Concha, B´arbara Dema, Rudolf S N. Fehrmann, Miguel Fern´andez-Arquero, Szilvia Fi- atal, Elvira Grandone, Peter M. Green, Harry J M. Groen, Rhian Gwilliam, Roderick H J. Houwen, Sarah E. Hunt, Katri Kaukinen, Dermot Kelleher, Ilma Korponay-Szabo, Kalle Kurppa, Padraic MacMathuna, Markku M¨aki, Maria Cristina Mazzilli, Owen T. McCann, M Luisa Mearin, Charles A. Mein, Muddassar M. Mirza, Vanisha Mistry, Barbara Mora, Katherine I. Morley, Chris J. Mulder, Joseph A. Murray, Concepci ´on N ´u ˜nez, Elvira Oos- terom, Roel A. Ophoff, Isabel Polanco, Leena Peltonen, Mathieu Platteel, Anna Rybak, Veikko Salomaa, Joachim J. Schweizer, Maria Pia Sperandeo, Greetje J. Tack, Graham Turner, Jan H. Veldink, Wieke H M. Verbeek, Rinse K. Weersma, Victorien M. Wolters, Elena Urcelay, Bozena Cukrowska, Luigi Greco, Susan L. Neuhausen, Ross McManus, Do- natella Barisani, Panos Deloukas, Jeffrey C. Barrett, Paivi Saavalainen, Cisca Wijmenga, and David A. van Heel. Multiple common variants for celiac disease influencing im- mune geneexpression. Nat Genet, 42(4):295–302, Apr 2010. doi: 10.1038/ng.543. URL
30-50 % enhanced expression values after a pulse of bichromatic irradiation in comparison to the one of blue light alone. This would indicate a faster and more efficient formation of the signaling form according to the above proposed mechanism. On the other hand the genes wctB and wctC show a 25 % lower transcriptional enhancement after bichromatic rather than blue light treatment, suggesting a preferable photoreversion of the red-absorbing species towards the ground state by red light and therefore a reduction of the active form (Fig. 4.3.1, p.106). Other genes like the cryA, wcoB and myc show equal photoactivation after a pulse of blue and bichromatic light exposure suggesting an individually specific mechanism of photoregulation. This conjecture is conform with the diverse gene expressions after continuous light exposure compared to the ones after a pulse of light, as well as the ones after the different irradiations and it demonstrates once again the complexity of the transcriptional photoresponses. The exceptional photoactivation of the genes hat10 and hsp90 only under continuous blue light is one example. Another is, the unique transcriptional behavior of the wctB gene, which is equally activated under continuous and after a pulse of blue and bichromatic light treatments. The other genes presented above show either, a positive synergism or antagonism after continuous bichromatic irradiations in comparison to each, red or blue light. Interestingly, none of the genes show a compensatory effect as obtained for the geneexpression of the carB and hsp90 gene. For all these transcriptional photoresponses the Phot1-LOV1 photocycle of C.
integrating environmental and genetic cues have not yet been fully elucidated. We aimed to test, whether the allele of an intronic 3.3 kb large variant esv3608688 (insertion / deletion), located within in a disease-associated haplotype at the FKBP5 locus, have an effect on glucocorticoid-dependent gene regulation and how this can be explained on a molecular level. We observed that the deletion allele was associated with a higher induction of FKBP5 expression after activation of the glucocorticoid receptor (GR) in lymphoblastoid cell lines (LCLs). Furthermore, we could show that the esv3608688 allele can moderate the effect on FKBP5 geneexpression regulation of a previously described functional single nucleotide polymorphism rs1360780 (Klengel et al, 2013). We identified that the stabilization of architectural and enhancer-promoter loops is a common feature of the factors (T- allele, deletion and GR activation) leading to increased FKBP5 mRNA expression. In addition, we observed differentially methylated sites depending on the allele status for esv3608688 within key regulatory sites of FKBP5. Our data proposes a model, that this geneexpression response is most likely result of an increased activity of GRE enhancers due to the stabilization of chromatin interactions. This study highlights molecular mechanism how genetic and environmental factors can be integrated fine-tune FKBP5 expression levels, potentially involved in leading to FKBP5 disinhibition and therefore shape the risk for developing a stress-related psychiatric disease.
Background: Lactobacillus plantarum constitutes a well-recognized food-grade system for the expression of recombinant proteins in the field of industrial and medical biotechnology. For applications in vivo or in biotechnological processes, the level of expression of e.g. antigens or enzymes is often critical, as expression levels should be of a certain effectiveness, yet, without putting too much strain to the overall system. The key factors that control geneexpression are promoter strength, gene copy number and translation efficiency. In order to estimate the impact of these adjusting screws in L. plantarum CD033, we have tested several constitutive promoters in combination with high and low copy number plasmid backbones and varying space between the Shine-Dalgarno sequence and the start-codon.
