Quantifying bias introduced by sample collection in relative and absolute microbiome measurements

Dissection of rhizosphere microbiome and exploiting strategies for sustainable agriculture

The rhizosphere microbiome plays critical roles in plant growth and provides promising solutions for sustainable agriculture. While the rhizosphere microbiome frequently fluctuates with the soil environment, recent studies have demonstrated that a small proportion of the microbiome is consistently assembled in the rhizosphere of a specific plant genotype regardless of the soil condition, which is determined by host genetics. Based on these breakthroughs, which involved exploiting the plant-beneficial function of the rhizosphere microbiome, we propose to divide the rhizosphere microbiome into environment-dominated and plant genetic-dominated components based on their different assembly mechanisms. Subsequently, two strategies to explore the different rhizosphere microbiome components for agricultural production are suggested, that is, the precise management of the environment-dominated rhizosphere microbiome by agronomic practices, and the elucidation of the plant genetic basis of the plant genetic-dominated rhizosphere microbiome for breeding microbiome-assisted crop varieties. We finally present the major challenges that need to be overcome to implement strategies for modulating these two components of the rhizosphere microbiome.

Oral bacteria relative abundance in faeces increases due to gut microbiota depletion and is linked with patient outcomes

The detection of oral bacteria in faecal samples has been associated with inflammation and intestinal diseases. The increased relative abundance of oral bacteria in faeces has two competing explanations: either oral bacteria invade the gut ecosystem and expand (the 'expansion' hypothesis), or oral bacteria transit through the gut and their relative increase marks the depletion of other gut bacteria (the 'marker' hypothesis). Here we collected oral and faecal samples from mouse models of gut dysbiosis (antibiotic treatment and DSS-induced colitis) and used 16S ribosomal RNA sequencing to determine the abundance dynamics of oral bacteria. We found that the relative, but not absolute, abundance of oral bacteria increases, reflecting the 'marker' hypothesis. Faecal microbiome datasets from diverse patient cohorts, including healthy individuals and patients with allogeneic haematopoietic cell transplantation or inflammatory bowel disease, consistently support the 'marker' hypothesis and explain associations between oral bacterial abundance and patient outcomes consistent with depleted gut microbiota. By distinguishing between the two hypotheses, our study guides the interpretation of microbiome compositional data and could potentially identify cases where therapies are needed to rebuild the resident microbiome rather than protect against invading oral bacteria.

Carbon starvation raises capacities in bacterial antibiotic resistance and viral auxiliary carbon metabolism in soils

Organic carbon availability in soil is crucial for shaping microbial communities, yet, uncertainties persist concerning microbial adaptations to carbon levels and the ensuing ecological and evolutionary consequences. We investigated organic carbon metabolism, antibiotic resistance, and virus-host interactions in soils subjected to 40 y of chemical and organic fertilization that led to contrasting carbon availability: carbon-poor and carbon-rich soils, respectively. Carbon-poor soils drove the enrichment of putative genes involved in organic matter decomposition and exhibited specialization in utilizing complex organic compounds, reflecting scramble competition. This specialization confers a competitive advantage of microbial communities in carbon-poor soils but reduces their buffering capacity in terms of organic carbon metabolisms, making them more vulnerable to environmental fluctuations. Additionally, in carbon-poor soils, viral auxiliary metabolic genes linked to organic carbon metabolism increased host competitiveness and environmental adaptability through a strategy akin to "piggyback the winner." Furthermore, putative antibiotic resistance genes, particularly in low-abundance drug categories, were enriched in carbon-poor soils as an evolutionary consequence of chemical warfare (i.e., interference competition). This raises concerns about the potential dissemination of antibiotic resistance from conventional agriculture that relies on chemical-only fertilization. Consequently, carbon starvation resulting from long-term chemical-only fertilization increases microbial adaptations to competition, underscoring the importance of implementing sustainable agricultural practices to mitigate the emergence and spread of antimicrobial resistance and to increase soil carbon storage.

