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 First Published: September 24: 2015
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Biochemistry Arkive Year Alpha September 24:2015-September 23: 2016

 

 

 

 

 

 

 

 

 

 

 

 

 

Biochemistry Arkive Year Alpha September 24:2015-September 23: 2016

 

 

If I were in Your Immunologic Memory: Would You Recognise Me?

Image: Dana-Farber Cancer Institute

|| September 19: 2016: Dana-Farber Cancer Institute News || ά.  Dana-Farber Cancer Institute investigators report they have discovered a type of immune antibody that can rapidly evolve to neutralise a wide array of influenza virus strains, including those the body hasn’t yet encountered. The body’s ability to make the adaptable antibody suggests potential strategies for creating improved or even “universal” influenza vaccines, according to a team led by Wayne A. Marasco, MD, PhD, a cancer immunologist and virologist at Dana-Farber, reporting in the journal Nature Communications.

The novel infection-fighting protein, named 3I14 mAb, is a “broadly neutralising antibody,” so-called because it can recognise and disable a diverse group of the 18 different strains of influenza virus that circulate around the globe. Marasco reported the discovery of broadly neutralising antibodies in 2009. According to the new report, the 3I14 antibody demonstrated it could neutralise the two main types of influenza A virus, group one and two, and protected mice against lethal doses of the virus.

The 3I14 antibody is made by the human immune system’s “memory” B cells, white blood cells that circulate in the blood and reside in the spleen and bone marrow. When a person is exposed to an infectious agent, or receives a vaccine made from pieces of that agent, B cells that respond to the invaders can generate a memory of the specific type or strain. Pools of these memory B cells constitute a reserve defensive force that can quickly recognise and attack the microbe or virus should it enter the body again.

Unlike most infectious agents that can be protected against with a one-time vaccination, influenza is a shape-shifting virus that constantly and rapidly mutates, and also combines with other flu viruses from animals and birds. The shape changing occurs every flu season and is responsible for seasonal flu that we are vaccinated against yearly. The more dramatic changes that occur when new viruses emerge from animal and bird reservoirs are responsible for potentially more serious pandemics such as occurred in 2009.

The discovery of the new broadly neutralising antibody came after Marasco and his colleagues took blood samples from seven blood bank donors that were shown to harbour these types of antibodies in their blood and challenged their immune B cells in the laboratory with an array of flu viruses. The researchers ultimately identified one B cell population “that recognised all the strains we screened against it,” Marasco said. Sorting through the B cells’ DNA, the scientists isolated the gene that carried the instructions for the 3I14 antibody.

The antibody proved that it could bind to the unchanging stem portion of flu viruses. And the antibody’s genetic makeup gave it the flexibility to adapt or evolve, through mutations, to neutralise a myriad of flu viruses.

The investigators challenged the B cells with a bird flu virus of the H5 type that the immune cells had never encountered. While the 3I14 antibody didn’t initially bind strongly to the virus, the researchers introduced a single DNA mutation, a change in one letter of genetic code, that increased its binding strength to H5 by 10 times. “To our delight, we made one mutation and it did the job,” Marasco said. “This was a simple mutation that would readily occur in nature.”

What these results suggest, he said, is that the memory B cells of the immune system may be continuously diversifying through mutations. As a result, he said, through this mechanism, B cells “may lay down immunologic memory that can recognise all virus strains, present and future.”

Ideally, Marasco said, vaccination strategies could be devised to build up in individuals a pool of memory B cells that aren’t committed to making a single antibody type; instead, “you would hope to store B cells that have broad activity that, with minimum activity, can recognize all strains.”
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The Endosialin Mechanism: Blood Vessel Cells are Helping Breast Cancers Enter Bloodstream and Spread Around the Body

Breast cancer cell: Image: National Cancer Institute


|| September 19: 2016: The Institute of  Cancer Research London News || Scientists have discovered that non-cancer cells that wrap around blood vessels, called ‘pericytes’, are helping breast cancer cells enter the bloodstream and spread around the body through the production of a key molecule called endosialin. The study represents an important step forward in researchers’ understanding of how and why breast cancer spreads.

Currently, once the disease spreads to another part of the body, known as secondary or metastatic breast cancer, it becomes incurable and is ultimately the reason that around 11,500 women and 80 men lose their lives to the disease each year in the UK. The research was led by scientists at the Breast Cancer Now Toby Robins Research Centre at The Institute of Cancer Research, London, and the German Cancer Research Centre DKFZ in Heidelberg. It was part-funded by Breast Cancer Now.

‘Spider-Like Cells’

Pericytes are large, spider-like cells that sit on the outside of blood vessels and support their growth and function. In a new study published in the journal Cancer Research, scientists have identified the important role the production of endosialin on the pericyte cell surface plays in breast cancer spreading to other organs.

For breast tumours to grow, they need a blood supply and so they look to attract the growth of nearby blood vessels. As well as providing the cancer cells with the oxygen and nutrients they need, this blood supply also provides an escape route into the rest of the body, but it has not been clear up until now what mechanism could be helping tumour cells escape through the vessel wall into the blood.

Using a combination of studies in mice, cells grown in the lab and samples donated by breast cancer patients, the research team, led by Professor Clare Isacke in the Breast Cancer Now Research Centre  at the ICR and Professor Hellmut Augustin at the DKFZ in Heidelberg, compared pericytes that produced endosialin with ones that couldn’t, and investigated how this affected the process of cancer spread.

Invading Blood Vessels

Having established in mice that a lack of endosialin prevented breast cancer cells from getting into the blood vessels, the researchers turned to lab experiments to understand how pericytes facilitated the invasion of cancer cells into blood vessels.

The researchers grew pericytes and endothelial cells, the cells that form blood vessels, in layers, to replicate the barrier that cancer cells have to cross to get into the bloodstream. They were able to show that far fewer breast cancer cells were able to migrate through these layers if the pericytes did not produce endosialin, and that the presence of endosialin was required for the pericytes to actively facilitate this mechanism.

Finally, the researchers investigated what clinical relevance endosialin could have. They split a set of 334 patient samples in half based on their levels of endosialin expression, finding that women with higher endosialin were significantly more likely to experience their cancer spreading to new sites than those with lower levels.

This research identifies endosialin as a potential biomarker in the future, testing patients’ tumours for the level of endosialin in the surrounding non-cancer cells could predict their risk of their disease spreading. By identifying those at high risk of spread, patients could be offered more intensive treatment.

Containing the spread of Cancer

Furthermore, while this is early research, the scientists believe that endosialin could be a therapeutic target and that drugs that block its function might be useful to help prevent and contain the spread of breast cancer. The next step for Professor Isacke’s team will be to try to uncover the exact mechanism by which pericytes begin producing endosialin, and precisely how endosialin helps cancer cells squeeze through the vessel wall into the blood.

Study co-leader Professor Clare Isacke, Professor of Molecular Cell Biology at the ICR, said: “Our study sheds valuable light on the role of pericytes, a type of cell that wraps around blood vessels, in helping breast cancer cells escape into the bloodstream and spread round the body. We found that a molecule called endosialin, which is produced on the surface of pericytes, plays a key role in aiding the getaway of cancer cells.

We believe that endosialin could be a useful marker of how likely a woman’s breast cancer is to spread around the body. And it might even be possible to block cancer spread by targeting this molecule with new drugs, something we plan to explore in future studies.”

'Exciting' Potential New Drug Target

Baroness Delyth Morgan, Chief Executive at Breast Cancer Now, said: “This discovery paves the way for research that could help prevent and contain the spread of breast cancer. We’re hopeful that this fundamental understanding could lead to new ways to identify patients at high risk of their breast cancer spreading, who could be offered more intensive treatment.

“That endosialin could also eventually be targeted by drugs to prevent and contain secondary breast cancer is a really exciting prospect. Secondary breast cancer, where the disease has spread, is ultimately the reason that around 11,500 women in the UK lose their lives to the disease each year. Uncovering the processes that allow tumours to spread is therefore one of the most crucial questions remaining in breast cancer research.”

The study was funded by Breast Cancer Now and the Medical Research Council in the United Kingdom, and the Deutsche Forschungsgemeinschaft and the Helmholtz Alliance in Germany. Breast Cancer Now thanked Walk the Walk and Future Dreams for their generous support towards Professor Isacke’s research. 
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Tapping the Unused Potential of Photosynthesis

Synechococcus brewing in a bioreactor. Image Modified from an Image by: Pacific Northwest National Laboratory

|| September 10: 2016: University of Southampton News || Scientists from the University of Southampton have re-engineered the fundamental process of photosynthesis to power useful chemical reactions that could be used to produce biofuels, pharmaceuticals and fine chemicals. Photosynthesis is the pivotal biological reaction on the planet, providing the food we eat, the oxygen we breathe and removing CO2 from the atmosphere.

Photosynthesis in plants and algae consists of two reactions, the light-reactions absorb light energy from the sun and use this to split water, H2O, into electrons, protons and oxygen and the dark-reactions which use the electrons and protons from the light reactions to ‘fix’ CO2 from the atmosphere into simple sugars that are the basis of the food chain. Importantly, the light reactions have a much higher capacity than the dark reactions resulting in much of the absorbed light energy being wasted as heat rather than being used to ‘fix’ CO2.

Co-author Dr Adokiye Berepiki, a Postdoctoral Research Fellow from Ocean and Earth Sciences at the University of Southampton, said: “In our study, we used synthetic biology methods to engineer an additional enzyme in-between the light-reactions and before the dark-reactions. We have therefore ‘rewired’ photosynthesis such that more absorbed light is used to power useful chemical reactions. This study therefore represents an innovation whereby a range of additional valuable chemical reactions can be powered by the sun in plants and algae.”

Professor Tom Bibby:L: Dr Adokiye Berepiki: Images: University of Southampton


In the study, published in ACS Synthetic Biology, the ‘wasted’ electrons were rewired to degrade the widespread environmental pollutant atrazine, a herbicide used in agriculture. Atrazine was banned from the EU over 20 years ago but is still one of the most prevalent pesticides in groundwater. The photosynthetic algae designed by the researchers may be used in the efficient bioremediation of such polluted wastewater areas.

Dr Berepiki said: “By taking a synthetic biology approach, combining science, technology and engineering to facilitate and accelerate the design, manufacture and modification of genetic materials in living organisms, we rewired electrons by introducing an enzyme from a brown rat into the photosynthetic machinery. This enzyme, which was encoded by a gene that was produced de novo using chemical synthesis rather than being taken from rat, was then able to serve as an electron sink that used photosynthetic electrons to power its activity.”

Co-author Professor Tom Bibby, Professor of Biological Oceanography from Ocean and Earth Sciences at the University of Southampton, said: “There has been much recent research into the potential of using photosynthetic species as sources of sustainable biofuels. While promising, this potential is not yet economically feasible. The ‘added value’ we have introduced into algal may therefore be a critical step toward the commercial realisation of using photosynthetic species to generate ‘biofuels’ that may one day replace our current dependence on fossil fuels.”
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Okayama University Researchers Find Keeping Cells in Shape to Fight Sepsis


|| August 08: 2016: Okayama University: Tokyo: Japan || Boosting levels of a protein that controls the shape and activity of a crucial group of white blood cells improves survival and recovery chances during sepsis. Blood poisoning following an infection or injury is known as sepsis, and is a major cause of death across the world. Sepsis occurs when the body’s immune system goes into overdrive, resulting in damage to its own tissues and organs through insufficient blood supply. However, the exact molecular mechanisms underpinning sepsis and its progression are unclear.

Now, Professor Masahiro Nishibori and co-workers at Okayama University, Shujitsu University and Kinki University, Japan, have shown that a naturally-occurring protein called histidine-rich glycoprotein:HRG plays a significant role in the prevention of sepsis. They found that HRG controls the shape and activity of white blood cells called neutrophils, enabling them to flow freely and respond correctly in the fight against sepsis.

Nishibori’s team aimed to verify the role of HRG, a protein produced and secreted by the liver, because HRG levels decrease rapidly in patients when sepsis takes hold. HRG is known to be involved in the regulation of immune responses, as well as prompting antibacterial and antifungal activity. The team induced sepsis in one group of mice, keeping a healthy group as controls. They purified HRG from human blood plasma, and treated some of the septic mice with a dose of the protein.

The researchers found that the HRG mice quickly regained locomotor activity, and began to recover from sepsis. Further investigations showed that the mice exhibited far less inflammation in the lungs than their non-treated counterparts. Neutrophils in the HRG mice were smooth and spherical in shape, allowing them to flow freely through microcapillaries and veins. The septic mice, however, had deformed neutrophils with irregular shapes. This in turn triggered unwanted activity because the deformed neutrophils became attached to other cells, creating cell clusters that limited blood flow.

“The decrease in plasma HRG constitutes the fundamental pathway for septic pathogenesis,” state the authors in their paper published in eBioMedicine, 2016. “Supplementary therapy with HRG may provide a novel strategy for the treatment of septic patients.”

