Comprehensive and unbiased multiparameter high-throughput screening by compaRe finds effective and subtle drug responses in AML models.

Large-scale multiparameter screening has become increasingly feasible and straightforward to perform thanks to developments in technologies such as high-content microscopy and high-throughput flow cytometry. The automated toolkits for analyzing similarities and differences between large numbers of tested conditions have not kept pace with these technological developments. Thus, effective analysis of multiparameter screening datasets becomes a bottleneck and a limiting factor in unbiased interpretation of results. Here we introduce compaRe, a toolkit for large-scale multiparameter data analysis, which integrates quality control, data bias correction, and data visualization methods with a mass-aware gridding algorithm-based similarity analysis providing a much faster and more robust analyses than existing methods. Using mass and flow cytometry data from acute myeloid leukemia and myelodysplastic syndrome patients, we show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles. By applying compaRe to high-throughput flow cytometry drug response data in AML models, we robustly identified multiple types of both deep and subtle phenotypic response patterns, highlighting how this analysis could be used for therapeutic discoveries. In conclusion, compaRe is a toolkit that uniquely allows for automated, rapid, and precise comparisons of large-scale multiparameter datasets, including high-throughput screens.

Biology has seen huge advances in technology in recent years. This has led to state-of-the-art techniques which can test hundreds of conditions simultaneously, such as how cancer cells respond to different drugs. In addition to this, each of the tens of thousands of cells studied can be screened for multiple variables, such as certain proteins or genes. This generates massive datasets with large numbers of parameters, which researchers can use to find similarities and differences between the tested conditions. Analyzing these 'high-throughput' experiments, however, is no easy task, as the data is often contaminated with meaningless information, or 'background noise', as well as sources of bias, such as non-biological variations between experiments. As a result, most analysis methods can only probe one parameter at a time, or are unautomated and require manual interpretation of the data. Here, Chalabi Hajkarim et al. have developed a new toolkit that can analyze multiparameter datasets faster and more robustly than current methods. The kit, which was named 'compaRe', combines a range of computational tools that automatically 'clean' the data of background noise or bias: the different conditions are then compared and any similarities are visually displayed using a graphical interface that is easy to explore. Chalabi Hajkarim et al. used their new method to study data from patients with acute myeloid leukemia (AML) and myelodysplastic syndrome, two forms of cancer that disrupt the production of functional immune cells. The toolkit was able to identify subtle differences between the patients and categorize them into groups based on the proteins present on immune cells. Chalabi Hajkarim et al. also applied compaRe to high-throughput data on cells from patients and mouse models with AML that had been treated with large numbers of specific drugs. This revealed that different cell types in the samples responded to the treatments in distinct ways. These findings suggest that the toolkit created by Chalabi Hajkarim et al. can automatically, rapidly and precisely compare large multiparameter datasets collected using high-throughput screens. In the future, compaRe could be used to identify drugs that illicit a specific response, or to predict how newly developed treatments impact different cell types in the body.

Cell-intrinsic glial pathology is conserved across human and murine models of Huntington's disease.

Glial pathology is a causal contributor to the striatal neuronal dysfunction of Huntington's disease (HD). We investigate mutant HTT-associated changes in gene expression by mouse and human striatal astrocytes, as well as in mouse microglia, to identify commonalities in glial pathobiology across species and models. Mouse striatal astrocytes are fluorescence-activated cell sorted (FACS) from R6/2 and zQ175 mice, which respectively express exon1-only or full-length mHTT, and human astrocytes are generated either from human embryonic stem cells (hESCs) expressing full-length mHTT or from fetal striatal astrocytes transduced with exon1-only mHTT. Comparison of differential gene expression across these conditions, all with respect to normal HTT controls, reveals cell-type-specific changes in transcription common to both species, yet with differences that distinguish glia expressing truncated mHTT versus full-length mHTT. These data indicate that the differential gene expression of glia expressing truncated mHTT may differ from that of cells expressing full-length mHTT, while identifying a conserved set of dysregulated pathways in HD glia.

Dysregulated Glial Differentiation in Schizophrenia May Be Relieved by Suppression of SMAD4- and REST-Dependent Signaling.

Astrocytic differentiation is developmentally impaired in patients with childhood-onset schizophrenia (SCZ). To determine why, we used genetic gain- and loss-of-function studies to establish the contributions of differentially expressed transcriptional regulators to the defective differentiation of glial progenitor cells (GPCs) produced from SCZ patient-derived induced pluripotent cells (iPSCs). Negative regulators of the bone morphogenetic protein (BMP) pathway were upregulated in SCZ GPCs, including BAMBI, FST, and GREM1, whose overexpression retained SCZ GPCs at the progenitor stage. SMAD4 knockdown (KD) suppressed the production of these BMP inhibitors by SCZ GPCs and rescued normal astrocytic differentiation. In addition, the BMP-regulated transcriptional repressor REST was upregulated in SCZ GPCs, and its KD similarly restored normal glial differentiation. REST KD also rescued potassium-transport-associated gene expression and K+ uptake, which were otherwise deficient in SCZ glia. These data suggest that the glial differentiation defect in childhood-onset SCZ, and its attendant disruption in K+ homeostasis, may be rescued by targeting BMP/SMAD4- and REST-dependent transcription.

Human ESC-Derived Chimeric Mouse Models of Huntington's Disease Reveal Cell-Intrinsic Defects in Glial Progenitor Cell Differentiation.

