Generalized anxiety disorder (GAD) is a highly prevalent, chronic disorder associated with impaired quality of life, societal burden, and poor treatment rates. Internet‐delivered interventions may improve the accessibility of treatments and are increasingly being used. This study aimed to update a previous meta‐analysis to determine the effectiveness of available Internet‐delivered interventions in treating symptoms of GAD.g was calculated at posttreatment and follow‐up.Systematic literature searches were conducted (through April 2020) using Embase, PubMed, PsychINFO, and Cochrane to find randomized controlled trials of Internet‐delivered interventions for GAD. Risk of bias was evaluated, and Hedge's g = 0.79) and worry (g = 0.75), favoring treatment. Effect sizes for depression, functional impairment, and quality of life were moderate to large. Maintenance of effects at follow‐up seems likely.Twenty studies met eligibility criteria and were included in the meta‐analysis. Random‐effect models detected large effect sizes for primary outcomes of anxiety (Results support the effectiveness of Internet‐delivered treatments for GAD. Considerable heterogeneity between studies appeared moderated by variability in the interventions themselves, highlighting the importance of further investigation into the characteristics that may optimize treatment outcomes. Overall, Internet‐delivery appears to be a viable mode of treatment for GAD with potential to relieve existing gaps in the provision of treatment. Remaining papers were assessed for eligibility by reading full texts and grounds for exclusion were recorded in line with a predefined hierarchy. Eligibility assessments were carried out independently by two researchers (N. E. and A. E. Dec’18’ searches/R. W. and N. E. April’20’ searches) and discrepancies in study selection that arose were solved through discussion and consultation with a senior researcher (D. R.).Eligibility criteria were established to solely include studies that were (1) an Internet‐delivered intervention for symptoms of GAD; (2) a randomized controlled trial (RCT); (3) compared with a wait‐list, placebo, or attention control; (4) had a sample with a confirmed clinical diagnosis of GAD who may have had comorbidities and/or impairment in functioning ; (5) had a sample of adults over 18 years of age; (6) published in a peer‐reviewed journal in English and; (7) included reliable and valid outcome measures of symptoms of GAD .In addition, for transdiagnostic protocols and studies reporting on outcomes across several anxiety disorders in total only, we contacted authors to request the discrete outcome data pertaining to participants with a confirmed diagnosis of GAD. Where authors were unable to provide the required data, studies were excluded from the meta‐analysis.2.3We extracted the following data from included studies: (a) country where study was conducted; (b) participant/sample characteristics ; (c) scope of intervention (transdiagnostic or disorder‐specific); (d) intervention type ; (e) type of control group; (f) support during intervention; (g) length of treatment phase and number of modules in intervention; (h) engagement metrics ; (i) means and standard deviation to calculate posttreatment and follow‐up effect sizes where relevant.As some trials used numerous instruments to measure outcomes related to our five constructs of interest , a hierarchy of instruments was developed before data analysis as per recommendations by the Cochrane Handbook and to facilitate uniformity where possible  was used to estimate between study variance and heterogeneity was assessed through the Q‐value, Thompson , an I2 Follow‐up between‐group effects were assessed through the same models as posttreatment effects where this was feasible . Where there were multiple follow‐up time points available for one study, the follow‐up time point closest to the ones used by other comparisons within an analysis was selected to ensure as much coherency as possible . Two sets of sensitivity analyses were conducted to assess the robustness of conclusions drawn from the analysis. To assess the influence of studies that may potentially act as confounders within the meta‐analysis  random‐effect models with and without these studies were compared. To assess the influence of potentially debatable methodological decisions  mixed‐effect models controlling for those decisions  were conducted.To explore various potential moderators, mixed‐effect models of primary outcomes (anxiety and worry) utilizing the Knapp–Hartung method to reduce the chance of type 1 error were used. As per Fu et al.  recommen33.1The electronic database searches resulted in 4165 records. Four records were identified through other sources. After duplicate removal 2401 records remained. Of these, 2252 records were excluded after reviewing title and abstract, leaving a total of 149 potentially eligible records. Full‐text versions of these articles were obtained and examined for eligibility. Of those considered eligible, it was necessary to exclude three transdiagnostic studies as the authors did not provide requested data for GAD participants. Finally, nine RCTs fulfilled all eligibility criteria and were included. In addition, 11 articles were carried over from the original meta‐analysis , the researchers reported an average amount of program completion between 76%–100%, six reported between 51% and 75%, and one study reported a percentage completion in the range of 26%–50%. The average amount of time supporters spent per person over the course of their treatment was 77.04 min across the 15 studies providing this information, ranging from 18.15 min  as some articles did not allow for the calculation of exact percentages. Program completion was addressed in 19 out of the 20 studies. One study did not include measures of program completion  CBT (seven of which were disorder‐specific), two on cognitive bias modification, one on psychodynamic therapy (disorder‐specific), one on extinction therapy, one on acceptance‐based behavior therapy (disorder‐specific), and one on affect‐focused psychodynamic psychotherapy. Most interventions consisted of eight modules . Specifically, there were 12 interventions with 8 modules, 4 with 6 modules, 2 with 10 modules, 3 with 7 modules, 1 with 5 modules, 1 with 4 modules, and 1 with a range of 3–6 modules.With the exception of the two interventions based on cognitive bias modification, most interventions included some combination of the following nonspecific intervention components: psychoeducation, case examples or vignettes, mindfulness and/or relaxation exercises, notification and/or reminder emails, homework, summaries, and relapse prevention and maintenance. In some interventions, participants had to have completed homework assignments or modules to progress to the next module. Several interventions included additional, supplementary information and resources on lifestyle factors, for example, sleep, interpersonal relationships, communication, problem‐solving, and stress.Given differences in the models used across interventions, there was variability between iCBT and other approaches in terms of the specific techniques used. In iCBT interventions, specific cognitive techniques included cognitive restructuring, cognitive distancing, thought and worry records or diaries, mood monitoring; and behavioral techniques included activity scheduling, behavioral experiments, graded exposure, worry exposure, scheduled worry time; and homework. Specific techniques used in other interventions included defense restructuring, extinction techniques, functional analysis, valued action, acceptance, affect‐experiencing techniques, affect expression, and cognitive bias modification. Intervention components that specifically addressed worry, like psychoeducational content, for example, types of worry, and worry‐related techniques, for example, cognitive restructuring of worry‐related beliefs, were reported in 15 interventions.3.5Based on the criteria outlined in the CLEAR NPT checklist guidelines, overall, methodological quality assessment ratings seemed good across included studies with 90% (18/20) or more reporting an adequate method of allocation sequence generation, providing clear descriptions of the intervention administered, quantitatively assessing participant adherence, and analyzing outcomes in line with the intention‐to‐treat principle. The percentage of studies that reported an adequate method of allocation concealment and appropriate level of care provider experience or skill was slightly less, at 75% (15/20) and 80% (16/20), respectively. Given the nature of nonpharmacological trials and self‐report outcome assessment, adequate blinding of participants, care providers, and outcome assessors to treatment allocation was often not feasible in the included studies. Related checklist items were, therefore, among the most poorly rated with participants and outcome assessors blinded in only 10% (2/20) of studies and care providers blinded in only 5% (1/20) of studies.However, in studies where participants and care providers were not blinded, the provision of all other treatments and care, and the number of withdrawals and loss to follow‐up were the same in each randomized group, which may have helped to minimize the risk of bias associated with lack of blinding. In studies where outcome assessors were not blinded, no study reported on specific methods used to avoid ascertainment bias . Only 35% (7/20) of studies adhered to the same follow‐up schedule for randomized groups with discrepancies often related to shorter follow‐up schedules for waiting list groups, possibly due to ethical considerations surrounding the withholding of treatment. Quality ratings for included studies are displayed in Figure 3.63.6.1g = −0.79; 95% confidence interval [CI], −1.03, −0.55; p < .0001; see Figure Q(22) = 56.76; p < .0001; I2 = 64.99%), with the wide prediction interval (PI) crossing the zero line of no effect . Model diagnostics suggested two potential outliers  = 37.89; p = .009; I2 = 49.89%) and narrower PI not crossing zero anymore .Based on 23 comparisons (across 19 studies), a large effect on symptoms of anxiety in favor of the Internet‐delivered treatment was detected  = 36.22; p = .004; I2 = 53.38%) and the PI ranged from −1.48 to −0.02. One potential outlier was detected  = 25.51; p = .061; I2 = 40.80%; 95% PI, −1.30, −0.09).For worry, outcomes across 18 comparisons (14 studies) suggested a large effect favoring the treatment  = 34.35; p = .045; I2 = 31.24%) with the PI reaching from −1.16 to −0.24.Drawing on 23 comparisons (across 19 studies) for depressive symptoms, a large effect of treatment was observed  = 9.1326; p = .42), I2 was 0.00%  and the PI was rather narrow .The overall effect of treatment on levels of functioning across 10 comparisons (eight studies) was also large  = 8.44; p = .21; I2 = 15.70%), the I2 95% CI  and the 95% PI was rather wide , suggesting some caution around the reliability of this result nonetheless.In terms of quality of life, outcomes across seven comparisons (six studies) suggested a small‐to‐moderate effect in favor of the treatment  with follow‐up periods ranging from 1‐ to 6‐months, follow‐up anxiety effect estimates were found to be large . Heterogeneity appeared to be very low according to the Q value (Q(5) = 3.40; p = .64), I2 metric  and PI . The same six comparisons (five studies) that included depression effect sizes suggested a moderate effect at follow‐up , with very low heterogeneity (Q(5) = 3.17; p = .67; I2 = 0%; I2 95% CI, 0%, 77.24%; 95% PI, −0.76, −0.05). In relation to worry, five comparisons  evaluated outcomes at follow‐up time points ranging from 1‐ to 6‐months. This revealed a nonsignificant moderate effect , with limited heterogeneity (Q(4) = 4.95; p = .29; I2 = 10.44%; I2 95% CI, 0%, 92.64%; 95% PI, −0.97, 0.28).Six of the studies included follow‐up assessments of treatment and