The conserved LEM-3/Ankle1 nuclease is involved in the combinatorial regulation of meiotic recombination repair and chromosome segregation in Caenorhabditis elegans

<div><p>Homologous recombination is essential for crossover (CO) formation and accurate chromosome segregation during meiosis. It is of considerable importance to work out how recombination intermediates are processed, leading to CO and non-crossover (NCO) outcome. Genetic analysis in budding yeast and <i>Caenorhabditis elegans</i> indicates that the processing of meiotic recombination intermediates involves a combination of nucleases and DNA repair enzymes. We previously reported that in <i>C</i>. <i>elegans</i> meiotic joint molecule resolution is mediated by two redundant pathways, conferred by the SLX-1 and MUS-81 nucleases, and by the HIM-6 Bloom helicase in conjunction with the XPF-1 endonuclease, respectively. Both pathways require the scaffold protein SLX-4. However, in the absence of all these enzymes, residual processing of meiotic recombination intermediates still occurs and CO formation is reduced but not abolished. Here we show that the LEM-3 nuclease, mutation of which by itself does not have an overt meiotic phenotype, genetically interacts with <i>slx-1</i> and <i>mus-81</i> mutants, the respective double mutants displaying 100% embryonic lethality. The combined loss of LEM-3 and MUS-81 leads to altered processing of recombination intermediates, a delayed disassembly of foci associated with CO designated sites, and the formation of univalents linked by SPO-11 dependent chromatin bridges (dissociated bivalents). However, LEM-3 foci do not colocalize with ZHP-3, a marker that congresses into CO designated sites. In addition, neither CO frequency nor distribution is altered in <i>lem-3</i> single mutants or in combination with <i>mus-81</i> or <i>slx-4</i> mutations. Finally, we found persistent chromatin bridges during meiotic divisions in <i>lem-3; slx-4</i> double mutants. Supported by the localization of LEM-3 between dividing meiotic nuclei, this data suggest that LEM-3 is able to process erroneous recombination intermediates that persist into the second meiotic division.</p></div

Loss of wobble uridine modification in tRNA anticodons interferes with TOR pathway signaling

Gefördert durch den Publikationsfonds der Universität Kasse

Insights into Diphthamide, key Diphtheria Toxin effector

Diphtheria toxin (DT) inhibits eukaryotic translation elongation factor 2 (eEF2) by ADP-ribosylation in a fashion that requires diphthamide, a modified histidine residue on eEF2. In budding yeast, diphthamide formation involves seven genes, DPH1-DPH7. In an effort to further study diphthamide synthesis and interrelation among the Dph proteins, we found, by expression in E. coli and co-immune precipitation in yeast, that Dph1 and Dph2 interact and that they form a complex with Dph3. Protein-protein interaction mapping shows that Dph1-Dph3 complex formation can be dissected by progressive DPH1 gene truncations. This identifies N- and C-terminal domains on Dph1 that are crucial for diphthamide synthesis, DT action and cytotoxicity of sordarin, another microbial eEF2 inhibitor. Intriguingly, dph1 truncation mutants are sensitive to overexpression of DPH5, the gene necessary to synthesize diphthine from the first diphthamide pathway intermediate produced by Dph1-Dph3. This is in stark contrast to dph6 mutants, which also lack the ability to form diphthamide but are resistant to growth inhibition by excess Dph5 levels. As judged from site-specific mutagenesis, the amidation reaction itself relies on a conserved ATP binding domain in Dph6 that, when altered, blocks diphthamide formation and confers resistance to eEF2 inhibition by sordarin

Differential growth is associated with wider transcriptome differences.

(A) A principal component analysis of log2 RPKM (reads per kilobase per million) values (S1 Data) for the transcriptome (n = 7255) during the RNA-seq time-course. The closed datapoints represent averages while the open datapoints represent each replicate. (B) The scatterplots show transcriptome differences between the day-5 and either day-9 or day-25 samples, highlighting transcripts encoding nucleolar proteins (n = 35), tRNA synthetases (n = 23) or glycolytic enzymes (n = 11). p values were derived using a χ2 test for increased v decreased expression of these 69 genes. The upper panels indicate data distribution. (C) The graphs show cell numbers for 36 clones and six of those clones selected for RNA-seq (right-hand panel), also indicating the VSG expressed by each clone. The data are shown as jittered dots and, when n>1, the horizontal line indicates the mean and the box indicates the range of values. (D) A principal component analysis of log2 RPKM values (S1 Data) for the transcriptomes of the six selected clones. (E) The scatterplots show transcriptome differences between the pairs of clones indicated. Other details as in B above.</p

Cas9-induced DNA breaks trigger antigenic variation.

