Prevention is Better Than Cure – Keeping Dementia at Bay

What do we know about dementia?

Recently Associate Professor Michael Valenzuela spoke at Macquarie University’s Australian Advanced School of Medicine about his work with elderly Australians. Associate Professor Venezuela leads a team of researchers at the Brain and Mind Research Institute, part of the University of Sydney. His work is important for determining how elderly people can be productive into old age rather than being confined to institutions or nursing homes. Dementia is not one disease, but a collection of diseases characterised by a decline in brain functions, such as perception, memory, language and cognitive skills1. Research has shown that shrinkage of the hippocampus (a brain section important for memory and spatial skills) is an indicator for dementia (Figure 1)2.

Figure 1. Shrinkage of the hippocampus in healthy elderly people and suffers of  Alzheimer’s Disease (one of the diseases under the umbrella of dementia)2.

Figure 1. Shrinkage of the hippocampus in healthy elderly people and sufferers of Alzheimer’s Disease (one of the diseases under the umbrella of dementia)2.

What are the risk factors?

So what determines whether an elderly person can maintain an independent lifestyle or become dependent on others for care and support? A major risk factor for dementia is being mentally lazy. A person’s cognitive lifestyle across the years of their life is a major factor in the risk of cognitive decline and developing dementia as a person ages. A survey of elderly people in Australia, the UK, the USA and France (Lifetime of Experience Questionnaire) is currently being conducted to find out more about how cognitive lifestyle correlates with risk of dementia3.

One recent finding of the Lifetime of Experience Questionnaire is that managerial experience during a person’s working life is correlated to a bigger hippocampus. Managing at least 10 people can be effective at preventing the onset of dementia4. The researchers analysing the data have postulated that interactions with people and the complex skill set required to successfully perform in a management position are what leads to this reduction in dementia risk.

What can we do to stave off the onset of dementia?

The findings from Associate Professor Valenzuela’s research show that one way to reduce the risk of dementia is to maintain an active cognitive lifestyle (ACL)5. Consistently ‘working out’ your brain can increase the time you live with a clear mind and reduce the amount of time living with dementia6. This pushing back of the onset of dementia is known as compression of cognitive morbidity. Pablo Picasso is a wonderful example of a person who was active and independent into old age. He continued to paint right up to his death at 73 years old (Figure 2).

Figure 2. Pablo Picasso adding paint to an artwork.

Figure 2. Pablo Picasso adding paint to an artwork.

How do we go about promoting active cognitive lifestyles in communities?

Associate Professor Valenzuela’s team has started a Brain Training Lab at the Montefiore Home in Randwick, Sydney (Figure 3). Here the program participants undergo computer-assisted cognitive training for 60 minutes, three times a week over a 12 week program. There are two groups – one does repeated standardized tasks and the other group watches National Geographic videos and answers questions about them. This study aims to answer questions about the minimum/maximum amount of training required to improve cognitive function and how long positive effects from brain training can last7.

Figure 3. Participants undertaking computerised tasks as part of the Brain Training Lab.

Figure 3. Participants undertaking computerised tasks as part of the Brain Training Lab.

Another experiment that has been established is the Study of Mental Activity and Regular Training (SMART) trial. This study combines mental and physical training to see if either type of training in isolation or the combination of both improves cognitive abilities8. The link between exercise and brain health has been observed in rat experiments. Exercise has been shown to improve brain organisation and object/place recognition in aged rats. This link has also been demonstrated in humans; exercise training has been shown to increase the size of the hippocampus9. The SMART study includes 100 Sydneysiders over the age of 55 and they conduct training twice a week for six months. Measurements of cognitive ability before any training, after six months of training and at an 18 month follow-up hope to determine if there are improvements in the brain, general health and quality of life of the participants8.

Wider implications

Only in the past 100 years have people been able to live beyond 65 years of age. The population of the world is aging fast due to the maturing of the baby boomer generation and medical advancements extending life expectancy. The combination of aging populations and increased life expectancy means more people than ever before are soon going to be retired. Retirement design needs to be carefully considered so the development of dementia in aging population doesn’t negatively impact on the economy and health care systems10. Studies on aging and cognitive ability have shown that it isn’t effective to retire and then do no ‘brain work’ for 20-30 years. Associate Professor Valenzuela warns that retirement design needs to take this into account. Opportunities need to be created where elderly people can replace the intensive cognitive and social interactions of a work environment when they retire. Only in this way can the older people retain their cognitive abilities and stave off the onset of dementia.

Want to learn more?

  1. Department of Health, Commonwealth of Australia (2013). Dementia, http://www.health.gov.au/dementia, accessed 23 May 2014.
  2. Thompson PM, Hayashi KM, de Zubicaray GI, Janke AL, Rose SE, et al. (2004). Mapping hippocampal and ventricular change in Alzheimer disease. NeuroImage, 22(4), 1754-1766. doi: 10.1016/j.neuroimage.2004.03.040.
  3. Valenzuela M & Sachdev P (2007). Assessment of Complex Mental Activity Across the Lifespan: Development of the Lifetime of Experiences Questionnaire. Psychological Medicine, 37, 1015-1026. doi: 10.1017/S003329170600938X.
  4. Suo C, Leon I, Brodaty H, Trollor J, Wen W, et al. (2012). Supervisory experience at work is linked to low rate of hippocampal atrophy in late life. NeuroImage, 63, 1542-1551. doi: 10.1016/j.neuroimage.2012.08.015.
  5. Marioni RE, van den Hout A, Valenzuela MJ, Brayne C, Matthews FE, et al. (2012). Active cognitive lifestyle associates with cognitive recovery and a reduced risk of cognitive decline. J Alzheimers Dis, 28(1), 223-230. doi: 10.3233/JAD-2011-110377.
  6. Marioni RE, Valenzuela MJ, van den Hout A, Brayne C, Matthews FE (2012).Active Cognitive Lifestyle Is Associated with Positive Cognitive Health Transitions and Compression of Morbidity from Age Sixty-Five.PLoS ONE, 7(12), e50940.doi: 10.1371/journal.pone.0050940.
  7. Lampit A, Suo C, Gates N, Kwok SSY, Naismith S et al. (2011). Temporal evolution of cognitive training-induced structural and functional brain plasticity. 10th National Emerging Researchers in Ageing Conference, University of New South Wales, Sydney. http://rng.org.au/timecourse/, Accessed 23 May 2014.
  8. Gates NJ, Valenzuela M, Sachdev PS, Singh NA, Baune BT, et al. (2011) Study of Mental Activity and Regular Training (SMART) in at risk individuals: A randomised double blind, sham controlled, longitudinal trial. BMC Geriatrics, 11(19), doi: 10.1186/1471-2318-11-19.
  9. Erickson KI, Voss MW, Prakash RS, Basak C, Szabo A, et al. (2011). Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences of the United States of America, 108(7), 3017-3022. doi: 10.1073/pnas.1015950108.
  10. Brookmeyer R, Johnson E, Ziegler-Graham K & Arrighi HM (2007). Forecasting the global burden of Alzheimer’s disease. Alzheimer’s & Dementia, 3(3), 186–191. doi: 10.1016/j.jalz.2007.04.381.

