Network analysis suggests a potentially 'evil' alliance of opportunistic pathogens inhibited by a cooperative network in human milk bacterial communities

Towards a unified medical microbiome ecology of the OMU for metagenomes and the OTU for microbes

BACKGROUND

Metagenomic sequencing technologies offered unprecedented opportunities and also challenges to microbiology and microbial ecology particularly. The technology has revolutionized the studies of microbes and enabled the high-profile human microbiome and earth microbiome projects. The terminology-change from microbes to microbiomes signals that our capability to count and classify microbes (microbiomes) has achieved the same or similar level as we can for the biomes (macrobiomes) of plants and animals (macrobes). While the traditional investigations of macrobiomes have usually been conducted through naturalists' (Linnaeus & Darwin) naked eyes, and aerial and satellite images (remote-sensing), the large-scale investigations of microbiomes have been made possible by DNA-sequencing-based metagenomic technologies. Two major types of metagenomic sequencing technologies-amplicon sequencing and whole-genome (shotgun sequencing)-respectively generate two contrastingly different categories of metagenomic reads (data)-OTU (operational taxonomic unit) tables representing microorganisms and OMU (operational metagenomic unit), a new term coined in this article to represent various cluster units of metagenomic genes.

RESULTS

The ecological science of microbiomes based on the OTU representing microbes has been unified with the classic ecology of macrobes (macrobiomes), but the unification based on OMU representing metagenomes has been rather limited. In a previous series of studies, we have demonstrated the applications of several classic ecological theories (diversity, composition, heterogeneity, and biogeography) to the studies of metagenomes. Here I push the envelope for the unification of OTU and OMU again by demonstrating the applications of metacommunity assembly and ecological networks to the metagenomes of human gut microbiomes. Specifically, the neutral theory of biodiversity (Sloan's near neutral model), Ning et al.stochasticity framework, core-periphery network, high-salience skeleton network, special trio-motif, and positive-to-negative ratio are applied to analyze the OMU tables from whole-genome sequencing technologies, and demonstrated with seven human gut metagenome datasets from the human microbiome project.

CONCLUSIONS

All of the ecological theories demonstrated previously and in this article, including diversity, composition, heterogeneity, stochasticity, and complex network analyses, are equally applicable to OMU metagenomic analyses, just as to OTU analyses. Consequently, I strongly advocate the unification of OTU/OMU (microbiomes) with classic ecology of plants and animals (macrobiomes) in the context of medical ecology.

Breastmilk microbiome changes associated with lactational mastitis and treatment with dandelion extract

Introduction

Dandelion (Pugongying) is one of the most frequently used Chinese herbs for treating lactational mastitis (LM). Pugongying granules, a patented medication primarily comprised of dandelion extract, have been approved by CFDA for LM treatment in China. The aims of this study were to investigate the etiology of LM and the mechanism by which Pugongying granules decrease LM symptoms, with a particular focus on the microbial communities found in breastmilk.

Methods

Participants were recruited from a previously performed randomized controlled trial (Identifier: NCT03756324, ClinicalTrials.gov). Between 2019 and 2020, women diagnosed with unilateral LM at the Beijing University of Chinese Medicine Third Affiliated Hospital were enrolled. In total, 42 paired breastmilk samples from the healthy and affected breasts of the participants were collected. Additionally, 37 paired pre- and post-treatment breastmilk samples from the affected breast were collected from women who received a 3-day course of either Pugongying granules (20 women) or cefdinir (17 women). Clinical outcomes [e.g., body temperature, visual analogue scale (VAS) score for breast pain, the percentage of neutrophils (NE%)] were analyzed pre- and post-treatment, and the breastmilk samples were subjected to 16S rRNA gene sequencing to analyze the alpha and beta diversities and identify significant bacteria. Finally, the relationship between microorganisms and clinical outcomes was analyzed.

Results

There was no significant difference in fever and pain between the Pugongying group and cefdinir group. The most prevalent bacterial genera in breastmilk were Streptococcus and Staphylococcus. Compared to healthy breastmilk, microbial diversity was reduced in affected breastmilk, and there was a higher relative abundance of Streptococcus. After Pugongying treatment, there was an increase in microbial diversity with significantly higher abundance of Corynebacterium. A negative correlation was found between Corynebacterium, VAS score, and NE%. Treatment with cefdinir did not affect microbial diversity. Taken together, our results show a correlation between LM and reduced microbial diversity, as well as an increased abundance of Streptococcus in affected breastmilk.

Conclusion

Pugongying granules enhanced microbial diversity in breastmilk samples. Given the substantial variation in individual microbiomes, identifying specific species of Streptococcus and Corynebacterium associated with LM may provide additional insight into LM pathogenesis and treatment.

A new hypothesis on BV etiology: dichotomous and crisscrossing categorization of complex versus simple on healthy versus BV vaginal microbiomes

IMPORTANCE

BV may influence as many as one-third of women, but its etiology remains unclear. A traditional view is that dominance by Lactobacillus is the hallmark of a healthy vaginal microbiome and lack of dominance may make women BV-prone. Recent studies show that the human VMs can be classified into five major types, four of which possess type-specific dominant species of Lactobacillus. The remaining one (type IV) is not dominated by Lactobacillus and contains a handful of strictly anaerobic bacteria. Nevertheless, exceptions to the first hypothesis have been noticed from the very beginning, and there is not a definite relationship, suggested yet, between the five VM types and BV status. Here, we propose and test a novel hypothesis that assumes the existence of four VM types from dichotomous crisscrossing of "complex versus simple (high diversity or low dominance versus low diversity or high dominance)" on "healthy versus BV." Consequently, there are simple BV versus complex BV.

Dietary supplementation of solubles from shredded, steam-exploded pine particles modulates cecal microbiome composition in broiler chickens

This study evaluated the effects of supplementing solubles from shredded, steam-exploded pine particles (SSPP) on growth performances, plasma biochemicals, and microbial composition in broilers. The birds were reared for 28 days and fed basal diets with or without the inclusion of SSPP from 8 days old. There were a total of three dietary treatments supplemented with 0% (0% SSPP), 0.1% (0.1% SSPP) and 0.4% (0.4% SSPP) SSPP in basal diets. Supplementation of SSPP did not significantly affect growth or plasma biochemicals, but there was a clear indication of diet-induced microbial shifts. Beta-diversity analysis revealed SSPP supplementation-related clustering (ANOSIM: r = 0.31, p < 0.01), with an overall lower (PERMDISP: p < 0.05) individual dispersion in comparison to the control group. In addition, the proportions of the Bacteroides were increased, and the relative abundances of the families Vallitaleaceae, Defluviitaleaceae, Clostridiaceae, and the genera Butyricicoccus and Anaerofilum (p < 0.05) were significantly higher in the 0.4% SSPP group than in the control group. Furthermore, the linear discriminant analysis effect size (LEfSe) also showed that beneficial bacteria such as Ruminococcus albus and Butyricicoccus pullicaecorum were identified as microbial biomarkers of dietary SSPP inclusion (p < 0.05; | LDA effect size | > 2.0). Finally, network analysis showed that strong positive correlations were established among microbial species belonging to the class Clostridia, whereas Erysipelotrichia and Bacteroidia were mostly negatively correlated with Clostridia. Taken together, the results suggested that SSPP supplementation modulates the cecal microbial composition of broilers toward a "healthier" profile.

