Age-correlated gene expression in normal and neurodegenerative human brain tissues

Convergence of Accelerated Brain Volume Decline in Normal Aging and Alzheimer's Disease Pathology

Background

Age represents the largest risk factor for Alzheimer's disease (AD) but is typically treated as a covariate. Still, there are similarities between brain regions affected in AD and those showing accelerated decline in normal aging, suggesting that the distinction between the two might fall on a spectrum.

Objective

Our goal was to identify regions showing accelerated atrophy across the brain and investigate whether these overlapped with regions involved in AD or where related to amyloid.

Methods

We used a longitudinal sample of 137 healthy older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI), who underwent magnetic resonance imaging (MRI). In addition, a total of 79 participants also had longitudinal positron emission tomography (PET) data. We computed linear-mixed effects models for brain regions declining faster than the average to investigate variability in the rate of change.

Results

23 regions displayed a 0.5 standard deviation (SD) above average decline over 2 years. Of these, 52% overlapped with regions showing similar decline in a matched AD sample. Beyond this, the left precuneus, right superior frontal, transverse temporal, and superior temporal sulcus showed accelerated decline. Lastly, atrophy in the precuneus was associated with increased amyloid load.

Conclusions

Accelerated decline in normal aging might contribute to the detection of early signs of AD among healthy individuals.

Genetic Causes of Alzheimer's Disease and the Neuroprotective Role of Melatonin in its Management

Dementia is a global health concern owing to its complexity, which also poses a great challenge to pharmaceutical scientists and neuroscientists. The global dementia prevalence is approximately 47 million, which may increase by three times by 2050. Alzheimer's disease (AD) is the most common cause of dementia. AD is a severe age-related neurodegenerative disorder characterized by short-term memory loss, aphasia, mood imbalance, and executive function. The etiology of AD is still unknown, and the exact origin of the disease is still under investigation. Aggregation of Amyloid β (Aβ) plaques or neurotoxic Aβo oligomers outside the neuron is the most common cause of AD development. Amyloid precursor protein (APP) processing by β secretase and γ secretase produces abnormal Aβ monomers. This aggregation of Aβ and NFT is promoted by various genes like BACE1, ADAM10, PIN1, GSK-3, APOE, PPARα, etc. Identification of these genes can discover several therapeutic targets that can be useful in studying pathogenesis and underlying treatments. Melatonin modulates the activities of these genes, thereby reducing Aβ production and increasing its clearance. Melatonin also reduces the expression of APP by attenuating cAMP, thereby enhancing the non-amyloidogenic process. Present communication explored and discussed the neuroprotective role of melatonin against Aβ-dependent AD pathogenesis. The manuscript also discussed potential molecular and genetic mechanisms of melatonin in the production and clearance of Aβ that could ameliorate neurotoxicity.

Longitudinal Analysis of Brain-Predicted Age in Amnestic and Non-amnestic Sporadic Early-Onset Alzheimer's Disease

