The Madness of Microbiome: Attempting To Find Consensus "Best Practice" for 16S Microbiome Studies

Evaluating the efficiency of 16S-ITS-23S operon sequencing for species level resolution in microbial communities

Rapid advancements in long-read sequencing have facilitated species-level microbial profiling through full-length 16S rRNA sequencing (~ 1500 bp), and more notably, by the newer 16S-ITS-23S ribosomal RNA operon (RRN) sequencing (~ 4500 bp). RRN sequencing is emerging as a superior method for species resolution, exceeding the capabilities of short-read and full-length 16S rRNA sequencing. However, being in its early stages of development, RRN sequencing has several underexplored or understudied elements, highlighting the need for a critical and thorough examination of its methodologies. Key areas that require detailed analysis include understanding how primer pairs, sequencing platforms, and classifiers and databases affect the accuracy of species resolution achieved through RRN sequencing. Our study addresses these gaps by evaluating the effect of primer pairs using four RRN primer combinations, and that of sequencing platforms by employing PacBio and Oxford Nanopore Technologies (ONT) systems. Furthermore, two classification methods (Minimap2 and OTU clustering), in combination with four RRN reference databases (MIrROR, rrnDB, and two versions of GROND) were compared to identify consistent and accurate classification methods with RRN sequencing. Here we demonstrate that RRN primer pair choice and sequencing platform do not substantially bias taxonomic profiles for most of the tested mock communities, while classification methods significantly impact the accuracy of species-level assignments. Of the classification methods tested, Minimap2 classifier in combination with the GROND database most consistently provided accurate species-level classification across the communities tested, irrespective of sequencing platform.

Variation of gene ratios in mock communities constructed with purified 16S rRNA during processing

16S ribosomal nucleic acid (16S rRNA) analysis allows to specifically target the metabolically active members of microbial communities. The stability of the ratios between target genes in the workflow, which is essential for the bioprocess-relevance of the data derived from this analysis, was investigated using synthetic mock communities constructed by mixing purified 16S rRNA from Bacillus subtilis (Bs), Staphylococcus aureus (Sa), Pseudomonas aeruginosa (Pa), Klebsiella pneumoniae (Kp) and Burkholderia cepacia (Bc) in different proportions. The RT reaction yielded one copy of cDNA per rRNA molecule for Pa, Bc and Sa but only 2/3 of the expected cDNA from 16S rRNAs of Bs and Kp. The combination of Taq DNA Platinum polymerase with subcycling PCR (scPCR) produced uniform yields of approximately 70% for second strand PCR synthesis from all target cDNAs. The proportion between templates in multicycle PCR was best preserved after 10 cycle scPCR followed by cloning. With MiSeq sequencing, correct proportions for about two thirds of templates were recovered after 10 cycle scPCR with Taq Platinum. 30 cycles standard PCR (stdPCR) or scPCR proved particularly harmful to proportion data and should be avoided.

Can Gut Microbiota Analysis Reveal Clostridioides difficile Infection? Evidence from an Italian Cohort at Disease Onset

A diverse and well-functioning gut microbiota normally serves as a protective shield against the invasion of harmful bacteria or the proliferation of opportunistic pathogens. Clostridioides difficile infection (CDI) is predominantly associated with the overuse of antibiotics, resulting in a significant alteration in the gut's microbial balance. Unfortunately, the lack of global standardization does not allow for the identification of a set of biomarkers associated with the onset and progression of this disease. In this study, we examined the composition of the gut microbiota in patients at the time of the initial detection of CDI compared to a control group of CDI-negative individuals, with a focus on identifying potential CDI biomarkers for diagnosis. While no significant differences in the alpha and beta diversity between CDI-negative and CDI-positive individuals were found, we found that certain genera (such as Clostridium XIVa and Clostridium XVIII) showed different abundance patterns in the two groups, indicating potential differences in gut microbiota balance. In conclusion, am enrichment in Clostridium XI and a decrease in Faecalibacterium emerged in the CDI-positive patients and following antibiotic treatment, indicating that changes in the Clostridium/Faecalibacterium ratio may be a promising biomarker that warrants further investigation for CDI diagnosis.

Impact of DNA extraction, PCR amplification, sequencing, and bioinformatic analysis on food-associated mock communities using PacBio long-read amplicon sequencing

BACKGROUND

Long-read 16S rRNA gene amplicon sequencing has a high potential for characterizing food-associated microbiomes. The advantage results from sequencing the full-length (1,500 bp) gene, enabling taxonomic resolution at species level. Here we present a benchmarking study using mock communities representative of milking machine biofilms and raw meat, revealing challenges relevant to food-associated habitats. These were varying species abundances, reliable intra-genus differentiation of species, and detection of novel species with < 98.7% sequence identity to type strains. By using mock communities at different levels of preparation - as mixed whole cells, mixed extracted DNA, and mixed PCR products - we systematically investigated the influence of DNA extraction using two different kits, PCR amplification of 16S rRNA genes, sequencing, and bioinformatics analysis including reference database and gene copy number normalization on bacterial composition and alpha diversity.

RESULTS

We demonstrated that PacBio ccs-reads allowed for correct taxonomic assignment of all species present within the mock communities using a custom Refseq database. However, choice of percent identity values for taxonomic assignment had a strong influence on identification and processing of reads from novel species. PCR amplification of 16S rRNA genes produced the strongest bias on the observed community composition, while sequencing alone reproduced the preset composition well. The PCR bias can in part be attributed to differences in mol% G + C content of 16S rRNA genes resulting in preferred amplification of low mol% G + C-containing taxa.

CONCLUSIONS

This study underlines the importance of benchmarking studies with mock communities representing the habitat of interest to evaluate the methodology prior to analyzing real samples of unknown composition. It demonstrates the advantage of long-read sequencing over short-read sequencing, as species level identification enables in-depth characterization of the habitat. One benefit is improved risk assessment by enabling differentiation between pathogenic and apathogenic species of the same genus.

Optimal storage time and temperature of human oral samples to minimize microbiome changes: A scoping review

Improper storage times and temperatures negatively impact the quality of biospecimens with oral microbiomes. This study aimed to determine the optimal storage time and temperature for maintaining the integrity of human dental plaque and saliva samples' microbial profiles. A comprehensive search yielded 5433 studies, with 12 meeting inclusion criteria. The number of studies on the storage time and temperature for plaque or saliva samples was extremely limited, with large variability in study designs and analytical tools. The best approach for dental plaque and saliva samples was to immediately freeze fresh specimens at - 80 °C or lower until DNA extraction, with a recommended storage time not exceeding 1-2 years, regardless of temperature. Checkerboard DNA-DNA hybridization-based studies suggested dental plaque storage at - 20 °C for 6 months, but a shorter duration was advised. Based on 16 S rRNA gene sequencing studies, dental plaque samples could be stored at - 80 °C for 6 months in 75 % ethanol or Bead Solution. Dental plaque and saliva samples could be stored at room temperature for 1-2 weeks without significant microbiome changes if stored in appropriate media. Further well-designed randomized controlled studies with longer-storage duration are necessary to establish more definitive guidelines.

