Transcription start-site (TSS) selection and alternative promoter (AP) usage contribute to gene expression complexity but little is known about their impact on translation. Here we performed TSS mapping of the translatome following energy stress. Assessing the contribution of cap-proximal TSS nucleotides, we found dramatic effect on translation only upon stress. As eIF4E levels were reduced, we determined its binding to capped-RNAs with different initiating nucleotides and found the lowest affinity to 5'cytidine in correlation with the translational stress-response. In addition, the number of differentially translated APs was elevated following stress. These include novel glucose starvation-induced downstream transcripts for the translation regulators eIF4A and Pabp, which are also translationally-induced despite general translational inhibition. The resultant eIF4A protein is N-terminally truncated and acts as eIF4A inhibitor. The induced Pabp isoform has shorter 5'UTR removing an auto-inhibitory element. Our findings uncovered several levels of coordination of transcription and translation responses to energy stress.DOI:http://dx.doi.org/10.7554/eLife.21907.001 The production of new proteins is a complex process that occurs in two steps known as transcription and translation. During transcription, the cell copies a section of DNA to make molecules of messenger ribonucleic acid (or mRNA for short) in the nucleus of the cell. The mRNA then leaves the nucleus and enters another cell compartment called the cytoplasm, where it serves as a template to make proteins during translation. A mRNA molecule contains a sequence of building blocks known as nucleotides. There are four different types of nucleotides in mRNA and the order they appear in the sequence determines how the protein is built.Both transcription and translation consume a lot of energy so they are highly regulated and sensitive to environmental changes. However, since transcription and translation happen in different cell compartments, it is not known if and how they are coordinated under stress.Tamarkin-Ben-Harush et al. studied transcription and translation in mouse cells that were starved of glucose. The experiments show that the identity of the very first nucleotide in the mRNA – which is dictated during transcription – has a dramatic influence on the translation of the mRNA, especially when the cells are starved of glucose. This first nucleotide affects the ability of a protein called eIF4E, which recruits the machinery needed for translation, to bind to the mRNA. The experiments also show that there is a dramatic increase in the number of distinct mRNAs that are transcribed from the same section of DNA but translated in a different way during glucose starvation.The findings of Tamarkin-Ben-Harush et al. show that transcription and translation are highly coordinated when cells are starved of glucose, allowing the cells to cope with the stress. The next step is to further analyze the data to find out more about how transcription and translation are linked.DOI:http://dx.doi.org/10.7554/eLife.21907.002 Transcription start site (TSS) selection and alternative promoter (AP) usage increase transcriptome diversity and its regulation. For example the level of transcription initiation can vary between different transcription start sites (TSS) under different growth conditions, in response to a specific signal or in different cell types and tissues. In addition, mRNA isoforms with different 5’ leaders can vary in their translation efficiency or their half-lives. Likewise, AP usage can lead to the generation of protein isoforms that differ in their N-termini and as a result have different or even opposite biological functions. Recent large-scale promoter analysis in hundreds of human and mouse primary cell types shed light on the prevalence of AP usage in mammals . SeveralThe process of protein synthesis is highly energy consuming and tightly regulated by the availability of nutrients, oxygen and growth factors. Downregulation of the translation machinery is a major mechanism that allows cells to preserve energy and cope with environmental deficiencies. Under these conditions translation of many mRNAs is inhibited but that of others is unchanged or even enhanced in order to survive the stress. The translation inhibition response is mediated by several mechanisms, in particular by the impairment of key initiation factors eIF2 and eIF4E . StresseIn the present study we aimed to obtain a global view of the effects of TSS selection on translation following metabolic energy stress. To this end, we combined polysomal profiling with quantitative assessment of the 5’ ends of mRNAs. By comparing transcript isoforms that differ in their 5’ end we identified hundreds of genes with APs that are differentially translated, in particular following energy stress, suggesting that a major determinant of the differential response is associated with transcription-induced APs. We also determined the contribution of TSS nucleotides to translation. Strikingly, while the cap-proximal nucleotides have no significant effect on translation under optimal growth conditions, they display dramatic effects on the translational response to stress. We demonstrate that eIF4E levels drop following the stress and that the binding affinity of eIF4E towards capped mRNA with different first nucleotide varies significantly, with a relatively lower affinity to 5’ polypyrimidine as in TOP mRNAs. We next characterized two genes encoding translation regulatory factors with differentially translated APs. The first is eIF4A, the helicase subunit of the cap complex eIF4F, in which we identified a novel glucose-starvation-induced intronic promoter. The induced isoform, which has distinct 5’UTR and initiating AUG, is efficiently translated in energy deficient cells in spite of the global translation inhibition. The resultant protein is N-terminally truncated and acts as an eIF4A inhibitor, most likely to facilitate the stress response. The second is poly-A binding protein  in which repression of the major TOP-containing isoform following stress is coupled with the induction of a downstream TSS that generates an mRNA isoform with a much shorter 5’UTR that is highly translated. Interestingly, the induced isoform lacks the well-characterized Pabp auto-inhibitory element . Our finTo obtain a global and quantitate view of the impact of transcription start site (TSS) selection on translation efficiency following cellular stress we combined polysomal profiling with quantitative assessment of the 5’ ends of mRNAs  note th and indeWhen the results of two independent experiments were combined and normalized using internal controls, 9286 promoters that corresponded to 6861 genes had at least 500 reads per promoter and were considered in further analysis . Under tWe next analyzed the translational response of individual promoters following the GS stress. For this we determined the ribosome occupancy  of transcripts emanating from each promoter and calculated the effect of GS on the RO (referred to as the RO effect) for each experiment. A promoter was considered as translationally affected if its RO was affected by at least two-fold in each of the two biological repeats . With thOur approach enabled us to compare the translation efficiency of transcript isoforms emanating from APs of the same gene as schematically shown in Among APs corresponding to the same ORF, those resulting in shorter 5’UTRs had higher RO under basal conditions . On the We also compared the mRNA levels of the transcript isoforms following GS stress. We calculated the change in overall mRNA levels of each promoter following GS, and compared the effects on the APs of each gene . 