New Publication: Reconstructing promoter activity from Lux bioluminescent reporters

Absolutely delighted to report that our paper has been published:

Iqbal M, Doherty N, Page AML, Qazi SNA, Ajmera I,  Lund PA, Kyraios T, Scott DJ, Hill PJ and Stekel DJ (2017) Reconstructing promoter activity from Lux bioluminescent reporters. PLOS Computational Biology 13(9): e1005731. https://doi.org/10.1371/journal.pcbi.1005731.

Abstract

The bacterial Lux system is used as a gene expression reporter. It is fast, sensitive and non-destructive, enabling high frequency measurements. Originally developed for bacterial cells, it has also been adapted for eukaryotic cells, and can be used for whole cell biosensors, or in real time with live animals without the need for euthanasia. However, correct interpretation of bioluminescent data is limited: the bioluminescence is different from gene expression because of nonlinear molecular and enzyme dynamics of the Lux system. We have developed a computational approach that, for the first time, allows users of Lux assays to infer gene transcription levels from the light output. This approach is based upon a new mathematical model for Lux activity, that includes the actions of LuxAB, LuxEC and Fre, with improved mechanisms for all reactions, as well as synthesis and turn-over of Lux proteins. The model is calibrated with new experimental data for the LuxAB and Fre reactions from Photorhabdus luminescens—the source of modern Lux reporters—while literature data has been used for LuxEC. Importantly, the data show clear evidence for previously unreported product inhibition for the LuxAB reaction. Model simulations show that predicted bioluminescent profiles can be very different from changes in gene expression, with transient peaks of light output, very similar to light output seen in some experimental data sets. By incorporating the calibrated model into a Bayesian inference scheme, we can reverse engineer promoter activity from the bioluminescence. We show examples where a decrease in bioluminescence would be better interpreted as a switching off of the promoter, or where an increase in bioluminescence would be better interpreted as a longer period of gene expression. This approach could benefit all users of Lux technology.

Author summary

Bioluminescent reporters are used in many areas of biology as fast, sensitive and non-destructive measures of gene expression. They have been developed for bacteria, adapted now for other kinds of organisms, and recently been used for whole cell biosensors, and for real-time live animal models for infection without the need for euthanasia. However, users of Lux technologies rely on the light output being similar to the gene expression they wish to measure. We show that this is not the case. Rather, there is a nonlinear relationship between the two: light output can be misleading and so limits the way that such data can be interpreted. We have developed a new computational method that, for the first time, allows users of Lux reporters to infer accurate gene transcription levels from bioluminescent data. We show examples where a small decrease in light would be better interpreted as promoter being switched off, or where an increase in light would be better interpreted as promoter activity for a longer time.

 

Thanks to all my brilliant collaborators and coauthors. Thanks also to the lovely referees (one of whom signed their review) who said of the article: “an extremely important contribution to the field” (Reviewer 1) and “a significant advance” (Reviewer 2) and  provided helpful and constructive feedback.

 

 

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New publication: microbial mass movements

Delighted that our perspective in Science has been published.

Zhu Y-G, Gillings M, Simonet P, Stekel DJ, Banwart S and Penuelas J. Microbial mass movements. Science 357: 1099-1100.

My involvement is relatively minor: we have written a much longer piece (which we are looking to publish also) to which I have contributed a fairly substantial section on modelling – and then when Michael Gillings put together this short perspective for Science, he compressed everything I wrote into a single sentence! Maybe it is an improvement 🙂 Anyway, it is a real privelege to have coauthored which such amazing international scientists, and a delight that we have had it published in such a great journal.

Summary

For several billion years, microorganisms and the genes they carry have mainly been moved by physical forces such as air and water currents. These forces generated biogeographic patterns for microorganisms that are similar to those of animals and plants (1). In the past 100 years, humans have changed these dynamics by transporting large numbers of cells to new locations through waste disposal, tourism, and global transport and by modifying selection pressures at those locations. As a consequence, we are in the midst of a substantial alteration to microbial biogeography. This has the potential to change ecosystem services and biogeochemistry in unpredictable ways.

