Contamination in Low Microbial Biomass Microbiome Studies: Issues and Recommendations

Contributions of the Microbiome-Derived Metabolome for Risk Assessment and Prognostication of Pancreatic Cancer

BACKGROUND

Increasing evidence implicates microbiome involvement in the development and progression of pancreatic ductal adenocarcinoma (PDAC). Studies suggest that reflux of gut or oral microbiota can lead to colonization in the pancreas, resulting in dysbiosis that culminates in release of microbial toxins and metabolites that potentiate an inflammatory response and increase susceptibility to PDAC. Moreover, microbe-derived metabolites can exert direct effector functions on precursors and cancer cells, as well as other cell types, to either promote or attenuate tumor development and modulate treatment response.

CONTENT

The occurrence of microbial metabolites in biofluids thereby enables risk assessment and prognostication of PDAC, as well as having potential for design of interception strategies. In this review, we first highlight the relevance of the microbiome for progression of precancerous lesions in the pancreas and, using liquid chromatography-mass spectrometry, provide supporting evidence that microbe-derived metabolites manifest in pancreatic cystic fluid and are associated with malignant progression of intraductal papillary mucinous neoplasm(s). We secondly summarize the biomarker potential of microbe-derived metabolite signatures for (a) identifying individuals at high risk of developing or harboring PDAC and (b) predicting response to treatment and disease outcomes.

SUMMARY

The microbiome-derived metabolome holds considerable promise for risk assessment and prognostication of PDAC.

A metatranscriptomics strategy for efficient characterization of the microbiome in human tissues with low microbial biomass

The high background of host RNA poses a major challenge to metatranscriptome analysis of human samples. Hence, metatranscriptomics has been mainly applied to microbe-rich samples, while its application in human tissues with low ratio of microbial to host cells has yet to be explored. Since there is no computational workflow specifically designed for the taxonomic and functional analysis of this type of samples, we propose an effective metatranscriptomics strategy to accurately characterize the microbiome in human tissues with a low ratio of microbial to host content. We experimentally generated synthetic samples with well-characterized bacterial and host cell compositions, and mimicking human samples with high and low microbial loads. These synthetic samples were used for optimizing and establishing the workflow in a controlled setting. Our results show that the integration of the taxonomic analysis of optimized Kraken 2/Bracken with the functional analysis of HUMAnN 3 in samples with low microbial content, enables the accurate identification of a large number of microbial species with a low false-positive rate, while improving the detection of microbial functions. The effectiveness of our metatranscriptomics workflow was demonstrated in synthetic samples, simulated datasets, and most importantly, human gastric tissue specimens, thus providing a proof of concept for its applicability on mucosal tissues of the gastrointestinal tract. The use of an accurate and reliable metatranscriptomics approach for human tissues with low microbial content will expand our understanding of the functional activity of the mucosal microbiome, uncovering critical interactions between the microbiome and the host in health and disease.

Evaluation of multiple displacement amplification for metagenomic analysis of low biomass samples

Combining multiple displacement amplification (MDA) with metagenomics enables the analysis of samples with extremely low DNA concentrations, making them suitable for high-throughput sequencing. Although amplification bias and nonspecific amplification have been reported from MDA-amplified samples, the impact of MDA on metagenomic datasets is not well understood. We compared three MDA methods (i.e. bulk MDA, emulsion MDA, and primase MDA) for metagenomic analysis of two DNA template concentrations (approx. 1 and 100 pg) derived from a microbial community standard "mock community" and two low biomass environmental samples (i.e. borehole fluid and groundwater). We assessed the impact of MDA on metagenome-based community composition, assembly quality, functional profiles, and binning. We found amplification bias against high GC content genomes but relatively low nonspecific amplification such as chimeras, artifacts, or contamination for all MDA methods. We observed MDA-associated representational bias for microbial community profiles, especially for low-input DNA and with the primase MDA method. Nevertheless, similar taxa were represented in MDA-amplified libraries to those of unamplified samples. The MDA libraries were highly fragmented, but similar functional profiles to the unamplified libraries were obtained for bulk MDA and emulsion MDA at higher DNA input and across these MDA libraries for the groundwater sample. Medium to low-quality bins were possible for the high input bulk MDA metagenomes for the most simple microbial communities, borehole fluid, and mock community. Although MDA-based amplification should be avoided, it can still reveal meaningful taxonomic and functional information from samples with extremely low DNA concentration where direct metagenomics is otherwise impossible.

Molecular ecology of microbiomes in the wild: Common pitfalls, methodological advances and future directions

The study of microbiomes across organisms and environments has become a prominent focus in molecular ecology. This perspective article explores common challenges, methodological advancements, and future directions in the field. Key research areas include understanding the drivers of microbiome community assembly, linking microbiome composition to host genetics, exploring microbial functions, transience and spatial partitioning, and disentangling non-bacterial components of the microbiome. Methodological advancements, such as quantifying absolute abundances, sequencing complete genomes, and utilizing novel statistical approaches, are also useful tools for understanding complex microbial diversity patterns. Our aims are to encourage robust practices in microbiome studies and inspire researchers to explore the next frontier of this rapidly changing field.

Metatranscriptomic characterization of six types of forensic samples and its potential application to body fluid/tissue identification: A pilot study

Microorganisms are potential markers for identifying body fluids (venous and menstrual blood, semen, saliva, and vaginal secretion) and skin tissue in forensic genetics. Existing published studies have mainly focused on investigating microbial DNA by 16 S rRNA gene sequencing or metagenome shotgun sequencing. We rarely find microbial RNA level investigations on common forensic body fluid/tissue. Therefore, the use of metatranscriptomics to characterize common forensic body fluids/tissue has not been explored in detail, and the potential application of metatranscriptomics in forensic science remains unknown. Here, we performed 30 metatranscriptome analyses on six types of common forensic sample from healthy volunteers by massively parallel sequencing. After quality control and host RNA filtering, a total of 345,300 unigenes were assembled from clean reads. Four kingdoms, 137 phyla, 267 classes, 488 orders, 985 families, 2052 genera, and 4690 species were annotated across all samples. Alpha- and beta-diversity and differential analysis were also performed. As a result, the saliva and skin groups demonstrated high alpha diversity (Simpson index), while the venous blood group exhibited the lowest diversity despite a high Chao1 index. Specifically, we discussed potential microorganism contamination and the "core microbiome," which may be of special interest to forensic researchers. In addition, we implemented and evaluated artificial neural network (ANN), random forest (RF), and support vector machine (SVM) models for forensic body fluid/tissue identification (BFID) using genus- and species-level metatranscriptome profiles. The ANN and RF prediction models discriminated six forensic body fluids/tissue, demonstrating that the microbial RNA-based method could be applied to BFID. Unlike metagenomic research, metatranscriptomic analysis can provide information about active microbial communities; thus, it may have greater potential to become a powerful tool in forensic science for microbial-based individual identification. This study represents the first attempt to explore the application potential of metatranscriptome profiles in forensic science. Our findings help deepen our understanding of the microorganism community structure at the RNA level and are beneficial for other forensic applications of metatranscriptomics.

Microbiome and pancreatic cancer: time to think about chemotherapy

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer characterized by late diagnosis, rapid progression, and a high mortality rate. Its complex biology, characterized by a dense, stromal tumor environment with an immunosuppressive milieu, contributes to resistance against standard treatments like chemotherapy and radiation. This comprehensive review explores the dynamic role of the microbiome in modulating chemotherapy efficacy and outcomes in PDAC. It delves into the microbiome's impact on drug metabolism and resistance, and the interaction between microbial elements, drugs, and human biology. We also highlight the significance of specific bacterial species and microbial enzymes in influencing drug action and the immune response in the tumor microenvironment. Cutting-edge methodologies, including artificial intelligence, low-biomass microbiome analysis and patient-derived organoid models, are discussed, offering insights into the nuanced interactions between microbes and cancer cells. The potential of microbiome-based interventions as adjuncts to conventional PDAC treatments are discussed, paving the way for personalized therapy approaches. This review synthesizes recent research to provide an in-depth understanding of how the microbiome affects chemotherapy efficacy. It focuses on elucidating key mechanisms and identifying existing knowledge gaps. Addressing these gaps is crucial for enhancing personalized medicine and refining cancer treatment strategies, ultimately improving patient outcomes.

