Fanconi anemia-independent DNA inter-strand crosslink repair in eukaryotes

Rogers, Cody M.; Simmons, Robert H.; Thornburg, Gabriella E. Fluhler; Buehler, Nicholas J.; Bochman, Matthew L.

doi:10.1016/j.pbiomolbio.2020.08.005

Cited by 19 publications

(9 citation statements)

References 138 publications

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“…To date, we have found that Pif1 is acetylated on one or more lysine residues, and the acetylation status of Pif1 affects toxicity upon over-expression as judged by cell growth with and without helicase over-expression and in wild-type, acetyltransferase mutant, and deacetylase mutant genetic backgrounds. [14,15] The above work entailed the comparison of growth among dozens of different strains and culturing conditions, and we sought to analyze and present the data in a quantitative manner. We were inspired by the work of Andis et al but wanted to avoid the need to perform the mathematically complex linear discriminant analyses [9] to rapidly F I G U R E 2 Problems with growth curves.…”

Section: Our Methodsmentioning

confidence: 99%

“…To date, we have found that Pif1 is acetylated on one or more lysine residues, and the acetylation status of Pif1 affects toxicity upon over‐expression as judged by cell growth with and without helicase over‐expression and in wild‐type, acetyltransferase mutant, and deacetylase mutant genetic backgrounds. [ 14,15 ]…”

Section: Our Methodsmentioning

confidence: 99%

“…Thus, we simply measured the OD 660 every 15 min for 48 h for each culture and averaged the OD 660 values across the entire growth curve to generate a single value representing the growth of each culture. [ 15 ] Because we were interested in the effects of Pif1 over‐expression, we normalized to data from a strain of the same genetic background but carrying an empty vector. Thus, the read out in the end was independent of the growth kinetics of individual wild‐type or mutant background, as well as changes in culturing parameters such as incubation temperature, and we could evaluate only the effect of helicase over‐expression.…”

Section: Our Methodsmentioning

confidence: 99%

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Overcoming stochastic variations in culture variables to quantify and compare growth curve data

Sausen

Bochman

2021

BioEssays

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The comparison of growth, whether it is between different strains or under different growth conditions, is a classic microbiological technique that can provide genetic, epigenetic, cell biological, and chemical biological information depending on how the assay is used. When employing solid growth media, this technique is limited by being largely qualitative and low throughput. Collecting data in the form of growth curves, especially automated data collection in multi-well plates, circumvents these issues. However, the growth curves themselves are subject to stochastic variation in several variables, most notably the length of the lag phase, the doubling rate, and the maximum expansion of the culture. Thus, growth curves are indicative of trends but cannot always be conveniently averaged and statistically compared. Here, we summarize a simple method to compile growth curve data into a quantitative format that is amenable to statistical comparisons and easy to graph and display. K E Y W O R D Sgrowth curve, microbe, Saccharomyces cerevisiae (e.g., the calculation of mutant fitness in synthetic genetic array [SGA]) analyses [8] ), but it is cumbersome, time consuming, and difficult to perform with spot dilution assays relative to SGA analyses.

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Section: Our Methodsmentioning

confidence: 99%

Section: Our Methodsmentioning

confidence: 99%

Section: Our Methodsmentioning

confidence: 99%

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Overcoming stochastic variations in culture variables to quantify and compare growth curve data

Sausen

Bochman

2021

BioEssays

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“…FA is caused by germline mutations in any of 22 genes (FANCA, FANCB, FANCC, FANCD1/BRCA2, FANCD2, FANCE, FANCF, FANCG, FANCI, FANCJ/BRIP1, FANCL, FANCM, FANCN/PALB2, FANCO/RAD51C, FANCP/SLX4, FANCQ/ERCC4/XPF, FANCR/RAD51, FANCS/BRCA1, FANCT/UBE2T, FANCU/XRCC2, FANCV/REV7, and FANCW/RFWD3) [59]. These FA gene products disrupt a cellular pathway (FA pathway) that is mainly involved in DNA repair mechanisms and is responsible for the resolution of DNA interstrand cross-links (ICLs) [60][61][62]. ICLs are a type of DNA damage and are primarily caused by cytotoxic chemotherapeutic agents.…”

