Amplicon Sequencing Data Research Articles

GNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes.

In recent years, omics technologies have offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user-friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to provide a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline. Here, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from 2 to 4 distinct omics data types, including 16S ribosomal RNA (rRNA) gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration, and visualization approaches, enhancing the toolkit for a more insightful analysis of microbiomes. The functionality of these new features is showcased through the use of 4 microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives. gNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, offering novel insights in both host-associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.

AmpliSAS and AmpliHLA: Web Server and Local Tools for MHC Typing of Non-model Species and Human Using NGS Data.

AmpliSAS and AmpliHLA are tools for automatic genotyping of MHC genes from high-throughput sequencing data. AmpliSAS is designed specifically to analyze amplicon sequencing data from non-model species and it is able to perform de novo genotyping without any previous knowledge of the reference alleles. AmpliHLA is a human specific version; it performs HLA typing by comparing sequenced variants against human reference alleles from the IMGT/HLA database. Both tools are available in AmpliSAT web-server as well as scripts for local/server installation. Here we describe the installation and deployment of AmpliSAS and AmpliHLA Perl scripts and dependencies on a local or a server computer. We will show how to run them in the command line using as examples four genotyping protocols: the first two use amplicon sequencing data to genotype the MHC genes of a passerine bird and human respectively; the third and fourth present the HLA typing of a human cell line starting from RNA and exome sequencing data respectively.

Human milk metals and metalloids shape infant microbiota.

Background: The profile of metal(loid)s in human milk is essential for infant growth and development, yet its impact on the development of the infant microbiota remains unclear. Elements, such as manganese, zinc, iron or copper, play crucial roles in influencing infant health. Aim: To investigate the metal(loid) content within human milk and its influence on the infant's gut microbiota within the first 2 months after birth. Methods: Human milk samples and infant stool samples from 77 mother-infant dyads in the MAMI cohort were collected at two time points: the early transitional stage and the mature stage. Metallomic profiling of human milk was conducted using inductively coupled plasma-mass spectrometry (ICP-MS). The infant gut microbiota was profiled through 16S rRNA amplicon sequencing and maternal-infant clinical data were available. Spearman's rank correlation coefficientsprovided insights into metal(loid)-microbiota relationships. Results: Independent cross-sectional analyses of mother-infant pairs at two time points, significant variations in metal concentrations and differences in microbial abundances and diversities were observed. Notably, Bifidobacterium genus abundance was higher during the mature lactation stage. During early lactation, we found a significant positive correlation between infant gut Corynebacterium and human milk nickel concentrations, and negative correlations between Veillonella spp. and antimony, and Enterobacter spp. and copper. Additionally, Simpson's diversity was negatively correlated with iron. In the mature lactation stage, we identified eleven significant correlations between metals and microbiota. Notably, Klebsiella genus showed multiple negative correlations with iron, antimony, and vanadium. Conclusion: Our study highlights the significance of metal(loid)-microbiota interactions in early infant development, indicating that infant gut Klebsiella genus may be particularly vulnerable to fluctuations in metal(loid) levels present in human milk, when compared to other genera. Future research should explore these interactions at a strain level and the implications on infant health and development. This trial was registered as NCT03552939.

RiboSnake - a user-friendly, robust, reproducible, multipurpose and documentation-extensive pipeline for 16S rRNA gene microbiome analysis.

Next-generation sequencing for microbial communities has become a standard technique. However, the computational analysis remains resource-intensive. With declining costs and growing adoption of sequencing-based methods in many fields, validated, fully automated, reproducible and flexible pipelines are increasingly essential in various scientific fields. We present RiboSnake, a validated, automated, reproducible QIIME2-based pipeline implemented in Snakemake for analysing 16S rRNA gene amplicon sequencing data. RiboSnake includes pre-packaged validated parameter sets optimized for different sample types, from environmental samples to patient data. The configuration packages can be easily adapted and shared, requiring minimal user input. RiboSnake is a new alternative for researchers employing 16S rRNA gene amplicon sequencing and looking for a customizable and user-friendly pipeline for microbiome analyses with in vitro validated settings. By automating the analysis with validated parameters for diverse sample types, RiboSnake enhances existing methods significantly. The workflow repository can be found on GitHub (https://github.com/IKIM-Essen/RiboSnake).

MHCtools 1.5: Analysis of MHC Sequencing Data in R.

