regulations, or the discovery that non coding RNA are loaded byribosome to induce Nonsense-Mediated Decay (NMD) [ 1 – 8 ].
Since the initial publication by Weissman’s laboratory ribo- some profi ling has been used in a variety of organisms to address a broad number of questions [ 7 – 11 ]. Despite the strong enthusiast generated by this fi rst technics allowing genome-wide translational changes, it should be keep in mind that this is a complicated approach with many pitfalls that can generate a number of misin- terpretations. Indeed small variations in growth culture, medium composition or low genome coverage can generate misinterpreta- tions. Ribosome profi ling cannot be the end of a story but instead should be the beginning of new questions. It is essential not to rely only on statistical analysis to validate data but also performing independent experiments on few genes. In this review we will describe in detail all steps needed to prepare high quality RPF and how to perform basic bioinformatics analysis to map them onto a Saccharomyces cerevisiae reference genome. Obviously most of the steps can be applied to other organisms since it is possible to extract polysomes.
capacity, the spatio-temporal expression patterns and the subcellular localization atthe protein level, this study provides new leads towards addressing the putative function and mode of action of tomato Aux/IAA genes. The tomato Aux/IAA family is slightly contracted, with 25 members compared with Arabidopsis (29 genes) (Liscum and Reed 2002). However, while overall the tomato Aux/IAA gene family comprises a lower number of genes than in Arabidopsis, two clades are substantially expanded. Clades A and J contain seven and three genes in tomato, respectively, but only four and one in Arabidopsis. As an illustration of the wide diversification of Aux/IAA proteins in higher plants, the two clades are also expanded in Populus trichocarpa, with six members in clade A and three members in clade J (Kalluri et al. 2007). This diversification is also reflected by important structural vari- ations found within Aux/IAA proteins. The accepted model for Aux/IAA function builds on auxin-mediated degradation of these short-lived proteins that typically have four conserved domains defining the gene family members. Notably, clade H comprising three non-canonical members (AtIAA20, AtIAA30 and AtIAA31) in Arabidopsis that lack the conserved domain II essential for protein degradation is not represented in tomato. In line with the absence or the alteration of domain II, AtIAA20 and AtIAA31 have been shown to be long-lived proteins compared with the canonical AtIAA17 (Dreher et al. 2006). The mechanism by which these non-canonical proteins impact auxin signaling remains unclear, even though the over- expression of AtIAA20, AtIAA30 or AtIAA31 results in aberrant auxin-related phenotypes in Arabidopsis (Sato and Yamamoto 2008). The tomato genome contains two non-canonical Aux/IAA genes (Sl-IAA32 and Sl-IAA33), whereas up to six are found in Arabidopsis. Sl-IAA32 protein lacks domain II, whereas both domain I and domain II are missing in Sl-IAA33. The pre- sent study shows that Sl-IAA32 is a functional repressor of auxin signaling and its expression is limited to the breaker stage of fruit development (data not shown). A search in the SGN data- base identified an EST sequence from a cell culture suspension corresponding to Sl-IAA33, suggesting that the expression of this gene is highly constrained. Attempts to detect Sl-IAA33 mRNA in the present study were unsuccessful in all tissues tested, further supporting the low level of expression of non-canonical Aux/IAA genes reported so far in Arabidopsis (Dreher et al. 2006). Considering their expression pattern ap- parently restricted to narrow developmental stages and their atypical long-lived feature due to the absence of domain II, the tomato non-canonical Aux/IAA proteins may have a specific function in mediating auxin responses during well-defined plant developmental events.