Background: Sepsis

Sepsis is caused by the body’s own response to an infection or an infected injury. Essentially, the immune system goes into overdrive and ends up damaging major organs and tissues by limiting the blood supply. Scientists theorised that sepsis must have its origins in the disruption of healthy cells in the blood, but the precise mechanisms are not yet clear.

The work by Nishibori and his team clarifies the role of histidine-rich glycoprotein:HRG in tackling sepsis. While it is not yet clear why HRG expression falls as sepsis sets in, this study shows that the loss of HRG in blood plasma is a key factor in sepsis. The decrease in HRG appears to trigger abnormal, deformed white blood cells, neutrophils, which clump together with other cells in the vasculature system and limit blood flow.

In this study, mice given a boost of human HRG during sepsis began to show rapid signs of improvement and increased chances of survival. The neutrophils in the treated mice regained the smooth and spherical shape needed to pass freely through capillaries and the veins, and the blood supply was reinstated.

Future work

Further investigations are needed into the upstream signalling processes involved in HRG action in both healthy and septic patients. Additional studies will also determine if doses of HRG could prove a valuable method of treatment for patients with sepsis.

Novel Understanding of Sepsis Physiology and a Proposal of New Treatment Strategy

Decrease in plasma HRG in septic condition triggers the deformation of circulating neutrophils and the tight attachment of these cells to vascular endothelial cells, associated with ROS production and endothelial cell injury . This in turn facilitates the NETosis, platelet aggregation and coagulation, leading to immunothrombosis and multiple organ failure.

Reference

Hidenori Wake, Shuji Mori, Keyue Liu, Yuta Morioka, Kiyoshi Teshigawara, Masakiyo Sakaguchi, Kosuke Kuroda, Yuan Gao, Hideo Takahashi, Aiji Ohtsuka, Tadashi Yoshino, Hiroshi Morimatsu, Masahiro Nishibori. Histidine-Rich Glycoprotein Prevents Septic Lethality through Regulation of Immunothrombosis and Inflammation. EBioMedicine, 2016 Jun 04. DOI : http://dx.doi.org/10.1016/j.ebiom.2016.06.003: the article can be read here.

Correspondence to: Professor Masahiro Nishibori, M.D., Ph.D. Department of Pharmacology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences,
Okayama University, Shikata-cho 2-5-1, Okayama city, Okayama 700-8558, Japan. E-mail: mbori@md.okayama-u.ac.jp

About Okayama University: Okayama University is one of the largest comprehensive universities in Japan with roots going back to the Medical Training Place sponsored by the Lord of Okayama and established in 1870. Now with 1,300 faculty and 14,000 students, the University offers courses in specialties ranging from medicine and pharmacy to humanities and physical sciences. Okayama University is located in the heart of Japan approximately 3 hours west of Tokyo by Shinkansen.
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Seeking to Understand Cellulose Biosynthesis Dundee and Manchester Team Discover New Insights

Image: University of Dundee

|| July 16: 2016: University of Dundee || A collaboration between the Universities of Dundee and Manchester has made new discoveries about one of the most abundant biological substances on the planet. Dr Piers Hemsley, a Principal Investigator within Dundee’s Division of Plant Sciences and the James Hutton Institute, and Manchester’s Professor Simon Turner have been studying cellulose, the major structural component in plants.

Cellulose essentially provides the plant with its skeleton. It is also one of the most widely used natural resources, best known in the form of wood, cotton and paper, but is increasingly important as a renewable raw material for industrial applications. Dr Hemsley and Professor Turner identified an important new process in cellulose synthesis called S-acylation. S-acylation involves adding fatty acids to proteins to change the proteins function. They found that when the proteins that create cellulose, known as the cellulose synthase complex, were not S-acylated, plants were no longer able to make cellulose. This makes S-acylation an extremely important part of the cellulose synthesis process.

Dr Hemsley said, “This work will help us to understand how the cellulose synthase complex works, how plants form cellulose and how they lay it down in the patterns that provide strength and structure to the plant.

“Plant cell walls have evolved to resist attack from microbes and insects, but this also means that the cellulose in plant cell walls is hard to break down and free up the sugars needed for fermentation into biofuels or use as industrial precursors.

“This work will help us to manipulate cellulose synthesis so that the cellulose structure is altered and therefore more open to processing. This will hopefully allow us to break down cellulose in cheaper, cleaner and more efficient ways.”

Professor Turner added, “Manipulating and understanding cellulose biosynthesis to provide renewable energy sources and industrial starting products while maintaining food yields is an important goal of plant science research.

“Our work highlights a critical aspect of cellulose synthesis that needs to be considered in fundamental research strategies that could help address food and energy issues in the future.”

The results of this work are published in the latest edition of Science and was supported by the BBSRC.
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The Intrinsically Disordered Protein Atg13 Mediates Supramolecular Assembly of Autophagy Initiation Complexes

Image: IMC
 

|| July 12: 2016 || ά. Tokyo Institute of Technology: Institute of Microbial Chemistry: || ά. Researchers revealed that Atg13 links autophagy initiation factors to each other using a string-like conformation, thereby promoting the association of diverse elements of the autophagy initiation machinery, initiating autophagosome formation through the recruitment of Atg9 vesicles and phosphorylation of various Atg factors.

In order to live, it is necessary for creatures not only to synthesize essential components, but also degrade harmful or superfluous components. Autophagy, an intracellular degradation system conserved among eukaryotes from yeast to humans, contributes to cell homeostasis via isolating and degrading various unnecessary components within cells. Since autophagy dysfunction is linked to severe diseases such as neurodegeneration and cancer, the artificial control of autophagy promises to facilitate the development of therapeutic and preventive treatment for these and severe diseases.

In budding yeast, the Atg1 complex, comprising Atg1, Atg13, Atg17, Atg29 and Atg31, mediates the initiation of autophagy (Figure 1). When autophagy is induced by starvation, these five components gather to form a dimer of pentamers. However, the formation of the dimer of pentamers alone is not sufficient for autophagy induction: and dozens of the pentameric complexes need to assemble into a supramolecular complex of autophagy initiation machinery elements. However, the molecular mechanism has been elusive.

Figure 1. Formation model of the autophagy initiation machinery.


Under nutrient-rich conditions, Atg1, Atg13 and the Atg17-29-31 subcomplex exist without interacting with each other. Upon autophagy induction, under conditions such as starvation, they form a pentameric complex, which further assembles into a giant autophagy initiation machinery complex.

This mystery has been addressed by Honorary Prof. Yoshinori Ohsumi and Dr. Hayashi Yamamoto at Tokyo Institute of Technology, and Drs. Nobuo N. Noda and Yuko Fujioka at Institute of Microbial Chemistry, in collaboration with other researchers. The researchers focused on Atg13 and analysed its structure and function in vitro. The data revealed that Atg13 has an intrinsically disordered, string-like conformation in solution and that Atg13 has two distinct binding regions for Atg17. Detailed analyses of the interaction between Atg13 and Atg17 by X-ray crystallography uncovered that Atg13 links two Atg17 molecules to each other using two binding regions. Analysis of the size of the Atg1 complex revealed that Atg1 complexes are linked to each other by Atg13, which results in the formation of a huge autophagy initiation complex (Figure 2). When point mutations that impair the formation of this giant complex were introduced to Atg13, association of the autophagy initiation machinery was completely blocked. These data suggest that the supramolecular complex resulting from the linkage of Atg1 complexes to each other by Atg13 functions as the autophagy initiation machinery in vivo.

Figure 2. Molecular mechanism of supramolecular autophagy initiaition complex assembly mediated by Atg13.


Atg13 (red) links Atg1 (blue) and two Atg17s (green) to each other, thereby promoting formation of the giant complex.

Next, the function of the autophagy initiation machinery was studied in budding yeast, revealing that the phosphorylation activity of Atg1 is markedly enhanced when it is incorporated into the supramolecular autophagy initiation complex. Moreover, it was revealed that the autophagy initiation machinery efficiently recruits Atg9 vesicles, which serve as membrane sources for autophagosome formation, and phosphorylates Atg9. These data collectively suggest that the autophagy initiation machinery not only mediates formation of initial isolation membrane through the recruitment of Atg9 vesicles, but also promotes autophagosome formation via phosphorylating autophagy-related factors including Atg9 (Figure 3).

Figure 3. Initiation model of autophagosome formation.


The autophagy initiation machinery mediates formation of the initial isolation membrane through the recruitment of Atg9 vesicles. At the same time, the machinery phosphorylates Atg factors including Atg9, thereby promoting autophagosome formation.

These results are an important step in our quest to fully understand the molecular mechanisms of autophagy initiation and autophagosome formation. A better understanding of the mechanisms of the initial steps of autophagy promises to allow the development of compounds that specifically regulate autophagy, with exciting clinical implications for the treatment of a range of serious conditions.

Reference
Yamamoto, H.1, Fujioka, Y.2, Suzuki, S. W.1, Noshiro, D.3, Suzuki, H.2, Kondo-Kakuta, C.1, Kimura, Y.4, Hirano, H.4, Ando, T.3, Noda, N. N.2,*, Ohsumi, Y.1,*, The intrinsically disordered protein Atg13 mediates supramolecular assembly of autophagy initiation complexes, Developmental Cell, DOI: 10.1016/j.devcel.2016.06.015


1: Frontier Research Center, Tokyo Institute of Technology, Yokohama 226-8503, Japan.
2: Institute of Microbial Chemistry (BIKAKEN), 3-14-23 Kamiosaki, Shinagawa-ku, Tokyo 141-0021, Japan.
3: Department of Physics, College of Science and Engineering, Kanazawa University, Kanazawa 920-1192, Japan.
4: Advanced Medical Research Center, Yokohama City University, Yokohama 236-0004, Japan.

About Tokyo Institute of Technology: Tokyo Institute of Technology stands at the forefront of research and higher education as the leading university for science and technology in Japan. Tokyo Tech researchers excel in a variety of fields, such as material science, biology, computer science and physics. Founded in 1881, Tokyo Tech has grown to host 10,000 undergraduate and graduate students who become principled leaders of their fields and some of the most sought-after scientists and engineers at top companies. Embodying the Japanese philosophy of “monotsukuri,” meaning technical ingenuity and innovation, the Tokyo Tech community strives to make significant contributions to society through high-impact research.

About Institute of Microbial Chemistry: Institute of Microbial Chemistry was established in 1962 by the Microbial Chemistry Research Foundation under the directorship of Dr. Umezawa. The institute has since given rise to numerous beneficial pharmaceuticals, including kasugamycin, which is highly effective against rice blast, and bleomycin, which was the world’s first target-specific anticancer agent. Furthermore, the institute pioneered research into the mechanism of resistance in strains resistant to aminoglycoside antibiotics such as streptomycin and kanamycin. This background has led to microbiology, medicine, and organic synthetic chemistry becoming the main pillars of the institute's research activities. ω.

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Building Antimicrobial Viruses from Breast Milk

Image: NPL

|| May 30: 2016 || ά. Scientists from the National Physical Laboratory:NPL and UCL have converted a breast milk protein into an artificial virus that kills bacteria on contact. As well as providing all the energy and nutrients that infants need for the first months of life, breast milk protects against infectious diseases. Lactoferrin is a protein in milk which provides antimicrobial protection to infants, effectively killing bacteria, fungi and even viruses.

The antimicrobial activities of this protein are mainly due to a tiny fragment, less than a nanometre across, made up of six amino acids. Based on the metrology of antimicrobial mechanisms, the team predicted that copies of this fragment gather at the same time, and at the same point, to attack bacterial cells by targeting and disrupting microbial membranes.

Recognising the potential applications in the fight against antimicrobial resistance, the team re-engineered the fragment into a nanoscale building block which self-assembles into virus-like capsules, to effectively target bacteria. Not only can these capsules recognise and bind to bacteria, but they also rapidly convert into membrane-damaging holes at precise landing positions.

Hasan Alkassem, a joint NPL:UCL EngD student who worked on the project, explains: "To monitor the activity of the capsules in real time we developed a high-speed measurement platform using atomic force microscopy. The challenge was not just to see the capsules, but to follow their attack on bacterial membranes. The result was striking: the capsules acted as projectiles porating the membranes with bullet speed and efficiency."

Remarkably, however, these capsules do not affect surrounding human cells. Instead, they infected them like viruses do. When viruses are inside human cells they release their genes, which then use the body's cellular machinery to multiply and produce more viruses. But if viral genes are replaced with drugs or therapeutic genes, viruses become effective tools in the pursuit of gene therapy to cure many diseases, from cancer to cystic fibrosis.

The research team explored this possibility and inserted model genes into the capsules. These genes were designed to switch off, or silence, a target process in human cells. The capsules harmlessly delivered the genes into the cells and effectively promoted the desired silencing. With therapeutic genes, this capability could be used to treat disorders resulting from a single mutated gene. Sickle-cell disease, cystic fibrosis or Duchenne muscular dystrophy are incurable at present, but can be cured by correcting corresponding mutated genes. The capsules therefore can serve as delivery vehicles for cures.

