Ribosome profiling experiments indicate pervasive translation of short open reading frames (ORFs) outside of annotated protein-coding genes. However, shotgun mass spectrometry (MS) experiments typically detect only a small fraction of the predicted protein products of this noncanonical translation. The rarity of detection could indicate that most predicted noncanonical proteins are rapidly degraded and not present in the cell; alternatively, it could reflect technical limitations. Here, we leveraged recent advances in ribosome profiling and MS to investigate the factors limiting detection of noncanonical proteins in yeast. We show that the low detection rate of noncanonical ORF products can largely be explained by small size and low translation levels and does not indicate that they are unstable or biologically insignificant. In particular, proteins encoded by evolutionarily young genes, including those with well-characterized biological roles, are too short and too lowly expressed to be detected by shotgun MS at current detection sensitivities. Additionally, we find that decoy biases can give misleading estimates of noncanonical protein false discovery rates, potentially leading to false detections. After accounting for these issues, we found strong evidence for 4 noncanonical proteins in MS data, which were also supported by evolution and translation data. These results illustrate the power of MS to validate unannotated genes predicted by ribosome profiling, but also its substantial limitations in finding many biologically relevant lowly expressed proteins. Ribosome profiling experiments suggest pervasive translation of short ORFs outside of annotated protein-coding genes. However, shotgun mass spectrometry experiments typically detect only a small fraction of the predicted protein products. This study shows that shotgun mass spectrometry has limited utility at this task due to its intrinsically low sensitivity for small, low-abundance proteins. Ribosome profiling (ribo-seq) experiments indicate that genomes are pervasively translated outside of annotated coding sequences . This “nOver the past decade, numerous studies have attempted to identify noncanonical proteins using bottom-up “shotgun” proteomics in which MS/MS spectra from a digested protein sample are matched to predicted spectra from a protein database ,17. ThesEven given the low rates of detection of noncanonical proteins predicted by ribo-seq, there are suggestions ,22 that Saccharomyces cerevisiae. He and colleagues [S. cerevisiae and investigate the biological and statistical factors affecting detection of noncanonical proteins.Several recent MS studies have aimed to improve detection of short, lowly expressed proteins in lleagues  used a clleagues  also uselleagues  searchedlleagues  integratUsing the MSFragger program , we searFig 1A). For noncanonical ORFs considered alone, it was not possible to generate a substantial list of detected proteins at a 1% FDR because too many decoys were detected relative to targets at all confidence thresholds . When the full proteome was considered together, 4,389 proteins were found at a 1% FDR, including 4,371 canonical proteins and 18 noncanonical . However, 10 noncanonical decoys also passed the 1% FDR expect score threshold, implying an estimated 56% FDR among these 18 noncanonical proteins. Thus, using a 1% proteome-wide FDR threshold, rather than a class-specific FDR strategy, results in a list of inferred noncanonical proteins of which a large fraction are false positives, as cautioned by Nesvizhskii [Among canonical ORFs considered alone, 4,391 of 5,968 had proteins detected at a 1% FDR . The small enrichment of targets above decoys gives little confidence in detection of noncanonical ORF products at the level of individual proteins but leaves open the possibility that MS data could contain a weak biological signal.In general, there is a trade-off in target-decoy approaches such that setting a weaker confidence threshold results in a longer list of proteins inferred as detected, but with a higher FDR. In the case of yeast noncanonical proteins, the decoy/target ratio never went below 60% for any list of inferred detected target proteins larger than 10, and this ratio also did not converge to 1 even with thresholds set to allow 10,000 target proteins to pass . In contrast, targets substantially outnumber decoys at all ranks for noncanonical ORFs . We reasoned that this bias could be explained by the short length of noncanonical proteins. Indeed, many predicted peptides derived from noncanonical ORFs include the starting methionine, while decoys, consisting of reversed sequences from the protein database, are more likely to end with methionine . To eliminate this large systematic difference, we constructed an alternative decoy database in which decoys for noncanonical proteins were reversed only after the leading methionine. When this database is used, the number of noncanonical targets and decoys at each rank is close to equal , and the target/decoy ratio converges to one as confidence thresholds are lowered . This behavior is consistent with expectations for a well-constructed decoy set. We therefore repeated our initial analysis using the alternative decoy set  and used it for all subsequent analyses.However, there is an alternative explanation for why targets are found at somewhat higher rates than decoys across a large range of confidence thresholds: decoy bias . The accFig 2G). We therefore sought to examine the strongest hits to determine if we could identify evidence that any were genuine detections. Two noncanonical proteins had peptides with stronger expect scores than any decoys . We gave the ORFs encoding these proteins systematic names YMR106W-A and YFR035W-A following SGD conventions [S1 Fig). Only a single YMR106W-A peptide was found at this threshold, but 3 additional YMR106W-A peptides had stronger matches than the next strongest decoy . Moreover, YMR106W and YFR035W-A both had translation rates (in-frame ribo-seq reads per codon) greater than 99% of noncanonical ORFs in the Wacholder and colleagues dataset [Using the alternative decoy set and standard MSFragger analysis, we remained unable to construct an FDR-controlled list of noncanonical proteins at a 10% FDR threshold because decoys were still detected at a similar rate as targets . In a multiple sequence alignment with other species in the Saccharomyces genus, the full span of the YFR035W-A amino acid sequence aligns between all species . Thus, evolutionary, translation, and proteomics evidence all indicate that unannotated ORF YFR035W-A is a better candidate for a conserved protein-coding gene than annotated ORF YFR035C.YMR106W-A is located 27 nt away from a Ty1 long terminal repeat. No homologs outside lleagues . It is tposition . This isposition . YFR035Wposition ,38, so iposition , but thi−5, similar to the level at which YMR106W-A was detected, or else the strongest candidates if another program was used.Aside from YMR106W-A and YFR035W-A, the standard MSFragger approach did not confidently detect proteins encoded by noncanonical ORFs supported by ribo-seq. We therefore considered some reasons we could miss noncanonical proteins present in the data and employed alternative approaches to test these possibilities. For each approach, we determined whether a substantial list of noncanonical ORFs could be constructed with FDR of 10% at the protein level. If not, we further investigated peptides with MSFragger expect scores <10Fig 4A). The only noncanonical proteins detected at a 10−5 expect score threshold were the same two as in the standard analysis.First, we hypothesized that a mismatch between the environmental conditions in which the ribo-seq and MS datasets were constructed may explain the low number of detected noncanonical proteins. To investigate this possibility, we reduced our analysis to consider only ribo-seq and MS experiments conducted on cells grown in YPD at 30°C. The target/decoy ratio looked similar to the analysis on the full dataset, with no noncanonical protein detection list generatable with a 10% FDR . The only noncanonical proteins detected at a 10−5 e-value threshold  were YMR106W-A and YFR035W-A, also found by MSFragger. We then applied the machine learning based MS2Rescore algorithm [Fig 4C). We also performed a search using MaxQuant [Fig 4D), with only 3 peptides given stronger scores than the strongest decoy; two belonged to YMR106W-A and one to a different hypothetical protein we named YPR195C-A following SGD conventions. However, this hypothetical protein was identified from a peptide found only once, showed no evidence of conservation in the Saccharomyces genus, and was not translated at high levels (Table 1); we therefore conclude that it may not be a genuine detection.Next, to ensure that our results were not specific to the search program MSFragger, we repeated our analysis using MS-GF+ . The patlgorithm  to rescoMaxQuant , which uMaxQuant  to indicFig 4E), and there were no PSMs with expect scores below 10−5.Work in other species has shown that use of multiple proteases, rather than trypsin alone, can improve detection of small or noncanonical proteins ,45. We t−5 expect score .One challenge in MS proteogenomics is that expanding searches to larger sequence database sizes raises the threshold for detection, which could limit discoveries . To redu−5 expect score threshold . Adding acetylation of lysine or N-terminal acetylation as variable modifications did not improve target/decoy ratios overall , but a single hit with an expect score of 8.37 × 10−6 was found, which we named YOR109W-A following SGD convention. However, this hypothetical protein was identified from a peptide found only once, showed no evidence of conservation in the Saccharomyces genus, and was translated at lower levels than other noncanonical protein detections (Table 1); we therefore conclude that it may not be a genuine detection.Next, we hypothesized that noncanonical proteins could have been missed from our searches due to posttranslational modification or cleavage. Allowing for phosphorylation of threonine, serine, or tyrosine as variable modifications did not improve