present similar insulating properties as the ones observed for TADs and interval-communities borders (Fig. 4 and Additional file 1: Figure S9) . In Human ES cells, master replication initiation zones are enriched in CTCF and pluripotent transcription factors NANOG and OCT4 that were recently shown to contribute to the overall folding of embryonic stem cells genome via specific long-range contacts [51, 52], and appear to be fundamental determinants of pluripotency maintenance [53, 54]. In particular they are atthe heart of the so-called consolidation phenomenon [17, 23, 55, 56] corresponding to early to late transitions from embryonic stem cells to differentiated cells coinciding with the emergence of compact heterochromatin atthe nuclear periphery . ES cell line are characterised by smaller replication U-domains . Here we observed in H1 ES cell line an excess of interval-communities in the range of scales from ∼ 500 kb to ∼ 1.5 Mb as compared to the differ- entiated cell lines (Fig. 3b). These domains not observed in differentiated cell lines might be subject to some structural consolidation scenario during cell differenti- ation, similar to the one described for replication timing domains. For example, the strutural community bor- der present in H1 ES and absent in IMR90 at position ∼ 84 Mb in Fig. 1 correspond to a replication timing U-domain border specific of ES cell line. Further analysis of the structural consolidation scenario is likely to shed a new light on the role of structural organisation in the epigenetically regulated chromatin reorganisation that underlies the loss of pluripotency and lineage commit- ment . It was shown that master origins of replication conserved between 6 cell lines are encoded in the DNA sequence via a local enrichment in nucleosome exclud- ing energy barriers [57, 58]. This raises the question whether borders of the conserved structural community borders (Fig. 6) might be specified by a similar genetic mechanism.
with a notable exception of the genus Badnavirus, in which the Met-tRNA primer binding site is located at a short distance from the pgRNA transcription start site, in front of the large stem-loop structure ( Pooggin et al., 1999 ; Geering et al., 2005 ; Rajeswaran et al., 2014b ). Indeed, deep small RNA sequencing from banana plants infected with six different badnaviruses revealed that, unlike CaMV and RTBV, viral siRNA production is not restricted to the pgRNA leader region of these badnaviruses ( Rajeswaran et al., 2014b ). As a consequence of the lack of a decoy-mediated silencing evasion, viral siRNA hotspots are distributed along the viral genome, with abundant siRNAs targeting the protein-coding sequences. Although in some badnaviruses, like, e.g., Banana streak OL virus (BSOLV), a short sequence of the pgRNA leader between the transcription and reverse transcription start sites is one of the hotspots for sense and antisense viral siRNAs, a potential short dsRNA precursor of those siRNAs (Figure 6; Rajeswaran et al., 2014b ) may not serve an effective decoy for sequestering DCLs. It should be noted, however, that, despite production of abundant 21-, 22-, and 24-nt viral siRNAs targeting both non-coding and coding sequences, those six badnaviruses could still persist in vegetative progeny of banana plants for many years and evade cytosine methylation of viral DNA and TGS ( Rajeswaran et al., 2014b ; reviewed in Pooggin, 2013 ). This implies other mechanisms evolved by badnaviruses to evade or suppress antiviral PTGS and TGS. In this regard, it is notable that members of the genus Badnavirus do not encode any homolog of the Caulimovirus TAV/P6 or the Tungrovirus P4 which serve as effector proteins suppressing RNAi and other plant defenses in the respective genera of plant pararetroviruses as reviewed below. Based on a position of the Met-tRNA primer binding site downstream of the leader-based stem-loop structure, the CaMV- and RTBV-type decoy strategy can also be predicted in all members of the genera Caulimovirus, Cavemovirus, Petuvirus, Soymovirus, Rosadnavirus, and Orendovirus as well as in the Solendovirus TVCV and the unassigned caulimovirids BFDaV and RuFDV (Figure 2, the Met-tRNA binding site highlighted in cyan). In the Solendovirus SPVCV and the Florendovirus PpersV-sc1, the Met-tRNA binding site is located upstream of the stem-loop structure or on its ascending arm, respectively, suggesting that the run-off transcript would be short, like in the genus Badnavirus. Regardless whether or not a dsRNA decoy is expressed, a large and stable stem-loop structure in the pgRNA leader of most plant pararetroviruses is expected to prevent access of the antiviral RISCs charged with viral siRNAs of antisense polarity that can potentially interfere with translation, splicing, packaging or reverse transcription processes regulated bythe leader-based cis-elements.
