RESUMO
Many of the gene regulatory processes of Plasmodium falciparum, the deadliest malaria parasite, remain poorly understood. To develop a comprehensive guide for exploring this organism's gene regulatory network, we generated a systems-level model of P. falciparum gene regulation using a well-validated, machine-learning approach for predicting interactions between transcription regulators and their targets. The resulting network accurately predicts expression levels of transcriptionally coherent gene regulatory programs in independent transcriptomic data sets from parasites collected by different research groups in diverse laboratory and field settings. Thus, our results indicate that our gene regulatory model has predictive power and utility as a hypothesis-generating tool for illuminating clinically relevant gene regulatory mechanisms within P. falciparum. Using the set of regulatory programs we identified, we also investigated correlates of artemisinin resistance based on gene expression coherence. We report that resistance is associated with incoherent expression across many regulatory programs, including those controlling genes associated with erythrocyte-host engagement. These results suggest that parasite populations with reduced artemisinin sensitivity are more transcriptionally heterogenous. This pattern is consistent with a model where the parasite utilizes bet-hedging strategies to diversify the population, rendering a subpopulation more able to navigate drug treatment.
Assuntos
Regulação da Expressão Gênica , Redes Reguladoras de Genes , Modelos Genéticos , Plasmodium falciparum/genética , Antimaláricos/farmacologia , Artemisininas/farmacologia , Resistência a Medicamentos/genética , Regulação da Expressão Gênica/efeitos dos fármacos , Aprendizado de Máquina , Plasmodium falciparum/efeitos dos fármacos , Biologia de Sistemas , Transcrição GênicaRESUMO
BACKGROUND: The tumor microenvironment (TME) is increasingly appreciated as an important determinant of cancer outcome, including in multiple myeloma (MM). However, most myeloma microenvironment studies have been based on bone marrow (BM) aspirates, which often do not fully reflect the cellular content of BM tissue itself. To address this limitation in myeloma research, we systematically characterized the whole bone marrow (WBM) microenvironment during premalignant, baseline, on treatment, and post-treatment phases. METHODS AND FINDINGS: Between 2004 and 2019, 998 BM samples were taken from 436 patients with newly diagnosed MM (NDMM) at the University of Arkansas for Medical Sciences in Little Rock, Arkansas, United States of America. These patients were 61% male and 39% female, 89% White, 8% Black, and 3% other/refused, with a mean age of 58 years. Using WBM and matched cluster of differentiation (CD)138-selected tumor gene expression to control for tumor burden, we identified a subgroup of patients with an adverse TME associated with 17 fewer months of progression-free survival (PFS) (95% confidence interval [CI] 5-29, 49-69 versus 70-82 months, χ2 p = 0.001) and 15 fewer months of overall survival (OS; 95% CI -1 to 31, 92-120 versus 113-129 months, χ2 p = 0.036). Using immunohistochemistry-validated computational tools that identify distinct cell types from bulk gene expression, we showed that the adverse outcome was correlated with elevated CD8+ T cell and reduced granulocytic cell proportions. This microenvironment develops during the progression of premalignant to malignant disease and becomes less prevalent after therapy, in which it is associated with improved outcomes. In patients with quantified International Staging System (ISS) stage and 70-gene Prognostic Risk Score (GEP-70) scores, taking the microenvironment into consideration would have identified an additional 40 out of 290 patients (14%, premutation p = 0.001) with significantly worse outcomes (PFS, 95% CI 6-36, 49-73 versus 74-90 months) who were not identified by existing clinical (ISS stage III) and tumor (GEP-70) criteria as high risk. The main limitations of this study are that it relies on computationally identified cell types and that patients were treated with thalidomide rather than current therapies. CONCLUSIONS: In this study, we observe that granulocyte signatures in the MM TME contribute to a more accurate prognosis. This implies that future researchers and clinicians treating patients should quantify TME components, in particular monocytes and granulocytes, which are often ignored in microenvironment studies.
