Data_Sheet_1_The ventral hippocampus is activated in olfactory but not auditory threat memory.PDF

Hippocampal networks required for associative memory formation are involved in cue- and context-dependent threat conditioning. The hippocampus is functionally heterogeneous at its dorsal and ventral poles, and recent investigations have focused on the specific roles required from each sub-region for associative conditioning. Cumulative evidence suggests that contextual and emotional information is processed by the dorsal and ventral hippocampus, respectively. However, it is not well understood how these two divisions engage in threat conditioning with cues of different sensory modalities. Here, we compare the involvement of the dorsal and ventral hippocampus in two types of threat conditioning: olfactory and auditory. Our results suggest that the dorsal hippocampus encodes contextual information and is activated upon recall of an olfactory threat memory only if contextual cues are relevant to the threat. Overnight habituation to the context eliminates dorsal hippocampal activation, implying that this area does not directly support cue-dependent threat conditioning. The ventral hippocampus is activated upon recall of olfactory, but not auditory, threat memory regardless of habituation duration. Concurrent activation of the piriform cortex is consistent with its direct connection with the ventral hippocampus. Together, our study suggests a unique role of the ventral hippocampus in olfactory threat conditioning.</p

Imaging CA1 pyramidal cell ensembles recruited by stimulation of Schaffer collateral afferent inputs.

<p>A, Calcium transients in Oregon Green-1 loaded CA1 pyramidal cells are action-potential dependent. A₁, DIC image of the pyramidal cell layer. The pyramidal cell marked by a yellow asterisk was recorded in the loose patch configuration and SC inputs were evoked via a stimulating electrode in stratum radiatum. Stimulus strength was set at threshold for evoking spikes in the targeted cell. Scale bar, 20 µm. A₂, SC stimulation evokes calcium transients revealed by the ΔF/F image averaged across 6 stimulus trials. A_3, Average dF/F image of 4 trials in which a calcium transient was detected in the targeted cell (Successes). Traces of individual trials show loose patch recordings of action potentials from the targeted cell (top) and time course of the dF/F signal of the same cell. A₄, average dF/F image of 2 trials in which a calcium transient was not evoked (Failures). Traces indicate that the failure to evoke action potentials on single trials (top) did not generate calcium transients in the targeted cell. Calcium transients were always associated with spiking in all cells tested with loose patch recording (n = 6). B, Steps diagramming methods used to construct activity maps of cell ensembles. C, Activity maps of SC-evoked cell ensembles are stable over time. Left, Representative experiment illustrating cell ensembles recruited by SC stimulation at two time points (T1 and T2, 30 minute interval). Activated neurons in the pyramidal cell layer are color-coded blue and field EPSPs recorded in stratum radiatum during each imaging period are shown above. The activity maps and field EPSPs from the two periods are overlaid (T1 + T2, image color code: blue cells are recruited during both imaging periods, white cells are those recruited during T1 but absent during T2, red cells are those recruited during T2 but absent during T1). Scale bar for activity maps, 50 µm. Right, summary (n = 5) of the stability of cell ensembles over a 30 min time period.</p

Table_7_Identification of biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis in alcoholic hepatitis by bioinformatics and experimental verification.xlsx

BackgroundsAlcoholic hepatitis (AH) is a major health problem worldwide. There is increasing evidence that immune cells, iron metabolism and copper metabolism play important roles in the development of AH. We aimed to explore biomarkers that are co-associated with M1 macrophages, ferroptosis and cuproptosis in AH patients.MethodsGSE28619 and GSE103580 datasets were integrated, CIBERSORT algorithm was used to analyze the infiltration of 22 types of immune cells and GSVA algorithm was used to calculate ferroptosis and cuproptosis scores. Using the “WGCNA” R package, we established a gene co-expression network and analyzed the correlation between M1 macrophages, ferroptosis and cuproptosis scores and module characteristic genes. Subsequently, candidate genes were screened by WGCNA and differential expression gene analysis. The LASSO-SVM analysis was used to identify biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis. Finally, we validated these potential biomarkers using GEO datasets (GSE155907, GSE142530 and GSE97234) and a mouse model of AH.ResultsThe infiltration level of M1 macrophages was significantly increased in AH patients. Ferroptosis and cuproptosis scores were also increased in AH patients. In addition, M1 macrophages, ferroptosis and cuproptosis were positively correlated with each other. Combining bioinformatics analysis with a mouse model of AH, we found that ALDOA, COL3A1, LUM, THBS2 and TIMP1 may be potential biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis in AH patients.ConclusionWe identified 5 potential biomarkers that are promising new targets for the treatment and diagnosis of AH patients.</p

DataSheet_1_Identification of biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis in alcoholic hepatitis by bioinformatics and experimental verification.pdf