Improvement and Validation of a Genomic DNA Extraction Method for Human Breastmilk

The human milk microbiota (HMM) of healthy women can vary substantially, as demonstrated by recent advances in DNA sequencing technology. However, the method used to extract genomic DNA (gDNA) from these samples may impact the observed variations and potentially bias the microbiological reconstruction. Therefore, it is important to use a DNA extraction method that is able to effectively isolate gDNA from a diverse range of microorganisms. In this study, we improved and compared a DNA extraction method for gDNA isolation from human milk (HM) samples to commercial and standard protocols. We evaluated the extracted gDNA using spectrophotometric measurements, gel electrophoresis, and PCR amplifications to assess its quantity, quality, and amplifiability. Additionally, we tested the improved method’s ability to isolate amplifiable gDNA from fungi, Gram-positive and Gram-negative bacteria to validate its potential for reconstructing microbiological profiles. The improved DNA extraction method resulted in a higher quality and quantity of the extracted gDNA compared to the commercial and standard protocols and allowed for polymerase chain reaction (PCR) amplification of the V3–V4 regions of the 16S ribosomal gene in all the samples and the ITS-1 region of the fungal 18S ribosomal gene in 95% of the samples. These results suggest that the improved DNA extraction method demonstrates better performance for gDNA extraction from complex samples such as HM.

Power law analysis of the human milk microbiome

The human breast milk microbiome (HMM) has far reached health implications for both mothers and infants, and understanding the structure and dynamics of milk microbial communities is therefore of critical biomedical importance. Community heterogeneity, which has certain commonalities with familiar diversity but also with certain fundamental differences, is an important aspect of community structure and dynamics. Taylor's (1961) power law (TPL) (Nature, 1961) was discovered to govern the mean-variance power function relationship of population abundances and can be used to characterize population spatial aggregation (heterogeneity) and/or temporal stability. TPL was further extended to the community level to measure community spatial heterogeneity and/or temporal stability (Ma 2015, Molecular Ecology). Here, we applied TPL extensions (TPLE) to analyze the heterogeneity of the human milk microbiome by reanalyzing 12 datasets (2115 samples) of the healthy human milk microbiome. Our analysis revealed that the TPLE heterogeneity parameter (b) is rather stable across the 12 datasets, and there were approximately no statistically significant differences among ¾ of the datasets, which is consistent with the hypothesis that the heterogeneity scaling (i.e., change across individuals) of the human microbiome, including HMM, is rather stable or even constant. For this, we built a TPLE model for the pooled 12 datasets (b = 1.906), which can therefore represent the scaling rate of community-level spatial heterogeneity of HMM across individuals. Similarly, we also analyzed mixed-species ("averaged virtual species") level heterogeneity of HMM, and it was found that the mixed-species level heterogeneity was smaller than the heterogeneity at the previously mentioned community level (1.620 vs. 1.906).

Ecology of Human Medical Enterprises: From Disease Ecology of Zoonoses, Cancer Ecology Through to Medical Ecology of Human Microbiomes

In nature, the interaction between pathogens and their hosts is only one of a handful of interaction relationships between species, including parasitism, predation, competition, symbiosis, commensalism, and among others. From a non-anthropocentric view, parasitism has relatively fewer essential differences from the other relationships; but from an anthropocentric view, parasitism and predation against humans and their well-beings and belongings are frequently related to heinous diseases. Specifically, treating (managing) diseases of humans, crops and forests, pets, livestock, and wildlife constitute the so-termed medical enterprises (sciences and technologies) humans endeavor in biomedicine and clinical medicine, veterinary, plant protection, and wildlife conservation. In recent years, the significance of ecological science to medicines has received rising attentions, and the emergence and pandemic of COVID-19 appear accelerating the trend. The facts that diseases are simply one of the fundamental ecological relationships in nature, and the study of the relationships between species and their environment is a core mission of ecology highlight the critical importance of ecological science. Nevertheless, current studies on the ecology of medical enterprises are highly fragmented. Here, we (i) conceptually overview the fields of disease ecology of wildlife, cancer ecology and evolution, medical ecology of human microbiome-associated diseases and infectious diseases, and integrated pest management of crops and forests, across major medical enterprises. (ii) Explore the necessity and feasibility for a unified medical ecology that spans biomedicine, clinical medicine, veterinary, crop (forest and wildlife) protection, and biodiversity conservation. (iii) Suggest that a unified medical ecology of human diseases is both necessary and feasible, but laissez-faire terminologies in other human medical enterprises may be preferred. (iv) Suggest that the evo-eco paradigm for cancer research can play a similar role of evo-devo in evolutionary developmental biology. (v) Summarized 40 key ecological principles/theories in current disease-, cancer-, and medical-ecology literatures. (vi) Identified key cross-disciplinary discovery fields for medical/disease ecology in coming decade including bioinformatics and computational ecology, single cell ecology, theoretical ecology, complexity science, and the integrated studies of ecology and evolution. Finally, deep understanding of medical ecology is of obvious importance for the safety of human beings and perhaps for all living things on the planet.

Metataxonomic Analysis of Milk Samples From SARS-CoV-2-Positive and SARS-CoV-2-Negative Women

Objective

To assess the impact of SARS-CoV-2 viral infection on the metataxonomic profile and its evolution during the first month of lactation.

Methods

Milk samples from 37 women with full-term pregnancies and mild SARS-CoV-2 infection and from 63 controls, collected in the first and fifth postpartum weeks, have been analyzed. SARS-CoV-2 RNA was assessed by reverse transcription polymerase chain reaction (RT-PCR) both in cases and controls. After DNA extraction, the V3-V4 hypervariable region of the gene 16S rRNA was amplified and sequenced using the MiSeq system of Illumina. Data were submitted for statistical and bioinformatics analyses after quality control.

Results

All the 1st week and 5th week postpartum milk samples were negative for SARS-CoV-2 RNA. Alpha diversity showed no differences between milk samples from the study and control group, and this condition was maintained along the observation time. Analysis of the beta-diversity also indicated that the study and control groups did not show distinct bacterial profiles. Staphyloccus and Streptococcus were the most abundant genera and the only ones that were detected in all the milk samples provided. Disease state (symptomatic or asymptomatic infection) did not affect the metataxonomic profile in breast milk.

Conclusion

These results support that in the non-severe SARS-CoV-2 pregnant woman infection the structure of the bacterial population is preserved and does not negatively impact on the human milk microbiota.

