This week in PLOS Biology

In PLOS Biology this week, you can read about neural activity in bird song, the utility of mathematical models in evolutionary biology, how diet can shape the genome and the robustness of protein interaction networks.


Oiseaux Exotiques: How Birds Encode Birdsong

Credit: Sam Sober

Credit: Sam Sober

One of the fundamental problems in neuroscience is understanding the relationship between neural activity and the behaviour it produces. For motor systems, firing rates of neurons are traditionally quantified, whereas in sensory systems the temporal patterns in neuron spikes have emerged as critical. New work by Claire Tang, Samuel Sober, and colleagues in this issue of PLOS Biology indicates that, just as in sensory systems, the motor output that controls bird song is dictated not only by firing rate but also by the precise firing pattern and the information inherent in those patterns greatly exceeds the information available in simple spike counts. Read more in the accompanying synopsis.


Mathematical Models in Evolutionary BiologyWhat Are They For?

In a new Essay, Maria Servedio, Justin Yeh and co-authors discuss the utility of proof-of-concept modeling in evolutionary biology. They argue that the complexity of the processes and the long timescales involved in evolutionary biology means that mathematical approaches have long been required. However some people are still sceptical about the value of mathematical models in the field. They attempt to clarify, using examples, the unique utility of proof-of-concept models.


Codons May Depend on Diet

Credit: 10.1371/journal.pbio.1002015

Credit: 10.1371/journal.pbio.1002015

Ribosomes translate mRNA into protein using tRNAs, but the genetic code contains multiple synonyms – with the same amino acid specified by differing codon triplets. However all codons are not created equal – as some tRNAs read them with differing speed and accuracy. John Zaborske, Allan Drummond and colleagues have found that the way in which proteins are encoded has changed systematically across several closely related fruit fly species. Variation in the availability of a specific nutrient – queuine (from bacteria), influences tRNA anticodon modification from guanosine to queuosine in multiple drosophilid species (they test 12 species). The effects of this on translational accuracy have left their mark on the way that protein sequences are encoded in the flies’ genomes. Read more in the accompanying synopsis.


Signaling Networks Tolerate Domain Rearrangements

Credit: 10.1371/journal.pbio.1002012

Credit: 10.1371/journal.pbio.1002012

Cells use complex protein interaction networks to sense and process external signals. The proteins involved often comprise a string of several functional units called domains. Mutations that rearrange these domains therefore have the potential to create novels proteins that can process different signals – useful on an evolutionary timescale, but how well is this tolerated at the shorter timescale of the individual lifespan? Using a yeast model system, Paloma Sato, Sergio Peisajovich and colleagues found that signalling complexes are essentially more malleable than we previously thought. This is useful not only for evolution, but for manipulation of signaling pathways by synthetic biologists.


Category: Biology, Cell biology, Cell signalling, Evolution, Genomics, Microbiology, Molecular biology, Neuroscience, PLOS Biology | Leave a comment

Predicting diseases with Wikipedia, how the brain modifies memories, and hypersynchrony: the PLOS Comp Biol November Issue

Here are our highlights from November’s PLOS Computational Biology.

Predicting Diseases with Wikipedia

Effective and timely disease surveillance is a critical component of prevention and mitigation strategies that can save lives. Nicholas Generous and colleagues have proposed a new approach for detecting and monitoring epidemics based on freely available Wikipedia article access logs. The authors’ proof-of-concept experiments suggest that Wikipedia is a broadly effective data source for predicting the present, as well as forecasting outbreaks up to 28 days in advance. The approach could help to overcome some of the key gaps in existing traditional and internet-based techniques.

How the Brain Modifies Memories

Rendering of Parkin's opening conformers resulting from molecular dynamics simulations. Image Credit: Thomas R. Caulfield

Rendering of Parkin’s opening conformers resulting from molecular dynamics simulations.
Image Credit: Thomas R. Caulfield

When do we modify old memories, and when do we create new ones? Samuel J. Gershman and colleagues suggest that the question can be answered statistically. When sensory data change gradually over time, the brain infers that the environment has slowly been evolving, and the current representation of the environment (an existing memory trace) is updated. In contrast, abrupt changes indicate transitions between different structures, leading to the formation of new memories. The authors use a new model of statistical inference to show that humans use temporal discontinuities in the structure of the environment to determine when to form new memory traces.