Objective: Predicted age difference (PAD) is a score computed by subtracting chronological age from "brain" age, which is estimated using neuroimaging data. The goal of this study was to evaluate the PAD as a marker of phenotypic heterogeneity and severity among early-onset Alzheimer's disease (EOAD) patients. Methods: We first used 3D T1-weighted (3D-T1) magnetic resonance images (MRI) of 3,227 healthy subjects aged between 18 and 85 years to train, optimize, and evaluate the brain age model. A total of 123 participants who met the criteria for early-onset (<65 years) sporadic form of probable Alzheimer's disease (AD) and presented with two distinctive clinical presentations [an amnestic form (n = 74) and a non-amnestic form (n = 49)] were included at baseline and followed-up for a maximum period of 4 years. All the participants underwent a work-up at baseline and every year during the follow-up period, which included clinical examination, neuropsychological testing and genotyping, and structural MRI. In addition, cerebrospinal fluid biomarker assay was recorded at baseline. PAD score was calculated by applying brain age model to 3D-T1 images of the EOAD patients and healthy controls, who were matched based on age and sex. At baseline, between-group differences for neuropsychological and PAD scores were assessed using linear models. Regarding longitudinal analysis of neuropsychological and PAD scores, differences between amnestic and non-amnestic participants were analyzed using linear mixed-effects modeling. Results: PAD score was significantly higher for non-amnestic patients (2.35 ± 0.91) when compared to amnestic patients (2.09 ± 0.74) and controls (0.00 ± 1). Moreover, PAD score was linearly correlated with the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating Sum of Boxes (CDR-SB), for both amnestic and non-amnestic sporadic forms. Longitudinal analyses showed that the gradual development of the disease in patients was accompanied by a significant increase in PAD score over time, for both amnestic and non-amnestic patients. Conclusion: PAD score was able to separate amnestic and non-amnestic sporadic forms. Regardless of the clinical presentation, as PAD score was a way of quantifying an early brain age acceleration, it was an appropriate method to detect the development of AD and follow the evolution of the disease as a marker of severity as MMSE and CDR-SB.

Factors associated with brain ageing - a systematic review

Background

Brain age is a biomarker that predicts chronological age using neuroimaging features. Deviations of this predicted age from chronological age is considered a sign of age-related brain changes, or commonly referred to as brain ageing. The aim of this systematic review is to identify and synthesize the evidence for an association between lifestyle, health factors and diseases in adult populations, with brain ageing.

Methods

This systematic review was undertaken in accordance with the PRISMA guidelines. A systematic search of Embase and Medline was conducted to identify relevant articles using search terms relating to the prediction of age from neuroimaging data or brain ageing. The tables of two recent review papers on brain ageing were also examined to identify additional articles. Studies were limited to adult humans (aged 18 years and above), from clinical or general populations. Exposures and study design of all types were also considered eligible.

Results

A systematic search identified 52 studies, which examined brain ageing in clinical and community dwelling adults (mean age between 21 to 78 years, ~ 37% were female). Most research came from studies of individuals diagnosed with schizophrenia or Alzheimer’s disease, or healthy populations that were assessed cognitively. From these studies, psychiatric and neurologic diseases were most commonly associated with accelerated brain ageing, though not all studies drew the same conclusions. Evidence for all other exposures is nascent, and relatively inconsistent. Heterogenous methodologies, or methods of outcome ascertainment, were partly accountable.

Conclusion

This systematic review summarised the current evidence for an association between genetic, lifestyle, health, or diseases and brain ageing. Overall there is good evidence to suggest schizophrenia and Alzheimer’s disease are associated with accelerated brain ageing. Evidence for all other exposures was mixed or limited. This was mostly due to a lack of independent replication, and inconsistency across studies that were primarily cross sectional in nature. Future research efforts should focus on replicating current findings, using prospective datasets.

Trial registration

A copy of the review protocol can be accessed through PROSPERO, registration number CRD42020142817.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02331-4.

MicroRNAs as Candidate Biomarkers for Alzheimer's Disease

The neurological damage of Alzheimer’s disease (AD) is thought to be irreversible upon onset of dementia-like symptoms, as it takes years to decades for occult pathologic changes to become symptomatic. It is thus necessary to identify individuals at risk for the development of the disease before symptoms manifest in order to provide early intervention. Surrogate markers are critical for early disease detection, stratification of patients in clinical trials, prediction of disease progression, evaluation of response to treatment, and also insight into pathomechanisms. Here, we review the evidence for a number of microRNAs that may serve as biomarkers with possible mechanistic insights into the AD pathophysiologic processes, years before the clinical manifestation of the disease.

The self-organization model reveals systematic characteristics of aging

BACKGROUND

Aging is a fundamental biological process, where key bio-markers interact with each other and synergistically regulate the aging process. Thus aging dysfunction will induce many disorders. Finding aging markers and re-constructing networks based on multi-omics data (i.e. methylation, transcriptional and so on) are informative to study the aging process. However, optimizing the model to predict aging have not been performed systemically, although it is critical to identify potential molecular mechanism of aging related diseases.