control groups . Only quality of life, excluding E. Andersson et al. ; however, this may have been due to the limited number of studies remaining within this analysis , accounting for 14.06% of heterogeneity among effect sizes then. Examination of effect estimates by subgroup suggested a larger effect among the 17 iCBT studies  than the six studies which implemented other interventions . There was more heterogeneity present among studies in the latter group (iCBT: Q(16) = 34.08; p = .005; I2 = 55.07%; other interventions: Q(5) = 17.75; p = .003; I2 = 70.87%), probably due to its theoretical diverseness.Several potential moderators of effects were explored across the primary outcomes of anxiety and worry using mixed‐effect models. Other study level moderators were deemed not feasible for inclusion due to the limited number of studies within each subgroup . A trend towards significance was observed for intervention type  in relation to anxiety outcomes  and worry outcomes , accounting for 66.21% and 54.39% of heterogeneity among studies, respectively, and suggesting that longer treatment was associated with better outcomes. The remaining moderators  remained nonsignificant across anxiety and worry models. For details see Appendix The length of treatment (in weeks) emerged as a significant moderator of effects for anxiety (3.10t(21) = 0.06; p = .95), it was concluded that publication bias was unlikely to be substantial. For worry, asymmetry within the funnel plot seemed minimal and Egger's test was not significant (t(16) = 0.73; p = .47). In terms of depression outcomes, the funnel plot flagged no asymmetry and this was confirmed by Egger's test (t(21) = −0.24; p = .81). For funnel plots, see Appendix A funnel plot generated across studies assessing anxiety suggested some possible asymmetry; however, given the heterogeneity found within the random‐effect model, as described above, and Egger's test being nonsignificant (4g = 0.79) and worry (g = 0.75) were found to be large and in favor of Internet‐delivered interventions, confirming the value of this mode of treatment among individuals experiencing GAD. In addition, Internet‐delivered interventions also appeared to be effective in treating comorbid symptoms of depression, to improve overall levels of functioning and have at least some effect on quality of life. While controlled follow‐up evaluations of Internet‐delivered interventions continue to be limited, the current study provides some evidence that outcomes are maintained into follow‐up, at least in terms of symptoms of anxiety and depression.The main aim of the current study was to evaluate the effectiveness of Internet‐delivered interventions in treating symptoms of GAD. We updated Richards et al.  meta‐anag = 0.76, n = 39; g = 0.80, n = 64), crucially neither focused on the effects of Internet‐delivered interventions on GAD specifically, with the first also including non‐Internet‐delivered interventions and the latter only including Internet‐delivered interventions based on CBT. This differentiation is key, as the sole focus on the Internet as a treatment modality, irrespective of ever debated psychotherapeutic models, is what may help to fill existing gaps in the provision of treatment to those suffering from GAD  were feasible, which did suggest somewhat larger effects across conventional CBT‐based interventions. Importantly, interventions following other theoretical models were also found to be effective though, and in light of users not necessarily choosing iCBT when given the option and the effects not meeting user preferences can have , type I error rates might have been inflated, making false positive findings more likely.5Internet‐delivered interventions are a viable option for the treatment of GAD and ought to be considered by clinicians and policy makers alike in the interest of addressing existing gaps in the provision of treatment of GAD. Since 2015, the field of Internet‐delivered interventions has matured and the current meta‐analysis was able to utilize data from 20 reasonably high quality studies, with the body of evidence as a whole appearing unlikely to have been unduly biased by methodological weaknesses at this point. Future research should investigate sources of heterogeneity in intervention effects to further develop, improve, and disseminate Internet‐delivered interventions.Nora Eilert, Angel Enrique, Rebecca Wogan, Olwyn Mooney, and Derek Richards are employees of SilverCloud Health, developer of computerized psychological interventions for depression, anxiety, stress, and comorbid long‐term conditions. Timulak serves as a research consultant for SilverCloud Health. One study included in the meta‐analysis was authored by Derek Richards, Angel Enrique, Nora Eilert, and Ladislav Timulak among others and part funded by SilverCloud Health.


The expression of several microglial specific genes, such as CX3CR1, CSF1R, IRF8, OLR1, and TMEM119 was decreased in SCZ , consistent with genome‐wide transcriptome meta‐analysis results. These results indicate a change in microglial phenotype rather than density, which was validated with the use of TMEM119/Iba1 immunostainings on temporal cortex of a separate cohort. Changes in microglial gene expression were overlapping between SCZ and other psychiatric disorders, but largely opposite from changes reported in Alzheimer's disease. This distinct microglial phenotype provides a crucial molecular hallmark for future research into the role of microglia in SCZ and other psychiatric disorders.Microglia, the immune cells of the brain, are important for neurodevelopment and have been hypothesized to play a role in the pathogenesis of schizophrenia (SCZ). Although previous postmortem studies pointed toward presence of microglial activation, this view has been challenged by more recent hypothesis‐driven and hypothesis‐free analyses. The aim of the present study is to further understand the observed microglial changes in SCZ. We first performed a detailed meta‐analysis on studies that analyzed microglial cell density, microglial morphology, and expression of microglial‐specific markers. We then further explored findings from the temporal cortex by performing immunostainings and qPCRs on an additional dataset. A random effect meta‐analysis showed that the density of microglial cells was unaltered in SCZ (ES: 0.144 95% CI: 0.102 to 0.390,  Microglia density is unaltered in postmortem brain tissue of schizophrenia patients, but several mature microglial markers are downregulated in schizophrenia. This expression pattern is largely opposite from microglial changes in Alzheimer's disease. Study design was rated for the following aspects: blinding researchers to diagnostic or clinical information, neuropathological assessment, the degree of matching of control and patient population, and whether correction of general (age/PMI) and other confounding factors, such as medication use, pH, cause of death, illness duration, age of onset, was applied. Methodology was assessed for complete description of technical methods and subsequent analyses. For reporting, studies were evaluated for whether important details regarding psychopathologic examination, population demographics, and main outcome variables could be retrieved .2.1.4https://automeris.io/WebPlotDigitizer/. The following variables were extracted for potential moderator analyses: sample size, methods, brain bank, brain region and area , age, sex, postmortem interval (PMI), and pH  was calculated to estimate the amount of heterogeneity. I2 reflects which proportion of the observed variance reflects differences in true effect size rather than sampling error (range 0–100%) . The potential for publication bias was assessed by visual examination of Funnel plots and by the Egger's test  . Cell density and gene expression studies were analyzed separately. For the microglial cell density studies, each study included only one marker (or a double staining) as outcome measurement, but various studies included multiple brain regions. Microglial cell density and gene expression measurements in different brain regions within the same cohort/study are not independent of each other. To prevent overrepresentation of the results from studies analyzing multiple brain regions in our analysis across all brain regions, we therefore used a conservative approach and nested data by computing combined scores from all measurements across different brain regions within one study. Forest plots of unnested data are depicted in the Figures of Meta‐analyses were carried out using the Comprehensive Meta‐Analysis (CMA) software developed by Biostat  and controls (N = 16). The sections were deparaffinized using a standard xylene and alcohol series, followed by blocking of endogenous peroxidase with PBS, 1% H2O2 . For antigen retrieval, sections were heated in 0.01 mM citrate buffer , 0.05% Tween‐20 , pH = 6.0 for 15 min. Subsequently, nonspecific binding was blocked in PBS with 1% normal horse serum , 0.1% bovine serum albumin , and 0.2% Triton X . Sections were subsequently incubated with a rabbit polyclonal anti‐Iba1 antibody  at 4°C. Next day, secondary goat‐anti‐rabbit biotin  was added, followed by avidin‐biotin‐peroxidase (AB) complex (Vector Laboratories). To visualize the microglia, the sections were incubated with a 3,3′ diaminobenzidine (DAB) substrate (DAKO). Finally, tissue sections were dehydrated using an alcohol and xylene series and embedded in Entellan . Iba1‐DAB stainings were visualized with a ZEISS Imager M2 light microscope. Per tissue section, six pictures of 615 x 450 μm were randomly taken throughout the cortex blinded for diagnosis. A qualitative assessment was done of the microglial morphology in each picture, as previously described by  immunohistochemistry as described before  and controls (N = 19). After deparaffinization, blocking of endogenous peroxidase, antigen retrieval, and blocking of nonspecific binding with normal horse serum, sections were incubated with a polyclonal goat anti‐Iba1 antibody Ab5076 (Abcam) and a polyclonal rabbit anti‐TMEM119 antibody HPA051870 (Atlas Antibodies). This was followed by staining with donkey anti‐rabbit Alexa Fluor 488 and donkey anti‐goat Cy3 antibodies (Jackson Immunoresearch Laboratories), as well as Hoechst (Thermo Scientific). Images (615 × 450 μm) of six randomly selected areas throughout the grey matter of the cortex were collected with a Zeiss Axio Scope A1 fluorescence microscope. Most cells were positive for both Iba1 and TMEM119. While we only rarely observed cells that were TMEM119+ and Iba1−, we found a variable degree of Iba1+/TMEM119− across donors. To analyze case–control differences, we calculated the percentage of TMEM119+/Iba1+ by dividing the number of TMEM119+/Iba1+ cells by the total number of Iba1+ cells, both manually counted.Immunofluorescence double staining with Iba1 and Transmembrane Protein 119 (TMEM119) antibodies was performed by sectioning paraffin‐embedded tissue of the superior temporal gyrus at 7 μm for SCZ patients (2.2.4N = 9) and controls (N = 14). RNA was isolated using Trizol reagent , followed by addition of 100 μl chloroform and centrifugation at 12,000 rcf at 7°C for 15 min. Subsequently, RNA was precipitated from 100–200 μl of the aqueous top phase by mixing with an equal volume of isopropanol, using 1 μl glycogen as a carrier, and stored overnight at −20°C. The next day, samples were centrifuged at maximum speed at 4°C for 60 min. The pellet was washed twice with 75% ETOH, air‐dried, and dissolved in 8 μl Milli‐Q water. Concentrations of extracted RNAs were measured using a nanodrop . RNA was reversed transcribed using a Quantitect Reverse Transcription kit  according to the manufacturer's protocol. RNA supplemented with Milli‐Q water to a volume of 6 μl was mixed with 1 μl gDNA Wipe‐Out buffer and incubated at 42°C for 2 min. After addition of the master mix containing 2 μl Reverse‐Transcriptase buffer, 0.5 μl