Biomedical subjects

Stefano Campanaro

Publications and source records attributed to Stefano Campanaro.

10 recordsLinked to original sources

PubMed2026

Adaptive Evolution Reveals Metabolic Plasticity and Functional Redundancy in an Anaerobic Microbiome under Extreme Ammonia Stress.

Ammonia toxicity represents a primary biochemical bottleneck governing microbial community structure and performance during the anaerobic digestion of the organic fraction of municipal solid waste. However, the mechanistic basis of microbial adaptation to chronic ammonia levels remains poorly characterized. In this study, a long-term sequential enrichment strategy under progressively increasing ammonia concentrations (350-1500 mgN L-1), integrated with genome-centric metagenomics and metatranscriptomics, was employed to resolve the response of an organic waste-degrading microbiome over a 240 day period. Increasing ammonia pressure induced a progressive decline in methanogenesis and accumulation of volatile fatty acids, particularly acetate. Despite these inhibitory pressures, methane production was only halved relative to the initial baseline reflecting a resilient methanogenic community. This stability was driven by a restructuring of the microbiome, where functional redundancy across divergent taxa preserved core metabolic functions. Key adaptive responses included the reconfiguration of carbon fixation pathways, specifically via a variant of the Wood-Ljungdahl pathway coupled with the glycine cleavage system acting as an alternative acetate oxidation route, as well as sustained osmoprotectant biosynthesis. Cellular homeostasis was preserved through H+ replenishment via multiple energy-converting complexes and K+ influx to maintain cation-proton balance. Collectively, these findings demonstrate that metabolic plasticity and the preservation of core metabolic functions are the primary determinants of ammonia resilience, sustaining methane production under inhibitory conditions.

Ammonia↗

PubMed2026

Discovering hidden candidate plastic-degrading enzymes: Combined multi-omics and machine learning strategy.

Plastic pollution poses a major threat to the stability of natural ecosystems as well as human health. Microbial enzymes have long been considered a potential resource for targeted biodegradation but, except for a few successful cases, the discovery of efficient enzymes has proved challenging. Aiming to accelerate the process, we propose an approach combining metagenomics, metatranscriptomics and semi-supervised learning that selects promising plastic-degrading candidate enzymes from the proteome of relevant microorganisms. Tested on a dataset of over 10,000 microbial proteins, ranking models consistently prioritize known plastic-degrading enzymes, achieving an area under the cumulative distribution function curve above 0.96, with leave-one-family-out cross-validation indicating that performance is largely retained across protein families. As a case study, this work focuses on mixed microbial cultures exposed for extended periods to polyethylene, polyethylene terephthalate, and polyurethane substrates. The prevalent species after selective enrichment were functionally characterized, finding Rhodococcus aetherivorans as the most relevant species in two of the five cultures under investigation. Among the top-ranked proteins, several have high structural similarity with known enzymes despite not being identified by sequence similarity search. Moreover, according to metatranscriptomics results, several of these enzymes were found to be expressed at the same level or above that of annotated enzymes, suggesting that they may have functional relevance. Overall, this work highlights the potential of integrating multi-omics with data-driven methods for enzyme discovery and for accelerating the development of biotechnological solutions to plastic pollution.

Biodegradation, Environmental↗

PubMed2025

Exploring genetic adaptation and microbial dynamics in engineered anaerobic ecosystems via strain-level metagenomics.

Genetic heterogeneity exists within all microbial populations, with sympatric cells of the same species often exhibiting single-nucleotide variations that influence phenotypic traits, including metabolic efficiency. However, the evolutionary dynamics of these strain-level differences in response to environmental stress remain poorly understood. Here, we present a first-of-its-kind study tracking the adaptive evolution of an anaerobic, carbon-fixing microbiota under a controlled engineered ecosystem focused on carbon dioxide bioconversion into methane. Leveraging strain-resolved metagenomics with an ad hoc variant calling and phasing approach, we mapped mutation trajectories and observed that the two dominant Methanothermobacter species maintained distinct sweeping haplotypes over time, most likely due to niche-specific metabolic roles. By combining population genetic statistics and peptide reconstruction, mer and mcrB genes emerged as potential drivers of archaeal strain-level competition. These findings pave the way for targeted engineering of microbial communities to enhance bioconversion efficiency, with significant implications for sustainable energy and carbon management in anaerobic systems.

