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GIT2 acts as a potential keystone protein in functional hypothalamic networks associated with age-related phenotypic changes in rats.

Chadwick W, Martin B, Chapter MC, Park SS, Wang L, Daimon CM, Brenneman R, Maudsley S - PLoS ONE (2012)

Bottom Line: However, the proteomic effects of aging in regions of the brain vital for integrating energy balance and neuronal activity are not well understood.Therefore, a greater understanding of the effects of aging in the hypothalamus may reveal important aspects of overall organismal aging and may potentially reveal the most crucial protein factors supporting this vital signaling integration.Using novel combinatorial bioinformatics analyses, we were able to gain a better understanding of the proteomic and phenotypic changes that occur during the aging process and have potentially identified the G protein-coupled receptor/cytoskeletal-associated protein GIT2 as a vital integrator and modulator of the normal aging process.

View Article: PubMed Central - PubMed

Affiliation: Receptor Pharmacology Unit, Laboratory of Neuroscience, National Institute on Aging, National Institutes of Health, Biomedical Research Center, Baltimore, Maryland, United States of America.

ABSTRACT
The aging process affects every tissue in the body and represents one of the most complicated and highly integrated inevitable physiological entities. The maintenance of good health during the aging process likely relies upon the coherent regulation of hormonal and neuronal communication between the central nervous system and the periphery. Evidence has demonstrated that the optimal regulation of energy usage in both these systems facilitates healthy aging. However, the proteomic effects of aging in regions of the brain vital for integrating energy balance and neuronal activity are not well understood. The hypothalamus is one of the main structures in the body responsible for sustaining an efficient interaction between energy balance and neurological activity. Therefore, a greater understanding of the effects of aging in the hypothalamus may reveal important aspects of overall organismal aging and may potentially reveal the most crucial protein factors supporting this vital signaling integration. In this study, we examined alterations in protein expression in the hypothalami of young, middle-aged, and old rats. Using novel combinatorial bioinformatics analyses, we were able to gain a better understanding of the proteomic and phenotypic changes that occur during the aging process and have potentially identified the G protein-coupled receptor/cytoskeletal-associated protein GIT2 as a vital integrator and modulator of the normal aging process.

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Latent semantic indexing correlations of KEGG signaling pathways terms with proteins.(A) Latent semantic indexing (LSI) interrogation matrix between input significantly-regulated KEGG signaling pathway terms. Colored blocks represent the individual LSI implicit correlation of the specific protein (vertically organized on left of heatmap: 1–2524 – see Table S20) with the respective KEGG term (1-Regulation of actin cytoskeleton, 2-Chemokine signaling, 3-Alzheimer's disease, 4-Focal adhesion, 5-MAPK signaling, 6-Gap junction, 7-GnRH signaling, 8-Long term potentiation, 9-Notch signaling, 10-VEGF signaling, 11-p53 signaling, 12-Calcium signaling). The number of KEGG signaling pathway correlations for each protein is indicated by the color of the respective heatmap blocks (9 correlations-red; 8 correlations-orange; 7 correlations-yellow; 6 correlations-green; 5 correlations-light blue; 4 correlations-dark blue; 3 correlations-purple; 2 correlations-grey). (B) Mean ± SEM for the total implicitly-correlating proteins for each of the 12 input KEGG signaling pathways. (C) Box and whisker plot with 1–99% statistical cut-offs (GraphPad Prism) of the number of specific correlations to KEGG pathways each protein possessed. Twelve proteins demonstrated a statistically-significantly greater number of KEGG pathway correlations compared to the total protein mean number of correlations (*** = p<0.001). (D) Expanded heatmap identification of specific proteins possessing a significantly greater number of KEGG pathway correlations compared to the mean number of KEGG pathway correlations for all implicit proteins. (E) Mean ± SEM of LSI correlation scores (across all 9 correlations) for Grit and GIT2.
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pone-0036975-g004: Latent semantic indexing correlations of KEGG signaling pathways terms with proteins.(A) Latent semantic indexing (LSI) interrogation matrix between input significantly-regulated KEGG signaling pathway terms. Colored blocks represent the individual LSI implicit correlation of the specific protein (vertically organized on left of heatmap: 1–2524 – see Table S20) with the respective KEGG term (1-Regulation of actin cytoskeleton, 2-Chemokine signaling, 3-Alzheimer's disease, 4-Focal adhesion, 5-MAPK signaling, 6-Gap junction, 7-GnRH signaling, 8-Long term potentiation, 9-Notch signaling, 10-VEGF signaling, 11-p53 signaling, 12-Calcium signaling). The number of KEGG signaling pathway correlations for each protein is indicated by the color of the respective heatmap blocks (9 correlations-red; 8 correlations-orange; 7 correlations-yellow; 6 correlations-green; 5 correlations-light blue; 4 correlations-dark blue; 3 correlations-purple; 2 correlations-grey). (B) Mean ± SEM for the total implicitly-correlating proteins for each of the 12 input KEGG signaling pathways. (C) Box and whisker plot with 1–99% statistical cut-offs (GraphPad Prism) of the number of specific correlations to KEGG pathways each protein possessed. Twelve proteins demonstrated a statistically-significantly greater number of KEGG pathway correlations compared to the total protein mean number of correlations (*** = p<0.001). (D) Expanded heatmap identification of specific proteins possessing a significantly greater number of KEGG pathway correlations compared to the mean number of KEGG pathway correlations for all implicit proteins. (E) Mean ± SEM of LSI correlation scores (across all 9 correlations) for Grit and GIT2.

