Evolving toward a human-cell based and multiscale approach to drug discovery for CNS disorders

Chlorpyrifos Disrupts Acetylcholine Metabolism Across Model Blood-Brain Barrier

Despite the significant progress in both scientific understanding and regulations, the safety of agricultural pesticides continues to be called into question. The need for complementary analytics to identify dysregulation events associated with chemical exposure and leverage this information to predict biological responses remains. Here, we present a platform that combines a model organ-on-chip neurovascular unit (NVU) with targeted mass spectrometry (MS) and electrochemical analysis to assess the impact of organophosphate (OP) exposure on blood-brain barrier (BBB) function. Using the NVU to simulate exposure, an escalating dose of the organophosphate chlorpyrifos (CPF) was administered. With up to 10 μM, neither CPF nor its metabolites were detected across the BBB (limit of quantitation 0.1 µM). At 30 µM CPF and above, targeted MS detected the main urinary metabolite, trichloropyridinol (TCP), across the BBB (0.025 µM) and no other metabolites. In the vascular chamber where CPF was directly applied, two primary metabolites of CPF, TCP and diethylthiophosphate (DETP), were both detected (0.1–5.7 µM). In a second experiment, a constant dose of 10 µM CPF was administered to the NVU, and though neither CPF nor its metabolites were detected across the BBB after 24 h, electrochemical analysis detected increases in acetylcholine levels on both sides of the BBB (up to 24.8 ± 3.4 µM) and these levels remained high over the course of treatment. In the vascular chamber where CPF was directly applied, only TCP was detected (ranging from 0.06 μM at 2 h to 0.19 μM at 24 h). These results provide chemical evidence of the substantial disruption induced by this widely used commercial pesticide. This work reinforces previously observed OP metabolism and mechanisms of impact, validates the use of the NVU for OP toxicology testing, and provides a model platform for analyzing these organotypic systems.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

Polygenic analysis suggests the involvement of calcium signaling in executive function in schizophrenia patients

Cognitive deficits are increasingly recognized as a core dimension rather than a consequence of schizophrenia (SCZ). The previous evidence supports the hypothesis of shared genetic factors between SCZ and cognitive ability. The objective of this study was to test whether and to what extent the variation of disease-relevant neurocognitive function in a sample of SCZ patients from the previous clinical interventional studies can be explained by SCZ polygenic risk scores (PRSs) or by hypothesis-driven and biomedical PRSs. The previous studies have described associations of the SNAP25 gene with cognition in SCZ. Likewise, the enrichment of several calcium signaling-related gene sets has been reported by genome-wide association studies (GWAS) in SCZ. Hypothesis-driven PRSs were calculated on the basis of the SNAP-25 interactome and also for genes regulated by phorbol myristate acetate (PMA), an activator of the signal transduction of protein kinase C (PKC) enzymes. In a cohort of 127 SCZ patients who had completed a comprehensive neurocognitive test battery as part of the previous antipsychotic intervention studies, we investigated the association between neurocognitive dimensions and PRSs. The PRS for SCZ and SNAP-25-associated genes could not explain the variance of neurocognition in this cohort. At a p value threshold of 0.05, the PRS for PMA was able to explain 2% of the variance in executive function (p = 0.05, uncorrected). The correlation between the PRS for PMA-regulated genes and cognition can give hints for further patient-derived cellular assays. In conclusion, incorporating biological information into PRSs and other en masse genetic analyses may help to close the gap between genetic vulnerability and the biological processes underlying neuropsychiatric diseases such as SCZ.

The genetics of bipolar disorder

Bipolar disorder (BD) is one of the most heritable mental illnesses, but the elucidation of its genetic basis has proven to be a very challenging endeavor. Genome-Wide Association Studies (GWAS) have transformed our understanding of BD, providing the first reproducible evidence of specific genetic markers and a highly polygenic architecture that overlaps with that of schizophrenia, major depression, and other disorders. Individual GWAS markers appear to confer little risk, but common variants together account for about 25% of the heritability of BD. A few higher-risk associations have also been identified, such as a rare copy number variant on chromosome 16p11.2. Large scale next-generation sequencing studies are actively searching for other alleles that confer substantial risk. As our understanding of the genetics of BD improves, there is growing optimism that some clear biological pathways will emerge, providing a basis for future studies aimed at molecular diagnosis and novel therapeutics.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Identification of Biomarkers in Neuropsychiatric Disorders Based on Systems Biology and Epigenetics

