Developing a Reproducible Microbiome Data Analysis Pipeline Using the Amazon Web Services Cloud for a Cancer Research Group: Proof-of-Concept Study

A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses

Environmental DNA (eDNA) metabarcoding has gained growing attention as a strategy for monitoring biodiversity in ecology. However, taxa identifications produced through metabarcoding require sophisticated processing of high-throughput sequencing data from taxonomically informative DNA barcodes. Various sets of universal and taxon-specific primers have been developed, extending the usability of metabarcoding across archaea, bacteria and eukaryotes. Accordingly, a multitude of metabarcoding data analysis tools and pipelines have also been developed. Often, several developed workflows are designed to process the same amplicon sequencing data, making it somewhat puzzling to choose one among the plethora of existing pipelines. However, each pipeline has its own specific philosophy, strengths and limitations, which should be considered depending on the aims of any specific study, as well as the bioinformatics expertise of the user. In this review, we outline the input data requirements, supported operating systems and particular attributes of thirty-two amplicon processing pipelines with the goal of helping users to select a pipeline for their metabarcoding projects.

Oral Microbiome, Mental Health, and Sleep Outcomes During the COVID-19 Pandemic: An Observational Study in Chinese and Korean American Immigrants

COVID-19 is a systemic disease whose effects are not limited to the respiratory system. The oral microbiome (OM)-brain axis is of growing interest in understanding the broader, neuropsychiatric, impacts of the COVID-19 pandemic through a systems biology lens. In this context, mental health and sleep disturbance are often reported by Asian Americans. In a cross-sectional observational study design, we examined the associations of the oral microbiome with mental health among Asian Americans during the COVID-19 pandemic (between November 2020 and April 2021). Participants (n = 20) were adult Chinese and Korean American immigrants in Atlanta, Georgia, and primarily born outside the United States (60%) with a mean age of 34.8 years ±14 (standard deviation). Participants reported depressive symptoms, anxiety, and sleep disturbance, as measured by standard questionnaires. The OM was characterized by 16S rRNA V3-V4 gene using saliva. Depressive symptoms and anxiety were reported by 60% (n = 12) of participants, whereas 35% (n = 7) reported sleep disturbance. The α-diversity was significantly associated with depressive symptoms, and marginally with anxiety. Participants with depressive symptoms and anxiety had enriched Rothia and Scardovia, respectively, whereas those without symptoms had enriched Fusobacterium. Individuals with sleep disturbance had enriched Kingella. In conclusion, this study suggests significant associations of the OM diversity with certain mental health dimensions such as depressive symptoms and anxiety. Specific taxa were associated with these symptoms. The present observations in a modest sample size suggest the possible relevance of the OM-brain axis in studies of mental health during COVID-19.

Associations between the Gut Microbiome and Migraines in Children Aged 7-18 Years: An Analysis of the American Gut Project Cohort

BACKGROUND

The gut microbiome seems to play a role in migraines through increasing intestinal epithelial permeability and pro-inflammatory processes. The associations between the gut microbiome and migraines are uncertain in children.

AIM

The purpose of this quantitative study was to examine the associations between the gut microbiome and migraines in children aged 7-18 years from the American Gut Project (AGP).

METHOD

A cohort of children aged 7-18 years from the AGP was analyzed. 16S rRNA V4 gene sequences for the gut microbiome, migraines, and demographics were obtained from the AGP Public Repository. After quality control of 16S rRNA gene sequences, α-diversity (Shannon, Faith's_PD, and evenness) and β-diversity metrics (Bray-Curtis and weighted-UniFrac distances), taxonomy, and abundance analyses were implemented using QIIME 2.

RESULTS

In total, 381 children (341 without migraines; 40 with professional or self-diagnosed migraines) were analyzed with a mean age of 11.5 years. Compared with those without migraines, children with migraines showed lower estimates in Shannon and Faith's_PD (p < .01). Both Bray-Curtis and weighted-UniFrac distances displayed the gut microbial dissimilarities between these two groups (p = .001). Children with migraines had higher abundances in genus of phylum Bacteroidetes (Bacteroides, Parabacteroides, Odoribacter), Actinobacteria (Eggerthella, Varibaculum), Firmicutes (SMB53, Lachnospira, Dorea, Veillonella, Anaerotruncus, Butyricicoccus, Coprobacillus, Eubacterium), and Proteobacteria (Sutterella) than children without migraines.

CONCLUSIONS

Associations of the gut microbiome diversity and abundances with migraines in children indicated potential biological mechanisms of migraines. Future work needs to confirm our findings in children.

Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data

This study aimed to explore the application value of the intelligent medical communication system based on the Apriori algorithm and cloud follow-up platform in out-of-hospital continuous nursing of breast cancer patients. In this study, the Apriori algorithm is optimized by Amazon Web Services (AWS) and graphics processing unit (GPU) to improve its data mining speed. At the same time, a cloud follow-up platform-based intelligent mobile medical communication system is established, which includes the log-in, my workstation, patient records, follow-up center, satisfaction management, propaganda and education center, SMS platform, and appointment management module. The subjects are divided into the control group (routine telephone follow-up, 163) and the intervention group (continuous nursing intervention, 216) according to different nursing methods. The cloud follow-up platform-based intelligent medical communication system is used to analyze patients' compliance, quality of life before and after nursing, function limitation of affected limb, and nursing satisfaction under different nursing methods. The running time of Apriori algorithm is proportional to the data amount and inversely proportional to the number of nodes in the cluster. Compared with the control group, there are statistical differences in the proportion of complete compliance data, the proportion of poor compliance data, and the proportion of total compliance in the intervention group (P < 0.05). After the intervention, the scores of the quality of life in the two groups are statistically different from those before treatment (P < 0.05), and the scores of the quality of life in the intervention group were higher than those in the control group (P < 0.05). The proportion of patients with limited and severely limited functional activity of the affected limb in the intervention group is significantly lower than that in the control group (P < 0.05). The satisfaction rate of postoperative nursing in the intervention group is significantly higher than that in the control group (P < 0.001), and the proportion of basically satisfied and dissatisfied patients in the control group was higher than that in the intervention group (P < 0.05).