A new direction in HIV therapy

HIV is a worldwide problem – 35.3 million people are living with HIV or AIDS and 36 million people have died from AIDS related illnesses1. As the HIV/AIDS epidemic is such a big problem, there are many agencies working hard to come up with effective and efficient treatments. In a recent seminar at Macquarie University’s Australian School of Advanced Medicine, Professor Anthony Kelleher discussed the cutting edge HIV research his team has been conducting. His research at The Kirby Institute, University of NSW, focuses on how HIV reservoirs are established and maintained and how this information can help to develop a cure.

Currently, the only option for HIV treatment is the use of antiviral therapy. This treatment suppresses the replication of HIV virus particles by targeting different stages of the virus lifecycle, such as the entry of the virus particles into the cell, creation of DNA from virus RNA, insertion of the virus DNA into the cell’s nuclear DNA or preventing mature virus particles from leaving the cell (Figure 1)2.

Figure 1. Anti-viral drugs target different stages of the HIV lifecycle.

Figure 1. Anti-viral drugs target different stages of the HIV lifecycle.

Antiviral therapies work well, but they don’t eliminate the virus from the body3. As soon as patients stop anti-viral drug treatment, HIV levels can rapidly rise due to the stockpile of virus particles lurking in cells (the viral reservoir)4. This means patients with HIV need anti-viral drugs for the rest of their lives, otherwise the virus will return with vigor and the patient will relapse (Figure 2).

Figure 2. HIV+ patients currently take a cocktail of drugs to suppress the virus.

Figure 2. HIV+ patients currently take a cocktail of drugs to suppress the virus.

Staying on anti-viral drugs for a lifetime can have a lot of unwanted side effects, such as metabolic diseases and osteoporosis5. There are research teams developing a vaccine for HIV, but this is still in phase 1 of clinical trials (preliminary safety tests). A vaccine needs multiple rounds of trials and will take approximately 15 years before it can be used in the general population6.

Only one person has been cured of HIV – a patient with HIV developed an additional disease which required a bone marrow transplant. The bone marrow donor had a deletion of his CCR5 gene, a receptor for HIV. As the HIV receptor was no longer present the HIV could no longer bind and the patient was cured4. Unfortunately, a bone marrow transplant from a donor with a CCR5 gene deletion is not an option for everyone with HIV, so other avenues are being explored.

Professor Kelleher and his team are interested in developing alternatives to long-term medications and are looking at the genes of HIV and the immune system for a possible solution. The team has just recently received ethics clearance to start human clinical trials using siRNAs (small interfering RNAs) to degrade the CCR5 gene receptor for HIV. This method aims to stop expression of the HIV genes (silencing) which stops the virus particles from replicating7 (Figure 3). This will remove the viral reservoir, but the genes will still be present in the patient’s genome. This method has been effective for other viruses, such as the Human Papilloma Virus, Polio, Hepatitis B and Hepatitis C, and oncogenes have also been silenced using this method8,9.

The way retroviruses, such as HIV, replicate is by inserting their genetic material into the host cell’s genome and taking over the DNA replication machinery to produce messenger RNA and then proteins. The siRNAs can bind to the HIV messenger RNAs (Figure 3) and degrade them, stopping the creation of the proteins that create HIV particles10. Alternatively the siRNAs can bind to the DNA and change the chemical structure so the HIV genes can’t be transcribed4. The siRNAs are so specifically targeted to HIV strains that there is no chance of detrimental impacts on other genes10.

Figure 3. siRNAs degrade messenger RNA so it can’t be translated into protein (McManus & Sharp 2002).

Figure 3. siRNAs degrade messenger RNA so it can’t be translated into protein (McManus & Sharp 2002).

Another research group has shown that treatment with siRNAs works to silence an HIV-type virus in mice11 so the next step is to try this method with human HIV. Professor Kelleher’s team will start the treatment with a small group of HIV patients, following these steps:

  1. Identify HIV+ patients currently taking anti-viral drugs to suppress the virus;
  2. Treat with siRNA therapy;
  3. Stop anti-viral treatment; and
  4. Monitor the patients to see if the HIV stays suppressed.

If the HIV symptoms don’t return then the siRNAs have eliminated the viral reservoir and silenced the HIV. The siRNA treatment has been shown to be effective in human cells for up to 30 days in a laboratory setting. If the same is shown in human clinical trials, this could lead to a significant improvement for the quality of life for millions of HIV+ patients in the future.

Want to know more?

