p62 degradation. LC3 is the mammalian homolog of the yeast Atg8 gene, which is lipidated when recruited for the double-membrane commitment and growth. p62 (SQSTM-1) is a polyubiquitin-binding protein involved in the proteasome system and that can either reside free in the cytosol and nucleus or occur within autophagosomes and lysosomes. The GFP-LC3 transgenic mouse model allows easy detection of autophagosomes by simply monitoring the presence of bright GFP-positive puncta inside the myofibrils and beneath the plasma membrane of the myofibers, thus investigate the activation of autophagy in skeletal muscles with different contents of slow and fast-twitching myofibers and in response to stimuli such as fasting. For example, in the fast-twiching extensor digitorum longus muscle few GFP-LC3 dots were observed before starvation, while many small GFP-LC3 puncta appeared between myofibrils and in the perinuclear regions after 24 h starvation. Conversely, in the slow-twitching soleus muscle, autophagic puncta were almost absent in standard condition and scarcely induced after 24 h starvation.
Autophagy in Muscle Homeostasis
The autophagic flux was found to be increased during certain catabolic conditions, such as fasting, atrophy , and denervation , thus contributing to protein breakdown. Food deprivation is one of the strongest stimuli known to induce autophagy in muscle. Indeed skeletal muscle, after the liver, is the most responsive tissue to autophagy activation during food deprivation. Since muscles are the biggest reserve of amino acids in the body, during fasting autophagy has the vital role to maintain the amino acid pool by digesting muscular protein and organelles. In mammalian cells, mTORC1, which consists of

  • mTOR and
  • Raptor,

is the nutrient sensor that negatively regulates autophagy.

During atrophy, protein breakdown is mediated by atrogenes, which are under the forkhead box O (FoxO) transcription factors control, and activation of autophagy seems to aggravate muscle loss during atrophy. In vivo and in vitro studies demonstrated that several genes coding for components of the autophagic machinery, such as

  • LC3,
  • Vps34,
  • Atg12 and
  • Bnip3,

are controlled by FoxO3 transcription factor. FoxO3 is able to regulate independently the ubiquitin-proteasome system and the autophagy-lysosome machinery in vivo and in vitro. Denervation is also able to induce autophagy in skeletal muscle, although at a slower rate than fasting. This effect is mediated by RUNX1, a transcription factor upregulated during autophagy; the lack of RUNX1 results in excessive autophagic flux in denervated muscle and leads to atrophy. The generation of Atg5 and Atg7 muscle-specific knockout mice have shown that with suppression of autophagy both models display muscle weakness and atrophy and a significant reduction of weight, which is correlated with the important loss of muscle tissue due to an atrophic condition. An unbalanced autophagy flux is highly detrimental for muscle, as too much induces atrophy whereas too little leads to muscle weakness and degeneration. Muscle wasting associated with autophagy inhibition becomes evident and symptomatic only after a number of altered proteins and dysfunctional organelles are accumulated, a condition that becomes evident after months or even years. On the other hand, the excessive increase of autophagy flux is able to induce a rapid loss of muscle mass (within days or weeks).
Alterations of autophagy are involved in the pathogenesis of several myopathies and dystrophies.

The maintenance of muscle homeostasis is finely regulated by the balance between catabolic and anabolic process. Macroautophagy (or autophagy) is a catabolic process that provides the degradation of protein aggregation and damaged organelles through the fusion between autophagosomes and lysosomes. Proper regulation of the autophagy flux is fundamental for the homeostasis of skeletal muscles during physiological situations and in response to stress. Defective as well as excessive autophagy is harmful for muscle health and has a pathogenic role in several forms of muscle diseases.
Grumati P, Bonaldo P. Autophagy in Skeletal Muscle Homeostasis and in Muscular Dystrophies. Cells 2012, 1, 325-345; doi:10.3390/cells1030325. ISSN 2073-4409. http://www.mdpi.com/journal/cells

Parkinson’s Disease Mutations
Mutations in parkin, a ubiquitin ligase, cause early-onset familial Parkinson’s disease (AR-JP). How Parkin suppresses Parkinsonism remains unknown. Parkin was recently shown to promote the clearance of impaired mitochondria by autophagy, termed mitophagy. Here, we show that Parkin promotes mitophagy by catalyzing mitochondrial ubiquitination, which in turn recruits ubiquitin-binding autophagic components, HDAC6 and p62, leading to mitochondrial clearance.

During the process, juxtanuclear mitochondrial aggregates resembling a protein aggregate-induced aggresome are formed. The formation of these “mito-aggresome” structures requires microtubule motor-dependent transport and is essential for efficient mitophagy. Importantly, we show that AR-JP–causing Parkin mutations are defective in supporting mitophagy due to distinct defects at

  • recognition,
  • transportation, or
  • ubiquitination of impaired mitochondria,

thereby implicating mitophagy defects in the development of Parkinsonism. Our results show that impaired mitochondria and protein aggregates are processed by common ubiquitin-selective autophagy machinery connected to the aggresomal pathway, thus identifying a mechanistic basis for the prevalence of these toxic entities in Parkinson’s disease.
Lee JY,Nagano Y, Taylor JP,Lim KL, and Yao TP. Disease-causing mutations in Parkin impair mitochondrial ubiquitination, aggregation, and HDAC6-dependent mitophagy. J Cell Biol 2010; 189(4):671-679. http://www.jcb.org/cgi/doi/10.1083/jcb.201001039

Drosophila Parkin Requires PINK1

Loss of the E3 ubiquitin ligase Parkin causes early onset Parkinson’s disease, a neurodegenerative disorder of unknown etiology. Parkin has been linked to multiple cellular processes including

  • protein degradation,
  • mitochondrial homeostasis, and
  • autophagy;

however, its precise role in pathogenesis is unclear. Recent evidence suggests that Parkin is recruited to damaged mitochondria, possibly affecting

  • mitochondrial fission and/or fusion,
  • to mediate their autophagic turnover.