the decoy/target ratio or yield detection of any noncanonical phosphorylated peptides at a 10Fig 4I), but a single additional noncanonical peptide was identified with a relatively strong expect score of 2.78 × 10−6 . This peptide was from the ORF YIL059C, annotated as “dubious” on SGD, indicating that, in the view of SGD, the ORF is “unlikely to encode a functional protein.” YIL059C is in the 88th percentile of translation rate and 99th percentile of length among noncanonical ORFs, at 366 nt (Table 1). It overlaps on the opposite strand of the ORF YIL060W, classified as “verified” on SGD. However, the references listed in support of YIL060W are all based on full deletion experiments, which would disturb both ORFs and therefore do not distinguish between them [Fig 5A), and YIL060W was not detected in our MS analysis of canonical ORFs. Given that the YIL059C peptide had one non-enzymatic end, we tested whether it could be a signal peptide using the TargetP program [Fig 5B), providing additional support that this is a genuine detection. Searching for homologs using TBLASTN, BLASTP, and BLASTN in the NCBI databases and TBLASTN and BLASTN in Saccharomyces genus genomes at a 10−4 e-value threshold, YIL059C and YIL060W have detected DNA homologs only in Saccharomyces species S. paradoxus, S. mikatae, and S. jurei. There was an intact protein alignment of YIL059C between S. cerevisiae and S. jurei , while YIL060W has no homologs that fully align in any species . YIL059C is located adjacent, and on the opposite strand, to a Ty2 long terminal repeat. These observations are consistent with a transposon-mediated horizontal transfer of YIL059C prior to divergence between S. cerevisiae and S. mikatae, followed by loss in S. paradoxus and S. mikatae and preservation in S. cerevisiae and S. jurei. We do not rule out a role for YIL060W, but all considered evidence provides greater support for the biological significance of YIL059C.Allowing for peptides to have one end that is not an enzymatic cut site to search for potential cleavage products did not improve target/decoy ratios overall . Thus, the Sun and colleagues results do not differ from ours because more high-confidence noncanonical PSMs were found. Rather, the difference is in statistical approach for FDR estimation. Sun and colleagues controlled FDR at a 1% proteome-wide level, rather than controlling a noncanonical-specific FDR as in our analysis. Moreover, Sun and colleagues analyzed multiple distinct datasets separately, each at a 1% FDR, and then constructed a combined list containing any protein found at 1% FDR in at least one analysis. Merging lists of detected proteins each constructed at 1% FDR is expected to generate a list with an FDR much higher than 1% [Finally, we wanted to investigate a class of noncanonical ORFs not present in the Wacholder and colleagues translated ORF dataset: noncanonical ORFs that overlap a canonical ORF on the same strand. These ORFs are difficult to identify by ribo-seq because it is challenging to distinguish noncanonical ORF-associated ribo-seq reads from those of the canonical gene; however, some proteins encoded by noncanonical ORFs that overlap canonical ORFs have been identified in previous analyses ,51, incllleagues , who fou than 1% .−5. We assigned it systematic name YNL155C-A following SGD conventions (Table 1).The stable translation product of YNL155C-A was supported by 2 distinct peptides, which together were detected 12 times with expect scores below the best decoy score of 5.69 × 10−7, with the strongest value of 5.77 × 10−9 .This 255-bp ORF overlaps canonical gene YNL156C for 57 of 255 bases. Its translation product was not identified in the Sun and colleagues’ analysis [Fig 6B). There also appears to be a triplet periodic pattern in a frame distinct from both YNL156C and YNL155C-A at the locus, suggesting that all 3 frames may be translated. Excluding the overlapping region, there are 265 reads per codon on the ORF that map to the first position of a codon in the YNL155C-A reading frame; this would put it in the 99.6th percentile of translation rate among translated noncanonical ORFs in the Wacholder and colleagues dataset. No homologs were found in more distantly related species in a TBLASTN search against the NCBI nonredundant protein database, but YNL155C-A was well conserved across Saccharomyces . As only 19 of 75 codons of YNL155C-A overlap YNL156C-A , the strong amino acid conservation across the length of the full protein  indicates purifying selection on YNL155C-A itself. Thus, proteomic, translation, and evolutionary evidence all support YNL155C-A as a protein-coding gene.In our analysis, only one overlapping ORF had associated PSMs with expect scores stronger than 10analysis . A clear−6 MSFragger expect score threshold). While 69% of canonical proteins have at least one peptide detected at this threshold, based on simulations, only 22% of noncanonical proteins would have a detectable peptide at this detection rate. These results illustrate how the short length of noncanonical proteins and the low numbers of potential tryptic peptides that result limit noncanonical detection.We sought to understand why the large majority of proteins predicted from translated noncanonical ORFs remained undetected across multiple computational search strategies. A major difference between canonical and noncanonical proteins is length: The average canonical protein is 503 residues compared to only 31 among noncanonical proteins. Short size can affect protein detection probability through distinct mechanisms: Shorter sequences provide fewer distinct peptides when digested, and the sample preparation steps of the MS experiment may be biased against small proteins . To inveS6 Fig). We therefore hypothesized that technical biases other than the number of potential tryptic peptides further limit the MS detectability of small proteins.Still, the larger challenge in noncanonical protein detection is not just the low number of possible peptides but the much lower detection rate among them than among canonical peptides. Our analyses only detect a handful of noncanonical proteins, far below the 22% that would be expected if lack of potential tryptic peptides was the only limitation. This is because, as a group, noncanonical proteins almost completely lack the high-confidence PSMs that support numerous canonical protein detections . We observe a division between canonical ORFs shorter versus longer than 150 nt. Among 27 canonical yeast ORFs shorter than 150 nt, none of 280 theoretically detectable peptides were detected at a 10−6 expect score threshold. This detection rate is significantly below expectation given the overall 6% rate at which canonical peptides are detected , suggesting that there may be technical biases limiting detection of proteins that are this short. As 83% of noncanonical ORFs  are shorter than 150 nt, short length can partially explain the low detectability of noncanonical ORF peptides. In contrast, however, among canonical ORFs longer than 150 nt, shorter lengths were associated with higher probabilities that a peptide was detected . This is likely due to a trend of higher translation rates among shorter ORFs , which is also observed among noncanonical ORFs . This observation suggests that short size should not be a barrier to detection of peptides encoded by noncanonical ORFs longer than 150 nt. There are 3,080 such ORFs, potentially encoding 35,392 detectable peptides, yet only one peptide was found at a 10−6 expect score threshold .To investigate this hypothesis, we calculated the peptide detection rate, out of all theoretically detectable peptides, among different ORF size classes . For example, 20% of canonical peptides scoring above 0.9 were detected at a 10−6 expect score threshold, compared to only 0.17% of peptides scoring below 0.1, an 85-fold increase. The distribution of detectability scores was similar between canonical and noncanonical peptides overall, with the major difference being that 6.3% of canonical peptides scored above .9 compared to only 3% of noncanonical peptides . If canonical peptides had the same distribution of scores as noncanonical peptides, the number of detected canonical peptides would be 83% of those found in actuality. Thus, amino acid composition does increase the difficulty in detection of noncanonical peptides, but this is a relatively small effect.In addition to length, noncanonical ORFs also differ from canonical ORFs in amino acid composition . The ami8 Fig). Among noncanonical proteins overall, only 11% are predicted to have transmembrane domains, below the 20% of canonical proteins predicted to have one. However, among noncanonical proteins longer than 50 amino acids, there is an excess of proteins with transmembrane domains that make up more than 20% of the protein . Thus, for some larger noncanonical proteins we would otherwise expect to be more likely to be detected, transmembrane domains likely hinder their detection.Transmembrane proteins are also more difficult to detect by MS . To dete−6 expect score threshold, at both the protein  and peptide  levels. As with protein length, we can use the canonical ORFs to infer an approximate detection limit: Among 439 canonical ORFs with fewer than 3 in-frame reads per codon, only 3 of 9,253 theoretically detectable peptides were detected at a 10−6 threshold. Thus, almost all canonical peptides, with only these 3 exceptions, are found among ORFs with reads per codon above 3 and longer than 150 nt. Yet, only 448 noncanonical ORFs  are in this category . Thus, almost all noncanonical ORFs are outside the limits in which canonical ORF products are detected by MS.Besides length and sequence composition, a major difference between canonical and noncanonical ORFs is expression level, and this too can affect the probability a protein is detected in MS data . We ther−6 expect score threshold under the assumption that detection probability depends only on translation rate. This probability was estimated as the peptide detection rate among canonical ORFs with a similar translation rate to the transient ORF . Given these estimates, the expected total count of detected peptides for