Data from: Rund et al, 2011 PNAS 108(32):E421-E430; and Rund et al, 2013 BMC Genomics 14(1): 218

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Tissue information:

Head and body tissue samples are from adult females mated, but not blood-fed An. gambiae (Pimperena S form) mosquitoes maintained at 85% relative humidity and 27 ± 1^oC in either constant dark (DD) conditions or a 12:12 light:dark (LD) cycle (11 hrs full light (~250 lux), 11 hrs darkness, and 1 hr dawn and 1 hr dusk transitions). Access to a 5% solution of fructose was provided ad libitum. Experiments began with female 7-9 days post emergence with 20-30 mosquitoes harvested every 4 hrs for 48 hrs.

Chart information:

y-axis - probe set fluorescence intensity after GC-RMA normalization.

x-axis - time in hours. The light:dark cycle (LD) experiments are described in Zeitgeber Time (ZT), with ZT12 defined as time of lights OFF under the LD cycle, and ZT0 defined as end of the dawn transition. Constant darkness (DD) experiments are described in Circadian Time (CT), with CT0 defined as the end of subjective dawn, inferred from ZT0 of the previous LD cycle.

Gene symbol and description

Gene names and description were annotated primarily from information stored at VectorBase, often using the closest homologue from Ae. aegypti (AAEG), Cx. quinquefasciatus (CQUI), Drosophila (DMEL) or C. elegans (CELG) (in that order), but also using published literature and the Database for Annotation, Visualization and Integrated Discovery (DAVID).

Expression values:

For gene profiles with values below an average fluorescence value of ~20, please be extra cautious when interpreting the data. For our paper, we chose to only include profiles with a mean fluorescent intensity across all 13 time points in both replicates of >20 (which would exclude >99% of Plasmodium genes on the microarray).

Statistical detection of rhythmic components:

You may filter by COSOPT p-value. In Rund et al (2011) a p-value < 0.1 was chosen as the maximum threshold for a rhythmic gene, in line with Panda et al (2002).

COSOPT measures the goodness-of-fit between our experimental data and a series of cosine curves with varying phases and (user defined) period lengths. P-values are determined by scrambling experimental data and re-fitting it to cosine curves to determine probability that the observed data matches a cosine curve by chance alone. In our analysis presented here, statistics are only reported for profiles with period lengths of 18.5 - 26.5 hrs for constant condition experiments (DD) or 20-28 hrs for LD experiments. COSOPT generates period length and phase estimates. Where a p-value is provided for only a single replicate time course, a "ONE" designate is provided in the far right column, and the COSOPT values provided (p-value, period length and phase) are derived from this single time course. The charted data always show the combined replicate data.

JTK_CYCLE is a nonparametric statistical algorithm designed to identify and characterize cycling variables in large data sets. It applies the Jonckheere-Terpstra-Kendall (JT) test and Kendall's tau (rank correlation) (K), finding the optimal combination of period and phase that minimizes the p-value of Kendall tau correction between the experimental time series and each tested cyclical ordering, this being derived from cosine curves. Analysis was restricted to 16 - 28 hr period lengths. JTK_CYCLE generates period length, phase and amplitude estimates, as well as corrects for multiple comparisons post hoc. The p-value for a given probe set was converted to a more stringent q-value, which takes into consideration the possible false positive rate across all probe sets. Amplitude value reflects the 1-cycle median sign-adjusted deviation from the median in relation to the optimal cosine pattern [for a perfect cosine wave, this is amplitude (median absolute derivation from the median)/sqrt(2)]. For example an amplitude of 0.45 represents approximately a 2.5-fold peak to trough rhythm (e.g.cdsA), 0.88 represents a 10-fold rhythm (e.g. CYP6M2), and 2.23 represents a 135-fold rhythm (e.g. CRY2).

Additional data formats and views:

Gene Expression Omnibus (GEO): Accession no. GSE22585

VectorBase Gene Expression: (Click here)

Relevant publications:

Anopheles gambiae diel and circadian transcriptional profiling:

Rund SSC, Hou TY, Ward SM, Collins FH and Duffield GE. 2011. Genome-wide profiling of diel and circadian gene expression of the malaria vector Anopheles gambiae. Proceedings of the National Academy of Sciences USA 108 (32): E421-E430 (Link)

Rund SSC, Gentile JE and Duffield GE. 2013. Extensive circadian and light regulation of the transcriptome in the malaria mosquito Anopheles gambiae. BMC Genomics 14: 218 (Link)

COSOPT algorithm:

Straume M. 2004. DNA microarray time series analysis: Automated statistical assessment of circadian rhythms in gene expression patterning. Methods in Enzymology 383:149-66

Panda S, Antoch MP, Miller BH, Su AI, Schook AB, Straume M, Schultz PG, Kay SA, Takahashi JS, Hogenesch JB. 2002. Coordinated transcription of key pathways in the mouse by the circadian clock. Cell 109:307-20

JTK_CYCLE algorithm:

Hughes ME, Hogenesch JB, Kornacker K. 2010. JTK_CYCLE: An efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets. Journal of Biological Rhythms 25:372-80

