Allergen-Encapsulating Nanoparticles Reprogram Pathogenic Allergen-Specific Th2 Cells to Suppress Food Allergy.

Food allergy is a prevalent, potentially deadly disease caused by inadvertent sensitization to benign food antigens. Pathogenic Th2 cells are a major driver for disease, and allergen-specific immunotherapies (AIT) aim to increase the allergen threshold required to elicit severe allergic symptoms. However, the majority of AIT approaches require lengthy treatments and convey transient disease suppression, likely due to insufficient targeting of pathogenic Th2 responses. Here, the ability of allergen-encapsulating nanoparticles to directly suppress pathogenic Th2 responses and reactivity is investigated in a mouse model of food allergy. NPs associate with pro-tolerogenic antigen presenting cells, provoking accumulation of antigen-specific, functionally suppressive regulatory T cells in the small intestine lamina propria. Two intravenous doses of allergen encapsulated in poly(lactide-co-glycolide) nanoparticles (NPs) significantly reduces oral food challenge (OFC)-induced anaphylaxis. Importantly, NP treatment alters the fates of pathogenic allergen-specific Th2 cells, reprogramming these cells toward CD25+FoxP3+ regulatory and CD73+FR4+ anergic phenotypes. NP-mediated reductions in the frequency of effector cells in the gut and mast cell degranulation following OFC are also demonstrated. These studies reveal mechanisms by which an allergen-encapsulating NP therapy and, more broadly, allergen-specific immunotherapies, can rapidly attenuate allergic responses by targeting pathogenic Th2 cells.

An engineered niche delineates metastatic potential of breast cancer.

Metastatic breast cancer is often not diagnosed until secondary tumors have become macroscopically visible and millions of tumor cells have invaded distant tissues. Yet, metastasis is initiated by a cascade of events leading to formation of the pre-metastatic niche, which can precede tumor formation by a matter of years. We aimed to distinguish the potential for metastatic disease from nonmetastatic disease at early times in triple-negative breast cancer using sister cell lines 4T1 (metastatic), 4T07 (invasive, nonmetastatic), and 67NR (nonmetastatic). We used a porous, polycaprolactone scaffold, that serves as an engineered metastatic niche, to identify metastatic disease through the characteristics of the microenvironment. Analysis of the immune cell composition at the scaffold was able to distinguish noninvasive 67NR tumor-bearing mice from 4T07 and 4T1 tumor-bearing mice but could not delineate metastatic potential between the two invasive cell lines. Gene expression in the scaffolds correlated with the up-regulation of cancer hallmarks (e.g., angiogenesis, hypoxia) in the 4T1 mice relative to 4T07 mice. We developed a 9-gene signature (Dhx9, Dusp12, Fth1, Ifitm1, Ndufs1, Pja2, Slc1a3, Soga1, Spon2) that successfully distinguished 4T1 disease from 67NR or 4T07 disease throughout metastatic progression. Furthermore, this signature proved highly effective at distinguishing diseased lungs in publicly available datasets of mouse models of metastatic breast cancer and in human models of lung cancer. The early and accurate detection of metastatic disease that could lead to early treatment has the potential to improve patient outcomes and quality of life.

Building-Block Size Mediates Microporous Annealed Particle Hydrogel Tube Microenvironment Following Spinal Cord Injury.

Spinal cord injury (SCI) is a life-altering event, which often results in loss of sensory and motor function below the level of trauma. Biomaterial therapies have been widely investigated in SCI to promote directional regeneration but are often limited by their pre-constructed size and shape. Herein, the design parameters of microporous annealed particles (MAPs) are investigated with tubular geometries that conform to the injury and direct axons across the defect to support functional recovery. MAP tubes prepared from 20-, 40-, and 60-micron polyethylene glycol (PEG) beads are generated and implanted in a T9-10 murine hemisection model of SCI. Tubes attenuate glial and fibrotic scarring, increase innate immune cell density, and reduce inflammatory phenotypes in a bead size-dependent manner. Tubes composed of 60-micron beads increase the cell density of the chronic macrophage response, while neutrophil infiltration and phenotypes do not deviate from those seen in controls. At 8 weeks postinjury, implantation of tubes composed of 60-micron beads results in enhanced locomotor function, robust axonal ingrowth, and remyelination through both lumens and the inter-tube space. Collectively, these studies demonstrate the importance of bead size in MAP construction and highlight PEG tubes as a biomaterial therapy to promote regeneration and functional recovery in SCI.

Simulating heterogeneous populations using Boolean models.

BACKGROUND: Certain biological processes, such as the development of cancer and immune activation, can be controlled by rare cellular events that are difficult to capture computationally through simulations of individual cells. Information about such rare events can be gleaned from an attractor analysis, for which a variety of methods exist (in particular for Boolean models). However, explicitly simulating a defined mixed population of cells in a way that tracks even the rarest subpopulations remains an open challenge. RESULTS: Here we show that when cellular states are described using a Boolean network model, one can exactly simulate the dynamics of non-interacting, highly heterogeneous populations directly, without having to model the various subpopulations. This strategy captures even the rarest outcomes of the model with no sampling error. Our method can incorporate heterogeneity in both cell state and, by augmenting the model, the underlying rules of the network as well (e.g., introducing loss-of-function genetic alterations). We demonstrate our method by using it to simulate a heterogeneous population of Boolean networks modeling the T-cell receptor, spanning ∼ 1020 distinct cellular states and mutational profiles. CONCLUSIONS: We have developed a method for using Boolean models to perform a population-level simulation, in which the population consists of non-interacting individuals existing in different states. This approach can be used even when there are far too many distinct subpopulations to model individually.