the 448 ORFs was 5.41. In reality, a single peptide was detected . To see whether observing only a single detection was surprising, we simulated the distribution of peptide detection counts under the estimated detection probabilities. The 95% confidence interval of noncanonical peptide detections ranged from 1 to 10. Thus, the single observed detection of a noncanonical peptide at a 10−6 expect score threshold is within range of expectations.For the 448 noncanonical translated ORFs displaying length and expression levels amenable to detection (longer than 150 nt and at least 3 reads per codon), we estimated the probability a peptide would be detected at a 10Table 1) were classified as evolutionarily transient. Among the 103 evolutionarily transient canonical ORFs, none were detected at a 10−5 expect score threshold, and similar numbers of ORFs and decoys were found at weaker thresholds .Wacholder and colleagues identified a class of rapidly evolving, evolutionarily novel ORFs termed “transient ORFs.” Despite lacking long-term evolutionary conservation, transient ORFs can express proteins that have major effects on phenotype . Of 18,9S. cerevisiae that plays a role in the yeast mating pathway [Table 2). These results indicate that MS detection appears to miss the entire class of evolutionary transient ORFs, whether canonical or not, including even those known to play important biological roles.Five transient canonical ORFs have been characterized in some depth , includi pathway . Yet, noBottom-up MS is an attractive approach for validating noncanonical ORFs supported by ribosome profiling due to the ease of testing large lists of predicted proteins but is limited by low sensitivity. Analyzing 3 MS experiments optimized to find small proteins, we identified 3 noncanonical proteins expressed from ORFs identified as translated in a recent analysis of yeast ribosome profiling studies . We additionally found MS evidence for an ORF not initially identified by ribo-seq, YNL155C-A, due to overlapping a canonical ORF on the same strand. All 4 proteins were translated at rates much higher than typical noncanonical ORFs, providing independent evidence that they are genuine protein-coding genes; 3 also showed evidence of evolutionary conservation. These findings illustrate the power of using proteomic, translation, and evolutionary evidence in combination to identify undiscovered genes at high confidence even in a well-annotated model organism.Nevertheless, the vast majority of ribo-seq-supported noncanonical ORFs showed no evidence of detection in MS datasets. We show that the low rates of detection of noncanonical ORFs can be explained primarily by their short size and low translation rate: Canonical ORFs at similar sizes and translation rates are also very rarely detected. The general amino acid composition of noncanonical ORFs, and the abundance of transmembrane domains among the longest ones, further contribute to hindering detection. As these factors explain the differences in detectability between canonical and noncanonical ORFs, little else about the biology of noncanonical ORFs can be inferred from their lack of detection in MS data. We cannot conclude that proteins expressed from noncanonical ORFs are less stable than canonical proteins, that they are targeted for degradation at higher rates, or that they are less likely to be functional, except to the extent that low expression already justifies these inferences.A majority of the yeast noncanonical translatome, and a small portion of the canonical, consist of evolutionarily young ORFs with little evolutionary conservation, classified as “evolutionary transient ORFs” in the Wacholder and colleagues dataset . No tranThere is considerable variability across studies that attempt to detect noncanonical proteins using MS, with some reporting detection of hundreds of proteins, while others, as in this study, find many fewer ,36,60–62How, then, can we use shotgun MS experiments to help us understand the biology of translated noncanonical ORFs and their potential protein products? We draw several lessons that may be applicable beyond yeast. For small-scale discovery of new protein-coding genes, the shotgun MS approach still provides value. Most noncanonical detections identified in this study were found on the basis of just 1 or 2 PSMs; additional support that these were genuine detections was provided from translation and evolution data. This suggests that further MS experiments conducted in a wide range of conditions will likely yield new discoveries of proteins that can be detected only rarely. To maximize these rare discoveries, it will be helpful to analyze MS data using different parameters, considering in particular a wide variety of posttranslational modifications. Given the short length of most noncanonical proteins, and that some noncanonical proteins lack tryptic peptides suitable for detection, it will likely also be helpful to use multi-enzyme digests or alternatives to digestion to maximize the probability that each noncanonical protein has at least one detectable peptide . HoweverAll MS data files were taken from 3 studies. The He and colleagues  dataset Searches were conducted using the MSFragger program . Unless Unless otherwise specified, FDR was calculated in a class-specific manner  by dividing the number of decoy proteins within the class that were detected at the expect score threshold from the number of target proteins in the class detected at the threshold. A protein was considered detected at a given expect score threshold if had at least one unique PSM with an expect score below the threshold. Decoys were either default (reverse of protein database sequence) or reversed after the starting methionine, as indicated. Peptides were excluded if they belonged to more than one predicted protein. Peptides were also excluded from supporting noncanonical proteins if the exact peptide sequence existed in a canonical protein, regardless of whether it would be a tryptic peptide of that protein. PSMs were excluded if the MSFragger hyperscore was less than 3 above the score for the next best peptide, in order to avoid using PSMs that did not uniquely support a single protein.2Rescore [In 2 analyses, searches were instead conducted either using the MS-GF+ program  or MaxQu2Rescore  was thenS. cerevisiae genome. All reads are mapped to ribosome P-sites as described by Wacholder and colleagues. A read was considered to map to an ORF only if the inferred P-site mapped to the first position of a codon in the reading frame of the ORF. The total read count for an ORF is the sum of reads mapping over all first codon positions, and the translation rate is the read count divided by the number of codons in the ORF.All ribo-seq data were taken from the analysis in Wacholder and colleagues . These d−4 e-value threshold to consider a match a homolog. BLAST searches conducted on NCBI databases were done on the NCBI website. Searches of the yeast genomes collected by Shen and colleagues [Saccharomyces species genomes were conducted on genomes acquired from the following sources: S. paradoxus from Liti and colleagues [S. arboricolus from Liti and colleagues [S. jurei from Naseeb and colleagues [S. mikatae, S. uvarum, S. eubayanus, and S. kudriavzevii from Scannell and colleagues [BLAST analyses were conducted with default settings and a 10lleagues  were conlleagues . TBLASTNlleagues , S. arbolleagues   were required, up to 2 missed cleavages were allowed, clipping to the N-terminal methionine was a variable modification, and methionine oxidation was a variable modification. As in the MSFragger analysis, peptides were restricted to 7 to 50 amino acids and peptide masses from 350 to 1,800 Da. Out of this list of theoretical peptides, the peptides that were detected in the MS analysis at a 10We used simulations to estimate the proportion of noncanonical proteins that would be detected if noncanonical peptides had the same detection probability as noncanonical proteins. Each simulation was conducted by randomly selecting noncanonical peptides at the canonical peptide detection rate and counting the proportion of noncanonical proteins with at least one selected peptide. The reported proportion is the average over 10 simulations.S. cerevisiae model provided with the program was used for scoring. Peptides were binned by AP3 detectability score, with 10 intervals evenly spaced between 0 and 1, to construct −6) within the bin. We then took the expected value of canonical peptide detections if the frequency distribution of canonical peptides among bins matched that of noncanonical peptides and divided this count by the number of canonical peptides actually detected.Every tryptic peptide was assessed for estimated detectability using the AP3 algorithm . SettingEvery protein was assessed for transmembrane domains using TMHMM . For eacS1 FigTwo spectra with stronger MSFragger expect scores than all decoys are shown. At top, the full protein sequence is shown, with peptide coverage colored in red. Spectra were visualized using the Universal Spectrum Identifier (USI) tool on ProteomeExchange  for the (PNG)Click here for additional data file.S2 FigA single PSM (top left) had a stronger MSFragger expect score than all decoys, while 3 others had stronger scores than the next strongest decoy. At top, the full protein sequence is shown, with peptide coverage colored in red. Spectra were visualized using the Universal Spectrum Identifier (USI) tool on ProteomeExchange for the USIs given in (PNG)Click here for additional data file.S3 FigA single PSM had a stronger MSFragger expect score than all decoys when a single non-enzymatic end was allowed. At top, the full protein sequence is shown, with peptide coverage colored in red. The spectrum was visualized using the Universal Spectrum Identifier (USI) tool on ProteomeExchange for the USI given in (PNG)Click here for additional data file.S4 FigPXD028623); we combined all PSMs from 11 different output files to create the plot. Each output file contains the PSMs passing a 1% FDR threshold, set at the whole-proteome level , in analyses conducted by Sun and colleagues using pFind. Each output file was