BackgroundsAlcoholic hepatitis (AH) is a major health problem worldwide. There is increasing evidence that immune cells, iron metabolism and copper metabolism play important roles in the development of AH. We aimed to explore biomarkers that are co-associated with M1 macrophages, ferroptosis and cuproptosis in AH patients.MethodsGSE28619 and GSE103580 datasets were integrated, CIBERSORT algorithm was used to analyze the infiltration of 22 types of immune cells and GSVA algorithm was used to calculate ferroptosis and cuproptosis scores. Using the “WGCNA” R package, we established a gene co-expression network and analyzed the correlation between M1 macrophages, ferroptosis and cuproptosis scores and module characteristic genes. Subsequently, candidate genes were screened by WGCNA and differential expression gene analysis. The LASSO-SVM analysis was used to identify biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis. Finally, we validated these potential biomarkers using GEO datasets (GSE155907, GSE142530 and GSE97234) and a mouse model of AH.ResultsThe infiltration level of M1 macrophages was significantly increased in AH patients. Ferroptosis and cuproptosis scores were also increased in AH patients. In addition, M1 macrophages, ferroptosis and cuproptosis were positively correlated with each other. Combining bioinformatics analysis with a mouse model of AH, we found that ALDOA, COL3A1, LUM, THBS2 and TIMP1 may be potential biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis in AH patients.ConclusionWe identified 5 potential biomarkers that are promising new targets for the treatment and diagnosis of AH patients.</p

Table_6_Identification of biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis in alcoholic hepatitis by bioinformatics and experimental verification.xlsx

BackgroundsAlcoholic hepatitis (AH) is a major health problem worldwide. There is increasing evidence that immune cells, iron metabolism and copper metabolism play important roles in the development of AH. We aimed to explore biomarkers that are co-associated with M1 macrophages, ferroptosis and cuproptosis in AH patients.MethodsGSE28619 and GSE103580 datasets were integrated, CIBERSORT algorithm was used to analyze the infiltration of 22 types of immune cells and GSVA algorithm was used to calculate ferroptosis and cuproptosis scores. Using the “WGCNA” R package, we established a gene co-expression network and analyzed the correlation between M1 macrophages, ferroptosis and cuproptosis scores and module characteristic genes. Subsequently, candidate genes were screened by WGCNA and differential expression gene analysis. The LASSO-SVM analysis was used to identify biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis. Finally, we validated these potential biomarkers using GEO datasets (GSE155907, GSE142530 and GSE97234) and a mouse model of AH.ResultsThe infiltration level of M1 macrophages was significantly increased in AH patients. Ferroptosis and cuproptosis scores were also increased in AH patients. In addition, M1 macrophages, ferroptosis and cuproptosis were positively correlated with each other. Combining bioinformatics analysis with a mouse model of AH, we found that ALDOA, COL3A1, LUM, THBS2 and TIMP1 may be potential biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis in AH patients.ConclusionWe identified 5 potential biomarkers that are promising new targets for the treatment and diagnosis of AH patients.</p

Associative LTP of two independent Schaffer collateral pathways merges the ensembles of pyramidal cells recruited by the two pathways.

<p>A₁, Summary plot of fEPSPs showing associative LTP induced by simultaneous (paired) theta burst stimulation (TBS) of two SC pathways, while prior independent (unpaired) TBS does not cause potentiation (n = 4). Inset, recording configuration. A₂, fEPSPs and cell ensembles evoked by each pathway (red, green) in one experiment at the times indicated on the summary plot. Scale bar, 0.2 mV and 20 ms. B, Associative LTP significantly increases the overlap ratio (OLR) of the two SC ensembles. B₁, OLR was measured as the cells common between the two ensembles (SC₁₊₂) divided by the total cells in the two ensembles (SC₁ + SC₂ - SC₁₊₂, we subtract SC₁₊₂ in order not to count cells common to both ensembles twice). Summary data plot the increase in total cells (SC₁ +SC₂) and OLR of the two ensembles normalized to control conditions (n = 4 slices; **, p<0.01). B₂, Overlay of the two SC-evoked neuronal ensembles (red, green) shown in (A₂). Yellow cells indicate neurons common to the two ensembles. (C,D) Increasing afferent input by increasing stimulus strength expands the size of cell ensembles but associative LTP causes a greater increase in overlap between two SC ensembles. C, Associative LTP was induced by pairing a weak stimulus (one TBS, black arrow) in one pathway (black traces) with a strong stimulus (four TBS, gray arrow) to the other pathway (not shown). Cell ensembles were measured under control conditions (i), following an increase in stimulus strength (ii), when stimulus strength was returned back to control (iii) and following associative LTP (iv). D, Summary data showing change in total number of cells and OLR relative to control conditions for changes in stimulus strength and associative LTP (n = 3; *, p<0.05).</p

Table_3_Identification of biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis in alcoholic hepatitis by bioinformatics and experimental verification.xlsx