ONC201 Targets AR and AR-V7 Signaling, Reduces PSA, and Synergizes with Everolimus in Prostate Cancer.

Androgen receptor (AR) signaling plays a key role in prostate cancer progression, and androgen deprivation therapy (ADT) is a mainstay clinical treatment regimen for patients with advanced disease. Unfortunately, most prostate cancers eventually become androgen-independent and resistant to ADT with patients progressing to metastatic castration-resistant prostate cancer (mCRPC). Constitutively activated AR variants (AR-V) have emerged as mediators of resistance to AR-targeted therapy and the progression of mCRPC, and they represent an important therapeutic target. Out of at least 15 AR-Vs described thus far, AR-V7 is the most abundant, and its expression correlates with ADT resistance. ONC201/TIC10 is the founding member of the imipridone class of small molecules and has shown anticancer activity in a broad range of tumor types. ONC201 is currently being tested in phase I/II clinical trials for advanced solid tumors, including mCRPC, and hematologic malignancies. There has been promising activity observed in patients in early clinical testing. This study demonstrates preclinical single-agent efficacy of ONC201 using in vitro and in vivo models of prostate cancer. ONC201 has potent antiproliferative and proapoptotic effects in both castration-resistant and -sensitive prostate cancer cells. Furthermore, the data demonstrate that ONC201 downregulates the expression of key drivers of prostate cancer such as AR-V7 and downstream target genes including the clinically used biomarker PSA (KLK3). Finally, the data also provide a preclinical rationale for combination of ONC201 with approved therapeutics for prostate cancer such as enzalutamide, everolimus (mTOR inhibitor), or docetaxel.Implications: The preclinical efficacy of ONC201 as a single agent or in combination, in hormone-sensitive or castration-resistant prostate cancer, suggests the potential for immediate clinical translation. Mol Cancer Res; 16(5); 754-66. ©2018 AACR.

Improved inference of chromosome conformation from images of labeled loci.

We previously published a method that infers chromosome conformation from images of fluorescently-tagged genomic loci, for the case when there are many loci labeled with each distinguishable color. Here we build on our previous work and improve the reconstruction algorithm to address previous limitations. We show that these improvements 1) increase the reconstruction accuracy and 2) allow the method to be used on large-scale problems involving several hundred labeled loci. Simulations indicate that full-chromosome reconstructions at 1/2 Mb resolution are possible using existing labeling and imaging technologies. The updated reconstruction code and the script files used for this paper are available at: https://github.com/heltilda/align3d.

Direct Detection of Unnatural DNA Nucleotides dNaM and d5SICS using the MspA Nanopore.

Malyshev et al. showed that the four-letter genetic code within a living organism could be expanded to include the unnatural DNA bases dNaM and d5SICS. However, verification and detection of these unnatural bases in DNA requires new sequencing techniques. Here we provide proof of concept detection of dNaM and d5SICS in DNA oligomers via nanopore sequencing using the nanopore MspA. We find that both phi29 DNA polymerase and Hel308 helicase are capable of controlling the motion of DNA containing dNaM and d5SICS through the pore and that single reads are sufficient to detect the presence and location of dNaM and d5SICS within single molecules.

Subangstrom single-molecule measurements of motor proteins using a nanopore.

Techniques for measuring the motion of single motor proteins, such as FRET and optical tweezers, are limited to a resolution of ∼300 pm. We use ion current modulation through the protein nanopore MspA to observe translocation of helicase Hel308 on DNA with up to ∼40 pm sensitivity. This approach should be applicable to any protein that translocates on DNA or RNA, including helicases, polymerases, recombinases and DNA repair enzymes.

Decoding long nanopore sequencing reads of natural DNA.

Nanopore sequencing of DNA is a single-molecule technique that may achieve long reads, low cost and high speed with minimal sample preparation and instrumentation. Here, we build on recent progress with respect to nanopore resolution and DNA control to interpret the procession of ion current levels observed during the translocation of DNA through the pore MspA. As approximately four nucleotides affect the ion current of each level, we measured the ion current corresponding to all 256 four-nucleotide combinations (quadromers). This quadromer map is highly predictive of ion current levels of previously unmeasured sequences derived from the bacteriophage phi X 174 genome. Furthermore, we show nanopore sequencing reads of phi X 174 up to 4,500 bases in length, which can be unambiguously aligned to the phi X 174 reference genome, and demonstrate proof-of-concept utility with respect to hybrid genome assembly and polymorphism detection. This work provides a foundation for nanopore sequencing of long, natural DNA strands.

Mutual information between discrete and continuous data sets.

Mutual information (MI) is a powerful method for detecting relationships between data sets. There are accurate methods for estimating MI that avoid problems with "binning" when both data sets are discrete or when both data sets are continuous. We present an accurate, non-binning MI estimator for the case of one discrete data set and one continuous data set. This case applies when measuring, for example, the relationship between base sequence and gene expression level, or the effect of a cancer drug on patient survival time. We also show how our method can be adapted to calculate the Jensen-Shannon divergence of two or more data sets.

Measuring chromosome conformation with degenerate labels.

Although DNA conformation plays an integral role in all genetic processes from transcription to chromosome segregation, there is as yet no tractable method for capturing the in vivo conformation of a chromosome at high resolution. Labeling and fluorescently imaging thousands of loci along the chromosome would readily yield a conformation if each locus could be uniquely distinguished in the image, but this would unrealistically require thousands of distinguishable labels and a tedious experimental process. Here we present a computational method for extracting conformations when the total number of labels far exceeds the number of distinguishable labels. We evaluate our technique using simulated conformations with lengths ranging from 10 to 100 kilobases, and discuss the prospects for an experiment.

Information flow analysis of interactome networks.

Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein-protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well.