Gradient Internal Standard Method for Absolute Quantification of Microbial Amplicon Sequencing Data

Impact of Staphylococcus carnosus DG06 inoculation on microbial and metabolic profiles during Cantonese soy sauce fermentation

The natural fermentation of soy sauce presents challenges in process control and raw material residue, limiting its suitability for modern production. This study utilized absolute quantification techniques to investigate inoculation parameters, fermentation performance, biological perturbation, and flavor regulation mechanisms of Staphylococcus carnosus DG06 in Cantonese soy sauce fermentation (CSSF). Results indicated that inoculating S. carnosus DG06 at 6.7 log CFU/g on day 7 significantly enhanced flavor development, particularly esters, alcohols, and ether compounds, while accelerating amino acid production. Although microbial composition and dynamic trends in traditional fermentation (TF) and inoculated fermentation (5IF7) were similar, 5IF7 significantly increased microbial abundance through synergistic interactions among Staphylococcus with Starmerella and Wickerhamiella. Correlation analysis revealed that these interactions promoted flavor production in CSSF. This study provides fermentation parameters of key functional strains for the application of synthetic microbial consortia in intelligent soy sauce brewing.

Identification of the saccharifying microbiota based on the absolute quantitative analysis in the batch solid-state fermentation system

The fermentation process of Chinese baijiu, a distinctive example of batch solid-state fermentation (BSSF), involves the recurrent use of the same raw material to optimize starch utilization. However, it is unclear which microorganisms are able to metabolize low concentration starch effectively. In this study, we successfully identified the key saccharifying microbiota that degraded low-concentration starch in the BSSF system by absolute quantification techniques. The results indicated a 61.93 % enhancement in the efficient utilization of starch, absolute quantification combined with correlation analysis revealed that Geotrichum, Aspergillus, Bacillus, Candida, and Kroppenstedtia were the saccharifying microbiota, with relative abundances exceeding 10 % during fermentation. In the KEGG metabolic pathway, these five saccharifying microbiota had a complete metabolic pathway for degrading starch to d-glucose-1p and d-glucose, including eight related enzymes: maltose phosphorylase, α-amylase, glucoamylase, oligo-1,6-glucosidase, α-glucosidase, pullulanase, α-glucosidase, and maltogenic α-amylase. These studies showed that the saccharifying microbiota can co-degrade starch by multiple saccharifying enzymes, thus improving the utilization of starch substrates.

The quest for environmental analytical microbiology: absolute quantitative microbiome using cellular internal standards

BACKGROUND

High-throughput sequencing has revolutionized environmental microbiome research, providing both quantitative and qualitative insights into nucleic acid targets in the environment. The resulting microbial composition (community structure) data are essential for environmental analytical microbiology, enabling characterization of community dynamics and assessing microbial pollutants for the development of intervention strategies. However, the relative abundances derived from sequencing impede comparisons across samples and studies.

RESULTS

This review systematically summarizes various absolute quantification (AQ) methods and their applications to obtain the absolute abundance of microbial cells and genetic elements. By critically comparing the strengths and limitations of AQ methods, we advocate the use of cellular internal standard-based high-throughput sequencing as an appropriate AQ approach for studying environmental microbiome originated from samples of complex matrices and high heterogeneity. To minimize ambiguity and facilitate cross-study comparisons, we outline essential reporting elements for technical considerations, and provide a checklist as a reference for environmental microbiome research.

CONCLUSIONS

In summary, we propose absolute microbiome quantification using cellular internal standards for environmental analytical microbiology, and we anticipate that this approach will greatly benefit future studies. Video Abstract.

Challenge of validation in whole-cell spike-in amplicon sequencing to comprehensively quantify food lactic acid bacteriota

Lactic acid bacteria (LAB) shape diverse communities in fermented foods. Developing comprehensive quantification methods for community structure will revolutionize our understanding of food LAB microbiome. For this purpose, 16S rRNA gene amplicon-based quantification, using spiked exogenous bacterial cells as an internal standard, shows potential for comprehensiveness and accuracy. We validated cell spike-in amplicon sequencing for quantifying LAB communities in food. Low efficiency of LAB DNA extraction underscores the importance of compensating for DNA loss by spiking internal standard cells. Quantitative equations generated using 15 selected LAB mock species showed positive relationships between the ratio of MiSeq read counts and the expected 16S rRNA gene copy numbers, with coefficients of determination (R2) ≥ 0.6823. The fold differences between observed and expected 16S copy numbers were within the range of 1/3 to 3-fold. Our validation highlights that accurate preparation of the LAB mock community is crucial for cell spike-in amplicon sequencing accuracy.