The precise mechanism of recruitment and the ubiquitination target are unclear. Here we show in Drosophila cells that PINK1 is required to recruit Parkin to dysfunctional mitochondria and promote their degradation. Furthermore, PINK1 and Parkin mediate the ubiquitination of the profusion factor Mfn on the outer surface of mitochondria. Loss of Drosophila PINK1 or parkin causes an increase in Mfn abundance in vivo and concomitant elongation of mitochondria. These findings provide a molecular mechanism by which the PINK1/Parkin pathway affects mitochondrial fission/fusion as suggested by previous genetic interaction studies. We hypothesize that Mfn ubiquitination may provide a mechanism by which terminally damaged mitochondria are labeled and sequestered for degradation by autophagy.

Ziviani E, Tao RN, and Whitworth AJ. Drosophila Parkin requires PINK1 for mitochondrial translocation and ubiquitinates Mitofusin. PNAS 2010. Pp6 http://www.pnas.org/cgi/doi/10.1073/pnas.0913485107

Dynamin-related protein 1 (Drp1) in Parkinson’s
Mutations in Parkin, an E3 ubiquitin ligase that regulates protein turnover, represent one of the major causes of familial Parkinson’s disease (PD), a neurodegenerative disorder characterized by the loss of dopaminergic neurons and impaired mitochondrial functions. The underlying mechanism by which pathogenic parkin mutations induce mitochondrial abnormality is not fully understood. Here we demonstrate that Parkin interacts with and subsequently ubiquitinates dynamin-related protein 1 (Drp1), for promoting its proteasome-dependent degradation. Pathogenic mutation or knockdown of Parkin inhibits the ubiquitination and degradation of Drp1, leading to an increased level of Drp1 for mitochondrial fragmentation. These results identify Drp1 as a novel substrate of Parkin and suggest a potential mechanism linking abnormal Parkin expression to mitochondrial dysfunction in the pathogenesis of PD.

Wang H, Song P, Du L, Tian W. Parkin ubiquitinates Drp1 for proteasome-dependent degradation: implication of dysregulated mitochondrial dynamics in Parkinson’s disease.
JBC Papers in Press. Published on February 3, 2011 as Manuscript M110.144238. http://www.jbc.org/cgi/doi/10.1074/jbc.M110.144238

Pink1, Parkin, and DJ-1 Form a Complex
Mutations in the genes PTEN-induced putative kinase 1 (PINK1), PARKIN, and DJ-1 cause autosomal recessive forms of Parkinson disease (PD), and the Pink1/Parkin pathway regulates mitochondrial integrity and function. An important question is whether the proteins encoded by these genes function to regulate activities of other cellular compartments. A study in mice, reported by Xiong et al. in this issue of the JCI, demonstrates that Pink1, Parkin, and DJ-1 can form a complex in the cytoplasm, with Pink1 and DJ-1 promoting the E3 ubiquitin ligase activity of Parkin to degrade substrates via the proteasome (see the related article, doi:10.1172/ JCI37617).

This protein complex in the cytosol may or may not be related to the role of these proteins in regulating mitochondrial function or oxidative stress in vivo.
Three models for the role of the PPD complex. In this issue of the JCI, Xiong et al. report that Pink1, Parkin, and DJ-1 bind to each other and form a PPD E3 ligase complex in which Pink1 and DJ-1 modulate Parkin-dependent ubiquitination and subsequent degradation of substrates via the proteasome. Previous work suggests that the Pink1/Parkin pathway regulates mitochondrial integrity and promotes mitochondrial fission in Drosophila.

(A) Parkin and DJ-1 may be recruited to the mitochondrial outer membrane during stress and interact with Pink1. These interactions may facilitate the ligase activity of Parkin, thereby facilitating the turnover of molecules that regulate mitochondrial dynamics and mitophagy. The PPD complex may have other roles in the cytosol that result in degradative ubiquitination and/or relay information from mitochondria to other cellular compartments.
(B) Alternatively, Pink1 may be released from mitochondria after cleavage to interact with DJ-1 and Parkin in the cytosol.
A and B differ in the site of action of the PPD complex and the cleavage status of Pink1.
The complex forms on the mitochondrial outer membrane potentially containing full-length Pink1 in A, and in the cytosol with cleaved Pink1 in B.
Lack of DJ-1 function results in phenotypes that are distinct from the mitochondrial phenotypes observed in null mutants of Pink1 or Parkin in Drosophila. Thus, although the PPD complex is illustrated here as regulating mitochondrial fission, the role of DJ-1 in vivo remains to be clarified.
(C) It is also possible that the action occurs in the cytosol and is independent of the function of Pink1/Parkin in regulating mitochondrial integrity and function.

