Nonsense-mediated mRNA decay (NMD) is a eukaryotic, translation-dependent degradation pathway that targets mRNAs with premature termination codons and also regulates the expression of some mRNAs that encode full-length proteins. Although many genes express NMD-sensitive transcripts, identifying them based on short-read sequencing data remains a challenge.To identify and analyze endogenous targets of NMD, we apply cDNA Nanopore sequencing and short-read sequencing to human cells with varying expression levels of NMD factors. Our approach detects full-length NMD substrates that are highly unstable and increase in levels or even only appear when NMD is inhibited. Among the many new NMD-targeted isoforms that our analysis identifies, most derive from alternative exon usage. The isoform-aware analysis reveals many genes with significant changes in splicing but no significant changes in overall expression levels upon NMD knockdown. NMD-sensitive mRNAs have more exons in the 3΄UTR and, for those mRNAs with a termination codon in the last exon, the length of the 3΄UTR per se does not correlate with NMD sensitivity. Analysis of splicing signals reveals isoforms where NMD has been co-opted in the regulation of gene expression, though the main function of NMD seems to be ridding the transcriptome of isoforms resulting from spurious splicing events.Long-read sequencing enables the identification of many novel NMD-sensitive mRNAs and reveals both known and unexpected features concerning their biogenesis and their biological role. Our data provide a highly valuable resource of human NMD transcript targets for future genomic and transcriptomic applications.The online version contains supplementary material available at 10.1186/s13059-021-02439-3. Ribonucleolytic activities are essential to dispose cells of defective RNAs, protect host cells from infections with RNA viruses, and regulate gene expression , 2. NonsAlthough many protein factors that recognize and degrade these mRNA substrates have been identified, how the recognition of substrates and NMD activation are accomplished remains unclear. In human cells, after the completion of at least one translation cycle of an NMD-sensitive mRNA , 6, 7, tWhile it has been established that NMD has a vital function in ridding transcriptomes of aberrant mRNAs with PTCs, cases in which NMD serves to rapidly switch off the expression of specific proteins have also emerged . AlternaThrough its quality control function in degrading aberrant endogenous mRNAs, NMD is also implicated in the development of various diseases. NMD inhibition has been reported in cancer, where it leads to the stabilization of transcripts that are important for tumorigenesis, such as KLF6 (Kruppel-like factor 6) in hepatocellular carcinoma and MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) in gastric cancer , 25. A sThese findings demonstrate that accurate identification of NMD-targeted RNAs is crucial to better understand how transcriptomes are remodeled in various diseases, where transcripts that in normal cells are rapidly degraded and thus have very low expression levels become more stable and alter cellular functions. Comprehensive catalogs of NMD-sensitive transcripts serve as a basis for identifying features that contribute to the recruitment of NMD factors and to their activity. They also enable the identification of regulatory circuits that operate on specific transcripts in different conditions. We and others have performed short-read sequencing from cells with decreased NMD activity to expose endogenous NMD targets A well-known caveat of short-read sequencing is that natural nucleic acid polymers that vary widely in length need to be reassembled from the short reads and quantified. This task is challenging when alternative splicing and polyadenylation lead to the expression of transcript isoforms that differ over only a small proportion of their length –32. LongPreviously, we applied short-read RNA-seq to knockdown (KD) and rescue conditions of three NMD factors  to detect genes with NMD-sensitive isoforms . To incrIn a previous study, we set out to identify NMD targets by Illumina sequencing. We provided a list of genes whose expression is sensitive to NMD, but the data did not allow us to unambiguously identify the NMD targets at isoform level . To resoFigure We extracted total RNA from cells, isolated polyA+ mRNAs, prepared cDNA libraries according to the Nanopore protocol and carried out direct cDNA sequencing. Nanopore sequencing was performed on a GridION using R9.4 flowcells, and the MinKNOW instrument software was used to record the Nanopore current. Basecalling was performed using GUPPY version 1.4.3-1 from Oxford Nanopore Technologies.The 0.8 to 2.4 million long reads that were obtained from the different samples  of SMG6 and SMG7 accompanied by rescues with SMG6 or SMG7 individually .StringTie2 [STAR (Spliced Transcripts Alignment to a Reference) to this reference transcriptome to find covered splice junctions [. The splicing analysis was carried out with Replicate Multivariate Analysis of Transcript Splicing (rMATs) [Salmon [The bioinformatics pipeline for the analysis of NMD-sensitive mRNAs is shown in Fig. ringTie2  to creatunctions . The spl (rMATs) . We used [Salmon  to calcu [Salmon  for the We first assessed to what extent NMD inhibition alters the expression of alternative transcript isoforms. We applied rMATS, a statistical model that allows detection of differential alternative splicing events from two groups of RNA-Seq samples. rMATS quantifies the usage of various types of isoforms in the "percent spliced in" (PSI) variable, indicating the proportion of transcripts that provide evidence for the inclusion of a specific alternatively spliced region among all pre-mRNAs that cover that region and taking into consideration the variability among the replicates .To further increase the chance of identifying functionally relevant events that react to NMD modulation, we selected those cases that (i) showed at least 10% change upon KD of NMD factors and (ii) changed in the opposite direction when the corresponding NMD factor was overexpressed (rescue experiment). The types of events that were analyzed are depicted schematically in Fig. To assess whether the usage of long reads facilitated the identification of novel NMD-sensitive isoforms, we calculated the percentage of exons that are included in our augmented human transcriptome but absent in the currently annotation. As shown in Fig. To further validate our approach, we compared the set of events that responded to either SMG6 or SMG7 rescue of cells with dKD of both of these factors. As shown in Fig. Next, we addressed the power of our approach in identifying NMD-sensitive isoforms beyond those found before with short-read sequencing. Short-read-based quantification of isoform expression depends on an accurate reference transcriptome, which is difficult to reconstruct from the short reads alone. Therefore, prior studies relied on gene-level quantification to detect cases that responded to perturbations in NMD factors. To illustrate the utility of the long-read-based approach, we asked whether there are changes in the expression of transcript isoforms that are not detected at the gene level. For this analysis, we focused on the most abundant type of NMD-inducing events, skipping exons, and in particular those that show a higher exon inclusion level upon dKD compared to control and whose level is again reduced by the rescue with SMG6. As shown in Fig. The apoptosis-modulating Bcl-2-associated athanogene-1 (BAG-1) has three main protein isoforms, deriving from alternative translation initiation on a single mRNA and showing different localization patterns . It has UTP11 is an example that illustrates the advantages of our approach to study NMD-sensitive mRNA isoforms using long-read sequencing. The UPT11 gene encodes the human homolog of U3 snoRNA-associated protein 11 that was identified in yeast and is implicated in the nucleolar processing of pre-18S ribosomal RNA . Our datThe features of NMD-sensitive mRNAs can guide the identification of molecular mechanisms of substrate recognition and can clarify the biological role of the NMD pathway in human cells. As our previous efforts to perform a transcript-specific analysis using only short reads were only partially successful, we address this caveat here, using the long read-based augmented transcriptome.As in general, multiple isoforms provide evidence for both inclusion and skipping of NMD-sensitive exons, the numbers varying widely between genes, we first constructed a non-redundant set of transcript pairs. First, we identified all transcripts related to significant alternative exon events that were enriched upon NMD inhibition and filtered out those without annotated coding regions. Next, we identified NMD-sensitive and NMD-insensitive transcripts providing evidence for a particular splicing event  has also been reported to lead to NMD , 52, 56.Splicing is a noisy process , which oIn this work, we applied Nanopore long-read sequencing in combination with Illumina short-read sequencing to identify endogenous human mRNAs that are targeted by NMD. By depleting NMD factors, we stabilized mRNA isoforms that in normal conditions are rapidly degraded by NMD and, therefore, difficult to detect in cells with normal NMD activity. This isoform-specific analysis showed that NMD-sensitive mRNAs derive mostly from the inclusion of alternative exons that have either canonical or non-canonical splice sites, indicating that NMD has evolved to serve two roles. As a quality control system, it can recognize and eliminate aberrantly processed transcripts, and as a posttranscriptional gene regulation mechanism, it controls the abundance of correctly processed, functional mRNAs. NMD-sensitive mRNAs contain more exon junctions in the 3΄UTR compared to NMD-insensitive transcripts, and, in contrast to previous studies –63, longA major caveat of prior analyses of alternatively spliced transcript isoforms of human genes is