Ultra-accurate microbial amplicon sequencing with synthetic long reads

Utility Analyses of AVITI Sequencing Chemistry

Background

DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist.

Results

Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate.

Conclusion

These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.

Short-read full-length 16S rRNA amplicon sequencing for characterisation of the respiratory bacteriome of captive and free-ranging African elephants (Loxodonta africana)

Hypervariable region sequencing of the 16S ribosomal RNA (rRNA) gene plays a critical role in microbial ecology by offering insights into bacterial communities within specific niches. While providing valuable genus-level information, its reliance on data from targeted genetic regions limits its overall utility. Recent advances in sequencing technologies have enabled characterisation of the full-length 16S rRNA gene, enhancing species-level classification. Although current short-read platforms are cost-effective and precise, they lack full-length 16S rRNA amplicon sequencing capability. This study aimed to evaluate the feasibility of a modified 150 bp paired-end full-length 16S rRNA amplicon short-read sequencing technique on the Illumina iSeq 100 and 16S rRNA amplicon assembly workflow by utilising a standard mock microbial community and subsequently performing exploratory characterisation of captive (zoo) and free-ranging African elephant (Loxodonta africana) respiratory microbiota. Our findings demonstrate that, despite generating assembled amplicons averaging 869 bp in length, this sequencing technique provides taxonomic assignments consistent with the theoretical composition of the mock community and respiratory microbiota of other mammals. Tentative bacterial signatures, potentially representing distinct respiratory tract compartments (trunk and lower respiratory tract) were visually identified, necessitating further investigation to gain deeper insights into their implication for elephant physiology and health.

Comparative analysis of gut microbiota in children with obstructive sleep apnea: assessing the efficacy of 16S rRNA gene sequencing in metabolic function prediction based on weight status

Background

Analyzing bacterial microbiomes consistently using next-generation sequencing (NGS) is challenging due to the diversity of synthetic platforms for 16S rRNA genes and their analytical pipelines. This study compares the efficacy of full-length (V1-V9 hypervariable regions) and partial-length (V3-V4 hypervariable regions) sequencing of synthetic 16S rRNA genes from human gut microbiomes, with a focus on childhood obesity.

Methods

In this observational and comparative study, we explored the differences between these two sequencing methods in taxonomic categorization and weight status prediction among twelve children with obstructive sleep apnea.

Results

The full-length NGS method by Pacbio® identified 118 genera and 248 species in the V1-V9 regions, all with a 0% unclassified rate. In contrast, the partial-length NGS method by Illumina® detected 142 genera (with a 39% unclassified rate) and 6 species (with a 99% unclassified rate) in the V3-V4 regions. These approaches showed marked differences in gut microbiome composition and functional predictions. The full-length method distinguished between obese and non-obese children using the Firmicutes/Bacteroidetes ratio, a known obesity marker (p = 0.046), whereas the partial-length method was less conclusive (p = 0.075). Additionally, out of 73 metabolic pathways identified through full-length sequencing, 35 (48%) were associated with level 1 metabolism, compared to 28 of 61 pathways (46%) identified through the partial-length method. The full-length NGS also highlighted complex associations between body mass index z-score, three bacterial species (Bacteroides ovatus, Bifidobacterium pseudocatenulatum, and Streptococcus parasanguinis ATCC 15912), and 17 metabolic pathways. Both sequencing techniques revealed relationships between gut microbiota composition and OSA-related parameters, with full-length sequencing offering more comprehensive insights into associated metabolic pathways than the V3-V4 technique.

Conclusion

These findings highlight disparities in NGS-based assessments, emphasizing the value of full-length NGS with amplicon sequence variant analysis for clinical gut microbiome research. They underscore the importance of considering methodological differences in future meta-analyses.

Metagenomics approaches in unveiling the dynamics of Plant Growth-Promoting Microorganisms (PGPM) vis-à-vis Phytophthora sp. suppression in various crop ecological systems

Phytophthora species are destructive pathogens causing yield losses in different ecological systems, such as potato, black pepper, pepper, avocado, citrus, and tobacco. The diversity of plant growth-promoting microorganisms (PGPM) plays a crucial role in disease suppression. Knowledge of metagenomics approaches is essential for assessing the dynamics of PGPM and Phytophthora species across various ecosystems, facilitating effective management strategies for better crop protection. This review discusses the dynamic interplay between PGPM and Phytophthora sp. using metagenomics approaches that sheds light on the potential of PGPM strains tailored to specific crop ecosystems to bolster pathogen suppressiveness.

Characterization of pediatric urinary microbiome at species-level resolution indicates variation due to sex, age, and urologic history

BACKGROUND

Recently, associations between recurrent urinary tract infections (UTI) and the urinary microbiome (urobiome) composition have been identified in adults. However, little is known about the urobiome in children. We aimed to characterize the urobiome of children with species-level resolution and to identify associations based on UTI history.

STUDY DESIGN

Fifty-four children (31 females and 21 males) from 3 months to 11 years of age participated in the study. Catheterized urine specimens were obtained from children undergoing a clinically indicated voiding cystourethrogram. To improve the analysis of the pediatric urobiome, we used a novel protocol using filters to collect biomass from the urine coupled with synthetic long-read 16S rRNA gene sequencing to obtain culture-independent species-level resolution data. We tested for differences in microbial composition between sex and history of UTIs using non-parametric tests on individual bacteria and alpha diversity measures.

RESULTS

We detected bacteria in 61% of samples from 54 children (mean age 40.7 months, 57% females). Similar to adults, urobiomes were distinct across individuals and varied by sex. The urobiome of females showed higher diversity as measured by the inverse Simpson and Shannon indices but not the Pielou evenness index or number of observed species (p = 0.05, p = 0.04, p = 0.35, and p = 0.11, respectively). Additionally, several species were significantly overrepresented in females compared to males, including those from the genera Anaerococcus, Prevotella, and Schaalia (p = 0.03, 0.04, and 0.02, respectively). Urobiome diversity increased with age, driven mainly by males. Comparison of children with a history of 1, 2, or 3+ UTIs revealed that urobiome diversity significantly decreases in the group that experienced 3+ UTIs as measured by the Simpson, Shannon, and Pielou indices (p = 0.03, p = 0.05, p = 0.01). Several bacteria were also found to be reduced in abundance.

DISCUSSION

In this study, we confirm that urobiome can be identified from catheter-collected urine specimens in infants as young as 3 months, providing further evidence that the pediatric bladder is not sterile. In addition to confirming variations in the urobiome related to sex, we identify age-related changes in children under 5 years of age, which conflicts with some prior research. We additionally identify associations with a history of UTIs.

CONCLUSIONS

Our study provides additional evidence that the pediatric urobiome exists. The bacteria in the bladder of children appear to be affected by early urologic events and warrants future research.

Characterization of pediatric urinary microbiome at species-level resolution indicates variation due to sex, age, and urologic history

Background

Recently, associations between recurrent urinary tract infections (UTI) and the urinary microbiome (urobiome) composition have been identified in adults. However, little is known about the urobiome in children. We aimed to characterize the urobiome of children with species-level resolution and to identify associations based on UTI history.

