Metabolic states following accumulation of intracellular aggregates: implications for neurodegenerative diseases

Tau-targeting therapies for Alzheimer disease: current status and future directions

Alzheimer disease (AD) is the most common cause of dementia in older individuals. AD is characterized pathologically by amyloid-β (Aβ) plaques and tau neurofibrillary tangles in the brain, with associated loss of synapses and neurons, which eventually results in dementia. Many of the early attempts to develop treatments for AD focused on Aβ, but a lack of efficacy of these treatments in terms of slowing disease progression led to a change of strategy towards targeting of tau pathology. Given that tau shows a stronger correlation with symptom severity than does Aβ, targeting of tau is more likely to be efficacious once cognitive decline begins. Anti-tau therapies initially focused on post-translational modifications, inhibition of tau aggregation and stabilization of microtubules. However, trials of many potential drugs were discontinued because of toxicity and/or lack of efficacy. Currently, the majority of tau-targeting agents in clinical trials are immunotherapies. In this Review, we provide an update on the results from the initial immunotherapy trials and an overview of new therapeutic candidates that are in clinical development, as well as considering future directions for tau-targeting therapies.

A biomimetic model of 3D fluid extracellular macromolecular crowding microenvironment fine-tunes ovarian cancer cells dissemination phenotype

An early fundamental step in ovarian cancer progression is the dissemination of cancer cells through liquid environments, one of them being cancer ascites accumulated in the peritoneal cavity. These biological fluids are highly crowded with a high total macromolecule concentration. This biophysical property of fluids is widely used in tissue engineering for a few decades now, yet is largely underrated in cancer biomimetic models. To unravel the role of fluids extracellular macromolecular crowding (MMC), we exposed ovarian cancer cells (OCC) to high molecular weight inert polymer solutions. High macromolecular composition of extracellular liquid presented a differential effect: i) it impeded non-adherent OCC aggregation in suspension and, decreased their adhesion; ii) it promoted adherent OCC migration by decreasing extracellular matrix deposition. Besides, there seemed to be a direct link between the extracellular MMC and intracellular processes, especially the actin cytoskeleton organization and the nucleus morphology. In conclusion, extracellular fluid MMC orients OCC dissemination phenotype. Integrating MMC seems crucial to produce more relevant mimetic 3D in vitro fluid models to study ovarian dissemination but also to screen drugs.

Structure of proteins: Evolution with unsolved mysteries

Evolution of macromolecules could be considered as a milestone in the history of life. Nucleic acids are the long stretches of nucleotides that contain all the possible codes and information of life. On the other hand, proteins are their actual translated outcomes, or reflections of modifications in their structure that have occurred at a slow, but steady rate over a very long period of evolution. Over the years of research, biophysicists, biochemists, molecular and structural biologists have unfurled several layers of the structural convolutions in these chemical molecules; however evolutionists look over their structures through a different prism, which may or may not coincide with others. There remains a need to outline several well-known, but less discussed features of protein structures, like intrinsically disordered states, degron signals and different types of ubiquitin chains providing degradation signals, which help the cellular proteolytic machinery to identify and target the proteins towards degradation pathways. There are several important factors, which are critical for folding of proteins into their native three-dimensional conformations by the cytoplasmic chaperones; but in real time how the chaperones fold the newly synthesized polypeptide sequences into a particular three-dimensional shape within a fraction of second is still a mystery for biologists as well as mathematicians. Multiple similar unsolved or unaddressed questions need to be addressed in detail so that future line of research can dig deeper into the finer details of these structures of the proteins.

Tau-targeting therapies for Alzheimer disease

Alzheimer disease (AD) is the most common form of dementia. Pathologically, AD is characterized by amyloid plaques and neurofibrillary tangles in the brain, with associated loss of synapses and neurons, resulting in cognitive deficits and eventually dementia. Amyloid-β (Aβ) peptide and tau protein are the primary components of the plaques and tangles, respectively. In the decades since Aβ and tau were identified, development of therapies for AD has primarily focused on Aβ, but tau has received more attention in recent years, in part because of the failure of various Aβ-targeting treatments in clinical trials. In this article, we review the current status of tau-targeting therapies for AD. Initially, potential anti-tau therapies were based mainly on inhibition of kinases or tau aggregation, or on stabilization of microtubules, but most of these approaches have been discontinued because of toxicity and/or lack of efficacy. Currently, the majority of tau-targeting therapies in clinical trials are immunotherapies, which have shown promise in numerous preclinical studies. Given that tau pathology correlates better with cognitive impairments than do Aβ lesions, targeting of tau is expected to be more effective than Aβ clearance once the clinical symptoms are evident. With future improvements in diagnostics, these two hallmarks of the disease might be targeted prophylactically.

