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.

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Pulse experiments in continuous-culture are a valuable tool in microbial physiology research. However, inferences become difficult when the cell response is followed by monitoring many biochemical variables or when several types of perturbations are compared. Moreover, there is no objective criterion to delimit the time-window, so that the recorded responses will render valid inferences. Hence, we have investigated the capability of a multivariate approach to deal with complex data from a previously described series of pulse experiments. Data are concerned with 12 biochemical variables that were monitored when an anaerobic, steady-continuous culture of E. coli O74K74 was disturbed by six types of pulses (glycerol, fumarate, acetate, crotonobetaine, hypersaline plus high-glycerol basal medium and crotonobetaine plus hypersaline basal medium). Our analysis determined the instantaneous uptake rate for the pulsed metabolite (Dynamical Chemical-Balances), reduced the multivariate observations to one response curve (Principal Component Analysis) and determined the optimal time-window (Cluster Analysis). Finally, input-output data were filtered (Orthogonal Signal Correction) while both blocks were mathematically connected (Partial Least-squares Regression). This systematic approach allowed us to detect several relevant patterns not previously revealed: (i) Glycerol uptake rate did not follow a Michaelian kinetics but showed a biphasic dependence on glycerol concentration; noticeably, net uptake decreased 136-fold despite the high availability of glycerol in the milieu. (ii) The structure of the bacterial response changed during time the glycerol-disturbance lasted (2 h), hence analyses had to be limited to the early response (time from 0 to 5 min). (iii) By mathematically relating the input (glycerol uptake rate) with the output (12 biochemical responses) it was possible to identify which of the monitored variables were primary targets of the glycerol disturbance (namely: ATP, formate, acetyl-CoA synthase, isocitrate dehydrogenase, and isocitrate lyase), which were secondarily responsive (ethanol) and those that were independent (acetate, carnitine, lactate, and NADH/NAD ratio). Identification was achieved even though all the analyzed variables were affected by the pulse. (iv) Some variables exhibited uncorrelated dynamics despite their close functional relationship (ATP and NADH/NAD ratio, ethanol and lactate; carnitine and the crotonobetaine hydratase complex; acetate and the enzymes phosphotransacetylase, acetyl-CoA synthase and isocitrate lyase). The results are discussed in terms of E. coli transcriptional control, and it is concluded that glycerol pulse produces a stressing effect. The consequent activation of the polyamine-dependent mechanisms involved in such stressing effect provides a unified explanation for how glycerol uptake is down-regulated in the presence of high glycerol availability and how acetate can be produced without de novo biosynthesis.

Microarray analysis is a powerful tool to simultaneously determine the pattern of transcription of large amounts of genes. For data post-processing distinct computational methods are currently used that, however, lead to different results regarding the genes expressed differentially. Herein, a new methodology for microarray data analysis named Q-GDEMAR is presented. It combines the quantile characterization of the entire distribution together with the Gaussian deconvolution of the central region of the microarray data distribution. Three discriminant variable variants are proposed that allow us to summarize data and compare groups even when their size is strongly unbalanced. In addition, a simple procedure to compute the false discovery rate (FDR) is also presented. The performance of the method is compared with that observed when using LIMMA (Linear Models Microarray) software as reference. In 58 out of 68 cases, Q-GDEMAR showed a higher sensitivity than LIMMA to detect differentially expressed genes (p = 1 × 10(-10)). The proposed method does not produce biased information, detecting genes with high sensitivity equally well at both tails of the distribution (p = 0.7428). Moreover, all detected genes were associated with very low levels of FDR (median value = 0.67%, interquartile range = 0.87%). Q-GDEMAR can be used as a general method for microarray analysis, but is particularly indicated when the conditions to be compared are unbalanced. The superior performance of Q-GEDEMAR is the consequence of its higher discriminative power and, the fact that it yields a univocal correspondence between the p-values and the values of the discriminating variable. Q-GDEMAR was tested only using Affymetrix microarrays. However, given that it operates after the step of data standardization, it can be used with the same quality features on any of the available mono- or dual-channel microarray platforms.

Cell adhesion in the normal colon is typically associated with differentiated cells, whereas in cancerous colon it is associated with advanced tumors. For advanced tumors growing evidence supports the existence of stem-like cells that have originated from transdifferentiation. Because stem cells can also be transformed in their own niche, at the base of the Lieberkühn's crypts, we conjectured that cell adhesion can also be critical in early tumorigenesis. To assess this hypothesis we built an annotated, multi-valued logic model addressing cell adhesion of normal and tumorigenic stem cells in the human colon. The model accounts for (i) events involving intercellular adhesion structures, (ii) interactions involving cytoskeleton-related structures, (iii) compartmental distribution of α/β/γ/δ-catenins, and (iv) variations in critical cell adhesion regulators (e.g., ILK, FAK, IQGAP, SNAIL, Caveolin). We developed a method that can deal with graded multiple inhibitions, something which is not possible with conventional logical approaches. The model comprises 315 species (including 26 genes), interconnected by 269 reactions. Simulations of the model covered six scenarios, which considered two types of colonic cells (stem vs. differentiated cells), under three conditions (normal, stressed and tumor). Each condition results from the combination of 92 inputs. We compared our multi-valued logic approach with the conventional Boolean approach for one specific example and validated the predictions against published data. Our analysis suggests that stem cells in their niche synthesize high levels of cytoplasmatic E-cadherin and CdhEP(Ser684,686,692), even under normal-mitogenic stimulus or tumorigenic conditions. Under these conditions, E-cadherin would be incorporated into the plasmatic membrane, but only as a non-adhesive CdhE_β-catenin_IQGAP complex. Under stress conditions, however, this complex could be displaced, yielding adhesive CdhE_β-catenin((cis/trans)) complexes. In the three scenarios tested with stem cells, desmosomes or tight junctions were not assembled. Other model predictions include expected levels of the nuclear complex β-catenin_TCF4 and the anti-apoptotic protein Survivin for both normal and tumorigenic colonic stem cells.

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