The investigation of splice site usage is relying on the accurate placement of RNA-Seq reads to a genomic reference sequence which requires an alignment with gaps at the intron positions. There are several tools dedicated to this purpose, which might show differences in terms of performance. Assessing the best available and frequently applied tools like STAR [158,286], HiSat2 , TopHat2 , and exonerate  could further facilitate research on splice sites and might reveal explanations for currently observed annotation differences . Such a benchmarking study should not just identify the most suitable tool for a specific application, but also provide insights into the best choice of parameters. The detection of novel non-canonical splice site combinations based on RNA-Seq data sets would benefit from these benchmarking results. This step from the assessment of existing annotations to the identification of novel non-canonical splice sites would substantially increase the accessible taxonomic diversity as genome sequences without annotation could be included in the analysis.
Among the non-canonical BCAA, norleucine is the one which accumulates more literature knowledge. The first evidence of the incorporation of exogenous norleucine into a recombinant protein by E. coli dates from 1956 (Munier and Cohen, 1956). Then, different studies reported that the mis-incorporation of exogenous norleucine into recombinant proteins by E. coli took place at positions where methionine is normally incorporated (Cohen and Munier, 1959; Cowie et al., 1959), thus confirming that norleucine is a structural analog of methionine. From that period onwards plenty of literature was made available demonstrating exogenous norleucine mis-incorporation into a wide range of recombinant proteins by the E. coli production platform in methionine positions, including recombinant adenylate kinase (Gilles et al., 1988), recombinant mammalian calmodulin (Yuan and Vogel, 1999) and recombinant cytochrome P450 BM-3 heme domain (Cirino et al., 2003). There were also cases reported where norleucine was not being supplied exogenously in the media, but was being naturally synthetized in E. coli cells and incorporated into recombinant proteins. In that case norleucine was found to be incorporated into recombinant interleukin-2 (IL-2) (Lu et al., 1988), recombinant bovine somatotropine (bST) (Bogosian et al., 1989), recombinant human macrophage colony stimulating factor (hM-CSF) (Randhawa et al., 1994), recombinant human brain- derived neurotrophic factor (Sunasara et al., 1999) and in a 41 kDa Met-rich recombinant protein vaccine candidate (Ni et al., 2015). Despite all cases described for recombinant proteins, there are no evidences in the literature regarding the synthesis and incorporation of norleucine into natural non- recombinant proteins by E. coli. However, the natural presence of norleucine has been reported in the field-growing parasitic fungi Claviceps purpurea (Cvak et al., 2005).
understand cancer progression, but the exact mechanisms of metastatic transformation remain elusive. TGFβ and activin A, two structurally related signaling proteins have been described to play a major role in CRC progression [155, 156]. However, their impact on malignant transformation seems to be related to advanced tumors, as early stage carcinomas are associated with tumor-suppressive effects of both molecules . Non-canonical, pro-tumorigenic pathways have been implied as the drivers of the cytokines’ pro-metastatic abilities. Activin A and TGFβ pathways are intertwined, and in CRC, pro-metastatic functions of TGFβ are depending on activin A . For in-depth understanding of cancer progression, the dissection of both ligand’s non-canonical pathways is crucial, however, the dependency of activin A and TGFβ on non-canonical targets has not been investigated. In our study, we utilized a model of TGFβ and activin A receptor restoration in an ACVR2/TGFBR2 deficient cell line. We showed that ACVR2 restoration in a CRC cell line leads to an exclusive response to activin A ligand and serves as a model to dissect the isolated effects of activin A, but not TGFβ. Accordingly, we investigated a CRC cell line that exclusively responds to TGFβ treatment with
Cells of the innate immune system detect danger signals via so called “Pattern recognition receptors” (PRRs). A multi-protein complex termed “inflammasome” is among the most important platforms integrating signalling by different PRRs and is mainly activated by molecules of the NOD-like receptor (NLR) family. Once assembled, inflammasome formation leads to the activation of inflammatory caspases and the subsequent secretion of the pro-inflammatory cytokines IL-1 and IL-18. Apart from the canonical activation by NLRs, recent research has outlined the importance of a non-canonical inflammasome activation by a direct sensing of LPS through the inflammatory caspases-4/5/11. The supreme importance of the inflammasome in the regulation of inflammation is underlined by the contribution of its deregulation in the pathogenesis of wide-spread diseases such as diabetes mellitus, COPD and asthma. Macrolides are a group of antibiotics mainly used in the treatment of respiratory diseases. In addition to their anti-infective effect, they are known to exhibit a broad range of anti-inflammatory properties, which are thought to contribute to their favourable clinical effects. The utilization of macrolides in low-dose regimen is increasingly discussed for the long-term treatment of inflammatory diseases with a bacteriological component, particularly COPD. As deregulated inflammasome signalling has been shown to be involved in many of these diseases, the aim of this study was to analyse the impact of macrolides on this signalling platform.
