S-adenosylmethionine decarboxylase (AMD1).Stop codon readthrough (SCR) occurs when the ribosome miscodes at a stop codon. Such readthrough events can be therapeutically desirable when a premature termination codon (PTC) is found in a critical gene. To study SCR in vivo in a genome-wide manner, we treated mammalian cells with aminoglycosides and performed ribosome profiling. We find that in addition to stimulating readthrough of PTCs, aminoglycosides stimulate readthrough of normal termination codons (NTCs) genome-wide. Stop codon identity, the nucleotide following the stop codon, and the surrounding mRNA sequence context all influence the likelihood of SCR. In comparison to NTCs, downstream stop codons in 3′UTRs are recognized less efficiently by ribosomes, suggesting that targeting of critical stop codons for readthrough may be achievable without general disruption of translation termination. Finally, we find that G418-induced miscoding alters gene expression with substantial effects on translation of histone genes, selenoprotein genes, and  Many genes provide a set of instructions needed to build a protein, which are read by structures called ribosomes through a process called translation. The genetic information contains a short, coded instruction called a stop codon which marks the end of the protein. When a ribosome finds a stop codon it should stop building and release the protein it has made.Ribosomes do not always stop at stop codons. Certain chemicals can actually prevent ribosomes from detecting stop codons correctly, and aminoglycosides are drugs that have exactly this effect. Aminoglycosides can be used as antibiotics at low doses because they interfere with ribosomes in bacteria, but at higher doses they can also prevent ribosomes from detecting stop codons in human cells. When ribosomes do not stop at a stop codon this is called readthrough. There are different types of stop codons and some are naturally more effective at stopping ribosomes than others.Wangen and Green have now examined the effect of an aminoglycoside called G418 on ribosomes in human cells grown in the laboratory. The results showed how ribosomes interacted with genetic information and revealed that certain stop codons are more affected by G418 than others. The stop codon and other genetic sequences around it affect the likelihood of readthrough. Wangen and Green also showed that sequences that encourage translation to stop are more common in the area around stop codons.These findings highlight an evolutionary pressure driving more genes to develop strong stop codons that resist readthrough. Despite this, some are still more affected by drugs like G418 than others. Some genetic conditions, like cystic fibrosis, result from incorrect stop codons in genes. Drugs that promote readthrough specifically in these genes could be useful new treatments. To complete synthesis of a mature protein, ribosomes must terminate translation accurately at the end of each coding sequence. Translation termination requires recognition of the normal termination codon (NTC) in the A site of the ribosome by release factors eRF1 and eRF3 in eukaryotes, a process distinct from the RNA-RNA mediated decoding of mRNAs by tRNAs during translation elongation . FollowiNonsense mutations perturb the process of translation by insertion of a premature termination codon (PTC) within the coding sequence (CDS) of a gene. Upon encountering a PTC, a ribosome will terminate translation prematurely resulting in the production of a truncated peptide, which typically lacks proper functionality, and may even exert dominant-negative effects . To guarAs nonsense mutations account for approximately 11% of inherited genetic disorders in humans , targeteWhile translation termination is generally the predominant reaction at stop codons, termination efficiencies do vary considerably between different stop codon contexts. Many factors have been reported to influence the probability of termination, readthrough, or frameshifting  including the identity of the stop codon and surrounding sequence contexts , proximaTo explore the processes of translation termination and stop codon readthrough, we utilized a class of compounds known as aminoglycosides (AG). AGs have been abundantly characterized in bacterial systems where they bind the decoding center of the ribosome  and promS-adenosylmethionine decarboxylase 1 (AMD1).Here, we performed ribosome profiling  to systeWe initially investigated the ability of AGs to promote readthrough of a PTC using a dual-luciferase reporter assay. To directly compare activity of various AGs, and additionally optimize AG concentrations for downstream ribosome profiling experiments, we developed a reporter expressing Firefly (FLuc) and Nano Luciferase (NLuc) from a bidirectional CMV promoter . Normal Stimulation of ribosome readthrough was tested for six different AGs  analysis, aligning all transcripts at their annotated stop codons and calculating normalized ribosome densities in this window. Examining translation in untreated cells . FinallyTwo immediate differences emerge when comparing global termination in untreated cells to AG-treated cells. First, the peak of terminating ribosomes decreases in cells treated with G418 and paromomycin . The redRibosome ReadThrough Score  tail  in untreated cells. More importantly, in G418-treated cells, we identify the same trend as in the reporter assays (UGA > UAG > UAA) but with strong significance as determined by our genome-wide analysis.To query readthrough as a function of stop codon identity genome-wide, we again utilized the deeper ribosome profiling data sets. We sorted transcripts by stop codon identity and measured RRTS values for all transcripts in the absence and presence of G418 . While iU). And, combining the highest-readthrough three-nucleotide codon (UGA) with the highest-readthrough +4 position (C) results in the highest observed readthrough for UGAC compared to all other 4 nt termination codons in both untreated (Numerous reports have identified an influence of the nucleotide immediately following the stop codon (+4 position) on SCR using reporter assays. Based on structural studies revealing compaction of the mRNA in the A site of the ribosome during termination , stop contreated  and G418ntreated .t-test using kpLogo  of the termination codon using two different approaches. As a first approach, we weighted each stop codon context by the RRTS for that transcript and performed a one-sided two-sample Student’s g kpLogo  to test rocedure  and plotrocedure  for untrrocedure . As an arocedure , a stratrocedure . These dLooking broadly across this defined sequence window, several features emerge that influence SCR. Generally, the presence of A’s or U’s increase SCR probability while C’s and G’s decreases SCR probability, especially in 3′UTRs, for both untreated , top andConsidering the influence of C’s and G’s in promoting efficient translation termination relative to A’s and U’s, we next examined the nucleotide usage of protein coding transcripts 40 nts upstream and 60 nts downstream of the stop codon . As nuclWe next asked what the likelihood of SCR would be at a different class of termination codon, the collection of first in-frame stop codons in the 3′UTR (3′TCs). While 3′TCs are rarely translated under normal conditions, the pervasive translation of 3′UTRs in G418-treated cells allowed for direct comparison between SCR efficiency at NTCs and 3′TCs. For this comparison, we calculated relative downstream ribosome density in a window 30 nts upstream and downstream of the stop codon for NTCs and the first in-frame 3′TC, excluding transcripts that overlapped additional stop codons . In compIn addition to investigating ribosome readthrough at NTCs and 3′UTR stop codons, we applied ribosome profiling to study readthrough of a natural PTC. For this set of experiments, we used Calu-6 cells that harbor a nonsense mutation (R196X – UGAG) in the tumor suppressor gene TP53. To initially verify that G418 was able to at least partially restore levels of full-length TP53 protein, we analyzed TP53 protein levels by western blotting. In untreated cells, no detectable band was observed for TP53, whereas G418 treatment restored full-length TP53 synthesis in a dose-dependent manner . We alsoWe next treated Calu-6 cells with G418 for 24 hr, generated lysates, and sequenced samples using ribosome profiling and RNA-seq. As with HEK293T cells, G418 stimulated global SCR in Calu-6 cells . CanonicTo verify that ribosomes were in fact reading through the PTC, we analyzed ribosome profiling reads on this message for both untreated  and G418To investigate dysregulation of gene expression by G418, we compared changes in RNA levels (RNA-seq) and translation  allowing calculation of ribosome occupancies (ROs) by taking the ratio of RPF densities to RNA-seq densities for each transcript . For theWhile the majority of mRNAs are neither dramatically changed in abundance nor differentially translated in response to G418, there are several interesting outliers that we highlight here. First, the histone mRNAs revealed consistent changes in gene expression. For these mRNAs we see that translation is overall decreased and mRNA levels are stabilized, even after only 10 min of G418 treatment . Uniquelst amino acid, selenocysteine tRNA decodes a consensus UGA stop codon as a sense codon, usually in the presence of a downstream SECIS element  which encodes a specific peptide (MAGDIS*) stalls ribosomes at its termination codon and thus prevents scanning 40S subunits from reaching the authentic start codon of the downstream CDS . We findHere