Jason Crain, Director of Research at NPL, said: "Antimicrobial resistance is an increasing public health threat which requires a strong and coordinated response. This work demonstrates the power of combining physics and engineering principles with innovative measurement methods to create new strategies for tackling the problem. It is exactly the sort of high priority problem that the National Physical Laboratory should be active in addressing in collaboration with others."

The findings are reported in Chemical Science - a journal of the Royal Society of Chemistry which publishes findings of exceptional significance from across the chemical sciences - and effectively demonstrate how measurement science can offer innovative solutions to healthcare, which build on and extend natural disease-fighting capabilities.

The study was funded by EPSRC, BBSRC and the Department for Business, Innovation and Skills, with measurements performed at the Diamond Light Source. ω.

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'Double Agent’ Cells Survival Factor Revealed

Electron microscopy image of an eosinophil: Image: University of Dundee

||April 17, 2016|| A group of `double agent’ cells, which both protect us from some infections while also contributing to tissue damage in various inflammatory conditions, could be manipulated to offer a new approach to treating conditions such as asthma, following research led by the University of Dundee.

Researchers led by Professor Colin Watts in the School of Life Sciences at Dundee have uncovered a critical survival factor for a group of white blood cells called eosinophils.

“Eosinophils play a key role in our immune system, protecting us from some infectious agents but, less desirably, also contributing to tissue damage in various inflammatory conditions,” said Professor Watts.

“We have found that a particular protein called cystatin F is critical for the survival for this group of cells. That makes it a potentially attractive target to inhibit the activity of these cells, which may offer a completely new approach to controlling the kinds of inflammatory disease where eosinophils can be harmful.”

These cells contain numerous internal structures called granules, which are filled with toxic proteins. These toxins are the weapons that eosinophils use to kill infectious agents such as, for example, nematode worms which cause diseases such as lymphatic filariasis, also known as elephantiasis.

However, these cells can also accumulate in the absence of infections and the same toxins cause substantial tissue damage. In cases of asthma, for example, it can result in damage to the airways.

“An intriguing puzzle has been how eosinophils safely handle and store these toxins and avoid killing themselves,” said Professor Watts. “The toxins are only dangerous once they have been activated by enzymes called proteases.

“This new work from our laboratory, carried out by Dr Steve Matthews, shows that the cystatin F protein works like a safety valve and keeps a brake on this protease activity. However, when cystatin F is missing, the `brake’ is released, resulting in failure of normal safe toxin packaging and disruption of the granules themselves.”

The end result of this process is that the lifespan of the eosinophil is much shorter and those that do survive have reduced granules and lower toxin levels. Working with Dr Rachel Lawrence at the Royal Veterinary College in London, the researchers have shown that loss of cystatin F results in much reduced ability to combat a nematode worm infection. However, there was an important positive benefit as well in that lung damage in a model of asthma was also significantly reduced.

“There are a large number of conditions where the production of excessive numbers of eosinophils can lead to damaging tissue reactions, asthma being a good example,” said Professor Watts. “Drugs that inhibit eosinophil survival are already being tested clinically so the discovery of a new factor that promotes survival of these cells is potentially important.

“More work is needed, but we think that blocking cystatin F function may offer a completely new approach to controlling this type of inflammatory disease.”

Results of the research have been published in the journal Immunity. The work has been supported by the Wellcome Trust.

The University of Dundee:

The University of Dundee is the top ranked University in the UK for biological sciences, according to the 2014 Research Excellence Framework. Dundee is internationally recognised for the quality of its teaching and research and has a core mission to transform lives across society. More than 17,000 students are enrolled at Dundee, helping make the city Scotland’s most student-friendly. The University is the central hub for a multi-million pound biotechnology sector in the east of Scotland, which now accounts for 16% of the local economy.

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Regulatory Consequences of Neuronal ELAV-like Protein Binding to Coding and Non-coding RNAs in Human Brain

Claudia Scheckel: Elodie Drapeau: Maria A Frias: Christopher Y Park: John Fak: Ilana Zucker-Scharff: Yan Kou: Vahram Haroutunian: Avi Ma'ayan: Joseph D Buxbaum: Robert B Darnell

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

eLife digest

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

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

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

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

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

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

Introduction

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

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

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

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

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

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

Results

Identification of nELAVL targets in human brain

To gain insight into nELAVL-mediated RNA regulation in human brain we performed CLIP on postmortem brain samples of eight human subjects (Supplementary file 1A). Tissue samples were derived from BA9, which is part of the dorsolateral prefrontal cortex (Figure 1A), a brain area that is damaged in later stages of AD and that is important for executive functions such as working memory, cognitive flexibility, planning, inhibition, and abstract reasoning (O'Reilly, 2010). Antibodies that specifically recognize individual ELAVL paralogs and that can be used for CLIP are currently not available. We therefore purified nELAVL-RNP complexes with antiserum reactive to all three nELAVL proteins (Figure 1B). 32P-labeled nELAVL-RNP complexes were not recovered with control serum or in the absence of UV-irradiation (Figure 1B).

nELAVL-crosslinked RNA tags were sequenced and mapped to the hg18 build of the human genome. We searched for and identified nELAVL RNA binding sites (peaks) that were significant (p<0.01) and that were present in at least five out of 8 individuals (n = 75,592). nELAVL binding at these sites correlated between individuals (Figure 1—figure supplement 1), and these peaks were further investigated. We determined the nELAVL binding motif by analyzing the top 500 nELAVL peaks (+/- 25nt) using MEME ChIP (Machanick and Bailey, 2011) and HOMER (Heinz et al., 2010). This revealed that nELAVL binds polyU RNA stretches in human brain, particularly when interrupted by a G (Figure 1C and Figure 1—figure supplement 2). This is in excellent agreement with the nELAVL binding motif identified in mouse brain using CLIP and in vitro binding assays (Ince-Dunn et al., 2012). Because nELAVL has been shown to regulate alternative splicing and mRNA abundance in mouse brain by binding to introns and 3’UTRs, respectively, we analyzed the genomic distribution of the peaks defined here. As previously reported for mouse nElavl (Ince-Dunn et al., 2012), nELAVL binding sites are found in 3’UTRs and introns (Figure 1D), with far higher per-nucleotide density in 3’UTR regions (Figure 1—figure supplement 3). Consistently we observed that nELAVL peaks in 3’UTRs were higher than intronic peaks (Figure 1—figure supplement 4). These results suggest that nELAVL could regulate splicing and mRNA abundance in the human brain.

To relate nELAVL binding to mRNA abundance, we performed RNAseq on the same brain samples used for CLIP analysis (Figure 1—figure supplement 5 and Supplementary file 1A/B). 74,423 nELAVL peaks mapped to 8681 expressed genes (Supplementary file 1C), referred to as nELAVL targets (Supplementary file 1D) hereafter, which are shown in Figure 1E. We observed that nELAVL binding correlated with mRNA abundance (Figure 1E). This was not unexpected, as nELAVL 3’ UTR binding has previously been shown to increase mRNA abundance, due to its role in mRNA stabilization. The correlation between nELAVL binding and mRNA abundance might therefore not only reflect the dependence of nELAVL binding on mRNA abundance, but also a role of nELAVL in mRNA stabilization. Consistently, we observed that intronic nELAVL binding correlated less with mRNA abundance than 3’UTR binding (Figure 1—figure supplement 6).

To identify genes most likely to be impacted by nELAVL, we defined the top 1000 nELAVL targets (colored in green in Figure 1E; Supplementary file 1E). Top targets were identified based on normalized nELAVL binding (binding sites were normalized for mRNA abundance and summarized per gene). Thirty-seven percent of nELAVL peaks (n = 27,581) mapped to these top targets. We constructed a subnetwork that connects top nELAVL target gene products based on a literature-based network of protein-protein interactions (PPI) created from multiple online databases (Chen et al., 2012). Six clusters were identified within the resulting network (Figure 1F) and gene set enrichment analyses were performed for top nELAVL targets found in the different clusters with Enrichr (Chen et al., 2013). Each cluster was examined for enrichment of Biological Processes (BP), Molecular Functions (MF), Cellular Components, and Mammalian Phenotypes (MP) terms (Supplementary file 1F). Enriched terms included RNA processing and transcription regulation, signal transduction, synaptic transmission, synaptic proteins, and abnormal neuron morphology and physiology. Three of the six clusters were particularly important for neuronal function. Cluster 1 is especially enriched in members of the TGFbeta/SMAD signaling pathways and the FOX protein family. Cluster 2 contains many actors of the IGF-I axis, which is important for neuronal development including neurogenesis, myelination, synaptogenesis, dendritic branching and neuroprotection after neuronal damage. Finally, cluster 3 is almost exclusively formed of synaptic proteins including many postsynaptic scaffolding proteins, members of the neuroligin/neurexin families and glutamatergic receptors or voltage-gated channels. Taken together, these data demonstrate that nELAVL associates with transcripts encoding proteins involved in key aspects of neuronal physiology.

nELAVL-mediated regulation is conserved

To investigate if nELAVL-mediated RNA regulation is conserved, we compared our dataset with previously published nELAVL targets in mice (Ince-Dunn et al., 2012; Bolognani et al., 2010). At the transcript level, we found that more than 90% of mouse nELAVL targets were among human nELAVL targets (Figure 2A). However, only 20% of human targets were bound by nELAVL in mouse brain, which is at least partly due to the 10-fold increased depth of the human dataset (more than 10 million human CLIP tags compare to less than a million mouse CLIP tags). Yet these differences could also reflect an increased functional complexity of nELAVL regulation in the human brain, and/or the fact that mouse targets were identified at different developmental stages.

We additionally investigated the overlap of nELAVL binding at individual binding sites. Surprisingly, only a small percentage of binding sites overlapped between mouse and human; 3% of human binding sites showed nELAVL binding in mouse, and 17% of mouse binding sites were bound by nELAVL in human brain (Figure 2B). The vast majority of these overlapping binding sites were in 3’UTRs (88%). These results indicate that many nELAVL targets are shared between mouse and human and that nELAVL binding at the transcript level is conserved, whereas individual binding sites have diverged drastically, especially within introns. These results reflect analogous observations of evolutionary conservation of transcriptional regulation at the gene rather than the positional level (Stergachis et al., 2014).

We further observed that nELAVL binding on entire transcripts correlated between mouse and human (Figure 2—figure supplement 1), which prompted us to overlay our human CLIP dataset with previously published transcriptome profiling of Elavl3/4 double KO mice (Ince-Dunn et al., 2012). 119 transcripts showed significant changes in their steady-state level in Elavl3/4 KO mice, 91 of which were expressed in human brain. 37 of these 91 transcripts were nELAVL 3’UTR targets in human brain (Supplementary file 2A), and the majority of them decreased in the absence of ELAVL3/4 (n = 26), including transcripts important for neuronal transport and excitation such as RAB6B, HCN3, and KCNMB2 (Figure 2C/D). This indicates that nELAVL 3’UTR binding is likely to be important for increasing the abundance of these transcripts in human brain, and likely has conserved functions across species.

Elavl3/4 KO mice also reported splicing defects and 59 alternative exons showed a significant change in their inclusion rate (delta inclusion rate, ΔI) between wildtype and Elavl3/4 KO mice (Ince-Dunn et al., 2012). 54 of the misregulated exons were conserved in the human genome, and 25 of them were adjacent to intronic nELAVL binding sites in human brain (Supplementary file 2B). We observed both increased and decreased inclusion of alternative exons – independently of the position of the peak relative to the exon, which has previously been observed for nELAVL mediated splicing regulation (Ince-Dunn et al., 2012). Three alternative exons are shown in Figure 2E,F, and whereas nELAVL seems to prevent splicing of DST by binding upstream and downstream of an alternative exon, nELAVL might promote the inclusion of alternative exons of NRXN1 and CELF2 by binding to downstream sequences. Given that nELAVL regulates the splicing of these 25 exons in mice and that we observe intronic nELAVL binding sites in human brain adjacent to these exons, we propose that nELAVL regulates the inclusion of these exons in human brain. Collectively, these analyses show that many confirmed functional nELAVL interactions in mouse brain show evidence for nELAVL binding in human brain.

nELAVL proteins regulate mRNA abundance of human brain targets

To further validate potential nELAVL targets, we analyzed the effect of nELAVL depletion on mRNA abundance and splicing in human neuroblastoma IMR-32 cells. We subjected IMR-32 cells to mock or ELAVL2/3/4 triple RNAi, achieving 70% knockdown of all three neuronal ELAVL proteins (Figure 3—figure supplement 1). These cells were then analyzed by RNAseq (Figure 3A, Supplementary file 1A/2C). The steady-state level of 784 transcripts was significantly changed in nELAVL RNAi treated cells (Figure 3A), with ~45% showing a decrease in mRNA abundance. Among those genes were all of the neuronal ELAVL paralogs (ELAVL2/3/4), while the ubiquitously expressed paralog ELAVL1 was not affected by nELAVL RNAi depletion.

