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 lightweight machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry to predict changes in the metabolite composition of 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. Despite its simplicity, the model learned captured shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting cellular adaptations associated with metastasis to specific organs. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.

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.

Antifungal heteroresistance causes prophylaxis failure and facilitates breakthrough Candida parapsilosis infections.

Breakthrough fungal infections in patients on antimicrobial prophylaxis during allogeneic hematopoietic cell transplantation (allo-HCT) represent a major and often unexplained cause of morbidity and mortality. Candida parapsilosis is a common cause of invasive candidiasis and has been classified as a high-priority fungal pathogen by the World Health Organization. In high-risk allo-HCT recipients on micafungin prophylaxis, we show that heteroresistance (the presence of a phenotypically unstable, low-frequency subpopulation of resistant cells (~1 in 10,000)) underlies breakthrough bloodstream infections by C. parapsilosis. By analyzing 219 clinical isolates from North America, Europe and Asia, we demonstrate widespread micafungin heteroresistance in C. parapsilosis. Standard antimicrobial susceptibility tests, such as broth microdilution or gradient diffusion assays, which guide drug selection for invasive infections, fail to detect micafungin heteroresistance in C. parapsilosis. To facilitate rapid detection of micafungin heteroresistance in C. parapsilosis, we constructed a predictive machine learning framework that classifies isolates as heteroresistant or susceptible using a maximum of ten genomic features. These results connect heteroresistance to unexplained antifungal prophylaxis failure in allo-HCT recipients and demonstrate a proof-of-principle diagnostic approach with the potential to guide clinical decisions and improve patient care.