Distractions and procrastination.

Distractions and procrastination.
There are lots of things to be embraced about being a Principal Investigator in Higher Education. You are free to direct your own research, you can be creative when devising your teaching sessions, and you can indulge your curiosity and passions, for example through public-engagement or immersing oneself in the literature.
But everyone knows that there is also the less enjoyable side to academic life – marking, ticking off marking criteria, providing student feedback, filling in marks moderation forms, attending exam boards – in general, the auditing and administration of mark-awarding.
These are things that need to be done to keep the external examiners happy, but they seem to have little obvious direct impact on the education of students.
And although ‘important’, they are tedious. So tedious that many, including myself, would succumb to any temptation to procrastinate during marking season.

At the best of times I love a good dataset to pore over – they usually jump right to the top of my ‘to do’ pile. But it’s heart-breaking when they arrive during marking season, when I’m most prone to distraction and procrastination, and yet subject to tight deadlines to get the mark-awarding paperwork completed.
So why do all the best datasets arrive during that marking season?
This marking season I’ve received ten genomes of novel bacterial isolates, the results of antimicrobial activity assays for 25 novel compounds, and a large set of transcriptome analyses, all of which need urgent analysis.
It’s like being a modern Tantalus, desperate to reach up to open those spreadsheets of insight and start analysing, while the chains of administration keep you grounded with moderation forms and marksheets.
So instead, I do neither and write a blog post.

Post by Dave Whitworth

Invasive Weed Species as a Source of Antimicrobials – Making the Best of a Bad Situation

Invasive Weed Species as a Source of Antimicrobials – Making the Best of a Bad Situation

Humans have always been dependant on nature to cater for their basic needs such a food and shelter but also for medicines. Initially medicines were in the form of crude treatments such as tinctures, teas, poultices, powders and other herbal formulations. The specific plants and methods of applications were originally passed down through oral history untill the information were recorded in herbals. In more recent history the use of natural products as medicines involves the isolation of active compounds [1]. The first active compound to be isolated in this way was morphine from opium by Friedrich Setürner in 1804 [2]. Drug discovery from plants also led to the isolation of many early drugs such as cocaine, codeine, digitoxin and quinine; some of which are still in use today. Due to the vast diversity of natural products ranging from teraestrial plants to marine organisms also incuding microorganisms and their infinite possible applications the isolation and characterisation for medicinal purposes continues today.
Plants have been the single most productive source of leads for the development of drugs, particularly as anti-cancer agents and anti-infectives [3]. Eventhough natural products have been a plentyful and continuous stream of useful drugs their use has dimished in the past two decade due to the major pharaceutical companies deminishing their interest in natural products. Due to slow nature of natural product discovery and its incompatiblity with high throughput screening (HTS) directed at moleculat targets [4]. Many large screening collections have been dissapointing in practice (these libraries containing a range of compounds from many different sources) natural products are the most diverse class of compounds with a significantly higher hit rate compared to fully synthetic and combinatorial libraries [5]. Furthermore, it has been shown that 83% of core ring scaffolds that are present in natural products are not present in commercially available screening libraries leading to fewer drug leads [6]. It is unsurprisingly that even with the introduction of new methods and technologies natural products have contributed massively to the drugs which have been approved in recent years (see Fig.1).

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Figure 1: Contributuion of Natural Products to Approved Drugs between 1981-2010; n=1355. (Adapted from Newman and Cragg 2012 [7])

My PhD funded by the Life Sciences Research Network Wales (http://www.lsrnw.ac.uk/). The project is based on the discovery of antimicrobal compounds form invasive weed species. Invasive non-native weed species are a significant global concern. These are resposible for a loss of biodiversity, altering ecological processes, impacting ecosystem services resulting in a cost of $35 billion annually in the USA [8-10]. If antimicrobial or any bioactive compounds could be sources from these problematic plants then we could at least draw one positive from their unwanted presence within our environment. This project includes the traditional extraction, isolation and characterisation of active compounds form plants followed by biological assays to test a range of biological activites of the compounds extracted. These techinques are also combined with the genomic and bioinformational approaches to aid and improve drug discovery. A wide range of plants were selected for this study and a range of compounds have been extracted from each with a range of interesting biological activites; especially antimicrobial activity. The most active plants tested were Japanese knotweed and Himalayan balsam.

