Alessio Milanese

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Milanese

First Name

Alessio

ORCID

0000-0002-7050-2239

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Publications1 - 10 of 14

Magnetosomes Extracted from Magnetospirillum gryphiswaldense as Theranostic Agents in an Experimental Model of Glioblastoma.
Mannucci, Silvia; Tambalo, Stefano; Conti, Giamaica; et al. (2018)
Contrast Media & Molecular Imaging
Magnetic fluid hyperthermia (MFH) with chemically synthesized nanoparticles is currently used in clinical trials as it destroys tumor cells with an extremely localized deposition of thermal energy. In this paper, we investigated an MFH protocol based on magnetic nanoparticles naturally produced by magnetotactic bacteria: magnetosomes. The efficacy of such protocol is tested in a xenograft model of glioblastoma. Mice receive a single intratumoral injection of magnetosomes, and they are exposed three times in a week to an alternating magnetic field with concurrent temperature measurements. MRI is used to visualize the nanoparticles and to monitor tumor size before and after the treatment. Statistically significant inhibition of the tumor growth is detected in subjects exposed to the alternating magnetic field compared to control groups. Moreover, thanks to magnetosomes high transversal relaxivity, their effective delivery to the tumor tissue is monitored by MRI. It is apparent that the efficacy of this protocol is limited by inhomogeneous delivery of magnetosomes to tumor tissue. These results suggest that naturally synthesized magnetosomes could be effectively considered as theranostic agent candidates for hyperthermia based on iron oxide nanoparticles.
Systematic analysis of drug combinations against Gram-positive bacteria
Cacace, Elisabetta; Kim, Vladislav; Varik, Vallo; et al. (2023)
Nature Microbiology
Drug combinations can expand options for antibacterial therapies but have not been systematically tested in Gram-positive species. We profiled similar to 8,000 combinations of 65 antibacterial drugs against the model species Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulated previously known drug interactions, but also identified ten times more novel interactions in the pathogen S. aureus, including 150 synergies. We showed that two synergies were equally effective against multidrug-resistant S. aureus clinical isolates in vitro and in vivo. Interactions were largely species-specific and synergies were distinct from those of Gram-negative species, owing to cell surface and drug uptake differences. We also tested 2,728 combinations of 44 commonly prescribed non-antibiotic drugs with 62 drugs with antibacterial activity against S. aureus and identified numerous antagonisms that might compromise the efficacy of antimicrobial therapies. We identified even more synergies and showed that the anti-aggregant ticagrelor synergized with cationic antibiotics by modifying the surface charge of S. aureus. All data can be browsed in an interactive interface (https://apps.embl.de/combact/).
proGenomes3: approaching one million accurately and consistently annotated high-quality prokaryotic genomes
Fullam, Anthony; Letunic, Ivica; Schmidt, Thomas S.B.; et al. (2023)
Nucleic Acids Research
The interpretation of genomic, transcriptomic and other microbial 'omics data is highly dependent on the availability of well-annotated genomes. As the number of publicly available microbial genomes continues to increase exponentially, the need for quality control and consistent annotation is becoming critical. We present proGenomes3, a database of 907 388 high-quality genomes containing 4 billion genes that passed stringent criteria and have been consistently annotated using multiple functional and taxonomic databases including mobile genetic elements and biosynthetic gene clusters. proGenomes3 encompasses 41 171 species-level clusters, defined based on universal single copy marker genes, for which pan-genomes and contextual habitat annotations are provided.
Cultivation-independent genomes greatly expand taxonomic-profiling capabilities of mOTUs across various environments
Ruscheweyh, Hans-Joachim; Milanese, Alessio; Paoli, Lucas Pierre Antoine; et al. (2022)
Microbiome
Background Taxonomic profiling is a fundamental task in microbiome research that aims to detect and quantify the relative abundance of microorganisms in biological samples. Available methods using shotgun metagenomic data generally depend on the deposition of sequenced and taxonomically annotated genomes, usually from cultures of isolated strains, in reference databases (reference genomes). However, the majority of microorganisms have not been cultured yet. Thus, a substantial fraction of microbial community members remains unaccounted for during taxonomic profiling, particularly in samples from underexplored environments. To address this issue, we developed the mOTU profiler, a tool that enables reference genome-independent species-level profiling of metagenomes. As such, it supports the identification and quantification of both “known” and “unknown” species based on a set of select marker genes. Results We present mOTUs3, a command line tool that enables the profiling