Breast and gut microbiome in health and cancer

The microbiota plays essential roles in health and disease, in both the intestine and the extra-intestine. Dysbiosis of the gut microbiota causes dysfunction in the intestine, which leads to inflammatory, immune, and infectious diseases. Dysbiosis is also associated with diseases beyond the intestine via microbial translocation or metabolisms. The in situ breast microbiome, which may be sourced from the gut through lactation and sexual contact, could be altered and cause breast diseases. In this review, we summarize the recent progress in understanding the interactions among the gut microbiome, breast microbiome, and breast diseases. We discuss the intestinal microbiota, microbial metabolites, and roles of microbiota in immune system. We emphasize the novel roles and mechanisms of the microbiome (both in situ and gastrointestinal sourced) and bacterial products in the development and progression of breast cancer. The intestinal microbial translocation suggests that the gut microbiome is translocated to the skin and subsequently to the breast tissue. The gut bacterial translocation is also due to the increased intestinal permeability. The breast and intestinal microbiota are important factors in maintaining healthy breasts. Micronutrition queuine (Q) is derived from a de novo synthesized metabolite in bacteria. All human cells use queuine and incorporate it into the wobble anticodon position of specific transfer RNAs. We have demonstrated that Q modification regulates genes critical in tight junctions and migration in human breast cancer cells and a breast tumor model. We further discuss the challenges and future perspectives that can move the field forward for prevention, diagnosis, and treatment of breast diseases.

Structure and Dynamics of the Breast Tissue Microbiomes Under Tumor Influences: An Approach With Neutral, Near-Neutral, and Niche-Neutral Hybrid Models

The structure and dynamics of breast tissue bacteria can have far-reaching influences on women’s health, particularly on breast tumor development. However, there is little understanding on the ecological processes that shape the structure and dynamics of breast tissue bacteria. Here, we fill the gap by applying three metacommunity models for investigating the community assembly and diversity maintenance, including Sloan near neutral model, Harris et al. multisite neutral and Tang & Zhou niche-neutral hybrid models to reanalyze the 16S-rRNA sequencing datasets of 23 healthy, 12 benign tumor, and 33 malignant tumor tissue samples. First, we found that, at the community/metacommunity levels, the mechanisms of bacteria assembly and diversity maintenance of breast tissue bacteria were moderately influenced by stochastic drifts of bacteria demography (division, death, and dispersal of bacterial cells). At species level, on average, approximately 10 and 5% species were above (positively selected) and below (negatively selected) neutral, respectively. Furthermore, malignant tumor may raise the positively selected species up to 17%. Second, malignant tumor appears to inhibit microbial dispersal as evidenced by lowered migration rates, compared with the migration in normal and benign tumor tissues. These theoretic findings can be inspirational for further investigating the relationships between tissue bacteria and breast tumor progression/development.

Comparative Analysis of Milk Microbiomes and Their Association with Bovine Mastitis in Two Farms in Central Russia

Bovine mastitis is a widespread infectious disease. In addition to the economic damages associated with reduced milk yield due to mastitis, the problem of food contamination by microorganism metabolites, in particular toxins, is also a concern. Horizontal transfer of microorganisms from animal populations to humans can also be complicated by antibiotic resistance. Therefore, bovine mastitis is relevant to the study of microbiology and veterinary medicine. In this study, we investigated the microbiome of milk samples from healthy cows and cows with different forms of mastitis from individual quarters of the udder of cows during first and second lactation. Total DNA was extracted from milk samples. The V3-V4 regions of the bacterial 16S rRNA genes from each sample were amplified to generate a library via high-throughput sequencing. We revealed significant dominance of several operational taxonomic units (OTUs) corresponding mostly to groups of Staphylococcus aureus, Aerococcus spp., and Streptococcus spp. In addition, we unexpectedly identified Streptococcus thermophilus in samples with high SCC quantities. We found some infectious agents that characterized summer mastitis. We demonstrated that in Central Russia, mastitis is associated with a wide variety of causal organisms. We observed some differences in the diversity of the two investigated farms. However, we did not find any significant difference among healthy, mastitis and subclinical samples according to their SCC status from either farms by principal component analysis. Linear discriminant analysis effect size (LEfSe) confirmed the presence of several indicator genera in farms from Moscow and the Tula Region. These results confirm the complex bacterial etiology of bovine mastitis.

In silico trio-biomarkers for bacterial vaginosis revealed by species dominance network analysis

BV (bacterial vaginosis) influences 20%–40% of women but its etiology is still poorly understood. An open question about the BV is which of the hundreds of bacteria found in the human vaginal microbiome (HVM) are the major force driving the vaginal microbiota dysbiosis. Here, we recast the question of microbial causality of BV by asking if there are any prevalent ‘signatures’ (network motifs) in the vaginal microbiome networks associated with it? We apply a new framework [species dominance network analysis by Ma & Ellison (2019): Ecological Monographs) to detect critical structures in HVM networks associated with BV risks and etiology. We reanalyzed the 16 s-rRNA gene sequencing datasets of a mixed-cohort of 25 BV patients and healthy women. In these datasets, we detected 15 trio-motifs that occurred exclusively in BV patients. We failed to find any of these 15 trio-motifs in three additional cohorts of 1535 healthy women. Most member-species of the 15 trio motifs are BV-associated anaerobic bacteria (BVAB), Ravel’s community-state type indicators, or the most dominant species; virtually all species interactions in these trios are high-salience skeletons, suggesting that those trios are strongly connected ‘cults’ associated with the occurrence of BV. The presence of the trio motifs unique to BV may act as indicators for its personalized diagnosis and could help elucidate a more mechanistic interpretation of its risks and etiology. We caution that scarcity of large longitudinal datasets of HVM also limited further verifications of our findings, and these findings require further clinical tests to launch their applications.

Microbial ecology in selenate-reducing biofilm communities: Rare biosphere and their interactions with abundant phylotypes

Selenate (SeO₄ ^2- ) reduction in hydrogen (H₂ )-fed membrane biofilm reactors (H₂ -MBfRs) was studied in combinations with other common electron acceptors. We employed H₂ -MBfRs with two distinctly different conditions: R1, with ample electron-donor availability and acceptors SeO₄ ^2- and sulfate (SO₄ ^2- ), and R2, with electron-donor limitation and the presence of electron acceptors SeO₄ ^2- , nitrate (NO₃ ^- ), and SO₄ ^2- . Even though H₂ was available to reduce all input SeO₄ ^2- and SO₄ ^2- in R1, SeO₄ ^2- reduction was preferred over SO₄ ^2- reduction. In R2, co-reduction of NO₃ ^- and SeO₄ ^2- occurred, and SO₄ ^2- reduction was mostly suppressed. Biofilms in all MBfRs had high microbial diversity that was influenced by the "rare biosphere" (RB), phylotypes with relative abundance less than 1%. While all MBfR biofilms had abundant members, such as Dechloromonas and Methyloversatilis, the bacterial communities were significantly different between R1 and R2. For R1, abundant genera were Methyloversatilis, Melioribacter, and Propionivibrio; for R2, abundant genera were Dechloromonas, Hydrogenophaga, Cystobacter, Methyloversatilis, and Thauera. Although changes in electron-acceptor or -donor loading altered the phylogenetic structure of the microbial communities, the biofilm communities were resilient in terms of SeO₄ ^2- and NO₃ ^- reductions, because interacting members of the RB had the capacity of respiring these electron acceptors.

Association of exacerbation phenotype with the sputum microbiome in chronic obstructive pulmonary disease patients during the clinically stable state

Background

Chronic obstructive pulmonary disease (COPD) is a progressive, life-threatening lung disease with increasing prevalence and incidence worldwide. Increasing evidence suggests that lung microbiomes might play a physiological role in acute exacerbations of COPD. The objective of this study was to characterize the association of the microbiota and exacerbation risk or airflow limitation in stable COPD patients.

