Caenorhabditis elegans across a timecourse of ethanol exposure, between 30 min and 8 h, to determine what genes and genetic pathways are regulated in response to ethanol in this model. We found that short exposures to ethanol (up to 2 h) induced expression of metabolic enzymes involved in metabolizing ethanol and retinol, while longer exposure (8 h) had much more profound effects on the transcriptome. Several genes that are known to be involved in the physiological response to ethanol, including direct ethanol targets, were regulated at 8 h of exposure. This longer exposure to ethanol also resulted in the regulation of genes involved in cilia function, which is consistent with an important role for the effects of ethanol on cilia in the deleterious effects of chronic ethanol consumption in humans. Finally, we found that food deprivation for an 8-h period induced gene expression changes that were somewhat ameliorated by the presence of ethanol, supporting previous observations that worms can use ethanol as a calorie source.Ethanol-induced transcriptional changes underlie important physiological responses to ethanol that are likely to contribute to the addictive properties of the drug. We examined the transcriptional responses of  Alcohol Use Disorder (AUD), the compulsive use of alcohol despite negative consequences, is the product of long-term misuse of alcohol (ethanol) that results in physiological changes in the brain and other organ systems. Such changes are likely to underlie both the pathological and the addictive characteristics of alcohol. Changes in gene expression can be important in the long-term consequences of ethanol exposure, so our goal is to understand the landscape of transcriptional responses to ethanol.Alcohol is a commonly abused drug worldwide. Abuse of alcohol causes significant harm, contributing to approximately 5% of the global burden of diseaseC. elegans as a model to study the physiological effects of ethanol. The molecular effects of ethanol in worms are directly relevant to the molecular effects of ethanol in mammals including humans2. C. elegans are intoxicated by exogenous ethanol, and they show a dynamic behavioral response to ethanol exposure. We observe behavioral depression within 10 min of exposure, which is a progressive slowing and loss of coordinated locomotion that reaches a plateau by 10 min of exposure4. The worms develop acute functional tolerance (AFT) to ethanol, which we first observe as an increase in locomotion speed between 10 and 30 min of exposure, despite a gradual increase in internal ethanol concentration over the exposure time6. We have extensively studied the mechanisms underlying these acute behavioral responses to ethanol9. However, we know less about the transcriptional modifications induced by ethanol, which are likely to underlie long term physiological adaptations to ethanol. We and others have observed that C. elegans develop physical dependence on ethanol over extended exposures; paradigms range between 6 and 24 h of exposure. In each case, dependence is observable as behavioral changes when the animals are withdrawn11. This suggests that there are important functional changes that occur over the exposure time, and we predict that modulation of transcription is likely to lead to a subset of these changes.We use the nematode worm C. elegans. We examine the very acute responses at 30 and 60 min of exposure, responses after 2 h of exposure, and later responses after 8 h of continuous exposure. We observe waves of gene expression responses to ethanol, and we find that the largest number of gene expression changes are observable after 8 h of ethanol exposure. Among these are transcriptional changes due to food deprivation that is moderated by ethanol exposure, demonstrating that worms can use ethanol as a calorie source.Here, we conduct a comprehensive analysis of the transcriptional responses across a time course of ethanol exposure in the nematode Caenorhabditis elegans strain wild-type N2  animals were maintained at 20 °C on nematode growth medium (NGM) plates in the presence of Escherichia coli strain OP5012.Populations were age-synchronized by allowing gravid adult hermaphrodites to lay eggs on a well-seeded plate for three hours. The adults were removed, and the resulting population was allowed to grow for three days at 20 °C until they were first-day adults.n = 4) for a given timepoint, were generated on different days. 15 mL conical treatment tubes were prepared by adding M9+/− 100% ethanol to yield 0 mM or 400 mM ethanol. Approximately 2000 age-synchronized well-fed young adult worms were washed off of culture plates with M9 buffer into 15 mL conical tubes. The animals were allowed to settle for 2 min, and the supernatant was removed. Worms were washed with 5 mL M9, allowed to settle for 2 min, and the supernatant was removed. Each population of worms was divided into two halves and each half was added to a prepared treatment tube so that matched 0 mM and 400 mM treatments were generated. Worms were placed on a rotator at room temperature for the treatment time . After the exposure was complete, tubes were removed from the rotator, the worms were allowed to settle for 2 min, and the supernatant was removed. Worms were washed twice with 5 mL M9, allowed to settle for 2 min, and the supernatant was removed. The resulting worm + M9 slurry was transferred to a microfuge tube and spun for one minute. Supernatant was removed to generate as dry a pellet as possible, and the worms were flash frozen in liquid nitrogen. Samples were then stored at − 80 °C. Worm samples were generated in the Bettinger laboratory  and shipped frozen on dry ice to the Kammenga laboratory  for processing and RNA analysis.Biological replicates,  following the manufacturer’s instructions with minor modifications. After the lysis step, 10 μL, instead of 25 μL, of a 20 mg/mL stock solution of proteinase K was added to each sample. Samples were incubated for 10 min at 65 °C and spun at 1000 rpm in a ThermoMixer . Samples were cooled on ice for 1 min before loading into the cartridge, after which the standard protocol was followed. RNA samples were stored at − 80 °C until further use.RNA was isolated as described previouslyC. elegans (V2) Gene Expression Microarray 4X44K slides. The microarrays were scanned with an Agilent High Resolution C Scanner using the recommended settings. After scanning, data was extracted with Agilent Feature Extraction Software (version 12.1.1.1), (https://www.agilent.com/en/product/mirna-microarray-platform/mirna-microarray-software/feature-extraction-software-228496).The microarray preparation was conducted as described in the ‘Two-Color Microarray-Based Gene Expression Analysis; Low Input Quick Amp Labeling’-protocol, version 6.0 from Agilent . Based on RNA quantification by NanoDrop, we used 100 or 200 ng RNA as input for cDNA synthesis. The microarrays used were the Agilent 14. As recommended, the within array normalization used the ‘LOESS’ method and between-array normalization used the ‘quantile’ method16. After normalization, the normalized intensities were log2 transformed (I) and also a log2 ratio with the mean of each spot was calculated (R).The microarray data was normalized using the Limma package in “R” I is the log2 normalized intensity of spot i  of sample j, which was explained over timepoint T , ethanol treatment E (0 mM or 400 mM), dye D (either Cy3 or Cy5), batch B (one out of four batches), and the interaction between timepoint and ethanol exposure, and residual variance e. Subsequently, the effects for dye and batch were subtracted from the normalized data.As we noticed a small dye-effect (from comparing the two dye-swaps) and batch effect based on an initial principal component analysis (< 5% of variance), we corrected the data for both dye and batch effects, by subtracting these effects as estimated based on the linear model17, accessible via https://git.wur.nl/published_papers/sterken_vanwijk_2020. The dplyr and tidyr packages were used for organization19, and plotting was done with ggplot220. For gene-level analysis the annotations from WS258 were used, spots were re-mapped versus WS258 and probes with double or ambiguous  hits were censored21. Transcriptome data were deposited at ArrayExpress under E-MTAB-9663.Data analysis was done in “R”  with custom written scriptsmod-1 at 30 min of exposure. We tested bas-1, che-3, fat-1, slo-1, unc-10, unc-13, unc-25, unc-47, and unc-49 at 480 min of exposure. We generated new cDNA from the biological samples tested in the microarray analysis. cDNA was made from 500 ng of RNA using the GoScript Reverse Transcriptase kit  following the recommended protocol with random hexamers . Gene expression was quantified by reverse transcriptase- quantitative PCR (RT-qPCR) using custom-designed primers  in technical duplicate22. Data was analyzed per time point. Data was transformed with the following formula:As a confirmation of our microarray results, we performed quantitative PCR analysis on selected genes. We tested To determine statistical significance over multiple samples we performed an ANOVA, as provided by R.prcomp function . Principal component analysis was done on the log2 ratio with the mean transformed normalized data.To understand sources of variance we used principal component analysis, as provided in “R” by the I is the log2 normalized intensity of spot i  of sample j, which was explained over ethanol treatment E (0 mM or 400 mM) and residual variance e. This model was used per time-point separately . For each treatment time the combination of the four biological replicates was assayed.To determine which gene-expression was affected by the experimental variables, we used the following linear modelY is the difference between 0 and 400 mM ethanol exposed samples of spot i  of batch k, which was explained over two subsequent timepoints T .To understand the between-timepoint dynamics, we calculated the difference between 0 and 400 mM ethanol exposed samples within the same batch and timepoint, and used the modelp.adjust function in “R”)23. This was done on a general threshold of − log10(p) > 3.7, to standardize effect-size detection over the various factors. This threshold resulted in false-discovery rates between q = 0.0020 (0 mM ethanol at 480 min) and q = 0.123 (0–400 mM ethanol between 60–120 min). Combined, this set of transcriptional differences was composed of 5968 