Optimizing methods and dodging pitfalls in microbiome research

Microbial DNA sample preservation and possible artifacts for field-based research in remote tropical peatlands

Surveying bacterial and archaeal microbial communities in host and environmental studies requires the collection and storage of samples. Many studies are conducted in distant locations challenging these prerequisites. The use of preserving buffers is an important alternative when lacking access to cryopreservation, however, its effectivity for samples with challenging chemistry or samples that provide opportunities for fast bacterial or archaeal growth upon exposure to an aerobic environment, like peat samples, requires methodological assessment. Here, in combination with an identified optimal DNA extraction kit for peat soil samples, we test the application of several commercial and a homemade preservation buffer and make recommendations on the method that can most effectively preserve a microbiome reflective of the original state. In treatments with a non-optimal buffer or in the absence, we observed notable community shifts beginning as early as three days post-preservation lowering diversity and community evenness, with growth-driven artifacts from a few specific phyla. However other buffers retain a very close composition relative to the original state, and we described several metrics to understand some variation across them. Due to the chemical effects of preservation buffers, it is critical to test their compatibility and reliability to preserve the original bacterial and archaeal community in different environments.

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.

Metagenomic discovery of microbial eukaryotes in stool microbiomes

Host-associated microbiota form complex microbial communities that are increasingly associated with host behavior and disease. While these microbes include bacterial, archaeal, viral, and eukaryotic constituents, most studies have focused on bacteria due to their dominance in the human host and available tools for investigation. Accumulating evidence suggests microbial eukaryotes in the microbiome play pivotal roles in host health, but our understandings of these interactions are limited to a few readily identifiable taxa because of technical limitations in unbiased eukaryote exploration. Here, we combined cell sorting, optimized eukaryotic cell lysis, and shotgun sequencing to accelerate metagenomic discovery and analysis of host-associated microbial eukaryotes. Using synthetic communities with a 1% microbial eukaryote representation, the eukaryote-optimized cell lysis and DNA recovery method alone yielded a 38-fold increase in eukaryotic DNA. Automated sorting of eukaryotic cells from stool samples of healthy adults increased the number of microbial eukaryote reads in metagenomic pools by up to 28-fold compared to commercial kits. Read frequencies for identified fungi increased by 10,000× on average compared to the Human Microbiome Project and allowed for the identification of novel taxa, de novo assembly of contigs from previously unknown microbial eukaryotes, and gene prediction from recovered genomic segments. These advances pave the way for the unbiased inclusion of microbial eukaryotes in deciphering determinants of health and disease in the host-associated microbiome.IMPORTANCEMicrobial eukaryotes are common constituents of the human gut where they can contribute to local ecology and host health, but they are often overlooked in microbiome studies. The lack of attention is due to current technical limitations that are heavily biased or poorly recovered DNA from microbial eukaryotes. We developed a method to increase the representation of these eukaryotes in metagenomic sequencing of microbiome samples that allows to improve their detection compared to prior methods and allows for the identification of new species. Application of the technique to gut microbiome samples improved detection of fungi, protists, and helminths. New eukaryotic taxa and their encoded genes could be identified by sequencing a small number of samples. This approach can improve the inclusion of eukaryotes into microbiome research.

Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis Perspective

As investigations of low-biomass microbial communities have become more common, so too has the recognition of major challenges affecting these analyses. These challenges have been shown to compromise biological conclusions and have contributed to several controversies. Here, we review some of the most common and influential challenges in low-biomass microbiome research. We highlight key approaches to alleviate these potential pitfalls, combining experimental planning strategies and data analysis methods.

Exposure to bacterial PAMPs before RSV infection exacerbates innate inflammation and disease via IL-1α and TNF-α

Respiratory syncytial virus (RSV) can cause severe lower respiratory tract infections. Understanding why some individuals get more serious disease may help with diagnosis and treatment. One possible risk factor underlying severe disease is bacterial exposure before RSV infection. Bacterial exposure has been associated with increased respiratory viral-induced disease severity but the mechanism remains unknown. Respiratory bacterial infections or exposure to their pathogen associated molecular patterns (PAMPs) trigger innate immune inflammation, characterised by neutrophil and inflammatory monocyte recruitment and the production of inflammatory cytokines. We hypothesise that these changes to the lung environment alter the immune response and disease severity during subsequent RSV infection. To test this, we intranasally exposed mice to LPS, LTA or Acinetobacter baumannii (an airway bacterial pathogen) before RSV infection and observed an early induction of disease, measured by weight loss, at days 1-3 after infection. Neutrophils or inflammatory monocytes were not responsible for driving this exacerbated weight loss. Instead, exacerbated disease was associated with increased IL-1α and TNF-α, which orchestrated the recruitment of innate immune cells into the lung. This study shows that exposure to bacterial PAMPs prior to RSV infection increases the expression of IL-1α and TNF-α, which dysregulate the immune response resulting in exacerbated disease.

From islands to infectomes: host-specific viral diversity among birds across remote islands

BACKGROUND

Accelerating biodiversity loss necessitates monitoring the potential pathogens of vulnerable species. With a third of New Zealand's avifauna considered at risk of extinction, a greater understanding of the factors that influence microbial transmission in this island ecosystem is needed. We used metatranscriptomics to determine the viruses, as well as other microbial organisms (i.e. the infectomes), of seven bird species, including the once critically endangered black robin (Petroica traversi), on two islands in the remote Chatham Islands archipelago, New Zealand.

RESULTS

We identified 19 likely novel avian viruses across nine viral families. Black robins harboured viruses from the Flaviviridae, Herpesviridae, and Picornaviridae, while introduced starlings (Sturnus vulgaris) and migratory seabirds (Procellariiformes) carried viruses from six additional viral families. Potential cross-species virus transmission of a novel passerivirus (family: Picornaviridae) between native (black robins and grey-backed storm petrels) and introduced (starlings) birds was also observed. Additionally, we identified bacterial genera, apicomplexan parasites, as well as a novel megrivirus linked to disease outbreaks in other native New Zealand birds. Notably, island effects were outweighed by host taxonomy as a significant driver of viral composition, even among sedentary birds.

CONCLUSIONS

These findings underscore the value of surveillance of avian populations to identify and minimise escalating threats of disease emergence and spread in these island ecosystems. Importantly, they contribute to our understanding of the potential role of introduced and migratory birds in the transmission of microbes and associated diseases, which could impact vulnerable island-endemic species.

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.

The microbiota of Amblyomma americanum reflects known westward expansion

Amblyomma americanum, a known vector of multiple tick-borne pathogens, has expanded its geographic distribution across the United States in the past decades. Tick microbiomes may play a role shaping their host's life history and vectorial capacity. Bacterial communities associated with A. americanum may reflect, or enable, geographic expansion and studying the microbiota will improve understanding of tick-borne disease ecology. We examined the microbiota structure of 189 adult ticks collected in four regions encompassing their historical and current geographic distribution. Both geographic region of origin and sex were significant predictors of alpha diversity. As in other tick models, within-sample diversity was low and uneven given the presence of dominant endosymbionts. Beta diversity analyses revealed that bacterial profiles of ticks of both sexes collected in the West were significantly different from those of the Historic range. Biomarkers were identified for all regions except the historical range. In addition, Bray-Curtis dissimilarities overall increased with distance between sites. Relative quantification of ecological processes showed that, for females and males, respectively, drift and dispersal limitation were the primary drivers of community assembly. Collectively, our findings highlight how microbiota structural variance discriminates the western-expanded populations of A. americanum ticks from the Historical range. Spatial autocorrelation, and particularly the detection of non-selective ecological processes, are indicative of geographic isolation. We also found that prevalence of Ehrlichia chaffeensis, E. ewingii, and Anaplasma phagocytophilum ranged from 3.40-5.11% and did not significantly differ by region. Rickettsia rickettsii was absent from our samples. Our conclusions demonstrate the value of synergistic analysis of biogeographic and microbial ecology data in investigating range expansion in A. americanum and potentially other tick vectors as well.

