Too much data

A Simple Screening Approach To Prioritize Genes for Functional Analysis Identifies a Role for Interferon Regulatory Factor 7 in the Control of Respiratory Syncytial Virus Disease

Scientists love data. It is like flower to florists, canvas to artists, money to bankers or ingredients to chefs. It answers the questions we have and sets the direction for new ones. From Mendel and his pea plants to Darwin and his finches through Rosalind Franklin and her X-ray crystals of DNA to CERN and their atom smashing tube thingy, the aim of experiments is to generate data to answer questions. We invest considerable time and effort to work out if the data we have is true and representative of the whole or a unique subset caused by chance (statistics) or the way we did the study (experimental design). We often repeat the same experiment multiple times to convince ourselves (and more importantly others) about the validity of our data. Without data, we are just messing around in a white coat.

Too much data

So you would think the more data the better. However, you can have too much of a good thing. Whereas before you would ask does my treatment increase or decrease a single factor, we can now measure 1000’s of things in a single experiment generating huge piles of data (datasets).In biology,, methods that generate large datasets are described as ‘omics. This is named after the genome (all the genes that make up an organism). We now have the transcriptome (all the mRNA – the messages that make proteins - at a certain timepoint), the proteome (all the proteins), the metabolome (all of the bacteria), the microbiome (all of the bacteria on or in the body) and the gnomeome (the number of garden ornaments per square metre). Each technique generates a long list of stuff that goes up or down after a certain treatment. These long lists of data are where the problems arise, being comprised of genes with weird short names like IFIT1, LILRB4, IIGP1 many of which have no known function. All of which leads to a mountain of data languishing in supplemental tables of half-read papers in obscure journals.

Biologist + computer = ???Xxx!!!

The surfeit of data has led to a whole new discipline to interpret these lists called bioinformatics. But bioinformatics requires special skills, knowledge of the mythical ‘R’ programming language, access to software tools with laborious jokey names based on forced acronyms like PICrust (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) and time. Faced with these datasets, I get a bit flustered: like many biologists, I type with 2 fingers, get nervous flushes if someone mentions Linux and can just about use Excel to add two numbers together. This is a problem because it means that there is a wealth of data out there that is inaccessible to me.

Bioinformatics for dummies

I am interested in how the body fights off viral infections in the lungs, particularly a virus called Respiratory Syncytial Virus (RSV). Part of the body’s defences is a family of proteins that restrict viruses ability to hijack our cells to make copies of themselves. There are a lot of these proteins, many of which we have no idea about how they work. A brief look at some of the ‘omics studies reveals long lists of these proteins, with no insight as to what they do. There are probably clever, but inaccessible, AI based algorithms that can search for all the relevant papers and compile them somehow; but I wouldn’t know how to use them or even where to start looking. Instead we used a ‘brute force’ approach, which meant that I/we/Jaq (first author on the paper) sat down and searched for every paper ever published on RSV that contained a big data. Having found the papers, we then harvested the gene lists from them. This was not trivial, some of the papers had to be ignored because they had inaccessible data locked behind pay walls, or were in Chinese, or were just rubbish papers or a combination of the three. But we were left with gene lists from 33 papers and stuck all the data in a big pile. At this point we employed the services of Derek, a bonafide bioinformatician, who through some computer wizardry wrote us a piece of software called geneIDs (which is freely available here, if you need such a thing), which handily counts and ranks the genes. This gave us a brand new list of all the other lists (sometimes called metadata) which can then be used as the basis for further analysis. Which we did and published the results here.

More data: better tools

First of all we compared our computer generated list to some new data from a clinical study. Children with severe RSV had higher levels of 56% of the genes on our list. This supports the approach demonstrating that the genes are important during infection. Taking a subset of these genes, we then performed experiments that showed that they are able to reduce RSV ability to infect cells and animals. In particular we demonstrated that a gene called IRF7 was central to the anti-RSV response. So ultimately the answer to the question, can you have too much data is no, but there is a need for tools to interpret it. In the current study we developed one such tool, which we feel is more accessible to biologists with little to no computer skills.

 

Sweeter lungs more bugs

Jam jar lungs

Why do some people, for example people with diabetes, get colds more often? We believe we have found a contributing factor – sugar, in particular glucose. Diabetes is defined by elevated blood glucose. 13 years ago, Prof Emma Baker and Prof Debbie Baines (at St George’s University of London) noticed that additionally, people with diabetes have increased airway glucose. Normally, the cells that line the airways pump any glucose that leaks into the lungs back into the blood. In diabetes, there is too much sugar in the blood and the pumps are overwhelmed, leading to a rise in airway glucose.  They hypothesized that the increased level of sugar in the lungs would allow more bacteria to grow in the lungs – the biological equivalent of leaving a jam jar open!

Diabetes = more lung bacteria

In our latest paper (Increased airway glucose increases airway bacterial load in hyperglycaemia) we set out to test this hypothesis using a number of different techniques. First we looked in hospitalised patients to see if there was a link between glucose and bacterial infection, and there was, patients with high blood sugar were twice as likely to have a bacterial lung infection. We know this thanks to our collaborators, Dr Luke Moore and Professor Alison Holmes, who have been tracking bacterial infections in London hospitals. This kind of a study is called an association or correlation study, and these studies are very good at showing that one thing is linked to another, but do not tell whether the link is causal and if it is how (the mechanism in scientific parlance).