Effects of different forms of amino acid supplementation on the performance and intestinal barrier function of laying hens fed a low-protein diet

The aim of this study was to investigate the effects of low-protein diets and the sustained release of synthetic amino acids (AA) on the performance, intestinal barrier function and nitrogen excretion of laying hens. Two hundred eighty-eight 39-week-old Hyline brown laying hens of were randomly divided into 3 groups with 8 replicates per group. The crude protein level in the control group (CON) was 16%, the crude protein levels in the crystal AA supplement group (LCP-CAA) and microencapsulated AA group (LCP-MAA) were both 13%, and the AA levels in the LCP-CAA and LCP-MAA groups were consistent with that in the CON group. The experiment lasted 12 wk, and production performance was assessed weekly. The FCR and ADFI values were significantly greater for the LCP-CAA group than for the CON and LCP-MAA groups (P < 0.05). Two hours after feeding, His levels were significantly greater in the LCP-CAA group than in the LCP-MAA group (P < 0.05); 4 h after feeding, the contents of Met, Thr, Leu and Val were significantly greater in blood from the LCP-MAA group (P < 0.05); 6 h after feeding, Trp, Ile and Arg levels were significantly greater in the LCP-MAA group (P < 0.05). The chylase content significantly decreased in the duodenum of the LCP-CAA group (P < 0.05), and the chylase and trypsin were contents increased in the ileum of the LCP-MAA group (P < 0.05). In the LCP-MAA group, significantly increased mRNA expression levels of Occludin, ZO-1 in duodenum; Occludin, ZO-1, y+LAT1 in jejunum; and ZO-1 in ileum were detected at 8 and 12 weeks (P < 0.05). The fecal nitrogen content significantly decreased in the low protein diet group (P < 0.01). In conclusion, reducing dietary crude protein levels and supplementing with microencapsulated AAs can improve intestinal barrier function, promote digestive enzyme secretion, increase the expression of AA transporters, improve dietary protein utilization efficiency, and reduce nitrogen emission in laying hens.

Biocontrol Agents Inhibit Banana Fusarium Wilt and Alter the Rooted Soil Bacterial Community in the Field

Banana is an important fruit and food crop in tropical and subtropical regions worldwide. Banana production is seriously threatened by Fusarium wilt of banana (FWB), a disease caused by Fusarium oxysporum f. sp. cubense, and biological control is an important means of curbing this soil-borne disease. To reveal the effects of biocontrol agents on inhibiting FWB and altering the soil bacterial community under natural ecosystems, we conducted experiments at a banana plantation. The control efficiency of a compound microbial agent (CM), Paenibacillus polymyxa (PP), Trichoderma harzianum (TH), and carbendazim (CA) on this disease were compared in the field. Meanwhile, the alterations in structure and function of the rooted soil bacterial community in different treatments during the vigorous growth and fruit development stages of banana were analyzed by microbiomics method. The results confirmed that the different biocontrol agents could effectively control FWB. In particular, CM significantly reduced the incidence of the disease and showed a field control efficiency of 60.53%. In terms of bacterial community, there were no significant differences in the richness and diversity of banana rooted soil bacteria among the different treatments at either growth stage, but their relative abundances differed substantially. CM treatment significantly increased the ratios of Bacillus, Bryobacter, Pseudomonas, Jatrophihabitans, Hathewaya, and Chujaibacter in the vigorous growth stage and Jatrophihabitans, Occallatibacter, Cupriavidus, and 1921-3 in the fruit development stage. Furthermore, bacterial community function in the banana rooted soil was affected differently by the various biocontrol agents. CM application increased the relative abundance of multiple soil bacterial functions, including carbohydrate metabolism, xenobiotic biodegradation and metabolism, terpenoid and polyketide metabolism, lipid metabolism, and metabolism of other amino acids. In summary, our results suggest that the tested biocontrol agents can effectively inhibit the occurrence of banana Fusarium wilt and alter the soil bacterial community in the field. They mainly modified the relative abundance of bacterial taxa and the metabolic functions rather than the richness and diversity. These findings provide a scientific basis for the use of biocontrol agents to control banana Fusarium wilt under field conditions, which serves as a reference for the study of the soil microbiological mechanisms of other biocontrol agents.

Variation in the metagenomic analysis of fecal microbiome composition calls for a standardized operating approach

The reproducibility in microbiome studies is limited due to the lack of one gold-standard operating procedure. The aim of this study was to examine the impact of protocol variations on microbiome composition using metagenomic data sets from a single center. We assessed the variation in a data set consisted of 2,722 subjects, including 9 subcohorts harboring healthy subjects and patients with various disorders, such as inflammatory bowel disease, colorectal cancer, and type 2 diabetes. Two different DNA extraction kits, with or without lyticase, and two sample storage methods were compared. Our results indicated that DNA extraction had the largest impact on gut microbiota diversity among all host factors and sample operating procedures. Healthy subjects matched by age, body mass index, and sample operating methods exhibited reduced, yet significant differences (PERMANOVA, P < 0.05) in gut microbiota composition across studies. The variations contributed by DNA extraction were primarily driven by different recovery efficiency of gram-positive bacteria, e.g., phyla Firmicutes and Actinobacteria. This was further confirmed by a parallel comparison of fecal samples from five healthy subjects and a standard mock community. In addition, the DNA extraction method influenced DNA biomass, quality, and the detection of specific lineage-associated diseases. Sample operating approach and batch effects should be considered for cohorts with large sample size or longitudinal cohorts to ensure that source data were appropriately generated and analyzed. Comparison between samples processed with inconsistent methods should be dealt with caution. This study will promote the establishment of a sample operating standard to enhance our understanding of microbiome and translating in clinical practice.IMPORTANCEThe reproducibility of human gut microbiome studies has been suboptimal across cohorts and study design choices. One possible reason for the disagreement is the introduction of systemic biases due to differences in methodologies. In our study, we utilized microbial metagenomic data sets from 2,722 fecal samples generated from a single research center to examine the extent to which sample storage and DNA extraction influence the quantification of microbial composition and compared this variable with other sources of technical and biological variation. Our research highlights the impact of DNA extraction methods when analyzing microbiome data and suggests that the microbiome profile may be influenced by differences in the extraction efficiency of bacterial species. With metagenomics sequencing being increasingly used in clinical biology, our findings provide insight into the challenges using metagenomics sequencing in clinical diagnostics, where the detection of certain species and its abundance relative to a "healthy reference" is key.

Optimal 16S rRNA gene amplicon sequencing analysis for oral microbiota to avoid the potential bias introduced by trimming length, primer, and database

16S rRNA gene amplicon sequencing analysis is used to investigate bacterial communities; however, the estimated bacterial composition can differ from the original due to experimental and analytical biases. Therefore, this study determines the optimal conditions to minimize the potential biases in 16S rRNA gene amplicon sequencing analysis using different DNA samples, trimming lengths, primers, and databases. The results of the mock1 community with 250 and 300 bp paired-end (PE), comprising 15 bacteria from various environments, showed the highest similarity between the theoretical value and the data using the Greengenes2 and the V3 region at the genus level and V5-V6 at the species level. In the 300 bp PE sequencing analysis of the mock2 community, comprising six major oral bacteria, the data using the V3-V4, V4, and V5-V6 regions with the SILVA, Greengenes2, and the Human Oral Microbiome Database (HOMD) showed the highest similarity to the theoretical values at the genus level. At the species level, the data using the V3-V4 and V4 regions with Greengenes2 and the data using the V1-V2 with HOMD exhibited the highest alignment with the theoretical values. In the species analysis of the dental calculus samples with 300 bp PE, the Shannon index value was higher with the V1-V2 region and HOMD than with others. Our results suggest that the optimal conditions for oral microbiome analysis are the combinations of 300 bp PE and the V3-V4 or V4 region with the Greengenes2 and the V1-V2 with HOMD.IMPORTANCEThe 16S rRNA gene amplicon sequencing analysis is frequently used in oral microbiome research. However, this method can have biases that distort the experimental data, which depend on the methodological steps, including sequencing length, trimming length, selected amplification regions, and referenced databases. In this study, a combination of 300 bp PE and the primer targeting the V3-V4 or V4 regions with the Greengenes2 and the V1-V2 region with the HOMD was the most bias-minimizing condition for oral microbiota analysis. In addition, this is the first report of such analyses in modern Japanese dental calculus. The methods used in this study will aid in setting appropriate conditions for sequence analysis of microbiota obtained not only from the oral cavity but also from any environment.