Assuntos
Medula Óssea/patologia , Mieloma Múltiplo/diagnóstico , Mieloma Múltiplo/patologia , Microambiente Tumoral , Adulto , Estudos de Coortes , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Mieloma Múltiplo/tratamento farmacológico , Prognóstico , Carga TumoralRESUMO
The interplay between cellular and molecular determinants that lead to severe malaria in adults is unexplored. Here, we analyzed parasite virulence factors in an infected adult population in India and investigated whether severe malaria isolates impair endothelial protein C receptor (EPCR), a protein involved in coagulation and endothelial barrier permeability. Severe malaria isolates overexpressed specific members of the Plasmodium falciparum var gene/PfEMP1 (P. falciparum erythrocyte membrane protein 1) family that bind EPCR, including DC8 var genes that have previously been linked to severe pediatric malaria. Machine learning analysis revealed that DC6- and DC8-encoding var transcripts in combination with high parasite biomass were the strongest indicators of patient hospitalization and disease severity. We found that DC8 CIDRα1 domains from severe malaria isolates had substantial differences in EPCR binding affinity and blockade activity for its ligand activated protein C. Additionally, even a low level of inhibition exhibited by domains from two cerebral malaria isolates was sufficient to interfere with activated protein C-barrier protective activities in human brain endothelial cells. Our findings demonstrate an interplay between parasite biomass and specific PfEMP1 adhesion types in the development of adult severe malaria, and indicate that low impairment of EPCR function may contribute to parasite virulence.
Assuntos
Malária Falciparum/parasitologia , Plasmodium falciparum/genética , Plasmodium falciparum/patogenicidade , Proteínas de Protozoários/genética , Adulto , Antígenos CD/genética , Antígenos CD/metabolismo , Biomassa , Receptor de Proteína C Endotelial , Feminino , Humanos , Aprendizado de Máquina , Malária Falciparum/genética , Malária Falciparum/metabolismo , Masculino , Pessoa de Meia-Idade , Proteína C/metabolismo , Domínios Proteicos , Proteínas de Protozoários/química , Receptores de Superfície Celular/genética , Receptores de Superfície Celular/metabolismo , Virulência , Adulto JovemRESUMO
Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.
Assuntos
Redes Reguladoras de Genes , Redes e Vias Metabólicas , Saccharomyces cerevisiae , Redes Reguladoras de Genes/genética , Redes Reguladoras de Genes/fisiologia , Redes e Vias Metabólicas/genética , Redes e Vias Metabólicas/fisiologia , Modelos Biológicos , Fenótipo , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo , Biologia de SistemasRESUMO
Malaria remains one of the most prevalent and lethal human infectious diseases worldwide. A comprehensive characterization of antibody responses to blood stage malaria is essential to support the development of future vaccines, sero-diagnostic tests, and sero-surveillance methods. We constructed a proteome array containing 4441 recombinant proteins expressed by the blood stages of the two most common human malaria parasites, P. falciparum (Pf) and P. vivax (Pv), and used this array to screen sera of Papua New Guinea children infected with Pf, Pv, or both (Pf/Pv) that were either symptomatic (febrile), or asymptomatic but had parasitemia detectable via microscopy or PCR. We hypothesized that asymptomatic children would develop antigen-specific antibody profiles associated with antidisease immunity, as compared with symptomatic children. The sera from these children recognized hundreds of the arrayed recombinant Pf and Pv proteins. In general, responses in asymptomatic children were highest in those with high parasitemia, suggesting that antibody levels are associated with parasite burden. In contrast, symptomatic children carried fewer antibodies than asymptomatic children with infections detectable by microscopy, particularly in Pv and Pf/Pv groups, suggesting that antibody production may be impaired during symptomatic infections. We used machine-learning algorithms to investigate the relationship between antibody responses and symptoms, and we identified antibody responses to sets of Plasmodium proteins that could predict clinical status of the donors. Several of these antibody responses were identified by multiple comparisons, including those against members of the serine enriched repeat antigen family and merozoite protein 4. Interestingly, both P. falciparum serine enriched repeat antigen-5 and merozoite protein 4 have been previously investigated for use in vaccines. This machine learning approach, never previously applied to proteome arrays, can be used to generate a list of potential seroprotective and/or diagnostic antigens candidates that can be further evaluated in longitudinal studies.