that they cannot be unambiguously assembled or accurately quantified from short reads, especially when their abundance is low, as is generally the case with NMD-sensitive transcripts , 64, 65.Several observations highlight the advantages of our approach in identifying new NMD targets. Approximately 13% of the short-read sequences obtained upon KD of NMD factors mapped exclusively to transcripts that were newly identified by the Nanopore sequencing. Interestingly, splicing level changes are also observed in genes with no significant changes in their overall expression levels. Thus, the number of NMD-sensitive mRNAs identified here is further increased by those cases that were previously missed in analyses that relied on gene-level changes in expression , 69, 70.The mRNAs that were stabilized by NMD inhibition derived mostly from exon-skipping events. This is in agreement with reports that animals have unusually high rates of alternative exons , likely Some of the advantages of long-read sequencing to detect NMD-sensitive isoforms are illustrated in the case of UTP11 Fig. B. NanopoIn mammals, the best-characterized NMD-triggering feature is the presence of an exon junction complex (EJC) located more than 30 nucleotides downstream of the termination codon. EJCs are usually deposited 20-24 nucleotides upstream of the splice junctions  and striHowever, there are also mRNAs with exon junctions in the 3΄UTR that evade NMD. This agrees with previous reports according to which genetic variations predicted to induce NMD had, in fact, no effect on mRNA levels –83. ThisSeveral additional features have been reported to stimulate NMD in vivo, including long 3΄UTRs , 85, uORIn our dataset, we found the presence of an uORF to be an orthogonal NMD-inducing feature, characteristic of transcripts that do not contain exon-exon junctions in their 3´UTRs. We hypothesize that in cases where the substantial translation of uORFs leads to termination events that are not accompanied by re-initiation, the corresponding mRNAs may be indeed sensed by the NMD mechanism. However, was shown recently that mammalian translation initiation factors eIF4G and eIF3 remain bound to elongating ribosomes and facilitate rescanning after translation of the uORF . AdditioStochasticity is an innate characteristic at all steps of gene expression that often leads to the production of inert or deleterious RNA molecules . SplicinA potential caveat of our experiments is that we cannot distinguish with certainty whether the enriched mRNAs are direct NMD targets or whether they result from altered pre-mRNA splicing due to changes in the concentration of splicing factors, which are often regulated by NMD , 52. HowLong-read sequencing proved to be highly valuable for identifying NMD-sensitive mRNAs under conditions of reduced NMD activity. The data presented here portray the diversity and multifaceted features of NMD-sensitive mRNAs and provide important information about the splicing origin, the features of these mRNAs as well as their biological role. We expect that this work will serve as an important source to investigate NMD targets at the transcript level and stimulate the usage of long-read sequencing to study the features of RNA degradation substrates in the future. Understanding the mRNA features that lead to NMD is important for understanding the impact of disease-related genetic mutations on the transcriptome and must be taken into consideration in the designing therapeutic schemes, disease models, and genome editing experiments , 94. Giv2. Knockdown experiments were performed in HeLa cells as described in [5 HeLa cells were seeded into Τ25 flasks, and 24 h later and the cells were transiently transfected using Dogtor (OZ Biosciences). For single-factor knockdowns, 1 μg of pSUPERpuro plasmids expressing shRNAs against UPF1, SMG6, SMG7, or control plasmids were transfected. For double knockdown experiments, 1 μg of each pSUPERpuro plasmid was added. The cells were split into a T25-cm2 cell culture flask and selected with puromycin at a concentration of 1.5 μg/μL. Twenty-four hours prior to harvesting, the cells were washed with PBS and the puromycin-containing medium was exchanged with normal DMEM–FCS medium. Cells were harvested 4 days after transfection. The shRNA target sequences for UPF1 and SMG6 were given in [. The rescue experiments that were used to produce short read sequencing data are described in [HeLa cells were cultured in Dulbecco's modified Eagles medium (DMEM) supplemented with 10% FBS and penicillin/streptomycin and maintained at a temperature of 37 °C in a humidified incubator with 5% COribed in . Brieflygiven in  and for given in . The resribed in .. Briefly, 2 × 105 cell equivalents were analyzed on a 10% PAGE, and detection was performed using Anti-RENT1 (UPF1) , anti-EST1 (SMG6) , Anti-SMG7 , and anti-Tyr-Tubulin  antibodies.Total RNA was extracted using the GenElute Mammalian Total RNA Miniprep Kit (Sigma-Aldrich) followed by polyA+ mRNA isolation using Dynabeads Oligo(dT)25 (Thermo Fisher) according to the manufacturer's guidelines. Cell harvesting for protein samples (derived from the same sample as RNA preparation) was done as previously described . BrieflyoC according to the manufacturer's suggestions. The total product was run in 1% agarose gels in TAE buffer, stained with ethidium bromide and visualized under UV light.Reverse transcription for PCR analysis was performed using 1 μg total RNA and 300 ng random hexamers using Affinity script multiple temperature reverse transcriptase (Agilent). For UTP11, PCR reactions were performed using isoform-specific primer pairs for the two NMD-sensitive isoforms  and a pair of primers that detects the protein-coding isoform of the UTP11 gene (EK161 ATGGCGGCGGCTTTTC and EK159 AACGTTTTCGACGACTCTGAAATT). BAG1 was amplified by PCR using a protein-coding-specific pair of primers (EK141 ACTCATATTTAAGGGAAAATCTCTG / EK38 TTGGGCAGAAAACCCTGCTG) and a pair of primers of the NMD-specific isoform (EK139 CATATTTAAGGTTCTTCAACAGATA / EK140 TGTTTCCATTTCCTTCAGAGA). PCR reactions were performed using CloneAmp™ HiFi PCR Premix (Takara) for 35 cycles with an annealing temperature of 52 The Oxford Nanopore Technology SQK-DCS108 kit was used for PCR-free direct cDNA sequencing. For each DCS108 library , 500 ng of polyA+RNA was used as input, following the instructions of the ONT protocol. The libraries were sequenced on a GridION using R9.4 flowcells and with the MinKNOW instrument software, we recorded the Nanopore current and generated fast5 files. Basecalling of the fast5 files was performed using GUPPY version (1.4.3-1 from Oxford Nanopore Technologies).The human reference genome and annotation were obtained from ENSEMBL  (versionPSI) score, indicating the proportion of transcripts that provide positive evidence for the event relative to all transcripts whose pre-mRNAs cover the genomic region of the event. PSI scores are used to calculate also 'ΔPSI' values, comparing two distinct conditions. Salmon [Reads were trimmed with the trimFastq.py script of rMATS  to obtai. Salmon   in the dKD vs. control comparison and a change in the opposite direction in the rescue vs. dKD comparison. For each category of events, we also distinguished two subclasses, depending on whether the 'inclusion' or 'exclusion' form of the event is enhanced when NMD is repressed.To identify NMD-sensitive events, we used the short-read-based To evaluate the effect of NMD on transcript and gene expression, we identified all transcripts related to significant events, in particular alternative exons, based on the corresponding pre-mRNA covering the genomic locus of the event. For events where the inclusion was associated with NMD, we selected the transcripts that contained the exon while for the events where the exclusion was associated with NMD, we selected the transcripts that did not include the exon.PSI was higher in the double knockdown compared to control samples, we considered the transcript including the exon to be NMD-sensitive and those that did not include the exon to be NMD-insensitive. Finally, to obtain a non-redundant set of transcripts, we further selected from each subset the transcript with the highest average abundance in control and double knockdown samples. These were used to determine the number of exons, 3' UTR length, etc. For the uORF analysis, we identified transcripts that contain an uORF defined by a start (AUG) and stop  codons, with a length of at least 15 nts (including the stop codon) and were located entirely upstream of the main annotated ORF. To focus on uORF that are indeed translated, we intersected the computationally predicted uORFs with recently published RiboSeq data [We started again from the significant events and identified all transcripts related to these events, as described above. We filtered out those transcripts without annotated coding regions and then separated the remaining transcripts into potentially NMD-sensitive or insensitive, depending on which form of the event was enhanced by the repression of NMD. That is, in the case of skipping exons, if the Seq data  and retaSeq data .We compared various features of NMD-inducing skipping exons and randomly chosen internal exons. The 'background' set of exons was chosen dependent on the feature. To compare exon length, we used all internal exons in transcripts with an annotated coding region. To compare splice signals and evolutionary conservation, we chose the same number of internal exons from any transcripts with an annotated coding region as we had NMD-inducing transcripts. We further imposed the restriction to have only one internal exon per gene.To calculate the strength of splice sites, we extracted 20 bases in the intron and 3 bases in the exon around a 3´splice site, and 3 bases in the exon and 6 bases in the intron around a 5´splice site. We then submitted these sequences to MaxEntScan  to obtaiAdditional file 1. Figures S1 and S2 and the uncropped Western blots in Figure S3.Additional file 2. Supplemental table 1.Additional file 3. Review history.


