Low pH immobilizes and kills human leukocytes and prevents transmission of cell-associated HIV in a mouse model

BACKGROUND: Both cell-associated and cell-free HIV virions are present in semen and cervical secretions of HIV-infected individuals. Thus, topical microbicides may need to inactivate both cell-associated and cell-free HIV to prevent sexual transmission of HIV/AIDS. To determine if the mild acidity of the healthy vagina and acid buffering microbicides would prevent transmission by HIV-infected leukocytes, we measured the effect of pH on leukocyte motility, viability and intracellular pH and tested the ability of an acidic buffering microbicide (BufferGel(®)) to prevent the transmission of cell-associated HIV in a HuPBL-SCID mouse model. METHODS: Human lymphocyte, monocyte, and macrophage motilities were measured as a function of time and pH using various acidifying agents. Lymphocyte and macrophage motilities were measured using video microscopy. Monocyte motility was measured using video microscopy and chemotactic chambers. Peripheral blood mononuclear cell (PBMC) viability and intracellular pH were determined as a function of time and pH using fluorescent dyes. HuPBL-SCID mice were pretreated with BufferGel, saline, or a control gel and challenged with HIV-1-infected human PBMCs. RESULTS: Progressive motility was completely abolished in all cell types between pH 5.5 and 6.0. Concomitantly, at and below pH 5.5, the intracellular pH of PBMCs dropped precipitously to match the extracellular medium and did not recover. After acidification with hydrochloric acid to pH 4.5 for 60 min, although completely immotile, 58% of PBMCs excluded ethidium homodimer-1 (dead-cell dye). In contrast, when acidified to this pH with BufferGel, a microbicide designed to maintain vaginal acidity in the presence of semen, only 4% excluded dye at 10 min and none excluded dye after 30 min. BufferGel significantly reduced transmission of HIV-1 in HuPBL-SCID mice (1 of 12 infected) compared to saline (12 of 12 infected) and a control gel (5 of 7 infected). CONCLUSION: These results suggest that physiologic or microbicide-induced acid immobilization and killing of infected white blood cells may be effective in preventing sexual transmission of cell-associated HIV

Consumer perceptions of co-branding alliances: Organizational dissimilarity signals and brand fit

This study explores how consumers evaluate co-branding alliances between dissimilar partner firms. Customers are well aware that different firms are behind a co-branded product and observe the partner firms’ characteristics. Drawing on signaling theory, we assert that consumers use organizational characteristics as signals in their assessment of brand fit and for their purchasing decisions. Some organizational signals are beyond the control of the co-branding partners or at least they cannot alter them on short notice. We use a quasi-experimental design and test how co-branding partner dissimilarity affects brand fit perception. The results show that co-branding partner dissimilarity in terms of firm size, industry scope, and country-of-origin image negatively affects brand fit perception. Firm age dissimilarity does not exert significant influence. Because brand fit generally fosters a benevolent consumer attitude towards a co-branding alliance, the findings suggest that high partner dissimilarity may reduce overall co-branding alliance performance

Cassini observations of planetary-period oscillations of Saturn's magnetopause

Examination of Cassini magnetic field and plasma data in the outer boundary regions of Saturn's magnetosphere shows that magnetopause oscillations at the planetary period commonly occur, in phase with plasma pressure variations inside the magnetosphere. The peak-to-trough amplitude of the boundary oscillations mapped to the planet-Sun line is estimated to be typically similar to 2 R-S, corresponding to a similar to 10% change in the boundary radius. The change in internal pressure required to produce such motions is estimated to be similar to 40% of the background values. A qualitative physical picture is proposed in which a compressive wave propagates outward through the sub-corotating outer magnetospheric plasma, originating from a corotating source in the nearer-planet region

Analysis and Computational Dissection of Molecular Signature Multiplicity

Molecular signatures are computational or mathematical models created to diagnose disease and other phenotypes and to predict clinical outcomes and response to treatment. It is widely recognized that molecular signatures constitute one of the most important translational and basic science developments enabled by recent high-throughput molecular assays. A perplexing phenomenon that characterizes high-throughput data analysis is the ubiquitous multiplicity of molecular signatures. Multiplicity is a special form of data analysis instability in which different analysis methods used on the same data, or different samples from the same population lead to different but apparently maximally predictive signatures. This phenomenon has far-reaching implications for biological discovery and development of next generation patient diagnostics and personalized treatments. Currently the causes and interpretation of signature multiplicity are unknown, and several, often contradictory, conjectures have been made to explain it. We present a formal characterization of signature multiplicity and a new efficient algorithm that offers theoretical guarantees for extracting the set of maximally predictive and non-redundant signatures independent of distribution. The new algorithm identifies exactly the set of optimal signatures in controlled experiments and yields signatures with significantly better predictivity and reproducibility than previous algorithms in human microarray gene expression datasets. Our results shed light on the causes of signature multiplicity, provide computational tools for studying it empirically and introduce a framework for in silico bioequivalence of this important new class of diagnostic and personalized medicine modalities

