author: David S Pisetsky
date: 2012
institute: 1Duke University Medical Center, 151G, Durham VA Medical Center, Durham, NC 27705, USA
title: 4th International Forum on Rheumatoid Arthritis: Great Times Near the Great Wall

Beijing, an incredible city, was the site for the 4th International Forum on Rheumatoid Arthritis (IFRA), an incredible meeting. Professor Zhan-guo Li, Professor of Medicine at Peking University and President of the Asia Pacific League Against Rheumatism, and his co-chairs, Professor Lars Klareskog, Professor of Rheumatology, Karolinska Institutet, and Professor Kazuhiko Yamamoto, Professor of Medicine, University of Tokyo, organized the meeting. They did a terrific job and created a program to showcase the excitement and dynamism of both modern China and modern rheumatology. Professor Li was a great host as hundreds of Chinese rheumatologists packed the Beijing Xinyuan Hotel to hear lectures by rheumatologists from China as well as invited speakers from around the world. I have attended few meetings where the energy was so high and the eagerness to learn so great.

While most of the international speakers had previously visited Beijing, the meeting was my first trip to China. To say the least, I was surprised by the city's modernity. Growing up in a very frosty time of the Cold War, China - usually called Red China or Communist China in the United States - was very much a forbidden country until Nixon thawed relationships with secret diplomacy, a ping-pong tournament and a memorable visit in 1972. I remain uncertain about the city's name because, during my lifetime, Beijing has also been called both Peking and Peiping. The images I have of modern China come mostly from the telecasts of the 2008 Olympics, although the NBC commentators seemed mostly interested in street food as if restaurants did not exist. I had few preconceived ideas of the real Beijing as opposed to the attractions I read about in *1000 Places to See Before You Die*.

While China is a very old country, Beijing is a very new city. I would say that Beijing has the ambience of a boom town - but boom town is a misnomer for a megalopolis that has a population of about 20 million people and a land area of 6,500 square miles. About half the size of Belgium, Beijing makes Chicago look like a quaint little village with its neighborhoods of row houses. High-rise apartments and office buildings are everywhere as if the only direction to grow is up. Alas, a dense haze often covers the landscape, blotting the outline of some beautiful buildings and preventing appreciation of the sheer magnitude of the construction that has marked the explosive growth of Beijing in recent years.

Throughout the city, wide avenues and highways are dense with cars at all hours. Driving around for sightseeing, I was gratified (and indeed mystified) to see many cars with Buick on the name plate. Indeed, I probably saw more Buicks in Beijing than I do in Durham, North Carolina. There used to be an advert that said 'This is not your father's Buick.' I guess in China the advert says 'This is not your American's Buick.' That would be true since the car is the product of GM Shanghai, a joint venture of General Motors and CDAI. Actually, GM sells more cars in China than in the United States. Volkswagens, Hyundais and Audi are also frequent vehicles and far outnumber the bicycles and scooters I was expecting.

As a tourist attraction, the ultra-modern Birds Nest Olympics stadium with its gleaming lattice shell designed by Swiss architects now competes for attention with the ancient Great Wall, the Forbidden City and the Summer Palace. The Egg is another landmark and a great building. For those who delight in stirring architecture, I recommend Beijing Airport. Airports are not usual venues for sightseers since they call forth memories of long lines at immigration, canceled flights and lost luggage. Nevertheless, Beijing Airport is a spectacular structure that is filled with light from an undulating ceiling of windows. The outside roof is painted red, the Chinese symbol for good luck. If you go to Beijing, you of course visit the airport, perhaps discombobulated by the time-zone change. My recommendation is to rouse yourself awake and look at the building and not just stumble through it.

Like China, rheumatology research is experiencing a boom and is on the go. As talks at the IFRA demonstrated clearly, modern science moves with incredible speed and is producing voluminous data at a dizzying pace. The technology keeps getting better and better, the analysis of the pathogenesis more and more precise. (Alas, while I saw the data presented by speakers from China, I will have to confine my comments to those talks that were in English. I want to make sure that my comments are reasonably correct and apologize to those whose work I cannot describe with confidence.)

Still in the midst of the genetics revolution, rheumatology is entering the epigenetics revolution. Studies by Professor Steffen Gay of Zurich and Professor Gary Firestein of San Diego described extraordinary progress in unraveling epigenetic changes in fibroblasts that may endow these cells with an invasive phenotype. These changes can result from base methylation reactions that lead to long-term changes in gene function; interestingly, some epigenetic changes may be transmitted through the generations. In this talk, Professor Gay raised the possibility of therapies to undo epigenetic modifications, including some agents that I can purchase at my local GNC store. Discussing another element of gene regulation, Professor Iain McInness of Glasgow illustrated how miRNA can regulate key events in immune activation. miRNA can be a target for therapy, as illustrated by findings from Professor Gay and colleagues on the use of antagomirs in a pulmonary hypertension model.

Modern technology is also providing new insights into B-cell abnormalities that underpin autoimmunity in rheumatoid arthritis (RA). Presenting a study of antibodies to citrullinated proteins (ACPA), Professor Klareskog described the extraordinary commitment of synovial B cells to ACPA production, with about 30% of these cells pumping out immunoglobulin to drive events in synovial inflammation. ACPA may have some unexpected roles in pathogenesis since, as Professor Klareskog showed, antibodies to citrullinated vimentin may trigger osteoclastogenesis, perhaps driving bone loss that is so characteristic of RA. Complementing these talks was a presentation by Professor Joachim Kalden of Erlangen on the treatment of autoimmunity with bortezemib, a proteasome inhibitor that can interfere with plasma cells. In both murine models as well as early studies on patients with lupus, this agent can reduce anti-DNA levels; effects on protective antibodies are of course a concern and will be closely watched as this therapy advances.

A presentation by Professor Ed Keystone of Toronto illustrated striking progress on the clinical front. Professor Keystone addressed two main topics, highlighting recent presentations from the European League Against Rheumatism meeting in Berlin. Two messages are clear. The first is that, in head-to-head trials, agents that differ in action can lead to similar clinical responses. Adalimumab and subcutaneous abatacept thus showed comparable efficacy; reference to prior trials suggests that the effects of subcutaneous abatacept may differ from those of the intravenous formulation with respect to the time course and magnitude of the response. The results of the head-to-head trials are also surprising in view of an impression that the efficacy of co-stimulatory blockade may differ from that of TNF blockade. These studies raise the question of why agents that attack fundamentally different cellular pathways act so similarly. While current terminology emphasizes the concept of targeted therapy, the action of treatments may converge so that the same bull's eye is hit even if the arrows come from different directions.

A second message of Professor Keystone's talks relates to remission. As the data he presented showed, treatment of RA with current agents and strategies (that is, treat to target and tight control) can induce remission in a significant number of patients as defined by well-established outcome measures. Furthermore, as he reported in patients achieving remission with a combination approach, elimination of a biological agent does not lead to relapse in many patients, with remission sustained at least over months. In the words of Professor Keystone, this is a game changer.

I think that the frequency of remission in recent clinical studies has caught many rheumatologists off guard. RA has long been conceptualized as a chronic, even relentless, disease with changes (?epigenetic) in synoviocytes leading to joint destruction even in the absence of inflammation. Interpretation of these clinical studies is, of course, dependent on the definition of remission and the extent of subclinical synovitis and radiological progression that may occur in a patient in whom arthritis seems quiescent.

Outcomes researchers will have ample opportunity to debate the criteria for remission and which composite measures (Disease Activity Score, Clinical Disease Activity Index, Simplified Disease Activity Index, Boolean), and their components, are most informative and reliable. I sometimes think rheumatologists do not really know how to quantify disease activity, for either clinical or research purposes, especially when inflammation is low. In my own clinical practice, I have thought that joint tenderness is important to this determination although Professor Keystone, an expert with decades of experience, gave the nod to joint swelling as a measure of active disease. During a discussion session, I asked Professor Keystone about his approach to assess swelling. He said ballottement of joint swelling is key, showing how he squeezes a joint with his fingers to determine its character and the presence of inflammation. If rheumatologists have to rely on ballottement to make crucial decisions to start or stop drugs, I have my concerns. Given the power and elegance of today's science, some biomarker or imaging modality must be out there to help with decision-making and tell whether the fire of inflammation smolders or has been successfully doused.

In addition to its importance clinically, the phenomenon of remission should drive inquiry into disease mechanisms, providing a unique setting to explore critical events in pathogenesis. Much research in rheumatology now focuses on preclinical autoimmunity, the time prior to the onset of clinical symptoms, in which immune cell abnormalities (for example, ACPA expression, cytokine production) are revving up for their full onslaught. With remission, there is perhaps now a state of postclinical autoimmunity in which signs and symptoms abate.

What is going on in patients with remission? Consider two examples. ACPA have a close relationship to RA, with some data, including animal studies, pointing to a direct role in joint inflammation. If ACPA titers do not change much during remission, then their role in pathogenesis is up for grabs. Perhaps during remission, ACPA specificity shifts in some subtle but significant way to nonpathogenic species. Since these antibodies emanate from long-lived plasma cells, however, I doubt such a change. Alternatively, an antigen required for immune complex formation may have disappeared during remission or, more probably, the downstream effectors have been quieted.

A second example relates to epigenetics. Many current studies on the epigenetic modification of fibroblasts involve operative specimens obtained at the time of replacement joint surgery. Synoviocytes in such tissues have been subject to years (?decades) of inflammation, the assault of biomechanical stress and even a slug or two of intra-articular glucocorticoids to go along with years of disease-modifying antirheumatic drugs. Until the advent of remission, it would be difficult to know which of these changes is primary or secondary - although, as Dr Firestein showed, IL-1 can affect DNA methylation. Now, with remission, epigenetic imprinting can be studied in a new way, although obtaining samples of synovium during remission may be challenging unless techniques for easy tissue retrieval become available.