spots representing 3603 genes. Excluding the analyses involving the 480-min timepoint, the set includes 959 spots representing 660 genes  > 6 and an absolute effect-size > 0.1 per hour) throughout development (41.5–72 h) . We subsequently calculated the average age from the inferred ages per gene to obtain an estimated age for each sample. As these ages are strictly relative, the estimated ages were subsequently transformed into a normal standard distribution for relative age estimation and sample comparison.The relative transcriptional age between the samples was estimated by their transcriptional profiles. We used a temporal expression ruler generated by Snoek et al., 2014, that consisted of 2195 microarray spots (1266 genes) that were linearly up- or down-regulated (log2 ratio with the mean transformed data (R) of the 959 spots that showed significant differences for the clustering analysis. This excluded the 480-min timepoints. To reliably identify patterns, we averaged values per spot per time per treatment over the four biological replicates . We used the “the elbow” method to determine the appropriate number of clusters by analyzing the within-cluster sum of square (WSS)25. The WSS measures the variability of the observations within each cluster. The number of clusters is generally considered appropriate when adding another cluster does not substantially improve the overall WSS. We used the function fviz_nbclust from the R package NbClust26. We clustered the selected spots using kmeans in R, using the Hartigan and Wong algorithm with 5 centers, 10,000 iterations and 50 random starts. These settings yielded a stable outcome when clustering was repeated 27; the ModERN transcription factor binding sites (http://epic.gs.washington.edu/modERN/)28, which were mapped to transcription start sites (as described by29)  into five distinct patterns . Serotonin signaling is a mechanism by which animals can modulate behavior based on external stimuli. In worms, serotonin activated MOD-1 channels play roles in the enhanced slowing response when worms that have been deprived of food encounter a new source of food54, and also in promoting a reduced exploration state called dwelling56; both are behavioral states that have reduced locomotion rates. It is interesting that a channel that is known to promote reduced locomotory states would be upregulated by exposure to ethanol at a concentration that depresses rates of locomotion; perhaps the pattern of mod-1 expression is distinct, such that serotonin is inhibiting inhibitory neurons in the case of 30-min ethanol exposure. Serotonin is also implicated in alcohol use disorder in humans, although its role is complex and not completely understood. A genetic polymorphism in the serotonin reuptake transporter that increases serotonin in the synapse is associated with alcohol dependence, suggesting that increasing serotonin signaling promotes consumption57. This is consistent with the observation that people with alcohol use disorder have relatively greater expression of the serotonin biosynthetic enzyme tryptophan hydroxylase58. However, drugs that impact serotonin signaling have been shown to both increase and decrease ethanol drinking in different paradigms , leading to the suggestion that particular serotonin pathways are associated with different aspects of alcohol use phenotypes. It is notable that we observe that among the, at least, four serotonin receptors in worms, only mod-1 was regulated at this timepoint, indicating that there is specificity in the ethanol responsiveness of this neurotransmitter pathway. We note that at the 480 min timepoint, there was downregulation of both the bas-1 (dopa decarboxylase)  and cat-1 (catecholamine symporter)  enzymes that are involved in regulating serotonin levels, suggesting that by this point, a global decrease in serotonin signaling is induced by ethanol.We found that different lengths of exposure to ethanol elicited distinct transcriptional profiles, and as expected, more genes were regulated at longer exposure times. Fewer than 100 genes were significantly regulated at 30 min and there were no overrepresented gene ontology categories, probably due to the small dataset. Interestingly, at this early timepoint, we observed an ethanol-induced increase in expression of 60.At 60 min of exposure, 220 genes were significantly regulated. As expected, overrepresented in this group are genes involved in alcohol metabolism . In addition, there was robust regulation of transcription factors; these are excellent candidate mediators of subsequent downstream expression changes, and suggest that by this time of exposure, a longer-term response to the effects of ethanol is evident. At 120 min of ethanol exposure, 379 genes were significantly regulated, and cytochrome P450 family members are overrepresented in the gene set. Cytochrome P450s are metabolic enzymes and are induced in many systems in response to drug treatments, including alcoholAt eight hours of ethanol exposure, we observed the largest number of and most diverse changes in gene expression; we found significant changes in expression of 2938 genes. There is a large effect of the experimental treatment at this timepoint independent of the ethanol concentration. We interpret this observation as the transcriptional response to food deprivation, which is considerably, although not completely, ameliorated by the presence of ethanol. This somewhat confounds the interpretation of the biological significance of the differentially expressed genes that are due to ethanol at this timepoint, but because our overall goal with this work is to identify the long-lasting adaptive changes that occur upon exposure to ethanol, we examined the biological actions of DEGs at this timepoint. We paid particular attention to genes whose expression was down in the ethanol-treated animals relative to the non-ethanol treated animals, because these may be less likely to reflect the potential moderating effect of ethanol on starvation-induced global down regulation of transcription.–3), and its binding partner, UNC-10  were both downregulated in ethanol treated animals. A mouse homolog of UNC-13, Munc13-1, has been demonstrated to be a direct molecular target of ethanol61. In Drosophila, reducing the levels of the unc-13 homolog Dunc13 generates resistance to the depressing effects of ethanol on synaptic transmission62.We found that among the overrepresented classes of genes were those involved in locomotory behavior and synaptic transmission. For example, the genes encoding the synaptic active zone protein, UNC-13  , unc-47 (GABA transporter) , and unc-49 (GABA receptor) , all of which were down-regulated with 8 h of ethanol exposure. The mammalian GABAA receptor is a well-described direct target of ethanol, and ethanol’s action as a positive allosteric modulator of GABAA receptors is an important component of the intoxicating effects of ethanol. The GABA system plays important roles in both acute and chronic consumption of ethanol63. Our observation that the GABA system is regulated with exposure to ethanol is consistent with an extensive literature in mammalian brains revealing that there is a complex manipulation of expression of GABA signaling components with ethanol treatments 64. This suggests that, like mammals, worms use transcriptional regulation of GABA system components to respond to ethanol exposure.Genes encoding several components of the GABA signaling pathway are also regulated, including 66 and, in worms, is a major contributor to the depressive effects of ethanol4. We found that expression of the BK encoding gene slo-1 was decreased with 8 h of ethanol exposure . These data are consistent with the observation in rats that ethanol causes a rapid downregulation of BK channel mRNA in supraoptic nucleus neurons67, and suggests a simple model in which downregulation of slo-1 and other direct ethanol targets could decrease the depressing effects of ethanol and, as such, could contribute to the development of tolerance to ethanol.We and others have previously demonstrated that the BK potassium channel SLO-1 is a direct target of ethanol in worms and mammalsslo-1, some of the genes that are regulated in response to ethanol exposure would already have been identified as having roles in acute ethanol response phenotypes. We have previously demonstrated that regulation of lipid levels is profoundly important in the process of development of acute functional tolerance to ethanol (AFT)68. We found that the triacylglyceride lipase gene, lips-7 was downregulated at 8 h of ethanol exposure . lips-7 acts antagonistically to the development of AFT; lips-7 loss of function mutants have enhanced development of AFT6. This suggests that down-regulation of lips-7 transcription after extended exposure to ethanol may enhance the development of tolerance to ethanol.We hypothesized that in addition to 7, and in mice, altering EPA levels in diet modifies both acute ethanol responses and voluntary consumption68. The fat-1 gene encodes a desaturase that generates EPA from arachidonic acid, and we found that the expression of fat-1 was significantly increased after 8 h of ethanol exposure . If this upregulation is a compensatory response to the effects of ethanol, then this suggests that increasing the amount of EPA may be an additional mechanism by which animals modify their systems to generate tolerance to extended ethanol exposure.In addition to triacylglycerides, the level of ω-3 polyunsaturated fatty acid eicosapentaenoic acid (EPA) also regulates the development of AFT. Depletion of EPA eliminates AFT, whereas supplementing additional dietary EPA enhances normal AFT in worms69. Alcohol Induced Ciliary Dysfunction (AICD) is one of the root causes of lung dysfunction because it disrupts the ability of the ciliated airways to move inhaled particles and pathogens out of the lungs. AICD is associated with the disruption of the regulation of cilia beating frequency, and this effect has been linked to the outer dynein arm of lung cilia71. Notably, we found that the C. elegans ortholog of the human dynein heavy chain, che-3, was significantly downregulated after 8 h of ethanol exposure .We were intrigued to observe that at 8 h of exposure, one of the most over-represented classes of DEGs included genes involved in cilia maintenance and function. In humans, there are significant effects of chronic ethanol exposure