Differences in the constituents of bacterial microbiota of soils collected from two fields of diverse potato blackleg and soft rot diseases incidences, a case study.

The presence of bacteria from the Dickeya spp. and Pectobacterium spp. in farmlands leads to global crop losses of over $420 million annually. Since 1982, the scientists have started to suspect that the development of disease symptoms in crops might be inhibited by bacteria present in the soil. Here, we characterized in terms of physicochemical properties and the composition of bacterial soil microbiota two fields differing, on the basis of long-term studies, in the occurrence of Dickeya spp.- and Pectobacterium spp.-triggered infections. Majority, i.e. 17 of the investigated physicochemical features of the soils collected from two fields of either low or high potato blackleg and soft rot diseases incidences turned out to be similar, in contrast to the observed 4 deviations in relation to Mg, Mn, organic C and organic substance contents. By performing microbial cultures and molecular diagnostics-based identification, 20 Pectobacterium spp. strains were acquired from the field showing high blackleg and soft rot incidences. In addition, 16S rRNA gene amplicon sequencing followed by bioinformatic analysis revealed differences at various taxonomic levels in the soil bacterial microbiota of the studied fields. We observed that bacteria from the genera Bacillus, Rumeliibacillus, Acidobacterium and Gaiella turned out to be more abundant in the soil samples originating from the field of low comparing to high frequency of pectinolytic bacterial infections. In the herein presented case study, it is shown for the first time that the composition of bacterial soil microbiota varies between two fields differing in the incidences of soft rot and blackleg infections.

The Many Lives of Auranofin: How an Old Anti-Rheumatic Agent May Become a Promising Antimicrobial Drug.

Auranofin (AF) is a gold-based compound with a well-known pharmacological and toxicological profile, currently used in the treatment of some severe forms of rheumatoid arthritis. Over the last twenty years, AF has also been repurposed as antiviral, antitumor, and antibacterial drug. In this review we focused on the antibacterial properties of AF, specifically researching the minimal inhibitory concentrations (MIC) of AF in both mono- and diderm bacteria reported so far in literature. AF proves to be highly effective against monoderm bacteria, while diderm are far less susceptible, probably due to the outer membrane barrier. We also reported the current mechanistic hypotheses concerning the antimicrobial properties of AF, although a conclusive description of its antibacterial mode of action is not yet available. Even if its mechanism of action has not been fully elucidated yet and further studies are required to optimize its delivery strategy, AF deserves additional investigation because of its unique mode of action and high efficacy against a wide range of pathogens, which could lead to potential applications in fighting antimicrobial resistance and improving therapeutic outcomes in infectious diseases.

Functional Genomics of a Collection of Gammaproteobacteria Isolated from Antarctica.

Antarctica, one of the most extreme environments on Earth, hosts diverse microbial communities. These microbes have evolved and adapted to survive in these hostile conditions, but knowledge on the molecular mechanisms underlying this process remains limited. The Italian Collection of Antarctic Bacteria (Collezione Italiana Batteri Antartici (CIBAN)), managed by the University of Messina, represents a valuable repository of cold-adapted bacterial strains isolated from various Antarctic environments. In this study, we sequenced and analyzed the genomes of 58 marine Gammaproteobacteria strains from the CIBAN collection, which were isolated during Italian expeditions from 1990 to 2005. By employing genome-scale metrics, we taxonomically characterized these strains and assigned them to four distinct genera: Pseudomonas, Pseudoalteromonas, Shewanella, and Psychrobacter. Genome annotation revealed a previously untapped functional potential, including secondary metabolite biosynthetic gene clusters and antibiotic resistance genes. Phylogenomic analyses provided evolutionary insights, while assessment of cold-shock protein presence shed light on adaptation mechanisms. Our study emphasizes the significance of CIBAN as a resource for understanding Antarctic microbial life and its biotechnological potential. The genomic data unveil new horizons for insight into bacterial existence in Antarctica.

COVID-19: A complex disease with a unique metabolic signature.