individually thresholded at a 1% FDR in the Sun and colleagues’ analysis and noncanonical proteins passing this threshold in any file were inferred to be detected. Lower scores indicate higher confidence given by the MS algorithm. The observation that decoys and claimed noncanonical detections have similar scores suggests that many claimed noncanonical detections (indicated in blue) may be false positives. Merging multiple lists of inferred detections that were each individually generated at a 1% FDR is expected to result in a combined list with a much higher FDR [For each protein and decoy passing the detection threshold in the Sun and colleagues  study, tgher FDR , which, (PNG)Click here for additional data file.S5 FigSpectra for the 4 strongest matches for YNL155C-A. At top, the full protein sequence is shown, with peptide coverage colored in red. The spectra were visualized using the Universal Spectrum Identifier (USI) tool on ProteomeExchange for the USIs given in (PNG)Click here for additional data file.S6 FigThe best peptide-spectrum match MSFragger expect score for each noncanonical protein and decoy in the database. Lower scores indicate stronger matches. The data underlying this Figure can be found in (PNG)Click here for additional data file.S7 FigA) Average log in-frame ribo-seq read count per codon among canonical ORFs of different size classes. (B) Average log in-frame ribo-seq read count per codon among noncanonical ORFs of different size classes. The data underlying this Figure can be found in ((PNG)Click here for additional data file.S8 FigThe proportion of peptides detected, among all eligible for detection, for canonical proteins binned by proportion of amino acids in predicted transmembrane domains. Predictions were made using TMHMM . The fir(PNG)Click here for additional data file.S9 FigPredicted proteins and decoys detected in MS data at a range of expect-score thresholds, among canonical proteins identified as evolutionarily transient by Wacholder and colleagues , using t(PNG)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


The neural cell adhesion molecule L1  functions not only in cell migration, but also in cell survival, differentiation, myelination, neurite outgrowth, and signaling during nervous system development and in adults. The proteolytic cleavage of L1 in its extracellular domain generates soluble fragments which are shed into the extracellular space and transmembrane fragments that are internalized into the cell and transported to various organelles to regulate cellular functions. To identify novel intracellular interaction partners of L1, we searched for protein–protein interaction motifs and found two potential microtubule-associated protein 1 light-chain 3 (LC3)-interacting region (LIR) motifs within L1, one in its extracellular domain and one in its intracellular domain. By ELISA, immunoprecipitation, and proximity ligation assay using L1 mutant mice lacking the 70 kDa L1 fragment (L1-70), we showed that L1-70 interacts with LC3 via the extracellular LIR motif in the fourth fibronectin type III domain, but not by the motif in the intracellular domain. The disruption of the L1-LC3 interaction reduces L1-mediated neurite outgrowth and neuronal survival. Neural cell adhesion molecules are membrane proteins that play important roles in cell adhesion, migration, and signaling. One prominent member of this group is the surface glycoprotein L1, which is involved in cell migration, proliferation, survival and differentiation, axon fasciculation, and myelination during development of the nervous system, as well as in neurogenesis, neural plasticity, learning and memory, and regeneration in the adult nervous system ,4,5,6,7.Proteolysis of full-length L1 was shown to be important for L1’s functions, and extracellular and intracellular L1 fragments were found in different locations and organelles and regulate different cellular functions ,44,45,46To identify novel interaction partners of intracellular L1 fragments and to further determine the function of these L1 fragments, we searched for protein interaction motifs in L1 and intracellular proteins interacting with these motifs. We found two potential microtubule-associated protein 1 light-chain 3-interacting region (LIR) motifs in L1: one in the fourth FNIII domain and one in the intracellular domain. Proteins containing a LIR motif bind to the LIR docking site of proteins belonging to the autophagy-related protein 8 (ATG8) family. The microtubule-associated protein 1 light chain 3 alpha  is a mammalian homolog of ATG8E. The LC3 conjugation system is required for elongation and maturation of the autophagosome  and mitoHere, we show by ELISA and proximity ligation assay that L1 interacts directly with LC3 and that this interaction is mediated by the LIR motif within the fourth FNIII domain, but not by the LIR motif in the intracellular domain. LC3 interacts mainly with L1-70 generated by myelin basic protein, as seen by a reduction of L1/LC3 interactions in non-stimulated cerebellar and cortical neurons from L1-70-lacking mutant mice, and L1 antibody-enhanced L1/LC3 interactions were not observed in L1-70-lacking neurons in contrast to wild-type neurons. The treatment of cultured cortical neurons with a cell-penetrating peptide containing the LIR motif of the fourth FNIII domain reduces the interaction as well as L1-dependent neurite outgrowth and neuronal cell survival.Previous research from our group showed that fragments of L1 are present inside of the cell in the cytosol, in nuclei, and in mitochondria, and that L1 regulates mitochondrial and nuclear functions ,45,46,480-X1-X2-[LVI]3 [-2-[DEGMSTV]-1-[WFY]0-[DEILQTV]1-[ADEFHIKLMPST]2-[ILV]3, which is a redefined LIR motif [-2-[DEGMSTV]-1-[WFY]0-[HKRN]1-[ADEFHIKLMPST]2-[ILV]3 with the conservative glutamine (Q) substitutions histidine (H), lysine (K), arginine (R), and asparagine (N) at position X1 and identified one motif in the fourth FNIII domain (952LSYHPV957) and one in the in the intracellular domain (1177GEYRSL1182) of mouse L1. These motifs were also found in the fourth FNIII domain and in the intracellular domains of rat , human , and bovine  L1. These findings raise the question of whether L1 directly interacts with LC3 and if this interaction is mediated by the LIR motifs in L1.To search for putative LIR motifs in L1, we used the canonical LIR core motif [WFY]2-[LVI]3  to scan IR motif , was useTo investigate if L1 interacts with LC3 and if this binding is mediated by the LIR motif in the extracellular or intracellular L1 domain, an ELISA was performed. First, recombinant LC3 was used as a substrate-coat and was incubated with the recombinant His-tagged intracellular domain of L1 (L1-ICD). The intracellular domain of CHL1 (CHL1-ICD), the close homolog of L1, served as negative control. A concentration-dependent binding of the L1-ICD, but not of the CHL1-ICD to LC3, was observed a. Next, WT peptide containing the extracellular LIR motif LSYHPV or the LIRmut peptide containing the mutated LIR motif LSAHPA for competition ELISA with L1/Fc and fusion proteins containing human Fc and either FNIII domains 1–5 (FN1–5/Fc) or Ig-like domains 1–6 (Ig1–6/Fc). L1/Fc and FN1–5/Fc, but not Fc, bound to LC3, while Ig1–6/Fc showed only low binding to LC3. Moreover, the LIRWT peptide reduced the binding of L1/Fc and FN1–5/Fc by 60–65%, whereas the LIRmut peptide did not affect the binding of L1/Fc and FN1–5/Fc to LC3  were tested by ELISA for binding to LC3. L1/Fc, but not CHL1/Fc, showed a concentration-dependent binding to LC3 a. Next, c to LC3 b. These WT peptide in the absence or presence of the function-triggering L1 antibody 557 and then subjected the neurons to proximity ligation assay with a mouse L1 antibody and a rabbit LC3 antibody. L1 antibody 557 enhanced the number of red spots, which indicate L1/LC3 interactions, approximately four times, while the tat-LIRWT peptide reduced the L1/LC3 interactions in non-stimulated and stimulated neurons by 69 ± 10% and 63 ± 10%, respectively  and phenylalanine (F) at that position: out of 100 LIR motifs only 10 contain Y at the [WFY]0 position, while 42 and 48 contain W or F at this position, respectively [0 are mitochondrial LC3 receptors, which are involved in autophagy and/or mitophagy: FUN14 Domain Containing 1 (FUNDC1), Autophagy Related 4B (ATG4B), Mitogen-Activated Protein Kinase 15 (MAPK15) and Neighbor of BRCA1 Gene 1 Protein (NBR1). The mitochondrial outer membrane protein FUNDC1 binds to LC3 through its LIR motif DSYEVL and acts as an activator of hypoxia-induced mitophagy, ATG4B with its LIR motif LTYDTL is a cysteine peptidase which is required for autophagy as well as for mitophagy, MAPK15 carrying the LIR motif RVYQMI is an atypical MAPK protein that regulates basal and starvation-induced autophagy in a kinase activity-dependent manner, and NBR1 with the LIR motif EDYIII is an autophagy cargo receptor. The phosphorylation of tyrosine (Tyr-18) at position X0 in the LIR motif of FUNDC1 inhibits the activation of mitophagy, while hypoxia-induced dephosphorylation of this tyrosine and phosphorylation of the preceding serine at position X-1 (Ser-17) stabilizes the interaction of FUNDC1 with LC3 and triggers mitophagy [After its generation at the plasma membrane, L1-70 is transported from the cytoplasm into nuclei and mitochondria and is also present at the mitochondrial surface . L1-70 iectively . Among pitophagy .-1 in the LIR motif GSWVEL of the mitochondrial LC3 receptor Bcl2 interacting protein 3 (BNIP3) promotes the interaction between BNIP3 and LC3 and induces mitophagy under oxidative stress conditions [Interestingly, the phosphorylation of serine (Ser-17) at position Xnditions .