Repeat Associated Non-AUG Translation (RAN Translation) Dependent on Sequence Downstream of the ATXN2 CAG Repeat.

Spinocerebellar ataxia type 2 (SCA2) is a progressive autosomal dominant disorder caused by the expansion of a CAG tract in the ATXN2 gene. The SCA2 disease phenotype is characterized by cerebellar atrophy, gait ataxia, and slow saccades. ATXN2 mutation causes gains of toxic and normal functions of the ATXN2 gene product, ataxin-2, and abnormally slow Purkinje cell firing frequency. Previously we investigated features of ATXN2 controlling expression and noted expression differences for ATXN2 constructs with varying CAG lengths, suggestive of repeat associated non-AUG translation (RAN translation). To determine whether RAN translation occurs for ATXN2 we assembled various ATXN2 constructs with ATXN2 tagged by luciferase, HA or FLAG tags, driven by the CMV promoter or the ATXN2 promoter. Luciferase expression from ATXN2-luciferase constructs lacking the ATXN2 start codon was weak vs AUG translation, regardless of promoter type, and did not increase with longer CAG repeat lengths. RAN translation was detected on western blots by the anti-polyglutamine antibody 1C2 for constructs driven by the CMV promoter but not the ATXN2 promoter, and was weaker than AUG translation. Strong RAN translation was also observed when driving the ATXN2 sequence with the CMV promoter with ATXN2 sequence downstream of the CAG repeat truncated to 18 bp in the polyglutamine frame but not in the polyserine or polyalanine frames. Our data demonstrate that ATXN2 RAN translation is weak compared to AUG translation and is dependent on ATXN2 sequences flanking the CAG repeat

<i>ATXN2-luc</i> expression driven by the native <i>ATXN2</i> promoter, dependent upon CAG length and the presence of a start codon.

<p>(A) Plasmid constructs used in luciferase assays. (B) Luciferase assays to evaluate <i>ATXN2</i> expression driven by 1062 bp of its native upstream sequence, demonstrated increasing expression with increasing CAG length (ATG constructs). When the start codon was mutated, expression significantly higher than the control was observed only for <i>ATXN2</i>s with CAG repeat lengths of 57 or 102 (CTG constructs). For the longest repeat expression was 25-fold reduced when the start codon was substituted with CTG. Values are mean±SD of three independent experiments. All constructs were cotransfected with SV40-Renilla luciferase and values are represented as mean FLuc / RLuc, the ratio of firefly luciferase to Renilla luciferase. (C) RAN translation products were not observed by western blotting using anti-luciferase (luc) or 1C2 antibodies. Note that polyglutamine proteins detected with the 1C2 anti-polyglutamine antibody are more easily seen as the length of the polyglutamine is increased. Loading was controlled by detecting actin. The mobilities of the smaller ataxin-2-luciferase bands are not consistent with RAN translation bands. (D) Analysis of the luciferase assay results for only the CTG-<i>ATXN2-luc</i> constructs in B revealed significantly increased expression for constructs with 22 or greater CAG repeats but no increasing luciferase expression with increasing CAG repeat length. P<0.001 (**), Bonferroni post-hoc probability of significance. Assays utilized HEK293T cells with assays made 24 hrs after transfection.</p

<i>ATXN2-luc</i> expression driven by the native <i>ATXN2</i> promoter, dependent upon CAG length and the presence of a start codon.

<p>(A) Plasmid constructs used in luciferase assays. (B) Luciferase assays to evaluate <i>ATXN2</i> expression driven by 1062 bp of its native upstream sequence, demonstrated increasing expression with increasing CAG length (ATG constructs). When the start codon was mutated, expression significantly higher than the control was observed only for <i>ATXN2</i>s with CAG repeat lengths of 57 or 102 (CTG constructs). For the longest repeat expression was 25-fold reduced when the start codon was substituted with CTG. Values are mean±SD of three independent experiments. All constructs were cotransfected with SV40-Renilla luciferase and values are represented as mean FLuc / RLuc, the ratio of firefly luciferase to Renilla luciferase. (C) RAN translation products were not observed by western blotting using anti-luciferase (luc) or 1C2 antibodies. Note that polyglutamine proteins detected with the 1C2 anti-polyglutamine antibody are more easily seen as the length of the polyglutamine is increased. Loading was controlled by detecting actin. The mobilities of the smaller ataxin-2-luciferase bands are not consistent with RAN translation bands. (D) Analysis of the luciferase assay results for only the CTG-<i>ATXN2-luc</i> constructs in B revealed significantly increased expression for constructs with 22 or greater CAG repeats but no increasing luciferase expression with increasing CAG repeat length. P<0.001 (**), Bonferroni post-hoc probability of significance. Assays utilized HEK293T cells with assays made 24 hrs after transfection.</p

RAN translation in <i>ATXN2</i> alternate reading frames.

