Brain-penetrant cyanoindane and cyanotetralin inhibitors of G2019S-LRRK2 kinase activity.

The G2019S variant of LRRK2, which causes an increase in kinase activity, is associated with the occurrence of Parkinson's disease (PD). Potent, mutation-selective, and brain penetrant inhibitors of LRRK2 can suppress the biological effects specific to G2019S-LRRK2 that cause pathogenicity. We report the discovery of a series of cyanoindane and cyanotetralin kinase inhibitors culminating in compound 34 that demonstrated selective inhibition of phosphorylation of LRRK2 in the mouse brain. These novel inhibitors may further enable the precision medicine path for future PD therapeutics.

Protein Networks Associated with Native Metabotropic Glutamate 1 Receptors (mGlu1) in the Mouse Cerebellum.

The metabotropic glutamate receptor 1 (mGlu1) plays a pivotal role in synaptic transmission and neuronal plasticity. Despite the fact that several interacting proteins involved in the mGlu1 subcellular trafficking and intracellular transduction mechanisms have been identified, the protein network associated with this receptor in specific brain areas remains largely unknown. To identify novel mGlu1-associated protein complexes in the mouse cerebellum, we used an unbiased tissue-specific proteomic approach, namely co-immunoprecipitation followed by liquid chromatography/tandem mass spectrometry analysis. Many well-known protein complexes as well as novel interactors were identified, including G-proteins, Homer, δ2 glutamate receptor, 14-3-3 proteins, and Na/K-ATPases. A novel putative interactor, KCTD12, was further investigated. Reverse co-immunoprecipitation with anti-KCTD12 antibodies revealed mGlu1 in wild-type but not in KCTD12-knock-out homogenates. Freeze-fracture replica immunogold labeling co-localization experiments showed that KCTD12 and mGlu1 are present in the same nanodomain in Purkinje cell spines, although at a distance that suggests that this interaction is mediated through interposed proteins. Consistently, mGlu1 could not be co-immunoprecipitated with KCTD12 from a recombinant mammalian cell line co-expressing the two proteins. The possibility that this interaction was mediated via GABAB receptors was excluded by showing that mGlu1 and KCTD12 still co-immunoprecipitated from GABAB receptor knock-out tissue. In conclusion, this study identifies tissue-specific mGlu1-associated protein clusters including KCTD12 at Purkinje cell synapses.

Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes.

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities.

Selective Inhibitors of G2019S-LRRK2 Kinase Activity.

Pathogenic variants in the leucine-rich repeat kinase 2 (LRRK2) gene have been identified that increase the risk for developing Parkinson's disease in a dominantly inherited fashion. These pathogenic variants, of which G2019S is the most common, cause abnormally high kinase activity, and compounds that inhibit this activity are being pursued as potentially disease-modifying therapeutics. Because LRRK2 regulates important cellular processes, developing inhibitors that can selectively target the pathogenic variant while sparing normal LRRK2 activity could offer potential advantages in heterozygous carriers. We conducted a high-throughput screen and identified a single selective compound that preferentially inhibited G2019S-LRRK2. Optimization of this scaffold led to a series of novel, potent, and highly selective G2019S-LRRK2 inhibitors.

Gene expression signature of antidepressant treatment response/non-response in Flinders Sensitive Line rats subjected to maternal separation.

Neurobiological underpinnings of treatment-resistant depression, a debilitating condition associated with significant functional impairment, have not been elucidated. Consequently, the aim of this study was to use animal models of response and resistance to antidepressant treatment, in an attempt to identify differences in associated transcriptional responses. Flinders Sensitive Line rats were subjected to maternal separation (MS) and chronically treated with Escitalopram or Nortriptyline. Antidepressants reduced immobility time in the forced swim test in non-MS rats, while lack of antidepressant behavioural response was observed in MS animals. We developed a novel bioinformatic algorithm that enabled identification of transcriptional signatures in hippocampus and pre-frontal cortex that discriminate vehicle- and antidepressant-treated subjects in both MS and non-MS rats. Functional annotation analysis showed that in antidepressant-responder rats the most enriched pathways included IQGAPs activation, toll-like receptor trafficking, energy metabolism, and regulation of endopeptidase activity. The analysis of interacting proteins implicated synaptic vesicles and neurotransmitter release, ubiquitin regulation, cytoskeleton organisation and carbohydrate metabolism. In contrast, in treatment-resistant MS rats, main expression changes were revealed in ribosomal proteins, inflammatory responses, transcriptional/epigenetic regulation, and small GTPases. Susceptibility signature shared Rtn1, Zdhhc5, Igsf6, and Sim1 genes with the latest depression GWAS meta-analysis, while antidepressant resistance signature shared Ctnnd1, Rbms3, Atp1a3, and Pla2r1 genes. In conclusion, this study demonstrated that distinct transcriptional signatures are associated with behavioural response or non-response to antidepressant treatment. The identification of genes involved in antidepressant response will increase the comprehension of the neurobiological underpinnings of treatment-resistant depression, thus contributing to identification of novel therapeutic targets.