Microbiome Profiling Demonstrates Concordance of Endotracheal Tube Aspirates With Direct Lower Airway Sampling in Intubated Patients

BACKGROUND

Endotracheal aspirates (ETAs) are widely used for microbiologic studies of the respiratory tract in intubated patients. However, they involve sampling through an established endotracheal tube using suction catheters, both of which can acquire biofilms that may confound results.

RESEARCH QUESTION

Does standard clinical ETA in intubated patients accurately reflect the authentic lower airway bacterial microbiome?

STUDY DESIGN AND METHODS

Comprehensive quantitative bacterial profiling using 16S rRNA V1-V2 gene sequencing was applied to compare bacterial populations captured by standard clinical ETA vs contemporaneous gold standard samples acquired directly from the lower airways through a freshly placed sterile tracheostomy tube. The study included 13 patients undergoing percutaneous tracheostomy following prolonged (median, 15 days) intubation. Metrics of bacterial composition, diversity, and relative quantification were applied to samples.

RESULTS

Pre-tracheostomy ETAs closely resembled the gold standard immediate post-tracheostomy airway microbiomes in bacterial composition and community features of diversity and quantification. Endotracheal tube and suction catheter biofilms also resembled cognate ETA and fresh tracheostomy communities.

INTERPRETATION

Unbiased molecular profiling shows that standard clinical ETA sampling has good concordance with the authentic lower airway microbiome in intubated patients.

Bacterial microbiota and proinflammatory cytokines in the anal sacs of treated and untreated atopic dogs: Comparison with a healthy control group

The pathogenesis of anal sacculitis has not been extensively investigated, although atopic dogs seem to be predisposed to the disease. The aim of this study was therefore to characterize and compare the bacterial microbiota and pro-inflammatory cytokines in the anal sacs of dogs from three groups (healthy dogs, untreated atopic dogs and atopic dogs receiving antipruritic treatment or allergen-specific immunotherapy) in order to determine whether changes could be at the origin of anal sacculitis in atopic dogs. Bacterial populations of anal sac secretions from fifteen healthy dogs, fourteen untreated and six treated atopic dogs were characterized by sequencing the V4 region of the 16S rRNA gene using Illumina technology. Proinflammatory cytokines were analyzed with the Luminex multiplex test. Community membership and structure were significantly different between the anal sacs of healthy and untreated atopic dogs (P = 0.002 and P = 0.003, respectively) and between those of untreated and treated atopic dogs (P = 0.012 and P = 0.017, respectively). However, the community structure was similar in healthy and treated atopic dogs (P = 0.332). Among the proinflammatory cytokines assessed, there was no significant difference between groups, except for interleukin 8 which was higher in the anal sacs of untreated atopic dogs compared to treated atopic dogs (P = 0.02), and tumor necrosis factor-alpha which was lower in the anal sacs of healthy dogs compared to treated atopic dogs (P = 0.04). These results reveal a dysbiosis in the anal sacs of atopic dogs, which may partially explain the predisposition of atopic dogs to develop bacterial anal sacculitis. Treatments received by atopic dogs (oclacitinib, desloratadine and allergen-specific immunotherapy) shift the microbiota of the anal sacs towards that of healthy dogs. Further studies are required to identify significant cytokines contributing to anal sacculitis in atopic dogs.

Dysregulated brain-gut axis in the setting of traumatic brain injury: review of mechanisms and anti-inflammatory pharmacotherapies

Traumatic brain injury (TBI) is a chronic and debilitating disease, associated with a high risk of psychiatric and neurodegenerative diseases. Despite significant advancements in improving outcomes, the lack of effective treatments underscore the urgent need for innovative therapeutic strategies. The brain-gut axis has emerged as a crucial bidirectional pathway connecting the brain and the gastrointestinal (GI) system through an intricate network of neuronal, hormonal, and immunological pathways. Four main pathways are primarily implicated in this crosstalk, including the systemic immune system, autonomic and enteric nervous systems, neuroendocrine system, and microbiome. TBI induces profound changes in the gut, initiating an unrestrained vicious cycle that exacerbates brain injury through the brain-gut axis. Alterations in the gut include mucosal damage associated with the malabsorption of nutrients/electrolytes, disintegration of the intestinal barrier, increased infiltration of systemic immune cells, dysmotility, dysbiosis, enteroendocrine cell (EEC) dysfunction and disruption in the enteric nervous system (ENS) and autonomic nervous system (ANS). Collectively, these changes further contribute to brain neuroinflammation and neurodegeneration via the gut-brain axis. In this review article, we elucidate the roles of various anti-inflammatory pharmacotherapies capable of attenuating the dysregulated inflammatory response along the brain-gut axis in TBI. These agents include hormones such as serotonin, ghrelin, and progesterone, ANS regulators such as beta-blockers, lipid-lowering drugs like statins, and intestinal flora modulators such as probiotics and antibiotics. They attenuate neuroinflammation by targeting distinct inflammatory pathways in both the brain and the gut post-TBI. These therapeutic agents exhibit promising potential in mitigating inflammation along the brain-gut axis and enhancing neurocognitive outcomes for TBI patients.

Effects of glucose and lactate on Streptococcus mutans abundance in a novel multispecies oral biofilm model

The oral microbiome plays an important role in protecting oral health. Here, we established a controlled mixed-species in vitro biofilm model and used it to assess the impact of glucose and lactate on the ability of Streptococcus mutans, an acidogenic and aciduric species, to compete with commensal oral bacteria. A chemically defined medium was developed that supported the growth of S. mutans and four common early colonizers of dental plaque: Streptococcus gordonii, Actinomyces oris, Neisseria subflava, and Veillonella parvula. Biofilms containing the early colonizers were developed in a continuous flow bioreactor, exposed to S. mutans, and incubated for up to 7 days. The abundance of bacteria was estimated by quantitative polymerase chain reaction (qPCR). At high glucose and high lactate, the pH in bulk fluid rapidly decreased to approximately 5.2, and S. mutans outgrew other species in biofilms. In low glucose and high lactate, the pH remained above 5.5, and V. parvula was the most abundant species in biofilms. By contrast, in low glucose and low lactate, the pH remained above 6.0 throughout the experiment, and the microbial community in biofilms was relatively balanced. Fluorescence in situ hybridization confirmed that all species were present in the biofilm and the majority of cells were viable using live/dead staining. These data demonstrate that carbon source concentration is critical for microbial homeostasis in model oral biofilms. Furthermore, we established an experimental system that can support the development of computational models to predict transitions to microbial dysbiosis based on metabolic interactions.IMPORTANCEWe developed a controlled (by removing host factor) dynamic system metabolically representative of early colonization of Streptococcus mutans not measurable in vivo. Hypotheses on factors influencing S. mutans colonization, such as community composition and inoculation sequence and the effect of metabolite concentrations, can be tested and used to predict the effect of interventions such as dietary modifications or the use of toothpaste or mouthwash on S. mutans colonization. The defined in vitro model (species and medium) can be simulated in an in silico model to explore more of the parameter space.

