Testing latent classes in gut microbiome data using generalized Poisson regression models

Human microbiome research has gained increasing importance due to its critical roles in comprehending human health and disease. Within the realm of microbiome research, the data generated often involves operational taxonomic unit counts, which can frequently present challenges such as over-dispersion and zero-inflation. To address dispersion-related concerns, the generalized Poisson model offers a flexible solution, effectively handling data characterized by over-dispersion, equi-dispersion, and under-dispersion. Furthermore, the realm of zero-inflated generalized Poisson models provides a strategic avenue to simultaneously tackle both over-dispersion and zero-inflation. The phenomenon of zero-inflation frequently stems from the heterogeneous nature of study populations. It emerges when specific microbial taxa fail to thrive in the microbial community of certain subjects, consequently resulting in a consistent count of zeros for these individuals. This subset of subjects represents a latent class, where their zeros originate from the genuine absence of the microbial taxa. In this paper, we introduce a novel testing methodology designed to uncover such latent classes within generalized Poisson regression models. We establish a closed-form test statistic and deduce its asymptotic distribution based on estimating equations. To assess its efficacy, we conduct an extensive array of simulation studies, and further apply the test to detect latent classes in human gut microbiome data from the Bogalusa Heart Study.

Community metabarcoding reveals the relative role of environmental filtering and spatial processes in metacommunity dynamics of soil microarthropods across a mosaic of montane forests

Disentangling the relative role of environmental filtering and spatial processes in driving metacommunity structure across mountainous regions remains challenging, as the way we quantify spatial connectivity in topographically and environmentally heterogeneous landscapes can influence our perception of which process predominates. More empirical data sets are required to account for taxon- and context-dependency, but relevant research in understudied areas is often compromised by the taxonomic impediment. Here we used haplotype-level community DNA metabarcoding, enabled by stringent filtering of amplicon sequence variants (ASVs), to characterize metacommunity structure of soil microarthropod assemblages across a mosaic of five forest habitats on the Troodos mountain range in Cyprus. We found similar β diversity patterns at ASV and species (OTU, operational taxonomic unit) levels, which pointed to a primary role of habitat filtering resulting in the existence of largely distinct metacommunities linked to different forest types. Within-habitat turnover was correlated to topoclimatic heterogeneity, again emphasizing the role of environmental filtering. However, when integrating landscape matrix information for the highly fragmented Quercus alnifolia habitat, we also detected a major role of spatial isolation determined by patch connectivity, indicating that stochastic and niche-based processes synergistically govern community assembly. Alpha diversity patterns varied between ASV and OTU levels, with OTU richness decreasing with elevation and ASV richness following a longitudinal gradient, potentially reflecting a decline of genetic diversity eastwards due to historical pressures. Our study demonstrates the utility of haplotype-level community metabarcoding for characterizing metacommunity structure of complex assemblages and improving our understanding of biodiversity dynamics across mountainous landscapes worldwide.

Bacterial communities of Antarctic lichens explored by gDNA and cDNA 16S rRNA gene amplicon sequencing

Recently, lichens came once more into the scientific spotlight due to their unique relations with prokaryotes. Several temperate region lichen species have been thoroughly explored in this regard yet, the information on Antarctic lichens and their associated bacteriobiomes is somewhat lacking. In this paper, we assessed the phylogenetic structure of the whole and active fractions of bacterial communities housed by Antarctic lichens growing in different environmental conditions by targeted 16S rRNA gene amplicon sequencing. Bacterial communities associated with lichens procured from a nitrogen enriched site were very distinct from the communities isolated from lichens of a nitrogen depleted site. The former were characterized by substantial contributions of Bacteroidetes phylum members and the elusive Armatimonadetes. At the nutrient-poor site the lichen-associated bacteriobiome structure was unique for each lichen species, with chlorolichens being occupied largely by Proteobacteria. Lichen species with a pronounced discrepancy in diversity between the whole and active fractions of their bacterial communities had the widest ecological amplitude, hinting that the nonactive part of the community is a reservoir of latent stress coping mechanisms. This is the first investigation to make use of targeted metatranscriptomics to infer the bacterial biodiversity in Antarctic lichens.

