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.
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.
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:
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.
- 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.
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.
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).
Compare the graph with the list of authors:
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.
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.
We are really very excited that our first paper on antimicrobial resistance in agriculture has been published. This is experimental work, carried out by Delveen Ibrahim in Jon Hobman and Chris Dodd’s laboratories, characterising AMR E. coli strains from the slurry tank of a dairy farm.
Ibrahim DR, Dodd CER, Stekel DJ, Ramsden SJ and Hobman JL (2016). Multi drug and extended spectrum beta-lactamase resistant Escherichia coli isolated from a dairy farm. FEMS Microbial Ecology DOI:10.1093/femsec/fiw013.
Escherichia coli strains were isolated from a single dairy farm as a sentinel organism for the persistence of antibiotic resistance genes in the farm environment. Selective microbiological media were used to isolate 126 E. coli isolates from slurry and faeces samples from different farm areas. Antibiotic resistance profiling for 17 antibiotics (seven antibiotic classes), showed 57.9% of the isolates were resistant to between 3 and 15 antibiotics. The highest frequency of resistance was to ampicillin (56.3%), and the lowest to imipenem (1.6%), which appeared to be an unstable phenotype and was subsequently lost. Extended spectrum beta-lactamase resistance (ESBL) was detected in 53 isolates and blaCTX-M, blaTEM and blaOXA genes were detected by PCR in twelve, four and two strains, respectively. Phenotypically most isolates showing resistance to cephalosporins were AmpC rather than ESBL, a number of isolates having both activities. Phenotypic resistance patterns suggested co-acquisition of some resistance genes within subsets of the isolates. Genotyping using ERIC PCR demonstrated these were not clonal, and therefore co-resistance may be associated with mobile genetic elements. These data show a snapshot of diverse resistance genes present in the E. coli population reservoir, including resistance to historically used antibiotics as well as cephalosporins in contemporary use.
Delighted to say that our first paper of 2016 is published. Matthias’s Biolog paper is now online-ready with the Journal of Bioinformatics and Computational Biology. This is also the first output from our Biolog grant – with a second paper detailing our newer software and analysis being planned.
Gerstgrasser M, Nicholls S, Stout M, Smart K, Powell C, Kypraios T and Stekel DJ. 2016. A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters. J Bioinform Comput Biol. DOI: 10.1142/S0219720016500074
Biolog phenotype microarrays (PMs) enable simultaneous, high throughput analysis of cell cultures in different environments. The output is high-density time-course data showing redox curves (approximating growth) for each experimental condition. The software provided with the Omnilog incubator/reader summarizes each time-course as a single datum, so most of the information is not used. However, the time courses can be extremely varied and often contain detailed qualitative (shape of curve) and quantitative (values of parameters) information. We present a novel, Bayesian approach to estimating parameters from Phenotype Microarray data, fitting growth models using Markov Chain Monte Carlo (MCMC) methods to enable high throughput estimation of important information, including length of lag phase, maximal “growth” rate and maximum output. We find that the Baranyi model for microbial growth is useful for fitting Biolog data. Moreover, we introduce a new growth model that allows for diauxic growth with a lag phase, which is particularly useful where Phenotype Microarrays have been applied to cells grown in complex mixtures of substrates, for example in industrial or biotechnological applications, such as worts in brewing. Our approach provides more useful information from Biolog data than existing, competing methods, and allows for valuable comparisons between data series and across different models.