  1. World Health Organisation (2014.) HIV/AIDS, http://www.who.int/gho/hiv/en/, accessed 22 May 2014.
  2. Mehellou Y & De Clercq E (2010). Twenty-Six Years of Anti-HIV Drug Discovery: Where Do We Stand and Where Do We Go? Journal of Medicinal Chemistry, 53(2), 521-538. doi: 10.1021/jm900492g.
  3. Suzuki K, Marks K, Symonds G, Cooper DA, Kelleher AD, et al. (2013). Promoter targeting shRNA suppresses HIV-1 infection in vivo through transcriptional gene silencing. Molecular Therapy – Nucleic Acids, 2, e137; doi: 10.1038/mtna.2013.64.
  4. Kent SJ, Reece JC, Petravic J, Martyushev A, Kramski M, et al. (2013). The search for an HIV cure: tackling latent infection. The Lancet, 13(7), 614-621. doi: 10.1016/S1473-3099(13)70043-4.
  5. Grund BA, Peng GA, Gibert CLB, Hoy JFC, Isaksson RLA, et al. (2009). Continuous antiretroviral therapy decreases bone mineral density. AIDS, 23(12), 1519-1529. doi: 10.1097/QAD.0b013e32832c1792.
  6. National Health and Medical Research Council, Commonwealth of Australia (2014). Australian Clinical Trials. http://www.australianclinicaltrials.gov.au/node/5, accessed 22 May 2014.
  7. McManus MT and Sharp PA (2002). Gene silencing in mammals by small interfering RNAs. Nature Reviews Genetics, 3, 737-747. doi: 10.1038/nrg908.
  8. Saleh MC, Van Rij RP, Andino R (2004). RNA silencing in viral infections: insights from poliovirus. Virus Research, 102, 11–17. doi: 10.1016/j.virusres.2004.01.010.
  9. Li S-D, Chono S & Huang L (2008). Efficient Oncogene Silencing and Metastasis Inhibition via Systemic Delivery of siRNA. Molecular Therapy, 16(5), 942-946. doi: 10.1038/mt.2008.51.
  10. Suzuki, K, Ishida T, Yamagishi M, Ahlenstiel C, Swaminathan S, et al. (2011). Transcriptional gene silencing of HIV-1 through promoter targeted RNA is highly specific. RNA Biology, 8(6), 1035-1046. doi: 10.4161/rna.8.6.16264.
  11. Mitsuyasu RT, Merigan TC, Carr A, Zack JA, Winters MA, et al. (2009). Phase 2 gene therapy trial of an anti-HIV ribozyme in autologous CD34+ cells. Nature Medicine, 15, 285-292. doi: 10.1038/nm.1932.

Going a bit batty – How do bats withstand so many viruses?

Dr Michelle Baker from the CSIRO Australian Animal Health Laboratory spoke at Macquarie University last week about her work on bat immune systems. Her lab recently contributed to the high impact Hendra virus vaccine for horses. Dr Baker’s work has implications for disease control and prevention and management of virus spillovers into human communities worldwide.

Bats make up 20% of mammalian diversity, are long lived for their body size and are the only mammals with powered flight. Bats are vital to ecosystem functions such as pollination, fertilisation and insect control (Calisher et al. 2006). Despite these unique characteristics they are not intensively studied like most other mammal groups. Before Dr Baker’s research group focused on bats not much was known about their immune systems (Baker et al. 2013).

Figure 1. Possible virus transmission routes from bats to humans. (adapted from image presented by Dr Michelle Baker)

Figure 1. Possible virus transmission routes
(adapted from image presented by Dr Michelle Baker).

Bats act as viral reservoirs, meaning they carry a range of viruses (He et al. 2010; Ng & Baker 2013). Fatal human viruses that can be traced back to bats include Rabies, Hendra, Ebola, Marburg and the SARS coronavirus (Ng & Baker 2013). These viruses occasionally spill over from bats into other animals and that’s normally how humans become infected (Figure 1). But even with this load of up to 100 different viruses, bats are hardly ever sick (Baker et al. 2013).

The bat’s lack of symptoms from viral infections was puzzling, so Dr Baker’s team looked more closely at bat immune systems. When a viral infection occurs in other mammals the immune system quickly delivers a generic response (innate response) and then a specific response occurs more slowly (adaptive response; Katze et al. 2002). The researchers observed that bats don’t develop many antibodies in response to infections, so they thought the bat immune system might be knocking down the viruses before the immune system could mount an adaptive response (Baker et al. 2013).

Figure 2. The Black Flying Fox, Pteropus alecto.

Figure 2. The Black Flying Fox, Pteropus alecto.

To test this idea the researchers sequenced the genomes and studied the immune responses of two species of bat: Myotis davidii, a micro bat, and Pteropus alecto, a megabat (Figure 2). They found the collection of immune genes in bats is different to other mammals. For example, bats have fewer genes for interferon production (Papenfuss et al. 2012).

Interferon is a protein produced by the immune system in response to the detection of viral invaders. It starts a signaling cascade that creates an anti-viral state in cells (He et al. 2010; Figure 3). High interferon levels can be toxic for cells, so normally the interferon level is very low. When a viral infection is detected, the interferon level is dramatically increased which signals cells to start fighting the infection (Katze et al. 2002). There are multiple types of interferon, but the type Dr Baker spoke most about is interferon alpha (IFNA).

Figure 3. Interferon signaling cascade - causes expression of immune system genes and creates an antiviral state in cells (Katze et al. 2002).

Figure 3. Interferon signaling cascade – causes expression of immune system genes and creates an antiviral state in cells (Katze et al. 2002).

In contrast to expectations, bat cells were found to have their IFNA genes constantly switched on and there is no increase when cells are infected with viruses (Figure 4). Even with this high IFNA level the toxicity effect observed in other mammals isn’t seen in bats. This IFNA level in bats may be part of the reason they can carry so many viruses, but don’t often get sick from them. Other research groups have found that bat IFNA genes have been positively selected which means they must have been beneficial to bats as they lived with viruses in the past (Calisher et al. 2006; He et al. 2010). Recent work has hypothesised that there is a link between the evolution of flight and the ability of bats to harbor viruses without becoming sick (Zhang et al. 2013; O’Shea et al. 2014).