Plasma of COVID-19 patients contains a strong metabolomic/lipoproteomic signature, revealed by the NMR analysis of a cohort of >500 patients sampled during various waves of COVID-19 infection, corresponding to the spread of different variants, and having different vaccination status. This composite signature highlights common traits of the SARS-CoV-2 infection. The most dysregulated molecules display concentration trends that scale with disease severity and might serve as prognostic markers for fatal events. Metabolomics evidence is then used as input data for a sex-specific multi-organ metabolic model. This reconstruction provides a comprehensive view of the impact of COVID-19 on the entire human metabolism. The human (male and female) metabolic network is strongly impacted by the disease to an extent dictated by its severity. A marked metabolic reprogramming at the level of many organs indicates an increase in the generic energetic demand of the organism following infection. Sex-specific modulation of immune response is also suggested.

Independent origins and evolution of the secondary replicons of the class Gammaproteobacteria.

Multipartite genomes, consisting of more than one replicon, have been found in approximately 10 % of bacteria, many of which belong to the phylum Proteobacteria. Many aspects of their origin and evolution, and the possible advantages related to this type of genome structure, remain to be elucidated. Here, we performed a systematic analysis of the presence and distribution of multipartite genomes in the class Gammaproteobacteria, which includes several genera with diverse lifestyles. Within this class, multipartite genomes are mainly found in the order Alteromonadales (mostly in the genus Pseudoalteromonas) and in the family Vibrionaceae. Our data suggest that the emergence of secondary replicons in Gammaproteobacteria is rare and that they derive from plasmids. Despite their multiple origins, we highlighted the presence of evolutionary trends such as the inverse proportionality of the genome to chromosome size ratio, which appears to be a general feature of bacteria with multipartite genomes irrespective of taxonomic group. We also highlighted some functional trends. The core gene set of the secondary replicons is extremely small, probably limited to essential genes or genes that favour their maintenance in the genome, while the other genes are less conserved. This hypothesis agrees with the idea that the primary advantage of secondary replicons could be to facilitate gene acquisition through horizontal gene transfer, resulting in replicons enriched in genes associated with adaptation to different ecological niches. Indeed, secondary replicons are enriched both in genes that could promote adaptation to harsh environments, such as those involved in antibiotic, biocide and metal resistance, and in functional categories related to the exploitation of environmental resources (e.g. carbohydrates), which can complement chromosomal functions.

Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling.

The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs' efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure.

Development of high-copy number plasmids in Pseudoalteromonas haloplanktis TAC125.

The Antarctic bacterium Pseudoalteromonas haloplanktis TAC125 (PhTAC125) is considered an interesting alternative host for the recombinant protein production, that can be explored when the conventional bacterial expression systems fail. Indeed, the manufacture of all the difficult-to-express proteins produced so far in this bacterial platform gave back soluble and active products. Despite these promising results, the low yield of recombinant protein production achieved is hampering the wider and industrial exploitation of this psychrophilic cell factory. All the expression plasmids developed so far in PhTAC125 are based on the origin of replication of the endogenous pMtBL plasmid and are maintained at a very low copy number. In this work, we set up an experimental strategy to select mutated OriR sequences endowed with the ability to establish recombinant plasmids at higher multiplicity per cell. The solution to this major production bottleneck was achieved by the construction of a library of psychrophilic vectors, each containing a randomly mutated version of pMtBL OriR, and its screening by fluorescence-activated cell sorting (FACS). The selected clones allowed the identification of mutated OriR sequences effective in enhancing the plasmid copy number of approximately two orders of magnitude, and the production of the recombinant green fluorescent protein was increased up to twenty times approximately. Moreover, the molecular characterization of the different mutant OriR sequences allowed us to suggest some preliminary clues on the pMtBL replication mechanism that deserve to be further investigated in the future. KEY POINTS: • Setup of an electroporation procedure for Pseudoalteromonas haloplanktis TAC125. • Two order of magnitude improvement of OriR-derived psychrophilic expression systems. • Almost twenty times enhancement in Green fluorescent protein production.

Metabolic Robustness to Growth Temperature of a Cold- Adapted Marine Bacterium.