Identification of intratumor bacteria-associated prognostic risk score in adrenocortical carcinoma

A landmark study by Poore et al. showed intratumor bacteria (ITBs) playing a critical role in most cancers by reproduction of The Cancer Genome Atlas (TCGA) transcriptome data. A recent study by Salzberg et al. argued that ITBs, being overstated as a methodology by Poore et al., were problematic. We previously reported that ITBs were prognostic in adrenocortical carcinoma (ACC), a highly aggressive rare disease using data by Poore et al., and here, we aimed to answer whether ITBs truly existed and were prognostic in ACC. ACC samples from our institutes underwent 16S rRNA sequencing [adrenocortical carcinoma blocks from Huashan Hospital and China Medical University (HS) cohort]. The ITB profile was compared to TCGA data processed by Poore et al. (TCGA-P) and TCGA data processed by Salzberg et al. (TCGA-S), respectively. The primary outcome was overall survival (OS). A total of 26 ACC cases (HS cohort) and 10 paraffin controls were sequenced. The TCGA cohort encompassed 77 cases. Two and four amid the top 10 abundant genera in HS cohort were not detected in TCGA-P and TCGA-S, respectively. Neither was alpha or beta diversity associated with survival nor could ACC be subtyped by ITB signature in the HS cohort. Notably, a five-genera ITB risk score (Corynebacterium, Mycoplasma, Achromobacter, Anaerococcus, and Streptococcus) for OS trained in the HS cohort was validated in both TCGA-P and TCGA-S cohorts and was independently prognostic. Whereas ITB signature on the whole may not be associated with ACC subtypes, certain ITB features are associated with prognosis, and a risk score could be generated and validated externally.

IMPORTANCE

In this report, we looked at the role of ITBs in ACC in patients with different race and sequencing platforms. We found a five-genera ITB risk score consistently predicted overall survival in all cohorts. We conclude that certain ITB features are universally pathogenic to ACC.

Deep learning methods in metagenomics: a review

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

Comparison of Daily versus Admission and Discharge Surveillance Cultures for Multidrug-Resistant Organism Detection in an Intensive Care Unit

BACKGROUND

Admission and discharge screening of patients for asymptomatic gut colonization with multidrug-resistant organisms (MDROs) is a traditional approach to active surveillance, but its sensitivity for detecting colonization is uncertain.

METHODS

Daily rectal or fecal swab samples and clinical data were collected over 12 months from patients in one 25-bed intensive care unit (ICU) in Chicago, IL USA and tested for the following multidrug-resistant organisms (MDROs): vancomycin-resistant enterococci (VRE); third-generation cephalosporin-resistant Enterobacterales, including extended-spectrum β-lactamase-producing Enterobacterales (ESBL); and carbapenem-resistant Enterobacterales (CRE). MDRO detection by (1) admission/discharge surveillance cultures or (2) clinical cultures were compared to daily surveillance cultures. Samples underwent 16S rRNA gene sequencing to measure the relative abundance of operational taxonomic units (OTUs) corresponding to each MDRO.

RESULTS

Compared with daily surveillance cultures, admission/discharge cultures detected 91% of prevalent MDRO colonization and 63% of incident MDRO colonization among medical ICU patients. Only a minority (7%) of MDRO carriers were identified by clinical cultures. Higher relative abundance of MDRO-associated OTUs and specific antibiotic exposures were independently associated with higher probability of MDRO detection by culture.

CONCLUSION

Admission and discharge surveillance cultures underestimated MDRO acquisitions in an ICU. These limitations should be considered when designing sampling strategies for epidemiologic studies that use culture-based surveillance.

Expanding the human gut microbiome atlas of Africa

Population studies are crucial in understanding the complex interplay between the gut microbiome and geographical, lifestyle, genetic, and environmental factors. However, populations from low- and middle-income countries, which represent ~84% of the world population, have been excluded from large-scale gut microbiome research. Here, we present the AWI-Gen 2 Microbiome Project, a cross-sectional gut microbiome study sampling 1,803 women from Burkina Faso, Ghana, Kenya, and South Africa. By intensively engaging with communities that range from rural and horticultural to urban informal settlements and post-industrial, we capture population diversity that represents a far greater breadth of the world's population. Using shotgun metagenomic sequencing, we find that study site explains substantially more microbial variation than disease status. We identify taxa with strong geographic and lifestyle associations, including loss of Treponema and Cryptobacteroides species and gain of Bifidobacterium species in urban populations. We uncover a wealth of prokaryotic and viral novelty, including 1,005 new bacterial metagenome-assembled genomes, and identify phylogeography signatures in Treponema succinifaciens. Finally, we find a microbiome signature of HIV infection that is defined by several taxa not previously associated with HIV, including Dysosmobacter welbionis and Enterocloster sp. This study represents the largest population-representative survey of gut metagenomes of African individuals to date, and paired with extensive clinical biomarkers, demographic data, and lifestyle information, provides extensive opportunity for microbiome-related discovery and research.

