Selection rules in symmetry-broken systems by symmetries in synthetic dimensions

Selection rules are often considered a hallmark of symmetry. Here, we employ symmetry-breaking degrees of freedom as synthetic dimensions to demonstrate that symmetry-broken systems systematically exhibit a specific class of symmetries and selection rules. These selection rules constrain the scaling of a system’s observables (non-perturbatively) as it transitions from symmetric to symmetry-broken. Specifically, we drive bi-elliptical high harmonic generation (HHG), and observe that the scaling of the HHG spectrum with the pump’s ellipticities is constrained by selection rules corresponding to symmetries in synthetic dimensions. We then show the generality of this phenomenon by analyzing periodically-driven (Floquet) systems subject to two driving fields, tabulating the resulting synthetic symmetries for (2 + 1)D Floquet groups, and deriving the corresponding selection rules for high harmonic generation (HHG) and other phenomena. The presented class of symmetries and selection rules opens routes for ultrafast spectroscopy of phonon-polarization, spin-orbit coupling, symmetry-protected dark bands, and more

Heritable genome editing in C. elegans via a CRISPR-Cas9 system

CRISPR-Cas systems have been used with single-guide RNAs for accurate gene disruption and conversion in multiple biological systems. Here we report the use of the endonuclease Cas9 to target genomic sequences in the C. elegans germline, utilizing single-guide RNAs that are expressed from a U6 small nuclear RNA promoter. Our results demonstrate that targeted, heritable genetic alterations can be achieved in C. elegans, providing a convenient and effective approach for generating loss-of-function mutants

Automating a framework to extract and analyse transport related social media content: The potential and the challenges

Harnessing the potential of new generation transport data and increasing public participation are high on the agenda for transport stakeholders and the broader community. The initial phase in the program of research reported here proposed a framework for mining transport-related information from social media, demonstrated and evaluated it using transport-related tweets associated with three football matches as case studies. The goal of this paper is to extend and complement the previous published studies. It reports an extended analysis of the research results, highlighting and elaborating the challenges that need to be addressed before a large-scale application of the framework can take place. The focus is specifically on the automatic harvesting of relevant, valuable information from Twitter. The results from automatically mining transport related messages in two scenarios are presented i.e. with a small-scale labelled dataset and with a large-scale dataset of 3.7 m tweets. Tweets authored by individuals that mention a need for transport, express an opinion about transport services or report an event, with respect to different transport modes, were mined. The challenges faced in automatically analysing Twitter messages, written in Twitter’s specific language, are illustrated. The results presented show a strong degree of success in the identification of transport related tweets, with similar success in identifying tweets that expressed an opinion about transport services. The identification of tweets that expressed a need for transport services or reported an event was more challenging, a finding mirrored during the human based message annotation process. Overall, the results demonstrate the potential of automatic extraction of valuable information from tweets while pointing to areas where challenges were encountered and additional research is needed. The impact of a successful solution to these challenges (thereby creating efficient harvesting systems) would be to enable travellers to participate more effectively in the improvement of transport services

Rapid generation of endogenously driven transcriptional reporters in cells through CRISPR/Cas9

CRISPR/Cas9 technologies have been employed for genome editing to achieve gene knockouts and knock-ins in somatic cells. Similarly, certain endogenous genes have been tagged with fluorescent proteins. Often, the detection of tagged proteins requires high expression and sophisticated tools such as confocal microscopy and flow cytometry. Therefore, a simple, sensitive and robust transcriptional reporter system driven by endogenous promoter for studies into transcriptional regulation is desirable. We report a CRISPR/Cas9-based methodology for rapidly integrating a firefly luciferase gene in somatic cells under the control of endogenous promoter, using the TGFβ-responsive gene PAI-1. Our strategy employed a polycistronic cassette containing a non-fused GFP protein to ensure the detection of transgene delivery and rapid isolation of positive clones. We demonstrate that firefly luciferase cDNA can be efficiently delivered downstream of the promoter of the TGFβ-responsive gene PAI-1. Using chemical and genetic regulators of TGFβ signalling, we show that it mimics the transcriptional regulation of endogenous PAI-1 expression. Our unique approach has the potential to expedite studies on transcription of any gene in the context of its native chromatin landscape in somatic cells, allowing for robust high-throughput chemical and genetic screens

