What’s the secret to a successful lab? New “About My Lab” Collection in PLOS Computational Biology

PLOS Computational Biology is pleased to announce the launch of ‘About My Lab’, a collection of informal interviews with Principal Investigators. The featured PI shares his or her experience of running a lab, covering everything from recruitment and funding, to the wild track towards tenure.

Image credit: PLOS

Image credit: PLOS

Unlike other aspects of working as a scientist, researchers rarely receive training in managing people and labs. We therefore aim to spark a dialogue where scientists learn from one another in an open platform for disseminating experience and opinions, and create a broader awareness of the role and importance of management in science.

As Theodore Alexandrov, the collection’s Guest Editor, explains: “The idea of About My Lab was motivated by an informal conversation with another PI at a conference dinner. She shared her experience on lab management and it helped me back then. I hope that reading such stories as can be heard at a conference dinner can help many readers, especially those like me who are in the early stages of their career.” The outline and aims of the collection, which launches with three Perspective articles and an Editorial, were further developed in discussions between Dr. Alexandrov and PLOS Computational Biology’s Founding Editor-in-Chief, Philip E. Bourne.

Learning how different labs operate will give readers the opportunity to see the variation between labs that may depend on geography or field. However, we also aim to highlight the common practices of Principal Investigators to provide insight for those starting out in their careers or about to embark on them. And who knows, even seasoned PIs may learn something!

You can visit the collection at: www.ploscollections.org/aboutmylab

By Chris Hall, Senior Publications Assistant, PLOS Computational Biology

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PLOS Biology at SfN 2013: Open for Neuroscience

BIO-neuro-300x250At PLOS Biology, we believe strongly that we are open for a reason: our aim is to publish high quality research in areas of broad significance, ensuring that it reaches the widest possible audience without any barriers to access. Neuroscience is an area of research that we believe should be as openly available as possible by being published in an open access, CC-BY journal, with associated data being mineable and reusable.

PLOS Biology publishes neuroscience research of exceptional significance, originality, and relevance that informs research in its field and influences thinking beyond. We encourage you to consider PLOS Biology as a high visibility outlet for your future research. We are interested in all areas of neuroscience, from molecular and cellular to systems and cognitive, and we welcome translational studies. To get a taste of the neuroscience research that we have recently published, check out the links below to access the latest research in this field.

I will also be attending Society for Neuroscience 2013 in San Diego, California, together with my colleagues from PLOS ONE, and while there I very much look forward to meeting with our Academic Editors, authors, and reviewers in the neuroscience research community.

If you are also attending SfN 2013 and would like to find out more about how to publish in an Open Access journal, please visit us at the PLOS booth, number 136, where you can meet with my PLOS colleagues and me. PLOS Biology is also planning a ‘meet the editor’ session on Sunday, November 10th from 12-2pm – so do come by then (or alternatively email me at biologue[at]plos.org to arrange a time to chat).

Looking forward to meeting you in San Diego!

 

OA-orange-squareIf you are interested in neuroscience, you might want to read the following research articles – all Open Access and available to read to all:

 

Dynactin Subunit p150Glued Is a Neuron-Specific Anti-Catastrophe Factor

Lazarus JE, Moughamian AJ,  Tokito MK, Holzbaur ELF (2013).

Common Features at the Start of the Neurodegeneration Cascade

Hervás R, Oroz J, Galera-Prat A, Goñi O, Valbuena A, et al. (2012).

Radial Glial Neural Progenitors Regulate Nascent Brain Vascular Network Stabilization Via Inhibition of Wnt Signaling

Ma S, Kwon HJ, Johng H, Zang K, Huang Z (2012).

Monoaminergic Orchestration of Motor Programs in a Complex C. elegans Behavior

Donnelly JL, Clark CM, Leifer AM, Pirri JK, Haburcak M, et al. (2013).

Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration O’Reilly JX, Jbabdi S, F. S. Rushworth MFS, Behrens TEJ. (2013).

Molecular Remodeling of Tip Links Underlies Mechanosensory Regeneration in Auditory Hair Cells

Indzhykulian AA, Stepanyan R, Nelina A, Spinelli KJ, Ahmed ZM, et al. (2013).

Neuronal Expression of Glucosylceramide Synthase in Central Nervous System Regulates Body Weight and Energy Homeostasis

Viola Nordström V, Monja Willershäuser M, Silke Herzer S, Jan Rozman J, Oliver von Bohlen und Halbach O, et al. (2013).

Evaluation of Excess Significance Bias in Animal Studies of Neurological Diseases

Tsilidis KK, Panagiotou OA, Emily S. Sena ES, Eleni Aretouli E, Evangelos Evangelou E, et al. (2013).

Neurotransmitter-Triggered Transfer of Exosomes Mediates Oligodendrocyte–Neuron Communication

Frühbeis C, Fröhlich D, Kuo WP, Amphornrat J, Thilemann S, et al. (2013).

Strength of Gamma Rhythm Depends on Normalization

Ray S, Ni AM, Maunsell JHR (2013).

