In silico evaluation of the role of lisdexamfetamine on attention-deficit/hyperactivity disorder common psychiatric comorbidities: mechanistic insights on binge eating disorder and depression.

Attention-deficit/hyperactivity disorder (ADHD) is a psychiatric condition well recognized in the pediatric population that can persist into adulthood. The vast majority of patients with ADHD present psychiatric comorbidities that have been suggested to share, to some extent, the pathophysiological mechanism of ADHD. Lisdexamfetamine (LDX) is a stimulant prodrug approved for treating ADHD and, in the US, also for binge eating disorder (BED). Herein, we evaluated, through a systems biology-based in silico method, the efficacy of a virtual model of LDX (vLDX) as ADHD treatment to improve five common ADHD psychiatric comorbidities in adults and children, and we explored the molecular mechanisms behind LDX's predicted efficacy. After the molecular characterization of vLDX and the comorbidities (anxiety, BED, bipolar disorder, depression, and tics disorder), we created a protein-protein interaction human network to which we applied artificial neural networks (ANN) algorithms. We also generated virtual populations of adults and children-adolescents totaling 2,600 individuals and obtained the predicted protein activity from Therapeutic Performance Mapping System models. The latter showed that ADHD molecular description shared 53% of its protein effectors with at least one studied psychiatric comorbidity. According to the ANN analysis, proteins targeted by vLDX are predicted to have a high probability of being related to BED and depression. In BED, vLDX was modeled to act upon neurotransmission and neuroplasticity regulators, and, in depression, vLDX regulated the hypothalamic-pituitary-adrenal axis, neuroinflammation, oxidative stress, and glutamatergic excitotoxicity. In conclusion, our modeling results, despite their limitations and although requiring in vitro or in vivo validation, could supplement the design of preclinical and potentially clinical studies that investigate treatment for patients with ADHD with psychiatric comorbidities, especially from a molecular point of view.

In silico clinical trial evaluating lisdexamfetamine's and methylphenidate's mechanism of action computational models in an attention-deficit/hyperactivity disorder virtual patients' population.

Introduction: Attention-deficit/hyperactivity disorder (ADHD) is an impairing psychiatric condition with the stimulants, lisdexamfetamine (LDX), and methylphenidate (MPH), as the first lines pharmacological treatment. Methods: Herein, we applied a novel in silico method to evaluate virtual LDX (vLDX) and vMPH as treatments for ADHD applying quantitative systems pharmacology (QSP) models. The objectives were to evaluate the model's output, considering the model characteristics and the information used to build them, to compare both virtual drugs' efficacy mechanisms, and to assess how demographic (age, body mass index, and sex) and clinical characteristics may affect vLDX's and vMPH's relative efficacies. Results and Discussion: We molecularly characterized the drugs and pathologies based on a bibliographic search, and generated virtual populations of adults and children-adolescents totaling 2,600 individuals. For each virtual patient and virtual drug, we created physiologically based pharmacokinetic and QSP models applying the systems biology-based Therapeutic Performance Mapping System technology. The resulting models' predicted protein activity indicated that both virtual drugs modulated ADHD through similar mechanisms, albeit with some differences. vMPH induced several general synaptic, neurotransmitter, and nerve impulse-related processes, whereas vLDX seemed to modulate neural processes more specific to ADHD, such as GABAergic inhibitory synapses and regulation of the reward system. While both drugs' models were linked to an effect over neuroinflammation and altered neural viability, vLDX had a significant impact on neurotransmitter imbalance and vMPH on circadian system deregulation. Among demographic characteristics, age and body mass index affected the efficacy of both virtual treatments, although the effect was more marked for vLDX. Regarding comorbidities, only depression negatively impacted both virtual drugs' efficacy mechanisms and, while that of vLDX were more affected by the co-treatment of tic disorders, the efficacy mechanisms of vMPH were disturbed by wide-spectrum psychiatric drugs. Our in silico results suggested that both drugs could have similar efficacy mechanisms as ADHD treatment in adult and pediatric populations and allowed raising hypotheses for their differential impact in specific patient groups, although these results require prospective validation for clinical translatability.

Application of systems biology to identify pharmacological mechanisms of thrombotic microangiopathy evoked by combined activated prothrombin complex concentrate and emicizumab.

