Lower female survival from an opportunistic infection reveals progesterone-driven sex bias in trained immunity.

Immune responses differ between females and males, although such sex-based variance is incompletely understood. Observing that bacteremia of the opportunistic pathogen Burkholderia gladioli caused many more deaths of female than male mice bearing genetic deficiencies in adaptive immunity, we determined that this was associated with sex bias in the innate immune memory response called trained immunity. Female attenuation of trained immunity varies with estrous cycle stage and correlates with serum progesterone, a hormone that decreases glycolytic capacity and recall cytokine secretion induced by antigen non-specific stimuli. Progesterone receptor antagonism rescues female trained immune responses and survival from controlled B. gladioli infection to magnitudes similar to those of males. These data demonstrate progesterone-dependent sex bias in trained immunity where attenuation of female responses is associated with survival outcomes from opportunistic infection.

Quantitative Biology at Community Colleges, a Network of Biology and Mathematics Faculty Focused on Improving Numerical and Quantitative Skills of Students.

Mastery of quantitative skills is increasingly critical for student success in life sciences, but few curricula adequately incorporate quantitative skills. Quantitative Biology at Community Colleges (QB@CC) is designed to address this need by building a grassroots consortium of community college faculty to 1) engage in interdisciplinary partnerships that increase participant confidence in life science, mathematics, and statistics domains; 2) generate and publish a collection of quantitative skills-focused open education resources (OER); and 3) disseminate these OER and pedagogical practices widely, in turn expanding the network. Currently in its third year, QB@CC has recruited 70 faculty into the network and created 20 modules. Modules can be accessed by interested biology and mathematics educators in high school, 2-year, and 4-year institutions. Here, we use survey responses, focus group interviews, and document analyses (principles-focused evaluation) to evaluate the progress in accomplishing these goals midway through the QB@CC program. The QB@CC network provides a model for developing and sustaining an interdisciplinary community that benefits participants and generates valuable resources for the broader community. Similar network-building programs may wish to adopt some of the effective aspects of the QB@CC network model to meet their objectives.

Quantitative imaging and dynamics of tumor therapy with viruses.

Cancer therapy remains challenging due to the myriad presentations of the disease and the vast genetic diversity of tumors that continuously evolve and often become resistant to therapy. Viruses can be engineered to specifically infect, replicate, and kill tumor cells (tumor virotherapy). Moreover, the viruses can be "armed" with therapeutic genes to enhance their oncolytic effect. Using viruses to treat cancer is exciting and novel and in principle can be used for a broad variety of tumors. However, the approach is distinctly different from other cancer therapies since success depends on establishment of an infection within the tumor and ongoing propagation of the oncolytic virus within the tumor itself. Therefore, the target itself amplifies the therapy. This introduces complex dynamics especially when the immune system is taken into consideration as well as the physical and other biological barriers to virus growth. Understanding these dynamics not only requires mathematical and computational models but also approaches for the noninvasive monitoring of the virus and tumor populations. In this perspective, we discuss strategies and current results to achieve this important goal of understanding these dynamics in pursuit of optimization of oncolytic virotherapy.

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation.

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic interactions among multiple proteins in a protein interaction network is technically difficult. Here, we present a protocol for Quantitative Multiplex Immunoprecipitation (QMI), which allows quantitative assessment of fold changes in protein interactions based on relative fluorescence measurements of Proteins in Shared Complexes detected by Exposed Surface epitopes (PiSCES). In QMI, protein complexes from cell lysates are immunoprecipitated onto microspheres, and then probed with a labeled antibody for a different protein in order to quantify the abundance of PiSCES. Immunoprecipitation antibodies are conjugated to different MagBead spectral regions, which allows a flow cytometer to differentiate multiple parallel immunoprecipitations and simultaneously quantify the amount of probe antibody associated with each. QMI does not require genetic tagging and can be performed using minimal biomaterial compared to other immunoprecipitation methods. QMI can be adapted for any defined group of interacting proteins, and has thus far been used to characterize signaling networks in T cells and neuronal glutamate synapses. Results have led to new hypothesis generation with potential diagnostic and therapeutic applications. This protocol includes instructions to perform QMI, from the initial antibody panel selection through to running assays and analyzing data. The initial assembly of a QMI assay involves screening antibodies to generate a panel, and empirically determining an appropriate lysis buffer. The subsequent reagent preparation includes covalently coupling immunoprecipitation antibodies to MagBeads, and biotinylating probe antibodies so they can be labeled by a streptavidin-conjugated fluorophore. To run the assay, lysate is mixed with MagBeads overnight, and then beads are divided and incubated with different probe antibodies, and then a fluorophore label, and read by flow cytometry. Two statistical tests are performed to identify PiSCES that differ significantly between experimental conditions, and results are visualized using heatmaps or node-edge diagrams.

