IGF2 prevents dopaminergic neuronal loss and decreases intracellular alpha-synuclein accumulation in Parkinson's disease models.

Parkinson's disease (PD) is the second most common late-onset neurodegenerative disease and the predominant cause of movement problems. PD is characterized by motor control impairment by extensive loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc). This selective dopaminergic neuronal loss is in part triggered by intracellular protein inclusions called Lewy bodies, which are composed mainly of misfolded alpha-synuclein (α-syn) protein. We previously reported insulin-like growth factor 2 (IGF2) as a key protein downregulated in PD patients. Here we demonstrated that IGF2 treatment or IGF2 overexpression reduced the α-syn aggregates and their toxicity by IGF2 receptor (IGF2R) activation in cellular PD models. Also, we observed IGF2 and its interaction with IGF2R enhance the α-syn secretion. To determine the possible IGF2 neuroprotective effect in vivo we used a gene therapy approach in an idiopathic PD model based on α-syn preformed fibrils intracerebral injection. IGF2 gene therapy revealed a significantly preventing of motor impairment in idiopathic PD model. Moreover, IGF2 expression prevents dopaminergic neuronal loss in the SN together with a decrease in α-syn accumulation (phospho-α-syn levels) in the striatum and SN brain region. Furthermore, the IGF2 neuroprotective effect was associated with the prevention of synaptic spines loss in dopaminergic neurons in vivo. The possible mechanism of IGF2 in cell survival effect could be associated with the decrease of the intracellular accumulation of α-syn and the improvement of dopaminergic synaptic function. Our results identify to IGF2 as a relevant factor for the prevention of α-syn toxicity in both in vitro and preclinical PD models.

Assessment of Bio-Compounds Content, Antioxidant Activity, and Neuroprotective Effect of Red Cabbage (Brassica oleracea var. Capitata rubra) Processed by Convective Drying at Different Temperatures.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, and no efficient therapy able to cure or slow down PD is available. In this study, dehydrated red cabbage was evaluated as a novel source of bio-compounds with neuroprotective capacity. Convective drying was carried out at different temperatures. Total phenolics (TPC), flavonoids (TFC), anthocyanins (TAC), and glucosinolates (TGC) were determined using spectrophotometry, amino acid profile by LC-DAD and fatty acid profile by GC-FID. Phenolic characterization was determined by liquid chromatography-high-resolution mass spectrometry. Cytotoxicity and neuroprotection assays were evaluated in SH-SY5Y human cells, observing the effect on preformed fibrils of α-synuclein. Drying kinetic confirmed a shorter processing time with temperature increase. A high concentration of bio-compounds was observed, especially at 90 °C, with TPC = 1544.04 ± 11.4 mg GAE/100 g, TFC = 690.87 ± 4.0 mg QE/100 g and TGC = 5244.9 ± 260.2 µmol SngE/100 g. TAC degraded with temperature. Glutamic acid and arginine were predominant. Fatty acid profiles were relatively stable and were found to be mostly C18:3n3. The neochlorogenic acid was predominant. The extracts had no cytotoxicity and showed a neuroprotective effect at 24 h testing, which can extend in some cases to 48 h. The present findings underpin the use of red cabbage as a functional food ingredient.

Different pieces of the same puzzle: a multifaceted perspective on the complex biological basis of Parkinson's disease.

The biological basis of the neurodegenerative movement disorder, Parkinson's disease (PD), is still unclear despite it being 'discovered' over 200 years ago in Western Medicine. Based on current PD knowledge, there are widely varying theories as to its pathobiology. The aim of this article was to explore some of these different theories by summarizing the viewpoints of laboratory and clinician scientists in the PD field, on the biological basis of the disease. To achieve this aim, we posed this question to thirteen "PD experts" from six continents (for global representation) and collated their personal opinions into this article. The views were varied, ranging from toxin exposure as a PD trigger, to LRRK2 as a potential root cause, to toxic alpha-synuclein being the most important etiological contributor. Notably, there was also growing recognition that the definition of PD as a single disease should be reconsidered, perhaps each with its own unique pathobiology and treatment regimen.