on cilia, and these effects underlie, at least in part, the profound dysfunction in lungs observed in many people who chronically abuse alcohol. Respiratory illness is an important cause of alcohol-related mortality; people with AUD are significantly more susceptible to serious pulmonary infections, as well as at higher risk of acute respiratory distress syndrome (ARDS), an often-fatal fluid buildup in the alveoli 72. Fetal alcohol exposure can cause hydrocephalus75, which can result in lasting consequences to the affected child. Al Omran et al. (2017) recently demonstrated that acute alcohol exposure in rat causes a decrease in the beating frequency of the ependymal cilia lining in the brain that are involved in moving CSF76. Our work raises the intriguing possibility that the regulation of cilia gene expression may be a response to the inhibition of cilia function that occurs during extended ethanol exposure.Recent work suggests that cilia dysfunction caused by alcohol may also underlie some of alcohol’s effects on the nervous system. Excess alcohol consumption is associated with a buildup of cerebral spinal fluid (CSF) resulting in hydrocephalus, which can cause pressure related brain damageC. elegans using quite different exposure conditions. Kwon et al. (2004)77 performed a microarray analysis at 15 min, 30 min, and 6 h of exposure to a high 1.5 M concentration of ethanol . Moreover, this study has only 2 or 1 replicates at 15 min and 30 min respectively, which may reduce the ability to eliminate false positives or detect more subtle expression changes. This study reported that the only biological category of genes whose expression increased in ethanol treatment was the heat shock protein family. Interestingly, in our study, several of these heat shock protein genes were not upregulated , suggesting that this heat shock response is characteristic of the very high concentration of ethanol used in the Kwon et al. study. Of the 230 genes identified in this study as being ethanol responsive, we found that 21 were similarly regulated in our study, suggesting that these are responding to the ethanol exposure regardless of the concentration of ethanol experienced but consistent with exposure time. Peltonen et al. (2013)78 performed an RNAseq analysis of a chronic developmental exposure to a lower ethanol concentration (200 mM). In this work, two analyses were performed; developing larvae were exposed to ethanol for 2 days throughout postembryonic development and were harvested before adulthood, and a second group were exposed for 7 days and harvested at approximately 4 days of adulthood. This group observed that approximately 1000 genes were regulated in response to ethanol over these long developmental times. Among the most overrepresented gene classes were those involved in cytochrome P450 drug metabolism. Our observation that the P450 enzymes were regulated in our studies suggests that this is a general ethanol response mechanism.This study examined the effects of a time course that culminated in 8 h of exposure to 400 mM ethanol. There have been at least two other examinations of the transcriptomics effects of exposure to ethanol in 81.An additional approach to understanding gene expression dynamics is to consider genes that are affected by the treatment in the same pattern across the samples because co-expression of genes can shed light on specific biological processes that are affected by treatment. We excluded the 8-h timepoint from this analysis to minimize the effect of starvation. We performed k-means clustering of the 959 spots that were significantly different in the 0-, 30-, 60-, and 120-min exposures and grouped them into 5 clusters, each of which had a distinct pattern over time. We performed an enrichment analysis for each cluster to see if there were processes and pathways that were overrepresented that might highlight important functions underlying the ethanol response over time82. Interestingly, alcohol consumption affects glycosylation levels83, and moderate alcohol consumption is associated with exacerbation of antithrombin deficiency in individuals with disorders of glycosylation84. In one study, in children diagnosed with fetal alcohol syndrome there were more incidents of alleles compromising function of N-glycosylation machinery, suggesting that decreasing function of N-glycosylation may increase prenatal sensitivity to the teratogenic effects of ethanol85.Cluster 1 contained genes that were down-regulated over time regardless of treatment; these are likely to reflect the transcriptional response to the food deprivation during the exposure, and are unlikely to be informative about the ethanol response. Cluster 2 consisted of genes that were upregulated in the early timepoints and were progressively more upregulated with longer exposures to ethanol. This cluster was enriched for pathways that are involved in glycosylation. Glycosylation pathways are important for posttranslational modifications86. In addition, alcohol induced dysregulation of retinol metabolism may contribute to the development of fetal alcohol syndrome87.Cluster 3 contained genes that were strongly upregulated at later timepoints during the ethanol exposure; this small cluster contained enzymes involved in drug metabolism. Genes in cluster 4 were weakly upregulated across the ethanol exposure timecourse; genes in this cluster are involved in multiple processes including retinol (vitamin A) metabolism. There is a complicated relationship between retinols and ethanol; enzymes that metabolize ethanol appear to have an impact on retinol metabolism and homeostasis, and ethanol drinking dysregulates retinol metabolism; this has important negative health consequences in people with AUD Cluster 5 consisted of genes whose expression was upregulated over time in animals not exposed to ethanol, presumably in response to the food deprivation during the assay. However, in animals exposed to ethanol, these genes were stably expressed, suggesting that ethanol suppressed the starvation-induced expression change. Among the over-represented classes were genes involved in neuronal function; one intriguing possibility is that these genes contribute to neuroadaptations to the food deprivation stress that are inhibited by ethanol.In conclusion, we have identified ethanol exposure-induced transcriptional regulation of genes involved in pathways predicted by our previous work, including several direct targets of ethanol and genes involved in alcohol and lipid metabolism, as well as novel gene groups, including those involved in cilia function, glycosylation and retinol metabolism. Regulation of these genes in humans by ethanol exposure may have significant consequences for the long-term abuse of the drug and health-related consequences of alcohol consumption.Supplementary Information.Supplementary Legend.Supplementary Figure 1.


Conducting numerous, rapid, and reliable PCR tests for SARS-CoV-2 is essential for our ability to monitor and control the current COVID-19 pandemic. Here, we tested the sensitivity and efficiency of SARS-CoV-2 detection in clinical samples collected directly into a mix of lysis buffer and RNA preservative, thus inactivating the virus immediately after sampling. We tested 79 COVID-19 patients and 20 healthy controls. We collected two samples  from each participant: one swab was inserted into a test tube with Viral Transport Medium (VTM), following the standard guideline used as the recommended method for sample collection; the other swab was inserted into a lysis buffer supplemented with nucleic acid stabilization mix (coined NSLB). We found that RT-qPCR tests of patients were significantly more sensitive with NSLB sampling, reaching detection threshold 2.1±0.6 (Mean±SE) PCR cycles earlier then VTM samples from the same patient. We show that this improvement is most likely since NSLB samples are not diluted in lysis buffer before RNA extraction. Re-extracting RNA from NSLB samples after 72 hours at room temperature did not affect the sensitivity of detection, demonstrating that NSLB allows for long periods of sample preservation without special cooling equipment. We also show that swirling the swab in NSLB and discarding it did not reduce sensitivity compared to retaining the swab in the tube, thus allowing improved automation of COVID-19 tests. Overall, we show that using NSLB instead of VTM can improve the sensitivity, safety, and rapidity of COVID-19 tests at a time most needed. Rapid and robust identification of individuals infected by COVID-19 is one of the mainstays of containment and mitigation efforts of this pandemic.The current guidelines of the CDC and WHO for SARS-CoV-2 tests are that collected swabs should be placed in a transport tube that is either empty or contains either Viral Transport Medium (VTM), Amies transport medium, or sterile saline , 2. UndeWe hypothesized that the collection of SARS-CoV-2 clinical specimens into a tube containing a Nucleic Acid Stabilization and Lysis Buffer (NSLB), instead of VTM, will streamline the clinical diagnostic workflow as this buffer both inactivates the virus and preserves viral RNA at room temperature. The use of NSLB will allow transport of the specimens without the need for cooling and improve the biosafety profile of the entire diagnostic workflow. Additionally, this buffer obviates the need for incubation in LB and the samples can be directly inserted into the RNA extraction step. This leads to a larger quantity of viral nucleic acid per sample in the diagnostic pathway.Here, we compared the performance of RT-qPCR on samples collected into VTM and NSLB from 77 COVID-19 patients and 20 healthy controls and observed a significantly higher sensitivity for NSLB over VTM.Recruitment took place during April 2020. Only adults (above 18 years old) were asked to participate. Participants were random Israelis who were hospitalized or quarantined after they have been found positive to SARS-CoV-2 by RT-qPCR prior to participating in this study. Participants were recruited from three different medical centers in Israel, and are representative of the Israeli general population. The participants included 26 hospitalized patients with moderate to severe symptoms from the Sheba Medical Center, 21 hospitalized patients from Hadassah Medical Center, and 32 asymptomatic or mildly symptomatic patients who were quarantined in a hotel . Hospitalized patients were recruited by medical staff in the relevant departments, and quarantined patients were recruited by the researchers at the hotel were the quarantine took place, after receiving permission from the medical staff in charge of the quarantine. In addition, we recruited 13 healthy volunteers from the medical staff at the Rambam Medical Center and 7 healthy volunteers from Hadassah Medical Center. All participants signed informed consent and the experiments were approved by the IRBs of the Sheba, Hadassah, and Rambam Medical Centers. Sampling and RT-qPCR for each of the three experiments were conducted by different teams at different labs. We conducted a power analysis with the Gpower software  and this difference was statistically significant . 