Complex hydrothermal vent microbial mat communities used to assess primer selection for targeted amplicon surveys from Kama'ehuakanaloa Seamount

The microbiota of hydrothermal vents has been widely implicated in the dynamics of oceanic biogeochemical cycling. Lithotrophic organisms utilize reduced chemicals in the vent effluent for energy, which fuels carbon fixation, and their metabolic byproducts can then support higher trophic levels and high-biomass ecosystems. However, despite the important role these microorganisms play in our oceans, they are difficult to study. Most are resistant to culturing in a lab setting, so culture-independent methods are necessary to examine community composition. Targeted amplicon surveying has become the standard practice for assessing the structure and diversity of hydrothermal vent microbial communities. Here, the performance of primer pairs targeting the V3V4 and V4V5 variable regions of the SSU rRNA gene was assessed for use on environmental samples from microbial mats surrounding Kama'ehuakanaloa Seamount, an iron-dominated hydrothermal vent system. Using the amplicon sequence variant (ASV) approach to taxonomic identification, the structure and diversity of microbial communities were elucidated, and both primer pairs generated robust data and comparable alpha diversity profiles. However, several distinct differences in community composition were identified between primer sets, including differential relative abundances of both bacterial and archaeal phyla. The primer choice was determined to be a significant driver of variation among the taxonomic profiles generated. Based on the higher quality of the raw sequences generated and on the breadth of abundant taxa found using the V4V5 primer set, it is determined as the most efficacious primer pair for whole-community surveys of microbial mats at Kama'ehuakanaloa Seamount.

Seabuckthorn polysaccharides mitigate hepatic steatosis by modulating the Nrf-2/HO-1 pathway and gut microbiota

Non-alcoholic fatty liver disease (NAFLD) is becoming a significant global public health threat. Seabuckthorn (Hippophae rhamnoides L.) has been used in traditional Chinese medicine (TCM). The hypolipidemic effects of Seabuckthorn polysaccharides (SP) against high-fat diets (HFD)-induced NAFLD were systematically explored and compared with that of Bifidobacterium lactis V9 (B. Lactis V9). Results showed that HFD-induced alanine transaminase (ALT) and aspartate aminotransferase (AST) levels decreased by 2.8-fold and 4.5-fold, respectively, after SP supplementation. Moreover, the alleviating effect on hepatic lipid accumulation is better than that of B. Lactis V9. The ACC and FASN mRNA levels were significantly reduced by 1.8 fold (P < 0.05) and 2.3 folds (P < 0.05), respectively, while the CPT1α and PPARα mRNA levels was significantly increased by 2.3 fold (P < 0.05) and 1.6 fold (P < 0.05), respectively, after SP administration. SP activated phosphorylated-AMPK and inhibited PPARγ protein expression, improved serum oxidative stress and inflammation (P < 0.05). SP supplementation leads to increased hepatic expression of nuclear factor erythroid 2-related factor 2 (Nrf-2), heme oxygenase-1 (HO-1) and Superoxide dismutase-2 (SOD-2). Furthermore, SP treatment improved HFD-induced intestinal dysbiosis. Lentisphaerae, Firmicutes, Tenericutes and Peptococcus sp., RC9_gut_group sp., and Parabacteroides sp. of the gut microbiota were significantly associated with hepatic steatosis and indicators related to oxidative stress and inflammation. Therefore, SP can mitigate hepatic lipid accumulation by regulating Nrf-2/HO-1 signaling pathways and gut microbiota. This study offers new evidence supporting the use of SP as a prebiotic treatment for NAFLD.

Gut microbes on the risk of advanced adenomas

BACKGROUND

More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC.

OBJECTIVE

To analyze the characteristic microbes in AA.

METHODS

Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and β diversity. α diversity included box diagram, and β diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed.

RESULTS

The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively.

CONCLUSION

Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing.

Harnessing human microbiomes for disease prediction

The human microbiome has been increasingly recognized as having potential use for disease prediction. Predicting the risk, progression, and severity of diseases holds promise to transform clinical practice, empower patient decisions, and reduce the burden of various common diseases, as has been demonstrated for cardiovascular disease or breast cancer. Combining multiple modifiable and non-modifiable risk factors, including high-dimensional genomic data, has been traditionally favored, but few studies have incorporated the human microbiome into models for predicting the prospective risk of disease. Here, we review research into the use of the human microbiome for disease prediction with a particular focus on prospective studies as well as the modulation and engineering of the microbiome as a therapeutic strategy.

The Role of the Urobiome in Kidney Transplantation: A Systematic Review

Background

The urinary microbiome, also known as the urobiome, was traditionally considered sterile. However, emerging evidence suggests its presence in the urinary tract. Urobiome dysbiosis has been associated with various urologic conditions, making it a topic of interest also in kidney transplantation. This systematic review examines the evidence of urobiome changes in kidney transplant recipients (KTRs).

Methods

Systematic literature searches in the PubMed and SCOPUS databases.

Results

Of the 770 articles identified, 8 met the inclusion criteria. The urobiome showed reduced diversity in KTRs compared with healthy controls and patients on dialysis. Proteobacteria enrichment was associated with graft stability or spontaneous tolerance in KTRs without immunological events. Kidney interstitial fibrosis and tubular atrophy were associated with changes in resident urinary microbes and increased pathogenic bacteria. Additionally, KTRs with chronic allograft dysfunction had a higher prevalence of Corynebacterium.

Conclusions

The review highlights the importance of studying the urobiome in KTRs and its potential impact on transplant outcomes. The field remains largely unexplored, and further research is needed to establish consistent study designs and objectives. Future studies could lead to biomarker discovery, personalized therapies, and improved outcomes and graft survival in KTRs.

Multi-species biofilms of environmental microbiota isolated from fruit packing facilities promoted tolerance of Listeria monocytogenes to benzalkonium chloride

Listeria monocytogenes may survive and persist in food processing environments due to formation of complex multi-species biofilms of environmental microbiota that co-exists in these environments. This study aimed to determine the effect of selected environmental microbiota on biofilm formation and tolerance of L. monocytogenes to benzalkonium chloride in formed biofilms. The studied microbiota included bacterial families previously shown to co-occur with L. monocytogenes in tree fruit packing facilities, including Pseudomonadaceae, Xanthomonadaceae, Microbacteriaceae, and Flavobacteriaceae. Biofilm formation ability and the effect of formed biofilms on the tolerance of L. monocytogenes to benzalkonium chloride was measured in single- and multi-family assemblages. Biofilms were grown statically on polystyrene pegs submerged in a R2A broth. Biofilm formation was quantified using a crystal violet assay, spread-plating, confocal laser scanning microscopy, and its composition was assessed using amplicon sequencing. The concentration of L. monocytogenes in biofilms was determined using the most probable number method. Biofilms were exposed to the sanitizer benzalkonium chloride, and the death kinetics of L. monocytogenes were quantified using a most probable number method. A total of 8, 8, 6, and 3 strains of Pseudomonadaceae, Xanthomonadaceae, Microbacteriaceae, and Flavobacteriaceae, respectively, were isolated from the environmental microbiota of tree fruit packing facilities and were used in this study. Biofilms formed by Pseudomonadaceae, Xanthomonadaceae, and all multi-family assemblages had significantly higher concentration of bacteria, as well as L. monocytogenes, compared to biofilms formed by L. monocytogenes alone. Furthermore, multi-family assemblage biofilms increased the tolerance of L. monocytogenes to benzalkonium chloride compared to L. monocytogenes mono-species biofilms and planktonic multi-family assemblages. These findings suggest that L. monocytogenes control strategies should focus not only on assessing the efficacy of sanitizers against L. monocytogenes, but also against biofilm-forming microorganisms that reside in the food processing built environment, such as Pseudomonadaceae or Xanthomonadaceae.

