Dataset Information

Monitoring Fe-S cluster occupancy across the E. coli proteome using chemoproteomics

ABSTRACT: Iron-sulfur (Fe-S) clusters are ubiquitous metallocofactors involved in redox chemistry, radical generation, and gene regulation. Common methods to monitor Fe-S clusters include spectroscopic analysis of purified proteins, and auto-radiographic visualization of radiolabeled iron distribution in proteomes. Here, we report a chemoproteomic strategy that monitors changes in the reactivity of Fe-S cysteine ligands to inform on Fe-S cluster occupancy. We highlight the utility of this platform in E. coli by: (1) demonstrating global disruptions in Fe-S incorporation in cells cultured under iron-depleted conditions; (2) determining Fe-S client proteins reliant on three scaffold/carrier proteins within the Isc Fe-S biogenesis pathway; and, (3) identifying two previously unannotated Fe-S proteins, TrhP and DppD. In summary, the chemoproteomic strategy described herein is a powerful tool that reports on Fe-S cluster incorporation directly within a native proteome, and enables the interrogation of Fe-S biogenesis pathways, and the identification of previously uncharacterized Fe-S proteins.

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Escherichia Coli

SUBMITTER: Daniel Bak

LAB HEAD: Eranthie Weerapana

PROVIDER: PXD026488 | Pride | 2024-05-22

REPOSITORIES: Pride

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Dataset's files

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			Action	DRS
	DTASelect-filter_TABLE_S2_TAB_All_Peptides_COLUMN_run_1_heavy.txt	Txt
	DTASelect-filter_TABLE_S2_TAB_All_Peptides_COLUMN_run_1_light.txt	Txt
	DTASelect-filter_TABLE_S2_TAB_All_Peptides_COLUMN_run_2_heavy.txt	Txt
	DTASelect-filter_TABLE_S2_TAB_All_Peptides_COLUMN_run_2_light.txt	Txt
	DTASelect-filter_TABLE_S2_TAB_All_Peptides_COLUMN_run_3_heavy.txt	Txt

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Publications

Monitoring Fe-S cluster occupancy across the E. coli proteome using chemoproteomics.

Bak Daniel W DW Weerapana Eranthie E

Nature chemical biology 20230112 3

Iron-sulfur (Fe-S) clusters are ubiquitous metallocofactors involved in redox chemistry, radical generation and gene regulation. Common methods to monitor Fe-S clusters include spectroscopic analysis of purified proteins and autoradiographic visualization of radiolabeled iron distribution in proteomes. Here, we report a chemoproteomic strategy that monitors changes in the reactivity of Fe-S cysteine ligands to inform on Fe-S cluster occupancy. We highlight the utility of this platform in Escheri ...[more]

PMID: 36635565

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Project description:Genes that are constitutively expressed across multiple environmental stimuli are crucial to quantifying differentially expressed genes, particularly when employing quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) assays. However, the identification of these potential reference genes in non-model organisms is challenging and is often guided by expression patterns in distantly related organisms. Here, transcriptome datasets from the diatom Thalassiosira pseudonana grown under replete, phosphorus-limited, iron-limited, and phosphorus and iron co-limited nutrient regimes were analyzed through literature-based searches for homologous reference genes, k-means clustering, and Analysis of Sequence Counts (ASC) to identify putative reference genes. A total of 9759 genes were identified and screened for stable expression. Literature-based searches surveyed 18 generally accepted reference genes, revealing 101 homologs in T. pseudonana with variable expression and a wide range of mean tags per million. K-means analysis parsed the whole transcriptome into 15 clusters. The two most stable clusters contained 709 genes but still had distinct patterns in expression. ASC analyses identified 179 genes that were stably expressed (posterior probability < 0.1 for 1.25 fold change). Genes known to have a stable expression pattern across the test treatments, like actin, were identified in this pool of 179 candidate genes. ASC can be employed on data without biological replicates and was more robust than the k-means approach in isolating genes with stable expression. The intersection of the genes identified through ASC with commonly used reference genes from the literature suggests that actin and ubiquitin ligase may be useful reference genes for T. pseudonana and potentially other diatoms. With the wealth of transcriptome sequence data becoming available, ASC can be easily applied to transcriptome datasets from other phytoplankton to identify reference genes. Axenic T. pseudonana CCMP 1335 was grown at 14Â°C under 24 hour light (120 Âµmol photons m-2 s-1) after Dyhrman et al. (2012) in f/2 plus silica chelated media made from surface Sargasso Sea water. Nitrate, silica, vitamins, and trace metals were at f/2 concentrations (Guillard and Ryther 1962), while iron and phosphate were modified across treatments. In brief, triplicate cultures of replete (36 ÂµM PO4, 400 nM Fe), P-limited (0.4 ÂµM PO4, 400 nM Fe), Fe-limited (36 ÂµM PO4, 40 nM Fe), and Co-limited (0.4 ÂµM PO4, 40 nM Fe) treatments were harvested when growth deviated from the replete control. Growth was monitored by cell counts. Biomass was harvested onto 0.2 Âµm filters and flash frozen in liquid nitrogen and total RNA was extracted as described in Dyhrman et al. (2012). Tag-seq sequencing of the transcriptome was performed by Illumina with a polyA selection and NlaIII digestion, resulting in 21 bp sequence reads or tags (Dyhrman et al., 2012). Libraries were of varied sizes as follows: replete (~12 million), P-limited (~13 million), Fe-limited (~23 million), and Co-limited (~26 million). Tags were mapped to gene models (predicted protein coding regions) with a pipeline designed by Genesifter Inc., requiring 100% identity and covering 9759 genes. Tag counts within a gene were pooled and normalized to the size of the library, with resulting data expressed in tags per million (tpm). Genes with normalized tag counts less than 2.5 tpm for each of the four treatments were excluded (Figure S1) , leaving 7380 genes in the analysis.