The in vivo application of CRISPR/Cas-based DNA editing technology will require the development of efficient delivery methods that likely will be dependent on adeno-associated virus (AAV)-based viral ...vectors. However, AAV vectors have only a modest, ∼4.7-kb packaging capacity, which will necessitate the identification and characterization of highly active Cas9 proteins that are substantially smaller than the prototypic Streptococcus pyogenes Cas9 protein, which covers ∼4.2 kb of coding sequence, as well as the development of single guide RNA (sgRNA) expression cassettes substantially smaller than the current ∼360 bp size. Here, we report that small, ∼70-bp tRNA promoters can be used to express high levels of tRNA:sgRNA fusion transcripts that are efficiently and precisely cleaved by endogenous tRNase Z to release fully functional sgRNAs. Importantly, cells stably expressing functional tRNA:sgRNA precursors did not show a detectable change in the level of endogenous tRNA expression. This novel sgRNA expression strategy should greatly facilitate the construction of effective AAV-based Cas9/sgRNA vectors for future in vivo use.
Previous work has demonstrated that the epitranscriptomic addition of m
A to viral transcripts can promote the replication and pathogenicity of a wide range of DNA and RNA viruses, including HIV-1, ...yet the underlying mechanisms responsible for this effect have remained unclear. It is known that m
A function is largely mediated by cellular m
A binding proteins or readers, yet how these regulate viral gene expression in general, and HIV-1 gene expression in particular, has been controversial. Here, we confirm that m
A addition indeed regulates HIV-1 RNA expression and demonstrate that this effect is largely mediated by the nuclear m
A reader YTHDC1 and the cytoplasmic m
A reader YTHDF2. Both YTHDC1 and YTHDF2 bind to multiple distinct and overlapping sites on the HIV-1 RNA genome, with YTHDC1 recruitment serving to regulate the alternative splicing of HIV-1 RNAs. Unexpectedly, while YTHDF2 binding to m
A residues present on cellular mRNAs resulted in their destabilization as previously reported, YTHDF2 binding to m
A sites on HIV-1 transcripts resulted in a marked increase in the stability of these viral RNAs. Thus, YTHDF2 binding can exert diametrically opposite effects on RNA stability, depending on RNA sequence context.
In classical study designs, the aim is often to learn about the effects of a treatment or intervention on a single outcome; in many modern studies, however, data on multiple outcomes are collected ...and it is of interest to explore effects on multiple outcomes simultaneously. Such designs can be particularly useful in patient-centered research, where different outcomes might be more or less important to different patients. In this paper, we propose scaled effect measures (via potential outcomes) that translate effects on multiple outcomes to a common scale, using mean-variance and median-interquartile range based standardizations. We present efficient, nonparametric, doubly robust methods for estimating these scaled effects (and weighted average summary measures), and for testing the null hypothesis that treatment affects all outcomes equally. We also discuss methods for exploring how treatment effects depend on covariates (i.e., effect modification). In addition to describing efficiency theory for our estimands and the asymptotic behavior of our estimators, we illustrate the methods in a simulation study and a data analysis. Importantly, and in contrast to much of the literature concerning effects on multiple outcomes, our methods are nonparametric and can be used not only in randomized trials to yield increased efficiency, but also in observational studies with high-dimensional covariates to reduce confounding bias.
Instrumental variable methods using dynamic interventions Mauro, Jacqueline A.; Kennedy, Edward H.; Nagin, Daniel
Journal of the Royal Statistical Society. Series A, Statistics in society,
October 2020, 2020-10-01, 20201001, Letnik:
183, Številka:
4
Journal Article
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Summary
Recent work on dynamic interventions has greatly expanded the range of causal questions that researchers can study. Simultaneously, this work has weakened identifying assumptions, yielding ...effects that are more practically relevant. Most work in dynamic interventions to date has focused on settings where we directly alter some unconfounded treatment of interest. In policy analysis, decision makers rarely have this level of control over behaviours or access to experimental data. Instead, they are often faced with treatments that they can affect only indirectly and whose effects must be learned from observational data. We propose new estimands and estimators of causal effects based on dynamic interventions with instrumental variables. This method does not rely on parametric models and does not require an experiment. Instead, we estimate the effect of treatment induced by a dynamic intervention on an instrument. This robustness should reassure policy makers that these estimates can be used to inform policy effectively. We demonstrate the usefulness of this estimation strategy in a case‐study examining the effect of visitation on recidivism.
