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Hippocampal neurons' cytosolic and membrane-bound ribosomal transcript profiles are differentially regulated by learning and subsequent sleep.

ABSTRACT: The hippocampus is essential for consolidating transient experiences into long-lasting memories. Memory consolidation is facilitated by postlearning sleep, although the underlying cellular mechanisms are largely unknown. We took an unbiased approach to this question by using a mouse model of hippocampally mediated, sleep-dependent memory consolidation (contextual fear memory). Because synaptic plasticity is associated with changes to both neuronal cell membranes (e.g., receptors) and cytosol (e.g., cytoskeletal elements), we characterized how these cell compartments are affected by learning and subsequent sleep or sleep deprivation (SD). Translating ribosome affinity purification was used to profile ribosome-associated RNAs in different subcellular compartments (cytosol and membrane) and in different cell populations (whole hippocampus, Camk2a+ neurons, or highly active neurons with phosphorylated ribosomal subunit S6 [pS6+]). We examined how transcript profiles change as a function of sleep versus SD and prior learning (contextual fear conditioning; CFC). While sleep loss altered many cytosolic ribosomal transcripts, CFC altered almost none, and CFC-driven changes were occluded by subsequent SD. In striking contrast, SD altered few transcripts on membrane-bound (MB) ribosomes, while learning altered many more (including long non-coding RNAs [lncRNAs]). The cellular pathways most affected by CFC were involved in structural remodeling. Comparisons of post-CFC MB transcript profiles between sleeping and SD mice implicated changes in cellular metabolism in Camk2a+ neurons and protein synthesis in highly active pS6+ (putative "engram") neurons as biological processes disrupted by SD. These findings provide insights into how learning affects hippocampal neurons and suggest that the effects of SD on memory consolidation are cell type and subcellular compartment specific.

SUBMITTER: Delorme J

PROVIDER: S-EPMC8640746 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe vast quantities of gene expression profiling data produced in microarray studies, and the more precise quantitative PCR, are often not statistically analysed to their full potential. Previous studies have summarised gene expression profiles using simple descriptive statistics, basic analysis of variance (ANOVA) and the clustering of genes based on simple models fitted to their expression profiles over time. We report the novel application of statistical non-linear regression modelling techniques to describe the shapes of expression profiles for the fungus Agaricus bisporus, quantified by PCR, and for E. coli and Rattus norvegicus, using microarray technology. The use of parametric non-linear regression models provides a more precise description of expression profiles, reducing the "noise" of the raw data to produce a clear "signal" given by the fitted curve, and describing each profile with a small number of biologically interpretable parameters. This approach then allows the direct comparison and clustering of the shapes of response patterns between genes and potentially enables a greater exploration and interpretation of the biological processes driving gene expression.ResultsQuantitative reverse transcriptase PCR-derived time-course data of genes were modelled. "Split-line" or "broken-stick" regression identified the initial time of gene up-regulation, enabling the classification of genes into those with primary and secondary responses. Five-day profiles were modelled using the biologically-oriented, critical exponential curve, y(t) = A + (B + Ct)Rt + epsilon. This non-linear regression approach allowed the expression patterns for different genes to be compared in terms of curve shape, time of maximal transcript level and the decline and asymptotic response levels. Three distinct regulatory patterns were identified for the five genes studied. Applying the regression modelling approach to microarray-derived time course data allowed 11% of the Escherichia coli features to be fitted by an exponential function, and 25% of the Rattus norvegicus features could be described by the critical exponential model, all with statistical significance of p < 0.05.ConclusionThe statistical non-linear regression approaches presented in this study provide detailed biologically oriented descriptions of individual gene expression profiles, using biologically variable data to generate a set of defining parameters. These approaches have application to the modelling and greater interpretation of profiles obtained across a wide range of platforms, such as microarrays. Through careful choice of appropriate model forms, such statistical regression approaches allow an improved comparison of gene expression profiles, and may provide an approach for the greater understanding of common regulatory mechanisms between genes.

Project description:The molecular mechanisms of iron trafficking in neurons have not been elucidated. In this study, we characterized the expression and localization of ferrous iron transporters Zip8, Zip14 and divalent metal transporter 1 (DMT1), and ferrireductases Steap2 and stromal cell-derived receptor 2 in primary rat hippocampal neurons. Steap2 and Zip8 partially co-localize, indicating these two proteins may function in Fe(3+) reduction prior to Fe(2+) permeation. Zip8, DMT1, and Steap2 co-localize with the transferrin receptor/transferrin complex, suggesting they may be involved in transferrin receptor/transferrin-mediated iron assimilation. In brain interstitial fluid, transferring-bound iron (TBI) and non-transferrin-bound iron (NTBI) exist as potential iron sources. Primary hippocampal neurons exhibit significant iron uptake from TBI (Transferrin-(59) Fe(3+)) and NTBI, whether presented as (59) Fe(2+) -citrate or (59) Fe(3+) -citrate; reductase-independent (59) Fe(2+) uptake was the most efficient uptake pathway of the three. Kinetic analysis of Zn(2+) inhibition of Fe(2+) uptake indicated that DMT1 plays only a minor role in the uptake of NTBI. In contrast, localization and knockdown data indicate that Zip8 makes a major contribution. Data suggest also that cell accumulation of (59) Fe from TBI relies at least in part on an endocytosis-independent pathway. These data suggest that Zip8 and Steap2 play a major role in iron accumulation from NTBI and TBI by hippocampal neurons. Analysis of the expression and localization of known iron uptake transporters demonstrated that Zip8 makes a major contribution to iron accumulation in primary cultures of rat embryonic hippocampal neurons. These cells exhibit uptake pathways for ferrous and ferric iron (non-transferrin-bound iron, NTBI in figure) and for transferrin-bound iron; the ferrireductases Steap2 and SDR2 support the uptake of ferric iron substrates. Zip8 and Steap2 are strongly expressed in the plasma membrane of both soma and processes, implying a crucial role in iron accumulation from NTBI and transferrin-bound iron (TBI) by hippocampal neurons.

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Hippocampal neurons' cytosolic and membrane-bound ribosomal transcript profiles are differentially regulated by learning and subsequent sleep.

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