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

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.

Metataxonomic insights in the distribution of Lactobacillaceae in foods and food environments

Members of the family Lactobacillaceae, which now includes species formerly belonging to the genera Lactobacillus and Pediococcus, but also Leuconostocaceae, are of foremost importance in food fermentations and spoilage, but also as components of animal and human microbiota and as potentially pathogenic microorganisms. Knowledge of the ecological distribution of a given species and genus is important, among other things, for the inclusion in lists of microorganisms with a Qualified Presumption of Safety or with beneficial use. The objective of this work is to use the data in FoodMicrobionet database to obtain quantitative insights (in terms of both abundance and prevalence) on the distribution of these bacteria in foods and food environments. We first explored the reliability of taxonomic assignments using the SILVA v138.1 reference database with full length and partial sequences of the 16S rRNA gene for type strain sequences. Full length 16S rRNA gene sequences allow a reasonably good classification at the genus and species level in phylogenetic trees but shorter sequences (V1-V3, V3-V4, V4) perform much worse, with type strains of many species sharing identical V4 and V3-V4 sequences. Taxonomic assignment at the genus level of 16S rRNA genes sequences and the SILVA v138.1 reference database can be done for almost all genera of the family Lactobacillaceae with a high degree of confidence for full length sequences, and with a satisfactory level of accuracy for the V1-V3 regions. Results for the V3-V4 and V4 region are still acceptable but significantly worse. Taxonomic assignment at the species level for sequences for the V1-V3, V3-V4, V4 regions of the 16S rRNA gene of members of the family Lactobacillaceae is hardly possible and, even for full length sequences, and only 49.9 % of the type strain sequences can be unambiguously assigned to species. We then used the FoodMicrobionet database to evaluate the prevalence and abundance of Lactobacillaceae in food samples and in food related environments. Generalist and specialist genera were clearly evident. The ecological distribution of several genera was confirmed and insights on the distribution and potential origin of rare genera (Dellaglioa, Holzapfelia, Schleiferilactobacillus) were obtained. We also found that combining Amplicon Sequence Variants from different studies is indeed possible, but provides little additional information, even when strict criteria are used for the filtering of sequences.

Selecting 16S rRNA Primers for Microbiome Analysis in a Host-Microbe System: The Case of the Jellyfish Rhopilema nomadica

Amplicon sequencing of the 16S rRNA gene is extensively used to characterize bacterial communities, including those living in association with eukaryotic hosts. Deciding which region of the 16S rRNA gene to analyze and selecting the appropriate PCR primers remains a major decision when initiating any new microbiome study. Based on a detailed literature survey of studies focusing on cnidarian microbiomes, we compared three commonly used primers targeting different hypervariable regions of the 16S rRNA gene, V1V2, V3V4, and V4V5, using the jellyfish Rhopilema nomadica as a model. Although all primers exhibit a similar pattern in bacterial community composition, the performance of the V3V4 primer set was superior to V1V2 and V4V5. The V1V2 primers misclassified bacteria from the Bacilli class and exhibited low classification resolution for Rickettsiales, which represent the second most abundant 16S rRNA gene sequence in all the primers. The V4V5 primer set detected almost the same community composition as the V3V4, but the ability of these primers to also amplify the eukaryotic 18S rRNA gene may hinder bacterial community observations. However, after overcoming the challenges possessed by each one of those primers, we found that all three of them show very similar bacterial community dynamics and compositions. Nevertheless, based on our results, we propose that the V3V4 primer set is potentially the most suitable for studying jellyfish-associated bacterial communities. Our results suggest that, at least for jellyfish samples, it may be feasible to directly compare microbial community estimates from different studies, each using different primers but otherwise similar experimental protocols. More generally, we recommend specifically testing different primers for each new organism or system as a prelude to large-scale 16S rRNA gene amplicon analyses, especially of previously unstudied host-microbe associations.

