RNA-Seq: Yellow Nutsedge (Cyperus esculentus) transcriptome analysis of lipid-accumulating tubers from early to late developmental stages

Thanks to high amounts of starch and oil amassed in the parenchyma of its tubers, yellow nutsedge (Cyperus esculentus) stands as a unique plant species with regards to nutrient biosynthesis and accumulation in underground organs. In the last decades, understanding of enzymatic processes in lipid, starch and sugar pathways underwent great improvements. Nevertheless, the underlying mechanisms of carbon allocation in sink tissues are still obscure, and insights may be rendered through the study of yellow nutsedge. Furthermore, in the global context of a still rising need for vegetable oils, Cyperus esculentus appears as a promising candidate for the introduction of novel high-yield oil species. Here is presented the first in-depth analysis of the yellow nutsedge tuber transcriptome, which was conducted using Roche 454 sequencing and targeted two developmental stages, coinciding with (i) the beginning of oil accumulation, but also (ii) an important increase of starch content, and finally (iii) a substantial drop in sugar amount. Denovo assembly led to a reference transcriptome of 37k transcripts, which underwent extensive functional and biological pathway annotation, leaving only 7 % of completely unknown sequences. A set of 186 differentially expressed genes (DEGs) was cross-confirmed by three different R packages. To cover the most important changes, top-30 rankings of up and down-regulated genes were investigated. Except a pronounced up-regulation of the WRI1 transcription factor (27-fold), no enzyme related to lipid, starch or sugar was found. Instead, massive changes in growth activity and stress response were observed. Analysis of expression at individual stages showed that several lipid, sugar and starch genes are actually abundant but would undergo changes of lower intensities, hence not visible in the top-30s. A private and user-friendly web-interface has been developed and compiles all the data and results generated through this study, providing with a convenient access for additional investigations, along with directives for further work

Emotional and Physical Health Impact in Children and Adolescents and Their Caregivers Using Open-source Automated Insulin Delivery: Qualitative Analysis of Lived Experiences

Background: Given the limitations in the access and license status of commercially developed automated insulin delivery (AID) systems, open-source AID systems are becoming increasingly popular among people with diabetes, including children and adolescents. Objective: This study aimed to investigate the lived experiences and physical and emotional health implications of children and their caregivers following the initiation of open-source AID, their perceived challenges, and sources of support, which have not been explored in the existing literature. Methods: Data were collected through 2 sets of open-ended questions from a web-based multinational survey of 60 families from 16 countries. The narratives were thematically analyzed, and a coding framework was identified through iterative alignment. Results: A range of emotions and improvements in quality of life and physical health were reported, as open-source AID enabled families to shift their focus away from diabetes therapy. Caregivers were less worried about hypoglycemia at night and outside their family homes, leading to increased autonomy for the child. Simultaneously, the glycemic outcomes and sleep quality of both the children and caregivers improved. Nonetheless, the acquisition of suitable hardware and technical setup could be challenging. The #WeAreNotWaiting community was the primary source of practical and emotional support. Conclusions: Our findings show the benefits and transformative impact of open-source AID and peer support on children with diabetes and their caregivers and families, where commercial AID systems are not available or suitable. Further efforts are required to improve the effectiveness and usability and facilitate access for children with diabetes, worldwide, to benefit from this innovative treatment

Organismal benefits of transcription speed control at gene boundaries

RNA polymerase II (RNAPII) transcription is crucial for gene expression. RNAPII density peaks at gene boundaries, associating these key regions for gene expression control with limited RNAPII movement. The connections between RNAPII transcription speed and gene regulation in multicellular organisms are poorly understood. Here, we directly modulate RNAPII transcription speed by point mutations in the second largest subunit of RNAPII in Arabidopsis thaliana. A RNAPII mutation predicted to decelerate transcription is inviable, while accelerating RNAPII transcription confers phenotypes resembling auto‐immunity. Nascent transcription profiling revealed that RNAPII complexes with accelerated transcription clear stalling sites at both gene ends, resulting in read‐through transcription. The accelerated transcription mutant NRPB2‐Y732F exhibits increased association with 5′ splice site (5′SS) intermediates and enhanced splicing efficiency. Our findings highlight potential advantages of RNAPII stalling through local reduction in transcription speed to optimize gene expression for the development of multicellular organisms.SynopsisRNAPII mutations that accelerate transcription cause auto‐immunity‐like phenotypes, read‐through transcription at RNAPII stalling sites and enhanced splicing in Arabidopsis, indicating that controlled transcription speed is required for optimal gene expression and plant development.A point mutation in RNAPII that increases the speed of RNAPII transcription triggers auto‐immunity‐like phenotypes.plaNET‐seq reveals reduced RNAPII stalling at gene boundaries in fast transcription mutants.Increasing the speed of transcription reduces the efficiency of transcriptional termination, resulting in read‐through transcription that blurs the spatial separation of genes.Accelerating RNAPII transcription enhances splicing efficiency in the multi‐cellular context.RNAPII mutations that accelerate transcription cause auto‐immunity‐like phenotypes, read‐through transcription at RNAPII stalling sites and enhanced splicing in Arabidopsis, indicating that controlled transcription speed is required for optimal gene expression and plant development.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154978/1/embr201949315-sup-0001-EVFigs.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154978/2/embr201949315.reviewer_comments.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154978/3/embr201949315.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154978/4/embr201949315_am.pd