Emicizumab is a bispecific monoclonal antibody that substitutes for the function of missing or deficient factor VIII (FVIII) in people with hemophilia A (PwHA). Long-term safety and efficacy of emicizumab have been demonstrated in several clinical trials. Nevertheless, in the first of these, three cases of thrombotic microangiopathy (TMA) occurred in PwHA treated with emicizumab receiving high doses of activated prothrombin complex concentrate (aPCC), a bypassing agent used for treating breakthrough bleeds when FVIII neutralizing antibodies (inhibitors) make FVIII replacement ineffective. The aim of the present work is to offer a method to elucidate the pathophysiological and pharmacological mechanisms involved in this treatment-induced TMA. Systems biology and machine learning-based Therapeutic Performance Mapping System is a validated in silico technology that allowed us to construct models of potential mechanisms behind induced TMA. Two drug combinations were modeled and assessed: emicizumab plus aPCC and emicizumab plus recombinant activated factor VII (another bypassing agent). Our models showed that both combinations were related to activation of the coagulation cascade. However, mechanisms involved mainly in platelet activation and possibly in complement activation were detected only for emicizumab plus aPCC, potentially explaining the occurrence of TMA only in this combination.

Artificial intelligence assessment of the potential of tocilizumab along with corticosteroids therapy for the management of COVID-19 evoked acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS), associated with high mortality rate, affects up to 67% of hospitalized COVID-19 patients. Early evidence indicated that the pathogenesis of COVID-19 evoked ARDS is, at least partially, mediated by hyperinflammatory cytokine storm in which interleukin 6 (IL-6) plays an essential role. The corticosteroid dexamethasone is an effective treatment for severe COVID-19 related ARDS. However, trials of other immunomodulatory therapies, including anti-IL6 agents such as tocilizumab and sarilumab, have shown limited evidence of benefit as monotherapy. But recently published large trials have reported added benefit of tocilizumab in combination with dexamethasone in severe COVID-19 related ARDS. In silico tools can be useful to shed light on the mechanisms evoked by SARS-CoV-2 infection and of the potential therapeutic approaches. Therapeutic performance mapping system (TPMS), based on systems biology and artificial intelligence, integrate available biological, pharmacological and medical knowledge to create mathematical models of the disease. This technology was used to identify the pharmacological mechanism of dexamethasone, with or without tocilizumab, in the management of COVID-19 evoked ARDS. The results showed that while dexamethasone would be addressing a wider range of pathological processes with low intensity, tocilizumab might provide a more direct and intense effect upon the cytokine storm. Based on this in silico study, we conclude that the use of tocilizumab alongside dexamethasone is predicted to induce a synergistic effect in dampening inflammation and subsequent pathological processes, supporting the beneficial effect of the combined therapy in critically ill patients. Future research will allow identifying the ideal subpopulation of patients that would benefit better from this combined treatment.

Female Sex, Age, and Unfavorable Response to Tumor Necrosis Factor Inhibitors in Patients With Axial Spondyloarthritis: Results of Statistical and Artificial Intelligence-Based Data Analyses of a National Multicenter Prospective Registry.

OBJECTIVE: Real-world studies are needed to identify factors associated with response to biologic therapies in patients with axial spondyloarthritis (SpA). The objective was to assess sex differences in response to tumor necrosis factor inhibitors (TNFi) and to explore possible risk factors associated with TNFi efficacy. METHODS: A total of 969 patients with axial SpA (315 females, 654 males) enrolled in the BIOBADASER registry (2000-2019) who initiated a TNFi (first, second, or further lines) were studied. Statistical and artificial intelligence (AI)-based data analyses were used to explore the association of sex differences and other factors to TNFi response, using the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), to calculate the BASDAI50, with an improvement of at least 50% of the BASDAI score, and using the Ankylosing Spondylitis Disease Activity Score, calculated using the C-reactive protein level (ASDAS-CRP). RESULTS: Females had a lower probability of reaching a BASDAI50 response with a first line TNFi treatment at the second year of follow-up (P = 0.018) and a lesser reduction of the ASDAS-CRP at this time point. The logistic regression model showed lower BASDAI50 responses to TNFi in females (P = 0.05). Other factors, such as older age (P = 0.004), were associated with unfavorable responses. The AI data analyses reinforced the idea that age at the beginning of the treatment was the main factor associated with an unfavorable response. The combination of age with other clinical characteristics (female sex or cardiovascular risk factors and events) potentially contributed to an unfavorable response to TNFi. CONCLUSION: In this national multicenter registry, female sex was associated with less response to a first-line TNFi by the second year of follow-up. A higher age at the start of the TNFi was the main factor associated with an unfavorable response to TNFi.