Nanopore Is Preferable over Illumina for 16S Amplicon Sequencing of the Gut Microbiota When Species-Level Taxonomic Classification, Accurate Estimation of Richness, or Focus on Rare Taxa Is Required

Nanopore sequencing is a promising technology used for 16S rRNA gene amplicon sequencing as it can provide full-length 16S reads and has a low up-front cost that allows research groups to set up their own sequencing workflows. To assess whether Nanopore with the improved error rate of the Kit 12 chemistry should be adopted as the preferred sequencing technology instead of Illumina for 16S amplicon sequencing of the gut microbiota, we used a mock community and human faecal samples to compare diversity, richness, and species-level community structure, as well as the replicability of the results. Nanopore had less noise, better accuracy with the mock community, a higher proportion of reads from the faecal samples classified to species, and better replicability. The difference between the Nanopore and Illumina results of the faecal bacterial community structure was significant but small compared to the variation between samples. The results show that Nanopore is a better choice for 16S rRNA gene amplicon sequencing when the focus is on species-level taxonomic resolution, the investigation of rare taxa, or an accurate estimation of richness. Illumina 16S sequencing should be reserved for communities with many unknown species, and for studies that require the resolution of amplicon sequence variants.

Mock community as an in situ positive control for amplicon sequencing of microbiotas from the same ecosystem

Metataxonomy has become the standard for characterizing the diversity and composition of microbial communities associated with multicellular organisms and their environment. Currently available protocols for metataxonomy assume a uniform DNA extraction, amplification and sequencing efficiency for all sample types and taxa. It has been suggested that the addition of a mock community (MC) to biological samples before the DNA extraction step could aid identification of technical biases during processing and support direct comparisons of microbiota composition, but the impact of MC on diversity estimates of samples is unknown. Here, large and small aliquots of pulverized bovine fecal samples were extracted with no, low or high doses of MC, characterized using standard Illumina technology for metataxonomics, and analysed with custom bioinformatic pipelines. We demonstrated that sample diversity estimates were distorted only if MC dose was high compared to sample mass (i.e. when MC > 10% of sample reads). We also showed that MC was an informative in situ positive control, permitting an estimation of the sample 16S copy number, and detecting sample outliers. We tested this approach on a range of sample types from a terrestrial ecosystem, including rhizosphere soil, whole invertebrates, and wild vertebrate fecal samples, and discuss possible clinical applications.

Optimization of Molecular Methods for Detecting Duckweed-Associated Bacteria

The bacterial colonization dynamics of plants can differ between phylogenetically similar bacterial strains and in the context of complex bacterial communities. Quantitative methods that can resolve closely related bacteria within complex communities can lead to a better understanding of plant-microbe interactions. However, current methods often lack the specificity to differentiate phylogenetically similar bacterial strains. In this study, we describe molecular strategies to study duckweed-associated bacteria. We first systematically optimized a bead-beating protocol to co-isolate nucleic acids simultaneously from duckweed and bacteria. We then developed a generic fingerprinting assay to detect bacteria present in duckweed samples. To detect specific duckweed-bacterium associations, we developed a genomics-based computational pipeline to generate bacterial strain-specific primers. These strain-specific primers differentiated bacterial strains from the same genus and enabled the detection of specific duckweed-bacterium associations present in a community context. Moreover, we used these strain-specific primers to quantify the bacterial colonization of duckweed by normalization to a plant reference gene and revealed differences in colonization levels between strains from the same genus. Lastly, confocal microscopy of inoculated duckweed further supported our PCR results and showed bacterial colonization of the duckweed root-frond interface and root interior. The molecular methods introduced in this work should enable the tracking and quantification of specific plant-microbe associations within plant-microbial communities.

