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| Status |
Public on Nov 30, 2022 |
| Title |
N2 krill day 1, replicate 2 |
| Sample type |
SRA |
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| Source name |
whole worm
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| Organism |
Caenorhabditis elegans |
| Characteristics |
tissue: whole worm
strain: N2
genotype: Wildtype
treatment: Krill
time: day 1 adult
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| Treatment protocol |
Animals were cultured on nematode growth medium (NGM) plates with the Escherichia coli strain OP50 at 20°C using standard procedure (Brenner, 1974). The adult animals were bleached to obtain synchronized populations. For all aging associated studies, L4 hermaphrodites were grown in NGM plates containing 0,5µl/ml krill oil (provided by Aker BioMarine) from day 1 to day 6. The aging population of worms were maintained in NGM plates without FdUrd. Instead, animals were washed with M9 buffer and filtered through Nylon Net Filter (catalog #NY4104700) everyday post adult day 2 stage, till the desired age. The adult Day 1 was defined as 24 hours post L4 stage. The following nematode strains were used in this study: N2: wild type; BY273 Is[pdat-1GFP; pdat-1 a-syn] to monitor dopaminergic neurons.
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| Extracted molecule |
total RNA |
| Extraction protocol |
Total RNA was isolated with RNeasy mini kit (Qiagen) following the manufacturer’s instructions. Reverse transcription was performed using High-Capacity cDNA Reverse Transcription kit (Applied Biosystems). Quantitative PCR was carried out on a QuantStudio 7 Flex detection system (Applied Biosystems) with the Power SYBR green PCR master mix (Applied Biosystems). Each sample was analysed in triplicate. Primer sequences are provided in Supplementary Table. 2.
Sequencing libraries were prepared from 200 ng total RNA using Nugen Universal Plus Total RNA-Seq library preparation kit (Tecan) with custom AnyDeplete design for C. elegans. Final libraries were pooled (10 samples/run) and paired-end sequencing (2 x 71 bp) performed using NextSeq High Output kits on the NextSeq 550 sequencer. The raw sequencing data were demultiplexed using BCL Convert (Illumina). Sequencing data preprocess starts right after being demultiplexed. Sequences were aligned to C.elegans WBcel235 dna reference (release-96)26 using annotations, general transfer format (.gtf) file from the same release, and with STAR aligner (version 2.7.10a)27.
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
NextSeq 2000 |
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| Data processing |
Demultiplexed using BCL Convert (Illumina).Sequencing data preprocess starts right after being demultiplexed. Sequences were aligned to C.elegans WBcel235 dna reference (release-96)26 using annotations, general transfer format (.gtf) file from the same release, and with STAR aligner (version 2.7.10a)27. First, an index was created using parameters --runMode genomeGenerate, --runThreadN 8 --genomeSAindexNbases 12, --sjdOverhang 69, and for any other parameter the default, in the specified version, was used. Second, reads were aligned using parameters --runThreadN 8, --outSAMtype BAM SortedByCoordinate --sjdbOverhang 69 and the ones suggested by STAR manual in ENCODE options section --outFilterType BySJout, --outFilterMultimapNmax 20, --alignSJoverhangMin 8, --alignSJDBoverhangMin 1, --outFilterMismatchNmax 999, --outFilterMismatchNoverReadLmax 0.04, --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000. From the BAM file obtained we created an index using samtools version 1.15.128 so we could remove UMI-duplicates using umi-tools version 1.1.229 , with parameters --log2stderr, --paired, --umi-separator=”:”. We then sorted the deduplicated BAM file by name and created new fastq files with the same parameters -@ 4, -n.
New fastq files were processed with fastp (version 0.23.2) with parameters -g -x -q 30 -e 30 -w 8. Then, reads were aligned with STAR aligner and parameters --quantMode GeneCounts --runThreadN 8, --outSAMtype BAM Unsorted --sjdbOverhang 69 and parameters suggested by STAR manual in ENCODE options to obtain the raw counts.
