Biology subjects

Sadacca, B.

Publications and source records attributed to Sadacca, B..

2 recordsLinked to original sources

bioRxiv2025

Neural correlates in basolateral and central amygdala during reward seeking in the face of punishment

The inability to suppress actions despite adverse consequences is a hallmark of compulsive behaviors and is amygdala dependent. To study the amygdalas role in responding despite adverse consequences, we compared single-unit activity in the basolateral (BLA) or central (CeA) amygdala before and after reward-seeking under punishment threat. Rats started each trial by pressing an initial lever, which triggered an outcome-specific 5-s auditory cue (white noise, pure tone, or clicker), signaling one of three reinforcement conditions: 100% sucrose reward, 20% reward/80% omission, or 80% reward/20% footshock punishment. Next, the active and inactive levers were extended, and pressing on the active lever terminated the auditory cue and triggered an outcome-specific 1-s visual cue predicting either reward, reward omission, or shock. After training, we implanted eight tetrodes in the BLA (n=7) or CeA (n=5) and recorded single-unit activity of [~]100 neurons per region during task performance. CeA neurons, and to a lesser extent BLA neurons, responded differently to the distinct auditory cues and outcomes. The discrimination between conditions partially explained the capacity of neuronal activity to predict the latency to lever press to complete the trial. The failure to suppress reward-seeking behavior in the face of punishment coincided with the reactivation of reward-seeking-sensitive and the loss of inhibition of punishment-sensitive neuronal populations. In conclusion, we found that after extended training, opposing populations of activated and inhibited neurons in CeA, and to a lesser extent in BLA, control completion of reward-seeking despite punishment.

neuroscience↗

bioRxiv2018

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications.\n\nIn the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables.\n\nIn conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.

bioinformatics↗