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Resveratrol was found to be the most active antimicrobial compounds present in Japanese Knotweed. This compounds is also found in spermatophytes, such as grapevines and has been linked to a wide variety of biological activites. It has been reported to have antioxidant, anticancer, anti-inflammatory, prevent post-menopausal bone loss, and a range of positive metabolic effects. Resveratrol has also been suggested as the causal link between increased red wine consuption and decreased risk of heart disease [11].
A key compound has been found in Himalayan balsam which is by far the most potant antimicrobial compound in all the plants studied. It has a minimum inhibitor concentration of between 3-15 µg/mL agaisnt a range of Staphylococcal species. This compound has also been found to be non-toxic against mammlian cells. Similar compounds have also been show to have anti-cancer and anti-fungal activity.
The mode of action of these compounds are currently being elucidated using genomic, metabolomic and proteominc approaches combined with novel assays and cytometric techniques. In addition to this I aim to improve the activity of these compounds using computer aided drug design (CADD) through the Life Sciences Reseach Network Wales CADD Platform (http://www.lsrnw.ac.uk/platform-technologies/welsh-computer-aided-drug-design-cadd-platform/).
Natural products have been a source of drugs which have revolutionalised treatment of disease. It is clear that natural sources will contiune to play a significant role in the fight against disease and should be combined with new inovative methods which are currently being developed to form a multidisciplinary approach to treat disease.

References
1. Balunas, M.J. and A.D. Kinghorn, Drug discovery from medicinal plants. Life sciences, 2005. 78(5): p. 431-441.
2. Schmitz, R., Friedrich Wilhelm Sertürner and the discovery of morphine. Pharmacy in history, 1985. 27(2): p. 61-74.
3. Harvey, A.L., Natural products in drug discovery. Drug discovery today, 2008. 13(19): p. 894-901.
4. Harvey, A.L., R. Edrada-Ebel, and R.J. Quinn, The re-emergence of natural products for drug discovery in the genomics era. Nature Reviews Drug Discovery, 2015. 14(2): p. 111-129.
5. Sukuru, S.C.K., et al., Plate-based diversity selection based on empirical HTS data to enhance the number of hits and their chemical diversity. Journal of biomolecular screening, 2009. 14(6): p. 690-699.
6. Hert, J., et al., Quantifying biogenic bias in screening libraries. Nature chemical biology, 2009. 5(7): p. 479-483.
7. Newman, D.J. and G.M. Cragg, Natural products as sources of new drugs over the 30 years from 1981 to 2010. Journal of natural products, 2012. 75(3): p. 311-335.
8. Simberloff, D., et al., Impacts of biological invasions: what’s what and the way forward. Trends in ecology & evolution, 2013. 28(1): p. 58-66.
9. Hulme, P.E., et al., Bias and error in understanding plant invasion impacts. Trends in ecology & evolution, 2013. 28(4): p. 212-218.
10. Pimentel, D., R. Zuniga, and D. Morrison, Update on the environmental and economic costs associated with alien- invasive species in the United States. Ecol. Econ., 2005. 52(3): p. 273-288.
11. King, R.E., J.A. Bomser, and D.B. Min, Bioactivity of resveratrol. Comprehensive Reviews in Food Science and Food Safety, 2006. 5(3): p. 65-70.

Post by Dai Fazakerley.
Dai is a PhD student with Prof. Luis Mur and is one of our Biochemistry BSc graduates.

