Alterations of Gut Microbiome in the Frontline Medical Staff Under the Stress

Overview

With the outbreak of 2019 novel coronavirus (2019-nCoV), the frontline medical workers faced enormous stress, including a high risk of infection and inadequate protection from contamination, isolation, patients with negative emotions, a lack of contact with their families, and exhaustion, which may cause mental health problems. The investigators plan to collect the faecal samples and clinical assessments from a part of frontline medical workers in three time points to analyse the changing profile of gut microbiome according to outcomes of 16s rRNA sequencing. The samples from the matched health controls will also be sequenced to compare with the exposed group in gut microbiome community.

In December, 2019, a novel coronavirus outbreak of pneumonia emerged in Wuhan, Hubei province, China, and has subsequently spread to more than 30 provinces in China and almost 100 countries in the world. In the fight against the 2019 novel coronavirus (2019-nCoV), medical workers in Wuhan have been facing enormous stress, including a high risk of infection and inadequate protection from contamination, isolation, patients with negative emotions, a lack of contact with their families, and exhaustion. The severe situation is causing mental health problems such as stress, anxiety, depressive symptoms, insomnia, denial, anger, and fear. These mental health problems could also cause posttraumatic stress (PTS) symptoms in a lasting time. A research examining the psychological impact of the 2003 outbreak of severe acute respiratory syndrome on hospital empoyees found that about 10% of the respondents had experienced high leves of PTS symptoms since the SARS outbreak. Microbiome-gut-brain (MGB) axis has been validated in expanding studies, which means there are bidirectional communication between commensal organisms within the gut and the brain. Gut microbiota may influence brain function through neural, endocrine, and immune pathways. For example, substances produced by the gut microbiota may be absorbed reaching the brain by the blood stream. The brain, in turn, may influence the gut microbiota trough neuronal and endocrine pathways. In recent years, many reseaches support the relevance of microbiota and mental health status. Bercik et al. transplanted microbiota from adult germ-free (GF) BALB/c mice (a high-anxiety mouse strain) into adult GF NIH Swiss mice (a low-anxiety mouse strain), then found the behavioral profile of the donor was evident in the recipient animal, showing that the microbiota can directly affect behavior. Moreover, preclinical studies have shown that stress and emotions, including maternal separation and restraint, heat, and acoustic stress, alters the composition of the gut microbiota, maybe through the release of stress hormones or sympathetic neurotransmitters that influence gut physiology and alter the habitat of the microbiota. In addition, researchers found that stress has the ability to increase intestinal permeability, probably through the involvement of corticotrophin releasing factor and its receptors (CRFR1 and CRFR2), which play a key role in stress-induced gut permeability dysfunction. Increased intestinal permeability provides bacteria an opportunity to translocate across the intestinal mucosa and directly access both the immune and neuronal cells of the enteric nervous system (ENS). Stress also activates the autonomic nervous system, which affects gastric acid, bile, and mucus secretion, as well as gut motility. Gut motility is of particular importance since it is strongly associated with gut microbiota composition and richness. Based on these researches, the gut microbiome increasingly deserve attention to understand psychiatric disorders. Therefore, the present study aim to collect the faecal samples and clinical assessments from a part of frontline medical workers in three time points to analyse the changing profile of gut microbiome according to outcomes of 16s rRNA sequencing. The samples from the matched health controls will also be sequenced to compare with the exposed group in gut microbiome community. Methods Study design and sample collection The frontline workers in First Affiliated Hospital of Xi'an Jiaotong University will be included if they conformed with (1) taking part in the medical team to support Wuhan, (2) of 18 to 50 years old, (3) did not take antibiotics within 3 months before sample collection, (4) 17.5\<body mass index (BMI)\<30. The healthy controls will be selected from the staffs in First Affiliated Hospital who didn't join in the medical team but fulfill the last three conditions above to match with the frontline staffs for age, gender, BMI, and diet. Any participant will be excluded if he/she fulfill the following criteria: (1) have serious cardiovascular disease, blood disease, and endocrine disease, (2) have a history of cancer or its complications, (3) have active gastrointestinal diseases or complications and serious systemic diseases, (4) have history of brain organic diseases or complications and mental retardation, (5) have mental disorders such as mood disorder and anxiety disorders, (6) pregnant or lactating, (7) drink in the past week (liquor\>250ml or beer\>1bottle) or the previous day (liquor\>50ml or beer\>50ml). All included persons should provide signed informed consent before sample collection, and the protocol was approved by the Ethics Committee of First Affiliated Hospital of Xi'an Jiaotong University (KYLLSL-2020-043). Faecal samples from each staff should be freshly collected at the hospital and frozen at -80℃ using specified faecal collector. Clinical assessmant PHQ-15, PHQ-9, GAD-7, PSQI, SCL-90, and IES-R will be used for the assessment of clinical symptoms related to psychiatric disorder for exposed group in three time point and non-exposed group. DNA extraction and 16S rRNA sequencing The investigators will perform 16S rRNA sequencing for all the collected faecal samples. Briefly, bacterial genomic DNA will be extracted, and the 16S rRNA whole region will be amplified by PCR, and then sequenced. Statistical analysis The 16S rRNA sequencing data will be analysed using Quantitative Insights Into Microbial Ecology (QIIME). The investigators then use usearch to cluster sequences into taxonomic units (OTUs) at 97% identity and construct the OTU table. Microbial community structure, alpha diversity, and beta diversity will be analysed. LEfSe analysis, and random forest analysis will be used to find the biomarker between different group. Spearman's rank correlation will be used to identify the corrolation between clinical assessments and stress-associated microbiome.