The implication of microbiome in lungs cancer: mechanisms and strategies of cancer growth, diagnosis and therapy

Available evidence illustrates that microbiome is a promising target for the study of growth, diagnosis and therapy of various types of cancer. Lung cancer is a leading cause of cancer death worldwide. The relationship of microbiota and their products with diverse pathologic conditions has been getting large attention. The novel research suggests that the microbiome plays an important role in the growth and progression of lung cancer. The lung microbiome plays a crucial role in maintaining mucosal immunity and synchronizing the stability between tolerance and inflammation. Alteration in microbiome is identified as a critical player in the progression of lung cancer and negatively impacts the patient. Studies suggest that healthy microbiome is essential for effective therapy. Various clinical trials and research are focusing on enhancing the treatment efficacy by altering the microbiome. The regulation of microbiota will provide innovative and promising treatment strategies for the maintenance of host homeostasis and the prevention of lung cancer in lung cancer patients. In the current review article, we presented the latest progress about the involvement of microbiome in the growth and diagnosis of lung cancer. Furthermore, we also assessed the therapeutic status of the microbiome for the management and treatment of lung cancer.

Influence of the gut and airway microbiome on asthma development and disease

There are ample data to suggest that early-life dysbiosis of both the gut and/or airway microbiome can predispose a child to develop along a trajectory toward asthma. Although individual studies show clear associations between dysbiosis and asthma development, it is less clear what (collection of) bacterial species is mechanistically responsible for the observed effects. This is partly due to issues related to the asthma diagnosis and the broad spectrum of anatomical sites, sample techniques, and analysis protocols that are used in different studies. Moreover, there is limited attention for potential differences in the genetics of individuals that would affect the outcome of the interaction between the environment and that individual. Despite these challenges, the first bacterial components were identified that are able to affect the transcriptional state of human cells, ergo the immune system. Such molecules could in the future be the basis for intervention studies that are now (necessarily) restricted to a limited number of bacterial species. For this transition, it might be prudent to develop an ex vivo human model of a local mucosal immune system to better and safer explore the impact of such molecules. With this approach, we might move beyond association toward understanding of causality.

Nine (not so simple) steps: a practical guide to using machine learning in microbial ecology

Due to the complex nature of microbiome data, the field of microbial ecology has many current and potential uses for machine learning (ML) modeling. With the increased use of predictive ML models across many disciplines, including microbial ecology, there is extensive published information on the specific ML algorithms available and how those algorithms have been applied. Thus, our goal is not to summarize the breadth of ML models available or compare their performances. Rather, our goal is to provide more concrete and actionable information to guide microbial ecologists in how to select, run, and interpret ML algorithms to predict the taxa or genes associated with particular sample categories or environmental gradients of interest. Such microbial data often have unique characteristics that require careful consideration of how to apply ML models and how to interpret the associated results. This review is intended for practicing microbial ecologists who may be unfamiliar with some of the intricacies of ML models. We provide examples and discuss common opportunities and pitfalls specific to applying ML models to the types of data sets most frequently collected by microbial ecologists.

Associations of maternal prenatal psychological symptoms and saliva cortisol with neonatal meconium microbiota: A cross-sectional study

Alterations in the diversity and relative abundances of the gut microbiome have been associated with a broad spectrum of medical conditions. Maternal psychological symptoms during pregnancy may impact on offspring development by altering the maternal and the foetal gut microbiome. We aimed to investigate whether self-reported maternal anxiety, depressive symptoms, and distress as well as saliva cortisol levels in late pregnancy alter the bacterial composition of the infant's meconium.

METHODS

A total of N = 100 mother-infant pairs were included. Maternal psychological symptoms were measured using psychological questionnaires (EPDS, PSS-10, STAI) at 34-36 weeks gestation and salivary cortisol was measured at 34-36 and 38 weeks gestation. Infant meconium samples were collected in the first five days postpartum and analysed using 16S rRNA amplicon sequencing.

RESULTS

Correlations showed that lower alpha diversity of the meconium microbiome was significantly associated with increased maternal prenatal depressive symptoms in late gestation (τ = -0.15, p = .04). Increased saliva cortisol AUCg at T2 was significantly related to higher beta diversity of the meconium samples (Pr(>F) = 0.003*). Pseudomonas was the most abundant phylum and was associated with maternal saliva cortisol total decline. No other associations were found.

CONCLUSIONS

Maternal prenatal depressive symptoms are associated with infant faecal microbiome alpha diversity, whereas maternal saliva cortisol AUCg is linked to increased beta diversity and total decline related to increased Psuedomonas. Future studies are warranted to understand how these microbiota community alterations are linked to child health outcomes.

Metabarcoding using nanopore long-read sequencing for the unbiased characterization of apicomplexan haemoparasites

Apicomplexan haemoparasites generate significant morbidity and mortality in humans and other animals, particularly in many low-to-middle income countries. Malaria caused by Plasmodium remains responsible for some of the highest numbers of annual deaths of any human pathogen, whilst piroplasmids, such as Babesia and Theileria can have immense negative economic effects through livestock loss. Diagnosing haemoparasites via traditional methods like microscopy is challenging due to low-level and transient parasitaemia. PCR-based diagnostics overcome these limitations by being both highly sensitive and specific, but they may be unable to accurately detect coinfections or identify novel species. In contrast, next-generation sequencing (NGS)-based methods can characterize all pathogens from a group of interest concurrently, although, the short-read platforms previously used have been limited in the taxonomic resolution achievable. Here, we used Oxford Nanopore Technologies' (ONT) long-read MinION™ sequencer to conduct apicomplexan haemoparasite metabarcoding via sequencing the near full-length 18S ribosomal RNA gene, demonstrating its ability to detect Babesia, Hepatozoon, Neospora, Plasmodium, Theileria and Toxoplasma species. This method was tested on blood-extracted DNA from 100 dogs and the results benchmarked against qPCR and Illumina-based metabarcoding. For two common haemoparasites, nanopore sequencing performed as well as qPCR (kappa agreement statistics > 0.98), whilst also detecting one pathogen, Hepatozoon felis, missed by the other techniques. The long-reads obtained by nanopore sequencing provide an improved species-level taxonomic resolution whilst the method's broad applicability mean it can be used to explore apicomplexan communities from diverse mammalian hosts, on a portable sequencer that easily permits adaptation to field use.

Respiratory and Gut Microbiome Modification during Respiratory Syncytial Virus Infection: A Systematic Review

BACKGROUND

Respiratory syncytial virus (RSV) infection is a major cause of lower respiratory tract infection, especially in infants, and increases the risk of recurrent wheezing and asthma. Recently, researchers have proposed a possible association between respiratory diseases and microbiome alterations. However, this connection has not been fully established. Herein, we conducted a systematic literature review to evaluate the reported evidence of microbiome alterations in patients with RSV infection.

METHODS

The systematic literature review on the association between RSV and microbiome in humans was conducted by searching PubMed, EMBASE, Scopus, and CINAHL from 2012 until February 2022. The results were analyzed qualitatively, focusing on the relationship between microbiome and RSV infection with available key microbiome-related parameters.

