References of "Wilmes, Paul 50003335"
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See detailIntegrated In Vitro and In Silico Modeling Delineates the Molecular Effects of a Synbiotic Regimen on Colorectal-Cancer-Derived Cells
Greenhalgh, Kacy UL; Ramiro Garcia, Javier UL; Heinken et al

in Cell Reports (2019), 27

By modulating the human gut microbiome, prebiotics and probiotics (combinations of which are called synbiotics) may be used to treat diseases such as colorectal cancer (CRC). Methodological limitations ... [more ▼]

By modulating the human gut microbiome, prebiotics and probiotics (combinations of which are called synbiotics) may be used to treat diseases such as colorectal cancer (CRC). Methodological limitations have prevented determining the potential combina- torial mechanisms of action of such regimens. We expanded our HuMiX gut-on-a-chip model to co-culture CRC-derived epithelial cells with a model probiotic under a simulated prebiotic regimen, and we integrated the multi-omic results with in silico metabolic modeling. In contrast to individual prebi- otic or probiotic treatments, the synbiotic regimen caused downregulation of genes involved in procarci- nogenic pathways and drug resistance, and reduced levels of the oncometabolite lactate. Distinct ratios of organic and short-chain fatty acids were produced during the simulated regimens. Treatment of primary CRC-derived cells with a molecular cocktail reflecting the synbiotic regimen attenuated self-renewal ca- pacity. Our integrated approach demonstrates the potential of modeling for rationally formulating synbi- otics-based treatments in the future. [less ▲]

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See detailDeep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition
Buetti-Dinh, Antoine; Galli, Vanni; Bellenberg, Sören et al

in Biotechnology Reports (2019)

Background Deep neural networks have been successfully applied to diverse fields of computer vision. However, they only outperform human capacities in a few cases. Methods The ability of deep neural ... [more ▼]

Background Deep neural networks have been successfully applied to diverse fields of computer vision. However, they only outperform human capacities in a few cases. Methods The ability of deep neural networks versus human experts to classify microscopy images was tested on biofilm colonization patterns formed on sulfide minerals composed of up to three different bioleaching bacterial species attached to chalcopyrite sample particles. Results A low number of microscopy images per category (<600) was sufficient for highly efficient computational analysis of the biofilm's bacterial composition. The use of deep neural networks reached an accuracy of classification of ∼90% compared to ∼50% for human experts. Conclusions Deep neural networks outperform human experts’ capacity in characterizing bacterial biofilm composition involved in the degradation of chalcopyrite. This approach provides an alternative to standard, time-consuming biochemical methods. [less ▲]

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See detailExtensive transmission of microbes along the gastrointestinal tract
Schmidt, Thomas; Hayward, Matthew; Coelho, Luis et al

in eLife (2019)

The gastrointestinal tract is abundantly colonized by microbes, yet the translocation of oral species to the intestine is considered a rare aberrant event, and a hallmark of disease. By studying salivary ... [more ▼]

The gastrointestinal tract is abundantly colonized by microbes, yet the translocation of oral species to the intestine is considered a rare aberrant event, and a hallmark of disease. By studying salivary and fecal microbial strain populations of 310 species in 470 individuals from five countries, we found that transmission to, and subsequent colonization of, the large intestine by oral microbes is common and extensive among healthy individuals. We found evidence for a vast majority of oral species to be transferable, with increased levels of transmission in colorectal cancer and rheumatoid arthritis patients and, more generally, for species described as opportunistic pathogens. This establishes the oral cavity as an endogenous reservoir for gut microbial strains, and oral-fecal transmission as an important process that shapes the gastrointestinal microbiome in health and disease. [less ▲]

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See detailInfluence of Macro-Substrate Composition in Wastewater on Micropollutant Removal
Christen, Anne UL; Gallé, Tom; Köhler, Christian et al

Scientific Conference (2019)

Detailed reference viewed: 7 (0 UL)
See detailMicrobiome: the environment within
Wilmes, Paul UL

