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ABSTRACT Plants modulate the soil microbiota by root exudation assembling a complex rhizosphere microbiome with organisms spanning different trophic levels. Here, we assessed the diversity of bacterial, fungal and cercozoan communities in landraces and modern varieties of wheat. The dominant taxa within each group were the bacterial phyla Proteobacteria, Actinobacteria and Acidobacteria; the fungi phyla Ascomycota, Chytridiomycota and Basidiomycota; and the Cercozoa classes Sarcomonadea, Thecofilosea and Imbricatea. We showed that microbial networks of the wheat landraces formed a more intricate network topology than that of modern wheat cultivars, suggesting that breeding selection resulted in a reduced ability to recruit specific microbes in the rhizosphere. The high connectedness of certain cercozoan taxa to bacteria and fungi indicated trophic network hierarchies where certain predators gain predominance over others. Positive correlations between protists and bacteria in landraces were preserved as a subset in cultivars as was the case for the Sarcomonadea class with Actinobacteria. The correlations between the microbiome structure and plant genotype observed in our results suggest the importance of top-down control by organisms of higher trophic levels as a key factor for understanding the drivers of microbiome community assembly in the rhizosphere. Protists as a key factor in rhizosphere microbiome assembly was demonstrated in landraces and modern cultivars of wheat.

High-throughput amplicon sequencing (HTAS) of conserved DNA regions is a powerful technique to characterize microbial communities. Recently, spike-in mock communities have been used to measure accuracy of sequencing platforms and data analysis pipelines. To assess the ability of sequencing platforms and data processing pipelines using fungal internal transcribed spacer (ITS) amplicons, we created two ITS spike-in control mock communities composed of cloned DNA in plasmids: a biological mock community, consisting of ITS sequences from fungal taxa, and a synthetic mock community (SynMock), consisting of non-biological ITS-like sequences. Using these spike-in controls we show that: (1) a non-biological synthetic control (e.g., SynMock) is the best solution for parameterizing bioinformatics pipelines, (2) pre-clustering steps for variable length amplicons are critically important, (3) a major source of bias is attributed to the initial polymerase chain reaction (PCR) and thus HTAS read abundances are typically not representative of starting values. We developed AMPtk, a versatile software solution equipped to deal with variable length amplicons and quality filter HTAS data based on spike-in controls. While we describe herein a non-biological SynMock community for ITS sequences, the concept and AMPtk software can be widely applied to any HTAS dataset to improve data quality.

Recently, a new annotation tool “FungalTraits” was created based on the previous FUNGuild and Fun Fun databases, which has attracted high attention in the scientific community. These databases were widely used to gain more information from fungal sequencing datasets by assigning fungal functional traits. More than 1500 publications so far employed FUNGuild and the aim of this study is to compare this successful database with the recent FungalTraits database. Quality and quantity of the assignment by FUNGuild and FungalTraits to a fungal internal transcribed spacer (ITS)–based amplicon sequencing dataset on amplicon sequence variants (ASVs) were addressed. Sequencing dataset was derived from leaves and needles of 12 temperate broadleaved and coniferous tree species. We found that FungalTraits assigned more functional traits than FUNGuild, and especially the coverage of saprotrophs, plant pathogens, and endophytes was higher while lichenized fungi revealed similar findings. Moreover, ASVs derived from leaves and needles of each tree species were better assigned to all available fungal traits as well as to saprotrophs by FungalTraits compared to FUNGuild in particular for broadleaved tree species. Assigned ASV richness as well as fungal functional community composition was higher and more diverse after analyses with FungalTraits compared to FUNGuild. Moreover, datasets of both databases showed similar effect of environmental factors for saprotrophs but for endophytes, unidentical patterns of significant corresponding factors were obtained. As a conclusion, FungalTraits is superior to FUNGuild in assigning a higher quantity and quality of ASVs as well as a higher frequency of significant correlations with environmental factors.

