Tiny life sticking to growing green things

Communicating science to non-scientists is important, but often the jargon scientists use makes their work impenetrable, even to other scientists. So how can scientific writing become less obscure and more approachable? Randall Monroe, the creator of xkcd webcomics, gave it a go with his annotation of a Saturn V rocket blueprint. The annotation used only the 1000 most commonly used words, so instead of Saturn V the name of the rocket became Up Goer Five.

So can scientific communication in my field (microbiology and genetics) be effective using only the 1000 most commonly used words? In the interests of simplifying my writing, I wrote a summary of my PhD project using only the 1000 most commonly used words (using this text editor):

This study wants to find the ‘small pieces’ which are important for tiny life (the helping ones) to stick to growing green things. Pseudomonas tiny life are some of the best helping tiny life and one of the most well-known ones, Pseudomonas protegens Pf-5, can control problems in growing green things used for food. But in the field, helping tiny life show does not stick to growing green things very often or very well. This study will look at the whole set of ‘small pieces’ important for P. protegens Pf-5 to stick to growing green things. Making tiny life stick better to growing green things will help lower problems with growing green things and better the return from growing green things used for food, which are important both here and around the world.

This is hilarious and obviously oversimplified (to the point of not making sense in a lot of places). For comparison, this is the ‘normal’ version of my project summary:

The project aims to identify the essential genes for colonisation of plant surfaces by biocontrol bacteria. Pseudomonas bacteria are some of the most successful biocontrol bacteria and one of the most well-known strains, Pseudomonas protegens Pf-5, has the ability to control diseases that affect cotton, wheat, pea, maize, tomatoes and potatoes. Despite this, field trials of biocontrol bacteria show a lack of reliability and persistence on plant surfaces. This project will conduct a genome-wide study of genes essential for P. protegens Pf-5 colonisation of plant surfaces. Enabling reliable colonisation of crop roots by biocontrol bacteria will contribute to lowering plant disease and increasing crop yields, which are important both in Australia and internationally.

From this exercise I learned that some level of complicated language is important to communicate a precise meaning (important in science), but not every complicated word is necessary. Sometimes the language I choose can be off-putting to the reader, make my work harder to understand and appear pretentious even when I don’t mean it to.

So overall, science writing in my field using the 1000 most used words is not practical and makes it harder, not easier to understand (even nonsensical in places). But it’s an interesting exercise to see just how much jargon you’ve used or if a simpler word will do in place of a complicated one. And wouldn’t we all like simpler rather than complex!

A minefield of data issues?

Even creating a small amount of data for my Masters project has brought home to me some of the issues around data – how to store it, where to store it, in what format to store it, how to ensure the appropriate people have access to it, how to stop people accessing it if they shouldn’t have access to it, how to future proof the storage, how to ensure the data and method used to collect it remain linked, who gets to keep it.

And that’s just for a few small plant biology experiments for my Masters. I’m sure there are many more levels of complexity for confidential data and big data. Some of these issue were discussed briefly with my Masters cohort, but it seems like that short conversation was only scratching the surface. I’m sure that as a (very) early career researcher there are a lot of things I don’t know and even more things I’m not even aware that I need to know.

 

Brightfield microscope image of Australian wild cotton (G. australe) leaf cross-section – one of the types of data from my Masters project (Photo: Belinda Fabian)

During my research break between my Masters and my PhD I’m working on up-skilling in a variety of areas; some directly related to my potential research topic(s), some which are generally related to study and/or my career (e.g. learning to code and using R) and others that just broaden my horizons (both scientifically and personally). One of the general study/career areas I’m learning about is data management through the 23 [research data] things program (see below for more information).

I see the 23 [research data] things program as helping me with generic study/career knowledge and skills and ideally it will will form part of a firm footing for me as a researcher. Awareness of the issues related to data management is important for researchers (and keeping digital data adds more concerns), but from my experience an understanding of it comes in a very piecemeal fashion during research training (as with many other things). So hopefully participating in this program will help me get out in front of the curve and make me aware of issues, solutions and strategies for managing data and where to find information down the track when the need becomes pressing.

Things you need to know:

The 23 [research data] things is a program run through ANDS (Australian National Data Service). More information can be found here. There’s an introductory webinar on tomorrow 1 March, 12.30-1.30 AEDT.

The program is free and runs from March to November 2016 (I know that sounds like a lot, but the FAQs suggest that it will only take about an hour a week and there will be breaks and catch-up opportunities during the year).

The program is advertised as being of interest to lots of different types of people – from the 23 [research data] things website: “If you are a person who cares for, and about, research data and want to fill in some gaps, learn more, find out what others are thinking… then this may be for you!” I’m getting involved as a research who will deal with data in my career, but if you’re a person who creates or cares for data then the program may be of interest to you too.

There’s a Meetup group for discussing the activities and other thoughts about the program and search #23RDThings on Twitter for all the buzz.

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.

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.

Finding the source journal article

I was pondering over a good topic for my first post and came across this article while floating around on the Internet. One of those timely and excellent finds from directionless link clicking.

The lack of full journal information in scientific news items frustrates the ‘ease of use’ and ‘knowledge should be shared’ person in me. It’s good to know that it’s not just me who is bothered by this.

This article has lots of good tips about how to find the original journal article fairly easily and without having to fork out a lot of cash. Fantastic for those of us who want to get ideas or news from easy to read and digest sources but also can delve deeper when needed.

Thanks to Bonnie Swoger who blogs as the Undergraduate Science Librarian.