Getting off tract: contributions of intraorgan microbiota to cancer in extraintestinal organs

The gastrointestinal ecosystem has received the most attention when examining the contributions of the human microbiome to health and disease. This concentration of effort is logical due to the overwhelming abundance of microbes in the gut coupled with the relative ease of sampling compared with other organs. However, the intestines are intimately connected to multiple extraintestinal organs, providing an opportunity for homeostatic microbial colonisation and pathogenesis in organs traditionally thought to be sterile or only transiently harbouring microbiota. These habitats are challenging to sample, and their low microbial biomass among large amounts of host tissue can make study challenging. Nevertheless, recent findings have shown that many extraintestinal organs that are intimately linked to the gut harbour stable microbiomes, which are colonised from the gut in selective manners and have highlighted not just the influence of the bacteriome but that of the mycobiome and virome on oncogenesis and health.

Shorebirds-driven trophic cascade helps restore coastal wetland multifunctionality

Ecosystem restoration has traditionally focused on re-establishing vegetation and other foundation species at basal trophic levels, with mixed outcomes. Here, we show that threatened shorebirds could be important to restoring coastal wetland multifunctionality. We carried out surveys and manipulative field experiments in a region along the Yellow Sea affected by the invasive cordgrass Spartina alterniflora. We found that planting native plants alone failed to restore wetland multifunctionality in a field restoration experiment. Shorebird exclusion weakened wetland multifunctionality, whereas mimicking higher predation before shorebird population declines by excluding their key prey - crab grazers - enhanced wetland multifunctionality. The mechanism underlying these effects is a simple trophic cascade, whereby shorebirds control crab grazers that otherwise suppress native vegetation recovery and destabilize sediments (via bioturbation). Our findings suggest that harnessing the top-down effects of shorebirds - through habitat conservation, rewilding, or temporary simulation of consumptive or non-consumptive effects - should be explored as a nature-based solution to restoring the multifunctionality of degraded coastal wetlands.

Pan-cancer analyses reveal the stratification of patient prognosis by viral composition in tumor tissues

The associations between cancer and bacteria/fungi have been extensively studied, but the implications of cancer-associated viruses have not been thoroughly examined. In this study, we comprehensively characterized the cancer virome of tissue samples across 31 cancer types, as well as blood samples from 23 cancer types. Our findings demonstrated the presence of viral DNA at low abundances in both tissue and blood across major human cancers, with significant differences in viral community composition observed among various cancer types. Furthermore, Cox regression analyses conducted on four cancers, including Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC), Stomach adenocarcinoma (STAD), and Uterine Corpus Endometrial Carcinoma (UCEC), revealed strong correlation between viral composition/abundance in tissues and patient survival. Additionally, we identified virus-associated prognostic signatures (VAPS) for these four cancers, and discerned differences in the interplay between VAPS and dominant bacteria in tissues among patients with varying survival risks. Notably, clinically relevant analyses revealed prognostic capacities of the VAPS in these four cancers. Taken together, our study provides novel insights into the role of viruses in tissue in the prognosis of multiple cancers and offers guidance on the use of tissue viruses to stratify prognosis for patients with cancer.

Anionic nanocellulose as competing agent in microbial DNA extraction from mine process samples

Microorganisms in flotation and minerals processing may significantly affect the grade and yield of metal concentrates. However, studying the phenomena requires working techniques to detach microorganisms and their DNA from mineral particles to which they strongly adhere. We developed a new method utilizing the competitive properties of anionic nanocellulose to block sorption of DNA to and detach microbial cells from mineral particles from ore processing. In general, up to one ng DNA mL-1 sample was obtained with the custom anionic nanocellulose method (CM) compared to DNA amounts below the Qubit assay's detection limit for extractions with a commercial kit (KIT). Similarly, 0.5-4 orders of magnitude more bacterial 16S and fungal 5.8S rRNA gene copies were detected by qPCR from CM treated samples compared to KIT extractions. A clear difference in the detected microbial community structure between CM and KIT extracted samples was also observed. Commercial kits optimized for mineral soils are easy to use and time efficient but may miss a considerable part of the microbial communities. A competing agent such as anionic nanocellulose may decrease the interaction between microorganisms or their DNA and minerals and provide a comprehensive view into the microbial communities in mineral processing environments.

Isolated Grauer's gorilla populations differ in diet and gut microbiome

The animal gut microbiome has been implicated in a number of key biological processes, ranging from digestion to behaviour, and has also been suggested to facilitate local adaptation. Yet studies in wild animals rarely compare multiple populations that differ ecologically, which is the level at which local adaptation may occur. Further, few studies simultaneously characterize diet and gut microbiome from the same sample, despite their probable interdependence. Here, we investigate the interplay between diet and gut microbiome in three geographically isolated populations of the critically endangered Grauer's gorilla (Gorilla beringei graueri), which we show to be genetically differentiated. We find population- and social group-specific dietary and gut microbial profiles and covariation between diet and gut microbiome, despite the presence of core microbial taxa. There was no detectable effect of age, and only marginal effects of sex and genetic relatedness on the microbiome. Diet differed considerably across populations, with the high-altitude population consuming a lower diversity of plants compared to low-altitude populations, consistent with plant availability constraining dietary choices. The observed pattern of covariation between diet and gut microbiome is probably a result of long-term social and environmental factors. Our study suggests that the gut microbiome is sufficiently plastic to support flexible food selection and hence contribute to local adaptation.

Survey of the infant male urobiome and genomic analysis of Actinotignum spp

The urinary bladder harbors a community of microbes termed the urobiome, which remains understudied. In this study, we present the urobiome of healthy infant males from samples collected by transurethral catheterization. Using a combination of enhanced culture and amplicon sequencing, we identify several common bacterial genera that can be further investigated for their effects on urinary health across the lifespan. Many genera were shared between all samples suggesting a consistent urobiome composition among this cohort. We note that, for this cohort, early life exposures including mode of birth (vaginal vs. Cesarean section), or prior antibiotic exposure did not influence urobiome composition. In addition, we report the isolation of culturable bacteria from the bladders of these infant males, including Actinotignum spp., a bacterial genus that has been associated with urinary tract infections in older male adults. Herein, we isolate and sequence 9 distinct strains of Actinotignum spp. enhancing the genomic knowledge surrounding this genus and opening avenues for delineating the microbiology of this urobiome constituent. Furthermore, we present a framework for using the combination of culture-dependent and sequencing methodologies for uncovering mechanisms in the urobiome.

Ancient dental calculus reveals oral microbiome shifts associated with lifestyle and disease in Great Britain

The prevalence of chronic, non-communicable diseases has risen sharply in recent decades, especially in industrialized countries. While several studies implicate the microbiome in this trend, few have examined the evolutionary history of industrialized microbiomes. Here we sampled 235 ancient dental calculus samples from individuals living in Great Britain (∼2200 BCE to 1853 CE), including 127 well-contextualized London adults. We reconstructed their microbial history spanning the transition to industrialization. After controlling for oral geography and technical biases, we identified multiple oral microbial communities that coexisted in Britain for millennia, including a community associated with Methanobrevibacter, an anaerobic Archaea not commonly prevalent in the oral microbiome of modern industrialized societies. Calculus analysis suggests that oral hygiene contributed to oral microbiome composition, while microbial functions reflected past differences in diet, specifically in dairy and carbohydrate consumption. In London samples, Methanobrevibacter-associated microbial communities are linked with skeletal markers of systemic diseases (for example, periostitis and joint pathologies), and their disappearance is consistent with temporal shifts, including the arrival of the Second Plague Pandemic. This suggests pre-industrialized microbiomes were more diverse than previously recognized, enhancing our understanding of chronic, non-communicable disease origins in industrialized populations.