M. hominis has already been shown to impact the geneexpression of infected HeLa cells (Hopfe et al. 2013 ). Until now, the effects of M. hominis on T. vaginalis have been mostly investigated phenotypically, e.g. M. hominis enhanc- ing ATP production, growth rate and haemolytic activity of T. vaginalis (Margarita et al. 2016 ), as well as altering the immune response of host cells to the infection (Mercer et al. 2016 ; Fiori et al. 2013 ). However, the influence of M. hominis on mRNA levels in T. vaginalis has not been investigated in depth so far. Morada et al. ( 2010 ) investigated mRNA expres- sion levels of three arginine deiminase (ADI) genes in a T. vaginalis strain artificially infected with M. hominis and Fig. 2 Metronidazole susceptibility of M. hominis-free T. vaginalis is
Reads were mapped onto the human reference genome release hg38 (GRCh38) [ 72 ] with Ensembl transcript annotation version 87 [ 48 ] using Tophat version 2.1.1 [ 73 ] with Bowtie version 2.2.9 [ 74 ]. Reads were counted with featureCounts [ 75 ] and geneexpression values (reads per kilobase exon per million mapped reads (RPKM)) were calculated with Cufflinks version 2.2 [ 76 ]. The differential expression between two sample groups was calculated with edgeR [ 77 ]. The filtering for differentially expressed genes is for p-value of 0.05 (FWER cor- rected) and minimal fold-change of 2. In the more specific analyses for single genes, for the dif- ferences in men and woman and for classification models, (healthy) age-related genes are removed. This is because the sampled healthy subjects are in average quite younger than the subjects with different arthritis conditions and at comparisons between them age-related genes are expected to be significantly different. Age-related genes are taken from Yang et. al. [ 78 ]. We performed also comparisons of geneexpression between groups adjusted for age. At the most changing adjustment in the comparison between healthy subjects and early RA (an average age of 35.2 vs. 55.9), we realized that many genes well known for RA are filtered (as CCL19 [ 79 ], CCL22 [ 80 ], CCR6 [ 81 ], CD6 [ 82 ], CDH11 [ 83 ], IFIT1B (as a paralog to IFIT1 [ 84 ]), IL26 [ 85 ], IL2RB [ 86 ], MMP10 [ 87 ], MMP12 [ 88 ], MMP8 [ 89 ] and MMP9 [ 90 ]). Similar worrying are the overlaps between unique DEGs in the comparison unadjusted and adjusted by age with the external age-related genes (as used for filtering from Yang et al. [ 78 ]), we see even a higher overlap between age-adjusted DEGs (healthy vs early RA) and the external age- related genes. Given that, we used the comparison without adjustment for further analyses. Age-adjusted comparisons are available in the Supplementary Archive.
Recent evidence from fully-sequenced genomes suggests that organismal complexity arises more from the elabo- rate regulation of geneexpression than from the gen- ome size itself . It is not surprising that determining the interactions between genes, which gives rise to parti- cular system’s function and behavior, represents the grand challenge of systems biology . In addition to structural information about the regulatory interactions, a comprehensive understanding of the dynamic behavior of these interactions requires specification of: (1) the type of regulation (i.e., activation or inhibition) , (2) kinetics of interactions , and (3) the specificity of the interactions with respect to the investigated tissue and/ or stress condition . The elucidation of a complete network of regulatory interactions parameterized with kinetic information leading to a particular gene expres- sion is, at present, still a challenging task even for well- studied model organisms whose networks have been partially assembled either for few selected processes and conditions or at the genome-wide level [6-9].