Methods

The sputum microbiota from 78 COPD outpatients during periods of clinical stability was investigated using 16S rRNA V3-V4 amplicon sequencing. The microbiome profiles were compared between patients with different risks of exacerbation, i.e., the low risk exacerbator (LRE) or high risk exacerbator (HRE) groups, and with different airflow limitation severity, i.e., mild to moderate (FEV1 ≥ 50; PFT I) or severe to very severe (FEV1 < 50; PFT II).

Results

The bacterial diversity (Chao1 and observed OTUs) was significantly decreased in the HRE group compared to that in the LRE group. The top 3 dominant phyla in sputum were Firmicutes, Actinobacteria, and Proteobacteria, which were similar in the HRE and LRE groups. At the genus level, compared to that in the LRE group (41.24%), the proportion of Streptococcus was slightly decreased in the HRE group (28.68%) (p = 0.007). However, the bacterial diversity and the proportion of dominant bacteria at the phylum and genus levels were similar between the PFT I and PFT II groups. Furthermore, the relative abundances of Gemella morbillorum, Prevotella histicola, and Streptococcus gordonii were decreased in the HRE group compared to those in the LRE group according to linear discriminant analysis effect size (LEfSe). Microbiome network analysis suggested altered bacterial cooperative regulation in different exacerbation phenotypes. The proportions of Proteobacteria and Neisseria were negatively correlated with the FEV1/FVC value. According to functional prediction of sputum bacterial communities through Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis, genes involved in lipopolysaccharide biosynthesis and energy metabolism were enriched in the HRE group.

Conclusion

The present study revealed that the sputum microbiome changed in COPD patients with different risks of exacerbation. Additionally, the bacterial cooperative networks were altered in the HRE patients and may contribute to disease exacerbation. Our results provide evidence that sputum microbiome community dysbiosis is associated with different COPD phenotypes, and we hope that by understanding the lung microbiome, a potentially modifiable clinical factor, further targets for improved COPD therapies during the clinically stable state may be elucidated.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02788-4.

Capturing the diversity of the human milk microbiota through culture-enriched molecular profiling: a feasibility study

Previous human milk studies have confirmed the existence of a highly diverse bacterial community using culture-independent and targeted culture-dependent techniques. However, culture-enriched molecular profiling of milk microbiota has not been done. Additionally, the impact of storage conditions and milk fractionation on microbiota composition is not understood. In this feasibility study, we optimized and applied culture-enriched molecular profiling to study culturable milk microbiota in eight milk samples collected from mothers of infants admitted to a neonatal intensive care unit. Fresh samples were immediately plated or stored at -80°C for 2 weeks (short-term frozen). Long-term samples were stored at -20°C for >6 months. Samples were cultured using 10 different culture media and incubated both aerobically and anaerobically. We successfully isolated major milk bacteria, including Streptococcus, Staphylococcus and Bifidobacterium, from fresh milk samples, but were unable to culture any bacteria from the long-term frozen samples. Short-term freezing shifted the composition of viable milk bacteria from the original composition in fresh samples. Nevertheless, the inter-individual variability of milk microbiota composition was observed even after short-term storage. There was no major difference in the overall milk microbiota composition between milk fractions in this feasibility study. This is among the first studies on culture-enriched molecular profiling of the milk microbiota demonstrating the effect of storage and fractionation on milk microbiota composition.

A Combined Metabolomic and Metagenomic Approach to Discriminate Raw Milk for the Production of Hard Cheese

The chemical composition of milk can be significantly affected by different factors across the dairy supply chain, including primary production practices. Among the latter, the feeding system could drive the nutritional value and technological properties of milk and dairy products. Therefore, in this work, a combined foodomics approach based on both untargeted metabolomics and metagenomics was used to shed light onto the impact of feeding systems (i.e., hay vs. a mixed ration based on hay and fresh forage) on the chemical profile of raw milk for the production of hard cheese. In particular, ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF) was used to investigate the chemical profile of raw milk (n = 46) collected from dairy herds located in the Po River Valley (Italy) and considering different feeding systems. Overall, a total of 3320 molecular features were putatively annotated across samples, corresponding to 734 unique compound structures, with significant differences (p < 0.05) between the two feeding regimens under investigation. Additionally, supervised multivariate statistics following metabolomics-based analysis allowed us to clearly discriminate raw milk samples according to the feeding systems, also extrapolating the most discriminant metabolites. Interestingly, 10 compounds were able to strongly explain the differences as imposed by the addition of forage in the cows’ diet, being mainly glycerophospholipids (i.e., lysophosphatidylethanolamines, lysophosphatidylcholines, and phosphatidylcholines), followed by 5-(3′,4′-Dihydroxyphenyl)-gamma-valerolactone-4′-O-glucuronide, 5a-androstan-3a,17b-diol disulfuric acid, and N-stearoyl glycine. The markers identified included both feed-derived (such as phenolic metabolites) and animal-derived compounds (such as lipids and derivatives). Finally, although characterized by a lower prediction ability, the metagenomic profile was found to be significantly correlated to some milk metabolites, with Staphylococcaceae, Pseudomonadaceae, and Dermabacteraceae establishing a higher number of significant correlations with the discriminant metabolites. Therefore, taken together, our preliminary results provide a comprehensive foodomic picture of raw milk samples from different feeding regimens, thus supporting further ad hoc studies investigating the metabolomic and metagenomic changes of milk in all processing conditions.

The Microbiota of the Human Mammary Ecosystem

Human milk contains a dynamic and complex site-specific microbiome, which is not assembled in an aleatory way, formed by organized microbial consortia and networks. Presence of some genera, such as Staphylococcus, Streptococcus, Corynebacterium, Cutibacterium (formerly known as Propionibacterium), Lactobacillus, Lactococcus and Bifidobacterium, has been detected by both culture-dependent and culture-independent approaches. DNA from some gut-associated strict anaerobes has also been repeatedly found and some studies have revealed the presence of cells and/or nucleic acids from viruses, archaea, fungi and protozoa in human milk. Colostrum and milk microbes are transmitted to the infant and, therefore, they are among the first colonizers of the human gut. Still, the significance of human milk microbes in infant gut colonization remains an open question. Clinical studies trying to elucidate the question are confounded by the profound impact of non-microbial human milk components to intestinal microecology. Modifications in the microbiota of human milk may have biological consequences for infant colonization, metabolism, immune and neuroendocrine development, and for mammary health. However, the factors driving differences in the composition of the human milk microbiome remain poorly known. In addition to colostrum and milk, breast tissue in lactating and non-lactating women may also contain a microbiota, with implications in the pathogenesis of breast cancer and in some of the adverse outcomes associated with breast implants. This and other open issues, such as the origin of the human milk microbiome, and the current limitations and future prospects are addressed in this review.