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.

Dancing the Nanopore limbo - Nanopore metagenomics from small DNA quantities for bacterial genome reconstruction

BACKGROUND

While genome-resolved metagenomics has revolutionized our understanding of microbial and genetic diversity in environmental samples, assemblies of short-reads often result in incomplete and/or highly fragmented metagenome-assembled genomes (MAGs), hampering in-depth genomics. Although Nanopore sequencing has increasingly been used in microbial metagenomics as long reads greatly improve the assembly quality of MAGs, the recommended DNA quantity usually exceeds the recoverable amount of DNA of environmental samples. Here, we evaluated lower-than-recommended DNA quantities for Nanopore library preparation by determining sequencing quality, community composition, assembly quality and recovery of MAGs.

RESULTS

We generated 27 Nanopore metagenomes using the commercially available ZYMO mock community and varied the amount of input DNA from 1000 ng (the recommended minimum) down to 1 ng in eight steps. The quality of the generated reads remained stable across all input levels. The read mapping accuracy, which reflects how well the reads match a known reference genome, was consistently high across all libraries. The relative abundance of the species in the metagenomes was stable down to input levels of 50 ng. High-quality MAGs (> 95% completeness, ≤ 5% contamination) could be recovered from metagenomes down to 35 ng of input material. When combined with publicly available Illumina reads for the mock community, Nanopore reads from input quantities as low as 1 ng improved the quality of hybrid assemblies.

CONCLUSION

Our results show that the recommended DNA amount for Nanopore library preparation can be substantially reduced without any adverse effects to genome recovery and still bolster hybrid assemblies when combined with short-read data. We posit that the results presented herein will enable studies to improve genome recovery from low-biomass environments, enhancing microbiome understanding.

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.

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.

Tumor-resident Lactobacillus iners confer chemoradiation resistance through lactate-induced metabolic rewiring

Tumor microbiota can produce active metabolites that affect cancer and immune cell signaling, metabolism, and proliferation. Here, we explore tumor and gut microbiome features that affect chemoradiation response in patients with cervical cancer using a combined approach of deep microbiome sequencing, targeted bacterial culture, and in vitro assays. We identify that an obligate L-lactate-producing lactic acid bacterium found in tumors, Lactobacillus iners, is associated with decreased survival in patients, induces chemotherapy and radiation resistance in cervical cancer cells, and leads to metabolic rewiring, or alterations in multiple metabolic pathways, in tumors. Genomically similar L-lactate-producing lactic acid bacteria commensal to other body sites are also significantly associated with survival in colorectal, lung, head and neck, and skin cancers. Our findings demonstrate that lactic acid bacteria in the tumor microenvironment can alter tumor metabolism and lactate signaling pathways, causing therapeutic resistance. Lactic acid bacteria could be promising therapeutic targets across cancer types.

The intratumor mycobiome promotes lung cancer progression via myeloid-derived suppressor cells

Although polymorphic microbiomes have emerged as hallmarks of cancer, far less is known about the role of the intratumor mycobiome as living microorganisms in cancer progression. Here, using fungi-enriched DNA extraction and deep shotgun metagenomic sequencing, we have identified enriched tumor-resident Aspergillus sydowii in patients with lung adenocarcinoma (LUAD). By three different syngeneic lung cancer mice models, we find that A. sydowii promotes lung tumor progression via IL-1β-mediated expansion and activation of MDSCs, resulting in suppressed activity of cytotoxic T lymphocyte cells and accumulation of PD-1+ CD8+ T cells. This is mediated by IL-1β secretion via β-glucan/Dectin-1/CARD9 pathway. Analysis of human samples confirms that enriched A. sydowii is associated with immunosuppression and poor patient outcome. Our findings suggest that intratumor mycobiome, albeit at low biomass, promotes lung cancer progression and could be targeted at the strain level to improve patients with LUAD outcome.

Recovery of microbial DNA by agar-containing solution from extremely low-biomass specimens including skin

Recovering a sufficient amount of microbial DNA from extremely low-biomass specimens, such as human skin, to investigate the community structure of the microbiome remains challenging. We developed a sampling solution containing agar to increase the abundance of recovered microbial DNA. Quantitative PCR targeting the 16S rRNA gene revealed a significant increase in the amount of microbial DNA recovered from the developed sampling solution compared with conventional solutions from extremely low-biomass skin sites such as the volar forearm and antecubital fossa. In addition, we confirmed that the developed sampling solution reduces the contamination rate of probable non-skin microbes compared to the conventional solutions, indicating that the enhanced recovery of microbial DNA was accompanied by a reduced relative abundance of contaminating microbes in the 16S rRNA gene amplicon sequencing data. In addition, agar was added to each step of the DNA extraction process, which improved the DNA extraction efficiency as a co-precipitant. Enzymatic lysis with agar yielded more microbial DNA than conventional kits, indicating that this method is effective for analyzing microbiomes of low-biomass specimens.

Association of Cutibacterium acnes with human thyroid cancer

Introduction

The diverse subtypes of thyroid carcinoma have distinct clinical outcomes despite a comparable spectrum of underlying genetic alterations. Beyond genetic alterations, sparse efforts have been made to characterize the microbes associated with thyroid cancer. In this study, we examine the microbial profile of thyroid cancer.

Methods

We sequenced the whole transcriptome of 70 thyroid cancers (40 papillary and 30 anaplastic). Using Infectious Pathogen Detector IPD 2.0, we analysed the relative abundance of 1060 microbes across 70 tumours from patients with thyroid cancer against 118 tumour samples from patients with breast, cervical, colorectal, and tongue cancer.

Results

Our analysis reveals a significant prevalence of Cutibacterium acnes in 58.6% thyroid cancer samples compared to other cancer types (p=0.00038). Immune cell fraction analysis between thyroid cancer samples with high and low Cutibacterium loads identify enrichment of immunosuppressive cells, including Tregs (p=0.015), and other anti-inflammatory cytokines in the tumour microenvironment, suggesting an immune evasion/immunosuppression milieu is associated with the infection. A higher burden of Cutibacterium acnes was also found to be associated with poor survival defining a distinct sub-group of thyroid cancer.

Conclusion

Cutibacterium acnes is associated with immune suppression and poor prognosis in a subpopulation of thyroid cancer. This study may help design novel therapeutic measures involving appropriate antibiotics to manage the disease better.

Analysis of microbial composition and sharing in low-biomass human milk samples: a comparison of DNA isolation and sequencing techniques

Human milk microbiome studies are currently hindered by low milk bacterial/human cell ratios and often rely on 16S rRNA gene sequencing, which limits downstream analyses. Here, we aimed to find a method to study milk bacteria and assess bacterial sharing between maternal and infant microbiota. We tested four DNA isolation methods, two bacterial enrichment methods and three sequencing methods on mock communities, milk samples and negative controls. Of the four DNA isolation kits, the DNeasy PowerSoil Pro (PS) and MagMAX Total Nucleic Acid Isolation (MX) kits provided consistent 16S rRNA gene sequencing results with low contamination. Neither enrichment method substantially decreased the human metagenomic sequencing read-depth. Long-read 16S-ITS-23S rRNA gene sequencing biased the mock community composition but provided consistent results for milk samples, with little contamination. In contrast to 16S rRNA gene sequencing, 16S-ITS-23S rRNA gene sequencing of milk, infant oral, infant faecal and maternal faecal DNA from 14 mother-infant pairs provided sufficient resolution to detect significantly more frequent sharing of bacteria between related pairs compared to unrelated pairs. In conclusion, PS or MX kit-DNA isolation followed by 16S rRNA gene sequencing reliably characterises human milk microbiota, and 16S-ITS-23S rRNA gene sequencing enables studies of bacterial transmission in low-biomass samples.