The Xiong et al. study offers an entry point for explorations of the role of Pink1, Parkin, and DJ-1 in the cytoplasm. It remains to be shown whether Parkin, in complex with Pink1 and DJ-1, carries out protein degradation in vivo.

Li H, and Guo M. Protein degradation in Parkinson disease revisited: it’s complex. commentaries. J Clin Invest.  doi:10.1172/JCI38619. http://www.jci.org

Xiong, H., et al. Parkin, PINK1, and DJ-1 form a ubiquitin E3 ligase complex promoting unfolded protein degradation. J. Clin. Invest. 2009; 119:650–660.

 Mitochondrial Ubiquitin Ligase, MITOL, protects neuronal cells

Nitric oxide (NO) is implicated in neuronal cell survival. However, excessive NO production mediates neuronal cell death, in part via mitochondrial dysfunction. Here, we report that the mitochondrial ubiquitin ligase, MITOL, protects neuronal cells from mitochondrial damage caused by accumulation of S-nitrosylated microtubule associated protein 1B-light chain 1 (LC1). S-nitrosylation of LC1 induces a conformational change that serves both to activate LC1 and to promote its ubiquination by MITOL, indicating that microtubule
stabilization by LC1 is regulated through its interaction with MITOL. Excessive NO production can inhibit MITOL, and MITOL inhibition resulted in accumulation of S-nitrosylated LC1 following stimulation of NO production by calcimycin and N-methyl-D-aspartate. LC1 accumulation under these conditions resulted in mitochondrial dysfunction and neuronal cell death. Thus, the balance between LC1 activation by S-nitrosylation and down-regulation by MITOL is critical for neuronal cell survival. Our findings may contribute significantly to an understanding of the mechanisms of neurological diseases caused by nitrosative stress-mediated mitochondrial dysfunction.

Yonashiro R, Kimijima Y, Shimura T, Kawaguchi K, et al. Mitochondrial ubiquitin ligase MITOL blocks S-nitrosylated MAP1B-light chain 1-mediated mitochondrial dysfunction and neuronal cell death. PNAS; 2012. pp 6. http://www.pnas.org/cgi/doi/10.1073/pnas.1114985109

Ubiquitin–Proteasome System in Neurodegeneration
A common histopathological hallmark of most neurodegenerative diseases is the presence of aberrant proteinaceous inclusions inside affected neurons. Because these protein aggregates are detected using antibodies against components of the ubiquitin–proteasome system (UPS), impairment of this machinery for regulated proteolysis has been suggested to be at the root of neurodegeneration. This hypothesis has been difficult to prove in vivo owing to the lack of appropriate tools. The recent report of transgenic mice with ubiquitous expression of a UPS-reporter protein should finally make it possible to test in vivo the role of the UPS in neurodegeneration.

Hernandez F, Dıaz-Hernandez M, Avila J and Lucas JJ. Testing the ubiquitin–proteasome hypothesis of neurodegeneration in vivo. TRENDS in Neurosciences 2004; 27(2): 66-68.

ALP in Parkinson’s
The ubiquitin-proteasome system (UPS) and autophagy-lysosome pathway (ALP) are the two most important mechanisms that normally repair or remove abnormal proteins. Alterations in the function of these systems to degrade misfolded and aggregated proteins are being increasingly recognized as playing a pivotal role in the pathogenesis of many neurodegenerative disorders such as Parkinson’s disease. Dysfunction of the UPS has been already strongly implicated in the pathogenesis of this disease and, more recently, growing interest has been shown in identifying the role of ALP in neurodegeneration. Mutations of a-synuclein and the increase of intracellular concentrations of non-mutant a-synuclein have been associated with Parkinson’s disease phenotype.

The demonstration that a-synuclein is degraded by both proteasome and autophagy indicates a possible linkage between the dysfunction of the UPS or ALP and the occurrence of this disorder.The fact that mutant a-synucleins inhibit ALP functioning by tightly binding to the receptor on the lysosomal membrane for autophagy pathway further supports the assumption that impairment of the ALP may be related to the development of Parkinson’s disease.

In this review, we summarize the recent findings related to this topic and discuss the unique role of the ALP in this neurogenerative disorder and the putative therapeutic potential through ALP enhancement.

Pan Y, Kondo S, Le W, Jankovic J. The role of autophagy-lysosome pathway in
neurodegeneration associated with Parkinson’s disease. Brain 2008; 131: 1969-1978. doi:10.1093/brain/awm318.

Ubiquitin-Proteasome System in Parkinson’s

There is growing evidence that dysfunction of the mitochondrial respiratory chain and failure of the cellular protein degradation machinery, specifically the ubiquitin-proteasome system, play an important role in the pathogenesis of Parkinson’s disease. We now show that the corresponding pathways of these two systems are linked at the transcriptomic level in Parkinsonian substantia nigra. We examined gene expression in medial and lateral substantia nigra (SN) as well as in frontal cortex using whole genome DNA oligonucleotide microarrays. In this study, we use a hypothesis-driven approach in analysing microarray data to describe the expression of mitochondrial and ubiquitin-proteasomal system (UPS) genes in Parkinson’s disease (PD).