 

 

 

 

 

 

 

 

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Controlling medically-induced comas, deciding not to decide, and a note from the Editor-in-Chief: the PLOS Comp Biol October issue

Here’s our pick of the best PLOS Computational Biology content for October.

Oct Summary Image

PLOS Comp Biol Featured Image for October. Image Credit: Fan Jin, Luhua Lai, and Zhirong Liu (College of Chemistry and Molecular Engineering, Peking University, China).

In a step towards improving the process of sedation, Maryam Shanechi and colleagues have developed a brain-machine interface that can automatically control the level of brain inactivation in medically-induced comas. The authors propose that their design could replace manual administration of anaesthetics in the intensive care unit and in surgeries.

Putting off that important decision until you feel better informed? You’re not alone… Sebastian Gluth and colleagues have shown that when faced with uncertainty, people often “decide not to decide”; rather than choose immediately, they search for more information that reduces the uncertainty.

Finally, Ruth Nussinov, PLOS Computational Biology Editor-in-Chief, marked the end of her first year at the helm with an Editorial discussing how the journal can help the biological sciences by ensuring that we focus on open questions, particularly those relating to disease, where the field of computational biology can drive progress.

Category: Bioinformatics, Computational biology, Neuroscience, PLOS Computational Biology | Tagged , | Leave a comment

A Faster Reset Button for Stem Cells

Modified logo wth textBeing able to restore function to organs damaged through disease or injury is a goal that has not only inspired work on programming robots to act on our thoughts (see ‘We have the technology…’) but has also stimulated scientists to explore whether our bodies’ cells can be reprogrammed to take on the missing functions. The 2012 Nobel Prize committee recognised the potential for therapy in adult cell reprogramming and rewarded John Gurdon and Shinya Yamanaka for their pioneering work in this area. These two stem cell biologists realised that a key step in the process is to return the adult cells to a primitive state before persuading them to take on another role, and that this would involve resetting the cells’ nuclear programme.

Shinya Yamanaka published his nobel prize winning work in 2006, in which he showed that treating fully developed adult cells with four protein factors would drive the necessary changes in the cells’ nucleus to return them to a stem cell-like state called pluripotency; and hence the name, “induced pluripotent stem cells”, iPS cells for short.  Since then teams the world over have sought to tweak and streamline this promising technology to obtain greater numbers of pluripotent stem cells, in shorter time, from adult tissue.

Reprogrammed adult cells  (Silva et al. PLOS Biology)

Reprogrammed adult cells
(Silva et al. PLOS Biology)

Austin Smith and colleagues at the Stem Cell Institute in Cambridge, UK asked whether adult stem cells, such as neural stem cells, which are naturally available in small numbers in adult organs, could be more rapidly and efficiently converted to a state of pluripotency than their fully differentiated neuronal neighbours. As published in their PLOS Biology article in 2008, the answer turned out to be yes – neural stem cells are easier to reprogram to a pluripotent state and therefore better starting material than mature fully developed adult brain cells are! The team also made some important discoveries along the way.

One such key discovery was that these neural stem cells don’t return to the pluripotent state in a single step. Using the reprogramming treatment identified by Yamanaka’s lab (the use of viral vectors to introduce genes encoding four reprogramming factors), Smith and his lab found that neural stem cells showed signs of reprogramming much earlier (3 days versus 3 weeks) and at higher frequency than did fully differentiated cells. There was a problem, however, in that these early appearing cells arrested on the verge of full pluripotency.

José Silva, the first author of the PLOS Biology article, told me of his initial disappointment “My passion has been the study of the biology of nuclear reprogramming for many years now. When Kazutoshi Takahashi and Shinya Yamanaka published their seminal work on induced pluripotency, my imagination ran loose with ideas. The irony is that it was the initial failure to generate Induced Pluripotent Stem cells using the conventional Yamanaka factors and traditional Embryonic Stem Cell culture conditions that guided the creation of the PLoS biology work. All we could make initially were highly proliferative cells that looked like Embryonic Stem Cells but were not like these at the molecular level. Somehow these cells were not able to go all the way.”

To tackle this issue, the team broke down the differentiation process  into several steps - starting from embryonic stem cell, and ending with neural stem cell (embryonic stem cells can form any cell type in the body, whereas neural stem cells are restricted to forming only nerve cells). They then worked out which signals might be getting in the way of complete reprogramming. Using chemicals to neutralize these signals (inhibitors ERK and GSK – termed “2i”) and adding a factor called LIF, which encourages self- renewal, the team found that this chemical cocktail (called 2i/LIF) could push the early appearing, partially reprogrammed cells to adopt a fully pluripotent state. 

José Silva explains: ‘we discovered the importance of the culture environment, together with the Yamanaka factors, in instructing the conversion of a differentiated cell back into an embryonic stem cell. “

That the group were able  to generate greater numbers of iPS cells is down to their recognising the potential in these partially reprogrammed “pre-iPS” cells which may have been  previously dismissed by others;  as Kathrin Plath and colleagues at UCLA wrote in 2012,While it is not absolutely clear that pre-iPS cells represent an intermediate that occurs transiently during the reprogramming process, they are not simply an aborted reprogramming artifact because pre-iPS cells can convert into iPS cells upon addition of ERK and GSK inhibitors.’