12/60 (20%) of the patients tested positive in NSLB but negative (below the limit of detection) in VTM, while only 6/60 (10%) tested positive in VTM but negative in NSLB. Overall, our results demonstrate a significantly higher sensitivity for sampling into NSLB over VTM. Importantly, NSLB was advantageous (lower Ct) over VTM in all the three different medical centers that participated in this study  of the patients, the NSLB sample showed lower Ct value  than its matched VTM sample, while in only 15/60 (25%) of patients, the Ct of VTM was lower (2(2.8) = 1.5 lower Ct for NSLB. The observed average Ct difference of 2.1±0.6 (Mean±SE) was not significantly higher than 1.5 . This result suggests that at least most of the advantage of NSLB in our study was due to using a more concentrated sample for RNA extraction since no inactivation in LB (and hence dilution) was needed. We observed no significant difference between the two commercial NSLBs that we used .When samples in VTM are processed in the lab they are typically inactivated in LB before entering the RNA extraction process. This inactivation dilutes the original sample, typically 2–3 fold. In our experiment, samples in VTM were diluted on average 2.8 fold in LB. Therefore, if NSLB is equal in performance with VTM with regards to biochemical properties alone, one would expect a baseline of logFig 1D–1F). Interestingly, this Ct difference was significantly higher than both zero  and the 1.5 Ct expected difference when dilution is controlled for . This suggests that the sampling into NSLB probably broke open more human cells over the time it took to transfer the samples to the lab (2–8 hours) compared to incubating VTM samples for 10–15 minutes in LB before RNA extraction. It also indicates that RNA in NSLB has not been degraded before reaching the lab.For 45 positive patients, we also performed RT-qPCR on human mRNA. NSLB samples showed on average 3.1 CI  lower Ct than their matched VTM samples .A possible concern when using NSLB for sampling is that RNA will be degraded prior to being processed in the lab, and thus the sensitivity of the test will decrease. To test the RNA stability over time, we repeated the RNA extraction of 24 samples from hospitalized patients  after 24 hours of storage at 4°C. Ct values were highly correlated for VTM samples between time 0h and time 24h (Pearson r = 0.96), and also for NSLB samples (Pearson r = 0.93), see Fig 2B). Both VTM and NSLB samples showed a mean decrease of 1 Ct value after 72 hours , indicating that not only RNA was not degraded in either medium, but that perhaps even more detectable RNA was released into the mediums during these 72 hours. Also, the advantage of NSLB over VTM remained similar at 72h as it was at time 0h .For 20 other patients (recovering quarantined patients) we kept for 72 hours the NSLB samples at room temperature and the VTM samples at 4°C and then extracted RNA a second time and performed another RT-qPCR. Correlation of Ct values between time 0h and 72h were Pearson r = 0.83 for VTM samples and Pearson r = 0.63 for NSLB samples , with some reports of up to 20%-30% FNR , 8. IncrWe showed the samples can be kept in NSLB at room temperature for at least 72 hours without degradation of RNA. This is important at times of high demand for COVID-19 testing and backlogs of samples waiting to be processed in labs. It can also simplify the transportation of large numbers of samples from remote areas, especially in countries where molecular diagnostic labs are far from the sampling sites.We showed that when sampling into NSLB, it is possible to discard the swab at the sampling site without sacrificing the test’s sensitivity. This removes a major obstacle for automation of COVID-19 tests (as pipetting robots can be used without the hindrance of the swab in the tube). Since NSLB also circumvents the need to lyse the sample in the lab before RNA extraction, it opens up the possibility of sampling into tubes that can be inserted directly into an RNA extraction robotic pipeline. This can help to significantly increase the number and shorten the turn-around time of COVID-19 tests, an important measure to curb the COVID-19 pandemic since patients are most infectious prior and only shortly after symptoms onset .Direct sampling into NSLB can reduce the risk of infecting the medical and lab personnel who conduct the sampling, transporting, and processing of the samples. In addition, the immediate inactivation of the virus in NSLB can improve the safety of a recently suggested swab pooling approach  that invThe method we describe here could be possibly improved further by direct sampling into a lysis buffer that allows for direct PCR . SuIt should be noted, however, that there are several disadvantages to using NSLB. First, it is more expensive than VTM. Second, it typically contains reagents that require more careful handling than VTM in cases of spillover. Third, there are certain RNA purification protocols that are not suitable for samples in NSLB. For example, RNA extraction protocols that use bleach are not safe if used with NSLB that contains Guanidine. Lastly, NSLB does not allow for the culturing of the virus in the lab. However, with the current need for high throughput and rapid SARS-CoV-2 tests, and since most samples are not currently cultured, this disadvantage is negligible.We demonstrated that sampling of suspected COVID-19 patients directly into NSLB and then discarding the swab has significant advantages over using the current guideline with VTM. It increases the sensitivity of the test, increases safety, and facilitates better automatic handling of samples in the lab. These advantages can help to increase the number and rapidity of COVID-19 tests at a time it is most needed.S1 DatasetA file containing Ct values of all the conducted experiments.(XLSX)Click here for additional data file.


Establishing accurate population size estimates (PSE) is important for prioritising and planning provision of services. Multiple source capture−recapture sampling method increases PSE accuracy and reliability. In August 2018, the three-source capture−recapture (3S-CRC) method was employed with a stringent assumption of sample independence to estimate the number of female sex workers (FSW) in Rwanda. Using Rwanda 2017 FSW hotspots mapping data, street and venue-based FSW were sampled at the sector level of each province and tagged with two unique gifts. Each capture was completed within one week to minimise FSW migration between provinces and recall bias. The three captures had 1042, 1204 and 1488 FSW. There were 111 FSW recaptured between captures 1 and 2; 237 between captures 2 and 3; 203 between captures 1 and 3 and 46 captured in all three. The PSE for street and venue-based FSW in Rwanda lies within 95% credible set: 8328–22 806 with corresponding median of 13 716 FSW. The 3S-CRC technique was low-cost and relatively easy to use for PSE in hard-to-reach populations. This estimate provides the basis for determining the denominators to assess HIV programme performance towards FSW and epidemic control and warrants further PSE for home- and cyber-based FSW in Rwanda. Globally, persons who exchange sex for money and other non-monetary items are at high risk of acquiring and transmitting HIV and other sexually transmitted infections. This risk is particularly high for those who exchange unprotected sex as their main source of income, such as female sex workers (FSW) . FSW plaIn Rwanda, the national HIV prevalence had reached a stable level of around 3% between 2005 and 2015  for RwanHowever, sex work is structured differently around the world and comprehensively capturing and describing the characteristics of this heterogeneous population is making PSE difficult . In mostIn the past decade, Rwanda conducted three PSE exercises among FSW using different methodologies: 2011, a household survey used the network scale-up method (NSUM) reporting the size of the FSW population ranged from 25 000 to 45 000 , 13. In In 2018, Rwanda Biomedical Center (RBC) and US Centers for Disease Control and Prevention (CDC) conducted a PSE among FSW using three-source CRC (3S-CRC) methods to gain more accurate and reliable national estimates.In recent years, two-source CRC (2S-CRC) and 3S-CRC methods are also being used to estimate the sizes of hard to reach population such as FSWs, men who have sex with other men (MSM) and people who inject drugs (PWID) in many countries , 18–20.Estimation is based on observations or ‘captures’ of population members on two occasions. The ‘captured’ population members are ‘marked’ by receiving a unique object during the first encounter. Population members who do and do not bear the mark are enumerated during the second encounter or ‘recapture’. The numbers of marks issued during the first encounter and the numbers of individuals who do and do not bear a mark are sufficient to estimate population size under the assumptions that: (1) the population members are neither added to or lost from the population during the sampling period; (2) the marks are identifiable with certainty and are not lost or duplicated and (3) each population member shares common fixed non-zero probability of encounter at both observation events and can be encountered independently with that probability. The first assumption of a fixed population size is unlikely to hold, but the adverse effects of violation are minimised by completing sampling over a short time interval. The second assumption is more vulnerable in human than in non-human populations because the latter are not able to refuse capture, and visible tags can be used as the marks. The third assumption is especially