Panicum virgatum which had 1,458 proteins with species-unique targeting. An average of 45% overlap was found in plastid-targeted protein-coding gene families compared with Arabidopsis, but an additional 20% of proteins matched against the full Arabidopsis proteome, indicating a unique evolution of plastid targeting. Neofunctionalization through subcellular relocalization is known to impart novel biological functions but has not been described before on a genome-wide scale for the plastid proteome. Further work to correlate these predicted novel plastid-targeted proteins to transcript abundance and high-throughput proteomics will uncover unique aspects of plastid biology and shed light on how the plastid proteome has evolved to influence plastid morphology and biochemistry.Plastids are morphologically and functionally diverse organelles that are dependent on nuclear-encoded, plastid-targeted proteins for all biochemical and regulatory functions. However, how plastid proteomes vary temporally, spatially, and taxonomically has been historically difficult to analyze at a genome-wide scale using experimental methods. A bioinformatics workflow was developed and evaluated using a combination of fast and user-friendly subcellular prediction programs to maximize performance and accuracy for chloroplast transit peptides and demonstrate this technique on the predicted proteomes of 15 sequenced plant genomes. Gene family grouping was then performed in parallel using modified approaches of reciprocal best BLAST hits (RBH) and UCLUST. A total of 628 protein families were found to have conserved plastid targeting across angiosperm species using RBH, and 828 using UCLUST. However, thousands of clusters were also detected where only one species had predicted plastid targeting, most notably in  However, the total chloroplast proteome conservatively contains 2,000–3,500 proteins as reported in Arabidopsis4, but as many as 4,875 plastid-targeted proteins are estimated in eSLDB5, and 5,136 by the Chloroplast 2010 project8. Less than 900 of 4,500 genes horizontally transferred from the ancestral cyanobacterium are predicted to be retargeted to the plastid in vivo9.Plastids represent biochemically and morphologically complex organelles and can change both form and function drastically in response to developmental and environmental cues. A vestigial but functional genome of 120–160 kb harboring ~90 protein-coding genes is present in the plastids of photosynthetic higher plants2. A similar result was obtained in a comparison of six crop plants against Arabidopsis, in which an average of 51.0% of the predicted plastid proteome of each species matched to the Arabidopsis predicted plastid proteome, while 67.5% matched against the full Arabidopsis proteome10. Thus, the plastid pan-proteome is extremely diverse and is composed of unique proteins at the species-level. Furthermore, as the number of conserved sequences across all the genomes analyzed closely mirrors the number of genes of cyanobacterial origin, the non-conserved plastid-targeted protein-coding genes most likely evolved from eukaryotic sequences. The variability in the predicted plastid proteome mirrors the observable diversity in plastid function and ultrastructure in different species or under different environmental and developmental conditions13. The diversity of plastid proteomes is evident even within the same plastid morphotype: the pigment-storing chromoplast alone has at least four described ultrastructural phenotypes across various species with unique sub-organellar membrane structures that can occur either singly or mixed within individual plastids14. Morphological differences in plastid shape and ultrastructure are noted even in genetically similar cultivars of the same species. Both chloroplasts and chromoplasts of developing apple peel differ significantly from tomato, which is used as a model reference for chromoplast differentiation in fruits16. Variation has also been documented between the apple cultivars and the epidermal and collenchymal plastids11.There seems to be a difference between the composition of plastid-targeted proteomes in dicots and monocots. Only 21% of plastid-targeted rice proteins have a predicted homolog in the predicted Arabidopsis plastid proteome, and in reciprocal comparison the number is 38%The observed phenotypic diversity of plastids could be explained by three potential molecular factors: (1) Differences in the expression of genes controlling the rate and total amount of protein accumulation or import. This aspect could lead to unique phenotypes without necessarily changing the subset of plastid-targeted proteins. (2) Mutations within a shared group of plastid-targeted proteins could lead to neofunctionalization. (3) Finally, gain or loss of transit peptides causing subcellular mistargeting could alter the total pool of plastid-targeted proteins.20. In support of the second mechanism, point mutations in the active site of plastid-targeted limonene synthase change the abundance and distribution of different monoterpenoid end products in bacterial expression systems21, and transplastomic expression of a delta-9 desaturase gene causes changes in fatty acid concentrations and levels of unsaturation, cold tolerance, leaf senescence, and seed yield22 are additional examples. While it is challenging to address the neofunctionalization of plastid-targeted proteins via mutation without detailed reverse genetics experiments, the other mechanisms can be evaluated with high-throughput sequencing and bioinformatics.These factors are not mutually exclusive, and examples of each mechanism are known. Gene expression differences, possibly caused by epigenetic DNA methylation patterns, are responsible for differential protein accumulation in mesophyll and bundle sheath cells of C4 plants, illustrating the first point23, PPDB24, SUBA425, and CROPPAL26. However, caution should be exercised in interpreting these datasets because MS is susceptible to high false positive errors due to contamination during plastid isolation, liberal mass tolerance, and errors in peptide mapping, among other problems29. While the use of reference genomes and transcriptomes can help overcome peptide mapping issues, other technical issues are more difficult to resolve. Use of fluorescent protein chimeras , though lower-throughput, typically have higher biological accuracy. Using these, localization of low-abundance, as well as proteins from species lacking robust plastid isolation methods, can be evaluated with higher efficiency. However, GFP techniques are not immune to experimental error either. Since the sequence of the mature protein partially influences localization , GFP fused to the native protein may alter localization in some cases. Furthermore, dual-targeted mitochondrial/chloroplast proteins can be mislocalized in GFP assays33. Alternative transcripts or alternative protein products may also produce differential subcellular localization that are either not captured in GFP assays or give ambiguous results. Given these experimental limitations, a robust bioinformatics workflow could enable rapid and cost-effective assessment of plastid proteomes with somewhat comparable accuracy. Though wet lab validation is still necessary, these datasets could narrow the focus to smaller subsets of proteins of interest which could be more manageably targeted for wet lab validation depending on the biological question being asked.High-throughput proteomics using mass spectrometry (MS) has been an important means of surveying organellar proteomes and comprises the majority of current plastid proteome evidence. However, these techniques have historically been limited to the chloroplast morphotype and a restricted number of plant species. Excellent databases for high-throughput plastid proteomes based largely on mass spectrometry are accessible at AT_CHLOROin silico predictions challenging. Plastid transit peptides, as with other signal peptides, are well-known to be more variable than downstream protein sequence but more conserved than noncoding sequence. Yet, patterns of loose conservation at the amino acid level if not at the sequence level reveal multiple subgroups of transit peptides37. However, sequence- and annotation-based approaches have yielded results with significant accuracy. Protein sequence-based prediction uses the amino acid content or the presence of conserved motifs in the peptide to make predictions. Use of the amino acid content alone, such as in the tool PCLR, is enough to predict many plastid-targeted proteins38. More complex sequence-based identify conserved motifs, such as in iPSORT39 and WoLF-PSORT40, or sliding-window searching algorithm such as Localizer41, make predictions based on the sum of prediction vectors to determine transit peptide similarity. Finally, tools that use neural networks such as ChloroP42, TargetP44, Predotar45, PredSL46, and Protein Prowler47 use multiple layers of nodes to identify the best-scoring localization. In contrast, annotation-based methods such as CLPFD48 and EpiLoc49, or simple text-based methods based on GO annotations50, use homology to proteins with known localization to designate subcellular predictions. While these methods offer advantages over sequence-based methods for proteins with annotated homologs, they perform poorly for novel proteins51. Hybrid approaches including MultiLoc252, Sherloc253, Y-Loc54, and Plant-mPLoc55 combine sequence- and annotation-based methods in an attempt to overcome this limitation. Unfortunately, the homology component of hybrid approaches is weighted more heavily, which can lead to the false prediction of proteins with transit peptide variation or for proteins with shared domains. Both high-throughput proteomics and bioinformatics approaches consistently indicate that the plastid proteome content is highly dynamic and likely has significant variability across the plant kingdom. With newer methods, ever-growing genomic resources, and availability of better gene annotation methods, previously reported estimates of conserved and non-conserved sets of the plastid proteome warrant an update.The semi-conserved and sometimes ambiguous nature of chloroplast transit peptides makes Amborella trichopoda, an early diverging species of the angiosperm clade. Two parallel approaches, Reciprocal-Best Blast Hit (RBH) and UCLUST56 were used to perform clustering, and the sub-cellular localization prediction for each cluster was analyzed to identify conserved, semi-conserved, and non-conserved plastid-targeted proteins. This approach also evaluated the hypothesis that a relative minority of plastid-targeted protein-coding genes are conserved among all species. It was found that natural selection and environmental influence has shaped the development of species-specific plastid proteomes.This study evaluated the hypothesis that bioinformatics methods could achieve similar accuracy to experimental methods by comprehensively testing previously published subcellular prediction algorithms both alone and in combination. A specific combination of methods was found to be most efficient, which was then used to globally predict nuclear-encoded plastid-targeted proteins for fifteen higher plant species including eight