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

ICU care through NGS - Based identification of infectious agents: A comparative study

Background

Sepsis claims 1 in 5 lives annually as per global statistics. Sepsis incidence in recent studies represents at least 35 % of all ICU admissions and has a high mortality rate, especially in the presence of co-existing morbidities. The challenge has been to accurately diagnose the causative organism, considering factors such as possible polymicrobial infections, commensals and environmental contaminants. Legacy techniques such as culture, automated culture systems or even newer species-specific PCR or film array these challenges difficult to overcome. The Bactfast® and Fungifast® assays along with the integrated workflow is based on next generation sequencing and have the ability to demarcate infecting pathogen from contamination and commensal. The unique ability to pinpoint the exact pathogen, considering the commensal and contamination in a variety of samples, with an extremely high sensitivity could lead it to be a tool of diagnostic choice for non-resolving ICU sepsis due to its comprehensive coverage and speed. The aim of this study was to evaluate the use of Bactfast® and Fungifast® as a last mile diagnostic tool in a ICU setting.

Method

This study was carried out considering access to four intensive care units (ICU). Legacy testing, mostly done on culture, was conducted at the various integrated microbiology facilities of the hospitals where the ICUs were located, in Chennai, India. NABL accredited laboratory Micro Genomics (India) Pvt Ltd, was established as the central processing facility for next generation sequencing to run the Bactfast® and Fungifast® assay. Co-relation of results for 490 samples was done retrospectively by a multi-disciplinary team of consultants which comprised of microbiologists, and infectious disease physicians.

Result

The diagnostic workflow established with the Bactfast® assay provided a sensitivity of 94.1 % and specificity of 86.6 %. Identification of pathogens in Bactfast® was better when compared to the data published in 2017, as reflected by positive co-relation with clinical confirmation. Although the Fungifast® specificity was high, at 99.4 %, only 12 samples were positive on fungal culture out of 490 samples. Therefore, it was concluded a further study for fungi based on multiple technologies with more true positive samples is required to evaluate the test.

Conclusion

Bactfast® can identify pathogens in a sample without any bias. Its introduction as diagnostic modality in life threatening ICU sepsis could reduce mortality and morbidity. Although the initial results of Fungifast® are encouraging a further research is required for more information on test sensitivity.

Comparative analysis of sensory features, microbial diversity, and their correlations in light-flavor Daqu from different regions

This study performed a comparative analysis of the sensory and microbial profiles of light-flavor Bijou (LFD) from Taiyuan (Shanxi Province) and Suizhou (Hubei Province) in China. The results of the electronic nose showed that the aromatic substances of the LFD from Taiyuan (TLFD) were significantly higher (p < .05), while alcohol and aldehyde substances were significantly lower (p < .05) compared with the LFD from Suizhou (SLFD). The average response values of sensors W1C (sensitive to aromatic hydrocarbons), W3C (sensitive to amine and aromatic components), W5C (sensitive to olefins, aromatics, and polar molecules), and W2S (sensitive to alcohol and aldehyde compounds) to TLFD were 0.26, 0.33, 0.34, and 7.72, whereas the response values to SLFD were 0.25, 0.32, 0.33, and 8.04, respectively. The electronic tongue results showed that the aftertaste A (bitter aftertaste) and aftertaste B (astringent aftertaste) of the TLFD were significantly higher (p < .05) and umami was significantly lower (p < .05) as compared to the SLFD. The relative intensities of the aftertaste A, aftertaste B, and umami indicators of TLFD were 0.10, -0.008, and -0.22, respectively, while those of SLFD were -0.23, -0.36, and 0.835, respectively. MiSeq high-throughput sequencing results showed that TLFD exhibited lower fungal richness and diversity compared to SLFD. The dominant bacterial genera were mainly Bacillus (58.12%), Kroppenstedtia (10.11%), and Weissella (6.26%), and the dominant fungal genera were Saccharomycopsis (67.53%), Rasamsonia (9.90%), and Thermoascus (7.10%). Streptomyces and Staphylococcus were identified as the key characteristic microorganisms in TLFD, while Kroppenstedtia, Rasamsonia, and Thermoascus were the key characteristic microorganisms in SLFD. Correlation analysis indicated a stronger correlation between microorganisms and sensory characteristics in SLFD samples. This study provides valuable insights into the sensory and microbiological characteristics of LFD from different regions and offers a new perspective for understanding the production of differently flavored light-flavor Baijiu.