Cross-disease analysis of Alzheimer's disease and type-2 Diabetes highlights the role of autophagy in the pathophysiology of two highly comorbid diseases.

Evidence is accumulating that the main chronic diseases of aging Alzheimer's disease (AD) and type-2 diabetes mellitus (T2DM) share common pathophysiological mechanisms. This study aimed at applying systems biology approaches to increase the knowledge of the shared molecular pathways underpinnings of AD and T2DM. We analysed transcriptomic data of post-mortem AD and T2DM human brains to obtain disease signatures of AD and T2DM and combined them with protein-protein interaction information to construct two disease-specific networks. The overlapping AD/T2DM network proteins were then used to extract the most representative Gene Ontology biological process terms. The expression of genes identified as relevant was studied in two AD models, 3xTg-AD and ApoE3/ApoE4 targeted replacement mice. The present transcriptomic data analysis revealed a principal role for autophagy in the molecular basis of both AD and T2DM. Our experimental validation in mouse AD models confirmed the role of autophagy-related genes. Among modulated genes, Cyclin-Dependent Kinase Inhibitor 1B, Autophagy Related 16-Like 2, and insulin were highlighted. In conclusion, the present investigation revealed autophagy as the central dys-regulated pathway in highly co-morbid diseases such as AD and T2DM allowing the identification of specific genes potentially involved in disease pathophysiology which could become novel targets for therapeutic intervention.

Cross-species evidence from human and rat brain transcriptome for growth factor signaling pathway dysregulation in major depression.

An enhanced understanding of the pathophysiology of depression would facilitate the discovery of new efficacious medications. To this end, we examined hippocampal transcriptional changes in rat models of disease and in humans to identify common disease signatures by using a new algorithm for signature-based clustering of expression profiles. The tool identified a transcriptomic signature comprising 70 probesets able to discriminate depression models from controls in both Flinders Sensitive Line and Learned Helplessness animals. To identify disease-relevant pathways, we constructed an expanded protein network based on signature gene products and performed functional annotation analysis. We applied the same workflow to transcriptomic profiles of depressed patients. Remarkably, a 171-probesets transcriptional signature which discriminated depressed from healthy subjects was identified. Rat and human signatures shared the SCARA5 gene, while the respective networks derived from protein-based significant interactions with signature genes contained 25 overlapping genes. The comparison between the most enriched pathways in the rat and human signature networks identified a highly significant overlap (p-value: 3.85 × 10-6) of 67 terms including ErbB, neurotrophin, FGF, IGF, and VEGF signaling, immune responses and insulin and leptin signaling. In conclusion, this study allowed the identification of a hippocampal transcriptional signature of resilient or susceptible responses in rat MDD models which overlapped with gene expression alterations observed in depressed patients. These findings are consistent with a loss of hippocampal neural plasticity mediated by altered levels of growth factors and increased inflammatory responses causing metabolic impairments as crucial factors in the pathophysiology of MDD.

Combined use of protein biomarkers and network analysis unveils deregulated regulatory circuits in Duchenne muscular dystrophy.

Although the genetic basis of Duchenne muscular dystrophy has been known for almost thirty years, the cellular and molecular mechanisms characterizing the disease are not completely understood and an efficacious treatment remains to be developed. In this study we analyzed proteomics data obtained with the SomaLogic technology from blood serum of a cohort of patients and matched healthy subjects. We developed a workflow based on biomarker identification and network-based pathway analysis that allowed us to describe different deregulated pathways. In addition to muscle-related functions, we identified other biological processes such as apoptosis, signaling in the immune system and neurotrophin signaling as significantly modulated in patients compared with controls. Moreover, our network-based analysis identified the involvement of FoxO transcription factors as putative regulators of different pathways. On the whole, this study provided a global view of the molecular processes involved in Duchenne muscular dystrophy that are decipherable from serum proteome.