Metagenomics and metabarcoding experimental choices and their impact on microbial community characterization in freshwater recirculating aquaculture systems

BACKGROUND

Microbial communities in recirculating aquaculture systems (RAS) play a role in system success, nutrient cycling, and water quality. Considering the increasing socio-economic role of fish farming, e.g., regarding food security, an in-depth understanding of aquaculture microbial communities is also relevant from a management perspective, especially regarding the growth, development, and welfare of the farmed animal. However, the current data on the composition of microbial communities within RAS is patchy, which is partly attributable to diverging method choices that render comparative analyses challenging. Therefore, there is a need for accurate, standardized, and user-friendly methods to study microbial communities in aquaculture systems.

RESULTS

We compared sequencing approach performances (3 types of 16S short amplicon sequencing, PacBio long-read amplicon sequencing, and amplification-free shotgun metagenomics) in the characterization of microbial communities in two commercial RAS fish farms. Results showed that 16S primer choice and amplicon length affect some values (e.g., diversity measures, number of assigned taxa or distinguishing ASVs) but have no impact on spatio-temporal patterns between sample types, farms and time points. This implies that 16S rRNA approaches are adequate for community studies. The long-read amplicons underperformed regarding the quantitative resolution of spatio-temporal patterns but were suited to identify functional services, e.g., nitrification cycling and the detection of pathogens. Finally, shotgun metagenomics extended the picture to fungi, viruses, and bacteriophages, opening avenues for exploring inter-domain interactions. All sequencing datasets agreed on major prokaryotic players, such as Actinobacteriota, Bacteroidota, Nitrospirota, and Proteobacteria.

CONCLUSION

The different sequencing approaches yielded overlapping and highly complementary results, with each contributing unique data not obtainable with the other approaches. We conclude that a tiered approach constitutes a strategy for obtaining the maximum amount of information on aquaculture microbial communities and can inform basic research on community evolution dynamics. For specific and/or applied questions, single-method approaches are more practical and cost-effective and could lead to better farm management practices.

Studying the pre-implantation uterine microbiota in cattle using transabdominal laparoscopic low-volume lavage: Aiming for zero-contamination

Recent studies have suggested that bacteria associated with the female reproductive tract - the uterine microbiota - may be important for reproductive health and pregnancy success. Therefore, uterine microbiome research gained much interest in the last few years. However, it is challenging to study late postpartum uterine samples, since they hold a low microbial biomass. Next-generation sequencing techniques are very sensitive for microbial identification, but they cannot make a distinction between actual microbiota and contaminant bacteria or their DNA. Our aim was to test a new method to sample the bovine uterine lumen in vivo, while minimizing the risk of cross-contamination. In order to evaluate this method, we performed a descriptive assessment of the microbial composition of the obtained samples. Transabdominal, laparoscopic sampling of the uterine lumen was conducted in five Holstein-Friesian cows. Uterine fluid from the uterine horns was collected by low-volume lavage. DNA from the samples was extracted using two different DNA extraction methods, and negative controls (sampling blank controls and DNA extraction blank controls) were included. Bacteria were identified using 16S rRNA gene amplicon sequencing. In this proof-of-concept study, no evidence for authentically present uterine microbiota could be found. During laparoscopic sampling, some practical challenges were encountered, and the reliability of low-volume-lavage for the collection of a low microbial biomass could be questioned. By comparing two DNA extraction methods, a significant contamination background could be noticed originating from the DNA extraction kits.

Microbiome-Based Stain Analyses in Crime Scenes

Recent advances in next-generation sequencing technologies (NGS) coupled with machine learning have demonstrated the potential of microbiome-based analyses in applied areas such as clinical diagnostics and forensic sciences. Particularly in forensics, microbial markers in biological stains left at a crime scene can provide valuable information for the reconstruction of crime scene cases, as they contain information on bodily origin, the time since deposition, and donor(s) of the stain. Importantly, microbiome-based analyses provide a complementary or an alternative approach to current methods when these are limited or not feasible. Despite the promising results from recent research, microbiome-based stain analyses are not yet employed in routine casework. In this review, we highlight the two main gaps that need to be addressed before we can successfully integrate microbiome-based analyses in applied areas with a special focus on forensic casework: one is a comprehensive assessment of the method's strengths and limitations, and the other is the establishment of a standard operating procedure. For the latter, we provide a roadmap highlighting key decision steps and offering laboratory and bioinformatic workflow recommendations, while also delineating those aspects that require further testing. Our goal is to ultimately facilitate the streamlining of microbiome-based analyses within the existing forensic framework to provide alternate lines of evidence, thereby improving the quality of investigations.

Detection and analysis of lung microbiota in mice with lung cancer lacking the NLRP3 gene

To explore whether the lung microbiota have changed in the process of NLRP3 inflammasome promoting cancer, we constructed a murine lung cancer model using tracheal instillation of benzo(a)pyrene and an equal volume of tricaprylin, and characterized lung microbiota in bronchoalveolar lavage fluid from 24 SPF wild-type and NLRP3 gene knockout (NLRP3-/-) C57BL/6 mice. 16SrDNA sequencing was used to analyze the changes in the microbiota. The wild-type and the NLRP3-/- lung cancer group had statistically significant differences in tumor formation rate, tumor number, and tumor size. At the phylum and the genus level, the relative abundance of Proteobacteria and Sphingomonas were the highest in each group respectively. Simpson (P = 0.002) and Shannon (P = 0.008) indexes showed that the diversity of microbiota in the lung cancer group was lower than that in the control group under the NLRP3-/- background. According to the ANOSIM and MRPP analysis, there was a difference between the NLRP3-/- lung cancer group and the NLRP3-/- control group (P < 0.05). The knockout of the NLRP3 gene caused changes in the lung microbiota of mice. There may be a regulatory relationship between the NLRP3 inflammasome and the lung microbiota, which affects the occurrence and development of lung cancer.

A Fast and Easy Method to Co-extract DNA and RNA from an Environmental Microbial Sample

We herein propose a fast and easy DNA and RNA co-extraction method for environmental microbial samples. It combines bead beating and phenol-chloroform phase separation followed by the separation and purification of DNA and RNA using the Qiagen AllPrep DNA/RNA mini kit. With a handling time of ~3 h, our method simultaneously extracted high-quality DNA (peak size >10-15‍ ‍kb) and RNA (RNA integrity number >6) from lake bacterioplankton filtered samples. The method is also applicable to low-biomass samples (expected DNA or RNA yield <50‍ ‍ng) and eukaryotic microbial samples, providing an easy option for more versatile eco-genomic applications.

Comparison of two molecular barcodes for the study of equine strongylid communities with amplicon sequencing

Basic knowledge on the biology and epidemiology of equine strongylid species still needs to be improved to contribute to the design of better parasite control strategies. Nemabiome metabarcoding is a convenient tool to quantify and identify species in bulk samples that could overcome the hurdle that cyathostomin morphological identification represents. To date, this approach has relied on the internal transcribed spacer 2 (ITS-2) of the ribosomal RNA gene, with a limited investigation of its predictive performance for cyathostomin communities. Using DNA pools of single cyathostomin worms, this study aimed to provide the first elements to compare performances of the ITS-2 and a cytochrome c oxidase subunit I (COI) barcode newly developed in this study. Barcode predictive abilities were compared across various mock community compositions of two, five and 11 individuals from distinct species. The amplification bias of each barcode was estimated. Results were also compared between various types of biological samples, i.e., eggs, infective larvae or adults. Bioinformatic parameters were chosen to yield the closest representation of the cyathostomin community for each barcode, underscoring the need for communities of known composition for metabarcoding purposes. Overall, the proposed COI barcode was suboptimal relative to the ITS-2 rDNA region, because of PCR amplification biases, reduced sensitivity and higher divergence from the expected community composition. Metabarcoding yielded consistent community composition across the three sample types. However, imperfect correlations were found between relative abundances from infective larvae and other life-stages for Cylicostephanus species using the ITS-2 barcode. While the results remain limited by the considered biological material, they suggest that additional improvements are needed for both the ITS-2 and COI barcodes.