140 genThe CapSeq data can provide insights into the impact of the exact nucleotide context of the TSS on translation efficiency. To determine the effect of the initiating nucleotides on translation efficiency we first analyzed all the non-redundant TSS positions in our data. We found that in MEFs, under basal and GS conditions, ~34% of TSSs initiated with adenosine (A), ~23% with cytidine (C), ~30% with guanosine (G) and ~13% with thymidine (T) , similarRepresentative examples of TSSs that are separated by just a few bases within the same promoter and display differential translational reaction to GS are shown in Another example is the p1 promoter of the Sars (Seryl-tRNA synthetase) gene that has multiple TSSs  binding protein (Pabp), a central translation initiation regulatory factor, has several annotated promoters. The major promoter under basal conditions is p1, which generates a TOP mRNA . This mRRenilla luciferase (RL) gene and transfected into MEFs , to act as a promoter. The upstream sequences of p2 were placed upstream to a promoter-less nto MEFs . The resArhgef2 (Rho/Rac guanine nucleotide exchange factor 2) is another interesting example of a gene with APs that are differentially regulated by GS  that can bypass the inhibitory effect of uAUGs. Alternatively, translation can be activated by uORF-mediated mechanisms in which phosphorylation of the subunit of eIF2α favors translation of genes containing multiple uAUGs .In summary, by measuring isoform-specific translation in basal and energy stress conditions we uncovered previously unknown regulatory mechanisms that broaden our understanding of how stress alters cellular translatome and transcriptome and how these processes are coordinated. We anticipate that further analysis of the data and focusing on specific examples will provide additional novel insights of transcription-translation links.Untreated and 8 hr glucose starved MEFs were incubated with 100 μg/ml Cycloheximide (Sigma) for 5 min and then washed twice with cold buffer containing 20 mM Tris pH 8, 140 mM KCl, 5 mM MgCl2 and 100 μg/ml Cycloheximide. The cells were collected and lyzed with 500 μl of same buffer that also contains 0.5% Triton, 0.5% DOC, 1.5 mM DTT, 150 units RNAse inhibitor (Eurx) and 5 μl of protease inhibitor (Sigma). The lyzed samples were centrifuged at 12,000g at 4°C for 5 min. The cleared lysates were loaded onto 10–50% sucrose gradient and centrifuged at 41,000 RPM in a SW41 rotor for 90 min at 4°C. Gradients were fractionated and the optical density at 254 nm was continuously recorded using ISCO absorbance detector UA-6. The collected samples were then merged to create three fractions: Polysome-free (F), Light (L) and Heavy (H). The use of three pools is sensitive to large changes in translation but relatively insensitive to smaller changes. RNA was isolated for each fraction using Trizol and Direc-Zol RNA mini-prep kits (Zymo Research). RNA spikes (0.25 ng GFP and 1pg Luciferase) that were transcribed  and capped  in-vitro using T7 RNA Polymerase were added into each fraction. Equivalent RNA volume was taken from each fraction for the library preparation for TSSs sequencing using the CapSeq method (with modifications) as previously described . SpecifiReads were mapped to the mouse genome (mm9 assembly) using STAR aligner and to the GFP and luciferase sequences using Bowtie2. The BAM file was altered to contain just the first base of the alignment using a custom Java script  and covemRNA differential effect = ftp://ftp-igor.weizmann.ac.il/pub/hubANA.txtThe data can be accessed in Gene Expression Omnibus (GEO) accession no. GSE93981 or tracked using UCSC genome browser at: MEFs from a WT mouse  and HEK293T (from ATCC) were maintained in DMEM supplemented with 10% fetal calf serum, 100 units/ml penicillin, 100 mg/ml streptomycin and 1 mM Sodium pyruvate (for MEFs only). Cells were tested negative for mycoplasma by PCR. Cells were harvested after the indicated period of glucose starvation [glucose free media  with 10% dialyzed FBS] or 24 hr after transfection and lysates were subjected to SDS-polyacrylamide gel electrophoresis followed by western blot. The levels of proteins were determined using the following antibodies: anti-GFP , anti-eIF4A , anti-eIF4E , anti-HA , anti-Pabp  and anti-Tubulin (Sigma).The pEGFP-N1 (Clontech), pEGFP-N1 with 5’UTR bearing secondary structure and the HA-eIF4A plasmids were previously described . To consFor knocking-down eIF4E, HEK293T cells were transfected with Dharmacon siGENOME SMART pool siRNA  using DharmaFECT1 transfection reagent. The Dharmacon ON-TARGETplus Nontargeting siRNA #3 was used as a negative control. 48 hr after the initial transfection, cells were transfected with the indicated GFP reporter plasmids. Cells were harvested 24 hr after the second transfection.eIF4e was purified from E.coli BL21(DE3) bacteria transformed with eIF4E-pET-30a construct . Bacteria were grown with Kanamycin at 37°C up to OD(600) = 0.8. Then, IPTG (0.5 mM) was added and bacteria were harvested after overnight incubation at 18°C. The samples were lysed using sonication in TPA buffer , 100 mM KCl) added with 2.5 mM PMSF, 10 mM β-mercaptoethanol and protease inhibitor cocktail (Sigma). All the buffers were prepared using Diethylpyrocarbonate (DEPC)-treated double distilled water. The lysate was subjected to centrifugation and subsequent ultracentrifugation in order to remove membrane associated RNAses. The soluble fraction was purified on gravity column using Ni-NTA His-Bind Resin , eluted with the TPA buffer containing 250 mM imidazole and subsequently dialyzed. Fractions containing eIF4E protein underwent gel filtration chromatography .Capped RNA oligos were chemically synthesized as previously described . For theTotal RNA extracted from control and 8 hr glucose starved cells was used as a template to create cDNA with superscript II (Invitrogen) according to the manufacturer’s instructions using gene-specific primers. The cDNA was purified using a PCR purification kit , followed by addition of polyG to the 5’-end using TdT enzyme (Promega) for 1 hr at 37°C. The reaction was terminated by heat inactivation for 15 min at 65°C, and the products were purified with the PCR purification kit (Qiagen). The modified cDNA was used as a template for PCR with Phusion polymerase (NEB) using nested reverse primers and forward PolyC primer. PCR products were run on 6% polyacrylamide gel. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been favorably evaluated by James Manley (Senior Editor) and three reviewers, one of whom, Timothy W Nilsen (Reviewer #1), is a member of our Board of Reviewing Editors. The following individual involved in review of your submission has agreed to reveal their identity: Orna Elroy-Stein (Reviewer #3).Thank you for submitting your article "TSS nucleotides via differential eIF4E binding and alternative promoter usage mediate translational response to stress" 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.As you will see all are positive about the work but some essential changes must be made via revision. In that regard please address the major comments of reviewer 2 as thoroughly as possible. Also explain CapSeq in the text and add the supplementary material requested during revision by reviewer 3.Reviewer #1:In this interesting manuscript authors provide new insight into translational dynamics that underlie cellular responses to energy stress.Major findings include the demonstration that under limiting eIF4E levels, different cap proximal nucleotides have different affinities for the factor with purine containing TSS's preferentially recognized.Additionally, the authors show that under stress a new isoform of eIF4A is synthesized that acts as an inhibitor of eIF4A and that PABP is upregulated via synthesis of isoforms with shorter 5'UTRs.eLife.Overall, the work is well performed and carefully presented and is viewed to be appropriate with minor revision for Major point the use of the term translational efficiency throughout should be changed to ribosome occupancy.Reviewer #2:In this manuscript, Dikstein and colleagues investigate the relationship between transcription start site usage and translational efficiency during energy stress. The authors perform a global analysis of transcription start sites (using CapSeq) across polysome fractions in the presence and absence of energy stress, with the goal of identifying changes in translational efficiency that correlate with changes in promoter usage. These data reveal a striking relationship between the identity of the cap-proximal nucleotides and the translational response to stress, with mRNAs beginning with purines being relatively refractory to stress. Biochemical analyses indicate that the cap-binding protein eIF4E has an intrinsic preference for purine-initiated capped mRNAs, which – together with the finding that eIF4E levels are reduced during stress – may explain the translational efficiency data. The authors further present data that stress-induced changes in start site usage for the eIF4A and PABP mRNAs also contribute to the stress response. Together the authors present a model in which changes in TSS selection induced by stress are a critical component of the translational response to stress, with the initiating nucleotides playing a particularly important but previously unappreciated mechanistic role.eLife after addressing all of the major concerns listed below.Overall, the manuscript reports an exciting finding that would be of interest to those interested in gene regulation. However, the current version of the manuscript lacks sufficient validation of the genome-wide datasets on which the entire paper is based. In addition, there are a number of caveats to both the experimental approaches and interpretations that should be addressed. For these reasons, I would only recommend publication in 1) The authors' measure of "translational efficiency" used for all analyses is poorly chosen and can be more accurately described as a measure of "ribosome occupancy". Rather than collecting many fractions from across a sucrose gradient in order to infer the number of ribosomes per mRNA , the authors only collected the "free", light and heavy polysomes. Even with these three pieces of data, the authors combined the light and heavy polysome data for unknown reasons and calculated "translational efficiency" as simply (light+heavy)/(light+heavy+free). The result is a metric that is extremely insensitive to changes in translation rate, since only if an mRNA shifts >2-fold in/out of the "free" fraction containing 0-1 ribosomes will an mRNA be labeled as down/up-regulated. This is problematic for the authors' conclusion that >70% of transcripts are unchanged at the translational level in response to stress  and impl2) There is an underlying assumption of the authors' analyses that is never explicitly stated: That transcript isoforms found to differ in the location of the 5' cap are identical in all other respects. Alternatively, transcripts generated by different promoters might also be spliced differently or use alternative polyadenylation sites, any of which could contribute to the measured translational efficiencies. By attributing any differences in translation to transcription start site, the authors are implying causation from what is only a correlation. In addition to raising this possibility in the text, the authors should undertake some validation experiments for some of their example genes to show that differences in transcription start site alone are sufficient to impart different polysome sedimentation to the mRNAs.3) Based on the examples of CapSeq reads depicted e.g., , the num4) From Reviewer #3:only in TOP-mRNAs, was never realized before. Thus, the current study points out for the first time at the importance of intrinsic properties of the translation machinery to TOP mRNA translation regulation by illuminating the critical importance of TSS/cap proximal nucleotides. Second important discovery is the stress-induced differential transcription of eIF4A and PABP genes which drives the (i) formation of inhibitory eIF4A isoform and (ii) high levels of PABP via translational regulation mediated by the alternative 5'UTR. Since both genes encode translation regulatory factors, this finding provides a demonstration of the tight linkage between transcription and translation in response to stress signals. Additional insights of the study relate to interesting examples many of which are relevant to gene expression during differentiation and cellular transformation.This elegant study address the usage of AP and TSS in response to energy stress and provides global and unbiased information about the translation status of each and every transcript. By doing so, an additional layer of complexity in gene expression is uncovered which refers to the contribution of transcription-translation linkage to proteome shaping in response to stress. This by itself is an important take-home message, which opens up the door to future similar studies concerning the transcription-translation linkage angle in response to other stressors and physiological signals. Moreover, there are additional novel findings, holding important implications. First, is the discovery that the affinity of eIF4E to capped mRNA with a proximal C nucleotide is significantly lower compared to its affinity to capped- proximal purines. The experiment was performed using cutting-edge label-free technology. This is a major contribution to the long-lasting question related to TOP-mRNAs translation regulation under conditions of limiting eIF4E. The purpose of proximal C nucleotide, Comments:The strengths of this study are several