-1 of L1’s extracellular LIR motif may become phosphorylated. We propose that L1 is a mitochondrial LC3 receptor like FUNDC1 and BNIP3 [-1 promotes mitophagy, as seen for the phosphorylation of serine at position X-1 in the LIR motifs of FUNDC1 and BNIP3. It is conceivable that the binding affinity of LIR motifs to LC3 in general is regulated through phosphorylation . Thus, serine at position Xnd BNIP3  and thatces, see ).L1 binds to dynamin-related protein 1 (DRP1) , which i953LSYHPL958. This mutation causes symptoms of the L1 syndrome, e.g., adducted thumbs, hydrocephalus, and spastic paraplegia [Since L1-stimulated neurite outgrowth and neuronal survival were abrogated in the presence of a peptide containing the LIR motif of the fourth FNIII domain and basal cell survival was also reduced in the presence of this peptide, it is plausible that L1-stimulated neurite outgrowth relies on the interaction of L1 with LC3 and that interference with the L1/LC3 interaction leads to enhanced neuronal cell death. Furthermore, one may conclude that neurite outgrowth and the protection of neurons against oxidative stress depends on mitophagy that is regulated by the interplay of LC3 with L1. The disturbance of this interaction may have severe functional consequences and may lead to the development of neurodegenerative diseases or L1-associated diseases, such as the L1 syndrome. Of note, T to G mutation in exon 21 of the human L1 gene (c.2858T > G) results in a leucine to arginine exchange at position 953 in the extracellular LIR motif raplegia .In summary, the extracellular LIR motif in the fourth FNIII domain of L1 is crucial for L1’s functions in the mouse brain.687 mutant) or with a mutation of the dibasic sequence RKHSKR to SKHSSS at positions 858–863 in the third FNIII domain (L1858–863 mutant) [687 mutant males, L1858–863 mutant males, L1-deficient males, and their wild-type male littermates, were used for all experiments. All animal experiments were conducted in accordance with the German and European Community laws on the protection of experimental animals and approved by the local authorities of the State of Hamburg . The manuscript was prepared following the ARRIVE guidelines for animal research [L1-deficient mice  and gene mutant)  have beeresearch .WT (SHNGVLTGYLLSYHPVEGESKEQ), and LIRmut (SHNGVLTGYLLSAHPAEGESKEQ), as well as tat-LIRWT (YGRKKRRQRRRSHNGVLTGYLLSYHPVEGESKEQ) and tat-LIRmut (YGRKKRRQRRRSHNGVLTGYLLSAHPAEGESKEQ), both containing the cell-penetrating HIV tat sequence YGRKKRRQRRR [Rabbit LC3 antibody  was from Proteintech Europe . Mouse L1 antibody C-2  against the L1-ICD, goat L1 antibody C-20 , and goat CHL1 antibody C-18  were from Santa Cruz Biotechnology . HRP-coupled goat anti-Fc antibody  was from Jackson ImmunoResearch . Rat monoclonal function-triggering L1 antibody 557 has been described . SecondaKKRRQRRR , were frhttps://prosite.expasy.org/scanprosite/; accessed on 10 August 2021) was used for scanning the sequences of mouse (L1CAM_mouse), rat (L1CAM_rat), human (L1CAM_human), and bovine (E1BBI3_BOVIN) L1 for putative LIR motifs. NetPhos prediction tool  was used to identify phosphorylation sites in L1.The Expasy ScanProsite tool  (PBS), treated with blocking solution  for 2  h, washed again with PBS containing 0.005% Tween 20 (PBST), and incubated with increasing concentrations of recombinant His-tagged L1-ICD and CHL1-ICD or L1/Fc and CHL1/Fc as ligands for 1 h under gentle agitation. For competition ELISA, 2.5 µM L1-ICD, L1/Fc, Ig1–6/Fc, or FN1–5/Fc were preincubated for 1 h without or with a 5-fold molar excess of peptide P1, P2, P3, P4, P5, Pa, Pb, Pc, Pd, Pe, LIRWT, or LIRmut. The mixtures were then incubated with substrate-coated recombinant LC3B. After washing two times with PBS and three times with PBST, L1 antibody C-2 (1:500), CHL1 antibody, or HRP-coupled anti-Fc antibody (1:2000) in blocking solution was applied for 1 h. When samples were incubated with L1 and CHL1 antibody they were washed two times with PBS and three times with PBST and then incubated with HRP-coupled anti-mouse and anti-goat antibody (diluted 1:2000 in blocking solution) for 1 h. All samples were washed two times with PBS and three times with PBST, and wells were incubated with 1 mg/mL ortho-phenylenediamine dihydrochloride  to detect bound proteins. The reaction was terminated by the addition of 25 µL 2.5 M sulphuric acid. Absorbance was measured at 492  nm with an ELISA reader .For ELISA, 25 µL of 10 µg/mL recombinant LC3B were incubated overnight at 4 °C in 384-well microtiter plates with a high binding surface . All the following steps were performed at room temperature. Wells were washed with Dulbecco’s phosphate-buffered saline with MgCl4 cells per well of a 48-well plate coated with poly-L-lysine (Sigma-Aldrich). For proximity ligation assay, cells were seeded onto 12 mm glass coverslips poly-L-lysine-coated in a 24-well plate at a density of 1.25 × 105 cells per well. For cell death analysis, cells were seeded onto poly-L-lysine-coated 48-well plates at a density of 2.5 × 105 cells per well.Cortical neurons were prepared from cerebral cortices of 15.5- to 16.5-day-old embryos. Dissected cerebral cortices were incubated in Hanks’ balanced salt solution (HBSS) containing 0.025% trypsin (Sigma-Aldrich) at 37 °C for 30 min. The cortices were then incubated in HBSS containing 1% BSA (Sigma-Aldrich) and 1% trypsin inhibitor  at 37 °C for 2 min. After washing in HBSS, the tissue was mechanically dissociated, and the dissociated cells were cultured in Neurobasal medium (Thermo Fisher Scientific) supplemented with 1% B27, 2 mM L-glutamine, 100 U/mL penicillin, and 100 μg/mL streptomycin . For the determination of neurite outgrowth, cells were seeded at a density of 5 × 105 cells per well. For the determination of neurite outgrowth, cells were seeded at a density of 5 × 104 cells per well of a 48-well plate coated with poly-L-lysine (Sigma-Aldrich), and for neuronal survival analysis cells were seeded at a density 1.25 × 105 cells per well of a 48-well plate coated with poly-L-lysine.Cerebellar neurons were prepared from the cerebella of 6- to 8-day-old mice. Cerebella were incubated with 10 mg/mL trypsin and 0.5 mg/mL DNase I (Sigma-Aldrich) in HBSS for 15 min at 37 °C, washed with HBSS, dissociated, and centrifuged at 100 × g for 15 min. Cells were then diluted in Neurobasal A medium (Thermo Fisher Scientific) supplemented with 2 mM L-glutamine (Thermo Fisher Scientific), 4 nM L-thyroxine (Sigma-Aldrich), 0.1 mg/mL BSA (Sigma-Aldrich), 12.5 μg/mL insulin (Sigma-Aldrich), 30 nM sodium selenite (Sigma-Aldrich), 100 μg/mL transferrin , 0.1 mg/mL streptomycin, and 100 U/mL penicillin (Thermo Fisher Scientific). For the proximity ligation assay, cells were seeded onto poly-L-lysine-coated 12 mm glass coverslips in a 24-well plate at a density of 2.5 × 10WT peptide (100 µg/mL in vehicle solution) for 16–24 h. Following that, cells were treated with PBS (unstimulated) or L1 antibody 557  for 2 h. Cultured cells were fixed for 15–30 min at room temperature in 4% formaldehyde, washed with PBS, and subjected to proximity ligation assay using Duolink PLA products according to the manufacturer’s protocol  with minor modifications. Cells were incubated with 1% Triton X-100 in PBS for 30 min, washed once with PBS, and blocked with Duolink Blocking solution for 30 min and incubated for 24 h at 4 °C with mouse L1 antibody C-2 (1:10 dilution) and rabbit antibody against LC3 (1:30 dilution) in Duolink Antibody Diluent. Cells were washed two times using Duolink Wash Buffer A and incubated with a mixture of secondary antibodies conjugated with oligonucleotides (Duolink PLA Anti-Rabbit Probe MINUS and Duolink Anti-Mouse PLA Probe PLUS). The proximity ligation reaction was then performed according to the manufacturer’s protocol using the Duolink In Situ Detection Reagent RED. Thereafter, the coverslips were incubated in 5 µg DAPI/mL PBS for 15 min, washed twice with PBS and mounted in Immuno-Mount (Thermo Fisher Scientific). Ten images per condition were taken using an Olympus F1000 confocal microscope  and analyzed using ImageJ software . Numbers of red dots and numbers of DAPI-stained nuclei were determined using ImageJ and the number of red dots per image was divided by the number of nuclei per image. The average values of red dots per cell (=nucleus) were determined in 10 images per condition.After seeding, cells were treated with vehicle solution or tat-LIRWT or tat-LIRmut peptides (50 µg/mL) and 557 antibody (50 µg/mL) 30 min after seeding. After 24 h in culture, cells were washed gently with pre-warmed culture medium, fixed in 2.5% glutaraldehyde for 30 min at room temperature, and stained with 1% toluidine blue and 1% methylene blue in 1% sodium tetraborate for 30 min at room temperature. Neurite outgrowth was analyzed by measuring the total length of neurites with a Zeiss AxioObserver.A1 microscope with the AxioVision 4.6 imaging system . For neurite outgrowth analysis for each condition, at least 100 neurons were counted.To determine neurite outgrowth, cells were treated with cell-penetrating tat-LIRWT or tat-LIRmut peptides (50 µg/mL) and L1 antibody 557 (50 µg/mL) and exposed to oxidative stress by the addition of 10 µM H2O2 for 24 h. Live and dead cells were then stained with calcein-AM (Thermo Fisher Scientific) and propidium iodide (Sigma-Aldrich) and imaged with a Zeiss AxioObserver.A1 microscope (Carl Zeiss) with a 20× objective (aperture 0.4) and the AxioVision 4.6 software (Carl Zeiss). Live and dead cells were counted in five images (containing 350–400 cells each) from each of the three wells per condition and experiment using ImageJ.To determine cell death, neurons were maintained overnight in serum-free medium and then treated with cell-penetrating tat-LIRFor the preparation of nuclear and non-nuclear fractions, the Subcellular Protein Fractionation Kit for Tissue (Thermo Fisher Scientific) was used. Fractions in cytoplasmic extraction buffer (CEB) and membrane extraction buffer (MEB) were pooled and taken as non-nuclear fractions. Fractions in nuclear extraction buffer (NEB) were taken as nuclear fractions. Nuclear and non-nuclear fractions were used for immunoprecipitation.For immunoprecipitation, Protein G magnetic beads (25 μL per sample) were washed twice in PBS, pH 7.4, and incubated in dilution buffer  for 10 min at 4 °C under rotation. The mouse L1 antibody C-2, rabbit LC3 antibody, or non-immune control rabbit or mouse antibodies (10 μg per sample) were diluted in dilution buffer. The diluted antibody solutions were incubated with the beads for 1 h at 4 °C under rotation. The beads were washed in dilution buffer for 5 min at 4 °C under rotation. A freshly prepared 13 mg/mL stock solution of dimethyl pimelimidate (DMP) was 1:1 diluted with wash buffer (0.2 M triethanolamine in PBS). After washing the beads in PBS, the diluted DMP solution (pH 8–9) was added to the beads, and beads were incubated for 30 min at room temperature (20–24 °C) under rotation. The beads were washed in wash buffer for 5 min at room temperature under rotation. The beads were then incubated in DMP solution and wash buffer for two further times. Finally, the beads were incubated twice in quenching buffer (50 mM ethanolamine in PBS) for 5 min at room temperature under rotation. After washing, the beads were incubated with brain fractions overnight at 4 °C under rotation. The beads were washed twice with lysis buffer and once with PBS. The beads were then boiled for 5 min in sample buffer .