Clinically useful biomarkers are available for some neuropsychiatric disorders like fragile X syndrome, Rett syndrome, and Huntington’s disease. Despite many decades of research on the pathogenesis of neuropsychiatric disorders like schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD), the exact pathogenesis of these disorders remains unclear, and there are no clinically useful biomarkers for these disorders. However, there is increasing evidence that abnormal epigenetic mechanisms of gene expression contribute to the pathogenesis of SZ, BD, and MDD. Both systems (or network) biology and epigenetics (a component of systems biology) attempt to make sense of biological systems that are highly dynamic and multi-compartmental. This article suggests that systems biology, emphasizing the epigenetic component of systems biology, could help identify clinically useful biomarkers in neuropsychiatric disorders like SZ, BD, and MDD.

Exploiting machine learning for end-to-end drug discovery and development

A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.

Studying and modulating schizophrenia-associated dysfunctions of oligodendrocytes with patient-specific cell systems

Postmortem studies in patients with schizophrenia (SCZ) have revealed deficits in myelination, abnormalities in myelin gene expression and altered numbers of oligodendrocytes in the brain. However, gaining mechanistic insight into oligodendrocyte (OL) dysfunction and its contribution to SCZ has been challenging because of technical hurdles. The advent of individual patient-derived human-induced pluripotent stem cells (hiPSCs), combined with the generation of in principle any neuronal and glial cell type, including OLs and oligodendrocyte precursor cells (OPCs), holds great potential for understanding the molecular basis of the aetiopathogenesis of genetically complex psychiatric diseases such as SCZ and could pave the way towards personalized medicine. The development of neuronal and glial co-culture systems now appears to enable the in vitro study of SCZ-relevant neurobiological endophenotypes, including OL dysfunction and myelination, with unprecedented construct validity. Nonetheless, the meaningful stratification of patients before the subsequent functional analyses of patient-derived cell systems still represents an important bottleneck. Here, to improve the predictive power of ex vivo disease modelling we propose using hiPSC technology to focus on representatives of patient subgroups stratified for genomic and/or phenomic features and neurobiological cell systems. Therefore, this review will outline the evidence for the involvement of OPCs/OLs in SCZ in the context of their proposed functions, including myelination and axon support, the implications for hiPSC-based cellular disease modelling and potential strategies for patient selection.

Expression-based drug screening of neural progenitor cells from individuals with schizophrenia

A lack of biologically relevant screening models hinders the discovery of better treatments for schizophrenia (SZ) and other neuropsychiatric disorders. Here we compare the transcriptional responses of 8 commonly used cancer cell lines (CCLs) directly with that of human induced pluripotent stem cell (hiPSC)-derived neural progenitor cells (NPCs) from 12 individuals with SZ and 12 controls across 135 drugs, generating 4320 unique drug-response transcriptional signatures. We identify those drugs that reverse post-mortem SZ-associated transcriptomic signatures, several of which also differentially regulate neuropsychiatric disease-associated genes in a cell type (hiPSC NPC vs. CCL) and/or a diagnosis (SZ vs. control)-dependent manner. Overall, we describe a proof-of-concept application of transcriptomic drug screening to hiPSC-based models, demonstrating that the drug-induced gene expression differences observed with patient-derived hiPSC NPCs are enriched for SZ biology, thereby revealing a major advantage of incorporating cell type and patient-specific platforms in drug discovery.

Unbiased large scale screening of small molecules for drug discovery in psychiatric disease is technically challenging and financially costly. Here, Readhead and colleagues integrate in silico and in vitro approaches to design and conduct transcriptomic drug screening in schizophrenia patient-derived neural cells, in order to survey novel pathologies and points of intervention.

Advances in Drug Discovery and Development in Geriatric Psychiatry

PURPOSE OF REVIEW

This article reviews recent advances in drug discovery and development for geriatric psychiatry. Drug discovery for disorders of the central nervous system is a long and challenging process, with a high attrition rate from the preclinical stages through to marketing a compound. Developing drugs for geriatric neuropsychiatric conditions presents additional challenges, due to the complexity of the symptoms, comorbid diagnoses, and the variability of the population. Despite there being limited success over the past two decades, a number of new approaches have identified potential targets for preclinical development and ultimately clinical testing.

