Predicting transcription factor activity using prior biological information.

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.

A framework for considering prior information in network-based approaches to omics data analysis.

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

Selective enrichment of plasma cell-free messenger RNA in cancer-associated extracellular vesicles.

Extracellular vesicles (EVs) have been shown as key mediators of extracellular small RNA transport. However, carriers of cell-free messenger RNA (cf-mRNA) in human biofluids and their association with cancer remain poorly understood. Here, we performed a transcriptomic analysis of size-fractionated plasma from lung cancer, liver cancer, multiple myeloma, and healthy donors. Morphology and size distribution analysis showed the successful separation of large and medium particles from other soluble plasma protein fractions. We developed a strategy to purify and sequence ultra-low amounts of cf-mRNA from particle and protein enriched subpopulations with the implementation of RNA spike-ins to control for technical variability and to normalize for intrinsic drastic differences in cf-mRNA amount carried in each plasma fraction. We found that the majority of cf-mRNA was enriched and protected in EVs with remarkable stability in RNase-rich environments. We observed specific enrichment patterns of cancer-associated cf-mRNA in each particle and protein enriched subpopulation. The EV-enriched differentiating genes were associated with specific biological pathways, such as immune systems, liver function, and toxic substance regulation in lung cancer, liver cancer, and multiple myeloma, respectively. Our results suggest that dissecting the complexity of EV subpopulations illuminates their biological significance and offers a promising liquid biopsy approach.

Disruption of the MYC Superenhancer Complex by Dual Targeting of FLT3 and LSD1 in Acute Myeloid Leukemia.

Mutations in Fms-like tyrosine kinase 3 (FLT3) are common drivers in acute myeloid leukemia (AML) yet FLT3 inhibitors only provide modest clinical benefit. Prior work has shown that inhibitors of lysine-specific demethylase 1 (LSD1) enhance kinase inhibitor activity in AML. Here we show that combined LSD1 and FLT3 inhibition induces synergistic cell death in FLT3-mutant AML. Multi-omic profiling revealed that the drug combination disrupts STAT5, LSD1, and GFI1 binding at the MYC blood superenhancer, suppressing superenhancer accessibility as well as MYC expression and activity. The drug combination simultaneously results in the accumulation of repressive H3K9me1 methylation, an LSD1 substrate, at MYC target genes. We validated these findings in 72 primary AML samples with the nearly every sample demonstrating synergistic responses to the drug combination. Collectively, these studies reveal how epigenetic therapies augment the activity of kinase inhibitors in FLT3-ITD (internal tandem duplication) AML. IMPLICATIONS: This work establishes the synergistic efficacy of combined FLT3 and LSD1 inhibition in FLT3-ITD AML by disrupting STAT5 and GFI1 binding at the MYC blood-specific superenhancer complex.

Asxl1 deletion disrupts MYC and RNA polymerase II function in granulocyte progenitors.

Mutations in the gene Additional Sex-Combs Like 1 (ASXL1) are recurrent in myeloid malignancies as well as the pre-malignant condition clonal hematopoiesis, where they are universally associated with poor prognosis. However, the role of ASXL1 in myeloid lineage maturation is incompletely described. To define the role of ASXL1 in myelopoiesis, we employed single cell RNA sequencing and a murine model of hematopoietic-specific Asxl1 deletion. In granulocyte progenitors, Asxl1 deletion leads to hyperactivation of MYC and a quantitative decrease in neutrophil production. This loss of granulocyte production was not accompanied by significant changes in the landscape of covalent histone modifications. However, Asxl1 deletion results in a decrease in RNAPII promoter-proximal pausing in granulocyte progenitors, indicative of a global increase in productive transcription. These results suggest that ASXL1 inhibits productive transcription in granulocyte progenitors, identifying a new role for this epigenetic regulator in myeloid development.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

The reactome pathway knowledgebase 2022.

The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied ('dark') proteins from analyzed datasets in the context of Reactome's manually curated pathways.

Author-sourced capture of pathway knowledge in computable form using Biofactoid.

Making the knowledge contained in scientific papers machine-readable and formally computable would allow researchers to take full advantage of this information by enabling integration with other knowledge sources to support data analysis and interpretation. Here we describe Biofactoid, a web-based platform that allows scientists to specify networks of interactions between genes, their products, and chemical compounds, and then translates this information into a representation suitable for computational analysis, search and discovery. We also report the results of a pilot study to encourage the wide adoption of Biofactoid by the scientific community.