METHODS

This paper aims to model the aging self-organization system using a series of supervised learning methods, and study complex molecular mechanisms of aging at system level: i.e. optimizing the aging network; summarizing interactions between aging markers; accumulating patterns of aging markers within module; finding order-parameters in the aging self-organization system.

RESULTS

In this work, the normal aging process is modeled based on multi-omics profiles across different tissues. In addition, the computational pipeline aims to model aging self-organizing systems and study the relationship between aging and related diseases (i.e. cancers), thus provide useful indicators of aging related diseases and could help to improve prediction abilities of diagnostics.

CONCLUSIONS

The aging process could be studied thoroughly by modelling the self-organization system, where key functions and the crosstalk between aging and cancers were identified.

Rate of brain aging and APOE ε4 are synergistic risk factors for Alzheimer's disease

This study describes a gauge for brain aging predictive of cognitive decline and Alzheimer's disease synergistic with the APOE ε4 allele.

Advanced age and the APOE ε4 allele are the two biggest risk factors for Alzheimer’s disease (AD) and declining cognitive function. We describe a universal gauge to measure molecular brain age using transcriptome analysis of four human postmortem cohorts (n = 673, ages 25–97) free of neurological disease. In a fifth cohort of older subjects with or without neurological disease (n = 438, ages 67–108), we show that subjects with brains deviating in the older direction from what would be expected based on chronological age show an increase in AD, Parkinson’s disease, and cognitive decline. Strikingly, a younger molecular age (−5 yr than chronological age) protects against AD even in the presence of APOE ε4. An established DNA methylation gauge for age correlates well with the transcriptome gauge for determination of molecular age and assigning deviations from the expected. Our results suggest that rapid brain aging and APOE ε4 are synergistic risk factors, and interventions that slow aging may substantially reduce risk of neurological disease and decline even in the presence of APOE ε4.

Temporal Integrative Analysis of mRNA and microRNAs Expression Profiles and Epigenetic Alterations in Female SAMP8, a Model of Age-Related Cognitive Decline

A growing body of research shows that epigenetic mechanisms are critically involved in normal and pathological aging. The Senescence-Accelerated Mouse Prone 8 (SAMP8) can be considered a useful tool to better understand the dynamics of the global epigenetic landscape during the aging process since its phenotype is not fully explained by genetic factors. Here we investigated dysfunctional age-related transcriptional profiles and epigenetic programming enzymes in the hippocampus of 2- and 9-month-old SAMP8 female mice using the Senescent-Accelerated Resistant 1 (SAMR1) mouse strain as control. SAMP8 mice presented 1,062 genes dysregulated at 2 months of age, and 1,033 genes at 9 months, with 92 genes concurrently dysregulated at both ages compared to age-matched SAMR1. SAMP8 mice showed a significant decrease in global DNA methylation (5-mC) at 2 months while hydroxymethylation (5-hmC) levels were increased in SAMP8 mice at 2 and 9 months of age compared to SAMR1. These changes were accompanied by changes in the expression of several enzymes that regulate 5-mC and methylcytosine oxidation. Acetylated H3 and H4 histone levels were significantly diminished in SAMP8 mice at 2-month-old compared to SAMR1 and altered Histone DeACetylase (HDACs) profiles were detected in both young and old SAMP8 mice. We analyzed 84 different mouse miRNAs known to be altered in neurological diseases or involved in neuronal development. Compared with SAMR1, SAMP8 mice showed 28 and 17 miRNAs differentially expressed at 2 and 9 months of age, respectively; 6 of these miRNAs overlapped at both ages. We used several bioinformatic approaches to integrate our data in mRNA:miRNA regulatory networks and functional predictions for young and aged animals. In sum, our study reveals interplay between epigenetic mechanisms and gene networks that seems to be relevant for the progression toward a pathological aging and provides several potential markers and therapeutic candidates for Alzheimer's Disease (AD) and age-related cognitive impairment.

