Biology subjects

Damiani, C.

Publications and source records attributed to Damiani, C..

13 recordsLinked to original sources

bioRxiv2026

Microbiota-driven, strain-specific Asaia colonisation modulates mosquito vector competence

Microbiota-based mosquito control strategies are founded on the ability of bacterial symbionts to modify host biology and/or pathogen transmission. However, symbionts do not operate in isolation. Their colonisation, persistence and biological effects arise within a complex ecological network comprising the host and its resident microbiota. We hypothesized that colonisation success is not only an intrinsic property of a bacterial symbiont but emerges from interactions between bacterial strain, host background, and the native microbial community. To test this, we compared recolonisation by endogenous and exogenous Asaia strains in the invasive mosquito Aedes koreicus using wild-type and axenic mosquitoes to experimentally manipulate the microbial composition. We found that endogenous and exogenous strains followed distinct colonisation trajectories. Depletion of the resident microbiota selectively rescued colonisation by the endogenous strain, whereas both strains successfully disseminated to transmission-relevant tissues despite contrasting early gut dynamics. Furthermore, these strain-specific colonisation dynamics were associated with increased Semliki Forest virus (SFV) transmission and enhanced adult longevity, particularly following colonisation by the exogenous strain. Our findings indicate that resident microbial communities act as selective ecological filters that shape symbiont colonisation in a strain-dependent manner. More broadly, this study demonstrates that the biological effects of mosquito symbionts emerge from interactions between bacterial strains and the microbial community context, providing an ecological framework for the rational and effective development of microbiota-based vector control strategies.

microbiology↗

bioRxiv2026

Thermal responses of an emerging temperate mosquito reshape arboviral transmission risk

Global warming is known to increase arboviral disease risk by enabling the expansion of tropical vectors such as Aedes aegypti and Aedes albopictus. However, whether climate-driven shifts in seasonal dynamics within temperate regions, such as warmer springs and shorter winters, create thermal settings favourable for additional vectors, which can reshape disease risk maps, is poorly understood. To answer this question, we used the invasive temperate mosquito species Aedes koreicus and tested its thermal developmental resilience, along with its vector competence for dengue and chikungunya viruses. We observed significant phenotypic variation in thermal tolerance across life stages, which translates into distinct thermal performance curves for life-history traits, ultimately shaping overall mosquito fitness. We further found that temperature effects are virus-specific, with differential impacts on infection and transmission between dengue and chikungunya viruses. These stage- and pathogen-specific responses generate carry-over effects across life stages, indicating that thermal responses may be constrained by life-history trade-offs rather than be defined by a single thermal optimum. Our results highlight the need to move beyond static, species-based risk assessments toward mechanistic frameworks that integrate thermal biology across life stages and vector-pathogen systems. As climate change increasingly reshapes seasonal structure rather than simply elevating mean temperatures, cold-adapted and temperate vectors such as Ae. koreicus, provide critical model systems to understand how transmission risk emerges outside mosquito classical thermal optima, calling for a broader paradigm shift in how global change and vector-borne disease risk are conceptualised.

ecology↗

bioRxiv2025

Mechanistically Informed Machine Learning Links Non-Canonical TCA Cycle Activity to Warburg Metabolism and Hallmarks of Malignancy

Cancer cells undergo extensive metabolic rewiring to support growth, survival, and phenotypic plasticity. A non-canonical variant of the tricarboxylic acid (TCA) cycle, characterized by mitochondrial-to-cytosolic citrate export, has emerged as critical for embryonic stem cell differentiation. However, its role in cancer remains poorly understood. Here, we present a two-step computational framework to systematically analyze the activity of this non-canonical TCA cycle across over 500 cancer cell lines and investigate its role in shaping hallmarks of malignancy. First, we applied constraint-based modeling to infer cycle activity, defining two complementary metrics: Cycle Propensity, measuring the likelihood of its engagement in each cell line, and Cycle Flux Intensity, quantifying average flux through the reaction identified as rate-limiting. We identified distinct tumor-specific patterns of pathway utilization. Notably, cells with high Cycle Propensity preferentially rerouted cytosolic citrate via aconitase 1 (ACO1) and isocitrate dehydrogenase 1 (IDH1), promoting -ketoglutarate (KG) and NADPH production. Elevated engagement of this cycle strongly correlated with Warburg-like metabolic shifts, including decreased oxygen consumption and increased lactate secretion. In the second step, to uncover non-metabolic transcriptional signatures associated with non-canonical TCA cycle activity, we performed machine learning-based feature selection using ElasticNet and Random Forest, identifying robust gene signatures predictive of cycle activity. Over-representation analysis revealed enrichment in genes involved in invasiveness, angiogenesis, stemness, and key oncogenic pathways. Analysis of DepMap gene dependency data revealed that TCA cycle activity correlates with differential vulnerability to perturbation of these oncogenic pathways, reinforcing the functional relevance of identified transcriptional signatures. To further interpret the predictive models, SHapley Additive exPlanations (SHAP) was applied to prioritize genes contributing most to non-canonical cycle activity, suggesting novel candidates for experimental investigation. Overall, our framework enables comprehensive analysis of non-canonical TCA cycle dynamics and uncovers potential links between metabolic plasticity and malignant phenotypes.

