Susanna Zucca
@engenome.com
ENGENOME SRL
ENGENOME SRL
RESEARCH INTERESTS
NGS, Bioinformatics, Next Generation Sequencing, Data Analysis
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ravi A. Shah, C. Anwar A. Chahal, Shaheryar Ranjha, Ghaith Sharaf Dabbagh, Babken Asatryan, Ivan Limongelli, Mohammed Khanji, Fabrizio Ricci, Federica De Paoli, Susanna Zucca,et al.
Elsevier BV
S. Zucca, G. Nicora, F. De Paoli, M. G. Carta, R. Bellazzi, P. Magni, E. Rizzo, and I. Limongelli
Springer Science and Business Media LLC
AbstractIdentifying disease-causing variants in Rare Disease patients’ genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called “Suggested Diagnosis”, whose aim is to prioritize genetic variants in an exome/genome based on the probability of being disease-causing. To do so, our method leverages standard guidelines for germline variant interpretation as defined by the American College of Human Genomics (ACMG) and the Association for Molecular Pathology (AMP), inheritance information, phenotypic similarity, and variant quality. Starting from (1) the VCF file containing proband’s variants, (2) the list of proband’s phenotypes encoded in Human Phenotype Ontology terms, and optionally (3) the information about family members (if available), the “Suggested Diagnosis” ranks all the variants according to their machine learning prediction. This method significantly reduces the number of variants that need to be evaluated by geneticists by pinpointing causative variants in the very first positions of the prioritized list. Most importantly, our approach proved to be among the top performers within the CAGI6 Rare Genome Project Challenge, where it was able to rank the true causative variant among the first positions and, uniquely among all the challenge participants, increased the diagnostic yield of 12.5% by solving 2 undiagnosed cases.
Giovanna Nicora, Susanna Zucca, Ivan Limongelli, Riccardo Bellazzi, and Paolo Magni
Springer Science and Business Media LLC
AbstractGenomic variant interpretation is a critical step of the diagnostic procedure, often supported by the application of tools that may predict the damaging impact of each variant or provide a guidelines-based classification. We propose the application of Machine Learning methodologies, in particular Penalized Logistic Regression, to support variant classification and prioritization. Our approach combines ACMG/AMP guidelines for germline variant interpretation as well as variant annotation features and provides a probabilistic score of pathogenicity, thus supporting the prioritization and classification of variants that would be interpreted as uncertain by the ACMG/AMP guidelines. We compared different approaches in terms of variant prioritization and classification on different datasets, showing that our data-driven approach is able to solve more variant of uncertain significance (VUS) cases in comparison with guidelines-based approaches and in silico prediction tools.
Ibrahim Taha, Federica De Paoli, Selena Foroni, Susanna Zucca, Ivan Limongelli, Marco Cipolli, Cesare Danesino, Ugo Ramenghi, and Antonella Minelli
MDPI AG
Introduction. Shwachman-Diamond Syndrome (SDS) is an autosomal-recessive disorder characterized by neutropenia, pancreatic exocrine insufficiency, skeletal dysplasia, and an increased risk for leukemic transformation. Biallelic mutations in the SBDS gene have been found in about 90% of patients. The clinical spectrum of SDS in patients is wide, and variability has been noticed between different patients, siblings, and even within the same patient over time. Herein, we present two SDS siblings (UPN42 and UPN43) carrying the same SBDS mutations and showing relevant differences in their phenotypic presentation. Study aim. We attempted to understand whether other germline variants, in addition to SBDS, could explain some of the clinical variability noticed between the siblings. Methods. Whole-exome sequencing (WES) was performed. Human Phenotype Ontology (HPO) terms were defined for each patient, and the WES data were analyzed using the eVai and DIVAs platforms. Results. In UPN43, we found and confirmed, using Sanger sequencing, a novel de novo variant (c.10663G > A, p.Gly3555Ser) in the KMT2A gene that is associated with autosomal-dominant Wiedemann–Steiner Syndrome. The variant is classified as pathogenic according to different in silico prediction tools. Interestingly, it was found to be related to some of the HPO terms that describe UPN43. Conclusions. We postulate that the KMT2A variant found in UPN43 has a concomitant and co-occurring clinical effect, in addition to SBDS mutation. This dual molecular effect, supported by in silico prediction, could help to understand some of the clinical variations found among the siblings. In the future, these new data are likely to be useful for personalized medicine and therapy for selected cases.
Ilaria Palmieri, Tino Emanuele Poloni, Valentina Medici, Susanna Zucca, Annalisa Davin, Orietta Pansarasa, Mauro Ceroni, Livio Tronconi, Antonio Guaita, Stella Gagliardi,et al.