 

We then compared this RNAseq dataset with the nELAVL CLIP analysis in human brain. 96% of the IMR-32 expressed transcripts were expressed in human brain (12,242 out of 12,743), and were used for subsequent analyses. Since nELAVL binding to 3’UTRs can mediate mRNA stabilization, we investigated the change in mRNA abundance of 3’UTR targets upon nELAVL RNAi, specifically examining 3’UTR targets that did not display any intronic binding. These transcripts were less abundant in nELAVL RNAi conditions (Figure 3B, left panel). In contrast, the mRNA abundance of intron targets (intron binding but no 3’UTR binding) slightly increased upon nELAVL RNAi (Figure 3B, right panel). This suggests that specifically nELAVL binding to 3’UTRs increases mRNA abundance. We further observed that nELAVL depletion affected top nELAVL 3’UTR targets as well as nELAVL 3’UTR targets in general (Figure 3C).

Out of 784 genes that changed significantly upon nELAVL RNAi, 743 genes were expressed in human brain, 327 of which decreased while 416 increased. We investigated which of these transcripts were direct targets of nELAVL based on nELAVL 3’UTR binding (Supplementary file 2D). Significantly changing transcripts that are top nELAVL 3’UTR targets are boxed in blue in Figure 3D. We observed that 68% of downregulated transcripts were 3’UTR targets (n = 226; p = 9.6e-13; hypergeometric test), and that 16% of downregulated transcripts were even among top 3’UTR targets (n = 51; p = 1.3e-6; hypergeometric test). In contrast, only 7% of upregulated transcripts were among top 3’UTR targets (p = 0.76; hypergeometric test), further supporting a role of nELAVL 3’UTR binding in positively regulating mRNA abundance. Several 3’UTR targets showed an mRNA abundance change in both mouse and IMR-32 datasets (n = 8), and in all but one case this change correlated positively between the datasets, providing support for the accuracy of target validation applied here. Because the abundance of multiple disease associated genes, including APPBP2, ATXN3, and SHANK2 (Figure 3E,F), is regulated by nELAVL, we propose that nELAVL mediated regulation of mRNA abundance plays an important role in the human brain.

nELAVL regulates splicing of human brain targets

To validate a role of nELAVL in the splicing regulation of human brain targets, we analyzed the inclusion rate of cassette exons in mock and nELAVL RNAi treated IMR-32 cells. We compared the change in exon inclusion of 7903 expressed cassette exons (Supplementary file 2E) and observed that 473 cassette exons were differentially spliced upon nELAVL depletion (FDR<0.05 and ΔI > 0.1; Supplementary file 2F). Many differentially spliced exons were adjacent (+/- 2.5 kb) to at least one intronic nELAVL binding site in human brain (n = 155; p = 1.3e-7; hypergeometric test; Figure 4A, Supplementary file 2F), indicating that these exons might be directly regulated by nELAVL. For example, downstream binding in BIN1 and PICALM was associated with lower exon inclusion upon nELAVL depletion, and binding in APP was associated with higher inclusion of both upstream and downstream exons upon nELAVL depletion (Figure 4B/C). Overall, three exons that were differentially spliced upon nELAVL RNAi depletion also changed in Elavl3/4 KO mice, and the splicing changes in both datasets changed in the same direction. We generated a map from intronic nELAVL binding sites that flanked the 155 nELAVL regulated exons as previously described (Licatalosi et al., 2008), revealing that upstream nELAVL binding can promote both exon inclusion and skipping (Figure 4D). In conclusion, these data indicate that intronic nELAVL binding regulates alternative splicing of numerous transcripts in human brain, including transcripts associated with central nervous system disorders.

RNA regulation changes in AD

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

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

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

Non-coding Y RNAs are bound by nELAVL in AD

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

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

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

Y RNPs are remodeled during UV stress

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

To determine if UV stress induced localization changes of Y RNP components, we investigated the distribution of nELAVL, RO60 as well as Y RNAs upon UV exposure using cell fractionation followed by western blot and qPCR analysis. The induction of a UV stress response was confirmed by measuring CDKN1A mRNA levels (Figure 7—figure supplement 3A). We did not observe a change in nucleocytoplasmic localization of the investigated RNAs or proteins (Figure 7—figure supplement 3B/C), suggesting that the increased nELAVL/Y RNA association upon UV exposure does not result from a difference in the nucleocytoplasmic distribution of nELAVL or Y RNAs. These results are consistent with previous observations that neuronal ELAVL proteins show a higher cytoplasmic localization than the ubiquitous paralog ELAVL1 (Kasashima et al., 1999), and that stress-induced nuclear-cytoplasmic shuttling might be limited to ELAVL1 (Burry and Smith, 2006). Nonetheless, these results do not rule out the possibility that there may be changes of nELAVL proteins within the nuclear or cytoplasmic compartments themselves with respect to Y RNA binding and localization.

We next sought to measure the proportion of nELAVL bound to Y RNAs in stressed and non-stressed conditions. Because Y RNAs are relatively short and have a high degree of similarity, our mapping strategy (reporting only unambiguous mapping events) discarded numerous reads that were assigned to multiple Y RNAs. We therefore re-mapped CLIP tags, allowing multiple alignments, but reporting only the best match, permitting a more accurate estimate of overall Y RNA binding. The fraction of the short CLIP reads mapping to Y RNAs was considerably higher using this strategy, revealing that up to 6% of nELAVL CLIP tags map to Y RNAs in AD and UV stressed cells, compared to less than 0.5% in control brain and ~1% in non-stressed cells (Figure 7—figure supplement 4). The significant increase in nELAVL/Y RNA association and our observation that up to 6% of nELAVL was bound to Y RNAs might in fact lead to a sequestration of nELAVL from its targets.

nELAVL/Y RNA association correlates with loss of nELAVL-mediated splicing

To investigate if the increased nELAVL/Y RNA association was linked to decreased intronic and 3’UTR nELAVL binding, we grouped AD subjects based on their Y RNA association and compared the two different AD groups to control subjects. We found that the majority of changing nELAVL binding sites decreased in AD subjects with high nELAVL/Y RNA, while nELAVL binding in AD subjects with low nELAVL/Y RNA association was mostly increasing (Figure 8A). Because nELAVL binding in UV-stressed cells also predominantly decreased (Figure 8A) and most of the decreased binding sites were in introns (85%, assessed by annotation of peak locations), we speculate that nELAVL/Y RNA association leads to a sequestration of nELAVL specifically from its intron targets, which might induce similar splicing changes as nELAVL depletion by RNAi. Of note is our observation that nELAVL binding decreased at only a subset of intronic binding sites.

DOI
Published February 19, 2016
Cite as eLife 2016;5:e10421

The Article can be read in full in eLife

The Auhors are from: The Rockefeller University, United States; Howard Hughes Medical Institute, The Rockefeller University, United States; Seaver Autism Center for Research and Treatment, United States; Icahn School of Medicine at Mount Sinai, United States; New York Genome Center, United States; James J. Peters VA Medical Center, United States

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ILC3 GM-CSF Production and Mobilisation Orchestrate Acute Intestinal Inflammation

Figure 4. IL-23R marks ILCs that are present in cryptopatches within the gut.

(A) Representative H&E and immunofluorescence staining of cryptopatches in transverse proximal colon sections in B6Rag1-/- mice from the steady state at 2.5x and 20x magnification. (B) Flow cytometry staining of Thy1.2 in the colon LPLs of steady-state Il23rgfp/+ Rag1-/-. (C) Representative image of intact tissue explant of proximal colon from Il23rgfp/+ Rag1-/- mice from the steady state. Left and middle panels show IL-23R (green) and collagen (blue). Right panel shows IL-23R alone (top) and collagen alone (bottom). (D) Quantification of steady-state Il23rgfp/+ Rag1-/- ILCs in clusters within the proximal colon from explant imaging. (Figure 4—figure supplement 1) shows modulation of RORγt and IL-23R after anti-CD40 treatment. Results are representative of 3-4 independent experiments.


In a research published in eLife Claire Pearson, Emily E Thornton, Brent McKenzie, Anna-Lena Schaupp, Nicky Huskens, Thibault Griseri, Nathaniel West, Sim Tung, Benedict P Seddon, Holm H Uhlig, Fiona Powrie writes that Innate lymphoid cells (ILCs) contribute to host defence and tissue repair but can induce immunopathology. Recent work has revealed tissue-specific roles for ILCs; however, the question of how a small population has large effects on immune homeostasis remains unclear. We identify two mechanisms that ILC3s utilise to exert their effects within intestinal tissue. ILC-driven colitis depends on production of granulocyte macrophage-colony stimulating factor (GM-CSF), which recruits and maintains intestinal inflammatory monocytes. ILCs present in the intestine also enter and exit cryptopatches in a highly dynamic process. During colitis, ILC3s mobilize from cryptopatches, a process that can be inhibited by blocking GM-CSF, and mobilization precedes inflammatory foci elsewhere in the tissue. Together these data identify the IL-23R/GM-CSF axis within ILC3 as a key control point in the accumulation of innate effector cells in the intestine and in the spatio-temporal dynamics of ILCs in the intestinal inflammatory response.

eLife digest

Crohn’s disease and ulcerative colitis are diseases in which the body’s own immune system causes inflammation of the large intestine. These autoimmune diseases can be severely debilitating and difficult to treat. However an improved understanding of the factors that contribute to the intestinal inflammation may lead to new and effective treatments.

Immune cells called innate lymphoid cells were discovered recently, and shown quickly to play a role in host defense, tissue repair and inflammation regulation. Several groups of innate lymphoid cells are now known; each group is characterized by the genes that control the cell’s development and the small proteins (called cytokines) that the cells release. One group of innate lymphoid cells, the ILC3s, are generally found in the intestinal tract, albeit in small numbers. Given that innate lymphoid cells are known to manage inflammatory responses, it is possible that ILC3s contribute to intestinal inflammation. However, it remains unclear how such a small population of cells could so dramatically inflame the gut.

Pearson et al. now reveal two mechanisms that these innate lymphoid cells use to amplify the inflammatory response and exacerbate intestinal inflammation. First, in both mice and humans, ILC3s were found to be a key source of a cytokine called GM-CSF, which recruits additional immune cells that further promote intestinal inflammation. Secondly, while ILC3s were traditionally regarded as immobile immune cells, Pearson et al. discovered that these cells can move within the intestinal tissue and mobilize from their starting points within this tissue if they are activated. These two mechanisms could explain how ILC3s can trigger inflammation that occurs throughout the gut.

The experiments suggest that blocking production of the GM-CSF cytokine or altering ILC3 movement or activity may help reduce intestinal inflammation. However, the use of GM-CSF blocking drugs to protect against colitis and similar conditions could be problematic, because GM-CSF also plays an important protective role in the intestines. Nevertheless, clinical trials are underway to investigate the use of anti-GM-CSF drugs to treat other inflammatory conditions (such as rheumatoid arthritis). These studies could offer insight into whether these drugs provide relief to trial participants who suffer from intestinal inflammation as well.

Introduction

Innate lymphoid cells (ILCs) are a recently defined family of evolutionarily ancient cells involved in many facets of host defence. As with conventional Th1, Th2 and Th17 T cells, ILCs can be functionally classified based on the expression of transcription factors and associated signature cytokines (Spits et al., 2013). ILC1 are defined by Th1-like and ILC2 by Th2-like cytokine responses, whereas ILC3 express RORγt, and can secrete IL-17 and/or IL-22 upon activation. The ILC3 family includes the prototypic foetal lymphoid tissue inducer (LTi) cells that play a non-redundant role in lymphoid tissue development (Mebius et al., 1997) via lymphotoxin-dependent interactions with stromal cells. This pathway is required not only for lymph node development but also for organised lymphoid structures in the gut such as dendritic cell (DC) and ILC containing cryptopatches (CP) (Eberl and Sawa, 2010), B cell, DC and ILC containing isolated lymphoid follicles (ILFs) (Tsuji et al., 2008), and small intestinal Peyer’s Patches (PP) that have a very similar structure to lymph nodes (Cornes, 1965Mebius et al., 1997). Postnatally, the LTi population can also make IL-17 and IL-22 (Cupedo et al., 2009) and contribute to host defence against pathogens, particularly in the gut (Sonnenberg et al., 2011).

The multi-functional roles of ILCs in disease development and pathogenesis (Buonocore et al., 2010), as well as host defence (Moro et al., 2010; Neill et al., 2010; Sonnenberg et al., 2011) and repair (Monticelli et al., 2011) have been the focus of much interest. Polyfunctional ILCs have also been described that do not fall into distinct ILC1 or ILC3 phenotypes but express both ILC1 and ILC3 lineage defining transcription factors Tbet and RORγt and secrete multiple cytokines such IL-17A, IL-22 as well as IFNγ (Buonocore et al., 2010).