Finally, Austin Smith’s team noticed that their 2i process enabled the complete reprogramming of neural stem cells that intriguingly contained only very few copies of the ‘Yamanaka factor’ genes, supporting the suggestion that genetic manipulation of cells might not be obligatory for reprogramming them to pluripotency. The use of genetic reprogramming has been a key concern for those in the field, as put forward by distinguished British stem cell biologist, Fiona Watt and her postdoc Ryan Driskell who wrote: ‘ Discovering how the pluripotent state can be efficiently and stably induced and maintained by treating cells with pharmacologically active compounds rather than by genetic manipulation is an important goal.

The paper by Smith and colleagues has gone on to be one of our research gems of the past 10 years and was picked out as a favourite by two of our Editorial Board members, Susan Gasser and Alfonso Martinez-Arias.

Looking back on this research, José Silva, who was a postdoctoral researcher at the time,  reminisces: “This work had a significant impact on my career, as it helped placing me on the path to becoming a Principal Investigator. Most importantly, it gave me a great platform to interrogate the underlying biology of nuclear reprogramming.


 

collection logoSee the Tenth Anniversary PLOS Biology Collection or read the Biologue blog posts highlighting the rest of our selected articles.

 

 
ResearchBlogging.org
Silva J, Barrandon O, Nichols J, Kawaguchi J, Theunissen TW, & Smith A (2008). Promotion of reprogramming to ground state pluripotency by signal inhibition. PLoS biology, 6 (10) PMID: 18942890

Category: Biology, Cell biology, Developmental biology, Epigenetics, PLOS Biology, Regeneration, Research, Stem cells | Tagged | Leave a comment

microRNAs: targeting seeds of destruction

Modified logo wth textA central dogma of biology goes like this: the DNA of genes is copied (‘transcribed’) to make messenger RNA (mRNA), and mRNA is then translated to make protein. Well, that’s what my textbooks used to say when I was a biology student.

This dogma remained unchallenged for decades, in fact until 1993, when Victor Ambros and colleagues identified a tiny RNA transcript in a worm. The worm was called C. elegans and the tiny transcript, lin-4. Without encoding any protein, this small RNA turned out to have a potent effect in the cells of  C. elegans – it could recognize and pair with  a complementary mRNA and prevent it from being translated into protein. Biologists call this effect ‘gene silencing‘.

It took several years before another of these tiny, silencing transcripts was found, and then the floodgates opened. In the years that followed, scientists discovered that these so-called microRNAs were present in lots of other animals and in plants too.

So how do these microRNAs work? Just 22 nucleotides long, these tiny transcripts control what proteins are made in a cell by preventing their translation from mRNAs. When a microRNA pairs with an mRNA, through complementary base pairing, the mRNA is destroyed or not translated.  But given how tiny microRNAs are, how do they find the right target to pair with and silence?  Early attempts to answer this question relied on computer programs but these tended to generate endless lists of targets, many of which turned out to be false.

So several years ago, Stephen Cohen and colleagues set about the laborious task of trying to answer this question. Their work culminated in a groundbreaking paper published in PLOS Biology, entitled Principles of MicroRNA–Target Recognition.

Using genetic tools and experimental approaches available to them in the model organism, Drosophila, Cohen and co discovered that miRNA targets can be divided into two overall categories: those that pair with just the 5′ end of microRNAs and those that additionally need to pair with its other end – the 3′ end.  Surprisingly, they also discovered that pairing with the 5′ end sometimes relied on so-called seed sites that consist of just seven or eight bases complementary to the microRNA 5′ end. The finding that so little sequence complementarity is needed revealed that microRNAs have many more target sites than had been previously imagined.  Indeed, Cohen and colleagues estimated that the Drosophila fly genome has about 100 sites for every microRNA in its genome.  That’s alot of targets, and this work hinted at the likelihood that microRNAs regulate many, many protein-coding genes by silencing their mRNAs.
With its discovery of how microRNAs accurately pair with their targets, this article had a far reaching impact on the field of microRNA biology and gene regulation. As PLOS Biology Editorial Board Member Avinash Bhandoola explains, this work offered“an important set of insights essential to the microRNA field”, including the development of computational tools better equipped to predict true microRNA targets.

Indeed, over the years, research on microRNAs has uncovered that these tiny regulators are involved in many key biological processes.

collection logoSee the Tenth Anniversary PLOS Biology Collection or read the Biologue blog posts highlighting the rest of our selected articles.

 

 

 

ResearchBlogging.org
Brennecke J, Stark A, Russell RB, & Cohen SM (2005). Principles of microRNA-target recognition. PLoS biology, 3 (3) PMID: 15723116

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From Jellyfish GFPs to Plant MiniSOGs; a Microscopy Revolution.

 

Modified logo wth textI’m a firm believer that a picture tells a thousand words, and in biomedical sciences advances in microscopy can only add to the story that every captured image can portray. As we work through the big reveal of our top ten papers, the biological resolution made possible by the advance detailed in this next paper honestly just astounds me in its physical capacity. The resulting overwhelming beauty of what we can image is awesome, and as a Brit who used to live in the US, I don’t use that word lightly.