Dominance network analysis of the healthy human vaginal microbiome not dominated by Lactobacillus species

Although Lactobacillus dominance is one of the commonest characteristics of many healthy vaginal microbiomes, a significant proportion of healthy women lack an appreciable amount of Lactobacillus in their microbiome. Indeed, the vaginal microbiomes of many BV (bacterial vaginosis) patients lack the dominance by Lactobacillus. One would wonder what are special with those healthy non-Lactobacillus dominated vaginal microbiomes (nLDVM)? Here we re-analyzed the vaginal microbiome datasets of 1107 postpartum women in rural Malawi Doyle et al. (2018) using species dominance network (SDN) analysis. We discovered that: (i) The DN of the nLDVM is predominantly mutualistic, where most competitive (negative) relationships were from bacterial vaginosis-associated bacteria (BVAB), >60% occurred between BVAB and non-BVAB genera. Gardnerella was inhibited by a mutualistic combination of 23 genera, and Lactobacillus by 15 genera. These may be possible mechanisms by which the microbiome maintains high diversity but avoids dominance by Gardnerella or Lactobacillus. Gardnerella and Lactobacillus were only cooperated with a few genera, but they were positively connected with each other. The suppressed Lactobacillus species positively associated with Gardnerella was Lactobacillus iners, indicating that L. iners might act as an “enemy” in the Lactobacillus-poor vaginal microbiome, and inhibition of Gardnerella and L. iners might be a self-protective mechanism to maintain stability and health of this microbiome. (ii) We identified skeletons of the DNs and separate pathways consisting of high salience skeletons. Finegoldia species and Staphylococcus epidermidis were the hubs of the skeleton network. The roles that they play in the nLDVM deserve more attention of future studies.

Critical Network Structures and Medical Ecology Mechanisms Underlying Human Microbiome-Associated Diseases

A fundamental problem in studies on human microbiome-associated diseases (MADs) is to understand the relationships between microbiome structures and health status of hosts. For example, species diversity metrics have been routinely evaluated in virtually all studies on MADs, yet a recent meta-analysis revealed that, in only approximately one-third of the cases, diversity and diseases were related. In this study, we ask whether Hubbell's neutral theory (supplemented with the normalized stochasticity ratio [NSR]) or critical microbiome network structures may offer better alternatives. Whereas neutral theory and NSR focus on stochastic processes, we use core/periphery and high-salience skeleton networks to evaluate deterministic, asymmetrical niche effects, assuming that all species or their interactions were not “born” equal and focusing on non-neutral, critical network structures. We found that properties of critical network structures are more indicative of disease effects. Finally, seven findings (mechanisms, interpretations, and postulations) regarding medical ecology mechanisms underlying MADs were summarized.

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Seven findings (mechanisms/interpretations/postulations) of medical ecology proposed

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Critical network structures more indicative of disease effects than ecology metrics

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One-third seems ceiling of diversity-disease relations, half to two-thirds of network structures

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Super resilience (unexplained one-third to half gap) is likely attributed to host genome

Integrative study of the upper and lower airway microbiome and transcriptome in asthma

Relatively little is known about interactions between the airway microbiome and airway host transcriptome in asthma. Since asthma affects and is affected by the entire airway, studying the upper (e.g., nasal) and lower (e.g., bronchial) airways together represents a powerful approach to understanding asthma. Here, we performed a systematic, integrative study of the nasal and bronchial microbiomes and nasal and bronchial host transcriptomes of children with severe persistent asthma and healthy controls. We found that (a) the microbiomes and host transcriptomes of asthmatic children are each distinct by site (nasal versus bronchial); (b) among asthmatic children, Moraxella and Alloiococcus are hub genera in the nasal microbiome, while there are no hubs among bronchial genera; (c) bronchial Actinomyces is negatively associated with bronchial genes for inflammation, suggesting Actinomyces may be protective; (d) compared with healthy children, asthmatic children express more nasal genes for ciliary function and harbor more nasal Streptococcus; and (e) nasal genera such as Corynebacterium are negatively associated with significantly more nasal genes for inflammation in healthy versus asthmatic children, suggesting a potentially stronger protective role for such nasal genera in healthy versus asthmatic children. Our systematic, integrative study provides a window into host-microbiome associations in asthma.

FBA Ecological Guild: Trio of Firmicutes-Bacteroidetes Alliance against Actinobacteria in Human Oral Microbiome

In a pioneering study, Zaura et al. (2009) found that majority of oral microbes fall within the five phyla including, Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes and Fusobacteria. Subsequent studies further identified a set of microbes that were commonly shared among unrelated individuals (i.e., core). However, these existing studies may have not been designed to investigate the interactions among various core species. Here by harnessing the power of ecological network analysis, we identified some important ecological guilds in the form of network clusters. In particular, we found that the strongest cluster is an alliance between Firmicutes and Bacteroidetes against Actinobacteria (FBA-guild). Within the guild, we further identified two sub-guilds, the Actinobacteria-dominant sub-guild (ASG) and Firmicutes-dominant allied with Bacteroidetes sub-guild (FBSG). Furthermore, we identified so-termed guard nodes in both sub-guilds, and their role may be to inhibit the peer sub-guild given they held competitive interactions only with the outside nodes only but held cooperative interactions only with the internal nodes, which we termed civilian nodes given that they only held cooperative interactions. We postulated that FBA-guild might be to do with protection of oral health against some opportunistic pathogens from Corynebacterium and Actinomyces, the two major genera of Actinobacteria (target of FB alliance).

Metataxonomic and immunological analysis of milk from ewes with or without a history of mastitis

Mastitis is a highly prevalent condition that has a great impact on milk production and animal welfare, and often requires substantial management efforts. For this reason, it is generally considered an important threat to the dairy industry. Many microbial, host, and environmental factors can protect against, predispose to, or influence the development of mastitis. The objective of this work was to characterize the milk microbiota of Manchega ewes, and to compare samples from animals with and without a history of mastitis. We analyzed milk samples from 36 ewes belonging to 2 different farms (18 ewes from each farm) using culture-dependent and culture-independent techniques. We also analyzed several immune compounds to investigate associations of mastitis with 3 main variables: farm; history of mastitis or no mastitis; and parity number. Both culture-dependent and culture-independent techniques showed that ewe milk harbored a site-specific complex microbiota and microbiome. Staphylococcus epidermidis was the main species driving the difference between farm A (where it was the dominant species) and B (where it was not). In contrast, samples from farm B were characterized by the presence of a wide spectrum of other coagulase-negative staphylococci. Some of these species have already been associated with subclinical intramammary infections in ruminants. Of the 10 immune compounds assayed in this study, 3 were related to a history of mastitis [IL-8, IFN-γ, and IFN-gamma-induced protein 10 (IP-10)]. Increases in IL-8 concentrations in milk seemed to be a feature of subclinical mastitis in sheep, and in this study, this immune factor was detected only in samples from ewes with some episodes of mastitis and from the group with the highest somatic cell count. We also observed a positive correlation between the samples with the highest somatic cell count and IFN-γ and IP-10 levels. Our results suggest that these 3 compounds could be used as biomarkers for the negative selection of mastitis-prone animals, particularly when somatic cell count is very high.

