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Triclocarban (TCC) is among the top 10 most commonly detected wastewater contaminants in both concentration and frequency. Its presence in water, as well as its propensity to bioaccumulate, has raised numerous questions about potential endocrine and developmental effects. Here, we investigated whether exposure to an environmentally relevant concentration of TCC could result in transfer from mother to offspring in CD-1 mice during gestation and lactation using accelerator mass spectrometry (AMS). 14C-TCC (100 nM) was administered to dams through drinking water up to gestation day 18, or from birth to post-natal day 10. AMS was used to quantify 14C-concentrations in offspring and dams after exposure. We demonstrated that TCC does effectively transfer from mother to offspring, both trans-placentally and via lactation. TCC-related compounds were detected in the tissues of offspring with significantly higher concentrations in the brain, heart and fat. In addition to transfer from mother to offspring, exposed offspring were heavier in weight than unexposed controls demonstrating an 11% and 8.5% increase in body weight for females and males, respectively. Quantitative real-time polymerase chain reaction (qPCR) was used to examine changes in gene expression in liver and adipose tissue in exposed offspring. qPCR suggested alterations in genes involved in lipid metabolism in exposed female offspring, which was consistent with the observed increased fat pad weights and hepatic triglycerides. This study represents the first report to quantify the transfer of an environmentally relevant concentration of TCC from mother to offspring in the mouse model and evaluate bio-distribution after exposure using AMS. Our findings suggest that early-life exposure to TCC may interfere with lipid metabolism and could have implications for human health.

BACKGROUND: Nanolipoprotein particles (NLPs) are discoidal, nanometer-sized particles comprised of self-assembled phospholipid membranes and apolipoproteins. NLPs assembled with human apolipoproteins have been used for myriad biotechnology applications, including membrane protein solubilization, drug delivery, and diagnostic imaging. To expand the repertoire of lipoproteins for these applications, insect apolipophorin-III (apoLp-III) was evaluated for the ability to form discretely-sized, homogeneous, and stable NLPs. METHODOLOGY: Four NLP populations distinct with regards to particle diameters (ranging in size from 10 nm to >25 nm) and lipid-to-apoLp-III ratios were readily isolated to high purity by size exclusion chromatography. Remodeling of the purified NLP species over time at 4 degrees C was monitored by native gel electrophoresis, size exclusion chromatography, and atomic force microscopy. Purified 20 nm NLPs displayed no remodeling and remained stable for over 1 year. Purified NLPs with 10 nm and 15 nm diameters ultimately remodeled into 20 nm NLPs over a period of months. Intra-particle chemical cross-linking of apoLp-III stabilized NLPs of all sizes. CONCLUSIONS: ApoLp-III-based NLPs can be readily prepared, purified, characterized, and stabilized, suggesting their utility for biotechnological applications.

Heterogeneity is a fact that plagues the characterization and application of many self-assembled biological constructs. The importance of obtaining particle homogeneity in biological assemblies is a critical goal, as bulk analysis tools often require identical species for reliable interpretation of the results—indeed, important tools of analysis such as x-ray diffraction typically require over 90% purity for effectiveness. This issue bears particular importance in the case of lipoproteins. Lipid-binding proteins known as apolipoproteins can self assemble with liposomes to form reconstituted high density lipoproteins (rHDLs) or nanolipoprotein particles (NLPs) when used for biotechnology applications such as the solubilization of membrane proteins. Typically, the apolipoprotein and phospholipids reactants are self assembled and even with careful assembly protocols the product often contains heterogeneous particles. In fact, size polydispersity in rHDLs and NLPs published in the literature are frequently observed, which may confound the accurate use of analytical methods. In this article, we demonstrate a procedure for producing a pure, monodisperse NLP subpopulation from a polydisperse self-assembly using size exclusion chromatography (SEC) coupled with high resolution particle imaging by atomic force microscopy (AFM). In addition, NLPs have been shown to self assemble both in the presence and absence of detergents such as cholate, yet the effects of cholate on NLP polydispersity and separation has not been systematically examined. Therefore, we examined the separation properties of NLPs assembled in both the absence and presence of cholate using SEC and native gel electrophoresis. From this analysis, NLPs prepared with and without cholate showed particles with well defined diameters spanning a similar size range. However, cholate was shown to have a dramatic affect on NLP separation by SEC and native gel electrophoresis. Furthermore, under conditions where different sized NLPs were not sufficiently separated or purified by SEC, AFM was used to deconvolute the elution pattern of different sized NLPs. From this analysis we were able to purify an NLP subpopulation to 90% size homogeneity by taking extremely fine elutions from the SEC. With this purity, we generate high quality NLP crystals that were over 100 μm in size with little precipitate, which could not be obtained utilizing the traditional size exclusion techniques. This purification procedure and the methods for validation are broadly applicable to other lipoprotein particles.