<p>(A and B) Luciferase assays performed using <i>ATXN2</i> constructs with expression driven by (A) the <i>CMV</i> promoter or (B) the native <i>ATXN2</i> promoter, with luciferase shifted into the PolyQ, PolyS, and PolyA frame. In all cases no <i>ATXN2</i> ATG start codons were included upstream of the CAG repeat that were in frame with luciferase, and the luciferase start codon was changed to CTG. Expression is shown as a percentage of the value determined for the <i>CMV</i> driven (A) or <i>ATXN2</i> promoter driven (B) polyglutamine frame constructs with the inclusion of the <i>ATXN2</i> ATG start codon. Bonferroni post-hoc probabilities of significance were P<0.001 (**) and P<0.01 (*). Values shown are mean±SD. The experiment was replicated 3 times. Assays were performed in HEK293T cells. Western blots of protein lysates from B and C revealed no bands detectable with anti-luciferase antibody. (C) <i>ATXN2</i> constructs with a 18 bp fragment of <i>ATXN2</i> sequence downstream of the CAG repeat followed by the 3T tag and western blotting detection. When the ATG was changed to CTG, a band resulting from <i>ATXN2</i> RAN translation was readily detected by western blotting of HEK293T cell lysates with anti-HA and anti-1C2 antibodies. No RAN translation bands were observed in the polyS or polyA frames. Note that only background banding was observed when using the anti-Myc antibody. Staufen-FLAG and Staufen-Myc was included as a positive control for FLAG and Myc detections.</p

Expression of <i>ATXN2-luc</i> driven by the <i>CMV</i> promoter.

<p>(A) Plasmid constructs used in luciferase assays. (B) Strong luciferase expression was observed for <i>CMV</i>-<i>ATXN2</i> with the non-mutant ATG. When the ATG was mutated to CTG expression was 7-fold higher than the vector control but 20-fold lower compared to when the ATG was present, consistent with the presence of weak RAN translation for <i>CMV-ATXN2-luc</i>. Values are mean±SD of three independent experiments. (C) RAN translation products were not observed by western blotting using anti-luciferase or anti-1C2 antibodies. Loading was controlled by detecting actin. (D) The reduced expression could not be attributed to reduced transcription, because qPCR assays comparing the expression of the transcripts indicated a non-significant trend toward higher transcription when the ATG➔CTG substitution was present. Assays utilized HEK293T cells with assays made 24 hrs after transfection.</p

<i>ATXN2</i> RAN translation was observed for <i>ATXN2</i> sequences with 91 or 102 CAG repeats with C-terminal epitopes driven by the <i>CMV</i> promoter but not the native <i>ATXN2</i> promoter.

<p>(A) <i>CMV</i> promoter driven <i>ATXN2</i> constructs including the <i>ATXN2</i> ATG start codon expressed proteins as expected, detected on western blots by anti-HA (lanes 1 and 3) and anti-FLAG when the epitope was included (lane 3), and anti-1C2 antibodies. When the <i>ATXN2</i> start codon was changed to CTG, <i>ATXN2</i> RAN translation bands were detected by anti-1C2 (lanes 2 and 4), but RAN translation products were not by anti-FLAG and for anti-HA the faintest RAN translation band is present for construct #4 but not #2. (B) <i>ATXN2</i> promoter (<i>ATXN2p</i>) driven <i>ATXN2</i> constructs including the <i>ATXN2</i> ATG start codon expressed proteins as expected, detected on western blots by anti-HA (lanes 5 and 7) and anti-FLAG when the epitope was included (lane 7), and anti-1C2 antibodies. When the <i>ATXN2</i> start codon was changed to CTG, <i>ATXN2</i> RAN translation bands were not observable by anti-1C2, anti-HA, or anti-FLAG antibodies (lanes 6 and 8). All constructs include the hygromycin phosphotransferase (HYG) gene, and uniformity of plasmid transfection and loading was ensured in A and B by detecting blots with anti-HYG and anti-Actin. Note that the intensity of upper bands detected by the anti-HA antibody follow actin band intensity but not HYG indicating that these are non-specific bands. Arrows indicate the specific bands detected by the anti-HA antibody. (C) Anti-HYG detected a doublet of bands in lysates from transfected cells that was absent in untransfected cells (UTC). For each of A, B, and C, we utilized HEK293T cells and 48 hr transfections.</p

Ataxin-2 regulates RGS8 translation in a new BAC-SCA2 transgenic mouse model.

Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant disorder with progressive degeneration of cerebellar Purkinje cells (PCs) and other neurons caused by expansion of a glutamine (Q) tract in the ATXN2 protein. We generated BAC transgenic lines in which the full-length human ATXN2 gene was transcribed using its endogenous regulatory machinery. Mice with the ATXN2 BAC transgene with an expanded CAG repeat (BAC-Q72) developed a progressive cellular and motor phenotype, whereas BAC mice expressing wild-type human ATXN2 (BAC-Q22) were indistinguishable from control mice. Expression analysis of laser-capture microdissected (LCM) fractions and regional expression confirmed that the BAC transgene was expressed in PCs and in other neuronal groups such as granule cells (GCs) and neurons in deep cerebellar nuclei as well as in spinal cord. Transcriptome analysis by deep RNA-sequencing revealed that BAC-Q72 mice had progressive changes in steady-state levels of specific mRNAs including Rgs8, one of the earliest down-regulated transcripts in the Pcp2-ATXN2[Q127] mouse line. Consistent with LCM analysis, transcriptome changes analyzed by deep RNA-sequencing were not restricted to PCs, but were also seen in transcripts enriched in GCs such as Neurod1. BAC-Q72, but not BAC-Q22 mice had reduced Rgs8 mRNA levels and even more severely reduced steady-state protein levels. Using RNA immunoprecipitation we showed that ATXN2 interacted selectively with RGS8 mRNA. This interaction was impaired when ATXN2 harbored an expanded polyglutamine. Mutant ATXN2 also reduced RGS8 expression in an in vitro coupled translation assay when compared with equal expression of wild-type ATXN2-Q22. Reduced abundance of Rgs8 in Pcp2-ATXN2[Q127] and BAC-Q72 mice supports our observations of a hyper-excitable mGluR1-ITPR1 signaling axis in SCA2, as RGS proteins are linked to attenuating mGluR1 signaling

Table1_A framework towards digital twins for type 2 diabetes.xlsx

IntroductionA digital twin is a virtual representation of a patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction of disease progression, optimization of care delivery, and improvement of outcomes.MethodsHere, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic models. By analyzing a substantial multiomic and clinical dataset, we constructed predictive machine learning models to forecast disease progression. Furthermore, knowledge graphs were employed to elucidate and contextualize multiomic–disease relationships.Results and discussionOur findings not only reaffirm known targetable disease components but also spotlight novel ones, unveiled through this integrated approach. The versatile components presented in this study can be incorporated into a digital twin system, enhancing our grasp of diseases and propelling the advancement of precision medicine.</p

Table2_A framework towards digital twins for type 2 diabetes.xlsx

IntroductionA digital twin is a virtual representation of a patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction of disease progression, optimization of care delivery, and improvement of outcomes.MethodsHere, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic models. By analyzing a substantial multiomic and clinical dataset, we constructed predictive machine learning models to forecast disease progression. Furthermore, knowledge graphs were employed to elucidate and contextualize multiomic–disease relationships.Results and discussionOur findings not only reaffirm known targetable disease components but also spotlight novel ones, unveiled through this integrated approach. The versatile components presented in this study can be incorporated into a digital twin system, enhancing our grasp of diseases and propelling the advancement of precision medicine.</p

Table3_A framework towards digital twins for type 2 diabetes.xlsx

IntroductionA digital twin is a virtual representation of a patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction of disease progression, optimization of care delivery, and improvement of outcomes.MethodsHere, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic models. By analyzing a substantial multiomic and clinical dataset, we constructed predictive machine learning models to forecast disease progression. Furthermore, knowledge graphs were employed to elucidate and contextualize multiomic–disease relationships.Results and discussionOur findings not only reaffirm known targetable disease components but also spotlight novel ones, unveiled through this integrated approach. The versatile components presented in this study can be incorporated into a digital twin system, enhancing our grasp of diseases and propelling the advancement of precision medicine.</p