RESULTS

In the 405 articles identified by searching databases, 12 (Respiratory tract: 9, Gut: 2, Both: 1) articles in line with the research aims were eligible for this qualitative review. The types of samples for the respiratory tract microbiome and the sequencing methods utilized varied from study to study. This review revealed that the overall microbial composition in both the respiratory tract and gut in RSV-infected patients was different from that in healthy controls. Our generated results demonstrated an increase in the abundance of Haemophilus and Streptococcus, which could contribute to the distinctive separation based on the beta diversity in the respiratory tract.

CONCLUSIONS

The respiratory tract and gut microbiome changed in patients with RSV infection. Further research with a well-organized longitudinal design is warranted to clarify the impact of microbiome alterations on disease pathogenesis.

Development and validation of a long-read metabarcoding platform for the detection of filarial worm pathogens of animals and humans

BACKGROUND

Filarial worms are important vector-borne pathogens of a large range of animal hosts, including humans, and are responsible for numerous debilitating neglected tropical diseases such as, lymphatic filariasis caused by Wuchereria bancrofti and Brugia spp., as well as loiasis caused by Loa loa. Moreover, some emerging or difficult-to-eliminate filarioid pathogens are zoonotic using animals like canines as reservoir hosts, for example Dirofilaria sp. 'hongkongensis'. Diagnosis of filariasis through commonly available methods, like microscopy, can be challenging as microfilaremia may wane below the limit of detection. In contrast, conventional PCR methods are more sensitive and specific but may show limited ability to detect coinfections as well as emerging and/or novel pathogens. Use of deep-sequencing technologies obviate these challenges, providing sensitive detection of entire parasite communities, whilst also being better suited for the characterisation of rare or novel pathogens. Therefore, we developed a novel long-read metabarcoding assay for deep-sequencing the filarial nematode cytochrome c oxidase subunit I gene on Oxford Nanopore Technologies' (ONT) MinION™ sequencer. We assessed the overall performance of our assay using kappa statistics to compare it to commonly used diagnostic methods for filarial worm detection, such as conventional PCR (cPCR) with Sanger sequencing and the microscopy-based modified Knott's test (MKT).

RESULTS

We confirmed our metabarcoding assay can characterise filarial parasites from a diverse range of genera, including, Breinlia, Brugia, Cercopithifilaria, Dipetalonema, Dirofilaria, Onchocerca, Setaria, Stephanofilaria and Wuchereria. We demonstrated proof-of-concept for this assay by using blood samples from Sri Lankan dogs, whereby we identified infections with the filarioids Acanthocheilonema reconditum, Brugia sp. Sri Lanka genotype and zoonotic Dirofilaria sp. 'hongkongensis'. When compared to traditionally used diagnostics, such as the MKT and cPCR with Sanger sequencing, we identified an additional filarioid species and over 15% more mono- and coinfections.

CONCLUSIONS

Our developed metabarcoding assay may show broad applicability for the metabarcoding and diagnosis of the full spectrum of filarioids from a wide range of animal hosts, including mammals and vectors, whilst the utilisation of ONT' small and portable MinION™ means that such methods could be deployed for field use.

The Associations Among Gut Microbiota, Branched Chain Amino Acids, and Parkinson's Disease: Mendelian Randomization Study

Background

In experimental and observational studies, the characteristics of gut microbiota have been associated with Parkinson's disease (PD), among which metabolic pathways played an important role. However, the causality remained unclear.

Objective

Herein, we aimed to determine the potential impact of gut microbiota and gut microbiota-derived metabolites on PD risk using a Mendelian randomization (MR) approach.

Methods

We included as exposures gut microbial taxa abundance and gut-derived metabolites (branched chain amino acids [BCAAs]), with PD as the outcome. In addition, we explored whether BCAAs act as a mediating factor in the pathway from gut microbiota to PD.

Results

We found evidence of a causality of 15 microbial taxa and PD before and after sensitivity analyses, but not after multiple testing correction. There was significant association between BCAAs levels and the risk of PD, especially isoleucine (OR = 0.995, 95% CI 0.992-0.999, p = 0.004, pFDR = 0.012). In addition, the causality of gut microbiota and BCAAs was also explored that the increased g_Coprococcus abundance can result in the decrease in isoleucine level (OR = 1.046; 95% CI, 1.009-1.085; p = 0.016).

Conclusions

Our findings indicated suggestive association between gut microbiota and its metabolites and PD. Furthermore, higher BCAAs levels were associated with the decreased PD risk. This study may provide new targets for PD treatment, such as dietary BCAAs supplementation.

Meta-analysis identifying gut microbial biomarkers of Qinghai-Tibet Plateau populations and the functionality of microbiota-derived butyrate in high-altitude adaptation

The extreme environmental conditions of a plateau seriously threaten human health. The relationship between gut microbiota and human health at high altitudes has been extensively investigated. However, no universal gut microbiota biomarkers have been identified in the plateau population, limiting research into gut microbiota and high-altitude adaptation. 668 16s rRNA samples were analyzed using meta-analysis to reduce batch effects and uncover microbiota biomarkers in the plateau population. Furthermore, the robustness of these biomarkers was validated. Mendelian randomization (MR) results indicated that Tibetan gut microbiota may mediate a reduced erythropoietic response. Functional analysis and qPCR revealed that butyrate may be a functional metabolite in high-altitude adaptation. A high-altitude rat model showed that butyrate reduced intestinal damage caused by high altitudes. According to cell experiments, butyrate may downregulate hypoxia-inducible factor-1α (HIF-1α) expression and blunt cellular responses to hypoxic stress. Our research found universally applicable biomarkers and investigated their potential roles in promoting human health at high altitudes.

The Role of Gut and Oral Microbiota in the Formation and Rupture of Intracranial Aneurysms: A Literature Review

In recent years, there has been a growing interest in the role of the microbiome in cardiovascular and cerebrovascular diseases. Emerging research highlights the potential role of the microbiome in intracranial aneurysm (IA) formation and rupture, particularly in relation to inflammation. In this review, we aim to explore the existing literature regarding the influence of the gut and oral microbiome on IA formation and rupture. In the first section, we provide background information, elucidating the connection between inflammation and aneurysm formation and presenting potential mechanisms of gut-brain interaction. Additionally, we explain the methods for microbiome analysis. The second section reviews existing studies that investigate the relationship between the gut and oral microbiome and IAs. We conclude with a prospective overview, highlighting the extent to which the microbiome is already therapeutically utilized in other fields. Furthermore, we address the challenges associated with the context of IAs that still need to be overcome.

Meta-analysis reveals the predictable dynamic development of the gut microbiota in commercial pigs

IMPORTANCE

The swine gut microbiome undergoes an age-dependent assembly pattern with a developmental phase at early ages and a stabilization phase at later ages. Shorter time intervals and a wider range of data sources provided a clearer understanding of the gut microbiota colonization and succession and their associations with pig growth and development. The rapidly changing microbiota of suckling and weaning pigs implies potential time targets for growth and health regulation through gut microbiota manipulation. Since swine gut microbiota development is predictable, swine microbiota age can be calculated and compared between animal treatment groups rather than relying only on static time-matched comparisons.

Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data

Sequencing-based approaches for the analysis of microbial communities are susceptible to contamination, which could mask biological signals or generate artifactual ones. Methods for in silico decontamination using controls are routinely used, but do not make optimal use of information shared across samples and cannot handle taxa that only partially originate in contamination or leakage of biological material into controls. Here we present Source tracking for Contamination Removal in microBiomes (SCRuB), a probabilistic in silico decontamination method that incorporates shared information across multiple samples and controls to precisely identify and remove contamination. We validate the accuracy of SCRuB in multiple data-driven simulations and experiments, including induced contamination, and demonstrate that it outperforms state-of-the-art methods by an average of 15-20 times. We showcase the robustness of SCRuB across multiple ecosystems, data types and sequencing depths. Demonstrating its applicability to microbiome research, SCRuB facilitates improved predictions of host phenotypes, most notably the prediction of treatment response in melanoma patients using decontaminated tumor microbiome data.

Methodologic Considerations for Studying the Ocular Surface Microbiome

The ocular surface microbiome, unlike that of the skin or gut, has not been well characterized. Culture experiments historically suggested a nearly sterile ocular surface, but initial application of molecular methods such as 16S ribosomal RNA and high-throughput sequencing demonstrated a surprisingly rich ocular surface microbiome. However, a major limitation in studying such a low-biomass niche is the potential for artifactual results when amplification-based techniques such as ribosomal polymerase chain reaction and shotgun sequencing are used. It will be essential to establish standards across the field for sample collection, positive and negative controls, and limitation of contamination in both the laboratory setting and computational analysis. New developments in ocular microbiome research, including the generation of reference reagents and fluoroscopic imaging techniques, provide improved means to validate sequencing results and to visualize complex interactions between host cells and bacteria. Through more thorough characterization of the ocular surface microbiome, the connections between a dysregulated surface and ophthalmic disease may be better understood.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

The core phageome and its interrelationship with preterm human milk lipids

Phages and lipids in human milk (HM) may benefit preterm infant health by preventing gastrointestinal pathobiont overgrowth and microbiome modulation. Lipid association may promote vertical transmission of phages to the infant. Despite this, interrelationships between lipids and phages are poorly characterized in preterm HM. Shotgun metagenomics and untargeted lipidomics of phage and lipid profiles from 99 preterm HM samples reveals that phages are abundant and prevalent from the first week and throughout the first 100 days of lactation. Phage-host richness of preterm HM increases longitudinally. Core phage communities characterized by Staphylococcus- and Propionibacterium-infecting phages are significantly correlated with long-chain fatty acid abundances over lactational age. We report here a phage-lipid interaction in preterm HM, highlighting the potential importance of phage carriage in preterm HM. These results reveal possible strategies for phage carriage in HM and their importance in early-life microbiota development.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Evaluation of a commercial NGS service for detection of bacterial and fungal pathogens in infectious ulcerative keratitis

OBJECTIVES

To compare results from a commercial next-generation sequencing (NGS) service to corneal cytology and culture for identification of causative organisms in veterinary patients presenting for infectious ulcerative keratitis (IUK).

PROCEDURE

Swabs for corneal aerobic and fungal cultures and DNA swabs for NGS were submitted for canine and equine normal controls (n = 11 and n = 4, respectively) and IUK patients (n = 22 and n = 8, respectively) for which microbrush cytology specimens confirmed the presence of infectious organisms. The sensitivity of the NGS results was compared with bacterial and fungal culture results. Concordance between the NGS and culture results was determined.

RESULTS

The NGS results were positive for bacterial and fungal organisms in 5 and 1 normal and 18 and 1 IUK cases, respectively. Bacterial and fungal cultures were positive for 7 and 2 normal and 20 and 5 IUK cases, respectively. Sensitivity of NGS was 82.14% (95% confidence interval (CI), 63.11% to 93.94%) and specificity was 76.47% (95% CI, 50.10% to 93.19%). Concordance (complete and partial) between identified bacterial and fungal organisms was found in 79% and 100% of cases, respectively. NGS identified organisms in 3 culture-negative IUK samples.

CONCLUSION

A commercial NGS service may be useful in the identification of causative agents in IUK cases with a sensitivity greater than the sensitivity previously reported for aerobic culture. Further testing is needed to determine the clinical significance of additional organisms isolated by NGS from infected cases, as well as organisms isolated from normal corneas.

Neuropeptide Y-Mediated Gut Microbiota Alterations Aggravate Postmenopausal Osteoporosis

Postmenopausal osteoporosis (PMO) is often accompanied by neuroendocrine changes in the hypothalamus, which closely associates with the microbial diversity, community composition, and intestinal metabolites of gut microbiota (GM). With the emerging role of GM in bone metabolism, a potential neuroendocrine signal neuropeptide Y (NPY) mediated brain-gut-bone axis has come to light. Herein, it is reported that exogenous overexpression of NPY reduced bone formation, damaged bone microstructure, and up-regulated the expressions of pyroptosis-related proteins in subchondral cancellous bone in ovariectomized (OVX) rats, but Y1 receptor antagonist (Y1Ra) reversed these changes. In addition, it is found that exogenous overexpression of NPY aggravated colonic inflammation, impaired intestinal barrier integrity, enhanced intestinal permeability, and increased serum lipopolysaccharide (LPS) in OVX rats, and Y1Ra also reversed these changes. Most importantly, NPY and Y1Ra modulated the microbial diversity and changed the community composition of GM in OVX rats, and thereby affecting the metabolites of GM (e.g., LPS) entering the blood circulation. Moreover, fecal microbiota transplantation further testified the effect of NPY-mediated GM changes on bone. In vitro, LPS induced pyroptosis, reduced viability, and inhibited differentiation of osteoblasts. The study demonstrated the existence of NPY-mediated brain-gut-bone axis and it might be a novel emerging target to treat PMO.

Gut microbiota alterations induced by intensive chemotherapy in acute myeloid leukaemia patients are associated with gut barrier dysfunction and body weight loss

BACKGROUND & AIMS

Acute myeloid leukaemia (AML) chemotherapy has been reported to impact gut microbiota composition. In this study, we investigated using a multi -omics strategy the changes in the gut microbiome induced by AML intense therapy and their association with gut barrier function and cachectic hallmarks.

METHODS

10 AML patients, allocated to standard induction chemotherapy (SIC), were recruited. Samples and data were collected before any therapeutic intervention (T0), at the end of the SIC (T1) and at discharge (T4). Gut microbiota composition and function, markers of inflammation, metabolism, gut barrier function and cachexia, as well as faecal, blood and urine metabolomes were assessed.

RESULTS

AML patients demonstrated decreased appetite, weight loss and muscle wasting during hospitalization, with an incidence of cachexia of 50%. AML intensive treatment transiently impaired the gut barrier function and led to a long-lasting change of gut microbiota composition characterized by an important loss of diversity. Lactobacillaceae and Campylobacter concisus were increased at T1 while Enterococcus faecium and Staphylococcus were increased at T4. Metabolomics analyses revealed a reduction in urinary hippurate and faecal bacterial amino acid metabolites (bAAm) (2-methylbutyrate, isovalerate, phenylacetate). Integration using DIABLO revealed a deep interconnection between all the datasets. Importantly, we identified bacteria which disappearance was associated with impaired gut barrier function (Odoribacter splanchnicus) and body weight loss (Gemmiger formicilis), suggesting these bacteria as actionable targets.

CONCLUSION

AML intensive therapy transiently impairs the gut barrier function while inducing enduring alterations in the composition and metabolic activity of the gut microbiota that associate with body weight loss.

TRIAL REGISTRATION

NCT03881826, https://clinicaltrials.gov/ct2/show/NCT03881826.