Assuntos
Malária Falciparum/imunologia , Malária Vivax/imunologia , Análise Serial de Proteínas/métodos , Proteínas de Protozoários/análise , Inteligência Artificial , Criança , Pré-Escolar , Humanos , Lactente , Malária Falciparum/parasitologia , Malária Falciparum/patologia , Malária Vivax/parasitologia , Malária Vivax/patologia , Nova Guiné , Plasmodium falciparum/imunologia , Plasmodium falciparum/metabolismo , Plasmodium vivax/imunologia , Plasmodium vivax/metabolismo , Proteínas de Protozoários/imunologiaRESUMO
Systems scale models provide the foundation for an effective iterative cycle between hypothesis generation, experiment and model refinement. Such models also enable predictions facilitating the understanding of biological complexity and the control of biological systems. Here, we demonstrate the reconstruction of a globally predictive gene regulatory model from public data: a model that can drive rational experiment design and reveal new regulatory mechanisms underlying responses to novel environments. Specifically, using â¼ 1500 publically available genome-wide transcriptome data sets from Saccharomyces cerevisiae, we have reconstructed an environment and gene regulatory influence network that accurately predicts regulatory mechanisms and gene expression changes on exposure of cells to completely novel environments. Focusing on transcriptional networks that induce peroxisomes biogenesis, the model-guided experiments allow us to expand a core regulatory network to include novel transcriptional influences and linkage across signaling and transcription. Thus, the approach and model provides a multi-scalar picture of gene dynamics and are powerful resources for exploiting extant data to rationally guide experimentation. The techniques outlined here are generally applicable to any biological system, which is especially important when experimental systems are challenging and samples are difficult and expensive to obtain-a common problem in laboratory animal and human studies.
Assuntos
Redes Reguladoras de Genes , Biologia de Sistemas/métodos , Perfilação da Expressão Gênica , Regulação Fúngica da Expressão Gênica , Saccharomyces cerevisiae/genéticaRESUMO
MOTIVATION: Protein phosphorylation is critical for regulating cellular activities by controlling protein activities, localization and turnover, and by transmitting information within cells through signaling networks. However, predictions of protein phosphorylation and signaling networks remain a significant challenge, lagging behind predictions of transcriptional regulatory networks into which they often feed. RESULTS: We developed PhosphoChain to predict kinases, phosphatases and chains of phosphorylation events in signaling networks by combining mRNA expression levels of regulators and targets with a motif detection algorithm and optional prior information. PhosphoChain correctly reconstructed â¼78% of the yeast mitogen-activated protein kinase pathway from publicly available data. When tested on yeast phosphoproteomic data from large-scale mass spectrometry experiments, PhosphoChain correctly identified â¼27% more phosphorylation sites than existing motif detection tools (NetPhosYeast and GPS2.0), and predictions of kinase-phosphatase interactions overlapped with â¼59% of known interactions present in yeast databases. PhosphoChain provides a valuable framework for predicting condition-specific phosphorylation events from high-throughput data. AVAILABILITY: PhosphoChain is implemented in Java and available at http://virgo.csie.ncku.edu.tw/PhosphoChain/ or http://aitchisonlab.com/PhosphoChain
Assuntos
Algoritmos , Sistema de Sinalização das MAP Quinases , Fosfoproteínas Fosfatases/metabolismo , Proteínas Quinases/metabolismo , Sequência de Aminoácidos , Regulação Enzimológica da Expressão Gênica , Genoma , Dados de Sequência Molecular , Fosfoproteínas Fosfatases/química , Fosfoproteínas Fosfatases/genética , Fosforilação , Proteínas Quinases/química , Proteínas Quinases/genética , RNA Mensageiro/biossíntese , Saccharomyces cerevisiae/química , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismoRESUMO
Immunochemotherapy has been the mainstay of treatment for newly diagnosed diffuse large B-cell lymphoma (ndDLBCL) yet is inadequate for many patients. In this work, we perform unsupervised clustering on transcriptomic features from a large cohort of ndDLBCL patients and identify seven clusters, one called A7 with poor prognosis, and develop a classifier to identify these clusters in independent ndDLBCL cohorts. This high-risk cluster is enriched for activated B-cell cell-of-origin, low immune infiltration, high MYC expression, and copy number aberrations. We compare and contrast our methodology with recent DLBCL classifiers to contextualize our clusters and show improved prognostic utility. Finally, using pre-clinical models, we demonstrate a mechanistic rationale for IKZF1/3 degraders such as lenalidomide to overcome the low immune infiltration phenotype of A7 by inducing T-cell trafficking into tumors and upregulating MHC I and II on tumor cells, and demonstrate that TCF4 is an important regulator of MYC-related biology in A7.