Hypersynchronous Neural Activity

In the study of neurological disorders, a number of approaches have been used to study the mechanisms of seizure activity. John R. Terry and colleagues have developed a new computational modelling framework to explain the interplay between local dynamics and global networks in the emergence of hypersynchronous neural activity. By applying this framework to collected data sets from people with idiopathic generalized epilepsy, the authors demonstrate that brain networks of people with epilepsy have a much greater tendency to hypersynchronize than do brain networks of people without epilepsy. This finding demonstrates a critical role for network structure in the tendency to have seizures.

Category: Community, Computational biology, Image, Infectious disease, Neuroscience, PLOS Computational Biology | Tagged , | Leave a comment

The Corrupting Power of Cancer

When we think of antioxidants, we think of good, protective things, like blueberries, red wine, and dark chocolate (God, I love antioxidants). But cancer, that nefarious creature, finds a way to corrupt even the most benign cellular functions, bending them to its will in its selfish pursuit of proliferation.

Image credit: kev-shine (Flickr)

Cancer researchers have found that certain types of cancer cells acquire what’s called multidrug resistance by producing lots of antioxidant enzymes and cellular pumps that export drugs out of the cell. This leads to cancers that are much harder to treat, as these cells can inactivate or expel drugs before they are able to perform their cancer-killing actions. How these cancers acquire multidrug resistance seemed to be linked to a type of cancer cell state known as a de-differentiation, but how de-differentiation leads to drug resistance was not understood until a recent publication in PLOS Biology from Catherine Del Vecchio, Piyush Gupta and co-authors at the Whitehead Institute.

So what is de-differentiation? To understand de-differentiation, one must first understand the process of differentiation. Stem cells have the capacity to become any specialized cell in the body, from a liver cell to a muscle cell to a skin cell. As a stem cell becomes a specialized cell, let’s say a skin cell, it loses the characteristics that make it a stem cell and gains the characteristics that make it a skin cell; this process is called differentiation. De-differentiation is the reverse of this process, where a skin cell loses its specialized characteristics and regains a more stem cell-like state. When de-differentiation occurs in cancer cells it results in the development of invasive and metastatic properties and is associated with poor prognosis.

Cancer promotes de-differentiation because less differentiated cells are better at proliferating than specialized cells, generally speaking. Additionally, as a tumor develops, the environment that the cancer cells experience becomes vastly different, forcing the cancer cells to adapt. Tumors are often nutrient-poor and lack adequate access to the blood stream, resulting in hypoxia and a buildup of cellular waste products. This toxic environment leads the cancer cells to turn on their antioxidant pathways to protect their ability to grow. And it turns out that it is this induction of the antioxidant pathways before chemotherapy that confers drug resistance on these de-differentiated cancer cells.

For the cell signaling nerds in the audience, the authors found that the master regulator of the oxidative stress response, the Nrf2 transcription factor, was activated in de-differentiated cancer cells by phosphorylation by the endoplasmic reticulum membrane kinase PERK in the absence of drug treatment and oxidative stress. Having Nrf2 constantly activated by PERK protected de-differentiated cells from chemotherapy by reducing reactive oxygen species and increasing drug efflux.

Now, back to the main point. While normal cells only activate their antioxidant pathways in response to oxidative stress (makes sense, huh?), cancer cells inhabit a stressful environment and thus already have their antioxidants on full bore. This makes the cancer cells able to withstand the additional stress caused by chemotherapy. Fortunately, now that we have a better grasp on the mechanism of drug resistance, therapies can be designed to target it. In fact, Del Vecchio and colleagues show that treatment with an inhibitor of the signaling pathway that turns on these antioxidants (i.e. a PERK inhibitor) re-sensitized cancer cells to chemotherapies. So we can reclaim antioxidants from the corrupting influence of cancer and return them to where they belong: our wine glass.


Category: Biology, Blog, Cancer, Cell biology, Cell signalling, PLOS Biology, Research, Stem cells | Tagged , , , , , | 1 Comment

This week in PLOS Biology

In PLOS Biology this week, you can read about how plants cope with arsenic in the soil, the lengthening of tubular biological structures, disrupting protein translocation, and the need for transparency in grant reviews.