The IFRA occurred in the midst of the US presidential election although, with current politics, it seems we are always in the midst of a presidential election. Talk of 2016 is already heating up. Palin versus Clinton? Whatever the outcome of the 2012 contest - red or blue - the prospects for the National Institutes of Health budget look bleak - gray if not black. Given the drive to shrink the federal budget and blot away red ink, stagnant funding may be the best we can do although genuine cutbacks may occur. Cutbacks on research now would be a great tragedy since technology is leading to unprecedented discoveries that can be translated into treatment advance. I wish some of our politicians would have attended the IFRA to hear lectures by Chinese investigators or to speak to Chinese students whose numbers have swelled with the influx of government money. Those politicians would have seen what a commitment to building research can accomplish in terms of innovation, economic growth and the inspiration provided to young people who are anxious to discuss the effects of IL-2 on regulatory T cells or the role of STING in the immune response to DNA in lupus.

According to de Tocqueville, the genius of America came from the pursuit of 'self-interest properly understood.' China seems to be taking a different path, however. Practically and philosophically, it seems that China wants to pursue national interest properly understood. In either system, proper understanding must balance the interests of the individual and those of the state, always a great challenge. China is an old country and America is a young one, but both are struggling to find a balance consistent with their histories and cultures. Given the importance of US and Chinese economies in the world, there will be many onlookers to this great political process.

I was honored to attend the IFRA and witness the extraordinary accomplishments of China in rheumatology research. It was a truly wonderful occasion of fellowship and cooperation, and I was excited to meet and toast new friends with potently delicious Chinese wine that had a strong resemblance to what in North Carolina we call white lightning. I look forward to many more years of international cooperation as we work together to advance rheumatology research and improve the health of people of the world properly understood.

Gan bei!

# Abbreviations

ACPA: antibodies to citrullinated proteins; IFRA: 4th International Forum on Rheumatoid Arthritis; IL: interleukin; miRNA: microRNA; RA: rheumatoid arthritis; STING: stimulator of interferon genes; TNF, tumor necrosis factor.

# Competing interests

The author declares that they have no competing interests.

# Suggested reading

1\. Barber GN: **Cytoplasmic DNA innate immune pathways**. *Immunol Rev* 2011, **243:**99-108.

2\. Boyman O, Sprent J: **The role of interleukin-2 during homeostasis and activation of the immune system**. *Nat Rev Immunol* 2012, **12:**180-190.

3\. Brock M, Samillan VJ, Trenkmann M, Schwarzwald C, Ulrich S, Gay RE, Gassmann M, Ostergaard L, Gay S, Speich R, Huber LC: **Antagomir directed against miR-20a restores functional BMPR2 signalling and prevents vascular remodeling in hypoxia-induced pulmonary hypertension**. *Eur Heart J* 2012. \[Epub ahead of print\]

4\. Felson DT, Smolen JS, Wells G, Zhang B, van Tuyl LH, Funovits J, Aletha D, Allaart CF, Bathon J, Bombardieri S, Brooks P, Brown A, Matucci-Cerinic M, Choi H, Combe B, de Wit M, Dougados M, Emery P, Furst D, Gomez-Reino J, Hawker G, Keystone E, Khanna D, Kirwan J, Kvien TK, Landewé R, Listing J, Michaud K, Martin-Mola E, Montie P, *et al*.; American College of Rheumatology; European League Against Rheumatism: **American College of Rheumatology/European League Against Rheumatism provisional definition of remission in rheumatoid arthritis for clinical trials**. *Arthritis Rheum* 2011, **63:**573-586.

5\. Harris U, Georgess D, Bang H, Bozec A, Axmann R, Ossipova E, Jakobsson PJ, Baum W, Nimmerjahn F, Szarka E, Sarmay G, Krumbholz G, Neumann E, Toes R, Scherer HU, Catrina AI, Klareskog L, Jurdic P, Schett G: **Induction of osteoclastogenesis and bone loss by human autoantibodies against citrullinated vimentin**. *J Clin Invest* 2012, **122:**1791-1802.

6\. Hughes-Austin JM, Deane KD, Derber LA, Kolfenbach JR, Zerbe GO, Sokolove J, Lahey LJ, Weisman MH, Buckner JH, Mikuls TR, O'Dell JR, Keating RM, Gregersen PK, Robinson WH, Holers VM, Norris JM: **Multiple cytokines and chemokines are associated with rheumatoid arthritis-related autoimmunity in first-degree relatives without rheumatoid arthritis: Studies of the Aetiology of Rheumatoid Arthritis (SERA)**. *Ann Rheum Dis* 2012. \[Epub ahead of print\]

7\. Keystone EC, Smolen J, van Riel P: **Developing an effective treatment algorithm for rheumatoid arthritis**. *Rheumatology* 2012, **51:**v48-v54.

8\. Nakano K, Whitaker JW, Boyle DL, Wang W, Firestein GS: **DNA methylome signature in rheumatoid arthritis**. *Ann Rheum Dis* 2012. \[Epub ahead of print\]

9\. Neubert K, Meister S, Moser K, Weisel F, Maseda D, Amann K, Wiethe C, Winkler TH, Kalden JR, Manz RA, Voll RE: **The proteasome inhibitor bortezomib depletes plasma cells and protects mice with lupus-like disease from nephritis**. *Nat Med* 2008, **14:**748-755.

10\. Sokolove J, Bromberg R, Deane KD, Lahey LJ, Derber LA, Chandra PE, Edison JD, Gilliland WR, Tibshirani RJ, Norris JM, Holers VM, Robinson WH: **Autoantibody epitope spreading in the pre-clinical phase predicts progression to rheumatoid arthritis**. *PLoS One* 2012, **7**:e35296.

11\. Studenic P, Smolen JS, Aletaha D: **Near misses of ACR/EULAR criteria for remission: effects of patient global assessment in Boolean and indexbased definitions**. *Ann Rheum Dis* 2012, **71:**1702-1705.


author: David T. JonesCorresponding author: David T. Jones, <firstname.lastname@example.com>.
date: 2012-07
references:
title: Neural Networks, Cognition, and Diabetes: What Is the Connection?

Diabetes has been associated not only with subtle cognitive deficits in mental speed and flexibility (1,2) but also with an increased risk for development of significant disruption in cognitive function in the form of dementia (3). In order to prevent subtle cognitive sequelae and potentially reduce the burden of dementia on an aging world population (4), a better understanding of the pathophysiological mechanisms underlying the cognitive dysfunction in diabetes is needed.

There are several factors associated with diabetes and its management beyond acute metabolic or vascular insults that are potentially involved in this cognitive disruption, a few of which will be briefly reviewed here. Hyperglycemia may affect cognitive function by altering synaptic plasticity in the brain (5), increasing levels of oxidative stress (6), and/or subtly altering the cerebral microvasculature (7). Treatment of diabetes may lead to slight or occasionally more severe periods of hypoglycemia, which may translate to structural and metabolic alterations of the central nervous systems and subsequent cognitive dysfunction. However, it has been observed that subtle diabetes-related cognitive changes are associated with chronic hyperglycemia rather than episodes of hypoglycemia (8). The same study also observed that there was no significant difference in cognitive function between aggressively managed subjects with reduced levels of microvascular complications and those with a greater degree of microvascular complications. Other studies have observed this as well (9). This may indicate that the association between chronic hyperglycemia and subtle cognitive dysfunction is related to factors outside of microvascular complications alone. In this context, the potential impact of chronic exposure to endogenous or exogenous insulin above the normal physiological range should be critically evaluated.

Mild cognitive impairment is associated not only with age of diabetes onset and disease duration but also with insulin treatment (10). The risk of dementia is also highest in people with diabetes treated with insulin (11). Hyperinsulinemia in nondiabetic individuals has also been associated with cognitive impairment (12). This association is confounded by other comorbid factors, disease duration, and disease severity. However, there are several sound biological mechanisms by which prolonged hyperinsulinemia may influence central nervous system function. Not only is insulin a vasoactive substance (13), but it also inhibits "housekeeping" processes important for healthy brain aging (i.e., autophagy) (14) and influences processing of proteins related to Alzheimer disease (15). Prolonged exposure of the brain to higher than physiological levels of insulin may alter metabolic pathways in a manner that is deleterious to cognitive circuitry, given that this circuitry is dependent on cells influenced by these metabolic processes. These factors likely impact the changes associated with the phenomenon of "brain insulin resistance" observed in Alzheimer disease.

It should be noted that comorbid conditions and confounding variables such as hypertension, brain infarcts, dyslipidemia, obesity, socioeconomic status, depression, and levels of mental and physical activity must also be taken into consideration when discussing diabetes-associated cognitive dysfunction.

Any factor that precipitates cognitive dysfunction must affect brain circuitry in some manner. Given this necessity, one potential strategy for elucidating the relevant pathophysiological link between diabetes and cognitive dysfunction is to isolate the factors discussed above and compare their effects not only on direct cognitive measures or on the risk of developing cognitive impairment but also on measures of the neural networks that are affected. Neural networks have been shown to be acutely altered by structural lesions causing cognitive deficits (16) and are altered in at-risk populations prior to structural changes such as atrophy (17) before clinically measurable effects on cognition. Therefore, measures of neural networks may be best suited to track the earliest effects of diabetes on brain function. In this vein, the study presented in this issue of *Diabetes* by van Duinkerken et al. (18) lays the groundwork for these investigations. This study is an extension of their work investigating the neural network correlates of cognitive deficits observed in type 1 diabetes mellitus (T1DM), in which they hypothesize that these changes are modified by the presence of microvascular disease. Previously these investigators explored functional connectivity within neural networks using magnetoencephalography (MEG) (19) and concluded that "chronic hyperglycemia, among other factors, may negatively affect brain functioning even before microvascular damage becomes manifest." In the current study, the authors skillfully use functional magnetic resonance imaging (fMRI) in the absence of an experimentally predetermined task, also known as resting-state fMRI, but referred to herein as task-free fMRI (TF-fMRI). (Vemuri et al. \[20\] review the application of this technique in a related cognitive context.) TF-fMRI allows for greater spatial resolution and visualization of networks of synchronized activity in the brain (intrinsic connectivity networks \[ICNs\]) than typically achieved by MEG.