eudicots, six monocots, and 43, WoLF PSORT40, PredSL46, Localizer41, Multiloc252, and PCLR38 was first evaluated using data from the original publications. Sensitivity, specificity, accuracy, and Matthew’s Correlation Coefficient (MCC) were evaluated for each program as it related to the prediction of plastid-targeted proteins  and 3,722 validated by GFP. Most prediction algorithms were found to have lower performance against biological data reported in the original reports, as shown in Table 28, program performance was expected to be much higher for GFP-validated proteins. While the dataset containing proteins experimentally validated by both GFP and mass spectrometry showed the highest apparent performance for the six subcellular prediction algorithms - and is likely closer to the biological accuracy of these programs - it contains roughly a third as many proteins as the GFP-validated dataset and is heavily biased by Arabidopsis sequences. Therefore, remaining comparisons focused on the GFP-validated dataset. Similarly, MCC was used as the primary measure of biological accuracy of in silico approaches to avoid problems due to drastically different dataset sizes.Next, cross-validation of subcellular prediction programs was performed against proteins with experimentally-determined subcellular localization retrieved from AT_CHLORO61 and over 500 are predicted to have ambiguous transit peptides62. Increased accuracy in the prediction of these sequences in Localizer could alone account for a portion of its higher performance. After Localizer, MultiLoc2 had the next-highest MCC and also had the highest specificity of any program, at 83% in GFP-validated proteins. MultiLoc is a hybrid method combining annotation and sequence analysis, so these findings support that the use of hybrid methods yields robust biological specificity. However, MultiLoc also had the worst sensitivity of any program, correctly predicting only 50% of bonafide plastid-targeted proteins validated by GFP or 31% of sequences validated by either GFP or mass spectrometry. TargetP, which has historically been the most popular subcellular prediction program for plants since its introduction, was found to perform at lower accuracy than earlier estimates: even when using the more conservative GFP-validated data, specificity was only 59% and sensitivity was 67%. Previous experiments using high-throughput shotgun proteomics have reported that the sensitivity of TargetP is as low as 62%65. Use of strictly-curated data improves the apparent sensitivity up to 86%, but false positive rates are still problematic as a specificity of about 65% is observed66. The results presented here suggest that the biological accuracy of TargetP is somewhat closer to the initial estimates on non-curated data. PredSL, PCLR, and WoLF-PSORT were the lowest-ranked programs by MCC for prediction of plastid-targeted proteins, in that order, but typically had higher sensitivity than Localizer or MultiLoc2.Overall, the highest-performing program in terms of MCC was Localizer, followed by MultiLoc2-HR, TargetP, PCLR, PredSL, WoLF PSORT, and MultiLoc2-LR. Of these, PredSL and MultiLoc2-LR performed poorly with GFP-validated proteins compared to the original reports, while other programs decreased marginally or performed similarly to the published MCC. Among the six programs that were evaluated, Localizer had the highest performance regardless of the experimental method used for validation, which is surprising since it is a simpler tool than annotation-based methods which have been at the forefront of subcellular prediction methods recently. Part of Localizer’s increased accuracy may be due to its unique capacity to predict dual-targeted mitochondrial/chloroplast proteins. Over 200 dual-localized proteins have been described in Arabidopsis66. Therefore, differences in the prediction of monocot or eudicot sequences were assessed, and different programs displayed significant bias  or to curate mass spectrometry data . Additionally, a combinatorial workflow using 22 prediction algorithms and four experimental techniques is used in the SUBAcon algorithm implemented for the SUBA4 database of Arabidopsis proteins which reportedly yields up to 97.5% accuracy for chloroplast localization and 90% for other compartments60. While SUBAcon does not strictly require experimental data to perform predictions, available evidence weighs heavily on the final prediction and contributes to the reported accuracy. Even if experimental evidence were to be ignored, the use of 22 separate subcellular prediction algorithms is not feasible for individual researchers or application to enormous datasets. Therefore, a bioinformatics-based workflow that can work efficiently would be desirable.Use of multiple prediction algorithms in combination is a powerful strategy to combine the strengths and overcome the limitations of single programs. Combinatorial approaches have been used to improve the accuracy of predictions in whole-genome analyses  supported the hypothesis that bioinformatics methods could reach parity with mass spectrometry in characterizing the plastid proteome. Due to the increased simplicity and comparable performance of the TargetP/Localizer consensus approach, this workflow was selected for subsequent genome-scale prediction of plastid-targeted proteins.Calculations were performed for each possible permutation of subcellular prediction algorithms and for all possible acceptable thresholds for each combination as applied to GFP-validated proteins. For example, for the combination of TargetP, PredSL, and Localizer, three thresholds were tested in which one, two, or all three programs needed to predict plastid localization to consider that protein as having a plastid transit peptide. To simplify analyses, the poorly-performing WoLF PSORT was removed from consideration   of predicted plastid-targeted Arabidopsis sequences in conserved RBH clusters were validated by GFP and 94.5%  were validated by MS. The same was true for 80.5% (154 proteins) and 92% (855 proteins) in conserved RBH and UCLUST clusters, respectively  annotations was performed in conserved clusters by finding GO terms shared in at least three individual sequences and for over 10% of sequences. Terms were compared to annotations extracted using the same criteria for all the clusters of the respective clustering method and GO term enrichment was performed using BLAST2GOTaken together, the good correlation of protein clusters with experimentally-validated sequences, the enrichment of expected annotation terms, and the presence of expected highly-abundant proteins or proteins critical to chloroplast biology suggest that both the RBH and UCLUST methods achieved good accuracy and sensitivity for genes with conserved chloroplast targeting which are likely critical in all photosynthetic plants for minimal chloroplast function. It is noteworthy that 194 clusters in RBH and 333 core clusters in UCLUST contain at least one Arabidopsis sequence but have no associated gene synonyms available . Since the chloroplast transit peptide is presumed to have arisen recently in each cluster, the term “nascent plastid-targeted proteins” (NPTPs) was coined to represent such proteins. Unsurprisingly, species with large and complex genomes possessed a more significant number of NPTPs: A. amnicola had the least, at just 52 in RBH and 97 in UCLUST, while P. virgatum had the most, with 682 NPTPs found in RBH and 1,458 in UCLUST. The predicted proteome of A. amnicola is based on transcriptomics data rather than genome-wide prediction, while P. virgatum has the largest genome and most extensive predicted proteome of the species in this analysis, so these trends are consistent with expectations.Finally, clusters with predicted plastid targeting only present in a single species were identified in RBH Table , Fig. 5 M. × domestica, and between 300–400 proteins had species-specific plastid transit peptides in B. distachyon, F. vesca, G. max, S. italica, and S. bicolor. Arabidopsis had some of the lowest estimates of NPTPs, with only 74 found in RBH and 166 in UCLUST. Species-unique plastid-targeted proteins had a moderately linear correlation with the total number of sequences in each species R2 = 0.73 in RBH and 0.72 in UCLUST, Fig. P. virgatum resulted in nonlinear correlation  has suggested that gene duplication is a prerequisite or at least greatly encourages neofunctionalization via novel subcellular targeting, and the generally linear correlation with proteome size suggests that this may indeed be the case. However, based on the data, the evolution of the plastid proteome is more likely to be driven by environmental adaptation and selection pressure114.Additionally, up to 728 proteins were uniquely targeted to the plastid in ion Fig. . ConsequWhile transit peptide structure and sequence were expected to be conserved within each thus-identified cluster, searching for shared homology between transit peptides of different clusters was not performed. Without experimental data to support such identification, the motifs thus identified would be unreliable predictions, and it would be hard to state if the observed convergent evolution detected in novel transit peptides has any cause-effect relationship.As with the conserved plastid-targeted clusters, the accuracy of targeting prediction in NPTPs was cross-validated against experimentally-validated proteins from Arabidopsis. For the RBH clusters, 75% (4 proteins) were validated to be true plastid proteins via GFP, and 53.8% (17 proteins) validated by MS. For UCLUST, 29.4% (17 proteins) were validated by GFP, and 41.4% validated by MS. Specificity was also very high: only 6.3% of 300 predicted non-plastid-targeted proteins in RBH-generated NPTP clusters were found to actually be plastid-targeted by GFP, while the rate in MS-validated proteins was 13.4% (967 proteins). UCLUST generated similar results, with false negative error rates of 3% (493 proteins) in GFP-validated data and 12.5%  for MS-validated data. The few false negatives in predicted NPTPs may be representative of ambiguous/intermediate sequences in clusters which are already predicted to be uniquely chloroplast-targeted in Arabidopsis and therefore represent missing links. More pertinently, the GFP estimates are likely more accurate due to the experimental specificity errors inherent in mass spectrometry, and the 3–6% error rates are within an acceptable range.10) due in part to the removal of singletons and single-species clusters. Homology to sequences in other species dramatically decreases the probability of pseudogenes and gene prediction errors. Remarkably, the monocot species had an average of 50–60% more species-unique plastid-targeted protein clusters than eudicot or Amborella counterparts. Even after removal of the outliers P. virgatum and A. amnicola, monocots still had 40% more plastid-targeted clusters than eudicots according to RBH methods, and over 80% more clusters using UCLUST. The reasons for this could be two-fold. First, the monocot species in this analysis have larger proteomes on average, increasing the overall likelihood for both de novo evolution of NPTPs and for retention of orphaned singleton/species-specific proteins. Secondly, monocots, and especially