® TGX™ Precast Protein Gels . PageRuler™ Plus Prestained Protein Ladder (Thermo Fisher Scientific) was used as molecular weight marker. Proteins were then transferred to 0.45 μm Protran™ nitrocellulose membranes  and stained with Ponceau S to control protein loading. The membranes were then incubated for 1 h in blocking solution   with 0.05% Tween 20 (TBST) and then incubated overnight with goat L1 antibody C-20 or rabbit LC3 antibody (1:1000) in blocking solution at 4 °C with shaking. After washing five times for 5 min in TBST, the membranes were incubated for 1 h with HRP-conjugated secondary anti-goat or anti-rabbit antibodies . Bands were detected using enhanced chemiluminescent solution  and a CCD camera .For Western blot analysis, samples were run on 4–20% Mini-PROTEANHere, we show that L1-70 interacts with LC3 via the extracellular LIR motif in the fourth fibronectin type III domain and that this interaction is required for L1-mediated neurite outgrowth and neuronal survival. Phosphorylation at the serine residue in L1’s extracellular LIR motif might alter binding affinity to LC3. Since L1 also interacts with the mitochondrial fission protein DRP1 that is involved in mitophagy, we propose that L1, together with LC3, is involved in mitophagy. Altogether, we provide evidence that the extracellular LIR motif in the fourth FNIII domain of L1 contributes to L1’s functions in the nervous system and that impaired L1/LC3 interactions can lead to neurodevelopmental and neurodegenerative diseases.


Sarcoidosis is a systemic granulomatous disease characterized by the hyperactivation of CD4 T cells, CD8 T cells, and macrophages. Clinical presentations of sarcoidosis are highly variable. Sarcoidosis is unknown in its etiology, but it suggests it may result from exposure to specific environmental agents in genetically susceptible people. Sarcoidosis commonly involves the lungs and lymphoid system. Bone marrow involvement in sarcoidosis is uncommon. Sarcoidosis rarely results in intracerebral hemorrhage due to severe thrombocytopenia secondary to bone marrow involvement. We present the case of a 72-year-old woman who has been in remission from sarcoidosis for 15 years and developed intracerebral hemorrhage secondary to severe thrombocytopenia due to sarcoidosis recurrence in the bone marrow. The patient presented to the emergency department with a generalized, non-blanching petechiae rash and nose and gum bleeding. Her labs showed a platelet count of less than 10.000/mcL, and computed tomography (CT) showed intracerebral hemorrhage. A bone marrow biopsy revealed a small, non-caseating granuloma indicative of a sarcoidosis relapse in the bone marrow. Sarcoidosis is a systemic inflammatory disorder that triggers granuloma formation in multiple organs with an unclear etiology . The preA 72-year-old woman with a past medical history of chronic obstructive pulmonary disease (COPD), a history of breast cancer with status post-radiation therapy, pulmonary sarcoidosis, and liver cirrhosis presented to the emergency department (ED) with intermittent nose bleeding, gum bleeding, and bright red blood per rectum for one week. She also complained of a progressive rash that started in her legs and spread to her torso, arms, and face for two days. She denied fever, weight loss, night sweats, and a loss of appetite. She had cancer on the right breast and completed five years of tamoxifen therapy following a lumpectomy and adjuvant radiotherapy. She was diagnosed with pulmonary sarcoidosis via bronchoscopy biopsy 25 years ago. She remained disease-free and had not been on any treatment for the last 15 years. She was treated with steroids for a brief period initially, but never with steroid-sparing drugs. She was regularly followed up with a pulmonologist and underwent computerized chest tomography (CT) yearly.On presentation to the ED, she was hypertensive with systolic blood pressure (SBP) at 150/75 mmHg and slightly tachycardic with a heart rate of 100-120 bpm. The physical examination revealed a non-blanching petechiae-like rash on the face, chest, torso, and bilateral lower extremities. The neurological examination was unremarkable. The bilateral lungs were clear, the abdomen was soft, and there was no hepatosplenomegaly.The lab tests revealed anemia with hemoglobin of 11.3 mg/dL and thrombocytopenia with platelets less than 10.000/mcL. The liver function tests were within the normal range. The lactate dehydrogenase level was 238 U/L, and the D-dimer level was 560 ng/mL. Coagulation panels showed an activated partial thromboplastin time (APTT) of 26 seconds, a fibrinogen level of 420 mg/dL, a prothrombin time (PT) of 13.5 seconds, and an international normalized ratio (INR) of 1.06. A peripheral blood smear revealed normochromic normocytic anemia with severe thrombocytopenia, adequate leukocytes, mild neutrophilia, several bands, and reactive monocytes and lymphocytes. There were no blasts or schistocytes. There was no clumping of platelets, but there were few megakaryocytes. Kidney function, electrolytes, acid-base balance, coagulation tests, and inflammatory markers were all within normal limits. Laboratory investigations on admission are shown in Table 1Her chest x-ray showed stable bilateral upper lung predominant fibrotic changes Figure . ComputeShe underwent further investigation for thrombocytopenia. Her serum protein electrophoresis (SPEP) showed an isolated elevation of alpha-2 globulins suggestive of acute inflammation. She had low levels of vitamin D 25 OH at 23 ng/ml (30-100 ng/ml). Ultrasonography of the abdomen showed no hepatosplenomegaly. A CT chest without contrast revealed stable bronchiectasis, most prominent at the left upper lobe, with no appreciable change in the extensive scarring and fibrosis within the apical prominence compared to last year's imaging Figures .A bone marrow biopsy revealed normocellular marrow showing maturing trilineage hematopoiesis and a small non-necrotizing granuloma consistent with known sarcoidosis Figures -6.It was negative for diagnostic evidence of dysplasia, lymphoproliferative disorder, plasma cell neoplasm, or acute leukemia. Ziehl-Neelsen staining for acid-fast bacilli and Grocott methenamine silver (GMS) staining for fungal organisms were negative. Her blood and urine cultures were also negative. Flow cytometric immunophenotypic analysis found no evidence of abnormal myeloid maturation or an increased blast population; there was no evidence of a lymphoproliferative disorder. She also had negative results for antinuclear antibody, Lyme panel, human immunodeficiency virus (HIV) antigen and antibodies, hepatitis C and B, Anaplasia, and Babesia.After the BM biopsy, she was started on prednisone 50 mg daily and was administered a dose of intravenous immunoglobulins at 1 mg/kg (50 mg). She also received multiple platelet transfusions. The patient was discharged with prednisone, tapering off once platelets stabilizedSarcoidosis is a granulomatous disease that usually affects the lungs and extrapulmonary organs, including the eyes, skin, liver, spleen, and lymph nodes . SarcoidThrombocytopenia can be due to decreased platelet production, increased platelet sequestration, destruction, or consumption . In our There are many causes of thrombocytopenia. It can be infectious, such as hepatitis, HIV, parvovirus B19, malaria, sepsis, or inflammatory or autoimmune, such as disseminated intravascular coagulation, hemolytic uraemic syndrome, bone marrow failure from acute leukemias, or a congenital defect [Severe thrombocytopenia leading to ICH is rare. This patient had ICH secondary to severe thrombocytopenia, and luckily, she had no neurological symptoms. The incidence of ICH in patients with thrombocytopenia due to immune thrombocytopenia (ITP) was 1.540% in all ages , and witThis patient suffers from relapsed sarcoidosis with bone marrow involvement. Bone marrow involvement in relapsed sarcoidosis is rare, and Jungmin Lee reported one case .Sarcoidosis is a diagnostic challenge. There is no universally accepted standardized method for diagnosis, but it is based on three major criteria. The criteria are based on clinical and/or radiological presentation, histological evidence of non-necrotizing granulomatous inflammation in one or more tissues, and the exclusion of alternative causes of granulomatous disease . Non-necThe first-line treatment for sarcoidosis is corticosteroids, but they have several serious adverse effects. Therefore, corticosteroids are indicated only when symptoms appear or cause life- or organ-threatening disease. Second-line therapy for sarcoidosis consists of disease-modifying antirheumatic drugs (DMARDs). Second-line therapies are indicated when patients’ conditions worsen using corticosteroids, such as decompensated heart failure, uncontrolled diabetes, severe obesity, uncontrolled hypertension, and glaucoma. Third-line therapy for sarcoidosis involves biological agents. These are recommended for patients with refractory disease who do not respond to glucocorticoids and DMARDs or cannot tolerate these agents -17. Our Bone marrow involvement in sarcoidosis is rare. Clinical presentations of sarcoidosis are diverse, and bleeding manifestations are an uncommon presentation of sarcoidosis. Intracranial hemorrhage secondary to severe thrombocytopenia can be life-threatening and requires urgent evaluation for the underlying cause. Here we present a case of relapsed sarcoidosis in the bone marrow leading to intracranial hemorrhage (ICH) due to severe thrombocytopenia. Clinicians need to be aware of these rare manifestations and maintain high susceptibility to reach the diagnosis earlier, especially when encountering a patient with a history of past sarcoidosis.