Unfolding the Human Milk Microbiome Landscape in the Omics Era

Studies conducted in the last years have demonstrated that human milk represents a continuous supply of beneficial bacteria to the infant gut, which contribute to the maturation of the digestive and immune functions in the developing infant. Nevertheless, the origin of bacterial populations in milk is not fully understood yet and they have been proposed to originate from maternal skin, infant’s mouth, and (or) endogenously, from the maternal digestive tract through a mechanism involving immune cells. Understanding the composition, functions and assembly of the human milk microbiota has important implications not only for the infant gut microbiota establishment, but also for the mammary health since dysbiosis in the milk bacteria may lead to mastitis. Besides, host, microbial, medical and environmental factors may affect the composition of the human milk microbiome, with implications for the mother-infant health. Application of both culture-dependent and -independent techniques to assess the milk microbiome faces some practical limitations but, together, have allowed providing novel and complementary views on its origin, composition and functioning as summarized in this minireview. In the next future, the application of the ultimate advances in next-generation sequencing and omics approaches, including culturomics, will allow a detailed and comprehensive understanding of the composition and functions of these microbial communities, including their interactions with other milk components, expanding the opportunities to design novel microbiome-based modulation strategies for this ecosystem.

Influence of Socio-Economic and Psychosocial Profiles on the Human Breast Milk Bacteriome of South African Women

The human breast milk (HBM) bacteriome is an important, continuous source of microbes to the neonate in early life, playing an important role in shaping the infant’s intestinal bacteriome. Study of the composition of the HBM bacteriome is an emerging area of research, with little information available, particularly from low- and middle-income countries. The aim of this study was to characterize the diversity of bacterial communities in HBM samples collected between 6–10 weeks postpartum from lactating South African women and to study potential influencing factors of the bacteriome. Using 16S rRNA gene sequencing of samples from 554 women, we demonstrated that the HBM bacteriome was largely dominated by the phyla Firmicutes (mean relative abundance: 71.1%) and Actinobacteria (mean relative abundance: 16.4%). The most abundant genera identified from the HBM bacteriome were Streptococcus (mean relative abundance: 48.6%), Staphylococcus (mean relative abundance: 17.8%), Rothia (mean relative abundance: 5.8%), and Corynebacterium (mean relative abundance: 4.3%). “Core” bacterial genera including Corynebacterium, Streptococcus, Staphylococcus, Rothia, Veillonella, Gemella, Acinetobacter, Micrococcus and a genus belonging to the Enterobacteriaceae family were present in 80% of samples. HBM samples were classified, according to their bacteriome, into three major clusters, dominated by the genera Staphylococcus (cluster 1), a combination of Staphylococcus and Streptococcus (cluster 2), and Streptococcus (cluster 3). The cluster groups differed significantly for Shannon and chao1 richness indices. Bacterial interactions were studied using co-occurrence networks with positive associations observed between the abundances of Staphylococcus and Corynebacteria (members of the skin microflora) and between Streptococcus, Rothia, Veillonella, and Gemella (members of the oral microflora). HBM from older mothers had a higher Shannon diversity index. The study site was associated with differences in HBM bacteriome composition (permutational multivariate analysis of variance using distance matrices (PERMANOVA), p < 0.05). No other tested socio-demographic or psychosocial factors were associated with HBM bacterial composition.

Supplemental Bacillus subtilis DSM 32315 manipulates intestinal structure and microbial composition in broiler chickens

Knowledge about the modulation of gut microbiota improves our understanding of the underlying mechanism by which probiotic treatment benefits the chickens. This study examined the effects of Bacillus subtilis DSM 32315 on intestinal structure and microbial composition in broilers. Broiler chicks were fed basal diets without or with B. subtilis supplementation (1.0 × 10⁹ spores/kg of diet). Supplemental B. subtilis increased average body weight and average daily gain, as well as elevated villus height and villus height to crypt depth ratio of ileum in broilers. Multi-dimension analysis showed a certain degree of separation between the cecal microbiota from treatment and control groups. Increased Firmicutes abundance and reduced Bacteroidetes abundance in cecum were observed responded to B. subtilis addition, which also increased the abundances of Christensenellaceae and Caulobacteraceae, and simultaneously decreased the abundances of potentially harmful bacteria such as Vampirovibrio, Escherichia/Shigella and Parabacteroides. Network analysis signified that B. subtilis addition improved the interaction pattern within cecal microbiota of broilers, however, it exerted little influence on the metabolic pathways of cecal microbiota by comparison of the functional prediction of metagenomes. In conclusion, supplemental B. subtilis DSM 32315 improved growth performance and intestinal structure of broilers, which could be at least partially responsible by the manipulation of cecal microbial composition.

Diversity time-period and diversity-time-area relationships exemplified by the human microbiome

We extend the ecological laws of species-time relationship (STR) and species-time-area relationship (STAR) to general diversity time-period relationship (DTR) and diversity-time-area relationship (DTAR), and test the extensions with the human vaginal microbiome datasets by building 1460 DTR/DTAR models. Our extensions were inspired by the observation that Hill numbers, well regarded as the most appropriate measure of alpha-diversity and also particularly suitable for multiplicative beta-diversity partitioning, are actually in the units of effective species, and therefore, should be able to substitute for species in the STR and STAR. We found that the traditional power law (PL) model is only applicable for DTR at diversity order zero (i.e., species richness); at higher diversity orders (q = 1–4), the power law with exponent cutoff (PLEC) and power law with inverse exponent cutoff (PLIEC) are more appropriate. In particular, PLEC has an advantage over PLIEC in predicting maximal accumulation diversity (MAD) over time. In fact, with the DTR extensions, we can construct DTR and MAD profiles. To the best of our knowledge, this is the first comprehensive investigation of the DTR/DTAR in human microbiome. Methodologically, our DTR/DTAR profiles can characterize general diversity scaling beyond species richness, covering both alpha- and beta-diversity regimes across different diversity orders.

The P/N (Positive-to-Negative Links) Ratio in Complex Networks-A Promising In Silico Biomarker for Detecting Changes Occurring in the Human Microbiome

Relatively little progress in the methodology for differentiating between the healthy and diseased microbiomes, beyond comparing microbial community diversities with traditional species richness or Shannon index, has been made. Network analysis has increasingly been called for the task, but most currently available microbiome datasets only allows for the construction of simple species correlation networks (SCNs). The main results from SCN analysis are a series of network properties such as network degree and modularity, but the metrics for these network properties often produce inconsistent evidence. We propose a simple new network property, the P/N ratio, defined as the ratio of positive links to the number of negative links in the microbial SCN. We postulate that the P/N ratio should reflect the balance between facilitative and inhibitive interactions among microbial species, possibly one of the most important changes occurring in diseased microbiome. We tested our hypothesis with five datasets representing five major human microbiome sites and discovered that the P/N ratio exhibits contrasting differences between healthy and diseased microbiomes and may be harnessed as an in silico biomarker for detecting disease-associated changes in the human microbiome, and may play an important role in personalized diagnosis of the human microbiome-associated diseases.

