Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation.

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

Batf3-dependent orchestration of the robust Th1 responses and fungal control during cryptococcal infection, the role of cDC1.

While type I conventional dendritic cells (cDC1s) are vital for generating adaptive immunity against intracellular pathogens and tumors, their role in defense against fungal pathogen Cryptococcus neoformans remains unclear. We investigated the role of the cDC1 subset in a fungus-restricting mouse model of cryptococcal infection. The cDC1 subset displayed a unique transcriptional signature with highly upregulated T-cell recruitment, polarization, and activation pathways compared to other DC subsets. Using Batf3-/- mice, which lack the cDC1 population, our results support that Batf3-dependent cDC1s are pivotal for the development of the effective immune response against cryptococcal infection, particularly within the lung and brain. Deficiency in Batf3 cDC1 led to diminished CD4 accumulation and decreased IFNγ production across multiple organs, supporting that cDC1s are a major driver of potent Th1 responses during cryptococcal infection. Consistently, mice lacking Batf3-cDC1 demonstrated markedly diminished fungicidal activity and weaker containment of the fungal pathogen. In conclusion, Batf3-dependent cDC1 can function as a linchpin in mounting Th1 response, ensuring effective fungal control during cryptococcal infection. Harnessing cDC1 pathways may present a promising strategy for interventions against this pathogen.IMPORTANCECryptococcus neoformans causes severe meningoencephalitis, accounting for an estimated 200,000 deaths each year. Central to mounting an effective defense against these infections is T-cell-mediated immunity, which is orchestrated by dendritic cells (DCs). The knowledge about the role of specific DC subsets in shaping anti-cryptococcal immunity is limited. Here, we demonstrate that Batf3 cDC1s are important drivers of protective Th1 CD4 T-cell responses required for clearance of cryptococcal infection. Deficiency of Batf3 cDC1 in the infected mice leads to significantly reduced Th1 response and exacerbated fungal growth to the point where depleting the remaining CD4 T cells no longer affects fungal burden. Unveiling this pivotal role of cDC1 in antifungal defense is likely to be important for the development of vaccines and therapies against life-threatening fungal pathogens.

Offline Two-dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome.

Compound identification is an essential task in the workflow of untargeted metabolomics since the interpretation of the data in a biological context depends on the correct assignment of chemical identities to the features it contains. Current techniques fall short of identifying all or even most observable features in untargeted metabolomics data, even after rigorous data cleaning approaches to remove degenerate features are applied. Hence, new strategies are required to annotate the metabolome more deeply and accurately. The human fecal metabolome, which is the focus of substantial biomedical interest, is a more complex, more variable, yet lesser-investigated sample matrix compared to widely studied sample types like human plasma. This manuscript describes a novel experimental strategy using multidimensional chromatography to facilitate compound identification in untargeted metabolomics. Pooled fecal metabolite extract samples were fractionated using offline semi-preparative liquid chromatography. The resulting fractions were analyzed by an orthogonal LC-MS/MS method, and the data were searched against commercial, public, and local spectral libraries. Multidimensional chromatography yielded more than a 3-fold improvement in identified compounds compared to the typical single-dimensional LC-MS/MS approach and successfully identified several rare and novel compounds, including atypical conjugated bile acid species. Most features identified by the new approach could be matched to features that were detectable but not identifiable in the original single-dimension LC-MS data. Overall, our approach represents a powerful strategy for deeper annotation of the metabolome that can be implemented with commercially-available instrumentation, and should apply to any dataset requiring deeper annotation of the metabolome.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics.

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.