fold: (I) generation of an important high quality dataset which is useful for scientists interested in further analyses; (II) important insightful information gathered by the analysis described in the manuscript; (III) experimental validation of several high impact examples. The weakness of the manuscript in its present form is the absence of Supplementary excel file Tables containing the information related to the performed analysis (item II). Such tables, containing list of genes grouped and itemized according to specific features described and discussed in this study should be added.The explanatory text to Legend to Polysomes association may reflect attenuation at the elongation level. See for example Shenton et al. JBC 2006 (showing translation attenuation at the elongation level in response to oxidative stress). This notion should be mentioned in the text. In addition, please add to the legend to Informative details on the genomic locus at the lower part of Subsection “The TSS nucleotides influence the cap-binding affinity of eIF4E”, last paragraph: should add after 'RNA oligos' "whose first three nucleotides are CCU, ACU or GCU".Discussion, second paragraph: The work of Tcherkezian et al. 2014 is incorrectly interpreted. In this paper LARP1 was identified in a proteomic screen for proteins that associate with the mRNA 5′ cap, but was shown to be associated with PABP, not with eIF4E or eIF4G (whereas it does co-immunoprecipitated with eIF4A).Subsection “Differential translation of transcript isoforms is part of the stress response”, second paragraph – add a reference to the statement that the truncated eIF4A retains its interaction with eIF4G1.Discussion, end of fourth paragraph: IRES is not the only possibility. The uAUG-burdened long 5'UTR of eIF4A might confer either IRES-independent or 5'cap-dependnet translation regulation .Discussion, last paragraph: To be specific, must change from stressful conditions to energy stress. Indeed it may imply that a similar mechanism is operative in response to other stressful conditions, but this should be added as a possibility. As you will see all are positive about the work but some essential changes must be made via revision. In that regard please address the major comments of reviewer 2 as thoroughly as possible. Also explain CapSeq in the text and add the supplementary material requested during revision by reviewer 3.Reviewer #1:[…] Major point the use of the term translational efficiency throughout should be changed to ribosome occupancy.We have changed the term ‘translation efficiency’ with ‘ribosome occupancy’ throughout.Reviewer #2:[…] Overall, the manuscript reports an exciting finding that would be of interest to those interested in gene regulation. However, the current version of the manuscript lacks sufficient validation of the genome-wide datasets on which the entire paper is based. In addition, there are a number of caveats to both the experimental approaches and interpretations that should be addressed. For these reasons, I would only recommend publication in eLife after addressing all of the major concerns listed below.1) The authors' measure of "translational efficiency" used for all analyses is poorly chosen and can be more accurately described as a measure of "ribosome occupancy". Rather than collecting many fractions from across a sucrose gradient in order to infer the number of ribosomes per mRNA , the authors only collected the "free", light and heavy polysomes. Even with these three pieces of data, the authors combined the light and heavy polysome data for unknown reasons and calculated "translational efficiency" as simply (light+heavy)/(light+heavy+free). The result is a metric that is extremely insensitive to changes in translation rate, since only if an mRNA shifts >2-fold in/out of the "free" fraction containing 0-1 ribosomes will an mRNA be labeled as down/up-regulated. This is problematic for the authors' conclusion that >70% of transcripts are unchanged at the translational level in response to stress  and implAs suggested we have changed the term ‘translation efficiency’ with ‘ribosome occupancy’ throughout. We accept the suggestion and added an explanation in the text that “the use of three pools  is sensitive to large changes in translation but relatively insensitive to smaller changes.”2) There is an underlying assumption of the authors' analyses that is never explicitly stated: That transcript isoforms found to differ in the location of the 5' cap are identical in all other respects. Alternatively, transcripts generated by different promoters might also be spliced differently or use alternative polyadenylation sites, any of which could contribute to the measured translational efficiencies. By attributing any differences in translation to transcription start site, the authors are implying causation from what is only a correlation. In addition to raising this possibility in the text, the authors should undertake some validation experiments for some of their example genes to show that differences in transcription start site alone are sufficient to impart different polysome sedimentation to the mRNAs.We have now added two examples that demonstrate that the TSS and not other aspects of the transcript, confers sensitivity to eIF4E availability. We used a GFP reporter either under a CMV promoter that initiates at an A nucleotide, or Rpl18 promoter that initiates at a C nucleotide. Upon eIF4E depletion by siRNA or its inactivation by the 4EGI-1 drug, we found a clear differential sensitivity of the same GFP transcripts see . In addi3) Based on the examples of CapSeq reads depicted e.g., , the numAs stated in our response to point 2 above, we now added validation experiments for differential translation using 5’RACE (without ligation) see  and 6E. 4) From The aim of the specific examples is to demonstrate that the global analysis is valid to specific genes and has potential biological consequences. A subset of those examples such as eIF4A, PABP and the newly added Rpl18 is also followed by experimental validation , 5 and 6Reviewer #3:[…] Comments:The strengths of this study are several fold: (I) generation of an important high quality dataset which is useful for scientists interested in further analyses; (II) important insightful information gathered by the analysis described in the manuscript; (III) experimental validation of several high impact examples. The weakness of the manuscript in its present form is the absence of Supplementary excel file Tables containing the information related to the performed analysis (item II). Such tables, containing list of genes grouped and itemized according to specific features described and discussed in this study should be added.We now provide an excel file containing several sheets with all the described gene lists .We now added explanatory sentences for determination of the 5’ends of transcripts in the polysomal profiling and additional details of the size selection procedure in the Methods.The explanatory text to We revised the text for better clarity. Thanks for noting the error with the numbers which was corrected.Legend to Thanks for