Behavior of antibiotic resistance genes during co-composting of swine manure with Chinese medicinal herbal residues

Swine manure is considered to be a reservoir for antibiotic resistance genes (ARGs) but little is known about the variations in ARGs during the co-composting of swine manure with Chinese medicinal herbal residues (CMHRs). Thus, this study explored the effects of CMHRs on the variations in ARGs during co-composting with swine manure. The results showed that CMHRs could reduce effectively most of the targeted ARGs (0.18-2.82logs) and mobile genetic elements (MGEs) (0.47-3.34logs). The correlations indicated that CMHRs might decrease the spread of ARGs via horizontal gene transfer. Redundancy analysis showed that the bacterial communities had more important effects on the variations in ARGs compared with environmental factors and MGEs. The results of this study demonstrate that CMHRs can decrease the abundances of ARGs and MGEs, as well as reducing the risk of ARGs spreading during the application of compost products to farmland.

Trios-promising in silico biomarkers for differentiating the effect of disease on the human microbiome network

Recent advances in the HMP (human microbiome project) research have revealed profound implications of the human microbiome to our health and diseases. We postulated that there should be distinctive features associated with healthy and/or diseased microbiome networks. Following Occam's razor principle, we further hypothesized that triangle motifs or trios, arguably the simplest motif in a complex network of the human microbiome, should be sufficient to detect changes that occurred in the diseased microbiome. Here we test our hypothesis with six HMP datasets that cover five major human microbiome sites (gut, lung, oral, skin, and vaginal). The tests confirm our hypothesis and demonstrate that the trios involving the special nodes (e.g., most abundant OTU or MAO, and most dominant OTU or MDO, etc.) and interactions types (positive vs. negative) can be a powerful tool to differentiate between healthy and diseased microbiome samples. Our findings suggest that 12 kinds of trios (especially, dominantly inhibitive trio with mixed strategy, dominantly inhibitive trio with pure strategy, and fully facilitative strategy) may be utilized as in silico biomarkers for detecting disease-associated changes in the human microbiome, and may play an important role in personalized precision diagnosis of the human microbiome associated diseases.

Milk microbiome and bacterial load following dry cow therapy without antibiotics in dairy cows with healthy mammary gland

Preventive infusion of antibiotics in the mammary gland of cows consumes 11 tons/year of medically relevant antimicrobials, yet, this practice might not be critical to prevent new infections in the healthy mammary gland of cows. Here, we used next-generation sequencing and quantitative real-time PCR to determine the impact of dry cow therapy without antibiotics on milk microbiome and bacterial load, respectively. Cows diagnosed as negative for mastitis at dry off were randomly allocated to receive antibiotic (intramammary ceftiofur hydrochloride) and teat sealant or just teat sealant. Firmicutes was the most abundant phylum, and Corynebacterium, Acinetobacter, and Staphylococcus, often involved in mastitis cases, were the most abundant genera across treatments and time. However, there were no effects of antimicrobial on milk microbiome and bacterial load. Bacterial load was greater at seven days postpartum than at dry off. Dry cow therapy based on teat sealant without antibiotics can be used with no detrimental impacts on milk microbiome and bacterial load in cows with a healthy mammary gland.

Bacterial Composition and Diversity in Breast Milk Samples from Mothers Living in Taiwan and Mainland China

Human breast milk is widely recognized as the best source of nutrients for healthy growth and development of infants; it contains a diverse microbiota. Here, we characterized the diversity of the microbiota in the breast milk of East Asian women and assessed whether delivery mode influenced the microbiota in the milk of healthy breast-feeding mothers. We profiled the microbiota in breast milk samples collected from 133 healthy mothers in Taiwan and in six regions of mainland China (Central, East, North, Northeast, South, and Southwest China) by using 16S rRNA pyrosequencing. Lactation stage (months postpartum when the milk sample was collected) and maternal body mass index did not influence the breast milk microbiota. Bacterial composition at the family level differed significantly among samples from the seven geographical regions. The five most predominant bacterial families were Streptococcaceae (mean relative abundance: 24.4%), Pseudomonadaceae (14.0%), Staphylococcaceae (12.2%), Lactobacillaceae (6.2%), and Oxalobacteraceae (4.8%). The microbial profiles were classified into three clusters, driven by Staphylococcaceae (abundance in Cluster 1: 42.1%), Streptococcaceae (Cluster 2: 48.5%), or Pseudomonadaceae (Cluster 3: 26.5%). Microbial network analysis at the genus level revealed that the abundances of the Gram-positive Staphylococcus, Streptococcus, and Rothia were negatively correlated with those of the Gram-negative Acinetobacter, Bacteroides, Halomonas, Herbaspirillum, and Pseudomonas. Milk from mothers who had undergone Caesarian section (C-section group) had a significantly higher abundance of Lactobacillus (P < 0.05) and a higher number of unique unclassified operational taxonomic units (OTUs) (P < 0.001) than that from mothers who had undergone vaginal delivery (vaginal group). These findings revealed that (i) geographic differences in the microbial profiles were found in breast milk from mothers living in Taiwan and mainland China, (ii) the predominant bacterial families Streptococcaceae, Staphylococcaceae, and Pseudomonadaceae were key components for forming three respective clusters, and (iii) a significantly greater number of unique OTUs was found in the breast milk from mothers who had undergone C-section than from those who had delivered vaginally.

Microbial Diversity in Milk of Women With Mastitis: Potential Role of Coagulase-Negative Staphylococci, Viridans Group Streptococci, and Corynebacteria

BACKGROUND

Lactational mastitis constitutes a significant cause of premature weaning. However, its etiology, linked to the presence of pathogenic microorganisms, has been scarcely reported. Research aim: The aim of this study was to describe the microbial diversity in milk samples from women suffering from lactational mastitis and to identify more accurately a collection of isolates belonging to coagulase-negative staphylococci, streptococci, and coryneform bacteria.

METHODS

This is a cross-sectional descriptive one-group study. A total of 5,009 isolates from 1,849 mastitis milk samples was identified by culture, biochemical, and/or molecular methods at the species or genus level. A more precise identification of a collection of 211 isolates was carried out by 16S rRNA gene sequencing.

RESULTS

Mean total bacterial count in milk samples was 4.11 log₁₀ colony-forming units/ml, 95% confidence interval [4.08, 4.15]. Staphylococcus epidermidis was the most common species being isolated from 91.56% of the samples, whereas Staphylococcus aureus was detected in 29.74%. Streptococci and corynebacteria constituted the second (70.20%) and third (16.60%) most prevalent bacterial groups, respectively, found in this study. In contrast, Candida spp. was present in only 0.54% of the samples. Sequencing of the 16S rRNA gene revealed a high diversity of bacterial species among identified isolates.

CONCLUSION

Many coagulase-negative staphylococci, viridans group streptococci, and corynebacteria, usually dismissed as contaminant bacteria, may play an important role as etiologic agents of mastitis. Proper diagnosis of mastitis should be established after performing microbiological testing of milk based on standardized procedures. A reliable analysis must identify the mastitis-causing pathogen(s) at the species level and its(their) concentration(s).