Abstract: Urm1 (ubiquitin related modifier 1) is a molecular fossil in the class of ubiquitin-like proteins (UBLs). It encompasses characteristics of classical UBLs, such as ubiquitin or SUMO (small ubiquitin-related modifier), but also of bacterial sulfur-carrier proteins (SCP). Since its main function is to modify tRNA, Urm1 acts in a non-canonical manner. Uba4, the activating enzyme of Urm1, contains two domains: a classical E1-like domain (AD), which activates Urm1, and a rhodanese homology domain (RHD). This sulfurtransferase domain catalyzes the formation of a C-terminal thiocarboxylate on Urm1. Thiocarboxylated Urm1 is the sulfur donor for 5-methoxycarbonylmethyl- 2-thiouridine (mcm 5 s 2 U), a chemical nucleotide modification at the wobble position in tRNA. This thio-modification is conserved in all domains of life and optimizes translation. The absence of Urm1 increases stress sensitivity in yeast triggered by defects in protein homeostasis, a hallmark of neurological defects in higher organisms. In contrast, elevated levels of tRNA modifying enzymes promote the appearance of certain types of cancer and the formation of metastasis. Here, we summarize recent findings on the unique features that place Urm1 at the intersection of UBL and SCP and make Urm1 an excellent model for studying the evolution of protein conjugation and sulfur-carrier systems.
I, Liliana H. Mochmann certify under penalty of perjury by my own signature that I have submitted the thesis on the topic Genome-wide screen reveals WNT11, a non-canonical WNT gene, as a direct target of ETS transcription factor ERG. I wrote this thesis independently and without assistance from third parties, I used no other aids than the listed sources and resources.
C / − 1 has been replaced by
its corresponding value as given by Equation (6). Then, the possibility of backward endogenous variables has thus been removed. Second, the optimal consumer’s condition, which was omitted in the government maximization’s problem, appears as forming part of the general equilibrium. This latter inclusion is relevant in terms of the logic behind my model. In fact, it should be (and it is, indeed) considered as a compulsory requirement for the model which supports the argument that the non-canonical strategy assumed in Section 2.3 for solving the government’s problem is appropriate and, more importantly, feasible to be tested under rational expectations.
only speculate about reasons in order to explain this reverse effect of hearing impairment on non-canonical sentence comprehension. One of the reasons might be that the two groups were not matches with regard to several factors: Means and ranges of severity of aphasia measured by the Token Test (NH 21. 8 (range 1-44), HI = 15.8 (3-26)), nor age or years of education (see also table 8.3) were different for both groups. What is more, the better performance of the hearing-impaired PWA may not have been based on the hearing status alone, but rather on the fact that almost all agrammatic PWA (13/15) belonged to the normal-hearing group (in contrast, the proportion of paragrammatic and anomic PWA was roughly equal for both groups, see table 13.8 in section 126.96.36.199). Furthermore, the optimal acoustic situation provided by modification of the stimuli, headphones and lack of background noise might have improved the perceptual abilities of the hearing-impaired participants with hearing aids. Those PWA might have relearned to rely on sensory cues due to regular usage of a hearing aid, as proposed by Uslar (2014) for non-brain-damaged hearing-impaired listeners (see chapter 5.6). In addition, a small degree of accuracy difference might have been caused by a therapy effect of auditory and/or syntactic training. Treatment of syntactic deficits might have heightened awareness and/or facilitated processing of object-first structures (see e.g., Breitenstein et al., 2014; Thompson & Shapiro, 2007). All of the hearing-impaired PWA received regular speech and language therapy, in contrast to six normal-hearing PWA who were not into therapy anymore. In addition, age had also reverse effect on as well general comprehension and performance on OVS for the whole group of PWA as well as for the agrammatic group, with older PWA demonstrating a higher degree of accuracy on OVS than their younger counterparts. Again, there is a lot of room for speculations for the possible explanations for these effect, but as the age variation was high (ranging from 22 to 82 years in the aphasic group, and from 23 to 75 years in the non-brain-damaged group), age effects may have only added to the general heterogeneity.
