Nature MicrobiologyMicrobiologyPeer-reviewed journal article
Multi-omics analysis of a traditional fermented food reveals a byproduct-associated subpopulation of Neurospora intermedia for waste-to-food upcycling
Vayu Maini RekdaliD, José Manuel Villalobos‐EscobedoiD, Nabila Rodríguez ValeróniD, Mikel Olaizola García, Diego Prado VásqueziD, Alexander Rosales, Pia M. SörenseniD, Edward E. K. BaidooiD, Ana Calheiros de CarvalhoiD, Robert W. RileyiD, Anna LipzeniD, Guifen HeiD, Mi YaniD, Sajeet HaridasiD, Chris G. DaumiD, Yuko YoshinagaiD, Vivian NgiD, Igor V. GrigorieviD, Rasmus Munk, Christofora Hanny WijayaiD, Lilis NuraidaiD, Irma Putri Damayanti, Pablo Cruz‐MoralesiD, Jay D. KeaslingiD
Abstract
Fungal solid-state fermentation (SSF) of byproducts has promise for increasing food sustainability and security, but fungal waste-to-food upcycling remains poorly understood at the molecular level. Here we use a multi-omics approach to characterize oncom - a fermented food traditionally produced from byproducts in Java, Indonesia - as a model system for understanding fungal waste conversion. Metagenomic sequencing of two oncom types (red and black) indicated that Neurospora intermedia is the fungus dominating red oncom. Further transcriptomic, metabolomic, and phylogenomic analysis revealed that oncom-derived N. intermedia utilizes pectin and cellulose degradation for substrate conversion and belongs to a distinct byproduct-associated subpopulation that differs from wild strains at the genetic and biochemical level. Finally, we found that N. intermedia grew on a range of industrially relevant byproducts, did not encode for any known mycotoxins, and could be used to create foods that were positively perceived by consumers outside Indonesia. This study uncovers the microbial and genetic basis of a traditional upcycled food, sheds light on human domestication of microbes for sustainability challenges, and establishes the edible N. intermedia as a promising fungus for byproduct upcycling in SSF and beyond.
- assay: metagenomics
- assay: metabolomics
- assay: proteomics
- assay: DNA methylation
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
- Citations
- 85
- FWCI
- 9.78
- Field percentile
- top 1%
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