Nutrition for the ageing brain: Towards evidence for an optimal diet.

As people age they become increasingly susceptible to chronic and extremely debilitating brain diseases. The precise cause of the neuronal degeneration underlying these disorders, and indeed normal brain ageing remains however elusive. Considering the limits of existing preventive methods, there is a desire to develop effective and safe strategies. Growing preclinical and clinical research in healthy individuals or at the early stage of cognitive decline has demonstrated the beneficial impact of nutrition on cognitive functions. The present review is the most recent in a series produced by the Nutrition and Mental Performance Task Force under the auspice of the International Life Sciences Institute Europe (ILSI Europe). The latest scientific advances specific to how dietary nutrients and non-nutrient may affect cognitive ageing are presented. Furthermore, several key points related to mechanisms contributing to brain ageing, pathological conditions affecting brain function, and brain biomarkers are also discussed. Overall, findings are inconsistent and fragmented and more research is warranted to determine the underlying mechanisms and to establish dose-response relationships for optimal brain maintenance in different population subgroups. Such approaches are likely to provide the necessary evidence to develop research portfolios that will inform about new dietary recommendations on how to prevent cognitive decline.

Integration of transcriptomic and genomic data suggests candidate mechanisms for APOE4-mediated pathogenic action in Alzheimer's disease.

Among the genetic factors known to increase the risk of late onset Alzheimer's diseases (AD), the presence of the apolipoproteine e4 (APOE4) allele has been recognized as the one with the strongest effect. However, despite decades of research, the pathogenic role of APOE4 in Alzheimer's disease has not been clearly elucidated yet. In order to investigate the pathogenic action of APOE4, we applied a systems biology approach to the analysis of transcriptomic and genomic data of APOE44 vs. APOE33 allele carriers affected by Alzheimer's disease. Network analysis combined with a novel technique for biomarker computation allowed the identification of an alteration in aging-associated processes such as inflammation, oxidative stress and metabolic pathways, indicating that APOE4 possibly accelerates pathological processes physiologically induced by aging. Subsequent integration with genomic data indicates that the Notch pathway could be the nodal molecular mechanism altered in APOE44 allele carriers with Alzheimer's disease. Interestingly, PSEN1 and APP, genes whose mutation are known to be linked to early onset Alzheimer's disease, are closely linked to this pathway. In conclusion, APOE4 role on inflammation and oxidation through the Notch signaling pathway could be crucial in elucidating the risk factors of Alzheimer's disease.

Systems biology integration of proteomic data in rodent models of depression reveals involvement of the immune response and glutamatergic signaling.

PURPOSE: The pathophysiological basis of major depression is incompletely understood. Recently, numerous proteomic studies have been performed in rodent models of depression to investigate the molecular underpinnings of depressive-like behaviours with an unbiased approach. The objective of the study is to integrate the results of these proteomic studies in depression models to shed light on the most relevant molecular pathways involved in the disease. EXPERIMENTAL DESIGN: Network analysis is performed integrating preexisting proteomic data from rodent models of depression. The IntAct mouse and the HRPD are used as reference protein-protein interaction databases. The functionality analyses of the networks are then performed by testing overrepresented GO biological process terms and pathways. RESULTS: Functional enrichment analyses of the networks revealed an association with molecular processes related to depression in humans, such as those involved in the immune response. Pathways impacted by clinically effective antidepressants are modulated, including glutamatergic signaling and neurotrophic responses. Moreover, dysregulations of proteins regulating energy metabolism and circadian rhythms are implicated. The comparison with protein pathways modulated in depressive patients revealed significant overlapping. CONCLUSIONS AND CLINICAL RELEVANCE: This systems biology study supports the notion that animal models can contribute to the research into the biology and therapeutics of depression.

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease.

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes.