this note. Indeed, the last sentence was moved to Polysomes association may reflect attenuation at the elongation level. See for example Shenton et al. JBC 2006 (showing translation attenuation at the elongation level in response to oxidative stress). This notion should be mentioned in the text. In addition, please add to the legend to We clarified that the inhibition of translation in response to energy stress is at the levels of initiation and elongation (see Introduction).Informative details on the genomic locus at the lower part of As suggested we added chromosomal location and genomic structure for each browser display presentation.eLife style is to place the supplementary figures adjacent to the main figure. As The Subsection “The TSS nucleotides influence the cap-binding affinity of eIF4E”, last paragraph: should add after 'RNA oligos' "whose first three nucleotides are CCU, ACU or GCU".Thanks for the suggestion, the phrase was added.Discussion, second paragraph: The work of Tcherkezian et al. 2014 is incorrectly interpreted. In this paper LARP1 was identified in a proteomic screen for proteins that associate with the mRNA 5′ cap, but was shown to be associated with PABP, not with eIF4E or eIF4G (whereas it does co-immunoprecipitated with eIF4A).Thanks, this was corrected.Subsection “Differential translation of transcript isoforms is part of the stress response”, second paragraph – add a reference to the statement that the truncated eIF4A retains its interaction with eIF4G1.A reference was added.Discussion, end of fourth paragraph: IRES is not the only possibility. The uAUG-burdened long 5'UTR of eIF4A might confer either IRES-independent or 5'cap-dependnet translation regulation .Thanks for raising this point, we added it to the Discussion.Discussion, last paragraph: To be specific, must change from stressful conditions to energy stress. Indeed it may imply that a similar mechanism is operative in response to other stressful conditions, but this should be added as a possibility.Corrected.


Percutaneous balloon mitral valvuloplasty (BMV) is an important option for the treatment of mitral valve stenosis. The crux of this process is choosing the appropriate Inoue balloon size. There are two methods to do this. One is an empirical formula based on the patient’s height, and other is to choose according to the maximal inter-commissural distance of the mitral valve provided by echocardiography.The study, performed between January 2006 and December 2011, included 128 patients who had moderate to severe mitral stenosis and whose valve morphology was suitable for BMV. Patients were randomised into two groups. One group was allocated to conventional height-based balloon reference sizing (the HBRS group) and the other was allocated to balloons sized by the echocardiographic measurement of the diastolic inter-commissural diameter (the EBRS group).p = 0.03). The mean of the calculated balloon reference sizes was significantly higher in the HBRS than in the EBRS group [26.3 ± 1.2 mm, 95% confidence interval (CI): 26.1–26.6 vs 25.2 ± 1.1, 95% CI: 25.0–25.4, respectively; p = 0.007). Final mitral valve areas (MVA) were larger and mitral regurgitation (MR) > 2+ was less in the EBRS group .BMV was assessed as successful in 60 (92.3%) patients in the HBRS group and in 61 (96.8%) in the EBRS group (EBRS is a method that is independent of body structure. Choosing Inoue balloon size by measuring maximal diastolic annulus diameter by echocardiography for BMV may be an acceptable method for appropriate final MVA and to avoid risk of significant MR. Although both the incidence and prevalence of rheumatic heart disease have seen a dramatic decrease in recent years, it is still the leading cause of mitral valve stenosis. It is known that rheumatic changes are present in 99% of stenotic mitral valves excised at the time of mitral valve surgery.1Mitral valve stenosis is the most serious sequel of rheumatic fever and it has unfavourable effects on survival rates and quality of life. The disease is endemic in developing countries, including Middle Eastern countries.Since the first publication of the Inoue balloon in 1984, balloon mitral valvuloplasty (BMV) has become the procedure of choice all over the world because of its lower cost and morbidity rate.52 with no significant MR (MR not more than 2/4 in over 80% of patients).7According to recently published valvular heart disease guidelines, BMV is recommended for symptomatic patients with moderate to severe mitral valve stenosis. Candidate patients for BMV should have a suitable valve structure with no mitral regurgitation (MR). BMV should not be administered to a patient with thrombus in the cardiac chambers. The main purpose of BMV is to provide an adequate mitral valve area (MVA) of > 1.5 cm2) and also for asymptomatic patients with pulmonary artery systolic pressure of > 50 mmHg at rest or > 60 mmHg with exercise.11Many studies have shown that acute procedural results, including final MVA and post-procedural MR, independently predict the long-term outcome after BMV.The aim of this procedure is to provide adequate valve area while protecting the mitral valve apparatus. For this purpose, selection of the appropriate balloon size is one of the most critical factors for BMV.A reasonable alternative approach is choosing balloon size according to the maximal inter-commissural distance of the mitral valve as provided by echocardiography. This is a more direct and conservative method of balloon sizing to avoid possible mistakes when determining the size of the balloon, and to prevent possible complications in the procedure.The aim of this study was to assess the early haemodynamic and echocardiograhic results of BMV according to these two different types of Inoue balloon selection strategy.2), whose valve morphology was suitable for BMV and who had no contraindications for BMV. Symptomatic and asymptomatic patients were included who had a pulmonary artery systolic pressure of > 50 mmHg at rest or 60 mmHg with exercise in the presence of favourable morphological criteria of the mitral valve, derived from the echocardiographic Wilkins scoring system.Between 1 January 2006 and 31 December 2011, 128 consecutive symptomatic patients were seen with moderate to severe mitral stenosis  and the other was allocated to balloons sized by the echocardiographic measurement of the diastolic intercommissural diameter (the EBRS group). BMV was performed in all patients.All patients were informed about the study, and written consent was obtained. The study was approved by the institution’s ethics committee and performed in accordance with the Helsinki Declaration.In accordance with the recommendations of the American Society of Echocardiography, all transthoracic echocardiographic (TTE) examinations were performed with the patient lying in the left lateral decubitus position, and two-dimensional images were recorded and measured in the apical four-chamber, two-chamber, and parasternal long- and short-axis views.B-mode, two-dimensional Doppler and colour-flow Doppler echocardiographic evaluations were performed using a Siemens Acuson CV70 system  