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.

Assessing Engraftment Following Fecal Microbiota Transplant.

Fecal Microbiota Transplant (FMT) is an FDA approved treatment for recurrent Clostridium difficile infections, and is being explored for other clinical applications, from alleviating digestive and neurological disorders, to priming the microbiome for cancer treatment, and restoring microbiomes impacted by cancer treatment. Quantifying the extent of engraftment following an FMT is important in determining if a recipient didn't respond because the engrafted microbiome didn't produce the desired outcomes (a successful FMT, but negative treatment outcome), or the microbiome didn't engraft (an unsuccessful FMT and negative treatment outcome). The lack of a consistent methodology for quantifying FMT engraftment extent hinders the assessment of FMT success and its relation to clinical outcomes, and presents challenges for comparing FMT results and protocols across studies. Here we review 46 studies of FMT in humans and model organisms and group their approaches for assessing the extent to which an FMT engrafts into three criteria: 1) Chimeric Asymmetric Community Coalescence investigates microbiome shifts following FMT engraftment using methods such as alpha diversity comparisons, beta diversity comparisons, and microbiome source tracking. 2) Donated Microbiome Indicator Features tracks donated microbiome features (e.g., amplicon sequence variants or species of interest) as a signal of engraftment with methods such as differential abundance testing based on the current sample collection, or tracking changes in feature abundances that have been previously identified (e.g., from FMT or disease-relevant literature). 3) Temporal Stability examines how resistant post-FMT recipient's microbiomes are to reverting back to their baseline microbiome. Individually, these criteria each highlight a critical aspect of microbiome engraftment; investigated together, however, they provide a clearer assessment of microbiome engraftment. We discuss the pros and cons of each of these criteria, providing illustrative examples of their application. We also introduce key terminology and recommendations on how FMT studies can be analyzed for rigorous engraftment extent assessment.

Fatty acid oxidation fuels natural killer cell responses against infection and cancer.

Natural killer (NK) cells are a vital part of the innate immune system capable of rapidly clearing mutated or infected cells from the body and promoting an immune response. Here, we find that NK cells activated by viral infection or tumor challenge increase uptake of fatty acids and their expression of carnitine palmitoyltransferase I (CPT1A), a critical enzyme for long-chain fatty acid oxidation. Using a mouse model with an NK cell-specific deletion of CPT1A, combined with stable 13C isotope tracing, we observe reduced mitochondrial function and fatty acid-derived aspartate production in CPT1A-deficient NK cells. Furthermore, CPT1A-deficient NK cells show reduced proliferation after viral infection and diminished protection against cancer due to impaired actin cytoskeleton rearrangement. Together, our findings highlight that fatty acid oxidation promotes NK cell metabolic resilience, processes that can be optimized in NK cell-based immunotherapies.

Machine learning of cellular metabolic rewiring.

Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry (GC/MS) to predict abundance changes in metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. The model learned captures shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting potential organ-tailored cellular adaptations. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.

The Ratio of Key Metabolic Transcripts Is a Predictive Biomarker of Breast Cancer Metastasis to the Lung.

Understanding the rewired metabolism underlying organ-specific metastasis in breast cancer could help identify strategies to improve the treatment and prevention of metastatic disease. Here, we used a systems biology approach to compare metabolic fluxes used by parental breast cancer cells and their brain- and lung-homing derivatives. Divergent lineages had distinct, heritable metabolic fluxes. Lung-homing cells maintained high glycolytic flux despite low levels of glycolytic intermediates, constitutively activating a pathway sink into lactate. This strong Warburg effect was associated with a high ratio of lactate dehydrogenase (LDH) to pyruvate dehydrogenase (PDH) expression, which correlated with lung metastasis in patients with breast cancer. Although feature classification models trained on clinical characteristics alone were unable to predict tropism, the LDH/PDH ratio was a significant predictor of metastasis to the lung but not to other organs, independent of other transcriptomic signatures. High lactate efflux was also a trait in lung-homing metastatic pancreatic cancer cells, suggesting that lactate production may be a convergent phenotype in lung metastasis. Together, these analyses highlight the essential role that metabolism plays in organ-specific cancer metastasis and identify a putative biomarker for predicting lung metastasis in patients with breast cancer. SIGNIFICANCE: Lung-homing metastatic breast cancer cells express an elevated ratio of lactate dehydrogenase to pyruvate dehydrogenase, indicating that ratios of specific metabolic gene transcripts have potential as metabolic biomarkers for predicting organ-specific metastasis.