The genes of the major histocompatibility complex (MHC) play a vital role in the vertebrate immune system and have attracted considerable interest in evolutionary biology. While the MHC has been characterized in detail in humans (human leukocyte antigen, HLA) and in model organisms such as the mouse, studies in non-model organisms often lack prior knowledge about structure, genetic variability, and evolutionary properties of this locus. MHC genotyping in non-model species commonly relies on PCR-based amplicon sequencing, and while several published protocols facilitate generation of MHC sequence data, there is a lack of transparent and standardized tools for downstream data analysis.Here, I present the R package MHCtools version 1.5, which contains 15 tools that (i) assist accurate MHC genotyping from high-throughput amplicon sequencing data, and provide standardized methods to analyze (ii) MHC diversity, (iii) MHC supertypes, and (iv) MHC haplotypes.I hope that MHCtools will be helpful in future studies of the MHC in non-model species and that it may help to advance our understanding of the important roles of the MHC in ecology and evolution.

Global citrus root microbiota unravels assembly cues and core members.

Citrus is one of the most important fruit crops worldwide, and the root-associated microbiota can have a profound impact on tree health and growth. In a collaborative effort, the International Citrus Microbiome Consortium investigated the global citrus root microbiota with samples collected from nine citrus-producing countries across six continents. We analyzed 16S rDNA and ITS2 amplicon sequencing data to identify predominant prokaryotic and fungal taxa in citrus root samples. Comparative analyses were conducted between root-associated microbial communities and those from the corresponding rhizosphere and bulk soil samples. Additionally, genotype-based group-wise comparisons were performed to assess the impact of citrus genotype on root microbiota composition. Ten predominant prokaryotic phyla, containing nine bacterial phyla including Proteobacteria, Actinobacteria, Acidobacteria, and Bacteroidetes and one archaeal phylum (Thaumarchaeota), and multiple fungal phyla including Ascomycota and Basidiomycota were identified in the citrus root samples. Compared with the microbial communities from the corresponding rhizosphere and bulk soil samples from the same trees, the prokaryotic and fungal communities in the roots exhibited lower diversity and complexity but greater modularity compared to those in the rhizosphere. In total, 30 root-enriched and 150 root-depleted genera in bacterial community were identified, whereas 21 fungal genera were enriched, and 147 fungal genera were depleted in the root niche compared with the rhizosphere. The citrus genotype significantly affected the root prokaryotic and fungal communities. In addition, we have identified the core root prokaryotic genera comprising Acidibacter, Allorhizobium, Bradyrhizobium, Chitinophaga, Cupriavidus, Devosia, Dongia, Niastella, Pseudomonas, Sphingobium, Steroidobacter and Streptomyces, and the core fungal genera including Acrocalymma, Cladosporium, Fusarium, Gibberella, Mortierella, Neocosmospora and Volutella. The potential functions of these core genera of root microbiota were predicted. Overall, this study provides new insights into the assembly of microbial communities and identifies core members of citrus root microbiota across a wide geographic range. The findings offer valuable information for manipulating root microbiota to enhance plant growth and health.

Comamonas-dominant microbial community in carbon poor aquitard sediments revealed by metagenomic-based growth rate investigation

The microbiological ecology of a low-nutrient shallow aquifer with high arsenic content in the Yinchuan Plain was investigated in this study. Amplicon sequencing data from five samples (depths: 1.5 m, 3.5 m, 11.2 m, 19.3 m, and 25.5 m) revealed diverse and adaptable microbial community. Among the microbial community, Comamonas was the most prominent, accounting for 10.52 % of the total. This genus displayed high growth rates, with a maximum growth rate of 12.06 d−1 and a corresponding doubling time of 1.38 days, as determined through an analysis of codon usage bias. Functional annotation of Metagenome-Assembled Genomes (MAGs) for samples at 1.5 m and 11.2 m depths revealed Comamonas' metabolic versatility, including various carbon pathways, assimilative sulfate reduction (ASR), and dissimilatory reduction to ammonium (DNRA). The TPM (Transcripts Per Kilobase of exon model per Million mapped reads) of MAGs at 11.2 m sample was 15.7 and 12.3. The presence of arsenic resistance genes in Comamonas aligns with sediment arsenic levels (65.8 mg/kg for 1.5 m depth, 32.8 mg/kg for 11.2 m depth). This study highlights the role of Comamonas as a ‘generalist’ bacteria in challenging oligotrophic sediments, emphasizing the significance of such organisms in community stability and ecological functions. Environmental implicationLow-biomass limits the microbial activity and biogeochemical study in oligotrophic environments, which is the typical condition for underground aquatic ecosystems. Facilitated by growth rate estimation, our research focuses on active functional microorganisms and their biogeochemical metabolic in oligotrophic aquifer sediments, revealing their impact on the environment and response to arsenic threats. Findings illuminate the metabolic advantage of a ‘generalist life-style’ in carbon-scarce environments and contribute to a broader understanding of bacterial ecosystems and environmental impacts in oligotrophic aquifer sediments worldwide.