Although a number of genes showed up-regulation, we found an overall decrease in expression affecting the majority of mitochondrial and UPS sequences. The down-regulated genes include genes that encode subunits of complex I and the Parkinson’s-disease-linked UCHL1. The observed changes in expression were very similar for both medial and lateral SN and also affected the PD cerebral cortex. As revealed by “gene shaving” clustering analysis, there was a very significant correlation between the transcriptomic profiles of both systems including in control brains.

Therefore, the mitochondria and the proteasome form a higher-order gene regulatory network that is severely perturbed in Parkinson’s disease. Our quantitative results also suggest that Parkinson’s disease is a disease of more than one cell class, i.e. that it goes beyond the catecholaminergic neuron and involves glia as well.

Duke DC, Moran LB, Kalaitzakis ME, Deprez M, et al. Transcriptome analysis reveals link between proteasomal and mitochondrial pathways in Parkinson’s disease. Neurogenetics 2006; 7:139-148.
Bax Degradation a Novel Mechanism for Survival in Bcl-2 overexpressed cancer cells
The authors previously reported that proteasome inhibitors were able to overcome Bcl-2-mediated protection from apoptosis, and now show that inhibition of the proteasome activity in Bcl-2-overexpressing cells accumulates the proapoptotic Bax protein to mitochondrial cytoplasm, where it interacts to Bcl-2 protein. This event was followed by release of mitochondrial cytochrome c into the cytosol and activation of caspase-mediated apoptosis. In contrast, proteasome inhibition did not induce any apparent changes in Bcl-2 protein levels. In addition, treatment with a proteasome inhibitor increased levels of ubiquitinated forms of Bax protein, without any effects on Bax mRNA expression. They also established a cell-free Bax degradation assay in which an in vitro-translated, 35S-labeled Bax protein can be degraded by a tumor cell protein extract, inhibitable by addition of a proteasome inhibitor or depletion of the proteasome or ATP. The Bax degradation activity can be reconstituted in the proteasome-depleted supernatant by addition of a purified 20S proteasome or proteasome-enriched fraction. Finally, by using tissue samples of human prostate adenocarcinoma, they demonstrated that increased levels of Bax degradation correlated well with decreased levels of Bax protein and increased Gleason scores of prostate cancer. These studies strongly suggest that ubiquitin-proteasome-mediated Bax degradation is a novel survival mechanism in human cancer cells and that selective targeting of this pathway should provide a unique approach for treatment of human cancers, especially those overexpressing Bcl-2.
In the current study, These investigators report that

  • (i) proteasome inhibition results in Bax accumulation before release of cytochrome c and induction of apoptosis, which is associated with the ability of proteasome inhibitors to overcome Bcl-2-mediated antiapoptotic function;
  • (ii) Bax is regulated by an ATP-ubiquitin-proteasome-dependent degradation pathway; and
  • (iii) decreased levels of Bax protein correlate with increased levels of Bax degradation in aggressive human prostate cancer.

Li B and Dou QP. Bax degradation by the ubiquitin-proteasome-dependent pathway: Involvement in tumor survival and progression. PNAS 2000; 97(8): 3851-3855. http://www.pnas.org

p97 and DBeQ, ATP-competitive p97 inhibitor
A major limitation to current studies on the biological functions of p97/Cdc48 is that there is no method to rapidly shut off its ATPase activity. Given the range of cellular processes in which Cdc48 participates, it is difficult to determine whether any particular phenotype observed in the existing mutants is due to a direct or indirect effect. Moreover, inhibition of p97 activity in animal cells by siRNA or expression of a dominant-negative version is challenged by its high abundance and is not suited to evaluating proximal phenotypic effects of p97 loss of function.

A specific small-molecule inhibitor of p97 would provide an important tool to investigate diverse functions of this essential ATPase associated with diverse cellular activities (AAA) ATPase and to evaluate its potential to be a therapeutic target in human disease. Cancer cells may be particularly sensitive to killing by suppression of protein degradation mechanisms, because they may exhibit a heightened dependency on these mechanisms to clear an elevated burden of quality-control substrates. For example, some cancers produce high levels of a specific protein that is a prominent quality-control substrate (e.g., Ig light chains in multiple myeloma) or produce high levels of reactive oxygen species, which can result in excessive protein damage via oxidation. Therefore, a specific p97 inhibitor would be a valuable research tool to investigate p97 function in cells.

We carried out a high-throughput screen to identify inhibitors of p97 ATPase activity. Dual-reporter cell lines that simultaneously express p97-dependent and p97-independent proteasome substrates were used to stratify inhibitors that emerged from the screen. N2,N4-dibenzylquinazoline-2,4-diamine (DBeQ) was identified as a selective,potent, reversible, and ATP-competitive p97 inhibitor.

DBeQ blocks multiple processes that have been shown by RNAi to depend on p97, including degradation of ubiquitin fusion degradation and endoplasmic reticulum-associated degradation pathway reporters, as well as autophagosome maturation. DBeQ also potently inhibits cancer cell growth and is more rapid than a proteasome inhibitor at mobilizing the executioner caspases-3 and -7.

