High-throughput sequencing of small RNA transcriptomes reveals critical biological features targeted by microRNAs in cell models used for squamous cell cancer research

Abstract\ud \ud \ud \ud Background\ud \ud The implication of post-transcriptional regulation by microRNAs in molecular mechanisms underlying cancer disease is well documented. However, their interference at the cellular level is not fully explored. Functional in vitro studies are fundamental for the comprehension of their role; nevertheless results are highly dependable on the adopted cellular model. Next generation small RNA transcriptomic sequencing data of a tumor cell line and keratinocytes derived from primary culture was generated in order to characterize the microRNA content of these systems, thus helping in their understanding. Both constitute cell models for functional studies of microRNAs in head and neck squamous cell carcinoma (HNSCC), a smoking-related cancer. Known microRNAs were quantified and analyzed in the context of gene regulation. New microRNAs were investigated using similarity and structural search, ab initio classification, and prediction of the location of mature microRNAs within would-be precursor sequences. Results were compared with small RNA transcriptomic sequences from HNSCC samples in order to access the applicability of these cell models for cancer phenotype comprehension and for novel molecule discovery.\ud \ud \ud \ud Results\ud \ud Ten miRNAs represented over 70% of the mature molecules present in each of the cell types. The most expressed molecules were miR-21, miR-24 and miR-205, Accordingly; miR-21 and miR-205 have been previously shown to play a role in epithelial cell biology. Although miR-21 has been implicated in cancer development, and evaluated as a biomarker in HNSCC progression, no significant expression differences were seen between cell types. We demonstrate that differentially expressed mature miRNAs target cell differentiation and apoptosis related biological processes, indicating that they might represent, with acceptable accuracy, the genetic context from which they derive. Most miRNAs identified in the cancer cell line and in keratinocytes were present in tumor samples and cancer-free samples, respectively, with miR-21, miR-24 and miR-205 still among the most prevalent molecules at all instances. Thirteen miRNA-like structures, containing reads identified by the deep sequencing, were predicted from putative miRNA precursor sequences. Strong evidences suggest that one of them could be a new miRNA. This molecule was mostly expressed in the tumor cell line and HNSCC samples indicating a possible biological function in cancer.\ud \ud \ud \ud Conclusions\ud \ud Critical biological features of cells must be fully understood before they can be chosen as models for functional studies. Expression levels of miRNAs relate to cell type and tissue context. This study provides insights on miRNA content of two cell models used for cancer research. Pathways commonly deregulated in HNSCC might be targeted by most expressed and also by differentially expressed miRNAs. Results indicate that the use of cell models for cancer research demands careful assessment of underlying molecular characteristics for proper data interpretation. Additionally, one new miRNA-like molecule with a potential role in cancer was identified in the cell lines and clinical samples.The authors acknowledge the contribution of GENCAPO (Brazilian Head and Neck Genome Project) for clinical samples and for clinical and pathological data collection (complete list of members and affiliations presented athttp://www.gencapo.famerp.br). This work was supported by FAPESP (grants 09/04166-5 and 10/51168-0) and by Hospital Israelita Albert Einstein

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

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Implementing Run-Time Systems in a Compiler

176 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997.This thesis presents a model for the implementation of run-time systems. We divide the implementation of the run-time system in 3 sub-tasks: implementing the data types, implementing the environment, and implementing the basic control flow. To each sub-task we present a model of how solutions are specified. We define visual tools to specify primitive data types and and enviroments. These tools automatically generate compiler components that generate code to implement the run-time system functionality. Finally, we present a generic interface for interfacing memory managers to run-time system. This interface can be used to write run-time system independent garbage collectors. The methods that support this independency are automatically generated by the components mentioned above.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Sequence and Structural Analysis of the 5 ` Noncoding Region of Hepatitis C Virus in Patients With Chronic Infection

Hepatitis C virus (HCV), exhibits considerable genetic diversity, but presents a relatively well conserved 5 ` noncoding region (5 ` NCR) among all genotypes. In this study, the structural features and translational efficiency of the HCV 5 ` NCR sequences were analyzed using the programs RNAfold, RNAshapes and RNApdist and with a bicistronic dual luciferase expression system, respectively. RNA structure prediction software indicated that base substitutions will alter potentially the 5 ` NCR structure. The heterogeneous sequence observed on 5 ` NCR led to important changes in their translation efficiency in different cell culture lines. Interactions of the viral RNA with cellular transacting factors may vary according to the cell type and viral genome polymorphisms that may result in the translational efficiency observed. J. Med. Virol. 81: 1212-1219, 2009. (C) 2009 Wiley-Liss, Inc

ToPS: A Framework to Manipulate Probabilistic Models of Sequence Data

<div><p>Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling. We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i) independent and identically distributed process; (ii) variable-length Markov chain; (iii) inhomogeneous Markov chain; (iv) hidden Markov model; (v) profile hidden Markov model; (vi) pair hidden Markov model; (vii) generalized hidden Markov model; and (viii) similarity based sequence weighting. The framework includes functionality for training, simulation and decoding of the models. Additionally, it provides two methods to help parameter setting: Akaike and Bayesian information criteria (AIC and BIC). The models can be used stand-alone, combined in Bayesian classifiers, or included in more complex, multi-model, probabilistic architectures using GHMMs. In particular the framework provides a novel, flexible, implementation of decoding in GHMMs that detects when the architecture can be traversed efficiently.</p></div