Field-Based Planetary Protection Operations for Melt Probes: Validation of Clean Access into the Blood Falls, Antarctica, Englacial Ecosystem

Subglacial environments on Earth offer important analogs to Ocean World targets in our solar system. These unique microbial ecosystems remain understudied due to the challenges of access through thick glacial ice (tens to hundreds of meters). Additionally, sub-ice collections must be conducted in a clean manner to ensure sample integrity for downstream microbiological and geochemical analyses. We describe the field-based cleaning of a melt probe that was used to collect brine samples from within a glacier conduit at Blood Falls, Antarctica, for geomicrobiological studies. We used a thermoelectric melting probe called the IceMole that was designed to be minimally invasive in that the logistical requirements in support of drilling operations were small and the probe could be cleaned, even in a remote field setting, so as to minimize potential contamination. In our study, the exterior bioburden on the IceMole was reduced to levels measured in most clean rooms, and below that of the ice surrounding our sampling target. Potential microbial contaminants were identified during the cleaning process; however, very few were detected in the final englacial sample collected with the IceMole and were present in extremely low abundances (∼0.063% of 16S rRNA gene amplicon sequences). This cleaning protocol can help minimize contamination when working in remote field locations, support microbiological sampling of terrestrial subglacial environments using melting probes, and help inform planetary protection challenges for Ocean World analog mission concepts.

The Microbiome, Ocular Surface, and Corneal Disorders

The ocular surface microbiome is an emerging field of study that seeks to understand how the community of microorganisms found on the ocular surface may help maintain homeostasis or can potentially lead to disease and dysbiosis. Initial questions include whether the organisms detected on the ocular surface inhabit that ecological niche and, if so, whether there exists a core microbiome found in most or all healthy eyes. Many questions have emerged around whether novel organisms and/or a redistribution of organisms play a role in disease pathogenesis, response to therapies, or convalescence. Although there is much enthusiasm about this topic, the ocular surface microbiome is a new field with many technical challenges. These challenges are discussed in this review as well as a need for standardization to adequately compare studies and advance the field. In addition, this review summarizes the current research on the microbiome of various ocular surface diseases and how these findings may impact treatments and clinical decision-making.

Untangling the web of intratumor microbiota in lung cancer

Microbes are pivotal in contemporary cancer research, influencing various biological behaviors in cancer. The previous notion that the lung was sterile has been destabilized by the discovery of microbiota in the lower airway and lung, even within tumor tissues. Advances of biotechnology enable the association between intratumor microbiota and lung cancer to be revealed. Nonetheless, the origin and tumorigenicity of intratumor microbiota in lung cancer still remain implicit. Additionally, accumulating evidence indicates that intratumor microbiota might serve as an emerging biomarker for cancer diagnosis, prognosis, and even a therapeutic target across multiple cancer types, including lung cancer. However, research on intratumor microbiota's role in lung cancer is still nascent and warrants more profound exploration. Herein, this paper provides an extensive review of recent advancements in the following fields, including 1) established and emerging biotechnologies utilized to study intratumor microbiota in lung cancer, 2) causation between intratumor microbiota and lung cancer from the perspectives of translocation, cancerogenesis and metastasis, 3) potential application of intratumor microbiota as a novel biomarker for lung cancer diagnosis and prognosis, and 4) promising lung cancer therapies via regulating intratumor microbiota. Moreover, this review addresses the limitations, challenges, and future prospects of studies focused on intratumor microbiota in lung cancer.

Alterations in sea urchin (Mesocentrotus nudus) microbiota and their potential contributions to host according to barren severity

Sea urchins are biotic factors driving the decline of kelp forests in marine ecosystems. However, few studies have analyzed the microbiota of surviving sea urchins in barren regions with scarce diet resources. Here, we analyzed the microbiota in the pharynx and gut of the sea urchin Mesocentrotus nudus located along the coast of an expanding barren region in South Korea. The ecological adaptation of genera in sea urchins was predicted using the neutral assembly model. The pharynx and gut microbiota were different, and microbes in the surrounding habitats dispersed more to the pharynx than to the gut. The gut microbiota in sea urchins is altered by barren severity and plays different roles in host energy metabolism. These findings help to understand the microbiota in sea urchins according to urchin barren and its contribution to the survival of sea urchins in severe barren regions with limited macroalgae.

Exploring gut microbiota in adult Atlantic salmon (Salmo salar L.): Associations with gut health and dietary prebiotics

BACKGROUND

The importance of the gut microbiota for physiological processes in mammals is well established, but the knowledge of their functional roles in fish is still limited. The aims of this study were to investigate associations between variation in taxonomical composition of the gut microbiota and gut health status in Atlantic salmon and to explore possible modulatory effects of dietary prebiotics in one net-pen farm in open water. The fish with initial mean body weight of around 240 g were fed diets based on the same basal composition, either without (Ref diet) or with (Test diet) yeast cell wall based-prebiotics, during the marine production phase from December to September the following year. Sampling was conducted at three sampling time points: January, April, and September, with average water temperature of 3.9 ℃, 3.4 ℃ and 9.6 ℃, respectively.

RESULTS

As the fish progressed towards September, growth, brush border membrane enzyme activities, and the expression in the gut of most of the observed genes involved in immune (e.g., il8, cd4a, myd88, il1b, gilt, tgfb, cd8b and cd3), barrier (e.g., zo1, occludin, ecad, claudin25b and claudin15), and metabolism increased significantly. Lipid accumulation in pyloric enterocytes decreased remarkably, suggesting improvement of gut health condition. The growth of the fish did not differ between dietary treatments. Further, dietary prebiotics affected the gut health only marginally regardless of duration of administration. Regarding gut microbiota composition, a decrease in alpha diversity (Observed species, Pielou and Shannon) over time was observed, which was significantly associated with an increase in the relative abundance of genus Mycoplasma and decrease in 32 different taxa in genus level including lactic acid bacteria (LAB), such as Lactobacillus, Leuconostoc, and Lactococcus. This indicates that developmental stage of Atlantic salmon is a determinant for microbial composition. Multivariate association analysis revealed that the relative abundance of Mycoplasma was positively correlated with gut barrier gene expression, negatively correlated with plasma glucose levels, and that its relative abundance slightly increased by exposure to prebiotics. Furthermore, certain LAB (e.g., Leuconostoc), belonging to the core microbiota, showed a negative development with time, and significant associations with plasma nutrients levels (e.g., triglyceride and cholesterol) and gene expression related to gut immune and barrier function.

CONCLUSIONS

As Atlantic salmon grew older under large-scale, commercial farm settings, the Mycoplasma became more prominent with a concomitant decline in LAB. Mycoplasma abundance correlated positively with time and gut barrier genes, while LAB abundance negatively correlated to time. Dietary prebiotics affected gut health status only marginally.

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.

Intratumor microbiota: Occult participants in the microenvironment of multiple myeloma

More recently, microbiota was detected in several tumorous tissues including multiple myeloma (MM), but the roles of which is still under-studied as paucity of research on tumor biology. Moreover, we also detected the presence of microbiota in the bone marrow of patients with MM by 2bRAD-M sequencing technology, which is an incurable hematological malignancy characterized by accumulation of abnormal plasma cells in the bone marrow. However, the roles of intratumor microbiota in tumor disease remains poorly understood. In this review, we critically reviewed recent literature about microbiota in the tumorigenesis and progression of MM. Importantly, we proposed that the emergence of microbiota in the microenvironment of multiple myeloma may be attributed to microbial dysbiosis and impaired intestinal barrier, due to the increased prevalence of MM in patients with obesity and diabetes, of which the characteristic phenotype is gut microbial dysbiosis and impaired intestinal barrier. When the intestinal barrier is damaged, dysbiotic microbiota and their metabolites, as well as dysregulated immune cells, may participate in the reshaping of the local immune microenvironment, and play pivotal roles in the tumorigenesis and development of multiple myeloma, probably by migrating to the bone marrow microenvironment from intestine. We also discuss the emerging microbiological manipulation strategies to improve long-term outcomes of MM, as well as the prospective of the state-of-the-art techniques to advance our knowledge about the biological implication in the microbiome in MM.