of metagenomes for >33,000 species-level operational taxonomic units. To achieve this, we leveraged the reconstruction of >600,000 draft genomes, most of which are metagenome-assembled genomes (MAGs), from diverse microbiomes, including soil, freshwater systems, and the gastrointestinal tract of ruminants and other animals, which we found to be underrepresented by reference genomes. Overall, two thirds of all species-level taxa lacked a reference genome. The cumulative relative abundance of these newly included taxa was low in well-studied microbiomes, such as the human body sites (6–11%). By contrast, they accounted for substantial proportions (ocean, freshwater, soil: 43–63%) or even the majority (pig, fish, cattle: 60–80%) of the relative abundance across diverse non-human-associated microbiomes. Using community-developed benchmarks and datasets, we found mOTUs3 to be more accurate than other methods and to be more congruent with 16S rRNA gene-based methods for taxonomic profiling. Furthermore, we demonstrate that mOTUs3 increases the resolution of well-known microbial groups into species-level taxa and helps identify new differentially abundant taxa in comparative metagenomic studies. Conclusions We developed mOTUs3 to enable accurate species-level profiling of metagenomes. Compared to other methods, it provides a more comprehensive view of prokaryotic community diversity, in particular for currently underexplored microbiomes. To facilitate comparative analyses by the research community, it is released with >11,000 precomputed profiles for publicly available metagenomes and is freely available at: https://github.com/motu-tool/mOTUs.
Microbial abundance, activity and population genomic profiling with mOTUs2
Milanese, Alessio; Mende, Daniel R.; Paoli, Lucas Pierre Antoine; et al. (2019)
Nature Communications
Metagenomic sequencing has greatly improved our ability to profile the composition of environmental and host-associated microbial communities. However, the dependency of most methods on reference genomes, which are currently unavailable for a substantial fraction of microbial species, introduces estimation biases. We present an updated and functionally extended tool based on universal (i.e., reference-independent), phylogenetic marker gene (MG)-based operational taxonomic units (mOTUs) enabling the profiling of >7700 microbial species. As more than 30% of them could not previously be quantified at this taxonomic resolution, relative abundance estimates based on mOTUs are more accurate compared to other methods. As a new feature, we show that mOTUs, which are based on essential housekeeping genes, are demonstrably well-suited for quantification of basal transcriptional activity of community members. Furthermore, single nucleotide variation profiles estimated using mOTUs reflect those from whole genomes, which allows for comparing microbial strain populations (e.g., across different human body sites).
Biosynthetic potential of the global ocean microbiome
Paoli, Lucas Pierre Antoine; Ruscheweyh, Hans-Joachim; Forneris, Clarissa C.; et al. (2022)
Nature
Natural microbial communities are phylogenetically and metabolically diverse. In addition to underexplored organismal groups1, this diversity encompasses a rich discovery potential for ecologically and biotechnologically relevant enzymes and biochemical compounds2,3. However, studying this diversity to identify genomic pathways for the synthesis of such compounds4 and assigning them to their respective hosts remains challenging. The biosynthetic potential of microorganisms in the open ocean remains largely uncharted owing to limitations in the analysis of genome-resolved data at the global scale. Here we investigated the diversity and novelty of biosynthetic gene clusters in the ocean by integrating around 10,000 microbial genomes from cultivated and single cells with more than 25,000 newly reconstructed draft genomes from more than 1,000 seawater samples. These efforts revealed approximately 40,000 putative mostly new biosynthetic gene clusters, several of which were found in previously unsuspected phylogenetic groups. Among these groups, we identified a lineage rich in biosynthetic gene clusters (‘Candidatus Eudoremicrobiaceae’) that belongs to an uncultivated bacterial phylum and includes some of the most biosynthetically diverse microorganisms in this environment. From these, we characterized the phospeptin and pythonamide pathways, revealing cases of unusual bioactive compound structure and enzymology, respectively. Together, this research demonstrates how microbiomics-driven strategies can enable the investigation of previously undescribed enzymes and natural products in underexplored microbial groups and environments.
Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community
Blasche, Sonja; Kim, Yongkyu; Mars, Ruben A.T.; et al. (2021)
Nature Microbiology
Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures or synthetic assemblies. Here, we show how kefir, a natural milk-fermenting community of prokaryotes (predominantly lactic and acetic acid bacteria) and yeasts (family Saccharomycetaceae), realizes stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microorganisms) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open the niche for the followers by making available metabolites such as amino acids and lactate. Through metabolomics, transcriptomics and large-scale mapping of inter-species interactions, we show how microorganisms poorly suited for milk survive in-and even dominate-the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence. Using kefir as a natural model microbial ecosystem, the authors apply metabolomics, transcriptomics and large-scale mapping of inter-species interactions to study the drivers of stable coexistence of species in space and time.
A genome analysis based on repeat sharing gene networks
Castellini, Alberto; Franco, Giuditta; Milanese, Alessio (2015)
Natural Computing
Motivated by an interest to understand how information is organized within genomes, and how genes communicate between each other in the transcription process, in this paper we propose a novel network based methodology for genomic sequence analysis, specifically applied to three organisms: Nanoarchaeum equitans, Escherichia coli, and Saccaromyces cerevisiae. A dictionary based approach previously introduced is here continued through a repeat analysis in genic and intergenic regions. Key results of this work have been found in a biological and computational analysis of novel parametrized gene networks, defined by means of motifs of fixed length occurring inside multiple genes. Cliques emerge as groups of genes sharing a long repeat with a clear biological interpretation, while a (complete, paralog) cluster analysis has outlined some unexpected regularity. Repeat sharing gene networks may be applied in contexts of comparative genomics, as an investigation methodology for a comprehension of evolutional and functional properties of genes. © Springer Science+Business Media Dordrecht 2014
The threshold q-gram distance: a simple, efficient, and effective distance measure for genomic sequence comparison
Cenzato, Davide; Franco, Giuditta; Lipták, Zsuzsanna; et al. (2025)
Natural Computing
The q–gram distance between two strings s, s', introduced by Ukkonen in 1992, is an alignment-free string similarity measure which can be computed in linear time, as opposed to the quadratic time necessary for alignment/edit distance. It is based on the L₁-distance, or Manhattan-distance, between the multiplicity vectors of fixed-length substrings (so-called q-grams or k-mers), and has been successfully applied in diverse bioinformatics settings. In this paper, we introduce the threshold q-gram distance (TqD), a new distance measure which is similar to the q-gram distance but uses reduced information on the multiplicities of the q-grams. The new measure retains the linear time computation of the q-gram distance but requires significantly less space. Storage space and accuracy of the measure can be controlled via a user-defined threshold t, which sets a limit on the maximum value of the integers in the multiplicity vectors. In particular, for, the comparison is made only on the basis of the sets of uniquely occurring q-grams on the one hand, and of repeated q-grams, on the other. We tested the new distance measure, using the benchmarking tool AFproject of Zielezinski et al. [Genome Biology, 2019], on several real-life data sets for phylogenetic reconstruction and compared the results with those of other k-mer based distance measures. Our experiments show that the new measure TqD compares well to other non-alignment based measures regarding accuracy, while requiring substantially less memory than the classic q-gram distance.
Changes in Plasma Short-Chain Fatty Acid Levels after Dietary Weight Loss among Overweight and Obese Adults over 50 Weeks
Sowah, Solomon A.; Hirche, Frank; Milanese, Alessio; et al. (2020)
Nutrients
Gut microbial-derived short-chain fatty acids (SCFAs) may regulate energy homeostasis and exert anti-carcinogenic, immunomodulatory and anti-inflammatory effects. Smaller trials indicate that dietary weight loss may lead to decreased SCFA production, but findings have been inconclusive. SCFA concentrations were measured by HPLC-MS/MS in plasma samples of 150 overweight or obese adults in a trial initially designed to evaluate the metabolic effects of intermittent (ICR) versus continuous (CCR) calorie restriction (NCT02449148). For the present post hoc analyses, participants were classified by quartiles of weight loss, irrespective of the dietary intervention. Linear mixed models were used to analyze weight-loss-induced changes in SCFA concentrations after 12, 24 and 50 weeks. There were no differential changes in SCFA levels across the initial study arms (ICR versus CCR versus control) after 12 weeks, but acetate concentrations significantly decreased with overall weight loss (mean log-relative change of −0.7 ± 1.8 in the lowest quartile versus. −7.6 ± 2 in the highest, p = 0.026). Concentrations of propionate, butyrate and other SCFAs did not change throughout the study. Our results show that weight-loss, achieved through calorie restriction, may lead to smaller initial decreases in plasma acetate, while plasma SCFAs generally remain remarkably stable over time.

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