Simultaneous inhibition of proteasome and histone deacetylase 6 (HDAC6) [which is required for autophagy results in synergistic killing of multiple myeloma cells]. Interestingly, more than one dozen human clinical trials (www.clinicaltrials.gov) combine bortezomib with the broad-spectrum HDAC inhibitor vorinostat, which is active toward HDAC6. Targeting p97
may provide an alternative route to achieving the same objective. Our results provide a rationale for targeting p97 in cancer therapy. Future work will provide molecular insight into how inhibition of p97 activity by DBeQ results in apoptosis and could strengthen the rationale for a p97-targeted cancer therapeutic.

Chou TF, Brown SJ, Minond D, Nordin BE, et al. Reversible inhibitor of p97, DBeQ, impairs both ubiquitin-dependent and autophagic protein clearance pathways. PNAS 2011; pp 6 http://www.pnas.org/cgi/doi/10.1073/pnas.1015312108

The causes of various neurodegenerative diseases, particularly sporadic cases, remain unknown, but increasing evidence suggests that these diseases may share similar molecular and cellular mechanisms of pathogenesis. One prominent feature common to most neurodegenerative diseases is the accumulation of misfolded proteins in the form of insoluble protein aggregates or inclusion bodies. Although these aggregates have different protein compositions, they all contain ubiquitin and proteasome subunits, implying a failure of the ubiquitin-proteasome system (UPS) in the removal of misfolded proteins.

A direct link between UPS dysfunction and neurodegeneration has been
provided by recent findings that genetic mutations in UPS components cause several rare, familial forms of neurodegenerative diseases. Furthermore, it is becoming increasingly clear that oxidative stress, which results from aging or exposure to environmental toxins, can directly damage UPS components, thereby contributing to the pathogenesis of sporadic forms of neurodegenerative diseases.

Aberrations in the UPS often result in defective proteasome-mediated protein degradation, leading to accumulation of toxic proteins and eventually to neuronal cell death. Interestingly, emerging evidence has begun to suggest that impairment in substrate-specific components of the UPS, such as E3 ubiquitin-protein ligases, may cause aberrant ubiquitination and neurodegeneration in a proteasome-independent manner. This provides an overview of the molecular components of the UPS and their impairment in familial and sporadic forms of neurodegenerative diseases, and summarizes present knowledge about the pathogenic mechanisms of UPS dysfunction in neurodegeneration.

Molecular mechanisms of protein ubiquitination and degradation by the UPS. Ubiquitination involves a highly specific enzyme cascade in which

  • ubiquitin (Ub) is first activated by the ubiquitinactivating enzyme (E1),
  • then transferred to an ubiquitin-conjugating enzyme (E2), and
  • finally covalently attached to the substrate by an ubiquitin-protein ligase (E3).

Ubiquitination is a reversible posttranslational modification in which the removal of Ub is mediated by a deubiquitinating enzyme (DUB).

  • Substrate proteins can be either monoubiquitinated or polyubiquitinated through successive conjugation of Ub moieties to an internal lysine residue in Ub.
  • K48-linked poly-Ub chains are recognized by the 26S proteasome, resulting in degradation of the substrate and recycling of Ub.
  • Monoubiquitination or K63-linked polyubiquitination plays a number of regulatory roles in cells that are proteasome-independent.


Loss-of-function mutations in parkin, a 465-amino-acid RING-type E3 ligase, were first identified as the cause for autosomal recessive juvenile Parkinsonism (AR-JP) and subsequently found to account for ~50% of all recessively transmitted early-onset PD cases. Interestingly, patients with parkin mutations do not exhibit Lewy body pathology.

Possible pathogenic mechanisms by which impaired UPS components cause neurodegeneration. Genetic mutations or oxidative stress from aging and/or exposure to environmental toxins have been shown to impair the ubiquitination machinery (particularly E3 ubiquitin-protein ligases) and deubiquitinating enzymes (DUBs), resulting in abnormal ubiquitination. Depending on the type of ubiquitination affected, the impairment could cause neurodegeneration through two different mechanisms.

In the first model, aberrant K48-linked polyubiquitination resulting from impaired E3s or DUBs alters protein degradation by the proteasome, leading to accumulation of toxic proteins and subsequent neurodegeneration. The proteasomes could be directly damaged by oxidative stress or might be inhibited by protein aggregation, which exacerbates the neurotoxicity.

In the second model, aberrant monoubiquitination or K63-linked polyubiquitination resulting from impaired E3s or DUBs alters crucial non-proteasomal functions, such as gene transcription and protein trafficking, thereby causing neurodegeneration without protein aggregation.

These two models are not mutually exclusive because a single E3 or DUB enzyme, such as parkin or UCH-L1, could regulate more than one type of ubiquitination. In addition, abnormal ubiquitination and neurodegeneration could also result from mutation or oxidative stress-induced structural changes in the protein substrates that alter their recognition and degradation by the UPS.