Mentions: To identify novel protein factors that may act as keystones by connecting the potentially complex series of functional networks involved in aging, we performed combinatorial LSI using multiple KEGG and GO term groups significantly populated by age-regulated hypothalamic proteins. We chose twelve KEGG signaling pathway text terms, including all functional subsets described in Fig. 3B–D, to use as input interrogation terms for a complete murine biomedical protein database (Computable Genomix, https://computablegenomix.com/geneindexer). This process yields lists of proteins that possess a quantitative LSI correlation score (cut off of >0.1 indicates at least an ‘implicit’ correlation) associated with the input text term. To maintain equality between the output lists of LSI-correlating proteins from the diverse input KEGG terms used (1-Regulation of actin cytoskeleton, 2-Chemokine signaling, 3-Alzheimer's disease, 4-Focal adhesion, 5-MAPK signaling, 6-Gap junction, 7-GnRH signaling, 8-Long term potentiation, 9-Notch signaling, 10-VEGF signaling, 11-p53 signaling, 12-Calcium signaling), we chose the top 1000 highest-scoring proteins in each case (terms 1–12). The individual protein LSI correlation scores for the input KEGG pathway terms (1–12) are listed in Tables S8, S9, S10, S11, S12, S13, S14, S15, S16, S17, S18, S19. To identify potentially multidimensional keystone factors in age-related protein patterns, we combined the LSI correlation results from the 12 input KEGG terms into a heatmap diagram. Only proteins that displayed an LSI correlation (>0.1 score) in at least two separate KEGG term outputs were used for heatmap analysis (Fig. 4A). Using a >2 KEGG pathway correlation cut-off, a matrix of 2524 proteins was generated (Table S20). The highest number of correlations (8 KEGG terms) was achieved by 2 proteins: Grit (Rho GTPase activating protein 32/Arhgap32) and GIT2 (G protein-coupled receptor kinase interacting protein 2) (Fig. 4A:Table S20). The mean LSI correlation scores for all correlated proteins, generated by the twelve input terms, were all significantly greater than 0.1, demonstrating a greater than implicit correlation for all proteins (Fig. 4B). After taking together the total number of proteins demonstrating multiple (>2) KEGG term correlations and performing a group statistical analysis, twelve were found to exist outside a 99% percentile of the mean results assuming a normal distribution (10 with 7 correlations, 2 with 8 correlations) (Fig. 4C: Box & Whiskers plot, p<0.001). These twelve proteins and their specific KEGG term correlations are highlighted in Fig. 4D. GIT2 possessed a greater mean LSI correlation score (across the 8 KEGG pathways linked to it) than Grit, despite a similar number of cross-KEGG pathway correlations (Fig. 4E). These unbiased results may suggest that cytoskeletal-organizing factors could play an important role in maintaining normal neuronal function with age in the hypothalamus.


