Validation of RNAseq Experiments by qPCR?

This post is in response to a couple of twitter discussions regarding whether its "useful" to do qPCR validation of RNAseq hits:

As preliminary data for a talk can I graph transcript counts of particular genes (transcriptome data) instead of qPCR (haven't done it yet)?
— Sciencegurl (@sciencegurlz0) October 27, 2014

In response to the that I posted a response I made in response to a similar query from a reviewer for a recent manuscript (original available here). Based on the fact that this came up a couple times in two weeks, I thought that I'd try to be more clear on my thoughts (and put them somewhere easier for others to find). This is how we address this issue experimentally, and in response to reviewer requests. Feel free to use any of these arguments yourself but your mileage with your supervisor/manuscript or grant reviewers may vary. Most importantly, if you have some suggestions/data/papers that we should include please comment below and I'll try to keep this post up to date.

The Answer We Gave:

We had considered performing qPCR studies to ‘re-validate’ some of our gene-expression findings but there is little evidence that qPCR analyses from the same samples will add any extra utility to our data so we decided to eschew those experiments. Previous studies have shown extremely close correlations between qPCR and RNAseq data [1-4]. Ideally, we would re-validate our findings (potentially by qPCR) in a separate cohort of samples, but due to the difficulty in accessing these samples, those experiments are not possible at this time.

What Do We Mean By Validation of RNAseq Results?

RNAseq, like microarrays before them generate a lot of data. Ideally you can determine the levels of every gene/transcript/exon in the genome, and given proper experimental design determine a large number of significantly differentially expressed genes. To follow up these findings, we often want to test how valid those observations are. In my interpretation, this can mean a few different things:

1. Are these transcripts really differentially expressed in these samples (technical reproducibility)?
2. Are these transcripts really generally differentially expressed in other samples (biological reproducibility)?
3. Do these transcriptional changes represent phenotypic differences (significance)?

For the third question, I'd say you'd want to perform some non-transcriptional response such as a western blot, enzymatic or cell based assay to show that the transcriptional change has some biological phenotype. Ideally, you may even go farther and manipulate the expression of a gene of interest back to the control condition, to test the hypothesis that a change in that gene is causative of some particular phenotype. 

Why was qPCR the traditional validation experiment from microarray studies?

Normally though, the question of validation stems from one of the first two questions. This is probably based on prior work with microarrays. Microarrays were/are great tools for transcriptomic analysis (though I am hard pressed to think of a reason to do them in lieuw of RNAseq). One major problem with microarrays was probe bias. What this means is that there was a limited number of hybridization probes in a microarray experiment, and its possible that this probe may not be representative of the transcript as a whole. Furthermore, not all transcripts of interest may be present on the microarray of choice. As a result, the standard in the field was to examine transcript abundance by qPCR to technically validate microarray results.

How similar are RNAseq and qPCR results?

Probe bias, poor sensitivity and reduced linear range are not as problematic in RNAseq experiments, since the entire transcript is assessed in a more or less unbiased manner [5]. Several studies have compared RNAseq results to qPCR data, and have found excellent correlation between these methods [1-4]. In cases where there are discrepancies exist, I would argue that it is most likely due to bias in the qPCR experiment (which has its own probe-bias based on what region of the cDNA is amplified). Therefore, it is unlikely to yield new information, and when it does, the information is probably worse than the quality of RNAseq data.

Under What Conditions Would qPCR Be a Good Validation Method?

This isn't to say qPCR isn't useful, we use it all the time in my lab. Its a great tool for looking at a small number of genes in samples for example. But when would it be good to use in the context of validating a RNAseq experiment? I would argue that it is most useful in the case where you have independent samples from those that you did your RNAseq studies on. For example, maybe for economical reasons, you examined 5 control samples and 5 drug-treated samples but you have another 20 samples available. qPCR would be a great way to test whether the differences observed are also true in separate samples, thus answering question #2. I think this is especially important as in my experience a lot of RNAseq studies are underpowered to answer the questions asked (if you want a quick and easy way to check the power of your experimental design I like Scotty).

References

Thanks to Matthew MacManes (@peroMHC) and Alejandro Montenegro (@aemonten) for spurring this discussion, and sending me towards a nice paper by Timothy Hughes [6], which is a great summary of similar, more general issues.

1. Griffith M, Griffith OL, Mwenifumbo J, Goya R, Morrissy a S, et al. (2010) Alternative expression analysis by RNA sequencing. Nat Methods 7: 843–847. doi:10.1038/nmeth.1503.
2. Asmann YW, Klee EW, Thompson EA, Perez E a, Middha S, et al. (2009) 3’ tag digital gene expression profiling of human brain and universal reference RNA using Illumina Genome Analyzer. BMC Genomics 10: 531. doi:10.1186/1471-2164-10-531.
3. Wu AR, Neff NF, Kalisky T, Dalerba P, Treutlein B, et al. (2014) Quantitative assessment of single-cell RNA-sequencing methods. Nat Methods 11: 41–46. doi:10.1038/nmeth.2694.
4. Shi Y, He M (2014) Differential gene expression identified by RNA-Seq and qPCR in two sizes of pearl oyster (Pinctada fucata). Gene 538: 313–322. doi:10.1016/j.gene.2014.01.031.
5. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10: 57–63. doi: 10.1038/nrg2484.
6. Hughes TR (2009) “Validation” in genome-scale research. J Biol 8: 3. doi:10.1186/jbiol104.

Preprints: Trying Something New in Publishing

As a trainee, having my papers reviewed by experts in the field has been both a frustrating and positive experience.  It has been positive, in that in nearly every case my publications have been improved by the process.  The enhancements from little embarrassing typos to new ways of conceptualizing our data, and certainly these papers are better for it.

On the other hand, some times it takes forever.  One paper went through 18 rounds of submission/resubmission, lasting over 3 years.  Another took almost 3 years and 10 submissions.  Some of these delays were certainly self-inflicted but in general it takes a really long time for papers to work through their pipeline.  Steven Royle recently looked at this more rigorously for the papers his group has published here.  In his experience, the average has been about 9 months

This can be bad for the careers of those involved, and for those for whom the data might help.  To get around this, we are trying something new with our next paper.  We submitted it to bioRxiv as a preprint.  The paper can be found here, so go ahead and take a look, I'll wait.  The posted version is identical to the submitted version, which was sent to a normal peer-reviewed journal.

What do I hope to gain?

This has been covered really well over the internet including in science, at Haldane's Sieve, and in PLOS Biology.  I hope that this will give people in my field a chance to read our work earlier.  I also hope that the people who may be interested in reading it will provide some feedback.  This paper, like all of our papers gets informally reviewed by colleagues and lab members before it goes out.  By putting it out online, I would like a broader audience to be empowered to take a look and give us their thoughts, before the 'final' version is done.