These findings were very new and unexpected so there were lots of questions from the audience after the seminar. It seems like everywhere Dr Baker turned there were more questions! These are the ‘top 5’ questions asked:

  1. Why aren’t the bats harmed by high levels of interferon in uninfected cells like other mammals?
  2. What triggers the spillover of viruses into other animals that cause outbreaks?
  3. Could there be a link between the interferon level and their long lifespan relative to their body size?
  4. What is the bat immune response to bacterial infection?
  5. Why doesn’t the bat immune response completely wipe out the viruses? How come the viruses can persist and then spill over into other animals?
Figure 4. Interferon alpha levels in infected and uninfected cells in bats and other mammals  (adapted from image presented by Dr Michelle Baker).

Figure 4. Interferon alpha levels in infected and
uninfected cells in bats and other mammals
(adapted from image by Dr Michelle Baker).

So much is currently unknown about how bats can carry so many viruses without being sick. Dr Baker and her team are working to find answers to these questions and more. As human populations increasingly overlap with bat habitats there is more chance of spillover events affecting human and animal populations. Dr Baker’s research could be used to understand human responses to viruses and develop anti-viral treatments in the future.

Learn more:

Baker ML, Schountz T & Wang L-F (2013). Antiviral Immune Responses of Bats: A Review. Zoonoses and Public Health, 60, 104-116. doi: 10.1111/j.1863-2378.2012.01528.x

Calisher CH, Childs JE, Field HE, Holmes KV & Schountz T (2006). Bats: Important Reservoir Hosts of Emerging Viruses. Clinical Microbiology Reviews, 19(3), 531-545. doi:  10.1128/CMR.00017-06

He G, He B, Racey PA & Cui J (2010). Positive Selection of the Bat Interferon Alpha Gene Family. Biochemical Genetics, 48(9-10), 840-846. doi: 10.1007/s10528-010-9365-9

Katze M, He Y & Gale M (2002). Viruses and interferon: A fight for supremacy. Nature Reviews Immunology, 2(9), 675-687. doi: 10.1038/nri888

Ng J & Baker ML (2013). Bats and bat-borne diseases: a perspective on Australian megabats. Australian Journal of Zoology, 61, 48-57. doi: 10.1071/ZO12126

O’Shea T, Cryan P, Cunningham A, Fooks A, Hayman D, Luis A, Peel A, Plowright R, & Wood J (2014). Bat Flight and Zoonotic Viruses. Emerging Infectious Diseases, 20(5), 741-745. doi: 10.3201/eid2005.130539

Papenfuss AT, Baker ML, Feng Z-P, Tachedjian M, Crameri G, Cowled C, Ng J, Janardhana V, Field HE, Wang L-F (2012). The immune gene repertoire of an important viral reservoir, the Australian black flying fox. BMC Genomics, 13:261, doi: 10.1186/1471-2164-13-261.

Zhang G, Cowled C, Shi Z, Huang Z, Bishop-Lilly KA, Fang X, Wynne JW, Xiong Z, Baker ML, Zhao W, Tachedjian M, Zhu Y, Zhou P, Jiang X, Ng J, Yang L, Wu L, Xiao J, Feng Y, Chen Y, Sun X, Zhang Y, Marsh GA, Crameri G, Broder CC, Frey KG, Wang L-F & Wang J (2013). Comparative Analysis of Bat Genomes Provides Insight into the Evolution of Flight and Immunity. Science, 339, 456-460. doi: 10.1126/science.1230835

Silhouette images in Figure 1 sourced from: horse, bat, pig, humans.

The magic of statistics: identifying micro RNAs and their target mRNAs

The speed of technological advancement and the development of high-throughput screening in recent years means a lot of biological investigations now result in large volumes of data. Biologists are often not experts in statistical or computational methods, so that’s where statisticians come in, such as Associate Professor Jean Yang from the University of Sydney. Her work focuses on developing methods for analysing large amounts of data to help biologists answer their questions of interest.

Last week Associate Professor Yang spoke at Macquarie University about a statistical technique to help biologists identify micro RNAs and their messenger RNA (mRNA) targets. Knowing if miRNAs are implicated in diseases is important for understanding disease mechanisms and in drug development (Goktug et al. 2013). To be able to explore how these collaborations work and the statistical method, a little background about miRNAs is necessary.

DNA is transcribed into mRNA, which is then translated into protein. Proteins create the observable characteristics of organisms (the phenotype). Micro RNAs (miRNAs) are small pieces of RNA about 22 nucleotides long which bind onto mRNA through complementary base pairing (Ambros 2004). Binding of miRNAs to mRNA can stop the translation of proteins. The machinery in the cell that ‘reads’ the mRNA and converts the message into proteins cannot get past the section where the miRNA is bound so a functional protein cannot be made (Bartel 2004; see Figure 1).

Process of how miRNAs can reduce the production of proteins in a cell  (Image: Steve Karp, Discover Magazine)

Figure 1. Process of how miRNAs can reduce the production of proteins in a cell
(Image: Steve Karp, Discover Magazine)

MiRNAs are relatively new to science – they were discovered in the 1990s and research implicating miRNAs in diseases has emerged since 2001 (Bartel 2004). Scientists found when there are more miRNAs present (they are upregulated) they can reduce the translation of mRNA into protein (Ambros 2004). This is referred to as downregulation of mRNA (shown with red arrows in Figure 2 below). This downregulation leads to a reduction in the phenotype.

