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Transformative technologies are enabling the construction of threedimensional (3D) maps of tissues with unprecedented spatial and molecularresolution. Over the next seven years, the NIH Common Fund Human BiomolecularAtlas Program (HuBMAP) intends to develop a widely accessible framework forcomprehensively mapping the human body at single-cell resolution by supportingtechnology development, data acquisition, and detailed spatial mapping. HuBMAPwill integrate its efforts with other funding agencies, programs, consortia,and the biomedical research community at large towards the shared vision of acomprehensive, accessible 3D molecular and cellular atlas of the human body, inhealth and various disease settings.

An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.

ObjectiveHIV-1 viral proteins and host inflammatory factors have a direct role in neuronal toxicity in vitro; however, the contribution of these factors in vivo in HIV-1-associated neurocognitive disorder (HAND) is not fully understood. We applied novel Systems Biology approaches to identify specific cellular and viral factors and their related pathways that are associated with different stages of HAND.DesignA cross-sectional study of individuals enrolled in the Multicenter AIDS Cohort Study including HIV-1-seronegative (N = 36) and HIV-1-seropositive individuals without neurocognitive symptoms (N = 16) or with mild neurocognitive disorder (MND) (N = 8) or HIV-associated dementia (HAD) (N = 16).MethodsA systematic evaluation of global transcriptome of peripheral blood mononuclear cells (PBMCs) obtained from HIV-1-seronegative individuals and from HIV-1-positive men without neurocognitive symptoms, or MND or HAD was performed.ResultsMND and HAD were associated with specific changes in mRNA transcripts and microRNAs in PBMCs. Comparison of upstream regulators and TimePath analyses identified specific cellular factors associated with MND and HAD, whereas HIV-1 viral proteins played a greater role in HAD. In addition, expression of specific microRNAs - miR-let-7a, miR-124, miR-15a and others - were found to correlate with mRNA gene expression and may have a potential protective role in asymptomatic HIV-1-seropositive individuals by regulating cellular signal transduction pathways downstream of chemokines and cytokines.ConclusionThese results identify signature transcriptome changes in PBMCs associated with stages of HAND and shed light on the potential contribution of host cellular factors and viral proteins in HAND development.

The growth factor interleukin-3 (IL-3) promotes the survival and growth of multipotent hematopoietic progenitors and stimulates myelopoiesis. It has also been reported to oppose terminal granulopoiesis and to support leukemic cell growth through autocrine or paracrine mechanisms. The degree to which IL-3 acts at the posttranscriptional level is largely unknown. We have conducted global mRNA decay profiling and bioinformatic analyses in 32Dcl3 myeloblasts indicating that IL-3 caused immediate early stabilization of hundreds of transcripts in pathways relevant to myeloblast function. Stabilized transcripts were enriched for AU-Response elements (AREs), and an ARE-containing domain from the interleukin-6 (IL-6) 3'-UTR rendered a heterologous gene responsive to IL-3-mediated transcript stabilization. Many IL-3-stabilized transcripts had been associated with leukemic transformation. Deregulated Abl kinase shared with IL-3 the ability to delay turnover of transcripts involved in proliferation or differentiation blockade, relying, in part, on signaling through the Mek/Erk pathway. These findings support a model of IL-3 action through mRNA stability control and suggest that aberrant stabilization of an mRNA network linked to IL-3 contributes to leukemic cell growth.

BackgroundSeveral years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.ResultsIn this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%.ConclusionWe performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.

While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer), a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic-anaerobic shift interface.

Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input-output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified temporal map. Applying our method to study yeast response to stress, we derive dynamic models that are able to recover many of the known aspects of these responses. Predictions made by our method have been experimentally validated leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress. The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation.

BackgroundTime series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3-8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data.ResultsWe introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology.ConclusionThe unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at http://www.cs.cmu.edu/~jernst/stem.

Abstract Background Regulation of meiosis and sporulation in Saccharomyces cerevisiae is a model for a highly regulated developmental process. Meiosis middle phase transcriptional regulation is governed by two transcription factors: the activator Ndt80 and the repressor Sum1. It has been suggested that the competition between Ndt80 and Sum1 determines the temporal expression of their targets during middle meiosis. Results Using a combination of ChIP-on-chip and expression profiling, we characterized a middle phase transcriptional network and studied the relationship between Ndt80 and Sum1 during middle and late meiosis. While finding a group of genes regulated by both factors in a feed forward loop regulatory motif, our data also revealed a large group of genes regulated solely by Ndt80. Measuring the expression of all Ndt80 target genes in various genetic backgrounds (WT, sum1Δ and MK-ER-Ndt80 strains), allowed us to dissect the exact transcriptional network regulating each gene, which was frequently different than the one inferred from the binding data alone. Conclusion These results highlight the need to perform detailed genetic experiments to determine the relative contribution of interactions in transcriptional regulatory networks.