What are the downsides?

There are a couple, we could be scooped, or it could affect our ability to publish it in another journal.  For the latter, we used SHERPA/ROMEO to pick a journal that has an established policy that pre-prints are acceptable.  As far as getting scooped, I am even less concerned about that.  The data is freely available for this paper on GitHub for anyone to use and I think the risk of scooping is dramatically overstated in science.

So take a look, and let us know here or at the paper what you think.

My Favorite Papers of 2012

These are the papers that I thought were the most interesting in my world this year. It was a big year for hormonal messengers involved in obesity and diabetes.  These include Fgf21 (Kim et. al) and VEGF (Hagberg et. al).  From a biochemistry perspective there was a lot of great work on the role of SREBP1 (Moon et. al) and its regulation both from a dietary (Haas et al.) perspective and from a mechanistic perspective (Owen et al.).  Finally both Kim et al. and Kusminski et al. highlighted the importance of mitochondrial function in the systemic response to obesity.



Haas, J., Miao, J., Chanda, D., Wang, Y., Zhao, E., Haas, M., Hirschey, M., Vaitheesvaran, B., Farese, R., Kurland, I., Graham, M., Crooke, R., Foufelle, F., & Biddinger, S. (2012). Hepatic Insulin Signaling Is Required for Obesity-Dependent Expression of SREBP-1c mRNA but Not for Feeding-Dependent Expression Cell Metabolism, 15 (6), 873-884 DOI: 10.1016/j.cmet.2012.05.002

Hagberg, C., Mehlem, A., Falkevall, A., Muhl, L., Fam, B., Ortsäter, H., Scotney, P., Nyqvist, D., Samén, E., Lu, L., Stone-Elander, S., Proietto, J., Andrikopoulos, S., Sjöholm, A., Nash, A., & Eriksson, U. (2012). Targeting VEGF-B as a novel treatment for insulin resistance and type 2 diabetes Nature, 490 (7420), 426-430 DOI: 10.1038/nature11464

Kusminski, C., Holland, W., Sun, K., Park, J., Spurgin, S., Lin, Y., Askew, G., Simcox, J., McClain, D., Li, C., & Scherer, P. (2012). MitoNEET-driven alterations in adipocyte mitochondrial activity reveal a crucial adaptive process that preserves insulin sensitivity in obesity Nature Medicine, 18 (10), 1539-1549 DOI: 10.1038/nm.2899

Moon, Y., Liang, G., Xie, X., Frank-Kamenetsky, M., Fitzgerald, K., Koteliansky, V., Brown, M., Goldstein, J., & Horton, J. (2012). The Scap/SREBP Pathway Is Essential for Developing Diabetic Fatty Liver and Carbohydrate-Induced Hypertriglyceridemia in Animals Cell Metabolism, 15 (2), 240-246 DOI: 10.1016/j.cmet.2011.12.017

Kim, K., Jeong, Y., Oh, H., Kim, S., Cho, J., Kim, Y., Kim, S., Kim, D., Hur, K., Kim, H., Ko, T., Han, J., Kim, H., Kim, J., Back, S., Komatsu, M., Chen, H., Chan, D., Konishi, M., Itoh, N., Choi, C., & Lee, M. (2012). Autophagy deficiency leads to protection from obesity and insulin resistance by inducing Fgf21 as a mitokine Nature Medicine DOI: 10.1038/nm.3014

Owen, J., Zhang, Y., Bae, S., Farooqi, M., Liang, G., Hammer, R., Goldstein, J., & Brown, M. (2012). From the Cover: Insulin stimulation of SREBP-1c processing in transgenic rat hepatocytes requires p70 S6-kinase Proceedings of the National Academy of Sciences, 109 (40), 16184-16189 DOI: 10.1073/pnas.1213343109

My Favorite Papers of 2012 by Dave Bridges is licensed under a Creative Commons Attribution 3.0 Unported License.

How is PtdIns(5)P Made?

For most phosphatidylinositides, the routes of synthesis and degradation have been largely elucidated.  However, due to difficulty in detecting PtdIns(5)P, only recently have investigators been able to assess the synthesis and degradation of this phospholipid.  The major stumbling block has been the separation of PtdIns(5)P from PtdIns(4)P, which migrate quite closely on HPLC/TLC based separations.  The two major advances in the field have been improved separation of these monophosphorylated lipids (for example see Sarkes and Rameh 2010 and Zolov et al. 2012) and separation-independent identification of PtdIns(5)P by an enzyme based phosphorylation assay. (see Jones et al., 2012).  I am a co-author on the Zolov paper and work closely with that group.

Which enzymes are involved?

Potential Routes for PtdIns(5)P Synthesis.

The simplest mechanism is through phosphorylation of PI directly by a PtdIns-5-Kinase.  There are two known classes of Ptdins-5-Kinases in mammalian cells, Pikfyve and three isoforms in the PtdIns(4)P-5-Kinase family (Pip5k1a, Pip5k1b and Pip5k1c).  Classically, Pikfyve is thought to convert PtdIns(3)P into PtdIns(3,5)P₂ wheras the other classes phosphorylate PtdIns(4)P into PtdIns(4,5)P₂.  I think that the strongest evidence is that Pifkve is essential for PtdIns(5)P levels in the cell, either directly or indirectly.

Biochemically, there seems to be three potential ways by which PtdIns(5)P could be made, through direct phosphorylation of PtdIns, or through dephosphorylation of either PtdIns(3,5)P₂ or PtdIns(4,5)P₂.  Of course, it is possible that in different contexts, each of these pathways could be involved.

Route 1: Direct Phosphorylation of PtdIns

Although there is limited evidence that the PtdIns(4)P-5-Kinases can phosphorylate PI, there is substantial evidence that PI(5)P can be generated by Pikfyve, in vitro (Sbrissa et. al, 1999).  Inside cells, it is less clear whether this is the case.  There is rapid and tightly correlated turnover of both PtdIns(3,5)P₂ and PtdIns(5)P in most cells (Zolov et. al, 2012, Sbrissa et al., 2012) to the point that it is difficult to tell if changes in PtdIns(3,5)P₂ preceed changes in PtdIns(5)P or correlate with them independently.  