High-resolution analyses of associations between medications, microbiome, and mortality in cancer patients.

Discerning the effect of pharmacological exposures on intestinal bacterial communities in cancer patients is challenging. Here, we deconvoluted the relationship between drug exposures and changes in microbial composition by developing and applying a new computational method, PARADIGM (parameters associated with dynamics of gut microbiota), to a large set of longitudinal fecal microbiome profiles with detailed medication-administration records from patients undergoing allogeneic hematopoietic cell transplantation. We observed that several non-antibiotic drugs, including laxatives, antiemetics, and opioids, are associated with increased Enterococcus relative abundance and decreased alpha diversity. Shotgun metagenomic sequencing further demonstrated subspecies competition, leading to increased dominant-strain genetic convergence during allo-HCT that is significantly associated with antibiotic exposures. We integrated drug-microbiome associations to predict clinical outcomes in two validation cohorts on the basis of drug exposures alone, suggesting that this approach can generate biologically and clinically relevant insights into how pharmacological exposures can perturb or preserve microbiota composition. The application of a computational method called PARADIGM to a large dataset of cancer patients' longitudinal fecal specimens and detailed daily medication records reveals associations between drug exposures and the intestinal microbiota that recapitulate in vitro findings and are also predictive of clinical outcomes.

The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia.

Longitudinal microbiome data provide valuable insight into disease states and clinical responses, but they are challenging to mine and view collectively. To address these limitations, we present TaxUMAP, a taxonomically informed visualization for displaying microbiome states in large clinical microbiome datasets. We used TaxUMAP to chart a microbiome atlas of 1,870 patients with cancer during therapy-induced perturbations. Bacterial density and diversity were positively associated, but the trend was reversed in liquid stool. Low-diversity states (dominations) remained stable after antibiotic treatment, and diverse communities had a broader range of antimicrobial resistance genes than dominations. When examining microbiome states associated with risk for bacteremia, TaxUMAP revealed that certain Klebsiella species were associated with lower risk for bacteremia localize in a region of the atlas that is depleted in high-risk enterobacteria. This indicated a competitive interaction that was validated experimentally. Thus, TaxUMAP can chart comprehensive longitudinal microbiome datasets, enabling insights into microbiome effects on human health.

Evolution and regulation of microbial secondary metabolism.

Microbes have disproportionate impacts on the macroscopic world. This is in part due to their ability to grow to large populations that collectively secrete massive amounts of secondary metabolites and alter their environment. Yet, the conditions favoring secondary metabolism despite the potential costs for primary metabolism remain unclear. Here we investigated the biosurfactants that the bacterium Pseudomonas aeruginosa makes and secretes to decrease the surface tension of surrounding liquid. Using a combination of genomics, metabolomics, transcriptomics, and mathematical modeling we show that the ability to make surfactants from glycerol varies inconsistently across the phylogenetic tree; instead, lineages that lost this ability are also worse at reducing the oxidative stress of primary metabolism on glycerol. Experiments with different carbon sources support a link with oxidative stress that explains the inconsistent distribution across the P. aeruginosa phylogeny and suggests a general principle: P. aeruginosa lineages produce surfactants if they can reduce the oxidative stress produced by primary metabolism and have excess resources, beyond their primary needs, to afford secondary metabolism. These results add a new layer to the regulation of a secondary metabolite unessential for primary metabolism but important to change physical properties of the environments surrounding bacterial populations.

Lactobacillus supports Clostridiales to restrict gut colonization by multidrug-resistant Enterobacteriaceae.

Infections by multidrug-resistant Enterobacteriaceae (MRE) are life-threatening to patients. The intestinal microbiome protects against MRE colonization, but antibiotics cause collateral damage to commensals and open the way to colonization and subsequent infection. Despite the significance of this problem, the specific commensals and mechanisms that restrict MRE colonization remain largely unknown. Here, by performing a multi-omic prospective study of hospitalized patients combined with mice experiments, we find that Lactobacillus is key, though not sufficient, to restrict MRE gut colonization. Lactobacillus rhamnosus and murinus increase the levels of Clostridiales bacteria, which induces a hostile environment for MRE growth through increased butyrate levels and reduced nutrient sources. This mechanism of colonization resistance, an interaction between Lactobacillus spp. and Clostridiales involving cooperation between microbiota members, is conserved in mice and patients. These results stress the importance of exploiting microbiome interactions for developing effective probiotics that prevent infections in hospitalized patients.

