Effect of bacterial inoculation, plant genotype and developmental stage on root-associated and endophytic bacterial communities in potato (Solanum tuberosum)

Beneficial bacteria interact with plants by colonizing the rhizosphere and roots followed by further spread through the inner tissues, resulting in endophytic colonization. The major factors contributing to these interactions are not always well understood for most bacterial and plant species. It is believed that specific bacterial functions are required for plant colonization, but also from the plant side specific features are needed, such as plant genotype (cultivar) and developmental stage. Via multivariate analysis we present a quantification of the roles of these components on the composition of root-associated and endophytic bacterial communities in potato plants, by weighing the effects of bacterial inoculation, plant genotype and developmental stage. Spontaneous rifampicin resistant mutants of two bacterial endophytes, Paenibacillus sp. strain E119 and Methylobacterium mesophilicum strain SR1.6/6, were introduced into potato plants of three different cultivars (Eersteling, Robijn and Karnico). Densities of both strains in, or attached to potato plants were measured by selective plating, while the effects of bacterial inoculation, plant genotype and developmental stage on the composition of bacterial, Alphaproteobacterial and Paenibacillus species were determined by PCR-denaturing gradient gel-electrophoresis (DGGE). Multivariate analyses revealed that the composition of bacterial communities was mainly driven by cultivar type and plant developmental stage, while Alphaproteobacterial and Paenibacillus communities were mainly influenced by bacterial inoculation. These results are important for better understanding the effects of bacterial inoculations to plants and their possible effects on the indigenous bacterial communities in relation with other plant factors such as genotype and growth stage

Cultivation of hitherto-uncultured bacteria belonging to the Verrucomicrobia subdivision 1 from the potato (Solanum tuberosum L.) rhizosphere

The role of dominant bacterial groups in the plant rhizosphere, e.g., those belonging to the phyla Acidobacteria and Verrucomicrobia, has, so far, not been elucidated, and this is mainly due to the lack of culturable representatives. This study aimed to isolate hitherto-uncultured bacteria from the potato rhizosphere by a combination of cultivation approaches. An agar medium low in carbon availability (oligotrophic agar medium) and either amended with potato root exudates or catalase or left unamended was used with the aim to improve the culturability of bacteria from the potato rhizosphere. The colony forming unit numbers based on colonies and microcolonies were compared with microscopically determined fluorescence-stained cell numbers. Taxonomical diversity of the colonies was compared with that of library clones made from rhizosphere DNA, on the basis of 16S rRNA gene comparisons. The oligotrophic media amended or not with catalase or rhizosphere extract recovered up to 33.6% of the total bacterial numbers, at least seven times more than the recovery observed on R2A. Four hitherto-uncultured Verrucomicrobia subdivision 1 representatives were recovered on agar, but representatives of this group were not found in the clone library. The use of oligotrophic medium and its modifications enabled the growth of colony numbers, exceeding those on classical agar media. Also, it led to the isolation of hitherto-uncultured bacteria from the potato rhizosphere. Further improvement in cultivation will certainly result in the recovery of other as-yet-unexplored bacteria from the rhizosphere, making these groups accessible for further investigation, e.g., with respect to their possible interactions with plants

Potential of microbiome-based solutions for agrifood systems

Host-associated microbiomes are central to food production systems and human nutrition and health. Harnessing the microbiome may help increase food and nutrient security, enhance public health, mitigate climate change and reduce land degradation. Although several microbiome solutions are currently under development or commercialized in the agrifood, animal nutrition, biotechnology, diagnostics, pharmaceutical and health sectors , fewer products than expected have been successfully commercialized beyond food processing, and fewer still have achieved wider adoption by farming, animal husbandry and other end-user communities. This creates concerns about the translatability of microbiome research to practical applications. Inconsistent efficiency and reliability of microbiome solutions are major constraints for their commercialization and further development, and demands urgent attention

Exploration of hitherto-uncultured bacteria from the rhizosphere

The rhizosphere environment selects a particular microbial community that arises from the one present in bulk soil due to the release of particular compounds in exudates and different opportunities for microbial colonization. During plant-microorganism coevolution, microbial functions supporting plant health and productivity have developed, of which most are described in cultured plant-associated bacteria. This review discusses the state of the art concerning the ecology of the hitherto-uncultured bacteria of the rhizosphere environment, focusing on Acidobacteria, Verrucomicrobia and Planctomycetes. Furthermore, a strategy is proposed to recover bacterial isolates from these taxa from the rhizosphere environment

High biodiversity in a benzene-degrading nitrate-reducing culture is sustained by a few primary consumers

A key question in microbial ecology is what the driving forces behind the persistence of large biodiversity in natural environments are. We studied a microbial community with more than 100 different types of species which evolved in a 15-years old bioreactor with benzene as the main carbon and energy source and nitrate as the electron acceptor. Using genome-centric metagenomics plus metatranscriptomics, we demonstrate that most of the community members likely feed on metabolic left-overs or on necromass while only a few of them, from families Rhodocyclaceae and Peptococcaceae, are candidates to degrade benzene. We verify with an additional succession experiment using metabolomics and metabarcoding that these few community members are the actual drivers of benzene degradation. As such, we hypothesize that high species richness is maintained and the complexity of a natural community is stabilized in a controlled environment by the interdependencies between the few benzene degraders and the rest of the community members, ultimately resulting in a food web with different trophic levels

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine. © 2021, The Author(s)