Honours and Masters projects available for 2017

Just a quick note to let any prospective students know, we have a range of interesting and exciting projects available starting next year in the Basic & Clinical Myology Laboratory, on the topics of skeletal muscle development, regeneration and atrophy. Full details can be found here. Also, we will be running an information session on Wednesday the 14th of September, so come and say hello!

Towards a road map of stem cell metabolism and epigenetics

Earlier this year I published work completed with Dr Vittorio Sartorelli at the National Institutes of Health, linking a change in metabolism to a change in identity in muscle stem cells (see Ryall et al. 2015 for details). Since that publication, several more studies have been published that further solidify the link between the metabolic status of stem cells to their epigenetic signature, particularly recent work by Craig Thompson’s group (see Carey et al. 2015) and Yaakov Nahmias’ group (see Moussaieff et al. 2015). Because of the exploding interest in metabolism and the epigenetic regulation of stem cells, Vittorio and I put together (with the help of Mr Tim Cliff and Prof Stephen Dalton) an extensive review on how metabolism, and specific metabolites, can directly influence gene expression in stem cells. As a change in environment often precedes a change in cell state, it is essential to investigate the metabolic status of cells when thinking about epigenetic changes and cell fate. With the increasing use of novel techniques such as the Seahorse XF bioanalyzer and metabolomics, I’m excited about the future of stem cell metabolism and being a part of the development of a “roadmap” of stem cell metabolism and epigenetics.Figure 3_new2

Refers to (free full text): Ryall JG, Cliff T, Dalton S & Sartorelli V (2015). Metabolic reprogramming of stem cell epigenetics. Cell Stem Cell 17, 651-662.

Tips for prospective Honours and Masters students

While this discussion is targeted at students studying Biomedical Sciences at The University of Melbourne, most of the general tips listed below should apply across the board for Australian Universities.

As an undergraduate student slowly approaching the end of your degree, you are hopefully thinking about your future beyond this year. Perhaps you’ve already lined up a job, or have plans to start a new degree (medicine, physiotherapy, dentistry etc), or perhaps you are just focused on graduating and you’ll worry about next year, next year. With over 8,000 students expected to complete their undergraduate degrees this year from The University of Melbourne alone, and an Australian unemployment rate of 6.3% (at time of writing), it is essential that you differentiate yourself from all of the other undergraduates. One way to do this is to complete an additional ‘Honours’ year, or a Masters degree. Additionally, my Honours year was the most rewarding and enjoyable year of my undergraduate degree, and I believe that this should be the norm – not the exception.

So lets assume that you are interested in completing an Honours year (or Masters degree), how do you go about ensuring that it is a successful year? Here is my cheat sheet for how to decide upon a supervisor/project to maximise your chances of having a successful year.

1.DO YOUR HOMEWORK

Start thinking about topics early. Think back to first semester, which lectures did you find the most interesting? Did lecturers present original research from their labs (this can give you an idea of the type of research they do)? Search online to see if your potential supervisor has a lab webpage (this often has links to ongoing research and projects available). Do a literature search to find out what your potential supervisor has published recently.

2.ATTEND AS MANY INFORMATION SESSIONS AS YOU CAN

At The University of Melbourne, you’ll see posters advertising for Honours (and Masters) information sessions for each of the different departments and research centres during August-October. Attend as many of these as you can, it will give you a chance to meet many of the supervisors, and you’ll also be able to find out more regarding the specifics of certain projects. These sessions are also an opportunity to meet existing honours, masters and PhD students. Talk to these people! Ask them about their supervisors, ask them about other supervisors, ask them about their projects, ask them if they are enjoying their research. This is where you will get your first real information about your preferred supervisors. I cannot stress how important this is (see point 5 below).

At the very least, most of these information sessions also have free pizza….