Identification of the Potential Molecular Mechanisms Linking RUNX1 Activity with Nonalcoholic Fatty Liver Disease, by Means of Systems Biology.

Nonalcoholic fatty liver disease (NAFLD) is the most prevalent chronic hepatic disease; nevertheless, no definitive diagnostic method exists yet, apart from invasive liver biopsy, and nor is there a specific approved treatment. Runt-related transcription factor 1 (RUNX1) plays a major role in angiogenesis and inflammation; however, its link with NAFLD is unclear as controversial results have been reported. Thus, the objective of this work was to determine the proteins involved in the molecular mechanisms between RUNX1 and NAFLD, by means of systems biology. First, a mathematical model that simulates NAFLD pathophysiology was generated by analyzing Anaxomics databases and reviewing available scientific literature. Artificial neural networks established NAFLD pathophysiological processes functionally related to RUNX1: hepatic insulin resistance, lipotoxicity, and hepatic injury-liver fibrosis. Our study indicated that RUNX1 might have a high relationship with hepatic injury-liver fibrosis, and a medium relationship with lipotoxicity and insulin resistance motives. Additionally, we found five RUNX1-regulated proteins with a direct involvement in NAFLD motives, which were NFκB1, NFκB2, TNF, ADIPOQ, and IL-6. In conclusion, we suggested a relationship between RUNX1 and NAFLD since RUNX1 seems to regulate NAFLD molecular pathways, posing it as a potential therapeutic target of NAFLD, although more studies in this field are needed.

Elucidating the Mechanism of Action of the Attributed Immunomodulatory Role of Eltrombopag in Primary Immune Thrombocytopenia: An In Silico Approach.

Eltrombopag is a thrombopoietin receptor (MPL) agonist approved for the treatment of primary immune thrombocytopenia (ITP). Recent evidence shows that some patients may sustain platelet counts following eltrombopag discontinuation. The systemic immunomodulatory response that resolves ITP in some patients could result from an increase in platelet mass, caused either by the direct action of eltrombopag on megakaryocytes through MPL stimulation, or potential MPL-independent actions on other cell types. To uncover the possible mechanisms of action of eltrombopag, in silico analyses were performed, including a systems biology-based approach, a therapeutic performance mapping system, and structural analyses. Through manual curation of the available bibliography, 56 key proteins were identified and integrated into the ITP interactome analysis. Mathematical models (94.92% mean accuracy) were obtained to elucidate potential MPL-dependent pathways in non-megakaryocytic cell subtypes. In addition to the effects on megakaryocytes and platelet numbers, the results were consistent with MPL-mediated effects on other cells, which could involve interferon-gamma, transforming growth factor-beta, peroxisome proliferator-activated receptor-gamma, and forkhead box protein P3 pathways. Structural analyses indicated that effects on three apoptosis-related proteins (BCL2L1, BCL2, BAX) from the Bcl-2 family may be off-target effects of eltrombopag. In conclusion, this study proposes new hypotheses regarding the immunomodulatory functions of eltrombopag in patients with ITP.

Application of systems biology-based in silico tools to optimize treatment strategy identification in Still's disease.