Study design

Fifty-four children (31 females and 21 males) from 3 months to 5 years of age participated in the study. Catheterized urine specimens were obtained from children undergoing a clinically indicated voiding cystourethrogram. To improve the analysis of the pediatric urobiome, we used a novel protocol using filters to collect biomass from the urine coupled with synthetic long-read 16S rRNA gene sequencing to obtain culture-independent species-level resolution data. We tested for differences in microbial composition between sex and history of UTIs using non-parametric tests on individual bacteria and alpha diversity measures.

Results

We detected bacteria in 61% of samples from 54 children (mean age 40.7 months, 57% females). Similar to adults, urobiomes were distinct across individuals and varied by sex. The urobiome of females showed higher diversity as measured by the inverse Simpson and Shannon indices but not the Pielou evenness index or number of observed species (p = 0.05, p=0.04, p = 0.35, and p = 0.11, respectively). Additionally, several species were significantly overrepresented in females compared to males, including those from the genera Anaerococcus, Prevotella, and Schaalia (p = 0.03, 0.04, and 0.02, respectively). Urobiome diversity increased with age, driven mainly by males. Comparison of children with a history of 1, 2, or 3+ UTIs revealed that urobiome diversity significantly decreases in the group that experienced 3+ UTIs as measured by the Simpson, Shannon, and Pielou indices (p = 0.03, p = 0.05, p = 0.01). Several bacteria were also found to be reduced in abundance.

Discussion

In this study, we confirm that urobiome can be identified from catheter-collected urine specimens in infants as young as 3 months, providing further evidence that the pediatric bladder is not sterile. In addition to confirming variations in the urobiome related to sex, we identify age-related changes in children under 5 years of age, which conflicts with some prior research. We additionally identify associations with a history of UTIs.

Conclusions

Our study provides additional evidence that the pediatric urobiome exists. The bacteria in the bladder of children appear to be affected by early urologic events and warrants future research.

Using short-read 16S rRNA sequencing of multiple variable regions to generate high-quality results to a species level

Introduction

Short-read amplicon sequencing studies have typically focused on 1-2 variable regions of the 16S rRNA gene. Species-level resolution is limited in these studies, as each variable region enables the characterisation of a different subsection of the microbiome. Although long-read sequencing techniques take advantage of all 9 variable regions by sequencing the entire 16S rRNA gene, they are substantially more expensive. This work assessed the feasibility of accurate species-level resolution and reproducibility using a relatively new sequencing kit and bioinformatics pipeline developed for short-read sequencing of multiple variable regions of the 16S rRNA gene. In addition, we evaluated the potential impact of different sample collection methods on our outcomes.

Methods

Using xGen™ 16S Amplicon Panel v2 kits, sequencing of all 9 variable regions of the 16S rRNA gene was carried out on an Illumina MiSeq platform. Mock cells and mock DNA for 8 bacterial species were included as extraction and sequencing controls respectively. Within-run and between-run replicate samples, and pairs of stool and rectal swabs collected at 0-5 weeks from the same participants, were incorporated. Observed relative abundances of each species were compared to theoretical abundances provided by ZymoBIOMICS. Paired Wilcoxon rank sum tests and distance-based intraclass correlation coefficients were used to statistically compare alpha and beta diversity measures, respectively, for pairs of replicates and stool/rectal swab sample pairs.

Results

Using multiple variable regions of the 16S ribosomal Ribonucleic Acid (rRNA) gene, we found that we could accurately identify taxa to a species level and obtain highly reproducible results at a species level. Yet, the microbial profiles of stool and rectal swab sample pairs differed substantially despite being collected concurrently from the same infants.

Conclusion

This protocol provides an effective means for studying infant gut microbial samples at a species level. However, sample collection approaches need to be accounted for in any downstream analysis.

Targeted phasing of 2-200 kilobase DNA fragments with a short-read sequencer and a single-tube linked-read library method

In the human genome, heterozygous sites refer to genomic positions with a different allele or nucleotide variant on the maternal and paternal chromosomes. Resolving these allelic differences by chromosomal copy, also known as phasing, is achievable on a short-read sequencer when using a library preparation method that captures long-range genomic information. TELL-Seq is a library preparation that captures long-range genomic information with the aid of molecular identifiers (barcodes). The same barcode is used to tag the reads derived from the same long DNA fragment within a range of up to 200 kilobases (kb), generating linked-reads. This strategy can be used to phase an entire genome. Here, we introduce a TELL-Seq protocol developed for targeted applications, enabling the phasing of enriched loci of varying sizes, purity levels, and heterozygosity. To validate this protocol, we phased 2-200 kb loci enriched with different methods: CRISPR/Cas9-mediated excision coupled with pulse-field electrophoresis for the longest fragments, CRISPR/Cas9-mediated protection from exonuclease digestion for mid-size fragments, and long PCR for the shortest fragments. All selected loci have known clinical relevance: BRCA1, BRCA2, MLH1, MSH2, MSH6, APC, PMS2, SCN5A-SCN10A, and PKI3CA. Collectively, the analyses show that TELL-Seq can accurately phase 2-200 kb targets using a short-read sequencer.

Innovations and trends in antibody repertoire analysis

Monoclonal antibodies have revolutionized the treatment of human diseases, which has made them the fastest-growing class of therapeutics, with global sales expected to reach $346.6 billion USD by 2028. Advances in antibody engineering and development have led to the creation of increasingly sophisticated antibody-based therapeutics (e.g. bispecific antibodies and chimeric antigen receptor T cells). However, approaches for antibody discovery have remained comparatively grounded in conventional yet reliable in vitro assays. Breakthrough developments in high-throughput single B-cell sequencing and immunoglobulin proteomic serology, however, have enabled the identification of high-affinity antibodies directly from endogenous B cells or circulating immunoglobulin produced in vivo. Moreover, advances in artificial intelligence offer vast potential for antibody discovery and design with large-scale repertoire datasets positioned as the optimal source of training data for such applications. We highlight advances and recent trends in how these technologies are being applied to antibody repertoire analysis.

Serovar-level identification of bacterial foodborne pathogens from full-length 16S rRNA gene sequencing

