Project description:In the fall, Eastern North American monarch butterflies (Danaus plexippus) undergo a magnificent long-range migration. In contrast to spring and summer butterflies, fall migrants are juvenile hormone deficient, which leads to reproductive arrest and increased longevity. Migrants also use a time-compensated sun compass to help them navigate in the south/southwesterly direction en route for Mexico. Central issues in this area are defining the relationship between juvenile hormone status and oriented flight, critical features that differentiate summer monarchs from fall migrants, and identifying molecular correlates of behavioral state. Here we show that increasing juvenile hormone activity to induce summer-like reproductive development in fall migrants does not alter directional flight behavior or its time-compensated orientation, as monitored in a flight simulator. Reproductive summer butterflies, in contrast, uniformly fail to exhibit directional, oriented flight. To define molecular correlates of behavioral state, we used microarray analysis of 9417 unique cDNA sequences. Gene expression profiles reveal a suite of 40 genes whose differential expression in brain correlates with oriented flight behavior in individual migrants, independent of juvenile hormone activity, thereby separating molecularly fall migrants from summer butterflies. Intriguing genes that are differentially regulated include the clock gene vrille and the locomotion-relevant tyramine beta hydroxylase gene. In addition, several differentially regulated genes (37.5% of total) are not annotated, suggesting unique functions associated with oriented flight behavior. We also identified 23 juvenile hormone-dependent genes in brain, which separate reproductive from non-reproductive monarchs; genes involved in longevity, fatty acid metabolism, and innate immunity are upregulated in non-reproductive (juvenile-hormone deficient) migrants. The results link key behavioral traits with gene expression profiles in brain that differentiate migratory from summer butterflies and thus show that seasonal changes in genomic function help define the migratory state.
Project description:In the fall, Eastern North American monarch butterflies (Danaus plexippus) undergo a magnificent long-range migration. In contrast to spring and summer butterflies, fall migrants are juvenile hormone deficient, which leads to reproductive arrest and increased longevity. Migrants also use a time-compensated sun compass to help them navigate in the south/southwesterly direction en route for Mexico. Central issues in this area are defining the relationship between juvenile hormone status and oriented flight, critical features that differentiate summer monarchs from fall migrants, and identifying molecular correlates of behavioral state. Here we show that increasing juvenile hormone activity to induce summer-like reproductive development in fall migrants does not alter directional flight behavior or its time-compensated orientation, as monitored in a flight simulator. Reproductive summer butterflies, in contrast, uniformly fail to exhibit directional, oriented flight. To define molecular correlates of behavioral state, we used microarray analysis of 9417 unique cDNA sequences. Gene expression profiles reveal a suite of 40 genes whose differential expression in brain correlates with oriented flight behavior in individual migrants, independent of juvenile hormone activity, thereby separating molecularly fall migrants from summer butterflies. Intriguing genes that are differentially regulated include the clock gene vrille and the locomotion-relevant tyramine beta hydroxylase gene. In addition, several differentially regulated genes (37.5% of total) are not annotated, suggesting unique functions associated with oriented flight behavior. We also identified 23 juvenile hormone-dependent genes in brain, which separate reproductive from non-reproductive monarchs; genes involved in longevity, fatty acid metabolism, and innate immunity are upregulated in non-reproductive (juvenile-hormone deficient) migrants. The results link key behavioral traits with gene expression profiles in brain that differentiate migratory from summer butterflies and thus show that seasonal changes in genomic function help define the migratory state. A total of 40 monarch butterflies were used for the microarray analysis. Of the 40, 10 (5 male/5 female) were summer butterflies (Designated as S) and 30 were fall butterflies. The fall butterflies were further divided into three groups: 10 (5 male/5 female) were untreated (F); 10 (5 male/5 female) were treated with methoprene (M), which is a juvenile hormone analog and induces the development of reproductive organs in migrant butterflies; and 10 (5 male/5 female) were treated with vehicle only (V). We collected total brain RNA from each of the 40 butterflies. The brain RNAs were amplified and then used to probe a custom Nimblegen array that was designed to analyze the 9,417 unique cDNA sequences established in our published EST library (http://titan.biotec.uiuc.edu/cgi-bin/ESTWebsite/estima_start?seqSet=butterfly). Our main interest is to find genes involved in migration. This includes genes regulating oriented flight behavior of the butterfly and genes that regulate reproductive status. To identify these genes, we approached the microarray data in two ways. First, we identified the potential genes involved in oriented flight behavior using the following strategy. We compared the summer group to each of the three fall groups (untreated, methoprene-treated, and vehicle-treated) for males and for females, and looked for gene regulation patterns common among the three comparisons for each sex. Because the comparisons were done separately for males and females, and our behavioral data did not show significant sex differences in flight orientation, we focused on the common differentially regulated genes that were shared between males and females. Accordingly, we identified 40 cDNAs that were differentially regulated between summer butterflies and fall migrants, irrespective of sex. Second, we looked for the juvenile hormone-response genes. Again, we performed sex-specific statistical analyses, and compared the summer and the fall groups, and the methoprene-treated and vehicle-treated migrants. We then screened for shared genes between the two groups for each sex. We next examined cDNAs that were differentially regulated in both males and females, to determine juvenile hormone-regulated genes involved in more global processes (e.g., longevity and fatty acid metabolism) that would not be expected to be sex-specific. We identified 23 putative juvenile hormone-response genes that were common between males and females.
