TY - JOUR
T1 - Gut microbiome-derived bacterial extracellular vesicles in patients with solid tumours
AU - Mishra, Surbhi
AU - Tejesvi, Mysore Vishakantegowda
AU - Hekkala, Jenni
AU - Turunen, Jenni
AU - Kandikanti, Niyati
AU - Kaisanlahti, Anna
AU - Suokas, Marko
AU - Leppä, Sirpa
AU - Vihinen, Pia
AU - Kuitunen, Hanne
AU - Sunela, Kaisa
AU - Koivunen, Jussi
AU - Jukkola, Arja
AU - Kalashnikov, Ilja
AU - Auvinen, Päivi
AU - Kääriäinen, Okko Sakari
AU - Peñate Medina, T.
AU - Peñate Medina, O.
AU - Saarnio, Juha
AU - Meriläinen, Sanna
AU - Rautio, Tero
AU - Aro, Raila
AU - Häivälä, Reetta
AU - Suojanen, Juho
AU - Laine, Mikael
AU - Erawijattari, Pande Putu
AU - Lahti, Leo
AU - Karihtala, Peeter
AU - Ruuska, Terhi S.
AU - Reunanen, Justus
N1 - Publisher Copyright:
© 2023
PY - 2025
Y1 - 2025
N2 - Introduction: Gut microbiome–derived nanoparticles, known as bacterial extracellular vesicles (bEVs), have garnered interest as promising tools for studying the link between the gut microbiome and human health. The diverse composition of bEVs, including their proteins, mRNAs, metabolites, and lipids, makes them useful for investigating diseases such as cancer. However, conventional approaches for studying gut microbiome composition alone may not be accurate in deciphering host–gut microbiome communication. In clinical microbiome research, there is a gap in the knowledge on the role of bEVs in solid tumor patients. Objectives: Analyzing the functionality of bEVs using (meta)genomics and proteomics could highlight the unique aspects of host–gut microbiome interactions in solid tumor patients. Therefore, we performed a comparative analysis of the proteome and microbiota composition of gut microbiome-derived bEVs isolated from patients with solid tumors and healthy controls. Methods: After isolating bEVs from the feces of solid tumor patients and healthy controls, we performed spectrometry analysis of their proteomes and next-generation sequencing (NGS) of the 16S gene. We also investigated the gut microbiomes of feces from patients and controls using 16S sequencing and used machine learning to classify the samples into patients and controls based on their bEVs and fecal microbiomes. Results: Solid tumor patients showed decreased microbiota richness and diversity in both the bEVs and feces. However, the bEV proteomes were more diverse in patients than in the controls and were enriched with proteins associated with the metabolism of amino acids and carbohydrates, nucleotide binding, and oxidoreductase activity. Metadata classification of samples was more accurate using fecal bEVs (100%) compared with fecal samples (93%). Conclusion: Our findings suggest that bEVs are unique functional entities. There is a need to explore bEVs together with conventional gut microbiome analysis in functional cancer research to decipher the potential of bEVs as cancer diagnostic or therapeutic biomarkers.
AB - Introduction: Gut microbiome–derived nanoparticles, known as bacterial extracellular vesicles (bEVs), have garnered interest as promising tools for studying the link between the gut microbiome and human health. The diverse composition of bEVs, including their proteins, mRNAs, metabolites, and lipids, makes them useful for investigating diseases such as cancer. However, conventional approaches for studying gut microbiome composition alone may not be accurate in deciphering host–gut microbiome communication. In clinical microbiome research, there is a gap in the knowledge on the role of bEVs in solid tumor patients. Objectives: Analyzing the functionality of bEVs using (meta)genomics and proteomics could highlight the unique aspects of host–gut microbiome interactions in solid tumor patients. Therefore, we performed a comparative analysis of the proteome and microbiota composition of gut microbiome-derived bEVs isolated from patients with solid tumors and healthy controls. Methods: After isolating bEVs from the feces of solid tumor patients and healthy controls, we performed spectrometry analysis of their proteomes and next-generation sequencing (NGS) of the 16S gene. We also investigated the gut microbiomes of feces from patients and controls using 16S sequencing and used machine learning to classify the samples into patients and controls based on their bEVs and fecal microbiomes. Results: Solid tumor patients showed decreased microbiota richness and diversity in both the bEVs and feces. However, the bEV proteomes were more diverse in patients than in the controls and were enriched with proteins associated with the metabolism of amino acids and carbohydrates, nucleotide binding, and oxidoreductase activity. Metadata classification of samples was more accurate using fecal bEVs (100%) compared with fecal samples (93%). Conclusion: Our findings suggest that bEVs are unique functional entities. There is a need to explore bEVs together with conventional gut microbiome analysis in functional cancer research to decipher the potential of bEVs as cancer diagnostic or therapeutic biomarkers.
KW - 16S rRNA gene sequencing
KW - Bacterial extracellular vesicles
KW - Cancer
KW - Gut microbiota
KW - Machine learning
KW - Proteome
U2 - 10.1016/j.jare.2024.03.003
DO - 10.1016/j.jare.2024.03.003
M3 - Article
AN - SCOPUS:85188597957
SN - 2090-1232
VL - 68
SP - 375
EP - 386
JO - Journal of Advanced Research
JF - Journal of Advanced Research
ER -