There are concerns that vaccine hesitancy may impede COVID-19 vaccine rollout and prevent the achievement of herd immunity. Vaccine hesitancy is a delay in acceptance or refusal of vaccines despite their availability.We aimed to identify which people are more and less likely to take a COVID-19 vaccine and factors associated with vaccine hesitancy to inform public health messaging.A Canadian cross-sectional survey was conducted in Canada in October and November 2020, prior to the regulatory approval of the COVID-19 vaccines. Vaccine hesitancy was measured by respondents answering the question “what would you do if a COVID-19 vaccine were available to you?” Negative binomial regression was used to identify the factors associated with vaccine hesitancy. Cluster analysis was performed to identify distinct clusters based on intention to take a COVID-19 vaccine, beliefs about COVID-19 and COVID-19 vaccines, and adherence to nonpharmaceutical interventions.Of 4498 participants, 2876 (63.9%) reported COVID-19 vaccine hesitancy. Vaccine hesitancy was significantly associated with (1) younger age (18-39 years), (2) lower education, and (3) non-Liberal political leaning. Participants that reported vaccine hesitancy were less likely to believe that a COVID-19 vaccine would end the pandemic or that the benefits of a COVID-19 vaccine outweighed the risks. Individuals with vaccine hesitancy had higher prevalence of being concerned about vaccine side effects, lower prevalence of being influenced by peers or health care professionals, and lower prevalence of trust in government institutions.These findings can be used to inform targeted public health messaging to combat vaccine hesitancy as COVID-19 vaccine administration continues. Messaging related to preventing COVID among friends and family, highlighting the benefits, emphasizing safety and efficacy of COVID-19 vaccination, and ensuring that health care workers are knowledgeable and supported in their vaccination counselling may be effective for vaccine-hesitant populations. In the fall of 2020, regions of Canada were experiencing a second wave of COVID-19 with rising case counts, hospitalizations, and deaths .Although there was great optimism about the potential emergence of safe and effective vaccines against SARS-CoV-2, COVID-19 vaccine hesitancy was becoming evident in the summer and fall of 2020 -7. VacciSeveral studies have looked at the risk factors for vaccine hesitancy in populations around the world and have found that many different factors, including sociodemographic variables and concerns about efficacy and safety of COVID-19 vaccines, may contribute to COVID-19 vaccine hesitancy . It has In the summer of 2020, we designed a mixed methods study to examine COVID-19 attitudes, beliefs, and behaviors among Canadians with an overreaching goal of informing targeted public health messaging to improve adherence to NPIs and vaccine uptake. We have previously published the initial phases of this mixed methods study including a pilot survey  and a quBased on the findings from the initial work in our mixed methods study ,5, we deWe used a cross-sectional survey to assess the attitudes and beliefs about vaccines and vaccine hesitancy among adults aged 18 years or older living in Canada. The survey was administered online by the Angus Reid Institute , a natioThis study was approved by the Conjoint Health Research Ethics Board at the University of Calgary REB20-1228). Informed consent was obtained from each participant prior to commencing the survey, and participation was voluntary. Responses were deidentified at the time of collection to ensure participant anonymity and privacy. If participants started the survey but did not complete it, it was assumed that consent was withdrawn and their survey responses were not saved. Consistent with Angus Reid Forum policy 28. Infor.The main outcome measure was vaccine hesitancy. Survey participants were asked what they would do if a COVID-19 vaccine were available to them and given the following 4 options: (1) get a vaccine as soon as possible, (2) eventually get a vaccine, but wait a while first, (3) not get a vaccine, or (4) not sure. Vaccine hesitancy was defined as any of the latter 3 responses consistent with the SAGE Working Group definition of vaccine hesitancy .We considered factors that could be associated with vaccine hesitancy in each of the domains of the SAGE framework   based onDescriptive statistics (percentage frequencies) were calculated for all participant characteristics, adherence to NPIs, attitudes toward COVID-19 and COVID-19 vaccines, and trusted sources of COVID-19 information. Respondents were excluded if they did not answer all survey questions, and therefore, there were no missing data. Negative binomial regression models were used to estimate crude prevalence ratios (PRs) for factors associated with being vaccine hesitant compared to not being vaccine hesitant. Each PR was reported with the associated 95% CI. We used multiple models to examine the association between vaccine hesitancy and each of the following: (1) sociodemographic characteristics, (2) attitudes toward COVID-19 vaccine, (3) adherence toward NPIs, (4) attitudes toward COVID-19, and (5) trusted sources of COVID-19 information. We also calculated adjusted prevalence ratios (aPRs) by using sociodemographic characteristics identified through a literature search ,14-16 asP value of <.05 was set as significant.To identify data-driven patterns in survey responses with respect to vaccine hesitancy, we used cluster analysis. The cluster analysis was based on intention to take a COVID-19 vaccine, beliefs about COVID-19 and COVID-19 vaccine, and adherence to NPIs. The K-means algorithm was used to partition the data set into distinct clusters. This iterative algorithm assigns observations to a cluster such that within each cluster, the sum of the squared distance between observations and the arithmetic mean of all observations is minimized. Cluster analysis was used to integrate COVID-19 vaccine intention, COVID-19 beliefs, and adherence to NPIs into similar like-minded groupings to identify insights that can be utilized for targeted messaging and interventions. By using several exposures to establish these clusters, we aimed to create clusters with greater similarity in motivations and attitudes for vaccine intention and gain a deeper understanding of vaccine hesitancy. Negative binomial regression was used to estimate crude PRs and 95% CI comparing sociodemographic characteristics between each of the clusters with cluster 2 as the reference. Analyses were conducted using STATA Version 15.1 (Stata Corp). A Of the 14,887 survey invitations distributed, 5893 (39.6%) invitations were accepted in the 7 days the survey was available. Of those, 4498 (76.3%) participants completed the survey and were included in the analysis , while 1Participant demographic and socioeconomic characteristics are presented in More than half of the participants  felt that the benefits of taking a vaccine outweigh its risks, while 969 (22%) were unsure and 1028 (22%) disagreed .Those who disagreed had higher prevalence of vaccine hesitancy compared with those who agreed  or if their friends or family took a vaccine . However, the prevalence of vaccine hesitancy was higher in participants who disagreed that they would take a vaccine if their friends/family do  or if it was recommended by a family doctor, pharmacist, or public health nurse . Numerous participants  said they would take a COVID-19 vaccine to protect their family; participants who disagreed with this statement had a higher prevalence of vaccine hesitancy compared with those who agreed . Many participants were concerned about the short-term side effects  and long-term side effects . Participants  who disagreed with the statement that vaccines were developed too fast had a lower prevalence of vaccine hesitancy compared with those who agreed .The majority of the participants reported physical distancing , wearing face masks , avoiding crowded spaces , and staying home when sick  all or most of the time . ParticiA small proportion of participants had tested positive for COVID-19  and almost one-third  knew someone who had tested positive for COVID-19 . ParticiParticipants who trusted chief medical officers of health  and public health websites  had lower prevalence of vaccine hesitancy compared with participants who did not . Those wThree distinct nonoverlapping clusters were identified through cluster analysis . ClusterCompared with participants in Cluster 2 (the vaccine waiting and NPI-accepting cluster), participants in Cluster 3 (the vaccine and NPI nonaccepting cluster) were more likely to be male , 35-54 years of age , have a household income of CAD $200,000  or more , and report a conservative political leaning . Participants in Cluster 1 (the vaccine and NPI-accepting cluster) were more likely to be Liberal leaning , have some university education , or a university degree , have an annual household income of CAD $100,000-$199,999  or CAD $200,000 or more , and male  compared with participants in Cluster 2 (the vaccine-waiting and NPI-accepting cluster).In this national cross-sectional survey completed in the fall of 2020 prior to the approval of COVID-19 vaccines in Canada, we found that 63.9% (2876/4498) of the participants reported COVID-19 vaccine hesitancy, ranging from delaying vaccine administration when offered to not planning to take a vaccine. Vaccine hesitancy was associated with several sociodemographic factors including (1) younger age (18-39 years), (2) lower education, and (3) non-Liberal political leaning. Participants who reported vaccine hesitancy had higher prevalence of reporting being concerned about vaccine side effects, did not believe that a COVID-19 vaccine would end the pandemic or that the benefits of a COVID-19 vaccine outweighed the risks, and had lower prevalence of reporting being influenced by peers or health care professionals. We identified 3 distinct participant clusters: (1) participants who reported adherence to NPIs and did not have vaccine hesitancy, (2) individuals who reported adherence to NPIs but did have vaccine hesitancy, and (3) individuals who reported less adherence to NPIs and vaccine hesitancy.The 3 distinct clusters of vaccine acceptance can inform targeted vaccination campaign messaging in a novel way by directing messages to address cluster-specific concerns with respect to vaccine hesitancy. The majority of the participants in Cluster 2 (the vaccine waiting and NPI-accepting cluster) planned to delay taking a vaccine when offered, while the majority in Cluster 3 (the vaccine and NPI nonaccepting cluster) did not intend to take a vaccine at all. Messaging related to preventing COVID among friends and family, highlighting the benefits, and ensuring health care workers are knowledgeable and supported in their vaccine counselling may be more helpful for those in Cluster 2 relative to those in Cluster 3. Participants in Cluster 3 were more likely to be male, 35-54 years of age, have an annual household income of CAD $200,000 or more, report Conservative political leaning, and live in Alberta compared with participants in Cluster 2. The characteristics of Cluster 3 are consistent with current trends in vaccine uptake in that less uptake has been seen among Albertans, males, and individuals aged 18 to 59 years as of October 23, 2021 . Based oAs of October 27, 2021, more than 1,700,000 Canadians have been infected with COVID-19 and more than 28,000 Canadians have died -23, and While there has been a decrease in vaccine hesitancy over time, many of the underlying predictors of hesitancy have remained stable over time ,10,26-29We did not find an association between ethnicity and COVID-19 vaccine hesitancy, although an association has been reported in several other studies ,30,33. IThe influences of COVID-19 vaccine characteristics and administration of COVID-19 vaccines on vaccine hesitancy are unique compared to annual influenza campaigns or childhood immunization schedules. In response to the COVID-19 pandemic, the scientific community has come together to develop safe and effective vaccines . At the Trust in government has been identified as a factor associated with acceptance of a COVID-19 vaccine . ConsistThe major limitation of this cross-sectional study was that it represents one snapshot in time in the fall of 2020 prior to the approval of COVID-19 vaccines in Canada and as the country was entering the second wave of the pandemic; therefore, the responses provided by participants at that time have evolved. The survey recruited participants from an existing voluntary nationwide panel designed to be representative of the Canadian population; however, by using a panel, there will be a component of selection bias as participants have volunteered to partake in research surveys through an electronic platform, which may lead to increased selection of individuals with higher socioeconomic status or education level leading to an underestimation of vaccine hesitancy. We included all provinces and territories in our sampling strata; however, we did oversample Alberta, which could lead to bias in the results and make these findings less generalizable to the Canadian population. To minimize this bias, province of residence was included in all adjusted analyses. Response bias should also be considered as individuals who chose to respond to the web-based survey may differ systematically from those who chose not to respond.COVID-19 vaccines are an important tool in the fight against the COVID-19 pandemic; yet, vaccine hesitancy is a concern. We have identified population segments that are associated with vaccine hesitancy  that can be targeted with public health messaging as well as attitudes toward COVID-19, COVID-19 vaccines, and NPIs that can inform messaging content. Effectively addressing vaccine hesitancy is important to increase vaccine uptake.