In vitro and in silico multidimensional modeling of oncolytic tumor virotherapy dynamics.

Tumor therapy with replication competent viruses is an exciting approach to cancer eradication where viruses are engineered to specifically infect, replicate, spread and kill tumor cells. The outcome of tumor virotherapy is complex due to the variable interactions between the cancer cell and virus populations as well as the immune response. Oncolytic viruses are highly efficient in killing tumor cells in vitro, especially in a 2D monolayer of tumor cells, their efficiency is significantly lower in a 3D environment, both in vitro and in vivo. This indicates that the spatial dimension may have a major influence on the dynamics of virus spread. We study the dynamic behavior of a spatially explicit computational model of tumor and virus interactions using a combination of in vitro 2D and 3D experimental studies to inform the models. We determine the number of nearest neighbor tumor cells in 2D (median = 6) and 3D tumor spheroids (median = 16) and how this influences virus spread and the outcome of therapy. The parameter range leading to tumor eradication is small and even harder to achieve in 3D. The lower efficiency in 3D exists despite the presence of many more adjacent cells in the 3D environment that results in a shorter time to reach equilibrium. The mean field mathematical models generally used to describe tumor virotherapy appear to provide an overoptimistic view of the outcomes of therapy. Three dimensional space provides a significant barrier to efficient and complete virus spread within tumors and needs to be explicitly taken into account for virus optimization to achieve the desired outcome of therapy.

The early proximal αß TCR signalosome specifies thymic selection outcome through a quantitative protein interaction network.

During αß T cell development, T cell antigen receptor (TCR) engagement transduces biochemical signals through a protein-protein interaction (PPI) network that dictates dichotomous cell fate decisions. It remains unclear how signal specificity is communicated, instructing either positive selection to advance cell differentiation or death by negative selection. Early signal discrimination might occur by PPI signatures differing qualitatively (customized, unique PPI combinations for each signal), quantitatively (graded amounts of a single PPI series), or kinetically (speed of PPI pathway progression). Using a novel PPI network analysis, we found that early TCR-proximal signals distinguishing positive from negative selection appeared to be primarily quantitative in nature. Furthermore, the signal intensity of this PPI network was used to find an antigen dose that caused a classic negative selection ligand to induce positive selection of conventional αß T cells, suggesting that the quantity of TCR triggering was sufficient to program selection outcome. Because previous work had suggested that positive selection might involve a qualitatively unique signal through CD3δ, we reexamined the block in positive selection observed in CD3δ0 mice. We found that CD3δ0 thymocytes were inhibited but capable of signaling positive selection, generating low numbers of MHC-dependent αß T cells that expressed diverse TCR repertoires and participated in immune responses against infection. We conclude that the major role for CD3δ in positive selection is to quantitatively boost the signal for maximal generation of αß T cells. Together, these data indicate that a quantitative network signaling mechanism through the early proximal TCR signalosome determines thymic selection outcome.

In Vivo Imaging of Oncolytic Measles Virus Propagation with Single-Cell Resolution.

Recombinant measles viruses (MVs) have oncolytic activity against a variety of human cancers. However, their kinetics of spread within tumors has been unexplored. We established an intravital imaging system using the dorsal skin fold chamber, which allows for serial, non-invasive imaging of tumor cells and replication of a fusogenic and a hypofusogenic MV. Hypofusogenic virus-infected cells were detected at the earliest 3 days post-infection (dpi), with peak infection around 6 dpi. In contrast, the fusogenic virus replicated faster: infected cells were detectable 1 dpi and cells were killed quickly. Infection foci were significantly larger with the fusogenic virus. Both viruses formed syncytia. The spatial relationships between cells have a major influence on the outcome of therapy with oncolytic viruses.