BACKGROUND: Systemic juvenile idiopathic arthritis (sJIA) and adult-onset Still's disease (AOSD) are manifestations of an autoinflammatory disorder with complex pathophysiology and significant morbidity, together also termed Still's disease. The objective of the current study is to set in silico models based on systems biology and investigate the optimal treat-to-target strategy for Still's disease as a proof-of-concept of the modeling approach. METHODS: Molecular characteristics of Still's disease and data on biological inhibitors of interleukin (IL)-1 (anakinra, canakinumab), IL-6 (tocilizumab, sarilumab), and glucocorticoids as well as conventional disease-modifying anti-rheumatic drugs (DMARDs, methotrexate) were used to construct in silico mechanisms of action (MoA) models by means of Therapeutic Performance Mapping System (TPMS) technology. TPMS combines artificial neuronal networks, sampling-based methods, and artificial intelligence. Model outcomes were validated with published expression data from sJIA patients. RESULTS: Biologicals demonstrated more pathophysiology-directed efficiency than non-biological drugs. IL-1 blockade mainly acts on proteins implicated in the innate immune system, while IL-6 signaling blockade has a weaker effect on innate immunity and rather affects adaptive immune mechanisms. The MoA models showed that in the autoinflammatory/systemic phases of Still's disease, in which the innate immunity plays a pivotal role, the IL-1ß-neutralizing antibody canakinumab is more efficient than the IL-6 receptor-inhibiting antibody tocilizumab. MoA models reproduced 67% of the information obtained from expression data. CONCLUSIONS: Systems biology-based modeling supported the preferred use of biologics as an immunomodulatory treatment strategy for Still's disease. Our results reinforce the role for IL-1 blockade on innate immunity regulation, which is critical in systemic autoinflammatory diseases. This further encourages early use on Still's disease IL-1 blockade to prevent the development of disease or drug-related complications. Further analysis at the clinical level will validate the findings and help determining the timeframe of the window of opportunity for canakinumab treatment.

Head to head evaluation of second generation ALK inhibitors brigatinib and alectinib as first-line treatment for ALK+ NSCLC using an in silico systems biology-based approach.

Around 3-7% of patients with non-small cell lung cancer (NSCLC), which represent 85% of diagnosed lung cancers, have a rearrangement in the ALK gene that produces an abnormal activity of the ALK protein cell signaling pathway. The developed ALK tyrosine kinase inhibitors (TKIs), such as crizotinib, ceritinib, alectinib, brigatinib and lorlatinb present good performance treating ALK+ NSCLC, although all patients invariably develop resistance due to ALK secondary mutations or bypass mechanisms. In the present study, we compare the potential differences between brigatinib and alectinib's mechanisms of action as first-line treatment for ALK+ NSCLC in a systems biology-based in silico setting. Therapeutic performance mapping system (TPMS) technology was used to characterize the mechanisms of action of brigatinib and alectinib and the impact of potential resistances and drug interferences with concomitant treatments. The analyses indicate that brigatinib and alectinib affect cell growth, apoptosis and immune evasion through ALK inhibition. However, brigatinib seems to achieve a more diverse downstream effect due to a broader cancer-related kinase target spectrum. Brigatinib also shows a robust effect over invasiveness and central nervous system metastasis-related mechanisms, whereas alectinib seems to have a greater impact on the immune evasion mechanism. Based on this in silico head to head study, we conclude that brigatinib shows a predicted efficacy similar to alectinib and could be a good candidate in a first-line setting against ALK+ NSCLC. Future investigation involving clinical studies will be needed to confirm these findings. These in silico systems biology-based models could be applied for exploring other unanswered questions.

In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm.

From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.

Repurposed Analog of GLP-1 Ameliorates Hyperglycemia in Type 1 Diabetic Mice Through Pancreatic Cell Reprogramming.

Type 1 diabetes is an autoimmune disease caused by the destruction of the insulin-producing ß-cells. An ideal immunotherapy should combine the blockade of the autoimmune response with the recovery of functional target cell mass. With the aim to develop new therapies for type 1 diabetes that could contribute to ß-cell mass restoration, a drug repositioning analysis based on systems biology was performed to identify the ß-cell regenerative potential of commercially available compounds. Drug repositioning is a strategy used for identifying new uses for approved drugs that are outside the scope of the medical indication. A list of 28 non-synonymous repurposed drug candidates was obtained, and 16 were selected as diabetes mellitus type 1 treatment candidates regarding pancreatic ß-cell regeneration. Drugs with poor safety profile were further filtered out. Lastly, we selected liraglutide for its predictive efficacy values for neogenesis, transdifferentiation of α-cells, and/or replication of pre-existing ß-cells. Liraglutide is an analog of glucagon-like peptide-1, a drug used in patients with type 2 diabetes. Liraglutide was tested in immunodeficient NOD-Scid IL2rg-/- (NSG) mice with type 1 diabetes. Liraglutide significantly improved the blood glucose levels in diabetic NSG mice. During the treatment, a significant increase in ß-cell mass was observed due to a boost in ß-cell number. Both parameters were reduced after withdrawal. Interestingly, islet bihormonal glucagon+insulin+ cells and insulin+ ductal cells arose during treatment. In vitro experiments showed an increase of insulin and glucagon gene expression in islets cultured with liraglutide in normoglycemia conditions. These results point to ß-cell replacement, including transdifferentiation and neogenesis, as aiding factors and support the role of liraglutide in ß-cell mass restoration in type 1 diabetes. Understanding the mechanism of action of this drug could have potential clinical relevance in this autoimmune disease.

