ATM deficiency alters in-frame and out-of-frame junctional sequences of TCRβ CDR3 in pre- and post-selection T cells.

<p>(A) Diagram of TCRβ CDR3 junctions. Nucleotide deletions and additions were sequenced from in-frame and out-of-frame TCRβ CDR3 regions isolated from ATMWT (grey bars) or KO (white bars) cells that were either TCRα KO DP thymocytes (B and C) or naïve CD4⁺ T cells (D and E). TCRαKO DP and naïve CD4⁺ T cells were sorted from two mice of each ATM genotype. Each data point represents >55,000 sequences. P values were calculated using Student’s unpaired 2-tailed t-test. *p<0.05; **p<0.01; ***p<0.001.</p

Rearrangement of TCRβ is impaired in ATMKO DN2/3 cells.

<p>(A) Genomic organization of the murine TCRβ locus. (B) A representative immuno-FISH image of an ATMKO DN2/3 cell showing a 53 BP1 focus (red) at one of two TCRβ loci (green). White bar denotes 1.5 micron. (C) Graph shows mean frequency (±SEM) of ATMWT (WT) and ATMKO (KO) DN2/3 cells expressing 53 BP1 foci at the TCRβ locus. 200 cells of each genotype were analyzed in two experiments. (D) Representative 3D-FISH images of two ATMKO DN2/3 cells hybridized with 5′TCRβ (red) and 3′TCRβ probes (white). In left image probes are separated by less than 1 µm at both TCRβ loci; whereas, in right image probes are separated by more than 1 µm at one of two TCRβ loci. (E) Summary of the frequency of ATMWT and KO DN2/3 cells in which the 5′ and 3′TCRβ probes are separated by more than 1 µm one of two TCRβ loci. More than 300 cells of each genotype were collected in two experiments. (F) Vβ-DJβ rearrangement in genomic DNA from ATMWT and KO DN2/3 cells was quantified using real-time PCR and normalized to invariant Cα. This plot is mean Vβ-DJβ rearrangements±SEM of seven ATMWT and KO data sets analyzed in five real-time PCR experiments. P values: Student’s unpaired (C) and paired (F) 1-tailed t-test and Fisher’ exact test (E). *p<0.05; **<0.01, ***p<0.001.</p

ATM deficiency alters cell survival and proliferation in DN cells.

<p>Freshly explanted thymocytes were stained for surface molecules, fixed, and permeabilized prior to intracellular staining. <b>(</b>A) As compared to ATMWT, ATMKO DN3 but not DN4 cells have an increased frequency of cleaved-Caspase-3⁺ cells (ATMWT vs KO DN3 p<0.03). Graphs show frequencies of ATMWT (grey bar) and KO (white bar) lineage negative DN3 and DN4 cells that express cleaved-Caspase-3. Data from four independent experiments analyzing ATMWT and KO pairs is shown. (B) Panels show representative lineage negative CD25 CD44 DN staining profiles for ATMWT (top panel) and KO (lower panel) thymi and DAPI staining profiles gated on DN3 and DN4 cells. (C) Frequency of DN3 and DN4 cells from individual ATMWT (black symbols) and KO (open symbols) mice that are in G2/M of the cell cycle. ATM deficiency results in increased cycling cells in both DN3 and DN4 stages (p<0.002 and 0.02, respectively). P values were calculated using Student’s paired 1-tailed t test from data collected from three-four ATMWT and KO pairs analyzed in three independent experiments.</p

Introduction of a rearranged TCRβ TG significantly improves the ATMKO defect in DN to DP development.

<p>Effects of TCRβ TG expression on ATMWT (WT) and ATMKO (KO) thymic cellularity (A), numbers of DP cells (B), and DN/DP ratios (C). Results in panels A–C are mean (± SEM) for three ATMWT and KO pairs analyzed in three independent experiments. For the parameters that were tested, ATMWT thymi with and without the TCRβ TG did not differ significantly. In contrast, ATMKO thymi with and without the TCRβ TG did differ significantly (p<0.05). P values were calculated using Student’s paired 1-tailed t test.</p

Competitive chimeras demonstrate that the ATMKO defect in DN to DP development is T cell-intrinsic.