Similar to the MEK inhibition, the treatment of glioma cell lines with a TAK1 inhibitor revealed an increased Akt phosphorylation in most cell lines as summarized in figure 4.2.D. This increased Akt phosphorylation did not correlate with ERK1/2 phosphorylation which was found not to be altered. Whereas TAK1 has been shown to activate p38 and JNK MAP kinases, there is no evidence for ERK1/2 activation by TAK1 . The repressive effect of TAK1 on the phosphorylation of Akt is in contrast with the findings of Lee et al. who reported TAK1 to activate Akt in response to LPS in a PI3K- dependent manner in pre-B cells . Furthermore, the existence of a TAK1-MEK-Akt pathway involved in survival has been claimed by Gingery and colleagues in osteoclasts in response to TGF-β . Since the combination of TAK1 inhibitor with BX795 did not show any further effects on the phosphorylation of Akt and ERK1/2 compared to the treatments only with BX795 or 5Z-7-oxozeaenol, it is likely that there is no crosstalk between the non-canonical IKK complex and TAK1 in glioma cell lines (figure 3.18). In order to confirm the role of TAK1 in Akt and ERK1/2 signaling in glioma cell lines, further investigations need to be done. Silencing of TAK1 followed by the measurement of Akt and ERK1/2 phosphorylation as well as the measurement of TBK1/IKKε activity could reveal better insights into the signaling crosstalk. Furthermore, it would also be interesting to assess the role of TAK1 in cellular functions in glioma cell lines by investigating proliferation, migration and apoptosis after silencing TAK1.
The aromatic amino acids phenylalanine, tyrosine and tryptophan are synthesized in E. coli via the shikimate pathway, a well-studied and widely conserved biosynthetic pathway that involves 17 enzymes, and multiple levels of regulation (Bentley, 1990; Knaggs, 1999; Pittard, 1996). When analysing similar pathways in novel microorganisms, particularly those with non-canonical enzymatic steps such as haloarchaea, a number of strategies need to be considered. At the transcriptional level one might use traditional methods for detecting gene expression changes such as Northen blotting or differential RNA display, which allow analysis of only one or few genes at a time. DNA microarrays on the other hand are a much more powerful tool for studying genome-wide differential gene expression. Microarrays allow high throughput, parallelism, speed and automation and by encompassing the whole genome it eliminates bias associated with preselecting a subset of genes believed to be involved in certain cellular event. Nonetheless, microarray experiments produce voluminous datasets which are frequently difficult to analyse and can lead to confusing hypotheses and conclusions (Dharmadi and Gonzalez, 2004).
TTL[1c], respectively, lost their maximal activities from 32 to 25 and 10 mU mg 1 , whereas that of TTL[1b] was negligibly aﬀected from 32 to 31 mU mg 1 (Table S1, ESI†). These findings suggest that 1b may facilitate the exposure of the active site for catalysis, a clear advantage in aqueous environments, but not in non-aqueous ones. Although 1c/1a may trade-oﬀ the activity of TTL under standard conditions, the potential advantages of using these ncAAs in organic media are evident. It is thus unadvisable to heat-activate the congeners prior to organic solvent exposure, since the likelihood of compromising their activity exists.