State of the art, more than three hundred years ago; Hooke's microscope from his "Micrographia".

State of the art, more than three hundred years ago; Hooke’s microscope from his “Micrographia”.

The problem with microscopy, from Hooke and van Leeuwenhoek onwards, is that most living things are inconveniently transparent. That is, photons (for light microscopy) or electrons (for electron microscopy) tend to pass through tissues without breaking a stride, lending very little contrast to the picture. So many crucial advances in microscopy have involved developing ways of staining tissue samples to enhance their interaction with light or electrons.

But what if you want to see specific proteins, instead of cells or organelles? The ingenious exploitation of antibodies opened up the fields of immunohistochemistry, immunofluorescence and immunoelectron microscopy, but these all suffer from the problem that you’re staining the tissue after it’s dead. Back in the 90′s, Roger Tsien and colleagues developed a way of engineering genes so that their protein product was spliced to a jellyfish protein, green fluorescent protein (GFP). When looked at under a high powered microscope those specific proteins (and nothing else) would glow green. GFP has since been used in tens of thousands of studies, and earned Tsien a share in the 2008 Nobel Prize for chemistry.

In 2011 Tsien published in PLOS Biology an analogous tagging system for protein visualization by way of electron microscopy (EM).  These tags are called miniSOG (mini-Singlet Oxygen Generator – I won’t go into much detail, read the paper for more info), and were obtained by re-purposing and mutating a plant protein called phototropin. The miniSOG tag is half the size of GFP, and not only fluoresces (allowing it to be seen in fluorescent light microscopy), but if you zap it with blue light, it generates a highly reactive chemical called singlet oxygen. This can be used to make an insoluble deposit that can be stained and then seen by EM. As noted in their abstract, the authors had high hopes for their new method, claiming that: “MiniSOG may do for EM what Green Fluorescent Protein did for fluorescence microscopy”.

miniSOG stains nematode mitochondria for fluorescent (top) and electron (middle) microscopy. The bottom panel shows a miniSOG-stained mouse brain synapse (Shu et al., PLOS Biology).

miniSOG stains nematode mitochondria for fluorescent (top) and electron (middle) microscopy. The bottom panel shows a miniSOG-stained mouse brain synapse (Shu et al., PLOS Biology).

The power of this method from Roger Tsien and colleagues is best exemplified with some images from their paper, just to try and capture your imagination. For an idea of relative size, the scale bar in the top panel shows 50 μm (micrometres), the width of an average human hair. This is a low-resolution fluorescent image of miniSOG that is targeted to the mitochondria of a nematode worm. You could easily have done this with good ol’-fashioned GFP, but the strength of miniSOGs is that you can switch to EM and zoom in much further. The scale bar in the middle panel shows 500 nanometres (nm; 1000 nm = 1μm), so we’ve zoomed in a hundred fold. Now we can see individual mitochondria, and that human hair would be wider than the computer screen you’re using. Yet other images in the paper (like the bottom panel here) use miniSOG to reveal the ultrastructural locations of specific individual labeled cell adhesion molecules – yes, individual proteins – in synapses of an intact mouse brain. As one of our editorial board members, Franck Polleux, puts it this is “a beautiful illustration of the power of combining chemistry, biology and electron microscopy. This new technique dramatically improves the ability of biologists to locate specific proteins inside cells and organelles at nanoscales.”

Xiaokun Shu, first author of the study who now runs a lab at UCSF told me that the success of this project was “a fruit of thinking outside the box.” Shu notes that many people were trying to engineer GFP to be an efficient SOG, but his insights into GFP’s biochemistry led him to conclude it would be a dead-end. And so they hit upon flavin, a cellular chemical that binds to an Arabidopsis protein, phototropin. But the drawback was that unfortunately there was no activation. Shu put his background in studying protein structure and function to good use and engineered this protein to function as an efficient SOG. And the rest, as they say, is history.

Actually, that’s not quite true. Since its publication, this method has already been widely applied to very good use. In another recent PLOS Biology paper from Ben Nichols and colleagues, miniSOG was applied to determine the molecular composition and ultrastructure of the caveolar coat complex. The incredible resolution made possible using the method – and how it compares with super resolution light microscopy – was also discussed in a really nice primer written by Jacomine Krijnse Locker and Sandra L. Schmid.

The landmark EM work of another Nobel laureate, George Palade, can arguably be seen to be the start of cell biology as we know it, but a resurgence in EM may now be underway as our ability to fully harness the technique is brought into a much sharper focus by way of canny biochemical tricks. We can now visualize some of the smallest molecular components of the cell truly at home in their natural habitats, and the combination of live cell light microscopy imaging with such high-resolution EM surely will provide a much clearer picture of cells than we have ever known before.

 

collection logoSee the Tenth Anniversary PLOS Biology Collection or read the Biologue blog posts highlighting the rest of our selected articles.