Metagenomic Analysis of a Concrete Bridge Reveals a Microbial Community Dominated by Halophilic Bacteria and Archaea

Concrete hosts a small but diverse microbiome that changes over time. Shotgun metagenomic sequencing would enable assessment of both the diversity and function of the microbial community in concrete, but a number of unique challenges make this difficult for concrete samples. The high concentration of divalent cations in concrete interferes with nucleic acid extraction, and the extremely low biomass in concrete means that DNA from laboratory contamination may be a large fraction of the sequence data. Here, we develop an improved method for DNA extraction from concrete, with higher yield and lower laboratory contamination. To show that this method provides DNA of sufficient quality and quantity to do shotgun metagenomic sequencing, DNA was extracted from a sample of concrete obtained from a road bridge and sequenced with an Illumina MiSeq system. This microbial community was dominated by halophilic Bacteria and Archaea, with enriched functional pathways related to osmotic stress responses. Although this was a pilot-scale effort, we demonstrate that metagenomic sequencing can be used to characterize microbial communities in concrete and that older concrete structures may host different microbes than recently poured concrete. IMPORTANCE Prior work on the microbial communities of concrete focused on the surfaces of concrete structures such as sewage pipes or bridge pilings, where thick biofilms were easy to observe and sample. Because the biomass inside concrete is so low, more recent analyses of the microbial communities inside concrete used amplicon sequencing methods to describe those communities. However, to understand the activity and physiology of microbes in concrete, or to develop living infrastructure, we must develop more direct methods of community analysis. The method developed here for DNA extraction and metagenomic sequencing can be used for analysis of microbial communities inside concrete and can likely be adapted for other cementitious materials.

Intratumoral Microbiota Changes with Tumor Stage and Influences the Immune Signature of Oral Squamous Cell Carcinoma

Characterization of the oral microbiota profile through various studies has shown an association between the microbiome and oral cancer; however, stage-specific determinants of dynamic changes in microbial communities of oral cancer remain elusive. Additionally, the influence of the intratumoral microbiota on the intratumoral immune system remains largely unexplored. Therefore, this study aims to stratify microbial abundance in the early-onset and subsequent stages of oral cancer and analyze their influence on clinical-pathological and immunological features. The microbiome composition of tissue biopsy samples was identified using 16S rRNA amplicon sequencing, while intratumoral and systemic immune profiling was done with flow cytometry and immunohistochemistry-based analysis. The bacterial composition differed significantly among precancer, early cancer, and late cancer stages with the enrichment of genera Capnocytophaga, Fusobacterium, and Treponema in the cancer group, while Streptococcus and Rothia were enriched in the precancer group. Late cancer stages were significantly associated with Capnocytophaga with high predicting accuracy, while Fusobacterium was associated with early stages of cancer. A dense intermicrobial and microbiome-immune network was observed in the precancer group. At the cellular level, intratumoral immune cell infiltration of B cells and T cells (CD4+ and CD8+) was observed with enrichment of the effector memory phenotype. Naive and effector subsets of tumor-infiltrating lymphocytes (TILs) and related gene expression were found to be distinctly associated with bacterial communities; most importantly, highly abundant bacterial genera of the tumor microenvironment were either negatively correlated or not associated with the effector lymphocytes, which led to the conclusion that the tumor microenvironment favors an immunosuppressive and nonimmunogenic microbiota. IMPORTANCE The gut microbiome has been explored extensively for its importance in the modulation of systemic inflammation and immune response; in contrast, the intratumoral microbiome is less studied for its influence on immunity in cancer. Given the established correlation between intratumoral lymphocyte infiltration and patient survival in cases of solid tumors, it was pertinent to explore the extrinsic factor influencing immune cell infiltration in the tumor. Modulation of intratumoral microbiota could have a beneficial effect on the antitumor immune response. This study stratifies the microbial profile of oral squamous cell carcinoma starting from precancer to late-stage cancer and provides evidence for their immunomodulatory role in the tumor microenvironment. Our results suggest combining microbiome study with immunological signatures of tumors for their prognostic and diagnostic application.

Dissecting Microbiome-Derived SCFAs in Prostate Cancer: Analyzing Gut Microbiota, Racial Disparities, and Epigenetic Mechanisms

Prostate cancer (PCa) continues to be the most diagnosed cancer and the second primary cause of fatalities in men globally. There is an abundance of scientific evidence suggesting that the human microbiome, together with its metabolites, plays a crucial role in carcinogenesis and has a significant impact on the efficacy of anticancer interventions in solid and hematological cancers. These anticancer interventions include chemotherapy, immune checkpoint inhibitors, and targeted therapies. Furthermore, the microbiome can influence systemic and local immune responses using numerous metabolites such as short-chain fatty acids (SCFAs). Despite the lack of scientific data in terms of the role of SCFAs in PCa pathogenesis, recent studies show that SCFAs have a profound impact on PCa progression. Several studies have reported racial/ethnic disparities in terms of bacterial content in the gut microbiome and SCFA composition. These studies explored microbiome and SCFA racial/ethnic disparities in cancers such as colorectal, colon, cervical, breast, and endometrial cancer. Notably, there are currently no published studies exploring microbiome/SCFA composition racial disparities and their role in PCa carcinogenesis. This review discusses the potential role of the microbiome in PCa development and progression. The involvement of microbiome-derived SCFAs in facilitating PCa carcinogenesis and their effect on PCa therapeutic response, particularly immunotherapy, are discussed. Racial/ethnic differences in microbiome composition and SCFA content in various cancers are also discussed. Lastly, the effects of SCFAs on PCa progression via epigenetic modifications is also discussed.

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

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

Revisiting the intrinsic mycobiome in pancreatic cancer

A growing body of literature suggests that alterations in the human microbiome are causative of disease initiation and progression. Aykut et al1 . present data supporting the argument that alterations in the gut fungal microbiome (the “mycobiome”), along with the presence of fungal elements within pancreatic tissue (specifically those of the genus Malassezia) , are associated with pancreatic oncogenesis. Upon analyzing the human sequencing data presented in the original manuscript, we found few fungal reads in pancreatic tissue samples and did not identify differences in pancreatic or gut mycobiome composition between healthy and pancreatic ductal adenocarcinoma (PDAC) patients. Our re-analysis of these data does not support an association between an intrinsic pancreatic mycobiome and the development of human PDAC, and illustrates the challenges in analyzing microbiome sequencing data from low biomass samples.

Caution regarding the specificities of pan-cancer microbial structure

Results published in an article by Poore et al. (Nature. 2020;579:567-574) suggested that machine learning models can almost perfectly distinguish between tumour types based on their microbial composition using machine learning models. Whilst we believe that there is the potential for microbial composition to be used in this manner, we have concerns with the paper that make us question the certainty of the conclusions drawn. We believe there are issues in the areas of the contribution of contamination, handling of batch effects, false positive classifications and limitations in the machine learning approaches used. This makes it difficult to identify whether the authors have identified true biological signal and how robust these models would be in use as clinical biomarkers. We commend Poore et al. on their approach to open data and reproducibility that has enabled this analysis. We hope that this discourse assists the future development of machine learning models and hypothesis generation in microbiome research.