Living with Arsenic


Credit: 10.1371/journal.pbio.1002009

Arsenic is nasty stuff, being toxic even at very low levels to most living organisms, and a hazardous environmental carcinogen for some human populations. Its abundance in some soils means that it can get into plants, where it runs the risk of contaminating human foodstuffs. Being able to control the levels of arsenic in food crops is therefore of some importance, and this involves knowing how plants have evolved to deal with the element. A genome-wide association study allowed Dai-Yin Chao, David Salt and colleagues to exploit natural variations in arsenic accumulation in wild Arabidopsis thaliana isolates. This identified the enzyme in plants that transforms arsenate into arsenite, as arsenate reductase HAC1. Formation of arsenite allows its extrusion into the soil and thereby controlling accumulation of the toxin – in the absence of HAC1, levels of arsenic in the plant rises 300-fold. Read more in the Synopsis.


Growing a Longer Tube

Credit: 10.1371/journal.pbio.1002013

Credit: 10.1371/journal.pbio.1002013

As animal embryos develop, many organs require the formation of tubular structures by orchestrated movements of cells. A new study by Aditya Saxena, Barry Denholm, Helen Skaer and colleagues, focuses on one such instance – the formation of renal tubules (“Malpighian tubules“) in the fruit fly. These originate as little buds on the hindgut, but then lengthen dramatically within a few hours. The authors show here how graded signalling by the epidermal growth factor Spitz provides the cells with axial information for polarized myosin pulses. These contractions shorten the cells in their circumferential dimension, driving them to intercalate and thereby causing elongation of the tubule.


Inhibiting Protein Translocation – Specifically

There are many ways in which one could in principle manipulate levels of a given protein. Kurt Vermeire, Kai-Uwe Kalies, Mark Marsh and colleagues study the small anti-HIV drug CADA, and find that it targets the membrane translocation of the precursor of the crucial T-cell protein CD4. They show that it does so in a novel fashion, by specifically binding to pre-CD4’s signal peptide during co-translational translocation (the step where the ribosome shoves the nascent protein through the protein-conducting Sec61 translocon in the endoplasmic reticulum membrane). This locks the signal peptide in the translocon, so instead of becoming embedded in the membrane and being routed to the cell surface, the CD4 precursor is diverted to the cytosolic degradation machinery. The effect seems to be specific to this protein and raises the possibility that other membrane proteins could be similarly targeted.


Grant Application Review: The Case for Transparency

How much do we know about the process by which the relative merits of research proposals are assessed? David Gurwitz, Elena Milanesi and Thomas Koenig propose that public funding agencies should be more transparent in awarding research grants to allow researchers and the public better insight into decision making.


Category: Biology, Cell biology, Cell signalling, Debate, Developmental biology, Genetics, Infectious disease, Molecular biology, Plant biology, PLOS Biology, Research, Review | Leave a comment

Understanding images: Keeping up in the wonderland of human evolution

This continues our series of blog posts from PLOS Genetics about our monthly issue images. Author Laurent Duret talks about November’s image from their article, Lesecque et al. 

Author: Laurent Duret

Competing interests: Laurent Duret is an author of the article discussed in this blog.

“Now, here, you see, it takes all the running you can do, to keep in the same place.”Lewis Carroll (1871) Through the Looking GlassImage credit: Laurent Duret

“Now, here, you see, it takes all the running you can do, to keep in the same place.”
Lewis Carroll (1871) Through the Looking Glass
Image credit: Pauline Sémon

The Red Queen hypothesis proposes that an organism must constantly evolve to allow the species to survive. In this issue of PLOS Genetics, we show that a similar process might account for the very rapid evolution of recombination hotspots within genomes. By analyzing the genome sequence of an archaic human (Denisova), we observed that recombination hotspots have a very short lifespan, caused by a strong process of self-destruction. This suggests that the molecular machinery that determines the location of hotspots must constantly evolve to find new target and thus compensate for the loss of hotspots.

Recombination hotspots

During meiosis, homologous recombination leads to the exchange of genetic information between chromosomes. This process contributes to population genetic diversity and is required for the segregation of chromosomes during meiosis. Recombination is initiated by the formation of double-strand breaks (DSBs), whose repair generates crossover or non-crossover recombination events. In humans and mice, meiotic DSBs occur at specific sites along chromosomes, called hotspots, whose location is primarily determined by the PRDM9 protein [1,2]. In primates and rodents, PRDM9 evolves extremely rapidly and is highly polymorphic, specifically in its DNA binding domain. There is clear evidence that this domain evolves under strong positive selective pressure to change of target [3], which leads to rapid changes in the location of hotspots. However, the reasons why there is a need for PRDM9 to evolve so rapidly are not known.