CIRCA System:

Hughes ME, DeHaro L, DiTacchio L, Hayes K, Pullivarthy S, Panda S, and Hogenesch JB. 2007. High resolution time course analysis of gene expression from the liver and pituitary. Cold Spring Harbor Symposia on Quantitative Biology 72:381-386

Hughes ME, DiTacchio L, Hayes K, Vollmers C, Pulivarthy S, Baggs J. 2009. Harmonics of circadian gene transcription in mammals. PLOS Genetics 5:e1000442

Pizarro A, Hayer K, Lahens NF, Hogenesch JB. 2013. CircaDB: a database of mammalian circadian gene expression profiles. Nucleic Acids Research 41(D1):D1009-D1013

DAVID:

Huang D, Sherman B, Lempicki R. 2008. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4:44-57

Photo Credit

Data from: Leming et al, 2014 BMC Genomics 15(1):1128

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Tissue information:

Mosquitoes were maintained at 85% relative humidity and 27 ± 1^oC in either constant dark (DD) conditions or a 12:12 light:dark (LD) cycle (11 hrs full light (~250 lux), 11 hrs darkness, with 1 hr dawn and 1 hr dusk transitions). Access to a 5% solution of fructose was provided ad libitum. Head tissue samples were collected from adult female presumed mostly mated but not blood-fed Ae. aegypti mosquitoes of the Wh (Higgs White Eye) strain. Experiments began with females 2-6 days post emergence with 20-30 mosquitoes harvested every 4 hrs for 44 hrs.

Chart information

y-axis - average probe set fluorescence intensity. These values were determined after normalization with the Robust Multi-array Average (RMA) as described by Irizarry et al (2003).

x-axis - time in hours. The light:dark cycle (LD) experiments are described in Zeitgeber Time (ZT), with ZT13 defined as the time of lights off under the LD cycle, and ZT1 defined as end of the dawn transition. Constant darkness (DD) experiments are described in Circadian Time (CT), with CT1 defined as the end of subjective dawn, inferred from ZT1 of the previous LD cycle.

Gene symbol and description:

Gene names and description were annotated from the information stored on VectorBase.org.

Expression values:

The RMA algorithm was used to determine expression values for all genes. Background levels of fluorescence was determined to be 52.8 in LD and 51.7 in DD. Therefore, for gene profiles with expression values below these please be extra cautious when interpreting the data. To determine background levels the expression data was log2 transformed, mean centered, and normalized across the time course for each gene. We then identified a group of genes that showed minimal expression across the experimental samples by using the Cluster 3.0 program. The Pearson correlation was used as a distance measure to cluster the expression variation for identification of different gene groups as previously described. From this we determined that the level of background for fluorescence was as mentioned above.

Statistical detection of rhythmic components:

In Leming et al (2014) a p-value of 0.05 was chosen as the maximum threshold for a rhythmic gene.

JTK_CYCLE is a nonparametric statistical algorithm designed to identify and characterize cycling variables in large data sets. This algorithm generates period length, phase and amplitude estimates, and also corrects for multiple comparisons post hoc. To achieve this, JTK_CYCLE applies the Jonckheere-Terpstra-Kendall (JT) test and Kendall's tau (rank correlation) (K). These tests find the optimal combination of period and phase that minimizes the p-value of the Kendall tau correction between the experimental time series and all tested cyclical orders, this being derived from cosine curves. Amplitude values reflect the 1-cycle median sign-adjusted deviation from the median in relation to the optimal cosine pattern [for a perfect cosine wave, this is amplitude (median absolute derivation from the median)/sqrt(2)]. Analysis was restricted to 20-28 hr period lengths in LD and 18-28 hr period lengths in DD.

Additional data formats and views:

Gene Expression Omnibus (GEO): Accession no. GSE60496

VectorBase Gene Expression: (Coming Soon)

Relevant publications:

Ae. aegypti diel and circadian transcriptional profiling:

Leming MT, Rund SSC, Behura SK, Duffield GE, O'Tousa JE. 2014. A database of circadian and diel rhythmic gene expression in the yellow fever mosquito Aedes aegypti. BMC Genomics 15(1):1128 (Link)

JTK_CYCLE algorithm:

Hughes ME, Hogenesch JB, Kornacker K. 2010. JTK_CYCLE: An efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets. Journal of Biological Rhythms 25:372-80

Statistical analysis:

Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, and Speed TP. 2003. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Research 31: e15

de Hoon MJ, Imoto S, Nolan J and Miyano S. 2004. Open source clustering software. Bioinformatics 20: 1453-4

Chauhan C, Behura SK, Debruyn B, Lovin DD, Harker BW, Gomez-Machorro C, Mori A, Romero-Severson J and Severson DW. 2012. Comparative expression profiles of midgut genes in dengue virus refractory and susceptible Aedes aegypti across critical period for virus infection. PLoS One 7: e47350

Eisen MB, Spellman PT, Brown PO and Botstein D. 1998. Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences USA 95: 14863-8

Photo Credit