The human brainome: network analysis identifies \u3ci\u3eHSPA2\u3c/i\u3e as a novel Alzheimer’s disease target

Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-B40 and amyloid-B42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes

The human brainome: network analysis identifies HSPA2 as a novel Alzheimer's disease target

Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer's disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer's disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65-105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66-107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-b40 and amyloid-b42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer's disease processes

Gender-specific effects of depression and suicidal ideation in prosocial behaviors.

Prosocial behaviors are essential to the ability to relate to others. Women typically display greater prosocial behavior than men. The impact of depression on prosocial behaviors and how gender interacts with those effects are not fully understood. We explored the role of gender in the potential effects of depression on prosocial behavior.We examined prosocial behaviors using a modified version of the Trust Game in a clinical population and community controls. Study participants were characterized on the severity of depression and anxiety, presence of suicidal ideation, history of childhood trauma, recent stressful life events, and impulsivity. We correlated behavioral outcomes with gender and clinical variables using analysis of variance and multiple regression analysis.The 89 participants comprised four study groups: depressed women, depressed men, healthy women and healthy men (n = 16-36). Depressed men exhibited reciprocity more frequently than healthy men. Depression induced an inversion of the gender-specific pattern of self-centered behavior. Suicidal ideation was associated with increased reciprocity behavior in both genders, and enhancement of the effect of depression on gender-specific self-centered behavior.Depression, particularly suicidal ideation, is associated with reversal of gender-specific patterns of prosocial behavior, suggesting abnormalities in sexual hormones regulation. This explanation is supported by known abnormalities in the hypothalamus-pituitary-adrenal and hypothalamus-pituitary-gonadal axes found in depression

Relationship between offer amount during the baseline condition and reciprocity behavior in healthy women, depressed women, healthy men and depressed men.

<p>Relationship between offer amount during the baseline condition and reciprocity behavior in healthy women, depressed women, healthy men and depressed men.</p

Demographic and clinical characteristics of depressed patients and healthy participants.

<p>Bonferroni correction was used for multiple comparison;</p>a<p>compared to suicide attempter group;</p>b<p>compared to suicidal ideation group;</p>c<p>compared to depressed control group;</p>d<p>compared to healthy control group;</p><p>*Yates chi square.</p><p>Demographic and clinical characteristics of depressed patients and healthy participants.</p

Effect of depression and presence of suicidal ideation in prosocial behavior in men and women.

<p>A. Reciprocity behavior during baseline condition in healthy participants and depressed patients. B. Self-centered behavior during emotionally challenging condition in healthy participants and depressed patients. C. Reciprocity behavior during baseline condition in depressed non suicidal and depressed suicidal patients. D. Self-centered behavior during emotionally challenging condition in in depressed non suicidal and depressed suicidal patients. * Different between healthy and depressed participants, # different from all other groups; p<0.05.</p

Behavior of depressed patients and healthy participants in the modified Trust Game.

<p>Bonferroni correction was used for multiple comparison;</p>a<p>compared to suicide attempter group;</p>b<p>compared to suicidal ideation group;</p>c<p>compared to depressed control group;</p>d<p>compared to healthy control group.</p><p>Behavior of depressed patients and healthy participants in the modified Trust Game.</p

The human brainome: network analysis identifies \u3ci\u3eHSPA2\u3c/i\u3e as a novel Alzheimer’s disease target

Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-B40 and amyloid-B42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes

Sources of Technical Variability in Quantitative LC–MS Proteomics: Human Brain Tissue Sample Analysis

To design a robust quantitative proteomics study, an understanding of both the inherent heterogeneity of the biological samples being studied as well as the technical variability of the proteomics methods and platform is needed. Additionally, accurately identifying the technical steps associated with the largest variability would provide valuable information for the improvement and design of future processing pipelines. We present an experimental strategy that allows for a detailed examination of the variability of the quantitative LC–MS proteomics measurements. By replicating analyses at different stages of processing, various technical components can be estimated and their individual contribution to technical variability can be dissected. This design can be easily adapted to other quantitative proteomics pipelines. Herein, we applied this methodology to our label-free workflow for the processing of human brain tissue. For this application, the pipeline was divided into four critical components: Tissue dissection and homogenization (extraction), protein denaturation followed by trypsin digestion and SPE cleanup (digestion), short-term run-to-run instrumental response fluctuation (instrumental variance), and long-term drift of the quantitative response of the LC–MS/MS platform over the 2 week period of continuous analysis (instrumental stability). From this analysis, we found the following contributions to variability: extraction (72%) ≫ instrumental variance (16%) > instrumental stability (8.4%) > digestion (3.1%). Furthermore, the stability of the platform and its suitability for discovery proteomics studies is demonstrated