Challenges and opportunities in sharing microbiome data and analyses

Microbiome data, metadata and analytical workflows have become 'big' in terms of volume and complexity. Although the infrastructure and technologies to share data have been established, the interdisciplinary and multi-omic nature of the field can make resources difficult to identify and use. Following best practices for data deposition requires substantial effort, with sometimes little obvious reward. Gaps remain where microbiome-specific resources for data sharing or reproducibility do not yet exist. We outline available best practices, challenges to their adoption and opportunities in data sharing in microbiome research. We showcase examples of best practices and advocate for their enforcement and incentivization for data sharing. This includes recognition of data curation and sharing endeavours by individuals, institutions, journals and funders. Opportunities for progress include enabling microbiome-specific databases to incorporate future methods for data analysis, integration and reuse.

Challenges and priorities for researching the gut microbiota in individuals living with anorexia nervosa

OBJECTIVE

The gut microbiota is implicated in several symptoms and biological pathways relevant to anorexia nervosa (AN). Investigations into the role of the gut microbiota in AN are growing, with a specific interest in the changes that occur in response to treatment. Findings suggest that microbial species may be associated with some of the symptoms common in AN, such as depression and gastrointestinal disturbances (GID). Therefore, researchers believe the gut microbiota may have therapeutic relevance. Whilst research in this field is rapidly expanding, the unique considerations relevant to conducting gut microbiota research in individuals with AN must be addressed.

METHOD

We provide an overview of the published literature investigating the relationship between the gut microbiota and symptoms and behaviors present in AN, discuss important challenges in gut microbiota research, and offer recommendations for addressing these. We conclude by summarizing research design priorities for the field to move forward.

RESULTS

Several ways exist to reduce participant burden and accommodate challenges when researching the gut microbiota in individuals with AN.

DISCUSSION

Recommendations from this article are foreseen to encourage scientific rigor and thoughtful protocol planning for microbiota research in AN, including ways to reduce participant burden. Employing such methods will contribute to a better understanding of the role of the gut microbiota in AN pathophysiology and treatment.

PUBLIC SIGNIFICANCE

The field of gut microbiota research is rapidly expanding, including the role of the gut microbiota in anorexia nervosa. Thoughtful planning of future research will ensure appropriate data collection for meaningful interpretation while providing a positive experience for the participant. We present current challenges, recommendations for research design and priorities to facilitate the advancement of research in this field.

Processing and storage methods affect oral and gut microbiome composition

In microbiome studies, fecal and oral samples are stored and processed in different ways, which could affect the observed microbiome composition. In this study, we compared storage and processing methods applied to samples prior to DNA extraction to determine how each affected microbial community diversity as assessed by 16S rRNA gene sequencing. We collected dental swabs, saliva, and fecal samples from 10 individuals, with three technical replicates per condition. We assessed four methods of storing and processing fecal samples prior to DNA extraction. We also compared different fractions of thawed saliva and dental samples to fresh samples. We found that lyophilized fecal samples, fresh whole saliva samples, and the supernatant fraction of thawed dental samples had the highest levels of alpha diversity. The supernatant fraction of thawed saliva samples had the second highest evenness compared to fresh saliva samples. Then, we investigated the differences in observed community composition at the domain and phylum levels and identified the amplicon sequence variants (ASVs) that significantly differed in relative abundance between the conditions. Lyophilized fecal samples had a greater prevalence of Archaea as well as a greater ratio of Firmicutes to Bacteroidetes compared to the other conditions. Our results provide practical considerations not only for the selection of storage and processing methods but also for comparing results across studies. Differences in processing and storage methods could be a confounding factor influencing the presence, absence, or differential abundance of microbes reported in conflicting studies.

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

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

Iron oxide nanozymes stabilize stannous fluoride for targeted biofilm killing and synergistic oral disease prevention

Dental caries is the most common human disease caused by oral biofilms despite the widespread use of fluoride as the primary anticaries agent. Recently, an FDA-approved iron oxide nanoparticle (ferumoxytol, Fer) has shown to kill and degrade caries-causing biofilms through catalytic activation of hydrogen peroxide. However, Fer cannot interfere with enamel acid demineralization. Here, we show notable synergy when Fer is combined with stannous fluoride (SnF2), markedly inhibiting both biofilm accumulation and enamel damage more effectively than either alone. Unexpectedly, we discover that the stability of SnF2 is enhanced when mixed with Fer in aqueous solutions while increasing catalytic activity of Fer without any additives. Notably, Fer in combination with SnF2 is exceptionally effective in controlling dental caries in vivo, even at four times lower concentrations, without adverse effects on host tissues or oral microbiome. Our results reveal a potent therapeutic synergism using approved agents while providing facile SnF2 stabilization, to prevent a widespread oral disease with reduced fluoride exposure.

From-Toilet-to-Freezer: A Review on Requirements for an Automatic Protocol to Collect and Store Human Fecal Samples for Research Purposes

The composition, viability and metabolic functionality of intestinal microbiota play an important role in human health and disease. Studies on intestinal microbiota are often based on fecal samples, because these can be sampled in a non-invasive way, although procedures for sampling, processing and storage vary. This review presents factors to consider when developing an automated protocol for sampling, processing and storing fecal samples: donor inclusion criteria, urine-feces separation in smart toilets, homogenization, aliquoting, usage or type of buffer to dissolve and store fecal material, temperature and time for processing and storage and quality control. The lack of standardization and low-throughput of state-of-the-art fecal collection procedures promote a more automated protocol. Based on this review, an automated protocol is proposed. Fecal samples should be collected and immediately processed under anaerobic conditions at either room temperature (RT) for a maximum of 4 h or at 4 °C for no more than 24 h. Upon homogenization, preferably in the absence of added solvent to allow addition of a buffer of choice at a later stage, aliquots obtained should be stored at either -20 °C for up to a few months or -80 °C for a longer period-up to 2 years. Protocols for quality control should characterize microbial composition and viability as well as metabolic functionality.

Integrated multi-omics analyses of microbial communities: a review of the current state and future directions

Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions.

Culturomics in Unraveling the Upper Female Reproductive Tract Microbiota

In recent years, the study of the human microbiome has surged, shedding light on potential connections between microbiome composition and various diseases. One specific area of intense interest within this research is the female reproductive tract, as it holds the potential to influence the process of embryo implantation. Advanced sequencing technologies have delivered unprecedented insights into the microbial communities, also known as microbiota, residing in the female reproductive tract. However, their efficacy encounters significant challenges when analyzing low-biomass microbiota, such as those present in the endometrium. These molecular techniques are susceptible to contamination from laboratory reagents and extraction kits, leading to sequencing bias that can significantly alter the perceived taxonomy of a sample. Consequently, investigating the microbiota of the upper female reproductive tract necessitates the exploration of alternative methods. In this context, the current review delves into the application of culturomics in unraveling the upper female reproductive tract microbiota. While culturomics holds value in research, its transition to routine clinical practice appears remote, at least in the foreseeable future.

The respiratory microbiota and its impact on health and disease in dogs and cats: A One Health perspective

Healthy lungs were long thought of as sterile, with presence of bacteria identified by culture representing contamination. Recent advances in metagenomics have refuted this belief by detecting rich, diverse, and complex microbial communities in the healthy lower airways of many species, albeit at low concentrations. Although research has only begun to investigate causality and potential mechanisms, alterations in these microbial communities (known as dysbiosis) have been described in association with inflammatory, infectious, and neoplastic respiratory diseases in humans. Similar studies in dogs and cats are scarce. The microbial communities in the respiratory tract are linked to distant microbial communities such as in the gut (ie, the gut-lung axis), allowing interplay of microbes and microbial products in health and disease. This review summarizes considerations for studying local microbial communities, key features of the respiratory microbiota and its role in the gut-lung axis, current understanding of the healthy respiratory microbiota, and examples of dysbiosis in selected respiratory diseases of dogs and cats.