Worldwide population is aging, and a large part of the growing burden associated with age-related conditions can be prevented or delayed by promoting healthy lifestyle and normalizing metabolic risk factors. However, a better understanding of the pleiotropic effects of available nutritional interventions and their influence on the multiple processes affected by aging is needed to select and implement the most promising actions. New methods of analysis are required to tackle the complexity of the interplay between nutritional interventions and aging, and to make sense of a growing amount of -omics data being produced for this purpose. In this paper, we review how various systems biology-inspired methods of analysis can be applied to the study of the molecular basis of nutritional interventions promoting healthy aging, notably caloric restriction and polyphenol supplementation. We specifically focus on the role that different versions of network analysis, molecular signature identification and multi-omics data integration are playing in elucidating the complex mechanisms underlying nutrition, and provide some examples on how to extend the application of these methods using available microarray data.

Novel drug target identification for the treatment of dementia using multi-relational association mining.

Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia worldwide is steadily increasing and regardless of the advances in the molecular characterization of the disease, current medical treatments for dementia are purely symptomatic and hardly effective. We present a novel multi-relational association mining method that integrates the huge amount of scientific data accumulated in recent years to predict potential novel targets for innovative therapeutic treatment of dementia. Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor. The predicted drug targets are mainly functionally related to metabolism, cell surface receptor signaling pathways, immune response, apoptosis, and long-term memory. Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95), and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies. These novel putative targets hold promises for the development of novel therapeutic approaches for the treatment of dementia.

Systems biology meets -omic technologies: novel approaches to biomarker discovery and companion diagnostic development.

The next generation of biomarkers and companion diagnostics will require the development of technologies capable of conjugating the advances in high-throughput techniques in biology with computational methods. Systems biology is poised to contribute through an integrated view, capturing the complexity of the system, both in terms of a collection of interacting molecular components and also in terms of multiple intersecting views. Following this system-centered view, novel approaches have been developed for the identification of signatures of both disease processes and drug modes of action with the promising perspectives of better diagnosis of disease and of the discovery of more efficacious and safe drugs. The application of systems biology to the development of companion diagnostics is very recent and to date a few pioneering steps have been made in this direction. In this review, we describe the ongoing studies and the potential developments in this area of research.

A systems biology investigation of neurodegenerative dementia reveals a pivotal role of autophagy.

BACKGROUND: Neurodegenerative dementia comprises chronic and progressive illnesses with major clinical features represented by progressive and permanent loss of cognitive and mental performance, including impairment of memory and brain functions. Many different forms of neurodegenerative dementia exist, but they are all characterized by death of specific subpopulation of neurons and accumulation of proteins in the brain. We incorporated data from OMIM and primary molecular targets of drugs in the different phases of the drug discovery process to try to reveal possible hidden mechanism in neurodegenerative dementia. In the present study, a systems biology approach was used to investigate the molecular connections among seemingly distinct complex diseases with the shared clinical symptoms of dementia that could suggest related disease mechanisms. RESULTS: Network analysis was applied to characterize an interaction network of disease proteins and drug targets, revealing a major role of metabolism and, predominantly, of autophagy process in dementia and, particularly, in tauopathies. Different phases of the autophagy molecular pathway appear to be implicated in the individual disease pathophysiology and specific drug targets associated to autophagy modulation could be considered for pharmacological intervention. In particular, in view of their centrality and of the direct association to autophagy proteins in the network, PP2A subunits could be suggested as a suitable molecular target for the development of novel drugs. CONCLUSION: The present systems biology investigation identifies the autophagy pathway as a central dis-regulated process in neurodegenerative dementia with a prevalent involvement in diseases characterized by tau inclusion and indicates the disease-specific molecules in the pathway that could be considered for therapy.

Network analysis of neurodegenerative disease highlights a role of Toll-like receptor signaling.

Despite significant advances in the study of the molecular mechanisms altered in the development and progression of neurodegenerative diseases (NDs), the etiology is still enigmatic and the distinctions between diseases are not always entirely clear. We present an efficient computational method based on protein-protein interaction network (PPI) to model the functional network of NDs. The aim of this work is fourfold: (i) reconstruction of a PPI network relating to the NDs, (ii) construction of an association network between diseases based on proximity in the disease PPI network, (iii) quantification of disease associations, and (iv) inference of potential molecular mechanism involved in the diseases. The functional links of diseases not only showed overlap with the traditional classification in clinical settings, but also offered new insight into connections between diseases with limited clinical overlap. To gain an expanded view of the molecular mechanisms involved in NDs, both direct and indirect connector proteins were investigated. The method uncovered molecular relationships that are in common apparently distinct diseases and provided important insight into the molecular networks implicated in disease pathogenesis. In particular, the current analysis highlighted the Toll-like receptor signaling pathway as a potential candidate pathway to be targeted by therapy in neurodegeneration.