MDPI AG
Alzheimer’s disease (AD) and Lewy body dementia (LBD) are two different forms of dementia, but their pathology may involve the same cortical areas with overlapping cognitive manifestations. Nonetheless, the clinical phenotype is different due to the topography of the lesions driven by the different underlying molecular processes that arise apart from genetics, causing diverse neurodegeneration. Here, we define the commonalities and differences in the pathological processes of dementia in two kindred cases, a mother and a son, who developed classical AD and an aggressive form of AD/LBD, respectively, through a neuropathological, genetic (next-generation sequencing), and transcriptomic (RNA-seq) comparison of four different brain areas. A genetic analysis did not reveal any pathogenic variants in the principal AD/LBD-causative genes. RNA sequencing highlighted high transcriptional dysregulation within the substantia nigra in the AD/LBD case, while the AD case showed lower transcriptional dysregulation, with the parietal lobe being the most involved brain area. The hippocampus (the most degenerated area) and basal ganglia (lacking specific lesions) expressed the lowest level of dysregulation. Our data suggest that there is a link between transcriptional dysregulation and the amount of tissue damage accumulated across time, assessed through neuropathology. Moreover, we highlight that the molecular bases of AD and LBD follow very different pathways, which underlie their neuropathological signatures. Indeed, the transcriptome profiling through RNA sequencing may be an important tool in flanking the neuropathological analysis for a deeper understanding of AD and LBD pathogenesis.
Daisy Sproviero, Stella Gagliardi, Susanna Zucca, Maddalena Arigoni, Marta Giannini, Maria Garofalo, Valentina Fantini, Orietta Pansarasa, Micol Avenali, Matteo Cotta Ramusino,et al.
Frontiers Media SA
ObjectivesThere is a lack of effective biomarkers for neurodegenerative diseases (NDs) such as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and frontotemporal dementia. Extracellular vesicle (EV) RNA cargo can have an interesting potential as a non-invasive biomarker for NDs. However, the knowledge about the abundance of EV-mRNAs and their contribution to neurodegeneration is not clear.MethodsLarge and small EVs (LEVs and SEVs) were isolated from plasma of patients and healthy volunteers (control, CTR) by differential centrifugation and filtration, and RNA was extracted. Whole transcriptome was carried out using next generation sequencing (NGS).ResultsCoding RNA (i.e., mRNA) but not long non-coding RNAs (lncRNAs) in SEVs and LEVs of patients with ALS could be distinguished from healthy CTRs and from other NDs using the principal component analysis (PCA). Some mRNAs were found in commonly deregulated between SEVs of patients with ALS and frontotemporal dementia (FTD), and they were classified in mRNA processing and splicing pathways. In LEVs, instead, one mRNA and one antisense RNA (i.e., MAP3K7CL and AP003068.3) were found to be in common among ALS, FTD, and PD. No deregulated mRNAs were found in EVs of patients with AD.ConclusionDifferent RNA regulation occurs in LEVs and SEVs of NDs. mRNAs and lncRNAs are present in plasma-derived EVs of NDs, and there are common and specific transcripts that characterize LEVs and SEVs from the NDs considered in this study.
Elisa Pischedda, Cristina Crava, Martina Carlassara, Susanna Zucca, Leila Gasmi, and Mariangela Bonizzoni
Springer Science and Business Media LLC
Abstract Background Several bioinformatics pipelines have been developed to detect sequences from viruses that integrate into the human genome because of the health relevance of these integrations, such as in the persistence of viral infection and/or in generating genotoxic effects, often progressing into cancer. Recent genomics and metagenomics analyses have shown that viruses also integrate into the genome of non-model organisms (i.e., arthropods, fish, plants, vertebrates). However, rarely studies of endogenous viral elements (EVEs) in non-model organisms have gone beyond their characterization from reference genome assemblies. In non-model organisms, we lack a thorough understanding of the widespread occurrence of EVEs and their biological relevance, apart from sporadic cases which nevertheless point to significant roles of EVEs in immunity and regulation of expression. The concomitance of repetitive DNA, duplications and/or assembly fragmentations in a genome sequence and intrasample variability in whole-genome sequencing (WGS) data could determine misalignments when mapping data to a genome assembly. This phenomenon hinders our ability to properly identify integration sites. Results To fill this gap, we developed ViR, a pipeline which solves the dispersion of reads due to intrasample variability in sequencing data from both single and pooled DNA samples thus ameliorating the detection of integration sites. We tested ViR to work with both in silico and real sequencing data from a non-model organism, the arboviral vector Aedes albopictus. Potential viral integrations predicted by ViR were molecularly validated supporting the accuracy of ViR results. Conclusion ViR will open new venues to explore the biology of EVEs, especially in non-model organisms. Importantly, while we generated ViR with the identification of EVEs in mind, its application can be extended to detect any lateral transfer event providing an ad-hoc sequence to interrogate.