Lian Li and Chin LS. IMPAIRMENT OF THE UBIQUITIN-PROTEASOME SYSTEM: A COMMON PATHOGENIC MECHANISM IN NEURODEGENERATIVE DISORDERS. In The Ubiquitin Proteasome System…Chapter 23. (Eds: Eds: Mario Di Napoli and Cezary Wojcik) 553-577 © 2007 Nova Science Publishers, Inc. ISBN 978-1-60021-749-4.

filedesc Schematic diagram of the ubiquitylati...

filedesc Schematic diagram of the ubiquitylation system. Created by Roger B. Dodd (Photo credit: Wikipedia)


Current Noteworthy Work

Nassif M and Hetz C.  Autophagy impairment: a crossroad between neurodegeneration and tauopathies.  BMC Biology 2012; 10:78. http://www.biomedcentral.com/1741-7007/10/78

Impairment of protein degradation pathways such as autophagy is emerging as a consistent and transversal pathological phenomenon in neurodegenerative diseases, including Alzheimer´s, Huntington´s, and Parkinson´s disease. Genetic inactivation of autophagy in mice has demonstrated a key role of the pathway in maintaining protein homeostasis in the brain, triggering massive neuronal loss and the accumulation of abnormal protein inclusions.  A paper in Molecular Neurodegeneration from Abeliovich´s group now suggests a role for phosphorylation of Tau and the activation of glycogen synthase kinase 3β (GSK3β) in driving neurodegeneration in autophagy-deficient neurons. We discuss the implications of this study for understanding the factors driving neurofibrillary tangle formation in Alzheimer´s disease and tauopathies.

Cajee UF, Hull R and Ntwasa M. Modification by Ubiquitin-Like Proteins: Significance in Apoptosis and Autophagy Pathways. Int. J. Mol. Sci. 2012, 13, 11804-11831; doi:10.3390/ijms130911804

Ubiquitin-like proteins (Ubls) confer diverse functions on their target proteins. The modified proteins are involved in various biological processes, including DNA replication, signal transduction, cell cycle control, embryogenesis, cytoskeletal regulation,
metabolism, stress response, homeostasis and mRNA processing. Modifiers such as SUMO, ATG12, ISG15, FAT10, URM1, and UFM have been shown to modify proteins thus conferring functions related to programmed cell death, autophagy and regulation of
the immune system. Putative modifiers such as Domain With No Name (DWNN) have been identified in recent times but not fully characterized. In this review, we focus on cellular processes involving human Ubls and their targets.

Aloy P. Shaping the future of interactome networks. (A report of the third Interactome Networks Conference, Hinxton, UK, 29 August-1 September 2007). Genome Biology 2007; 8:316 (doi:10.1186/gb-2007-8-10-316)

Complex systems are often networked, and biology is no exception. Following on from the genome sequencing projects,
experiments show that proteins in living organisms are highly connected, which helps to explain how such great complexity
can be achieved by a comparatively small set of gene products. At a recent conference on interactome networks held outside
Cambridge, UK, the most recent advances in research on cellular networks were discussed. This year’s conference focused on
identifying the strengths and weaknesses of currently resolved interaction networks and the techniques used to determine
them – reflecting the fact that the field of mapping interaction networks is maturing.

Peroutka RJ, Orcutt SJ, Strickler JE, and Butt TR. SUMO Fusion Technology for Enhanced Protein Expression and Purification in Prokaryotes and Eukaryotes. Chapter 2. in T.C. Evans, M.-Q. Xu (eds.), Heterologous Gene Expression in E. coli, Methods in Molecular Biology 705:15-29. DOI 10.1007/978-1-61737-967-3_2, © Springer Science+Business Media, LLC 2011

The preparation of sufficient amounts of high-quality protein samples is the major bottleneck for structural proteomics. The use of recombinant proteins has increased significantly during the past decades. The most commonly used host, Escherichia coli, presents many challenges including protein misfolding, protein degradation, and low solubility. A novel SUMO fusion technology appears to enhance protein expression and solubility (www.lifesensors.com). Efficient removal of the SUMO tag by SUMO protease in vitro facilitates the generation of target protein with a native N-terminus. In addition to its physiological relevance in eukaryotes, SUMO can be used as a powerful biotechnology tool for enhanced functional protein expression in prokaryotes and eukaryotes.

Juang YC, Landry MC, et al. OTUB1 Co-opts Lys48-Linked Ubiquitin Recognition to Suppress E2 Enzyme Function. Molecular Cell 2012; 45: 384–397. DOI 10.1016/j.molcel.2012.01.011

Ubiquitylation entails the concerted action of E1, E2, and E3 enzymes. We recently reported that OTUB1, a deubiquitylase, inhibits the DNA damage response independently of its isopeptidase activity. OTUB1 does so by blocking ubiquitin transfer by UBC13, the cognate E2 enzyme for RNF168. OTUB1 also inhibits E2s of the UBE2D and UBE2E families. Here we elucidate the structural mechanism by which OTUB1 binds E2s to inhibit ubiquitin transfer. OTUB1 recognizes ubiquitin-charged E2s through contacts with both donor ubiquitin and the E2 enzyme. Surprisingly, free ubiquitin associates with the canonical distal ubiquitin-binding site on OTUB1 to promote formation of the inhibited E2 complex. Lys48 of donor ubiquitin lies near the OTUB1 catalytic site and the C terminus of free ubiquitin, a configuration that mimics the products of Lys48-linked ubiquitin chain cleavage. OTUB1 therefore co-opts Lys48-linked ubiquitin chain recognition to suppress ubiquitin conjugation and the DNA damage response.

