Microhomology Selection for Microhomology Mediated End Joining in Saccharomyces cerevisiae.

Microhomology-mediated end joining (MMEJ) anneals short, imperfect microhomologies flanking DNA breaks, producing repair products with deletions in a Ku- and RAD52-independent fashion. Puzzlingly, MMEJ preferentially selects certain microhomologies over others, even when multiple microhomologies are available. To define rules and parameters for microhomology selection, we altered the length, the position, and the level of mismatches to the microhomologies flanking homothallic switching (HO) endonuclease-induced breaks and assessed their effect on MMEJ frequency and the types of repair product formation. We found that microhomology of eight to 20 base pairs carrying no more than 20% mismatches efficiently induced MMEJ. Deletion of MSH6 did not impact MMEJ frequency. MMEJ preferentially chose a microhomology pair that was more proximal from the break. Interestingly, MMEJ events preferentially retained the centromere proximal side of the HO break, while the sequences proximal to the telomere were frequently deleted. The asymmetry in the deletional profile among MMEJ products was reduced when HO was induced on the circular chromosome. The results provide insight into how cells search and select microhomologies for MMEJ in budding yeast.

ESTIMATION OF EXPOSURE DOSES FOR THE SAFE MANAGEMENT OF NORM WASTE DISPOSAL.

Naturally occurring radioactive materials (NORM) wastes with different radiological characteristics are generated in several industries. The appropriate options for NORM waste management including disposal options should be discussed and established based on the act and regulation guidelines. Several studies calculated the exposure dose and mass of NORM waste to be disposed in landfill site by considering the activity concentration level and exposure dose. In 2012, the Korean government promulgated an act on the safety control of NORM around living environments to protect human health and the environment. For the successful implementation of this act, we suggest a reference design for a landfill for the disposal of NORM waste. Based on this reference landfill, we estimate the maximum exposure doses and the relative impact of each pathway to exposure dose for three scenarios: a reference scenario, an ingestion pathway exclusion scenario, and a low leach rate scenario. Also, we estimate the possible quantity of NORM waste disposal into a landfill as a function of the activity concentration level of U series, Th series and 40K and two kinds of exposure dose levels, 1 and 0.3 mSv/y. The results of this study can be used to support the establishment of technical bases of the management strategy for the safe disposal of NORM waste.

Identification of Unannotated Small Genes in Salmonella.

Increasing evidence indicates that many, if not all, small genes encoding proteins ≤100 aa are missing in annotations of bacterial genomes currently available. To uncover unannotated small genes in the model bacterium Salmonella enterica Typhimurium 14028s, we used the genomic technique ribosome profiling, which provides a snapshot of all mRNAs being translated (translatome) in a given growth condition. For comprehensive identification of unannotated small genes, we obtained Salmonella translatomes from four different growth conditions: LB, MOPS rich defined medium, and two infection-relevant conditions low Mg2+ (10 µM) and low pH (5.8). To facilitate the identification of small genes, ribosome profiling data were analyzed in combination with in silico predicted putative open reading frames and transcriptome profiles. As a result, we uncovered 130 unannotated ORFs. Of them, 98% were small ORFs putatively encoding peptides/proteins ≤100 aa, and some of them were only expressed in the infection-relevant low Mg2+ and/or low pH condition. We validated the expression of 25 of these ORFs by western blot, including the smallest, which encodes a peptide of 7 aa residues. Our results suggest that many sequenced bacterial genomes are underannotated with regard to small genes and their gene annotations need to be revised.

Development of engineered Escherichia coli whole-cell biocatalysts for high-level conversion of L-lysine into cadaverine.