An example of such a cascade is a change in the regulation of tumor suppressor genes. These genes are expressed all the time in cells and this prevents tumors from forming. Upregulation of specific miRNAs that target tumor suppressor genes can reduce the expression of these genes (less mRNA translated into protein). As the tumor suppression mechanism is now less effective there is a chance a tumor could form (Shenouda & Alahari 2009).

Inhibitory cascade initiated by upregulation of miRNA (adapted from image presented by Associate Professor Yang)

Figure 2. Inhibitory cascade initiated by upregulation of miRNA
(adapted from image presented by Associate Professor Yang)

[As a side note: I wonder what starts the upregulation of miRNAs? Why are they present at low levels most of the time and then undergo a dramatic upregulation? Is it a genetic trigger or is there something in the environment triggering this change?]

Now we know what miRNAs do in cells, so we can go back to how Associate Professor Yang is using statistics to help biologists. When biologists and statisticians collaborate there are specialised tasks for each person, but both parties need to understand a little (or a lot) of the other person’s work so they can communicate effectively and address the question of interest together.

Steps for determining which mRNAs are targets of miRNAs.

Figure 3. Steps for determining which mRNAs are targets of miRNAs.

Using our miRNA example, the biologist starts by identifying an organism which is showing the phenotype of interest (for example, a disease). Samples are taken from this organism using a high-throughput method (such as a microarray) and the biologist presents the statistician with information on which miRNAs and mRNAs are expressed in the disease state but not in a healthy organism (referred to as differential expression; Jayaswal et al. 2012). At this point the biologist knows there is a mixture of miRNAs and mRNAs present, but the interactions are unknown. Figure 3 outlines the process described below

Using databases, such as TargetScan, the statistician can work out which genes the miRNAs may be targeting and therefore causing the phenotype. TargetScan uses the miRNA nucleotide sequence to find corresponding sequences in mRNA across the whole genome (Witkos et al. 2011). This step doesn’t confer any meaning on these matches; it just presents all possibilities (which can amount to thousands of pairs of miRNAs and mRNAs).

Associate Professor Yang is developing a statistical method, pMimCor, to whittle down the possible pairs of miRNAs and mRNAs to those most likely to be causing the phenotype. The statistician passes this information back to the biologist who can experimentally test the small number of pairs (Jayaswal et al. 2012). The biologist is looking to see if the upregulation of a specific miRNA causes a downregulation in the target mRNA and the disease phenotype.

The method used in this example was only possible due to cooperation between a biologist and a statistician, Associate Professor Yang. Using statistical methods for analysing vast amount of data from high-throughput methods can add value to the work conducted by many types of scientists. Collaborations are ‘very dear to the heart’ of Associate Professor Yang as she knows that cooperation between disciplines can provide an efficient and effective way to answer scientific questions.

 

Learn more:

Click here for a video lecture about micro RNA by David Bartel, Professor of Biology at MIT.

Ambros V (2004). The functions of animal microRNAs. Nature, 431, 350-355.

Bartel DP (2004). MicroRNAs: Genomics, Biogenesis, Mechanism, and Function. Cell, 116, 281-297.

Goktug AN, Chai SC and Chen T (2013). ‘Data Analysis Approaches in High Throughput Screening’, in HA El-Shemy (ed), Drug Discovery, InTech, DOI: 10.5772/52508.

Jayaswal V, Lutherborrow M and Yang YH (2012). Measures of Association for Identifying MicroRNA-mRNA Pairs of Biological Interest. PLoS One, 7(1), e29612.

Shenouda SK and Alahari SK (2009). MicroRNA function in cancer: oncogene or a tumor suppressor? Cancer Metastasis Review, 28(3-4), 369-378.

Witkos TM, Koscianska E and Krzyzosiak WJ (2011). Practical Aspects of microRNA Target Prediction. Current Molecular Medicine, 11, 93-109.

Is it safe to come out? Fish responses to changes in predator density

Overfishing can cause changes in fish populations over time by removing the big fish (often predators) from marine ecosystems. Reducing predator density can have consequences for ecosystem function, including movement of prey and reproduction of the predators themselves (Madin et al. 2012). Often marine parks are established to protect marine ecosystems by preventing these changes or allowing communities to recover from fishing.

Example of a food web and the responses of lower trophic levels to a reduction in the number of top-level predators  (Cury et al. 2001).

Example of a food web and the responses of lower trophic levels to a reduction in the number of top-level predators
(Cury et al. 2001).

Professor Robert Warner from the University of California, Santa Barbara and his team of researchers study fish behaviour. Professor Warner spoke last week at Macquarie University about fish behavioural responses to predators and what happens when predators are removed from ecosystems.

Removing predators can cause a trophic cascade, which is a change in the abundance of animals and plants in the lower levels of the food chain (Baum & Worm 2009). For example, the removal of sharks means predatory fish can increase in numbers which can lead to a decrease in smaller herbivorous fish population sizes and a boom in the amount of seaweed and/or algae on the reef.

As well as these direct changes to the food chain there are also indirect changes in the ecosystem, such as the behaviour of fish (Madin et al. 2010). Prey fish change their behaviour to avoid predators and reduce their risk of being eaten. This avoidance behaviour changes in response to their environment, so when there are more predators around the prey fish are more risk averse. The behaviour can change both temporally (over time) and spatially (over an area).

The extent to which prey fish will range from shelter (blue line) in fished (lower predator density) and unfished (higher predator density) areas. Photo: Belinda Fabian.

The extent to which prey fish will range from shelter (blue line) in fished (lower predator density) and unfished (higher predator density) areas. Photo: Belinda Fabian.

The researchers compared the distances prey fish ventured from shelter and the density of predators in both fished and unfished areas. The predators in fished areas are smaller and a lower density compared to the predators in unfished areas. In unfished areas they found the prey fish ranged over a shorter distance from shelter than in fished areas (Madin et al. 2012).