Identification of transcriptional regulatory mechanisms and signaling networks involved in the response of host cells to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable the identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, our goal was to identify a transcriptional regulatory network that is associated with gene expression changes between samples infected by SARS-CoV-2 and those that are infected by other respiratory viruses to narrow the results on those enriched or specific to SARS-CoV-2. We combined a series of recently developed computational tools to identify transcriptional regulatory mechanisms involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus when compared to other viruses. In addition, using network-guided analyses, we identified kinases associated with this network. The results identified pathways associated with regulation of inflammation (MAPK14) and immunity  that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory network identified herein reflects a combination of known hits and novel candidate pathways supporting the novel computational pipeline presented herein to quickly narrow down promising avenues of investigation when facing an emerging and novel disease such as COVID-19. This response must be kept in balance as viral clearance mechanisms can lead to tissue damage if not kept in check2. This balance can be especially hard to maintain in the presence of new emerging infections such as the novel coronavirus severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) responsible for coronavirus disease 2019 (COVID-19), for which the host is naïve. Under those conditions, excessive activation of the immune system can lead to cytokine release syndrome, pulmonary edema, multiorgan failure, and shock3, like the severe acute respiratory distress syndrome (ARDS) observed in COVID-19 patients1. The TRIF, RIG-I and MDA-5-mediated activation of Interferon response factors (IRFs) is responsible for the expression of antiviral genes, such as type I, II and III IFNs, critical regulators of antiviral immunity. In turn, Type I, II and III interferons activate JAK-STAT signaling to further promote antiviral host responses4, which can contribute to the cytokine release syndrome if the response is excessive. Accordingly, JAK-STAT inhibitors have been proposed as attractive drug targets to mitigate the effect of severe COVID-195.Host responses to various insults are coordinated by distinct sets of regulatory networks matching the response to the insult. Viral infections of human cells lead to the production of interferons (IFNs) as an antiviral mechanismUnraveling the gene expression programs involved in the response of the host to the infection by SARS-CoV-2, by mapping the corresponding transcriptional regulatory network (TRN), can provide a fundamental understanding of COVID-19 and its complications and can enable the identification of therapeutic targets and novel treatments. TRNs represent the regulatory relationships between transcription factors and their target genes, which play significant roles in regulating the gene expression programs in a cell. In this study, our goal was to identify the TRN that was associated with infection by SARS-CoV-2, as opposed to those that are common among infection by different respiratory viruses.11. However, none of these studies have incorporated data corresponding to multiple viruses to identify regulatory mechanisms that are enriched or specifically associated with SARS-CoV-2. Moreover, in many of these studies, the TF-gene regulatory relationships are identified based on data corresponding to cancer or normal samples, and not cells infected by SARS-CoV-211. On the other hand, recognizing the importance of considering the response of host to other respiratory viruses, several studies have sought to identify shared differentially expressed genes, shared pathways or shared gene networks corresponding to multiple respiratory viruses (including SARS-CoV-2)14.Several studies have attempted building TRNs associated with COVID-19 and the response of the host to the infection by SARS-CoV-2 virusHowever, in this study, our goal was to analyze samples infected by multiple respiratory viruses (including SARS-CoV-2) and build a TRN that, while capturing the regulatory influence of TFs on genes, highlights the relationships that are associated with gene expression changes between samples infected by SARS-CoV-2 and those that are infected by other respiratory viruses. Conceptually, such a TRN is different from those constructed only based on samples infected by SARS-CoV-2, those that compare infected samples against uninfected control, or those that try to identify shared mechanisms among the response to different viruses. This TRN would allow us to understand biological mechanisms and gene expression programs that are prominent in the response of host epithelial cells to SARS-CoV-2 that may lead to the identification of biomarkers that are otherwise unidentifiable.15. SARS-CoV-2 is a positive-sense single-stranded RNA virus, part of the broad coronaviridae family. Similar to SARS-Cov-1 and the Middle East respiratory syndrome (MERS), SARS-CoV-2 can cause severe acute respiratory disease in humans1. Respiratory syncytial virus (RSV) is a single-stranded negative-sense virus, a common cause of mostly mild respiratory disease in children part of the pneumomviridae family. However, both in children16 and adults2, it can lead to serious lung diseases, including ARDS. The influenza A virus H1N1, a negative-sense RNA virus member of the orthomyxoviridae family, was responsible for the 2009 swine flu pandemic. Human parainfluenza viruses (HPIV) are negative-sense RNA viruses that cause lower respiratory infections in children, chronically ill and elderly patients17 and members of the paramyxoviridae family. This dataset is particularly valuable for our study, since it contains gene expression profiles of multiple human lung cell lines infected by four different respiratory viruses, measured using the same gene expression profiling protocol by the same lab. As a result, this dataset enables achieving the goals of this study without introducing confounders such as different protocols or different labs that may distort the analysis.For this purpose, we identified a dataset containing gene expression profiles of human lung epithelial cell lines that were infected by four different respiratory viruses 18 to integrate sample-level phenotypic or clinical labels  and scores  scores in pharmacogenomics studies) in the TRN reconstruction procedure using gene expression datasets and identify ‘phenotype-relevant’ TRNs .The main challenge in reconstructing TRNs associated with gene expression changes between samples infected by SARS-CoV-2 and samples infected by other respiratory viruses is how to systematically integrate the association between gene expression data and the virus type used for infection in the TRN reconstruction analysis. Recently, we developed and extensively validated a new computational algorithm called InPheRNo19, we observed that these kinases indeed influence the expression of genes in the reconstructed TRN in epithelial cells. Our analyses using network-based algorithms and machine learning tools provided a systems biology perspective of the response of the epithelial cells to infection by SARA-CoV-2 and identified regulatory mechanisms specific to this virus. In addition, our results implicated important families of kinases (including JAK and MAPK family) that may be used as therapeutic targets for COVID-19.First, using InPheRNo, we reconstructed a TRN associated with gene expression changes between samples infected by SARS-CoV-2 and those that were infected by other respiratory viruses  Fig. A. Our re15. Details of the data are provided in “We sought to identify transcriptional regulatory mechanisms that are involved in the host response to SARS-CoV-2 infection, but also distinguish this response from response to other respiratory viruses . For this purpose, we obtained gene expression profiles of human lung epithelial cells that were infected by SARS-CoV-2, respiratory syncytial virus (RSV), human parainfluenza virus type 3 (HPIV3), influenza A/Puerto Rico/8/1934 (H1N1) virus (IAV), and IAV that lacks the NS1 protein (IAVdNS1) from a recent study18, a method that we recently developed to identify ‘phenotype-relevant’ TRNs using gene expression profiles of multiple samples and their phenotypic labels . InPheRNo is based on a probabilistic graphical model (PGM) designed to integrate the collective regulatory influence of multiple TFs on a gene with the association of the gene’s expression with a phenotype to identify regulatory mechanisms that are phenotype-relevant (as opposed to phenotype-independent). In this approach, first, the p-values of gene-phenotype associations  and p-values of gene-TF associations  are obtained and provided as input ‘observed variables’ to the PGM. The PGM is then trained on the data to obtain posterior probabilities for each TF-gene pair determining whether the TF regulates the gene in a phenotype-relevant manner .To reconstruct the SvOV TRN, we used InPheRNo20) and then reconstructed the SvOV TRN based on the top 500 differentially expressed genes  < 1.43E−3, Supplementary Table 21 and STAT222, as well as some other well-established TFs such as TP5323 or IRF924.Using data corresponding to SARS-CoV-2 infected samples and samples infected by the other viruses mentioned above, we first performed differential expression analysis  were present in the GTRD dataset, which we used for the evaluation of our network. Out of the 45 targets found for these TFs by InPheRNo, 37 were confirmed using GTRD , showing a high degree of overlap between the SvOV TRN (identified using RNA-seq) and the global TRN (identified using ChIP-seq).Since our analysis seeks to pinpoint regulatory relationships that are specific to SARS-CoV-2, a systematic evaluation of the results is difficult, since most of the comprehensive databases of TF-gene relationships are formed by integrating data from many different conditions that may not reflect the response of host to SARS-CoV-2 . However, one can still expect an enrichment of the identified edges in the SvOV network with such global TRNs. To assess this enrichment, we compared the targets of these TFs identified by InPheRNo, with their targets determined using ChIP-seq data available in the Gene Transcription Regulation Database (GTRD) database26, which enables ‘standard’ gene-set enrichment analysis (using Fisher’s exact test), as well as advanced ‘network-guided’ analysis (using a user-selected gene interaction network).In order to determine the functional characteristics of gene expression programs involved in the response of host to SARS-CoV-2, we performed pathway enrichment analysis for the implicated TFs and their identified target genes in the SvOV network. For this purpose, we used the gene set characterization (GSC) computational pipeline of KnowEnG (Knowledge Engine for Genomics) analytical platform27) pathway enrichment analysis (using Reactome pathways28) with default parameters  network by aggregating kinase-substrate relationships from three previous studies32 , and a query set containing the top TFs obtained from the SvOV TRN (21 TFs) vOV TRN 2 TFs . For our evaluations, we only used the 978 landmark genes, since these are the only genes that are experimentally measured in the LINCS database, while the other genes are computationally predicted. Since this database does not provide p-values of differential gene expression, we used a threshold-based approach to select genes that