Hunter T. The Age of Crosstalk: Phosphorylation, Ubiquitination, and Beyond. Molecular Cell  2007; 28:730-738. DOI 10.1016/ j.molcel.2007.11.019.

Crosstalk between different types of posttranslational modification is an emerging theme in eukaryotic biology. Particularly prominent are the multiple connections between phosphorylation and ubiquitination, which act either positively or negatively in both directions to regulate these processes.

Tu Y, Chen C, et al. The Ubiquitin Proteasome Pathway (UPP) in the regulation of cell cycle control and DNA damage repair and its implication in tumorigenesis. Int J Clin Exp Pathol 2012;5(8):726-738. www.ijcep.com /ISSN:1936-2625/IJCEP1208018

Accumulated evidence supports that the ubiquitin proteasome pathway (UPP) plays a crucial role in protein
metabolism implicated in the regulation of many biological processes such as cell cycle control, DNA damage
response, apoptosis, and so on. Therefore, alterations for the ubiquitin proteasome signaling or functional impairments
for the ubiquitin proteasome components are involved in the etiology of many diseases, particularly in cancer
development.The authors discuss the ubiquitin proteasome pathway in the regulation of cell cycle control and DNA
damage response, the relevance for the altered regulation of these signaling pathways in tumorigenesis, and finally
assess and summarize the advancement for targeting the ubiquitin proteasome pathway in cancer therapy.

Cebollero E , Reggiori F  and Kraft C.  Ribophagy: Regulated Degradation of Protein Production Factories. Int J Cell Biol. 2012; 2012: 182834. doi:  10.1155/2012/182834 (online).

During autophagy, cytosol, protein aggregates, and organelles are sequestered into double-membrane vesicles called autophagosomes and delivered to the lysosome/vacuole for breakdown and recycling of their basic components. In all eukaryotes this pathway is important for adaptation to stress conditions such as nutrient deprivation, as well as to regulate intracellular homeostasis by adjusting organelle number and clearing damaged structures. For a long time, starvation-induced autophagy has been viewed as a nonselective transport pathway; however, recent studies have revealed that autophagy is able to selectively engulf specific structures, ranging from proteins to entire organelles. In this paper, we discuss recent findings on the mechanisms and physiological implications of two selective types of autophagy: ribophagy, the specific degradation of ribosomes, and reticulophagy, the selective elimination of portions of the ER.

Lee JH, Yu WH,…, Nixon RA.  Lysosomal Proteolysis and Autophagy Require Presenilin 1 and Are Disrupted by Alzheimer-Related PS1 Mutations. Cell 2010; 141, 1146–1158. DOI 10.1016/j.cell.2010.05.008.

Macroautophagy is a lysosomal degradative pathway essential for neuron survival. Here, we show that macroautophagy requires the Alzheimer’s disease (AD)-related protein presenilin-1 (PS1). In PS1 null blastocysts, neurons from mice hypomorphic for PS1 or
conditionally depleted of PS1, substrate proteolysis and autophagosome clearance during macroautophagy are prevented as a result of a selective impairment of autolysosome acidification and cathepsin activation. These deficits are caused by failed PS1-dependent
targeting of the v-ATPase V0a1 subunit to lysosomes. N-glycosylation of the V0a1 subunit, essential for its efficient ER-to-lysosome delivery, requires the selective binding of PS1 holoprotein to the unglycosylated subunit and the  sec61alpha/ oligosaccharyltransferase complex. PS1 mutations causing early-onset AD produce a similar lysosomal/autophagy phenotype in
fibroblasts from AD patients. PS1 is therefore essential for v-ATPase targeting to lysosomes, lysosome acidification, and proteolysis during autophagy. Defective lysosomal proteolysis represents a basis for pathogenic protein accumulations and neuronal cell death in AD and suggests previously unidentified therapeutic targets.

Pohl C and Jentsch S. Midbody ring disposal by autophagy is a post-abscission event of cytokinesis. nature cell biology 2009; 11 (1): 65-70.  DOI: 10.1038/ncb1813.

At the end of cytokinesis, the dividing cells are connected by an intercellular bridge, containing the midbody along with a single,
densely ubiquitylated, circular structure called the midbody ring (MR). Recent studies revealed that the MR serves as a target
site for membrane delivery and as a physical barrier between the prospective daughter cells. The MR materializes in telophase,
localizes to the intercellular bridge during cytokinesis, and moves asymmetrically into one cell after abscission. Daughter
cells rarely accumulate MRs of previous divisions, but how these large structures finally disappear remains unknown.
Here, we show that MRs are discarded by autophagy, which involves their sequestration into autophagosomes and delivery to
lysosomes for degradation. Notably, autophagy factors, such as the ubiquitin adaptor p62 and the ubiquitin-related protein Atg8 , associate with the MR during abscission, suggesting that autophagy is coupled to cytokinesis. Moreover, MRs accumulate in cells of patients with lysosomal storage disorders, indicating that defective MR disposal is characteristic of these diseases. Thus our findings suggest that autophagy has a broader role than previously assumed, and that cell renovation by clearing from superfluous large macromolecular assemblies, such as MRs, is an important autophagic function.


