Abstract
Induction chemotherapy for patients with acute myeloid leukemia (AML) is a unique clinical scenario. These patients spend several weeks in the hospital, receiving multiple antibiotics, experiencing gastrointestinal mucosal damage, and suffering severe impairments in their immune system and nutrition. These factors cause major disruptions to the gut microbiota to a level rarely seen in other clinical conditions. Thus, the study of the gut microbiota in these patients can reveal novel aspects of microbiota-host relationships. When combined with the circulating metabolome, such studies could shed light on gut microbiota contribution to circulating metabolites. Collectively, gut microbiota and circulating metabolome are known to regulate host physiology. We have previously deposited amplicon sequences from 566 fecal samples from 68 AML patients. Here, we provide sample-level details and a link, using de-identified patient IDs, to additional data including serum metabolomics (260 samples from 36 patients) and clinical metadata. The detailed information provided enables comprehensive multi-omics analysis. We validate the technical quality of these data through 3 examples and demonstrate a method for integrated analysis.
| Original language | English (US) |
|---|---|
| Article number | 468 |
| Journal | Scientific Data |
| Volume | 9 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2022 |
Bibliographical note
Funding Information:Sequence data from UMN samples were analyzed using the resources of the Minnesota Supercomputing Institute. Serum samples were analyzed for metabolomics by Metabolon, Inc. We thank Dr. Sivapriya Ramamoorthy from Metabolon for her assistance with Figure 1. This work was supported by the National Institutes of Health’s National Center for Advancing Translational Sciences grants KL2TR002492 and UL1TR002494. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health’s National Center for Advancing Translational Sciences.
Funding Information:
Sequence data from UMN samples were analyzed using the resources of the Minnesota Supercomputing Institute. Serum samples were analyzed for metabolomics by Metabolon, Inc. We thank Dr. Sivapriya Ramamoorthy from Metabolon for her assistance with Figure 1. This work was supported by the National Institutes of Health’s National Center for Advancing Translational Sciences grants KL2TR002492 and UL1TR002494. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health’s National Center for Advancing Translational Sciences.
Publisher Copyright:
© 2022, The Author(s).