Neuroprotective Drug for Nerve Trauma Revealed Using Artificial Intelligence.

Here we used a systems biology approach and artificial intelligence to identify a neuroprotective agent for the treatment of peripheral nerve root avulsion. Based on accumulated knowledge of the neurodegenerative and neuroprotective processes that occur in motoneurons after root avulsion, we built up protein networks and converted them into mathematical models. Unbiased proteomic data from our preclinical models were used for machine learning algorithms and for restrictions to be imposed on mathematical solutions. Solutions allowed us to identify combinations of repurposed drugs as potential neuroprotective agents and we validated them in our preclinical models. The best one, NeuroHeal, neuroprotected motoneurons, exerted anti-inflammatory properties and promoted functional locomotor recovery. NeuroHeal endorsed the activation of Sirtuin 1, which was essential for its neuroprotective effect. These results support the value of network-centric approaches for drug discovery and demonstrate the efficacy of NeuroHeal as adjuvant treatment with surgical repair for nervous system trauma.

Novel Neuroprotective Multicomponent Therapy for Amyotrophic Lateral Sclerosis Designed by Networked Systems.

Amyotrophic Lateral Sclerosis is a fatal, progressive neurodegenerative disease characterized by loss of motor neuron function for which there is no effective treatment. One of the main difficulties in developing new therapies lies on the multiple events that contribute to motor neuron death in amyotrophic lateral sclerosis. Several pathological mechanisms have been identified as underlying events of the disease process, including excitotoxicity, mitochondrial dysfunction, oxidative stress, altered axonal transport, proteasome dysfunction, synaptic deficits, glial cell contribution, and disrupted clearance of misfolded proteins. Our approach in this study was based on a holistic vision of these mechanisms and the use of computational tools to identify polypharmacology for targeting multiple etiopathogenic pathways. By using a repositioning analysis based on systems biology approach (TPMS technology), we identified and validated the neuroprotective potential of two new drug combinations: Aliretinoin and Pranlukast, and Aliretinoin and Mefloquine. In addition, we estimated their molecular mechanisms of action in silico and validated some of these results in a well-established in vitro model of amyotrophic lateral sclerosis based on cultured spinal cord slices. The results verified that Aliretinoin and Pranlukast, and Aliretinoin and Mefloquine promote neuroprotection of motor neurons and reduce microgliosis.

Methods for diagnosing perceived age on the basis of an ensemble of phenotypic features.

BACKGROUND: Perceived age has been defined as the age that a person is visually estimated to be on the basis of physical appearance. In a society where a youthful appearance are an object of desire for consumers, and a source of commercial profit for cosmetic companies, this concept has a prominent role. In addition, perceived age is also an indicator of overall health status in elderly people, since old-looking people tend to show higher rates of morbidity and mortality. However, there is a lack of objective methods for quantifying perceived age. METHODS: In order to satisfy the need of objective approaches for estimating perceived age, a novel algorithm was created. The novel algorithm uses supervised mathematical learning techniques and error retropropagation for the creation of an artificial neural network able to learn biophysical and clinically assessed parameters of subjects. The algorithm provides a consistent estimation of an individual's perceived age, taking into account a defined set of facial skin phenotypic traits, such as wrinkles and roughness, number of wrinkles, depth of wrinkles, and pigmentation. A nonintervention, epidemiological cross-sectional study of cases and controls was conducted in 120 female volunteers for the diagnosis of perceived age using this novel algorithm. Data collection was performed by clinical assessment of an expert panel and biophysical assessment using the ANTERA 3D(®) device. RESULTS AND DISCUSSION: Employing phenotype data as variables and expert assignments as objective data, the algorithm was found to correctly classify the samples with an accuracy of 92.04%. Therefore, we have developed a method for determining the perceived age of a subject in a standardized, consistent manner. Further application of this algorithm is thus a promising approach for the testing and validation of cosmetic treatments and aesthetic surgery, and it also could be used as a screening method for general health status in the population.