Gut microbiota signatures in inflammatory bowel disease.

Inflammatory bowel diseases (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), affect millions of people worldwide with increasing incidence. Several studies have shown a link between gut microbiota composition and IBD, but results are often limited by small sample sizes. We aimed to re-analyze publicly available fecal microbiota data from IBD patients. We extracted original fecal 16S rRNA amplicon sequencing data from 45 cohorts of IBD patients and healthy individuals using the BioProject database at the National Center for Biotechnology Information. Unlike previous meta-analyses, we merged all study cohorts into a single dataset, including sex, age, geography, and disease information, based on which microbiota signatures were analyzed, while accounting for varying technical platforms. Among 2518 individuals in the combined dataset, we discovered a hitherto unseen number of genera associated with IBD. A total of 77 genera associated with CD, of which 38 were novel associations, and a total of 64 genera associated with UC, of which 28 represented novel associations. Signatures were robust across different technical platforms and geographic locations. Reduced alpha diversity in IBD compared to healthy individuals, in CD compared to UC, and altered microbiota composition (beta diversity) in UC and especially in CD as compared to healthy individuals were found. Combining original microbiota data from 45 cohorts, we identified a hitherto unseen large number of genera associated with IBD. Identification of microbiota features robustly associated with CD and UC may pave the way for the identification of new treatment targets.

Effects of error, chimera, bias, and GC content on the accuracy of amplicon sequencing.

Amplicon sequencing of targeted genes is the predominant approach to estimate the membership and structure of microbial communities. However, accurate reconstruction of community composition is difficult due to sequencing errors, and other methodological biases and effective approaches to overcome these challenges are essential. Using a mock community of 33 phylogenetically diverse strains, this study evaluated the effect of GC content on sequencing results and tested different approaches to improve overall sequencing accuracy while characterizing the pros and cons of popular amplicon sequence data processing approaches. The sequencing results from this study can serve as a benchmarking data set for future algorithmic improvements. Furthermore, the new insights on sequencing error, chimera formation, and GC bias from this study will help enhance the quality of amplicon sequencing studies and support the development of new data analysis approaches.

Influence of the Antibiotic Oxytetracycline on the Morphometric Characteristics and Endophytic Bacterial Community of Lettuce (Lactuca sativa L.).

Antibiotics enter the soil with compost prepared from livestock manures and other sources. There is concern that they may influence plant growth and cause antibiotic resistance in soil and plant endospheric microbiomes. In the present work, lettuce plants were cultivated in soil and hydroponics spiked with oxytetracycline (0, 15, and 300 mg × kg-1 and 0, 15, and 50 mg × L-1, respectively) during a 28-day greenhouse experiment. It was revealed that the antibiotic reduced the chlorophyll content, the biomass, and the length of the roots and stems by 1.4-4.7, 1.8-39, 2.5-3.2, and 1.8-6.3 times in soil and in hydroponics. The copy numbers of the tet(A) and tet(X) genes were revealed to be 4.51 × 103-1.58 × 105 and 8.36 × 106-1.07 × 108 copies × g-1, respectively, suggesting the potential migration of these genes from soil/hydroponics to plant roots and leaves. According to a non-metric multidimensional scaling (NMDS) analysis of the 16S rRNA amplicon sequencing data, endospheric bacterial communities were similar in leaves and roots independent of the growing substrate and antibiotic concentration. While soil bacterial communities were unaffected by the presence of antibiotics, hydroponic communities exhibited dependency, likely attributable to the absence of the mitigating effect of soil particle absorption.

Isolation, identification, and significance of salivary Veillonella spp., Prevotella spp., and Prevotella salivae in patients with inflammatory bowel disease.