Prediction of response to specific cancer treatments is complicated by significant heterogeneity between tumors in terms of mutational profiles, gene expression, and clinical measures. Here we focus on the response of Estrogen Receptor (ER)+ post-menopausal breast cancer tumors to aromatase inhibitors (AI). We use a network smoothing algorithm to learn novel features that integrate several types of high throughput data and new cell line experiments. These features greatly improve the ability to predict response to AI when compared to prior methods. For a subset of the patients, for which we obtained more detailed clinical information, we can further predict response to a specific AI drug. [ABSTRACT FROM AUTHOR]

The Dynamic Regulatory Events Miner (DREM) software reconstructs dynamic regulatory networks by integrating static protein-DNA interaction data with time series gene expression data. In recent years, several additional types of high-throughput time series data have been profiled when studying biological processes including time series miRNA expression, proteomics, epigenomics and single cell RNA-Seq. Combining all available time series and static datasets in a unified model remains an important challenge and goal. To address this challenge we have developed a new version of DREM termed interactive DREM (iDREM). iDREM provides support for all data types mentioned above and combines them with existing interaction data to reconstruct networks that can lead to novel hypotheses on the function and timing of regulators. Users can interactively visualize and query the resulting model. We showcase the functionality of the new tool by applying it to microglia developmental data from multiple labs. [ABSTRACT FROM AUTHOR]

Background: Latent HIV-1 reservoirs are identified as one of the major challenges to achieve HIV-1 cure. Currently available strategies are associated with wide variability in outcomes both in patients and CD4+ T cell models. This underlines the critical need to develop innovative strategies to predict and recognize ways that could result in better reactivation and eventual elimination of latent HIV-1 reservoirs. Results and discussion: In this study, we combined genome wide transcriptome datasets post activation with Systems Biology approach (Signaling and Dynamic Regulatory Events Miner, SDREM analyses) to reconstruct a dynamic signaling and regulatory network involved in reactivation mediated by specific activators using a latent cell line. This approach identified several critical regulators for each treatment, which were confirmed in follow-up validation studies using small molecule inhibitors. Results indicate that signaling pathways involving JNK and related factors as predicted by SDREM are essential for virus reactivation by suberoylanilide hydroxamic acid. ERK1/2 and NF-κB pathways have the foremost role in reactivation with prostratin and TNF-α, respectively. JAK-STAT pathway has a central role in HIV-1 transcription. Additional evaluation, using other latent J-Lat cell clones and primary T cell model, also confirmed that many of the cellular factors associated with latency reversing agents are similar, though minor differences are identified. JAK-STAT and NF-κB related pathways are critical for reversal of HIV-1 latency in primary resting T cells. Conclusion: These results validate our combinatorial approach to predict the regulatory cellular factors and pathways responsible for HIV-1 reactivation in latent HIV-1 harboring cell line models. JAK-STAT have a role in reversal of latency in all the HIV-1 latency models tested, including primary CD4+ T cells, with additional cellular pathways such as NF-κB, JNK and ERK 1/2 that may have complementary role in reversal of HIV-1 latency. [ABSTRACT FROM AUTHOR]

Reconstructing regulatory and signaling response networks is one of the major goals of systems biology. While several successful methods have been suggested for this task, some integrating large and diverse datasets, these methods have so far been applied to reconstruct a single response network at a time, even when studying and modeling related conditions. To improve network reconstruction we developed MT-SDREM, a multi-task learning method which jointly models networks for several related conditions. In MT-SDREM, parameters are jointly constrained across the networks while still allowing for condition-specific pathways and regulation. We formulate the multi-task learning problem and discuss methods for optimizing the joint target function. We applied MT-SDREM to reconstruct dynamic human response networks for three flu strains: H1N1, H5N1 and H3N2. Our multi-task learning method was able to identify known and novel factors and genes, improving upon prior methods that model each condition independently. The MT-SDREM networks were also better at identifying proteins whose removal affects viral load indicating that joint learning can still lead to accurate, condition-specific, networks. Supporting website with MT-SDREM implementation: [ABSTRACT FROM AUTHOR]

Biological processes are often dynamic, thus researchers must monitor their activity at multiple time points. The most abundant source of information regarding such dynamic activity is time-series gene expression data. These data are used to identify the complete set of activated genes in a biological process, to infer their rates of change, their order and their causal effects and to model dynamic systems in the cell. In this Review we discuss the basic patterns that have been observed in time-series experiments, how these patterns are combined to form expression programs, and the computational analysis, visualization and integration of these data to infer models of dynamic biological systems. [ABSTRACT FROM AUTHOR]

Characterization of the transcriptional regulatory network of the normal cell cycle is essential for understanding the perturbations that lead to cancer. However, the complete set of cycling genes in primary cells has not yet been identified. Here, we report the results of genome-wide expression profiling experiments on synchronized primary human foreskin fibroblasts across the cell cycle. Using a combined experimental and computational approach to deconvolve measured expression values into "single-cell" expression profiles, we were able to overcome the limitations inherent in synchronizing nontransformed mammalian cells. This allowed us to identify 480 periodically expressed genes in primary human foreskin fibroblasts. Analysis of the reconstructed primary cell profiles and comparison with published expression datasets from synchronized transformed cells reveals a large number of genes that cycle exclusively in primary cells. This conclusion was supported by both bioinformatic analysis and experiments performed on other cell types. We suggest that this approach will help pinpoint genetic elements contributing to normal cell growth and cellular transformation. [ABSTRACT FROM AUTHOR]