To evaluate the potential influence of the mini-Tn insertions on gene expression from these operon-like structures, we analyzed the 4 kb co-linear gene cluster pip-nifS-nifU-mucB (MAG0710 to MAG0740) (Fig. 2). This cluster was of particular interest because: (i) nifS has been shown to be essential for proliferation of M. agalactiae on cell cultures, and two knockout mutants, NifS1 and NifS2, were identified by high-throughput screening on caprine and human cells (Table 3); and (ii) these co- linear genes are likely to be co-transcribed by a promoter located upstream of pip, based on genome data indicating short intergenic distances (Fig. 2A). Indeed, the operon-like structure of the pip- nifS-nifU-mucB cluster was further supported by our transcrip- tional analyses, which detected overlapping transcripts by RT- PCR (Fig. 2A). Two mutants, Pip and MucB, with a transposon inserted into the corresponding genes, were searched for and found in the mutant library (see Materials and Methods), but both are able to grow on all three cell lines. When tested individually with TIGMEC, both mutants had the wild-type phenotype (Fig. 2B). This result was surprising, at least for the Pip mutant, which was expected to have a similar phenotype to that of the NifSs mutants because of the predicted polar effect of the transposon insertion (Fig. 2A).
Ribosomeprofiling confirms translation of annotated
CDSes and identifies novel translated ORFs in Toxoplasma
RNA-seq alone cannot distinguish translated from non- translated transcripts. Additionally, it is not clear whether some annotated non-coding RNAs contain translated small open reading frames. These problems are exacerbated in non-model organisms, such as Toxo- plasma, with incompletely annotated genomes. Because Ribo-seq captures ribosome-engaged mRNAs, it is often used to not only estimate thetranslation efficiencies of annotated coding regions, but also to identify novel translated ORFs. Consequently, we used RiboTaper, as previously described , to identify translated ORFs based on 3-nt periodicity and P-site positions in the expressed Toxoplasma genes. Because the current anno- tation of Toxoplasma gene structures (ToxoDB.org; GT1 v28 ) is incomplete, and RiboTaper classifies ORFs based on known coding regions, we initially used RNA- seq reads (~500 million paired-end reads from this and a parallel study ) to update GT1 gene structures. To do this, we performed genome-guided transcript assem- bly using Trinity , followed by transcript structure resolution using the Program to Assemble Spliced Alignments (PASA) , as previously described . Subsequently, we updated the structures of 6442 tran- scripts, mostly due to the addition of 5′ and 3′ UTRs (mean lengths of 435-nt and 508-nt, respectively) (Fig. 2a). Next, we used RiboTaper and identified 4224 ORFs in 4195 genes based on the updated transcript structures. Noteworthy, the identification of ORFs in RiboTaper is based on codon resolution on the Ribo-seq
The second important parameter for ensuring a high quality physical map is to limit as much as possible the number of chimerical contigs. Here, we estimated that, atthe final cut-off value of 1e -11 a number of chimerical contigs is 0.6 for 10 Mb of sequence. Van Oeveren et al.  have developed a methodology and a tool to iden- tify chimerical contigs on the basis of the fraction of BAC pairs within a contig sharing at least one tag (C1) and the average tag density in a contig (C2). The authors empirically determined a threshold for which the square of C1 divided by C2 provided a value that discriminated between chimerical and non-chimerical contigs. Problematic BACs, then, can be identified and discarded by iteratively removing each BAC of the con- tig and testing whether BAC removal will break up the contig (23). We tested this approach on the wheat chro- mosome 3B dataset but it did not detect any of the chi- merical contigs identified by comparison with the reference sequences. Moreover, only two contigs were identified as chimerical in the whole dataset with this approach while 14 were present based on our estimation of the number of chimerical contigs in 10 Mb. The threshold used to choose chimerical and contiguous contigs was defined from the WGP experiment on Ara- bidopsis  and it is likely that new parameters need to be established for wheat reinforcing the idea that parameters in the WGP analysis need to be adapted to the complexity of the target genome. With our dataset, we did not have sufficient sequence information to esti- mate a robust threshold value for wheat. The access to the entire 3B sequence in the near future (C. Feuillet, pers. comm.) will help in this regard.
