Transfer RNA-derived small RNAs in the cancer transcriptome

The cellular lifetime includes stages such as differentiation, proliferation, division, senescence and apoptosis.These stages are driven by a strictly ordered process of transcription dynamics. Molecular disruption to RNA polymerase assembly, chromatin remodelling and transcription factor binding through to RNA editing, splicing, post-transcriptional regulation and ribosome scanning can result in significant costs arising from genome instability. Cancer development is one example of when such disruption takes place. RNA silencing is a term used to describe the effects of post-transcriptional gene silencing mediated by a diverse set of small RNA molecules. Small RNAs are crucial for regulating gene expression and microguarding genome integrity.RNA silencing studies predominantly focus on small RNAs such as microRNAs, short-interfering RNAs and piwi-interacting RNAs. We describe an emerging renewal of inter-est in a‘larger’small RNA, the transfer RNA (tRNA).Precisely generated tRNA-derived small RNAs, named tRNA halves (tiRNAs) and tRNA fragments (tRFs), have been reported to be abundant with dysregulation associated with cancer. Transfection of tiRNAs inhibits protein translation by displacing eukaryotic initiation factors from messenger RNA (mRNA) and inaugurating stress granule formation.Knockdown of an overexpressed tRF inhibits cancer cell proliferation. Recovery of lacking tRFs prevents cancer metastasis. The dual oncogenic and tumour-suppressive role is typical of functional small RNAs. We review recent reports on tiRNA and tRF discovery and biogenesis, identification and analysis from next-generation sequencing data and a mechanistic animal study to demonstrate their physiological role in cancer biology. We propose tRNA-derived small RNA-mediated RNA silencing is an innate defence mechanism to prevent oncogenic translation. We expect that cancer cells are percipient to their ablated control of transcription and attempt to prevent loss of genome control through RNA silencing

Monoclonal antibody-conjugated dendritic nanostructures for siRNA delivery

Small interfering RNA (siRNA) is a promising tool for gene therapy-based disease treatments. However, delivery of siRNA to the target cells requires a specific and reliable carrier system. Herein we describe a targeted carrier system that can deliver siRNA to cancer cells overexpressing the human epidermal growth factor 2 (HER2) receptor. Trastuzumab-conjugated poly(amido)amine dendrimers can be synthesized using the protocols described here

Nutrient uptake dynamics of Gloriosa for cut flower

Gloriosa superba L. is a recently introduced tropical species in Argentina, cultivated as a cut flower. It is extremely important to know the nutritional demands of the crop to provide the optimal amounts of nutrients at each stage, achieving quality and good yield in flowers, reducing production costs and environmental impact. The objective of this work was to determine the dynamics of nutrient absorption in the cultivation of G. superba for cut flowers, to facilitate the creation of a fertilization program, in order to avoid crop deficiencies and contribute to sustainable production. Tuber composition analyzes were carried out and, on the other hand, an essay was installed in greenhouse beds, taking samples at seven moments of the cycle. Fresh matter and dry matter of stems, leaves, flowers, tubers, roots and chemical analysis of aerial organs were measured to obtain the absorption curve. It was verified that only around 20% of each nutrient is provided by the tuber, being necessary the external contribution from initial stages of the crop. The rate of growth and accumulation of dry matter was shown as a double sigmoid, with maximum peaks      in the vegetative stage of stem elongation and beginning of flowering. Nutrient amounts were absorbed in the following order: N>K>Mg>Ca>P>Fe>Mn>Zn>Cu. Fertilization rich in N, P, and Fe is recommended in the vegetative stage, balanced during the visible shoot stage, and rich in Ca, K, Mg, Mn, Zn, and Cu during flowering

A triple helix model of medical innovation: supply, demand, and technological capabilities in terms of medical subject headings

We develop a model of innovation that enables us to trace the interplay among three key dimensions of the innovation process: (i) demand of and (ii) supply for innovation, and (iii) technological capabilities available to generate innovation in the forms of products, processes, and services. Building on triple helix research, we use entropy statistics to elaborate an indicator of mutual information among these dimensions that can provide indication of reduction of uncertainty. To do so, we focus on the medical context, where uncertainty poses significant challenges to the governance of innovation. We use the Medical Subject Headings (MeSH) of MEDLINE/PubMed to identify publications classified within the categories “Diseases" (C), "Drugs and Chemicals" (D), "Analytic, Diagnostic, and Therapeutic Techniques and Equipment" (E) and use these as knowledge representations of demand, supply, and technological capabilities, respectively. Three case-studies of medical research areas are used as representative 'entry perspectives' of the medical innovation process. These are: (i) human papilloma virus, (ii) RNA interference, and (iii) magnetic resonance imaging. We find statistically significant periods of synergy among demand, supply, and technological capabilities (C-D-E) that point to three-dimensional interactions as a fundamental perspective for the understanding and governance of the uncertainty associated with medical innovation. Among the pairwise configurations in these contexts, the demand-technological capabilities (C-E) provided the strongest link, followed by the supply-demand (D-C) and the supply-technological capabilities (D-E) channels

