HughesLab:Research: Difference between revisions
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== | == Understanding the mechanism and physiological significance of CCG regulation == | ||
[[Image: | [[Image:Fig. 2.png|left|thumb|'''Fig. 1''': [http://www.ncbi.nlm.nih.gov/pubmed/27856350 Li et al. (2016)]<br>RNA-seq data showed an activation of immune responsive genes in ''Achl'' RNAi flies.]] | ||
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One of the most important questions to be addressed in the field is to identify CCGs that mediate clock output of physiological processes and to understand how they mediate clock output of physiological processes.</p> | |||
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Here we found ''Achilles'' (''Achl''), a previously uncharacterized CCG, is rhythmically expressed in the fly brain, and ''Achl'' is likely to mediate the rhythmic immune response in flies. We found that the expression of ''Achl'' in neurons, represses the expression of immune responsive genes in the fat body (Fig. 2) and consequently the resistance of flies against bacterial infection. Notably, we noticed a decreased overall lifespan and starvation resistance in ''Achl'' knockdown flies, suggesting a a behavioral or metabolic cost of constitutively activating immune pathways and furthermore underscores the significance of maintaining rhythmic homeostasis.</p> | |||
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Ongoing work is focused on identifying the significance of ''Achl'' in the rhythmic regulation of immune system and deciphering the signaling mechanism downstream of ''Achl'' that conveys information from neurons to the fat body. | |||
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== RNA-seq parameters in circadian rhythms == | |||
[[Image:Read-depth.png|left|thumb|'''Fig. 2''': [http://www.ncbi.nlm.nih.gov/pubmed/25662464 Li et al. (2015)]<br>Many cycling transcripts are detectable with fewer reads per sample.]] | |||
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The development of next-generation sequencing, especially RNA-seq, has significantly accelerated circadian studies in profiling tissue-specific CCGs (clock controlled genes) as well as elucidating the mechanism of circadian output pathways. However, there are technical challenges await when conducting these experiments.</p> | |||
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To address these challenges, we performed computational simulations and figured out the significance of some major challenges such as the depth of sequencing coverage, read-depth (See an example in Fig. 1), and choice of alignment algorithms in using RNA-seq analysis in circadian rhythms using ''mice'' and ''Drosophila'', two most commonly used model organisms. | |||
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== Discovery of circadian harmonics == | == Discovery of circadian harmonics == | ||
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== System-driven circadian oscillations == | == System-driven circadian oscillations == | ||
[[Image:HughesFig2.png|left|thumb|'''Fig. 4''': [http://www.ncbi.nlm.nih.gov/pubmed/22844252 Hughes et al. (2012b)]<br>~100 genes oscillate in the mouse liver, despite ablation of the local, peripheral clock.]] | [[Image:HughesFig2.png|left|thumb|'''Fig. 4''': [http://www.ncbi.nlm.nih.gov/pubmed/22844252 Hughes et al. (2012b)]<br>~100 genes oscillate in the mouse liver, despite ablation of the local, peripheral clock.]] | ||
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Since snoRNAs are involved in ribosomal maturation, this observation raises the possibility that these daily oscillations directly influence the assembly and stability of the ribosome, one of the most fundamental processes of cell biology. Alternatively, these snoRNAs may exert their influence over mRNAs instead, thereby regulating the stability or translation of circadian target genes. Consistent with this possibility, we found that snoRNAs can target several known clock genes in the fly, such as ''Takeout'' (''to''). | Since snoRNAs are involved in ribosomal maturation, this observation raises the possibility that these daily oscillations directly influence the assembly and stability of the ribosome, one of the most fundamental processes of cell biology. Alternatively, these snoRNAs may exert their influence over mRNAs instead, thereby regulating the stability or translation of circadian target genes. Consistent with this possibility, we found that snoRNAs can target several known clock genes in the fly, such as ''Takeout'' (''to''). | ||
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Hughes Lab<br> | Hughes Lab<br> | ||
[ | [https://pulmonary.wustl.edu/ Pulmonary and Critical Care Medicine]<br> | ||
University of | Washington University School of Medicine | ||
Revision as of 14:07, 6 February 2017
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Understanding the mechanism and physiological significance of CCG regulation
RNA-seq data showed an activation of immune responsive genes in Achl RNAi flies.