Reverse‐Transcriptase enzyme, and 0.5 μl RT random hexamers primer mix, samples were incubated at 42°C for 30 min, followed by 95°C for 3 min. Samples were subsequently stored at −20°C until further use. Quantitative real‐time polymerase chain reaction (qPCR) was performed on a QuantStudio™ 6 Flex Real‐Time PCR System . Per reaction, 3.5 ng input of cDNA was mixed with Milli‐Q water, 5 μl SYBRgreen PCR Master Mix , and 1 μl primer mix  and the microglial gene expression analysis. Subsequently, an ANCOVA analysis was performed to study the interaction effects of these covariates on microglial density and microglial gene expression. In the case of non‐normally distributed data, log transformation was used. Results were adjusted for multiple testing by dividing the probability level of p < .05 by the number of performed tests.Statistical analysis was performed with SPSS IBM 25. Assumption of normality was tested by visual checks using histograms and by the Shapiro Wilk test. Mann–Whitney 2.3N = 249 genes that are specifically expressed by microglia compared to other CNS cell‐types . Correction for these confounders was also often applied. As expected, more recent studies scored higher on these quality measures than some of the initial studies. As microglia activation can be caused by a variety of reasons, a full description of demographics can be helpful. However, this was present in only half of the studies.3.3p = .258). Unnested data displayed similar results , and the variability between studies was not significant . Inspection of the funnel plot and the Egger's test showed indication for publication bias (p = .01) . An exploratory subgroup analysis was performed to assess possible sources of variation. No significant differences were observed in microglia density between different microglia markers , automated or manual counting methods, or cortical or subcortical studies (data not shown). When segregating the analysis per brain regions, we found a significant increase in microglial density in the temporal cortex  in SCZ patients, but this was not observed in the other brain regions  . Two stup = .01) ). Sensit3.43.5+ cells/mm2. The correlation between these parameters was 0.57 (p = .008). The number of Iba1+ cells/mm2 and total Iba1‐covered area were not different from controls . Age, PMI and pH were associated with microglial density and Iba1‐covered area. Controlling for these covariates using an ANCOVA did not change this result . The current study was included in the meta‐analysis described before , but no change in subcortical tissues  and frontal cortex  was observed, but no change in regions related to the limbic system , but not for HLA‐DR . The Q‐value indicated no significant variability between studies . The funnel plot and Egger's test indicated the presence of publication bias . None of the potential moderators  were associated with the ES or significantly changed heterogeneity (data not shown).A random‐effect meta‐analysis revealed a significant decrease in gene expression of microglial markers for unnested outcomes . No significant differences were observed for AIF1, CD68, GPR34, ITGB2, TGFB2, TREM2, TYROBP between SCZ patients and controls. We included our novel data of the NBB/EBB cohort in our meta‐analysis of published studies   Figure , which i3.9N = 243)  . Although we found several studies reporting on morphological changes, the results were inconsistent, which is probably related to the large variety of read‐outs that were used on studies with small sample sizes and the challenges in quantifying morphological assessments.Previous reviews by Trepanier and Van Kesteren reported evidence for an increased microglial density in SCZ , suggesting that CX3CR1 in human microglia may contribute to microglial homeostasis by regulating inflammatory response and phagocytosis . Abnormalities in synaptic maturation, synapse elimination and spine density are thought to underlie SCZ, but also contribute to other psychiatric disorders, including ASD and BD (Gandal, Zhang, et al., P2RY12 and related genes have been shown to play an important role in dopamine signaling (Krügel, Kittner, Franke, & Illes, Whether the changes that we observed are involved in causing the disease is not known yet. In Figure 4.5Evidence for inflammation in postmortem brain tissue of SCZ patients has been reviewed before (Trépanier et al., 4.6Altogether we have found evidence for the presence of a distinct microglia phenotype in SCZ with potential overlap with other psychiatric disorders. The microglial gene expression changes identified in bulk brain tissue of SCZ patients need to be confirmed through sequencing of pure microglia populations by either isolating them from non‐frozen brain tissue (Melief et al., All authors reported no financial, biomedical, or potential conflict of interest to declare.AppendixS1. Supporting Information.Click here for additional data file.AppendixS2. Figures.Click here for additional data file.AppendixS3. Tables.Click here for additional data file.


Coronavirus disease 19 (COVID‐19) has caused over 200 000 deaths worldwide. Thailand announced the first confirmed case outside mainland China in January 2020. The disease also spread widely across Association of Southeast Asian Nations (ASEAN). Gastrointestinal manifestations could be presenting symptoms of COVID‐19. This study aimed to determine the prevalence of gastrointestinal manifestations of COVID‐19 patients in Thailand and review important aspects of this disease in ASEAN.Thai patients diagnosed with COVID‐19 at Thammasat University Hospital, Thailand, were evaluated between 1 January 2020 and 30 April 2020. Patients' data, clinical presentation, exposure risk, past medical history, laboratory results, and treatment outcomes were extensively reviewed.P = 0.009). Gastrointestinal symptoms were present in 12 patients (30%). The most common presenting symptoms were anorexia (17.5%) and diarrhea (15%). Gastrointestinal symptoms developed before (9.1%), concurrent with (63.6%), and after (27.3%) respiratory symptoms. Diarrhea was significantly associated with severe COVID‐19 . Twenty‐four patients (60%) received antiviral drugs; 40% had only supportive care. Only one patient required intensive care. No patient died.A total of 352 COVID‐19 tests were performed, and 40 patients with positive tests were studied. The mean age was 30.5 years, and 55% were female. Most (82.5%) had no underlying diseases. Comorbidities were associated with severe COVID‐19 (odds ratio [OR] 29.93; 95% confidence interval [CI] 2.31–388.40, Gastrointestinal manifestations in COVID‐19 patients are common symptoms and can occur anytime during the disease course. Patients presenting with only gastrointestinal symptoms should raise clinical suspicion for COVID‐19 in areas with high disease incidence. Clinically severe COVID‐19 was associated with comorbidities and diarrhea. Gastrointestinal manifestations in COVID‐19 patients are common symptoms and can occur anytime during the disease course. Patients presenting with only gastrointestinal symptoms should raise clinical suspicion for COVID‐19 in areas with high disease incidence. Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is an enveloped, single‐stranded, positive‐sense RNA virus with high transmissibility.The Association of Southeast Asian Nations (ASEAN) is composed of 11 countries with a total population of more than 655 million people, ranking third among all subregions in the world. The coronavirus outbreak was recognized in Wuhan, China, in December 2019. Thailand announced the first confirmed case outside mainland China during the Chinese New Year on 13 January 2020, and the disease continued to spread widely across ASEAN.COVID‐19 has continued to be responsible for high health‐care resource utilization and economic burden throughout ASEAN. This study aimed to determine the prevalence of gastrointestinal manifestations of COVID‐19 patients in Thailand and review important aspects of this disease in ASEAN.This retrospective study was conducted at Thammasat University Hospital between 1 January 2020 and 30 April 2020. The inclusion criteria were patients aged more than 15 years old who were diagnosed with COVID‐19. All patients in this study were of Thai ethnicity. Demographic data, clinical presentation, exposure risk, past medical history, laboratory results including a complete blood count and a comprehensive metabolic panel, and treatment outcomes were extracted from a medical database and extensively reviewed. The clinical data and blood investigation were recorded within 24 h of hospital presentation. Data were analyzed and interpreted by the authors.The diagnosis of COVID‐19 was defined as confirmed detection of SARS‐CoV‐2 in either upper or lower respiratory tract samples using real‐time reverse‐transcription polymerase chain reaction (RT‐PCR). Upper respiratory tract samples could be obtained through nasopharyngeal wash/aspirate, nasal wash/aspirate, or nasal swab. The lower respiratory tract specimens were obtained using tracheal aspirate or bronchoalveolar lavage.Comorbidity was defined as the presence of one or more underlying medical conditions  in addition to a current diagnosis of COVID‐19.Occupational risk for COVID‐19 was defined as including health‐care workers, retail store workers, tourism staff, bus or taxi drivers, multinational business staff, transport and security workers, and construction workers.Incubation period was defined as the time between exposure to SARS‐CoV‐2 and symptom onset.COVID‐19 with mild symptoms was defined as having a normal chest radiograph according to the Thai practice guideline for diagnosis, treatment, and prevention of nosocomial transmission of COVID‐19 (revision date 1 May 2020).COVID‐19 with severe symptoms was defined as having signs and symptoms of pneumonia or an abnormal chest radiograph.Gastrointestinal manifestations of COVID‐19 included anorexia, nausea and vomiting, watery diarrhea, bloody diarrhea, or abdominal pain.Diarrhea was defined as an abrupt onset of the passage of ≥3 loose stools per day.P value of <0.05 was indicated statistical significance. Ethical approval for this study was obtained from the Human Research Ethics Committee of Thammasat University, Thailand. The research was conducted according to the good clinical practice guideline, as well as the Declaration of Helsinki.All data were analyzed using SPSS version 22 . The demographic data were analyzed by Fisher's exact test or Chi‐square test, where appropriate. Laboratory results were compared between patients with mild symptoms and those with severe symptoms using Student's t‐test. The univariate and multivariate analyses were used for determining the association between factors and severity of symptoms. A P = 0.009). The diagnosis of COVID‐19 was confirmed by positive RT‐PCR assays of respiratory specimens in all patients. In Thammasat University Hospital, a total of 352 tests were performed, and 40 patients tested positive. All 40 patients were admitted to Thammasat University Hospital, a major referral center located north of Bangkok. Most patients in this study had close contact with a confirmed case (40%), followed by unknown exposure (30%). The mean incubation period of patients with mild symptoms was not different from those with severe disease . The mean time from symptom onset to hospital admission was also not different between groups of mild and severe symptoms .Forty Thai patients were studied, including 18 men and 22 women, with a mean age of 30.5 ± 9.2 (range 20–55) years. The majority (82.5%) had no underlying medical conditions. The presence of comorbidities was associated with more severe symptoms of COVID‐19 . Clinical symptoms of COVID‐19 patients in ASEAN are given in Table There were 12 patients 30%) with gastrointestinal symptoms. The most common presenting gastrointestinal symptoms were anorexia (17.5%), followed by diarrhea (15%). Nausea and vomiting