We previously identified a critical role for a phenotypically distinct population of IL-23R+ RORγt+ CD4- NKp46- ILCs in the development of innate intestinal inflammation in Rag-/- mice following Helicobacter hepaticus infection or αCD40 stimulation (Buonocore et al., 2010). Similar ILC populations were enriched in the colonic mucosa of patients with inflammatory bowel disease (IBD) (Geremia et al., 2011), implicating IL-23-responsive, RORγt expressing ILCs in the pathogenesis of inflammatory gut disease in mice and humans. However, it remains unclear how ILCs, that are numerically sparse in vivo can initiate inflammatory processes that lead to colitis.

Despite advances in understanding of the functions of ILCs, little is known about their location in tissue at different stages of the inflammatory response, and how putative structural and cytokine-mediated functions are co-ordinated. Since its description in 2006 (Uhlig et al., 2006), the induction of colitis by injecting agonistic anti-CD40 antibody has become an important tool to assess ILC-driven acute colitis (Buonocore et al., 2010; Vonarbourg et al., 2010Fuchs et al., 2013; Kim et al., 2013; Song et al., 2015). By contrast with other models, anti-CD40 induced colitis follows discrete phases at well-defined time points following initiation, offering the opportunity to probe the role of leukocytes in the development and amplification of the inflammatory response. Experiments have demonstrated that intestinal inflammation was mediated via Thy1+ ILCs in a rorc dependent manner, making it an ideal system to study how ILCs contribute to pathogenesis (Buonocore et al., 2010). A recent study investigating potential biomarkers for anti-IL-23 therapy described similar changes in the colons of both anti-CD40-treated mice and patients with active Crohn’s disease (Cayatte et al., 2012).

Many recent publications have focused on the specific functions of ILC subsets within effector sites, and the location of ILCs has been proposed to contribute to their ability to affect systemic cytokine levels (Nussbaum et al., 2013). Despite histological and flow cytometry data demonstrating the presence of ILCs within lymphoid structures in the gut (Eberl and Sawa, 2010), it isn’t clear whether they function as sedentary, cytokine producing cells or play a more active role in cell interactions and organization. In vivo microscopy is a tool that provides an opportunity to look at the behaviour of ILCs within the tissue. By combining anti-CD40 stimulation with intra-vital microscopy we are able to reliably track cellular changes at discrete phases of disease and capture cell movement at key timepoints.

Our results show two novel mechanisms through which the small number of ILCs found in vivo orchestrate the intestinal inflammatory response. IL-23-driven GM-CSF production by ILC3s is critical for the development of colitis, and ILCs mobilise from cryptopatches after activation in a GM-CSF-dependent manner. Both of these behaviours likely contribute to the ability of ILCs to coordinate the immune response in the gut. Initiation and perpetuation of disease occur in distinct anatomical compartments, indicating both a temporal and spatial switch of ILC function during inflammatory conditions.

Results

GM-CSF is a critical cytokine mediator in the pathogenesis of innate colitis

Anti-CD40 induced colitis is dependent on a RORγt/IL-23 axis but key downstream cytokines are less well understood (Uhlig et al., 2006; Buonocore et al., 2010). As IL-17 and IL-22 are major downstream effectors of the IL-23 signalling axis (Zheng et al., 2007; McGeachy et al., 2009) we first investigated their role in anti-CD40 colitis. However, blockade of IL-17A failed to modify anti-CD40-induced systemic or intestinal disease (Figure 1A,B), indicating that IL-17A is dispensable for development of acute colitis in this model. Blocking the closely related molecule IL-17F also failed to modify disease (Figure 1—figure supplement 1).

Like IL-17 blockade, treatment with a neutralising anti-IL-22 mAb had no effect on systemic disease or colitis in the proximal colon (Figure 1A&B), indicating that in our facility, IL-22 is also redundant for disease induction in this model. However, blockade of IL-17A and anti-IL-22 reduced intestinal inflammation but not systemic disease indicating redundant roles for IL-17A and IL-22 in the intestinal inflammatory response (Figure 1—figure supplement 1).

As we have described an important role for GM-CSF in IL-23 driven T cell dependent colitis we next investigated the role of GM-CSF in innate colitis models. Strikingly, administration of an anti-GM-CSF blocking mAb reduced both weight loss (Figure 1C) and severity of colitis (Figure 1D). Indeed amelioration of colitis with GM-CSF blockade was similar to that observed with anti-IL-23R mAb treatment. Similar effects of GM-CSF blockade were also observed in bacteria-induced innate colitis following Helicobacter hepaticus infection of 129SvEv Rag2-/- mice (Figure1—figure supplement 2) supporting a pivotal role for this cytokine in both T cell dependent and innate colitis. GM-CSF has been shown to promote CNS inflammation through effects on inflammatory monocytes (Croxford et al., 2015). Consistent with this and in line with a recent report (Song et al., 2015) GM-CSF blockade led to a marked reduction in the number of inflammatory monocytes in the colon of anti-CD40 treated mice (Figure 1E). The number of neutrophils and eosinophils also decreased, but this was not significant. These changes were accompanied by an increase in both the percentage and total number of CD11b- CD103+ dendritic cells (Figure 1E).

ILCs are an important source of GM-CSF in mouse and human

We next investigated GM-CSF expression by flow cytometry to identify the cellular source in intestinal inflammation. Only a small proportion of epithelial, stromal or lineage positive (CD11b, CD11c, Gr-1, B220, CD49b) cells expressed GM-CSF (Figure 2A). The majority of these lineage positive GM-CSF producers were NK cells, and they expressed a lower amount of GM-CSF than ILCs (Figure 2—figure supplement 1A & B). However, using Rag and IL-15 receptor double deficient mice that lack NK cells (Lodolce et al., 1998), we found, as previously reported (Vonarbourg et al., 2010), that NK cells were not required for the development of anti-CD40 mediated colitis (Figure 2—figure supplement 1C–F). By contrast, a significant proportion of ILCs were capable of producing GM-CSF, even in the healthy intestine, and this proportion increased during colitis (Figure 2B). Consistent with an IL-23-dependent ILC3 phenotype, GM-CSF+ ILCs were CCR6-, cKit- and NKR-, and expressed only low amounts of T-bet (Figure 2A).

We next investigated the cytokine milieu that regulates GM-CSF production by ILCs in vivo. To determine whether IL-23 signalling induces GM-CSF, we cultured total colonic lamina propria cells overnight with IL-12 or IL-23 and measured GM-CSF production in the cultures. IL-12 had no effect on either the proportion of GM-CSF-expressing ILCs or the amount of GM-CSF per cell (Figure 2C). By contrast and contrary to a previous study using sorted ILC3s (Mortha et al., 2014), culture with IL-23 increased both the proportion of GM-CSF expressing ILCs as well as GM-CSF production per cell (Figure 2C).

Analysis of Il23a mRNA expression within colonic tissue revealed a peak in expression at day 1 following anti-CD40 treatment that rapidly declined but was followed by increased expression of Csf2, the gene for GM-CSF, at day 3 (Figure 2D). The increase in both Il23a and Csf2 was abrogated in mice treated with a blocking antibody to GM-CSF, indicating that GM-CSF may be required to maintain and amplify expression of Il23a.

To assess whether GM-CSF production may also be relevant in human IBD, we first analyzed GM-CSF production from blood ILCs. PMA and ionomycin stimulated blood ILC3s produced GM-CSF with very little production from other ILC subsets (Figure 3A&B, gating strategy in Figure 3—figure supplement 1). Indeed, a greater proportion of ILCs produced GM-CSF than did T cells from the same healthy donors (Figure 3C). ILCs secreting GM-CSF are further enriched in the colon (Figure 3D). Critically, the proportion of blood ILCs capable of GM-CSF production along with other disease-associated cytokines such as IFNγ and TNFα was greater in IBD patients than in healthy controls (Figure 3E).

Analysis of a publically available dataset of colon tissue from healthy controls and IBD patients showed a significant increase in CSF2 gene expression in the colons of patients with either Crohn’s disease or ulcerative colitis compared with controls (Figure 3F). This was confirmed by gene expression analysis of colon biopsies from the Oxford cohort of IBD patients (Figure 3G). CSF2 expression was also higher in uninflamed biopsies from IBD patients compared with controls. There was significantly greater expression again in lesional areas in active disease, suggesting that GM-CSF expression correlates with, and may be a driver of, inflammation in IBD.

ILCs are present within cryptopatches but mobilize during colitis induction

To date attention has focussed on the capacity of ILCs in tissue to produce immune modulatory cytokines with little emphasis on how ILC positioning within a tissue may impact on functional outcomes. Under non-inflammatory conditions, ILCs are found within lymphoid structures in the colon (Eberl and Sawa, 2010), and in Rag-deficient hosts these structures are limited to cryptopatches as there are no B cells to form ILFs. Under homeostatic conditions ILCs primarily reside within lymphoid aggregate cryptopatch (CP) structures (Figure 4A) and are found only rarely within the non-inflamed lamina propria. Within CP, RORγt+ and IL-7Rα+ ILC3s are present at high density surrounded by CD11c and MHCII expressing cells. Some of these ILCs within CP also express CD4, suggesting that some but not all are classical LTi cells that are known to be involved in lymphoid organogenesis (Figure 4A). In addition to RORγt expression, ILCs are known to express IL-23R (Buonocore et al., 2010). FACS analysis of Rag-deficient mice that express GFP under control of the Il23r demonstrates that these mice can be used to study ILC behaviour in vivo (Figure 4B). Two-photon imaging of Il23rgfp/+ Rag-/- mice shows ILCs (green) present within a cryptopatch (Figure 4C, and Video 1). Quantification of ILC localization in 3D indicates that greater than 90% of ILCs are present within cryptopatches under steady-state conditions (Figure 4D). This suggests that IL-23R+ ILCs are perfectly positioned in close proximity to a high density of CD11c+ DCs that can be activated by colitogenic stimuli. Indeed Il23r mRNA increased in the colon 6 hr following anti-CD40 injection indicating that the first ILC changes occur in the hours following CD40 stimulation (Figure 4—figure supplement 1A).

We further examined the changes in IL-23R expression following anti-CD40 stimulation, and found a decrease in IL-23R+ ILCs at day 3 (Figure 4—figure supplement 1B&C), which may indicate a change in ILC phenotype. In support of this hypothesis, analysis of RORγt protein revealed a transient decrease in the proportion of ILCs expressing it 3 days following anti-CD40 stimulation (Figure 4—figure supplement 1D&E), similar to a previous report (Vonarbourg et al., 2010). Whilst these data suggested that ILCs are reacting to changes in the environment, it remained unclear how this leads to the downstream inflammatory cascade.

It is possible that ILCs that are resident within lymphoid structures produce cytokines locally to carry out their effects. Alternatively, ILCs may be able to mobilize into the tissue to coordinate the immune response. To understand which mechanism ILCs utilize, we adopted the strategy of McDole et al (McDole et al., 2012) used to study leukocytes in the gut. In the steady-state, ILCs were much more motile than expected. While little motility could be observed within the cryptopatch, ILCs can be observed to enter and exit the cluster near the basal collagen layer (Figure 5A and Videos 2 and 3). Interestingly, the cryptopatch did not have an obvious point of entry or exit, implying that trafficking into and out of the structure may be governed by mechanisms different to that of the lymph node. By gating on motile cells outside the cluster, we could see that the average velocity of motile ILCs (approximately 9 μm/min) is grossly similar to that of other lymphocytes and while cell velocity decreases after treatment with anti-CD40 (Figure 5B), this difference does not appear to affect overall cell trafficking. To determine whether ILC trafficking changed in colitis, we plotted the displacements of ILCs in steady-state and after anti-CD40 treatment (Figure 5C, Video 4, and Video 5). While no gross changes appeared in track displacements, the overall movement of cells shifted. In steady-state, roughly equal numbers of cells move toward and away from the cluster; however, just 4–6 hr after anti-CD40 treatment, a greater proportion of cells exit the tissue and a smaller proportion enter (Figure 5D). The ratio of entering to exiting cells indicates a significant skewing toward egress from cryptopatches (Figure 5E). To assess whether GM-CSF may be playing a role in ILC mobilization, we treated mice with anti-GM-CSF 24 hr before imaging. The shift toward exit that is normally observed after anti-CD40 treatment was strikingly reversed when mice were treated with a GM-CSF blocking antibody 24 hr before imaging (Figure 5D and E, Video 6). It is possible that many of these cells move through blood vessels, but by labelling the blood vessels with texas-red dextran, ILCs appeared to be adjacent to blood vessels, not within them (Figure 5F). This indicates that ILCs may be mobilizing into the adjacent tissue from cryptopatches.