The global prevalence of inflammatory bowel disease (IBD) is on the rise, prompting significant attention from researchers worldwide. IBD entails chronic inflammatory disorders of the intestinal tract, characterized by alternating flares and remissions. Through high-throughput sequencing, numerous studies have unveiled a potential microbial signature for IBD patients showing intestinal enrichment of oral-associated bacteria. Simultaneously, the oral microbiome can be perturbed by intestinal inflammation. Our prior investigation, based on 16S rRNA amplicon sequencing, underscored elevated abundance of Veillonella spp. and Prevotella spp. in the salivary microbiomes of IBD patients. Noteworthy, Prevotella salivae emerged as a distinct species significantly associated with IBD. P. salivae is an under-recognized pathogen that was found to play a role in both oral and systemic diseases. In this study, we delve deeper into the salivary microbiomes of both IBD patients and healthy controls. Employing diverse cultivation techniques and real-time quantitative polymerase chain reactions (RT-qPCR), we gauged the prevalence and abundance of Veillonella spp., Prevotella spp., and P. salivae. Our isolation efforts yielded 407 and 168 strains of Veillonella spp., as well as 173 and 90 strains of Prevotella spp., from the saliva samples of IBD patients and healthy controls, respectively. Veillonella-vancomycin agar emerged as the discerning choice for optimal Veillonella spp. cultivation, while Schaedler kanamycin-vancomycin agar proved to be the most suitable medium for cultivating Prevotella spp. strains. Comparing our RT-qPCR findings to the previous 16S rRNA amplicon sequencing data, the results corroborated the higher abundance of Veillonella spp., Prevotella spp., and P. salivae in the saliva of IBD patients compared to healthy controls. However, it's worth noting that in contrast to RT-qPCR, the 16S rRNA amplicon sequencing data revealed greater absolute abundance of all three bacterial groups in both IBD patients and controls.

Recovery of microbial DNA by agar-containing solution from extremely low-biomass specimens including skin

Recovering a sufficient amount of microbial DNA from extremely low-biomass specimens, such as human skin, to investigate the community structure of the microbiome remains challenging. We developed a sampling solution containing agar to increase the abundance of recovered microbial DNA. Quantitative PCR targeting the 16S rRNA gene revealed a significant increase in the amount of microbial DNA recovered from the developed sampling solution compared with conventional solutions from extremely low-biomass skin sites such as the volar forearm and antecubital fossa. In addition, we confirmed that the developed sampling solution reduces the contamination rate of probable non-skin microbes compared to the conventional solutions, indicating that the enhanced recovery of microbial DNA was accompanied by a reduced relative abundance of contaminating microbes in the 16S rRNA gene amplicon sequencing data. In addition, agar was added to each step of the DNA extraction process, which improved the DNA extraction efficiency as a co-precipitant. Enzymatic lysis with agar yielded more microbial DNA than conventional kits, indicating that this method is effective for analyzing microbiomes of low-biomass specimens.

Genomic characterization of the C. tuberculostearicum species complex, a prominent member of the human skin microbiome.

Amplicon sequencing data combined with isolate whole genome sequencing have expanded our understanding of Corynebacterium on the skin. Healthy human skin is colonized by a diverse collection of Corynebacterium species, but Corynebacterium tuberculostearicum predominates on many skin sites. Our work supports the emerging idea that C. tuberculostearicum is a species complex encompassing several distinct species. We produced a collection of genomes that help define this complex, including a potentially new species we term Corynebacterium hallux based on a preference for sites on the feet, whole-genome average nucleotide identity, pangenomic analysis, and growth in skin-like media. This isolate collection and high-quality genome resource set the stage for developing engineered strains for both basic and translational clinical studies.