with a 2.5–4.0-MHz transducer with second harmonic capabilities. Transoesophageal echocardiographic (TEE) examinations were performed on all patients with a 5-MHz multiplane endoscopic probe.2 + right atrial pressure). V is the maximum velocity of the tricuspid valve regurgitant jet, measured by continuous-wave Doppler, added to the estimated right atrial pressure. Right atrial pressure was calculated using the caval respiratory index, as described by Kircher et al.16MVAs were calculated using the direct planimetry and pressure half-time method. Diastolic trans-mitral gradients were measured by continuous-wave Doppler echocardiography. Systolic pulmonary artery pressure (sPAP) was measured with continuous-wave Doppler. Tricuspid regurgitation velocity (V) was recorded from any view and used to determine sPAP . The right and left heart haemodynamic study was performed to evaluate sPAP and mitral valve pressure gradients.In the HBRS group, balloons were selected using the height-based reference size by calculating according to the standard height-based formula [0.1 × height (cm) + 10]. In the EBRS group, balloons were selected according to the echocardiographic inter-commissural distance measurement in mid-diastole.The classic antegrade Inoue balloon technique was used for BMV by two experienced cardiologists in a standard and similar fashion. Procedures were performed with TEE guidance. After the atrial septostomy and appropriate septal dilation, 100 IU/kg of heparin was administered to achieve an activating clotting time of > 250 seconds. Left ventriculography was performed before and after BMV to evaluate mitral regurgitation. A successful BMV was defined as an uncomplicated procedure yielding a final mitral valve area of > 1.5 cm2 and post-valvuloplasty mitral regurgitation of < 3+.p 0.05 was considered significant, and two-tailed p-values were used for all statistics.Continuous variables are given as mean ± SD. Categorical variables were defined as percentages and compared with the chi-square test to compare the measurements before and after BMV, and a Student’s paired t‑test was used. A probability value of p In this study, 128 patients participated. In 65 patients, Inoue balloon sizes were calculated according to HBRS, while in the remaining 63 patients, sizes were provided by EBRS. The study patients’ baseline clinical, echocardiographic and catheterisation characteristics are shown in p = 0.16) between the groups; 72.7% of the patients were female. There was no difference between the groups in terms of weight and height . Therefore it can be considered that both groups were similar in terms of body mass index.The mean age of the patients was 32 years in the HBRS group and 31 in the EBRS group, and there was no statistically significant difference .Echocardiographic and catheterisation assessment of patients before the procedure showed no significant differences between the basic features of the two groups. The degree of mitral valve stenosis, mitral valve scores and MR (2+) were similar between the two groups . The mean of the calculated balloon reference sizes was significantly higher in the HBRS than in the EBRS group . The final inflated balloon sizes were similar between the groups .BMV was assessed as successful in 60 (92.3%) patients in the HBRS group and in 61 (96.8%) in the EBRS group (p = 0.06). In the EBRS group, larger MVAs were achieved than in the HBRS group . The incidence of significant MR (3–4+) was lower than in the HBRS group (pp = 0.05 by echocardiography and p = 0.03 by ventriculography). Also, 2+ degree MR was significantly less developed in the EBRS patients (p = 0.01).The post-procedural results of the patients are presented in In recent years, Inoue balloon mitral commissurotomy has become the treatment of choice in many patients with rheumatic mitral stenosis. The main target of this procedure is to resolve the stenotic mitral orifice without causing extensive damage to the commissures, leaflets and subvalvular apparatus, thus leading to excessive mitral regurgitation. The most common serious complication is haemopericardium, with an incidence of 0 to 2.0%.18Many studies have shown that acute procedural results, including final MVA and post-procedural MR, independently predict the long-term outcome after BMV.Although there are many data for MBV, there is no consensus in determining the optimal size of the balloon.22Nobuyoshi and colleagues recommended selecting the balloon size by directly measuring the mitral annular diameter using two-dimensional echocardiography to avoid undesirable extensive injury to the mitral valve apparatus.In our study we have shown that selecting Inoue balloon size according to echocardiographic maximal diastolic diameter is as efficient as using the height-based formula. Final balloon sizes were similar between the HBRS and EBRS groups but were smaller in the EBRS group. There was a significant difference between calculated and final balloon sizes in the HBRS group. We achieved sufficient MVAs by echocardiography-derived balloon sizing in this study, associated with lesser degrees of MR change.Sanati and colleagues had the same clinical results with the EBRS method. Their final balloon sizes were similar between the two groups, and they achieved better valve areas with less MR.Considering the fact that severe MR is also infrequent with the height-based method, but in our study severe MR was seen less in the EBRS group, we believe that applying invasive treatments for heart diseases using imaging methods could be better than using empirical methods. So in order to achieve effective MVA without severe MR, we suggest the EBRS method to select Inoue balloon size. In the future, the importance and correlation of MR developing after MBV with prognosis of patients will be better understood.The participants in this study were all suitable for MBV, and we did not include patients who had a borderline Wilkins score. The number of patients in our study was higher than in other studies in the literature; however, the number of patients should perhaps have been greater. Also, long-term follow-up results, especially those concerning MR, are yet to be published.EBRS is a method that is independent of body structure. Some patients are tall, some are short, and some are obese or asthenic, so patients who have discordance between heart size and body structure may benefit from this method. Choosing Inoue balloon size for BMV by measuring maximal diastolic annulus diameter using echocardiography is a reasonable method with acceptable final MVAs to avoid the risk of significant MR.Echocardiographic balloon sizing for BMV should be used, especially in patients with discordance between height and heart size. We believe that all types of invasive procedures may be planned according to the dimensions of cardiac structures. Our study sheds some light on this issue.