A compilation of fecal microbiome shotgun metagenomics from hematopoietic cell transplantation patients.

Hospitalized patients receiving hematopoietic cell transplants provide a unique opportunity to study the human gut microbiome. We previously compiled a large-scale longitudinal dataset of fecal microbiota and associated metadata, but we had limited that analysis to taxonomic composition of bacteria from 16S rRNA gene sequencing. Here we augment those data with shotgun metagenomics. The compilation amounts to a nested subset of 395 samples compiled from different studies at Memorial Sloan Kettering. Shotgun metagenomics describes the microbiome at the functional level, particularly in antimicrobial resistances and virulence factors. We provide accession numbers that link each sample to the paired-end sequencing files deposited in a public repository, which can be directly accessed by the online services of PATRIC to be analyzed without the users having to download or transfer the files. Then, we show how shotgun sequencing enables the assembly of genomes from metagenomic data. The new data, combined with the metadata published previously, enables new functional studies of the microbiomes of patients with cancer receiving bone marrow transplantation.

Mathematical models to study the biology of pathogens and the infectious diseases they cause.

Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.

Optimal Strategy and Benefit of Pulsed Therapy Depend On Tumor Heterogeneity and Aggressiveness at Time of Treatment Initiation.

Therapeutic resistance is a fundamental obstacle in cancer treatment. Tumors that initially respond to treatment may have a preexisting resistant subclone or acquire resistance during treatment, making relapse theoretically inevitable. Here, we investigate treatment strategies that may delay relapse using mathematical modeling. We find that for a single-drug therapy, pulse treatment-short, elevated doses followed by a complete break from treatment-delays relapse compared with continuous treatment with the same total dose over a length of time. For tumors treated with more than one drug, continuous combination treatment is only sometimes better than sequential treatment, while pulsed combination treatment or simply alternating between the two therapies at defined intervals delays relapse the longest. These results are independent of the fitness cost or benefit of resistance, and are robust to noise. Machine-learning analysis of simulations shows that the initial tumor response and heterogeneity at the start of treatment suffice to determine the benefit of pulsed or alternating treatment strategies over continuous treatment. Analysis of eight tumor burden trajectories of breast cancer patients treated at Memorial Sloan Kettering Cancer Center shows the model can predict time to resistance using initial responses to treatment and estimated preexisting resistant populations. The model calculated that pulse treatment would delay relapse in all eight cases. Overall, our results support that pulsed treatments optimized by mathematical models could delay therapeutic resistance.

Optimizing the future: how mathematical models inform treatment schedules for cancer.

For decades, mathematical models have influenced how we schedule chemotherapeutics. More recently, mathematical models have leveraged lessons from ecology, evolution, and game theory to advance predictions of optimal treatment schedules, often in a personalized medicine manner. We discuss both established and emerging therapeutic strategies that deviate from canonical standard-of-care regimens, and how mathematical models have contributed to the design of such schedules. We first examine scheduling options for single therapies and review the advantages and disadvantages of various treatment plans. We then consider the challenge of scheduling multiple therapies, and review the mathematical and clinical support for various conflicting treatment schedules. Finally, we propose how a consilience of mathematical and clinical knowledge can best determine the optimal treatment schedules for patients.

Spatial-temporal dynamics of a microbial cooperative behavior resistant to cheating.

Much of our understanding of bacterial behavior stems from studies in liquid culture. In nature, however, bacteria frequently live in densely packed spatially-structured communities. How does spatial structure affect bacterial cooperative behaviors? In this work, we examine rhamnolipid production-a cooperative and virulent behavior of Pseudomonas aeruginosa. Here we show that, in striking contrast to well-mixed liquid culture, rhamnolipid gene expression in spatially-structured colonies is strongly associated with colony specific growth rate, and is impacted by perturbation with diffusible quorum signals. To interpret these findings, we construct a data-driven statistical inference model which captures a length-scale of bacterial interaction that develops over time. Finally, we find that perturbation of P. aeruginosa swarms with quorum signals preserves the cooperating genotype in competition, rather than creating opportunities for cheaters. Overall, our data demonstrate that the complex response to spatial localization is key to preserving bacterial cooperative behaviors.