The Article can be read in full in eLife DOI
 

Authors are from University of Oxford, United Kingdom; John Radcliffe Hospital, University of Oxford, United Kingdom; CSL Ltd., Australia; National Institute for Medical Research, United Kingdom

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Blue Light-induced LOV Domain Dimerization Enhances the Affinity of Aureochrome 1a for Its Target DNA Sequence



Udo Heintz and Ilme Schlichting of Max Planck Institute for Medical Research, Germany, write in a newly published paper in eLife that the design of synthetic optogenetic tools that allow precise spatiotemporal control of biological processes previously inaccessible to optogenetic control has developed rapidly over the last years. Rational design of such tools requires detailed knowledge of allosteric light signaling in natural photoreceptors. To understand allosteric communication between sensor and effector domains, characterization of all relevant signaling states is required. Here, we describe the mechanism of light-dependent DNA binding of the light-oxygen-voltage (LOV) transcription factor Aureochrome 1a from Phaeodactylum tricornutum (PtAu1a) and present crystal structures of a dark state LOV monomer and a fully light-adapted LOV dimer. In combination with hydrogen/deuterium-exchange, solution scattering data and DNA-binding experiments, our studies reveal a light-sensitive interaction between the LOV and basic region leucine zipper DNA-binding domain that together with LOV dimerization results in modulation of the DNA affinity of PtAu1a. We discuss the implications of these results for the design of synthetic LOV-based photosensors with application in optogenetics.

eLife digest

The ability to react to sunlight is important for the survival of a wide range of lifeforms. Many organisms, including humans, plants, bacteria and algae, sense light using specialized proteins called photoreceptors. These proteins are able to translate the information transported by light into various biological activities.

The structure of a photoreceptor can be broken down into different parts, each with a specialized role. For example, the light-sensing region of a photoreceptor typically binds to small molecules called chromophores that are able to absorb light. This light absorption causes changes in the photoreceptor that are ultimately transmitted to a part of the protein that can bind to DNA or perform some other type of biological activity. This activity triggers further processes that build up to the organism’s reaction to the incoming light.

Aureochromes are photoreceptors that detect blue light and are found in algae. The light-sensing and DNA-binding parts of aureochromes are arranged in a different way to the arrangement seen in most related photoreceptors. This raises questions about how the light signal is transmitted to the DNA-binding part of the protein and how this affects the DNA binding of aureochromes.

By using a combination of biophysical and structural methods, Heintz and Schlichting now provide detailed information about the structural changes that blue light causes in the Aureochrome 1a photoreceptor found in the algae Phaeodactylum tricornutum. This shows that when exposed to light, the light-sensing part of the photoreceptor, called LOV domain, detaches from the DNA binding part and binds to the LOV region of a second molecule. This helps the protein to bind to DNA.

Recently, synthetic photoreceptors have been engineered that use the light-sensing part of aureochromes. Therefore, as well as contributing to the fundamental understanding of light signaling in photoreceptors, Heintz and Schlichting’s findings can be used to help develop light-controllable artificial proteins for use in research, medicine or industry.

Introduction

Light-sensing is essential for the survival of organisms from all kingdoms of life and plays an important role in their adaptation to different habitats. Prokaryotes, higher plants, fungi, animals and algae use light-sensing systems that encompass a variety of sensory photoreceptors that respond to different wavelengths of light. Recently, a new type of blue light photoreceptor termed Aureochrome (Aureo) was discovered in the photosynthetic stramenopile alga Vaucheria frigida that has been suggested to function as blue light-regulated transcription factor. Originally, two Aureo homologs, named Aureo 1 and 2, were identified, but only Aureo1 was shown to bind DNA in a light-dependent manner (Takahashi et al., 2007). Since the discovery of the first Aureos, several orthologs from other stramenopile algae such as Ochromonas danica, Fucus distichus, Saccharina japonica and Phaeodactylum tricornutum have been identified (Ishikawa et al., 2009; Deng et al., 2014; Schellenberger Costa et al., 2013). The diatom P. tricornutum has four genes encoding aureochromes: three orthologs of type 1 (PtAu1a, b and c) and one of type 2 (Depauw et al., 2012). Only PtAu1a has been functionally characterized so far and is shown to be involved in light-dependent mitosis regulation (Huysman et al., 2013) and repress high-light acclimation (Schellenberger Costa et al., 2013).

Aureos typically consist of an N-terminal domain with unknown function, a basic region leucine zipper (bZIP) DNA-binding domain, and a C-terminal light-oxygen-voltage (LOV) sensing domain. LOV domains are a subgroup of the Per-Arnt-Sim (PAS) superfamily that sense blue light using a noncovalently bound flavin cofactor (Zoltowski and Gardner, 2011; Herrou and Crosson, 2011; Losi and Gartner, 2012; Conrad et al., 2014). Photon absorption of the flavin results in formation of a flavin-C4(a)-cysteinyl adduct with a conserved cysteine residue (Salomon et al., 2000), which initiates a cascade of structural rearrangements within the LOV core that are propagated to the domain boundaries. LOV domains can be found as isolated entities, but are often part of multidomain proteins where they are coupled to a variety of different effector domains. The effector-sensor topology observed in Aureos differs from the domain topology found in most other LOV photoreceptors where the sensory LOV domain is located N-terminally to the effector domain. This rare domain topology raises the question of how light signaling is achieved in Aureos compared with other LOV proteins.

Recent biochemical and spectroscopic experiments on V. frigida Aureo1 (VfAu1) and PtAu1a led to the hypothesis that DNA binding of Aureos might be influenced by light-induced LOV domain dimerization and that structural changes of the N– (A´α) and C-terminal (Jα) helices flanking the LOV core play a key role in this process (Toyooka et al., 2011; Herman et al., 2013; Mitra et al., 2012; Herman and Kottke, 2015). This hypothesis was supported to some extent by the crystal structure of the VfAu1 LOV domain that showed an unexpected dimeric arrangement (Mitra et al., 2012). However, this structure was determined from crystals grown in the dark and it remains unclear whether the observed dimeric LOV arrangement represents the biologically relevant light state dimer. To obtain insights into structural rearrangements within VfAu1 LOV upon light activation, dark state crystals were illuminated to study light-induced conformational changes (Mitra et al., 2012). However, crystal lattice constraints can prevent large conformational changes that limit this approach. Therefore, the mechanism of light-induced LOV dimerization and its consequences on DNA binding in Aureos remain unclear.

Here, we present crystal structures of a fully light-adapted LOV dimer as well as of a dark state LOV monomer of PtAu1a. We combine these results with hydrogen/deuterium-exchange coupled to mass spectrometry (HDX-MS) and small-angle X-ray scattering (SAXS) experiments of full-length PtAu1a and of a truncated construct that lacks the N-terminal domain, respectively. Together with functional studies, this integrative structural approach enabled us to establish a model for light signaling in Aureos where, in the dark, the LOV domain directly interacts with the bZIP domain and thereby impedes its DNA binding function. Illumination with blue light triggers intramolecular bZIP–LOV dissociation and subsequent LOV dimerization, thus enhancing the affinity of PtAu1a for its target DNA sequence. Together, these results provide insight into the molecular mechanism of Aureo function and implicate a new model of light-dependent gene regulation by Aureos in stramenopiles. In addition, they offer new design strategies for synthetic Aureo–LOV based photosensors for applications in optogenetics.

Results

Dark state recovery of PtAu1afull, PtAu1abZIP-LOV and PtAu1aLOV

To understand how blue light-sensing of the LOV domain influences PtAu1a DNA binding, we used the full-length protein (PtAu1afull) and additionally generated N- and C-terminally truncated PtAu1a variants encompassing the bZIP and LOV domain (PtAu1abZIP-LOV), the bZIP domain (PtAu1abZIP) as well as the LOV domain (PtAu1aLOV) containing its N- and C-terminal α-helical extensions A´α (in the context of PAS domains often referred to as N-cap) and Jα, respectively (Figure 1a). UV-vis spectra of dark adapted LOV domain containing PtAu1a variants show the typical signature of an oxidized flavin mononucleotide (FMN) chromophore with a main absorption maximum at 448 nm and several subsidiary peaks (Figure 1b). Upon light activation, the intensities of these absorption bands decrease and a new maximum appears at 390 nm indicating FMN-cysteine adduct formation. Investigation of the dark state recovery kinetics of PtAu1afull, PtAu1abZIP-LOV and PtAu1aLOV yielded traces that were analyzed by fitting an exponential function (Figure 1c). For PtAu1afull, PtAu1abZIP-LOV and PtAu1aLOV, time constants of 826 ± 19 s, 811 ± 4 s and 1500 ± 7 s were determined, respectively, indicating that the presence of the bZIP DNA binding domain accelerates the recovery kinetics of the LOV domain about 1.8-fold.

Blue light illumination induces dimerization of PtAu1aLOV, but not of PtAu1afull and PtAu1abZIP-LOV

To investigate the effect of blue light illumination on the oligomerization of PtAu1afull, PtAu1abZIP-LOV, PtAu1aLOV we performed size-exclusion chromatography coupled to multi-angle light scattering (MALS) in the dark and under continuous blue light illumination. Quantification of the average molar mass of PtAu1afull (Figure 2a) and PtAu1abZIP-LOV (Figure 2b) in the dark and light state yielded similar values of 55.1, 53.3, 31.7 and 33.7 kDa, respectively, which are between the theoretically expected molar masses of dimers (83.6 kDa for PtAu1afull and 52.6 for PtAu1abZIP-LOV) and monomers (41.8 kDa for PtAu1afull and 26.3 kDa for PtAu1abZIP-LOV). Peak tailing and a continuous decrease of the molar mass signal in the dark and light state of PtAu1afull and PtAu1abZIP-LOV suggested an equilibrium between dimers and monomers irrespective of the light conditions. Illumination-induced small differences in the elution volumes, indicating conformational changes of both protein variants have occurred as also reported for VfAu1 (Toyooka et al., 2011; Hisatomi et al., 2013). In contrast to PtAu1afull and PtAu1abZIP-LOV, light activation of PtAu1aLOV shifts the oligomerization state from monomers to dimers (Figure 2c). The change in oligomerization state is reflected by a decrease of the elution volume from 16.2 ml (dark) to 15.2 ml (light) and an increase of the calculated average molar mass from 20 kDa (dark) to 28 kDa (light). Quantification of the monomer–dimer transition using microscale thermophoresis (MST) revealed a Kd of 13.6 ± 1.4 µM for the dark adapted protein (Figure 2d) and pre-illumination of PtAu1aLOV increased the dimerization ability as expected from the MALS measurements (data not shown). However, it was not possible to determine a reliable Kd value for the monomer–dimer transition of light-activated PtAu1aLOV, as continuous illumination of the protein is not possible during the measurements. Together, our results are in line with previous reports on the oligomerization states of the LOV domain of PtAu1a (Herman et al., 2013; Herman and Kottke, 2015) as well as truncated and full-length variants of VfAu1 in their dark and light states (Toyooka et al., 2011; Hisatomi et al., 2013). It was recently reported that VfAu1 dimerizes in a light-dependent manner and that the redox potential influences the oligomerization state by formation of disulfide bonds between the bZIP domains and the bZIP–LOV linker regions (Hisatomi et al., 2014). We did not observe such light-dependent oligomerization in our experiments and can also rule out an influence of the redox potential on the oligomerization state of PtAu1a, as PtAu1a does not possess cysteine residues outside the LOV domain.

Blue light illumination enhances the affinity of PtAu1a for its target DNA sequence

To characterize the effect of blue light on the DNA binding properties of PtAu1afull we performed electrophoretic mobility shift assays (EMSAs) in the dark as well as under continuous blue light illumination and measured the binding of PtAu1a to a 24-base pair (bp) DNA fragment of the diatom-specific cyclin 2 (dsCYC2, GenBank XM_002179247) promoter sequence of P. tricornutum containing the TGACGT binding motif reported for VfAu1 (Takahashi et al., 2007) (Figure 3a,b). Semi-quantitative data evaluation using the Hill equation revealed an effective concentration for 50% response (EC50) of 860 nM in the dark and a Hill coefficient of 1.35 (Figure 3—figure supplement 1). Illumination with blue light results in a decrease of the EC50 to 90 nM (Hill coefficient of 1.65), revealing a 9.6-fold higher affinity of PtAu1afull to DNA in its light compared with its dark state. To verify sequence-specific DNA binding of PtAu1afull, we performed the same experiments with a 24-bp DNA fragment lacking the PtAu1a target sequence (Figure 3—figure supplement 2). In the dark as well as light experiments, PtAu1afull displayed no or only weak binding to the DNA probe lacking the target sequence, confirming sequence-specific DNA binding of PtAu1afull. The presence of MgCl2 in the EMSA experiments is essential for PtAu1afull sequence specificity, as also described for other bZIP transcription factors (Moll, 2002). In the absence of MgCl2, sequence specificity and light-dependence of DNA binding are negligible (Figure 3—figure supplement 3).

