Strategies to elicit Abs that can neutralize diverse strains of a highly mutable pathogen are likely to result in a potent vaccine. Broadly neutralizing Abs (bnAbs) against HIV have been isolated from patients, proving that the human immune system can evolve them. Using computer simulations and theory, we study immunization with diverse mixtures of variant antigens (Ags). Our results show that particular choices for the number of variant Ags and the mutational distances separating them maximize the probability of inducing bnAbs. The variant Ags represent potentially conflicting selection forces that can frustrate the Darwinian evolutionary process of affinity maturation. An intermediate level of frustration maximizes the chance of evolving bnAbs. A simple model makes vivid the origin of this principle of optimal frustration. Our results, combined with past studies, suggest that an appropriately chosen permutation of immunization with an optimally designed mixture (using the principles that we describe) and sequential immunization with variant Ags that are separated by relatively large mutational distances may best promote the evolution of bnAbs.HIV | broadly neutralizing antibodies | statistical mechanics | evolutionary biology | biophysics

Background: A number of gene therapy applications would benefit from vectors capable of expressing multiple genes. In this study we explored the feasibility and efficiency of expressing two or three transgenes in HIV-1 based lentiviral vector. Bicistronic and tricistronic self-inactivating lentiviral vectors were constructed employing the internal ribosomal entry site (IRES) sequence of encephalomyocarditis virus (EMCV) and/or foot-and-mouth disease virus (FMDV) cleavage factor 2A. We employed enhanced green fluorescent protein (eGFP), O 6 -methylguanine-DNAmethyltransferase (MGMT), and homeobox transcription factor HOXB4 as model genes and their expression was detected by appropriate methods including fluorescence microscopy, flow cytometry, immunocytochemistry, biochemical assay, and western blotting.

In addition to the well-established effector functions of IgGs, including direct cytotoxicity and antibody-dependent cellular cytotoxicity, some populations of IgGs may exert anti-inflammatory effects. Here, we describe a population of antibodies that form in the natural course of metastatic cancer and contain glycans that terminate with sialic acid. We demonstrate that both the titer of these antibodies and their level of sialylation are relatively stable throughout the progression of metastatic melanoma. The sialylation pattern of these antibodies somehow correlates with their specificity for tumor-associated antigens, as IgGs targeting several antigens associated with infectious agents are relatively poor of sialic acid. We also show that some antibodies targeting the melanoma-associated antigen NY-ESO-1 bind to the human C-type lectin CD209 (DC-SIGN). We propose that these antibodies are candidate anti-inflammatory antibodies. The presence of anti-inflammatory antibodies in cancer patients may explain, at least in part, why tumors persist and spread in the host despite strong tumor-specific humoral responses. The elucidation of the cellular and molecular pathways involved in the induction of anti-inflammatory antibodies specific for tumor-associated antigens and their function may yield important insights into how tumors evade immune detection and progress.

Information about an extensive set of health conditions on a well-defined sample of subjects is essential for assessing population health, gauging the impact of various policies, modeling costs, and studying health disparities. Unfortunately, there is no single data source that provides accurate information about health conditions. We combine information from several administrative and survey data sets to obtain model-based dummy variables for 107 health conditions (diseases, preventive measures, and screening for diseases) for elderly (age 65 and older) subjects in the Medicare Current Beneficiary Survey (MCBS) over the fourteen-year period, 1999–2012. The MCBS has prevalence of diseases assessed based on Medicare claims and provides detailed information on all health conditions but is prone to underestimation bias. The National Health and Nutrition Examination Survey (NHANES), on the other hand, collects self-reports and physical/laboratory measures only for a subset of the 107 health conditions. Neither source provides complete information, but we use them together to derive model-based corrected dummy variables in MCBS for the full range of existing health conditions using a missing data and measurement error model framework. We create multiply imputed dummy variables and use them to construct the prevalence rate and trend estimates. The broader goal, however, is to use these corrected or modeled dummy variables for a multitude of policy analysis, cost modeling, and analysis of other relationships either using them as predictors or as outcome variables.