Apobec-Mediated Retroviral Hypermutation In Vivo is Dependent on Mouse Strain.

Replication of the complex retrovirus mouse mammary tumor virus (MMTV) is antagonized by murine Apobec3 (mA3), a member of the Apobec family of cytidine deaminases. We have shown that MMTV-encoded Rem protein inhibits proviral mutagenesis by the Apobec enzyme, activation-induced cytidine deaminase (AID) during viral replication in BALB/c mice. To further study the role of Rem in vivo , we have infected C57BL/6 (B6) mice with a superantigen-independent lymphomagenic strain of MMTV (TBLV-WT) or a mutant strain (TBLV-SD) that is defective in Rem and its cleavage product Rem-CT. Unlike MMTV, TBLV induced T-cell tumors in µMT mice, indicating that mature B cells, which express the highest AID levels, are not required for TBLV replication. Compared to BALB/c, B6 mice were more susceptible to TBLV infection and tumorigenesis. The lack of Rem expression accelerated B6 tumorigenesis at limiting doses compared to TBLV-WT in either wild-type B6 or AID-deficient mice. However, unlike proviruses from BALB/c mice, high-throughput sequencing indicated that proviral G-to-A or C-to-T changes did not significantly differ in the presence and absence of Rem expression. Ex vivo stimulation showed higher levels of mA3 relative to AID in B6 compared to BALB/c splenocytes, but effects of agonists differed in the two strains. RNA-Seq revealed increased transcripts related to growth factor and cytokine signaling in TBLV-SD-induced tumors relative to those from TBLV-WT, consistent with a third Rem function. Thus, Rem-mediated effects on tumorigenesis in B6 mice are independent of Apobec-mediated proviral hypermutation.

Massively Parallel Selection of NanoCluster Beacons.

NanoCluster Beacons (NCBs) are multicolor silver nanocluster probes whose fluorescence can be activated or tuned by a proximal DNA strand called the activator. While a single-nucleotide difference in a pair of activators can lead to drastically different activation outcomes, termed polar opposite twins (POTs), it is difficult to discover new POT-NCBs using the conventional low-throughput characterization approaches. Here, a high-throughput selection method is reported that takes advantage of repurposed next-generation-sequencing chips to screen the activation fluorescence of ≈40 000 activator sequences. It is found that the nucleobases at positions 7-12 of the 18-nucleotide-long activator are critical to creating bright NCBs and positions 4-6 and 2-4 are hotspots to generate yellow-orange and red POTs, respectively. Based on these findings, a "zipper-bag" model is proposed that can explain how these hotspots facilitate the formation of distinct silver cluster chromophores and alter their chemical yields. Combining high-throughput screening with machine-learning algorithms, a pipeline is established to design bright and multicolor NCBs in silico.

Covalent JNK Inhibitor, JNK-IN-8, Suppresses Tumor Growth in Triple-Negative Breast Cancer by Activating TFEB- and TFE3-Mediated Lysosome Biogenesis and Autophagy.

The heterogeneity and aggressiveness of triple-negative breast cancer (TNBC) contribute to its early recurrence and metastasis. Despite substantial research to identify effective therapeutic targets, TNBC remains elusive in terms of improving patient outcomes. Here, we report that a covalent JNK inhibitor, JNK-IN-8, suppresses TNBC growth both in vitro and in vivo. JNK-IN-8 reduced colony formation, cell viability, and organoid growth in vitro and slowed patient-derived xenograft and syngeneic tumor growth in vivo. Cells treated with JNK-IN-8 exhibited large, cytoplasmic vacuoles with lysosomal markers. To examine the molecular mechanism of this phenotype, we looked at the master regulators of lysosome biogenesis and autophagy transcription factor EB (TFEB) and TFE3. JNK-IN-8 inhibited TFEB phosphorylation and induced nuclear translocation of unphosphorylated TFEB and TFE3. This was accompanied by an upregulation of TFEB/TFE3 target genes associated with lysosome biogenesis and autophagy. Depletion of both TFEB and TFE3 diminished the JNK-IN-8-driven upregulation of lysosome biogenesis and/or autophagy markers. TFEB and TFE3 are phosphorylated by a number of kinases, including mTOR. JNK-IN-8 reduced phosphorylation of mTOR targets in a concentration-dependent manner. Knockout of JNK1 and/or JNK2 had no impact on TFEB/TFE3 activation or mTOR inhibition by JNK-IN-8 but inhibited colony formation. Similarly, reexpression of either wildtype or drug-nonbinding JNK (C116S) in JNK knockout cells did not reverse JNK-IN-8-induced TFEB dephosphorylation. In summary, JNK-IN-8 induced lysosome biogenesis and autophagy by activating TFEB/TFE3 via mTOR inhibition independently of JNK. Together, these findings demonstrate the efficacy of JNK-IN-8 as a targeted therapy for TNBC and reveal its novel lysosome- and autophagy-mediated mechanism of action.