A Comprehensive View of Food Microbiota: Introducing FoodMicrobionet v5

Amplicon-targeted metagenomics is now the standard approach for the study of the composition and dynamics of food microbial communities. Hundreds of papers on this subject have been published in scientific journals and the information is dispersed in a variety of sources, while raw sequences and their metadata are available in public repositories for some, but not all, of the published studies. A limited number of web resources and databases allow scientists to access this wealth of information but their level of annotation on studies and samples varies. Here, we report on the release of FoodMicrobionet v5, a comprehensive database of metataxonomic studies on bacterial and fungal communities of foods. The current version of the database includes 251 published studies (11 focusing on fungal microbiota, 230 on bacterial microbiota, and 10 providing data for both bacterial and fungal microbiota) and 14,035 samples with data on bacteria and 1114 samples with data on fungi. The new structure of the database is compatible with interactive apps and scripts developed for previous versions and allows scientists, R&D personnel in industries and regulators to access a wealth of information on food microbial communities.

Microbiota of healthy dogs demonstrate a significant decrease in richness and changes in specific bacterial groups in response to supplementation with resistant starch, but not psyllium or methylcellulose, in a randomized cross-over trial

Even though dietary fibres are often used as prebiotic supplements in dogs, the effect of individual types of fibres on canine microbiota composition is unknown. The objective of this study was to assess changes in faecal microbiota richness, diversity and taxonomic abundance with three different fibre supplements in dogs. These were psyllium husk, resistant starch from banana flour and methylcellulose. They were administered to 17 healthy dogs in a cross-over trial after transition to the same complete feed. Faecal scores and clinical activity indices were recorded, and faecal samples were collected before and at the end of supplementation, as well as 2 weeks after each supplement (washout). Illumina NovaSeq paired-end 16S rRNA gene sequencing was performed on all samples. After quality control and chimera removal, alpha diversity indices were calculated with QIIME. Differences in specific taxa between groups were identified using Metastats. Methylcellulose significantly increased faecal scores but had no effect on microbiota. Psyllium resulted in minor changes in the abundance of specific taxa, but with questionable biological significance. Resistant starch reduced microbiota richness and resulted in the most abundant changes in taxa, mostly a reduction in short-chain fatty acid-producing genera of the phylum Bacillota, with an increase in genera within the Bacteroidota, Pseudomonadota, Actinomycetota and Saccharibacteria. In conclusion, while psyllium and methylcellulose led to few changes in the microbiota composition, the taxonomic changes seen with resistant starch may indicate a less favourable composition. Based on this, the type of resistant starch used here cannot be recommended as a prebiotic in dogs.

Characterizing the blood microbiota in healthy and febrile domestic cats via 16s rRNA sequencing

This study aimed to evaluate the blood bacterial microbiota in healthy and febrile cats. High-quality sequencing reads from the 16S rRNA gene variable region V3-V4 were obtained from genomic blood DNA belonging to 145 healthy cats, and 140 febrile cats. Comparisons between the blood microbiota of healthy and febrile cats revealed dominant presence of Actinobacteria, followed by Firmicutes and Proteobacteria, and a lower relative abundance of Bacteroidetes. Upon lower taxonomic levels, the bacterial composition was significantly different between healthy and febrile cats. The families Faecalibacterium and Kineothrix (Firmicutes), and Phyllobacterium (Proteobacteria) experienced increased abundance in febrile samples. Whereas Thioprofundum (Proteobacteria) demonstrated a significant decrease in abundance in febrile. The bacterial composition and beta diversity within febrile cats was different according to the affected body system (Oral/GI, systemic, skin, and respiratory) at both family and genus levels. Sex and age were not significant factors affecting the blood microbiota of febrile cats nor healthy ones. Age was different between young adult and mature adult healthy cats. Alpha diversity was unaffected by any factors. Overall, the findings suggest that age, health status and nature of disease are significant factors affecting blood microbiota diversity and composition in cats, but sex is not.

Consistent signatures in the human gut microbiome of old- and young-onset colorectal cancer

The incidence of young-onset colorectal cancer (yCRC) has been increasing in recent decades, but little is known about the gut microbiome of these patients. Most studies have focused on old-onset CRC (oCRC), and it remains unclear whether CRC signatures derived from old patients are valid in young patients. To address this, we assembled the largest yCRC gut metagenomes to date from two independent cohorts and found that the CRC microbiome had limited association with age across adulthood. Differential analysis revealed that well-known CRC-associated taxa, such as Clostridium symbiosum, Peptostreptococcus stomatis, Parvimonas micra and Hungatella hathewayi were significantly enriched (false discovery rate <0.05) in both old- and young-onset patients. Similar strain-level patterns of Fusobacterium nucleatum, Bacteroides fragilis and Escherichia coli were observed for oCRC and yCRC. Almost all oCRC-associated metagenomic pathways had directionally concordant changes in young patients. Importantly, CRC-associated virulence factors (fadA, bft) were enriched in both oCRC and yCRC compared to their respective controls. Moreover, the microbiome-based classification model had similar predication accuracy for CRC status in old- and young-onset patients, underscoring the consistency of microbial signatures across different age groups.

Comparison of DNA extraction methods on different sample matrices within the same terrestrial ecosystem

Metataxonomic studies of ecosystem microbiotas require the simultaneous processing of samples with contrasting physical and biochemical traits. However, there are no published studies of comparisons of different DNA extraction kits to characterize the microbiotas of the main components of terrestrial ecosystems. Here, and to our knowledge for the first time, five DNA extraction kits were used to investigate the composition and diversity of the microbiota of a subset of samples typically studied in terrestrial ecosystems such as bulk soil, rhizosphere soil, invertebrate taxa and mammalian feces. DNA extraction kit was associated with changes in the relative abundance of hundreds of ASVs, in the same samples, resulting in significant differences in alpha and beta diversity estimates of their microbiotas. Importantly, the impact of DNA extraction kit on sample diversity varies according to sample type, with mammalian feces and soil samples showing the most and least consistent diversity estimates across DNA extraction kits, respectively. We show that the MACHEREY-NAGEL NucleoSpin® Soil kit was associated with the highest alpha diversity estimates, providing the highest contribution to the overall sample diversity, as indicated by comparisons with computationally assembled reference communities, and is recommended to be used for any large-scale microbiota study of terrestrial ecosystems.

Considerations for the inclusion of metabarcoding data in the plant protection product risk assessment of the soil microbiome

There is increasing interest in further developing the plant protection product (PPP) environmental risk assessment, particularly within the European Union, to include the assessment of soil microbial community composition, as measured by metabarcoding approaches. However, to date, there has been little discussion as to how this could be implemented in a standardized, reliable, and robust manner suitable for regulatory decision-making. Introduction of metabarcoding-based assessments of the soil microbiome into the PPP risk assessment would represent a significant increase in the degree of complexity of the data that needs to be processed and analyzed in comparison to the existing risk assessment on in-soil organisms. The bioinformatics procedures to process DNA sequences into community compositional data sets currently lack standardization, while little information exists on how these data should be used to generate regulatory endpoints and the ways in which these endpoints should be interpreted. Through a thorough and critical review, we explore these challenges. We conclude that currently, we do not have a sufficient degree of standardization or understanding of the required bioinformatics and data analysis procedures to consider their use in an environmental risk assessment context. However, we highlight critical knowledge gaps and the further research required to understand whether metabarcoding-based assessments of the soil microbiome can be utilized in a statistically and ecologically relevant manner within a PPP risk assessment. Only once these challenges are addressed can we consider if and how we should use metabarcoding as a tool for regulatory decision-making to assess and monitor ecotoxicological effects on soil microorganisms within an environmental risk assessment of PPPs. Integr Environ Assess Manag 2024;20:337-358. © 2023 SETAC.