Synthetic DNA spike-in standards for cross-domain absolute quantification of microbiomes by rRNA gene amplicon sequencing

Microbiome studies using high-throughput sequencing are increasingly incorporating absolute quantitative approaches to overcome the inherent limitations of relative abundances. In this study, we have designed and experimentally validated a set of 12 unique synthetic rRNA operons, which we refer to as rDNA-mimics, to serve as spike-in standards for quantitative profiling of fungal/eukaryotic and bacterial microbiomes. The rDNA-mimics consist of conserved sequence regions from natural rRNA genes to act as binding sites for common universal PCR primers, and bioinformatically designed variable regions that allow their robust identification in any microbiome sample. All constructs cover multiple rRNA operon regions commonly targeted in fungal/eukaryotic microbiome studies (SSU-V9, ITS1, ITS2, and LSU-D1D2) and two of them also include an artificial segment of the bacterial 16S rRNA gene (SSU-V4) for cross-domain application. We validated the quantitative performance of the rDNA-mimics using defined mock communities and representative environmental samples. In particular, we show that rDNA-mimics added to extracted DNA or directly to the samples prior to DNA extraction precisely reflects the total amount of fungal and/or bacterial rRNA genes in the samples. We demonstrate that this allows accurate estimation of differences in microbial loads between samples, thereby confirming that the rDNA-mimics are suitable for absolute quantitative analyses of differential microbial abundances.

Impacts of Aspergillus oryzae 3.042 on the flavor formation pathway in Cantonese soy sauce koji

To investigate the mechanism of flavor formation during the traditional preparation Cantonese soy sauce koji (TP), the changes of microorganisms, physicochemical properties, and flavor compounds in TP were comprehensively and dynamically monitored by absolute quantitative methods. Results demonstrated that inoculating Aspergillus oryzae 3.042 in TP was crucial role in enhancing enzyme activity properties. Absolute quantification of flavor combined with multivariate statistical analysis yielded 5 organic acids, 15 amino acids, and 2 volatiles as significantly different flavors of TP. Amplicon sequencing and RT-qPCR revealed that the dominant genera were Staphylococcus, Weissella, Enterobacter, Lactic streptococci, Lactobacillus, and Aspergillus, which exhibited a increasing trend in TP. Correlation analysis exhibited that Staphylococcus and Aspergillus were the pivotal genera contributing to the enzyme activities and flavor of TP. The flavor formation network involved lipid and protein degradation, carbohydrate metabolism and other pathways. Simultaneously, TP can appropriately increase the fermentation time to improve product quality.

Quantitative characterization and dynamics of bacterial communities in ready-to-eat chicken using high-throughput sequencing combined with internal standard-based absolute quantification

Ready-to-eat (RTE) chicken products are prone to bacterial contamination, posing foodborne illness risks. High-throughput sequencing (HTS) has been widely used to study the distribution of pathogenic and spoilage bacteria in RTE chicken products but lacks quantitative data on taxa abundances. In this study, we employed a method combining HTS with absolute quantification, using Edwardsiella tarda as an internal standard strain, to achieve the relative and absolute abundances of microbiota in RTE chicken products stored at 4 and 25 °C. The results showed that the addition of appropriate concentration of internal standard strains exhibited no significant impact on the structure composition, relative abundance, and absolute abundance of bacterial communities in chicken meat, achieving comprehensive absolute quantification in RTE chicken products. Furthermore, the absolute abundance of bacterial genera at the end of storage followed a log-normal distribution, with most genera having an absolute abundance between 103 and 105 CFU/g. This study provides insights into the quantification of bacterial communities in RTE chicken products, laying a foundation for the development of strategies to extend the shelf life of RTE products.

Comprehensive analysis of flavor formation mechanisms in the mechanized preparation Cantonese soy sauce koji using absolute quantitative metabolomics and microbiomics approaches

Based on the widespread application and under-research of mechanized preparation Cantonese soy sauce koji (MP), absolute quantitative approaches were utilized to systematically analyze the flavor formation mechanism in MP. The results indicated that the enzyme activities increased greatly during MP fermentation, and 4 organic acids, 15 amino acids, and 2 volatiles were identified as significantly different flavor actives. The flavor parameters of MP4 were basically identical to those of MP5. Furthermore, microorganisms were dominated by Staphylococcus, Weissella, and Aspergillus in MP, and their biomass demonstrated an increasing trend. A precise enumeration of microorganisms exposed the inaccuracy of relative quantitative data. Concurrently, Staphylococcus and Aspergillus were positively correlated with numerous enzymes and flavor compounds, and targeted strains for enhancing MP quality. The flavor formation network comprises pathways including carbohydrate metabolism, lipid metabolism and oxidation, and protein degradation and amino acid metabolism. In summary, the fermentation period of MP can be substantially shortened without compromising the product quality. These findings lay the groundwork for refining parameters in modern production processes.