A gut microbial signature for combination immune checkpoint blockade across cancer types

Immune checkpoint blockade (ICB) targeting programmed cell death protein 1 (PD-1) and cytotoxic T lymphocyte protein 4 (CTLA-4) can induce remarkable, yet unpredictable, responses across a variety of cancers. Studies suggest that there is a relationship between a cancer patient's gut microbiota composition and clinical response to ICB; however, defining microbiome-based biomarkers that generalize across cohorts has been challenging. This may relate to previous efforts quantifying microbiota to species (or higher taxonomic rank) abundances, whereas microbial functions are often strain specific. Here, we performed deep shotgun metagenomic sequencing of baseline fecal samples from a unique, richly annotated phase 2 trial cohort of patients with diverse rare cancers treated with combination ICB (n = 106 discovery cohort). We demonstrate that strain-resolved microbial abundances improve machine learning predictions of ICB response and 12-month progression-free survival relative to models built using species-rank quantifications or comprehensive pretreatment clinical factors. Through a meta-analysis of gut metagenomes from a further six comparable studies (n = 364 validation cohort), we found cross-cancer (and cross-country) validity of strain-response signatures, but only when the training and test cohorts used concordant ICB regimens (anti-PD-1 monotherapy or combination anti-PD-1 plus anti-CTLA-4). This suggests that future development of gut microbiome diagnostics or therapeutics should be tailored according to ICB treatment regimen rather than according to cancer type.

Methane flux at the water-gas interface is influenced by complex interactions among phytoplankton, phosphorus inputs and methane-functional bacteria: A microcosm systems study

Phytoplankton affect carbon cycling and emissions in eutrophic reservoirs dramatically, but our knowledge about carbon emissions response to phytoplankton bloom and phosphorus enrichment is rather limited. Here we performed a microcosm experiment with five treatments to investigate how phytoplankton blooms and phosphorus addition will impact the carbon emissions and the methane-functional bacterial community. During the 43-day incubation, the CH4 and CO2 flux at the water-air interface in the five water columns fluctuated between 7.536 and 16.689 μmol and between 2788.501 and 4142.726 μmol, respectively. The flux of CH4 and CO2 during phytoplankton decay was 1.542 to 10.397 times and 4.203 to 8.622 times higher, respectively, compared to that during phytoplankton growth. Furthermore, exogenous phosphorus increases bloom biomass of phytoplankton and subsequent CH4 production, even with low nitrogen concentrations. The addition of 1 mg KH2PO4 resulted in a conservative increase of 0.0715 μmol in CH4 emission and 11.911 μmol in CO2 emission in the water column, respectively, compared to the in-situ water column. High throughput sequencing determined that hydrogenotrophic Methanoregula dominated methanogens (MPB) and Methylocystaceae dominated methanotrophs (MOB) in the sediment. Phosphorus inhibited the relative abundance of Methanoregula after incubation, resulting in a significant decrease. Real-time quantitative polymerase chain reaction indicated that the absolute abundance of MPB and MOB (i.e., the mcrA gene and the pmoA gene) in the sediments ranged from 5.1354E+06 to 6.3176E+07 copies·g-1 and 1.1656E+06 to 9.5056E+06 copies·g-1, respectively. The mcrA gene showed a preference for sediments with high organic carbon content. The effect of eutrophication on CH4 emissions is closely related to nutrient load and distinct niche of methane-functional bacteria.

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models

Every step in common microbiome profiling protocols has variable efficiency for each microbe. For example, different DNA extraction kits may have different efficiency for Gram-positive and -negative bacteria. These variable efficiencies, combined with technical variation, create strong processing biases, which impede the identification of signals that are reproducible across studies and the development of generalizable and biologically interpretable prediction models. "Batch-correction" methods have been used to alleviate these issues computationally with some success. However, many make strong parametric assumptions which do not necessarily apply to microbiome data or processing biases, or require the use of an outcome variable, which risks overfitting. Lastly and importantly, existing transformations used to correct microbiome data are largely non-interpretable, and could, for example, introduce values to features that were initially mostly zeros. Altogether, processing bias currently compromises our ability to glean robust and generalizable biological insights from microbiome data. Here, we present DEBIAS-M (Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using benchmarks of HIV and colorectal cancer classification from gut microbiome data, and cervical neoplasia prediction from cervical microbiome data, we demonstrate that DEBIAS-M outperforms batch-correction methods commonly used in the field. Notably, we show that the inferred bias-correction factors are stable, interpretable, and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M allows for better modeling of microbiome data and identification of interpretable signals that are reproducible across studies.