In Vivo Estimation of Oncolytic Virus Populations within Tumors.

The use of replication-competent viruses as oncolytic agents is rapidly expanding, with several oncolytic viruses approved for cancer therapy. As responses to therapy are highly variable, understanding the dynamics of therapy is critical for optimal application of virotherapy in practice. Although mathematical models have been developed to understand the dynamics of tumor virotherapy, a scarcity of in vivo data has made difficult parametrization of these models. To tackle this problem, we studied the in vitro and in vivo spread of two oncolytic measles viruses that induce expression of the sodium iodide symporter (NIS) in cells. NIS expression enabled infected cells to concentrate radioactive isotopes that could be reproducibly and quantitatively imaged using SPECT/CT. We observed a strong linear relationship in vitro between infectious virus particles, viral N and NIS gene expression, and radioactive isotope uptake. In vivo radioisotope uptake was highly correlated with viral N and NIS gene expression. Similar expression patterns between viral N and NIS gene expression in vitro and in vivo implied that the oncolytic virus behaved similarly in both scenarios. Significant titers of viable virus were consistently isolated from tumors explanted from mice that had been injected with oncolytic measle viruses. We observed a weaker but positive in vivo relationship between radioisotope uptake and the viable virus titer recovered from tumors; this was likely due to anisotropies in the viral distribution in vivo These data suggest that methods that enable quantitation of in vivo anisotropies are required for continuing development of oncolytic virotherapy.Significance: These findings address a fundamental gap in our knowledge of oncolytic virotherapy by presenting technology that gives insight into the behavior of oncolytic viruses in vivo Cancer Res; 78(20); 5992-6000. ©2018 AACR.

Harnessing uncertainty to approximate mechanistic models of interspecific interactions.

Because the Lotka-Volterra competitive equations posit no specific competitive mechanisms, they are exceedingly general, and can theoretically approximate any underlying mechanism of competition near equilibrium. In practice, however, these models rarely generate accurate predictions in diverse communities. We propose that this difference between theory and practice may be caused by how uncertainty propagates through Lotka-Volterra systems. In approximating mechanistic relationships with Lotka-Volterra models, associations among parameters are lost, and small variation can correspond to large and unrealistic changes in predictions. We demonstrate that constraining Lotka-Volterra models using correlations among parameters expected from hypothesized underlying mechanisms can reintroduce some of the underlying structure imposed by those mechanisms, thereby improving model predictions by both reducing bias and increasing precision. Our results suggest that this hybrid approach may combine some of the generality of phenomenological models with the broader applicability and meaningful interpretability of mechanistic approaches. These methods could be useful in poorly understood systems for identifying important coexistence mechanisms, or for making more accurate predictions.

Gene Expression Signatures Characterized by Longitudinal Stability and Interindividual Variability Delineate Baseline Phenotypic Groups with Distinct Responses to Immune Stimulation.

Human immunity exhibits remarkable heterogeneity among individuals, which engenders variable responses to immune perturbations in human populations. Population studies reveal that, in addition to interindividual heterogeneity, systemic immune signatures display longitudinal stability within individuals, and these signatures may reliably dictate how given individuals respond to immune perturbations. We hypothesize that analyzing relationships among these signatures at the population level may uncover baseline immune phenotypes that correspond with response outcomes to immune stimuli. To test this, we quantified global gene expression in peripheral blood CD4+ cells from healthy individuals at baseline and following CD3/CD28 stimulation at two time points 1 mo apart. Systemic CD4+ cell baseline and poststimulation molecular immune response signatures (MIRS) were defined by identifying genes expressed at levels that were stable between time points within individuals and differential among individuals in each state. Iterative differential gene expression analyses between all possible phenotypic groupings of at least three individuals using the baseline and stimulated MIRS gene sets revealed shared baseline and response phenotypic groupings, indicating the baseline MIRS contained determinants of immune responsiveness. Furthermore, significant numbers of shared phenotype-defining sets of determinants were identified in baseline data across independent healthy cohorts. Combining the cohorts and repeating the analyses resulted in identification of over 6000 baseline immune phenotypic groups, implying that the MIRS concept may be useful in many immune perturbation contexts. These findings demonstrate that patterns in complex gene expression variability can be used to define immune phenotypes and discover determinants of immune responsiveness.