Scientific Conference (2018, December)

Detailed reference viewed: 30 (3 UL)
See detailSystems ecology of microbiomes
Wilmes, Paul UL

Scientific Conference (2018, December)

Detailed reference viewed: 39 (9 UL)
See detailSystems ecology of the human microbiome
Wilmes, Paul UL

Scientific Conference (2018, November)

Detailed reference viewed: 18 (0 UL)
See detailUnderstanding the role of the microbiome in Parkinson’s disease
Wilmes, Paul UL

Scientific Conference (2018, October)

Detailed reference viewed: 12 (0 UL)
See detailThe ecology of the unseen
Wilmes, Paul UL

Scientific Conference (2018, October)

Detailed reference viewed: 11 (0 UL)
See detailLe microbiote humain et son impacte sur la santé
Wilmes, Paul UL

Scientific Conference (2018, October)

Detailed reference viewed: 10 (0 UL)
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See detailA multi-omic view of invasive genetic elements and their linked prokaryotic population dynamics within a mixed microbial community
Martinez Arbas, Susana UL; Narayanasamy, Shaman; Herold, Malte et al

Poster (2018, September 11)

Detailed reference viewed: 50 (5 UL)
See detailUnraveling the combinatorial mechanisms linking the gut microbiome to Parkinson’s disease
Wilmes, Paul UL

Scientific Conference (2018, September)

Detailed reference viewed: 7 (1 UL)
See detailMicrobial Systems Ecology for identifying key functions driving host-microbiome-diet interactions
Wilmes, Paul UL

Scientific Conference (2018, September)

Detailed reference viewed: 11 (0 UL)
See detailThe integration of quantitative big data from microbial consortia: what is there to gain?
Wilmes, Paul UL

Scientific Conference (2018, August)

Detailed reference viewed: 8 (1 UL)
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See detailIntegrated time-resolved multi-omics for understanding microbial niche ecology
Herold, Malte UL; Narayanasamy, Shaman UL; Martinez Arbas, Susana UL et al

Poster (2018, August)

Microbial communities are strongly shaped by the niche breadths of their constituent populations. However, a detailed understanding of microbial niche ecology is typically lacking. Integrated multi-omic ... [more ▼]

Microbial communities are strongly shaped by the niche breadths of their constituent populations. However, a detailed understanding of microbial niche ecology is typically lacking. Integrated multi-omic analyses of host- or environment-derived samples offer the prospect of resolving fundamental and realised niches in situ. In turn, this is considered a prerequisite for niche engineering in order to drive an individual population or a community towards a specific phenotype, e.g., improvement of a biotechnological process. Here, we sampled floating islets on the surface of an activated sludge tank in a time-series spanning 51 time-points over 14 months. Multi-omics datasets (metagenomics, metatranscriptomics, metaproteomics, and (meta-)metabolomics) were generated for all time-points. Leveraging nucleotide sequencing data, we analyzed the community structure and reconstructed genomes for specific populations of interest. Moreover, based on their metabolic potential, three major groups emerged, serving as proxies for their respective fundamental niches . Time-resolved linkage of the proteomic and transcriptomic data to the reconstructed genomes revealed a fine-grained picture of niche realization. In particular, environmental factors (temperature, metabolites, oxygen) were significantly associated with gene expression of individual populations. Furthermore, we subjected the community to controlled oxygen conditions (stable or dynamic) in a bioreactor experiment and measured the transcriptomic response. Our results suggest short-term adaptations of populations of interest with respect to lipid metabolism, among other pathways. In conclusion, our work demonstrates how longitudinal multi-omic datasets can be integrated in order to further our understanding of microbial niche ecology within a biotechnological process with potential applications beyond waste water treatment. [less ▲]

Detailed reference viewed: 190 (14 UL)
See detailSystems Ecology of microbiomes: identifying key functions
Wilmes, Paul UL

Scientific Conference (2018, August)

Detailed reference viewed: 6 (0 UL)