3.CONTACT YOUR PREFERRED SUPERVISORS

Now that you have done your homework, you know which supervisors and which projects are of interest, it is time to contact your preferred supervisors. Do not send out a mass proforma email, your supervisors will likely get a dozen of these, Instead, think about why you want to do honours (or masters) in this lab, and communicate that in a short email. You will hopefully have met this person at one of the information sessions, and you can state that in the email. At the end of the email you should request a time to meet with the supervisor in person to discuss the project and honours in more detail. While it’s not essential, you can also state your average grade (keep in mind, your supervisor will probably look this up before they agree to meet with you).

4.INTERVIEWING WITH YOUR POTENTIAL SUPERVISOR

This may be your only chance to meet with your prospective supervisor before they decide which students to take, so you need to make a good impression. More importantly though, this may be your only chance to determine what sort of mentor/supervisor this person is going to be. You should be broadly familiar with the research the lab does, and you should have a clear idea as to why you want to do honours. Don’t be suprised if you are asked what your long term plans are (I accept that the majority of students I interview want to do Medicine after doing Honours, I see that as a challenge to try and convince you to stick around and do a PhD!).

Here are a list of questions that I feel are important for you (as a potential student) to ask your potential supervisor:

  • How many people are currently in your lab? (great question to determine whether there are postdocs/PhDs/research assistants that will be available to help you out when your supervisor isn’t around) 
  • How many Honours/Masters students have come through your lab in the last two years? What grade did they get? (This is an important question, if a supervisor um’s and ah’s in response to this – run for the hills!)
  • How often do you meet with your students? (This will be different for every supervisors, some will have set meetings, others will have an open door policy. Make sure that you will get regular access to your potential supervisor)
  • How many students are you likely to supervise next year? (Be careful with any supervisor who is planning on supervising several [4+] students, this may be a warning sign of someone who just wants cheap labor)
  • What sort of techniques will I learn throughout my project? (Many supervisors won’t have decided upon a specific project, but they should be able to give you a rough idea of the different techniques you’ll get to learn. Make sure it sounds like you’ll get to try several different techniques)
  • Where does the funding for my research project come from? (This is a good question to ask to find out how successful the lab has been in terms of getting funding, but tread carefully here, some supervisors will not want to talk about grant successes/failures with students).
  • Can I meet with some of your current students? (If you only ask one question, make sure it is this one. Any supervisor who will not let you talk to current students should be immediately crossed off your list).

5.MEETING WITH EXISTING STUDENTS

 This is so important, and yet so many prospective students fail to do this. You should aim to meet with as many students as possible (hopefully all of them from your preferred labs). Ask them directly about their mentor (your soon to be supervisor). Ask them whether they get along with their mentor, do they get enough supervision (or too much). Ask them about their research (are they excited about the project? are they enjoying research?). Ask them what it’s like working in the lab? Is it a good environment? Do people get along with each other? Ask them what they know about other labs and other supervisors. Current students are like gold, offer to take them out for a coffee and pick their brains.

6. COME AND SEE ME

Once you have done all of the above, come and see me. I’m happy to meet with prospective students, and I am always on the look out for new students who want to study stem cell metabolism and change the world!

A quantitative analysis of track record?

Yesterday The Conversation published an article titled “Science funding should go to people, not projects” which has as its leading hypothesis that we should shift from a project funding model to a person funding model. The reason for this is that it is difficult to compare the relative merits of projects from different fields, and in times of extremely low success rates leads to the preferential funding of “safe” projects (often projects which are essentially complete already). Immediately following publication of this article, the twitterverse exploded, with several very interesting reply threads arising (unfortunately the discussion became a bit ad-hoc, and no # was devised at the time, however @Dr_Mel_Thomson has created a great Storify page).

Let me say straight out that I am against funding people in the absence of projects. My concern (similar to those raised by several others) is it unfairly biases against young Early and Mid-career Researchers (EMCRs) and women. It encourages funding to go to senior academics in large established labs. To counter this, many will argue that track record will be judged ‘relative to opportunity’, and this leads me to the topic of this post, can we shift away from the current qualitative measure of track record to a more quantitative metric that more accurately reflects the idea of ‘relative to opportunity’?