Prokaryotic and eukaryotic microbial diversity from three soda lakes in the East African Rift Valley determined by amplicon sequencing

Soda lakes are unique poly-extreme environments with high alkalinity and salinity that support diverse microbial communities despite their extreme nature. In this study, prokaryotic and eukaryotic microbial diversity in samples of the three soda lakes, Lake Abijata, Lake Chitu and Lake Shala in the East African Rift Valley, were determined using amplicon sequencing. Culture-independent analysis showed higher diversity of prokaryotic and eukaryotic microbial communities in all three soda lakes than previously reported. A total of 3,603 prokaryotic and 898 eukaryotic operational taxonomic units (OTUs) were found through culture-independent amplicon sequencing, whereas only 134 bacterial OTUs, which correspond to 3%, were obtained by enrichment cultures. This shows that only a fraction of the microorganisms from these habitats can be cultured under laboratory conditions. Of the three soda lakes, samples from Lake Chitu showed the highest prokaryotic diversity, while samples from Lake Shala showed the lowest diversity. Pseudomonadota (Halomonas), Bacillota (Bacillus, Clostridia), Bacteroidota (Bacteroides), Euryarchaeota (Thermoplasmata, Thermococci, Methanomicrobia, Halobacter), and Nanoarchaeota (Woesearchaeia) were the most common prokaryotic microbes in the three soda lakes. A high diversity of eukaryotic organisms were identified, primarily represented by Ascomycota and Basidiomycota. Compared to the other two lakes, a higher number of eukaryotic OTUs were found in Lake Abijata. The present study showed that these unique habitats harbour diverse microbial genetic resources with possible use in biotechnological applications, which should be further investigated by functional metagenomics.

Testing latent class of subjects with structural zeros in negative binomial models with applications to gut microbiome data

Human microbiome research has become a hot-spot in health and medical research in the past decade due to the rapid development of modern high-throughput. Typical data in a microbiome study consisting of the operational taxonomic unit counts may have over-dispersion and/or structural zero issues. In such cases, negative binomial models can be applied to address the over-dispersion issue, while zero-inflated negative binomial models can be applied to address both issues. In practice, it is essential to know if there is zero-inflation in the data before applying negative binomial or zero-inflated negative binomial models because zero-inflated negative binomial models may be unnecessarily complex and difficult to interpret, or may even suffer from convergence issues if there is no zero-inflation in the data. On the other hand, negative binomial models may yield invalid inferences if the data does exhibit excessive zeros. In this paper, we develop a new test for detecting zero-inflation resulting from a latent class of subjects with structural zeros in a negative binomial regression model by directly comparing the amount of observed zeros with what would be expected under the negative binomial regression model. A closed form of the test statistic as well as its asymptotic properties are derived based on estimating equations. Intensive simulation studies are conducted to investigate the performance of the new test and compare it with the classical Wald, likelihood ratio, and score tests. The tests are also applied to human gut microbiome data to test latent class in microbial genera.

Impact of mitotype diversity on metabarcoding biodiversity estimations in Insecta and Arachnida using different sample preparation strategies

DNA barcoding and metabarcoding have been increasingly used in species delimitation and species diversity assessment, respectively, and the molecular markers used in animals are mainly derived from mitochondrial DNA. It is well known that the phenomenon of multiple mitochondrial haplotypes within the same specimen (hereafter referred to as "mitotype diversity") may have a negative impact on the proper assessment of biodiversity by metabarcoding. However, few studies have focused on the incidence of this phenomenon and its effects on metabarcoding results using different sample preparation strategies, such as mock community construction using pooled High-Throughput Sequencing (HTS) data, DNA-pooling, and Tissue-pooling. In this study, we tried to investigate mitotype diversity and its influence on metabarcoding based on 398 specimens from 66 species of Insecta and 82 specimens from 16 species of Arachnida by HTS of mitochondrial cox1 gene fragment. The results revealed that mitotype diversity was common in the studied taxa and significantly increased the number of Operational Taxonomic Units (OTUs) using the three sample preparation strategies. The results also showed that the bioinformatics pipeline based on authentic Amplicon Sequence Variants (ASVs) was more reliable than the pipeline based on OTUs. Regarding the sample preparation strategies of DNA-pooling and Tissue-pooling commonly used in metabarcoding, our results revealed that their results of metabarcoding were quite similar, and the Tissue-pooling strategy was therefore preferred because of its simplicity. Our study calls for additional attention in the interference of mitotype diversity on the results of DNA metabarcoding in biodiversity assessment.