Hfq-binding sRNAs control geneexpression by base-pairing with trans-encoded target transcripts ( Kavita et al., 2018 ). To determine the targets of OppZ in V. cholerae, we cloned the sRNA (starting from the RNase E cleavage site) on a plasmid under the control of the pBAD promoter. Induction of the pBAD promoter for 15 min resulted in a strong increase in OppZ levels (~30 fold, Figure 3—fig- ure supplement 1A ) and RNA-seq experiments of the corresponding samples revealed four repressed genes ( Figure 3A and Figure 3—figure supplement 1B ). Interestingly, these genes were oppBCDF, i.e. the same transcript that OppZ is processed from. We validated OppZ-mediated repression of all four genes using qRT-PCR ( Figure 3—figure supplement 1C ), which also confirmed that the first gene of the operon, oppA, is not affected by OppZ. Despite the reduced transcript lev- els of oppBCDF, OppZ over-expression did not reduce the stability of the oppB messenger ( Fig- ure 3—figure supplement 1D ). Using the RNA-hybrid algorithm ( Rehmsmeier et al., 2004 ), we were able to predict RNA duplex formation of the oppB translation initiation site with the 5’ end of the OppZ sRNA ( Figure 3B ). We confirmed this interaction using a variant of a previously reported post-transcriptional reporter system ( Corcoran et al., 2012 ). Here, the first gene of the operon is replaced by the red-fluorescent mKate2 protein, followed by the oppAB intergenic sequence and the first five codons of oppB, which were fused to gfp ( Figure 3C , top). Transfer of this plasmid into E. coli and co-transformation of the OppZ over-expression plasmid resulted in strong repression of GFP (~7 fold), while mKate2 levels remained constant. Mutation of either OppZ or oppB (mutations M1, see Figure 3B ) abrogated regulation of GFP and combination of both mutants restored control ( Figure 3C , bottom). In contrast, OppZ-mediated repression of OppB::GFP was strongly reduced in E. coli lacking hfq ( Figure 3—figure supplement 2A–B ). We also generated three additional variants of the reporter plasmids in which we included the oppBC, oppBCD, and oppBCDF sequences fused to GFP ( Figure 3—figure supplement 2C ). In all cases, OppZ readily inhibited GFP but did not affect mKate2. These results confirm that OppZ promotes discoordinate expression of the oppABCDF operon.
that were not useable for a quantitative analysis. Consequently, it is not surprising that the results of the Microarray experiments and the real-time PCR show just a weak correlation. Additionally, the differential expression of some further genes was analyzed by real-time PCR. Some of these genes were previously investigated by members of our research group that examined the hypoxia dependant geneexpression in lung vessels and alveolar septa. A small number of the genes, which seemed to play an important role in the adaptation of lung vessels to hypoxia, were also examined in this work. Examples are CD 36 that was found to be initially upregulated under hypoxia in intrapulmonary arteries while at later time period (21 days) downregulated (Kwapiszewska et al., 2005). Additionally, Prosaposin and Cytochrome b-245 α polypeptide was shown to be regulated by hypoxia in intrapulmonary arteries (Fink et al., 2002). Because of the same reasons as discussed above, the real-time PCR did not provide enough evidence to identify a differential expression of these genes as well. The data do neither allow to conclude that these genes are not regulated in AM, nor that they are regulated. However, this work demonstrates that the sample size must be considerably increased to study hypoxia-dependant gene regulation in AM in future studies.
Gene activation in germ cells is mainly a result of CREM (cAMP response element modulator) activity. It was shown that the serine residue at position 117 of CREM can be phosphorylated by PKA and by other kinases (Fimia GM and Sassone-Corsi P, 2001). However, in testis-tissue CREM can be activated in a phosphorylation independent manner by the activator of CREM in testis (ACT), a protein which is co-expressed with CREM in round spermatides. ACT has an autonomous activation domain and via binding to CREM it can be activated without Ser-117 phosphorylation and CBP binding (Don J and Stelzer G, 2002). The CREM-ACT complex activates geneexpression via interaction with CRE in the gene promoter and recruitment of the general transcription machinery (Kimmins S et al. 2004).
In such a representation, an interaction between one or more TFs and a TG is character- ized in dependence on the activation context of the TFs and by the semi-quantitative effect on corresponding TGs. This seems to strike the balance between striving for a detailed model granularity, and optimally and comprehensively exploiting the available knowledge on the other hand. This also enables a model-based data view, i.e. the model can be tested whether the annotated, and thus expected, behavior of regulations agrees with the observed behavior in a particular dataset of geneexpression measurements under investigation. The suggested representation is exploited in our resulting diauxic shift network, compris- ing >300 multi-input regulations that also account for combinatorial control by more than one regulator. Available in a machine-readable flat format, it is readily usable in network- based approaches for the interpretation of geneexpression data. As a front end, we further provide interactive pathways maps, enabling intuitive exploration of the network modules integrated into our annotation system, where the evidence for each regulation can be en- tered or retrieved down to the exact reference position in the primary literature. Our system can serve as a starting point to similarly annotate and incorporate additional pro- cesses, e.g. all processes subject to glucose control, as the addition of new annotations to existing transitions and pathway maps is straightforward and can be interconnected to the already existing maps.