 

 

 

ResearchBlogging.orgShu X, Lev-Ram V, Deerinck TJ, Qi Y, Ramko EB, Davidson MW, Jin Y, Ellisman MH, & Tsien RY (2011). A genetically encoded tag for correlated light and electron microscopy of intact cells, tissues, and organisms. PLoS biology, 9 (4) PMID: 21483721

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Naïve, Crazy Idealists Make Good

Modified logo wth text2Ten years ago this month, PLOS ventured into the world of scientific publishing by launching its flagship journal, PLOS Biology. When Harold Varmus, Pat Brown, and Michael Eisen founded PLOS in 2000, they did so with the grand vision of making the vast stores of scientific information available to anyone with an interest—teachers, students, physicians, patients, scientists, and the general public—who didn’t have access to a research library or couldn’t afford to pay for journal subscriptions.

Frustrated by the unwillingness of publishers to make their content freely available without restriction, the founders decided to become publishers to show the world how it could be done. As they explained in their editorial in PLOS Biology’s inaugural issue: “Our aim is to catalyze a revolution in scientific publishing by providing a compelling demonstration of the value and feasibility of open-access publication.”

To mark the occasion of PLOS Biology’s 10th anniversary, we asked co-founder Michael Eisen and his brother, Jonathan Eisen, who has served first as Academic Editor-in-Chief of PLOS Biology and now as Chair of the Advisory Board for the journal, to reflect on the early days of PLOS and PLOS Biology, open access, and the future of scientific publishing.

“Ten years ago, everybody said it wasn’t going to make it a year,” Michael Eisen says. “I don’t think anybody thought that PLOS or PLOS Biology would still be around after a decade. They thought we were naïve, crazy idealists who were playing around with some foundation’s money to start a journal.”

“Have we accomplished everything we wanted to accomplish 10 years ago? No. Have we fundamentally changed the way people think about science publishing? I think the answer is yes.”

 

 

collection logoSee the Tenth Anniversary PLOS Biology Collection or read Biologue blog posts highlighting our ten selected articles.

Category: Advocacy, Biology, Community, Debate, Editorial policy, Interview, PLOS Biology, Publishing, Video | Tagged | Leave a comment

The First Individual Genome: One Is the Loneliest Number

Modified logo wth text

When Craig Venter published the complete sequence of his genome in PLOS Biology in 2007, in some ways, it was old news. Though Venter didn’t admit it until he left Celera as president in 2002, many scientists suspected that the human genome his company sequenced far ahead of schedule, thanks to his groundbreaking shotgun sequencing approach, was his own. (Venter and his colleague Ham Smith supplied the male portion of Celera’s genome.)

But the real news when Venter published his genome (highlighted here as part of PLOS Biology’s 10th anniversary) wasn’t whose genome had been sequenced, but the fact that it had been done.

J. Craig Venter says "nothing but benefit" has come from the publication of his personal genome in 2007. And because the data is publicly available, students routinely sequence his genome and DNA in science class. His DNA has also been used to develop stem cell lines to explore genetic contributions to Alzheimer's. "It's had an even broader impact than I'd ever imagined," he says.

J. Craig Venter says “nothing but benefit” has come from the publication of his personal genome in 2007. And because the data is publicly available, students routinely sequence his genome and DNA in science class. His DNA has also been used to develop stem cell lines to explore genetic contributions to Alzheimer’s. “It’s had an even broader impact than I’d ever imagined,” he says.

Seven years earlier, when Venter and Francis Collins of the government-funded Human Genome Project announced the completion of the draft human genome amid great fanfare at the White House, President Bill Clinton called it “the most important, most wondrous map ever produced by humankind.” As wondrous as the map was, it actually conflated several related maps into an unnatural chimera. The Celera and HGP genomes are both composite haploid assemblies: they represent a single set of chromosomes derived from several individuals. The assemblies provide a map of all of our genes and where they occur on the chromosomes, a herculean task, given the 3 billion base pairs that make up the human genome. But as diploid organisms, we inherit two sets of chromosomes, 23 from mom and 23 from dad. They carry (mostly) the same genes, of course, but the genes aren’t exactly the same (between the two copies and between individuals). And it’s these variations that spell the difference between short and tall, lean and stout—and most importantly for biomedical researchers, sickness and health.

To figure out how variant forms of the same gene, or alleles, contribute to disease, you need to sequence diploid genomes—both sets of chromosomes—to see first how the gene pairs differ and then how the differences might contribute to disease. But the prospect of sequencing a known individual’s genome was, and still is, a highly sensitive affair. What if someone carries alleles associated with a high risk of breast cancer, Alzheimer’s or another potentially fatal disease? How should that information be communicated? Might it affect a person’s ability to get insurance at a fair price?

Venter thinks such concerns have impeded the field’s progress. “That’s the reason we turned to my genome in the first place,” he told me. “I decided it’s not fair to ask other people to do something I’m unwilling to do.” (It’s also why Venter helped pass The Genetic Information Nondiscrimination Act of 2008, which prohibits discrimination in employment or health coverage based on genetic data.)