and abdominal pain were observed equally at 5%. None had bloody diarrhea. Gastrointestinal symptoms could develop before (9.1%), concurrent with (63.6%), or after (27.3%) respiratory symptoms. Patients most frequently had only one gastrointestinal symptom (75%). Of nine patients with a single gastrointestinal symptom, five patients (55.6%) had anorexia, and four (44.4%) had diarrhea. There was one patient with all five gastrointestinal manifestations. That patient was a 51‐year‐old woman without comorbidity. She had close contact with her husband who died of complications from COVID‐19 pneumonia. Two weeks before admission, she had intermittent low‐grade fever with myalgia and reported loss of appetite. She developed shortness of breath, productive cough, watery diarrhea, and epigastric pain 2 days before admission. She was subsequently diagnosed with pneumonia. Her chest radiograph demonstrated bilateral reticular infiltration with predominant right lower lobe consolidation. She was started on darunavir, ritonavir, favipiravir, hydroxychloroquine, and azithromycin on the first day of admission. During hospitalization, she had nausea and vomiting. All symptoms subsided after 10 days of admission. This patient recovered and was discharged from the hospital 23 days after admission. Another patient had only gastrointestinal symptoms without respiratory abnormality. This patient was a 31‐year‐old man with COVID‐19 gastroenteritis and was admitted with generalized abdominal discomfort, bloating, and watery diarrhea. He denied having fever or respiratory symptoms. He received antiviral treatment for 10 days, and his symptoms improved. This patient was discharged after 11 days of admission. In multivariate analysis, diarrhea was significantly associated with severe symptoms of COVID‐19 , sore throat (47.5%), and nasal congestion (37.5%). Fever defined as body temperature ≥37.5°C was present in 20% of patients. Headache was present only in patients with mild symptoms, whereas fatigue was found only in the group with severe symptoms. In this study, patients were mostly diagnosed with nasopharyngitis (37.5%), followed by pneumonia (25%), pharyngitis (22.5%), and bronchitis (12.5%).9/L vs 6.0 ± 1.7 × 109/L, P = 0.013) and neutrophil counts  than the mild symptom group. Lymphopenia, defined as lymphocyte count of less than 1.5 × 109/L, was present in three patients with mild symptoms and one patient with severe symptoms. There was no difference in renal or liver function test between patients with mild and severe symptoms.Patients (10) with pneumonia had abnormal findings on chest radiographs. Six had focal ground‐glass opacity, and the others had diffuse ground‐glass opacities. Patients with severe symptoms were inclined to have more white blood cells (8.2 ± 2.6 × 10Twenty‐four patients (60%) received antiviral drugs, including either darunavir/ritonavir or lopinavir/ritonavir, favipiravir, hydroxychloroquine, and azithromycin; 40% had only supportive care. Thirty‐nine patients demonstrated improvement of their symptoms without any complications and were discharged home after a mean of 16.5 ± 7.8 days. One patient had a complication from COVID‐19. A 37‐year‐old woman with hypertension had acute dyspnea, nasal congestion, and productive cough for 1 day before admission. She denied having any fever, myalgia, or gastrointestinal symptom. She was diagnosed with pneumonia and required ventilator support for respiratory failure. Medications including darunavir/ritonavir, favipiravir, hydroxychloroquine, and azithromycin were immediately started. On the same day, she developed myocarditis and cardiogenic shock. The echocardiogram showed global hypokinesia with left ventricular ejection fraction of 20%. One gram of methylprednisolone daily for 3 consecutive days was started, along with inotropic drugs and diuretics. Her symptoms gradually improved. She was extubated 6 days after admission and was later discharged home. No mortality was observed in this study.The COVID‐19 pandemic has had an enormous impact on global community since January 2020. The ASEAN was one of the first sites where the virus began spreading, and the number of the infected patients in this region has increased rapidly.The gastrointestinal tract is affected by SARS‐CoV‐2 infection.In conclusion, gastrointestinal manifestations in COVID‐19 patients are common presenting symptoms and can occur anytime during the disease course, particularly in ASEAN. Patients presenting with only gastrointestinal symptoms should raise clinical suspicion for COVID‐19 in areas with high disease incidence. Comorbidities and diarrhea might be good predictors for the development of severe COVID‐19.


A key aim for current genome‐wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole‐genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture a wider set of variants. However, imputation quality depends crucially on reference panel size and genetic distance from the target population. Here, we consider sequencing a subset of GWAS participants and imputing the rest using a reference panel that includes both sequenced GWAS participants and an external reference panel. We investigate how imputation quality and GWAS power are affected by the number of participants sequenced for admixed populations (African and Latino Americans) and European population isolates (Sardinians and Finns), and identify powerful, cost‐effective GWAS designs given current sequencing and array costs. For populations that are well‐represented in existing reference panels, we find that array genotyping alone is cost‐effective and well‐powered to detect common‐ and rare‐variant associations. For poorly represented populations, sequencing a subset of participants is often most cost‐effective, and can substantially increase imputation quality and GWAS power. For our analysis of isolated populations, we used WGS data on 2,995 Finns  and 3,445 Sardinians (participants of the SardiNIA study) in the HRC (EGAS00001001710).2.2We considered three imputation strategies: (a) using sequenced study participants as a study‐specific reference panel, (b) using an external reference panel alone , and (c) using an augmented panel that comprises sequenced study participants and an external panel.For African Americans, who are underrepresented in the current version 1.1 of the HRC, we constructed population‐specific and HRC‐augmented reference panels with 0–2,000 African Americans. For Latino Americans, we used the same approach but restricted the study‐specific panel size to <1,500 due to the more limited available sample of sequenced Latino American individuals. For Finns and Sardinians, which are present in the HRC, we constructed augmented reference panels that comprised the 29,470 non‐Finnish or 29,020 non‐Sardinian individuals in the HRC together with 0–2,000 Finns or Sardinians from the HRC.r2, the squared Pearson correlation between true genotype and imputed dosage, and (b) imputation coverage, the proportion of variants with imputation r2 > 0.3 and minor allele count (MAC) ≥5 . While common variant associations are likely to be captured by LD proxy single nucleotide polymorphisms (SNPs) even when the causal variant is not directly genotyped or imputed, rare variant associations are much less likely to be captured by proxy SNPs 1/2Zmimp, where cnm=n/(n+r2m). The asymptotic distribution of Znm−ηn+r2m is normal with mean 0 and variance 1, where r2 is the squared correlation between imputed dosages and true genotypes, and η is an effect size parameter which is equal to 0 under the null hypothesis of no association. The form of η depends on the association model , RR or odds ratio, MAF, and population prevalence and, for binary traits, the case–control ratio. Under an arbitrary association model for binary traits, we can writepcase and pcontrol are the alternate allele frequencies in the disease‐positive and disease‐negative populations, vcase and vcontrol are the variances of genotypes in the disease‐positive and disease‐negative populations, and s is the GWAS case–control ratio.We assume that the r2 and the possibility that a variant is not imputable, we average empirical imputation r2 values and MACs across variants from experiments with real data described in the previous section. Specifically, we estimate power to detect association when n individuals are sequenced and m are genotyped and imputed asϕ(u)=e−u22/2π is the standard normal density function, z1−α/2 is the α‐level significance threshold, rnj2 is the imputation r2 value for the jth variant, Cnj=I is an indicator equal to 1 if the jth variant was imputable and 0 otherwise, and MACnjpanel is the reference panel MAC for the jth variant when the n sequenced individuals from the target population were included in the reference panel.To estimate power while accounting for variability in imputation wjMAF=PNGWAS(pˆj)/PˆN(pˆj), where N is the total number of samples used in our analysis for the given population , pˆj is the sample MAF for the jth variant in the total sample, PˆN(x) is the proportion of variants with MAF = x, and PNGWAS(x) is the probability of observing sample MAF = x in a sample of size N given the specified association model. For example, in a GWAS with sample size N and case–control ratio s, the sample MAC  is approximately Poisson distributed with mean 2N(spcase+pcontrol)/(s+1), where pcase=pγ/[1+p(γ−1)] and pcontrol=(p−Kpcase)/(1−K) for a variant with population MAF p and RR γ for a disease with prevalence K. This weighting approach adjusts for differences between the empirical distribution of MACs across variants in real data, and the theoretical MAC distribution for a variant with the specified MAF, effect size, prevalence in a GWAS with sample size N and case–control ratio s.We define the first weight term wjPS accounts for the probability that a variant with the specified population MAF p is population‐specific (monomorphic outside the target population), and is definedPˆPS(x) is the fraction of variants that are population‐specific among variants with MAF = x in the target population. This adjustment factor ensures that the weight assigned to population‐specific variants in power calculations reflects the probability that a variant with the specified population MAF p is population‐specific.The second weighting term 3First, we compare strategies to improve imputation using study‐specific WGS data for African Americans, Latino Americans, Sardinians, and Finns. Next, we assess the effects of genotyping array on imputation quality and coverage for each population and reference panel. We then use these results to estimate statistical power to detect association as a function of study‐specific panel size, number of participants imputed, external reference panel, and genotyping array. Finally, we identify cost‐effective study designs by comparing statistical power and total experimental (sequencing and genotyping) costs for sequencing‐only, imputation‐only, and sequencing‐and‐imputation GWAS designs for each population and genotyping array.3.1r2 and coverage (proportion of variants with imputation r2 > 0.3 and reference MAC ≥ 5) for three imputation strategies: (a) using an external reference panel (the HRC or HRC subset) alone, (b) using an augmented reference panel that combines the study‐specific and external panels, and (c) using a study‐specific reference panel alone.We compared imputation ≥ 0.25% variants  for Finns, 84% coverage for Sardinians, 86% coverage for Latino Americans, and 77% coverage for African Americans  provided 96% imputation coverage for MAF s Figure , top rows Figure , and thes Figure .Augmenting an external reference panel with even a relatively small number of sequenced individuals substantially increased coverage, particularly for African Americans and Sardinians, and for variants with lower MAF. For example, augmenting the external panel with 500 sequenced individuals from the study population improved