@article{14449, abstract = {The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.}, author = {D’Elia, Domenica and Truu, Jaak and Lahti, Leo and Berland, Magali and Papoutsoglou, Georgios and Ceci, Michelangelo and Zomer, Aldert and Lopes, Marta B. and Ibrahimi, Eliana and Gruca, Aleksandra and Nechyporenko, Alina and Frohme, Marcus and Klammsteiner, Thomas and Pau, Enrique Carrillo De Santa and Marcos-Zambrano, Laura Judith and Hron, Karel and Pio, Gianvito and Simeon, Andrea and Suharoschi, Ramona and Moreno-Indias, Isabel and Temko, Andriy and Nedyalkova, Miroslava and Apostol, Elena Simona and Truică, Ciprian Octavian and Shigdel, Rajesh and Telalović, Jasminka Hasić and Bongcam-Rudloff, Erik and Przymus, Piotr and Jordamović, Naida Babić and Falquet, Laurent and Tarazona, Sonia and Sampri, Alexia and Isola, Gaetano and Pérez-Serrano, David and Trajkovik, Vladimir and Klucar, Lubos and Loncar-Turukalo, Tatjana and Havulinna, Aki S. and Jansen, Christian and Bertelsen, Randi J. and Claesson, Marcus Joakim}, issn = {1664-302X}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, title = {{Advancing microbiome research with machine learning: Key findings from the ML4Microbiome COST action}}, doi = {10.3389/fmicb.2023.1257002}, volume = {14}, year = {2023}, } @article{12469, abstract = {Hosts can carry many viruses in their bodies, but not all of them cause disease. We studied ants as a social host to determine both their overall viral repertoire and the subset of actively infecting viruses across natural populations of three subfamilies: the Argentine ant (Linepithema humile, Dolichoderinae), the invasive garden ant (Lasius neglectus, Formicinae) and the red ant (Myrmica rubra, Myrmicinae). We used a dual sequencing strategy to reconstruct complete virus genomes by RNA-seq and to simultaneously determine the small interfering RNAs (siRNAs) by small RNA sequencing (sRNA-seq), which constitute the host antiviral RNAi immune response. This approach led to the discovery of 41 novel viruses in ants and revealed a host ant-specific RNAi response (21 vs. 22 nt siRNAs) in the different ant species. The efficiency of the RNAi response (sRNA/RNA read count ratio) depended on the virus and the respective ant species, but not its population. Overall, we found the highest virus abundance and diversity per population in Li. humile, followed by La. neglectus and M. rubra. Argentine ants also shared a high proportion of viruses between populations, whilst overlap was nearly absent in M. rubra. Only one of the 59 viruses was found to infect two of the ant species as hosts, revealing high host-specificity in active infections. In contrast, six viruses actively infected one ant species, but were found as contaminants only in the others. Disentangling spillover of disease-causing infection from non-infecting contamination across species is providing relevant information for disease ecology and ecosystem management.}, author = {Viljakainen, Lumi and Fürst, Matthias and Grasse, Anna V and Jurvansuu, Jaana and Oh, Jinook and Tolonen, Lassi and Eder, Thomas and Rattei, Thomas and Cremer, Sylvia}, issn = {1664-302X}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, title = {{Antiviral immune response reveals host-specific virus infections in natural ant populations}}, doi = {10.3389/fmicb.2023.1119002}, volume = {14}, year = {2023}, } @article{12478, abstract = {In Gram negative bacteria, the multiple antibiotic resistance or mar operon, is known to control the expression of multi-drug efflux genes that protect bacteria from a wide range of drugs. As many different chemical compounds can induce this operon, identifying the parameters that govern the dynamics of its induction is crucial to better characterize the processes of tolerance and resistance. Most experiments have assumed that the properties of the mar transcriptional network can be inferred from population measurements. However, measurements from an asynchronous population of cells can mask underlying phenotypic variations of single cells. We monitored the activity of the mar promoter in single Escherichia coli cells in linear micro-colonies and established that the response to a steady level of inducer was most heterogeneous within individual colonies for an intermediate value of inducer. Specifically, sub-lineages defined by contiguous daughter-cells exhibited similar promoter activity, whereas activity was greatly variable between different sub-lineages. Specific sub-trees of uniform promoter activity persisted over several generations. Statistical analyses of the lineages suggest that the presence of these sub-trees is the signature of an inducible memory of the promoter state that is transmitted from mother to daughter cells. This single-cell study reveals that the degree of epigenetic inheritance changes as a function of inducer concentration, suggesting that phenotypic inheritance may be an inducible phenotype.