Metagenomic Methods for Addressing NASA's Planetary Protection Policy Requirements on Future Missions: A Workshop Report

Molecular biology methods and technologies have advanced substantially over the past decade. These new molecular methods should be incorporated among the standard tools of planetary protection (PP) and could be validated for incorporation by 2026. To address the feasibility of applying modern molecular techniques to such an application, NASA conducted a technology workshop with private industry partners, academics, and government agency stakeholders, along with NASA staff and contractors. The technical discussions and presentations of the Multi-Mission Metagenomics Technology Development Workshop focused on modernizing and supplementing the current PP assays. The goals of the workshop were to assess the state of metagenomics and other advanced molecular techniques in the context of providing a validated framework to supplement the bacterial endospore-based NASA Standard Assay and to identify knowledge and technology gaps. In particular, workshop participants were tasked with discussing metagenomics as a stand-alone technology to provide rapid and comprehensive analysis of total nucleic acids and viable microorganisms on spacecraft surfaces, thereby allowing for the development of tailored and cost-effective microbial reduction plans for each hardware item on a spacecraft. Workshop participants recommended metagenomics approaches as the only data source that can adequately feed into quantitative microbial risk assessment models for evaluating the risk of forward (exploring extraterrestrial planet) and back (Earth harmful biological) contamination. Participants were unanimous that a metagenomics workflow, in tandem with rapid targeted quantitative (digital) PCR, represents a revolutionary advance over existing methods for the assessment of microbial bioburden on spacecraft surfaces. The workshop highlighted low biomass sampling, reagent contamination, and inconsistent bioinformatics data analysis as key areas for technology development. Finally, it was concluded that implementing metagenomics as an additional workflow for addressing concerns of NASA's robotic mission will represent a dramatic improvement in technology advancement for PP and will benefit future missions where mission success is affected by backward and forward contamination.

Potential role of intratumor bacteria outside the gastrointestinal tract: More than passengers

INTRODUCTION

Tumor-associated bacteria and gut microbiota have gained significant attention in recent years due to their potential role in cancer development and therapeutic response. This review aims to discuss the contributions of intratumor bacteria outside the gastrointestinal tract, in addition to exploring the mechanisms, functions, and implications of these bacteria in cancer therapy.

METHODS

We reviewed current literature on intratumor bacteria and their impact on tumorigenesis, progression, metastasis, drug resistance, and anti-tumor immune modulation. Additionally, we examined techniques used to detect intratumor bacteria, precautions necessary when handling low microbial biomass tumor samples, and the recent progress in bacterial manipulation for tumor treatment.

RESULTS

Research indicates that each type of cancer uniquely interacts with its microbiome, and bacteria can be detected even in non-gastrointestinal tumors with low bacterial abundance. Intracellular bacteria have the potential to regulate tumor cells' biological behavior and contribute to critical aspects of tumor development. Furthermore, bacterial-based anti-tumor therapies have shown promising results in cancer treatment.

CONCLUSIONS

Understanding the complex interactions between intratumor bacteria and tumor cells could lead to the development of more precise cancer treatment strategies. Further research into non-gastrointestinal tumor-associated bacteria is needed to identify new therapeutic approaches and expand our knowledge of the microbiota's role in cancer biology.

Machine learning-based approaches for cancer prediction using microbiome data

Emerging evidence of the relationship between the microbiome composition and the development of numerous diseases, including cancer, has led to an increasing interest in the study of the human microbiome. Technological breakthroughs regarding DNA sequencing methods propelled microbiome studies with a large number of samples, which called for the necessity of more sophisticated data-analytical tools to analyze this complex relationship. The aim of this work was to develop a machine learning-based approach to distinguish the type of cancer based on the analysis of the tissue-specific microbial information, assessing the human microbiome as valuable predictive information for cancer identification. For this purpose, Random Forest algorithms were trained for the classification of five types of cancer-head and neck, esophageal, stomach, colon, and rectum cancers-with samples provided by The Cancer Microbiome Atlas database. One versus all and multi-class classification studies were conducted to evaluate the discriminative capability of the microbial data across increasing levels of cancer site specificity, with results showing a progressive rise in difficulty for accurate sample classification. Random Forest models achieved promising performances when predicting head and neck, stomach, and colon cancer cases, with the latter returning accuracy scores above 90% across the different studies conducted. However, there was also an increased difficulty when discriminating esophageal and rectum cancers, failing to differentiate with adequate results rectum from colon cancer cases, and esophageal from head and neck and stomach cancers. These results point to the fact that anatomically adjacent cancers can be more complex to identify due to microbial similarities. Despite the limitations, microbiome data analysis using machine learning may advance novel strategies to improve cancer detection and prevention, and decrease disease burden.

Longitudinal development of the airway metagenome of preterm very low birth weight infants during the first two years of life

Preterm birth is accompanied with many complications and requires severe therapeutic regimens at the neonatal intensive care unit. The influence of the above-mentioned factors on the premature-born infants' respiratory metagenome or more generally its maturation is unknown. We therefore applied shotgun metagenome sequencing of oropharyngeal swabs to analyze the airway metagenome development of 24 preterm infants from one week postpartum to 15 months of age. Beta diversity analysis revealed a distinct clustering of airway microbial communities from hospitalized preterms and samples after hospital discharge. At nine and 15 months of age, the preterm infants lost their hospital-acquired individual metagenome signatures towards a common taxonomic structure. However, ecological network analysis and Random Forest classification of cross-sectional data revealed that by this age the preterm infants did not succeed in establishing the uniform and stable bacterial community structures that are characteristic for healthy full-term infants.

Anopheline mosquito saliva contains bacteria that are transferred to a mammalian host through blood feeding

Introduction

Malaria transmission occurs when Plasmodium sporozoites are transferred from the salivary glands of anopheline mosquitoes to a human host through the injection of saliva. The need for better understanding, as well as novel modes of inhibiting, this key event in transmission has driven intense study of the protein and miRNA content of saliva. Until now the possibility that mosquito saliva may also contain bacteria has remained an open question despite the well documented presence of a rich microbiome in salivary glands.

Methods

Using both 16S rRNA sequencing and MALDI-TOF approaches, we characterized the composition of the saliva microbiome of An. gambiae and An. stephensi mosquitoes which respectively represent two of the most important vectors for the major malaria-causing parasites P. falciparum and P. vivax.

Results

To eliminate the possible detection of non-mosquito-derived bacteria, we used a transgenic, fluorescent strain of one of the identified bacteria, Serratiamarcescens, to infect mosquitoes and detect its presence in mosquito salivary glands as well as its transfer to, and colonization of, mammalian host tissues following a mosquito bite. We also showed that Plasmodium infection modified the mosquito microbiota, increasing the presence of Serratia while diminishing the presence of Elizabethkingia and that both P. berghei and Serratia were transferred to, and colonized mammalian tissues.

Discussion

These data thus document the presence of bacteria in mosquito saliva, their transfer to, and growth in a mammalian host as well as possible interactions with Plasmodium transmission. Together they raise the possible role of mosquitoes as vectors of bacterial infection and the utility of commensal mosquito bacteria for the development of transmission-blocking strategies within a mammalian host.

Ecoevolutionary processes structure milk microbiomes across the mammalian tree of life

Milk production is an ancient adaptation that unites all mammals. Milk contains a microbiome that can contribute to offspring health and microbial-immunological development. We generated a comprehensive milk microbiome dataset (16S rRNA gene) for the class Mammalia, representing 47 species from all placental superorders, to determine processes structuring milk microbiomes. We show that across Mammalia, milk exposes offspring to maternal bacterial and archaeal symbionts throughout lactation. Deterministic processes of environmental selection accounted for 20% of milk microbiome assembly processes; milk microbiomes were similar from mammals with the same host superorder (Afrotheria, Laurasiathera, Euarchontoglires, and Xenarthra: 6%), environment (marine captive, marine wild, terrestrial captive, and terrestrial wild: 6%), diet (carnivore, omnivore, herbivore, and insectivore: 5%), and milk nutrient content (sugar, fat, and protein: 3%). We found that diet directly and indirectly impacted milk microbiomes, with indirect effects being mediated by milk sugar content. Stochastic processes, such as ecological drift, accounted for 80% of milk microbiome assembly processes, which was high compared to mammalian gut and mammalian skin microbiomes (69% and 45%, respectively). Even amid high stochasticity and indirect effects, our results of direct dietary effects on milk microbiomes provide support for enteromammary trafficking, representing a mechanism by which bacteria are transferred from the mother's gut to mammary gland and then to offspring postnatally. The microbial species present in milk reflect both selective pressures and stochastic processes at the host level, exemplifying various ecological and evolutionary factors acting on milk microbiomes, which, in turn, set the stage for offspring health and development.