Translating Big Data into Smart Data for Veterinary Epidemiology.

The increasing availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing "big" data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the incorporation of new skills into veterinary epidemiology training, including, for example, machine learning and coding, to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having "big data" to create "smart data," with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.

An analytical workflow for accurate variant discovery in highly divergent regions.

BACKGROUND: Current variant discovery methods often start with the mapping of short reads to a reference genome; yet, their performance deteriorates in genomic regions where the reads are highly divergent from the reference sequence. This is particularly problematic for the human leukocyte antigen (HLA) region on chromosome 6p21.3. This region is associated with over 100 diseases, but variant calling is hindered by the extreme divergence across different haplotypes. RESULTS: We simulated reads from chromosome 6 exonic regions over a wide range of sequence divergence and coverage depth. We systematically assessed combinations between five mappers and five callers for their performance on simulated data and exome-seq data from NA12878, a well-studied individual in which multiple public call sets have been generated. Among those combinations, the number of known SNPs differed by about 5 % in the non-HLA regions of chromosome 6 but over 20 % in the HLA region. Notably, GSNAP mapping combined with GATK UnifiedGenotyper calling identified about 20 % more known SNPs than most existing methods without a noticeable loss of specificity, with 100 % sensitivity in three highly polymorphic HLA genes examined. Much larger differences were observed among these combinations in INDEL calling from both non-HLA and HLA regions. We obtained similar results with our internal exome-seq data from a cohort of chronic lymphocytic leukemia patients. CONCLUSIONS: We have established a workflow enabling variant detection, with high sensitivity and specificity, over the full spectrum of divergence seen in the human genome. Comparing to public call sets from NA12878 has highlighted the overall superiority of GATK UnifiedGenotyper, followed by GATK HaplotypeCaller and SAMtools, in SNP calling, and of GATK HaplotypeCaller and Platypus in INDEL calling, particularly in regions of high sequence divergence such as the HLA region. GSNAP and Novoalign are the ideal mappers in combination with the above callers. We expect that the proposed workflow should be applicable to variant discovery in other highly divergent regions.

Multiplex matrix network analysis of protein complexes in the human TCR signalosome.

Multiprotein complexes transduce cellular signals through extensive interaction networks, but the ability to analyze these networks in cells from small clinical biopsies is limited. To address this, we applied an adaptable multiplex matrix system to physiologically relevant signaling protein complexes isolated from a cell line or from human patient samples. Focusing on the proximal T cell receptor (TCR) signalosome, we assessed 210 pairs of PiSCES (proteins in shared complexes detected by exposed surface epitopes). Upon stimulation of Jurkat cells with superantigen-loaded antigen-presenting cells, this system produced high-dimensional data that enabled visualization of network activity. A comprehensive analysis platform generated PiSCES biosignatures by applying unsupervised hierarchical clustering, principal component analysis, an adaptive nonparametric with empirical cutoff analysis, and weighted correlation network analysis. We generated PiSCES biosignatures from 4-mm skin punch biopsies from control patients or patients with the autoimmune skin disease alopecia areata. This analysis distinguished disease patients from the controls, detected enhanced basal TCR signaling in the autoimmune patients, and identified a potential signaling network signature that may be indicative of disease. Thus, generation of PiSCES biosignatures represents an approach that can provide information about the activity of protein signaling networks in samples including low-abundance primary cells from clinical biopsies.

Tipping the mutation-selection balance: Limited migration increases the frequency of deleterious mutants.

Typical mutation-selection models assume well-mixed populations, but dispersal and migration within many natural populations is spatially limited. Such limitations can lead to enhanced variation among locations as different types become clustered in different places. Such clustering weakens competition between unlike types relative to competition between like types; thus, the rate by which a fitter type displaces an inferior competitor can be affected by the spatial scale of movement. In this paper, we use a birth-death model to show that limited migration can affect asexual populations by creating competitive refugia. We use a moment closure approach to show that as population structure is introduced by limiting migration, the equilibrial frequency of deleterious mutants increases. We support and extend the model through stochastic simulation, and we use a spatially explicit cellular automaton approach to corroborate the results. We discuss the implications of these results for standing variation in structured populations and adaptive valley crossing in Wright's "shifting balance" process.