Lineage diversity within a widespread endemic Australian skink to better inform conservation in response to regional-scale disturbance

Much attention is paid in conservation planning to the concept of a species, to ensure comparability across studies and regions when classifying taxa against criteria of endangerment and setting priorities for action. However, various jurisdictions now allow taxonomic ranks below the level of species and nontaxonomic intraspecific divisions to be factored into conservation planning—subspecies, key populations, evolutionarily significant units, or designatable units. Understanding patterns of genetic diversity and its distribution across the landscape is a key component in the identification of species boundaries and determination of substantial geographic structure within species. A total of 12,532 reliable polymorphic SNP loci were generated from 63 populations (286 individuals) covering the distribution of the Australian eastern three‐lined skink, Bassiana duperreyi, to assess genetic population structure in the form of diagnosable lineages and their distribution across the landscape, with particular reference to the recent catastrophic bushfires of eastern Australia. Five well‐supported diagnosable operational taxonomic units (OTUs) existed within B. duperreyi. Low levels of divergence of B. duperreyi between mainland Australia and Tasmania (no fixed allelic differences) support the notion of episodic exchange of alleles across Bass Strait (ca 60 m, 25 Kya) during periods of low sea level during the Upper Pleistocene rather than the much longer period of isolation (1.7 My) indicated by earlier studies using mitochondrial sequence variation. Our study provides foundational work for the detailed taxonomic re‐evaluation of this species complex and the need for biodiversity assessment to include an examination of cryptic species and/or cryptic diversity below the level of species. Such information on lineage diversity within species and its distribution in the context of disturbance at a regional scale can be factored into conservation planning regardless of whether a decision is made to formally diagnose new species taxonomically and nomenclaturally.

City-Scale Meta-Analysis of Indoor Airborne Microbiota Reveals that Taxonomic and Functional Compositions Vary with Building Types

Currently, there is a lack of understanding on the variations of the indoor airborne microbiotas of different building types within a city, and how operational taxonomic unit (OTU)- and amplicon sequence variant (ASV)-based analyses of the 16S rRNA gene sequences affect interpretation of the indoor airborne microbiota results. Therefore, in this study, the indoor airborne bacterial microbiotas between commercial buildings, residences, and subways within the same city were compared using both OTU- and ASV-based analytic methods. Our findings suggested that indoor airborne bacterial microbiota compositions were significantly different between building types regardless of the bioinformatics method used. The processes of ecological drift and random dispersal consistently played significant roles in the assembly of the indoor microbiota across building types. Abundant taxa tended to be more centralized in the correlation network of each building type, highlighting their importance. Taxonomic changes between the microbiotas of different building types were also linked to changes in their inferred metabolic function capabilities. Overall, the results imply that customized strategies are necessary to manage indoor airborne bacterial microbiotas for each building type or even within each specific building.

Identification of bacterial communities in extreme sites of Pakistan using high throughput barcoded amplicon sequencing

Microorganisms thrive nearly everywhere including extreme environments where few other forms of life can exist. Geochemistry of extreme sites plays a major role in shaping these microbial communities and microbes thriving in such harsh conditions are untapped sources of novel biomolecules. To understand the structure and composition of such microbial communities, culture-independent bacterial diversity was characterised for two extreme sites in Pakistan, Khewra salt range and Murtazaabad hot spring. Barcoded amplicon sequencing technique was used to study the microbial communities. Physicochemical analysis of these sites was also conducted to study the dynamics of microbial communities under stressed conditions. Metagenomic sequencing of salt range soil samples yielded of 40,433 16S rRNA sequences, while hot spring sediments produced 76,449 16S rRNA sequence reads. Proteobacteria were predominant in saline soil while Firmicutes were most abundant in hot spring sediment. The taxonomic analysis of saline samples revealed 914 operational taxonomic units (OTUs) while that of hot spring sequences were clustered into 726 distinct OTUs. OTUs from genus Alkalibacillus were most abundant in hot spring sediments, whereas Haloarcula were more prevalent in saline soil. Some unidentified sequences were also present at each taxonomic level. Multivariate analysis indicated that electrical conductivity and pH are the major environmental factors involved in modelling microbial communities. This study revealed a poly-extremophilic microbial community in the Murtazaabad hot spring and characterised the unexplored halophilic microbial diversity of saline soil of Pakistan.

Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences

With the advent of next-generation sequencing technology, it has become convenient and cost efficient to thoroughly characterize the microbial diversity and taxonomic composition in various environmental samples. Millions of sequencing data can be generated, and how to utilize this enormous sequence resource has become a critical concern for microbial ecologists. One particular challenge is the OTUs (operational taxonomic units) picking in 16S rRNA sequence analysis. Lucky, this challenge can be directly addressed by sequence clustering that attempts to group similar sequences. Therefore, numerous clustering methods have been proposed to help to cluster 16S rRNA sequences into OTUs. However, each method has its clustering mechanism, and different methods produce diverse outputs. Even a slight parameter change for the same method can also generate distinct results, and how to choose an appropriate method has become a challenge for inexperienced users. A lot of time and resources can be wasted in selecting clustering tools and analyzing the clustering results. In this study, we introduced the recent advance of clustering methods for OTUs picking, which mainly focus on three aspects: (i) the principles of existing clustering algorithms, (ii) benchmark dataset construction for OTU picking and evaluation metrics, and (iii) the performance of different methods with various distance thresholds on benchmark datasets. This paper aims to assist biological researchers to select the reasonable clustering methods for analyzing their collected sequences and help algorithm developers to design more efficient sequences clustering methods.