The Red Queen hypothesis

In this paper, we test a hypothesis, initially proposed by Myers and colleagues [2], that the evolution of PRDM9 might be a consequence of a Red Queen process. This model stems of the fact that recombination hotspots are subject to self-destruction, by the phenomenon of biased gene conversion (BGC) [2]. Indeed, the repair of DSBs leads to the conversion of recombination-prone alleles by hotspot-disrupting alleles (we call this form of BGC ‘dBGC’, for DSB-driven BGC). Over generations, the progressive degradation of recombination hotspots through dBGC is expected to lead to a loss of fitness, because the lack recombination can cause a loss of fertility, due to improper chromosome disjunction. Thus, according to this model, the evolution of recombination hotspots would be the consequence of a Red Queen process , in which PRDM9 has to evolve constantly to compensate for the loss of its targets and maintain a sufficient number of recombination hotspots in the genome.


Insights from an archaic human genome

The Red Queen model  is a very elegant hypothesis, but up to now has never been tested quantitatively. Is this process of dBGC strong enough to cause a significant depletion of PRDM9 target motifs within the genome in just a few million years (i.e. at the scale of the divergence between human and chimpanzee)? To address this question, we analyzed the genome sequence of an archaic human (Denisova) that diverged from modern humans about 400,000-800,000 years ago [4]. Our results show that human hotspots are younger than previously thought, as the onset of human hotspots activity can be dated to the last 10% of time since the human-chimpanzee split (i.e. 0.7 to 1.3 MYR ago, depending on the estimate of the chimpanzee/human divergence time). Furthermore, multiple lines of evidence reveal that recombination hotspots were not shared between Denisovans and modern humans, indicating that the hotspot turnover can be very fast. We analyzed polymorphism data in human populations to quantify the strength of dBGC on the major PRDM9 target motif (HM). We found that the dBGC process is very strong, and if it remained constant over time (i.e. if PRDM9 alleles remained at the same frequency as in present-day human populations), about 90% of HM motifs located in recombination hotspots would be lost over the next 100,000 generations. In less than 3 MYRs, the dBGC process would therefore lead to a total change in recombination hotspot activity. These observations are fully consistent with the Red Queen hypothesis of recombination hotspot evolution.


1.        Baudat F, Buard J, Grey C, Fledel-Alon A, Ober C, et al. (2010) PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science 327: 836–840. doi:10.1126/science.1183439.

2.        Myers S, Bowden R, Tumian A, Bontrop RE, Freeman C, et al. (2010) Drive against hotspot motifs in primates implicates the PRDM9 gene in meiotic recombination. Science 327: 876–879. doi:10.1126/science.1182363.

3.        Ponting CP (2011) What are the genomic drivers of the rapid evolution of PRDM9? Trends Genet 27: 165–171. doi:10.1016/j.tig.2011.02.001.

4.         Lesecque, Y, Glémin S, Lartillot N, Mouchiroud D., and Duret L (2014) The Red Queen Model of Recombination Hotspots Evolution in the Light of Archaic and Modern Human Genomes. PLoS Genet, 10: e1004790. doi:10.1371/journal.pgen.1004790

Category: Blog, Genetics, Image, PLOS Genetics, Research, Uncategorized | Tagged , , | Leave a comment

Measuring the success of an online bioinformatics resource

Like many other journals, PLOS Computational Biology aims to publish research that helps to further the field and develop the community that the journal serves. From time to time, however, we publish a paper that makes a significant impact not as research but as a resource. One such article is a Perspective entitled ‘An Online Bioinformatics Curriculum’ by David Searls, one of our Associate Editors, who presents a bioinformatics curriculum in the form of a virtual ‘course list’, together with editorial commentary, and an assessment of strengths, weaknesses, and likely future directions for open online learning in bioinformatics.

Image courtesy of David Searls

Image courtesy of David Searls

Published in September 2012 as part of the PLOS Computational Biology Education collection, the article has picked up just over 77,000 hits, if you look at both PLOS and PubMed views and downloads combined. Not only that, but it’s been shared widely via social media – 204 times on Twitter and 303 times on Facebook – and saved over 300 times using bookmarking tools. These high article-level metrics (ALMs) – for comparison, see other Education articles published in 2012 – give us a fascinating insight into the specific ways readers have interacted with this article.

