- Award ID(s):
- NSF-PAR ID:
- Date Published:
- Journal Name:
- BMC cancer
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Claesen, Jan (Ed.)ABSTRACT The capacity of the human microbiome to modulate inflammation in the context of cancer is becoming increasingly clear. Myeloproliferative neoplasms (MPNs) are chronic hematologic malignancies in which inflammation plays a key role in disease initiation, progression, and symptomatology. To better understand the composition of the gut microbiome in patients with MPN, triplicate fecal samples were collected from 25 MPN patients and 25 non-MPN controls. Although most of the variance between the microbial community compositions could be attributed to the individual (permutational analysis of variance [PERMANOVA], R 2 = 0.92, P = 0.001), 1.7% of the variance could be attributed to disease status (MPN versus non-MPN). When a more detailed analysis was performed, significantly fewer reads mapping to a species of Phascolarctobacterium , a microbe previously associated with reduced inflammation, were found in MPNs. Further, our data revealed an association between Parabacteroides and tumor necrosis factor alpha (TNF-α), an inflammatory cytokine elevated in MPNs. Taken together, our results indicate a significant difference in the microbiome of MPN patients compared to non-MPN controls, and we identify specific species which may have a role in the chronic inflammation central to this disease. IMPORTANCE MPNs are chronic blood cancers in which inflammation plays a key role in disease initiation, progression, and symptomatology. The gut microbiome modulates normal blood development and inflammation and may also impact the development and manifestation of blood cancers. Therefore, the microbiome may be an important modulator of inflammation in MPN and could potentially be leveraged therapeutically in this disease. However, the relationship between the gut microbiome and MPNs has not been defined. Therefore, we performed an evaluation of the MPN microbiome, comparing the microbiomes of MPN patients with healthy donors and between MPN patients with various states of disease.more » « less
Immune checkpoint inhibitor (ICI) treatment has created the opportunity of improved outcome for patients with hepatocellular carcinoma (HCC). However, only a minority of HCC patients benefit from ICI treatment owing to poor treatment efficacy and safety concerns. There are few predictive factors that precisely stratify HCC responders to immunotherapy. In this study, we developed a tumour microenvironment risk (TMErisk) model to divide HCC patients into different immune subtypes and evaluated their prognosis. Our results indicated that virally mediated HCC patients who had more common tumour protein P53 (TP53) alterations with lower TMErisk scores were appropriate for ICI treatment. HCC patients with alcoholic hepatitis who more commonly harboured catenin beta 1 (CTNNB1) alterations with higher TMErisk scores could benefit from treatment with multi-tyrosine kinase inhibitors. The developed TMErisk model represents the first attempt to anticipate tumour tolerance of ICIs in the TME through the degree of immune infiltration in HCCs.
Because microbes use carotenoids as an antioxidant for protection, dietary carotenoids could be associated with gut microbiota composition. We aimed to determine associations among reported carotenoid intake, plasma carotenoid concentrations, and fecal bacterial communities in pregnant women. Pregnant women (
n= 27) were enrolled in a two‐arm study designed to assess feasibility of biospecimen collection and delivery of a practical nutrition intervention. Plasma and fecal samples were collected and women were surveyed with a 24‐hr dietary checklist and recalls. Plasma carotenoids were analyzed by HPLC using photodiode array detection. Fecal bacteria were analyzed by 16S rRNA DNA sequencing. Results presented are cross‐sectional from the 36‐week gestational study visit combined across both study arms due to lack of significant differences between intervention and usual care groups ( n= 23 women with complete data). Recent intake of carotenoid‐containing foods included carrots, sweet potatoes, mangos, apricots, and/or bell peppers for 48% of women; oranges/orange juice (17%); egg (39%); tomato/tomato‐based sauces (52%); fruits (83%); and vegetables (65%). Average plasma carotenoid concentrations were 6.4 µg/dL α‐carotene (AC), 17.7 µg/dL β‐carotene (BC), 11.4 µg/dL cryptoxanthin, 39.0 µg/dL trans‐lycopene, and 29.8 µg/dL zeaxanthin and lutein. AC and BC concentrations were higher in women who recently consumed foods high in carotenoids. CR concentrations were higher in women who consumed oranges/orange juice. Microbiota α‐diversity positively correlated with AC and BC. Microbiota β‐diversity differed significantly across reported intake of carotenoid containing foods and plasma concentrations of AC. This may reflect an effect of high fiber or improved overall dietary quality, rather than a specific effect of carotenoids. Practical Application
Little is known about the association between the gut microbiome and specific dietary microconstituents, such as carotenoids, especially during pregnancy. This research demonstrates that a carotenoid‐rich diet during pregnancy supports a diverse microbiota, which could be one mechanism by which carotenoids promote health.
