Long Term Culture of Human Kidney Proximal Tubule Epithelial Cells Maintains Lineage Functions and Serves as an Ex vivo Model for Coronavirus Associated Kidney Injury

The mechanism of how SARS-CoV-2 causes severe multi-organ failure is largely unknown. Acute kidney injury(AKI) is one of the frequent organ damage in severe COVID-19 patients. Previous studies have shown that human renal tubule cells could be the potential host cells targeted by SARS-CoV-2. Traditional cancer cell lines or immortalized cell lines are genetically and phenotypically different from host cells. Animal models are widely used, but often fail to reflect a physiological and pathogenic status because of species tropisms. There is an unmet need for normal human epithelial cells for disease modeling. In this study, we successfully established long term cultures of normal human kidney proximal tubule epithelial cells(KPTECs) in 2 D and 3 D culture systems using conditional reprogramming(CR) and organoids techniques.These cells had the ability to differentiate and repair DNA damage, and showed no transforming property. Importantly, the CR KPTECs maintained lineage function with expression of specific transporters(SLC34 A3 and cubilin). They also expressed angiotensin-converting enzyme 2(ACE2), a receptor for SARS-CoV and SARS-CoV-2. In contrast, cancer cell line did not express endogenous SLC34 A3, cubilin and ACE2. Very interestingly, ACE2 expression was around twofold higher in 3 D organoids culture compared to that in 2 D CR culture condition. Pseudovirion assays demonstrated that SARS-CoV spike(S) protein was able to enter CR cells with luciferase reporter. This integrated 2 D CR and 3 D organoid cultures provide a physiological ex vivo model to study kidney functions, innate immune response of kidney cells to viruses, and a novel platform for drug discovery and safety evaluation.     

Testing for spatial correlation in nonstationary binary data, with application to aberrant crypt foci in colon carcinogenesis

In an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen, with half the animals also being exposed to radiation. Spatially, we measured the existence of what are referred to as aberrant crypt foci (ACF), namely, morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score-type methods based upon the Matern and conditionally autoregressive (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score-type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran's test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage.     

Structural time series models with feedback mechanisms

Structural time series models have applications in many different fields such as biology, economics, and meteorology. A structural times series model can be represented as a state-space model where the states of the system represent the unobserved components and the structural parameters have clear interpretations. This paper introduces a class of structural time series models that incorporate feedback from the latent components of the history. An iterative procedure is proposed for estimation. These models allow flexible and robust feedback mechanisms, have clear interpretations, and have a computationally efficient estimation procedure. They are applied to hormone data to characterize hormone secretion and to explore a potential feedback mechanism.     

Parametric spatial cure rate models for interval-censored time-to-relapse data

Several recent papers (e.g., Chen, Ibrahim, and Sinha, 1999, Journal of the American Statistical Association 94, 909-919; Ibrahim, Chen, and Sinha, 2001a, Biometrics 57, 383-388) have described statistical methods for use with time-to-event data featuring a surviving fraction (i.e., a proportion of the population that never experiences the event). Such cure rate models and their multivariate generalizations are quite useful in studies of multiple diseases to which an individual may never succumb, or from which an individual may reasonably be expected to recover following treatment (e.g., various types of cancer). In this article we extend these models to allow for spatial correlation (estimable via zip code identifiers for the subjects) as well as interval censoring. Our approach is Bayesian, where posterior summaries are obtained via a hybrid Markov chain Monte Carlo algorithm. We compare across a broad collection of rather high-dimensional hierarchical models using the deviance information criterion, a tool recently developed for just this purpose. We apply our approach to the analysis of a smoking cessation study where the subjects reside in 53 southeastern Minnesota zip codes. In addition to the usual posterior estimates, our approach yields smoothed zip code level maps of model parameters related to the relapse rates over time and the ultimate proportion of quitters (the cure rates).     

Exploring dependence with data on spatial lattices

Summary .  The application of Markov random field models to problems involving spatial data on lattice systems requires decisions regarding a number of important aspects of model structure. Existing exploratory techniques appropriate for spatial data do not provide direct guidance to an investigator about these decisions. We introduce an exploratory quantity that is directly tied to the structure of Markov random field models based on one-parameter exponential family conditional distributions. This exploratory diagnostic is shown to be a meaningful statistic that can inform decisions involved in modeling spatial structure with statistical dependence terms. In this article, we develop the diagnostic, illustrate its use in guiding modeling decisions with simulated examples, and reexamine a previously published application.     

The relationship between virologic and immunologic responses in AIDS clinical research using mixed-effects varying-coefficient models with measurement error

In this article we study the relationship between virologic and immunologic responses in AIDS clinical trials. Since plasma HIV RNA copies (viral load) and CD4+ cell counts are crucial virologic and immunologic markers for HIV infection, it is important to study their relationship during HIV/AIDS treatment. We propose a mixed-effects varying-coefficient model based on an exploratory analysis of data from a clinical trial. Since both viral load and CD4+ cell counts are subject to measurement error, we also consider the measurement error problem in covariates in our model. The regression spline method is proposed for inference for parameters in the proposed model. The regression spline method transforms the unknown nonparametric components into parametric functions. It is relatively simple to implement using readily available software, and parameter inference can be developed from standard parametric models. We apply the proposed models and methods to an AIDS clinical study. From this study, we find an interesting relationship between viral load and CD4+ cell counts during antiviral treatments. Biological interpretations and clinical implications are discussed.     

Blood-brain barrier is involved in the efflux transport of a neuroactive steroid, dehydroepiandrosterone sulfate, via organic anion transporting polypeptide 2

We have investigated the transport characteristics of dehydroepiandrosterone sulfate (DHEAS), a neuroactive steroid, at the blood-brain barrier (BBB) in a series of functional in vivo and in vitro studies. The apparent BBB efflux rate constant of [(3)H]DHEAS evaluated by the brain efflux index method was 2.68 x 10(-2) min(-1). DHEAS efflux transport was a saturable process with a Michaelis constant (K:(m)) of 32.6 microM: Significant amounts of [(3)H]DHEAS were determined in the jugular venous plasma by HPLC, providing direct evidence that most of the DHEAS is transported in intact form from brain to the circulating blood across the BBB. This efflux transport of [(3)H]DHEAS was significantly inhibited by common rat organic anion-transporting polypeptide (oatp) substrates such as taurocholate, cholate, sulfobromophthalein, and estrone-3-sulfate. Moreover, the apparent efflux clearance of [(3)H]DHEAS across the BBB (118 microl/min-g of brain) was 10.4-fold greater than its influx clearance estimated by the in situ brain perfusion technique (11.4 microl/min-g of brain), suggesting that DHEAS is predominantly transported from the brain to blood across the BBB. In cellular uptake studies using a conditionally immortalized mouse brain capillary endothelial cell line (TM-BBB4), [(3)H]DHEAS uptake by TM-BBB4 cells exhibited a concentration dependence with a K:(m) of 34.4 microM: and was significantly inhibited by the oatp2-specific substrate digoxin. Conversely, [(3)H]digoxin uptake by TM-BBB4 cells was significantly inhibited by DHEAS. Moreover, the net uptake of [(3)H]DHEAS at 30 min was significantly increased under ATP-depleted conditions, suggesting that an energy-dependent efflux process may also be involved in TM-BBB4. RT-PCR and sequence analysis suggest that an oatp2 is expressed in TM-BBB4 cells. In conclusion, DHEAS efflux transport takes place across the BBB, and studies involving in vitro DHEAS uptake and RT-PCR suggest that there is oatp2-mediated DHEAS transport at the BBB.