The central role of AMP-kinase and energy homeostasis impairment in Alzheimer's disease: a multifactor network analysis.

Alzheimer's disease is the most common cause of dementia worldwide, affecting the elderly population. It is characterized by the hallmark pathology of amyloid-ß deposition, neurofibrillary tangle formation, and extensive neuronal degeneration in the brain. Wealth of data related to Alzheimer's disease has been generated to date, nevertheless, the molecular mechanism underlying the etiology and pathophysiology of the disease is still unknown. Here we described a method for the combined analysis of multiple types of genome-wide data aimed at revealing convergent evidence interest that would not be captured by a standard molecular approach. Lists of Alzheimer-related genes (seed genes) were obtained from different sets of data on gene expression, SNPs, and molecular targets of drugs. Network analysis was applied for identifying the regions of the human protein-protein interaction network showing a significant enrichment in seed genes, and ultimately, in genes associated to Alzheimer's disease, due to the cumulative effect of different combinations of the starting data sets. The functional properties of these enriched modules were characterized, effectively considering the role of both Alzheimer-related seed genes and genes that closely interact with them. This approach allowed us to present evidence in favor of one of the competing theories about AD underlying processes, specifically evidence supporting a predominant role of metabolism-associated biological process terms, including autophagy, insulin and fatty acid metabolic processes in Alzheimer, with a focus on AMP-activated protein kinase. This central regulator of cellular energy homeostasis regulates a series of brain functions altered in Alzheimer's disease and could link genetic perturbation with neuronal transmission and energy regulation, representing a potential candidate to be targeted by therapy.

Differential effects of glycogen synthase kinase 3 (GSK3) inhibition by lithium or selective inhibitors in the central nervous system.

Glycogen synthase kinase (GSK3) is a constitutively active serine-threonine kinase associated to neurological and psychiatric disorders. GSK3 inhibition is considered a mediator of the efficacy of the mood-stabiliser lithium. This study aimed at comparing the central nervous system effect of lithium with the selective GSK3 inhibitors AZ1080 and compound A in biochemical, cellular, and behavioural tests. Collapsin response mediator protein 2 is a neuron-specific GSK3 substrate. Lithium, AZ1080, and compound A inhibited its phosphorylation in rat primary neurons with different pIC50. After systemic treatments with lithium or GSK3 inhibitors to assess specific functional responses, phosphorylation was unchanged in adult rat brain, while it was strongly inhibited by GSK3 inhibitors in pups, differently from lithium. Lithium may exert neurotrophic effect by increasing brain-derived neurotrophic factor (BDNF) levels: in the present experimental conditions, lithium exerted opposite effects on plasma BDNF levels compared to GSK3 inhibitors, suggesting this effect might not be necessarily mediated by GSK3 inhibition alone. While plasma thyroid-stimulating hormone and luteinising hormone were not affected by lithium, they were decreased by selective inhibitors. GH and prolactin displayed similar responses towards reduction. Follicle-stimulating hormone levels were not altered by treatments, whereas melatonin was specifically increased by AZ1080. Lithium impaired mouse spontaneous locomotion and decreased amphetamine-induced hyper-locomotion. AZ1080 had no effects on locomotion, while compound A reduced spontaneous locomotor activity without effects on amphetamine-induced hyper-locomotion. The present results indicate that a broad correlation between the effects of lithium and selective GSK3 inhibitors could not be devised, suggesting alternative mechanisms, whereas overlapping results could be obtained in specific assays.

The identification of a series of novel, soluble non-peptidic neuropeptide Y Y2 receptor antagonists.

The identification and subsequent optimisation of a selective non-peptidic NPY Y2 antagonist series is described. This led to the development of amine 2, a selective, soluble NPY Y2 receptor antagonist with enhanced CNS exposure.