The authors used TF-fMRI to identify ICNs in 49 T1DM patients with microangiopathy (MA^+^) and 52 without microangiopathy (MA^−^) compared with 48 control (CN) subjects and their relationship to various cognitive and disease variables. They found increases in connectivity in visual processing and sensorimotor networks in MA^−^ not present in MA^+^ relative to CN subjects, and decreases in connectivity in attentional, auditory/language processing, and executive control networks that progressed in degree going from CN\>MA^−^\> MA^+^. Given that the MA^+^ subjects were significantly older and had greater depressive symptoms, higher systolic blood pressure, and an earlier disease onset and longer disease duration, a subsequent analysis was conducted matching 27 MA^+^ and 28 MA^−^ on disease onset and duration. In this analysis, the increased connectivity in the MA^−^ group in the visual processing and sensorimotor networks was the only statistically significant finding. It should be emphasized that the MA^+^ group did not differ from the CN group in these two networks (MA^+^ results are reported as a relative decrease to MA^−^, just as the CN group would appear as a relative decrease to MA^−^, both of which being primarily driven by the MA^−^ group's increase in connectivity in these two networks).

This study will certainly advance the field in the search for pathophysiological mechanisms of cognitive decline in diabetes. The authors focused on a T1DM population, which may be less confounded by metabolic syndrome comorbidities than a type 2 diabetic population. They also had a relatively large sample of well-characterized subjects with expert acquisition and analysis of TF-fMRI data. The study's weaknesses lie in the inherent difficulties in this and other TF-fMRI studies. TF-fMRI is reliant on the blood oxygenation level–dependent signal and the fidelity of neurovascular coupling, which may be altered by microvascular changes. However, the similar results the authors report using MEG (which is not dependent on neurovascular coupling) are reassuring. In this regard, future TF-fMRI studies may benefit from inclusion of additional neuroimaging measures of white and gray matter integrity, which will also help tease apart other etiological factors. TF-fMRI also needs a model of the organization of the brain when it is not being experimentally directed (i.e., in a "resting state" during a task-free experimental paradigm). This will allow for a priori hypothesis generation and also circumvent some of the difficulties of TF-fMRI experimental design and interpretation.

One common difficulty in TF-fMRI interpretation is the phenomenon of increased functional connectivity, which is typically ascribed to "compensation." However, this link has not been firmly established nor is it based on a model of undirected brain activity measured by TF-fMRI. In Alzheimer dementia, the earliest functional changes detected in at-risk populations appear to be increases in functional connectivity in posterior limbic circuitry as early as the third decade of life (21). The same circuitry displays decreased functional connectivity later in life and is accompanied by increased functional connectivity in anterior/frontal limbic pathways (22), which in turn also decreases as Alzheimer disease progresses (23). Therefore, the increases in connectivity observed in the MA^−^ group studied by van Duinkerken et al. may be related to an intermediate stage of connectivity change. This is clearly evident in the networks displaying decreased connectivity in a dose-dependent manner going from CN\>MA^−^\> MA^+^. This line of reasoning is also supported by the fact that the difference between MA^+^ and MA^−^ was not present when they were matched on disease duration and age of onset. Therefore, the results of this study do not strongly implicate microvascular disease as the only, or most prominent, factor related to diabetes-associated cognitive disruption. However, this study does implicate disease severity and duration, suggesting that metabolic derangements associated with chronic exposure to either hyperglycemia and/or hyperinsulinemia may be more prominent in the development of diabetes-associated cognitive decline.

Future studies should focus on differentiating the effects of chronic hyperglycemia and hyperinsulinemia on measures of neural network integrity. Such studies will not only advance our understanding of the pathophysiological mechanisms underlying the cognitive dysfunction in diabetes but will also refine the use of neural network metrics as biomarkers of cognition. This will facilitate tracking of neural network responses to early intervention strategies targeting key pathophysiological mechanisms before the appearance of any cognitive dysfunction, thereby refining strategies aimed at reducing the cognitive burden of diabetes.

## ACKNOWLEDGMENTS

No potential conflicts of interest relevant to this article were reported.

# REFERENCES


abstract: A report on the 11th European Conference on Computational Biology (ECCB), Basel, Switzerland, September 9-12, 2012.
author: Evangelos Bellos; Lachlan JM Coin; Myrsini Kaforou
date: 2012
institute: 1Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Hospital, Norfolk Place, London, W2 1PG, UK; 2Department of Genomics of Common Disease, School of Public Health, Imperial College London, The Hammersmith Hospital, Du Cane road, London, W12 0NN, UK; 3BGI-Shenzhen, Beishan Road, Shenzhen, 518083, China; 4Section of Paediatrics and Wellcome Trust Centre for Clinical Tropical Medicine, Division of Infectious Diseases, Department of Medicine, Imperial College London, St Mary's Hospital, Norfolk Place, London, W2 1PG, UK
title: Bioinformatics: living on the edge

# Meeting report

Can music be decomposed to single notes on a piece of paper? Can life be fully understood by the function of genes in isolation? These are some of the questions that Denis Noble (University of Oxford, UK) posed in one of the more philosophical keynote addresses of this year's European Conference on Computational Biology (ECCB). Noble, a famed critic of genetic reductionism, in concert with renowned guitarist Christoph Denoth, set out to explore how synergy begets complexity both in art and in science. And in doing so, he captured a central theme of the conference: holism.

From gene prediction to comparative genomics to protein structure prediction, the early days of post-genomic bioinformatics research were arguably characterized by a reductionist approach to understanding life. Systems biology, on the other hand, started on the opposite end of the spectrum, attempting to directly model complex behavior arising from biomolecular interplay, largely abstracted from the individual components. In this post-ENCODE era, in which tremendous progress has been made in understanding each biomolecule individually, the focus now seems to have shifted toward a middle ground: leveraging the treasure trove of information about the nodes of the network to elucidate the intricate web of interactions represented by the network edges.

Given the sustained development of next-generation sequencing technologies and their essential role in the ascendancy of genomics, it is perhaps surprising that talks on this subject were conspicuously scarce at ECCB. However, this is not to say that genomics was not pervasive throughout the conference; it was, from keynotes to posters, just indirectly so. Sequencing technologies appear to be maturing into stable tools that are ubiquitously used to obtain deep knowledge of biomolecules themselves, as well as intermolecular relationships. As a silent partner, genomics is providing both the starting point and the means for investigating networks, but without stealing the spotlight.

# Genetic interactions: within genomes

The systematic identification and characterization of genome-wide genetic interactions is one area that has already been transformed by the availability of sequence data for multiple species. Colm Ryan (University of California, San Francisco, USA) compared experimentally derived epistasis maps of the fission yeast *Schizosaccharomyces pombe* and the budding yeast *Saccharomyces cerevisiae* to show that the overall level of genetic cross-talk between different biological processes tends to be more conserved than the underlying interactions. He further demonstrated how epistasis maps can be used to annotate uncharacterized genes, drawing on the observation that genes with similar genetic interaction profiles are often involved in the same functional pathway. Similar approaches may be used as a model for genetic interaction analysis in more complex organisms, in an effort to understand not only biological functions, but also the mechanisms that underlie disease phenotypes. Philipp Bucher (École Polytechnique Fédérale de Lausanne, Switzerland) exploited lineage-specific whole-genome duplication events in teleost fish (including zebrafish and pufferfish lineages) to investigate the post-duplication fate of ultraconserved non-coding elements. These elements tend to be organized in large clusters around developmental genes, and were found mostly to be retained together in only one of the two genome copies. This winner-takes-all scenario suggests that ultraconserved non-coding elements operate as part of a dense cooperativity network, which could be responsible for their high levels of conservation.

# Genetic interactions: between genomes

In a *tour de force* keynote presentation focusing on the social organization of fire ants (*Solenopsis invicta*), Laurent Keller (University of Lausanne, Switzerland) demonstrated that an ant's behavior is determined not only by its own genome but also by other genomes in the population. By way of example, Keller described what might be called a selfish allele in a 13.9 Mbp non-recombining region, encoding the odorant protein Gp-9, that regulates its own frequency by mediating altruistic and aggressive instincts. Michal Linial (The Hebrew University of Jerusalem, Israel) explored interactions between virus and host genomes from an evolutionary perspective. Linial demonstrated how both amino acid and codon usage of viral proteomes have adapted to maximize translational efficiency in specific host environments. Linial also showed that host-acquired genes in virus genomes tend to have fewer protein domains and shorter linker regions than their host counterparts. Understanding these evolutionary adaptations may prove essential to the effective management of viral infections.

# Protein-RNA interactions

The recently published results of the ENCODE project established that at least three quarters of the human genome is capable of being transcribed. One approach to understanding the functional role of this pervasive transcription may be the identification of new RNA-binding proteins, as presented by Cristoph Dieterich (Max Delbrück Center for Molecular Medicine, Germany). By purifying proteins that have been cross-linked to polyadenylated mRNA, followed by quantitative mass spectrometry, Dieterich identified 797 mRNA-bound proteins, one third of which were previously unknown and likely participate in post-transcriptional gene-regulation networks. Moreover, Dieterich used PAR-CLIP to sequence the mRNA-binding sites of these proteins. Mihaela Zavolan (Biozentrum, Switzerland) also reported a novel application of PAR-CLIP, in which the Argonaute component of the RISC complex was used as bait to discover non-canonical microRNA (miRNA)-target interactions. Intriguingly, Zavolan showed how a single miRNA molecule (miR-294) can target components of the chromatin-remodeling complex and prevent differentiation in embryonic stem cells, thus concluding that a handful of miRNAs may be sufficient to determine cell fate.