grasses, have been described to have many presence/absence variants (PAV’s) and copy number variants (CV’s) in their genomes. Pan-genome sequencing of B. distachyon revealed over 7,000 pan-genes that are not present in the reference genome, and an average of 9 Mb of sequence in each accession does not align to the reference genome115. Similar rates of PAV’s have been reported for cereal crops: only half of the pan-genome diversity of maize is present in the reference genome116, over 21,000 predicted wheat genes are not represented in the reference genome117, and 8,000 predicted rice genes are not represented in the Nipponbare reference genome118. In contrast, pan-genomes of Arabidopsis119 and tomato121 describe variation primarily at the SNP and small insertion/deletion levels, although one report described that 14.9 Mb of the Columbia-0 genome was absent in one or more other accessions122. In Brassica oleracea, less than 20% of genes were affected by presence/absence variation123. Somewhat higher variation is observed in legumes: 302 soybean lines including varieties, landraces, and wild accessions revealed 1,614 copy number variants and 6,388 segmental deletions, and 51.4% of gene families were dispensable124 while in Medicago truncatula, 67% of annotated genes may be dispensable125. It bears consideration that the pangenomes of the grasses are primarily within cultivated accessions and have already passed through a domestication filter which already significantly reduces genomic diversity, whereas the pangenomes of most of the eudicots include wild and landrace accessions. These trends suggest that PAV’s and CV’s are significant drivers of plastid proteome evolution, either by retention of orphaned genes or by de novo evolution of transit peptides in duplicated genes. Despite the smaller number of species-unique clusters, conserved plastid-targeted proteins are still outnumbered up to 25-fold by species-unique or semi-conserved proteins. If even a fraction of these sequences is accurate and expressed in vivo, each could impart novel biological functions because escape from the evolutionarily established biochemical and regulatory environment could impart a different function in a new subcellular environment without changing the functional sequence of the protein. Thus, each of these is an excellent candidate for further characterization to determine if unique phenotypes are created by relocalization to the plastid. Conversely, species-specific plastid-targeted protein-coding genes in model systems could yield misleading interpretations because the same phenotypes for those genes would not be observed in species where homologs do not have plastid-specific localization. Such a situation is potentially problematic for the unique plastid-targeted proteins detected for Arabidopsis, B. distachyon, and rice because it is likely that some of these genes already have a described gene function that is being inaccurately ascribed to plants as a whole. Indeed, out of 113 Arabidopsis proteins with predicted species-specific plastid-targeting, 18 have a described phenotype, and 100 are cited in previous research reports 24, CropPAL version 58839ba26, and CropPAL2 version 7486696726. Headers which could not be referenced to the most up-to-date reference proteomes were discarded. For AT_CHLORO, Suba4, and PPDB databases, all genes located on the chloroplast and mitochondrial genomes were removed, and redundant headers were merged. Subsets of data including sequences confirmed by mass spectrometry, GFP fusion, either GFP or mass spectrometry, or both were extracted from each database by filtering for the keywords “Chloroplast” or “Plastid.” All ambiguous results containing experimental evidence for both plastids and at least one other subcellular fraction were removed.Test datasets for cross-comparison of subcellular prediction algorithms were retrieved from PPDB , AT_CHLORO 44, WoLF PSORT Command Line Version 0.240, PredSL Web Server46, Localizer v.1.0.241, MultiLoc2 version 2-26-10-200952, and PCLR update 2011-11-24 release 0.938. Additionally, NLStradamus v.1.8126 was used as part of the Localizer algorithm, while Python v.2.7.5, LIBSVM v.2.8, BLAST v.2.2.30, and Interproscan v.5.25-64.0 were used as part of MultiLoc2. Results for each workflow were converted into binary classification and evaluated for Sensitivity (SE), Specificity (SP), Matthew’s Correlation Coefficient (MCC), and accuracy (ACC) as related to plastid localization prediction based on the number of true positives, false positives, true negatives, and false negatives compared to the annotations in the corresponding experimental dataset (see equations below). Combinatorial approaches were performed for each possible combination of programs from two up to all six programs, and different thresholds were evaluated based on the number of programs in agreement for plastid localization. Complete records of individual and combinatorial workflows for each experimental dataset are available in Supplementary File tp is the number of sequences correctly identified as plastid-targeted, tn is the number of sequences correctly predicted to be non-plastid-targeted, fp is the number of non-plastid-targeted sequences incorrectly predicted as plastid-targeted, and fn is the number of plastid-targeted sequences that were predicted as non-plastid-targeted. Note that these categorizations are based on the accuracy of the database annotation and any filtering that was applied to data subsets, and they may not reflect biological accuracy.Experimentally validated protein sequences were analyzed with TargetP v.1.1Amborella trichopoda, Arabidopsis thaliana, Brachypodium distachyon, Fragaria vesca, Glycine max, Malus × domestica, Oryza sativa, Panicum virgatum, Populus trichocarpa, Prunus persica, Setaria italica, Solanum lycopersicum, and Sorghum bicolor were downloaded from Phytozome87. The proteome of Anthurium amnicola was obtained by personal correspondence with Dr. Jon Suzuki, USDA-ARS, Hilo, Hawaii, in advance of the publication76. For Vitis vinifera, an expanded proteome version was obtained from94. For Malus × domestica, modifications to the predicted proteome were made because over 15,000 sequences, representing over 20% of the predicted proteome, were determined to have no significant matches to proteins from other species (See Supplementary File 127. These sources are described further in Supplementary File de novo with default settings. Assembled contigs of >300 bp were kept and used to predict open reading frames (ORF’s). Non-overlapping ORF’s with at least 5x average base coverage and >300 bp were extracted and translated into protein sequences. Finally, extracted protein sequences were compared against the existing Malus × domestica v.1.0 predicted gene set81 downloaded from Rosaceae.org. All hits with greater than 98% ID and coverage (as per85) were tagged as potential duplications or alleles of the original headers but were kept in the peptide dataset in case minor mutations caused differential localization prediction. All sequences generated from this transcriptome assembly are available in Supplementary File 6. In total, 36,477 sequences were obtained, of which 26,881 sequences were determined to be unique in comparison with the apple genome 81. Addition of the unique genes from the de novo transcriptome created a final dataset of 64,680 unique proteins. Redundant sequences from the resulting transcriptome were retained in case minor differences resulted in differential targeting.Predicted proteomes for 44 and Localizer v.1.0.241. All sequences predicted by both methods to have a chloroplast transit peptide were classified as plastid-targeted, and all sequences with either “1 or 2” or “0 of 2” chloroplast transit peptide predictions were classified as non-plastid-targeted.The predicted proteomes of all species were filtered to remove any sequences less than 100 residues and which did not begin with methionine. Post-analysis filtering was accomplished by removing singleton sequences that failed to find matches with both the USEARCH method and BLAST (indicated for each sequence in Supplementary File 129) was performed for each species’ predicted proteome set against that of every other species in both forward and reverse directions. These results were filtered for hits in which identity and coverage parameters exceeded 40%. Of these, only hits in which two sequences from different genomes were the respective best hit were kept. Next, better-BLAST hits within each species were performed by conducting pairwise BLAST-P of the predicted proteome against itself. Hits exceeding 90% coverage and identity and which was reciprocal within the first 10 hits were collected. Cluster merging was performed by iterating through each possible header and collapsing all pairwise hits containing that header.Reciprocal Best-BLAST hit clustering was performed as follows: Pairwise BLAST-P  with 40% identity and 40% query coverage. Next, random seeds were constructed by extracting a single random sequence from each cluster, sorting the resulting sequences by length, and appending them to a length-sorted FASTA of the full sequence list used in the initial “Cluster_Fast” analysis. 100 randomly-seeded FASTA files were then analyzed with “Cluster_Fast” set to 90% sequence identity. Target and query coverage were additionally set to 0.4 to avoid problems with small query sequences acting as centroids for much larger sequences as a result of USEARCH being performed in sequential rather than length-sorted order. Cluster merging was performed by iteratively searching through each possible sequence header and collapsing all clusters containing that header. Custom scripts were developed for automating program workflows, referencing and translating sequences or headers, performing seed randomization for the modified UCLUST technique, performing cluster expansion, calculating statistics on clustering outputs, and referencing headers to respective clusters for both workflows. Sequence members within merged clusters from RBH and UCLUST methods were referenced to the predicted plastid targeting phenotype, and all clusters containing plastid-targeted members were extracted. Conserved plastid-targeted protein-coding gene families were defined as clusters containing at least 13 species and in which all had either predicted plastid transit peptides or at least three additional sequences. Semi-conserved plastid-targeted gene families were defined as clusters containing plastid-targeted sequences from at least 2 species within each family or clade and no predicted plastid-targeted sequences from species outside that clade. Non-conserved plastid-targeted protein-coding gene families were defined as all clusters containing a minimum of three species in which only one species had a plastid-targeted sequence.Clustering using the UCLUST algorithm proceeded as follows: An initial run on a length-sorted FASTA file containing all sequences was performed using ‘Cluster_Fast’ function of UCLUST . GO terms were converted into GOslim annotations using BLAST2GO, and for each cluster, all terms shared by at least three species and present in over 10% of a cluster’s sequences were extracted to develop query datasets. In parallel, the same methods were used to extract GO terms from the total list of clusters to serve as reference datasets. Enrichment of GO terms in the shared plastid-targeted clusters was performed using BLAST2GO, with Fisher’s Exact Test was used to calculate significance using a false discovery rate (FDR) of less than 0.05 as a minimum significance threshold108. Graphical analyses of enriched GO terms were produced in BLAST2GO.Annotations for NPTPs were retrieved from Phytozome87. Gene names were referenced from the annotation file for Arabidopsis sequences present in conserved plastid-targeted protein clusters. Phenotype information for species-unique plastid-targeted proteins was referenced on NCBI130.Full gene annotations include described gene names were downloaded for the TAIR10 Arabidopsis genome from PhytozomeSupplementary File 1Supplementary File 2Supplementary File 3Supplementary File 4Supplementary File 5Supplementary File 6