Differential gene expression was performed using the DESeq2 R package 30, version 1.32.0. Count files were split into their respective groups and attributed defining conditions, such as ‘control’ and ‘experiment’. The counts for the respective conditions are merged into one dataframe, from this dataframe, genes which have no counts for every sample are removed. A sample defining file containing the sample names along with their associated condition is also created, this file contains the relevant information for the samples within the merged count file. The values are then normalized using the DESeq2 normalization method. The standard DESeq2 pipeline was used to obtain the differential gene expression results. It starts by creating the DESeqDataSet-class by calling the DESeqDataSetFromMatrix function, using the merged count file, and the sample defining file are used as inputs, while the condition is used as the design. The design always uses the ‘experiment’ and compares it with the ‘control’. Using the estimateSizeFactors function, the median ratio method is utilized to estimate the size factors of the DESeqDataSet object. The DESeq2 function is called, which performs an estimate of dispersion followed by a negative binomial generalized linear model fitting and wal statistics in order to obtain the differential gene expression results. Selection of significant differentially expressed genes was made based on an adjusted p value (false discovery rate) below 0.05 and a log 2-fold change value greater than 1 or smaller than -1.
The timeseries analysis was performed using a package develop in our lab (Lefol et al., manuscript in preparation). The package takes in raw RNAseq files and creates a matrix of raw counts, these counts are then normalized using the DESeq2 method. (REF DESeq2 processing and normalization seen above) It then performs differential gene expression analyses for both the conditional and temporal elements of the timeseries analysis. Where conditional elements are the comparison of the experiment versus the control at every time point. Temporal elements are the comparison of subsequent timepoints, where the later time point is used as the experiment and the earlier time point is used as the control. The significant genes from all differential gene experiments are pooled together and clustered using the clusterGenomics (Identifying clusters in genomics data by recursive partitioning package, version 1.0.). A functional enrichment of each cluster is obtained using gprofiler (g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)).
The WGCNA package 31, version 1.70-3 was used to obtain a weighted correlation association between gene modules and behavioral parameters. WGCNA takes in RNAseq count data. A sample file was manually prepared for the WGCNA package, the sample file details which behavioral values are associated with the samples inputted. The pipeline first removes any samples with too many missing values in proportion with the amount of samples and values inputted. The samples are then clustered and their power is evaluated. Based on the indications of 31, the power value used is the first value above the 0.8 threshold which was a value of 4. The blockwise Modules function is used to calculate the gene modules and obtains the correlation values with the clinical parameters. Each gene module is run through gprofiler 32 in order to extract potential biological relevance of each gene module.
Biological age predictions were performed using the fastq files as raw data as input in the preprocessing specified in the Bit age pipeline33. First, the data were processed with fastp version 0.23.234 with parameters -g -x -q 30 -e 30 -w 8. And second, reads were aligned with STAR aligner and parameters --quantMode GeneCounts --runThreadN 8, --outSAMtype BAM Unsorted --sjdbOverhang 69 and again the ones suggested by STAR manual in ENCODE options, as above. For predicting biological age we took raw counts from the STAR aligner output and computed count per million(CPM), as required by bit age method, with edgeR library version 3.38.135. We then computed predicted biological age using elastic net coefficients and code provided in the bit age material33.
Assembly: C.elegans WBcel235 dna reference (release-96)
Supplementary files format and content: tab-delimited text files containing raw counts for each sample.
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| Submission date |
Jun 29, 2022 |
| Last update date |
Dec 01, 2022 |
| Contact name |
Hilde Loge Nilsen |
| E-mail(s) |
h.l.nilsen@medisin.uio.no
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| Organization name |
Akershus University Hospital
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| Street address |
Sykehusveien 25
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| City |
Lørenskog |
| ZIP/Postal code |
1148 |
| Country |
Norway |
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| Platform ID |
GPL32326 |
| Series (1) |
| GSE207152 |
Krill oil protects dopaminergic neurons from age-related degeneration through temporal transcriptome rewiring and suppression of several hallmarks of aging |
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| Relations |
| BioSample |
SAMN29416660 |
| SRA |
SRX15938538 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM6280615_N2_Krill_day1_2.counts.tab.gz |
180.1 Kb |
(ftp)(http) |
TAB |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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