Transcriptional and Histone Acetylation Changes Associated with CRE Elements Expose Key Factors Governing the Regulatory Circuit in the Early Stage of Huntington's Disease Models.

Huntington's disease (HD) is a disorder caused by an abnormal expansion of trinucleotide CAG repeats within the huntingtin (Htt) gene. Under normal conditions, the CREB Binding Protein interacts with CREB elements and acetylates Lysine 27 of Histone 3 to direct the expression of several genes. However, mutant Htt causes depletion of CBP, which in turn induces altered histone acetylation patterns and transcriptional deregulation. Here, we have studied a differential expression analysis and H3K27ac variation in 4- and 6-week-old R6/2 mice as a model of juvenile HD. The analysis of differential gene expression and acetylation levels were integrated into Gene Regulatory Networks revealing key regulators involved in the altered transcription cascade. Our results show changes in acetylation and gene expression levels that are related to impaired neuronal development, and key regulators clearly defined in 6-week-old mice are proposed to drive the downstream regulatory cascade in HD. Here, we describe the first approach to determine the relationship among epigenetic changes in the early stages of HD. We determined the existence of changes in pre-symptomatic stages of HD as a starting point for early onset indicators of the progression of this disease.

Are cytoskeleton changes observed in astrocytes functionally linked to aging?

Astrocytes are active participants in the performance of the Central Nervous System (CNS) in both health and disease. During aging, astrocytes are susceptible to reactive astrogliosis, a molecular state characterized by functional changes in response to pathological situations, and cellular senescence, characterized by loss of cell division, apoptosis resistance, and gain of proinflammatory functions. This results in two different states of astrocytes, which can produce proinflammatory phenotypes with harmful consequences in chronic conditions. Reactive astrocytes and senescent astrocytes share morpho-functional features that are dependent on the organization of the cytoskeleton. However, such changes in the cytoskeleton have yet to receive the necessary attention to explain their role in the alterations of astrocytes that are associated with aging and pathologies. In this review, we summarize all the available findings that connect changes in the cytoskeleton of the astrocytes with aging. In addition, we discuss future avenues that we believe will guide such a novel topic.

Comprehensive Evaluation of the Bioactive Composition and Neuroprotective and Antimicrobial Properties of Vacuum-Dried Broccoli (Brassica oleracea var. italica) Powder and Its Antioxidants.

In this study, vacuum drying (VD) was employed as an approach to protect the bioactive components of and produce dried broccoli powders with a high biological activity. To achieve these goals, the effects of temperature (at the five levels of 50, 60, 70, 80 and 90 °C) and constant vacuum pressure (10 kPa) were evaluated. The results show that, with the increasing temperature, the drying time decreased. Based on the statistical tests, the Brunauer-Emmett-Teller (BET) model was found to fit well to sorption isotherms, whereas the Midilli and Kucuk model fit well to the drying kinetics. VD has a significant impact on several proximate composition values. As compared with the fresh sample, VD significantly reduced the total phenol, flavonoid and glucosinolate contents. However, it was shown that VD at higher temperatures (80 and 90 °C) contributed to a better antioxidant potential of broccoli powder. In contrast, 50 °C led to a better antimicrobial and neuroprotective effects, presumably due to the formation of isothiocyanate (ITC). Overall, this study demonstrates that VD is a promising technique for the development of extracts from broccoli powders that could be used as natural preservatives or as a neuroprotective agent.

Interpretable by Design: Learning Predictors by Composing Interpretable Queries.

There is a growing concern about typically opaque decision-making with high-performance machine learning algorithms. Providing an explanation of the reasoning process in domain-specific terms can be crucial for adoption in risk-sensitive domains such as healthcare. We argue that machine learning algorithms should be interpretable by design and that the language in which these interpretations are expressed should be domain- and task-dependent. Consequently, we base our model's prediction on a family of user-defined and task-specific binary functions of the data, each having a clear interpretation to the end-user. We then minimize the expected number of queries needed for accurate prediction on any given input. As the solution is generally intractable, following prior work, we choose the queries sequentially based on information gain. However, in contrast to previous work, we need not assume the queries are conditionally independent. Instead, we leverage a stochastic generative model (VAE) and an MCMC algorithm (Unadjusted Langevin) to select the most informative query about the input based on previous query-answers. This enables the online determination of a query chain of whatever depth is required to resolve prediction ambiguities. Finally, experiments on vision and NLP tasks demonstrate the efficacy of our approach and its superiority over post-hoc explanations.