vulnerable in studies of human populations. Human behaviour is complex, and therefore it is difficult to assume that all humans will share a common, constant and non-zero encounter probability , 19.The assumption that each population member shares a common, constant encounter probability can be relaxed, with three or more encounters, with estimation models including conventional log-linear models , and Bay13 were used as the sampling frame to generate a range of the number of objects to be distributed in each of 30 administrative districts in the five provinces of Rwanda.Data from the Rwanda 2017 hotspot mapping exercise for FSW size estimation conducted in 2017 in RwandaA FSW was eligible if she met following criteria: (1) self-reported having sex with men in exchange for goods or money, (2) estimated age 15 years and above  and (3) present at the visited venue/street at the time of data collection and identified by peer informants who worked in the same area. The age of consent in Rwanda is 18 years and above and because anyone under the age of 18 who is engaged in sex work is considered to be a victim of sexual exploitation, we included age 15–17 with a waiver of written informed consent from the local IRB. Including FSWs below 15 years would have required parental consent.The objects used were small, inexpensive and branded with messages so they would have a memorable design and only be available from our staff who distributed them. The local civil society organisations were consulted to determine the type of objects and the unique messages in the local language (Kinyarwanda). A key holder with a message ‘Rinda Ubuzima’ (‘Protect your life’). and a bracelet with a message ‘♥ Ubuzima’ (‘Love your life’) were used for the initial two capture stages.Thirty trained data collectors were assigned to the administrative sector within 30 districts in each province. In each sector, a data collector was paired with a local FSW key informant who served as a guide per sampling event. A total of 576 informants assisted as local guides. To achieve independence between captures, each data distributor and local guide were assigned to different sector hotspots in each capture stage. FSW who presented in the street and venue-based hotspots were approached by the data collectors and guides.The 3S-CRC started with a capture stage (capture 1) where FSW encountered at hotspots were captured and tagged (marked) by accepting a unique object. After a one-week period, a second capture began during which FSW were asked if they had received unique objects during the previous week. If an FSW reported that she had received a unique object in the previous week, but could not show the object, the data collector showed her a laminated card with the distributed unique objects mixed with several incorrect objects. If the FSW correctly described and identified the object received in the last one week from the picture, she was considered as a recapture at capture 2. FSW who confirmed they have not received any objects or could not identify the object were considered newly captured.The third capture was initiated one week after the second. Sampled FSW were approached and asked whether they had received unique gift(s) in the last two weeks. Visiting time depended on the days and hours selected by key informants for each venue/street. The estimation exercise was conducted in the first three weeks in August of 2018 in all five provinces.We used 3S-CRC sampling to estimate the numbers of FSW in Rwanda using Bayesian non-parametric latent-class model , which eData were collected with Android tablets that had a preprogramed Open Data Kit (ODK) form loaded (version 1.7). Data entered were sent in real-time to a server and backed up at midnight daily. Stata 15.0 and SAS 9.4 were used for sampling and descriptive analysis. We used the R 3.5.1  RCaptureThis survey was reviewed and approved by the Rwanda National Ethics Committee. It was also reviewed in accordance with the US Centers for Disease Control and Prevention (CDC) human research protection procedures and was determined to be non-research. We received a waiver of informed consent for all participants because FSWs' age or other personal identifying information (PII) were not collected. Verbal consent was obtained as data collection was restricted to recording whether or not the FSWs accepted the gift/objects, at subsequent captures, whether FSWs can present/identify the gift they had received (age was estimated by the peer distributor). FSWs who were under 18 years (15–17) were provided with referral form to the nearest health facility for psychosocial support and HIV prevention and treatment intervention.The encounter with a FSW took less than 5 min per person. The anonymity was ensured as no name of survey participants was collected on the distributor form and no other data tool was used. All data collected were retrieved from PDA and stored in a computer protected with a password. Employees exposed to privileged survey participant information, during implementation of the survey signed an Employee confidentiality agreement prior to the survey.A total number of 1042 unique objects were distributed countrywide during capture 1 and 1204 during capture 2. In a three-week survey implementation exercise, 192 sectors were visited countrywide in each capture. Among 1135 women approached during capture 1, 1080 (95.2%) self-reported to be FSW, 1071 (99.2%) of them were offered the object and 1042 (97.3%) accepted it. In capture 2, 1278 women were approached, among them 1234 (96.6%) self-reported to be FSWs, 7 (0.6%) of them reported having been approached in the same capture with the same object (defined as those who were captured in a different hotspot in the same capture), 1227 (99.4%) were approached for the first time in capture 2. Among those approached in capture 2, objects acceptance rate was higher 1204 (98.1%) compared to capture 1. During capture 3, 1515 women were approached, 1494 (98.6%) self-reported to be FSW and among them 1488 (99.6%) answered questions about having received the object in previous captures and counted as captured .Table 1The results for the numbers of single, double and triple captures are presented in Venn diagram in The PSE and uncertainty intervals with their corresponding 95% confidence intervals (CI) were calculated using the exact hypergeometric method presented in fi plot is convex-up, suggesting individual heterogeneity in capture probabilities (25); the ui plot in the same To check for heterogeneity assumption, an exploratory heterogeneity graph was plotted. In After fitting the Bayesian model, the population size of street and venue-based FSWs in Rwanda was estimated to be within a credible set ranging from 7752 to 22 230 with corresponding median of 13 140 . As the The Rwanda FSW PSE 2018 data provide a national estimate of the FSW population. This nationwide FSW PSE using a 3S-CRC method estimated a credible set ranging from 8328 to 22 806 with a median of 13 716 FSW. This is the first time a 3S-CRC method was used to estimate the population size of FSW at the national level in Rwanda. As of January 2019, HIV prevention program data indicate that, approximately 15 000 FSW were enrolled in a key population program in 18 districts of Rwanda. In this study, a population size of FSW was estimated to range from 8328 to 22 806 FSW. Given the number of FSW enrolled in HIV prevention programme, using the upper bound of the credible set provides the best-case scenario for FSW programming.In 2010, Rwanda's FSW PSE was estimated using multiple methods namely capture−recapture, enumeration and multiplier methods, and generated estimates of 3205, 3348 and 2253 FSW, respectively . In 2012Although these previous four PSE for Rwanda FSW conducted since 2010 used different methods and cannot be directly compared, they can be triangulated with other estimates to better estimate the population size of FSW in Rwanda. In addition, size estimation conducted in Côte d'Ivoire, Western Kenya, Nigeria and Iran used 2S-CRC –17, whilOne of critical assumption for CRC experiments is that the population is closed during capture period. However, FSW are dynamic – both in time/space, and membership, they can move from one province to the other, change hotspots often or leave FS work at anytime. To minimise the effects, the estimation exercise was implemented within a short period where all the three captures were completed within three subsequent weeks. Because current estimates are limited to FSW who congregated at hotspots (i.e. street and venue-based FSW), the FSW who do not congregate at hotspot venues, such as home-based FSW, were not able to be included. A survey, such as an immediate follow-up BBS survey would provide the visibility of non-home-based FSW and improve the estimating results.We anticipate underreporting of FSW because this profession is illegal in Rwanda and subject to a certain level of stigma and discrimination. In addition, the study was designed to generate national level estimates and cannot support reliable subnational estimation. Furthermore, FSWs were not interviewed or asked questions during data collection, this might have caused some error.Size estimation of key populations at risk of HIV (e.g. FSW and their clients) is essential for understanding the magnitude and burden of the HIV epidemic and estimating the gap in the coverage of HIV prevention programme in these populations. Knowing the estimated size of FSW in Rwanda enables HIV prevention programmes targeting FSW to estimate the proportion of FSW living with HIV aware of their HIV status. This will inform the development of appropriate prevention and treatment programmes, measurement of service coverage and informing strategic planning and resource allocation to reach FSWs with HIV services.We estimate that the population size of FSW in Rwanda ranges from 8328 to 22 806. This estimate provides a basis for determining the denominators to assess HIV program performance among FSW in Rwanda relative to UNAIDS 90-90-90 targets. This estimate also provides the size of FSWs to be targeted for HIV prevention programmes because we know how many we need to support. The current estimate underestimates the total number of FSW because it is limited to street and venue-based FSW. In the future, an improved FSW-PSE should be conducted to overcome this limitation and gain more precise estimates for Rwanda.