Medical coding is used for a variety of activities, from observational studies to hospital billing. However, comorbidities tend to be under-reported by medical coders. The aim of this study was to develop an algorithm to detect comorbidities in electronic health records (EHR) by using a clinical data warehouse (CDW) and a knowledge database.We enriched the Theriaque pharmaceutical database with the French national Comorbidities List to identify drugs associated with at least one major comorbid condition and diagnoses associated with a drug indication. Then, we compared the drug indications in the Theriaque database with the ICD-10 billing codes in EHR to detect potentially missing comorbidities based on drug prescriptions. Finally, we improved comorbidity detection by matching drug prescriptions and laboratory test results. We tested the obtained algorithm by using two retrospective datasets extracted from the Rennes University Hospital (RUH) CDW. The first dataset included all adult patients hospitalized in the ear, nose, throat (ENT) surgical ward between October and December 2014 (ENT dataset). The second included all adult patients hospitalized at RUH between January and February 2015 . We reviewed medical records to find written evidence of the suggested comorbidities in current or past stays.Among the 22,132 Common Units of Dispensation (CUD) codes present in the Theriaque database, 19,970 drugs (90.2%) were associated with one or several ICD-10 diagnoses, based on their indication, and 11,162 (50.4%) with at least one of the 4878 comorbidities from the comorbidity list. Among the 122 patients of the ENT dataset, 75.4% had at least one drug prescription without corresponding ICD-10 code. The comorbidity diagnoses suggested by the algorithm were confirmed in 44.6% of the cases. Among the 4312 patients of the general dataset, 68.4% had at least one drug prescription without corresponding ICD-10 code. The comorbidity diagnoses suggested by the algorithm were confirmed in 20.3% of reviewed cases.This simple algorithm based on combining accessible and immediately reusable data from knowledge databases, drug prescriptions and laboratory test results can detect comorbidities.The online version of this article (10.1186/s12911-018-0586-x) contains supplementary material, which is available to authorized users. Medical coding  is used With the increasing use of electronic health records (EHR), such data become more easily available, especially through clinical data warehouses (CDW) that are great sources of integrated heterogeneous and exhaustive information , 10. In The aim of this study was to develop an algorithm to detect comorbidities in EHR using a clinical data warehouse and knowledge databases. We focused on two different types of data, drug prescriptions and laboratory tests, to assess how they contribute and complement each other for identifying comorbidities.We used three data sources: clinical data from the Rennes University Hospital (RUH) CDW, knowledge data from a pharmaceutical database and the French reference list of comorbidities.The EHOP , 13 CDW The Theriaque database  is one oThe French Comorbidity List (“Liste des Comorbidités et Morbidités Associées”) is a reference list from the French Program of Medicalization of the Information Systems  that is based on the Diagnosis-Related-Group (DRG) system. The Program of Medicalization of Information Systems takes comorbidities into account for hospital reimbursement and the major comorbid conditions are identified in this reference list. The aim of this list is to identify comorbidities (ICD-10-coded) with a financial impact on the patient’s hospital stay.To detect comorbidities, we developed a three-step algorithm that we then tested in a retrospective observational study Figure .Fig. 1ThWe paired the Theriaque database with the Comorbidity List to identify drugs associated with at least one major comorbid condition. This step also identified ICD-10 codes associated with a drug indication and eliminated all ICD-10 codes that could not be paired with at least one drug indication. It also excluded drugs with very broad  or only imprecise  indications, based on their ATC classification , because this drug is only prescribed in the case of hyperkalemia (ICD-10 code E87.5). As the French Nephrology Society defines hyperkalemia as a plasma potassium level higher than 5.0 mmol/L , we compWe retrospectively tested the algorithm using two different datasets extracted from the EHOP CDW. The first dataset included all stays of adult patients hospitalized at RUH ear, nose, throat (ENT) surgical ward between October and December 2014 and with at least one drug prescription (ENT dataset). We chose this unit because comorbidities unrelated to the cause of hospitalization are rarely mentioned in the surgical record and thus, it represented a good test for our algorithm.The second dataset included the stays of all adult patients hospitalized at RUH between January and February 2015 and with at least one drug prescription .We reviewed all the diagnoses suggested by the algorithm using the data from the medical records extracted from the EHOP CDW, to find written evidence about such comorbidities in the current or past hospital stays.We evaluated the algorithm performance by computing Precision, Recall and F-Score metrics for the ENT dataset. For the general dataset, we did not compute Recall because the value obtained for the ENT dataset suggested that the algorithm could efficiently detect comorbidities based on the drug prescriptions.Precision is the ratio between the number of stays with confirmed diagnoses and all stays with suggested diagnoses. Recall is the ratio between the number of stays with confirmed diagnoses and all stays with missing diagnoses. The F-Score is the harmonic mean of Precision and Recall. The detailed description of the evaluation process can be found in Additional file We described quantitative variables using means and standard deviation and categorical variables using counts and percentages. We calculated the inter-expert agreement using the Cohen’s Kappa (κ) coefficient. We performed all statistical analyses with R 3.3.1 .The suggested diagnoses were confirmed or not by one medical expert for the ENT dataset and by two medical experts for the general dataset. These investigators had a public health background and were familiar with the EHOP CDW and the Program of Medicalization of Information Systems. They also knew well the coding guidelines because they have been involved in coding for a few years before this study. The two experts reviewed all records from the ENT dataset and only a random sample from the general dataset, to estimate the algorithm metrics and inter-observer agreement. We calculated that the size of the general dataset sample required to have a 90% inter-observer agreement rate, with β = 0.80 and α = 0.05, was 285 stays.For each drug prescription, the two experts had to confirm at least one of the suggested comorbidities (ICD-10 codes) or to reject all of them. To confirm a diagnosis, they compared the title of the suggested ICD-10 code or its synonyms (based on the list of synonyms provided by ICD-10) with the diseases/symptoms recorded in the EHR. If they rejected all diagnoses suggested by the algorithm based on the drug prescriptions (structured data), they examined the discharge notes (unstructured data) to