What are the most important research priorities in concussion from the perspective of patients, caregivers, and clinicians?This survey study used 2 cross-sectional surveys and a consensus workshop with a nominal group technique to gather perspectives from patients, caregivers, and clinicians to identify the top 10 concussion research questions. The main question themes focused on accurate concussion diagnosis, effective symptom management, and prediction of poor outcomes.This research priority list provides a guide to the concussion research community and can help prioritize funding for research that matters most to those living with concussion and those who care for them. This cross-sectional survey study uses priority-setting partnership methods to identify the most important unanswered research questions in concussion from the perspectives of patients, caregivers, and clinicians. Identifying research priorities of patients with concussion, their caregivers, and their clinicians is important to ensure future concussion research reflects the needs of those who will benefit from the research.To prioritize concussion research questions from the perspectives of patients, caregivers, and clinicians.This cross-sectional survey study used the standardized James Lind Alliance priority-setting partnership methods . Data were collected between October 1, 2020, and May 26, 2022, from people with lived concussion experience (patients and caregivers) and clinicians who treat concussion throughout Canada.The first survey collected unanswered questions about concussion that were compiled into summary questions and checked against research evidence to ensure they were unanswered. A second priority-setting survey generated a short list of questions, and 24 participants attended a final priority-setting workshop to decide on the top 10 research questions.Top 10 concussion research questions.The first survey had 249 respondents , including 145 with lived experience and 104 clinicians. A total of 1761 concussion research questions and comments were collected and 1515 (86%) were considered in scope. These were combined into 88 summary questions, of which 5 were considered answered following evidence review, 14 were further combined to form new summary questions, and 10 were removed for being submitted by only 1 or 2 respondents. The 59 unanswered questions were circulated in a second survey, which had 989 respondents , including 654 people who identified as having lived experience and 327 who identified as clinicians (excluding 8 who did not record type of participant). This resulted in 17 questions short-listed for the final workshop. The top 10 concussion research questions were decided by consensus at the workshop. The main research question themes focused on early and accurate concussion diagnosis, effective symptom management, and prediction of poor outcomes.This priority-setting partnership identified the top 10 patient-oriented research questions in concussion. These questions can be used to provide direction to the concussion research community and help prioritize funding for research that matters most to patients living with concussion and those who care for them. To address this, multidisciplinary research networks, including the Canadian Concussion Network, are being established to generate high-quality research.Concussion is a mild traumatic brain injury affecting 2% of the population each year.18 and the Patient-Centered Outcomes Research Institute in the US19 have encouraged researchers to seek out the perspectives of those with lived experience to determine important and unanswered questions. This approach leads to priority setting that is relevant to patients and more readily implemented in clinical practice.19In this context, an important first step is to establish research priorities. In the past, research priorities were typically set by academic researchers, pharmaceutical companies, medical device industries, or funding bodies. More recently, the Strategy for Patient-Oriented Research in Canada20 The JLA process is grounded in principles such as equal involvement of patients, caregivers, and clinicians; transparency; mandatory sharing of an audit trail; and exclusion of researchers and groups with competing interests from the prioritization process.20 The JLA-PSP method has engaged patients in setting research priorities for more than 150 discrete health conditions.21 Our aim was to conduct a research prioritization study using JLA-PSP methods to identify the most important unanswered questions in concussion from the perspectives of patients, caregivers, and clinicians.The James Lind Alliance (JLA) brings together patients, caregivers, and clinicians to form priority-setting partnerships (PSPs) to identify and prioritize important unanswered questions in health care.22 Steps included establishing a steering group for the partnership; identifying research uncertainties through a national cross-sectional survey; summarizing top-ranked questions and determining their inclusion via evidence verification; interim priority setting through a second national cross-sectional survey; and a priority-setting workshop to rank the questions in order of priority. The study followed the Reporting Guideline for Priority Setting of Health Research (REPRISE) and the American Association of Public Opinion Research (AAPOR) Standards Best Practices. The project was conducted from October 1, 2020, to May 26, 2022. Research ethics board approval was obtained from the Children’s Hospital of Eastern Ontario. Potential respondents were informed of the risks and benefits of the study in the survey introduction and informed that by completing the survey they were agreeing to participate in the study.In this cross-sectional survey study, we used standardized JLA-PSP methods, which draw on modified Delphi and nominal group techniques.17 Patients of all ages, any mechanism of injury, any symptom burden, and any duration of postinjury symptoms were included. The scope of questions included those relating to diagnosis, treatment, management, prognosis, and prevention of concussion. We excluded questions related to moderate or severe brain injury, brain injury not caused by physical trauma, and health care resourcing.This pan-Canadian PSP sought individuals diagnosed with or suspected of having a concussion or mild traumatic brain injury, as well as caregivers of people with concussion and clinicians of all disciplines who treat concussion. We used the 5th Consensus Statement on Concussion in Sport to define concussion as “a traumatic brain injury induced by biomechanical forces” that results in 1 or more symptoms in the clinical domains of somatic, cognitive, emotional and/or behavioral, or sleep.23 The PSP lead (M.H.O.) and senior JLA advisor (K.C.) were coleaders of the steering group. The steering group met monthly and was responsible for (1) finalizing the scope; (2) designing and piloting the surveys; (3) overseeing a plan to publicize the online surveys; (4) assisting with classification and verification of summary questions; and (5) identifying the short list of questions for the final workshop.The steering group, established to oversee the operations of the PSP, included 6 patients with concussion, 1 caregiver, 6 clinicians who treat concussion, and 1 concussion stakeholder organization member.24 and stored on a password-protected laptop in a locked room. The survey , anonymous, web-based survey. Study data were encrypted and securely collected on REDCapThe survey was open from February 16 to May 17, 2021, to patients of all ages with concussion, caregivers, and clinicians across Canada. The survey was distributed broadly through websites, emails, newsletters, and social media by strategic partners . Participant characteristics were analyzed using descriptive statistics.Questions from survey 1 were categorized into themes by the steering group through an iterative process. Out-of-scope questions were removed. Patient-clinician dyads formulated summary questions. The steering group reviewed summary questions, and the final wording of the questions was settled by consensus.17 or national concussion evidence-based practice guidelines.16 Unanswered questions were included for the interim priority-setting survey. Questions considered answered by previous research were compiled separately.Evidence checking was completed by a team convened by one of us (L.M.L.) who provided perspectives from specialized fields, including knowledge translation, research, public policy, and clinical practice. These information specialists examined recent research, databases, and current guidelines. For pragmatic reasons, focused searches to identify systematic reviews, primary research, clinical guidelines, and consensus statements were conducted. We searched the MEDLINE, Embase, CINAHL, Epistemonikos, and Cochrane Central Register of Controlled Trials databases from 2010 onward for English language publications. Questions were classified as unanswered if the search found no systematic review, if recent systematic reviews found the question unanswered or yielded contradictory results, if there was a lack of primary research, or if there was insufficient evidence in the 5th International Consensus Statement on Concussion in SportA second anonymous online survey (available in English and French) shared the long list of unanswered summary questions  and requested that participants choose the 10 most important questions from their perspective. The order in which the list of questions was presented was randomized weekly to minimize bias. The survey was pilot tested on 5 people for comprehension, clarity, and time for completion. Demographic data were also collected in survey 2, which was open from January 10 to April 10, 