MiR-34 inhibits polycomb repressive complex 2 to modulate chaperone expression and promote healthy brain aging

Aging is a prominent risk factor for neurodegenerative disease. Defining gene expression mechanisms affecting healthy brain aging should lead to insight into genes that modulate susceptibility to disease. To define such mechanisms, we have pursued analysis of miR-34 mutants in Drosophila. The miR-34 mutant brain displays a gene expression profile of accelerated aging, and miR-34 upregulation is a potent suppressor of polyglutamine-induced neurodegeneration. We demonstrate that Pcl and Su(z)12, two components of polycomb repressive complex 2, (PRC2), are targets of miR-34, with implications for age-associated processes. Because PRC2 confers the repressive H3K27me3 mark, we hypothesize that miR-34 modulates PRC2 activity to relieve silencing of genes promoting healthful aging. Gene expression profiling of the brains of hypomorphic mutants in Enhancer of zeste (E(z)), the enzymatic methyltransferase component of PRC2, revealed a younger brain transcriptome profile and identified the small heat shock proteins as key genes reduced in expression with age.

miR-34 is known to regulate age-related gene expression in the Drosophila brain, and miR-34 overexpression can attenuate neurodegeneration induced by polyQ-expanded proteins. Here, Kennerdell and colleagues show that miR-34 confers longevity and neuroprotection via an epigenetic regulator Polycomb Repressive Complex 2 and molecular chaperone expression.

Integrative analysis of methylation and transcriptional profiles to predict aging and construct aging specific cross-tissue networks

BACKGROUND

Aging is a complex process relating multi-scale omics data. Finding key age markers in normal tissues could help to provide reliable aging predictions in human. However, predicting age based on multi-omics data with both accuracy and informative biological function has not been performed systematically, thus relative cross-tissue analysis has not been investigated entirely, either.

RESULTS

Here we have developed an improved prediction pipeline, the Integrating and Stepwise Age-Prediction (ISAP) method, to regress age and find key aging markers effectively. Furthermore, we have performed a serious of network analyses, such as the PPI network, cross-tissue networks and pathway interaction networks.

CONCLUSION

Our results find important coordinated aging patterns between different tissues. Both co-profiling and cross-pathway analyses identify more thorough functions of aging, and could help to find aging markers, pathways and relative aging disease researches.

Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases

Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer's disease (AD) and Huntington's disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10⁻¹²), while Dnmt3a KO signature does not (P = 0.017).

Chaperoning proteins for destruction: diverse roles of Hsp70 chaperones and their co-chaperones in targeting misfolded proteins to the proteasome

Molecular chaperones were originally discovered as heat shock-induced proteins that facilitate proper folding of proteins with non-native conformations. While the function of chaperones in protein folding has been well documented over the last four decades, more recent studies have shown that chaperones are also necessary for the clearance of terminally misfolded proteins by the Ub-proteasome system. In this capacity, chaperones protect misfolded degradation substrates from spontaneous aggregation, facilitate their recognition by the Ub ligation machinery and finally shuttle the ubiquitylated substrates to the proteasome. The physiological importance of these functions is manifested by inefficient proteasomal degradation and the accumulation of protein aggregates during ageing or in certain neurodegenerative diseases, when chaperone levels decline. In this review, we focus on the diverse roles of stress-induced chaperones in targeting misfolded proteins to the proteasome and the consequences of their compromised activity. We further discuss the implications of these findings to the identification of new therapeutic targets for the treatment of amyloid diseases.