Using the traditional citation metric, the article doesn’t particularly stand out, as it has only been cited a handful of times. The high pageviews, saves and social media shares, on the other hand, indicate that people are showing an interest in the article, passing it on via their networks and bookmarking it for future use. The acceptance of these ‘altmetrics’ as measures of impact is not yet well established, perhaps because they are not recognised as having a direct effect on researchers’ careers – though this is changing, according to a Perspective article out last week in PLOS Biology. What these newer ALMs do demonstrate, however, is an awareness within the community that the Searls article is a useful resource to be shared and saved for another day. And for an educational article intended to benefit the community, that’s surely an indicator of success.

So what’s the attraction of this particular article? Well, bioinformatics is a relatively new field and one that hasn’t traditionally been taught widely in universities, on-the-job learning having been the main route into the field.  The existence of an online resource where bioinformatics courses are systematically listed follows a general pattern whereby self-directed learning is becoming available across multiple disciplines, according to Searls’ introduction, and is especially relevant for a field that has embraced an online movement that includes open-access publishing.

As the first of its kind, it’s perhaps not surprising that the article has attracted attention. And, importantly, this is regular, long-term attention: Figure 1, produced using, shows the total article views over time. After an initial spike, followed by some ups and downs, you can see that the article is being viewed as frequently over two years on as it was three months after publication. This continuous use suggests the article is well established as a valuable resource for members of the community.

Figure 1: Article use as a function of age

Figure 1: Article usage as a function of age

The success of ‘An Online Bioinformatics Curriculum’ prompted Searls to write a follow-up article, which we published in June this year. In ‘A New Online Computational Biology Curriculum’, Searls updates the curriculum he originally provided in response to a surge in the development of course content and availability. He explains in the introduction why the terminology used in the title has changed: ‘the sizeable expansion of courses available[…]has been deemed sufficient to widen the scope of this edition to encompass the more expansive term “computational biology” as opposed to “bioinformatics” (for those who consider the distinction important)’.

In fact, it was the original article’s popularity as indicated through the less traditional ALMs that prompted Searls to write the latest instalment, as he explains below:

The article is obviously unique in its format, and it was great fun to see how much information I could fit into the framework of a typical university course catalog. The first edition of the “catalog” that I wrote two years ago was an opportunity to let the unusual format spontaneously evolve from the data I wanted to convey, and the level of interest in that initial effort was the major factor that spurred me to do the follow-up. Interestingly, although I had a scattering of personal contacts from readers that were very encouraging, I never would have had an inkling of just how great the uptake was if it hadn’t been for the various article-level metrics that PLOS tracks. It’s obviously not the sort of publication that people are going to cite widely, and even citations would not have provided the granularity and immediacy of feedback that would have inspired me to write the sequel so soon after the initial effort. That speed of turnaround was particularly important given the rate at which the MOOC universe has evolved, and it strikes me that there’s a nice symmetry there between the subject matter of the article and the way it was published and measured — all reflecting the online nature of science and education today. Since the piece appeared, I’ve had several invitations to speak and inquiries from institutions that are interested in using online educational resources systematically, so I have every reason to believe that free online education, like free online publication, will have real staying power.”

Making educational resources as freely available as possible is important for building a future for the computational biology community, and the use of ALMs such as pageviews, social media shares and bookmarks provides a way to measure their success that traditional measures of impact fail to capture.

Category: Bioinformatics, Community, Computational biology, Education, Interview, PLOS Computational Biology, Resources | Tagged | 2 Comments

This week in PLOS Biology

In PLOS Biology this week, you can read about the sequence of the centipede genome, alternative publication metrics, how we pay attention to our sense of touch, and the information bandwidth of human neurons.


What Goes “99-Thump”? The Centipede Genome

Image credit: 10.1371/journal.pone.0052623

Image credit: 10.1371/journal.pone.0052623

The arthropods are one of Earth’s real success stories—there are more species of arthropod than in any other animal phylum, but our knowledge of the genomic basis of arthropod biology is massively skewed towards insects. Myriapoda (e.g. centipedes and millipedes) is the only arthropod class without a sequenced genome. In PLOS Biology this week, Ariel Chipman, David Ferrier and a large international consortium of researchers rectify this by publishing the genome of Strigamia maritima: a Northern European beach-dwelling centipede. For more on some of the fascinating insights they have begun to unravel, see the accompanying synopsis.