Cecilia Pandini, Maria Garofalo, Federica Rey, Jessica Garau, Susanna Zucca, Daisy Sproviero, Matteo Bordoni, Giulia Berzero, Annalisa Davin, Tino Emanuele Poloni,et al.
Elsevier BV
The multitasking nature of lncRNAs allows them to play a central role in both physiological and pathological conditions. Often the same lncRNA can participate in different diseases. Specifically, the MYC-induced Long non-Coding RNA MINCR is upregulated in various cancer types, while downregulated in Amyotrophic Lateral Sclerosis patients. Therefore, this work aims to investigate MINCR potential mechanisms of action and its implications in cancer and neurodegeneration in relation to its expression levels in SH-SY5Y cells through RNA-sequencing approach. Our results show that MINCR overexpression causes massive alterations in cancer-related genes, leading to disruption in many fundamental processes, such as cell cycle and growth factor signaling. On the contrary, MINCR downregulation influences a small number of genes involved in different neurodegenerative disorders, mostly concerning RNA metabolism and inflammation. Thus, understanding the cause and functional consequences of MINCR deregulation gives important insights on potential pathogenetic mechanisms both in cancer and in neurodegeneration.
Maria Garofalo, Stella Gagliardi, Susanna Zucca, Cecilia Pandini, Francesca Dragoni, Daisy Sproviero, Orietta Pansarasa, Tino Emanuele Poloni, Valentina Medici, Annalisa Davin,et al.
Elsevier BV
Since the association of SARS-Cov-2 infection with Nervous System (NS) manifestations, we performed RNA-sequencing analysis in Frontal Cortex of COVID-19 positive or negative individuals and affected or not by Dementia individuals. We examined gene expression differences in individuals with COVID-19 and Dementia compared to Dementia only patients by collecting transcript counts in each sample and performing Differential Expression analysis. We found eleven genes satisfying our significance criteria, all of them being protein coding genes.
These data are suitable for integration with supplemental samples and for analysis according to different individuals’ classification. Also, differential expression evaluation may be implemented with other scientific purposes, such as research of unannotated genes, mRNA splicing and genes isoforms.
The analysis of Differential Expressed genes in COVID-19 positive patients compared to non-COVID-19 patients is published in: S. Gagliardi, E.T. Poloni, C. Pandini, M. Garofalo, F. Dragoni, V. Medici, A. Davin, S.D. Visonà, M. Moretti, D. Sproviero, O. Pansarasa, A. Guaita, M. Ceroni, L. Tronconi, C. Cereda, Detection of SARS-CoV-2 genome and whole transcriptome sequencing in frontal cortex of COVID-19 patients., Brain. Behav. Immun. (2021). https://doi.org/10.1016/j.bbi.2021.05.012.
Daisy Sproviero, Stella Gagliardi, Susanna Zucca, Maddalena Arigoni, Marta Giannini, Maria Garofalo, Martina Olivero, Michela Dell’Orco, Orietta Pansarasa, Stefano Bernuzzi,et al.
MDPI AG
Identifying biomarkers is essential for early diagnosis of neurodegenerative diseases (NDs). Large (LEVs) and small extracellular vesicles (SEVs) are extracellular vesicles (EVs) of different sizes and biological functions transported in blood and they may be valid biomarkers for NDs. The aim of our study was to investigate common and different miRNA signatures in plasma derived LEVs and SEVs of Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic Lateral Sclerosis (ALS) and Fronto-Temporal Dementia (FTD) patients. LEVs and SEVs were isolated from plasma of patients and healthy volunteers (CTR) by filtration and differential centrifugation and RNA was extracted. Small RNAs libraries were carried out by Next Generation Sequencing (NGS). MiRNAs discriminate all NDs diseases from CTRs and they can provide a signature for each NDs. Common enriched pathways for SEVs were instead linked to ubiquitin mediated proteolysis and Toll-like receptor signaling pathways and for LEVs to neurotrophin signaling and Glycosphingolipid biosynthesis pathway. LEVs and SEVs are involved in different pathways and this might give a specificity to their role in the spreading of the disease. The study of common and different miRNAs transported by LEVs and SEVs can be of great interest for biomarker discovery and for pathogenesis studies in neurodegeneration.