Documenting the microbiome diversity and distribution in selected fleas from South Africa with an emphasis on the cat flea, Ctenocephalides f. felis

The factors that influence parasite associated bacterial microbial diversity and the geographic distributions of bacteria are not fully understood. In an effort to gain a deeper understanding of the relationship between the bacterial diversity of Ctenocephalides fleas and host species and the external environment, we conducted a metagenetic analysis of 107 flea samples collected from 8 distinct sampling sites in South Africa. Pooled DNA samples mostly comprising of 2 or 3 individuals sampled from the same host, and belonging to the same genetic cluster, were sequenced using the Ion PGM™ Hi-Q™ Kit and the Ion 316™ Chip v2. Differences were detected in the microbiome compositions between Ctenocephalides felis, Ctenocephalides canis and Ctenocephalides connatus. Although based on a small sample, C. connatus occurring on wildlife harboured a higher bacterial richness when compared to C. felis on domestic animals. Intraspecific differences in the microbial OTU diversity were detected within C. f. felis that occurred on domestic cats and dogs. Different genetic lineages of C. f. felis were similar in microbial compositions but some differences exist in the presence or absence of rare bacteria. Rickettsia and Bartonella OTU's identified in South African cat fleas differ from those identified in the USA and Australia. Intraspecific microbial compositions also differ across geographic sampling sites. Generalized dissimilarity modelling showed that temperature and humidity are potentially important environmental factors explaining the pattern obtained.

Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing

Assuring that cell therapy products are safe before releasing them for use in patients is critical. Currently, compendial sterility testing for bacteria and fungi can take 7-14 days. The goal of this work was to develop a rapid untargeted approach for the sensitive detection of microbial contaminants at low abundance from low volume samples during the manufacturing process of cell therapies. We developed a long-read sequencing methodology using Oxford Nanopore Technologies MinION platform with 16S and 18S amplicon sequencing to detect USP <71> organisms and other microbial species. Reads are classified metagenomically to predict the microbial species. We used an extreme gradient boosting machine learning algorithm (XGBoost) to first assess if a sample is contaminated, and second, determine whether the predicted contaminant is correctly classified or misclassified. The model was used to make a final decision on the sterility status of the input sample. An optimized experimental and bioinformatics pipeline starting from spiked species through to sequenced reads allowed for the detection of microbial samples at 10 colony-forming units (CFU)/mL using metagenomic classification. Machine learning can be coupled with long-read sequencing to detect and identify sample sterility status and microbial species present in T-cell cultures, including the USP <71> organisms to 10 CFU/mL. IMPORTANCE This research presents a novel method for rapidly and accurately detecting microbial contaminants in cell therapy products, which is essential for ensuring patient safety. Traditional testing methods are time-consuming, taking 7-14 days, while our approach can significantly reduce this time. By combining advanced long-read nanopore sequencing techniques and machine learning, we can effectively identify the presence and types of microbial contaminants at low abundance levels. This breakthrough has the potential to improve the safety and efficiency of cell therapy manufacturing, leading to better patient outcomes and a more streamlined production process.

Next-generation sequencing analysis of semen microbiome taxonomy in men with nonobstructive azoospermia vs. fertile controls: a pilot study

OBJECTIVES

To study how the semen microbiome profile in men with nonobstructive azoospermia (NOA) differs from that of fertile controls (FCs).

DESIGN

Using quantitative polymerase chain reaction and 16S ribosomal RNA, we sequenced semen samples from men with NOA (follicle-stimulating hormone >10 IU/mL, testis volume <10 mL) and FCs and performed a comprehensive taxonomic microbiome analysis.

SETTING

All patients were identified during evaluation at the outpatient male andrology clinic at the University of Miami.

PATIENTS

In total, 33 adult men, including 14 diagnosed with NOA and 19 with proven paternity undergoing vasectomy, were enrolled.

MAIN OUTCOME MEASURES

Bacterial species in the semen microbiome were identified.

RESULTS

Alpha-diversity was similar between the groups, suggesting similar diversity within samples, whereas beta-diversity was different, suggesting differences in taxa between samples. In the NOA men, the phyla Proteobacteria and Firmicutes were underrepresented, and Actinobacteriota were overrepresented compared with FC men. At the genus level, Enterococcus was the most common amplicon sequence variant in both groups, whereas 5 genera differed significantly between the groups, including Escherichia and Shigella, Sneathia, and Raoutella.

CONCLUSION

Our study showed significant differences in the seminal microbiome between men with NOA and fertile men. These results suggest a loss of functional symbiosis may be associated with NOA. Further research into the characterization and clinical utility of the semen microbiome and its causal role in male infertility is necessary.

Pragmatic Considerations When Extracting DNA for Metagenomics Analyses of Clinical Samples

Microbiome analyses are essential for understanding microorganism composition and diversity, but interpretation is often challenging due to biological and technical variables. DNA extraction is a critical step that can significantly bias results, particularly in samples containing a high abundance of challenging-to-lyse microorganisms. Taking into consideration the distinctive microenvironments observed in different bodily locations, our study sought to assess the extent of bias introduced by suboptimal bead-beating during DNA extraction across diverse clinical sample types. The question was whether complex targeted extraction methods are always necessary for reliable taxonomic abundance estimation through amplicon sequencing or if simpler alternatives are effective for some sample types. Hence, for four different clinical sample types (stool, cervical swab, skin swab, and hospital surface swab samples), we compared the results achieved from extracting targeted manual protocols routinely used in our research lab for each sample type with automated protocols specifically not designed for that purpose. Unsurprisingly, we found that for the stool samples, manual extraction protocols with vigorous bead-beating were necessary in order to avoid erroneous taxa proportions on all investigated taxonomic levels and, in particular, false under- or overrepresentation of important genera such as Blautia, Faecalibacterium, and Parabacteroides. However, interestingly, we found that the skin and cervical swab samples had similar results with all tested protocols. Our results suggest that the level of practical automation largely depends on the expected microenvironment, with skin and cervical swabs being much easier to process than stool samples. Prudent consideration is necessary when extending the conclusions of this study to applications beyond rough estimations of taxonomic abundance.

Rapid metagenomic sequencing for diagnosis and antimicrobial sensitivity prediction of canine bacterial infections

Antimicrobial resistance is a major threat to human and animal health. There is an urgent need to ensure that antimicrobials are used appropriately to limit the emergence and impact of resistance. In the human and veterinary healthcare setting, traditional culture and antimicrobial sensitivity testing typically requires 48-72 h to identify appropriate antibiotics for treatment. In the meantime, broad-spectrum antimicrobials are often used, which may be ineffective or impact non-target commensal bacteria. Here, we present a rapid, culture-free, diagnostics pipeline, involving metagenomic nanopore sequencing directly from clinical urine and skin samples of dogs. We have planned this pipeline to be versatile and easily implementable in a clinical setting, with the potential for future adaptation to different sample types and animals. Using our approach, we can identify the bacterial pathogen present within 5 h, in some cases detecting species which are difficult to culture. For urine samples, we can predict antibiotic sensitivity with up to 95 % accuracy. Skin swabs usually have lower bacterial abundance and higher host DNA, confounding antibiotic sensitivity prediction; an additional host depletion step will likely be required during the processing of these, and other types of samples with high levels of host cell contamination. In summary, our pipeline represents an important step towards the design of individually tailored veterinary treatment plans on the same day as presentation, facilitating the effective use of antibiotics and promoting better antimicrobial stewardship.