we used ribosome profiling to examine readthrough of stop codons genome-wide in human cell lines in untreated and aminoglycoside-treated cells. We find that aminoglycosides, and especially G418, generally disrupted the normally accurate process of translation termination leading to high levels of SCR and, in turn, broad perturbation of several cellular processes. Genome-wide levels of NTC readthrough varied considerably between different stop codons allowing investigation into sequence features driving the differences in SCR on the various stop codons. Examination of both 3 nt and 4 nt termination codons revealed a clear relationship between stop codon identity and SCR in response to G418 treatment; we define genome-wide an increased likelihood of G418-induced SCR with the order UGA > UAG > UAA for the 3 nt stop codons and C > U > A > G for the +4 nt position . In addiWe also noted that treatment of cells with G418 changed the relative importance of features that impact SCR. In untreated cells, SCR is strongly driven by the identity of the +4 and +5 nucleotide of the stop codon motif , top. ByThe high levels of readthrough stimulated by G418 disrupted several biological processes with one of the most substantial consequences being perturbation of histone gene expression resulting in mRNA stabilization and decreased ribosome density . Unique In contrast to the histones, ribosome density was substantially increased on the selenoprotein mRNAs following G418 treatment. The large peaks of ribosomes normally stalled at the UGA codon sites for selenocysteine insertion were decreased in G418-treated cells; this likely reflects the fact that UGA now becomes recoded by an nc-tRNA instead of the cognate selenocysteine tRNA. Simple replacement of selenocysteine with other amino acids has been shown to decrease function of several of these proteins . Thus, wS-adenosylmethionine levels in the cell. At the earliest time point, we see AMD1 is the most translationally upregulated mRNA in the transcriptome while at the later time point, translation still remains very high, but mRNA levels decrease substantially , it simTargeting a central process such as translation without causing deleterious effects remains challenging. General toxicity may result from AGs for multiple reasons including dominant effects from miscoding during elongation or from SCR leading to gain-of-function effects arising from C-terminal extensions . Moreove2. Antibiotics were not added to the media at any point, with the exception of the AGs tested in the study. Cell lines were purchased directly from ATCC and passaged fewer than 15 times. HEK293T cells tested negative for mycoplasma contamination.HEK293T cells and Calu-6 cells, purchased from ATCC, were cultured in Dulbecco’s Modified Eagle Medium with high glucose, L-glutamine, and sodium pyruvate, supplemented with 10% fetal bovine serum, certified endotoxin-free (Thermo Fisher Scientific). Cells were grown in a 37 °C cell culture incubator in the presence of 5% COAGs  were prepared by dissolving compounds in either water or 150 mM Tris pH 8.0 to prepare 50 mg/mL stock solutions of each compound. Liquid preparations were also purchased for G418 and Gentamicin (Thermo Fisher Scientific). Activity of compounds was not influenced by preparation methods (data not shown), so compounds prepared in 150 mM Tris pH 8.0 were used for the majority of experiments. Prior to addition of AGs for ribosome profiling and RNA-seq, AGs were added to media and equilibrated in a cell culture incubator to allow stabilization of pH and temperature before addition to cells.All plasmids in this study were generated using the pcDNA5/FRT/TO vector (Thermo Fisher Scientific). The dual expression cassette containing a shared enhancer and minCMV promoters was subcloned from the pBI-CMV1 (Clontech) into the pcDNA5 vector using restriction enzyme digest and Gibson assembly. Nano luciferase (Promega) and Firefly luciferase (a gift from David Bedwell) were inserted into this construct also using Gibson assembly to generate a template expressing full-length versions of both luciferase constructs. PTCs were then inserted via site-directed mutagenesis using the QuikChange Lightning Multi Site-Directed Mutagenesis Kit (Agilent) to generate the constructs tested here.To measure luciferase activity, HEK293T cells were transfected using Lipofectamine 3000 transfection reagent (Thermo Fisher Scientific) in 96 well plate format. Lipofectamine 3000 (0.225 µL/well) and P3000 reagent (0.2 µL/well) was diluted in serum free media , and mixed with plasmid DNA (100 ng/well). Four hours after transfection, AGs were added to each well testing all concentrations in triplicate. After 24 hr of AG treatment, cells were removed from the incubator and equilibrated to room temperature. Luciferase activity was measured using the Nano-Glo Dual-Luciferase Reporter Assay System (Promega). Cells were lysed through addition of Firefly luciferase reagent by direct injection, followed by measurement of Nano luciferase activity using a Synergy H1 microplate reader (BioTek).g, 4°C, for 10 min and soluble fractions were flash frozen in liquid nitrogen and stored at −80°C. Total protein levels were quantified using a standard curve by BCA Protein Assay (Thermo Fisher Scientific) and absorbance was measured on a Synergy H1 microplate reader (BioTek). 6x SDS loading buffer was added to lysates, samples were denatured at 98°C for 10 min, and 20 µg of protein were loaded to 4–12% Criterion XT Bis-Tris protein gels in 1x XT MES buffer (BioRad). Proteins were then separated using SDS-PAGE at 150V for 1 hr, and transferred to PVDF membrane via Trans-Blot Turbo Transfer System (BioRad). Following blocking with 2.5% milk (Santa Cruz Biotechnology) dissolved in 1x TBS-T for 1 hr at room temperature, the primary antibody to TP53 ) diluted 1:100 in 2.5% milk, 1x TBS-T was added and membranes were incubated overnight at 4°C with gentle agitation. Membranes were then washed 3x with 1x TBS-T, incubated with HRP-conjugated secondary antibody  diluted 1:1000 for 1 hr at room temperature, and washed again 3x with 1x TBS-T. HRP activity was probed by adding SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific), and chemiluminescence was detected using a G:BOX (Syngene). Membranes were stripped using OneMinute Advance Western Blot Stripping Buffer (GM Biosciences), reprobed with β-Actin-HRP  diluted 1:5000 for 1 hr at room temperature, and imaged again for detection of luminescent signal.Cells were lysed in RIPA lysis buffer  supplemented with Roche cOmplete, mini, EDTA-free (1 tablet per 3 mL of lysis buffer), 100 uM PMSF, 1x mammalian protease inhibitor cocktail (Sigma), after brief wash with 1x phosphate buffered saline (Thermo Fisher Scientific). Cells were scraped in the presence of ice-cold lysis buffer and lysed on ice for 10 min. Lysates were then clarified by centrifugation at 21,000 2, 1 mM DTT, 100 µg/mL cycloheximide, 1% Triton X-100, 2.5 U/mL Turbo DNase (Thermo Fisher Scientific)) on ice for 10 min. Lysates were clarified via centrifugation at 21,000 g, 4°C, for 10 min. The soluble ribosome-containing supernatant was removed, and separate aliquots were flash-frozen with liquid nitrogen for later quantification, ribosome profiling, and RNA-seq.Ribosome profiling samples were prepared as previously described 2, 1 mM DTT, 100 µg/mL cycloheximide) and digested with 750 U of RNase I (Ambion) for generation of ribosome protected fragments. After 1 hr of digestion at 25°C while shaking at 400 RPM on a thermomixer, digestions were stopped with 10 uL of SUPERase*In (Thermo Fisher Scientific). Nuclease-treated lysates were underlaid with 0.9 mL of sucrose dissolved in polysome buffer in 13 × 51 mm polycarbonate ultracentrifuge tubes, centrifuged at 100,000 RPM, 4°C, for 1 hr in a TLA 100.3 rotor using a Beckmann-Coulter Optima MAX ultracentrifuge, and RPFs were extracted using the miRNeasy mini kit (Qiagen).Sample lysates were quantified using the Quant-iT RiboGreen RNA Assay (Thermo Fisher Scientific) and fluorescence  was measured using a Synergy H1 microplate reader (BioTek). 20 µg of total RNA was diluted to a final volume of 300 uL using polysome buffer  were excised. RNA fragments were dephosphorylated using T4 PNK (New England Biolabs), and ligated to a 3′ oligonucleotide adapter containing a unique molecular identifier (UMI) hexanucleotide degenerate sequence using T4 RNA ligase 2 – truncated (New England Biolabs) for 3 hr at 37°C. Ribosomal RNA was depleted using RiboZero (Illumina) omitting the final 50°C incubation step in an effort to reduce contamination of short fragments of rRNA. RNA was then reverse-transcribed to cDNA using Superscript III (Thermo Fisher Scientific) using RT primer with a second four-nucleotide UMI sequence, RNNN. cDNA was then circularized using circLigase (Lucigen) and amplified by PCR using Phusion high-fidelity polymerase (New England Biolabs). PCR-amplified libraries were quantified with a Bioanalyzer 2100 (Agilent) using the High Sensitivity DNA kit and pooled in equimolar ratios. Sequencing was performed using a HiSeq2500 (Illumina) at the Johns Hopkins Institute of Genetic Medicine.Total RNA was extracted from 50 or 100 µL of lysate using 1 mL of TRIzol (Thermo Fisher Scientific). Following precipitation, the RNeasy kit (Qiagen) was used to clean-up RNA prior to library preparation. Starting with 1.0 µg of total RNA, RNA-seq libraries were prepared using the low-throughput TruSeq Stranded Total RNA Library Prep Gold kit (Illumina). As for ribosome profiling, cDNA libraries were quantified using a Bioanalyzer 2100 (Agilent) with the High