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses

Programmable Ligand Detection System in Plants through a Synthetic Signal Transduction Pathway

There is an unmet need to monitor human and natural environments for substances that are intentionally or unintentionally introduced. A long-sought goal is to adapt plants to sense and respond to specific substances for use as environmental monitors. Computationally re-designed periplasmic binding proteins (PBPs) provide a means to design highly sensitive and specific ligand sensing capabilities in receptors. Input from these proteins can be linked to gene expression through histidine kinase (HK) mediated signaling. Components of HK signaling systems are evolutionarily conserved between bacteria and plants. We previously reported that in response to cytokinin-mediated HK activation in plants, the bacterial response regulator PhoB translocates to the nucleus and activates transcription. Also, we previously described a plant visual response system, the de-greening circuit, a threshold sensitive reporter system that produces a visual response which is remotely detectable and quantifiable.We describe assembly and function of a complete synthetic signal transduction pathway in plants that links input from computationally re-designed PBPs to a visual response. To sense extracellular ligands, we targeted the computational re-designed PBPs to the apoplast. PBPs bind the ligand and develop affinity for the extracellular domain of a chemotactic protein, Trg. We experimentally developed Trg fusions proteins, which bind the ligand-PBP complex, and activate intracellular PhoR, the HK cognate of PhoB. We then adapted Trg-PhoR fusions for function in plants showing that in the presence of an external ligand PhoB translocates to the nucleus and activates transcription. We linked this input to the de-greening circuit creating a detector plant.Our system is modular and PBPs can theoretically be designed to bind most small molecules. Hence our system, with improvements, may allow plants to serve as a simple and inexpensive means to monitor human surroundings for substances such as pollutants, explosives, or chemical agents

Stable interference of EWS–FLI1 in an Ewing sarcoma cell line impairs IGF-1/IGF-1R signalling and reveals TOPK as a new target

BACKGROUND: Ewing sarcoma is a paradigm of solid tumour -bearing chromosomal translocations resulting in fusion proteins that act as deregulated transcription factors. Ewing sarcoma translocations fuse the EWS gene with an ETS transcription factor, mainly FLI1. Most of the EWS–FLI1 target genes still remain unknown and many have been identified in heterologous model systems

Psychosocial risk factors for suicidality in children and adolescents

Suicidality in childhood and adolescence is of increasing concern. The aim of this paper was to review the published literature identifying key psychosocial risk factors for suicidality in the paediatric population. A systematic two-step search was carried out following the PRISMA statement guidelines, using the terms 'suicidality, suicide, and self-harm' combined with terms 'infant, child, adolescent' according to the US National Library of Medicine and the National Institutes of Health classification of ages. Forty-four studies were included in the qualitative synthesis. The review identified three main factors that appear to increase the risk of suicidality: psychological factors (depression, anxiety, previous suicide attempt, drug and alcohol use, and other comorbid psychiatric disorders); stressful life events (family problems and peer conflicts); and personality traits (such as neuroticism and impulsivity). The evidence highlights the complexity of suicidality and points towards an interaction of factors contributing to suicidal behaviour. More information is needed to understand the complex relationship between risk factors for suicidality. Prospective studies with adequate sample sizes are needed to investigate these multiple variables of risk concurrently and over time

Persistent activation of interlinked type 2 airway epithelial gene networks in sputum-derived cells from aeroallergen-sensitized symptomatic asthmatics

© 2018 The Author(s). Atopic asthma is a persistent disease characterized by intermittent wheeze and progressive loss of lung function. The disease is thought to be driven primarily by chronic aeroallergen-induced type 2-associated inflammation. However, the vast majority of atopics do not develop asthma despite ongoing aeroallergen exposure, suggesting additional mechanisms operate in conjunction with type 2 immunity to drive asthma pathogenesis. We employed RNA-Seq profiling of sputum-derived cells to identify gene networks operative at baseline in house dust mite-sensitized (HDM S ) subjects with/without wheezing history that are characteristic of the ongoing asthmatic state. The expression of type 2 effectors (IL-5, IL-13) was equivalent in both cohorts of subjects. However, in HDM S -wheezers they were associated with upregulation of two coexpression modules comprising multiple type 2- and epithelial-associated genes. The first module was interlinked by the hubs EGFR, ERBB2, CDH1 and IL-13. The second module was associated with CDHR3 and mucociliary clearance genes. Our findings provide new insight into the molecular mechanisms operative at baseline in the airway mucosa in atopic asthmatics undergoing natural aeroallergen exposure, and suggest that susceptibility to asthma amongst these subjects involves complex interactions between type 2- and epithelial-associated gene networks, which are not operative in equivalently sensitized/exposed atopic non-asthmatics