<p>This graph shows the ratio of thymocytes derived from test (ATMWT or KO CD45.2⁺) and control (ATMWT CD45.1⁺) bone marrow for each thymic subset. When both test (CD45.2⁺) and control (CD45.1⁺) bone marrows are ATMWT (solid black line), the ratio of test/control cells is not significantly changed during thymic development. In contrast, when test bone marrow is ATMKO (CD45.2⁺) (dashed black line) the ratio of test/control (ATMKO/ATMWT) cells decreases as thymocytes develop from DN to DP cells (p<0.007) and from DP to CD8 (p<0.005) or CD4 (p<0.02) SP cells. Data from two independent sets of chimeras consisting of 16–20 mice in each group were combined for this analysis. P values were calculated using Student’s unpaired 1-tailed t-test.</p

Active DNA demethylation promotes cell fate specification and the DNA damage response

Neurons harbor high levels of single-strand DNA breaks (SSBs) that are targeted to neuronal enhancers, but the source of this endogenous damage remains unclear. Using two systems of postmitotic lineage specification—induced pluripotent stem cell–derived neurons and transdifferentiated macrophages—we show that thymidine DNA glycosylase (TDG)–driven excision of methylcytosines oxidized with ten-eleven translocation enzymes (TET) is a source of SSBs. Although macrophage differentiation favors short-patch base excision repair to fill in single-nucleotide gaps, neurons also frequently use the long-patch subpathway. Disrupting this gap-filling process using anti-neoplastic cytosine analogs triggers a DNA damage response and neuronal cell death, which is dependent on TDG. Thus, TET-mediated active DNA demethylation promotes endogenous DNA damage, a process that normally safeguards cell identity but can also provoke neurotoxicity after anticancer treatments

Somatic mutations in ATM, identified in PTCL patients, involve highly conserved residues.

<p>(A) Schematic representation of ATM protein domains showing location of somatic mutations in ATM from 5 different PTCL samples. (B) Multiple sequence alignment across species around the 6 mutations in ATM found in 5 samples from patients with PTCL. Conserved mutated residue highlighted in black, other conserved residues highlighted in grey.</p

Functional algorithms, MutationAssessor, PolyPhen2, and PROVEAN, predict the majority of somatic mutations identified to significantly impact protein function.

<p>(A) Bar graph shows percent of non-synonymous somatic mutations and their probability to impact protein function, as predicted by MutationAssessor, PolyPhen2, and PROVEAN. (B) Venn diagram shows number of non-synonymous somatic mutations predicted to significantly impact protein function by each algorithm or combination of algorithms: MutationAssessor, PolyPhen2, and PROVEAN. Mutations were considered significant if selected as “high” or “medium”, “probably damaging” or “possibly damaging,” and “deleterious,” respectively.</p

104 somatic mutations predicted to be cancer drivers that significantly alter protein function by all four algorithms.

<p>CHASM significance determined by p≤0.05. If a mutation was previously identified, the appropriate citation is listed as a footnote [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141906#pone.0141906.ref027" target="_blank">27</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141906#pone.0141906.ref032" target="_blank">32</a>].</p><p>¹ Camacho, <i>et</i>. <i>al</i>. 2002; Navrkalova, <i>et</i>. <i>al</i>. 2013; Biankin, <i>et</i>. <i>al</i>. 2012; Fang, <i>et</i>. <i>al</i>. 2003; Gronbaek, <i>et</i>. <i>al</i>. 2002</p><p>² Achatz, <i>et</i>. <i>al</i>. 2007</p><p>104 somatic mutations predicted to be cancer drivers that significantly alter protein function by all four algorithms.</p

Concurrent Mutations in <i>ATM</i> and Genes Associated with Common γ Chain Signaling in Peripheral T Cell Lymphoma

<div><p>Peripheral T cell lymphoma (PTCL) is a heterogeneous malignancy with poor response to current therapeutic strategies and incompletely characterized genetics. We conducted whole exome sequencing of matched PTCL and non-malignant samples from 12 patients, spanning 8 subtypes, to identify potential oncogenic mutations in PTCL. Analysis of the mutations identified using computational algorithms, CHASM, PolyPhen2, PROVEAN, and MutationAssessor to predict the impact of these mutations on protein function and PTCL tumorigenesis, revealed 104 somatic mutations that were selected as high impact by all four algorithms. Our analysis identified recurrent somatic missense or nonsense mutations in 70 genes, 9 of which contained mutations predicted significant by all 4 algorithms: <i>ATM</i>, <i>RUNX1T1</i>, <i>WDR17</i>, <i>NTRK3</i>, <i>TP53</i>, <i>TRMT12</i>, <i>CACNA2D1</i>, <i>INTS8</i>, and <i>KCNH8</i>. We observed somatic mutations in <i>ATM</i> (ataxia telangiectasia-mutated) in 5 out of the 12 samples and mutations in the common gamma chain (γ_c) signaling pathway (<i>JAK3</i>, <i>IL2RG</i>, <i>STAT5B</i>) in 3 samples, all of which also harbored mutations in <i>ATM</i>. Our findings contribute insights into the genetics of PTCL and suggest a relationship between γ_c signaling and ATM in T cell malignancy.</p></div