Lens Free Holographic Imaging for Urinary Tract Infection Screening.

OBJECTIVE: The diagnosis of urinary tract infection (UTI) currently requires precise specimen collection, handling infectious human waste, controlled urine storage, and timely transportation to modern laboratory equipment for analysis. Here we investigate holographic lens free imaging (LFI) to show its promise for enabling automatic urine analysis at the patient bedside. METHODS: We introduce an LFI system capable of resolving important urine clinical biomarkers such as red blood cells, white blood cells, crystals, and casts in 2 mm thick urine phantoms. RESULTS: This approach is sensitive to the particulate concentrations relevant for detecting several clinical urine abnormalities such as hematuria and pyuria, linearly correlating to ground truth hemacytometer measurements with R 2 = 0.9941 and R 2 = 0.9973, respectively. We show that LFI can estimate E. coli concentrations of 10 3 to 10 5 cells/mL by counting individual cells, and is sensitive to concentrations of 10 5 cells/mL to 10 8 cells/mL by analyzing hologram texture. Further, LFI measurements of blood cell concentrations are relatively insensitive to changes in bacteria concentrations of over seven orders of magnitude. Lastly, LFI reveals clear differences between UTI-positive and UTI-negative urine from human patients. CONCLUSION: LFI is sensitive to clinically-relevant concentrations of bacteria, blood cells, and other sediment in large urine volumes. SIGNIFICANCE: Together, these results show promise for LFI as a tool for urine screening, potentially offering early, point-of-care detection of UTI and other pathological processes.

Adaptive sparse reconstruction for lensless digital holography via PSF estimation and phase retrieval.

In-line lensless digital holography has great potential in multiple applications; however, reconstructing high-quality images from a single recorded hologram is challenging due to the loss of phase information. Typical reconstruction methods are based on solving a regularized inverse problem and work well under suitable image priors, but they are extremely sensitive to mismatches between the forward model and the actual imaging system. This paper aims to improve the robustness of such algorithms by introducing the adaptive sparse reconstruction method, ASR, which learns a properly constrained point spread function (PSF) directly from data, as opposed to solely relying on physics-based approximations of it. ASR jointly performs holographic reconstruction, PSF estimation, and phase retrieval in an unsupervised way by maximizing the sparsity of the reconstructed images. Like traditional methods, ASR uses the image formation model along with a sparsity prior, which, unlike recent deep learning approaches, allows for unsupervised reconstruction with as little as one sample. Experimental results in synthetic and real data show the advantages of ASR over traditional reconstruction methods, especially in cases where the theoretical PSF does not match that of the actual system.

Unfolded protein response IRE1/XBP1 signaling is required for healthy mammalian brain aging.

Aging is a major risk factor to develop neurodegenerative diseases and is associated with decreased buffering capacity of the proteostasis network. We investigated the significance of the unfolded protein response (UPR), a major signaling pathway activated to cope with endoplasmic reticulum (ER) stress, in the functional deterioration of the mammalian brain during aging. We report that genetic disruption of the ER stress sensor IRE1 accelerated age-related cognitive decline. In mouse models, overexpressing an active form of the UPR transcription factor XBP1 restored synaptic and cognitive function, in addition to reducing cell senescence. Proteomic profiling of hippocampal tissue showed that XBP1 expression significantly restore changes associated with aging, including factors involved in synaptic function and pathways linked to neurodegenerative diseases. The genes modified by XBP1 in the aged hippocampus where also altered. Collectively, our results demonstrate that strategies to manipulate the UPR in mammals may help sustain healthy brain aging.