A quantitative sequencing method using synthetic internal standards including functional and phylogenetic marker genes

The method of spiking synthetic internal standard genes (ISGs) to samples for amplicon sequencing, generating sequences and converting absolute gene numbers from read counts has been used only for phylogenetic markers and has not been applied to functional markers. In this study, we developed ISGs, including gene sequences of the 16S rRNA, pmoA, encoding a subunit of particulate methane monooxygenase and amoA, encoding a subunit of ammonia monooxygenase. We added ISGs to the samples, amplified the target genes and performed amplicon sequencing. For the mock community, the copy numbers converted from read counts using ISGs were equivalent to those obtained by the quantitative real-time polymerase chain reaction (4.0 × 104 versus 4.1 × 104 and 3.0 × 103 versus 4.0 × 103 copies μL-DNA-1 for 16S rRNA and pmoA genes, respectively), but we also identified underestimation, possibly due to primer coverage (7.8 × 102 versus 3.7 × 103  μL-DNA-1 for amoA gene). We then applied this method to environmental samples and analysed phylogeny, functional diversity and absolute quantities. One Methylocystis population was most abundant in the sludge samples [16S rRNA gene (3.8 × 109 copies g-1 ) and the pmoA gene (2.3 × 109 copies g-1 )] and were potentially interrelated. This study demonstrates that ISG spiking is useful for evaluating sequencing data processing and quantifying functional markers.

Challenges and perspectives of quantitative microbiome profiling in food fermentations

Spontaneously fermented foods are consumed and appreciated for thousands of years although they are usually produced with fluctuated productivity and quality, potentially threatening both food safety and food security. To guarantee consistent fermentation productivity and quality, it is essential to control the complex microbiota, the most crucial factor in food fermentations. The prerequisite for the control is to comprehensively understand the structure and function of the microbiota. How to quantify the actual microbiota is of paramount importance. Among various microbial quantitative methods evolved, quantitative microbiome profiling, namely to quantify all microbial taxa by absolute abundance, is the best method to understand the complex microbiota, although it is still at its pioneering stage for food fermentations. Here, we provide an overview of microbial quantitative methods, including the development from conventional methods to the advanced quantitative microbiome profiling, and the application examples of these methods. Moreover, we address potential challenges and perspectives of quantitative microbiome profiling methods, as well as future research needs for the ultimate goal of rational and optimal control of microbiota in spontaneous food fermentations. Our review can serve as reference for the traditional food fermentation sector for stable fermentation productivity, quality and safety.

Phospholipid fatty acid (PLFA) analysis as a tool to estimate absolute abundances from compositional 16S rRNA bacterial metabarcoding data

Microbial biodiversity monitoring through the analysis of DNA extracted from environmental samples is increasingly popular because it is perceived as being rapid, cost-effective, and flexible concerning the sample types studied. DNA can be extracted from diverse media before high-throughput sequencing of the prokaryotic 16S rRNA gene is used to characterize the taxonomic diversity and composition of the sample (known as metabarcoding). While sources of bias in metabarcoding methodologies are widely acknowledged, previous studies have focused mainly on the effects of these biases within a single substrate type, and relatively little is known of how these vary across substrates. We investigated the effect of substrate type (water, microbial mats, lake sediments, stream sediments, soil and a mock microbial community) on the relative performance of DNA metabarcoding in parallel with phospholipid fatty acid (PLFA) analysis. Quantitative estimates of the biomass of different taxonomic groups in samples were made through the analysis of PLFAs, and these were compared to the relative abundances of microbial taxa estimated from metabarcoding. Furthermore, we used the PLFA-based quantitative estimates of the biomass to adjust relative abundances of microbial groups determined by metabarcoding to provide insight into how the biomass of microbial taxa from PLFA analysis can improve understanding of microbial communities from environmental DNA samples. We used two sets of PLFA biomarkers that differed in their number of PLFAs to evaluate how PLFA biomarker selection influences biomass estimates. Metabarcoding and PLFA analysis provided significantly different views of bacterial composition, and these differences varied among substrates. We observed the most notable differences for the Gram-negative bacteria, which were overrepresented by metabarcoding in comparison to PLFA analysis. In contrast, the relative biomass and relative sequence abundances aligned reasonably well for Cyanobacteria across the tested freshwater substrates. Adjusting relative abundances of microbial taxa estimated by metabarcoding with PLFA-based quantification estimates of the microbial biomass led to significant changes in the microbial community compositions in all substrates. We recommend including independent estimates of the biomass of microbial groups to increase comparability among metabarcoding libraries from environmental samples, especially when comparing communities associated with different substrates.