Workarounds, KISS and the dangers of overcomplicating things

Workarounds, KISS and the dangers of overcomplicating things

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Around 6 months ago I was asked to look into the program PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) for a data set produced by an amplicon sequencing run on the Ion Torrent. Basically, PICRUSt takes a 16S OTU table that has been classified (taxonomically, not some top-secret government thing, unfortunately) and uses information from sequenced genomes of known or closely related organisms to predict the potential genomic and metabolic functionality of the community identified in the 16S dataset. As complex as the process sounds, in reality, it actually only consists of three steps: Normalisation of the dataset by 16S copy number, this corrects for any potential under/over representation of functionality due to variation in the number of copies of the 16S gene in different bacterial genomes; prediction of the metagenome, basically multiplying the occurrences of functional genes, in this case KOs (KEGG orthologs), within the known genomes by the corrected OTU table from the previous step; finally categorising by function, collapsing the table of thousands of functional KOs into hierarchical KEGG pathways:

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Despite my background in sequencing, population genetics and phylogenetics, and having learned/taught myself many different analysis packages and programs over the course of my career, and having a solid, reliable, method of producing and analysing OTU tables from the data obtained from the ion torrent and other sequencing platforms, I’ve never considered myself as a bioinformatician. But four steps should be easy enough… right?

The little yellow arrow in the workflow now represents around 2-3 months of probably the steepest learning curve I have ever ventured on to.

OTU tables are simply a table of counts of OTUs (operational taxonomic units i.e. species/observations etc.) for each sample within your dataset. Despite their simplicity, the method used by myself and others in the research group to construct the OTU table was different to that in the online PICRUSt guide and the information contained therein, also different. I could already sense the increase in learning curve gradient, but carried on forward anyway not quite realising the dangers that lay ahead!

Operating system (UNIX/Windows) cross-compatibility, version control, system resources, version control, new programming languages, version control, more system resources, overloading system resources, complete system failure, starting from scratch again, installation-and-compilation-of-new-algebraic-libraries-for-your-systems-mathematical-calculations, version control, new programming languages, manual editing of enormous databases, scripts, packages and version control. These points are a hint at some of the processes I went through and the problems I had to deal with, in the creation of what I now call ‘The Workaround’. I still don’t consider myself a bioinformatician.

‘The Workaround’
The workaround consists of a small number of R scripts and processes for the reformatting and production of new files that are so simple they belie the amount of work that went into producing them. The steps are straightforward enough that anyone with any sort of experience of working with OTU tables or sequencing data should be able to complete them. The entire workflow is robust and repeatable and I have since worked with a few different ways of visualising and representing the data for publication.

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Using STAMP to identify SEED subsystems which are differentially abundant between Candidatus Accumulibacter phosphatis sequences obtained from a pair of enhanced biological phosphorus removal (EBPR) sludge metagenomes(data originally described in Parks and Beiko, 2010).

PICRUSt appears to becoming an ever more popular tool in the analysis of microbiomes and one that compliments many of the studies and analyses already performed by many of the members of our research group. I am currently in the process of writing up ‘The Workaround’ into a step-by-step guide to be placed on the bioinformatics wiki for anyone to access but in the meantime if anyone would like to speak to me about the possibilities of applying this type of analysis to any existing or future experiments I’m more than happy to help!

Post by Toby Wilkinson.
About Toby:
I am a postdoc and perpetual resident of Aberystwyth, having come here as an undergrad in 2002 to study Zoology, worked through a PhD in parasitology starting in 2005 and then holding various positions as technician/research assistant/PDRA since, I’ve never quite been able to bring myself to leave Aberystwyth. Over the last few years I’ve worked in various roles in the Herbivore Gut Environment group working on the microbiome of ruminants building up my experience in NGS and bioinformatics, and more recently with Sharon Huws on the further characteristaion of novel antimicrobial peptides, but also continuing work in NGS and the study of the dynamics of various bacterial communities in a number of environments.