Advances in high-throughput sequencing (HTS) have fostered rapid developments in the field of microbiome research, and massive microbiome datasets are now being generated. However, the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field. Here, we systematically summarize the advantages and limitations of microbiome methods. Then, we recommend specific pipelines for amplicon and metagenomic analyses, and describe commonly-used software and databases, to help researchers select the appropriate tools. Furthermore, we introduce statistical and visualization methods suitable for microbiome analysis, including alpha- and beta-diversity, taxonomic composition, difference comparisons, correlation, networks, machine learning, evolution, source tracing, and common visualization styles to help researchers make informed choices. Finally, a step-by-step reproducible analysis guide is introduced. We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.

As an acute febrile and inflammatory disease, Kawasaki disease (KD) could develop Kawasaki disease shock syndrome (KDSS) sometimes. However its pathogenesis was still not well known. This study was to learn more about the clinical features and evaluate the role of cytokines in the pathogenesis of KDSS. We collected clinical and laboratory data retrospectively for all patients with KDSS(KDSS, n = 27)who were hospitalized at our hospital from Jan 2014 to Oct 2017. For patient with KDSS, we randomly identified 43 patients with KD as control subjects (KD, n = 43). Clinical features, laboratory evaluations were collected. Cytokines IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ in serum were assayed using flow cytometric bead array. The patients with KDSS were older age (43.41 ± 31.42 vs 28.81 ± 21.51 months, P < 0.05), longer duration of fever (10.63 ± 5.12 vs 6.98 ± 2.45 days, P < 0.05), higher WBC count, neutrophils, CRP, ESR, PCT and D-dimer, and lower hemoglobin and albumin, more severe hyponatremia and hypokalemia, more refractory to IVIG therapy, more coronary artery abnormalities (CAAs), aseptic meningitis, and longer duration of hospitalization than patients with KD (all P < 0.05). The levels of serum IL-6 [184.1 (27.7–2577.3) vs 54.1 (4–425) pg/ml], IL-10 [42.6 (5–236.7) vs 9.4 (3–94) pg/ml], TNF-α [2.6 (1.0–23.4) vs 2.1 (1–6) pg/ml] and IFN-γ [18.3 (4.5–94.4) vs 6.7 (2–56) pg/ml] in KDSS patients were significant higher than KD patients (all P < 0.05). ROC curves showed that 66.7 pg/ml of IL-6, 20.85 pg/ml of IL-10 and 8.35 pg/ml of IFN-γ had sensitivity and specificity for identifying KDSS as 85.2 and 62.8%; 66.7 and 83.7%; 74.1 and 74.4% respectively. No fatality was recorded in this series. KDSS were characteristic as more cytokine production and prone to developing IVIG non-responsiveness and CAAs. KD patients with IL-6 above 66.7 pg/ml, IL-10 above 20.85 pg/ml, and IFN-γ above 8.35 pg/ml suggested higher risk for KDSS.

The purpose of this review is to provide medical researchers, especially those without a bioinformatics background, with an easy-to-understand summary of the concepts and technologies used in microbiome research. First, we define primary concepts such as microbiota, microbiome, and metagenome. Then, we discuss study design schemes, the methods of sample size calculation, and the methods for improving the reliability of research. We emphasize the importance of negative and positive controls in this section. Next, we discuss statistical analysis methods used in microbiome research, focusing on problems with multiple comparisons and ways to compare β-diversity between groups. Finally, we provide step-by-step pipelines for bioinformatics analysis. In summary, the meticulous study design is a key step to obtaining meaningful results, and appropriate statistical methods are important for accurate interpretation of microbiome data. The step-by-step pipelines provide researchers with insights into newly developed bioinformatics analysis methods.

The current document is based on a consensus reached by a panel of experts from the Chinese Society of Allergy and the Chinese Society of Otorhinolaryngology-Head and Neck Surgery, Rhinology Group. Chronic rhinosinusitis (CRS) affects approximately 8% of Chinese adults. The inflammatory and remodeling mechanisms of CRS in the Chinese population differ from those observed in the populations of European descent. Recently, precision medicine has been used to treat inflammation by targeting key biomarkers that are involved in the process. However, there are no CRS guidelines or a consensus available from China that can be shared with the international academia. The guidelines presented in this paper cover the epidemiology, economic burden, genetics and epigenetics, mechanisms, phenotypes and endotypes, diagnosis and differential diagnosis, management, and the current status of CRS in China. These guidelines-with a focus on China-will improve the abilities of clinical and medical staff during the treatment of CRS. Additionally, they will help international agencies in improving the verification of CRS endotypes, mapping of eosinophilic shifts, the identification of suitable biomarkers for endotyping, and predicting responses to therapies. In conclusion, these guidelines will help select therapies, such as pharmacotherapy, surgical approaches and innovative biotherapeutics, which are tailored to each of the individual CRS endotypes.

Background: Recent studies have suggested that the gut microbiota is altered in children with juvenile idiopathic arthritis (JIA). However, age, sex, and body mass index (BMI) were not matched in the previous studies, and the results are inconsistent. We conducted an age-, sex-, and BMI-matched cross-sectional study to characterize the gut microbiota in children with JIA, and evaluate its potential in clinical prediction. Methods: A total of 40 patients with JIA and 42 healthy controls, ranging from 1 to 16 years, were enrolled in this study. Fecal samples were collected for 16S rDNA sequencing. The data were analyzed using QIIME software and R packages. Specifically, the random forest model was used to identify biomarkers, and the receiver operating characteristic curve and the decision curve analysis were used to evaluate model performance. Results: A total of 39 fecal samples from patients with JIA, and 42 fecal samples from healthy controls were sequenced successfully. The Chao 1 and Shannon–Wiener index in the JIA group were significantly lower than those in the control group, and the Bray-Curtis dissimilarity also differed significantly between the two groups. The relative abundance of 4 genera, Anaerostipes, Dialister, Lachnospira, and Roseburia, decreased significantly in the JIA group compared to those in the control group. The 4 genera included microbes that produce short-chain fatty acids (SCFAs) and were negatively correlated with some rheumatic indices. Moreover, 12 genera were identified as potential biomarkers by using the nested cross-validation function of the random forest. A random forest model constructed using these genera was able to differentiate the patients with JIA from the healthy controls, and the area under the receiver operating characteristic curve was 0.7975. The decision curve analysis indicated that the model had usefulness in clinical practice. Conclusions: The gut microbiota in patients with JIA is altered and characterized by a decreased abundance of 4 SCFA-producing genera. The decreases in the 4 genera correlated with more serious clinical indices. Twelve genera could be used as biomarkers and predictors in clinical practice. Trial registration: The study is registered online at the Chinese Clinical Trial Registry on 11 May 2018 (registration number: ChiCTR1800016110).