Ridinilazole, a narrow spectrum antibiotic for treatment of Clostridioides difficile infection, enhances preservation of microbiota-dependent bile acids.

Antibiotic treatment is a standard therapy for Clostridioides difficile infection, but dysbiosis of the gut microbiota due to antibiotic exposure is also a major risk factor for the disease. Following an initial episode of C. difficile infection, a relentless cycle of recurrence can occur, where persistent treatment-related dysbiosis predisposes the patient to subsequent relapse. This study uses a longitudinal study design to compare the effects of a narrow-spectrum (ridinilazole) or broad-spectrum antibiotic (vancomycin) on intestinal bile acid profiles and their associations with gut bacteria over the course of C. difficile infection treatment. At the end of treatment (day 10), subjects receiving vancomycin showed a nearly 100-fold increase in the ratio of conjugated to secondary bile acids in their stool compared with baseline, whereas subjects receiving ridinilazole maintained this ratio near baseline levels. Correlation analysis detected significant positive associations between secondary bile acids and several Bacteroidales and Clostridiales families. These families were depleted in the vancomycin group but preserved at near-baseline abundance in the ridinilazole group. Enterobacteriaceae, which expanded to a greater extent in the vancomycin group, correlated negatively and positively with secondary and conjugated primary bile acids, respectively. Bile acid ratios at the end of treatment were significantly different between those who recurred and those who did not. These results indicate that a narrow-spectrum antibiotic maintains an intestinal bile acid profile associated with a lowered risk of recurrence.NEW & NOTEWORTHY This is the first study to demonstrate in humans the relationships between Clostridioides difficile antibiotic treatment choice and bile acid metabolism both during therapy and after treatment cessation. The results show a microbiota- and metabolome-preserving property of a novel narrow-spectrum agent that correlates with the agent's favorable sustained clinical response rates compared with broad-spectrum antibiotic treatment.

Using Metabolomics to Identify Cell Line-Independent Indicators of Growth Inhibition for Chinese Hamster Ovary Cell-based Bioprocesses.

Chinese hamster ovary (CHO) cells are widely used for the production of biopharmaceuticals. Efforts to improve productivity through medium design and feeding strategy optimization have focused on preventing the depletion of essential nutrients and managing the accumulation of lactate and ammonia. In addition to ammonia and lactate, many other metabolites accumulate in CHO cell cultures, although their effects remain largely unknown. Elucidating these effects has the potential to further improve the productivity of CHO cell-based bioprocesses. This study used untargeted metabolomics to identify metabolites that accumulate in fed-batch cultures of monoclonal antibody (mAb) producing CHO cells. The metabolomics experiments profiled six cell lines that are derived from two different hosts, produce different mAbs, and exhibit different growth profiles. Comparing the cell lines' metabolite profiles at different growth stages, we found a strong negative correlation between peak viable cell density (VCD) and a tryptophan metabolite, putatively identified as 5-hydroxyindoleacetaldehyde (5-HIAAld). Amino acid supplementation experiments showed strong growth inhibition of all cell lines by excess tryptophan, which correlated with the accumulation of 5-HIAAld in the culture medium. Prospectively, the approach presented in this study could be used to identify cell line- and host-independent metabolite markers for clone selection and bioprocess development.

Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics.

Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF was applied to untargeted LC-MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model.

Environmental Chemical Diethylhexyl Phthalate Alters Intestinal Microbiota Community Structure and Metabolite Profile in Mice.

Exposure to environmental chemicals during windows of development is a potentially contributing factor in gut microbiota dysbiosis and linked to chronic diseases and developmental disorders. We used a community-level model of microbiota metabolism to investigate the effects of diethylhexyl phthalate (DEHP), a ubiquitous plasticizer implicated in neurodevelopmental disorders, on the composition and metabolite outputs of gut microbiota in young mice. Administration of DEHP by oral gavage increased the abundance of Lachnoclostridium, while decreasing Clostridium sensu stricto Addition of DEHP to in vitro-cultured cecal microbiota increased the abundance of Paenibacillus and Lachnoclostridium Untargeted metabolomics showed that DEHP broadly altered the metabolite profile in the culture. Notably, DEHP enhanced the production of p-cresol while inhibiting butyrate synthesis. Metabolic model-guided correlation analysis indicated that the likely sources of p-cresol are Clostridium species. Monoculture of Lachnoclostridium bolteae confirmed that it is capable of producing p-hydroxyphenylacetic acid, the immediate precursor of p-cresol, and that the species' growth is enhanced upon DEHP exposure. Taken together, these findings suggest a model where DEHP increases production of p-cresol, a bacterial metabolite linked with neurodevelopmental disorders, by expanding the abundance of species that synthesize the metabolite's precursor.IMPORTANCE Several previous studies have pointed to environmental chemical exposure during windows of development as a contributing factor in neurodevelopmental disorders and correlated these disorders with microbiota dysbiosis; however, little is known about how the chemicals specifically alter the microbiota to interfere with development. The findings reported in this paper unambiguously establish that a pollutant linked with neurodevelopmental disorders can directly modify the microbiota to promote the production of a potentially toxic metabolite (p-cresol) that has also been correlated with neurodevelopmental disorders. Furthermore, we used a novel modeling strategy to identify the responsible enzymes and bacterial sources of this metabolite. To the best of our knowledge, the present study is the first to characterize the functional consequence of phthalate exposure on a developed microbiota. Our results suggest that specific bacterial pathways could be developed as diagnostic and therapeutic targets against health risks posed by ingestion of environmental chemicals.

Biologically Consistent Annotation of Metabolomics Data.

Annotation of metabolites remains a major challenge in liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics. The current gold standard for metabolite identification is to match the detected feature with an authentic standard analyzed on the same equipment and using the same method as the experimental samples. However, there are substantial practical challenges in applying this approach to large data sets. One widely used annotation approach is to search spectral libraries in reference databases for matching metabolites; however, this approach is limited by the incomplete coverage of these libraries. An alternative computational approach is to match the detected features to candidate chemical structures based on their mass and predicted fragmentation pattern. Unfortunately, both of these approaches can match multiple identities with a single feature. Another issue is that annotations from different tools often disagree. This paper presents a novel LC-MS data annotation method, termed Biologically Consistent Annotation (BioCAn), that combines the results from database searches and in silico fragmentation analyses and places these results into a relevant biological context for the sample as captured by a metabolic model. We demonstrate the utility of this approach through an analysis of CHO cell samples. The performance of BioCAn is evaluated against several currently available annotation tools, and the accuracy of BioCAn annotations is verified using high-purity analytical standards.

Pathways and functions of gut microbiota metabolism impacting host physiology.

The bacterial populations in the human intestine impact host physiological functions through their metabolic activity. In addition to performing essential catabolic and biotransformation functions, the gut microbiota produces bioactive small molecules that mediate interactions with the host and contribute to the neurohumoral axes connecting the intestine with other parts of the body. This review discusses recent progress in characterizing the metabolic products of the gut microbiota and their biological functions, focusing on studies that investigate the responsible bacterial pathways and cognate host receptors. Several key areas are highlighted for future development: context-based analysis targeting pathways; integration of analytical approaches; metabolic modeling; and synthetic systems for in vivo manipulation of microbiota functions. Prospectively, these developments could further our mechanistic understanding of host-microbiota interactions.