Genome-Scale Brain Metabolic Networks as Scaffolds for the Systems Biology of Neurodegenerative Diseases: Mapping Metabolic Alterations

Systems-based investigation of diseases requires integrated analysis of cellular networks and high-throughput data of gene products. The use of genome-scale metabolic networks for such integration has led to the elucidation of cellular mechanisms for several cell types from microorganisms to plants. It has become easier and cheaper to generate high-throughput data over years in the form of transcriptome, proteome and metabolome. This has tremendously improved the quality and quantity of information extracted from such data enabling the documentation of active pathways and reactions in cell metabolism. A number of omics-based datasets for several neurodegenerative diseases are now available in public repositories. This increases the potential of using genome-scale brain metabolic networks as a scaffold for this type of data to map metabolic alterations for the purpose of elucidating disease mechanisms and for the diagnosis and treatment of such disorders. This chapter first reviews omics data collected for neurodegenerative diseases to map their effect on metabolism. Later, the potential for genome-scale metabolic modeling of such data is reviewed and discussed in light of recently reconstructed brain metabolic networks at genome-scale.

Metabolic network modeling with model organisms

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions.

Computational Flux Balance Analysis Predicts that Stimulation of Energy Metabolism in Astrocytes and their Metabolic Interactions with Neurons Depend on Uptake of K+ Rather than Glutamate

Brain activity involves essential functional and metabolic interactions between neurons and astrocytes. The importance of astrocytic functions to neuronal signaling is supported by many experiments reporting high rates of energy consumption and oxidative metabolism in these glial cells. In the brain, almost all energy is consumed by the Na⁺/K⁺ ATPase, which hydrolyzes 1 ATP to move 3 Na⁺ outside and 2 K⁺ inside the cells. Astrocytes are commonly thought to be primarily involved in transmitter glutamate cycling, a mechanism that however only accounts for few % of brain energy utilization. In order to examine the participation of astrocytic energy metabolism in brain ion homeostasis, here we attempted to devise a simple stoichiometric relation linking glutamatergic neurotransmission to Na⁺ and K⁺ ionic currents. To this end, we took into account ion pumps and voltage/ligand-gated channels using the stoichiometry derived from available energy budget for neocortical signaling and incorporated this stoichiometric relation into a computational metabolic model of neuron-astrocyte interactions. We aimed at reproducing the experimental observations about rates of metabolic pathways obtained by ¹³C-NMR spectroscopy in rodent brain. When simulated data matched experiments as well as biophysical calculations, the stoichiometry for voltage/ligand-gated Na⁺ and K⁺ fluxes generated by neuronal activity was close to a 1:1 relationship, and specifically 63/58 Na⁺/K⁺ ions per glutamate released. We found that astrocytes are stimulated by the extracellular K⁺ exiting neurons in excess of the 3/2 Na⁺/K⁺ ratio underlying Na⁺/K⁺ ATPase-catalyzed reaction. Analysis of correlations between neuronal and astrocytic processes indicated that astrocytic K⁺ uptake, but not astrocytic Na⁺-coupled glutamate uptake, is instrumental for the establishment of neuron-astrocytic metabolic partnership. Our results emphasize the importance of K⁺ in stimulating the activation of astrocytes, which is relevant to the understanding of brain activity and energy metabolism at the cellular level.

Mevalonate Pathway Regulates Cell Size Homeostasis and Proteostasis through Autophagy

Balance between cell growth and proliferation determines cell size homeostasis, but little is known about how metabolic pathways are involved in the maintenance of this balance. Here, we perform a screen with a library of clinically used drug molecules for their effects on cell size. We find that statins, inhibitors of the mevalonate pathway, reduce cell proliferation and increase cell size and cellular protein density in various cell types, including primary human cells. Mevalonate pathway effects on cell size and protein density are mediated through geranylgeranylation of the small GTPase RAB11, which is required for basal autophagic flux. Our results identify the mevalonate pathway as a metabolic regulator of autophagy and expose a paradox in the regulation of cell size and proteostasis, where inhibition of an anabolic pathway can cause an increase in cell size and cellular protein density.

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Mevalonate pathway regulates cell size, proliferation, and cellular protein density

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Geranylgeranylation of RAB11 links mevalonate pathway to autophagy

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Mevalonate pathway is required for basal autophagic flux

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Cell size and protein density effects are dependent on RAB11 and autophagy

Proteome response at the edge of protein aggregation

Proteins adopt defined structures and are crucial to most cellular functions. Their misfolding and aggregation is associated with numerous degenerative human disorders such as type II diabetes, Huntington's or Alzheimer's diseases. Here, we aim to understand why cells promote the formation of protein foci. Comparison of two amyloid-β-peptide variants, mostly insoluble but differently recruited by the cell (inclusion body versus diffused), reveals small differences in cell fitness and proteome response. We suggest that the levels of oxidative stress act as a sensor to trigger protein recruitment into foci. Our data support a common cytoplasmic response being able to discern and react to the specific properties of polypeptides.

