Coordination of meristem and boundary functions by transcription factors in the SHOOT MERISTEMLESS regulatory network

The Arabidopsis homeodomain transcription factor SHOOT MERISTEMLESS (STM) is crucial for shoot apical meristem (SAM) function, yet the components and structure of the STMgene regulatory network (GRN) are largely unknown. Here, we show that transcriptional regulators are overrepresented among STM-regulated genes and, using these as GRN components in Bayesian network analysis, we infer STM GRN associations and reveal regulatory relationships between STM and factors involved in multiple aspects of SAM function. These include hormone regulation, TCP-mediated control of cell differentiation, AIL/PLT-mediated regulation of pluripotency and phyllotaxis, and specification of meristem-organ boundary zones via CUC1. We demonstrate a direct positive transcriptional feedback loop between STM and CUC1, despite their distinct expression patterns in the meristem and organ boundary, respectively. Our further finding that STM activates expression of the CUC1-targeting microRNA miR164c combined with mathematical modelling provides a potential solution for this apparent contradiction, demonstrating that these proposed regulatory interactions coupled with STM mobility could be sufficient to provide a mechanism for CUC1 localisation at the meristem-organ boundary. Our findings highlight the central role for the STM GRN in coordinating SAM functions

Association of IGF1 and KDM5A polymorphisms with performance, fatness and carcass traits in chickens

Two functional and positional candidate genes were selected in a region of chicken chromosome 1 (GGA1), based on their biological roles, and also where several quantitative trait loci (QTL) have been mapped and associated with performance, fatness and carcass traits in chickens. The insulin-like growth factor 1 (IGF1) gene has been associated with several physiological functions related to growth. The lysine (K)-specific demethylase 5A (KDM5A) gene participates in the epigenetic regulation of genes involved with the cell cycle. Our objective was to find associations of selected single-nucleotide polymorphisms (SNPs) in these genes with performance, fatness and carcass traits in 165 F chickens from a resource population. In the IGF1 gene, 17 SNPs were detected, and in the KDM5A gene, nine SNPs were detected. IGF1 SNP c. 47673G > A was associated with body weight and haematocrit percentage, and also with feed intake and percentages of abdominal fat and gizzard genotype × sex interactions. KDM5A SNP c. 34208C > T genotype × sex interaction affected body weight, feed intake, percentages of abdominal fat (p = 0. 0001), carcass, gizzard and haematocrit. A strong association of the diplotype × sex interaction (p < 0. 0001) with abdominal fat was observed, and also associations with body weight, feed intake, percentages of carcass, drums and thighs, gizzard and haematocrit. Our findings suggest that the KDM5A gene might play an important role in the abdominal fat deposition in chickens. The IGF1 and KDM5A genes are strong candidates to explain the QTL mapped in this region of GGA1

Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

Essentially all biological processes depend on protein-protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner

Harmonics of Circadian Gene Transcription in Mammals

The circadian clock is a molecular and cellular oscillator found in most mammalian tissues that regulates rhythmic physiology and behavior. Numerous investigations have addressed the contribution of circadian rhythmicity to cellular, organ, and organismal physiology. We recently developed a method to look at transcriptional oscillations with unprecedented precision and accuracy using high-density time sampling. Here, we report a comparison of oscillating transcription from mouse liver, NIH3T3, and U2OS cells. Several surprising observations resulted from this study, including a 100-fold difference in the number of cycling transcripts in autonomous cellular models of the oscillator versus tissues harvested from intact mice. Strikingly, we found two clusters of genes that cycle at the second and third harmonic of circadian rhythmicity in liver, but not cultured cells. Validation experiments show that 12-hour oscillatory transcripts occur in several other peripheral tissues as well including heart, kidney, and lungs. These harmonics are lost ex vivo, as well as under restricted feeding conditions. Taken in sum, these studies illustrate the importance of time sampling with respect to multiple testing, suggest caution in use of autonomous cellular models to study clock output, and demonstrate the existence of harmonics of circadian gene expression in the mouse

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

A system for generating reversible knockout cell lines

AbstractA powerful use of gene editing technology is the generation of genetic-null (knockout) mutants of human immortalized cell lines. However, a problem that arises in the process of generating such edited lines is that to ensure edit homogeneity, clonal selection must often be performed and, given the genomic instability of immortalized cell lines and the high division number inherent to the clonal selection process, the resulting clone can exhibit genotypic and phenotypic differences from the parental line. Here, we present a system that allows for the generation of genetic null cell lines with an excisable cassette. This system allows for the generation of suitable controls without the need of further rounds of clonal selection.</jats:p

The effect of AMPK activation on Alzheimer's-like symptoms in APP mice

Research toward understanding Alzheimer's disease has revealed that it is strongly linked with metabolic dysfunction, such as that found in Type II Diabetes. For instance, diabetic patients have a doubled risk of developing AD, while Alzheimer's patients have disrupted insulin signaling in the brain. Moreover, some lifestyle modifications beneficial to diabetic patients - specifically, increased physical activity and caloric restriction - have shown promise as tools to manage AD. Both of these interventions create an energy deficit whereby the cellular energy sensor AMPK becomes activated. In turn, AMPK activation promotes energy availability, insulin sensitivity and cellular survival during times of stress. We activated AMPK both pharmacologically and genetically in a transgenic mouse model of AD and measured the effect on learning and memory function by the Morris water maze. Surprisingly, our results revealed a gender- specific response in Alzheimer's phenotypes. Pharmacological activation of AMPK by the anti-diabetic drug metformin in male APP mice worsened learning and memory function. This finding was reproduced with a genetic model of AMPK activation, requiring only liver- specific activity to occur. Remarkably, activation of AMPK in this murine model, either by genetic means or metformin treatment, had beneficial effects for female animals. The results herein demonstrate the effect of AMPK activation in the APP mouse model of AD, the gender-specific differences in cognitive function in response to treatment, the importance of liver function in mental health and the need to assess these factors in human patients as there are currently millions of patients already taking metformin that may be altering their risk of Alzheimer's diseas