Sensitivity DNA kit and sequenced with a HiSeq2500 (Illumina).Following sequencing of ribosome profiling libraries, we performed several pre-processing steps. Reads were deduplicated, collapsing reads with identical sequences and UMIs, using tally . UMI’s wFollowing alignment to the hg38 genome, reads were assigned to a custom transcriptome annotation based on the GENCODE (version 30)  annotatiThe 5′ ends of all aligned reads, between 15 and 40 nucleotides in length, were then assigned to protein coding transcripts without data normalization. These uniquely assigned reads were summed to calculate the total quantity of reads mapped to valid transcripts. Reads were assigned a second time to calculate normalized read counts in reads per million (RPM) by dividing raw counts by millions of mapped reads for every read length.Mapping to ribosomal A or P sites was next performed by shifting RPF reads from the 5′ end to the position in the center of the A site or P site codon. Density was calculated for all full-length (28–35 nt reads) and empty A site (20–23 nt reads)  ribosomeAverage gene plots were calculated for regions surrounding the start or stop codons as previously reported . For eacU test , the ribosome density within the 3 nt window of a queried codon (provided that the codon was not within the first or last two codons of the mRNA) was divided by the average ribosome density for the CDS of each transcript (excluding the first 15 and last 15 nts). This ratio was then calculated for all instances in the transcriptome of a queried codon, and the average of these ratios was determined to be the codon occupancy for that codon.The fraction of full length RPFs (28–35 nts) were assigned to the frame of translation in the CDS (frame 0), or the two other two possible frames . For calculation of reading frame in t-test was performed testing the difference between two biological replicates.For comparison between NTCs and first in-frame 3′TCs presented in t-tests to ask whether a given nucleotide increased or decreased RRTS values relative to all other nucleotides at this position. We used the Benjamini-Hochberg procedure  was retrieved for all transcripts along with the RRTS for that transcript. Any transcripts with 3′UTRs shorter than 12 nts were discarded. For all positions in this sequence window, we used kpLogo , to perfrocedure  to controgomaker .d (and d is the number of transcripts) and y is the vector for RRTS values for each transcript. A value of 103 was used for alpha, as this minimized residual error. Regression coefficients were plotted using Logomaker (Regression coefficients for each position within the defined window 15 nt upstream and 12 nt downstream of the stop codon were estimated using a regularized least-squares (‘ridge’) regression  of RRTS ogomaker .Nucleotide frequencies were calculated in a window surrounding the NTC (40 nt upstream to 60 nt downstream) discarding all transcripts with 3′UTRs shorter than 60 nt, and also for the first in-frame 3′TC (12 nt upstream to 12 nt downstream) discarding transcripts with 3′UTRs shorter than 12 nt downstream of the 3′TC in addition to transcripts with other 3′TCs in this window. The fraction of each nucleotide at every position (stop codons were collapsed to a single position) was then calculated and plotted using Logomaker .Differential expression of RNA-seq and ribosome profiling datasets were analyzed using DESeq2  data no. For estAll statistical analysis was performed using the SciPy package in python . Statisthttps://github.com/jrw24/G418_readthrough (https://github.com/elifesciences-publications/G418_readthrough).Raw sequencing data and count tables for each sample were deposited in the GEO database (GSE138643). Data analysis was performed using custom software written in Python 2.7 and R 3.4.4 available at  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:The paper describes the investigation of stop codon readthrough in mammalian cells on a genome-wide scale in the absence and presence of aminoglycosides using ribosome profiling. The study shows that aminoglycosides stimulate readthrough of both normal termination codons and premature termination. The study unveils parameters which are important for readthrough efficiencies. In addition, the authors studied the readthrough of downstream 3'UTR stop codons and show that the latter are inefficiently terminated. The study is highly significant for a better understanding of many genetic diseases that are caused by premature termination codons. Most importantly, the study should prompt the search for compounds to augment the readthrough of premature termination codons to restore protein synthesis of the mutated genes.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and James Manley as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: David Bedwell (Reviewer #1); Joseph D Puglisi (Reviewer #2); Jonathan S Weissman (Reviewer #3).Thank you for submitting your article "Stop codon context influences genome-wide stimulation of termination codon readthrough by aminoglycosides" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:The authors demonstrate that aminoglycosides stimulate readthrough of both normal termination codons (NTCs) and premature termination codons (PTCs). Downstream stop codons in 3′UTRs are recognized less efficiently by ribosomes, as compared to NTCs, suggesting that NTCs may have evolved features that resist readthrough. Factors that modulate readthrough include stop codon identity, the nucleotide following the stop codon, and the surrounding mRNA sequence context. They show that different mRNAs, including a histones and stress-related genes, that respond robustly to aminoglycoside treatment, including selenoprotein genes, and S-adenosylmethionine decarboxylase (AMD1).Essential revisions:eLife. They offer some thoughtful advice to make amendments and expand the Discussion, in addition to clarifying some text and figures. Please see below the full text of the comments.The three reviewers agree that this work is of great interest and merits publication in Reviewer #1:S-adenosylmethionine decarboxylase (AMD1). I think this is an outstanding study that carries out a thorough analysis of stop codon suppression in a genome-wide manner. The analysis is carefully and rigorous, and the results are thoughtfully discussed.The authors analyzed stop codon readthrough in vivo in a genome-wide manner in the absence and presence of aminoglycosides using ribosome profiling. They found that aminoglycosides stimulate readthrough of both normal termination codons (NTCs) and premature termination codons (PTCs) genome-wide. Factors that influenced the efficiency of this process included stop codon identity, the nucleotide following the stop codon, and the surrounding mRNA sequence context. As compared to NTCs, downstream stop codons in 3′UTRs were found to be recognized less efficiently by ribosomes, suggesting that NTCs may have evolved features that resist readthrough. Finally, the authors also found that G418 treatment alters diverse gene expression with particularly striking effects on histone genes, selenoprotein genes, and Reviewer #2:Life after some modifications to address minor points and presentation issues outlined below.The manuscript of Wangen and Green is a lovely and rigorous study of stop-codon suppression induced by aminoglycoside antibiotics. Such suppression has been known for decades, related to the known miscoding inducing activity for these antibiotics in both prokaryotic and eukaryotic translation. In addition, there is renewed interest in stop codon readthrough agents to treat diseases caused by premature stop codons. As such, this study is both timely, and sorely needed, as to my knowledge there has been no genome wide study of suppression. Here the authors first test a range of aminoglycosides in a standard reporter suppression assay, which showed the known effects of G418 and gentamicin, and established dosages for the subsequent ribosome profiling experiments . The ribosome profiling is of exceptional quality and shows clear readthrough into the 3'UTR upon treatment of drug. The results show that this readthrough is clearly from translating ribosomes (three-nucleotide repeat) within the correct zero frame (not caused by frameshifting). They could then calculate at readthrough score and correlate a range of genome wide mRNA signatures for efficient readthrough, including local sequence context around a stop codon. Using a premature stop codon reporter system, they show efficient readthrough of a premature stop codon in p53, and suppression of NMD pathways. They then study a range of different specific RNAs, including a histones and stress related genes, that respond robustly to aminoglycoside treatment. The results are carefully presented, and not overly interpreted, and represent a large leap forward in our understanding of stop codon suppression. The manuscript as such deserves publication in e1) The authors should discuss whether they observe any hints of elongation inhibition in the presence of aminoglycosides. This was not apparent from their data.2) The construction of the Discussion section should be rethought. The authors dive immediately into aminoglycoside effects on specific classes of mRNAs. This seems too detailed to begin. They may want to consider starting with global conclusions about aminoglycosides-role for 4,6 deoxystreptamines (gent), UGA as a winner, the fact there is readthrough etc. and then present the more specific effects of different mRNA subclasses.3) Preference here, but I think it would be easier for a reader to have names of aminoglycosides