Costs and underuse of insulin and diabetes supplies: Findings from the 2020 T1 International cross-sectional web-based survey

Aims: To investigate self-reported out-of-pocket expenses (OoPE) associated with insulin and diabetes supplies for people living with type 1 diabetes (T1D) worldwide. Methods: A web-based, cross-sectional survey was conducted from August to December 2020. The analysis included comparisons between responses from countries with no, partial, and full healthcare coverage. Results: 1,066 participants from 64 countries took part in the study. ~25% of respondents reported having underused insulin at least once within the last year due to perceived cost. A significant correlation was observed between OoPEs and reported household income for respondents with partial healthcare coverage. 63.2% of participants reported disruption of insulin supplies and 25.3% reported an increase of prices related to the COVID-19 pandemic. Conclusions: This study confirms previous reports of ~25% of people in the United States with T1D using less insulin and/or fewer supplies at least once in the last year due to cost, a trend associated with the extent of healthcare coverage. Similar trends were observed in some middle/low income countries. Moreover, patients reported an increase in insulin prices and disruption of supplies during the COVID-19 pandemic. This study highlights the importance of self-reported OoPEs and its association with underuse/rationing of insulin.Arnold VenturesCenter for Translational Science and Trainin

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

Background: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. Results: We present the Systems Biology Markup Language (SBML) Qualitative Models Package (“qual”), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. Conclusions: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks

Computing paths and cycles in biological interaction graphs

<p>Abstract</p> <p>Background</p> <p>Interaction graphs (signed directed graphs) provide an important qualitative modeling approach for Systems Biology. They enable the analysis of causal relationships in cellular networks and can even be useful for predicting qualitative aspects of systems dynamics. Fundamental issues in the analysis of interaction graphs are the enumeration of paths and cycles (feedback loops) and the calculation of shortest positive/negative paths. These computational problems have been discussed only to a minor extent in the context of Systems Biology and in particular the shortest signed paths problem requires algorithmic developments.</p> <p>Results</p> <p>We first review algorithms for the enumeration of paths and cycles and show that these algorithms are superior to a recently proposed enumeration approach based on elementary-modes computation. The main part of this work deals with the computation of shortest positive/negative paths, an NP-complete problem for which only very few algorithms are described in the literature. We propose extensions and several new algorithm variants for computing either exact results or approximations. Benchmarks with various concrete biological networks show that exact results can sometimes be obtained in networks with several hundred nodes. A class of even larger graphs can still be treated exactly by a new algorithm combining exhaustive and simple search strategies. For graphs, where the computation of exact solutions becomes time-consuming or infeasible, we devised an approximative algorithm with polynomial complexity. Strikingly, in realistic networks (where a comparison with exact results was possible) this algorithm delivered results that are very close or equal to the exact values. This phenomenon can probably be attributed to the particular topology of cellular signaling and regulatory networks which contain a relatively low number of negative feedback loops.</p> <p>Conclusion</p> <p>The calculation of shortest positive/negative paths and cycles in interaction graphs is an important method for network analysis in Systems Biology. This contribution draws the attention of the community to this important computational problem and provides a number of new algorithms, partially specifically tailored for biological interaction graphs. All algorithms have been implemented in the <it>CellNetAnalyzer </it>framework which can be downloaded for academic use at <url>http://www.mpi-magdeburg.mpg.de/projects/cna/cna.html</url>.</p

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

Nuclear and cytoplasmic RNA exosomes and PELOTA1 prevent miRNA-induced secondary siRNA production in Arabidopsis

Amplification of short interfering RNA (siRNAs) via RNA-dependent RNA polymerases (RdRPs) is of fundamental importance in RNA silencing. Plant microRNA (miRNA) action generally does not involve engagement of RdRPs, in part thanks to a poorly understood activity of the cytoplasmic exosome adaptor SKI2. Here, we show that inactivation of the exosome subunit RRP45B and SKI2 results in similar patterns of miRNA-induced siRNA production. Furthermore, loss of the nuclear exosome adaptor HEN2 leads to secondary siRNA production from miRNA targets largely distinct from those producing siRNAs in ski2. Importantly, mutation of the Release Factor paralogue PELOTA1 required for subunit dissociation of stalled ribosomes causes siRNA production from miRNA targets overlapping with, but distinct from, those affected in ski2 and rrp45b mutants. We also show that in exosome mutants, miRNA targets can be sorted into producers and non-producers of illicit secondary siRNAs based on trigger miRNA levels and miRNA:target affinity rather than on presence of 5′-cleavage fragments. We propose that stalled RNA-Induced Silencing Complex (RISC) and ribosomes, but not mRNA cleavage fragments released from RISC, trigger siRNA production, and that the exosome limits siRNA amplification by reducing RISC dwell time on miRNA target mRNAs while PELOTA1 does so by reducing ribosome stalling