In 16S rRNA gene sequencing, certain bacterial genera were found to be underrepresented or even missing in taxonomic profiles when using unsuitable primer combinations, outdated reference databases, or inadequate pipeline settings. Concerning the last, quality thresholds as well as bioinformatic settings (i.e., clustering approach, analysis pipeline, and specific adjustments such as truncation) are responsible for a number of observed differences between studies. Short-amplicon 16S rRNA gene sequencing is currently the method of choice for studies investigating microbiomes. However, comparative studies on differences in procedures are scarce. We sequenced human stool samples and mock communities with increasing complexity using a variety of commonly used protocols. Short amplicons targeting different variable regions (V-regions) or ranges thereof (V1-V2, V1-V3, V3-V4, V4, V4-V5, V6-V8, and V7-V9) were investigated for differences in the composition outcome due to primer choices. Next, the influence of clustering (operational taxonomic units [OTUs], zero-radius OTUs [zOTUs], and amplicon sequence variants [ASVs]), different databases (GreenGenes, the Ribosomal Database Project, Silva, the genomic-based 16S rRNA Database, and The All-Species Living Tree), and bioinformatic settings on taxonomic assignment were also investigated. We present a systematic comparison across all typically used V-regions using well-established primers. While it is known that the primer choice has a significant influence on the resulting microbial composition, we show that microbial profiles generated using different primer pairs need independent validation of performance. Further, comparing data sets across V-regions using different databases might be misleading due to differences in nomenclature (e.g., Enterorhabdus versus Adlercreutzia ) and varying precisions in classification down to genus level. Overall, specific but important taxa are not picked up by certain primer pairs (e.g., Bacteroidetes is missed using primers 515F-944R) or due to the database used (e.g., Acetatifactor in GreenGenes and the genomic-based 16S rRNA Database). We found that appropriate truncation of amplicons is essential and different truncated-length combinations should be tested for each study. Finally, specific mock communities of sufficient and adequate complexity are highly recommended. IMPORTANCE In 16S rRNA gene sequencing, certain bacterial genera were found to be underrepresented or even missing in taxonomic profiles when using unsuitable primer combinations, outdated reference databases, or inadequate pipeline settings. Concerning the last, quality thresholds as well as bioinformatic settings (i.e., clustering approach, analysis pipeline, and specific adjustments such as truncation) are responsible for a number of observed differences between studies. Conclusions drawn by comparing one data set to another (e.g., between publications) appear to be problematic and require independent cross-validation using matching V-regions and uniform data processing. Therefore, we highlight the importance of a thought-out study design including sufficiently complex mock standards and appropriate V-region choice for the sample of interest. The use of processing pipelines and parameters must be tested beforehand.

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, open‐source, and community‐supported analysis pipeline EasyAmplicon. EasyAmplicon 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). EasyAmplicon is a user‐friendly, cross‐platform, and community‐supported pipeline for amplicon data analysis. It has most of the modules for data processing and visualization in microbiome research. The pipeline is maintained and updated regularly. We encourage users to contribute appropriate code. Highlights EasyAmplicon is a user‐friendly, cross‐platform, and community‐supported pipeline for amplicon data analysis. It has most of the modules for data processing and visualization in microbiome research. The pipeline is maintained and updated regularly, and we encourage users to contribute appropriate code.

Background Amplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (less OTU scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences. Results In LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios. We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines, yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxon composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified taxa and a higher fraction of reads assigned to true taxa (48% and 57% at species; 83% and 98% at genus level, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reported 16S sequences. Conclusion LotuS2 is a lightweight and user-friendly pipeline that is fast, precise, and streamlined, using extensive pre- and post-ASV/OTU clustering steps to further increase data quality. High data usage rates and reliability enable high-throughput microbiome analysis in minutes. Availability LotuS2 is available from GitHub, conda, or via a Galaxy web interface, documented at http://lotus2.earlham.ac.uk/ . 6hTcPpJ8wvT9bQx45qQZLR Video Abstract

Data visualization plays a crucial role in illustrating results and sharing knowledge among researchers. Though many types of visualization tools are widely used, most of them require enough coding experience or are designed for specialized usages, or are not free. Here, we present ImageGP, a specialized visualization platform designed for biology and chemistry data illustration. ImageGP could generate generalized plots like lines, bars, scatters, boxes, sets, heatmaps, and histograms with the most common input content in a user‐friendly interface. Normally plotting using ImageGP only needs a few mouse clicks. For some plots, one only needs to just paste data and click submit to get the visualization results. Additionally, ImageGP supplies up to 26 parameters to meet customizable requirements. ImageGP also contains specialized plots like volcano plot, functional enrichment plot for most omics‐data analysis, and other four specialized functions for microbiome analysis. Since 2017, ImageGP has been running for nearly 5 years and serving 336,951 visits from all over the world. Together, ImageGP (http://www.ehbio.com/ImageGP/) is an effective and efficient tool for experimental researchers to comprehensively visualize and interpret data generated from wet‐lab and dry‐lab. Representative visualization results of ImageGP. ImageGP supports 16 types of images (including heatmap, volcano plot, enrichment bubble plot) and four types of online analysis with up to 26 parameters for customization. Highlights Publication‐quality visualization results. Easy to use and customize. Reproducible results with scripts.