Maria Garofalo, Cecilia Pandini, Matteo Bordoni, Orietta Pansarasa, Federica Rey, Alfredo Costa, Brigida Minafra, Luca Diamanti, Susanna Zucca, Stephana Carelli,et al.
MDPI AG
Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS) are neurodegenerative disorders characterized by a progressive degeneration of the central or peripheral nervous systems. A central role of the RNA metabolism has emerged in these diseases, concerning mRNAs processing and non-coding RNAs biogenesis. We aimed to identify possible common grounds or differences in the dysregulated pathways of AD, PD, and ALS. To do so, we performed RNA-seq analysis to investigate the deregulation of both coding and long non-coding RNAs (lncRNAs) in ALS, AD, and PD patients and controls (CTRL) in peripheral blood mononuclear cells (PBMCs). A total of 293 differentially expressed (DE) lncRNAs and 87 mRNAs were found in ALS patients. In AD patients a total of 23 DE genes emerged, 19 protein coding genes and four lncRNAs. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses, we found common affected pathways and biological processes in ALS and AD. In PD patients only five genes were found to be DE. Our data brought to light the importance of lncRNAs and mRNAs regulation in three principal neurodegenerative disorders, offering starting points for new investigations on deregulated pathogenic mechanisms.
Jessica Garau, Vanessa Cavallera, Marialuisa Valente, Davide Tonduti, Daisy Sproviero, Susanna Zucca, Domenica Battaglia, Roberta Battini, Enrico Bertini, Silvia Cappanera,et al.
MDPI AG
Aicardi-Goutières syndrome (AGS) is a genetically determined early onset encephalopathy characterized by cerebral calcification, leukodystrophy, and increased expression of interferon-stimulated genes (ISGs). Up to now, seven genes (TREX1, RNASEH2B, RNASEH2C, RNASEH2A, ADAR1, SAMHD1, IFIH1) have been associated with an AGS phenotype. Next Generation Sequencing (NGS) analysis was performed on 51 AGS patients and interferon signature (IS) was investigated in 18 AGS patients and 31 healthy controls. NGS identified mutations in 48 of 51 subjects, with three patients demonstrating a typical AGS phenotype but not carrying mutations in known AGS-related genes. Five mutations, in RNASEH2B, SAMHD1 and IFIH1 gene, were not previously reported. Eleven patients were positive and seven negatives for the upregulation of interferon signaling (IS > 2.216). This work presents, for the first time, the genetic data of an Italian cohort of AGS patients, with a higher percentage of mutations in RNASEH2B and a lower frequency of mutations in TREX1 than those seen in international series. RNASEH2B mutated patients showed a prevalence of negative IS consistent with data reported in the literature. We also identified five novel pathogenic mutations that warrant further functional investigation. Exome/genome sequencing will be performed in future studies in patients without a mutation in AGS-related genes.
Lorenzo Pasotti, Massimo Bellato, Nicolo Politi, Michela Casanova, Susanna Zucca, Maria Gabriella Cusella De Angelis, and Paolo Magni
Institute of Electrical and Electronics Engineers (IEEE)
Feedback control is ubiquitous in biological systems. It can also play a crucial role in the design of synthetic circuits implementing novel functions in living systems, to achieve self-regulation of gene expression, noise reduction, rise time decrease, or adaptive pathway control. Despite in vitro, in vivo, and ex vivo implementations have been successfully reported, the design of biological close-loop systems with quantitatively predictable behavior is still a major challenge. In this work, we tested a model-based bottom-up design of a synthetic close-loop controller in engineered Escherichia coli, aimed to automatically regulate the concentration of an extracellular molecule, N-(3-oxohexanoyl)-L-homoserine lactone (HSL), by rewiring the elements of heterologous quorum sensing/quenching networks. The synthetic controller was successfully constructed and experimentally validated. Relying on mathematical model and experimental characterization of individual regulatory parts and enzymes, we evaluated the predictability of the interconnected system behavior in vivo. The culture was able to reach an HSL steady-state level of 72 nM, accurately predicted by the model, and showed superior capabilities in terms of robustness against cell density variation and disturbance rejection, compared with a corresponding open-loop circuit. This engineering-inspired design approach may be adopted for the implementation of other close-loop circuits for different applications and contribute to decreasing trial-and-error steps.
Daisy Sproviero, Sabrina La Salvia, Federico Colombo, Susanna Zucca, Orietta Pansarasa, Luca Diamanti, Alfredo Costa, Luca Lova, Marta Giannini, Stella Gagliardi,et al.