wise counts for traditional exon mapping, one for intron and one for exon+intron counts. If a user chooses the downsam- pling option, 6 additional count-tables per target read count are provided. To evaluate library quality zUMIs summarizes the mapping statistics of the reads. While exon and intron mapping reads likely represent mRNA quantities, a high frac- tion of intergenic and unmapped reads indicates low-quality libraries. Another measure of RNA-seq library quality is the complexity of the library, for which the number of detected genes and the number of identified UMIs are good measures (Figure 1). We processed 227 million reads with zUMIs and quantified expression levels for exon and intron counts on a unix machine using up to 16 threads, which took barely 3 hours. Increasing the number of reads increases the processing time approximately linearly, where filtering, mapping and count- ing each take up roughly one third of the total time (Figure 3 E). We also observe that the peak RAM usage for process- ing datasets of 227, 500 and 1000 million pairs was 42 Gb, 89 Gb and 172 Gb, respectively. Finally, zUMIs could process the largest scRNA-seq dataset reported to date with around 1.3 million brain cells and 25 billion read pairs generated with 10xGenomics Chromium https://support.10xgenomics.com/ single-cell-gene-expression/datasets/1.3.0/1M_neurons on a 22-core Intel Xeon E5-2699 processor in only 7 days. Intron Counting
The different heart preservation methods during the surgery had a significant influence on IL-5 and IL-6 expression. Geneexpression for both cytokines was lower in the group of non-ischaemic perfused hearts. Myocardial ischemia is known to induce IL-6 production in human patients . Similar to other inflammatory reactions, CD4+ TH1 cells may play a key role in the pathogenesis of ischemia through releasing pro-inflammatory cytokines, whereas CD4+ TH2 cells may play a protective role through anti-inflammatory cytokines such as IL-5 . In our study, the samples were taken from the animal on the last day of survival. However, survival in the ischemic group was very short (PAV2, PAV4, and PAV6 were survival one day) and upregulation during the transplantation process can only be seen in this group. In further experiments, the immediate inflammatory geneexpression could be measured by myocardial biopsies to prove the difference between the two different heart preservation methods.
Description. The Data Quality Checks & Normalization tool uses primarily methods implemented in the Bioconductor packages ‘affy’ (14) and ‘affyPLM’. To quality check the perfect match (PM), probe levels are summarized in spatial and dens- ity plots. Individual probes in each probe set are numbered starting from the 5 0 end of the transcript, and the mean 5 0 to 3 0 probe intensity bias for each array is determined. The probe- level intensities for probe sets are summarized to define a measure of the individual geneexpression. To make data from different arrays comparable, RACE provides several normalization methods. The first of these is MAS 5.0, the current Affymetrix default algorithm. However, several studies (15,16) suggest that measures based only on the PM probes outperform the MAS 5.0 algorithm. For this reason RACE also provides access to two of the most prominent PM-based algorithms: RMA (Robust Multichip Average; 17) and gcRMA (see the Bioconductor website: http://www. Bioconductor.org). RMA includes quantile normalization and a robust multi-array probe-level fit, and gcRMA addi- tionally exploits sequence information for the background adjustment. Based on the normalized expression values the Pearson correlation and the standard deviation of gene-wise expression differences between two arrays are calculated to evaluate similarities of the geneexpression profile for each pair of samples. Moreover, a hierarchical sample cluster is built using Ward’s minimum variance method.
The Gram-positive bacterium Bacillus subtilis has long been used as a host for production and secretion of industrially relevant enzymes like amylases and proteases. It is imperative for optimal efficiency, to balance protein yield and correct folding. While there are numerous ways of doing so on protein or mRNA level, our approach aims for the underlying number of coding sequences. Gene copy numbers are an important tuning valve for the optimization of heterologous geneexpression. While some genes are best expressed from many gene copies, for other genes, medium or even single copy numbers are the only way to avoid formation of inclusion bodies, toxic gene dosage effects or achieve desired levels for metabolic engineering. In order to provide a simple and robust method to address above-mentioned issues in the Gram-positive bacterium Bacillus subtilis, we have developed an automatable system for the tuning of heterologous geneexpression based on the host’s intrinsic natural competence and homologous recombination capabilities. Strains are transformed with a linearized, low copy number plasmid containing an antibiotic resistance marker and homology regions up- and downstream of the gene of interest. Said gene is copied onto the vector, rendering it circular and replicative and thus selectable. We could show an up to 3.6-fold higher gfp (green fluorescent protein) expression and up to 1.3-fold higher mPLC (mature phospholipase C) expression after successful transformation. Furthermore, the plasmid-borne gfp expression seems to be more stable, since over the whole cultivation period the share of fluorescent cells compared to all measured cells is consistently higher. A major benefit of this method is the ability to work with very large regions of interest, since all relevant steps are carried out in vivo and are thus far less prone to mechanical DNA damage.