The Genographic Project Public Participation Mitochondrial DNA Database

The Genographic Project is studying the genetic signatures of ancient human migrations and creating an open-source research database. It allows members of the public to participate in a real-time anthropological genetics study by submitting personal samples for analysis and donating the genetic results to the database. We report our experience from the first 18 months of public participation in the Genographic Project, during which we have created the largest standardized human mitochondrial DNA (mtDNA) database ever collected, comprising 78,590 genotypes. Here, we detail our genotyping and quality assurance protocols including direct sequencing of the mtDNA HVS-I, genotyping of 22 coding-region SNPs, and a series of computational quality checks based on phylogenetic principles. This database is very informative with respect to mtDNA phylogeny and mutational dynamics, and its size allows us to develop a nearest neighbor–based methodology for mtDNA haplogroup prediction based on HVS-I motifs that is superior to classic rule-based approaches. We make available to the scientific community and general public two new resources: a periodically updated database comprising all data donated by participants, and the nearest neighbor haplogroup prediction tool

Tissue-specific regulation of mouse MicroRNA genes in endoderm-derived tissues

MicroRNAs fine-tune the activity of hundreds of protein-coding genes. The identification of tissue-specific microRNAs and their promoters has been constrained by the limited sensitivity of prior microRNA quantification methods. Here, we determine the entire microRNAome of three endoderm-derived tissues, liver, jejunum and pancreas, using ultra-high throughput sequencing. Although many microRNA genes are expressed at comparable levels, 162 microRNAs exhibited striking tissue-specificity. After mapping the putative promoters for these microRNA genes using H3K4me3 histone occupancy, we analyzed the regulatory modules of 63 microRNAs differentially expressed between liver and jejunum or pancreas. We determined that the same transcriptional regulatory mechanisms govern tissue-specific gene expression of both mRNA and microRNA encoding genes in mammals

Continuous and Long-Term Volume Measurements with a Commercial Coulter Counter

We demonstrate a method to enhance the time resolution of a commercial Coulter counter and enable continuous and long-term cell size measurements for growth rate analyses essential to understanding basic cellular processes, such as cell size regulation and cell cycle progression. Our simple modifications to a commercial Coulter counter create controllable cell culture conditions within the sample compartment and combine temperature control with necessary adaptations to achieve measurement stability over several hours. We also wrote custom software, detailed here, to analyze instrument data files collected by either this continuous method or standard, periodic sampling. We use the continuous method to measure the growth rate of yeast in G1 during a prolonged arrest and, in different samples, the dependency of growth rate on cell size and cell cycle position in arrested and proliferating cells. We also quantify with high time resolution the response of mouse lymphoblast cell culture to drug treatment. This method provides a technique for continuous measurement of cell size that is applicable to a large variety of cell types and greatly expands the set of analysis tools available for the Coulter counter.National Institutes of Health (U.S.) (EUREKA Exceptional, Unconventional Research Enabling Knowledge Acceleration (R01GM085457))National Institutes of Health (U.S.) (contract R21CA137695)National Cancer Institute (U.S.). Physical Sciences-Oncology Center (U54CA143874

A Discrete Time Model for the Analysis of Medium-Throughput C. elegans Growth Data