Frontiers Media SA
The lack of biomarkers in Amyotrophic Lateral Sclerosis (ALS) makes it difficult to determine the stage of the disease in patients and, therefore, it delays therapeutic trials. Microvesicles (MVs) are possible biomarkers implicated in physiological and pathological functions, however, their role in ALS remains unclear. We investigated whether plasma derived microvesicles could be overrepresented in a group of 40 patients affected by ALS compared to 28 Alzheimer’s Disease (AD) patients and 36 healthy volunteers. Leukocyte derived MVs (LMVs) compared to endothelial, platelet, erythrocyte derived MVs, were mostly present in ALS patients compared to AD patients and healthy donors. Correlation analysis corrected for the presence of confounding variables (riluzole, age at onset, site of onset, gender) was tested between PRL (Progression Rate at the Last visit) and LMVs, and a statistically significant value was found (Pearson partial correlation r = 0.407, p = 0.006). We also investigated SOD1, TDP-43 intravesicular protein level in LMVs. Misfolded SOD1 was selectively transported by LMVs and its protein level was associated with the percentage of LMVs in slow progressing patients (r = 0.545, p = 0.033). Our preliminary findings suggest that LMVs are upregulated in ALS patients and they can be considered possible markers of disease progression.
Susanna Zucca, Stella Gagliardi, Cecilia Pandini, Luca Diamanti, Matteo Bordoni, Daisy Sproviero, Maddalena Arigoni, Martina Olivero, Orietta Pansarasa, Mauro Ceroni,et al.
Springer Science and Business Media LLC
Coding and long non-coding RNA (lncRNA) metabolism is now revealing its crucial role in Amyotrophic Lateral Sclerosis (ALS) pathogenesis. In this work, we present a dataset obtained via Illumina RNA-seq analysis on Peripheral Blood Mononuclear Cells (PBMCs) from sporadic and mutated ALS patients (mutations in FUS, TARDBP, SOD1 and VCP genes) and healthy controls. This dataset allows the whole-transcriptome characterization of PBMCs content, both in terms of coding and non-coding RNAs, in order to compare the disease state to the healthy controls, both for sporadic patients and for mutated patients. Our dataset is a starting point for the omni-comprehensive analysis of coding and lncRNAs, from an easy to withdraw, manage and store tissue that shows to be a suitable model for RNA profiling in ALS.Design Type(s)parallel group design • individual genetic characteristics comparison designMeasurement Type(s)transcription profiling assayTechnology Type(s)RNA sequencingFactor Type(s)diagnosis • protein_altering_variantSample Characteristic(s)Homo sapiens • peripheral blood mononuclear cellMachine-accessible metadata file describing the reported data (ISA-Tab format)
Stella Gagliardi, Susanna Zucca, Cecilia Pandini, Luca Diamanti, Matteo Bordoni, Daisy Sproviero, Maddalena Arigoni, Martina Olivero, Orietta Pansarasa, Mauro Ceroni,et al.
Springer Science and Business Media LLC
Alteration in RNA metabolism, concerning both coding and long non-coding RNAs (lncRNAs), may play an important role in Amyotrophic Lateral Sclerosis (ALS) pathogenesis. In this work, we performed a whole transcriptome RNA-seq analysis to investigate the regulation of non-coding and coding RNAs in Sporadic ALS patients (SALS), mutated ALS patients (FUS, TARDBP and SOD1) and matched controls in Peripheral Blood Mononuclear Cells (PBMC). Selected transcripts were validated in spinal cord tissues. A total of 293 differentially expressed (DE) lncRNAs was found in SALS patients, whereas a limited amount of lncRNAs was deregulated in mutated patients. A total of 87 mRNAs was differentially expressed in SALS patients; affected genes showed an association with transcription regulation, immunity and apoptosis pathways. Taken together our data highlighted the importance of extending the knowledge on transcriptomic molecular alterations and on the significance of regulatory lncRNAs classes in the understanding of ALS disease. Our data brought the light on the importance of lncRNAs and mRNAs regulation in central and peripheral systems, offering starting points for new investigations about pathogenic mechanism involved in ALS disease.
Stella Gagliardi, Valentina Franco, Stefano Sorrentino, Susanna Zucca, Cecilia Pandini, Paola Rota, Stefano Bernuzzi, Alfredo Costa, Elena Sinforiani, Orietta Pansarasa,et al.