Cutting edge tools in the field of soil microbiology

The study of the whole of the genetic material contained within the microbial populations found in a certain environment is made possible by metagenomics. This technique enables a thorough knowledge of the variety, function, and interactions of microbial communities that are notoriously difficult to research. Due to the limitations of conventional techniques such as culturing and PCR-based methodologies, soil microbiology is a particularly challenging field. Metagenomics has emerged as an effective technique for overcoming these obstacles and shedding light on the dynamic nature of the microbial communities in soil. This review focuses on the principle of metagenomics techniques, their potential applications and limitations in soil microbial diversity analysis. The effectiveness of target-based metagenomics in determining the function of individual genes and microorganisms in soil ecosystems is also highlighted. Targeted metagenomics, including high-throughput sequencing and stable-isotope probing, is essential for studying microbial taxa and genes in complex ecosystems. Shotgun metagenomics may reveal the diversity of soil bacteria, composition, and function impacted by land use and soil management. Sanger, Next Generation Sequencing, Illumina, and Ion Torrent sequencing revolutionise soil microbiome research. Oxford Nanopore Technology (ONT) and Pacific Biosciences (PacBio)'s third and fourth generation sequencing systems revolutionise long-read technology. GeoChip, clone libraries, metagenomics, and metabarcoding help comprehend soil microbial communities. The article indicates that metagenomics may improve environmental management and agriculture despite existing limitations.Metagenomics has revolutionised soil microbiology research by revealing the complete diversity, function, and interactions of microorganisms in soil. Metagenomics is anticipated to continue defining the future of soil microbiology research despite some limitations, such as the difficulty of locating the appropriate sequencing method for specific genes.

Co-Occurrence Analysis of Citrus Root Bacterial Microbiota under Citrus Greening Disease

Candidatus Liberibacter asiaticus (CLas) is associated with Citrus Huanglongbing (HLB), a devastating disease in the US. Previously, we conducted a two-year-long monthly HLB survey by quantitative real-time PCR using root DNA fractions prepared from 112 field grapefruit trees grafted on sour orange rootstock. Approximately 10% of the trees remained CLas-free during the entire survey period. This study conducted 16S metagenomics using the time-series root DNA fractions, monthly prepared during twenty-four consecutive months, followed by microbial co-occurrence network analysis to investigate the microbial factors contributing to the CLas-free phenotype of the aforementioned trees. Based on the HLB status and the time when the trees were first diagnosed as CLas-positive during the survey, the samples were divided into four groups, Stage H (healthy), Stage I (early), II (mid), and III (late) samples. The 16S metagenomics data using Silva 16S database v132 revealed that HLB compromised the diversity of rhizosphere microbiota. At the phylum level, Actinobacteria and Proteobacteria were the predominant bacterial phyla, comprising >93% of total bacterial phyla, irrespective of HLB status. In addition, a temporal change in the rhizosphere microbe population was observed during a two-year-long survey, from which we confirmed that some bacterial families differently responded to HLB disease status. The clustering of the bacterial co-occurrence network data revealed the presence of a subnetwork composed of Streptomycetaceae and bacterial families with plant growth-promoting activity in Stage H and III samples. These data implicated that the Streptomycetaceae subnetwork may act as a functional unit against HLB.

