Genome Sequences of the Race 1 and Race 4 Xanthomonas campestris pv. campestris Strains CFBP 1869 and CFBP 5817

Xanthomonas campestris pv. campestris is the causal agent of black rot on Brassicaceae. The draft genome sequences of strains CFBP 1869 and CFBP 5817 have been determined and are the first ones corresponding to race 1 and race 4 strains, which have a predominant agronomic and economic impact on cabbage cultures worldwide

Genomic and Evolutionary Features of the SPI-1 Type III Secretion System That Is Present in Xanthomonas albilineans but Is Not Essential for Xylem Colonization and Symptom Development of Sugarcane Leaf Scald

Xanthomonas albilineans is the causal agent of sugarcane leaf scald. Interestingly, this bacterium, which is not known to be insect or animal associated, possesses a type III secretion system (T3SS) belonging to the injectisome family Salmonella pathogenicity island 1 (SPI-1). The T3SS SPI-1 of X. albilineans shares only low similarity with other available T3SS SPI-1 sequences. Screening of a collection of 128 plant-pathogenic bacteria revealed that this T3SS SPI-1 is present in only two species of Xanthomonas: X. albilineans and X. axonopodis pv. phaseoli. Inoculation of sugarcane with knockout mutants showed that this system is not required by X. albilineans to spread within xylem vessels and to cause disease symptoms. This result was confirmed by the absence of this T3SS SPI-1 in an X. albilineans strain isolated from diseased sugarcane. To investigate the importance of the T3SS SPI-1 during the life cycle of X. albilineans, we analyzed T3SS SPI-1 sequences from 11 strains spanning the genetic diversity of this species. No nonsense mutations or frameshifting indels were observed in any of these strains, suggesting that the T3SS SPI-1 system is maintained within the species X. albilineans. Evolutionary features of T3SS SPI-1 based on phylogenetic, recombination, and selection analyses are discussed in the context of the possible functional importance of T3SS SPI-1 in the ecology of X. albilineans

Natural Genetic Variation of Xanthomonas campestris pv. campestris Pathogenicity on Arabidopsis Revealed by Association and Reverse Genetics

The pathogenic bacterium Xanthomonas campestris pv. campestris, the causal agent of black rot of Brassicaceae, manipulates the physiology and the innate immunity of its hosts. Association genetic and reverse-genetic analyses of a world panel of 45 X. campestris pv. campestris strains were used to gain understanding of the genetic basis of the bacterium’s pathogenicity to Arabidopsis thaliana. We found that the compositions of the minimal predicted type III secretome varied extensively, with 18 to 28 proteins per strain. There were clear differences in aggressiveness of those X. campestris pv. campestris strains on two Arabidopsis natural accessions. We identified 3 effector genes (xopAC, xopJ5, and xopAL2) and 67 amplified fragment length polymorphism (AFLP) markers that were associated with variations in disease symptoms. The nature and distribution of the AFLP markers remain to be determined, but we observed a low linkage disequilibrium level between predicted effectors and other significant markers, suggesting that additional genetic factors make a meaningful contribution to pathogenicity. Mutagenesis of type III effectors in X. campestris pv. campestris confirmed that xopAC functions as both a virulence and an avirulence gene in Arabidopsis and that xopAM functions as a second avirulence gene on plants of the Col-0 ecotype. However, we did not detect the effect of any other effector in the X. campestris pv. campestris 8004 strain, likely due to other genetic background effects. These results highlight the complex genetic basis of pathogenicity at the pathovar level and encourage us to challenge the agronomical relevance of some virulence determinants identified solely in model strains.IMPORTANCE The identification and understanding of the genetic determinants of bacterial virulence are essential to be able to design efficient protection strategies for infected plants. The recent availability of genomic resources for a limited number of pathogen isolates and host genotypes has strongly biased our research toward genotype-specific approaches. Indeed, these do not consider the natural variation in both pathogens and hosts, so their applied relevance should be challenged. In our study, we exploited the genetic diversity of Xanthomonas campestris pv. campestris, the causal agent of black rot on Brassicaceae (e.g., cabbage), to mine for pathogenicity determinants. This work evidenced the contribution of known and unknown loci to pathogenicity relevant at the pathovar level and identified these virulence determinants as prime targets for breeding resistance to X. campestris pv. campestris in Brassicaceae

Back to Massey: Impressively fast, scalable and tight security evaluation tools

None of the existing rank estimation algorithms can scale to large cryptographic keys, such as 4096-bit (512 bytes) RSA keys. In this paper, we present the first solution to estimate the guessing entropy of arbitrarily large keys, based on mathematical bounds, resulting in the fastest and most scalable security evaluation tool to date. Our bounds can be computed within a fraction of a second, with no memory overhead, and provide a margin of only a few bits for a full 128-bit AES key

Transforming growth factor β receptor 1 is a new candidate prognostic biomarker after acute myocardial infarction

<p>Abstract</p> <p>Background</p> <p>Prediction of left ventricular (LV) remodeling after acute myocardial infarction (MI) is clinically important and would benefit from the discovery of new biomarkers.</p> <p>Methods</p> <p>Blood samples were obtained upon admission in patients with acute ST-elevation MI who underwent primary percutaneous coronary intervention. Messenger RNA was extracted from whole blood cells. LV function was evaluated by echocardiography at 4-months.</p> <p>Results</p> <p>In a test cohort of 32 MI patients, integrated analysis of microarrays with a network of protein-protein interactions identified subgroups of genes which predicted LV dysfunction (ejection fraction ≤ 40%) with areas under the receiver operating characteristic curve (AUC) above 0.80. Candidate genes included transforming growth factor beta receptor 1 (TGFBR1). In a validation cohort of 115 MI patients, TGBFR1 was up-regulated in patients with LV dysfunction (P < 0.001) and was associated with LV function at 4-months (P = 0.003). TGFBR1 predicted LV function with an AUC of 0.72, while peak levels of troponin T (TnT) provided an AUC of 0.64. Adding TGFBR1 to the prediction of TnT resulted in a net reclassification index of 8.2%. When added to a mixed clinical model including age, gender and time to reperfusion, TGFBR1 reclassified 17.7% of misclassified patients. TGFB1, the ligand of TGFBR1, was also up-regulated in patients with LV dysfunction (P = 0.004), was associated with LV function (P = 0.006), and provided an AUC of 0.66. In the rat MI model induced by permanent coronary ligation, the TGFB1-TGFBR1 axis was activated in the heart and correlated with the extent of remodeling at 2 months.</p> <p>Conclusions</p> <p>We identified TGFBR1 as a new candidate prognostic biomarker after acute MI.</p

One trace is all it takes: Machine Learning-based Side-channel Attack on EdDSA

Profiling attacks, especially those based on machine learning proved as very successful techniques in recent years when considering side-channel analysis of block ciphers implementations. At the same time, the results for implementations public-key cryptosystems are very sparse. In this paper, we consider several machine learning techniques in order to mount a power analysis attack on EdDSA using the curve Curve25519 as implemented in WolfSSL. The results show all considered techniques to be viable and powerful options. The results with convolutional neural networks (CNNs) are especially impressive as we are able to break the implementation with only a single measurement in the attack phase while requiring less than 500 measurements in the training phase. Interestingly, that same convolutional neural network was recently shown to perform extremely well for attacking the AES cipher. Our results show that some common grounds can be established when using deep learning for profiling attacks on distinct cryptographic algorithms and their corresponding implementations