spelled out rather than using abbreviations .4) The last sentence of the Abstract is very specific, and it may not be immediately clear to a reader why these genes are interesting with respect to stop codon readthrough.5) Introduction paragraph three citing Mort et al. should read 11% rather than 10% for accuracy.6) “As NTC readthrough is normally a rare event, the vast majority of transcripts showed zero RPFs in 3′UTRs of untreated cells in these initial libraries. Treatment with AGs broadly increased RRTS values  indicating that stimulation of NTC readthrough by AGs is a global phenomenon.” – This statement seems to suggest that a small minority of transcripts showed RRTS values that were > 0 in untreated cells. Of those with non-zero RRTS scores, were these repeatable across replicates or is it just noisy signal? Furthermore, were those transcripts especially susceptible to readthrough upon treatment with AGs?P = 4.84 x10-7, Mann-Whitney U) in untreated cells” – In the absence of G418 the authors mention that UGA were more likely to be read through than UAG. Can the same be said of UGA and UAG relative to UAA in untreated cells? If not, why?7) “… we did find that UGA stop codons were significantly more likely to be readthrough than UAG stop codons (8) In Figure 5—figure supplement 1A, it is very difficult to read the identity of the stop codon – can only discern sequence from the colors.9) In Figure 5A, untreated cells: the significance of the +4 base in readthrough is not surprising given structures from Ramakrishnan and coworkers, but the authors should discuss the significance of the +5 base? It seems to be highlighted by the analysis and significantly stronger than the favorability of A/U from +7 to +15.Reviewer #3:Drosophila. The present study, however, goes light years beyond what was done previously using both aminoglycosides to enhance read through at natural stop codons as well as read through at premature termination codons and stop codons in 3'UTRs. The paper is filled with interesting observations; the work is well done, the analysis thoughtful and the presentation clear. I particularly like the three-nucleotide periodicity in the footprints from the 3'UTRs as well as the evidence suggesting evolution has tuned the 3'UTRs to enable efficient translation termination. There is also a bit of a cautionary tale as G418 results in considerable disruption to cell physiology including perturbations to histone gene expression. Overall, I am highly enthusiastic about this paper and recommend publication without delay.The notion that stop codons can in some cases be read through has been appreciated for decades and of course forms the basis of a great deal of classic genetics. There has over the years been much interest in understanding the context of what makes for a robust stop codon and how readthrough is influenced by small molecules. This has been motivated both by its fundamental importance to biology and for therapeutic applications including antibiotics and small molecules that enable preferential read through of disease-causing nonsense mutations (nonsense mutations account for approximately 10% of inherited genetic disorders). Classically these studies have relied on reporter genes but more recently there have been efforts based on ribosome profiling including studies from my own lab on read through in  Essential revisions:The three reviewers agree that this work is of great interest and merits publication in eLife. They offer some thoughtful advice to make amendments and expand the Discussion, in addition to clarifying some text and figures. Please see below the full text of the comments.We thank the reviewers for their positive feedback and enthusiasm for publication of this manuscript. The suggestions from each reviewer have been included in this revised version of the manuscript that has clarified and strengthened the presentation of the data. Several additions to the manuscript include an additional supplemental figure detailing the effects of aminoglycosides on translation initiation and elongation , another subfigure added to Figure 2—figure supplement 2 showing the dose-dependent increase in 3′UTR ribosomes from G418 treatment, and Venn diagrams comparing the reproducibility of readthrough detection in untreated and G418-treated cells . We have also reorganized the Discussion as suggested by multiple reviewers.Reviewer #2:[…]1) The authors should discuss whether they observe any hints of elongation inhibition in the presence of aminoglycosides. This was not apparent from their data.We did indeed observe effects on codon occupancy in cells treated with aminoglycosides and have added a supplemental figure to illustrate these genome-wide effects on translation elongation. Furthermore, we also observe enrichment of ribosomes at start codons in cells treated with G418. We have added a second additional supplemental figure  to illustrate these effects.2) The construction of the Discussion section should be rethought. The authors dive immediately into aminoglycoside effects on specific classes of mRNAs. This seems too detailed to begin. They may want to consider starting with global conclusions about aminoglycosides-role for 4,6 deoxystreptamines (gent), UGA as a winner, the fact there is readthrough etc. and then present the more specific effects of different mRNA subclasses.We struggled with this and appreciate the feedback. The Discussion has been restructured to better highlight the role of aminoglycosides on stimulating stop codon readthrough at the outset. The order of the Discussion now features genomic features that influence stop codon readthrough, followed by biological consequences of readthrough , and finally implications of the findings here for development of nonsense-suppression therapeutics.3) Preference here, but I think it would be easier for a reader to have names of aminoglycosides spelled out rather than using abbreviations .The abbreviations for aminoglycosides have been removed and replaced with the full name of each compound in the text.4) The last sentence of the Abstract is very specific, and it may not be immediately clear to a reader why these genes are interesting with respect to stop codon readthrough.The final sentence of the Abstract has been modified to highlight the role of miscoding in disruption of gene expression for these genes.5) Introduction paragraph three citing Mort et al. should read 11% rather than 10% for accuracy.This value has been corrected in the text.6) “As NTC readthrough is normally a rare event, the vast majority of transcripts showed zero RPFs in 3′UTRs of untreated cells in these initial libraries. Treatment with AGs broadly increased RRTS values  indicating that stimulation of NTC readthrough by AGs is a global phenomenon.” – This statement seems to suggest that a small minority of transcripts showed RRTS values that were > 0 in untreated cells. Of those with non-zero RRTS scores, were these repeatable across replicates or is it just noisy signal? Furthermore, were those transcripts especially susceptible to readthrough upon treatment with AGs?The rarity of stop codon readthrough in untreated cells presented a challenge for capturing these events using ribosome profiling. With libraries sequenced extremely deeply, we anticipate that most if not all transcripts would show detectable levels of stop codon readthrough. A nonzero RRTS value in untreated cells correlates very strongly with the number of raw sequencing reads assigned to the CDS of a given transcript, and only minimally with the actual readthrough level of that transcript. To address this question, we have added Venn diagrams  showing the number of transcripts with nonzero RRTS values for each sequencing libraries in our deepest datasets analyzed in Figure 3. These show strong agreement between replicates in the ability to detect readthrough on identical transcripts, even in untreated cells. There are more transcripts in replicate 1 that are not detected in replicate 2 because replicate 1 had more uniquely assigned sequencing reads .-7, Mann-Whitney U) in untreated cells” – In the absence of G418 the authors mention that UGA were more likely to be read through than UAG. Can the same be said of UGA and UAG relative to UAA in untreated cells? If not, why?7) “… we did find that UGA stop codons were significantly more likely to be readthrough than UAG stop codons  to the text. While UGA codons had higher average RRTS values than UAA codons, these results were not statistically significant (P = 0.0585) as indicated in Figure 4B.We added the comparison between UAA to UAG and the corresponding 8) In Figure 5—figure supplement 1 panel A, it is very difficult to read the identity of the stop codon – can only discern sequence from the colors.While the stop codons may be difficult to see here, this figure is a magnified image of Figure 5A which clearly illustrates the position of the stop codons. The purpose of presenting this magnified view is to allow comparison between less influential nucleotides in the surrounding stop codon context.9) In Figure 5A, untreated cells: the significance of the +4 base in readthrough is not surprising given structures from Ramakrishnan and coworkers, but the authors should discuss the significance of the +5 base? It seems to be highlighted by the analysis and significantly stronger than the favorability of A/U from +7 to +15.We extended our Discussion of the influence of the +5 nucleotide in untreated cells. We hope that, together with two lines in the results addressing the +5 position data, the importance of this position is now apparent to the reader.