Multicenter evaluation of gut microbiome profiling by next-generation sequencing reveals major biases in partial-length metabarcoding approach

Next-generation sequencing workflows, using either metabarcoding or metagenomic approaches, have massively contributed to expanding knowledge of the human gut microbiota, but methodological bias compromises reproducibility across studies. Where these biases have been quantified within several comparative analyses on their own, none have measured inter-laboratory reproducibility using similar DNA material. Here, we designed a multicenter study involving seven participating laboratories dedicated to partial- (P1 to P5), full-length (P6) metabarcoding, or metagenomic profiling (MGP) using DNA from a mock microbial community or extracted from 10 fecal samples collected at two time points from five donors. Fecal material was collected, and the DNA was extracted according to the IHMS protocols. The mock and isolated DNA were then provided to the participating laboratories for sequencing. Following sequencing analysis according to the laboratories' routine pipelines, relative taxonomic-count tables defined at the genus level were provided and analyzed. Large variations in alpha-diversity between laboratories, uncorrelated with sequencing depth, were detected among the profiles. Half of the genera identified by P1 were unique to this partner and two-thirds of the genera identified by MGP were not detected by P3. Analysis of beta-diversity revealed lower inter-individual variance than inter-laboratory variances. The taxonomic profiles of P5 and P6 were more similar to those of MGP than those obtained by P1, P2, P3, and P4. Reanalysis of the raw sequences obtained by partial-length metabarcoding profiling, using a single bioinformatic pipeline, harmonized the description of the bacterial profiles, which were more similar to each other, except for P3, and closer to the profiles obtained by MGP. This study highlights the major impact of the bioinformatics pipeline, and primarily the database used for taxonomic annotation. Laboratories need to benchmark and optimize their bioinformatic pipelines using standards to monitor their effectiveness in accurately detecting taxa present in gut microbiota.

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

The multi-batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision-making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene-derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome-based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome-specific methods for accounting for or correcting them.

Comparison of Methods for Biological Sequence Clustering

Recent advances in sequencing technology have considerably promoted genomics research by providing high-throughput sequencing economically. This great advancement has resulted in a huge amount of sequencing data. Clustering analysis is powerful to study and probe the large-scale sequence data. A number of available clustering methods have been developed in the last decade. Despite numerous comparison studies being published, we noticed that they have two main limitations: only traditional alignment-based clustering methods are compared and the evaluation metrics heavily rely on labeled sequence data. In this study, we present a comprehensive benchmark study for sequence clustering methods. Specifically, i) alignment-based clustering algorithms including classical (e.g., CD-HIT, UCLUST, VSEARCH) and recently proposed methods (e.g., MMseq2, Linclust, edClust) are assessed; ii) two alignment-free methods (e.g., LZW-Kernel and Mash) are included to compare with alignment-based methods; and iii) different evaluation measures based on the true labels (supervised metrics) and the input data itself (unsupervised metrics) are applied to quantify their clustering results. The aims of this study are to help biological analyzers in choosing one reasonable clustering algorithm for processing their collected sequences, and furthermore, motivate algorithm designers to develop more efficient sequence clustering approaches.

Best Practices in Designing, Sequencing, and Identifying Random DNA Barcodes

Random DNA barcodes are a versatile tool for tracking cell lineages, with applications ranging from development to cancer to evolution. Here, we review and critically evaluate barcode designs as well as methods of barcode sequencing and initial processing of barcode data. We first demonstrate how various barcode design decisions affect data quality and propose a new design that balances all considerations that we are currently aware of. We then discuss various options for the preparation of barcode sequencing libraries, including inline indices and Unique Molecular Identifiers (UMIs). Finally, we test the performance of several established and new bioinformatic pipelines for the extraction of barcodes from raw sequencing reads and for error correction. We find that both alignment and regular expression-based approaches work well for barcode extraction, and that error-correction pipelines designed specifically for barcode data are superior to generic ones. Overall, this review will help researchers to approach their barcoding experiments in a deliberate and systematic way.

Optimal sequence similarity thresholds for clustering of molecular operational taxonomic units in DNA metabarcoding studies