Section: Pathogenesis Of Famentioning

confidence: 99%

Cell and Gene Therapy for Anemia: Hematopoietic Stem Cells and Gene Editing

Anurogo

Budi

Ngo

et al. 2021

IJMS

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Hereditary anemia has various manifestations, such as sickle cell disease (SCD), Fanconi anemia, glucose-6-phosphate dehydrogenase deficiency (G6PDD), and thalassemia. The available management strategies for these disorders are still unsatisfactory and do not eliminate the main causes. As genetic aberrations are the main causes of all forms of hereditary anemia, the optimal approach involves repairing the defective gene, possibly through the transplantation of normal hematopoietic stem cells (HSCs) from a normal matching donor or through gene therapy approaches (either in vivo or ex vivo) to correct the patient’s HSCs. To clearly illustrate the importance of cell and gene therapy in hereditary anemia, this paper provides a review of the genetic aberration, epidemiology, clinical features, current management, and cell and gene therapy endeavors related to SCD, thalassemia, Fanconi anemia, and G6PDD. Moreover, we expound the future research direction of HSC derivation from induced pluripotent stem cells (iPSCs), strategies to edit HSCs, gene therapy risk mitigation, and their clinical perspectives. In conclusion, gene-corrected hematopoietic stem cell transplantation has promising outcomes for SCD, Fanconi anemia, and thalassemia, and it may overcome the limitation of the source of allogenic bone marrow transplantation.

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“…Similarly, RECQL4 is difficult to study in vitro because the protein is large (∼135 kD) with a natively disordered N-terminus ( Keller et al 2014 ), making the generation of recombinant protein for biochemistry arduous ( Macris et al 2006 ; Bochman et al 2014 ). Thus, although RECQL4 is reported to be involved in telomere maintenance ( Ghosh et al 2011 ) and DNA inter-strand crosslink (ICL) repair ( Jin et al 2008 ; Rogers et al 2020b ), its mechanism of action in these pathways is unknown.…”

mentioning

confidence: 99%

The Genetic and Physical Interactomes of theSaccharomyces cerevisiaeHrq1 Helicase

Rogers

Sanders

Nguyen

et al. 2020

G3 Genes|Genomes|Genetics

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The human genome encodes five RecQ helicases (RECQL1, BLM, WRN, RECQL4, and RECQL5) that participate in various processes underpinning genomic stability. Of these enzymes, the disease-associated RECQL4 is comparatively understudied due to a variety of technical challenges. However, Saccharomyces cerevisiae encodes a functional homolog of RECQL4 called Hrq1, which is more amenable to experimentation and has recently been shown to be involved in DNA inter-strand crosslink (ICL) repair and telomere maintenance. To expand our understanding of Hrq1 and the RecQ4 subfamily of helicases in general, we took a multi-omics approach to define the Hrq1 interactome in yeast. Using synthetic genetic array analysis, we found that mutations of genes involved in processes such as DNA repair, chromosome segregation, and transcription synthetically interact with deletion of HRQ1 and the catalytically inactive hrq1-K318A allele. Pull-down of tagged Hrq1 and mass spectrometry identification of interacting partners similarly underscored links to these processes and others. Focusing on transcription, we found that hrq1 mutant cells are sensitive to caffeine and that mutation of HRQ1 alters the expression levels of hundreds of genes. In the case of hrq1-K318A, several of the most highly upregulated genes encode proteins of unknown function whose expression levels are also increased by DNA ICL damage. Together, our results suggest a heretofore unrecognized role for Hrq1 in transcription, as well as novel members of the Hrq1 ICL repair pathway. These data expand our understanding of RecQ4 subfamily helicase biology and help to explain why mutations in human RECQL4 cause diseases of genomic instability.

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Fanconi anemia-independent DNA inter-strand crosslink repair in eukaryotes

Cited by 19 publications

References 138 publications

Overcoming stochastic variations in culture variables to quantify and compare growth curve data

Overcoming stochastic variations in culture variables to quantify and compare growth curve data

Cell and Gene Therapy for Anemia: Hematopoietic Stem Cells and Gene Editing

The Genetic and Physical Interactomes of theSaccharomyces cerevisiaeHrq1 Helicase

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