Willem M. de Vos studied Biochemistry and obtained a PhD at Groningen University, partly at the Max-Planck Institute for Molecular Genetics in Berlin. He has been over 30 years Professor (Bacterial Genetics and Microbiology) at Wageningen University, was Chair of Microbiology for 25 years and also served as Wageningen Distinguished Professor. He also served over 12 years at the University of Helsinki, first as Finland Distinguished Professor and subsequently Finland Academy Professor, and now as Professor of Human Microbiomics and Director of the Human Microbiome Research Program at the Faculty of Medicine. He has supervised >120 PhD students and published >750 peer-reviewed publications (including Nature, Science & Cell papers) with a Google Scholar h-index of >175. Moreover, he is an inventor in over 50 patent (families). He is a member of the Netherlands Royal Academy of Arts and Sciences, the European Academy of Microbiology, and received an honorary doctorate in Medicine. He obtained early-career fellowships from EMBO and FEBS, and various career awards, including the Miles Marschall Rhone-Poulenc Science Award, the NWO Spinoza Award an ERC Advanced Grant. He also received a Royal Knighthood in the Order of the Netherlands Lion and obtained the Netherlands’ Most Entrepreneurial Scientist Award. In the last ten years he (co)founded 5 start-ups in field of microbial diagnostics and therapeutics. He serves in various scientific, advisory and supervisory boards of international research organizations, multinational companies, and start-ups.
LotuS2: An ultrafast and highly accurate tool for amplicon sequencing analysis
Amplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (Less OTU Scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences. In LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios. We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines – yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxa composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified genera and species (98% and 57%, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reconstructed 16S sequences. In conclusion, LotuS2 is a lightweight and user-friendly pipeline that is fast, precise and streamlined. High data usage rates and reliability enable high-throughput microbiome analysis in minutes.
Quadram Institute Bioscience & Earlham Institute
Anatomy promotes neutral coexistence of strains in the human skin microbiome
What enables strains of the same species to coexist in a microbiome? Here, we investigate whether host anatomy can explain strain co-residence of Cutibacterium acnes, the most abundant species on human skin. We reconstruct on-person evolution and migration using whole-genome sequencing of C. acnes colonies acquired from healthy subjects, including from individual skin pores, and find considerable spatial structure at the level of pores. Although lineages (sets of colonies separated by <100 mutations) with in vitro fitness differences coexist within centimeter-scale regions, each pore is dominated by a single lineage. Moreover, colonies from a pore typically have identical genomes. An absence of adaptive signatures suggests a genotype-independent source of low within-pore diversity. We therefore propose that pore anatomy imposes random single-cell bottlenecks; the resulting population fragmentation reduces competition and promotes coexistence. Our findings suggest that therapeutic interventions involving pore-dwelling species might focus on removing resident populations over optimizing probiotic fitness.
Massachusetts Institute of Technology
How heavy metals influence bacterial and fungal alpha-diversity in soil, sediment, and rhizosphere: A meta-analysis
Heavy metals (HMs) accumulation in soil affects plant growth and reduces the soil fauna diversity. Contrarily, the effect of HMs on microbial alpha-diversity remains an open question. Literature is contrasting, most likely because the microbial alpha-diversity variations strongly depend on heterogeneous and study-specific experimental conditions (e.g. soil type, soil chemical, physical and biological characteristics, type of metal considered, dose added etc.). Here, we report the first meta-analysis upon the response of soil microbial alpha-diversity to the experimental addition of cadmium (Cd) and copper (Cu). We considered the studies using the DNA metabarcoding of bacterial and fungal communities conducted between 2013 and 2021 to overcome some limitations of other techniques such as Denaturing Gradient Gel Electrophoresis (DGGE), or cultivation. This resulted in 66 independent experiments reported in 32 primary papers located over four continents. We found dose-dependent response to HMs for microbial alpha-diversity in bulk soil, rhizosphere soil and sediments. We further estimated that, on average, 17.5 mg kg-1 was the minimum inhibitory dose necessary to register a first significant loss (0.03 %) in microbial alpha-diversity compared to control conditions. However, such decline in alpha-diversity reached a maximum of 14.3 % for HMs additions peaking 5000 mg kg-1, irrespectively of the different experimental conditions. Our results suggest that extreme doses of Cu and Cd are necessary to have a consistent diversity loss, highlighting how the behaviour of microbial communities diverges from macro-organisms.