Neuronal Rubicon Represses Extracellular APP/Amyloid ß Deposition in Alzheimer's Disease.

Alzheimer's disease (AD) is the most prevalent age-associated neurodegenerative disease. A decrease in autophagy during aging contributes to brain disorders by accumulating potentially toxic substrates in neurons. Rubicon is a well-established inhibitor of autophagy in all cells. However, Rubicon participates in different pathways depending on cell type, and little information is currently available on neuronal Rubicon's role in the AD context. Here, we investigated the cell-specific expression of Rubicon in postmortem brain samples from AD patients and 5xFAD mice and its impact on amyloid ß burden in vivo and neuroblastoma cells. Further, we assessed Rubicon levels in human-induced pluripotent stem cells (hiPSCs), derived from early-to-moderate AD and in postmortem samples from severe AD patients. We found increased Rubicon levels in AD-hiPSCs and postmortem samples and a notable Rubicon localization in neurons. In AD transgenic mice lacking Rubicon, we observed intensified amyloid ß burden in the hippocampus and decreased Pacer and p62 levels. In APP-expressing neuroblastoma cells, increased APP/amyloid ß secretion in the medium was found when Rubicon was absent, which was not observed in cells depleted of Atg5, essential for autophagy, or Rab27a, required for exosome secretion. Our results propose an uncharacterized role of Rubicon on APP/amyloid ß homeostasis, in which neuronal Rubicon is a repressor of APP/amyloid ß secretion, defining a new way to target AD and other similar diseases therapeutically.

Contribution of Autophagy-Lysosomal Pathway in the Exosomal Secretion of Alpha-Synuclein and Its Impact in the Progression of Parkinson's Disease.

Parkinson's disease (PD) is caused by the degeneration of dopaminergic neurons due to an accumulation of intraneuronal abnormal alpha-synuclein (α-syn) protein aggregates. It has been reported that the levels of exosomal α-syn of neuronal origin in plasma correlate significantly with motor dysfunction, highlighting the exosomes containing α-syn as a potential biomarker of PD. In addition, it has been found that the selective autophagy-lysosomal pathway (ALP) contributes to the secretion of misfolded proteins involved in neurodegenerative diseases. In this review, we describe the evidence that supports the relationship between the ALP and α-syn exosomal secretion on the PD progression and its implications in the diagnosis and progression of this pathology.

Insulin-like growth factor 2 and autophagy gene expression alteration arise as potential biomarkers in Parkinson's disease.

Insulin-like growth factor 2 (IGF2) and autophagy-related genes have been proposed as biomolecules of interest related to idiopathic Parkinson's disease (PD). The objective of this study was to determine the IGF2 and IGF1 levels in plasma and peripheral blood mononuclear cells (PBMCs) from patients with moderately advanced PD and explore the potential correlation with autophagy-related genes in the same blood samples. IGF1 and IGF2 levels in patients' plasma were measured by ELISA, and the IGF2 expression levels were determined by real-time PCR and Western blot in PBMCs. The expression of autophagy-related genes was evaluated by real-time PCR. The results show a significant decrease in IGF2 plasma levels in PD patients compared with a healthy control group. We also report a dramatic decrease in IGF2 mRNA and protein levels in PBMCs from PD patients. In addition, we observed a downregulation of key components of the initial stages of the autophagy process. Although IGF2 levels were not directly correlated with disease severity, we found a correlation between its levels and autophagy gene profile expression in a sex-dependent pattern from the same samples. To further explore this correlation, we treated mice macrophages cell culture with α-synuclein and IGF2. While α-synuclein treatment decreased levels Atg5, IGF2 treatment reverted these effects, increasing Atg5 and Beclin1 levels. Our results suggest a relationship between IGF2 levels and the autophagy process in PD and their potential application as multi-biomarkers to determine PD patients' stages of the disease.

The c-Abl/p73 pathway induces neurodegeneration in a Parkinson's disease model.