Microbial communities experience continuous environmental changes, with temperature fluctuations being the most impacting. This is particularly important considering the ongoing global warming but also in the "simpler" context of seasonal variability of sea-surface temperature. Understanding how microorganisms react at the cellular level can improve our understanding of their possible adaptations to a changing environment. In this work, we investigated the mechanisms through which metabolic homeostasis is maintained in a cold-adapted marine bacterium during growth at temperatures that differ widely (15 and 0°C). We have quantified its intracellular and extracellular central metabolomes together with changes occurring at the transcriptomic level in the same growth conditions. This information was then used to contextualize a genome-scale metabolic reconstruction, and to provide a systemic understanding of cellular adaptation to growth at 2 different temperatures. Our findings indicate a strong metabolic robustness at the level of the main central metabolites, counteracted by a relatively deep transcriptomic reprogramming that includes changes in gene expression of hundreds of metabolic genes. We interpret this as a transcriptomic buffering of cellular metabolism, able to produce overlapping metabolic phenotypes, despite the wide temperature gap. Moreover, we show that metabolic adaptation seems to be mostly played at the level of few key intermediates (e.g., phosphoenolpyruvate) and in the cross talk between the main central metabolic pathways. Overall, our findings reveal a complex interplay at gene expression level that contributes to the robustness/resilience of core metabolism, also promoting the leveraging of state-of-the-art multi-disciplinary approaches to fully comprehend molecular adaptations to environmental fluctuations. IMPORTANCE This manuscript addresses a central and broad interest topic in environmental microbiology, i.e. the effect of growth temperature on microbial cell physiology. We investigated if and how metabolic homeostasis is maintained in a cold-adapted bacterium during growth at temperatures that differ widely and that match measured changes on the field. Our integrative approach revealed an extraordinary robustness of the central metabolome to growth temperature. However, this was counteracted by deep changes at the transcriptional level, and especially in the metabolic part of the transcriptome. This conflictual scenario was interpreted as a transcriptomic buffering of cellular metabolism, and was investigated using genome-scale metabolic modeling. Overall, our findings reveal a complex interplay at gene expression level that contributes to the robustness/resilience of core metabolism, also promoting the use of state-of-the-art multi-disciplinary approaches to fully comprehend molecular adaptations to environmental fluctuations.

Towards a mechanistic understanding of microalgae-bacteria interactions: integration of metabolomic analysis and computational models.

Interactions amongst marine microalgae and heterotrophic bacteria drive processes underlying major biogeochemical cycles and are important for many artificial systems. These dynamic and complex interactions span the range from cooperative to competitive, and it is the diverse and intricate networks of metabolites and chemical mediators that are predicted to principally dictate the nature of the relationship at any point in time. Recent advances in technologies to identify, analyze, and quantify metabolites have allowed for a comprehensive view of the molecules available for exchange and/or reflective of organismal interactions, setting the stage for development of mechanistic understanding of these systems. Here, we (i) review the current knowledge landscape of microalgal-bacterial interactions by focusing on metabolomic studies of selected, simplified model systems; (ii) describe the state of the field of metabolomics, with specific focus on techniques and approaches developed for microalga-bacterial interaction studies; and (iii) outline the main approaches for development of mathematical models of these interacting systems, which collectively have the power to enhance interpretation of experimental data and generate novel testable hypotheses. We share the viewpoint that a comprehensive and integrated series of -omics approaches that include theoretical formulations are necessary to develop predictive and mechanistic understanding of these biological entities.

Fast, Ungapped Reads Mapping Using Squid.

Advances in Next Generation Sequencing technologies allow us to inspect and unlock the genome to a level of detail that was unimaginable only a few decades ago. Omics-based studies are casting a light on the patterns and determinants of disease conditions in populations, as well as on the influence of microbial communities on human health, just to name a few. Through increasing volumes of sequencing information, for example, it is possible to compare genomic features and analyze the modulation of the transcriptome under different environmental stimuli. Although protocols for NGS preparation are intended to leave little to no space for contamination of any kind, a noticeable fraction of sequencing reads still may not uniquely represent what was intended to be sequenced in the first place. If a natural consequence of a sequencing sample is to assess the presence of features of interest by mapping the obtained reads to a genome of reference, sometimes it is useful to determine the fraction of those that do not map, or that map discordantly, and store this information to a new file for subsequent analyses. Here we propose a new mapper, which we called Squid, that among other accessory functionalities finds and returns sequencing reads that match or do not match to a reference sequence database in any orientation. We encourage the use of Squid prior to any quantification pipeline to assess, for instance, the presence of contaminants, especially in RNA-Seq experiments.