Clustering approaches are pivotal to handle the many sequence variants obtained in DNA metabarcoding data sets, and therefore they have become a key step of metabarcoding analysis pipelines. Clustering often relies on a sequence similarity threshold to gather sequences into molecular operational taxonomic units (MOTUs), each of which ideally represents a homogeneous taxonomic entity (e.g., a species or a genus). However, the choice of the clustering threshold is rarely justified, and its impact on MOTU over-splitting or over-merging even less tested. Here, we evaluated clustering threshold values for several metabarcoding markers under different criteria: limitation of MOTU over-merging, limitation of MOTU over-splitting, and trade-off between over-merging and over-splitting. We extracted sequences from a public database for nine markers, ranging from generalist markers targeting Bacteria or Eukaryota, to more specific markers targeting a class or a subclass (e.g., Insecta, Oligochaeta). Based on the distributions of pairwise sequence similarities within species and within genera, and on the rates of over-splitting and over-merging across different clustering thresholds, we were able to propose threshold values minimizing the risk of over-splitting, that of over-merging, or offering a trade-off between the two risks. For generalist markers, high similarity thresholds (0.96-0.99) are generally appropriate, while more specific markers require lower values (0.85-0.96). These results do not support the use of a fixed clustering threshold. Instead, we advocate careful examination of the most appropriate threshold based on the research objectives, the potential costs of over-splitting and over-merging, and the features of the studied markers.

Predicting the effects of winter water warming in artificial lakes on zooplankton and its environment using combined machine learning models

This work deals with the consequences of climate warming on aquatic ecosystems. The study determined the effects of increased water temperatures in artificial lakes during winter on predicting changes in the biomass of zooplankton taxa and their environment. We applied an innovative approach to investigate the effects of winter warming on zooplankton and physico-chemical factors. We used a modelling scheme combining hierarchical clustering, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) algorithms. Under the influence of increased water temperatures in winter, weight- and frequency-dominant Crustacea taxa such as Daphnia cucullata, Cyclops vicinus, Cryptocyclops bicolor, copepodites and nauplii, and the Rotifera: Polyarthra longiremis, Trichocerca pusilla, Keratella quadrata, Asplanchna priodonta and Synchaeta spp. tend to decrease their biomass. Under the same conditions, Rotifera: Lecane spp., Monommata maculata, Testudinella patina, Notholca squamula, Colurella colurus, Trichocerca intermedia and the protozoan species Centropyxis acuelata and Arcella discoides with lower size and abundance responded with an increase in biomass. Decreases in chlorophyll a, suspended solids and total nitrogen were predicted due to winter warming. Machine learning ensemble models used in innovative ways can contribute to the research utility of studies on the response of ecological units to environmental change.

Microbial mechanisms to improve immune checkpoint blockade responsiveness

The human microbiota acts as a diverse source of molecular cues that influence the development and homeostasis of the immune system. Beyond endogenous roles in the human holobiont, host-microbial interactions also alter outcomes for immune-related diseases and treatment regimens. Over the past decade, sequencing analyses of cancer patients have revealed correlations between microbiota composition and the efficacy of cancer immunotherapies such as checkpoint inhibitors. However, very little is known about the exact mechanisms that link specific microbiota with patient responses, limiting our ability to exploit these microbial agents for improved oncology care. Here, we summarize current progress towards a molecular understanding of host-microbial interactions in the context of checkpoint inhibitor immunotherapies. By highlighting the successes of a limited number of studies focused on identifying specific, causal molecules, we underscore how the exploration of specific microbial features such as proteins, enzymes, and metabolites may translate into precise and actionable therapies for personalized patient care in the clinic.

PhyloFunDB: A Pipeline to Create and Update Functional Gene Taxonomic Databases

The increase in sequencing capacity has amplified the number of taxonomically unclassified sequences in most databases. The classification of such sequences demands phylogenetic tree construction and comparison to currently classified sequences, a process that demands the processing of large amounts of data and use of several different software. Here, we present PhyloFunDB, a pipeline for extracting, processing, and inferring phylogenetic trees from specific functional genes. The goal of our work is to decrease processing time and facilitate the grouping of sequences that can be used for improved taxonomic classification of functional gene datasets.