GIT2 acts as a potential keystone protein in functional hypothalamic networks associated with age-related phenotypic changes in rats.

Chadwick W, Martin B, Chapter MC, Park SS, Wang L, Daimon CM, Brenneman R, Maudsley S - PLoS ONE (2012)

Latent semantic indexing correlations of KEGG signaling pathways terms with proteins.(A) Latent semantic indexing (LSI) interrogation matrix between input significantly-regulated KEGG signaling pathway terms. Colored blocks represent the individual LSI implicit correlation of the specific protein (vertically organized on left of heatmap: 1–2524 – see Table S20) with the respective KEGG term (1-Regulation of actin cytoskeleton, 2-Chemokine signaling, 3-Alzheimer's disease, 4-Focal adhesion, 5-MAPK signaling, 6-Gap junction, 7-GnRH signaling, 8-Long term potentiation, 9-Notch signaling, 10-VEGF signaling, 11-p53 signaling, 12-Calcium signaling). The number of KEGG signaling pathway correlations for each protein is indicated by the color of the respective heatmap blocks (9 correlations-red; 8 correlations-orange; 7 correlations-yellow; 6 correlations-green; 5 correlations-light blue; 4 correlations-dark blue; 3 correlations-purple; 2 correlations-grey). (B) Mean ± SEM for the total implicitly-correlating proteins for each of the 12 input KEGG signaling pathways. (C) Box and whisker plot with 1–99% statistical cut-offs (GraphPad Prism) of the number of specific correlations to KEGG pathways each protein possessed. Twelve proteins demonstrated a statistically-significantly greater number of KEGG pathway correlations compared to the total protein mean number of correlations (*** = p<0.001). (D) Expanded heatmap identification of specific proteins possessing a significantly greater number of KEGG pathway correlations compared to the mean number of KEGG pathway correlations for all implicit proteins. (E) Mean ± SEM of LSI correlation scores (across all 9 correlations) for Grit and GIT2.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3351446&req=5

pone-0036975-g004: Latent semantic indexing correlations of KEGG signaling pathways terms with proteins.(A) Latent semantic indexing (LSI) interrogation matrix between input significantly-regulated KEGG signaling pathway terms. Colored blocks represent the individual LSI implicit correlation of the specific protein (vertically organized on left of heatmap: 1–2524 – see Table S20) with the respective KEGG term (1-Regulation of actin cytoskeleton, 2-Chemokine signaling, 3-Alzheimer's disease, 4-Focal adhesion, 5-MAPK signaling, 6-Gap junction, 7-GnRH signaling, 8-Long term potentiation, 9-Notch signaling, 10-VEGF signaling, 11-p53 signaling, 12-Calcium signaling). The number of KEGG signaling pathway correlations for each protein is indicated by the color of the respective heatmap blocks (9 correlations-red; 8 correlations-orange; 7 correlations-yellow; 6 correlations-green; 5 correlations-light blue; 4 correlations-dark blue; 3 correlations-purple; 2 correlations-grey). (B) Mean ± SEM for the total implicitly-correlating proteins for each of the 12 input KEGG signaling pathways. (C) Box and whisker plot with 1–99% statistical cut-offs (GraphPad Prism) of the number of specific correlations to KEGG pathways each protein possessed. Twelve proteins demonstrated a statistically-significantly greater number of KEGG pathway correlations compared to the total protein mean number of correlations (*** = p<0.001). (D) Expanded heatmap identification of specific proteins possessing a significantly greater number of KEGG pathway correlations compared to the mean number of KEGG pathway correlations for all implicit proteins. (E) Mean ± SEM of LSI correlation scores (across all 9 correlations) for Grit and GIT2.
Mentions: To identify novel protein factors that may act as keystones by connecting the potentially complex series of functional networks involved in aging, we performed combinatorial LSI using multiple KEGG and GO term groups significantly populated by age-regulated hypothalamic proteins. We chose twelve KEGG signaling pathway text terms, including all functional subsets described in Fig. 3B–D, to use as input interrogation terms for a complete murine biomedical protein database (Computable Genomix, https://computablegenomix.com/geneindexer). This process yields lists of proteins that possess a quantitative LSI correlation score (cut off of >0.1 indicates at least an ‘implicit’ correlation) associated with the input text term. To maintain equality between the output lists of LSI-correlating proteins from the diverse input KEGG terms used (1-Regulation of actin cytoskeleton, 2-Chemokine signaling, 3-Alzheimer's disease, 4-Focal adhesion, 5-MAPK signaling, 6-Gap junction, 7-GnRH signaling, 8-Long term potentiation, 9-Notch signaling, 10-VEGF signaling, 11-p53 signaling, 12-Calcium signaling), we chose the top 1000 highest-scoring proteins in each case (terms 1–12). The individual protein LSI correlation scores for the input KEGG pathway terms (1–12) are listed in Tables S8, S9, S10, S11, S12, S13, S14, S15, S16, S17, S18, S19. To identify potentially multidimensional keystone factors in age-related protein patterns, we combined the LSI correlation results from the 12 input KEGG terms into a heatmap diagram. Only proteins that displayed an LSI correlation (>0.1 score) in at least two separate KEGG term outputs were used for heatmap analysis (Fig. 4A). Using a >2 KEGG pathway correlation cut-off, a matrix of 2524 proteins was generated (Table S20). The highest number of correlations (8 KEGG terms) was achieved by 2 proteins: Grit (Rho GTPase activating protein 32/Arhgap32) and GIT2 (G protein-coupled receptor kinase interacting protein 2) (Fig. 4A:Table S20). The mean LSI correlation scores for all correlated proteins, generated by the twelve input terms, were all significantly greater than 0.1, demonstrating a greater than implicit correlation for all proteins (Fig. 4B). After taking together the total number of proteins demonstrating multiple (>2) KEGG term correlations and performing a group statistical analysis, twelve were found to exist outside a 99% percentile of the mean results assuming a normal distribution (10 with 7 correlations, 2 with 8 correlations) (Fig. 4C: Box & Whiskers plot, p<0.001). These twelve proteins and their specific KEGG term correlations are highlighted in Fig. 4D. GIT2 possessed a greater mean LSI correlation score (across the 8 KEGG pathways linked to it) than Grit, despite a similar number of cross-KEGG pathway correlations (Fig. 4E). These unbiased results may suggest that cytoskeletal-organizing factors could play an important role in maintaining normal neuronal function with age in the hypothalamus.