Fig. 1 a Volcano plot of differentially expressed genes in substantia nigra. The x axis shows differential expression (DE; logarithmic scale), the y axis indicates the respective p value of each sequence. The sequences coloured in red show a differential expression greater or smaller than one (logarithm with base 2, i.e. one log unit representing a 100% difference) and p values smaller than 0.001. There are 570 probe sets fulfilling these criteria. b Comparison of the number of "significant genes" as detected by GC-RMA-, PLIER- and MAS5.0-based analysis procedures. At p<0.05 (no variance inflation, no Bonferroni or Benjamini-Hochberg FDR correction) the PLIER algorithm which takes into account Affymetrix proprietary information on probe behaviour stands out by producing a very large number of significantly regulated genes. The number of significantly regulated genes as detected bythe different algorithms compares rather well at
presence of GMP-PNP, we see efficient accumulation of 48S particles. In contrast, in presence of edeine, which interferes specifically in the P-site with the codon- anticodon interaction between the initiator tRNA and the start codon, no 80S ribosomal complexes are assembled and only 43S complexes, presumably scanning complexes, are observed (Supplemental Figure 1). These experiments demonstrate that theribosome complexes that are assembled on SARS-CoV-2 5’UTR are fully functional and that non-productive complexes are absent. In order to determine whether NSP1 is removed from the assembled pre-initiation complexes, we used a previously established protocol that yields purified pre-initiation complexes programmed with SARS-CoV-2 5’UTR (Chicher et al., 2015; Martin et al., 2016; Prongidi-Fix et al., 2013). The principle is to use a chimeric molecule composed on one hand bythe RNA region encompassing the SARS-CoV-2 5’UTR followed by a small coding sequence and a DNA oligonucleotide coupled to Biotin at its 3’ end (Figure 5A). The hybrid molecules are then immobilised on magnetic streptavidin beads and incubated in RRL in the presence of cycloheximide. Cycloheximide blocks the first translocation step and, therefore, incubation of SARS-CoV-2 5’UTR in RRL, previously treated with cycloheximide, leads to the accumulation of 80S ribosomes that are stalled on the start codon. The complexes are then eluted by DNase digestion that removes the Biotin and the DNA linker. The composition of the eluted ribosomal complexes is determined by mass spectrometry analysis. We performed in parallel two experiments with the SARS-CoV-2 5’UTR in the presence or in the absence of NSP1. Each experiment was repeated three times (Figure 5B). Since cycloheximide induces the stalling of 80S complexes on the start codon, the 5’ cap of the mRNA is still accessible. Therefore, another scanning complex can also be present on the mRNA, meaning that we can in fact purify 43S, 48S and 80S complexes atthe same time. Concerning elongation, it is well described that cycloheximide does not allow a 100%-blockage and that a small proportion of disomes is always observed, which explains the presence of elongation factors in our complexes. The important point here is that NSP1 is still present together with disomes, 80S and scanning complexes. Since all these complexes are assembled with the mRNA in the mRNA channel, we have to conclude on the presence of NSP1 on these ribosomal complexes but with its C-terminal domain displaced from the mRNA channel to enable the presence of mRNA.
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21.1 Introduction 479 function. The utilization of biomass components to satisfy the objective function is represented bythe drain of these metabolites out of the system. Since in ﬂux bal- ance analysis (FBA), biochemical reactions are assumed to occur rapidly, steady state is achieved instantaneously and hence is assumed in all reactions. Linear programming is used to incorporate the constraints in the model to identify ﬂux values that will result in the maximized production of biomass components at steady state. This process deﬁnes FBA. Flux variability analysis (FVA), on the other hand, aims to identify the minimum and maximum ﬂux values on reactions that correspond to similar optimal values for the objective function. This identiﬁes alternate pathways that contribute to achieving the objective function. It also gives an idea of the ﬂexibility of the organism metabolism. For instance, this may help in identifying essential pathways where only small changes in ﬂuxes through reac- tions are allowed. One of the major drawbacks of this technique is that since it does not take into consideration the reaction kinetics and the initial concentration of each metabolite, it will be unable to predict the concentration of these metabo- lites over time as in kinetic modeling . There has, however, been a release of algorithms such as dynamic FBA, which tries to address the issue of varying con- centration of medium components [10, 11].