Performance characterization and optimization of mobile augmented reality on handheld platforms

Abstract — The introduction of low power general purpose processors (like the Intel ® Atom ™ processor) expands the capability of handheld and mobile internet devices (MIDs) to include compelling visual computing applications. One rapidly emerging visual computing usage model is known as mobile augmented reality (MAR). In the MAR usage model, the user is able to point the handheld camera to an object (like a wine bottle) or a set of objects (like an outdoor scene of buildings or monuments) and the device automatically recognizes and displays information regarding the object(s). Achieving this on the handheld requires significant compute processing resulting in a response time in the order of several seconds. In this paper, we analyze a MAR workload and identify the primary hotspot functions that incur a large fraction of the overall response time. We also present a detailed architectural characterization of the hotspot functions in terms of CPI, MPI, etc. We then implement and analyze the benefits of several software optimizations: (a) vectorization, (b) multi-threading, (c) cache conflict avoidance and (d) miscellaneous code optimizations that reduce the number of computations. We show that a 3X performance improvement in execution time can be achieved by implementing these optimizations. Overall, we believe our analysis provides a detailed understanding of the processing for a new domain of visual computing workloads (i.e. MAR) running on low power handheld compute platforms. 1

DARIO: a ncRNA detection and analysis tool for next-generation sequencing experiments

Small non-coding RNAs (ncRNAs) such as microRNAs, snoRNAs and tRNAs are a diverse collection of molecules with several important biological functions. Current methods for high-throughput sequencing for the first time offer the opportunity to investigate the entire ncRNAome in an essentially unbiased way. However, there is a substantial need for methods that allow a convenient analysis of these overwhelmingly large data sets. Here, we present DARIO, a free web service that allows to study short read data from small RNA-seq experiments. It provides a wide range of analysis features, including quality control, read normalization, ncRNA quantification and prediction of putative ncRNA candidates. The DARIO web site can be accessed at http://dario.bioinf.uni-leipzig.de/

Integrative Deep Sequencing of the Mouse Lung Transcriptome Reveals Differential Expression of Diverse Classes of Small RNAs in Response to Respiratory Virus Infection

We previously reported widespread differential expression of long non-protein-coding RNAs (ncRNAs) in response to virus infection. Here, we expanded the study through small RNA transcriptome sequencing analysis of the host response to both severe acute respiratory syndrome coronavirus (SARS-CoV) and influenza virus infections across four founder mouse strains of the Collaborative Cross, a recombinant inbred mouse resource for mapping complex traits. We observed differential expression of over 200 small RNAs of diverse classes during infection. A majority of identified microRNAs (miRNAs) showed divergent changes in expression across mouse strains with respect to SARS-CoV and influenza virus infections and responded differently to a highly pathogenic reconstructed 1918 virus compared to a minimally pathogenic seasonal influenza virus isolate. Novel insights into miRNA expression changes, including the association with pathogenic outcomes and large differences between in vivo and in vitro experimental systems, were further elucidated by a survey of selected miRNAs across diverse virus infections. The small RNAs identified also included many non-miRNA small RNAs, such as small nucleolar RNAs (snoRNAs), in addition to nonannotated small RNAs. An integrative sequencing analysis of both small RNAs and long transcripts from the same samples showed that the results revealing differential expression of miRNAs during infection were largely due to transcriptional regulation and that the predicted miRNA-mRNA network could modulate global host responses to virus infection in a combinatorial fashion. These findings represent the first integrated sequencing analysis of the response of host small RNAs to virus infection and show that small RNAs are an integrated component of complex networks involved in regulating the host response to infection

deepBlockAlign: a tool for aligning RNA-seq profiles of read block patterns

Motivation: High-throughput sequencing methods allow whole transcriptomes to be sequenced fast and cost-effectively. Short RNA sequencing provides not only quantitative expression data but also an opportunity to identify novel coding and non-coding RNAs. Many long transcripts undergo post-transcriptional processing that generates short RNA sequence fragments. Mapped back to a reference genome, they form distinctive patterns that convey information on both the structure of the parent transcript and the modalities of its processing. The miR-miR* pattern from microRNA precursors is the best-known, but by no means singular, example