A comparative study of the bacterial diversity and composition of nursery piglets' oral fluid, feces, and housing environment

The oral cavity is the portal of entry for many microorganisms that affect swine, and the swine oral fluid has been used as a specimen for the diagnosis of several infectious diseases. The oral microbiota has been shown to play important roles in humans, such as protection against non-indigenous bacteria. In swine, studies that have investigated the microbial composition of the oral cavity of pigs are scarce. This study aimed to characterize the oral fluid microbiota of weaned pigs from five commercial farms in Brazil and compare it to their respective fecal and environmental microbiotas. Bacterial compositions were determined by 16S rRNA gene sequencing and analyzed in R Studio. Oral fluid samples were significantly less diverse (alpha diversity) than pen floor and fecal samples (P < 0.01). Alpha diversity changed among farms in oral fluid and pen floor samples, but no differences were observed in fecal samples. Permutational ANOVA revealed that beta diversity was significantly different among sample types (P = 0.001) and farms (P = 0.001), with separation of sample types (feces, pen floor, and oral fluid) on the principal coordinates analysis. Most counts obtained from oral fluid samples were classified as Firmicutes (80.4%) and Proteobacteria (7.7%). The genera Streptococcus, members of the Pasteurellaceae family, and Veillonella were differentially abundant in oral fluid samples when compared to fecal samples, in which Streptococcus was identified as a core genus that was strongly correlated (SparCC) with other taxa. Firmicutes and Bacteroidota were the most relatively abundant phyla identified in fecal and pen floor samples, and Prevotella_9 was the most classified genus. No differentially abundant taxa were identified when comparing fecal samples and pen floor samples. We concluded that under the conditions of our study, the oral fluid microbiota of weaned piglets is different (beta diversity) and less diverse (alpha diversity) than the fecal and environmental microbiotas. Several differentially abundant taxa were identified in the oral fluid samples, and some have been described as important colonizers of the oral cavity in human microbiome studies. Further understanding of the relationship between the oral fluid microbiota and swine is necessary and would create opportunities for the development of innovative solutions that target the microbiota to improve swine health and production.

Techniques, procedures, and applications in microbiome analysis

Microbiota is a complex community of microorganisms living in a defined environment. Until the 20th century, knowledge of microbiota was partial, as the techniques available for their characterization were primarily based on bacteriological culture. In the last twenty years, the development of DNA sequencing technologies, multi-omics, and bioinformatics has expanded our understanding of microorganisms. We have moved from mainly considering them isolated disease-causing agents to recognizing the microbiota as an essential component of host biology. These techniques have shown that the microbiome plays essential roles in various host phenotypes, influencing development, physiology, reproduction, and evolution. This chapter provides researchers with a summary of the primary concepts, sample collection, experimental techniques, and bioinformatics analysis commonly used in microbiome research. The main features, applications in microbiome studies, and their advantages and limitations are included in each section.

COVID-19 associated oral and oropharyngeal microbiome: Systematic review and meta-analysis

Three years into the coronavirus disease 2019 (COVID-19) pandemic, there are still growing concerns with the emergence of different variants, unknown long- and short-term effects of the virus, and potential biological mechanisms underlying etiopathogenesis and increased risk for morbidity and mortality. The role of the microbiome in human physiology and the initiation and progression of several oral and systemic diseases have been actively studied in the past decade. With the proof of viral transmission, carriage, and a potential role in etiopathogenesis, saliva and the oral environment have been a focus of COVID-19 research beyond diagnostic purposes. The oral environment hosts diverse microbial communities and contributes to human oral and systemic health. Several investigations have identified disruptions in the oral microbiome in COVID-19 patients. However, all these studies are cross-sectional in nature and present heterogeneity in study design, techniques, and analysis. Therefore, in this undertaking, we (a) systematically reviewed the current literature associating COVID-19 with changes in the microbiome; (b) performed a re-analysis of publicly available data as a means to standardize the analysis, and (c) reported alterations in the microbial characteristics in COVID-19 patients compared to negative controls. Overall, we identified that COVID-19 is associated with oral microbial dysbiosis with significant reduction in diversity. However, alterations in specific bacterial members differed across the study. Re-analysis from our pipeline shed light on Neisseria as the potential key microbial member associated with COVID-19.

Assessment of DNA extraction methods for human gut mycobiome analysis

The gut mycobiome plays an important role in the health and disease of the human gut, but its exact function is still under investigation. While there is a wealth of information available on the bacterial community of the human gut microbiome, research on the fungal community is still relatively limited. In particular, technical methodologies for mycobiome analysis, especially the DNA extraction method for human faecal samples, varied in different studies. In the current study, two commercial kits commonly used in DNA extraction, the QIAamp® Fast DNA Stool Mini Kit and DNeasy PowerSoil Pro Kit, and one manual method, the International Human Microbiome Standards Protocol Q, were compared. Furthermore, the effectiveness of two different bead-beating machines, the Mini-Beadbeater-16 and FastPrep-24TM 5G, was compared in parallel. A mock fungal community with a known composition of fungal strains was also generated and included to compare different DNA extraction methods. Our results suggested that the method using the DNeasy PowerSoil Pro Kit and Mini-Beadbeater-16 provides the best results to extract DNA from human faecal samples. Based on our data, we propose a standard operating procedure for DNA extraction from human faecal samples for mycobiome analysis.

The potential role of the microbiota in prostate cancer pathogenesis and treatment

The human body hosts a complex and dynamic population of trillions of microorganisms - the microbiota - which influences the body in homeostasis and disease, including cancer. Several epidemiological studies have associated specific urinary and gut microbial species with increased risk of prostate cancer; however, causal mechanistic data remain elusive. Studies have associated bacterial generation of genotoxins with the occurrence of TMPRSS2-ERG gene fusions, a common, early oncogenic event during prostate carcinogenesis. A subsequent study demonstrated the role of the gut microbiota in prostate cancer endocrine resistance, which occurs, at least partially, through the generation of androgenic steroids fuelling oncogenic signalling via the androgen receptor. These studies present mechanistic evidence of how the host microbiota might be implicated in prostate carcinogenesis and tumour progression. Importantly, these findings also reveal potential avenues for the detection and treatment of prostate cancer through the profiling and modulation of the host microbiota. The latter could involve approaches such as the use of faecal microbiota transplantation, prebiotics, probiotics, postbiotics or antibiotics, which can be used independently or combined with existing treatments to reverse therapeutic resistance and improve clinical outcomes in patients with prostate cancer.

A coral-associated actinobacterium mitigates coral bleaching under heat stress

BACKGROUND

The positive effects of exposing corals to microorganisms have been reported though how the benefits are conferred are poorly understood. Here, we isolated an actinobacterial strain (SCSIO 13291) from Pocillopora damicornis with capabilities to synthesize antioxidants, vitamins, and antibacterial and antiviral compounds supported with phenotypic and/or genomic evidence. Strain SCSIO 13291 was labeled with 5 (and - 6)-carboxytetramethylrhodamine, succinimidyl ester and the labeled cell suspension directly inoculated onto the coral polyp tissues when nubbins were under thermal stress in a mesocosm experiment. We then visualized the labelled bacterial cells and analyzed the coral physiological, transcriptome and microbiome to elucidate the effect this strain conferred on the coral holobiont under thermal stress.

RESULTS

Subsequent microscopic observations confirmed the presence of the bacterium attached to the coral polyps. Addition of the SCSIO 13291 strain reduced signs of bleaching in the corals subjected to heat stress. At the same time, alterations in gene expression, which were involved in reactive oxygen species and light damage mitigation, attenuated apoptosis and exocytosis in addition to metabolite utilization, were observed in the coral host and Symbiodiniaceae populations. In addition, the coral associated bacterial community altered with a more stable ecological network for samples inoculated with the bacterial strain.