Route 2: De-Phosphorylation of PtdIns(3,5)P₂

Two main lines of evidence support the possibility that PtdIns(3,5)P₂ could be the source of some or all of the PtdIns(5)P in the cell:  

1. Myotubularins, which are 3-phoshphatses leads to increased PtdIns(5)P and their deletion may lead to reductions in PtdIns(5)P (Vaccari, et al., 2011, Oppelt et al., 2012).
2. The kinetics of acute PtdIns(5)P synthesis or degradation may lag slightly behind the synthesis or degradation of PtdIns(5)P.  In any case, the levels of PtdIns(5)P and PtdIns(3,5)P₂ are very tightly correlated (Zolov et al., 2012).

The killer experiment here would be to test whether ablation of PtdIns(3)P levels would have direct effects on PtdIns(5)P levels, but since it is not clear whether PI3K inhibitors such as Wortmannin would affect Pikfyve in vivo that experiment may not be interpretable without ruling out direct effects first.

Route 3: De-Phosphorylation of PtdIns(4,5)P₂

An alternate theory has suggested that some or all of PtdIns(5)P is derived by the activity of a 4-Phosphatase which convertes PtdIns(4,5)P₂ into PtdIns(5)P.  The exact identity of this 4-phosphatase is not yet known.  Jones et al. show that peroxide increases PtdIns(5)P levels, and propose a role for PtdIns(4,5)P₂ dephosphorylation in that process.  However, in contrast to our findings (Zolov et al., 2012), this paper finds no role for Pikfyve in the synthesis of PtdIns(5)P, using similar approaches but a different assay to measure PtdIns(5)P (see below).

What is the Best Way to Measure PtdIns(5)P?

Regarding the role of Pikfyve, there seems to be a controversy here.  I've summarized the assays and their results in the table below.

Assay	Inositol Labelling	Mass Assay
Summary	Cells are grown in inositol depleted media with radioactive inositol. Cells are lysed and lipid headgroups are separated by HPLC based on charge.	Cells are grown in any condition, lipids are extracted and phosphorylated with PIP4K and radioactive ATP. Only PtdIns(5)P can be phosphorylated by this enzyme, so all hot PIP2 (based on TLC and counting) is derived from PtdIns(5)P.
Normalization	Total phosphatidylinosotol	Total cellular phospholipids
Result	Pikfyve knockdown/inhibition nearly completely decreases PtdIns(5)P levels.	Pikfyve knockdown/inhibition does not affect PtdIns(5)P levels.

Setting aside the role of peroxide in PtdIns(5)P as potentially a special case, you could make arguments for both methods.  Hopefully this can be resolved quickly since knowing where this lipid comes from is the first step in figuring out what it does.

References

Jones, D., Foulger, R., Keune, W., Bultsma, Y., & Divecha, N. (2012). PtdIns5P is an oxidative stress-induced second messenger that regulates PKB activation The FASEB Journal DOI: 10.1096/fj.12-218842

Oppelt, A., Lobert, V. H., Haglund, K., Mackey, A. M., Rameh, L. E., Liestøl, K., Oliver Schink, K., et al. (2012). Production of phosphatidylinositol 5-phosphate via PIKfyve and MTMR3 regulates cell migration. EMBO reports. doi:10.1038/embor.2012.183

Sarkes, D., & Rameh, L. E. (2010). A Novel HPLC-Based Approach Makes Possible the Spacial Characterization of Cellular PtdIns5P and Other Phosphoinositides.The Biochemical journal384, 375–384. doi:10.1042/BJ20100129

Sbrissa, D., Ikonomov, O. C., & Shisheva, A. (1999). PIKfyve, a mammalian ortholog of yeast Fab1p lipid kinase, synthesizes 5-phosphoinositides. Effect of insulin. J Biol Chem, 274(31), 21589–21597. pmid:10419465

Sbrissa, D., Ikonomov, O. C., Filios, C., Delvecchio, K., & Shisheva, A. (2012). Functional dissociation between PIKfyve-synthesized PtdIns5P and PtdIns(3,5)P2 by means of the PIKfyve inhibitor YM201636. American journal of physiology. Cell physiology, (313). doi:10.1152/ajpcell.00105.2012

Vaccari, I., Dina, G., Tronchère, H., Kaufman, E., Chicanne, G., Cerri, F., Wrabetz, L., et al. (2011). Genetic interaction between MTMR2 and FIG4 phospholipid phosphatases involved in Charcot-Marie-Tooth neuropathies. PLoS genetics, 7(10), e1002319. doi:10.1371/journal.pgen.1002319

Zolov, S. N., Bridges, D., Zhang, Y., Lee, W., Riehle, E., Verma, R., Lenk, G. M., et al. (2012). In vivo, Pikfyve generates PI(3,5)P2, which serves as both a signaling lipid and the major precursor for PI5P. Proceedings of the National Academy of Sciences of the United States of America, 109(43), 17472–7. doi:10.1073/pnas.1203106109


How is PtdIns(5)P Made? by Dave Bridges is licensed under a Creative Commons Attribution 3.0 Unported License.

How is SREBP Regulated by Insulin?

SREBP is a transcription factor which integrates anabolic signals and drives transcription of several important lipogenic genes such as Fatty Acid Synthase, Acetyl-CoA Carboxylase and the LDL Receptor.  In liver tissue ,this serves to enhance the uptake, synthesis and storage of lipid in the postprandial state.  The biochemical mechanisms by which this happens is unclear but a recent paper from the Brown and Goldstein laboratory has shed some light on this.

Figure: SREBP Processing and Translocation (from http://en.wikipedia.org/wiki/Srebp)

Previously it had been known that insulin causes both the cleavage and nuclear translocation of SREBP in hepatocytes (see Figure). Confusing the issue was the fact that SREBP was also increased transcriptionally. Owing to the presence of a SRE element in the promoter, I thought that the transcriptional effects were likely due to a positive feedback loop where insulin causes SREBP processing, which in turn causes more transcription of the mRNA.  Adding credence to this hypothesis was the fact that inhibitors of the PI3K->mTORC1 pathways (Wortmannin and Rapamycin) inhibited both transcription and processing of SREBP1.

In the Owen et al. paper, a transgenic rat is generated which puts SREBP1c under the control of a non-insulin responsive promoter, allowing for examination of the processing of SREBP1c independent of the SREBP1c promoter.  Consistent with previous findings, they show that both Wortmannin and Rapamycin block processing and mRNA synthesis, but that another inhibitor LYS6K2 which is specific for S6K (a target of mTORC1) blocks only processing and not mRNA levels.



This not only suggests that S6K is the proximal effector of the PI3K-mTORC1 pathway with respect to processing, but that S6K plays no role in the transcriptional regulation.  This also, for the most part, excludes a role for the SREBP -> SRE positive feedback loop, since under LYS6K conditions, SREBP cleavage is blocked but mRNA levels are unchanged.  Put another way, if the SREBP positive feedback loop was important, then this would suggest that mRNA of SREBP would be reduced under all conditions in which SREBP processing is blocked.