With the rebuttal period for the major funding rounds for the Australian National Health & Medical Research Council (NH&MRC) and Australian Research Council (ARC) just about to finish, I’m sure everyone has their own anecdotal story to tell about the evaluation of track record. From a personal perspective, I’ve had comments suggesting I had a “moderate” track record from one assessor, and an “outstanding” track record from another – on the same grant. Regardless of what my track record may or may not be, these comments indicate the problems associated with a qualitative assessment of track record.

Assessors for NH&MRC are told to ignore journal impact factors and other metrics such as H-index when assessing track record, and they are told to assess the candidate relative to opportunity. In the absence of guidelines, this becomes haphazard at best (through no fault of the assessors, having assessed several grants over the last three years I find it tough). While it is easy to pick the investigators/teams with the absolute top track records (6/7’s for NH&MRC and A’s for ARC), and the non-competitive track records, the next tier down becomes difficult (think 5’s for NH&MRC.and B/C’s for ARC).

Imagine logging on to RMS (for ARC) or RGMS (for NH&MRC) and being able to quickly calculate a researcher score (lets go with the current NH&MRC system of 1-7). If your score comes out as less than 4.5 you are ineligible to submit a grant as a lead investigator. Straight away, this would reduce the burden placed on external reviewers by triaging applicants who are deemed not-competitive. A score of 4.5+ allows you to submit your project proposal for review. During the rejoinder process, RGMS/RMS allows you to update your track record and recalculate your score, at this point a minimum of 5.5 is required to progress. This would allow those researchers who have that big, exciting paper published in May/June to significantly improve their score.

Following these two rounds of culling based on track record, the remaining grants could go to the grant review panels (NH&MRC) or College of Experts (ARC) for discussion. Track record scores could then be adjusted by the respective panels when deemed necessary.

Now the big question, what variables do we plug in to generate the magical score? Can we limit the potential for researchers to game the system? Can we make the quantitative score fair across a broad range of disciplines? Importantly, any quantitative value/metric MUST improve on the current system. This is where I make my disclaimer, these variables are simply a list of things I would like to see incorporated, it is by no means exhaustive and there will be plenty I have forgotten (feel free to point out things I’ve missed in the comments), but these are things I believe are important. Any score should be a combination of both track record (numerator) and time/opportunity (denominator).

NUMERATOR VARIABLES

FIRST/LAST AUTHOR PUBLICATIONS – First and foremost, any track record score needs to take in to account both the quantity and quality of publications. Generating a fair score for this is difficult, impact factors can bias against certain smaller fields, citations are not necessarily a true indication of the impact of a piece of work, Altmetrics can be gamed (to some extent). A significant improvement in recent times are the inclusion of article level metrics (how many times an article has been viewed, downloaded, cited). Each one of these variables is imperfect on its own, but a combination of journal impact factor, article level metrics, and online impact (altmetrics) could give a reasonable measure of the “impact” of the work.

SECONDARY AUTHOR PUBLICATIONS – The number of first/last author manuscripts are particularly important, but we also need to recognise the importance of secondary author manuscripts as well. Science is supposed to be collaborative, and the last thing we want to do is encourage researchers to work in isolation, and yet so many reviewers count up the number of first and last author manuscripts and ignore everything else. This removes the impetus for collaboration. Could the publication metric be weighed by position in the author list? (1.0 for first/last, 0.2 for secondary author?).

PREVIOUS FUNDING – As you’ll see, I’ve included this in the calculation of the NUMERATOR and the DENOMINATOR below, as funding often begets more funding. But it is important to recognise a track record of being able to successfully obtain funding, from national funding agencies, international sources and industry.