Alternative Views: ALMs, Altmetrics, Funders and Impact

A perspective publishing this week by Adam Dinsmore, Liz Allen and Kevin Dolby revisits ALMs and Altmetrics. They describe how more evidence of the meaning and validity of ALMs and altmetrics, coupled with greater consistency and transparency in their presentation, would enable research funders to explore their potential value and identify appropriate use cases.


How Neurons Pay Attention

Image credit: Flickr user jinterwas

Image credit: Flickr user jinterwas

We know a lot about how attention affects the firing rates of neurons and their synchronization in the visual system, but very little is known about the effects of attention in the other senses. In a new study of the sense of touch in rhesus macaques, Manuel Gomez-Ramirez, Steven Hsiao and colleagues found that top-down selective attention mediates feature selection.  It does this by reducing the noise correlations in neural populations and enhancing the synchronized activity across subpopulations that encode the relevant features of sensory stimuli.


Human Neurons Are Faster

 Our knowledge of neuronal information transfer is based on rodent neurons – properties of synaptic information transfer and synaptic depression in humans are not known. In this research paper by Guilherme Testa-Silva, Michele Giugliano, Huibert Mansvelder and colleagues, they discover that because of fast recovery from synaptic depression and fast-initiated action potentials, neuronal information transfer can have a substantially higher bandwidth in human neocortical circuits than in those of rodents.


Category: Advocacy, Biology, Debate, Evolution, Genomics, Neuroscience, PLOS Biology, Publishing | Leave a comment

PLOS @ BSI Annual Congress 2014

Will you be attending the 2014 Annual Congress of the British Society for Immunology? If so, we look forward to meeting you and hearing your thoughts about immunology at PLOS, Open Access, open data, and open science.

Image Credit: National Cancer Institute (NCI) / Creator: Don Bliss, Sriram Subramaniam / PLOS

Image Credit: National Cancer Institute (NCI) / Creator: Don Bliss, Sriram Subramaniam / PLOS

You will find PLOS at booth 43 in the Exhibit Hall The Brighton Centre, Brighton, UK – please drop by any time. PLOS Biology’s Chief Editor, Chris Ferguson, and Nathaniel Gore, Manager of PLOS Collections, will be there to answer any questions you may have.

To coincide with BSI 2014 we will be launching the PLOS Immunobiology Collection. The Collection collates some of the best and most recent immunobiology articles published at PLOS, featuring papers that examine cellular and molecular immunology, evolutionary immunology, animal models of the human immune system and ontogeny of the immune system. The biology featured in this collection serves to complement the studies in the Clinical Immunology Collection that highlight immune-related challenges faced by individuals and the health care profession.

PLOS welcomes submissions in this field.

For more information about the Immunobiology Collection and Clinical Immunology Collection please contact

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This week in PLOS Biology

In PLOS Biology this week, you can read about brain folding in mammals, misfolded proteins in human genetic disease, and mechano-transduction.


Evolution of the Mammalian Cortex – Folded or Unfolded?



The neocortex of the brain is highly expanded in humans, and is involved in high-level functions such as language and conscious thought. However the adaptive mechanism that evolved along certain mammalian lineages to produce a large and folded neocortex has not been clear. In a study published this week, Wieland Huttner, Iva Kelava, Eric Lewitus and colleagues found that mammals fall into two principal groups associated with distinct ecological niches: those with less folding (such as mice and tarsiers) and more folding (such as dolphins and humans). It seems that a highly folded neocortex requires a specific class of progenitor cell-type to adopt a special mode of cell division, and that folding is in fact the ancestral state for mammals. Read more in the accompanying synopsis.


When Stress Management Becomes the Problem

The function of all proteins in our bodies is dependent on their correct folding. Therefore emergency systems exist to manage protein folding if something goes wrong. But what happens if levels of misfolded proteins are always high, as in the case of some genetic diseases, such as cystic fibrosis, where mutations disrupt folding? A new research article by Daniela Martino Roth, William Balch, and colleagues, suggests that chronic activation of the stress response can be detrimental, exacerbating the disease phenotype. They call this effect a ‘maladaptive stress response’. Their provocative message is that we should ask ourselves if stress responses can sometimes be part of the problem, as well as being helpful in acute situations. Read more in the accompanying synopsis.