Parkinson's disease is the second most common neurodegenerative disorder. Although it is clear that dopaminergic neurons degenerate, the underlying molecular mechanisms are still unknown, and thus, successful treatment is still elusive. One pro-apoptotic pathway associated with several neurodegenerative diseases is the tyrosine kinase c-Abl and its target p73. Here, we evaluated the contribution of c-Abl and p73 in the degeneration of dopaminergic neurons induced by the neurotoxin 6-hydroxydopamine as a model for Parkinson's disease. First, we found that in SH-SY5Y cells treated with 6-hydroxydopamine, c-Abl and p73 phosphorylation levels were up-regulated. Also, we found that the pro-apoptotic p73 isoform TAp73 was up-regulated. Then, to evaluate whether c-Abl tyrosine kinase activity is necessary for 6-hydroxydopamine-induced apoptosis, we co-treated SH-SY5Y cells with 6-hydroxydopamine and Imatinib, a c-Abl specific inhibitor, observing that Imatinib prevented p73 phosphorylation, TAp73 up-regulation, and protected SH-SY5Y cells treated with 6-hydroxydopamine from apoptosis. Interestingly, this observation was confirmed in the c-Abl conditional null mice, where 6-hydroxydopamine stereotaxic injections induced a lesser reduction of dopaminergic neurons than in the wild-type mice significantly. Finally, we found that the intraperitoneal administration of Imatinib prevented the death of dopaminergic neurons induced by injecting 6-hydroxydopamine stereotaxically in the mice striatum. Thus, our findings support the idea that the c-Abl/p73 pathway is involved in 6-hydroxydopamine degeneration and suggest that inhibition of its kinase activity might be used as a therapeutical drug in Parkinson's disease.

Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces.

Finding a small set of representatives from an unlabeled dataset is a core problem in a broad range of applications such as dataset summarization and information extraction. Classical exemplar selection methods such as k-medoids work under the assumption that the data points are close to a few cluster centroids, and cannot handle the case where data lie close to a union of subspaces. This paper proposes a new exemplar selection model that searches for a subset that best reconstructs all data points as measured by the l1 norm of the representation coefficients. Geometrically, this subset best covers all the data points as measured by the Minkowski functional of the subset. To solve our model efficiently, we introduce a farthest first search algorithm that iteratively selects the worst represented point as an exemplar. When the dataset is drawn from a union of independent subspaces, our method is able to select sufficiently many representatives from each subspace. We further develop an exemplar based subspace clustering method that is robust to imbalanced data and efficient for large scale data. Moreover, we show that a classifier trained on the selected exemplars (when they are labeled) can correctly classify the rest of the data points.

The Fastest l1,∞ Prox in the West.

Proximal operators are of particular interest in optimization problems dealing with non-smooth objectives because in many practical cases they lead to optimization algorithms whose updates can be computed in closed form or very efficiently. A well-known example is the proximal operator of the vector l1 norm, which is given by the soft-thresholding operator. In this paper we study the proximal operator of the mixed l1,∞ matrix norm and show that it can be computed in closed form by applying the well-known soft-thresholding operator to each column of the matrix. However, unlike the vector l1 norm case where the threshold is constant, in the mixed l1,∞ norm case each column of the matrix might require a different threshold and all thresholds depend on the given matrix. We propose a general iterative algorithm for computing these thresholds, as well as two efficient implementations that further exploit easy to compute lower bounds for the mixed norm of the optimal solution. Experiments on large-scale synthetic and real data indicate that the proposed methods can be orders of magnitude faster than state-of-the-art methods.

Small molecules to perform big roles: The search for Parkinson's and Huntington's disease therapeutics.