Metagenomics↗

PubMed2006

Piezophilic adaptation: a genomic point of view.

Two-thirds of Earth's surface is covered by oceans, yet the study of this massive integrated living system is still in its infancy. Various environmental variables, such as high salinity, low and changeable nutrient availability and depth-correlated gradients of light, temperature, nutrients and pressure shape the diversity, physiology and ecology of marine species. As oceans present an average depth of 3800 m, deep-sea ecosystems represent the most common marine ecological niche. One of the key environment variables that influences the life and evolution of deep-sea organisms is high pressure. This extreme widespread condition requires specific adaptations, the nature of which remains largely unknown. Recent advances in genomic approaches, such as in sequencing technologies and global expression profiling, are rapidly increasing the data available to understand microbial evolution, biochemistry, physiology and diversity. This review summarises the analysis of the results published so far about microbial high pressure adaptation from a genomic point of view. Understanding high pressure adaptation mechanisms is not just a scientific exercise but has important biotechnological implications. For example, hydrostatic pressure is a reality for food science and technology, both for food preparation and preservation. An understanding of the effects of pressure on biomolecules will expand its use in the medical, industrial and biotechnological fields.

Acclimatization↗

PubMed2006

Quantitative proteomic comparison of rat mitochondria from muscle, heart, and liver.

Mitochondria, through oxidative phosphorylation, are the primary source of energy production in all tissues under aerobic conditions. Although critical to life, energy production is not the only function of mitochondria, and the composition of this organelle is tailored to meet the specific needs of each cell type. As an organelle, the mitochondrion has been a popular subject for proteomic analysis, but quantitative proteomic methods have yet to be applied to tease apart subtle differences among mitochondria from different tissues or muscle types. Here we used mass spectrometry-based proteomics to analyze mitochondrial proteins extracted from rat skeletal muscle, heart, and liver tissues. Based on 689 proteins identified with high confidence, mitochondria from the different tissues are qualitatively quite similar. However, striking differences emerged from the quantitative comparison of protein abundance between the tissues. Furthermore we applied similar methods to analyze mitochondrial matrix and intermembrane space proteins extracted from the same mitochondrial source, providing evidence for the submitochondrial localization of a number of proteins in skeletal muscle and liver. Several proteins not previously thought to reside in mitochondria were identified, and their presence in this organelle was confirmed by protein correlation profiling. Hierarchical clustering of microarray expression data provided further evidence that some of the novel mitochondrial candidates identified in the proteomic survey might be associated with mitochondria. These data reveal several important distinctions between mitochondrial and submitochondrial proteomes from skeletal muscle, heart, and liver tissue sources. Indeed approximately one-third of the proteins identified in the soluble fractions are associated predominantly to one of the three tissues, indicating a tissue-dependent regulation of mitochondrial proteins. Furthermore a small percentage of the mitochondrial proteome is unique to each tissue.

Amino Acid Sequence↗

PubMed2005

Laterally transferred elements and high pressure adaptation in Photobacterium profundum strains.

BACKGROUND: Oceans cover approximately 70% of the Earth's surface with an average depth of 3800 m and a pressure of 38 MPa, thus a large part of the biosphere is occupied by high pressure environments. Piezophilic (pressure-loving) organisms are adapted to deep-sea life and grow optimally at pressures higher than 0.1 MPa. To better understand high pressure adaptation from a genomic point of view three different Photobacterium profundum strains were compared. Using the sequenced piezophile P. profundum strain SS9 as a reference, microarray technology was used to identify the genomic regions missing in two other strains: a pressure adapted strain (named DSJ4) and a pressure-sensitive strain (named 3TCK). Finally, the transcriptome of SS9 grown under different pressure (28 MPa; 45 MPa) and temperature (4 degrees C; 16 degrees C) conditions was analyzed taking into consideration the differentially expressed genes belonging to the flexible gene pool. RESULTS: These studies indicated the presence of a large flexible gene pool in SS9 characterized by various horizontally acquired elements. This was verified by extensive analysis of GC content, codon usage and genomic signature of the SS9 genome. 171 open reading frames (ORFs) were found to be specifically absent or highly divergent in the piezosensitive strain, but present in the two piezophilic strains. Among these genes, six were found to also be up-regulated by high pressure. CONCLUSION: These data provide information on horizontal gene flow in the deep sea, provide additional details of P. profundum genome expression patterns and suggest genes which could perform critical functions for abyssal survival, including perhaps high pressure growth.

