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27 Apr 2021

Coronavirus Seroprevalence Among Villagers Exposed To Bats In Thailand

Hein Min Tun signed with colleagues from the Center of Excellence for Emerging and Re‐emerging Infectious Diseases in Animals of the Chulalongkorn University, Bangkok, a paper on a serological survey of human coronavirus antibodies among villagers in 10 provinces of Thailand that was conducted between 2016–2018 in the Wiley Online Library.
 
They showed that 10.44% (38/364) of the villagers had developed antibodies against human coronaviruses.
 
The odds ratio for coronavirus positivity in the villagers in the central region who were exposed to bats was 4.75 (95% CI 1.04–21.70) compared to that for the non‐exposed villagers.
 
Their results showed that 62.36% (227/364) of the villagers had been exposed to bats at least once in the past six months and that low monthly family income was statistically significant in increasing the risk for coronavirus seropositivity among the villagers.
 
Abstract
A serological survey of human coronavirus antibodies among villagers in 10 provinces of Thailand was conducted during 2016–2018. Serum samples (n = 364) were collected from participants from the villages and tested for coronavirus antibodies using a human coronavirus IgG ELISA kit. Our results showed that 10.44% (38/364; 21 males and 17 females) of the villagers had antibodies against human coronaviruses. The odds ratio for coronavirus positivity in the villagers in the central region who were exposed to bats was 4.75, 95% CI 1.04–21.70, when compared to that in the non‐exposed villagers. The sociodemographics, knowledge, attitudes and practices (KAP) of the villagers were also recorded and analysed by using a quantitative structured questionnaire. Our results showed that 62.36% (227/364) of the villagers had been exposed to bats at least once in the past six months. Low monthly family income was statistically significant in increasing the risk for coronavirus seropositivity among the villagers (OR 2.91, 95% CI 1.13–7.49). In‐depth interviews among the coronavirus‐positive participants (n = 30) showed that cultural context, local norms and beliefs could influence to bat exposure activities. In conclusion, our results provide baseline information on human coronavirus antibodies and KAP regarding to bat exposure among villagers in Thailand.
 
 
Map of study provinces in Thailand and percentages of CoV seropositivity among villagers.

20 Apr 2021

Understanding the microbial composition of the respiratory tract in hospitalized Covid-19 patients

Hein Min Tun’s team, in collaboration with colleagues from Denmark, Guangzhou, and Shenzhen among others, have studied the characterization of respiratory microbial dysbiosis in hospitalized COVID-19 patients. This study published in Nature’s Cell Discovery focuses on the microbial composition of the respiratory tract and other infected tissues as well as their possible pathogenic contributions to varying degrees of disease severity in COVID-19.

From the identification of distinct signatures of microbial dysbiosis among severely ill Covid-19 patients, their work highlights the need for adapted detection and tracking strategies to prevent the spread of antimicrobial resistance and improve the clinical outcomes of hospitalized patients.

>>> Characterization of respiratory microbial dysbiosis in hospitalized COVID-19 patients

Abstract:

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of Coronavirus disease 2019 (COVID-19). However, the microbial composition of the respiratory tract and other infected tissues as well as their possible pathogenic contributions to varying degrees of disease severity in COVID-19 patients remain unclear. Between 27 January and 26 February 2020, serial clinical specimens (sputum, nasal and throat swab, anal swab and feces) were collected from a cohort of hospitalized COVID-19 patients, including 8 mildly and 15 severely ill patients in Guangdong province, China. Total RNA was extracted and ultra-deep metatranscriptomic sequencing was performed in combination with laboratory diagnostic assays. We identified distinct signatures of microbial dysbiosis among severely ill COVID-19 patients on broad spectrum antimicrobial therapy. Co-detection of other human respiratory viruses (including human alphaherpesvirus 1, rhinovirus B, and human orthopneumovirus) was demonstrated in 30.8% (4/13) of the severely ill patients, but not in any of the mildly affected patients. Notably, the predominant respiratory microbial taxa of severely ill patients were Burkholderia cepacia complex (BCC), Staphylococcus epidermidis, or Mycoplasma spp. (including M. hominis and M. orale). The presence of the former two bacterial taxa was also confirmed by clinical cultures of respiratory specimens (expectorated sputum or nasal secretions) in 23.1% (3/13) of the severe cases. Finally, a time-dependent, secondary infection of B. cenocepacia with expressions of multiple virulence genes was demonstrated in one severely ill patient, which might accelerate his disease deterioration and death occurring one month after ICU admission. Our findings point to SARS-CoV-2-related microbial dysbiosis and various antibiotic-resistant respiratory microbes/pathogens in hospitalized COVID-19 patients in relation to disease severity. Detection and tracking strategies are needed to prevent the spread of antimicrobial resistance, improve the treatment regimen and clinical outcomes of hospitalized, severely ill COVID-19 patients.

 

a Presence/absence profile of nonviral microbial genera in mild and severe cases. Orange, mild; brick red, severe. Only common genera detected in over 60% of patients in the mild cases (n > 4) or severe cases (n > 7) are shown. b Bar plot showing the relative expression levels of nonviral microbes in all respiratory specimens of mild (orange, n = 7) and severe cases (brick red, n = 40). c Relative expression levels of selected genera differing between mild and severe cases. The bar chart and black error bars denote the mean and standard error values of expression levels in mild (orange) and severe (brick red) cases for each genus. ***P < 0.001; **P < 0.01; *P < 0.05; Wilcoxon rank-sum test. For patients with multiple respiratory specimens (all were severe cases), the presence of a given genus is considered when at least one sample from this patient was positive for the taxon (relative abundance > 0) (a), and the comparisons between relative expression levels of selected genera are conducted across all collected respiratory samples between mild and severe cases (c).

19 Apr 2021

Multi-platform omics analysis reveals molecular signature for COVID-19 pathogenesis, prognosis and drug target discovery

Hein Min Tun and colleagues published in Nature's Signal Transduction and Targeted Therapy about a multi-platform omics analysis to get a depper insight about potential strategies for severe COVID-19 patient treatment.

>>> Multi-platform omics analysis reveals molecular signature for COVID-19 pathogenesis, prognosis and drug target discovery

Abstract:
Disease progression prediction and therapeutic drug target discovery for Coronavirus disease 2019 (COVID-19) are particularly important, as there is still no effective strategy for severe COVID-19 patient treatment. Herein, we performed multi-platform omics analysis of serial plasma and urine samples collected from patients during the course of COVID-19. Integrative analyses of these omics data revealed several potential therapeutic targets, such as ANXA1 and CLEC3B. Molecular changes in plasma indicated dysregulation of macrophage and suppression of T cell functions in severe patients compared to those in non-severe patients. Further, we chose 25 important molecular signatures as potential biomarkers for the prediction of disease severity. The prediction power was validated using corresponding urine samples and plasma samples from new COVID-19 patient cohort, with AUC reached to 0.904 and 0.988, respectively. In conclusion, our omics data proposed not only potential therapeutic targets, but also biomarkers for understanding the pathogenesis of severe COVID-19.

 

Overview of samples for multi-omics study. a Multi-omics analysis design with three datasets. The training dataset combined with severe, non-severe, and healthy controls, proteins, lipids, and amino acids were quantified in plasma and used for biomarker discovery, using random forest. The validation cohort 1 contained ten plasma samples from from non-severe and five severe patients, 25 molecules were targeted quantified for prediction evaluation. The validation cohort 2 contained urine samples corresponding to plasma samples in the training dataset, and prediction precision was further evaluated using targeted quantification. b Sample information of COVID-19 patients in the training dataset with time annotation from onset of disease to admission or from admission to discharge