Early identification of severe dengue patients is important regarding patient management and resource allocation. We investigated the association of 10 biomarkers  with the development of severe/moderate dengue (S/MD).We performed a nested case-control study from a multi-country study. A total of 281 S/MD and 556 uncomplicated dengue cases were included.On days 1–3 from symptom onset, higher levels of any biomarker increased the risk of developing S/MD. When assessing together, SDC-1 and IL-1RA were stable, while IP-10 changed the association from positive to negative; others showed weaker associations. The best combinations associated with S/MD comprised IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1 for children, and SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163 for adults.Our findings assist the development of biomarker panels for clinical use and could improve triage and risk prediction in dengue patients.This study was supported by the EU's Seventh Framework Programme (FP7-281803 IDAMS), the WHO, and the Bill and Melinda Gates Foundation. Dengue is the most common arboviral disease to affect humans globally. In 2019, the World Health Organization (WHO) identified dengue as one of the top 10 threats to global health . TransmiIn many dengue-endemic settings, seasonal epidemics can rapidly overwhelm fragile health systems. Although most symptomatic dengue infections are self-limiting, a small proportion of patients develop complications, most of which manifest at around 4–6 days from symptom onset. Thus, large numbers of patients require regular assessments to identify complications should they arise. The accurate and early identification of such patients, particularly within the first 3 days of illness in the febrile phase, should allow for appropriate care to be provided and potentially increase health system effectiveness. Although the 2009 WHO dengue guidelines set out specific warning signs for use in patient triage, utility of these guidelines at identifying those at risk for complications remains limited .The pathogenesis of dengue involves a complex interplay between viral factors and the host response. It is hypothesized that an excessive immune response acting through inflammatory mediators can lead to the observed manifestations of bleeding, shock, and organ dysfunction. Studies have shown that in secondary infections, adaptive immune activation can result in high circulating levels of plasma cytokines and chemokines . BindingThe role of blood biomarkers in predicting severe outcomes has been investigated in many studies, but mostly at later time-points or at hospital admission and many of these biomarkers either peak too late in the disease course or have too short a half-life to be clinically useful . AcknowlThe aims of this study were: (1) to investigate the association of these ten biomarkers with development of more severe dengue outcomes, (2) to find the best combination of biomarkers associated with more severe dengue outcomes. The results of the second aim could help in developing multiplex panels for use in outpatient settings to rapidly identify patients who require hospitalization.We conducted a nested case-control study using the samples and clinical information from a large multi-country observational study named ‘Clinical evaluation of dengue and identification of risk factors for severe disease’  . The IDAOf the 2694 laboratory-confirmed dengue cases in the IDAMS study, 38 and 266 cases were classified as severe and moderate dengue respectively. For this study, we selected all severe and moderate cases from five study sites in four countries , as residual plasma from these countries’ sample sets was available at the Oxford University Clinical Research Unit (OUCRU) in Ho Chi Minh City, Vietnam. For the control group, we selected patients with uncomplicated dengue with similar geographic and demographic characteristics at a 2:1 ratio. In total 281 cases and 556 controls were included in the analysis .The biomarkers were measured at two time points: at enrollment (illness day 1–3) and after recovery (day 10–31 post-symptom onset), if available. Eight biomarkers (CRP and ferritin excepted) were combined in a premixed magnetic bead panel . CRP was measured using a separate commercial magnetic bead panel . These panels were analyzed using the Luminex200 analyzer with the Luminex calibration (Cat. No. LX200-CAL-K25) and verification kits (Cat. No. LX200-CON-K25). Ferritin was measured using the Human Ferritin ELISA kit . All tests were done according to the manufacturer’s specifications.The primary endpoint was combined severe and moderate dengue (S/MD), defined by the development of severe or moderate grades of any of the following – plasma leakage, haemorrhage, or organ impairment  . We combPlasma levels of all biomarkers were transformed to the base-2 logarithm (log-2) before analysis as a right skewed distribution was apparent. We used a logistic regression model for all endpoints. We investigated the non-linear effects of all biomarkers and age on the endpoints, using restricted cubic splines with three knots at the 10th, 50th, and 90th percentiles.For the first aim, that is to investigate the association of all biomarkers with the primary and secondary endpoints, we performed two different analyses: (1) fitting models for each biomarker separately (‘single models’) and (2) fitting models including all biomarkers together . In the ‘single models’ for a particular biomarker, only that biomarker along with age and their interaction were included, whereas in the ‘global models’ all the biomarkers along with their interactions with age were included. We performed the ‘global model’ in order to investigate the influence of the biomarkers when considering them together and this was also the initial step to develop models for the second aim. Results are reported as odds ratio (OR) and presented graphically.For the second aim to find the best combination of biomarkers associated with the primary endpoint, we built upon the results from the first aim to fit separate models for children and adults (<15 versus ≥15 years of age), as differences were apparent by age. We used variable selection based on the ‘best subset’ approach . BrieflyWe carried out several sensitivity analyses. First, we fitted the single and global models taking into account potential differences between serotypes by including serotype variable along with its interaction with the biomarkers. Second, we included viremia  levels as an additional biomarker and performed the single model, global model and best subset procedure. Higher viremia levels have been associated with worse disease outcomes; however, viral load was not considered in the main analysis as the focus was on host markers with the potential for combining in a biomarker rapid test.GitHub (swh:1:rev:847d8e0f564eeb3f075b443205fb3384598bc2b4). All analyses were done using the statistical software R version 3.6.3  and the The majority of the patients were from Vietnam . Median  age of the case and control groups were 12  and 16  years. Among the S/MD group, 127 cases (45%) were children and 154 cases (55%) were adults. Male gender was predominant (60% and 54% in the case and control groups respectively). Serotype distribution was similar between the S/MD and control groups, with DENV-1 predominating (42%), particularly in children (48%). Host immune status however differed: there was a higher proportion of secondary infections in the S/MD group compared with controls  and this was consistent in both children and adults. The S/MD had a slightly lower percentage of obese patients than the control group (10% versus 14%). As expected, hospitalization was more common in the S/MD group (57% versus 31%) . OverallOn average, the patients who progressed to S/MD had higher levels of the biomarkers in both children and adult patients, both at enrollment and at follow-up . For mosIn the single models, higher levels of each biomarker on illness days 1, 2, or 3 increased the risk of developing S/MD, with the exception of ferritin in adults where there was a downward trend at higher values , Table 2In the global model there were some differences compared to the single models , Table 2The sensitivity analysis showed that the association between the biomarkers and S/MD did not differ between DENV-1 and other serotypes . SimilarFor children, the best subset that showed the clearest association with S/MD was the combination of the six markers IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1 with an AIC of 465.9. This model was selected most often in the bootstrap procedure, but was not highly robust (it was selected in 134 of the 1000 samples) . Over thFor adults, the best subset included the seven markers SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163. This model was selected 79 times among 1000 bootstrap samples, but still was selected more often than the other models . Over thIn the sensitivity analysis, viremia was not selected in any of the best combinations for children, and the marker combinations remained the same as the main analysis. For adults, the best subset included five markers SDC-1, IL-8, ferritin, viremia and sCD163. The best of two and three were the same as the main analysis; viremia was selected in the best of four and five .This nested case-control study has shown that a range of endothelial, immune activation and inflammatory biomarkers measured during the early febrile phase of dengue are associated with progression to worse clinical outcomes in both children and adults. In children we found IL-1RA to have the most robust association with S/MD, whereas in adults we found SDC-1 and IL-8 to have the most robust association. For children, the best combination (ordered by robustness) included six biomarkers IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1; for adults the best combination identified comprised seven biomarkers SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163.Our results add to the current literature on biomarkers in severe/moderate dengue compared with uncomplicated dengue, by including early time-points prior to the development of the severe manifestations, as well as providing data on the use of biomarker combinations, which takes into consideration the complex inflammatory-vascular pathogenesis of severe dengue. We observed that there were marked changes in the associations between individual biomarkers and outcomes when considering them together, while other biomarkers showed consistent associations. Particularly, the association of IP-10 with S/MD changed significantly from the single to global model, which may be because another biomarker in our model is a mediator or confounder of IP-10 in the pathway to the outcome. This could be IL-1RA as its association with S/MD was similar between the single and global model, and the correlation between IP-10 and IL-1RA was strong (Spearman’s rank correlation coefficient was 0.75). Nonetheless, changing the direction of the association from the single to global model does not diminish the possibility of that biomarker being selected in the best combinations.Our study also demonstrates some key differences between pediatric and adult dengue. Clinical phenotypes of dengue in children and adults differ, with children experiencing more shock and adults more organ impairment and bleeding, with distinct clinical management guidelines published by the WHO. Our results imply dengue pathogenesis may differ by age, with distinct combinations of immune-activation and vascular markers demonstrated between children and adults. Specifically, the association of IL-8 and ferritin differed between children and adults, which is likely to be due to the composite endpoint of severe and moderate dengue. As shown in the analysis of severe dengue alone , the effThe use of biomarker panels for the prediction of