An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways

Epigenetic changes have been associated with ageing and cancer. Identifying and interpreting epigenetic changes associated with such phenotypes may benefit from integration with protein interactome models. We here develop and validate a novel integrative epigenome-interactome approach to identify differential methylation interactome hotspots associated with a phenotype of interest. We apply the algorithm to cancer and ageing, demonstrating the existence of hotspots associated with these phenotypes. Importantly, we discover tissue independent age-associated hotspots targeting stem-cell differentiation pathways, which we validate in independent DNA methylation data sets, encompassing over 1000 samples from different tissue types. We further show that these pathways would not have been discovered had we used a non-network based approach and that the use of the protein interaction network improves the overall robustness of the inference procedure. The proposed algorithm will be useful to any study seeking to identify interactome hotspots associated with common phenotypes.

Gender-specific impact of personal health parameters on individual brain aging in cognitively unimpaired elderly subjects

Aging alters brain structure and function. Personal health markers and modifiable lifestyle factors are related to individual brain aging as well as to the risk of developing Alzheimer's disease (AD). This study used a novel magnetic resonance imaging (MRI)-based biomarker to assess the effects of 17 health markers on individual brain aging in cognitively unimpaired elderly subjects. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for developing AD. Within this cross-sectional, multi-center study 228 cognitively unimpaired elderly subjects (118 males) completed an MRI at 1.5Tesla, physiological and blood parameter assessments. The multivariate regression model combining all measured parameters was capable of explaining 39% of BrainAGE variance in males (p < 0.001) and 32% in females (p < 0.01). Furthermore, markers of the metabolic syndrome as well as markers of liver and kidney functions were profoundly related to BrainAGE scores in males (p < 0.05). In females, markers of liver and kidney functions as well as supply of vitamin B₁₂ were significantly related to BrainAGE (p < 0.05). In conclusion, in cognitively unimpaired elderly subjects several clinical markers of poor health were associated with subtle structural changes in the brain that reflect accelerated aging, whereas protective effects on brain aging were observed for markers of good health. Additionally, the relations between individual brain aging and miscellaneous health markers show gender-specific patterns. The BrainAGE approach may thus serve as a clinically relevant biomarker for the detection of subtly abnormal patterns of brain aging probably preceding cognitive decline and development of AD.

Rare Variants and Transcriptomics in Alzheimer disease

Alzheimer disease (AD) is the most common dementia in the elderly, still without effective treatment. Early-onset AD (EOAD) is caused by mutations in the genes APP, PSEN1 and PSEN2. Genome-wide association studies have identified >20 late-onset AD (LOAD) susceptibility genes with common variants of small risk, with the exception of APOE. We review rare susceptibility variants in LOAD with larger effects that have been recently identified in the EOAD gene APP and the newly discovered AD genes TREM2 and PLD3. Human genetic studies now consistently support the amyloid hypothesis of AD for both EOAD and LOAD. Moreover, they identified biological processes that overlap with human transcriptomics studies in AD across different tissues, such as inflammation, cytoskeletal organization, synaptic functions, etc. Transcriptomic profiles of pre-symptomatic AD-associated variant carriers already reflect specific molecular mechanisms reminiscent to those of AD patients. This might provide an avenue for personalized medicine.

Analysis of nonlinear gene expression progression reveals extensive pathway and age-specific transitions in aging human brains

Several recent gene expression studies identified hundreds of genes that are correlated with age in brain and other tissues in human. However, these studies used linear models of age correlation, which are not well equipped to model abrupt changes associated with particular ages. We developed a computational algorithm for age estimation in which the expression of each gene is treated as a dichotomized biomarker for whether the subject is older or younger than a particular age. In addition, for each age-informative gene our algorithm identifies the age threshold with the most drastic change in expression level, which allows us to associate genes with particular age periods. Analysis of human aging brain expression datasets from three frontal cortex regions showed that different pathways undergo transitions at different ages, and the distribution of pathways and age thresholds varies across brain regions. Our study reveals age-correlated expression changes at particular age points and allows one to estimate the age of an individual with better accuracy than previously published methods.