confirm their decision. This was done to ensure that the rejection was based on the absence of data on a given disease/symptom and not on the CDW inability to extract all available data. Any disagreement between experts required a consensus session with a third expert to reach a final decision.Among the 22,132 CUD codes presents in the Theriaque database, 19,970 (90.2%) were associated with one or more ICD-10 codes, based on the drug indication(s). Moreover, 11,162 (50.4%) CUD codes were associated with at least one of the 4878 comorbidities from the Comorbidity List. Among the 40,000 codes included in the French ICD-10, only 4407 (11.0%) were present in the Theriaque database as a drug indication.After excluding all hospital stays without at least one drug prescription, or with prescriptions of excluded drugs or without at least one ICD-10 code associated with a drug, the ENT dataset included 122 stays and the general dataset 4312 stays  from the general dataset . The two medical experts reached an agreement on 280 cases . For the five remaining stays, the experts’ disagreement could be explained by the Program of Medicalization of the Information Systems coding rules. For instance, the reimbursement guidelines do not allow coding some diagnoses/symptoms. Accordingly, even if present in the medical record , these diagnoses will not be coded in the hospital stay .We assessed the inter-expert agreement using a random sample  were detected by the algorithm. The algorithm suggested a possible missing comorbidity in 92 of the 122 stays (75.4%)  and added 58 ICD-10 codes to the stay ; ii) the drug indication was based on implicit judgments and, therefore, could not be added to the stay .The medical expert did not confirm the suggested diagnoses in 51 of the 92 stays for two main reasons: i) the stays did not contain information concerning the suggested comorbidity (Drug prescriptions could not be combined with the laboratory test results (step 3) because only one stay in this dataset included a SPS prescription.Among the 4932 ICD-10 billing codes present in the 4312 stays of the general dataset, 1271 (25.8%) were detected by the algorithm. The algorithm suggested a possible missing comorbidity in 2950 of the 4312 stays (68.4%)  and added 76 ICD-10 codes ; ii) the indication for the drug prescription was implicit and therefore could not be added to the stay .The two experts could not confirm the suggested diagnoses in 227 reviewed stays for two mains reasons: i) the EHR had no information concerning the suggested comorbidity (Combining SPS prescriptions and laboratory results (plasma potassium concentration) improved the detection of hyperkalemia by 66.8%, compared with using only the laboratory results. Specifically, 1270 stays in the general dataset had laboratory results including blood potassium values and in 152 of them (12%) this value was higher than 5 mmol/L, but without a hyperkalemia ICD-10 billing code. This means that for 12% of stays in this dataset the hyperkalemia ICD-10 code could be added only on the basis of the laboratory test results. Moreover, 299 stays in this dataset included an SPS prescription. Among them, 85 did not have a hyperkalemia ICD-10 billing code and 67 had a potassium concentration value higher than 5 mmol/L, which means that in 78.8% (67/85) of stays, the algorithm could detect a hyperkalemia ICD-10 code by combining the laboratory test result and the SPS prescription.This study demonstrates that an algorithm based on clinical data and knowledge databases can detect comorbidities in EHR. The algorithm suggested missing comorbidities for more than half of the stays and, depending on the dataset, billing codes were improved by 6–8% after expert review. Moreover, by combining drug prescriptions and laboratory test results hyperkalemia detection was improved by 67%.To develop this algorithm, we applied strategies similar to those used for adverse drug event detection –22. FirsThen, we combined laboratory test results and prescription of drugs with narrow indications  to improve comorbidity detection . This reWe then used data extracted from the EHOP CDW to confirm the diagnoses suggested by the algorithm. The EHOP CDW gives the opportunity to get back to the free text reports (unstructured data) . This alAlthough the algorithm can identify missing comorbidities, the method has some limitations. First, some drug prescriptions are not covered by the Theriaque database, because it only records indications approved by regulatory agencies for the drug original marketing authorizations, but not “off-label” usage . Yet, doSecond, the Cohen’s kappa coefficient value (0.74) indicates a substantial agreement between observers, which could be explained by their common background and similar training. This value could have been improved by involving more human experts, but very few were available at the time of the study.Furthermore, the suggested diagnoses were confirmed only in 20% of cases. The remaining diagnoses could not be confirmed mainly because information was often implicit, a recurring issue in healthcare , particuNevertheless, our algorithm can improve the patients’ characterization using EHR data . This isWe developed a fairly simple algorithm based on accessible and immediately reusable data. This study highlights that knowledge databases, drug prescriptions and laboratory test results can be used together to improve comorbidity detection. Combining this approach with NLP methods focused on implicit information should improve the algorithm performance.Additional file 1:List of excluded drugs with broad or imprecise indications according to their Anatomical Therapeutic Chemical (ATC) class. This table lists all drugs that were not included in the first step of the algorithm, with their ATC code and label. (DOCX 12 kb)Additional file 2:Algorithm Evaluation: Inter-observer agreement process. This file explains in detail inter-observer agreement process: the data used, all the different steps, the formalization, the process in case of disagreement between the experts. (DOCX 19 kb)Additional file 3:Characteristics of all ICD-10 codes suggested by the Theriaque database. This table describes the aggregated characteristics of all ICD-10 codes suggested by the algorithm for each dataset. The codes are separated between “Match” codes (codes added after expert review) and “Non-match” codes (codes that could not been added after expert review). (DOCX 15 kb)Additional file 4:Characteristics of the ICD-10 codes added after expert review. This table describes the aggregated characteristics of the ICD-10 codes added to each dataset after the manual review by the two experts. It also count the number of ICD-10 codes that were part of the CMA list. (DOCX 13 kb)Additional file 5:List of the ICD-10 codes added after expert review. This table lists all ICD-10 codes that were added after expert review, the number of stays were they were added, and specify if these codes are part of the CMA list. (DOCX 17 kb)Additional file 6:Ethics committee approval. This file (in French) is the official ethics committee approval for the study. (DOC 85 kb)