RECENT FINDINGS

Recent approaches have tried to address specific mechanisms that relate to the disease progression. These approaches include combining a number of ligands into to multi-target compounds, or targeting specific types of cells such as protein kinases or myeloid cells. In addition, the increased use of induced pluripotent stem cell cultures has enabled new compounds to be tested on disease-specific tissues, increasing the success rate of the lead compounds going through the preclinical stages. New pharmacological agents designed with advanced screening techniques and the shift towards systems pharmacology is changing the landscape of drug discovery in geriatric psychiatry. There is potential for these new agents to produce targeted effects in the framework of disorders that have long been untreatable.

Brain imaging genetics in ADHD and beyond - Mapping pathways from gene to disorder at different levels of complexity

Attention-deficit/hyperactivity disorder (ADHD) is a common and often persistent neurodevelopmental disorder. Beyond gene-finding, neurobiological parameters, such as brain structure, connectivity, and function, have been used to link genetic variation to ADHD symptomatology. We performed a systematic review of brain imaging genetics studies involving 62 ADHD candidate genes in childhood and adult ADHD cohorts. Fifty-one eligible research articles described studies of 13 ADHD candidate genes. Almost exclusively, single genetic variants were studied, mostly focussing on dopamine-related genes. While promising results have been reported, imaging genetics studies are thus far hampered by methodological differences in study design and analysis methodology, as well as limited sample sizes. Beyond reviewing imaging genetics studies, we also discuss the need for complementary approaches at multiple levels of biological complexity and emphasize the importance of combining and integrating findings across levels for a better understanding of biological pathways from gene to disease. These may include multi-modal imaging genetics studies, bioinformatic analyses, and functional analyses of cell and animal models.

Genetic tests in major psychiatric disorders-integrating molecular medicine with clinical psychiatry-why is it so difficult?

With the advent of post-genomic era, new technologies create extraordinary possibilities for diagnostics and personalized therapy, transforming todays' medicine. Rooted in both medical genetics and clinical psychiatry, the paper is designed as an integrated source of information of the current and potential future application of emerging genomic technologies as diagnostic tools in psychiatry, moving beyond the classical concept of patient approach. Selected approaches are presented, starting from currently used technologies (next-generation sequencing (NGS) and microarrays), followed by newer options (reverse phenotyping). Next, we describe an old concept in a new light (endophenotypes), subsequently coming up with a sophisticated and complex approach (gene networks) ending by a nascent field (computational psychiatry). The challenges and barriers that exist to translate genomic research to real-world patient assessment are further discussed. We emphasize the view that only a paradigm shift can bring a fundamental change in psychiatric practice, allowing to disentangle the intricacies of mental diseases. All the diagnostic methods, as described, are directed at uncovering the integrity of the system including many types of relations within a complex structure. The integrative system approach offers new opportunity to connect genetic background with specific diseases entities, or concurrently, with symptoms regardless of a diagnosis. To advance the field, we propose concerted cross-disciplinary effort to provide a diagnostic platform operating at the general level of genetic pathogenesis of complex-trait psychiatric disorders rather than at the individual level of a specific disease.

High content analysis in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a devastating disease characterized by the progressive loss of motor neurons. Neurons, astrocytes, oligodendrocytes and microglial cells all undergo pathological modifications in the onset and progression of ALS. A number of genes involved in the etiopathology of the disease have been identified, but a complete understanding of the molecular mechanisms of ALS has yet to be determined. Currently, people affected by ALS have a life expectancy of only two to five years from diagnosis. The search for a treatment has been slow and mostly unsuccessful, leaving patients in desperate need of better therapies. Until recently, most pre-clinical studies utilized the available ALS animal models. In the past years, the development of new protocols for isolation of patient cells and differentiation into relevant cell types has provided new tools to model ALS, potentially more relevant to the disease itself as they directly come from patients. The use of stem cells is showing promise to facilitate ALS research by expanding our understanding of the disease and help to identify potential new therapeutic targets and therapies to help patients. Advancements in high content analysis (HCA) have the power to contribute to move ALS research forward by combining automated image acquisition along with digital image analysis. With modern HCA machines it is possible, in a period of just a few hours, to observe changes in morphology and survival of cells, under the stimulation of hundreds, if not thousands of drugs and compounds. In this article, we will summarize the major molecular and cellular hallmarks of ALS, describe the advancements provided by the in vitro models developed in the last few years, and review the studies that have applied HCA to the ALS field to date.