Intestinal toxicity alleviation and efficacy potentiation through therapeutic administration of Lactobacillus paracasei GY-1 in the treatment of gout flares with colchicine

Gout flares have emerged as a significant public health concern. Colchicine (COL) is a first-line and standard drug for treating gout flares. However, its clinical use is limited due to various adverse effects. Besides, COL fails to adequately meet the needs of patients, particularly young patients. In this study, we investigate the therapeutic administration of Lactobacillus paracasei GY-1 (GY-1) to overcome the limitations of COL. Our results demonstrate that GY-1 attenuates COL toxicity in terms of body weight loss, decreased feed intake, mortality, reduced locomotor activity, colon shortening, increased oxidative stress, histological damage, and impaired gut permeability. Meanwhile, we demonstrate that GY-1 enhances the therapeutic effect for gout flares when combined with COL, as evidenced by the reduction in paw swelling, decreased levels of proinflammatory cytokines including IL-1β and TNF-α, and an increase in the anti-inflammatory cytokine IL-10. Additionally, the absolute quantification of the gut microbiota shows that GY-1 restores the gut microbiota imbalance caused by COL. Furthermore, GY-1 reduces the abundance of 4 Alistipes species and 6 Porphyromonadaceae species, which may be responsible for toxicity alleviation. At the same time, GY-1 increases the abundance of Bacteroides sartorii and Enterococcus sp., which may contribute to its therapeutic efficacy. This study demonstrates the feasibility of developing probiotic-based adjuvant therapy or bacteriotherapy for treating gout flares. To our knowledge, GY-1 is the first probiotic that could be used as an alternative synergetic agent with COL for the therapeutic treatment of gout flares.

The gut microbiota and its biogeography

Biogeography is the study of species distribution and diversity within an ecosystem and is at the core of how we understand ecosystem dynamics and interactions at the macroscale. In gut microbial communities, a historical reliance on bulk sequencing to probe community composition and dynamics has overlooked critical processes whereby microscale interactions affect systems-level microbiota function and the relationship with the host. In recent years, higher-resolution sequencing and novel single-cell level data have uncovered an incredible heterogeneity in microbial composition and have enabled a more nuanced spatial understanding of the gut microbiota. In an era when spatial transcriptomics and single-cell imaging and analysis have become key tools in mammalian cell and tissue biology, many of these techniques are now being applied to the microbiota. This fresh approach to intestinal biogeography has given important insights that span temporal and spatial scales, from the discovery of mucus encapsulation of the microbiota to the quantification of bacterial species throughout the gut. In this Review, we highlight emerging knowledge surrounding gut biogeography enabled by the observation and quantification of heterogeneity across multiple scales.

Viroid-like colonists of human microbiomes

Here, we describe the "Obelisks," a previously unrecognised class of viroid-like elements that we first identified in human gut metatranscriptomic data. "Obelisks" share several properties: (i) apparently circular RNA ~1kb genome assemblies, (ii) predicted rod-like secondary structures encompassing the entire genome, and (iii) open reading frames coding for a novel protein superfamily, which we call the "Oblins". We find that Obelisks form their own distinct phylogenetic group with no detectable sequence or structural similarity to known biological agents. Further, Obelisks are prevalent in tested human microbiome metatranscriptomes with representatives detected in ~7% of analysed stool metatranscriptomes (29/440) and in ~50% of analysed oral metatranscriptomes (17/32). Obelisk compositions appear to differ between the anatomic sites and are capable of persisting in individuals, with continued presence over >300 days observed in one case. Large scale searches identified 29,959 Obelisks (clustered at 90% nucleotide identity), with examples from all seven continents and in diverse ecological niches. From this search, a subset of Obelisks are identified to code for Obelisk-specific variants of the hammerhead type-III self-cleaving ribozyme. Lastly, we identified one case of a bacterial species (Streptococcus sanguinis) in which a subset of defined laboratory strains harboured a specific Obelisk RNA population. As such, Obelisks comprise a class of diverse RNAs that have colonised, and gone unnoticed in, human, and global microbiomes.

Inference of the demographic histories and selective effects of human gut commensal microbiota over the course of human history

Despite the importance of gut commensal microbiota to human health, there is little knowledge about their evolutionary histories, including their population demographic histories and their distributions of fitness effects (DFE) of new mutations. Here, we infer the demographic histories and DFEs of 27 of the most highly prevalent and abundant commensal gut microbial species in North Americans over timescales exceeding human generations using a collection of lineages inferred from a panel of healthy hosts. We find overall reductions in genetic variation among commensal gut microbes sampled from a Western population relative to an African rural population. Additionally, some species in North American microbiomes display contractions in population size and others expansions, potentially occurring at several key historical moments in human history. DFEs across species vary from highly to mildly deleterious, with accessory genes experiencing more drift compared to core genes. Within genera, DFEs tend to be more congruent, reflective of underlying phylogenetic relationships. Taken together, these findings suggest that human commensal gut microbes have distinct evolutionary histories, possibly reflecting the unique roles of individual members of the microbiome.