Nearly new publication: Metal Resistance and Its Association With Antibiotic Resistance. Advances in Microbial Physiology

Last month the review that Sankalp and I contributed to was published on line by Advances in Microbial Physiology. This review was led by Jon Hobman, with considerable writing by Chandan Pal. It is a real honour to have co-authored with the amazing Joakim Larsson. My own contribution was small: Sankalp contributed some review material on modelling, and I got stuck in with Joakim and Jon in the editing phase to ensure we had a coherent story. Overall, this is a very nice and timely review, and we have had a lot of interest in it already. Citation and abstract:

Pal C, Asiani K, Arya S, Rensing C, Stekel DJ, Larsson DGJ and Hobman JL 2017. Metal Resistance and Its Association With Antibiotic Resistance. Advances in Microbial Physiology. DOI: https://doi.org/10.1016/bs.ampbs.2017.02.001.

Abstract

Antibiotic resistance is recognised as a major global threat to public health by the World Health Organization. Currently, several hundred thousand deaths yearly can be attributed to infections with antibiotic-resistant bacteria. The major driver for the development of antibiotic resistance is considered to be the use, misuse and overuse of antibiotics in humans and animals. Nonantibiotic compounds, such as antibacterial biocides and metals, may also contribute to the promotion of antibiotic resistance through co-selection. This may occur when resistance genes to both antibiotics and metals/biocides are co-located together in the same cell (co-resistance), or a single resistance mechanism (e.g. an efflux pump) confers resistance to both antibiotics and biocides/metals (cross-resistance), leading to co-selection of bacterial strains, or mobile genetic elements that they carry. Here, we review antimicrobial metal resistance in the context of the antibiotic resistance problem, discuss co-selection, and highlight critical knowledge gaps in our understanding.

First use of bioRxiv: Reconstructing Promoter Activity From Lux Bioluminescent Reporters

Today I have made a new publication foray and submitted a manuscript to bioRxiv. This is the main paper to have come out of work on our BBSRC Lux grant. We are yet to find a peer-review home – but one of our co-authors has already had a conversation with someone who wants to use the method – so it was time to put the manuscript out there while we continue with the peer-review process. R code and Biomodels submission will follow. The manuscript details are:

New publication:So why have you added me? Adolescent girls’ technology-mediated attachments and relationships.

Work from Di’s PhD has just been published! This is very much Di’s work. My contribution was making the figures in R. Very proud of Di! This is my first social research article – my publication record becomes increasingly eclectic.

Levine DT and Stekel DJ 2016. So why have you added me? Adolescent girls’ technology-mediated attachments and relationships. Computers in Human Behaviour 63:25-34.

Highlights

  • Adolescent girls can develop attachment with others through, and with, technology.
  • Adolescent girls use technology to meet others and mediate relationships.
  • Facets of relationships can be understood as functions of secure relationships.
  • Functions include proximity-seeking, trust, exploration and return to secure base.
  • Technology use can amplify girls’ secure relationships with peers and parents.

Abstract

Technology plays an almost ubiquitous role in contemporary British society. Despite this, we do not have a well-theorised understanding of the ways adolescent girls use digital devices in the context of their developing secure relationships with their families and friends. This study aims to address this gap in understanding. Fifteen young women based in the Midlands and from across the socio-economic spectrum participated between 2012 and 2013. Participants completed three research tools exploring technology-mediated attachment and relationships, and participated in a face-to-face interview. The findings suggest that it is possible for girls to develop attachments with others through, and with, technology; technology use brings people together and mediates relationships in a range of ways encapsulated by attachment functions. The study highlights the ongoing importance of parental and peer relationships by suggesting that technology can act as a means by which the positive and negative attributes of existing relationships can be amplified.