are influenced by the kinase knockdown. We defined a landmark gene to be positively (negatively) influenced by the knockdown of a kinase if its expression increased (decreased) as a result of the knockdown and also if its normalized expression change (captured in the signature) was among the top (bottom) 15% of all landmark genes.Since foRWaRD incorporates both direct and indirect interactions to identify kinases, we sought to determine whether the knockdown of identified kinases directly influences the expression of the TFs and their target genes in the SvOV TRN. To this end, we obtained gene expression signatures corresponding to shRNA knockdown experiments from the LINCS databaseKnockdown signatures were only available for 7 (out of 15) implicated kinases in Table Investigation of the regulatory networks specifically associated with SARS-CoV-2 can help better understand either acute complications or long-term consequences of COVID-19. This is particularly important for a novel disease, for which limited information is available. Considering that as of June 2021, more than 176 million people have been infected, the potential of diverging responses is huge and can have a long-lasting impact on the health of many. Therefore, better understanding the peculiarities of SARS-CoV-2 compared to other viruses is paramount to deal with the coming fall out of the pandemic. The aim of this study was to identify such signaling and transcriptional regulatory networks that play key roles in transducing signals specific to SARS-CoV-2 infection of airway epithelial cells in order to better understand the pathophysiology of COVID-19 and provide a list of potential molecular targets for therapies aimed at altering the clinical courses of the disease. The response was studied in comparison to other viruses , which allows to pinpoint mechanisms that are specific to SARS-CoV-2. A systematic comparison with known regulatory mechanisms based on ChIP-seq data confirmed the regulatory relationships between the identified TFs and their target genes. In addition, pathway enrichment analysis identified immune system, cytokines and interferon signaling pathways associated with the reconstructed TRN.36. This family of kinases is involved in the transduction of cytokine-mediated signals through the JAK-STAT pathway. The members of the Janus family and the JAK-STAT pathway have been suggested as potential therapeutic targets in COVID-1939, supporting the findings of our analysis. Additionally, published evidence exists for a link between several other implicated kinases and COVID-19. MAPK11 and MAPK14 both belong to the p38 MAPK family which are involved in the cellular responses to extracellular stimuli including proinflammatory cytokines such as IL-640. Previously, it has been shown that one of the proteins expressed by SARS-CoV virus upregulates p38 MAPK41. A global phosphorylation analysis of SARS-CoV-2 infected epithelial cells also identified targets of MAPK14 and MAPK11 as kinases upregulated during infection that make important contributions to host responses35. Accordingly, inhibition of MAPK14 and MAPK11 family has been proposed as a potential therapeutic approach in COVID-1940. In addition to being a downstream target of TLR-signaling pathways, including TLR3 in airway epithelial cells42, MAPK14 regulates IL-6 expression and mRNA stability43. IL-6 circulating levels are elevated in severe COVID-1944. Moreover, MAPK14 is an important signal transducer of IL-17 in endothelial cells45, involved in neutrophilic inflammation, that may be important mediators of thrombosis in COVID-19 via the release of Neutrophil-Extracellular-Traps46. MAPK14 is an important target of the potent corticosteroid dexamethasone47, that was shown to decrease mortality in severe COVID-1948. BMX and BTK are non-receptor tyrosine kinases that belong to the Tec kinase family. The Tec family has been shown to be involved in the intracellular signaling mechanisms of cytokine receptors and antigen receptor signaling in lymphocytes49. BMX has been shown to link both MYD88, another TLR-signaling adapter, and Focal Adhesion Kinases, a kinase associated with integrin activation, to the synthesis of IL-650. This published evidence makes BMX and BTK candidate contributors to the cytokine release storm. Accordingly, the inhibition of genes in this family, and particularly BTK43, has been proposed as a therapeutic approach to protect COVID-19 patients against pulmonary injury44 and to block thrombo-inflammation45. Two potential downstream targets of BTK according to our regulatory networks , an important regulator of inflammatory signaling and apoptosis, that increases TRAIL-mediated apoptosis in response to IFNβ53. While these responses are likely desirable during early phases of the infection, whether they can also contribute to immunopathology in the second sustained phase of the disease warrants further investigation. While published data supports the current pipeline, it is expected that highly studied gene families such as JAK-STAT and p38 MAPK would feature more prominently, based on the greater availability of information on these kinases. One of the benefits of the current approach is to highlight the potential role of much less studied kinases that may nevertheless be important contributors following experimental validation. In this regard, the pseudokinase VRK3, which has not been studied in the context of human viral infections, can negatively regulate the MAPK ERK, known for its role in regulating inflammation downstream of the protein kinase Tumor Promoting Locus-256. Another example is the top hit identified, MYO3A, a motor and kinase activity containing protein that can be found in actin bundle-based structures of cells57, for which no information is available in the context of infection or inflammation.Using a kinase-substrate network, which we formed by aggregating data from three separate studies, we identified several families of kinases associated with the SvOV network Table . JAK1, JIn this study, we focused on data corresponding to cell lines as opposed to patient data. The reason for this choice was the availability of data on several epithelial cell lines infected by different respiratory viruses generated by the same lab using an identical experimental procedure. Repeating the same analysis based on patient data requires a similarly consistent data acquisition setup corresponding to patients infected by different viruses; otherwise, the differences between experimental protocols and batch effects may introduce significant confounders in the analysis.One of the limitations of this study was using only transcriptomic profiles of the samples in reconstructing the TRN. While we used ChIP-seq data for validation of our findings, the ChIP-seq data did not correspond to cells infected by SARS-CoV-2. A multi-omics approach that incorporates matched molecular profiles of the same set of samples can improve the analysis and identify other regulatory mechanisms involved in the response of host to SARS-CoV-2. As new datasets become available in this domain, we will apply a multi-omics variation of the proposed pipeline to achieve this goal. Another limitation of this study was the difficulty in validating the relationship between the implicated kinases and their target genes and TFs. While the LINCS dataset provides an extremely useful resource, the fact that the mRNA expressions of only 978 genes are measured as a result of kinase knockdowns, limited our ability to validate all our identified relationships. In spite of these limitations, we believe that building TRNs associated with the response of host to SARS-CoV-2, when compared against mechanisms common among other respiratory viruses, is essential in better understanding the biological processes involved in this infection. This study serves as the first step in achieving this goal.In conclusion, the results obtained in this manuscript are the product of a novel data analysis pipeline that strings together three powerful computational tools , which identified regulatory networks, pathways, and kinases, many of which have already been associated with COVID-19 in previous studies. These results also provided further information on putative regulatory mechanisms underpinning the infection of epithelial cells by SARS-CoV-2 and identified novel potential therapeutic targets that can serve as the basis for future identification and development of drugs that mitigate the impact of COVID-19 in individuals at risk of severe complications.We downloaded RNA-seq gene expression profiles of human lung epithelial cells infected by respiratory viruses from the Gene Expression Omnibus (GEO) database (accession number: GSE147507). More specifically, we used 33 samples corresponding to independent biological replicates of A549 cells infected with SARS-CoV-2, RSV, IAV, and HPIV3, NHBE cells infected with SARS-CoV-2, IAV, and IAVdNS, A549-ACE2 cells infected with SARS-CoV-2, and Calu3 cells infected with SARS-CoV-2.58. Experimentally verified protein–protein interaction network from the STRING database27 and HumanNet integrated network34 were downloaded from KnowEnG’s knowledge network (version 17.06) available at the address https://github.com/KnowEnG/KN_Fetcher/blob/master/Contents.md. The list of target genes for the top TFs (identified using ChIP-seq) was downloaded from the GTRD database . In this dataset, a gene is considered to be a target of a TF if its promoter region  contains at least one GTRD meta-cluster for the TF. The meta-clusters reflect ChIP peaks for the same TF-gene but integrated from different experimental conditions and peak-calling methods25. We used this dataset since it uses a consistent and principled computational pipeline to integrate ChIP-seq data from many different studies and, to the best of our knowledge, is the most comprehensive database of such regulatory evidence.We downloaded the list of human TFs from AnimalTFDBhttp://www.phosphosite.org)30, PhosphoNetworks (http://www.phosphonetworks.org)31, and the supplementary material of an independent study32. After removing duplicate edges, we formed a KSI involving 29,594 kinase-substrate relationships corresponding to 406 unique kinases and 3942 unique substrates. LINCS Level 5 consensus signatures (‘trt_sh.cgs’) corresponding to shRNA knockdowns in A549 cell line were obtained from GEO with the accession number (GSE92742). We used the Consensus Gene Signatures (CGS) data since they correspond to gene expression changes that are common among multiple shRNAs that target the same gene, mitigating off-target effects59.To form the aggregated KSI network, we obtained kinase-substrate relationships from three previous studies. The interactions corresponding to homo sapiens were downloaded from PhosphoSitePlus database  of gene-TF associations (from their expression profiles) with information on the significance  of gene-phenotype associations to construct phenotype-relevant TRNs. As input, it accepts a list of TFs, the expression profiles of genes and TFs (in multiple samples), and the p-value of association between genes’ expression and a phenotype.InPheRNo20 with the cell type and the time of measurement post-infection as confounding factors. Next, we quantile normalized the gene expression profiles using voom-limma60 and then z-score normalized the results. We ran InPheRNo (downloaded from https://github.com/KnowEnG/InPheRNo) with 1000 iterations, 500 repeats and default values for other parameters.To construct the SvOV TRN using InPheRNo, we first performed differential expression analysis using EdgeR26  to perform pathway enrichment analysis. For