Owen JL, Zhang Y, Bae SH, Farooqi MS, Liang G, Hammer RE, Goldstein JL, & Brown MS (2012). Insulin stimulation of SREBP-1c processing in transgenic rat hepatocytes requires p70 S6-kinase. Proceedings of the National Academy of Sciences of the United States of America PMID: 22927400


How is SREBP Regulated by Insulin? by Dave Bridges is licensed under a Creative Commons Attribution 3.0 Unported License.

Rab5 Knockdown In Vivo

This paper from the Zerial Laboratory describes a study in which the three mammalian isoforms of Rab5 are reduced in whole livers or cultured hepatocytes by a lipid nanoparticle mediated knockdown treatment. They then analyse images from these cells and liver sections and determine that there are reduced early endosomes, late endosomes and lysosomes in cells depleted of Rab5.

They also identify two functional defects in these tissues; reduced LDL uptake and impaired apical polarity of the hepatocytes. They also generate a simplified model of Rab5/early endosomal dynamics and suggest a role for Rab5 in vesicle fission as well as fusion. Although this model fits the available data quite well, it ignores several other key components of endosomal formation and maturation.

The authors also present the curious finding that while Rab5 depletion reduces both LDL uptake and endosome numbers, dynamin depletion only affects LDL uptake but not the number of endosomes. One possible explanation for this defect is that they quantify the presence of endosomes in this assay largely by EEA1 puncta. Since EEA1 is a Rab5 effector, it is possible that in the Rab5 depleted cells there is still a population of early endosomes, but that these are not detected by EEA1 staining.

Together this study presents a convincing picture for the role of Rab5 in endosomal dynamics, and highlight the important role of Rab5 in both endocytic pathways and in the establishment of polarity in vivo.

Citation

Zeigerer A, Gilleron J, Bogorad RL, Marsico G, Nonaka H, Seifert S, Epstein-Barash H, Kuchimanchi S, Peng CG, Ruda VM, Del Conte-Zerial P, Hengstler JG, Kalaidzidis Y, Koteliansky V, & Zerial M (2012). Rab5 is necessary for the biogenesis of the endolysosomal system in vivo. Nature, 485 (7399), 465-70 PMID: 22622570

Disclosure: I have received constructs and yeast strains from the Zerial laboratory in the past.



Rab5 Knockdown In Vivo by Dave Bridges is licensed under a Creative Commons Attribution 3.0 Unported License.

Why Isn't There Anonymous Post-Publication Peer Review

If pre-publication review is anonymous, and it almost always is why isnt there anonymous post-publication peer review? If there is a benefit to anonymous review, then isn't it odd that the Faculty of 1000 and most journals commenting/letter to editor mechanisms require the submitter to provide a real name and appointment. Would post-publication review of articles suck less if it were anonymous?

Why Blog

Based on the twitter meme #whydoiblog. This is a good question, seeing as I do this infrequently and without a ton of focus. I guess I'd say to get something off my chest that I think other people might want to hear. I aspire towards being able to talk about research in a more public and open way, but until I am independent that is unfair to people who might not want to operate that way. Until then I'll continue to use this as a sporadic forum for me to speak out to the ether.

Why Blog by Dave Bridges is licensed under a Creative Commons Attribution 3.0 Unported License.

What Should Be Done about Reproducibility

A recent Commentary and linked editorial in Nature regarding reproducible science (or rather the lack thereof in science) has been troubling me for a few days now. The article brings to light a huge problem in the current academic science enterprise.

What am I talking about?

In the comment, two former Amgen researchers describe some of the efforts of that company to reproduce "landmark studies" in cancer biology. Amgen had a team of about a hundred researchers called the reproducibility team and their job was to test new basic science findings prior to investing in following up these targets. Shockingly, according to the authors, only 6/53 of these landmark studies were actually reproduced. When things were not reproduced they contacted the authors to attempt to work through the potential problems. This is an incredibly dismal 11% reproducibility rate!

Could it really be that bad?

The first problem is what exactly is meant by reproducibility. In the commentary the authors acknowledge that they did attempt to use additional models in the validation process and that technical issues may have under-lied some of these differences. They also point out that their sample set is biased with respect to the findings. These were often novel and cutting edge type findings and typically more surprising than the general research finding. Also, their definition of reproducibility is unclear. If researcher says drug X has a 10 fold effect on something and the Amgen guys say it has a 3X effect on the process is that a reproducible finding. My initial reaction was that the 89% were thing where the papers said something like thing X does thing Y and there was no evidence supporting that. We don't know, and in a bit of an ironic twist, since no data is provided (either which papers were good and which were bad, or within those, which findings were good and bad) this commentary could be considered both unscientific and non-reproducible itself (also we are awfully close to April Fools Day).

So there is some bad papers out there, who cares?

Reproducibility is at the heart of everything we do as scientists. No one cares if you did something once and for reasons you cant really explain, were never able to do it again. If something is not replicable and reproducble for all intents and purposes it should be ignored. We need measures of these to be able to evaluate research claims, and we need context specificity to understand the breadth of claims. Ill toss out few reasons why this problem really matters both to those of us who do science, and to everyone else.

This is a massive waste of time and money

From the commentary:

Some non-reproducible preclinical papers had spawned an entire field, with hundreds of secondary publications that expanded on elements of the original observation, but did not actually seek to confirm or falsify its fundamental basis.

Wow, really? Whole fields have been built on these? In a way I don't feel bad for these fields at all. If you are going to work in a field, and are never going to bother even indirectly testing the axioms on which your field is built then you are really not so good at the science. If you are going to rely on everyone else being correct and never test it then your entire research enterprise might as well be made from tissue paper. More importantly, if you are on top of these things you are going to waste time and money figuring out not to follow this up. Hopefully this is the more common case. This really goes back to the difficulty in publishing negative data to let people know which conditions work and which don't.

The reward system for science is not in sync with the goals of the enterprise

Why are people publishing things that they know only happen one out of six times? Why are they over-extending their hypotheses and why are they reluctant to back away from their previous findings? All of these things are because we are judged for jobs and for tenure and for grants on our ability to do these things. The person who spends 3 years proving that a knockout mouse model does not actually extend lifespan walks away with nothing, the one who shows it (even if done incorrectly) gets a high impact paper and a job. Even if it didn't take an unreasonable amount time and effort to publish non-reproducible data, the risk of insulting another researcher or not contributing anything new might be enough to prevent this. Until the rewards of publishing negative or contravening data are on par with the effort, people just won't do it.