Self-assembly of purified apolipoproteins and phospholipids results in the formation of nanometer-sized lipoprotein complexes, referred to as nanolipoprotein particles (NLPs). These bilayer constructs are fully soluble in aqueous environments and hold great promise as a model system to aid in solubilizing membrane proteins. Size variability in the self-assembly process has been recognized for some time, yet limited studies have been conducted to examine this phenomenon. Understanding the source of this heterogeneity may lead to methods to mitigate heterogeneity or to control NLP size, which may be important for tailoring NLPs for specific membrane proteins. Here, we have used atomic force microscopy, ion mobility spectrometry, and transmission electron microscopy to quantify NLP size distributions on the single-particle scale, specifically focusing on assemblies with 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) and a recombinant apolipoprotein E variant containing the N-terminal 22 kDa fragment (E422k). Four discrete sizes of E422k/DMPC NLPs were identified by all three techniques, with diameters centered at ∼14.5, 19, 23.5, and 28 nm. Computer simulations suggest that these sizes are related to the structure and number of E422k lipoproteins surrounding the NLPs and particles with an odd number of lipoproteins are consistent with the double-belt model, in which at least one lipoprotein adopts a hairpin structure.

Abstract Nerve agents have experienced a resurgence in recent times with their use against civilian targets during the attacks in Syria (2012), the poisoning of Sergei and Yulia Skripal in the United Kingdom (2018) and Alexei Navalny in Russia (2020), strongly renewing the importance of antidote development against these lethal substances. The current standard treatment against their effects relies on the use of small molecule-based oximes that can efficiently restore acetylcholinesterase (AChE) activity. Despite their efficacy in reactivating AChE, the action of drugs like 2-pralidoxime (2-PAM) is primarily limited to the peripheral nervous system (PNS) and, thus, provides no significant protection to the central nervous system (CNS). This lack of action in the CNS stems from their ionic nature that, on one end makes them very powerful reactivators and on the other renders them ineffective at crossing the Blood Brain Barrier (BBB) to reach the CNS. In this report, we describe the use of an iterative approach composed of parallel chemical and in silico syntheses, computational modeling, and a battery of detailed in vitro and in vivo assays that resulted in the identification of a promising, novel CNS-permeable oxime reactivator. Additional experiments to determine acute and chronic toxicity are ongoing.

Osteoarthritis (OA) is a painful and debilitating disease characterized by the chronic and progressive degradation of articular cartilage. Post-traumatic OA (PTOA) is a secondary form of OA that develops in ~50% of cases of severe articular injury. Inflammation and re-occurring injury have been implicated as contributing to the progression of PTOA after the initial injury. However, there is very little known about external factors prior to injury that could affect the risk of PTOA development. To examine how the gut microbiome affects PTOA development we used a chronic antibiotic treatment regimen starting at weaning for six weeks prior to ACL rupture, in mice. A six-weeks post-injury histological examination showed more robust cartilage staining on the antibiotic (AB)-treated mice than the untreated controls (VEH), suggesting slower disease progression in AB cohorts. Injured joints also showed an increase in the presence of anti-inflammatory M2 macrophages in the AB group. Molecularly, the phenotype correlated with a significantly lower expression of inflammatory genes Tlr5, Ccl8, Cxcl13, and Foxo6 in the injured joints of AB-treated animals. Our results indicate that a reduced state of inflammation at the time of injury and a lower expression of Wnt signaling modulatory protein, Rspo1, caused by AB treatment can slow down or improve PTOA outcomes.

We tested environmental data from remote sensing and geographic information system maps as indicators of Sin Nombre virus (SNV) infections in deer mouse (Peromyscus maniculatus) populations in the Walker River Basin, Nevada and California. We determined by serologic testing the presence of SNV infections in deer mice from 144 field sites. We used remote sensing and geographic information systems data to characterize the vegetation type and density, elevation, slope, and hydrologic features of each site. The data retroactively predicted infection status of deer mice with up to 80% accuracy. If models of SNV temporal dynamics can be integrated with baseline spatial models, human risk for infection may be assessed with reasonable accuracy.