Single-cell analysis of habituation in Stentor coeruleus.

Although learning is often viewed as a unique feature of organisms with complex nervous systems, single-celled organisms also demonstrate basic forms of learning. The giant ciliate Stentor coeruleus responds to mechanical stimuli by contracting into a compact shape, presumably as a defense mechanism. When a Stentor cell is repeatedly stimulated at a constant level of force, it will learn to ignore that stimulus but will still respond to stronger stimuli. Prior studies of habituation in Stentor reported a graded response, suggesting that cells transition through a continuous range of response probabilities. By analyzing single cells using an automated apparatus to deliver calibrated stimuli, we find that habituation occurs via a single step-like switch in contraction probability within each cell, with the graded response in a population arising from the random distribution of switching times in individual cells. This step-like response allows Stentor behavior to be represented by a simple two-state model whose parameters can be estimated from experimental measurements. We find that transition rates depend on stimulus force and also on the time between stimuli. The ability to measure the behavior of the same cell to the same stimulus allowed us to quantify the functional heterogeneity among single cells. Together, our results suggest that the behavior of Stentor is governed by a two-state stochastic machine whose transition rates are sensitive to the time series properties of the input stimuli.

Safety and efficacy of pioglitazone for the delay of cognitive impairment in people at risk of Alzheimer's disease (TOMMORROW): a prognostic biomarker study and a phase 3, randomised, double-blind, placebo-controlled trial.

BACKGROUND: The identification of people at risk of cognitive impairment is essential for improving recruitment in secondary prevention trials of Alzheimer's disease. We aimed to test and qualify a biomarker risk assignment algorithm (BRAA) to identify participants at risk of developing mild cognitive impairment due to Alzheimer's disease within 5 years, and to evaluate the safety and efficacy of low-dose pioglitazone to delay onset of mild cognitive impairment in these at-risk participants. METHODS: In this phase 3, multicentre, randomised, double-blind, placebo-controlled, parallel-group study, we enrolled cognitively healthy, community living participants aged 65-83 years from 57 academic affiliated and private research clinics in Australia, Germany, Switzerland, the UK, and the USA. By use of the BRAA, participants were grouped as high risk or low risk. Participants at high risk were randomly assigned 1:1 to receive oral pioglitazone (0·8 mg/day sustained release) or placebo, and all low-risk participants received placebo. Study investigators, site staff, sponsor personnel, and study participants were masked to genotype, risk assignment, and treatment assignment. The planned study duration was the time to accumulate 202 events of mild cognitive impairment due to Alzheimer's disease in White participants who were at high risk (the population on whom the genetic analyses that informed the BRAA development was done). Primary endpoints were time-to-event comparisons between participants at high risk and low risk given placebo (for the BRAA objective), and between participants at high risk given pioglitazone or placebo (for the efficacy objective). The primary analysis included all participants who were randomly assigned, received at least one dose of study drug, and had at least one valid post-baseline visit, with significance set at p=0·01. The safety analysis included all participants who were randomly assigned and received at least one dose of study medication. An efficacy futility analysis was planned for when approximately 33% of the anticipated events occurred in the high-risk, White, non-Hispanic or Latino group. This trial is registered with ClinicalTrials.gov, NCT01931566. FINDINGS: Between Aug 28, 2013, and Dec 21, 2015, we enrolled 3494 participants (3061 at high risk and 433 at low risk). Of those participants, 1545 were randomly assigned to pioglitazone and 1516 to placebo. 1104 participants discontinued treatment (464 assigned to the pioglitazone group, 501 in the placebo high risk group, and 139 in the placebo low risk group). 3399 participants had at least one dose of study drug or placebo and at least one post-baseline follow-up visit, and were included in the efficacy analysis. 3465 participants were included in the safety analysis (1531 assigned to the pioglitazone group, 1507 in the placebo high risk group, and 427 in the placebo low risk group). In the full analysis set, 46 (3·3%) of 1406 participants at high risk given placebo had mild cognitive impairment due to Alzheimer's disease, versus four (1·0%) of 402 participants at low risk given placebo (hazard ratio 3·26, 99% CI 0·85-12·45; p=0·023). 39 (2·7%) of 1430 participants at high risk given pioglitazone had mild cognitive impairment, versus 46 (3·3%) of 1406 participants at high risk given placebo (hazard ratio 0·80, 99% CI 0·45-1·40; p=0·307). In the safety analysis set, seven (0·5%) of 1531 participants at high risk given pioglitazone died versus 21 (1·4%) of 1507 participants at high risk given placebo. There were no other notable differences in adverse events between groups. The study was terminated in January, 2018, after failing to meet the non-futility threshold. INTERPRETATION: Pioglitazone did not delay the onset of mild cognitive impairment. The biomarker algorithm demonstrated a 3 times enrichment of events in the high risk placebo group compared with the low risk placebo group, but did not reach the pre-specified significance threshold. Because we did not complete the study as planned, findings can only be considered exploratory. The conduct of this study could prove useful to future clinical development strategies for Alzheimer's disease prevention studies. FUNDING: Takeda and Zinfandel.