Atmospheric Pressure↗

PubMed2005

The Ankrd2, Cdkn1c and calcyclin genes are under the control of MyoD during myogenic differentiation.

Skeletal muscle development requires the coordinated expression of numerous transcription factors to control the specification of the muscle fate in mesodermal cells and the differentiation of the committed myoblasts into functional contractile fibers. The bHLH transcription factor MyoD plays a key role in these processes, since its forced expression is sufficient to induce the myogenesis in a variety of non-muscle cells in culture. Consistent with this observation, the majority of skeletal muscle genes require MyoD to activate their own transcription. In order to identify novel MyoD-target genes we generated C2C12 MyoD-silenced clones, and used a muscle-specific cDNA microarray to study the induced modifications of the transcriptional profile. Gene expression was analyzed at three different stages in differentiating MyoD(-)C2C12 myoblasts. These microarray data sets identified many additional uncharacterized downstream MyoD transcripts that may play important functions in muscle cell differentiation. Among these genes, we concentrated our study on the cell cycle regulators Cdkn1c and calcyclin and on the muscle-specific putative myogenic regulator Ankrd2. Bioinformatic and functional studies on the promoters of these genes clarified their dependence on MyoD activity. Clues of other regulatory mechanisms that might interact with the principal bHLH transcription factor have been revealed by the unexpected up-regulation in MyoD(-) cells of these novel (and other) target transcripts, at the differentiation stage in which MyoD became normally down-regulated.

Animals↗

PubMed2003

Pattern recognition in gene expression profiling using DNA array: a comparative study of different statistical methods applied to cancer classification.

Large-scale parallel measurements of the expression of many thousands genes are now available with high-density array made with collections of cDNA fragments, or oligonucleotide corresponding to different transcripts. These technologies have been applied to cancer investigations since the availability of such a large number of markers makes DNA array a powerful diagnostic tool for tumour and patient classification. Over the last two years, a series of computational tools have been developed for the analysis of different aspects of gene profiling. Our work tries to compare a series of supervised statistical techniques on the basis of their ability to correctly classify different types of tumours. A simulation approach was initially used to control the huge source of variation among and between patients, and to evaluate the ability of algorithms to classify tumours in relation to different types of experimental variables. Different techniques for reduction of data dimension were then added to the discriminant analysis and compared according to their ability to capture the main genetic information. The simulation results have been tested by applying the selected classification algorithms to two experimental microarray datasets of human cancers, and by measuring the correspondent rates of misclassification. Our analyses identify in these datasets a series of genes principally involved in tumour characterization. The functional role of these discriminant transcripts is discussed.

Algorithms↗

PubMed2002

Gene expression profiling in dysferlinopathies using a dedicated muscle microarray.

We have performed expression profiling to define the molecular changes in dysferlinopathy using a novel dedicated microarray platform made with 3'-end skeletal muscle cDNAs. Eight dysferlinopathy patients, defined by western blot, immunohistochemistry and mutation analysis, were investigated with this technology. In a first experiment RNAs from different limb-girdle muscular dystrophy type 2B patients were pooled and compared with normal muscle RNA to characterize the general transcription pattern of this muscular disorder. Then the expression profiles of patients with different clinical traits were independently obtained and hierarchical clustering was applied to discover patient-specific gene variations. MHC class I genes and genes involved in protein biosynthesis were up-regulated in relation to muscle histopathological features. Conversely, the expression of genes codifying the sarcomeric proteins titin, nebulin and telethonin was down-regulated. Neither calpain-3 nor caveolin, a sarcolemmal protein interacting with dysferlin, was consistently reduced. There was a major up-regulation of proteins interacting with calcium, namely S100 calcium-binding proteins and sarcolipin, a sarcoplasmic calcium regulator.

Adolescent↗

PubMed2002

Functional profiling of the Saccharomyces cerevisiae genome.

Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed 'molecular bar codes' uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.

Cell Size↗