CONCLUSIONS

Our results provide insights into the benefits of a putative actinobacterial probiotic strain that mitigate coral bleaching signs. This study suggests that the inoculation of bacteria can potentially directly benefit the coral holobiont through conferring metabolic activities or through indirect mechanisms of suppling additional nutrient sources.

Benchmarking MicrobIEM - a user-friendly tool for decontamination of microbiome sequencing data

BACKGROUND

Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls.

RESULTS

We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (108-103 cells, 0.4-80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 106 cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden's index. In the skin dataset, the Decontam prevalence filter and MicrobIEM's ratio filter effectively reduced common contaminants while keeping skin-associated genera.

CONCLUSIONS

MicrobIEM's ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.

Mastering DNA chromatogram analysis in Sanger sequencing for reliable clinical analysis

BACKGROUND

Sanger dideoxy sequencing is vital in clinical analysis due to its accuracy, ability to analyze genetic markers like SNPs and STRs, capability to generate reliable DNA profiles, and its role in resolving complex clinical cases. The precision and robustness of Sanger sequencing contribute significantly to the scientific basis of clinical investigations. Though the reading of chromatograms seems to be a routine step, many errors conducted in PCR may lead to consequent limitations in the readings of AGCT peaks. These errors are possibly associated with improper DNA amplification and its subsequent interpretation of DNA sequencing files, such as noisy peaks, artifacts, and confusion between double-peak technical errors, heterozygosity, and double infection potentials. Thus, it is not feasible to read nucleic acid sequences without giving serious attention to these technical problems. To ensure the accuracy of DNA sequencing outcomes, it is also imperative to detect and rectify technical challenges that may lead to misinterpretation of the DNA sequence, resulting in errors and incongruities in subsequent analyses.

SHORT CONCLUSION

This overview sheds light on prominent technical concerns that can emerge prior to and during the interpretation of DNA chromatograms in Sanger sequencing, along with offering strategies to address them effectively. The significance of identifying and tackling these technical limitations during the chromatogram analysis is underscored in this review. Recognizing these concerns can aid in enhancing the quality of downstream analyses for Sanger sequencing results, which holds notable improvement in accuracy, reliability, and ability to provide crucial genetic information in clinical analysis.

Soil as a transdisciplinary research catalyst: from bioprospecting to biorespecting

The vast microbial biodiversity of soils is beginning to be observed and understood by applying modern DNA sequencing techniques. However, ensuring this potentially valuable information is used in a fair and equitable way remains a challenge. Here, we present a public engagement project that explores this topic through collaborative research of soil microbiomes at six urban locations using nanopore-based DNA sequencing. The project brought together researchers from the disciplines of synthetic biology, environmental humanities and microbial ecology, as well as school students aged 14-16 years old, to gain a broader understanding of views on the use of data from the environment. Discussions led to the transformation of 'bioprospecting', a metaphor with extractive connotations which is often used to frame environmental DNA sequencing studies, towards a more collaborative approach-'biorespecting'. This shift in terminology acknowledges that genetic information contained in soil arises as a result of entire ecosystems, including the people involved in its creation. Therefore, any use of sequence information should be accountable to the ecosystems from which it arose. As knowledge can arise from ecosystems and communities, science and technology should acknowledge this link and reciprocate with care and benefit-sharing to help improve the wellbeing of future generations.

Establishing reference material for the quest towards standardization in environmental microbial metagenomic studies

Breakthroughs in DNA-based technologies, especially in metagenomic sequencing, have drastically enhanced researchers' ability to explore environmental microbiome and the associated interplays within. However, as new methodologies are being actively developed for improvements in different aspects, metagenomic workflows become diversified and heterogeneous. Through a single-variable control approach, we quantified the microbial profiling variations arising from 6 common technical variables associated with metagenomic workflows for both simple and complex samples. The incurred variations were constantly the lowest in replicates of DNA isolation and DNA sequencing library construction. Different DNA extraction kits often caused the highest variation among all the tested variables. Additionally, sequencing run batch was an important source of variability for targeted platforms. As such, the development of an environmental reference material for complex environmental samples could be beneficial in benchmarking accrued non-biological variability within and between protocols and insuring reliable and reproducible sequencing outputs immediately upstream of bioinformatic analysis. To develop an environment reference material, sequencing of a well-homogenized environmental sample composed of activated sludge was performed using different pre-analytical assays in replications. In parallel, a certified mock community was processed and sequenced. Assays were ranked based on the reconstruction of the theoretical mock community profile. The reproducibility of the best-performing assay and the microbial profile of the reference material were further ascertained. We propose the adoption of our complex environmental reference material, which could reflect the degree of diversity in environmental microbiome studies, to facilitate accurate, reproducible, and comparable environmental metagenomics-based studies.

Ocular Surface Microbiota in Naïve Keratoconus: A Multicenter Validation Study

In the field of Ophthalmology, the mNGS 16S rRNA sequencing method of studying the microbiota and ocular microbiome is gaining more and more weight in the scientific community. This study aims to characterize the ocular microbiota of patients diagnosed with keratoconus who have not undergone any prior surgical treatment using the mNGS 16S rRNA sequencing method. Samples of naïve keratoconus patients were collected with an eNAT with 1 mL of Liquid Amies Medium (Copan Brescia, Italy), and DNA was extracted and analyzed with 16S NGS. The microbiota analysis showed a relative abundance of microorganisms at the phylum level in each sample collected from 38 patients with KC and 167 healthy controls. A comparison between healthy control and keratoconus samples identified two genera unique to keratoconus, Pelomonas and Ralstonia. Our findings suggest that alterations in the microbiota may play a role in the complex scenario of KC development.

The microbiota of uterine biopsies, cytobrush and vaginal swabs at artificial insemination in Norwegian red cows

The individual resistance or tolerance against uterine disease in dairy cattle might be related to variations in the uterine tract microbiota. The uterine tract microbiota in dairy cattle is a field of increasing interest. However, its specific taxonomy and functional aspects is under-explored, and information about the microbiota in the endometrium at artificial insemination (AI) is still missing. Although uterine bacteria are likely to be introduced via the vaginal route, it has also been suggested that pathogens can be transferred to the uterus via a hematogenous route. Thus, the microbiota in different layers of the uterine wall may differ. Norwegian Red (NR) is a high fertility breed that also has a high prevalence of subclinical endometritis (SCE), an inflammation of the uterus that has a negative effect on dairy cattle fertility. However, in this breed the negative effect is only moderate, raising the question of whether this may be due to a favorable microbiota. In the present study we investigated the endometrial microbiota in NR at AI by biopsy and cytobrush samples, and comparing this to the vaginal microflora. The second objective was to describe potential differences at both distinct depths of the endometrium, in healthy vs SCE positive NR cows. We sampled 24 lactating and clinically healthy Norwegian red cows in their second heat or more after calving, presented for first AI. First, we obtained a vaginal swab and a cytobrush sample, in addition to a cytotape to investigate the animal's uterine health status with respect to SCE. Secondly, we acquired a biopsy sample from the uterine endometrium. Bacterial DNA from the 16S rRNA gene was extracted and sequenced with Illumina sequencing of the V3-V4 region. Alpha and beta diversity and taxonomic composition was investigated. Our results showed that the microbiota of endometrial biopsies was qualitatively different and more even than that of cytobrush and vaginal swab samples. The cytobrush samples and the vaginal swabs shared a similar taxonomic composition, suggesting that vaginal swabs may suffice to sample the surface-layer uterine microbiota at estrus. The current study gave a description of the microbiota in the healthy and SCE positive NR cows at AI. Our results are valuable as we continue to explore the mechanisms for high fertility in NR, and possible further improvements.