Long-term moderate fertilization increases the complexity of soil microbial community and promotes regulation of phosphorus cycling genes to improve the availability of phosphorus in acid soil

Microbial community and functional genes associated with phosphorus (P) cycling are intrinsic to soil P transformation. Yet, how the P fertilization rates affect the interactions of the microbial community and functional genes of P profiles is urgently needed to improve fertilizer P utilization efficiency. To address this, we conducted a long-term field experiment on purple acid soil in southwestern China using five mineral P fertilization rates. We analyzed the amplicon and metagenomic sequencing data to investigate the impact of P application rates on the bacterial and fungal communities as well as P cycling genes. Our results indicate that the community structure of both microbes and functional genes was significantly affected by P fertilization. Compared to other P levels, P application at 33 kg P ha−1 significantly increased the average degree and cohesion in the bacteria-fungi network, while the interactions between P cycling genes and microbes were also complex. Meanwhile, P application up to 16 kg P ha−1 increased the abundance of dominant functional genes (gcd and phoD), which were negatively correlated with Po and Pi. Nocardioides, Candidatus Solibacter, and Gaiella were the dominant taxon that induced the P cycling. The partial least squares path model (PLS-PM) revealed that SOM and pH were the main factors influencing AP by affecting the diversity and cohesion of the bacteria-fungi community, as well as gene abundance of P cycling genes under long-term gradient P fertilization. Overall, these results highlight the potential of moderate P fertilization to promote the stability of the microbiology and P transformation in acid soil.

PSXIII-20 The Effect of Hemp Varieties and Cannabidiol Concentrations on the in Vitro Ruminal Microbiome

Abstract This study aimed to evaluate the effect of hemp varieties and cannabidiol (CBD) concentrations on in vitro ruminal microbiota diversity and abundance relative do alfalfa (Medicago sativa). Two ruminally cannulated Angus steers were used as inoculum donors for in vitro incubation. Two 160-mL serum bottles containing 200 mg air-dried of either alfalfa, fedora 17 (F17) and felina 32 after late harvest (F32S) or without seeds (F32O) were incubated in 14 mL of buffering media and 4 mL of rumen inoculum. A stock solution of 1mg/mL of CBD was prepared and added in increasing concentrations into each bottle to reach a final concentration of 0.001%, 0.002%, 0.003%, and 0.004% of CBD. After a period of 48 hours, the whole content inside of the serum bottles was used immediately for DNA extraction followed by 16S rRNA sequencing with an Oxford Nanopore technologies MiniON. Amplicon sequence data were bioinformatically processed in QIIME2 version 2022.2. Alpha diversity was measured by the Shannon diversity index. Bray-Curtis’s dissimilarity matrix was constructed to estimate the differences among the samples and CBD levels, and differential abundance was then evaluated using analysis of compositions of microbiomes with bias correction (ANCOM) testing. Statistical significance was established at P < 0.05. All data were visualized in R version 4.1.2 (R Core Team, 2022) using the qiime2R package. Shannon diversity index was the highest for alfalfa (P < 0.05), but no difference was observed among the different CBD inclusion levels. When looking at beta diversity through Bray-Curtis, differences were only observed when comparing the different alfalfa and hemp varieties (P < 0.05), where alfalfa grouped separately from all the hemp samples. The most abundant phyla included Firmicute, Bacteroidetes, Proteobacteria, and Fusobacteria (P < 0.05). At family level, alfalfa samples had greater Aerococcaceae and Enterobacteriaceae than all the hemp treatments, but less Lactobacillales (unclassified), Streptococcaceae, Succinivibrionaceae, and Bacilli (unclassified) than F32S and F17 (P < 0.05). In conclusion, these results suggest that sample type might play a larger role on microbiome than CBD lconcentrations. Further studies are required to determine if greater concentrations of CBD than those examined herein could also lead to a shift on rumen microbes.

Microbial dark matter sequences verification in amplicon sequencing and environmental metagenomics data.

Although microorganisms constitute the most diverse and abundant life form on Earth, in many environments, the vast majority of them remain uncultured. As it is based on information gleaned mainly from cultivated microorganisms, our current body of knowledge regarding microbial life is partial and does not reflect actual microbial diversity. That diversity is hidden in the uncultured microbial majority, termed by microbiologists as "microbial dark matter" (MDM), a term borrowed from astrophysics. Metagenomic sequencing analysis techniques (both 16S rRNA gene and shotgun sequencing) compare gene sequences to reference databases, each of which represents only a small fraction of the existing microorganisms. Unaligned sequences lead to groups of "unknown microorganisms" that are usually ignored and rarefied from diversity analysis. To address this knowledge gap, we analyzed the 16S rRNA gene sequences of microbial communities from four different environments-a living organism, a desert environment, a natural aquatic environment, and a membrane bioreactor for wastewater treatment. From those datasets, we chose representative sequences of potentially unknown bacteria for additional examination as "microbial dark matter sequences" (MDMS). Sequence existence was validated by specific amplification and re-sequencing. These sequences were screened against databases and aligned to the Genome Taxonomy Database to build a comprehensive phylogenetic tree for additional sequence classification, revealing potentially new candidate phyla and other lineages. These putative MDMS were also screened against metagenome-assembled genomes from the explored environments for additional validation and for taxonomic and metabolic characterizations. This study shows the immense importance of MDMS in environmental metataxonomic analyses of 16S rRNA gene sequences and provides a simple and readily available methodology for the examination of MDM hidden behind amplicon sequencing results.