In-Silico Detection of Oral Prokaryotic Species With Highly Similar 16S rRNA Sequence Segments Using Different Primer Pairs

Although clustering by operational taxonomic units (OTUs) is widely used in the oral microbial literature, no research has specifically evaluated the extent of the limitations of this sequence clustering-based method in the oral microbiome. Consequently, our objectives were to: 1) evaluate in-silico the coverage of a set of previously selected primer pairs to detect oral species having 16S rRNA sequence segments with ≥97% similarity; 2) describe oral species with highly similar sequence segments and determine whether they belong to distinct genera or other higher taxonomic ranks. Thirty-nine primer pairs were employed to obtain the in-silico amplicons from the complete genomes of 186 bacterial and 135 archaeal species. Each fasta file for the same primer pair was inserted as subject and query in BLASTN for obtaining the similarity percentage between amplicons belonging to different oral species. Amplicons with 100% alignment coverage of the query sequences and with an amplicon similarity value ≥97% (ASI97) were selected. For each primer, the species coverage with no ASI97 (SC-NASI97) was calculated. Based on the SC-NASI97 parameter, the best primer pairs were OP_F053-KP_R020 for bacteria (region V1-V3; primer pair position for Escherichia coli J01859.1: 9-356); KP_F018-KP_R002 for archaea (V4; undefined-532); and OP_F114-KP_R031 for both (V3-V5; 340-801). Around 80% of the oral-bacteria and oral-archaea species analyzed had an ASI97 with at least one other species. These very similar species play different roles in the oral microbiota and belong to bacterial genera such as Campylobacter, Rothia, Streptococcus and Tannerella, and archaeal genera such as Halovivax, Methanosarcina and Methanosalsum. Moreover, ~20% and ~30% of these two-by-two similarity relationships were established between species from different bacterial and archaeal genera, respectively. Even taxa from distinct families, orders, and classes could be grouped in the same possible OTU. Consequently, regardless of the primer pair used, sequence clustering with a 97% similarity provides an inaccurate description of oral-bacterial and oral-archaeal species, which can greatly affect microbial diversity parameters. As a result, OTU clustering conditions the credibility of associations between some oral species and certain health and disease conditions. This significantly limits the comparability of the microbial diversity findings reported in oral microbiome literature.

Analyzing the human gut mycobiome – a short guide for beginners

The human body is a dynamic ecosystem consisting of millions of microbes which are often comprised under the term microbiome. Compared to bacteria, which count for the overwhelming majority of the microbiome, the number of human-associated fungi is small and often underestimated. Nonetheless, they can be found in different host niches such as the gut, the oral cavity and the skin. The fungal community has several potential roles in health and disease of the human host. In this review we will focus on intestinal fungi and their interaction with the host as well as bacteria. We also summarize technical challenges and possible biases researchers must be aware of when conducting mycobiome analysis.

Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities

Background

Taxonomic classification of genetic markers for microbiome analysis is affected by the numerous choices made from sample preparation to bioinformatics analysis. Paired-end read merging is routinely used to capture the entire amplicon sequence when the read ends overlap. However, the exclusion of unmerged reads from further analysis can result in underestimating the diversity in the sequenced microbial community and is influenced by bioinformatic processes such as read trimming and the choice of reference database. A potential solution to overcome this is to concatenate (join) reads that do not overlap and keep them for taxonomic classification. The use of concatenated reads can outperform taxonomic recovery from single-end reads, but it remains unclear how their performance compares to merged reads. Using various sequenced mock communities with different amplicons, read length, read depth, taxonomic composition, and sequence quality, we tested how merging and concatenating reads performed for genus recall and precision in bioinformatic pipelines combining different parameters for read trimming and taxonomic classification using different reference databases.

Results

The addition of concatenated reads to merged reads always increased pipeline performance. The top two performing pipelines both included read concatenation, with variable strengths depending on the mock community. The pipeline that combined merged and concatenated reads that were quality-trimmed performed best for mock communities with larger amplicons and higher average quality sequences. The pipeline that used length-trimmed concatenated reads outperformed quality trimming in mock communities with lower quality sequences but lost a significant amount of input sequences for taxonomic classification during processing. Genus level classification was more accurate using the SILVA reference database compared to Greengenes.

Conclusions

Merged sequences with the addition of concatenated sequences that were unable to be merged increased performance of taxonomic classifications. This was especially beneficial in mock communities with larger amplicons. We have shown for the first time, using an in-depth comparison of pipelines containing merged vs concatenated reads combined with different trimming parameters and reference databases, the potential advantages of concatenating sequences in improving resolution in microbiome investigations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04410-2.