Authorship networks as an alternative to authorship lists

I am starting to think about different barriers to multidisciplinary research. One of the barriers is the traditional list of authors on journal research articles. The problem is that one has a linear list – and as a consequence the position on that list becomes hierarchical. Typically in our field, that means that being the first or final author on the list is over-valued, while other authorship locations are less valued. This then has an impact on jobs, promotions, etc.

Where research is genuinely multidisciplinary, this then becomes very problematic. Journals have tried to respond to this in various ways, including having joint first (or last) authorships and lists of author contributions (usually an afterthought at the end of the paper).

I propose here a radical alternative to an author list: an authorship network. This would replace the list of authors with a network (or graph) showing how the people have contributed to the work. Nodes on the graph could represent people, activities or grant codes. Edges could connect people to activities, people to grants (either as authors of the grant, or employed by the grant), people to each other (e.g. supervision relationships).

I have had a go at representing my most complex paper in this way. Here it is. Rectangular nodes are the authors. Rounded rectangles are the grants. Ovals are activities. Arrows between people link who is supervised by whom. Edges between people and grants represent grant authorship (blue) or employment (arrows). Edges from people to activities show who has done what, with thicker edges for the main contributors (i.e. Hiroki doing most of the modelling and Taku doing most of the experiments).

takahashi_et_alCompare the graph with the list of authors:

Hiroki Takahashi, Taku Oshima, Jon L. Hobman, Neil Doherty, Selina R. Clayton, Mudassar Iqbal, Philip J. Hill, Toru Tobe, Naotake Ogasawara, Shigehiko Kanaya, Dov J. Stekel.
I think the graph is far more informative. It is immediately clear that there are two main activities in experiments and modelling. It is clear that the BBSRC/JST is the central grant, while there is a contribution from the other BBSRC grant. Jon Hobman’s centrality to the research is also much clearer from the graphical view than appears from his position as third author on the list.
I’d appreciate some feedback and ideas.

 

New publication: Mathematical modelling of antimicrobial resistance in agricultural waste highlights importance of gene transfer rate

We are delighted that our second paper – and first modelling paper – on antimicrobial resistance in slurry has been pubished, also in FEMS Microbial Ecology.

Baker M, Hobman JL, Dodd CER, Ramsden SJ and Stekel DJ (2016). Mathematical modelling of antimicrobial resistance in agricultural waste highlights importance of gene transfer rate. FEMS Microbial Ecology DOI:10.1093/femsec/fiw040.

The work came from the very short post that Michelle spent with us – funded by pump prime money from the school. Both the experimental paper (led by Jon Hobman) and the modelling paper have been accepted for the Virtual Issue of FEMS Microbial Ecology: Environmental Dimension of Antibiotic Resistance associated with the EDAR 2015 conference we attended last year. These papers can show the value and importance of timely institutional pump prime support.

Abstract

Antimicrobial resistance is of global concern. Most antimicrobial use is in agriculture; manures and slurry are especially important because they contain a mix of bacteria, including potential pathogens, antimicrobial resistance genes and antimicrobials. In many countries, manures and slurry are stored, especially over winter, before spreading onto fields as organic fertilizer. Thus these are a potential location for gene exchange and selection for resistance. We develop and analyze a mathematical model to quantify the spread of antimicrobial resistance in stored agricultural waste. We use parameters from a slurry tank on a UK dairy farm as an exemplar. We show that the spread of resistance depends in a subtle way on the rates of gene transfer and antibiotic inflow. If the gene transfer rate is high, then its reduction controls resistance, while cutting antibiotic inflow has little impact. If the gene transfer rate is low, then reducing antibiotic inflow controls resistance. Reducing length of storage can also control spread of resistance. Bacterial growth rate, fitness costs of carrying antimicrobial resistance and proportion of resistant bacteria in animal faeces have little impact on spread of resistance. Therefore effective treatment strategies depend critically on knowledge of gene transfer rates.