# Protein-protein interactions

Although protein-protein interaction (PPI) networks have been extremely valuable as a starting point for modeling cellular processes, it is well known that PPI databases have a high false-positive rate, while omitting many genuine interactions. The gold standard for the field, as described by Barry Honig (Columbia University, USA), is a three-dimensional structure of a complex that incorporates the interacting proteins, but this level of confirmation is available for less than 0.5% of all recorded PPIs. Honig presented a computationally efficient method for superimposing homologous proteins onto known structural interactions in order to generate interaction models, which are subsequently combined with co-expression and functional similarity information. This approach makes clever use of homology to known structures; however, as Chris Sander (Memorial Sloan-Kettering Cancer Center, USA) noted, only half of the well-characterized protein families in the Pfam database have a known three-dimensional structure for any of their members. To address this limitation, Sander identified co-evolving residues from multiple sequence alignments in order to predict residue-residue proximity in folded protein structures. Philip Kim (University of Toronto, Canada) illustrated the tissue-specific nature of PPI networks by monitoring how alternative splicing events impact upon them. Kim presented evidence that as much as a third of all tissue-specific alternatively spliced exons lead to differential PPI through both the creation of new and the destruction of existing interactions.

# Protein-drug interactions

Chemogenomics is an emerging research area that is focused on identifying novel drug-protein interactions by screening libraries of small molecules against the proteome, through the computational analysis of high-throughput data. Yoshihiro Yamanishi (Kyushu University, Japan) presented a method, based on sparsity-induced binary classifiers, that incorporates data on the chemical structures of drugs, genomic information about target proteins and all known protein-ligand interactions across different protein families into a predictive model. The model can predict underlying interactions between drug chemical substructures and protein functional sites, which are involved in drug-target interaction networks. With a view to understanding and minimizing drug side effects in patients, Sayaka Mizutani (Kyoto University, Japan) presented an approach for integrating drug side effect data with drug-protein binding information, using sparse canonical correlation. This technique was used to derive highly correlated sets of drug-targeted proteins and side effects, along with the drugs that drive their correlation. Such methods can play an important role in predicting potential side-effects and their mediating pathways even at the drug design phase.

# Conclusions

The opening keynote lecture of ECCB was delivered by Nobel laureate Aaron Ciechanover (Technion - Israel Institute of Technology, Israel) who described his *magnum opus*: unraveling the previously unknown cellular mechanism of protein degradation. Ciechanover outlined many aspects of ubiquitin-mediated protein degradation, including the role of E3 ubiquitin ligases in ensuring high target specificity. He further highlighted the link between dysregulation of the ubiquitin-proteasome pathway and disease. Various levels of this pathway have been identified as drug targets to treat or suspend the progression of diseases such as myeloma, leukemia and viral encephalitis. While this talk was inspiring, the fact that these discoveries were made without the assistance of high-throughput technologies or bioinformatics was sobering, and raises the question of whether computational analyses of high-throughput data can lead to the elucidation of still unknown, fundamental cellular mechanisms. In other words, when will bioinformatics have its Nobel Prize moment? The talks presented at this conference suggest that by transforming information on complex biomolecular interactions, obtained at high cellular and temporal resolution, into sequence data, bioinformaticians may be well on the way to achieving such a feat.

# Abbreviations

ECCB: European Conference on Computational Biology; ENCODE: Encyclopedia of DNA Elements; Mbp: megabase pair; miRNA: microRNA; PAR-CLIP: photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation; PPI: protein-protein interaction; RISC: RNA-induced silencing complex.

# Competing interests

The authors declare that they have no competing interests.


abstract: Precise regulation of β-cell function is crucial for maintaining blood glucose homeostasis. Pax6 is an essential regulator of β-cell–specific factors like insulin and Glut2. Studies in the developing eye suggest that Pax6 interacts with Mitf to regulate pigment cell differentiation. Here, we show that Mitf, like Pax6, is expressed in all pancreatic endocrine cells during mouse postnatal development and in the adult islet. A *Mitf* loss-of-function mutation results in improved glucose tolerance and enhanced insulin secretion but no increase in β-cell mass in adult mice. Mutant β-cells secrete more insulin in response to glucose than wild-type cells, suggesting that Mitf is involved in regulating β-cell function. In fact, the transcription of genes critical for maintaining glucose homeostasis (insulin and *Glut2*) and β-cell formation and function (*Pax4* and *Pax6*) is significantly upregulated in *Mitf* mutant islets. The increased Pax6 expression may cause the improved β-cell function observed in *Mitf* mutant animals, as it activates insulin and Glut2 transcription. Chromatin immunoprecipitation analysis shows that Mitf binds to *Pax4* and *Pax6* regulatory regions, suggesting that Mitf represses their transcription in wild-type β-cells. We demonstrate that Mitf directly regulates *Pax6* transcription and controls β-cell function.
author: Magdalena A. Mazur; Marcus Winkler; Elvira Ganić; Jesper K. Colberg; Jenny K. Johansson; Hedvig Bennet; Malin Fex; Ulrike A. Nuber; Isabella ArtnerCorresponding author: Isabella Artner, <firstname.lastname@example.com>.
date: 2013-08
references:
title: Microphthalmia Transcription Factor Regulates Pancreatic β-Cell Function

The islets of Langerhans consist of α-, β-, δ-, ε-, and pancreatic polypeptide (PP) cells, which produce the hormones glucagon, insulin, somatostatin, ghrelin, and PP, respectively. Together these hormones regulate fuel metabolism, with insulin and glucagon being essential for glucose homeostasis. Thus, glucagon secreted from α-cells stimulates the mobilization of glucose through gluconeogenesis and glycogenolysis, while β-cell–secreted insulin promotes glucose storage. Defects in α- and β-cell function play a significant role in the ability of diabetic individuals to maintain glycemic control.

Characterization of the transcription factors regulating insulin and glucagon expression has demonstrated their significance to islet cell function and endocrine cell development. For example, pancreatic and duodenal homeobox 1 (Pdx1) is crucial for pancreas development; pancreatic endodermal progenitors fail to proliferate in the absence of Pdx1, resulting in a small pancreatic rudiment (1). Pdx1 is also critical for mature β-cell activity because deletion of this factor from adult β-cells results in a severe diabetic phenotype, which is at least partially caused by reduced insulin and Glut2 expression (2). Pax6 and NeuroD1 are also required for insulin and Glut2 expression, and loss of these transcription factors affects β-cell function and endocrine cell differentiation (3,4). Significantly, mutations in *PDX1* (5), *PAX6* (6), and *NEUROD1* (7) also cause maturity-onset diabetes of the young in humans.

β-Cell function and glucose responsiveness are established during late embryonic and postnatal development (8). Recently, the MafA and MafB transcription factors have been identified as key regulators of these processes because of their ability to regulate genes essential for endocrine cell function, such as insulin, glucagon, *Pdx1*, and *Glut2* (9–11). Embryonic β-cells initially express *MafB*, but *MafB* expression is lost in postnatal β-cells, which express *MafA* instead (12). The switch between MafB and MafA expression in β-cells is associated with the development of functional β-cells. Recent gene profiling studies have shown that MafB regulates genes required for β-cell function during embryonic β-cell differentiation while the same genes are activated by MafA in adult β-cells (12). These gene expression experiments have shown that microphthalmia transcription factor (Mitf) expression is reduced in *MafA* and *MafB* mutant embryonic pancreata (12). Mitf, a basic-helix-loop-helix-leucine-zipper transcription factor, regulates melanogenesis by activating transcription of pigment cell-specific genes in both the skin and retina (13,14). In addition, it can act as a transcriptional repressor (14,15) and controls the expression of cell survival (*Bcl2*) (16) and cell cycle regulatory (*Cdk2* and *p16/Ink4a*) genes (17,18).

In this study, we show that Mitf is produced in the developing pancreas and adult islets. *Mitf* loss-of-function mice have lower blood glucose levels than wild-type animals in response to an intraperitoneal glucose challenge but also during nonfasted conditions. Mutant islets secrete more insulin upon exposure to high glucose concentrations, and *Mitf* mutant animals have higher circulating insulin levels in fasted conditions. Additionally, the expression of genes regulating blood glucose levels (insulin and *Glut2*) and β-cell formation and function (*Pax4* and *Pax6*) is significantly higher in *Mitf* mutant than in wild-type islets. Promoter occupancy studies show that Mitf binds to the pancreas-specific *Pax4* and *Pax6* regulatory regions, which suggests that Mitf directly regulates the transcription of these genes in β-cells and thereby modulates β-cell function.

# RESEARCH DESIGN AND METHODS

Mice with a point mutation (C to T at nucleotide 916) in the *Mitf* gene have previously been characterized (19). The *Mitf* cloudy eye (*Mitf^ce/ce^*) mutation results in a lack of the Mitf zipper domain due to the presence of a stop codon between the helix-loop-helix and zipper motif. This leads to the synthesis of a truncated protein incapable of DNA binding (19). *Mitf^ce/ce^* mice have white coat color and small eyes. Heterozygous and wild-type animals both have black coat color. For distinguishing between the latter, DNA samples were sequenced (forward, ggtccattgtcttgttttatcacag; reverse, gtatccaccctctgccat). Wild-type, heterozygous, and homozygous animals are distinguished by C, C/T, and T at bp 916 of the *Mitf* gene (<http://www.informatics.jax.org/searches/accession_report.cgi?id=mgi:1856532>). Wild-type and heterozygous *Mitf* animals were indistinguishable in physiological and histological studies (data not shown), and therefore the data for them are combined as "wt/het" data. All animal work was approved by a local ethics committee for animal research.

## Immunohistochemistry.

Pancreata from E15.5 and E18.5 embryos, postnatal day P0, P7, and P21, and 12 week-old mice were fixed for 2 h at 4°C in 4% paraformaldehyde in PBS, washed with PBS, dehydrated, and embedded in paraffin. Sections (6 μm) were used for immunohistochemical analysis as previously described (9).