Elevation in GABA concentrations not only naturally decreases neuronal spikes but also enhances astrocytic glutamate release, which leads to an increase in astrocyte-mediated presynaptic release and postsynaptic slow inward currents. Neuronal excitation induced by GABA-activated astrocytes partly counteracts the inhibitory effect of GABA. Overall, the model helps to increase knowledge regarding the involvement of astrocytes in neuronal regulation using simulated bath perfusion of GABA, which may be useful for exploring the effects of GABA-type antiepileptic drugs.Gamma-aminobutyric acid (GABA) is critical for proper neural network function and can activate astrocytes to induce neuronal excitability; however, the mechanism by which astrocytes transform inhibitory signaling to excitatory enhancement remains unclear. Computational modeling can be a powerful tool to provide further understanding of how GABA-activated astrocytes modulate neuronal excitation. In the present study, we implemented a biophysical neuronal network model to investigate the effects of astrocytes on excitatory pre- and postsynaptic terminals following exposure to increasing concentrations of external GABA. The model completely describes the effects of GABA on astrocytes and excitatory presynaptic terminals within the framework of glutamatergic gliotransmission according to neurophysiological findings. Utilizing this model, our results show that astrocytes can rapidly respond to incoming GABA by inducing Ca BR agonist  = 0 μM, 1 μM, 5 μM, and 10 μM) into the extracellular space. The goal of this paper was to describe neuronal population activity and investigate the modulation of astrocytes on the neurons in stimulus conditions by lumping the microscale tripartite synapse model and macroscale neuronal-astrocytic network. On the cell level, the GABA-activated astrocyte model was incorporated into a modeling framework of glutamatergic gliotransmission ∗ = 0.16 μM. The second and third equations describe the production rate of cytoplasmic IP3 induced by exogenous GABA and presynaptic glutamate, respectively. [GABAex] and [Glu] are the exogenous GABA and synaptic glutamate concentration, respectively. vgaba3IP and vglu3IP represent the rate of IP3 production via GABA and glutamate, respectively, and kgaban1 and kglun2 are their respective dissociation constants with Hill coefficients n1 and n2. Given the amplification interaction between the GABA and glutamate signaling pathways  and GA represent the concentrations of exogenous GABA and astrocyte-released glutamate, respectively, given by Equation , defined as rS = uS∙xS. The estimated glutamate concentration GS from presynaptic release can be represented mathematically as follows:gSc is the clearance rate of glutamate via neuronal and astrocytic uptake or spillover (flowing out of the synaptic cleft). ϱc is the vesicular to mixing volume ratio, and YT represents the total vesicular glutamate concentration in the presynaptic bouton. Akin to those described in glutamate release process in Equations  in Equation  and the membrane potential v by the following equations  = 1 mM  = 1 mM :(26)Mgv=Isic elicited by astrocytic glutamate can be defined as follows  = 0 μM, neuronal activity can also trigger relatively slower astrocytic Ca2+ oscillations. In fact, astrocyte Ca2+ events occur in a slow and prolonged manner under physiological condition [3 production, astrocyte Ca2+ elevation, and astrocytic gliotransmitter release. Consistent with [2+.Our research yielded several predictions. Firstly, the period and amplitude of Caondition . This isent with , our res2+-dependent exocytosis may be the most widely examined. Considering the spontaneous astrocytic Ca2+ oscillations [2+ increase [2+ concentration results in glutamate release and subsequent neuronal regulation. Yet, Ca2+-dependent mechanism remains a controversial topic. Recent study suggests that Ca2+-dependent gliotransmission is a pharmacological phenomenon rather than a physiological process [2+ activity [2+ signaling [2+-dependent mechanism as the method of glutamate release from astrocytes. Numerical results in this paper verify astrocyte function as the conversion of GABA inhibition into glutamatergic excitation, in which astrocytic Ca2+ activity is a crucial factor contributing to network excitability. Although the release of synaptic glutamate was inhibited by GABA, astrocyte Ca2+ response in this model was enhanced, which may be the amplifying interaction between the GABABR and mGluR-induced Ca2+ signaling [2+ signaling may be a potential mechanism for some drug targets. For instance, anticonvulsants reduce neuronal excitability by blocking astrocytic Ca2+ signaling [It is known that astrocytes release glutamate through multiple pathways , in whicllations , neuronaincrease , there s process . Given tactivity , it is nignaling . Our modignaling . If thesignaling .Another significant finding of our model is that GABA and astrocytic glutamate play opposing roles at excitatory presynaptic terminals. We used neurotransmitter depletion and reintegration to describe the modulation of basal release probability by these two factors. The net effect of GABA and astrocytic glutamate on excitatory presynaptic terminals is due to the coexistence of mixed scenarios. The results suggest that the enhanced release of astrocytic glutamate results in stronger vesicle recruitment and release as compared with the inhibitory effect of GABA. From the perspective of information transmission, the probability of presynaptic glutamate release depends on the pattern of presynaptic activity that integrates the contradictory effects of GABA and astrocytic glutamate. Thus, the synapse can be thought of as a filter , determiOur model illustrates that astrocyte-elicited SIC by activation of extrasynaptically located postsynaptic NMDA receptors is the other mechanism that affects neuronal activity. Unlike the results of postsynaptic high frequency activity produced by SICs in pathological conditions , 66, 75,Overall, the model described in the present study attempts to mimic the bidirectional neuron-astrocyte interaction under exogenous stimuli. These results demonstrate the involvement of astrocytes in synaptic transmission and suggest the excitatory drive of GABA-activated astrocytes in the neuronal network. Indeed, there exists a fine balance between excitation and inhibition in the central nervous system. Astrocytes have the potential ability to modulate synaptic activity and maintain this excitatory-inhibitory balance in particular brain states, which is far-reaching within brain function.