Genome analysis of a new biosurfactants source: The Antarctic bacterium Psychrobacter sp. TAE2020.

Biosurfactants are considered a possible green alternative to chemical surfactants for countless commercial products including detergents and cleaners, personal care products, cosmetics, pharmaceuticals and therapeutics, food additives, emulsifiers, and dispersants for bioremediation. Organisms from extreme environments are well-adapted to the harsh conditions and represent an exciting avenue of discovery of naturally occurring biosurfactants. In this study, we report the genome analysis of Psychrobacter sp. TAE2020, an aerobic Æ´-proteobacterium isolated from an Antarctic coastal seawater sample collected in the vicinity of the French Antarctic station Dumont d'Urville, Terre Adelie (66°40' S; 140° 01' E) which has been shown to produce biosurfactants. Biochemical assays indicate that Psychrobacter sp. TAE2020 can produce one or more excellent emulsifiers and a biosurfactant which is able to reduce the surface tension of a Gut medium. Next generation sequencing and genome mining allowed the identification of a plethora of biosynthetic gene clusters possibly involved in the production of emulsifying agents, just waiting to be isolated and characterized. This study paves the way for a more thorough investigation into the potential biotechnological applications of this new Antarctic strain.

Crossing Bacterial Genomic Features and Methylation Patterns with MeStudio: An Epigenomic Analysis Tool.

DNA methylation is one of the most observed epigenetic modifications. It is present in eukaryotes and prokaryotes and is related to several biological phenomena, including gene flow and adaptation to environmental conditions. The widespread use of third-generation sequencing technologies allows direct and easy detection of genome-wide methylation profiles, offering increasing opportunities to understand and exploit the epigenomic landscape of individuals and populations. Here, we present a pipeline named MeStudio, with the aim of analyzing and combining genome-wide methylation profiles with genomic features. Outputs report the presence of DNA methylation in coding sequences (CDSs) and noncoding sequences, including both intergenic sequences and sequences upstream of the CDS. We apply this novel tool, showing the usage and performance of MeStudio, on a set of single-molecule real-time sequencing outputs from strains of the bacterial species Sinorhizobium meliloti.

Whole-genome sequencing of Pseudomonas sp. TAE6080, a strain capable of inhibiting Staphylococcus epidermidis biofilm.

Antarctic bacteria are able to survive under extreme environmental conditions and have adapted to exploit some of the most ephemeral nutrient pockets. Importantly, such strains have been often shown to be capable of synthesizing compounds of valuable biotechnological importance. Here we show that Pseudomonas sp. TAE6080, a possibly new bacterium isolated in 1994 during water column samplings near the French Antarctic station Dumont d'Urville, is capable of inhibiting the formation of Staphylococcus epidermidis biofilm, known to be an important opportunistic pathogen in infections associated to medical devices. A better understanding of this bacterium can therefore provide useful insight on new bioactive molecules that could play a role against chronic infections. To this end, the anti-biofilm effect of cell-free supernatant of Pseudomonas sp. TAE6080 was evaluated on S. epidermidis RP62A biofilm formation, demonstrating that it significantly reduced its aggregation. Furthermore, genome sequencing, assembly and mining revealed a plethora of putative biosynthetic gene clusters that might be involved in biofilm disruption. The experimental and genomic data presented here open the venue to further investigations on the molecular basis underlying biofilm inhibition.

Scaling down the microbial loop: data-driven modelling of growth interactions in a diatom-bacterium co-culture.

