Neisseria gonorrhoeae (Ng) encounters different microenvironments during its life-cycle. Some of these niches have different concentrations of oxygen, which influences the rate of Ng growth; as well as iron, an element essential for Ng survival. Differential expression of several proteins allows the bacteria to adapt to the diverse conditions it comes encounters. One protein affected by environmental changes during Ng growth is Gcp, a tRNA-modification enzyme essential for protein synthesis. To study the regulation of expression of Gcp, we first analyzed the sequence of its ORF, gcp. Orthologs of this gene are found in all kingdoms of life. In silico analysis shows that among Neisseria species, gcp ranges in homology from 76% to 99%, at the nucleotide level. Reverse transcription PCR indicates that gcp is expressed as part of an operon, together with three cytochrome-associated genes cyc4, resB and resC. Rapid amplification of complementary DNA ends determined the start of transcription of cyc4 (and possibly of the cyc4-gcp operon) at 95 nucleotides from the gene start codon. Transcriptional fusions determined that the promoter region upstream of cyc4 is the strongest promoter in the operon. However, the region directly upstream of gcp also has low level of promoter activity, suggesting that the gene may be expressed from two different promoters. Semi-quantitative determination of the concentration of gcp mRNA indicates that the transcription of the gene is significantly repressed when Ng is grown under low iron or low oxygen conditions. Analysis of an fnr mutant, grown under the same conditions as its parental wild type, indicates that the FNR transcriptional regulator is involved in the repression of gcp in low iron or low oxygen conditions. Contrary to expectation, the cyc4 promoter is upregulated when Ng is grown under low oxygen or low iron conditions. However, these results cannot be compared to the original promoter strength. Determination of which was performed on bacteria grown in liquid medium. Coregulation of gcp with cytochrome genes can guarantee low levels of protein synthesis when Ng encounters adverse microenvironments and needs its energy redirected to the expression of genes that would allow it to survive.

Nuclear hormone receptors (NHR) are important transcription factors that regulate development and metabolism in the large group of animals. Caenorhabditis elegans contains 284 nuclear receptors, which is unusually large amount compared to receptors of Drosophila melanogaster (18) and humans (48). 15 receptors of the C. elegans have homologous receptor structure with receptors of D. melanogaster and mammals. The remaining 269 NHR are specific to nematodes and belong to the group of supplementary nuclear receptors (SupNRs), the evolutionary precursor of the HNF4 - an important transcription factor in humans. In this work we describe the nuclear hormone receptor nhr-97 C. elegans, whose expression and function have not yet been studied. The gene is encoded in the genome of C. elegans and is among SupNRs. Nhr-97 consists of two isoforms A and B, whose expression in C. elegans tissues is different. Localization of gene expression in vivo was determined using lines expressing nhr-97:: GFP. For the A isoform expression of nhr-97::GFP was localized in neurons in the pharynx and the tail, in the intestine and hypodermis, in isoform B in the pharynx, in neurons around the corpus of pharynx, the head mesodermal cell and in anal sphincter. Nhr-97 expression during development of C. elegans was determined by...

As the effector component of the Notch signaling pathway the transcription factors of the CSL family (CBF1/RBP-Jκ/Suppressor of Hairless/Lag-1) are essential for many developmental processes in metazoan organisms, but they can function also independently of Notch. Recently, their presence was proved in fungal organisms lacking the Notch pathway as well as most of the known metazoan interacting partners. Cbf11 and Cbf12, the CSL proteins of the unicellular yeast Schizosaccharomyces pombe, were determined experimentally as non-essential nuclear transcription factors, which regulate cell adhesion, extracellular material production, colony morphology, septation and daughter cell separation, coordination of nuclear and cell division, and ploidy maintenance in an antagonistic way. The responsive genes of these factors are not known yet. In this study, genes of S. pombe, whose promoter regions represent potential direct targets for the Cbf proteins binding, were predicted. The binding of the Cbf11 and Cbf12 proteins, and of a truncated version Cbf12∆N to CSL response elements contained in the regulatory regions of selected S. pombe genes was tested in vitro by EMSA, and consequently, in the case of the Cbf11 protein, also in vivo by ChIP. Cbf11 and Cbf12∆N recognize specifically the response elements in...

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This paper investigates the use of deep Convolutional Neural Networks for modeling the intronic regulation of Alternative Splicing on the basis of DNA sequence. By training the CNN on massively parallel synthetic DNA libraries of Alternative 5'-splicing and Alternatively Skipped exon events, the model is capable of predicting the relative abundance of alternatively spliced mRNA isoforms on held-out library data to a very high accuracy (R2 = 0.77 for Alt. 5'-splicing). Furthermore, the CNN is shown to generalize alternative splicing across cell lines efficiently. The Convolutional Neural Net is tested against a Logistic regression model and the results show that while prediction accuracy on the synthetic library is notably higher compared to the LR model, the CNN is worse at generalizing to new intronic contexts. Tests on non-synthetic human SNP genes suggest the CNN is dependent on the relative position of the intronic region it was trained for, a problem which is alleviated with LR. The increased library prediction accuracy of the CNN compared to Logistic regression is concluded to come from the non-linearity introduced by the deep layer architecture. It adds the capacity to model complex regulatory interactions and combinatorial RBP effects which studies have shown largely affect alternative splicing. However, the architecture makes interpreting the CNN hard, as the regulatory interactions are encoded deep within the layers. Nevertheless, high-performance modeling of alternative splicing using CNNs may still prove useful in numerous Synthetic biology applications, for example to model differentially spliced genes as is done in this paper. / Den här uppsatsen undersöker hur djupa neurala nätverk baserade på faltning ("Convolutions") kan användas för att modellera den introniska regleringen av Alternativ Splicing med endast DNA-sekvensen som indata. Nätverket tränas på ett massivt parallelt bibliotek av syntetiskt DNA innehållandes Alternativa Splicing-event där delar av de introniska regionerna har randomiserats. Uppsatsen visar att nätverksarkitekturen kan förutspå den relativa mängden alternativt splicat RNA till en mycket hög noggrannhet inom det syntetiska biblioteket. Modellen generaliserar även alternativ splicing mellan mänskliga celltyper väl. Hursomhelst, tester på icke-syntetiska mänskliga gener med SNP-mutationer visar att nätverkets prestanda försämras när den introniska region som används som indata flyttas i jämförelse till den relativa position som modellen tränats på. Uppsatsen jämför modellen med Logistic regression och drar slutsatsen att nätverkets förbättrade prestanda grundar sig i dess förmåga att modellera icke-linjära beroenden i datan. Detta medför dock svårigheter i att tolka vad modellen faktiskt lärt sig, eftersom interaktionen mellan reglerande element är inbäddat i nätverkslagren. Trots det kan högpresterande modellering av alternativ splicing med hjälp av neurala nät vara användbart, exempelvis inom Syntetisk biologi där modellen kan användas för att kontrollera regleringen av splicing när man konstruerar syntetiska gener.