One of the most important questions to be addressed in the field is to identify CCGs that mediate clock output of physiological processes and to understand how they mediate clock output of physiological processes.
Here we found Achilles (Achl), a previously uncharacterized CCG, is rhythmically expressed in the fly brain, and Achl is likely to mediate the rhythmic immune response in flies. We found that the expression of Achl in neurons, represses the expression of immune responsive genes in the fat body (Fig. 2) and consequently the resistance of flies against bacterial infection. Notably, we noticed a decreased overall lifespan and starvation resistance in Achl knockdown flies, suggesting a a behavioral or metabolic cost of constitutively activating immune pathways and furthermore underscores the significance of maintaining rhythmic homeostasis.
Ongoing work is focused on identifying the significance of Achl in the rhythmic regulation of immune system and deciphering the signaling mechanism downstream of Achl that conveys information from neurons to the fat body.
RNA-seq parameters in circadian rhythms
Many cycling transcripts are detectable with fewer reads per sample.
The development of next-generation sequencing, especially RNA-seq, has significantly accelerated circadian studies in profiling tissue-specific CCGs (clock controlled genes) as well as elucidating the mechanism of circadian output pathways. However, there are technical challenges await when conducting these experiments.
To address these challenges, we performed computational simulations and figured out the significance of some major challenges such as the depth of sequencing coverage, read-depth (See an example in Fig. 1), and choice of alignment algorithms in using RNA-seq analysis in circadian rhythms using mice and Drosophila, two most commonly used model organisms.
Discovery of circadian harmonics
Transcriptional profiling of the mouse liver identified rhythmic transcripts with period lengths of ~8, ~12, and ~24 hours.
We profiled global gene expression over two full days using Affymetrix microarrays. We identified rhythmic transcripts in the mouse liver and pituitary, as well as fibroblasts (NIH3T3) and osteosarcoma cells (U2OS). These data have been made freely available on CircaDB as a resource to the field. To our surprise, we found several hundred genes cycling with period lengths much shorter than 24 hours (Fig. 1). These ultradian rhythms had period lengths of ~8 and ~12 hours -- i.e., the second and third harmonics of 24 hour oscillations.
Subsequently, we have shown that these rhythms are found in tissues throughout the body. Moreover, they are found in fruit flies as well, suggesting that circadian harmonics are a common feature of animal transcriptional rhythms. At a mechanistic level, 12 hour rhythms require both a central and peripheral circadian oscillator, indicating that these rhythms are ultimately downstream of the conventional circadian clock. Typically, they are involved in cellular responses to stress, suggesting that ultradian transcriptional rhythms respond to twice daily stresses.
System-driven circadian oscillations
~100 genes oscillate in the mouse liver, despite ablation of the local, peripheral clock.
In collaboration with the Takahashi laboratory, we profiled transcriptional rhythms in mice with and without a defective circadian clock in the liver. Although most rhythmic genes are lost due to the ablation of the local circadian oscillator, nearly 100 genes continue to cycle with appropriate period lengths and phases (Fig. 2). Consequently, these persistent rhythms may form the molecular basis by which the central clock drives peripheral oscillations. Strikingly, many core clock genes continue to oscillate in the absence of a local clock, implying that the promoters of these genes have evolved to be directly responsive to circulating, systemic cues from the central nervous system.
Rhythms of snoRNA host genes
Non-coding, snoRNA host genes oscillate in the fly brain
We used Illumina sequencing to systematically profile RNA expression in fly heads over two consecutive days. We identified hundreds of cycling genes, including many non-coding RNAs (ncRNAs) that had not been identified in previous microarray studies. Most interestingly, an entire family of ncRNAs, the Uhg genes, oscillated over the course of 24 hours. These genes are hosts for snoRNA expression -- i.e., their exons are spliced together, and their introns are further processed to generate mature snoRNAs.
Since snoRNAs are involved in ribosomal maturation, this observation raises the possibility that these daily oscillations directly influence the assembly and stability of the ribosome, one of the most fundamental processes of cell biology. Alternatively, these snoRNAs may exert their influence over mRNAs instead, thereby regulating the stability or translation of circadian target genes. Consistent with this possibility, we found that snoRNAs can target several known clock genes in the fly, such as Takeout (to).
Hughes Lab
Pulmonary and Critical Care Medicine
Washington University School of Medicine