Empirical field studies allow us to view how ecological and environmental processes shape the biodiversity of our planet, but collecting samples in situ creates inherent challenges. The majority of empirical vertebrate gut microbiome research compares multiple host species against abiotic and biotic factors, increasing the potential for confounding environmental variables. To minimize these confounding factors, we focus on a single species of passerine bird found throughout the geologically complex island of Sulawesi, Indonesia. We assessed the effects of two environmental factors, geographic Areas of Endemism (AOEs) and elevation, as well as host sex on the gut microbiota assemblages of the Sulawesi Babbler,
Pellorneum celebense,from three different mountains across the island. Using cloacal swabs, high-throughput-amplicon sequencing, and multiple statistical models, we identified the core microbiome and determined the signal of these three factors on microbial composition. Results
The five most prevalent bacterial phyla within the gut microbiome of
P. celebensewere Proteobacteria(32.6%), Actinobacteria(25.2%), Firmicutes(22.1%), Bacteroidetes(8.7%), and Plantomycetes(2.6%). These results are similar to those identified in prior studies of passeriform microbiomes. Overall, microbiota diversity decreased as elevation increased, irrespective of sex or AOE. A single ASV of Clostridiumwas enriched in higher elevation samples, while lower elevation samples were enriched with the genera Perlucidibaca(Family Moraxellaceae), Lachnoclostridium(Family Lachnospiraceae), and an unidentified species in the Family Pseudonocardiaceae. Conclusions
While the core microbiota families recovered here are consistent with other passerine studies, the decreases in diversity as elevation increases has only been seen in non-avian hosts. Additionally, the increased abundance of
Clostridiumat high elevations suggests a potential microbial response to lower oxygen levels. This study emphasizes the importance of incorporating multiple statistical models and abiotic factors such as elevation in empirical microbiome research, and is the first to describe an avian gut microbiome from the island of Sulawesi.
We performed various analyses on the taxonomic and functional features of the gut microbiome from NSCLC patients treated with immunotherapy to establish a model that may predict whether a patient will benefit from immunotherapy. We collected 65 published whole metagenome shotgun sequencing samples along with 14 samples from our previous study. We systematically studied the taxonomical characteristics of the dataset and used both the random forest (RF) and the multilayer perceptron (MLP) neural network models to predict patients with progression-free survival (PFS) above 6 months versus those below 3 months. Our results showed that the RF classifier achieved the highest F-score (85.2%) and the area under the receiver operating characteristic curve (AUC) (95%) using the protein families (Pfam) profile, and the MLP neural network classifier achieved a 99.9% F-score and 100% AUC using the same Pfam profile. When applying the model trained in the Pfam profile directly to predict the treatment response, we found that both trained RF and MLP classifiers significantly outperformed the stochastic predictor in F-score. Our results suggested that such a predictive model based on functional (e.g., Pfam) rather than taxonomic profile might be clinically useful to predict whether an NSCLC patient will benefit from immunotherapy, as both the F-score and AUC of functional profile outperform that of taxonomic profile. In addition, our model suggested that interactive biological processes such as methanogenesis, one-carbon, and amino acid metabolism might be important in regulating the immunotherapy response that warrants further investigation.more » « less