Metabolic characterization of directly reprogrammed renal tubular epithelial cells (iRECs)

Fibroblasts can be directly reprogrammed to induced renal tubular epithelial cells (iRECs) using four transcription factors. These engineered cells may be used for disease modeling, cell replacement therapy or drug and toxicity testing. Direct reprogramming induces drastic changes in the transcriptional landscape, protein expression, morphological and functional properties of cells. However, how the metabolome is changed by reprogramming and to what degree it resembles the target cell type remains unknown. Using untargeted gas chromatography-mass spectrometry (GC-MS) and targeted liquid chromatography-MS, we characterized the metabolome of mouse embryonic fibroblasts (MEFs), iRECs, mIMCD-3 cells, and whole kidneys. Metabolic fingerprinting can distinguish each cell type reliably, revealing iRECs are most similar to mIMCD-3 cells and clearly separate from MEFs used for reprogramming. Treatment with the cytotoxic drug cisplatin induced typical changes in the metabolic profile of iRECs commonly occurring in acute renal injury. Interestingly, metabolites in the medium of iRECs, but not of mIMCD-3 cells or fibroblast could distinguish treated and non-treated cells by cluster analysis. In conclusion, direct reprogramming of fibroblasts into renal tubular epithelial cells strongly influences the metabolome of engineered cells, suggesting that metabolic profiling may aid in establishing iRECs as in vitro models for nephrotoxicity testing in the future

Is It Rational to Assume that Infants Imitate Rationally? A Theoretical Analysis and Critique

It has been suggested that preverbal infants evaluate the efficiency of others' actions (by applying a principle of rational action) and that they imitate others' actions rationally. The present contribution presents a conceptual analysis of the claim that preverbal infants imitate rationally. It shows that this ability rests on at least three assumptions: that infants are able to perceive others' action capabilities, that infants reason about and conceptually represent their own bodies, and that infants are able to think counterfactually. It is argued that none of these three abilities is in place during infancy. Furthermore, it is shown that the idea of a principle of rational action suffers from two fallacies. As a consequence, is it suggested that it is not rational to assume that infants imitate rationally. Copyright (C) 2012 S. Karger AG, Base

Tuning the 3D microenvironment of reprogrammed tubule cells enhances biomimetic modeling of polycystic kidney disease

Renal tubular cells frequently lose differentiation markers and physiological properties when propagated in conventional cell culture conditions. Embedding cells in 3D microenvironments or controlling their 3D assembly by bioprinting can enhance their physiological properties, which is beneficial for modeling diseases in vitro. A potential cellular source for modeling renal tubular physiology and kidney diseases in vitro are directly reprogrammed induced renal tubular epithelial cells (iRECs). iRECs were cultured in various biomaterials and as bioprinted tubular structures. They showed high compatibility with the embedding substrates and dispensing methods. The morphology of multicellular aggregates was substantially influenced by the 3D microenvironment. Transcriptomic analyses revealed signatures of differentially expressed genes specific to each of the selected biomaterials. Using a new cellular model for autosomal-dominant polycystic kidney disease, Pkd1(−/−) iRECs showed disrupted morphology in bioprinted tubules and a marked upregulation of the Aldehyde dehydrogenase 1a1 (Aldh1a1). In conclusion, 3D microenvironments strongly influence the morphology and expression profiles of iRECs, help to unmask disease phenotypes, and can be adapted to experimental demands. Combining a direct reprogramming approach with appropriate biomaterials will facilitate construction of biomimetic kidney tubules and disease models at the microscale

Value chain transformation: Taking stock of WorldFish research on value chains and markets

The goal of WorldFish’s research on markets and value chains is to increase the benefits to resource-poor people from fisheries and aquaculture value chains by researching (1) key barriers to resource-poor men, women and other marginalized groups gaining greater benefits from participation in value chains, including barriers related to the availability, affordability and quality of nutrient-rich fish for resource-poor consumers; (2) interventions to overcome those barriers; and (3) mechanisms that are most effective for scaling up of value chain interventions. This paper aims to promote and document learning across WorldFish’s value chain research efforts in Asia and Africa. It has three main objectives: (1) to take stock of WorldFish’s past and ongoing research on value chains; (2) to draw out commonalities and differences between these projects; and (3) to provide a synthesis of some learning that can guide future work

Deep learning is widely applicable to phenotyping embryonic development and disease

Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can automate segmentation tasks in various imaging modalities, and we quantify phenotypes of altered renal, neural and craniofacial development in Xenopus embryos in comparison with normal variability. We demonstrate the utility of this approach in embryos with polycystic kidneys (pkd1 and pkd2) and craniofacial dysmorphia (six1). We highlight how in toto light-sheet microscopy facilitates accurate reconstruction of brain and craniofacial structures within X. tropicalis embryos upon dyrk1a and six1 loss of function or treatment with retinoic acid inhibitors. These tools increase the sensitivity and throughput of evaluating developmental malformations caused by chemical or genetic disruption. Furthermore, we provide a library of pre-trained networks and detailed instructions for applying deep learning to the reader's own datasets. We demonstrate the versatility, precision and scalability of deep neural network phenotyping on embryonic disease models. By combining light-sheet microscopy and deep learning, we provide a framework for higher-throughput characterization of embryonic model organisms. This article has an associated 'The people behind the papers' interview

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data