BackgroundsAlcoholic hepatitis (AH) is a major health problem worldwide. There is increasing evidence that immune cells, iron metabolism and copper metabolism play important roles in the development of AH. We aimed to explore biomarkers that are co-associated with M1 macrophages, ferroptosis and cuproptosis in AH patients.MethodsGSE28619 and GSE103580 datasets were integrated, CIBERSORT algorithm was used to analyze the infiltration of 22 types of immune cells and GSVA algorithm was used to calculate ferroptosis and cuproptosis scores. Using the “WGCNA” R package, we established a gene co-expression network and analyzed the correlation between M1 macrophages, ferroptosis and cuproptosis scores and module characteristic genes. Subsequently, candidate genes were screened by WGCNA and differential expression gene analysis. The LASSO-SVM analysis was used to identify biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis. Finally, we validated these potential biomarkers using GEO datasets (GSE155907, GSE142530 and GSE97234) and a mouse model of AH.ResultsThe infiltration level of M1 macrophages was significantly increased in AH patients. Ferroptosis and cuproptosis scores were also increased in AH patients. In addition, M1 macrophages, ferroptosis and cuproptosis were positively correlated with each other. Combining bioinformatics analysis with a mouse model of AH, we found that ALDOA, COL3A1, LUM, THBS2 and TIMP1 may be potential biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis in AH patients.ConclusionWe identified 5 potential biomarkers that are promising new targets for the treatment and diagnosis of AH patients.</p

Table_2_Identification of biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis in alcoholic hepatitis by bioinformatics and experimental verification.xlsx

BackgroundsAlcoholic hepatitis (AH) is a major health problem worldwide. There is increasing evidence that immune cells, iron metabolism and copper metabolism play important roles in the development of AH. We aimed to explore biomarkers that are co-associated with M1 macrophages, ferroptosis and cuproptosis in AH patients.MethodsGSE28619 and GSE103580 datasets were integrated, CIBERSORT algorithm was used to analyze the infiltration of 22 types of immune cells and GSVA algorithm was used to calculate ferroptosis and cuproptosis scores. Using the “WGCNA” R package, we established a gene co-expression network and analyzed the correlation between M1 macrophages, ferroptosis and cuproptosis scores and module characteristic genes. Subsequently, candidate genes were screened by WGCNA and differential expression gene analysis. The LASSO-SVM analysis was used to identify biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis. Finally, we validated these potential biomarkers using GEO datasets (GSE155907, GSE142530 and GSE97234) and a mouse model of AH.ResultsThe infiltration level of M1 macrophages was significantly increased in AH patients. Ferroptosis and cuproptosis scores were also increased in AH patients. In addition, M1 macrophages, ferroptosis and cuproptosis were positively correlated with each other. Combining bioinformatics analysis with a mouse model of AH, we found that ALDOA, COL3A1, LUM, THBS2 and TIMP1 may be potential biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis in AH patients.ConclusionWe identified 5 potential biomarkers that are promising new targets for the treatment and diagnosis of AH patients.</p

Bidirectional synaptic plasticity can merge neuronal ensembles without altering ensemble size.

<p>A₁, Summary plot of fEPSPs showing that low frequency stimulation (LFS, 300 pulses, 1 Hz) of two SC pathways (red, green) induces LTD and subsequent paired TBS induces LTP that returns the fEPSP to control conditions. A₂, Images and traces from one experiment collected at the time points indicated on the summary plot. LTD and LTP of fEPSPs were accompanied, respectively, by a reduction and a restoration of the size of neuronal ensembles recruited by the two SC pathways. Red and green represent the neuronal ensembles recruited by two independent SC pathways. Scale bars, 0.5 mV, 20 ms. B, Comparison of change in total number of cells and overlap between the two neuronal ensembles following LFS and subsequent paired TBS normalized to control conditions (n = 8; **, P<0.01). Schematics show the redistribution of the neuronal ensembles.</p

Pairing-induced synaptic plasticity selectively recruits cells from a defined population.

<p>A₁, Summary plot showing increases in fEPSPs following pairing-induced LTP and subsequent increase in SC stimulus strength (n = 6). Example fEPSPs (top traces) from one experiment at the indicated time points (scale bars, 0.2 mV, 20 ms). A₂, Cell activity maps from one experiment at the indicated time points (cameras, scale = 50 µm). Top row, Images show cells activated by the SC stimulation (blue) before (i) and after (i) pairing along with the new cells recruited (Cells added 1). Middle row, Cells activated following pairing (ii) and after increasing stimulus strength (iii) along with new cells recruited by the stimulus increase (Cells added II). Bottom row, images show cells activated by the alveus stimulation (orange) superimposed with those of the SC ensembles recruited by pairing-induced plasticity (Alv stim + I) and the increase in stimulus strength (Alv stim + II). Cells color-coded white belong to both the SC and alveus ensembles. B, Left, Summary showing that a larger fraction of newly added cells belong to the alveus population following LTP induction compared to those recruited by increased stimulation strength (n = 6; **, p<0.01). Right, diagram illustrating the dynamics of neuronal ensembles in this experiment. Blue and orange outlines represent the neuronal populations activated by SC and alveus stimulation, respectively. Hatched areas indicate cells that belong to both ensembles.</p