Another change in fish behaviour is their foraging patterns. When predators are present, fish can change the location of their foraging and/or the time when they forage. For example, a fish that normally feeds on the reef can avoid a predator by moving to the mangroves or feed at night to avoid a predator which is active during the day.

These changes in prey fish behaviour can have flow on effects for other parts of the reef. The restriction in the distance the fish are willing to range from shelter during feeding can have an impact on the distribution pattern of algae (food of the prey fish). When there is low predator density prey fish are willing to range far and wide which leads to even consumption of algae over the reef. In contrast when there are more predators the prey fish are more risk averse and only forage close to shelter (Madin et al. 2012). This means the algae is heavily cropped close to shelter and there is low cropping at further distances from shelter. This uneven distribution and overgrowth of algae can negatively impact other organisms on the reef such as coral (Coyer et al. 1993). The heavy cropping close to shelter means some of food the fish is consuming may be less than ideal and their growth and reproduction may be limited due to energy and/or nutrient deficiencies (Heithaus et al. 2008).

Algae growing over coral in Suva, Fiji. Photo: Belinda Fabian.

Algae growing over coral in Suva, Fiji.
Photo: Belinda Fabian.

Understanding the impacts of predator density in marine ecosystems is important for fisheries management and the establishment of marine sanctuaries. The sites used in these studies include currently fished, long established protected areas (no previous fishing) and new protected areas (recently fished). The researchers included these types of areas in the study as they wanted to determine the impacts of predator removal on prey behaviour and if these effects can be reversed through the cessation of fishing and a resulting increase in predator density (Madin et al. 2012).

Reef environments have a delicate balance of species, interactions and environmental variables. Professor Warner and his team have shown that a change such as overfishing of a predator species could have far-reaching impacts on the distribution and abundance of organisms on the reef. If the interactions are permanently changed then there could be negative impacts on the functioning of the reef, especially in the current context where there are many other challenges for reefs such as pollution and climate change.

To learn more:

Baum JK and Worm B (2009). Cascading top-down effects of changing oceanic predator abundances. Journal of Animal Ecology, 78, 699-714.

Coyer JA, Ambrose RF, Engle JM and Carroll JC (1993). Interactions between corals and algae on a temperate zone rocky reef: mediation by sea urchins. Journal of Experimental Marine Biology and Ecology, 167 (1): 21-37.

Cury P, Shannon L and Shin Y-J (2001). ‘The Functioning of Marine Ecosystems’, Reykjavik Conference on Responsible Fisheries in the Marine Ecosystem, Reykjavik, Iceland, 1-4 October.

Heithaus MR, Frid A, Wirsing AJ and Worm B (2008). Predicting ecological consequences of marine top predator declines. Trends in Ecology and Evolution, 23 (4), 202-210.

Madin EMP, Gaines SD and Warner RR (2010) Field evidence for pervasive indirect effects of fishing on prey foraging behaviour. Ecology, 91 (12), 3563-3571.

Madin EMP, Gaines SD, Madin JS, Link A-K, Lubchenco PJ, Selden RL and Warner RR (2012). Do Behavioral Foraging Responses of Prey to Predators Function Similarly in Restored and Pristine Foodwebs? PLoS ONE, doi: 10.1371/journal.pone.0032390.

Playing well with others? Sociality in huntsman spiders

Why do some huntsman spiders live in groups while the majority are perfectly happy living a solitary life? Dr Linda Rayor from Cornell University in New York spoke at Macquarie University recently about her work with Australian social huntsman spiders (Delena cancerides) and her excitement about finding more social huntsman species in Australia last year. Dr Rayor is passionate about social spiders and her findings could shed light on the evolution of parental care in a broad range of animals.

Sociality in arachnids is very rare – less than 1% are social beyond a short time of maternal care just after hatching. This may be due to the challenges a spider species has to overcome to become social. Aggressiveness in spiders means the majority can’t tolerate any other spiders being around them and this can lead to cannibalism (Riechert & Lockley 1984). The existence of three social huntsman species in Australia is a ‘big deal’ according to Dr Rayor as they have overcome the inbuilt aggressiveness that seems to come naturally to so many spiders.

Sociality in spiders has evolved independently at least 18 times (Yip & Rayor 2013b), so there must be something in it. Social huntsman spiders are found in south-west and south-eastern Australia and live in groups of 20 to 200 individuals, with a dominant female and her offspring of all different ages. In contrast to social insects, such as bees and termites, living together doesn’t increase the reproductive output of social spiders so there must be a different driving force behind their sociality (Whitehouse & Lubin 2005).

Delena Cancerides collected at Mount Ainslie, ACT in March 2014 Source: Canberra Times / Photo: Jay Cronan

Delena cancerides collected at Mount Ainslie, ACT – March 2014
Source: Canberra Times
Photo: Jay Cronan

Research from Dr Rayor’s group suggests a lack of available habitat was the driving force for the evolution of sociality in these spiders. Their preferred habitat of tight spaces under peeling acacia bark is normally 80-100% occupied, so there’s not much room to spread out. The spiders are forced together due to a lack of available housing.

Social huntsman spiders aren’t attached to their family members; they only live together because there’s no other option. This is demonstrated when a colony has to move because their home is destroyed (usually the bark falling off the tree). The family doesn’t move as a group – the spiders go in search of new homes by themselves with no regard for their siblings and the small ones generally get eaten by predators.

These social spiders live (mostly) peacefully in family groups, but if there are big spiders trying to immigrate into their colony they will become aggressive and deny entry to the invaders (Beavis et al. 2007). This is consistent with the limited habitat concept as the spiders are protecting their home (a valuable resource). Young spiders don’t leave the family home until they are big enough to compete with others for the sparse housing options.

To test this idea the researchers looked at the relationship between the availability of suitable habitat and the occupants of the bark spaces. They found that as suitable habitat becomes rarer the number of spiders in each colony increases and there are more large spiders in the colonies (Yip 2012). In addition, the frequency of takeovers of bark spaces also increases when available habitat decreases.