To this end, the process of translation itself is known to exhibit substantial heterogeneity (Sauert et al., 2015). Eukaryotic cells contain up to 108 ribosomes (Milo et al., 2010), and variation in composition, modification, interaction, and localization can specialize their functionality (Guo, 2018; Hui & Boer, 1987; Xue & Barna, 2012). Such specialized ribosomes have been found for instance to prioritize the efficient translation of proteins destined for export to mitochondria (Shi et al., 2017). Similarly, ER-targeted proteins are preferentially translated by a select pool of translocation competent ribosomes (Chartron et al., 2016). Heterogeneity equally exists atthe level of translation elongation. The cellular abundances of tRNAs affect the effiency of diffusion to theribosome, thus codon-specific translation rates (Reis et al., 2004). Long considered stable in their expression, the principles and functional implications of regulating tRNA abundances are only starting to emerge (Goodarzi et al., 2016). Another mechanism that directly affects the decoding efficiency is achieved through RNA modifications at or near the tRNA stem loop (60, 61). Of note, tRNA modifications are well characterized biochemically but their physiological roles in most cases remain poorly understood (Grosjean et al., 2010; Quax et al., 2015). An example of a universally conserved tRNA modification is the modification of the anticodon wobble uridine (U34) in the tRNA genes tEUUC, tKUUU , and tQUUG that increases the efficiency of translating AAA, CAA, and GAA codons (Nedialkova & Leidel, 2015, 2015). Taken together, a plethora of processes within the cell exists that can render translation heterogeneous, which should be reflected in Ribo-Seq data.
An important topic in the study of the neural basis of brain functions is the interregional asymmetry between the left and the right hemisphere and its relation to the factors that modulate cognitive specialization in the brain, such as language and motor control. The specialization of the two hemispheres is termed lateralization.
The central nervous system (CNS) comprises the brain (cerebrum and cerebellum) and the spinal cord 1 . The brain contains neurons that receive, analyze and store information. It is also the source of conscious and uncon- scious thoughts and behaviors. The cerebrum is one of the most important parts of the brain; it controls emotion, hearing, vision, personality, amongst other things. The cerebrum, which accounts for 85% of total brain weight, is divided into left and right hemispheres. The hemispheres communicate with each other through the corpus callosum (a bundle of fibers between the hemispheres). Of particular interest is the modularity of the hemi- spheres, which allows one hemisphere to take over a specific function controlled bythe other hemisphere if the latter is damaged. It should be noted, however, that this ability depends on the area damaged and the patient’s age. Generally speaking, the left hemisphere controls the right side of the body and vice versa. Typically, in humans, the left hemisphere handles linear reasoning and language functions, such as vocabulary and grammar, while the right hemisphere accounts for different language functions, such as intonation and accentuations, as well as for the processing of visual and audiological stimuli, spatial manipulation, facial perception, and artistic ability 2 – 4 . Other integrative functions, including arithmetic, sound localization and emotions, seem to be controlled more bilaterally 5 , 6 .
Figure 2: Evolution of the reduced flame speed sL/s 0
L with the gaseous agent mass fraction Y IOH,u for lean (φ = 0.6), stoichiometric and rich (φ = 1.4) methane/air flames.
38R sub-mechanisms, a drastic decrease of the flame speed is observed, which illustrates the catalytic potential of alkali hydroxides. Figure 1 also shows that potassium hydroxyde is more effective, per mass basis, at reducing the flame speed than sodium hydroxide. The results are in fair agreement with the experimental data of Rosser et al. .
b Institut de M´ ecanique des Fluides de Toulouse (I.M.F.T)
A time scaleanalysis of the homogeneous flame inhibition problem is carried out to identify the main parameters controlling the gas phase chemical inter- action of the alkali metal inhibitors with the flame chemistry. First, kinetic sub-models for the interaction of alkali metals with the flame are analyzed to show that a simplified 2-step inhibition cycle can capture the essential fea- tures of this interaction. Second, it is shown that this cycle is auto-catalytic, which explains the high efficiency of alkali metals in inhibiting flames even at low concentrations. Third, the time scales associated to this inhibition cycle are linked to the free flame termination time scale via a non-dimensional parameter characterizing the efficiency of an inhibitor at promoting radical scavenging. It is shown that this parameter accounts for the main trends observed in the literature and can also be used to provide estimates for the chemical flame suppression limit.