overall imputation coverage for MAF = 0.25–0.5% variants by 4% for Finns, 9% for Latino Americans, 16% for African Americans, and 23% for Sardinians genotyped using the OmniExpress relative to the external panel alone ; however, augmented panels provided higher imputation coverage overall for low MAF variants . For example, augmented panels with 2,000 individuals from the target population  provided 86%, 80%, 79%, and 86% coverage for 0.1–0.25% MAF variants for Finns, Latino Americans, African Americans, and Sardinians respectively, whereas population‐specific panels alone provided 72%, 51%, 78%, and 72% coverage using the Omni Express array. However, imputation coverage for variants with MAF > 0.25% differed by <1% between augmented and population‐specific panels with 2,000 individuals from the target population  for all populations and genotyping arrays. When a smaller number (less than 500) of individuals from the target population are sequenced, augmented reference panels provided substantially higher imputation coverage and r2 than population‐specific panels alone. For example, augmented panels with 500 individuals from the target population provided 90%, 85%, 65%, and 85% coverage for 0.25–0.5% MAF variants for Finns, Latino Americans, African Americans, and Sardinians, respectively, whereas population‐specific panels of 500 individuals provided <30% coverage using the Omni Express array.With 2,000 individuals from the target population , population‐specific panels provided roughly equivalent imputation >70% imputation coverage for MAF = 0.1–0.25% variants required a study‐specific panel of >1,800 individuals for African Americans, 1,000 for Latino Americans, 700 for Sardinians, and 0 for Finns using the OmniExpress. These increases in imputation coverage primarily reflect increasing numbers of population‐specific variants captured in the reference panel, which are absent from or present in low copy number in the external panel.Even very rare variants (MAF = 0.1–0.25%) attained high coverage across all populations given a sufficient number of population‐matched individuals in the reference panel. For example, attaining 3.2r2≥0.3. Imputation r2 varied more across genotyping arrays than did imputation coverage  minimizing total experimental (sequencing and genotyping) cost while attaining power at or above a given threshold, or equivalently (b) maximizing power while maintaining cost no greater than a specified constraint.The cost‐effectiveness of sequencing a subset of study participants varied greatly across populations. For Finns, imputation‐only designs were most powerful to detect association and adding sequenced individuals increased power only minimally, even for low‐frequency and rare variants. For Sardinians, Latino Americans, and African Americans, sequencing a subset of study participants was optimal, and often achieved substantially greater power than imputation‐only or sequencing‐only studies. For example, a GWAS of African Americans with equal numbers of cases and controls in which 400 participants are sequenced and 11,100 are imputed using the Illumina Infinium Core array has 90% power to detect a risk variant with MAF = 0.5% and RR = 4 for a disease with prevalence 1%, whereas an imputation‐only GWAS with the same total cost  has only 68% power Figure . This is3.4Imputation coverage and power to detect association can be increased by using denser genotyping arrays, which provide a more informative framework for imputation, or by sequencing population‐matched individuals and augmenting the reference panel. We assessed the cost‐effectiveness of these two strategies by comparing power to detect association across genotyping arrays for study designs that have the same total cost assuming $1,000 for WGS and current list prices for genotyping arrays , and population prevalence Sham and Purcell . These p≥80% power to detect association at minimum total cost increases with decreasing MAF  provides nearly equivalent imputation quality . Thus, study‐specific WGS data can be used to improve imputation even when directly augmenting an external panel is not feasible.We assumed that sequenced participants are randomly selected from the overall set of GWAS participants. When array‐based genotype data are collected before sequencing, samples could be strategically selected to maximize imputation quality or minimize redundancy in the reference panel. Indeed, strategic selection of individuals to be sequenced for an imputation reference panel has been explored in several previous studies. For example, Zhang, Zhan, Rosenberg, and Zollner  proposedFGFR3 , which is open source and freely available at 5Here, we assessed the genomic coverage, statistical power, and cost‐effectiveness of sequencing and imputation‐based designs for GWAS in four populations across a range of genetic models. We developed a novel method to account for available reference haplotype data in power calculations using empirical data, which can be applied to inform GWAS planning and design. For European populations that are well‐represented in current reference panels, our results suggest that imputation‐based GWAS is cost‐effective and well‐powered to detect both common‐ and rare‐variant associations. For populations with limited representation in current reference panels, we found that sequencing a subset of study participants can substantially increase genomic coverage and power to detect association, particularly for rare and population‐specific variants. Our results also suggest that larger and more diverse reference panels will be important to facilitate array‐based GWAS in global populations.Whole genome sequencing (WGS) for the Trans‐Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). WGS for NHLBI TOPMed: Genes‐Environments and Admixture and Latino Americans (GALA II) Study (phs000920.v1.p1) was performed at the New York Genome Center (3R01HL117004‐01S3). WGS for NHLBI TOPMed: The Jackson Heart Study (phs000964.v1.p1) was performed at the University of Washington Northwest Genomics Center (HHSN268201100037C). WGS for NHLBI TOPMed: The Genetic Epidemiology of Asthma in Costa Rica (phs000988.v1.p1) was performed at the University of Washington Northwest Genomics Center (3R37HL066289‐13S1). Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL‐117626‐02S1). Phenotype harmonization, data management, sample‐identity QC, and general study coordination, were provided by the TOPMed Data Coordinating Center (3R01HL‐120393‐02S1). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed.The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201300049C and HHSN268201300050C), Tougaloo College (HHSN268201300048C), and the University of Mississippi Medical Center (HHSN268201300046C and HHSN268201300047C) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute for Minority Health and Health Disparities (NIMHD). The authors also wish to thank the staffs and participants of the JHS.The Genetics of Asthma in Costa Rica project is supported by R‐37 HL066289 (PI‐Scott T. Weiss) and P01 HL132825‐01 (PI‐Scott T. Weiss) both from the National Heart Lung and Blood Institute. The investigators wish to acknowledge the families who contributed data to this study and to Juan C. Celedon, MD, DPH, Lydia Avila MD, and Manuel Soto‐Queros, MD who led the data collection team in Costa Rica.The Genes‐Environments and Admixture in Latino Americans (GALA II) Study and E. G. B. are supported by the Sandler Family Foundation, the American Asthma Foundation, the RWJF Amos Medical Faculty Development Program, the Harry Wm. and Diana V. Hind Distinguished Professor in Pharmaceutical Sciences II, the National Heart, Lung, and Blood Institute (NHLBI) R01HL117004, R01HL128439, R01HL135156, X01HL134589, the National Institute of Environmental Health Sciences R01ES015794, R21ES24844, the National Institute on Minority Health and Health Disparities (NIMHD) P60MD006902, R01MD010443, RL5GM118984 and the Tobacco‐Related Disease Research Program under Award Number 24RT‐0025. The authors wish to acknowledge the following GALA II coinvestigators for subject recruitment, sample processing and quality control: Celeste Eng, Scott Huntsman, MSc, Donglei Hu, PhD, Angel CY Mak, MPhil, PhD, Shannon Thyne, MD, Harold J. Farber, MD, MSPH, Pedro C. Avila, MD, Denise Serebrisky, MD, William Rodriguez‐Cintron, MD, Jose R. Rodriguez‐Santana, MD, Rajesh Kumar, MD, Luisa N. Borrell, DDS, PhD, Emerita Brigino‐Buenaventura, MD, Adam Davis, MA, MPH, Michael A. LeNoir, MD, Kelley Meade, MD, Saunak Sen, PhD and Fred Lurmann, MS. The authors also wish to thank the staffs and participants contributed to the GALA II study.http://www.terveys2000.fi). Both studies are grateful for the THL DNA laboratory for its skillful work to produce the DNA samples used in this study. We thank the Sanger Institute sequencing facilities for whole genome sequencing of the Kuusamo subset. A. P. and S. R. are supported by the Academy of Finland , Juselius Foundation, Finnish Foundation for Cardiovascular Research, NordForsk eScience NIASC (grant no. 62721) and Biocentrum Helsinki (to SR).The SISu and Kuusamo data set comprise of selected study samples from the FINRISK and Health2000 (H2000) studies. FINRISK study has been primarily funded by budgetary funds of THL  with important additional funding from the Academy of Finland (grant number 139635 for VS) and from the Finnish Foundation for Cardiovascular Research. The H2000 study was funded by the National Institute for Health and Welfare (THL), the Finnish Centre for Pensions (ETK), the Social Insurance Institution of Finland (KELA), the Local Government Pensions Institution (KEVA) and other organizations listed on the website of the survey (Funding for this study was provided by: The Academy of Finland (139635); Action on Hearing Loss ; The Ahokas Foundation; The Andrea and Charles Bronfman Philanthropies; The British Heart Foundation (SP/04/002); The Canadian Institutes of Health Research; The Estonian Government ; The European Commission (ENGAGE: HEALTH‐F4‐2007‐201413); The European Community's Seventh Framework Programme ; The European Regional Development Fund (3.2.0304.11‐0312); The European Research Council (ADG20110310#293574); The European Union (EXGENESIS); The Finnish Diabetes Research Foundation; The Finnish Foundation for Cardiovascular Research; The Finnish Medical Society; The Folkhälsan Research Foundation (Finland); The Fonds de la Recherche en Santé du Québec (Canada); The Foundation for Life and Health in Finland; The German Federal Ministry of Education and Research (BMBF) The German Federal Ministry of Health (BMG); The German Center for Diabetes Research (DZD); Helmholtz Zentrum München , which is supported by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria; The Helsinki University Central Hospital Research Foundation; The KG Jebsen Foundation (Norway); The Knut och Alice Wallenberg Foundation (Sweden); The Medical Research Council ; The Ministry of Science and Research of the State of North Rhine‐Westphalia (MIWF NRW); The Munich Center of Health Sciences ; The Närpes Health Care Foundation (Finland); The National Institute for Health Research ; The National Institutes of Health ; The Nordic Center of Excellence in Disease Genetics; Novo Nordisk; The Ollqvist Foundation (Finland); The Paavo Nurmi Foundation (Finland); The Påhlssons Foundation (Sweden); The Perklén Foundation (Finland); The Research Council of Norway; The Signe and Ane Gyllenberg Foundation (Finland); The Sigrid Juselius Foundation (Finland); The Skåne Regional Health Authority (Sweden); The Swedish Cultural Foundation in Finland; The Swedish Diabetes Foundation ; The Swedish Heart‐Lung Foundation (20140422); The Swedish Research Council; The University of Bergen; The University of Tartu (SP1GVARENG); Uppsala University; The Wellcome Trust ; The Western Norway Regional Health Authority (Helse Vest).Supporting informationClick here for additional data file.