Haematopoietic cell transplantation outcomes are linked to intestinal mycobiota dynamics and an expansion of Candida parapsilosis complex species.

Allogeneic haematopoietic cell transplantation (allo-HCT) induces profound shifts in the intestinal bacterial microbiota. The dynamics of intestinal fungi and their impact on clinical outcomes during allo-HCT are not fully understood. Here we combined parallel high-throughput fungal ITS1 amplicon sequencing, bacterial 16S amplicon sequencing and fungal cultures of 1,279 faecal samples from a cohort of 156 patients undergoing allo-HCT to reveal potential trans-kingdom dynamics and their association with patient outcomes. We saw that the overall density and the biodiversity of intestinal fungi were stable during allo-HCT but the species composition changed drastically from day to day. We identified a subset of patients with fungal dysbiosis defined by culture positivity (n = 53) and stable expansion of Candida parapsilosis complex species (n = 19). They presented with distinct trans-kingdom microbiota profiles, characterized by a decreased intestinal bacterial biomass. These patients had worse overall survival and higher transplant-related mortality independent of candidaemia. This expands our understanding of the clinical significance of the mycobiota and suggests that targeting fungal dysbiosis may help to improve long-term patient survival.

Unclouding Clostridiodes difficile virulence with systems biology.

Systems biology studies how complex dynamics emerge from many elements interacting with each other in biological systems. This definition might sound abstract, but the applications are concrete. In this issue of Cell Host & Microbe, two studies apply systems biology to study Clostridioides difficile, a major cause of hospital-acquired infections.

Cooperation between melanoma cell states promotes metastasis through heterotypic cluster formation.

Melanomas can have multiple coexisting cell states, including proliferative (PRO) versus invasive (INV) subpopulations that represent a "go or grow" trade-off; however, how these populations interact is poorly understood. Using a combination of zebrafish modeling and analysis of patient samples, we show that INV and PRO cells form spatially structured heterotypic clusters and cooperate in the seeding of metastasis, maintaining cell state heterogeneity. INV cells adhere tightly to each other and form clusters with a rim of PRO cells. Intravital imaging demonstrated cooperation in which INV cells facilitate dissemination of less metastatic PRO cells. We identified the TFAP2 neural crest transcription factor as a master regulator of clustering and PRO/INV states. Isolation of clusters from patients with metastatic melanoma revealed a subset with heterotypic PRO-INV clusters. Our data suggest a framework for the co-existence of these two divergent cell populations, in which heterotypic clusters promote metastasis via cell-cell cooperation.

Listening in on the conversation between the human gut microbiome and its host.

The gut microbiome is an ecosystem. Natural selection favored microbes fit for the gut, which can utilize and convert molecules produced by the host for their own benefit. But natural selection also favored the host's mechanisms to sense and respond to the microbial ecosystem for its own benefit. We can listen in on the host-microbiome 'conversation' in the simultaneous responses of the microbiome and the host to strong perturbations. In laboratory animals a perturbation can be done for research; in human patients a perturbation can be caused by disease or therapy. Advances in metagenomics, metabolomics and computation amplify our means to listen in on the conversation between the gut microbiome and its host.

Compilation of longitudinal microbiota data and hospitalome from hematopoietic cell transplantation patients.

The impact of the gut microbiota in human health is affected by several factors including its composition, drug administrations, therapeutic interventions and underlying diseases. Unfortunately, many human microbiota datasets available publicly were collected to study the impact of single variables, and typically consist of outpatients in cross-sectional studies, have small sample numbers and/or lack metadata to account for confounders. These limitations can complicate reusing the data for questions outside their original focus. Here, we provide comprehensive longitudinal patient dataset that overcomes those limitations: a collection of fecal microbiota compositions (>10,000 microbiota samples from >1,000 patients) and a rich description of the "hospitalome" experienced by the hosts, i.e., their drug exposures and other metadata from patients with cancer, hospitalized to receive allogeneic hematopoietic cell transplantation (allo-HCT) at a large cancer center in the United States. We present five examples of how to apply these data to address clinical and scientific questions on host-associated microbial communities.