Peptides as surface coatings of nanoparticles that penetrate human cystic fibrosis sputum and uniformly distribute in vivo following pulmonary delivery.

Therapeutic delivery of drug and gene delivery systems have to traverse multiple biological barriers to achieve efficacy. Mucosal administration, such as pulmonary delivery in cystic fibrosis (CF) disease, remains a significant challenge due to concentrated viscoelastic mucus, which prevents drugs and particles from penetrating the mucus barrier. To address this problem, we used combinatorial peptide-presenting phage libraries and next-generation sequencing (NGS) to identify hydrophilic, net-neutral charged peptide coatings that enable penetration through human CF mucus ex vivo with ~600-fold better penetration than control, improve uptake into lung epithelial cells compared to uncoated or PEGylated-nanoparticles, and exhibit enhanced uniform distribution and retention in the mouse lung airways. These peptide coatings address multiple delivery barriers and effectively serve as excellent alternatives to standard PEG surface chemistries to achieve mucus penetration and address some of the challenges encountered using these chemistries. This biomolecule-based strategy can address multiple delivery barriers and hold promise to advance efficacy of therapeutics for diseases like CF.

Identification of peptide coatings that enhance diffusive transport of nanoparticles through the tumor microenvironment.

In solid tumors, increasing drug penetration promotes their regression and improves the therapeutic index of compounds. However, the heterogeneous extracellular matrix (ECM) acts as a steric and interaction barrier that hinders effective transport of therapeutics, including nanomedicines. Specifically, the interactions between the ECM and surface physicochemical properties of nanomedicines (e.g. charge, hydrophobicity) affect their diffusion and penetration. To address the challenges using existing surface chemistries, we used peptide-presenting phage libraries as a high-throughput approach to screen and identify peptides as coatings with desired physicochemical properties that improve diffusive transport through the tumor microenvironment. Through iterative screening against the ECM and identification by next-generation DNA sequencing and analysis, we selected individual clones and quantify their transport by diffusion assays. Here, we identified a net-neutral charge, hydrophilic peptide P4 that facilitates significantly higher diffusive transport of phage than negative control through in vitro tumor ECM. Through alanine mutagenesis, we confirmed that the hydrophilicity, charge, and spatial ordering impact diffusive transport. The P4 phage clone exhibited almost 200-fold improved uptake in ex vivo pancreatic tumor xenografts compared to the negative control. Nanoparticles coated with P4 exhibited ∼40-fold improvement in diffusivity in pancreatic tumor tissues, and P4-coated particles demonstrated less hindered diffusivity through the ECM compared to functionalized control particles. By leveraging the power of molecular diversity using phage display, we can greatly expand the chemical space of surface chemistries that can improve the transport of nanomedicines through the complex tumor microenvironment to ultimately improve their efficacy.

Nineteen Years of Consistently Positive and Strong Female Mate Preferences despite Individual Variation.

