BackgroundMicroRNAs (miRNAs) are short, non-coding RNA regulators of protein coding genes. miRNAs play a very important role in diverse biological processes and various diseases. Many algorithms are able to predict miRNA genes and their targets, but their transcription regulation is still under investigation. It is generally believed that intragenic miRNAs (located in introns or exons of protein coding genes) are co-transcribed with their host genes and most intergenic miRNAs transcribed from their own RNA polymerase II (Pol II) promoter. However, the length of the primary transcripts and promoter organization is currently unknown.MethodologyWe performed Pol II chromatin immunoprecipitation (ChIP)-chip using a custom array surrounding regions of known miRNA genes. To identify the true core transcription start sites of the miRNA genes we developed a new tool (CPPP). We showed that miRNA genes can be transcribed from promoters located several kilobases away and that their promoters share the same general features as those of protein coding genes. Finally, we found evidence that as many as 26% of the intragenic miRNAs may be transcribed from their own unique promoters.ConclusionmiRNA promoters have similar features to those of protein coding genes, but miRNA transcript organization is more complex.

We report the results of genetic analysis on a series of 51 patients attending this Haemophilia Comprehensive Care Centre. The most common cause of severe haemophilia A--the factor VIII intron 22 inversion was detected in eight families and the factor VIII intron 1 inversion in three families. Mutation analysis was carried out on the remaining patients by nucleotide sequencing of genomic DNA after screening with conformation-sensitive gel electrophoresis (CSGE) or denaturing high-performance liquid chromatography (dHPLC). A total of 27 different FVIII non-inversion mutations were detected. Severe haemophilia was associated with 12 null mutations (six nonsense, six frameshift) and four missense mutations. A further 11 different missense mutations were associated with moderate or mild disease. To our knowledge, six null mutations [1950del 4(tttg), 3270-75insA, 4416del 10, 6735-38delA, W1029X, Y1792X] and four missense mutations (E1682K, M1947V, P2048L, P2143L) have not been previously published. Each novel missense mutation occurred at a highly conserved residue, no other candidate mutation was detected on screening the entire coding region of the FVIII gene and they were not detected in a screen of individuals without haemophilia A. The genotype-phenotype correlations of the FVIII mutations detected will be discussed.

SummaryThe genetic basis of factor XI (FXI) deficiency was investigated in 30 patients from 13 different families of non-Jewish origin. Twelve different mutations were detected (including six novel changes), seven missense mutations and three mutations leading to null alleles. Haplotype analysis suggested a large gene deletion in one family. We confirmed the presence of a recently reported Alu-mediated FXI gene deletion. An unrelated patient with severe deficiency was shown to be compound heterozygous for A412V and this whole gene deletion. We suggest that this recurrent gene deletion should be included in the genetic analysis of FXI deficiency.

Genetic circuits are designed to implement certain logic in living cells, keeping burden on the host cell minimal. However, manipulating the genome often will have a significant impact for various reasons (usage of the cell machinery to express new genes, toxicity of genes, interactions with native genes, etc.). In this work we utilize Koopman operator theory to construct data-driven models of transcriptomic-level dynamics from noisy and temporally sparse RNAseq measurements. We show how Koopman models can be used to quantify impact on genetic circuits. We consider an experimental example, using high-throughput RNAseq measurements collected from wild-type E. coli, single gate components transformed in E. coli, and a NAND circuit composed from individual gates in E. coli, to explore how Koopman subspace functions encode increasing circuit interference on E. coli chassis dynamics. The algorithm provides a novel method for quantifying the impact of synthetic biological circuits on host-chassis dynamics.