## Antibodies.

Primary antibodies used were as follows: rabbit anti-Mitf (1:1,000; gift from S. Saule, Institut Curie, Paris), rabbit anti-Mitf (1:1,000; Abcam), guinea pig anti-insulin (1:2,000; Millipore), mouse anti-glucagon (1:2,000; SIGMA), sheep anti-somatostatin (1:2,000; American Research Products), guinea pig anti-PP (1:2,000; Millipore), goat anti-ghrelin (1:500; Santa Cruz), rabbit anti-Glut2 (1:500; Chemicon International), rabbit anti-MafA (1:1,000; Bethyl Laboratories), rabbit anti-Nkx6.1 (1:1,000; β-Cell Biology Consortium), rabbit anti-Pax6 (1:1,000; Covance), mouse anti-Pax6 (1:100; Developmental Hybridoma Bank), goat anti-Pdx1 (1:1,000; β-Cell Biology Consortium), mouse anti-islet 1 (1:100; Developmental Hybridoma Bank), and mouse anti-Synaptophysin (1:100; Abcam). Secondary antibodies used were as follows: (1:500) diluted CY2, CY3, CY5 conjugated anti-mouse, -goat, -guinea pig, -rabbit, and -sheep (Jackson ImmunoResearch). DAPI (Invitrogen) was used for nuclear counterstaining (1:6,000).

## Blood glucose level measurements.

Random-fed blood glucose samples were collected at a consistent time of the day (1200–1300 h). Fasted blood glucose levels were measured after a 6-h fasting period (800–1400 h) with a handheld glucometer (OneTouch; Lifescan). Mean difference between wild-type and *Mitf^ce/ce^* was tested with Student *t* test.

## Intraperitoneal glucose tolerance test and insulin secretion measurements.

Intraperitoneal glucose tolerance test (IPGTT) was performed on 12-week- and 6-month-old *Mitf^ce/ce^* and wild-type mice after overnight fasting (12 h) using an injection of 2 g glucose/kg body wt. Blood glucose levels were measured using a handheld glucometer (OneTouch; Lifescan). Blood was collected from an incision in the distal part of the tail. Measurements were taken at 0, 5, 15, 30, 60, and 120 min after glucose administration for the IPGTT and at 0, 2, 5, and 15 min for glucose-stimulated insulin secretion studies. For the latter, blood was collected into heparin-coated tubes (Sarstedt) and the serum fractions were analyzed with a Mouse Insulin ELISA (Mercodia) according to the manufacturer's instructions.

## Insulin secretion assay.

Islets were isolated by collagenase digestion and handpicked under a stereomicroscope (20). Batches (*n* = 8) of three islets for each condition were kept in secretion assay buffer (SAB) (114 mmol/L NaCl, 4.7 mmol KCl, 1.2 mmol/L KH~2~PO~4~, 1.16 mmol/L MgSO~4~, 20 mmol/L HEPES, 2.5 mmol/L CaCl~2~, 25.5 mmol/L NaHCO~3~, and 0.2% BSA; pH 7.2) containing 2.8 mmol/L glucose for 60 min in an incubator at 37°C. Three islets per well were transferred to a 96-well plate containing 200 μL/well SAB but with the addition of 2.8, 8.3, and 16.7 mmol/L glucose with or without 35 mmol/L KCl. After transfer of all islets, the plate was placed in an incubator at 37°C. At 60 min, a buffer sample was removed for measurement of insulin by ELISA.

## Glucagon secretion measurements.

Blood samples from random-fed and 12-h-fasted wild-type and Mitf mutant mice were collected into heparin-coated tubes. Samples were chilled immediately and centrifuged at 4°C. Plasma aliquots were stored at −20°C. Glucagon concentrations were determined using a glucagon radioimmunoassay kit (Millipore). Statistical analysis was conducted using Student *t* test.

## Islet isolation, RNA extraction, RT-PCR, and quantitative PCR.

Islet isolation has previously been described (20). RNA from E18.5 and P7 pancreas and islets of 12 week-old mice was extracted with ToTally RNA (Ambion) or RNeasy mini kit (Qiagen). RNA concentrations were measured with a NanoDrop ND-1000 spectrophotometer (NanoDrop), and RNA quality was assessed using a Bioanalyzer (Agilent). Only samples with a RNA Integrity Number \>7.5 were further analyzed. Quantitative PCR was performed in two steps. First, reverse transcription was carried out with SuperScript III (Invitrogen) using 500 ng RNA. For the quantitative PCR reaction, cDNA was diluted 1:200 and all assays were performed with Fast SYBR Green Master Mix on a StepOnePlus Real-Time PCR instrument (Applied Biosystems). Primer sequences are available upon request. Formation of expected PCR products was confirmed by electrophoresis and melting curve analysis. Gene expression data were normalized to the expression of the internal control gene hypoxanthine-guanine phosphoribosyltransferase (*HPRT*). Experiments were repeated at least five times—each in triplicate. Raw data from real-time PCR measurements were exported from StepOne Software v2.1 and analyzed in Microsoft Excel. The data are shown as mean expression with SEM. Graphs represent the fold change in comparison with the wild-type control samples, set as 1. Mean difference was tested with Student *t* test.

## Image analysis, quantification, and statistical analysis.

Immunofluorescence images were collected with Zeiss Axioplan 2 imaging (Zeiss, Jena, Germany) in AxioVision Rel 4.9 software. Immunohistochemistry and quantification were performed from at least three adult wild-type and *Mitf^ce/ce^* pancreata. β-Cell mass was assessed by quantifying the pancreatic and insulin-stained area in sections at a 720-μm interval throughout the whole pancreatic organ. The percentage of endocrine cells expressing ghrelin was assessed by quantifying Pax6 and ghrelin-producing cells at a 96 (E18.5) or 120 (P7) μm interval throughout the whole pancreas. Synaptophysin-expressing cells were measured in every 20th section of adult pancreas and normalized to the pancreatic weight. Mean differences were tested with Student *t* test; *n* ≥ 3.

## Chromatin immunoprecipitation.

β-Cell–derived β-TC6 cells and α-cell–derived α-TC6 (American Type Culture Collection) were maintained in Dulbecco's modified Eagle's medium (Invitrogen) supplemented with 10% calf serum. Cells were transfected with FLAG-tagged Mitf expression plasmid (True ORF cDNA clone MR227086; ORIGene) using Metafectene Pro (Biontex). Cells were harvested 48 h after transfection, fixed in 1% formaldehyde in Dulbecco's modified Eagle's medium without serum, and treated with lysis buffer (10 mmol/L Tris-HCl, pH 8.0; 10 mmol/L NaCl; 3 mmol/L MgCl~2~; 1% NP-40; 1% SDS; and 0.5% sodium deoxycholate \[DOC\]) and sonicated four times for 5 min on high (30 s on and 30 s off) settings in a Bioruptor sonicator (Diagenode). Protein/DNA chromatin fragments were immunoprecipitated with mouse anti-FLAG antibody (Sigma) or mouse IgG (Santa Cruz) as previously described (21).

Nonquantitative PCR reactions were performed using purified immunoprecipitated DNA with 0.8 μmol/L primers specific for *Pax6^EE^* (AGGCACGTCCTGGATGTTAG and CCCCAACCTCATTCTTTTCA), *Pax6^PE^* (ACTCAGGCCTGTGGTTATGC and TCAAGAGCGAAGCTGGAAAC), *Pax4* (AGGGACAATTAGCCCCAAAC and ACAGAAGCTTTCGACCCAGA), *Runx2* (GTTGTTTGATTTGTTTTGAAGG and TTTTACCTAAAATGTGGTTTTTG), and *PEPCK* (GAGTGACACCTCACAGCTGTGG and GGCAGGCCTTTGGATCATAGCC) transcriptional control region primers. PCR products were visualized on a 1.5% agarose gel in 1× Tris-Borate-EDTA buffer. Each chromatin immunoprecipitation (ChIP) experiment was repeated at least three times using independent chromatin preparations.

## Transfection assays.

The Pax6 P0 sequence (−3.8 to 0.2 kb) was PCR amplified from mouse genomic DNA and inserted upstream of a promoterless firefly luciferase plasmid (pGL2basic; Promega). Human embryonic kidney (HEK)293 cells were transfected with pPax6P0-LUC, cytomegalovirus-driven Mitf, and/or Pax6, and Renilla luciferase plasmids (phRL-CMV; Promega) using Metafectene Pro reagent (Biontex). Extracts were prepared 40–48 h later and analyzed for firefly and Renilla luciferase activity using the Dual-Luciferase Reporter Assay System (Promega). Mean difference was tested with one-way ANOVA; *n* = 3.

# RESULTS

## Mitf is specifically expressed in endocrine cells in the postnatal and adult pancreas.

Immunohistochemical analysis was performed to determine which cell types produce Mitf during pancreas development and in the adult. Mitf expression was not detected at E15.5 (data not shown) but was initially observed at E18.5 ([Supplementary Fig. 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1464/-/DC1)). At P0, Mitf was found in nuclei of endocrine, exocrine, and ductal cells (Fig. 1A–D and [Supplementary Fig. 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1464/-/DC1)). Postnatally, Mitf expression becomes restricted to islet endocrine cells ([Supplementary Fig. 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1464/-/DC1)). Analysis of adult wild-type pancreata showed that Mitf is expressed in all hormone-producing cells (Fig. 1E–H). Mitf expression is restricted to nuclei of insulin-producing cells, whereas it is also detected in the cytoplasm of ∼20% glucagon cells ([Supplementary Fig. 2](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1464/-/DC1)).