Sexual selection driven by mate choice has generated some of the most astounding diversity in nature, suggesting that population-level preferences should be strong and consistent over many generations. On the other hand, mating preferences are among the least repeatable components of an individual animal's phenotype, suggesting that consistency should be low across an animal's lifetime. Despite decades of intensive study of sexual selection, there is almost no information about the strength and consistency of preferences across many years. In this study, we present the results of more than 5,000 mate choice tests with a species of wild frog conducted over 19 consecutive years. Results show that preferences are positive and strong and vary little across years. This consistency occurs despite the fact that there are substantial differences among females in their strength of preference. We also suggest that mate preferences in populations that are primarily the result of sensory exploitation might be more stable over time than preferences that are primarily involved in assessing male quality.

eIFiso4G Augments the Synthesis of Specific Plant Proteins Involved in Normal Chloroplast Function.

The plant-specific translation initiation complex eIFiso4F is encoded by three genes in Arabidopsis (Arabidopsis thaliana)-genes encoding the cap binding protein eIFiso4E (eifiso4e) and two isoforms of the large subunit scaffolding protein eIFiso4G (i4g1 and i4g2). To quantitate phenotypic changes, a phenomics platform was used to grow wild-type and mutant plants (i4g1, i4g2, i4e, i4g1 x i4g2, and i4g1 x i4g2 x i4e [i4f]) under various light conditions. Mutants lacking both eIFiso4G isoforms showed the most obvious phenotypic differences from the wild type. Two-dimensional differential gel electrophoresis and mass spectrometry were used to identify changes in protein levels in plants lacking eIFiso4G. Four of the proteins identified as measurably decreased and validated by immunoblot analysis were two light harvesting complex binding proteins 1 and 3, Rubisco activase, and carbonic anhydrase. The observed decreased levels for these proteins were not the direct result of decreased transcription or protein instability. Chlorophyll fluorescence induction experiments indicated altered quinone reduction kinetics for the double and triple mutant plants with significant differences observed for absorbance, trapping, and electron transport. Transmission electron microscopy analysis of the chloroplasts in mutant plants showed impaired grana stacking and increased accumulation of starch granules consistent with some chloroplast proteins being decreased. Rescue of the i4g1 x i4g2 plant growth phenotype and increased expression of the validated proteins to wild-type levels was obtained by overexpression of eIFiso4G1. These data suggest a direct and specialized role for eIFiso4G in the synthesis of a subset of plant proteins.

SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing.

MOTIVATION: We set out to develop an algorithm that can mine differential gene expression data to identify candidate cell type-specific DNA regulatory sequences. Differential expression is usually quantified as a continuous score-fold-change, test-statistic, P-value-comparing biological classes. Unlike existing approaches, our de novo strategy, termed SArKS, applies non-parametric kernel smoothing to uncover promoter motif sites that correlate with elevated differential expression scores. SArKS detects motif k-mers by smoothing sequence scores over sequence similarity. A second round of smoothing over spatial proximity reveals multi-motif domains (MMDs). Discovered motif sites can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing. RESULTS: We applied SArKS to published gene expression data representing distinct neocortical neuron classes in Mus musculus and interneuron developmental states in Homo sapiens. When benchmarked against several existing algorithms using a cross-validation procedure, SArKS identified larger motif sets that formed the basis for regression models with higher correlative power. AVAILABILITY AND IMPLEMENTATION: https://github.com/denniscwylie/sarks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Functional Access to Neuron Subclasses in Rodent and Primate Forebrain.

Viral vectors enable foreign proteins to be expressed in brains of non-genetic species, including non-human primates. However, viruses targeting specific neuron classes have proved elusive. Here we describe viral promoters and strategies for accessing GABAergic interneurons and their molecularly defined subsets in the rodent and primate. Using a set intersection approach, which relies on two co-active promoters, we can restrict heterologous protein expression to cortical and hippocampal somatostatin-positive and parvalbumin-positive interneurons. With an orthogonal set difference method, we can enrich for subclasses of neuropeptide-Y-positive GABAergic interneurons by effectively subtracting the expression pattern of one promoter from that of another. These methods harness the complexity of gene expression patterns in the brain and significantly expand the number of genetically tractable neuron classes across mammals.