Prostate Cancer (PCa) is the second most common cancer in men where advancements have been made for early detection using imaging techniques, however these are limited by lesion size. Immune surveillance has emerged as an effective approach for early detection and to monitor disease progression. In recent studies, we have shown that host peripheral blood immune cells undergo changes in DNA methylation in liver and breast cancer.In the current study, we examined the DNA methylation status of peripheral blood T cells of men with positive biopsy for PCa versus men with negative biopsy having benign prostate tissue, defined as controls. T cells DNA was isolated and subjected to Illumina Infinium methylation EPIC array and validated using Illumina amplicon sequencing and pyrosequencing platforms.p < 0.05). Two hundred twenty-three differentially methylated CGs between control and PCa , were enriched in pathways involved in immune surveillance system. Three CGs which were found differentially methylated following DMP  analysis of ChAMP remained significant after BH (Benjamini-Hochberg) correction, of which, 2 CGs were validated. Predictive ability of combination of these 3 CGs  to detect PCa had high sensitivity, specificity and overall accuracy. PMS also showed strong positive correlation with Gleason score and tumor volume of PCa patients.Differential methylation of 449 CG sites between control and PCa T cell DNA showed a correlation with Gleason score (Results from the current study provide for the first-time a potential role of DNA methylation changes in peripheral T cells in PCa. This non-invasive methodology may allow for early intervention and stratification of patients into different prognostic groups to reduce PCa associated morbidity from repeat invasive prostate biopsies and design therapeutic strategy to reduce PCa associated mortality. Prostate Cancer (PCa) is the second most common cancer in men and has been estimated to affect more than a million men worldwide in 2018 . PCa is DNA methylation has emerged as a major area of investigation for the identification of cancer specific biomarkers at an early time point which can be utilized as a diagnostic or prognostic biomarker in numerous cancers including PCa –18. DNA Host immuno-surveillance plays an important role in the recognition and elimination of transformed and tumor cells –22. T-ceIn the current study, we have carried out genomic DNA methylation assay using peripheral blood T cells from negative-biopsy men having benign prostate tissue, defined as controls, and positive biopsy PCa patients. T cells DNA isolated from control and PCa subjects was analyzed by Illumina Infinium methylation EPIC array platform and validated using amplicon- and pyro- sequencing. We have identified a DNA methylation signature that not only distinguishes healthy controls from PCa patients but also different stages of PCa according to Gleason score.p > 0.05). The percentage tumor volume (%) was calculated as the total volume of positive cores divided by all cores tissue and is reflective of the potential cancer volume in the prostate gland.The study design was approved by the ethics committee of Alberta PCa Research Initiative (APCaRI). Buffy coats for positive-biopsy PCa patients and healthy negative-biopsy, having benign prostate tissue defined as controls, men were obtained through APCaRI. All patients were enrolled at the time of diagnosis before initiation of any treatment and had strict exclusion criterion of any autoimmune disease, asthma or infection which could alter T cells characteristics. The study had 12 controls and 20 PCa samples; 9 of which were low Gleason PCa (LGPCa) having a Gleason score ≤ 7 and; 11 of which were high Gleason PCa (HGPCa) having a Gleason score > 7 (Cohort 1)  from the buffy coats of PCa and healthy controls according to manufacturer’s protocol. Genomic DNA was extracted from the T cells using AllPrep DNA/RNA mini kit  according to manufacture’s protocol as previously described , 25.Methylome assay was performed on the genomic DNA (gDNA) extracted from T cells using Infinium® MethylationEPIC BeadChip assay from Illumina . Following sodium bisulfite conversion of gDNA Infinium® MethylationEPIC BeadChip assay was carried out as previously described , 25.Pyrosequencing was performed according to PyroMark Q24 (QIAGEN) following manufacturer’s protocol except a few modifications according to Tost et al  All dataThe bisulfite converted DNA was quantified by a Nanodrop Spectrophotometer  followed by two rounds of polymerase chain reaction (PCR) targeting the cg14713996 and cg05133736 regions using Bio-Rad C1000 Touch Thermal Cycler  for multiplex sequencing. The pooled library was then purified twice using AMPure XP Beads  and quantified by RealTime PCR using NEBNext® Library Quant Kit for Illumina  and sequencing was performed on the Illumina platform using MiSeq Reagent Nano Kit V2 .P < 0.05) variation arising from the Slide variable were completely removed after running the ComBat function  ; where β1 = methylation level of cg14713996, β2 = methylation level of cg02701909 and β3 = methylation level of cg05133736). This polyvariable linear regression equation, composed of weighted methylation level of three CG sites, was highly significant for the prediction of Gleason score in PCa patients (We generated receiver operating characteristic (ROC) curves and measured sensitivity, specificity and the area under the ROC curve (AUC) for each CG  and for PMS. Figure r2 = 0.4032; p < 0.0001). However, the data points best fit was a non-linear exponential curve . Indeed, when stratified into healthy controls (Ctrl), LGPCa and HGPCa, the HGPCa showed significantly higher mean tumor volumes (%) than both the other groups  indicating that PMS was able to predict progressiveness of the tumor. Hence, a higher PMS in peripheral T cells’ DNA would potentially indicate a higher tumor volume in the prostate of that patient and vice versa.Percentage tumor volume, which is reflective of the potential cancer volume in the prostate gland of PCa patients, also showed a strong significant positive correlation with Gleason score , as well as amplicon sequencing and pyrosequencing   difference in methylation between the controls and PCa in the Illumina EPIC (850 K) assay  difference in DNA methylation between the controls and PCa in the Illumina EPIC (850 K) assay Fig. . ValidatDNA methylation alterations in leukocytes have been reported by us and others in many cancers including head and neck squamous cell carcinoma, ovarian , 35, colPrevious studies have shown differential methylation of individual gene promoters in PCa. A study investigating clinical significance of gene promoter DNA methylation in blood DNA found six genes promoter methylation of PCa patients to be significantly different from controls . HoweverDespite these convincing results we are aware of the limitation of our study due to the small sample size. However, these results are robust in differentiating normal and PCa subjects, identifying several CGs which were validated using amplicon sequencing and pyrosequencing , 41–43. Results of this study are the first to identify the DNA methylation signature in immune cells of PCa subjects using non-invasive approach for early detection and stratification of PCa. It is imperative to carry out follow up studies using a larger cohort of subjects which will also consider history of smoking, alcohol intake and stratify the subjects based on their racial background. Future studies will also monitor differences among different T cells, PCa sub-types, stages and Gleason score. It is anticipated that the T cell DNA methylation signature will be identifiable in whole blood for large scale screens, active surveillance, early intervention with targeted therapies including the use of epigenetic based therapeutic agents.The current study provides for the first-time a potential role of DNA methylation changes in peripheral T cells in PCa. Using this non-invasive approach, changes in the DNA methylation status of T cells can predict men at risk of developing PCa and disease progression. Furthermore, the non-invasive methodology may allow for early intervention and stratification of patients into different prognostic groups to reduce PCa associated morbidity from repeat invasive prostate biopsies and design therapeutic strategy to reduce PCa associated mortality.Additional file 1: Supplementary Figure 1. (A) The number of T cell DNA samples used in the cohort 1. Ctrl, Negative-biopsy healthy control; LGPCa, Positive-biopsy low Gleason PCa; HGPCa, Positive biopsy high Gleason PCa. (B) Outline of DNA methylation signature determination in DNA of peripheral T cells in buffy coat of PCa patients and healthy controls using Illumina Infinium EPIC methylation array followed by validation with Illumina MiSeq and Pyrosequencing platforms. Supplementary Figure 2. Quality control (QC) of the data from Illumina Infinium EPIC methylation array using QC in ChAMP pipeline. (A) Density plot of raw data showing the beta distributions for each healthy control and PCa sample. None of the samples deviate from each other. (B) Singular value decomposition analysis (SVD) plot shows the significant components of variation in T cell DNA methylation. SVD analysis before (Pre) and after (Post) running the ComBat function in ChAMP pipeline removes the batch effects such as “Slide” variations as shown here. Supplementary Figure 3. Global distribution of the differentially methylated CGs that showed correlation with Gleason score in combined cohort. 1181 significant (p <0.05) differentially methylated CGs that had 10% methylation difference  in T cell DNA between PCa patients (with Gleason score 9) and in healthy controls were taken and input into IGV browser. Each row represents a Gleason score in a sequence of 6, 7, 8 and 9 while the first row is Chromosome numbers . Hypomethylation (Blue) and hypermethylation (Red). Supplementary Figure 4. Venn diagram showing the overlap of CGs between breast, liver and prostate cancer studies. Methylated CGs in T cell DNA that were correlating with Gleason score in this study were overlapped with CGs that were correlating with breast cancer and liver cancer progression in breast and liver cancer studies, respectively. ns, not significant; ****, p <0.0001. Supplementary Figure 5. Global distribution of the differentially methylated regions (DMRs). DMRs of the T cell DNA between the PCa and healthy control obtained using DMR function in the ChAMP pipeline. The data for the IGV input was obtained from Supplementary Table 3. The first row represents Chromosome numbers , second row shows DMR positions and the last row shows the reference genome H19. Supplementary Figure 6. Correlation analysis of percentage tumor volume and Gleason score. The percentage tumor volume was predicted from core prostate biopsies as described in Methods. Negative-biopsy healthy controls had no tumor and therefore assigned a 0% tumor volume and a Gleason score of 0. (A) Linear regression analysis of the percentage tumor volume of the PCa patients with the Gleason score. The red dotted lines show the 95% confidence band of the line of best fit. (B) Non-linear regression analysis of the percentage tumor volume of the PCa patients with Gleason score. The equation for the graph was an exponential growth curve with least square method as the fitting method. Supplementary Figure 7. Identification and validation of cg02701909 as a DNA methylation marker using Illumina EPIC (850K) and pyrosequencing. (A-B) Percentage methylation of T cell DNA at cg02701909 plotted as; (A) Ctrl  and PCa ; (B) Ctrl , LGPCa  and HGPCa . The number of samples for pyrosequencing were; Ctrl (n = 9), LGPCa (n = 9) and HGPCa (n = 9). Statistical significance was obtained in GraphPad Prism and are represented by asterisks . (C) Correlation graph between Illumina EPIC (850K) and pyrosequencing.Additional file 2: Supplementary Table 1. List of negative-biopsy healthy controls and positive-biopsy PCa patient sample ids with their respective Gleason score.Additional file 3: Supplementary Table 2. List of the primers used for pyrosequencing and amplicon sequencing (Illumina MiSeq system).Additional file 4: Supplementary Table 3. Differentially methylated regions (DMRs) between the PCa T cell DNA and healthy control T cell DNA. The data was obtained using DMR function in the ChAMP pipeline and input into the IGV browser to identify nearby genes and location of each DMR manually. Reference genome H19 was used for alignment. TSS, Transcription start site.Additional file 5.Additional file 6.Additional file 7.