cancer biology↗

bioRxiv2025

Do heatwaves impact mosquitoes? Multi-level analyses across developmental stages in Aedes albopictus

The increase in the frequency and intensity of heatwaves (HWs) under global warming is expected to have more dramatic biological impacts on insects than mean temperature increases by challenging their thermal thresholds. However, experimental evidence that quantifies the impact of HWs on insects in their ecological context is limited. Here, we measured across multiple biological scales (e.g. fitness, physiology, transcriptomic and microbiota) the stage-specific responses of the arboviral vector Aedes albopictus when experiencing an ecologically relevant HW. In arboviral vectors, only females require a blood-meal thus contributing to the transmission cycle, but males, eggs and larvae can also be the target of control strategies to reduce population size. As such the understanding of the responses to a HW during mosquito development has both biological and epidemiological relevance. We observed stage-specific responses, starting at the egg stage. We saw egg hatching decreasing and delaying after HW exposure. On the contrary, larvae showed to be resilient to HW. Larvae repurposed their energy resulting in trivial mortality, but delayed development. At the adult stage, we observed a marked sex difference, with extensive (>50%) male mortality, accompanied by a small (16) number of genes elicited following HW exposure, which indicates that males have limited coping mechanisms against hot events. In females, we observed a reduced reproductive output, but only when HW occurred after a blood-meal. Finally, we saw extensive HW-dependent changes in the microbial composition of larvae, but female microbiota remained dominated by Wolbachia regardless of the thermal challenge. Our results have relevant implications for both the understanding of mosquito biology and the implementation of vector control strategies as the climate crisis progresses.

ecology↗

bioRxiv2024

Spatial Flux Balance Analysis reveals tissue-of-origin and spatially dependent metabolic rewiring in renal and colorectal cancer

1To fully understand how cancer metabolism differs between primary tumors and metastases, resolving cell metabolism with spatial precision is essential. Yet, spatial fluxomics lags behind advancements in spatial transcriptomics. To address this gap, we generated high-resolution spatial transcriptomics datasets from paired primary colorectal tumors and liver metastases, designed to capture metabolic adaptations across distinct tumor sites. Concurrently, we developed the Spatial Flux Balance Analysis (spFBA) computational framework to leverage them. Since broad metabolic differences between tumors and healthy tissues are established, we first validated spFBA on a publicly available renal cancer dataset, including tumor-normal interface samples. spFBA detected cancer metabolic hallmarks, like enhanced glucose uptake and metabolic growth, but with unprecedented resolution, revealing lactate production with sustained oxygen consumption at the tumor interface and with reduced respiration in the core. Next, applying spFBA to our colorectal cancer dataset, we provided biological insights, confirming that metastases mimic the metabolic traits of their tissue of origin. Additionally, our approach uncovered the first in vivo evidence of lactate-consuming cancer cells, marking a significant advance in understanding cancer metabolism. spFBA stands out as a powerful approach to unravel the spatial metabolic complexity of cancer and beyond, leveraging the expanding landscape of spatial transcriptomics datasets.

systems biology↗

bioRxiv2023

De-novo genome assembly of the invasive mosquito species Aedes japonicus and Aedes koreicus

Recently, two invasive Aedes mosquito species, Ae. japonicus and Ae. koreicus, are circulating in several European countries posing potential health risks to humans and animals. Vector control is the main option to prevent mosquito-borne diseases, and an accurate genome sequence of these mosquitoes is essential to better understand their biology and to develop effective control strategies. Here, we present a de novo genome assembly of the Ae. japonicus (Ajap1) and Ae. koreicus (Akor1) based on a hybrid approach that combines Oxford Nanopore long reads and Illumina short reads data. Their quality was ascertained using various metrics. Masking of repetitive elements, gene prediction and functional annotation was performed. Sequence analysis revealed a very high presence of repetitive DNA and, among others, thermal adaptation genes and insecticide-resistance genes. The RNA sequencing analysis of larvae and adults of Ae. koreicus and Ae. japonicus exposed to different temperatures revealed genes showing a thermal-dependent activation. The assembly of Akor1 and Ajap1 genomes constitutes the first updated collective knowledge of the genomes of both mosquito species, providing the possibility to understand key mechanisms of their biology such as the ability to adapt to harsh climates and to develop insecticide-resistance mechanisms.