AMAnD: an automated metagenome anomaly detection methodology utilizing DeepSVDD neural networks

The composition of metagenomic communities within the human body often reflects localized medical conditions such as upper respiratory diseases and gastrointestinal diseases. Fast and accurate computational tools to flag anomalous metagenomic samples from typical samples are desirable to understand different phenotypes, especially in contexts where repeated, long-duration temporal sampling is done. Here, we present Automated Metagenome Anomaly Detection (AMAnD), which utilizes two types of Deep Support Vector Data Description (DeepSVDD) models; one trained on taxonomic feature space output by the Pan-Genomics for Infectious Agents (PanGIA) taxonomy classifier and one trained on kmer frequency counts. AMAnD's semi-supervised one-class approach makes no assumptions about what an anomaly may look like, allowing the flagging of potentially novel anomaly types. Three diverse datasets are profiled. The first dataset is hosted on the National Center for Biotechnology Information's (NCBI) Sequence Read Archive (SRA) and contains nasopharyngeal swabs from healthy and COVID-19-positive patients. The second dataset is also hosted on SRA and contains gut microbiome samples from normal controls and from patients with slow transit constipation (STC). AMAnD can learn a typical healthy nasopharyngeal or gut microbiome profile and reliably flag the anomalous COVID+ or STC samples in both feature spaces. The final dataset is a synthetic metagenome created by the Critical Assessment of Metagenome Annotation Simulator (CAMISIM). A control dataset of 50 well-characterized organisms was submitted to CAMISIM to generate 100 synthetic control class samples. The experimental conditions included 12 different spiked-in contaminants that are taxonomically similar to organisms present in the laboratory blank sample ranging from one strain tree branch taxonomic distance away to one family tree branch taxonomic distance away. This experiment was repeated in triplicate at three different coverage levels to probe the dependence on sample coverage. AMAnD was again able to flag the contaminant inserts as anomalous. AMAnD's assumption-free flagging of metagenomic anomalies, the real-time model training update potential of the deep learning approach, and the strong performance even with lightweight models of low sample cardinality would make AMAnD well-suited to a wide array of applied metagenomics biosurveillance use-cases, from environmental to clinical utility.

Exploring breast tissue microbial composition and the association with breast cancer risk factors

BACKGROUND

Microbial dysbiosis has emerged as an important element in the development and progression of various cancers, including breast cancer. However, the microbial composition of the breast from healthy individuals, even relative to risk of developing breast cancer, remains unclear. Here, we performed a comprehensive analysis of the microbiota of the normal breast tissue, which was analyzed in relation to the microbial composition of the tumor and adjacent normal tissue.

METHODS

The study cohorts included 403 cancer-free women (who donated normal breast tissue cores) and 76 breast cancer patients (who donated tumor and/or adjacent normal tissue samples). Microbiome profiling was obtained by sequencing the nine hypervariable regions of the 16S rRNA gene (V1V2, V2V3, V3V4, V4V5, V5V7, and V7V9). Transcriptome analysis was also performed on 190 normal breast tissue samples. Breast cancer risk score was assessed using the Tyrer-Cuzick risk model.

RESULTS

The V1V2 amplicon sequencing resulted more suitable for the analysis of the normal breast microbiome and identified Lactobacillaceae (Firmicutes phylum), Acetobacterraceae, and Xanthomonadaceae (both Proteobacteria phylum) as the most abundant families in the normal breast. However, Ralstonia (Proteobacteria phylum) was more abundant in both breast tumors and histologically normal tissues adjacent to malignant tumors. We also conducted a correlation analysis between the microbiome and known breast cancer risk factors. Abundances of the bacterial taxa Acetotobacter aceti, Lactobacillus vini, Lactobacillus paracasei, and Xanthonomas sp. were associated with age (p < 0.0001), racial background (p < 0.0001), and parity (p < 0.0001). Finally, transcriptome analysis of normal breast tissues showed an enrichment in metabolism- and immune-related genes in the tissues with abundant Acetotobacter aceti, Lactobacillus vini, Lactobacillus paracasei, and Xanthonomas sp., whereas the presence of Ralstonia in the normal tissue was linked to dysregulation of genes involved in the carbohydrate metabolic pathway.

CONCLUSIONS

This study defines the microbial features of normal breast tissue, thus providing a basis to understand cancer-related dysbiosis. Moreover, the findings reveal that lifestyle factors can significantly affect the normal breast microbial composition.

Important role of endogenous microbial symbionts of fish gills in the challenging but highly biodiverse Amazonian blackwaters

Amazonian blackwaters are extremely biodiverse systems containing some of Earth's most naturally acidic, dissolved organic carbon -rich and ion-poor waters. Physiological adaptations of fish facing these ionoregulatory challenges are unresolved but could involve microbially-mediated processes. Here, we characterize the physiological response of 964 fish-microbe systems from four blackwater Teleost species along a natural hydrochemical gradient, using dual RNA-Seq and 16 S rRNA of gill samples. We find that host transcriptional responses to blackwaters are species-specific, but occasionally include the overexpression of Toll-receptors and integrins associated to interkingdom communication. Blackwater gill microbiomes are characterized by a transcriptionally-active betaproteobacterial cluster potentially interfering with epithelial permeability. We explore further blackwater fish-microbe interactions by analyzing transcriptomes of axenic zebrafish larvae exposed to sterile, non-sterile and inverted (non-native bacterioplankton) blackwater. We find that axenic zebrafish survive poorly when exposed to sterile/inverted blackwater. Overall, our results suggest a critical role for endogenous symbionts in blackwater fish physiology.

A bacterial signature-based method for the identification of seven forensically relevant human body fluids

Detection and identification of body fluids plays a crucial role in criminal investigation, as it provides information on the source of the DNA as well as corroborative evidence regarding the crime committed, scene, and/or association with persons of interest. Historically, forensic serological methods have been chemical, immunological, catalytic, spectroscopic, and/or microscopic in nature. However, most of these methods are presumptive, with few robust confirmatory exceptions. In recent years several new molecular methods (mRNA, miRNA, DNA methylation, etc.) have been proposed; although promising, these methods require high quality human DNA or RNA. Additional steps are required in RNA based methods. Additionally, RNA based methods cannot be used for old cases where only DNA extracts remain to sample from. In this study, a novel non-human DNA (microbiome) based method was developed for the identification of the majority of forensically relevant human biological samples. Eight hundred and twelve (n = 812) biological samples (semen, vaginal fluid, menstrual blood, saliva, feces, urine, and blood) were collected and preserved using methods commonly used in forensic laboratories for evidence collection. Variable region four (V4) of 16 S ribosomal DNA (16 S rDNA) was amplified using a dual-indexing strategy and then sequenced on the MiSeq FGx sequencing platform using the MiSeq Reagent Kit v2 (500 cycles) and following the manufacturer's protocol. Machine learning prediction models were used to assess the classification accuracy of the newly developed method. As there was no significant difference in bacterial communities between vaginal fluid, menstrual blood, and female urine, these were combined as female intimate samples. Except in urine, the bacterial structures associated with male and female body fluid samples were not significantly different from one another. The newly developed method accurately identified human body fluid samples with an overall accuracy of more than 88%. This newly developed bacterial signature-based method is fast (no additional steps are needed as the same DNA can be used for both body fluid identification and STR typing), efficient (consume less sample as a single test can identify all major body fluids), sensitive (needs only 5 pg of bacterial DNA), accurate, and can be easily added into a forensic high throughput sequencing (HTS) panel.