Using hiPSCs to model neuropsychiatric copy number variations (CNVs) has potential to reveal underlying disease mechanisms

Schizophrenia is a neuropsychological disorder with a strong heritable component; genetic risk for schizophrenia is conferred by both common variants of relatively small effect and rare variants with high penetrance. Genetically engineered mouse models can recapitulate rare variants, displaying some behavioral defects associated with schizophrenia; however, these mouse models cannot recapitulate the full genetic architecture underlying the disorder. Patient-derived human induced pluripotent stem cells (hiPSCs) present an alternative approach for studying rare variants, in the context of all other risk alleles. Genome editing technologies, such as CRISPR-Cas9, enable the generation of isogenic hiPSC lines with which to examine the functional contribution of single variants within any genetic background. Studies of these rare variants using hiPSCs have the potential to identify commonly disrupted pathways in schizophrenia and allow for the identification of new therapeutic targets. This article is part of a Special Issue entitled SI:StemsCellsinPsychiatry.

Stage-specific roles of FGF2 signaling in human neural development

This study elucidated the stage-specific roles of FGF2 signaling during neural development using in-vitro human embryonic stem cell-based developmental modeling. We found that the dysregulation of FGF2 signaling prior to the onset of neural induction resulted in the malformation of neural rosettes (a neural tube-like structure), despite cells having undergone neural induction. The aberrant neural rosette formation may be attributed to the misplacement of ZO-1, which is a polarized tight junction protein and shown co-localized with FGF2/FGFR1 in the apical region of neural rosettes, subsequently led to abnormal neurogenesis. Moreover, the FGF2 signaling inhibition at the stage of neural rosettes caused a reduction in cell proliferation, an increase in numbers of cells with cell-cycle exit, and premature neurogenesis. These effects may be mediated by NUMB, to which expression was observed enriched in the apical region of neural rosettes after FGF2 signaling inhibition coinciding with the disappearance of PAX6+/Ki67+ neural stem cells and the emergence of MAP2+ neurons. Moreover, our results suggested that the hESC-based developmental system reserved a similar neural stem cell niche in vivo.

CNS Target Identification and Validation: Avoiding the Valley of Death or Naive Optimism?

There are many challenges along the path to the approval of new drugs to treat CNS disorders, one of the greatest areas of unmet medical need with a large societal burden and health-care impact. Unfortunately, over the past two decades, few CNS drug approvals have succeeded, leading many pharmaceutical companies to deprioritize this therapeutic area. The reasons for the failures in CNS drug discovery are likely to be multifactorial. However, selecting the most biologically plausible molecular targets that are relevant to the disorder is a critical first step to improve the probability of success. In this review, we outline previous methods for identifying and validating novel targets for CNS drug discovery, and, cognizant of previous failures, we discuss potential new strategies that may improve the probability of success of developing novel treatments for CNS disorders.

Microfabric Vessels for Embryoid Body Formation and Rapid Differentiation of Pluripotent Stem Cells

Various scalable three-dimensional culture systems for regenerative medicine using human induced pluripotent stem cells (hiPSCs) have been developed to date. However, stable production of hiPSCs with homogeneous qualities still remains a challenge. Here, we describe a novel and simple embryoid body (EB) formation system using unique microfabricated culture vessels. Furthermore, this culture system is useful for high throughput EB formation and rapid generation of differentiated cells such as neural stem cells (NSCs) from hiPSCs. The period of NSC differentiation was significantly shortened under high EB density culture conditions. Simultaneous mass production of a pure population of NSCs was possible within 4 days. These results indicate that the novel culture system might not only become a unique tool to obtain new insights into developmental biology based on human stem cells, but also provide an important tractable platform for efficient and stable production of NSCs for clinical applications.

Establishment of Reporter Lines for Detecting Fragile X Mental Retardation (FMR1) Gene Reactivation in Human Neural Cells

Human patient-derived induced pluripotent stem cells (hiPSCs) provide unique opportunities for disease modeling and drug development. However, adapting hiPSCs or their differentiated progenies to high throughput assays for phenotyping or drug screening has been challenging. Fragile X syndrome (FXS) is the most common inherited cause of intellectual disability and a major genetic cause of autism. FXS is caused by mutational trinucleotide expansion in the FMR1 gene leading to hypermethylation and gene silencing. One potential therapeutic strategy is to reactivate the silenced FMR1 gene, which has been attempted using both candidate chemicals and cell-based screening. However, molecules that effectively reactivate the silenced FMR1 gene are yet to be identified; therefore, a high throughput unbiased screen is needed. Here we demonstrate the creation of a robust FMR1-Nluc reporter hiPSC line by knocking in a Nano luciferase (Nluc) gene into the endogenous human FMR1 gene using the CRISPR/Cas9 genome editing method. We confirmed that luciferase activities faithfully report FMR1 gene expression levels and showed that neural progenitor cells derived from this line could be optimized for high throughput screening. The FMR1-Nluc reporter line is a good resource for drug screening as well as for testing potential genetic reactivation strategies. In addition, our data provide valuable information for the generation of knockin human iPSC reporter lines for disease modeling, drug screening, and mechanistic studies. Stem Cells 2017;35:158-169.