The resolution of variation within species is critical for interpreting and acting on many microbial measurements. In the key foodborne pathogens Salmonella and Escherichia coli, the primary subspecies classification scheme used is serotyping: differentiating variants within these species by surface antigen profiles. Serotype prediction from whole-genome sequencing (WGS) of isolates is now seen as comparable or preferable to traditional laboratory methods where WGS is available. However, laboratory and WGS methods depend on an isolation step that is time-consuming and incompletely represents the sample when multiple strains are present. Community sequencing approaches that skip the isolation step are, therefore, of interest for pathogen surveillance. Here, we evaluated the viability of amplicon sequencing of the full-length 16S rRNA gene for serotyping Salmonella enterica and E. coli. We developed a novel algorithm for serotype prediction, implemented as an R package (Seroplacer), which takes as input full-length 16S rRNA gene sequences and outputs serovar predictions after phylogenetic placement into a reference phylogeny. We achieved over 89% accuracy in predicting Salmonella serotypes on in silico test data and identified key pathogenic serovars of Salmonella and E. coli in isolate and environmental test samples. Although serotype prediction from 16S rRNA gene sequences is not as accurate as serotype prediction from WGS of isolates, the potential to identify dangerous serovars directly from amplicon sequencing of environmental samples is intriguing for pathogen surveillance. The capabilities developed here are also broadly relevant to other applications where intraspecies variation and direct sequencing from environmental samples could be valuable.IMPORTANCEIn order to prevent and stop outbreaks of foodborne pathogens, it is important that we can detect when pathogenic bacteria are present in a food or food-associated site and identify connections between specific pathogenic bacteria present in different samples. In this work, we develop a new computational technology that allows the important foodborne pathogens Escherichia coli and Salmonella enterica to be serotyped (a subspecies level classification) from sequencing of a single-marker gene, and the 16S rRNA gene often used to surveil bacterial communities. Our results suggest current limitations to serotyping from 16S rRNA gene sequencing alone but set the stage for further progress that we consider likely given the rapid advance in the long-read sequencing technologies and genomic databases our work leverages. If this research direction succeeds, it could enable better detection of foodborne pathogens before they reach the public and speed the resolution of foodborne pathogen outbreaks.

cloudrnaSPAdes: isoform assembly using bulk barcoded RNA sequencing data

MOTIVATION

Recent advancements in long-read RNA sequencing have enabled the examination of full-length isoforms, previously uncaptured by short-read sequencing methods. An alternative powerful method for studying isoforms is through the use of barcoded short-read RNA reads, for which a barcode indicates whether two short-reads arise from the same molecule or not. Such techniques included the 10x Genomics linked-read based SParse Isoform Sequencing (SPIso-seq), as well as Loop-Seq, or Tell-Seq. Some applications, such as novel-isoform discovery, require very high coverage. Obtaining high coverage using long reads can be difficult, making barcoded RNA-seq data a valuable alternative for this task. However, most annotation pipelines are not able to work with a set of short reads instead of a single transcript, also not able to work with coverage gaps within a molecule if any. In order to overcome this challenge, we present an RNA-seq assembler that allows the determination of the expressed isoform per barcode.

RESULTS

In this article, we present cloudrnaSPAdes, a tool for assembling full-length isoforms from barcoded RNA-seq linked-read data in a reference-free fashion. Evaluating it on simulated and real human data, we found that cloudrnaSPAdes accurately assembles isoforms, even for genes with high isoform diversity.

AVAILABILITY AND IMPLEMENTATION

cloudrnaSPAdes is a feature release of a SPAdes assembler and version used for this article is available at https://github.com/1dayac/cloudrnaSPAdes-release.

Characterizing the Effect of Campylobacter jejuni Challenge on Growth Performance, Cecal Microbiota, and Cecal Short-Chain Fatty Acid Concentrations in Broilers

This study aimed to understand the effect of C. jejuni challenge on the cecal microbiota and short-chain fatty acid (SCFA) concentration to form a better understanding of the host-pathogen interaction. Sixty broilers were randomly allocated into two treatments: control and challenge. Each treatment was replicated in six pens with five birds per pen. On day 21, birds in the challenge group were orally gavaged with 1 × 108C. jejuni/mL, while the control group was mock challenged with PBS. The C. jejuni challenge had no effect on body weight, feed intake, and feed conversion ratio compared to the control group. On day 28, the C. jejuni challenge decreased the observed features and Shannon index compared to the control group. On the species level, the C. jejuni challenge decreased (p = 0.02) the relative abundance of Sellimonas intestinalis on day 28 and increased (p = 0.04) the relative abundance of Faecalibacterium sp002160895 on day 35 compared to the control group. The C. jejuni challenge did not change the microbial function and the cecal concentrations of SCFA on days 28 and 35 compared to the control group. In conclusion, C. jejuni might alter the gut microbiota's composition and diversity without significantly compromising broilers' growth.

Soil quality and microbial communities in subtropical slope lands under different agricultural management practices

Land degradation is a major threat to ecosystem. Long-term conventional farming practices can lead to severe soil degradation and a decline in crop productivity, which are challenging for both local and global communities. This study was conducted to clarify the responses on soil physicochemical properties and microbial communities to changes in farming practices. Slope land orchards under three agricultural management practices-conventional farming (CF), organic farming (OF), and ecofriendly farming (EFF)-were included in this study. We found that soil carbon stock increased by 3.6 and 5.1 times in surface soils (0-30 cm) under EFF and OF treatments, respectively. EFF and OF significantly increased the contents of total nitrogen by 0.33-0.46 g/kg, ammonia-N by 3.0-7.3 g/kg, and microbial biomass carbon by 0.56-1.04 g/kg but reduced those of pH by 0.6 units at least, and available phosphorous by 104-114 mg/kg. The application of phosphorous-containing herbicides and chemical fertilizers might increase the contents of phosphorous and nitrate in CF soil. High abundances of Acidobacteria and Actinobacteria were observed in EFF and OF soils, likely because of phosphorous deficiency in these soils. The abundance of fungi in OF soil indicated that plants' demand for available soil phosphorous induced the fungus-mediated mineralization of organic phosphorous. High abundances of Gammaproteobacteria, Planctomycetes, Firmicutes, and Nitrospirae were observed in CF soil, possibly because of the regular use of herbicides containing phosphorous and chemical fertilizers containing high total nitrogen contents.

Diverse Microbial Hot Spring Mat Communities at Black Canyon of the Colorado River

The thermophilic microbial mat communities at hot springs in the Black Canyon of the Colorado River, thought to harbor the protistan human pathogen Naegleria fowleri, were surveyed using both culture-independent and -dependent methods to further understand the ecology of these hot spring microbiomes. Originating from Lake Mead source water, seven spring sites were sampled, varying in temperature from 25 to 55 °C. Amplicon-based high-throughput sequencing of twelve samples using 16S rRNA primers (hypervariable V4 region) revealed that most mats are dominated by cyanobacterial taxa, some but not all similar to those dominating the mats at other studied hot spring systems. 18S rRNA amplicon sequencing (V9 region) demonstrated a diverse community of protists and other eukaryotes including a highly abundant amoebal sequence related to Echinamoeba thermarum. Additional taxonomic and diversity metric analyses using near full-length 16S and 18S rRNA gene sequencing allowed a higher sequence-based resolution of the community. The mat sequence data suggest a major diversification of the cyanobacterial orders Leptolyngbyales, as well as microdiversity among several cyanobacterial taxa. Cyanobacterial isolates included some representatives of ecologically abundant taxa. A Spearman correlation analysis of short-read amplicon sequencing data supported the co-occurrences of populations of cyanobacteria, chloroflexi, and bacteroidetes providing evidence of common microbial co-occurrences across the Black Canyon hot springs.

Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.

Ariadne: synthetic long read deconvolution using assembly graphs

Synthetic long read sequencing techniques such as UST's TELL-Seq and Loop Genomics' LoopSeq combine 3[Formula: see text] barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. However, the lack of a 1:1 correspondence between a long fragment and a 3[Formula: see text] unique molecular identifier confounds the assignment of linkage between short reads. We introduce Ariadne, a novel assembly graph-based synthetic long read deconvolution algorithm, that can be used to extract single-species read-clouds from synthetic long read datasets to improve the taxonomic classification and de novo assembly of complex populations, such as metagenomes.