Low levels of copper disrupt brain amyloid-ß homeostasis by altering its production and clearance.

Whereas amyloid-ß (Aß) accumulates in the brain of normal animals dosed with low levels of copper (Cu), the mechanism is not completely known. Cu could contribute to Aß accumulation by altering its clearance and/or its production. Because Cu homeostasis is altered in transgenic mice overexpressing Aß precursor protein (APP), the objective of this study was to elucidate the mechanism of Cu-induced Aß accumulation in brains of normal mice and then to explore Cu's effects in a mouse model of Alzheimer's disease. In aging mice, accumulation of Cu in brain capillaries was associated with its reduction in low-density lipoprotein receptor-related protein 1 (LRP1), an Aß transporter, and higher brain Aß levels. These effects were reproduced by chronic dosing with low levels of Cu via drinking water without changes in Aß synthesis or degradation. In human brain endothelial cells, Cu, at its normal labile levels, caused LRP1-specific down-regulation by inducing its nitrotyrosination and subsequent proteosomal-dependent degradation due in part to Cu/cellular prion protein/LRP1 interaction. In APP(sw/0) mice, Cu not only down-regulated LRP1 in brain capillaries but also increased Aß production and neuroinflammation because Cu accumulated in brain capillaries and, unlike in control mice, in the parenchyma. Thus, we have demonstrated that Cu's effect on brain Aß homeostasis depends on whether it is accumulated in the capillaries or in the parenchyma. These findings should provide unique insights into preventative and/or therapeutic approaches to control neurotoxic Aß levels in the aging brain.

The proton-pump inhibitor lansoprazole enhances amyloid beta production.

A key event in the pathogenesis of Alzheimer's disease (AD) is the accumulation of amyloid-ß (Aß) species in the brain, derived from the sequential cleavage of the amyloid precursor protein (APP) by ß- and γ-secretases. Based on a systems biology study to repurpose drugs for AD, we explore the effect of lansoprazole, and other proton-pump inhibitors (PPIs), on Aß production in AD cellular and animal models. We found that lansoprazole enhances Aß37, Aß40 and Aß42 production and lowers Aß38 levels on amyloid cell models. Interestingly, acute lansoprazole treatment in wild type and AD transgenic mice promoted higher Aß40 levels in brain, indicating that lansoprazole may also exacerbate Aß production in vivo. Overall, our data presents for the first time that PPIs can affect amyloid metabolism, both in vitro and in vivo.

Brain oligomeric ß-amyloid but not total amyloid plaque burden correlates with neuronal loss and astrocyte inflammatory response in amyloid precursor protein/tau transgenic mice.

It has long been assumed that ß-amyloid (Aß) had to assemble into fibrillar amyloid plaques to exert its neurotoxic effects in Alzheimer disease. An alternative hypothesis is that soluble oligomers ofAß play a much larger role in neuronal damage than the insoluble component. We have tested these competing hypotheses in vivo by studying the clinicopathologic correlates of oligomeric Aß species and classic fibrillar amyloid plaques in the brains of double-transgenic APP-tau mice up to 17 months of age. Biochemical and immunohistochemical measures of brain oligomeric Aß exponentially increased with age. Oligomeric Aß load correlated with morphological markers of fibrillar Aß deposition. In contrast to total amyloid plaque burden, the amount of oligomeric Aß deposits labeled by the conformational epitope-specific antibody Nab61 closely correlated with neuronal loss and numbers of astrocytes in the entorhinal cortex and the CA1 hippocampal subfield. However, like other morphological Aß measurements, brain oligomeric Aß burden did not correlate well with memory deficits in these mice. The number of glial fibrillary acidic protein-positive astrocytes in entorhinal cortex and CA1 most tightly correlated with memory impairment and neuronal cell loss. Based on these findings, we hypothesize that the astrocyte response, which is likely triggered by brain oligomeric Aß accumulation, adversely affects cognition and might also contribute to neuronal cell death in this model.