Assuntos
Regulação Neoplásica da Expressão Gênica , Fator de Transcrição Ikaros , Lenalidomida , Linfoma Difuso de Grandes Células B , Proteínas Proto-Oncogênicas c-myc , Fator de Transcrição 4 , Transcriptoma , Linfoma Difuso de Grandes Células B/genética , Linfoma Difuso de Grandes Células B/imunologia , Linfoma Difuso de Grandes Células B/patologia , Humanos , Proteínas Proto-Oncogênicas c-myc/genética , Proteínas Proto-Oncogênicas c-myc/metabolismo , Lenalidomida/uso terapêutico , Lenalidomida/farmacologia , Fator de Transcrição Ikaros/genética , Fator de Transcrição Ikaros/metabolismo , Fator de Transcrição 4/genética , Fator de Transcrição 4/metabolismo , Linfócitos B/metabolismo , Linfócitos B/imunologia , Prognóstico , Animais , Linhagem Celular Tumoral , Perfilação da Expressão Gênica/métodos , Camundongos , Linfócitos T/imunologia , Linfócitos T/metabolismo , Variações do Número de Cópias de DNARESUMO
In Saccharomyces cerevisiae, subtelomeric silencing is involved in the propagation of Silent Information Regulator (SIR) proteins toward euchromatin. Numerous mechanisms are involved in antagonizing the local spread of Sir-dependent silent chromatin into neighboring euchromatin. Here, we identified a novel role for sumoylation E3 ligase Mms21 in the maintenance of subtelomeric silencing. We found that disruption of E3 ligase activity of Mms21 results in the de-repression of subtelomeric silencing. Deletion of E3 ligase domain of Mms21 led to decreased binding of Sir2p, Sir3p and Sir4 at subtelomeric chromatins and increased H3K4 tri-methylation at telomere-distal euchromatin regions, correlating with increased gene expression in two subtelomeric reporter genes. In addition, a mms21Δsl mutant caused a severe growth defect in combination with htz1Δ deletion and showed an enhanced association of Htz1 with telomere proximal regions. Taken together, our findings suggest an important role of Mms21p; it contributes to subtelomeric silencing during the formation of a heterochromatin boundary.
Assuntos
Regulação Fúngica da Expressão Gênica , Proteína SUMO-1/metabolismo , Proteínas de Saccharomyces cerevisiae/metabolismo , Saccharomyces cerevisiae/genética , Proteínas Reguladoras de Informação Silenciosa de Saccharomyces cerevisiae/metabolismo , Telômero/metabolismo , Ubiquitina-Proteína Ligases/metabolismo , Sequência de Aminoácidos , Inativação Gênica , Histonas/metabolismo , Metilação , Ligação Proteica , Proteína SUMO-1/genética , Saccharomyces cerevisiae/metabolismo , Proteínas de Saccharomyces cerevisiae/genética , Deleção de Sequência , Telômero/genética , Ubiquitina-Proteína Ligases/genéticaRESUMO
In vitro scanning mutagenesis strategies are valuable tools to identify critical residues in proteins and to generate proteins with modified properties. We describe the fast and simple All-Codon Scanning (ACS) strategy that creates a defined gene library wherein each individual codon within a specific target region is changed into all possible codons with only a single codon change per mutagenesis product. ACS is based on a multiplexed overlapping mutagenesis primer design that saturates only the targeted gene region with single codon changes. We have used ACS to produce single amino-acid changes in small and large regions of the human tumor suppressor protein p53 to identify single amino-acid substitutions that can restore activity to inactive p53 found in human cancers. Single-tube reactions were used to saturate defined 30-nt regions with all possible codon changes. The same technique was used in 20 parallel reactions to scan the 600-bp fragment encoding the entire p53 core domain. Identification of several novel p53 cancer rescue mutations demonstrated the utility of the ACS approach. ACS is a fast, simple and versatile method, which is useful for protein structure-function analyses and protein design or evolution problems.