Image credit: journal.pbio. 1000617


Mechano-Transduction: From Molecules to Tissues

All cells and tissues of the body are subject to external forces, such as fluid shear stress, osmotic forces and stretch. Changes in these forces, or how cells respond to them, can result in abnormal embryonic development and diseases in adults. In this new essay, Beth Pruitt, James Nelson and colleagues discuss mechano-transduction – the cellular process that converts a mechanical input, for example stretching, into intracellular signal transduction – as it applies to protein conformation, cellular organization, and multi-cellular tissue function.



Category: Biology, Cell biology, Developmental biology, Disease, Evolution, Molecular biology, Neuroscience, PLOS Biology | Leave a comment

Monitoring progress in translational bioinformatics

It is with great enthusiasm that the PLOS Computational Biology Education Editors present this invited blog post from Russ Altman, in what we hope will be a yearly feature for PLOS Biologue. It is a recapture of his annual review on translational bioinformatics, a topic very close to our interests and the focus of the first PLOS online book, part of the PLOS Computational Biology Education section. We hope you enjoy this blog post, and find this a useful and convenient way for us to share this information and perspective with you.

Joanne Fox and B.F. Francis Ouellette, Education Editors, PLOS Computational Biology


Image courtesy of Russ Altman

Image courtesy of Russ Altman

Each year the American Medical Informatics Association (AMIA) holds a meeting on Translational Bioinformatics as part of its “Joint Summits on Translational Science”, along with a meeting on Clinical Research Informatics. For the last several years, I have been invited to present a “Year in Review” for Translational Bioinformatics in which I summarize notable papers within translational bioinformatics during the previous 12-14 months. The meeting happens in March, so the summary usually covers the preceding calendar year plus some extra weeks.


This activity is both rewarding and quite stressful, as there is always a large body of work to review, and I would like to do a good job highlighting the work that is novel and exciting, while not just automatically choosing papers published in high impact journals. I have developed some rules to make this manageable. Primarily, I have a fairly strict definition of translational bioinformatics: the candidate papers should present a novel methodological approach to combining clinical entities (patients, diseases, drugs, signs, symptoms) and molecular/cellular entities (genes, proteins, RNA, DNA, small molecules, pathways, networks). After all, translational science is the study of how we move discoveries from bench to bedside, so translational bioinformatics should be informatics work that does the same. If there are no clinical entities, then the paper is not eligible. If there are no biological entities, then the paper is not eligible. I prefer novel informatics methodological content, but will allow papers that use off-the-shelf methods to do something really remarkable.


I try to track papers all year, but honestly have to do a lot of reviewing in the few weeks before the talk. The main sources of papers are two: (1) the recommendations of colleagues from whom I request nominations around January (self-nominations are OK, but nominating others is particularly valued); and (2) a set of somewhat ad hoc PubMED searches seeking papers that combine clinical/biological/informatics concepts. It is also fine for anyone to send me nominations throughout the year by email (russ.altman[at] or even twitter (@rbaltman) and I have a system for tracking them. I then review several hundred articles, first at the title/abstract level (mostly to triage the ineligible ones), and then more deeply to find the contributions that most excite me. I do as many passes through the list as needed to reduce it to a length that allows me to present it in one hour. This is usually about 35 papers with an additional 10-15 “shout outs”, which I mention only by title.


After I have the final list of papers, I assemble them into 5-8 ad hoc groupings that provide some structure for the talk. I then create a slide deck with a rigid format: every paper is summarized by a single slide with title, first author, journal and PubMED ID, and then my bulleted summary of:

  • Goal: what are they trying to do?
  • Method: how did they approach the problem methodologically?
  • Result: what did they find?
  • Conclusion: what should we take away from the paper?

I stress that the “conclusion” is my own conclusion, not necessarily the conclusion of the authors, and functions as my pulpit to justify why I chose the paper. The next 1-2 slides are always key graphics from the paper that illustrate or summarize what impressed me about it.


