Public Health Disparities and Genomics
Millions of people all over the world are exposed to arsenic and other metals in drinking water or foods. My graduate research combined toxicity assays with high throughput sequencing to study the effect of naturally occurring metals on pathways associated with metabolic dysfunction.
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Metabolism (or the breakdown of a substance) has long been thought of as the body’s way of keeping us safe from the buildup of harmful chemicals… but could arsenic metabolism actually make things worse?
Using a cohort from Mexico, I was able measure circulating (plasma) arsenic levels in individuals exposed to heavy metal contamination. I showed that exposure to arsenic is associated with an altered profile of circulating microRNAs (short non-coding genetic material), several of which have ties to diabetes.
These microRNAs could potentially be used to diagnose diabetes or other metabolic diseases in individuals exposed to arsenic before any symptoms are present!
Read about this here.
Further studies were completed utilizing this cohort to determine that urine and plasma arsenicals reflect different aspects of iAs toxicokinetics, including metabolism and excretion.
Read about this study here.
Arsenic, cadmium, and manganese all have similar effects in dampening insulin secretion; However, arsenic exposure caused a greater change in the microRNA expression than cadmium or manganese.
This means that while the physical effects might look the same when you ingest various heavy metals, they may work through different actions.
Read about this study here.
Utilizing genomic technologies to help us answer questions.
Quantifying small non-coding RNAs
miRquant pipeline to align and annotate miRNA reads
Tools used in this workflow: Cutadapt trimming; Bowtie alignment; SHRiMP alignment; Bedtools
Discoveries using this pipeline:
As exposure is associated with an altered profile of circulating miRNAs
6 miRNAs were identified as associated with a methylated metabolite of iAs, MAs
Several miRNAs have been previously reported to be linked to a risk of diabetes
Findings are in non-diabetic individuals making these miRNAs potential early biomarkers of diabetes risk
Quantification & Differential Expression of genes
Tools used in this workflow: STAR/Salmon alignment and quantification followed by DESeq2 for differential expression analysis
Discoveries using this pipeline:
iAs altered upregulated genes are enriched in NFKB signaling
NFkB is a master regulator of inflammatory processes; It is implicated in insulin resistance and pancreatic β cell dysfunction
Genes down-regulated by iAs are involved in beta cell function, including Camk2a, a predicted target of miR-146a
Both miR-146a and Camk2a have been associated with insulin secretion, and therefore merit further functional investigation
Mapping the transcriptional regulatory landscape of arsenic-exposed beta cells
Tools used in this workflow: Proseq 2.0: ProseqMapper, Proseq 2.0: Merge bigWig files from mapping to call transcriptional regulatory elements (TREs), Detection of Regulatory DNA using Pro-seq, GRO-seq (dREG) Data to call a universal set of TREs, Transcription factor motif enrichment analysis using: 1) RNA Transcription Factor Binding Site Data Base (RTFBS_DB) and 2) Hypergeometric Optimization of Motif EnRichment (HOMER)
Discoveries using this pipeline:
1 µM iAs, 0.5 µM MAs both significantly inhibit insulin secretion
Each exposure affects a unique set of TREs
Unique transcription factor binding motifs have been identified for iAs and MAs exposed cells along with the transcription factors predicted to bind at these sites