An intricate set of interactions characterizes marine ecosystems. One of the most important is represented by the microbial loop, which includes the exchange of dissolved organic matter (DOM) from phototrophic organisms to heterotrophic bacteria. Here, it can be used as the major carbon and energy source. This interaction is one of the foundations of the entire ocean food-web. The carbon fixed by phytoplankton can be redirected to bacteria in two main ways; either (i) bacteria feed on dead phytoplankton cells or (ii) DOM is actively released by phytoplankton (a process resulting in up to 50% of the fixed carbon leaving the cell). Here, we have set up a co-culture of the diatom Phaeodactylum tricornutum and the chemoheterotrophic bacterium Pseudoalteromonas haloplanktis TAC125 and used this system to study the interactions between these two representatives of the microbial loop. We show that the bacterium can thrive on diatom-derived carbon and that this growth can be sustained by both diatom dead cells and diatom-released compounds. These observations were formalized in a network of putative interactions between P. tricornutum and P. haloplanktis and implemented in a model that reproduces the observed co-culture dynamics, revealing an overall accuracy of our hypotheses in explaining the experimental data.

Modelling hCDKL5 Heterologous Expression in Bacteria.

hCDKL5 refers to the human cyclin-dependent kinase like 5 that is primarily expressed in the brain. Mutations in its coding sequence are often causative of hCDKL5 deficiency disorder, a devastating neurodevelopmental disorder currently lacking a cure. The large-scale recombinant production of hCDKL5 is desirable to boost the translation of preclinical therapeutic approaches into the clinic. However, this is hampered by the intrinsically disordered nature of almost two-thirds of the hCDKL5 sequence, making this region more susceptible to proteolytic attack, and the observed toxicity when the enzyme is accumulated in the cytoplasm of eukaryotic host cells. The bacterium Pseudoalteromonas haloplanktis TAC125 (PhTAC125) is the only prokaryotic host in which the full-length production of hCDKL5 has been demonstrated. To date, a system-level understanding of the metabolic burden imposed by hCDKL5 production is missing, although it would be crucial for upscaling of the production process. Here, we combined experimental data on protein production and nutrients assimilation with metabolic modelling to infer the global consequences of hCDKL5 production in PhTAC125 and to identify potential overproduction targets. Our analyses showed a remarkable accuracy of the model in simulating the recombinant strain phenotype and also identified priority targets for optimised protein production.

A Protocol for Teaching Basic Next Generation Sequencing (NGS) Analysis Skills to Undergraduate Students Using Bash and R.

Next generation sequencing (NGS) is routinely used to study crucial aspects of biological systems, including differentially expressed genes identification, microbiome taxonomic composition and structure, enrichment of specific cellular functions in a given environment, and so on. Current research laboratories are facing a serious lack in the availability of properly trained researchers capable of carrying out basic NGS analysis computational pipelines. This reflects a gap in most academic curricula concerning the basics of NGS data management, analysis, and interpretation. Indeed, most of the times, the knowledge necessary to undertake these tasks is acquired through the use of one-shot tutorial, without a thorough explanation of the concepts behind the practical steps. With this protocol we aim to fill this gap by providing teachers with a hands-on protocol to guide bachelor and master students in a more focused analysis of NGS data, from basic and standard operations on sequencing reads (e.g., quality check and trimming) to more advanced analysis techniques (e.g., data normalization).

Tn-Core: Functionally Interpreting Transposon-Sequencing Data with Metabolic Network Analysis.

Transposon-sequencing (Tn-seq) is a powerful tool facilitating the genome-scale identification of genes required for bacterial growth or survival in an environment of interest. However, Tn-seq suffers from two primary drawbacks: (1) genetic interactions masking phenotypes thereby resulting in important cellular functions remaining undiscovered and (2) a difficulty in easily going from a list of essential genes to a functional understanding of cell physiology. Tn-Core is a computational toolbox to help overcome these limitations through combining the output of Tn-seq studies with in silico genome-scale metabolic networks. In this chapter, we outline how to use Tn-Core to contextualize Tn-seq data (and optionally RNA-seq data) with metabolic models to: (1) generate a complete view of essential metabolism, (2) prepare context-specific metabolic models for further computational analyses, and (3) refine genome-scale metabolic models. All functions of Tn-Core are provided for download from a freely available repository ( github.com/diCenzo-GC/Tn-Core ), and a web-app requiring limited computational experience is also available ( combo.dbe.unifi.it /tncore).