}, author = {Guet, Calin C and Bruneaux, L and Oikonomou, P and Aldana, M and Cluzel, P}, issn = {1664-302X}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, title = {{Monitoring lineages of growing and dividing bacteria reveals an inducible memory of mar operon expression}}, doi = {10.3389/fmicb.2023.1049255}, volume = {14}, year = {2023}, } @article{10271, abstract = {Understanding interactions between antibiotics used in combination is an important theme in microbiology. Using the interactions between the antifolate drug trimethoprim and the ribosome-targeting antibiotic erythromycin in Escherichia coli as a model, we applied a transcriptomic approach for dissecting interactions between two antibiotics with different modes of action. When trimethoprim and erythromycin were combined, the transcriptional response of genes from the sulfate reduction pathway deviated from the dominant effect of trimethoprim on the transcriptome. We successfully altered the drug interaction from additivity to suppression by increasing the sulfate level in the growth environment and identified sulfate reduction as an important metabolic determinant that shapes the interaction between the two drugs. Our work highlights the potential of using prioritization of gene expression patterns as a tool for identifying key metabolic determinants that shape drug-drug interactions. We further demonstrated that the sigma factor-binding protein gene crl shapes the interactions between the two antibiotics, which provides a rare example of how naturally occurring variations between strains of the same bacterial species can sometimes generate very different drug interactions.}, author = {Qi, Qin and Angermayr, S. Andreas and Bollenbach, Mark Tobias}, issn = {1664-302X}, journal = {Frontiers in Microbiology}, keywords = {microbiology}, publisher = {Frontiers}, title = {{Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli}}, doi = {10.3389/fmicb.2021.760017}, volume = {12}, year = {2021}, } @article{9380, abstract = {Shigella are pathogens originating within the Escherichia lineage but frequently classified as a separate genus. Shigella genomes contain numerous insertion sequences (ISs) that lead to pseudogenisation of affected genes and an increase of non-homologous recombination. Here, we study 414 genomes of E. coli and Shigella strains to assess the contribution of genomic rearrangements to Shigella evolution. We found that Shigella experienced exceptionally high rates of intragenomic rearrangements and had a decreased rate of homologous recombination compared to pathogenic and non-pathogenic E. coli. The high rearrangement rate resulted in independent disruption of syntenic regions and parallel rearrangements in different Shigella lineages. Specifically, we identified two types of chromosomally encoded E3 ubiquitin-protein ligases acquired independently by all Shigella strains that also showed a high level of sequence conservation in the promoter and further in the 5′-intergenic region. In the only available enteroinvasive E. coli (EIEC) strain, which is a pathogenic E. coli with a phenotype intermediate between Shigella and non-pathogenic E. coli, we found a rate of genome rearrangements comparable to those in other E. coli and no functional copies of the two Shigella-specific E3 ubiquitin ligases. These data indicate that the accumulation of ISs influenced many aspects of genome evolution and played an important role in the evolution of intracellular pathogens. Our research demonstrates the power of comparative genomics-based on synteny block composition and an important role of non-coding regions in the evolution of genomic islands.}, author = {Seferbekova, Zaira and Zabelkin, Alexey and Yakovleva, Yulia and Afasizhev, Robert and Dranenko, Natalia O. and Alexeev, Nikita and Gelfand, Mikhail S. and Bochkareva, Olga}, issn = {1664-302X}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, title = {{High rates of genome rearrangements and pathogenicity of Shigella spp}}, doi = {10.3389/fmicb.2021.628622}, volume = {12}, year = {2021}, }