R2C2+UMI: Combining concatemeric consensus sequencing with unique molecular identifiers enables ultra-accurate sequencing of amplicons on Oxford Nanopore Technologies sequencers

The sequencing of PCR amplicons is a core application of high-throughput sequencing technology. Using unique molecular identifiers (UMIs), individual amplified molecules can be sequenced to very high accuracy on an Illumina sequencer. However, Illumina sequencers have limited read length and are therefore restricted to sequencing amplicons shorter than 600bp unless using inefficient synthetic long-read approaches. Native long-read sequencers from Pacific Biosciences and Oxford Nanopore Technologies can, using consensus read approaches, match or exceed Illumina quality while achieving much longer read lengths. Using a circularization-based concatemeric consensus sequencing approach (R2C2) paired with UMIs (R2C2+UMI) we show that we can sequence ~550nt antibody heavy-chain (IGH) and ~1500nt 16S amplicons at accuracies up to and exceeding Q50 (<1 error in 100,0000 sequenced bases), which exceeds accuracies of UMI-supported Illumina paired sequencing as well as synthetic long-read approaches.

Differences in Microbial Community Composition between Uterine Horns Ipsilateral and Contralateral to the Corpus Luteum in Beef Cows on Day 15 of the Estrous Cycle

This study evaluated differences in uterine microbiota composition between uterine horns ipsilateral and contralateral to the corpus luteum of beef cows on day 15 of the estrous cycle. Cows (n = 23) were exposed to an estrus synchronization protocol to exogenously induce synchronized ovulation. Cows were then euthanized on day 15 of the estrous cycle, and individual swabs were collected from uterine horns ipsilateral and contralateral to the corpus luteum using aseptic techniques. DNA was extracted, and the entire (V1-V9 hypervariable regions) 16s rRNA gene was sequenced. Sequences were analyzed, and amplicon sequence variants (ASVs) were determined. Across all samples, 2 bacterial domains, 24 phyla, and 265 genera were identified. Butyribirio, Cutibacterium, BD7-11, Bacteroidales BS11 gut group, Ruminococcus, Bacteroidales RF16 group, and Clostridia UCG-014 differed in relative abundances between uterine horns. Rikenellaceae RC9 gut group, Bacteroidales UCG-001, Lachnospiraceae AC2044 group, Burkholderia-Caballeronia-Paraburkholderia, Psudobutyribibrio, and an unidentified genus of the family Chitinophagaceae and dgA-11 gut group differed between cows that expressed estrus and those that did not. The composition of the microbial community differed between the ipsilateral and contralateral horns and between cows that expressed estrus and cows that failed to express estrus, indicating that the uterine microbiota might play a role in cow fertility.

Serovar-level Identification of Bacterial Foodborne Pathogens From Full-length 16S rRNA Gene Sequencing

The resolution of variation within species is critical for interpreting and acting on many microbial measurements. In the key foodborne pathogens Escherichia coli and Salmonella, the primary sub-species classification scheme used is serotyping: differentiating variants within these species by surface antigen profiles. Serotype prediction from whole-genome sequencing (WGS) of isolates is now seen as comparable or preferable to traditional laboratory methods where WGS is available. However, laboratory and WGS methods depend on an isolation step that is time-consuming and incompletely represents the sample when multiple strains are present. Community sequencing approaches that skip the isolation step are therefore of interest for pathogen surveillance. Here we evaluated the viability of amplicon sequencing of the full-length 16S rRNA gene for serotyping S. enterica and E. coli. We developed a novel algorithm for serotype prediction, implemented as an R package (Seroplacer), which takes as input full-length 16S rRNA gene sequences and outputs serovar predictions after phylogenetic placement into a reference phylogeny. We achieved over 89% accuracy in predicting Salmonella serotypes on in silico test data, and identified key pathogenic serovars of Salmonella and E. coli in isolate and environmental test samples. Although serotype prediction from 16S sequences is not as accurate as serotype prediction from WGS of isolates, the potential to identify dangerous serovars directly from amplicon sequencing of environmental samples is intriguing for pathogen surveillance. The capabilities developed here are also broadly relevant to other applications where intra-species variation and direct sequencing from environmental samples could be valuable.

High speciation rate of niche specialists in hot springs

Ecological and evolutionary processes simultaneously regulate microbial diversity, but the evolutionary processes and their driving forces remain largely unexplored. Here we investigated the ecological and evolutionary characteristics of microbiota in hot springs spanning a broad temperature range (54.8-80 °C) by sequencing the 16S rRNA genes. Our results demonstrated that niche specialists and niche generalists are embedded in a complex interaction of ecological and evolutionary dynamics. On the thermal tolerance niche axis, thermal (T) sensitive (at a specific temperature) versus T-resistant (at least in five temperatures) species were characterized by different niche breadth, community abundance and dispersal potential, consequently differing in potential evolutionary trajectory. The niche-specialized T-sensitive species experienced strong temperature barriers, leading to completely species shift and high fitness but low abundant communities at each temperature ("home niche"), and such trade-offs thus reinforced peak performance, as evidenced by high speciation across temperatures and increasing diversification potential with temperature. In contrast, T-resistant species are advantageous of niche expansion but with poor local performance, as shown by wide niche breadth with high extinction, indicating these niche generalists are "jack-of-all-trades, master-of-none". Despite of such differences, the T-sensitive and T-resistant species are evolutionarily interacted. Specifically, the continuous transition from T-sensitive to T-resistant species insured the exclusion probability of T-resistant species at a relatively constant level across temperatures. The co-evolution and co-adaptation of T-sensitive and T-resistant species were in line with the red queen theory. Collectively, our findings demonstrate that high speciation of niche specialists could alleviate the environmental-filtering-induced negative effect on diversity.

Fine scale sampling reveals early differentiation of rhizosphere microbiome from bulk soil in young Brachypodium plant roots

For a deeper and comprehensive understanding of the composition and function of rhizosphere microbiomes, we need to focus at the scale of individual roots in standardized growth containers. Root exudation patterns are known to vary along distinct parts of the root even in juvenile plants giving rise to spatially distinct microbial niches. To address this, we analyzed the microbial community from two spatially distinct zones of the developing primary root (tip and base) in young Brachypodium distachyon grown in natural soil using standardized fabricated ecosystems known as EcoFABs as well as in more conventional pot and tubes. 16S rRNA based community analysis showed a strong rhizosphere effect resulting in significant enrichment of several OTUs belonging to Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria. However, microbial community composition did not differ between root tips and root base or across different growth containers. Functional analysis of bulk metagenomics revealed significant differences between root tips and bulk soil. The genes associated with different metabolic pathways and root colonization were enriched in root tips. On the other hand, genes associated with nutrient-limitation and environmental stress were prominent in the bulk soil compared to root tips, implying the absence of easily available, labile carbon and nutrients in bulk soil relative to roots. Such insights into the relationships between developing root and microbial communities are critical for judicious understanding of plant-microbe interactions in early developmental stages of plants.

