A stem-cell-derived platform enables complete Cryptosporidium development in vitro and genetic tractability

Despite being a frequent cause of severe diarrheal disease in infants and an opportunistic infection in immunocompromised patients, Cryptosporidium research has lagged due to a lack of facile experimental methods. Here, we describe a platform for complete life cycle development and long-term growth of C. parvum in vitro using air-liquid interface (ALI) cultures derived from intestinal epithelial stem cells. Transcriptomic profiling revealed that differentiating epithelial cells grown under ALI conditions undergo profound changes in metabolism and development that enable completion of the parasite life cycle in vitro. ALI cultures support parasite expansion \u3e 100-fold and generate viable oocysts that are transmissible in vitro and to mice, causing infection and animal death. Transgenic parasite lines created using CRISPR/Cas9 were used to complete a genetic cross in vitro, demonstrating Mendelian segregation of chromosomes during meiosis. ALI culture provides an accessible model that will enable innovative studies into Cryptosporidium biology and host interactions

Milk fat globule membrane isolated from buttermilk or whey cream and their lipid components inhibit infectivity of rotavirus in vitro

Milk fat is encapsulated in a milk fat globule membrane (MFGM) that contains bioactive glycoproteins and glycolipids. The MFGM inhibits infectivity of rotavirus (RV), activity that has been attributed to its glycoprotein and carbohydrate components. However, previous studies of proteins and oligosaccharides in the MGFM have not accounted for all the bioactivity associated with the complete MFGM. The lipid fraction of the MFGM accounts for half of its composition by weight, and we postulate that this fraction should be tested by itself to determine if it plays a role in antiviral activity. Herein, the anti-RV activity of an organic extract of MFGM was tested. Natural and whey buttermilk powders containing bovine MFGM enriched in polar lipids were prepared by microfiltration and supercritical fluid extraction treatment to reduce the triglyceride content of the powders. Lipid fractions were then extracted from the MFGM using both single- and dual-phase extraction methods. Whole MFGM and organic extracts were screened in MA-104 cells for anti-infective activity against a neuraminidase-sensitive rotavirus using a focus- forming unit assay. Dose-dependent inhibition was observed for whole buttermilk and cheese whey MFGM against the rotavirus. In general, buttermilk MFGM exhibited greater RV percentage inhibition than cheese whey MFGM. Organic-soluble anti-RV compounds were identified in bovine MFGM. The most active fraction, isolated by dual-phase extraction and iatrobead chromatography, was free of proteins and highly nonpolar. Further separation of this fraction in a less polar solvent (30:1 chloroform:methanol) resolved at least 5 lipid-containing compounds, which likely contribute to the anti-RV activity associated with bovine MFGM. In summary, lipid components associated with MFGM appear to contribute in large part to the anti-RV activity associated with the bovine MFGM

Smoothing With Couplings of Conditional Particle Filters

In state-space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and CI can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly informative observations, and for a realistic Lotka-Volterra model with an intractable transition density. Supplementary materials for this article are available online.Funding Agencies|Swedish Foundation for Strategic Research (SSF) via the project Probabilistic Modeling and Inference for Machine Learning [ICA16-0015]; Swedish Foundation for Strategic Research (SSF) via the project ASSEMBLE [RIT15-0012]; Swedish Research Council (VR) via the project Learning of Large-Scale Probabilistic Dynamical Models [2016-04278]; Swedish Research Council (VR) via the project NewLEADS-New Directions in Learning Dynamical Systems [621-2016-06079]; National Science Foundation [DMS-1712872]</p