Global Geographic and Temporal Analysis of SARS-CoV-2 Haplotypes Normalized by COVID-19 Cases During the Pandemic

Since the identification of SARS-CoV-2, a large number of genomes have been sequenced with unprecedented speed around the world. This marks a unique opportunity to analyze virus spreading and evolution in a worldwide context. Currently, there is not a useful haplotype description to help to track important and globally scattered mutations. Also, differences in the number of sequenced genomes between countries and/or months make it difficult to identify the emergence of haplotypes in regions where few genomes are sequenced but a large number of cases are reported. We propose an approach based on the normalization by COVID-19 cases of relative frequencies of mutations using all the available data to identify major haplotypes. Furthermore, we can use a similar normalization approach to tracking the temporal and geographic distribution of haplotypes in the world. Using 171,461 genomes, we identify five major haplotypes or operational taxonomic units (OTUs) based on nine high-frequency mutations. OTU_3 characterized by mutations R203K and G204R is currently the most frequent haplotype circulating in four of the six continents analyzed (South America, North America, Europe, Asia, Africa, and Oceania). On the other hand, during almost all months analyzed, OTU_5 characterized by the mutation T85I in nsp2 is the most frequent in North America. Recently (since September), OTU_2 has been established as the most frequent in Europe. OTU_1, the ancestor haplotype, is near to extinction showed by its low number of isolations since May. Also, we analyzed whether age, gender, or patient status is more related to a specific OTU. We did not find OTU's preference for any age group, gender, or patient status. Finally, we discuss structural and functional hypotheses in the most frequently identified mutations, none of those mutations show a clear effect on the transmissibility or pathogenicity.

Data-Driven Modeling for Species-Level Taxonomic Assignment From 16S rRNA: Application to Human Microbiomes

With the emergence of next-generation sequencing (NGS) technology, there have been a large number of metagenomic studies that estimated the bacterial composition via 16S ribosomal RNA (16S rRNA) amplicon sequencing. In particular, subsets of the hypervariable regions in 16S rRNA, such as V1-V2 and V3-V4, are targeted using high-throughput sequencing. The sequences from different taxa are assigned to a specific taxon based on the sequence homology. Since such sequences are highly homologous or identical between species in the same genus, it is challenging to determine the exact species using 16S rRNA sequences only. Therefore, in this study, homologous species groups were defined to obtain maximum resolution related with species using 16S rRNA. For the taxonomic assignment using 16S rRNA, three major 16S rRNA databases are independently used since the lineage of certain bacteria is not consistent among these databases. On the basis of the NCBI taxonomy classification, we re-annotated inconsistent lineage information in three major 16S rRNA databases. For each species, we constructed a consensus sequence model for each hypervariable region and determined homologous species groups that consist of indistinguishable species in terms of sequence homology. Using a k-nearest neighbor method and the species consensus sequence models, the species-level taxonomy was determined. If the species determined is a member of homologous species groups, the species group is assigned instead of a specific species. Notably, the results of the evaluation on our method using simulated and mock datasets showed a high correlation with the real bacterial composition. Furthermore, in the analysis of real microbiome samples, such as salivary and gut microbiome samples, our method successfully performed species-level profiling and identified differences in the bacterial composition between different phenotypic groups.

Fused Omics Data Models Reveal Gut Microbiome Signatures Specific of Inactive Stage of Juvenile Idiopathic Arthritis in Pediatric Patients

Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease in children. Herein, we evaluated the relationship between the gut microbiome (GM) and disease phenotype by an integrated omics fused approach. In a multicenter, observational cohort study, stools from Italian JIA patients were collected at baseline, active, and inactive disease stages, and their GM compared to healthy controls (CTRLs). The microbiota metabolome was analyzed to detect volatile- and non-volatile organic compounds (VOCs); the data were fused with operational taxonomic units (OTUs) from 16S RNA targeted-metagenomics and classified by chemometric models. Non-VOCs did not characterize JIA patients nor JIA activity stages compared to CTRLs. The core of VOCs, (Ethanol, Methyl-isobutyl-ketone, 2,6-Dimethyl-4-heptanone and Phenol) characterized patients at baseline and inactive disease stages, while the OTUs represented by Ruminococcaceae, Lachnospiraceae and Clostridiacea discriminated between JIA inactive stage and CTRLs. No differences were highlighted amongst JIA activity stages. Finally, the fused data discriminated inactive and baseline stages versus CTRLs, based on the contribution of the invariant core of VOCs while Ruminococcaceae concurred for the inactive stage versus CTRLs comparison. In conclusion, the GM signatures enabled to distinguish the inactive disease stage from CTRLs.