So what papers did I highlight from Jan 2013 to March 2014? Well, the complete slide deck is available at my blog “Building Confidence”, as are all the slide decks from 2008 to 2014 (see QR code in the final slide of this blog). Just to provide some comparison and baseline for this year, the topics last year (January 2012 to March 2013) were:

  • Omics medicine
  • Cool methods
  • Cancer
  • Drugs
  • Delivery (of healthcare)

And the topics this year were:Slide 9

  • Controversies

This was a new category this year, and I used iSlide 11t to highlight two non-publications. I lead off with the FDA letter to the direct-to-consumer genetic testing company, 23andme, which is mandatory reading for anyone interested in translational genomics.  Next, I highlighted the blog of colleague Lior Pachter, as he engaged in an entertaining (and informative) polemic about network science applied to cell biology.




  • Clinical genomicsSlide 26

Here I highlight informatics papers pushing the agenda of clinical genomics. This included the much-awaited results of the warfarin dosing pharmacogenomics vs. clinical algorithm trials, which were split, and an analysis of the ubiquity of pharmacogenomic variants in the general population. Another good paper, shown here, by Kircher et al., introduces the Combined Annotation-Dependent Depletion (CADD) score for evaluating the probable impact of a SNP on health.Slide 30


  • Drugs

There are always many good papers about using informatics to learn things about drugs, including side effects and new uses. This category had several excellent papers. One paper reported the manual curation of 88,000 scientific articles, mined for drug-disease and drug-phenotype interactions. That was impressive because of the scale, if nothing else.  Another favorite Slide 42was a paper showing how publicly-available gene expression data suggested that an antidepressant may be a useful adjuvant drug in lung cancer—and it’s now in clinical trials!


  • Genetic basis of disease

These are all informatics papers showing methods for inferring new things about the Slide 47underlying mechanisms and genetics of diseases. My favorite in this category was a paper showing the many complex diseases that co-occur with Mendelian diseases, and suggesting deep genetic overlaps—as if there is a “code” in which complex diseases result from less-severe mutations in the same genes that are associated with Mendelian diseases. The implications of this work for how we interpret genome-wide association studies and think about complex disease are significant.


  • Emerging data sourcesSlide 63

The interesting aspect of this topic is that we get a preview of the future—emerging work in the roles of long non-coding RNA, immune diversity, metabolomics, and the microbiome in disease. One exciting paper looked at the DNA sequences of B-cell antibodies and created lineage for 17 volunteers, comparing young to old in the pre- and post-vaccine state. The future is now.


  • Mice.  Can’t live with ‘em, can’t live without ‘em.

Slide 75One of my hard rules (usually) is that the talk will only focus on human studies and disease.  This year I made an exception because of several relevant papers in mice. One praised the ability of mouse knockouts to discover/model new drugs. My favorite, however, showed that gene expression measurements on mice after burn, trauma and endotoxemia—all inflammatory conditions—not only do not correlate with similar measurements made in humans, but do not correlate with each other!


  • Scientific processSlide 80

This was a fun category that included a paper about why some publications achieve high impact, based on citation analysis. Of course, my favorite is the PLOS Computational Biology decision to publish a textbook of translational bioinformatics as a series of individual chapters. A spectacularly good idea, and a credit to PLOS.


  • Odds and ends

Slide 84Many years require a final bucket to include important papers that don’t fit into neat categories.  This year included such a category, which featured papers on social networks for tracking infections in a hospital, and for global tracking of disease based on airplanes as efficient infection-dissemination vehicles. A very important set of papers this year were those reporting the genome sequence of the HeLa cell. We all owe a debt to Henrietta Lacks for providing such a valuable biomedical research resource (under very non-optimal conditions), and these sequences help us interpret the results of HeLa experiments more precisely and with knowledge of the genome alterations underlying the cell line.


Each talk ends with a “crystal ball” section where I speculate what we may see in the coming year, and do a scorecard for my predictions the previous year. As a wise person once said, predictions are very hard, especially about the future. This year’s predictions are:

  • We will see more emphasis on non-European descent populations for discovery of disease associations;
  • There will be a crowd-based discovery in translational bioinformatics;
  • Methods will emerge for recommending treatment for cancer based on genome/transcriptome;
  • There will be more “trained systems” (like IBM Watson) applications in the field;
  • There will be drug repurposing methods that suggest more than one drug used synergistically;
  • There will be more cost-effectiveness evidence for genomic medicine;
  • Powerful methods focused on linking genes, targets and drug response will emerge.

That’s the summary of this year’s talk.  I have been invited to repeat the talk next year, and hope to see you there.

Slide 93

By Russ Altman, PLOS Computational Biology Editorial Advisor.

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