In the inhibition and recovery from inhibition measurements, we found that in human α9α10 nAChR-expressing oocytes, ACh-mediated current responses were blunted in the presence of LPC and G-PC. The slow kinetics of inhibition and recovery from inhibition by LPC and G-PC suggest that both compounds might also function as silent agonists of the heteromeric α9α10 nAChR. However, we cannot formally exclude that LPC and G-PC act as partial antagonists at these nAChR. Interestingly, while LPC decreased the ACh-evoked current responses to 69 ± 2% in heteromeric α9α10 nAChR expressing oocytes, the ACh-evoked currents in homomeric α7 nAChR expressing oocytes were decreased only to 89 ± 7%. These findings are in accordance with our observation on the monocytic U937 cells, in which the immuno-modulatory function of LPC requires the expression of nAChR subunits α9 and α10 but not α7. In summary, LPC, G-PC and PC are potent agonists of non-classical nicotinic receptors of monocytes that provoke metabotropic functions. LPC, G-PC and PC do not induce ion currents, but they seem to act as silent agonists.
A major factor that differentiates professional from non-professional phagocytes is the multitude of surface receptors pattern-recognition receptors such as the Toll-like receptors (TLRs), C-type lectin receptors (CLRs), NOD-like receptors (NLRs), DNA sensing receptors or retinoic acid inducible gene I (RIG-I) like receptors that detect signals that are not normally found in healthy tissue (Murray and Wynn, 2011). Receptor triggering by foreign material like pathogens results in their engulfment in phagosomes. During phagosome maturation, phagosomes fuse with lysosomes resulting in pH acidification (4 – 4.5) and the influx of proteases generically called cathepsins (Blum et al., 2013). These conditions mediate the denaturation of engulfed material by proteolytical cleavage producing peptides of >11 amino acids for presentation on MHC-II molecules (van Kasteren and Overkleeft, 2014; Rossjohn et al., 2015). MHC-II molecules are restricted to antigen presenting cells (APC), whereas MHC-I molecules are abundantly expressed on all cell types as they are loaded with cytosolic, proteasomal processed peptides of 8-10 amino acids containing a broad spectrum of self-peptides which are presented to CD8 + T cells (van Kasteren and Overkleeft, 2014; Rossjohn et al., 2015). Prior of antigen loading to MHC-II molecules, the invariant chain (Ii) which occupies the binding groove needs to be removed. MHC- II, being assembled in the endoplasmatic reticulum of α- and β-chains associated with the Ii, is transported to MHC class II compartments (MIIC) (Neefjes et al., 1990). The Ii is processed by Cathepsin S, resulting in a 25 aa class-II associated invariant chain peptide (CLIP) that is exchanged by Cathepsin V and the chaperone HLA-DM for a high affinity peptide (Tolosa et al., 2003; van Kasteren and Overkleeft, 2014). Subsequently, the peptide-MHC complex is transported to the cell surface for immune surveillance by CD4 + T cells (Neefjes et al., 2011). T cell activation requires at least two signals namely the complex of a peptide and a MHC molecule binding the T cell receptor (TCR) and a second co-stimulatory signal e.g. the binding of CD80 (B7- 1)/CD86 (B7-2) or CD40 on APCs to CD28 or CD40L on T cells (Medzhitov and Janeway, JR, 2000) (Figure 4). The surface expression of co-stimulatory molecules on APCs is induced by TLRs upon recognition of their cognate pathogen-associated molecular pattern (PAMP) in the presence of infection leading to the activation of pathogen-specific T cells (Medzhitov and Janeway, JR, 2000).