Analyzing causal relationships in proteomic profiles using CausalPath.

CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).

Causal interactions from proteomic profiles: Molecular data meet pathway knowledge.

We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at http://causalpath.org.

The AML microenvironment catalyzes a stepwise evolution to gilteritinib resistance.

Our study details the stepwise evolution of gilteritinib resistance in FLT3-mutated acute myeloid leukemia (AML). Early resistance is mediated by the bone marrow microenvironment, which protects residual leukemia cells. Over time, leukemia cells evolve intrinsic mechanisms of resistance, or late resistance. We mechanistically define both early and late resistance by integrating whole-exome sequencing, CRISPR-Cas9, metabolomics, proteomics, and pharmacologic approaches. Early resistant cells undergo metabolic reprogramming, grow more slowly, and are dependent upon Aurora kinase B (AURKB). Late resistant cells are characterized by expansion of pre-existing NRAS mutant subclones and continued metabolic reprogramming. Our model closely mirrors the timing and mutations of AML patients treated with gilteritinib. Pharmacological inhibition of AURKB resensitizes both early resistant cell cultures and primary leukemia cells from gilteritinib-treated AML patients. These findings support a combinatorial strategy to target early resistant AML cells with AURKB inhibitors and gilteritinib before the expansion of pre-existing resistance mutations occurs.

Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes.

BACKGROUND: Genes implicated in tumorigenesis often exhibit diverse sets of genomic variants in the tumor cohorts within which they are frequently mutated. For many genes, neither the transcriptomic effects of these variants nor their relationship to one another in cancer processes have been well-characterized. We sought to identify the downstream expression effects of these mutations and to determine whether this heterogeneity at the genomic level is reflected in a corresponding heterogeneity at the transcriptomic level. RESULTS: By applying a novel hierarchical framework for organizing the mutations present in a cohort along with machine learning pipelines trained on samples' expression profiles we systematically interrogated the signatures associated with combinations of mutations recurrent in cancer. This allowed us to catalogue the mutations with discernible downstream expression effects across a number of tumor cohorts as well as to uncover and characterize over a hundred cases where subsets of a gene's mutations are clearly divergent in their function from the remaining mutations of the gene. These findings successfully replicated across a number of disease contexts and were found to have clear implications for the delineation of cancer processes and for clinical decisions. CONCLUSIONS: The results of cataloguing the downstream effects of mutation subgroupings across cancer cohorts underline the importance of incorporating the diversity present within oncogenes in models designed to capture the downstream effects of their mutations.

Ribavirin shows antiviral activity against SARS-CoV-2 and downregulates the activity of TMPRSS2 and the expression of ACE2 in vitro.

Ribavirin is a guanosine analog with broad-spectrum antiviral activity against RNA viruses. Based on this, we aimed to show the anti-SARS-CoV-2 activity of this drug molecule via in vitro, in silico, and molecular techniques. Ribavirin showed antiviral activity in Vero E6 cells following SARS-CoV-2 infection, whereas the drug itself did not show any toxic effect over the concentration range tested. In silico analysis suggested that ribavirin has a broad-spectrum impact on SARS-CoV-2, acting at different viral proteins. According to the detailed molecular techniques, ribavirin was shown to decrease the expression of TMPRSS2 at both mRNA and protein levels 48 h after treatment. The suppressive effect of ribavirin in ACE2 protein expression was shown to be dependent on cell types. Finally, proteolytic activity assays showed that ribavirin also showed an inhibitory effect on the TMPRSS2 enzyme. Based on these results, we hypothesized that ribavirin may inhibit the expression of TMPRSS2 by modulating the formation of inhibitory G-quadruplex structures at the TMPRSS2 promoter. As a conclusion, ribavirin is a potential antiviral drug for the treatment against SARS-CoV-2, and it interferes with the effects of TMPRSS2 and ACE2 expression.

2D MXenes with antiviral and immunomodulatory properties: A pilot study against SARS-CoV-2.