You get what you screen for: on the value of fermentation characterization in high-throughput strain improvements in industrial settings.

While design and high-throughput build approaches in biotechnology have increasingly gained attention over the past decade, approaches to test strain performance in high-throughput have received less discussion in the literature. Here, we describe how fermentation characterization can be used to improve the overall efficiency of high-throughput DBTAL (design-build-test-analyze-learn) cycles in an industrial context. Fermentation characterization comprises an in-depth study of strain performance in a bioreactor setting and involves semi-frequent sampling and analytical measurement of substrates, cell densities and viabilities, and (by)products. We describe how fermentation characterization can be used to (1) improve (high-throughput) strain design approaches; (2) enable the development of bench-scale fermentation processes compatible with a wide diversity of strains; and (3) inform the development of high-throughput plate-based strain testing procedures for improved performance at larger scales.

The TOMMORROW study: Design of an Alzheimer's disease delay-of-onset clinical trial.

INTRODUCTION: Alzheimer's disease (AD) is a continuum with neuropathologies manifesting years before clinical symptoms; thus, AD research is attempting to identify more disease-modifying approaches to test treatments administered before full disease expression. Designing such trials in cognitively normal elderly individuals poses unique challenges. METHODS: The TOMMORROW study was a phase 3 double-blind, parallel-group study designed to support qualification of a novel genetic biomarker risk assignment algorithm (BRAA) and to assess efficacy and safety of low-dose pioglitazone to delay onset of mild cognitive impairment due to AD. Eligible participants were stratified based on the BRAA (using TOMM40 rs 10524523 genotype, Apolipoprotein E genotype, and age), with high-risk individuals receiving low-dose pioglitazone or placebo and low-risk individuals receiving placebo. The primary endpoint was time to the event of mild cognitive impairment due to AD. The primary objectives were to compare the primary endpoint between high- and low-risk placebo groups (for BRAA qualification) and between high-risk pioglitazone and high-risk placebo groups (for pioglitazone efficacy). Approximately 300 individuals were also asked to participate in a volumetric magnetic resonance imaging substudy at selected sites. RESULTS: The focus of this paper is on the design of the study; study results will be presented in a separate paper. DISCUSSION: The design of the TOMMORROW study addressed many key challenges to conducting a dual-objective phase 3 pivotal AD clinical trial in presymptomatic individuals. Experiences from planning and executing the TOMMORROW study may benefit future AD prevention/delay-of-onset trials.

A Toolkit for Rapid Modular Construction of Biological Circuits in Mammalian Cells.

The ability to rapidly assemble and prototype cellular circuits is vital for biological research and its applications in biotechnology and medicine. Current methods for the assembly of mammalian DNA circuits are laborious, slow, and expensive. Here we present the Mammalian ToolKit (MTK), a Golden Gate-based cloning toolkit for fast, reproducible, and versatile assembly of large DNA vectors and their implementation in mammalian models. The MTK consists of a curated library of characterized, modular parts that can be assembled into transcriptional units and further weaved into complex circuits. We showcase the capabilities of the MTK by using it to generate single-integration landing pads, create and deliver libraries of protein variants and sgRNAs, and iterate through dCas9-based prototype circuits. As a biological proof of concept, we demonstrate how the MTK can speed the generation of noninfectious viral circuits to enable rapid testing of pharmacological inhibitors of emerging viruses that pose a major threat to human health.