Project description:This study evaluated the ammonium oxidizing communities (COA) associated with a potato crop (Solanum phureja) rhizosphere soil in the savannah of Bogotá (Colombia) by examining the presence and abundance of amoA enzyme genes and transcripts by qPCR and next-generation sequence analysis. amoA gene abundance could not be quantified by qPCR due to problems inherent in the primers; however, the melting curve analysis detected increased fluorescence for Bacterial communities but not for Archaeal communities. Transcriptome analysis by next-generation sequencing revealed that the majority of reads mapped to ammonium-oxidizing Archaea, suggesting that this activity is primarily governed by the microbial group of the Crenarchaeota phylum. In contrast,a lower number of reads mapped to ammonia-oxidizing bacteria.
Project description:The goals of this study are to use Next-generation sequencing (NGS)to detect bacterial mRNA profiles of original E. coli K-12 MG1655 and fluoxetine induced E. coli mutants in response to 100 mg/L fluoxetine for 8 h, in triplicate, using Illumina HiSeq 2500.The NGS QC toolkit (version 2.3.3) was used to treat the raw sequence reads to trim the 3’-end residual adaptors and primers, and the ambiguous characters in the reads were removed. Then, the sequence reads consisting of at least 85% bases were progressively trimmed at the 3’-ends until a quality value ≥ 20 were kept. Downstream analyses were performed using the generated clean reads of no shorter than 75 bp. The clean reads of each sample were aligned to the E. coli reference genome (NC_000913) using SeqAlto (version 0.5). Cufflinks (version 2.2.1) was used to calculate the strand-specific coverage for each gene, and to analyze the differential expression in triplicate bacterial cell cultures. The statistical analyses and visualization were conducted using CummeRbund package in R (http://compbio.mit.edu/cummeRbund/). Gene expression was calculated as fragments per kilobase of a gene per million mapped reads (FPKM, a normalized value generated from the frequency of detection and the length of a given gene.
Project description:The goals of this study are to use Next-generation sequencing (NGS)to detect bacterial mRNA profiles of E.coli DH5a in response to 0, 20ug/L and 2 mg/L triclosan for 2 h, in triplicate, using Illumina HiSeq 2500.The NGS QC toolkit (version 2.3.3) was used to treat the raw sequence reads to trim the 3’-end residual adaptors and primers, and the ambiguous characters in the reads were removed. Then, the sequence reads consisting of at least 85% bases were progressively trimmed at the 3’-ends until a quality value ≥ 20 were kept. Downstream analyses were performed using the generated clean reads of no shorter than 75 bp. The clean reads of each sample were aligned to the E. coli reference genome (NC_000913) using SeqAlto (version 0.5). Cufflinks (version 2.2.1) was used to calculate the strand-specific coverage for each gene, and to analyze the differential expression in triplicate bacterial cell cultures. The statistical analyses and visualization were conducted using CummeRbund package in R (http://compbio.mit.edu/cummeRbund/). Gene expression was calculated as fragments per kilobase of a gene per million mapped reads (FPKM, a normalized value generated from the frequency of detection and the length of a given gene.
Project description:The goals of this study are to use Next-generation sequencing (NGS)to detect bacterial mRNA profiles of Pseudomonas aeruginosa PAO1 in response to 0, 1, 20 and 25 mg/L AgNPs or 0, 1,30 and 300 mg/L AgNRs for 2 h, using Illumina HiSeq 2500.The NGS QC toolkit (version 2.3.3) was used to treat the raw sequence reads to trim the 3’-end residual adaptors and primers, and the ambiguous characters in the reads were removed. Then, the sequence reads consisting of at least 85% bases were progressively trimmed at the 3’-ends until a quality value ≥ 20 were kept. Downstream analyses were performed using the generated clean reads of no shorter than 75 bp. The clean reads of each sample were aligned to the E. coli reference genome (NC_000913) using SeqAlto (version 0.5). Cufflinks (version 2.2.1) was used to calculate the strand-specific coverage for each gene, and to analyze the differential expression in triplicate bacterial cell cultures. The statistical analyses and visualization were conducted using CummeRbund package in R (http://compbio.mit.edu/cummeRbund/). Gene expression was calculated as fragments per kilobase of a gene per million mapped reads (FPKM, a normalized value generated from the frequency of detection and the length of a given gene.
Project description:The goals of this study are to use Next-generation sequencing (NGS) to detect bacterial mRNA profiles of wild-type E. coli K-12 MG1655 and triclosan induced E. coli mutants in response to 0.2 mg/L triclosan for 8 h, in triplicate, using Illumina HiSeq 2500.The NGS QC toolkit (version 2.3.3) was used to treat the raw sequence reads to trim the 3’-end residual adaptors and primers, and the ambiguous characters in the reads were removed. Then, the sequence reads consisting of at least 85% bases were progressively trimmed at the 3’-ends until a quality value ≥ 20 were kept. Downstream analyses were performed using the generated clean reads of no shorter than 75 bp. The clean reads of each sample were aligned to the E. coli reference genome (NC_000913) using SeqAlto (version 0.5). Cufflinks (version 2.2.1) was used to calculate the strand-specific coverage for each gene, and to analyze the differential expression in triplicate bacterial cell cultures. The statistical analyses and visualization were conducted using CummeRbund package in R (http://compbio.mit.edu/cummeRbund/). Gene expression was calculated as fragments per kilobase of a gene per million mapped reads (FPKM, a normalized value generated from the frequency of detection and the length of a given gene.