Hanai JI, Cao P, Tanksale P, Imamura S, et al. The muscle-specific ubiquitin ligase atrogin-1/MAFbx mediates statin-induced muscle toxicity. The Journal of Clinical Investigation  2007; 117(12):3930-3951.    http://www.jci.org

Statins inhibit HMG-CoA reductase, a key enzyme in cholesterol synthesis, and are widely used to treat hypercholesterolemia.
These drugs can lead to a number of side effects in muscle, including muscle fiber breakdown; however, the mechanisms of muscle injury by statins are poorly understood. We report that lovastatin induced the expression of atrogin-1, a key gene involved in skeletal muscle atrophy, in humans with statin myopathy, in zebrafish embryos, and in vitro in murine skeletal muscle cells. In cultured mouse myotubes, atrogin-1 induction following lovastatin treatment was accompanied by distinct morphological changes, largely absent in
atrogin-1 null cells. In zebrafish embryos, lovastatin promoted muscle fiber damage, an effect that was closely mimicked by knockdown of zebrafish HMG-CoA reductase. Moreover, atrogin-1 knockdown in zebrafish embryos prevented lovastatin-induced muscle injury. Finally, overexpression of PGC-1α, a transcriptional coactivator that induces mitochondrial biogenesis and protects against the development of muscle atrophy, dramatically prevented lovastatin-induced muscle damage and abrogated atrogin-1 induction both in fish and in cultured mouse myotubes. Collectively, our human, animal, and in vitro findings shed light on the molecular mechanism of statin-induced myopathy and suggest that atrogin-1 may be a critical mediator of the muscle
damage induced by statins.

Inami Y, Waguri S, Sakamoto A, Kouno T, et al.  Persistent activation of Nrf2 through p62 in hepatocellular carcinoma cells. J. Cell Biol. 2011; 193(2): 275–284. http://www.jcb.org/cgi/doi/10.1083/jcb.201102031

Macroautophagy (hereafter referred to as autophagy) is a cellular degradation system in which cytoplasmic components, including
organelles, are sequestered by double membrane structures called autophagosomes and the sequestered materials are
degraded by lysosomal hydrolases for supply of amino acids and for cellular homeostasis. Although autophagy has generally been considered nonselective, recent studies have shed light on another indispensable role for basal autophagy in cellular homeostasis, which is mediated by selective degradation of a specific substrate(s).  p62 is a ubiquitously expressed cellular protein that is conserved in metazoa but not in plants and fungi, and recently it has been known as one of the selective substrates for autophagy.
This protein is localized at the autophagosome formation site  and directly interacts with LC3, an autophagosome localizing protein . Subsequently, the p62 is incorporated into the autophagosome and then degraded. Therefore, impaired autophagy is accompanied by
accumulation of p62 followed by the formation of p62 and ubiquitinated protein aggregates because of the nature of both self- oligomerization and ubiquitin binding of p62.


Kima K, Khayrutdinov BI, Leeb CK, et al. Solution structure of the Zβ domain of human DNA-dependent activator of IFN-regulatory factors and its binding modes to B- and Z-DNAs. PNAS 2010; Early Edition ∣ pp 6. www.pnas.org/cgi/doi/10.1073/pnas.1014898107

The DNA-dependent activator of IFN-regulatory factors (DAI), also known as DLM-1/ZBP1, initiates an innate immune response by binding to foreign DNAs in the cytosol. For full activation of the immune response, three DNA binding domains at the N terminus are required: two Z-DNA binding domains (ZBDs), Zα and Zβ, and an adjacent putative B-DNA binding domain. The crystal structure of the Zβ domain of human DAI (hZβDAI) in complex with Z-DNA revealed structural features distinct from other known Z-DNA binding proteins, and it was classified as a group II ZBD. To gain structural insights into the DNA binding mechanism of hZβDAI, the solution structure of the free hZβDAI was solved, and its bindings to B- and Z-DNAs were analyzed by NMR spectroscopy. Compared to the Z-DNA–bound structure, the conformation of free hZβDAI has notable alterations in the α3 recognition helix, the “wing,” and Y145, which are critical in Z-DNA recognition. Unlike some other Zα domains, hZβDAI appears to have conformational flexibility, and structural adaptation is required for Z-DNA binding. Chemical-shift perturbation experiments revealed that hZβDAI also binds weakly to B-DNA via a different binding mode. The C-terminal domain of DAI is reported to undergo a conformational change on B-DNA binding; thus, it is possible that these changes are correlated. During the innate immune response, hZβDAI is likely to play an active role in binding to DNAs in both B and Z conformations in the recognition of foreign DNAs.



This extensive review leaves little left unopened. We have seen the central role that the UPS system plays in normal organelle proteolysis in concert with autophagy. Impaired ubiquitination occurs from aging, and/or toxins, under oxidative stress involving E3s or DUBs.

This leads to altered gene transcripton, altered protein trafficking, and plays a role in neurodegenative disease, muscle malfunction, and cancer as well.

English: A cartoon representation of a lysine ...

English: A cartoon representation of a lysine 48-linked diubiquitin molecule. The two ubiquitin chains are shown as green cartoons with each chain labelled. The components of the linkage are indicated and shown as orange sticks. Image was created using PyMOL from PDB id 1aar. (Photo credit: Wikipedia)

Different forms of protein ubiquitylation

Different forms of protein ubiquitylation (Photo credit: Wikipedia)

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