Huntsman family

Delena cancerides siblings of varying ages sharing food / Photo: Linda Rayor

Instead of using a web to trap prey, huntsman spiders roam around at night and hunt their prey (hence the name ‘huntsman’). This food is brought back to the colony for consumption. Food is shared about 5% of the time – mainly between mothers and children and sometime older siblings even share with their younger brothers and sisters (Yip & Rayor 2013a). Even this small amount of sharing is very different to solitary spiders who share food less than 1% of the time.

Prey sharing means all the spiders in the group have some food often, so there is less variability in the amount of food they consume. This is very different to spiders living alone that can have erratic food availability. D. cancerides has a lower metabolic rate than solitary spiders which means they can survive on lower amounts of food (Zimmerman 2007). The researchers aren’t sure whether sociality arose because of the lower metabolic rate in these spiders or whether sociality allows them to share prey and they have developed a lower metabolic rate as a result – it’s a bit of a ‘which came first: the chicken or the egg?’ discussion in the research group at the moment.

It would be interesting to study the metabolic rate of some closely related social and solitary huntsman species to see if all social huntsman species have a low metabolic rate or just D. cancerides. This could possibly shed some light on the evolution of sociality in this group.

To learn more:

Click here to see Linda Rayor talking about social huntsman prey sharing dynamics and click here to learn about her field collection of spiders in Canberra this month.

Beavis AS, Rowell DM and Evans T (2007). Cannibalism and kin recognition in Delena cancerides (Araneae: Sparassidae), a social huntsman spider. Journal of Zoology, 271:2, 233-237.

Riechert SE and Lockley T (1984). Spiders as Biological Control Agents. Annual Review of Entomology, 29, 299-320.

Whitehouse MEA & Lubin Y (2005). The functions of societies and the evolution of group living: spider societies as a test case. Biological Reviews. 80, 347-361.

Yip E (2012). ‘Costs and benefits of group living in an unusual social spider, Delena cancerides’. PhD thesis, Cornell University, New York.

Yip EC & Rayor LS (2013a). The influence of siblings on body condition in a social spider: is prey sharing cooperation or competition? Animal Behaviour. 85, 1161-1168.

Yip EC & Rayor LS (2013b). Maternal care and subsocial behaviour in spiders. Biological Reviews, doi: 10.1111/brv: 12060

Zimmerman A (2007). ‘Assessing the Costs of Group Living: Comparing Metabolic Physiology and Growth in Social and Solitary Spiders’. PhD thesis, Cornell University, New York.

Weaker Mussels in Warm Water?

You’d be forgiven for thinking that mussels attach to rocks and other substrates using a muscular foot. After all, that’s what their name implies. But mussels actually hang on using byssal threads – small fibres constructed by the mussel that are very strong while also being highly flexible.

MusselFoot300x267Researchers at the University of Washington are looking at the impacts of environmental conditions on the strength of byssal threads. They found the strength and flexibility of the threads varies with temperature and ocean pH, which could have far reaching consequences in the not too distant future.

Mussel foot (right) and byssal thread (left). Photo: Laura Coutts

The researchers compared the strength of byssal threads at 10oC and 25oC. In warmer water the mussels produced fewer threads and those that were produced were weaker than the corresponding threads created in cooler conditions. These changes were seen even as a result of short-term variations in temperature.

The warming of the ocean due to climate change could impact mussel populations by reducing their attachment strength. They may not be able to hold on as tightly to the substrate and could be washed away by waves and currents. Existing sites may no longer be habitable by mussels and there could be increased mortality if feeding is impacted by the inability to remain attached to the substrate.

Byssus threads 300x271When these temperature impacts are combined with other environmental stressors, such as ocean acidification and a change in the frequency and intensity of storms, mussels could be detrimentally affected. Mussels have a larval stage at the beginning of their life cycle, so the colonisation of cooler and calmer environments is theoretically possible.

Mussels attaching to substrate using byssal threads. Photo: Emily Carrington

Mussel migration due to changes in ocean temperature has the potential to dramatically impact intertidal ecosystem composition and dynamics. Changes in water temperature and mussel attachment strength will also have ramifications for the aquaculture industry as mussel attachment to ropes is important for productive mussel farming.

The mussel species in these experiments was Mytilus trossulus which lives mainly in the intertidal zone of the northern Pacific Ocean. Mussels are also found in warmer environments around the world, but these findings seem to imply that they may not be able to hang on to the substrate in turbulent conditions as well as their counterparts in cooler environments.

Maybe mussels in warmer environments may be more successful in habitats with calmer conditions? It would be interesting to extend these experiments to warmer conditions and possibly freshwater mussels to see if the same limitations apply to their byssal threads.

To find out more:

Newcomb LA, Carrington E, George MN & O’Donnell MJ (2014). Short−term exposure to elevated temperature and low pH alters mussel attachment strength. Abstract of presentation to The Society of Integrative & Comparative Biology, Austin, Texas, 3-7 January.

O’Donnell MJ, George MN & Carrington E (2013). Mussel Byssus Attachment Weakened by Ocean Acidification. Nature Climate Change. doi: 10.1038/nclimate1846.

Hear Professor Emily Carrington discussing this research and Professor Phillip Messersmith talking about the applications of mussel attachment for medical research on the ‘The Science Show’ Radio National podcast here.

Unrealistic current estimates of climate change mitigation?

Current energy and climate policies around the world aim to deliver climate change mitigation in order to limit global warming to 2 degrees above pre-industrial levels. The backbone of this policy is that this mitigation will be delivered by increased energy efficiency and ‘clean’ energy technology. The underlying force of increased energy consumption is being driven by larger populations with more resource intensive lifestyles.