Amplicon sequencing data profiling of rhizospheric microbiome associated with Capsicum annuum L. var. Kulai cultivated under organic farming.

Data on the 16S rRNA and 18S rRNA amplicon sequencing from the rhizosphere of Capsicum annuum L. var. Kulai cultivated under organic farming are unveiled. The most dominant phyla for the 16S rRNA gene were Actinobacteriota and Proteobacteria. As for the 18S rRNA gene amplicon, Charophyta and Fungi were the major taxonomic groups.

ASVmaker: A New Tool to Improve Taxonomic Identifications for Amplicon Sequencing Data.

The taxonomic assignment of sequences obtained by high throughput amplicon sequencing poses a limitation for various applications in the biomedical, environmental, and agricultural fields. Identifications are constrained by the length of the obtained sequences and the computational processes employed to efficiently assign taxonomy. Arriving at a consensus is often preferable to uncertain identification for ecological purposes. To address this issue, a new tool called "ASVmaker" has been developed to facilitate the creation of custom databases, thereby enhancing the precision of specific identifications. ASVmaker is specifically designed to generate reference databases for allocating amplicon sequencing data. It uses publicly available reference data and generates specific sequences derived from the primers used to create amplicon sequencing libraries. This versatile tool can complete taxonomic assignments performed with pre-trained classifiers from the SILVA and UNITE databases. Moreover, it enables the generation of comprehensive reference databases for specific genes in cases where no directly applicable database exists for taxonomic classification tools.

Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts.

There are challenges in merging microbiome data from diverse research groups due to the intricate and multifaceted nature of such data. To address this, we utilized a combination of machine-learning (ML) models to analyze 16S sequencing data from a substantial set of gut microbiome samples, sourced from 12 distinct infant cohorts that were gathered prospectively. Our initial focus was on the mode of delivery due to its prior association with changes in infant gut microbiomes. Through ML analysis, we demonstrated the effective merging and comparison of various gut microbiome data sets, facilitating the identification of robust microbiome biomarkers applicable across varied study populations.

Depth-Dependent Distribution of Prokaryotes in Sediments of the Manganese Crust on Nazimov Guyots of the Magellan Seamounts.

Deep ocean polymetallic nodules, rich in cobalt, nickel, and titanium which are commonly used in high-technology and biotechnology applications, are being eyed for green energy transition through deep-sea mining operations. Prokaryotic communities underneath polymetallic nodules could participate in deep-sea biogeochemical cycling, however, are not fully described. To address this gap, we collected sediment cores from Nazimov guyots, where polymetallic nodules exist, to explore the diversity and vertical distribution of prokaryotic communities. Our 16S rRNA amplicon sequencing data, quantitative PCR results, and phylogenetic beta diversity indices showed that prokaryotic diversity in the surficial layers (0-8 cm) was > 4-fold higher compared to deeper horizons (8-26 cm), while heterotrophs dominated in all sediment horizons. Proteobacteria was the most abundant taxon (32-82%) across all sediment depths, followed by Thaumarchaeota (4-37%), Firmicutes (2-18%), and Planctomycetes (1-6%). Depth was the key factor controlling prokaryotic distribution, while heavy metals (e.g., iron, copper, nickel, cobalt, zinc) can also influence significantly the downcore distribution of prokaryotic communities. Analyses of phylogenetic diversity showed that deterministic processes governing prokaryotic assembly in surficial layers, contrasting with stochastic influences in deep layers. This was further supported from the detection of a more complex prokaryotic co-occurrence network in the surficial layer which suggested more diverse prokaryotic communities existed in the surface vs. deeper sediments. This study expands current knowledge on the vertical distribution of benthic prokaryotic diversity in deep sea settings underneath polymetallic nodules, and the results reported might set a baseline for future mining decisions.