Mutation rate, selection, and epistasis inferred from RNA virus haplotypes via neural posterior estimation

RNA viruses are particularly notorious for their high levels of genetic diversity, which is generated through the forces of mutation and natural selection. However, disentangling these two forces is a considerable challenge, and this may lead to widely divergent estimates of viral mutation rates, as well as difficulties in inferring the fitness effects of mutations. Here, we develop, test, and apply an approach aimed at inferring the mutation rate and key parameters that govern natural selection, from haplotype sequences covering full-length genomes of an evolving virus population. Our approach employs neural posterior estimation, a computational technique that applies simulation-based inference with neural networks to jointly infer multiple model parameters. We first tested our approach on synthetic data simulated using different mutation rates and selection parameters while accounting for sequencing errors. Reassuringly, the inferred parameter estimates were accurate and unbiased. We then applied our approach to haplotype sequencing data from a serial passaging experiment with the MS2 bacteriophage, a virus that parasites Escherichia coli. We estimated that the mutation rate of this phage is around 0.2 mutations per genome per replication cycle (95% highest density interval: 0.051-0.56). We validated this finding with two different approaches based on single-locus models that gave similar estimates but with much broader posterior distributions. Furthermore, we found evidence for reciprocal sign epistasis between four strongly beneficial mutations that all reside in an RNA stem loop that controls the expression of the viral lysis protein, responsible for lysing host cells and viral egress. We surmise that there is a fine balance between over- and underexpression of lysis that leads to this pattern of epistasis. To recap, we have developed an approach for joint inference of the mutation rate and selection parameters from full haplotype data with sequencing errors and used it to reveal features governing MS2 evolution.

The Skin Microbiome: Current Techniques, Challenges, and Future Directions

Skin acts as a barrier that promotes the colonization of bacteria, fungi, archaea, and viruses whose membership and function may differ depending on the various specialized niches or micro-environments of the skin. The group of microorganisms inhabiting the skin, also known as the skin microbiome, offers protection against pathogens while actively interacting with the host's immune system. Some members of the skin microbiome can also act as opportunistic pathogens. The skin microbiome is influenced by factors such as skin site, birth mode, genetics, environment, skin products, and skin conditions. The association(s) of the skin microbiome with health and disease has (have) been identified and characterized via culture-dependent and culture-independent methods. Culture-independent methods (such as high-throughput sequencing), in particular, have expanded our understanding of the skin microbiome's role in maintaining health or promoting disease. However, the intrinsic challenges associated with the low microbial biomass and high host content of skin microbiome samples have hindered advancements in the field. In addition, the limitations of current collection and extraction methods and biases derived from sample preparation and analysis have significantly influenced the results and conclusions of many skin microbiome studies. Therefore, the present review discusses the technical challenges associated with the collection and processing of skin microbiome samples, the advantages and disadvantages of current sequencing approaches, and potential future areas of focus for the field.

Multi-factorial examination of amplicon sequencing workflows from sample preparation to bioinformatic analysis

BACKGROUND

The development of sequencing technologies to evaluate bacterial microbiota composition has allowed new insights into the importance of microbial ecology. However, the variety of methodologies used among amplicon sequencing workflows leads to uncertainty about best practices as well as reproducibility and replicability among microbiome studies. Using a bacterial mock community composed of 37 soil isolates, we performed a comprehensive methodological evaluation of workflows, each with a different combination of methodological factors spanning sample preparation to bioinformatic analysis to define sources of artifacts that affect coverage, accuracy, and biases in the resulting compositional profiles.

RESULTS

Of the workflows examined, those using the V4-V4 primer set enabled the highest level of concordance between the original mock community and resulting microbiome sequence composition. Use of a high-fidelity polymerase, or a lower-fidelity polymerase with an increased PCR elongation time, limited chimera formation. Bioinformatic pipelines presented a trade-off between the fraction of distinct community members identified (coverage) and fraction of correct sequences (accuracy). DADA2 and QIIME2 assembled V4-V4 reads amplified by Taq polymerase resulted in the highest accuracy (100%) but had a coverage of only 52%. Using mothur to assemble and denoise V4-V4 reads resulted in a coverage of 75%, albeit with marginally lower accuracy (99.5%).

CONCLUSIONS

Optimization of microbiome workflows is critical for accuracy and to support reproducibility and replicability among microbiome studies. These considerations will help reveal the guiding principles of microbial ecology and impact the translation of microbiome research to human and environmental health.

Effective microbial molecular diagnosis of periodontitis-related pathogen Porphyromonas gingivalis from salivary samples using rgpA gene

Importance of accurate molecular diagnosis and quantification of particular disease-related pathogenic microorganisms is highlighted as an introductory step to prevent and care for diseases. In this study, we designed a primer/probe set for quantitative real-time polymerase chain reaction (qRT-PCR) targeting rgpA gene, known as the specific virulence factor of periodontitis-related pathogenic bacteria ‘Porphyromonas gingivalis’, and evaluated its diagnostic efficiency by detecting and quantifying relative bacterial load of P. gingivalis within saliva samples collected from clinical subjects. As a result of qRT-PCR, we confirmed that relative bacterial load of P. gingivalis was detected and quantified within all samples of positive control and periodontitis groups. On the contrary, negative results were confirmed in both negative control and healthy groups. Additionally, as a result of comparison with next-generation sequencing (NGS)–based 16S metagenome profiling data, we confirmed relative bacterial load of P. gingivalis, which was not identified on bacterial classification table created through 16S microbiome analysis, in qRT-PCR results. It showed that an approach to quantifying specific microorganisms by applying qRT-PCR method could solve microbial misclassification issues at species level of an NGS-based 16S microbiome study. In this respect, we suggest that P. gingivalis–specific primer/probe set introduced in present study has efficient applicability in various oral healthcare industries, including periodontitis-related microbial molecular diagnosis field.

Unveiling the Human Gastrointestinal Tract Microbiome: The Past, Present, and Future of Metagenomics

Over 10¹⁴ symbiotic microorganisms are present in a healthy human body and are responsible for the synthesis of vital vitamins and amino acids, mediating cellular pathways and supporting immunity. However, the deregulation of microbial dynamics can provoke diverse human diseases such as diabetes, human cancers, cardiovascular diseases, and neurological disorders. The human gastrointestinal tract constitutes a hospitable environment in which a plethora of microbes, including diverse species of archaea, bacteria, fungi, and microeukaryotes as well as viruses, inhabit. In particular, the gut microbiome is the largest microbiome community in the human body and has drawn for decades the attention of scientists for its significance in medical microbiology. Revolutions in sequencing techniques, including 16S rRNA and ITS amplicon sequencing and whole genome sequencing, facilitate the detection of microbiomes and have opened new vistas in the study of human microbiota. Especially, the flourishing fields of metagenomics and metatranscriptomics aim to detect all genomes and transcriptomes that are retrieved from environmental and human samples. The present review highlights the complexity of the gastrointestinal tract microbiome and deciphers its implication not only in cellular homeostasis but also in human diseases. Finally, a thorough description of the widely used microbiome detection methods is discussed.

