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Carrella, D.

Publications and source records attributed to Carrella, D..

3 recordsLinked to original sources

Functional specialization of the hippocampus along its dorso-ventral axis: from span to supraspan in working memory capacity

Working memory (WM) is characterized by a limited capacity or span, beyond which performance deteriorates, the supraspan condition. Human neuropsychology and neuroimaging studies have implicated the medial temporal lobe (MTL), including the hippocampus (HPC), in WM particularly when memory load exceeds span, yet no animal model has been established to investigate supraspan mechanisms. Here, we present a novel rodent model of supraspan memory and examine the distinct roles of dorsal and ventral HPC. Using selective NMDA lesions in mice combined with object recognition tasks and a modified radial arm maze (RAM), we manipulated memory load and strategy constraints to assess span and supraspan performance. Dorsal HPC lesions impaired object memory span, while ventral lesions spared this ability. In the RAM, span performance (number of correct arm visits before the first error) was unaffected by either lesion under allocentric conditions. However, once animals exceeded span, following the first error, here defined as the supraspan condition, both dorsal and ventral lesions caused catastrophic disorientation and error escalation. Under free-choice conditions, ventral HPC lesions selectively reduced the occurrence of trials with no errors in the first 5 choices, increased errors overall, and disrupted sequential egocentric strategies. Together, these results demonstrate that supraspan performance can be modeled in rodents, revealing a dorsal contribution to object memory under high load, a ventral contribution to egocentric spatial strategies, and a shared hippocampal role in supraspan memory performance. This paradigm provides a translational framework for investigating supraspan deficits observed in MTL patients and Alzheimers disease.

neuroscience↗

gene2drug: a Computational Tool for Pathway-based Rational Drug Repositioning

MotivationDrug repositioning has been proposed as an effective shortcut to drug discovery. The availability of large collections of transcriptional responses to drugs enables computational approaches to drug repositioning directly based on measured molecular effects.\n\nResultsWe introduce a novel computational methodology for rational drug repositioning, which exploits the transcriptional responses following treatment with small molecule. Specifically, given a therapeutic target gene, a prioritisation of potential effective drugs is obtained by assessing their impact on the transcription of genes in the pathway(s) including the target. We performed in silico validation and comparison with a state-of-art technique based on similar principles. We next performed experimental validation in two different real-case drug repositioning scenarios: (i) upregulation of the glutamate-pyruvate transaminase, which has been shown to induce reduction of oxalate levels in a mouse model of primary hyperoxaluria, and (ii) activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy, whose modulation may be beneficial in neurodegenerative disorders.\n\nAvailabilityfree at http://gene2drug.tigem.it\n\nContactdibernardo@tigem.it

bioinformatics↗

Integrated Structure-Transcription analysis of small molecules reveals widespread noise in drug-induced transcriptional responses and a transcriptional signature for drug-induced phospholipidosis

We performed an integrated analysis of drug chemical structures and drug-induced transcriptional responses. We demonstrated that a network representing 3D structural similarities among 5,452 compounds can be used to automatically group together drugs with similar scaffolds and mode-of-action. We then compared the structural network to a network representing transcriptional similarities among a subset of 1,309 drugs for which transcriptional response were available in the Connectivity Map dataset. Analysis of structurally similar, but transcriptionally different, drugs sharing the same mode of action (MOA) enabled us to detect and remove weak and noisy transcriptional responses, greatly enhancing the reliability and usefulness of transcription-based approaches to drug discovery and drug repositioning. Analysis of transcriptionally similar, but structurally different drugs with unrelated MOA, led us to the identification of a \"toxic\" transcriptional signature indicative of lysosomal stress (lysosomotropism) and lipid accumulation (phospholipidosis) partially masking the target-specific transcriptional effects of these drugs. We further demonstrated by High Content Screening that this transcriptional signature is caused by the activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy. Our results show that chemical structures and transcriptional profiles provide complementary information and that combined analysis can lead to new insights on on- and off-target effects of small molecules.

bioinformatics↗