Gut Microbiota Reconstruction Following Host Infection with Blood-stage Plasmodium berghei ANKA Strain in a Murine Model

Malaria remains a global health problem. The relationship between Plasmodium spp. and the gut microbiota as well as the impact of Plasmodium spp. on the gut microbiota in vertebrate hosts is unclear. The aim of the current study was to evaluate the effect of blood-stage Plasmodium parasites on the gut microbiota of mice. The gut microbiota was analyzed by 16S rRNA sequencing and bioinformatic analyses at three stages. The gut microbiota changed during the three phases: the healthy stage, the infection stage, and the cure stage (on the 9th day after malarial elimination). Moreover, the gut microbiota of these infected animals did not recover after malaria infection. There were 254 operational taxonomic units (OTUs) across all three stages, and there were unique strains or OTUs at each stage of the experiment. The percentages of community abundance of 8 OTUs changed significantly (P<0.05). The dominant OTU in both the healthy mice and the mice with malaria was OTU265, while that in the cured mice was OTU234. In addition, the changes in OTU147 were the most noteworthy. Its percentage of community abundance varied greatly, with higher values during malaria than before malaria infection and after malaria elimination. These results indicated that the external environment influenced the gut microbiota after host C57BL/6 mice were infected with blood-stage P. berghei ANKA and that the same was true during and after elimination of blood-stage P. berghei ANKA. In addition, we could not isolate OTU147 for further study. This study identified gut microbiota components that were reconstructed after infection by and elimination of blood-stage P. berghei ANKA in host C57BL/6 mice, and this process was affected by P. berghei ANKA and the external environment of the host.

DMSC: A Dynamic Multi-Seeds Method for Clustering 16S rRNA Sequences Into OTUs

Next-generation sequencing (NGS)-based 16S rRNA sequencing by jointly using the PCR amplification and NGS technology is a cost-effective technique, which has been successfully used to study the phylogeny and taxonomy of samples from complex microbiomes or environments. Clustering 16S rRNA sequences into operational taxonomic units (OTUs) is often the first step for many downstream analyses. Heuristic clustering is one of the most widely employed approaches for generating OTUs. However, most heuristic OTUs clustering methods just select one single seed sequence to represent each cluster, resulting in their outcomes suffer from either overestimation of OTUs number or sensitivity to sequencing errors. In this paper, we present a novel dynamic multi-seeds clustering method (namely DMSC) to pick OTUs. DMSC first heuristically generates clusters according to the distance threshold. When the size of a cluster reaches the pre-defined minimum size, then DMSC selects the multi-core sequences (MCS) as the seeds that are defined as the n-core sequences (n ≥ 3), in which the distance between any two sequences is less than the distance threshold. A new sequence is assigned to the corresponding cluster depending on the average distance to MCS and the distance standard deviation within the MCS. If a new sequence is added to the cluster, dynamically update the MCS until no sequence is merged into the cluster. The new method DMSC was tested on several simulated and real-life sequence datasets and also compared with the traditional heuristic methods such as CD-HIT, UCLUST, and DBH. Experimental results in terms of the inferred OTUs number, normalized mutual information (NMI) and Matthew correlation coefficient (MCC) metrics demonstrate that DMSC can produce higher quality clusters with low memory usage and reduce OTU overestimation. Additionally, DMSC is also robust to the sequencing errors. The DMSC software can be freely downloaded from https://github.com/NWPU-903PR/DMSC.

Alterations of the Mice Gut Microbiome via Schistosoma japonicum Ova-Induced Granuloma