Frontiers Media SA
Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that is associated with the most common type of dementia and is characterized by the presence of deposits of the protein fragment amyloid beta (Aβ) in the brain. The natural product mixture of curcuminoids that improves certain defects in innate immune cells of AD patients may selectively enhance Aβ phagocytosis by alteration of gene transcription. In this work, we evaluated the protective effects of curcuminoids in cells from AD patients by investigating the effect on NF-κB and BACE1 signaling pathways. These results were compared to the gene expression profile of the clearance of Aβ. The minor curcumin constituent, bisdemethoxycurcumin (BDC) showed the most potent protective action to decrease levels of NF-κB and BACE1, decrease the inflammatory cascade and diminish Aβ aggregates in cells from AD patients. Moreover, mannosyl-glycoprotein 4-beta-N-acetylglucosaminyltransferase (MGAT3) and vitamin D receptor (VDR) gene mRNAs were up-regulated in peripheral blood mononuclear cells from AD patients treated with BDC. BDC treatment impacts both gene expression including Mannosyl (Beta-1,4-)-Glycoprotein Beta-1,4-N-Acetylglucosaminyltransferase, Vitamin D and Toll like receptor mRNA and Aβ phagocytosis. The observation of down-regulation of BACE1 and NF-κB following administration of BDC to cells from AD patients as a model system may have utility in the treatment of asymptomatic AD patients.
Lorenzo Pasotti, Massimo Bellato, Michela Casanova, Susanna Zucca, Maria Gabriella Cusella De Angelis, and Paolo Magni
Springer Science and Business Media LLC
BackgroundThe study of simplified, ad-hoc constructed model systems can help to elucidate if quantitatively characterized biological parts can be effectively re-used in composite circuits to yield predictable functions. Synthetic systems designed from the bottom-up can enable the building of complex interconnected devices via rational approach, supported by mathematical modelling. However, such process is affected by different, usually non-modelled, unpredictability sources, like cell burden.MethodsHere, we analyzed a set of synthetic transcriptional cascades in Escherichia coli. We aimed to test the predictive power of a simple Hill function activation/repression model (no-burden model, NBM) and of a recently proposed model, including Hill functions and the modulation of proteins expression by cell load (burden model, BM). To test the bottom-up approach, the circuit collection was divided into training and test sets, used to learn individual component functions and test the predicted output of interconnected circuits, respectively.ResultsAmong the constructed configurations, two test set circuits showed unexpected logic behaviour. Both NBM and BM were able to predict the quantitative output of interconnected devices with expected behaviour, but only the BM was also able to predict the output of one circuit with unexpected behaviour. Moreover, considering training and test set data together, the BM captures circuits output with higher accuracy than the NBM, which is unable to capture the experimental output exhibited by some of the circuits even qualitatively. Finally, resource usage parameters, estimated via BM, guided the successful construction of new corrected variants of the two circuits showing unexpected behaviour.ConclusionsSuperior descriptive and predictive capabilities were achieved considering resource limitation modelling, but further efforts are needed to improve the accuracy of models for biological engineering.
Lorenzo Pasotti, Susanna Zucca, Michela Casanova, Giuseppina Micoli, Maria Gabriella Cusella De Angelis, and Paolo Magni
Springer Science and Business Media LLC
BackgroundWhey permeate is a lactose-rich effluent remaining after protein extraction from milk-resulting cheese whey, an abundant dairy waste. The lactose to ethanol fermentation can complete whey valorization chain by decreasing dairy waste polluting potential, due to its nutritional load, and producing a biofuel from renewable source at the same time. Wild type and engineered microorganisms have been proposed as fermentation biocatalysts. However, they present different drawbacks (e.g., nutritional supplements requirement, high transcriptional demand of recombinant genes, precise oxygen level, and substrate inhibition) which limit the industrial attractiveness of such conversion process. In this work, we aim to engineer a new bacterial biocatalyst, specific for dairy waste fermentation.ResultsWe metabolically engineered eight Escherichia coli strains via a new expression plasmid with the pyruvate-to-ethanol conversion genes, and we carried out the selection of the best strain among the candidates, in terms of growth in permeate, lactose consumption and ethanol formation. We finally showed that the selected engineered microbe (W strain) is able to efficiently ferment permeate and concentrated permeate, without nutritional supplements, in pH-controlled bioreactor. In the conditions tested in this work, the selected biocatalyst could complete the fermentation of permeate and concentrated permeate in about 50 and 85 h on average, producing up to 17 and 40 g/l of ethanol, respectively.ConclusionsTo our knowledge, this is the first report showing efficient ethanol production from the lactose contained in whey permeate with engineered E. coli. The selected strain is amenable to further metabolic optimization and represents an advance towards efficient biofuel production from industrial waste stream.