An efficient method for high molecular weight bacterial DNA extraction suitable for shotgun metagenomics from skin swabs

The human skin microbiome represents a variety of complex microbial ecosystems that play a key role in host health. Molecular methods to study these communities have been developed but have been largely limited to low-throughput quantification and short amplicon-based sequencing, providing limited functional information about the communities present. Shotgun metagenomic sequencing has emerged as a preferred method for microbiome studies as it provides more comprehensive information about the species/strains present in a niche and the genes they encode. However, the relatively low bacterial biomass of skin, in comparison to other areas such as the gut microbiome, makes obtaining sufficient DNA for shotgun metagenomic sequencing challenging. Here we describe an optimised high-throughput method for extraction of high molecular weight DNA suitable for shotgun metagenomic sequencing. We validated the performance of the extraction method, and analysis pipeline on skin swabs collected from both adults and babies. The pipeline effectively characterised the bacterial skin microbiota with a cost and throughput suitable for larger longitudinal sets of samples. Application of this method will allow greater insights into community compositions and functional capabilities of the skin microbiome.

The Diagnostic Potential of the Human Blood Microbiome: Are We Dreaming or Awake?

Human blood has historically been considered a sterile environment. Recently, a thriving microbiome dominated by Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes phyla was detected in healthy blood. The localization of these microbes is restricted to some blood cell populations, particularly the peripheral blood mononuclear cells and erythrocytes. It was hypothesized that the blood microbiome originates from the skin-oral-gut axis. In addition, many studies have evaluated the potential of blood microbiome dysbiosis as a prognostic marker in cardiovascular diseases, cirrhosis, severe liver fibrosis, severe acute pancreatitis, type 2 diabetes, and chronic kidney diseases. The present review aims to summarize current findings and most recent evidence in the field.

Detection of bile acids in bronchoalveolar lavage fluid defines the inflammatory and microbial landscape of the lower airways in infants with cystic fibrosis

BACKGROUND

Cystic Fibrosis (CF) is a genetic condition characterized by neutrophilic inflammation and recurrent infection of the airways. How these processes are initiated and perpetuated in CF remains largely unknown. We have demonstrated a link between the intestinal microbiota-related metabolites bile acids (BA) and inflammation in the bronchoalveolar lavage fluid (BALF) from children with stable CF lung disease. To establish if BA indicate early pathological processes in CF lung disease, we combined targeted mass spectrometry and amplicon sequencing-based microbial characterization of 121 BALF specimens collected from 12-month old infants with CF enrolled in the COMBAT-CF study, a multicentre randomized placebo-controlled clinical trial comparing azithromycin versus placebo. We evaluated whether detection of BA in BALF is associated with the establishment of the inflammatory and microbial landscape of early CF lung disease, and whether azithromycin, a motilin agonist that has been demonstrated to reduce aspiration of gastric contents, alters the odds of detecting BA in BALF. We also explored how different prophylactic antibiotics regimens impact the early life BALF microbiota.

RESULTS

Detection of BA in BALF was strongly associated with biomarkers of airway inflammation, more exacerbation episodes during the first year of life, increased use of oral antibiotics with prolonged treatment periods, a higher degree of structural lung damage, and distinct microbial profiles. Treatment with azithromycin, a motilin agonist, which has been reported to reduce aspiration of gastric contents, did not reduce the odds of detecting BA in BALF. Culture and molecular methods showed that azithromycin does not alter bacterial load or diversity in BALF. Conversely, penicillin-type prophylaxis reduced the odds of detecting BAs in BALF, which was associated with elevated levels of circulating biomarkers of cholestasis. We also observed that environmental factors such as penicillin-type prophylaxis or BAs detection were linked to distinct early microbial communities of the CF airways, which were associated with different inflammatory landscapes but not with structural lung damage.

CONCLUSIONS

Detection of BA in BALF portend early pathological events in CF lung disease. Benefits early in life associated with azithromycin are not linked to its antimicrobial properties. Video Abstract.

The role of microbiome-host interactions in the development of Alzheimer´s disease

Alzheimer`s disease (AD) is the most prevalent cause of dementia. It is often assumed that AD is caused by an aggregation of extracellular beta-amyloid and intracellular tau-protein, supported by a recent study showing reduced brain amyloid levels and reduced cognitive decline under treatment with a beta-amyloid-binding antibody. Confirmation of the importance of amyloid as a therapeutic target notwithstanding, the underlying causes of beta-amyloid aggregation in the human brain, however, remain to be elucidated. Multiple lines of evidence point towards an important role of infectious agents and/or inflammatory conditions in the etiology of AD. Various microorganisms have been detected in the cerebrospinal fluid and brains of AD-patients and have thus been hypothesized to be linked to the development of AD, including Porphyromonas gingivalis (PG) and Spirochaetes. Intriguingly, these microorganisms are also found in the oral cavity under normal physiological conditions, which is often affected by multiple pathologies like caries or tooth loss in AD patients. Oral cavity pathologies are mostly accompanied by a compositional shift in the community of oral microbiota, mainly affecting commensal microorganisms and referred to as 'dysbiosis'. Oral dysbiosis seems to be at least partly mediated by key pathogens such as PG, and it is associated with a pro-inflammatory state that promotes the destruction of connective tissue in the mouth, possibly enabling the translocation of pathogenic microbiota from the oral cavity to the nervous system. It has therefore been hypothesized that dysbiosis of the oral microbiome may contribute to the development of AD. In this review, we discuss the infectious hypothesis of AD in the light of the oral microbiome and microbiome-host interactions, which may contribute to or even cause the development of AD. We discuss technical challenges relating to the detection of microorganisms in relevant body fluids and approaches for avoiding false-positives, and introduce the antibacterial protein lactoferrin as a potential link between the dysbiotic microbiome and the host inflammatory reaction.

Metatranscriptome analysis of blood in healthy individuals and irritable bowel syndrome patients

Introduction. Although the presence of micro-organisms in the blood of healthy humans is a relatively new concept, there is a growing amount of evidence that blood might have its own microbiome.Gap Statement. Previous research has targeted the taxonomic composition of the blood microbiome using DNA-based sequencing methods, while little information is known about the presence of microbial transcripts obtained from the blood and their relation to conditions connected with increased gut permeability.Aim. To detect potentially alive and active micro-organisms and investigate differences in taxonomic composition between healthy people and patients with irritable bowel syndrome (IBS), we used the metatranscriptomics approach.Methodology. We collected blood samples from 23 IBS patients and 26 volunteers from the general population, and performed RNAseq on the isolated RNA. Reads corresponding to microbial genomes were identified with Kraken 2's standard plus protozoa and fungi database, and re-estimated at genus level with Bracken 2.7. We looked for trends in the taxonomic composition, making a comparison between the IBS and control groups, accounting for other different factors.Results. The dominant genera in the blood microbiome were found to be Cutibacterium, Bradyrhizobium, Escherichia, Pseudomonas, Micrococcus, Delftia, Mediterraneibacter, Staphylococcus, Stutzerimonas and Ralstonia. Some of these are typical environmental bacteria and could partially represent contamination. However, analysis of sequences from the negative controls suggested that some genera which are characteristic of the gut microbiome (Mediterraneibacter, Blautia, Collinsella, Klebsiella, Coprococcus, Dysosmobacter, Anaerostipes, Faecalibacterium, Dorea, Simiaoa, Bifidobacterium, Alistipes, Prevotella, Ruminococcus) are less likely to be a result of contamination. Differential analysis of microbes between groups showed that some taxa associated with the gut microbiome (Blautia, Faecalibacterium, Dorea, Bifidobacterium, Clostridium, Christensenella) are more prevalent in IBS patients compared to the general population. No significant correlations with any other factors were identified.Conclusion. Our findings support the existence of the blood microbiome and suggest the gut and possibly the oral microbiome as its origin, while the skin microbiome is a possible but less certain source. The blood microbiome is likely influenced by states of increased gut permeability such as IBS.