Ageing and inflammation - A central role for mitochondria in brain health and disease

To develop successful therapies that prevent or treat neurodegenerative diseases requires an understanding of the upstream events. Ageing is by far the greatest risk factor for most of these diseases, and to clarify their causes will require an understanding of the process of ageing itself. Starting with the question Why do we age as individual organisms, but the line of pluripotent embryonic stem cells and germ cells carried by individuals and transmitted to descendants is immortal? this review discusses how the process of cellular differentiation leads to the accumulation of biological imperfections with ageing, and how these imperfections may be the cause of chronic inflammatory responses to stress that undermine cellular function. Both differentiation and inflammation involve drastic metabolic changes associated with alterations in mitochondrial dynamics that shift the balance between aerobic glycolysis and oxidative phosphorylation. With ageing, mitochondrial dysfunction can be both the cause and consequence of inflammatory processes and elicit metabolic adaptations that might be either protective or become progressively detrimental. It is argued here that an understanding of the relationship between metabolism, differentiation and inflammation is essential to understand the pathological mechanisms governing brain health and disease during ageing.

Glycolysis in patients with age-related macular degeneration

Purpose:

Retinal adenosine triphosphate is mainly produced via glycolysis, so inhibition of glycolysis may promote the onset and progression of age-related macular degeneration (AMD). When glycolysis is inhibited, pyruvate is metabolized by lactic acid fermentation instead of entering the mitochondrial tricarboxylic acid (TCA) cycle. We measured urinary pyruvate and lactate levels in patients with AMD.

Methods:

Eight patients with typical AMD (tAMD group) and 9 patients with polypoidal choroidal vasculopathy (PCV group) were enrolled. Urinary levels of pyruvate, lactate, α-hydroxybutyrate, and β-hydroxybutyrate were measured in all patients.

Results:

The mean urinary levels of pyruvate and lactate were 8.0 ± 2.8 and 7.5 ± 8.3 μg/mg creatinine (reference values: 0.5-6.6 and 0.0-1.6), respectively, with the mean increase over the reference value being 83.6 ± 51.1% and 426.5 ± 527.8%, respectively. In 12 patients (70.6%), the lactate/pyruvate ratio was above the reference range. Urinary levels of α-hydroxybutyrate and β-hydroxybutyrate were decreased by -31.9 ± 15.2% and -33.1 ± 17.5% compared with the mean reference values. There were no significant differences of any of these glycolysis metabolites between the tAMD and PCV groups. Multivariate analysis revealed that none of the variables tested, including patient background factors (age, hypertension, diabetes, hyperlipidemia, cerebrovascular disease, alcohol, smoking, visual acuity, and AMD phenotype), were significantly associated with the lactate/pyruvate ratio.

Conclusion:

A high lactate/pyruvate ratio is a well-known marker of mitochondrial impairment, and it indicates poor oxidative function in AMD. Our results suggest that increased lactate levels may be implicated in the pathogenesis of AMD.

Transcriptional and metabolic adaptation of human neurons to the mitochondrial toxicant MPP(+)

Assessment of the network of toxicity pathways by Omics technologies and bioinformatic data processing paves the road toward a new toxicology for the twenty-first century. Especially, the upstream network of responses, taking place in toxicant-treated cells before a point of no return is reached, is still little explored. We studied the effects of the model neurotoxicant 1-methyl-4-phenylpyridinium (MPP⁺) by a combined metabolomics (mass spectrometry) and transcriptomics (microarrays and deep sequencing) approach to provide unbiased data on earliest cellular adaptations to stress. Neural precursor cells (LUHMES) were differentiated to homogeneous cultures of fully postmitotic human dopaminergic neurons, and then exposed to the mitochondrial respiratory chain inhibitor MPP⁺ (5 μM). At 18–24 h after treatment, intracellular ATP and mitochondrial integrity were still close to control levels, but pronounced transcriptome and metabolome changes were seen. Data on altered glucose flux, depletion of phosphocreatine and oxidative stress (e.g., methionine sulfoxide formation) confirmed the validity of the approach. New findings were related to nuclear paraspeckle depletion, as well as an early activation of branches of the transsulfuration pathway to increase glutathione. Bioinformatic analysis of our data identified the transcription factor ATF-4 as an upstream regulator of early responses. Findings on this signaling pathway and on adaptive increases of glutathione production were confirmed biochemically. Metabolic and transcriptional profiling contributed complementary information on multiple primary and secondary changes that contribute to the cellular response to MPP⁺. Thus, combined ‘Omics' analysis is a new unbiased approach to unravel earliest metabolic changes, whose balance decides on the final cell fate.