He blames “hysteria and misinformation” for spreading fears about posting an individual’s genome data online. When some still thought the genome had 100,000 to 300,000 genes, rather than the roughly 22,000 we’ve come to discover, “you had this genetic determinism point of view that there was going to be a gene for everything,” Venter says. People thought once you knew which alleles someone had, you’d know their destiny. Though there are some alleles that accurately foretell a person’s risk of disease, he says, “other than Huntington’s disease I’m not sure there are any other examples.”

When James Watson (one of the main instigators of the Human Genome Project) had his genome sequenced, he withheld the data about his ApoE gene, which has an allele long associated with increased risk of late-onset Alzheimer’s disease. Venter withheld nothing about his genome. He learned that he’s a heterozygote for ApoE4—he inherited a single copy of the allele, tripling his risk of getting the disease. Having two copies of ApoE4 is associated with a even greater risk of disease than having one, but predicting what this means for a given individual is incredibly difficult. Releasing his genome has produced “nothing but benefit,” Venter says, by giving researchers an ability to match gene variants to phenotype. But at this point, “the reality is nobody knows how to interpret a human genome. Nobody can tell accurately what it means to be a heterozygote for ApoE4.”

Although Venter’s genome data places him at risk of developing Alzheimer’s, a recent brain scan looking for the amyloid plaques characteristic of the disease came out “100 percent negative,” he says. “What works statistically for a population with genomics does not work statistically for individuals. Either you have something or you don’t. You don’t have 30 percent of Alzheimer’s.”

Venter's chromosomes (from Levy et al., PLOS Biology).

Venter’s chromosomes (from Levy et al., PLOS Biology).

Venter published his genome to set an example and allay fears about the potential risks of doing so and to show the research community the type of data you can produce. Venter’s team, led by Samuel Levy, did what’s called haplotype phasing—they mapped the gene variants at the same loci on matched chromosome pairs. To understand how our genes contribute to our biology, researchers have to separate out the linear arrays of genes we get from each of our parents, figure out how the genes differ and how the differences sort with phenotypes, like male pattern baldness or Alzheimer’s disease. The PLOS Biology paper “had more haplotype phasing than anything before,” Venter says. “Then we’ve added to that by sequencing from sperm cells to get a high percentage of it separated into parental chromosomes.”

But in the end, one genome doesn’t tell you a lot, he says. Real progress in personal genomics depends on the law of large numbers. “The goal is to take not one or two or ten people, but tens of thousands.”

There’s no denying that sequencing your genome can come with risks—like finding out the man you grew up calling Dad is not your biological father. And there’s no denying that the genomics community has a long way to go to build the necessary infrastructure so that people who share their genomes fully understand what their genetic information does–and does not–mean. But Venter thinks that amassing more and more genome sequences without phenotypic information is largely a waste of time. “It’s not just a nice thing or curious thing to link the genome back to an individual,” he says. “It’s absolutely essential to understand the genome.”

 

collection logoSee the Tenth Anniversary PLOS Biology Collection or read the Biologue blog posts highlighting the rest of our selected articles.

 

 

 

ResearchBlogging.org
Levy S, Sutton G, Ng PC, Feuk L, Halpern AL, Walenz BP, Axelrod N, Huang J, Kirkness EF, Denisov G, Lin Y, MacDonald JR, Pang AW, Shago M, Stockwell TB, Tsiamouri A, Bafna V, Bansal V, Kravitz SA, Busam DA, Beeson KY, McIntosh TC, Remington KA, Abril JF, Gill J, Borman J, Rogers YH, Frazier ME, Scherer SW, Strausberg RL, & Venter JC (2007). The Diploid Genome Sequence of an Individual Human PLoS Biology, 5 (10) DOI: 10.1371/journal.pbio.0050254

Category: Bioinformatics, Biology, Data, Disease, Genetics, Genomics, PLOS Biology, Policy, Resources | Tagged , | 1 Comment

The Significance of the 2013 Nobel Prize in Chemistry and the Challenges Ahead

Following the announcement of the 2013 Nobel Prize in Chemistry, PLOS Computational Biology Editor-in-Chief Ruth Nussinov discusses the implications of this award for both the computational biology and wider biological community.

Last week, the 2013 Nobel Prize in Chemistry was awarded to Martin Karplus, Michael Levitt and Arieh Warshel for “the development of multiscale models for complex chemical systems”. As the Royal Swedish Academy of Sciences noted “Chemists used to create models of molecules using plastic balls and sticks. Today, the modelling is carried out in computers. In the 1970s, Martin Karplus, Michael Levitt and Arieh Warshel laid the foundation for the powerful programs that are used to understand and predict chemical processes. Computer models mirroring real life have become crucial for most advances made in chemistry today.” Further, “Today the computer is just as important a tool for chemists as the test tube. Simulations are so realistic that they predict the outcome of traditional experiments.”

Ruth Nussinov

Image Credit: Ruth Nussinov

This event is a milestone for the broad community that PLOS Computational Biology represents. Along with Philip E. Bourne, the Founding Editor-in-Chief, and our Editorial Board, which proudly lists Michael Levitt among its members, I extend the warmest congratulations to the winners. Beyond the specific personal scientific achievements that have already been widely discussed, we must consider the more general and broader context of this unique prize. Here, I would like to present this Nobel Prize within this framework, emphasizing its magnitude and far reaching implications not only for computational biology, but for the biological community at large.