## *Mitf^ce/ce^* mice have improved blood glucose clearance and elevated insulin secretion.

Blood glucose measurements showed that *Mitf^ce/ce^* mice had significantly lower blood glucose levels than their wild-type littermates whether they were random-fed or fasted (Fig. 2A). In addition, *Mitf^ce/ce^* animals had improved blood glucose clearance after glucose injection at 12 weeks and 6 months. Blood glucose levels stayed below 20 mmol/L even at 15–30 min after the glucose injection (Fig. 2C and D) and returned faster to homeostatic conditions. Glucose-stimulated serum insulin levels were measured to determine whether this improved glucose clearance is a direct result of altered β-cell function. Interestingly, *Mitf* mutant animals have significantly higher serum insulin levels in fasting conditions than wild-type animals (0 min \[Fig. 2E\]), which suggests that Mitf is required for proper β-cell function. In contrast, plasma glucagon levels were not significantly changed in *Mitf^ce/ce^* animals compared with wild-type littermates in both fasted and random fed conditions (Fig. 2B). Stimulation of *Mitf^ce/ce^* and wild-type isolated islets with 16.7 mmol/L glucose and 35 mmol/L KCl resulted in a 60% increased insulin secretory response (Fig. 2F). Our results demonstrate that *Mitf* mutant mice have enhanced glucose tolerance and are protected from high blood glucose levels by elevated insulin secretion.

## Number of endocrine cells is unchanged in *Mitf^ce/ce^* animals.

Immunohistochemical analysis of wild-type and *Mitf^ce/ce^* pancreatic sections showed no obvious changes in the appearance of pancreatic islets (Fig. 3A). For determination of whether the elevated serum insulin levels observed in *Mitf^ce/ce^* mice result from an increase in β-cell mass, quantitative immunohistochemical analysis was performed. The percentage of insulin-labeled wild-type and *Mitf^ce/ce^* cell area and total islet cell area were comparable (Fig. 3B and [Supplementary Fig. 4*C* and *D*](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1464/-/DC1)) suggesting that Mitf is not required for β-cell specification or regulation of β-cell mass. Insulin and *PP* mRNA levels were significantly increased in *Mitf^ce/ce^* islets (Fig. 3C), while ghrelin transcription was decreased to only 20% of wild-type (Fig. 3C). Immunohistochemical quantification of ghrelin-producing cells revealed no differences in the percentage of ghrelin cells within the endocrine Pax6^+^ cell population at E18.5 and P7 ([Supplementary Fig. 3*A*–*E*](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1464/-/DC1)), while ghrelin mRNA levels were already reduced by P7 ([Supplementary Fig. 3*F*](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1464/-/DC1)). In accordance with previous reports (22) ghrelin cells were almost absent in adult wild-type and *Mitf^ce/ce^* islets, which precluded a quantitative analysis at this time point. Quantitative immunohistochemical analysis showed no significant increase in PP cell number (data not shown) in *Mitf^ce/ce^* islets. These findings suggest that insulin, ghrelin, and PP production is altered in individual hormone-expressing cells. *Mitf^ce/ce^* and wild-type β-cells were analyzed using electron microscopy to determine whether the increase in insulin mRNA levels results in enhanced production of insulin secretory granules. *Mitf^ce/ce^* β-cells have the same number of insulin granules per cell, and granule morphology is comparable with the wild-type ([Supplementary Table 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1464/-/DC1) and [Supplementary Fig. 4*A* and *B*](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1464/-/DC1)). Our findings illustrate that Mitf is critical for proper hormone expression but not essential for endocrine cell specification and maintenance.

## Gene expression of key β-cell genes is increased in *Mitf^ce/ce^* islets.

Gene expression analysis and immunohistochemistry were performed to further characterize *Mitf^ce/ce^* islets. First, we analyzed the expression level and distribution of transcription factors involved in β-cell development and/or function. The cellular distribution of known β-cell factors Pax6, Pdx1, MafA, and Nkx6.1 was unchanged (Fig. 4A–H). However, a twofold increase in *Pax6* and *Pax4* mRNA levels was observed in *Mitf^ce/ce^* islet samples (Figs. 4K and 5). Interestingly, β-Cell–specific expression of *Glut2,* a known Pax6 target gene (3) was increased twofold in mutant islets (Fig. 4O), while the transcription of other glucose-sensing, metabolizing, and insulin secretory genes was unaffected (Fig. 5). These results suggest that Mitf is controlling β-cell function by directly regulating genes required for β-cell function and differentiation.

## Mitf binds to the pancreas-specific regulatory sequences of the *Pax4* and *Pax6* genes.

Previous studies have established that Mitf acts as a transcriptional activator and repressor in the developing eye (13–15). We hypothesize that Mitf directly controls *Pax6* transcription, since *Pax6* mRNA levels were upregulated in *Mitf^ce/ce^* mice (Fig. 4K) and *Mitf* overexpression prevents basic fibroblast growth factor (bFGF)-induced *Pax6* upregulation in cultured retinal pigmented epithelial cells (14,23). The mouse *Pax6* gene contains several regulatory elements that confer tissue-specific expression in the pancreas, eye, and brain (24). Among those, the P0 proximal regulatory element (PE) is required for embryonic pancreas-specific *Pax6* expression at physiological levels (25), while ectodermal expression is controlled by the EE element (EE). ChIP analysis was performed in mouse α-TC6 and β-TC6 cells transfected with FLAG-tagged Mitf to determine whether Mitf occupied the *Pax6* EE and PE sequences (*Pax6^EE^* and *Pax6^PE^*). An enrichment of both *Pax6^EE^* and *Pax6^PE^* was found using primer pairs that span the *Pax6^PE^* and *Pax6^EE^* regions compared with control sequences only after α-FLAG immunoprecipitation (Fig. 6B and C). A similar analysis demonstrated that flag-tagged Mitf also immunoprecipitates *Pax4* regulatory elements in β-TC6 cells (Fig. 6B). For further assessment of functional regulation of the *Pax6* P0 promoter, Pax6 and Mitf were cotransfected with a *Pax6P0*-driven reporter construct in HEK293 cells (Fig. 6A and D). Pax6P0 firefly luciferase (pPax6P0-LUC) activity was enhanced upon cotransfection with Pax6, while cotransfection with Mitf resulted in transcriptional repression. Cotransfection of Mitf and Pax6 resulted in loss of Mitf-mediated repression, suggesting that interactions between these factors are critical for the transcriptional control of the *Pax6P0* regulatory region. The ChIP and luciferase reporter data are complimentary and demonstrate that Mitf directly binds to and regulates *Pax6* regulatory elements, suggesting that increased *Pax6* mRNA levels in *Mitf^ce/ce^* are a direct effect of the loss of Mitf.

# DISCUSSION

Previous studies have established Mitf as a master regulator of pigment cell differentiation (rev. in 26). Our previous results have shown that Mitf expression is reduced in *MafA* and *MafB* mutant embryonic pancreata (12) suggesting that Mitf may be involved in β-cell differentiation and function. In this study, we demonstrate that Mitf is initially expressed in the pancreatic epithelium at E18.5 and that its expression becomes restricted to adult islet cells. Mitf inactivation results in altered β-cell function, due to an enhanced insulin secretory response, without changes in β-cell mass. In contrast, the transcription of genes critical for the regulation of blood glucose levels (insulin and *Glut2*) and β-cell formation and function (*Pax4* and *Pax6*) was significantly upregulated in *Mitf^ce/ce^* islets, suggesting that Mitf is critical for adult β-cell function.

During development, Mitf was initially detected in all pancreatic epithelial cells from E18.5 onward ([Supplementary Fig. 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1464/-/DC1)). Interestingly, *Mitf* expression increases and becomes progressively restricted to islet cells postnatally and is not detected in ductal and exocrine cells after the first weeks of birth. Our findings are supported by a gene-profiling study of Ngn3^+^ endocrine progenitor cells and their descendants, which showed that *Mitf* transcription increases postnatally, with the highest expression observed in adult islet cells (27). The late onset of *Mitf* expression and localization in endocrine cells coincides with profound morphological and physiological changes in the pancreas: endocrine cells cluster into mature islets, with β-cells in the core and other hormone-producing cells at the periphery. In addition, and perhaps most importantly, the glucose responsiveness of β-cells is established at this time (8). During specification of zebrafish melanocytes, Mitf is specifically required for late steps of differentiation (28), which also supports a role for Mitf in β-cell maturation or function.

To determine Mitf's function in the developing and adult pancreas, we analyzed a *Mitf* mutant mouse model. *Mitf^ce/ce^* pancreata were histologically indistinguishable from wild-type littermates, β-cell area was comparable, and no changes in adult islet cell composition were observed. However, as evident from our in vitro studies on isolated islets, the insulin response to high glucose and KCl concentrations was significantly greater in *Mitf^ce/ce^* animals than in wild-type, whereas insulin response to a stimulation with lower glucose concentrations was not significantly affected (Fig. 2F). This shows that the total β-cell secretory capacity is increased in mutant mice. Interestingly, *Mitf^ce/ce^* animals also had fasting hyperinsulinemia in the presence of slight hypoglycemia (Fig. 2A and E), again showing increased insulin secretion. Plasma glucagon levels were unchanged in the fasted and fed condition (Fig. 2B) suggesting that α-cell function is not affected in *Mitf^ce/ce^* animals. After intraperitoneal glucose challenge, glucose tolerance was improved (Fig. 2C and D), which also reflects increased glucose-stimulated insulin secretion. However, when glucose-stimulated insulin secretion was determined the only significant difference was observed at 0 min (Fig. 2E). Mitf function in peripheral tissues may also influence glucose responsiveness and partially account for the altered glucose clearance in *Mitf^ce/ce^*. However, thus far, no data supporting a role of Mitf in muscle, liver, and adipose glucose metabolism have been reported, and transcription in these tissues is relatively low (gene expression atlas, <http://www.ebi.ac.uk/arrayexpress/>). Collectively, these observations suggest that loss of *Mitf* specifically alters β-cell function by increasing glucose responsiveness and insulin secretion.