We describe the clinical benefit of immune checkpoint inhibitors using the European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS) and ASCO VF.We identify all approved indications of immune checkpoint inhibitors based on RCTs between January 1, 2011 and September 30, 2018 by FDA. Information including medians and HR of OS (PFS or DFS) and 95% CI, grade 3 or 4 toxicities in each arm, QOL data, survival probability at fixed time were extracted.Immune checkpoint inhibitors were approved for 18 indications based on RCTs. All the indications meet the ESMO-MCBS 1.1 threshold for meaningful benefit. By the updated ASCO-VF, the median Net Health Benefit (NHB) of these agents was 55.3 (range 17.4–77.1). Two third of the indication gained the bonus points for durable survival benefits by updated ASCO VF. When updated results were incorporated in the assessment, clinical benefit of most approved immune checkpoint inhibitors increased with a median improvement of NHB of 10 (range 2–20).Approved immune checkpoint inhibitors provided clinical meaningful benefit by ESMO-MCBS 1.1, and most of these agents reach the threshold for bonus points for durable survival in the updated ASCO VF. Knowledge of the potential benefits and risks associated with the use of anticancer therapies is fundamental for making treatment-related recommendations and decisions. Two important oncology societies have recently taken a step forward to quantize the clinical benefit. The American Society of Clinical Oncology (ASCO) Value Framework (ASCO-VF) , which wThe growing wave of progress using cancer immunotherapy, which has extended and improved the lives of patients, many of whom had few other effective treatment options has yielded high expectations from all stakeholders. However, there are also concerns about the value of check point inhibitors. Many immune checkpoint inhibitors were approved based on single-arm studies, only recently more RCTs were finished and reported.Patient-reported outcomes (PROs), such as symptoms, quality of life (QOL), and patient-perceived health status supplement clinical data and are now more important during decision-making in oncology because they provide a holistic understanding of patient experience and treatment effectiveness , 9. BothIn this study, we aimed to describe the clinical benefit of checkpoint inhibitors that were recently approved by the FDA based on RCTs using ESMO-MCBS and ASCO VF, and whether these agents reach defined thresholds of long-term benefit in the two value frameworks. We also compare the values based on primary reports with those assessed based on updated reports including long-term survival reports and/or QOL reports.We identify all approved indications of immune checkpoint inhibitors  between January 1, 2011 and September 30, 2018 by searching FDA website . Only inInformation including medians and HR of OS (PFS or DFS) and 95% CI, grade 3 or 4 toxicities in each arm, QOL data, survival probability at fixed or specified time were extracted from the reports of pivotal clinical trials supporting the FDA approval and FDA documents (drug labels and review summary retrieved from Drugs@fda website ). SurvivASCO-VF and ESMO-MCBS both quantify treatment benefit in a survival endpoint. ESMO-MCBS grade was assigned based on the lower limit of the 95% confidence interval of the hazard ratio (HR), and in conjunction with the minimum absolute gain differences in median survival or by the increase in survival at a fixed time, and further adjusted on QOL, toxicity and long term plateau of survival curve. ESMO-MCBS grades, in the non-curative setting, range from 1 to 5, with grades 4 and 5 representing meaningful clinical benefit, in the curative setting, range from A to C, with A and B representing meaningful clinical benefit. ASCO-VF score was assigned primarily on the point estimate of the HR with adjustment on toxicity and bonus points including tail of the curve, palliation, QOL and treatment-free interval. ASCO-VF score is continuous with a higher score representing a better score, and no cut-off value was provided to define clinical benefit. Both value frameworks incorporated amendments to introduce tail-of-the curve credits for progression-free survival and overall survival. For ESMO-MCBS, credit is given for a 10% or greater absolute gain at prognostically weighted specified time points in the true tail of the curve. Grading based on “long term” survival points differs depending on a PFS or OS endpoint . None of the trials actually meet this OS upgrade given the length of time required for the data to mature. ASCO-VF awarded 20 points of tail-of-the-curve bonus points if, at twice the median survival time (or DFS) in the control arm, there was an improvement of at least 50% in survival provided the survival in the control group was at least 20% and award 16 points (0.8 × 20) if the improvement is in PFS. ASCO-VF further awarded treatment with a statistically significant improvement in cancer-related symptoms (10 points) or improvement in treatment-free interval (10 points).Two review authors (F.L. and S.Z.) independently scored each indication using ESMO-MCBS and the ASCO-VF with discrepancies resolved by a third investigator. We used the k coefficient to determine degree of agreement between reviewers. For trials with two or more immunotherapy arms, we scored each arm separately, but only the arm with higher score was used to represent the value of the specific indication in all analysis.Value of approved drugs may change as long-term follow-up data or QOL data  become available. Particularly, drugs that failed to qualify the tail of the curve bonus due to limited follow-up time can show long term plateauing of survival with longer follow-up time.We searched latest drug label or PubMed to identify if updated reports of survival, toxicity or quality of life data and assigned updated score for these indications. When multiple reports of updated reports of survival were published, the most up-to-date one was used.Eighteen indications for 5 immune checkpoint inhibitors were approved by the FDA for metastatic solid tumors based on RCTs from March 2011 to September 2018 , and 8 trials grade four. For the two trials in the adjuvant setting, both were grade A. Thus, all trials met the ESMO-MCBS meaningful benefit threshold. Three trials met the ESMO-MCBS long term benefit criteria, all with the primary endpoint of PFS. Twelve of trials meet the criteria of improved toxicity (less grade 3–4 toxicities impacting on daily well-being) and only one trial was considered as increased toxic death.By the ASCO-VF, the median Net Health Benefit (NHB) of drugs was 55.3 range 17.4–77.1). The median treatment effect score was 34.4 range 25–58) and the median toxicity score was 3.8 (range − 7.6 to 11.3), with 13 trials have positive toxicity score and 5 trials with negative toxicity score , while for those with ESMO-MCBS grade of 5 or A, the median NHB was 56.8 (range 47.7–71.1).Fourteen trials reported updated survival results or toxicity data or quality of life data. ESMO-MCBS grades were changed for two trials, both of which increased from 4 to 5 Table . The ESMBy the ASCO-VF, the NHB were changed for 13 trials with updated results Fig. , Table 3A previous study by Vivot A et al. , which aIn our analysis, all trials met the ESMO-MCBS meaningful benefit threshold and by the ASCO-VF, the median NHB of drugs was 55.3 (range 17.4–77.1). Although caution should be taken in interpreting across study comparisons, due to the fact we used updated ASCO-VF and ESMO-MCBS, the clinical benefit seems greater in immune checkpoint inhibitors than other approved cancer drugs. Only two trials in the adjuvant setting were included in our study. Both trial meet the ESMO-MCBS 1.1 threshold for meaningful benefit. NHB of the two agents were 37.8 and 17.4, which seems lower than those in the metastatic setting. Further studies are need to evaluate whether the clinical benefit of immune checkpoint inhibitors in the adjuvant setting is consistent with those in the metastatic setting with more agents were approved in the adjuvant setting.Recently, Ben-Aharon et al. , tried tAlthough our study did not aim to or was powered to assess the consistency of updated ASCO-VF and the ESMO-MCBS 1.1 due to limited number of RCTs included, Clinical benefits by updated ASCO-VF and the ESMO-MCBS 1.1 yielded some sorts of consistencies. For trials with ESMO-MCBS grade of 5 or A, the median NHB was numerical higher than those with ESMO-MCBS grade of 4. A recent study  that evaWe found that 12 of 18 indications gained the bonus points for tails of the curve, while only 3 indications met the ESMO-MCBS long term benefit criteria, all with the primary endpoint of PFS. This discrepancy is not surprising given the differences in their criteria. To qualify for the long-term plateau by ESMO-MCBS 1.1 , overallWhen updated results were incorporated in the assessment, clinical benefit of most approved immune checkpoint inhibitors increased, largely due to the statistically significant improvement in the QoL or/and cancer-related symptoms that were not available in the primary reports but reported subsequently. Thus, the score may change when data mature. Our results emphasized the importance of PROs in accurately evaluating the clinical benefit of immune checkpoint inhibitors.Our study has several limitations. First, toxicities information were extracted from published articles, which often reported only adverse events that occurred in at least 10% of the treated patients, thus, the toxicity grade by ASCO VF may change with complete toxicity information. Second, although we conducted comprehensive research, PROs reports were not available for all approved agents, clinical benefit of these agents may change when PROs report were available. Third, we focused on clinical benefit of immune checkpoint inhibitors, and no comparisons to approved chemotherapy or other agents over a similar time period were conducted.In summary, all of the approved immune checkpoint inhibitors based on RCTs meet the ESMO-MCBS threshold for clinical benefit, and two thirds of these approved agents fulfilled the durable benefit thresholds in the updated ASCO VF. This information may be used in future analysis to better define clinical benefits of immunotherapies.