Revealing the molecular relationship between type 2 diabetes and the metabolic changes induced by a very-low-carbohydrate low-fat ketogenic diet.

BACKGROUND: The prevalence of type 2 diabetes is increasing worldwide, accounting for 85-95% of all diagnosed cases of diabetes. Clinical trials provide evidence of benefits of low-carbohydrate ketogenic diets in terms of clinical outcomes on type 2 diabetes patients. However, the molecular events responsible for these improvements still remain unclear in spite of the high amount of knowledge on the primary mechanisms of both the diabetes and the metabolic state of ketosis. Molecular network analysis of conditions, diseases and treatments might provide new insights and help build a better understanding of clinical, metabolic and molecular relationships among physiological conditions. Accordingly, our aim is to reveal such a relationship between a ketogenic diet and type 2 diabetes through systems biology approaches. METHODS: Our systemic approach is based on the creation and analyses of the cell networks representing the metabolic state in a very-low-carbohydrate low-fat ketogenic diet. This global view might help identify unnoticed relationships often overlooked in molecule or process-centered studies. RESULTS: A strong relationship between the insulin resistance pathway and the ketosis main pathway was identified, providing a possible explanation for the improvement observed in clinical trials. Moreover, the map analyses permit the formulation of some hypothesis on functional relationships between the molecules involved in type 2 diabetes and induced ketosis, suggesting, for instance, a direct implication of glucose transporters or inflammatory processes. The molecular network analysis performed in the ketogenic-diet map, from the diabetes perspective, has provided insights on the potential mechanism of action, but also has opened new possibilities to study the applications of the ketogenic diet in other situations such as CNS or other metabolic dysfunctions.

Clinical, neuropathologic, and biochemical profile of the amyloid precursor protein I716F mutation.

We report the clinical, pathologic, and biochemical characteristics of the recently described amyloid precursor protein (APP) I716F mutation. We present the clinical findings of individuals carrying the APP I716F mutation and the neuropathologic examination of the proband. The mutation was found in a patient with Alzheimer disease with onset at the age of 31 years and death at age 36 years and who had a positive family history of early-onset Alzheimer disease. Neuropathologic examination showed abundant diffuse amyloid plaques mainly composed of amyloid-beta42 and widespread neurofibrillary pathology. Lewy bodies were found in the amygdala. Chinese hamster ovary cells transfected with this mutation showed a marked increase in the amyloid-beta42/40 ratio and APP C-terminal fragments and a decrease in APP intracellular domain production, suggesting reduced APP proteolysis by gamma-secretase. Taken together, these findings indicate that the APP I716F mutation is associated with the youngest age of onset for this locus and strengthen the inverse association between amyloid-beta42/40 ratio and age of onset. The mutation leads to a protein that is poorly processed by gamma-secretase. This loss of function may be an additional mechanism by which some mutations around the gamma-secretase cleavage site lead to familial Alzheimer disease.

Mild cholesterol depletion reduces amyloid-beta production by impairing APP trafficking to the cell surface.

It has been suggested that cellular cholesterol levels can modulate the metabolism of the amyloid precursor protein (APP) but the underlying mechanism remains controversial. In the current study, we investigate in detail the relationship between cholesterol reduction, APP processing and gamma-secretase function in cell culture studies. We found that mild membrane cholesterol reduction led to a decrease in Abeta(40) and Abeta(42) in different cell types. We did not detect changes in APP intracellular domain or Notch intracellular domain generation. Western blot analyses showed a cholesterol-dependent decrease in the APP C-terminal fragments and cell surface APP. Finally, we applied a fluorescence resonance energy transfer (FRET)-based technique to study APP-Presenilin 1 (PS1) interactions and lipid rafts in intact cells. Our data indicate that cholesterol depletion reduces association of APP into lipid rafts and disrupts APP-PS1 interaction. Taken together, our results suggest that mild membrane cholesterol reduction impacts the cleavage of APP upstream of gamma-secretase and appears to be mediated by changes in APP trafficking and partitioning into lipid rafts.