Die vorliegende Arbeit untersucht den Einfluss von Acetylcystein (ACC) auf die Transient Rezeptor Potential Canonical Subtyp 6 (TRPC6)- Kanal- Expression. Andere Studien haben sich bereits mit Auswirkungen von verschiedenen Erkrankungen auf die Expression von TRPC- Kanälen befasst, wobei bisher intensiver mit TRPC3 gearbeitet wurde, wodurch für diesen Subtyp z. Z. eher verlässliche Ergebnisse vorliegen als für TRPC6. Untersuchungen an humanen Monozyten von Diabetikern haben ergeben, dass es unter hyperglykämischen Bedingungen zu einer Erhöhung der TRPC3- und TRPC6- Expression kommt. Gleichzeitig lag ein erhöhter Calciuminflux vor, der auf die vermehrte TRPC- Expression zurückgeführt wurde (Wuensch et al., 2010). Liu et. al. konnten bei Patienten mit essentieller Hypertonie eine vermehrte Expression von TRPC3 und TRPC5, aber keinen signifikanten Unterschied in der Expression von TRPC6 im Vergleich zur Kontrollgruppe ermitteln. Infolge der erhöhten Anzahl an Kationenkanälen ließ sich eine Steigerung der intrazellulären Calcium- Konzentration feststellen. Wurde die Kanalexpression mittels siRNA knockdown (small interference Ribonukleinsäure) down- reguliert, ließ sich bei geringerer Anzahl von TRPC3 und TRPC5 ein verminderter Calciuminflux feststellen. Hingegen änderte sich nach spezifischem TRPC6 knockdown die intrazelluläre Calcium- Konzentration nicht signifikant (Liu et al., 2007).
We apply the ParMitISEM algorithm with different number of IS draws, M , and for each number of draws
we record the execution time and compare them between CPU and the GPU. Moreover we calculate the Numerical Standard Error (NSE) for the CPU and GPU version of the program. Figure 2 reports the results of this experiment. The top panel in Figure 2 shows the target density kernel for the Gelman-Meng function with a ‘banana shaped’ contour and the step-by-step approximations of this kernel using ParMitISEM. The target kernel has two clear modes and the ParMitISEM approximation stops with 3 mixture components. Even with this relatively low number of mixture components the contour of the ParMitISEM approximation are similar to the contour of the target density. Gains from each additional component, presented in the top-right panel of Figure 2, according to the CoV shows that the non-standard ‘banana shaped’ contour of Gelman-Meng is well approximated with 3 mixture components, and the major improvement in this approx- imation is obtained by adding the second mixture component in ParMitISEM.
Several recent papers use and extend the MitISEM algorithm for Bayesian inference. Reference [ 10 ] incorporates the MitISEM algorithm to the estimation of non-Gaussian state space models, [ 11 ] uses MitISEM for Value-at-Risk estimation, [ 12 , 13 ] estimates non-causal models using MitISEM and [ 14 ] uses MitISEM for Bayesian inference of latent variable models. Recently, [ 15 ] provided the R package MitISEM, together with routines to use MitISEM and its sequential extension for Bayesian inference of model parameters and model probabilities. Speeding up computations in such econometric models is appealing for several reasons. First, the amount of data used in these models are typically increasing in areas such as finance, macroeconomics and marketing. Second, such increases in data are often accompanied by construction of more complex models as soon as estimation of these models is possible. For some applications, such as in macroeconomics, estimations taking days or weeks are common. Last but not least, decision making based on econometric models often needs to be performed in a timely manner in areas such as financial risk management. These requirements bring out the necessity to perform quick computations of the econometric models.
U t = U t −1 + η t (2.2)
with innovation η t . The second one, V t is the transitory state which follows the ARMA(p q) process:
V t = ρ t1 V t −1 + ρ t2 V t −2 · · · + ρ tp V t−p + G t (ε t ε t −1 ε t−q ) (2.3) For a shorthand notation, we write the vector of the AR coefficients by ρ t = (ρ t1 ρ tp ) . Note that the time effect is the source of nonstationarity in this model both through the time-varying ARMA specifications (i.e., ρ t and G t ) and through arbitrary time variations in the distributions of the primitives (i.e., η t and ε t ). Because of the non- parametric specification of these time-varying distributions of the primitives, the time effect may appear in higher-order moments as well as in the first moment, for example, as commonly introduced by additive time effects in ( 2.1 ), as is common in applications. In contrast to much of the literature, we allow arbitrarily high-order ARMA processes and this will be a major feature of our empirical application in Section 6 .