Schistosomiasis, also called bilharziasis, is a neglected tropical disease induced by Schistosoma spp. that causes hundreds of millions of infections. Although Schistosoma ova-induced granulomas commonly cause inflammation, hyperplasia, ulceration, micro abscess formation, and polyposis, the role of the egg granuloma on the gut microbiome remains unclear. To explore the role, gut microbial communities in mice infected with Schistosoma japonicum were surveyed. Female C57BL/6 and BALB/c mice were exposed to cercariae of S. japonicum for 45 and 65 days and then sacrificed. Intestinal contents and feces were collected, DNA was extracted, and high-throughput 16S rRNA gene-based pyrosequencing was used to provide a comparative analysis of gut microbial diversity. The intestinal mucosal tissues were also examined. Histopathologic analysis demonstrated that the basic structure of the colonic mucosa was damaged by ova-induced granuloma. Regarding the gut microbiome, 2,578,303 good-quality sequences were studied and assigned to 25,278 Operational Taxonomic Units (OTUs) at a threshold of 97% similarity. The average number of OTUs for C57BL/6 and BALB/c were 545 and 530, respectively. At the phylum level, intestinal microbial communities were dominated by Firmicutes, Bacteroidetes, Proteobacteria, and Verrucomicrobia. Infection with S. japonicum modified bacterial richness in the fecal associated microbiota. Exposure significantly modified bacterial community composition among different groups. At the phylogenetic levels, LEfSe analysis revealed that several bacterial taxa were significantly associated with the S. japonicum-infected mice. The present results suggest that egg granulomas in the intestine influence differentiation of the gut microbial community under pathophysiological conditions. This result suggests that intestinal microbiome-based strategies should be considered for early diagnosis, clinical treatment, and prognosis evaluation of schistosomiasis.

Sample Preservation, DNA or RNA Extraction and Data Analysis for High-Throughput Phytoplankton Community Sequencing

Phytoplankton is the basis for aquatic food webs and mirrors the water quality. Conventionally, phytoplankton analysis has been done using time consuming and partly subjective microscopic observations, but next generation sequencing (NGS) technologies provide promising potential for rapid automated examination of environmental samples. Because many phytoplankton species have tough cell walls, methods for cell lysis and DNA or RNA isolation need to be efficient to allow unbiased nucleic acid retrieval. Here, we analyzed how two phytoplankton preservation methods, three commercial DNA extraction kits and their improvements, three RNA extraction methods, and two data analysis procedures affected the results of the NGS analysis. A mock community was pooled from phytoplankton species with variation in nucleus size and cell wall hardness. Although the study showed potential for studying Lugol-preserved sample collections, it demonstrated critical challenges in the DNA-based phytoplankton analysis in overall. The 18S rRNA gene sequencing output was highly affected by the variation in the rRNA gene copy numbers per cell, while sample preservation and nucleic acid extraction methods formed another source of variation. At the top, sequence-specific variation in the data quality introduced unexpected bioinformatics bias when the sliding-window method was used for the quality trimming of the Ion Torrent data. While DNA-based analyses did not correlate with biomasses or cell numbers of the mock community, rRNA-based analyses were less affected by different RNA extraction procedures and had better match with the biomasses, dry weight and carbon contents, and are therefore recommended for quantitative phytoplankton analyses.

From reads to operational taxonomic units: an ensemble processing pipeline for MiSeq amplicon sequencing data

The development of high-throughput sequencing technologies has provided microbial ecologists with an efficient approach to assess bacterial diversity at an unseen depth, particularly with the recent advances in the Illumina MiSeq sequencing platform. However, analyzing such high-throughput data is posing important computational challenges, requiring specialized bioinformatics solutions at different stages during the processing pipeline, such as assembly of paired-end reads, chimera removal, correction of sequencing errors, and clustering of those sequences into Operational Taxonomic Units (OTUs). Individual algorithms grappling with each of those challenges have been combined into various bioinformatics pipelines, such as mothur, QIIME, LotuS, and USEARCH. Using a set of well-described bacterial mock communities, state-of-the-art pipelines for Illumina MiSeq amplicon sequencing data are benchmarked at the level of the amount of sequences retained, computational cost, error rate, and quality of the OTUs. In addition, a new pipeline called OCToPUS is introduced, which is making an optimal combination of different algorithms. Huge variability is observed between the different pipelines in respect to the monitored performance parameters, where in general the amount of retained reads is found to be inversely proportional to the quality of the reads. By contrast, OCToPUS achieves the lowest error rate, minimum number of spurious OTUs, and the closest correspondence to the existing community, while retaining the uppermost amount of reads when compared to other pipelines. The newly introduced pipeline translates Illumina MiSeq amplicon sequencing data into high-quality and reliable OTUs, with improved performance and accuracy compared to the currently existing pipelines.