The relationship between electrocardiographic evaluation and circadian blood pressure (BP) variation in young and middle-aged hypertensive patients remains unknown.A total of 171 hypertensive patients were included in the study. First, patients were divided into a young and middle-aged group and an elderly group. The two groups were then separately classified into three subgroups on the basis of circadian variation of BP as dippers, non-dippers and reverse-dippers. The electrocardiographic evaluation was calculated from 12-lead electrocardiography (ECG).QTc intervals were shortest in the dippers and longest in the reverse-dippers in the young and middle-aged group . QTc interval was found to be an independent risk factor for the non-dipper BP pattern  and reverse-dipper BP pattern  in young and middle-aged hypertensive patients. No significant differences in other ECG parameters were found among the three subgroups in the young and middle-aged group.Our study suggested that QTc interval might serve as a risk factor for non-dipper BP pattern and reverse-dipper BP pattern in young and middle-aged hypertensive patients. To prevThere have been several studies investigating the association between electrocardiographic evaluation  and the circadian variation of BP , 9, 10. 22.1This single-center study retrospectively included hypertensive patients admitted to the Cardiology department at the first Affiliated Hospital of Soochow University who simultaneously underwent ECG and ABPM from January 2017 to January 2019. Hypertension was defined as previously diagnosed hypertension, currently using antihypertensive drugs, office-measured systolic BP (SBP)/diastolic BP (DBP)≥140/90 mmHg, 24h ABPM SBP/DBP≥130/80 mmHg, daytime (or awake) ABPM SBP/DBP≥135/85 mmHg or nighttime (or sleep) ABPM SBP/DBP≥120/70 mmHg, 12. BodThere were only two extreme-dippers meeting the above inclusion and exclusion criteria. Due to the limited numbers, the two extreme-dippers were not included in our study. In the end, a total of 171 patients were included in the study. They were first divided into two groups, the young and middle-aged group and the elderly group. Then, the above two groups were separately classified into three subgroups on the basis of circadian variation of BP, as dipper, non-dipper and reverse-dipper.2.2ABPM was performed to record the circadian variation of blood pressure in all included patients by an ABPM 6100 device . The cuff was placed on the right arm of patients. Patients were asked to keep their daily routine and stay calm when feeling the inflation of cuff. During the daytime (8:00AM to 11:00PM), BP was measured every 15 minutes. During the nighttime (11:00PM to 8:00AM), BP was measured every 30 minutes. Only recordings with more than 70% of valid BP measurements were included in the final analysis. The means of SBP and DBP were calculated at 24 h, awake and sleep. The nocturnal dip rate was calculated as follows: (%) 100× [1− (average night SBP/average awake SBP)]. Then, the circadian variation of blood pressure status was defined as: extreme-dipper , dipper , non-dipper  and reverse-dipper  [2.3All patients included in the study underwent a resting 12-lead ECG  with a 25mm/s paper speed and 10 mm/mV height. Heart rate and frontal QRS-T angle were obtained directly from ECG reports. The QT intervals were measured using a software program . Then we recorded the longest QT interval from all limb and precordial leads and QTc interval was calculated using Bazett’s formula. QTc int2.4Baseline demographic, clinical and laboratory data were collected from the hospital medical records and case collection and scientific research system for clinical cardiology (CCSSSCC). Peripheral blood samples were drawn from patients when they were in a fasting state. Serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST) and other biochemical parameters were determined at the Biochemistry Department in the first Affiliated Hospital of Soochow University.2.5Statistical analysis was performed using IBM SPSS for Windows version 25.0 . Continuous variables were tested for a normal distribution using the Shapiro-Wilk test. Continuous variables with a normal distribution were defined as mean±standard deviation (SD), while continuous variables without a normal distribution were defined as median . Continuous variables were compared using the one way analysis of variance (ANOVA) or the Kruskal-Wallis test, if appropriate. If the results of ANOVA were statistically significant, post-hoc Bonferroni test was performed. If the results of Kruskal-Wallis test were statistically significant, the results of pairwise comparison were corrected by Bonferroni correction. Categorical variables, defined as percentages, were compared using the χ2-test or the Fisher’s exact test, if appropriate. To assess the correlation between continuous variables, Pearson’s correlation coefficients were calculated. To explore the relationship between relevant variables and circadian blood pressure rhythm, multivariate logistic regression analyses were applied. For all analyses, values of P less than 0.05 were regarded to be statistically significant.Ethical issues: Our study was in accordance with the principles outlined in the Declaration of Helsinki and approved by the ethics committee of Soochow University.Informed consent: Informed consent has been obtained from all individuals included in this study.3In the end, a total of 171 patients were included in our study. The baseline demographic and clinical characteristics are shown in The laboratory and electrocardiographic variables of the study population are shown in the Age, QTc interval, TpTe, frontal QRS-T angle, K and creatinine were included in the multivariate logistic regression analysis. Age and QTc interval were found to be significantly different when comparing reverse-dipper BP pattern with dipper pattern or non-dipper BP pattern with dipper pattern . QTc intTo investigate whether there were associations between age or indices of ABPM and QTc in the young and middle-aged patients, Pearson’s correlation coefficients were calculated . QTc int4ABPM, which records the variation of blood pressure in 24h, has attracted more and more attention in recent years. Due to its higher reproducibility than office-measured blood pressure, ABPM has been proposed as a clinical tool to confirm office-measured hypertension diagnosis in several international guidelines . Based oQTc is one of the most widely used ECG parameters to assess ventricular repolarization. Previous studies have revealed that abnormal prolongation of QTc interval was correlated to ventricular arrhythmia and sudden cardiac death, 19, 20.In our study, the main finding was that QTc interval was significantly longer in non-dippers and reverse-dippers than in dippers in young and middle-aged hypertensive patients. However, QTc was similar among dippers, non-dippers and reverse-dippers in elderly patients with essential hypertension. What’s more, QTc interval duration was negatively correlated with the decline rate of nocturnal SBP in young and middle-aged hypertensive patients. Last but not least, QTc interval and age were found to be independent risk factors for non-dipper BP pattern and reverse-dipper BP pattern in young and middle-aged hypertensive patients.The relationship between QTc interval and circadian BP variation might be explained by LVH and impaired autonomic nervous system functions. Loss of normal nocturnal BP drop is correlated to left ventricular hypertrophy. Ivanovic et al demonstrated that LVH was most prevalent among reverse-dippers compared to dippers, non-dippers, extreme-dippers . HypertrIn our study, the correlation between QTc prolongation and blunted nocturnal blood pressure drop was observed in young and middle-aged patients but not in elderly patients. Older age itself is a risk factor for QTc interval prolongation . ElderlyTanriverdi et al demonstrated that frontal QRS-T angle was smaller in patients with dipper hypertension than in patients with non-dipper hypertension . On the In our study, heart rate was significantly different among dippers, non-dippers and reverse-dippers in elderly patients with essential hypertension. Moreover, heart rate was lowest in revere-dippers and highest in non-dippers in elderly hypertensive patients, which was inconsistent with previous studies , 28. TheRecently, the Monitorización Ambulatoria para Predicción de Eventos Cardiovasculares (MAPEC) study found that the reduction of sleep time SBP and correction of blunted night blood pressure drop through a night time antihypertensive treatment strategy can most efficiently lower the risks of stroke and cardiovascular diseases , 30. In 5Our study suggests that there is a correlation between QTc interval and the circadian variation of blood pressure in young and middle-aged hypertensive patients but not in the elderly. We found that QTc interval is a risk factor for non-dipper and reverse-dipper status in young and middle-aged hypertensive patients. What’s more, QTc interval is negatively correlated with decline rate of nocturnal SBP. The finding of our study may help to explain the conflicting results of previous studies.