The art of adapting to extreme environments: The model system Pseudoalteromonas.

Extremophilic microbes have adapted to thrive in ecological niches characterized by harsh chemical/physical conditions such as, for example, very low/high temperature. Living organisms inhabiting these environments have developed peculiar mechanisms to cope with extreme conditions, in such a way that they mark the chemical-physical boundaries of life on Earth. Studying such mechanisms is stimulating from a basic research viewpoint and because of biotechnological applications. Pseudoalteromonas species are a group of marine gamma-proteobacteria frequently isolated from a range of extreme environments, including cold habitats and deep-sea sediments. Since deep-sea floors constitute almost 60% of the Earth's surface and cold temperatures represent the most common of the extreme conditions, the genus Pseudoalteromonas can be considered one of the most important model systems for studying microbial adaptation. Particularly, among all Pseudoalteromonas representatives, P. haloplanktis TAC125 has recently gained a central role. This bacterium was isolated from seawater sampled along the Antarctic ice-shell and is considered one of the model organisms of cold-adapted bacteria. It is capable of thriving in a wide temperature range and it has been suggested as an alternative host for the soluble overproduction of heterologous proteins, given its ability to rapidly multiply at low temperatures. In this review, we will present an overview of the recent advances in the characterization of Pseudoalteromonas strains and, more importantly, in the understanding of their evolutionary and chemical-physical strategies to face such a broad array of extreme conditions. A particular attention will be given to systems-biology approaches in the study of the above-mentioned topics, as genome-scale datasets (e.g. genomics, proteomics, phenomics) are beginning to expand for this group of organisms. In this context, a specific section dedicated to P. haloplanktis TAC125 will be presented to address the recent efforts in the elucidation of the metabolic rewiring of the organisms in its natural environment (Antarctica).

Diauxie and co-utilization of carbon sources can coexist during bacterial growth in nutritionally complex environments.

It is commonly thought that when multiple carbon sources are available, bacteria metabolize them either sequentially (diauxic growth) or simultaneously (co-utilization). However, this view is mainly based on analyses in relatively simple laboratory settings. Here we show that a heterotrophic marine bacterium, Pseudoalteromonas haloplanktis, can use both strategies simultaneously when multiple possible nutrients are provided in the same growth experiment. The order of nutrient uptake is partially determined by the biomass yield that can be achieved when the same compounds are provided as single carbon sources. Using transcriptomics and time-resolved intracellular 1H-13C NMR, we reveal specific pathways for utilization of various amino acids. Finally, theoretical modelling indicates that this metabolic phenotype, combining diauxie and co-utilization of substrates, is compatible with a tight regulation that allows the modulation of assimilatory pathways.

Genome-scale metabolic reconstruction of the symbiosis between a leguminous plant and a nitrogen-fixing bacterium.

The mutualistic association between leguminous plants and endosymbiotic rhizobial bacteria is a paradigmatic example of a symbiosis driven by metabolic exchanges. Here, we report the reconstruction and modelling of a genome-scale metabolic network of Medicago truncatula (plant) nodulated by Sinorhizobium meliloti (bacterium). The reconstructed nodule tissue contains five spatially distinct developmental zones and encompasses the metabolism of both the plant and the bacterium. Flux balance analysis (FBA) suggests that the metabolic costs associated with symbiotic nitrogen fixation are primarily related to supporting nitrogenase activity, and increasing N2-fixation efficiency is associated with diminishing returns in terms of plant growth. Our analyses support that differentiating bacteroids have access to sugars as major carbon sources, ammonium is the main nitrogen export product of N2-fixing bacteria, and N2 fixation depends on proton transfer from the plant cytoplasm to the bacteria through acidification of the peribacteroid space. We expect that our model, called 'Virtual Nodule Environment' (ViNE), will contribute to a better understanding of the functioning of legume nodules, and may guide experimental studies and engineering of symbiotic nitrogen fixation.