PRIZES/AWARDS – At the moment I’m not convinced this section really adds much to the track record score, as assessors tend to focus on publications above all else. But prizes and awards are important, they are often awarded by our peers in recognition of outstanding work. Again, difficult (but not impossible) to put in to a numerical score. Any scoring system should take in to account whether the prize/award was internationally/nationally/locally competitive with the scores weighted appropriately.

PATENTS – Beyond my area of expertise, but could be assigned a value in the same way as prizes/awards.

SPEAKER INVITATIONS – As for prizes/awards, speaker/chair invitations should be weighted based on location (international/national/local), with emphasis on any international speaker invitations.

SUPERVISION OF STUDENTS – A simple count of the number of active and completed Honours, Masters and PhD students an individual has/is supervising (0.25 for Honours, 0.5 for Masters and 1.0 for PhD?).

COMMUNITY ENGAGEMENT – This may be a qualitative variable not considered until the application reaches the panel review stage (that’s not to say this is not important, but rather speaks to the huge range in which individuals communicate/engage the community). Would be happy to hear suggestions about this.

DENOMINATOR VARIABLES

TIME – NH&MRC evaluates track record over five years, ARC uses ten years. Personally I prefer ten years, as it can demonstrate a sustained level of achievement, while also minimising the influence of a single bad year. This is also where relative to opportunity can be directly applied. If you are three years post-PhD then you could be evaluated over 3 years (or perhaps since the date of your first publication included above?).

CAREER INTERRUPTIONS – If you’ve had time out of research due to maternity leave, illness, carers leave etc. then this time can be deducted from the value above.

LAB CHANGE – One of the things that has always frustrated me is the disadvantage faced by early/mid-career researchers who might change labs 2-3+ times in the space of ten years before they establish themselves. In contrast, an established senior researcher has the advantage of not moving from lab-to-lab, and does not experience the same decrease in productivity for this (hopefully short) period of time. Therefore, I would propose a value (one month? two months?) for each time a researcher has changed labs (up to a maximum).

LAB SIZE – This is a difficult one, but the point I want to make here is that a senior researcher with ten post-docs would absolutely be expected to have a better track record than a new investigator with a single post-doc.

PREVIOUS FUNDING – On a similar note, a lab that has a track record of receiving a large amount of continuous funding will be expected to have a better track record than a lab with less funding.

CONCLUSIONS

This is by no means an exhaustive list, and is not meant to be. I simply want to push the conversation about how we can more accurately measure track record (relative to opportunity), and the much larger conversation about how to reduce the workload put on external assessors reviewing grants. Please let me know your thoughts in the comments below.

EDIT: I’ve included full definitions for my abbreviations

Effects of GFP on C2C12 metabolism

I wanted to share some results we recently generated on the Seahorse Bioanalyzer, looking at the effect of GFP on cell metabolism. I was really surprised at the size of the response, and how reproducible it was. Hopefully others will find this data useful/interesting when interpreting their own results. Please let me know in the comments section below if you have found something similar.

This year my BSc(Hons) student (the very talented Ms Alex Webster) has been working on a C2C12 cell based project involving transient overexpression of a GFP tagged protein. Alex has completed several experiments so far looking at the effect of her protein on rates of proliferation and differentiation. Because of my ongoing interest in cell metabolism, Alex also used the Seahorse XF24 Bioanalyzer to examine the effect of her protein on OxPhos and glycolysis. She compared metabolism across three groups, control, GFP alone and her protein tagged with GFP, the results of the first two groups are presented below (we are planning on publishing a study with the tagged protein, so it isn’t included).

GFP_bioenergeticsMETHODS: The two graphs are from a single mitochondrial stress-test, run on a Seahorse XF24(3) machine, measuring oxygen consumption rate (OCR, top graph), and extracellular acidification rate (ECAR, bottom graph). In each graph, the green line/points indicate the GFP group, and black the control. Cells were seeded at a density of 25,000 cells/well and transfected with a commercially available GFP plasmid from Origene (pCMV6-AC-GFP) using Lipofectamine3000. The assay was run the next day as previously described by Dr David Nicholls (I will provide a full outline of our methodology in the ‘Muscle Methods’ section at a later date).