Ustilaginomycotina, Pucciniomycotina, Agaricomycotina, which were not resolved until very recently  already challenge the current view by placing the Ustilaginomycotina as a sister group of the Pucciniomycotina instead of the Agaricomycotina. Using the alternative approach of the NeighbourNet algorithm implemented in SplitsTree, the same general pattern is recovered with two additional notes (Fig 2). First, there are considerably more possible splits within the Pucciniales, which could indicate a source of genuine conflicting phylogenetic signal in our data or simply reflect the higher proportion of missing data for this group with the inclusion of EST data. Second, the position of the Ustilaginomycotina is again ambiguous in relation to the other two sub-phyla. Solving these problems will most likely require that addition of more high quality data from further loci and taxa and the application of additional filtering and quality control steps on the data matrix [13,14]. Establishing a robust and well resolved phylogeny will then ensure the first and most important step in our project, by laying the basis from which further analyses can be undertaken such as divergence time estimation, detection of natural selection and mapping of pathogenic traits or evolution of gene families on the evolutionary history of the rust fungi and H. vastatrix.
( 88 ) which are based on thermodynamical data generate
secondary structure models using experimental structure probing data as constraints. These softwares generate many different models, rank them according to their predicted free energy and yield one or several models for the exper- imenter to choose. Most of the time, the resulting models are mostly, but not completely in agreement with the prob- ing data. This can be due to model inaccuracy or to exper- imental artefacts, but in many cases most probably reflects the inherent property of RNA to fold into several confor- mations. In addition, each probe has its own specificity and can reflect slightly different things. For example some ‘sin- gle strand’ specific reagents react with nucleotides involved in non-canonical base-pairs while others do not ( 81 ). As a result, the modelling process often leaves the experimenter with different RNA structure models among which it is of- ten difficult to choose. In addition, when modelling the sec- ondary structure with multiple constraints sets, one faces the necessity to compare many different structures (20 more stable for each data set) in order to identify structures (ide- ally one) that can hold most of the probing information. This step is extremely time-consuming when manually per- formed and necessitates a rationalization that cannot be ob- tained with available structure prediction softwares. Here, we have developed an integrative approach which identi- fies the model that fits the best with the reactivity map ob- tained with different structural probes. The structure prob- ing and modelling of this portion of Gag ORF has been reported in two previous studies ( 89 , 90 ). Both rely on the SHAPE technology and have been conducted in the con- text of the full length genomic RNA. In the first report, po- sitions reactive to 1M7 were resolved through electrophore- sis as in this study, while the second used the recently devel- oped SHAPE-Map technology which relies on Next Gener- ation Sequencing ( 90 ). The 1M7 reactivity maps presented in both reports are in good agreement although not identi- cal. Both are also globally comparable to the 1M7 reactivity map reported in this study, but for two noticeable excep- tions. The nucleotides between U 559 and U 570 and those in-
A Possible Model for the Interactions Between RPL36AL, the CCA-arm of P-tRNA and eRF1, atthe Active Site of Human 80s Ribosome