REST is not only negatively regulating the geneexpression of chromogranin A and synaptophysin but also the expression of miR-9. Indeed, we could show that miR-9 and miR-9* are abundantly present in REST-negative MCPyV-positive MCC cell lines. Recently, miR-9 was found to be upregulated in 20 MCC tissues compared to cutaneous lesions of melanoma, squamous cell carcinoma, and basal cell carcinoma . In cervical carcinoma and oropharyngeal squamous cell carcinoma it was shown that miR-9 is activated by HPV [26,44]. It is tempting to speculate that a comparable mechanism might be applicable to MCPyV and miR-9 expression in MCC. In the context of the unknown cellular origin of MCC the expression of miR-9 and the absence of REST might be the first step towards understanding the regulation of neuroendocrine geneexpression in MCC and might help to identify the cellular ancestry of MCC [13,14].
To help biologists to compare and analyze 3D geneexpression data, we developed Point- CloudXplore (PCX) a tool speciﬁcally designed for exploration of PointCloud data. In PCX we have linked physical and information visualization views via the concept of brush- ing (cell selection). Here we are going to introduce PointCloudXplore 2 (PCX2), the latest publicly available version of PCX. We are going to discuss all views available in PCX2 and describe how linking of the views via cell selection is used for effective data exploration. Afterwards, we will explain different dedicated strategies for performing cell selection and describe the basic architecture of PCX2. The following parts of this manuscript are struc- tured as follows. In Section 2 we will provide an overview of related work. The different visualization techniques available in PCX2 are then described in Section 3. In Section 4.1 and 4.2, linking of the views via cell selection and all operations for performing and com- bining cell selections will be explained. A brief overview of the implementation of PCX2 will be provided in Section 5. In Section 6 we present our conclusions and describe future plans.
The recently developed microarray technology allows for measuring expression levels of thousands of genes simul- taneously. We focus on the case where the experiments monitor geneexpression values of different individuals or tissue samples, and where each experiment is equipped with an additional categorical outcome variable describ- ing a cancer (pheno)type. In such a supervised setting, our goal is to predict the unknown class label of a new individ- ual on the basis of its geneexpression profile, since pre- cise diagnosis of cancer type is often crucial for successful treatment. Given the availability of efficient classification techniques, bio-molecular information could become as, or even more important than traditional clinical factors.
A precise diagnosis of cancerous malignancies is diffi- cult but often crucial for successful treatment. Given the large-scale, high-throughput geneexpression technology and accurate statistical methods, biomolecular information could become as, or even more, important for cancer diagnosis than the traditional clinical factors. The challenge is that microarray experiments generate large datasets with expres- sion values for thousands of genes, but usually not more than a few dozens of arrays. The situation with so many more predictor variables than experiments raises new stat- istical challenges and has leads us to a wealth of research. The task of diagnosing cancer on the basis of microarray data has been termed class prediction in the literature, and encompasses methods ranging from modified versions of traditional discriminant analysis, over penalized regression
Besides genetic factors, epigenetic mechanisms are important for MT gene regulation in vertebrates [ 53 – 56 ]. However, methylation in CG pairs of a selected promotor region of the L. terrestris wMT-2 gene were not observed (Table A2 ). It is known that, in general, invertebrate genomes are only sparsely methylated compared to the heavily methylated vertebrate genomes [ 57 ]. Nevertheless, genes involved in stress and environmental responses can be methylated, taking over regulatory functions as shown in the Pacific oyster C. gigas [ 58 ]. A positive correlation between promotor methylation and geneexpression has been shown in C. gigas [ 59 ]. In Ciona intestinalis, however, the methylation level in promotor regions and gene bodies were comparable, but the nearby regions of the methylated promoters were of particular importance: if the methylated promotor was adjacent to methylated gene bodies, geneexpression was depressed, whereas when adjacent to non-methylated gene bodies, expression was slightly increased [ 57 ]. Herein, we focused on a part of the wMT-2 promotor region, indicating a lack of epigenetic control mechanism. However, without examining the gene body region, the role of DNA methylation in wMT-2 gene regulation in earthworms cannot be eliminated. However, we recently showed that low Cd concentrations caused hypermethylation and induced persistent changes in genome-wide DNA methylation levels in earthworms [ 8 ].