RESULTS: As you can see, overexpression of GFP in C2C12 cells results in a significant 3-fold increase in basal glycolysis (bottom graph) and a minor decrease in maximal oxidative capacity (peak observed between points B and C in top graph). Alex has repeated this experiment several times, and in every case experiment she has observed a 2-3 fold increase in glycolysis.

These results have important implications for the use of fluorescent tagging in cell culture studies. Altered cellular metabolism can lead to altered metabolite levels which can directly influence the transcriptional profile of the cell. Importantly, these results also indicate the need to confirm the metabolism of cells isolated in lineage tracing models (such as the often used ROSA26eYFP mouse).

As we have only used a single plasmid at this stage, I would be really interested to hear what experiences others have had when using fluorescent tags and the Seahorse.

Reflections on metabolism and skeletal muscle stem cells

The last few months have been a bit of a whirlwind at work, with plenty of successes to celebrate – the biggest of which has been the acceptance and publication of my postdoctoral research in Cell Stem Cell  (Ryall et al. 2015) investigating the process of metabolic reprogramming in skeletal muscle stem cells. This project began in 2008 in Dr Vittorio Sartorelli’s Laboratory of Muscle Stem Cells & Gene Regulation, when we noticed that there was a distinct metabolic gene profile in quiescent compared to proliferating muscle stem cells. What was particularly clear was the increased expression of glycolytic genes in the proliferating population (nearly every glycolytic enzyme was increased 2-20 fold). A similar upregulation can be observed in other previously published datasets, such as those from Margaret Buckingham and Tom Rando. This shift towards glycolysis is not unexpected in a proliferating cell population, and similar changes in metabolism have been observed in other stem cell and cancer cell populations. One of the reasons for this change is the requirement for new biomass (nucleotides, proteins and phospholipids for the generation of new cells, see here and here for recent reviews). However, what was particularly exciting was the link between this change in metabolism, gene transcription, and cell identity. We observed that the increase in glycolysis led to a reduction in the availability of NAD (a substrate required for the deactylase activity of SIRT1) and, as a result, an increase in the acetylation of one of the histone targets of SIRT1, histone H4 lysine 16 (H4K16). Using the Pax7cre X SIRT1fl/fl mouse model we could demonstrate elevated global H4K16ac in skeletal muscle stem cells, while culturing cells in galactose based growth media (instead of glucose) could prevent/delay the decline in NAD and H4K16ac. These results suggest that skeletal muscle stem cells can undergo a process of metabolic reprogramming during activation, and clearly link metabolism to cell identity (see our graphical abstract below).

Print
While the last two-decades have focused extensively on the molecular revolution, and defining the transcriptional networks that regulate skeletal muscle stem cell identity and the processes of activation and proliferation, I expect the next decade to reveal the underlying cellular signals that initiate and regulate these networks. I’m excited about the role metabolism may play in regulating these processes, particularly the importance of metabolite availability (NAD for SIRT mediated deacetylation, acetyl-coA for acetylation, methionine for DNA/histone etc).

Refers to Ryall JG, Dell’Orso S, Derfoul A, Juan A, Zare H, Feng X, Clermont D, Koulnis M, Gutierrez-Cruz G, Fulco M, Sartorelli V (2015). The NAD+-Dependent SIRT1 Deacetylase Translates a Metabolic Switch into Regulatory Epigenetics in Skeletal Muscle Stem Cells. Cell Stem Cell Feb 5;16(2):171-183.

Congratulations!

Just wanted to quickly post a big congratulations to all of the students in the Department of Physiology who handed in their Honours theses on Friday. A huge amount of work goes in to an Honours year (by both student and supervisor!), and such an effort should be recognised. In particular, I want to acknowledge the efforts of my own student, an awesome effort over the whole year…… now the real work begins!

