mgieasy environmental microbiome dna extraction kit  (Complete Genomics Inc)

Journal: Frontiers in Microbiology

Article Title: Multi-omics reveals glutinous rice varieties shape Baijiu flavor via microbial and metabolic modulation

doi: 10.3389/fmicb.2025.1721127

Figure Lengend Snippet: Metagenomic analysis of microbial communities in Zaopei. (A) Stacked bar chart of species-level relative abundances showing the top 30 most abundant taxa; others were grouped as “Other,” and kingdom-level classification. (B) Alpha diversity indices represent species richness and evenness. (C) Heatmap of significantly different functional categories. Non-parametric statistical tests (Wilcoxon/Kruskal–Wallis) were applied, and the top 30 categories with p < 0.05 were visualized. (D) LDA score plot based on LEfSe analysis, indicating taxa with significant differences among groups; the x -axis shows LDA scores, and the y -axis lists discriminatory taxa. (E) Principal component analysis (PCA) plot with ANOSIM test assessing group-level differences. (F) Venn diagram of differentially abundant species. (G) Boxplot of beta diversity illustrating within-group variability.

Article Snippet: Total microbial DNA was extracted from Zaopei samples collected on day 8 of fermentation, with three biological replicates for each sample, using the MGIEasy Microbiome DNA & RNA Extraction Kit (MGI Tech, China).

Techniques: Functional Assay

Journal: Endocrine Connections

Article Title: Rapid and selective gut microbiome modulation by polyherbal formulation in type 2 diabetes

doi: 10.1530/EC-25-0463

Figure Lengend Snippet: CCA and taxonomic composition of the gut microbiome. (A) CCA of gut microbiome profiles in PHF-treated patients (salmon) and the placebo group (pale green) before and after the 7-day intervention. Time points are distinguished by shape: circles indicate the baseline (before intervention) and triangles indicate the post-intervention period. The colored ellipses represent grouping by study arm (PHF or placebo) and time (samples collected pre- or post-intervention). The arrows represent the centroids of the constrained factors (arm and time) in the CCA model. Only arm was identified as a significant contributor to microbiome variation by PERMANOVA; time is shown for visualization. (B) Taxonomic composition of the 20 most abundant bacterial species across all samples, shown as relative abundances in stacked bar plots. The samples are grouped by study arm and time (placebo group before intervention, placebo group after intervention, polyherbal formulation group before intervention, and polyherbal formulation group after intervention), with taxa ordered by the mean abundance within each group. CCA: canonical correspondence analysis; PHF: polyherbal formulation.

Article Snippet: DNA extraction was performed with the MGIEasy Stool Microbiome DNA Extraction Kit on the MGISP-960 automated platform (MGI Tech Co., Ltd, China).

Techniques: Formulation

Journal: Cell

Article Title: Microbial ecosystems and ecological driving forces in the deepest ocean sediments.

doi: 10.1016/j.cell.2024.12.036

Figure Lengend Snippet: Figure 1. Sampling and extraordinary nov- elty of the Deepest Ocean microbiome re- vealed by the MEER dataset (A) Sampling sites of hadal zones using the hu- man-occupied vehicle (HOV) Fendouzhe in this study, including the Philippine Basin (PB), the Yap Trench (YT), and the Mariana Trench (MT), as well as the bottom (Bt), northern slope (NS), and southern slope (SS) within the Mariana Trench. (B) Comparative analysis between the species- level representative genomes (SRGs) from the MEER dataset and the species-level genomes in the Ocean Microbiomics Database (OMD), the typical deep-sea and hadal sediment. The OMD included isolated reference genome (ISO) of Global Ocean Reference Genomes (GORG), sin- gle-cell amplified genome (SAG) of MAR Database (MARD), as well as seawater metagenomes of abyssopelagic layer (ABY, 4,500–6,000 m), bathypelagic layer (BAT, 1,000–4,500 m), meso- pelagic layer (MES, 200–1,000 m), and epipelagic layer (EPI, 0–200 m). Doughnut charts illustrate the sample habitat distribution in each habitat. Bar plots depict the distribution of MEER SRGs de- tected in reference habitats. (C) Taxonomic novelty of the MEER dataset against the Genome Taxonomy Database (GTDB, release 220). Light bars extending leftward indi- cate the finest taxonomic level to which SRGs or 16S rRNA gene amplicon sequence variants (ASVs) can be annotated. Solid bars extending rightward represent unreported taxa at each taxonomic level. (D) De novo phylogenetic tree of SRGs, color- coded by the 10 most abundant phyla in the MEER dataset. Purple shapes indicate the placement of three bacterial SRGs that could not be assigned to known phyla according to GTDB, with subtrees showing their neighboring clades and relative evolutionary divergence (RED) values. Note that the bacterial and archaeal trees were combined for visualization purpose but were not rooted together. (E) Distribution of novelty among SRGs and 16S rRNA gene ASVs across the 10 most abundant phyla. Stacked bar plots represent the number of unreported and known SRGs or ASVs, while or- ange lines and dots indicate the percentage of novelty. See also Figure S1 and Tables S1 and S2.