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Image courtesy of digitalart at freedigitalphotos.net

Arvesen, Bright and Hertwich (2011) argue that the current policies are based on simplified models of complex social and physical systems that don’t include links between climate and other environmental pressures or the indirect effects of the mitigation measures themselves. Using a narrow view of systems and mitigation effects means environmental impacts can be underestimated and mitigation success can be overestimated.

The Copenhagen Accord national emissions-reduction pledges are not sufficient for global warming to be limited to 2 degrees, especially in the face of lopsided CO2 emissions for 2000-2009 (321 Gt emitted out of 1000 Gt goal for 2000-2049). Of great concern is the speed of climate change and combined with the disregard of long term feedbacks the modelled amount of climate change mitigation may be grossly overestimated. In addition there are many other environmental factors, such as habitat change and loss of biodiversity, which could impact on the rate of climate change.

The authors examine six areas they believe are not sufficiently considered in the development of energy and climate policy:

1. Transitioning to ‘clean’ energy supply will reduce climate impacts

Even though there is no fossil fuel combustion in the operation of energy converters (e.g. photovoltaic solar cells converting solar energy into electricity) emissions still occur in processes that support these ‘clean’ technologies, such as the manufacturing of solar cells.

2. Realised net climate change mitigation from energy efficiency is unlikely to live up to its expectations

Negative costs – modelling often shows that negative costs are associated with reducing emissions, but individual end consumers can often be faced with real costs even if the modelling shows this is not the case in aggregate.

Rebound effects – the reduction in the price of energy from increased efficiency may not reduce the amount of energy consumed as the lower price may result in increased demand and/or the income available for consumption may increase.

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Image courtesy of ponsulak at freedigitalphotos.net

3. Developing fossil energy with carbon capture and storage (CCS) and renewable energy in parallel may lower system-wide performance

Technological, institutional or social factors can hinder the implementation of greenhouse gas saving mechanisms, leading to the continuation of fossil fuel dependence.

4. The notion of absolute decoupling is not supported by historical records (absolute decrease in environmental impact as income grows).

5. Linkages between environmental pressures are likely to complicate mitigation

Biophysical and social systems are highly complex (so much so that a large portion of the complexity is not understood) so models of their function don’t encompass all of the complexity. A risk of this reduction in complexity is that interactions that aren’t modelled could lead to unforseen impacts. This could lead to problem shifting (generating a problem while solving another) and/or the hindering of solutions to overcome a biophysical limit by other physical constraints.

6. Future demands for energy services may be underestimated

Current energy models account for upscaling demand in existing categories of energy consumption, but not new categories of demand that may arise. There may also be unexpected growth in existing areas of energy demand (e.g. energy for pumping, treatment and desalination of water).

In this paper Arvesen, Bright and Hertwich dispute the idea that energy efficiency and ‘clean’ energy technologies (without social and economic structural changes) can produce the amount of climate change mitigation necessary to limit global warming to 2 degrees. The complexity of environmental and social systems doesn’t seem to be taken into account in the principles underlying energy and climate policy. Combining this complexity and other impacts on climate change could lead to unforeseen consequences for energy consumption and global warming in the future.

Read more:
Arvesen A, Bright RM, Hertwich EG (2011) Considering only first-order effects? How simplifications lead to unrealistic technology optimism in climate change mitigation. Energy Policy, 39, 7448-7454.

Travelling geckos – coping with climate change

Researchers from Macquarie University have been studying geckos (Gehyra variegata) in arid areas of Australia to determine the impacts of climate change and the possible responses of gecko populations.

The pace of the change in climate expected over the next 70 years is greater than any other change in climate in human history. Even with effective climate policy and major changes in greenhouse gas emissions the world over there is still a significant possibility of exceeding a 2 degree temperature increase that will have major negative impact on ecosystems.

Increases in global average temperature, changes in rainfall patterns and more extreme events (such as drought, fire, flood and cyclones) are the major factors that all organisms on earth potentially have to face. Each of these factors will have differing levels of impacts so the actual changes in climate and ecosystems will differ between areas.

Animals have an advantage over plants when it comes to adapting to and coping with climate change as they can move to new areas. To achieve a 1 degree temperature change an animal needs to move 100m upwards in altitude or 125km south (in the southern hemisphere or north in the northern hemisphere). This means that to combat a 2 degree temperature rise a shift of 250km would be required if a mountainous habitat was not suitable or available (and Australia is a very flat country – 99% percent of the continent is <1000m above sea level).

Paul Duckett and other scientists from Macquarie University used models to identify suitable habitats for the geckos and what proportion would make it to these new habitats. A startling conclusion they came to was that although there are places for the geckos to move to which would mitigate the effects of climate change the problem would be in them actually getting there.

Gehyra_variegata,_Sturt_National_Park_NSW_Australia,_June_2012
Gehyra variegata – Sturt National Park NSW Australia (Wikipedia)

The modeling showed that over 40% of the gecko populations would not reach suitable areas before climate change has negative impacts on the populations, such as small population sizes and the associated genetic consequences. There are also suitable areas to colonise that won’t be used as the geckos won’t be able to migrate that far within the time span of the change in climate.

As the data used in these models is based on past conditions it is possible that the rates of gecko dispersal may differ from the model under actual climate change conditions. For example, the geckos in the past may have dispersed under specific rainfall and aridity conditions, but these may not be the same conditions under which the geckos will disperse in times of climate change as the Australian continent is expected to experience increasing aridity. In addition the predicted future distribution of these geckos is expected to overlap with areas utilised by humans, so fragmented environments may have additional impacts on the persistance of gecko populations.

And even if the geckos do make it to their new and suitable habitats far to the south of their current locations what is the chance that their food source also made the journey successfully?

Read more:

Duckett PE, Wilson PD, Stow AJ (2013). Keeping up with the neighbours: using a genetic measurement of dispersal and species distribution modelling to assess the impact of climate change on an Australian arid zone gecko (Gehyra variegata). Diversity and Distributions, DOI: 10.1111/ddi.12071