Gene expression analysis showed increased transcription of key β-cell genes such as *Pax4*, *Pax6*, insulin, and *Glut2*, which could contribute to the increased insulin secretory response. Thus elevated insulin mRNA levels may result in increased total insulin content and in a larger pool of insulin granules that are ready to be released, which could influence insulin secretion. However, *Mitf^ce/ce^* β-cells have no change in the total number of insulin granules ([Supplementary Fig. 4](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1464/-/DC1) and [Supplementary Table 1](http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db12-1464/-/DC1)), and the percentage of ready releasable granules stays the same suggesting that mutant β-cell function is most likely improved owing to changes in glucose sensing. Thus, elevated *Glut2* expression may positively influence this process, since it is the only glucose transporter expressed in β-cells that mediates glucose-stimulated insulin secretion (29,30).

Upregulation of *Pax6* transcription may be the key to the improved β-cell function observed in *Mitf^ce/ce^*, since it has been shown that this transcription factor regulates the expression of several β-cell genes like insulin, *Glut2*, *PC1/3*, and glucokinase (31). Interestingly, we observed enhanced transcriptional activity of only some Pax6 target genes like insulin and *Glut2* but not others, which suggests that a twofold increase in Pax6 expression is not sufficient to augment the transcription of all Pax6 target genes in this context.

Loss-of-function studies have established a critical role for Pax4 in β-cell development (32), while ectopic Pax4 expression in adult β-cells is associated with increased proliferation and protection against apoptosis (33). Mitf binds to *Pax4* regulatory elements, and *Mitf^ce/ce^* islets have twofold higher *Pax4* mRNA levels than wild-type islets but no difference in β-cell mass and thus no difference in β-cell proliferation and specification. This may be due to the moderate upregulation of *Pax4* transcription in *Mitf^ce/ce^* islets compared with *Pax4* overexpression models used in recent work, which has studied the effects of 15-fold increased *Pax4* (33). However, the moderately increased Pax4 expression in *Mitf^ce/ce^* islets may still positively influence β-cell function and contribute to improved glucose clearance.

Previous studies have demonstrated that Mitf acts both as transcriptional activator and repressor (14,15,34): it activates genes required for cell pigmentation (13,14), while ectopic Mitf expression prevents *Pax6* transcription in the developing eye (14,23) and Pax6 and Mitf cooperatively repress common target genes in the retinal pigmented epithelium (15). Thus far, Mitf repressor function has only been described in the context of coexpression with Pax6 but not with other transcriptional repressors like groucho (15,35). Here, we demonstrate that Mitf binds to and represses the *Pax6 P0* regulatory region, which is required for high *Pax6* expression during pancreas development (25,36). This finding suggests that Mitf is partially responsible for inactivation of the embryonic pancreatic *Pax6 P0* promoter.

In vitro transfection and gel shift experiments have demonstrated that direct protein-protein interaction between Pax6 and Mitf results in an inactivation of both proteins (37). The Mitf^ce^ protein lacks the leucine zipper domain, which results in loss of DNA binding and reduced interaction with cofactors (19). The leucine zipper domain is also required for interactions with Pax6 (37); thus, Mitf^ce^ protein not only has lost its transcriptional activity but also fails to interact and repress Pax6 function in mutant β-cells. These in vitro studies support our observation that the expression of the newly identified direct Mitf target gene *Pax6* is elevated in *Mitf^ce/ce^* animals and that enhanced *Pax6* levels and transcriptional activity may result in increased *Glut2*, *Pax4*, and insulin transcription.

Our data identify Mitf as a novel transcriptional repressor in adult β-cells and suggest that regulation of β-cell function depends on interplay between the Mitf and Pax6 transcription factors.

## ACKNOWLEDGMENTS

This work was supported by the Swedish Research Council, the JDRF, Diabetesfonden, Lindhés, and Jeanssons Stiftelse.

No potential conflicts of interest relevant to this article were reported.

M.A.M. designed research, researched data, and wrote the manuscript. M.W., E.G., and J.K.C. researched data. J.K.J. reviewed the manuscript and contributed to discussion. H.B. researched data. M.F. researched data, reviewed the manuscript, and contributed to discussion. U.A.N. reviewed the manuscript and contributed to discussion. I.A. designed research and wrote the manuscript. M.A.M. and I.A. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

The authors thank S. Saule (Institut Curie), C. Wright (Department of Cell and Developmental Biology, Vanderbilt University Medical Center), and the Beta Cell Biology Consortium (BCBC) for providing antibodies; B. Ahrén (Department of Clinical Sciences, Lund University), H. Semb (Stem Cell Center, Lund University, and The Danish Stem Cell Center, Copenhagen University), and R. Stein (Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine) for comments on the manuscript; M. Magnusson for mouse strain maintenance; B.-M. Nilsson (Clinical Research Center, Lund University) for plasma glucagon measurements; and R. Wallén (Lund University) for transmission electron microscopy analysis.

# REFERENCES


abstract: Genome annotation currently tends to represent a static snapshot. Routine re-annotation, perhaps using wiki software, would help.
author: Steven L Salzberg
date: 2007
institute: 1Center for Bioinformatics and Computational Biology and Department of Computer Science, 3125 Biomolecular Sciences Building, University of Maryland, College Park, MD 20742, USA
references:
title: Genome re-annotation: a wiki solution?

So you think that gene you just retrieved from GenBank \[1\] is correct? Are you certain? If it is a eukaryotic gene, and especially if it is from an unfinished genome, there is a pretty good chance that the amino acid sequence is wrong. And depending on when the genome was sequenced and annotated, there is a chance that the description of its function is wrong too.

Large-scale genome sequencing has revolutionized biology over the past ten years, generating vast amounts of new information that has radically transformed our understanding of hundreds of species, including ourselves. Sequencing centers continue to churn out new DNA sequences for a fantastic variety of species, covering more and more of the tree of life. Along with these sequences, the centers also produce genome annotation, which includes the locations and descriptions of all identifiable genes. These gene lists are the first pictures we get of what's inside a newly sequenced genome, and they can reveal key insights into what makes an organism distinctive. Sometimes the gene lists themselves are part of the story; for example, when the human genome was published \[2,3\], the headline was that humans have 'only' 25,000 genes, in contrast to earlier estimates of 100,000 or more. For many microbial species, the genome helps us to understand how the organism can accomplish something particularly difficult, such as how *Deinococcus radiodurans* (to cite just one of many examples) can withstand exposure to radiation levels far in excess of what a human could tolerate \[4\]. With each new human pathogen, the gene list helps us determine how the organism infects humans, how it causes sickness and (sometimes) how it becomes resistant to antibiotics. For these and other reasons, the accuracy of the gene list is tremendously important.

# What is genome annotation?

Before addressing the problems with annotation, I will first summarize how it is done. The process of sequencing and annotating the DNA of a bacterial species has become highly automated in recent years, but the major steps are quite similar to what was done for the very first bacterial genome, *Haemophilus influenzae*, in 1995 \[5\].

Figure 1 shows an outline of the main steps of the whole-genome shotgun sequencing and annotation process for a bacterial genome. Similar procedures - for both sequencing and annotation - are followed for much larger genomes, including the human genome, although the details vary. The laboratory steps have not changed greatly since *H. influenzae*: they begin with DNA purification, followed by shearing the DNA into countless small fragments (the 'shotgun' step). These fragments are then cloned and sequenced from both ends and assembled, usually resulting in a set of contiguous DNA sequences (contigs) joined together into larger scaffolds. The annotation pipeline can be applied immediately to these contigs, but in projects where the genome will be finished, the annotation software is usually run later, when the gaps between contigs have been filled in.

Most annotation pipelines are considerably more complex than shown in Figure 1, but they share the same outline. First a gene finder (such as, for bacteria, Glimmer \[6\] or GeneMark \[7\]) is run over the genome, producing a set of predicted protein-coding genes. These programs are very accurate, though not perfect. They are far more accurate than eukaryotic gene finders, however, primarily because the problem is far more difficult in eukaryotic genomes. In either case, the next step in the pipeline is to take the set of predictions and search them against one or more protein databases using BLAST \[8\], HMMer \[9\] or other programs. For each gene that has a significant match, the BLAST output can be used to assign a name and function to the protein. The accuracy of this step depends not only on the annotation software, but also on the quality of the annotations already in the database. For genes with no match, the pipeline might keep them and label them as 'hypothetical', or it might discard them based on criteria as simple as minimum length.

Annotation pipelines also run separate searches for tRNA and rRNA genes, and they may include other components as well. The pipeline software will usually take extra steps to find any genes missed by earlier steps; typically this involves running a translated search, aligning all six possible translations of the unannotated sections to a database.

# Partial and draft genomes

Finishing a genome - sequencing every remaining nucleotide of every chromosome and creating a gap-free assembly - is considerably slower and more expensive than the high-throughput shotgun-sequencing phase. As a result, a growing number of genomes are being released in 'draft' form and will remain in this form indefinitely. These include many bacteria and the majority of eukaryotic genomes. (In fact, only a handful of eukaryotic genomes, such as those of *Saccharomyces cerevisiae* and *Caenorhabditis elegans* but not including the human, are truly finished.)

The effect of draft genomes upon annotation is considerable: many genes will 'run off' the end of contigs or appear on two or more separate contigs. This in turn complicates the subsequent steps of annotation and is likely to lead to additional errors in assigning gene function. For example, a gene fragment is liable to match a small protein domain, and functions based on a single domain hit are not reliable. A gene that is split across two contigs might be annotated twice. Draft sequences also have much higher sequencing error rates, which can introduce erroneous stop codons in the middle of genes or improperly merge adjacent but distinct genes.