Amyotrophic lateral sclerosis (ALS) is a complex neurodegenerative disorder characterized by the loss of the upper and lower motor neurons. Approximately 10% of cases are caused by specific mutations in known genes, with the remaining cases having no known genetic link. As such, sporadic cases have been more difficult to model experimentally. Here, we describe the generation and differentiation of ALS induced pluripotent stem cells reprogrammed from discordant identical twins. Whole genome sequencing revealed no relevant mutations in known ALS-causing genes that differ between the twins. As protein aggregation is found in all ALS patients and is thought to contribute to motor neuron death, we sought to characterize the aggregation phenotype of the sporadic ALS induced pluripotent stem cells (iPSCs). Motor neurons from both twins had high levels of insoluble proteins that commonly aggregate in ALS that did not robustly change in response to exogenous glutamate. In contrast, established genetic ALS iPSC lines demonstrated insolubility in a protein- and genotype-dependent manner. Moreover, whereas the genetic ALS lines failed to induce autophagy after glutamate stress, motor neurons from both twins and independent controls did activate this protective pathway. Together, these data indicate that our unique model of sporadic ALS may provide key insights into disease pathology and highlight potential differences between sporadic and familial ALS. Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder caused by the selective death of the upper and lower motor neurons (MNs). MN loss causes a progressive paralysis that affects a patient’s ability to move, speak, and breathe; ALS typically leads to death within 3 to 5 years of diagnosis. The vast majority of cases (approximately 90%) occur in the absence of a family history, denoted as sporadic ALS (sALS) . HoweverProtein aggregates can be found in virtually all ALS patients ,4,5,6. MInduced pluripotent stem cells (iPSCs) represent a powerful tool to model a wide array of diseases as iPSCs can theoretically be generated from any individual regardless of health status or genetics. Like embryonic stem cells, iPSCs can be differentiated into virtually every cell type of the body , therebyIn this study, we generated iPSCs from discordant identical twin brothers to model sALS in an isogenic manner. We found that the brothers have no pathogenic mutations in known ALS-causing genes, and that MNs generated from both twins exhibit high levels of insoluble proteins. Additionally, whereas MNs derived from iPSCs expressing pathogenic SOD1 or C9orf72 mutations alter protein burden in response to excess glutamate in a protein- and genotype-dependent manner, the MNs from the identical twins do not robustly change their aggregate levels after the same stress. They are, however, capable of inducing autophagic flux at levels seen in healthy control cells in response to excess glutamate, which is a mechanism that the SOD1 and C9orf72 MNs do not activate. Together, these data indicate that these novel iPSC lines may provide key insights into disease pathology and identify convergent and divergent mechanisms between fALS and sALS.Identical twin males discordant for ALS were identified from the ALS Multi-Disciplinary Clinic at Froedtert Hospital and consented to sample collection in accordance to the procedures outlined by IRB PRO00024167 (approved 29 September 2015). The affected individual was diagnosed with ALS with left leg onset at the age of 59 and collection occurred at age 61. The disease duration was 64 months from symptom onset.Peripheral blood mononuclear cells (PBMCs) were isolated and reprogrammed as described previously  using thWhole genome libraries were constructed by standard automated methods using Illumina TruSeq technology. Quantitative Real-time PCR was used to assess the overall amount of DNA library. Samples were sequenced on the Illumina HiSeq 2500  with 125 bp paired-end reads with an average depth of coverage of 43X. Raw sequencing data were converted to the fastq format by bcl2fastq conversion software v 1.8.4  and were aligned to Human reference GRCh37 by the Burrows–Wheeler Alignment Minimal Exact Matches (BWA-MEM) algorithm v0.7.15 . VariantInduced pluripotent stem cells (iPSCs) were maintained in Essential 8  on Matrigel  coated 6-well plates  and passaged every 4–6 days with Versene . Cultures were routinely tested for mycoplasm using the Mycoalert Detection Kit . Two independent control lines ,20, one Motor neurons (MNs) were generated as described previously  and wereMotor neurons (MNs) were fixed after four weeks of differentiation and stained as previously described . Images Protein was isolated using the Triton-X 100 fractionation method. Briefly, cell pellets were resuspended in a 1% Triton X-100 lysis buffer and briefly sonicated. Protein concentration was determined through a bicinchoninic acid (BCA) assay  according to manufacturer’s instructions. Insoluble protein was isolated via ultra-centrifugation at 45,000 rpm for 30 min and the resulting pellet was resuspended with 2% SDS. Insoluble protein was quantified using a BCA assay. 10 µg of soluble protein or 30–40 µL of insoluble protein were used for Western Blot analysis . Blots wt-test or a one-way ANOVA with Tukey’s post test, as appropriate. p < 0.05 was considered significant.A minimum of three independent experiments each with a minimum of three biological replicates were analyzed. The data were analyzed with a Student’s We identified identical twin males who are discordant for ALS. The affected twin was diagnosed at age 59 with no family history of ALS and died at age 63. The unaffected individual is still ALS-free at the time of writing. Whole blood samples from both individuals were collected when the twins were 61 years old. Peripheral blood mononuclear cells (PBMCs) were then isolated and reprogrammed using Sendai virus expressing the Yamanaka factors . IndividOPTN). However, this particular variant has been linked to open-angle glaucoma [SIGMAR1. However, mutations in SIGMAR1 are linked to juvenile-onset ALS, so we speculate that this mutation is likely not disease-causing in these patients. Additionally, we found two missense mutations in chromogranin B (CHGB) that have been linked to Schizophrenia [NIPA1, a known risk allele for ALS [Sporadic ALS (sALS) generally lacks known genetic mutations; however, de novo mutations in known ALS-causing genes have occurred in sporadic patients . We therglaucoma  and is fophrenia ,27. Fina for ALS . HoweverWe next differentiated these iPSCs into MNs to determine whether any phenotypic alterations existed between the affected and unaffected samples. Using an established MN protocol , we founSince there were no overt phenotypes in the affected MN cultures compared to the unaffected control and baseline, we tested how the MNs reacted to exogenous stress. There are several non-cell autonomous contributors to ALS pathogenesis, and glutamate excitotoxicity is considered one of the prominent features . We therConsistent with our previous results , SOD1 anWe next tested the impact of excess glutamate on fALS iPSC-derived motor neurons. Like the MNs derived from the identical twins, a 96-h treatment with 100 µM glutamate did not alter overall morphology or viability in mutant SOD1, C9orf72, and control iPSC-derived MNs (data not shown). Yet, this treatment was sufficient to induce a consistent change in insoluble TDP-43 protein levels C. Both tSince autophagy is a common mechanism by which a cell degrades misfolded and aggregated proteins, we next tested whether autophagic flux was disrupted in ALS iPSC-derived MNs. We found that the ratio of LC3-II/LC3-I is not changed in the affected MNs compared to the unaffected twin at baseline but does increase after glutamate treatment A. This sInduced pluripotent stem cells (iPSCs) are a powerful modeling tool to study both sporadic and genetically linked disease. Still, few studies have generated iPSC lines from sALS patients ,36,37,38While sALS is generally believed to occur in the absence of genetic mutations, previous data have found de novo mutations in sporadic patients . Still, Like most MNs derived from ALS iPSC lines , the affConsistent with our previous work , we founInterestingly, we also observed a 35 kDa fragment of TDP-43 within the soluble fraction in the C9orf72 MNs A. EvidenWhile the field has largely taken a MN-centric view of ALS, there are undoubtably multifaceted interactions between additional cell types within the brain and spinal cord of ALS patients. Indeed, it is well established that astrocytes can drive disease pathogenesis . AlthougThe iPSC-derived MNs from discordant identical twins seem to exist in a phenotypic space between “ALS-like” and “control-like”. MNs from both twins exhibit evidence of protein aggregation that are not significantly different from each other but do appear higher than independent controls . AdditioTogether, these data suggest that glutamate may contribute to protein aggregation in a protein- and genotype-dependent manner. Moreover, sALS and fALS may follow different pathological mechanisms and should be separated into distinct subgroups when developing and validating new therapies. As such, the iPSC lines from discordant identical twins generated in this study will be a valuable tool for studying sALS in an isogenic manner in order to better understand the phenotypes and pathological mechanisms in ALS.


