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ABSTRACT: transcriptomics of lead-contaminated leeches

PROVIDER: PRJNA739220 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR14873371_1.fastq.gz	Fastqsanger.gz
	SRR14873371_2.fastq.gz	Fastqsanger.gz
	SRR14873372_1.fastq.gz	Fastqsanger.gz
	SRR14873372_2.fastq.gz	Fastqsanger.gz
	SRR14873373_1.fastq.gz	Fastqsanger.gz

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Project description:75 Diosophilia Roo lines, recominant inbred lines, were raised to adulthood either on conventional diet or diet supplemented with lead acetate. Whole flies were used for RNA extraction. RNA was run on Dros Genome 2 arrays. Each line also genotyped for 88 markers; Control food consisted of standard cornmeal, agar, sugar, yeast, and 250 microM NaAc (Ashburner 1989). Lead-contaminated food consisted of standard food plus 250 microM PbAc (lead exposure at this concentration has been shown to affect locomotion in adults (Hirsch et al. 2003)). Experiment Overall Design: 75 RI lines each line treated and untreated with lead acetate

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Project description:Spatial regulation analysis across multiple condition comparisons revealed distinct patterns of gene expression. We combined these transcriptome data with spatial CNS data to produce the spatio-transcripto map of the ganglia chain. The Hirudo Medicinalis set of transcripts generated here provides a resource for gene discovery and gene regulation within the nervous system. In addition, the strategy for de novo assembly of transcriptome data presented here may be helpful in other similar transcriptome studies. Examination of 3 different ganglia in 3 different leeches.

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Project description:Metagenomic diversity of soil co-contaminated with lead and amended with microbes and root exudates

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Project description:Metagenomic diversity of soil co-contaminated with lead and amended with microbes and root exudates

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Early biomarkers of renal damage due to prolonged exposure to embedded metal.

Project description:Background: Prolonged exposure to toxic heavy metals leads to deleterious health outcomes such as kidney injury and irreversible progression to chronic kidney disease. For example, veterans return from the battlefield with increasing amounts of retained metal fragments. Certain community water sources in the US are contaminated with varying levels of heavy metals, including uranium and lead. One of the key challenges is to detect damage to kidney tissue before glomerular filtration rate is affected. Methods: High-throughput transcriptomics (HTT) has recently been demonstrated have high sensitivity and specificity as a rapid and cost-effective assay for detecting tissue toxicity. To better understand the molecular signature of early kidney damage, we performed RNA-seq analysis on renal tissue using a rat model of soft tissue-embedded metal exposure. We them performed small RNA-seq analysis on serum samples from the same animals in an effort to identify miRNA biomarkers of kidney damage. Results: We found that metals, especially lead and depleted uranium, induces oxidative damage that mainly cause dysregulated mitochondrial gene expression. Utilizing publicly available single-cell RNA-seq datasets, we demonstrate that deep learning-based cell type decomposition effectively identified cells within the kidney that were affected by metal exposure. By combining random forest feature selection and statistical methods, we further identify miRNA-423 as a promising early systemic marker of kidney injury. Conclusion: Our data suggests that combining HTT and deep learning represents a promising approach for identifying cell injury in kidney tissue. We propose miRNA-423 as a potential serum biomarker for early detection of kidney injury.

2022-12-01 | GSE203624 | GEO

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