TBX6 dosage contributes to a substantial proportion of CS, the molecular etiology for the majority of CS remains largely unknown. TBX6‐mediated genes involved in the process of somitogenesis represent promising candidates.Congenital scoliosis (CS) is a spinal deformity due to vertebral malformations. Although insufficiency of TBX6‐mediated candidate genes, namely MEOX1, MEOX2, MESP2, MYOD1, MYF5, RIPPLY1, and RIPPLY2.Individuals affected with CS and without a positive genetic finding were referred to this study. Proband‐only exome sequencing (ES) were performed on the recruited individuals, followed by analysis of RIPPLY1 and MYF5 were identified from two individuals, respectively. In addition, we identified five deleterious missense variants  in TBX6‐mediated genes. We observed a significant mutational burden of MYOD1 in CS (p = 0.032) compared with the in‐house controls (n = 1854). Moreover, a potential oligogenic disease‐causing mode was proposed based on the observed mutational co‐existence of MYOD1/MEOX1 and MYOD1/RIPPLY1.A total of 584 patients with CS of unknown molecular etiology were recruited. After ES analysis, protein‐truncating variants in TBX6‐mediated genes, prioritized core candidate genes/variants, and provided insight into a potential oligogenic disease‐causing mode in CS.Our study characterized the mutational spectrum of  Our study characterized the mutational spectrum of TBX6‐mediated genes, prioritized core candidate genes/variants, and provided insight into a potential oligogenic disease‐causing mode in CS. Recessive MYF5 variants are recently reported to be associated with External Ophthalmoplegia, Rib, and Vertebral Anomalies (MIM#618155), whose spine phenotype highly resembles that of Spondylocostal Dysostosis  on 584 individuals with congenital scoliosis without a prior molecular diagnosis, and then, studied both heterozygous variants and combinations of variant alleles.To explore the mutational landscape of 22.1Approval for the study was obtained from the ethics committee at Peking Union Medical College Hospital (JS‐098). Written informed consent was provided by each participant.2.2http://www.discostudy.org/).We consecutively recruited individuals who are affected with CS in Peking Union Medical College Hospital from 2009 to 2016, as part of the Deciphering Disorders Involving Scoliosis and COmorbidities (DISCO) study  without a molecular diagnosis by any other known CS‐causing gene. In brief, genomic DNA was extracted from the peripheral blood. Illumina paired‐end libraries were prepared from DNA samples and subjected to exome capture using one of four capture kits , depending on the time of enrollment in our cohort, followed by sequencing on an Illumina HiSeq 2000/4000 platform.Proband‐only exome sequencing was performed on patients (1) without a molecular diagnosis of TACS, that is, not caused by defects of 2.4http://www.internationalgenome.org/), the Exome variant server, NHLBI GO Exome Sequencing Project (ESP) (http://evs.gs.washington.edu/EVS/), the Exome Aggregation Consortium (ExAC) (http://exac.broadinstitute.org/), and the Genome Aggregation Database (gnomAD) (https://gnomad.broadinstitute.org/).Raw data from exome sequencing were processed using PUMCH Pipeline (PUMP)   was used for the statistical analysis. Fisher's Exact Test was used to compare the burden of deleterious missense variants with MAF ≤0.001 between cases and controls.33.1TBX6 or other CS‐causing genes .We recruited 584 individuals affected with CS that could not be explained by s Figure . After E Figures  and 4. O3.2TBX6‐mediated genes were identified from two patients  with multiple segmentation failures from T8 to L1  in an autosomal recessive mode. In addition, knock‐out of Ripply1/Ripply2 in mouse results in disrupted somitogenesis and causes gross spinal deformity . The spinal phenotypes of this syndrome include cervical and thoracic scoliosis, cervical fusions, clivus malformations, basilar invagination, and narrow disc spaces, which represents a more complex condition than that in our patient. Therefore, we propose that heterozygous loss‐of‐function of MYF5 might be associated with a non‐syndromic form of CS.A heterozygous stop‐gain variant in 3.3n = 4 for MYOD1, n = 1 for RIPPLY2) in TBX6‐mediated genes . These findings suggest the possible involvement of MYOD1 in the pathogenesis of CS. MYOD1 encodes an early transcriptional factor during somitogenesis, and is required for MYF5 expression in the early mesoderm  in MEOX1  in RIPPLY1, as described above, and a heterozygous missense variant c.464G>T(p.Arg155Leu) in MYOD1 was also identified. Although no direct interaction between RIPPLY1 and MYOD1 has been reported, they may together dysregulate the TBX6 pathway given the deleterious nature of both variants  of the candidate genes in the gnomAD database, a deleterious variant alone in any of these genes might not be disease causing. However, the combined effect of deleterious variants in multiple genes might synergistically lead to the disease. Although experimental validation of the oligogenic model still needs to be conducted, our data give insight into the complex disease‐causing mode of CS and suggest the combined effect of mutations in TBX6‐mediated genes as an important mechanism in the pathogenesis of CS.Due to the fact that CS represents a relatively milder phenotype spectrum than the autosomal recessive syndromes caused by biallelic loss‐of‐function of the candidate genes, the variant spectrum and disease‐causing mode of these genes in CS are assumed to be more complicated. More recently, there has been a surge of interest in digenic/oligogenic inheritance mode in complex disease (Schaffer, TBX6‐mediated genes in CS, prioritized core candidate genes/variants, and provided insight into a potential oligogenic disease‐causing mode.In conclusion, our study characterized the mutational spectrum of The authors declare that they have no conflicts of interest.Jianguo Zhang, Nan Wu, Zhihong Wu, and Xisheng Weng conceived of the project and designed the study, Yang Yang, Sen Zhao, Yuanqiang Zhang, Shengru Wang, Jiashen Shao, Bowen Liu, Yaqi Li, and Zihui Yan collected and interpreted the data, Lianlei Wang, Yongyu Ye, Jiachen L, Hengqiang Z, Zihui Y, Zefu C, and Gang L conducted the statistical analysis. Yuchen Niu and Ziaoxin Li conducted the bioinformatic analyses. Yangyang, Yuanqiang Zhang, and Lianlei Wang recruited patients. Sen Zhao, Yangyang, and Nan Wu wrote the first draft of the manuscript, Jianguo Zhang, Zhihong Wu, and Xisheng Weng critically revised the work for important intellectual content.


Klebsiella pneumoniae (hvKp), which causes surgical site infections (SSIs), and describe the microbiological and molecular characteristics of hvKp isolates.We aim to determine the prevalence of hypervirulent K. pneumoniae strains were isolated from wound drainage specimens of postoperative patients at the Chinese PLA General Hospital between September 2008 and July 2017. Antimicrobial susceptibility, string test, pulsed‐field gel electrophoresis (PFGE), and genome sequencing analyses were performed.Non‐duplicate K. pneumoniae strains were isolated from wound drainage specimens collected from postoperative patients. Twenty‐six hvKp strains, including 17  and 9  hvKp strains, were isolated from 37 and 14 patients with SSIs and community‐acquired infections (CAIs), respectively. Notably, 4 extended‐spectrum beta‐lactamase (ESBL)‐producing hvKp strains  and 2 carbapenem‐resistant hvKp strains  were found. Thirteen K1 serotype  and 7 K2 serotype  strains were identified. Phylogenetic analysis results showed that 13 K1 serotype isolates exhibited a high degree of clonality, while 7 K2 serotype strains were genetically unrelated. MLST analysis indicated that there was a strong correlation between ST23 and the K1 serotype. ST65, ST86, and ST375 were prevalent in K2 serotype strains. Almost all hvKp strains  harbored large virulence plasmids with a high degree of homology to pNTUH‐K2044 and sizes ranging from 140 to 220 kbp.Fifty‐one HvKp strains were prevalent in SSIs. Effective surveillance and control measures should be implemented to prevent the dissemination of such organisms, including the ESBL‐producing and carbapenem‐resistant hvKp strains. However, extended‐spectrum beta‐lactamase (ESBL)‐producing and carbapenem‐resistant hvKp strains have been observed, especially with healthcare‐associated infections (HAIs), in long‐term care facilities.22.1K. pneumoniae strains were isolated from wound drainage specimens of postoperative patients at the Chinese PLA General Hospital, between September 2008 and July 2017. Bacterial identification was performed using MALDI‐TOF (bioMérieux), and results were further confirmed by 16S rRNA gene sequencing. The ESBL production level and antimicrobial susceptibility to ceftazidime, ceftriaxone, cefepime, aztreonam, ertapenem, imipenem, amikacin, gentamicin, ciprofloxacin, levofloxacin, and sulfamethoxazole‐trimethoprim were assessed using Vitek II (bioMérieux), and results were interpreted according to the 2018 Clinical and Laboratory Standards Institute (CLSI) guidelines.Fifty‐one non‐duplicate 2.2According to the US Centers for Disease Control and Prevention (CDC), SSI is an infection that occurs within 30 days after the operative procedure and involves at least one of the following: purulent drainage; organisms isolated in wound culture; at least one of the following signs of infection: erythema, heat, pain, and swelling of surgical incision; or diagnosis of a SSI by the surgeon.2.3The string test was used to identify the hypermucoviscosity phenotype. Positive results were obtained if a viscous string with a length >5mm was formed, by the stretching of bacterial colonies on a blood agar plate after overnight incubation, using an inoculation loop.2.4For all hvKp isolates, PFGE was performed to analyze the genetic relatedness, and S1‐PFGE was used to determine the number and size of plasmids carried by these isolates, as described previously.2.5https://github.com/katholt/Kleborate). BLASTN atlases of the pNTUH‐K2044 virulence plasmid were constructed using BLAST Ring Image Generator (BRIG) v0.95 and manually inspected with mauve.Genomic DNA was extracted using the TIANamp Bacteria DNA Kit (Tiangen Biotech (Beijing) Co., Ltd.), according to the manufacturer's instructions; fragmented via ultrasonication; and sequenced using the Illumina HiSeqXTen system, using a 2 × 150‐bp paired‐end approach. The sequencing reads were assembled de novo using SPAdes 3.12, and subsequently annotated with Prokka 1.12. Resistance genes and plasmid incompatibility groups were predicted using ResFinder 2.1 and PlasmidFinder 1.2 softwares at the Centre for Genomic Epidemiology. The capsular type, multilocus sequence type (MLST), virulence genes, and Yersiniabactin ICEKp structures were predicted using Kleborate  were identified as hvKp strains. Moreover, 17  and 9  hvKp strains were isolated from 37 SSI and 14 CAI patients, respectively. Demographic data and clinical characteristics have been shown in Table Fifty‐one P < .05), while there were no significant differences in antimicrobial resistance rates of amikacin, cefepime, ertapenem, imipenem, ciprofloxacin, levofloxacin, and sulfamethoxazole/trimethoprim for patients infected with hvKp and cKp , while 7 K2 serotype strains harbored a virulence plasmid with a size ranging from 140 to 220 kbp. The virulence plasmids were found to occur in a growing number of distinct lineages, suggesting that acquisition had occurred, albeit at a very low frequency,K1 and, to a lesser extent, K2 were the major serotypes of hvKp strains. Among the 26 hvKp isolates, 13 K1 serotype  and 7 K2 serotype  strains were identified, respectively. PFGE analysis showed that 13 K1 serotype isolates exhibited a high degree of clonality, while 7 K2 serotype strains were genetically unrelated. MLST analysis indicated a strong correlation between ST23 and the K1 serotype. ST65, ST86, and ST375 were prevalent in K2 serotype strains. Despite their different genetic backgrounds, almost all of the hvKp strains  harbored a large virulence plasmid with a high homology to pNTUH‐K2044. This plasmid encodes two siderophores, aerobactin and salmochelin, along with rmpA (regulator of the mucoid phenotype); these were found to be restricted to hvKp isolates.In summary, we have shown that hvKp (defined as aerobactin positive) strains were prevalent in SSI infections in China. K1 and K2 were the major serotypes of hvKp, and almost all the hvKp strains  harbored a large virulence plasmid with a high homology to pNTUH‐K2044. Effective surveillance and control measures should be implemented to prevent the dissemination of such organisms, including the ESBL‐producing and carbapenem‐resistant hvKp strains.Not applicable.


