<HashMap><database>biostudies-arrayexpress</database><scores/><additional><submitter>Reshma Menon</submitter><organism>Drosophila melanogaster</organism><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/E-MTAB-15735</full_dataset_link><description>Organisms in the wild constantly encounter fluctuations in temperature and food availability, pathogens, and other stressors that disrupt their physiological balance. To counteract these disruptions, organisms initiate stress responses that vary in nature depending on the intensity and duration of the stressor. While severe stress can be harmful or even fatal, moderate stress can activate adaptive mechanisms, a phenomenon known as hormesis. Hormesis enhances resilience to stress and has been associated with improved aging, immunity, and metabolism. Short-term exposures to mild stress, such as heat or oxidative stress, have been shown to extend Drosophila lifespan and promote cross-tolerance to other stressors. Among various environmental stressors, starvation poses a significant and recurring challenge that has driven the evolution of energy-conserving strategies essential for survival. Prior exposure to starvation has been shown to influence longevity, resilience to starvation, physiological status and stress tolerance. However, the mechanisms underlying these hormetic effects remain poorly understood. In this study, we investigate how short-term starvation enhances resistance to prolonged food deprivation in Drosophila. Our findings reveal that metabolic rewiring, including changes in energy utilization, insulin signaling, and transcriptomic profiles underpins this adaptive plasticity. These insights will improve our understanding of the molecular and metabolic mechanisms driving hormesis, with broader implications for stress resilience and organismal health.</description><repository>biostudies-arrayexpress</repository><sample_protocol>Sequencing - The original raw data from the Illumina platform is transformed into sequenced reads by base calling. Raw data are recorded in a FASTQ file containing sequence information (reads) and corresponding sequencing quality information. The quality of the reads was assessed using FastQC v 0.11.9 (1) before proceeding with the downstream analysis. Raw reads were passed through fastp (2) to remove adaptors, low-quality bases (Q&lt;30), and short reads (&lt; 36 bp).</sample_protocol><sample_protocol>Nucleic Acid Extraction - RNA extraction was done using Qiagen miRNeasy Mini kit (Cat. No. 1038703).</sample_protocol><sample_protocol>Library Construction - Drosophila melanogaster (https://ftp.ensembl.org/pub/release-112/fasta/drosophila_melanogaster/dna/) reference genome and annotation were downloaded from NCBI. HISAT2 (4) was used to create the reference index and the trimmed &amp; filtered reads were mapped to the indexed genome using HISAT2. Quantification was performed with HTSeq-count (mode, union) (5).</sample_protocol><sample_protocol>Sample Collection - The samples were collected on day 13 post the training protocol. Day 13 pre-starvation samples are labelled as t0 trained or t0 fed. The samples post 28 hours of starvation are labelled as t28 trained or t28 fed.</sample_protocol><figure_sub>Organization</figure_sub><figure_sub>MINSEQE Score</figure_sub><figure_sub>Assays and Data</figure_sub><figure_sub>Processed Data</figure_sub><figure_sub>MAGE-TAB Files</figure_sub><data_protocol>Data Transformation - A total of 24278 features were assigned in the raw count file. The raw count file is made into raw counts for individual sets (6 sample each) and rest of the differential analysis is done according to the sets. The library sizes are plotted (with and without log) for individual sets. The transcripts with low read counts (&lt; count of at least 10 for a minimum of 3 samples) were removed. The number of filtered counts varied among the sets and have been tabulated (Table 8). The filtered read counts were normalized by transforming to the rlog transformation function (rlog) of DESeq2 (6) and are used for further analysis. Differential gene expression was analysed using the DESeq function from the DESeq2 package with a false discovery rate (FDR) cut-off of &lt; 0.05 and a minimum expression log2-fold change (FC) of ≥ 2/1.</data_protocol><omics_type>Unknown</omics_type><omics_type>Transcriptomics</omics_type><omics_type>Genomics</omics_type><omics_type>Proteomics</omics_type><instrument_platform>Illumina NovaSeq 6000</instrument_platform><study_type>RNA-seq of coding RNA</study_type><species>Drosophila melanogaster</species><pubmed_authors>Jishy Varghese</pubmed_authors><pubmed_authors>Reshma Menon</pubmed_authors></additional><is_claimable>false</is_claimable><name>Moderate nutritional stress reprograms insulin responses to drive enhanced starvation tolerance in Drosophila melanogaster</name><description>Organisms in the wild constantly encounter fluctuations in temperature and food availability, pathogens, and other stressors that disrupt their physiological balance. To counteract these disruptions, organisms initiate stress responses that vary in nature depending on the intensity and duration of the stressor. While severe stress can be harmful or even fatal, moderate stress can activate adaptive mechanisms, a phenomenon known as hormesis. Hormesis enhances resilience to stress and has been associated with improved aging, immunity, and metabolism. Short-term exposures to mild stress, such as heat or oxidative stress, have been shown to extend Drosophila lifespan and promote cross-tolerance to other stressors. Among various environmental stressors, starvation poses a significant and recurring challenge that has driven the evolution of energy-conserving strategies essential for survival. Prior exposure to starvation has been shown to influence longevity, resilience to starvation, physiological status and stress tolerance. However, the mechanisms underlying these hormetic effects remain poorly understood. In this study, we investigate how short-term starvation enhances resistance to prolonged food deprivation in Drosophila. Our findings reveal that metabolic rewiring, including changes in energy utilization, insulin signaling, and transcriptomic profiles underpins this adaptive plasticity. These insights will improve our understanding of the molecular and metabolic mechanisms driving hormesis, with broader implications for stress resilience and organismal health.</description><dates><release>2026-01-14T00:00:00Z</release><modification>2026-01-14T12:32:39.814Z</modification><creation>2025-10-15T14:31:18.486Z</creation></dates><accession>E-MTAB-15735</accession><cross_references><ENA>ERP182240</ENA><EFO>EFO_0002944</EFO><EFO>EFO_0004170</EFO><EFO>EFO_0005518</EFO><EFO>EFO_0003816</EFO><EFO>EFO_0003738</EFO><EFO>EFO_0004184</EFO></cross_references></HashMap>