Abstract
Effect measure modification is often evaluated using parametric models. These models, although efficient when correctly specified, make strong parametric assumptions. While nonparametric ...models avoid important functional form assumptions, they often require larger samples to achieve a given accuracy. We conducted a simulation study to evaluate performance tradeoffs between correctly specified parametric and nonparametric models to detect effect modification of a binary exposure by both binary and continuous modifiers. We evaluated generalized linear models and doubly robust (DR) estimators, with and without sample splitting. Continuous modifiers were modeled with cubic splines, fractional polynomials, and nonparametric DR-learner. For binary modifiers, generalized linear models showed the greatest power to detect effect modification, ranging from 0.42 to 1.00 in the worst and best scenario, respectively. Augmented inverse probability weighting had the lowest power, with an increase of 23% when using sample splitting. For continuous modifiers, the DR-learner was comparable to flexible parametric models in capturing quadratic and nonlinear monotonic functions. However, for nonlinear, nonmonotonic functions, the DR-learner had lower integrated bias than splines and fractional polynomials, with values of 141.3, 251.7, and 209.0, respectively. Our findings suggest comparable performance between nonparametric and correctly specified parametric models in evaluating effect modification.
Conditional effect estimation has great scientific and policy importance because interventions may impact subjects differently depending on their characteristics. Most research has focused on ...estimating the conditional average treatment effect (CATE). However, identification of the CATE requires that all subjects have a non-zero probability of receiving treatment, or positivity, which may be unrealistic in practice. Instead, we propose conditional effects based on incremental propensity score interventions, which are stochastic interventions where the odds of treatment are multiplied by some factor. These effects do not require positivity for identification and can be better suited for modeling scenarios in which people cannot be forced into treatment. We develop a projection approach and a flexible nonparametric estimator that can each estimate all the conditional effects we propose and derive model-agnostic error guarantees showing that both estimators satisfy a form of double robustness. Further, we propose a summary of treatment effect heterogeneity and a test for any effect heterogeneity based on the variance of a conditional derivative effect and derive a nonparametric estimator that also satisfies a form of double robustness. Finally, we demonstrate our estimators by analyzing the effect of intensive care unit admission on mortality using a dataset from the (SPOT)light study.
Abstract SAMHD1 limits HIV-1 infection in non-dividing myeloid cells by decreasing intracellular dNTP pools. HIV-1 restriction by SAMHD1 in these cells likely prevents activation of antiviral immune ...responses and modulates viral pathogenesis, thus highlighting a critical role of SAMHD1 in HIV-1 physiopathology. Here, we explored the function of SAMHD1 in regulating cell proliferation, cell cycle progression and apoptosis in monocytic THP-1 cells. Using the CRISPR/Cas9 technology, we generated THP-1 cells with stable SAMHD1 knockout. We found that silencing of SAMHD1 in cycling cells stimulates cell proliferation, redistributes cell cycle population in the G1 /G0 phase and reduces apoptosis. These alterations correlated with increased dNTP levels and more efficient HIV-1 infection in dividing SAMHD1 knockout cells relative to control. Our results suggest that SAMHD1, through its dNTPase activity, affects cell proliferation, cell cycle distribution and apoptosis, and emphasize a key role of SAMHD1 in the interplay between cell cycle regulation and HIV-1 infection.
We biochemically simulated HIV-1 DNA polymerization in physiological nucleotide pools found in two HIV-1 target cell types: terminally differentiated/non-dividing macrophages and activated/dividing ...CD4+ T cells. Quantitative tandem mass spectrometry shows that macrophages harbor 22–320-fold lower dNTP concentrations and a greater disparity between ribonucleoside triphosphate (rNTP) and dNTP concentrations than dividing target cells. A biochemical simulation of HIV-1 reverse transcription revealed that rNTPs are efficiently incorporated into DNA in the macrophage but not in the T cell environment. This implies that HIV-1 incorporates rNTPs during viral replication in macrophages and also predicts that rNTP chain terminators lacking a 3′-OH should inhibit HIV-1 reverse transcription in macrophages. Indeed, 3′-deoxyadenosine inhibits HIV-1 proviral DNA synthesis in human macrophages more efficiently than in CD4+ T cells. This study reveals that the biochemical landscape of HIV-1 replication in macrophages is unique and that ribonucleoside chain terminators may be a new class of anti-HIV-1 agents specifically targeting viral macrophage infection.
Effect modification occurs when the effect of a treatment on an outcome differsaccording to the level of some pre-treatment variable (the effect modifier). Assessing an effect modifier is not a ...straight-forward task even for a subject matter expert. In this paper, we propose a two-stageprocedure to automatically selecteffect modifying variables in a Marginal Structural Model (MSM) with a single time point exposure based on the two nuisance quantities (the conditionaloutcome expectation and propensity score). We highlight the performance of our proposal in a simulation study. Finally, to illustrate tractability of our proposed methods, we apply them to analyze a set of pregnancy data. We estimate the conditional expected difference in the counterfactual birth weight if all women were exposed to inhaled corticosteroids during pregnancy versus the counterfactual birthweight if all women were not, using data from asthma medications during pregnancy.
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