Faculty of Science and Technology, Free University of Bolzano, Italy
Effect of cecal microbiota transplantation between different broiler breeds on the chick flora in the first week of life
Microbiota transplantation studies in chickens represent a fascinating opportunity to study the impact of early-life microbiota manipulation on important phenotypes such as pathogen resistance and nutrition. When chicks are raised by a maternal hen, they quickly develop a gastrointestinal microbiota that is similar to their mother. However, commercially-raised chicks have no direct contact with maternal hens or their faeces. They subsequently develop a less-diverse microbiota that is seeded from their environment. By delivering microbiota transplants from chickens with a desired phenotype to chicks raised without maternal contact we can define the trajectory of their microbiota development. This allows us to examine the impact of the transplanted microbiota on the development of desired phenotypes. However, studies have shown varying success when attempting to conduct microbiota transplants in chickens, potentially due to the variability of techniques used. We investigated whether it was possible to transplant the caecal microbiota between 40 week old adult chickens and freshly hatched chicks from a different chicken breed. Caecal contents were collected from the adult birds and within 30 minutes were administered orally to chicks. The microbiota of treated and control chicks was characterised via 16S rRNA gene sequencing at 1, 2, 3, 4, and 7 days post-hatch. At all timepoints, the microbiota of treated chicks was significantly different in composition and richer and more diverse than control birds. Transplantation clearly changed the trajectory of the treated birds so that they developed a microbiota which was far more similar to the donor birds than the control chicks. The most notable taxonomic difference between control and treated birds was the abundance of the phylum Bacteroidota. This phylum was present in high relative abundance in the donor samples (38%) and also in the treated samples (day 7: 41%). However, it was almost completely absent from the control birds (Day7: 0.02%). We therefore demonstrate that our technique is highly effective at transplanting the caecal microbiota between adult donors and chicks from a different chicken breed. This technique could be further used to study the role of the microbiota in chicken breeds with differing phenotypes.
The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh
Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications
Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strategy into the study of microbial patterns, in particular from 16S rRNA microbiome datasets, applying ARM on real case studies and providing guidelines for future usage. Our results highlighted issues related to the type of input and the use of metadata in microbial pattern extraction, identifying the key steps that must be considered to apply ARM consciously on 16S rRNA microbiome data. To promote the use of ARM and the visualization of microbiome patterns, specifically, we developed microFIM (microbial Frequent Itemset Mining), a versatile Python tool that facilitates the use of ARM integrating common microbiome outputs, such as taxa tables. microFIM implements interest measures to remove spurious information and merges the results of ARM analysis with the common microbiome outputs, providing similar microbiome strategies that help scientists to integrate ARM in microbiome applications. With this work, we aimed at creating a bridge between microbial ecology researchers and ARM technique, making researchers aware about the strength and weaknesses of association rule mining approach.
University of Milano-Bicocca
Different Effects of Mineral Versus Vegetal Granular Activated Carbon Filters on the Microbial Community Composition of a Drinking Water Treatment Plant
Drinking water quality and safety is strictly regulated and constantly monitored, but little is known about the microorganisms inhabiting drinking water treatment plants (DWTPs). This lack of knowledge prevents optimization of designs and operational controls. Here we investigated the drinking water microbial community harbored by a groundwater-derived DWTP, involving mineral and vegetal granular activated carbon filters (GACs). We used 16S rRNA gene sequencing to analyze water microbiome variations through the potabilization process, considering (i) different GAC materials and (ii) time from GAC regeneration. Our results revealed the predominance of Cand. Patescibacteria, uncultivable bacteria with limited metabolic capacities and small genomes, from source to downstream water. Microbial communities clustered per sampling date, with the noteworthy exception of groundwater samples. If the groundwater microbiome showed no significant variations over time, the community structure of water downstream GACs (both mineral and vegetal) seemed to be affected by time from GAC regeneration. Looking at a finer scale, different GAC material affected microbiome assembly over time with significant variation in the relative abundances of specific taxa. The significance of our research is in identifying the environmental microorganisms intrinsic of deep groundwater and the community shift after the perturbations induced by potabilization processes. Which microorganisms colonize different GACs and become abundant after GACs regeneration and over time is a first step toward advanced control of microbial communities, improving drinking water safety and management of operational costs.
University of Milano-Bicocca