Assuntos
Substituição de Aminoácidos , Códon , Genes Neoplásicos , Genes p53 , Sequência de Bases , Linhagem Celular , Biblioteca Gênica , Humanos , Dados de Sequência Molecular , Mutação , Reação em Cadeia da PolimeraseRESUMO
Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to multiple lines of therapies. Therefore, we not only need the ability to predict which patients are at high risk for disease progression but also a means to understand the mechanisms underlying their risk. Here, we report a transcriptional regulatory network (TRN) for MM inferred from cross-sectional multi-omics data from 881 patients that predicts how 124 chromosomal abnormalities and somatic mutations causally perturb 392 transcription regulators of 8549 genes to manifest in distinct clinical phenotypes and outcomes. We identified 141 genetic programs whose activity profiles stratify patients into 25 distinct transcriptional states and proved to be more predictive of outcomes than did mutations. The coherence of these programs and accuracy of our network-based risk prediction was validated in two independent datasets. We observed subtype-specific vulnerabilities to interventions with existing drugs and revealed plausible mechanisms for relapse, including the establishment of an immunosuppressive microenvironment. Investigation of the t(4;14) clinical subtype using the TRN revealed that 16% of these patients exhibit an extreme-risk combination of genetic programs (median progression-free survival of 5 months) that create a distinct phenotype with targetable genes and pathways.
RESUMO
Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques have been optimized to iteratively improve classifier accuracy, not to quickly discover biologically significant results. We report here a novel active learning technique, Most Informative Positive (MIP), which is tailored to biological problems because it seeks novel and informative positive results. MIP active learning differs from traditional active learning methods in two ways: (1) it preferentially seeks Positive (functionally active) examples; and (2) it may be effectively extended to select gene regions suitable for high throughput combinatorial mutagenesis. We applied MIP to discover mutations in the tumor suppressor protein p53 that reactivate mutated p53 found in human cancers. This is an important biomedical goal because p53 mutants have been implicated in half of all human cancers, and restoring active p53 in tumors leads to tumor regression. MIP found Positive (cancer rescue) p53 mutants in silico using 33% fewer experiments than traditional non-MIP active learning, with only a minor decrease in classifier accuracy. Applying MIP to in vivo experimentation yielded immediate Positive results. Ten different p53 mutations found in human cancers were paired in silico with all possible single amino acid rescue mutations, from which MIP was used to select a Positive Region predicted to be enriched for p53 cancer rescue mutants. In vivo assays showed that the predicted Positive Region: (1) had significantly more (p<0.01) new strong cancer rescue mutants than control regions (Negative, and non-MIP active learning); (2) had slightly more new strong cancer rescue mutants than an Expert region selected for purely biological considerations; and (3) rescued for the first time the previously unrescuable p53 cancer mutant P152L.
Assuntos
Biologia Computacional/métodos , Análise Mutacional de DNA/métodos , Proteína Supressora de Tumor p53/genética , Algoritmos , Inteligência Artificial , Simulação por Computador , Proteínas Fúngicas/genética , Proteínas Fúngicas/metabolismo , Humanos , Modelos Genéticos , Modelos Moleculares , Mutação , Engenharia de Proteínas , Reprodutibilidade dos Testes , Proteína Supressora de Tumor p53/metabolismo , Leveduras/genética , Leveduras/metabolismoRESUMO
BACKGROUND: Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer. RESULTS: By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type-specific molecular interactions and cytokine-mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. CONCLUSIONS: The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.