Genetic variants in Alzheimer disease - molecular and brain network approaches

Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.

Integrating Multiscale Modeling with Drug Effects for Cancer Treatment

In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.

A Next Generation Multiscale View of Inborn Errors of Metabolism

Inborn errors of metabolism (IEM) are not unlike common diseases. They often present as a spectrum of disease phenotypes that correlates poorly with the severity of the disease-causing mutations. This greatly impacts patient care and reveals fundamental gaps in our knowledge of disease modifying biology. Systems biology approaches that integrate multi-omics data into molecular networks have significantly improved our understanding of complex diseases. Similar approaches to study IEM are rare despite their complex nature. We highlight that existing common disease-derived datasets and networks can be repurposed to generate novel mechanistic insight in IEM and potentially identify candidate modifiers. While understanding disease pathophysiology will advance the IEM field, the ultimate goal should be to understand per individual how their phenotype emerges given their primary mutation on the background of their whole genome, not unlike personalized medicine. We foresee that panomics and network strategies combined with recent experimental innovations will facilitate this.

A Dishful of a Troubled Mind: Induced Pluripotent Stem Cells in Psychiatric Research

Neuronal differentiation of induced pluripotent stem cells and direct reprogramming represent powerful methods for modeling the development of neurons in vitro. Moreover, this approach is also a means for comparing various cellular phenotypes between cell lines originating from healthy and diseased individuals or isogenic cell lines engineered to differ at only one or a few genomic loci. Despite methodological constraints and initial skepticism regarding this approach, the field is expanding at a fast pace. The improvements include the development of new differentiation protocols resulting in selected neuronal populations (e.g., dopaminergic, GABAergic, hippocampal, and cortical), the widespread use of genome editing methods, and single-cell techniques. A major challenge awaiting in vitro disease modeling is the integration of clinical data in the models, by selection of well characterized clinical populations. Ideally, these models will also demonstrate how different diagnostic categories share overlapping molecular disease mechanisms, but also have unique characteristics. In this review we evaluate studies with regard to the described developments, to demonstrate how differentiation of induced pluripotent stem cells and direct reprogramming can contribute to psychiatry.

Mesenchymal stem cells and induced pluripotent stem cells as therapies for multiple sclerosis. Int J Mol Sci. 2015;16:9283-9302. [PMID: 25918935 PMCID: PMC4463588 DOI: 10.3390/ijms16059283]

Multiple sclerosis (MS) is a chronic, autoimmune, inflammatory demyelinating disorder of the central nervous system that leads to permanent neurological deficits. Current MS treatment regimens are insufficient to treat the irreversible neurological disabilities. Tremendous progress in the experimental and clinical applications of cell-based therapies has recognized stem cells as potential candidates for regenerative therapy for many neurodegenerative disorders including MS. Mesenchymal stem cells (MSC) and induced pluripotent stem cell (iPSCs) derived precursor cells can modulate the autoimmune response in the central nervous system (CNS) and promote endogenous remyelination and repair process in animal models. This review highlights studies involving the immunomodulatory and regenerative effects of mesenchymal stem cells and iPSCs derived cells in animal models, and their translation into immunomodulatory and neuroregenerative treatment strategies for MS.

Systems approach to the study of brain damage in the very preterm newborn

Background: A systems approach to the study of brain damage in very preterm newborns has been lacking.

Methods: In this perspective piece, we offer encephalopathy of prematurity as an example of the complexity and interrelatedness of brain-damaging molecular processes that can be initiated inflammatory phenomena.

Results: Using three transcription factors, nuclear factor-kappa B (NF-κB), Notch-1, and nuclear factor erythroid 2 related factor 2 (NRF2), we show the inter-connectedness of signaling pathways activated by some antecedents of encephalopathy of prematurity.

Conclusions: We hope that as biomarkers of exposures and processes leading to brain damage in the most immature newborns become more readily available, those who apply a systems approach to the study of neuroscience can be persuaded to study the pathogenesis of brain disorders in the very preterm newborn.