A simple method for gene phasing using mate pair sequencing.

BACKGROUND: Recessive genes cause disease when both copies are affected by mutant loci. Resolving the cis/trans relationship of variations has been an important problem both for researchers, and increasingly, clinicians. Of particular concern are patients who have two heterozygous disease-causing mutations and could be diagnosed as affected (one mutation on each allele) or as phenotypically normal (both mutations on the same allele). Several methods are currently used to phase genes, however due to cost, complexity and/or low sensitivity they are not suitable for clinical purposes. METHODS: Long-range amplification was used to select and enrich the target gene (CYP21A2) followed by modified mate-pair sequencing. Fragments that mapped coincidently to two heterozygous sites were identified and used for statistical analysis. RESULTS: Probabilities for cis/trans relationships between heterozygous positions were calculated along with 99% confidence intervals over the entire length of our 10 kb amplicons. The quality of phasing was closely related to the depth of coverage and the number of erroneous reads. Most of the error was found to have been introduced by recombination in the PCR reaction. CONCLUSIONS: We have developed a simple method utilizing massively parallel sequencing that is capable of resolving two alleles containing multiple heterozygous positions. This method stands out among other phasing tools because it provides quantitative results allowing confident haplotype calls.

The influence of desmocollin 1-3 expression on prognosis after curative resection of colorectal liver metastases.

PURPOSE: Prognosis after curative resection of colorectal liver metastases is hard to determine based on clinical parameters; biomarkers are therefore needed. The purpose of this study was to determine the value of desmocollins (DSC) as potential biomarkers. Desmocollins are responsible for cell-cell adhesion in epithelial tissue; their loss may lead to reduced cellular adhesion and facilitate cellular migration, enabling tumor cells to form distant metastases. We analyzed DSC expression in colorectal liver metastases with respect to the risk of recurrence following liver resection. METHODS: Tissue microarrays from 257 consecutive patients who underwent R0-resection of colorectal liver metastases were constructed. RESULTS: Low expression of DSC 1, 2, and 3 was observed in 55, 54, and 79 % of liver metastases. There was no correlation between site or stage of the primary tumor, presence of extrahepatic tumor, grading, size or number of metastases, and desmocollin expression. Primary tumor stage I or II (p = 0.005) and no or few lymph node metastases (p < 0.001) were associated with a significantly better disease-free survival on univariate analysis. These parameters reached only marginal significance on multivariate analysis (p = 0.059 and p = 0.052, respectively), as did desmocollin 3 expression (p = 0.050). In the subgroup of patients with stages III-IV primary tumors, however, multivariate analysis showed a significant correlation between DSC 3 expression and disease-free survival after liver resection (p = 0.009). CONCLUSIONS: Reduced expression of DSC3 correlated with an increased risk of developing tumor recurrence after resection of liver metastases. These findings may be helpful in selecting high-risk patients who might benefit from multimodal therapy.

Toward a mechanistic understanding of how natural bacterial communities respond to changes in temperature in aquatic ecosystems.

We examine how heterotrophic bacterioplankton communities respond to temperature by mathematically defining two thermally adapted species and showing how changes in environmental temperature affect competitive outcome in a two-resource environment. We did this by adding temperature dependence to both the respiration and uptake terms of a two species, two-resource model rooted in Droop kinetics. We used published literature values and results of our own work with experimental microcosms to parameterize the model and to quantitatively and qualitatively define relationships between temperature and bacterioplankton physiology. Using a graphical resource competition framework, we show how physiological adaptation to temperature can allow organisms to be more, or less, competitive for limiting resources across a thermal gradient (2-34 degrees C). Our results suggest that the effect of temperature on bacterial community composition, and therefore bacterially mediated biogeochemical processes, depends on the available resource pool in a given system. In addition, our results suggest that the often unclear relationship between temperature and bacterial metabolism, as reported in the literature, can be understood by allowing for changes in the relative contribution of thermally adapted populations to community metabolism.