(A) The schematic shows two examples of VSGs with salient features highlighted; the N-terminal signal sequence, Cys residues (red), predicted N-glycosylation sites (black) and C-terminal GPI anchor signal. Length, in amino acids, and class (B3 or C2) are indicated. See S2 Fig for more examples. (B) The schematic shows the active telomeric VSG (VSG-2) and the sites targeted by Cas9-sgRNAs to introduce DNA double-strand breaks. Recombination within 70-bp repeats triggers gene conversion and duplicative replacement of VSG-2 with a new VSG. (C) The protein blot shows robust inducible expression of Cas9. EF1α was used as loading control. (D) Immunofluorescence microscopy reveals switched and intermediate switching cells following Cas9-induction. Two switched sub-clones assessed by immunofluorescence microscopy are also shown. (E) An immunofluorescence microscopy time-course assay, with continuous Cas9 induction. Two biological replicates for each sgRNA. n > 90 cells for each sample at each time-point.</p

VSG dynamics and relationship to other features of VSGs.

(A) The graphs show fold-change in read-counts for VSGs between the day-5 and day-9 time-points relative to VSG mass. (B) As in A but relative to predicted N-glycosylation sites. (C) As in A but relative to number of Cys residues. (D) The graphs show read-counts for VSGs at the day-5 time-point (a measure of VSG activation rate) relative to VSG length. (E) The graphs show fold-change in read-counts for VSGs between the day-9 and day-13 time-points relative to VSG length. Further comparisons between the day-13/17 and day-17/25 time-points and VSG length yielded R2 values p values >0.3. All R2 and p values were derived using regression analysis in Excel.</p

Filtering of the minichromosomal <i>VSG</i> set.

The VSG set was filtered based on RNA-seq read-mapping profiles. VSGs displaying full coding sequence activation were included in the analysis. VSGs with mapped reads restricted to the C-terminal coding sequence were excluded from further analysis. These results are explained by the presence of common sequences in multiple VSGs. The 3’-terminal 268 nucleotides of VSG-444 are shared with VSG-18, while the 3’-terminal 584 nucleotides of VSG-514 are shared with VSG-17, for example. VSGs included in the analysis were truncated to 1,200 bp to remove shared sequence. (TIF)</p

Reproducibility of VSG activation and MC-<i>VSG</i> associated competitive advantage.

(A) The graph shows VSG expression levels as determined by RNA-seq read-count on day 5 after inducing switching in two independent biological replicate strains. n = 36 VSGs. (B) The graph shows relative VSG expression levels as determined by relative RNA-seq read-count over 20 days of growth following switching in two independent biological replicate strains. n = 36 VSGs. (C) Relative read-counts for ES-VSGs (n = 16) and MC-VSGs (n = 20) at day-5 and day-25 in the second replicate strain, and as determined by RNA-seq; three replicates, error bars, SD (not visible). (D) Read-counts for individual ES-VSGs (n = 16) and MC-VSGs (n = 20) at day-25 relative to the day-5 values in the second replicate strain. (TIF)</p

VSG dynamics are related to VSG template location and VSG length.

(A) A principal component analysis of read-counts (S1 Data) for the activated VSGs (n = 36) in each replicate during the RNA-seq time-course. (B) Relative read-counts for ES-VSGs (n = 16) and MC-VSGs (n = 20) over the RNA-seq time-course. Error bars, SD; three replicates. (C) Read-counts for individual ES-VSGs and MC-VSGs over the RNA-seq time-course. The upper panels show bloodstream ES-VSGs, red; metacyclic ES-VSGs, black; MC-VSGs, blue. Error bars, SD; three replicates. The lower panels show the same dataset but displayed relative to the day-5 values to emphasize the different behavior for the two sets of VSGs. (D) The graphs show fold-change in read-counts for either ES-VSGs or MC-VSGs between the day-5 and day-9 time-points relative to VSG length. R2 and p values were derived using regression analysis in Excel.</p