Targeted Phasing of 2-200 Kilobase DNA Fragments with a Short-Read Sequencer and a Single-Tube Linked-Read Library Method

In the human genome, heterozygous sites are genomic positions with different alleles inherited from each parent. On average, there is a heterozygous site every 1-2 kilobases (kb). Resolving whether two alleles in neighboring heterozygous positions are physically linked-that is, phased-is possible with a short-read sequencer if the sequencing library captures long-range information. TELL-Seq is a library preparation method based on millions of barcoded micro-sized beads that enables instrument-free phasing of a whole human genome in a single PCR tube. TELL-Seq incorporates a unique molecular identifier (barcode) to the short reads generated from the same high-molecular-weight (HMW) DNA fragment (known as 'linked-reads'). However, genome-scale TELL-Seq is not cost-effective for applications focusing on a single locus or a few loci. Here, we present an optimized TELL-Seq protocol that enables the cost-effective phasing of enriched loci (targets) of varying sizes, purity levels, and heterozygosity. Targeted TELL-Seq maximizes linked-read efficiency and library yield while minimizing input requirements, fragment collisions on microbeads, and sequencing burden. To validate the targeted protocol, we phased seven 180-200 kb loci enriched by CRISPR/Cas9-mediated excision coupled with pulse-field electrophoresis, four 20 kb loci enriched by CRISPR/Cas9-mediated protection from exonuclease digestion, and six 2-13 kb loci amplified by PCR. The selected targets have clinical and research relevance (BRCA1, BRCA2, MLH1, MSH2, MSH6, APC, PMS2, SCN5A-SCN10A, and PKI3CA). These analyses reveal that targeted TELL-Seq provides a reliable way of phasing allelic variants within targets (2-200 kb in length) with the low cost and high accuracy of short-read sequencing.

Abundant fungi dominate the complexity of microbial networks in soil of contaminated site: High-precision community analysis by full-length sequencing

During the past decade, the characterization of microbial community in soil of contaminated sites was primarily done by high-throughput short-read amplicon sequencing. However, due to the similarity of 16S rRNA and ITS genes amplicon sequences, the short-read approach often limits the microbial composition analysis at the species level. Here, we simultaneously performed full-length and short-read amplicon sequencing to clarify the community composition and ecological status of different microbial taxa in contaminated soil from a high-resolution perspective. We found that (1) full-length 16S rRNA gene sequencing gave better resolution for bacterial identification at all levels, while there were no significant differences between the two sequencing platforms for fungal identification in some samples. (2) Abundant taxa were vital for microbial co-occurrences network constructed by both full-length and short-read sequencing data, and abundant fungal species such as Mortierella alpine, Fusarium solani, Mrakia frigida, and Chaetomium homopilatum served as the keystone species. (3) Heavy metal correlated with the microbial community significantly, and bacterial community and its abundant taxa were assembled by deterministic process, while the other taxa were dominated by stochastic process. These findings contribute to the understanding of the ecological mechanisms and microbial interactions in site soil ecosystems and demonstrate that full-length sequencing has the potential to provide more details of microbial community.

Large-Scale Complete Sequencing and Haplotyping of 1-10 kb DNA Molecules Using Short Massively Parallel Reads

In this chapter, we describe a simple, low-cost method for making many copies of a single DNA molecule (1-10 kb in length) as a concatemer on a long DNA strand. This can enable applications requiring high-quality contiguous sequence and haplotype data from long single DNA molecules at large scale.

The Application of Metagenomics to Study Microbial Communities and Develop Desirable Traits in Fermented Foods

The microbial communities present within fermented foods are diverse and dynamic, producing a variety of metabolites responsible for the fermentation processes, imparting characteristic organoleptic qualities and health-promoting traits, and maintaining microbiological safety of fermented foods. In this context, it is crucial to study these microbial communities to characterise fermented foods and the production processes involved. High Throughput Sequencing (HTS)-based methods such as metagenomics enable microbial community studies through amplicon and shotgun sequencing approaches. As the field constantly develops, sequencing technologies are becoming more accessible, affordable and accurate with a further shift from short read to long read sequencing being observed. Metagenomics is enjoying wide-spread application in fermented food studies and in recent years is also being employed in concert with synthetic biology techniques to help tackle problems with the large amounts of waste generated in the food sector. This review presents an introduction to current sequencing technologies and the benefits of their application in fermented foods.

Are short-read amplicons suitable for the prediction of microbiome functional potential? A critical perspective

Taxonomic marker gene analysis allows uncovering taxonomic profiles of microbial communities at low cost, making it omnipresent in microbiome research. There is an ever-expanding set of tools to extract further biological information from this kind of data. In this perspective, we enunciate several concerns regarding the biological validity of predicting functional potential from taxonomic profiles, especially when they are generated by short-read sequencing. The taxonomic resolution of marker genes, intragenomic variability of marker genes, and the compositional nature of microbiome data are discussed. Combining actual measurements of microbiome functions with predicted functional potentials is proposed as a powerful approach to better understand microbiome functioning. In this context, the significance of predicted functional potentials for generating and testing hypotheses is highlighted. We argue that functions of microbiomes predicted from microbiome DNA read count data generated by short-read amplicon sequencing should not serve as the only basis to draw biological inferences.

Updating Urinary Microbiome Analyses to Enhance Biologic Interpretation

Objective

An approach for assessing the urinary microbiome is 16S rRNA gene sequencing, where analysis methods are rapidly evolving. This re-analysis of an existing dataset aimed to determine whether updated bioinformatic and statistical techniques affect clinical inferences.

Methods

A prior study compared the urinary microbiome in 123 women with mixed urinary incontinence (MUI) and 84 controls. We obtained unprocessed sequencing data from multiple variable regions, processed operational taxonomic unit (OTU) tables from the original analysis, and de-identified clinical data. We re-processed sequencing data with DADA2 to generate amplicon sequence variant (ASV) tables. Taxa from ASV tables were compared to the original OTU tables; taxa from different variable regions after updated processing were also compared. Bayesian graphical compositional regression (BGCR) was used to test for associations between microbial compositions and clinical phenotypes (e.g., MUI versus control) while adjusting for clinical covariates. Several techniques were used to cluster samples into microbial communities. Multivariable regression was used to test for associations between microbial communities and MUI, again while adjusting for potentially confounding variables.

Results

Of taxa identified through updated bioinformatic processing, only 40% were identified originally, though taxa identified through both methods represented &gt;99% of the sequencing data in terms of relative abundance. Different 16S rRNA gene regions resulted in different recovered taxa. With BGCR analysis, there was a low (33.7%) probability of an association between overall microbial compositions and clinical phenotype. However, when microbial data are clustered into bacterial communities, we confirmed that bacterial communities are associated with MUI. Contrary to the originally published analysis, we did not identify different associations by age group, which may be due to the incorporation of different covariates in statistical models.

Conclusions

Updated bioinformatic processing techniques recover different taxa compared to earlier techniques, though most of these differences exist in low abundance taxa that occupy a small proportion of the overall microbiome. While overall microbial compositions are not associated with MUI, we confirmed associations between certain communities of bacteria and MUI. Incorporation of several covariates that are associated with the urinary microbiome improved inferences when assessing for associations between bacterial communities and MUI in multivariable models.