Knockout bugs

In order to understand the how, we investigated how bacteria use glucose in the lung. The way we do this is to delete individual bacterial genes and compare the function of these gene deleted mutant bacteria to bacteria with all their genes (wild type). We deleted four different genes that based on their shape and similarities to genes from other bacteria were predicted to be important for the bug to be able to use glucose. These studies were performed using a bacteria called Pseudomonas aeruginosa, which, unless you have cystic fibrosis, you’ve probably never heard of, but causes many cases of pneumonia each year, especially in hospitalised patients. The first step was to demonstrate that deleting the genes affected Pseudomonas ability to use glucose to grow. Great news, they do.

Hypothesis - tested

The final step was to link everything - high glucose, in the lungs and bacteria - together. We did this using mice with diabetes (yes they do exist). As seen in people with diabetes, diabetic mice get more severe bacterial lung infections, unless you infect them with bacteria that can’t use glucose. When these bacteria were used, there was no difference in the bacterial lung infection. Boom, job done.

Drugs for bugs

But why stop there, understanding the factors that increase infection gives us new ways to fight infection. This is particularly important for bacterial infections because our arsenal of antibiotics is rapidly being depleted and we desperately need new treatments. If increased lung glucose increases infection it follows that drugs that reduce lung glucose should reduce infection. We tested the common anti-diabetic drug, metformin. Diabetic mice treated with metformin had lower lung glucose and less bacterial infection.

In conclusion, we have linked increased bacterial infection in people with diabetes to the level of glucose in the lungs, and used this finding to test new antibacterial treatments. If you want to read more details the paper is here.

Failing to fail gracefully

Advice: easier to give than to follow

This time last year, I wrote ten strategies to improve mental health in academic life. I think they’re worth reading, if you haven’t already. You’d think that having given all this advice, I would have followed it, and maintained a Zen-like calm. Not so.

In the last year I have allowed failure (and the prospect of failure) to define my mood, compared my progress with researchers several leagues above me and found myself wanting, got too obsessed with work to appreciate anything else, taken on more than I can manage, unsuccessfully disguised my jealousy about colleagues’ success, taken criticism as a personal attack, and not spoken to anyone about what was going on in my head.
Whilst reflecting on my inability to follow my own advice, this year I wanted to come up with something that I could follow to improve my own mental health. Then I had (another) grant bounce and realised that, for me, the major contributor to mental health issues in academia is failure. Yes, failure is relative and, yes, there are clearly bigger problems in the world. But in that bitter moment of rejection it’s hard to step back and see that.

Do take it personally

Failure is distressing. The process of grant writing is long and hard; the time it takes to get over grant rejection is long and hard. Even the most thorough, fair and supportive reviewer will not spend as long destroying your hard work as you’ve spent creating it. Failure is stressful. Sadly we are judged on our inputs and outputs, if we are not bringing in money or putting out papers, we feel exposed. And failure is personal. Not only because it is your ideas that are being rejected but also because grants are judged in part on your CV – it is you who is being rejected.
All of which is to say failure sucks. If you are anywhere in an academic environment, you don’t need me to tell you that. But based on the success of the CV of failures, it can help to know that other people are having a bad time too. So if it helps you, I am, currently, having a bad time.

The 24 hour rule

I hope to be having a better time soon. Normally, I allow myself 24 hours to wallow in failure (I’m writing this at hour four). I warn my students that I have a grant decision coming up to give them time to avoid me. I go and find other things to be cross or sad about. I have a drawer of failed applications that I stare at; I contemplate quitting; I swear more and sometimes kick things. Then, at the end of the cycle, I start again with the next application.

Fail less?

So where does that leave me? Essentially, I need to reduce the impact of failure on my mental state. The first approach would be to fail less – either by applying less (a very short term strategy, with a guaranteed result of no job) or by being more successful (essentially impossible – funding rates and paper acceptance rates are both in decline).
The second, more realistic, approach is to find real ways to cope better with failure when it, inevitably, happens. Some of the tools I’ve suggested before – mindset, perseverance, not taking it personally, getting support, taking a step back, getting some perspective (it is only one grant after all) and not acting like a spoilt child – should all help. But sometimes they don’t. Maybe it’s because there are no quick fixes.

Ready player one

However, I have had a moment of clarity. I am (in the gap between work, childcare, running, gardening and husbanding) a gamer. Not a lock-yourself-in-a-darkened-room-for-a-week-to-be-the-first-to-finish-gamer; nor an online gamer, because my reflexes are too slow and I don’t like losing. But a reasonable amount of my spare time is spent killing dragons, fighting aliens and losing to my son at FIFA.
Currently, the game I’m playing most is Dark Souls 2. It’s designed to be hard; and it is really, really, really hard. I repeatedly fail and have to start again; and again, and again, and again. Despite this much failure, I don’t throw my controller down in disgust and quit (that often), in fact, I pay money for the opportunity to fail. So what’s the difference between gaming and grants?
The major difference is that gaming is more enjoyable. Setting aside the personal nature of rejection, failed grants hurt because they feel like time wasted. Admittedly the stakes are lower – I am not going to lose my job if I don’t save the Kingdom of Drangleic from King Vendrick (a gaming reference so nerdy, I am embarrassed even typing it).
Next time, I’m going to try something new: I’m going to try to enjoy the grant writing process. Instead of seeing it as time lost, I will use it as a springboard to do thought experiments, create new ideas, read the literature more widely and improve my writing. Then, when it is rejected, it won’t hurt so much. Probably.

Author’s note
Having written what I believed to be an uplifting end to this, my games console broke, leaving me unable to ever finish the game, which feels like a metaphor for something.

This article first appeared on the NatureJobs Blog: