Microbiota data from low biomass milk samples is markedly affected by laboratory and reagent contamination

Field and laboratory guidelines for reliable bioinformatic and statistical analysis of bacterial shotgun metagenomic data

Shotgun metagenomics is an increasingly cost-effective approach for profiling environmental and host-associated microbial communities. However, due to the complexity of both microbiomes and the molecular techniques required to analyze them, the reliability and representativeness of the results are contingent upon the field, laboratory, and bioinformatic procedures employed. Here, we consider 15 field and laboratory issues that critically impact downstream bioinformatic and statistical data processing, as well as result interpretation, in bacterial shotgun metagenomic studies. The issues we consider encompass intrinsic properties of samples, study design, and laboratory-processing strategies. We identify the links of field and laboratory steps with downstream analytical procedures, explain the means for detecting potential pitfalls, and propose mitigation measures to overcome or minimize their impact in metagenomic studies. We anticipate that our guidelines will assist data scientists in appropriately processing and interpreting their data, while aiding field and laboratory researchers to implement strategies for improving the quality of the generated results.

The impact of kit, environment, and sampling contamination on the observed microbiome of bovine milk

In low-microbial biomass samples such as bovine milk, contaminants can outnumber endogenous bacteria. Because of this, milk microbiome research suffers from a critical knowledge gap, namely, does non-mastitis bovine milk contain a native microbiome? In this study, we sampled external and internal mammary epithelia and stripped and cisternal milk and used numerous negative controls, including air and sampling controls and extraction and library preparation blanks, to identify the potential sources of contamination. Two algorithms were used to mathematically remove contaminants and track the potential movement of microbes among samples. Results suggest that the majority (i.e., >75%) of sequence data generated from bovine milk and mammary epithelium samples represents contaminating DNA. Contaminants in milk samples were primarily sourced from DNA extraction kits and the internal and external skin of the teat, while teat canal and apex samples were mainly contaminated during the sampling process. After decontamination, the milk microbiome displayed a more dispersed, less diverse, and compositionally distinct bacterial profile compared with epithelial samples. Similar microbial compositions were observed between cisternal and stripped milk samples, as well as between teat apex and canal samples. Staphylococcus and Acinetobacter were the predominant genera detected in milk sample sequences, and bacterial culture showed growth of Staphylococcus and Corynebacterium spp. in 50% (7/14) of stripped milk samples and growth of Staphylococcus spp. in 7% (1/14) of cisternal milk samples. Our study suggests that microbiome data generated from milk samples obtained from clinically healthy bovine udders may be heavily biased by contaminants that enter the sample during sample collection and processing workflows.IMPORTANCEObtaining a non-contaminated sample of bovine milk is challenging due to the nature of the sampling environment and the route by which milk is typically extracted from the mammary gland. Furthermore, the very low bacterial biomass of bovine milk exacerbates the impacts of contaminant sequences in downstream analyses, which can lead to severe biases. Our finding showed that bovine milk contains very low bacterial biomass and each contamination event (including sampling procedure and DNA extraction process) introduces bacteria and/or DNA fragments that easily outnumber the native bacterial cells. This finding has important implications for our ability to draw robust conclusions from milk microbiome data, especially if the data have not been subjected to rigorous decontamination procedures. Based on these findings, we strongly urge researchers to include numerous negative controls into their sampling and sample processing workflows and to utilize several complementary methods for identifying potential contaminants within the resulting sequence data. These measures will improve the accuracy, reliability, reproducibility, and interpretability of milk microbiome data and research.

Microbiome: Mammalian milk microbiomes: sources of diversity, potential functions, and future research directions

Abstract

Milk is an ancient, fundamental mammalian adaptation that provides nutrition and biochemical communication to offspring. Microbiomes have been detected in milk of all species studied to date. In this review, we discuss: (a) routes by which microbes may enter milk; (b) evidence for proposed milk microbiome adaptive functions; (c) variation in milk microbiomes across mammals; and (d) future research directions, including suggestions for how to address outstanding questions on the viability and functionality of milk microbiomes. Milk microbes may be sourced from the maternal gastrointestinal tract, oral, skin, and mammary gland microbiomes and from neonatal oral and skin microbiomes. Given the variety of microbial sources, stochastic processes strongly influence milk microbiome assembly, but milk microbiomes appear to be influenced by maternal evolutionary history, diet, environment, and milk nutrients. Milk microbes have been proposed to colonize the neonatal intestinal tract and produce gene and metabolic products that influence physiology, metabolism, and immune system development. Limited epidemiological data indicate that early-life exposure to milk microbes can result in positive, long-term health outcomes. Milk microbiomes can be modified by dietary changes including providing the mother with probiotics and prebiotics. Milk replacers (i.e. infant formula) may benefit from supplementation with probiotics and prebiotics, but data are lacking on probiotics' usefulness, and supplementation should be evidence based. Overall, milk microbiome literature outside of human and model systems is scarce. We highlight the need for mechanistic studies in model species paired with comparative studies across mammals to further our understanding of mammalian milk microbiome evolution. A broader study of milk microbiomes has the potential to inform animal care with relevance to ex situ endangered species.

Lay summary

Milk is an ancient adaptation that supports the growth and development of mammalian neonates and infants. Beyond its fundamental nutritional function, milk influences all aspects of neonatal development, especially immune function. All kinds of milks so far studied have contained a milk microbiome. In this review, we focus on what is known about the collection of bacterial members found in milk microbiomes. Milk microbiomes include members sourced from maternal and infant microbiomes and they appear to be influenced by maternal evolutionary history, diet, milk nutrients, and environment, as well as by random chance. Once a neonate begins nursing, microbes from milk colonize their gut and produce byproducts that influence their physiology, metabolism, and immune development. Empirical data on milk microbiomes outside of humans and model systems are sparse. Greater study of milk microbiomes across mammals will expand our understanding of mammalian evolution and improve the health of animals under human care.

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.

Sample Collection Methods in Upper Gastrointestinal Research

In recent years, significant translational research advances have been made in the upper gastrointestinal (GI) research field. Endoscopic evaluation is a reasonable option for acquiring upper GI tissue for research purposes because it has minimal risk and can be applied to unresectable gastric cancer. The optimal number of biopsy samples and sample storage is crucial and might influence results. Furthermore, the methods for sample acquisition can be applied differently according to the research purpose; however, there have been few reports on methods for sample collection from endoscopic biopsies. In this review, we suggested a protocol for collecting study samples for upper GI research, including microbiome, DNA, RNA, protein, single-cell RNA sequencing, and organoid culture, through a comprehensive literature review. For microbiome analysis, one or two pieces of biopsied material obtained using standard endoscopic forceps may be sufficient. Additionally, 5 mL of gastric fluid and 3-4 mL of saliva is recommended for microbiome analyses. At least one gastric biopsy tissue is necessary for most DNA or RNA analyses, while proteomics analysis may require at least 2-3 biopsy tissues. Single cell-RNA sequencing requires at least 3-5 tissues and additional 1-2 tissues, if possible. For successful organoid culture, multiple sampling is necessary to improve the quality of specimens.

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.

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.

Monitoring the Milk Composition, Milk Microbiota, and Blood Metabolites of Jersey Cows throughout a Lactation Period

This study aimed to determine how milk composition, milk microbiota, and blood metabolites may change during the lactation period in Jersey cows. Milk and jugular blood samples were collected from eight healthy cows every other month from the beginning to the end of their lactation period. Samples of airborne dust were also collected to determine whether the cowshed microbiota could affect milk microbiota. Milk yield peaked in the first two months and gradually decreased as the lactation period progressed. Milk fat, protein, and solids-not-fat contents were low in the first month, and then increased during the middle and late lactation periods. In the first month, plasma non-esterified fatty acids (NEFA), haptoglobin (Hp), and aspartate transaminase (AST) levels were elevated, and high abundances of Burkholderiaceae and Oxalobacteraceae were observed in milk and airborne dust microbiota. The finding that contamination of the environmental microbiota in milk was coupled with elevated plasma NEFA, Hp, and AST levels indicated that impaired metabolic function during the early lactation period may increase the invasion of opportunistic bacteria. This study can affirm the importance of feeding and cowshed management and should provide a helpful addition to improving Jersey cow farming.

Comparison of four DNA extraction kits efficiency for 16SrDNA microbiota profiling of diverse human samples

Aim: The current study investigated the performance of 4 widely used DNA extraction kits using different types of high (stool) and low biomass samples (chyme, broncho alveolar lavage and sputum). Methods: Qiagen Powerfecal Pro DNA kit, Macherey Nucleospin Soil kit, Macherey Nucleospin Tissue Kit and MagnaPure LC DNA isolation kit III were evaluated in terms of DNA quantity, quality, diversity and composition profiles. Results: The quantity and quality of DNA varied among the four kits. The microbiota of the stool samples showed similar diversity and compositional profiles for the 4 kits. Conclusion: Despite differences in DNA quality and quantity, the 4 kits yielded similar results for stool samples, while all kits were not sensitive enough for low biomass samples.

Interrogating the role of the milk microbiome in mastitis in the multi-omics era

There is growing interest in a functional understanding of milk-associated microbiota as there is ample evidence that host-associated microbial communities play an active role in host health and phenotype. Mastitis, characterized by painful inflammation of the mammary gland, is prevalent among lactating humans and agricultural animals and is associated with significant clinical and economic consequences. The etiology of mastitis is complex and polymicrobial and correlative studies have indicated alterations in milk microbial community composition. Recent evidence is beginning to suggest that a causal relationship may exist between the milk microbiota and host phenotype in mastitis. Multi-omic approaches can be leveraged to gain a mechanistic, molecular level understanding of how the milk microbiome might modulate host physiology, thereby informing strategies to prevent and ameliorate mastitis. In this paper, we review existing studies that have utilized omics approaches to investigate the role of the milk microbiome in mastitis. We also summarize the strengths and challenges associated with the different omics techniques including metagenomics, metatranscriptomics, metaproteomics, metabolomics and lipidomics and provide perspective on the integration of multiple omics technologies for a better functional understanding of the milk microbiome.

The Influence of Holder Pasteurization on the Diversity of the Human Milk Bacterial Microbiota Using High-Throughput DNA Sequencing

BACKGROUND

Human milk is the best food for infants; however, when breastfeeding is not possible, pasteurized milk from human milk banks is the best alternative. Little has been reported about variations in the bacterial microbiota composition of human milk after pasteurization.

RESEARCH AIM

To characterize and compare the bacterial microbiota composition and diversity within human milk among Mexican mothers before and after the Holder pasteurization process.

METHODS

A cross-sectional, observational, and comparative design was used. The effect of the pasteurization process on the bacterial composition and diversity of human milk samples of donors (N = 42) from a public milk bank was assessed before and after pasteurization by high throughput deoxyribonucleic acid sequencing of V3-16S rRNA gene libraries. Sequencing data were examined using the Quantitative Insights into Microbial Ecology software and Phyloseq in R environment.

RESULTS

A varied community of bacteria was found in both raw and pasteurized human milk. The bacterial diversity of the milk samples was increased by the pasteurization, where some thermoduric bacteria of the phyla Proteobacteria, Firmicutes, and Actinobacteria were more abundant. The source tracker analysis indicated that at most 1.0% of bacteria may have come from another source, showing the safety of the process used to treat milk samples.

CONCLUSION

The pasteurization process increased the bacterial diversity. We selected taxa capable of surviving the process, which could proliferate after the treatment without being a risk for infants.

Future Applications of Metagenomic Next-Generation Sequencing for Infectious Diseases Diagnostics

Metagenomic next-generation sequencing (mNGS) has the theoretical capacity to detect any microbe present in a host. mNGS also has the potential to infer a pathogen's phenotypic characteristics, including the ability to colonize humans, cause disease, and resist treatment. Concurrent host nucleic acid sequencing can assess the infected individual's physiological state, including characterization and appropriateness of the immune response. When the pathogen cannot be identified, host RNA sequencing may help infer the organism's nature. While the full promise of mNGS remains far from realization, the potential ability to identify all microbes in a complex clinical sample, assess each organism's virulence and antibiotic susceptibility traits, and simultaneously characterize the host's response to infection provide opportunities for mNGS to supplant existing technologies and become the primary method of infectious diseases diagnostics.

ddPCR allows 16S rRNA gene amplicon sequencing of very small DNA amounts from low-biomass samples

BACKGROUND

One limiting factor of short amplicon 16S rRNA gene sequencing approaches is the use of low DNA amounts in the amplicon generation step. Especially for low-biomass samples, insufficient or even commonly undetectable DNA amounts can limit or prohibit further analysis in standard protocols.

RESULTS

Using a newly established protocol, very low DNA input amounts were found sufficient for reliable detection of bacteria using 16S rRNA gene sequencing compared to standard protocols. The improved protocol includes an optimized amplification strategy by using a digital droplet PCR. We demonstrate how PCR products are generated even when using very low concentrated DNA, unable to be detected by using a Qubit. Importantly, the use of different 16S rRNA gene primers had a greater effect on the resulting taxonomical profiles compared to using high or very low initial DNA amounts.

CONCLUSION

Our improved protocol takes advantage of ddPCR and allows faithful amplification of very low amounts of template. With this, samples of low bacterial biomass become comparable to those with high amounts of bacteria, since the first and most biasing steps are the same. Besides, it is imperative to state DNA concentrations and volumes used and to include negative controls indicating possible shifts in taxonomical profiles. Despite this, results produced by using different primer pairs cannot be easily compared.

The evidence for placental microbiome and its composition in healthy pregnancies: A systematic review

OBJECTIVE

To assess the available scientific evidence regarding the placental microbial composition of a healthy pregnancy, the quality of this evidence, and the potential relation between placental and oral microbiome.

MATERIALS AND METHODS

Data sources: MEDLINE and EMBASE up to August 1, 2019.

STUDY ELIGIBILITY CRITERIA

Human subjects; healthy women; term deliveries; healthy normal birth weight; assessment of microorganisms (bacteria) in placental tissue; full research papers in English. The quality of the included studies was assessed by a modified Joanna Briggs Institute checklist for analytical cross-sectional studies.

RESULTS

57 studies passed the inclusion criteria. Of these, 33 had a high risk of quality bias (e.g., insufficient infection control, lack of negative controls, poor description of the healthy cases). The remaining 24 studies had a low (N = 12) to moderate (N = 12) risk of bias and were selected for in-depth analysis. Of these 24 studies, 22 reported microorganisms in placental tissues, where Lactobacillus (11 studies), Ureaplasma (7), Fusobacterium (7), Staphylococcus (7), Prevotella (6) and Streptococcus (6) were among the most frequently identified genera. Methylobacterium (4), Propionibacterium (3), Pseudomonas (3) and Escherichia (2), among others, although frequently reported in placental samples, were often reported as contaminants in studies that used negative controls.

CONCLUSIONS

The results support the existence of a low biomass placental microbiota in healthy pregnancies. Some of the microbial taxa found in the placenta might have an oral origin. The high risk of quality bias for the majority of the included studies indicates that the results of individual papers should be interpreted with caution.

Milk microbiome in dairy cattle and the challenges of low microbial biomass and exogenous contamination

Background

The blanket usage of antimicrobials at the end of lactation (or “drying off”) in dairy cattle is under increasing scrutiny due to concerns about antimicrobial resistance. To lower antimicrobial usage in dairy farming, farmers are now encouraged to use “selective dry cow therapy” whereby only cows viewed as at high risk of mastitis are administered antimicrobial agents. It is important to gain a better understanding of how this practice affects the udder-associated microbiota and the potential knock-on effects on antimicrobial-resistant bacterial populations circulating on the farm. However, there are challenges associated with studying low biomass environments such as milk, due to known contamination effects on microbiome datasets. Here, we obtained milk samples from cattle at drying off and at calving to measure potential shifts in bacterial load and microbiota composition, with a critical assessment of contamination effects.

Results

Several samples had no detectable 16S rRNA gene copies and crucially, exogenous contamination was detected in the initial microbiome dataset. The affected samples were removed from the final microbiome analysis, which compromised the experimental design and statistical analysis. There was no significant difference in bacterial load between treatments (P > 0.05), but load was lower at calving than at drying off (P = 0.039). Escherichia coli counts by both sequence and culture data increased significantly in the presence of reduced bacterial load and a decreasing trend of microbiome richness and diversity. The milk samples revealed diverse microbiomes not reflecting a typical infection profile and were largely comprised of gut- and skin-associated taxa, with the former decreasing somewhat after prolonged sealing of the teats.

Conclusions

The drying off period had a key influence on microbiota composition and bacterial load, which appeared to be independent of antimicrobial usage. The interactions between drying off treatment protocol and milk microbiome dynamics are clearly complex, and our evaluations of these interactions were restricted by low biomass samples and contamination effects. Therefore, our analysis will inform the design of future studies to establish whether different selection protocols could be implemented to further minimise antimicrobial usage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-021-00144-x.

Technical challenges regarding the use of formalin-fixed paraffin embedded (FFPE) tissue specimens for the detection of bacterial alterations in colorectal cancer

Background

Formalin-fixed paraffin embedded (FFPE) tissues may provide an exciting resource to study microbial associations in human disease, but the use of these low biomass specimens remains challenging. We aimed to reduce unintentional bacterial interference in molecular analysis of FFPE tissues and investigated the feasibility of conducting quantitative polymerase chain reaction (qPCR) and 16S rRNA amplicon sequencing using 14 colorectal cancer, 14 normal adjacent and 13 healthy control tissues.

Results

Bacterial contaminants from the laboratory environment and the co-extraction of human DNA can affect bacterial analysis. The application of undiluted template improves bacterial DNA amplification, allowing the detection of specific bacterial markers (Escherichia coli and Faecalibacterium prausnitzii) by qPCR. Nested and non-nested PCR-based 16S rRNA amplicon sequencing approaches were employed, showing that bacterial communities of tissues and paired paraffin controls cluster separately at genus level on weighted Unifrac in both non-nested (R2 = 0.045; Pr(> F) = 0.053) and nested (R2 = 0.299; Pr(> F) = 0.001) PCR datasets. Nevertheless, considerable overlap of bacterial genera within tissues was seen with paraffin, DNA extraction negatives (non-nested PCR) or PCR negatives (nested PCR). Following mathematical decontamination, no differences in α- and β diversity were found between tumor, normal adjacent and control tissues.

Conclusions

Bacterial marker analysis by qPCR seems feasible using non-normalized template, but 16S rRNA amplicon sequencing remains challenging. Critical evaluation of laboratory procedures and incorporation of positive and negative controls for bacterial analysis of FFPE tissues are essential for quality control and to account for bacterial contaminants.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02359-z.

Milking system and premilking routines have strong effect on the microbial community in bulk tank milk

In this study, we investigated the variation in the microbial community present in bulk tank milk samples and the potential effect of different farm management factors. Bulk tank milk samples were collected repeatedly over one year from 42 farms located in northern Sweden. Total and thermoresistant bacteria counts and 16S rRNA gene-based amplicon sequencing were used to characterize microbial community composition. The microbial community was in general heterogeneous both within and between different farms and the community composition in the bulk tank milk was commonly dominated by Pseudomonas, Acinetobacter, Streptococcus, unclassified Peptostreptococcaceae, and Staphylococcus. Principal component analysis including farm factor variables and microbial taxa data revealed that the microbial community in milk was affected by type of milking system. Milk from farms using an automatic (robot) milking system (AMS) and loose housing showed different microbial community composition compared with milk from tiestall farms. A discriminant analysis model revealed that this difference was dependent on several microbial taxa. Among farms using an automatic milking system, there were further differences in the microbial community composition depending on the brand of the milking robot used. On tiestall farms, routines for teat preparation and cleaning of the milking equipment affected the microbial community composition in milk. Total bacteria count (TBC) in milk differed between the farm types, and TBC were higher on AMS than tiestall farms (log 4.05 vs. log 3.79 TBC/mL for AMS and tiestalls, respectively). Among tiestall farms, milk from farms using a chemical agent in connection to teat preparation and a more frequent use of acid to clean the milking equipment had lower TBC in milk, than milk from farms using water for teat preparation and a less frequent use of acid to clean the milking equipment (log 3.68 vs. 4.02 TBC/mL). There were no significant differences in the number of thermoresistant bacteria between farm types. The evaluated factors explained only a small proportion of total variation in the microbiota data, however, despite this, the study highlights the effect of routines associated with teat preparation and cleaning of the milking equipment on raw milk microbiota, irrespective of type of milking system used.

Borrelia afzelii Infection in the Rodent Host Has Dramatic Effects on the Bacterial Microbiome of Ixodes ricinus Ticks

The microbiome of blood-sucking arthropods can shape their competence to acquire and maintain infections with vector-borne pathogens. We used a controlled study to investigate the interactions between Borrelia afzelii, which causes Lyme borreliosis in Europe, and the bacterial microbiome of Ixodes ricinus, its primary tick vector. We applied a surface sterilization treatment to I. ricinus eggs to produce dysbiosed tick larvae that had a low bacterial abundance and a changed bacterial microbiome compared to those of the control larvae. Dysbiosed and control larvae fed on B. afzelii-infected mice and uninfected control mice, and the engorged larvae were left to molt into nymphs. The nymphs were tested for B. afzelii infection, and their bacterial microbiome underwent 16S rRNA amplicon sequencing. Surprisingly, larval dysbiosis had no effect on the vector competence of I. ricinus for B. afzelii, as the nymphal infection prevalence and the nymphal spirochete load were the same between the dysbiosed group and the control group. The strong effect of egg surface sterilization on the tick bacterial microbiome largely disappeared once the larvae molted into nymphs. The most important determinant of the bacterial microbiome of I. ricinus nymphs was the B. afzelii infection status of the mouse on which the nymphs had fed as larvae. Nymphs that had taken their larval blood meal from an infected mouse had a less abundant but more diverse bacterial microbiome than the control nymphs. Our study demonstrates that vector-borne infections in the vertebrate host shape the microbiome of the arthropod vector. IMPORTANCE Many blood-sucking arthropods transmit pathogens that cause infectious disease. For example, Ixodes ricinus ticks transmit the bacterium Borrelia afzelii, which causes Lyme disease in humans. Ticks also have a microbiome, which can influence their ability to acquire and transmit tick-borne pathogens such as B. afzelii. We sterilized I. ricinus eggs with bleach, and the tick larvae that hatched from these eggs had a dramatically reduced and changed bacterial microbiome compared to that of control larvae. These larvae fed on B. afzelii-infected mice, and the resultant nymphs were tested for B. afzelii and for their bacterial microbiome. We found that our manipulation of the bacterial microbiome had no effect on the ability of the tick larvae to acquire and maintain populations of B. afzelii. In contrast, we found that B. afzelii infection had dramatic effects on the bacterial microbiome of I. ricinus nymphs. Our study demonstrates that infections in the vertebrate host can shape the tick microbiome.

Identification and Removal of Potential Contaminants in 16S rRNA Gene Sequence Data Sets from Low-Microbial-Biomass Samples: an Example from Mosquito Tissues

The bacterial microbiota of the mosquito influences numerous physiological processes of the host. As low-microbial-biomass ecosystems, mosquito tissues are prone to contamination from the laboratory environment and from reagents commonly used to isolate DNA from tissue samples. In this report, we analyzed nine 16S rRNA data sets, including new data obtained by us, to gain insight into the impact of potential contaminating sequences on the composition, diversity, and structure of the mosquito tissue microbial community. Using a clustering-free approach based on the relative abundance of amplicon sequence variants (ASVs) in tissue samples and negative controls, we identified candidate contaminating sequences that sometimes differed from, but were consistent with, results found using established methodologies. Some putative contaminating sequences belong to bacterial taxa previously identified as contaminants that are commonly found in metagenomic studies but that have also been identified as part of the mosquito core microbiota, with putative physiological relevance for the host. Using different relative abundance cutoffs, we show that contaminating sequences have a significant impact on tissue microbiota diversity and structure analysis. IMPORTANCE The study of tissue-associated microbiota from mosquitoes (primarily from the gut) has grown significantly in the last several years. Mosquito tissue samples represent a challenge for researchers given their low microbial biomass and similar taxonomic composition commonly found in the laboratory environment and in molecular reagents. Using new and published data sets that identified mosquito tissue microbiota from gut and reproductive tract tissues (and their respective negative controls), we developed a simple method to identify contamination microbiota. This approach uses an initial taxonomic identification without operational taxonomic unit (OTU) clustering and evaluates the relative abundance of control sample sequences, allowing the identification and removal of purported contaminating sequences in data sets obtained from low-microbial-biomass samples. While it was exemplified with the analysis of tissue microbiota from mosquitos, it can be extended to other data sets dealing with similar technical artifacts.

Full-Length SSU rRNA Gene Sequencing Allows Species-Level Detection of Bacteria, Archaea, and Yeasts Present in Milk

Full-length SSU rRNA gene sequencing allows species-level identification of the microorganisms present in milk samples. Here, we used bulk-tank raw milk samples of two German dairies and detected, using this method, a great diversity of bacteria, archaea, and yeasts within the samples. Moreover, the species-level classification was improved in comparison to short amplicon sequencing. Therefore, we anticipate that this approach might be useful for the detection of possible mastitis-causing species, as well as for the control of spoilage-associated microorganisms. In a proof of concept, we showed that we were able to identify several putative mastitis-causing or mastitis-associated species such as Streptococcusuberis, Streptococcusagalactiae, Streptococcusdysgalactiae, Escherichiacoli and Staphylococcusaureus, as well as several Candida species. Overall, the presented full-length approach for the sequencing of SSU rRNA is easy to conduct, able to be standardized, and allows the screening of microorganisms in labs with Illumina sequencing machines.

Amplicon-sequencing of raw milk microbiota: impact of DNA extraction and library-PCR

Abstract

The highly complex raw milk matrix challenges the sample preparation for amplicon-sequencing due to low bacterial counts and high amounts of eukaryotic DNA originating from the cow. In this study, we optimized the extraction of bacterial DNA from raw milk for microbiome analysis and evaluated the impact of cycle numbers in the library-PCR. The selective lysis of eukaryotic cells by proteinase K and digestion of released DNA before bacterial lysis resulted in a high reduction of mostly eukaryotic DNA and increased the proportion of bacterial DNA. Comparative microbiome analysis showed that a combined enzymatic and mechanical lysis procedure using the DNeasy^® PowerFood^® Microbial Kit with a modified protocol was best suitable to achieve high DNA quantities after library-PCR and broad coverage of detected bacterial biodiversity. Increasing cycle numbers during library-PCR systematically altered results for species and beta-diversity with a tendency to overrepresentation or underrepresentation of particular taxa. To limit PCR bias, high cycle numbers should thus be avoided. An optimized DNA extraction yielding sufficient bacterial DNA and enabling higher PCR efficiency is fundamental for successful library preparation. We suggest that a protocol using ethylenediaminetetraacetic acid (EDTA) to resolve casein micelles, selective lysis of somatic cells, extraction of bacterial DNA with a combination of mechanical and enzymatic lysis, and restriction of PCR cycles for analysis of raw milk microbiomes is optimal even for samples with low bacterial numbers.

Key points

• Sample preparation for high-throughput 16S rRNA gene sequencing of raw milk microbiota.

• Reduction of eukaryotic DNA by enzymatic digestion.

• Shift of detected microbiome caused by high cycle numbers in library-PCR.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-021-11353-4.

Salmon gut microbiota correlates with disease infection status: potential for monitoring health in farmed animals

BACKGROUND

Infectious diseases cause significant production losses in aquaculture every year. Since the gut microbiota plays an essential role in regulating the host immune system, health and physiology, altered gut microbiota compositions are often associated with a diseased status. However, few studies have examined the association between disease severity and degree of gut dysbiosis, especially when the gut is not the site of the primary infection. Moreover, there is a lack of knowledge on whether bath treatment with formalin, a disinfectant commonly used in aquaculture to treat external infections, might affect the gut microbiome as a consequence of formalin ingestion. Here we investigate, through 16S rRNA gene metabarcoding, changes in the distal gut microbiota composition of a captive-reared cohort of 80 Atlantic salmon (Salmo salar L.), in consequence of an external bacterial skin infection due to a natural outbreak and subsequent formalin treatment.

RESULTS

We identified Tenacibaculum dicentrarchi as the causative disease pathogen and we show that the distal gut of diseased salmon presented a different composition from that of healthy individuals. A new, yet undescribed, Mycoplasma genus characterized the gut of healthy salmon, while in the sick fish we observed an increase in terms of relative abundance of Aliivibrio sp., a strain regarded as opportunistic. We also noticed a positive correlation between fish weight and Mycoplasma sp. relative abundance, potentially indicating a beneficial effect for its host. Moreover, we observed that the gut microbiota of fish treated with formalin was more similar to those of sick fish than healthy ones.

CONCLUSIONS

We conclude that external Tenacibaculum infections have the potential of indirectly affecting the host gut microbiota. As such, treatment optimization procedures should account for that. Formalin treatment is not an optimal solution from a holistic perspective, since we observe an altered gut microbiota in the treated fish. We suggest its coupling with a probiotic treatment aimed at re-establishing a healthy community. Lastly, we have observed a positive correlation of Mycoplasma sp. with salmon health and weight, therefore we encourage further investigations towards its potential utilization as a biomarker for monitoring health in salmon and potentially other farmed fish species.

Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human milk microbiota

BACKGROUND

Quality control including assessment of batch variabilities and confirmation of repeatability and reproducibility are integral component of high throughput omics studies including microbiome research. Batch effects can mask true biological results and/or result in irreproducible conclusions and interpretations. Low biomass samples in microbiome research are prone to reagent contamination; yet, quality control procedures for low biomass samples in large-scale microbiome studies are not well established.

RESULTS

In this study, we have proposed a framework for an in-depth step-by-step approach to address this gap. The framework consists of three independent stages: (1) verification of sequencing accuracy by assessing technical repeatability and reproducibility of the results using mock communities and biological controls; (2) contaminant removal and batch variability correction by applying a two-tier strategy using statistical algorithms (e.g. decontam) followed by comparison of the data structure between batches; and (3) corroborating the repeatability and reproducibility of microbiome composition and downstream statistical analysis. Using this approach on the milk microbiota data from the CHILD Cohort generated in two batches (extracted and sequenced in 2016 and 2019), we were able to identify potential reagent contaminants that were missed with standard algorithms and substantially reduce contaminant-induced batch variability. Additionally, we confirmed the repeatability and reproducibility of our results in each batch before merging them for downstream analysis.

CONCLUSION

This study provides important insight to advance quality control efforts in low biomass microbiome research. Within-study quality control that takes advantage of the data structure (i.e. differential prevalence of contaminants between batches) would enhance the overall reliability and reproducibility of research in this field. Video abstract.

Effect of storage, temperature, and extraction kit on the phylogenetic composition detected in the human milk microbiota

Human milk is considered the optimum feeding regime for newborns and is a source of bacteria for the developing infant gastrointestinal tract. However, as with all low biomass samples, standardization across variabilities such as sample collection, storage, and extraction methods is needed to eliminate discrepancies in microbial composition across studies. The aim of this study was to investigate how different storage methods, temperatures, preservatives, and extraction kits influence the human milk microbiome, compared to fresh samples. Breast milk samples were processed via six different methods: fresh (Method 1), frozen at -80°C (Method 2), treated with RNAlater and stored at 4°C or -80°C (Methods 3 and 4), and treated with Milk Preservation Solution at room temperature (Methods 5 and 6). Methods 1-5 were extracted using PowerFoodTM Microbial DNA Isolation kit (Mobio), and Method 6 was extracted using Milk DNA Preservation and Isolation kit (Norgen BioTek). At genus level, the most abundant genera were shared across Methods 1-5. Samples frozen at -80°C had fewest significant changes while samples treated and extracted using Milk Preservation and Isolation kit had the most significant changes when compared to fresh samples. Diversity analysis indicated that variation in microbiota composition was related to the method and extraction kit used. This study highlighted that, when extraction from fresh milk samples is not an option, freezing at -80°C is the next best option to preserve the integrity of the milk microbiome. Furthermore, our results demonstrate that choice of extraction kit had a profound impact on the microbiota populations detected in milk.

A core microbiota dominates a rich microbial diversity in the bovine udder and may indicate presence of dysbiosis

The importance of the microbiome for bovine udder health is not well explored and most of the knowledge originates from research on mastitis. Better understanding of the microbial diversity inside the healthy udder of lactating cows might help to reduce mastitis, use of antibiotics and improve animal welfare. In this study, we investigated the microbial diversity of over 400 quarter milk samples from 60 cows sampled from two farms and on two different occasions during the same lactation period. Microbiota analysis was performed using amplicon sequencing of the 16S rRNA gene and over 1000 isolates were identified using MALDI-TOF MS. We detected a high abundance of two bacterial families, Corynebacteriaceae and Staphylococcaceae, which accounted for almost 50% of the udder microbiota of healthy cows and were detected in all the cow udders and in more than 98% of quarter milk samples. A strong negative correlation between these bacterial families was detected indicating a possible competition. The overall composition of the udder microbiota was highly diverse and significantly different between cows and between quarter milk samples from the same cow. Furthermore, we introduced a novel definition of a dysbiotic quarter at individual cow level, by analyzing the milk microbiota, and a high frequency of dysbiotic quarter samples were detected distributed among the farms and the samples. These results emphasize the importance of deepening the studies of the bovine udder microbiome to elucidate its role in udder health.

Biological observations in microbiota analysis are robust to the choice of 16S rRNA gene sequencing processing algorithm: case study on human milk microbiota

BACKGROUND

In recent years, the microbiome field has undergone a shift from clustering-based methods of operational taxonomic unit (OTU) designation based on sequence similarity to denoising algorithms that identify exact amplicon sequence variants (ASVs), and methods to identify contaminating bacterial DNA sequences from low biomass samples have been developed. Although these methods improve accuracy when analyzing mock communities, their impact on real samples and downstream analysis of biological associations is less clear.

RESULTS

Here, we re-processed our recently published milk microbiota data using Qiime1 to identify OTUs, and Qiime2 to identify ASVs, with or without contaminant removal using decontam. Qiime2 resolved the mock community more accurately, primarily because Qiime1 failed to detect Lactobacillus. Qiime2 also considerably reduced the average number of ASVs detected in human milk samples (364 ± 145 OTUs vs. 170 ± 73 ASVs, p < 0.001). Compared to the richness, the estimated diversity measures had a similar range using both methods albeit statistically different (inverse Simpson index: 14.3 ± 8.5 vs. 15.6 ± 8.7, p = 0.031) and there was strong consistency and agreement for the relative abundances of the most abundant bacterial taxa, including Staphylococcaceae and Streptococcaceae. One notable exception was Oxalobacteriaceae, which was overrepresented using Qiime1 regardless of contaminant removal. Downstream statistical analyses were not impacted by the choice of algorithm in terms of the direction, strength, and significance of associations of host factors with bacterial diversity and overall community composition.

CONCLUSION

Overall, the biological observations and conclusions were robust to the choice of the sequencing processing methods and contaminant removal.

Effect of Sample Collection (Manual Expression vs. Pumping) and Skimming on the Microbial Profile of Human Milk Using Culture Techniques and Metataxonomic Analysis

Human milk microbiota is a unique bacterial community playing a relevant role in infant health, but its composition depends on different factors (woman health, lactation stage, and geographical lactation). However, information is lacking regarding some other factors that may affect the bacterial community of human milk. In this study we aimed to study the impact of the sample collection method and the skimming procedure using culture-dependent and culture-independent techniques to study the human milk microbial profile. One set of milk samples was provided by women (n = 10) in two consecutive days; half of the samples were collected the first day by manual expression and the other half on the second day by pumping. The rest of the participants (n = 17) provided milk samples that were fractionated by centrifugation; the bacterial profiles of whole milk and skimmed milk were compared by culture techniques in 10 milk samples, while those of whole milk, fat and skimmed milk were subjected to metataxonomic analysis in seven samples. Globally, the results obtained revealed high interindividual variability but that neither the use of single-use sterile devices to collect the sample nor the skimming procedure have a significant impact of the microbial profile of human samples.

Influence of PCR cycle number on 16S rRNA gene amplicon sequencing of low biomass samples

The objective of this study was to evaluate the effects of increased PCR cycle number on sequencing results from samples with low microbial biomass, including bovine milk, and murine pelage and blood. We hypothesized that subjecting DNA from such samples to higher PCR cycle numbers would increase 16S rRNA sequencing coverage. DNA was extracted from matched samples of each type and multiple PCR cycle numbers were evaluated to generate a total of 96 libraries from 24 milk samples, 46 libraries from 23 pelage samples, and 170 libraries from 85 blood samples. 16S rRNA sequencing was performed on the Illumina MiSeq platform, and the coverage per sample, detected richness, and beta-diversity were evaluated. Across all sample types, higher PCR cycle numbers were associated with increased coverage. Surprisingly however, while higher PCR cycle numbers resulted in greater number of useable datapoints, no differences were detected in metrics of richness or beta-diversity. While reagent controls amplified for 40 cycles yielded similarly increased coverage, control and experimental samples were clearly differentiated based on beta-diversity. The results from this study support the use of higher PCR cycle numbers to evaluate samples with low microbial biomass.

Microbiota of bovine milk, teat skin, and teat canal: Similarity and variation due to sampling technique and milk fraction

The aim of this study was to evaluate the effect of sampling technique and milk fraction on bovine milk microbiota data and to compare the microbiota in milk to microbiota on the teat end and in the teat canal. Representative milk samples are highly important for assessment of bacteriological findings and microbiota in milk. Samples were obtained from 5 healthy lactating dairy cows at udder quarter level during 1 milking. Swab samples from the teat end and teat canal, and milk samples collected using different techniques and in different milk fractions were included. Milk was collected by hand stripping and through a teat canal cannula before and after machine milking, through a trans-teat wall needle aspirate after milking, and from udder quarter composite milk. The microbiota of the samples was analyzed with sequencing of the V1-V3 region of the 16S rRNA gene. In addition, somatic cell counts and bacterial cultivability were analyzed in the milk samples. Microbiota data were analyzed using multivariate methods, and differences between samples were tested using analysis of similarity (ANOSIM). Differences between samples were further explored via individual studies of the 10 most abundant genera. The microbiota on the teat end, in the teat canal, and in udder quarter composite milk, collected using a milking machine, differed in composition from the microbiota in milk collected directly from the udder quarter. No differences in milk microbiota composition were detected between hand-stripped milk samples, milk samples taken through a teat canal cannula, or milk samples taken as a trans-teat wall needle aspirate before or after milking. We conclude that for aseptic milk samples collected directly from the lactating udder quarter, sampling technique or milk fraction has minor effect on the microbiota composition.

Optimizing 16S rRNA gene profile analysis from low biomass nasopharyngeal and induced sputum specimens

BACKGROUND

Careful consideration of experimental artefacts is required in order to successfully apply high-throughput 16S ribosomal ribonucleic acid (rRNA) gene sequencing technology. Here we introduce experimental design, quality control and "denoising" approaches for sequencing low biomass specimens.

RESULTS

We found that bacterial biomass is a key driver of 16S rRNA gene sequencing profiles generated from bacterial mock communities and that the use of different deoxyribonucleic acid (DNA) extraction methods [DSP Virus/Pathogen Mini Kit® (Kit-QS) and ZymoBIOMICS DNA Miniprep Kit (Kit-ZB)] and storage buffers [PrimeStore® Molecular Transport medium (Primestore) and Skim-milk, Tryptone, Glucose and Glycerol (STGG)] further influence these profiles. Kit-QS better represented hard-to-lyse bacteria from bacterial mock communities compared to Kit-ZB. Primestore storage buffer yielded lower levels of background operational taxonomic units (OTUs) from low biomass bacterial mock community controls compared to STGG. In addition to bacterial mock community controls, we used technical repeats (nasopharyngeal and induced sputum processed in duplicate, triplicate or quadruplicate) to further evaluate the effect of specimen biomass and participant age at specimen collection on resultant sequencing profiles. We observed a positive correlation (r = 0.16) between specimen biomass and participant age at specimen collection: low biomass technical repeats (represented by < 500 16S rRNA gene copies/μl) were primarily collected at < 14 days of age. We found that low biomass technical repeats also produced higher alpha diversities (r = - 0.28); 16S rRNA gene profiles similar to no template controls (Primestore); and reduced sequencing reproducibility. Finally, we show that the use of statistical tools for in silico contaminant identification, as implemented through the decontam package in R, provides better representations of indigenous bacteria following decontamination.

CONCLUSIONS

We provide insight into experimental design, quality control steps and "denoising" approaches for 16S rRNA gene high-throughput sequencing of low biomass specimens. We highlight the need for careful assessment of DNA extraction methods and storage buffers; sequence quality and reproducibility; and in silico identification of contaminant profiles in order to avoid spurious results.

Comparison of DNA Extraction Methods for Optimal Recovery of Metagenomic DNA from Human and Environmental Samples

Metagenomics is the study of gene pool of an entire community in a particular niche. This provides valuable information about the functionality of host-microbe interaction in a biological ecosystem. Efficient metagenomic DNA extraction is a critical pre-requisite for a successful sequencing run in a metagenomic study. Although isolation of human stool metagenomic DNA is fairly standardized, the same protocol does not work as efficiently in fecal DNA from other organisms. In this study, we report a comparison of manual and commercial DNA extraction methods for diverse samples such as human stool, fish gut and soil. Fishes are known to have variable microbial diversity based on their food habits, so the study included two different varieties of fishes. A modified protocol for effective isolation of metagenomic DNA from human milk samples is also reported, highlighting critical precautions. Recent studies have emphasized the importance of studying functionality of human milk metagenome to understand its influence on infants' health. While manual method works well with most samples and therefore can be a method of choice for testing new samples, broad-range commercial kit offers advantage of high purity and quality. DNA extraction of different samples would go a long way in unraveling the unexplored association between microbes and host in a biological system.