A whole-cell biocatalytic system for the production of cadaverine from L-lysine has been developed. Among the investigated lysine decarboxylases from different microorganisms, Escherichia coli LdcC showed the best performance on cadaverine synthesis when E. coli XL1-Blue was used as the host strain. Six different strains of E. coli expressing E. coli LdcC were investigated and recombinant E. coli XL1-Blue, BL21(DE3) and W were chosen for further investigation since they showed higher conversion yield of lysine into cadaverine. The effects of substrate pH, substrate concentrations, buffering conditions, and biocatalyst concentrations have been investigated. Finally, recombinant E. coli XL1-Blue concentrated to an OD(600) of 50, converted 192.6 g/L (1317 mM) of crude lysine solution, obtained from an actual lysine manufacturing process, to 133.7 g/L (1308 mM) of cadaverine with a molar yield of 99.90 %. The whole-cell biocatalytic system described herein is expected to be applicable to the development of industrial bionylon production process.

Before It Gets Started: Regulating Translation at the 5' UTR.

Translation regulation plays important roles in both normal physiological conditions and diseases states. This regulation requires cis-regulatory elements located mostly in 5' and 3' UTRs and trans-regulatory factors (e.g., RNA binding proteins (RBPs)) which recognize specific RNA features and interact with the translation machinery to modulate its activity. In this paper, we discuss important aspects of 5' UTR-mediated regulation by providing an overview of the characteristics and the function of the main elements present in this region, like uORF (upstream open reading frame), secondary structures, and RBPs binding motifs and different mechanisms of translation regulation and the impact they have on gene expression and human health when deregulated.

SIDEKICK: Genomic data driven analysis and decision-making framework.

BACKGROUND: Scientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation. RESULTS: Sidekick, a genomic data driven analysis and decision making framework, is a web-based tool that provides a user-friendly intuitive solution to the problem of information inaccessibility. Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated with disease X also influence other diseases." Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing the multi-step research tasks needed to answer these questions. We demonstrate Sidekick's effectiveness by showing how to accomplish a complex published analysis in a fraction of the original time with no computational effort using Sidekick. CONCLUSIONS: Sidekick is an easy-to-use web-based tool that organizes and facilitates complex genomic research, allowing scientists to explore genomic relationships and formulate hypotheses without computational effort. Possible analysis steps include gene list discovery, gene-pair list discovery, various enrichments for both types of lists, and convenient list manipulation. Further, Sidekick's ability to characterize pairs of genes offers new ways to approach genomic analysis that traditional single gene lists do not, particularly in areas such as interaction discovery.

Over-represented sequences located on 3' UTRs are potentially involved in regulatory functions.

Eukaryotic gene expression must be coordinated for the proper functioning of biological processes. This coordination can be achieved both at the transcriptional and post-transcriptional levels. In both cases, regulatory sequences placed at either promoter regions or on UTRs function as markers recognized by regulators that can then activate or repress different groups of genes according to necessity. While regulatory sequences involved in transcription are quite well documented, there is a lack of information on sequence elements involved in post-transcriptional regulation. We used a statistical over-representation method to identify novel regulatory elements located on UTRs. An exhaustive search approach was used to calculate the frequency of all possible n-mers (short nucleotide sequences) in 16,160 human genes of NCBI RefSeq sequences and to identify any peculiar usage of n-mers on UTRs. After a stringent filtering process, we identified 2,772 highly over-represented n-mers on 3' UTRs. We provide evidence that these n-mers are potentially involved in regulatory functions. Identified n-mers overlap with previously identified binding sites for HuR and TIA-1 and, ARE and GRE sequences. We determine also that n-mers overlap with predicted miRNA target sites. Finally, a method to cluster n-mer groups allowed the identification of putative gene networks.

IGF2 knockout mice are resistant to kainic acid-induced seizures and neurodegeneration.

Insulin-like growth factor 2 (Igf2), a member of the insulin gene family, is important for brain development and has known neurotrophic properties. Though Igf2, its receptors, and binding proteins, are expressed in the adult CNS, their role in the adult brain is less well-understood. Here we studied how Igf2 deficiency affects brains of adult Igf2 knockout (Igf2(-/-)) mice following neurotoxic insult produced by the glutamate analog kainic acid (KA). Igf2(-/-) mice exhibited attenuated epileptiform activity in response to KA and were less susceptible to hippocampal neurodegeneration compared with Igf2(+/+) mice. Other brain areas protected by the lack of Igf2 included the amygdala complex, septal nuclei, and thalamic region. Apoptosis, as determined by TUNEL and Hoechst 33342 staining, was accordingly less for Igf2(-/-) mice. Hippocampal slices from Igf2(-/-) mice also were protected against the effects epileptogenic effects of KA compared to Igf2(+/+) mice suggesting that neuroprotection afforded by a lack of Igf2 may be developmental in origin and experiments demonstrating enhanced synaptic inhibition in slices taken from Igf2(-/-) mice support this hypothesis. Taken together, these results suggest that Igf2 may be important for mechanisms and circuits that contribute to neurodegeneration and epilepsy.