the standard mode of GSC pipeline (without the use of any gene interaction network), we chose Reactome pathways28 as the option for target pathway collection and the rest of parameters were left as default. We excluded pathways with smaller than 10 genes and adjusted the enrichment p-values (Fisher’s exact test) for multiple tests using Benjamini–Hochberg false discovery rate (FDR). Pathways with FDR < 0.05 were considered statistically significant.We used KnowEnG’s gene set characterization (GSC) pipeline61, which utilizes random walk with restarts (RWR) algorithm on a user-selected gene interaction network to rank pathways based on their relevance to a query gene-set. Including a gene interaction network  network) enriches the analysis and enables identification of important pathways that may not be detectable using simple overlap-based Fisher’s exact test. For this mode of the GSC pipeline, we used ‘STRING Experimental PPI’ option for the knowledge network  and the amount of network smoothing was set to the default 50%. Reactome was used as the target pathway collection. The network-guided mode of this pipeline does not provide a p-value for the pathways, since the task of identification of pathways is done using an algorithm called DRaWR61, which is based on random walk with restarts (RWR) on graphs . Instead, it provides a score for each pathway called the ‘difference score’. Difference score values larger than 0.5 correspond to pathways that have a higher steady-state RWR probability when the input query gene set is used as the restart set, compared to their steady-state probability when the query gene set is not used as the restart set in the random walk. The difference scores ensure that a pathway is not simply scored high due to the network bias and having many edges in the network.The network-guided mode of this pipeline is an implementation of an algorithm called DRaWRFor pathway enrichment analysis of the set of top TFs, the universe  was considered to be the set of all TFs present in our study. For pathway enrichment analysis of a TF and its target genes, the universe was considered to be the set of all genes and TFs present in our study.34, and a query set containing the top TFs obtained from the SvOV TRN (21 TFs). foRWaRD first forms a heterogenous network by superimposing the substrates on their corresponding gene nodes in the gene–gene interaction network. Then, it performs two runs of the RWR algorithm on this heterogenous network: one run with the query set (set of TFs) as the restart set of the RWR and another run with all nodes as the restart set (to be used as control). Each run of the RWR provides a probability score for each node (including those corresponding to kinases), representing the relevance of the node to the restart set. Finally, a normalized score for each kinase is obtained by comparing the scores of the two runs of the RWR, and kinases are ranked based on how much their query set score is higher than their background  score.To use foRWaRD to rank kinases associated with the SvOV network, we provided it with the aggregated KSI, the HumanNet integrated networkSupplementary Figure S1.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.Supplementary Table S7.Supplementary Table S8.


A high incidence of thromboembolic phenomena has been widely reported in patients with coronavirus disease 2019 (COVID-19) pneumonia. There is, however, a paucity of data detailing the incidence and characteristics of pulmonary emboli (PE) in COVID-19 patients in the South African setting.To describe the incidence and characteristics of PE confirmed by CT pulmonary angiogram (CTPA) in patients with COVID-19 pneumonia admitted to a tertiary hospital in the Western Cape, South Africa.This was a retrospective-, descriptive study of all adult patients with COVID-19 pneumonia confirmed by polymerase chain reaction (PCR) undergoing CTPA for suspected PE while admitted to Groote Schuur Hospital. The study period was from 01 April 2020 to 30 September 2020.The study cohort consisted of 116 patients, 59% being female, of whom 29% were pregnant or in the postpartum period. The median age for both genders combined was 49.5 years. The overall incidence of PE was 19%, with 20% in our subset of pregnant and postpartum patients. The majority (64%) of PE’s were reported as being segmental in anatomical location.The noteworthy cohort included patients with pulmonary tuberculosis (PTB), HIV as well as pregnant and postpartum patients. The overall incidence of PE was 19% with no significant differences in demographics, comorbidities or D-dimer levels between patients with or without PE. The importance of a high clinical index of suspicion together with the role of CTPA in diagnosing PE in hospitalised COVID-19 patients is emphasised. The study included all adult patients admitted to Groote Schuur Hospital (GSH) from 01 April 2020 to 30 September 2020 with confirmed COVID-19 pneumonia who underwent CTPA scans. This was defined by a positive reverse transcriptase polymerase chain reaction (RT-PCR) test on a nasopharyngeal swab for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The indication for CTPA in all patients was suspected PE.Radiological images and reports on scans performed in the Division of Radiology, Groote Schuur Hospital are stored in the Picture Archiving and Communications System (PACS) database system. A detailed search using query builder  database search tool was used to access all CTPA reports/images on the PACS system for the study period. The search phrases ‘covid’, ‘SARS’, ‘corona’, ‘Cov 2’, ‘person under investigation (PUI)’ and ‘PCR’ were used to refine the search results.The initial search results with these search phrases revealed 269 CTPA studies. The study investigators manually filtered all 269 CTPA studies, and those patients with a history of COVID-19 and confirmed positive RT-PCR SARS-CoV-2 results were included in the search criteria. All PUI patients with positive RT-PCR SARS-CoV-2 results were re-classified as COVID-19 and included in the study.Of the 269 studies, a total of 116 CTPA studies satisfied the search criteria and were enrolled in the study. The included cases either had a confirmed RT-PCR SARS-CoV-2 result on the National Health Laboratory Service (NHLS) or a confirmed result documented on the CTPA clinical request form.All CTPA studies were performed on a 160-slice Toshiba Aquillion PRIME  160 multidetector computed tomography (MDCT) scanner. The scans were acquired with intravenous injection of 100 mL of contrast (Omnipaque 350), using a bolus tracking technique. The CTPA acquisition was initiated once the contrast bolus in the pulmonary trunk reached a Hounsfield unit (HU) of 180.The CTPA images were transferred to the PACS database for viewing of both soft tissue and lung window reconstructions. All the CTPA studies were either reported by a Radiology consultant or reported by Radiology registrar, and the final report was approved by a Radiology consultant. The final CTPA report was used to confirm the presence or absence of a PE.10 The anatomical location of the PE was documented. In the case of multiple PEs, the most proximal anatomical location was noted.Pulmonary embolism was defined as a filling defect within the main pulmonary artery/arteries, and/or their lobar, segmental or subsegmental branches on the CTPA study.We further retrospectively collected data on patient demographics , whether the patient was admitted to a general ward, high care or intensive care unit (ICU) and whether the patient had any recorded comorbidities on the clinical request form, such as diabetes, hypertension, cardiovascular disease, dyslipidaemia, HIV, malignancy, pulmonary tuberculosis, other respiratory diseases or a history of deep vein thrombosis (DVT).11Only D-dimer levels performed within three days of the CTPA study were included in the study. In a case where more than one D-dimer level was performed, the D-dimer result closest to the date of the CTPA study was recorded. In the absence of contraindications, all patients in the cohort received either prophylactic or therapeutic doses of enoxaparin according to our hospital protocol. As per hospital protocol at the time, therapeutic anticoagulation doses were reserved for patients who required high flow oxygen or mechanical ventilation, a high index of suspicion for PE or DVT, a rising D-dimer level on serial D-dimer measurements or a single D-dimer level > 1.5 times the upper limit of normal.We recorded the incidence of CTPA confirmed PE in all patients with proven COVID-19 pneumonia with clinical suspicion for PE undergoing CTPA at tertiary hospital during the specified reporting period.Furthermore, we investigated the association between demographic and clinical variables, and CTPA confirmed PE in patients with COVID-19 pneumonia. The appropriate non-parametric tests were conducted where the data violated assumptions of normality. Mann-Whitney U tests were used to compare age and D-dimer levels between COVID-19 patients with and without PE. Chi-square tests investigated the association between PE (positive or negative) and gender, anatomical location of PE, whether the patient was admitted to the ICU, high care or general ward, and whether the patient had comorbidities or not. Sub-analyses compared the female patients who were pregnant or within the postpartum period, versus those who were not pregnant.p = 0.05.Data was analysed using Statistical Package for the Social Sciences (SPSS) version 27, with the significance level set at Ethical approval to conduct the study was obtained from the Faculty of Health Sciences Human Research Ethics Committee of the University of Cape Town (HREC reference number: 109/2021) and from Groote Schuur Hospital. All patient data were anonymised.Of the study cohort of 116 patients, 69 (59%) were female and of these, 18 (26%) were pregnant and two (3%) were in the postpartum period. The median age for both genders was 49.5 years. Nineteen percent (19%) of patients undergoing CTPA for suspected PE had radiological confirmation of PE, with the majority (64%) reported as segmental in anatomical location.A large proportion (85%) of the patients in the study were admitted to general wards, with 13% admitted to a high care unit and only 2% patients in ICU. The majority (71%) of patients in the cohort had comorbidities , with hyThere was no significant difference in demographic or clinical variables, including D-dimer levels between patients with and without PE . The inc12The incidence of PE in hospitalised patients with COVID-19 pneumonia undergoing CTPA for clinical suspicion of PE at our institution was 19.0%. This is similar to the incidence of 23.9% reported in a meta-analysis by Kwee et al. (2021). Their pooled incidence included studies with similar study design, as well as similar indications for CTPA, and likewise only included studies with CTPA confirmed PE.12 Most of the patients in the studied cohort were admitted to general wards, with only 2.0% admitted to the ICU. The general assumption was that patients with severe COVID-19 pneumonia were admitted to either high care or ICU, leading us to postulate that the majority had non-severe COVID-19 pneumonia. Peripheral PE was the most frequent anatomical location reported in international studies.15 This study had similar findings, with segmental PE the most common anatomical location.The pooled incidence of those admitted to general wards was 23.9%, with a higher incidence (48.6%) of PE reported in patients admitted to ICU.17 We observed a female predominance, which further consisted of a small subset of young pregnant patients and