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Biological samples Sediment samples Collected from the Mariana Trench, the Yap Trench and the Philippine Basin MEER Critical commercial assays MGIEasy Environmental Microbiome DNA Extraction Kit MGI-Tech, China Cat#940-001731-00 MGIEasy Fast FS DNA Library Prep Set MGI-Tech, China Cat#940-000030-00 Deposited data Raw data This paper CNSA: CNP0004890 Raw data This paper NODE: OEP004067 Shotgun metagenome-derived specieslevel representative genomes This paper eLMSG: LMSG_G000037053.1 – LMSG_G000044616.1 Source code for quality control, assembly, binning, classification, and abundance profiling This paper https://github.com/meer-trench/genome_ catalogue Source code for ecological analyses This paper https://doi.org/10.5281/zenodo.13317475 Ocean Microbiomics Database Paoli et al.30 https://microbiomics.io/ocean/ GTDB database release 220 Parks et al.49 https://gtdb.ecogenomic.org/ SILVA database version 138 Pruesse et al.79 https://www.arb-silva.de/ Greengenes2 database release 2022.10 McDonald et al.52 https://ftp.microbio.me/greengenes_ release/2022.10/ Oligonucleotides 515F (Parada), Forward 5’-GTGYCAGCMGCCGCGGTAA-3’ N/A 806R (Apprill), Reverse 5’-GGACTACNVGGGTWTCTAAT-3’ N/A Software and algorithms QIMME2 Bolyen et al.80 https://github.com/qiime2 RESCRIPt (2024.10.0.dev0+7.g3c179ca) Robeson et al.81 https://github.com/bokulich-lab/RESCRIPt Cutadapt Martin et al.82 https://cutadapt.readthedocs.io/en/stable/ DADA2 Callahan et al.83 https://github.com/benjjneb/dada2 fastp (v0.23.2) Chen et al.84 https://github.com/OpenGene/fastp MEGAHIT (v1.2.9) Li et al.85 https://github.com/voutcn/megahit MetaBAT2 (v2.12.1) Kang et al.86 https://bitbucket.org/berkeleylab/metabat BWA (v0.7.17-r1188) Li et al.87 https://github.com/lh3/bwa dRep (v3.4.0) Olm et al.76 https://github.com/MrOlm/drep CoverM (v0.7.0) N/A https://github.com/wwood/CoverM GTDB-Tk (v2.4.0) Chaumeil et al.88 https://github.com/Ecogenomics/GTDBTk Prodigal Hyatt et al.89 https://github.com/hyattpd/Prodigal Mantis Queirós et al.90 https://github.com/PedroMTQ/mantis R (v4.1.0) R Project https://www.r-project.org/ R Studio Server v2023.12.1.402 Posit https://posit.co/products/open-source/ rstudio-server/ ggtree (v3.10.1) Yu et al.91 https://github.com/YuLab-SMU/ggtree ggtreeExtra (v1.12.0) Xu et al.92 https://github.com/YuLab-SMU/ ggtreeExtra vegan (v2.6-4) Oksanen et al.93 https://github.com/vegandevs/vegan (Continued on next page) e1 Cell 188, 1363–1377.e1–e3, March 6, 2025

Techniques: Sampling, Northern Blot, Isolation, Amplification, Sequencing