genomics↗

bioRxiv2021

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation

Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. First, metabolic regulation controls metabolic fluxes (i.e., the rate of individual metabolic reactions) through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. A second regulatory layer sets the maximal theoretical level for each enzyme-controlled reaction by controlling the expression level of the catalyzing enzyme. In isolation, high-throughput data, such as metabolomics and transcriptomics data do not allow for accurate characterization of the hierarchical regulation of metabolism outlined above. Hence, they must be integrated in order to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we proposes INTEGRATE, a computational pipeline that integrates metabolomics (intracellular and optionally extracellular) and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomic data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients. Author summaryThe study of metabolism and its regulation finds increasing application in various fields, including biotransformations, wellness, and health. Metabolism can be studied using post-genomic technologies, notably transcriptomics and metabolomics, that provide snapshots of transcripts and metabolites in specific physio-pathological conditions. In the health field, the transcriptome and, more recently, the metabolome have been broadly profiled at the pre-clinical and clinical levels. The informative power of single omic technologies is inadequate since metabolism regulation involves a complex interplay of regulatory steps. While gene expression regulates metabolism by setting the upper level of metabolic enzymes, the interaction of metabolites with metabolic enzymes directly auto-regulates metabolism. Therefore there is a need for methods that integrate multiple data sources. We present INTEGRATE, a computational pipeline that captures dynamic features from the static snapshots provided by transcriptomic and metabolomic data. Through integration in a steady-state metabolic model, the pipeline predicts which reactions are controlled purely by metabolic control rather than by gene expression or a combination of the two. This knowledge is crucial in a clinical setting to develop personalized therapies in patients of multifactorial diseases, such as cancer. Besides cancer, INTEGRATE can be applied to different fields in which metabolism plays a driving role.

systems biology↗

bioRxiv2021

GPRuler: metabolic Gene-Protein-Reaction rules automatic reconstruction

BackgroundMetabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. ResultsBy mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. ConclusionsBy complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.

systems biology↗

bioRxiv2021

Increased biting rate and decreased Wolbachia density in irradiated mosquitoes: safety issues in the framework of genetic control strategies

BackgroundReleasing considerable numbers of radiation-sterilized males is a promising strategy to suppress mosquito vectors. However, releases may also include small percentages of biting females which translate to large numbers when releases are large. Currently, the effects of irradiation on the host-seeking and host-biting behaviors have not been exhaustively investigated. Information is also lacking regarding the effects of sterilizing treatment on the endosymbiotic bacterium Wolbachia, which is known to affect the vector competence of infected mosquitos. MethodsTo ascertain the effects of irradiation on females, the pupae of two Aedes albopictus strains, differing in their natural or artificial Wolbachia infection type, and Ae. aegypti--which is not infected by Wolbachia--were treated with various doses of X-rays and monitored for key fitness parameters and biting behavior over a period of two weeks. The effect of radiation on Wolbachia was investigated by qPCR and FISH analysis. ResultsPartial Ae. albopictus female sterility was achieved at 28 Gy but the number of weekly bites more than doubled compared to that of the controls. Radiation doses of 35 and 45 Gy completely inhibited progeny production but did not significantly affect the survival or flight ability of Ae. albopictus females and caused a tripling of the number of bites per female per week (compared to untreated controls). These results were also confirmed in Ae. aegypti after treatment at 50 Gy. Wolbachia density decreased significantly in 45-Gray-irradiated females, with the greatest decreases in the early irradiation group (26{+/-}2-hour-old pupae). Wolbachia density also decreased as adults aged. This trend was confirmed in ovaries but not in extra-ovarian tissues. FISH analysis showed a strongly reduced Wolbachia-specific fluorescence in the ovaries of 13{+/-}1-day-old females. ConclusionsThese results suggest that, under SIT programs, the vector capacity of a target population could increase with the frequency of the irradiated females co-released with the sterile males due to an increased biting rate. In the context of a successful suppression, the related safety issues could be generally negligible, but they should be conservatively evaluated when large scale programs relying on imperfect sexing and high overflooding release ratios are run for long time in areas endemic for arboviral diseases. Also, the effects of irradiation on the vector competence deserve further investigation.

microbiology↗

bioRxiv2020

Phylogenomics reveals that Asaia symbionts from insects underwent convergent genome reduction, preserving an insecticide-degrading gene