Now we are in the situation to propose a model for the interactions between the ribosomal L36AL protein, the CCA-arm of P-site bound tRNA and thetranslation termination factor eRF1 bound to an A-site stop codon (Fig. 11), taking into account recently published structural and photocrosslinking data as well as the chemical crosslinking results reported here. We have recently proposed that the tRNAox species used in the present report might bind first to the classical P/P site before they flip spontaneously into the P/E site, where they are sampled bythe crosslinking with Lys-53 of RPL36AL , in agreement with recent cryo-electron microscopy and single-molecule FRET data . Our crosslinking data agree well with photocrosslinking data using a tRNAAsp analogue substituted with 4-thiouridine in position 76 (tRNAAspp- s4U76), which could be cross-linked to C4335 located very close to Lys-53 of RPL36AL in the E-site region of human 80S ribosome . Crosslinking of the CCA-arm of a P- tRNA with both RPL36AL and eRF1 implies that the P- tRNA is present in the P/P state, while the L36AL protein due to the flexibility of its loop extension is capable of reaching the P-site, where it could interact with the CCA- arm and the acceptor stem of tRNA. Accordingly, we propose the model in Fig. (11), where the CCA end of the P- tRNA is sandwiched between the methylated GGQ motifs of RPL36AL and the A-site bound eRF1. In this model, the GGQ loop of eRF1 faces nucleotide A76 of P-tRNA, since atthe end of thetranslation process, this nucleotide which bears the ester carbone of the peptidyl-tRNA is the natural target of the GGQ motif of eRF1 for the nucleophilic attack (via a water molecule) leading to the release of the polypeptide chain. Therefore, this model is compatible with the observation that the crosslinking yields of the ternary complexes “b” decrease as a consequence of the removal of A76 of the CCA-arm of tRNA. Decreasing of the crosslinking yield of the ternary complex “b” upon removal of A76 is in striking contrast to the crosslinking yields of the
resonance may provide deeper insight on discriminating interaction features among various TPL isoforms.
Functional redundancy among Arabidopsis TPL family members is supported bythe absence of obvious phenotypes in single loss-of-function mutants of AtTPL/TPR genes and bythe requirement for downregulation of all five AtTPL- TPRs in order to phenocopy the dominant mutation tpl-1 ( Long et al., 2006 ). However, this assumption is in contrast to the situation prevailing in rice and maize, where genetic evidence seems to support a more specialized functionality for TPL genes. Thus, in rice ( Yoshida et al., 2012 ), a single recessive mutation in Asp1, a TPL-like gene close to SlTPL2, exhibited several pleiotropic phenotypes, such as altered phyllotaxy and spikelet morphology. While these phenotypes suggest a close association of Asp1 with auxin action, they clearly reveal that the specialization of TPL-related pro- teins in some organisms can differ from that in Arabidopsis. Further evidence sustaining a diversified function for TPL Fig. 6. PPI maps between SlTPLs and SlIAAs established by a Y2H screen. (A) Yeast growth of co-transformed BD–TPLs and AD–IAAs.
Initially, all Illumina and 454 reads were filtered for adapter contamination, PCR duplicates, ambiguous resi- dues (N residues) and low quality regions. The initial backbone of the draft genome was assembled with Illu- mina reads using De Bruijn graph-based SOAPdenovo (version 1.05) assembler [8,35], run with a k-mer param- eter of 47 and each library ranked according to insert size from smallest to largest. The gaps within assembled scaffolds were filled with the short insert PE reads using GapCloser (version 1.12). The resulting assembly con- sisted of a total of 35,436 contigs and short scaffolds, with a sequence span of 488 Mb and an N50 size of 265 kb. BAC end sequences for TO1434 were downloaded from NCBI (LIBGSS_011756) and trimmed for quality, ambiguous bases and adapter sequences. Bambus  was used to overlay all the 454 MP information and the BAC end sequence data onto SOAPdenovo scaffolds to improve scaffold lengths as described in . In short, all 454 MP reads and BAC end sequence reads (Table S1 in Additional file 2) were aligned to the scaffolds using a genomic mapping and alignment program (GMAP) . The output from GMAP was used to create a Bambus- compatible GDE formatted contig file that indicated scaffold links. Redundant or multi-mapped mates, mates where only one read mapped, and those where both mates mapped to a single scaffold were considered invalid links. Each link was considered in Bambus in as- cending order of their length, with scaffolding parame- ters including a redundancy level of 3 and link size error of 5%. Any potentially ambiguous scaffolds were re- solved using the 'untangle' utility of Bambus. Bambus was able to order, orient and merge 2,623 of these pre- assembled SOAPdenovo scaffolds into 646 superscaf- folds, resulting in a greatly improved assembly with an N50 size of 850 kb.