Distinctive topology of age-associated epigenetic drift in the human interactome

Recently, it has been demonstrated that DNA methylation, a covalent modification of DNA that can regulate gene expression, is modified as a function of age. However, the biological and clinical significance of this age-associated epigenetic drift is unclear. To shed light on the potential biological significance, we here adopt a systems approach and study the genes undergoing age-associated changes in DNA methylation in the context of a protein interaction network, focusing on their topological properties. In contrast to what has been observed for other age-related gene classes, including longevity- and disease-associated genes, as well as genes undergoing age-associated changes in gene expression, we here demonstrate that age-associated epigenetic drift occurs preferentially in genes that occupy peripheral network positions of exceptionally low connectivity. In addition, we show that these genes synergize topologically with disease and longevity genes, forming unexpectedly large network communities. Thus, these results point toward a potentially distinct mechanistic and biological role of DNA methylation in dictating the complex aging and disease phenotypes.

BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease

Alzheimer’s disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging patterns. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for conversion to AD. Here, the BrainAGE framework was applied to predict the individual brain ages of 195 subjects with MCI at baseline, of which a total of 133 developed AD during 36 months of follow-up (corresponding to a pre-test probability of 68%). The ability of the BrainAGE framework to correctly identify MCI-converters was compared with the performance of commonly used cognitive scales, hippocampus volume, and state-of-the-art biomarkers derived from cerebrospinal fluid (CSF). With accuracy rates of up to 81%, BrainAGE outperformed all cognitive scales and CSF biomarkers in predicting conversion of MCI to AD within 3 years of follow-up. Each additional year in the BrainAGE score was associated with a 10% greater risk of developing AD (hazard rate: 1.10 [CI: 1.07–1.13]). Furthermore, the post-test probability was increased to 90% when using baseline BrainAGE scores to predict conversion to AD. The presented framework allows an accurate prediction even with multicenter data. Its fast and fully automated nature facilitates the integration into the clinical workflow. It can be exploited as a tool for screening as well as for monitoring treatment options.

Ubiquitin conjugation triggers misfolded protein sequestration into quality control foci when Hsp70 chaperone levels are limiting

Ubiquitylation of partially misfolded proteins by the yeast Doa10 E3 ligase requires the Hsp40 cochaperone Sis1, whereas the Hsp70 chaperones Ssa1 and Ssa2 are dispensable. Elimination of the Hsp70 chaperones prevents proteasomal degradation, resulting in ubiquitin-dependent sequestration of the misfolded proteins in Hsp42-positive foci.

Ubiquitin accumulation in amyloid plaques is a pathological marker observed in the vast majority of neurodegenerative diseases, yet ubiquitin function in these inclusions is controversial. It has been suggested that ubiquitylated proteins are directed to inclusion bodies under stress conditions, when both chaperone-mediated refolding and proteasomal degradation are compromised or overwhelmed. Alternatively, ubiquitin and chaperones may be recruited to preformed inclusions to promote their elimination. We address this issue using a yeast model system, based on expression of several mildly misfolded degradation substrates in cells with altered chaperone content. We find that the heat shock protein 70 (Hsp70) chaperone pair Ssa1/Ssa2 and the Hsp40 cochaperone Sis1 are essential for degradation. Substrate ubiquitylation is strictly dependent on Sis1, whereas Ssa1 and Ssa2 are dispensable. Remarkably, in Ssa1/Ssa2-depleted cells, ubiquitylated substrates are sequestered into detergent-insoluble, Hsp42-positive inclusion bodies. Unexpectedly, sequestration is abolished by preventing substrate ubiquitylation. We conclude that Hsp40 is required for the targeting of misfolded proteins to the ubiquitylation machinery, whereas the decision to degrade or sequester ubiquitylated proteins is mediated by the Hsp70s. Accordingly, diminished Hsp70 levels, as observed in aging or certain pathological conditions, might be sufficient to trigger ubiquitin-dependent sequestration of partially misfolded proteins into inclusion bodies.