Unique Bacteria Community Composition and Co-occurrence in the Milk of Different Ruminants

Lactation provides the singular source of nourishment to the offspring of mammals. This nutrition source also contains a diverse microbiota affecting the development and health of the newborn. Here, we examined the milk microbiota in water deer (Hydropotes inermis, the most primitive member of the family Cervidae), reindeer (Rangifer tarandus, the oldest semi-domesticated cervid), and the dairy goat (Capra aegagrus, member of the family Bovidae), to determine if common milk microbiota species were present across all three ruminant species. The results showed that water deer had the highest bacterial diversity, followed by reindeer, and then goat. Unifrac distance and correspondence analyses revealed that water deer harbored an increased abundance of Pseudomonas spp. and Acinetobacter spp., while milk from reindeer and goat was dominated by unclassified bacteria from the family Hyphomicrobiaceae and Bacillus spp., respectively. These data indicate significant differences in the composition of milk-based bacterial communities. The presence of Halomonas spp. in three distinct co-occurrence networks of bacterial interactions revealed both common and unique features in milk niches. These results suggest that the milk of water deer and reindeer harbor unique bacterial communities compared with the goat, which might reflect host microbial adaptation caused by evolution.

Microbiome-wide association studies link dynamic microbial consortia to disease

Rapid advances in DNA sequencing, metabolomics, proteomics and computational tools are dramatically increasing access to the microbiome and identification of its links with disease. In particular, time-series studies and multiple molecular perspectives are facilitating microbiome-wide association studies, which are analogous to genome-wide association studies. Early findings point to actionable outcomes of microbiome-wide association studies, although their clinical application has yet to be approved. An appreciation of the complexity of interactions among the microbiome and the host's diet, chemistry and health, as well as determining the frequency of observations that are needed to capture and integrate this dynamic interface, is paramount for developing precision diagnostics and therapies that are based on the microbiome.

Integrated network-diversity analyses suggest suppressive effect of Hodgkin's lymphoma and slightly relieving effect of chemotherapy on human milk microbiome

We aim to investigate the effects of Hodgkin’s lymphoma and the chemotherapy for treating the disease on the human milk microbiome through integrated network and community diversity analyses. Our analyses suggest that Hodgkin’s lymphoma seems to have a suppressing effect on the milk microbiome by lowering the milk microbial community diversity, as measured by the Hill numbers profiles. Although the diversity analysis did not reveal an effect of chemotherapy on community diversity, bacterial species interaction network analysis shows that chemotherapy may help to slightly restore the milk microbiome impacted by Hodgkin’s lymphoma through its influence on the interactions among species (or OTUs). We further constructed diversity-metabolites network, which suggests that the milk microbial diversity is positively correlated with some beneficial milk metabolites such as DHA (DocosaHexaenoic Acid), and that the diversity is negatively correlated with some potentially harmful metabolites such as Butanal. We hence postulate that higher milk microbial diversity should be a signature of healthy mothers and beneficial to infants. Finally, we constructed metabolites OTU correlation networks, from which we identified some special OTUs. These OTUs deserve further investigations given their apparent involvements in regulating the levels of critical milk metabolites such as DHA, Inositol and Butanal.

Pyrosequencing Unveils Cystic Fibrosis Lung Microbiome Differences Associated with a Severe Lung Function Decline

Chronic airway infection is a hallmark feature of cystic fibrosis (CF) disease. In the present study, sputum samples from CF patients were collected and characterized by 16S rRNA gene-targeted approach, to assess how lung microbiota composition changes following a severe decline in lung function. In particular, we compared the airway microbiota of two groups of patients with CF, i.e. patients with a substantial decline in their lung function (SD) and patients with a stable lung function (S). The two groups showed a different bacterial composition, with SD patients reporting a more heterogeneous community than the S ones. Pseudomonas was the dominant genus in both S and SD patients followed by Staphylococcus and Prevotella. Other than the classical CF pathogens and the most commonly identified non-classical genera in CF, we found the presence of the unusual anaerobic genus Sneathia. Moreover, the oligotyping analysis revealed the presence of other minor genera described in CF, highlighting the polymicrobial nature of CF infection. Finally, the analysis of correlation and anti-correlation networks showed the presence of antagonism and ecological independence between members of Pseudomonas genus and the rest of CF airways microbiota, with S patients showing a more interconnected community in S patients than in SD ones. This population structure suggests a higher resilience of S microbiota with respect to SD, which in turn may hinder the potential adverse impact of aggressive pathogens (e.g. Pseudomonas). In conclusion, our findings shed a new light on CF airway microbiota ecology, improving current knowledge about its composition and polymicrobial interactions in patients with CF.

Bovine Teat Microbiome Analysis Revealed Reduced Alpha Diversity and Significant Changes in Taxonomic Profiles in Quarters with a History of Mastitis

Mastitis is a mammary gland inflammatory disease often due to bacterial infections. Like many other infections, it used to be considered as a host-pathogen interaction driven by host and bacterial determinants. Until now, the involvement of the bovine mammary gland microbiota in the host-pathogen interaction has been poorly investigated, and mainly during the infectious episode. In this study, the bovine teat microbiome was investigated in 31 quarters corresponding to 27 animals, which were all free of inflammation at sampling time but which had different histories regarding mastitis: from no episode of mastitis on all the previous lactations (Healthy quarter, Hq) to one or several clinical mastitis events (Mastitic quarter, Mq). Several quarters whose status was unclear (possible history of subclinical mastitis) were classified as NDq. Total bacterial DNA was extracted from foremilk samples and swab samples of the teat canal. Taxonomic profiles were determined by pyrosequencing on 16s amplicons of the V3-4 region. Hq quarters showed a higher diversity compared to Mq ones (Shannon index: ~8 and 6, respectively). Clustering of the quarters based on their bacterial composition made it possible to separate Mq and Hq quarters into two separate clusters (C1 and C2, respectively). Discriminant analysis of taxonomic profiles between these clusters revealed several differences and allowed the identification of taxonomic markers in relation to mastitis history. C2 quarters were associated with a higher proportion of the Clostridia class (including genera such as Ruminococcus, Oscillospira, Roseburia, Dorea, etc.), the Bacteroidetes phylum (Prevotella, Bacteroides, Paludibacter, etc.), and the Bifidobacteriales order (Bifidobacterium), whereas C1 quarters showed a higher proportion of the Bacilli class (Staphylococcus) and Chlamydiia class. These results indicate that microbiota is altered in udders which have already developed mastitis, even far from the infectious episode. Microbiome alteration may have resulted from the infection itself and or the associated antibiotic treatment. Alternatively, differences in microbiome composition in udders with a history of mastitis may have occurred prior to the infection and even contributed to infection development. Further investigations on the dynamics of mammary gland microbiota will help to elucidate the contribution of this endogenous microbiota to the mammary gland health.

Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome

We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth.

The community of bacteria that live in our intestines (called the “gut microbiome”) is important to normal intestinal function, and destruction of this community has a causative role in diseases including obesity, diabetes, and even neurological disorders. Clostridum difficile is an opportunistic pathogenic bacterium that causes potentially life-threatening intestinal inflammation and diarrhea and frequently occurs after antibiotic treatment, which wipes out the normal intestinal bacterial community. We use a mathematical model to identify how the normal bacterial community interacts and how this community changes with antibiotic treatment and C. difficile infection. We use this model to identify bacteria that may inhibit C. difficile growth. Our model and subsequent experiments indicate that Barnesiella intestinihominis inhibits C. difficile growth. This result suggests that B. intestinihominis could potentially be used as a probiotic to treat or prevent C. difficile infection.