Enhancing the role of veterinary vaccines reducing zoonotic diseases of humans: linking systems biology with vaccine development.

The aim of research on infectious diseases is their prevention, and brucellosis and salmonellosis as such are classic examples of worldwide zoonoses for application of a systems biology approach for enhanced rational vaccine development. When used optimally, vaccines prevent disease manifestations, reduce transmission of disease, decrease the need for pharmaceutical intervention, and improve the health and welfare of animals, as well as indirectly protecting against zoonotic diseases of people. Advances in the last decade or so using comprehensive systems biology approaches linking genomics, proteomics, bioinformatics, and biotechnology with immunology, pathogenesis and vaccine formulation and delivery are expected to enable enhanced approaches to vaccine development. The goal of this paper is to evaluate the role of computational systems biology analysis of host:pathogen interactions (the interactome) as a tool for enhanced rational design of vaccines. Systems biology is bringing a new, more robust approach to veterinary vaccine design based upon a deeper understanding of the host-pathogen interactions and its impact on the host's molecular network of the immune system. A computational systems biology method was utilized to create interactome models of the host responses to Brucella melitensis (BMEL), Mycobacterium avium paratuberculosis (MAP), Salmonella enterica Typhimurium (STM), and a Salmonella mutant (isogenic ΔsipA, sopABDE2) and linked to the basis for rational development of vaccines for brucellosis and salmonellosis as reviewed by Adams et al. and Ficht et al. [1,2]. A bovine ligated ileal loop biological model was established to capture the host gene expression response at multiple time points post infection. New methods based on Dynamic Bayesian Network (DBN) machine learning were employed to conduct a comparative pathogenicity analysis of 219 signaling and metabolic pathways and 1620 gene ontology (GO) categories that defined the host's biosignatures to each infectious condition. Through this DBN computational approach, the method identified significantly perturbed pathways and GO category groups of genes that define the pathogenicity signatures of the infectious agent. Our preliminary results provide deeper understanding of the overall complexity of host innate immune response as well as the identification of host gene perturbations that defines a unique host temporal biosignature response to each pathogen. The application of advanced computational methods for developing interactome models based on DBNs has proven to be instrumental in elucidating novel host responses and improved functional biological insight into the host defensive mechanisms. Evaluating the unique differences in pathway and GO perturbations across pathogen conditions allowed the identification of plausible host-pathogen interaction mechanisms. Accordingly, a systems biology approach to study molecular pathway gene expression profiles of host cellular responses to microbial pathogens holds great promise as a methodology to identify, model and predict the overall dynamics of the host-pathogen interactome. Thus, we propose that such an approach has immediate application to the rational design of brucellosis and salmonellosis vaccines.

Multi-comparative systems biology analysis reveals time-course biosignatures of in vivo bovine pathway responses to B.melitensis, S.enterica Typhimurium and M.avium paratuberculosis.

BACKGROUND: To decipher the complexity and improve the understanding of host-pathogen interactions, biologists must adopt new system level approaches in which the hierarchy of biological interactions and dynamics can be studied. This paper presents the application of systems biology for the cross-comparative analysis and interactome modeling of three different infectious agents, leading to the identification of novel, unique and common molecular host responses (biosignatures). METHODS: A computational systems biology method was utilized to create interactome models of the host responses to Brucella melitensis (BMEL), Salmonella enterica Typhimurium (STM) and Mycobacterium avium paratuberculosis (MAP). A bovine ligated ileal loop biological model was employed to capture the host gene expression response at four time points post infection. New methods based on Dynamic Bayesian Network (DBN) machine learning were employed to conduct a systematic comparative analysis of pathway and Gene Ontology category perturbations. RESULTS: A cross-comparative assessment of 219 pathways and 1620 gene ontology (GO) categories was performed on each pathogen-host condition. Both unique and common pathway and GO perturbations indicated remarkable temporal differences in pathogen-host response profiles. Highly discriminatory pathways were selected from each pathogen condition to create a common system level interactome model comprised of 622 genes. This model was trained with data from each pathogen condition to capture unique and common gene expression features and relationships leading to the identification of candidate host-pathogen points of interactions and discriminatory biosignatures. CONCLUSIONS: Our results provide deeper understanding of the overall complexity of host defensive and pathogen invasion processes as well as the identification of novel host-pathogen interactions. The application of advanced computational methods for developing interactome models based on DBN has proven to be instrumental in conducting multi-conditional cross-comparative analyses. Further, this approach generates a fully simulateable model with capabilities for predictive analysis as well as for diagnostic pattern recognition. The resulting biosignatures may represent future targets for identification of emerging pathogens as well as for development of antimicrobial drugs, immunotherapeutics, or vaccines for prevention and treatment of diseases caused by known, emerging/re-emerging infectious agents.