patients within the postpartum period. This could potentially explain and partially account for the difference in age observed in this study compared to the other studies. Not surprisingly, the pregnant and postpartum group was significantly younger when compared to the non-pregnant females, and had less age-related comorbidities. Other study populations with similar study designs have not commented on pregnant or postpartum patients. It is unclear whether pregnant patients were excluded from these studies or whether there were no pregnant patients in their respective study populations.17Interestingly, the mean age of the study participants was younger than that reported in similar study populations undergoing CTPA for suspected PE.18 However, there is a paucity of published work on the incidence and characteristics of PE in pregnant patients with COVID-19 pneumonia. A literature review conducted by Czeresnia et al. reported no difference in the severity of COVID-19 infection or symptomatology in pregnant patients compared to the non-pregnant population.19 It is unclear whether, given the hypercoagulable state of pregnancy, COVID-19 pneumonia or interplay between these two entities could have contributed to PE in this subgroup of patients. Larger studies including post-mortem studies would need to be conducted to comment on the true underlying pathophysiology. The incidence of PE in the subset of pregnant and postpartum patients in this study was 20%.Pregnancy and the postpartum period in the general population are associated with an increased risk of VTE, especially in the immediate postpartum period.The mean D-dimer levels in the PE group were higher than in the non-PE group, but this was not statistically significant. No significant differences in demographic features or comorbidities between patients with and without PE were found.16 In addition, a similar study to assess PE in COVID-19 patients undergoing CTPA reported a median of 14 days duration of symptoms prior to CTPA.17 This highlights the proposal for potentially using a lower threshold for requesting CTPA during the 2nd- and 3rd week of COVID 19 infection. Unfortunately, we did not collect information on the delay between the onset of symptoms to the time of CTPA, and only reported on the number of days from the RT-PCR CoV-2 test to CTPA. This precluded a direct comparison to the above mentioned studies with the assumption that symptom onset most likely preceded the date of RT-PCR CoV-2 test.17There was a median 13-day interval between positive RT-PCR results for SARS-CoV-2 and the diagnosis of PE on CTPA. In a study by S. Meiler et al., the majority of PE’s were detected on days 11–20 after symptom onset.Identifying a time period when patients are most at risk of developing PE during COVID-19 infection could alert the clinician to have a lower threshold for requesting a CTPA study. This information would be invaluable, especially in view of the clinical challenges created by symptom overlap between PE and severe COVID-19 pneumonia.Sample size was limited by the retrospective nature of the study.Caution should be taken regarding the true incidence of PE in hospitalised COVID-19 patients, as only patients who underwent CTPA for suspicion of PE were included. The incidence reported does not reflect the true incidence in all hospitalised COVID-19 patients.All patients included in the study received at least prophylactic anticoagulation, as per our institution guidelines at the time. However, no distinction was made between prophylactic or therapeutic doses, nor were exact doses included in the analysis.A small subset of pregnant and postpartum patients was included in our study. Larger studies are required to comment on the true incidence and characteristics of PE in COVID-19 pregnant and postpartum patients.We only included D-dimer levels performed within three days of the CTPA which resulted in a smaller sample size compared to our overall cohort. This was especially true for the non-PE group.18We further acknowledge that D-dimer cut off levels used in the non-pregnant population are not reliable in pregnancy.According to our knowledge, this study is the first to describe the incidence and characteristics of PE confirmed on CTPA in hospitalised COVID-19 patients in South Africa. The incidence of PE in patients with COVID-19 pneumonia undergoing CTPA for suspected PE at our institution was 19% with the majority of PE’s reported as segmental in anatomical location. The study cohort was unique as it included patients with pulmonary tuberculosis (PTB), HIV, as well as a subset of pregnant and postpartum patients.We report no significant differences in demographic features, comorbidities or D-dimer levels between patients with and without PE. Our observations highlight the importance of CTPA, together with a high clinical index of suspicion, in diagnosing PE in hospitalised COVID-19 patients at our institution.Larger multi-centre studies exploring the characteristics of PE, together with laboratory markers and historical data to help identify a time interval during the course of COVID-19 infection, when patients are most at risk of developing PE, could help with clinical decision making and in refining local hospital protocols, especially in resource constrained settings.


Background: Lower urinary tract symptoms (LUTS) affect more than half of men over age 70 and contribute to both poor health-related quality of life and polypharmacy. Tamsulosin hydrochloride, a selective α1-blocker, is the most common medication used to treat LUTS due to presumed benign prostatic hyperplasia and is often prescribed indefinitely, although not all men benefit from long-term therapy. N-of-1 trials allow for individualized estimates of benefit and harm and could facilitate decisions regarding chronic tamsulosin therapy for LUTS, particularly among older men. Our team developed the PERSONAL  app to track daily urinary symptoms and medication side effects for n-of-1 trials among older men with LUTS.Materials and Methods: We will conduct a feasibility study of 20 individual randomized n-of-1 trials using the PERSONAL app to compare tamsulosin (0.4 or 0.8 mg) vs. placebo among older men taking tamsulosin for LUTS. We will include men over age 65 with a smartphone for whom temporary discontinuation of tamsulosin is safe, . Participants will work with research staff to prospectively identify the most important urinary symptoms and medication side effects that they would like to digitally track. Men will then be randomized to 2-week treatment periods of tamsulosin or placebo followed by a 1-week wash-out with placebo, for 4 distinct treatment periods and 3 wash-out periods, totaling 11 weeks. Study medications will be blinded using over-encapsulation of tamsulosin pills and matching placebo. Our primary outcomes for this study will be recruitment and retention of eligible men, completion rates of n-of-1 trials and daily questionnaires using the PERSONAL app, and participants' perceived usefulness of their n-of-1 trial for determining whether tamsulosin is effective for them. Linear mixed effects models with individual-specific intercepts and intervention effects will also be used to estimate within-individual effects of tamsulosin.Discussion: The goal of this innovative study is to establish feasibility and acceptability of using a mobile health app and n-of-1 trials to provide older men with individualized estimates of benefits and harms of chronic tamsulosin therapy for LUTS. LUTS arblockers , 6, whicblockers . Althougom Score ], averagom Score , 12. Theom Score . Harms older men –16. Unfolder men , 15, 17.1-blocker therapy.N-of-1 trials, or multiple crossover trials conducted within a single individual, are a powerful yet underused tool that could be used to optimize prescriptions for symptomatic conditions such as LUTS . This stThe goal of this study is to establish feasibility and acceptability of using the PERSONAL  mobile health app to conduct placebo-controlled n-of-1 trials among older men receiving chronic tamsulosin therapy for LUTS to facilitate deprescribing decisions. This study will include a total of 20 men who will undergo individualized n-of-1 trials in order to collect and report the parameters necessary to plan an optimal and adequately powered full study of drug effectiveness.Our research team is following a mixed methods approach  to develThe proposed study will be located within the San Francisco Bay Area with recruitment occurring at multiple clinical sites within the University of California, San Francisco (UCSF) Medical Center. Participants' UCSF clinicians will not be routinely informed of their participation in this study.The primary study hypothesis is that it is feasible to conduct a series of individual placebo-controlled n-of-1 trials among older men receiving tamsulosin for LUTS using PERSONAL app. Specifically, we hypothesize that it is possible to recruit and retain 20 eligible men from a single healthcare system within a reasonable timeframe , >70% of enrolled participants will complete n-of-1 trials, participants will complete >50% of daily questionnaires, and >50% of participants will describe the PERSONAL app as “extremely helpful” or “very helpful” for deciding whether tamsulosin is an effective medication for them to continue or discontinue. Secondary and tertiary outcomes will include standardized measures of LUTS severity, global urinary bother, satisfaction with LUTS treatment, attitude toward deprescribing, medication adherence, and health-related quality of life.Study participants include a broad diversity of patients recruited from the UCSF Medical Center electronic health record (EHR) at 3 UCSF-affiliated clinical sites: Mission Bay Campus, Parnassus Heights Campus, and Zuckerberg San Francisco General Hospital. Participants must meet the following eligibility criteria : EnglishThe participant flow diagram is shown in Eligibility will be determined via telephone screening. Research staff will explain the study and ask questions to determine which inclusion and exclusion criteria are met, including whether the patient has an eligible phone device. Once a patient is deemed eligible, they will be asked to schedule a study orientation visit. They will then receive a confirmatory email with a link to complete the baseline questionnaire using Research Electronic Data Capture (RedCap), a secure online portal. They will also receive instructions to download the PERSONAL app (available for free on Google Play and Apple's App Store) on their smartphone prior to the orientation visit.The orientation visit will be conducted by trained research staff who will first obtain informed consent to participate in the study and a Health Insurance Portability and Accountability Act (HIPPA) authorization form. Research staff will first describe the n-of-1 study design, daily questionnaires, and real-time data visualization and will then ensure successful installation of the PERSONAL app. Each participant will then receive a unique study ID and login credentials for the PERSONAL app. Participants and research staff will customize the app together by selecting symptoms and side effects to track and setting reminder notifications based on the preferences of the participant . The participants will then select a start date for their n-of-1 trial which will be entered directly into the PERSONAL app. Participants will receive a bubble pack with 11 weeks of tamsulosin (at their previously prescribed dose) or matching placebo and will be instructed to start taking the study medications after successfully completing the run-in period. At the end of the orientation