Emu: species-level microbial community profiling of full-length 16S rRNA Oxford Nanopore sequencing data

16S ribosomal RNA-based analysis is the established standard for elucidating the composition of microbial communities. While short-read 16S rRNA analyses are largely confined to genus-level resolution at best, given that only a portion of the gene is sequenced, full-length 16S rRNA gene amplicon sequences have the potential to provide species-level accuracy. However, existing taxonomic identification algorithms are not optimized for the increased read length and error rate often observed in long-read data. Here we present Emu, an approach that uses an expectation-maximization algorithm to generate taxonomic abundance profiles from full-length 16S rRNA reads. Results produced from simulated datasets and mock communities show that Emu is capable of accurate microbial community profiling while obtaining fewer false positives and false negatives than alternative methods. Additionally, we illustrate a real-world application of Emu by comparing clinical sample composition estimates generated by an established whole-genome shotgun sequencing workflow with those returned by full-length 16S rRNA gene sequences processed with Emu.

DNA barcoding of fungal specimens using PacBio long-read high-throughput sequencing

Molecular methods are increasingly used to identify species that lack conspicuous macro- or micromorphological characters. Taxonomic and ecological research teams barcode large numbers of collected voucher specimens annually. In this study we assessed the efficiency of long-read high throughput sequencing (HTS) as opposed to the traditionally used Sanger method for taxonomic identification of multiple vouchered fungal specimens. We also evaluated whether this method can provide reference information about intra-individual gene polymorphism. We developed a workflow based on a test set of 423 basidiomycete specimens (representing 195 species), the PacBio HTS method, and ribosomal rRNA operon internal transcribed spacer (ITS) and 28S rRNA gene (LSU) markers. The PacBio HTS had a higher success rate than Sanger sequencing at a comparable cost. Species identification based on PacBio reads was usually straightforward, because the dominant operational taxonomic unit (OTU) typically represented the targeted organism. The PacBio HTS also enabled us to detect widespread polymorphism within the ITS marker. We conclude that multiplex DNA barcoding of the fungal ITS and LSU markers using PacBio HTS is a useful tool for taxonomic identification of large amounts of collected voucher specimens at a competitive price. Furthermore, PacBio HTS accurately recovers various alleles and paralogs, which can provide crucial information for species delimitation and population-level studies.

Best practices in metabarcoding of fungi: From experimental design to results

The development of high-throughput sequencing (HTS) technologies has greatly improved our capacity to identify fungi and unveil their ecological roles across a variety of ecosystems. Here we provide an overview of current best practices in metabarcoding analysis of fungal communities, from experimental design through molecular and computational analyses. By reanalysing published data sets, we demonstrate that operational taxonomic units (OTUs) outperform amplified sequence variants (ASVs) in recovering fungal diversity, a finding that is particularly evident for long markers. Additionally, analysis of the full-length ITS region allows more accurate taxonomic placement of fungi and other eukaryotes compared to the ITS2 subregion. Finally, we show that specific methods for compositional data analyses provide more reliable estimates of shifts in community structure. We conclude that metabarcoding analyses of fungi are especially promising for integrating fungi into the full microbiome and broader ecosystem functioning context, recovery of novel fungal lineages and ancient organisms as well as barcoding of old specimens including type material.

Particle Size, Mass Concentration, and Microbiota in Dental Aerosols

Many dental procedures are considered aerosol-generating procedures that may put the dental operator and patients at risk for cross-infection due to contamination from nasal secretions and saliva. This aerosol, depending on the size of the particles, may stay suspended in the air for hours. The primary objective of the study was to characterize the size and concentrations of particles emitted from 7 different dental procedures, as well as estimate the contribution of the nasal and salivary fluids of the patient to the microbiota in the emitted bioaerosol. This cross-sectional study was conducted in an open-concept dental clinic with multiple operators at the same time. Particle size characterization and mass and particle concentrations were done by using 2 direct reading instruments: Dust-Trak DRX (Model 8534) and optical particle sizer (Model 3330). Active bioaerosol sampling was done before and during procedures. Bayesian modeling (SourceTracker2) of long-reads of the 16S ribosomal DNA was used to estimate the contribution of the patients’ nasal and salivary fluids to the bioaerosol. Aerosols in most dental procedures were sub-PM₁ dominant. Orthodontic debonding and denture adjustment consistently demonstrated more particles in the PM₁, PM_2.5, PM₄, and PM₁₀ ranges. The microbiota in bioaerosol samples were significantly different from saliva and nasal samples in both membership and abundance (P < 0.05) but not different from preoperative ambient air samples. A median of 80.15% of operator exposure was attributable to sources other than the patients’ salivary or nasal fluids. Median operator’s exposure from patients’ fluids ranged from 1.45% to 2.75%. Corridor microbiota showed more patients’ nasal bioaerosols than oral bioaerosols. High-volume saliva ejector and saliva ejector were effective in reducing bioaerosol escape. Patient nasal and salivary fluids are minor contributors to the operator’s bioaerosol exposure, which has important implications for COVID-19. Control of bioaerosolization of nasal fluids warrants further investigation.

Effects of Waterlogging on Soybean Rhizosphere Bacterial Community Using V4, LoopSeq, and PacBio 16S rRNA Sequence

Waterlogging causes a significant reduction in soil oxygen levels, which in turn negatively affects soil nutrient use efficiency and crop yields. Rhizosphere microbes can help plants to better use nutrients and thus better adapt to this stress, while it is not clear how the plant-associated microbes respond to waterlogging stress. There are also few reports on whether this response is influenced by different sequencing methods and by different soils. In this study, using partial 16S rRNA sequencing targeting the V4 region and two full-length 16S rRNA sequencing approaches targeting the V1 to V9 regions, the effects of waterlogging on soybean rhizosphere bacterial structure in two types of soil were examined. Our results showed that, compared with the partial 16S sequencing, full-length sequencing, both LoopSeq and Pacific Bioscience (PacBio) 16S sequencing, had a higher resolution. On both types of soil, all the sequencing methods showed that waterlogging significantly affected the bacterial community structure of the soybean rhizosphere and increased the relative abundance of Geobacter. Furthermore, modular analysis of the cooccurrence network showed that waterlogging increased the relative abundance of some microorganisms related to nitrogen cycling when using V4 sequencing and increased the microorganisms related to phosphorus cycling when using LoopSeq and PacBio 16S sequencing methods. Core microorganism analysis further revealed that the enriched members of different species might play a central role in maintaining the stability of bacterial community structure and ecological functions. Together, our study explored the role of microorganisms enriched at the rhizosphere under waterlogging in assisting soybeans to resist stress. Furthermore, compared to partial and PacBio 16S sequencing, LoopSeq offers improved accuracy and reduced sequencing prices, respectively, and enables accurate species-level and strain identification from complex environmental microbiome samples. IMPORTANCE Soybeans are important oil-bearing crops, and waterlogging has caused substantial decreases in soybean production all over the world. The microbes associated with the host have shown the ability to promote plant growth, nutrient absorption, and abiotic resistance. High-throughput sequencing of partial 16S rRNA is the most commonly used method to analyze the microbial community. However, partial sequencing cannot provide correct classification information below the genus level, which greatly limits our research on microbial ecology. In this study, the effects of waterlogging on soybean rhizosphere microbial structure in two soil types were explored using partial 16S rRNA and full-length 16S gene sequencing by LoopSeq and Pacific Bioscience (PacBio). The results showed that full-length sequencing had higher classification resolution than partial sequencing. Three sequencing methods all indicated that rhizosphere bacterial community structure was significantly impacted by waterlogging, and the relative abundance of Geobacter was increased in the rhizosphere in both soil types after suffering waterlogging. Moreover, the core microorganisms obtained by different sequencing methods all contain species related to nitrogen cycling. Together, our study not only explored the role of microorganisms enriched at the rhizosphere level under waterlogging in assisting soybean to resist stress but also showed that LoopSeq sequencing is a less expensive and more convenient method for full-length sequencing by comparing different sequencing methods.

