Biology subjects

Ceccarani, C.

Publications and source records attributed to Ceccarani, C..

3 recordsLinked to original sources

bioRxiv2023

Gut microbiota profile in CDKL5 deficiency disorder patients as a potential marker of clinical severity

CDKL5 deficiency disorder (CDD) is a neurodevelopmental condition characterized by global developmental delay, early-onset seizures, intellectual disability, visual and motor impairments. Unlike Rett Syndrome (RTT), CDD lacks a clear regression period. CDD patients frequently encounter gastrointestinal (GI) disturbances and exhibit signs of subclinical immune dysregulation. However, the underlying causes of these conditions remain elusive. Emerging studies indicate a potential connection between neurological disorders and gut microbiota, an area completely unexplored in CDD. We conducted a pioneering study, analyzing fecal microbiota composition in CDD patients and their healthy relatives. Notably, differences in intestinal bacterial diversity and composition were identified in CDD patients. We further investigated microbiota changes based on the severity of GI issues, seizure frequency, sleep disorders, food intake type, impairment in neuro-behavioral features (assessed through the RTT Behaviour Questionnaire - RSBQ), and ambulation capacity. Our findings hint at a potential connection between CDD, microbiota, and symptom severity. This study marks the first exploration of the gut-microbiota-brain axis in CDD patients. It adds to the growing body of research emphasizing the role of the gut microbiota in neurodevelopmental disorders and opens doors to potential interventions that target intestinal microbes with the aim of improving the lives of CDD patients.

microbiology↗

bioRxiv2023

A comparison between Greengenes, SILVA, RDP, and NCBI reference databases in four published microbiota datasets

Inaccurate bacterial taxonomic assignment in 16S-based microbiota experiments could have deleterious effects on research results, as all downstream analyses heavily rely on the accurate assessment of microbial taxonomy: a bias in the choice of the reference database can deeply alter microbiota biodiversity (alpha-diversity), composition (beta-diversity), and taxa profile (bacterial relative abundances). In this paper, we explored the influence of the reference 16S rRNA collection by performing a classification against four of the main databases used by the scientific community (i.e. Greengenes, SILVA, RDP, NCBI); the consequences of database clustering at 97% were also explored. To investigate the effects of the database choice on real and representative microbiome samples from different ecosystems, we performed a comparative analysis on four already published datasets from various sources: stools from a mouse model experiment, bovine milk, human gut microbiota stool samples, and swabs from the human vaginal environment. We took into consideration the computational time needed to perform the taxonomic classification as well. Although values in both alpha- and beta-diversity varied a lot, sometimes even statistically, according to the dataset chosen and the eventual clustering, the final outcome of the analysis was a concordance in the capability to retrieve the original experimental group differences over the various datasets. However, in the taxonomy classification, we found several inconsistencies with taxonomies correctly assigned in only some of the four databases. The degree of concordance among the databases was related to both the complexity of the environment and its degree of completeness in the reference databases. IMPORTANCE16S rRNA sequencing is, nowadays, the most commonly used strategy for microbiota profiling in many different ecosystems, ranging from human-associated to animal models, food matrices, and environmental samples. The ability of this kind of analysis to correctly capture differences in the microbiota composition is related to the taxonomic classification of the fragments obtained from sequencing and, thus, to the choice of the best reference database. This paper deals with four of the most popular microbial databases, which were evaluated in their ability to reproduce the experimental evidence from four already published datasets. The knowledge of the advantages and drawbacks of the database choice can be pivotal for planning future experiments in the field, making researchers aware of the repercussions of such a choice according to the different environments under scrutiny. Moreover, this work can also shed new light upon past results, partially explaining discordant evidence.

bioinformatics↗

bioRxiv2020

Exogenous and endogenous HDAC inhibitor effects in Rubinstein-Taybi syndrome models

Rubinstein-Taybi syndrome (RSTS) is an autosomal dominant disorder with specific clinical signs and neurodevelopmental impairment. The two known proteins altered in the majority of RSTS patients are the histone acetylation regulators CBP and p300. For assessing possible ameliorative effects of exogenous and endogenous HDAC inhibitors (HDACi), we exploited in vivo and in vitro RSTS models. First, HDACi effects were tested on Drosophila melanogaster, showing molecular rescue. In the same model, we observed a shift in gut microbiota composition. We then studied HDACi effects in RSTS cell lines compared to healthy donor cells. We observed patients-specific molecular rescue of acetylation defects at subtoxic concentrations. Finally, we assessed commensal gut microbiota composition in a cohort of RSTS patients compared to healthy siblings. Intriguingly, we observed a significant depletion in butyrate-producing bacteria in RSTS patients. In conclusion, this study reports the possibility of modulating acetylation equilibrium by HDACi treatments and the importance of microbiota composition in a chromatinopathy.

genetics↗