An Induced Hypersensitive-Like Response Limits Expression of Foreign Peptides via a Recombinant TMV-Based Vector in a Susceptible Tobacco

BACKGROUND: By using tobacco mosaic virus (TMV)-based vectors, foreign epitopes of the VP1 protein from food-and-month disease virus (FMDV) could be fused near to the C-terminus of the TMV coat protein (CP) and expressed at high levels in susceptible tobacco plants. Previously, we have shown that the recombinant TMV vaccines displaying FMDV VP1 epitopes could generate protection in guinea pigs and swine against the FMDV challenge. Recently, some recombinant TMV, such as TMVFN20 that contains an epitope FN20 from the FMDV VP1, were found to induce local necrotic lesions (LNL) on the inoculated leaves of a susceptible tobacco, Nicotiana tabacum Samsun nn. This hypersensitive-like response (HLR) blocked amplification of recombinant TMVFN20 in tobacco and limited the utility of recombinant TMV vaccines against FMDV. METHODOLOGY/PRINCIPAL FINDINGS: Here we investigate the molecular mechanism of the HLR in the susceptible Samsun nn. Histochemical staining analyses show that these LNL are similar to those induced in a resistant tobacco Samsun NN inoculated with wild type (wt) TMV. The recombinant CP subunits are specifically related to the HLR. Interestingly, this HLR in Samsun nn (lacking the N/N'-gene) was able to be induced by the recombinant TMV at both 25°C and 33°C, whereas the hypersensitive response (HR) in the resistant tobacco plants induced by wt TMV through the N/N'-gene pathways only at a permissive temperature (below 30°C). Furthermore, we reported for the first time that some of defense response (DR)-related genes in tobacco were transcriptionally upregulated during HLR. CONCLUSIONS: Unlike HR, HLR is induced in the susceptible tobacco through N/N'-gene independent pathways. Induction of the HLR is associated with the expression of the recombinant CP subunits and upregulation of the DR-related genes

Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size

Abstract Background Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required. Thus, the performances of co-expression network analysis methods on large co-expression networks consisting of a few thousand nodes, with only a small number of profiles with a single perturbation, which more accurately reflect normal experimental conditions, are generally uncharacterized and unknown. Methods We proposed a novel network inference methods based on Relevance Low order Partial Correlation (RLowPC). RLowPC method uses a two-step approach to select on the high-confidence edges first by reducing the search space by only picking the top ranked genes from an intial partial correlation analysis and, then computes the partial correlations in the confined search space by only removing the linear dependencies from the shared neighbours, largely ignoring the genes showing lower association. Results We selected six co-expression-based methods with good performance in evaluation studies from the literature: Partial correlation, PCIT, ARACNE, MRNET, MRNETB and CLR. The evaluation of these methods was carried out on simulated time-series data with various network sizes ranging from 100 to 3000 nodes. Simulation results show low precision and recall for all of the above methods for large networks with a small number of expression profiles. We improved the inference significantly by refinement of the top weighted edges in the pre-inferred partial correlation networks using RLowPC. We found improved performance by partitioning large networks into smaller co-expressed modules when assessing the method performance within these modules. Conclusions The evaluation results show that current methods suffer from low precision and recall for large co-expression networks where only a small number of profiles are available. The proposed RLowPC method effectively reduces the indirect edges predicted as regulatory relationships and increases the precision of top ranked predictions. Partitioning large networks into smaller highly co-expressed modules also helps to improve the performance of network inference methods. The RLowPC R package for network construction, refinement and evaluation is available at GitHub: https://github.com/wyguo/RLowPC