visit, research staff will assess understanding of the app features and provide verbal and written instructions for the rest of the study. Research staff contact information will be provided for reporting severe or concerning symptoms or technical app support for the duration of the study.Participants will be asked at baseline to identify their most bothersome urinary symptoms and perceived medication side effects. Using responses from the baseline questionnaire as a guide, research staff will guide participants to select up to 2 symptoms and up to 2 side effects to track during their n-of-1 study (see Assessments and Outcome Measures below). We will consider allowing participants to track additional symptoms and side effects if they perceive the burden of additional daily questions to be low. Once selected, these outcomes will be entered directly into the PERSONAL app to create a personalized n-of-1 trial focused on the specific outcomes of interest for each participant. If participants cannot identify a preferred symptom or side effect for tracking, this will be recorded, and the default urinary symptom will be their most frequent symptom identified on the baseline questionnaire. The default medication side effect will be dizziness/lightheadedness because it is one of the most common (15% to 17%) and serious side effects of tamsulosin among older men .Participants will start with 1-week open label run-in period where they will use the PERSONAL app to track daily symptoms and side effects while not taking their tamsulosin or any study pills. Based on the pharmacokinetics and expected timeframe of symptomatic relief from tamsulosin  , all n-ohttps://www.overlaphealth.com/), has developed and tested the PERSONAL app among older men with LUTS due to presumed BPH. The PERSONAL app presents participants with a daily questionnaire to track their individually selected urinary symptoms and medication side effects  will also be shared using patient-friendly graphs and text. We will obtain qualitative feedback on the orientation, PERSONAL app, and usefulness of the data and visualization at the end of study completion visit.We will evaluate 4 primary outcomes that will be used to determine feasibility and acceptability of a larger future n-of-1 study:Recruitment and retention of 20 eligible men within reasonable timeframe N-of-1 trial completion rate Daily questionnaire completion rate Percentage of participants who describe the PERSONAL app as “extremely helpful” or “very helpful” for deciding whether tamsulosin is an effective medication for them to continue or discontinue .To calculate the recruitment timeframe, we will log the start date of each individual n-of-1 trial. N-of-1 trials will be considered complete when participants complete the end-of-study questionnaire. Completion of daily questionnaires is tracked by the PERSONAL app along with session duration and distribution of time spent with each component of the app to further quantify user engagement. To characterize the experience of participants using the PERSONAL app, we will ask them at the end of the study to rate the helpfulness of the app from 1 (extremely helpful) to 5  across multiple domains based on prior mobile health studies . We willParticipants will select from daily urinary symptom measures that were adapted from the LURN SI-10 . SpecifiWe adapted the American Urologic Association Symptom Index global bother question and changed the recall period from the past month to the past 24 h . All parTo evaluate side effects of tamsulosin, we further adapted the global bother question and will ask participants to quantify how bothered they were by specific perceived side effects. Participants will be asked “Over the past 24 h, how bothered were you by [side effect]?” for each of the medication side effects selected to track during the study orientation visit. Responses rangeD from “Not at all bothered” to “Extremely bothered.” Specific wording and response options for each question are listed in At baseline, we will collect demographic data on age, marital status, race, ethnicity, employment, income, and educational attainment via questionnaire. We will ask participants about their smoking history, alcohol and caffeine intake, and physical activity as well as history of medical conditions, including cardiovascular disease, diabetes, prostate cancer, and prostatitis. At baseline and the end of study, participants attitudes toward deprescribing will be assessed using Revised Patients' Attitudes Toward Deprescribing (rPATD)  and healBased on prior mobile health studies , 34–36, t-test for continuous measures and McNemar tests for binary variables. We will use multivariable-adjusted linear mixed effects models, with random intercepts and slopes and an unstructured variance-covariance matrix, to estimate variation in daily urinary symptoms or medications side effects. We are aware that there is unlikely to be a sufficient sample size of men who tracked the same urinary symptom or medication side effect needed to calculate valid between-person estimates in this feasibility study, however, participants may have sufficient data to calculate valid within-person variability for each treatment group as well as within-person treatment effects. These estimates will be more accurate and precise in a larger future trial where data from other participants tracking the same symptoms or side effects is incorporated into the linear mixed effects models and contributes to within-person estimates.Primary outcomes will be assessed as binary variables  and the feasibility study will be considered successful if all 4 primary outcome objectives are met. We will describe the primary outcomes as well as baseline demographic and clinical characteristics using percentages, means ± standard deviations for normally distributed variables, and medians with interquartile ranges for skewed variables. The change in secondary and tertiary outcomes from baseline to end-of-study, including LUTS treatment satisfaction, attitudes toward deprescribing, and health-related quality of life, will be evaluated using paired samples Data entered into the PERSONAL app will be hosted on the Overlap Health secure environment and will contain no personal health information. To protect participant confidentiality, Overlap Health will only have access to participant study ID numbers. The raw data collected by the PERSONAL app will not be available to other applications. Data transfers will use HIPPA-compliant file encryption (at rest and in transit), secure file transfer (SFTP), Secure Sockets Layer (SSL) for interface data transfers, predefined authentication routes, and a role-based permission system. Questionnaires will be collected electronically via RedCap surveys managed in secure environments behind institutional firewalls. All study staff will be trained in good clinical practice, HIPPA procedures, and participant confidentiality.A unblinded Safety Monitoring Committee will be established to review unanticipated or serious adverse events for the duration of the study and will report directly to the University of California, San Francisco Institutional Review Board.ClinicalTrials.gov once finalized.Ethical approval was granted to our team by the University of California, San Francisco Institutional Review Board for a PERSONAL app pilot study (#19-28557). The feasibility study protocol will build off this prior work and will be submitted for approval by the University of California, San Francisco Institutional Review Board as well as registered on The PERSONAL study leverages mobile health technology, n-of-1 trials, patient-selected outcomes, and placebo controls to provide older men with personalized information regarding the benefits and harms of continuing or discontinuing chronic tamsulosin therapy. This study protocol seeks to evaluate the feasibility and acceptability of using the PERSONAL app to conduct a series of n-of-trials. We will collect the data needed to plan a larger future n-of-1 study to provide individualized estimates of patient-selected benefits and harms of chronic tamsulosin therapy among older men with LUTS.1-blocker therapy for LUTS, n-of-1 trials could be used to personalize LUTS treatments by quantifying both benefits and harms of continuing or discontinuing chronic tamsulosin therapy.There is likely both undertreatment and overtreatment of LUTS with chronic tamsulosin therapy. Potential contributors to overtreatment include situations where there is minimal or no benefit of long-term treatment  and situations where harms exceed benefit . Harms from chronic tamsulosin therapy are often insidious; tamsulosin or polypharmacy-related side effects may be attributed to other medications and comorbidities or inappropriately tolerated as a “normal process of aging.” Conversely, men who would benefit from chronic tamsulosin therapy may prematurely discontinue due to misattributed harm  or perceived lack of benefit due to overlapping conditions . Since there is currently no recommended minimum or maximum duration of α1-blocker monotherapy, mean symptom severity were not increased up to 6 months after unblinded discontinuation and more than two-thirds of men remained off medication (1-blocker monotherapy demonstrated stable symptoms for up to 12 months after discontinuation with only 30% of men requesting re-initiation of treatment (1-blockers plus 5α-reductase inhibitor therapy, both observational and randomized studies have demonstrated no symptomatic progression in the majority of men who discontinue α1-blockers but continue 5α-reductase inhibitor monotherapy (1-blocker, 74% of men in both groups had no worsening of symptoms after 12 months (1-blockers can be safely discontinued in men with a wide range of LUTS severity without significant worsening of symptoms, the effects of chronic tamsulosin discontinuation remain unknown and rigorous placebo-controlled studies of α1-blocker discontinuation are lacking. We also know that the effects averaged over large numbers of participants in traditional placebo-controlled RCTs do not translate directly to individuals, particularly older adults (Several observational and small open-label randomized clinical trials provide evidence that discontinuation of chronic tamsulosin therapy will not lead to worsening symptoms in many men. Among 33 men who initially experienced symptomatic improvement with αdication . Anotherreatment . Even amotherapy –44. In t2 months . Despiter adults , 21. NewN-of-1 trials have the potential to greatly increase the accuracy and precision with which urologic medications are prescribed for symptomatic conditions such as LUTS. Mobile health technology has lowered many of the barriers to implementing this powerful study design in research, clinical, and non-clinical settings by decreasing the burden of frequent data collection and facilitating data interpretation through instantaneous visualization, however it remains unknown if placebo-controlled n-of-1 trials using patient-selected outcomes are feasible. To address this gap, we will establish the feasibility and acceptability of placebo-controlled n-of-1 trials using the PERSONAL app and patient-selected outcomes among older men with LUTS.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.SB, BB, AO-O, MS, IS, and SK conceptualized the study and contributed to the study design. SB and CM developed the analytic plan. BB, AO-O, MS, IS, and SK provided administrative, technical, or material support. SB wrote the manuscript and obtained funding. All authors contributed to the refinement of the study protocol and the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.