A qRT-PCR Method Capable of Quantifying Specific Microorganisms Compared to NGS-Based Metagenome Profiling Data

Metagenome profiling research using next-generation sequencing (NGS), a technique widely used to analyze the diversity and composition of microorganisms living in the human body, especially the gastrointestinal tract, has been actively conducted, and there is a growing interest in the quantitative and diagnostic technology for specific microorganisms. According to recent trends, quantitative real-time PCR (qRT-PCR) is still a considerable technique in detecting and quantifying bacteria associated with the human oral and nasal cavities, due to the analytical cost and time burden of NGS technology. Here, based on NGS metagenome profiling data produced by utilizing 100 gut microbiota samples, we conducted a comparative analysis for the identification and quantification of five bacterial genera (Akkermansia, Bacteroides, Bifidobacterium, Phascolarctobacterium, and Roseburia) within same metagenomic DNA samples through qRT-PCR assay in parallel. Genus-specific primers, targeting the particular gene of each genus for qRT-PCR assay, allowed a statistically consistent quantification pattern with the metagenome profiling data. Furthermore, results of bacterial identification through Sanger validation demonstrated the high genus-specificity of each primer set. Therefore, our study suggests that an approach to quantifying specific microorganisms by applying the qRT-PCR method can compensate for the concerns (potential issues) of NGS while also providing efficient benefits to various microbial industries.

Defining and quantifying the core microbiome: Challenges and prospects

The term "core microbiome" has become widely used in microbial ecology over the last decade. Broadly, the core microbiome refers to any set of microbial taxa, or the genomic and functional attributes associated with those taxa, that are characteristic of a host or environment of interest. Most commonly, core microbiomes are measured as the microbial taxa shared among two or more samples from a particular host or environment. Despite the popularity of this term and its growing use, there is little consensus about how a core microbiome should be quantified in practice. Here, we present a brief history of the core microbiome concept and use a representative sample of the literature to review the different metrics commonly used for quantifying the core. Empirical analyses have used a wide range of metrics for quantifying the core microbiome, including arbitrary occurrence and abundance cutoff values, with the focal taxonomic level of the core ranging from phyla to amplicon sequence variants. However, many of these metrics are susceptible to sampling and other biases. Developing a standardized set of metrics for quantifying the core that accounts for such biases is necessary for testing specific hypotheses about the functional and ecological roles of core microbiomes.

Microdiversity characterizes prevalent phylogenetic clades in the glacier-fed stream microbiome

Glacier-fed streams (GFSs) are extreme and rapidly vanishing ecosystems, and yet they harbor diverse microbial communities. Although our understanding of the GFS microbiome has recently increased, we do not know which microbial clades are ecologically successful in these ecosystems, nor do we understand potentially underlying mechanisms. Ecologically successful clades should be more prevalent across GFSs compared to other clades, which should be reflected as clade-wise distinctly low phylogenetic turnover. However, methods to assess such patterns are currently missing. Here we developed and applied a novel analytical framework, “phyloscore analysis”, to identify clades with lower spatial phylogenetic turnover than other clades in the sediment microbiome across twenty GFSs in New Zealand. These clades constituted up to 44% and 64% of community α-diversity and abundance, respectively. Furthermore, both their α-diversity and abundance increased as sediment chlorophyll a decreased, corroborating their ecological success in GFS habitats largely devoid of primary production. These clades also contained elevated levels of putative microdiversity than others, which could potentially explain their high prevalence in GFSs. This hitherto unknown microdiversity may be threatened as glaciers shrink, urging towards further genomic and functional exploration of the GFS microbiome.

Wastewater-Based Epidemiology and Long-Read Sequencing to Identify Enterovirus Circulation in Three Municipalities in Maricopa County, Arizona, Southwest United States between June and October 2020

We used wastewater-based epidemiology and amplicon-based long-read high-throughput sequencing for surveillance of enteroviruses (EVs) in Maricopa County, Arizona, Southwest United States. We collected 48 samples from 13 sites in three municipalities between 18 June and 1 October 2020, and filtered (175 mL each; 0.45 µm pore size) and extracted RNA from the filter-trapped solids. The RNA was converted to cDNA and processed through two workflows (Sanger sequencing (SSW) and long-read Illumina sequencing (LRISW)) each including a nested polymerase chain reaction (nPCR) assay. We subjected the ~350 bp amplicon from SSW to Sanger sequencing and the ~1900-2400 bp amplicon from LRISW to Illumina sequencing. We identified EV contigs from 11 of the 13 sites and 41.67% (20/48) of screened samples. Using the LRISW, we detected nine EV genotypes from three species (Enterovirus A (CVA4, EV-A76, EV-A90), Enterovirus B (E14) and Enterovirus C (CVA1, CVA11, CVA13, CVA19 and CVA24)) with Enterovirus C representing approximately 90% of the variants. However, the SSW only detected the five Enterovirus C types. Similarity and phylogenetic analysis showed that multiple Enterovirus C lineages were circulating, co-infecting and recombining in the population during the season despite the SARS-CoV-2 pandemic and the non-pharmaceutical public health measures taken to curb transmission.

Perspectives and Benefits of High-Throughput Long-Read Sequencing in Microbial Ecology

Short-read, high-throughput sequencing (HTS) methods have yielded numerous important insights into microbial ecology and function. Yet, in many instances short-read HTS techniques are suboptimal, for example, by providing insufficient phylogenetic resolution or low integrity of assembled genomes. Single-molecule and synthetic long-read (SLR) HTS methods have successfully ameliorated these limitations. In addition, nanopore sequencing has generated a number of unique analysis opportunities, such as rapid molecular diagnostics and direct RNA sequencing, and both Pacific Biosciences (PacBio) and nanopore sequencing support detection of epigenetic modifications. Although initially suffering from relatively low sequence quality, recent advances have greatly improved the accuracy of long-read sequencing technologies. In spite of great technological progress in recent years, the long-read HTS methods (PacBio and nanopore sequencing) are still relatively costly, require large amounts of high-quality starting material, and commonly need specific solutions in various analysis steps. Despite these challenges, long-read sequencing technologies offer high-quality, cutting-edge alternatives for testing hypotheses about microbiome structure and functioning as well as assembly of eukaryote genomes from complex environmental DNA samples.