This reflects really poorly on science and scientists

Science is always and probably has always been under some type of "attack". Science as an entity and scientists as their representatives need to not shirk this off or ignore it. We have to deal with this problem head-on, whether it be at the review level or at the post-publication level. People who are distrustful of science are rightful to point at this and say, why are we giving tens of billions of dollars to the NIH when they are 89% wrong. Why not just give that money to Amgen, who seem to be the ones actually searching for the truth (not that they will share that data with anyone else).

Can anything be done?

The short answer is its really going to be difficult and its going to rely on a lot of moving parts. Reviewers should (and in my experience do) ask for explicit reprodicibility statements in the papers. This can go farther, if someone says this blot is representative of 5 experiments then there is no reason the other 4 couldnt be put in the supplement. If they looked at 100 cells and show just one, then why cant the rest be quantified in some way. Post-publication, there should be open (ie not just in lab meetings) discussion of papers and the problems and where they match or mismatch with the rest of the literature. Things like blogs and the Faculty of 1000 are great, but how often have you seen a negative F1000 review? Finally, eventually there ought to be some type of network of research findings. If I am reading a paper, and I would like to know what other results agree or disagree with this, it would be fantastic to get there in a reasonable way. This is probably the most complicated, as it requires not only publication of disagreeing findings, but also some network to link them together.



Begley, C., & Ellis, L. (2012). Drug development: Raise standards for preclinical cancer research Nature, 483 (7391), 531-533 DOI: 10.1038/483531a


What Should Be Done about Reproducibility by Dave Bridges is licensed under a Creative Commons Attribution 3.0 Unported License.

Future Bridges Lab Rules version 0.1

Lab Rules

Version 0.1.3 on July 14, 2012 by Dave Bridges

Remember when you were growing up and you would say, well when I’m older I will (or won’t) do that.
I have been thinking of that, for my future when I run my own group.
It is fairly easy (and as a bit of a blowhard I do this all the time) to say I would do this, or I would do that.
I think posting this publicly will encourage me to stick to these rules.
Below are some roles and a bit of rationale and caveats.
This is the first version of this post but future versions will include links to the previous versions.
The version numbering is described in the Lab Policies README.
Check out the GitHub Repository for more granular changes.

Supervision of Trainees

Trainee-Advisor Contract

Both myself and trainees (either mine, or co-supervised trainees) will read, discuss and sign a contract describing our roles and responsibilities both as a trainee/employee and a mentor. This will include data dissemination/publishing rules, expectations of productivity, note keeping and time commitment, rules for dealing with other members both in my group and in collaborations, rules for sharing of reagents and data, rules for adjucating disagreements and grounds and procedures for termination. These rules will be in conformity with any institutional rules. Exceptions can be discussed and the agreement can be modified throughout the term of the relationship. I will post a generic version of this agreement in a publicly viewable location.

Online Presence

All trainees will appear on the laboratory website and write a blurb about their research interests and goals. Trainees will be strongly encouraged to blog, tweet and otherwise engage in social networking tools regarding their research and the work of others, but this is not required. Links to their publicly available social network profiles will be posted on the laboratory website.

Open Access Policy

Trainees will be made aware of the open publishing, dissemination, software and data/reagent sharing policies of the laboratory at the outset and will have to agree to these standards.

Reagents, Software and Tools

Software Usage

Wherever possible, free open source software will be used for data acquisition, analysis and dissemination. Exceptions will be made if necessary, but trainees will be encouraged to use/incorporate/development free tools.

Software Development

If software, scripts or the like are generated they will be released under a permissible open source license such as CC-BY and the license will be attached explicitly to the source code. Scripts and programs will be uploaded to a public revision control database such as GitHub or similar (my GitHub profile is here).

Publishing of Protocols and Scripts

When not present in the published article, detailed step by step protocols, data analysis scripts and other things which cannot fit into either methods and materials sections or supplementary materials will be posted online and linked to the publication’s online presence (post or as a comment on the paper’s website).

Protocol Sharing

Protocols will be made available online in a wiki format in a publicly available location, whether they have been published on or not. Editing will be restricted to laboratory members and collaborators.

Reagent and Tool Sharing

Reagents generated by my group will be shared upon request without condition (aside from potential restrictions placed by other collaborators, funding agencies and the institution). These reagents will be shipped with an explicit statement of free use/sharing/modification. Once a reagent sharing license is generated/identified it will be linked to in this document. This policy includes unpublished reagents and will never require attribution as a condition. If a reagent is obtained from another group and modified, we will offer the modified reagent back to the originator immediately.

Publishing and Data Dissemination

Open Access Journals

I believe that all work should be available to the public to read, evaluate and discuss. I am strongly against the mentality that data/knowledge should be restricted to experts and the like. I will therefore send all papers in which I am corresponding author and have supervised the majority of the work to journals (or their equivalent) which are publicly available. The two major caveats will be for work in which I am a minor (less than 50% effort) collaborator and the primary group leader wants to submit the work elsewhere. This will not exempt any potential major impact publications, no matter how awesome they may be. Delayed open access does not count in this respect.

Open Peer Review

Journals will be selected which publish non-anonymous reviewer comments alongside the articles whenever possible. If this is not done, and if permissible by the publisher and/or reviewers I will re-post the reviewer comments online without any modifications.

Public Forum for Article Discussion

Although I will encourage discussion of articles to occur at the point of publication (for example via the posting of comments directly at the website of the publisher), I will also provide a publicly available summary of every published finding from which I am an author (corresponding or not) and allow commenting at that point too. This discussion post will also link to or contain the reviewer and editor comments where possible. This summary might be a blog post, a facebook post or a google plus post or anything else that might come up in the future. If I am not the primary author or corresponding author I will encourage the first or corresponding author to write the post and link/quote that directly.

Presentations

All presentations of published data will be posted on an online repository such as Slideshare or something similar. My slideshare profile is here. If unpublished or preliminary data is presented privately and then is later published, then those slides will be presented upon publication. Similar to papers, an online blog post or the like will also accompany that upload. If audio or video of the presentation is available, that will be uploaded as well.

Data Sets

All datasets, once published will be made available in manipulable (preferably non-proprietary) formats for further analysis. Based on the scheme set out by the Linked Data Research Center Laboratory, all data will be provided at level 2 or above.