Susanna Zucca, Margherita Villaraggia, Stella Gagliardi, Gaetano Salvatore Grieco, Marialuisa Valente, Cristina Cereda, and Paolo Magni
Springer Science and Business Media LLC
BackgroundAmplicon-based targeted resequencing is a commonly adopted solution for next-generation sequencing applications focused on specific genomic regions. The reliability of such approaches rests on the high specificity and deep coverage, although sequencing artifacts attributable to PCR-like amplification can be encountered. Between these artifacts, allele drop-out, which is the preferential amplification of one allele, causes an artificial increase in homozygosity when heterozygous mutations fall on a primer pairing region.Here, a procedure to manage such artifacts, based on a pipeline composed of two steps of alignment and variant calling, is proposed. This methodology has been compared to the Illumina Custom Amplicon workflow, available on Illumina MiSeq, on the analysis of data obtained with four newly designed TruSeq Custom Amplicon gene panels.ResultsFour gene panels, specific for Parkinson disease, for Intracerebral Hemorrhage Diseases (COL4A1 and COL4A2 genes) and for Familial Hemiplegic Migraine (CACNA1A and ATP1A2 genes) were designed.A total of 119 samples were re-sequenced with Illumina MiSeq sequencer and panel characterization in terms of coverage, number of variants found and allele drop-out potential impact has been carried out. Results show that 14 % of identified variants is potentially affected by allele drop-out artifacts and that both the Custom Amplicon workflow and the procedure proposed here could correctly identify them.Furthermore, a more complex configuration in presence of two mutations was simulated in silico. In this configuration, our proposed methodology outperforms Custom Amplicon workflow, being able to correctly identify two mutations in all the studied configurations.ConclusionsAllele drop-out plays a crucial role in amplicon-based targeted re-sequencing and specific procedures in data analysis of amplicon data should be adopted. Although a consensus has been established in the elimination of primer sequences from aligned data (e.g., via primer sequence trimming or soft clipping), more complex configurations need to be managed in order to increase the retrieved information from available data. Our method shows how to manage one of these complex configurations, when two mutations occur.
Lucia Bandiera, Alice Pasini, Lorenzo Pasotti, Susanna Zucca, Giuliano Mazzini, Paolo Magni, Emanuele Giordano, and Simone Furini
Elsevier BV
The small number of molecules, unevenly distributed within an isogenic cell population, makes gene expression a noisy process, and strategies have evolved to deal with this variability in protein concentration and to limit its impact on cellular behaviors. As translational efficiency has a major impact on biological noise, a possible strategy to control noise is to regulate gene expression processes at the post-transcriptional level. In this study, fluctuations in the concentration of a green fluorescent protein were compared, at the single cell level, upon transformation of an isogenic bacterial cell population with synthetic gene circuits implementing either a transcriptional or a post-transcriptional control of gene expression. Experimental measurements showed that protein variability is lower under post-transcriptional control, when the same average protein concentrations are compared. This effect is well reproduced by stochastic simulations, supporting the hypothesis that noise reduction is due to the control mechanism acting on the efficiency of translation. Similar strategies are likely to play a role in noise reduction in natural systems and to be useful for controlling noise in synthetic biology applications.
Michela Casanova, Lorenzo Pasotti, Susanna Zucca, Nicolò Politi, Ilaria Massaiu, Cinzia Calvio, Maria Gabriella Cusella De Angelis, and Paolo Magni
Springer Science and Business Media LLC
BackgroundCircular plasmid-mediated homologous recombination is commonly used for marker-less allelic replacement, exploiting the endogenous recombination machinery of the host. Common limitations of existing methods include high false positive rates due to mutations in counter-selection genes, and limited applicability to specific strains or growth media. Finally, solutions compatible with physical standards, such as the BioBrick™, are not currently available, although they proved to be successful in the design of other replicative or integrative plasmids.FindingsWe illustrate pBBknock, a novel BioBrick™-compatible vector for allelic replacement in Escherichia coli. It includes a temperature-sensitive replication origin and enables marker-less genome engineering via two homologous recombination events. Chloramphenicol resistance allows positive selection of clones after the first event, whereas a colorimetric assay based on the xylE gene provides a simple way to screen clones in which the second recombination event occurs. Here we successfully use pBBknock to delete the lactate dehydrogenase gene in E. coli W, a popular host used in metabolic engineering.ConclusionsCompared with other plasmid-based solutions, pBBknock has a broader application range, not being limited to specific strains or media. We expect that pBBknock will represent a versatile solution both for practitioners, also among the iGEM competition teams, and for research laboratories that use BioBrick™-based assembly procedures.