Huntington's disease (HD) is characterized by hypomyelination and neuronal loss. To assess the basis for myelin loss in HD, we generated bipotential glial progenitor cells (GPCs) from human embryonic stem cells (hESCs) derived from mutant Huntingtin (mHTT) embryos or normal controls and performed RNA sequencing (RNA-seq) to assess mHTT-dependent changes in gene expression. In human GPCs (hGPCs) derived from 3 mHTT hESC lines, transcription factors associated with glial differentiation and myelin synthesis were sharply downregulated relative to normal hESC GPCs; NKX2.2, OLIG2, SOX10, MYRF, and their downstream targets were all suppressed. Accordingly, when mHTT hGPCs were transplanted into hypomyelinated shiverer mice, the resultant glial chimeras were hypomyelinated; this defect could be rescued by forced expression of SOX10 and MYRF by mHTT hGPCs. The mHTT hGPCs also manifested impaired astrocytic differentiation and developed abnormal fiber architecture. White matter involution in HD is thus a product of the cell-autonomous, mHTT-dependent suppression of glial differentiation.

Human iPSC Glial Mouse Chimeras Reveal Glial Contributions to Schizophrenia.

In this study, we investigated whether intrinsic glial dysfunction contributes to the pathogenesis of schizophrenia (SCZ). Our approach was to establish humanized glial chimeric mice using glial progenitor cells (GPCs) produced from induced pluripotent stem cells derived from patients with childhood-onset SCZ. After neonatal implantation into myelin-deficient shiverer mice, SCZ GPCs showed premature migration into the cortex, leading to reduced white matter expansion and hypomyelination relative to controls. The SCZ glial chimeras also showed delayed astrocytic differentiation and abnormal astrocytic morphologies. When established in myelin wild-type hosts, SCZ glial mice showed reduced prepulse inhibition and abnormal behavior, including excessive anxiety, antisocial traits, and disturbed sleep. RNA-seq of cultured SCZ human glial progenitor cells (hGPCs) revealed disrupted glial differentiation-associated and synaptic gene expression, indicating that glial pathology was cell autonomous. Our data therefore suggest a causal role for impaired glial maturation in the development of schizophrenia and provide a humanized model for its in vivo assessment.

Human glia can both induce and rescue aspects of disease phenotype in Huntington disease.

The causal contribution of glial pathology to Huntington disease (HD) has not been heavily explored. To define the contribution of glia to HD, we established human HD glial chimeras by neonatally engrafting immunodeficient mice with mutant huntingtin (mHTT)-expressing human glial progenitor cells (hGPCs), derived from either human embryonic stem cells or mHTT-transduced fetal hGPCs. Here we show that mHTT glia can impart disease phenotype to normal mice, since mice engrafted intrastriatally with mHTT hGPCs exhibit worse motor performance than controls, and striatal neurons in mHTT glial chimeras are hyperexcitable. Conversely, normal glia can ameliorate disease phenotype in transgenic HD mice, as striatal transplantation of normal glia rescues aspects of electrophysiological and behavioural phenotype, restores interstitial potassium homeostasis, slows disease progression and extends survival in R6/2 HD mice. These observations suggest a causal role for glia in HD, and further suggest a cell-based strategy for disease amelioration in this disorder.

Automated protein motif generation in the structure-based protein function prediction tool ProMOL.

ProMOL, a plugin for the PyMOL molecular graphics system, is a structure-based protein function prediction tool. ProMOL includes a set of routines for building motif templates that are used for screening query structures for enzyme active sites. Previously, each motif template was generated manually and required supervision in the optimization of parameters for sensitivity and selectivity. We developed an algorithm and workflow for the automation of motif building and testing routines in ProMOL. The algorithm uses a set of empirically derived parameters for optimization and requires little user intervention. The automated motif generation algorithm was first tested in a performance comparison with a set of manually generated motifs based on identical active sites from the same 112 PDB entries. The two sets of motifs were equally effective in identifying alignments with homologs and in rejecting alignments with unrelated structures. A second set of 296 active site motifs were generated automatically, based on Catalytic Site Atlas entries with literature citations, as an expansion of the library of existing manually generated motif templates. The new motif templates exhibited comparable performance to the existing ones in terms of hit rates against native structures, homologs with the same EC and Pfam designations, and randomly selected unrelated structures with a different EC designation at the first EC digit, as well as in terms of RMSD values obtained from local structural alignments of motifs and query structures. This research is supported by NIH grant GM078077.

Estimation of protein function using template-based alignment of enzyme active sites.

BACKGROUND: The accumulation of protein structural data occurs more rapidly than it can be characterized by traditional laboratory means. This has motivated widespread efforts to predict enzyme function computationally. The most useful/accurate strategies employed to date are based on the detection of motifs in novel structures that correspond to a specific function. Functional residues are critical components of predictively useful motifs. We have implemented a novel method, to complement current approaches, which detects motifs solely on the basis of distance restraints between catalytic residues. RESULTS: ProMOL is a plugin for the PyMOL molecular graphics environment that can be used to create active site motifs for enzymes. A library of 181 active site motifs has been created with ProMOL, based on definitions published in the Catalytic Site Atlas (CSA). Searches with ProMOL produce better than 50% useful Enzyme Commission (EC) class suggestions for level 1 searches in EC classes 1, 4 and 5, and produce some useful results for other classes. 261 additional motifs automatically translated from Jonathan Barker's JESS motif set [Bioinformatics 19:1644-1649, 2003] and a set of NMR motifs is under development. Alignments are evaluated by visual superposition, Levenshtein distance and root-mean-square deviation (RMSD) and are reasonably consistent with related search methods. CONCLUSION: The ProMOL plugin for PyMOL provides ready access to template-based local alignments. Recent improvements to ProMOL, including the expanded motif library, RMSD calculations and output selection formatting, have greatly increased the program's usability and speed, and have improved the way that the results are presented.