Chickens and Eggs

Yesterday two posts appeared in my feed both challenging the requirement of glamor mag (Nature, Science, Cell) level publications for career advancement.  Michael Eisen (@mbeisen) wrote a post in response to this idea suggesting that this is not a criteria for hiring in his experience (he is referring to job applications where he is, at UC Berkeley as well experiences with his  trainees).  Key point:

My own lab provides several examples that demonstrate this reality. My graduate students have gone on to great postdocs and many have landed prestigious fellowships “despite” having only published in open access journals. More curiously, I have had four postdoctoral fellows go out onto the academic job market, who  all got great jobs: at Wash U., Wisconsin, Idaho and Harvard Medical School. Not only did none of them have glamour mag publications from my lab. None of them had yet published the work on the basis of which they were hired! They got their interviews on the basis of my letters and their research statements, and got the jobs because they are great scientists who had done outstanding, as of yet unpublished, work. If anything demonstrates the fallacy of the glamour mag or bust mentality this is it.

In fact, as a co-founder of PLOS and a strong, vocal advocate of open science his group primarily publishes in PLOS journals.  He hasn't been the last author on a non-PLOS paper since a PNAs paper in 1998, so he is certainly putting his science where his mouth is. Earlier in the day, William Gunn (@mrgunn) made a similar argument:

I'm starting to think that the plodding careerists who always raise the "but I have to publish in X journal for my career" criticism just need to be routed around. You shouldn't be in science because you want a stable career, you should be here because you can't fathom doing anything else.

Now these are both admirable positions to take.  But as the title alludes to, this is a chicken and egg problem.  If a postdoc decides to only publish in open access journals then he hopes that prospective departments and grant committees agree with his stance.  If a faculty member takes this stance he hopes that grants and tenure/promotion committees agree.  If tenure and promotion committees agree, then they hope granting agencies agree.  If granting agencies agree then they hope that the public (or their foundations or government agencies agree).  If any link breaks, then its a risk.  As someone who agrees with this, I might find a department happy with this policy, but if my NIH study section isn't on board then I am in some trouble.

Taking this further, I thought why should this even only apply to open access and open review.  Lets say I do all my research totally in the open, self-publishing it online either on my own site or on a pre-print server like ArXiv or the newer Faculty of 1000 Research and engaging in discussion on these forums.  If I completely ignored journals entirely, would anyone accept this as being ok?  I posted it on twitter, and there were positive responses, but that is really hard to imagine.

Without anonymous peer-review how could I (or the reader) be assured that controls were done properly and the context of the work was appropriately stated.  If that is done via peer review posted with the article anonymously how could the reader be sure I didn't just delete the bad reviews or comments.  If I post some data on a blog or pre-print server and some other person finds it, expands on it and publishes it in Science then do I have any right to feel aggrieved?  Who should get the credit?

In an ideal world, things might work analogously to how Rosie Redfield (@rosieredfield) has addressed the arsenic life question.  After posting an initial rebuttal online, Dr. Redfield did some experiments, engaged with the community about the data and put it all together.  This was (to me at least) the first archetypical evidence of the open evaluation of a research claim.  It was done in the open, public suggestions were incorporated, the work was posted to a preprint archive and then in the end.... it was submitted to Science.

Now this is not entirely fair to Dr. Redfield, Science was chosen as that was where the first arsenic paper was published and where her (and other) critiques were published.  But if this, most open and public scientific re-evaluation, still needs glamour mag validation what hope does the rest of research have?

So who should mandate this?  Various policies of public access have helped make publicly funded research open access and a new generation of scientists has shown more proclivity towards goal but who needs to take the first steps.  Dr. Eisen suggests it has to be everyone.  For this change to happen, primary researchers, group leaders, departments, granting agencies and the public all need to take that step, and leave those who are betrothed to impact factor chasing looking like relics of the past.


Chickens and Eggs by Dave Bridges is licensed under a Creative Commons Attribution 3.0 Unported License.

PI(3)P and Exocytosis

The classic opinion regarding PI(3)P’s role in intracellular trafficking is that it is synthesized and functions primarily on early endosomes. Several recent publications have highlighted a potential second role for this lipid in exocytosis. Extending previous work by this group and others on the positive role of PI3K-C2a in neurosecretory pathways (1,2) and GLUT4 exoctyosis (3,4) this paper from Tania Maffucci's group interrogates the role of this lipid kinase in insulin secretion in INS1 cells. Combined these results suggest a general role for PI3K-C2a and its product, PI(3)P in exocytotic events.

This group has been investigating the role of the class II PI3K isoforms in exocytosis in a variety of systems. In this paper they investigate the role of PI3K-C2a in insulin secretion using INS1 rat insulinoma cells as a model. Stable knockdown cells did not show any defects in proliferation, calcium signaling, intracellular insulin levels or the expression levels and sub-cellular localization of exocytotic proteins. The PI3K-C2a knockdown cells did however show significant defects in insulin secretion, stimulated by either a secretagogue cocktail or potassium chloride.

Mechanistically, the authors show that there is no defect in insulin granules proximal to the plasma membrane at the resting state. They do detect a decrease in the amount of SNAP25 hydrolysis induced by the secretagogue cocktail. SNAP25 hydrolysis has been proposed to be an important step in the fusion of exocytic vesicles (5). This proteolytic event has not been established as a major mechanism in exocytosis, and it may only correlate with defects in PI3K-C2a signaling. However if reduced SNAP25 proteolysis is the mechanistic defect resulting from PI3K-C2a knockdown, then this suggests a role in protease regulation by PI(3)P or another PI(3)P-derived molecule as a key part of the general exocytotic machinery. It is also possible that there is another, as of yet unstudied role of PI3K-C2a and PI(3)P in exocytosis.



Dominguez, V., Raimondi, C., Somanath, S., Bugliani, M., Loder, M., Edling, C., Divecha, N., da Silva-Xavier, G., Marselli, L., Persaud, S., Turner, M., Rutter, G., Marchetti, P., Falasca, M., & Maffucci, T. (2010). Class II Phosphoinositide 3-Kinase Regulates Exocytosis of Insulin Granules in Pancreatic Cells Journal of Biological Chemistry, 286 (6), 4216-4225 DOI: 10.1074/jbc.M110.200295

References

(1) Meunier, Frederic, Shona Osborne, Gerald Hammond, Frank Cooke, Peter Parker, Jan Domin, and Giampietro Schiavo. “Phosphatidylinositol 3-Kinase C2{alpha} Is Essential for ATP-dependent Priming of Neurosecretory Granule Exocytosis.” Molecular Biology of the Cell 16, no. 10 (2005): 4841-4851. PubMed, DOI.

(2) Wen, Peter J, Shona L Osborne, Isabel C Morrow, Robert G Parton, Jan Domin, and Frederic A Meunier. “Ca2+-regulated pool of phosphatidylinositol-3-phosphate produced by phosphatidylinositol 3-kinase C2alpha on neurosecretory vesicles.” Molecular biology of the cell 19, no. 12 (December 2008): 5593-603. PMC, PubMed, DOI.