Molecular Detection and Environment-Specific Diversity of Glycosyl Hydrolase Family 1 β-Glucosidase in Different Habitats

β-glucosidase is a crucial element of the microbial cellulose multienzyme complex since it is responsible for the regulation of the entire cellulose hydrolysis process. Therefore, the aim of the present work was to explore the diversity and distribution of glycosyl hydrolase family 1 β-glucosidase genes in three different environmental niches including, Himalayan soil, cow dung and compost by metagenomic approach. Preliminary evaluation through metabolic profiling using BIOLOG based utilization patterns of carbon, nitrogen, phosphorus and sulfur revealed the environment and substrate specific nature of the indigenous microbial population. Furthermore, clonal library selection, screening and sequence analysis revealed that most of the GH1 β-glucosidase proteins had low identities with the available database. Analysis of the distribution of GH1 β-glucosidase gene fragments and β-glucosidase producing microbial community revealed the environment specific nature. The OTUs obtained from Himalayan soil and compost metagenomic libraries were grouped into 19 different genera comprising 6 groups. The cow dung sample displayed the least diversity of GH1 β-glucosidase sequences, with only 14 genera, distributed among three groups- Bacteroidetes, Firmicutes, and Actinobacteria. The metagenomic study coupled with metabolic profiling of GH1 β-glucosidase illustrated the existence of intricate relationship between the geochemical environmental factors and inherent microbial community.

Assessing the performance of DNA barcoding using posterior predictive simulations

Accurate estimates of biodiversity are required for research in a broad array of biological subdisciplines including ecology, evolution, systematics, conservation and biodiversity science. The use of statistical models and genetic data, particularly DNA barcoding, has been suggested as an important tool for remedying the large gaps in our current understanding of biodiversity. However, the reliability of biodiversity estimates obtained using these approaches depends on how well the statistical models that are used describe the evolutionary process underlying the genetic data. In this study, we utilize data from the Barcode of Life Database and posterior predictive simulations to assess the performance of DNA barcoding under commonly used substitution models. We demonstrate that the success of DNA barcoding varies widely across DNA substitution models and that model choice has a substantial impact on the number of operational taxonomic units identified (changing results by ~4-31%). Additionally, we demonstrate that the widely followed practice of a priori assuming the Kimura 2-parameter model for DNA barcoding is statistically unjustified and should be avoided. Using both data-based and inference-based test statistics, we detect variation in model performance across taxonomic groups, clustering algorithms, genetic divergence thresholds and substitution models. Taken together, these results illustrate the importance of considering both model selection and model adequacy in studies quantifying biodiversity.

Open-Source Sequence Clustering Methods Improve the State Of the Art.mSystems. 2016;1. [PMID: 27822515 DOI: 10.1128/mSystems.00003-15]

Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http://dx.doi.org/10.1186/s12915-014-0069-1).

Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH’s most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release.

IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http://dx.doi.org/10.1186/s12915-014-0069-1).

Comparative study of the validity of three regions of the 18S-rRNA gene for massively parallel sequencing-based monitoring of the planktonic eukaryote community

The nuclear 18S-rRNA gene has been used as a metabarcoding marker in massively parallel sequencing (MPS)-based environmental surveys for plankton biodiversity research. However, different hypervariable regions have been used in different studies, and their utility has been debated among researchers. In this study, detailed investigations into 18S-rRNA were carried out; we investigated the effective number of sequences deposited in international nucleotide sequence databases (INSDs), the amplification bias, and the amplicon sequence variability among the three variable regions, V1-3, V4-5 and V7-9, using in silico polymerase chain reaction (PCR) amplification based on INSDs. We also examined the primer universality and the taxonomic identification power, using MPS-based environmental surveys in the Sea of Okhotsk, to determine which region is more useful for MPS-based monitoring. The primer universality was not significantly different among the three regions, but the number of sequences deposited in INSDs was markedly larger for the V4-5 region than for the other two regions. The sequence variability was significantly different, with the highest variability in the V1-3 region, followed by the V7-9 region, and the lowest variability in the V4-5 region. The results of the MPS-based environmental surveys showed significantly higher identification power in the V1-3 and V7-9 regions than in the V4-5 region, but no significant difference was detected between the V1-3 and V7-9 regions. We therefore conclude that the V1-3 region will be the most suitable for future MPS-based monitoring of natural eukaryote communities, as the number of sequences deposited in INSDs increases.

DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity

Documenting the diversity of marine life is challenging because many species are cryptic, small, and rare, and belong to poorly known groups. New sequencing technologies, especially when combined with standardized sampling, promise to make comprehensive biodiversity assessments and monitoring feasible on a large scale. We used this approach to characterize patterns of diversity on oyster reefs across a range of geographic scales comprising a temperate location [Virginia (VA)] and a subtropical location [Florida (FL)]. Eukaryotic organisms that colonized multilayered settlement surfaces (autonomous reef monitoring structures) over a 6-mo period were identified by cytochrome c oxidase subunit I barcoding (>2-mm mobile organisms) and metabarcoding (sessile and smaller mobile organisms). In a total area of ∼ 15.64 m(2) and volume of ∼ 0.09 m(3), 2,179 operational taxonomic units (OTUs) were recorded from 983,056 sequences. However, only 10.9% could be matched to reference barcodes in public databases, with only 8.2% matching barcodes with both genus and species names. Taxonomic coverage was broad, particularly for animals (22 phyla recorded), but 35.6% of OTUs detected via metabarcoding could not be confidently assigned to a taxonomic group. The smallest size fraction (500 to 106 μm) was the most diverse (more than two-thirds of OTUs). There was little taxonomic overlap between VA and FL, and samples separated by ∼ 2 m were significantly more similar than samples separated by ∼ 100 m. Ground-truthing with independent assessments of taxonomic composition indicated that both presence-absence information and relative abundance information are captured by metabarcoding data, suggesting considerable potential for ecological studies and environmental monitoring.

Identification of Human Intestinal Microbiota of 92 Men by Data Mining for 5 Characteristics, i.e., Age, BMI, Smoking Habit, Cessation Period of Previous Smokers and Drinking Habit

The intestinal microbiota compositions of 92 men living in Japan were identified following consumption of identical meals for 3 days. Fecal samples were analyzed by terminal restriction fragment length polymorphism with 4 primer-restriction enzyme systems, and the 120 obtained operational taxonomic units (OTUs) were analyzed by Data mining software focusing on the following 5 characteristics, namely, age, body mass index, present smoking habit, cessation period of previous smokers and drinking habit, according to the answers of the subjects. After performing Data mining analyses with each characteristic, the details of the constructed Decision trees precisely identified the subjects or discriminated them into various suitable groups. Through the pathways to reach the groups, practical roles of the related OTUs and their quantities were clearly recognized. Compared with the other identification methods for OTUs such as bicluster analyses, correlation coefficients and principal component analyses, the clear difference of this Data mining technique was that it set aside most OTUs and emphasized only some closely related ones. For example for a selected characteristic, such as smoking habit, only 7 OTUs out of 120 were able to identify all smokers, and the remaining 113 OTUs were thought of as data noise for smoking. Data mining analyses were affirmed as an effective method of subject discrimination for various physiological constitutions. The species of bacteria that were closely related to heavy smokers, i.e., HaeIII-291, were also discussed.

Employing 454 amplicon pyrosequencing to reveal intragenomic divergence in the internal transcribed spacer rDNA region in fungi

The rDNA internal transcribed spacer (ITS) region has been accepted as a DNA barcoding marker for fungi and is widely used in phylogenetic studies; however, intragenomic ITS variability has been observed in a broad range of taxa, including prokaryotes, plants, animals, and fungi, and this variability has the potential to inflate species richness estimates in molecular investigations of environmental samples. In this study 454 amplicon pyrosequencing of the ITS1 region was applied to 99 phylogenetically diverse axenic single-spore cultures of fungi (Dikarya: Ascomycota and Basidiomycota) to investigate levels of intragenomic variation. Three species (one Basidiomycota and two Ascomycota), in addition to a positive control species known to contain ITS paralogs, displayed levels of molecular variation indicative of intragenomic variation; taxon inflation due to presumed intragenomic variation was ≈9%. Intragenomic variability in the ITS region appears to be widespread but relatively rare in fungi (≈3–5% of species investigated in this study), suggesting this problem may have minor impacts on species richness estimates relative to PCR and/or pyrosequencing errors. Our results indicate that 454 amplicon pyrosequencing represents a powerful tool for investigating levels of ITS intragenomic variability across taxa, which may be valuable for better understanding the fundamental mechanisms underlying concerted evolution of repetitive DNA regions.

Computational methods for high-throughput comparative analyses of natural microbial communities

One of the most widely employed methods in metagenomics is the amplification and sequencing of the highly conserved ribosomal RNA (rRNA) genes from organisms in complex microbial communities. rRNA surveys, typically using the 16S rRNA gene for prokaryotic identification, provide information about the total diversity and taxonomic affiliation of organisms present in a sample. Greatly enhanced by high-throughput sequencing, these surveys have uncovered the remarkable diversity of uncultured organisms and revealed unappreciated ecological roles ranging from nutrient cycling to human health. This chapter outlines the best practices for comparative analyses of microbial community surveys. We explain how to transform raw data into meaningful units for further analysis and discuss how to calculate sample diversity and community distance metrics. Finally, we outline how to find associations of species with specific metadata and true correlations between species from compositional data. We focus on data generated by next-generation sequencing platforms, using the Illumina platform as a test case, because of its widespread use especially among researchers just entering the field.