Activity-Dependent Genes in Mouse Olfactory Sensory Neurons

Fischl, Adrian M.; Heron, Paula; Stromberg, Arnold J.; McClintock, Timothy S.

doi:10.1093/chemse/bju015

Cited by 30 publications

(40 citation statements)

References 46 publications

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“…Lastly, Figure 4 presents the p -values of an experiment by Fischl et al [ 3 ] where paired t -tests were used to compare dependent samples. From the histogram, it appears as though something has gone wrong: there is an abundance not of low p -values but of p -values near 0.3.…”

Section: Introductionmentioning

confidence: 99%

p-Value Histograms: Inference and Diagnostics

2018

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It is increasingly common for experiments in biology and medicine to involve large numbers of hypothesis tests. A natural graphical method for visualizing these tests is to construct a histogram from the p-values of these tests. In this article, we examine the shapes, both regular and irregular, that these histograms can take on, as well as present simple inferential procedures that help to interpret the shapes in terms of diagnosing potential problems with the experiment. We examine potential causes of these problems in detail, and discuss potential remedies. Throughout, examples of irregular-looking p-value histograms are provided and based on case studies involving real biological experiments.

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Section: Introductionmentioning

confidence: 99%

p-Value Histograms: Inference and Diagnostics

2018

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“…With a binwidth of 0.05, this amounts to checking 20 bins, and therefore using a corrected significance threshold of 0.05/20=0.0025, or equivalently, a frequency threshold of F .9975 (m, b). For the data from the study by Fischl et al [3] in Figure 4, m = 23, 332 and b = 0.05, so the frequency threshold is 1261.…”

Section: Quality Controlmentioning

confidence: 98%

“…The other issue is that permutation approaches cannot be applied to very small samples. For example, we cannot use a permutation approach to investigate the Fischl et al [3] data from Figure 6, which involves a one-sample t-test with only 3 pairs of subjects. Although the idea can be extended to paired data (by randomly assigning signs to the differences rather than permuting rows), in this case there are only Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 5 August 2018 doi:10.20944/preprints201808.0091.v1…”

Section: Incorrect Distributional Assumptionsmentioning

confidence: 99%

<em>P</em>-Value Histograms: Inference and Diagnostic

Breheny¹,

Stromberg²,

Lambert³

2018

Preprint

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It is increasingly common for experiments in biology and medicine to involve large numbers of hypothesis tests. A natural graphical method for visualizing these tests is to construct a histogram from the p-values of these tests. In this article, we examine the shapes, both normal and abnormal, that these histograms can take on, as well as present simple inferential procedures that help to interpret the shapes in terms of diagnosing potential problems with the experiment. We examine potential causes of these problems in detail, and discuss potential remedies. Throughout, examples of abnormal-looking p-value histograms are provided and based on case studies involving real biological experiments.

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“…S100A5 is expressed primarily in the olfactory bulb and olfactory sensory neurons (OSNs). Its expression is dramatically upregulated by odor stimulation [ 8 – 10 ]. It has been proposed that S100A5 is actively involved in olfactory signalling due to its expression profile [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Human S100A5 binds Ca2+ and Cu2+ independently

Wheeler

Harms

2017

BMC Biophys

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BackgroundS100A5 is a calcium binding protein found in a small subset of amniote tissues. Little is known about the biological roles of S100A5, but it may be involved in inflammation and olfactory signaling. Previous work indicated that S100A5 displays antagonism between binding of Ca2+ and Cu2+ ions—one of the most commonly cited features of the protein. We set out to characterize the interplay between Ca2+ and Cu2+ binding by S100A5 using isothermal titration calorimetry (ITC), circular dichroism spectroscopy (CD), and analytical ultracentrifugation (AUC).ResultsWe found that human S100A5 is capable of binding both Cu2+ and Ca2+ ions simultaneously. The wildtype protein was extremely aggregation-prone in the presence of Cu2+ and Ca2+. A Cys-free version of S100A5, however, was not prone to precipitation or oligomerization. Mutation of the cysteines does not disrupt the binding of either Ca2+ or Cu2+ to S100A5. In the Cys-free background, we measured Ca2+ and Cu2+ binding in the presence and absence of the other metal using ITC. Saturating concentrations of Ca2+ or Cu2+ do not disrupt the binding of one another. Ca2+ and Cu2+ binding induce structural changes in S100A5, which are measurable using CD spectroscopy. We show via sedimentation velocity AUC that the wildtype protein is prone to the formation of soluble oligomers, which are not present in Cys-free samples.ConclusionsS100A5 can bind Ca2+ and Cu2+ ions simultaneously and independently. This observation is in direct contrast to previously-reported antagonism between binding of Cu2+ and Ca2+ ions. The previous result is likely due to metal-dependent aggregation. Little is known about the biology of S100A5, so an accurate understanding of the biochemistry is necessary to make informed biological hypotheses. Our observations suggest the possibility of independent biological functions for Cu2+ and Ca2+ binding by S100A5.Electronic supplementary materialThe online version of this article (doi:10.1186/s13628-017-0040-y) contains supplementary material, which is available to authorized users.

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Activity-Dependent Genes in Mouse Olfactory Sensory Neurons

Cited by 30 publications

References 46 publications

p-Value Histograms: Inference and Diagnostics

p-Value Histograms: Inference and Diagnostics

<em>P</em>-Value Histograms: Inference and Diagnostic

Human S100A5 binds Ca2+ and Cu2+ independently

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