(3) Maffucci, Tania, Anna Brancaccio, Enza Piccolo, Robert C Stein, and Marco Falasca. “Insulin induces phosphatidylinositol-3-phosphate formation through TC10 activation.” The EMBO journal 22, no. 16 (August 15, 2003): 4178-89. PubMed, DOI.

(4) Falasca, Marco, William E Hughes, Veronica Dominguez, Gianluca Sala, Florentia Fostira, Michelle Q Fang, Rosanna Cazzolli, Peter R Shepherd, David E James, and Tania Maffucci. “The role of phosphoinositide 3-kinase C2alpha in insulin signaling.” The Journal of biological chemistry 282, no. 38 (September 21, 2007): 28226-36. PubMed, DOI.

Teaching, whats the point?

First a caveat, I dont teach right now, and havent formally taught in several years, even then it was a GTA sort of experience not a lecture hall. I'm also actively looking or jobs with a teaching component, so its not like I have a totally unbiased perspective here.

However, a recent trip forced me to think (in slightly more coherent terms) about the role of teaching in a scientists life. In my field at least, there seems to be, at times, a selection against competent teachers ever getting in front of undergrads. The people who are excellent communicators, listeners, original thinkers, have the best breadth of knowledge about their topic and are the hardest workers (ie the best scientists) are often directly or indirectly encouraged to do only science. These skills are of course the things that make the best educators.

Now I used to think primarily about a teaching load as what that experience could do for me. I could teach a course relevant to my field of interest and I could harvest the fact-questioning and creativity of the students to further my own research program. That sounds great for me, but maybe not so great for them. There is another perspective though, which I think might be disregarded quite a bit. The public has funded my training as a scientist (through both University funding and more directly through NSERC in Canada and NIH/ADA in the States) and its my responsibility to pay that investment back.

Its great to think that my research someday may cure some devastating disease, or will directly improve the human condition. At the end of a scientific career, even a very good one, how many people can point to a specific direct impact. Most researchers (especially basic scientists) can improve a field, and provide insights that then may go on to help people more directly but most of us are not inclined, or capable to directly impact the public.

How then can we have an impact and pay back the public investment in our careers. It seems to me that a teaching responsibility might be the most efficient way of repaying that investment. An inspiring teacher, who can convey both the benefits of the scientific enterprise and point out how it improves the human condition can have an incredible impact on an undergraduate's future opinions of science. If that student does not end up as a scientist (as most won't) that experience will carry with them to wherever they go and whatever they do. If that student has learned an appreciation for science (or a more specific field in science) then they will carry that to their colleagues and their friends and their families and will spread that appreciation. In the end that kind of viral spreading will help pay back that investment in me much more efficiently than anything I am likely to directly accomplish through research.

IRS Serine Phosphorylation and Insulin Sensitivity?

This is part of my last year in science series. Click here for the rationale.

Insulin signalling passes through a cascade of signaling proteins starting with the recruitment of the Insulin Receptor Substrate (IRS) to the Insulin Receptor.  Insulin resistance, or impaired insulin signaling is a hallmark of obesity and diabetes.  One of the ways in which was thought to happen was through phosphorylation of Serine 307 on IRS, a phosphorylation event which has been proposed to be inhibitory to insulin signaling.  As an example, it has been proposed that inflammation (via JNK or IKKbeta), overnutrition (via S6K) or several other factors can lead to the phosphorylation of this protein.  Since several of these factors correlate with reduced insulin signaling, and the ablation of these factors leads to both increased insulin signaling and reduced serine phosphorylation, the obvious hypothesis was that serine phosphorylation of IRS is causative of insulin signaling.

Correlation does not equal causation, so in order to test this hypothesis in vivo, Morris White's group at Harvard Medical School generated knockin mice, in which Serine 307 of IRS1 is mutated to an alanine (and is therefore unable to be phosphorylated).  The idea would be that these mice would be unable to phosphorylated IRS1 on Serine 307, and therefore would be resistant to the deleterious effects of this phosphorylation.  This serine to alanine knockin model is considered the gold standard for translating an observational protein phosphorylation site into an in vivo phenotype.  Unfortunately for the prevailing hypothesis, the opposite was true.

The paper, from Copps et al. published in January of 2010 show that on a normal diet S307A mice showed modest reductions in insulin sensitivity, and increased fasted insulin levels.  Both of these effects were amplified by high fat diet, and were associated with a reduction in weight gain, in contradiction to the previous hypothesis, that these effects would be ameliorated.  Mechanistically, when on a high fat diet (or coupled with liver specific IRS2 knockout) the S307A mouse had decreased tyrosine phosphorylation of IRS, but no effect of the downstream insulin targets Akt and S6K. Therefore it is unclear exactly how the insulin intolerance is propagated into effects on glucose homeostasis.

In the past year this article has been cited 10 times (according to Google Scholar), mostly in review articles, but the major upshot here, is that the models which showed effects on IRS Serine 307 phosphorylation and insulin resistance, and concluded that insulin resistance was mediated by increased phosphorylation may need re-interpretation.  IRS is phosphorylated on several other sites, so the general hypothesis that serine phosphorylation of IRS causes insulin resistance could still be true, but that again might need to wait until such a knockin model can be generated.  This work also points out the risks of correlating phenotypes with incompletely characterized phosphorylation sites.



Copps KD, Hancer NJ, Opare-Ado L, Qiu W, Walsh C, & White MF (2010). Irs1 serine 307 promotes insulin sensitivity in mice. Cell metabolism, 11 (1), 84-92 PMID: 20074531 DOI

Last Year in Science

I hope to put together a series of posts on papers from about a year ago. Quite often the context of a paper can get lost in the flurry surrounding the initial release of a paper. My hope is that I can provide a little bit of insight on these papers with a little bit more since publication. If you have any ideas for things that might be interesting to go over (again) just let me know. For now I'll try to read some of the glamor mags in my field (Cell, Cell Metabolism, Nature Cell Biology, Nature and Science) and see if anything strikes my interest.

Inositol Phosphates and Insulin Signaling

When most people think of the role of inositols in Akt signaling, they immediately think about the role of PIP3 in the PDK1-Akt signaling axis.  A recent paper published in Cell by Solomon Snyder's group at John's Hopkins highlights the role of soluble inositol phosphates in insulin signaling.

Soluble Inositol Phosphates

Inositol is best known as a lipid head group, that can be phosphorylated to form 8 potential phosphorylated phosphatidylinositols. These membrane bound signaling lipids have many important roles in cell biology, including in signal transduction. In addition to these 8 membrane bound lipids, the inositol headgroup can be solubilized from the lipid tails by phospholipases.  This leads to the important second messenger IP3, which can then be further phosphorylated to yield IP4, IP5 and IP6.  Adding even more to the complexity, these rings can be pyrophosphorylated to yield even more species including IP7 and IP8 among others. The functions of these phosphorylated inositol rings are largely unknown.