The Interplay between Microbiota and Chemotherapy-Derived Metabolites in Breast Cancer

The most common cancer in women is breast cancer, which is also the second leading cause of death in this group. It is, however, important to note that some women will develop or will not develop breast cancer regardless of whether certain known risk factors are present. On the other hand, certain compounds are produced by bacteria in the gut, such as short-chain fatty acids, secondary bile acids, and other metabolites that may be linked to breast cancer development and mediate the chemotherapy response. Modeling the microbiota through dietary intervention and identifying metabolites directly associated with breast cancer and its complications may be useful to identify actionable targets and improve the effect of antiangiogenic therapies. Metabolomics is therefore a complementary approach to metagenomics for this purpose. As a result of the combination of both techniques, a better understanding of molecular biology and oncogenesis can be obtained. This article reviews recent literature about the influence of bacterial metabolites and chemotherapy metabolites in breast cancer patients, as well as the influence of diet.

Lacticaseibacillus rhamnosus Strain GG (LGG) Regulate Gut Microbial Metabolites, an In Vitro Study Using Three Mature Human Gut Microbial Cultures in a Simulator of Human Intestinal Microbial Ecosystem (SHIME)

In the present research, we investigated changes in the gut metabolome that occurred in response to the administration of the Laticaseibacillus rhamnosus strain GG (LGG). The probiotics were added to the ascending colon region of mature microbial communities established in a human intestinal microbial ecosystem simulator. Shotgun metagenomic sequencing and metabolome analysis suggested that the changes in microbial community composition corresponded with changes to metabolic output, and we can infer linkages between some metabolites and microorganisms. The in vitro method permits a spatially-resolved view of metabolic transformations under human physiological conditions. By this method, we found that tryptophan and tyrosine were mainly produced in the ascending colon region, while their derivatives were detected in the transverse and descending regions, revealing sequential amino acid metabolic pathways along with the colonic tract. The addition of LGG appeared to promote the production of indole propionic acid, which is positively associated with human health. Furthermore, the microbial community responsible for the production of indole propionic acid may be broader than is currently known.

MPrESS: An R-Package for Accurately Predicting Power for Comparisons of 16S rRNA Microbiome Taxa Distributions including Simulation by Dirichlet Mixture Modeling

Deep sequencing has revealed that the 16S rRNA gene composition of the human microbiome can vary between populations. However, when existing data are insufficient to address the desired study questions due to limited sample sizes, Dirichlet mixture modeling (DMM) can simulate 16S rRNA gene predictions from experimental microbiome data. We examined the extent to which simulated 16S rRNA gene microbiome data can accurately reflect the diversity within that identified from experimental data and calculate the power. Even when experimental and simulated datasets differed by less than 10%, simulation by DMM consistently overestimates power, except when using only highly discriminating taxa. Admixtures of DMM with experimental data performed poorly compared to pure simulation and did not show the same correlation with experimental data p-value and power values. While multiple replications of random sampling remain the favored method of determining the power, when the estimated sample size required to achieve a certain power exceeds the sample number, then simulated samples based on DMM can be used. We introduce an R-Package, MPrESS, to assist in power calculation and sample size estimation for a 16S rRNA gene microbiome dataset to detect a difference between populations. MPrESS can be downloaded from GitHub.

A tool to assess the mock community samples in 16S rRNA gene-based microbiota profiling studies

Inclusion and investigation of technical controls in microbiome sequencing studies is important for understanding technical biases and errors. Here, we present chkMocks, a general R-based tool that allows researchers to compare the composition of mock communities that are processed along with samples to their theoretical composition. A visual comparison between experimental and theoretical community composition and their correlation is provided for researchers to assess the quality of their sample processing workflows.

Iron oxide nanozymes stabilize stannous fluoride for targeted biofilm killing and synergistic oral disease prevention

Dental caries (tooth decay) is the most prevalent human disease caused by oral biofilms, affecting nearly half of the global population despite increased use of fluoride, the mainstay anticaries (tooth-enamel protective) agent. Recently, an FDA-approved iron oxide nanozyme formulation (ferumoxytol, Fer) has been shown to disrupt caries-causing biofilms with high specificity via catalytic activation of hydrogen peroxide, but it is incapable of interfering with enamel acid demineralization. Here, we find notable synergy when Fer is combined with stannous fluoride (SnF 2 ), markedly inhibiting both biofilm accumulation and enamel damage more effectively than either alone. Unexpectedly, our data show that SnF 2 enhances the catalytic activity of Fer, significantly increasing reactive oxygen species (ROS) generation and antibiofilm activity. We discover that the stability of SnF 2 (unstable in water) is markedly enhanced when mixed with Fer in aqueous solutions without any additives. Further analyses reveal that Sn 2+ is bound by carboxylate groups in the carboxymethyl-dextran coating of Fer, thus stabilizing SnF 2 and boosting the catalytic activity. Notably, Fer in combination with SnF 2 is exceptionally effective in controlling dental caries in vivo , preventing enamel demineralization and cavitation altogether without adverse effects on the host tissues or causing changes in the oral microbiome diversity. The efficacy of SnF 2 is also enhanced when combined with Fer, showing comparable therapeutic effects at four times lower fluoride concentration. Enamel ultrastructure examination shows that fluoride, iron, and tin are detected in the outer layers of the enamel forming a polyion-rich film, indicating co-delivery onto the tooth surface. Overall, our results reveal a unique therapeutic synergism using approved agents that target complementary biological and physicochemical traits, while providing facile SnF 2 stabilization, to prevent a widespread oral disease more effectively with reduced fluoride exposure.

Multistability and hysteresis in states of oral microbiota: Is it impacting the dental clinical cohort studies?

INTRODUCTION

Microbiome from a "healthy cohort" is used as a reference for comparison to cases and intervention. However, the studies with cohort-based clinical research have not sufficiently accounted for the multistability in oral microbial community. The screening is limited to phenotypic features with marked variations in microbial genomic markers. Herein, we aimed to assess the stability of the oral microbiome across time from an intervention-free "healthy" cohort.

METHODS

We obtained 33 supragingival samples of 11 healthy participants from the biobank. For each participant, we processed one sample as baseline (T0) and two samples spaced at 1-month (T1) and 3-month (T2) intervals for 16S ribosomal RNA gene sequencing analysis.

RESULTS

We observed that taxonomic profiling had a similar pattern of dominant genera, namely, Rothia, Prevotella, and Hemophilus, at all time points. Shannon diversity revealed a significant increase from T0 (p < .05). Bray Curtis dissimilarity was significant (R = -.02, p < .01) within the cohort at each time point. Community stability had negative correlation to synchrony (r = -.739; p = .009) and variance (r = -.605; p = .048) of the species. Clustering revealed marked differences in the grouping patterns between the three time points. For all time points, the clusters presented a substantially dissimilar set of differentially abundant taxonomic and functional biomarkers.

CONCLUSION

Our observations indicate towards the presence of multistable states within the oral microbiome in an intervention-free healthy cohort. For a conclusive and meaningful long-term reference, dental clinical research should account for multistability in the personalized therapy approach to improve the identification and classification of reliable markers.