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Colombo, R.

Publications and source records attributed to Colombo, R..

6 recordsLinked to original sources

Tracing the origin of Finnish gelsolin amyloidosis using haplotype sharing trees

Finnish gelsolin amyloidosis (AGel amyloidosis) is an autosomal dominant systemic amyloidosis caused by GSN c.640G>A p.D187N (rs121909715) founder variant. The disease was first described in 1969, and it was hypothesized that the Finnish patients share a common ancestor dating back to the 14th century. The link between two Finnish regions with high AGel incidence (Kanta-Hame and Kymenlaakso) has been hypothesized to have occurred in 1365 by a settler moving from Kanta-Hame to Kymenlaakso. Here, we used haplotype sharing tree (HST) to analyze Finnish AGel amyloidosis haplotypes to trace the geographic origin of the variant. We also estimated the time from the most recent common ancestor (MRCA) using single nucleotide polymorphism and short tandem repeat data. The HST -based analyses leveraging AGel amyloidosis cohorts from different Finnish geographic regions indicated, that the variant more likely appeared first in Kymenlaakso, not Kanta-Hame, contrary to the original hypothesis. The MRCA estimates for Finnish AGel ranged from 15 to 40 generations using four different methods, the mean of all estimates (27 generations) dated back to the 14th century. Thus, the data supports the original hypothesis on the variants spreading temporally, but not geographically. These results illustrate the use of HSTs in the analysis of haplotype structures and in tracing the ancestry of a founder variant.

genetics↗

Effects of transcranial photobiomodulation on peripheral biomarkers associated with oxidative stress and complex IV activity in the prefrontal cortex in rats subjected to chronic mild stress

BackgroundThis study addresses the urgent need for effective alternatives to treat major depressive disorder (MDD) in patients who do not respond to conventional therapies Transcranial photobiomodulation therapy (tPBM) shows promise by enhancing mitochondrial function and reducing oxidative stress, as demonstrated in the chronic mild stress (CMS) rat model. To analyze the impact of tPBM with two wavelengths (red and infrared) on behavioral and biological parameters related to MDD in a CMS model. MethodsMale Wistar rats were subjected to CMS for five weeks and categorized into resilient (CMS-R) and susceptible (CMS-S) groups using the sucrose consumption test (SCT). The CMS-S group received tPBM treatment (600nm and 840nm) for five weeks. Biological measures included lipid damage (TBARS), antioxidant defense (TEAC), mitochondrial complex IV activity (CCO), and nitric oxide (NO) concentration in the prefrontal cortex and blood were measured. ResultsAs expected, post-tPBM treatment (both red and infrared groups) exhibited increased sucrose consumption compared to the sham (Kruskal Wallis chi-squared=26.131; p<0.001). The red and infrared presented higher serum TEAC levels than the sham and control groups, but these effects did not reach statistical significance (p=0.306). In contrast, the red group showed lower peripheral TBARS levels (M = 9.50, SD = 2.87) than the sham group (M = 13.66, SD = 2.20) (p=0.0048); such effect was similar to the control non-stress group. The infrared group showed higher NO levels within the hippocampus than the sham group (Mean = 107.83; SD = 6.48, Dunn Test p = 0.0134) and higher prefrontal CCO activity levels than the red group (p=0.012), which was similar to the control non-stress group. ConclusionsOur study demonstrated that tPBM using both red and infrared wavelengths significantly improved behavioral and biological parameters in the chronic mild stress (CMS) rat model. In particular, tPBM may offer therapeutic benefits by ameliorating oxidative stress and enhancing mitochondrial function, thereby presenting a promising alternative for the management of MDD.

neuroscience↗

Post-vaccination Omicron infections induce broader immunity across antigenic space than prototype mRNA COVID-19 booster vaccination or primary infection

The rapid emergence of new SARS-CoV-2 variants challenges vaccination strategies. Here, we measured antigenic diversity among variants and interpreted neutralizing antibody responses following single and multiple exposures in longitudinal infection and vaccine cohorts. Antigenic cartography using primary infection antisera showed that BA.2, BA.4/BA.5, and BA.2.12.1 are distinct from BA.1 and closer to the Beta cluster. Three doses of an mRNA COVID-19 vaccine increased breadth to BA.1 more than to BA.4/BA.5 or BA.2.12.1. Omicron BA.1 post-vaccination infection elicited antibody landscapes characterized by broader immunity across antigenic space than three doses alone, although with less breadth than expected to BA.2.12.1 and BA.4/BA.5. Those with Omicron BA.1 infection after two or three vaccinations had similar neutralizing titer magnitude and antigenic breadth. Accounting for antigenic differences among variants of concern when interpreting neutralizing antibody titers aids understanding of complex patterns in humoral immunity and informs selection of future COVID-19 vaccine strains.

microbiology↗

Severity of SARS-CoV-2 infection as a function of the interferon landscape across the respiratory tract of COVID-19 patients.

The COVID-19 outbreak driven by SARS-CoV-2 has caused more than 2.5 million deaths globally, with the most severe cases characterized by over-exuberant production of immune-mediators, the nature of which is not fully understood. Interferons of the type I (IFN-I) or type III (IFN-III) families are potent antivirals, but their role in COVID-19 remains debated. Our analysis of gene and protein expression along the respiratory tract shows that IFNs, especially IFN-III, are over-represented in the lower airways of patients with severe COVID-19, while high levels of IFN-III, and to a lesser extent IFN-I, characterize the upper airways of patients with high viral burden but reduced disease risk or severity; also, IFN expression varies with abundance of the cell types that produce them. Our data point to a dynamic process of inter- and intra-family production of IFNs in COVID-19, and suggest that IFNs play opposing roles at distinct anatomical sites.

immunology↗

Neutrophil extracellular traps induce the epithelial-mesenchymal transition: implications in post-COVID-19 fibrosis

The release of neutrophil extracellular traps (NETs), a process termed NETosis, avoids pathogen spread but may cause tissue injury. NETs have been found in severe COVID-19 patients, but their role in disease development is still unknown. The aim of this study is to assess the capacity of NETs to drive epithelial-mesenchymal transition (EMT) of lung epithelial cells and to analyze the involvement of NETs in COVID-19. Neutrophils activated with PMA (PMA-Neu), a stimulus known to induce NETs formation, induce both EMT and cell death in the lung epithelial cell line, A549. Notably, NETs isolated from PMA-Neu induce EMT without cell damage. Bronchoalveolar lavage fluid of severe COVID-19 patients showed high concentration of NETs. Thus, we tested in an in vitro alveolar model the hypothesis that virus-induced NET may drive EMT. Co-culturing A549 at air-liquid interface with alveolar macrophages, neutrophils and SARS-CoV2, we demonstrated a significant induction of the EMT in A549 together with high concentration of NETs, IL8 and IL1{beta}, best-known inducers of NETosis. Lung tissues of COVID-19 deceased patients showed that epithelial cells are characterized by increased mesenchymal markers. These results show for the first time that NETosis plays a major role in triggering lung fibrosis in COVID-19 patients.

immunology↗

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↗