severe outcomes in dengue has been investigated in previous studies, using several statistical approaches . HoweverMethods of variable selection have been discussed previously but there remains no clear consensus regarding the best approach . We adopStrengths of our study include the large sample size and use of a nested case-control dataset from a prospective multi-country cohort study with consistent data collection and standardized outcome definitions and laboratory methodologies. The biomarker panel we selected was guided by pathogenesis studies, focusing on pathways activated early in the disease course, thus ensuring clinical relevance.There are some limitations in our study. One being we analysed the biomarkers at only one time-point in the early phase; limited financial resources did not allow us to evaluate the full range of biomarkers across the whole IDAMS population and at more time-points. Secondly, this study was not designed to build prediction models so we did not use a measure of predictive value as a criterion, which was motivated by the nested case-control design. Our findings need to be validated in new studies.In conclusion, higher levels of the ten biomarkers , when considered individually, are associated with increased risk of adverse clinical outcomes in both children and adults with dengue. The best biomarker combination for children includes IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1; for adults, SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163 were selected. These findings serve to assist the development of biomarker panels to improve future triage and early assessment of dengue patients. This would aid not only individual patient management and facilitate healthcare allocation which would be of major public health benefit especially in outbreak settings, but could also serve as potential biological endpoints for dengue clinical trials. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Acceptance summary:The paper is relevant as dengue diagnosis and prognosis remains a major problem in several parts of the world. This paper should help the development of biomarker panels for clinical use and could improve triage and risk prediction in dengue patients.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by Balram Bhargava as the Senior and Reviewing Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Combination of inflammatory and vascular markers in the febrile phase of dengue is associated with more severe outcomes" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:Please address the weaknesses of the study which have been highlighted in the reviewers comments as below.Reviewer #2 (Recommendations for the authors):Page 8, line 5. Could the authors provide a rationale on why a 1 case:2 control ratio was chosen for this study?Table 1. The inclusion of results from statistical tests would be useful to accompany the description provided in page 12, lines 1-6.Table 1. Is the biomarker validation affected by the preponderance of DENV-1 in the study population? Although the number of dengue cases caused by the other serotypes is relatively lower, is it possible to show that at least in terms of trends, there is no difference in the expression of the biomarkers by serotype? This could be a very useful supplementary information to address any concern on whether the findings reported here could be applied to dengue cases caused by non-DENV-1 infection.Table 1. Most of the severe dengue cases were in children whereas there were proportionately more moderately severe cases in adults. Could the different sets of biomarkers for paediatric and adult dengue cases be predictive for severe vs moderately severe dengue?Tables 2 and 3 can be shown as supplementary tables since the same datasets are shown as Figures 2 and 3. It may be busy but it would also be useful to indicate in Figure 2 where the statistically significant differences are.Page 17, line 8. I do not understand how the global model was developed and how it was useful for biomarker development. Indeed, the example highlighted in this paragraph was IP-10, where the direction of correlation changed in the global compared to single model. Despite this finding, Tables 4 and 5 went on to consider IP-10 as amongst the possible biomarkers to predict severe/moderately severe dengue. Please provide a clearer explanation for readers without deep expertise in statistics to appreciate the findings reported here.The lack of viraemia and NS1 antigenaemia measurements is a striking omission. I think it would be of great advantage to include these parameters into the analysis. If not possible, the authors should provide a discussion on why these parameters were excluded and whether they should be considered for future studies.Reviewer #3 (Recommendations for the authors):1. The model can be confusing to understand. it appears to this non statistician reviewer that global model did not improve sensitivity of the test? If so, how does that impact the interpretation of the data?2. Certain sections of the discussion are repetitive and can be more succinctly summarised. Reviewer #2 (Recommendations for the authors):Page 8, line 5. Could the authors provide a rationale on why a 1 case:2 control ratio was chosen for this study?As there were a limited number of cases with the primary endpoint (severe/moderate dengue) in the clinical study, our aim was to increase the power of the study by doubling the number of controls (uncomplicated dengue).Table 1. The inclusion of results from statistical tests would be useful to accompany the description provided in page 12, lines 1-6.We did not perform statistical tests for table 1 because of the following:– This was for descriptive analysis only and there was no hypothesis to test– Performing statistical analysis for table 1 would mean that the independent variable is severity (severe/moderate versus uncomplicated dengue) and the outcomes are the clinical characteristics, which reverses the causal pathway.Table 1. Is the biomarker validation affected by the preponderance of DENV-1 in the study population? Although the number of dengue cases caused by the other serotypes is relatively lower, is it possible to show that at least in terms of trends, there is no difference in the expression of the biomarkers by serotype? This could be a very useful supplementary information to address any concern on whether the findings reported here could be applied to dengue cases caused by non-DENV-1 infection.Thank you – please see our response to the public review for reviewer 2, the results of the single and global models are similar between DENV-1 and non-DENV-1 infections.Table 1. Most of the severe dengue cases were in children whereas there were proportionately more moderately severe cases in adults. Could the different sets of biomarkers for paediatric and adult dengue cases be predictive for severe vs moderately severe dengue?Because the number of severe dengue cases is limited (38 cases), it is not possible to analyse severe versus moderate dengue. In our analysis for severe dengue alone (severe versus moderate/uncomplicated dengue) as shown in appendix 6 (figure S5 and table S6) in the supplementary file, we could see a trend in some of the biomarkers, but confidence intervals were very wide.Tables 2 and 3 can be shown as supplementary tables since the same datasets are shown as Figures 2 and 3. It may be busy but it would also be useful to indicate in Figure 2 where the statistically significant differences are.We agree that table 2 is similar to figure 2 and we have moved it to the supplementary file (table S3). However, we would like to keep table 3 in the main file (which is changed to table 2 of the revised manuscript) as it contains important and distinct results which are not represented in figure 3, such as numerical results, p-values for the overall effect and the interaction between biomarkers and age.Similar to our second response to reviewer 2 above, we did not perform statistical tests for figure 2 as this is for descriptive analysis only; we do not want to test for differences in trend over time. Also, performing tests would result in reversal of the causal pathway, with the biomarker as dependent variable and dengue outcome as predictor. Note that the relationships with p-values and confidence intervals can be found in table 2 and figure 3 of the revised manuscript .Page 17, line 8. I do not understand how the global model was developed and how it was useful for biomarker development. Indeed, the example highlighted in this paragraph was IP-10, where the direction of correlation changed in the global compared to single model. Despite this finding, Tables 4 and 5 went on to consider IP-10 as amongst the possible biomarkers to predict severe/moderately severe dengue. Please provide a clearer explanation for readers without deep expertise in statistics to appreciate the findings reported here.When performing variable selection, a backward approach is preferable over a forward approach. Therefore, we started variable selection with a global model that contained all the biomarkers along with the interaction with age. The global model allows investigation of the association between biomarkers and the clinical endpoint when considering them together (aim #1). It was the initial step to develop a set of biomarkers that is most strongly associated with the primary endpoint (aim #2). We have added this point to the Statistical analysis section of the revised manuscript .We fitted the single models because it may provide further information on disease mechanisms when compared with results from the global model . The observation that the direction of the association between IP-10 and the endpoint changed in the global model compared to single model suggests that there might be confounders or intermediate variables of IP-10 in the global model. It could be because of IL-1RA since both markers show a fairly strong correlation (Spearman’s correlation coefficient was 0.75). However, changing the direction of the association from the single to global model does not diminish the possibility of that biomarker being selected in the best combinations. See also the reply to comment #3 of reviewer #1. This explanation was added to the Discussion section of the revised manuscript .The lack of viraemia and NS1 antigenaemia measurements is a striking omission. I think it would be of great advantage to include these parameters into the analysis. If not possible, the authors should provide a discussion on why these parameters were excluded and whether they should be considered for future studies.Thank you – please see our response to the public review for reviewer 2. We have added viremia data, but NS1 antigenaemia data was not available.Reviewer #3 (Recommendations for the authors):1. The model can be confusing to understand. it appears to this non statistician reviewer that global model did not improve sensitivity of the test? If so, how does that impact the interpretation of the data?Thank you, please also see our response to comment #10 of reviewer #2. The global model is the model to start with and contains all the biomarkers combined, taking into account the interaction with age. In this study, building the global model is important because: (1) it allows investigation into the association between biomarkers and clinical endpoint when considering them together (aim #1), and (2) it was the first step to develop a set of biomarkers that is most strongly associated with the primary endpoint (aim #2). We did not analyse the predictive performance of the models. Choosing a cutoff value for the score to compute the sensitivity is better done in a separate study .2. Certain sections of the discussion are repetitive and can be more succinctly summarised.Thank you for this suggestion, we have revised and cut down the discussion to make it more concise.