Two-dimensional transition metal carbides/carbonitrides known as MXenes are rapidly growing as multimodal nanoplatforms in biomedicine. Here, taking SARS-CoV-2 as a model, we explored the antiviral properties and immune-profile of a large panel of four highly stable and well-characterized MXenes - Ti3C2Tx, Ta4C3T x , Mo2Ti2C3T x and Nb4C3T x . To start with antiviral assessment, we first selected and deeply analyzed four different SARS-CoV-2 genotypes, common in most countries and carrying the wild type or mutated spike protein. When inhibition of the viral infection was tested in vitro with four viral clades, Ti3C2T x in particular, was able to significantly reduce infection only in SARS-CoV-2/clade GR infected Vero E6 cells. This difference in the antiviral activity, among the four viral particles tested, highlights the importance of considering the viral genotypes and mutations while testing antiviral activity of potential drugs and nanomaterials. Among the other MXenes tested, Mo2Ti2C3T x also showed antiviral properties. Proteomic, functional annotation analysis and comparison to the already published SARS-CoV-2 protein interaction map revealed that MXene-treatment exerts specific inhibitory mechanisms. Envisaging future antiviral MXene-based drug nano-formulations and considering the central importance of the immune response to viral infections, the immune impact of MXenes was evaluated on human primary immune cells by flow cytometry and single-cell mass cytometry on 17 distinct immune subpopulations. Moreover, 40 secreted cytokines were analyzed by Luminex technology. MXene immune profiling revealed i) the excellent bio and immune compatibility of the material, as well as the ability of MXene ii) to inhibit monocytes and iii) to reduce the release of pro-inflammatory cytokines, suggesting an anti-inflammatory effect elicited by MXene. We here report a selection of MXenes and viral SARS-CoV-2 genotypes/mutations, a series of the computational, structural and molecular data depicting deeply the SARS-CoV-2 mechanism of inhibition, as well as high dimensional single-cell immune-MXene profiling. Taken together, our results provide a compendium of knowledge for new developments of MXene-based multi-functioning nanosystems as antivirals and immune-modulators.

Newt: a comprehensive web-based tool for viewing, constructing and analyzing biological maps.

MOTIVATION: Visualization of cellular processes and pathways is a key recurring requirement for effective biological data analysis. There is a considerable need for sophisticated web-based pathway viewers and editors operating with widely accepted standard formats, using the latest visualization techniques and libraries. RESULTS: We developed a web-based tool named Newt for viewing, constructing and analyzing biological maps in standard formats such as SBGN, SBML and SIF. AVAILABILITY AND IMPLEMENTATION: Newt's source code is publicly available on GitHub and freely distributed under the GNU LGPL. Ample documentation on Newt can be found on http://newteditor.org and on YouTube.

Author Correction: COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Phosphoproteomic quantitation and causal analysis reveal pathways in GPVI/ITAM-mediated platelet activation programs.

Platelets engage cues of pending vascular injury through coordinated adhesion, secretion, and aggregation responses. These rapid, progressive changes in platelet form and function are orchestrated downstream of specific receptors on the platelet surface and through intracellular signaling mechanisms that remain systematically undefined. This study brings together cell physiological and phosphoproteomics methods to profile signaling mechanisms downstream of the immunotyrosine activation motif (ITAM) platelet collagen receptor GPVI. Peptide tandem mass tag (TMT) labeling, sample multiplexing, synchronous precursor selection (SPS), and triple stage tandem mass spectrometry (MS3) detected >3000 significant (false discovery rate < 0.05) phosphorylation events on >1300 proteins over conditions initiating and progressing GPVI-mediated platelet activation. With literature-guided causal inference tools, >300 site-specific signaling relations were mapped from phosphoproteomics data among key and emerging GPVI effectors (ie, FcRγ, Syk, PLCγ2, PKCδ, DAPP1). Through signaling validation studies and functional screening, other less-characterized targets were also considered within the context of GPVI/ITAM pathways, including Ras/MAPK axis proteins (ie, KSR1, SOS1, STAT1, Hsp27). Highly regulated GPVI/ITAM targets out of context of curated knowledge were also illuminated, including a system of >40 Rab GTPases and associated regulatory proteins, where GPVI-mediated Rab7 S72 phosphorylation and endolysosomal maturation were blocked by TAK1 inhibition. In addition to serving as a model for generating and testing hypotheses from omics datasets, this study puts forth a means to identify hemostatic effectors, biomarkers, and therapeutic targets relevant to thrombosis, vascular inflammation, and other platelet-associated disease states.

The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

Pathway Commons 2019 Update: integration, analysis and exploration of pathway data.

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g. metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (i) tools to search this comprehensive resource, (ii) a download site offering integrated bulk sets of pathway data (e.g. tables of interactions and gene sets), (iii) reusable software libraries for working with pathway information in several programming languages (Java, R, Python and Javascript) and (iv) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4794 detailed human biochemical processes (i.e. pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.