In recent decades, molecular biology has progressed by leaps and bounds. Huge technological advances have taken place in sequencing, in mapping structure and dynamics via EM, X-ray and NMR, in manipulating imaging of nuclei and cells, in sequencing single biomolecules, and more.  These have led to fundamental new insights; biology and medicine have soared to new heights with the DNA double helix providing the molecular basis for genetics and Darwinism. Many steps were required to identify and untangle DNA-RNA-protein sequence -structure- function and reverse transcription processes, RNA enzymes, key multi-partnered scaffolding molecules important under normal physiological conditions and in disease, their structures, mutations, and the principles and mechanisms of their dynamic regulation, and other landmark developments. These involved technological breakthroughs and greater understanding of the specific mechanisms involved. Most of the Nobel prizes in chemistry and medicine in recent years have been awarded at these junctures.

Vast amounts of information on sequences and structures are yet to be explained and pose a challenge for computational biology. Recently this has been compounded by interdisciplinary studies of the nervous system, posing questions such as how it is structured, how it develops, how it works, the mechanisms of signal processing, and more, all at multiple levels, ranging from the molecular and cellular levels to the systems and cognitive levels. Thus, even if we gain in-depth insight into static properties such as the genomic data and structural snapshots of proteins (DNA and RNA) at different levels of resolution, the truly monumental challenge of understanding their dynamics still looms ahead. And eventually, this is how cells, tissues and organisms develop and work.

The systems in question operate at all scales:  force fields and free energy landscapes relevant for protein folding and function, large complexes, biomolecular recognition involving proteins, DNA, RNA, lipids, post-translational (and DNA) modifications, and interactions with small molecules. On a larger scale we see cellular locomotion, cell division and trafficking, and cell-cell recognition. Further, beyond these, lurks the working of the complex cell as a cohesive unit: the cellular network controls metabolism and regulation, intra- and inter-cellular signaling and the neural circuits of nerve cells, where the activity of one cell directly influences many others. All are dynamic, all change with the cellular environment and all present a daunting challenge. The relevant time scales range from femtosecond for simple chemical reactions to the eons of evolution; however, all operate with the same underlying physical principles of conformational variability and selection.

At each time scale and corresponding physical size we strive to identify the relevant moving parts and degrees of freedom and to formulate effective – though often approximate- rules for their mutual interactions and resulting motion. Solving, understanding, and computing the dynamic behavior at any given scale is of great interest in its own right and provides approximate dynamical input for the next scale, which is one rung above it. Only at the lowest, most basic scale of individual atoms and electrons are the dynamical rules (electrostatics and Schrödinger’s equation) completely well defined. And the all-important work cited by the Nobel Prize Committee and which is carried out by our community is roughly at the first/second level, making it of fundamental importance.

This Nobel Prize is the first given to work in computational biology, indicating that the field has matured and is on a par with experimental biology. It may also be the very first prize given in any area of the exact sciences for calculations.  What is different in the present case?  I believe that the answer is simple: the present calculations are of much greater interest to a much broader community. In endeavoring to imitate the basic processes of life in silico, great strides are being made toward understanding the secret of life. Computational biology, and simulations, for which Martin Karplus, Michael Levitt and Arieh Warshel shared the Nobel Prize, can carry the torch leading the sciences to decipher the elemental processes and help alleviate human suffering.

What are the challenges ahead? Are simulations with time scales of microseconds, milliseconds or beyond, under the current force field framework, capable of producing results in agreement with experiment, also for large and complex proteins like membrane receptors? Do the challenges also lie in the type of questions which are asked, for which such long time scale simulations can be useful in providing answers? Or is it the biology behind the questions that is also the key? Ultimately, as in experimental biology which also exploits methods and machines, it is likely to be all of the above. Computations are our treasured tool; they are not our aim. Merely running long molecular dynamics trajectories is unlikely to advance science.

PLOS Computational Biology joins the International Society of Computational Biology (ISCB) and our computational biology community in congratulating the awardees and celebrating this momentous event.

Category: Announcement, Biology, Community, Computational biology, News, PLOS Computational Biology | Tagged , , , | 1 Comment

Scanning for Recent Human Evolution

Modified logo wth textSurvival of the fittest is a concept that is well known to most of us. Heaven knows, many of us strive to remain fit enough to try and extend our life expectancy and survive. But in its original context this relates to natural selection and evolution. A lot of the time natural selection is a conservative force (“negative selection“), trying to keep an organism close to a previously achieved state of perfection. However, occasionally circumstances can change, and selection can then favour genetic changes that will fit an organism better for the new regime. This is what we call “positive selection“, and unlike its rather boring conservative cousin, it can be very interesting indeed.

Continuing the unveiling of our top ten papers selected from the papers published in PLOS Biology over the last decade, the latest choice morsel comes from 2006. The authors developed a new statistical method to search for features that flag those regions of the human genome that may have aided our own adaptation to changing fortunes. They applied this method to the then recently available single nucleotide polymorphism (SNP) data from the International HapMap Project – a catalogue of subtle (but potentially important) genetic variations in different human populations.