Background Detecting species at low abundance, including aquatic invasive species (AIS), is critical for making informed management decisions. Environmental DNA (eDNA) methods have become a powerful tool for rare or cryptic species detection; however, many eDNA assays offer limited utility for community‐level analyses due to their use of species‐specific (presence/absence) ‘barcodes’. Metabarcoding methods provide information on entire communities based on sequencing of all taxon‐specific barcodes within an eDNA sample. Aims Evaluate measures of fish species detections, community diversity, and estimates of relative abundance based on eDNA metabarcoding and traditional fisheries sampling approaches in the context of fish community characterization and AIS survellience. Materials and Methods In 2016, eight limnologically diverse lakes (surface area range: 13 – 1,728 ha) in Michigan, USA were sampled using a variety of traditional fisheries gears to characterize fish community composition. Environmental DNAs from surface (33 ± 6, mean ± 1 SD) and benthic (14 ± 2) water samples from each lake were isolated and amplified for two metabarcoding markers (mitochondrial 12S and 16S rDNA loci) using fish‐specific primers. Fish species detected within each lake were determined by comparing the sequencing data to a database of sequences from native Michigan fish species and 19 AIS on the Michigan's Watch List. Results Analysis of species accumulation curves indicated multi‐locus eDNA metabarcoding assays can enhance species detection capacities and characterize 95% of a fish community in fewer sampling efforts than traditional gear (range: 2 – 62, median: 14). In addition, all AIS detected in traditional gear samples were also detected by eDNA, while some AIS detected by eDNA assays were absent from traditional gear samples. Discussion Results reported here are, in part, driven by the lack of species‐selectivity during eDNA sampling events. Given the efficacy of eDNA assays, we suggest multi‐locus eDNA metabarcoding assays be implemented in early detection efforts. The early detection of aquatic invasive species (AIS) is critical for a range of conservation issues. Environmental DNA (eDNA) metabarcoding methods (dark grey) were compared to traditional fisheries gears (light grey) to evaluate how each characterized a fish community. Results suggest that fish communities, including low abundance AIS, could be characterized more fully and in fewer samples with a multi‐locus eDNA metabarcoding assay.

Background Amplicon sequencing of Mycobacterium tuberculosis resistance-associated genes offers a cost-effective alternative to whole-genome sequencing for rapid profiling of infections and guiding clinical management. However, existing assays require frequent manual updates to accommodate emerging resistance mutations, limiting scalability and responsiveness. Results We present TOAST (Tuberculosis Optimised Amplicon Sequencing Tool), a novel software tool that automates primer design by integrating mutation frequencies from a curated database of over 68,000 drug-resistant M. tuberculosis genomes. TOAST prioritises regions with the highest clinical relevance, accounting for single-nucleotide polymorphisms, insertions, and deletions. The software supports customisation of design parameters such as amplicon length, melting temperature, and GC content, while screening for undesirable primer properties, including self-dimers and off-target binding. Using TOAST, we designed a multiplex panel of 33 amplicons targeting mutations associated with resistance to 13 anti-TB drugs. These amplicons covered over 97% of resistance mutations in a 68 K isolate database and were validated using Oxford Nanopore sequencing of two clinical samples, achieving high uniform coverage with a minimum sequencing depth exceeding 50-fold across all targets. Conclusions TOAST represents a major advancement in targeted TB sequencing by integrating large-scale clinical genomic data directly into assay design. This enables rapid, high-coverage, and adaptable amplicon sequencing, enhancing diagnostic precision and surveillance capabilities for drug-resistant TB. TOAST’s framework is also extensible to other pathogens, supporting broader applications in infectious disease genomics.

Spaeth, M.K.; Miller, T.E.; Gornish, E.S., and Barberán, A., 2025. Soil microbial community diversity varies by coastal dune successional stage. Journal of Coastal Research, 41(5), 799–810. Charlotte (North Carolina), ISSN 0749-0208. Coastal sand dunes play a vital role in protecting inland ecosystems and urban areas against storm surges and sea-level rise, which will become increasingly important under human-induced climate change. While plant community dynamics have been found to benefit coastal dune resilience and resistance to disturbances, there has been relatively little investigation of soil microbial communities. Soil microorganisms play a vital role in coastal ecosystems by contributing to soil development, stabilization, and the mediation of plant productivity and diversity. In nutrient-poor environments like sand dunes, plant-microbe interactions are crucial due to their reliance on resource exchange. This study investigated soil microbial communities and whether they are primarily driven by abiotic or biotic conditions across successional habitats of a coastal dune system at St. George Island, Florida, southeastern United States, to help elucidate dune function and resilience. By assessing bacterial and fungal amplicon sequences across a disturbance gradient, it was found that different successional habitats harbored distinct soil communities, which varied in taxonomic diversity and microbial functional groups. Bacteria showed the greatest diversity in interdunes, whereas fungal diversity was greatest in the foredunes. Additionally, it was found that plant and soil characteristics were differentially associated with the soil microbial community based on habitat age, with pH and soil moisture as important compositional drivers. Overall, these findings highlight how coastal dune dynamics differentially influence above- and belowground biotic communities.