BACKGROUND: As part of a program to predict the toxicity of environmental agents on human health using alternative methods, several in vivo high- and medium-throughput assays are being developed that use C. elegans as a model organism. C. elegans-based toxicological assays utilize the COPAS Biosort flow sorting system that can rapidly measure size, extinction (EXT) and time-of-flight (TOF), of individual nematodes. The use of this technology requires the development of mathematical and statistical tools to properly analyze the large volumes of biological data. METHODOLOGY/PRINCIPAL FINDINGS: Findings A Markov model was developed that predicts the growth of populations of C. elegans. The model was developed using observations from a 60 h growth study in which five cohorts of 300 nematodes each were aspirated and measured every 12 h. Frequency distributions of log(EXT) measurements that were made when loading C. elegans L1 larvae into 96 well plates (t = 0 h) were used by the model to predict the frequency distributions of the same set of nematodes when measured at 12 h intervals. The model prediction coincided well with the biological observations confirming the validity of the model. The model was also applied to log(TOF) measurements following an adaptation. The adaptation accounted for variability in TOF measurements associated with potential curling or shortening of the nematodes as they passed through the flow cell of the Biosort. By providing accurate estimates of frequencies of EXT or TOF measurements following varying growth periods, the model was able to estimate growth rates. Best model fits showed that C. elegans did not grow at a constant exponential rate. Growth was best described with three different rates. Microscopic observations indicated that the points where the growth rates changed corresponded to specific developmental events: the L1/L2 molt and the start of oogenesis in young adult C. elegans. CONCLUSIONS: Quantitative analysis of COPAS Biosort measurements of C. elegans growth has been hampered by the lack of a mathematical model. In addition, extraneous matter and the inability to assign specific measurements to specific nematodes made it difficult to estimate growth rates. The present model addresses these problems through a population-based Markov model

The mTOR inhibitor rapamycin down-regulates the expression of the ubiquitin ligase subunit Skp2 in breast cancer cells

INTRODUCTION: Loss of the cyclin-dependent kinase inhibitor p27 is associated with poor prognosis in breast cancer. The decrease in p27 levels is mainly the result of enhanced proteasome-dependent degradation mediated by its specific ubiquitin ligase subunit S phase kinase protein 2 (Skp2). The mammalian target of rapamycin (mTOR) is a downstream mediator in the phosphoinositol 3' kinase (PI3K)/Akt pathway that down-regulates p27 levels in breast cancer. Rapamycin was found to stabilize p27 levels in breast cancer, but whether this effect is mediated through changes in Skp2 expression is unknown. METHODS: The expression of Skp2 mRNA and protein levels were examined in rapamycin-treated breast cancer cell lines. The effect of rapamycin on the degradation rate of Skp2 expression was examined in cycloheximide-treated cells and in relationship to the anaphase promoting complex/Cdh1 (APC\C) inhibitor Emi1. RESULTS: Rapamycin significantly decreased Skp2 mRNA and protein levels in a dose and time-dependent fashion, depending on the sensitivity of the cell line to rapamycin. The decrease in Skp2 levels in the different cell lines was followed by cell growth arrest at G1. In addition, rapamycin enhanced the degradation rate of Skp2 and down-regulated the expression of the APC\C inhibitor Emi1. CONCLUSION: These results suggest that Skp2, an important oncogene in the development and progression of breast cancer, may be a novel target for rapamycin treatment

Mitochondrial Variability as a Source of Extrinsic Cellular Noise

We present a study investigating the role of mitochondrial variability in generating noise in eukaryotic cells. Noise in cellular physiology plays an important role in many fundamental cellular processes, including transcription, translation, stem cell differentiation and response to medication, but the specific random influences that affect these processes have yet to be clearly elucidated. Here we present a mechanism by which variability in mitochondrial volume and functionality, along with cell cycle dynamics, is linked to variability in transcription rate and hence has a profound effect on downstream cellular processes. Our model mechanism is supported by an appreciable volume of recent experimental evidence, and we present the results of several new experiments with which our model is also consistent. We find that noise due to mitochondrial variability can sometimes dominate over other extrinsic noise sources (such as cell cycle asynchronicity) and can significantly affect large-scale observable properties such as cell cycle length and gene expression levels. We also explore two recent regulatory network-based models for stem cell differentiation, and find that extrinsic noise in transcription rate causes appreciable variability in the behaviour of these model systems. These results suggest that mitochondrial and transcriptional variability may be an important mechanism influencing a large variety of cellular processes and properties