Similarity in milk microbiota in replicates

Receiving the same results from repeated analysis of the same sample is a basic principle in science. The inability to reproduce previously published results has led to discussions of a reproducibility crisis within science. For studies of microbial communities, the problem of reproducibility is more pronounced and has, in some fields, led to a discussion on the very existence of a constantly present microbiota. In this study, DNA from 44 bovine milk samples were extracted twice and the V3-V4 region of the 16S rRNA gene was sequenced in two separate runs. The FASTQ files from the two data sets were run through the same bioinformatics pipeline using the same settings and results from the two data sets were compared. Milk samples collected maximally 2 h apart were used as replicates and permitted comparisons to be made within the same run. Results show a significant difference in species richness between the two sequencing runs although Shannon and Simpson's diversity was the same. Multivariate analyses of all samples demonstrate that the sequencing run was a driver for variation. Direct comparison of similarity between samples and sequencing run showed an average similarity of 42%-45% depending on whether binary or abundance-based similarity indices were used. Within-run comparisons of milk samples collected maximally 2 h apart showed an average similarity of 39%-47% depending on the similarity index used and that similarity differed significantly between runs. We conclude that repeated DNA extraction and sequencing significantly can affect the results of a low microbial biomass microbiota study.

Impact of Experimental Bias on Compositional Analysis of Microbiome Data

Microbiome data are subject to experimental bias that is caused by DNA extraction and PCR amplification, among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis, and Callahan (2019) proposed a model for how such biases affect the observed taxonomic profiles; this model assumes the main effects of bias without taxon-taxon interactions. Our newly developed method for testing the differential abundance of taxa, LOCOM, is the first method to account for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicate that LOCOM remained robust to a reasonable range of interaction biases. The other methods tend to have an inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods could not control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.

A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance

Next-generation sequencing (NGS) and metabarcoding approaches are increasingly applied to wild animal populations, but there is a disconnect between the widely applied generalized linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology typically applied to metabarcoding data. Here, we describe the suitability of a novel GLMM-based approach for analyzing the taxon-specific sequence read counts derived from standard metabarcoding data. This approach allows decomposition of the contribution of different drivers to variation in community composition (e.g., age, season, individual) via interaction terms in the model random-effects structure. We provide guidance to implementing this approach and show how these models can identify how responsible specific taxonomic groups are for the effects attributed to different drivers. We applied this approach to two cross-sectional data sets from the Soay sheep population of St. Kilda. GLMMs showed agreement with dissimilarity-based approaches highlighting the substantial contribution of age and minimal contribution of season to microbiota community compositions, and simultaneously estimated the contribution of other technical and biological factors. We further used model predictions to show that age effects were principally due to increases in taxa of the phylum Bacteroidetes and declines in taxa of the phylum Firmicutes. This approach offers a powerful means for understanding the influence of drivers of community structure derived from metabarcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate contributions of additional factors such as host or microbe phylogeny to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities. IMPORTANCE NGS and fecal metabarcoding methods have provided powerful opportunities to study the wild gut microbiome. A wealth of data is, therefore, amassing across wild systems, generating the need for analytical approaches that can appropriately investigate simultaneous factors at the host and environmental scale that determine the composition of these communities. Here, we describe a generalized linear mixed-effects model (GLMM) approach to analyze read count data from metabarcoding of the gut microbiota, allowing us to quantify the contributions of multiple host and environmental factors to within-host community structure. Our approach provides outputs that are familiar to a majority of field ecologists and can be run using any standard mixed-effects modeling packages. We illustrate this approach using two metabarcoding data sets from the Soay sheep population of St. Kilda investigating age and season effects as worked examples.

Human Microbiomes and Disease for the Biomedical Data Scientist

The human microbiome is complex, variable from person to person, essential for health, and related to both the risk for disease and the efficacy of our treatments. There are robust techniques to describe microbiota with high-throughput sequencing, and there are hundreds of thousands of already-sequenced specimens in public archives. The promise remains to use the microbiome both as a prognostic factor and as a target for precision medicine. However, when used as an input in biomedical data science modeling, the microbiome presents unique challenges. Here, we review the most common techniques used to describe microbial communities, explore these unique challenges, and discuss the more successful approaches for biomedical data scientists seeking to use the microbiome as an input in their studies.

Exploring Long-Read Metagenomics for Full Characterization of Shiga Toxin-Producing Escherichia coli in Presence of Commensal E. coli

The characterization of Shiga toxin-producing Escherichia coli (STEC) is necessary to assess their pathogenic potential, but isolation of the strain from complex matrices such as milk remains challenging. In previous work, we have shown the potential of long-read metagenomics to characterize eae-positive STEC from artificially contaminated raw milk without isolating the strain. The presence of multiple E. coli strains in the sample was shown to potentially hinder the correct characterization of the STEC strain. Here, we aimed at determining the STEC:commensal ratio that would prevent the characterization of the STEC. We artificially contaminated pasteurized milk with different ratios of an eae-positive STEC and a commensal E. coli and applied the method previously developed. Results showed that the STEC strain growth was better than the commensal E. coli after enrichment in acriflavine-supplemented BPW. The STEC was successfully characterized in all samples with at least 10 times more STEC post-enrichment compared to the commensal E. coli. However, the presence of equivalent proportions of STEC and commensal E. coli prevented the full characterization of the STEC strain. This study confirms the potential of long-read metagenomics for STEC characterization in an isolation-free manner while refining its limit regarding the presence of background E. coli strains.

Touching the (almost) untouchable: a minimally invasive workflow for microbiological and biomolecular analyses of cultural heritage objects

Microbiological and biomolecular approaches to cultural heritage research have expanded the established research horizon from the prevalent focus on the cultural objects' conservation and human health protection to the relatively recent applications to provenance inquiry and assessment of environmental impacts in a global context of a changing climate. Standard microbiology and molecular biology methods developed for other materials, specimens, and contexts could, in principle, be applied to cultural heritage research. However, given certain characteristics common to several heritage objects-such as uniqueness, fragility, high value, and restricted access, tailored approaches are required. In addition, samples of heritage objects may yield low microbial biomass, rendering them highly susceptible to cross-contamination. Therefore, dedicated methodology addressing these limitations and operational hurdles is needed. Here, we review the main experimental challenges and propose a standardized workflow to study the microbiome of cultural heritage objects, illustrated by the exploration of bacterial taxa. The methodology was developed targeting the challenging side of the spectrum of cultural heritage objects, such as the delicate written record, while retaining flexibility to adapt and/or upscale it to heritage artifacts of a more robust constitution or larger dimensions. We hope this tailored review and workflow will facilitate the interdisciplinary inquiry and interactions among the cultural heritage research community.

Influence of scat ageing on the gut microbiome: how old is too old?

BACKGROUND

The study of the host-microbiome by the collection of non-invasive samples has the potential to become a powerful tool for conservation monitoring and surveillance of wildlife. However, multiple factors can bias the quality of data recovered from scats, particularly when field-collected samples are used given that the time of defecation is unknown. Previous studies using scats have shown that the impact of aerobic exposure on the microbial composition is species-specific, leading to different rates of change in microbial communities. However, the impact that this aging process has on the relationship between the bacterial and fungal composition has yet to be explored. In this study, we measured the effects of time post-defecation on bacterial and fungal compositions in a controlled experiment using scat samples from the endangered koala (Phascolarctos cinereus).

RESULTS

We found that the bacterial composition remained stable through the scat aging process, while the fungal composition did not. The absence of an increase in facultative anaerobes and the stable population of obligate anaerobic bacteria were likely due to our sampling from the inner portion of the scat. We report a cluster of fungal taxa that colonises scats after defecation which can dilute the genetic material from the autochthonous mycoflora and inhibit recovery.

CONCLUSION

We emphasize the need to preserve the integrity of scat samples collected in the wild and combat the effects of time and provide strategies for doing so.