The morbidity and mortality of cryptococcal meningoencephalitis (CM) in previously healthy, HIV-negative individuals is increasingly recognized. We administered a healthcare associated quality of life (QOL) survey to the largest longitudinally followed cohort of these patients in the United States. We identified moderate or severe self-reported impairment in at least one QOL domain in 61% of subjects at least one year following diagnosis. Self-reported cognitive impairment was noted in 52% and sleep disturbance was noted in 55%. This is the first comprehensive study of cross-sectional long-term QOL in previously healthy patients following cryptococcal infection. Cryptococcus is a cause of fatal meningoencephalitis that kills 250,000 people worldwide each year 1. Mortality of CM is high in both immunocompetent and immunocompromised populations, reaching 42% and 72% at one year respectively2. Between 20–70% of CM survivors are estimated to have long-term disability following infection, similar to other causes of acute infectious meningitis like tuberculosis, although the number and quality of current studies is limited2.1. This group has some of the highest mortality rates in the U.S.3 and significant disease sequelae including a fronto-subcortical syndrome4, hearing loss5, vision loss, and spinal arachnoiditis6, associated with direct fungal damage as well as a post-infectious inflammatory response syndrome (PIIRS)7. The effect of these sequelae on quality of life (QOL) is not known. Here, we examined QOL of HIV-negative, previously healthy individuals at least one year following CM diagnosis using the patient-reported survey instrument Quality of Life in Neurological Disorders or Neuro-QoL.In the U.S., 17–30% of non-HIV infected individuals with CM have no apparent underlying condition, accounting for approximately 1000 cases per year Of 66 subjects eligible for survey participation, 56 (85%) completed the survey between January 2020 and June 2020. Ten surveyed subjects 18%) did not have CNS disease; 9 had isolated pulmonary disease and one had confirmed osteomyelitis of the thoracic vertebrae (T4-T5). The demographic and clinical characteristics of the surveyed population are shown in Table 8% did noThe distributions of the population scaled Neuro-QoL T-scores with CM and non-CNS cryptococcosis are summarized in Fig. In the CNS cryptococcosis group, sleep disturbance (55% with at least mild symptoms) and cognitive function (52% with at least mild impairment) were reported as the most affected domains , anxiety , depression , as well as negatively correlated with positive affect and wellbeing . Depression T-scores were positively correlated with anxiety  and negatively correlated with positive affect and wellbeing . All correlations between Neuro-QoL T-score domains are shown in Fig. In the CNS cryptococcosis group, sleep disturbance T-scores were positively correlated with fatigue  and sleep disturbance (50% with at least mild symptoms) were the most reported affected domains. The domains with the lowest frequency of reported symptoms or impairment were mobility, communication, and stigma at 10%, respectively .The presence of a neurosurgical intervention in the CNS group during hospital course was not associated with a difference in the median number of moderately or severely self-reported impaired domains  or CSF glucose . CSF:blood glucose ratios at 100 + /- 50 days past diagnosis were not significantly associated with cognitive function T-scores . Longitudinal plots of CSF:blood glucose ratios and CSF white blood cell count are shown in Supplementary Fig. Self-reported cognitive function T-scores were not significantly associated with diagnostic CSF cryptococcal antigen , with declines in function in both. In the same way, Beck Depression Inventory (BDI) scores were positively correlated with the depression T-scores  and Beck Anxiety Inventory (BAI) scores were positively correlated with anxiety T-scores .Among the overlapping CNS group patients who underwent comprehensive neuropsychological (NP) testing, self-reported cognitive function T-score was positively correlated with global NP T-score , and satisfaction with SRA domains was also identified; these four Neuro-QoL domains correlate with common measures of activities of daily living (ADL) and instrumental ADL completion , summative NP T-score trended towards a positive correlation with self-reported cognitive function T-score. Given the varied distribution of responses to the cognitive function Neuro-QoL measure and the lack of a clear ceiling effect in the data , we believe it is likely that cryptococcal infection may be driving this symptomatology. Although self-reported impaired sleep is known to be a long-term consequence of viral, bacterial19. We identified that BAI and BDI scores were moderately correlated with their respective self-reported QOL domain scores in the 12 subjects with overlapping data. Interestingly, sleep disturbance may be linked with the prevalence of anxiety and depression in this population; given the highly correlated QOL scores on these metrics . All patients had been seen at the National Institutes of Health Clinical Center, Bethesda, Maryland, and informed consent was obtained. All experimental protocols were approved by the research ethics committee and Institutional Review Board of the NIAID (NIH). All methods were carried out in accordance with relevant guidelines and regulations. Exclusion criteria included being within 1 year of cryptococcal diagnosis, HIV seropositivity, chemotherapeutic agent use, underlying malignancy, and/or monoclonal antibody use prior to cryptococcal diagnosis. Patients received standard antifungal therapy after diagnosis, at least one course of amphotericin B followed by prolonged maintenance therapy with fluconazole. A minority of patients also received immunosuppressive therapy during their hospitalization. Patients were consented and mailed a survey packet to complete at home. All methodology was approved by the National Institute of Allergy and Infectious Disease Intramural Institutional Review Board.All previously healthy patients > 18 years old with a confirmed diagnosis of cryptococcosis by histopathology, culture, or cryptococcal antigen evaluated at the NIH Clinical Center from January 2013-January 2019 were invited to participate in this study and were recruited from an ongoing National Institute of Allergy and Infectious Disease study, 8. These questions have been validated, and shown adequate internal consistency and re-test reliability, in patients with other conditions that confer chronic neurologic morbidity, such as epilepsy, multiple sclerosis, Parkinson Disease, and stroke. We utilized a paper survey battery consisting of 13 Adult Neuro-QoL short-forms assessing 7 functional and 6 symptomatic domains in English  is a patient-reported measurement system that evaluates and monitors the physical, mental, and social effects experienced by adults and children living with neurological conditionshttps://www.assessmentcenter.net/ac_scoringservice). Reported T-scores have a mean of 50 and a standard deviation (SD) of 10 centered and calibrated to a general or clinical United States population . Comprehensive neuropsychological (NP) evaluation data, as reported in Traino et al. 2019, was used to compare functional measures with self-reported QOL domain T-scores. Briefly, NP evaluation assessed psychomotor, information processing, executive function, learning, memory, language, attention, and visuospatial function, summarized in a demographically-corrected global T-score. Mood symptoms were assessed with the Beck Depression Inventory II (BDI) and the Beck Anxiety Inventory (BAI)4.Descriptive data from participants was collected through the Biomedical Translational Research Information System (Continuous data were compared with two-tailed, non-paired Mann–Whitney U tests and frequency data with chi-square test. Associations between T-scores and clinical continuous metrics were calculated with simple linear regressions. Significance was considered alpha < 0.05. Spearman’s rank correlation coefficient was calculated between individual domain T-scores and NP test scores. Although statistical comparisons between the CNS and non-CNS groups were not performed given the small samples sizes in the latter group, the groups were displayed separately given the notable differences in clinical presentation, treatment, and sequalae between the populations.Supplementary Information.


Introduction: Substance use disorder has been frequently reported to increase the risk of infectious diseases, which might be owing to the sharing of contaminated inhalation, smoking, vaping, or injection equipment. Aim: This review analyzes the recent literature with the aim to put in light the possible relationship between the abuse of different substances  with coronavirus disease (COVID-19). Tobacco: Multiple studies confirmed that cigarette smoking affects the respiratory system by increasing the expression of angiotensin-converting enzyme-2 (ACE2) receptors, which have a significant association with COVID-19 infection rate and disease severity. Opioid: Studies conducted regarding the association of opioid use disorder (OUD) and COVID-19 infection severity are limited; however, opioids can lead to both respiratory depression and kidney injuries, causing poor prognosis for those with COVID-19 infections. Alcohol: People with alcohol use disorders are at risk of developing acute lung injury and severe COVID-19 infection. Alcohol consumption during the COVID-19 pandemic has two possible scenarios: either increased or decreased based on situations. Conclusion: SUD has been frequently reported to have a positive relationship with COVID-19 severity Further studies are needed to understand the effects of opioids and alcohol abuse on COVID-19. Coronavirus disease 2019 (COVID-19) caused by a recently discovered coronavirus . Within Substance use disorders (SUD) have been frequently reported to increase the risk of infectious diseases . This inPrevious studies assumed an increase in the substance of abuse consumption during the COVID-19 related lockdown compared to the period before the lockdown. This review analyzes the recent literature with the aim to put in light the possible relationship between the abuse of different drugs and coronavirus disease (COVID-19).Cigarette smoking can affect multiple organs, thereby leading to different diseases, and in general, reduces the overall health of smokers . This seThe largest study population of 1099 patients with COVID-19 was provided by Guan et al.  from mulp = 0.21) with regard to mortality from COVID-19. Similarly, Zhang et al. [n = 58), 3.4% were current smokers and 6.9% were former smokers, in contrast to non-severe patients (n = 82) among which 0% were current smokers and 3.7% were former smokers, leading to an OR of 2.23; .Another study by Zhou et al.  studied g et al.  presenten = 13) was a current smoker. In contrast, three patients from the non-ICU group were current smokers, with no statistically significant difference between the two groups of patients (p = 0.31), albeit the small sample size of the study. Finally, Liu et al. found among their population of 78 patients with COVID-19 that the adverse outcome group had a significantly higher proportion of patients with a history of smoking (27.3%) than the group that showed improvement or stabilization (3.0%), with this difference statistically significant at the p = 0.018 level [p= 0.018) as shown in Huang et al. studied the epidemiological characteristics of COVID-19 among 41 patients . In this18 level . In theid = 0.21) and smoking more cigarettes (d = 0.13) than before the COVID-19 (both p < 0.001) [The risk factor relation attributed to that Smoking has been reported to increase the expression of angiotensin-converting enzyme-2 (ACE2) receptors, which has been known to be a target for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and human respiratory coronavirus as shown in < 0.001) . The stu< 0.001) ,21.Interestingly, smoking could be a potential factor for the doubled death rates observed in men than women due to COVID-19. A study of 140 patients with COVID-19 conducted in China  more menUnlawful opioid use has a significant impact on society, including increased mortality and morbidity, marginalization, and criminal behaviors, including the enormous black-market economy from opiate trafficking . Opioid Studies conducted regarding the association of opioid use disorder (OUD) and COVID-19 infection or its severity are limited. A review paper explained how opioids could increase the severity of COVID-19 infection, with its effect on different body systems leading to complications . OpioidsIt is necessary to note that opioids are usually used safely in anesthesia and as a pain killer in the perioperative period in patients with kidney disease. The renal toxicity appears owing to the improper use of opioids: accidental higher doses, in the presence of other toxins, with preexisting dehydration, or prostate enlargement . ConstanChronic kidney disease may result owing to the method of drug administration: skin popping resulting in amyloidosis. Heroin-associated nephropathy is now considered to be linked to a toxin introduced into heroin during drug processing . One recElderly patients have higher risks, as age-related changes can alter opioid pharmacokinetics, thereby resulting in undesired side effects. Reduced organ function of the liver and kidney, alterations in adipose tissue composition, and altering opioid pharmacokinetics allow metabolites to accumulate and exist for a longer duration . The actThere are nearly 20 identified opioid peptide receptors that can be activated by endorphins . The actAnother study also confirmed that alcohol, amphetamine, and cocaine abusers are more likely to suffer from renal failure ,41,42. FAlcohol use disorder (AUD) is a chronic and relapsing disorder . People COVID-19 pandemic has affected different vulnerable health populations; one of them is patients with SUDs. There are many barriers related to substance use treatment, which already exist not only in the USA but worldwide . Within Other concerns regarding SUD patients during the pandemic include following physical/social distancing in face-to-face group treatments and other mutual support groups that are critical for their recovery . On 12 MSubstances abuse might increase the risk of developing COVID-19 and the severity of this infection. Further studies are needed to address such risk and severity of COVID-19 in patients with SUD. People with a history of SUD should receive attention from healthcare providers during such pandemics which can cause stress that is one of the main reasons for relapse to drug use. Moreover, special care should be provided for current patients with SUD to make sure their therapeutic plan and medications are not affected during such pandemics. This can be done by establishing a virtual consultations and follow-up to support them and address their concerns. Group counseling and other rehabilitation programs can also be continued virtually during such pandemics to ensure drug-free lifestyle.The relationship between smoking and the incidence of COVID-19 infection and its severity was recognized in different studies, which is a direct relationship. Patients of COVID-19 with OUD might have increased risk of worsening respiratory and renal functions especially in older patients. In addition, patients with AUD are at risk of developing severe COVID-19 infections and superinfections due to the impairments of their immune system. Further studies are needed to investigate the incidence of COVID-19 and possible complications following exposure to different drugs of abuse.