Bottom Line: However, the proteomic effects of aging in regions of the brain vital for integrating energy balance and neuronal activity are not well understood.Therefore, a greater understanding of the effects of aging in the hypothalamus may reveal important aspects of overall organismal aging and may potentially reveal the most crucial protein factors supporting this vital signaling integration.Using novel combinatorial bioinformatics analyses, we were able to gain a better understanding of the proteomic and phenotypic changes that occur during the aging process and have potentially identified the G protein-coupled receptor/cytoskeletal-associated protein GIT2 as a vital integrator and modulator of the normal aging process.

View Article: PubMed Central - PubMed

Affiliation: Receptor Pharmacology Unit, Laboratory of Neuroscience, National Institute on Aging, National Institutes of Health, Biomedical Research Center, Baltimore, Maryland, United States of America.

ABSTRACT
The aging process affects every tissue in the body and represents one of the most complicated and highly integrated inevitable physiological entities. The maintenance of good health during the aging process likely relies upon the coherent regulation of hormonal and neuronal communication between the central nervous system and the periphery. Evidence has demonstrated that the optimal regulation of energy usage in both these systems facilitates healthy aging. However, the proteomic effects of aging in regions of the brain vital for integrating energy balance and neuronal activity are not well understood. The hypothalamus is one of the main structures in the body responsible for sustaining an efficient interaction between energy balance and neurological activity. Therefore, a greater understanding of the effects of aging in the hypothalamus may reveal important aspects of overall organismal aging and may potentially reveal the most crucial protein factors supporting this vital signaling integration. In this study, we examined alterations in protein expression in the hypothalami of young, middle-aged, and old rats. Using novel combinatorial bioinformatics analyses, we were able to gain a better understanding of the proteomic and phenotypic changes that occur during the aging process and have potentially identified the G protein-coupled receptor/cytoskeletal-associated protein GIT2 as a vital integrator and modulator of the normal aging process.

Show MeSH
Related in: MedlinePlus