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It is difficult for beginners to learn and use amplicon analysis software because there are so many software tools to choose from, and all of them need multiple steps of operation. Herein, we provide a cross-platform, opensource, and community-supported analysis pipeline EasyAmplicon. Easy-Amplicon has most of the modules needed for an amplicon analysis, including data quality control, merging of paired-end reads, dereplication, clustering or denoising, chimera detection, generation of feature tables, taxonomic diversity analysis, compositional analysis, biomarker discovery, and publication-quality visualization. EasyAmplicon includes more than 30 cross-platform modules and R packages commonly used in the field. All steps of the pipeline are integrated into RStudio, which reduces learning costs, keeps the flexibility of the analysis process, and facilitates personalized analysis. The pipeline is maintained and updated by the authors and editors of WeChat official account "Meta-genome." Our team will regularly release the latest tutorials both in Chinese and English, read the feedback from users, and provide help to them in the WeChat account and GitHub. The pipeline can be deployed on various platforms, and the installation time is less than half an hour. On an ordinary laptop, the whole analysis process for dozens of samples can be completed within 3 h. The pipeline is available at GitHub (https://github.com/YongxinLiu/EasyAmplicon) and Gitee (https:// gitee.com/YongxinLiu/EasyAmplicon).

Cucumber plants subjected to consecutive monoculture for 9 years were found to suffer from severe Fusarium wilt disease, caused by the soil-borne fungus Fusarium oxysporum f. sp. Cucumerinum J. H. Owen. In the present study, greenhouse experiments were performed to evaluate the influence of ammonia gas fumigation on Fusarium wilt suppression, fungal abundance and fungal community composition. Results showed that ammonia gas fumigation remarkably reduced disease incidence from 80% to 27%, resulting in a four-fold increase in yield, compared to the control. Total fungal abundance declined dramatically after fumigation and reached the lowest level at day 32, at 243 times lower than the control. Moreover, fumigation significantly increased soil fungal diversity, though it also decreased considerably coinciding with cucumber growth. Fumigation also significantly altered soil fungal community composition, relative to the control. Fusarium was strongly inhibited by fumigation in both relative abundance (3.8 times lower) and targeted quantification (a decrease of 167 fold). Collectively, the application of ammonia gas fumigation to control Fusarium wilt of cucumber resulted in a re-assembly of the fungal community to resemble that of a non-disease conducive consortium. Additional strategies, such as bioorganic fertilizer application, may still be required to develop sustainable disease suppression following fumigation.

The gut microbiota undergoes rapid changes during infancy in response to early-life exposures. We have investigated how the infant gut bacterial community matures over time and how exposures such as human milk and antibiotic treatment alter gut microbiota development. We used the LonGP program to create predictive models to determine the contribution of exposures on infant gut bacterial abundances from one month to two years of age. These models indicate that infant antibiotic use, human milk intake, maternal pre-pregnancy BMI, and sample shipping time were associated with changes in gut microbiome composition. In most infants, Bacteroides, Lachnospiraceae unclassified, Faecalibacterium, Akkermansia, and Phascolarctobacterium abundance increased rapidly after 6 months, while Escherichia, Bifidobacterium, Veillonella, and Streptococcus decreased in abundance over time. Individual, time-varying, random effects explained most of the variation in the LonGP models. Multivariate association with linear models (MaAsLin) displayed partial agreement with LonGP in the predicted trajectories over time and in relation to significant factors such as human milk intake. Multiple factors influence the dynamic changes in bacterial composition of the infant gut. Within-individual differences dominate the temporal variations in the infant gut microbiome, suggesting individual temporal variability is an important feature to consider in studies with a longitudinal sampling design.

Background Maternal pre-pregnancy obesity and human milk feeding have been associated with altered infant gut microbiota. Research aim Determine the relationships between maternal pre-pregnancy BMI, human milk exposure, and their influence on the infant microbiota simultaneously. Methods This was a cross-sectional study of infants at 6 months of age ( N = 36), a time when many infants are fed a mixed diet of human milk and other foods. Fecal samples and participant information were collected from a subset of dyads enrolled in two related prospective cohorts (ARCHGUT and BABYGUT) in Michigan. Sequencing the V4 region of the 16S gene was used to analyze fecal bacterial samples collected from 6-month-old infants. Participants were grouped into four categories designated by their extent of human milk exposure (100%, 80%, 50%–80%, ≤ 20% human milk in the infant diet) and by maternal pre-pregnancy BMI category (normal, overweight, obese). Results Fewer participants with pre-pregnancy obesity were breastfeeding at 6 months postpartum compared to non-obese participants (35.7% and 81.8%, respectively). In univariate analyses, maternal pre-pregnancy BMI and human milk exposure were both significantly associated with alpha and beta diversity of the infant microbiota. However, in multivariate analyses, human milk exposure accounted for 20% of the variation in alpha diversity, but pre-pregnancy BMI was not significantly associated with any form of microbiota diversity. Conclusions The proportion of the infant diet that was human milk at 6 months was the major determinant of alpha and beta diversity of the infant. Maternal obesity contributes to the gut microbiota by its association with the extent of human milk feeding.

Microorganisms are the most abundant and diverse organisms in soils and have important effects on soil fertility. In this study, effects of the long-term fertilization treatments no fertilizer (CK), chemical fertilizer (nitrogen–phosphorus–potassium (NPK)), and organic–inorganic fertilizer (NPK and organic fertilizer (NPKM)) on rice yield and soil bacterial and fungal community diversity, structure, composition, and interaction networks were evaluated. Of the three treatments, the highest rice yield was in NPKM. Bacterial richness was significantly higher in NPKM than in NPK. Fertilization treatment significantly altered β diversity of communities, species composition of bacterial and fungal communities, and structure of soil microbial networks. The most complex bacterial and fungal interaction co-occurrence network with the highest average degree and numbers of edges and nodes was in NPKM. Relative abundance of the plant growth-promoting fungus Trichoderma increased significantly in NPKM compared with CK and NPK. The results of the study indicate that bacterial richness and microbial community member interactions (network complexity) might be suitable indicators of soil biological fertility. This research provides new insights on the effects of different fertilization regimes on responses of soil bacterial and fungal communities and their contributions to crop yield. New indicators such as bacterial richness and complexity of microbial interaction networks are also identified that can be used to evaluate soil biological fertility.

iMeta" is an open-access Wiley partner journal launched by iMeta Science Society consisting of worldwide scientists in bioinformatics and metagenomics. iMeta aims to promote microbiome and bioinformatics research by publishing original research, methods/protocols, and reviews. The goal is to publish high-quality papers (top 10%) targeting a broad audience. Unique features include video submission, reproducible analysis, figure polishing, APC waiver, and promotion by social media with 500,000 followers [1]. In 2022, iMeta released four issues (Figure 1), including 60 publications with a total of 340 citations (https://app.dimensions.ai/ by December 31, 2022). The top three highly cited papers were ImageGP [2], Majorbio Cloud [3], and Sangerbox [4], with more than 50 citations, respectively. Since the first paper was published in February and the first issue was released on March 2022, publications This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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