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Tortarolo, D.

Publications and source records attributed to Tortarolo, D..

4 recordsLinked to original sources

The cerebrospinal fluid virome in people with HIV: links to neuroinflammation and cognition

Despite effective HIV suppression, neuroinflammation and neurocognitive issues are prevalent in people with HIV (PWH) yet poorly understood. HIV infection alters the human virome, and virome perturbations have been linked to neurocognitive issues in people without HIV. Once thought to be sterile, the cerebrospinal fluid (CSF) hosts a recently discovered virome, presenting an unexplored avenue for understanding brain and mental health in PWH. This cross-sectional study analyzed 85 CSF samples (74 from PWH on suppressive antiretroviral therapy, and 11 from controls without HIV, CWH) through shotgun metagenomics for DNA/RNA viruses. Taxonomic composition (reads and contigs), and {beta} diversity, and relative abundance (RA) of prokaryotic (PV), human eukaryotic (hEV), and non-human eukaryotic viruses (nhEV) were evaluated in relation to HIV infection, markers of neuroinflammation and neurodegeneration, cognitive functions, and depressive symptoms. Sensitivity analyses and post-hoc cluster analysis on the RA of viral groups and blood-brain barrier permeability were also performed. Of 46 read-positive CSF samples, 93.5% contained PV sequences, 47.8% hEV, and 45.6% nhEV. Alpha diversity was lower in PWH versus CWH, although p>0.05. At {beta} diversity analysis, HIV status explained 3.3% of the variation in viral composition (p=0.016). Contigs retained 13 samples positive for 8 hEV, 2 nhEV, and 6 PV. Higher RA of PV was correlated with higher CSF S100{beta} (p=0.002) and {beta}-Amyloid 1-42 fragment ({beta}A-42, p=0.026), while higher RA of nhEV with poorer cognitive performance (p=0.022). Conversely, higher RA of hEV correlated with better cognition (p=0.003) and lower {beta}A-42 (p=0.012). Sensitivity analyses in virome-positive samples only confirmed these findings. Three CSF clusters were identified and showed differences in astrocytosis, {beta}A-42, tau protein, and cognitive functions. Participants with hEV-enriched CSF showed better cognitive performance compared to those with virus-devoid and nhEV-enriched CSF (modelsp<0.05). This study provides the first comprehensive description of the CSF virome in PWH, revealing associations with neuroinflammation and cognition. These findings highlight the potential involvement of the CSF virome in brain health and inform about its composition, origin, and potential clinical implications in people with and without HIV. Author SummaryHIV can affect brain health and mental well-being, even in people on successful antiretroviral therapy. The reasons behind this are still unclear. HIV also influences the communities of microbes and viruses living in the human body, and recent research suggests that the human virome, the collection of all viruses within the body, may play a role in cognitive functions, mood, and brain health. For a long time, scientists believed that the cerebrospinal fluid (CSF), which surrounds the brain, was sterile, while robust evidence has shown that the CSF hosts its own unique virome. Via advanced genetic sequencing (shotgun metagenomics), we analyzed the CSF virome in people with and without HIV looking for possible links to neuroinflammation, cognitive performance, and depression. We found that while HIV infection does affect the composition of CSF viral communities, there were no remarkable differences in the CSF virome of individuals with and without HIV. Most viral sequences appeared to come outside the brain. A higher abundance of non-human viral sequences, such as viruses of bacteria, plants, fungi, and animals, was associated with neuroinflammation and poorer cognitive performance. On the other hand, a higher abundance of human viruses correlated with better cognitive function and healthier signature of neuromarkers. These findings provide new insights into the presence and characteristics of the human CSF virome and how it might influence brain health. They also suggest new potential mechanisms of HIV-associated neuropathology.

microbiology↗

UnifiedGreatMod: A New Holistic Modeling Paradigm for Studying Biological Systems on a Complete and Harmonious Scale

MotivationComputational models are crucial for addressing critical questions about systems evolution and deciphering system connections. The pivotal feature of making this concept recognisable from the biological and clinical community is the possibility of quickly inspecting the whole system, bearing in mind the different granularity levels of its components. This holistic view of system behaviour expands the evolution study by identifying the heterogeneous behaviours applicable, for example, to the cancer evolution study. ResultsTo address this aspect, we propose a new modelling paradigm, UnifiedGreatMod, which allows modellers to integrate fine-grained and coarse-grained biological information into a unique model. It allows for functional studies, in which the understanding of the systems multi-level stable condition and the systems fluctuating condition are combined to investigate the functional dependencies among the biological entities in the system under study. This is achieved thanks to the hybridisation of two analysis approaches that capture a systems different granularity levels. The proposed paradigm was then implemented into the open-source, general modelling framework GreatMod, in which a graphical meta-formalism is exploited to simplify the model creation phase and R languages to define user-defined analysis workflows. The proposals effectiveness was demonstrated by mechanistically simulating the metabolic output of Echerichia coli under environmental nutrient perturbations and integrating a gene expression dataset. Additionally, the UnifiedGreatMod was used to examine the responses of luminal epithelial cells to Clostridium difficile infection.

systems biology↗

OmniReprodubileCellAnalysis: a comprehensive toolbox for the analysis of cellular biology data.

Open science and reproducibility are two key pillars of modern scientific research. Open science is making scientific research and data accessible and transparent to the broader scientific community and the public. Reproducibility, on the other hand, is the ability to replicate and confirm research results by following the same methods and procedures. Reproducibility is thus crucial because it ensures the reliability and validity of scientific findings. The relationship between open science and reproducibility is intertwined; indeed open science practices, such as sharing raw data, detailed methodologies, and code, greatly facilitate the reproducibility of research. In recent years, concerns about the reproducibility of scientific research have gained prominence, and indeed scientists still lament the lack of details in the methods sections of published papers and the unavailability of raw data from the authors. To assist cellular biologists and immunologists and to promote a more transparent, open and reproducible research practice, we developed OmniReproducibleCellAnalysis (ORCA), a new Shiny Application based in R, for the semi-automated analysis of Western Blot (WB), Reverse Transcription-quantitative PCR (RT-qPCR), Enzyme-Linked ImmunoSorbent Assay (ELISA), Endocytosis and Cytotoxicity experiments. ORCA is open-source and approachable by scientists without advanced R language knowledge. Our application automatically compiles a report containing the finalized data analysis and all its preliminary and intermediate steps, ensuring data analysis standardization and reproducibility. Furthermore, ORCA allows to upload raw data and results directly on the data repository Harvard Dataverse, a valuable tool for promoting transparency and data accessibility in scientific research. By employing ORCA, scientists will cut down analysis time and human-dependent errors, while taking a step towards a research practice compliant with Open Science and FAIR principle.

bioinformatics↗

CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.

The transition from the evaluation of a single time point to the examination of the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and data clustering challenging. Here we present CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft (PDX) growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. Through the lens of a new perspective of mechanism interpretation, CONNECTOR shed light onto novel model aggregations and identified unanticipated molecular associations with response to clinically approved therapies.

bioinformatics↗