Haiku: New paradigm for the reverse genetics of emerging RNA viruses.

Reverse genetics is key technology for producing wild-type and genetically modified viruses. The ISA (Infectious Subgenomic Amplicons) method is a recent versatile and user-friendly reverse genetics method to rescue RNA viruses. The main constraint of its canonic protocol was the requirement to produce (e.g., by DNA synthesis or fusion PCR) 5' and 3' modified genomic fragments encompassing the human cytomegalovirus promoter (pCMV) and the hepatitis delta virus ribozyme/simian virus 40 polyadenylation signal (HDR/SV40pA), respectively. Here, we propose the ultimately simplified "Haiku" designs in which terminal pCMV and HDR/SV40pA sequences are provided as additional separate DNA amplicons. This improved procedure was successfully applied to the rescue of a wide range of viruses belonging to genera Flavivirus, Alphavirus and Enterovirus in mosquito or mammalian cells using only standard PCR amplification techniques and starting from a variety of original materials including viral RNAs extracted from cell supernatant media or animal samples. We also demonstrate that, in specific experimental conditions, the presence of the HDR/SV40pA is not necessary to rescue the targeted viruses. These ultimately simplified "Haiku" designs provide an even more simple, rapid, versatile and cost-effective tool to rescue RNA viruses since only generation of overlapping amplicons encompassing the entire viral genome is now required to generate infectious virus. This new approach may completely modify our capacity to obtain infectious RNA viruses

New reverse genetics and transfection methods to rescue arboviruses in mosquito cells

Abstract Reverse genetics is a critical tool to decrypt the biological properties of arboviruses. However, whilst reverse genetics methods have been usually applied to vertebrate cells, their use in insect cells remains uncommon due to the conjunction of laborious molecular biology techniques and of specific difficulties surrounding the transfection of such cells. To leverage reverse genetics studies in both vertebrate and mosquito cells, we designed an improved DNA transfection protocol for insect cells and then demonstrated that the simple and flexible ISA (Infectious Subgenomic Amplicons) reverse-genetics method can be efficiently applied to both mammalian and mosquito cells to generate in days recombinant infectious positive-stranded RNA viruses belonging to genera Flavivirus (Japanese encephalitis, Yellow fever, West Nile and Zika viruses) and Alphavirus (Chikungunya virus). This method represents an effective option to potentially overcome technological issues related to the study of arboviruses

MALDI-ToF mass spectrometry for the rapid diagnosis of cancerous lung nodules.

Recently, tissue-based methods for proteomic analysis have been used in clinical research and appear reliable for digestive, brain, lymphomatous, and lung cancers classification. However simple, tissue-based methods that couple signal analysis to tissue imaging are time consuming. To assess the reliability of a method involving rapid tissue preparation and analysis to discriminate cancerous from non-cancerous tissues, we tested 141 lung cancer/non-tumor pairs and 8 unique lung cancer samples among the stored frozen samples of 138 patients operated on during 2012. Samples were crushed in water, and 1.5 µl was spotted onto a steel target for analysis with the Microflex LT analyzer (Bruker Daltonics). Spectra were analyzed using ClinProTools software. A set of samples was used to generate a random classification model on the basis of a list of discriminant peaks sorted with the k-nearest neighbor genetic algorithm. The rest of the samples (n = 43 cancerous and n = 41 non-tumoral) was used to verify the classification capability and calculate the diagnostic performance indices relative to the histological diagnosis. The analysis found 53 m/z valid peaks, 40 of which were significantly different between cancerous and non-tumoral samples. The selected genetic algorithm model identified 20 potential peaks from the training set and had 98.81% recognition capability and 89.17% positive predictive value. In the blinded set, this method accurately discriminated the two classes with a sensitivity of 86.7% and a specificity of 95.1% for the cancer tissues and a sensitivity of 87.8% and a specificity of 95.3% for the non-tumor tissues. The second model generated to discriminate primary lung cancer from metastases was of lower quality. The reliability of MALDI-ToF analysis coupled with a very simple lung preparation procedure appears promising and should be tested in the operating room on fresh samples coupled with the pathological examination

Characterization of the recovered viruses.

<p>Replication of recovered viruses was assessed using a combination of several criteria: (i) the presence or absence of cytopathic effect (CPE) is highlighted in green or red, respectively, (ii) the amount of viral RNAs in cell supernatant at the second passage, assessed using a real-time RT-PCR assay is reported as mean Log₁₀ copies/mL ±SD (iii) the infectious titer of each of the rescued virus at the second passage is expressed as a mean log₁₀ TCID₅₀/mL ±SD.</p

Figure 1

<p><b>top:</b> Representative spectra of each subclass: Non-tumor, Primary and Metastasis. <b>bottom:</b> Gel images in grayscale from the same samples as above.</p

Average intensity versus mass-to-charge ratio of 40 significantly different peaks averaged from the whole cohort between Cancer and Non-tumor samples.

<p>Arrows show the mass values for the 20 peaks selected by the Cancer versus Non-tumor GA. The peaks #3370.75, 3442.75, 4963.85, 7004.95, 7487.13, 7567.21, 8454.18, 8563.21, and 9952.85 were up-regulated in the Cancer set, whereas the others were up-regulated in the Non-tumor set.</p

Average intensity versus mass-to-charge ratio of 49 peaks significantly different between Non-tumor, Primary and Metastasis subclasses averaged from the whole cohort.

<p>Arrows show the mass values for the 15 discriminating peaks selected by the Primary cancer versus Metastasis GA model. The peaks 2136.75, 2829.90, 5291.86, 6175.21, 6551.37, 6748.52, 8181.01, 10092.47 and 12685.50 were up-regulated in the Primary Cancer set, whereas the others were up-regulated in the Metastasis set.</p

Diagnostic performances of the 20 peaks of the two class (Cancer versus Non-tumor) GA model using Reference and Blind sets of lung samples.

<p>RC: Recognition Capability, PPV: Predictive Positive Value; Se: Sensibility; Sp: Specificity. Accuracy  =  TP + TN/TP + FN + FP + TN.</p><p>RC and PPV were calculated by testing the training cohort (n = 206). Se and Sp were calculated by testing the Blinded cohort (n = 84 samples).</p