The origin of chromosome imbalances in neuroblastoma.

Many recurrent large-scale chromosome abnormalities associated with poor clinical outcomes have been identified in neuroblastoma, a pediatric tumor that accounts for 15% of childhood cancer deaths. We have previously used high-resolution oligonucleotide array comparative genomic hybridization to map 461 chromosome breakpoints leading to large-scale chromosome imbalances in 56 primary neuroblastoma tumors and cell lines. Here, we analyze the distribution of DNA sequence elements and genomic landmarks found within these breakpoint intervals and in 15,800 randomly generated intervals of similar size. The most consistent finding was that neuroblastoma chromosome breakpoints occur preferentially in GC-rich regions of the genome. It is not unsurprising that these regions have fewer (AT)(n) microsatellite repeat sequences. In addition, chromosome breakpoints occurring in neuroblastoma also appeared to be preferentially associated with ancestral chromosome breakpoint regions on several chromosomes, suggesting that such sites also act as hotspots for chromosome rearrangement in somatic cells. Very little evidence for the enrichment of Alu and other types of repeats in breakpoint intervals was obtained. Overall, our results are consistent with a mechanistic model involving nonhomologous end joining of DNA double-strand breaks that have been generated in a nonrandom manner.

Transcriptional analysis of the gum gene cluster from Xanthomonas oryzae pathovar oryzae.

Genome sequence analysis of Xanthomonas oryzae pv. oryzae KACC10331 provides insight into the X. oryzae gum gene cluster that is composed of 14 open-reading frames (ORFs), designated gumB, -C, -D, -E, -F, -G, -H, -I, -J, -K, -L, -M, XOO3167, and -N. We analyzed the transcriptional linkage of the X. oryzae gum gene cluster by using RT-PCR. Analyses of the gum gene cluster by RT-PCR with the wild-type and mutant strains, which carried a deletion of the promoter-like region upstream of gumB or an insertion of the rrnB transcriptional terminator into the gumF gene, revealed that the ORFs of this gene cluster were transcribed as polycistronic mRNA, from gumB to gumN, and the secondary promoter was located upstream of gumG. Taken together, these results suggest that the genes of this cluster constitute an operon expressed from overlapping transcripts.

Protein subcellular localization prediction using a hybrid of similarity search and error-correcting output code techniques that produces interpretable results.

In silico prediction of protein subcellular localization based on amino acid sequence can reveal valuable information about the protein's innate roles in the cell. Unfortunately, such prediction is made difficult because of complex protein sorting signals. Some prediction methods are based on searching for similar proteins with known localization, assuming that known homologs exist. However, it may not perform well on proteins with no known homolog. In contrast, machine learning-based approaches attempt to infer a predictive model that describes the protein sorting signals. Alas, in doing so, it does not take advantage of known homologs (if they exist) by doing a simple "table lookup". Here, we capture the best of both worlds by combining both approaches. On a dataset with 12 locations, similarity-based and machine learning independently achieve an accuracy of 83.8% and 72.6%, respectively. Our hybrid approach yields an improved accuracy of 85.9%. We compared our method with three other methods' published results. For two of the methods, we used their published datasets for comparison. For the third we used the 12 location dataset. The Error Correcting Output Code algorithm was used to construct our predictive model. This algorithm gives attention to all the classes regardless of number of instances and led to high accuracy among each of the classes and a high prediction rate overall. We also illustrated how the machine learning classifier we use, built over a meaningful set of features can produce interpretable rules that may provide valuable insights into complex protein sorting mechanisms.