Brain maturation: predicting individual BrainAGE in children and adolescents using structural MRI

BACKGROUND

Neural development during human childhood and adolescence involves highly coordinated and sequenced events, characterized by both progressive and regressive processes. Despite a multitude of results demonstrating the age-dependent development of gray matter, white matter, and total brain volume, a reference curve allowing prediction of structural brain maturation is still lacking but would be clinically valuable. For the first time, the present study provides a validated reference curve for structural brain maturation during childhood and adolescence, based on structural MRI data.

METHODS AND FINDINGS

By employing kernel regression methods, a novel but well-validated BrainAGE framework uses the complex multidimensional maturation pattern across the whole brain to estimate an individual's brain age. The BrainAGE framework was applied to a large human sample (n=394) of healthy children and adolescents, whose image data had been acquired during the NIH MRI study of normal brain development. Using this approach, we were able to predict individual brain maturation with a clinically meaningful accuracy: the correlation between predicted brain age and chronological age resulted in r=0.93. The mean absolute error was only 1.1 years. Moreover, the predicted brain age reliably differentiated between all age groups (i.e., preschool childhood, late childhood, early adolescence, middle adolescence, late adolescence). Applying the framework to preterm-born adolescents resulted in a significantly lower estimated brain age than chronological age in subjects who were born before the end of the 27th week of gestation, demonstrating the successful clinical application and future potential of this method.

CONCLUSIONS

Consequently, in the future this novel BrainAGE approach may prove clinically valuable in detecting both normal and abnormal brain maturation, providing important prognostic information.

Prevention of age-related changes in hippocampal levels of 5-methylcytidine by caloric restriction

Aberrant DNA methylation patterns have been linked to molecular and cellular alterations in the aging brain. Caloric restriction (CR) and upregulation of antioxidants have been proposed as interventions to prevent or delay age-related brain pathology. Previously, we have shown in large cohorts of aging mice, that age-related increases in DNA methyltransferase 3a (Dnmt3a) immunoreactivity in the mouse hippocampus were attenuated by CR, but not by overexpression of superoxide dismutase 1 (SOD1). Here, we investigated age-related alterations of 5-methylcytidine (5-mC), a marker of DNA methylation levels, in a hippocampal subregion-specific manner. Examination of 5-mC immunoreactivity in 12- and 24-month-old wild type (WT) mice on control diet, mice overexpressing SOD1 on control diet, wild type mice on CR, and SOD1 mice on CR, indicated an age-related increase in 5-mC immunoreactivity in the hippocampal dentate gyrus, CA3, and CA1-2 regions, which was prevented by CR but not by SOD1 overexpression. Moreover, positive correlations between 5-mC and Dnmt3a immunoreactivity were observed in the CA3 and CA1-2. These findings suggest a crucial role for DNA methylation in hippocampal aging and in the mediation of the beneficial effects of CR on aging.

RNA-Seq and human complex diseases: recent accomplishments and future perspectives

The availability of the human genome sequence has allowed identification of disease-causing mutations in many Mendelian disorders, and detection of significant associations of nucleotide polymorphisms to complex diseases and traits. Despite these progresses, finding the causative variations for most of the common diseases remains a complex task. Several studies have shown gene expression analyses provide a quite unbiased way to investigate complex traits and common disorders' pathogenesis. Therefore, whole-transcriptome analysis is increasingly acquiring a key role in the knowledge of mechanisms responsible for complex diseases. Hybridization- and tag-based technologies have elucidated the involvement of multiple genes and pathways in pathological conditions, providing insights into the expression of thousand of coding and noncoding RNAs, such as microRNAs. However, the introduction of Next-Generation Sequencing, particularly of RNA-Seq, has overcome some drawbacks of previously used technologies. Identifying, in a single experiment, potentially novel genes/exons and splice isoforms, RNA editing, fusion transcripts and allele-specific expression are some of its advantages. RNA-Seq has been fruitfully applied to study cancer and host-pathogens interactions, and it is taking first steps for studying neurodegenerative diseases (ND) as well as neuropsychiatric diseases. In addition, it is emerging as a very powerful tool to study quantitative trait loci associated with gene expression in complex diseases. This paper provides an overview on gene expression profiling of complex diseases, with emphasis on RNA-Seq, its advantages over conventional technologies for studying cancer and ND, and for linking nucleotide variations to gene expression changes, also discussing its limitations.