Assuntos
Algoritmos , Carcinoma Ductal Pancreático/etiologia , Carcinoma Ductal Pancreático/patologia , Suscetibilidade a Doenças , Modelos Biológicos , Comunicação Autócrina , Carcinoma Ductal Pancreático/metabolismo , Comunicação Celular/genética , Citocinas/metabolismo , Regulação Neoplásica da Expressão Gênica , Humanos , Especificidade de Órgãos , Comunicação Parácrina , FenótipoRESUMO
While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.
Assuntos
Biomarcadores Tumorais/metabolismo , Ensaios Clínicos como Assunto/estatística & dados numéricos , Proteínas de Ligação a DNA/metabolismo , Epigênese Genética , Regulação Neoplásica da Expressão Gênica , Modelos Estatísticos , Mieloma Múltiplo/patologia , Fatores de Transcrição/metabolismo , Biomarcadores Tumorais/genética , Ciclo Celular , Proliferação de Células , Proteínas de Ligação a DNA/genética , Bases de Dados Factuais , Conjuntos de Dados como Assunto , Humanos , Mieloma Múltiplo/genética , Mieloma Múltiplo/metabolismo , Fatores de Transcrição/genética , Células Tumorais CultivadasRESUMO
Immune cell infiltration of tumors and the tumor microenvironment can be an important component for determining patient outcomes. For example, immune and stromal cell presence inferred by deconvolving patient gene expression data may help identify high risk patients or suggest a course of treatment. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from single cell type purified gene expression data. Many methods from this family have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are difficult to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.
Assuntos
Biologia Computacional/métodos , Neoplasias/genética , RNA-Seq/métodos , Análise de Célula Única/métodos , Software , Análise por Conglomerados , Conjuntos de Dados como Assunto , Regulação Neoplásica da Expressão Gênica/imunologia , Humanos , Neoplasias/imunologia , Neoplasias/patologia , Máquina de Vetores de Suporte , Microambiente Tumoral/genética , Microambiente Tumoral/imunologiaRESUMO
MOTIVATION: Many biomedical projects would benefit from reducing the time and expense of in vitro experimentation by using computer models for in silico predictions. These models may help determine which expensive biological data are most useful to acquire next. Active Learning techniques for choosing the most informative data enable biologists and computer scientists to optimize experimental data choices for rapid discovery of biological function. To explore design choices that affect this desirable behavior, five novel and five existing Active Learning techniques, together with three control methods, were tested on 57 previously unknown p53 cancer rescue mutants for their ability to build classifiers that predict protein function. The best of these techniques, Maximum Curiosity, improved the baseline accuracy of 56-77%. This article shows that Active Learning is a useful tool for biomedical research, and provides a case study of interest to others facing similar discovery challenges.
Assuntos
Algoritmos , Inteligência Artificial , Biomarcadores Tumorais/metabolismo , Análise Mutacional de DNA/métodos , Proteínas de Neoplasias/genética , Neoplasias/genética , Proteína Supressora de Tumor p53/genética , Animais , Humanos , Mutação , Neoplasias/metabolismoRESUMO
Despite global eradication efforts over the past century, malaria remains a devastating public health burden, causing almost half a million deaths annually (WHO, 2016). A detailed understanding of the mechanisms that control malaria infection has been hindered by technical challenges of studying a complex parasite life cycle in multiple hosts. While many interventions targeting the parasite have been implemented, the complex biology of Plasmodium poses a major challenge, and must be addressed to enable eradication. New approaches for elucidating key host-parasite interactions, and predicting how the parasite will respond in a variety of biological settings, could dramatically enhance the efficacy and longevity of intervention strategies. The field of systems biology has developed methodologies and principles that are well poised to meet these challenges. In this review, we focus our attention on the Liver Stage of the Plasmodium lifecycle and issue a "call to arms" for using systems biology approaches to forge a new era in malaria research. These approaches will reveal insights into the complex interplay between host and pathogen, and could ultimately lead to novel intervention strategies that contribute to malaria eradication.