PtAu1aLOV light and dark state structures

To investigate the underlying molecular mechanism for light-regulated gene transcription by PtAu1a and the effect of LOV dimerization, we solved the crystal structure of PtAu1aLOV in its dark and light state (Figure 4a and b and Table 1). The dark state structure revealed a LOV monomer that adopts a typical PAS fold consisting of a five-stranded antiparallel β-sheet flanked by several helices. The LOV core forms the chromophore binding pocket and closely resembles the structure of VfAu1 LOV (Mitra et al., 2012) (root-mean-square deviation (r.m.s.d) between 0.42 and 0.58 Å for 101 Cα atoms and molecules A and B PtAu1a and A,B,C,D,E,F for VfAu1a). The LOV core is flanked at the N- and C-termini by prominent α-helical extensions denoted A´α and Jα, respectively. As observed for VfAu1 LOV (Mitra et al., 2012), the C-terminal Jα helix partially folds back onto the surface of the β-sheet and interacts with the LOV core via hydrogen bonds between the conserved residue Gln365 and the carbonyl and amine group of Cys316 as well as the side chains of Tyr357 and Gln330. A´α forms an amphipathic three-turn helix and interacts with the LOV core through a highly conserved 4 amino acid linker with Ala248, Glu249, Glu250 and Gln251 in the hinge region. In addition to Jα, A´α also folds back across the surface of the β-sheet and covers a large hydrophobic patch (Figure 4c). The chromophore binding pocket is mainly formed by hydrophobic residues that stabilize the FMN chromophore together with Gln350, Gln291, Asn319, Asn329, which form hydrogen bonds with the heteroatoms of the isoalloxazine ring. FMN is additionally stabilized by Arg304 and Arg288, which interact with the phosphate group of the ribityl chain. The conserved photoreactive Cys287, which forms a covalent bond with FMN upon illumination, is located in the Eα helix on the opposite site of the core β-sheet.

The Article can be read in full in eLife DOI

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P: 180116

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Protein Biogenesis Machinery is a Driver of Replicative Aging in Yeast


In a research published in eLife recently Georges E Janssens, Anne C Meinema, Javier González, Justina C Wolters, Alexander Schmidt, Victor Guryev, Rainer Bischoff, Ernst C Wit, Liesbeth M VeenhoffCorresponding Author, Matthias Heinemann writes that an integrated account of the molecular changes occurring during the process of cellular aging is crucial towards understanding the underlying mechanisms. Here, using novel culturing and computational methods as well as latest analytical techniques, we mapped the proteome and transcriptome during the replicative lifespan of budding yeast. With age, we found primarily proteins involved in protein biogenesis to increase relative to their transcript levels. Exploiting the dynamic nature of our data, we reconstructed high-level directional networks, where we found the same protein biogenesis-related genes to have the strongest ability to predict the behavior of other genes in the system. We identified metabolic shifts and the loss of stoichiometry in protein complexes as being consequences of aging. We propose a model whereby the uncoupling of protein levels of biogenesis-related genes from their transcript levels is causal for the changes occurring in aging yeast. Our model explains why targeting protein synthesis, or repairing the downstream consequences, can serve as interventions in aging.

Aging is a complex process, and so many scientists use baker’s yeast as a simpler model to understand it. Although many genes that influence aging have been found, all the generated knowledge is still rather fragmented. It also remains difficult to disentangle cause and consequence. That is to say, sometimes a gene that looks like it might cause aging could simply be a gene that responds to an age related phenomenon. To unravel this puzzle of cause and effect, it is necessary to first get an idea on a system level of everything that changes as an organism ages.

Now, Janssens, Meinema et al. have managed to map many of the molecular changes that occur as baker’s yeast ages; this is something that has yet to be achieved for any other organism. The work first involved developing a new way of growing baker’s yeast to keep and generate large cohorts of aging yeast cells in a constant environment. It also required the use of a mathematical ‘un-mixing’ tool to separate the data obtained from the aging cohort from the data from the young offspring that the yeast produce while they age.

Janssens, Meinema et al. measured both the majority of the transcriptome and much of the proteome of baker’s yeast throughout its reproductive lifespan. The “transcriptome” refers to the collection of RNA molecules in the cell, which are produced whenever a gene is expressed. The “proteome” refers to all the proteins in the cell, which are translated from the RNA transcripts by the cell’s so-called “translational machinery”. These experiments revealed that this yeast’s proteome reflects its transcriptome less and less as it ages. In particular, this ‘uncoupling’ of the proteome from the transcriptome was seen most strongly for the proteins related to the cell’s translational machinery; these proteins accumulated with age relative to their transcripts.

Janssens, Meinema et al. then conducted a computational network-based analysis of the data. This indicated that the uncoupling is the driving force behind the aging process. Many of the other molecular changes that occur with aging were predicted to be consequences of this uncoupling.

These findings give a framework for many observations in the existing literature. However, it remains unclear why proteins related to translational machinery are overrepresented in aging yeast in the first place. This question should be explored in future work.

Aging, the gradual decrease in function occurring at the molecular, cellular, and organismal level, is a main risk factor for cardiovascular disease, neurodegeneration, and cancer (Niccoli and Partridge, 2012). Understanding its driving force is the required step towards enabling interventions that might delay age-related disorders (de Magalhães et al., 2012). While this remains an unsolved problem in biology (Medawar, 1952; Mccormick and Kennedy, 2012), significant advances in the field have shown the process of aging to be malleable at both the genetic and environmental levels, indicating that it is possible for its causal elements to be dissected. The rate of aging, however, is influenced by diverse factors, including protein translation, protein quality control, mitochondrial dysfunction, and metabolism (Kennedy and Kaeberlein, 2009; Webb and Brunet, 2014; Lagouge and Larsson, 2013; Barzilai et al., 2012). The multitude of factors involved indicates that aging is a complex and multifactorial process, where ultimately an integrated and systems-level approach might be necessary to untangle the causal forces.

Important insights into the complex process of aging originate from research on the unicellular eukaryote Saccharomyces cerevisiae, which can produce 20–30 daughter cells before its death (Mortimer and Johnston, 1959, and see Wasko and Kaeberlein, 2014; Denoth Lippuner et al., 2014 for recent reviews). Significant contributions towards global mapping of the aging process have been demonstrated through transcriptome studies (Egilmez et al., 1989; Lin et al., 2001; Lesur and Campbell, 2004; Koc et al., 2004; Yiu et al., 2008) and genome-wide single-gene deletion lifespan measurements (reviewed in Mccormick and Kennedy, 2012). However, a major task remains to comprehensively describe the molecular changes that accompany the aging process. As the exponential increase in daughter cells represents a major challenge in terms of generating sufficient numbers of aged cells, to date no comprehensive description of the changes on both the proteome and transcriptome level has been provided. Assuming that the molecular changes occurring along the replicative lifespan of yeast are, in part, responsible for its decreased viability that occurs over time, we reason that revealing the dynamic and interdependent changes that accompany this process would allow us to distinguish cause from consequence in aging.

Here, we developed a novel column-based cultivation method that allowed us to generate large numbers of advanced-age cells in a constant environment. Applying next-generation RNA sequencing and shotgun proteomics, we mapped the molecular phenotypes of aging yeast cells at 12 time points, well into advanced age where the majority of cells had died due to aging. Analysis of these dynamic and comprehensive datasets allowed us to identify a general uncoupling of protein levels from their corresponding messenger RNA (mRNA) levels. This uncoupling was most apparent in protein biogenesis-related proteins, which we found over-represented relative to their transcripts. Using computational network-based inference methods, we found that changes in these genes had the strongest ability to predict the behavior of other genes, thereby suggesting their causal role in replicatively aging yeast. On the basis of these analyses, we provide a systems-level model of aging unifying and integrating diverse observations made within the field.

Results

Novel culture and computational methods to determine aged cell phenotypes

To obtain aged yeast cells, we bound streptavidin-conjugated iron beads to biotinylated cells (adapted from Smeal et al., 1996) from an exponentially growing culture. This starting cohort of mother cells was put into a column containing stainless steel mesh that was positioned within a magnetic field (Figure 1A, Figure 1—figure supplement 1). The daughter cells do not inherit the iron beads, as the yeast cell wall remains with the mother during mitosis (Smeal et al., 1996). By running a constant flow of medium through the column, we washed away the majority of emerging daughter cells. The flowing medium also provided fresh nutrients and oxygen and ensured constant culture conditions, as confirmed for pH, glucose, and oxygen levels (Figure 1—figure supplement 2A–C). By maintaining multiple columns simultaneously, we could harvest cells from the same starting cohort at different time points and thus at different replicative ages (Figure 1—figure supplement 2D). Because we could retain up to 109 mother cells per column (Figure 1—figure supplement 3), we could produce sufficient numbers of aged cells for performing parallel proteome and transcriptome analyses. Computer simulations showed that the age distribution broadened over time (Figure 1—figure supplement 4A,B). The broadened age distribution results in a lower resolution making detecting the actual changes occurring at later time points more difficult, and we therefore harvested cells at exponentially increasing time intervals to maximize the differences between time points at later ages.

To assess whether our column-based cultivation method generated correctly aged cells in a reproducible manner, we developed flow cytometric assays to determine the typical phenotypes of aging cells. Avidin-fluorescein isothiocyanate (AvF) binding to the biotin-labeled cells distinguished the starting cohort of mother cells from daughter cells (Figure 1—figure supplement 5A). Dead cells were identified using propidium iodide (PI), which fluoresces upon intercalating with the DNA of membrane-permeable dead cells (Figure 1—figure supplement 5A). These two assays were used to determine the fractions of daughters, mothers, and dead cells in a population (Figure 1—figure supplement 5B). From this data, we derived the viability of the mother cells over time, which we found to be in excellent agreement with the lifespan curve of yeast as observed in a microfluidic device (Huberts et al., 2014) (Figure 1B). Using the forward scatter of the flow cytometer as a rough proxy for cell size, we could qualitatively observe the cell size increase of live mothers that is known to occur in aging mother cells (Egilmez et al., 1990) (Figure 1C). Similarly, using fluorophore-conjugated wheat-germ agglutinin, which labels bud scars that appear after every division (Powell et al., 2003), we observed an increase of bud scar staining on mother cells in the column, as also visualized by confocal microscopy (Figure 1D, Figure 1—figure supplement 2D). These analyses confirmed known changes that characterize aging yeast: increased cell size and bud scars, and decreased population viability (Figure 1B–C).

Next, we developed a combined experimental and mathematical method to determine the molecular phenotype of aging mother cells without contributions from daughter or dead cells. The approach exploits the fact that a system of linear equations can be solved when the number of unknowns equals the number of independent equations. Specifically, while we could determine the number of mothers, daughters, and dead cells in a sample using flow cytometry, the contribution of each type of cells to the measured abundance of a particular protein or transcript was unknown. Therefore, by measuring protein and transcript abundances in three mixed samples with various proportions of mothers, daughters, and dead cells, we could mathematically un-mix the abundances. This resulted in un-mixed data for the aging mother cells. Experiments using samples containing mixed cell populations with known molecular phenotypes validated this mathematical un-mixing method for the RNA sequencing (RNAseq) transcriptome, targeted (selected reaction monitoring) proteome, and global (shotgun) proteome data with a <16% average error (Figure 2—figure supplement 1 and 2; Supplementary file 1).

To use this data un-mixing approach, we harvested three mixed samples for each time point (Figure 2A, Figure 2—figure supplement 3). One sample was collected from the column effluent (Mix 3, mainly daughter cells). Harvesting all cells from the column and applying a further enrichment step on a larger magnet produced the two other samples: one sample contained mainly aged mother cells (Mix 2, 80–99% mothers), while the other contained an intermediate composition compared to Mixes 2 and 3 (wash fraction, Mix 1). In each of these mixed-cell samples, we determined the fraction of mothers, daughters, and dead cells and generated the mixed-population proteomes and transcriptomes. Then, we mathematically un-mixed the proteomes and transcriptomes to obtain the molecular phenotype of aging mother cells. The data was corrected for sampling artefacts related to bead labeling and cell harvesting (Figure 2—figure supplement 4; supplemental notes 2 and 3 in Supplementary file 1). Together, through this approach, we obtained pure data for aging mother cells and daughter cells.

In two experimental series with overlapping time points, we generated 61 samples for both the proteome and the transcriptome as required for un-mixing. After data processing, we obtained high quality data at 12 unique time points during the lifespan of replicatively aging yeast (Figure 2—figure supplement 5). We found the replicates to be in excellent agreement (Spearman correlations >0.85) (Figure 2B,C). A unified dataset was generated for both the proteome and the transcriptome by fitting the replicate datasets with a polynomial regression (Figure 2D,E), only retaining highly reproducible expression profiles (∼85% of genes, Figure 2—figure supplement 6), and resampling the fit at the actual time points of the experiment. This yielded profiles for 1494 proteins and 4904 transcripts from aging mother cells. The raw data (Janssens et al., 2015a; Janssens et al., 2015b) and the data for each processing step are provided in the supplementary Tables S2 and S3 (Figure 2—source data 1 and 2). The final datasets for aging mother cells are presented in Table S4 (proteome) and Table S5 (transcriptome) (Figure 2—source data 3 and 4).