Real-Time Genetic Compensation Defines the Dynamic Demands of Feedback Control.

Biological signaling networks use feedback control to dynamically adjust their operation in real time. Traditional static genetic methods such as gene knockouts or rescue experiments can often identify the existence of feedback interactions but are unable to determine what feedback dynamics are required. Here, we implement a new strategy, closed-loop optogenetic compensation (CLOC), to address this problem. Using a custom-built hardware and software infrastructure, CLOC monitors, in real time, the output of a pathway deleted for a feedback regulator. A minimal model uses these measurements to calculate and deliver-on the fly-an optogenetically enabled transcriptional input designed to compensate for the effects of the feedback deletion. Application of CLOC to the yeast pheromone response pathway revealed surprisingly distinct dynamic requirements for three well-studied feedback regulators. CLOC, a marriage of control theory and traditional genetics, presents a broadly applicable methodology for defining the dynamic function of biological feedback regulators.

Robust Synthetic Circuits for Two-Dimensional Control of Gene Expression in Yeast.

Cellular phenotypes are the result of complex interactions between many components. Understanding and predicting the system level properties of the resulting networks requires the development of perturbation tools that can simultaneously and independently modulate multiple cellular variables. Here, we develop synthetic modules that use different arrangements of two transcriptional regulators to achieve either concurrent and independent control of the expression of two genes, or decoupled control of the mean and variance of a single gene. These modules constitute powerful tools to probe the quantitative attributes of network wiring and function.

Modeling gene expression evolution with an extended Ornstein-Uhlenbeck process accounting for within-species variation.

Much of the phenotypic variation observed between even closely related species may be driven by differences in gene expression levels. The current availability of reliable techniques like RNA-Seq, which can quantify expression levels across species, has enabled comparative studies. Ornstein-Uhlenbeck (OU) processes have been proposed to model gene expression evolution as they model both random drift and stabilizing selection and can be extended to model changes in selection regimes. The OU models provide a statistical framework that allows comparisons of specific hypotheses of selective regimes, including random drift, constrained drift, and expression level shifts. In this way, inferences may be made about the mode of selection acting on the expression level of a gene. We augment this model to include within-species expression variance, allowing for modeling of nonevolutionary expression variance that could be caused by individual genetic, environmental, or technical variation. Through simulations, we explore the reliability of parameter estimates and the extent to which different selective regimes can be distinguished using phylogenies of varying size using both the typical OU model and our extended model. We find that if individual variation is not accounted for, nonevolutionary expression variation is often mistaken for strong stabilizing selection. The methods presented in this article are increasingly relevant as comparative expression data becomes more available and researchers turn to expression as a primary evolving phenotype.

Building robust functionality in synthetic circuits using engineered feedback regulation.

The ability to engineer novel functionality within cells, to quantitatively control cellular circuits, and to manipulate the behaviors of populations, has many important applications in biotechnology and biomedicine. These applications are only beginning to be explored. In this review, we advocate the use of feedback control as an essential strategy for the engineering of robust homeostatic control of biological circuits and cellular populations. We also describe recent works where feedback control, implemented in silico or with biological components, was successfully employed for this purpose.

The evolution of gene expression levels in mammalian organs.

Changes in gene expression are thought to underlie many of the phenotypic differences between species. However, large-scale analyses of gene expression evolution were until recently prevented by technological limitations. Here we report the sequencing of polyadenylated RNA from six organs across ten species that represent all major mammalian lineages (placentals, marsupials and monotremes) and birds (the evolutionary outgroup), with the goal of understanding the dynamics of mammalian transcriptome evolution. We show that the rate of gene expression evolution varies among organs, lineages and chromosomes, owing to differences in selective pressures: transcriptome change was slow in nervous tissues and rapid in testes, slower in rodents than in apes and monotremes, and rapid for the X chromosome right after its formation. Although gene expression evolution in mammals was strongly shaped by purifying selection, we identify numerous potentially selectively driven expression switches, which occurred at different rates across lineages and tissues and which probably contributed to the specific organ biology of various mammals.