Sick and edgy: walk-counting as a metric of epidemic spreading on networks.

A network structure metric is herein suggested for the investigation of the behaviour of epidemic spreading processes in general network-structured populations. This simple measure, based on the algebraic powers of the adjacency matrix associated with the network in question, is shown to admit a heuristic interpretation as a representation of a spreading process similar to standard epidemic models. It is further shown that the values of this metric may be of use in understanding the dynamic pattern of epidemic spread on networks of greatly varying structural properties (e.g. the degree distribution, the assortativity/dissortativity and the clustering).

Sensitivity of T cells to antigen and antagonism emerges from differential regulation of the same molecular signaling module.

Activation of T helper cells is necessary for the adaptive immune response to pathogens, and spurious activation can result in organ-specific autoimmunity (e.g., multiple sclerosis). T cell activation is initiated by membrane-proximal signaling that is predicated on the binding of the T cell receptor expressed on the T cell surface to peptide major histocompatibility complex (pMHC) molecules presented on the surface of antigen-presenting cells. These signaling processes regulate diverse outcomes, such as the ability of T cells to discriminate sensitively between stimulatory pMHC molecules and those that are characteristic of "self," and the phenomenon of antagonism (wherein the presence of certain pMHC molecules impairs T cell receptor signaling). We describe a molecular model for membrane-proximal signaling in T cells from which these disparate observations emerge as two sides of the same coin. This development of a unified mechanism that is consistent with diverse data would not have been possible without explicit consideration of the stochastic nature of the pertinent biochemical events. Our studies also reveal that certain previously proposed concepts are not dueling ideas but rather are different stimuli-dependent manifestations of a unified molecular model for membrane-proximal signaling. This model may provide a conceptual framework for further investigations of early events that regulate T cell activation in response to self and foreign antigens and for the development of intervention protocols to inhibit aberrant signaling.

A hybrid deterministic-stochastic algorithm for modeling cell signaling dynamics in spatially inhomogeneous environments and under the influence of external fields.

Cell signaling dynamics mediate myriad processes in biology. It has become increasingly clear that inter- and intracellular signaling reactions often occur in a spatially inhomogeneous environment and that it is important to account for stochastic fluctuations of certain species involved in signaling reactions. The importance of these effects enhances the difficulty of gleaning mechanistic information from observations of a few experimental reporters and highlights the significance of synergistic experimental and computational studies. When both stochastic fluctuations and spatial inhomogeneity must be included in a model simultaneously, however, the resulting computational demands quickly become overwhelming. In many situations the failure of standard coarse-graining methods (i.e., ignoring spatial variation or stochastic fluctuations) when applied to all components of a complex system does not exclude the possibility of successfully applying such coarse-graining to some components of the system. Following this approach alleviates computational cost but requires "hybrid" algorithms where some variables are treated at a coarse-grained level while others are not. We present an efficient algorithm for simulation of stochastic, spatially inhomogeneous reaction-diffusion kinetics coupled to coarse-grained fields described by (stochastic or deterministic) partial differential equations (PDEs). The PDEs could represent mean-field descriptions of reactive species present in large copy numbers or evolution of hydrodynamic variables that influence signaling (e.g., membrane shape or cytoskeletal motion). We discuss the approximations made to derive our algorithm and test its efficacy by applying it to problems that include many features typical of realistic cell signaling processes.