Leptoria phrygia colonies between two sites with significantly different temperature regimes and monitored them for 12 months. Our ITS2 amplicon deep sequencing demonstrated that L. phrygia acclimatized to maintain a strong and stable association with Durusdinium D17, D. trenchii, and D. glynnii, but also remained flexible and formed a short-term association with different Cladocopium. Most interestingly, two colonies shuffled between Durusdinium and Cladocopium without the occurrence of bleaching; one colony even switched its dominant Cladocopium after generic shuffling. Both dominant Cladocopium were originally rare with relative abundances as low as 0.024%. This is the first record of adult corals switching dominant symbiont without bleaching.Symbiodiniaceae communities in some corals often shuffle or switch after severe bleaching events, one of the major threats to coral survival in a world with climate change. In this study we reciprocally transplanted five  Interruption of such symbioses may force corals to expel their associated algae, which results in coral whitening, a phenomenon best known as bleaching. Temporary bleaching may not kill corals immediately, but recurrent and prolonged bleaching can be fatal3. Rising sea surface temperatures (SSTs) are one of the major contributors to coral bleaching3. Tropical/subtropical SSTs have increased 0.25–0.75 °C since 1880 due to anthropogenic global warming4. Over the past three decades, three mass bleaching events have occurred on a global scale: 1997–1998, 2010, and 2015–175. The intervals between bleaching events are decreasing and the duration of each event is lengthening. To survive, both corals and their symbiotic algae need to adapt to the rising SSTs.Stable symbioses between scleractinian corals and dinoflagellate algae (family: Symbiodiniaceae) are key for coral reefs to thrive in tropical oligotrophic waters. More than 90% of a coral’s nutritional requirements are provided by its symbiotic algae6. Two common genera in the Indo-Pacific—Cladocopium (formerly clade C) and Durusdinium (formerly clade D)—are the only Symbiodiniaceae detected in coral colonies from Taiwanese waters10. Cladocopium is the most diverse genus11, while members of Durusdinium are known to be resilient to hostile environments, e.g., high temperature or turbidity27.Coral-associated Symbiodiniaceae currently comprises seven genera and several unnamed but genetically distinct lineages29. Some coral species host different algal symbionts when living in more hostile environments32, and some host multiple algal species and adjust the relative abundance of their symbionts, or shuffle between completely different symbionts when necessary36. Most studies have identified the dominant and codominant algal species in corals using conventional Sanger sequencing, restriction fragment length polymorphism (RFLP), quantitative polymerase chain reaction (qPCR), or temperature gradient gel electrophoresis (DGGE). However, these methods have limited resolution and very often fail to detect background species that might be ecologically important but extremely low in quantity 37. High-throughput sequencing of the internal transcribed spacer 2 (ITS2) region is a promising approach to better understand intragenomic diversity and differentiate symbionts39. It can detect rare symbionts with relative abundances <1%. For example, using this approach, Boulotte et al.40 recorded rare symbionts in adult coral colonies and showed that symbiont switching occurred—one colony became dominated by one of the rare symbionts—after two consecutive bleaching events, a rare phenomenon in adult corals. In this study we applied the same approach to investigate Symbiodiniaceae dynamics in Leptoria phrygia, which is known to host Cladocopium or Durusdinium23, or both41, within the same colony.In response to rising SSTs, corals and their algal symbionts use different strategies to acclimatize. In some cases, corals expel suboptimal algal species and host more optimal partnersL. phrygia colonies—13 in Wanlitong (WLT) and 12 in the outlet of the third nuclear power plant (OL) in southern Taiwan—to investigate spatial and temporal variation in Symbiodiniaceae communities41. We collected five cores from each colony; each core was 10~20 cm apart longitudinally and was sampled from the top to the bottom of the colony. Our survey demonstrated that, within individual colonies, there were no correlations between sampling position and the Symbiodiniaceae community; there were, however, positive correlations between the relative abundance of Durusdinium and mean maximum temperature, and between the relative abundance of Durusdinium and temperature variability41. Moreover, L. phrygia colonies native to OL were dominated exclusively by Durusdinium glynnii (formerly D1), while those native to WLT were dominated by Cladocopium C3w or C21a, or co-dominated by Cladocopium sp. and D. glynnii41. WLT is a sea surface temperature (SST) steady site (SS) with an average summer SST of 29–30 °C and daily fluctuation of <3 °C41, while OL is a SST variable site (VS) with an average summer SST of 32–33 °C—2–3 °C higher than any adjacent reef sites10—and daily fluctuations up to 10 °C42. Seasonal variation was only observed in colonies co-dominated by both genera; however, despite changes in relative abundance, the dominant species in the colonies remained the same41. Background information acquired from this study suggested: (1) L. phrygia can only modify the relative abundance of its symbionts if multiple symbionts exist in the same colony, but dominant symbionts do not change over time. (2) L. phrygia copes with variable environments better by hosting Durusdinium, and only associates with Cladocopium in more stable environments.We tagged and sampled 25 L. phrygia colonies? Does Symbiodiniaceae composition within the same colony change through time? Can L. phrygia respond to a new habitat completely different from its native one and, if so, how long does it take to adapt? To answer these questions, we narrowed our sampling to 10 colonies, five each randomly selected from the SS and VS. We reciprocally transplanted those colonies and monitored the dynamics of the Symbiodiniaceae community in each for 12 months.The background above begs several questions. Which Symbiodiniaceae are present in individual All deployed nubbins survived the entire experimental period, except the five transplanted Wanlitong (WLT) nubbins of colony V, which died before the first sampling time in July 2015. Therefore, WLT colony V was excluded from subsequent analyses.The mean monthly temperatures for July 2015, March 2016, and August 2016 were 29.8 ± 0.4 °C, 26.6 ± 0.7 °C, 24.4 ± 0.8 °C, and 29.8 ± 0.5 °C, respectively, in WLT and 29.4 ± 1.2 °C, 27.7 ± 0.8 °C, 25.9 ± 1.0 °C, and 30.4 ± 1.1 °C, respectively, in the third nuclear power plant (OL). The Mann-Whitney-Wilcoxon test demonstrated that the mean temperatures were significantly different between two sites for all four months (p < 0.05). It is noteworthy that the mean temperatures in OL were unseasonably low in July 2015, possibly due to a super typhoon passing by southern Taiwan and reducing the mean summer temperature in OL.Durusdinium and only 12.6% belonged to Cladocopium. Within individual nubbins, the first sampling in July 2015 showed that all nubbins, including native (control) and transplanted ones from both sites, exhibited an extremely high ratio of Durusdinium to Cladocopium  in summer (July 2015 and August 2016), when the SST was the highest (ca. 29.8 °C). In fall-winter (November 2015) and spring (March 2016), when the SST was cooler , the Durusdinium to Cladocopium ratios decreased slightly in colonies I, II, and IV. In colony III, however, the Durusdinium to Cladocopium ratio decreased dramatically to as low as 6%, while the Cladocopium to Durusdinium ratio increased to 94%, becoming the dominant Symbiodiniaceae genus in November , without seasonal variation, except in colony V, which had no summer samples but did have a Durusdinium to Cladocopium ratio as low as 6 and 14% in November 2015 and March 2016, respectively.Relative abundance analysis demonstrated that, in native WLT colonies, ratios of Durusdinium and Cladocopium were significantly (p < 0.05) or highly significantly (p < 0.001) different  at all sampling times , and D. glynnii (D1)  Figs. . MoreoveCladocopium-dominant stage, however, the relative abundances of the top three were more variable. For example, in WLT colony III, the top three most abundant taxa changed from Cladocopium C3s, C50, and C3b in November 2015 to C50, D17, and C3s in March 2016  and March . However, those sequences were transient, and none survived to the next sampling month , all colonies at both sites were particularly dominated by Durusdinium (relative abundance >90%). Our results concur with those of Carballo-Bolaños et al.41 in that colonies able to adjust their relative abundances of Durusdinium and Cladocopium mostly retained the same dominant symbiont through time; there were two exceptions: (1) native WLT colony III and transplanted third nuclear power plant (OL) colony V both shuffled from Durusdinium- to Cladocopium-dominated in November 2015 and March 2016, when the SST decreased  of the Symbiodiniaceae. In contrast, Cladocopium-dominated colonies depended on the accumulation of different Cladocopium, each with a low relative abundance; even the most abundant taxon made up only ca. 21~22%  in March. Based on the results above, we can infer that L. phrygia has acclimatized to maintain a strong and stable association with three Durusdinium— D17, D. trenchii, and D. glynnii—but remained flexible enough to form a short-term association with Cladocopium.Moreover, our Symbiodiniaceae profiling demonstrated that, for colonies dominated by Durusdinium, however, comes at a cost. Durusdinium is known as an opportunist that seizes the chance to become dominant in corals facing adverse conditions, and Durusdinium dominancy often has negative effects