Neurological motor disorders (NMDs) such as Parkinson's disease and Huntington's disease are characterized by the accumulation and aggregation of misfolded proteins that trigger cell death of specific neuronal populations in the central nervous system. Differential neuronal loss initiates the impaired motor control and cognitive function in the affected patients. Although major advances have been carried out to understand the molecular basis of these diseases, to date there are no treatments that can prevent, cure, or significantly delay the progression of the disease. In this context, strategies such as gene editing, cellular therapy, among others, have gained attention as they effectively reduce the load of toxic protein aggregates in different models of neurodegeneration. Nevertheless, these strategies are expensive and difficult to deliver into the patients' nervous system. Thus, small molecules and natural products that reduce protein aggregation levels are highly sought after. Numerous drug discovery efforts have analyzed large libraries of synthetic compounds for the treatment of different NMDs, with a few candidates reaching clinical trials. Moreover, the recognition of new druggable targets for NMDs has allowed the discovery of new small molecules that have demonstrated their efficacy in pre-clinical studies. It is also important to recognize the contribution of natural products to the discovery of new candidates that can prevent or cure NMDs. Additionally, the repurposing of drugs for the treatment of NMDs has gained huge attention as they have already been through clinical trials confirming their safety in humans, which can accelerate the development of new treatment. In this review, we will focus on the new advances in the discovery of small molecules for the treatment of Parkinson's and Huntington's disease. We will begin by discussing the available pharmacological treatments to modulate the progression of neurodegeneration and to alleviate the motor symptoms in these diseases. Then, we will analyze those small molecules that have reached or are currently under clinical trials, including natural products and repurposed drugs.

A phenolic-rich extract from Ugni molinae berries reduces abnormal protein aggregation in a cellular model of Huntington's disease.

Accumulation of misfolded proteins in the brain is a common hallmark of most age-related neurodegenerative diseases. Previous studies from our group identified the presence of anti-inflammatory and antioxidant compounds in leaves derived from the Chilean berry Ugni molinae (murtilla), in addition to show a potent anti-aggregation activity in models of Alzheimer´s disease. However, possible beneficial effects of berry extracts of murtilla was not investigated. Here we evaluated the efficacy of fruit extracts from different genotypes of Chilean-native U. molinae on reducing protein aggregation using cellular models of Huntington´s disease and assess the correlation with their chemical composition. Berry extraction was performed by exhaustive maceration with increasing-polarity solvents. An unbiased automatic microscopy platform was used for cytotoxicity and protein aggregation studies in HEK293 cells using polyglutamine-EGFP fusion proteins, followed by secondary validation using biochemical assays. Phenolic-rich extracts from murtilla berries of the 19-1 genotype (ETE 19-1) significantly reduced polyglutamine peptide aggregation levels, correlating with the modulation in the expression levels of autophagy-related proteins. Using LC-MS and molecular network analysis we correlated the presence of flavonoids, phenolic acids, and ellagitannins with the protective effects of ETE 19-1 effects on protein aggregation. Overall, our results indicate the presence of bioactive components in ethanolic extracts from U. molinae berries that reduce the load of protein aggregates in living cells.

Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization.

Optimization is at the heart of machine learning, statistics, and many applied scientific disciplines. It also has a long history in physics, ranging from the minimal action principle to finding ground states of disordered systems such as spin glasses. Proximal algorithms form a class of methods that are broadly applicable and are particularly well-suited to nonsmooth, constrained, large-scale, and distributed optimization problems. There are essentially five proximal algorithms currently known, each proposed in seminal work: Forward-backward splitting, Tseng splitting, Douglas-Rachford, alternating direction method of multipliers, and the more recent Davis-Yin. These methods sit on a higher level of abstraction compared to gradient-based ones, with deep roots in nonlinear functional analysis. In this paper we show that all of these methods are actually different discretizations of a single differential equation, namely, the simple gradient flow which dates back to Cauchy (1847). An important aspect behind many of the success stories in machine learning relies on "accelerating" the convergence of first-order methods. However, accelerated methods are notoriously difficult to analyze, counterintuitive, and without an underlying guiding principle. We show that similar discretization schemes applied to Newton's equation with an additional dissipative force, which we refer to as accelerated gradient flow, allow us to obtain accelerated variants of all these proximal algorithms-the majority of which are new although some recover known cases in the literature. Furthermore, we extend these methods to stochastic settings, allowing us to make connections with Langevin and Fokker-Planck equations. Similar ideas apply to gradient descent, heavy ball, and Nesterov's method which are simpler. Our results therefore provide a unified framework from which several important optimization methods are nothing but simulations of classical dissipative systems.