Fecal Fungal Microbiota (Mycobiome) Study as a Potential Tool for Precision Medicine in Inflammatory Bowel Disease

There is growing evidence of the role of fungal microbiota in the pathogenesis of inflammatory bowel disease (IBD). Fungi can exert direct pro-inflammatory effects or modify the bacterial composition via interkingdom interactions. Although several studies have demonstrated alterations in the fecal fungal microbiota composition in IBD, there is a wide variation in the mycobiome in different populations, with no definite pattern that can define the mycobiome in IBD having yet been identified. Recent work has suggested that characterizing the fecal fungal composition may influence therapeutic decisions and help to predict outcomes in a subset of IBD patients. In this study, we review the current literature on the emerging role of the fecal mycobiome as a potential tool for precision medicine in IBD.

The Association between Gestational Diabetes and the Microbiome: A Systematic Review and Meta-Analysis

Gestational diabetes, affecting about 10% of pregnancies, is characterized by impaired glucose regulation and can lead to complications for health of pregnant women and their offspring. The microbiota, the resident microbes within the body, have been linked to the development of several metabolic conditions. This systematic review with meta-analysis aims to summarize the evidence on the differences in microbiota composition in pregnant women with gestational diabetes and their offspring compared to healthy pregnancies. A thorough search was conducted in the PubMed, Scopus, and Web of Science databases, and data from 21 studies were analyzed utilizing 41 meta-analyses. In the gut microbiota, Bifidobacterium and Alistipes were found to be more abundant in healthy pregnancies, while Roseburia appears to be more abundant in gestational diabetes. The heterogeneity among study findings regarding the microbiota in the meconium is considerable. The placental microbiota exhibited almost no heterogeneity, with an increased abundance of Firmicutes in the gestational diabetes group and a higher abundance of Proteobacteria in the control. The role of the microbiota in gestational diabetes is reinforced by these findings, which additionally point to the potential of microbiome-targeted therapies. To completely comprehend the interactions between gestational diabetes and the microbiome, standardizing methodologies and further research is necessary.

Challenges and strategies for preventing intestinal damage associated to mercury dietary exposure

Food represents the major risk factor for exposure to mercury in most human populations. Therefore, passage through the gastrointestinal tract plays a fundamental role in its entry into the organism. Despite the intense research carried out on the toxicity of Hg, the effects at the intestinal level have received increased attention only recently. In this review we first provide a critical appraisal of the recent advances on the toxic effects of Hg at the intestinal epithelium. Next, dietary strategies aimed to diminish Hg bioavailability or modulate the epithelial and microbiota responses will be revised. Food components and additives, including probiotics, will be considered. Finally, limitations of current approaches to tackle this problem and future lines of research will be discussed.

Absence of changes in the milk microbiota during Escherichia coli endotoxin induced experimental bovine mastitis

Changes in the milk microbiota during the course of mastitis are due to the nature of a sporadic occurring disease difficult to study. In this study we experimentally induced mastitis by infusion of Escherichia coli endotoxins in one udder quarter each of nine healthy lactating dairy cows and assessed the bacteriological dynamics and the milk microbiota at four time points before and eight time points after infusion. As control, saline was infused in one udder quarter each of additionally nine healthy cows that followed the same sampling protocol. The milk microbiota was assessed by sequencing of the 16 S rRNA gene and a range of positive and negative controls were included for methodological evaluation. Two different data filtration models were used to identify and cure data from contaminating taxa. Endotoxin infused quarters responded with transient clinical signs of inflammation and increased SCC while no response was observed in the control cows. In the milk microbiota data no response to inflammation was identified. The data analysis of the milk microbiota was largely hampered by laboratory and reagent contamination. Application of the filtration models caused a marked reduction in data but did not reveal any associations with the inflammatory reaction. Our results indicate that the microbiota in milk from healthy cows is unaffected by inflammation.

The use of different 16S rRNA gene variable regions in biogeographical studies

16S rRNA gene amplicon sequencing is routinely used in environmental surveys to identify microbial diversity and composition of the samples of interest. The dominant sequencing technology of the past decade (Illumina) is based on the sequencing of 16S rRNA hypervariable regions. Online sequence data repositories, which represent an invaluable resource for investigating microbial distributional patterns across spatial, environmental or temporal scales, contain amplicon datasets from diverse 16S rRNA gene variable regions. However, the utility of these sequence datasets is potentially reduced by the use of different 16S rRNA gene amplified regions. By comparing 10 Antarctic soil samples sequenced for five different 16S rRNA amplicons, we explore whether sequence data derived from diverse 16S rRNA variable regions can be validly used as a resource for biogeographical studies. Patterns of shared and unique taxa differed among samples as a result of variable taxonomic resolutions of the assessed 16S rRNA variable regions. However, our analyses also suggest that the use of multi-primer datasets for biogeographical studies of the domain Bacteria is a valid approach to explore bacterial biogeographical patterns due to the preservation of bacterial taxonomic and diversity patterns across different variable region datasets. We deem composite datasets useful for biogeographical studies.

Investigating the oral microbiome in retrospective and prospective cases of prostate, colon, and breast cancer

The human microbiome has been proposed as a potentially useful biomarker for several cancers. To examine this, we made use of salivary samples from the Atlantic Partnership for Tomorrow's Health (PATH) project and Alberta's Tomorrow Project (ATP). Sample selection was divided into both a retrospective and prospective case control design examining prostate, breast, and colon cancer. In total 89 retrospective and 260 prospective cancer cases were matched to non-cancer controls and saliva samples were sequenced using 16S rRNA gene sequencing. We found no significant differences in alpha diversity. All beta diversity measures were insignificant except for unweighted UniFrac profiles in retrospective breast cancer cases and weighted UniFrac, Bray-Curtis and Robust Atchinson's distances in colon cancer after testing with age and sex adjusted MiRKAT models. Differential abundance (DA) analysis showed several taxa that were associated with previous cancer in all three groupings. Only one genus (Clostridia UCG-014) in breast cancer and one ASV (Fusobacterium periodonticum) in colon cancer was identified by more than one DA tool. In prospective cases three ASVs were associated with colon cancer, one ASV with breast cancer, and one ASV with prostate cancer. Random Forest classification showed low levels of signal in both study designs in breast and prostate cancer. Contrastingly, colon cancer did show signal in our retrospective analysis (AUC: 0.737) and in one of two prospective cohorts (AUC: 0.717). Our results indicate that it is unlikely that reliable microbial oral biomarkers for breast and prostate cancer exist.. However, further research into the oral microbiome and colon cancer could be fruitful.

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.

Evaluation of DNA extraction methods and direct PCR in metabarcoding of mock and marine bacterial communities

Recent advances in new molecular biology methods and next-generation sequencing (NGS) technologies have revolutionized metabarcoding studies investigating complex microbial communities from various environments. The inevitable first step in sample preparation is DNA extraction which introduces its own set of biases and considerations. In this study, we assessed the influence of five DNA extraction methods [B1: phenol/chloroform/isoamyl extraction, B2 and B3: isopropanol and ethanol precipitations, respectively-both modifications of B1, K1: DNeasy PowerWater Kit (QIAGEN), K2: modified DNeasy PowerWater Kit (QIAGEN) and direct PCR approach (P) that completely circumvents this step on community composition and DNA yield of mock and marine sample communities from the Adriatic Sea]. B1-B3 methods generally produced higher DNA yields and more similar microbial communities, but with higher interindividual variability. Each method demonstrated significant differences in a specific community structure, where rare taxa seem to play a crucial role. There was not one superior method closest to the theoretically expected mock community composition, they all demonstrated skewed ratios, but in a similar way which might be attributed to other factors, such as primer bias or 16S rRNA gene count for specific taxa. Direct PCR represents an interesting approach when high throughput in sample processing is required. We emphasize the importance of making a cautious decision about the choice of the extraction method or direct PCR approach, but even more importantly its consistent application throughout the study.