on coral health43. However, for corals living on the edge—e.g., the VS in our study—retaining considerable quantities of Durusdinium seems unavoidable. Our transplantation experiment validated the advantages of associating with Durusdinium when environmental conditions deteriorate: one colony died and the four surviving colonies all hosted Durusdinium exclusively (ca. 97% on average) after they were transplanted from the SS to VS. On the other hand, four of the five OL colonies remained Durusdinium-dominant, and the last one shuffled to become Cladocopium-dominant after transplantation from the VS (OL) to SS (WLT).Hosting a great quantity of Durusdinium to Cladocopium ratio was so high (>92%) in all colonies in July due to the high SST combined with stress resulting from relocation, indicating aggravating ambient conditions. Although L. phrygia was able to shuffle to Cladocopium when the environment was less adverse—e.g., SST decreased—even switching to different dominant Cladocopium, the association with Cladocopium was only temporary. As soon as the SST increased again the next summer, the colony shuffled back to the same top three Durusdinium. Our results concur with another study on three coral species  that possess a unique Symbiodiniaceae signature: a suite of permanent Symbiodiniaceae genera in each colony44.The evidence above suggests that the 45, nor do they change their dominant algal genus easily46. Moreover, before NGS techniques, the uptake of novel symbionts was only reported in corals at the juvenile stage, when the association was non-specific and flexible48. Even today, novel symbiont acquisition and switching in adult corals has only been reported in one case study—on Pocillopora damicornis after two consecutive bleaching events40. Our study is unique in that we monitored individual colonies and recorded the shuffling between Durusdinium and Cladocopium, as well as between dominant Cladocopium that switched within one particular colony over a period as short as 12 months without bleaching. This is the first record of a dominant symbiont switch in adult corals not triggered by bleaching.Previous studies show that corals do not take up algal symbionts indiscriminatelyet al.40 (driven by bleaching) and our study (not driven by bleaching), whether or not the two cases truly constitute a switch is still equivocal. By definition, switching involves acquiring novel symbionts from the environment that later increase in abundance and dominate the colony49. The symbionts in these studies that switched actually existed in the colony before they were taken up, although their relative abundance was extremely low—0.03% in Boulotte et al.40 and 0.024% in our study. Nonetheless, we did detect new symbionts for each sampling month, but they never survived to the next sampling.However, although the uptake of novel symbionts by adult corals was reported in both Boulotte L. phrygia has acclimatized to associate with three particular Durusdinium , but keeps numerous background Cladocopium in stock that may increase in abundance to dominate the colony when ambient conditions are less adverse. Sequences of new taxa are sporadically acquired from the environment, but they are transient and do not survive long enough to increase much in abundance. Consequently, L. phrygia may stand a chance of resisting recurrent and prolonged bleaching by solidly associating with Durusdinium supplemented with sporadic shuffling between Durusdinium and background Cladocopium. This coral is also resilient and recovers quickly from bleaching due to its swift responses to changing environments.In conclusion, our study elucidated that L. phrygia colonies at 3–5 m deep from OL (21°55′53.7″N–120°44′42.7″E) and five colonies at 3–5 m deep from WLT (21°59′43.9″N–120°42′23.2″E) were haphazardly selected for subsample coring in March 2015. Sampled colonies were separated by at least 7–10 m to avoid sampling clones.The study was conducted in Kenting National Park, southern Taiwan. We received a field permit (number: 1040008112) to conduct a reciprocal transplant experiment (spanning four seasons over 1 year) between Wanlitong (WLT) and the outlet of the third nuclear power plant (OL). The permit allowed us to sample 10 colonies from the two sites  and collect a maximum of 10 subsamples from each colony, which was sufficient for our experimental design . Five et al.50. Briefly, 10 cores (2.5 cm in diameter) were taken from each colony using a set of underwater drills. To reduce sampling effect, all cores were brought back to the laboratory in the National Museum of Marine Biology and Aquarium for processing and acclimatizing. In the laboratory, individual cores were attached to PVC tubes with epoxy (nubbins). Fifty nubbins collected from the same site were equally distributed on two fiberglass racks (25 nubbins each). On each rack, five nubbins from the same colony were arranged on the same row. Four racks (100 nubbins) were acclimatized for 96 hours and then returned to their native sites: two racks with WLT nubbins back to WLT and two racks with OL nubbins back to OL. The racks were fixed to the reefs at a depth of 3–5 m and allowed to acclimate for four weeks. In April 2015, one of the racks from each site was reciprocally transplanted to the other site, i.e., one WLT rack was transplanted to OL and vice versa. The other rack remained at the native site as the experimental control.Procedures for coring and nubbin production were modified from Kao 51. Briefly, about 30 mg of coral tissue (~3–4 polyps) was fragmented and incubated at 55–60 °C overnight in a 200-μl lysis buffer mixture  and pronase E (10 μl at 10 mg/ml). To precipitate the DNA, 210 μl NaCl (7 M) was added to the lysis solution and mixed by inverting the tubes several times. The mixture was centrifuged at 10,000 × g for 30 min. The resulting supernatant was transferred into a new tube, mixed with 100% isopropanol (420 μl) for 5 min, and incubated at −20 °C for at least two hours. To clean the DNA, samples were centrifuged for 30 min at 16,000 × g and the supernatant was discarded. The DNA pellet was rinsed with 70% cold ethanol (150 μl) three times and dried in a laminar hood for 1–2 hr. A total volume of 150 μl preheated (65 °C) TE buffer (1×) was added to elute the DNA.Data loggers  deployed at 3 m deep recorded ambient SST hourly from April 2015 to August 2016. Seasonal variation in the Symbiodiniaceae community compositions of native and transplanted nubbins were examined by collecting one nubbin per colony every three to four months. Nubbins were sampled in July 2015 (summer), November 2015 , March 2016 (spring), and August 2016 (summer). A total of 80 nubbins were collected from the racks by scuba diving, but some were lost during the sampling process. Consequently, fewer than 80 nubbins were brought back to the laboratory for molecular study. In the laboratory, a small piece of coral tissue was collected from each nubbin and fixed in 100% ethanol for DNA extraction using a modified high salt protocolThe primers ITSintfor2 (5′-GAATTGCAGAACTCCGTG -3′) and ITS2 (5′-GGGATCCATATGCTTAAGTTCAGCGGGT -3′) were used to amplify the ITS2 spacer along with partial ribosomal RNA genes (5.8 S and 28 S). Sixteen unique barcodes (6-bp) were attached to the 5’ end of the forward primer (ITSinfor2) for Illumina multiplex sequencing. Taq DNA polymerase RED (Ampliqon) was used for PCR amplification using the following conditions: initial denaturation at 95 °C for 5 min, followed by 35 cycles at 95 °C for 30 s, 53~55 °C for 30 s, 72 °C for 30 s, and a final extension at 72 °C for 10 min. The resulting amplicons were purified using the Gel/PCR DNA Purification Mini Kit  and measured with Nanodrop to determine the concentration and purity. A total of five tubes were prepared and delivered to Yourgene Bioscience, Co.  for library construction and amplicon sequencing using Illumina MiSeq, 2 × 300 bp paired-end . Each tube contained 16 samples that could be distinguished by the unique barcode attached to the 5’ end of the forward primer.52. For each sample, the merged reads were imported independently into QIIME53 for operational taxonomic unit (OTU) selection using the UCLUST method and a 97% similarity threshold. A previous study demonstrated that 97% within sample clustering collapses intragenomic sequences and only preserves sequences that are most likely interspecific54. The most abundant sequence variant in each 97% cluster was selected as the representative OTU. Only representative OTUs were used in downstream analyses. The identity of each representative OTU was determined by BLAST search against reference sequences from Arif et al.38 and Tong et al.55 with an e-value threshold of 10−5. The output with length coverage <250 bp or identity <97% were removed from further analyses. The relative abundances of Cladocopium and Durusdinium were calculated by the equation: total Cladocopium OTUs or total Durusdinium OTUs divided by total OTUs. All raw reads were deposited into GenBank and will be accessible once this manuscript is published (accession number PRJNA580156).Initial read trimming (error probability <0.05); barcode sorting; and adaptor, primer, and barcode removal were performed by Yourgene Bioscience, Co. . Read lengths shorter than 35 bp and unpaired reads were removed from downstream analyses. Paired forward and reverse reads were further trimmed  and then merged using PEAR v0.9.10http://bioinformatics.psb.ugent.be/webtools/Venn/).Mean monthly temperature was calculated for each site, and the differences between the two sites over four sampling months were determined by non-parametric Mann-Whitney-Wilcoxon test. Differences in relative abundance between native and transplanted samples of each colony were evaluated by chi-squared tests. All statistical analyses were performed in R (version 3.4.2). Shared and unique symbionts across seasons were calculated through the Bioinformatics & Evolutionary Genomics online server (Table S1. Summary of MiSeq paired-end sequencing.