Role of IP7 in Akt Activation

The IP6-Kinase 1 phosphorylates IP6 to form IP7 (pyrophospho-IP5). The current paper, Chakraborty et al. (2010), describes insulin signaling in cells in which IP6 is knocked out. As expected, IP7 concentrations are reduced in these cells, but the major finding is that Akt phosphorylation and activation is increased. The proposed mechanism for this effect is that IP7 acts as an endogenous, physiological inhibitor of Akt, likely by competitively inhibiting the ability of PIP3 to bind to the same site in its PH domain. Once IP7 is reduced, this inhibition is released, and Akt can be activated more easily.

Consistent with hyperactivation of Akt, these knockout mice exhibit increased insulin sensitivity and a reduction in diet-induced obesity. Akt and its downstream targets are known to be major mediators of insulin signaling, and so increased insulin signaling through the Akt pathway leads to increased glucose disposal and a resistance to diet-induced weight gain, insulin resistance, hyperinsulinemia and hyperglycemia. These data are consistent with a role of IP7 as a negative regulator of insulin signaling and the authors propose that IP6K1 may be a novel potential therapeutic target to improve insulin sensitivity.



Chakraborty, A., Koldobskiy, M., Bello, N., Maxwell, M., Potter, J., Juluri, K., Maag, D., Kim, S., Huang, A., & Dailey, M. (2010). Inositol Pyrophosphates Inhibit Akt Signaling, Thereby Regulating Insulin Sensitivity and Weight Gain Cell, 143 (6), 897-910 DOI: 10.1016/j.cell.2010.11.032

The Web of Data and Experimental Observations

A few things this week got me thinking about the idealized best way to think of experimental data.  One was a technical problem I had been pondering.  If i wanted to publish some experimental observation (not a paper just a single observation) what is the best way to do this.  It got me thinking a little about the Semantic Web (or the Web of Data) and how it could related to 'wet' biology. I am far from an expert on any of these things, so feel free to make public your thoughts

The Web of Data and Ontologies

One of the new things the architechts of the internet have been concerning themselves lately is the semantic web. Some authoritative links are here, here and here.   The idea is that there are lots of things out there which are data but the web considers mostly things that are documents.  The world will be a better place when computers can make connections between these things.  This involves two concepts, one of which is the thing and the other of which is the connection

Things on the Web

Not everything is on the web.  I for example, am sitting in my living room and am definately not on the web.  Therefore to locate me, I need some kind of identifier.  These are called Uniform Resource Identifiers (URI).  Mine could be something like http://davebridges.github.com#davebridges.  URI's need to be unique and they need to be available on the internet.  Anything could have a URI, and something could have several URI's. The key is that a URI should not belong to more than one thing.  Things which have multiple URI's can be crossreferenced with specific vocabularies (ie owl:sameAs).

Connections (Ontologies)

Once things are on the internet, the basis of linked data is how these things relate to one another.  For example, this blog post was created by me.  So if there was some kind of explicit statement connecting this, any computer could figure out that I wrote this post, or inversely that this post was written by me.  The connections are defined by specific vocabularies or ontologies.  For example dublincore is a vocabulary about documents, and includes a term "creator".  Therefore one could create a link between me and this post by writing something like this:

This Blog Post has a Creator named Dave Bridges

The important thing is that the ontology specifically defines the relationship between two URI's.  Given this knowledge, a computer could generate the creator of the page, or all pages created by me.

How Would This Work in Science

What got me thinking about this was how it would be great to have defined vocabularies to describe experimental results.  For example if there was an ontology that described a protein-protein interaction (there is, its at http://bioportal.bioontology.org/ontologies/39508), one could use, for example two PubMed links as URI's to could indicate a molecular interaction and the two proteins.  Given a large enough catalog of these it would be possible to get a list of all molecular interactions for a particular protein.

What About Non-Cannonical Findings

I might talk about this later, but one thing important would be to not just be able to obtain a list of interactions, but also links to the specific data supporting (or refuting that point).  Ideally this would go deeper than just a link to the paper, but maybe a link to a separate URI describing a particular experiment.

The things that got me thinking about this were a question i posted on BioStar, a blog post on MolBio Research Highlights and a paper at Nature Preceedings

Plans and Ethics

This is my first post here and my second stab at blogging.  I am still not sure what sorts of things that will go here so bear with me.  Some guidelines that I will try to stick to are below:

Journalistic-like Ethics

• Posts will not be deleted.  I said it, I cant unsay it.
• Comments will left to stand, unless they are obviously unrelated sales pitches.  I reserve the right to eventually approve comments, but lets hope not.
• Minor changes and typos will be made without notification.  If you see a typo just email me or leave a comment.
• Major changes, corrections and omissions will be made in a separate post and both posts will be linked.
• Assertions and generalizations about individuals will be made very carefully and with thoughts of consequences.  If I say something less than positive about a model, result, or datum it is not personal.
• All posts regarding published materials will be tagged with a http://www.researchblogging.org/ symbol and DOI resolvable links (preferably) or PubMed links to source materials.  Summaries of research blogging materials can be found here
• Unpublished and/or confidential materials, thoughts or ideas will be not used unless permission is obtained from the scientists involved.
• Disclosures will be made if I have collaborated with or obtained reagents with a group in the past. Also if I am worried that I am allowing my personal feelings affect my interpretation.

Web Community Ethics

• I will link to all material that leads to a post, and will amend posts with relevant discussions.  Even if I am linking to someone just to say I am agree.  If I don't, its an accidental omission and please let me know.
• I will quote portions of, but rarely all of other posts, but will provide links to the full post.
• If you email me directly, and I forget to respond, please keep at me.  Chances are I have forgotten to get back to you.  Any information provided in confidence will remain in confidence unless explicit permission is obtained.
• If I promised to do something and didn't, I absolutely forgot.  My bad, just remind me.
• Until I find a voice on this thing, I am totally open to ideas.  Drop me a comment or an email.

General Rules

• I will not discuss politics, and will discourage others from talking about it.  There is no doubt that there is an intersection between science (and especially science funding) and politics, but there are thousands of other people who would rather talk about that.
• I will try to keep discussion of personal lives and trials and tribulations of my current career to a minimum.
• I will try to keep a positive tone wherever possible.  Its too easy to be negative and critical all the time.

These are based on the code of conduct of the fantastic mgoblog.  Any further suggestions are welcome.