Biology subjects

Mihaljevic, B.

Publications and source records attributed to Mihaljevic, B..

3 recordsLinked to original sources

bioRxiv2020

Comparing the electophysiology and morphology ofhuman and mouse layer 2/3 pyramidal neurons withBayesian networks

Pyramidal neurons are the most common neurons in the cerebral cortex. Understanding how they differ between species is a key challenge in neuroscience. We compared human temporal cortex and mouse visual cortex pyramidal neurons from the Allen Cell Types Database in terms of their electrophysiology and basal dendrites morphology. We found that, among other differences, human pyramidal neurons had a higher threshold voltage, a lower input resistance, and a larger basal dendritic arbor. We learned Gaussian Bayesian networks from the data in order to identify correlations and conditional independencies between the variables and compare them between the species. We found strong correlations between electrophysiological and morphological variables in both species. One result is that, in human cells, dendritic arbor width had the strongest effect on input resistance after accounting for the remaining variables. Electrophysiological variables were correlated, in both species, even with morphological variables that are not directly related to dendritic arbor size or diameter, such as mean bifurcation angle and mean branch tortuosity. Contrary to previous results, cortical depth was correlated with both electrophysiological and morphological variables, and its effect on electrophysiological could not be explained in terms of the morphological variables. Overall, the correlations among the variables differed strikingly between human and mouse neurons. Besides identifying correlations and conditional independencies, the learned Bayesian networks might be useful for probabilistic reasoning regarding the morphology and electrophysiology of pyramidal neurons.

neuroscience↗

bioRxiv2020

Increased p53 signaling impairs neural differentiation causing HUWE1-promoted intellectual disabilities

Essential E3 ubiquitin ligase HUWE1 (HECT, UBA and WWE domain containing 1) regulates key factors, as p53. Mutations in HUWE1 have been associated with neurodevelopmental X-linked intellectual disabilities (XLIDs), however the pathomechanism at the onset of heterogenous XLIDs remains unknown. In this work, we identify p53 signaling as the process hyperactivated in lymphoblastoid cells from patients with HUWE1-promoted XLIDs. The hiPSCs-based modeling of the severe HUWE1-promoted XLID, the Juberg Marsidi syndrome (JMS), reviled majorly impaired neural differentiation, accompanied by increased p53 signaling. The impaired differentiation results in loss of cortical patterning and overall undergrowth of XLID JMS patient-specific cerebral organoids, thus closely recapitulating key symptoms, as microcephaly. Importantly, the neurodevelopmental potential of JMS hiPSCs is successfully rescued by restoring p53 signaling, upon reduction of p53 levels. In summary, our findings indicate that increased p53 signaling leads to impaired neural differentiation and is the common cause of neurodevelopmental HUWE1-promoted XLIDs.

cell biology↗

bioRxiv2020

Comparing basal dendrite branches in human and mouse hippocampal CA1 pyramidal neurons with Bayesian networks

Pyramidal neurons are the most common cell type in the cerebral cortex. Understanding how they differ between species is a key challenge in neuroscience. A recent study provided a unique set of human and mouse pyramidal neurons of the CA1 region of the hippocampus, and used it to compare the morphology of apical and basal dendritic branches of the two species. The study found inter-species differences in the magnitude of the morphometrics and similarities regarding their variation with respect to morphological determinants such as branch type and branch order. We use the same data set to perform additional comparisons of basal dendrites. In order to isolate the heterogeneity due to intrinsic differences between species from the heterogeneity due to differences in morphological determinants, we fit multivariate models over the morphometrics and the determinants. In particular, we use conditional linear Gaussian Bayesian networks, which provide a concise graphical representation of the independencies and correlations among the variables. We also extend the previous study by considering additional morphometrics and by formally testing test whether a morphometric increases or decreases with the distance from the soma. This study introduces a multivariate methodology for inter-species comparison of morphology.

neuroscience↗