The microRNA miR-34 modulates ageing and neurodegeneration in Drosophila

Human neurodegenerative diseases possess the temporal hallmark of afflicting the elderly population. Hence, aging is among the most significant factors to impinge on disease onset and progression¹, yet little is known of molecular pathways that connect these processes. Central to understanding this connection is to unmask the nature of pathways that functionally integrate aging, chronic maintenance of the brain and modulation of neurodegenerative disease. microRNAs (miRNA) are emerging as critical players in gene regulation during development, yet their role in adult-onset, age-associated processes are only beginning to be revealed. Here we report that the conserved miRNA miR-34 regulates age-associated events and long-term brain integrity in Drosophila, presenting such a molecular link between aging and neurodegeneration. Fly miR-34 expression is adult-onset, brain-enriched and age-modulated. Whereas miR-34 loss triggers a gene profile of accelerated brain aging, late-onset brain degeneration and a catastrophic decline in survival, miR-34 upregulation extends median lifespan and mitigates neurodegeneration induced by human pathogenic polyglutamine (polyQ) disease protein. Some of the age-associated effects of miR-34 require adult-onset translational repression of Eip74EF, an essential ETS domain transcription factor involved in steroid hormone pathways. These studies indicate that miRNA-dependent pathways may impact adult-onset, age-associated events by silencing developmental genes that later have a deleterious influence on adult life cycle and disease, and highlight fly miR-34 as a key miRNA with a role in this process

Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longitudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer’s disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive functioning in the future.

Molecular insights into the pathogenesis of Alzheimer's disease and its relationship to normal aging

Alzheimer's disease (AD) is a complex neurodegenerative disorder that diverges from the process of normal brain aging by unknown mechanisms. We analyzed the global structure of age- and disease-dependent gene expression patterns in three regions from more than 600 brains. Gene expression variation could be almost completely explained by four transcriptional biomarkers that we named BioAge (biological age), Alz (Alzheimer), Inflame (inflammation), and NdStress (neurodegenerative stress). BioAge captures the first principal component of variation and includes genes statistically associated with neuronal loss, glial activation, and lipid metabolism. Normally BioAge increases with chronological age, but in AD it is prematurely expressed as if some of the subjects were 140 years old. A component of BioAge, Lipa, contains the AD risk factor APOE and reflects an apparent early disturbance in lipid metabolism. The rate of biological aging in AD patients, which cannot be explained by BioAge, is associated instead with NdStress, which includes genes related to protein folding and metabolism. Inflame, comprised of inflammatory cytokines and microglial genes, is broadly activated and appears early in the disease process. In contrast, the disease-specific biomarker Alz was selectively present only in the affected areas of the AD brain, appears later in pathogenesis, and is enriched in genes associated with the signaling and cell adhesion changes during the epithelial to mesenchymal (EMT) transition. Together these biomarkers provide detailed description of the aging process and its contribution to Alzheimer's disease progression.

microRNA-34c is a novel target to treat dementias

MicroRNAs are key regulators of transcriptome plasticity and have been implicated with the pathogenesis of brain diseases. Here, we employed massive parallel sequencing and provide, at an unprecedented depth, the complete and quantitative miRNAome of the mouse hippocampus, the prime target of neurodegenerative diseases such as Alzheimer's disease (AD). Using integrative genetics, we identify miR-34c as a negative constraint of memory consolidation and show that miR-34c levels are elevated in the hippocampus of AD patients and corresponding mouse models. In line with this, targeting miR-34 seed rescues learning ability in these mouse models. Our data suggest that miR-34c could be a marker for the onset of cognitive disturbances linked to AD and indicate that targeting miR-34c could be a suitable therapy.