By TimVickers at en.wikipedia [Public domain], from Wikimedia Commons The source of this image is the frontispiece of Huxley's 1863 book Man's Place in Nature.

By TimVickers at en.wikipedia [Public domain], from Wikimedia Commons
The source of this image is the frontispiece of Huxley’s 1863 book Man’s Place in Nature.

What do we mean by ‘recent positive selection‘? Well, we’re not talking about how humans evolved from apes, nor are we considering how the last 100 years have changed us, but instead ‘recent’ here means the last ~100,000 years, give or take, during which time modern human populations experienced dramatic environment and lifestyle shifts; Homo sapiens would have left Africa and probably encountered distant cousins H. erectus in Asia and H. neanderthalensis in Europe. They will have needed to adapt to climate and habitat changes as well as to successfully exploit novel food sources and evade newly emerging pathogens. Crucially the authors’ approach specifically looks for genetic variants that are still under ongoing selection, and haven’t yet become universal in the population (haven’t reached “fixation”, in the parlance).

Senior author of the study, Jonathan Pritchard, commented that “this was an important paper for my lab. It really represented the start of our transition into thinking about genome-wide variation data.” He notes also that “[this project] brought me back to thinking about how positive selection shapes the genome… the first area that excited me when I got into biology.” The study was groundbreaking. It presented both the authors’ new statistical method to scan the genome, and many intriguing insights about what had been (and is still being) selected for in humans.

I fully intended to describe the paper to you in some detail, but luckily for me long-time Editorial Board member, and the academic editor who advised us through the review process back then, Laurence Hurst, remembers it well:

“I recall that this paper came at an interesting time.  Plenty of folks were attempting between-species trawls for domains under selection (using Ka/Ks etc) but this was one of the first to consider genome-wide trawls for selection based on SNP/haplotype data and so capture much more recent selection events – potentially events that reflect different selection pressures in different populations.”

He goes on to note that the author came up with “a simple but elegant solution” and that ”all methods of this variety also have problems with demography (which can give false signals), but the paper makes a good crack at doing simulations to try and weed out these issues.”

“I liked this study,” continues Hurst, “not least because it is novel, clever, rigorous and careful, but it is interesting to see what selection is doing.  I find it intriguing, for example, that skin pigment genes appear to be under selection in Europeans. But this isn’t just academic interest: as the authors note, alleles under recent selection are often associated with complex phenotypes of medical relevance. Indeed they identify alleles associated with alcohol susceptibility and salt-sensitive hypertension.”

A map of positive selection along chromosome 2. The blue peak surrounds the lactase gene, already known to be involved in the adaptation to dairy farming. Voight et al., PLOS Biology.

A map of positive selection along chromosome 2. The blue peak surrounds the lactase gene, already known to be involved in the adaptation to dairy farming. Voight et al., PLOS Biology.

In short, the authors  compared SNP data from three populations – Yorubans from sub-Saharan Africa, a combination of Japanese and Chinese individuals from Asia, and a cohort from Europe. They specifically looked across the genome for long blocks of co-inherited DNA (haplotypes) that indicate that a particular SNP (or a nearby feature in the genome) confers some positive advantage on the individual that carries it, and that it is increasing in prevalence in that population. Their new method was a breath of fresh air, and has been widely adopted since then, and they found lots of intriguing biologically and medically relevant results. What’s not to like?

PLOS Biology editorial board member Chris Tyler-Smith noted that this is “A classic paper in my own field, introducing a method that has now become standard”. One of the two co-first authors, Ben Voight, was a graduate student when undertaking this research and is now an Assistant Professor at U. Penn. Voight notes that this paper probably “was a non-trivial factor in getting me where I am today”. In recalling his work on this study, he remembers that given the competitive nature of the field at that time and as a newcomer they were very careful in “crafting the description of the framework, model, and inferences possible. This took a great deal of effort, but in some way was only really possible by a publication framework where we weren’t unnecessarily limited in making that description”.

And it seems that this area is rife for future additional discovery as Pritchard muses “More recently my lab [and others] has argued that adaptation by polygenic selection is likely much more important than standard sweep models… in the next few years it will be possible to make a lot of progress on untangling these issues, helped … by the much richer sequence data that are available today”. Voight told me that “it is clear that this work opened the door to a number of new questions which follow that, even today, have not been flushed out completely: the times these sweeps occurred in human history, the targets, mechanisms, the phenotypes subject to fitness consequences, and the relationship to complex traits and diseases.” And so as our resources and tools improve and advance, our ability to decipher and decode backwards progresses in tandem, leading us to better understand how we came to be who, what, and where we are today. The fascinating research will continue… 

If you want to read more about the original research article, we published both a synopsis and an editorial in the same issue of the journal way back when.

 

collection logoSee the Tenth Anniversary PLOS Biology Collection or read the Biologue blog posts highlighting the rest of our selected articles.

 

 

 
ResearchBlogging.org
Voight BF, Kudaravalli S, Wen X, & Pritchard JK (2006). A map of recent positive selection in the human genome. PLoS biology, 4 (3) PMID: 16494531

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