Evidence on whether leisure-time physical activity (LTPA) facilitates individuals’ adoption of multiple healthy behaviors remains scarce. This study investigated the associations of diverse longitudinal LTPA trajectories from childhood to adulthood with diet, screen time, smoking, binge drinking, sleep difficulties, and sleep duration in adulthood.N = 3553; 51% females) in 1980 and 33–49 years at the latest follow-up in 2011. The LTPA trajectories were identified using a latent profile analysis. Differences in self-reported health-related behaviors across the LTPA trajectories were studied separately for women and men by using the Bolck-Croon-Hagenaars approach. Models were adjusted for age, body mass index, education level, marital status, total energy intake and previous corresponding behaviors.Data were drawn from the Cardiovascular Risk in Young Finns Study. Participants were aged 9–18 years  > 0.50). The low-active men followed an unhealthier diet than the persistently and increasingly active men . Compared to their inactive and low-active peers, smoking frequency was lower in the increasingly active women and men  and persistently active men . The increasingly active men reported lower screen time than the low-active  and persistently active  men. The increasingly and persistently active women reported fewer sleep difficulties than the inactive  and low-active  women. Sleep duration and binge drinking were not associated with the LTPA trajectories in either gender, nor were sleep difficulties in men and screen time in women.Persistently active, persistently low-active, decreasingly and increasingly active trajectories were identified in both genders and an additional inactive trajectory for women. After adjusting the models with the above-mentioned covariates, the inactive women had an unhealthier diet than the women in the other trajectories (Not only persistently higher LTPA but also an increasing tendency to engage in LTPA after childhood/adolescence were associated with healthier diet and lower smoking frequency in both genders, having less sleep difficulties in women and lower screen time in increasingly active men. Inactivity and low activity were associated with the accumulation of several unhealthy behaviors in adulthood. Associations were stronger in women.The online version contains supplementary material available at 10.1186/s12889-021-10554-w. Non-communicable diseases, such as cardiovascular disease, diabetes mellitus, and several cancers, cause over 70% of deaths worldwide with unhealthy behaviors usually causing these diseases . PhysicaBehavioral risk factors tend to accumulate to same individuals which may have synergistic effects on health . A studyChanging one behavior may translate into changing other behavior as well, especially if both behaviors are health-enhancing or health-risky . PreviouHowever, previous research has mainly focused on the association of PA or LTPA with single health behavior. Moreover, longitudinal studies on whether a specific LTPA life-course developmental pathway facilitates the adoption of several other health-enhancing behaviors in adulthood are lacking. This study aimed to investigate whether cross-sectionally measured health-related behaviors in adulthood – including diet, screen time, smoking, binge drinking, sleep difficulties, and sleep duration – differ by LTPA pathways measured from childhood to adulthood in men and women. LTPA was defined as participation in PA and sport by querying intensity, duration and frequency of PA, and participation in organized sports. Occupational PA was not queried. Data-driven trajectory modeling  was chosThe data were drawn from the ongoing population-based longitudinal Cardiovascular Risk in Young Finns Study (YFS). The study involves five Finnish university cities  and their rural surroundings. Participants with different socioeconomic backgrounds were randomly selected from the population register of these areas to produce a representative sample of Finnish children and adolescents . In 1980N = 3553) were included in the present study  and medium to large effect sizes in adulthood   / 7) after which the mean values of the two sedentary behaviors were summed to form an index of daily screen time during leisure (hours/day).The categories for current smoking status were: (1) non-smoker (never smoked), (2) former smoker (on strike or stopped smoking), (3) occasional smoker (less than once a week), and (4) regular smoker (once or more a week). Binge drinking was determined as consuming six or more alcoholic drinks on a single occasion. The categories for binge drinking frequency were: (1) six times a year or less, (2) one to three times a month, (3) once a week, and (4) twice a week or more often.Sleep difficulties were assessed with Jenkins’s scale which has proven valid and reliable , 35. TheHours of usual sleep duration per night were self-reported within half an hour accuracy. The recommended duration of sleep for young adults and adults is 7–9 h per night . The resAge, body mass index (BMI), level of education and marital status were elicited in 2011 and used as covariates, as they may affect health behaviors –41. TotaDescriptive statistics were calculated by using IBM SPSS Statistics for Windows, version 24.0  and expressed as means and standard deviations or as percentages. Differences in the study variables between women and men were tested by using an independent-samples t-test.Further modeling was performed with Mplus, version 8.0 . All anaIn the current study, mean differences in the diet index, screen time, smoking status, binge drinking, sleep difficulties, and sleep duration were studied across the LTPA trajectories with the 2011 data. The Bolck-Croon-Hagenaars (BCH) approach was utilized for this purpose –48. In tp-value of <.05. In the second step (model 2), the mean values of the health-related behaviors were adjusted for age, BMI, education level and marital status. Model 2 was also adjusted for total energy intake when studying the mean diet index across the LTPA trajectories. In the third step (model 3), the mean values of the health-related behaviors were additionally adjusted with those of the previous corresponding health-related behaviors. When adjusting models 2 and 3, the mean values of the health behaviors were regressed on the above-mentioned covariates for the distinct LTPA trajectories. Differences in the regression intercepts  were studied across the trajectory classes. Models were adjusted using standardized values of the covariates.In the first step (model 1), the mean values of the health-related behaviors studied across the LTPA trajectory classes were unadjusted. To interpret mean differences required a Wald test Cohen’s d  and Cohep <  0.05). In 1986, the sleep difficulties variables were only assessed in the three oldest age cohorts. Thus, sleep difficulties in women were assessed at a higher mean age in model 3 than in model 1. However, model 3 was considered to be adequately reliable as it included all the available data with the relevant adjustments.The sample sizes dropped, especially in model 3. To verify the robustness of the models, sensitivity analyses were performed. Unadjusted analyses were performed for the same sample as in model 3 and then compared to the original results of model 1. The results of the sensitivity analyses closely resembled those of the main analyses. The main differences were in the associations between the LTPA trajectories and sleep difficulties in women: two additional significant associations were observed   Supplem. The GuiThe sample size of the study was 3553 51% females). All eight LTPA measurements had been completed by 4% of the participants, seven by 8%, six by 16%, five by 23%, four by 18%, three by 15%, two by 10% and one by 6%. Descriptive information on the participants is shown in Table % femalesFour similar LTPA trajectories were identified for both men and women from childhood to adulthood: persistently low-active, decreasingly active, increasingly active, and persistently active Fig. a and b. d and h) can be found in the supplementary material [Supplementary file After all adjustments, the highest mean values of the diet index range 0–27) were observed in the increasingly active (mean 16.8) and the lowest in the persistently inactive trajectory (12.6) among women while men in the persistently active trajectory had the highest mean values (13.0) and men in the persistently low-active one the lowest (10.7)  were highest in the persistently inactive and low-active women (mean 1.9) and the persistently low-active men 2.2) whereas the increasingly active women and men had the lowest mean values of smoking  after full adjustments  were highest among the persistently inactive women (mean 1.6), and persistently low-active (1.9) and decreasingly active (1.9) men. The lowest mean values of binge drinking were observed in the increasingly and persistently active women 1.2) and increasingly active men (1.7)  was highest in the persistently inactive trajectory of women mean 2.0) and decreasingly active trajectory for men (1.8) while the persistently active women and men had the lowest mean values: 1.1 and 1.3, respectively  of LTPA from childhood to adulthood were associated with diet, screen time, smoking, binge drinking, sleep difficulties and sleep duration in adulthood in men and women. Higher LTPA was associated with non-smoking and following a healthier diet in both genders and with less frequent sleep difficulties in women. Men whose LTPA increased in young adulthood reported lower screen time than persistently active and low-active men. Thus, those in the persistently, and especially increasingly active, life-course trajectories had accumulated several health-enhancing behaviors. Simultaneously, those in the inactive or low-active life-course trajectories had accumulated health-compromising behaviors. This is a concern from the public health perspective, as the biggest proportion of participants, of both sexes, were in the low-active trajectories. Our results are supported by previous cross-sectional , 53 and The poorest diet was found in the inactive trajectory for women and the low-active trajectory for men. This supports our previous findings  and thosThe strongest associations between LTPA trajectories and sleep difficulties were detected in the fully adjusted model for women that included only the three oldest age cohorts (42–49-year-old participants). Sleep difficulties were less prevalent among the increasingly and persistently active women than among their persistently low-active and inactive peers, while no associations were found among men. Generally, poor sleep quality is more prevalent among women than men . In our Several studies have reported an inverse association between PA and smoking: for example, smoking  and nicoSomewhat unexpectedly, not only the persistently low-active but also the persistently active men reported higher leisure screen time than their increasingly active counterparts. Earlier studies have found that men watch more sport on television , engage The strengths of the current longitudinal study were its six age cohorts and several measurement points during a follow-up of over 30 years, enabling the study of the associations between life-course LTPA trajectories and several other health behaviors in adulthood. In this study, we expanded our previous research on health behaviors by including sleeping behavior, using an overall healthy diet index instead of measuring only fruit and vegetable consumption, and adding screen time to television viewing as sedentary behavior. Finally, instead of regular alcohol consumption we studied binge drinking, since this has been found to have marked effects on cardiovascular disease morbidity and mortality .The study had its limitations. The use of self-reports may bias the results and lead to under- and over-reporting –82. The In past studies, the previous PA level has been found to predict the future PA level , 90. TheThe present findings underline the different associations between life-course LTPA trajectories and various other health-related behaviors in adulthood and the sex differences in these associations. While the findings corroborated previous findings on the accumulation of healthy behaviors with persistent activity and of unhealthy behaviors with persistent inactivity and low activity, they also highlighted the meaning of increase in LTPA level. Especially, the study offered insight into the association between persistent LTPA during life-course and increased LTPA after childhood or adolescence with healthy eating and non-smoking in both genders and having less sleep difficulties in middle-aged women. The results suggest that to be physically active in adulthood does not necessarily require the initiation of a physically active lifestyle already in childhood. Promoting LTPA at all ages is important, not only because it may increase the level of PA, but because of its potential associations with healthy lifestyle choices later in life.Additional file 1: Supplementary file 1. Statistical analyses used for identifying the leisure-time physical activity trajectories and the description of the trajectories.Additional file 2: Supplementary file 2. Description of the covariates.Additional file 3: Supplementary file 3. STROBE-nut checklist.Additional file 4: Supplementary file 4. Effect sizes (Cohen’s d and h) corresponding to adjusted models two and three for both genders.