The mosquito microbiota is composed by several lineages of microorganisms whose ecological roles and evolutionary histories have yet to be investigated in depth. Among these microorganisms, Asaia bacteria play a prominent role, given its abundance in the gut, reproductive organs and salivary glands of different mosquito species, while its presence has also been reported in several other insects. Notably, Asaia has great potential as a tool for the control of mosquito-borne diseases. Here, we present a wide phylogenomic analysis of Asaia strains isolated from different species of mosquito vectors and from different populations of the Mediterranean fruit fly (medfly) Ceratitis capitata, an insect pest of worldwide economic importance. We show that phylogenetically distant lineages of Asaia experienced independent genome reductions, despite following a common pattern, characterized by the early loss of genes involved in genome stability. This result highlights the role of specific metabolic pathways in the symbiotic relationship between Asaia and the insect host. Finally, we discovered that all but one of the Asaia strains included in the study possess the pyrethroid hydrolase gene. Phylogenetic analysis revealed that this gene is ancestral in Asaia, strongly suggesting that it played a role in the establishment of the symbiotic association between these bacteria and the mosquito hosts. We propose that this gene from the symbiont contributed to initial pyrethroid resistance in insects harboring Asaia, also considering the widespread production of pyrethrins by several plants. ImportanceWe have studied genome reduction within several strains of the insect symbiont Asaia, isolated from different species/strains of mosquito and medfly. Phylogenetically distant strains of Asaia, despite following a common pattern involving the loss of genes related to genome stability, have undergone independent genome reductions, highlighting the peculiar role of specific metabolic pathways in the symbiotic relationship between Asaia and its host. We also show that the pyrethroid hydrolase gene is present in all the Asaia strains isolated except for the South American malaria vector An. darlingi, for which resistance to pyrethroids has never been reported, suggesting a possible involvement of Asaia in determining the resistance to insecticides.

microbiology↗

bioRxiv2018

Genome-scale metabolic construction of the stress-tolerant hybrid yeast Zygosaccharomyces parabailii

Genome-scale metabolic models are powerful tools to understand and engineer cellular systems facilitating their use as cell factories. This is especially true for microorganisms with known genome sequences from which nearly complete sets of enzymes and metabolic pathways are determined, or can be inferred. Yeasts are highly diverse eukaryotes whose metabolic traits have long been exploited in industry, and although many of their genome sequences are available, few genome-scale metabolic models have so far been produced. For the first time, we reconstructed the genome-scale metabolic model of the hybrid yeast Zygosaccharomyces parabailii, which is a member of the Z. bailii sensu lato clade notorious for stress-tolerance and therefore relevant to industry. The model comprises 3096 reactions, 2091 metabolites, and 2413 genes. Our own laboratory data were then used to establish a biomass synthesis reaction, and constrain the extracellular environment. Through constraint-based modeling, our model reproduces the co-consumption and catabolism of acetate and glucose posing it as a promising platform for understanding and exploiting the metabolic potential of Z. parabailii.

systems biology↗

bioRxiv2018

Integration of single-cell RNA-seq data into metabolic models to characterize tumour cell populations

MotivationMetabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. Computational models hold the promise to bridge this gap, by estimating fluxes across metabolic pathways. Yet they currently portray the average behavior of intermixed subpopulations, masking their inherent heterogeneity known to hinder cancer diagnosis and treatment. If complemented with the information on single-cell transcriptome, now enabled by RNA sequencing (scRNA-seq), metabolic models of cancer populations are expected to empower the characterization of the mechanisms behind metabolic heterogeneity. To this aim, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate sc-transcriptomes into single-cell fluxomes.\n\nResultsWe show that the integration of scRNA-seq profiles of cells derived from lung ade-nocarcinoma and breast cancer patients, into a multi-scale stoichiometric model of cancer population: 1) significantly reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites.\n\nAvailabilityThe scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.\n\nContactchiara.damiani@unimib.it

systems biology↗

bioRxiv2018

MaREA: Metabolic feature extraction, enrichment and visualization of RNAseq data

The characterization of the metabolic deregulations that distinguish cancer phenotypes, and which might be effectively targeted by ad-hoc strategies, is a key open challenge. To this end, we here introduce MaREA (Metabolic Reaction Enrichment Analysis), a computational pipeline that processes cross-sectional RNAseq data to identify the metabolic reactions that are significantly up-/ down-regulated in different sample subgroups. MaREA relies on the definition of a Reaction Activity Score, computed as a function of the expression level of genes encoding for reaction enzymes, which can also be used as an effective metrics to cluster samples into distinct metabolic subgroups. MaREA finally allows to visualize the results in a graphical form directly on metabolic maps. We apply MaREA to distinct cancer datasets and we show that it can produce useful information and new experimental hypotheses on metabolic deregulation of cancer cells, also allowing to stratify patients in metabolic clusters with significantly different survival expectancy.

bioinformatics↗