Sexual Dimorphism and Aging in the Human Hyppocampus: Identification, Validation, and Impact of Differentially Expressed Genes by Factorial Microarray and Network Analysis

Abstract

Motivation: In the brain of elderly-healthy individuals, the effects of sexual dimorphism and those due to normal aging appear overlapped. Discrimination of these two dimensions would powerfully contribute to a better understanding of the etiology of some neurodegenerative diseases, such as "sporadic" Alzheimer. Methods: Following a system biology approach, top-down and bottom-up strategies were combined. First, public transcriptome data corresponding to the transition from adulthood to the aging stage in normal, human hippocampus were analyzed through an optimized microarray post-processing (Q-GDEMAR method) together with a proper experimental design (full factorial analysis). Second, the identified genes were placed in context by building compatible networks. The subsequent ontology analyses carried out on these networks clarify the main functionalities involved. Results: Noticeably we could identify large sets of genes according to three groups: those that exclusively depend on the sex, those that exclusively depend on the age, and those that depend on the particular combinations of sex and age (interaction). The genes identified were validated against three independent sources (a proteomic study of aging, a senescence database, and a mitochondrial genetic database). We arrived to several new inferences about the biological functions compromised during aging in two ways: by taking into account the sex-independent effects of aging, and considering the interaction between age and sex where pertinent. In particular, we discuss the impact of our findings on the functions of mitochondria, autophagy, mitophagia, and microRNAs. Conclusions: The evidence obtained herein supports the occurrence of significant neurobiological differences in the hippocampus, not only between adult and elderly individuals, but between old-healthy women and old-healthy men. Hence, to obtain realistic results in further analysis of the transition from the normal aging to incipient Alzheimer, the features derived from the sexual dimorphism in hippocampus should be explicitly considered.

Keywords: aging; autophagia; hippocampus; microRNAs; microarray; mitochondria; senescence; sexual differences.

Figure 1

Sequence of operations performed upon the microarray data to achieve separated networks corresponding to genetic regulation, protein-protein interactions (PP), and metabolic regulation at two different levels of complexity (augmented and core network), for each one of the conditions analyzed (sex ratio positive and negative, age ratio positive and negative, and interaction positive and negative).

Figure 2

Venn diagram showing how the data of microarray GSE11882 (Berchtold et al., 2008) are disaggregated by Q-GDEMAR (Guebel et al., 2016). The factorial micro-array analysis allows to identify; (A) Genes operating under the age-sex interaction mode; (B) Genes operating under the sex-dependent mode; (C) Genes operating under the age-dependent mode. The values between parentheses indicate the level of False Discovery Rate (FDR) associated to each class of genes.

Figure 3

Networks isolated as strongly connected components (SCC) from the Protein-Protein (P-P) connections identified in nucleus 1 under the interaction mode. The nucleus 1 is the network that arises from known inter-relationships between nodes without need to add any additional connector to the list of differential genes detected. (A) Case of positive interaction (n = 229 nodes); (B) Case of negative interaction (n = 549 nodes).

Figure 4

Correspondence among the genes detected as differentially expressed under the different modes identified after Q-GDEMAR factorial analysis without summarization and the genes identified in three independent sources of data.

Figure 5

Comparison between the core of circuits leading to senescence depending on the type of interaction. (A) Network arising from the nodes with negative value of super-ratio coefficient. (B) Network arising from the nodes with positive value of super-ratio coefficient. The nodes fulfilling the condition imposed (sign of the significant super-ratio values) are colored in gray, while colorless nodes represent genes not detected as differentially expressed. These have been added by the computing algorithm for the sake of network completeness because they have well-known interactions with some of the gray nodes.

Figure 6

Comparison between the core of circuits leading to senescence depending on the value of the age effect. (A) Network of nodes corresponding to samples from the “younger” group; (B) Network of nodes corresponding to the samples from the “older” group.

Figure 7

Comparison between the core of the circuits leading to senescence depending on the sex effect. (A) Network arising from the genes differentially expressed in the group of “Males”; (B) Network arising from the genes differentially expressed in the group of “Females.”

Figure 8

Distribution of ontology classes in the mitochondrial genes identified by the microarray analysis, according to their association to sex, age, and interaction effects (the values of the FDR associated to each ontology class were corrected for the multiple comparisons).

Figure 9

Simplified diagram mapping the process of mitophagy in relation to the general machinery of autophagy. The specifically dysregulated genes are marked with asterisk. The blue arrows indicate positive regulation, whereas the red arrows indicate inhibitory effects (For numeric details and disaggregation effects, see Table 5). In mitophagy, there are two complementary mechanisms. One is Parkin-independent, comprising the Drp1 path, (Kageyama et al., 2014) and the RNF185-BNIP1 path (Tang et al., 2011), whereas the other is a group of Parkin-dependent variants: the PARK2-BINP3-NRB1 path (Lee et al., ; Pratt and Annabi, 2014), PARK2-BINP3L path (Gao et al., 2015), PARK2-SMURF1 path (Orvedahl et al., 2011). This last path is also used to degrade viral capsids. Actually, the Parkin-independent process is driven by the Dynein-related Protein 1 (Drp1) and regulated through the process of mitochondrial fission (see Section Impact of Sexual Dimorphism and Aging on Mitochondrial Function). It aims to diminish the size of the damaged mitochondria to facilitate their engulfment. Importantly, the PARKIN-dependent mitophagy variants require of the proteins PINK1 (Vives-Bauza et al., 2010), and some of the VDACs isoforms (Sun et al., 2012) to be effective. Both proteins contribute to the recruitment of PARK2 from the cytoplasm to the mitochondria. The mitochondrial PARK2 acts as E3Ub ligase on several adaptor molecules, triggering mitophagy. In addition, note that where PARKIN2 route is used, a final set of proteins such as HDAC6 (Lee et al., 2010) and TBK1 together with SQSTML1 (Matsumoto et al., 2015), which allows the step of engulfment of the mitochondrion.