People with mental illness may be vulnerable to decline in mental health andreduced physical activity because of the COVID-19 pandemic and associatedrestrictions. The aim of this study was to inform the design of physicalactivity interventions for implementation under these conditions toimprove/maintain well-being and physical activity in this population.People with mental illness who had participated in a physical activityprogram prior to the pandemic were invited to complete a survey about theimpact of COVID-19 on mental health and physical activity and theirpreferences for engaging in a physical activity program underpandemic-related restrictions.More than half the 59 respondents reported worse mental health and lowerphysical activity during the pandemic. The preferred format for a physicalactivity program was one-on-one exercise instruction in-person in a park.Program components endorsed as helpful included incentivization, provisionof exercise equipment and fitness devices, and daily exercise programs.About a third of the participants reported limitations in using technologyfor a physical activity program.In-person exercise support is preferred by people with mental illnessesduring pandemic-related restrictions. Enablement strategies such asproviding equipment and self-monitoring devices should be utilized;assistance may be needed to incorporate the use of technology in exerciseprograms. It is foreseeablethat until a vaccine is available, Australia will be subject to fluctuating communityrestrictions disruptive to usual program delivery. This study aimed to inform exerciseprogram design and implementation for people with mental illness during pandemic-relatedrestrictions. The primary objective was to profile mental health, changes in PA levels,access to resources for exercise programs, and preferences and opinions for engaging inPA programs under pandemic-related restrictions.This was a questionnaire-based cross-sectional study of people with mental illnesswho had, prior to the pandemic, participated in a community exercise program.Ethical approval was obtained from the QIMR Berghofer Medical Research HumanResearch Ethics Committee (P3616).Potentially eligible individuals were identified from a register of participantsof community-based lifestyle programs at the Queensland Police-Citizens YouthWelfare Association in Australia. Participants of these programs were (i) agedover 18 years and (ii) referred to the program from public mental healthservices, mental health non-government organizations, and private or generalpractice clinics. The lead author (JC) contacted potentially eligibleindividuals, and emailed or text messaged the link to the online survey to thosewho agreed to participate. Completing the survey implied consent forparticipation. Participants were offered a $20 gift card for completing thesurvey. Survey responses were received in May 2020, during which time outdoorgatherings of up to 10 people were permitted in Queensland, Australia(reinstated 15 May).before and afterCOVID-19 were collected. Outcome measures are outlined in Self-reported sociodemographic , psychosocial, and health information ,and information on usual PA sufficiently orinsufficiently active . A related-samples McNemar test was used to test fordifferences in PA participation before and during COVID-19. All analyses wereconducted in SPSS v23.Summary statistics are presented for all measures. Estimates for weekly exercisewere calculated by multiplying the number of reported exercise days by theduration of exercise and dichotomized into muchworse mental health. Most  had highpsychological distress , andone-third (34%) had the highest loneliness rating. Health and demographicstatistics are shown in Most of the 59 respondents (58%) reported that their mental health was negativelyaffected by COVID-19, with about one-fifth (19%) reporting p = .001 andp = .013, respectively).A total of 30 (51%) respondents reported exercising at least 150 min/week beforeCOVID-19, which reduced to 12 (20%) during COVID-19. High-intensity exercise wasreported by 32 participants before COVID-19 , and by 21 participants since COVID-19 . A related-samples McNemar test confirmed that the reduction inparticipants exercising at least 150 min/week and those doing any high-intensityexercise was statistically significant (not having a pedometer (80%), fitness tracker (76%),or equipment for aerobic (76%) or resistance exercise (63%); however, most saidthey have enough space for home-based exercise (63%). A high proportion ofrespondents reported having a phone (95%) or smartphone (90%); fewer reportedhaving a computer (68%) or tablet (44%). However, only 63% reported having aninternet connection suitable for supporting video streaming or calls .When asked about access to equipment that could be used for PA, most respondentsreported disagreed that they were asactive as they would like to be (83%). Reported self-efficacy for exercise waslow: 64% disagreed that they would be confident exercising ontheir own and 76% disagreed that they could make themselves exercise if having abad day. When asked about confidence in using online mediums for exercisesupport, about a quarter of those with an internet connection reported beingnot at all confident in their ability to access exercisevideos (26%), use video messaging services (24%), or use smartphone applicationsrelated to exercise (30%).A high proportion of respondents unhelpful rating (34% rated unhelpful); results areprovided in In relation to exercise program formats, one-on-one exercise instruction outdoorsin a park was the most preferred (81%); other formats were endorsed by fewerthan 40% of participants. The most disliked suggestions were one-on-one exerciseinstruction via video and group educational sessions over phone or video ; results are provided in 10 and that m-Health and wearable technologies are feasible when ongoingindividual technical assistance is provided.11The COVID-19 pandemic has had a major impact on society. Participants in this studyhad high psychological distress and loneliness and reported worse mental health andlower PA during the pandemic. Limited access to resources must be considered indesign of PA interventions. Relatively low access to exercise equipment and asuitable internet service in this group limits the potential for remoteinterventions  to enable home-based exercise. Interventionsemploying online mediums for people with appropriate internet access should includeassistance to establish the service (e.g. set up user accounts) and navigateapplications; however, other mediums are required to reach people with unsuitableinternet access, which may be pertinent to rural communities. Participants perceiveduse of fitness trackers or phone apps for self-monitoring as helpful programcomponents; however, low ownership of fitness devices and low confidence in usingtechnology imply that more practical support would be required to incorporate thesecomponents. This is consistent with a recent survey study demonstrating highsmartphone ownership but low app usage in people with mental illness,12 Although internet or phone formats would arguably reduce chances of virustransmission, participants still prefer in-person support such as one-on-oneexercise instruction. Education strategies to encourage family or carers of peoplewith mental illness to provide in-person exercise support  could be effective. Helpful program components included incentives (e.g.gift cards), home exercise equipment delivered, use of fitness devices or phone appsfor self-monitoring, and daily exercise programs sent to them. Providing homeexercise equipment combined with prescribed exercise programs to utilize theequipment could enable home-based exercise and facilitate skill building.Practical support and enablement strategies were preferred, consistent with researchrelated to exercise preferences during non-pandemic conditions.Limitations of this research are a small sample that limits generalizability and lackof clinically confirmed diagnoses or other information relevant to condition . Participants were recruited from a register ofprevious exercise participants with the view of informing program adaptation forpandemic-related restrictions.People with mental illness may be vulnerable to decline in mental health and lowerlevels of PA because of the COVID-19 pandemic and associated restrictions. Membersof this group want to be more active and prefer in-person exercise support duringgovernment-imposed restrictions. Participation in PA can be promoted byincentivization, enablement via provisions of equipment and professionalinstruction, and self-monitoring strategies. A high proportion of people with mentalillness own smartphones, which could be utilized in exercise interventions forself-monitoring or exercise instruction; however, there may be problems withinternet limitations, and some may need additional support because of low confidencein using this medium.