Leucoraja erinacea). Using cell lineage tracing, we show that skate trunk vertebrae arise through tetrapod-like resegmentation, with anterior and posterior halves of each vertebra deriving from adjacent somites. We further show that tail vertebrae also arise through resegmentation, though with a duplication of the number of vertebrae per body segment. These findings resolve axial resegmentation as an ancestral feature of the jawed vertebrate body plan.The vertebral skeleton is a defining feature of vertebrate animals. However, the mode of vertebral segmentation varies considerably between major lineages. In tetrapods, adjacent somite halves recombine to form a single vertebra through the process of ‘resegmentation’. In teleost fishes, there is considerable mixing between cells of the anterior and posterior somite halves, without clear resegmentation. To determine whether resegmentation is a tetrapod novelty, or an ancestral feature of jawed vertebrates, we tested the relationship between somites and vertebrae in a cartilaginous fish, the skate ( Axial segmentation is key to the body plan organization of many metazoan groups and has arisen repeatedly throughout animal evolution . Within Cell lineage tracing studies in tetrapods have shown that there is not a 1:1 correspondence between somites and vertebrae. Rather, cells from adjacent somite halves recombine to give rise to a single vertebra, through a process known as ‘resegmentation’ . Somite The vertebral skeleton in cartilaginous fishes consists of a series of neural and intercalary arches and neural spines, tri-layered centra and haemal arches . We also test whether the diplospondyly observed in the tails of cartilaginous fishes arises through duplication of skeletal derivatives from tail vs. trunk somites. We find that skate tail somites do, indeed, duplicate their vertebral derivatives relative to trunk somites, and that resegmentation occurs along the entire body axis. These findings point to a stem-gnathostome origin of axial column resegmentation, and allow us to reconstruct the evolutionary history of vertebral column development.Here, we test whether tetrapod-like resegmentation is the ancestral mode of vertebral segmentation in gnathostomes using an outgroup to the bony fishes, a cartilaginous fish   or SpDiOC of DiO) , and theElasmobranch fishes show a diplospondylous condition in their caudal vertebrae, in which two vertebrae are present for each set of spinal nerve roots. As each tail vertebra is comparable in anatomical organization to trunk vertebrae  24. Early skate embryos were staged according to Portions of the trunk and tail of a S34 skate embryo were stained with iodine potassium iodide (IKI) according to Vertebrae in hatchling skates were visualized by skeletal preparation, as described in L. erinacea embryos were embedded in paraffin wax and sectioned at 8 µm thickness for mRNA in situ hybridization as described in Uncx4.1 (GenBank accession number MN478366) and Tbx18 (GenBank accession number MN478367) were performed on sections as described in 18 (ThermoFisher). Concentrated stocks of CM-DiI (5 µg/µL in absolute ethanol) and SpDiOC18 (2.23 µg/µL in dimethylformamide) were diluted 1:10 in 0.3 molar sucrose for injection. After injection embryos were returned to their egg cases and maintained in a flow-through seawater table at 15°C for 8–12 weeks post-injection.Two-color somite fate mapping experiments were performed as described in Injected skate embryos were euthanized using an overdose of tricaine (1 g/L in seawater) and fixed in 4% paraformaldehyde overnight at 4°C. Embryos were then rinsed 3 × 5 min in phosphate-buffered saline (PBS), embedded in 15% gelatin in PBS and post-fixed in 4% paraformaldehyde in PBS for 4 nights at 4°C before vibratome sectioning. A Leica VT1000S vibratome was used to cut 100 µm sections of tissue in sagittal plane, which were then DAPI-stained (1 µg/mL), coverslipped with Fluoromount-G (Southern Biotech) and imaged on an Olympus FV3000  or Leica Sp5 (tail vertebrae) confocal microscope. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This elegant study focuses on elucidating resegmentation-based vertebral patterning in vertebrate evolution. The novel findings are consistent with resegmentation, and as such provide new and exciting insight into this evolutionary process.Decision letter after peer review:eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Clifford Rosen as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Roger Keynes (Reviewer #1).Thank you for submitting your article "Resegmentation is an ancestral feature of the gnathostome vertebral skeleton" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Essential revisions:Please include some discussion of whether there are other known or hypothesized A-P patterning genes regulating each half-somite, as assessed by in situ hybridisation for Tbx18/Uncx4.1 expression.And please discuss whether there is evidence for skate myotome derivatives that straddle the intervertebral joints, and include discussion of possible advantages/disadvantages of diplospondyly. Essential revisions:Please include some discussion of whether there are other known or hypothesized A-P patterning genes regulating each half-somite, as assessed by in situ hybridisation for Tbx18/Uncx4.1 expression.Many other AP patterning genes have been demonstrated to regulate somite polarity in bony vertebrates. We have added to our discussion of these in two places: First, we have added text on the molecular basis of somite AP polarity with respect to axonal migration and patterning of the peripheral nervous system; and secondly, we have expanded our discussion of additional signaling pathways and transcription factors that establish somite AP polarity during development (Discussion).And please discuss whether there is evidence for skate myotome derivatives that straddle the intervertebral joints, and include discussion of possible advantages/disadvantages of diplospondyly.While it has been suggested that a functional consequence of axial resegmentation is the offset between myotome and sclerotome derivatives , there is actually little evidence for this in amniotes . Additionally, we examined our existing collection of histological sections from skate embryos, but we were unable to confidently draw any conclusions regarding spatial relationships between myotome and sclerotome derivatives originating from the same or neighboring somites. Differentiated gnathostome myomeres are very complex in morphology, and we believe that the relationship between these structures and sclerotomal derivatives is complicated and difficult to infer from adult morphology alone. We suspect that clearly understanding this relationship would require a much more extensive study, for example using simultaneous lineage tracing of myotome and sclerotome, with fixation and analysis at multiple intermediate developmental time points .However, in light of the reviewer’s comment, we have substantially revised the final paragraph of our Discussion to address this, along with an additional discussion of the hypothesized functional advantage of resegmentation and diplospondyly.