Biogenesis proteins increase relative to transcript levels during aging

Correlation analyses between the proteomes of young cells and the proteomes of aging mother cells confirmed the expected divergence of the aging cell away from the youthful state (Figure 3A, Figure 3—figure supplement 1). Daughters from later time points showed a partially aged signature (Figure 3—figure supplement 2), consistent with the notion that rejuvenation of daughter cells is incomplete later in a mother’s life (Kennedy et al., 1994). Furthermore, we found agreement between specific proteome changes detected by us and observations present in literature, including changes related to glycolysis, gluconeogenesis (Lin et al., 2001), increased expression levels in energy reserve pathway proteins (Levy et al., 2012), increases in stress response protein levels (Erjavec et al., 2007; Crane et al., 2014), and mitochondrial changes (Hughes and Gottschling, 2012) (Figure 3B, Figure 3—figure supplement 3). Also, we confirmed that changes detected in our population-level study similarly occurred at the single-cell level, which excluded the possibility that our observed changes may reflect a gradual enrichment of a long lived subpopulation. Specifically, we see the levels of the stress-related chaperone Hsp104 and the translation elongation factor Tef1 to increase with age (Figure 3—figure supplement 4), similar to what was shown using a microfluidic platform tracking single cells (Zhang et al., 2012). Also, other single protein changes reported to occur in literature match well (Koc et al., 2004; Lee et al., 2012; Hughes and Gottschling, 2012; Zhang et al., 2012; Lord et al., 2015; Denoth-Lippuner et al., 2014; Eldakak et al., 2010; Sun et al., 1994) (Figure 3—figure supplement 4). Together, these observations confirm the validity of our novel experimental design.

The Article can be read in full in eLife DOI

( The Authors are from European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, The Netherlands; Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, The Netherlands; Probability and Statistics, Johann Bernoulli Institute of Mathematics and Computer Science, University of Groningen, The Netherlands; Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, The Netherlands; Biozentrum, University of Basel, Switzerland)

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Posted: December 14, 2015

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Imaging and Energetics of Single SSB-ssDNA Molecules Reveal Intramolecular Condensation and Insight into RecOR Function

Membrane Protein Topology: The Messy Process of Guiding Proteins Into Membranes

Stephen H White  (University of California, Irvine, United States) writes in an article published in eLife last week that one of the keys to predicting the three-dimensional structure of a membrane protein from its sequence of amino acid residues is to understand how structures called translocons guide the protein to its final folded state. Translocons are generally thought of as channels that allow proteins to cross cell membranes. In eukaryotes, it is thought that newly-formed secreted proteins pass through the Sec61 translocon as they emerge from the ribosome. New membrane proteins are thought to follow a similar path, except that the hydrophobic transmembrane helices in these proteins are diverted sideways so that they become embedded in the cell membrane. This ‘sequential-insertion’ scheme seems logical in the context of what we know about the structure of translocons (Rapoport et al., 2004; Cymer et al., 2015), but is it correct?

We cannot answer this question because we do not have experimental methods that can follow, residue-by-residue, the insertion and folding of the protein chains as they pass from the ribosome and into the membrane. The alternative is to simulate the process. However, a newly-formed protein chain elongates at a rate of about one residue every 50–100 milliseconds, which is orders of magnitude faster than can be modeled using standard molecular dynamics simulation methods. Now, in eLife, Reid van Lehn, Bin Zhang and Thomas Miller of the California Institute of Technology report a simplified approach that allows insertion and folding to be simulated on biological time scales (Van Lehn et al., 2015). Their results suggest that the membrane protein insertion/folding process is more complicated than commonly depicted in the sequential-insertion scheme.

Van Lehn et al. modeled a protein called EmrE that sits in the inner membrane of Escherichia coli bacteria and is able to transport a wide range of antibiotic drugs out of the cell. This helps to make the bacteria resistant to these treatments. EmrE is a homodimer, and each monomer has four transmembrane helices (Chen et al., 2007). EmrE is unusual in that the two monomers are oriented in opposite directions (Figure 1A): this is known as dual topology.

The topology (orientation) of membrane proteins is largely determined by the positive-inside rule (von Heijne, 1986). This rule suggests that if the connecting loops that join the transmembrane regions of the protein are rich in lysine and arginine residues, then these loops tend to orient inward, toward the cytoplasm of the cell. This is known as the K+R bias. EmrE, which is encoded in a single gene, has a weak K+R bias, and this means that the monomers can be inserted into the membrane in one of two opposite orientations (Rapp et al., 2006, 2007).

In 2010, researchers at Stockholm University reported, based on extensive mutation studies, that a single positively charged residue placed in different positions throughout the protein can control the topology of EmrE monomers and affect whether parallel or anti-parallel dimers form (Seppälä et al., 2010). Given the positive-inside rule and the sequential-insertion scheme, one would expect positive charges in the C-terminal region of a membrane protein to have a smaller influence on topology than charges in the N-terminal region. However, Seppälä et al. discovered that a single positive charge at the C-terminus itself could determine the orientation of EmrE!

Because the positive-inside rule was robustly verified in the Stockholm experiments, a logical conclusion is that the sequential-insertion scheme does not describe accurately how EmrE, and perhaps other membrane proteins, fold inside cells. The simulations now performed by Van Lehn et al. divulge the missing ingredients of membrane protein folding: stochastic insertion and post-insertion annealing. By stochastic insertion, I mean that protein chains can have various topologies after they have been made, creating what Van Lehn et al. refer to as an ‘end-of-translation ensemble’ (Figure 1A). After being inserted into the membrane, the members of the ensemble that are not initially in their lowest thermodynamic free energy state subsequently relax to their preferred topology through a process called annealing. In the case of EmrE, antiparallel dimers can form because there are two final topologies that have similar free energies.

Van Lehn et al. increased the speed of the simulations by treating the nascent protein chain as a sequence of coarse-grained beads, with each bead representing several amino acids (Figure 1B). Four beads were used to represent the transmembrane helices and five beads were to used represent the loops that connect these helices. Certain properties of the amino acid residues that are known to affect the topology of a protein were also incorporated into the simulation: for example, hydrophobicities were assigned to the beads using an experimentally-determined hydrophobicity scale (Wimley et al., 1996). Particularly important was the assignment of positive charges in the connecting loops between the transmembrane helices to mimic the mutation experiments of Seppälä et al. (2010). The ribosome and translocon were also represented by simple two-dimensional structures composed of coarse-grained beads (Zhang and Miller, 2012; Figure 1B). Crucially, the model translocon used in the simulations had two negative charges on its cytoplasmic side and two positive charges on its periplasmic side to mimic the known net charge distribution of the translocon (Goder et al., 2004).

The simulations were performed by adding a new bead at the C-terminal of the nascent chain every 125 milliseconds. In this way, van Lehn et al. simulated the insertion and folding of the many mutant EmrE proteins studied by Seppälä et al. (2010) and found remarkable agreement with the experimentally determined topologies.

The simulations of van Lehn et al. show that the stochastic insertion of newly-formed protein chains into the membrane, followed by thermodynamics-driven annealing, is a viable alternative to the current sequential-insertion view. What is needed now is direct experimental verification of how transmembrane proteins are inserted into the membrane. This will require new methods that can directly follow insertion and folding on the biological time scale.

The article can be read in full in DOI

The article by Van Lehn RC, Zhang B, Miller TF 2015 that has been referred to, titled, Regulation of Multispanning Membrane Protein Topology Via post-translational Annealing can be read in  eLife DOI

Posted on: November 12, 2015

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ESA's Next Earth Explorer FLEX to Map Vegetation Fluorescence to Quantify Photosynthetic Activity

 

ESA’s Member States have selected FLEX as the eighth Earth Explorer mission, upon recommendation from the Earth Science Advisory Committee.

The Fluorescence Explorer (FLEX) mission will map vegetation fluorescence to quantify photosynthetic activity.

The conversion of atmospheric carbon dioxide and sunlight into energy-rich carbohydrates through photosynthesis is one of the most fundamental processes on Earth – and one on which we all depend.

Information from FLEX will improve our understanding of the way carbon moves between plants and the atmosphere and how photosynthesis affects the carbon and water cycles.

In addition, information from FLEX will lead to better insight into plant health and stress. This is of particular relevance since the growing global population is placing increasing demands on the production of food and animal feed.

Although most people have heard of photosynthesis, the process involves an extremely complex chain of events.

Working in sequence, there are two different ‘solar power systems’ inside plant and algae cells. They collect energy in sunlight and produce chemical energy for photosynthesis, heat and a faint fluorescence, subject to environmental conditions and the health of the plant.

So far, it has not been possible to measure photosynthetic activity from space, but FLEX’s novel fluorescence imaging spectrometer will observe this faint glow, which serves as an indicator of photosynthesis.

The FLEX satellite will orbit in tandem with one of the Copernicus Sentinel-3 satellites, taking advantage of its optical and thermal sensors to provide an integrated package of measurements.

Jan Woerner, ESA’s Director General, said, “FLEX will give us new information on the actual productivity of vegetation that can be used to support agricultural management and the development of a sustainable bioeconomy. It will therefore help to understand our ecosystem.”

“With the selection of the FLEX mission, ESA Member States have continued to show their determination to provide essential data to the scientific community to better understand our planet while at the same time serving society.”

Volker Liebig, ESA’s Director of Earth Observation Programmes, added, “The selection of FLEX is an important milestone in our series of Earth Explorer missions.

“FLEX will give us a better understanding of an important part of the carbon cycle and provide important information about the health and stress of the planet’s vegetation.

“Through this, FLEX might make a contribution to the understanding of feeding the increasing population of our planet.”

The planned launch date for the FLEX mission is in 2022.

 “With the selection of the FLEX mission, ESA Member States have continued to show their determination to provide essential data to the scientific community to better understand our planet while at the same time serving society.”

Volker Liebig, ESA’s Director of Earth Observation Programmes, added, “The selection of FLEX is an important milestone in our series of Earth Explorer missions.

“FLEX will give us a better understanding of an important part of the carbon cycle and provide important information about the health and stress of the planet’s vegetation.

“Through this, FLEX might make a contribution to the understanding of feeding the increasing population of our planet.”
 

Harnessing Europe’s technological excellence, ESA’s family of Earth Explorer missions is designed to exploit new ways of observing Earth from space to improve our understanding of how our planet works as a system, as well as a better appreciation of the impact human activity is having on the natural world.

They are defined, developed and operated in close cooperation with the scientific community so that pressing Earth-science questions are addressed as effectively as possible.

The completed GOCE mission mapped variations in Earth’s gravity with extreme detail and accuracy. Three missions in orbit now are providing new insight into Earth’s cryosphere, soil moisture and ocean salinity, and the magnetic field. Future Explorers will provide new insight into wind, forest biomass and the effect of clouds and aerosols on Earth’s handling of sunlight.
 

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Posted on: November 20, 2015

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Charing Cross Hospital: Please, Note, The CCH Clock Does Work Perfectly

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

University College Hospital London

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Eastman Dental Hospital

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The 
Earth

 

  The 
Moon

 

The Lake Eden Eye

 

 

 

 

The Window of the Heavens Always Open and Calling: All We Have to Do Is: To Choose to Be Open, Listen and Respond

 

 

 

Imagine a Rose-Boat

Imagine a rose floating like a tiny little boat on this ocean of infinity
And raise your soul-sail on this wee-little boat and go seeking out
All along feed on nothing but the light that you gather only light
Fear shall never fathom you nor greed can tempt nor illusion divert
For Love you are by name by deeds you are love's working-map

 

 

Only in the transparent pool of knowledge, chiselled out by the sharp incision of wisdom, is seen the true face of what truth is: That what  beauty paints, that what music sings, that what love makes into a magic. And it is life: a momentary magnificence, a-bloom like a bubble's miniscule exposition, against the spread of this awe-inspiring composition of the the Universe. Only through the path of seeking, learning, asking and developing, only through the vehicles and vesicles of knowledge, only through listening to the endless springs flowing beneath, outside, around and beyond our reach, of wisdom, we find the infinite ocean of love which is boundless, eternal, and being infinite, it makes us, shapes us and frees us onto the miracle of infinite liberty: without border, limitation or end. There is nothing better, larger or deeper that humanity can ever be than to simply be and do love. The Humanion

 

Poets' Letter Magazine Archive Poetry Pearl

About The Humanion The Humanion Team Home Contact Submission Guidelines
The Humanion Online Daily from the United Kingdom for the World: To Inspire Souls to Seek

At Home in the Universe : One Without Frontier. Editor: Munayem Mayenin

All copyrights @ The Humanion: London: England: United Kingdom: Contact Address: editor at thehumanion dot com

First Published: September 24: 2015