Lorenzo Pasotti, Susanna Zucca, Michela Casanova, Nicolo' Politi, Ilaria Massaiu, Giuliano Mazzini, Giuseppina Micoli, Cinzia Calvio, Maria Gabriella Cusella De Angelis, and Paolo Magni
IEEE
Whey is an abundant by-product of cheese production process and it is considered a special waste due to its high nutritional load and hypertrophic potential. Technologies for whey valorization are available. They can convert such waste into high-value products, like whey proteins. However, the remaining liquid (called permeate) is still considered as a polluting waste due to its high lactose concentration. The alcoholic fermentation of lactose into ethanol will simultaneously achieve two important goals: safe disposal of a pollutant waste and green energy production. This methodology paper illustrates the workflow carried out to design and realize an optimized microorganism that can efficiently perform the lactose-to-ethanol conversion, engineered via synthetic biology experimental and computational approaches.
Ilaria Massaiu, Lorenzo Pasotti, Michela Casanova, Nicolò Politi, Susanna Zucca, Maria Gabriella Cusella De Angelis, and Paolo Magni
Springer Science and Business Media LLC
Small RNAs (sRNAs) are genetic tools for the efficient and specific tuning of target genes expression in bacteria. Inspired by naturally occurring sRNAs, recent works proposed the use of artificial sRNAs in synthetic biology for predictable repression of the desired genes. Their potential was demonstrated in several application fields, such as metabolic engineering and bacterial physiology studies. Guidelines for the rational design of novel sRNAs have been recently proposed. According to these guidelines, in this work synthetic sRNAs were designed, constructed and quantitatively characterized in Escherichia coli. An sRNA targeting the reporter gene RFP was tested by measuring the specific gene silencing when RFP was expressed at different transcription levels, under the control of different promoters, in different strains, and in single-gene or operon architecture. The sRNA level was tuned by using plasmids maintained at different copy numbers. Results demonstrated that RFP silencing worked as expected in an sRNA and mRNA expression-dependent fashion. A mathematical model was used to support sRNA characterization and to estimate an efficiency-related parameter that can be used to compare the performance of the designed sRNA. Gene silencing was also successful when RFP was placed in a two-gene synthetic operon, while the non-target gene (GFP) in the operon was not considerably affected. Finally, silencing was evaluated for another designed sRNA targeting the endogenous lactate dehydrogenase gene. The quantitative study performed in this work elucidated interesting performance-related and context-dependent features of synthetic sRNAs that will strongly support predictable gene silencing in disparate basic or applied research studies.
Nicolò Politi, Lorenzo Pasotti, Susanna Zucca, and Paolo Magni
Springer Science and Business Media LLC
BackgroundThe interconnection of quantitatively characterized biological devices may lead to composite systems with apparently unpredictable behaviour. Context-dependent variability of biological parts has been investigated in several studies, measuring its entity and identifying the factors contributing to variability. Such studies rely on the experimental analysis of model systems, by quantifying reporter genes via population or single-cell approaches. However, cell-to-cell variability is not commonly included in predictability analyses, thus relying on predictive models trained and tested on central tendency values. This work aims to study in silico the effects of cell-to-cell variability on the population-averaged output of interconnected biological circuits.MethodsThe steady-state deterministic transfer function of individual devices was described by Hill equations and lognormal synthetic noise was applied to their output. Two- and three-module networks were studied, where individual devices implemented inducible/repressible functions. The single-cell output of such networks was simulated as a function of noise entity; their population-averaged output was computed and used to investigate the expected variability in transfer function identification. The study was extended by testing different noise models, module logic, intrinsic/extrinsic noise proportions and network configurations.ResultsFirst, the transfer function of an individual module was identified from simulated data of a two-module network. The estimated parameter variability among different noise entities was limited (14%), while a larger difference was observed (up to 62%) when estimated and true parameters were compared. Thus, low-variability parameter estimates can be obtained for different noise entities, although deviating from the true parameters, whose measurement requires noise knowledge. Second, the black-box input-output function of a two/three-module network was predicted from the knowledge of the transfer function of individual modules, identified in the presence of noise. Estimates variability was low (16%); however, differences up to 68% were observed by simulating a typical experimental study where the predictions obtained above were compared to network outputs generated in the presence of noise. Network predictions can, thus, deviate from real outputs when modules are characterized and re-used in different noise contexts.ConclusionsThe adopted approach can support predictability studies in synthetic biology by distinguishing between actual unpredictability and contribution of noise and by guiding researchers in the design of suitable experimental measurement for gene networks.