# The role of GenBank

Once a genome - draft or complete - is annotated, the DNA sequence along with the annotation is normally deposited in GenBank. Countless researchers rely on GenBank \[1\], EMBL \[10\] and DDBJ \[11\] (which mirror one another) as their primary source for genome annotation, and for a good reason: these databases are the world's largest public repositories of genome information. GenBank now contains over 65 billion base pairs (Gbp) of sequence, up from just 2 Gbp in 1998 and 10 Gbp in 2000, and it continues to grow at an astonishing rate. If you want to find a gene, GenBank should definitely be your first stop. Yet I frequently hear claims within the bioinformatics community that the 'GenBank annotation' of a particular genome is fraught with problems, and that the speaker can fix them.

Is the GenBank annotation perfect? Of course not. How good it is, though, depends on many variables, and the consumer of GenBank data would be wise to be aware of them (in other words, *caveat emptor*). The first and most important point to understand is that GenBank is not simply a database; it is also a library. A scientist who submits a sequence to GenBank is the owner of that sequence and is listed on the 'author' line in the GenBank entry. Just as with any article published in a journal, the author (and only the author) has the right to submit an erratum. Because GenBank is an electronic library, an erratum is really an update: new sequences or annotations replace the old ones, although GenBank keeps a record of the changes so that the original entry can still be retrieved if necessary. This notion of GenBank as a library (or an electronic journal) is frequently misunderstood, especially when a scientist discovers an annotation error. Even if the error is overwhelmingly obvious, the custodians of GenBank cannot simply fix it, any more than the editor of a journal can correct one of the papers published in that journal. Another way to think of this is to recognize that a 'GenBank annotation' is not 'GenBank's' annotation, but rather the annotation of whoever deposited the sequence in the first place.

When confronted with this problem, some scientists react by suggesting that GenBank (and DDBJ and EMBL) should allow scientists to fix errors that they find. But this would quickly destroy the archival function of GenBank, as original entries would be erased over time. It would also violate the agreement that GenBank has with all its submitters that their entries belong to them and can only be changed by them. This agreement has been crucial in GenBank's near-universal acceptance by the genomics community as the central resource for DNA sequences. The idea of allowing others to alter GenBank annotation also immediately begs the question of who should be permitted to make such alterations.

This leaves us with a problem: users go to GenBank expecting to find the authoritative annotation for a genome, and what they find might be far less than that. Most genome annotation deposited in GenBank remains static for years, and many annotations have never been changed since their initial publication. Nonetheless, many scientists assume that GenBank annotation is kept up to date, and they are surprised to hear that it is not.

For example, 479 genes in the *H. influenzae* Rd genome are currently listed as hypothetical proteins. Of these, 217 have at least one extremely strong BLAST hit to another species (*E*-value \< 10-100), which means they should at least be called 'conserved hypothetical' proteins. And 40 of these have matches to a gene with an assigned function, meaning that a re-annotation would result in these genes having a more meaningful name than 'hypothetical protein'.

# Some inconvenient truths

Even considering all of the issues above, one might reasonably expect that as protein databases have grown, annotation has improved and that recently annotated genomes (at least) will be of the highest quality. This is not quite true. What is true is that a BLAST search of a protein that is run today will yield far more results than it would have five or ten years ago, and these results in turn should lead to better annotation. Not all software is equally good, however, and the annotation pipelines vary considerably in their quality. There is also wide variation in the skills and experience of those operating the pipelines. Further complicating matters, some genomes are subjected to careful curation and review, whereas others receive only automated annotation. In the early days of sequencing, the sequencing teams included experts on the biology of each genome, and their manual curation dramatically improved the annotation of those species. Today that is no longer true: high-throughput sequencing centers are large, efficient factories with unique expertise in the methods necessary for sequencing, but they sometimes have very little expertise on the biology of the species they are sequencing. The inconvenient truth is that, as a result of these factors and others, some genomes are poorly annotated even today.

There are several ways in which genome annotation can be erroneous. The first and most fundamental is simply that the gene models may be wrong. Although bacterial gene-finding systems \[6,7\] are highly accurate, finding 98-99% of protein-coding genes in most species, they still occasionally miss genes. Their accuracy at placing the start site is a bit lower, probably closer to 90%, which is excellent but far from the perfect accuracy that some might expect. In the past, the accuracy of (bacterial) start-site prediction was closer to 80%, and many of the genomes in GenBank were predicted with earlier versions of gene finders. Note that all these accuracy figures are much lower for eukaryotic annotation. Some annotation pipelines include algorithms to adjust start sites, which can be done by looking closely at the boundaries of alignments to homologous proteins.

False positives represent another type of erroneous annotation: when the prediction of a gene-finding program does not match any previously known protein, the annotators (or the annotation pipeline software) must decide whether or not to include that prediction in the gene list. Over the years, annotation groups have used a variety of rules to make this decision, and they have inevitably included thousands of false predictions in the publicly available genome annotation. These predictions are mostly harmless unless they result in effort being expended trying to verify them. In some cases, too, they might 'hide' functional RNA genes or true genes in a different reading frame from that of the false prediction.

Perhaps the biggest problem with genome annotation is erroneous and inconsistent naming of genes. Much of this is due to the simple fact that our knowledge of genes has improved but the annotation has remained static. Thus a gene labeled 'hypothetical protein' a few years ago might now have a known function. A second problem is what's known as transitive catastrophe: the phenomenon whereby a name is transferred from one gene to another on the basis of sequence similarity (usually from a BLAST search) but where the original name is incorrect. As more genomes are annotated, and more BLAST searches are run, the name gets transferred to other proteins, and the original source of the name quickly becomes lost. It is well known in the genomics community that thousands of such transitive errors have propagated through sequence databases, and efforts are under way to try to clean up some of the mess. In the meantime, though, many genes remain incorrectly annotated.

Let us consider just one example, selected more or less at random from the bacterium *H. influenzae* Rd \[4\]. The gene *fdxH* encodes formate dehydrogenase, β subunit, GenBank accession number NP438180. When the genome was sequenced in 1995, this gene (encoding a 312 amino acid protein) was similar to very few other genes; even the orthologous *Escherichia coli* gene was not yet sequenced. It is very difficult today to reconstruct what the best BLAST hit was back then, but today there are 197 highly significant BLAST hits to 123 distinct species. Thus, it is pretty clear that this gene today should be well-annotated because of the multitude of highly similar proteins. Yet if we look at the list of matching proteins, we find a variety of names given, including not only the name found on NP438180 itself, but also: formate dehydrogenase-O β subunit; formate dehydro-genase, nitrate-inducible, iron-sulfur subunit; HybA protein; formate dehydrogenase-N, Fe-S β subunit, nitrate-inducible; hypothetical protein PaerPA_01004979; hypothetical protein Bpse11_03005113; 4Fe-4S ferredoxin, iron-sulfur binding; and Twin-arginine translocation pathway signal. Some of these names seem to be synonymous, but others clearly are not. To decide properly among them, we need to look at the source of each annotation and at the species to which it is attached.

# Possible solutions

So if we can't always trust GenBank, what can we do? Clearly we cannot just ignore it. The scientific community must have a resource that contains the genes from all the species that have been sequenced. For the past 25 years, GenBank, EMBL and DDBJ have been enormously successful at providing these data. The pace of sequencing has changed the rules of the game, however: sequencing centers are pouring out genomes, annotating them rapidly and moving on. An archive of these annotations may be useful, but a static archive is insufficient.

One part of the solution is obvious: annotation must be regularly re-computed using the latest databases and software. For a small number of model organisms, this is already happening, but these species represent a tiny proportion of all known genes. Simply re-running an automated pipeline on all genomes is not sufficient, though, because that would over-write many of the carefully curated, manually annotated genes that have been produced in the past. Unfortunately, there is no standard label attached to such genes, so there is no way for an automated pipeline to know that they should be trusted. Therefore, we also need to launch an effort to start identifying those genes that are well annotated and, beyond that, to start recording the evidence used to annotate each gene.

Another solution is to create a new, expanded database that can display all the alternative annotations for any locus in a genome. If this were available, then scientists could be provided with links from any gene to alternative or overlapping gene predictions as well as alternative gene names. Along with each annotation could be a link to the evidence supporting it; for example, the date of a BLAST search or a citation to experiments contained in a journal article.

# A wiki solution?

Various members of the genomics community have considered these and other solutions, but so far none have emerged as the standard. Several new databases have been developed with alternative genome annotation, or with re-annotation (for example, the TIGR Comprehensive Microbial Resource \[12\]), but none of them has attracted nearly as much web traffic as GenBank or the other databases at NCBI \[13\]. The difficulties in changing this system are many: first, for example, there are some genomes for which GenBank is still the best source, and second, if another, better source of annotation exists, how is someone to discover it?

A relatively new model of sharing expertise through the Internet might offer a solution. This model is the 'wiki': a shared resource that anyone can edit. This open-editing framework for websites and data was first introduced in 1995, and it was initially viewed with skepticism by many in the Internet community, who argued that wiki-based websites would be filled with unreliable, inaccurate information. But the success of the online encyclopedia Wikipedia \[14\] has demonstrated that, despite the skeptics, a wiki site can be accurate, up-to-date and incredibly useful. Genome annotation has many of the same features of an encyclopedia: the information required to produce it is broad-based and the expertise is scattered around the scientific community in a very wide range of laboratories, most of whom are not connected to genome projects. I therefore propose that a 'genome wiki' might provide just the solution we need for genome annotation. A wiki would allow the community of experts to work out the best name for each gene, to indicate uncertainty where appropriate and to discuss alternative annotations. Although wikis will not (and should not) supplant well-curated model-organism databases, for the majority of species they might represent our best chance for creating accurate, up-to-date genome annotation.

Whether or not a genome wiki emerges, we will probably need an archival repository of annotation for many years to come. The international database consortium represented by GenBank, EMBL and DDBJ has served that purpose remarkably well for a long time and will continue to do so. Despite this success, the genomics community needs an accurate, continually updated source of genome annotation for every species, and we can hope that a solution to this problem will emerge in the near future.

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