On a related note, for students interested in doing Honours or Masters in the Department of Physiology at The University of Melbourne starting in 2014 please see the link here 

Musings on the future of academic publishing

I have read a number of really great articles recently, regarding the future of academic publishing, and I have long been excited by the potential of altmetrics as a mechanism to judge the true impact of a research paper. But having spent the last week chatting with my partner (who just happens to be a rising star at one of the largest publishing houses in NYC) about the future of publishing in general, I am convinced that we need to completely reinvent the way we approach presenting and sharing data. The current system of peer-review and publishing in academic journals was perfectly suited to the environment 20 years ago, it was appropriate for the volume of publications and the size of the data sets presented. This is no longer the case.

The sheer size of some data-sets (think next-gen sequencing, or next-gen imaging), and type (3D modelling, video) cannot be presented in print editions of journals. The current solution is to present these files as supplemental data which are not available in print editions, and are (generally) separate to the main document online. Similarly, there are problems related to journal paywalls (for publicly funded research), lack of detailed methods sections (when did we arrive at the point of a single sentence being acceptable for particular methods?), impact factors (see some great blog posts from Stephen Curry and Michael Eisen), author lists and author credit, time between submission and eventual publication, the single-blind peer-review system, and many more. The open-access revolution has begun to address many of these problems (I am a massive fan of the article-level metrics and the comments section – even if this is currently underused, in the PLOS journals), but these journals are still limited to the standard article format (abstract, introduction, methods, results, discussion).

At the risk of oversimplifying and generalizing, labs tend to have a particular research focus (a disease, a protein/transcription factor, a model organism/cell etc), and that lab will build on that research focus over an extended period of time. A lab may publish several smaller papers (or one large paper) that build around a central issue, and then branch out in a new direction. The several smaller papers may be published in different journals with different first authors. The single large paper may take years from inception to publication. As each lab builds upon each publication, wouldn’t it be great to be able to see a story develop from inception to conclusion (and not the “future studies will investigate….” conclusion,  but rather “this has led us to a completely new area….” or “we are sufficiently satisfied with our conclusions and have shifted our attention to…”). Wouldn’t it be great to be able to access the entire story from a single destination, and to be able to comment on the work prior to publication? Wouldn’t it be great to see some of the raw data?

What I’m leading to with this discussion is a proposal to change the way science is presented, shared and judged. What if we had a Facebook of science? What if we took something like ResearchGate a step further and uploaded results directly on a pre-publication weekly/monthly/quarterly basis? Each researcher could have their own page which would include a short summary of their research interest, an introduction that puts their research in context, a detailed methods section (this would include the type of protocols used in the lab, step-by-step protocols to allow for reproducibility), and then (well organised!) figures and tables that are regularly updated with both raw data files and condensed figures. Finally, a discussion of the results obtained and what the next step/s will be. Importantly, all of these sections would be open for comments (from registered users). Researchers would have the option of ‘following’ pages/projects that were of interest to them and could receive notifications of updates. The relative impact of the science being conducted could be judged via page views, links to pages, number of followers, this could be judged on both an immediate level (a researcher uploads an incredibly exciting result once), and across the course of their career (a researcher who consistently uploads interesting results).

A system such as this would increase peer-review from 2-3 reviewers, to however many people find the topic interesting. It would allow for the generation of a complex story over a number of years, will still providing clear evidence of progress (especially important for early- and mid-career researchers). It should encourage the publication of both positive and negative results (imagine how many experiments have been repeated by different labs but never published? Think of how much money is wasted because we don’t publish negative results). It should reduce the likelihood of getting ‘scooped’ after spending several years on a single project (or at least reducing the pain to getting scooped on months rather than years work).  It should encourage more open collaboration between research groups, and it should encourage uploading of raw data, thus reducing the likelihood of data manipulation.

I think some of the open-access journals have started this revolution, but I also believe that the revolution is only just beginning.