2022; promotion, distribution, and data storage paralleled that of survey 1. A scoring system was used to analyze the data. One point was assigned for each question in a respondent’s top 10 list. Points were tallied separately for 2 distinct groups: (1) those with lived experience  and (2) clinicians who treat concussion. To ensure equal influence for each group, questions were ranked from highest to lowest for each group and the 2 scores were summed to give a total score which determined the combined rank order for each question. The steering group reviewed the final rankings and reached consensus on the short list of questions for the priority-setting workshop.26 Three additional JLA advisors facilitated breakout sessions to ensure balanced participation and respectful discussion across participants. To enable a wide exchange of knowledge and consider all perspectives, participants rotated through different combinations of breakout groups. Consensus on the top 10 questions was reached using established JLA methods.22 Degree of participant engagement was assessed through a postworkshop survey.A workshop consisting of 2 half-day sessions  and conducted through video teleconference (Zoom Video Communications) was used to rank the final list of unanswered research questions by order of importance and generate a final top 10 list. Twenty-four participants were invited to attend . The workshop sought equal representation from those with lived experience and clinicians and sought diversity in age, sex, geography, concussion mechanism, and clinician expertise. Two weeks prior to the workshop, participants were sent the short list of 17 final questions via email and post. The workshop was led by a senior JLA advisor (K.C.) and used a modified nominal group technique.Two hundred forty-nine participants completed survey 1 . Of thesA total of 1761 concussion questions and comments were collected from the 249 participants . Of thesOf the 88 summary questions, 5 (6%) were determined to be sufficiently answered by existing research evidence . This leBox. What is the effect of a delay in concussion diagnosis on symptom severity and outcomes?What relationship exists between neck injuries  and concussions?Are impact sensors  effective tools to help diagnose and assess concussions?What criteria are required for a health care provider to request a CT scan for a child with a head injury and possible concussion?What type of family support, education, and resources result in the best outcomes for families coping with concussion?Abbreviation: CT, computed tomography.A total of 989 participants completed survey 2 . Of thesTwenty-four participants (13 with lived experience and 11 clinicians) attended the workshop. Of the 13 with lived experience, 8 (62%) identified as female and 5 (38%) as male. One participant was younger than 18 years, and the mechanism of concussion injury included sports (6 [46%]), non–sport-related falls (4 [31%]), motor vehicle–related injuries (2 [15%]), and assault (1 [8%]). Clinicians included 2 physiotherapists, 2 neuropsychologists, 1 nurse, 1 athletic therapist, and 5 physicians . Consensus was reached on the final ranking of the top 10 unanswered research questions in concussion .A postworkshop anonymous survey was completed by all 24 attendees. Overall, all attendees (100%) strongly agreed or agreed that they were able to share thoughts and opinions effectively, 23 (96%) strongly agreed or agreed that the facilitators were fair and impartial, and 22 (92%) strongly agreed or agreed that the process of determining the top 10 questions was fair and robust.This JLA PSP gathered opinions from more than 1000 patients, caregivers, and clinicians and, through a multistep process, identified the top 10 unanswered research questions that matter most to those affected by concussion. The project brought together a broad and diverse group of patients, caregivers, and clinicians from across Canada and, to our knowledge, is the first patient-oriented research priority-setting project in concussion. The top 10 questions identify priority areas on which researchers and research networks can focus to improve outcomes for people living with concussion.Taken as a whole, the list of research priorities is broad in scope and reflects the diverse concerns of patients, caregivers, and clinicians. Priorities include early and accurate diagnosis after a head injury, specific symptom management, approaches to ensure a safe return to physical and cognitive activities after concussion, predicting and preventing prolonged symptoms and poor outcomes, and understanding the pathophysiology of concussion.6 early and accurate identification of risk factors for prolonged symptoms was deemed the highest priority. Risk factors associated with prolonged recovery have been identified, and a clinical risk score has been validated for pediatric patients14 and is now in development for adult patients.27 However, despite considerable research, there are no validated physiological, serological, or radiological biomarkers that can accurately diagnose concussion and predict recovery.31 Therefore, more research is needed to implement existing validated tools into clinical pathways and to develop additional biomarkers and strategies to refine diagnosis and personalize therapies to improve outcomes.The top-ranked question focuses on identifying patients at high risk for prolonged symptoms at the time of diagnosis and developing a personalized management strategy. With up to 30% of people with concussion experiencing symptoms longer than 1 month,Three questions focused on improving early diagnosis and management of concussion. The first concerns the role of interdisciplinary teams of health care professionals  in assessing and managing concussion and asks which patients should be referred early to these teams to achieve the best outcomes. A second question focused on the need to develop approaches to differentiate symptoms due to concussion from those of nonconcussion medical problems that may have similar symptoms but require different therapies. The third question concerns the variation in abilities of clinicians to recognize and manage concussion. With concussion diagnosis and management recommendations changing frequently, educating clinicians on concussion was considered important to improve recognition and standardize management.32 Some patients have difficulty increasing mental and physical activity without exacerbating postconcussion symptoms. Effective strategies to reintroduce cognitive and physical activities in those with refractory symptoms, to reduce time away from school, work, and/or physical activity, and to improve recovery time are needed. Two additional management-related questions focused on the effectiveness of interventions to manage concussion-related chronic headache and sleep disturbances.Two highly ranked questions focused on identifying the best approach for a safe and effective return to activities. Recent evidence has shown that after a short period of initial rest (24-48 hours), mild to moderate physical and cognitive activity is beneficial for recovery.Five questions were deemed to be sufficiently answered by literature review. These answered questions highlight gaps in knowledge translation and should be a priority for dissemination to stakeholders and the public to improve awareness and enhance implementation of existing research evidence.22 Results were analyzed transparently and are shared openly on the project website. Other strengths include a mix of ages of survey participants across Canada, including speakers of both official languages and those in urban and rural areas. The steering group, survey respondents, and workshop participants included a diverse group of clinicians who treat people with concussion, allowing for different perspectives across professions.Strengths of this study include the methodological rigor and transparent nature of the JLA-PSP methods. The JLA PSP is a widely used and highly cited approach for facilitating collaboration between patients and clinicians to prioritize research questions.33 Second, we may have had difficulty reaching groups who do not have internet access. However, both a paper version of the survey and telephone completion were offered. Third, greater than 50% of survey participants had concussion symptoms lasting longer than 1 year, and this may have resulted in research questions reflecting more chronic concerns. Despite this limitation, many questions in the top 10 focused on acute concussion management. Fourth, as the surveys were completely anonymous, we were not able to determine whether people completed them more than once. Fifth, although many questions on concussion prevention came from survey 1, they were not highly ranked by participants in survey 2 and therefore not selected as priorities to include in the final workshop. We believe that this omission does not reflect a lack of importance of research on concussion prevention. Rather, we believe it reflects the overwhelming importance of diagnosis, treatment, and prognosis as the major concerns of individuals living with concussion and clinicians managing concussion. Finally, this is a Canadian study, and the research priorities may not reflect people from countries with different health care expertise, access, and resourcing.This study has some limitations. First, it was challenging to recruit people from some racial, ethnic, and cultural backgrounds, particularly those who identify as Black, Latino, and Middle Eastern. Our study was able to recruit 4% of individuals with concussion who self-identified as Indigenous (5% of Canadians identify as being Indigenous).This PSP of patients, caregivers, and clinicians worked together in an equal partnership to identify the top 10 patient-oriented research questions in concussion. These priority questions help inform the concussion research agenda and may be useful to stakeholders, researchers, clinicians, funders, hospital leaders, and policy makers. The goal is that these questions will stimulate funding for concussion research that will answer questions that matter most to those living with concussion and those who care for them.