The second study, conducted on rats (n = 64), shows that rewarding with food also translates into higher calorie intake over a 24-hour period. These results suggest that the common practice of rewarding children with calorie-dense sweet foods is a plausible contributing factor to obesity and might therefore be ill advised.Sweet foods are commonly used as rewards for desirable behavior, specifically among children. This study examines whether such practice may contribute to reinforce the valuation of these foods. Two experiments were conducted, one with children, the other with rats. The first study, conducted with first graders ( Poor nutrition is associated with three of the five highest risks for mortality in the world: high blood pressure; high blood glucose; and obesity . Dietarycognitive dissonance – 5 whereby R is equal to the number of rewards already earned plus 1 (the next reward). It went through the following series: 1, 2, 4, 9, 12, 15, 20, 25, 32, 40, 50, 62, 77, 95, 118, 145, 178, 219 and so on. The final completed response ratio is the breakpoint, i.e. when a rat ceased to engage in lever-pressing and fails to earn any more rewards within 60 min. During PR sessions the operant chambers were therefore programmed to shut down automatically if a rat had failed to earn a reward within 60 min, but all rats remained in the operant chambers for the entire session length of 90 min regardless of the session finishing earlier. On average the sessions were ending after 73.9 ± 0.7 minutes. During all PR sessions we measured the active/inactive lever presses, the response ratio (breakpoint) and the number of sucrose pellets earned. During the last 3 days of PR conditioning, rats pressed the active lever 123.8 ± 11.1 times, with a response ratio of 35.8 ± 2.6 and received 8.0 ± 0.3 sucrose pellets. The number of sucrose pellets earned by the treatment rats during the last stage varied between 4 and 12, the control rats received the average amount of pellets for free. Chow and water were withheld during all conditioning sessions.Once the rats had passed the FR stage, they progressed to the PR stage. This consisted of 90 min PR sessions with mild food restriction once a day over 3 days, followed by 90 min PR sessions without food restriction once a day over 8 days. Performance on the PR schedule was considered stable when the number of rewards (sucrose pellets) earned in a 90 min session deviated by ≤10% for at least 3 consecutive days. In each PR session the number of presses required to receive the next reward was progressively increased in order to determine the amount of work the rat is willing to put into obtaining the reward. The response requirement increased according to the following equation: response ratio =  and comparison , but no significant difference between the groups for the measure of liking , as well as between the first and third assessment . Overall, we find that the 24-hour calorie intake of the treatment group is significantly larger than that of the control group   more sucrose pellets over three hours than the control rats  see .ad libitum sucrose intake over the 3 hours. The higher the sucrose intake, the more effort was excerted during the conditioning process measured by: the number of active lever presses , the response ratio  and the number of sucrose pellets earned .For the rats in the treatment group, we observed a positive relationship between measures from the PR sessions (mean of the last 3 sessions) and the ad libitum access to sucrose . Controlling for the difference in body weight reduces the treatment effect for group differences in sucrose intake by one fourth, with a p-value of 0.08 instead of 0.03. Similarly, differences in 24-hour calorie intake reduced from 5.2 kcal  to 3.46 kcal .While the PR sessions did not lead to an overall difference in the body weight of rats between the two groups measured regularly during the conditioning process , the average weight of the rats in the treatment group was slightly higher than those in the control group measured directly before their evaluative conditioning whereby the value of a conditioned stimulus (here a food used as a reward) is affected by previous pairing with another positive or negative unconditioned stimulus [In two experiments, we show that using a sweet food as a reward increases its subsequent appeal. The results from the experiment with children are consistent with the theory of stimulus , 9. Whilstimulus , our stuAn important question is whether the effect of using food as a reward on valuation translates into an increase in overall calorie intake. Children may be more inclined to consume the food used as a reward but, triggered by homeostatic regulators, their consumption of other foods might be reduced such that the overall calorie intake remains unchanged . In humaOur experimental study with rats allows us to evaluate whether using food as a reward affects the subsequent consumption of this food and the 24-hour calorie intake. By measuring chow intake post treatment and for a period of 24 hours, we evaluate whether increasing the value of the sucrose by the reward paradigm affects the intake of other foods. The results indicate that the increase in valuation of a reward leads to an increase in total calorie intake.Combining the results of these two studies, we conclude that the common practice of rewarding children with sweet foods may be contributing factor to weight gain among children.Matching an animal study with a human study is a challenging task. The experiments were both designed to test the hypothesis that rewarding with a (sweet) food changes the valuation of that food. The protocols differ, however, and we remain cautious in the translational conclusions that are possible to draw. Here we discuss three important differences between the two studies and briefly explain the choices made: 1) the human experiment was performed in children of both sexes, whereas the rat experiment was undertaken in adult males; 2) the schedules of reinforcement were different and 3), the food reinforcer was different.In the rodent study, it was not an option to match the age and gender to that of the study in children. Rats are considered infants from birth until 3 weeks of age at which point they are weaned. The “juvenile period” occurs from 3 weeks to 8 weeks old and our experiment  is not possible to undertake in this short time frame. Gender matching would only be relevant if we had the opportunity to explore the juvenile period in rats after which we would need to take into account the stage of the oestrus cycle.Regarding the schedule of reinforcement, it should be noted that children can be explicitly told that their effort will be rewarded. The results from comparing the two treatment groups with different effort levels suggest that evaluative conditioning is a more plausible explanation for the observed treatment effect in children. Rats, however, need to learn the reinforcing value of a food by undertaking a motivational task . Given that effort was not manipulated in the animal experimental design, we cannot draw direct conclusions about the mechanisms for increased valuation for the rats.Finally, regarding the choice of food reinforcer: for children, we chose a sweet palatable food. Dried apples comprise 56% sugar and have an energy density of 2.8 kcal/g. Rats are usually fed a grain-based diet. Foods rich in fat and/or sugar are considered as palatable or rewarding to rats. There are only a few options of food reinforcers available in tablet form, which are mostly either grain-based or sucrose-based. Therefore, our choice of reinforcer for the rats was sucrose-based.The fact that both studies point to a similar effect, despite differences in protocols, is in our view evidence that the effect is likely to be robust. A question for further research is whether this mechanism of food preference formation extends to other tastes, non- sweet tastes in particular, and to foods that have a lower calorie content or a higher nutrient density. If that is the case, such mechanism may be exploited to design interventions aimed at fostering the formation of “healthier” food preferences, e.g. preferences for foods with a high nutrient calorie ratio.S1 File(DOCX)Click here for additional data file.S1 FigShare of participants reporting that in their family sweat treats are at least rarely used to reward for (1) doing something difficult, (2) doing something good or performing well, or (3) an achievement. Data are presented as mean ± 1 SEM.(TIF)Click here for additional data file.S1 Tablea Values based on a parental survey response with varying sample size (N): Control (35); Low-Effort (48); High-effort (47). b Average frequency of attendance to the 6 treatment sessions. c Average frequency of trying the food reward to the 6 treatment sessions. d Average value at baseline. e P-values based on a two-sample Wilcoxon rank-sum (Mann-Whitney) test for ordinal variables. Fisher’s exact test is used for binary variables.(DOCX)Click here for additional data file.S2 TableNotes: Linear probability model predicting missing values in the data. P-values below 0.1 in bold based on clustered on the class level in parenthesis. Binary dependent variable = 1 if at least 1 of 3 outcomes is missing during at least one assessment.(DOCX)Click here for additional data file.S3 TableNotes: Dependent variables are: choice (change in percentage of children choosing the dried apple), liking , and comparison (change in number of times the dried apple is preferred in 5 pairwise comparisons). Independent variables are the binary Treatment-group indicator and baseline values are the respective food valuations in the first assessment; we also control for school specific fixed-effects. Model based on OLS. P-values below the coefficients based on:  clustered standard errors on the class level following ;  clus(DOCX)Click here for additional data file.S4 TableNotes: Dependent variables are: choice (change in percentage of children choosing the dried apple), liking , and comparison (change in number of times the dried apple is preferred in 5 pairwise comparisons). Independent variables are the binary Treatment-group indicator and baseline values are the respective food valuations in the first assessment; we also control for school specific fixed-effects. Estimates for choice based on a logit model, liking and comparison estimated by an ordered logit. P-values below the coefficients based on clustered standard errors on the class level. P-values below 0.1 in bold. Columns (1), (2) and (3) refer to the second assessment, columns (4), (5) and (6) to the follow-up.(DOCX)Click here for additional data file.S5 TableNotes: Table provides the frequencies of change in the three outcome variables between baseline and the two following assessments. For each outcome variable we provide non-parametric tests whether changes are significantly different from zero (within-subject) and for the difference in the changes of valuation between control and treatment groups (between-subject). P-values below 0.1 in bold.(DOCX)Click here for additional data file.S6 TableNotes: Dependent variables are: choice (change in percentage of children choosing the dried apple), liking , and comparison (change in number of times the dried apple is preferred in 5 pairwise comparisons). Independent variables a set of dummy variables indicating the assessment, with the 1st assessment as reference. We also control for school specific fixed-effects. Estimates for choice based on a logit model, liking and comparison estimated by an ordered logit. P-values below the coefficients based on:  clustered standard errors on the individual level following [ollowing ; {} hete(DOCX)Click here for additional data file.S7 TableNotes: Dependent variables are: Choice (change in percentage of children choosing the dried apple), Liking , and Comparison (change in number of times the dried apple is preferred in 5 pairwise comparisons). Independent variables are: Reward (binary treatment-group indicator), Effort (binary indicator capturing the difference related to higher effort), Baseline value  and School (control for school specific fixed-effects). P-values below the coefficients based on:  clustered standard errors on the class level following ;  clus(DOCX)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.S2 AppendixExperiment 1 (children): Child questionnaire (translated to English).(DOCX)Click here for additional data file.