Methodology Considerations in Studying Mental Health, Sleep Quality, and Biopsychosocial Determinants Among Chinese and Korean Americans During the COVID-19 Pandemic

Asians are one of the fastest-growing racial groups in the United States. The mental health of Asian Americans, particularly regarding depression and anxiety, needs significant attention. Various biopsychosocial factors interact to influence the risks of depression, anxiety, and sleep quality among Asian Americans. Currently, multiple methodological issues exist in the research of Asian Americans, such as limited data collection using Asian languages and inconsistent reporting of race and ethnicity data, which may be lacking entirely. All these methodological issues in research may account for the seemingly low prevalence rates of mental health problems among Asian Americans. In our study on mental health and sleep quality among Chinese and Korean Americans, we adopted multiple data collection strategies during the COVID-19 pandemic, including using culturally adaptive and validated measures as well as operating culture-sensitive procedures in the recruitment and data collection. The successful use of these strategies could promote early detection and personalized treatment of depression, anxiety, and sleep disturbance among Asian Americans. These strategies would further improve health care service use in this population.

International Registered Report Identifier IRRID

RR2-10.1136/bmjopen-2020-047281.

How stress, discrimination, acculturation and the gut microbiome affect depression, anxiety and sleep among Chinese and Korean immigrants in the USA: a cross-sectional pilot study protocol

INTRODUCTION

Although a considerable proportion of Asians in the USA experience depression, anxiety and poor sleep, these health issues have been underestimated due to the model minority myth about Asians, the stigma associated with mental illness, lower rates of treatment seeking and a shortage of culturally tailored mental health services. Indeed, despite emerging evidence of links between psychosocial risk factors, the gut microbiome and depression, anxiety and sleep quality, very few studies have examined how these factors are related in Chinese and Korean immigrants in the USA. The purpose of this pilot study was to address this issue by (a) testing the usability and feasibility of the study's multilingual survey measures and biospecimen collection procedure among Chinese and Korean immigrants in the USA and (b) examining how stress, discrimination, acculturation and the gut microbiome are associated with depression, anxiety and sleep quality in this population.

METHOD AND ANALYSIS

This is a cross-sectional pilot study among first and second generations of adult Chinese and Korean immigrants in the greater Atlanta area (Georgia, USA). We collected (a) gut microbiome samples and (b) data on psychosocial risk factors, depression, anxiety and sleep disturbance using validated, online surveys in English, Chinese and Korean. We aim to recruit 60 participants (30 Chinese, 30 Korean). We will profile participants' gut microbiome using 16S rRNA V3-V4 sequencing data, which will be analysed by QIIME 2. Associations of the gut microbiome and psychosocial factors with depression, anxiety and sleep disturbance will be analysed using descriptive and inferential statistics, including linear regression.

ETHICS AND DISSEMINATION

This study has been approved by the Institutional Review Board at Emory University (IRB ID: STUDY00000935). Results will be made available to Chinese and Korean community members, the funder and other researchers and the broader scientific community.

Gut Microbiome Associated with the Psychoneurological Symptom Cluster in Patients with Head and Neck Cancers

Cancer patients experience a cluster of co-occurring psychoneurological symptoms (PNS) related to cancer treatments. The gut microbiome may affect severity of the PNS via neural, immune, and endocrine signaling pathways. However, the link between the gut microbiome and PNS has not been well investigated in cancer patients, including those with head and neck cancers (HNCs). This pilot study enrolled 13 patients with HNCs, who reported PNS using the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (CTCAEs). Stool specimens were collected to analyze patients' gut microbiome. All data were collected pre- and post-radiation therapy (RT). Associations between the bacterial abundances and the PNS clusters were analyzed using the linear discriminant analysis effect size; functional pathway analyses of 16S rRNA V3-V4 bacterial communities were conducted using Tax4fun. The high PNS cluster had a greater decrease in microbial evenness than the low PNS cluster from pre- to post-RT. The high and low PNS clusters showed significant differences using weighted UniFrac distance. Those individuals with the high PNS cluster were more likely to have higher abundances in phylum Bacteroidetes, order Bacteroidales, class Bacteroidia, and four genera (Ruminiclostridium9, Tyzzerella, Eubacterium_fissicatena, and DTU089), while the low PNS cluster had higher abundances in family Acidaminococcaceae and three genera (Lactococcus, Phascolarctobacterium, and Desulfovibrio). Both glycan metabolism (Lipopolysaccharide biosynthesis) and vitamin metabolism (folate biosynthesis and lipoic acid metabolism) were significantly different between the high and low PNS clusters pre- and post-RT. Our preliminary data suggest that the diversity and abundance of the gut microbiome play a potential role in developing PNS among cancer patients.