RESUMO
We have established proof of principle for the Indicator Cell Assay Platform™ (iCAP™), a broadly applicable tool for blood-based diagnostics that uses specifically-selected, standardized cells as biosensors, relying on their innate ability to integrate and respond to diverse signals present in patients' blood. To develop an assay, indicator cells are exposed in vitro to serum from case or control subjects and their global differential response patterns are used to train reliable, disease classifiers based on a small number of features. In a feasibility study, the iCAP detected pre-symptomatic disease in a murine model of amyotrophic lateral sclerosis (ALS) with 94% accuracy (p-Value = 3.81E-6) and correctly identified samples from a murine Huntington's disease model as non-carriers of ALS. Beyond the mouse model, in a preliminary human disease study, the iCAP detected early stage Alzheimer's disease with 72% cross-validated accuracy (p-Value = 3.10E-3). For both assays, iCAP features were enriched for disease-related genes, supporting the assay's relevance for disease research.
Assuntos
Doença de Alzheimer/diagnóstico , Esclerose Lateral Amiotrófica/diagnóstico , Bioensaio/métodos , Doença de Alzheimer/genética , Esclerose Lateral Amiotrófica/genética , Animais , Modelos Animais de Doenças , Corpos Embrioides/metabolismo , Humanos , Masculino , Camundongos , Sensibilidade e Especificidade , Superóxido Dismutase-1/genéticaRESUMO
Brain swelling is a major predictor of mortality in pediatric cerebral malaria (CM). However, the mechanisms leading to swelling remain poorly defined. Here, we combined neuroimaging, parasite transcript profiling, and laboratory blood profiles to develop machine-learning models of malarial retinopathy and brain swelling. We found that parasite var transcripts encoding endothelial protein C receptor (EPCR)-binding domains, in combination with high parasite biomass and low platelet levels, are strong indicators of CM cases with malarial retinopathy. Swelling cases presented low platelet levels and increased transcript abundance of parasite PfEMP1 DC8 and group A EPCR-binding domains. Remarkably, the dominant transcript in 50% of swelling cases encoded PfEMP1 group A CIDRα1.7 domains. Furthermore, a recombinant CIDRα1.7 domain from a pediatric CM brain autopsy inhibited the barrier-protective properties of EPCR in human brain endothelial cells in vitro. Together, these findings suggest a detrimental role for EPCR-binding CIDRα1 domains in brain swelling.
Assuntos
Edema Encefálico/metabolismo , Receptor de Proteína C Endotelial/metabolismo , Malária Cerebral/metabolismo , Proteínas de Neoplasias/metabolismo , Plasmodium falciparum/metabolismo , Plasmodium falciparum/patogenicidade , Receptores de Superfície Celular/metabolismo , Encéfalo/parasitologia , Edema Encefálico/parasitologia , Adesão Celular , Criança , Pré-Escolar , Feminino , Humanos , Lactente , Malária Cerebral/parasitologia , Malária Falciparum/metabolismo , Malária Falciparum/parasitologia , Malária Falciparum/fisiopatologia , Malaui , Masculino , Ligação Proteica , Domínios Proteicos , Proteínas de Protozoários/metabolismoRESUMO
Many biomedical problems relate to mutant functional properties across a sequence space of interest, e.g., flu, cancer, and HIV. Detailed knowledge of mutant properties and function improves medical treatment and prevention. A functional census of p53 cancer rescue mutants would aid the search for cancer treatments from p53 mutant rescue. We devised a general methodology for conducting a functional census of a mutation sequence space by choosing informative mutants early. The methodology was tested in a double-blind predictive test on the functional rescue property of 71 novel putative p53 cancer rescue mutants iteratively predicted in sets of three (24 iterations). The first double-blind 15-point moving accuracy was 47 percent and the last was 86 percent; r = 0.01 before an epiphanic 16th iteration and r = 0.92 afterward. Useful mutants were chosen early (overall r = 0.80). Code and data are freely available (http://www.igb.uci.edu/research/research.html, corresponding authors: R.H.L. for computation and R.K.B. for biology).